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(message truncated) [%s : %d] RAW: %s%.*s~~~~~~~~~~~~~~~~w0` `0` ` `0` `0` `!x-sys-default-locale        ŀ0C0EA?Invalid data type tensor(seq(optional(map(sparse_tensor(Unsuported type proto value case.Invalid tensor data type optionalmapopaque'.DataTypeUtils::FromDataTypeString - Received invalid data type string 'complex64complex128uint4\ `??@-@<LyX domain: Attempting to deregister an unregistered schema with name: `8, has unsupported type: typestr: has inconsistent type has unsupported type ' has been deprecated since version Operator ') in op definition.) than declared () has more inputs ( is marked single but has an empty string in the graph)'s input ) has more outputs ()'s output ' appeared multiple times.Attribute ' for operator Unrecognized attribute: ', actual: ''. Expected: 'Mismatched attribute type in '' is expected to have field 't'' is expected to have field 'sparse_tensor'' is expected to have field 'g'' is expected to have field 'type_proto' has unknown expected type' is missing.Required attribute ':: with schema(, max= not in range [min= has input size not in allowed input sizes. has output size not in allowed output sizes.Attribute specification type mismatch.Duplicate type constraint name, opset_version = Cannot find a function builder that satisfies the requested opset version: op_type = Error parsing function body:Extra unparsed input unexpected.(inputs_.size() - 1) == i: failed validating the check: ONNX Schema (outputs_.size() - 1) == i!(it.GetName().empty())P@8|pp@`#PPP@0#Dp@ #??@-@x0` `ЛpD `$pD `ti i0iphhh\tiiiPjhhunk__H$̀̀̀s̀̀̀̀s̀̀`s0̀̀̀̀̀̀̀I0OQD0Wf fsc g h] diW is out of bounds.GraphProto attribute inferencing is not enabled in this InferenceContextImpl instance. does not contain a graph.node Vf`eg@g_I already exists.Data for input p??B@>@@@@P$p???@>@@@@poptional_typeopaque_typesparse_tensor_typeNOT_SET inferred=type case mismatch. existing=key type mismatch from MapProto. existing=type case unsupported. existing=type case unsupported for symbolic shape inference. inferred= optype . No opset import for domain Cannot infer type and shape for node name Container for generated shape data cannot be nullptr when enable_data_propagation option is set.Inference error(s): 0   P  @         xhUnable to save inferred model to the target path: were provided inputs but Graph has Cannot use the same name as both a subgraph initializer and subgraph input: The number of graph input cannot be smaller than the number of node input were provided.Graph initializer names must appear after the actual inputs: in initializers. Cannot find missing input: , node name: ): (op_type:) vs (Inferred elem type differs from existing elem type: (Inferred shape and existing shape differ in rank: (: (Inferred shape and existing shape differ in dimension . Please check if it is a valid proto. Unable to open proto file: . Please check if it is a valid protobuf file of proto. Unable to parse proto from file: Ą#pCCDpIC# This operator is DEPRECATED. Please use TreeEnsemble instead which provides the same functionality.
Tree Ensemble regressor. Returns the regressed values for each input in N.
All args with nodes_ are fields of a tuple of tree nodes, and it is assumed they are the same length, and an index i will decode the tuple across these inputs. Each node id can appear only once for each tree id.
All fields prefixed with target_ are tuples of votes at the leaves.
A leaf may have multiple votes, where each vote is weighted by the associated target_weights index.
All fields ending with _as_tensor can be used instead of the same parameter without the suffix if the element type is double and not float. All trees must have their node ids start at 0 and increment by 1.
Mode enum is BRANCH_LEQ, BRANCH_LT, BRANCH_GTE, BRANCH_GT, BRANCH_EQ, BRANCH_NEQ, LEAF Tree Ensemble operator. Returns the regressed values for each input in a batch. Inputs have dimensions `[N, F]` where `N` is the input batch size and `F` is the number of input features. Outputs have dimensions `[N, num_targets]` where `N` is the batch size and `num_targets` is the number of targets, which is a configurable attribute. The encoding of this attribute is split along interior nodes and the leaves of the trees. Notably, attributes with the prefix `nodes_*` are associated with interior nodes, and attributes with the prefix `leaf_*` are associated with leaves. The attributes `nodes_*` must all have the same length and encode a sequence of tuples, as defined by taking all the `nodes_*` fields at a given position. All fields prefixed with `leaf_*` represent tree leaves, and similarly define tuples of leaves and must have identical length. This operator can be used to implement both the previous `TreeEnsembleRegressor` and `TreeEnsembleClassifier` nodes. The `TreeEnsembleRegressor` node maps directly to this node and requires changing how the nodes are represented. The `TreeEnsembleClassifier` node can be implemented by adding a `ArgMax` node after this node to determine the top class. To encode class labels, a `LabelEncoder` or `GatherND` operator may be used. Creates a map from the input and the attributes.
The values are provided by the input tensor, while the keys are specified by the attributes. Must provide keys in either classlabels_strings or classlabels_int64s (but not both).
The columns of the tensor correspond one-by-one to the keys specified by the attributes. There must be as many columns as keys.
Select elements of the input tensor based on the indices passed.
The indices are applied to the last axes of the tensor. Maps the values of the input tensor to either 0 or 1, element-wise, based on the outcome of a comparison against a threshold value. Converts a map to a tensor.
The map key must be an int64 and the values will be ordered in ascending order based on this key.
The operator supports dense packing or sparse packing. If using sparse packing, the key cannot exceed the max_map-1 value. Converts strings to integers and vice versa.
Two sequences of equal length are used to map between integers and strings, with strings and integers at the same index detailing the mapping.
Each operator converts either integers to strings or strings to integers, depending on which default value attribute is provided. Only one default value attribute should be defined.
If the string default value is set, it will convert integers to strings. If the int default value is set, it will convert strings to integers. Uses an index mapping to convert a dictionary to an array.
Given a dictionary, each key is looked up in the vocabulary attribute corresponding to the key type. The index into the vocabulary array at which the key is found is then used to index the output 1-D tensor 'Y' and insert into it the value found in the dictionary 'X'.
The key type of the input map must correspond to the element type of the defined vocabulary attribute. Therefore, the output array will be equal in length to the index mapping vector parameter. All keys in the input dictionary must be present in the index mapping vector. For each item in the input dictionary, insert its value in the output array. Any keys not present in the input dictionary, will be zero in the output array.
For example: if the ``string_vocabulary`` parameter is set to ``["a", "c", "b", "z"]``, then an input of ``{"a": 4, "c": 8}`` will produce an output of ``[4, 8, 0, 0]``. ??@-@ Concatenates input tensors into one continuous output.
All input shapes are 2-D and are concatenated along the second dimension. 1-D tensors are treated as [1,C]. Inputs are copied to the output maintaining the order of the input arguments.
All inputs must be integers or floats, while the output will be all floating point values. Replaces inputs that equal one value with another, leaving all other elements alone.
This operator is typically used to replace missing values in situations where they have a canonical representation, such as -1, 0, NaN, or some extreme value.
One and only one of imputed_value_floats or imputed_value_int64s should be defined -- floats if the input tensor holds floats, integers if the input tensor holds integers. The imputed values must all fit within the width of the tensor element type. One and only one of the replaced_value_float or replaced_value_int64 should be defined, which one depends on whether floats or integers are being processed.
The imputed_value attribute length can be 1 element, or it can have one element per input feature.
In other words, if the input tensor has the shape [*,F], then the length of the attribute array may be 1 or F. If it is 1, then it is broadcast along the last dimension and applied to each feature. Maps each element in the input tensor to another value.
The mapping is determined by the two parallel attributes, 'keys_*' and 'values_*' attribute. The i-th value in the specified 'keys_*' attribute would be mapped to the i-th value in the specified 'values_*' attribute. It implies that input's element type and the element type of the specified 'keys_*' should be identical while the output type is identical to the specified 'values_*' attribute. Note that the 'keys_*' and 'values_*' attributes must have the same length. If an input element can not be found in the specified 'keys_*' attribute, the 'default_*' that matches the specified 'values_*' attribute may be used as its output value. The type of the 'default_*' attribute must match the 'values_*' attribute chosen.
Let's consider an example which maps a string tensor to an integer tensor. Assume and 'keys_strings' is ["Amy", "Sally"], 'values_int64s' is [5, 6], and 'default_int64' is '-1'. The input ["Dori", "Amy", "Amy", "Sally", "Sally"] would be mapped to [-1, 5, 5, 6, 6].
Since this operator is an one-to-one mapping, its input and output shapes are the same. Notice that only one of 'keys_*'/'values_*' can be set.
Float keys with value 'NaN' match any input 'NaN' value regardless of bit value. If a key is repeated, the last key takes precedence. Linear classifier Generalized linear regression evaluation.
If targets is set to 1 (default) then univariate regression is performed.
If targets is set to M then M sets of coefficients must be passed in as a sequence and M results will be output for each input n in N.
The coefficients array is of length n, and the coefficients for each target are contiguous. Intercepts are optional but if provided must match the number of targets. Normalize the input. There are three normalization modes, which have the corresponding formulas, defined using element-wise infix operators '/' and '^' and tensor-wide functions 'max' and 'sum':

Max: Y = X / max(X)
L1: Y = X / sum(X)
L2: Y = sqrt(X^2 / sum(X^2)}
In all modes, if the divisor is zero, Y == X.
For batches, that is, [N,C] tensors, normalization is done along the C axis. In other words, each row of the batch is normalized independently. Replace each input element with an array of ones and zeros, where a single one is placed at the index of the category that was passed in. The total category count will determine the size of the extra dimension of the output array Y.
For example, if we pass a tensor with a single value of 4, and a category count of 8, the output will be a tensor with ``[0,0,0,0,1,0,0,0]``.
This operator assumes every input feature is from the same set of categories.
If the input is a tensor of float, int32, or double, the data will be cast to integers and the cats_int64s category list will be used for the lookups. Rescale input data, for example to standardize features by removing the mean and scaling to unit variance. Support Vector Machine classifier Support Vector Machine regression prediction and one-class SVM anomaly detection. This operator is DEPRECATED. Please use TreeEnsemble with provides similar functionality. In order to determine the top class, the ArgMax node can be applied to the output of TreeEnsemble. To encode class labels, use a LabelEncoder operator. Tree Ensemble classifier. Returns the top class for each of N inputs.
The attributes named 'nodes_X' form a sequence of tuples, associated by index into the sequences, which must all be of equal length. These tuples define the nodes.
Similarly, all fields prefixed with 'class_' are tuples of votes at the leaves. A leaf may have multiple votes, where each vote is weighted by the associated class_weights index.
One and only one of classlabels_strings or classlabels_int64s will be defined. The class_ids are indices into this list. All fields ending with _as_tensor can be used instead of the same parameter without the suffix if the element type is double and not float. Unspecified required attribute.' must have type ' must have Data to be selectedThe indices, based on 0 as the first index of any dimension.Selected output data as an arrayThe input must be a tensor of a numeric type or string. The output will be of the same tensor type.D:\a\_work\_temp\onnx\src\v1.17.0-7e28176e64.clean\onnx\defs\traditionalml\defs.ccData to be binarizedBinarized output dataThe input must be a tensor of a numeric type. The output will be of the same tensor type.Values greater than this are mapped to 1, others to 0.The input map that is to be cast to a tensorA tensor representing the same data as the input map, ordered by their keysThe input must be an integer map to either string or float.map(int64, string)map(int64, float)The output is a 1-D tensor of string, float, or integer.A string indicating the desired element type of the output tensor, one of 'TO_FLOAT', 'TO_STRING', 'TO_INT64'.Indicates whether to only output as many values as are in the input (dense), or position the input based on using the key of the map as the index of the output (sparse).
One of 'DENSE', 'SPARSE'.If the value of map_form is 'SPARSE,' this attribute indicates the total length of the output tensor.Input dataOutput data. If strings are input, the output values are integers, and vice versa.The input must be a tensor of strings or integers, either [N,C] or [C].The output is a tensor of strings or integers. Its shape will be the same as the input shape.The strings of the map. This sequence must be the same length as the 'cats_int64s' sequenceThe integers of the map. This sequence must be the same length as the 'cats_strings' sequence.A string to use when an input integer value is not found in the map.
One and only one of the 'default_*' attributes must be defined.An integer to use when an input string value is not found in the map.
One and only one of the 'default_*' attributes must be defined.A dictionary.A 1-D tensor holding values from the input dictionary.The input must be a map from strings or integers to either strings or a numeric type. The key and value types cannot be the same.map(string, int64)map(int64, double)map(string, float)map(string, double)The output will be a tensor of the value type of the input map. It's shape will be [1,C], where C is the length of the input dictionary.A string vocabulary array.
One and only one of the vocabularies must be defined.An integer vocabulary array.
One and only one of the vocabularies must be defined.An ordered collection of tensors, all with the same element type.The output array, elements ordered as the inputs.The input type must be a tensor of a numeric type.The size of each input in the input listData to be processed.Imputed output dataThe input type must be a tensor of a numeric type, either [N,C] or [C]. The output type will be of the same tensor type and shape.Value(s) to change toA value that needs replacing.Value(s) to change to.At least one of keys_tensor, keys_strings, keys_int64s, keys_floats must be set. are different, which is not permitted for LabelEncoders. and the key type The input type was At least one of values_tensor, values_strings, values_int64s, values_floats must be set. must be the same in the LabelEncoder. and the number of values The number of keys and the value type The default tensor type The default tensor must be a singleton 1D tensor.Input data. It must have the same element type as the keys_* attribute set.Output data. This tensor's element type is based on the values_* attribute set.The input type is a tensor of any shape.Output type is determined by the specified 'values_*' attribute.Keys encoded as a 1D tensor. One and only one of 'keys_*'s should be set.A list of strings.A list of ints.A list of floats.Values encoded as a 1D tensor. One and only one of 'values_*'s should be set.A string.An integer.A float.A default tensor. {"_Unused"} if values_* has string type, {-1} if values_* has integral type, and {-0.f} if values_* has float type.Input's shape should be 1D or 2DData to be classified.Classification outputs (one class per example).Classification scores ([N,E] - one score for each class and exampleThe input must be a tensor of a numeric type, and of shape [N,C] or [C]. In the latter case, it will be treated as [1,C]The output will be a tensor of strings or integers.A collection of weights of the model(s).A collection of intercepts.Indicates whether to do OvR or multinomial (0=OvR is the default).Class labels when using string labels. One and only one 'classlabels' attribute must be defined.Class labels when using integer labels. One and only one 'classlabels' attribute must be defined.Indicates the transform to apply to the scores vector.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'Data to be regressed.Regression outputs (one per target, per example).The input must be a tensor of a numeric type.Indicates the transform to apply to the regression output vector.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'Weights of the model(s).Weights of the intercepts, if used.The total number of regression targets, 1 if not defined.Data to be encoded, a tensor of shape [N,C] or [C]Encoded output dataOne of 'MAX,' 'L1,' 'L2'Exactly one of 'cats_*' attributes must be provided.Data to be encoded.Encoded output data, having one more dimension than X.List of categories, ints.
One and only one of the 'cats_*' attributes must be defined.List of categories, strings.
One and only one of the 'cats_*' attributes must be defined.If true and category is not present, will return all zeros; if false and a category if not found, the operator will fail.Data to be scaled.Scaled output data.First, offset by this.
Can be length of features in an [N,F] tensor or length 1, in which case it applies to all features, regardless of dimension count.Second, multiply by this.
Can be length of features in an [N,F] tensor or length 1, in which case it applies to all features, regardless of dimension count.
Must be same length as 'offset'Class scores (one per class per example), if prob_a and prob_b are provided they are probabilities for each class, otherwise they are raw scores.The input must be a tensor of a numeric type, either [C] or [N,C].The output type will be a tensor of strings or integers, depending on which of the classlabels_* attributes is used. Its size will match the bactch size of the input.The kernel type, one of 'LINEAR,' 'POLY,' 'RBF,' 'SIGMOID'.List of 3 elements containing gamma, coef0, and degree, in that order. Zero if unused for the kernel.First set of probability coefficients.Second set of probability coefficients. This array must be same size as prob_a.
If these are provided then output Z are probability estimates, otherwise they are raw scores.Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'Class labels if using string labels.
One and only one of the 'classlabels_*' attributes must be defined.Class labels if using integer labels.
One and only one of the 'classlabels_*' attributes must be defined.Regression outputs (one score per target per example).The input type must be a tensor of a numeric type, either [C] or [N,C].Chosen support vectorsFlag indicating whether the regression is a one-class SVM or not.Support vector coefficients.The number of support vectors.Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'Input of shape [N,F]N, Top class for each pointThe class score for each class, for each point, a tensor of shape [N,E].The output type will be a tensor of strings or integers, depending on which of the classlabels_* attributes is used.Tree id for each node.Node id for each node. Ids may restart at zero for each tree, but it not required to.Feature id for each node.Thresholds to do the splitting on for each node.Popularity of each node, used for performance and may be omitted.The node kind, that is, the comparison to make at the node. There is no comparison to make at a leaf node.
One of 'BRANCH_LEQ', 'BRANCH_LT', 'BRANCH_GTE', 'BRANCH_GT', 'BRANCH_EQ', 'BRANCH_NEQ', 'LEAF'Child node if expression is true.Child node if expression is false.For each node, define what to do in the presence of a missing value: if a value is missing (NaN), use the 'true' or 'false' branch based on the value in this array.
This attribute may be left undefined, and the default value is false (0) for all nodes.The id of the tree that this node is in.node id that this weight is for.The index of the class list that each weight is for.The weight for the class in class_id.Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'Base values for classification, added to final class score; the size must be the same as the classes or can be left unassigned (assumed 0)N classesNode id for each node. Node ids must restart at zero for each tree and increase sequentially.Child node if expression is trueChild node if expression is falseFor each node, define what to do in the presence of a NaN: use the 'true' (if the attribute value is 1) or 'false' (if the attribute value is 0) branch based on the value in this array.
This attribute may be left undefined and the default value is false (0) for all nodes.The id of the tree that each node is in.The node id of each weightThe index of the target that each weight is forThe weight for each targetThe total number of targets.Defines how to aggregate leaf values within a target.
One of 'AVERAGE,' 'SUM,' 'MIN,' 'MAX.'Base values for regression, added to final prediction after applying aggregate_function; the size must be the same as the classes or can be left unassigned (assumed 0)Attribute 'nodes_splits' is required.Attribute 'nodes_splits' must be 1D. and attribute type is Attribute 'nodes_splits' must have same type as input. Input type is Attribute 'membership_values' must have same type as input. Input type is and 'leaf_weights' length is Attribute 'leaf_targetids' must have same length as attribute 'leaf_weights'. 'leaf_targetids' length is Attributes 'leaf_targetids' and 'leaf_weights' must both be set.Attribute 'leaf_weights' must have same type as input. Input type is Input of shape [Batch Size, Number of Features]Output of shape [Batch Size, Number of targets]Thresholds to do the splitting on for each node with mode that is not 'BRANCH_MEMBER'.The comparison operation performed by the node. This is encoded as an enumeration of 0 ('BRANCH_LEQ'), 1 ('BRANCH_LT'), 2 ('BRANCH_GTE'), 3 ('BRANCH_GT'), 4 ('BRANCH_EQ'), 5 ('BRANCH_NEQ'), and 6 ('BRANCH_MEMBER'). Note this is a tensor of type uint8.If `nodes_trueleafs` is false at an entry, this represents the position of the true branch node. This position can be used to index into a `nodes_*` entry. If `nodes_trueleafs` is false, it is an index into the leaf_* attributes.If `nodes_falseleafs` is false at an entry, this represents the position of the false branch node. This position can be used to index into a `nodes_*` entry. If `nodes_falseleafs` is false, it is an index into the leaf_* attributes.1 if true branch is leaf for each node and 0 an interior node. To represent a tree that is a leaf (only has one node), one can do so by having a single `nodes_*` entry with true and false branches referencing the same `leaf_*` entry1 if false branch is leaf for each node and 0 if an interior node. To represent a tree that is a leaf (only has one node), one can do so by having a single `nodes_*` entry with true and false branches referencing the same `leaf_*` entryFor each node, define whether to follow the true branch (if attribute value is 1) or false branch (if attribute value is 0) in the presence of a NaN input feature. This attribute may be left undefined and the default value is false (0) for all nodes.Index into `nodes_*` for the root of each tree. The tree structure is derived from the branching of each node.Members to test membership of for each set membership node. List all of the members to test again in the order that the 'BRANCH_MEMBER' mode appears in `node_modes`, delimited by `NaN`s. Will have the same number of sets of values as nodes with mode 'BRANCH_MEMBER'. This may be omitted if the node doesn't contain any 'BRANCH_MEMBER' nodes.The index of the target that this leaf contributes to (this must be in range `[0, n_targets)`).The weight for each leaf.Indicates the transform to apply to the score.
One of 'NONE' (0), 'SOFTMAX' (1), 'LOGISTIC' (2), 'SOFTMAX_ZERO' (3) or 'PROBIT' (4), defaults to 'NONE' (0)Defines how to aggregate leaf values within a target.
One of 'AVERAGE' (0) 'SUM' (1) 'MIN' (2) 'MAX (3) defaults to 'SUM' (1)ZipMap input shape should be 1D or 2D.The input valuesThe output mapThe output will be a sequence of string or integer maps to float.seq(map(string, float))seq(map(int64, float))The keys when using string keys.
One and only one of the 'classlabels_*' attributes must be defined.The keys when using int keys.
One and only one of the 'classlabels_*' attributes must be defined. Ђ ЂЂтC#PЂPЂттC#TςςpЂ тC#ςςЂ0тC#ςςЂ@тC#8ЂЂЂ`тC#0Ђ0ЂЂтC#ςς`ЂтC#@Ђ@ЂЂтC#hЂЂЂpтC#ςςЂPтC# Converts strings to integers and vice versa.
If the string default value is set, it will convert integers to strings. If the int default value is set, it will convert strings to integers.
Each operator converts either integers to strings or strings to integers, depending on which default value attribute is provided. Only one default value attribute should be defined.
When converting from integers to strings, the string is fetched from the 'classes_strings' list, by simple indexing.
When converting from strings to integers, the string is looked up in the list and the index at which it is found is used as the converted value. Tree Ensemble classifier. Returns the top class for each of N inputs.
The attributes named 'nodes_X' form a sequence of tuples, associated by index into the sequences, which must all be of equal length. These tuples define the nodes.
Similarly, all fields prefixed with 'class_' are tuples of votes at the leaves. A leaf may have multiple votes, where each vote is weighted by the associated class_weights index.
One and only one of classlabels_strings or classlabels_int64s will be defined. The class_ids are indices into this list. Tree Ensemble classifier. Returns the top class for each of N inputs.
The attributes named 'nodes_X' form a sequence of tuples, associated by index into the sequences, which must all be of equal length. These tuples define the nodes.
Similarly, all fields prefixed with 'class_' are tuples of votes at the leaves. A leaf may have multiple votes, where each vote is weighted by the associated class_weights index.
One and only one of classlabels_strings or classlabels_int64s will be defined. The class_ids are indices into this list. All fields ending with _as_tensor can be used instead of the same parameter without the suffix if the element type is double and not float. Tree Ensemble regressor. Returns the regressed values for each input in N.
All args with nodes_ are fields of a tuple of tree nodes, and it is assumed they are the same length, and an index i will decode the tuple across these inputs. Each node id can appear only once for each tree id.
All fields prefixed with target_ are tuples of votes at the leaves.
A leaf may have multiple votes, where each vote is weighted by the associated target_weights index.
All trees must have their node ids start at 0 and increment by 1.
Mode enum is BRANCH_LEQ, BRANCH_LT, BRANCH_GTE, BRANCH_GT, BRANCH_EQ, BRANCH_NEQ, LEAF Tree Ensemble regressor. Returns the regressed values for each input in N.
All args with nodes_ are fields of a tuple of tree nodes, and it is assumed they are the same length, and an index i will decode the tuple across these inputs. Each node id can appear only once for each tree id.
All fields prefixed with target_ are tuples of votes at the leaves.
A leaf may have multiple votes, where each vote is weighted by the associated target_weights index.
All fields ending with _as_tensor can be used instead of the same parameter without the suffix if the element type is double and not float. All trees must have their node ids start at 0 and increment by 1.
Mode enum is BRANCH_LEQ, BRANCH_LT, BRANCH_GTE, BRANCH_GT, BRANCH_EQ, BRANCH_NEQ, LEAF Maps each element in the input tensor to another value.
The mapping is determined by the two parallel attributes, 'keys_*' and 'values_*' attribute. The i-th value in the specified 'keys_*' attribute would be mapped to the i-th value in the specified 'values_*' attribute. It implies that input's element type and the element type of the specified 'keys_*' should be identical while the output type is identical to the specified 'values_*' attribute. If an input element can not be found in the specified 'keys_*' attribute, the 'default_*' that matches the specified 'values_*' attribute may be used as its output value.
Let's consider an example which maps a string tensor to an integer tensor. Assume and 'keys_strings' is ["Amy", "Sally"], 'values_int64s' is [5, 6], and 'default_int64' is '-1'. The input ["Dori", "Amy", "Amy", "Sally", "Sally"] would be mapped to [-1, 5, 5, 6, 6].
Since this operator is an one-to-one mapping, its input and output shapes are the same. Notice that only one of 'keys_*'/'values_*' can be set.
For key look-up, bit-wise comparison is used so even a float NaN can be mapped to a value in 'values_*' attribute.
??@-@Input data.The input type must be a tensor of integers or strings, of any shape.The output type will be a tensor of strings or integers, and will have the same shape as the input.A list of labels.D:\a\_work\_temp\onnx\src\v1.17.0-7e28176e64.clean\onnx\defs\traditionalml\old.ccOnly one of the attributes 'nodes_values', 'nodes_values_as_tensor' should be specified.Only one of the attributes 'nodes_hitrates', 'nodes_hitrates_as_tensor' should be specified.Only one of the attributes 'class_weights', 'class_weights_as_tensor' should be specified.Only one of the attributes 'base_values', 'base_values_as_tensor' should be specified.Non of classlabels_int64s or classlabels_strings is set.Only one of the attributes 'target_weights', 'target_weights_as_tensor' should be specified.Label encoder has only one input.Label encoder has only one output.Only one of keys_*'s can be set in label encoder.Input type is not string tensor but key_strings is setInput type is not int64 tensor but keys_int64s is setInput type is not float tensor but keys_floats is setOnly one of values_*'s can be set in label encoder.Input data. It can be either tensor or scalar.Output data.A list of strings. One and only one of 'keys_*'s should be set.A list of strings. One and only one of 'value_*'s should be set.C#LppЂC# C#0C#0@C# GatherElements takes two inputs `data` and `indices` of the same rank r >= 1 and an optional attribute `axis` that identifies an axis of `data` (by default, the outer-most axis, that is axis 0). It is an indexing operation that produces its output by indexing into the input data tensor at index positions determined by elements of the `indices` tensor. Its output shape is the same as the shape of `indices` and consists of one value (gathered from the `data`) for each element in `indices`. For instance, in the 3-D case (r = 3), the output produced is determined by the following equations: ``` out[i][j][k] = input[index[i][j][k]][j][k] if axis = 0, out[i][j][k] = input[i][index[i][j][k]][k] if axis = 1, out[i][j][k] = input[i][j][index[i][j][k]] if axis = 2, ``` This operator is also the inverse of ScatterElements. It is similar to Torch's gather operation. Example 1: ``` data = [ [1, 2], [3, 4], ] indices = [ [0, 0], [1, 0], ] axis = 1 output = [ [1, 1], [4, 3], ] ``` Example 2: ``` data = [ [1, 2, 3], [4, 5, 6], [7, 8, 9], ] indices = [ [1, 2, 0], [2, 0, 0], ] axis = 0 output = [ [4, 8, 3], [7, 2, 3], ] ``` Remove single-dimensional entries from the shape of a tensor. Takes an input `axes` with a list of axes to squeeze. If `axes` is not provided, all the single dimensions will be removed from the shape. If an axis is selected with shape entry not equal to one, an error is raised. Insert single-dimensional entries to the shape of an input tensor (`data`). Takes one required input `axes` - which contains a list of dimension indices and this operator will insert a dimension of value `1` into the corresponding index of the output tensor (`expanded`). For example, given an input tensor (`data`) of shape [3, 4, 5], then Unsqueeze(data, axes=[0, 4]) outputs a tensor (`expanded`) containing same data as `data` but with shape [1, 3, 4, 5, 1]. The input `axes` should not contain any duplicate entries. It is an error if it contains duplicates. The rank of the output tensor (`output_rank`) is the rank of the input tensor (`data`) plus the number of values in `axes`. Each value in `axes` should be within the (inclusive) range [-output_rank , output_rank - 1]. The order of values in `axes` does not matter and can come in any order. SpaceToDepth rearranges blocks of spatial data into depth. More specifically, this op outputs a copy of the input tensor where values from the height and width dimensions are moved to the depth dimension. DepthToSpace rearranges (permutes) data from depth into blocks of spatial data. This is the reverse transformation of SpaceToDepth. More specifically, this op outputs a copy of the input tensor where values from the depth dimension are moved in spatial blocks to the height and width dimensions. By default, `mode` = `DCR`. In the DCR mode, elements along the depth dimension from the input tensor are rearranged in the following order: depth, column, and then row. The output y is computed from the input x as below: ``` b, c, h, w = x.shape tmp = np.reshape(x, [b, blocksize, blocksize, c // (blocksize**2), h, w]) tmp = np.transpose(tmp, [0, 3, 4, 1, 5, 2]) y = np.reshape(tmp, [b, c // (blocksize**2), h * blocksize, w * blocksize]) ``` In the CRD mode, elements along the depth dimension from the input tensor are rearranged in the following order: column, row, and the depth. The output y is computed from the input x as below: ``` b, c, h, w = x.shape tmp = np.reshape(x, [b, c // (blocksize ** 2), blocksize, blocksize, h, w]) tmp = np.transpose(tmp, [0, 1, 4, 2, 5, 3]) y = np.reshape(tmp, [b, c // (blocksize ** 2), h * blocksize, w * blocksize]) ``` Constructs a tensor by tiling a given tensor. This is the same as function `tile` in Numpy, but no broadcast. For example A = [[1, 2], [3, 4]], B = [1, 2], tile(A, B) = [[1, 2, 1, 2], [3, 4, 3, 4]] Upsample the input tensor. Each dimension value of the output tensor is: output_dimension = floor(input_dimension * scale). Resize the input tensor. In general, it calculates every value in the output tensor as a weighted average of neighborhood (a.k.a. sampling locations) in the input tensor. Each dimension value of the output tensor is: ``` output_dimension = floor(input_dimension * (roi_end - roi_start) * scale) ``` if input \"sizes\" is not specified. This attribute describes how to transform the coordinate in the resized tensor to the coordinate in the original tensor. The coordinate of each dimension is transformed individually. Let's describe a case using axis x as an example. Denote `x_resized` as the coordinate of axis x in the resized tensor, `x_original` as the coordinate of axis x in the original tensor, `length_original` as the length of the original tensor in axis x, `length_resized` as the length of the resized tensor in axis x, `scale = length_resized / length_original`, `output_width` the target length on the axis x which can be a fractional number when it is calculated out of a scale factor, and `output_width_int` the effective output width as an integer. if coordinate_transformation_mode is `"half_pixel"`, ``` x_original = (x_resized + 0.5) / scale - 0.5 ``` if coordinate_transformation_mode is `"half_pixel_symmetric"`, ``` adjustment = output_width_int / output_width center = input_width / 2 offset = center * (1 - adjustment) x_ori = offset + (x + 0.5) / scale - 0.5 ``` if coordinate_transformation_mode is `"pytorch_half_pixel"`, ``` x_original = length_resized > 1 ? (x_resized + 0.5) / scale - 0.5 : 0 ``` if coordinate_transformation_mode is `"align_corners"`, ``` x_original = x_resized * (length_original - 1) / (length_resized - 1) ``` if coordinate_transformation_mode is `"asymmetric"`, ``` x_original = x_resized / scale ``` if coordinate_transformation_mode is `"tf_crop_and_resize"`, ``` x_original = length_resized > 1 ? start_x * (length_original - 1) + x_resized * (end_x - start_x) * (length_original - 1) / (length_resized - 1) : 0.5 * (start_x + end_x) * (length_original - 1) ``` . This attribute describes how to interpret the `sizes` input with regard to keeping the original aspect ratio of the input, and it is not applicable when the `scales` input is used. Given a set of `sizes`, associated with a subset of `axes` (explicitly provided or default), and assuming `d = axes[i]`, with `i` being the index of the provided `sizes`. If `keep_aspect_ratio_policy` is `"stretch"`, the original aspect ratio is disregarded, and the input is resized to the specified size: `out_size[d] = sizes[i]` If `keep_aspect_ratio_policy` is `"not_larger"`, the sizes are adjusted so that no extent of the output is larger than the specified size, while keeping the original aspect ratio: ``` scale = Min(sizes[i] / in_size[d]) out_size[d] = round_int(scale * in_size[i]) ``` If `keep_aspect_ratio_policy` is `"not_smaller"`, the sizes are adjusted so that no extent of the output is smaller than the specified size, while keeping the original aspect ratio: ``` scale = Max(sizes[i] / in_size[d]) out_size[d] = round_int(scale * in_size[i]) ``` For non-resizable axes (those not specified in `axes`), the output size will be equal to the input size. Note: `round_int` stands for computing the nearest integer value, rounding halfway cases up. Given an input `X` and a flow-field `grid`, computes the output `Y` using `X` values and pixel locations from the `grid`. For spatial input `X` with shape (N, C, H, W), the `grid` will have shape (N, H_out, W_out, 2), the output `Y` will have shape (N, C, H_out, W_out). For volumetric input `X` with shape (N, C, D, H, W), the `grid` will have shape (N, D_out, H_out, W_out, 3), the output `Y` will have shape (N, C, D_out, H_out, W_out). More generally, for an input `X` of rank r+2 with shape (N, C, d1, d2, ..., dr), the `grid` will have shape (N, D1_out, D2_out, ..., Dr_out, r), the output `Y` will have shape (N, C, D1_out, D2_out, ..., Dr_out). The tensor `X` contains values at centers of square pixels (voxels, etc) locations such as (n, c, d1_in, d2_in, ..., dr_in). The (n, d1_out, d2_out, ..., dr_out, :) values from the tensor `grid` are the normalized positions for interpolating the values at the (n, c, d1_out, d2_out, ..., dr_out) locations from the output tensor `Y` using a specified interpolation method (the mode) and a padding mode (for `grid` positions falling outside the 2-dimensional image). For example, the values in `grid[n, h_out, w_out, :]` are size-2 vectors specifying normalized positions in the 2-dimensional space of `X`. They are used to interpolate output values of `Y[n, c, h_out, w_out]`. The GridSample operator is often used in doing grid generator and sampler in the [Spatial Transformer Networks](https://arxiv.org/abs/1506.02025). See also in [torch.nn.functional.grid_sample](https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html). Generates a 2D or 3D flow field (sampling grid), given a batch of affine matrices theta (https://pytorch.org/docs/stable/generated/torch.nn.functional.affine_grid.html). An affine matrix `theta` is applied to a position tensor represented in its homogeneous expression. Here is an example in 3D: ``` [r00, r01, r02, t0] [x] [x'] [r10, r11, r12, t1] * [y] = [y'] [r20, r21, r22, t2] [z] [z'] [0, 0, 0, 1 ] [1] [1 ] ``` where `(x, y, z)` is the position in the original space, `(x', y', z')` is the position in the output space. The last row is always `[0, 0, 0, 1]` and is not stored in the affine matrix. Therefore we have `theta` of shape `(N, 2, 3)` for 2D or `(N, 3, 4)` for 3D. Input `size` is used to define grid of positions evenly spaced in the original 2D or 3D space, with dimensions ranging from `-1` to `1`. The output `grid` contains positions in the output space. When `align_corners=1`, consider `-1` and `1` to refer to the centers of the corner pixels (mark `v` in illustration). ``` v v v v |-------------------|------------------| -1 0 1 ``` When `align_corners=0`, consider `-1` and `1` to refer to the outer edge of the corner pixels. ``` v v v v |------------------|-------------------| -1 0 1 ``` The operator casts the elements of a given input tensor to a data type specified by the 'to' argument and returns an output tensor of the same size in the converted type. The 'to' argument must be one of the data types specified in the 'DataType' enum field in the TensorProto message. Casting from string tensor in plain (e.g., "3.14" and "1000") and scientific numeric representations (e.g., "1e-5" and "1E8") to float types is supported. For example, converting string "100.5" to an integer may yield result 100. There are some string literals reserved for special floating-point values; "+INF" (and "INF"), "-INF", and "NaN" are positive infinity, negative infinity, and not-a-number, respectively. Any string which can exactly match "+INF" in a case-insensitive way would be mapped to positive infinite. Similarly, this case-insensitive rule is applied to "INF" and "NaN". When casting from numeric tensors to string tensors, plain floating-point representation (such as "314.15926") would be used. Converting non-numerical-literal string such as "Hello World!" is an undefined behavior. Cases of converting string representing floating-point arithmetic value, such as "2.718", to INT is an undefined behavior. Conversion from a numerical type to any numerical type is always allowed. User must be aware of precision loss and value change caused by range difference between two types. For example, a 64-bit float 3.1415926459 may be round to a 32-bit float 3.141592. Similarly, converting an integer 36 to Boolean may produce 1 because we truncate bits which can't be stored in the targeted type. In more detail, the conversion among numerical types should follow these rules if the destination type is not a float 8 type. * Casting from floating point to: * floating point: +/- infinity if OOR (out of range). * fixed point: undefined if OOR. * bool: +/- 0.0 to False; all else to True. * Casting from fixed point to: * floating point: +/- infinity if OOR. (+ infinity in the case of uint) * fixed point: when OOR, discard higher bits and reinterpret (with respect to two's complement representation for signed types). For example, 200 (int16) -> -56 (int8). * bool: zero to False; nonzero to True. * Casting from bool to: * floating point: `{1.0, 0.0}`. * fixed point: `{1, 0}`. * bool: no change. Float 8 type were introduced to speed up the training of deep models. By default the conversion of a float *x* obeys to the following rules. `[x]` means the value rounded to the target mantissa width. | x | E4M3FN | E4M3FNUZ | E5M2 | E5M2FNUZ | |------|----|----|----|----| | 0 | 0 | 0 | 0 | 0 | |-0 | -0 | 0 | -0 | 0 | | NaN | NaN | NaN | NaN | NaN | | +/- Inf | +/- FLT_MAX | NaN | FLT_MAX | NaN | | [x] > FLT_MAX | FLT_MAX | FLT_MAX | FLT_MAX | FLT_MAX | | [x] < -FLT_MAX | -FLT_MAX | -FLT_MAX | -FLT_MAX | -FLT_MAX | | else | RNE | RNE | RNE | RNE | The behavior changes if the parameter 'saturate' is set to False. The rules then become: | x | E4M3FN | E4M3FNUZ | E5M2 | E5M2FNUZ | |------|----|----|----|----| | 0 | 0 | 0 | 0 | 0 | |-0 | -0 | 0 | -0 | 0 | | NaN | NaN | NaN | NaN | NaN | | +/- Inf | NaN | NaN | +/- Inf | NaN | | [x] > FLT_MAX | NaN | NaN | Inf | NaN | | [x] < -FLT_MAX | NaN | NaN | -Inf | NaN | | else | RNE | RNE | RNE | RNE | The operator casts the elements of a given input tensor (the first input) to the same data type as the elements of the second input tensor. See documentation of the Cast operator for further details. Reshape the input tensor similar to numpy.reshape. First input is the data tensor, second input is a shape tensor which specifies the output shape. It outputs the reshaped tensor. At most one dimension of the new shape can be -1. In this case, the value is inferred from the size of the tensor and the remaining dimensions. A dimension could also be 0, in which case the actual dimension value is unchanged (i.e. taken from the input tensor). If 'allowzero' is set, and the new shape includes 0, the dimension will be set explicitly to zero (i.e. not taken from input tensor). Shape (second input) could be an empty shape, which means converting to a scalar. The input tensor's shape and the output tensor's shape are required to have the same number of elements. If the attribute 'allowzero' is set, it is invalid for the specified shape to contain both a zero value and -1, as the value of the dimension corresponding to -1 cannot be determined uniquely. Selects slices from an input tensor along a given axis where condition evaluates to True for each axis index. In case axis is not provided, input is flattened before elements are selected. Compress behaves like numpy.compress: https://docs.scipy.org/doc/numpy/reference/generated/numpy.compress.html Produces a one-hot tensor based on inputs. The locations represented by the index values in the 'indices' input tensor will have 'on_value' and the other locations will have 'off_value' in the output tensor, where 'on_value' and 'off_value' are specified as part of required input argument 'values', which is a two-element tensor of format [off_value, on_value]. The rank of the output tensor will be one greater than the rank of the input tensor. The additional dimension is for one-hot representation. The additional dimension will be inserted at the position specified by 'axis'. If 'axis' is not specified then then additional dimension will be inserted as the innermost dimension, i.e. axis=-1. The size of the additional dimension is specified by required scalar input 'depth'. The type of the output tensor is the same as the type of the 'values' input. Any entries in the 'indices' input tensor with values outside the range [-depth, depth-1] will result in one-hot representation with all 'off_value' values in the output tensor. when axis = 0: output[input[i, j, k], i, j, k] = 1 for all i, j, k and 0 otherwise. when axis = -1: output[i, j, k, input[i, j, k]] = 1 for all i, j, k and 0 otherwise. Takes a tensor as input and outputs an 1D int64 tensor containing the shape of the input tensor. Optional attributes start and end can be used to compute a slice of the input tensor's shape. If start axis is omitted, the slice starts from axis 0. The end axis, if specified, is exclusive (and the returned value will not include the size of that axis). If the end axis is omitted, the axes upto the last one will be included. Negative axes indicate counting back from the last axis. Note that axes will be clamped to the range [0, r-1], where r is the rank of the input tensor if they are out-of-range (after adding r in the case of negative axis). Thus, specifying any end value > r is equivalent to specifying an end value of r, and specifying any start value < -r is equivalent to specifying a start value of 0. Examples: ``` Input tensor with shape: [2, 3, 4] No attributes specified. Output: [2, 3, 4] ``` ``` Input tensor with shape: [2, 3, 4] start: -1 Output: [4] ``` ``` Input tensor with shape: [2, 3, 4] end: -1 Output: [2, 3] ``` ``` Input tensor with shape: [2, 3, 4] start: 1 end: 2 Output: [3] ``` Return elements, either from X or Y, depending on condition. Where behaves like [numpy.where](https://docs.scipy.org/doc/numpy/reference/generated/numpy.where.html) with three parameters. Takes a tensor as input and outputs a int64 scalar that equals to the total number of elements of the input tensor. Reverse batch of sequences having different lengths specified by `sequence_lens`. For each slice i iterating on batch axis, the operator reverses the first sequence_lens[i] elements on time axis, and copies elements whose index's beyond sequence_lens[i] to the output. So the output slice i contains reversed sequences on the first sequence_lens[i] elements, then have original values copied for the other elements. Example 1: input = [[0.0, 4.0, 8.0, 12.0], [1.0, 5.0, 9.0, 13.0], [2.0, 6.0, 10.0, 14.0], [3.0, 7.0, 11.0, 15.0]] sequence_lens = [4, 3, 2, 1] time_axis = 0 batch_axis = 1 output = [[3.0, 6.0, 9.0, 12.0], [2.0, 5.0, 8.0, 13.0], [1.0, 4.0, 10.0, 14.0], [0.0, 7.0, 11.0, 15.0]] Example 2: input = [[0.0, 1.0, 2.0, 3.0 ], [4.0, 5.0, 6.0, 7.0 ], [8.0, 9.0, 10.0, 11.0], [12.0, 13.0, 14.0, 15.0]] sequence_lens = [1, 2, 3, 4] time_axis = 1 batch_axis = 0 output = [[0.0, 1.0, 2.0, 3.0 ], [5.0, 4.0, 6.0, 7.0 ], [10.0, 9.0, 8.0, 11.0], [15.0, 14.0, 13.0, 12.0]] Find the unique elements of a tensor. When an optional attribute 'axis' is provided, unique subtensors sliced along the 'axis' are returned. Otherwise the input tensor is flattened and unique values of the flattened tensor are returned. This operator returns the unique values or sliced unique subtensors of the input tensor and three optional outputs. The first output tensor 'Y' contains all unique values or subtensors of the input. The second optional output tensor 'indices' contains indices of 'Y' elements' first occurrence in 'X'. The third optional output tensor 'inverse_indices' contains, for elements of 'X', its corresponding indices in 'Y'. The fourth optional output tensor 'counts' contains the count of each element of 'Y' in the input. Outputs are either sorted in ascending order or optionally in the order of the first occurrence of the values in the input. https://docs.scipy.org/doc/numpy/reference/generated/numpy.unique.html Example 1: ``` input_X = [2, 1, 1, 3, 4, 3] attribute_sorted = 0 attribute_axis = None output_Y = [2, 1, 3, 4] output_indices = [0, 1, 3, 4] output_inverse_indices = [0, 1, 1, 2, 3, 2] output_counts = [1, 2, 2, 1] ``` Example 2: ``` input_X = [[1, 3], [2, 3]] attribute_sorted = 1 attribute_axis = None output_Y = [1, 2, 3] output_indices = [0, 2, 1] output_inverse_indices = [0, 2, 1, 2] output_counts = [1, 1, 2] ``` Example 3: ``` input_X = [[1, 0, 0], [1, 0, 0], [2, 3, 4]] attribute_sorted = 1 attribute_axis = 0 output_Y = [[1, 0, 0], [2, 3, 4]] output_indices = [0, 2] output_inverse_indices = [0, 0, 1] output_counts = [2, 1] ``` Example 4: ``` input_x = [[[1., 1.], [0., 1.], [2., 1.], [0., 1.]], [[1., 1.], [0., 1.], [2., 1.], [0., 1.]]] attribute_sorted = 1 attribute_axis = 1 ``` intermediate data are presented below for better understanding: there are 4 subtensors sliced along axis 1 of input_x (shape = (2, 4, 2)): ``` A: [[1, 1], [1, 1]], [[0, 1], [0, 1]], [[2, 1], [2, 1]], [[0, 1], [0, 1]]. ``` there are 3 unique subtensors: ``` [[1, 1], [1, 1]], [[0, 1], [0, 1]], [[2, 1], [2, 1]]. ``` sorted unique subtensors: ``` B: [[0, 1], [0, 1]], [[1, 1], [1, 1]], [[2, 1], [2, 1]]. ``` output_Y is constructed from B: ``` [[[0. 1.], [1. 1.], [2. 1.]], [[0. 1.], [1. 1.], [2. 1.]]] ``` output_indices is to map from B to A: ``` [1, 0, 2] ``` output_inverse_indices is to map from A to B: ``` [1, 0, 2, 0] ``` output_counts: ``` [2, 1, 1] ``` Given `data` tensor of rank `r` >= 1, `indices` tensor of rank `q` >= 1, and `batch_dims` integer `b`, this operator gathers slices of `data` into an output tensor of rank `q + r - indices_shape[-1] - 1 - b`. `indices` is an q-dimensional integer tensor, best thought of as a `(q-1)`-dimensional tensor of index-tuples into `data`, where each element defines a slice of `data` `batch_dims` (denoted as `b`) is an integer indicating the number of batch dimensions, i.e the leading `b` number of dimensions of `data` tensor and `indices` are representing the batches, and the gather starts from the `b+1` dimension. Some salient points about the inputs' rank and shape: 1) r >= 1 and q >= 1 are to be honored. There is no dependency condition to be met between ranks `r` and `q` 2) The first `b` dimensions of the shape of `indices` tensor and `data` tensor must be equal. 3) b < min(q, r) is to be honored. 4) The `indices_shape[-1]` should have a value between 1 (inclusive) and rank `r-b` (inclusive) 5) All values in `indices` are expected to be within bounds [-s, s-1] along axis of size `s` (i.e.) `-data_shape[i] <= indices[...,i] <= data_shape[i] - 1`. It is an error if any of the index values are out of bounds. The output is computed as follows: The output tensor is obtained by mapping each index-tuple in the `indices` tensor to the corresponding slice of the input `data`. 1) If `indices_shape[-1] > r-b` => error condition 2) If `indices_shape[-1] == r-b`, since the rank of `indices` is `q`, `indices` can be thought of as `N` `(q-b-1)`-dimensional tensors containing 1-D tensors of dimension `r-b`, where `N` is an integer equals to the product of 1 and all the elements in the batch dimensions of the indices_shape. Let us think of each such `r-b` ranked tensor as `indices_slice`. Each *scalar value* corresponding to `data[0:b-1,indices_slice]` is filled into the corresponding location of the `(q-b-1)`-dimensional tensor to form the `output` tensor (Example 1 below) 3) If `indices_shape[-1] < r-b`, since the rank of `indices` is `q`, `indices` can be thought of as `N` `(q-b-1)`-dimensional tensor containing 1-D tensors of dimension `< r-b`. Let us think of each such tensors as `indices_slice`. Each *tensor slice* corresponding to `data[0:b-1, indices_slice , :]` is filled into the corresponding location of the `(q-b-1)`-dimensional tensor to form the `output` tensor (Examples 2, 3, 4 and 5 below) This operator is the inverse of `ScatterND`. **Example 1** ``` batch_dims = 0 data = [[0,1],[2,3]] # data_shape = [2, 2] indices = [[0,0],[1,1]] # indices_shape = [2, 2] output = [0,3] # output_shape = [2] ``` **Example 2** ``` batch_dims = 0 data = [[0,1],[2,3]] # data_shape = [2, 2] indices = [[1],[0]] # indices_shape = [2, 1] output = [[2,3],[0,1]] # output_shape = [2, 2] ``` **Example 3** ``` batch_dims = 0 data = [[[0,1],[2,3]],[[4,5],[6,7]]] # data_shape = [2, 2, 2] indices = [[0,1],[1,0]] # indices_shape = [2, 2] output = [[2,3],[4,5]] # output_shape = [2, 2] ``` **Example 4** ``` batch_dims = 0 data = [[[0,1],[2,3]],[[4,5],[6,7]]] # data_shape = [2, 2, 2] indices = [[[0,1]],[[1,0]]] # indices_shape = [2, 1, 2] output = [[[2,3]],[[4,5]]] # output_shape = [2, 1, 2] ``` **Example 5** ``` batch_dims = 1 data = [[[0,1],[2,3]],[[4,5],[6,7]]] # data_shape = [2, 2, 2] indices = [[1],[0]] # indices_shape = [2, 1] output = [[2,3],[4,5]] # output_shape = [2, 2] ``` Given a tensor containing the data to be padded (`data`), a tensor containing the number of start and end pad values for axis (`pads`), (optionally) a `mode`, and (optionally) `constant_value`, a padded tensor (`output`) is generated. The three supported `modes` are (similar to corresponding modes supported by `numpy.pad`): 1) `constant`(default) - pads with a given constant value as specified by `constant_value` (which defaults to 0, empty string, or False) 2) `reflect` - pads with the reflection of the vector mirrored on the first and last values of the vector along each axis 3) `edge` - pads with the edge values of array 4) `wrap` - wrap-around padding as if the data tensor forms a torus Example 1 (`constant` mode): Insert 0 pads to the beginning of the second dimension. ``` data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] mode = 'constant' constant_value = 0.0 output = [ [0.0, 0.0, 1.0, 1.2], [0.0, 0.0, 2.3, 3.4], [0.0, 0.0, 4.5, 5.7], ] ``` Example 2 (`reflect` mode): ``` data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] mode = 'reflect' output = [ [1.0, 1.2, 1.0, 1.2], [2.3, 3.4, 2.3, 3.4], [4.5, 5.7, 4.5, 5.7], ] ``` Example 3 (`edge` mode): ``` data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] mode = 'edge' output = [ [1.0, 1.0, 1.0, 1.2], [2.3, 2.3, 2.3, 3.4], [4.5, 4.5, 4.5, 5.7], ] ``` Example 4 (`wrap` mode): ``` data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [2, 1, 1, 1] mode = 'wrap' output = [ [3.4, 2.3, 3.4, 2.3], [5.7, 4.5, 5.7, 4.5], [1.2, 1.0, 1.2, 1.0], [3.4, 2.3, 3.4, 2.3], [5.7, 4.5, 5.7, 4.5], [1.2, 1.0, 1.2, 1.0], ] ``` Split a tensor into a list of tensors, along the specified 'axis'. Either input 'split' or the attribute 'num_outputs' should be specified, but not both. If the attribute 'num_outputs' is specified, then the tensor is split into equal sized parts. If the tensor is not evenly splittable into `num_outputs`, the last chunk will be smaller. If the input 'split' is specified, it indicates the sizes of each output in the split. Given a 2-D matrix or batches of 2-D matrices, returns the upper or lower triangular part of the tensor(s). The attribute "upper" determines whether the upper or lower part is retained. If set to true, the upper triangular matrix is retained. Lower triangular matrix is retained otherwise. Default value for the "upper" attribute is true. Trilu takes one input tensor of shape [*, N, M], where * is zero or more batch dimensions. The upper triangular part consists of the elements on and above the given diagonal (k). The lower triangular part consists of elements on and below the diagonal. All other elements in the matrix are set to zero. If k = 0, the triangular part on and above/below the main diagonal is retained. If upper is set to true, a positive k retains the upper triangular matrix excluding the main diagonal and (k-1) diagonals above it. A negative k value retains the main diagonal and |k| diagonals below it. If upper is set to false, a positive k retains the lower triangular matrix including the main diagonal and k diagonals above it. A negative k value excludes the main diagonal and (|k|-1) diagonals below it. Center crop or pad an input to given dimensions. The crop/pad dimensions can be specified for a subset of the `axes`. Non-specified dimensions will not be cropped or padded. If the input dimensions are bigger than the crop shape, a centered cropping window is extracted from the input. If the input dimensions are smaller than the crop shape, the input is padded on each side equally, so that the input is centered in the output. Produces a slice of the input tensor along multiple axes. Similar to numpy: https://numpy.org/doc/stable/user/basics.indexing.html?highlight=slice#slicing-and-striding Slice uses the `starts`, `ends`, `axes` and `steps` inputs to select a sub-tensor of its input `data` tensor. An effective `starts[i]`, `ends[i]`, and `steps[i]` must be computed for each `i` in `[0, ... r-1]` where `r = rank(input)` as follows: If `axes` are omitted, they are set to `[0, ..., r-1]`. If `steps` are omitted, they are set to `[1, ..., 1]` of length `len(starts)` The effective values are initialized as `start[i] = 0`, `ends[i] = dims[i]` where `dims` are the dimensions of `input` and `steps[i] = 1`. All negative elements of `axes` are made non-negative by adding `r` to them, where `r =rank(input)`. All negative values in `starts[i]` and `ends[i]` have `dims[axes[i]]` added to them, where `dims` are the dimensions of `input`. Then `start[axes[i]]` is the adjusted `starts[i]` is clamped into the range `[0, dims[axes[i]]]` for positive stepping and `[0, dims[axes[i]]-1]` for negative stepping. The clamping for the adjusted `ends[i]` depends on the sign of `steps[i]` and must accommodate copying 0 through `dims[axes[i]]` elements, so for positive stepping `ends[axes[i]]` is clamped to `[0, dims[axes[i]]]`, while for negative stepping it is clamped to `[-1, dims[axes[i]]-1]`. Finally, `steps[axes[i]] = steps[i]`. For slicing to the end of a dimension with unknown size, it is recommended to pass in `INT_MAX` when slicing forward and 'INT_MIN' when slicing backward. Example 1: ``` data = [ [1, 2, 3, 4], [5, 6, 7, 8], ] axes = [0, 1] starts = [1, 0] ends = [2, 3] steps = [1, 2] result = [ [5, 7], ] ``` Example 2: ``` data = [ [1, 2, 3, 4], [5, 6, 7, 8], ] starts = [0, 1] ends = [-1, 1000] result = [ [2, 3, 4], ] ``` ??@-@ Transpose the input tensor similar to numpy.transpose. For example, when perm=(1, 0, 2), given an input tensor of shape (1, 2, 3), the output shape will be (2, 1, 3). This operator is deprecated. Please use ScatterElements, which provides the same functionality. Scatter takes three inputs `data`, `updates`, and `indices` of the same rank r >= 1 and an optional attribute axis that identifies an axis of `data` (by default, the outer-most axis, that is axis 0). The output of the operation is produced by creating a copy of the input `data`, and then updating its value to values specified by `updates` at specific index positions specified by `indices`. Its output shape is the same as the shape of `data`. For each entry in `updates`, the target index in `data` is obtained by combining the corresponding entry in `indices` with the index of the entry itself: the index-value for dimension = axis is obtained from the value of the corresponding entry in `indices` and the index-value for dimension != axis is obtained from the index of the entry itself. For instance, in a 2-D tensor case, the update corresponding to the [i][j] entry is performed as below: ``` output[indices[i][j]][j] = updates[i][j] if axis = 0, output[i][indices[i][j]] = updates[i][j] if axis = 1, ``` This operator is the inverse of GatherElements. It is similar to Torch's Scatter operation. Example 1: ``` data = [ [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], ] indices = [ [1, 0, 2], [0, 2, 1], ] updates = [ [1.0, 1.1, 1.2], [2.0, 2.1, 2.2], ] output = [ [2.0, 1.1, 0.0] [1.0, 0.0, 2.2] [0.0, 2.1, 1.2] ] ``` Example 2: ``` data = [[1.0, 2.0, 3.0, 4.0, 5.0]] indices = [[1, 3]] updates = [[1.1, 2.1]] axis = 1 output = [[1.0, 1.1, 3.0, 2.1, 5.0]] ``` ScatterND takes three inputs `data` tensor of rank r >= 1, `indices` tensor of rank q >= 1, and `updates` tensor of rank q + r - indices.shape[-1] - 1. The output of the operation is produced by creating a copy of the input `data`, and then updating its value to values specified by `updates` at specific index positions specified by `indices`. Its output shape is the same as the shape of `data`. `indices` is an integer tensor. Let k denote indices.shape[-1], the last dimension in the shape of `indices`. `indices` is treated as a (q-1)-dimensional tensor of k-tuples, where each k-tuple is a partial-index into `data`. Hence, k can be a value at most the rank of `data`. When k equals rank(data), each update entry specifies an update to a single element of the tensor. When k is less than rank(data) each update entry specifies an update to a slice of the tensor. Index values are allowed to be negative, as per the usual convention for counting backwards from the end, but are expected in the valid range. `updates` is treated as a (q-1)-dimensional tensor of replacement-slice-values. Thus, the first (q-1) dimensions of updates.shape must match the first (q-1) dimensions of indices.shape. The remaining dimensions of `updates` correspond to the dimensions of the replacement-slice-values. Each replacement-slice-value is a (r-k) dimensional tensor, corresponding to the trailing (r-k) dimensions of `data`. Thus, the shape of `updates` must equal indices.shape[0:q-1] ++ data.shape[k:r-1], where ++ denotes the concatenation of shapes. The `output` is calculated via the following equation: ``` output = np.copy(data) update_indices = indices.shape[:-1] for idx in np.ndindex(update_indices): output[indices[idx]] = updates[idx] ``` The order of iteration in the above loop is not specified. In particular, indices should not have duplicate entries: that is, if idx1 != idx2, then indices[idx1] != indices[idx2]. This ensures that the output value does not depend on the iteration order. `reduction` allows specification of an optional reduction operation, which is applied to all values in `updates` tensor into `output` at the specified `indices`. In cases where `reduction` is set to "none", indices should not have duplicate entries: that is, if idx1 != idx2, then indices[idx1] != indices[idx2]. This ensures that the output value does not depend on the iteration order. When `reduction` is set to some reduction function `f`, `output` is calculated as follows: ``` output = np.copy(data) update_indices = indices.shape[:-1] for idx in np.ndindex(update_indices): output[indices[idx]] = f(output[indices[idx]], updates[idx]) ``` where the `f` is `+`, `*`, `max` or `min` as specified. This operator is the inverse of GatherND. (Opset 18 change): Adds max/min to the set of allowed reduction ops. Example 1: ``` data = [1, 2, 3, 4, 5, 6, 7, 8] indices = [[4], [3], [1], [7]] updates = [9, 10, 11, 12] output = [1, 11, 3, 10, 9, 6, 7, 12] ``` Example 2: ``` data = [[[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]], [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]], [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]], [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]] indices = [[0], [2]] updates = [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]], [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]]] output = [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]], [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]], [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]], [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]] ``` ScatterElements takes three inputs `data`, `updates`, and `indices` of the same rank r >= 1 and an optional attribute axis that identifies an axis of `data` (by default, the outer-most axis, that is axis 0). The output of the operation is produced by creating a copy of the input `data`, and then updating its value to values specified by `updates` at specific index positions specified by `indices`. Its output shape is the same as the shape of `data`. For each entry in `updates`, the target index in `data` is obtained by combining the corresponding entry in `indices` with the index of the entry itself: the index-value for dimension = axis is obtained from the value of the corresponding entry in `indices` and the index-value for dimension != axis is obtained from the index of the entry itself. `reduction` allows specification of an optional reduction operation, which is applied to all values in `updates` tensor into `output` at the specified `indices`. In cases where `reduction` is set to "none", indices should not have duplicate entries: that is, if idx1 != idx2, then indices[idx1] != indices[idx2]. For instance, in a 2-D tensor case, the update corresponding to the [i][j] entry is performed as below: ``` output[indices[i][j]][j] = updates[i][j] if axis = 0, output[i][indices[i][j]] = updates[i][j] if axis = 1, ``` When `reduction` is set to some reduction function `f`, the update corresponding to the [i][j] entry is performed as below: ``` output[indices[i][j]][j] = f(output[indices[i][j]][j], updates[i][j]) if axis = 0, output[i][indices[i][j]] = f(output[i][indices[i][j]], updates[i][j]) if axis = 1, ``` where the `f` is `+`, `*`, `max` or `min` as specified. This operator is the inverse of GatherElements. It is similar to Torch's Scatter operation. (Opset 18 change): Adds max/min to the set of allowed reduction ops. Example 1: ``` data = [ [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], ] indices = [ [1, 0, 2], [0, 2, 1], ] updates = [ [1.0, 1.1, 1.2], [2.0, 2.1, 2.2], ] output = [ [2.0, 1.1, 0.0] [1.0, 0.0, 2.2] [0.0, 2.1, 1.2] ] ``` Example 2: ``` data = [[1.0, 2.0, 3.0, 4.0, 5.0]] indices = [[1, 3]] updates = [[1.1, 2.1]] axis = 1 output = [[1.0, 1.1, 3.0, 2.1, 5.0]] ``` Given `data` tensor of rank r >= 1, and `indices` tensor of rank q, gather entries of the axis dimension of `data` (by default outer-most one as axis=0) indexed by `indices`, and concatenates them in an output tensor of rank q + (r - 1). If `axis = 0`, let `k = indices[i_{0}, ..., i_{q-1}]` then `output[i_{0}, ..., i_{q-1}, j_{0}, ..., j_{r-2}] = input[k , j_{0}, ..., j_{r-2}]`: ``` data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] indices = [ [0, 1], [1, 2], ] output = [ [ [1.0, 1.2], [2.3, 3.4], ], [ [2.3, 3.4], [4.5, 5.7], ], ] ``` If `axis = 1`, let `k = indices[i_{0}, ..., i_{q-1}]` then `output[j_{0}, i_{0}, ..., i_{q-1}, j_{1}, ..., j_{r-2}] = input[j_{0}, k, j_{1}, ..., j_{r-2}]`: ``` data = [ [1.0, 1.2, 1.9], [2.3, 3.4, 3.9], [4.5, 5.7, 5.9], ] indices = [ [0, 2], ] axis = 1, output = [ [[1.0, 1.9]], [[2.3, 3.9]], [[4.5, 5.9]], ] ``` type: does not match type of output: Input: expected to have tensor or sparse tensor type in and Output indices must be in [-rank, rank-1].axis must be in [-rank, rank-1].optional(seq(tensor(uint8)))optional(seq(tensor(uint16)))optional(seq(tensor(uint32)))optional(seq(tensor(uint64)))optional(seq(tensor(int8)))optional(seq(tensor(int16)))optional(seq(tensor(int32)))optional(seq(tensor(int64)))optional(seq(tensor(float16)))optional(seq(tensor(float)))optional(seq(tensor(double)))optional(seq(tensor(string)))optional(seq(tensor(bool)))optional(seq(tensor(complex64)))optional(seq(tensor(complex128)))optional(tensor(uint8))optional(tensor(uint16))optional(tensor(uint32))optional(tensor(uint64))optional(tensor(int8))optional(tensor(int16))optional(tensor(int32))optional(tensor(int64))optional(tensor(float16))optional(tensor(float))optional(tensor(double))optional(tensor(string))optional(tensor(bool))optional(tensor(complex64))optional(tensor(complex128))This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).The data type to which the elements of the input tensor are cast. Strictly must be one of the types from DataType enum in TensorProtoThe parameter defines how the conversion behaves if an input value is out of range of the destination type. It only applies for float 8 conversion (float8e4m3fn, float8e4m3fnuz, float8e5m2, float8e5m2fnuz). It is true by default. All cases are fully described in two tables inserted in the operator description.Input tensor to be cast.Output tensor with the same shape as input with type specified by the 'to' argumentConstrain input types. Casting from complex is not supported.Constrain output types. Casting to complex is not supported.D:\a\_work\_temp\onnx\src\v1.17.0-7e28176e64.clean\onnx\defs\tensor\defs.cc, saturate: int = @saturate> (input)output = Cast = 0.Sum of the values must be equal to the dim value at 'axis' specified.One or more outputs forming list of tensors after splittingWhich axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1] where r = rank(input).Number of outputs to split parts of the tensor into. If the tensor is not evenly splittable the last chunk will be smaller.'step' cannot be 0 for SliceOnly supports `int32_t` or `int64_t` inputs for starts/ends/axes/stepsSlice op must have either three, four or five inputs.Incorrect or missing input value for starts and endsInput axes has incorrect lengthInput steps has incorrect lengthInput rank for starts and ends should be the same: (1-D tensor of ending indices (exclusive) of corresponding axis in `axes`1-D tensor of axes that `starts` and `ends` apply to. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data). Behavior is undefined if an axis is repeated.1-D tensor of slice step of corresponding axis in `axes`. Negative value means slicing backward. 'steps' cannot be 0. Defaults to 1s.stepsInvalid attribute perm {}, input shape = {Attribute perm for Transpose has repeated value: A list of integers. By default, reverse the dimensions, otherwise permute the axes according to the values given. Its length must be equal to the rank of the input.Transposed output.transposedWhich axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).Tensor of int32/int64 indices, of r >= 1 (same rank as input). All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.Tensor of rank r >=1 (same rank and shape as indices)updatesTensor of rank r >= 1 (same rank as input).Input and output types can be of any tensor type.Type of reduction to apply: none (default), add, mul, max, min. 'none': no reduction applied. 'add': reduction using the addition operation. 'mul': reduction using the addition operation. 'max': reduction using the maximum operation.'min': reduction using the minimum operation.Tensor of rank q + r - indices_shape[-1] - 1.Type of reduction to apply: none (default), add, mul, max, min. 'none': no reduction applied. 'add': reduction using the addition operation. 'mul': reduction using the multiplication operation.'max': reduction using the maximum operation.'min': reduction using the minimum operation.axis must be in [-r, r-1]Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).Tensor of rank q + (r - 1).Tensor of int32/int64 indices, with the same rank r as the input. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.Tensor of the same shape as indices.Tensors with at least max(dims) dimensions.List of integers indicating the dimensions to squeeze. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).Reshaped tensor with same data as input.squeezedConstrain input and output types to all tensor types up to IRv10.Original tensorList of integers indicating the dimensions to be inserted. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(expanded).expandedBlocksize must be positiveInput tensor must be 4-dimensionalBlocks of [blocksize, blocksize] are moved.Input tensor of [N,C,H,W], where N is the batch axis, C is the channel or depth, H is the height and W is the width.Output tensor of [N, C * blocksize * blocksize, H/blocksize, W/blocksize].DCR (default) for depth-column-row order re-arrangement. Use CRD for column-row-depth order.Output tensor of [N, C/(blocksize * blocksize), H * blocksize, W * blocksize].'Repeats' input must be 1D tensor of type int64'Repeats' input has incorrect number of values. The number of values in 'repeats' must be equal to the number of input dimensions.Input tensor of any shape.1D int64 tensor of the same length as input's dimension number, includes numbers of repeated copies along input's dimensions.repeatsOutput tensor of the same dimensions and type as tensor input. output_dim[i] = input_dim[i] * repeats[i]Constrain repeat's type to int64 tensors.Two interpolation modes: nearest (default), and linear (including bilinear, trilinear, etc)N-D tensorThe scale array along each dimension. It takes value greater than or equal to 1. The number of elements of 'scales' should be the same as the rank of input 'X'.N-D tensor after resizingConstrain input 'X' and output 'Y' to all tensor types.Three interpolation modes: "nearest" (default), "linear" and "cubic". The "linear" mode includes linear interpolation for 1D tensor and N-linear interpolation for N-D tensor (for example, bilinear interpolation for 2D tensor). The "cubic" mode includes cubic interpolation for 1D tensor and N-cubic interpolation for N-D tensor (for example, bicubic interpolation for 2D tensor).The coefficient 'a' used in cubic interpolation. Two common choice are -0.5 (in some cases of TensorFlow) and -0.75 (in PyTorch). Check out Equation (4) in https://ieeexplore.ieee.org/document/1163711 for the details. This attribute is valid only if mode is "cubic".If set to 1, the weight of sampling locations outside the tensor will be set to 0 and the weight will be renormalized so that their sum is 1.0. The default value is 0.Four modes: "round_prefer_floor" (default, as known as round half down), "round_prefer_ceil" (as known as round half up), "floor", "ceil". Only used by nearest interpolation. It indicates how to get "nearest" pixel in input tensor from x_original, so this attribute is valid only if "mode" is "nearest".When coordinate_transformation_mode is "tf_crop_and_resize" and x_original is outside the range [0, length_original - 1], this value is used as the corresponding output value. Default is 0.0f.If set to 1, "linear" and "cubic" interpolation modes will use an antialiasing filter when downscaling. Antialiasing is achieved by stretching the resampling filter by a factor max(1, 1 / scale), which means that when downsampling, more input pixels contribute to an output pixel.If provided, it specifies a subset of axes that 'roi', 'scales' and 'sizes' refer to. If not provided, all axes are assumed [0, 1, ..., r-1], where r = rank(data). Non-specified dimensions are interpreted as non-resizable. Negative value means counting dimensions from the back. Accepted range is [-r, r-1], where r = rank(data). Behavior is undefined if an axis is repeated.1-D tensor given as [start1, ..., startN, end1, ..., endN], where N is the rank of X or the length of axes, if provided. The RoIs' coordinates are normalized in the coordinate system of the input image. It only takes effect when coordinate_transformation_mode is "tf_crop_and_resize"roiThe scale array along each dimension. It takes value greater than 0. If it's less than 1, it's sampling down, otherwise, it's upsampling. The number of elements of 'scales' should be the same as the rank of input 'X' or the length of 'axes', if provided. One of 'scales' and 'sizes' MUST be specified and it is an error if both are specified. If 'sizes' is needed, the user can use an empty string as the name of 'scales' in this operator's input list.Target size of the output tensor. Its interpretation depends on the 'keep_aspect_ratio_policy' value.The number of elements of 'sizes' should be the same as the rank of input 'X', or the length of 'axes', if provided. Only one of 'scales' and 'sizes' can be specified. sizesConstrain roi type to float or double.Three interpolation modes: linear (default), nearest and cubic. The "linear" mode includes linear and N-linear interpolation modes depending on the number of spatial dimensions of the input tensor (i.e. linear for 1 spatial dimension, bilinear for 2 spatial dimensions, etc.). The "cubic" mode also includes N-cubic interpolation modes following the same rules. The "nearest" mode rounds to the nearest even index when the sampling point falls halfway between two indices.Support padding modes for outside grid values: `zeros`(default), `border`, `reflection`. zeros: use 0 for out-of-bound grid locations, border: use border values for out-of-bound grid locations, reflection: use values at locations reflected by the border for out-of-bound grid locations. If index 0 represents the margin pixel, the reflected value at index -1 will be the same as the value at index 1. For location far away from the border, it will keep being reflected until becoming in bound. If pixel location x = -3.5 reflects by border -1 and becomes x' = 1.5, then reflects by border 1 and becomes x'' = 0.5.If align_corners=1, the extrema (-1 and 1) are considered as referring to the center points of the input's corner pixels (voxels, etc.). If align_corners=0, they are instead considered as referring to the corner points of the input's corner pixels (voxels, etc.), making the sampling more resolution agnostic.Input tensor of rank r+2 that has shape (N, C, D1, D2, ..., Dr), where N is the batch size, C is the number of channels, D1, D2, ..., Dr are the spatial dimensions.Input offset of shape (N, D1_out, D2_out, ..., Dr_out, r), where D1_out, D2_out, ..., Dr_out are the spatial dimensions of the grid and output, and r is the number of spatial dimensions. Grid specifies the sampling locations normalized by the input spatial dimensions. Therefore, it should have most values in the range of [-1, 1]. If the grid has values outside the range of [-1, 1], the corresponding outputs will be handled as defined by padding_mode. Following computer vision convention, the coordinates in the length-r location vector are listed from the innermost tensor dimension to the outermost, the opposite of regular tensor indexing.gridOutput tensor of rank r+2 that has shape (N, C, D1_out, D2_out, ..., Dr_out) of the sampled values. For integer input types, intermediate values are computed as floating point and cast to integer at the end.Constrain input `X` and output `Y` types to all tensor types.Constrain grid types to float tensors.. It must be 4 for 2D or 5 for 5D.Length of input 'size' is if align_corners=1, consider -1 and 1 to refer to the centers of the corner pixels. if align_corners=0, consider -1 and 1 to refer to the outer edge the corner pixels.input batch of affine matrices with shape (N, 2, 3) for 2D or (N, 3, 4) for 3Dthetathe target output image size (N, C, H, W) for 2D or (N, C, D, H, W) for 3Doutput tensor of shape (N, H, W, 2) of 2D sample coordinates or (N, D, H, W, 3) of 3D sample coordinates.Constrain size's type to int64 tensors. { # naming one: 1, one_f: 1.0, one_1d: [1], one_f_1d: [1.0] one = Constant () two = Constant () zero = Constant () four = Constant () one_1d = Constant () zero_1d = Constant () minus_one = Constant () minus_one_f = CastLike (minus_one, theta) zero_f = CastLike (zero, theta) one_f = CastLike (one, theta) two_f = CastLike (two, theta) constant_align_corners = Constant () constant_align_corners_equal_zero = Equal (constant_align_corners, zero) size_ndim = Size (size) condition_is_2d = Equal (size_ndim, four) N, C, D, H, W = If (condition_is_2d) < then_branch = g1 () => (N_then, C_then, D_then, H_then, W_then) { N_then, C_then, H_then, W_then = Split (size) D_then = Identity (one_1d) }, else_branch = g2 () => (N_else, C_else, D_else, H_else, W_else) { N_else, C_else, D_else, H_else, W_else = Split (size) } > size_NCDHW = Concat (N, C, D, H, W) theta_3d = If (condition_is_2d) < then_branch = g3 () => (theta_then) { # theta: N by 2 by 3 => N by 3 by 4 # use of thetaN23 is a way to make shape inference happy when theta is N by 3 by 4. gather_idx_6 = Constant () shape_23 = Constant () gather_idx_23 = Reshape (gather_idx_6, shape_23) shape_N23 = Concat (N, shape_23) gather_idx_N23 = Expand (gather_idx_23, shape_N23) thetaN23 = GatherElements (theta, gather_idx_N23) # N by 2 by 3 => N by 3 by 2 r1, r2 = Split (thetaN23) # N by 1 by 3 r1_ = Squeeze (r1) # N by 3 r2_ = Squeeze (r2) r11, r12, t1 = Split (r1_) # N by 1 r21, r22, t2 = Split (r2_) r11_shape = Shape (r21) float_zero_1d_ = ConstantOfShape (r11_shape) # N by 1 float_zero_1d = CastLike (float_zero_1d_, theta) float_one_1d = Add (float_zero_1d, one_f) # N by 1 R1 = Concat (r11, r12, float_zero_1d, t1) # N by 4 R2 = Concat (r21, r22, float_zero_1d, t2) R3 = Concat (float_zero_1d, float_zero_1d, float_one_1d, float_zero_1d) R1_ = Unsqueeze (R1, one_1d) # N by 1 by 4 R2_ = Unsqueeze (R2, one_1d) R3_ = Unsqueeze (R3, one_1d) theta_then = Concat (R1_, R2_, R3_) # N by 3 by 4 # theta_then = Identity (theta) }, else_branch = g4 () => (theta_else) { theta_else = Identity (theta) } > two_1d = Constant () three_1d = Constant () five_1d = Constant () constant_D_H_W_shape = Slice (size_NCDHW, two_1d, five_1d) # [N, C, D, H, W] => [D, H, W] zeros_D_H_W_ = ConstantOfShape (constant_D_H_W_shape) zeros_D_H_W = CastLike (zeros_D_H_W_, theta) ones_D_H_W = Add (zeros_D_H_W, one_f) D_float = CastLike (D, zero_f) H_float = CastLike (H, zero_f) W_float = CastLike (W, zero_f) start_d, step_d, start_h, step_h, start_w, step_w = If (constant_align_corners_equal_zero) < then_branch = h1 () => (start_d_then, step_d_then, start_h_then, step_h_then, start_w_then, step_w_then) { # => (float, float, float, float, float, float) step_d_then = Div (two_f, D_float) step_h_then = Div (two_f, H_float) step_w_then = Div (two_f, W_float) step_d_half = Div (step_d_then, two_f) start_d_then = Add (minus_one_f, step_d_half) step_h_half = Div (step_h_then, two_f) start_h_then = Add (minus_one_f, step_h_half) step_w_half = Div (step_w_then, two_f) start_w_then = Add (minus_one_f, step_w_half) }, else_branch = h2 () => (start_d_else, step_d_else, start_h_else, step_h_else, start_w_else, step_w_else) { # => (float, float, float, float, float, float) D_float_nimus_one = Sub (D_float, one_f) H_float_nimus_one = Sub (H_float, one_f) W_float_nimus_one = Sub (W_float, one_f) # avoid divide by 0 D_equals_one = Equal (D, one) step_d_else = If (D_equals_one) < then_branch = g5 () => (step_d_else_then) { step_d_else_then = Identity (zero_f) }, else_branch = g6 () => (step_d_else_else) { step_d_else_else = Div (two_f, D_float_nimus_one) } > step_h_else = Div (two_f, H_float_nimus_one) step_w_else = Div (two_f, W_float_nimus_one) start_d_else = Identity (minus_one_f) start_h_else = Identity (minus_one_f) start_w_else = Identity (minus_one_f) } > grid_w_steps_int = Range (zero, W, one) grid_w_steps_float = CastLike (grid_w_steps_int, step_w) grid_w_steps = Mul (grid_w_steps_float, step_w) grid_w_0 = Add (start_w, grid_w_steps) grid_h_steps_int = Range (zero, H, one) grid_h_steps_float = CastLike (grid_h_steps_int, step_h) grid_h_steps = Mul (grid_h_steps_float, step_h) grid_h_0 = Add (start_h, grid_h_steps) grid_d_steps_int = Range (zero, D, one) grid_d_steps_float = CastLike (grid_d_steps_int, step_d) grid_d_steps = Mul (grid_d_steps_float, step_d) grid_d_0 = Add (start_d, grid_d_steps) zeros_H_W_D = Transpose (zeros_D_H_W) grid_d_1 = Add (zeros_H_W_D, grid_d_0) grid_d = Transpose (grid_d_1) zeros_D_W_H = Transpose (zeros_D_H_W) grid_h_1 = Add (zeros_D_W_H, grid_h_0) grid_h = Transpose (grid_h_1) grid_w = Add (grid_w_0, zeros_D_H_W) grid_w_usqzed = Unsqueeze (grid_w, minus_one) grid_h_usqzed = Unsqueeze (grid_h, minus_one) grid_d_usqzed = Unsqueeze (grid_d, minus_one) ones_D_H_W_usqzed = Unsqueeze (ones_D_H_W, minus_one) original_grid = Concat (grid_w_usqzed, grid_h_usqzed, grid_d_usqzed, ones_D_H_W_usqzed) constant_shape_DHW_4 = Constant () original_grid_DHW_4 = Reshape (original_grid, constant_shape_DHW_4) original_grid_4_DHW_ = Transpose (original_grid_DHW_4) original_grid_4_DHW = CastLike (original_grid_4_DHW_, theta_3d) grid_N_3_DHW = MatMul (theta_3d, original_grid_4_DHW) grid_N_DHW_3 = Transpose (grid_N_3_DHW) N_D_H_W_3 = Concat (N, D, H, W, three_1d) grid_3d_else_ = Reshape (grid_N_DHW_3, N_D_H_W_3) grid_3d = CastLike (grid_3d_else_, theta_3d) # grid = Identity (grid_3d) grid = If (condition_is_2d) < then_branch = g1 () => (grid_then) { # [N, D=1, H, W, 3] => [N, H, W, 2] grid_squeezed = Squeeze (grid_3d, one_1d) # [N, H, W, 3] grid_then = Slice (grid_squeezed, zero_1d, two_1d, three_1d) # [N, H, W, 2] }, else_branch = g2 () => (grid_else) { grid_else = Identity (grid_3d) } > } Identity operatorTensor to copy input into.Constrain input and output types to all tensor, sequence, and optional types.Indices tensor must have rank >= 1'axis' must be in [-rank(indices), rank(indices)-1](Optional) Axis along which to take slices. If not specified, input is flattened before elements being selected. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).Rank 1 tensor of booleans to indicate which slices or data elements to be selected. Its length can be less than the input length along the axis or the flattened input size if axis is not specified. In such cases data slices or elements exceeding the condition length are discarded.Tensor of rank r if axis is specified. Otherwise output is a Tensor of rank 1.Constrain to boolean tensors.OneHot node must have three inputs.Input 'depth' must be a scalar or rank 1 tensor.Input 'depth' must have exactly one element.Input 'values' must be rank 1 tensor.Input 'values' must have exactly two elements.(Optional) Axis along which one-hot representation in added. Default: axis=-1. axis=-1 means that the additional dimension will be inserted as the innermost/last dimension in the output tensor. Negative value means counting dimensions from the back. Accepted range is [-r-1, r] where r = rank(indices).Input tensor containing indices. Any entries in the 'indices' input tensor with values outside the range [-depth, depth-1] will result in one-hot representation with all 'off_value' values in the output tensor.In case 'indices' is of non-integer type, the values will be casted to int64 before use.Scalar or Rank 1 tensor containing exactly one element, specifying the number of classes in one-hot tensor. This is also the size of the one-hot dimension (specified by 'axis' attribute) added on in the output tensor. The values in the 'indices' input tensor are expected to be in the range [-depth, depth-1]. In case 'depth' is of non-integer type, it will be casted to int64 before use.depthRank 1 tensor containing exactly two elements, in the format [off_value, on_value], where 'on_value' is the value used for filling locations specified in 'indices' input tensor, and 'off_value' is the value used for filling locations other than those specified in 'indices' input tensor. Tensor of rank one greater than input tensor 'indices', i.e. rank(output) = rank(indices) + 1. The data type for the elements of the output tensor is the same as the type of input 'values' is used.Constrain input to only numeric types.Constrain to any tensor type.Returns which elements of the input are NaN.Constrain input types to float tensors.Constrain output types to boolean tensors.Map infinity to true and other values to false.(Optional) Whether map positive infinity to true. Default to 1 so that positive infinity induces true. Set this attribute to 0 if positive infinity should be mapped to false.(Optional) Whether map negative infinity to true. Default to 1 so that negative infinity induces true. Set this attribute to 0 if negative infinity should be mapped to false.When True (nonzero), yield X, otherwise yield Yvalues selected at indices where condition is Truevalues selected at indices where condition is FalseTensor of shape equal to the broadcasted shape of condition, X, and Y.Constrain input and output types to all tensor types (including bfloat).Constrain to all tensor types.'input' must have rank >= 2'sequence_lens' must have rank of 1(Optional) Specify which axis is time axis. Must be one of 0 (default), or 1.(Optional) Specify which axis is batch axis. Must be one of 1 (default), or 0.Tensor of rank r >= 2.Tensor specifying lengths of the sequences in a batch. It has shape `[batch_size]`.Tensor with same shape of input.Invalid value for attribute axis(Optional) Whether to sort the unique elements in ascending order before returning as output. Must be one of 0, or 1 (default).(Optional) The dimension to apply unique. If not specified, the unique elements of the flattened input are returned. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).A N-D input tensor that is to be processed.A tensor of the same type as 'X' containing all the unique values or subtensors sliced along a provided 'axis' in 'X', either sorted or maintained in the same order they occur in input 'X'A 1-D INT64 tensor containing indices of 'Y' elements' first occurrence in 'X'. When 'axis' is provided, it contains indices to subtensors in input 'X' on the 'axis'. When 'axis' is not provided, it contains indices to values in the flattened input tensor. A 1-D INT64 tensor containing, for elements of 'X', its corresponding indices in 'Y'. When 'axis' is provided, it contains indices to subtensors in output 'Y' on the 'axis'. When 'axis' is not provided, it contains indices to values in output 'Y'. inverse_indicesA 1-D INT64 tensor containing the count of each element of 'Y' in input 'X'Both `data` and `indices` input tensors in GatherND op need to have rank larger than 0.Last dimension of `indices` input tensor in GatherND op must not be larger than the rank of `data` tensorThe number of batch dimensions. The gather of indexing starts from dimension of data[batch_dims:]Tensor of rank q >= 1. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.Supported modes: `constant`(default), `reflect`, `edge`, `wrap`A 0-D tensor containing a single value corresponding to the number diagonals above or below the main diagonal to exclude or include. Default value is 0 if it's not specified.CenterCropPad op must have 2 inputs.`shape` only supports `int32_t` or `int64_t` inputs) does not match the number of axes (Number of elements of input 'shape' (axes_input = Constant ()x_shape_alldims = Shape (input_data)x_shape = Gather (x_shape_alldims, axes_input)x_shape = Shape (input_data)padded_sh = Max(x_shape, shape)pad_amount = Sub(padded_sh, x_shape)pad_amount_left = Div(pad_amount, k2)pad_amount_right = Sub(pad_amount, pad_amount_left)pads = Concat (pad_amount_left, pad_amount_right)padded_input = Pad (input_data, pads, , axes_input)padded_input = Pad (input_data, pads)x_shape_alldims2 = Shape (padded_input)x_shape2 = Gather (x_shape_alldims2, axes_input)x_shape2 = Shape (padded_input)sh_diff = Sub (x_shape2, shape)start_dims = Div (sh_diff, k2)end_dims = Add (start_dims, shape)output_data = Slice (padded_input, start_dims, end_dims, axes_input)output_data = Slice (padded_input, start_dims, end_dims)Input to extract the centered crop from.input_data1-D tensor representing the cropping window dimensions.output_dataIf provided, it specifies a subset of axes that 'shape' refer to. If not provided, all axes are assumed [0, 1, ..., r-1], where r = rank(data). Negative value means counting dimensions from the back. Accepted range is [-r, r-1], where r = rank(data). Behavior is undefined if an axis is repeated.CenterCropPad. Expected range [Unexpected axis value: is referred to more than once.Axis |00`C#``C#pC#`C#`C#@C#Dpp C#@C#``C#( C#t0C#``C# PPC#XPC#@C#PPтC#<ppтC#  тPC#@C# 0C#l  PC#00`C#C#PC#C#PC#400`C#@@pC#0C#0C#d`C# C#@@pC#H@@pC#C#PPC#,@C#xC#C# C#\C#  PC#pp0C#@pC# Computes an one-layer simple RNN. This operator is usually supported via some custom implementation such as CuDNN. Notations: * `X` - input tensor * `i` - input gate * `t` - time step (t-1 means previous time step) * `Wi` - W parameter weight matrix for input gate * `Ri` - R recurrence weight matrix for input gate * `Wbi` - W parameter bias vector for input gate * `Rbi` - R parameter bias vector for input gate * `WBi` - W parameter weight matrix for backward input gate * `RBi` - R recurrence weight matrix for backward input gate * `WBbi` - WR bias vectors for backward input gate * `RBbi` - RR bias vectors for backward input gate * `H` - Hidden state * `num_directions` - 2 if direction == bidirectional else 1 Activation functions: * Relu(x) - max(0, x) * Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x}) * Sigmoid(x) - 1/(1 + e^{-x}) NOTE: Below are optional * Affine(x) - alpha*x + beta * LeakyRelu(x) - x if x >= 0 else alpha * x * ThresholdedRelu(x) - x if x >= alpha else 0 * ScaledTanh(x) - alpha*Tanh(beta*x) * HardSigmoid(x) - min(max(alpha*x + beta, 0), 1) * Elu(x) - x if x >= 0 else alpha*(e^x - 1) * Softsign(x) - x/(1 + |x|) * Softplus(x) - log(1 + e^x) Equations (Default: f=Tanh): * Ht = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Wbi + Rbi) Computes an one-layer GRU. This operator is usually supported via some custom implementation such as CuDNN. Notations: * `X` - input tensor * `z` - update gate * `r` - reset gate * `h` - hidden gate * `t` - time step (t-1 means previous time step) * `W[zrh]` - W parameter weight matrix for update, reset, and hidden gates * `R[zrh]` - R recurrence weight matrix for update, reset, and hidden gates * `Wb[zrh]` - W bias vectors for update, reset, and hidden gates * `Rb[zrh]` - R bias vectors for update, reset, and hidden gates * `WB[zrh]` - W parameter weight matrix for backward update, reset, and hidden gates * `RB[zrh]` - R recurrence weight matrix for backward update, reset, and hidden gates * `WBb[zrh]` - W bias vectors for backward update, reset, and hidden gates * `RBb[zrh]` - R bias vectors for backward update, reset, and hidden gates * `H` - Hidden state * `num_directions` - 2 if direction == bidirectional else 1 Activation functions: * Relu(x) - max(0, x) * Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x}) * Sigmoid(x) - 1/(1 + e^{-x}) NOTE: Below are optional * Affine(x) - alpha * x + beta * LeakyRelu(x) - x if x >= 0 else alpha * x * ThresholdedRelu(x) - x if x >= alpha else 0 * ScaledTanh(x) - alpha * Tanh(beta * x) * HardSigmoid(x) - min(max(alpha * x + beta, 0), 1) * Elu(x) - x if x >= 0 else alpha * (e^x - 1) * Softsign(x) - x/(1 + |x|) * Softplus(x) - log(1 + e^x) Equations (Default: f=Sigmoid, g=Tanh): * zt = f(Xt*(Wz^T) + Ht-1*(Rz^T) + Wbz + Rbz) * rt = f(Xt*(Wr^T) + Ht-1*(Rr^T) + Wbr + Rbr) * ht = g(Xt*(Wh^T) + (rt (.) Ht-1)*(Rh^T) + Rbh + Wbh) # default, when linear_before_reset = 0 * ht = g(Xt*(Wh^T) + (rt (.) (Ht-1*(Rh^T) + Rbh)) + Wbh) # when linear_before_reset != 0 * Ht = (1 - zt) (.) ht + zt (.) Ht-1 Computes an one-layer LSTM. This operator is usually supported via some custom implementation such as CuDNN. Notations: * `X` - input tensor * `i` - input gate * `o` - output gate * `f` - forget gate * `c` - cell gate * `t` - time step (t-1 means previous time step) * `W[iofc]` - W parameter weight matrix for input, output, forget, and cell gates * `R[iofc]` - R recurrence weight matrix for input, output, forget, and cell gates * `Wb[iofc]` - W bias vectors for input, output, forget, and cell gates * `Rb[iofc]` - R bias vectors for input, output, forget, and cell gates * `P[iof]` - P peephole weight vector for input, output, and forget gates * `WB[iofc]` - W parameter weight matrix for backward input, output, forget, and cell gates * `RB[iofc]` - R recurrence weight matrix for backward input, output, forget, and cell gates * `WBb[iofc]` - W bias vectors for backward input, output, forget, and cell gates * `RBb[iofc]` - R bias vectors for backward input, output, forget, and cell gates * `PB[iof]` - P peephole weight vector for backward input, output, and forget gates * `H` - Hidden state * `num_directions` - 2 if direction == bidirectional else 1 Activation functions: * Relu(x) - max(0, x) * Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x}) * Sigmoid(x) - 1/(1 + e^{-x}) NOTE: Below are optional * Affine(x) - alpha*x + beta * LeakyRelu(x) - x if x >= 0 else alpha * x * ThresholdedRelu(x) - x if x >= alpha else 0 * ScaledTanh(x) - alpha*Tanh(beta*x) * HardSigmoid(x) - min(max(alpha*x + beta, 0), 1) * Elu(x) - x if x >= 0 else alpha*(e^x - 1) * Softsign(x) - x/(1 + |x|) * Softplus(x) - log(1 + e^x) Equations (Default: f=Sigmoid, g=Tanh, h=Tanh): * it = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Pi (.) Ct-1 + Wbi + Rbi) * ft = f(Xt*(Wf^T) + Ht-1*(Rf^T) + Pf (.) Ct-1 + Wbf + Rbf) * ct = g(Xt*(Wc^T) + Ht-1*(Rc^T) + Wbc + Rbc) * Ct = ft (.) Ct-1 + it (.) ct * ot = f(Xt*(Wo^T) + Ht-1*(Ro^T) + Po (.) Ct + Wbo + Rbo) * Ht = ot (.) h(Ct) ??@-@This operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted. First input tensor must have rank 3The shape format of inputs X, initial_h and outputs Y, Y_h. If 0, the following shapes are expected: X.shape = [seq_length, batch_size, input_size], Y.shape = [seq_length, num_directions, batch_size, hidden_size], initial_h.shape = Y_h.shape = [num_directions, batch_size, hidden_size]. If 1, the following shapes are expected: X.shape = [batch_size, seq_length, input_size], Y.shape = [batch_size, seq_length, num_directions, hidden_size], initial_h.shape = Y_h.shape = [batch_size, num_directions, hidden_size].One (or two if bidirectional) activation function for input gate. The activation function must be one of the activation functions specified above. Optional: Default `Tanh` if not specified.The weight tensor for input gate. Concatenation of `Wi` and `WBi` (if bidirectional). The tensor has shape `[num_directions, hidden_size, input_size]`.The recurrence weight tensor. Concatenation of `Ri` and `RBi` (if bidirectional). The tensor has shape `[num_directions, hidden_size, hidden_size]`.The bias tensor for input gate. Concatenation of `[Wbi, Rbi]` and `[WBbi, RBbi]` (if bidirectional). The tensor has shape `[num_directions, 2*hidden_size]`. Optional: If not specified - assumed to be 0.D:\a\_work\_temp\onnx\src\v1.17.0-7e28176e64.clean\onnx\defs\rnn\defs.ccA list of 2 (or 4 if bidirectional) activation functions for update, reset, and hidden gates. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified.When computing the output of the hidden gate, apply the linear transformation before multiplying by the output of the reset gate.The weight tensor for the gates. Concatenation of `W[zrh]` and `WB[zrh]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 3*hidden_size, input_size]`.The recurrence weight tensor. Concatenation of `R[zrh]` and `RB[zrh]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 3*hidden_size, hidden_size]`.The bias tensor for the gates. Concatenation of `[Wb[zrh], Rb[zrh]]` and `[WBb[zrh], RBb[zrh]]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 6*hidden_size]`. Optional: If not specified - assumed to be 0The shape format of inputs X, initial_h, initial_c and outputs Y, Y_h, Y_c. If 0, the following shapes are expected: X.shape = [seq_length, batch_size, input_size], Y.shape = [seq_length, num_directions, batch_size, hidden_size], initial_h.shape = Y_h.shape = initial_c.shape = Y_c.shape = [num_directions, batch_size, hidden_size]. If 1, the following shapes are expected: X.shape = [batch_size, seq_length, input_size], Y.shape = [batch_size, seq_length, num_directions, hidden_size], initial_h.shape = Y_h.shape = initial_c.shape = Y_c.shape = [batch_size, num_directions, hidden_size].<<<<C# Region of Interest (RoI) align operation described in the [Mask R-CNN paper](https://arxiv.org/abs/1703.06870). RoiAlign consumes an input tensor X and region of interests (rois) to apply pooling across each RoI; it produces a 4-D tensor of shape (num_rois, C, output_height, output_width). RoiAlign is proposed to avoid the misalignment by removing quantizations while converting from original image into feature map and from feature map into RoI feature; in each ROI bin, the value of the sampled locations are computed directly through bilinear interpolation. Filter out boxes that have high intersection-over-union (IOU) overlap with previously selected boxes. Bounding boxes with score less than score_threshold are removed. Bounding box format is indicated by attribute center_point_box. Note that this algorithm is agnostic to where the origin is in the coordinate system and more generally is invariant to orthogonal transformations and translations of the coordinate system; thus translating or reflections of the coordinate system result in the same boxes being selected by the algorithm. The selected_indices output is a set of integers indexing into the input collection of bounding boxes representing the selected boxes. The bounding box coordinates corresponding to the selected indices can then be obtained using the Gather or GatherND operation. ??@-@Multiplicative spatial scale factor to translate ROI coordinates from their input spatial scale to the scale used when pooling, i.e., spatial scale of the input feature map X relative to the input image. E.g.; default is 1.0f. default 1; Pooled output Y's height.default 1; Pooled output Y's width.Number of sampling points in the interpolation grid used to compute the output value of each pooled output bin. If > 0, then exactly sampling_ratio x sampling_ratio grid points are used. If == 0, then an adaptive number of grid points are used (computed as ceil(roi_width / output_width), and likewise for height). Default is 0.The pooling method. Two modes are supported: 'avg' and 'max'. Default is 'avg'.Allowed values are 'half_pixel' and 'output_half_pixel'. Use the value 'half_pixel' to pixel shift the input coordinates by -0.5 (the recommended behavior). Use the value 'output_half_pixel' to omit the pixel shift for the input (use this for a backward-compatible behavior).RoIs (Regions of Interest) to pool over; rois is 2-D input of shape (num_rois, 4) given as [[x1, y1, x2, y2], ...]. The RoIs' coordinates are in the coordinate system of the input image. Each coordinate set has a 1:1 correspondence with the 'batch_indices' input.RoI pooled output, 4-D tensor of shape (num_rois, C, output_height, output_width). The r-th batch element Y[r-1] is a pooled feature map corresponding to the r-th RoI X[r-1].D:\a\_work\_temp\onnx\src\v1.17.0-7e28176e64.clean\onnx\defs\object_detection\defs.ccAn input tensor with shape [num_batches, spatial_dimension, 4]. The single box data format is indicated by center_point_box.An input tensor with shape [num_batches, num_classes, spatial_dimension]Integer representing the maximum number of boxes to be selected per batch per class. It is a scalar. Default to 0, which means no output.max_output_boxes_per_classFloat representing the threshold for deciding whether boxes overlap too much with respect to IOU. It is scalar. Value range [0, 1]. Default to 0.Float representing the threshold for deciding when to remove boxes based on score. It is a scalar.selected indices from the boxes tensor. [num_selected_indices, 3], the selected index format is [batch_index, class_index, box_index].selected_indicesInteger indicate the format of the box data. The default is 0. 0 - the box data is supplied as [y1, x1, y2, x2] where (y1, x1) and (y2, x2) are the coordinates of any diagonal pair of box corners and the coordinates can be provided as normalized (i.e., lying in the interval [0, 1]) or absolute. Mostly used for TF models. 1 - the box data is supplied as [x_center, y_center, width, height]. Mostly used for Pytorch models.IIIIC#$IIIIC# This is layer normalization defined in ONNX as function. The overall computation can be split into two stages. The first stage is standardization, which makes the normalized elements have zero mean and unit variances. The computation required by standardization can be described by the following equations. ``` Mean = ReduceMean(X) D = Sub(X, Mean) DD = Mul(D, D) Var = ReduceMean(DD) VarEps = Add(Var, epsilon) StdDev = Sqrt(VarEps) InvStdDev = Reciprocal(StdDev) Normalized = Mul(D, InvStdDev) ``` where `normalized_axes` is `[axis, ..., rank of X - 1]`. The variables `Var` and `StdDev` stand for variance and standard deviation, respectively. The second output is `Mean` and the last one is `InvStdDev`. Depending on `stash_type` attribute, the actual computation must happen in different floating-point precision. For example, if `stash_type` is 1, this operator casts all input variables to 32-bit float, perform the computation, and finally cast `Normalized` back to the original type of `X`. The second stage then scales and shifts the outcome of the first stage using ``` NormalizedScaled = Mul(Normalized, Scale) Y = Add(NormalizedScaled, B) ``` The second stage doesn't depends on `stash_type`. All equations are in [this syntax](https://github.com/onnx/onnx/blob/main/docs/Syntax.md). The same variable (i.e., input, output, and attribute) uses the same name in the equations above and this operator's definition. Let `d[i]` indicate the i-th dimension of `X`. If `X`'s shape is `[d[0], ..., d[axis-1], d[axis], ..., d[rank-1]]`, the shape of `Mean` and `InvStdDev` is `[d[0], ..., d[axis-1], 1, ..., 1]`. `Y` and `X` have the same shape. This operator supports unidirectional broadcasting (tensors `Scale` and `B` should be unidirectional broadcastable to tensor `X`); for more details please check [the doc](Broadcasting.md). A GroupNormalization function. Carries out group normalization as described in the paper https://arxiv.org/abs/1803.08494 This operator transforms input according to ``` y = scale * (x - mean) / sqrt(variance + epsilon) + bias, ``` where the mean and variance are computed per instance per group of channels, and `scale` and `bias` should be specified for each group of channels. The number of groups `num_groups` should be divisible by the number of channels so that there are an equal number of channels per group. The overall computation has two stages: the first stage normalizes the elements to have zero mean and unit variance for each instance in each group, and the second stage scales and shifts the results of the first stage. The floating-point precision used in the first stage is determined by the `stash_type` attribute. For example, if `stash_type` is 1, the operator casts all input variables to 32-bit float, performs the computation, and finally casts the normalized results back to the original type of `X`. The second stage does not depend on `stash_type`. When the number of groups is the same as the number of channels, this operator is equivalent to InstanceNormalization. When there is only one group, this operator is equivalent to LayerNormalization. auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that `output_shape[i] = ceil(input_shape[i] / strides[i])` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER.auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that `output_shape[i] = input_shape[i] * strides[i]` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER.??@-@ MaxUnpool essentially computes the partial inverse of the MaxPool op. The input information to this op is typically the output information from a MaxPool op. The first input tensor X is the tensor that needs to be unpooled, which is typically the pooled tensor (first output) from MaxPool. The second input tensor, I, contains the indices to the (locally maximal) elements corresponding to the elements in the first input tensor X. Input tensor I is typically the second output of the MaxPool op. The third (optional) input is a tensor that specifies the output size of the unpooling operation. MaxUnpool is intended to do 'partial' inverse of the MaxPool op. 'Partial' because all the non-maximal values from the original input to MaxPool are set to zero in the output of the MaxUnpool op. Pooling the result of an unpooling operation should give back the original input to the unpooling op. MaxUnpool can produce the same output size for several input sizes, which makes unpooling op ambiguous. The third input argument, output_size, is meant to disambiguate the op and produce output tensor of known/predictable size. In addition to the inputs, MaxUnpool takes three attributes, namely kernel_shape, strides, and pads, which define the exact unpooling op. The attributes typically have the same values as the corresponding pooling op that the unpooling op is trying to invert. The convolution operator consumes a quantized input tensor, its scale and zero point, a quantized filter, its scale and zero point, and output's scale and zero point, and computes the quantized output. Each scale and zero-point pair must have same shape. It means they must be either scalars (per tensor) or 1-D tensors (per output channel). Each input or output and its related zero point must have same type. When bias is present it must be quantized using scale = input scale * weight scale and zero point as 0. The integer convolution operator consumes an input tensor, its zero-point, a filter, and its zero-point, and computes the output. The production MUST never overflow. The accumulation may overflow if and only if in 32 bits. Performs deformable convolution as described in https://arxiv.org/abs/1703.06211 and https://arxiv.org/abs/1811.11168. This operator specification supports the general N-D case. Note that most common use cases have 2D or 3D data. Carries out batch normalization as described in the paper https://arxiv.org/abs/1502.03167. Depending on the mode it is being run, There are five required inputs 'X', 'scale', 'B', 'input_mean' and 'input_var'. Note that 'input_mean' and 'input_var' are expected to be the estimated statistics in inference mode (training_mode=False, default), and the running statistics in training mode (training_mode=True). There are multiple cases for the number of outputs, which we list below: * Output case #1: Y, running_mean, running_var (training_mode=True) * Output case #2: Y (training_mode=False) When training_mode=False, extra outputs are invalid. The outputs are updated as follows when training_mode=True: ``` running_mean = input_mean * momentum + current_mean * (1 - momentum) running_var = input_var * momentum + current_var * (1 - momentum) Y = (X - current_mean) / sqrt(current_var + epsilon) * scale + B ``` where: ``` current_mean = ReduceMean(X, axis=all_except_channel_index) current_var = ReduceVar(X, axis=all_except_channel_index) ``` Notice that `ReduceVar` refers to the population variance, and it equals to `sum(sqrd(x_i - x_avg)) / N` where `N` is the population size (this formula does not use sample size `N - 1`). The computation of ReduceMean and ReduceVar uses float to avoid overflow for float16 inputs. When training_mode=False: ``` Y = (X - input_mean) / sqrt(input_var + epsilon) * scale + B ``` For previous (depreciated) non-spatial cases, implementors are suggested to flatten the input shape to (N x C * D1 * D2 * ... * Dn) before a BatchNormalization Op. Carries out instance normalization as described in the paper https://arxiv.org/abs/1607.08022. y = scale * (x - mean) / sqrt(variance + epsilon) + B, where mean and variance are computed per instance per channel. Given a matrix, apply Lp-normalization along the provided axis. Dropout takes an input floating-point tensor, an optional input ratio (floating-point scalar) and an optional input training_mode (boolean scalar). It produces two tensor outputs, output (floating-point tensor) and mask (optional `Tensor`). If `training_mode` is true then the output Y will be a random dropout; Note that this Dropout scales the masked input data by the following equation, so to convert the trained model into inference mode, the user can simply not pass `training_mode` input or set it to false. ``` output = scale * data * mask, ``` where ``` scale = 1. / (1. - ratio). ``` Shrink takes one input data (Tensor) and produces one Tensor output, having same datatype and shape with input. It has two attributes, lambd and bias. The formula of this operator is: If x < -lambd, y = x + bias; If x > lambd, y = x - bias; Otherwise, y = 0. Flattens the input tensor into a 2D matrix. If input tensor has shape (d_0, d_1, ... d_n) then the output will have shape (d_0 X d_1 ... d_(axis-1), d_axis X d_(axis+1) ... X dn). Local Response Normalization proposed in the [AlexNet paper](https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf). It normalizes over local input regions. The local region is defined across the channels. For an element `X[n, c, d1, ..., dk]` in a tensor of shape `(N x C x D1 x D2, ..., Dk)`, its region is `{X[n, i, d1, ..., dk] | max(0, c - floor((size - 1) / 2)) <= i <= min(C - 1, c + ceil((size - 1) / 2))}`. `square_sum[n, c, d1, ..., dk] = sum(X[n, i, d1, ..., dk] ^ 2)`, where `max(0, c - floor((size - 1) / 2)) <= i <= min(C - 1, c + ceil((size - 1) / 2))`. `Y[n, c, d1, ..., dk] = X[n, c, d1, ..., dk] / (bias + alpha / size * square_sum[n, c, d1, ..., dk] ) ^ beta` This transform extracts n-grams from the input sequence and save them as a vector. Input can be either a 1-D or 2-D tensor. For 1-D input, output is the n-gram representation of that input. For 2-D input, the output is also a 2-D tensor whose i-th row is the n-gram representation of the i-th input row. More specifically, if input shape is [C], the corresponding output shape would be [max(ngram_indexes) + 1]. If input shape is [N, C], this operator produces a [N, max(ngram_indexes) + 1]-tensor. In contrast to standard n-gram extraction, here, the indexes of extracting an n-gram from the original sequence are not necessarily consecutive numbers. The discontinuity between indexes are controlled by the number of skips. If the number of skips is 2, we should skip two tokens when scanning through the original sequence. Let's consider an example. Assume that input sequence is [94, 17, 36, 12, 28] and the number of skips is 2. The associated 2-grams are [94, 12] and [17, 28] respectively indexed by [0, 3] and [1, 4]. If the number of skips becomes 0, the 2-grams generated are [94, 17], [17, 36], [36, 12], [12, 28] indexed by [0, 1], [1, 2], [2, 3], [3, 4], respectively. The output vector (denoted by Y) stores the count of each n-gram; Y[ngram_indexes[i]] indicates the times that the i-th n-gram is found. The attribute ngram_indexes is used to determine the mapping between index i and the corresponding n-gram's output coordinate. If pool_int64s is [94, 17, 17, 36], ngram_indexes is [1, 0], ngram_counts=[0, 0], then the Y[0] (first element in Y) and Y[1] (second element in Y) are the counts of [17, 36] and [94, 17], respectively. An n-gram which cannot be found in pool_strings/pool_int64s should be ignored and has no effect on the output. Note that we may consider all skips up to S when generating the n-grams. The examples used above are true if mode is "TF". If mode is "IDF", all the counts larger than 1 would be truncated to 1 and the i-th element in weights would be used to scale (by multiplication) the count of the i-th n-gram in pool. If mode is "TFIDF", this operator first computes the counts of all n-grams and then scale them by the associated values in the weights attribute. Only one of pool_strings and pool_int64s can be set. If pool_int64s is set, the input should be an integer tensor. If pool_strings is set, the input must be a string tensor. A MeanVarianceNormalization Function: Perform mean variance normalization on the input tensor X using formula: `(X-EX)/sqrt(E(X-EX)^2)` The operator rearranges column blocks back into a multidimensional image Col2Im behaves similarly to PyTorch's fold https://pytorch.org/docs/stable/generated/torch.nn.Fold.html, but it only supports *batched* multi-dimensional image tensors. Another implementation in Python with N-dimension support can be found at https://github.com/f-dangel/unfoldNd/. NOTE: Although specifying image_shape looks redundant because it could be calculated from convolution formulas, it is required as input for more advanced scenarios as explained at PyTorch's implementation (https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/Col2Im.cpp#L10) Input tensor must have at least 2 dimensionsAttribute dilations has incorrect sizeAttribute strides has incorrect sizeAttribute kernel_shape has incorrect sizeAttribute kernel_shape must be specifiedAttribute pads has incorrect size values in kernel shape should have Second input tensor has wrong dimension {name} consumes an input tensor X and applies {opName} pooling across the tensor according to kernel sizes, stride sizes, and pad lengths. {opName} pooling consisting of computing the {opName} on all values of a subset of the input tensor according to the kernel size and downsampling the data into the output tensor Y for further processing. The output spatial shape is calculated differently depending on whether explicit padding is used, where pads is employed, or auto padding is used, where auto_pad is utilized. With explicit padding (https://pytorch.org/docs/stable/generated/torch.nn.MaxPool2d.html?highlight=maxpool#torch.nn.MaxPool2d): ``` output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - dilation[i] * (kernel_shape[i] - 1) - 1) / strides_spatial_shape[i] + 1) ``` or ``` output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - dilation[i] * (kernel_shape[i] - 1) - 1) / strides_spatial_shape[i] + 1) ``` if ceil_mode is enabled. `pad_shape[i]` is the sum of pads along axis `i`. Sliding windows that would start in the right padded region are ignored. `auto_pad` is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following when ceil_mode is enabled: ``` VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i] - {kernelSpatialShape} + 1) / strides_spatial_shape[i]) SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides_spatial_shape[i]) ``` or when ceil_mode is disabled (https://www.tensorflow.org/api_docs/python/tf/keras/layers/AveragePooling2D): ``` VALID: output_spatial_shape[i] = floor((input_spatial_shape[i] - {kernelSpatialShape}) / strides_spatial_shape[i]) + 1 SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = floor((input_spatial_shape[i] - 1) / strides_spatial_shape[i]) + 1 ``` And pad shape will be following if `SAME_UPPER` or `SAME_LOWER`: ``` pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial_shape[i] + {kernelSpatialShape} - input_spatial_shape[i] ``` {additionalDescription} {opName}{additionalDescription}((kernel_spatial_shape[i] - 1) * dilations[i] + 1)kernel_spatial_shape[i]{kernelSpatialShape}Constrain input and output types to float and 8 bit tensors.The output of each pooling window is divided by the number of elements (exclude pad when attribute count_include_pad is zero).averageDilation value along each spatial axis of filter. If not present, the dilation defaults to 1 along each spatial axis.D:\a\_work\_temp\onnx\src\v1.17.0-7e28176e64.clean\onnx\defs\nn\defs.ccThe output of each pooling window is maximum number of elements exclude pad. The storage order of the tensor. 0 is row major, and 1 is column major. This attribute is used only to convert an n-tuple index value into a single integer value for producing the second output. Indices tensor from max pooling across the input tensor. The dimensions of indices are the same as output tensor. The values in indices of are the indices of the selected values during pooling. The indices are computed as flatten 1-D tensor, and the indices do not consider padding. So the values in indices are in [0, N x C x D1 x ... x Dn).IndicesConstrain index tensor to int64MaxUnpool op must have either two or three inputs.Input tensor X must have at least 2 dimensions.Attribute pads has incorrect size.Attribute strides has incorrect size.Attribute kernel_shape has incorrect size.Attribute kernel_shape must be specified.'output_shape' must be rank 1 tensor.'output_shape' must have same number of elements as the shape of input tensor X.Input data tensor that has to be unpooled. This tensor is typically the first output of the MaxPool op.Dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non-image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].Input data tensor containing the indices corresponding to elements in the first input tensor X.This tensor is typically the second output of the MaxPool op.Dimensions must be the same as input tensor X. The indices are linear, i.e. computed considering the tensor as flattened 1-D tensor, assuming row-major storage. Also, the linear indices should not consider padding. So the values in indices are in the range [0, N x C x D1 x ... x Dn).The shape of the output can be explicitly set which will cause pads values to be auto generated. If 'output_shape' is specified, 'pads' values are ignored.Output data tensor that contains the result of the unpooling. {name} consumes an input tensor X and applies Lp pooling across the tensor according to kernel sizes, stride sizes, and pad lengths. Lp pooling consisting of computing the Lp norm on all values of a subset of the input tensor according to the kernel size and downsampling the data into the output tensor Y for further processing. The output spatial shape will be following: ``` output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - {kernelSpatialShape}) / strides_spatial_shape[i] + 1) ``` or ``` output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - {kernelSpatialShape}) / strides_spatial_shape[i] + 1) ``` if ceil_mode is enabled `pad_shape[i]` is the sum of pads along axis `i`. `auto_pad` is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following: ``` VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i] - {kernelSpatialShape} + 1) / strides_spatial_shape[i]) SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides_spatial_shape[i]) ``` And pad shape will be following if `SAME_UPPER` or `SAME_LOWER`: ``` pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial_shape[i] + {kernelSpatialShape} - input_spatial_shape[i] ```p value of the Lp norm used to pool over the input data.Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size.Output data tensor from Lp pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes.RoIs tensor must have 2 dimensionsAttribute pooled_shape has incorrect lengthAttribute pooled_shape must be specified ROI {name} pool consumes an input tensor X and region of interests (RoIs) to apply {name} pooling across each RoI, to produce output 4-D tensor of shape (num_rois, channels, pooled_shape[0], pooled_shape[1]).ROI pool output shape (height, width).Multiplicative spatial scale factor to translate ROI coordinates from their input scale to the scale used when pooling.Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data.RoIs (Regions of Interest) to pool over. Should be a 2-D tensor of shape (num_rois, 5) given as [[batch_id, x1, y1, x2, y2], ...].RoI pooled output 4-D tensor of shape (num_rois, channels, pooled_shape[0], pooled_shape[1]). The convolution operator consumes an input tensor and {filter_desc}, and computes the output.{filter_desc}a filterinputs are expected to have tensor type in input and zero_point pair is expected to have be same type in weight and zero_point pair is expected to have same type in The weight tensor that will be used in the convolutions; has size (M x C/group x kH x kW), where C is the number of channels, and kH and kW are the height and width of the kernel, and M is the number of feature maps. For more than 2 dimensions, the kernel shape will be (M x C/group x k1 x k2 x ... x kn), where (k1 x k2 x ... kn) is the dimension of the kernel. Optionally, if dimension denotation is in effect, the operation expects the weight tensor to arrive with the dimension denotation of [FILTER_OUT_CHANNEL, FILTER_IN_CHANNEL, FILTER_SPATIAL, FILTER_SPATIAL ...]. X.shape[1] == (W.shape[1] * group) == C (assuming zero based indices for the shape array). Or in other words FILTER_IN_CHANNEL should be equal to DATA_CHANNEL. The shape of the convolution kernel. If not present, should be inferred from input 'w'.Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0.The value represent the number of pixels added to the beginning and end part of the corresponding axis.`pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number ofpixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaultsto 0 along start and end of each spatial axis.number of groups input channels and output channels are divided into. default is 1.Zero point tensor for input 'x'. It's optional and default value is 0. It's a scalar, which means a per-tensor/layer quantization.Zero point tensor for input 'w'. It's optional and default value is 0. It could be a scalar or a 1-D tensor, which means a per-tensor/layer or per output channel quantization. If it's a 1-D tensor, its number of elements should be equal to the number of output channels (M)Constrain input x and its zero point data type to 8-bit integer tensor.Constrain input w and its zero point data type to 8-bit integer tensor.Constrain output y data type to 32-bit integer tensor.dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each axis.Stride along each spatial axis. If not present, the stride defaults to 1 along each axis.The pads attribute cannot be used simultaneously with auto_pad attribute The convolution transpose operator consumes an input tensor and {filter_desc}, and computes the output. If the pads parameter is provided the shape of the output is calculated via the following equation: output_shape[i] = stride[i] * (input_size[i] - 1) + output_padding[i] + ((kernel_shape[i] - 1) * dilations[i] + 1) - pads[start_i] - pads[end_i] output_shape can also be explicitly specified in which case pads values are auto generated using these equations: total_padding[i] = stride[i] * (input_size[i] - 1) + output_padding[i] + ((kernel_shape[i] - 1) * dilations[i] + 1) - output_shape[i] If (auto_pads == SAME_UPPER): pads[start_i] = total_padding[i]/2; pads[end_i] = total_padding[i] - (total_padding[i]/2) Else: pads[start_i] = total_padding[i] - (total_padding[i]/2); pads[end_i] = (total_padding[i]/2). The weight tensor that will be used in the convolutions; has size (C x M/group x kH x kW), where C is the number of channels, and kH and kW are the height and width of the kernel, and M is the number of feature maps. For more than 2 dimensions, the weight shape will be (C x M/group x k1 x k2 x ... x kn), where (k1 x k2 x ... x kn) is the dimension of the kernel. The number of channels in the output should be equal to W.shape[1] * group (assuming zero based indices of the shape array)Output data tensor that contains the result of the convolution. The output dimensions are functions of the kernel size, stride size, pad lengths and group count. The number of channels in the output should be equal to W.shape[1] * group (assuming zero based indices of the shape array)The shape of the output can be explicitly set which will cause pads values to be auto generated. If output_shape is specified pads values are ignored. See doc for details for equations to generate pads. Note that the output_shape attribute value should not include dimensions for batch size and channels, which are automatically inferred.Input data tensor. For 2D image data, it has shape (N, C, H, W) where N is the batch size, C is the number of input channels, and H and W are the height and width. In general, the shape is (N, C, D1, D2, ... , Dn) for n-dimensional data, where D1 to Dn are the spatial dimension sizes. Most common use cases have n = 2 or 3.Weight tensor that will be used in the convolutions. It has shape (oC, C/group, kH, kW), where oC is the number of output channels and kH and kW are the kernel height and width. For more than 2 dimensions, it has shape (oC, C/group, k1, k2, ... , kn).Offset tensor denoting the offset for the sampling locations in the convolution kernel. It has shape (N, offset_group * kH * kW * 2, oH, oW) for 2D data or (N, offset_group * k1 * k2 * ... * kn * n, o1, o2, ... , on) for nD data. Use linear interpolationfor fractional offset values. Sampling locations outside of the padded input tensor gives zero.Optional 1D bias of length oC to be added to the convolution. Default is a tensor of zeros.The mask tensor to be applied to each position in the convolution kernel. It has shape (N, offset_group * kH * kW, oH, oW) for 2D data or (N, offset_group * k1 * k2 * ... * kn * n, o1, o2, ... , on) for nD data. Default is a tensor of ones.Output data tensor that contains the result of convolution. It has shape (N, oC, oH, oW) for 2D data or (N, oC, o1, o2, ..., on) for nD dataDilation value along each spatial axis of the kernel. Default is 1 along each axis.Number of groups the input and output channels, C and oC, are divided into. C and oC must both be divisible by group. Default is 1.Shape of the convolution kernel. If not present, it is inferred from the shape of input W.Number of groups of offset. C must be divisible by offset_group. Default is 1.offset_groupPadding for the beginning and end along each spatial axis. The values represent the number of pixels added to the beginning and end of the corresponding axis and can take any nonnegative value. The format should be as follows: [x1_begin, x2_begin, ..., x1_end, x2_end, ...], where xi_begin is the number of pixels added at the beginning of axis `i` and xi_end is the number of pixels added at the end of axis `i`. Default is 0 along each axis.Stride along each spatial axis. Default is 1 along each axis.DeformConv Global{op_type} consumes an input tensor X and applies {op} pooling across the values in the same channel. This is equivalent to {op_type} with kernel size equal to the spatial dimension of input tensor.{op_type}{op}Output data tensor from pooling across the input tensor. The output tensor has the same rank as the input. The first two dimensions of output shape are the same as the input (N x C), while the other dimensions are all 1.lp poolThis number of op outputs should be 3 when Training_mode = True, but it is not.This number of op outputs should be 1 when Training_mode = False, but it is not.Factor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum).If set to true, it indicates BatchNormalization is being used for training, and outputs 1 and 2 are to be computed.Input data tensor from the previous operator; dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size, C is the number of channels. Statistics are computed for every channel of C over N and D1 to Dn dimensions. For image data, input dimensions become (N x C x H x W). The op also accepts single dimension input of size N in which case C is assumed to be 1Scale tensor of shape (C).Bias tensor of shape (C).running (training) or estimated (testing) mean tensor of shape (C).input_meanrunning (training) or estimated (testing) variance tensor of shape (C).input_varThe running mean after the BatchNormalization operator.running_meanThe running variance after the BatchNormalization operator. This op uses the population size (N) for calculating variance, and not the sample size N-1.running_varConstrain scale and bias types to float tensors.Constrain mean and variance types to float tensors.The input 1-dimensional scale tensor of size C.The input 1-dimensional bias tensor of size C.The output tensor of the same shape as input.Input matrixMatrix after normalizationThe axis on which to apply normalization, -1 mean last axis.The order of the normalization, only 1 or 2 are supported.Ratio of Dropout must be a scalar.training_mode of Dropout must be a scalar.The output mask.The lambd value for the Shrink formulation. Default is 0.5.The bias value added to output. Default is 0. { Lambd = Constant () LambdCast = CastLike (Lambd, input) Bias = Constant () BiasCast = CastLike (Bias, input) Zero = Constant () ZeroCast = CastLike (Zero, input) NegLmbda = Neg (LambdCast) InputLessThanNegLambda = Less (input, NegLmbda) InputAddBias = Add (input, BiasCast) InputSubBias = Sub (input, BiasCast) LambdaLessThanInput = Less (LambdCast, input) InputSubBiasOrZero = Where (LambdaLessThanInput, InputSubBias, ZeroCast) output = Where(InputLessThanNegLambda, InputAddBias, InputSubBiasOrZero) } ) for attribute 'axis'Invalid value(A tensor of rank >= axis.A 2D tensor with the contents of the input tensor, with input dimensions up to axis flattened to the outer dimension of the output and remaining input dimensions flattened into the inner dimension of the output.Constrain input and output to all tensor types up to IRv10.Indicate up to which input dimensions (exclusive) should be flattened to the outer dimension of the output. The value for axis must be in the range [-r, r], where r is the rank of the input tensor. Negative value means counting dimensions from the back. When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n), where the shape of the input tensor is (d_0, d_1, ... d_n). The number of channels to sum overScaling parameter.The exponent.Output tensor, which has the shape and type as input tensorConstrain input and output types to float tensors.ngram_indexes must be non-empty with no negative valuesInput tensor must have rank 1 or 2Input for n-gram extractionNgram resultsInput is ether string UTF-8 or int32/int641-D tensor of floatsMaximum n-gram length. If this value is 3, 3-grams will be used to generate the output.Minimum n-gram length. If this value is 2 and max_gram_length is 3, output may contain counts of 2-grams and 3-grams.Maximum number of items (integers/strings) to be skipped when constructing an n-gram from X. If max_skip_count=1, min_gram_length=2, max_gram_length=3, this operator may generate 2-grams with skip_count=0 and skip_count=1, and 3-grams with skip_count=0 and skip_count=1List of strings n-grams learned from the training set. Either this or pool_int64s attributes must be present but not both. It's an 1-D tensor starting with the collections of all 1-grams and ending with the collections of n-grams. The i-th element in pool stores the n-gram that should be mapped to coordinate ngram_indexes[i] in the output vector.List of int64 n-grams learned from the training set. Either this or pool_strings attributes must be present but not both. It's an 1-D tensor starting with the collections of all 1-grams and ending with the collections of n-grams. The i-th element in pool stores the n-gram that should be mapped to coordinate ngram_indexes[i] in the output vector.The starting indexes of 1-grams, 2-grams, and so on in pool. It is useful when determining the boundary between two consecutive collections of n-grams. For example, if ngram_counts is [0, 17, 36], the first index (zero-based) of 1-gram/2-gram/3-gram in pool are 0/17/36. This format is essentially identical to CSR (or CSC) sparse matrix format, and we choose to use this due to its popularity.list of int64s (type: AttributeProto::INTS). This list is parallel to the specified 'pool_*' attribute. The i-th element in ngram_indexes indicate the coordinate of the i-th n-gram in the output tensor.list of floats. This attribute stores the weight of each n-gram in pool. The i-th element in weights is the weight of the i-th n-gram in pool. Its length equals to the size of ngram_indexes. By default, weights is an all-one tensor.This attribute is used when mode is "IDF" or "TFIDF" to scale the associated word counts.The weighting criteria. It can be one of "TF" (term frequency), "IDF" (inverse document frequency), and "TFIDF" (the combination of TF and IDF)A list of integers, along which to reduce. The default is to calculate along axes [0,2,3] for calculating mean and variance along each channel. Two variables with the same C-coordinate are associated with the same mean and variance. { Exponent = Constant () Epsilon = Constant () X_RM = ReduceMean (X) EX_squared = Pow (X_RM, Exponent) X_squared = Pow (X, Exponent) E_Xsquared = ReduceMean (X_squared) Variance = Sub (E_Xsquared, EX_squared) STD = Sqrt (Variance) X_variance = Sub (X, X_RM) Processed_STD = Add (STD, Epsilon) Y = Div (X_variance, Processed_STD) } { Exponent = Constant () Epsilon = Constant () axes = Constant () X_RM = ReduceMean (X, axes) EX_squared = Pow (X_RM, Exponent) X_squared = Pow (X, Exponent) E_Xsquared = ReduceMean (X_squared, axes) Variance = Sub (E_Xsquared, EX_squared) STD = Sqrt (Variance) X_variance = Sub (X, X_RM) Processed_STD = Add (STD, Epsilon) Y = Div (X_variance, Processed_STD) } Attribute pads must have an even sizeinput must have rank 3.1-dimensional tensor with dilation value along each spatial axis of the image. If not present, the dilation defaults to 1 along each spatial axis of the image.1-dimensional tensor with padding value for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin is the number of pixels added at the beginning of axis `i` and xi_end is the number of pixels added at the end of axis `i`. If not present, the padding defaults to 0 along start and end of each spatial axis.1-dimensional tensor with stride value along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.Input data tensor to be rearranged from column blocks back into an image. This is a 3-dimensional tensor containing [N, C * n-ary-product(block_shape), L], where N is batch dimension, C is image channel dimension and L is number of blocks.The blocks are enumerated in increasing lexicographic-order of their indices.For example, with an image-size 10*20 and block-size 9*18, there would be 2*3 blocks, enumerated in the order block(0, 0), block(0, 1), block(0, 2), block(1, 0), block(1, 1), block(1, 2).The shape of the spatial dimensions of the image after rearranging the column blocks.This is a 1-dimensional tensor with size of at least 2, containing the value [H_img, W_img] for a 2-D image or [dim_i1, dim_i2, ..., dim_iN] for a N-D image.image_shapeThe shape of the block to apply on the input.This is a 1-dimensional tensor of size of at least 2, containing the value [H_block, W_block] for a 2-D image or [dim_b1, dim_b2, ..., dim_bN] for a N-D block.This is the block-shape before dilation is applied to it.block_shapeOutput tensor produced by rearranging blocks into an image.Constrain input and output types to all numeric tensor types.sinceVersion == 17 || sinceVersion == 18BuildContextDependentFunctionBodyLayerNormalization%s:%u: %s: Assertion `%s` failed.FloatEpsilonEpsilon = Cast (FloatEpsilon)Axes_1 = Constant()Mean2D = ReduceMean (XU, Axes_1)MeanOfSquare = ReduceMean (Square, Axes_1)NormalizedT = Cast (Normalized)Scaled = Mul (NormalizedT, Scale2D)) rank of first input is Unexpected axis value (The first normalization dimension. If rank(X) is r, axis' allowed range is [-r, r). Negative value means counting dimensions from the back.Type of Mean and InvStdDev. This also specifies stage one's computation precision.Tensor to be normalized.Normalized tensor.Constrain input types and output Y type to float tensors.num_groupsXU = Cast (X)XShape = Shape (XU)C = Shape (X)NumGroupsGroupSize = Div (C, NumGroups)N = Shape (X)InstanceShape = Shape (X)NewShape = Concat (N, NumGroups, GroupSize, InstanceShape)XReshaped = Reshape (XU, NewShape)Shape3D = Constant ()X3D = Reshape (XReshaped, Shape3D)Axes2Mean = ReduceMean (X3D, Axes2)Square = Mul (X3D, X3D)MeanOfSquare = ReduceMean (Square, Axes2)SquareOfMean = Mul (Mean, Mean)Deviation = Sub (X3D, Mean)NormalizedU = Div (Deviation, StdDev)NormalizedOriginalShape = Reshape (NormalizedU, XShape)NormalizedNC = Reshape (NormalizedOriginalShape, Shape3D)NormalizedT = Cast (NormalizedNC)ScaleShape = Constant ()ScaleT = Cast (scale)BiasT = Cast (bias)ScaleReshaped = Reshape (ScaleT, ScaleShape)BiasReshaped = Reshape (BiasT, ScaleShape)Scaled = Mul (ScaleReshaped, NormalizedT)Biased = Add (Scaled, BiasReshaped)The number of groups of channels. It should be a divisor of the number of channels `C`.The floating-point precision used in stage one of the computation.Input data tensor. Dimensions for image cases are `(N x C x H x W)`, where `N` is the batch size, `C` is the number of channels, and `H` and `W` are the height and width of the data. Statistics are computed for every group of channels over `C`, `H`, and `W`. For non-image cases, the dimensions are in the form of `(N x C x D1 x D2 ... Dn)`.Scale tensor of shape `(C)`.Bias tensor of shape `(C)`.The output tensor of the same shape as `X`.GroupNormalizationpττ҄@ԄC#PτPτфԄЄ# τ τфӄC#T0τ0τфӄC#̈́̈́pЄ҄C#΄΄`фӄC#8΄΄@фӄC#ττЄ`ԄC#΄΄0фӄC#΄΄ЄӄC#hP΄P΄Є@ӄC#pτpτф ԄC#ττ҄pԄC#L΄΄ЄӄC#ττpфӄC#0΄0΄Є0ӄτ#0ττpфPԄC#|΄΄ фpӄτ#΄΄PфӄC#̈́̈́`Є҄C#`ττ`Є0ԄC#ττфӄC# ΄ ΄Є ӄC#Dp΄p΄ф`ӄC#`΄`΄фPӄC#̈́̈́0Є҄C#(@τ@τфԄC#ẗ́̈́Є҄C#??@-@ An einsum of the form `term1, term2 -> output-term` produces an output tensor using the following equation ``` output[output-term] = reduce-sum( input1[term1] * input2[term2] ) ``` where the reduce-sum performs a summation over all the indices occurring in the input terms (term1, term2) that do not occur in the output-term. The Einsum operator evaluates algebraic tensor operations on a sequence of tensors, using the Einstein summation convention. The equation string contains a comma-separated sequence of lower case letters. Each term corresponds to an operand tensor, and the characters within the terms correspond to operands dimensions. This sequence may be followed by "->" to separate the left and right hand side of the equation. If the equation contains "->" followed by the right-hand side, the explicit (not classical) form of the Einstein summation is performed, and the right-hand side indices indicate output tensor dimensions. In other cases, output indices are (implicitly) set to the alphabetically sorted sequence of indices appearing exactly once in the equation. When a dimension character is repeated in the left-hand side, it represents summation along the dimension. The equation may contain ellipsis ("...") to enable broadcasting. Ellipsis must indicate a fixed number of dimensions. Specifically, every occurrence of ellipsis in the equation must represent the same number of dimensions. The right-hand side may contain exactly one ellipsis. In implicit mode, the ellipsis dimensions are set to the beginning of the output. The equation string may contain space (U+0020) character. Performs element-wise binary modulus (with Numpy-style broadcasting support). The sign of the remainder is the same as that of the Divisor. Mod operator can also behave like C fmod() or numpy.fmod. In this case, the sign of the remainder however, will be the same as the Dividend (in contrast to integer mod). To force a behavior like numpy.fmod() an 'fmod' Attribute is provided. This attribute is set to 0 by default causing the behavior to be like integer mod. Setting this attribute to 1 causes the remainder to be calculated similar to that of numpy.fmod(). If the input type is floating point, then `fmod` attribute must be set to 1. In case of dividend being zero, the results will be platform dependent. This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). Type of reduction to apply to loss: none, sum, mean(default). 'none': no reduction will be applied, 'sum': the output will be summed. 'mean': the sum of the output will be divided by the number of elements in the output.Loss function that measures the softmax cross entropy between 'scores' and 'labels'. This operator first computes a loss tensor whose shape is identical to the labels input. If the input is 2-D with shape (N, C), the loss tensor may be a N-element vector L = (l_1, l_2, ..., l_N). If the input is N-D tensor with shape (N, C, D1, D2, ..., Dk), the loss tensor L may have (N, D1, D2, ..., Dk) as its shape and L[i,][j_1][j_2]...[j_k] denotes a scalar element in L. After L is available, this operator can optionally do a reduction operator. * shape(scores): (N, C) where C is the number of classes, or (N, C, D1, D2,..., Dk), with K >= 1 in case of K-dimensional loss. * shape(labels): (N) where each value is 0 <= labels[i] <= C-1, or (N, D1, D2,..., Dk), with K >= 1 in case of K-dimensional loss. The loss for one sample, l_i, can calculated as follows: ``` l[i][d1][d2]...[dk] = -y[i][c][d1][d2]..[dk], where i is the index of classes. ``` or ``` l[i][d1][d2]...[dk] = -y[i][c][d1][d2]..[dk] * weights[c], if 'weights' is provided. ``` loss is zero for the case when label-value equals ignore_index. ``` l[i][d1][d2]...[dk] = 0, when labels[n][d1][d2]...[dk] = ignore_index ``` where: ``` p = Softmax(scores) y = Log(p) c = labels[i][d1][d2]...[dk] ``` Finally, L is optionally reduced: * If reduction = 'none', the output is L with shape (N, D1, D2, ..., Dk). * If reduction = 'sum', the output is scalar: Sum(L). * If reduction = 'mean', the output is scalar: ReduceMean(L), or if weight is provided: `ReduceSum(L) / ReduceSum(W)`, where tensor W is of shape `(N, D1, D2, ..., Dk)` and `W[n][d1][d2]...[dk] = weights[labels[i][d1][d2]...[dk]]`. Neg takes one input data (Tensor) and produces one output data (Tensor) where each element flipped sign, y = -x, is applied to the tensor elementwise. Computes the discrete Fourier Transform (DFT) of the input. Assuming the input has shape `[M, N]`, where `N` is the dimension over which the DFT is computed and `M` denotes the conceptual "all other dimensions," the DFT `y[m, k]` of shape `[M, N]` is defined as $$y[m, k] = \sum_{n=0}^{N-1} e^{-2 \pi j \frac{k n}{N} } x[m, n] ,$$ and the inverse transform is defined as $$x[m, n] = \frac{1}{N} \sum_{k=0}^{N-1} e^{2 \pi j \frac{k n}{N} } y[m, k] ,$$ where $j$ is the imaginary unit. The actual shape of the output is specified in the "output" section. Reference: https://docs.scipy.org/doc/scipy/tutorial/fft.html Absolute takes one input data (Tensor) and produces one output data (Tensor) where absolute value, y = abs(x), is applied to the tensor elementwise. Reciprocal takes one input data (Tensor) and produces one output data (Tensor) where the reciprocal is, y = 1/x, is applied to the tensor elementwise. Floor takes one input data (Tensor) and produces one output data (Tensor) where the floor is, y = floor(x), is applied to the tensor elementwise. If x is integral, +0, -0, NaN, or infinite, x itself is returned. Ceil takes one input data (Tensor) and produces one output data (Tensor) where the ceil is, y = ceil(x), is applied to the tensor elementwise. If x is integral, +0, -0, NaN, or infinite, x itself is returned. Square root takes one input data (Tensor) and produces one output data (Tensor) where the square root is, y = x^0.5, is applied to the tensor elementwise. If x is negative, then it will return NaN. Relu takes one input data (Tensor) and produces one output data (Tensor) where the rectified linear function, y = max(0, x), is applied to the tensor elementwise. LeakyRelu takes input data (Tensor) and an argument alpha, and produces one output data (Tensor) where the function `f(x) = alpha * x for x < 0`, `f(x) = x for x >= 0`, is applied to the data tensor elementwise. ThresholdedRelu takes one input data (Tensor) and produces one output data (Tensor) where the rectified linear function, y = x for x > alpha, y = 0 otherwise, is applied to the tensor elementwise. Selu takes one input data (Tensor) and produces one output data (Tensor) where the scaled exponential linear unit function, `y = gamma * (alpha * e^x - alpha) for x <= 0`, `y = gamma * x for x > 0`, is applied to the tensor elementwise. Elu takes one input data (Tensor) and produces one output data (Tensor) where the function `f(x) = alpha * (exp(x) - 1.) for x < 0`, `f(x) = x for x >= 0`., is applied to the tensor elementwise. Mish: A Self Regularized Non-Monotonic Neural Activation Function. Perform the linear unit element-wise on the input tensor X using formula: ``` mish(x) = x * tanh(softplus(x)) = x * tanh(ln(1 + e^{x})) ``` Continuously Differentiable Exponential Linear Units: Perform the linear unit element-wise on the input tensor X using formula: ``` max(0,x) + min(0,alpha*(exp(x/alpha)-1)) ``` Generate a MelWeightMatrix that can be used to re-weight a Tensor containing a linearly sampled frequency spectra (from DFT or STFT) into num_mel_bins frequency information based on the [lower_edge_hertz, upper_edge_hertz] range on the mel scale. This function defines the mel scale in terms of a frequency in hertz according to the following formula: mel(f) = 2595 * log10(1 + f/700) In the returned matrix, all the triangles (filterbanks) have a peak value of 1.0. The returned MelWeightMatrix can be used to right-multiply a spectrogram S of shape [frames, num_spectrogram_bins] of linear scale spectrum values (e.g. STFT magnitudes) to generate a "mel spectrogram" M of shape [frames, num_mel_bins]. Gelu takes one input data (Tensor) and produces one output data (Tensor) where the gaussian error linear units function, $y = 0.5 * x * (1 + erf(x/sqrt(2)))$ is applied to the tensor elementwise. If the attribute "approximate" is set to "tanh", the function estimation, $y = 0.5 * x * (1 + Tanh(sqrt(2/\pi) * (x + 0.044715 * x^3)))$ is used and applied to the tensor elementwise. Computes the Short-time Fourier Transform of the signal. Calculates the exponential of the given input tensor, element-wise. Calculates the natural log of the given input tensor, element-wise. Calculates the hyperbolic tangent of the given input tensor element-wise. Pow takes input data (Tensor) and exponent Tensor, and produces one output data (Tensor) where the function `f(x) = x^exponent`, is applied to the data tensor elementwise. PRelu takes input data (Tensor) and slope tensor as input, and produces one output data (Tensor) where the function `f(x) = slope * x for x < 0`, `f(x) = x for x >= 0`., is applied to the data tensor elementwise. Sigmoid takes one input data (Tensor) and produces one output data (Tensor) where the sigmoid function, y = 1 / (1 + exp(-x)), is applied to the tensor elementwise. HardSigmoid takes one input data (Tensor) and produces one output data (Tensor) where the HardSigmoid function, y = max(0, min(1, alpha * x + beta)), is applied to the tensor elementwise. HardSwish takes one input data (Tensor) and produces one output data (Tensor) where the HardSwish function, y = x * max(0, min(1, alpha * x + beta)) = x * HardSigmoid(x), where alpha = 1/6 and beta = 0.5, is applied to the tensor elementwise. Clip operator limits the given input within an interval. The interval is specified by the inputs 'min' and 'max'. They default to numeric_limits::lowest() and numeric_limits::max(), respectively. Calculates the softsign (x/(1+|x|)) of the given input tensor element-wise. Softplus takes one input data (Tensor) and produces one output data (Tensor) where the softplus function, y = ln(exp(x) + 1), is applied to the tensor elementwise. General Matrix multiplication: https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms#Level_3 * A' = transpose(A) if transA else A * B' = transpose(B) if transB else B Compute Y = alpha * A' * B' + beta * C, where input tensor A has shape (M, K) or (K, M), input tensor B has shape (K, N) or (N, K), input tensor C is broadcastable to shape (M, N), and output tensor Y has shape (M, N). A will be transposed before doing the computation if attribute transA is non-zero, same for B and transB. Matrix product that behaves like [numpy.matmul](https://numpy.org/doc/stable/reference/generated/numpy.matmul.html). Retrieve the top-K largest or smallest elements along a specified axis. Given an input tensor of shape [a_0, a_1, ..., a_{n-1}] and integer argument k, return two outputs: * Value tensor of shape [a_0, a_1, ..., a_{axis-1}, k, a_{axis+1}, ... a_{n-1}] which contains the values of the top k elements along the specified axis * Index tensor of shape [a_0, a_1, ..., a_{axis-1}, k, a_{axis+1}, ... a_{n-1}] which contains the indices of the top k elements (original indices from the input tensor). * If "largest" is 1 (the default value) then the k largest elements are returned. * If "sorted" is 1 (the default value) then the resulting k elements will be sorted. * If "sorted" is 0, order of returned 'Values' and 'Indices' are undefined. Given two equivalent values, this operator uses the indices along the axis as a tiebreaker. That is, the element with the lower index will appear first. Calculates the sine of the given input tensor, element-wise. Calculates the cosine of the given input tensor, element-wise. Calculates the tangent of the given input tensor, element-wise. Calculates the arcsine (inverse of sine) of the given input tensor, element-wise. Calculates the arccosine (inverse of cosine) of the given input tensor, element-wise. Calculates the arctangent (inverse of tangent) of the given input tensor, element-wise. Broadcast the input tensor following the given shape and the broadcast rule. The broadcast rule is similar to numpy.array(input) * numpy.ones(shape): Dimensions are right alignment; Two corresponding dimensions must have the same value, or one of them is equal to 1. Also, this operator is similar to numpy.broadcast_to(input, shape), but the major difference is numpy.broadcast_to() does not allow shape to be smaller than input.size(). It is possible that the output.shape is not equal to shape, when some dimensions in shape is equal to 1, or the shape.ndim < input.shape.ndim. Calculates the hyperbolic sine of the given input tensor element-wise. Calculates the hyperbolic cosine of the given input tensor element-wise. Calculates the hyperbolic arcsine of the given input tensor element-wise. Calculates the hyperbolic arccosine of the given input tensor element-wise. Calculates the hyperbolic arctangent of the given input tensor element-wise. Calculate the sign of the given input tensor element-wise. If input > 0, output 1. if input < 0, output -1. if input == 0, output 0. Computes the error function of the given input tensor element-wise. Matrix product that behaves like [numpy.matmul](https://numpy.org/doc/stable/reference/generated/numpy.matmul.html). The production MUST never overflow. The accumulation may overflow if and only if in 32 bits. Performs cumulative sum of the input elements along the given axis. By default, it will do the sum inclusively meaning the first element is copied as is. Through an `exclusive` attribute, this behavior can change to exclude the first element. It can also perform summation in the opposite direction of the axis. For that, set `reverse` attribute to 1. Example: ``` input_x = [1, 2, 3] axis=0 output = [1, 3, 6] exclusive=1 output = [0, 1, 3] exclusive=0 reverse=1 output = [6, 5, 3] exclusive=1 reverse=1 output = [5, 3, 0] ``` Round takes one input Tensor and rounds the values, element-wise, meaning it finds the nearest integer for each value. In case of halves, the rule is to round them to the nearest even integer. If input x is integral, +0, -0, NaN, or infinite, x itself is returned. The output tensor has the same shape and type as the input. Examples: ``` round([0.9]) = [1.0] round([2.5]) = [2.0] round([2.3]) = [2.0] round([1.5]) = [2.0] round([-4.5]) = [-4.0] ``` Det calculates determinant of a square matrix or batches of square matrices. Det takes one input tensor of shape `[*, M, M]`, where `*` is zero or more batch dimensions, and the inner-most 2 dimensions form square matrices. The output is a tensor of shape `[*]`, containing the determinants of all input submatrices. e.g., When the input is 2-D, the output is a scalar(shape is empty: `[]`). A NegativeLogLikelihoodLoss operator computes (weighted) negative log likelihood loss. Its "input" tensor has the shape of (N, C, d1, d2, ..., dk) where k >= 0. The "input" tensor contains log-probabilities for input[n, :, d_1, d_2,..., d_k] being in a class of [0, C). The operator's "target" input tensor has the shape of (N, d1, d2, ..., dk). It encodes class labels (one of C classes) or it may contain a special value (indicated by an attribute ignore_index) for N x d1 x d2 x ... x dk samples. The loss value for input[n, :, d_1, d_2,...d_k] being classified as class c = target[n][d_1][d_2]...[d_k] is computed as: ``` loss[n][d_1][d_2]...[d_k] = -input[n][c][d_1][d_2]...[d_k]. ``` When an optional "weight" is provided, the sample loss is calculated as: ``` loss[n][d_1][d_2]...[d_k] = -input[n][c][d_1][d_2]...[d_k] * weight[c]. ``` loss is zero for the case when target-value equals ignore_index. ``` loss[n][d_1][d_2]...[d_k] = 0, when target[n][d_1][d_2]...[d_k] = ignore_index ``` If "reduction" attribute is set to "none", the operator's output will be the above loss with shape (N, d1, d2, ..., dk). If "reduction" attribute is set to "mean" (the default attribute value), the output loss is (weight) averaged: ``` mean(loss), if "weight" is not provided, ``` or if weight is provided, ``` sum(loss) / sum(weight[target[n][d_1][d_2]...[d_k]]]), for all samples. ``` If "reduction" attribute is set to "sum", the output is a scalar: `sum(loss)`. See also https://pytorch.org/docs/stable/nn.html#torch.nn.NLLLoss. Example 1: ``` // negative log likelihood loss, "none" reduction N, C, d1 = 2, 3, 2 input = [[[1.0, 2.0], [2.0, 2.0], [3.0, 2.0]], [[0.0, 1.0], [2.0, 2.0], [1.0, 2]]] target = [[2, 1], [0, 2]] loss = np.zeros((N, d1)) for n in range(N): for d_1 in range(d1): c = target[n][d_1] loss[n][d_1] = -input[n][c][d_1] // print(loss) // [[-3. -2.] // [-0. -2.]] ``` Example 2: ``` // weighted negative log likelihood loss, sum reduction N, C, d1 = 2, 3, 2 input = [[[1.0, 2.0], [2.0, 2.0], [3.0, 2.0]], [[0.0, 1.0], [2.0, 2.0], [1.0, 2]]] target = [[2, 1], [0, 2]] weight = [0.2, 0.3, 0.1] loss = np.zeros((N, d1)) for n in range(N): for d_1 in range(d1): c = target[n][d_1] loss[n][d_1] = -input[n][c][d_1] * weight[c] loss = np.sum(loss) // print(loss) // -1.1 ``` Example 3: ``` // weighted negative log likelihood loss, mean reduction N, C, d1 = 2, 3, 2 input = [[[1.0, 2.0], [2.0, 2.0], [3.0, 2.0]], [[0.0, 1.0], [2.0, 2.0], [1.0, 2]]] target = [[2, 1], [0, 2]] weight = [0.2, 0.3, 0.1] loss = np.zeros((N, d1)) weight_total = 0 for n in range(N): for d_1 in range(d1): c = target[n][d_1] loss[n][d_1] = -input[n][c][d_1] * weight[c] weight_total = weight_total + weight[c] loss = np.sum(loss) / weight_total // print(loss) // -1.57 ``` This operator supports **unidirectional broadcasting** (); for more details please check [the doc](Broadcasting.md). should be unidirectional broadcastable to broadcasting: ( Performs element-wise binary {name} (with Numpy-style broadcasting support). {broadcast_doc} (Opset 14 change): Extend supported types to include uint8, int8, uint16, and int16. {broadcast_doc}D:\a\_work\_temp\onnx\src\v1.17.0-7e28176e64.clean\onnx\defs\math\defs.ccsubtractionWhether the operator should behave like fmod (default=0 meaning it will do integer mods); Set this to 1 to force fmod treatmentDividend tensorDivisor tensorRemainder tensorConstrain input and output types to high-precision numeric tensors.divisionConstrain input and output types to signed numeric tensors. { Zero = Constant () ZeroCast = CastLike (Zero, X) Y = Max (X, ZeroCast) } { Alpha = Constant () AlphaCast = CastLike (Alpha, X) Zero = Constant () ZeroCast = CastLike(Zero, X) XLessThanZero = Less(X, ZeroCast) AlphaMulX = Mul (AlphaCast, X) Y = Where (XLessThanZero, AlphaMulX, X) } { Alpha = Constant () AlphaCast = CastLike (Alpha, X) Zero = Constant () ZeroCast = CastLike (Zero, X) AlphaLessThanX = Less(AlphaCast, X) Y = Where(AlphaLessThanX, X, ZeroCast) } Coefficient of SELU default to 1.67326319217681884765625 (i.e., float32 approximation of 1.6732632423543772848170429916717).Coefficient of SELU default to 1.05070102214813232421875 (i.e., float32 approximation of 1.0507009873554804934193349852946). { Alpha = Constant () AlphaCast = CastLike (Alpha, X) Gamma = Constant () GammaCast = CastLike (Gamma, X) Zero = Constant () ZeroCast = CastLike (Zero, X) ExpX = Exp (X) AlphaMulExpX = Mul(AlphaCast, ExpX) AlphaMulExpXSubAlpha = Sub (AlphaMulExpX, AlphaCast) Neg = Mul (GammaCast, AlphaMulExpXSubAlpha) Pos = Mul (GammaCast, X) XLessThanZero = Less (X, ZeroCast) Y = Where(XLessThanZero, Neg, Pos) } Coefficient of ELU. { Alpha = Constant () AlphaCast = CastLike (Alpha, X) Zero = Constant () ZeroCast = CastLike (Zero, X) One = Constant () OneCast = CastLike (One, X) XLessThanZero = Less (X, ZeroCast) ExpX = Exp (X) ExpXSubOne = Sub (ExpX, OneCast) AlphaMulExpXSubOne = Mul (AlphaCast, ExpXSubOne) Y = Where(XLessThanZero, AlphaMulExpXSubOne, X) } Constrain input X and output types to float tensors. { Softplus_X = Softplus (X) TanHSoftplusX = Tanh (Softplus_X) Y = Mul (X, TanHSoftplusX) } Mish X_alpha = Div (X, alpha) Elu_Result = Elu (X_alpha) Y = Mul (alpha, Elu_Result) The Alpha value in Celu formula which control the shape of the unit. The default value is 1.0. Half = Constant () HalfCast = CastLike (Half, X) One = Constant () OneCast = CastLike (One, X) TwoOverPi = Constant () TwoOverPiCast = CastLike (TwoOverPi, X) C0 = Constant () C0Cast = CastLike (C0, X) SqrtTwoOverPi = Sqrt (TwoOverPiCast) Three = Constant () ThreeCast = CastLike (Three, X) XCubed = Pow (X, ThreeCast) XCubedC0 = Mul (C0Cast, XCubed) XC0XCubed = Sum (X, XCubedC0) TanhInput = Mul (SqrtTwoOverPi, XC0XCubed) ErfApprox = Tanh (TanhInput) PhiApprox = Sum (OneCast, ErfApprox) MultX = Mul (HalfCast, X) Y = Mul (MultX, PhiApprox) Half = Constant () HalfCast = CastLike (Half, X) One = Constant () OneCast = CastLike (One, X) Two = Constant () TwoCast = CastLike (Two, X) SqrtTwo = Sqrt (TwoCast) XSqrt = Div (X, SqrtTwo) ErfXSqrt = Erf(XSqrt) Phi = Sum (OneCast, ErfXSqrt) MultX = Mul (HalfCast, X) Y = Mul (MultX, Phi) Gelu approximation algorithm: `"tanh"`, `"none"`(default).`"none"`: do not use approximation.`"tanh"`: use tanh approximation.The exponential of the input tensor computed element-wiseThe natural log of the input tensor computed element-wiseThe hyperbolic tangent values of the input tensor computed element-wiseFirst operand, base of the exponent.Second operand, power of the exponent.Constrain input X and output types to float/int tensors.Constrain input Y types to float/int tensors.input tensor Xtensor slopeSlope tensor. The shape of slope can be smaller than first input X; if so, its shape must be unidirectional broadcastable to XslopeOutput tensor (same size as X) { Zero = Constant () ZeroCast = CastLike(Zero, X) XLessThanZero = Less (X, ZeroCast) SlopeMulX = Mul (slope, X) Y = Where(XLessThanZero, SlopeMulX, X) } Value of alpha.Value of beta. { Alpha = Constant () AlphaCast = CastLike (Alpha, X) Beta = Constant () BetaCast = CastLike (Beta, X) Zero = Constant () ZeroCast = CastLike (Zero, X) One = Constant () OneCast = CastLike (One, X) AlphaMulX = Mul (X, AlphaCast) AlphaMulXAddBeta = Add (AlphaMulX, BetaCast) MinOneOrAlphaMulXAddBeta = Min (AlphaMulXAddBeta, OneCast) Y = Max(MinOneOrAlphaMulXAddBeta, ZeroCast) } { HS_X = HardSigmoid(X) Y = Mul (X, HS_X) } Element-wise {name} of each of the input tensors (with Numpy-style broadcasting support). All inputs and outputs must have the same data type. {broadcast_doc} List of tensors for Constrain input and output types to numeric tensors.output = Identity (input)input_less_than_min = Less (input, min)output = Where (input_less_than_min, min, input)input_large_than_max = Less (max, input)output = Where (input_large_than_max, max, input)tmp = Where (input_less_than_min, min, input)output_large_than_max = Less (max, tmp)output = Where (output_large_than_max, max, tmp)Input tensor whose elements to be clippedMinimum value, under which element is replaced by min. It must be a scalar(tensor of empty shape).Maximum value, above which element is replaced by max. It must be a scalar(tensor of empty shape).Output tensor with clipped input elements The operator computes the {description} values for the given input: {equation} The "axis" attribute indicates the dimension along which {name} will be performed. The output tensor has the same shape and contains the {name} values of the corresponding input. {description}{equation} Describes the dimension {name} will be performed on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input). The input tensor of rank >= axis.The output values with the same shape as the input tensor.X_ReduceMax = ReduceMax (input) X_Sub = Sub (input, X_ReduceMax) X_Exp = Exp (X_Sub) X_ReduceSum = ReduceSum (X_Exp, axes) output = Div (X_Exp, X_ReduceSum) X_ReduceMax = ReduceMax (input, axes)Softmax(input, axis) = Exp(input) / ReduceSum(Exp(input), axis=axis, keepdims=1) normalized exponential X_Sub = Sub (input, X_ReduceMax) X_Exp = Exp (X_Sub) X_ReduceSum = ReduceSum (X_Exp, axes) X_Log = Log (X_ReduceSum) output = Sub (X_Sub, X_Log) LogSoftmax(input, axis) = Log(Softmax(input, axis=axis))log of softmaxHardmax(element in input, axis) = 1 if the element is the first maximum value along the specified axis, 0 otherwisehardmaxThe softsign (x/(1+|x|)) values of the input tensor computed element-wise { One = Constant () OneCast = CastLike (One, input) AbsInput = Abs(input) OneAddAbsInput = Add (OneCast, AbsInput) output = Div(input, OneAddAbsInput) } { exp_x = Exp (X) one = Constant () one_cast = CastLike (one, X) exp_x_add_one = Add (exp_x, one_cast) Y = Log (exp_x_add_one) } tensor A * Btensor COptional input tensor C. If not specified, the computation is done as if C is a scalar 0. The shape of C should be unidirectional broadcastable to (M, N).K input must be a one-dimensional tensor of size 1.K input must be of type int64.Axis has less than the requested k elements.Tensor of shape [a_0, a_1, ..., a_{n-1}]A 1-D tensor containing a single positive value corresponding to the number of top elements to retrieveTensor of shape [a_0, a_1, ..., a_{axis-1}, k, a_{axis+1}, ... a_{n-1}] containing top K values from the input tensorValuesTensor of shape [a_0, a_1, ..., a_{axis-1}, k, a_{axis+1}, ... a_{n-1}] containing the corresponding input tensor indices for the top K values.Dimension on which to do the sort. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).Whether to return the top-K largest or smallest elements.Whether to return the elements in sorted order.The sine of the input tensor computed element-wiseThe cosine of the input tensor computed element-wiseThe tangent of the input tensor computed element-wiseThe arcsine of the input tensor computed element-wiseThe arccosine of the input tensor computed element-wiseThe arctangent of the input tensor computed element-wiseA 1-D tensor indicates the shape you want to expand to, following the broadcast ruleConstrain input and output types to all tensors.The hyperbolic sine values of the input tensor computed element-wiseThe hyperbolic cosine values of the input tensor computed element-wiseThe hyperbolic arcsine values of the input tensor computed element-wiseThe hyperbolic arccosine values of the input tensor computed element-wiseThe hyperbolic arctangent values of the input tensor computed element-wiseThe sign of the input tensor computed element-wise. It has the same shape and type of the input.The error function of the input tensor computed element-wise. It has the same shape and type of the input.N-dimensional quantized matrix ascale of quantized input azero point of quantized input aN-dimensional quantized matrix bscale of quantized input bzero point of quantized input bscale of quantized output yzero point of quantized output yQuantized matrix multiply results from a * bConstrain scales.The type of input a and its zeropoint.The type of input b and its zeropoint.The type of the output and its zeropoint.Zero point tensor for input 'A'. It's optional and default value is 0. It could be a scalar or N-D tensor. Scalar refers to per tensor quantization whereas N-D refers to per row quantization. If the input is 2D of shape [M, K] then zero point tensor may be an M element vector [zp_1, zp_2, ..., zp_M]. If the input is N-D tensor with shape [D1, D2, M, K] then zero point tensor may have shape [D1, D2, M, 1]. Zero point tensor for input 'B'. It's optional and default value is 0. It could be a scalar or a N-D tensor, Scalar refers to per tensor quantization whereas N-D refers to per col quantization. If the input is 2D of shape [K, N] then zero point tensor may be an N element vector [zp_1, zp_2, ..., zp_N]. If the input is N-D tensor with shape [D1, D2, K, N] then zero point tensor may have shape [D1, D2, 1, N]. Constrain output Y data type as 32-bit integer tensor.If set to 1 will return exclusive sum in which the top element is not included. In other terms, if set to 1, the j-th output element would be the sum of the first (j-1) elements. Otherwise, it would be the sum of the first j elements.If set to 1 will perform the sums in reverse direction.An input tensor that is to be processed.A 0-D tensor. Must be in the range [-rank(x), rank(x)-1]. Negative value means counting dimensions from the back.Output tensor of the same type as 'x' with cumulative sums of the x's elementsaxis tensor can be int32 or int64 onlyConstrain input and output types to floating-point tensors.const_zeroconst_oneexpanded_target = Unsqueeze (target, axes)ignore_index input_gather_element = GatherElements (input, expanded_target) loss_NCdd = Neg (input_gather_element) loss_N1dd = Slice (loss_NCdd, const_zero, const_one, const_one) loss = Squeeze (loss_N1dd, axes)loss_Ndd = Squeeze (loss_N1dd, axes)loss = ReduceMean (loss_Ndd)loss = ReduceSum (loss_Ndd)weight_gather = Gather (weight, target)loss_unweighted = Squeeze (loss_N1dd, axes)loss = Mul (loss_unweighted, weight_gather)loss_Ndd = Mul (loss_unweighted, weight_gather) loss_sum = ReduceSum (loss_Ndd) weight_gather_sum = ReduceSum (weight_gather) loss = Div (loss_sum, weight_gather_sum) const_ignore_index const_zero_target_typed = Sub (expanded_target, expanded_target) expanded_target_int64 = Cast (expanded_target) mask = Equal (expanded_target_int64, const_ignore_index) transform_targets = Where (mask, const_zero_target_typed, expanded_target) input_gather_element = GatherElements (input, transform_targets)const_zero_floatconst_zero_casted = Cast (const_zero_float)input_gather_element_transform = Where (mask, const_zero_casted, input_gather_element)input_gather_element_transform = Where (mask, const_zero_float, input_gather_element)loss_NCdd = Neg (input_gather_element_transform)loss_N1dd = Slice (loss_NCdd, const_zero, const_one, const_one)squeeze_mask = Squeeze (mask, axes)const_one_floatconst_one_casted = Cast (const_one_float)weight_gather = Where (squeeze_mask, const_zero_casted, const_one_casted)weight_gather = Where (squeeze_mask, const_zero_float, const_one_float)weight_gather_temp = Gather (weight, transform_targets)weight_gather_temp_1 = Where (mask, const_zero_float, weight_gather_temp)weight_gather_temp_1 = Where (mask, const_zero_casted, weight_gather_temp)weight_gather = Squeeze (weight_gather_temp_1, axes) loss_sum = ReduceSum (loss_Ndd) weight_gather_sum = ReduceSum (weight_gather) loss = Div (loss_sum, weight_gather_sum) Input rank must be >= 2. input_rank=, target_rank=Target rank must be 1 less than the input rank. input_rank= target_dim_value=Input and target dimension value mismatch. input_dim_value=Weight rank must be 1. weight_rank=Input tensor of shape (N, C) or (N, C, d1, d2, ..., dk).Target tensor of shape (N) or (N, d1, d2, ..., dk). Target element value shall be in range of [0, C). If ignore_index is specified, it may have a value outside [0, C) and the target values should either be in the range [0, C) or have the value ignore_index.targetOptional rescaling weight tensor. If given, it has to be a tensor of size C. Otherwise, it is treated as if having all ones.The negative log likelihood losslossType of reduction to apply to loss: none, sum, mean (default). 'none': the output is the loss for each sample. 'sum': the output will be summed. 'mean': the sum of the output will be divided by the sum of applied weights.Specifies a target value that is ignored and does not contribute to the input gradient. It's an optional value.Constrain input, weight, and output types to floating-point tensors.Constrain target to integer typesNegativeLogLikelihoodLossNumber of input tensors does not match the operands in the equation.Ellipsis represents incompatible dimensions. does not match the equation indices.Rank of input Einsum expression string.OperandsInputsConstrain input and output types to all numerical tensor types.Shape3D X_NCD = Reshape (scores, Shape3D) X_NDC = Transpose (X_NCD) X_LogSM = LogSoftmax (X_NDC) X_LogSM_NCD = Transpose (X_LogSM) X_shape = Shape (scores) X_Log = Reshape (X_LogSM_NCD, X_shape) log_prob = Identity (X_Log)output = NegativeLogLikelihoodLoss (X_Log, labels, weights)output = NegativeLogLikelihoodLoss (X_Log, labels)The predicted outputs with shape [batch_size, class_size], or [batch_size, class_size, D1, D2 , ..., Dk], where K is the number of dimensions.The ground truth output tensor, with shape [batch_size], or [batch_size, D1, D2, ..., Dk], where K is the number of dimensions. Labels element value shall be in range of [0, C). If ignore_index is specified, it may have a value outside [0, C) and the label values should either be in the range [0, C) or have the value ignore_index.labelsA manual rescaling weight given to each class. If given, it has to be a 1D Tensor assigning weight to each of the classes. Otherwise, it is treated as if having all ones.Weighted loss float Tensor. If reduction is 'none', this has the shape of [batch_size], or [batch_size, D1, D2, ..., Dk] in case of K-dimensional loss. Otherwise, it is a scalar.Log probability tensor. If the output of softmax is prob, its value is log(prob).log_probSoftmaxCrossEntropyLossis_onesided and inverse attributes cannot be enabled at the same timeinput tensor must have rank >= 2, including the complex dimension.rank == static_cast(new_shape_proto.dim_size())operator ()%s:%u: %s: Assertion `%s` failed: rank should be equal to new_shape_proto.dim_size()axis_tensor != nullptr%s:%u: %s: Assertion `%s` failed: axis should not be nullptr at this pointaxis input must be a scalar.. Valid values are '-rank <= axis && axis != -1 && axis < rank - 1' is invalid for a tensor of rank axis attribute value dft_length input must be a scalar.If `onesided` is `1` and input is real, only values for `k` in `[0, 1, 2, ..., floor(n_fft/2) + 1]` are returned because the real-to-complex Fourier transform satisfies the conjugate symmetry, i.e., `X[m, k] = X[m, n_fft-k]*`, where `m` denotes "all other dimensions" DFT was not applied on. If the input tensor is complex, onesided output is not possible. Value can be `0` or `1`. Default is `0`.Whether to perform the inverse discrete Fourier Transform. Default is 0, which corresponds to `false`.For real input, the following shape is expected: `[signal_dim0][signal_dim1][signal_dim2]...[signal_dimN][1]`. For complex input, the following shape is expected: `[signal_dim0][signal_dim1][signal_dim2]...[signal_dimN][2]`. The final dimension represents the real and imaginary parts of the value in that order.The length of the signal as a scalar. If greater than the axis dimension, the signal will be zero-padded up to `dft_length`. If less than the axis dimension, only the first `dft_length` values will be used as the signal. dft_lengthThe axis as a scalar on which to perform the DFT. Default is `-2` (last signal axis). Negative value means counting dimensions from the back. Accepted range is $[-r, -2] \cup [0, r-2]$ where `r = rank(input)`. The last dimension is for representing complex numbers and thus is an invalid axis.The Fourier Transform of the input vector. If `onesided` is `0`, the following shape is expected: `[signal_dim0][signal_dim1][signal_dim2]...[signal_dimN][2]`. If `axis=0` and `onesided` is `1`, the following shape is expected: `[floor(signal_dim0/2)+1][signal_dim1][signal_dim2]...[signal_dimN][2]`. If `axis=1` and `onesided` is `1`, the following shape is expected: `[signal_dim0][floor(signal_dim1/2)+1][signal_dim2]...[signal_dimN][2]`. If `axis=N` and `onesided` is `1`, the following shape is expected: `[signal_dim0][signal_dim1][signal_dim2]...[floor(signal_dimN/2)+1][2]`. The `signal_dim` at the specified `axis` is equal to the `dft_length`.Constrain scalar length types to integers.size input must be a scalar.size input must be greater than 0. Generates a {name} window as described in the paper https://ieeexplore.ieee.org/document/1455106. The data type of the output tensor. Strictly must be one of the values from DataType enum in TensorProto whose values correspond to T2. The default value is 1 = FLOAT. If 1, returns a window to be used as periodic function. If 0, return a symmetric window. When 'periodic' is specified, hann computes a window of length size + 1 and returns the first size points. The default value is 1. A scalar value indicating the length of the window.A {name} window with length: size. The output has the shape: [size].HannConstrain the input size to int64_t.Constrain output types to numeric tensors. { A0 = Constant () A1 = Constant () A2 = Constant () Zero = Constant () One = Constant () Two = Constant () Tau = Constant () Periodic_Size_FP = Cast (size) Symmetric_Size_FP = Sub(Periodic_Size_FP, One) IsPeriodic = Constant () IsPeriodic_FP = Cast (IsPeriodic) IsSymmetric_FP = Sub(One, IsPeriodic_FP) Periodic_Component = Mul(Periodic_Size_FP, IsPeriodic_FP) Symmetric_Component = Mul(Symmetric_Size_FP, IsSymmetric_FP) Size_FP = Add(Periodic_Component, Symmetric_Component) AngularIncrement = Div (Tau, Size_FP) Range = Range (Zero, Periodic_Size_FP, One) RangeAngular = Mul (Range, AngularIncrement) TwoRangeAngular = Mul (RangeAngular, Two) CosTwoRangeAngular = Cos (TwoRangeAngular) A2_Component = Mul (A2, CosTwoRangeAngular) CosRangeAngular = Cos (RangeAngular) A1_Component = Mul (A1, CosRangeAngular) Temp0 = Sub (A0, A1_Component) Temp1 = Add (Temp0, A2_Component) output = Cast (Temp1) } Hamming { A0 = Constant () A1 = Constant () A2 = Constant () Zero = Constant () One = Constant () Two = Constant () Tau = Constant () Periodic_Size_FP = Cast (size) Symmetric_Size_FP = Sub(Periodic_Size_FP, One) IsPeriodic = Constant () IsPeriodic_FP = Cast (IsPeriodic) IsSymmetric_FP = Sub(One, IsPeriodic_FP) Periodic_Component = Mul(Periodic_Size_FP, IsPeriodic_FP) Symmetric_Component = Mul(Symmetric_Size_FP, IsSymmetric_FP) Size_FP = Add(Periodic_Component, Symmetric_Component) AngularIncrement = Div (Tau, Size_FP) Range = Range (Zero, Periodic_Size_FP, One) RangeAngular = Mul (Range, AngularIncrement) TwoRangeAngular = Mul (RangeAngular, Two) CosTwoRangeAngular = Cos (TwoRangeAngular) A2_Component = Mul (A2, CosTwoRangeAngular) CosRangeAngular = Cos (RangeAngular) A1_Component = Mul (A1, CosRangeAngular) Temp0 = Sub (A0, A1_Component) Temp1 = Add (Temp0, A2_Component) output = Cast (Temp1) } Blackman { A0 = Constant () A1 = Constant () A2 = Constant () Zero = Constant () One = Constant () Two = Constant () Tau = Constant () Periodic_Size_FP = Cast (size) Symmetric_Size_FP = Sub(Periodic_Size_FP, One) IsPeriodic = Constant () IsPeriodic_FP = Cast (IsPeriodic) IsSymmetric_FP = Sub(One, IsPeriodic_FP) Periodic_Component = Mul(Periodic_Size_FP, IsPeriodic_FP) Symmetric_Component = Mul(Symmetric_Size_FP, IsSymmetric_FP) Size_FP = Add(Periodic_Component, Symmetric_Component) AngularIncrement = Div (Tau, Size_FP) Range = Range (Zero, Periodic_Size_FP, One) RangeAngular = Mul (Range, AngularIncrement) TwoRangeAngular = Mul (RangeAngular, Two) CosTwoRangeAngular = Cos (TwoRangeAngular) A2_Component = Mul (A2, CosTwoRangeAngular) CosRangeAngular = Cos (RangeAngular) A1_Component = Mul (A1, CosRangeAngular) Temp0 = Sub (A0, A1_Component) Temp1 = Add (Temp0, A2_Component) output = Cast (Temp1) } num_mel_bins input must be scalar.dft_length input must be scalar.The data type of the output tensor. Strictly must be one of the values from DataType enum in TensorProto whose values correspond to T3. The default value is 1 = FLOAT. The number of bands in the mel spectrum.num_mel_binsThe size of the original DFT. The size of the original DFT is used to infer the size of the onesided DFT, which is understood to be floor(dft_length/2) + 1, i.e. the spectrogram only contains the nonredundant DFT bins.Samples per second of the input signal used to create the spectrogram. Used to figure out the frequencies corresponding to each spectrogram bin, which dictates how they are mapped into the mel scale.sample_rateLower bound on the frequencies to be included in the mel spectrum. This corresponds to the lower edge of the lowest triangular band.lower_edge_hertzThe desired top edge of the highest frequency band.upper_edge_hertzThe Mel Weight Matrix. The output has the shape: [floor(dft_length/2) + 1][num_mel_bins].Constrain to integer tensors.Constrain to float tensorsConstrain to any numerical types.First input should have at least 2 dimensions in frame_length input must be scalar.window input must have rank = 1.If STFT has both a window input and frame_length specified, the dimension of the window must match the frame_length specified!If onesided is 1, only values for w in [0, 1, 2, ..., floor(n_fft/2) + 1] are returned because the real-to-complex Fourier transform satisfies the conjugate symmetry, i.e., X[m, w] = X[m,w]=X[m,n_fft-w]*. Note if the input or window tensors are complex, then onesided output is not possible. Enabling onesided with real inputs performs a Real-valued fast Fourier transform (RFFT).When invoked with real or complex valued input, the default value is 1. Values can be 0 or 1.Input tensor representing a real or complex valued signal. For real input, the following shape is expected: [batch_size][signal_length][1]. For complex input, the following shape is expected: [batch_size][signal_length][2], where [batch_size][signal_length][0] represents the real component and [batch_size][signal_length][1] represents the imaginary component of the signal.signalThe number of samples to step between successive DFTs.frame_stepA tensor representing the window that will be slid over the signal.The window must have rank 1 with shape: [window_shape]. It's an optional value. A scalar representing the size of the DFT. It's an optional value.frame_lengthThe Short-time Fourier Transform of the signals.If onesided is 1, the output has the shape: [batch_size][frames][dft_unique_bins][2], where dft_unique_bins is frame_length // 2 + 1 (the unique components of the DFT) If onesided is 0, the output has the shape: [batch_size][frames][frame_length][2], where frame_length is the length of the DFT.Constrain signal and output to float tensors.Constrain scalar length types to int64_t.  C# @@C#XC#``C#0C#<@C#``C#  C# p@C#l00C#`P #ppC#P0 C#pC#C#4PC#p`C#C#P@C#dC# C#PPC#H@@C#C#PC#, @0C#x ppC# ` C# 00C# This operator produces a constant tensor. Exactly one of the provided attributes, either value, sparse_value, or value_* must be specified. ??@-@ Generate a tensor with given value and shape. Generate a 2D tensor (matrix) with ones on the diagonal and zeros everywhere else. Only 2D tensors are supported, i.e. input T1 must be of rank 2. The shape of the output tensor is the same as the input tensor. The data type can be specified by the 'dtype' argument. If 'dtype' is not specified, then the type of input tensor is used. By default, the main diagonal is populated with ones, but attribute 'k' can be used to populate upper or lower diagonals. The 'dtype' argument must be one of the data types specified in the 'DataType' enum field in the TensorProto message and be valid as an output type. Generate a tensor with random values drawn from a uniform distribution. The shape of the tensor is specified by the `shape` argument and the range by `low` and `high`. The data type is specified by the 'dtype' argument. The 'dtype' argument must be one of the data types specified in the 'DataType' enum field in the TensorProto message. Generate a tensor with random values drawn from a normal distribution. The shape of the tensor is specified by the `shape` argument and the parameter of the normal distribution specified by `mean` and `scale`. The data type is specified by the 'dtype' argument. The 'dtype' argument must be one of the data types specified in the 'DataType' enum field in the TensorProto message. Generate a tensor with random values drawn from a uniform distribution. The shape of the output tensor is copied from the shape of the input tensor, and the parameters of the uniform distribution are specified by `low` and `high`. The data type is specified by the 'dtype' argument, or copied from the input tensor if not provided. The 'dtype' argument must be one of the data types specified in the 'DataType' enum field in the TensorProto message and be valid as an output type. Generate a tensor with random values drawn from a normal distribution. The shape of the output tensor is copied from the shape of the input tensor, and the parameters of the normal distribution are specified by `mean` and `scale`. The data type is specified by the 'dtype' argument, or copied from the input tensor if not provided. The 'dtype' argument must be one of the data types specified in the 'DataType' enum field in the TensorProto message, and be valid as an output type. Generate a tensor of samples from a multinomial distribution according to the probabilities of each of the possible outcomes. Generate a tensor containing a sequence of numbers that begin at `start` and extends by increments of `delta` up to `limit` (exclusive). The number of elements in the output of range is computed as below: ``` number_of_elements = max( ceil( (limit - start) / delta ) , 0 ) ``` The pseudocode determining the contents of the output is shown below: ``` for(int i=0; i= 0.Output tensor of shape specified by 'input'.If attribute 'value' is specified, the value and datatype of the output tensor is taken from 'value'.If attribute 'value' is not specified, the value in the output defaults to 0, and the datatype defaults to float32.Constrain input types.Constrain output types to be numerics or boolean.Input tensor must be 2-dimensional(Optional) Index of the diagonal to be populated with ones. Default is 0. If T2 is the output, this op sets T2[i, i+k] = 1. k = 0 populates the main diagonal, k > 0 populates an upper diagonal, and k < 0 populates a lower diagonal.(Optional) The data type for the elements of the output tensor. If not specified,the data type of the input tensor T1 is used. If input tensor T1 is also notspecified, then type defaults to 'float'.2D input tensor to copy shape, and optionally, type information from.Output tensor, same shape as input tensor T1.Constrain input types. Strings and complex are not supported.Constrain output types. Strings and complex are not supported.Lower boundary of the output values.Upper boundary of the output values.The data type for the elements of the output tensor. If not specified, default is TensorProto::FLOAT.The shape of the output tensor.Output tensor of random values drawn from uniform distributionThe mean of the normal distribution.The standard deviation of the normal distribution.The data type for the elements of the output tensor. Default is TensorProto::FLOAT.Output tensor of random values drawn from normal distribution(Optional) The data type for the elements of the output tensor, if not specified, we will use the data type of the input tensor.Input tensor to copy shape and optionally type information from.Output type must be int32 or int64Input tensor must have rank 2Number of times to sample.(Optional) The data type for the elements of the output tensor, if not specified, we will use int32.Input tensor with shape [batch_size, class_size], where class_size is the number of all possible outcomes. Each value along the axis zero represents the unnormalized log-probability of each corresponding outcome in a batch.Output tensor with shape [batch_size, sample_size], where sample_size is the number of times to sample. Each value along the axis zero represents the outcome of the corresponding sample in a batch.Constrain output types to integral tensors.All inputs to 'Range' op must be of the same typeScalar. First entry for the range of output values.Scalar. Exclusive upper limit for the range of output values.Scalar. Value to step by.A 1-D tensor with same type as the inputs containing generated range of values.Constrain input types to common numeric type tensors. { sub_result = Sub (limit, start) sub_result_casted = Cast (sub_result) delta_casted = Cast (delta) div_result = Div (sub_result_casted, delta_casted) ceil_result = Ceil (div_result) ceil_result_relu = Relu (ceil_result) ceil_result_relu_int = Cast (ceil_result_relu) ceil_result_relu_bool = Cast (ceil_result_relu) variadic_output, output = Loop (ceil_result_relu_int, ceil_result_relu_bool, start) (cond_out, current, range) { cond_out = Identity (cond) current = Add (prev, delta) range = Identity (prev) }> } X_random = RandomUniformLike (input)X_greater = Greater (X_random, input)output = Cast (X_greater)The data type for the elements of the output tensor. if not specified, we will use the data type of the input tensor.All values in input have to be in the range:[0, 1].The returned output tensor only has values 0 or 1, same shape as input tensor.Constrain output types to all numeric tensors and bool tensors.BernoulliInput to 'Range' op should be scalars (Tensor with only one element and shape empty)\ HH0IIC# HH@IIC# @H@HHPIC#@ HHHIC# PHPHH`IC# HHHIC#$ pHpHIIC#p `H`HIpIC# HHHIC# HH IIC# Generic Looping construct. This loop has multiple termination conditions: 1) Trip count. Iteration count specified at runtime. Set by specifying the input M. Optional. Set to empty string to omit. Note that a static trip count (specified at graph construction time) can be specified by passing in a constant node for input M. 2) Loop termination condition. This is an input to the op that determines whether to run the first iteration and also a loop-carried dependency for the body graph. The body graph must yield a value for the condition variable, whether this input is provided or not. This table summarizes the operating modes of this operator with equivalent C-style code: Operator inputs defined as (max_trip_count, condition_var). * input ("", ""): for (int i=0; ; ++i) { cond = ... // Note this value is ignored, but is required in the body } * input ("", cond) // Note this is analogous to a while loop bool cond = ...; for (int i=0; cond; ++i) { cond = ...; } * input ("", 1) // Note this is analogous to a do-while loop bool cond = true for (int i=0; cond; ++i) { cond = ...; } * input (trip_count, "") // Note this is analogous to a for loop int trip_count = ... for (int i=0; i < trip_count; ++i) { cond = ...; // ignored } * input (trip_count, cond) int trip_count = ...; bool cond = ...; for (int i=0; i < trip_count && cond; ++i) { cond = ...; } *Sample usage - cond as well as trip count* graph predict-net { %a = Constant[value = ]() %b = Constant[value = ]() %keepgoing = Constant[value = ]() %max_trip_count = Constant[value = ]() %keepgoing_out, %b_out, %user_defined_vals = Loop[body = ](%max_trip_count, %keepgoing, %b) return } graph body-net ( %i[INT32, scalar] // iteration number %keepgoing_in[BOOL, scalar] // incoming loop-termination-condition; not used %b_in[INT32, scalar] // incoming value of loop-carried-dependency b ) { %my_local = Add(%a, %b_in) %b_out = Sub(%a, %b_in) // outgoing value of loop-carried-dependency b %keepgoing_out = Greater(%my_local, %b_out) // outgoing loop-termination-condition %user_defined_val = Add(%b_in, %b_in) // scan-output value to be accumulated return %keepgoing_out, %b_out, %user_defined_val } *Sample equivalent C code* { /* User-defined code (enclosing scope) */ int a = 3, b = 6; bool keepgoing = true; // Analogous to input cond /* End user-defined code */ /* Implicitly-defined code */ const int max_trip_count = 10; // Analogous to input M int user_defined_vals[]; // Imagine this is resizable /* End implicitly-defined code */ /* initialize loop-carried variables and scan-output variables */ bool keepgoing_out = keepgoing int b_out = b for (int i=0; i < max_trip_count && keepgoing_out; ++i) { /* Implicitly-defined code: bind actual parameter values to formal parameter variables of loop-body */ bool keepgoing_in = keepgoing_out; bool b_in = b_out; /* User-defined code (loop body) */ int my_local = a + b_in; // Reading value "a" from the enclosing scope is fine b_out = a - b_in; keepgoing_out = my_local > b_out; user_defined_val = b_in + b_in; // b_in and b_out are different variables /* End user-defined code */ /* Implicitly defined-code */ user_defined_vals[i] = user_defined_val // accumulate scan-output values } // int t = my_local; // Can't do this. my_local is not accessible here. // The values below are bound to the output variables of the loop and therefore accessible // b_out; user_defined_vals; keepgoing_out; } There are several things of note in this code snippet: 1) Values from the enclosing scope (i.e. variable "a" here) are in scope and can be referenced in the inputs of the loop. 2) Any values computed in the loop body that needs to be used in a subsequent iteration or after the loop are modelled using a pair of variables in the loop-body, consisting of an input variable (eg., b_in) and an output variable (eg., b_out). These are referred to as loop-carried dependences. The loop operation node supplies the input value of the input variable for the first iteration, and returns the output value of the output variable produced by the final iteration. 3) Scan_output variables are used to implicitly concatenate values computed across all the iterations. In the above example, the value of user_defined_val computed over all iterations are concatenated and returned as the value of user_defined_vals after the loop. 4) Values created in the body cannot be accessed in the enclosing scope, except using the mechanism described above. Note that the semantics of this op support "diagonal" or "wavefront" execution. (See Step 3 here for an example: https://devblogs.nvidia.com/optimizing-recurrent-neural-networks-cudnn-5/). Frontends should emit multi-layer RNNs as a series of While operators (with time being the inner looping dimension), with each successive layer consuming the scan_outputs from the previous layer, possibly going through several point-wise operators (e.g. dropout, residual connections, linear layer). The input/output of subgraph (produced by loop node) matching is based on order instead of name. The implementation will figure out the names based on this order. Scan can be used to iterate over one or more scan_input tensors, constructing zero or more scan_output tensors. It combines ideas from general recurrences, functional programming constructs such as scan, fold, map, and zip, and is intended to enable generalizations of RNN-like constructs for sequence-to-sequence processing. Other tensors (referred to as state_variables here) can be used to carry a state when iterating from one element to another (similar to hidden-state in RNNs, also referred to as loop-carried dependences in the context of loops). Many common usages involve a single scan_input tensor (where functionality similar to scan, fold and map can be obtained). When more than one scan_input is used, a behavior similar to zip is obtained. The attribute body must be a graph, specifying the computation to be performed in every iteration. It takes as input the current values of the state_variables and the current iterated element of the scan_inputs. It must return the (updated) values of the state_variables and zero or more scan_output_element tensors. The values of the scan_output_element tensors are concatenated over all the iterations to produce the scan_output values of the scan construct (similar to the concatenated intermediate hidden-state values of RNN-like constructs). All the output tensors (state_variables as well as scan_output_element tensors) are required to have the same shape in each iteration of the loop (a restriction imposed to enable efficient memory allocation). Note that the iterated element passed to the body subgraph does not have a sequence axis. It will have a rank one less than the rank of the corresponding scan_input. The scan operation returns the final values of the state_variables as well as the scan_outputs. The optional attribute scan_input_directions specifies the direction (forward or backward) for each scan input. If this attribute is omitted, all sequences are scanned in the forward direction. A bidirectional scan may be performed by specifying the same tensor input twice in the scan_inputs, once with a forward direction, and once with a backward direction. The scan_output of the operation is produced by concatenating the scan_output_element values produced by the body in each iteration. The optional attribute scan_output_directions specifies the direction in which scan_output is constructed (by appending or prepending the scan_output_element to scan_output in each iteration) for each scan_output. If this attribute is omitted, the scan_output_element is appended to the scan_output in each iteration. The optional attribute scan_input_axes specifies the axis to be scanned for each scan_input. If omitted, every scan_input will be scanned in axis 0. For example, if axis 0 is the batch axis and axis 1 is the time axis (to be scanned), specify an axis value of 1. Note that scanning a non-zero axis may be less efficient than scanning axis zero. The optional attribute scan_output_axes specifies the axis along which the scan_outputs are accumulated for each scan_output. For example, if axis 1 is the time axis (to be scanned) for both inputs and outputs, specify a scan_input axis and scan_output axis value of 1. Note that because of the ONNX restriction that only the last parameter of an operator can be variadic, the initial-states and scan-inputs are listed together as one input parameter. Similarly, the final-states and scan-outputs are listed together as one output parameter. The attribute num_scan_inputs indicates the number M of scan-inputs. The behavior of Scan < num_scan_inputs = m, body = loop-body, scan_input_axes = [axis_1, ..., axis_m] > (init_1, ..., init_n, scan_1, ..., scan_m) is equivalent to the following pseudo-code: // scan_i.shape[axis_i] denotes the (max) sequence-length of scan_i // scan_i.shape[axis_i] is required to be equal to scan_j.shape[axis_j] for all i,j. sequence_length = scan_1.shape[axis_1]; // initialize state-variables st_1 = init_1; ... st_n = init_n; // initialize scan-output variables: [] denotes an empty tensor scan_out_1 = []; ...; scan_out_k = []; // identify number of iterations: // execute loop for (int t = 0; t < sequence_length; ++t) { // generate the scan-input elements: the notation T[t] indicates the sub-tensor // of rank one less than T obtained by indexing T at position t along axis k. si_1 = scan_1[t]; ... ; si_m = scan_m[t]; // execute loop-body st_1, ..., st_n, so_1, ..., so_k = loop-body(st_1, ..., st_n, si_1, ..., si_m) // accumulate the scan-output elements scan_out_1 = Concat(scan_out_1, so_1); ... ; scan_out_k = Concat(scan_out_k, so_k); } return st_1, ..., st_n, scan_out_1, ..., scan_out_k; *Sample usage: Encoding RNN using a Scan* The following example shows how a simple RNN over an input tensor %X, with weight tensor %Wi, recurrence weight tensor %Ri, bias tensors %Wbi and %Rbi, and initial hidden-state %H_0 can be encoded as a ScanLoop. Note that the loop-body is a nested graph, and it directly computes %Wi, %Ri, %Wbi, and %Rbi (typically constants or initializers in the body graph). If these values are computed in the outer graph, they need to be passed in as extra state_variables. graph rnn-encoding { %H_0 = ... %X = ... %Y_h, %Y = Scan[body = , num_scan_inputs=1](%H_0, %X) return %Y, %Y_h } graph rnn-cell-1 ( %H_tminus1[FLOAT, tensor] %X_t[FLOAT, tensor] ) { %Wi = ... %Ri = ... %Wbi = ... %Rbi = ... %t1 = X_t * (Wi^T) %t2 = H_tminus1*(Ri^T) %t3 = Add(%t1, %t2) %t4 = Add(%t3, %Wbi) %t5 = Add(%t4, %Rbi) %Ht = Tanh(%t5) %Accumulate = Identity(%Ht) return %Ht, %Accumulate } ??@-@seq(tensor(float8e4m3fn))seq(tensor(float8e4m3fnuz))seq(tensor(float8e5m2))seq(tensor(float8e5m2fnuz))optional(seq(tensor(bfloat16)))optional(tensor(bfloat16))optional(tensor(float8e4m3fn))optional(tensor(float8e4m3fnuz))optional(tensor(float8e5m2))optional(tensor(float8e5m2fnuz))optional(tensor(uint4))optional(tensor(int4))If conditionalCondition for the if. The tensor must contain a single element.condValues that are live-out to the enclosing scope. The return values in the `then_branch` and `else_branch` must be of the same data type. The `then_branch` and `else_branch` may produce tensors with the same element type and different shapes. If corresponding outputs from the then-branch and the else-branch have static shapes S1 and S2, then the shape of the corresponding output variable of the if-node (if present) must be compatible with both S1 and S2 as it represents the union of both possible shapes.For example, if in a model file, the first output of `then_branch` is typed float tensor with shape [2] and the first output of `else_branch` is another float tensor with shape [3], If's first output should have (a) no shape set, or (b) a shape of rank 1 with neither `dim_value` nor `dim_param` set, or (c) a shape of rank 1 with a unique `dim_param`. In contrast, the first output cannot have the shape [2] since [2] and [3] are not compatible.Graph to run if condition is true. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the else_branch.Graph to run if condition is false. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the then_branch.All Tensor, Sequence(Tensor), Optional(Tensor), and Optional(Sequence(Tensor)) types up to IRv10.Only boolD:\a\_work\_temp\onnx\src\v1.17.0-7e28176e64.clean\onnx\defs\controlflow\defs.ccA maximum trip-count for the loop specified at runtime. Optional. Pass empty string to skip.A boolean termination condition. Optional. Pass empty string to skip.The initial values of any loop-carried dependencies (values that change across loop iterations)v_initialFinal N loop carried dependency values then K scan_outputs. Scan outputs must be Tensors.v_final_and_scan_outputsThe graph run each iteration. It has 2+N inputs: (iteration_num, condition, loop carried dependencies...). It has 1+N+K outputs: (condition, loop carried dependencies..., scan_outputs...). Each scan_output is created by concatenating the value of the specified output value at the end of each iteration of the loop. It is an error if the dimensions or data type of these scan_outputs change across loop iterations.tensor of int64, which should be a scalar.tensor of bool, which should be a scalar.Initial values of the loop's N state variables followed by M scan_inputsinitial_state_and_scan_inputsFinal values of the loop's N state variables followed by K scan_outputsfinal_state_and_scan_outputsThe graph run each iteration. It has N+M inputs: (loop state variables..., scan_input_elts...). It has N+K outputs: (loop state variables..., scan_output_elts...). Each scan_output is created by concatenating the value of the specified scan_output_elt value at the end of each iteration of the loop. It is an error if the dimensions of these values change across loop iterations.An attribute specifying the number of scan_inputs M. An optional list of M flags. The i-th element of the list specifies the direction to be scanned for the i-th scan_input tensor: 0 indicates forward direction and 1 indicates reverse direction. If omitted, all scan_input tensors will be scanned in the forward direction.An optional list of K flags, one for each scan_output. The i-th element of the list specifies whether the i-th scan_output should be constructed by appending or prepending a new value in each iteration: 0 indicates appending and 1 indicates prepending. If omitted, all scan_output tensors will be produced by appending a value in each iteration.An optional list of M flags. The i-th element of the list specifies the axis to be scanned (the sequence axis) for the i-th scan_input. If omitted, 0 will be used as the scan axis for every scan_input. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).An optional list of K flags. The i-th element of the list specifies the axis for the i-th scan_output. The scan outputs are accumulated along the specified axis. If omitted, 0 will be used as the scan axis for every scan_output. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1].All Tensor types up to IRv10. The linear quantization operator consumes a high-precision tensor, a scale, and a zero point to compute the low-precision/quantized tensor. The scale factor and zero point must have the same shape, determining the quantization granularity. The quantization formula is `y = saturate((x / y_scale) + y_zero_point)`. Saturation is done according to: - uint16: [0, 65535] - int16: [-32768, 32767] - uint8: [0, 255] - int8: [-128, 127] - uint4: [0, 15] - int4: [-8, 7] For `(x / y_scale)`, it rounds to the nearest even. Refer to https://en.wikipedia.org/wiki/Rounding for details. `y_zero_point` and `y` must have the same type. `y_zero_point` is usually not used for quantization to float8 types, but the quantization formula remains the same for consistency, and the type of the attribute `y_zero_point` still determines the quantization type. There are three supported quantization granularities, determined by the shape of `y_scale`. In all cases, `y_zero_point` must have the same shape as `y_scale`. - Per-tensor (per-layer) quantization: `y_scale` is a scalar. - Per-axis quantization: The scale must be a 1-D tensor, with the length of the quantization axis. For an input shape `(D0, ..., Di, ..., Dn)` and `axis=i`, `y_scale` is a 1-D tensor of length `Di`. - Blocked quantization: The scale's shape is identical to the input's shape, except for one dimension, in which blocking is performed. Given `x` shape `(D0, ..., Di, ..., Dn)`, `axis=i`, and block size `B`: `y_scale` shape is `(D0, ..., ceil(Di/B), ..., Dn)`. ??@-@ The linear dequantization operator. It consumes a quantized tensor, a scale, and a zero point to compute the full-precision tensor. The dequantization formula is `y = (x - x_zero_point) * x_scale`. `x_scale` and `x_zero_point` must have the same shape, determining the quantization's granularity: a scalar for per-tensor/per-layer quantization, a 1-D tensor for per-axis quantization, or have a rank identical to the input for blocked quantization. See QuantizeLinear for details on quantization granularity. `x_zero_point` and `x` must have the same type. `x` and `y` must have the same shape. In the case of dequantizing `int32`, there's no zero point (zero point is supposed to be 0). `zero-point` is usually not used in the case of float8 types quantization, but the dequantization formula remains the same for consistency, and `x_scale` still determines the output type. A Function to fuse calculation for Scale, Zero Point and FP32->8Bit conversion of FP32 Input data. Outputs Scale, ZeroPoint and Quantized Input for a given FP32 Input. Scale is calculated as: ``` y_scale = (maximum(0, max(x)) - minimum(0, min(x))) / (qmax - qmin) ``` * where qmax and qmin are max and min values for quantization range i.e. [0, 255] in case of uint8 * data range is adjusted to include 0. Zero point is calculated as: ``` intermediate_zero_point = qmin - min(x)/y_scale y_zero_point = cast(round(saturate(itermediate_zero_point))) ``` * where qmax and qmin are max and min values for quantization range .i.e [0, 255] in case of uint8 * for saturation, it saturates to [0, 255] if it's uint8, or [-127, 127] if it's int8. Right now only uint8 is supported. * rounding to nearest ties to even. Data quantization formula is: ``` y = saturate (round (x / y_scale) + y_zero_point) ``` * for saturation, it saturates to [0, 255] if it's uint8, or [-127, 127] if it's int8. Right now only uint8 is supported. * rounding to nearest ties to even. does not match y_zero_point type output_dtype Scale for doing quantization to get `y`. For per-tensor/layer quantization the scale is a scalar, for per-axis quantization it is a 1-D Tensor and for blocked quantization it has the same shape as the input, except for one dimension in which blocking is performed.Zero point for doing quantization to get `y`. Shape must match `y_scale`.Default is uint8 with zero point of 0 if it's not specified.N-D quantized output tensor. It has same shape as input `x`.(Optional) The axis of the dequantizing dimension of the input tensor. Used only for per-axis and blocked quantization. Negative value means counting dimensions from the back. Accepted range is `[-r, r-1]` where `r = rank(input)`. When the rank of the input is 1, per-tensor quantization is applied, rendering the axis unnecessary in this scenario.The parameter defines how the conversion behaves if an input value is out of range of the destination type. It only applies for float 8 quantization (float8e4m3fn, float8e4m3fnuz, float8e5m2, float8e5m2fnuz). It is true by default. All cases are fully described in two tables inserted in the operator description.(Optional) The size of the quantization block (number of times every scale is replicated). Used only for blocked quantization. The block size is a positive integer. Given `x` shape `(D0, ..., Di, ..., Dn)`, `y_scale` shape `(S0, ... Si, ...Sn)` and `axis=i`, the accepted range is `[ceil(Di/Si), ceil(Di/(Si-1))-1]`(Optional) The output data type. If not supplied, the output data type is inferred from `y_zero_point` data type (`T2`). If neither `output_dtype` nor `y_zero_point` are supplied, output data type is uint8. If both `output_dtype` and `y_zero_point` are specified, `output_dtype` must be `T2`.The type of the input 'x'.The type of the input `y_zero_point` and the output `y`.D:\a\_work\_temp\onnx\src\v1.17.0-7e28176e64.clean\onnx\defs\quantization\defs.ccN-D quantized input tensor to be de-quantized.Scale for input `x`. For per-tensor/layer dequantization the scale is a scalar, for per per-axis dequantization it is a 1-D Tensor and for blocked dequantization it has the same shape as the input, except for one dimension in which blocking is performed.Zero point for input `x`. Shape must match x_scale. It's optional. Zero point is 0 when it's not specified.N-D full precision output tensor. It has same shape as input `x`.(Optional) The axis of the dequantizing dimension of the input tensor. Used for per-axis and blocked quantization. Negative value means counting dimensions from the back. Accepted range is `[-r, r-1]` where `r = rank(input)`.The type of the inputs 'x_zero_point' and 'x'.'x_scale' determines the output type.Quantized output tensorOutput zero point. It's a scalar, which means a per-tensor/layer quantization.Constrain 'x' to float tensor.Constrain 'y_zero_point' and 'y' to 8-bit unsigned integer tensor. { Q_Min = Constant() Q_Max = Constant() X_Min = ReduceMin (x) X_Min_Adjusted = Min (X_Min, Q_Min) X_Max = ReduceMax (x) X_Max_Adjusted = Max (X_Max, Q_Min) X_Range = Sub (X_Max_Adjusted, X_Min_Adjusted) Scale = Div (X_Range, Q_Max) Min_Scaled = Div (X_Min_Adjusted, Scale) Initial_ZeroPoint_FP = Sub (Q_Min, Min_Scaled) Clipped_ZeroPoint_FP = Clip (Initial_ZeroPoint_FP, Q_Min, Q_Max) Rounded_ZeroPoint_FP = Round (Clipped_ZeroPoint_FP) Zeropoint = Cast (Rounded_ZeroPoint_FP) y_scale = Identity (Scale) y_zero_point = Identity (Zeropoint) y = QuantizeLinear (x, Scale, Zeropoint) } T   PC# @@pЂC# 00`C#StringConcat concatenates string tensors elementwise (with NumPy-style broadcasting support)RegexFullMatch performs a full regex match on each element of the input tensor. If an element fully matches the regex pattern specified as an attribute, the corresponding element in the output is True and it is False otherwise. [RE2](https://github.com/google/re2/wiki/Syntax) regex syntax is used.StringSplit splits a string tensor's elements into substrings based on a delimiter attribute and a maxsplit attribute. The first output of this operator is a tensor of strings representing the substrings from splitting each input string on the `delimiter` substring. This tensor has one additional rank compared to the input tensor in order to store the substrings for each input element (where the input tensor is not empty). Note that, in order to ensure the same number of elements are present in the final dimension, this tensor will pad empty strings as illustrated in the examples below. Consecutive delimiters are not grouped together and are deemed to delimit empty strings, except if the `delimiter` is unspecified or is the empty string (""). In the case where the `delimiter` is unspecified or the empty string, consecutive whitespace characters are regarded as a single separator and leading or trailing whitespace is removed in the output. The second output tensor represents the number of substrings generated. `maxsplit` can be used to limit the number of splits performed - after the `maxsplit`th split if the string is not fully split, the trailing suffix of input string after the final split point is also added. For elements where fewer splits are possible than specified in `maxsplit`, it has no effect. StringNormalization performs string operations for basic cleaning. This operator has only one input (denoted by X) and only one output (denoted by Y). This operator first examines the elements in the X, and removes elements specified in "stopwords" attribute. After removing stop words, the intermediate result can be further lowercased, uppercased, or just returned depending the "case_change_action" attribute. This operator only accepts [C]- and [1, C]-tensor. If all elements in X are dropped, the output will be the empty value of string tensor with shape [1] if input shape is [C] and shape [1, 1] if input shape is [1, C]. ??@-@Tensor to prepend in concatenationTensor to append in concatenationConcatenated string tensorInputs and outputs must be UTF-8 stringsD:\a\_work\_temp\onnx\src\v1.17.0-7e28176e64.clean\onnx\defs\text\defs.ccTensor with strings to match on.Regex pattern to match on. This must be valid RE2 syntax.Tensor of bools indicating if each input string fully matches the regex pattern specified.Inputs must be UTF-8 stringsOutputs are bools and are True where there is a full regex match and False otherwise.Tensor of strings to split.Delimiter to split on. If left unset or set to the empty string (""), the input is split on consecutive whitespace.Maximum number of splits (from left to right). If left unset (or if the number of possible splits are less than maxsplit), it will make as many splits as possible. Note that the maximum possible number of substrings returned with `maxsplit` specified is `maxsplit+1` since the remaining suffix after the `maxsplit`th split is included in the output.Tensor of substrings representing the outcome of splitting the strings in the input on the delimiter. Note that to ensure the same number of elements are present in the final rank, this tensor will pad any necessary empty strings.The number of substrings generated for each input element.The input must be a UTF-8 string tensorTensor of substrings.The number of substrings generated.Input shape must have either [C] or [1,C] dimensions where C > 0UTF-8 strings to normalizeUTF-8 Normalized stringsstring enum that cases output to be lowercased/uppercases/unchanged. Valid values are "LOWER", "UPPER", "NONE". Default is "NONE"Boolean. Whether the identification of stop words in X is case-sensitive. Default is falseList of stop words. If not set, no word would be removed from X.Environment dependent string that denotes the locale according to which output strings needs to be upper/lowercased.Default en_US or platform specific equivalent as decided by the implementation.8 `C#@C#PC#pp0C#$f` ??@-@minus infinity (if supported by the datatype) or the minimum value of the data type otherwiseplus infinity (if supported by the datatype) or the maximum value of the data type otherwiseminus infinity (if supported by the datatype) or undefined otherwise { data_square = Mul(data, data) reduced = ReduceSum(data_square, axes) } { reduced_sum = ReduceSum(data, axes) reduced = Log (reduced_sum) } { data_double = Cast(data) data_exp = Exp (data_double) reduced_sum = ReduceSum(data_exp, axes) reduced_double = Log (reduced_sum) reduced = CastLike(reduced_double, data) } { data_abs = Abs(data) reduced = ReduceSum(data_abs, axes) } { data_square = Mul(data, data) sum_square = ReduceSum(data_square, axes) sum_square_dbl = Cast (sum_square) sqrt = Sqrt(sum_square_dbl) reduced = CastLike(sqrt, data) } undefinedD:\a\_work\_temp\onnx\src\v1.17.0-7e28176e64.clean\onnx\defs\reduction\defs.ccsum squareproductlog sumlog sum exponentL1 normL2 norm Computes the indices of the {name} elements of the input tensor's element along the provided axis. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equals 0, then the resulting tensor has the reduced dimension pruned. If select_last_index is True (default False), the index of the last occurrence of the {name} is selected if the {name} appears more than once in the input. Otherwise the index of the first occurrence is selected. The type of the output tensor is integer.The axis in which to compute the arg indices. Accepted range is [-r, r-1] where r = rank(data).Keep the reduced dimension or not, default 1 means keep reduced dimension.Whether to select the last index or the first index if the {name} appears in multiple indices, default is False (first index).Reduced output tensor with integer data type.h @C#आआ0C#??@-@Loads and decodes and image from a file. If it can't decode for any reason (e.g. corrupted encoded stream, invalid format, it will return an empty matrix). The following image formats are supported: * BMP * JPEG (note: Lossless JPEG support is optional) * JPEG2000 * TIFF * PNG * WebP * Portable image format (PBM, PGM, PPM, PXM, PNM) Decoded images follow a channel-last layout: (Height, Width, Channels). **JPEG chroma upsampling method:** When upsampling the chroma components by a factor of 2, the pixels are linearly interpolated so that the centers of the output pixels are 1/4 and 3/4 of the way between input pixel centers. When rounding, 0.5 is rounded down and up at alternative pixels locations to prevent bias towards larger values (ordered dither pattern). Considering adjacent input pixels A, B, and C, B is upsampled to pixels B0 and B1 so that ``` B0 = round_half_down((1/4) * A + (3/4) * B) B1 = round_half_up((3/4) * B + (1/4) * C) ``` This method, is the default chroma upsampling method in the well-established libjpeg-turbo library, also referred as "smooth" or "fancy" upsampling. Input tensor must be 1-dimensionalRGBPixel format. Can be one of "RGB", "BGR", or "Grayscale".pixel_formatEncoded streamencoded_streamDecoded imageimageConstrain input types to 8-bit unsigned integer tensor.Constrain output types to 8-bit unsigned integer tensor.ImageDecoderD:\a\_work\_temp\onnx\src\v1.17.0-7e28176e64.clean\onnx\defs\image\defs.ccppC# Given `data` tensor of rank `r` >= 1, `indices` tensor of rank `q` >= 1, and `batch_dims` integer `b`, this operator gathers slices of `data` into an output tensor of rank `q + r - indices_shape[-1] - 1 - b`. `indices` is an q-dimensional integer tensor, best thought of as a `(q-1)`-dimensional tensor of index-tuples into `data`, where each element defines a slice of `data` `batch_dims` (denoted as `b`) is an integer indicating the number of batch dimensions, i.e the leading `b` number of dimensions of `data` tensor and `indices` are representing the batches, and the gather starts from the `b+1` dimension. Some salient points about the inputs' rank and shape: 1) r >= 1 and q >= 1 are to be honored. There is no dependency condition to be met between ranks `r` and `q` 2) The first `b` dimensions of the shape of `indices` tensor and `data` tensor must be equal. 3) b < min(q, r) is to be honored. 4) The `indices_shape[-1]` should have a value between 1 (inclusive) and rank `r-b` (inclusive) 5) All values in `indices` are expected to be within bounds [-s, s-1] along axis of size `s` (i.e.) `-data_shape[i] <= indices[...,i] <= data_shape[i] - 1`. It is an error if any of the index values are out of bounds. The output is computed as follows: The output tensor is obtained by mapping each index-tuple in the `indices` tensor to the corresponding slice of the input `data`. 1) If `indices_shape[-1] > r-b` => error condition 2) If `indices_shape[-1] == r-b`, since the rank of `indices` is `q`, `indices` can be thought of as `N` `(q-b-1)`-dimensional tensors containing 1-D tensors of dimension `r-b`, where `N` is an integer equals to the product of 1 and all the elements in the batch dimensions of the indices_shape. Let us think of each such `r-b` ranked tensor as `indices_slice`. Each *scalar value* corresponding to `data[0:b-1,indices_slice]` is filled into the corresponding location of the `(q-b-1)`-dimensional tensor to form the `output` tensor (Example 1 below) 3) If `indices_shape[-1] < r-b`, since the rank of `indices` is `q`, `indices` can be thought of as `N` `(q-b-1)`-dimensional tensor containing 1-D tensors of dimension `< r-b`. Let us think of each such tensors as `indices_slice`. Each *tensor slice* corresponding to `data[0:b-1, indices_slice , :]` is filled into the corresponding location of the `(q-b-1)`-dimensional tensor to form the `output` tensor (Examples 2, 3, 4 and 5 below) This operator is the inverse of `ScatterND`. `Example 1` batch_dims = 0 data = [[0,1],[2,3]] # data_shape = [2, 2] indices = [[0,0],[1,1]] # indices_shape = [2, 2] output = [0,3] # output_shape = [2] `Example 2` batch_dims = 0 data = [[0,1],[2,3]] # data_shape = [2, 2] indices = [[1],[0]] # indices_shape = [2, 1] output = [[2,3],[0,1]] # output_shape = [2, 2] `Example 3` batch_dims = 0 data = [[[0,1],[2,3]],[[4,5],[6,7]]] # data_shape = [2, 2, 2] indices = [[0,1],[1,0]] # indices_shape = [2, 2] output = [[2,3],[4,5]] # output_shape = [2, 2] `Example 4` batch_dims = 0 data = [[[0,1],[2,3]],[[4,5],[6,7]]] # data_shape = [2, 2, 2] indices = [[[0,1]],[[1,0]]] # indices_shape = [2, 1, 2] output = [[[2,3]],[[4,5]]] # output_shape = [2, 1, 2] `Example 5` batch_dims = 1 data = [[[0,1],[2,3]],[[4,5],[6,7]]] # data_shape = [2, 2, 2] indices = [[1],[0]] # indices_shape = [2, 1] output = [[2,3],[4,5]] # output_shape = [2, 2] Given a tensor containing the data to be padded (`data`), a tensor containing the number of start and end pad values for axis (`pads`), (optionally) a `mode`, and (optionally) `constant_value`, a padded tensor (`output`) is generated. The three supported `modes` are (similar to corresponding modes supported by `numpy.pad`): 1) `constant`(default) - pads with a given constant value as specified by `constant_value` (which defaults to 0) 2) `reflect` - pads with the reflection of the vector mirrored on the first and last values of the vector along each axis 3) `edge` - pads with the edge values of array Example 1 (`constant` mode): Insert 0 pads to the beginning of the second dimension. data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] mode = 'constant' constant_value = 0.0 output = [ [0.0, 0.0, 1.0, 1.2], [0.0, 0.0, 2.3, 3.4], [0.0, 0.0, 4.5, 5.7], ] Example 2 (`reflect` mode): data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] mode = 'reflect' output = [ [1.0, 1.2, 1.0, 1.2], [2.3, 3.4, 2.3, 3.4], [4.5, 5.7, 4.5, 5.7], ] Example 3 (`edge` mode): data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] mode = 'edge' output = [ [1.0, 1.0, 1.0, 1.2], [2.3, 2.3, 2.3, 3.4], [4.5, 4.5, 4.5, 5.7], ] The operator casts the elements of a given input tensor to a data type specified by the 'to' argument and returns an output tensor of the same size in the converted type. The 'to' argument must be one of the data types specified in the 'DataType' enum field in the TensorProto message. NOTE: Casting to and from strings is not supported yet. Concatenate a list of tensors into a single tensorSplit a tensor into a list of tensors, along the specified 'axis'. The lengths of the split can be specified using argument 'axis' or optional second input blob to the operator. Otherwise, the tensor is split to equal sized parts. Given `data` tensor, paddings, mode, and value. Example: Insert 0 paddings to the beginning of the second dimension. data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] paddings = [0, 0, 2, 0] output = [ [ [0.0, 0.0, 1.0, 1.2], [0.0, 0.0, 2.3, 3.4], [0.0, 0.0, 4.5, 5.7], ], ] Reshape the input tensor similar to numpy.reshape. It takes a tensor as input and an argument `shape`. It outputs the reshaped tensor. At most one dimension of the new shape can be -1. In this case, the value is inferred from the size of the tensor and the remaining dimensions. A dimension could also be 0, in which case the actual dimension value is unchanged (i.e. taken from the input tensor). Shape (second input) could be an empty shape, which means converting to a scalar. The input tensor's shape and the output tensor's shape are required to have the same number of elements. Upsample the input tensor. The width and height of the output tensor are: output_width = floor(input_width * width_scale), output_height = floor(input_height * height_scale). Example: Given `data` tensor, width_scale, height_scale, mode, Upsample the input 4-D tensor in nearest mode: data = [[[ [1, 2], [3, 4] ]]] width_scale = 2 height_scale = 2 mode = "nearest" output = [[[ [1, 1, 2, 2], [1, 1, 2, 2], [3, 3, 4, 4], [3, 3, 4, 4] ]]] Resize the input tensor. Each dimension value of the output tensor is: output_dimension = floor(input_dimension * scale). Produces a slice of the input tensor along multiple axes. Similar to numpy: https://numpy.org/doc/stable/reference/routines.indexing.html Slices uses `axes`, `starts` and `ends` attributes to specify the start and end dimension for each axis in the list of axes, it uses this information to slice the input `data` tensor. If a negative value is passed for any of the start or end indices, it represent number of elements before the end of that dimension. If the value passed to start or end is larger than the `n` (the number of elements in this dimension), it represents `n`. For slicing to the end of a dimension with unknown size, it is recommended to pass in `INT_MAX`. If `axes` are omitted, they are set to `[0, ..., ndim-1]`. Example 1: data = [ [1, 2, 3, 4], [5, 6, 7, 8], ] axes = [0, 1] starts = [1, 0] ends = [2, 3] result = [ [5, 6, 7], ] Example 2: data = [ [1, 2, 3, 4], [5, 6, 7, 8], ] starts = [0, 1] ends = [-1, 1000] result = [ [2, 3, 4], ] Produces a slice of the input tensor along multiple axes. Similar to numpy: https://numpy.org/doc/stable/reference/routines.indexing.html Slices uses `starts`, `ends`, `axes` and `steps` inputs to specify the start and end dimension and step for each axis in the list of axes, it uses this information to slice the input `data` tensor. If a negative value is passed for any of the start or end indices, it represent number of elements before the end of that dimension. If the value passed to start or end is larger than the `n` (the number of elements in this dimension), it represents `n`. For slicing to the end of a dimension with unknown size, it is recommended to pass in `INT_MAX`. If a negative value is passed for step, it represents slicing backward. If `axes` are omitted, they are set to `[0, ..., ndim-1]`. If `steps` are omitted, they are set to `[1, ..., 1]` of length `len(starts)` Example 1: data = [ [1, 2, 3, 4], [5, 6, 7, 8], ] axes = [0, 1] starts = [1, 0] ends = [2, 3] steps = [1, 2] result = [ [5, 7], ] Example 2: data = [ [1, 2, 3, 4], [5, 6, 7, 8], ] starts = [0, 1] ends = [-1, 1000] result = [ [2, 3, 4], ] Given `data`, `updates` and `indices` input tensors of rank r >= 1, write the values provided by `updates` into the first input, `data`, along `axis` dimension of `data` (by default outer-most one as axis=0) at corresponding `indices`. For each entry in `updates`, the target index in `data` is specified by corresponding entry in `indices` for dimension = axis, and index in source for dimension != axis. For instance, in a 2-D tensor case, data[indices[i][j]][j] = updates[i][j] if axis = 0, or data[i][indices[i][j]] = updates[i][j] if axis = 1, where i and j are loop counters from 0 up to the respective size in `updates` - 1. Example 1: data = [ [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], ] indices = [ [1, 0, 2], [0, 2, 1], ] updates = [ [1.0, 1.1, 1.2], [2.0, 2.1, 2.2], ] output = [ [2.0, 1.1, 0.0] [1.0, 0.0, 2.2] [0.0, 2.1, 1.2] ] Example 2: data = [[1.0, 2.0, 3.0, 4.0, 5.0]] indices = [[1, 3]] updates = [[1.1, 2.1]] axis = 1 output = [[1.0, 1.1, 3.0, 2.1, 5.0]] DepthToSpace rearranges (permutes) data from depth into blocks of spatial data. This is the reverse transformation of SpaceToDepth. More specifically, this op outputs a copy of the input tensor where values from the depth dimension are moved in spatial blocks to the height and width dimensions. Given `data` tensor of rank r >= 1, and `indices` tensor of rank q, gather entries of the axis dimension of `data` (by default outer-most one as axis=0) indexed by `indices`, and concatenates them in an output tensor of rank q + (r - 1). Example 1: ``` data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] indices = [ [0, 1], [1, 2], ] output = [ [ [1.0, 1.2], [2.3, 3.4], ], [ [2.3, 3.4], [4.5, 5.7], ], ] ``` Example 2: ``` data = [ [1.0, 1.2, 1.9], [2.3, 3.4, 3.9], [4.5, 5.7, 5.9], ] indices = [ [0, 2], ] axis = 1, output = [ [[1.0, 1.9]], [[2.3, 3.9]], [[4.5, 5.9]], ] ``` ScatterND takes three inputs `data` tensor of rank r >= 1, `indices` tensor of rank q >= 1, and `updates` tensor of rank q + r - indices.shape[-1] - 1. The output of the operation is produced by creating a copy of the input `data`, and then updating its value to values specified by `updates` at specific index positions specified by `indices`. Its output shape is the same as the shape of `data`. `indices` is an integer tensor. Let k denote indices.shape[-1], the last dimension in the shape of `indices`. `indices` is treated as a (q-1)-dimensional tensor of k-tuples, where each k-tuple is a partial-index into `data`. Hence, k can be a value at most the rank of `data`. When k equals rank(data), each update entry specifies an update to a single element of the tensor. When k is less than rank(data) each update entry specifies an update to a slice of the tensor. Index values are allowed to be negative, as per the usual convention for counting backwards from the end, but are expected in the valid range. `updates` is treated as a (q-1)-dimensional tensor of replacement-slice-values. Thus, the first (q-1) dimensions of updates.shape must match the first (q-1) dimensions of indices.shape. The remaining dimensions of `updates` correspond to the dimensions of the replacement-slice-values. Each replacement-slice-value is a (r-k) dimensional tensor, corresponding to the trailing (r-k) dimensions of `data`. Thus, the shape of `updates` must equal indices.shape[0:q-1] ++ data.shape[k:r-1], where ++ denotes the concatenation of shapes. The `output` is calculated via the following equation: output = np.copy(data) update_indices = indices.shape[:-1] for idx in np.ndindex(update_indices): output[indices[idx]] = updates[idx] The order of iteration in the above loop is not specified. In particular, indices should not have duplicate entries: that is, if idx1 != idx2, then indices[idx1] != indices[idx2]. This ensures that the output value does not depend on the iteration order. `reduction` allows specification of an optional reduction operation, which is applied to all values in `updates` tensor into `output` at the specified `indices`. In cases where `reduction` is set to "none", indices should not have duplicate entries: that is, if idx1 != idx2, then indices[idx1] != indices[idx2]. This ensures that the output value does not depend on the iteration order. When `reduction` is set to "add", `output` is calculated as follows: output = np.copy(data) update_indices = indices.shape[:-1] for idx in np.ndindex(update_indices): output[indices[idx]] += updates[idx] When `reduction` is set to "mul", `output` is calculated as follows: output = np.copy(data) update_indices = indices.shape[:-1] for idx in np.ndindex(update_indices): output[indices[idx]] *= updates[idx] This operator is the inverse of GatherND. Example 1: ``` data = [1, 2, 3, 4, 5, 6, 7, 8] indices = [[4], [3], [1], [7]] updates = [9, 10, 11, 12] output = [1, 11, 3, 10, 9, 6, 7, 12] ``` Example 2: ``` data = [[[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]], [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]], [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]], [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]] indices = [[0], [2]] updates = [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]], [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]]] output = [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]], [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]], [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]], [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]] ``` The operator casts the elements of a given input tensor to a data type specified by the 'to' argument and returns an output tensor of the same size in the converted type. The 'to' argument must be one of the data types specified in the 'DataType' enum field in the TensorProto message. Casting from string tensor in plain (e.g., "3.14" and "1000") and scientific numeric representations (e.g., "1e-5" and "1E8") to float types is supported. For example, converting string "100.5" to an integer may yield result 100. There are some string literals reserved for special floating-point values; "+INF" (and "INF"), "-INF", and "NaN" are positive infinity, negative infinity, and not-a-number, respectively. Any string which can exactly match "+INF" in a case-insensitive way would be mapped to positive infinite. Similarly, this case-insensitive rule is applied to "INF" and "NaN". When casting from numeric tensors to string tensors, plain floating-point representation (such as "314.15926") would be used. Converting non-numerical-literal string such as "Hello World!" is an undefined behavior. Cases of converting string representing floating-point arithmetic value, such as "2.718", to INT is an undefined behavior. Conversion from a numerical type to any numerical type is always allowed. User must be aware of precision loss and value change caused by range difference between two types. For example, a 64-bit float 3.1415926459 may be round to a 32-bit float 3.141592. Similarly, converting an integer 36 to Boolean may produce 1 because we truncate bits which can't be stored in the targeted type. In more detail, the conversion among numerical types should follow these rules: * Casting from floating point to: * floating point: +/- infinity if OOR (out of range). * fixed point: undefined if OOR. * bool: +/- 0.0 to False; all else to True. * Casting from fixed point to: * floating point: +/- infinity if OOR. (+ infinity in the case of uint) * fixed point: when OOR, discard higher bits and reinterpret (with respect to two's complement representation for signed types). For example, 200 (int16) -> -56 (int8). * bool: zero to False; nonzero to True. * Casting from bool to: * floating point: `{1.0, 0.0}`. * fixed point: `{1, 0}`. * bool: no change. ScatterND takes three inputs `data` tensor of rank r >= 1, `indices` tensor of rank q >= 1, and `updates` tensor of rank q + r - indices.shape[-1] - 1. The output of the operation is produced by creating a copy of the input `data`, and then updating its value to values specified by `updates` at specific index positions specified by `indices`. Its output shape is the same as the shape of `data`. Note that `indices` should not have duplicate entries. That is, two or more `updates` for the same index-location is not supported. `indices` is an integer tensor. Let k denote indices.shape[-1], the last dimension in the shape of `indices`. `indices` is treated as a (q-1)-dimensional tensor of k-tuples, where each k-tuple is a partial-index into `data`. Hence, k can be a value at most the rank of `data`. When k equals rank(data), each update entry specifies an update to a single element of the tensor. When k is less than rank(data) each update entry specifies an update to a slice of the tensor. Index values are allowed to be negative, as per the usual convention for counting backwards from the end, but are expected in the valid range. `updates` is treated as a (q-1)-dimensional tensor of replacement-slice-values. Thus, the first (q-1) dimensions of updates.shape must match the first (q-1) dimensions of indices.shape. The remaining dimensions of `updates` correspond to the dimensions of the replacement-slice-values. Each replacement-slice-value is a (r-k) dimensional tensor, corresponding to the trailing (r-k) dimensions of `data`. Thus, the shape of `updates` must equal indices.shape[0:q-1] ++ data.shape[k:r-1], where ++ denotes the concatenation of shapes. The `output` is calculated via the following equation: output = np.copy(data) update_indices = indices.shape[:-1] for idx in np.ndindex(update_indices): output[indices[idx]] = updates[idx] The order of iteration in the above loop is not specified. In particular, indices should not have duplicate entries: that is, if idx1 != idx2, then indices[idx1] != indices[idx2]. This ensures that the output value does not depend on the iteration order. This operator is the inverse of GatherND. Example 1: ``` data = [1, 2, 3, 4, 5, 6, 7, 8] indices = [[4], [3], [1], [7]] updates = [9, 10, 11, 12] output = [1, 11, 3, 10, 9, 6, 7, 12] ``` Example 2: ``` data = [[[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]], [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]], [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]], [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]] indices = [[0], [2]] updates = [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]], [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]]] output = [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]], [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]], [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]], [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]] ``` Insert single-dimensional entries to the shape of a tensor. Takes one required argument `axes`, a list of dimensions that will be inserted. Dimension indices in `axes` are as seen in the output tensor. For example: Given a tensor such that tensor with shape [3, 4, 5], then Unsqueeze(tensor, axes=[0, 4]) has shape [1, 3, 4, 5, 1] ScatterElements takes three inputs `data`, `updates`, and `indices` of the same rank r >= 1 and an optional attribute axis that identifies an axis of `data` (by default, the outer-most axis, that is axis 0). The output of the operation is produced by creating a copy of the input `data`, and then updating its value to values specified by `updates` at specific index positions specified by `indices`. Its output shape is the same as the shape of `data`. For each entry in `updates`, the target index in `data` is obtained by combining the corresponding entry in `indices` with the index of the entry itself: the index-value for dimension = axis is obtained from the value of the corresponding entry in `indices` and the index-value for dimension != axis is obtained from the index of the entry itself. `reduction` allows specification of an optional reduction operation, which is applied to all values in `updates` tensor into `output` at the specified `indices`. In cases where `reduction` is set to "none", indices should not have duplicate entries: that is, if idx1 != idx2, then indices[idx1] != indices[idx2]. For instance, in a 2-D tensor case, the update corresponding to the [i][j] entry is performed as below: ``` output[indices[i][j]][j] = updates[i][j] if axis = 0, output[i][indices[i][j]] = updates[i][j] if axis = 1, ``` When `reduction` is set to "add", the update corresponding to the [i][j] entry is performed as below: ``` output[indices[i][j]][j] += updates[i][j] if axis = 0, output[i][indices[i][j]] += updates[i][j] if axis = 1, ``` When `reduction` is set to "mul", the update corresponding to the [i][j] entry is performed as below: ``` output[indices[i][j]][j] *= updates[i][j] if axis = 0, output[i][indices[i][j]] *= updates[i][j] if axis = 1, ``` This operator is the inverse of GatherElements. It is similar to Torch's Scatter operation. Example 1: ``` data = [ [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], ] indices = [ [1, 0, 2], [0, 2, 1], ] updates = [ [1.0, 1.1, 1.2], [2.0, 2.1, 2.2], ] output = [ [2.0, 1.1, 0.0] [1.0, 0.0, 2.2] [0.0, 2.1, 1.2] ] ``` Example 2: ``` data = [[1.0, 2.0, 3.0, 4.0, 5.0]] indices = [[1, 3]] updates = [[1.1, 2.1]] axis = 1 output = [[1.0, 1.1, 3.0, 2.1, 5.0]] ``` Produces a one-hot tensor based on inputs. The locations represented by the index values in the 'indices' input tensor will have 'on_value' and the other locations will have 'off_value' in the output tensor, where 'on_value' and 'off_value' are specified as part of required input argument 'values', which is a two-element tensor of format [off_value, on_value]. The rank of the output tensor will be one greater than the rank of the input tensor. The additional dimension is for one-hot representation. The additional dimension will be inserted at the position specified by 'axis'. If 'axis' is not specified then then additional dimension will be inserted as the innermost dimension, i.e. axis=-1. The size of the additional dimension is specified by required scalar input 'depth'. The type of the output tensor is the same as the type of the 'values' input. Any entries in the 'indices' input tensor with values outside the range [0, depth) will result in one-hot representation with all 'off_value' values in the output tensor. ScatterElements takes three inputs `data`, `updates`, and `indices` of the same rank r >= 1 and an optional attribute axis that identifies an axis of `data` (by default, the outer-most axis, that is axis 0). The output of the operation is produced by creating a copy of the input `data`, and then updating its value to values specified by `updates` at specific index positions specified by `indices`. Its output shape is the same as the shape of `data`. For each entry in `updates`, the target index in `data` is obtained by combining the corresponding entry in `indices` with the index of the entry itself: the index-value for dimension = axis is obtained from the value of the corresponding entry in `indices` and the index-value for dimension != axis is obtained from the index of the entry itself. For instance, in a 2-D tensor case, the update corresponding to the [i][j] entry is performed as below: ``` output[indices[i][j]][j] = updates[i][j] if axis = 0, output[i][indices[i][j]] = updates[i][j] if axis = 1, ``` This operator is the inverse of GatherElements. It is similar to Torch's Scatter operation. Example 1: ``` data = [ [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], ] indices = [ [1, 0, 2], [0, 2, 1], ] updates = [ [1.0, 1.1, 1.2], [2.0, 2.1, 2.2], ] output = [ [2.0, 1.1, 0.0] [1.0, 0.0, 2.2] [0.0, 2.1, 1.2] ] ``` Example 2: ``` data = [[1.0, 2.0, 3.0, 4.0, 5.0]] indices = [[1, 3]] updates = [[1.1, 2.1]] axis = 1 output = [[1.0, 1.1, 3.0, 2.1, 5.0]] ``` Given `data` tensor of rank r >= 1, and `indices` tensor of rank q, gather entries of the axis dimension of `data` (by default outer-most one as axis=0) indexed by `indices`, and concatenates them in an output tensor of rank q + (r - 1). axis = 0 : Let k = indices[i_{0}, ..., i_{q-1}] Then output[i_{0}, ..., i_{q-1}, j_{0}, ..., j_{r-2}] = input[k , j_{0}, ..., j_{r-2}] ``` data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] indices = [ [0, 1], [1, 2], ] output = [ [ [1.0, 1.2], [2.3, 3.4], ], [ [2.3, 3.4], [4.5, 5.7], ], ] ``` axis = 1 : Let k = indices[i_{0}, ..., i_{q-1}] Then output[j_{0}, i_{0}, ..., i_{q-1}, j_{1}, ..., j_{r-2}] = input[j_{0}, k, j_{1}, ..., j_{r-2}] ``` data = [ [1.0, 1.2, 1.9], [2.3, 3.4, 3.9], [4.5, 5.7, 5.9], ] indices = [ [0, 2], ] axis = 1, output = [ [[1.0, 1.9]], [[2.3, 3.9]], [[4.5, 5.9]], ] ``` The operator casts the elements of a given input tensor to a data type specified by the 'to' argument and returns an output tensor of the same size in the converted type. The 'to' argument must be one of the data types specified in the 'DataType' enum field in the TensorProto message. Casting from string tensor in plain (e.g., "3.14" and "1000") and scientific numeric representations (e.g., "1e-5" and "1E8") to float types is supported. For example, converting string "100.5" to an integer may yield result 100. There are some string literals reserved for special floating-point values; "+INF" (and "INF"), "-INF", and "NaN" are positive infinity, negative infinity, and not-a-number, respectively. Any string which can exactly match "+INF" in a case-insensitive way would be mapped to positive infinite. Similarly, this case-insensitive rule is applied to "INF" and "NaN". When casting from numeric tensors to string tensors, plain floating-point representation (such as "314.15926") would be used. Converting non-numerical-literal string such as "Hello World!" is an undefined behavior. Cases of converting string representing floating-point arithmetic value, such as "2.718", to INT is an undefined behavior. Conversion from a numerical type to any numerical type is always allowed. User must be aware of precision loss and value change caused by range difference between two types. For example, a 64-bit float 3.1415926459 may be round to a 32-bit float 3.141592. Similarly, converting an integer 36 to Boolean may produce 1 because we truncate bits which can't be stored in the targeted type. Given an input `X` and a flow-field `grid`, computes the output `Y` using `X` values and pixel locations from `grid`. Currently, only spatial (4-D) inputs are supported. For input `X` with shape (N, C, H, W) and `grid` with shape (N, H_out, W_out, 2), the output `Y` will have shape (N, C, H_out, W_out). The tensor `X` contains values at centers of square pixels in a H by W 2-dimensional image. The tensor `grid` describes normalized positions where the output `Y` is to be computed using a specified interpolation method (the mode) and a padding mode (for grid positions falling outside the 2-dimensional image). Elements in `grid[N, H_out, W_out]` are size-2 vectors specifying positions in the 2-dimensional space of `X`. They are used to interpolate output values of `Y[N, C, H_out, W_out]`. The GridSample operator is often used in doing grid generator and sampler in the [Spatial Transformer Networks](https://arxiv.org/abs/1506.02025). See also in [torch.nn.functional.grid_sample](https://pytorch.org/docs/master/generated/torch.nn.functional.grid_sample.html#torch-nn-functional-grid-sample). GatherElements takes two inputs `data` and `indices` of the same rank r >= 1 and an optional attribute `axis` that identifies an axis of `data` (by default, the outer-most axis, that is axis 0). It is an indexing operation that produces its output by indexing into the input data tensor at index positions determined by elements of the `indices` tensor. Its output shape is the same as the shape of `indices` and consists of one value (gathered from the `data`) for each element in `indices`. For instance, in the 3-D case (r = 3), the output produced is determined by the following equations: ``` out[i][j][k] = input[index[i][j][k]][j][k] if axis = 0, out[i][j][k] = input[i][index[i][j][k]][k] if axis = 1, out[i][j][k] = input[i][j][index[i][j][k]] if axis = 2, ``` This operator is also the inverse of ScatterElements. It is similar to Torch's gather operation. Example 1: ``` data = [ [1, 2], [3, 4], ] indices = [ [0, 0], [1, 0], ] axis = 1 output = [ [ [1, 1], [4, 3], ], ] ``` Example 2: ``` data = [ [1, 2, 3], [4, 5, 6], [7, 8, 9], ] indices = [ [1, 2, 0], [2, 0, 0], ] axis = 0 output = [ [ [4, 8, 3], [7, 2, 3], ], ] ``` Given `data` tensor, pads, mode, and value. Example: Insert 0 pads to the beginning of the second dimension. data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] output = [ [ [0.0, 0.0, 1.0, 1.2], [0.0, 0.0, 2.3, 3.4], [0.0, 0.0, 4.5, 5.7], ], ] Given `data` tensor of rank `r` >= 1, and `indices` tensor of rank `q` >= 1, this operator gathers slices of `data` into an output tensor of rank `q + r - indices_shape[-1] - 1`. `indices` is an q-dimensional integer tensor, best thought of as a `(q-1)`-dimensional tensor of index-tuples into `data`, where each element defines a slice of `data` Some salient points about the inputs' rank and shape: 1) r >= 1 and q >= 1 are to be honored. There is no dependency condition to be met between ranks `r` and `q` 2) The `indices_shape[-1]` should have a value between 1 (inclusive) and rank `r` (inclusive) 3) All values in `indices` are expected to be within bounds [-s, s-1] along axis of size `s` (i.e.) `-data_shape[i] <= indices[...,i] <= data_shape[i] - 1`. It is an error if any of the index values are out of bounds. The output is computed as follows: The output tensor is obtained by mapping each index-tuple in the `indices` tensor to the corresponding slice of the input `data`. 1) If `indices_shape[-1] > r` => error condition 2) If `indices_shape[-1] == r`, since the rank of `indices` is `q`, `indices` can be thought of as a `(q-1)`-dimensional tensor containing 1-D tensors of dimension `r`. Let us think of each such `r` ranked tensor as `indices_slice`. Each *scalar value* corresponding to `data[indices_slice]` is filled into the corresponding location of the `(q-1)`-dimensional tensor to form the `output` tensor (Example 1 below) 3) If `indices_shape[-1] < r`, since the rank of `indices` is `q`, `indices` can be thought of as a `(q-1)`-dimensional tensor containing 1-D tensors of dimension `< r`. Let us think of each such tensors as `indices_slice`. Each *tensor slice* corresponding to `data[indices_slice , :]` is filled into the corresponding location of the `(q-1)`-dimensional tensor to form the `output` tensor (Examples 2, 3, and 4 below) This operator is the inverse of `ScatterND`. `Example 1` data = [[0,1],[2,3]] # data_shape = [2, 2] indices = [[0,0],[1,1]] # indices_shape = [2, 2] output = [0,3] # output_shape = [2] `Example 2` data = [[0,1],[2,3]] # data_shape = [2, 2] indices = [[1],[0]] # indices_shape = [2, 1] output = [[2,3],[0,1]] # output_shape = [2, 2] `Example 3` data = [[[0,1],[2,3]],[[4,5],[6,7]]] # data_shape = [2, 2, 2] indices = [[0,1],[1,0]] # indices_shape = [2, 2] output = [[2,3],[4,5]] # output_shape = [2, 2] `Example 4` data = [[[0,1],[2,3]],[[4,5],[6,7]]] # data_shape = [2, 2, 2] indices = [[[0,1]],[[1,0]]] # indices_shape = [2, 1, 2] output = [[[2,3]],[[4,5]]] # output_shape = [2, 1, 2] Remove single-dimensional entries from the shape of a tensor. Takes a parameter `axes` with a list of axes to squeeze. If `axes` is not provided, all the single dimensions will be removed from the shape. If an axis is selected with shape entry not equal to one, an error is raised. Given a tensor containing the data to be padded (`data`), a tensor containing the number of start and end pad values for axis (`pads`), (optionally) a `mode`, and (optionally) `constant_value`, a padded tensor (`output`) is generated. The three supported `modes` are (similar to corresponding modes supported by `numpy.pad`): 1) `constant`(default) - pads with a given constant value as specified by `constant_value` (which defaults to 0, empty string, or False) 2) `reflect` - pads with the reflection of the vector mirrored on the first and last values of the vector along each axis 3) `edge` - pads with the edge values of array Example 1 (`constant` mode): Insert 0 pads to the beginning of the second dimension. data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] mode = 'constant' constant_value = 0.0 output = [ [0.0, 0.0, 1.0, 1.2], [0.0, 0.0, 2.3, 3.4], [0.0, 0.0, 4.5, 5.7], ] Example 2 (`reflect` mode): data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] mode = 'reflect' output = [ [1.0, 1.2, 1.0, 1.2], [2.3, 3.4, 2.3, 3.4], [4.5, 5.7, 4.5, 5.7], ] Example 3 (`edge` mode): data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] mode = 'edge' output = [ [1.0, 1.0, 1.0, 1.2], [2.3, 2.3, 2.3, 3.4], [4.5, 4.5, 4.5, 5.7], ] Given a tensor containing the data to be padded (`data`), a tensor containing the number of start and end pad values for axis (`pads`), (optionally) a `mode`, and (optionally) `constant_value`, a padded tensor (`output`) is generated. The three supported `modes` are (similar to corresponding modes supported by `numpy.pad`): 1) `constant`(default) - pads with a given constant value as specified by `constant_value` (which defaults to 0, empty string, or False) 2) `reflect` - pads with the reflection of the vector mirrored on the first and last values of the vector along each axis 3) `edge` - pads with the edge values of array Example 1 (`constant` mode): Insert 0 pads to the beginning of the second dimension. ``` data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] mode = 'constant' constant_value = 0.0 output = [ [0.0, 0.0, 1.0, 1.2], [0.0, 0.0, 2.3, 3.4], [0.0, 0.0, 4.5, 5.7], ] ``` Example 2 (`reflect` mode): ``` data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] mode = 'reflect' output = [ [1.0, 1.2, 1.0, 1.2], [2.3, 3.4, 2.3, 3.4], [4.5, 5.7, 4.5, 5.7], ] ``` Example 3 (`edge` mode): ``` data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] mode = 'edge' output = [ [1.0, 1.0, 1.0, 1.2], [2.3, 2.3, 2.3, 3.4], [4.5, 4.5, 4.5, 5.7], ] ``` Insert single-dimensional entries to the shape of an input tensor (`data`). Takes one required argument `axes` - which contains a list of dimension indices and this operator will insert a dimension of value `1` into the corresponding index of the output tensor (`expanded`). For example: Given an input tensor (`data`) of shape [3, 4, 5], then Unsqueeze(data, axes=[0, 4]) outputs a tensor (`expanded`) containing same data as `data` but with shape [1, 3, 4, 5, 1]. The attribute `axes` should not contain any duplicate entries. It is an error if it contains duplicates. The rank of the output tensor (`output_rank`) is the rank of the input tensor (`data`) plus the number of values in `axes`. Each value in `axes` should be within the (inclusive) range [-output_rank , output_rank - 1]. The order of values in `axes` does not matter and can come in any order. Reshape the input tensor similar to numpy.reshape. First input is the data tensor, second input is a shape tensor which specifies the output shape. It outputs the reshaped tensor. At most one dimension of the new shape can be -1. In this case, the value is inferred from the size of the tensor and the remaining dimensions. A dimension could also be 0, in which case the actual dimension value is unchanged (i.e. taken from the input tensor). Shape (second input) could be an empty shape, which means converting to a scalar. The input tensor's shape and the output tensor's shape are required to have the same number of elements.DepthToSpace rearranges (permutes) data from depth into blocks of spatial data. This is the reverse transformation of SpaceToDepth. More specifically, this op outputs a copy of the input tensor where values from the depth dimension are moved in spatial blocks to the height and width dimensions. By default, `mode` = `DCR`. In the DCR mode, elements along the depth dimension from the input tensor are rearranged in the following order: depth, column, and then row. The output y is computed from the input x as below: b, c, h, w = x.shape tmp = np.reshape(x, [b, blocksize, blocksize, c // (blocksize**2), h, w]) tmp = np.transpose(tmp, [0, 3, 4, 1, 5, 2]) y = np.reshape(tmp, [b, c // (blocksize**2), h * blocksize, w * blocksize]) In the CRD mode, elements along the depth dimension from the input tensor are rearranged in the following order: column, row, and the depth. The output y is computed from the input x as below: b, c, h, w = x.shape tmp = np.reshape(x, [b, c // (blocksize ** 2), blocksize, blocksize, h, w]) tmp = np.transpose(tmp, [0, 1, 4, 2, 5, 3]) y = np.reshape(tmp, [b, c // (blocksize ** 2), h * blocksize, w * blocksize]) ??@-@ Takes a tensor as input and outputs an 1D int64 tensor containing the shape of the input tensor. Resize the input tensor. In general, it calculates every value in the output tensor as a weighted average of neighborhood (a.k.a. sampling locations) in the input tensor. Each dimension value of the output tensor is:
`output_dimension = floor(input_dimension * (roi_end - roi_start) * scale)`
if input \"sizes\" is not specified. This attribute describes how to transform the coordinate in the resized tensor to the coordinate in the original tensor.
The coordinate of each dimension is transformed individually. Let's describe a case using axis x as an example. Denote x_resized as the coordinate of axis x in the resized tensor, x_original as the coordinate of axis x in the original tensor, `length_original` as the length of the original tensor in axis x, length_resized as the length of the resized tensor in axis x, roi_x = (start_x, end_x) of the axis x in input "roi", `scale = length_resized / length_original`,
if coordinate_transformation_mode is `"half_pixel"`,
`x_original = (x_resized + 0.5) / scale - 0.5`
if coordinate_transformation_mode is `"pytorch_half_pixel"`,
`x_original = length_resized > 1 ? (x_resized + 0.5) / scale - 0.5 : 0`
if coordinate_transformation_mode is `"align_corners"`,
`x_original = x_resized * (length_original - 1) / (length_resized - 1)`
if coordinate_transformation_mode is `"asymmetric"`,
`x_original = x_resized / scale`
if coordinate_transformation_mode is `"tf_crop_and_resize"`,
`x_original = length_resized > 1 ? start_x * (length_original - 1) + x_resized * (end_x - start_x) * (length_original - 1) / (length_resized - 1) : 0.5 * (start_x + end_x) * (length_original - 1)` . This attribute describes how to interpret the `sizes` input with regard to keeping the original aspect ratio of the input, and it is not applicable when the `scales` input is used.
Given a set of `sizes`, associated with a subset of `axes` (explicitly provided or default), and assuming `d = axes[i]`, with `i` being the index of the provided `sizes`.
If `keep_aspect_ratio_policy` is `"stretch"`, the original aspect ratio is disregarded, and the input is resized to the specified size:
`out_size[d] = sizes[i]`
If `keep_aspect_ratio_policy` is `"not_larger"`, the sizes are adjusted so that no extent of the output is larger than the specified size, while keeping the original aspect ratio:
`scale = Min(sizes[i] / in_size[d])`
`out_size[d] = round_int(scale * in_size[i])`
If `keep_aspect_ratio_policy` is `"not_smaller"`, the sizes are adjusted so that no extent of the output is smaller than the specified size, while keeping the original aspect ratio:
`scale = Max(sizes[i] / in_size[d])`
`out_size[d] = round_int(scale * in_size[i])`
For non-resizable axes (those not specified in `axes`), the output size will be equal to the input size. Note: `round_int` stands for computing the nearest integer value, rounding halfway cases up. Resize the input tensor. In general, it calculates every value in the output tensor as a weighted average of neighborhood (a.k.a. sampling locations) in the input tensor. Each dimension value of the output tensor is: output_dimension = floor(input_dimension * (roi_end - roi_start) * scale) if input \"sizes\" is not specified. This attribute describes how to transform the coordinate in the resized tensor to the coordinate in the original tensor.
The coordinate of each dimension is transformed individually. Let's describe a case using axis x as an example. Denote x_resized as the coordinate of axis x in the resized tensor, x_original as the coordinate of axis x in the original tensor, length_original as the length of the original tensor in axis x, length_resized as the length of the resized tensor in axis x, roi_x = (start_x, end_x) of the axis x in input "roi", scale = length_resized / length_original,
if coordinate_transformation_mode is "half_pixel",
x_original = (x_resized + 0.5) / scale - 0.5,
if coordinate_transformation_mode is "pytorch_half_pixel",
x_original = length_resized > 1 ? (x_resized + 0.5) / scale - 0.5 : 0,
if coordinate_transformation_mode is "align_corners",
x_original = x_resized * (length_original - 1) / (length_resized - 1),
if coordinate_transformation_mode is "asymmetric",
x_original = x_resized / scale,
if coordinate_transformation_mode is "tf_crop_and_resize",
x_original = length_resized > 1 ? start_x * (length_original - 1) + x_resized * (end_x - start_x) * (length_original - 1) / (length_resized - 1) : 0.5 * (start_x + end_x) * (length_original - 1). This attribute describes how to transform the coordinate in the resized tensor to the coordinate in the original tensor.
The coordinate of each dimension is transformed individually. Let's describe a case using axis x as an example. Denote x_resized as the coordinate of axis x in the resized tensor, x_original as the coordinate of axis x in the original tensor, length_original as the length of the original tensor in axis x, length_resized as the length of the resized tensor in axis x, roi_x = (start_x, end_x) of the axis x in input "roi", scale = length_resized / length_original,
if coordinate_transformation_mode is "half_pixel",
x_original = (x_resized + 0.5) / scale - 0.5,
if coordinate_transformation_mode is "pytorch_half_pixel",
x_original = length_resized > 1 ? (x_resized + 0.5) / scale - 0.5 : 0,
if coordinate_transformation_mode is "align_corners",
x_original = x_resized * (length_original - 1) / (length_resized - 1),
if coordinate_transformation_mode is "asymmetric",
x_original = x_resized / scale,
if coordinate_transformation_mode is "tf_half_pixel_for_nn",
x_original = (x_resized + 0.5) / scale,
if coordinate_transformation_mode is "tf_crop_and_resize",
x_original = length_resized > 1 ? start_x * (length_original - 1) + x_resized * (end_x - start_x) * (length_original - 1) / (length_resized - 1) : 0.5 * (start_x + end_x) * (length_original - 1).Split a tensor into a list of tensors, along the specified 'axis'. Lengths of the parts can be specified using argument 'split'. Otherwise, the tensor is split to equal sized parts. Split a tensor into a list of tensors, along the specified 'axis'. Lengths of the parts can be specified using input 'split'. Otherwise, the tensor is split to equal sized parts. Returns the indices of the elements that are non-zero (in row-major order - by dimension). NonZero behaves similar to numpy.nonzero: https://docs.scipy.org/doc/numpy/reference/generated/numpy.nonzero.html, but for scalar input, NonZero produces output shape (0, N) instead of (1, N), which is different from Numpy's behavior. Produces a slice of the input tensor along multiple axes. Similar to numpy: https://numpy.org/doc/stable/reference/routines.indexing.html Slices uses `starts`, `ends`, `axes` and `steps` inputs to specify the start and end dimension and step for each axis in the list of axes, it uses this information to slice the input `data` tensor. If a negative value is passed for any of the start or end indices, it represents number of elements before the end of that dimension. If the value passed to start or end is larger than the `n` (the number of elements in this dimension), it represents `n`. For slicing to the end of a dimension with unknown size, it is recommended to pass in `INT_MAX` when slicing forward and 'INT_MIN' when slicing backward. If a negative value is passed for step, it represents slicing backward. However step value cannot be 0. If `axes` are omitted, they are set to `[0, ..., ndim-1]`. If `steps` are omitted, they are set to `[1, ..., 1]` of length `len(starts)` Example 1: data = [ [1, 2, 3, 4], [5, 6, 7, 8], ] axes = [0, 1] starts = [1, 0] ends = [2, 3] steps = [1, 2] result = [ [5, 7], ] Example 2: data = [ [1, 2, 3, 4], [5, 6, 7, 8], ] starts = [0, 1] ends = [-1, 1000] result = [ [2, 3, 4], ] D:\a\_work\_temp\onnx\src\v1.17.0-7e28176e64.clean\onnx\defs\tensor\old.ccoutput = Cast (input)4-D tensor of shape (N, C, H_out, W_out) of sampled values. For integer input types, intermediate values are computed as floating point and cast to integer at the end.Target shape may not have multiple -1 dimensionsInvalid position of 0Invalid Target shape product of 0The input is not evenly splittablelength of each output. Values should be >= 0.Input axes has invalid data'step' cannot be 01-D tensor of axes that `starts` and `ends` apply to. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).1-D tensor of slice step of corresponding axis in `axes`. Negative value means slicing backward. 'steps' cannot be 0. Defaults to 1.A list of integers. By default, reverse the dimensions, otherwise permute the axes according to the values given.Type of reduction to apply: none (default), add, mul. 'none': no reduction applied. 'add': reduction using the addition operation. 'mul': reduction using the multiplication operation.'axes' attribute must not contain any duplicatesvalues in 'axes' are beyond the bounds of the computed output shapeThree interpolation modes: nearest (default), linear and cubic. The "linear" mode includes linear interpolation for 1D tensor and N-linear interpolation for N-D tensor (for example, bilinear interpolation for 2D tensor). The "cubic" mode includes cubic interpolation for 1D tensor and N-cubic interpolation for N-D tensor (for example, bicubic interpolation for 2D tensor).The coefficient 'a' used in cubic interpolation. Two common choice are -0.5 (in some cases of TensorFlow) and -0.75 (in PyTorch). Check out Equation (4) in https://ieeexplore.ieee.org/document/1163711 for the details. This attribute is valid only if "mode" is "cubic".Four modes: round_prefer_floor (default, as known as round half down), round_prefer_ceil (as known as round half up), floor, ceil. Only used by nearest interpolation. It indicates how to get "nearest" pixel in input tensor from x_original, so this attribute is valid only if "mode" is "nearest".1-D tensor given as [start1, ..., startN, end1, ..., endN], where N is the rank of X. The RoIs' coordinates are normalized in the coordinate system of the input image. It only takes effect when coordinate_transformation_mode is "tf_crop_and_resize"The scale array along each dimension. It takes value greater than 0. If it's less than 1, it's sampling down, otherwise, it's upsampling. The number of elements of 'scales' should be the same as the rank of input 'X'. One of 'scales' and 'sizes' MUST be specified and it is an error if both are specified. If 'sizes' is needed, the user can use an empty string as the name of 'scales' in this operator's input list.The size of the output tensor. The number of elements of 'sizes' should be the same as the rank of input 'X'. Only one of 'scales' and 'sizes' can be specified.The scale array along each dimension. It takes value greater than 0. If it's less than 1, it's sampling down, otherwise, it's upsampling. The number of elements of 'scales' should be the same as the rank of input 'X'. If 'size' is needed, the user must set 'scales' to an empty tensor.The size of the output tensor. The number of elements of 'sizes' should be the same as the rank of input 'X'. May only be set if 'scales' is set to an empty tensor.'pads' input must be a 1D (shape: [2 * input_rank]) tensor of type int64Supported modes: `constant`(default), `reflect`, `edge`Tensor of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D input tensor, it is the number of pixels. `pads` should be a 1D tensor of shape [2 * input_rank]. `pads` format should be: [x1_begin, x2_begin,...,x1_end, x2_end,...], where xi_begin is the number of pad values added at the beginning of axis `i` and xi_end, the number of pad values added at the end of axis `i`.(Optional) A scalar value to be used if the mode chosen is `constant` (by default it is 0).constant_valueConstrain input and output to only numeric types.Constrain input types. Casting from strings and complex are not supported.Constrain output types. Casting to strings and complex are not supported.Which axis to concat on. Default value is 1.rank must be greater than axisOptional list of output lengths (see also arg 'split')outputs...Which axis to split onlength of each outputList of integers indicate the padding element count at the beginning and end of each axis, for 2D it is the number of pixel. `paddings` rank should be double of the input's rank. `paddings` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.paddingsThree modes: constant(default), reflect, edgeOne float, indicates the value to be filled, default is 0New shapelegacy optimization attribute.consumed_inputsRepeat the elements of a tensor along an axis.Number of repeated copies to make of the input tensor.tilesAxis along which to repeat.Output tensor of same shape and type as input.Constrain tiles and axis's type to int64 tensors.The scale along width dimension. It takes value greater than or equal to 1.width_scaleThe scale along height dimension. It takes value greater than or equal to 1.height_scaleTwo interpolation modes: nearest(default), bilinear4-D tensor, [N,C,H,W]4-D tensor after resizing, [N,C,H,W]Constrain output types to bool, int32, int64, float16, float, double tensors.) is not equal to the existing rank value (Ranks inferred (Number of elements of attribute 'scales' must be same as rank of input 'X'Attribute 'scales' must have floats type.Attribute 'scales' is required.The scale array along each dimension. It takes value greater than 0. If it's less than 1, it's sampling down, otherwise, it's upsampling. The number of elements of 'scales' should be the same as the rank of input 'X'.Incorrect or missing attribute value for starts and endsAttribute axes has incorrect lengthAxes that `starts` and `ends` apply to. It's optional. If not present, will be treated as [0, 1, ..., len(`starts`) - 1].Starting indices of corresponding axis in `axes`Ending indices (exclusive) of corresponding axis in axes`1-D tensor of slice step of corresponding axis in `axes`. Default to 1. Which axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1]Tensor of int32/int64 indices, of r >= 1 (same rank as input).Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1]Tensor of int32/int64 indices, of any rank q. All index values are expected to be within bounds. It is an error if any of the index values are out of bounds.List of non-negative integers, indicate the dimensions to squeeze.List of non-negative integers, indicate the dimensions to be inserted'axis' must be in [-rank(indices)-1, rank(indices)](Optional) Axis along which one-hot representation in added. Default: axis=-1. axis=-1 means that the additional dimension will be inserted as the innermost/last dimension in the output tensor.Input tensor containing indices. The values must be non-negative integers. Any entries in the 'indices' input tensor with values outside the range [0, depth) will result in one-hot representation with all 'off_value' values in the output tensor.In case 'indices' is of non-integer type, the values will be casted to int64 before use.Scalar or rank 1 tensor containing exactly one element, specifying the number of classes in one-hot tensor. This is also the size of the one-hot dimension (specified by 'axis' attribute) added on in the output tensor. The values in the 'indices' input tensor are expected to be in the range [0, depth). In case 'depth' is of non-integer type, it will be casted to int64 before use.(Optional) Axis along which to take slices. If not specified, input is flattened before elements being selected.Rank 1 tensor of booleans to indicate which slices or data elements to be selected. Its length can be less than the input length alone the axis or the flattened input size if axis is not specified. In such cases data slices or elements exceeding the condition length are discarded.Which axis to split on. Attribute value for pads is requiredAttribute pads has incorrect lengthList of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D it is the number of pixels. `pads` rank should be double of the input's rank. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.One float, indicates the value to be filled.Constrain input and output types to all tensor and sequence types.(Optional) A scalar value to be used if the mode chosen is `constant` (by default it is 0, empty string or False).Lpp`C#@@PC#C#0C#|PP`C#pC#`C#`PPp`C#C#C#D`C#`C#  0C#(@@PC#tpC#PC# pppC#XPC#00@@C#00`@C#<@@PC#C#@@ PC#   0C#lPC#т C#т C#PтC#тC#``тpC#4тC#@C#0C#``pC#d C#C#@@PC#HC#C#C#,P C#xC#00@C#pC#\``ppC#  0C#  0C#@C#C#C#$C#p00@C# C# ppC#T 0C# C# 0C#8!0C#!``pC#!PP `C#"тC#h"PP`C#"pp@C##0C#L#pp@C##C##C#0$C#|$PP0`C#$00@C#%C#`%C#%`C#%@C#D&C#&@C#&C#('``@pC#t'C#'@C# (  0C#??@-@A constant tensor. A constant tensor. Exactly one of the two attributes, either value or sparse_value, must be specified. D:\a\_work\_temp\onnx\src\v1.17.0-7e28176e64.clean\onnx\defs\generator\old.ccAttribute 'value' of Constant node must exist with 'Tensor' data.Only one of the attributes 'value' or 'sparse_value' must be specified for a Constant node.One of the attributes 'value' or 'sparse_value' must be specified for a Constant node.Constrain output types to be numerics.X(``H`C#(   I C#(PPIPC#<)HC#)ppHpC#)HC# *@@H@C#l*@IC#*C#+C#P+C#+0IC#+000I0C#??@-@ Scan can be used to iterate over one or more scan_input tensors, constructing zero or more scan_output tensors. It combines ideas from general recurrences, functional programming constructs such as scan, fold, map, and zip, and is intended to enable generalizations of RNN-like constructs for sequence-to-sequence processing. Other tensors (referred to as state_variables here) can be used to carry a state when iterating from one element to another (similar to hidden-state in RNNs, also referred to as loop-carried dependences in the context of loops). All these tensors are required to have the same shape in each iteration of the loop (a restriction imposed to enable efficient memory allocation). Many common usages involve a single scan_input tensor (where functionality similar to scan, fold and map can be obtained). When more than one scan_input is used, a behavior similar to zip is obtained. The attribute body must be a graph, specifying the computation to be performed in every iteration. It takes as input the current values of the state_variables and the current iterated element of the scan_inputs. It must return the (updated) values of the state_variables and zero or more scan_output_element tensors. The values of the scan_output_element tensors are concatenated over all the iterations to produce the scan_output values of the scan construct (similar to the concatenated intermediate hidden-state values of RNN-like constructs). The scan operation returns the final values of the state_variables as well as the scan_outputs. The operation supports batching, and the batch-axis is required to be 0. When multiple scan_input tensors are used, they must all have the same batch-size, and they must all have the same maximum-sequence-length (the dimensionality of the sequence axis or scan axis). The sequence axis or scan axis is required to be 1. The operation has an optional sequence_lens input (of shape [BATCH_SIZE]) to allow variable length sequences of length <= the maximum-sequence-length. If this input is not specified, all sequences are assumed to be of length equal to maximum-sequence-length. For variable length input sequences, the scan_outputs will consist of a sequence of same length as the input, padded to the maximum-sequence-length. The optional attribute directions can be used to scan a sequence in the reverse direction. If this attribute is omitted, all sequences are scanned in the forward direction. A bidirectional scan be performed by specifying the same tensor input twice in the scan_inputs, once with a forward direction, and once with a backward direction. Note that because of the ONNX restriction that only the last parameter of an operator can be variadic, the initial-states and scan-inputs are listed together as one input parameter. Similarly, the final-states and scan-outputs are listed together as one output parameter. The attribute num_scan_inputs indicates the number M of scan-inputs. The behavior of Scan < num_scan_inputs = m, body = loop-body > (sequence_lengths, init_1, ..., init_n, scan_1, ..., scan_m) is equivalent to the following pseudo-code: // T.shape[0] denotes the batch-size of T // The batch-size of scan_1, ..., scan_m are all required to be equal batch_size = scan_1.shape[0]; // scan_i.shape[1] denotes the (max) sequence-length of scan_i // scan_i.shape[1] is required to be equal to scan_j.shape[1] for all i,j. max_sequence_length = scan_1.shape[1]; for (int batch = 0; batch < batch_size; ++batch) { // initialize state-variables st_1 = init_1; ... st_n = init_n; // initialize scan-output variables: [] denotes an empty tensor scan_out_1 = []; ...; scan_out_k = []; // identify number of iterations: N = (sequence_lengths specified) ? sequence_lengths[batch] : max_sequence_length; // execute loop for (int t = 0; t < N; ++t) { // generate the scan-input elements: the notation T[t] indicates the sub-tensor // of rank one less than T obtained by indexing T at position t along axis k. si_1 = (scan_1[batch])[t]; ... ; si_m = (scan_m[batch])[t]; // execute loop-body st_1, ..., st_n, so_1, ..., so_k = loop-body(st_1, ..., st_n, si_1, ..., si_m) // accumulate the scan-output elements scan_out_1 = Concat(scan_out_1, so_1); ... ; scan_out_k = Concat(scan_out_k, so_k); } // accumulate the outputs for this batch: bst_1[batch] = st_1; ..., bst_n[batch] = st_n; // Note scan-outputs will have size max_sequence_length, but only first N values will be meaningful. // The remaining values have an undefined value. b_scan_out_1[batch] = scan_out_1; ...; b_scan_out_k[batch] = scan_out_k; } return bst_1, ..., bst_n, b_scan_out_1, ..., b_scan_out_k; *Sample usage: Encoding RNN using a Scan* The following example shows how a simple RNN over an input tensor %X, with weight tensor %Wi, recurrence weight tensor %Ri, bias tensors %Wbi and %Rbi, and initial hidden-state %H_0 can be encoded as a ScanLoop. Note that the loop-body is a nested graph, and it directly computes %Wi, %Ri, %Wbi, and %Rbi (typically constants or initializers in the body graph). If these values are computed in the outer graph, they need to be passed in as extra state_variables. graph rnn-encoding { %H_0 = ... %X = ... %Y_h, %Y = Scan[body = , num_scan_inputs=1]("", %H_0, %X) return %Y, %Y_h } graph rnn-cell-1 ( %H_tminus1[FLOAT, tensor] %X_t[FLOAT, tensor] ) { %Wi = ... %Ri = ... %Wbi = ... %Rbi = ... %t1 = X_t * (Wi^T) %t2 = H_tminus1*(Ri^T) %t3 = Add(%t1, %t2) %t4 = Add(%t3, %Wbi) %t5 = Add(%t4, %Rbi) %Ht = Tanh(%t5) %Accumulate = Identity(%Ht) return %Ht, %Accumulate } Generic Looping construct. This loop has multiple termination conditions: 1) Trip count. Iteration count specified at runtime. Set by specifying the input M. Optional. Set to empty string to omit. Note that a static trip count (specified at graph construction time) can be specified by passing in a constant node for input M. 2) Loop termination condition. This is an input to the op that determines whether to run the first iteration and also a loop-carried dependency for the body graph. The body graph must yield a value for the condition variable, whether this input is provided or not. This table summarizes the operating modes of this operator with equivalent C-style code: Operator inputs defined as (max_trip_count, condition_var). input ("", ""): for (int i=0; ; ++i) { cond = ... // Note this value is ignored, but is required in the body } input ("", cond) // Note this is analogous to a while loop bool cond = ...; for (int i=0; cond; ++i) { cond = ...; } input ("", 1) // Note this is analogous to a do-while loop bool cond = true for (int i=0; cond; ++i) { cond = ...; } input (trip_count, "") // Note this is analogous to a for loop int trip_count = ... for (int i=0; i < trip_count; ++i) { cond = ...; // ignored } input (trip_count, cond) int trip_count = ...; bool cond = ...; for (int i=0; i < trip_count && cond; ++i) { cond = ...; } *Sample usage - cond as well as trip count* graph predict-net { %a = Constant[value = ]() %b = Constant[value = ]() %keepgoing = Constant[value = ]() %max_trip_count = Constant[value = ]() %keepgoing_out, %b_out, %user_defined_vals = Loop[body = ](%max_trip_count, %keepgoing, %b) return } graph body-net ( %i[INT32, scalar] %keepgoing[BOOL, scalar] %b[INT32, scalar] ) { %my_local = Add(%a, %b) %b_out = Sub(%a, %b) %keepgoing_out = Greater(%my_local, %b_out) %user_defined_vals = Add(%b, %b) return %keepgoing_out, %b_out, %user_defined_vals } *Sample equivalent C code* { /* User-defined code (enclosing scope) */ int a = 3, b = 6; bool keepgoing = true; // Analogous to input cond /* End user-defined code */ /* Implicitly-defined code */ const int max_trip_count = 10; // Analogous to input M int user_defined_vals[]; // Imagine this is resizable /* End implicitly-defined code */ for (int i=0; i < max_trip_count && keepgoing; ++i) { /* User-defined code (loop body) */ int my_local = a + b; // Reading values in the enclosing scope is fine b = a - b; // writes fine if we specify b as a loop-carried dependency keepgoing = my_local > b; // keepgoing is a loop-carried dependency user_defined_vals[i] = b + b; /* End user-defined code */ } // my_local = 123; // Can't do this. my_local was defined in the body // These below values are live-out from the loop and therefore accessible b_out; user_defined_vals; keepgoing_out; } There are several things of note in this code snippet: 1) Values from the enclosing scope (i.e. variable a here) are in scope and can be referenced in the inputs of the loop. 2) Any variables which you wish to make available in the enclosing scope (i.e. the variables b and keepgoing) must be declared as either loop-carried dependencies (both at the op inputs and output and at the body net input and output) or scan_outputs. 3) Values created in the body cannot be accessed in the enclosing scope. Note that the semantics of this op support "diagonal" or "wavefront" execution. (See Step 3 here for an example: https://devblogs.nvidia.com/optimizing-recurrent-neural-networks-cudnn-5/). Frontends should emit multi-layer RNNs as a series of While operators (with time being the inner looping dimension), with each successive layer consuming the scan_outputs from the previous layer, possibly going through several point-wise operators (e.g. dropout, residual connections, linear layer). Generic Looping construct. This loop has multiple termination conditions: 1) Trip count. Iteration count specified at runtime. Set by specifying the input M. Optional. Set to empty string to omit. Note that a static trip count (specified at graph construction time) can be specified by passing in a constant node for input M. 2) Loop termination condition. This is an input to the op that determines whether to run the first iteration and also a loop-carried dependency for the body graph. The body graph must yield a value for the condition variable, whether this input is provided or not. This table summarizes the operating modes of this operator with equivalent C-style code: Operator inputs defined as (max_trip_count, condition_var). input ("", ""): for (int i=0; ; ++i) { cond = ... // Note this value is ignored, but is required in the body } input ("", cond) // Note this is analogous to a while loop bool cond = ...; for (int i=0; cond; ++i) { cond = ...; } input ("", 1) // Note this is analogous to a do-while loop bool cond = true for (int i=0; cond; ++i) { cond = ...; } input (trip_count, "") // Note this is analogous to a for loop int trip_count = ... for (int i=0; i < trip_count; ++i) { cond = ...; // ignored } input (trip_count, cond) int trip_count = ...; bool cond = ...; for (int i=0; i < trip_count && cond; ++i) { cond = ...; } *Sample usage - cond as well as trip count* graph predict-net { %a = Constant[value = ]() %b = Constant[value = ]() %keepgoing = Constant[value = ]() %max_trip_count = Constant[value = ]() %keepgoing_out, %b_out, %user_defined_vals = Loop[body = ](%max_trip_count, %keepgoing, %b) return } graph body-net ( %i[INT32, scalar] // iteration number %keepgoing_in[BOOL, scalar] // incoming loop-termination-condition; not used %b_in[INT32, scalar] // incoming value of loop-carried-dependency b ) { %my_local = Add(%a, %b_in) %b_out = Sub(%a, %b_in) // outgoing value of loop-carried-dependency b %keepgoing_out = Greater(%my_local, %b_out) // outgoing loop-termination-condition %user_defined_val = Add(%b_in, %b_in) // scan-output value to be accumulated return %keepgoing_out, %b_out, %user_defined_val } *Sample equivalent C code* { /* User-defined code (enclosing scope) */ int a = 3, b = 6; bool keepgoing = true; // Analogous to input cond /* End user-defined code */ /* Implicitly-defined code */ const int max_trip_count = 10; // Analogous to input M int user_defined_vals[]; // Imagine this is resizable /* End implicitly-defined code */ /* initialize loop-carried variables and scan-output variables */ bool keepgoing_out = keepgoing int b_out = b for (int i=0; i < max_trip_count && keepgoing_out; ++i) { /* Implicitly-defined code: bind actual parameter values to formal parameter variables of loop-body */ bool keepgoing_in = keepgoing_out; bool b_in = b_out; /* User-defined code (loop body) */ int my_local = a + b_in; // Reading value "a" from the enclosing scope is fine b_out = a - b_in; keepgoing_out = my_local > b_out; user_defined_val = b_in + b_in; // b_in and b_out are different variables /* End user-defined code */ /* Implicitly defined-code */ user_defined_vals[i] = user_defined_val // accumulate scan-output values } // int t = my_local; // Can't do this. my_local is not accessible here. // The values below are bound to the output variables of the loop and therefore accessible // b_out; user_defined_vals; keepgoing_out; } There are several things of note in this code snippet: 1) Values from the enclosing scope (i.e. variable "a" here) are in scope and can be referenced in the inputs of the loop. 2) Any values computed in the loop body that needs to be used in a subsequent iteration or after the loop are modelled using a pair of variables in the loop-body, consisting of an input variable (eg., b_in) and an output variable (eg., b_out). These are referred to as loop-carried dependences. The loop operation node supplies the input value of the input variable for the first iteration, and returns the output value of the output variable produced by the final iteration. 3) Scan_output variables are used to implicitly concatenate values computed across all the iterations. In the above example, the value of user_defined_val computed over all iterations are concatenated and returned as the value of user_defined_vals after the loop. 4) Values created in the body cannot be accessed in the enclosing scope, except using the mechanism described above. Note that the semantics of this op support "diagonal" or "wavefront" execution. (See Step 3 here for an example: https://devblogs.nvidia.com/optimizing-recurrent-neural-networks-cudnn-5/). Frontends should emit multi-layer RNNs as a series of While operators (with time being the inner looping dimension), with each successive layer consuming the scan_outputs from the previous layer, possibly going through several point-wise operators (e.g. dropout, residual connections, linear layer). Generic Looping construct. This loop has multiple termination conditions: 1) Trip count. Iteration count specified at runtime. Set by specifying the input M. Optional. Set to empty string to omit. Note that a static trip count (specified at graph construction time) can be specified by passing in a constant node for input M. 2) Loop termination condition. This is an input to the op that determines whether to run the first iteration and also a loop-carried dependency for the body graph. The body graph must yield a value for the condition variable, whether this input is provided or not. This table summarizes the operating modes of this operator with equivalent C-style code: Operator inputs defined as (max_trip_count, condition_var). input ("", ""): for (int i=0; ; ++i) { cond = ... // Note this value is ignored, but is required in the body } input ("", cond) // Note this is analogous to a while loop bool cond = ...; for (int i=0; cond; ++i) { cond = ...; } input ("", 1) // Note this is analogous to a do-while loop bool cond = true for (int i=0; cond; ++i) { cond = ...; } input (trip_count, "") // Note this is analogous to a for loop int trip_count = ... for (int i=0; i < trip_count; ++i) { cond = ...; // ignored } input (trip_count, cond) int trip_count = ...; bool cond = ...; for (int i=0; i < trip_count && cond; ++i) { cond = ...; } *Sample usage - cond as well as trip count* graph predict-net { %a = Constant[value = ]() %b = Constant[value = ]() %keepgoing = Constant[value = ]() %max_trip_count = Constant[value = ]() %keepgoing_out, %b_out, %user_defined_vals = Loop[body = ](%max_trip_count, %keepgoing, %b) return } graph body-net ( %i[INT32, scalar] // iteration number %keepgoing_in[BOOL, scalar] // incoming loop-termination-condition; not used %b_in[INT32, scalar] // incoming value of loop-carried-dependency b ) { %my_local = Add(%a, %b_in) %b_out = Sub(%a, %b_in) // outgoing value of loop-carried-dependency b %keepgoing_out = Greater(%my_local, %b_out) // outgoing loop-termination-condition %user_defined_val = Add(%b_in, %b_in) // scan-output value to be accumulated return %keepgoing_out, %b_out, %user_defined_val } *Sample equivalent C code* { /* User-defined code (enclosing scope) */ int a = 3, b = 6; bool keepgoing = true; // Analogous to input cond /* End user-defined code */ /* Implicitly-defined code */ const int max_trip_count = 10; // Analogous to input M int user_defined_vals[]; // Imagine this is resizable /* End implicitly-defined code */ /* initialize loop-carried variables and scan-output variables */ bool keepgoing_out = keepgoing int b_out = b for (int i=0; i < max_trip_count && keepgoing_out; ++i) { /* Implicitly-defined code: bind actual parameter values to formal parameter variables of loop-body */ bool keepgoing_in = keepgoing_out; bool b_in = b_out; /* User-defined code (loop body) */ int my_local = a + b_in; // Reading value "a" from the enclosing scope is fine b_out = a - b_in; keepgoing_out = my_local > b_out; user_defined_val = b_in + b_in; // b_in and b_out are different variables /* End user-defined code */ /* Implicitly defined-code */ user_defined_vals[i] = user_defined_val // accumulate scan-output values } // int t = my_local; // Can't do this. my_local is not accessible here. // The values below are bound to the output variables of the loop and therefore accessible // b_out; user_defined_vals; keepgoing_out; } There are several things of note in this code snippet: 1) Values from the enclosing scope (i.e. variable "a" here) are in scope and can be referenced in the inputs of the loop. 2) Any values computed in the loop body that needs to be used in a subsequent iteration or after the loop are modelled using a pair of variables in the loop-body, consisting of an input variable (eg., b_in) and an output variable (eg., b_out). These are referred to as loop-carried dependences. The loop operation node supplies the input value of the input variable for the first iteration, and returns the output value of the output variable produced by the final iteration. 3) Scan_output variables are used to implicitly concatenate values computed across all the iterations. In the above example, the value of user_defined_val computed over all iterations are concatenated and returned as the value of user_defined_vals after the loop. 4) Values created in the body cannot be accessed in the enclosing scope, except using the mechanism described above. Note that the semantics of this op support "diagonal" or "wavefront" execution. (See Step 3 here for an example: https://devblogs.nvidia.com/optimizing-recurrent-neural-networks-cudnn-5/). Frontends should emit multi-layer RNNs as a series of While operators (with time being the inner looping dimension), with each successive layer consuming the scan_outputs from the previous layer, possibly going through several point-wise operators (e.g. dropout, residual connections, linear layer). The input/output of subgraph (produced by loop node) matching is based on order instead of name. The implementation will figure out the names based on this order. All Tensor, Sequence(Tensor), Optional(Tensor), and Optional(Sequence(Tensor)) types up to IRv9.D:\a\_work\_temp\onnx\src\v1.17.0-7e28176e64.clean\onnx\defs\controlflow\old.ccAll Tensor, Sequence(Tensor), Optional(Tensor), and Optional(Sequence(Tensor)) types up to IRv4.All Tensor types up to IRv9.All Tensor types up to IRv4. was not a tensor.Scan input outputs. Expected Graph attribute inferencing returned type information for was notScan 'body' subgraph outputs should all be tensors but output axis value ) is not equal to number of scan inputs (Number of scan input axes specified () is not equal to number of scan outputs (Number of scan output axes specified (Optional tensor specifying lengths of the sequences in a batch. If this input is not specified, all sequences are assumed to be of the maximum sequence length (the dimension of the sequence axis of the scan_input tensors).Int64 tensorAll Tensor types was Loop 'body' subgraph outputs should all be tensors but output Final N loop carried dependency values then K scan_outputsAn optional list of M flags. The i-th element of the list specifies the axis to be scanned (the sequence axis) for the i-th scan_input. If omitted, 0 will be used as the scan axis for every scan_input.An optional list of K flags. The i-th element of the list specifies the axis for the i-th scan_output. The scan outputs are accumulated along the specified axis. If omitted, 0 will be used as the scan axis for every scan_output.then_branch and else_branch produce different number of outputs. but subgraphs produce If node has else= then=Mismatched type for output Mismatched tensor element type for output Values that are live-out to the enclosing scope. The return values in the `then_branch` and `else_branch` must be of the same shape and same data type.All Tensor and Sequence typesLoop 'body' subgraph outputs should all be tensors or sequences but output Loop 'body' subgraph scan outputs should all be tensors but output The linear quantization operator. It consumes a high precision tensor, a scale, and a zero point to compute the low precision / quantized tensor. The scale factor and zero point must have same shape, and can be either a scalar for per-tensor / per layer quantization, or a 1-D tensor for per-axis quantization. The quantization formula is `y = saturate ((x / y_scale) + y_zero_point)`. For saturation, it saturates to [0, 255] if it's uint8, or [-128, 127] if it's int8. For (x / y_scale), it's rounding to the nearest even. Refer to https://en.wikipedia.org/wiki/Rounding for details. 'y_zero_point' and 'y' must have same type. 'y_zero_point' is usually not used for quantization to float8e4m3fn, float8e4m3fnuz, float8e5m2, float8e5m2fnuz, but the quantization formula remains the same for consistency and the type of the attribute 'y_zero_point' still determines the quantization type. ??@-@ The linear dequantization operator. It consumes a quantized tensor, a scale, and a zero point to compute the full precision tensor. The dequantization formula is `y = (x - x_zero_point) * x_scale`. `x_scale` and `x_zero_point` must have same shape, and can be either a scalar for per-tensor / per layer quantization, or a 1-D tensor for per-axis quantization. `x_zero_point` and `x` must have same type. `x` and `y` must have same shape. In the case of dequantizing int32, there's no zero point (zero point is supposed to be 0). `zero-point` is usually not used in the case of float8e4m3fn, float8e4m3fnuz, float8e5m2, float8e5m2fnuz quantization, but the dequantization formula remains the same for consistency and 'x_scale' still determines the output type. The linear quantization operator. It consumes a high precision tensor, a scale, and a zero point to compute the low precision / quantized tensor. The scale factor and zero point must have same shape, and can be either a scalar for per-tensor / per layer quantization, or a 1-D tensor for per-axis quantization. The quantization formula is y = saturate ((x / y_scale) + y_zero_point). For saturation, it saturates to [0, 255] if it's uint8, or [-128, 127] if it's int8. For (x / y_scale), it's rounding to the nearest even. Refer to https://en.wikipedia.org/wiki/Rounding for details. 'y_zero_point' and 'y' must have same type. The linear dequantization operator. It consumes a quantized tensor, a scale, and a zero point to compute the full precision tensor. The dequantization formula is `y = (x - x_zero_point) * x_scale`. `x_scale` and `x_zero_point` must have same shape, and can be either a scalar for per-tensor / per layer quantization, or a 1-D tensor for per-axis quantization. `x_zero_point` and `x` must have same type. `x` and `y` must have same shape. In the case of dequantizing int32, there's no zero point (zero point is supposed to be 0). The linear per-tensor/layer quantization operator. It consumes a high precision tensor, a scale, a zero point to compute the low precision / quantized tensor. The quantization formula is y = saturate ((x / y_scale) + y_zero_point). For saturation, it saturates to [0, 255] if it's uint8, or [-128, 127] if it's int8. For (x / y_scale), it's rounding to the nearest even. Refer to https://en.wikipedia.org/wiki/Rounding for details. 'y_zero_point' and 'y' must have same type. The linear dequantization operator. It consumes a quantized tensor, a scale, a zero point to compute the full precision tensor. The dequantization formula is y = (x - x_zero_point) * x_scale. 'x_scale' and 'x_zero_point' are both scalars. 'x_zero_point' and 'x' must have same type. 'x' and 'y' must have same shape. In the case of dequantizing int32, there's no zero point (zero point is supposed to be 0). Scale for doing quantization to get 'y'. It can be a scalar, which means per-tensor/layer quantization, or a 1-D Tensor for per-axis quantization.(Optional) The axis of the quantization dimension of the input tensor. Ignored for per-tensor quantization. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).Constrain 'x' to float, float16, bfloat16 or int32 tensor.Constrain 'y_zero_point' and 'y' to 8-bit integer/float tensor.D:\a\_work\_temp\onnx\src\v1.17.0-7e28176e64.clean\onnx\defs\quantization\old.cc(Optional) The axis of the dequantizing dimension of the input tensor. Used only for per-axis quantization. Negative value means counting dimensions from the back. Accepted range is `[-r, r-1]` where `r = rank(input)`. When the rank of the input is 1, per-tensor quantization is applied, rendering the axis unnecessary in this scenario.Constrain 'x_zero_point' and 'x' to 8-bit integer or float, or /32-bit integer tensor.Constrain 'x' to float or int32 tensor.Constrain 'y_zero_point' and 'y' to 8-bit integer tensor.(Optional) The axis of the dequantizing dimension of the input tensor. Ignored for per-tensor quantization. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).Constrain 'x_zero_point' and 'x' to 8-bit/32-bit integer tensor.Scale for doing quantization to get 'y'. It's a scalar, which means a per-tensor/layer quantization.Zero point for doing quantization to get 'y'. It's a scalar, which means a per-tensor/layer quantization. Default value is uint8 typed 0 if it's not specified.Scale for input 'x'. It's a scalar, which means a per-tensor/layer quantization.Zero point for input 'x'. It's a scalar, which means a per-tensor/layer quantization. It's optional. 0 is the default value when it's not specified.4,ЈЈш0шC#,ЈЈppшC#,ЈЈш@шC#-ЈЈш`шC#d-ЈЈшPшC#-ЈЈш шC#Padding for the beginning and ending along each axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute.auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding. DEPRECATION NOTE: auto_pad is only intended to support legacy uses, and for framework authors, one is explicitly encouraged to use explicit padding specified in the pads attribute. LpPool consumes an input tensor X and applies Lp pooling across the the tensor according to kernel sizes, stride sizes, and pad lengths. Lp pooling consisting of computing the Lp norm on all values of a subset of the input tensor according to the kernel size and downsampling the data into the output tensor Y for further processing. GlobalLpPool consumes an input tensor X and applies lp pool pooling across the the values in the same channel. This is equivalent to LpPool with kernel size equal to the spatial dimension of input tensor.??@-@ Carries out batch normalization as described in the paper https://arxiv.org/abs/1502.03167. Depending on the mode it is being run, there are multiple cases for the number of outputs, which we list below: Output case #1: Y, mean, var, saved_mean, saved_var (training mode) Output case #2: Y (test mode) Carries out batch normalization as described in the paper https://arxiv.org/abs/1502.03167. Depending on the mode it is being run, there are multiple cases for the number of outputs, which we list below: Output case #1: Y, mean, var, saved_mean, saved_var (training mode) Output case #2: Y (test mode) For previous (depreciated) non-spatial cases, implementors are suggested to flatten the input shape to (N x C*D1*D2 ..*Dn) before a BatchNormalization Op. Carries out batch normalization as described in the paper https://arxiv.org/abs/1502.03167. Depending on the mode it is being run, There are five required inputs 'X', 'scale', 'B', 'input_mean' and 'input_var'. Note that 'input_mean' and 'input_var' are expected to be the estimated statistics in inference mode (training_mode=False, default), and the running statistics in training mode (training_mode=True). There are multiple cases for the number of outputs, which we list below: Output case #1: Y, running_mean, running_var (training_mode=True) Output case #2: Y (training_mode=False) When training_mode=False, extra outputs are invalid. The outputs are updated as follows when training_mode=True: ``` running_mean = input_mean * momentum + current_mean * (1 - momentum) running_var = input_var * momentum + current_var * (1 - momentum) Y = (X - current_mean) / sqrt(current_var + epsilon) * scale + B where: current_mean = ReduceMean(X, axis=all_except_channel_index) current_var = ReduceVar(X, axis=all_except_channel_index) Notice that ReduceVar refers to the population variance, and it equals to sum(sqrd(x_i - x_avg)) / N where N is the population size (this formula does not use sample size N - 1). ``` When training_mode=False: ``` Y = (X - input_mean) / sqrt(input_var + epsilon) * scale + B ``` For previous (depreciated) non-spatial cases, implementors are suggested to flatten the input shape to (N x C * D1 * D2 * ... * Dn) before a BatchNormalization Op. Dropout takes one input data (Tensor) and produces two Tensor outputs, output (Tensor) and mask (Tensor). Depending on whether it is in test mode or not, the output Y will either be a random dropout, or a simple copy of the input. Note that our implementation of Dropout does scaling in the training phase, so during testing nothing needs to be done. Dropout takes one input floating tensor and produces two tensor outputs, output (floating tensor) and mask (`Tensor`). Depending on whether it is in test mode or not, the output Y will either be a random dropout, or a simple copy of the input. Note that our implementation of Dropout does scaling in the training phase, so during testing nothing needs to be done. Carries out batch normalization as described in the paper https://arxiv.org/abs/1502.03167. Depending on the mode it is being run, there are multiple cases for the number of outputs, which we list below: Output case #1: Y, mean, var, saved_mean, saved_var (training mode) Output case #2: Y (test mode) Carries out batch normalization as described in the paper https://arxiv.org/abs/1502.03167. Depending on the mode it is being run, there are multiple cases for the number of outputs, which we list below: Output case #1: Y, mean, var, saved_mean, saved_var (training mode) Output case #2: Y (test mode) A GroupNormalization function. Carries out group normalization as described in the paper https://arxiv.org/abs/1803.08494 This operator transforms input according to ``` y = scale * (x - mean) / sqrt(variance + epsilon) + bias, ``` where the mean and variance are computed per instance per group of channels, and `scale` and `bias` should be specified for each group of channels. The number of groups `num_groups` should be divisible by the number of channels so that there are an equal number of channels per group. When the number of groups is the same as the number of channels, this operator is equivalent to InstanceNormalization. When there is only one group, this operator is equivalent to LayerNormalization. Local Response Normalization proposed in the [AlexNet paper](https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf). It normalizes over local input regions. The local region is defined across the channels. For an element X[n, c, d1, ..., dk] in a tensor of shape (N x C x D1 x D2, ..., Dk), its region is {X[n, i, d1, ..., dk] | max(0, c - floor((size - 1) / 2)) <= i <= min(C - 1, c + ceil((size - 1) / 2))}. square_sum[n, c, d1, ..., dk] = sum(X[n, i, d1, ..., dk] ^ 2), where max(0, c - floor((size - 1) / 2)) <= i <= min(C - 1, c + ceil((size - 1) / 2)). Y[n, c, d1, ..., dk] = X[n, c, d1, ..., dk] / (bias + alpha / size * square_sum[n, c, d1, ..., dk] ) ^ beta A MeanVarianceNormalization Function: Perform mean variance normalization on the input tensor X using formula:
``` (X-EX)/sqrt(E(X-EX)^2) ``` D:\a\_work\_temp\onnx\src\v1.17.0-7e28176e64.clean\onnx\defs\nn\old.cc {name} consumes an input tensor X and applies {opName} pooling across the tensor according to kernel sizes, stride sizes, and pad lengths. {opName} pooling consisting of computing the {opName} on all values of a subset of the input tensor according to the kernel size and downsampling the data into the output tensor Y for further processing. The output spatial shape is calculated differently depending on whether explicit padding is used, where pads is employed, or auto padding is used, where auto_pad is utilized. With explicit padding (https://pytorch.org/docs/stable/generated/torch.nn.MaxPool2d.html?highlight=maxpool#torch.nn.MaxPool2d): ``` output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - dilation[i] * (kernel_shape[i] - 1) - 1) / strides_spatial_shape[i] + 1) ``` or ``` output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - dilation[i] * (kernel_shape[i] - 1) - 1) / strides_spatial_shape[i] + 1) ``` if ceil_mode is enabled. `pad_shape[i]` is the sum of pads along axis `i`. `auto_pad` is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following when ceil_mode is enabled: ``` VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i] - {kernelSpatialShape} + 1) / strides_spatial_shape[i]) SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides_spatial_shape[i]) ``` or when ceil_mode is disabled (https://www.tensorflow.org/api_docs/python/tf/keras/layers/AveragePooling2D): ``` VALID: output_spatial_shape[i] = floor((input_spatial_shape[i] - {kernelSpatialShape}) / strides_spatial_shape[i]) + 1 SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = floor((input_spatial_shape[i] - 1) / strides_spatial_shape[i]) + 1 ``` And pad shape will be following if `SAME_UPPER` or `SAME_LOWER`: ``` pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial_shape[i] + {kernelSpatialShape} - input_spatial_shape[i] ``` {additionalDescription} Constrain input and output to all tensor types.EX_squaredX_squaredE_XsquaredSTDX_varianceProcessed_STD {name} consumes an input tensor X and applies {opName} pooling across the tensor according to kernel sizes, stride sizes, and pad lengths. {opName} pooling consisting of computing the {opName} on all values of a subset of the input tensor according to the kernel size and downsampling the data into the output tensor Y for further processing. The output spatial shape will be following: ``` output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - kernel_spatial_shape[i]) / strides_spatial_shape[i] + 1) * pad_shape[i] is sum of pads along axis i ``` `auto_pad` is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following: ``` VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i] - kernel_spatial_shape[i] + 1) / strides_spatial_shape[i]) SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides_spatial_shape[i]) ``` And pad shape will be following if `SAME_UPPER` or `SAME_LOWER`: ``` pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial_shape[i] + kernel_spatial_shape[i] - input_spatial_shape[i] ``` {additionalDescription} Stride along each spatial axis. {name} consumes an input tensor X and applies {opName} pooling across the tensor according to kernel sizes, stride sizes, and pad lengths. {opName} pooling consisting of computing the {opName} on all values of a subset of the input tensor according to the kernel size and downsampling the data into the output tensor Y for further processing. The output spatial shape will be following: ``` output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - {kernelSpatialShape}) / strides_spatial_shape[i] + 1) ``` or ``` output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - {kernelSpatialShape}) / strides_spatial_shape[i] + 1) ``` if ceil_mode is enabled ``` * pad_shape[i] is sum of pads along axis i ``` `auto_pad` is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following: ``` VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i] - {kernelSpatialShape} + 1) / strides_spatial_shape[i]) SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides_spatial_shape[i]) ``` And pad shape will be following if `SAME_UPPER` or `SAME_LOWER`: ``` pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial_shape[i] + {kernelSpatialShape} - input_spatial_shape[i] ``` {additionalDescription} {name} consumes an input tensor X and applies {opName} pooling across the tensor according to kernel sizes, stride sizes, and pad lengths. {opName} pooling consisting of computing the {opName} on all values of a subset of the input tensor according to the kernel size and downsampling the data into the output tensor Y for further processing. The output spatial shape will be following: ``` output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - {kernelSpatialShape}) / strides_spatial_shape[i] + 1) ``` or ``` output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - {kernelSpatialShape}) / strides_spatial_shape[i] + 1) ``` if ceil_mode is enabled ``` * pad_shape[i] is sum of pads along axis i ``` `auto_pad` is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following when ceil_mode is enabled: ``` VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i] - {kernelSpatialShape} + 1) / strides_spatial_shape[i]) SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides_spatial_shape[i]) ``` or when ceil_mode is disabled: ``` VALID: output_spatial_shape[i] = floor((input_spatial_shape[i] - {kernelSpatialShape} + 1) / strides_spatial_shape[i]) SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = floor(input_spatial_shape[i] / strides_spatial_shape[i]) ``` And pad shape will be following if `SAME_UPPER` or `SAME_LOWER`: ``` pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial_shape[i] + {kernelSpatialShape} - input_spatial_shape[i] ``` {additionalDescription} The output of each pooling window is divided by the number of elements exclude pad.The output of each pooling window is maximum number of elements exclude pad.The storage order of the tensor. 0 is row major, and 1 is column major.Dilation value along each spatial axis of filter.Stride along each axis.p value of the Lp norm used to pool over the input data, default is 2.0.Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimension are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. {name} consumes an input tensor X and applies Lp pooling across the tensor according to kernel sizes, stride sizes, and pad lengths. Lp pooling consisting of computing the Lp norm on all values of a subset of the input tensor according to the kernel size and downsampling the data into the output tensor Y for further processing.dilation value along each spatial axis of the filter. The convolution transpose operator consumes an input tensor and {filter_desc}, and computes the output. If the pads parameter is provided the shape of the output is calculated via the following equation: output_shape[i] = stride[i] * (input_size[i] - 1) + output_padding[i] + ((kernel_shape[i] - 1) * dilations[i] + 1) - pads[start_i] - pads[end_i] output_shape can also be explicitly specified in which case pads values are auto generated using these equations: total_padding[i] = stride[i] * (input_size[i] - 1) + output_padding[i] + ((kernel_shape[i] - 1) * dilations[i] + 1) - output_shape[i] If (auto_pads != SAME_UPPER): pads[start_i] = total_padding[i]/2; pads[end_i] = total_padding[i] - (total_padding[i]/2) Else: pads[start_i] = total_padding[i] - (total_padding[i]/2); pads[end_i] = (total_padding[i]/2). The zero-padding added to one side of the output. This is also called adjs/adjustment in some frameworks.Output data tensor from pooling across the input tensor. Dimensions will be N x C x 1 x 1If true, compute the mean and variance across all spatial elements If false, compute the mean and variance across per feature.Default is 1.If set to nonzero, run spatial batch normalization in test mode, default is 0.is_testThe epsilon value to use to avoid division by zero, default is 1e-5f.Factor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum), default is 0.9f.The input 4-dimensional tensor of shape NCHW.The scale as a 1-dimensional tensor of size C to be applied to the output.The bias as a 1-dimensional tensor of size C to be applied to the output.The running mean (training) or the estimated mean (testing) as a 1-dimensional tensor of size C.The running variance (training) or the estimated variance (testing) as a 1-dimensional tensor of size C.The output 4-dimensional tensor of the same shape as X.The running mean after the BatchNormalization operator. Must be in-place with the input mean. Should not be used for testing.The running variance after the BatchNormalization operator. Must be in-place with the input var. Should not be used for testing.Saved mean used during training to speed up gradient computation. Should not be used for testing.Saved variance used during training to speed up gradient computation. Should not be used for testing.The running variance after the BatchNormalization operator.Saved mean used during training to speed up gradient computation.Saved variance used during training to speed up gradient computation.If set to true, it indicates BatchNormalization is being used for training, and outputs 1, 2, 3, and 4 would be populated.Constrain mean and variance types to float tensors. It allows all float type for U.The output 4-dimensional tensor of the same shape as input.(float, default 0.5) the ratio of random dropout(int, default 0) if nonzero, run dropout in test mode where the output is simply Y = X.The output mask. If is_test is nonzero, this output is not filled.The ratio of random dropoutConstrain output mask types to boolean tensors.The output tensor of the same shape as X.Indicate up to which input dimensions (exclusive) should be flattened to the outer dimension of the output. The value for axis must be in the range [0, R], where R is the rank of the input tensor. When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n), where the shape of the input tensor is (d_0, d_1, ... d_n). If true, compute the mean and variance across per activation. If false, compute the mean and variance across per feature over each mini-batch.If spatial is true, the dimension of scale is (C). If spatial is false, the dimensions of scale are (C x D1 x ... x Dn)If spatial is true, the dimension of bias is (C). If spatial is false, the dimensions of bias are (C x D1 x ... x Dn)If spatial is true, the dimension of the running mean (training) or the estimated mean (testing) is (C). If spatial is false, the dimensions of the running mean (training) or the estimated mean (testing) are (C x D1 x ... x Dn).If spatial is true, the dimension of the running variance(training) or the estimated variance (testing) is (C). If spatial is false, the dimensions of the running variance(training) or the estimated variance (testing) are (C x D1 x ... x Dn).XReshaped = Reshape (X, NewShape)X3D = Reshape(XReshaped, Shape3D)Scaled = Mul (ScaleReshaped, Normalized)Scale tensor of shape `(num_groups)`.Bias tensor of shape `(num_groups)`.-pфІC#H.  Єτ#.  фC#.`ЄC#,/}}`Є C#x/pф`C#/p~p~0τ#0C#\0`C#0}}Ё@C#0`` C#@1}}C#1`фC#1}}0C#$2  C#p2`C#20~0~ф`C#3pp0C#T3@@0 Є#3фC#3PC#84PC#400 C#4@@ #5`pC#h5`#5~~`C#6~~PЅЄ#L600фC#6pC#6@@C#07@~@~pC#|7PC#7}}C#8P~P~ C#`8pC#8~~PC#8~~`ЄC#D9~~PфC#9@C#9}}`ЄC#(:``@0C#t:@PC#:pp`Є@C# ;~~@C#??@-@ Returns the negation of the input tensor element-wise. Bitwise shift operator performs element-wise operation. For each input element, if the attribute "direction" is "RIGHT", this operator moves its binary representation toward the right side so that the input value is effectively decreased. If the attribute "direction" is "LEFT", bits of binary representation moves toward the left side, which results the increase of its actual value. The input X is the tensor to be shifted and another input Y specifies the amounts of shifting. For example, if "direction" is "Right", X is [1, 4], and S is [1, 1], the corresponding output Z would be [0, 2]. If "direction" is "LEFT" with X=[1, 2] and S=[1, 2], the corresponding output Y would be [2, 8]. Because this operator supports Numpy-style broadcasting, X's and Y's shapes are not necessarily identical. Returns the bitwise not of the input tensor element-wise. Returns the tensor resulted from performing the `{name}` logical operation elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support). {broadcast_doc} First input operand for the logical operator.Second input operand for the logical operator.Result tensor.andConstrain input to boolean tensor.Constrain output to boolean tensor.D:\a\_work\_temp\onnx\src\v1.17.0-7e28176e64.clean\onnx\defs\logical\defs.ccorxorgreaterConstrain input types to all numeric tensors.lessequalConstrain input types to all (non-complex) tensors.Constrain input/output to boolean tensors.First operand, input to be shifted.Second operand, amounts of shift.Constrain input and output types to integer tensors.Direction of moving bits. It can be either "RIGHT" (for right shift) or "LEFT" (for left shift).less_equal { O1 = Less (A, B) O2 = Equal (A, B) C = Or (O1, O2) } greater_equal { O1 = Greater (A, B) O2 = Equal (A, B) C = Or (O1, O2) } Constrain input/output to integer tensors. Returns the tensor resulting from performing the bitwise `{name}` operation elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support). {broadcast_doc} First input operand for the bitwise operator.Second input operand for the bitwise operator.Constrain input to integer tensors.X;PC#;  `C#;C#<<pC#<@C#$f` ??@-@ Computes the {name} of the input tensor's element along the provided axes. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then the resulted tensor have the reduced dimension pruned. The above behavior is similar to numpy, with the exception that numpy defaults keepdims to False instead of True.A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data).Constrain input and output types to high-precision and 8 bit numeric tensors.D:\a\_work\_temp\onnx\src\v1.17.0-7e28176e64.clean\onnx\defs\reduction\old.cc Computes the indices of the {name} elements of the input tensor's element along the provided axis. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then the resulting tensor has the reduced dimension pruned. If select_last_index is True (default False), the index of the last occurrence of the {name} is selected if the {name} appears more than once in the input. Otherwise the index of the first occurrence is selected. The type of the output tensor is integer. Computes the {name} of the input tensor's element along the provided axes. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then the resulted tensor have the reduced dimension pruned. Input tensors of rank zero are valid. Reduction over an empty set of values yields {empty_value}. The above behavior is similar to numpy, with the exception that numpy defaults keepdims to False instead of True.{empty_value} Computes the indices of the {name} elements of the input tensor's element along the provided axis. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then the resulted tensor have the reduced dimension pruned. The type of the output tensor is integer.The axis in which to compute the arg indices. Computes the indices of the {name} elements of the input tensor's element along the provided axis. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then the resulting tensor has the reduced dimension pruned. The input tensor must not be empty. The type of the output tensor is integer.<020223C# =22033τ#l=112`3C#=2223C#>@2@233C#P>p2p2 33 #>222p3C#>P2P233C#4?112@3C#?112P3C#??@-@ Constructs an optional-type value containing either an empty optional of a certain type specified by the attribute, or a non-empty value containing the input element. Returns true if (1) the input is an optional-type and contains an element, or, (2) the input is a tensor or sequence type. If the input is not provided or is an empty optional-type, this op returns false. If the input is a tensor or sequence type, it returns the input. If the input is an optional type, it outputs the element in the input. It is an error if the input is an empty optional-type (i.e. does not have an element) and the behavior is undefined in this case. Optional is expected to have an output.Attribute 'type' should be a TypeProto and it should specify a type.Input type is null. Type information is expected for the input.Optional is expected to have either an input or the type attribute set.The input element.Type of the element in the optional outputThe optional output enclosing the input element.Constrain input type to all tensor and sequence types.Constrain output type to all optional tensor or optional sequence types.D:\a\_work\_temp\onnx\src\v1.17.0-7e28176e64.clean\onnx\defs\optional\defs.ccOptionalHasElement is expected to have 0 or 1 input.OptionalHasElement is expected to have 1 output.The optional input.A scalar boolean tensor. If true, it indicates that optional-type input contains an element. Otherwise, it is empty.Constrain input type to optional tensor and optional sequence types.Constrain output to a boolean tensor.OptionalGetElement must have an input element.Input type is null. Input must have Type information.Optional-type input must contain an element with type information.Output element in the optional input.Constrain output type to all tensor or sequence types.?AAABC#@AAB0BC#d@AAA BC# If necessary the right-hand-side argument will be broadcasted to match the shape of left-hand-side argument. When broadcasting is specified, the second tensor can either be of element size 1 (including a scalar tensor and any tensor with rank equal to or smaller than the first tensor), or having its shape as a contiguous subset of the first tensor's shape. The starting of the mutually equal shape is specified by the argument "axis", and if it is not set, suffix matching is assumed. 1-dim expansion doesn't work yet. For example, the following tensor shapes are supported (with broadcast=1): shape(A) = (2, 3, 4, 5), shape(B) = (,), i.e. B is a scalar tensor shape(A) = (2, 3, 4, 5), shape(B) = (1, 1), i.e. B is an 1-element tensor shape(A) = (2, 3, 4, 5), shape(B) = (5,) shape(A) = (2, 3, 4, 5), shape(B) = (4, 5) shape(A) = (2, 3, 4, 5), shape(B) = (3, 4), with axis=1 shape(A) = (2, 3, 4, 5), shape(B) = (2), with axis=0 Attribute `broadcast=1` needs to be passed to enable broadcasting. PRelu takes input data (Tensor) and slope tensor as input, and produces one output data (Tensor) where the function `f(x) = slope * x for x < 0`, `f(x) = x for x >= 0`., is applied to the data tensor elementwise. Element-wise max of each of the input tensors. All inputs and outputs must have the same shape and data type. Element-wise min of each of the input tensors. All inputs and outputs must have the same shape and data type. Element-wise sum of each of the input tensors. All inputs and outputs must have the same shape and data type. Element-wise mean of each of the input tensors. All inputs and outputs must have the same shape and data type. Clip operator limits the given input within an interval. The interval is specified with arguments 'min' and 'max'. They default to numeric_limits::lowest() and numeric_limits::max() respectively. General Matrix multiplication: https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms#Level_3 Compute Y = alpha * A * B + beta * C, where input tensor A has dimension (M X K), input tensor B has dimension (K X N), input tensor C and output tensor Y have dimension (M X N). If attribute broadcast is non-zero, input tensor C will be broadcasted to match the dimension requirement. A will be transposed before doing the computation if attribute transA is non-zero, same for B and transB. ??@-@ Retrieve the top-K elements along a specified axis. Given an input tensor of shape [a_0, a_1, ..., a_{n-1}] and integer argument k, return two outputs: -Value tensor of shape [a_0, a_1, ..., a_{axis-1}, k, a_{axis+1}, ... a_{n-1}] which contains the values of the top k elements along the specified axis -Index tensor of shape [a_0, a_1, ..., a_{axis-1}, k, a_{axis+1}, ... a_{n-1}] which contains the indices of the top k elements (original indices from the input tensor). Given two equivalent values, this operator uses the indices along the axis as a tiebreaker. That is, the element with the lower index will appear first. Retrieve the top-K elements along a specified axis. Given an input tensor of shape [a_0, a_1, ..., a_{n-1}] and integer argument k, return two outputs: -Value tensor of shape [a_0, a_1, ..., a_{axis-1}, k, a_{axis+1}, ... a_{n-1}] which contains the values of the top k elements along the specified axis -Index tensor of shape [a_0, a_1, ..., a_{axis-1}, k, a_{axis+1}, ... a_{n-1}] which contains the indices of the top k elements (original indices from the input tensor). Given two equivalent values, this operator uses the indices along the axis as a tiebreaker. That is, the element with the lower index will appear first. Computes the discrete Fourier transform of input. Performs element-wise binary modulus (with Numpy-style broadcasting support). The sign of the remainder is the same as that of the Divisor. Mod operator can also behave like C fmod() or numpy.fmod. In this case, the sign of the remainder however, will be the same as the Dividend (in contrast to integer mod). To force a behavior like numpy.fmod() an 'fmod' Attribute is provided. This attribute is set to 0 by default causing the behavior to be like integer mod. Setting this attribute to 1 causes the remainder to be calculated similar to that of numpy.fmod(). If the input type is floating point, then `fmod` attribute must be set to 1. In case of dividend being zero, the results will be platform dependent. This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). Absolute takes one input data (Tensor) and produces one output data (Tensor) where the absolute is, y = abs(x), is applied to the tensor elementwise. Floor takes one input data (Tensor) and produces one output data (Tensor) where the floor is, y = floor(x), is applied to the tensor elementwise. Ceil takes one input data (Tensor) and produces one output data (Tensor) where the ceil is, y = ceil(x), is applied to the tensor elementwise. General Matrix multiplication: https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms#Level_3 A' = transpose(A) if transA else A B' = transpose(B) if transB else B Compute Y = alpha * A' * B' + beta * C, where input tensor A has shape (M, K) or (K, M), input tensor B has shape (K, N) or (N, K), input tensor C is broadcastable to shape (M, N), and output tensor Y has shape (M, N). A will be transposed before doing the computation if attribute transA is non-zero, same for B and transB. A NegativeLogLikelihoodLoss operator computes (weighted) negative log likelihood loss. Its "input" tensor has the shape of (N, C, d1, d2, ..., dk) where k >= 0. The "input" tensor contains log-probabilities for input[n, :, d_1, d_2,..., d_k] being in a class of [0, C). The operator's "target" input tensor has the shape of (N, d1, d2, ..., dk). It encodes class labels (one of C classes) or it may contain a special value (indicated by an attribute ignore_index) for N x d1 x d2 x ... x dk samples. The loss value for input[n, :, d_1, d_2,...d_k] being classified as class c = target[n][d_1][d_2]...[d_k] is computed as: loss[n][d_1][d_2]...[d_k] = -input[n][c][d_1][d_2]...[d_k]. When an optional "weight" is provided, the sample loss is calculated as: loss[n][d_1][d_2]...[d_k] = -input[n][c][d_1][d_2]...[d_k] * weight[c]. loss is zero for the case when target-value equals ignore_index. loss[n][d_1][d_2]...[d_k] = 0, when target[n][d_1][d_2]...[d_k] = ignore_index If "reduction" attribute is set to "none", the operator's output will be the above loss with shape (N, d1, d2, ..., dk). If "reduction" attribute is set to "mean" (the default attribute value), the output loss is (weight) averaged: mean(loss), if "weight" is not provided, or if weight is provided, sum(loss) / sum(weight[target[n][d_1][d_2]...[d_k]]]), for all samples. If "reduction" attribute is set to "sum", the output is a scalar: sum(loss). See also https://pytorch.org/docs/stable/nn.html#torch.nn.NLLLoss. Example 1: // negative log likelihood loss, "none" reduction N, C, d1 = 2, 3, 2 input = [[[1.0, 2.0], [2.0, 2.0], [3.0, 2.0]], [[0.0, 1.0], [2.0, 2.0], [1.0, 2]]] target = [[2, 1], [0, 2]] loss = np.zeros((N, d1)) for n in range(N): for d_1 in range(d1): c = target[n][d_1] loss[n][d_1] = -input[n][c][d_1] // print(loss) // [[-3. -2.] // [-0. -2.]] Example 2: // weighted negative log likelihood loss, sum reduction N, C, d1 = 2, 3, 2 input = [[[1.0, 2.0], [2.0, 2.0], [3.0, 2.0]], [[0.0, 1.0], [2.0, 2.0], [1.0, 2]]] target = [[2, 1], [0, 2]] weight = [0.2, 0.3, 0.1] loss = np.zeros((N, d1)) for n in range(N): for d_1 in range(d1): c = target[n][d_1] loss[n][d_1] = -input[n][c][d_1] * weight[c] loss = np.sum(loss) // print(loss) // -1.1 Example 3: // weighted negative log likelihood loss, mean reduction N, C, d1 = 2, 3, 2 input = [[[1.0, 2.0], [2.0, 2.0], [3.0, 2.0]], [[0.0, 1.0], [2.0, 2.0], [1.0, 2]]] target = [[2, 1], [0, 2]] weight = [0.2, 0.3, 0.1] loss = np.zeros((N, d1)) weight_total = 0 for n in range(N): for d_1 in range(d1): c = target[n][d_1] loss[n][d_1] = -input[n][c][d_1] * weight[c] weight_total = weight_total + weight[c] loss = np.sum(loss) / weight_total // print(loss) // -1.57 Loss function that measures the softmax cross entropy between 'scores' and 'labels'. This operator first computes a loss tensor whose shape is identical to the labels input. If the input is 2-D with shape (N, C), the loss tensor may be a N-element vector L = (l_1, l_2, ..., l_N). If the input is N-D tensor with shape (N, C, D1, D2, ..., Dk), the loss tensor L may have (N, D1, D2, ..., Dk) as its shape and L[i,][j_1][j_2]...[j_k] denotes a scalar element in L. After L is available, this operator can optionally do a reduction operator. shape(scores): (N, C) where C is the number of classes, or (N, C, D1, D2,..., Dk), with K >= 1 in case of K-dimensional loss. shape(labels): (N) where each value is 0 <= labels[i] <= C-1, or (N, D1, D2,..., Dk), with K >= 1 in case of K-dimensional loss. The loss for one sample, l_i, can calculated as follows: l[i][d1][d2]...[dk] = -y[i][c][d1][d2]..[dk], where i is the index of classes. or l[i][d1][d2]...[dk] = -y[i][c][d1][d2]..[dk] * weights[c], if 'weights' is provided. loss is zero for the case when label-value equals ignore_index. l[i][d1][d2]...[dk] = 0, when labels[n][d1][d2]...[dk] = ignore_index where: p = Softmax(scores) y = Log(p) c = labels[i][d1][d2]...[dk] Finally, L is optionally reduced: If reduction = 'none', the output is L with shape (N, D1, D2, ..., Dk). If reduction = 'sum', the output is scalar: Sum(L). If reduction = 'mean', the output is scalar: ReduceMean(L), or if weight is provided: ReduceSum(L) / ReduceSum(W), where tensor W is of shape (N, D1, D2, ..., Dk) and W[n][d1][d2]...[dk] = weights[labels[i][d1][d2]...[dk]]. Target rank must be 1 less than the input rank.Input and target dimension value mismatch.Weight rank must be 1.D:\a\_work\_temp\onnx\src\v1.17.0-7e28176e64.clean\onnx\defs\math\old.cc Performs element-wise binary {name} (with Numpy-style broadcasting support). {broadcast_doc} The operator computes the {name} ({description}) values for each layer in the batch of the given input. The input does not need to explicitly be a 2D vector; rather, it will be coerced into one. For an arbitrary n-dimensional tensor input \in [a_0, a_1, ..., a_{k-1}, a_k, ..., a_{n-1}] and k is the axis provided, then input will be coerced into a 2-dimensional tensor with dimensions [a_0 * ... * a_{k-1}, a_k * ... * a_{n-1}]. For the default case where axis=1, this means the input tensor will be coerced into a 2D tensor of dimensions [a_0, a_1 * ... * a_{n-1}], where a_0 is often the batch size. In this situation, we must have a_0 = N and a_1 * ... * a_{n-1} = D. Each of these dimensions must be matched correctly, or else the operator will throw errors. The output tensor has the same shape and contains the {name} values of the corresponding input. Describes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_size. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).The input tensor that's coerced into a 2D matrix of size (NxD) as described above.The output values with the same shape as input tensor (the original size without coercion).softmaxlogsoftmax1 for the first maximum value, and 0 for all others'shape' input must be 1D tensorexpanded_targetinput_gather_elementloss_NCddloss_N1ddloss_Nddweight_gatherloss_unweightedloss_sumweight_gather_sumconst_zero_target_typedexpanded_target_int64transform_targetsconst_zero_castedinput_gather_element_transformsqueeze_maskconst_one_castedweight_gather_tempweight_gather_temp_1X_NCDX_NDCX_LogSMX_LogSM_NCDX_shapeX_Log The operator computes the {name} ({description}) values for each layer in the batch of the given input. The input is a 2-D tensor (Tensor) of size (batch_size x input_feature_dimensions). The output tensor has the same shape and contains the {name} values of the corresponding input. Input does not need to explicitly be a 2D vector; rather, it will be coerced into one. For an arbitrary n-dimensional tensor input \in [a_0, a_1, ..., a_{k-1}, a_k, ..., a_{n-1}] and k is the axis provided, then input will be coerced into a 2-dimensional tensor with dimensions [a_0 * ... * a_{k-1}, a_k * ... * a_{n-1}]. For the default case where axis=1, this means the input tensor will be coerced into a 2D tensor of dimensions [a_0, a_1 * ... * a_{n-1}], where a_0 is often the batch size. In this situation, we must have a_0 = N and a_1 * ... * a_{n-1} = D. Each of these dimensions must be matched correctly, or else the operator will throw errors. Describes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_size Performs element-wise binary {name} (with limited broadcast support). {broadcast_doc}Pass 1 to enable broadcastingIf set, defines the broadcast dimensions. See doc for details.First operand, should share the type with the second operand.Second operand. With broadcasting can be of smaller size than A. If broadcasting is disabled it should be of the same size.Result, has same dimensions and type as AInput tensor of any shape, base of the exponent.Input tensor of any shape broadcastable to X shape, the exponent component.Coefficient of leakage default to 0.01.Coefficient of SELU default to 1.6732.Coefficient of SELU default to 1.0507.Coefficient of ELU default to 1.0.1-D input tensorSlope tensor. If `Slope` is of size 1, the value is sharedacross different channelsValue of alpha default to 0.2Value of beta default to 0.5List of tensors for Max.Output tensor. Same dimension as inputs.List of tensors for MinList of tensors for Sum.List of tensors for Mean.Minimum value, under which element is replaced by minMaximum value, above which element is replaced by maxInput tensor AInput tensor BInput tensor C, can be inplace.Whether C should be broadcastedScalar multiplier for the product of input tensors A * B, the default value is 1.0.Scalar multiplier for input tensor C, the default value is 1.0.Input tensor CInvalid value for attribute kNumber of top elements to retrieveDimension on which to do the sort.If onesided is 1, only values for w in [0, 1, 2, ..., floor(n_fft/2) + 1] are returned because the real-to-complex Fourier transform satisfies the conjugate symmetry, i.e., X[m, w] = X[m, n_fft-w]*. Note if the input or window tensors are complex, then onesided output is not possible. Enabling onesided with real inputs performs a Real-valued fast Fourier transform (RFFT). When invoked with real or complex valued input, the default value is 0. Values can be 0 or 1.The axis on which to perform the DFT. By default this value is set to 1, which corresponds to the first dimension after the batch index. Negative value means counting dimensions from the back. Accepted range is $[-r, -2] \cup [0, r-2]$ where `r = rank(input)`. The last dimension is for representing complex numbers and thus is an invalid axis.Whether to perform the inverse discrete fourier transform. By default this value is set to 0, which corresponds to false.For real input, the following shape is expected: [batch_idx][signal_dim1][signal_dim2]...[signal_dimN][1]. For complex input, the following shape is expected: [batch_idx][signal_dim1][signal_dim2]...[signal_dimN][2]. The first dimension is the batch dimension. The following N dimensions correspond to the signal's dimensions. The final dimension represents the real and imaginary parts of the value in that order.The length of the signal as a scalar. If greater than the axis dimension, the signal will be zero-padded up to dft_length. If less than the axis dimension, only the first dft_length values will be used as the signal. It's an optional value. The Fourier Transform of the input vector. If onesided is 0, the following shape is expected: [batch_idx][signal_dim1][signal_dim2]...[signal_dimN][2]. If axis=1 and onesided is 1, the following shape is expected: [batch_idx][floor(signal_dim1/2)+1][signal_dim2]...[signal_dimN][2]. If axis=2 and onesided is 1, the following shape is expected: [batch_idx][signal_dim1][floor(signal_dim2/2)+1]...[signal_dimN][2]. If axis=N and onesided is 1, the following shape is expected: [batch_idx][signal_dim1][signal_dim2]...[floor(signal_dimN/2)+1][2]. The signal_dim at the specified axis is equal to the dft_length.Constrain input a and its zero point data type to 8-bit integer tensor.Constrain input b and its zero point data type to 8-bit integer tensor.Constrain output y and its zero point data type to 8-bit integer tensor.@C#@PP ЦC#HAC#A  C#A@C#,BC#xB`C#B00τ#CppC#\CЧC#CУУ@C#C@োC#@DpPC#DpC#DC#$EpC#pE``ঋC#E࣋࣋PC#Fो0τ#TFऋऋ`0C#F@@pC#F00`C#8G@@ C#GpC#GpC#HХ C#hHФФP C#HC#Ipp@C#LIPP0C#I`C# Construct an empty tensor sequence, with given data type. ??@-@ Construct a tensor sequence containing 'inputs' tensors. All tensors in 'inputs' must have the same data type. Outputs a tensor sequence that inserts 'tensor' into 'input_sequence' at 'position'. 'tensor' must have the same data type as 'input_sequence'. Accepted range for 'position' is in `[-n, n]`, where `n` is the number of tensors in 'input_sequence'. Negative value means counting positions from the back. 'position' is optional, by default it inserts 'tensor' to the back of 'input_sequence'. Outputs a tensor copy from the tensor at 'position' in 'input_sequence'. Accepted range for 'position' is in `[-n, n - 1]`, where `n` is the number of tensors in 'input_sequence'. Negative value means counting positions from the back. Outputs a tensor sequence that removes the tensor at 'position' from 'input_sequence'. Accepted range for 'position' is in `[-n, n - 1]`, where `n` is the number of tensors in 'input_sequence'. Negative value means counting positions from the back. 'position' is optional, by default it erases the last tensor from 'input_sequence'. Produces a scalar(tensor of empty shape) containing the number of tensors in 'input_sequence'. Split a tensor into a sequence of tensors, along the specified 'axis'. Lengths of the parts can be specified using the optional argument 'split'. If the argument `split' is not specified, a default scalar value of 1 is used as the value of `split'. 'split' must contain only positive numbers. 'split' is either a scalar (tensor of empty shape), or a 1-D tensor. If 'split' is a scalar, then 'input' will be split into chunks all of size 'split' if possible. The last chunk alone may be smaller than 'split' if the 'input' size along the given axis 'axis' is not divisible by 'split'. If 'split' is a 1-dimensional tensor, the input tensor is split into 'size(split)' chunks, with lengths of the parts on 'axis' specified in 'split'. In this scenario, the sum of entries in 'split' must be equal to the dimension size of input tensor on 'axis'. Concatenate a sequence of tensors into a single tensor. All input tensors must have the same shape, except for the dimension size of the axis to concatenate on. By default 'new_axis' is 0, the behavior is similar to numpy.concatenate. When 'new_axis' is 1, the behavior is similar to numpy.stack. Applies a sub-graph to each sample in the input sequence(s). Inputs can be either tensors or sequences, with the exception of the first input which must be a sequence. The length of the first input sequence will determine the number of samples in the outputs. Any other sequence inputs should have the same number of samples. The number of inputs and outputs, should match the one of the subgraph. For each i-th element in the output, a sample will be extracted from the input sequence(s) at the i-th position and the sub-graph will be applied to it. The outputs will contain the outputs of the sub-graph for each sample, in the same order as in the input. This operator assumes that processing each sample is independent and could executed in parallel or in any order. Users cannot expect any specific ordering in which each subgraph is computed.Attribute dtype should be of integer type and specify a type.(Optional) The data type of the tensors in the output sequence. The default type is 'float'.Empty sequence.D:\a\_work\_temp\onnx\src\v1.17.0-7e28176e64.clean\onnx\defs\sequence\defs.ccSequenceConstruct is expected to have at least 1 input. is null. Type info is expected.Input type for input at index Element type of inputs are expected to be the same.Tensors.Sequence enclosing the input tensors.output_sequenceConstrain input types to any tensor type.Input Sequence and Tensor are expected to have type info. Current type is null. Tensor=Input Sequence and Tensor are expected to have the same elem type. Sequence=Input sequence.input_sequenceInput tensor to be inserted into the input sequence.Position in the sequence where the new tensor is inserted. It is optional and default is to insert to the back of the sequence. Negative value means counting positions from the back. Accepted range in `[-n, n]`, where `n` is the number of tensors in 'input_sequence'. It is an error if any of the index values are out of bounds. It must be a scalar(tensor of empty shape).positionOutput sequence that contains the inserted tensor at given position.Constrain position to integral tensor. It must be a scalar(tensor of empty shape).Input type for input at index 0 is null. Type info is expected.Position of the tensor in the sequence. Negative value means counting positions from the back. Accepted range in `[-n, n - 1]`, where `n` is the number of tensors in 'input_sequence'. It is an error if any of the index values are out of bounds. It must be a scalar(tensor of empty shape).Output tensor at the specified position in the input sequence.Output sequence that has the tensor at the specified position removed.Length of input sequence. It must be a scalar(tensor of empty shape).Constrain output to integral tensor. It must be a scalar(tensor of empty shape).Only supports `int32_t` or `int64_t` inputs for splitInput 'split' can not be empty. sum of split values=Sum of split values not equal to 'input' dim size on 'axis'. 'axis' dim size=Length of each output. It can be either a scalar(tensor of empty shape), or a 1-D tensor. All values must be >= 0. One or more outputs forming a sequence of tensors after splittingConstrain split size to integral tensor.Constrain output types to all tensor types.Which axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1].Keep the split dimension or not. Default 1, which means we keep split dimension. If input 'split' is specified, this attribute is ignored.new_axis must be either 0 or 1], Value=Invalid value of attribute 'axis'. Accepted range=[Which axis to concat on. Accepted range in `[-r, r - 1]`, where `r` is the rank of input tensors. When `new_axis` is 1, accepted range is `[-r - 1, r]`. Insert and concatenate on a new axis or not, default 0 means do not insert new axis.Sequence of tensors for concatenation expected to have type info expected to be a sequence typeGraph attribute inferencer for "body" not availableInvalid ``body`` argument. Expected a graphExpected 1 or more inputs.Expected 1 or more outputs.Input 0 expected but not providedExpected a sequence type for input 0 expected but not providedSequenceMap_loop_body_itercount_cond_in_cond_out_inSequenceMap__seqlen_cond_seqemptyThe graph to be run for each sample in the sequence(s). It should have as many inputs and outputs as inputs and outputs to the SequenceMap function.Additional inputs to the graphadditional_inputsOutput sequence(s)out_sequenceConstrain input types to any sequence type.Constrain to any tensor or sequence type.SequenceMapIC#0JpppC#|JC#J C#K@@@C#`K```C#KPPPC#K000C#??@-@D:\a\_work\_temp\onnx\src\v1.17.0-7e28176e64.clean\onnx\defs\object_detection\old.ccDL@@PC#L  PpC#L00`C# Returns true if the optional-type input contains an element. If it is an empty optional-type, this op returns false. ??@-@ Outputs the element in the optional-type input. It is an error if the input value does not have an element and the behavior is undefined in this case. OptionalHasElement is expected to have 1 input.D:\a\_work\_temp\onnx\src\v1.17.0-7e28176e64.clean\onnx\defs\optional\old.ccInput must be an optional-type value containing an element with type information.(MP&P&p&&C#tM@&@&`&&C# Computes an one-layer GRU. This operator is usually supported via some custom implementation such as CuDNN. Notations: `X` - input tensor `z` - update gate `r` - reset gate `h` - hidden gate `t` - time step (t-1 means previous time step) `W[zrh]` - W parameter weight matrix for update, reset, and hidden gates `R[zrh]` - R recurrence weight matrix for update, reset, and hidden gates `Wb[zrh]` - W bias vectors for update, reset, and hidden gates `Rb[zrh]` - R bias vectors for update, reset, and hidden gates `WB[zrh]` - W parameter weight matrix for backward update, reset, and hidden gates `RB[zrh]` - R recurrence weight matrix for backward update, reset, and hidden gates `WBb[zrh]` - W bias vectors for backward update, reset, and hidden gates `RBb[zrh]` - R bias vectors for backward update, reset, and hidden gates `H` - Hidden state `num_directions` - 2 if direction == bidirectional else 1 Activation functions: Relu(x) - max(0, x) Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x}) Sigmoid(x) - 1/(1 + e^{-x}) (NOTE: Below are optional) Affine(x) - alpha*x + beta LeakyRelu(x) - x if x >= 0 else alpha * x ThresholdedRelu(x) - x if x >= alpha else 0 ScaledTanh(x) - alpha*Tanh(beta*x) HardSigmoid(x) - min(max(alpha*x + beta, 0), 1) Elu(x) - x if x >= 0 else alpha*(e^x - 1) Softsign(x) - x/(1 + |x|) Softplus(x) - log(1 + e^x) Equations (Default: f=Sigmoid, g=Tanh): - zt = f(Xt*(Wz^T) + Ht-1*Rz + Wbz + Rbz) - rt = f(Xt*(Wr^T) + Ht-1*Rr + Wbr + Rbr) - ht = g(Xt*(Wh^T) + (rt (.) Ht-1)*Rh + Rbh + Wbh) # default, when linear_before_reset = 0 - ht = g(Xt*(Wh^T) + (rt (.) (Ht-1*Rh + Rbh) + Wbh) # when linear_before_reset != 0 - Ht = (1 - zt) (.) ht + zt (.) Ht-1 Computes an one-layer simple RNN. This operator is usually supported via some custom implementation such as CuDNN. Notations: `X` - input tensor `i` - input gate `t` - time step (t-1 means previous time step) `Wi` - W parameter weight matrix for input gate `Ri` - R recurrence weight matrix for input gate `Wbi` - W parameter bias vector for input gate `Rbi` - R parameter bias vector for input gate `WBi` - W parameter weight matrix for backward input gate `RBi` - R recurrence weight matrix for backward input gate `WBbi` - WR bias vectors for backward input gate `RBbi` - RR bias vectors for backward input gate `H` - Hidden state `num_directions` - 2 if direction == bidirectional else 1 Activation functions: Relu(x) - max(0, x) Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x}) Sigmoid(x) - 1/(1 + e^{-x}) (NOTE: Below are optional) Affine(x) - alpha*x + beta LeakyRelu(x) - x if x >= 0 else alpha * x ThresholdedRelu(x) - x if x >= alpha else 0 ScaledTanh(x) - alpha*Tanh(beta*x) HardSigmoid(x) - min(max(alpha*x + beta, 0), 1) Elu(x) - x if x >= 0 else alpha*(e^x - 1) Softsign(x) - x/(1 + |x|) Softplus(x) - log(1 + e^x) Equations (Default: f=Tanh): - Ht = f(Xt*(Wi^T) + Ht-1*Ri + Wbi + Rbi) Computes an one-layer LSTM. This operator is usually supported via some custom implementation such as CuDNN. Notations: `X` - input tensor `i` - input gate `o` - output gate `f` - forget gate `c` - cell gate `t` - time step (t-1 means previous time step) `W[iofc]` - W parameter weight matrix for input, output, forget, and cell gates `R[iofc]` - R recurrence weight matrix for input, output, forget, and cell gates `Wb[iofc]` - W bias vectors for input, output, forget, and cell gates `Rb[iofc]` - R bias vectors for input, output, forget, and cell gates `P[iof]` - P peephole weight vector for input, output, and forget gates `WB[iofc]` - W parameter weight matrix for backward input, output, forget, and cell gates `RB[iofc]` - R recurrence weight matrix for backward input, output, forget, and cell gates `WBb[iofc]` - W bias vectors for backward input, output, forget, and cell gates `RBb[iofc]` - R bias vectors for backward input, output, forget, and cell gates `PB[iof]` - P peephole weight vector for backward input, output, and forget gates `H` - Hidden state `num_directions` - 2 if direction == bidirectional else 1 Activation functions: Relu(x) - max(0, x) Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x}) Sigmoid(x) - 1/(1 + e^{-x}) (NOTE: Below are optional) Affine(x) - alpha*x + beta LeakyRelu(x) - x if x >= 0 else alpha * x ThresholdedRelu(x) - x if x >= alpha else 0 ScaledTanh(x) - alpha*Tanh(beta*x) HardSigmoid(x) - min(max(alpha*x + beta, 0), 1) Elu(x) - x if x >= 0 else alpha*(e^x - 1) Softsign(x) - x/(1 + |x|) Softplus(x) - log(1 + e^x) Equations (Default: f=Sigmoid, g=Tanh, h=Tanh): - it = f(Xt*(Wi^T) + Ht-1*Ri + Pi (.) Ct-1 + Wbi + Rbi) - ft = f(Xt*(Wf^T) + Ht-1*Rf + Pf (.) Ct-1 + Wbf + Rbf) - ct = g(Xt*(Wc^T) + Ht-1*Rc + Wbc + Rbc) - Ct = ft (.) Ct-1 + it (.) ct - ot = f(Xt*(Wo^T) + Ht-1*Ro + Po (.) Ct + Wbo + Rbo) - Ht = ot (.) h(Ct) Computes an one-layer simple RNN. This operator is usually supported via some custom implementation such as CuDNN. Notations: `X` - input tensor `i` - input gate `t` - time step (t-1 means previous time step) `Wi` - W parameter weight matrix for input gate `Ri` - R recurrence weight matrix for input gate `Wbi` - W parameter bias vector for input gate `Rbi` - R parameter bias vector for input gate `WBi` - W parameter weight matrix for backward input gate `RBi` - R recurrence weight matrix for backward input gate `WBbi` - WR bias vectors for backward input gate `RBbi` - RR bias vectors for backward input gate `H` - Hidden state `num_directions` - 2 if direction == bidirectional else 1 Activation functions: Relu(x) - max(0, x) Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x}) Sigmoid(x) - 1/(1 + e^{-x}) (NOTE: Below are optional) Affine(x) - alpha*x + beta LeakyRelu(x) - x if x >= 0 else alpha * x ThresholdedRelu(x) - x if x >= alpha else 0 ScaledTanh(x) - alpha*Tanh(beta*x) HardSigmoid(x) - min(max(alpha*x + beta, 0), 1) Elu(x) - x if x >= 0 else alpha*(e^x - 1) Softsign(x) - x/(1 + |x|) Softplus(x) - log(1 + e^x) Equations (Default: f=Tanh): - Ht = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Wbi + Rbi) Computes an one-layer GRU. This operator is usually supported via some custom implementation such as CuDNN. Notations: `X` - input tensor `z` - update gate `r` - reset gate `h` - hidden gate `t` - time step (t-1 means previous time step) `W[zrh]` - W parameter weight matrix for update, reset, and hidden gates `R[zrh]` - R recurrence weight matrix for update, reset, and hidden gates `Wb[zrh]` - W bias vectors for update, reset, and hidden gates `Rb[zrh]` - R bias vectors for update, reset, and hidden gates `WB[zrh]` - W parameter weight matrix for backward update, reset, and hidden gates `RB[zrh]` - R recurrence weight matrix for backward update, reset, and hidden gates `WBb[zrh]` - W bias vectors for backward update, reset, and hidden gates `RBb[zrh]` - R bias vectors for backward update, reset, and hidden gates `H` - Hidden state `num_directions` - 2 if direction == bidirectional else 1 Activation functions: Relu(x) - max(0, x) Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x}) Sigmoid(x) - 1/(1 + e^{-x}) (NOTE: Below are optional) Affine(x) - alpha*x + beta LeakyRelu(x) - x if x >= 0 else alpha * x ThresholdedRelu(x) - x if x >= alpha else 0 ScaledTanh(x) - alpha*Tanh(beta*x) HardSigmoid(x) - min(max(alpha*x + beta, 0), 1) Elu(x) - x if x >= 0 else alpha*(e^x - 1) Softsign(x) - x/(1 + |x|) Softplus(x) - log(1 + e^x) Equations (Default: f=Sigmoid, g=Tanh): - zt = f(Xt*(Wz^T) + Ht-1*(Rz^T) + Wbz + Rbz) - rt = f(Xt*(Wr^T) + Ht-1*(Rr^T) + Wbr + Rbr) - ht = g(Xt*(Wh^T) + (rt (.) Ht-1)*(Rh^T) + Rbh + Wbh) # default, when linear_before_reset = 0 - ht = g(Xt*(Wh^T) + (rt (.) (Ht-1*(Rh^T) + Rbh)) + Wbh) # when linear_before_reset != 0 - Ht = (1 - zt) (.) ht + zt (.) Ht-1 Computes an one-layer LSTM. This operator is usually supported via some custom implementation such as CuDNN. Notations: `X` - input tensor `i` - input gate `o` - output gate `f` - forget gate `c` - cell gate `t` - time step (t-1 means previous time step) `W[iofc]` - W parameter weight matrix for input, output, forget, and cell gates `R[iofc]` - R recurrence weight matrix for input, output, forget, and cell gates `Wb[iofc]` - W bias vectors for input, output, forget, and cell gates `Rb[iofc]` - R bias vectors for input, output, forget, and cell gates `P[iof]` - P peephole weight vector for input, output, and forget gates `WB[iofc]` - W parameter weight matrix for backward input, output, forget, and cell gates `RB[iofc]` - R recurrence weight matrix for backward input, output, forget, and cell gates `WBb[iofc]` - W bias vectors for backward input, output, forget, and cell gates `RBb[iofc]` - R bias vectors for backward input, output, forget, and cell gates `PB[iof]` - P peephole weight vector for backward input, output, and forget gates `H` - Hidden state `num_directions` - 2 if direction == bidirectional else 1 Activation functions: Relu(x) - max(0, x) Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x}) Sigmoid(x) - 1/(1 + e^{-x}) (NOTE: Below are optional) Affine(x) - alpha*x + beta LeakyRelu(x) - x if x >= 0 else alpha * x ThresholdedRelu(x) - x if x >= alpha else 0 ScaledTanh(x) - alpha*Tanh(beta*x) HardSigmoid(x) - min(max(alpha*x + beta, 0), 1) Elu(x) - x if x >= 0 else alpha*(e^x - 1) Softsign(x) - x/(1 + |x|) Softplus(x) - log(1 + e^x) Equations (Default: f=Sigmoid, g=Tanh, h=Tanh): - it = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Pi (.) Ct-1 + Wbi + Rbi) - ft = f(Xt*(Wf^T) + Ht-1*(Rf^T) + Pf (.) Ct-1 + Wbf + Rbf) - ct = g(Xt*(Wc^T) + Ht-1*(Rc^T) + Wbc + Rbc) - Ct = ft (.) Ct-1 + it (.) ct - ot = f(Xt*(Wo^T) + Ht-1*(Ro^T) + Po (.) Ct + Wbo + Rbo) - Ht = ot (.) h(Ct) ??@-@D:\a\_work\_temp\onnx\src\v1.17.0-7e28176e64.clean\onnx\defs\rnn\old.ccfowardOptional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM.The sequence output for the hidden is optional if 0. Default 0.A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`. It is optional if `output_sequence` is 0.M[[\@\C# N[[\P\C#XN[[[0\C#N[[[ \C#??@-@D:\a\_work\_temp\onnx\src\v1.17.0-7e28176e64.clean\onnx\defs\logical\old.cc Returns the tensor resulted from performing the `{name}` logical operation elementwise on the input tensors `A` and `B`. If broadcasting is enabled, the right-hand-side argument will be broadcasted to match the shape of left-hand-side argument. See the doc of `Add` for a detailed description of the broadcasting rules. Enable broadcastingIf set, defines the broadcast dimensions.Left input tensor for the logical operator.Right input tensor for the logical operator.Constrain input to float tensors.Constrain input to integral tensors.N{{P0|C# Context: utf8str_to_wstring: string is too long for converting to wstring.wstring_to_utf8str: string is too long for converting to UTF-8.' is required to be non-empty.value_info' of 'nameField '' is required but missing.elem_typekey_typevalue_typeUnrecognized type value case (value_info name: data_type) to UNDEFINED is not allowedsetting data_type field (tensor name: float_dataint32_datastring_dataint64_datadouble_datauint64_data) is stored externally and should not have data field.Data of TensorProto ( tensor name: ) is stored externally but doesn't have a location.TensorProto ( tensor name: ) is 0-element but contains data!TensorProto (tensor name: ) should contain one and only one value field.) should not be stored in raw_data fieldSTRING data (tensor name: ' instead of '' should be stored in field 'values of data_type 'Unrecognized data_type (tensor name: values, but NNZ is ) has Sparse tensor indices (] out of range [0, ) index value at position [Sparse tensor (] not in sorted order.) first dimension size does not equal NNZ.) second dimension size does not match rank of tensor.] out of range.] not in lexicographic sorted order.sparse_tensor_proto) must have rank 1.Sparse tensor values () must have a dense-rank > 0) dimensions are not positive.) must have INT64 type.) must have rank 1 or 2.) has no index values.attrtype field and data field mismatch in attribute ) should not contain more than one value field.Attribute (name: ) should refer to attribute in parent node.Warning: Model contains experimental ops:op_type) has zero input and zero output., type: NodeProto (name: No opset import for domain ' with domain_version of No Op registered for is deprecated in domain_version of Op registered for OpType: Bad node spec for node. Name: ' has been used as graph input names multiple times.Graph must be in single static assignment (SSA) form, however 'initTensor initializers must have a non-empty name initializer name is not unique in initializer but not in graph inputSparse tensor initializers must have a non-empty name sparse initializer name is not unique across initializers and sparse_initializers is not output of any previous nodes.name: ' of node: Nodes in a graph must be topologically sorted, however input '' has been used as output names multiple times.' is not an output of any node in graph.Graph output ') should be a relative path, but it is an absolute path: Location of external TensorProto ( tensor name: ' points outside the directory, but the ') should be file inside the , but it doesn't exist or is not accessible.) should be stored in invalid stol argumentstol argument out of rangeinttype_protofloatsstringstensorsgraphssparse_tensorstype_protosir_versionopset_importmetadata_props column: (line: Expected character Integer value expected, but not found.Unexpected literal type.String value expected, but not found.Identifier expected but not found.Encountered invalid float literal!Incomplete string literal.Value expected but not found.Expecting primitive type as map key type.Unexpected type in sparse-tensor element type.Unexpected type.Error parsing TensorProto (expected a tensor type).Error parsing TensorProto (expected a tensor shape).Error parsing TensorProto shape (expected numeric dimension).Unhandled type: %d and specified value's typeMismatch between expected type Unexpected attribute type.Empty list attribute value requires type annotation.Singleton attribute value cannot be specified as a list.Error context: [ParseError at position Wrong op_type name for running propagation: input and zero_point pair is expected to have same type. Matrix product that behaves like [numpy.matmul](https://numpy.org/doc/stable/reference/generated/numpy.matmul.html). It consumes two quantized input tensors, their scales and zero points, scale and zero point of output, and computes the quantized output. The quantization formula is y = saturate((x / y_scale) + y_zero_point). For (x / y_scale), it is rounding to nearest ties to even. Refer to https://en.wikipedia.org/wiki/Rounding for details. Scale and zero point must have same shape. They must be either scalar (per tensor) or N-D tensor (per row for 'a' and per column for 'b'). Scalar refers to per tensor quantization whereas N-D refers to per row or per column quantization. If the input is 2D of shape [M, K] then zero point and scale tensor may be an M element vector [v_1, v_2, ..., v_M] for per row quantization and K element vector of shape [v_1, v_2, ..., v_K] for per column quantization. If the input is N-D tensor with shape [D1, D2, M, K] then zero point and scale tensor may have shape [D1, D2, M, 1] for per row quantization and shape [D1, D2, 1, K] for per column quantization. Production must never overflow, and accumulation may overflow if and only if in 32 bits. ??@-@Got grid tensor rank: Got input tensor rank: The input tensor and grid tensor must have the same rank for GridSample. Got input tensor and grid tensor ranks: The input tensor and grid tensor ranks must be >= 3. Got the last dimension of the grid tensor: The last dimension of the grid tensor must be the rank of the grid tensor - 2. ) is not equal to the existing dim value (Dimension value inferred (Either `sizes` or `scales` must be provided, but not both of themUnknown value for `keep_aspect_ratio_policy`: Providing `scales` is incompatible with a `keep_aspect_ratio_policy` other than "stretch".Number of elements of input 'sizes' () must be same as rank of input 'X' (Number of elements of input 'scales' (Number of elements of input 'scales' must be same as rank of input 'X'Input 'scales' must have float element type.'pads' input must be a 1D (shape: [2 * num_axes]) tensor of type int64 values. values. Got Pads has incorrect number of values. Expected 2 * Tensor of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D input tensor, it is the number of pixels. `pads` should be a 1D tensor of shape [2 * num_axes] where `num_axes` refers to the number of elements in the `axes` input or the input rank if `axes` are not provided explicitly. `pads` format should be: [x1_begin, x2_begin, ..., x1_end, x2_end,...], where xi_begin is the number of pad values added at the beginning of axis `axes[i]` and xi_end, the number of pad values added at the end of axis `axes[i]`.1-D tensor of axes that `pads` apply to. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data). Behavior is undefined if an axis is repeated. If not provided, all axes are assumed (`[0, 1, ..., input_rank-1]`).PཌྷC#PP#8Q@@C#Q00C#Q `??@-@One and only one of the attributes 'value', 'value_*' or 'sparse_value' must be specified for a Constant node.Attribute 'value_int' expect an integer.Attribute 'value_float' expect a float.Attribute 'value_string' expect a string.TypeAndShapeInferenceFunction implementation incomplete: this line should never be reached.??@-@Loop 'body' subgraph outputs should all be tensors or sequences or optionals, but output ??@-@axes as an input and attribute cannot be specified at the same time. Computes the {name} of the input tensor's elements along the provided axes. The resulting tensor has the same rank as the input if `keepdims` equals 1. If `keepdims` equals 0, then the resulting tensor has the reduced dimension pruned. Input tensors of rank zero are valid. Reduction over an empty set of values yields {empty_value}. If the input data type is Boolean, the comparison should consider `False < True`. The above behavior is similar to numpy, with the exception that numpy defaults `keepdims` to `False` instead of `True`.Defines behavior if 'axes' is empty. Default behavior with 'false' is to reduce all axes. When axes is empty and this attribute is set to true, input tensor will not be reduced,and the output tensor would be equivalent to input tensor.Optional input list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor if 'noop_with_empty_axes' is false, else act as an Identity op when 'noop_with_empty_axes' is true. Accepted range is [-r, r-1] where r = rank(data).Constrain input and output types to numeric and Boolean tensors. R   @ C#lR 0 P  @??@-@`p0$*4,8GdPZ8^oxz$ $("$,.<CK M_@@0(x onnx/onnx-ml.protoonnx" AttributeProto name (  ref_attr_name (  doc_string ( 0 type (2".onnx.AttributeProto.AttributeType f ( i ( s (  t ( 2.onnx.TensorProto g ( 2.onnx.GraphProto. sparse_tensor ( 2.onnx.SparseTensorProto tp ( 2.onnx.TypeProto floats ( ints ( strings ( " tensors ( 2.onnx.TensorProto graphs ( 2.onnx.GraphProto/ sparse_tensors ( 2.onnx.SparseTensorProto$ type_protos ( 2.onnx.TypeProto" AttributeType UNDEFINED FLOAT INT STRING TENSOR GRAPH SPARSE_TENSOR  TYPE_PROTO  FLOATS INTS STRINGS TENSORS  GRAPHS  SPARSE_TENSORS  TYPE_PROTOSJ  JRv" ValueInfoProto name (  type ( 2.onnx.TypeProto doc_string ( 4 metadata_props ( 2.onnx.StringStringEntryProto" NodeProto input (  output (  name (  op_type (  domain (  overload ( ' attribute ( 2.onnx.AttributeProto doc_string ( 4 metadata_props ( 2.onnx.StringStringEntryProto" TrainingInfoProto( initialization ( 2.onnx.GraphProto# algorithm ( 2.onnx.GraphProto< initialization_binding ( 2.onnx.StringStringEntryProto4 update_binding ( 2.onnx.StringStringEntryProto" ModelProto ir_version (. opset_import ( 2.onnx.OperatorSetIdProto producer_name (  producer_version (  domain (  model_version ( doc_string (  graph ( 2.onnx.GraphProto4 metadata_props ( 2.onnx.StringStringEntryProto. training_info ( 2.onnx.TrainingInfoProto& functions ( 2.onnx.FunctionProto"4 StringStringEntryProto key (  value ( "k TensorAnnotation tensor_name ( B quant_parameter_tensor_names ( 2.onnx.StringStringEntryProto" GraphProto node ( 2.onnx.NodeProto name ( & initializer ( 2.onnx.TensorProto3 sparse_initializer ( 2.onnx.SparseTensorProto doc_string ( # input ( 2.onnx.ValueInfoProto$ output ( 2.onnx.ValueInfoProto( value_info ( 2.onnx.ValueInfoProto7 quantization_annotation ( 2.onnx.TensorAnnotation4 metadata_props ( 2.onnx.StringStringEntryProtoJJJ R ir_versionRproducer_versionR producer_tagRdomain" TensorProto dims ( data_type (* segment ( 2.onnx.TensorProto.Segment float_data (B int32_data (B string_data (  int64_data (B name (  doc_string (  raw_data ( 3 external_data ( 2.onnx.StringStringEntryProto5 data_location (2.onnx.TensorProto.DataLocation double_data (B uint64_data (B4 metadata_props ( 2.onnx.StringStringEntryProto% Segment begin ( end (" DataType UNDEFINED FLOAT UINT8 INT8 UINT16 INT16 INT32 INT64 STRING BOOL  FLOAT16  DOUBLE  UINT32  UINT64  COMPLEX64 COMPLEX128 BFLOAT16 FLOAT8E4M3FN FLOAT8E4M3FNUZ FLOAT8E5M2 FLOAT8E5M2FNUZ UINT4 INT4") DataLocation DEFAULT EXTERNAL"h SparseTensorProto! values ( 2.onnx.TensorProto" indices ( 2.onnx.TensorProto dims (" TensorShapeProto- dim ( 2 .onnx.TensorShapeProto.DimensionR Dimension dim_value (H dim_param ( H denotation ( B value" TypeProto- tensor_type ( 2.onnx.TypeProto.TensorH1 sequence_type ( 2.onnx.TypeProto.SequenceH' map_type ( 2.onnx.TypeProto.MapH1 optional_type ( 2.onnx.TypeProto.OptionalH: sparse_tensor_type ( 2.onnx.TypeProto.SparseTensorH- opaque_type ( 2.onnx.TypeProto.OpaqueH denotation ( B Tensor elem_type (% shape ( 2.onnx.TensorShapeProto. 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OperatorStatus EXPERIMENTAL STABLEp(00`4x|(8HhX  ($ PT\`0X@04 hPTXp\` 0@  p0@P`(8HtXd  @@@@@@@@ 0`@Pp6p:; =`>$AA@DS`@ 00c@Vp@ 0c@@0pU 0c@p`T#`   @!0c#@## U0$# 0c@$#U$P$@ 0c@p$`$T@($`% %0&0c'@((onnx/onnx-ml.proto0T@`(* @* ,0c.@//R3 00 0P10c2@3p3@S@@`% %0&0c'@@@SG0DD D@E0cG@GG`VՎ` 0׎0c@Ypu ֎ 0c@W`Վ `0c @`00Y֎P ୎@0c@PPW0ՎP  0c @0 @XՎฎ `0c@PWՎഎ @0c@XՎP P0c @`WsՎ Ц0c @@XД֎ 0c0@0֎ pVՎ` 0@ߎ0c@п`d 0@P,40( $pP h8$1, onnx/onnx-data.protoonnxonnx/onnx-ml.proto" SequenceProto name (  elem_type (( tensor_values ( 2.onnx.TensorProto5 sparse_tensor_values ( 2.onnx.SparseTensorProto, sequence_values ( 2.onnx.SequenceProto" map_values ( 2.onnx.MapProto, optional_values ( 2.onnx.OptionalProto"] DataType UNDEFINED TENSOR SPARSE_TENSOR SEQUENCE MAP OPTIONAL"r MapProto name (  key_type ( keys ( string_keys ( # values ( 2.onnx.SequenceProto" OptionalProto name (  elem_type (' tensor_value ( 2.onnx.TensorProto4 sparse_tensor_value ( 2.onnx.SparseTensorProto+ sequence_value ( 2.onnx.SequenceProto! map_value ( 2.onnx.MapProto+ optional_value ( 2.onnx.OptionalProto"] DataType UNDEFINED TENSOR SPARSE_TENSOR SEQUENCE MAP OPTIONALY`#@ 0c"@0# #onnx/onnx-data.protopZ+01@/ ,`40c9@0P0 Z+ 1. +@10cP8@`0@0INVALID_CPPTYPECPPTYPE_INT32CPPTYPE_INT64CPPTYPE_UINT32CPPTYPE_UINT64CPPTYPE_DOUBLECPPTYPE_FLOATCPPTYPE_BOOLCPPTYPE_ENUMCPPTYPE_STRINGCPPTYPE_MESSAGED:\a\_work\_temp\protobuf\src\v3.21.12-8f95413c2b.clean\src\google/protobuf/descriptor.hCHECK failed: is_extension_: D:\a\_work\_temp\protobuf\src\v3.21.12-8f95413c2b.clean\src\google/protobuf/map_field.hProtocol Buffer map usage error: type does not match Expected : Actual : MapValueConstRef::type MapValueConstRef is not initialized.MapValueRef::MutableMessageValueD:\a\_work\_temp\protobuf\src\v3.21.12-8f95413c2b.clean\src\google\protobuf\generated_message_reflection.ccProtocol Buffer reflection usage error: Method : google::protobuf::Reflection:: Message type: Field : Problem : Problem : Field is not the right type for this message: Expected : Field type: Problem : Enum value did not match field type: Expected : Actual : Unimplemented type: CHECK failed: (message1->GetReflection()) == (this): First argument to Swap() (of type "") is not compatible with this reflection object (which is for type ""). Note that the exact same class is required; not just the same descriptor.CHECK failed: (message2->GetReflection()) == (this): Second argument to Swap() (of type "Field does not match message type.HasFieldField is repeated; the method requires a singular field.CHECK failed: ((lhs_donated_array[0] & 0x1u) == 0) == ((rhs_donated_array[0] & 0x1u) == 0): FieldSizeField is singular; the method requires a repeated field.ClearFieldGetInt32SetInt32GetRepeatedInt32AddInt32GetInt64SetInt64GetRepeatedInt64AddInt64GetUInt32SetUInt32GetRepeatedUInt32AddUInt32GetUInt64SetUInt64GetRepeatedUInt64AddUInt64GetFloatSetFloatGetRepeatedFloatAddFloatGetDoubleSetDoubleGetRepeatedDoubleAddDoubleGetBoolSetBoolGetRepeatedBoolAddBoolGetStringGetStringReferenceSetStringGetRepeatedStringGetRepeatedStringReferenceAddStringGetEnumValueSetEnumSetEnumValueGetRepeatedEnumValueAddEnumGetMessageMutableMessageSetAllocatedMessageReleaseMessageGetRepeatedMessageAddMessage"MutableRawRepeatedField"MutableRawRepeatedFieldCHECK failed: (field->message_type()) == (desc): wrong submessage type"GetRawRepeatedField"GetRawRepeatedFieldCHECK failed: (field->options().ctype()) == (ctype): subtype mismatchField is not a map field."LookupMapValue""MapBegin""MapEnd"Reached impossible case in HasBit().CHECK failed: field->is_repeated(): CHECK failed: field->cpp_type() == cpp_type || (field->cpp_type() == FieldDescriptor::CPPTYPE_ENUM && cpp_type == FieldDescriptor::CPPTYPE_INT32): The type parameter T in RepeatedFieldRef API doesn't match the actual field type (for enums T should be the generated enum type or int32_t).CHECK failed: (message_type) == (field->message_type()): "GetMapData"CHECK failed: file != nullptr: unimplemented type: MapValueConstRef::GetMessageValueD:\a\_work\_temp\protobuf\src\v3.21.12-8f95413c2b.clean\src\google\protobuf\reflection_ops.ccMessage does not support reflection (type CHECK failed: (&from) != (to): CHECK failed: (to->GetDescriptor()) == (descriptor): Tried to merge messages of different types (merge Z̀<[00 `@` p`D:\a\_work\_temp\protobuf\src\v3.21.12-8f95413c2b.clean\src\google/protobuf/reflection_internal.hCHECK failed: this == other_mutator: [pP@  `T\ 0  p`D:\a\_work\_temp\protobuf\src\v3.21.12-8f95413c2b.clean\src\google\protobuf\message.ccCHECK failed: (from.GetDescriptor()) == (descriptor): : Tried to copy from a message with a different type. to: , from: \`0File is already registered: Type is already registered: File appears to be in generated pool but wasn't registered: Type appears to be in generated pool but wasn't registered: 0]`P `]`P `P^`@ `^`@ `p_`@p ``` ``` 0 `Should not reach here.D:\a\_work\_temp\protobuf\src\v3.21.12-8f95413c2b.clean\src\google/protobuf/dynamic_message.hInvalid key for map field.MapKey::type MapKey is not initialized. Call set methods to initialize MapKey.MapKey::GetInt64ValueMapKey::GetUInt64ValueMapKey::GetInt32ValueMapKey::GetUInt32ValueMapKey::GetBoolValueMapKey::GetStringValueUnsupportedMapValueConstRef::GetInt64ValueMapValueConstRef::GetUInt64ValueMapValueConstRef::GetInt32ValueMapValueConstRef::GetUInt32ValueMapValueConstRef::GetBoolValueMapValueConstRef::GetEnumValueMapValueConstRef::GetStringValueMapValueConstRef::GetFloatValueMapValueConstRef::GetDoubleValueD:\a\_work\_temp\protobuf\src\v3.21.12-8f95413c2b.clean\src\google\protobuf\wire_format.ccExtensions of MessageSets must be optional messages.Can't reachInvalid descriptorCannot get hereѪ 0 E$EDDxѪѪ E ѪѪDѪѪѪѪѪ0 DDDE$E EѪ Ѫ0 0*ѪѪfixed64fixed32messagebytesenumsfixed32sfixed64sint32sint64requiredrepeatedgoogle.protobuf.EmptyCHECK failed: false: google.protobuf.ExtensionRangeOptionsgoogle.protobuf.FileOptionsgoogle.protobuf.MessageOptionsgoogle.protobuf.FieldOptionsgoogle.protobuf.OneofOptionsgoogle.protobuf.EnumOptionsgoogle.protobuf.EnumValueOptionsgoogle.protobuf.ServiceOptionsgoogle.protobuf.MethodOptions ab̀'hac0ypyyD:\a\_work\_temp\protobuf\src\v3.21.12-8f95413c2b.clean\src\google\protobuf\descriptor.ccproto2proto3can't reach here.FileOptionsMessageOptionsFieldOptionsEnumOptionsEnumValueOptionsServiceOptionsMethodOptionsOneofOptionsExtensionRangeOptionsgoogle.protobuf.2.protogoogle.protobuf.DoubleValuegoogle.protobuf.FloatValuegoogle.protobuf.Int64Valuegoogle.protobuf.UInt64Valuegoogle.protobuf.Int32Valuegoogle.protobuf.UInt32Valuegoogle.protobuf.StringValuegoogle.protobuf.BytesValuegoogle.protobuf.BoolValuegoogle.protobuf.Anygoogle.protobuf.FieldMaskgoogle.protobuf.Durationgoogle.protobuf.Timestampgoogle.protobuf.Valuegoogle.protobuf.ListValuegoogle.protobuf.StructUNKNOWN_ENUM_VALUE_%s_%dCHECK failed: GeneratedDatabase()->Add(encoded_file_descriptor, size): CHECK failed: has_default_value(): No default valueMessages can't have default values!Can't get here: failed to get default value as string(. = Found invalid proto option data for: $0option $1; $0// $1 extend .$0 { $0message $1 { $0 extensions $1 to $2; $0 } $0 extend .$1 { $0 reserved $0, $0 to max, $0 to $1, ; "$0", $0} map<$0, $1>$0$1$2 $3 = $4 [default = $0 [json_name = " { ... }; $0oneof $1 { ... } $0enum $1 { $0$1 = $2 [$0]CHECK failed: out_location != nullptr: Invalid proto descriptor for file "" is not defined.". To use it here, please add the necessary import.", which is not imported by "" seems to be defined in "") to start from the outermost scope.", which is not defined. The innermost scope is searched first in name resolution. Consider using a leading '.'(i.e., "." is resolved to ".placeholder.protoPLACEHOLDER_VALUE.PLACEHOLDER_VALUE" contains null character." not previously defined in symbols_by_name_, but was defined in symbols_by_parent_; this shouldn't be possible." is already defined." is already defined in "" is already defined in file "" is already defined (as something other than a package) in file "Missing name." is not a valid identifier..dummyFile recursively imports itself: -> " was listed twice.Import "" has not been loaded." was not found or had errors.Package name is too longMissing field: FileDescriptorProto.name.Unrecognized syntax: A file with this name is already in the pool.Exceeds Maximum Package DepthInvalid public dependency index.Invalid weak dependency index.Reached maximum recursion limit for nested messages.Reserved range $0 to $1 overlaps with already-defined range $2 to $3.Field name "$0" is reserved multiple times.Extension range $0 to $1 includes field "$2" ($3).Field "$0" uses reserved number $1.Field name "$0" is reserved.Extension range $0 to $1 overlaps with reserved range $2 to $3.Extension range $0 to $1 overlaps with already-defined range $2 to $3.The [proto3_optional=true] option may only be set on proto3fields, not cannot be required.The extension Repeated fields can't have default values.Boolean default must be true or false.Messages can't have default values.Couldn't parse default value "Field numbers must be positive integers.Field numbers cannot be greater than $0.Field numbers $0 through $1 are reserved for the protocol buffer library implementation.FieldDescriptorProto.extendee not set for extension field.FieldDescriptorProto.oneof_index should not be set for extensions.FieldDescriptorProto.extendee set for non-extension field.FieldDescriptorProto.oneof_index $0 is out of range for type "$1".Extension numbers must be positive integers.Extension range end number must be greater than start number.Reserved numbers must be positive integers.Reserved range end number must be greater than start number. if you ignore case and strip out the enum name prefix (if any). This is error-prone and can lead to undefined behavior. Please avoid doing this. If you are using allow_alias, please assign the same numeric value to both enums. has the same name as Enum name Enums must contain at least one value.Enum value "$0" is reserved multiple times.Enum value "$0" uses reserved number $1.Enum value "$0" is reserved.the global scope, not just within "" must be unique within Note that enum values use C++ scoping rules, meaning that enum values are siblings of their type, not children of it. Therefore, "Fields in the same oneof must be defined consecutively. "$0" cannot be defined before the completion of the "$1" oneof definition.CHECK failed: (out_oneof_decl.fields_ + out_oneof_decl.field_count_) == (message->field(i)): Oneof must have at least one field.Fields with proto3_optional set must be a member of a one-field oneofSynthetic oneofs must be after all other oneofs" is not a message type.google.protobuf.bridge.MessageSet"$0" does not declare $1 as an extension number.Fields of oneofs must themselves have label LABEL_OPTIONAL." is not a type." is not an enum type.Default value for an enum field must be an identifier." has no value named "Enum type "Field with primitive type has type_name.Field with message or enum type missing type_name.Extension number $0 has already been used in "$1" by extension "$2".Field number $0 has already been used in "$1" by field "$2".Extension number $0 has already been used in "$1" by extension "$2" defined in $3.Suggested field numbers for " which is.Files that do not use optimize_for = LITE_RUNTIME cannot import files which do use this option. This file is not lite, but it imports "Extension ranges are not allowed in proto3.MessageSet is not supported in proto3.allowed in proto3.". This is not " conflicts with field "The JSON camel-case name of field "Extensions in proto3 are only allowed for defining options.Required fields are not allowed in proto3.Explicit default values are not allowed in proto3." which is a proto3 message type." is not a proto3 enum, but is used in "Groups are not supported in proto3 syntax.The first enum value must be zero in proto3.Extension numbers cannot be greater than $0.[lazy = true] can only be specified for submessage fields.[packed = true] can only be specified for repeated primitive fields.MessageSets cannot have fields, only extensions.Extensions to non-lite types can only be declared in non-lite files. Note that you cannot extend a non-lite type to contain a lite type, but the reverse is allowed.map_entry should not be set explicitly. Use map instead.option json_name is not allowed on extension fields.". If this is intended, set 'option allow_alias = true;' to the enum definition." uses the same enum value as "Files with optimize_for = LITE_RUNTIME cannot define services unless you set both options cc_generic_services and java_generic_services to false.EntryKey in map fields cannot be enum types.Key in map fields cannot be float/double, bytes or message types.Enum value in map must define 0 as the first value. conflicts with an existing nested message type.Expanded map entry type conflicts with an existing field. conflicts with an existing enum type. conflicts with an existing oneof type.Illegal jstype for int64, uint64, sint64, fixed64 or sfixed64 field: jstype is only allowed on int64, uint64, sint64, fixed64 or sfixed64 fields.CHECK failed: builder_: uninterpreted_optionCHECK failed: uninterpreted_options_field != nullptr: No field named "uninterpreted_option" in the Options proto.CHECK failed: original_uninterpreted_options_field != nullptr: Parsing attempt: Some options could not be correctly parsed using the proto descriptors compiled into this binary. Unparsed options: Option must have a name.Option must not use reserved name "uninterpreted_option".CHECK failed: options_descriptor: )", which is not defined. The innermost scope is searched first in name resolution. Consider using a leading '.'(i.e., "(." is resolved to "(Option " definition file imports the proto which defines the option." unknown. Ensure that your proto" is not a field or extension of message "Option field "" is an atomic type, not a message." is a repeated message. Repeated message options must be initialized using an aggregate value.CHECK failed: unknown_fields->SerializeToString(outstr): Unexpected failure while serializing option submessage Invalid wire type for CPPTYPE_MESSAGE: CHECK failed: field != nullptr: " was already set.Value out of range for int32 option "Value must be integer for int32 option "Value out of range for int64 option "Value must be integer for int64 option "Value out of range for uint32 option "Value must be non-negative integer for uint32 option "Value must be non-negative integer for uint64 option "Value must be number for float option "Value must be number for double option "Value must be identifier for boolean option "Value must be "true" or "false" for boolean option "Value must be identifier for enum-valued option "". This appears to be a value from a sibling type." for option "Value must be quoted string for string option "a@bppy`a bd'; .foo = value". = { }". To set fields within it, use syntax like "" is a message. To set the entire message, use syntax like "CHECK failed: dynamic.get() != nullptr: Could not create an instance of Error while parsing option value for "CHECK failed: (option_field->type()) == (FieldDescriptor::TYPE_GROUP): Invalid wire type for CPPTYPE_INT32: Invalid wire type for CPPTYPE_INT64: Invalid wire type for CPPTYPE_UINT32: Invalid wire type for CPPTYPE_UINT64: is unused.Import CHECK failed: file()->finished_building_ == true: CHECK failed: enum_type->value_count(): CHECK failed: finished_building_ == true: CHECK failed: !once_: CHECK failed: !descriptor_: CHECK failed: file && file->pool_: CHECK failed: file->pool_->lazily_build_dependencies_: CHECK failed: !file->finished_building_: CHECK failed: file->finished_building_: CHECK failed: has_allocated(): CHECK failed: !has_allocated(): CHECK failed: (used) <= (total_.template Get()): Uninterpreted option is missing name or value.CHECK failed: (total_.template Get()) == (used_.template Get()): D:\a\_work\_temp\protobuf\src\v3.21.12-8f95413c2b.clean\src\google\protobuf\extension_set_heavy.ccCHECK failed: prototype != nullptr: CHECK failed: output->message_info.prototype != nullptr: Extension factory's GetPrototype() returned nullptr; extension: Unsupported: type mismatchHb̀̀̀̀̀̀̀̀̀̀p 0̀̀̀̀b0˓ ГPӓ0ΓΓӓԓ`ԓ ʓ@@`ϓΓp˓0ϓϓߓ0`ǓMapValueRef::SetInt64ValueMapValueRef::SetUInt64ValueMapValueRef::SetInt32ValueMapValueRef::SetUInt32ValueMapValueRef::SetBoolValueMapValueRef::SetEnumValueMapValueRef::SetStringValueMapValueRef::SetFloatValueMapValueRef::SetDoubleValueD:\a\_work\_temp\protobuf\src\v3.21.12-8f95413c2b.clean\src\google\protobuf\map_field.ccD:\a\_work\_temp\protobuf\src\v3.21.12-8f95413c2b.clean\src\google/protobuf/map_field_inl.hCHECK failed: map_iter->iter_ != nullptr: DO NOT PARSE: fields may be stripped and missing. c0'00z̀`c`'@Р0 0D:\a\_work\_temp\protobuf\src\v3.21.12-8f95413c2b.clean\src\google\protobuf\text_format.cc@d'P(`(Error parsing text-format Warning parsing text-format ><" stored in google.protobuf.Any.Could not find type "Non-repeated Any specified multiple times." is not defined or is not an extension of "Extension "" which is not defined or is not an extension of "Ignoring extension "" has no field named "Message type "CHECK failed: allow_unknown_field_ || allow_unknown_extension_ || reserved_field: " is specified multiple times.Non-repeated field "", another member of oneof "" is specified along with field "Field "text format contains deprecated field "Message is too deep, the parser exceeded the configured recursion limit of ". Value: "Invalid value for boolean field "Expected integer or identifier, got: " for field "Unknown enumeration value of "Reached an unintended state: CPPTYPE_MESSAGECannot skip field value, unexpected token: infinityInvalid float number: Expected identifier, got: Expected string, got: Expected integer, got: Integer out of range (Expect a decimal number, got: Expected double, got: " stored in google.protobuf.Any has missing required fieldsValue of type "", found "Expected "c'y}y Outdent() without matching Indent().c`'@Р0 0@{ > Input size too large: Message missing required fields: { } Can't print proto content: proto type not found: failed to parse contentsNot supported......." 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:@[GIlq0-g-o-p---pz|ƤGAdlamAhomAnatolian_HieroglyphsArabicArmenianAvestanBalineseBamumBassa_VahBatakBengaliBhaiksukiBopomofoBrahmiBrailleBugineseBuhidCanadian_AboriginalCarianCaucasian_AlbanianCcCfChakmaChamCherokeeChorasmianCoCommonCopticCsCuneiformCypriotCypro_MinoanCyrillicDeseretDevanagariDives_AkuruDograDuployanEgyptian_HieroglyphsElbasanElymaicEthiopicGeorgianGlagoliticGothicGranthaGreekGujaratiGunjala_GondiGurmukhiHanHangulHanifi_RohingyaHanunooHatranHebrewHiraganaImperial_AramaicInheritedInscriptional_PahlaviInscriptional_ParthianJavaneseKaithiKannadaKatakanaKawiKayah_LiKharoshthiKhitan_Small_ScriptKhmerKhojkiKhudawadiLLaoLatinLepchaLimbuLinear_ALinear_BLisuLycianLydianMahajaniMakasarMalayalamMandaicManichaeanMarchenMasaram_GondiMeMedefaidrinMeetei_MayekMende_KikakuiMeroitic_CursiveMeroitic_HieroglyphsMiaoModiMongolianMroMultaniMyanmarNabataeanNag_MundariNandinagariNew_Tai_LueNewaNkoNoNushuNyiakeng_Puachue_HmongOghamOl_ChikiOld_HungarianOld_ItalicOld_North_ArabianOld_PermicOld_PersianOld_SogdianOld_South_ArabianOld_TurkicOld_UyghurOriyaOsageOsmanyaPahawh_HmongPalmyrenePau_Cin_HauPdPePfPhags_PaPhoenicianPiPoPsPsalter_PahlaviRejangRunicSamaritanSaurashtraScSharadaShavianSiddhamSignWritingSinhalaSkSmSoSogdianSora_SompengSoyomboSundaneseSyloti_NagriSyriacTagalogTagbanwaTai_LeTai_ThamTai_VietTakriTamilTangsaTangutTeluguThaanaThaiTibetanTifinaghTirhutaTotoUgariticVaiVithkuqiWanchoWarang_CitiYezidiYiZanabazar_SquareZlZpZspp||(4@LXdp| $0<PPTT``09 09AZ__az09AZazAZaz 09!~az ~!/:@[`{~ AZ09AZ__az09AFaf\d\D\s\S\w\W[:alnum:][:^alnum:][:alpha:][:^alpha:][:ascii:][:^ascii:][:blank:][:^blank:][:cntrl:][:^cntrl:][:digit:][:^digit:][:graph:][:^graph:][:lower:][:^lower:][:print:][:^print:][:punct:][:^punct:][:space:][:^space:][:upper:][:^upper:][:word:][:^word:][:xdigit:][:^xdigit:]p?p66666и฀*** Check failure stack trace: *** (stacktrace: %c0000 00:00:%02d.%06d %7d RAW: 0 H P`l| h pTryLock succeeded TryLock failed ReaderTryLock succeeded ReaderTryLock failed Lock blocking Lock returning ReaderLock blocking ReaderLock returning Unlock ReaderUnlock Wait on Wait unblocked Signal on SignalAll on Check %s failed: %sD:\a\_work\_temp\abseil\src\20240722.0-08b1a9128d.clean\absl\synchronization\mutex.cc @ %p%s%p %s %sdetected illegal recursion into Mutex codes->waitp == nullptr || s->waitp == waitp || s->suppress_fatal_errorsbad skip in DequeueAllWakeablepw->skip == nullptrdetected illegal recursion in Mutex codes->waitp != nullptr || s->suppress_fatal_errorscondition untrue on return from LockSlowthis->LockSlowWithDeadline(how, cond, KernelTimeout::Never(), flags)Check (v & (kMuWriter | kMuReader)) != (kMuWriter | kMuReader) failed: %s: Mutex corrupt: both reader and writer lock held: %pCheck (v & (kMuWait | kMuWrWait)) != kMuWrWait failed: %s: Mutex corrupt: waiting writer with no waiters: %pwaitp->thread->waitp == nullptr || waitp->thread->suppress_fatal_errorsLockEnqueue to empty list failednew_h != nullptrEnqueue to list failedUnlockwaitp == nullptr || waitp->thread->waitp == nullptr || waitp->thread->suppress_fatal_errorsUnlockSlow is confusedwaitp != nullptrwaiters disappeared during Enqueue()!Mutex queue changed beneath usold_h == nullptr || h->maybe_unlockingillegal skip from headold_h->skip == nullptrh->skip == nullptrpw not w's predecessorpw->next == wunexpected empty wake listwake_list != kPerThreadSynchNullslow releasethread should hold at least a read lock on Mutex %p %swaiting when shouldn't bewaitp->thread->waitp == nullptr{e{eD:\a\_work\_temp\abseil\src\20240722.0-08b1a9128d.clean\absl\synchronization\internal\win32_waiter.ccSleepConditionVariableSRW failed: %lu   %#*.*NAN0123456789ABCDEF0123456789abcdef-00Y@j@חA܀ vH7B nsusmsmh@@@@@@@@@hHHHH@@@@@@@@@@@@@@@@@@(@  !"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`ABCDEFGHIJKLMNOPQRSTUVWXYZ{|}~  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7o>WfHH!T9D:\a\_work\_temp\abseil\src\20240722.0-08b1a9128d.clean\absl\base\internal\low_level_alloc.ccblock not big enough for even one levellevel >= 1element not in freeliste == foundhaven't left Arena regionleft_LowLevelAlloc arithmetic overflowsum >= atoo few levels in Next()i < prev->levelsbad magic number in Next()next->header.magic == Magic(kMagicUnallocated, &next->header)bad arena pointer in Next()next->header.arena == arenaunordered freelistprev < nextmalformed freelistreinterpret_cast(prev) + prev->header.size < reinterpret_cast(next)bad magic number in AddToFreelist()f->header.magic == Magic(kMagicAllocated, &f->header)bad arena pointer in AddToFreelist()f->header.arena == arenanothing in arena to freearena->allocation_count > 0VirtualAlloc failednew_pages != nullptrs->header.arena == arena{e{eRtlCaptureStackBackTracentdll.dll{e{e{e{eLq 00WARNING: All log messages before absl::InitializeLog() is called are written to STDERR q 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)x#@/'x'x&x&x#|8xxxpx#7PxPxxpx#10%x0%x %x%x#$3"x"x"x"x#6#x#x#x#x#l0`x`x@x0x#3"x"x x x#.&x&x%x&x#8#x#x@#x#x#X0`*x`*xP*x@*x#2@"x@"x0"x "x#>xxPx@x#=xxЇxx#< x xxx#H:@x@xxЈx#>xx0x x#<pxpx`xPx#=xxpx`x#; x xxx#:@x@xxx#4>xxxx#?xx`xPx#:@x@xx0x#<xxxx#p=ЋxЋx@x0x#H>xxxx#>ЌxЌxxx#L?0x0xxx#?xxxx#H<xxxЍx#T@ИxИxxx#?xxЗxx#Cyyyy#4Ayyyy#A y y y y#D`y`y`yPy#lA@y@y0y y#E y y y y#A y y y y#C y yyy#Byyyy#@ y y yp y`#E@y@yyy#E y y y y#@pypy`yPy#@ y y y y#xFyyyy#Byyyy#D` y` y y y#HAyyypy#CP yP y y y#dCp yp yx` y#G%y%y %y%y#G &y &y&y&y# GP&yP&y@&y0&y#(H&y&yp&y`&y#H%y%y$y$y#dH$y$y$y$y#}Z`H}`H}`CPH}p#Z0H}0H}H}G}`#@\̀H}0̀̀`I}]`q}H}0l}Pu}@q} q}n}l}r}r}T\0}0}@}0}#L[}}}p}#Z@}@}}})#|[Х}Х}`}P}#[ }}}}@[} }}}@8[}}Ъ}}PN#8]@}@}p}`}p#^0c9~^0` `^`~_0>~66 >~>~=~p=~ =~Unknown exceptionbad array new lengthstring too longDirectML.dllDirectML.dll,,bad castnarrowing_errorgenericsystem/\ : was false. Stacktrace: \\?\...: "VIWEFAttempt to use DefaultLogger but none has been registered.onnxruntime::logging::LoggingManager::DefaultLoggerD:\a\_work\1\s\include\onnxruntime\core/common/logging/logging.hExceptionReturnNtReturnHrLogNtLogHrFailFast%hs(%u)\%hs!%p: %hs!%p: (caller: %p) %hs(%d) tid(%x) %08X %ws Msg:[%ws] CallContext:[%hs] [%hs(%hs)] [%hs] kernelbase.dllRaiseFailFastExceptionWIL Exceptionstd::exception: %hs error msg: unmap view of file failed. error code: onnxruntime::UnmapFileD:\a\_work\1\s\onnxruntime\core\platform\windows\env.cc/\Negative thread index is not allowedindex >= 0onnxruntime::WindowsThread::WindowsThreadcustom_create_thread_fn returned invalid handle. doserrno: message:WindowThread:_beginthreadex failed with errno:kernel32.dllSetThreadDescriptiononnxruntime- does not exist, processor Cannot set affinity for thread onnxruntime::WindowsThread::ThreadMain, then on , first on group Cannot set cross-group affinity for thread , mask: , group_id: SetThreadAffinityMask done for thread: . Specify the number of threads explicitly so the affinity is not set., error msg: , error code: , index: SetThreadAffinityMask failed for thread: - fail, errcode = open file GetFileSizeEx GetFileLength: File is too largeInvalid fd was supplied: Received negative size from stat callFile is too large.file_path == nullptronnxruntime::WindowsEnv::ReadFileIntoBufferoffset < 0length > buffer.size()SetFilePointerEx ReadFile fail: unexpected endonnxruntime::WindowsEnv::MapFileIntoMemoryopen file mapping , errcode = , allocation_granularity = , mapped_offset = mapped offset must be a multiple of the allocation granularity\/\/DeleteFile() failed - path: onnxruntime::WindowsEnv::DeleteFolderRemoveDirectory() failed - path: GetFinalPathNameByHandle() failed: onnxruntime::WindowsEnv::GetCanonicalPathUNC\") (Error FreeLibrary failed with error Kernel32.dllK32EnumProcessModules" " in library, error code: Failed to find symbol is not implementedonnxruntime::WindowsEnv::FormatLibraryFileNameFailed to calculate byte size for saving cpu info on windowsonnxruntime::WindowsEnv::InitializeCpuInfoFailed to fetch cpu info on windows consist of logical processors: core core(s) from windows system:Found total bytes Detected L2 cache size: vector too longinvalid string position\*onnxruntime::LoopDirD:\a\_work\1\s\onnxruntime\core/platform/path_lib.habsl::container_internal::raw_hash_map<>::atXunknown errorPhError loading which is missing. No import directory found. which depends on But no dependency issue could be determined.length overflowonnxruntime::ToUTF8StringD:\a\_work\1\s\onnxruntime\core\common\helper.cconnxruntime::ToWideString{, }Failed to get error messageonnxruntimeISink must be provided.onnxruntime::logging::LoggingManager::LoggingManagerD:\a\_work\1\s\onnxruntime\core\common\logging\logging.ccdefault_logger_id must be provided if instance_type is InstanceType::DefaultOnly one instance of LoggingManager created with InstanceType::Default can exist at any point in time.Default logger already set. onnxruntime::logging::LoggingManager::CreateDefaultLogger[...truncated...] ERROR LOG MSG NOTIFICATION: Failure to successfully parse the messageSUCCESSFAILINVALID_ARGUMENTNO_SUCHFILENO_MODELENGINE_ERRORRUNTIME_EXCEPTIONINVALID_PROTOBUFMODEL_LOADEDNOT_IMPLEMENTEDINVALID_GRAPHEP_FAILMODEL_LOAD_CANCELEDMODEL_REQUIRES_COMPILATIONGENERAL ERRORcode != static_cast(common::OK)onnxruntime::common::Status::StatusD:\a\_work\1\s\onnxruntime\core\common\status.ccOKSystemError : [ONNXRuntimeError]1.22.1Error in ETW registration: Unexpected Severity of onnxruntime::logging::EtwSink::SendImplD:\a\_work\1\s\onnxruntime\core\platform\windows\logging\etw_sink.ccmap/set too longonnxruntime::logging::LoggingManager::SetDefaultLoggerSeverityInteger overflowSafeIntExceptionHandler::SafeIntOnOverflowD:\a\_work\1\s\onnxruntime\core/common/safeint.hCpuDMLai.onnx.mlai.onnx.trainingai.onnx.preview.trainingai.onnxonnxruntime::IDataTransfer::CopyTensorAsyncD:\a\_work\1\s\include\onnxruntime\core/framework/data_types_internal.h!=D:\a\_work\1\s\include\onnxruntime\core/framework/tensor.h)Trying to get a Tensor, but got: IsTensor()OrtValue::GetD:\a\_work\1\s\include\onnxruntime\core/framework/ort_value.hOrtValue::GetMutableTrying to get a TensorSeq, but got: IsTensorSequence()Trying to get a SparseTensor, but got: IsSparseTensor()Tensor sequence must contain only primitive typeselem_type_ != nullptronnxruntime::TensorSeq::SetTypeD:\a\_work\1\s\onnxruntime\core/framework/TensorSeq.hi < tensors_.size()onnxruntime::TensorSeq::GetAtTensorSeq: tensor to be added has a different data type.IsSameDataType(tensor.Get())onnxruntime::TensorSeq::Addtried creating tensor with negative value in shape, got not enough space: expected Got:p_data_len was smaller than expected. Expected:Unknown ExceptionNot able to find appropriate IDataTransfer to copy sparse data`anonymous-namespace'::GetDataTransferD:\a\_work\1\s\onnxruntime\core\session\onnxruntime_c_api.ccStrings can only reside in CPU memory`anonymous-namespace'::ValidateFillInputArgstried Filling sparse tensor with negative value in values shapeOrtApis::FillSparseTensorCooOrtApis::FillSparseTensorCsrtried Filling sparse tensor with negative value in block sparse indices shapeOrtApis::FillSparseTensorBlockSparseCan not use strings in pre-allocated memory. Use CreateSparseTensorAsOrtValue() to allocate memory inside and copyOrtApis::UseCooIndicesOrtApis::UseCsrIndicesOrtApis::UseBlockSparseIndicesthe ort_value must contain a constructed tensorUse GetStringTensor*() API to retrieve stringsRegisterCustomOpsLibrary: Failed to load libraryRegisterCustomOpsRegisterCustomOpsLibrary: Entry point RegisterCustomOps not found in libraryRegisterCustomOpsUsingFunction: Registration function name must be specified.RegisterCustomOpsUsingFunction: Registration function was not foundEnableOrtCustomOps: Custom operators in onnxruntime-extensions are not enabledno imputs were passedRunAsync() active adapters specified, but won't have an effectOrtApis::RunAsyncRunWithBinding() has active adapters specified, but won't have an effectOrtApis::RunWithBindinglengths allocation failedstring buffer allocation failedOutput buffer allocation failedinput array doesn't equal tensor sizeelement index is out of boundsOrtValue should contain a Tensor or a Sparse TensorSparse Tensor does not contain sparse dataThis API supports Tensors or SparseTensorsshape is invalidindex is out of boundsoffsets buffer is not equal to tensor sizeoutput buffer is too small. Use GetStringTensorDataLength.buffer size is too small for string elementout of indexinternal errorindex out of rangeInput is not of one of the supported sequence types.Input is not of type sequence or map.input array is too shortInput is not of one of the supported map types.Expecting all elements to be tensors. Got: in[idx]->IsTensor()OrtCreateValueImplSeqHelperSequences must have tensors of the same data type. There was at least one tensor in the input that was different.Each element of the sequence should be either tensor or map.At least one element in the sequence is of a type different from others.Unsupported input typeFor map type num_values MUST be 2Either the key tensor or the value tensor has NumDimensions > 1Key and value tensors have unequal number of elements.Key type is not supported yet.Number of values should be at least 1.opaque(,Specified domain and type names combination does not refer to a registered opaque typeml_type != nullptrOrtApis::CreateOpaqueValueOpaque type is not a non_tensor type!!!non_tensor_base != nullptrOrtApis::GetOpaqueValueInvalid build with no providers availableSpecified version is not supported for the DirectML provider.Specified provider is not supported.this API does not support stringslocation dimensions do not match shape sizeinvalid location rangemax_memarena_extend_strategyinitial_chunk_size_bytesmax_dead_bytes_per_chunkinitial_growth_chunk_size_bytesmax_power_of_two_extend_bytesInvalid key found: All OrtEpDevice values in ep_devices must have the same execution provider.EP is not currently supported by this APIThe requested API version [%u] is not available, only API versions [1, %u] are supported in this build. Current ORT Version is: %s ORT Build Info: git-branch=HEAD, git-commit-id=89746dc19a, build type=RelWithDebInfo, cmake cxx flags: /MP /guard:cf /DWIN32 /D_WINDOWS /DWINAPI_FAMILY=100 /DWINVER=0x0A00 /D_WIN32_WINNT=0x0A00 /DNTDDI_VERSION=0x0A000000 /Qspectre /O2 /Ob1 /DNDEBUG /EHsc /Zc:__cplusplus /EHsc /wd26812 -DEIGEN_HAS_C99_MATH -DCPUINFO_SUPPORTEDinvalid vector subscriptinvalid hash bucket countT Tensor type mismatch. utils::IsPrimitiveDataType(dtype_)onnxruntime::Tensor::MutableDataonnxruntime::Tensor::DataAsSpanstatic_cast(num_kv_pairs) < std::numeric_limits::max()OrtGetValueImplMapHelperInvalid index requested for map type.Tensor must always contain primitive types. Found: value_type != nullptrOrtCreateValueImplMapHelperValue type is not supported yet: Map is missing type entry for its value++index < c.size()onnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,struct std::less<__int64>,class std::allocator,class std::allocator > > > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,double,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,double> > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,float,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,float> > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,__int64,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,__int64> > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,class std::basic_string,class std::allocator >,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,class std::basic_string,class std::allocator > > > > >::checkSequence is missing type entry for its elementonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator > >,class std::allocator,class std::allocator > > > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,float,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,float> > >,class std::allocator,class std::allocator >,float,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,float> > > > > >::checkunordered_map/set too long != onnxruntime::DataTypeImpl::GetType() == type_len >= 0 && static_cast(len) < std::numeric_limits::max()OrtCreateMapMLValueonnxruntime::Tensor::Data1ORT_LOAD_CONFIG_FROM_MODEL`anonymous-namespace'::TestAutoSelectEPsImplIExecutionProvider that wraps OrtEp has not been implemented.D:\a\_work\1\s\onnxruntime\core\session\utils.ccInference session was configured with EPContext model generation enabled but without a valid location (e.g., file or buffer) for the output model. Please specify a valid ep.context_file_path via SessionOption configs or use the OrtCompileApi to compile a model to a file or buffer.onnxruntime_profile_session_logger != nullptrSession logger is invalid, but should have been initialized during session construction.test.ep_to_selectInitializeSession as a plugin as a provider bridgeonnxruntime::LoadPluginOrProviderBridgeLoading EP library: name cannot be null or empty stringOnly tensor types are supported currentlytype_info cannot be nullinputs cannot contain null entriestensor_type_info cannot be nullOnly Tensor is currently supported.outputs cannot contain null entriesOnly CPU based tensors are currently supported.Tensor must be allocated.graph cannot be nullExternal initializer should only be used for data >= 128 bytes. Please use CreateTensorAsOrtValue instead.Domain not used by model.: .D:\a\_work\1\s\onnxruntime\core\session\abi_session_options.ccep.OrtSessionOptions::AddProviderOptionsToConfigOptionsFailed to load dynamic library OrtSessionOptions::RegisterCustomOpsLibraryFailed to unload handle for dynamic library graph_optimization_level is not validexecution_mode is not validSession config entry '' was not found. contains null pointersOutput buffer is not large enough for session config entryInput index: VitisAIExecutionProviderOpenVINOExecutionProviderNvTensorRTRTXExecutionProviderQNNExecutionProviderDmlExecutionProviderJsExecutionProviderCoreMLExecutionProviderSNPEExecutionProviderWebNNExecutionProviderXnnpackExecutionProviderWebGpuExecutionProviderAzureExecutionProvider. Provider option key: Provider options key/value cannot be emptyProvider option key length is but the limit is QNNProvider option value length is SNPEOpenVINOWEBNNXNNPACKAZUREWebGPUVitisAIJSNvTensorRtRtxCoreMLFailed to load provider execution provider is not supported in this build. '. Currently supported values are 'Unknown provider name ', and '/'OrtApis::SessionOptionsAppendExecutionProviderep_to_append.id != EpID::INVALIDD:\a\_work\1\s\onnxruntime\core\session\provider_registration.ccD:\a\_work\1\b\RelWithDebInfo\vcpkg_installed\x86-windows-static-md\include\google/protobuf/repeated_ptr_field.hOutput in [TypeInferenceError] expected to have tensor or sparse tensor type: is null expected to have tensor or sparse type in expected to have tensor type in ", domain: "Trying to add a domain to LastReleaseVersion map, but the domain is already exist with last version: ). domain: "Trying to add a domain to DomainToVersion map, but the domain is already exist with version range (CPUExecutionProvideronnxruntime::OpKernel::ComputeAsyncvalue_case == ONNX_NAMESPACE::TypeProto::kTensorTypeOrtShapeInferContext::OrtShapeInferContextshape inference not yet supported for non-tensor typesOrtShapeInferContext::SetOutputTypeShapesymbolic and integer dims mismatch!D:\a\_work\1\s\onnxruntime\core\session\custom_ops.ccInvalid shape infoInvalid contextFailed to fetch type shape info for the index.No requested allocator availableFailed to fetch a stream hosting the requested resourceSize of data not large enough to hold a float.Attribute has no float value.Attribute does not exist.Invalid attribute.Attribute has no int64 value.Size of data not large enough to hold the array of int64.Size of data not large enough to hold the array of floats.Size of data not large enough to hold an int64.Unexpected attribute type. Result buffer is not large enoughSize of data not large enough to hold the string.Size of data not large enough to hold the array of strings.::OrtKernelInfo output index is out of boundsOutput buffer is not large enough for ::OrtKernelInfo output name::OrtKernelInfo input index is out of boundsOutput buffer is not large enough for ::OrtKernelInfo input nameOutput buffer is not large enough for ::OrtKernelInfo node name::OrtKernelInfo does not have an execution provider::OrtKernelInfo input does not have a type::OrtKernelInfo output does not have a typeUnsupported version 'onnxruntime::CustomOpKernel::CustomOpKernel::OrtKernelInfo cannot get a valid logger from its execution provider' in custom op ' to a custom op may be marked variadic.inputInputOutputi == count - 1onnxruntime::CreateSchema::::operator ()outputOnly the last . onnxruntime allows the overloading of an operator if all versions have the same number of declared 'has a different number of inputsoutputsdefined list of typesall typesAnother version of operator 'static_cast(i) != (is_input ? o->GetInputTypeCount(o) : o->GetOutputTypeCount(o))onnxruntime::CreateSchemacustom op registered at runtimeNo kernels to registers.ops.size() > 0input count does not matchonnxruntime::IsCompatibleThe type of a dynamically typed output can be inferred from a single dynamically typed input, or by a user provided OrtCustomOp->InferOutputShapeFn that sets the output type.num_inputs_with_dynamic_type == 1 || have_shape_infer_fnndth input to be of optional typest input to keep same homogeneity input to keep same aritycustom op schemas mismatch, expecting input to be of variadic type output to be of optional type output to be of variadic type input to be of single typeoutput count does not match output to be of single type is absent, but not optional. Op : output to keep same homogeneity output to keep same arity Op: [CustomOP type inferencing error]: no type constraint found for input: [CustomOP type inferencing error]: kernel Input: onnxruntime::InferOutputTypesonnxruntime::CreateCustomRegistry[CustomOP type inferencing error]: no kernel def matches node inputs for Op: [CustomOP type inferencing error]: no type constraint found for output: `InlinedVector::at(size_type) const` failed bounds checkinvalid unordered_map keyCHECK failed: (index) >= (0): CHECK failed: (index) < (current_size_): Bad optional accessfalsetrueonnxruntime::Tensor::MutableDataRawonnxruntime::Tensor::DataRawTensor type mismatch.type == dtype_shape_.Size() == new_shape.Size()onnxruntime::Tensor::Reshape) != new size (Tensor size ( Max:Validating no unexpected access using an invalid node_index. Got:D:\a\_work\1\s\include\onnxruntime\core/graph/graph.hDEFAULTnode_index < nodes_.size()onnxruntime::Graph::NodeAtIndexImpl is not present.onnxruntime::OpKernelContext::RequiredOutputD:\a\_work\1\s\include\onnxruntime\core/framework/op_kernel_context.hRequired output at index output_ptronnxruntime::OpKernelContext::OutputPlease fetch output tensor with specified shape.p_ml_valueIsSameDataType(tensor)0ep.context_enableep.context_file_pathep.context_embed_modeuser_compute_streamFailed to get optimizer onnxruntime_providers_shared.dllonnxruntime::LoadDynamicLibraryFromProviderD:\a\_work\1\s\onnxruntime\core\session\provider_bridge_ort.cconnxruntime::ProviderSharedLibrary::UnloadAn absolute path must be specified.onnxruntime::ProviderSharedLibrary::EnsureProvider_SetHostonnxruntime::ProviderLibrary::Getonnxruntime::ProviderLibrary::Unloadonnxruntime::ProviderLibrary::LoadGetProvideronnxruntime_providers_cann.dllonnxruntime_providers_rocm.dllonnxruntime_providers_cuda.dllonnxruntime_providers_cuda_ut.dllonnxruntime_providers_openvino.dllonnxruntime_providers_tensorrt.dllonnxruntime_providers_dnnl.dllonnxruntime_providers_vitisai.dllonnxruntime_providers_qnn.dllonnxruntime::CudaProviderFactoryCreator::Createonnxruntime_providers_nv_tensorrt_rtx.dllonnxruntime_providers_migraphx.dlldevice_typenum_of_threadsonnxruntime::TensorrtProviderFactoryCreator::Createonnxruntime::NvProviderFactoryCreator::Createenable_opencl_throttlingdisable_dynamic_shapescache_dircontextnum_streamsload_configenable_npu_fast_compile option is deprecated. Skipping this optiononnxruntime::OrtOpenVINOProviderOptionsToOrtOpenVINOProviderOptionsV2enable_causallmonnxruntime::QNNProviderFactoryCreator::Createmodel_priorityenable_qdq_optimizeronnxruntime::TryGetProviderInfo_TensorRTonnxruntime::OpenVINOProviderFactoryCreator::Createonnxruntime::VitisAIProviderFactoryCreator::CreateTensorRT Provider not available, can't get interface for itonnxruntime::GetProviderInfo_TensorRTCUDA Provider not available, can't get interface for itonnxruntime::GetProviderInfo_CUDAonnxruntime::TryGetProviderInfo_CUDA is already in session option.The custom op domain name SessionOptionsAppendExecutionProvider_MIGraphX: Failed to load shared libraryAddTensorRTCustomOpDomainToSessionOptionSessionOptionsAppendExecutionProvider_OpenVINO_V2: Failed to load shared libraryCUDA and/or ROCM execution provider is either not enabled or not available.SessionOptionsAppendExecutionProvider_OpenVINO: Failed to load shared libraryMaximum string length for a provider options key/value is 1024.Context cache enable: OrtApis::SessionOptionsAppendExecutionProvider_TensorRT_V2OrtSessionOptionsAppendExecutionProvider_Cuda: Failed to load shared libraryOrtSessionOptionsAppendExecutionProvider_Rocm: Failed to load shared libraryInvalid ep.context_embed_mode: User specified context cache embed mode: User specified context cache path: only 0 or 1 allowed. Set to 1.=;OrtSessionOptionsAppendExecutionProvider_TensorRT: Failed to load shared librarykey/value cannot be emptySessionOptionsAppendExecutionProvider_CANN: Failed to load shared libraryCANN execution provider is not enabled in this build.trt_onnx_bytestreamtrt_onnx_bytestream_sizeROCm execution provider is not enabled in this build.session_optionsSessionOptionsAppendExecutionProvider_Dnnl: Failed to load shared libraryoneDNN execution provider is not enabled in this build.SessionOptionsAppendExecutionProvider_VitisAI: Failed to load shared libraryRequired input at index input_ptrMissing Input: onnxruntime::OpKernelContext::Inputonnxruntime::OpKernelContext::RequiredInputCreate_State_Release_State_Compute_kernel already mapped to existing nodeonnxruntime::standalone::NodeRepo::RegisterCustomOpNodeSchemasD:\a\_work\1\s\onnxruntime\core\session\standalone_op_invoker.cc' op_type:Unable to find schema for node. Domain:'invalid node input count: invalid node output countmatching node is missing, expect: onnxruntime::standalone::StandAloneKernelContext::NumVariadicInputsAttribute type not supported yet.invalid arg_num.arg_num < static_cast(input_count_)Kernel definition was not found for node Domain:'onnxruntime::standalone::InvokeOpstandalone_onnxruntime::standalone::CreateOpOrtMemoryInfo is nullProvided allocator is nullEnv is nullPlease register the allocator as OrtDeviceAllocator even if the provided allocator has arena logic built-in. OrtArenaAllocator is reserved for internal arena logic based allocators only.Provided OrtMemoryInfo is nullCudaD:\a\_work\1\s\onnxruntime\core\session\lora_adapters.cconnxruntime::lora::CreateOrtValueOnDeviceParams absentData transfer is not available for the specified device allocator, it also must not be a CPU allocatoronnxruntime::lora::LoraAdapter::InitializeParamsValuesAdapter is not loaded yet.params != nullptrThe env argument must be a non-null pointerInvalid input model: data size is 0Invalid input model: data pointer is nullInvalid input model: path string is emptyThe session_options argument must be a non-null pointerInvalid output model buffer: size pointer is nullInvalid output model buffer: data pointer is nullInvalid external initializer file: path is emptyInvalid output model path: path is emptyInvalid allocator for output model buffer: allocator pointer is nullD:\a\_work\1\s\onnxruntime\core\session\ort_env.ccOrtEnv::Releaseenv_ptr == p_instance_.get()[ShapeInferenceError] Unsupported Source/Target type=Mismatch between inferred and declared type. Inferred= Declared=tensor(uint32)unknowntensor(float16)tensor(int64)tensor(int32)tensor(uint64)tensor(float8e4m3fn)tensor(bfloat16)tensor(double)tensor(float)tensor(uint8)tensor(float8e5m2fnuz)tensor(float8e5m2)tensor(float8e4m3fnuz)tensor(int16)tensor(int8)tensor(uint16)seq(tensor(uint32))seq(tensor(uint16))seq(tensor(uint8))seq(tensor(int32))seq(tensor(int16))seq(tensor(int8))seq(tensor(uint64))seq(tensor(double))seq(tensor(float))seq(tensor(float16))seq(tensor(int64))tensor(complex128)tensor(complex64)tensor(bool)tensor(string)seq(tensor(complex128))seq(tensor(complex64))seq(tensor(bool))seq(tensor(string))seq(tensor(bfloat16))Schema error: version: ) from file , but it is already registered from file line , but its domain is not known by the checker.Trying to register schema with name (domain: ] (usually, this means you bumped the operator version but forgot to update the version range in DomainToVersionRange in onnx/defs/schema.h)., but its version is not in the inclusive range [Xcom.microsoftcom.ms.internal.nhwccom.microsoft.nchwccom.microsoft.experimentalorg.pytorch.atenCUDAExecutionProvidercom.microsoft.dmlReceived invalid value for arena extend strategy. Valid values can be either 0, 1 or -1.Only CPU devices are supported. Please call CreateAndRegisterAllocatorV2() for other device.An allocator for this device has already been registered for sharing.intra-opException caught: stringNo allocator for this device has been registered for sharing.TMemcpyFromHostD:\a\_work\1\s\onnxruntime\core\session\environment.ccinter-opMemcpyToHost Internal copy node Constrain to all fixed size tensor and sequence types. If the dtype attribute is not provided this must be a valid output type.YFailed to register EP library under '' with error: is not implemented in CreateAndRegisterAllocatorV2()onnxruntime::Environment::Initialize with error: onnxruntime::Environment::CreateAndRegisterInternalEpsonnxruntime::Environment::RegisterExecutionProviderLibrarylibrary is already registered under EpLibrary was nullExecution provider library: was not registered.Failed to unregister EP library: Failed to unload EP library registered under 'factory_ptr != nullptrFactory pointer was null. EpLibrary should prevent this. Library:onnxruntime::Environment::EpInfo::Createonnxruntime::Environment::EpInfo::~EpInfo index= it.second=D:\a\_work\1\s\onnxruntime\core\session\IOBinding.cconnxruntime::IOBinding::BindInputonnxruntime::IOBinding::SynchronizeInputsonnxruntime::SyncProvidersmapped_feed_names_.size() == feed_names_.size()Size mismatch:onnxruntime::IOBinding::BindOutputImplmapped_output_names_.size() == output_names_.size()Size mismatchonnxruntime::IOBinding::SynchronizeOutputsp_providerinvalid stoi argumentstoi argument out of range*UNKNOWNDeviceType:Device:[ OrtAllocatorType: OrtMemType:] DeviceId: MemoryType: id:name:OrtMemoryInfo:[ROCMExecutionProviderTensorrtExecutionProviderPRIORITY_BASEDMEMORY_EFFICIENT thread_pool_allow_spinning: use_per_session_threads: inter_op_param: intra_op_param: } config_options: { ep_selection_policy: use_deterministic_compute: session_logid: profile_file_prefix: enable_cpu_mem_arena: enable_mem_reuse: graph_optimization_level: max_num_graph_transformation_steps: session_log_verbosity_level: session_log_severity_level: execution_mode:Session Options { has already been registered. enable_mem_pattern: optimized_model_filepath: enable_profiling: execution_order:!delimiter.empty()onnxruntime::utils::SplitStringD:\a\_work\1\s\onnxruntime\core/common/string_utils.hProvider onnxruntime::ExecutionProviders::AddD:\a\_work\1\s\onnxruntime\core/framework/execution_providers.hdelimiter must not be emptyonnxruntime::FeedsFetchesInfo::FeedsFetchesInfoD:\a\_work\1\s\onnxruntime\core/framework/feeds_fetches_manager.hCould not find OrtValue with name 'Cached EP instance for graph replay is not set yet before calling ReplayGraph()IsOptionalTensor(type)onnxruntime::utils::GetElementTypeFromOptionalTensorD:\a\_work\1\s\onnxruntime\core/framework/mldata_type_utils.hProvided type is not an optional sequence tensorProvided type is not an optional tensorIsOptionalSeqTensor(type)onnxruntime::utils::GetElementTypeFromOptionalSeqTensorMemcpyTransformerntdll.dllep.dml.disable_graph_fusionep.dml.enable_graph_serializationD:\a\_work\1\b\RelWithDebInfo\vcpkg_installed\x86-windows-static-md\include\wil\resource.h_p0RtlDisownModuleHeapAllocationwilWilError_03] [json.exception.ORTMsession.use_env_allocatorssession.load_model_formatsession.save_model_formatsession.set_denormal_as_zerosession.disable_aot_function_inliningoptimization.disable_specified_optimizerssession.inter_op.allow_spinningsession.disable_quant_qdqoptimization.minimal_build_optimizationssession.dynamic_block_basesession.intra_op.allow_spinningsession.use_ort_model_bytes_directlysession.use_ort_model_bytes_for_initializerssession.intra_op_thread_affinitiessession.debug_layout_transformationsession.disable_cpu_ep_fallbacksession.optimized_model_external_initializers_file_namesession.force_spinning_stopsession.strict_shape_type_inferencesession.allow_released_opsets_onlysession.optimized_model_external_initializers_min_size_in_bytessession.model_external_initializers_file_folder_pathsession.collect_node_memory_stats_to_filememory.enable_memory_arena_shrinkagedisable_synchronize_execution_providers%Y-%m-%d_%H-%M-%SShapesavegpu_graph_id are '0' and '1'. The only supported values for the environment variable Reading the provided model for the ORT configonnxruntime::FinalizeSessionOptions value of 'save' is only valid when saving an ORT format model.applyInvalid value for The environment variable contained the value: session_options.session_log_severity_level >= 0 && session_options.session_log_severity_level <= static_cast(logging::Severity::kFATAL)onnxruntime::GetSeverityonoffD:\a\_work\1\s\onnxruntime\core\session\inference_session.ccModelProto needs to be parsed to check for ORT config within itis_model_proto_parsedInvalid session log severity level. Not a valid onnxruntime::logging::Severity value: Done Adding ETW Sink to logger with severity level: Removing ETW Sink from loggerDone Removing ETW Sink from loggerCould not finalize session options while constructing the inference session. Error Message: Flush-to-zero and denormal-as-zero are onnxruntime::InferenceSession::ConstructorCommon::::operator ()Adding ETW Sink to logger with severity level: onnxruntime::InferenceSession::ConstructorCommon::::operator ()-intra-opsession-Dynamic block base set to Affinity string must not be emptystatus.IsOK()onnxruntime::InferenceSession::ConstructorCommonCreating and using per session threadpools since use_per_session_threads_ is true-custom join thread function not set for inter op thread poolFailed to create the inter-op thread pool for the parallel executor, setting ExecutionMode to SEQUENTIALUsing global/env threadpools since use_per_session_threads_ is falseWhen the session is not configured to use per session threadpools, the env must be created with the the CreateEnvWithGlobalThreadPools API.!to.affinity_str.empty()custom join thread function not set for intra op thread poolto.custom_join_thread_fn-inter-opCould not parse model successfully while constructing the inference sessionresultError during EndProfiling(): session_env.EnvCreatedWithGlobalThreadPools()onnxruntime::InferenceSession::TraceSessionOptionsGiven model could not be parsed while creating inference session. Error message: onnxruntime::InferenceSession::InferenceSessionExecution providers must be registered before the session is initialized. onnxruntime::InferenceSession::RegisterExecutionProviderExecution providers must be registered before the session is initialized.So disabling it for this session since it uses onnxruntime::InferenceSession::{dtor}::::operator ()Unknown error during EndProfiling()onnxruntime::InferenceSession::~InferenceSessionReceived nullptr for exec providerCan't register custom op domains with ORT for onnxruntime::InferenceSession::AddCustomOpDomainsReceived nullptr for custom registry. Having memory pattern enabled is not supported while using So making the execution mode sequential for this session since it uses the DML Execution Provider.Parallel execution mode does not support the DML Execution Provider. Exception during loading: This session already contains a loaded model.onnxruntime::InferenceSession::LoadWithLoaderEncountered unknown exception in LoadWithLoader()onnxruntime::InferenceSession::SaveToOrtFormatFailed to save ORT format model to file: ModelProto corresponding to the model to be loaded has already been parsed. Invoke Load().Failed to load model because protobuf parsing failed./virtual_model.onnxmodel_loading_arraymodel_loading_uri failed:Load model from ORTonnxruntime::InferenceSession::LoadThis session has already been initialized.This session does not contain a loaded model.onnxruntime::InferenceSession::ApplyUpdatesModelProto corresponding to the model to be loaded has not been parsed yet. This API should be called in conjunction with a ctor that takes a model abstraction.model_loading_from_saved_protoonnxruntime::InferenceSession::TransformGraphCastFloat16Transformeronnxruntime::LoadOrtModelBytes bytes were able to be read.onnxruntime::InferenceSession::TransformGraph::::operator ().onnxpost_layout_transform_step_onnxruntime::InferenceSession::TransformGraph::::operator ()ORT model verification failed.InferenceSession is null. Invalid ORT format model.Serialized version info is null. Invalid ORT format model.) has saved runtime optimizations. They will be ignored./ failed. Only onnxruntime::InferenceSession::LoadOrtModel::::operator ()onnxruntime::InferenceSession::LoadOrtModelWithLoaderThe provided PrePackedWeightsContainer instance to be added to the session is nullThe session already has a PrePackedWeightsContainer instanceThe session already has an instance of NodeStatsRecorderonnxruntime::`anonymous-namespace'::PartitionOrtFormatModelThe old ORT format model (version ] is not supported in this build The ORT format model version [Missing Model. Invalid ORT format model.onnxruntime::InferenceSession::Initialize::::operator ()Session initialization canceled due to user request.Initializing session.onnxruntime::InferenceSession::Initializeonnxruntime::`anonymous-namespace'::ApplyOrtFormatModelRuntimeOptimizationsDML EP can be used with only CPU EP.Exception during initialization: onnxruntime::InferenceSession::Initialize::::operator ()This session will use the allocator registered with the environment.DmlRuntimeGraphFusionTransformerDmlRuntimeGraphFusionTransformer is nullptrDmlGraphFusionTransformerModel was not loadedModel was not loaded.Session has already been initialized.Adding default CPU execution provider.This session cannot use the graph capture feature as requested by the user This session cannot use the graph capture feature as requested by the user as the model has control flow nodes which can't be supported by as all compute graph nodes have not been partitioned to the This session cannot use the graph capture feature as requested by the user as all compute graph nodes have not been partitioned to the DmlGraphFusionTransformer is nullptrDmlOperatorFusionTransformerDmlOperatorFusionTransformer is nullptras the model has control flow nodes which can't be supported by As long as the intermediate shapes produced in the model Use the graph capture feature with caution. This model has shape massaging nodes that will execute on CPU. as all the graph nodes have not been assigned to it is safe to use the graph capture feature.of the same shape as the representative input (common case), will match the shapes produced in the model for other inputs using the representative input used to capture the graph, This session contains graph nodes that are assigned to the default CPU EP, but fallback to CPU EP has been explicitly disabled by the user.Unable to serialize model as it contains compiled nodes. Please disable any execution providers which generate compiled nodes.NchwcTransformerSerializing optimized model with Graph Optimization level greater than ORT_ENABLE_EXTENDED and the NchwcTransformer enabled. The generated model may contain hardware specific optimizations, and should only be used in the same environment the model was optimized in.This session cannot use the CUDA Graph feature as requested by the user This session cannot use the CUDA Graph feature as requested by the user as all the graph nodes have not been assigned to This session will use the CUDA/HIP Graph feature as requested by the user.Conflicting session configuration: explicitly added the CPU EP to the session, but also disabled fallback to the CPU EP via session configuration options. Please fix either the inputs/outputs or the model. Expected: Got: Invalid rank for 1024Session successfully initialized.Encountered unknown exception in Initialize()session_initialization()) , expected: ( data type. Actual: (Unexpected for the following indices Got invalid dimensions for index: ))fetch elements. has elements, but Valid values kInput, kOutputarg_type == ArgType::kInput || arg_type == ArgType::kOutputonnxruntime::InferenceSession::ValidateInputsOutputsfeed' expected to be of type: with name: 'tensorsparse_tensor names has name: Invalid but received a tensorInvalid output name:Session was not initializedonnxruntime::InferenceSession::SetEpDynamicOptionsSession not initialized. but received a sparse tensor but received a tensor sequenceseqAt least one output should be requested.onnxruntime::InferenceSession::RunRunning with tag: Encountered unknown exception in Run()model_runFailed to parse the cuda graph annotation id: with graph annotation id: CUDA Graph for this model with tag: Replaying the captured fetch_unique_ptrs[i] != nullptrunknown exceptionintra op thread pool must have at least one thread for RunAsynconnxruntime::InferenceSession::GetModelMetadataStart another run for necessary memory allocation or graph capture.input name cannot be emptyNULL input supplied for input output name cannot be emptyProfiler is disabled.onnxruntime::InferenceSession::EndProfilingCould not write a profile because no model was loaded.Cannot find execution provider onnxruntime::InferenceSession::GetModelInputsonnxruntime::InferenceSession::GetOverridableInitializersonnxruntime::InferenceSession::GetModelOutputsonnxruntime::InferenceSession::NewIOBinding). Reason: Failed to load TuningResults (index=, enable TunableOp for usingCorrectly set TuningResults for onnxruntime::InferenceSession::SetTuningResults does not support TunableOp.). Invalid TuningResults (index= combination in the memory arena shrink list: Did not find an arena based allocator registered for device-id combination is not an arena based allocator: The registered allocator for device-id cpugpuUnsupported device specified in the memory arena shrink list: Unsupported device id in the memory arena shrink list: run_options.run_log_severity_level >= 0 && run_options.run_log_severity_level <= static_cast(logging::Severity::kFATAL)onnxruntime::InferenceSession::CreateLoggerForRunonnxruntime::InferenceSession::AddPredefinedTransformers error message: Unable to shrink arena: onnxruntime::InferenceSession::ShrinkMemoryArenasInvalid run log severity level. Not a valid onnxruntime::logging::Severity value: Local\SM0:%lu:%lu:%hs961c151d2e87f2686a955a9be24d316f1362bf21 3.11.3bad conversioninvalid string_view positionD:\a\_work\1\s\include\onnxruntime\core/common/parse_string.hlist too long.json_onnxruntime::ParseStringWithClassicLocaleother_errorlocaltime_s(&local_tm, &in_time_t) == 0onnxruntime::`anonymous-namespace'::GetCurrentTimeStringFailed to parse value: "p  !"#  !"#`0CreateProvider without parameters is not supported.D:\a\_work\1\s\onnxruntime\core\session\ep_factory_internal.ccEpFactoryInternal currently only supports one device at a time.Internal error. No ReleaseEp call is required for EpFactoryInternal.onnxruntime::EpFactoryInternal::ReleaseEpInternal error. CreateIExecutionProvider should be used for EpFactoryInternal.onnxruntime::EpFactoryInternal::CreateEpError creating execution provider: onnxruntime::InternalExecutionProviderFactory::CreateProviderFailed to load execution provider library: Failed to unload execution provider library: onnxruntime::EpLibraryPlugin::Load::::operator ()ReleaseEpFactory failed for: onnxruntime::EpLibraryPlugin::UnloadFailed releasing EP factories from factories were not released due to errors. This may cause memory leaks. Please check the error details in the log.D:\a\_work\1\s\onnxruntime\core\session\ep_library_plugin.cconnxruntime::EpLibraryPlugin::LoadCreateEpFactoriesReleaseEpFactoryUnloading EpLibraryProviderBridge has been unloaded. Please create a new instance using LoadPluginOrProviderBridge.onnxruntime::EpLibraryProviderBridge::UnloadD:\a\_work\1\s\onnxruntime\core\session\ep_library_provider_bridge.ccDiscreteMicrosoftproducer_versiondomainmodel_versiondoc_stringUnexpected combination of devicesonnxruntime::`anonymous-namespace'::OrderDevices::::operator ()D:\a\_work\1\s\onnxruntime\core\session\provider_policy_context.ccproducer_nameNo execution providers selected. Please check the device policy and available devices.onnxruntime::ProviderPolicyContext::SelectEpsForSessiononnxruntime::ProviderPolicyContext::AddEpDefaultOptionsToSessionNo CPU based execution providers were found.graph_namegraph_descriptionEP selection delegate failed: EP selection delegate did not select anything.first_cpu != sorted_devices.end()onnxruntime::PreferCpuEpPolicy::SelectProvidersForDevices", "existssession.disable_model_compilesession_options_.value.config_options.AddConfigEntry(kOrtSessionOptionEpContextEnable, "1").IsOK()onnxruntime::ModelCompilationOptions::ModelCompilationOptionsORT will still generated the expected output file, but EPs will see an empty characters.) exceeds limit of Output model path length (D:\a\_work\1\s\onnxruntime\core\session\model_compilation_options.ccsession_options_.value.config_options.AddConfigEntry(kOrtSessionOptionsDisableModelCompile, "0").IsOK()onnxruntime::ModelCompilationOptions::SetOutputModelPathoutput model path in SessionOption's ConfigOptions.Input model to compile must be loaded from either a file or a memory buffer, Input model path does not exist: Buffer for input model data has size 0Output model to compile must be saved either to a file or to a buffer, but not both.onnxruntime::ModelCompilationOptions::SetOutputModelBufferonnxruntime::ModelCompilationOptions::SetEpContextEmbedModeInput model to compile must be loaded from either a file or a memory bufferbut not both.session_options_.value.config_options.GetConfigOrDefault(kOrtSessionOptionsDisableModelCompile, "0") == "0"Invalid buffer configuration for output model: size pointer is nullInvalid buffer configuration for output model: allocator is nullsession_options_.value.ep_context_gen_options.enableonnxruntime::ModelCompilationOptions::CheckCPU EP factory currently only supports one device at a time.DxgiAdapterNumberdevice_idDML EP factory currently only supports one device at a time.value, column at line Setting intra_op_num_threads to D:\a\_work\1\s\onnxruntime\core\session\inference_session_utils.cconnxruntime::SetInterOpNumThreadsUnsupported value for inter_op_num_threads: tuning_resultsort_configonnxruntime::SetIntraOpNumThreadsUnsupported value for intra_op_num_threads: Setting execution_mode to Parallel modeonnxruntime::SetGraphOptimizationLevelSetting graph_optimization_level to ORT_DISABLE_ALLUnsupported execution_mode value in ORT config: Setting inter_op_num_threads to Sequential modeonnxruntime::SetExecutionModeonnxruntime::SetEnableProfilingUnsupported value for enable_profiling option: epSetting enable_profiling to Setting graph_optimization_level to ORT_ENABLE_EXTENDEDSetting graph_optimization_level to ORT_ENABLE_BASICUnsupported graph_optimization_level value in ORT config: Setting graph_optimization_level to ORT_ENABLE_ALLFound session/run/environment configuration in the model file to be used while running the modelThe Model Proto has already been checked for the ORT config json.ORT config json from the model: onnxruntime::inference_session_utils::JsonConfigParser::ParseOrtConfigJsonInModelProtovalidatorsresultsonnxruntime::inference_session_utils::JsonConfigParser::ParseOrtConfigJsonInModelProto::::operator ()Json stored in the `ort_config` key cannot be parsed. Error message: inter_op_num_threadsintra_op_num_threads option in the model file must be an integerexecution_modeinter_op_num_threads option in the model file must be an integerDid not find session options in the model file to be used while running the modelThe Model Proto hasn't been checked for the ORT config json.intra_op_num_threadsonnxruntime::inference_session_utils::JsonConfigParser::ParseSessionOptionsFromModelProtoIgnoring unsupported session option in ORT config: enable_profiling option in the model file must be an integer. Ignoring...graph_optimization_levelexecution_mode option in the model file must be an integerenable_profilinggraph_optimization_level option in the model file must be an integerFound tuning results in the model file to be used while loading the modelTuning results stored in the model file cannot be parsed. Error message: onnxruntime::inference_session_utils::ParseTuningResultsFromModelMetadatacannot use key() for non-object iteratorscannot get value' not foundcannot use at() with parse errorparse_errorkey 'invalid_iteratorbooleanarraydiscardedbinaryobjectnullunexpected ; last read: '; expected syntax error number- while parsing null literalfalse literalnumber literalstring literalinvalid literalinvalid BOM; must be 0xEF 0xBB 0xBF if giventrue literal','':'end of input'{''[''}'']'invalid number; expected digit after exponent signinvalid number; expected '+', '-', or digit after exponentinvalid comment; expecting '/' or '*' after '/'invalid comment; missing closing '*/'unknown token'[', '{', or a literalinvalid number; expected digit after '.'invalid number; expected digit after '-'invalid string: control character U+0000 (NUL) must be escaped to \u0000invalid string: forbidden character after backslashinvalid string: control character U+0002 (STX) must be escaped to \u0002invalid string: control character U+0001 (SOH) must be escaped to \u0001invalid string: '\u' must be followed by 4 hex digitsinvalid string: missing closing quoteinvalid string: surrogate U+DC00..U+DFFF must follow U+D800..U+DBFFinvalid string: surrogate U+D800..U+DBFF must be followed by U+DC00..U+DFFFinvalid string: control character U+0008 (BS) must be escaped to \u0008 or \binvalid string: control character U+0007 (BEL) must be escaped to \u0007invalid string: control character U+000A (LF) must be escaped to \u000A or \ninvalid string: control character U+0009 (HT) must be escaped to \u0009 or \tinvalid string: control character U+0004 (EOT) must be escaped to \u0004invalid string: control character U+0003 (ETX) must be escaped to \u0003invalid string: control character U+0006 (ACK) must be escaped to \u0006invalid string: control character U+0005 (ENQ) must be escaped to \u0005invalid string: control character U+0010 (DLE) must be escaped to \u0010invalid string: control character U+000F (SI) must be escaped to \u000Finvalid string: control character U+0012 (DC2) must be escaped to \u0012invalid string: control character U+0011 (DC1) must be escaped to \u0011invalid string: control character U+000C (FF) must be escaped to \u000C or \finvalid string: control character U+000B (VT) must be escaped to \u000Binvalid string: control character U+000E (SO) must be escaped to \u000Einvalid string: control character U+000D (CR) must be escaped to \u000D or \rinvalid string: control character U+0018 (CAN) must be escaped to \u0018invalid string: control character U+0017 (ETB) must be escaped to \u0017invalid string: control character U+001A (SUB) must be escaped to \u001Ainvalid string: control character U+0019 (EM) must be escaped to \u0019invalid string: control character U+0014 (DC4) must be escaped to \u0014invalid string: control character U+0013 (DC3) must be escaped to \u0013invalid string: control character U+0016 (SYN) must be escaped to \u0016invalid string: control character U+0015 (NAK) must be escaped to \u0015invalid string: ill-formed UTF-8 byteinvalid string: control character U+001F (US) must be escaped to \u001Finvalid string: control character U+001C (FS) must be escaped to \u001Cinvalid string: control character U+001B (ESC) must be escaped to \u001Binvalid string: control character U+001E (RS) must be escaped to \u001Einvalid string: control character U+001D (GS) must be escaped to \u001Dtype_errorvector too longattempting to parse an empty input; check that your input string or stream contains the expected JSONnumber overflow parsing 'cannot compare iterators of different containersout_of_rangeobject separatorobject keytype must be string, but is iterator does not fit current valuecannot use erase() with iterator out of rangetype must be number, but is type must be object, but is type must be array, but is Failed to load dxcore.dll. Expected on older Windows version that do not support dxcore.dxcore.dllDXCoreCreateAdapterFactoryonnxruntime::DMLProviderFactoryCreator::CreateFromDeviceOptionsep.dml.enable_cpu_sync_spinningep.dml.enable_graph_captureD:\a\_work\1\s\onnxruntime\core\providers\dml\dml_provider_factory.ccep.dml.disable_memory_arenadefaultperformance_preferenceminimum_powerhigh_performanceDXCore is not available on this platform. This is expected on older versions of Windows.Failed to get DXCoreCreateAdapterFactory function address.No devices detected that match the filter criteria.No GPUs or NPUs detected.onnxruntime::ParseFilterInvalid Filter provided for DirectML EP device selection.FalseTrueonnxruntime::ParsePerformancePreferenceInvalid PerformancePreference provided for DirectML EP device selection.anydevice_filter[ERROR] [DirectML] The value for the key '' should be 'True' or 'False'. Default value is 'False'. disable_metacommandsonnxruntime::ParseBooleanD:\a\_work\1\b\RelWithDebInfo\vcpkg_installed\x86-windows-static-md\include\wil/wrl.hfkJrNFBMΞ n/O3G($$G#WKT!3vwlR@E*m(7dm?B^|*W?6OF !9FWKT!3p~]"L[vA 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R_ELEMENT_WISE_CLIP_GRAD1DML_OPERATOR_ELEMENT_WISE_CLIP1DML_OPERATOR_QUANTIZED_LINEAR_AVERAGE_POOLINGDML_OPERATOR_MULTIHEAD_ATTENTIONDML_OPERATOR_MEAN_VARIANCE_NORMALIZATION2DML_OPERATOR_MATRIX_MULTIPLY_INTEGER_TO_FLOATDML_OPERATOR_RESAMPLE3DML_OPERATOR_RESAMPLE2DML_OPERATOR_DIAGONAL_MATRIX1DML_OPERATOR_RESAMPLE_GRAD1DML_OPERATOR_UNFOLDDML_OPERATOR_FOLDDML_OPERATOR_QUANTIZEDML_OPERATOR_MULTIHEAD_ATTENTION1DML_OPERATOR_ROI_ALIGN_GRADDML_OPERATOR_DEQUANTIZEATensorScaleBiasMinBTensorOutputTensorInputTensorExponentExponentTensorZeroPointTensorScaleTensorMinMaxDataTypeMaxOutputGradientTensorInputGradientTensorStridesDimensionCountStartPaddingDilationsBiasTensorFilterTensorDirectionModeTransACTensorAlphaTransBOutputPaddingEndPaddingFusedActivationGroupCountWindowSizeAxisDirectionPIncludePaddingFunctionBetaAxesAxisCountSizesOffsetsOutputTensorsOutputCountROITensorOutputIndicesTensorPooledSizeSpatialScalePaddingValueDataTypePaddingValueChannelCountScaleInputCountAxisPaddingModeInputTensorsBlockSizeIndexDimensionsRepeatsRepeatsCountScaleSizeBiasIndicesTensorInterpolationModeSpatialVarianceTensorOutputScaleGradientTensorEpsilonOutputIndexTensorOutputValueTensorMeanTensorKRecurrenceTensorWeightTensorSequenceLengthsTensorHiddenInitTensorCrossChannelOutputBiasGradientTensorLocalSizeNormalizeVariancePeepholeTensorCellMemInitTensorClipThresholdOutputCellSingleTensorOutputSingleTensorOutputSequenceTensorActivationDescsActivationDescCountValueOffsetValuesTensorUpdatesTensorCoupleInputForgetUseClipThresholdConditionTensorLinearBeforeResetValueStartValueDataTypeHasExclusiveSumValueDeltaScalesScaleCountInfinityModeRoundingModeInputWindowStridesInputWindowSizesInputPixelOffsetsOrderInputDimensionCountHasExclusiveProductInputWindowOffsetsIndicesDimensionCountOutputScaleTensorBScaleTensorInputZeroPointTensorOutputZeroPointTensorAZeroPointTensorOutputPixelOffsetsAScaleTensorBZeroPointTensorTypeOutputStateTensorOutputCoordinatesTensorOutputCountTensorInputScaleTensorFilterZeroPointTensorInputStateTensorFilterScaleTensorOutputParametersTensorTrainingStepTensorOutputSecondMomentTensorOutputFirstMomentTensorInputFirstMomentTensorInputParametersTensorGradientTensorInputSecondMomentTensorSpatialScaleXReductionFunctionOutOfBoundsInputValueSpatialScaleYBeta1LearningRateBatchIndicesTensorBeta2BatchDimensionCountAlignRegionsToCornersFusedAddTensorOutputROIGradientTensorMaximumSamplesPerOutputMinimumSamplesPerOutputOutputPixelOffsetInputPixelOffsetDiagonalFillEndDiagonalFillBeginKeyTensorQueryTensorOutputVarianceTensorOutputMeanTensorAntialiasedRoundingDirectionAttentionBiasTensorMaskTensorPastValueTensorPastKeyTensorStackedQueryKeyTensorValueTensorStackedQueryKeyValueTensorStackedKeyValueTensorWindowSizesMaskTypeUseVarianceUseMeanOutputPresentValueTensorOutputPresentKeyTensorHeadCountMaskFilterValueQuantizationTensorsQuantizationTensorCountGammaSlopeTensorQueryHeadCountPastSequenceLengthsTensorQuantizationTypeKeyValueHeadCountThresholdSteepnessSigmoidInputScaleD:\a\_work\1\s\onnxruntime\core/providers/dml/OperatorAuthorHelper/MLOperatorAuthorHelper.hD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\GpuEvent.hequationSequenceEraseSequenceLengthOptionalGetElementSequenceInsertSequenceAtD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\ExecutionProvider.cppSequenceEmptySequenceConstructGridSampleSTFTFailed to parse the dml graph annotation id: Dml::ExecutionProviderImpl::CopyTensorsIfOptionalHasElementDFTConcatFromSequenceDml::ExecutionProviderImpl::OnRunStartonnxruntime::TryParseStringWithClassicLocale(*graphAnnotationStr, dmlGraphAnnotationId)onnxruntime::KernelLookup::KernelLookup!provider_type_.empty()D:\a\_work\1\s\onnxruntime\core/framework/kernel_lookup.hprovider_type must be specified.D:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\DmlGraphFusionTransformer.cppDml::DmlGraphFusionTransformer::ApplyImplHelperfused_activation_domainfused_activation_since_versionfused_activationfused op ( ') + (Dml::GraphTransformer::ApplyImplD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\GraphTransformer.cppD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\DmlRuntimeGraphFusionTransformer.cppDml::DmlRuntimeGraphFusionTransformer::ApplyImplHelperD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\ExecutionContext.cppallowzeroaxisauto_padaxeschannels_lastceil_modeborderendendsdilationskernel_shapekkeepdimsnum_outputsnoop_with_empty_axessample_sizepadsoutput_shapeoutput_paddingselect_last_indexscalesscalestridesstartssplitstarttransBtransBatchBtransAtransBatchAtraining_modenum_headskv_num_headsqkv_hidden_sizesNNOTSETD:\a\_work\1\s\onnxruntime\core\providers\dml\OperatorAuthorHelper\MLOperatorAuthorHelper.hD:\a\_work\1\s\onnxruntime\core\providers\dml\OperatorAuthorHelper\OperatorHelper.hUnknown AutoPadType Stringonnxruntime::StringToAutoPadTypeSAME_UPPERSAME_LOWERD:\a\_work\1\s\onnxruntime\core/providers/common.hVALIDonnxruntime::MatMulComputeHelper::ComputeD:\a\_work\1\s\onnxruntime\core/providers/cpu/math/matmul_helper.hleft_num_dims and right_num_dims must be >= 1M_ == 1 && N_ == 1 was falsenum_dims_with_pad - 1 != num_output_dimsright operand cannot broadcast on dim num_dims_with_pad != num_output_dimsleft_num_dims > 2 && left_num_dims == right_num_dimsleft operand cannot broadcast on dim MatMul dimension mismatchTwo inputs should have same rank and rank >= 3 if transBatchA or transBatchB is truepermD:\a\_work\1\s\onnxruntime\core\providers\dml\OperatorAuthorHelper\OperatorHelper.cppnum_dims_with_pad - 2 != num_output_dims$H'8)$&u$"(|+ H7XBlM# pl`$x$%$x%%% &%$$% &$($&|~HGRSA$$ h$$ t$ $ x8I$H'$$$'$$&&%,%%$$0(\(% &@'ZN$$ h$$/$$$$$$$% I0)$B,<u$$% &g>!@\$$$$&& @'`$x$$`$x$$`$x$$$+***$%%%,%%<%%s$$&%)#P$$ h$`$x$$,$w$$cCBL"P`0"`EeNF<T(;HBonFC$$%%%,%%<%%M`c$H'8)$D**$$&&D_Ȃ9}$$$$X') )$$&&,#]pXn  \f| DATO7 mJhXN`$x$$$$%%%,%%%$$$H'$& '%$%%%,%%%$$@'/`$x$$[(z`$x$$ -RHR"q@$ ))~`$x$$$lnH$xbad variant accessD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\External/DirectMLHelpers/GeneratedSchemaHelpers.hseq(tensor(uint4))tensor(int4)tensor(uint4)seq(tensor(int4))*/_ORT_MEM_ADDR_/*seq(tensor(complext64))seq(tensor(complext128))tensor(complext64)tensor(complext128)D:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\MLOperatorAuthorImpl.hD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\MLOperatorAuthorImpl.cpp`$x$$$$%/$$ h$$$$ h$!(Mf$$\# ?`$x$$$$&&%$%$0(\(% &@'\CTfx=$$ h$ r0r8L$$%@D`$x$$$$&%X*$''&'@T{lAݣ1Q +$@+8+yHT0$H'$&$'t'$H%$l''%$$%.%<%,%%0h s!fM߈&$T&,$,X',,,x-`-$$H%8-$ .-'%SizeD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\AbiCustomRegistry.cpp-*rFS =@{A4D:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\BucketizedBufferAllocator.cppGlaIO{cQT9ҮKSDXBC20|T>R6 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B@??@F @@?@?F6@?6"*6B6"FF  @>D:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\External/DirectMLHelpers/ApiHelpers.halign_cornersalphabroadcastblocksizebetabatch_dimsgrouphidden_sizepadding_modemodepooled_shapeoutput_widthoutput_heightScaledTanhTanhThresholdedReluLeakyReluReluSoftplusSoftsignEluHardSigmoidSigmoidD:\a\_work\1\s\onnxruntime\core/providers/dml/OperatorAuthorHelper/OperatorHelper.honesided and inverse attributes cannot be enabled at the same timeonesidedinverseD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlDFT.hzerosD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlGridSample.hbicubicnearestbilinearD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlSTFT.hT2T1GridResourceOutputResourceInputResourcereflectionBOIST8TFTindT4T3UMVGlobalMaxPoolMaxPoolGlobalAveragePoolAveragePoolConvTransposeNhwcConvConvTVLRNLayerNormalizationBatchNormalizationInstanceNormalizationRoiAlignMaxRoiPoolGlobalLpPoolLpPoolTransposeSplitConvTransposeWithDynamicPadsLSTMGRURNNLpNormalizationMeanVarianceNormalizationConstantOfShapeExpandTileDepthToSpaceSpaceToDepthPadSliceConcatTriluEyeLikeScatterNDScatterElementsScatterGatherNDGatherElementsGatherReciprocalSqrtReshapeUnsqueezeSqueezeFlattenIdentityCol2ImAddClipFloorCeilAbsLogExpPowTanSinCosMeanSumDivMulSubIsNaNSignDequantizeLinearQuantizeLinearAffineAtanAsinAcosReduceSumWhereErfAtanhAcoshAsinhCoshSinhReduceL2ReduceL1ReduceSumSquareReduceLogSumExpReduceLogSumReduceProdReduceMeanEinsumLessGreaterNegGemmArgMinArgMaxReduceMinReduceMaxBitwiseAndXorOrAndNotEqualLessOrEqualGreaterOrEqualPReluResizeUpsampleImageScalerCropBitwiseNotBitwiseXorBitwiseOrShrinkDropoutParametricSoftplusHardmaxLogSoftmaxSoftmaxSeluCeluDmlFusedConvTransposeDmlFusedConvNonZeroOneHotTopKCastLikeCastMatMulGeluDmlFusedSumDmlFusedAddDmlFusedMatMulDmlFusedGemmDmlFusedMeanVarianceNormalizationDmlFusedBatchNormalizationDmlFusedInstanceNormalizationAttentionQAttentionQLinearSigmoidFusedMatMulActivationDynamicQuantizeMatMulFusedMatMulFastGeluBiasGeluReverseSequenceRoundBitShiftModIsInfQLinearConcatRotaryEmbeddingMultiHeadAttentionQLinearMatMulQLinearConvQLinearAddQLinearGlobalAveragePoolQLinearAveragePoolMaxUnpoolRangeCumSumEmbedLayerNormalizationSkipSimplifiedLayerNormalizationSkipLayerNormalizationSimplifiedLayerNormalizationDynamicQuantizeLinearConvIntegerMatMulIntegerToFloatMatMulIntegerD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\OperatorRegistration.cppDml::RegisterCpuOperatorsAsDmlGroupQueryAttentionMatMulNBitsGroupNormQuickGeluBiasAddBiasSplitGeludeque too longD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\DmlCommon.cppD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\PooledUploadHeap.cppD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\DmlCommittedResourceAllocator.cppD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\ReadbackHeap.cppiostream stream errorOUC]Y(aY KMs$iostreamcreate_directory/ could not be opened File named: D:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\DmlGraphFusionHelper.cpp.binPartition_Dml::DmlGraphFusionHelper::FusePartitionAndRegisterKernelDml::DmlGraphFusionHelper::RegisterDynamicKernelDmlFusedNode_DmlFusedNodeDomainD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\External/DirectMLHelpers/ApiTraits.hD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\External/DirectMLHelpers/SchemaHelpers.h%hsnodeIdx:-outputIdx:-nodeIdx:_dmlEp_FusedNode's nodeArgList does not contain one of the nodeArgD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\GraphDescBuilder.cppbiaslambdactivationactivation_axisfused_alphafused_betafused_gammaDmlFusedD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\OperatorUtility.cppLinearfused_NEARESTnnlinearBILINEARNNDCRCRDD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\DmlCommandRecorder.cppD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\CommandAllocatorRing.h|B\CFvM]rD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\CommandQueue.cpp=u đKZ`Zvunidirectionalpast_present_share_bufferD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorQAttention.cppdo_rotarymask_filter_valueD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorGather.cppcount_include_padD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorQLinearAveragePooling.cppD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorMaxUnpool.cppD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorGemm.cppfmoddetect_positivedetect_negativedirectionminmaxRIGHTD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorElementWise.cppLEFTD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorExpand.cppD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorConvolution.cppconstantreflectwrapedgeD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorPadding.cppD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorReduce.cpppstorage_orderD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorPooling.cppgammaD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorActivation.cppD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorBiasAdd.cppacross_channelsnormalize_varianceD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorMeanVarianceNormalization.cppD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorCast.cppepsilonD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorLayerNormalization.cppreductionnoneD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorScatter.cppD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorSkipLayerNormalization.cppspatialD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorBatchNormalization.cppD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorFusedMatMul.cppD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorSplit.cppD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorQLinearConcat.cppD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorQLinearSigmoid.cppD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorMatMul.cppD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorQLinearConv.cppexclude_outsidecoordinate_transformation_modeantialiasnearest_modeextrapolation_valuecubicD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorResize.cppround_prefer_ceilround_prefer_floorceilfloorasymmetricpytorch_half_pixeltf_crop_and_resizetf_half_pixel_for_nnhalf_pixel_symmetrichalf_pixell, T@-諸D:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorConstantOfShape.cppD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorQLinearAdd.cppD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorMatMulNBits.cppblock_sizebitsD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorMemcpy.cppspatial_scaleD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorRoiPooling.cppsampling_ratioD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorRoiAlign.cppavgoutput_half_pixelD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorGroupNorm.cppgroupsD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorAttention.cppD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorBiasGelu.cppclipactivationsactivation_betaactivation_alphareverselayoutlinear_before_resetinput_forgetD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorRecurrentNeuralNetwork.cppforwardbidirectionalD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorSlice.cppbatch_axisD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorReverseSequence.cpptime_axisD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorLpNormalization.cppD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorConcatFromSequence.cppCannot concatenate scalarsDml::DmlOperatorConcatFromSequence::DmlOperatorConcatFromSequencenew_axisD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorTopk.cpplargestnumHeads > 0Dml::DmlOperatorRotaryEmbedding::DmlOperatorRotaryEmbeddingWhen the input has 4 dimensions, num_heads must be 0 or have the same value as the second dimension of the inputnumHeads == inputDataSizes[1]Updating cos_cache and sin_cache in RotaryEmbedding is not currently supportedinterleavedrotary_embedding_dimis_packed_batchingD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorRotaryEmbedding.cppnum_heads must be provided if rotary_embedding_dim is specifiedD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorCopy.cppD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorEyeLike.cppD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorQuickGelu.cppD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorBiasSplitGelu.cppD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorEinSum.cppD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorCol2Im.cppD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorAffine.cppD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorGroupQueryAttention.cppD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorShape.cppD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorInstanceNormalization.cppexclusiveD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorCumSum.cppD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorConvInteger.cppD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorDepthToSpace.cppD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorNonZero.cppD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorNeg.cppD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorMatMulIntegerToFloat.cppupperD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorTrilu.cppD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorDynamicQuantizeMatMul.cppD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorOneHot.cppD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorMatMulInteger.cppD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorSpaceToDepth.cppD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorMultiHeadAttention.cppD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorCrop.cppD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorSize.cppD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorDynamicQuantizeLinear.cppD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorTile.cppsizeD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorLocalResponseNormalization.cppD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorConcat.cppD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorQLinearMatMul.cppD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorRange.cppD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorTranspose.cppD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorValueScale2D.cppD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorEmbedLayerNormalization.cppD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\FusedGraphKernel.cppD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\DmlRuntimeFusedGraphKernel.cppDML_TENSOR_DATA_TYPE_UINT8DML_TENSOR_DATA_TYPE_INT32DML_TENSOR_DATA_TYPE_UINT32DML_TENSOR_DATA_TYPE_UINT16DML_TENSOR_DATA_TYPE_FLOAT32DML_TENSOR_DATA_TYPE_FLOAT16DML_TENSOR_DATA_TYPE_UNKNOWNDML_TENSOR_DATA_TYPE_INT4DML_TENSOR_DATA_TYPE_INT64DML_TENSOR_DATA_TYPE_UINT4DML_TENSOR_DATA_TYPE_FLOAT64DML_TENSOR_DATA_TYPE_UINT64DML_TENSOR_DATA_TYPE_INT16DML_TENSOR_DATA_TYPE_INT8Given graph is not acyclic.Graph intermediate edge from nodeIndex: has more than 1 names. doesn't have name. & nodeOutputIndex: does not have any name.Graph node at index: doesn't have the constant data name.Graph constant node at index: does not have name.Graph input or output edge at index %$%l** *@?D($l+T+$$**$$$$ h$@!P$%$# $$$''L,4,t,d,++,,,,-'`$x$$  Ҹ$$% &p Xָb$*T%+$%%<%,%%%<dϸ$%'t'$$'@(' 8и@ظ$$%%%,%%<%%$$`|(\6 $$ h$$$)$$H%$&&..'%%`$$" $$$$ h$$T%H%$p%d%%%<%,%%%%%  0$''H%(((()(d%)$$ h$t$$''&'%$H'$D**\$# --....H%T.@.p.`.$ //&D/8/.A@C@,иָ`$x$$`$x$$$$,uX$$$$$$'<*%$X'% ) (* +Ix  h`$x$$`$x$$$$ h$,:'@Dx%$X',.% ) (* +d"$$%l** *`$x$$ `$x$$$$%0&(&%$$ h$`$x$$do׸$$\^$%T&,$H-,X',,--x-`- -xN `ѸQ$ θx 3ո$$ h$" ޸$$ h$<E$$$$ h$$$$ h$ 5hи$$)$$% &  @͸ |iPK`$$`&%%%,%%ɸ$T&,$,X',,--,x-`- -$$ h$$$%%%,%%%$+*T%++H%**$%%<%,%%%$$`&%%%,%%<% $$&ϸ(FʸP  ]`v $$&&$$%.%<%,%%$$% &$$ h$t$$p)`$x$$$%$$ t$ $!ٸ.hH $$ h$DW!˸dV 0$/h/T/$  ʸH'()$&taԸ|$H'$&$) ) )<}$T&$& t& xn P<"ɸ0h8$$%.%<%,%%$'t'$H%$l''%!@ո$$H%8-$ .-'%$T&,$,X',,,x-`-+8$H'8)$&H7hl ظ$"ݸ|~$($&# 0ϸ`$x$%$x%%% &%$$% &$X XD TB&&$&Rd۸`$x$$ $$%xt/θ$%$t$$xP߸$$X',.% ) (* + .$) )$$%s S$$'$H&8&&۸$$&& &%,%%$$%%%,%%$T(4$G$H'$D** -T>$$ h$$ t$ $ ܸ$${˸8&$$&&$$'$$$$$$%$$ h$ 0)$/$$H'$$$&&%,%%$$0(\(% &@'Z$$$$&& @'`$x$$!P,<ظ$$% &gԸ$$&%)#$+***$%%%,%%<%%`$x$$`$x$$<\"( $$%%%,%%<%%M$H'8)$D**$$ h$`$x$$0"e,$ٸ$$$$&&D_ m,# p`Ҹ  (ʸhX`$x$$9޸$$X') )$$&&$$%$%%%,%%%$$@'/`$x$$[ݸ$$%%%,%%%$$$H'$& '$ ))"8Ӹ`$x$$ -x`$x$$H$($lҸInvalid attribute type. either has empty or invalid operator type.attributes for graph operator node at index:Given pointer to flatbuffer blob is nullMissing output names for node at index:output tensor desc for graph operator node at index:input tensor desc for graph operator node at index:Expecting at least Graph operator node at index:Missing input names for node at index:Constant node at index: doesn't have any name doesn't have constant data name. nor there is any node which has Neither there is any graph input with name as one of the output.$$ h$`$x$$$$$ h$$$%/\# `\$$&&%$%$0(\(% &@'! lʸ`$x$$$$@(`$x$$$''&'Tf\$$ h$$$% rո8t#T0ʸ$$&%X*y\+$@+8+$$&'%#(׸$$2x `$+*x$*$+**$z(ٸjx$%'t'$$'@('ո#$$/$$ h$Y $$ h$`$x$$`$*x$$+$h ۸$$ 4'X'$$ h$$$ h$$$X'% ) (* +$$X',.% ) (* +`$x$$$$p!dظqh$''H%(((()(d%$$H%$0(h('%$$) )--cLܸ=#@Uָ t*P`$x$$`$+*x$*$+H%$$%$%%%,%%<%$$0--....H%T.@.p.`./$ //&D///.<w x̸k$$ h$`$x$$%`$+*x$*$+**$d0$$ h$0L $$&&%$$'<*$''H%(((|(((()(d%(\)H)$/h/T/$8 ɸTt`X1`XO۸;`$x$$Jp ͸H$$H%$&&h('%T(ܸ$`,$$ h$D:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\DescriptorPool.cppm_dmlDevice->QueryInterface(IID_PPV_ARGS(&dmlDevice1))D:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperator.cppD:\a\_work\1\s\onnxruntime\core\providers\dml\DmlExecutionProvider\src\TensorDesc.cppDML_OPERATOR_INVALIDonnxruntime::GraphOptimizerRegistry::GetSelectionFuncCan't find selection function of D:\a\_work\1\s\onnxruntime\core\optimizer\graph_optimizer_registry.cconnxruntime::GraphOptimizerRegistry::GraphOptimizerRegistryCould not create pre-defined selection functions. Error Message: ConstantFoldingDQMultiple entries for operator is not supported. OpType=HardSwishonnxruntime::QDQ::SelectorManager::GetQDQSelectionsOp version is not supported forD:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\selectors_actions\shared\utils.cconnxruntime::QDQ::SelectorManager::InitializeSelectorsMapinsertedD:\a\_work\1\s\onnxruntime\core\optimizer\insert_cast_transformer.cconnxruntime::IsIsolatedFp16NodeOnCpuidx < input_defs.size()tocast node to cast from float16 to float32 on cpuInsertedPrecisionFreeCast_dtype_attribute->second.has_i()InsertCastTransformer works on the assumption that `dtype` attribute holds an integer.dtypeonnxruntime::InsertCastTransformer::ApplyImplonnxruntime::RemoveDuplicateCastTransformer::ApplyImplRemoveDuplicateCastTransformerD:\a\_work\1\s\include\onnxruntime\core/optimizer/graph_transformer.honnxruntime::GraphTransformer::RecurseQDQFinalCleanupTransformersession.disable_double_qdq_removersession.enable_quant_qdq_cleanupACLExecutionProviderArmNNExecutionProviderConstantSharingConvAddFusionConvBNFusionConvMulFusionDivMulFusionDoubleQDQPairsRemoverEliminateDropoutBiasDropoutFusionBiasGeluFusionBiasSoftmaxFusionCastEliminationCommonSubexpressionEliminationsession.qdq_matmulnbits_accuracy_levelAttentionFusionoptimization.enable_gelu_approximationsession.qdqisint8allowedsession.x64quantprecisionMatMulActivationFusionMatMulAddFusionMatMul_BatchNormalization_FusionMatMulIntegerToFloatFusionMatMulScaleFusionMatmulTransposeFusionNoopEliminationNotWhereFusionEliminateIdentityLabelEncoderFusionLabelEncoderLayerNormFusionL1LayerNormFusionL2LayerNormFusionSimplifiedLayerNormFusionGeluFusionL2GeluFusionGemmActivationFusionGemmSumFusionGemmTransposeFusionGroupQueryAttentionFusionDynamicQuantizeMatMulFusionEmbedLayerNormFusionExpandEliminationFastGeluFusionGatherSliceToSplitFusionGatherToSliceFusionGeluApproximationGeluFusionL1onnxruntime::optimizer_utils::GenerateTransformersonnxruntime::optimizer_utils::GenerateTransformersForMinimalBuildUnsqueezeElimination_RuleBasedTransformerLevelUnsupported optimization level: onnxruntime::optimizer_utils::GenerateRewriteRulesD:\a\_work\1\s\onnxruntime\core\optimizer\graph_transformer_utils.cconnxruntime::optimizer_utils::GenerateRuleBasedGraphTransformer4QuickGeluFusionFuseReluClipReshapeFusionRocmBlasAltImplSkipLayerNormFusionEliminateSliceTransposeOptimizerTransposeOptimizer_Pad_FusionPreShapeNodeEliminationAvx2WeightS8ToU8TransformerWeightBiasQuantizationClipQuantRewriteQDQPropagationTransformerQDQS8ToU8TransformerReluQuantRewriteAdded by onnxruntime::`anonymous-namespace'::EnsureUniqueDQForEachExplicitOutputEdgeD:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\ensure_unique_dq_for_node_unit.cconnxruntime::EnsureUniqueDQForNodeUnit::ApplyImplEnsureUniqueDQForNodeUnit/duplicated!onnxruntime::Node::ForEachWithIndexMIGraphXExecutionProvideronnxruntime::TransformerMemcpyImpl::AddCopyNodeCopy from/to host memorydup_replacements.find(&arg) == dup_replacements.end()onnxruntime::TransformerMemcpyImpl::ProcessInitializers::::operator ()onnxruntime::TransformerMemcpyImpl::ProcessInitializers Memcpy nodes are added to the graph onnxruntime::MemcpyTransformer::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\transformer_memcpy.cconnxruntime::TransformerMemcpyImpl::ProcessDefsMemcpy after before Add Set session_options.log_severity_level=1 to see the detail logs before this message.. It might have negative impact on performance (including unable to run CUDA graph). for onnxruntime::GraphTransformer::ApplyD:\a\_work\1\s\onnxruntime\core\optimizer\graph_transformer.cc with status: modified: GraphTransformer stft_imaginary_conv_weightsstft_signal_shapestft_output_reshapedstft_window_shapeSTFTDecompositiononnxruntime::STFTDecomposition::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\stft_decomposition.ccstft_real_conv_weightsFusedConv failed: Layout/Transpose optimization for This transformer is already registered Graph transformation canceled due to user request.onnxruntime::GraphTransformerManager::ApplyTransformersD:\a\_work\1\s\onnxruntime\core\optimizer\graph_transformer_mgr.cconnxruntime::ConstantFoldingDQFuncs::OptimizeD:\a\_work\1\s\onnxruntime\core\optimizer\selection_and_optimization_func.cconnxruntime::GatherToSliceFusion::ApplyImplSplit for Fused Gather nodesunsqueeze_output_range_input_defs.size() == 3Unsqueeze_Unsqueeze for Fused Gather nodesSliceAxesInitializerUnsqueezeAxesInitializeronnxruntime::GatherSliceToSplitFusion::ApplyImplsplit_outputD:\a\_work\1\s\onnxruntime\core\optimizer\gather_fusion.ccSqueeze for Fused Gather nodessqueeze_axes/GatherSliceToSplitFusionsplitsSlice for Fused Gather nodesUnsupported output type of . Can't constant fold ': D:\a\_work\1\s\onnxruntime\core\optimizer\constant_folding.ccUnable to constant fold. InlineIfSubgraph failed node 'can't constant fold onnxruntime::ConstantFolding::ApplyImplfetches.size() == node->OutputDefs().size()Could not find a CPU kernel and hence ' is expected to have a single boolean elementConstantFoldingonnxruntime::ConstantFoldIfNodeIf node condition initializer: `Unsupported data type: onnxruntime::MatMulScaleFusion::ApplyImplmul_inputs.size() == 2onnxruntime::`anonymous-namespace'::GetScaleFromNodeonnxruntime::`anonymous-namespace'::GetInputNodeMergesinput_node.InputDefs().size() == 2 && scale_and_index->second < 2onnxruntime::`anonymous-namespace'::GetOutputNodeMergesoutput_node.OutputDefs().size() == 1/MatMulScaleFusion/Fused MatMul and ScaleConstant initializer NodeArg shape should not be null. NodeArg: onnxruntime::`anonymous-namespace'::GetScalarConstantInitializershapediv_inputs.size() == 2D:\a\_work\1\s\onnxruntime\core\optimizer\matmul_scale_fusion.cconnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget<__int64>::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::utils::mltype_dispatcher_internal::UnsupportedTypeDefaultPolicy::operator ()Output edge count not expected for nodes in gemm pathInput of reshape_before_gemm is not the input of subgraphGemm bias is not constantGemm does not have 3 inputsCheckSliceParameters return falseGemm bias shape not expectedFaild to match concat node for Gather pathsconcat first input value is not -1Slice does not have enough number of inputsD:\a\_work\1\s\onnxruntime\core/optimizer/attention_fusion_helper.hExpected value:Slice ends is less than INT_MAXStart MatchGemmSubgraphSlice parameter is not expected. Input index:Faild to match gemm pathonnxruntime::AttentionFusionHelper::MatchGemmSubgraphonnxruntime::AttentionFusionHelper::CheckSliceParametersinput_indices.size() == expected_values.size() && input_indices.size() > 0Output edge count not expected for nodes in path 1 of unidirectional maskFaild to match path 1 for unidirectional maskCheckSliceParameters returns false for last_sliceDiv and Shape1 does not have edgeValidateUnidirMask returns false for mask_sliceCheckSliceParameters returns false for mask_sliceFaild to match path 2 for unidirectional maskCheckSliceParameters returns false for slice1unidir mask shape not expectedonnxruntime::AttentionFusionHelper::ValidateUnidirMaskMask is neither unidirectional nor all onesThis optimizer does not support external data for unidirectional mask right nowStart MatchUnidirMaskSubgraphExpect mask data type is uint8 or floatFaild to match the path (Div-->Where-->Add) for unidirectional maskonnxruntime::AttentionFusionHelper::MatchUnidirMaskSubgraphonnxruntime::AttentionFusionHelper::ValidateGemmInitializerStart ValidateGemmInitializerGemm bias shape is not expectedGemm bias is not constant initializerGemm weight shape is not expectedGemm weight is not constant initializerunidir mask is not constantPass ValidateGemmInitializerconcat_after_gather input 2 does not have expected valueconcat_after_gather does not have expected number of inputs or output edgesOutput edge count not expected for nodes in gemm gather pathFaild to match gemm gather pathgather axis value not expectedunsqueeze_after_gather axes value not expectedPass MatchGemmSubgraphgather input 1 value is not expectedFailed to find reshape shape path 1equal const not matched.gather indices not matched.Failed to find reshape shape path 2Start MatchPastSubgraphPass MatchInputMaskSubgraphDistilBertFailed to find path for past_konnxruntime::AttentionFusionHelper::MatchPastSubgraphmask_sub const input not matchedmask_unsqueeze_2 axes not matched. Expect: 2Pass MatchInputMaskSubgraphmask_mul const input not matchedFailed to find mask pathStart MatchInputMaskSubgraphDistilBertFailed to find shape pathwhere const not matched.onnxruntime::AttentionFusionHelper::MatchInputMaskSubgraphStart MatchInputMaskSubgraphOutput edge count not expected for SoftmaxFailed to find Softmax nodeOutput edge count not expected for mask nodesFailed to find path for maskmask_unsqueeze_1 axes not matched. Expect: 1Softmax attribute axis is expected to be 3Faild to match path 3 for unidirectional maskOutput edge count not expected for unsqueeze2 of unidirectional maskFaild to match path 4 for unidirectional maskOutput edge count not expected for unsqueeze3 of unidirectional maskOutput edge count not expected for squeeze_2/slices2/shape2 of unidirectional maskDiv and Shape does not have edgePass MatchUnidirMaskSubgraphCheckSliceParameters return false for slice2qk_div const not matched.q_reshape const not matchedPass CheckNodesInPathQq_transpose perm attribute not matchedonnxruntime::AttentionFusionHelper::CheckNodesInPathKStart CheckNodesInPathKk_transpose perm attribute not matchedk_transpose has not perm attributeFailed in match v_transpose attribute perm. Expected: 0, 2, 1, 3Failed in match Transpose attribute perm. Expected: 0, 2, 1, 3v_reshape initializer value is not expectedhidden_size != num_heads * head_sizereshape initializer value is not expectedPass CheckNodesInPathVonnxruntime::AttentionFusionHelper::CheckNodesInPathQStart CheckNodesInPathQpast_v_gather and past_k_gather does not have same past inputpast_k_gather indices != 0Pass MatchPastSubgraphOutput edge count not expected for nodes in past subgraphStart CheckNodesInPathVonnxruntime::AttentionFusionHelper::CheckDistilBertReshapeShapeOutput edge count not expected for nodes in path vonnxruntime::AttentionFusionHelper::CheckNodesInPathVFailed to find path for present_v and past_vFailed to find path for present_kpast_k_transpose perm attribute not matchedFailed to match v_concatpresent_k_unsqueeze axes value not expectedpresent_k_transpose perm attribute not matchedpast_v_gather indices != 1present_v_unsqueeze axes value not expectedMask shape is unknown or not 2D, or data type unknownqkv_biasD:\a\_work\1\s\onnxruntime\core\optimizer\attention_fusion.cconnxruntime::ConvertMaskToInt32onnxruntime::AttentionFusion::ApplyImplMask data type is not int32 or int64 or float32Total fused Attention node count: shape of layer norm bias tensor not expectedFailed to find path for kopt_k_transpose perm attribute not matchedCheckNodesInPathK returns falsek and v are not from same Split nodeFused Attention subgraphs MatchPastSubgraph returns falseqkv_weightsFused an attention node for GPT.CheckNodesInPathV return falseFaild to find path v to SplitMatchUnidirMaskSubgraph returns NULLMatchInputMaskSubgraph returns falseq and v are not from same Split nodeFailed to find path for qUsing transpose optimized patternCheckNodesInPathQ returns falsePass CheckNodesInPathKk_reshape const not matchedCast mask from int64 to int32Mask_Int32Start FuseGptAttentionMaskCastFaild to find path to qkv_matmulonnxruntime::AttentionFusionHelper::FuseGptAttentionFused an attention node.onnxruntime::FuseSubGraphQKFaild to find path vonnxruntime::FuseSubGraphQKDistilBertOutput edge count not expected for Add or MatMul in path vonnxruntime::AttentionFusion::FuseSubGraphFailed in match input mask subgraphFailed in match v_matmul and v_add input shapeq root should be layer normalizationonnxruntime::FuseSubGraphQKImplk root is not layer normq_matmul and q_add shape not matchedFailed to load Q, K and V weights, or data type is not float or float16.k_matmul and k_add shape not matchedFailed to convert mask to int32Failed to load Q, K and V bias tensors, or data type is not float or float16.ConvBnFusion_BN_B_D:\a\_work\1\s\onnxruntime\core\optimizer\conv_bn_fusion.cconnxruntime::ConvBNFusion::Applybn_mean_tensor_protobn_B_tensor_protoconv_W_tensor_protobn_var_tensor_protoConvBnFusion_W_conv_B_tensor_protobn_scale_tensor_protoD:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\qdq_s8_to_u8.cconnxruntime::QDQS8ToU8Transformer::ApplyImplqdq_s8_to_u8_quantqdq_s8_to_u8_zp_conversion*DQ2DQ_DMLQDQSelectorActionTransformerDQMatMulToMatMulNBitsdropSplitQDQdrop_no_int16_supportdropdropDQdrop_no_int16_support_and_positive_scale2DQ1DQTConvActConvActivationFusionactivation != nullptronnxruntime::`anonymous-namespace'::actions::FuseConvActivationAction::ExtraAttributesD:\a\_work\1\s\onnxruntime\core\optimizer\conv_activation_fusion.ccExpected activation node.activation_paramsFailed to get Clip min/max constants.optimizer_utils::GetClipConstantMinMax(state.graph, *activation, min, max)Unsupported operator: onnxruntime::`anonymous-namespace'::actions::FuseConvActivationAction::OpTypeNhwcFusedConv and domain: D:\a\_work\1\s\onnxruntime\core/optimizer/selectors_actions/helpers.hindex < nodes_.size() && ((node = nodes_[index]) != nullptr || !required)onnxruntime::NodesToOptimize::GetNodeonnxruntime::MatMulActivationFusion::ApplyImpl_FusedActivationactivation_D:\a\_work\1\s\onnxruntime\core\optimizer\matmul_activation_fusion.cc with activation D:\a\_work\1\s\onnxruntime\core\optimizer\unsqueeze_elimination.ccUnsqueezeElimination cannot remove node due to invalid axesUnsqueezeElimination cannot remove node onnxruntime::UnsqueezeElimination::ApplyUnsqueezeElimination_/QuickGeluFusion/onnxruntime::QuickGeluFusion::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\quick_gelu_fusion.ccD:\a\_work\1\s\onnxruntime\core\optimizer\relu_clip_fusion.cc_min_zero_constantUnexpected data type for Clip 'min' input of onnxruntime::FuseReluClip::ApplyFuseReluClip_onnxruntime::GeluApproximation::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\gelu_approximation.ccGelu approximationTotal Gelu Approximation (FastGelu) node count: p_bias_scaleBias scale node_weight_dqWeight DQ nodeBias div node_bias_div_round_scale_bias_divonnxruntime::WeightBiasQuantization::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\weight_bias_quantization.cc_weight_qWeight Q node_weight_scale_weight_zpBias INT32 node_bias_dqBias div round node_bias_int32Bias DQ node_new_shapeReshape for Cannot replace concat node with initializer:onnxruntime::ReshapeFusion::Fuse_Subgraph_new_reshapeonnxruntime::ReshapeFusion::ApplyImplTotal fused reshape node count: ConcatTrainingD:\a\_work\1\s\onnxruntime\core\optimizer\reshape_fusion.ccFused reshape node: sum_node.GetOutputEdgesCount() == 0graph.RemoveNode(gemm_node.Index())!sum_input_movedsum_output_edge.src_arg_index == 0onnxruntime::GemmSumFusion::SatisfyConditionother_sum_input != nullptrgraph.RemoveNode(sum_node.Index())D:\a\_work\1\s\onnxruntime\core\optimizer\gemm_sum_fusion.cconnxruntime::GemmSumFusion::Applynew_gemm_input_defs.size() == 3gemm_input_edge.src_arg_index < 2/GemmSumFusion/Fused Gemm with Sumnew_gemm_output_defs.size() == 1 and node != nullptrInvalid node index at NodeArg "".dst node ("node ("_pre_qonnxruntime::`anonymous-namespace'::InsertQDQPairsInserted Q/DQ pair between src node (" but expected NodeArg QDQ insertion edge [0] for NodeArg must have a source or a destination nodeQDQ insertion edge does not have a valid graph NodeArg for ", index: ] for NodeArg QDQ insertion edge [] has NodeArg D:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\qdq_propagation.cconnxruntime::`anonymous-namespace'::ValidateQDQInsertionEdgesExpected at least one edge into which to insert QDQ pair.node_input_def_idx >= 0 && static_cast(node_input_def_idx) < node_inputs.size()onnxruntime::graph_utils::ExtendedGraphEdge::GetMutableNodeAtEndD:\a\_work\1\s\onnxruntime\core/graph/extended_graph_edge.honnxruntime::graph_utils::ExtendedGraphEdge::GetNodeAtEndonnxruntime::graph_utils::ExtendedGraphEdge::TryCreateFromNodeToOutputnode_output_def_idx >= 0 && static_cast(node_output_def_idx) < node_outputs.size()onnxruntime::graph_utils::ExtendedGraphEdge::TryCreateFromInputOrInitializerToNodeonnxruntime::`anonymous-namespace'::PropagateDQForwardonnxruntime::`anonymous-namespace'::GetNextPropagationEdgesdst_node != nullptronnxruntime::`anonymous-namespace'::GetPreviousPropagationEdgeonnxruntime::QDQPropagationTransformer::ApplyImplonnxruntime::`anonymous-namespace'::PropagateQBackwardFailed to set op schema for added Q node._qInserted by QDQPropagationTransformer_q_to_dqsrc_node != nullptrFailed to set op schema for added DQ node._dq_post_dqonnxruntime::MatchInputToConcatSubgraphFailed to find path 1 of position shape.Input data type is not int32 or int64D:\a\_work\1\s\onnxruntime\core\optimizer\embed_layer_norm_fusion.ccOutput edge count not expected for nodes in path 2 of position shape.Failed to find path 2 of position shape.Second input of Gather in path 1 of position shape should be a constant with value 0.Output edge count not expected for nodes in path 1 of position shape._Int32onnxruntime::CheckInputInput shape is unknown or not 2D, or data type unknown_CastCast Input from int64 to int32Segment id is not valid. Input id is not valid. Position embedding shape is not expected.Failed to get position embedding weights.onnxruntime::FuseSubGraphDistilBertBeta should be of shape (hidden_size). Gamma should be of shape (hidden_size). Input_ids and segment id should have the same shape. Input is expected to have dim value in all dimensions.onnxruntime::FuseSubGraphWord embedding shape not expected.fused EmbedLayerNorm subgraphs Failed to match position embedding subgraph.Position embedding data type shall be float or float16.Position embedding shape not matched.Failed to get initializer tensor.Optional position subgraph nodes number of outputs unexpected.The parent of shape nodes are expected to be input_ids.Failed to match Shape node. Second input of Gather should be a constant with value 1. mask_indexposition_embeddingsFailed to match position subgraph.Optional position subgraph nodes Where node is expected to be the parent of Reshape.two paths share the same shapeThe parent of two shape nodes are expected to be input_ids.Second input of Gather in path 2 of position shape should be a constant with value 1.Gather node in path 2 is not linked to another subgraph.The third input of Range should be a constant with value 1.The first input of Range should be a constant with value 0.onnxruntime::MatchPositionEmbeddingSubgraphsFromGatherOutput edge count not expected for nodes in path1.onnxruntime::EmbedLayerNormFusion::ApplyImplYieldOponnxruntime::DynamicQuantizeMatMulFusion::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\dynamic_quantize_matmul_fusion.cconnxruntime::FreeDimensionOverrideTransformer::ApplyImplThe model has input '' with a fixed dimension size Invalid free dimension override.FreeDimensionOverrideTransformerwhich does not equal the specified override of Conflicting free dimension overrides.D:\a\_work\1\s\onnxruntime\core\optimizer\free_dim_override_transformer.cconnxruntime::FreeDimensionOverrideTransformer::FreeDimensionOverrideTransformergemm_input_reshape_reshape_arg_shapegemm_output/MatMulAddFusionfused Matmul and AddD:\a\_work\1\s\onnxruntime\core\optimizer\matmul_add_fusion.cconnxruntime::MatMulAddFusion::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\gemm_activation_fusion.cconnxruntime::GemmActivationFusion::ApplyImplfused FusedGemmfused Gemm with activation Unexpected data type for QuantizeLinear input y_zero_point of onnxruntime::GetQConstantLowerUpperD:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\clip_quantizelinear.cconnxruntime::`anonymous-namespace'::actions::FuseConvAddActivationAction::ValueMovesConvAddActConvAddActivationFusiononnxruntime::`anonymous-namespace'::actions::FuseConvAddActivationAction::ExtraAttributesD:\a\_work\1\s\onnxruntime\core\optimizer\conv_add_act_fusion.ccExpected Conv then Add.conv.GetOutputEdgesCount() == 1 && conv.OutputNodesBegin()->OpType() == "Add"onnxruntime::Avx2WeightS8ToU8Transformer::ApplyImplDynamicQuantizeLSTM unable to locate recurrence tensor or its zero point value, int8 overflow might impact precision ! cannot locate recurrence tensor of const int8 type,Unable transforming DynamicQuantizeLSTM operator,onnxruntime::TryConvertDynamicQuantizeLSTMD:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\avx2_weight_s8_to_u8.ccweight_zp_s8_2_u8_s8_2_u8QGemm/GemmTransposeFusion/Fused Gemm with Transpose_transformedonnxruntime::MatmulTransposeFusion::ApplyImplfused MatMul and Transpose onnxruntime::ReorderCastAndTranspose/MatmulTransposeFusion/Created a new Cast node to interchange Cast and Transpose nodesCreated a new Transpose node to interchange Cast and Transpose nodesD:\a\_work\1\s\onnxruntime\core\optimizer\matmul_transpose_fusion.cc!node_consumers.empty()onnxruntime::UpdateConsumerCountcast != nullptrtranspose_node.InputDefs().size() == 1onnxruntime::GetTransposePermsD:\a\_work\1\s\onnxruntime\core\optimizer\conv_mul_fusion.ccmul_B_tensor_protoConvMulFusion_W_ConvMulFusion_Mul_B_onnxruntime::ConvMulFusion::Applyonnxruntime::RocmBlasAltImpl::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\rocm_blas_alt_impl.cc__backwardpass/LayerNormFusion/stash_typeonnxruntime::SimplifiedLayerNormFusion::ApplyImpl/SimplifiedLayerNormFusion/onnxruntime::LayerNormFusion::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\layer_norm_fusion.ccfused LayerNorm subgraphs  pp_FloatCast Input to floatonnxruntime::SkipLayerNormFusion::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\skip_layer_norm_fusion.ccfused SkipLayerNorm subgraphs D:\a\_work\1\s\onnxruntime\core\optimizer\nchwc_transformer.cconnxruntime::NchwcTransformerImpl::TransformConvreshape_nchwcReorderInputreorderoutput_channels <= nchwc_output_channelsBuffer overflowonnxruntime::NchwcTransformer::ApplyImplchannelsbn_Bbn_scaleReorderOutput_bn_nchwcBiasSoftmax into softmax(input + bias)onnxruntime::BiasSoftmaxFusion::ApplyImplis_inner_broadcastonnxruntime::HandleNegativeAxis is not in valid range [-IsAxisInRange(axis, tensor_rank)axis D:\a\_work\1\s\onnxruntime\core\optimizer\bias_softmax_fusion.ccdoutput_dtypeDoubleQDQRemoved_MatMulNBits fusion nodeMatMul fusion nodeSome of the required tensors were not able to loadD:\a\_work\1\s\onnxruntime\core\optimizer\group_query_attention_fusion.ccMatMul_outputThe newly created node does not follow output def shape of [batch_size, sequence_length, hidden_size]qkv_scaleqkv_weightonnxruntime::GroupQueryAttentionFusion::ApplyImplqkv_zpNot eliminating output ] because it's the graph's output.representative.output_index != kInvalidOutputIndex of node [D:\a\_work\1\s\onnxruntime\core\optimizer\common_subexpression_elimination.cconnxruntime::CommonSubexpressionElimination::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\qdq_final_cleanup.cconnxruntime::`anonymous-namespace'::CleanUpNodeSequenceonnxruntime::QDQFinalCleanupTransformer::ApplyImplFound back-to-back nodes: with name "Transpose optimizer failed: D:\a\_work\1\s\onnxruntime\core\optimizer\transpose_optimizer.cconnxruntime::TransposeOptimizer::ApplyImplMatMulBnFusion_GemmMatMulBnFusion_GemmB_matmul_b_tensorGenerated from Matmul BatchNormalization fusionMatMulBnFusion_GemmBiasbias_tensorD:\a\_work\1\s\onnxruntime\core\optimizer\matmul_bn_fusion.ccvar_tensormean_tensoronnxruntime::MatmulBNFusion::Applyscale_tensorD:\a\_work\1\s\onnxruntime\core\optimizer\nhwc_transformer.cconnxruntime::NhwcTransformer::ApplyImplNhwcTransformeronnxruntime::BiasDropoutFusion::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\bias_dropout_fusion.ccBitmaskBiasDropoutfused Add-Dropout-(Add) for BitmaskDropoutBiasDropoutFuseBiasMatMulNBitsFusiononnxruntime::`anonymous-namespace'::actions::BiasFusion::ValueMovesD:\a\_work\1\s\onnxruntime\core\optimizer\matmul_nbits_fusion.cctarget.GetOutputEdgesCount() == 1D:\a\_work\1\s\onnxruntime\core\optimizer\matmul_integer_to_float.cconnxruntime::MatMulIntegerToFloatFusion::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\rule_based_graph_transformer.cconnxruntime::RuleBasedGraphTransformer::ApplyImplonnxruntime::RuleBasedGraphTransformer::ApplyRulesOnNodefused Gelu subgraphs onnxruntime::GeluFusion::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\gelu_fusion.ccsonnxruntime::OpNodeProtoHelper::GetAttrD:\a\_work\1\s\include\onnxruntime\core/framework/op_node_proto_helper.hint64default_keys_values_tanhfused Add and GeluD:\a\_work\1\s\onnxruntime\core\optimizer\bias_gelu_fusion.ccapproximateonnxruntime::BiasGeluFusion::ApplyImplGPT2Gelufused GPT2Gelu subgraphs fast_gelu_outputD:\a\_work\1\s\onnxruntime\core\optimizer\fast_gelu_fusion.cconnxruntime::FastGeluFusion::ApplyImplpD:\a\_work\1\s\onnxruntime\core\optimizer\constant_sharing.cconnxruntime::ConstantSharing::ApplyImplTotal shared scalar initializer count:  ConvAddFusion_Add_B_ConvAddFusion_B_add_B_tensor_protoD:\a\_work\1\s\onnxruntime\core\optimizer\conv_add_fusion.cconnxruntime::ConvAddFusion::Applyonnxruntime::QDQ::IsQOrDQScalePositiveConstantScalarD:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\qdq_util.cconnxruntime::QDQ::QOrDQNodeHasConstantScalarScaleAndZeroPointq_or_dq_input_defs.size() >= 2Expecting the same data type!model_path.empty()model_path must not be empty. Ensure that a path is provided when the model is created or loaded.D:\a\_work\1\s\onnxruntime\core\optimizer\initializer.cconnxruntime::Initializer::Initializerutils::HasDataType(tensor_proto)Initializer must have a datatypeonnxruntime::utils::mltype_dispatcher_internal::CallableDispatchableHelper::CheckCalledOncecalled_ == 1scalers.size() == 1 || (column_major ? scalers.size() == block_size : scalers.size() == num_blocks)Invalid other(scalers) sizeonnxruntime::Initializer::scale_by_axisaxis >= 0Axis must be non-negativeonnxruntime::Initializer::divonnxruntime::Initializer::mulonnxruntime::Initializer::subsize() == other.size()Expecting the same sizeonnxruntime::Initializer::adddata_type() == other.data_type()onnxruntime::Tensor::MutableDataAsSpancom.microsoft.QLinearSoftmaxcom.microsoft.QLinearSigmoidcom.microsoft.QLinearReduceMeancom.microsoft.QLinearMulcom.microsoft.QLinearLeakyRelucom.microsoft.QLinearGlobalAveragePoolcom.microsoft.QLinearConcatcom.microsoft.QLinearAveragePoolcom.microsoft.QLinearAddcom.microsoft.DequantizeLinearcom.microsoft.QuantizeLinearNhwcMaxPool to have different number of elementsonnxruntime::ApiGraph::ReshapeInitializerFailed to find initializer to reshape with name onnxruntime::ApiGraph::TransposeInitializersuccessFailed to find initializer for name: onnxruntime::ApiGraph::GetValueInfonode_arg_ != nullptrNo NodeArg found for name onnxruntime::ApiTensor::Dataonnxruntime::ApiTensor::NumElementssize >= 0Failed to get size of TensorProto0 <= p && p_int < shape_proto->dim_size()Permutation entry out of bounds for shape D:\a\_work\1\s\onnxruntime\core\optimizer\transpose_optimization\ort_optimizer_api_impl.cconnxruntime::ApiValueInfo::PermuteDimsperm.size() == gsl::narrow_cast(shape_proto->dim_size())Permutation length does not match rank onnxruntime::ApiGraph::CopyValueInfodst_type->value_case() == src_type->value_case() && (!dst_data_element_type_present || dst_data_element_type == src_data_element_type)Existing destination type is not compatible with source type.const_transpose_optimizersince_version.has_value()Transpose Optimizer is adding an unexpected node: An entry for this node should be added in kLayoutTransformationPotentiallyAddedOps.onnxruntime::GetSinceVersionForNewOpopset_import_iter != domain_to_version_map.end() domain not found in opset imports.Added in transpose optimizer_outnew_num_elts == old_num_eltsCannot reshape initializer - - ---<-< -< -|-| -|-|-|-|- - ---l-l -l -l-( -( -( -` -` -` -` T T  T  T Unsupported ONNX opset: saturateRandomUniformRandomNormalRandomUniformLikeRandomNormalLikeMultinomialUnexpected data type for Clip input of onnxruntime::optimizer_utils::GetClipConstantMinMax::::operator ()D:\a\_work\1\s\onnxruntime\core\optimizer\utils.ccP`ponnxruntime::NodesToOptimizeIndicesBuilder::Buildonnxruntime::MoveInputOutputIndex out of range input arg counts but there are only Expected at least onnxruntime::`anonymous-namespace'::MoveInputOutputImpl::::operator ()D:\a\_work\1\s\onnxruntime\core\optimizer\selectors_actions\helpers.cc, actual input arg count: Expected input arg count of zero for input onnxruntime::`anonymous-namespace'::MoveInputOutputImplA target node must be set.target_node != NodesToOptimizeIndices::kEmptyNodeIndexonnxruntime::OptimizerExecutionFrame::Info::{ctor}::::operator ()D:\a\_work\1\s\onnxruntime\core\optimizer\optimizer_execution_frame.ccFailed to get allocator for optimizerallocator_ptr_onnxruntime::OptimizerExecutionFrame::Info::Infoonnxruntime::TryCreateKernelTried to allocate without valid type information, ort_value index=Existing registration with name inserted_in_name_to_entryonnxruntime::SelectorActionRegistry::RegisterSelectorAndActionD:\a\_work\1\s\onnxruntime\core\optimizer\selectors_actions\selector_action_transformer.ccMatched onnxruntime::MatchAndProcessonnxruntime::SelectorActionTransformer::ApplySelectorsAndActionsNot enough produced nodes in the runtime optimization record.onnxruntime::SetOpSinceVersionForProducedNodes).) does not match produced node op in runtime optimization record (New node op (Too many produced nodes in the runtime optimization record.onnxruntime::SelectorActionTransformer::ApplySavedRuntimeOptimizations for transformer Applying runtime optimization action Missing action Nodes to optimize are not valid, skipping action.Target node is not assigned to a compatible execution provider, skipping action.QLinearinit_optional_zero_point_int8_b33fd0fa-cd7b-4b10-ae5a-df64cabfe1f8init_optional_zero_point_uint8_b33f88f7-c464-43e3-8692-97ac832bb14aopsetMatMulNBits accuracy level must be between 0 and 4accuracy_level_ >= 0 && accuracy_level_ <= 4onnxruntime::QDQ::DQMatMulToMatMulNBitsAction::DQMatMulToMatMulNBitsActionD:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\selectors_actions\qdq_actions.ccaccuracy_levelBuffered tensors map cannot be nullonnxruntime::QDQ::DQMatMulToMatMulNBitsAction::ProcessNewNode_Tfused_DQ_MatMul_zero_point_TFailed to add buffered tensor generated at runtimeonnxruntime::MergeIntoTarget::RunD:\a\_work\1\s\onnxruntime\core\optimizer\selectors_actions\actions.cconnxruntime::CreateReplacementNodeonnxruntime::ReplaceWithNew::Runonnxruntime::ReplaceWithNew::RunForSaveFailed to set node op schema.Failed to remove node.,m|"dmllLm̟|mmdmmmm ntrDnnlExecutionProviderNnapiExecutionProviderRknpuExecutionProviderCANNExecutionProviderVSINPUExecutionProvider name shorterMake the EP:provider.name.size() <= kMaxExecutionProviderNameLenD:\a\_work\1\s\onnxruntime\core\providers\get_execution_providers.cconnxruntime::GetAvailableExecutionProviderNames::::operator ()Node support test is required.onnxruntime::utils::`anonymous-namespace'::CreateSupportedPartitionNodeGroupsis_node_supported_fn nodes. Expected to process D:\a\_work\1\s\onnxruntime\core\providers\partitioning_utils.ccnum_nodes_processed == in_degree.size()Processed onnxruntime::RegisterOnnxOperatorKernelsonnxruntime::ml::RegisterOnnxMLOperatorKernelsD:\a\_work\1\s\onnxruntime\core\providers\cpu\cpu_execution_provider.cconnxruntime::CPUExecutionProvider::GetKernelRegistryonnxruntime::RegisterCPUKernelsMust have 1 or more inputsData type of the input tensor MUST be same as that of the input sequence. Sequence data type (onnxruntime::SequenceConstruct::Computenum_inputs >= 1SplitToSequenceViolation of the requirement that all input tensors must have the same data type.split_size_sum () != split_dim_size (onnxruntime::GetScalarSplitInputInvalid data type for split tensor Split should be > 0onnxruntime::GetSplitSizesInputonnxruntime::SplitToSequence::ComputeImplsplit_scalar > 0std::all_of(split_sizes.cbegin(), split_sizes.cend(), [](int64_t value) { return value >= 0; })Invalid value in 'split' input. All values must be >= 0onnxruntime::GetSeqIdx) specified for sequence of size (D:\a\_work\1\s\onnxruntime\core\providers\cpu\sequence\sequence_ops.cconnxruntime::SequenceAt::ComputeInvalid sequence index (onnxruntime::SequenceEmpty::ComputeUnsupported 'dtype' value: ), input tensor data type (onnxruntime::CloneTensorOptional op must have a TypeProto in the 'type' attribute if the attribute is presenttypeonnxruntime::Optional::Optionalattr->has_tp()D:\a\_work\1\s\onnxruntime\core\providers\cpu\optional\optional_ops.honnxruntime::Optional::ComputeOnly Optional type OrtValues containing Tensors and Sequence Tensors are acceptableThe TypeProto attribute in the Optional op can only be of type(tensor) or (seq(tensor))onnxruntime::OptionalGetElement::ComputeTrying to use OptionalGetElement on an optional type OrtValue which contains no dataOptionalD:\a\_work\1\s\onnxruntime\core\providers\cpu\optional\optional_ops.cconnxruntime::PropagateInputOrtValueToFirstOutput outputs which doesn't match the subgraph's outputs.num_subgraph_outputs == static_cast(num_outputs)'If' node has D:\a\_work\1\s\onnxruntime\core\providers\cpu\controlflow\if.cconnxruntime::If::Info::Infoinfo.GetAttr("then_branch", &proto).IsOK()then_branchelse_branchonnxruntime::If::InitSetupSubgraphExecutionInfo should only be called once for each subgraph.info.GetAttr("else_branch", &proto).IsOK()onnxruntime::If::SetupSubgraphExecutionInfoinfo == nullptrthen_feeds_fetches_manager_ && else_feeds_fetches_manager_CreateFeedsFetchesManager must be called prior to execution of graph.If nodes condition input must have exactly one elementonnxruntime::If::ComputeSubgraph SessionState was not found for '' attribute.onnxruntime::IfImpl::Initializesession_stateOnly tensors, tensor sequence, optional tensor, and optional tensor sequence types are supportedFailed to create output tensor for onnxruntime::IfImpl::ExecuteFailed to create output tensor for If output perm: onnxruntime::TransposeBase::TransposeBasev >= 0 && static_cast(v) <= std::numeric_limits::max() is outside range.D:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/transpose.h is repeated.Attribute perm of Transpose has an invalid value. Value perm size: does not match input rank: does not align with rank of input data: [ onnxruntime::TypedDoTransposeEltWise(local_source >= source) && (local_source < source + sizeof(T) * num_blocks)Expected to transpose int4 tensorFailed to pack 8-bit Tensor into 4-bit Tensoronnxruntime::DoTransposeInt4Method IncrementIndexAndComputeOffset assumes this value is strictly positive.onnxruntime::IncrementIndexAndComputeOffsetSetupnaxes > 0(local_source >= source) && (local_source < source + num_blocks * blocksize)D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\transpose.ccTranspose not implemented for empty tensors.onnxruntime::DoTransposeImpl(local_source >= source) && (local_source < source + num_blocks * num_elts_in_block)num_axes > 0onnxruntime::DoTransposeEltWiseTranspose of element size not supported in this build. Size=(local_source >= source) && (local_source < source + num_blocks)input_tensor_ptr != nullptrMismatched data types between input and output Tensors. onnxruntime::Transpose::ComputeFailed to unpack Int4x2 Tensor to an int8_t Tensoronnxruntime::UnpackInt4TensorSubgraph in 'body' produces outputs but Scan expects Output OrtValue has not been created for loop state variable output onnxruntime::Scan8Impl::AllocateOutputTensorsonnxruntime::Scan8Impl::CreateLoopStateVariablesp_mlvalueScanonnxruntime::Scan8Impl::Executeonnxruntime::OrtValueTensorSlicer::Iterator::operator *position_ >= 0 && position_ < sequence_length_D:\a\_work\1\s\onnxruntime\core/framework/ort_value_tensor_slicer.hbodyScan<8> spec does not support transpose of output. This should never be called.onnxruntime::Scan<8>::Initinfo.GetAttr("body", &proto).IsOK()num_scan_inputsD:\a\_work\1\s\onnxruntime\core\providers\cpu\controlflow\scan_8.ccdirectionsinfo.GetAttr("num_scan_inputs", &num_scan_inputs_).IsOK()onnxruntime::Scan<8>::SetupSubgraphExecutionInfoinfo_ == nullptronnxruntime::Scan<8>::Computefeeds_fetches_manager_ && info_onnxruntime::Scan8Impl::InitializeSubgraph SessionState was not found for 'body' attribute. Expected dimensions or more but input had shape of has batch size of Invalid scan input:Scan inputs have inconsistent batch size. Previous value was but Scan inputs have inconsistent sequence lengths. Previous value was has length of did not match batch size of onnxruntime::Scan8Impl::ValidateInputInvalid entries in sequence_lens. Max sequence length was sequence_lens length of * D:\a\_work\1\s\include\onnxruntime\core/framework/allocator.honnxruntime::IAllocator::ValidatedCalcMemSizeForArrayInvalid size requested for allocation: onnxruntime::IAllocator::ValidateAllocationMemory allocation failed. Size=p != nullptr || size == 0D:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/upsamplebase.hwhen anti-aliasing is set, Resize only supports mode `LINEAR` and `CUBIC`.info.GetAttr("mode", &mode).IsOK()onnxruntime::UpsampleBase::UpsampleBasekeep_aspect_ratio_policy model uses the deprecated attribute(UpsampleMode::LINEAR == mode_ || UpsampleMode::CUBIC == mode_)stretchcubic_coeff_a and anti-aliasing is set to yet this opset `tf_half_pixel_for_nn` is deprecated since opset 13, (default) or . It can only be exclude_outside can be set to 1 when (1 mode is CUBIC. (2 mode is CUBIC or LINEAR when anti-aliasing is on. Current mode is set to or not_largernot_smallermode attribute is onnxruntime::UpsampleBase::StringToUpsampleModeonnxruntime::UpsampleBase::StringToKeepAspectRatioPolicycoordinate_transform_mode:[] is not supported!keep_aspect_ratio of [Invalid roi input index.onnxruntime::Upsample::ComputeEither scales or sizes MUST be provided as input.onnxruntime::Upsample::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\upsample.ccOnly one of scales or sizes must be provided as input.onnxruntime::Upsample::Computeonnxruntime::Upsample::Computeonnxruntime::UpsampleBase::StringToNearestModeScale value should be greater than or equal to 1.onnxruntime::UpsampleBase::StringToCoordinateTransformationModenearest_mode:[Resize operatorUpsample operatoronnxruntime::UpsampleBase::ScalesValidationScale value should be greater than 0.scales size should be greater than 0.onnxruntime::UpsampleBase::ParseScalesData'Linear' mode only supports: * 2-D inputs or * 3-D inputs ('Bilinear', 'Trilinear') or * 4-D inputs with the corresponding outermost 2 scale values being 1 or the corresponding outermost and innermost scale values being 1 or * 5-D inputs with the corresponding outermost 2 scale values being 1in the 'Cubic' mode only supports: * 2-D inputs ('Bicubic') or * 4-D inputs with the corresponding outermost 2 scale values being 1 or the corresponding outermost and innermost scale values being 1 with antialias attribute or the corresponding outermost and innermost scale values being 1 and other scales >= 1 without antialias attribute in the onnxruntime::UpsampleBase::ParseSizesDataaxes should be less than output_dims.size()all values in axes should be less than rank of the dataResize: input tensor's rank does not match the output tensor's rank.Dimension: Cannot scale 0 by any factor to generate a non-zero value. Output dim value: Input dim value: Resize: input/output value is nullptrUpsample: input/output value is nullptrInput dim is zero but required output dim is non-zero. onnxruntime::UpsampleBase::ParseScalesDataAndAdjustOutputSizeResize: input shape needs to be at least a single dimensionUpsample: input shape needs to be at least a single dimension.Resize: input/output value's dimension mismatchUpsample: input/output value's dimension mismatchonnxruntime::IAllocator::ValidateAllocatorResize: input tensor's dimension does not match the scales.Upsample: input tensor's dimension does not match the scales.Rank of input and output tensor should be same.onnxruntime::Upsample::BaseCompute: 'Linear' mode only support 2-D inputs or 3-D inputs ('Bilinear', 'Trilinear') or 4-D inputs or 5-D inputs with the corresponding outermost 2 scale values being 1.: 'Cubic' mode only support 2-D inputs ('Bicubic') or 4-D inputs with the corresponding outermost 2 scale values being 1.Resize: size of roi array should be 2 * N where N is the rank of input tensor X.4-D input with innermost scale (usually channel of NHWC) as 1.onnxruntime::Upsample::BaseComputeonnxruntime::Upsample::BaseComputeResize: unexpected modeUpsample: unexpected modeonnxruntime::UpsampleNearestonnxruntime::Upsample::BaseComputeallocator != nullptrand will be fixed in the next ORT 1.13 release. Thus the results of RoiAlign The existing summation for max mode and sampling ratios besides 1 is incorrect sampling_ratio_ >= 0will be different.Null input X ptrNull rois_ptrNumber of dimensions for rois should be exactly Second dimension for rois should be exactly Null batch_indices_ptrNumber of dimensions for batch indices should be exactly 1First dimension (num_rois) of batch_indices and rois don't match specified. It should be either avg or maxD:\a\_work\1\s\onnxruntime\core\providers\cpu\object_detection\roialign.hSampling ratio should be >=0, but it was Invalid mode of value onnxruntime::RoiAlignBase::RoiAlignBase'axes' has an axis outside of the tensor dimension count'axes' has duplicates'step' value cannot be 0has_starts && has_ends && attr_starts_.size() == attr_ends_.size()onnxruntime::SliceBase::SliceBaseMissing or invalid starts and ends attribute!has_axes || attr_axes_.size() == attr_starts_.size()D:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/slice.hInvalid axes attribute, axes attribute (if present) should have the same size as starts/ends attributesonnxruntime::SliceImpl::::operator ()output == output_enddims.size() == extents.size() && dims.size() >= steps.size()onnxruntime::SliceSkips::SliceSkipsonnxruntime::SliceIteratorBase::InitUnexpected element size of D:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/utils.hdims_size == starts.size() && dims_size == extents_.size() && dims_size >= steps.size()D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\slice.ccStarts must be a 1-D arrayonnxruntime::SliceIteratorBase::CopyInnermostAxisNonSolitaryInnerSteponnxruntime::SliceBase::PrepareForComputeStarts and ends shape mismatchStarts and axes shape mismatchonnxruntime::SliceBase::FillVectorsFromInputEnds must be a 1-D arrayCannot slice scalarsonnxruntime::SliceBase::ComputeStarts and steps shape mismatchData type for starts and ends inputs' is not supported in this build. Got Unsupported input data type of onnxruntime::SliceImpl::::operator ()onnxruntime::SliceImpl::::operator ()onnxruntime::SliceImpl::::operator ()onnxruntime::SliceImpl::::operator ()D:\a\_work\1\s\onnxruntime\core\providers\cpu\math\einsum_utils\einsum_compute_preprocessor.ccNumber of subscripts in the input equation does not match number of input tensorsonnxruntime::EinsumComputePreprocessor::RunEllipsis must indicate a fixed number of dimensions across all inputsFound '.' not part of an ellipsis in input: Found a '.' not part of an ellipsis in input: Einsum subscripts string contains too many subscript labels when compared to the rank of the inputAnother operand has a dim value of . The shape is: The only subscript labels allowed are lower-cased letters (a-z) and upper-cased letters (A-Z) in the same dimensionEinsum subscripts string contains too many subscript labels when compared to the rank of the input Einsum subscripts does not contain enough subscript labels and there is no ellipsis for input is incompatible in the dimension Einsum operands could not be broadcast together. Please check input shapes/equation provided.Input shape of operand The broadcasted dimensions of the inputs are incompatibledim_iter == ranknum_broadcasted_indices < num_of_ellipsis_dims_onnxruntime::EinsumComputePreprocessor::PostProcessBroadcastedDimsFound a '.' not part of an ellipsis in the output subscript providedFound '.' not part of an ellipsis in the output subscript provided...Inputs have ellipses in them but the provided output subscript does not contain an ellipsisRank of the input must match number of subscript labels corresponding to the inputOutput subscript contains repeated lettersOutput subscript contains letters not seen in the inputsonnxruntime::Einsum::EinsumD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\einsum.hMissing 'equation' attributeinfo.GetAttr("equation", &equation_).IsOK()onnxruntime::Einsum::DeviceComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\einsum.ccEinsum op: There must be atleast one inputThere was a problem acquiring temporary memory allocator in Einsum op is not supported yetEinsum op: An implementation for the input type ->center_point_boxcenter_point_box only support 0 or 1D:\a\_work\1\s\onnxruntime\core\providers\cpu\object_detection\non_max_suppression.hNonMaxSuppression0 == center_point_box_ || 1 == center_point_box_onnxruntime::NonMaxSuppressionBase::NonMaxSuppressionBaseD:\a\_work\1\s\onnxruntime\core\providers\cpu\object_detection\non_max_suppression.ccscores_tensorboxes_tensoronnxruntime::NonMaxSuppressionBase::PrepareComputeboxes and scores should have same num_batches.boxes and scores should have same spatial_dimension.boxes must be a 3D tensor.scores must be a 3D tensor.onnxruntime::NonMaxSuppressionBase::GetThresholdsFromInputsonnxruntime::NonMaxSuppression::ComputeThe most inner dimension in boxes must have 4 data.iou_threshold must be in range [0, 1].output != nullptrD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\padbase.hInvalid 'pads' attribute valueInvalid 'mode' attribute valueonnxruntime::PadBase::PadBaseInput tensor has no dimensions'pads' has wrong number of valuesdata_rank * 2 == pads.size()data_rank > 0onnxruntime::PadImplUnexpected mode of Axes tensor should be a 1D tensor Cannot use 'edge' mode to pad dimension with a value of 0. Input shape:Cannot use 'reflect' mode to pad dimension with a value of 0. Input shape:D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\pad.ccPads tensor size should be equal to twice the number of explicitly provided axes.num_axes_dims == 1onnxruntime::PadBase::ComputePadspads_data.size() == 2 * data_rankonnxruntime::PadValueFromFloatpads_data.size() == narrow(2 * num_axes)Pads tensor size should be equal to twice the input dimension count onnxruntime::Pad::ComputeValue tensor should be a 1D tensor of size 1 with the same type as that of the input tensorPads tensor should be a 1D tensor of shape [2 * num_axes] or a 2D tensor of shape [1, 2 * num_axes]pads_tensor_dims.size() == 1 || (pads_tensor_dims.size() == 2 && pads_tensor_dims[0] == 1)value_tensor->DataType() == data_type && value_tensor->Shape().Size() == 1onnxruntime::PadInputWithDimValueOfZeroonnxruntime::EinsumTypedComputeProcessor::FinalizeOutputonnxruntime::EinsumTypedComputeProcessor<__int64>::PairwiseOperandProcessonnxruntime::EinsumTypedComputeProcessor::FinalizeOutputonnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcessonnxruntime::EinsumTypedComputeProcessor::FinalizeOutputonnxruntime::EinsumTypedComputeProcessor<__int64>::FinalizeOutputonnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcess Left shape override: Left shape: onnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcessD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\einsum_utils\einsum_typed_compute_processor.ccThe override dims are not compatible with given tensor's shape. left.Shape().Size() == left_shape_override.Size()right.Shape().Size() == right_shape_override.Size() Right shape: Right shape override: Right shape: Einsum op: Input dimensions must be equal along an axis to be reduced across all inputsleft_dim == right_dimRanks of pair-wise operands must be equal. left_rank == right_rankcandidate_output.Shape().Size() == output_shape.Size()onnxruntime::EinsumTypedComputeProcessor::FinalizeOutputEinsum op: Input shapes do not alignEinsum op: The candidate output cannot be reshaped into the op's outputEinsum op: Could not copy the intermediate output's buffer into the op's output buffer. Error: onnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcessNot all dimensions to be reduced have been reduced in the candidate output. Candidate output dims: candidate_output_dims[iter] == 1 NumOutputs=Invalid num_outputs value of . Size of dimension being split is Num entries in 'split' (must equal number of outputs) was Sum of sizes in 'split' (must equal size of selected axis) was Input cannot be split evenly on selected axis. Input shape= Axis=split_tensor->Shape().NumDimensions() == 1The split tensor must be a vector tensor.Cannot split using values in 'split' attribute. Axis= Input shape=D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\split.cconnxruntime::SplitImpl::ComputeInvalid value in 'split' attribute. All values must be > 0If 'num_outputs' is specified, the 'split' input should not be provided.D:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/split.honnxruntime::SplitBase::SplitBasestd::all_of(split_sizes_.cbegin(), split_sizes_.cend(), [](int64_t value) { return value >= 0; })onnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopy::::operator ()src and dst types must matchD:\a\_work\1\s\onnxruntime\core/framework/copy.honnxruntime::DispatchStridedCopydst.DataType() == src.DataType()last >= firstsrc and dst must have same shape and not be rank 0.onnxruntime::StridedCopy::::operator ()counter.current_offset == lastonnxruntime::StridedCopy::::operator ()total_num_elements_to_copy >= 0copy shape must have non-negative sizeonnxruntime::StridedCopydst_strides.size() == src_strides.size() && src_strides.size() == copy_shape.size() && !copy_shape.empty()Input count of Tile OP mismatch, the second one is emptyInput count of Tile OP mismatch, the first one is emptyTile doesn't have an implementation yet for the type: 'repeat' input tensor must have the same length as the 'input' tensor'repeat' input tensor must be 1 dimensionalthe tensor to be tiled using Tile OP must be atleast 1 dimensionalD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\tile.cconnxruntime::Tile::ComputeMust have valid 'axis' attributeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\concatbase.honnxruntime::ConcatBase::ConcatBaseinput count mismatchD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\concat.cconnxruntime::ConcatBase::PrepareForComputeinput_rank == reference_rankRanks of input data are different, cannot concatenate them. expected rank: got: input != nullptronnxruntime::ConcatBase::ComputeImplData type mismatchNon concat axis dimensions must match: Axis has mismatched dimensions of input shape: , indices shape: updates shape: , data shape: last dimension of indices must not be larger than rank of input tensorinput tensor and indices tensor must has rank larger than 0. CPU execution provider: string data type is not supported with ScatterND opset 16 when reduction is 'mul'.CPU execution provider: BFloat16 data type is not supported with ScatterND opset 16 when reduction is 'add'.CPU execution provider: MLFloat16 data type is not supported with ScatterND opset 16 when reduction is 'add'.updates tensor should have shape equal to indices.shape[:-1] + data.shape[indices.shape[-1]:]. CPU execution provider: string data type is not supported with ScatterND opset 18 when reduction is 'min'.CPU execution provider: bool data type is not supported with ScatterND opset 18 when reduction is 'min'.CPU execution provider: BFloat16 data type is not supported with ScatterND opset 16 when reduction is 'mul'.CPU execution provider: MLFloat16 data type is not supported with ScatterND opset 16 when reduction is 'mul'.addmulonnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\scatter_nd.cconnxruntime::ScatterNDDispatchTarget,class std::allocator > >::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()CPU execution provider: string data type is not supported with ScatterND opset 18 when reduction is 'max'.CPU execution provider: bool data type is not supported with ScatterND opset 18 when reduction is 'max'.CPU execution provider: BFloat16 data type is not supported with ScatterND opset 18 when reduction is 'min'.CPU execution provider: MLFloat16 data type is not supported with ScatterND opset 18 when reduction is 'min'.CPU execution provider: BFloat16 data type is not supported with ScatterND opset 18 when reduction is 'max'.CPU execution provider: MLFloat16 data type is not supported with ScatterND opset 18 when reduction is 'max'.onnxruntime::PrepareForComputeonnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget<__int64>::operator ()invalid indice found, indice = onnxruntime::scan::detail::OutputIterator::GetOutputfinal_output_mlvalue_Attempt to retrieve final output before it was set.D:\a\_work\1\s\onnxruntime\core/providers/cpu/controlflow/scan_utils.honnxruntime::ScanImpl::TransposeOutputOutputs from Scan are not optional and should never be null.Invalid value in scan_output_axes for output D:\a\_work\1\s\onnxruntime\core\providers\cpu\controlflow\scan_9.cconnxruntime::Scan<9>::Init but expected scan_input_axesscan_output_directionsscan_input_directionsNumber of entries in 'scan_output_axes' was scan_output_axesgsl::narrow_cast(input_axes_.size()) == num_scan_inputs_Number of entries in 'scan_input_axes' was onnxruntime::ScanImpl::Initializeonnxruntime::Scan<9>::Computeonnxruntime::Scan<9>::SetupSubgraphExecutionInfogsl::narrow_cast(output_axes_.size()) == num_scan_outputs of . Input tensor rank was but input '' dimension onnxruntime::ScanImpl::AllocateOutputTensorsonnxruntime::ScanImpl::SetupInputsonnxruntime::ScanImpl::ValidateInputInvalid value in scan_input_axes for input . Output tensor rank was onnxruntime::ScanImpl::Executeoutput_mlvalueonnxruntime::ScanImpl::CreateLoopStateVariablesSafeIntExceptionHandler::SafeIntOnDivZeroDivide by zeroinfo.GetAttr("axis", &axis_).IsOK()Missing/Invalid 'axis' attribute valueGatherElements op: Rank of input 'data' needs to be equal to rank of input 'indices'GatherElements op: Cannot operate on scalar inputD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\gather_elements.honnxruntime::GatherElements::GatherElementsonnxruntime::core_implGatherElements op: Unsupported tensor type, size:GatherElements op: Out of range value in index tensorD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\gather_elements.cconnxruntime::GetIndexGatherElements op: Data type of input 'data' should match the data type of the outputGatherElements op: 'indices' shape should have values within bounds of 'data' shape. Invalid value in indices shape is: indices element out of data bounds, idx= must be within the inclusive range [onnxruntime::Gather::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\gatherbase.honnxruntime::GatherBase::GatherBaseGather Tind type not supported in this build.D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\gather.ccaxes_tensor != nullptrAxes input is nullD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\unsqueeze.cconnxruntime::UnsqueezeBase::PrepareCompute'axes' has a duplicate axis'axes' has an out of range axisaxes_tensor->Shape().NumDimensions() == 0 || axes_tensor->Shape().NumDimensions() == 1An axes tensor must be a scalar or a 1-D tensor.onnxruntime::Unsqueeze::Computenullptr != p.output_tensorD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/unsqueeze.hX != nullptronnxruntime::UnsqueezeBase::UnsqueezeBaseinfo.GetAttrs("axes", axes_).IsOK()Missing/Invalid 'axes' attribute valueonnxruntime::CumSum::Computeonnxruntime::CumSum<__int64>::ComputeCannot apply CumSum operator on a scalaronnxruntime::CumSum::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\cumsum.cconnxruntime::CumSum::ComputeAxis tensor must be provided to the CumSum opAxis tensor should be 0D or 1DAxis tensor should be of type `int32_t` or `int64_t`bad allocationInvalid argument for depth; it's not a scalar.Invalid argument for values; either it's rank is more than 1 or it has more than 2 elementsonnxruntime::OneHotOp<__int64,__int64,__int64>::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\onehot.ccDepth is negative.onnxruntime::OneHotOp::Computeonnxruntime::OneHotOp::Computeonnxruntime::OneHotOp::Computeonnxruntime::OneHotOp<__int64,int,float>::Computeonnxruntime::OneHotOp::Computeonnxruntime::OneHotOp<__int64,class std::basic_string,class std::allocator >,__int64>::Computeonnxruntime::OneHotOp,class std::allocator >,__int64>::Computeonnxruntime::OneHotOp<__int64,float,__int64>::Computeonnxruntime::OneHotOp<__int64,float,float>::Computeonnxruntime::OneHotOp<__int64,float,int>::ComputeLoop subgraph input 0 has unknown shape: onnxruntime::LoopImpl::InitializeLoop subgraph input 1 has unknown shape: All scan outputs MUST be tensorsonnxruntime::Loop::Computefeeds_fetches_manager_'Loop' input 'M' should be a scalar tensor. Got shape of 'Loop' input 'cond' should be a scalar tensor. Got shape of onnxruntime::LoopImpl::Execute was not found. Defaulting to a rank 1 shape of {0}.Loop had zero iterations and the shape of subgraph output last_outputs[j + 1].IsTensor()onnxruntime::LoopImpl::SaveOutputsAndUpdateFeedsonnxruntime::LoopImpl::ConcatenateLoopOutputonnxruntime::LoopImpl::Execute::::operator ()Graph in 'body' attribute of Loop should have static_cast(num_subgraph_inputs) == subgraph_inputs.size()onnxruntime::Loop::Info::InfoD:\a\_work\1\s\onnxruntime\core\providers\cpu\controlflow\loop.ccLoop inputs. Found: Expected:Inconsistent shape in loop output for output. onnxruntime::Loop::Initonnxruntime::Loop::SetupSubgraphExecutionInfo but has outputs so the subgraph requires 'Loop' node has num_subgraph_outputs - 1 == num_outputsInputs 'past' dimension 0 shall have length of 2Inputs 'past' dimension 1 shall have same length as dimension 0 of input 0Inputs 'past' dimension 2 shall have length of num_headsInputs 'past' dimension 2 shall have length of q_hidden_size=Input 'bias' dimension 0 should have same length as sum of Q/K/V hidden sizes:Input 'past' expect k_hidden_size == v_hidden_sizeInput 'past' is expected to have 5 dimension, got max_sequence_length not matching from mask and past when past_present_share_buffer_ is setInputs 'mask_index' with 1D data shall have length of batch_size or 2 * batch_size or 3 * batch_size + 2Inputs 'mask_index' with 2D data shall have shape batch_size x total_sequence_lengthInputs 'mask_index' with 3D data shall have shape batch_size x sequence_length x total_sequence_lengthpast_sequence_length tensor must be of one element when past_present_share_buffer is setwhen past_present_share_buffer, past tensor sequence must not smaller than total_sequqnce_length onnxruntime::contrib::AttentionBase::CheckInputsD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\attention_base.ccExpect to have present state output when past state input is givenonnxruntime::contrib::AttentionBase::GetPresentInputs 'mask_index' with 4D data shall have shape batch_size x 1 x max_sequence_length x max_sequence_length)Inputs 'mask_index' with 4D data shall have is_unidirectional set to falseInput 'mask_index' is expected to have 1, 2, 3 or 4 dimensions, got num_heads should be no larger than Input 'attention_bias' dimension 1 should be same as number of heads or 1, got Input 'attention_bias' dimension 2 should be same as sequence_length, got Input 'attention_bias' dimension 3 should be same as total_sequence_length, got Input 'attention_bias' is expected to have 4 dimensions, got Input 'attention_bias' dimension 0 should be batch_size or 1, got Input 'input' is expected to have 3 dimensions, got Input 'bias' is expected to have 1 dimension, got Input 'weights' is expected to have 2 dimensions, got Input 1 dimension 0 should have same length as dimension 2 of input 0Attention cannot have both past and attention_biasHidden size of Q, K and V shall be samebias_dims[0]= v_hidden_size= k_hidden_size=Input 'bias' dimension 0 should have same length as dimension 1 of input 'weights'qkv_hidden_sizes attribute should have 3 elementshidden_size should be divisible by num_heads:qkv_hidden_sizes first element should be same as the secondInput 0 and 7 (mask) shall have same shapeinput_ids is expected to have 2 dimensions, got word_embedding is expected to have 2 dimensions, got position_embedding is expected to have 2 dimensions, got input_ids and position_ids shall have same sequence_lengthposition_ids's first dimension shall be 1 or batch_sizeInput 0 and 1 shall have same shapegamma is expected to have size of beta is expected to have 1 dimensions, got beta is expected to have size of segment_embedding is expected to have 2 dimensions, got word_embedding and segment_embedding shall have same dimension 1word_embedding and position_embedding shall have same dimension 1gamma is expected to have 1 dimensions, got onnxruntime::contrib::transformers::BeamSearch::Computedecoder_feeds_fetches_manager_init_run_decoder_session_stateinit_run_decoder_feeds_fetches_manager_Encoder subgraph shall have 2 inputs (encoder_input_ids, decoder_input_ids)whisper_decoder_subgraph_ == nullptrSubgraph SessionState was not found for 'decoder' attribute.decoder_session_stateencoder_feeds_fetches_manager_Model type is not supported.encoder_hidden_statesencoder_subgraph_.subgraph_output_names[1] == "encoder_hidden_states"past_present_share_buffer mode must be same for init decoder and decoder subgraphesinit_run_gpt_subgraph_ && gpt_subgraph_ && init_run_gpt_subgraph_->past_present_share_buffer_ == gpt_subgraph_->past_present_share_buffer_Subgraph SessionState was not found for 'encoder' attribute.encoder_session_stateonnxruntime::contrib::transformers::BeamSearchT5::BeamSearchT5D:\a\_work\1\s\onnxruntime\contrib_ops/cpu/transformers/beam_search_impl_t5.hencoder_subgraph_.num_layers == decoder_subgraph_.num_layersonnxruntime::contrib::transformers::BeamSearchT5::BeamSearchT5logitsBeamSearchWhisperBeamSearchBeamSearch does not support float16 model on CPU execution provider. Use float32 model or CUDA execution provider instead.parameters_->model_type == IGenerationParameters::kModelTypeGpt || parameters_->model_type == IGenerationParameters::kModelTypeT5 || parameters_->model_type == IGenerationParameters::kModelTypeWhisperdecoder_output_cross_qk should be a scalar. Got shape of Node input is requiredinit_decoderdecoderinfo.GetAttr("decoder", &proto).IsOK()gpt_subgraph_ == nullptronnxruntime::contrib::transformers::BeamSearch::InitD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\beam_search.ccencoderinfo.GetAttr("encoder", &proto).IsOK()Encoder subgraph shall have 2 inputs when decoder_start_token_id attribute is emptyEncoder subgraph shall have 3 inputs when decoder_start_token_id attribute is availablet5_decoder_subgraph_ == nullptrwhisper_encoder_subgraph_ == nullptronnxruntime::contrib::transformers::BeamSearch::SetupSubgraphExecutionInfoinit_run_gpt_subgraph_ == nullptrt5_encoder_subgraph_ == nullptrparameters_->decoder_start_token_id >= 0parameters->sequence_length == 1onnxruntime::contrib::transformers::BeamSearchT5::Executecurrent_length == cpu_state.sequences.GetSequenceLength()onnxruntime::contrib::transformers::BeamSearchT5::Executeonnxruntime::contrib::transformers::BeamSearchBase::CheckInputsonnxruntime::contrib::transformers::BeamSearchGpt::Executeonnxruntime::contrib::transformers::BeamSearchBase::Initializeonnxruntime::contrib::transformers::BeamSearchBase::CheckInputsD:\a\_work\1\s\onnxruntime\contrib_ops/cpu/transformers/beam_search_impl_whisper.hdecoder subgraph: output_cross_qk could only work with has_decoder_masked_attentiondecoder_subgraph_.has_decoder_masked_attention_decoder subgraph: output_cross_qk could only work with past_present_share_buffer, vocab_size is no_speech_token_id is out of range, it is parameters->no_speech_token_id >= 0 && parameters->no_speech_token_id < parameters->vocab_sizeonnxruntime::contrib::transformers::BeamSearchWhisper::Executedecoder_subgraph_.past_present_share_buffer_Must specify input cross_qk_layer_headinput_tensor_cross_qk_layer_head != nullptronnxruntime::contrib::transformers::BeamSearchWhisper::Executeonnxruntime::contrib::transformers::BeamSearchBase::GenerateNextTokenonnxruntime::contrib::transformers::BeamSearchBase::GenerateNextTokenonnxruntime::contrib::transformers::BeamSearchGpt::ExecuteD:\a\_work\1\s\onnxruntime\contrib_ops/cpu/transformers/beam_search_impl_gpt.hnum_beamsnum_return_sequenceslength_penalty'num_return_sequences' has to be smaller or equal to 'num_beams'.onnxruntime::contrib::transformers::BeamSearchBase::InitializeD:\a\_work\1\s\onnxruntime\contrib_ops/cpu/transformers/beam_search_impl_base.hmin_lengthmax_lengthInput 'vocab_mask' is expected to have 1 dimension, got Input 'vocab_mask' dimension 0 does not match with vocab_size's, got Input 'prefix_vocab_mask' is expected to be 2 dimensions, got input_ids and prefix_vocab_mask must have the same batch_sizeInput 'input_features' is expected to have 3 dimensions, got Input 'decoder_input_ids' is expected to have 2 dimensions, got Input 'input_ids' is expected to have 2 dimensions, got Input 'presence_mask' is expected to have 2 dimensions, got input_ids and presence_mask must have the same batch_sizeInput 'presence_mask' shape[1] shall be vocab_size, got Input 'prefix_vocab_mask' shape[1] shall be vocab_size, got Input 'attention_mask' is expected to have 3 dimensions, got Input 'attention_mask' is expected to have 2 dimensions, got Input 'attention_mask' is expected to have same shape as input_idsD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\beam_search_parameters.ccmodel_typeORT_BEAM_SEARCH_USE_FAST_TOPKno_repeat_ngram_sizevocab_sizecontext != nullptronnxruntime::contrib::transformers::BeamSearchParameters::ParseFromInputsearly_stoppingeos_token_idpad_token_iddecoder_start_token_idno_speech_token_idno_timestamps_token_idbeginning_timestamp_token_idinput_ids shall have 2 dimensions. Got dims.size() == 2extra_decoder_tensor shall have 2 dimensions. Got extra_decoder_tensor_dims.size() == 2input_features shall have 3 dimensions. Got dims.size() == 3decoder_input_ids shall have 2 dimensions. Got decoder_dims.size() == 2max_length (max_length > sequence_length) shall be no more than max_length <= kMaxSequenceLength, expecting extra_decoder_tensor first dim not same as batch_size. Got extra_decoder_tensor_dims[0] == batch_size) shall be greater than input sequence length () shall be be no more than num_beams (num_return_sequences (num_beams >= num_return_sequencesrepetition_penalty shall be greater than 0, got num_beams shall be a positive integer no more than num_beams >= 1 && num_beams <= kMaxNumBeamsnum_return_sequences shall be a positive integer, got num_return_sequences >= 1onnxruntime::contrib::transformers::WhisperBeamSearchParameters::ParseFromAttributestranslate_token_idtranscribe_token_idstart_of_lm_token_idrepetition_penalty > 0.0flogits_processor shall be a non-negative integer, got logits_processor >= 0model_type == IGenerationParameters::kModelTypeWhisperTryParseStringWithClassicLocale(value_str, parsed_value)onnxruntime::ParseEnvironmentVariableD:\a\_work\1\s\onnxruntime\core/platform/env_var_utils.h", value: "Failed to parse environment variable - name: "Seed must be >= 0seed >= 0onnxruntime::contrib::transformers::SamplingParameters::ParseFromInputsD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\sampling_parameters.ccfilter_valuemin_tokens_to_keeppresence_penaltycustomtemperaturetop_pD:\a\_work\1\s\onnxruntime\contrib_ops/cpu/transformers/greedy_search_impl_base.honnxruntime::contrib::transformers::GreedySearchBase::CheckInputsonnxruntime::contrib::transformers::GreedySearchGpt::Executeonnxruntime::contrib::transformers::GreedySearchBase::Initializeonnxruntime::contrib::transformers::GreedySearchGpt::ExecuteD:\a\_work\1\s\onnxruntime\contrib_ops/cpu/transformers/greedy_search_impl_gpt.honnxruntime::contrib::transformers::GreedySearchBase::InitializeGreedySearchNot Implementedonnxruntime::contrib::transformers::GreedySearch::Computeparameters_.model_type == IGenerationParameters::kModelTypeGptonnxruntime::contrib::transformers::GreedySearch::InitD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\greedy_search.cconnxruntime::contrib::transformers::GreedySearch::SetupSubgraphExecutionInfoonnxruntime::contrib::transformers::GreedySearchBase::GenerateNextTokenonnxruntime::contrib::transformers::GreedySearchBase::GenerateNextTokenonnxruntime::contrib::transformers::GreedySearchBase::CheckInputsonnxruntime::contrib::transformers::Sampling::SetupSubgraphExecutionInfoD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\sampling.cconnxruntime::contrib::transformers::Sampling::Computeonnxruntime::contrib::transformers::Sampling::InitSamplingonnxruntime::contrib::transformers::GreedySearchGpt::Executeonnxruntime::contrib::transformers::GreedySearchBase::CheckInputsonnxruntime::contrib::transformers::GreedySearchBase::CheckInputsonnxruntime::contrib::transformers::GreedySearchBase::Initializeonnxruntime::contrib::transformers::GreedySearchBase::Initializeonnxruntime::contrib::transformers::GreedySearchGpt::Executeonnxruntime::contrib::transformers::GreedySearchBase::GenerateNextTokenonnxruntime::contrib::transformers::GreedySearchBase::GenerateNextTokenD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\greedy_search_parameters.cconnxruntime::contrib::transformers::GreedySearchParameters::ParseFromInputs@Input 1 dimension 0 should have same length as the last dimension of input 0Input 1 is expected to have 1 dimensions, got Input 0 is expected to have 1 or more dimensions, got Input 'input' dimension 1 should be divisible by 2W, where W is value of the window attribute.Inputs 'attention_mask' shape shall be (batch_size, sequence_length)Input 'bias' shape should be (5 * hidden_size) for format 0Input 'global_weights' shape should be (3, hidden_size, hidden_size) for format 0Input 'global_weights' shape should be (hidden_size, 3 * hidden_size) for format 1Input 'weights' shape should be (hidden_size, 3 * hidden_size) for format 1Input 'input' dimension 2 should be divisible by value of the num_heads attribute.Input 'bias' shape should be (3 * hidden_size) for format 1Input 'weights' shape should be (3, hidden_size, hidden_size) for format 0Input 'global_bias' shape should be (3 * hidden_size) for format 1Input 'global_bias' is expected to have 1 dimension, got Input 'global_attention_mask' shape shall be (batch_size, sequence_length)Input 'global_bias' shape should be (hidden_size) for format 0Pad should be smaller than kernel.kernel_shape[dim] > 0strides.size() == kernel_shape.size()pads[dim] < kernel_shape[dim] && pads[dim + kernel_shape.size()] < kernel_shape[dim]info.GetAttr("auto_pad", &auto_padding).IsOK()D:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/pool_attributes.hinfo.GetAttr("storage_order", &storage_order).IsOK()info.GetAttr("count_include_pad", &temp).IsOK()input_dims.size() >= 2onnxruntime::PoolAttributes::SetOutputSizeUnsupported AutoPad Type.onnxruntime::PoolAttributes::InferOutputSizedilations.size() == kernel_shape.size()Dilations dimensions should match kernel shapeinput_shape.Size() > 0 || input_shape[0] == 0Invalid input shape. Only N can be zero. Got:onnxruntime::PoolBase::ComputeInput dimension cannot be less than 3.Unsupported pooling size.D:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\pool.ccinfo.GetAttr("p", &p_).IsOK()onnxruntime::PoolAttributes::ComputeSizePadDilationsD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/pool_base.honnxruntime::PoolProcessContext::initkernel_shape num_dims is not compatible with X num_dims.`InlinedVector::at(size_type)` failed bounds checkNo kernel shape is set.onnxruntime::PoolAttributes::PoolAttributesinfo.GetAttrs("kernel_shape", kernel_shape).IsOK()onnxruntime::MaxPoolV8::ComputeImplUnsupported pooling size : onnxruntime::Pool::Computeonnxruntime::LpPoolV18::ComputeUnsupported kernel dimension : onnxruntime::AveragePoolV19::Computeonnxruntime::LpPoolV18::LpPoolV18D:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/pool.hAttribute name and type don't match for 'No attribute with name:''is defined.onnxruntime::functors::ElementWiseRangedTransform::Createunknown kernel typeD:\a\_work\1\s\onnxruntime\core\providers\cpu\activation\activations.cconnxruntime::functors::ParametricSoftplus::InitD:\a\_work\1\s\onnxruntime\contrib_ops/cpu/activations.honnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::functors::ScaledTanh::InitD:\a\_work\1\s\onnxruntime\core/providers/cpu/activation/activations.honnxruntime::functors::Selu::Initonnxruntime::ElementWiseKernel >::ComputeD:\a\_work\1\s\onnxruntime\core/providers/cpu/element_wise_ranged_transform.honnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeinput_size < std::numeric_limits::max()onnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::functors::HardSigmoid::Initonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::TensorAllocator::TensorAllocatorAttempting to broadcast an axis by a dimension other than 1. by onnxruntime::BroadcastIterator::Initaxis == 1 || axis == largestonnxruntime::Broadcaster::Broadcasterlargest <= 1offset % span_size_ == 0InputBroadcaster can only start at span boundary!onnxruntime::BroadcastIterator::AppendD:\a\_work\1\s\onnxruntime\core/providers/cpu/math/element_wise_ops.hCan broadcast 0 by 0 or 1. is invalid.) are not at boundary of span with size:onnxruntime::OutputBroadcaster::OutputBroadcasterstart_offset % span_size == 0 && real_end % span_size == 0Broadcast Output range [) for tensor of length:onnxruntime::InputBroadcaster::AdvanceBystart_offset >= 0 && real_end >= 0 && start_offset <= real_end && real_end <= lenInvalid start/ending offset [onnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\element_wise_ops.cconnxruntime::Min_6::Computedata_n.Shape() == shapeAll inputs must have the same shapeinputCount >= 1Unsupported X type: onnxruntime::ExpandBroadcastLooper!helper.HaveTwoTensorInputs()shape_data_tensor.Shape().GetDims().size() == 1Tensor with shape information must be 1 dimensional.onnxruntime::Mean_6::Computeonnxruntime::Max_6::ComputeExpandBroadcastLooper should only have a shape for the second input.data_1.Shape() == shapeonnxruntime::mod_internal::CallModImpl::operator ()fmod attribute must be true for floating point typesonnxruntime::ElementWiseKernel >::Computefmod must have value either 0 or 1onnxruntime::UntypedExpandonnxruntime::Mod::Mod(fmod == 0) || (fmod == 1)onnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computehelper.HaveTwoTensorInputs()BroadcastLooper requires two tensors as input.onnxruntime::BroadcastLooperonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelinput_count >= 1onnxruntime::Sum_6::Computeonnxruntime::Sum_6::ComputeUnsupported Y type: onnxruntime::BitShift::Compute::::operator ()onnxruntime::Expand_8,class std::allocator > >::Compute::::operator ()Invalid usage. Input 1 is a shape with no data.onnxruntime::BitShift::Compute::::operator ()cur1 == end1onnxruntime::BitShift::Compute::::operator ()cur_out == end_outonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernel'. Valid values are 'LEFT' or 'RIGHT'.onnxruntime::BitShift::BitShiftonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::BitShift::BitShiftInvalid direction value of 'onnxruntime::BitShift::BitShiftonnxruntime::mod_internal::CallModImpl::operator ()onnxruntime::mod_internal::CallModImpl::operator ()((`(`(``JJ J J } M}7M}p7M}6p7M6p76pf6OfOfOfOstrides_.size() == kernel_shape_.size()kernel_shape_[dim] > 0pads_[dim] < kernel_shape_[dim] && pads_[dim + kernel_shape_.size()] < kernel_shape_[dim]onnxruntime::MaxUnpool::MaxUnpoolD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/unpool.h inferred output shape:output_shape is smaller than minimum required. output_shape:Index tensor shape should be same as that of the input data tensor to unpool.Shape must be 1 dimensional as it's tensor data of a shapeonnxruntime::MaxUnpool::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\Unpool.cconnxruntime::InstanceNorm::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\instance_norm.ccInvalid input B: number of dimensions is not 1: Mismatch between input data and B: size of B != input channel count vs. Mismatch between input data and scale: size of scale != input channel count Invalid input data: number of dimensions is less than 3: Invalid input scale: number of dimensions is not 1: D:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/instance_norm.hop_kernel_info.GetAttr("epsilon", &epsilon_).IsOK()onnxruntime::InstanceNorm::InstanceNormonnxruntime::RNN::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\rnn.ccactivations_.size() == static_cast(num_directions)RNN op: Invalid activation attribute - info.GetAttrs("activations", activations_).IsOK()info.GetAttr("hidden_size", &hidden_size_).IsOK()D:\a\_work\1\s\onnxruntime\core/providers/cpu/rnn/rnn.hallowed_directions.find(direction_) != allowed_directions.end()info.GetAttr("direction", &direction_).IsOK()onnxruntime::RNN::RNNlayout_ == 0allowed_activations.find(activations_[direction]) != allowed_activations.end()Batchwise recurrent operations (layout == 1) are not supported. If you need support create a github issue with justification.info.GetAttr("direction", &direction).IsOK()onnxruntime::LSTMBase::LSTMBaseonnxruntime::rnn::detail::MakeDirectionD:\a\_work\1\s\onnxruntime\core/providers/cpu/rnn/rnn_helpers.h'. Must be one of 'forward', 'reverse', or 'bidirectional'.Invalid 'direction' argument of 'D:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\deep_cpu_lstm.ccLSTM operator does not support double yetactivation_func_names.size() == static_cast(num_directions_) * 3onnxruntime::DeepCpuLstmOp::PrePackclip_ > 0.fsigmoidD:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\lstm_base.hinfo.GetAttr("hidden_size", &int64_value).IsOK() && int64_value > 0Invalid data type for LSTM operator of onnxruntime::DeepCpuLstmOp::ComputeInvalid data type for GRU operator of GRU operator does not support double yetactivation_func_names.size() == static_cast(num_directions_) * 2info.GetAttr("linear_before_reset", &int64_value).IsOK()D:\a\_work\1\s\onnxruntime\core/providers/cpu/rnn/deep_cpu_gru.honnxruntime::DeepCpuGruOp::DeepCpuGruOpD:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\deep_cpu_gru.cconnxruntime::DeepCpuGruOp::ComputeB + (N * ldb - (ldb - K)) <= B_endA + (M * lda - (lda - K)) <= A_endonnxruntime::rnn::detail::ComputeGemmlda >= K && ldb >= K && ldc >= Nonnxruntime::rnn::detail::SafeRawPointeroffset + size <= size_t(span.size())onnxruntime::rnn::detail::SafeRawConstPointercur + size <= endC + (M * ldc - (ldc - N)) <= C_endonnxruntime::DeepCpuGruOp::ComputeImplonnxruntime::rnn::detail::GemmWeights::GetUnpackedSpan!is_prepacked_Can not get unpacked span from prepacked weightsOnly support GridSample Cubic mode in 4-D cases.grid_dims[0] == NGrid batch size does not match input batch size input_dims.NumDimensions() == 4 || input_dims.NumDimensions() == 5Only 4-D or 5-D tensor is supportedgrid_dims[grid_dims.NumDimensions() - 1] == data_dimsLast dimension of grid: onnxruntime::GridSample::GridSamplemode "" not supported, expect linear, nearest or cubiconnxruntime::GridSample::ComputeOnly support GirdSample in 4-D or 5-D cases.mode_ != Cubiconnxruntime::GridSample::GridSamplepadding_mode "" not supported, expect zeros, border or reflection" not supported, expect bilinear, nearest or bicubicD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/grid_sample.h, expect D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\grid_sample.cconnxruntime::GridSample::Computestatic_cast(grid_dims.NumDimensions()) == data_dims + 2grid dimensions must be for input dimension of D:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/dropout_op.honnxruntime::Dropout::Compute!mask || mask->Shape() == X_shapeX and mask should have the same shapeonnxruntime::Dropout::Computeonnxruntime::Dropout::Computeonnxruntime::Dropout::Computeonnxruntime::`anonymous-namespace'::GetRatioOrDefaultratio_tensor->Shape().Size() == 1ratio input should have a single value.seed0.0f <= ratio_value && ratio_value < 1.0fratio must be in the range [0, 1)EyeLike : Input tensor dimension is not 2Matrix dimensions are not equal. Square matrix is expectedInput tensor should have a rank of at least 2D:\a\_work\1\s\onnxruntime\core\providers\cpu\math\det.cconnxruntime::Det::ComputeDetInvalid input shape: dim_size > 0D:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/conv_transpose_attributes.honnxruntime::ConvTransposeAttributes::PrepareForComputeInput channels is not divisible by group.filter number not equal to input channel number. filter_number: num_input_channels: group count is <= 0onnxruntime::ConvTransposeAttributes::ComputeTransposePadAndOutputShape*out_size >= 0onnxruntime::ConvTransposeAttributes::ComputePadsAndOutputShape channels_last: kernel_shape num_dims is not compatible with W num_dims. kernel_shape: W: D:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/conv_attributes.honnxruntime::ConvAttributes::ConvAttributesauto_pad == AutoPadType::NOTSETA Conv/ConvTranspose node has both 'auto_pad' and 'pads' attributes group: X num_dims does not match W num_dims. X: kernel_shape is not compatible with W shape.D:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\conv_transpose.cconnxruntime::ConvTranspose::DoConvTranspose kernel channels: onnxruntime::ComputePadAndOutputShapeComputePad: pad type not supported.Dilation not supported for AutoPadType::SAME_UPPER or AutoPadType::SAME_LOWER.Not enough elements in pads. Expected: Not enough elements in dilations. Expected: Not enough elements in kernel shape. Expected: Not enough elements in strides. Expected: Output channels M is not divisible by group. M: Input channels C is not equal to kernel channels * group. C: onnxruntime::ConvAttributes::InferPadsAndOutputShapeoutput and sum shape must matchD:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\conv.cconnxruntime::Conv::Computex_zero_point and x_scale must have the same shape for blocked quantizationscale.Shape()[i] == input_shape[i]x_scale and x must have the same shape despite the quantize axis for blocked quantizationscale.Shape()[i] == (input_shape[i] + quant_block_size - 1) / quant_block_sizex_scale must be ceil(Di/block_size) on the quantize axis i for blocked quantizationzero_point_ptr == nullptr || zero_point_ptr->Shape().NumDimensions() == input_shape.NumDimensions()x_zero_point must be null or have the same rank as x for blocked quantizationscale.Shape().NumDimensions() == input_shape.NumDimensions()zero_point_ptr == nullptr || (zero_point_ptr->Shape().NumDimensions() == 1 && zero_point_ptr->Shape()[0] == broadcast_dim)For per axis quantization, x_zero_point must be null or 1D tensor with size scale.Shape().NumDimensions() == 1 && scale.Shape()[0] == broadcast_dimFor per axis quantization, scale must be 1D tensor with size zero_point_ptr->Shape()[i] == scale.Shape()[i]x_scale and x must have the same rank for blocked quantizationquant_block_size == 0block_size must be 0 for per-tensor quantization.D:\a\_work\1\s\onnxruntime\core\providers\cpu\quantization\quantize_linear.cconnxruntime::PrepareForQDQzero_point_ptr == nullptr || IsScalarOr1ElementVector(zero_point_ptr)x_zero_point must be null or a scalar or 1D tensor or size 1.onnxruntime::QuantizeLinear::QuantizeLinearonnxruntime::QuantizeLinear::QuantizeLinearonnxruntime::QuantizeLinear::QuantizeLinearblock_size_ >= 0'block_size' must be non-negative.onnxruntime::QuantizeLinear::Computeonnxruntime::DequantizeLinear::DequantizeLinearonnxruntime::QuantizeLinear::QuantizeLinearonnxruntime::QuantizeLinear::QuantizeLinearonnxruntime::QuantizeLinear::QuantizeLinearonnxruntime::QuantizeLinear::QuantizeLinearonnxruntime::QuantizeLinear >::QuantizeLinearonnxruntime::QuantizeLinear >::QuantizeLinearonnxruntime::QuantizeLinear::QuantizeLinearonnxruntime::DequantizeLinear::DequantizeLinearonnxruntime::DequantizeLinear::DequantizeLinearonnxruntime::DequantizeLinear::DequantizeLinearonnxruntime::DequantizeLinear >::DequantizeLinearonnxruntime::DequantizeLinear >::DequantizeLinearonnxruntime::DequantizeLinear::DequantizeLinearonnxruntime::DequantizeLinear::DequantizeLinearonnxruntime::DequantizeLinear::DequantizeLinearonnxruntime::DequantizeLinear::DequantizeLinearonnxruntime::DequantizeLinear::DequantizeLinearDequantizeLinear into BFLOAT16 is not implemented yet.onnxruntime::DequantizeLinear::Computezero_point == nullptr || std::all_of(zero_point, zero_point + x_zero_point->Shape().Size(), [](T zp) { return zp == T{0}; })DequantizeLinear with type int32 or float8 should have no zero point or all zero points should be 0onnxruntime::DequantizeLinear::Computeonnxruntime::DequantizeLinear::Computeonnxruntime::DequantizeLinear::ComputeDequantizeLinear only outputs FLOAT16, FLOAT or BFLOAT16.onnxruntime::DequantizeLinear::Computeonnxruntime::QuantizeLinear::ComputeUnsupported input type.onnxruntime::DequantizeLinear::Computeonnxruntime::DequantizeLinear::Computeonnxruntime::DequantizeLinear::Computeonnxruntime::DequantizeLinear::Computeonnxruntime::DequantizeLinear >::Computeonnxruntime::DequantizeLinear >::Computeonnxruntime::QuantizeLinear::Computeonnxruntime::QuantizeLinear::Computeonnxruntime::QuantizeLinear::Computeonnxruntime::QuantizeLinear >::Computeonnxruntime::QuantizeLinear >::Computeonnxruntime::QuantizeLinear::Computeonnxruntime::QuantizeLinear::Computeonnxruntime::QuantizeLinear::ComputeIsScalarOr1ElementVector(y_offset)QLinearMatmul : input scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(a_scale)QLinearMatmul : weight scale must be a scalar, 1D tensor of size 1, or last to second dimension is 1IsBQuantParamSupported(b_scale->Shape(), b ? b->Shape() : b_shape_)QLinearMatmul : result scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(y_scale)TSQLinearMatmul : input zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(a_offset)onnxruntime::QLinearMatMul::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\quantization\quantize_linear_matmul.ccQLinearMatmul : weight zero point must be a scalar, 1D tensor of size 1, or last to second dimension is 1IsBQuantParamSupported(b_offset->Shape(), b ? b->Shape() : b_shape_)QLinearMatmul : result zero point must be a scalar or 1D tensor of size 1Per-column quantization parameter of batched matrix should have same dimension as the matrix,and its size by K should be equal to the matrix's size.onnxruntime::MatMulComputeHelper::Compute::::operator (). shape= must be 1 instead of Dimension of input input_shape[i] == 1onnxruntime::SqueezeBase::ComputeOutputShapeD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/squeeze.honnxruntime::Squeeze::ComputeAn axes tensor must be a vector tensor.axes_tensor->Shape().NumDimensions() == 1onnxruntime::Flatten::FlattenD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/flatten.hThe rank of input tensor must be >= axisgsl::narrow_cast(X_shape.NumDimensions()) >= axisonnxruntime::Flatten::Computeunknown_dim == -1The dimension with value zero exceeds the dimension size of the input tensor.i < input_shape.NumDimensions(), requested shape:The input tensor cannot be reshaped to the requested shape. Input shape:size != 0 && (input_shape_size % size) == 0input_shape_size == sizeThe input tensor must not have any dynamic (-1) dimensions. Input shape:input_shape_size != -1onnxruntime::ReshapeHelper::ReshapeHelperD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\reshape_helper.hA dimension cannot be less than -1, got requested_shape[i] >= -1At most one dimension can be -1.A shape tensor must be a vector tensor.shapeTensor->Shape().NumDimensions() == 1onnxruntime::Reshape::ComputeD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/reshape.hAttribute shape is not set.onnxruntime::Reshape_1::Reshape_1onnxruntime::IdentityOp<0>::ComputeD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/identity_op.hUnable to get an allocatoronnxruntime::IdentityOp<1>::Computeonnxruntime::ConstantOfShapeBase >::ConstantOfShapeBaseMust have a single dimension of 1t_proto_p->dims()[0] == 1onnxruntime::`anonymous-namespace'::ConstantOfShape::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\generator\constant_of_shape.ccUnsupported output datatype with size: Must have a valid input shape.onnxruntime::ConstantOfShapeBase >::PrepareComputeD:\a\_work\1\s\onnxruntime\core/providers/cpu/generator/constant_of_shape_base.hMust have a single dimensiont_proto_p->dims_size() == 1Tensor proto with external data for value attribute is not supported.!utils::HasExternalData(t_proto)Unsupported value attribute datatype: utils::HasDataType(t_proto)onnxruntime::ConstantOfShapeBase >::SetValueFromTensorProtoONNX_NAMESPACE::TensorProto::DataType_IsValid(t_proto.data_type())stoull argument out of rangeinvalid stod argumentstod argument out of rangeinvalid stoll argumentstoll argument out of rangeinvalid stoull argumentAttribute to is not set.onnxruntime::`anonymous-namespace'::Cast::CastD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\cast_op.ccAttribute saturate is only used for cast to float 8 types.%.8gsnprintf() failed with return value: snprintf_result > 0onnxruntime::`anonymous-namespace'::CastToStringFailed to write value with snprintf().snprintf_result > 0 && gsl::narrow_cast(snprintf_result) == buffer_span.size() - 1NaN-INFINFStringSplitdelimitermaxsplitD:\a\_work\1\s\onnxruntime\core\providers\cpu\text\regex_full_match.cconnxruntime::RegexFullMatch::RegexFullMatchpatternRegexFullMatchre_.ok()Invalid regex pattern: StringConcatNull input ptrUnsupported approximation_algorithm: AffineGridAffineGrid : Invalidate size - length of size should be 4 or 5.AffineGrid : Input theta tensor dimension is not 3onnxruntime::short_time_fourier_transformsignal_components == 1 || signal_components == 2frame_length == window_lengthIf both frame_length and window are set, then the size of the window must be equal to the frame_length.window_size <= signal_sizeEnsure that the dft size is smaller than the signal.onnxruntime::signal::get_scalar_value_from_tensortensor->Shape().Size() == 1signal shape must end in 1 (real) or 2 (real, imaginary).D:\a\_work\1\s\onnxruntime\core/providers/cpu/signal/utils.hUnsupported input signal shape. The signal's first dimenstion must be the batch dimension and its second dimension must be the signal length dimension. It may optionally include a 3rd dimension of size 2 for complex inputs.onnxruntime::STFT::Computeonnxruntime::discrete_fourier_transformdft_length must be a scalar value.dft_length must be greater than zero.D:\a\_work\1\s\onnxruntime\core\providers\cpu\signal\dft.cconnxruntime::DFT::ComputeUnsupported input signal shape. The signal's first dimension must be the batch dimension and its second dimension must be the signal length dimension. It may optionally include a 3rd dimension of size 2 for complex inputs.onnxruntime::dft_bluestein_z_chirponnxruntime::bit_reverseUnsupported bit size.@ `P0pH(hX8xD$dT4t L,l\<|B"bR2r J*jZ:zF&fV6vN.n^>~A!aQ1q I)iY9yE%eU5u M-m]=}C#cS3s K+k[;{G'gW7wO/o_?onnxruntime::ValidateNoTransposeReduceonnxruntime::CommonFastReduceCopyReduction on all axes, output size should be 1.count == 1Can't reduce on dim with value of 0 if 'keepdims' is false. Invalid output shape would be produced. input_shape:onnxruntime::ValidateKeepDimsonnxruntime::ReduceKernelBase<1>::ReduceKernelBaseD:\a\_work\1\s\onnxruntime\core/providers/cpu/reduction/reduction_kernel_base.hinfo.GetAttr("keepdims", &keepdims).IsOK()onnxruntime::ResultsNoTransposePrepareForReduce::ValidateNotEmptyD:\a\_work\1\s\onnxruntime\core\providers\cpu\reduction\reduction_ops.cclast_loop_red_size > 0fast_shape[1] == output.Shape().Size()Only works on matrices with three dimensions.fast_shape[0] == output.Shape().Size()onnxruntime::ValidateFastReduceRKfast_shape[0] * fast_shape[2] == output.Shape().Size()onnxruntime::ValidateFastReduceRKRfast_shape.size() == 3onnxruntime::ValidateFastReduceKRKmust be overloaded.onnxruntime::ValidateMustBeOverloadedlast_loop_size > 0projected_index.size() > 0onnxruntime::ValidateFastReduceKROutput size mismatch.Only works on matrices with two dimensions.fast_shape.size() == 2onnxruntime::ReduceKernelBase<0>::ReduceKernelBaseAxes input and attribute should not both be present for reduction.axes.empty()onnxruntime::check_and_reduce_empty_set_input. Input rank=Indices must have the same rank as Input. Indices rank= vs Indices vs updates dimensions differs at position=Indices dim=Indices type is not supported. is greater than input dim= at pos=data type is different from updates typeIndices and updates must have the same rankCPU execution provider: BFloat16 data type is not supported with ScatterElements opset 18 when reduction is 'min'.CPU execution provider: bool data type is not supported with ScatterElements opset 18 when reduction is 'max'.CPU execution provider: bool data type is not supported with ScatterElements opset 18 when reduction is 'min'.CPU execution provider: string data type is not supported with ScatterElements opset 18 when reduction is 'min'.CPU execution provider: string data type is not supported with ScatterElements opset 18 when reduction is 'max'.CPU execution provider: BFloat16 data type is not supported with ScatterElements opset 18 when reduction is 'max'.CPU execution provider: MLFloat16 data type is not supported with ScatterElements opset 16 when reduction is 'add'.CPU execution provider: MLFloat16 data type is not supported with ScatterElements opset 16 when reduction is 'mul'.CPU execution provider: BFloat16 data type is not supported with ScatterElements opset 16 when reduction is 'mul'.CPU execution provider: BFloat16 data type is not supported with ScatterElements opset 16 when reduction is 'add'.CPU execution provider: string data type is not supported with ScatterElements opset 16 when reduction is 'mul'.onnxruntime::ScatterDataScatterElements op: input tensor must have at least one dimensiononnxruntime::Scatter,class std::allocator > > >::ScatterD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\scatter.ccsize of 'dilations' attribute, if provided, should equal to the number of image dimmensions.D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\col2im.ccsize of 'pads' attribute, if provided, should equal to twice the number of image dimmensions.dilations_.size() == image_dim_numberonnxruntime::Col2Im::Computedilations_.empty()pads_.empty()strides_.size() == image_dim_numberpads_.size() == 2 * image_dim_numbersize of 'strides' attribute, if provided, should equal to the number of image dimmensions.onnxruntime::Col2Im::Col2ImD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/col2im.hstrides_.empty()onnxruntime::CreateMelWeightMatrix::operator ()onnxruntime::CreateMelWeightMatrix::operator ()highest_index >= 0 && highest_index < num_spectrogram_binsonnxruntime::CreateMelWeightMatrix::operator ()onnxruntime::CreateMelWeightMatrix::operator ()onnxruntime::CreateMelWeightMatrix::operator ()onnxruntime::CreateMelWeightMatrix<__int64>::operator ()onnxruntime::CreateMelWeightMatrix::operator ()lower_edge_hertz produces a mel triangle filter bank that is out of range given the dft_length and the sample_rate.D:\a\_work\1\s\onnxruntime\core\providers\cpu\signal\window_functions.ccupper_edge_hertz produces a mel triangle filter bank that is out of range given the dft_length and the sample_rate.lowest_index >= 0 && lowest_index < num_spectrogram_binsonnxruntime::CreateMelWeightMatrix::operator ()output_datatypeperiodicBlackmanWindowMelWeightMatrixHannWindowHammingWindowonnxruntime::CreateMelWeightMatrix::operator ()onnxruntime::CreateMelWeightMatrix::operator ()Invalid input X: NumDimensions() < 1Invalid input scale: dimension != Invalid input scale: 0th dimension != Invalid input scale: NumDimensions() != Training mode only supports spatial BNmomentumonnxruntime::BatchNorm::BatchNormonnxruntime::BatchNorm::BatchNormis_spatial_Invalid input var: Invalid number of outputs for BN trainingD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/batch_norm.honnxruntime::BatchNorm::ComputeInvalid input mean: NumDimensions() != Invalid input B: Invalid input B: 0th dimension != Invalid input B: NumDimensions() != Invalid input var: 0th dimension != Invalid input var: NumDimensions() != Invalid input mean: Invalid input mean: 0th dimension != onnxruntime::BatchNorm::Compute!is_train_ || ((!saved_mean && !saved_inv_std) || (saved_mean && saved_inv_std))D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\trilu.honnxruntime::Trilu::Triluinfo.GetAttr("upper", &temp).IsOK()D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\trilu.cconnxruntime::Trilu::ComputeIsScalarOr1ElementVector(k)k should be a 1-D or 0-D tensor.onnxruntime::Softmax::ComputeImplOpset13D:\a\_work\1\s\onnxruntime\core\providers\cpu\math\softmax.cconnxruntime::Softmax::ComputeImplOpset13Hardmax inputs N, D and N * D must be < . N=, D=D:\a\_work\1\s\onnxruntime\core\providers\cpu\math\hardmax.cconnxruntime::Hardmax::Computeinvalid index found, index = indices tensor must has rank larger than 0GatherNDBase PrepareForCompute: Input count mismatchindicesindices tensor data type not supportedD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\gather_nd.cconnxruntime::GatherND::Computeinput_tensor != nullptr && indices_tensor != nullptrX input is required!onnxruntime::NonZero::Computefailed to get first output!D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\nonzero_op.cconnxruntime::NonZero::Computeonnxruntime::NonZero::Computeonnxruntime::NonZero<__int64>::Computeonnxruntime::NonZero::ComputeUnsupported input type in SpaceToDepth op: D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\space_depth_ops.cconnxruntime::SpaceToDepth::ComputeUnsupported input type in DepthToSpace op: onnxruntime::DepthToSpace::ComputeDepthToSpace op: only 'DCR' and 'CRD' modes are supportedD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/space_depth_ops.honnxruntime::SpaceDepthBase::SpaceDepthBaseinfo.GetAttr("blocksize", &blocksize_).IsOK()onnxruntime::DepthToSpace::DepthToSpaceAttribute blocksize is not set.SpaceToDepth requires input height to be a multiple of block_sizeSpaceDepth ops require a 4-D input. Provided rank: DepthToSpace requires input depth to be a multiple of (block_size * block_size)SpaceToDepth requires input width to be a multiple of block_sizeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\mean_variance_normalization.cconnxruntime::`anonymous-namespace'::ComputeMeanVarianceNormalization2DX and Y must both have M * N elements.onnxruntime::MeanVarianceNormalization::ComputeNo valid axes are specified. This is not handled now.size_ % 2 == 1size_ > 0D:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/lrn.honnxruntime::LRN::LRNbeta_ > 0.0finfo.GetAttr("beta", &beta_).IsOK()alpha_ > 0.0finfo.GetAttr("alpha", &alpha_).IsOK()info.GetAttr("size", &size).IsOK()onnxruntime::LRN::ComputeX->Shape().NumDimensions() == 4D:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\lrn.ccinfo.GetAttr("transA", &temp).IsOK()onnxruntime::GemmBase::GemmBaseD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\gemm_base.hinfo.GetAttr("transB", &temp).IsOK()c_shape is required if c_data is providedc_shape != nullptronnxruntime::GemmBroadcastBiasM_ >= 0 && K_ >= 0 && N_ >= 0 N: K: GEMM: Dimension mismatch, W: Gemm: Invalid bias shape for broadcastleft[i] >= 0left[i] <= std::numeric_limits::max()right[i] >= 0right[i] <= std::numeric_limits::max()left.NumDimensions() == 2 || left.NumDimensions() == 1onnxruntime::GemmHelper::GemmHelperD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\gemm_helper.hright.NumDimensions() == 2onnxruntime::MatMul::Computeonnxruntime::MatMul::Computeonnxruntime::MatMul<__int64>::Computeonnxruntime::MatMul::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\matmul.ccmin_ <= max_onnxruntime::clip_internal::Clip_6Base::Clip_6BaseD:\a\_work\1\s\onnxruntime\core/providers/cpu/math/clip.honnxruntime::Clip::ComputeImpl::operator ()onnxruntime::Clip::ComputeImpl::operator ()onnxruntime::Clip::ComputeImpl::operator ()onnxruntime::Clip::ComputeImpl::operator ()max->Shape().IsScalar()onnxruntime::Clip::ComputeImpl<__int64>::operator ()onnxruntime::Clip::ComputeImpl::operator ()onnxruntime::Clip::ComputeImpl::operator ()min->Shape().IsScalar()onnxruntime::Clip::ComputeImpl::operator ()D:\a\_work\1\s\onnxruntime\core\providers\cpu\math\clip.ccmax should be a scalar.min should be a scalar.onnxruntime::Clip::ComputeImpl::operator ()invalid expand shapeop_kernel_info.GetAttr("largest", &largest_temp).IsOK()sortedop_kernel_info.GetAttr("sorted", &sorted_temp).IsOK()k_temp > 0op_kernel_info.GetAttr("axis", &axis_temp).IsOK()onnxruntime::TopkOpset10ConstructorCommononnxruntime::TopkOpset11ConstructorCommonop_kernel_info.GetAttr("k", &k_temp).IsOK()onnxruntime::TopkOpset9ConstructorCommonD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\top_k.ccoutput count mismatch, expected 2 outputs to be present for TopK operatorvalue of k must not be negativek argument [input count mismatch, expected 1 input - the tensor to be processedinput count mismatch, expected 2 inputs - the tensor to be processed and a tensor containing k valuek tensor should be a 1D tensor of size 1] should not be greater than specified axis dim value [UniqueUnsupported tensor type of Data type is not supported in this build.delta in Range operator can not be zero!start in Range operator should be scalar like tensor, yet got shape:limit in Range operator should be scalar like tensor, yet got shape:delta in Range operator should be scalar like tensor, yet got shape:Got nullptr for sequence input.onnxruntime::ConcatFromSequence::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\sequence\concat_from_sequence.cconnxruntime::Compress::Computeaxes_right_stride >= 0 && static_cast(axes_right_stride) < std::numeric_limits::max()size overflowD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\compress.ccCompressx_ptr != nullptrD:\a\_work\1\s\onnxruntime\core\providers\cpu\quantization\dynamicquantizelinear.cconnxruntime::DynamicQuantizeLinear::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\reverse_sequence.cconnxruntime::ReverseSequenceOp::Compute. batch_size=Unknown tensor type of sequence_lens shape must be {batch_size}. Got:time_axis < 2Invalid time_axis of batch_axis != time_axistime_axis and batch_axis must have different values but both are . Must be 0 or 1info.GetAttr("time_axis", &time_axis).IsOK()batch_axis < 2Invalid batch_axis of info.GetAttr("batch_axis", &batch_axis).IsOK()D:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/reverse_sequence.honnxruntime::ReverseSequenceOp::ReverseSequenceOpInvalid sequence length: . Value must be in range [0,onnxruntime::QLinearConv::ComputeOutputScaleW_zero_point_data[i] == W_zero_point_valueonnxruntime::QLinearConv::ComputeOffsetQLinearConv : filter zero point shape invalidIsScalarOr1ElementVector(Y_zero_point)QLinearConv : zero point of per-channel filter must be same. This happens by design if the quantization is symmetric.IsValidQuantParam(W_zero_point, M)onnxruntime::QLinearConv::Computeprepacked_buffers[0].get() == nullptrD:\a\_work\1\s\onnxruntime\core\providers\cpu\quantization\qlinearconv.ccIsScalarOr1ElementVector(X_zero_point)QLinearConv : input zero point must be a scalar or 1D tensor of size 1QLinearConv : result zero point must be a scalar or 1D tensor of size 1onnxruntime::QLinearConv::ComputeOffsetIsScalarOr1ElementVector(Y_scale)QLinearConv : result scale must be a scalar or 1D tensor of size 1IsValidQuantParam(W_scale, M)QLinearConv : filter scale shape invalidQLinearConv : input scale must be a scalar or 1D tensor of size 1onnxruntime::QLinearConv::ComputeOutputScaleIsScalarOr1ElementVector(X_scale)onnxruntime::QLinearConv::Computeonnxruntime::QLinearConv::UseSharedPrePackedBuffersonnxruntime::QLinearConv::UseSharedPrePackedBuffersIsScalarOr1ElementVector(W_Zero_Point)Non per-tensor quantization is not supported now.IsScalarOr1ElementVector(X_Zero_Point)Must be a scalar or 1D tensor or size 1.D:\a\_work\1\s\onnxruntime\core\providers\cpu\quantization\conv_integer.cconnxruntime::ConvInteger::ComputeIsScalarOr1ElementVector(a_zero_point)MatmulInteger : input1 zero point must be a scalar or 1D tensor of size 1D:\a\_work\1\s\onnxruntime\core\providers\cpu\quantization\matmul_integer.cconnxruntime::MatMulInteger::ComputeIsBQuantParamSupported(b_zero_point->Shape(), b ? b->Shape() : b_shape_)MatmulInteger : B zero point is not validStringNormalizerFailed to construct locale with name::Please, install necessary language-pack-XX and configure localesWideCharToMultiByte failed errcode = onnxruntime::string_normalizer::Locale::LocaleD:\a\_work\1\s\onnxruntime\core\providers\cpu\text\string_normalizer.cclocaleattribute case_change_action has invalid valueonnxruntime::StringNormalizer::Compute::::operator ()stopwordsLOWERattribute case_change_action is not setNONEUPPERattribute is_case_sensitive is not setis_case_sensitivecase_change_actiononnxruntime::StringNormalizer::StringNormalizerMultiByteToWideChar failed errcode = onnxruntime::string_normalizer::Utf8ConverterWindows::ComputeRequiredSizeToUtf8en-USonnxruntime::string_normalizer::Utf8ConverterWindows::from_bytesSingle dimension value must be greater than 0onnxruntime::StringNormalizer::Compute::::operator ()onnxruntime::StringNormalizer::ComputeInput dimensions are either[C > 0] or [1][C > 0] allowedmode: is unrecognized, acceptable values are TF,IDF,TFIDFmin_gram_lengthimpl_->weighting_criteria_ != kNoneD:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\tfidfvectorizer.cconnxruntime::TfIdfVectorizer::TfIdfVectorizerTFIDFIDFmode is requiredTfIdfVectorizerInput shape must have either [C] or [B,C] dimensions with B > 0.end_idx >= start_idx && end_idx <= total_itemsn-gram counts out of bounds for (items % ngram_size == 0)Number of items must compose whole non-empty pool_int64s is required if pool_strings not providedpool_int64s-gramsstatus.IsOK() && !pool_int64s.empty()status.IsOK() && !impl_->ngram_counts_.empty()Non-empty ngram_counts is requiredmin_gram_length must be inbounds of ngram_counts: <= max_skip_count must be non-negative: max_skip_count is requiredngram_countsimpl_->max_skip_count_ >= 0min_gram_length >= max_gram_length required: >= max_skip_countimpl_->max_gram_length_ >= impl_->min_gram_length_Required min_gram_length must be positive: min_gram_length is requiredmax_gram_lengthimpl_->min_gram_length_ > 0pool_stringsimpl_->weights_.size() == impl_->ngram_indexes_.size()!pool_strings.empty()pool_strings must not be empty if specified must be of equal sizeweightsGot weights of size: but ngram_indexes size: status.IsOK() && !impl_->ngram_indexes_.empty()Non-empty ngram_indexes is requiredstd::all_of(impl_->ngram_indexes_.begin(), impl_->ngram_indexes_.end(), [](int64_t i) { return i >= 0; })Negative ngram_indexes values are not allowedmax_gram_length must be inbounds of ngram_counts: size_t(impl_->min_gram_length_) <= impl_->ngram_counts_.size()ngram_indexessize_t(impl_->max_gram_length_) <= impl_->ngram_counts_.size()onnxruntime::ngram_details::PopulateGrams id: p.first->second->id_ == 0Duplicate ngram detected, size: op_kernel_info.GetAttr("lambd", &lambd_temp).IsOK()op_kernel_info.GetAttr("bias", &bias_temp).IsOK()D:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/shrink.honnxruntime::Shrink::Shrinkroi_batch_id >= 0roi_batch_id < batch_sizeonnxruntime::RoiPool::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\roi_pool.ccR->Shape()[1] == 5spatial_scale_ > 0pooled_width_ > 0info.GetAttr("spatial_scale", &spatial_scale_).IsOK()pooled_shape.size() == 2pooled_height_ > 0onnxruntime::RoiPool::RoiPoolD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/roi_pool.honnxruntime::LpNorm::LpNormop_kernel_info.GetAttr("p", &p_).IsOK()p_ == 1 || p_ == 2onnxruntime::LpNorm::LpNormD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/lp_norm.hop_kernel_info.GetAttr("axis", &axis_).IsOK()info.GetAttr("high", &high_).IsOK()onnxruntime::RandomUniform::RandomUniformonnxruntime::RandomNormalLike::RandomNormalLikehighONNX_NAMESPACE::TensorProto::DataType_IsValid(dtype_) && dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINEDinfo.GetAttrs("shape", shape).IsOK()info.GetAttr("dtype", &dtype).IsOK()Invalid dtype of D:\a\_work\1\s\onnxruntime\core/providers/cpu/generator/random.hinfo.GetAttr("scale", &scale_).IsOK()info.GetAttr("mean", &mean_).IsOK()onnxruntime::RandomNormal::RandomNormalmeanInvalid data type of Output type not supported in this build: onnxruntime::MultinomialComputeonnxruntime::Multinomial::MultinomialONNX_NAMESPACE::TensorProto::DataType_IsValid(output_dtype_) && output_dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINEDonnxruntime::RandomUniformLike::RandomUniformLikeinfo.GetAttr("sample_size", &num_samples_).IsOK()lowinfo.GetAttr("low", &low_).IsOK()num_classes is < 1num_samples is < 1Empty dimensions for input tensorbatch_size is < 1Could not infer data type from input tensor with data type onnxruntime::RandomUniformLike::Computeonnxruntime::RandomNormalLike::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\generator\random.cconnxruntime::contrib::RegisterCpuContribKernelsD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\cpu_contrib_kernels.cconnxruntime::contrib::RegisterQuantizationKernelsonnxruntime::contrib::RegisterNchwcKernelsinfo.GetAttr("default_string", &default_string_).IsOK()default_int64D:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/label_encoder.hdefault_stringclasses_stringsonnxruntime::ml::LabelEncoder::LabelEncoderonnxruntime::ml::LabelEncoder_4::LabelEncoder_4onnxruntime::ml::LabelEncoder_4,class std::allocator >,short>::LabelEncoder_4onnxruntime::ml::LabelEncoder_4,class std::allocator >,double>::LabelEncoder_4onnxruntime::ml::LabelEncoder_4,class std::allocator > >::LabelEncoder_4onnxruntime::ml::LabelEncoder_4<__int64,double>::LabelEncoder_4onnxruntime::ml::LabelEncoder_4::LabelEncoder_4Keys and values must have the same length.keys.size() == values.size()keys_tensorvalues_tensorresult.IsOK()onnxruntime::ml::GetDefaultdefault_tensorLabelEncoder could not unpack default tensor values_int64sdefault_floatkeys_int64skeys_stringsvalues_floatsvalues_strings_UnusedInput of tensor(int64) must have output of tensor(string)keys_floatsinfo.GetAttr("default_int64", &default_int_).IsOK()Input of tensor(string) must have output of tensor(int64)(name: attributes in LabelEncoder However, the number of key is ) must have the same length. values is and the number of onnxruntime::ml::LabelEncoder_4<__int64,__int64>::LabelEncoder_4onnxruntime::ml::LabelEncoder_2<__int64,__int64>::LabelEncoder_2onnxruntime::ml::LabelEncoder_4<__int64,float>::LabelEncoder_4onnxruntime::ml::LabelEncoder_4<__int64,class std::basic_string,class std::allocator > >::LabelEncoder_4onnxruntime::ml::LabelEncoder_4,class std::allocator > >::LabelEncoder_4onnxruntime::ml::LabelEncoder_4::LabelEncoder_4onnxruntime::ml::LabelEncoder_4,class std::allocator >,__int64>::LabelEncoder_4onnxruntime::ml::LabelEncoder_4::LabelEncoder_4onnxruntime::ml::LabelEncoder_4,class std::allocator >,class std::basic_string,class std::allocator > >::LabelEncoder_4onnxruntime::ml::LabelEncoder_4,class std::allocator >,float>::LabelEncoder_4onnxruntime::ml::GetAttributeLabelEncoder could not unpack tensor attribute LabelEncoder is missing attribute onnxruntime::ml::LabelEncoder_2,class std::allocator >,float>::LabelEncoder_2onnxruntime::ml::LabelEncoder_2,class std::allocator > >::LabelEncoder_2onnxruntime::ml::LabelEncoder_2::LabelEncoder_2onnxruntime::ml::LabelEncoder_2<__int64,float>::LabelEncoder_2onnxruntime::ml::LabelEncoder_2::LabelEncoder_2onnxruntime::ml::LabelEncoder_2,class std::allocator >,class std::basic_string,class std::allocator > >::LabelEncoder_2onnxruntime::ml::LabelEncoder_2,class std::allocator >,__int64>::LabelEncoder_2onnxruntime::ml::LabelEncoder_2<__int64,class std::basic_string,class std::allocator > >::LabelEncoder_2The num_keys == num_valuesD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\tree_ensemble_aggregator.hTreeEnsembleUnexpected value for node_modeonnxruntime::ml::detail::Convert_NODE_MODE_ONNX_to_ORTMAXLOGISTICSOFTMAXBRANCH_EQBRANCH_MEMBERBRANCH_GTEBRANCH_GTLEAFBRANCH_LTBRANCH_LEQSUMMINSOFTMAX_ZEROAVERAGEclass_weightsclasslabels_stringsclass_nodeidsclass_treeidspost_transformclass_idsnodes_treeidsnodes_valuesbase_valuesnodes_nodeidstarget_weights_as_tensoraggregate_functionnodes_values_as_tensorclass_weights_as_tensorD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\tree_ensemble_attribute.hnodes_hitrates_as_tensortarget_class_ids.size() == target_class_treeids.size()target_class_weights.empty() || target_class_ids.size() == target_class_weights.size()nodes_falsenodeids.size() == nodes_values.size() || nodes_falsenodeids.size() == nodes_values_as_tensor.size()target_class_ids.size() == target_class_nodeids.size()nodes_falsenodeids.size() == nodes_treeids.size()nodes_falsenodeids.size() == nodes_truenodeids.size()nodes_falsenodeids.size() == nodes_modes_string.size()nodes_falsenodeids.size() == nodes_nodeids.size()n_targets_or_classes > 0nodes_falsenodeids.size() == nodes_featureids.size()target_treeidstarget_weightstarget_idstarget_nodeidsclasslabels_int64sn_targetsnodes_missing_value_tracks_truenodes_modesnodes_featureidsnodes_hitratesnodes_falseleafsnodes_falsenodeidsleaf_weightsmembership_valuesInput shape needs to be at least a single dimension.leaf_targetidsbase_values_as_tensoronnxruntime::ml::detail::TreeEnsembleAttributesV3::TreeEnsembleAttributesV3D:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\tree_ensemble_common.honnxruntime::ml::detail::TreeEnsembleCommon::computeUnknown aggregation function in TreeEnsemble.onnxruntime::ml::detail::TreeEnsembleCommon::computeD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\tree_ensemble.cconnxruntime::ml::TreeEnsemble::TreeEnsembleonnxruntime::ml::TreeEnsemble::TreeEnsemblenodes_truenodeidsnodes_splitsnodes_trueleafsonnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePredictiononnxruntime::ml::detail::TreeAggregator::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorAverage::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorSum::MergePredictiononnxruntime::ml::detail::TreeAggregatorMax::MergePredictiononnxruntime::ml::detail::TreeAggregatorMin::MergePredictionpredictions.size() == (size_t)n_targets_or_classes_onnxruntime::ml::detail::TreeAggregator::FinalizeScoresit->i < (int64_t)predictions.size()onnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePredictiononnxruntime::ml::detail::TreeAggregatorAverage::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorSum::MergePredictiononnxruntime::ml::detail::TreeAggregatorMin::MergePredictionthis->base_values_.size() == predictions.size()onnxruntime::ml::detail::TreeEnsembleCommon::AddNodes with flags False node must always be the next node, but it isn't at index Tree id mismatch. Expected onnxruntime::ml::detail::TreeEnsembleCommon::AddNodes at position but got onnxruntime::ml::detail::TreeEnsembleAttributesV5::aggregateFunctionToStringonnxruntime::ml::detail::TreeEnsembleAttributesV5::aggregateFunctionToStringonnxruntime::ml::detail::TreeEnsembleAttributesV5::postTransformToStringonnxruntime::ml::detail::TreeEnsembleAttributesV5::postTransformToStringUnknown value for aggregate_function.PROBITUnknown value for post_transform. in tree Node onnxruntime::ml::detail::TreeEnsembleCommon::Init is already there.onnxruntime::ml::detail::TreeEnsembleCommonV5::Initattributes.base_values.empty()onnxruntime::ml::detail::TreeEnsembleAttributesV5::TreeEnsembleAttributesV5tree_rootsonnxruntime::ml::detail::TreeEnsembleAttributesV3::TreeEnsembleAttributesV3nodes_modes_string.size() < std::numeric_limits::max()onnxruntime::ml::detail::TreeEnsembleAttributesV5::TreeEnsembleAttributesV5nodes_values.empty() || nodes_values_as_tensor.empty()target_class_weights.empty() || target_class_weights_as_tensor.empty()base_values.empty() || base_values_as_tensor.empty()nodes_hitrates.empty() || nodes_hitrates_as_tensor.empty()predictions.size() == predictions2.size()onnxruntime::ml::detail::TreeAggregatorMax::MergePredictionOne path in the graph requests feature onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg features. but input tensor has TreeEnsemble only works on 1D, 2D tensors.onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAggonnxruntime::ml::detail::TreeEnsembleCommonV5::Initonnxruntime::ml::detail::TreeEnsembleCommon::Init (falsenode). (weights).Unable to find node A node cannot point to itself: (coor.node_id >= 0 && coor.node_id < n_nodes_) (truenode).TreeEnsembleRegressoronnxruntime::ml::TreeEnsembleRegressor::TreeEnsembleRegressortarget_weightsbase_values_as_tensoronnxruntime::ml::detail::TreeEnsembleCommon::AddNodesonnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePredictiononnxruntime::ml::detail::TreeAggregator::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorAverage::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorSum::MergePredictiononnxruntime::ml::detail::TreeAggregatorMax::MergePredictiononnxruntime::ml::detail::TreeAggregatorMin::MergePredictiononnxruntime::ml::detail::TreeEnsembleCommon::Initonnxruntime::ml::detail::TreeEnsembleCommon::ComputeAggonnxruntime::ml::detail::TreeEnsembleCommon::computeD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\treeregressor.cconnxruntime::ml::TreeEnsembleRegressor::TreeEnsembleRegressoronnxruntime::ml::TreeEnsembleClassifier::TreeEnsembleClassifieronnxruntime::ml::TreeEnsembleClassifier::TreeEnsembleClassifieronnxruntime::ml::TreeEnsembleClassifier<__int64>::TreeEnsembleClassifieronnxruntime::ml::TreeEnsembleClassifier::TreeEnsembleClassifierD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\tree_ensemble_classifier.ccX dims is empty.classlabels_int64sbase_values_as_tensoronnxruntime::ml::detail::TreeEnsembleCommon<__int64,float,float>::ComputeAggonnxruntime::ml::detail::TreeEnsembleCommon::ComputeAggonnxruntime::ml::detail::TreeEnsembleCommonClassifier::Initonnxruntime::ml::detail::TreeEnsembleCommon::Initonnxruntime::ml::detail::TreeEnsembleCommonClassifier<__int64,float,float>::Initonnxruntime::ml::detail::TreeEnsembleCommon<__int64,float,float>::Initonnxruntime::ml::detail::TreeEnsembleCommonClassifier::Initonnxruntime::ml::detail::TreeEnsembleCommonClassifier::Initonnxruntime::ml::detail::TreeEnsembleCommonClassifier::computeonnxruntime::ml::detail::TreeEnsembleCommonClassifier<__int64,float,float>::computeonnxruntime::ml::detail::TreeEnsembleCommonClassifier::computeonnxruntime::ml::detail::TreeEnsembleCommonClassifier::computeTreeEnsembleClassifieronnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorSum<__int64,float,float>::MergePredictiononnxruntime::ml::detail::TreeAggregatorSum<__int64,float,float>::ProcessTreeNodePredictiononnxruntime::ml::detail::TreeAggregatorClassifier<__int64,float,float>::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePredictiononnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorSum::MergePredictionpredictions.size() == 2onnxruntime::ml::detail::TreeEnsembleCommon::AddNodesonnxruntime::ml::detail::TreeEnsembleCommon<__int64,float,float>::AddNodesonnxruntime::ml::detail::TreeAggregatorClassifier::_set_score_binaryonnxruntime::ml::detail::TreeAggregatorClassifier::_set_score_binaryonnxruntime::ml::detail::TreeAggregatorClassifier::_set_score_binaryonnxruntime::ml::detail::TreeAggregatorClassifier<__int64,float,float>::_set_score_binaryclasses.size() == 2 || classes.size() == 1onnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScoresInput features_per_batch[] != number of classlabels[Zipmap only supports 1D or 2D input tensorsZipmap does not support empty dim countD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\zipmap.cconnxruntime::ml::ZipMapOp::ZipMapOpclasslabels_strings_.empty() ^ classlabels_int64s_.empty()Must provide classlabels_strings or classlabels_int64s but not both.ZipMapD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\svmclassifier.honnxruntime::ml::SVMCommon::SVMCommonkernel_paramskernel_typeRBFPOLYLINEARone_class!coefficients_.empty()coefficientsrhoonnxruntime::ml::SVMRegressor::SVMRegressorn_supportssupport_vectorsSVMRegressorUnexpected mode:D:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\svmregressor.cconnxruntime::ml::SVMRegressor::ComputeInvalid argumentonnxruntime::IAllocator::AllocArrayUnsupported data type of onnxruntime::ml::SVMClassifier::Computecoefficients_.size() > 0classlabels_strings_.size() > 0 || classlabels_ints_.size() > 0D:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\ml_common.hinfo.GetAttrs("classlabels_strings", classlabels_strings_).IsOK() || info.GetAttrs("classlabels_ints", classlabels_ints_).IsOK()classlabels_intsproba_.size() == probb_.size()D:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\svmclassifier.cconnxruntime::ml::SVMClassifier::SVMClassifierprob_bprob_avectors_per_classSVMClassifierUnexpected value for 'add_second_class' of onnxruntime::ml::batched_update_scores_inplacescores.size() == static_cast(expected_num_scores)onnxruntime::ml::ScalerOp::ScalerOponnxruntime::ml::ScalerOp<__int64>::ScalerOponnxruntime::ml::ScalerOp::ScalerOpscale_.size() == offset_.size()Scale size: () != (D:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\scaler.cconnxruntime::ml::ScalerOp::ScalerOp!scale_.empty()Empty scale in attributesoffsetScalerEither both scale and offset can be of feature size () or 1Invalid argument: input has empty dimensions.One and only one of the 'cats_*' attributes must be definedcats_stringscats_int64sonnxruntime::ml::OneHotEncoderOp,class std::allocator > >::OneHotEncoderOponnxruntime::ml::OneHotEncoderOp::OneHotEncoderOponnxruntime::ml::OneHotEncoderOp::OneHotEncoderOpnum_categories_ > 0D:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\onehotencoder.cconnxruntime::ml::OneHotEncoderOp<__int64>::OneHotEncoderOptmp_cats_int64s.empty() || tmp_cats_strings.empty()OneHotEncoderUnknown Category and zeros = 0.D:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/ml_common.hNormalizerD:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/normalizer.honnxruntime::ml::Normalizer::Normalizerinfo.GetAttr("norm", &norm).IsOK()normonnxruntime::ml::MakeNormalizeInvalid normalize value of L2L1Unexpected NORMALIZE value of Rank of input to Normalized must be less than 2. Got Input shape had more than 2 dimension. Dims=D:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\linearregressor.cconnxruntime::ml::LinearRegressor::LinearRegressortargetsinterceptsLinearRegressorUnsupported input element type of onnxruntime::ml::LinearClassifier::ComputeImplscores_output_data.size() >= scores_output_sizeScores output is incorrect size. Expected: Found:D:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\linearclassifier.cconnxruntime::ml::LinearClassifier::LinearClassifiermulti_classLinearClassifieronnxruntime::ml::CastInputToFloatshape_size == out.size()Empty value of imputed values.Empty input dimensions.Must provide imputed_values_float_ or imputed_values_int64_ but not both.imputed_values_float_.empty() ^ imputed_values_int64_.empty()onnxruntime::ml::ImputerOp::ComputeInvalid typeImputerimputed_value_floatsimputed_value_int64sreplaced_value_floatExpected 'replaced_value_float' attribute since 'imputed_value_floats' is specifiedonnxruntime::ml::ImputerOp::ImputerOpD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\imputer.ccreplaced_value_int64Expected 'replace_value_int64' attribute since 'imputed_values_int64' is specifiedinputdimensionsinputdimensions attribute must be providedstatus.IsOK() && !input_dimensions_.empty()onnxruntime::ml::FeatureVectorizer::FeatureVectorizerD:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/feature_vectorizer.hFeatureVectorizer) does not match number of inputdimensions values (Number of inputs (input_count >= 0 && static_cast(input_count) == input_dimensions_.size()onnxruntime::ml::FeatureVectorizer::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\feature_vectorizer.ccInvalid input type:DictVectorizerstring_vocabularyint64_vocabularyinfo.GetAttrs(std::is_same::value ? "string_vocabulary" : "int64_vocabulary", vocabulary_).IsOK()onnxruntime::ml::DictVectorizerOp<__int64,double>::DictVectorizerOpD:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/dictvectorizer.honnxruntime::ml::DictVectorizerOp<__int64,float>::DictVectorizerOponnxruntime::ml::DictVectorizerOp<__int64,class std::basic_string,class std::allocator > >::DictVectorizerOponnxruntime::ml::DictVectorizerOp,class std::allocator >,double>::DictVectorizerOponnxruntime::ml::DictVectorizerOp,class std::allocator >,float>::DictVectorizerOponnxruntime::ml::DictVectorizerOp,class std::allocator >,__int64>::DictVectorizerOponnxruntime::ml::CategoryMapper::CategoryMapperD:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/category_mapper.hnum_entries == int_categories.size()CategoryMapperInput of string must have output of int64Input of int64 must have output of string %finvalid stof argumentstof argument out of rangeUnexpected CAST_TO value of Invalid PACK_MAP value of onnxruntime::ml::MakePackcast_toinfo.GetAttr("cast_to", &attr).IsOK()onnxruntime::ml::CastMap::CastMapD:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/cast_map.hmap_forminfo.GetAttr("map_form", &attr).IsOK()max_mapinfo.GetAttr("max_map", &max_map_).IsOK()max_map must be > 0 if map_form is SPARSEmap_form_ != PACK_MAP::SPARSE || max_map_ > 0CastMap Expected std::map or std::mapInvalid input type of value: 0.fTO_FLOATTO_STRINGTO_INT64 Expected TO_FLOAT, TO_STRING or TO_INT64Invalid CAST_TO value of onnxruntime::ml::MakeCastDENSESPARSE Expected DENSE or SPARSENegative index values are not permitted. First entry in map has index value of cur_input == end_input || cur_input->first >= 0onnxruntime::ml::CastMap::ComputeImplD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\cast_map.cc is NaNInput data with index: thresholdBinarizerArrayFeatureExtractorInvalid argument: X input has empty dimensions.Invalid Y argument: num_indices = 0) >=] (Invalid Y argument: index is out of range: Y[ inputs but Scan was only given The subgraph in 'body' requires num_variadic_inputs == num_subgraph_inputsonnxruntime::scan::detail::Info::InfoD:\a\_work\1\s\onnxruntime\core\providers\cpu\controlflow\scan_utils.cc' was Number of entries in 'directions.size() == num_entriesFailed to create output tensor for output #onnxruntime::scan::detail::OutputIterator::AllocateFinalBufferIf shape was concrete we shouldn't be using a custom allocator!is_concrete_shape_onnxruntime::scan::detail::OutputIterator::AllocateFinalOutputcur_iteration_ < num_iterations_onnxruntime::scan::detail::OutputIterator::operator *Expected AllocateFinalOutput to have been called to before we read the OrtValue from the iterator.is_concrete_shape_Expected AllocateFinalOutput to have been called to before we increment the iteratoronnxruntime::scan::detail::OutputIterator::operator ++onnxruntime::OrtValueTensorSlicer::Iterator::operator *onnxruntime::scan::detail::ReadDirections'. 0 == forward. 1 == reverse.Invalid values in 'valid did not.Subgraph must have the shape set for all outputs but onnxruntime::scan::detail::AllocateOutputonnxruntime::scan::detail::CreateFeedsFetchesManageronnxruntime::scan::detail::IterateSequence::::operator ()onnxruntime::scan::detail::IterateSequenceMisuse of LoopStateVariable. Attempt to move beyond end of sequenceiteration_num_ < sequence_len_onnxruntime::scan::detail::LoopStateVariable::Next is not compatible with Mismatch between expected shape and shape from first outputonnxruntime::scan::detail::OutputIterator::Initializeinput_dims[rank - 2] == input_dims[rank - 1]onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::DiagonalInnermostDimsEinsum op: Unsupported data type for Diagonal for input shape Cannot parse the diagonal elements along dims rank >= 2 && dim_1 != dim_2 && input_dims[onnxruntime::narrow(dim_1)] == input_dims[onnxruntime::narrow(dim_2)]onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::DiagonalThe rank of the input must match permutation size for Transposeinput_rank == permutation.size()onnxruntime::EinsumOp::IsTransposeRequiredLength of permutation must match the rank of the input to be permutatedonnxruntime::EinsumOp::TransposeEinsum op: Transpose failed: Einsum op: The candidate output does not match the actual output's shapeoutput.SizeInBytes() == input.SizeInBytes()onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::DataCopyD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\einsum_utils\einsum_auxiliary_ops.ccThe innermost dims should have the same dim value to parse the diagonal elementsEinsum op: Exception during MatMul operation: Data types of the inputs must match for MatMulinput_1.DataType() == input_2.DataType()onnxruntime::EinsumOp::MatMulOnly 1 batch dimension is allowed for MatMulinput_shape_1_override.size() == 3 && input_shape_2_override.size() == 3Batch dimension should match for MatMul;input_shape_1_override[0] == input_shape_2_override[0]Incompatible matrix dimensions for matMulinput_shape_1_override[2] == input_shape_2_override[1]Unsupported typetop_k <= beams_used_output_scores->IsDataType()next_scores.size() == next_tokens.size()D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\beam_search_scorer.cconnxruntime::contrib::transformers::BeamSearchScorer::ProcessBatch can only be done if all beams have been generatednext_scores.size() == next_indices.size()beam_idx == num_beams_beam_hyp.beams_used_ == gsl::narrow_cast(num_beams_)output_sequences != nullptrstatic_cast(hypothesis_buffer_used_) <= hypothesis_buffer_.size()output_sequence_scores->IsDataType()onnxruntime::contrib::transformers::BeamSearchScorer::Finalizeonnxruntime::contrib::transformers::BeamSearchScorer::OutputScorestarget.size() == final_scores.size()onnxruntime::contrib::transformers::BeamHypotheses::Outputnumber of outputs expected to be 1 + 2 * layers, got:number of inputs expected to be kFirstPastInputIndex + 4 * layers + 1, got:decoder subgraph input 0 shall be named as input_ids, got: input_ids shall be named as encoder_attention_mask, got: encoder_attention_mask shall be named as encoder_hidden_states, got: decoder subgraph input decoder subgraph input 0 (input_ids) shall have int32 typedecoder subgraph output 0 shall be named as logits, got: (encoder_attention_mask) shall have int32 typedecoder subgraph input 1 (encoder_input_ids) shall have int32 typedecoder subgraph past inputs shall have same data type as that of encoder_hidden_states (encoder_hidden_states) shall have float or float16 typeSetup must be called before CreateInitialFeedsdecoder subgraph output shall have same data type as that of encoder_hidden_statesdecoder_masked_attention shall use with past_present_share_bufferencoder_input_idsD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\subgraph_t5_decoder.cconnxruntime::contrib::transformers::T5DecoderSubgraph::Validatenumber of inputs expected to be kFirstPastInputIndex + 4 * layers + 3, got:number of inputs expected to be kFirstPastInputIndex + 4 * layers, got:onnxruntime::contrib::transformers::T5DecoderSubgraph::CreateInitialFeedssession_state_ != nullptrcross_shape.NumDimensions() == 4onnxruntime::contrib::transformers::WhisperDecoderSubgraph::Validate + 2 * layers, got:D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\subgraph_whisper_decoder.cc + 3 * layers, got:number of outputs expected to be first_present_output_index_decoder subgraph input 1 shall be named as encoder_hidden_states, got: When outputing cross qk, number of outputs expected to be first_present_output_index_decoder subgraph past inputs shall have same data type as that of encoder_hidden_states.decoder subgraph input 1 (encoder_hidden_states) shall have float or float16 typeInvalid hidden_states expension: has_hidden_state_ == falseonnxruntime::contrib::transformers::WhisperDecoderSubgraph::CreateInitialFeedsonnxruntime::contrib::transformers::WhisperEncoderSubgraph::Validateexpect 2 inputs, got:expect >=6 outputs, got:D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\subgraph_whisper_encoder.ccencoder subgraph input 0 shall be named as encoder_input_ids, got: number of outputs expected to be 2 + 4 * layers, got:encoder subgraph input 2 shall be named as decoder_input_ids, got: decoder_input_idsencoder subgraph output 1 shall be named encoder_hidden_states, got: encoder subgraph output 0 shall be named as logits, got: encoder subgraph output 2 shall be named as present_key_self_0, got: present_key_self_0encoder subgraph output 3 shall be named as present_value_self_0, got: present_value_self_0encoder subgraph input 1 (decoder_input_ids) shall have int32 typeencoder subgraph input 0 (encoder_input_features) shall have float32 or float16 typeencoder subgraph outputs 1, 2, ... shall have same data typeencoder subgraph output 0 (logits) shall be float or float16 data typecpu_allocator shouldn't be nullptronnxruntime::contrib::transformers::WhisperEncoderSubgraph::CreateInitialFeedsonnxruntime::contrib::transformers::T5EncoderSubgraph::Validateexpect 2 or 3 inputs, got:encoder subgraph input 1 shall be named as encoder_attention_mask, got: D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\subgraph_t5_encoder.ccencoder subgraph input 1 (encoder_attention_mask) shall have int32 typeencoder subgraph input 0 (encoder_input_ids) shall have int32 typepresent_key_cross_0number of outputs expected to be 2 * layers, got:present_value_cross_0encoder subgraph output 0 shall be named as present_key_cross_0, got: encoder subgraph input 2 (decoder_input_ids) shall have int32 typeencoder subgraph output 1 shall be named as present_value_cross_0, got: onnxruntime::contrib::transformers::T5EncoderSubgraph::CreateInitialFeedsencoder subgraph outputs shall have same data typeonnxruntime::contrib::transformers::NoRepeatNGramLogitsProcessor::Processprefix.size() == narrow(prefix_length)D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\logits_processor.ccD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\subgraph_base.ccsubgraph past state is expected to have 5 dimension, got beam_widthonnxruntime::contrib::transformers::Subgraph::Setupsubgraph past state dimension 2 shall have a positive value for number of headssubgraph past state dimension 4 shall have a positive value for hidden size per headonnxruntime::contrib::transformers::Subgraph::GetParameterssubgraph past state dimension 0 shall have length of 2subgraph past state dimension 2 shall have a positive value for vocabulary sizesubgraph output present_key_self_0 is expected to have 4 dimension, got subgraph logits output is expected to have 3 dimension, got DecoderMaskedMultiHeadAttentionpast_sequence_lengthDecoderMaskedSelfAttentionpast_0subgraph input 3 shall be named as past_0, got: attention_masksubgraph input 2 shall be named as attention_mask, got: present_0subgraph input 1 shall be named as present_0, got: subgraph past state cannot be nullptrsubgraph output 0 shall be named as logits, got: subgraph input 2 (attention_mask) shall have int32 typesubgraph output 0 (logits) shall be float or float16 data typesubgraph input 0 (input_ids) shall have int32 typesubgraph input 1 (position_ids) shall have int32 typesubgraph input 3 (past_0) shall shall have same data type of logits outputsubgraph output 1 (present_0) shall shall have same data type of logits outputonnxruntime::contrib::transformers::GptSubgraph::CreateInitialFeedsInvalid GPT-2 subgraph: number of outputs shall be larger than 1 (Need past state in outputs).onnxruntime::contrib::transformers::GptSubgraph::ValidateD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\subgraph_gpt.ccinput_ids_shape.NumDimensions() == 2position_idssubgraph input 1 shall be named as position_ids, got: Invalid GPT-2 subgraph: number of inputs shall be number of outputs plus 2 or 3 (if past_present_share_buffer) or 5 (if past_present_share_buffer and use_decoder_masked_self_attention for BeamSearch)subgraph input 0 shall be named as input_ids, got: BeamSearch op: An implementation for the input type start_token_id >= 0original_decoder_input_ids_shape.NumDimensions() == 2onnxruntime::contrib::SamplingCpuHelper::SampleD:\a\_work\1\s\onnxruntime\contrib_ops/cpu/transformers/sampling_cpu_helper.honnxruntime::contrib::GenerationCpuDeviceHelper::ExpandInputslogits_shape.NumDimensions() == 3element_type == DataTypeImpl::GetType()last_outputs.size() >= static_cast(1) + num_present_tensorsonnxruntime::contrib::GenerationCpuDeviceHelper::UpdateDecoderFeedsonnxruntime::contrib::GenerationCpuDeviceHelper::ProcessLogitsonnxruntime::contrib::GenerationCpuDeviceHelper::GreedySearchProcessLogitsinput_features_shape.NumDimensions() == 3onnxruntime::contrib::GenerationCpuDeviceHelper::CreateWhisperEncoderInputsonnxruntime::contrib::GenerationCpuDeviceHelper::ExpandBufferis_kv_cacheonnxruntime::contrib::GenerationCpuDeviceHelper::CreateEncoderInputsCPU beam search current not support output cross QK.onnxruntime::contrib::GenerationCpuDeviceHelper::CreateGptInputsD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\generation_device_helper.ccsoftsignsoftplushardsigmoideluD:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\rnn_helpers.ccA + (M * K) <= A_endExpecting activation to be one of Affine, Relu, LeakyRelu, ThresholdedRelu, Tanh, ScaledTanh, Sigmoid, HardSigmoid, Elu, Softsign, Softplus. Got onnxruntime::rnn::detail::NormalizeActivationArgumentAndGetAlphaBetaCountalpha == 1.0f && (beta == 0.0f || beta == 1.0f)Invalid activation function of weights.quant_para_Quantized GEMM only support alpha equal to 1.0f and beta equal to 0.0f or 1.0fonnxruntime::rnn::detail::deepcpu::LstmMergeGatesFuncByNameInvalid GRU reset gate activation function: onnxruntime::rnn::detail::deepcpu::ActivationFuncByNameInvalid LSTM merge activation function of Input X must have 3 dimensions only. Actual:Input R must have shape {Input B must have shape {}. Actual:Input W must have shape {Input initial_h must have shape {affineInput sequence_lens must have shape {Invalid value/s in sequence_lens. All values must be > 0 and < seq_length. seq_length=thresholdedreluscaledtanhreluleakyreluonnxruntime::rnn::detail::deepcpu::GruOutputGateFuncByNameonnxruntime::rnn::detail::deepcpu::GruResetGateFuncByNameInvalid GRU hidden gate activation function: onnxruntime::LSTMBase::ComputeImplD:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\lstm_base.ccInput P must have shape {Input initial_c must have shape {op_kernel_info.GetAttr("axis", &axis_).IsOK()onnxruntime::LayerNormImpl::LayerNormImplD:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\layer_norm_impl.ccSize of scale and bias (if provided) must match X.shape[axis:], or scale and bias (with same shape) can be broadcasted to X when axis is 2.Size of X.shape[axis:] must be larger than 1, got scale.shape= X.shape= and axis= bias.shape=onnxruntime::LayerNormImpl::ComputeWithoutContextonnxruntime::LayerNormImpl::ComputeImplSoftmaxCPU inputs N, D and N * D must be < FDynamicTimeWarpingCurrently input rank must be 2, or (3 with first dim equal to 1), but got:D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\tensor\dynamic_time_warping.ccInvalid trace value: rank == 2 || (rank == 3 && input_dims[0] == 1)onnxruntime::contrib::DynamicTimeWarping::Compute is not bigger than attribut specified dim: input rank:UnfoldTensorstepstep must greater than zero!dimD:\a\_work\1\s\onnxruntime\contrib_ops/cpu/tensor/unfold.hinfo.GetAttr("size", &temp_size).IsOK()step_ > 0onnxruntime::contrib::UnfoldTensor::UnfoldTensorD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\tensor\unfold.cc is less than unfold size:dim < rankonnxruntime::contrib::UnfoldTensor::Computedimsize:input_dims[dim] >= size_Inverseonnxruntime::contrib::SkipLayerNorm::Compute!simplifiedSkipSimplifiedLayerNormalization should only has 4 inputs (input, skip, gamma, and beta). Got 5.D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\skip_layer_norm.cconnxruntime::contrib::SkipLayerNorm::Computeonnxruntime::contrib::SkipLayerNorm::PrePackonnxruntime::contrib::SkipLayerNorm::Computeskip is expected but not providedinput is expected to have 3 or 2 dimensions, got onnxruntime::contrib::SkipLayerNorm::SkipLayerNormlast two dimensions of skip needs to be same as inputskip is expected to have same shape as input or, a batch size of 1 or no batch size when input has 3 dimensionsskip is expected to have 3 or 2 dimensions, got gamma is expected but not providedLast dimension of beta and input does not matchbeta is expected to have 1 dimension, got Last dimension of gamma and input does not matchgamma is expected to have 1 dimension, got Last dimension of bias and input does not matchbias is expected to have 1 dimension, got onnxruntime::contrib::SkipLayerNorm::Computeonnxruntime::contrib::SkipLayerNorm::PrePackonnxruntime::contrib::SkipLayerNorm::Computeonnxruntime::contrib::SkipLayerNorm::PrePackonnxruntime::contrib::SkipLayerNorm::Computeepsilon_ >= 0onnxruntime::contrib::SkipLayerNorm::SkipLayerNormonnxruntime::contrib::SkipLayerNorm::SkipLayerNormonnxruntime::contrib::SkipLayerNorm::SkipLayerNormonnxruntime::contrib::SkipLayerNorm::SkipLayerNormonnxruntime::contrib::SkipLayerNorm::SkipLayerNorminfo.GetAttr("scale", &scale_).IsOK()onnxruntime::contrib::Affine::Affineinfo.GetAttr("alpha", &alpha_).IsOK()D:\a\_work\1\s\onnxruntime\contrib_ops/cpu/element_wise_ops.honnxruntime::contrib::Scale::Scaleinfo.GetAttr("beta", &beta_).IsOK()onnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::contrib::ImageScaler::ImageScalerD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\image_scaler.hBias size () does not match the number of channels (Input is expected to have four dimensions corresponding to [N,C,H,W], got DynamicSliceAttribute border needs to be specified with four border elements, got Input's height () needs to be greater than or equal to the topBorder () + bottomBorder ( input dimensions instead) + scale_[0] (Input's width () needs to be greater than or equal to the leftBorder () + rightBorder (D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\crop.honnxruntime::contrib::Crop::Compute) + scale_[1] (onnxruntime::contrib::AttentionBase::AttentionBaseinfo.GetAttr("num_heads", &num_heads).IsOK() && num_heads > 0D:\a\_work\1\s\onnxruntime\contrib_ops/cpu/bert/attention_base.houtput_qkonnxruntime::contrib::DecoderMaskedMultiHeadAttention::DecoderMaskedMultiHeadAttentionInput 'value' shall absent when 'key' is absentD:\a\_work\1\s\onnxruntime\contrib_ops/cpu/bert/multihead_attention_helper.honnxruntime::contrib::multihead_attention_helper::CheckInputsInput 'query' is expected to have 3 or 5 dimensions, got onnxruntime::contrib::DecoderMaskedMultiHeadAttention::ComputeDecoderMaskedMultiHeadAttention only supports no mask or 2D key padding mask of shape [batch, total_seq_length] currentlyQK head size should be same as V head size to use DecoderMaskedMultiHeadAttentionInput sequence length should be 1 to use DecoderMaskedMultiHeadAttention. Actual length is D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\decoder_masked_multihead_attention.ccIf beam width is greater than 1, then cache indirection buffer MUST be presentpast_key != nullptr && past_value != nullptrpast_present_share_buffer_DecoderMaskedMultiHeadAttention does not support attention bias for cross-attentionInput 'cache_indirection' dimension 2 should be same as (or less than) max_sequence_length, got Input 'cache_indirection' dimension 0 should be batch_size, got Input 'cache_indirection' dimension 1 should be num_beams, got Input 'cache_indirection' is expected to have 3 dimensions, got Input 'past_value' is expected to have 4 dimensions, got Input 'past_key' is expected to have 4 dimensions, got Expect 'query' shape (batch_size, sequence_length, num_heads, 3, head_size) for packed qkvInput 'key' and 'value' shall have same shape (batch_size, num_heads, kv_sequence_length, head_size)Input 'past_value' dimension 1 should be same as number of heads, got Input 'past_key' dimension 1 should be same as number of heads, got Input 'past_value' dimension 0 should be batch_size, got Input 'past_key' dimension 0 should be batch_size, got 4D mask in attention cpu kernel is not supportedInput 'past_value' dimension 3 should be same as head_size, got Input 'past_key' dimension 3 should be same as head_size, got Input 'past_key' and 'past_value' shall have same dim 2 (past_sequence_length or max_sequence_length). Input 'bias' length is expected to be 2 * hidden_size + hidden_size_v, got Input 'bias' shall be empty when packed kv is usedPacked qkv of 3D BS3NH format is not support by MultiHeadAttentionInput 'past_key' and 'past_value' shall be both present or both absentonnxruntime::contrib::AttentionCPUBase::ApplyAttentionWithBeamsD:\a\_work\1\s\onnxruntime\contrib_ops/cpu/bert/attention_cpu_base.honnxruntime::contrib::AttentionCPUBase::ApplyAttentionInput 'key_padding_mask' shape is not expected.Expect 'key' shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kvExpect rank of key be 5 for packed kvExpect rank of query be 3 for packed kvInput 'key' and 'value' shall have same dim 1 (kv_sequence_length)Input 'query' and 'key' shall have same dim 2 (hidden_size)Input 'query', 'key' and 'value' shall have same dim 0 (batch_size)Expect rank of key and value be same, and either 3 or 4D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\bifurcation_detector.hmin_ngram_size_ > 0max_ngram_sizeinfo.GetAttr("max_ngram_size", &max_ngram_size_).IsOK()min_ngram_sizeinfo.GetAttr("min_ngram_size", &min_ngram_size_).IsOK()onnxruntime::contrib::BifurcationDetector::BifurcationDetectorpred_tokens_len == (src_tokens_len + 1 - prev_suffix_match_idx_data)BifurcationDetectormax_ngram_size_ > 0max_ngram_size_ >= min_ngram_size_src_tokens_len >= prev_suffix_match_idx_dataonnxruntime::contrib::BifurcationDetector::Computeinput_ids_dims.size() == 2onnxruntime::contrib::NGramRepeatBlock::Computescores_dims.size() == 2scores_dims[0] == batch_sizeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\ngram_repeat_block.hngram_size_ > 0token_id < vocab_sizeonnxruntime::contrib::NGramRepeatBlock::Compute::::operator ()TidNGramRepeatBlockngram_sizeinfo.GetAttr("ngram_size", &ngram_size_).IsOK()onnxruntime::contrib::NGramRepeatBlock::NGramRepeatBlockonnxruntime::contrib::BiasGelu::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\bias_gelu.ccuse_approximationonnxruntime::contrib::BiasGelu::ComputeCDistmetricinfo.GetAttr("metric", &metric).IsOK()onnxruntime::contrib::CDist::CDistD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\cdist.hThe second input of CDist kernel has wrong shape: Input shape dimensions mismatch:sqeuclideaneuclideanonnxruntime::contrib::CDist::CDistThe first input of CDist kernel has wrong shape: CropAndResizeonnxruntime::contrib::CropAndResize::CropAndResizeD:\a\_work\1\s\onnxruntime\contrib_ops/cpu/crop_and_resize.hNumber of dimensions for crop size should be exactly 1 specified. It should be either bilinear or nearestNull crop_size_ptrInput tensor to Unique op should be 1DD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\maxpool_with_mask.hMaxpoolWithMaskonnxruntime::contrib::MaxpoolWithMask::Computeonnxruntime::contrib::MatMulFpQ4::Computeblob_shape.NumDimensions() == 1Right hand side of MatMulFpQ4 must be a 2D matrix!D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\matmul_fpq4.ccSecond input of MatMulFpQ4 must be a 1D blob!blk_quant_typeMatMulFpQ4(size_t)blob_len == buf_sizeOperator MatMulFpQ4 not yet supported on this hardware platform.b_shape.NumDimensions() == 2Quantized and packed blob size differ from expected!buf_size > 0Unsupported dequantized type: D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\gather_block_quantized.cconnxruntime::contrib::GatherBlockQuantized,int>::Computeonnxruntime::contrib::GatherBlockQuantized::Computeonnxruntime::contrib::GatherBlockQuantized::ComputeGatherBlockQuantizedonnxruntime::contrib::GatherBlockQuantized,__int64>::CopyDataAndDequantize::::operator ()onnxruntime::contrib::GatherBlockQuantized,__int64>::CopyDataAndDequantize::::operator ()onnxruntime::contrib::GatherBlockQuantized,int>::CopyDataAndDequantize::::operator ()onnxruntime::contrib::GatherBlockQuantized,int>::CopyDataAndDequantize::::operator ()onnxruntime::contrib::GatherBlockQuantized::CopyDataAndDequantize::::operator ()onnxruntime::contrib::GatherBlockQuantized::CopyDataAndDequantize::::operator ()onnxruntime::contrib::GatherBlockQuantized,int>::CopyDataAndDequantize::::operator ()onnxruntime::contrib::GatherBlockQuantized,int>::CopyDataAndDequantize::::operator ()onnxruntime::contrib::GatherBlockQuantized,__int64>::CopyDataAndDequantize::::operator ()onnxruntime::contrib::GatherBlockQuantized,__int64>::CopyDataAndDequantize::::operator ()scales and zero_points must have the same shape.scales and zero_points must have the same rank.onnxruntime::contrib::GatherBlockQuantized,int>::PrepareForComputeonnxruntime::contrib::GatherBlockQuantized::PrepareForComputedata and scales must have the same rank.data and scales do not match shapes.onnxruntime::contrib::GatherBlockQuantized::PrepareForComputeindices_val >= -gather_axis_dim && indices_val < gather_axis_dimonnxruntime::contrib::GatherBlockQuantized::CopyDataAndDequantize::::operator ()onnxruntime::contrib::GatherBlockQuantized::CopyDataAndDequantize::::operator ()onnxruntime::contrib::GatherBlockQuantized,int>::PrepareForComputeonnxruntime::contrib::GatherBlockQuantized,__int64>::PrepareForComputeonnxruntime::contrib::GatherBlockQuantized,__int64>::PrepareForComputegather_axis'block_size' must be 2's power and not less than 16.quantize_axisonnxruntime::contrib::GatherBlockQuantized,int>::Computeonnxruntime::contrib::GatherBlockQuantized,__int64>::Computeonnxruntime::contrib::GatherBlockQuantized,__int64>::Computeonnxruntime::contrib::GatherBlockQuantized::GatherBlockQuantizedonnxruntime::contrib::GatherBlockQuantized,int>::GatherBlockQuantizedonnxruntime::contrib::GatherBlockQuantized::GatherBlockQuantizedonnxruntime::contrib::GatherBlockQuantized,__int64>::GatherBlockQuantizedblock_size_ >= 16 && ((block_size_ - 1) & block_size_) == 0onnxruntime::contrib::GatherBlockQuantized,__int64>::GatherBlockQuantizedonnxruntime::contrib::GatherBlockQuantized,int>::GatherBlockQuantizedMatMulBnb4onnxruntime::contrib::MatMulBnb4::MatMulBnb4Status::OK() == info.GetAttr("K", &K_)Invalid quant_type, only 0 (FP4) and 1 (NF4) are supported.Status::OK() == info.GetAttr("quant_type", &quant_type_)onnxruntime::contrib::MatMulBnb4::Computequant_type_ == FP4 || quant_type_ == NF4Status::OK() == info.GetAttr("N", &N_)D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\matmul_bnb4.ccquant_typeStatus::OK() == info.GetAttr("block_size", &block_size_)only block size 16, 32, 64, 128, 256 are supported.D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\dequantize_blockwise_bnb4.honnxruntime::contrib::DequantizeBlockwiseBnb4quant_type == FP4 || quant_type == NF4920k2ʾM?5=qx=$>|>:>>?9??;*??>?*>>**nbits_ == 4Only 4b quantization is supported for unpacked compute.D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\matmul_nbits.cconnxruntime::contrib::MatMulNBits::ComputeBUnpackedcolumn_wise_quant_Row-wise quantization is not supported for nowonnxruntime::contrib::MatMulNBits::ComputeBUnpackedonnxruntime::contrib::MatMulNBits::ComputeOnly 4b and 8b quantization is supported for MatMulNBits op, additional bits support is planned.onnxruntime::contrib::MatMulNBits::Computeonnxruntime::contrib::MatMulNBits::MatMulNBitsnbits_ == 4 || nbits_ == 8onnxruntime::contrib::MatMulNBits::MatMulNBitsTransposeMatMulinput_num_bytes % 4 == 0D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\murmur_hash3.cconnxruntime::contrib::MurmurHash3::Computesizeof(uint32_t) == output_element_bytesInvalid assumption of output element sizepositivekeysMurmurHash3Can not multiply A and B as inner dimension does not match. inner_A: vs inner_B: Expecting 2xValues == indicesExpecting COO 2-D indices shapeCurrently supporting only 2-D matricesSparseToDenseMatMulD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\math\sparse_dense_matmul.cconnxruntime::contrib::SparseToDenseMatMul::ComputeOuter size must be M + 1Expecting the same number NNZ == size of Inner indicesCurrently support only COO and CSR(x64) formatsonnxruntime::contrib::`anonymous-namespace'::SparseToDenseCoo::operator ()onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCoo::operator ()onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCoo::operator ()COO m index: is out of bounds of out_left: onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCoo::operator ()onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCoo<__int64>::operator ()onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCoo::operator ()COO indices must be 2-D, got: COO k index: is out of bounds of lhs_right: D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\word_conv_embedding.cconnxruntime::contrib::WordConvEmbedding::ComputeWordConvEmbedding conv filter size: Conv filter size does not match embedding_size attribute. embedding_size attribute: Char embedding size does not match char_embedding_size attribute. char_embedding_size attribute: Char embedding size does not match conv kernal size 2. Conv kernal size 2 : conv_window_size attribute: conv kernal size 1: Char embedding size: Conv kernal size 1 does not match conv_window_size attribute .conv_window_sizeembedding_sizechar_embedding_sizeInput dimensions are either [C] or [N][C] allowedtensor(string) expected as inputseparators must not be empty!tokenexp.empty()mincharnum is too big for char level tokenezation!separators.empty()tokenexpseparatorsExpecting a non-empty tokenexpEither one of the separators OR tokenexp attributes required but none is setonnxruntime::contrib::Tokenizer::SeparatorExpressionTokenizerInput string contains invalid utf8 chars:onnxruntime::contrib::Tokenizer::TokenExpressionMatch contains invalid utf8 chars: Can not digest separators: !char_tokenezation_ || mincharnum_ < 2Input string contains invalid utf8 chars: Can not digest tokenexp: Tokenizerattribute mark is not setmarkattribute mincharnum is not setmincharnummincharnum > 0attribute mincharnum must have a positive valueD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\tokenizer.cconnxruntime::contrib::Tokenizer::Tokenizerattribute pad_value is not setpad_valueAttention v weight shape error! Expected:{, aw_attn_size}. Got:Attention query layer weight shape error! Expected:{}. Got: Attention memory layer weight shape error! Expected:{}, Got: Attention mechanism memory sequence lengths value must in (0, , am_attn_size}, Got:Attention layer weight shape error! Expected: {onnxruntime::contrib::DeepCpuAttnLstmOp::ValidateInputsD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\attnlstm\deep_cpu_attn_lstm.hstatic_cast(activation_func_names.size()) == num_directions_ * 3onnxruntime::contrib::DeepCpuAttnLstmOp::DeepCpuAttnLstmOp found!], while Attention mechanism memory shape error! Expected: {Attention mechanism memory sequence lengths must have shape {D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\attnlstm\deep_cpu_attn_lstm.cc}, actural: AttnLSTMonnxruntime::contrib::DeepCpuAttnLstmOp::Computeonnxruntime::contrib::DeepCpuAttnLstmOp::ComputeImplhead_size / 2 or rotary_embedding_dim / 2, got Input 'cos_cache' dimension 1 should be same as max_sequence_length, got Input 'cos_cache' dimension 0 should be same as sequence_length, got Input 'position_ids' dimension 1 should be of size Input 'cos_cache' is expected to have 2 dimensions, got Input 'sin_cache' is expected to have 2 dimensions, got dimensions, got Input 'position_ids' is expected to have 0, 1, or 2 onnxruntime::contrib::RotaryEmbedding::RotaryEmbeddingInput 'x' is expected to have 3 or 4 dimensions, got num_heads > 0onnxruntime::contrib::RotaryEmbedding::RotaryEmbeddingbatch_size, got Input 'position_ids' dimension 0 should be of size head_sizerotary_embedding_dim must be less than or equal to specifiednum_heads must be provided if rotary_embedding_dim is the same shapeInputs 'cos_cache' and 'sin_cache' are expected to have D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\rotary_embedding.cconnxruntime::contrib::RotaryEmbedding::Computeonnxruntime::contrib::RotaryEmbedding::ComputeSparseAttentioncos_cache and sin_cache dimension 1 must be the same., max_cache_sequence_length:max_cache_sequence_length should be no less than total_sequence_length:Input 'past_key' dimension 0 should be batch_size Input 'past_key' shall have kv_num_headsD:\a\_work\1\s\onnxruntime\contrib_ops/cpu/sparse/sparse_attention_helper.hInput 'past_key' and 'past_value' shall have same shapemax_sequence_length should be no less than total_sequence_length:onnxruntime::contrib::sparse_attention_helper::CheckInputscos_cache dimension 1 must be <= head_size / 2 and a multiple of 8.sin_cache dimension 1 must be <= head_size / 2 and a multiple of 8., max_rotary_sequence_length:max_rotary_sequence_length should be no less than total_sequence_length:head_size shall be a multiple of 16. Got head_size = cos_cache and sin_cache dimension 0 should be same size.key_total_sequence_lengths must have shape (batch_size).cos_cache and sin_cache must be passed to SparseAttention when do_rotary = 1Input 'key' is expected to have 3 dimensions, got Input 'query' and 'key' shall have same dim 0 (batch size)head_size must be a multiple of 8. Got head_size = Input 'key' and 'value' shall be both present, or both absent in the case of packed qkv.info.GetAttr("sparse_block_size", &sparse_block_size).IsOK()Input 'query' is expected to have 3 dimensions, got rotary_interleavedsparse_block_sizeblock_col_indices must have shape (num_layout, max_nnz), where max_nnz <= max_blocks * max_blocks., max_sequence_length deduced from block_row_indices:total_sequence_length tensor must be of one element.block_row_indices must have shape (num_layout, max_blocks + 1) where num_heads is divisible by num_layout. does not match num_heads and kv_num_headspacked qkv hidden size= Input 'query' and 'key' shall have same dim 1 (sequence length)Input 'query' and 'value' shall have same shapeD:\a\_work\1\s\onnxruntime\contrib_ops/cpu/sparse/sparse_attention_base.hinfo.GetAttr("kv_num_heads", &kv_num_heads).IsOK() && kv_num_heads > 0onnxruntime::contrib::SparseAttentionBase::SparseAttentionBaseonnxruntime::contrib::SparseAttention::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\sparse\sparse_attention.ccpast_key->DataRaw() == present_key->DataRaw() && past_value->DataRaw() == present_value->DataRaw()onnxruntime::contrib::SparseAttention::Computeseqlens_k must be shape (batch_size).head_size shall be a multiple of 16. Got head_size % 16 == Input 'past_value' shall have kv_num_headsInput 'past_key' and 'past_value' shall be both present or both absent.Input 'value' is expected to have same hidden size as key.BNSH Input 'past_key' and 'past_value' should have same dimension 2 (max sequencelength or past sequence length), got Input 'query' and 'value' shall have same dim 0 (batch size)Input 'query' and 'value' shall have same dim 1 (sequence length)position_ids dimension 1 must be atleast sequence length, got attention_bias dimension 0 must be equal to the batch size or 1, got sequence_length shall be 1 when it is not prompt.position_ids dimension 0 must be equal to the batch size, got Input 'cos_cache' and 'sin_cache' shall be both present or both absent.batch_size must be 1 when sequence_length > 1 and past context is given.cos_cache dimension 0 shall not be less than total_sequence_length.sin_cache dimension 0 shall not be less than total_sequence_length.D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\group_query_attention.cconnxruntime::contrib::GroupQueryAttention::Computeonnxruntime::contrib::GroupQueryAttention::Computehead_size must be a multiple of 8. Got head_size % 8 == Input 'value' is expected to have 3 dimensions, got num_heads must be a multiple of kv_num_heads. Got num_heads % kv_num_heads == smooth_softmaxlocal_window_sizeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\gqa_attention_base.hsoftcaponnxruntime::contrib::GQAAttentionBase::GQAAttentionBaseattention_bias dimension 3 must be equal to total_sequence_length, got attention_bias dimension 1 must be equal to the num heads or 1, got attention_bias dimension 2 must be equal to the sequence length, got ORT_DISABLE_DECODER_ATTENTIONORT_DISABLE_FLASH_ATTENTIONonnxruntime::contrib::MultiHeadAttention::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\multihead_attention.ccPacked QKV of shape (B, L, N, 3, H) not implemented for CPUPacked KV not implemented for CPUonnxruntime::contrib::MultiHeadAttention::MultiHeadAttentiononnxruntime::contrib::FusedGemm::FusedGemmD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\fused_gemm.ccD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\fused_conv.ccGetFusedActivationAttr(info, activation_).IsOK()onnxruntime::contrib::FusedConvFloat::FusedConvFloataxis <= X_NumDims && axis >= -X_NumDimsExpandDims].Axis must be within range [D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\expand_dims.h Axis is axis_tensor->Shape().IsScalar()onnxruntime::contrib::ExpandDims::ComputeAn axis tensor must be a scalar tensor.input index out of rangeonnxruntime::contrib::EmbedLayerNorm::Computeonnxruntime::contrib::EmbedLayerNormBase::EmbedLayerNormBaseD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\embed_layer_norm.ccRotary embedding is not supported in Attention CPU kernel. Please fuse the model with MHA + RotaryEmbedding.D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\attention.cconnxruntime::contrib::Attention::ComputeSampleOpTAQGemm : scale of y must be null or a scalar or 1D tensor of size 1QGemm : zero point of y must be null or a scalar or 1D tensor of size 1QGemm : zero point and scale of input b should have same shape sizeQGemm : scale of input b must be a scalar or 1D tensor of size 1 or NQGemm : zero point of input b must be a scalar or 1D tensor of size 1 or NQGemm : zero point of input a must be a scalar or 1D tensor of size 1onnxruntime::contrib::QGemm::CheckInputsTYTYZTCTBQGemm : scale of input a must be a scalar or 1D tensor of size 1D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\quant_gemm.cconnxruntime::contrib::QGemm::ComputeD:\a\_work\1\s\onnxruntime\core/providers/cpu/math/gemm_helper.hPosition embedding zero point must be a scalar or 1D tensor of size 1Word embedding zero point must be a scalar or 1D tensor of size 1Beta scale must be a scalar or 1D tensor of size 1Gamma scale must be a scalar or 1D tensor of size 1Segment embedding scale must be a scalar or 1D tensor of size 1Position embedding scale must be a scalar or 1D tensor of size 1Word embedding scale must be a scalar or 1D tensor of size 1QEmbedLayerNormalizationBeta zero point must be a scalar or 1D tensor of size 1Gamma zero point must be a scalar or 1D tensor of size 1Segment embedding zero point must be a scalar or 1D tensor of size 1D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\qembed_layer_norm.cconnxruntime::contrib::QEmbedLayerNorm::Computeonnxruntime::contrib::NhwcMaxPool::Computeinput_shape.Size() > 0 || N == 0D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\nhwc_max_pool.cconnxruntime::contrib::NhwcMaxPool::Computeonnxruntime::contrib::DynamicQuantizeMatMul::ComputeIsBQuantParamSupported(b_zp_tensor->Shape(), b_tensor ? b_tensor->Shape() : b_shape_)MatmulInteger : b zero point is not validD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\dynamic_quantize_matmul.cconnxruntime::contrib::MatMulIntegerToFloatBase::ComputeCommononnxruntime::contrib::MatMulIntegerToFloat::ComputeIsScalarOr1ElementVector(a_zero_point_tensor)MatMulIntegerToFloat : input a zero point must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet.Input Weight point must be constantDynamicQuantizeLSTM : Weight zero point must be zeroR_scaleW_scaleR_zero_pointW_zero_point must have shape {Recurrent} for per-tensor/layer quantization or shape {, 4*} for per-channel quantization. Actual:D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\dynamic_quantize_lstm.cconnxruntime::contrib::DynamicQuantizeLSTM::PrePackinput zero point must be a scalar or 1D tensor of size 1.input scale must be a scalar or 1D tensor of size 1D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\attention_quant.cconnxruntime::contrib::QAttention::ComputeQLinearMultensor_b_zero_point == nullptr || IsScalarOr1ElementVector(tensor_b_zero_point)MatmulInteger : input1 B_zero_point must be a scalar or 1D tensor of size 1 if givenIsScalarOr1ElementVector(tensor_b_scale)MatmulInteger : input1 B_scale must be a scalar or 1D tensor of size 1tensor_a_zero_point == nullptr || IsScalarOr1ElementVector(tensor_a_zero_point)MatmulInteger : input1 A_zero_point must be a scalar or 1D tensor of size 1 if givenD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\qlinear_binary_op.cconnxruntime::contrib::`anonymous-namespace'::QLinearImpltensor_c_zero_point == nullptr || IsScalarOr1ElementVector(tensor_c_zero_point)MatmulInteger : input1 C_zero_point must be a scalar or 1D tensor of size 1 if givenIsScalarOr1ElementVector(tensor_c_scale)MatmulInteger : input1 C_scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(tensor_a_scale)MatmulInteger : input1 A_scale must be a scalar or 1D tensor of size 1tensor_x_scale == nullptr || IsScalarOr1ElementVector(tensor_x_scale)onnxruntime::contrib::`anonymous-namespace'::BuildLookupTableIfFixedD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\qlinear_softmax.ccopset must be existed in attributes of QlinearSoftmaxonnxruntime::contrib::QLinearSoftmax::QLinearSoftmaxonnxruntime::contrib::QLinearSoftmax::ComputeImplOpset13QLinearSoftmaxQlinearBuildLookupTable : input X_scale must be a scalar or 1D tensor of size 1QLinearLeakyReluInput x_scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(tensor_x_scale)onnxruntime::contrib::QLinearAveragePool::ComputeImplD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\qlinear_pool.ccinput x_zero_point must be a scalar or 1D tensor of size 1 if giventensor_x_zero_point == nullptr || IsScalarOr1ElementVector(tensor_x_zero_point)input y_scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(tensor_y_scale)input y_zero_point must be a scalar or 1D tensor of size 1 if giventensor_y_zero_point == nullptr || IsScalarOr1ElementVector(tensor_y_zero_point)QLinear Pooling unsupported pooling size!Wrong input type encountered for zero point of quantized input @QLinearWheretensor_x_zero_point->GetElementType() == tensor_y_zero_point->GetElementType() && tensor_x_zero_point->GetElementType() == tensor_z_zero_point->GetElementType() && tensor_y_zero_point->GetElementType() == tensor_z_zero_point->GetElementType()Input scale is not float for quantized input x @ 2tensor_x_scale->IsDataType()onnxruntime::contrib::QLinearWhere::ComputeInput scale is not float for quantized input y @ 5tensor_y_scale->IsDataType()Input scale is not float for quantized output z @ 7tensor_z_scale->IsDataType() inputs! (condition, x, x_scale, x_zero_point, y, y_scale, y_zero_point, z_scale, z_zero_point)There must be input_def_count == kExpected_input_countonnxruntime::contrib::QLinearWhere::QLinearWhereD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\qlinear_where.ccWrong input type encountered for zero point input def of x, y, z onnxruntime::contrib::QLinearConcat::ComputeInput scale is not float for quantized input @Each input must be (tensor, scale, zero_point) tuple!input_def_count >= 5 && (input_def_count - 2) % 3 == 0onnxruntime::contrib::QLinearConcat::QLinearConcatD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\qlinear_concat.ccInput scale is not float for input def @Wrong input type encountered for zero point input def @tensor_x_zero_point->GetElementType() == tensor_y_zero_point->GetElementType()input_count_x3 >= 3 && input_count_x3 % 3 == 0onnxruntime::contrib::QLinearGlobalAveragePool::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\qlinear_global_average_pool.ccIsScalarOr1ElementVector(tensor_x_zero_point)IsScalarOr1ElementVector(tensor_y_zero_point)MatMulInteger16A != nullptr && B != nullptronnxruntime::contrib::MatMulInteger16::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\matmul_integer16.cc(channels % 4) == 0onnxruntime::contrib::ReorderOutput::Computechannels_ <= X_shape[1]onnxruntime::contrib::NchwcConv::ComputeX_shape.NumDimensions() == 4(static_cast(X_shape[1]) < nchwc_block_size) || ((X_shape[1] % nchwc_block_size) == 0)Unsupported convolution size.onnxruntime::contrib::NchwcPoolBase::NchwcPoolinfo.GetAttr("coordinate_transformation_mode", &transformation_mode).IsOK()' for NCHWc UpsampleUnsupported transformation mode 'transformation_mode_ == TransformationMode::ASYMMETRICUnsupported mode 'X_rank == 4onnxruntime::contrib::ReorderInput::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\nchwc_ops.ccchannels_ > 0onnxruntime::contrib::NchwcConv::NchwcConvpool_attrs_.kernel_shape.size() == 2onnxruntime::contrib::NchwcPoolBase::NchwcPoolBaseinfo.GetAttrs("scales", scales_).IsOK()onnxruntime::contrib::NchwcUpsample::NchwcUpsamplescales_.size() == 4scales_[0] == 1 && scales_[1] == 1 && scales_[2] >= 1 && scales_[3] >= 1info.GetAttr("channels_last", &channels_last_).IsOK()onnxruntime::contrib::ReorderInput::ReorderInputD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\nchwc_ops.hinfo.GetAttr("channels", &channels_).IsOK()onnxruntime::contrib::ReorderOutput::ReorderOutputinvalid channel count(X_shape[1] % MlasNchwcGetBlockSize()) == 0X_shape.size() == 4onnxruntime::contrib::NchwcUpsample::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\tree_ensemble_helper.ccTreeEnsemble could not unpack tensor attribute onnxruntime::ml::GetAnyVectorAttrsOrDefaultonnxruntime::contrib::MaybeTransposeToBNSHonnxruntime::contrib::AddBiasTransposeonnxruntime::contrib::AddBiasReshapeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\attention_utils.cconnxruntime::contrib::MaybeTransposeToBNSHAndAddBias.. row[ rows[ [seqno=Real memory steps is not in (0, onnxruntime::contrib::BahdanauAttention::PrepareMemorymem_steps <= max_memory_steps_ && mem_steps > 0!normalize_not support normalize yet.D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\attnlstm\bahdanau_attention.cconnxruntime::contrib::BahdanauAttention::BahdanauAttentionunimplemented activation: activation_params count mismatchQlinearBuildLookupTable : input Y_zero_point must be a scalar or 1D tensor of size 1QlinearBuildLookupTable : input Y_scale must be a scalar or 1D tensor of size 1onnxruntime::contrib::QlinearBuildLookupTableQlinearBuildLookupTable : input X_zero_point must be a scalar or 1D tensor of size 1D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\qlinear_lookup_table.ccTORTD:\a\_work\1\s\onnxruntime\lora\adapter_format_utils.ccThe buffer does not appear to be a valid lora parameter formatonnxruntime::adapters::utils::MemoryMapAdapterFileThe buffer fails lora adapter format verificationonnxruntime::adapters::utils::ValidateAndGetAdapterFromBytesUnsupported lora format version?Tensor types should have been handled alreadyD:\a\_work\1\s\onnxruntime\core\framework\onnxruntime_typeinfo.ccThis TypeProto does not have ValueCase setThis OrtValue is neither Tensor, SparseTensor, Map or Sequence typeThe type is not tensor, sparse tensor, sequence, map or optional typeOrtTypeInfo::FromTypeProtoOrtValue is TensorSequence type but has no element Tensor DataType.OrtTypeInfo::FromOrtValuetensor_data_type != nullptrOpenVINO_GPUQnnHtpSharedOpenVINO_RT_NPUWebNN_TensorWebGPU_BufferCudaPinnedHipHipPinned size= alignment=onnxruntime::IAllocator::CalcMemSizeForArrayWithAlignment::::operator () nmemb=Specified device is not supported.D:\a\_work\1\s\onnxruntime\core\framework\allocator.ccUnsupported indices_format passedD:\a\_work\1\s\onnxruntime\core\framework\tensor_type_and_shape.cc`anonymous-namespace'::GetIndicesTensordim_values must be -1 (symbolic dimension) or larger.the ort_value must contain a constructed tensor or sparse tensorTensor type is undefinedOrtApis::GetTensorTypeAndShapeArgument is not a tensortype_proto is not of type sequence!value_case == ONNX_NAMESPACE::TypeProto::kSequenceTypeOrtSequenceTypeInfo::FromTypeProtoD:\a\_work\1\s\onnxruntime\core\framework\onnxruntime_sequence_type_info.ccOrtOptionalTypeInfo::FromTypeProtoD:\a\_work\1\s\onnxruntime\core\framework\onnxruntime_optional_type_info.cctype_proto is not of optional typevalue_case == ONNX_NAMESPACE::TypeProto::kOptionalTypeOrtMapTypeInfo::FromTypeProtoD:\a\_work\1\s\onnxruntime\core\framework\onnxruntime_map_type_info.cctype_proto is not of type map!value_case == ONNX_NAMESPACE::TypeProto::kMapTypedtype_ != nullptronnxruntime::Tensor::InitTensor is expected to contain one of the primitive data types. Got: Calculation for Tensor storage size overflowedelt_type != nullptrD:\a\_work\1\s\onnxruntime\core\framework\tensor.ccTensor shape is too largeonnxruntime::Tensor::SizeInBytesshape.Size() must >=0onnxruntime::Tensor::Tensortensor size overflowTensor shape.Size() must be >= 0onnxruntime::Tensor::CalculateTensorStorageSizesrc.SizeInBytes() == dst.SizeInBytes()onnxruntime::CPUDataTransfer::CopyTensorD:\a\_work\1\s\onnxruntime\core\framework\data_transfer.cconnxruntime::IDataTransfer::CopySparseTensorsonnxruntime::IDataTransfer::CopyTensoronnxruntime::IDataTransfer::CopyTensorsonnxruntime::SequenceTensorTypeBase::GetElementTypeonnxruntime::data_types_internal::SequenceTypeHelper::Setonnxruntime::data_types_internal::OptionalTypeHelper::SetD:\a\_work\1\s\include\onnxruntime\core/framework/data_types.helem_proto != nullptronnxruntime::OptionalTypeBase::GetDeleteFunconnxruntime::OptionalTypeBase::GetElementTypeonnxruntime::TensorTypeBase::GetElementTypeonnxruntime::SparseTensorTypeBase::GetElementTypevalue_proto != nullptronnxruntime::data_types_internal::MapTypeHelper::Setexpected a registered ONNX typeuint64float16int32uint32Float8E4M3FNUZFloat8E5M2bfloat16Float8E4M3FNbooldouble(null)floatint16uint16int8uint8onnxruntime::NonTensorTypeBase::IsSequenceCompatibleonnxruntime::NonTensorTypeBase::IsMapCompatibleutils::HasKeyType(thisProto->map_type())onnxruntime::NonTensorTypeBase::FromDataContaineronnxruntime::NonTensorTypeBase::ToDataContainerNot implementedonnxruntime::SequenceTensorTypeBase::IsCompatibleutils::HasElemType(thisProto->sequence_type())utils::HasElemType(thisProto->sparse_tensor_type())thisProto->value_case() == TypeProto::ValueCase::kSequenceTypeutils::HasElemType(thisProto->optional_type())thisProto->value_case() == TypeProto::ValueCase::kMapTypethisProto->value_case() == TypeProto::ValueCase::kOptionalTypeonnxruntime::OptionalTypeBase::IsCompatiblep.secondthisProto->value_case() == TypeProto::ValueCase::kTensorTypeonnxruntime::data_types_internal::DataTypeRegistry::RegisterDataTypeWe do not expect duplicate registration of types for: thisProto->value_case() == TypeProto::ValueCase::kSparseTensorTypeonnxruntime::SparseTensorTypeBase::IsCompatibleonnxruntime::TensorTypeBase::IsCompatibleutils::HasElemType(thisProto->tensor_type())Only ONNX MLDataType can be registeredproto != nullptronnxruntime::data_types_internal::IsCompatibleD:\a\_work\1\s\onnxruntime\core\framework\data_types.cconnxruntime::utils::ContainerChecker::ContainerCheckerMLDataType for: Invalid DataTypeImpl TypeProto definitionUInt4x2 is not supportedFloat8E5M2FNUZInt4x2sparse tensor type is not currently registered or supportedtensor type Must have the same size. Got src_size: onnxruntime::`anonymous-namespace'::CopyData dst_size: onnxruntime::SparseTensor::MakeBlockSparseStrings to device type: Use MakeBlockSparseStringsonnxruntime::SparseTensor::MakeBlockSparseDataThis instance should not be emptyDestination should be emptyUnable to find a data transfer for copying from device type: onnxruntime::SparseTensor::CopyIndices shape must have dim[0] == 2 to be equal to values blocks: onnxruntime::SparseTensor::ValidateBlockSparseShapesExpecting indices to have 2-D shape . Got: Expecting fully sparse tensors to have indices shape {0}onnxruntime::SparseTensor::UseBlockSparseIndicesExpecting index blocks: Expecting fully sparse tensors to have value shape {0}onnxruntime::SparseTensor::MakeCsrStringsMust contain BlockSparse format. Got: Use MakeCsrStringsonnxruntime::SparseTensor::MakeCsrDataExpecting one index. Got: Expecting to have at lest 3-D shape. Got:Format() == SparseFormat::kBlockSparseonnxruntime::SparseTensor::AsBlockSparse the same as values size: Expecting inner index size: onnxruntime::SparseTensor::ValidateCsrIndicesInner and Outer indices must either be both zero or non-zeroThis method does not expect allocator to be setonnxruntime::SparseTensor::UseCsrIndices rows: Outer index count must be rows + 1 or zero. Got: Must contain Csr format. Contains: Format() == SparseFormat::kCsrcThis method should follow a call to constructor that supplies the allocatorallocator_ != nullptrformat_data_.size() == 2Udense shape must 2-D. Got: onnxruntime::SparseTensor::AsCsrExpecting two indices. Got: Not expecting an allocator setUse MakeCooStringsSparse format must not be set. Already contains format: onnxruntime::SparseTensor::UseCooIndicesonnxruntime::SparseTensor::MakeCooStringsFormat() == SparseFormat::kUndefinedonnxruntime::SparseTensor::MakeCooDataExpecting data type to be set as stringExpecting to contain one index, got: format_data_.size() == 1UFormat() == SparseFormat::kCooonnxruntime::SparseTensor::AsCoovalues_count == index_sizeonnxruntime::SparseTensor::GetCooIndexDims must be equal to or twice the values size: Index size: onnxruntime::SparseTensor::GetSparseTensorFromOrtValuethis tensor already has populated sparse_indicesD:\a\_work\1\s\onnxruntime\core\framework\sparse_tensor.ccthe ort_value must contain a constructed sparse tensoronnxruntime::SparseTensor::AllocateBufferMust contain Coo format. Got: must be less than total buffer size: Values size Src and Dst must be of the same typeMust have the same shapeDestination must have a CPU allocator setX-device copy of strings not supportedD:\a\_work\1\s\onnxruntime\core\framework\config_options.cc Config with key [onnxruntime::ConfigOptions::AddConfigEntry]. It will be overwritten] already exists with value [Config key is empty or longer than maximum length Config value is longer than maximum length:  dimensions. for SizeToDimension. Tensor has onnxruntime::TensorShape::Sliceonnxruntime::TensorShape::SizeToDimensionD:\a\_work\1\s\onnxruntime\core\framework\tensor_shape.ccInvalid dimension of dimension <= num_dimsInvalid tensor shape slice argument.dimstart <= dimend && dimend <= values_.size() for SizeFromDimension. Tensor has onnxruntime::TensorShape::SizeFromDimension./.\\onnxruntime::SessionOptions::AddExternalInitializers.//onnxruntime::SessionOptions::AddExternalInitializersFromFilesInMemoryAn entry for this name has already been added: .\num_files == files_buffers.size()Buffer containing the initializer must be owned by the user.onnxruntime::SessionOptions::AddInitializerReceived nullptr for OrtValueReceived OrtValue is not a tensor. Only tensors are supported.Expecting same size spansinit_num == values.size()D:\a\_work\1\s\onnxruntime\core\framework\session_options.ccAn OrtValue for this name has already been added: Received nullptr for nameep.context_model_external_initializers_file_name(YXXY is expected to have type: Requested attribute: Attribute: D:\a\_work\1\s\onnxruntime\core\framework\op_node_proto_helper.cconnxruntime::OpNodeProtoHelper::GetAttrsGetAttrs failed. Expect values.size()=onnxruntime::OpNodeProtoHelper::GetAttrs but is of type: expected to be of type: is defined.No attribute with name: Attribute name and type don't matchNo attribute with this name is defined.D:\a\_work\1\s\onnxruntime\core/framework/node_index_info.hnode_offsets_index < node_offsets_size_onnxruntime::NodeIndexInfo::GetNodeOffsetInvalid arg_num of arg_num < arg_counts.size()onnxruntime::OpKernelContext::OutputMLValue. Num args is CPU allocator not foundonnxruntime::OpKernelContext::GetOrCreateOutputMLValueonnxruntime::OpKernelContext::NumVariadicInputsTempSpace allocator not foundExecution frame was nullframe != nullptrOpKernel was nullkernel != nullptronnxruntime::OpKernelContext::OpKernelContextD:\a\_work\1\s\onnxruntime\core\framework\op_kernel.ccfile_size type: onnxruntime::`anonymous-namespace'::TensorProtoToOrtValueImpl, Got string tensor requires allocator to be provided., external_data.length: External data type cannot be UNDEFINED or STRING.onnxruntime::utils::GetExternalDataInfoTensor does not have external data to read from.onnxruntime::`anonymous-namespace'::UnpackTensorWithRawDatanullptr == p_dataUnpackTensor: the pre-allocated size does not match the raw data size, expected onnxruntime::`anonymous-namespace'::ReadExternalDataForTensorUnexpected number of packed int4 pairsD:\a\_work\1\s\onnxruntime\core\framework\tensorprotoutils.ccMust have a valid data typeUnsupported sparse tensor data type of Element_size of: is not supported.onnxruntime::utils::UnpackInitializerData data_type: onnxruntime::utils::DenseTensorToSparseTensorProtoHasDataType(dense_proto)Invalid SparseTensor indices. Should one of the following types: int8, int16, int32 or int64Invalid SparseTensor indices. INT8 indices must be in the raw data of indices tensorInvalid SparseTensor indices. INT16 indices must be in the raw data of indices tensorSparse indices int32 data size does not match expectedonnxruntime::utils::SparseTensorProtoToDenseTensorProtoInvalid SparseTensor indices. Should be rank 0 or 1. Got:cur_index == indices_data.end()indices_shape[1] > 0 && static_cast(indices_shape[1]) == dims.size()NodeProto for Constant should have 1 output. Got:Unsupported attribute value type of in 'Constant' node 'onnxruntime::utils::ConstantNodeProtoToTensorProtoSparse indices int64 data size does not match expectedonnxruntime::utils::CopySparseDataSparse Indices raw data size does not match expected.node.output_size() == 1Missing raw data for tensor proto. Invalid tensor.Initialized tensor with unexpected type: tensor can't contain negative dimsstring tensor can not have raw dataConstant node: has no data attributesonnxruntime::utils::TensorToTensorProtoraw_dataMemory address tag is not supported by custom external data loader. does not match the tensor size: onnxruntime::utils::LoadExtDataToTensorFromTensorProtoPre-packed blob: onnxruntime::utils::TensorProtoToTensorTensorProto type can not be written into Tensor type TensorProtoToTensor() tensor shape mismatch! size to read: given file_length: are out of bounds or can not be read in full.onnxruntime::utils::GetExtDataFromTensorProto file_length: is out of bounds and can not read in fullExternal initializer: offset: data overflowUnpackTensor: the pre-allocate size does not match the size in protocorrupted protobuf data: tensor shape size() does not match the data size(utils::HasExternalData(tensor_proto)onnxruntime::utils::GetFileContentonnxruntime::utils::UnpackTensorUnpackTensor: the pre-allocated size does not match the size in protoTensorProto: external data size mismatch. Computed size: ) in protoExternal data type cannot be STRING.onnxruntime::utils::UnpackTensorWithExternalDataImplInvalid TensorProtoonnxruntime::utils::GetSizeInBytesFromTensorProtoOut of bounds dimensions in TypeProto_TensorUnsupported type: PD:\a\_work\1\s\onnxruntime\core\framework\kernel_def_builder.cconnxruntime::KernelDefBuilder::VariadicAliasinput_offset >= 0 && output_offset >= 0Failed to add kernel for : Conflicting with a registered kernel with op versions. the since version is: kernel def can't be NULLOp type ( and type ( kernel is not supported in Encountered following errors: (Kernel found kernel However the types are incompatible.Kernel not foundOp with name ( domain ( node_version: kernel start version: kernel_end_version: type_proto != nullptr in the supported version range (node_version: However the types are incompatible. Version mismatch.),This op has been implemented only for the following types (static_cast(first_arg_idx) < actual_inputs.size()D:\a\_work\1\s\onnxruntime\core\framework\kernel_registry.cconnxruntime::`anonymous-namespace'::MatchKernelDefTypes but the node in the model has the following type (ORT optimization- Force fallback to CPU execution for node: because the CPU execution path is deemed faster than overhead involved with execution on other EPs capable of executing this nodefloat8e5m2fnuzkernel_info != nullptrD:\a\_work\1\s\onnxruntime\core\framework\fallback_cpu_capability.cconnxruntime::GetCpuPreferredNodes::::operator ()Candidate for fallback CPU execution: float8e5m2float8e4m3fnuzfloat8e4m3fnonnxruntime::GetCpuPreferredNodesIExecutionProvider::Compile with FusedNodeAndGraph is not implemented by onnxruntime::CreateAllocatorReceived invalid value of arena_extend_strategy D:\a\_work\1\s\onnxruntime\core\framework\allocator_utils.cconnxruntime::sparse_utils::DenseTensorToSparseCooInvalid index: outer_num == (rows + 1)Outer indices must be M + 1. Got: onnxruntime::sparse_utils::SparseCsrToDenseTensorinner_num == src.Values().Shape().Size() > dense_size: onnxruntime::sparse_utils::SparseCooToDenseTensorExpecting indices to be equal the number of values or be twice as manyInput must be of COO formatUnsupported element size: D:\a\_work\1\s\onnxruntime\core\framework\sparse_utils.cconnxruntime::sparse_utils::DenseTensorToSparseCsrUnable to convert strings tensor to a sparse tensor that not on CPUExpecting inner indices to be same as nnz. Got: Unable to convert strings tensor to a sparse tensor that is not on CPUInput must be of CSR formatSupport 2-D matrices onlyCurrently do not support dims higher than 2 dimensions: The QLinear op [onnxruntime::NodeUnit::InitForSingleNode output idx:QDQ node group cannot have an output from the target node that is consumed by a Q node and a graph output. target node: onnxruntime::NodeUnit::NodeUnit] is not supportedQDQ node group cannot have DQ that doesn't have a single output edge to the target node. DQ node: QDQ node group cannot have target node with more than one output edge if there is redunant clip node. target node: QDQ node group cannot have redudant clip node that doesn't have a single output edge to a Q node. redundant clip node: QDQ node group cannot have an output from the target node being consumed by a Q node and a non-Q node. target node: , target node: QDQ node group cannot have DQ node that produces a graph output. DQ node: onnxruntime::QDQ::NodeGroup::CanCreateNodeGroupD:\a\_work\1\s\onnxruntime\core\framework\node_unit.ccQLinearReduceMeanonnxruntime::DataTransferManager::CopySparseTensors to There's no data transfer registered for copying tensors from onnxruntime::DataTransferManager::CopyTensorsD:\a\_work\1\s\onnxruntime\core\framework\data_transfer_manager.ccdata_transfer registered is nullptr.Tensor size mismatchInternalTestingExecutionProvideronnxruntime::utils::CopyOneInputAcrossDevicesonnxruntime::utils::CopyOutputsAcrossDevicesonnxruntime::utils::ExecuteGraphImplonnxruntime::utils::ExecuteGraph, this is not supported yet. while parent graph node running on device: Subgraph has nodes running on device: onnxruntime::utils::UpdateWithParentStreamfetch_alloc_info.size() == copy_info.size()onnxruntime::utils::FinalizeCopyInfoForFetchescopy_info.size() == num_feedsonnxruntime::utils::CopyInputsAcrossDevicesonnxruntime::utils::CalculateStaticCopyInfoForFeedsonnxruntime::utils::InitializeFeedFetchCopyInfofeed_locations.size() == copy_info.size()onnxruntime::utils::FinalizeCopyInfoForFeedsexec_plan_ptronnxruntime::utils::CalculateStaticCopyInfoForFeednode_info.stream_index < 0node_info_vec[i].stream_index < 0onnxruntime::utils::BatchOrCopyMLValueD:\a\_work\1\s\onnxruntime\core\framework\utils.ccUnsupported OrtValue type to copy between device.onnxruntime::utils::FindDeviceForValueinvalid allocator.Unsupported OrtValue type.Failed to find allocator for device onnxruntime::KernelTypeStrResolver::RegisterOpSchemaOp schema must be available.onnxruntime::KernelTypeStrResolver::RegisterNodeOpSchemaonnxruntime::KernelTypeStrResolver::RegisterGraphNodeOpSchemasFailed to find args for kernel type string '', but the existing argument with that formal parameter name has a different formal parameter type string.Kernel type string already exists for formal parameter name 'onnxruntime::KernelTypeStrResolver::RegisterOpSchema::::operator ()D:\a\_work\1\s\onnxruntime\core\framework\kernel_type_str_resolver.ccensure that they are used in the kernel def type constraints instead of op input or output names. Not doing so will result in this error.If type constraint names are available, ' for node type 'Invalid ORT format model.Failed to find op_id: onnxruntime::KernelTypeStrResolver::ResolveKernelTypeStrargs entry is null.Duplicate entry for kernel type str: Duplicate entry for op id: onnxruntime::OpSchemaKernelTypeStrResolver::ResolveKernelTypeStrkernel_type_str_args is null.kernel_type_str_args entry is null.kernel_type_str is null.args is null.onnxruntime::KernelTypeStrResolver::SaveToOrtFormatop_kernel_type_str_args is null.op_kernel_type_str_args entry is null.op_id is null.$-Caught exception while loading custom ops with message: has already been loaded.A dso with name onnxruntime::ExLibLoader::LoadExternalLibD:\a\_work\1\s\onnxruntime\core\framework\ex_lib_loader.ccUnloading DSO onnxruntime::ExLibLoader::~ExLibLoaderFailed to unload DSO: Caught exception while destructing CustomOpsLoader with message: onnxruntime::ExLibLoader::{dtor}::::operator ()session.save_external_prepacked_constant_initializerssession.disable_prepackingsession.node_partition_config_fileNode placements]. Number of nodes: All nodes placed on [ Node(s) placed on [) node with name 'Could not find an implementation for onnxruntime::VerifyEachNodeIsAssignedToAnEpImplonnxruntime::VerifyEachNodeIsAssignedToAnEponnxruntime::SessionState::GetNodeIndexInfoMain Graph instance should have populated all subgraphs when being resolved.subgraphonnxruntime::SessionState::CreateSubgraphSessionStateexisting_entries.find(attribute_name) == existing_entries.cend()onnxruntime::SessionState::AddSubgraphSessionStateSetGraphAndCreateKernels must be called prior to GetExecutionInfo.node_index_info_.has_value()onnxruntime::SessionState::AddOutputNameToNodeInfoMapping for attribute Entry exists in node Using an input in multiple nodes on different devices is not supported currently. Input:Failed to find input name in the mapping: Only one node should produce an output. Existing entry for output_names_to_nodeinfo.empty()Unable to write the provided PrePackedWeights instance into the container () and node is used by node !op_type.empty() which is of op type: used in the node: Using cached version of pre-packed weight for constant initializer: onnxruntime::SessionState::PrepackConstantInitializedTensors::::operator () doesn't have an implementation that can cache computed pre-packed weightsweights_to_be_filled_in.buffers_.size() > 0The op type of a node cannot be emptyThe kernel corresponding to the node +Weight pre-packing was canceled due to user request.allocator_for_caching.get() != nullptr. Do you have duplicated calls to SessionState::AddInitializedTensor function?duplicated ort_value index:onnxruntime::KernelUseSharedPrePackedBuffers doesn't have an implementation that can consume provided pre-packed weightsamong attributes: '), removable attributes removed ' ('for node 'failed at retrieving the removable attributesonnxruntime::SessionState::PruneRemovableAttributesentry != kernel_create_info_map_.cend()onnxruntime::SessionState::GetNodeKernelCreateInfoonnxruntime::SessionState::CreateKernels').onnxruntime::SessionState::CreateGraphInfoD:\a\_work\1\s\onnxruntime\core\framework\session_state.ccDone saving OrtValue mappings.onnxruntime::SessionState::PopulateKernelCreateInfoSaveMLValueNameIndexMappingp_op_kernel Index:' OpType:Missing session state for subgraph. Node:'entry != node_to_subgraph_ss.second.cend()onnxruntime::SessionState::FinalizeSessionStateImpl::::operator ()onnxruntime::SessionState::FinalizeSessionStateImplSessionState finalize is canceled due to user request Attribute:onnxruntime::OuterScopeNodeArgLocationAccumulator::::operator ()onnxruntime::OuterScopeNodeArgLocationAccumulatorsubgraphs_kernel_create_info_maps.find(local_subgraph_kernel_create_info_map_key) == subgraphs_kernel_create_info_maps.end()onnxruntime::AccumulateAllNestedSubgraphsInfoonnxruntime::SessionState::GetSaveModeForPrepacks Ignoring the flag.Serializing optimized model in ORT format with external pre-packed constant initializers is not supported.onnxruntime::OuterScopeNodeArgLocationAccumulator::::operator ()Some nodes were not assigned to the preferred execution providers which may or may not have an negative impact on performance. e.g. ORT explicitly assigns shape related ops to CPU to improve perf.Rerunning with verbose output on a non-minimal build will show node assignments.onnxruntime::SessionState::FinalizeSessionStateSavePrePackedConstantInitializers is set to true but the model is not being saved. Ignoring the flag.onnxruntime::DeviceStreamCollectionImpl::GetStreamidx < num_streams_onnxruntime::DeviceStreamCollectionImpl::AddDeviceStreamonnxruntime::DeviceStreamCollectionImpl::SetDeviceStreamstream_idx < num_streams_onnxruntime::DeviceStreamCollectionImpl::CleanUpD:\a\_work\1\s\onnxruntime\core\framework\device_stream_collection.ccThe node is not placed on any Execution Provider. Failed to find kernel for found duplicated provider '). ' ep:' (node:' in KernelRegistryManagernullptr != type_protoonnxruntime::utils::GetMLDataTypeD:\a\_work\1\s\onnxruntime\core\framework\mldata_type_utils.ccktsrD:\a\_work\1\s\onnxruntime\core\framework\kernel_type_str_resolver_utils.ccFailed to verify KernelTypeStrResolver flatbuffers data.onnxruntime::kernel_type_str_resolver_utils::LoadKernelTypeStrResolverFromBufferonnxruntime::kernel_type_str_resolver_utils::AddLayoutTransformationRequiredOpsToKernelTypeStrResolverexternal_data_loader registered is nullptr.D:\a\_work\1\s\onnxruntime\core\framework\feeds_fetches_manager.cconnxruntime::FeedsFetchesInfo::MapNamesToMLValueIdxsError mapping output names: Error mapping feeds: onnxruntime::FeedsFetchesManager::SetDeviceCopyChecksinput_copy_needed != DeviceCopyCheck::Unknown && output_copy_needed != DeviceCopyCheck::Unknown was inserted using the NHWC format as requested by , but was not selectedNode ' with domain:Graph partitioning was canceled by user requestD:\a\_work\1\s\onnxruntime\core\framework\graph_partitioner.cc by that EP. This means the graph is now invalid as there will not be an EP able to run the node. This could be a bug in layout transformer, or in the GetCapability implementation of the EP.GetCapabilities was canceled by user requestonnxruntime::GetCapabilityForEP cannot be used with this model due to its ONNX opset not being supported by the layout transformer.API VERSION Create state function failed. Return value:D:\a\_work\1\s\onnxruntime\core/framework/func_kernel.honnxruntime::FunctionKernel::CreateAOT inlining completed. () functions of (onnxruntime::GraphPartitioner::PartitionNo provider specified.This model does not have any local functions defined. AOT Inlining is not performedonnxruntime::PartitionOrtFormatModel) pruned.onnxruntime::GraphPartitioner::InlineFunctionsAOTonnxruntime::PartitionOnnxFormatModelCannot serialize ONNX ModelProto larger than 2GBsingle_node_compute_func should have 1 element.onnxruntime::PartitionOrtFormatModelImplonnxruntime::CreateEpContextModelCompiled model does not contain any EPContext nodes. Check that the session EPs support compilation and can execute at least one model subgraph../model_ext_ini.binCompiled model does not contain any EPContext nodes. Either the session EPs do not support compilation or no subgraphs were able to be compiled.Both ep_context_path and model_path are empty._ctx.onnxFailed to generate EP context model since the file '' exist already. Please remove the EP context model if you want to re-generate it.onnxruntime::InlineFunctionsAOTImplonnxruntime::InlineNodescontext_file_path should not point to a folder.AOT inlining is canceled due to user request.User disabled EP compilation but EP '' needs to compile one or more nodes. did not return correct number of compiled functionsGraph partitioning is canceled due to user request.onnxruntime::PartitionOnnxFormatModelImplEPContextThe Function Style fusion is deprecated.The optimization function failed to finish.dFailed to find a free memory block despite calling Extend. rounded_bytes=Extending BFCArena for (actual) rounded_bytes:onnxruntime::BFCArena::AllocateRawInternal. bin_num: (requested) num_bytes: h != kInvalidChunkHandletried to allocate 0 bytesReserving memory in BFCArena for size: reserved_chunks_.find(ptr) == reserved_chunks_.end()onnxruntime::BFCArena::ReserveAllocated memory at Total allocated bytes: bytes.Failed to allocate memory for requested buffer of size onnxruntime::BFCArena::ExtendExtended allocation by onnxruntime::BFCArena::Extend::::operator ()Incorrect arena extend strategy.Available memory of is smaller than requested bytes of onnxruntime::BFCArena::ChunkFromHandleh < chunks_.size()hipMalloccudaMallocBinForSize(bin_size + 255) == BinFromIndex(b)BinForSize(bin_size) == BinFromIndex(b)BinForSize(bin_size * 2) != BinFromIndex(b)BinForSize(bin_size * 2 - 1) == BinFromIndex(b)D:\a\_work\1\s\onnxruntime\core\framework\bfc_arena.cconnxruntime::BFCArena::BFCArenaCreating bins of max chunk size max_dead_bytes_per_chunk: initial_growth_chunk_size_bytes: Creating BFCArena for with following configs: initial_chunk_size_bytes: arena_extend_strategy: onnxruntime::BFCArena::RegionManager::RegionFor max_power_of_two_extend_bytes: memory limit: entry != regions_.end()Could not find Region for: Could not find Region for onnxruntime::BFCArena::RegionManager::RemoveAllocationRegionp_int >= base_intD:\a\_work\1\s\onnxruntime\core/framework/bfc_arena.hp_int < base_int + memory_size_onnxruntime::BFCArena::AllocationRegion::IndexForonnxruntime::BFCArena::AllocationRegion::AllocationRegion0 == memory_size % kMinAllocationSizeonnxruntime::BFCArena::Coalesce!c->in_use()BinFromIndex(c->bin_num)->free_chunks.erase(h) > 0Could not find chunk in binonnxruntime::BFCArena::FreeAndMaybeCoalescec->in_use() && (c->bin_num == kInvalidBinNum)!c->in_use() && (c->bin_num != kInvalidBinNum)onnxruntime::BFCArena::InsertFreeChunkIntoBinonnxruntime::BFCArena::RemoveFreeChunkFromBinonnxruntime::BFCArena::RemoveFreeChunkIterFromBin!c1->in_use() && !c2->in_use() && c1->stream == c2->streamonnxruntime::BFCArena::DeallocateRawInternalc2->prev == h1onnxruntime::BFCArena::Merge bytes. The total allocated bytes is now onnxruntime::BFCArena::Shrink BFC Arena shrunk by onnxruntime::BFCArena::FindChunkPtr!chunk->in_use()onnxruntime::BFCArena::SplitChunk!c->in_use() && (c->bin_num == kInvalidBinNum)Invalid format for: Invalid resource partitioning settingssplits.size() == 5 : expecting comma separated fieldsonnxruntime::NodeStatsRecorder::CreateAccountantsFailed to open file D:\a\_work\1\s\onnxruntime\core\framework\resource_accountant.ccInvalid line in the file onnxruntime::LoadNodeAllocationStatsFailed to open file: #name,input_sizes,initializers_sizes,total_dynamic_sizes,total_temp_allocations onnxruntime::NodeStatsRecorder::DumpStatsofs.is_open()session.resource_cuda_partitioning_settingsonnxruntime::NodeIndexInfo::GetMLValueIndexoffset >= 0 && static_cast(offset) < node_values_size_, block in memory pattern size is: but the actual size is: Failed to get allocator for For ort_value with index: Trying to allocate memory for unused optional inputs/outputs returned nullptr, fall back to default allocation behavioronnxruntime::ExecutionFrame::AllocateMLValueTensorSelfOwnBufferHelperonnxruntime::ExecutionFrame::{ctor}::::operator ()Allocation of memory pattern buffer for onnxruntime::ExecutionFrame::ExecutionFramebuffers_.find(location) == buffers_.end() entries which doesn't match the number of fetches the frame was initialized with of fetches.empty() || fetches.size() == fetch_mlvalue_idxs_.size() failed. Error:Fetches vector passed to GetOutputs contains Get feed size: but expected feed size: onnxruntime::IExecutionFrame::Initfeeds.size() == feed_mlvalue_idxs.size()onnxruntime::IExecutionFrame::GetOrCreateNodeOutputMLValueshape && tensor.Shape() == *shapeinvalid index shape && sp_tensor.DenseShape() == *shapeonnxruntime::IExecutionFrame::IExecutionFramenode_index_info_.GetMaxMLValueIdx() == ort_value_idx_map.MaxIdx()OrtValue shape verification failed. Current shape: Requested shape:onnxruntime::AsStreamBasedAllocatorallocator.get() != nullptrnode_index_info and ort_value_idx_map are out of sync and cannot be usedD:\a\_work\1\s\onnxruntime\core\framework\execution_frame.ccallocator is nullptrCould not find OrtValue with idx 'D:\a\_work\1\s\onnxruntime\core/framework/execution_frame.honnxruntime::IExecutionFrame::GetMLValueort_value_index >= 0 && static_cast(ort_value_index) < all_values_size_Memory pattern planner is not enabled on this execution framework.TraceFree for ort_value_idx=onnxruntime::ExecutionFrame::TraceAllocateTraceAllocation for ort_value_idx=onnxruntime::ExecutionFrame::TraceFreeort_value_idx >= 0 && static_cast(ort_value_idx) < alloc_plan.size()Expected shape from model of does not match actual shape of onnxruntime::ExecutionFrame::ReleaseMLValueImplonnxruntime::ExecutionFrame::VerifyOutputSizesAllocation of tensor types requires a shape.We don't expect custom allocators for non-tensor types, so a shape is mandatory here. for output Invalid allocation kind: onnxruntime::ExecutionFrame::AllocateReusedOrtValueIfNotAllocatedHelperShape mismatch attempting to re-use buffer. onnxruntime::ExecutionFrame::AllocateAsPerAllocationPlanort_value_index >= 0 && static_cast(ort_value_index) < alloc_plan.size()!is_strided_tensoralloc && alloc.get() != nullptr. Validate usage of dim_value (values should be > 0) and dim_param (all values with the same string should equate to the same size) in shapes in the model.onnxruntime::ExecutionFrame::AllocateMLValueTensorPreAllocateBufferD:\a\_work\1\s\onnxruntime\core\framework\node_index_info.cconnxruntime::NodeIndexInfo::Init::::operator ()onnxruntime::NodeIndexInfo::Init::::operator ()D:\a\_work\1\s\onnxruntime\core\framework\transpose_helper.cconnxruntime::TransposeSingleAxisOutwardsonnxruntime::OrtValueTensorSlicer::Iterator::Iteratordim0_offset < dim0_sizeInvalid dim0_offset of onnxruntime::OrtValueTensorSlicer::Createonnxruntime::OrtValueTensorSlicer::Iterator::IteratorInsufficient dimensions to slice on . Shape:. Dimension 0 is gsl::narrow_cast(tensor_shape.NumDimensions()) >= slice_dimensionort_value.IsAllocated()OrtValue has not been allocated so can't be sliced.!prim_type->HasSubElems()Can't slice a tensor with a subbyte element typeort_value.IsTensor()Can't slice a non-tensor OrtValue. Type was D:\a\_work\1\s\onnxruntime\core\framework\ort_value_tensor_slicer.cconnxruntime::OrtValueTensorSlicer::CreateD:\a\_work\1\s\onnxruntime\core\framework\library_handles.cconnxruntime::LibraryHandles::UnloadLibrariesonnxruntime::utils::detail::CopyLittleEndiansource and destination buffer size mismatchD:\a\_work\1\s\onnxruntime\core\framework\endian_utils.ccprepacked_;0Prepacked weights not found for key model format error! Missing 'location'onnxruntime::ExternalDataInfo::WritePrepackedToFileAndAddToProtoprepacked_weights != nullptrprepackedchecksummodel format error!|onnxruntime::ExternalDataInfo::CreatelocationlengthD:\a\_work\1\s\onnxruntime\core\framework\tensor_external_data_info.ccmodel format error! Need a value for the external data infomodel format error! Need a key for the external data infoD:\a\_work\1\s\onnxruntime\core\framework\prepacked_weights_container.ccUnsupported device allocator in the context of pre-packed weights caching: onnxruntime::PrepackedWeightsContainer::GetOrCreateAllocatoronnxruntime::ExecuteThePlan node. Name:'Non-zero status code returned while running D:\a\_work\1\s\onnxruntime\core\framework\sequential_executor.cc' Status Message: Async Kernel Support is not implemented yet.onnxruntime::ExecuteKernelthread_scheduling_stats_kernel_timeinput_type_shapeoutput_type_shapeparameter_sizeoutput_sizenode_indexactivation_sizeop_nameprovider{"SequentialExecutor::Execute]}":[onnxruntime::OpKernelContextInternal::OpKernelContextInternalD:\a\_work\1\s\onnxruntime\core/framework/op_kernel_context_internal.hAll implicit inputs should have OrtValue instances by now. entry != nullptr does not. cannot reuse input buffer for node nullptr != tensor_type_baseonnxruntime::PlannerImpl::FindReusableInput as it has external outputs is reusing input buffer of node Be noted Node Be cautious the reuse MUST be a read-only usage. which has external outputs. reused != reused_foronnxruntime::PlannerImpl::Reuseid >= 0 && static_cast(id) < ort_value_info_.size()onnxruntime::PlannerImpl::ProcessDefn >= 0 && static_cast(n) < plan_.allocation_plan.size()onnxruntime::PlannerImpl::AllocPlanonnxruntime::PlannerImpl::UseCountonnxruntime::PlannerImpl::Bufferinvalid value index: n >= 0 && static_cast(n) < ort_value_info_.size()onnxruntime::PlannerImpl::Index against size onnxruntime::GetKernelCreateInfoD:\a\_work\1\s\onnxruntime\core\framework\allocation_planner.ccSessionState should have saved the KernelCreateInfo prior to this running. NodeIndex:entry != kernel_create_info_map.cend()cannot use insert() with onnxruntime::IGraphPartitioner::CreateGraphPartitionerUse DeviceBasedPartition as defaultonnxruntime::DeviceBasedPartitioner::SaveConfigCaught exception when reading partition config file: node_names_by_stream_.size() == device_types_.size()Caught exception during saving DeviceBasedPartitioner config: devicesNumber of streams does not equal to number of device types!onnxruntime::DeviceBasedPartitioner::Initializestreamsonnxruntime::DeviceBasedPartitioner::PartitionGraphPartitioner type is not DeviceBasedPartitionerFailed to find node "iter != node_stream_map.end()DeviceBasedPartitioner" in node-stream maptrigger_point_it != node_to_trigger_points.end()onnxruntime::PlannerImpl::CreatePlanonnxruntime::PlannerImpl::BuildExecutionPlanexecution_plan[plan_.node_stream_map_[node_index]]->device_.Type() == node_device_mem_location.Type()onnxruntime::PlannerImpl::GenerateDeallocationPlanonnxruntime::PlannerImpl::PartitionIntoStreamsonnxruntime::PlannerImpl::GenerateDeallocationPlan::::operator ()onnxruntime::PlannerImpl::ComputeSingleStreamReusePlanStrided tensor is not supported in non-training build for now.Only tensors are supported for external outputs for now.!IsNonTensor(*node_output)onnxruntime::PlannerImpl::OptimizeReusePlanForMultiStreamonnxruntime::PlannerImpl::ComputeReusePlan as input reused by Be noted input buffer onnxruntime::PlannerImpl::OptimizeReusePlanForMultiStream::::operator ()onnxruntime::PlannerImpl::OptimizeReusePlanForMultiStream::::operator () which has external outputs is reused. specific_subgraph_kernel_create_info_map != subgraphs_kernel_create_info_maps_.end()onnxruntime::PlannerImpl::GeneratePlanForWeightsHelperkernel_create_info.kernel_def != nullptronnxruntime::PlannerImpl::GetAliasMaponnxruntime::PlannerImpl::GetLocationForNodeInputKernelDef is null for node: onnxruntime::PlannerImpl::ComputeValueLocationCan not find the execution provider Should not have entry in kernel create info with nullptr for kernel_defp_kernel_deffound_in_outer_scope_location_maponnxruntime::PlannerImpl::ComputeValueLocation::::operator ()onnxruntime::PlannerImpl::ComputeReuseCountThere is no location for this node arg in the outer scope location maponnxruntime::PlannerImpl::GetElementSizeCan not find the node cannot use operator[] with a string argument with 0123456789ABCDEFcannot use operator[] with a numeric argument with FF�invalid UTF-8 byte at index incomplete UTF-8 string; last byte: 0x: 0x\u%04xnull}{"bytes":[],"subtype":], "subtype": [ "bytes": [":[]": , {}{ 00010203040506070809101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899ƚpOܼw kAV<Ѝ,U1(\QFɦqaˋ#w"{mSx@I̮ Wζ]y<7VM6OH8oꖐ$:%˅t,φ4* 45<5*g8PD;?ȄkL'DŅT%Οk\b}$lۺd_ Xfl&ޓt |J|l_b%S04`?U&NZ~)p$wt帟ߦ}t_ϛpDk11eU%{?;+*\.Ӓsi$$Icd~̈Po ̼,eXѳ@ bx x9?{Η8$p\{2~S,h髤8m4E"&'O<'1cDȌ8eްLeǃTqB]\X,iM dpd'lJwmBtk}{x \|wyTwś[[=]S5Ȭ\*_F%94›\rξTSܷ1A"Lx\ӛ fS!{Z:0ܵ\S٨second->data_type() != ONNX_NAMESPACE::TensorProto_DataType_STRINGOrtValue index: entry != initialized_tensors_to_allocate.end()ort_value_name_idx_map.MaxIdx() > -1 from initializer_allocation_order not found among initialized tensorsonnxruntime::session_state_utils::SaveInitializedTensorsOrtValue indexes should have been populated.onnxruntime::session_state_utils::SaveInitializedTensors::::operator ()Saving initialized tensors.) because the ORT planned memory location device Cannot use user supplied initializer with name: (Failed to copy tensor to ) is different from what is supplied (Internal error. The preallocated buffer is too small. Requires onnxruntime::session_state_utils::AllocateTensorOnDeviceOrMemoryonnxruntime::session_state_utils::DeserializeTensorProtostring tensor is not supported for copying between allocatorsDeserializeTensorProto() takes either pre-allocated buffer or an allocator!With buffered tensor, tensor proto must use external location and point to buffered tensorD:\a\_work\1\s\onnxruntime\core\framework\session_state_utils.cconnxruntime::session_state_utils::ExtDataTensorProtoToTensoronnxruntime::session_state_utils::AllocateBufferUsingDeviceAllocatorFromShapeAndTypesession.use_device_allocator_for_initializersGraph input with name Subgraphonnxruntime::session_state_utils::SaveInputOutputNamesToNodeMapping is not used by any node.onnxruntime::session_state_utils::SaveInputOutputNamesToNodeMapping::::operator ()onnxruntime::session_state_utils::SaveInputOutputNamesToNodeMapping::::operator ()Deserialize tensor Done saving initialized tensorsUsing user supplied initializer with name ( failed.Saving session state weights is canceled due to user request.Skipping entry for missing optional value at idx bytes for [Memory] SessionStateInitializer statically allocates Get preallocated buffer for initializer 'onnxruntime::TensorAllocatorWithMemPattern::TraceWeight buffer for initializer '' failedMem pattern for initializer ' is not foundInternal error. is not foundonnxruntime::TensorAllocatorWithMemPattern::FinalizePlanD:\a\_work\1\s\onnxruntime\core\framework\tensor_allocator_with_mem_pattern.hFailed to get allocator for location: duplicated locationD:\a\_work\1\s\onnxruntime\core\framework\fuse_nodes_funcs.cc not found.onnxruntime::FuncManager::GetFuncsfunc info for node: Can't use func with null ptr already exist.current <= buffer_size_onnxruntime::MemPatternPlanner::TraceAllocationD:\a\_work\1\s\onnxruntime\core/framework/mem_pattern_planner.h!using_counters_D:\a\_work\1\s\onnxruntime\core\framework\stream_execution_context.ccframe_.ReleaseMLValue(static_cast(execution_plan->release_actions[idx].value_index)).IsOK()idx < device_stream_map_->NumStreams()onnxruntime::StreamExecutionContext::GetDeviceStreamD:\a\_work\1\s\onnxruntime\core/framework/stream_execution_context.hv >= 0onnxruntime::StreamExecutionContext::CountDownBarrier::Setsince == endonnxruntime::RunSinceonnxruntime::StreamExecutionContext::RecycleNodeInputsExiting due to terminate flag being set to true.NodeIndex: , Name: WaitOnEP - NotificationId: stream wait on Notification with id: D:\a\_work\1\s\onnxruntime\core\framework\execution_steps.cconnxruntime::WaitOnEPStep::Executewait_handle_WaitOnEPStep.wait_handle is nullBarrier - BarrierId: , Count: TriggerDownstream - TriggerPointIndex: ActivateNotification - NotificationId: onnxruntime::ActivateNotificationStep::Execute activate notification with index LaunchKernel - ConstantNoOpALLOW_RELEASED_ONNX_OPSET_ONLYD:\a\_work\1\s\onnxruntime\core/framework/tensorprotoutils.honnxruntime::utils::GetShapeshape != nullptrTypeProto must have shape for this to runNode::LoadEdgesFromOrtFormat, edge is missing for Node::LoadFromOrtFormat, input_arg_counts is missingSerialization error. Graph attribute was serialized without Graph instancefbs_attr cannot be nullonnxruntime::Node::LoadFromOrtFormatLoadNodeArgsFromOrtFormat: Node [], could not find NodeArg node_arg_name cannot be nullonnxruntime::Node::LoadFromOrtFormat::::operator ()fbs_node_arg_names cannot be nullonnxruntime::Node::SaveToOrtFormatdoes not have the graph for key Removable attributes were removed before the node is saved.Serialization of fused function body is not currently supported, Node [] op_type [onnxruntime::Node::ToProtoRemovable attributes were removed before the conversion is started.Optional Type mismatch. Expected: . Got: onnxruntime::NodeArg::UpdateTypeAndShapeType mismatch. Current= Input=Tensor element type mismatch. D:\a\_work\1\s\onnxruntime\core\graph\graph.cconnxruntime::MergeShapeInfoError merging shape info for output. '' source: target:. Falling back to lenient merge. , or sparse tensors , or optional typed entitiesSource and target must both be tensorsOutput:Node:Size mismatch validating subgraph inputs. Got inputs but subgraph has inputs and requires inputs. Either provide all subgraph inputs, or just the required inputs.No Graph instance was found for attribute in node Graph attribute inferencing failed: This is an invalid model. Error: the graph is not acyclic.onnxruntime::Graph::KahnsTopologicalSortSome nodes are not included in the topological sort, graph have a cycle.Invalid model. Node input '' is not a graph input, initializer, or output of a previous node.This is an invalid model. Failed to find NodeArg in all parent graphs. Name=This is an invalid model. At top level graph without matching NodeArg that subgraph consumes. Name= Graph may not conform to the ONNX spec and contain initializers that are not graph inputs.onnxruntime::Graph::BuildConnectionsArgument mismatch when removing edge.Invalid destination node arg slot specified when removing edge.Invalid source node arg slot specified when removing edge.onnxruntime::Graph::RemoveEdgeInvalid node indexes specified when removing edge.Argument type mismatch when adding edge.Invalid destination node arg slot specified when adding edge.Invalid source node arg slot specified when adding edge.onnxruntime::Graph::AddEdgeInvalid node indexes specified when adding edge.This is an invalid model. Error: Duplicate definition of name (This is an invalid model. Error: two nodes with same node name (This is an invalid model. Subgraph output () is an outer scope value being returned directly. Please update the model to add an Identity node between the outer scope value and the subgraph output.This is an invalid model. Graph output () does not exist in the graph.node_argGraph ctor should have created NodeArg for initializer. Missing:onnxruntime::Graph::InitializeStateFromModelFileGraphProtograph_inputs_excluding_initializers_.empty() && graph_inputs_including_initializers_.empty() && value_info_.empty() && graph_outputs_.empty()Graph state to be loaded into must be empty.Initializer appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.This is an invalid model. Tensor does not have type information.Duplicate initializer (dense, sparse or ConstantNode): '' the model will use the latest encountered initializer. Please, fix your model.Duplicate sparse_tensor_initializer: 'utils::HasName(sparse_tensor)Sparse initializer must have a name. This model is invalidGraph loading canceled due to user request.Duplicate constant node sparse initializer name: '' Model is invalid.onnxruntime::Graph::Graphgraph_proto != nullptrgraph_proto cannot be nullThis is an invalid model. The sum of input arg count is not equal to size of input defs in node (output edgesinput edgesonnxruntime::Node::LoadEdgesFromOrtFormatinput index: is not the same as this node's index:onnxruntime::Node::LoadEdgesFromOrtFormat::::operator ()Replacement tensor's data type does not match.Replacement tensor's dimensions do not match.Trying to replace non-external initializer with external dataonnxruntime::Graph::ReplaceInitializedTensorImplFailed to find existing initializer with name !foundgraph_proto_ is not in sync with name_to_initial_tensor_.onnxruntime::Graph::RemoveInitializedTensorsparse_tensor_names_.count(tensor_name) == 0sparse_tensor_names_ not in sync with name_to_initial_tensor_onnxruntime::Graph::ForThisAndAllSubgraphsonnxruntime::Graph::AddInitializedTensorexisting->second == &tensorAddInitializedTensor already has tensor with name but different TensorProto.outer_scope_node_args_consumed.empty()Shouldn't be possible to have NodeArgs that haven't been handled already.onnxruntime::Graph::Resolveonnxruntime::Graph::PerformTypeAndShapeInferencingonnxruntime::Graph::InitInputsInitializersOutputsError: Duplicate definition-site for () attribute () is required but not specified.Fatal error: ) is not a registered function/oponnxruntime::Graph::VerifyNodeAndOpMatchThis is an invalid model. In Node, , Error Type Error: Shape of initializer does not match. Type Error: Data in initializer '' has element type but usage of initializer in graph expects This is an invalid model. Model input () does not have type information.Type Error: Type () of output arg () of node () does not match expected type () output arg () type inference failed, inferred types: index=onnxruntime::Graph::InferAndVerifyTypeMatchType Error: Type parameter () of Optype () bound to different types ( in node (This is an invalid model. Type Error: Type '' of input parameter () of operator () in node () is invalid.This is an invalid model. Node () input arg () does not have type information set by parent node.Node () Op () onnxruntime::Graph::UpdateShapeInferencenode.GetAttributeNameToMutableSubgraphMap().empty()UpdateTypeShapeInference is not intended to be used with control flow nodes containing subgraphsonnxruntime::Graph::InferAndVerifySubgraphTypesSubgraph input missing type. conflicts with graph initializer. Check name generation above._if_onnxruntime::Graph::InlineIfSubgraphUnable to constant fold If node: '' Unable to fetch: insert_result.secondConstant node name: conflicts with graph initializer. Check that the node names have been made unique.onnxruntime::Graph::AddConstantProtoAsInitializerdst_implicit_input_idx < (int)node->ImplicitInputDefs().size()onnxruntime::Graph::FinalizeFuseSubGraphSetOpSchemaFromRegistryForNode(fused_node)Schema was not found for fused node. Domain: OpType: SinceVersion:onnxruntime::Graph::CreateFusedSubGraphNodenullptr != func_meta_defonnxruntime::Graph::AllocateNodenodes_.size() < static_cast(std::numeric_limits::max()) must be either specified in graph inputs or graph initializers.Removing NodeArg 'outer_scope_node_arg != nullptr'. It is no longer used by any node.Removing initializer ''. It is not used by any node and should be removed from the model.onnxruntime::Graph::CleanUnusedInitializersAndNodeArgsinitializer_node_arg != nullptrCannot find NodeArgs for [onnxruntime::Graph::ToGraphProtoInternalOuter scope node arg name ''was added but does not exist. Failed to flush file with external initializers: external_stream.is_open()Failed to open for writing:onnxruntime::Graph::ToGraphProtoWithExternalInitializersexternal_file_path.is_relative()processed_weights.find(weight_name) != processed_weights.end()Failed to write pre-packed blobs to external fileFailed to write external initializers to file: Failed writing external data to: Subgraph is referred to in GetAttributeNameToSubgraphMap, but not found in node while attempting to recurse into it.onnxruntime::Graph::AddExternalInitializersToGraphProtoImpl not found in output_graph_protoonnxruntime::Graph::ToGraphProtoFailed to convert dense initializer to sparseonnxruntime::Graph::ToGraphProto::::operator ()onnxruntime::Graph::RemoveNodenode->GetOutputEdgesCount() == 0Can't remove node as it still has output edges._token_onnxruntime::Graph::SaveToOrtFormatExternal file: not found from the table user provided.onnxruntime::Graph::InjectExternalInitializersFromFilesInMemoryReplaced external initializer: onnxruntime::Graph::InjectExternalInitializedTensorsexisting_entry != mutable_initializers.pointer_end()graph_proto_ is not in sync with name_to_initial_tensor_D:\a\_work\1\s\include\onnxruntime\core/common/const_pointer_container.honnxruntime::ConstPointerContainer > >::atindex < data_.size()Domain version can not be changed for ''. Current version: onnxruntime::Graph::LoadFromModelEditorApiModel is an invalid 'Constant' node. Must have no inputs, one attribute and one output. onnxruntime::Graph::LoadFromModelEditorApiModel::::operator ()v.IsTensor()Initializers must be Tensorsonnxruntime::`anonymous-namespace'::OrtValueInfoToOnnxvi.type_info->type == ONNX_TYPE_TENSORInternal error. Model Editor API should only allow OrtValueInfo for tensor to be created.NodeEdge is missing. Invalid ORT format model.Node index is out of rangeNode is missing. Invalid ORT format model.NodeArg is missing. Invalid ORT format model.Sparse Initializer tensor is missing. Invalid ORT format model.Duplicate initializer (dense or ConstantNode): 'Initializer tensor is missing. Invalid ORT format model.onnxruntime::Graph::LoadFromOrtFormat::::operator ()NodeArg Name is missing. Invalid ORT format model.onnxruntime::Graph::LoadFromOrtFormat) -> (""(") : (onnxruntime::Graph::SetInputsinput->Exists()Input to set must exist. in inlined subgraph: conflicts with graph initializer. Check Specializing code.onnxruntime::Graph::InlineFunctioncallnode.TryGetFunctionProto(inlined_fp)Node has no function body and cannot be inlined._inlfunc_onnxruntime::Graph::InlineFunctionProtoConstant folded (inlined) for If node: Node's: input: is not If node's input or previous node output in this subgraphInitializer name: from graph: Ponnxruntime::Model::LoadNo graph was found in the protobuf.Failed to load model with error: ONNX Runtime only *guarantees* support for models stamped with opset version 7 or above for opset domain 'ai.onnx'. Please upgrade your model to opset 7 or higher. For now, this opset model may run depending upon legacy support of some older opset version operators.Unsupported model IR version: , max supported IR version: Missing model IR version.Missing opset in the model. All ModelProtos MUST have at least one entry that specifies which version of the ONNX OperatorSet is being imported.D:\a\_work\1\s\onnxruntime\core\graph\model.cconnxruntime::Model::ModelModelProto does not have a graph. is under development and support for this is limited. The operator schemas and or other functionality could possibly change before next ONNX release and in this case ONNX Runtime will not guarantee backward compatibility. Current official support for domain onnxruntime::model_load_utils::ValidateOpsetForDomainONNX Runtime only *guarantees* support for models stamped with official released onnx opset versions. Opset is under development and support for this is limited. The operator schemas and or other functionality may change before next ONNX release and in this case ONNX Runtime will not guarantee backward compatibility. Current official support for domain is till opset D:\a\_work\1\s\onnxruntime\core/graph/model_load_utils.honnxruntime::model_load_utils::IsAllowReleasedONNXOpsetsOnlySet are '0' and '1'. The environment variable contained the value: system error number failedLoad model failed. File doesn't existonnxruntime::SaveModelWithExternalInitializersonnxruntime::SaveModelGraph is null. Invalid ORT format model.onnxruntime::Model::LoadFromOrtFormatNull entry in metadata_props. Invalid ORT format model.onnxruntime::Model::SaveToOrtFormatonnxruntime::Model::SaveWithExternalInitializers is less than 0.Protobuf serialization failed.onnxruntime::Model::Save is less than 0.onnxruntime::Model::LoadFromModelEditorApiModel_GRAPH_API_MODEL_ less than 0.Protobuf parsing failed., but it its version is higherthan the operator set version , but it its domain is notknown by the checker.onnxruntime::OnnxRuntimeOpSchemaRegistry::RegisterOpSchemaInternalD:\a\_work\1\s\onnxruntime\core\graph\schema_registry.cconnxruntime::OnnxRuntimeOpSchemaRegistry::RegisterOpSetDomain already set in registryD:\a\_work\1\s\onnxruntime\core\graph\graph_proto_serializer.cconnxruntime::GraphViewerToProtoInvalid ExecutionOrderonnxruntime::GraphViewer::GetNodesInTopologicalOrderMemory efficient topological order is not enabled for non-training build.nodeMismatch between Graph and IndexedSubGraph. Node not found: Mismatch between Graph and IndexedSubGraph. Output not found:nodeargMismatch between Graph and IndexedSubGraph. Input not found:D:\a\_work\1\s\onnxruntime\core\graph\graph_viewer.cconnxruntime::GraphViewer::GraphViewergraph_->GetNode(idx) != nullptrIndexedSubGraph contains values not present in the GraphXCIncompatible dimensions must have a non null shape in expected to be a tensor or a sparse tensor type in saved_varsaved_meanvarDirectML fused BatchNormalization+ActivationDirectML fused InstanceNormalization+ActivationDirectML fused ConvTranspose+ActivationWDirectML fused Conv+ActivationD:\a\_work\1\s\onnxruntime\core\graph\dml_ops\dml_defs.ccIncompatible dimensions for matrix multiplicationInput tensors of wrong rank (0).Second input does not have rank 2First input does not have rank 2fused_ratiosumdata_0DirectML fused Sum+ActivationDirectML fused Add+ActivationDirectML fused MatMul+ActivationADirectML fused Gemm+ActivationDirectML fused MeanVarianceNormalization+ActivationTensor must have at least 3 dimensions to convert between channels first and channels last.Output tensor must have at least 3 dimensionsNegative values are not allowed in a shape specification in should specify a shape in Attribute Input data to be scaled Scale takes one input data (Tensor) and produces one output data (Tensor) whose value is the input data tensor scaled element-wise. GivenTensorFillvaluesThe filled tensorThe shape of filled tensorNegative values are not allowed in a shape specificationextra_shapeinput_as_shapeConstrain indices to integer typesConstrain input and output types to all tensor types.Sliced data tensor.1-D tensor of axes that `starts` and `ends` apply to.1-D tensor of ending indices (exclusive) of corresponding axis in axes1-D tensor of starting indices of corresponding axis in `axes`dataTensor of data to extract slices from. Produces a slice of the input tensor along multiple axes. Similar to numpy: https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html Slices uses `axes`, `starts` and `ends` inputs to specify the start and end dimension for each axis in the list of axes, it uses this information to slice the input `data` tensor. If a negative value is passed for any of the start or end indices, it represent number of elements before the end of that dimension. If the value passed to start or end is larger than the `n` (the number of elements in this dimension), it represents `n`. For slicing to the end of a dimension with unknown size, it is recommended to pass in `INT_MAX`. If `axes` are omitted, they are set to `[0, ..., ndim-1]`. Example 1: data = [ [1, 2, 3, 4], [5, 6, 7, 8], ] axes = [0, 1] starts = [1, 0] ends = [2, 3] result = [ [5, 6, 7], ] Example 2: data = [ [1, 2, 3, 4], [5, 6, 7, 8], ] starts = [0, 1] ends = [-1, 1000] result = [ [2, 3, 4], ] Output tensorInput tensorThreshold value ThresholdedRelu takes one input data (Tensor) and produces one output data (Tensor) where the rectified linear function, y = x for x > alpha, y = 0 otherwise, is applied to the tensor elementwise. Result, has same type as input, with H and W dimensions reduced.A 1-D values of (height, width).A 1-D values of (leftBorder, topBorder, rightBorder, bottomBorder).Crop and image to the specified spatial dimensions. If scale is given, then optionally start the crop offset by the left/top border amounts. If scale is not provided, crop the borders as provided.Result, has same shape and type as inputInput tensor of shape [N,C,H,W]The scale to apply.Bias applied to each channel, same size as C.Scale and bias the input image. Bias values are stored in the same ordering as the image pixel format. ParametricSoftplus takes one input data (Tensor) and produces one output data (Tensor) where the softplus function, y = alpha * ln(exp(beta * x) + 1), is applied to the tensor elementwise. D:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\onnx_deprecated_operators.ccConstrain input and output types to float tensors.1D output tensor1D input tensorValue of betaValue of alpha Affine takes one input data (Tensor) and produces one output data (Tensor) where the affine function, y = alpha * x + beta, is applied to the tensor elementwise. ) + scale[1] () + scale[0] () needs to be greater than or equal to the left_border () + right_border () needs to be greater than or equal to the top_border () + bottom_border ('Scale' must contain exactly 2 values - (height, width)'Border' attribute must be present and must contain exactly 4 values - (left_border, top_border, right_border, bottom_border)Input's shape must be 4-DThe scaled hyperbolic tangent values of the input tensor computed element-wiseScaling valueIf 0, normalize the mean only. Default is 1.If 1, mean and variance are computed across channels. Default is 0.Perform mean variance normalization.GRUUnithiddenThe new GRU hidden state calculated by this op.tThe timestep for this operation.seq_lengthsArray of sequence lengths. len(seq_lengths) should equal batch size N.gatesUnactivated gate outputs from forget, update, and output gates, pre-activation.hidden_prevThe previous GRU hidden state.drop_statesBool to determine if hidden state is zeroes or passed along for timesteps past the given sequence_length. GRUUnit computes the activations of a standard GRU, in a sequence-length aware fashion. Concretely, given the (fused) inputs X (TxNxD), the previous hidden state (NxD), and the sequence lengths (N), computes the GRU activations, avoiding computation if the input is invalid (as in, the value at X[t][n] >= seqLengths[n]. Output data after scaling expected to have rank >Dimension mismatch in unification between expected to have rank but has rank does not specify a valid type in should be of integer type and specify a type in Value of attribute not specified in = Constant()Error unexpected extra input in node:Error parsing node:xConstrain output 'mask' types to uint32 tensors.Constrain input 'training_mode' types to boolean tensors.output, dropout_bitmask = Dropout(data + bias, ratio) + residual, Intended to specialize the dropout pattern commonly found in transformer models.Constrain output 'mask' types to boolean tensors.Constrain input 'ratio' types to float tensors.maskThe output mask of dropout.If set to true then it indicates dropout is being used for training. It is an optional value hence unless specified explicitly, it is false. If it is false, ratio is ignored and the operation mimics inference mode where nothing will be dropped from the input data and if mask is requested as output it will contain all ones.ratioThe ratio of random dropout, with value in [0, 1). If this input was not set, or if it was set to 0, the output would be a simple copy of the input. If it's non-zero, output will be a random dropout of the scaled input, which is typically the case during training. It is an optional value, if not specified it will default to 0.5.residualThe residual input, must have the same shape as dataThe bias input, a vector with the same shape as last dim of data OR same shape with data(Optional) Seed to the random generator, if not specified we will auto generate one.output, dropout_mask = Dropout(data + bias, ratio) + residual, Intended to specialize the dropout pattern commonly found in transformer models.The bias (or mask) as Tensor.true if broadcast bias across input for dimensions broadcast_axis to axis-1, otherwise broadcast bias across input for dimensions 0 to broadcast_axis - 1apply softmax to elements for dimensions axis or higherY = softmax(scores + bias)) with simple broadcast on bias. Intended to specialize softmax(scores + additive_mask) commonly found in transformer models.Constrain input and output types to all numeric tensors and bool tensors.A 0-D tensor containing a single value corresponding to the number diagonals above or the main diagonal to exclude or include.Default value is 0 if it's not specified.Input tensor of rank 2 or higher.Boolean. Indicates whether upper or lower part of matrix is retained. Default is true. Returns the upper or lower triangular part of a 2-D matrix, or batches of 2-D matrices. If the attribute "upper" is set to true, the upper triangular matrix is retained. Lower triangular matrix is retained otherwise. Default value for upper is true. Trilu takes one input tensor of shape [*, N, M], where * is zero or more batch dimensions. The upper triangular part consists of the elements on and above the given diagonal (k). The lower triangular part consists of elements on and below the diagonal. All other elements in the matrix are set to zero. If k = 0, the triangular part on and above/below the main diagonal is retained. If upper is set to true, a positive k retains the upper triangular matrix excluding k diagonals above the main diagonal. A negative k value includes as many diagonals below the main diagonal. If upper is set to false, a positive k retains the lower triangular matrix including k diagonals above the main diagonal. A negative k value excludes as many diagonals below the main diagonal. TorchEmbeddingConstrain input and output types to all numeric tensors.Output tensor of the same type as the input tensor. Shape of the output is * x M, where '*' is the shape of input indices, and 'M' is the embedding size.scale_grad_by_freqA 0-D bool tensor. If given, this will scale gradients by the inverse of frequency of the indices (words) in the mini-batch. Default is ``False``padding_idxA 0-D scalar tensor. If specified, the entries at `padding_idx` do not contribute to the gradient; therefore, the embedding vector at `padding_idx` is not updated during training, i.e. it remains as a fixed pad.Long tensor containing the indices to extract from embedding matrix.weightThe embedding matrix of size N x M. 'N' is equal to the maximum possible index + 1, and 'M' is equal to the embedding size Based on Torch operator Embedding, creates a lookup table of embedding vectors of fixed size, for a dictionary of fixed size. Output tensor of the same type and shape as the input tensor.Input tensor. Every matrix in the batch must be invertible.The inner-most 2 dimensions must have the same size (mat_w: != mat_h:Input rank must be >= 2.Alpha value. CX = Mul (Alpha, X) SIGMOIDCX = Sigmoid (CX) Y = Mul (X, SIGMOIDCX) Compute x * Sigmoid(alpha * x).The bias input data that is a 1D tensor.The normal input data.Bias Gelu. It's an extension of Gelu. It takes the sum of input A and bias input B as the input of Gelu activation. D:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\contrib_defs.ccThe output.The input data as Tensor. CX = Mul (C, X) ERFCX = Erf (CX) ERFCXPlus1 = Add (ERFCX, One) PhiX = Mul (ERFCXPlus1, Half) Y = Mul (X, PhiX) OneHalfGaussian Error Linear Unit. A high-performing neural network activation function.The GELU nonlinearity is the expected transformation of a stochastic regularizer which randomly applies the identity or zero map to a neuron's input. The GELU nonlinearity weights inputs by their magnitude, rather than gates inputs by their sign as in ReLUs.Failed to parse num_return_sequences or it is not positive integer scalarFailed to parse num_beams or it is not positive integer scalarFailed to parse max_length or it is not positive integer scalarInputs 0 shall be 2 dimensionsInputs 0 shall be 3 dimensions in whisper graphPads has incorrect number of values'pads' input must be a 1D (shape: [2 * n_input_dims]) tensor of type int64Final beam score of the generated sequences. Shape is (batch_size, num_return_sequences)sequencesWord IDs of generated sequences. Shape is (batch_size, num_return_sequences, max_sequence_length)logits_processorSpecific logits processor for different types of beamsearch models. Default value 0 means no specific logit processor. Accepts value >= 0. Shape is (1)The forced input id sequence for the decoder subgraph. Shape is (batch_size, initial_sequence_length)Custom attention mask. Shape is (batch_size, sequence_length)prefix_vocab_maskMask of vocabulary for first step. Words that masked with 0 are not allowed to be generated, and 1 is allowed. Shape is (batch_size, vocab_size)vocab_maskMask of vocabulary. Words that masked with 0 are not allowed to be generated, and 1 is allowed. Shape is (vocab_size)repetition_penaltyThe parameter for repetition penalty. Default value 1.0 means no penalty. Accepts value > 0.0. Shape is (1)Exponential penalty to the length. Default value 1.0 means no penalty.Value > 1.0 encourages longer sequences, while values < 1.0 produces shorter sequences.Shape is (1,)The number of returned sequences in the batch. Shape is (1)Number of beams for beam search. 1 means no beam search. Shape is (1)The minimum length below which the score of eos_token_id is set to -Inf. Shape is (1)The maximum length of the sequence to be generated. Shape is (1)The sequence used as a prompt for the generation in the encoder subgraph. Shape is (batch_size, sequence_length)Size of the vocabulary. If not provided, it will be inferred from the decoder subgraph's output shapeDecoder subgraph to execute in a loop.The subgraph for the first decoding run. It will be called once before `decoder` subgraph. This is relevant only for the GPT2 model. If this attribute is missing, the `decoder` subgraph will be used for all decoding runsThe subgraph for initialization of encoder and decoder. It will be called once before decoder subgraph.model type: 0 for GPT-2; 1 for encoder decoder like T5early stop or notno repeat ngrams sizeThe id of the token that indicates decoding starts.The id of the padding tokenThe id of the end-of-sequence tokenBeam Search for text generation. Supports GPT-2 decoder.Constrain to integer types.Constrain to float tensors.Output tensor. shape is [2, x], where max(M, N) <= x < M + NInput cost tensor, it must be 2D tensor of shape M x N, or 1 x M x NInput is cost matrix where each value in input[r][c] is the cost for pass the point (r, c). From current point(r, c), points (r+1, c), (r+1, c+1) or (r, c+1) could be arrived in next move. Given such cost matrix, return dynamic time warping of shape [2, x], where the path made by all points (output[0][t], output[1][t])have the lowest cost among all paths from (0, 0) to (M-1, N-1).Allow inputs and outputs to be any kind of tensor.Output tensor.input tensorspecify the step.specify the sizespecify the dimension to unfoldReturns a tensor which contains all slices of size `size` from input tensor in the dimension `dim`. Step between two slices is given by `step`. If `sizedim` is the size of dimension `dim` for input tensor, the size of dimension `dim` in the returned tensor will be `(sizedim - size) / step + 1`. An additional dimension of size `size` is appended in the returned tensor.size attribute in UnfoldTensor not positive and less than the dim size!size attribute in UnfoldTensor not specified!size attribute in UnfoldTensor must greater than 0.Constrain output types to float tensors.Constrain input types to all tensor types.4-D tensor of shape (N, C, H_out, W_out).GridInput offset, 4-D tensor of shape (N, H_out, W_out, 2), where H_out and W_out are the height and width of grid and output, Grid specifies the sampling pixel locations normalized by the input spatial dimensions. Therefore, it should have most values in the range of [-1, 1]. If grid has values outside the range of [-1, 1], the corresponding outputs will be handled as defined by padding_mode.4-D tensor of shape (N, C, H, W), where N is the batch size, C is the numbers of channels, H and W are the height and width of the input data.If align_corners=1, the extrema (-1 and 1) are considered as referring to the center points of the input's corner pixels. If align_corners=0, they are instead considered as referring to the corner points of the input's corner pixels, making the sampling more resolution agnostic.Support padding modes for outside grid values: `zeros`(default), `border`, `reflection`. zeros: use 0 for out-of-bound grid locations, border: use border values for out-of-bound grid locations, reflection: use values at locations reflected by the border for out-of-bound grid locations.Three interpolation modes: bilinear (default), nearest and bicubic. Given an `input` and a flow-field `grid`, computes the `output` using `input` values and pixel locations from `grid`. Currently, only spatial (4-D) inputs are supported. For `input` with shape (N, C, H, W) and `grid` with shape (N, H_out, W_out, 2), the `output` will have shape (N, C, H_out, W_out). For each output location `output[n, :, h, w]`, the size-2 vector `grid[n, h, w]` specifies `input` pixel locations `x` and `y`, which are used to interpolate the output value `output[n, :, h, w]`. The GridSample operator is often used in doing grid generator and sampler in the [Spatial Transformer Networks](https://arxiv.org/abs/1506.02025). See also in [torch.nn.functional.grid_sample](https://pytorch.org/docs/master/generated/torch.nn.functional.grid_sample.html#torch-nn-functional-grid-sample). The output scalar. Its value is true if all input tensors are finite. Otherwise, the output value would be false.Input tensors to check.Constrain the output to a boolean tensor.If true, check only for NaN.If true, check only for Inf, -Inf.IsAllFiniteBoth attributes isinf_only and isnan_only cannot be set. Unset both to check for both conditions.isnan_onlyisinf_only2D optional input tensor with shape (num_experts, hidden_size)fc2_experts_weights3D input tensor with shape (num_experts, inter_size, hidden_size)fc1_experts_bias2D optional input tensor with shape (num_experts, inter_size)fc1_experts_weights3D input tensor with shape (num_experts, hidden_size, inter_size)router_probs2D input tensor with shape (num_rows, num_experts)2D input tensor with shape (num_rows, hidden_size) or 3D input tensor with shape (batch_size, sequence_length, hidden_size)use_sparse_mixerWhether to use sparse mixernormalize_routing_weightsWhether to normalize routing weightsNumber of top experts to select from expert poolactivation_typeActivation function to use. Choose from relu, gelu, silu and identity. Default is relu Mixture of experts. Examples: Switch transformer(https://arxiv.org/pdf/2101.03961.pdf) use top 1, GLaM(https://arxiv.org/abs/2112.06905) activates top 2 FFN, Vision MOE(https://arxiv.org/pdf/2106.05974.pdf) usually uses top 32 experts and Mixtral(https://huggingface.co/blog/mixtral). filtered_logitsFiltered logits as input to the mutinomial function for debug purpose. Shape is (batch_size, vocab_size)Seed for random number generator. Shape is (1)presence_maskPresence penalty mask. Shape is (batch_size, vocab_size)Model type: 0 for decoder only like GPT-2; 1 for encoder decoder like BartIf 1 custom sampling logicPresence penalty for custom samplingMinimumber of tokens we keep per batch example in the output.All filtered values will be set to this float value.If set to float < 1, only the smallest set of most probable tokens with probabilities that add up to `top_p` or higher are kept for generation.The value used to module the next token probabilities.Greedy Sampling for text generation.Word IDs of generated sequences. Shape is (batch_size, max_sequence_length)The sequence used as a prompt for the generation. Shape is (batch_size, sequence_length)The subgraph for initialization of encoder and decoder. It will be called once before `decoder` subgraph.model type: 0 for decoder only like GPT-2; 1 for encoder decoder like BartGreedy Search for text generation.Constrain cross_qk to float32 tensors.non_speech_probsFor whisper model, output the probabilities from logits after encoder and context decoding for the no_speech_token_id. The shape of non_speech_probs is [B]cross_qkOutput the accumulated stacked Q*K in cross attentions. Let H = number of Head of cross attention, F = the frames or kv-seq-len of the cross attention input, T = real decoded token length, L = number of layers, B = batch size, R = num_return_sequences. It then should return tensor of shape [B, R, L*H, T, F]. If cross_qk_layer_head is given, shape is [B, R, cross_qk_layer_head.shape[0], T, F]Processed beam scores for each vocabulary token at each generation step. Beam scores consisting of log softmax scores for each vocabulary token and sum of log softmax of previously generated tokens in this beam. Shape is (max_length - sequence_length, batch_size, num_beams, vocab_size)Temperature value to apply to logits processing during this execution's decoding. Shape is (1)extra_decoding_idsPart of the decoder_input_ids that we need cross qk for it. it is of shape (batch_size, extra_decoding_ids_len).In such case, we should remove this from the tail of the decoder_input_ids, and put it here. ids < 0 in it (for multiple batch) are treated as stop of the extra_decoding_ids for corresponding batch.cross_qk_layer_headOnly keep this list of (layer, head) of QK in the final cross_qk output when use_cross_qk is set. Default collect all its shape is (number of (layer, head) to keep, 2), i.e., [[layer_id1, head_id1], [layer_id2, head_id2]......]Exponential penalty to the length. Default value 1.0 means no penalty. Value > 1.0 encourages longer sequences, while values < 1.0 produces shorter sequences. Shape is (1,)If nozero, decoder subgraph contains output Q*K from cross attentions. Default 0.Must be 2 for whisperThe id of the first timestampThe id of the token that indicates no timestampsThe token in whisper model that marks all sequence empty. With this model, whisper could output no_speech_prob after. Default -1.The id of the token that indicates LM startsThe id of the transcribe taskThe id of the translate taskThe id of the token that indicates decoding starts (i.e. the start of transcription token id)Beam Search for whisper model, especiall with cross_qk features etc.Constrain mask to integer typesConstrain to integer typesConstrain input type to float or int tensors.scoresProcessed beam scores for each vocabulary token at each generation step.Beam scores consisting of log softmax scores for each vocabulary token and sum of log softmax of previously generated tokens in this beam.Shape is (max_length - sequence_length, batch_size, num_beams, vocab_size)sequences_scoresTokenized stringsStrings to tokenize Tokenizer divides each string in X into a vector of strings along the last axis. Allowed input shapes are [C] and [N, C]. If the maximum number of tokens found per input string is D, the output shape would be [N, C, D] when input shape is [N, C]. Similarly, if input shape is [C] then the output should be [C, D]. Tokenizer has two different operation modes. The first mode is selected when "tokenexp" is not set and "separators" is set. If "tokenexp" is set and "separators" is not set, the second mode will be used. The first mode breaks each input string into tokens by matching and removing separators. "separators" is a list of strings which are regular expressions. "tokenexp" is a single regular expression. Let's assume "separators" is [" "] and consider an example. If input is ["Hello World", "I love computer science !"] whose shape is [2], then the output would be [["Hello", "World", padvalue, padvalue, padvalue], ["I", "love", "computer", "science", "!"]] whose shape is [2, 5] because you can find at most 5 tokens per input string. Note that the input at most can have two axes, so 3-D and higher dimension are not supported. If "separators" contains a single empty string, the Tokenizer will enter into character tokenezation mode. This means all strings will be broken part into individual characters. For each input string, the second mode searches matches of "tokenexp" and each match will be a token in Y. The matching of "tokenexp" is conducted greedily (i.e., a match should be as long as possible). This operator searches for the first match starting from the beginning of the considered string, and then launches another search starting from the first remained character after the first matched token. If no match found, this operator will remove the first character from the remained string and do another search. This procedure will be repeated until reaching the end of the considered string. Let's consider another example to illustrate the effect of setting "mark" to true. If input is ["Hello", "World"], then the corresponding output would be [0x02, "Hello", "World", 0x03]. This implies that if mark is true, [C]/[N, C] - input's output shape becomes [C, D+2]/[N, C, D+2]. If tokenizer removes the entire content of [C]-input, it will produce [[]]. I.e. the output shape should be [C][0] or [N][C][0] if input shape was [N][C]. If the tokenizer receives empty input of [0] then the output is [0] if empty input of [N, 0] then [N, 0]. ExpandDims echo operator.Specified axis to insert a dimensionInput axis is invalid: activation_gammaScalar multiplier for input tensor C.Scalar multiplier for the product of input tensors A * B.Whether B should be transposedWhether A should be transposedConstrain input and output types to float/int tensors.Output tensor of shape (M, N).Input tensor C. The shape of C should be unidirectional broadcastable to (M, N).Input tensor B. The shape of B should be (K, N) if transB is 0, or (N, K) if transB is non-zero.Input tensor A. The shape of A should be (M, K) if transA is 0, or (K, M) if transA is non-zero. The FusedGemm operator schema is the same as Gemm besides it includes attributes activation and leaky_relu_alpha.Z The fused convolution operator schema is the same as Conv besides it includes an attribute activation.Constrain input and output types to float tensorsPadsComplexMulConjComplexMuloutput tensorinput_1input_0Irfftoutput tensor with size n in the signal dimnumber of dimensions comprising the signalinput tensor with size (n//2 + 1) in the signal dim and 2 in the last dimension for the real and complex partsThis function computes the inverse of the one-dimensional n-point RFFT computed in 'com.microsoft.rfft'.RfftConstrain input and output types to float or half tensors.output tensor of size (n//2 + 1) in the signal dim and 2 in the last dimension for the real and complex partsmust be 1, only one sided FFTs supportednormalizedmust be 0, normalization currently not supportedsignal_ndimnumber of dimensions comprising the signal, collected in reverse order (e.g. 1 = last dimension is the signal)input tensor of size n in the signal dimThis function computes the n-point one dimensional Fourier transform for a real-valued input where n is an even number.Constrain input0 and output types to float tensorsFor internal use. Sample echo operator.Constrain to any tensor type. If the dtype attribute is not provided this must be a valid output type.QMoEConstrain weights type to uint8 tensors.fc3_scales3D optional input tensor with shape (num_experts, hidden_size, inter_size) or (num_experts, hidden_size, inter_size / 2)fc2_scales2D input tensor with shape (num_experts, hidden_size)3D input tensor with shape (num_experts, inter_size, hidden_size) or (num_experts, inter_size, hidden_size / 2)fc1_scales2D input tensor with shape (num_experts, inter_size)3D input tensor with shape (num_experts, hidden_size, inter_size) or (num_experts, hidden_size, inter_size / 2)expert_weight_bitsNumber of bits used in quantized weights. Default is 4 bitsQuantized MoEMoEConstrain input and output types to float or float16 tensors.fc3_experts_biasfc3_experts_weights3D optional input tensor with shape (num_experts, hidden_size, inter_size)fc2_experts_biasSpecify bias of convSpecify weights of convSequenceSpecify batchs of sequence words to embeddingInteger representing the embedding vector size for each char.If not provide, use the char embedding size of embedding vector.This operator applies convolution to word from left to right with window equal to conv_window_size and stride to 1.Take word 'example' for example, with conv_window_size equal to 2, conv is applied to [ex],[xa], [am], [mp]...If not provide, use the first dimension of conv kernel shape.Integer representing the embedding vector size for each word.If not provide, use the filter size of conv weight Given `data` tensor of rank r >= 1, and `indices` tensor of rank q >= 1, gather slices of `data` into an output tensor of rank q - 1 + r - indices[-1]. Example 1: data = [[0,1],[2,3]] indices = [[0,0],[1,1]] output = [0,3] Example 2: data = [[0,1],[2,3]] indices = [[1],[0]] output = [[2,3],[0,1]] Example 3: data = [[[0,1],[2,3]],[[4,5],[6,7]]] indices = [[0,1],[1,0]] output = [[2,3],[4,5]] Example 4: data = [[[0,1],[2,3]],[[4,5],[6,7]]] indices = [[[0,1]],[[1,0]]] output = [[[2,3]],[[4,5]]] Constrain indice type to int32 or int64Constrain input and output types to any tensor type.Tensor of rank q-1+r-indices[-1].Tensor of rank q >= 1.Tensor of rank r >= 1.last dimension of indices must not be larger and rank of data tensorboth data and indices tensor need to have rank larger than zero.If value is 1, output type is uint32_t, else int32_t. Default value is 1.Seed for the hashing algorithm, unsigned 32-bit integer, default to 0.Constrain output type to unsigned and signed 32-bit integer tensor.Constrain input type to unsigned or signed 32-bit integer tensor, or string tensor. It should be utf-8 encoded if using unicode.32-bit hash value.An input tensor to hash.The underlying implementation is MurmurHash3_x86_32 generating low latency 32bits hash suitable for implementing lookup tables, Bloom filters, count min sketch or feature hashing.sparse_tensor(uint32)sparse_tensor(uint64)sparse_tensor(int32)sparse_tensor(int64)sparse_tensor(double)sparse_tensor(float)N-dimensional dense matrix B2-dimensional sparse matrix A. Either COO or CSR formatWhether B should be transposed on the 1st dimension and batch dimensions (dim-1 to dim-rank-2) before doing multiplicationWhether A should be transposed on the 1st dimension and batch dimensions (dim-1 to dim-rank-2) before doing multiplicationMatrix multiply resultsWhether B should be transposed on the last two dimensions before doing multiplicationWhether A should be transposed on the last two dimensions before doing multiplicationScalar multiplier for the product of the input tensors. Executes the same operation as FusedMatMul, but also has an activation function fused to its output. Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html Duplicate of FusedMatMul. Going forward FusedMatMul should be used. This OP will be supported for backward compatibility. Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html Constrain shape of B must be int64 tensor.Constrain input B data types as data blobConstrain input matrix data types as single precision float tensorB_shapeShape information of B1-dimensional data blobQuantization type Matrix product with right hand matrix being pre-packed and quantized int4 data blob. During quantization, the matrix is divided into blocks, where each block is a continguous subset inside each column. Each block is quantized into a sequence of 4b integers with a scaling factor and an optional offset. Currently 3 quantization types are supported: (0): block size 32, no offset, (1): block size 32, with offset, (2): block size 64, no offset Input q4 tensors of wrong size!4b quantization not yet supported on this hardware platform!B input for MatMul must be a 2-D matrix!Constrain output Y data types as 32-bit integer tensor.T3 must be tensor(uint32) when both T1 and T2 are tensor(uint16),or must be tensor(int32) when either T1 or T2 is tensor(int16).Constrain input B data types as 16-bit integer tensorConstrain input A data types as 16-bit integer tensorMatrix multiply results from A * BN-dimensional matrix BN-dimensional matrix A Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html. The production MUST never overflow. The accumulation may overflow if and only if in 32 bits.inputs are expected to have tensor type and output type should not be null.Minimum number of characters allowed in the output. For example, if mincharnum is 2, tokens such as "A" and "B" would be ignoredan optional list of strings attribute that contains a list of separators - regular expressions to match separators Two consecutive segments in X connected by a separator would be divided into two tokens. For example, if the input is "Hello World!" and this attribute contains only one space character, the corresponding output would be ["Hello", "World!"]. To achieve character-level tokenization, one should set the 'separators' to [""], which contains an empty string.An optional string. Token's regular expression in basic POSIX format (pubs.opengroup.org/onlinepubs/9699919799/basedefs/V1_chap09.html#tag_09_03). If set, tokenizer may produce tokens matching the specified pattern. Note that one and only of 'tokenexp' and 'separators' should be set.The string used to pad output tensors when the tokens extracted doesn't match the maximum number of tokens found. If start/end markers are needed, padding will appear outside the markers.Boolean whether to mark the beginning/end character with start of text character (0x02)/end of text character (0x03).Input/Output is a string tensorRank = Size (XShape)XShape = Shape (X)GemmFloat8Constrain type for all input scales (scaleA, scaleB, scaleY).Constrain type to result type.Constrain type to input C.Constrain type to input B.Constrain type to input A.TRscaleYScale of the output tensor if A or B is float 8.scaleBScale of tensor B if B is float 8 tensorscaleAScale of tensor A if A is float 8 tensorInput tensor C.Activation function, RELU or GELU or NONE (default).Output Type. Same definition as attribute 'to' for operator Cast.Scalar multiplier for the product of input bias C.Whether B should be transposed. Float 8 only supprted transB=1.Whether A should be transposed. Float 8 only supprted transA=0.Generic Gemm for float and float 8. Extracts crops from the input image tensor and resizes them using bilinear sampling or nearest neighbor sampling (possibly with aspect ratio change) to a common output size specified by crop_height and crop_width. Returns a tensor with crops from the input image at positions defined at the bounding box locations in boxes. The cropped boxes are all resized (with bilinear or nearest neighbor interpolation) to a fixed size = [crop_height, crop_width]. The result is a 4-D tensor [num_boxes, crop_height, crop_width, depth]. The resizing is corner aligned.Constrain types to int tensors.Constrain types to float tensors.RoI pooled output, 4-D tensor of shape (num_rois, C, crop_height, crop_width). The r-th batch element Y[r-1] is a pooled feature map corresponding to the r-th RoI X[r-1].crop_size1-D tensor of 2 elements: [crop_height, crop_width]. All cropped image patches are resized to this size. Both crop_height and crop_width need to be positive.batch_indices1-D tensor of shape (num_rois,) with each element denoting the index of the corresponding image in the batch.roisRoIs (Regions of Interest) to pool over; rois is 2-D input of shape (num_rois, 4) given as [[y1, x1, y2, x2], ...]. The RoIs' coordinates are normalized in the coordinate system of the input image. Each coordinate set has a 1:1 correspondence with the 'batch_indices' input.Input data tensor from the previous operator; 4-D feature map of shape (N, C, H, W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data.Value used for extrapolation, when applicable. Default is 0.0f. The pooling method. Two modes are supported: 'bilinear' and 'nearest'. Default is 'bilinear'.crop_size shape input tensor has wrong dimensionbatch_indices shape input tensor has wrong dimensionrois input tensor has wrong dimensionfirst input tensor has wrong dimensionConstrains input to only numeric types.A 2D Matrix that represents the distance between each pair of the two collections of inputs.2D matrix with shape (K,N)2D matrix with shape (M,N)The distance metric to use. If a string, the distance function can be "braycurtis", "canberra", "chebyshev", "cityblock", "correlation", "cosine", "dice", "euclidean", "hamming", "jaccard", "jensenshannon", "kulsinski", "mahalanobis", "matching", "minkowski", "rogerstanimoto", "russellrao", "seuclidean", "sokalmichener", "sokalsneath", "sqeuclidean", "wminkowski", "yule". Finds all the unique values (deduped list) present in the given input tensor. This operator returns 3 outputs. The first output tensor 'uniques' contains all of the unique elements of the input, sorted in the same order that they occur in the input. The second output tensor 'idx' is the same size as the input and it contains the index of each value of the input in 'uniques'. The third output tensor 'counts' contains the count of each element of 'uniques' in the input. Example: input_x = [2, 1, 1, 3, 4, 3] output_uniques = [2, 1, 3, 4] output_idx = [0, 1, 1, 2, 3, 2] output_counts = [1, 2, 2, 1] Input can be of any tensor type.countsA 1-D INT64 tensor containing the the count of each element of 'uniques' in the input 'x'idxA 1-D INT64 tensor of the same size as 'x' containing the indices for each value in 'x' in the output 'uniques'yA 1-D tensor of the same type as 'x' containing all the unique values in 'x' sorted in the same order that they occur in the input 'x'A 1-D input tensor that is to be processed. Given `data` tensor, pads, mode, and value. Example: Insert 0 pads to the beginning of the second dimension. data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] output = [ [ [0.0, 0.0, 1.0, 1.2], [0.0, 0.0, 2.3, 3.4], [0.0, 0.0, 4.5, 5.7], ], ] Tensor after padding.(Optional) A scalar or rank 1 tensor containing a single value to be filled if the mode chosen is `constant` (by default it is 0.0).Tensor of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D input tensor, it is the number of pixels. `pads` should be a 1D tensor of shape [2 * input_rank] or a 2D tensor of shape [1, 2 * input_rank]. `pads` format (1D example) should be as follow [x1_begin, x2_begin,...,x1_end, x2_end,...], where xi_begin is the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.Input tensor.Three modes: `constant`(default) - pads with a given constant value, `reflect` - pads with the reflection of the vector mirrored on the first and last values of the vector along each axis, `edge` - pads with the edge values of array'pads' input must be a 1D (shape: [input_rank]) or 2D tensor (shape: [1, input_rank]) of type int64The WordConvEmbedding takes in a batch of sequence words and embed each word to a vector.Constrain to tensor(float).Constrain to tensor(int32).Specify embedding vector of charInvStdDevSaved inverse standard deviation used during training to speed up gradient computation.Saved mean used during training to speed up gradient computationOutput data tensor.Bias tensor.Scale tensor.Input data tensor from the previous layer.type used for stash mean/inv_std_varThe epsilon value to use to avoid division by zero.The first normalization dimension: normalization will be performed along dimensions axis : rank(inputs).zero points shape and scales shape do not matchzero points must have the same rank as datazero_points shape must be knownzero_points are not supported for uint8_t data typedata shape and scales shape do not matchscales must have the same rank as datagather_axis must be 0, for uint8 datablock_size must be non-negativequantize_axis must be in [-r, r-1]gather_axis must be in [-r, r-1]data tensor must have rank >= 1 GatherBlockQuantized is a Gather with data quantized. It is similar to Gather (https://github.com/onnx/onnx/blob/main/docs/Operators.md#gather) with differences: 1. Input `data` is a constant. It is quantized block-wise along attribute `quantize_axis` with block size specified by attribute `block_size`. `block_size must` be a power of 2 and not smaller than 16, like 16, 32, 64, 128, .. 2. Input `data`'s scale and zero point are specified by input `scales` and `zero_points`. `scales` and `zero_points` are also constants. If `zero_points` is not provided, 0 is the zero point except when data is uint8 type then the default zero point is 8. 3. During the op execution, `data` and `indices` are first used to generate the quantized output. Then, `scales` and `zero_points` are used to dequantize the output. 4. The `output` and `scales` have the same type. The `data` and `zero_points` have the same type. 5. For uint8 data, the `gather_axis` must be 0. MatMulBnb4 is a MatMul with weight quantized with 4 bits using either FP4 or NF4 data type (https://arxiv.org/pdf/2305.14314.pdf). It does Matrix Multiplication like MatMul (https://github.com/onnx/onnx/blob/main/docs/Operators.md#matmul) with differences: 1. Input B is a 2D constant Matrix. Its input feature count and output feature count are specified by attribute 'K' and 'N'. 2. Input B is quantized with 4 bits with quantization data type specified by attribute 'quant_type'. It is transposed, flattened and quantized blockwisely with block size specified by attribute 'block_size'. And block_size is not an arbitrary number and must be a power of 2 and not smaller than 16, like 16, 32, 64, 128,.. 3. Input B's quantization constants or scales are specified by input 'absmax'. Input B is stored as uint8_t with shape: [(N * K + 1) / 2]. Input absmax is stored in same type as original type of B(float32, float16) with shape like: [(N * K + block_size - 1) / block_size]. 1. (Default value) transB=True (Majorly used for forward pass) Shape of A: [D0, D1, ..., Dn, K] Shape of Dequanted B: [N, K], this is aligned with how PyTorch defined the linear weight, .e.g [out_features, in_features]. The computation math: dequant_B = dequant(B, absmax, quant_type, block_size) transposed_dequant_B = dequant_B^T output = A @ transposed_dequant_B Shape of output: [D0, D1, ..., Dn, N] 2. transB=False (Majorly used for backward pass) Shape of A: [D0, D1, ..., Dn, N] Shape of Dequanted B: [N, K], this is aligned with how PyTorch defined the linear weight, .e.g [out_features, in_features]. The computation math: dequant_B = dequant(B, absmax, quant_type, block_size) output = A @ dequant_B Shape of output: [D0, D1, ..., Dn, K] bias shape must be [N] where N = bias shape must be known MatMulNBits is a MatMul with weight quantized with N bits(e.g., 2, 3, 4, 5, 6, 7).It does Matrix Multiplication like MatMul (https://github.com/onnx/onnx/blob/main/docs/Operators.md#matmul) with differences: 1. Input B is a 2D constant Matrix. Its input feature count and output feature count are specified by attribute 'K' and 'N'. 2. Input B is quantized with x bits which is specified by attribute 'bits'. It is quantized blockwisely along dimension 0 (e.g. column) with block size specified by attribute block_size. And block_size is not an arbitrary number and must be a power of 2 and not smaller than 16, like 16, 32, 64, 128,.. 3. Input B's scale and zero point are specified by input scales and zero_points. Input B is stored as uint8_t with shape: [N][n_blocks_per_col][blob_size] in which: - n_blocks_per_col = (K + block_size - 1) / block_size - blob_size = CeilDiv(block_size * bits, bitsof(uint8_t)<8>) For all bits from 2-8, a row of data is stored squeezely and represented by uint8_t. - for 2,4,8 bits, 4x2bit,2x4bit,1x8bit are stored in one uint8_t. 4bit example: |.|.|.|.| .|.|.|.| =uint8_t (2x4bit) - for 3,5,6,7 bits, 32x3bit,32x5bit,16x6bit,32x7bit are stored in 12xuint8_t,20xuint8_t,12xuint8_t,28xuint8_t separately. no bits are wasted. 3bit example: |.|.|. |.|.|. |.|.|. = 9bit, which across 2 uint8_t, the highest bit for the second uint8_t is used. The last uint_8 may have some bits unused. Input scales is stored in same type as original type of B(float32, float16) with shape like: [N * n_blocks_per_col] Input zero_points is stored as uint8_t or same as type(A). It has the same packing method as input B. - [N * CeilDiv(n_blocks_per_col * bits, 8)] If zero_points has same type as A, it's not packed and has the same shape as Scales. BitmaskDropout takes an input floating-point tensor, an optional input ratio (floating-point scalar) and an optional input training_mode (boolean scalar). It produces two tensor outputs: output (floating-point tensor) and mask (optional `Tensor`). If `training_mode` is true then the output Y will be a random dropout. Note that this Dropout scales the masked input data by the following equation, so to convert the trained model into inference mode, the user can simply not pass `training_mode` input or set it to false. ``` output = scale * data * mask, ``` where ``` scale = 1. / (1. - ratio). ``` This op functions in much the same was as Dropout-11 and Dropout-13 do, execpt that the mask is output as a bit-packed uint32 tensor, instead of a boolean tensor. Input 0 shall be 3 dimensionsDisentangled Attention TensorRT Plugin.Pyramid ROI Align TensorRT Plugin.Attribute 'pooled_size' must be >= 1.pooled_sizeMultilevel Crop and Resize TensorRT Plugin.Attribute 'max_output_boxes' must be >= 1.max_output_boxesEfficient NMS TensorRT Plugin.InvStdDev = Reshape (InvStdDev2D, ReducedShape)Mean = Reshape (Mean2D, ReducedShape)InvStdDev2D = Reciprocal (StdDev)Y = Reshape (Biased, XShape)Biased = Identity (Scaled)Biased = Add (Scaled, B2D)B2D = Flatten (B)Scaled = Mul (NormalizedV, Scale2D)Scale2D = Flatten (Scale)NormalizedV = Cast (Normalized)Normalized = Div (Deviation, StdDev)Deviation = Sub (XU, Mean2D)StdDev = Sqrt (VarPlusEpsilon)VarPlusEpsilon = Add (Var, Epsilon)Var = Sub (MeanOfSquare, SquareOfMean)SquareOfMean = Mul (Mean2D, Mean2D)MeanOfSquare = ReduceMean (Square)Square = Mul (XU, XU)Mean2D = ReduceMean (XU)XU = Cast (X2D)X2D = Flatten (X)ReducedShape = Concat (PrefixShape, SuffixShape)SuffixShape = ConstantOfShape (NumReducedAxes)NumReducedAxes = Neg (Axis1D)NumReducedAxes = Sub (Rank, Axis1D)PrefixShape = Slice (XShape, Zero1D, Axis1D)Axis1D = Constant()Zero1D = Constant()Onnx node for SNPE.SnpefactorScaling factor applied to attention values, 1/sqrt(3d). d is hidden size per head = H/N. H is hidden size, N is number of heads.spanMaximum relative distance, k.disentangled_attentionThe disentangled attention output tensor.p2c_attentionposition-to-content attention tensor, KcQr^T.c2p_attentioncontent-to-position attention tensor, QcKr^T.c2c_attentioncontent-to-content attention tensor, QcKc^T.DisentangledAttention_TRTPyramidROIAlign_TRTPooled size.image_sizeImage size.patchesThe cropped patches output tensor.feature_map_3The fourth feature map input tensor.feature_map_2The third feature map input tensor.feature_map_1The second feature map input tensor.feature_map_0The first feature map input tensor.MultilevelCropAndResize_TRTscore_thresholdScore threshold value.score_activationActivation function to apply to the scores input.plugin_versionVersion number of the TRT plugin.Max detections to output.iou_thresholdBox IOU threshold value.box_codingEncoding type for the boxes or anchors inputs.background_classBackground class ID.detection_classesThe detection_classes output tensor.detection_scoresThe detection_scores output tensor.detection_boxesThe detection_boxes output tensor.num_detectionsThe num_detections output tensor.anchorsThe anchors input tensor.The scores input tensor.boxesThe boxes input tensor.EfficientNMS_TRTConstrain output Y and scale type to float tensors.Constrain mean and inv_std_var to be float tensors.inv_std_varSaved inverse standard variance used during training to speed up gradient computation.Constrain output Y, scale and bias type to float tensors.Type of Mean and InvStdDev tensors.Constrain input X type to float tensors.(Optional) Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).Constrain quantized weight types to uint8.Constrain input and output types to float/half_float/brain_float tensors.absmaxquantization constants1-dimensional quantized data for weightWhether B should be transposed on the last two dimensions before doing multiplication. Default to be 1.Indicate if the ops run in training_mode, by default, False.quantization data type. 0 for FP4, 1 for NF4.number of groupsize used for weight quantization. It needs to be a power of 2 and not smaller than 16.the index tensor.Constrain quantized zero point types to uint8/int32/float16/float.Constrain quantized weight types to uint8/int32.Constrain input and output types to float/half_float tensors.tensor. The output tensor has the same rank as the input. Bias to add to result. It should have shape [N].g_idxgroup_idxzero_pointsquantization zero pointsquantization scale1 or 2 dimensional data blobThe input tensor, not quantizedThe minimum accuracy level of input A, can be: 0(unset), 1(fp32), 2(fp16), 3(bf16), or 4(int8) (default unset). It is used to control how input A is quantized or downcast internally while doing computation, for example: 0 means input A will not be quantized or downcast while doing computation. 4 means input A can be quantized with the same block_size to int8 internally from type T1.number of groupsize used for weight quantization,(default 128). It needs to be a power of 2 and not smaller than 16.number of bits used for weight quantization (default 4)size of each output featuresize of each input featureConstrain output 'mask' types to bit-packed uint32 tensor.Constrain 'training_mode' to boolean tensor.The bit-packed output mask.Constrain input and output types.One or more outputs, list of tensors for outputsList of tensors for inputsmax_sizemax size in the context. Usage depend on the EP.source(Optional) the source used to generate the engine/context cache file. Ort EP or native SDK tool chainpartition_name(Optional) partitioned graph name.hardware_architecture(Optional) Hardware architecture.onnx_model_filename(Optional) Filename of the original ONNX model.ep_sdk_version(Optional) SDK version used to convert the model.embed_mode1: indicate ep_cache_context is the context content. 0: indicate ep_cache_context is the file path to the context content.The path is relative to this Onnx file. Default is 1.ep_cache_contextpayload of the execution provider context if embed_mode=1, or path to the context file if embed_mode=0.main_contextUsually each single EPContext associate with a graph partition.But for some case like QNN, it has single EPContext contains all partitions.In that case, the node with ep_cache_context should set main_context=1. Other nodes set main_context=0 and skip ep_cache_context.The path is relative to this Onnx file. Default is 1.Onnx node container for EP context.Constrain input and output types to uint8, uint16, float tensors.One or more outputs, list of tensors for DLC outputList of tensors for SNPE DLC inputnotes(Optional) Some notes for the modeltarget_device(Optional) Target device like CPU, DSP, etc.snpe_version(Optional) SNPE version used to convert the model.DLCpayload of the SNPE DLC file.Constrain indices to integer types.Constrain dequantized types.Constrain quantized types.Dequantized output tensor of rank q + (r - 1).Tensor of int32/int64 indices, of any rank q. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.Tensor of rank r >= 1. Block-wise quantized.(Optional) block size used for weight quantization. It needs to be a power of 2 and not smaller than 16.(Optional) Which axis to block-wise quantize. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).Whether to use rotary position embedding. Default value is 0.Dimension of rotary embedding. Limited to 32, 64 or 128. Default value is head_sizeThe value to be filled in the attention mask. Default value is -10000.0fCustom scale will be used if specified. Default value is 1/sqrt(head_size)Input tensor with shape (batch_size, sequence_length, input_hidden_size)Merged Q/K/V weights with shape (input_hidden_size, hidden_size + hidden_size + v_hidden_size)Bias tensor with shape (hidden_size + hidden_size + v_hidden_size) for input projectionAttention mask with shape (batch_size, 1, max_sequence_length, max_sequence_length), (batch_size, total_sequence_length) or (batch_size, sequence_length, total_sequence_length), or index with shape (batch_size) or (2 * batch_size) or (3 * batch_size + 2)past state for key and value with shape (2, batch_size, num_heads, past_sequence_length, head_size)When past_present_share_buffer is set, its shape is (2, batch_size, num_heads, max_sequence_length, head_size)pastadditional add to QxK' with shape (batch_size or 1, num_heads or 1, sequence_length, total_sequence_length)attention_biasWhen past_present_share_buffer is used, it is required to specify past_sequence_length (could be 0).3D output tensor with shape (batch_size, sequence_length, v_hidden_size)past state for key and value with shape (2, batch_size, num_heads, total_sequence_length, head_size). If past_present_share_buffer is set, its shape is (2, batch_size, num_heads, max_sequence_length, head_size), while effective_seq_length = (past_sequence_length + kv_sequence_length).presentConstrain mask index to integer typesD:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\bert_defs.cc This is the packed version of Attention. Sequences in one batch usually don't have same length and they are padded to have same length, e.g., below is a batch with 3 sequences and tokens* are padded. Sequence_0: 0, 1*, 2*, 3* Sequence_1: 4, 5, 6*, 7* Sequence_2: 8, 9, 10, 11 PackedAttention is designed to takes in packed input, i.e., only the real tokens without padding. An input as above will be packed into 3 tensors like below: - input ([h0, h4, h5, h8, h9, h10, h11]) - token_offset: 0, 4, 5, 8, 9, 10, 11, 1*, 2*, 3*, 6*, 7* - cumulated_token_count: 0, 1, 1+2, 1+2+4 Input tensors contains the hidden embedding of real tokens. Token_offset records the offset of token in the unpacked input. cumulated_token_count records cumulated length of each sequence length. The operator only supports BERT like model with padding on right now. Invalid bias shapeqkv_hidden_sizes should have 3 elementsInput tensor with shape (token_count, input_hidden_size)In packing mode, it specifies the offset of each token(batch_size, sequence_length).token_offsetA tensor with shape (batch_size + 1). It specifies the cumulative sequence length.cumulative_sequence_lengthA tensor with shape (batch_size or 1, num_heads or 1, sequence_length, sequence_length).It specifies the additional bias to QxK'2D output tensor with shape (token_count, v_hidden_size)PackedAttention This is the packed version of MultiHeadAttention. Sequences in one batch usually don't have same length and they are padded to have same length, e.g., below is a batch with 3 sequences and * is padding token. Sequence_0: 0, 1*, 2*, 3* Sequence_1: 4, 5, 6*, 7* Sequence_2: 8, 9, 10, 11 PackedMultiHeadAttention is designed to takes in packed input, i.e., only the real tokens without padding. An input as above will be packed into 3 tensors like below: - query ([q0, q4, q5, q8, q9, q10, q11]) - key ([k0, k4, k5, k8, k9, k10, k11]) - value ([v0, v4, v5, v8, v9, v10, v11]) - token_offset: 0, 4, 5, 8, 9, 10, 11, 1*, 2*, 3*, 6*, 7* - cumulative_sequence_length: 0, 1, 1+2, 1+2+4 The query, key and value tensors contain result of hidden embedding of real tokens after input projections. Token_offset records the offset of token in the unpacked input. cumulative_sequence_length records cumulated length of each sequence length. The operator only supports BERT like model with padding on right now. Inputs 0 (query) shall be 2 or 4 dimensionsInputs 2 (value) shall be 2 dimensionsQuery with shape (token_count, hidden_size) or packed qkv with shape (token_count, num_heads, 3, head_size)queryKey with shape (token_count, hidden_size)keyValue with shape (token_count, v_hidden_size)Bias tensor with shape (hidden_size + hidden_size + v_hidden_size) from input projectionOffset of each token before packing, with shape (batch_size, sequence_length).It specifies the additional bias to QxK'. The shape is (batch_size or 1, num_heads or 1, sequence_length, sequence_length)output tensor with shape (token_count, v_hidden_size)Constrain input and output to float tensors.Constrain mask, offset and sequence length to integer typesPackedMultiHeadAttention Self attention that supports input sequence length of 1. The weights for input projection of Q, K and V are merged. The data is stacked on the second dimension. Its shape is (input_hidden_size, hidden_size + hidden_size + v_hidden_size). Here hidden_size is the hidden dimension of Q and K, and v_hidden_size is that of V. The mask_index is optional. If it is provided, only raw attention mask with shape (batch_size, total_sequence_length) is supported currently. Both past and present state need to be provided. The qkv_hidden_sizes is required only when K and V have different hidden sizes. The total_sequence_length is past_sequence_length + kv_sequence_length. Here kv_sequence_length is the length of K or V. Currently, only self attention is supported which means that kv_sequence_length equals to sequence_length (sequence length of Q). Input tensor with shape (batch_size, 1, input_hidden_size)Mask values of shape (batch_size, total_sequence_length)past state for key and value with shape (2, batch_size, num_heads, past_sequence_length, head_size)When past_present_share_buffer is set, its shape is (2, batch_size, num_heads, max_sequence_length, head_size). The first `batch_size * num_heads * max_sequence_length * head_size` elements correspond to keys and the next `batch_size * num_heads * max_sequence_length * head_size` elements correspond to values. The keys buffer is re-ordered in such a way that its virtual sub-tensor of shape (batch_size, num_heads, max_sequence_length, head_size) which may be perceived as being of shape (batch_size, num_heads, max_sequence_length, head_size / x, x) is reordered to become (batch_size, num_heads, head_size / x, max_sequence_length, x) where `x = 16 / sizeof(T)`.The beam width that is being used while decoding. If not provided, the beam width will be assumed to be 1.A buffer of shape [batch_size, beam_width, max_output_length] where an `[i, j, k]` entry specifies which beam the `k`-th token came from for the `j`-th beam for batch `i` in the current iterationcache_indirection Multihead attention that supports input sequence length of 1. Similar to DecoderMaskedSelfAttention but this op excludes QKV MatMul and Bias. This op supports both Self and Cross Attention. Corresponding past and present are same tensor, its size is (batch_size, num_heads, max_sequence_length, head_size)Need output the cross attention MatMul(Q, K)Query with shape (batch_size, 1, hidden_size) or packed QKV with shape (batch_size, 1, 2 * hidden_size + v_hidden_size)Key with shape (batch_size, 1, hidden_size) for self attention or past_key with shape (batch_size, num_heads, kv_sequence_length, head_size) for cross attentionValue with shape (batch_size, 1, v_hidden_size) for self attention or past_value with shape (batch_size, num_heads, kv_sequence_length, head_size) for cross attentionMask values of shape (batch_size, total_sequence_length) or (batch_size, kv_sequence_length)past state for key with shape (batch_size, num_heads, past_sequence_length, head_size) for self attentionWhen past_present_share_buffer is set, its shape is (batch_size, num_heads, max_sequence_length, head_size). The keys buffer is re-ordered in such a way that its virtual sub-tensor of shape (batch_size, num_heads, max_sequence_length, head_size) which may be perceived as being of shape (batch_size, num_heads, max_sequence_length, head_size / x, x) is reordered to become (batch_size, num_heads, head_size / x, max_sequence_length, x) where `x = 16 / sizeof(T)`.past_keypast state for value with shape (batch_size, num_heads, past_sequence_length, head_size) for self attentionWhen past_present_share_buffer is set, its shape is (batch_size, num_heads, max_sequence_length, head_size). past_valueWhen past_present_share_buffer is used, it is required to specify past_sequence_length (could be 0).Cross Attention doesn't need this input.present state for key with shape (batch_size, num_heads, total_sequence_length, head_size). If past_present_share_buffer is set, its shape is (batch_size, num_heads, max_sequence_length, head_size), while effective_seq_length = (past_sequence_length + kv_sequence_length).key and value cache shall be 4 dimensionsinput shall be 3 dimensionsinput shall be 2 dimensionstoken_offset shall be 2 dimensionsInputs 0 (query) shall be 3 or 5 dimensionsInputs 2 (value) shall be 3 or 4 dimensionsThe past_key input shall be 4 dimensionsInputs 0 (query) shall be 3 dimensions Multi-Head Attention that can be either unidirectional (like GPT-2) or bidirectional (like BERT). The weights for input projection of Q, K and V are merged. The data is stacked on the second dimension. Its shape is (input_hidden_size, hidden_size + hidden_size + v_hidden_size). Here hidden_size is the hidden dimension of Q and K, and v_hidden_size is that of V. The mask_index is optional. Besides raw attention mask with shape (batch_size, total_sequence_length) or (batch_size, sequence_length, total_sequence_length) with value 0 for masked and 1 otherwise, we support other two formats: When input has right-side padding, mask_index is one dimension with shape (batch_size), where value is actual sequence length excluding padding. When input has left-side padding, mask_index has shape (2 * batch_size), where the values are the exclusive end positions followed by the inclusive start positions. When unidirectional is 1, each token only attends to previous tokens. Both past and present state are optional. They shall be used together, and not allowed to use only one of them. The qkv_hidden_sizes is required only when K and V have different hidden sizes. When there is past state, hidden dimension for Q, K and V shall be the same. The total_sequence_length is past_sequence_length + kv_sequence_length. Here kv_sequence_length is the length of K or V. For self attention, kv_sequence_length equals to sequence_length (sequence length of Q). For cross attention, query and key might have different lengths. Number of attention headsWhether every token can only attend to previous tokens. Default value is 0.Hidden dimension of Q, K, V: hidden_size, hidden_size and v_hidden_sizeCorresponding past and present are same tensor, its size is (2, batch_size, num_heads, max_sequence_length, head_size)Sin cache of rotary with shape (max_rotary_sequence_length, head_size / 2).3D output tensor with shape (batch_size, sequence_length, num_heads * head_size)Updated key cache with shape (batch_size, kv_num_heads, max_cache_sequence_length, head_size).Updated value cache with shape (batch_size, kv_num_heads, max_cache_sequence_length, head_size).Constrain integer type. Longformer Self Attention with a local context and a global context. Tokens attend locally: Each token attends to its W previous tokens and W succeeding tokens with W being the window length. A selected few tokens attend globally to all other tokens. The attention mask is of shape (batch_size, sequence_length), where sequence_length is a multiple of 2W after padding. Mask value < 0 (like -10000.0) means the token is masked, 0 otherwise. Global attention flags have value 1 for the tokens attend globally and 0 otherwise. One sided attention windows length W, or half of total window lengthwindow3D input tensor with shape (batch_size, sequence_length, hidden_size), hidden_size = num_heads * head_size2D input tensor with shape (hidden_size, 3 * hidden_size)1D input tensor with shape (3 * hidden_size)Attention mask with shape (batch_size, sequence_length)global_weightglobal_biasGGlobal attention flags with shape (batch_size, sequence_length)globalLongformerAttention This DecoderAttention supports self attention and cross attention, key and value cache, and key_padding_mask. The attention mask is not support at the moment. Some boolean parameters are passed by runtime input for generic purpose 3D input tensor with shape (sequence_length, batch_size, hidden_size), hidden_size = num_heads * head_size3D input tensor with shape (total_sequence_length, batch_size, hidden_size)2D input tensor with shape (hidden_size, hidden_size)q_weight2D input tensor with shape (hidden_size, 2 * hidden_size)kv_weight2D input tensor with shape (batch_size, total_sequence_length)input tensor with shape (batch_size, num_heads, sequence_length or total_sequence_length, head_size)key_cachevalue_cacheIf static_kv = true, cross-attention; else self-attentionstatic_kvIf use_past = true, use cache; else no cacheuse_pastIf has_layer_state = true, layer_state = {} or [a,b]; else layer_state = Nonehas_layer_statehas_key_padding_mask or nothas_key_padding_mask3D output tensor with shape (sequence_length, batch_size, hidden_size)output tensor with shape (batch_size, num_heads, new sequence_length, head_size)new_key_cachenew_value_cacheConstrain input and output types to float and float16 tensors.Constrain key_padding_mask to bool tensors.DecoderAttention RotaryEmbedding is the implementation of rotary positional embeddings (RoPE). The positions are represented as rotation matrices that are multiplied to query and key before the inner product of query and key is taken. Custom scale will be used if specified. Default value is 1.0Indicates whether the input has real and imaginary parts interleaved. Default value is 0 (False), meaning the first half of the input consists of real values and the second half consists of imaginary values.Rotary embedding dimension. Default value is 0.Number of attention heads. Default value is 0. Must use with rotary_embedding_dimragged batch inputs or not. Default value is 03D tensor with shape (batch_size, sequence_length, hidden_size) or 4D with shape (batch_size, num_heads, sequence_length, head_size)1D tensor with shape (1) or 2D tensor with shape (batch_size, sequence_length)2D tensor with shape (max_sequence_length, head_size / 2) or (max_sequence_length, rotary_embedding_dim / 2)tensor with same shape as input.Constrain input and output types to integer tensors GemmaRotaryEmbedding is the implementation of below part of rotary positional embeddings (RoPE). It implements below from modeling_gemma.py. Here's onnxscript that was tested from onnxscript import FLOAT, FLOAT16, script from onnxscript import opset18 as op @script() def gemma_rotary_embedding(emb: FLOAT["bs", "seq_len", "dim"], q: FLOAT16["bs", "num_heads", "seq_len", "dim"], q_rot: FLOAT16["bs", "num_heads", "seq_len", "dim"], k: FLOAT16["bs", "num_heads", "seq_len", "dim"], k_rot: FLOAT16["bs", "num_heads", "seq_len", "dim"]): sin_val = op.Sin(emb) casted_sin = op.Cast(sin_val, to=10) # for fp16 mix-precision training. Other types are not supported. cos_val = op.Cos(emb) casted_cos = op.Cast(cos_val, to=10) unsqueezed_sin = op.Unsqueeze(casted_sin, [1]) unsqueezed_cos = op.Unsqueeze(casted_cos, [1]) q_embed = (q * casted_cos) + (q_rot * casted_sin) k_embed = (k * casted_cos) + (k_rot * casted_sin) return q_embed, k_embed onnx_model = gemma_rotary_embedding.to_model_proto() embeddding - 3D tensor with shape (batch_size, seq_len, dim)embq state - 4D tensor with shape (batch_size, num_heads, seq_len, dim)qhalf rotated q state - 4D tensor with shape (batch_size, num_heads, seq_len, dim)q_rotk state - 4D tensor with shape (batch_size, num_heads, seq_len, dim)k_rotpresent_keypresent state for value with shape (batch_size, num_heads, total_sequence_length, head_size). If past_present_share_buffer is set, its shape is (batch_size, num_heads, max_sequence_length, head_size), while effective_seq_length = (past_sequence_length + kv_sequence_length).present_valueQKnormalized Q * K, of shape (batch_size, num_heads, 1, total_sequence_length). qkConstrain QK output to float32 or float16 tensors, independent of input type or output type. Multi-Head Self/Cross Attention. Bias from input projection is included. The key padding mask is optional. When its shape is (batch_size, kv_sequence_length), value 0 means padding or 1 otherwise. When key has right-side padding, its shape could be (batch_size): it is actual length of each key sequence excluding paddings. Query with shape (batch_size, sequence_length, hidden_size), or packed QKV with shape (batch_size, kv_sequence_length, num_heads, 3, head_size)Key with shape (batch_size, kv_sequence_length, hidden_size), or packed KV with shape (batch_size, kv_sequence_length, num_heads, 2, head_size), or past_key with shape (batch_size, num_heads, kv_sequence_length, head_size)Value with shape (batch_size, kv_sequence_length, v_hidden_size), or past_value with shape (batch_size, num_heads, kv_sequence_length, head_size)Key padding mask with shape (batch_size), (3 * batch_size + 2), (batch_size, kv_sequence_length), (batch_size, total_sequence_length), or (batch_size, sequence_length, total_sequence_length)key_padding_maskbias added to QxK' with shape (batch_size or 1, num_heads or 1, sequence_length, total_sequence_length)past state for key with shape (batch_size, num_heads, past_sequence_length, head_size) or (batch_size, num_heads, max_sequence_length, head_size) when buffer sharing is usedpast state for value with shape (batch_size, num_heads, past_sequence_length, head_size) or (batch_size, num_heads, max_sequence_length, head_size) when buffer sharing is usedThe past_sequence_length buffer sharing is used withA buffer of shape [batch_size, beam_width, max_sequence_length] where an [i, j, k] entry specifieswhich beam the 'k' th token came from for the 'j' th beam for batch 'i' in the current iterationpresent state for key with shape (batch_size, num_heads, total_sequence_length, head_size) or (batch_size, num_heads, max_sequence_length, head_size) when buffer sharing is usedpresent state for value with shape (batch_size, num_heads, total_sequence_length, head_size) or (batch_size, num_heads, max_sequence_length, head_size) when buffer sharing is usednormalized Q * K, of shape (batch_size, num_heads, sequence_length, total_sequence_length). Group Query Self/Cross Attention. *Highly recommend using k-v cache share buffer for both CPU and CUDA. Enabled through IOBinding past and present kv. Supports different number of heads for q and kv for CPU and CUDA. Only supports causal and local attention. Supports rotary position embedding for CPU and CUDA. Supports packed input for CPU and CUDA. Supports continuous decoding for batch_size == 1 for CPU and CUDA. Number of attention heads for qNumber of attention heads for k and vSoftcap value for attention weights. Default value is 0.left_window_size for local attention (like Mistral). Default value is -1 meaning unused.Rotate using interleaved pattern. Default value is 0 (False).Use a smooth factor in softmax.Query with shape (batch_size, sequence_length, hidden_size), or packed QKV with shape(batch_size, sequence_length, d) where d is (num_heads * head_size + 2 * kv_num_heads * head_size).Key with shape (batch_size, kv_sequence_length, kv_hidden_size) Value with shape (batch_size, kv_sequence_length, kv_hidden_size)past state key with support for format BNSH. When past_key uses same tensor as present_key(k-v cache), it is of length max_sequence_length... otherwise of length past_sequence_length.past state value with support for format BNSH. When past_value uses same tensor as present_value(k-v cache), it is of length max_sequence_length... otherwise of length past_sequence_length.1D Tensor of shape (batch_size). Equivalent to (total_sequence_lengths - 1).seqlens_kScalar tensor equivalent to the maximum total sequence length (past + new) of the batch. Used for checking inputs and determining prompt vs token generation case.total_sequence_length2D tensor with shape (max_sequence_length, head_size / 2).cos_cachesin_cache2D tensor with shape (batch_size, sequence_length). When processing the first prompt the kernel uses only the first element3D output tensor with shape (batch_size, sequence_length, hidden_size)present state key with support for format BNSH. When past_key uses same tensor as present_key(k-v buffer), it is of length max_sequence_length... otherwise of length past_sequence_length +kv_sequence_length.present state value with support for format BNSH. When past_value uses same tensor as present_value(k-v buffer), it is of length max_sequence_length... otherwise of length past_sequence_length +kv_sequence_length.Constrain mask to int tensor. Block Sparse Attention used in Phi-3-small (https://arxiv.org/pdf/2404.14219). It is inspired by Sparse Transformers (https://arxiv.org/pdf/1904.10509) and BigBird (https://arxiv.org/pdf/2007.14062). block_mask can be used to configure sparse layout for different head. When number of sparse layout is 1, all heads have same sparse layout. Otherwise, different layouts are used cyclically. For example, given 4 layouts (S0, S1, S2, S3), 8 heads will have layouts like (S0, S1, S2, S3, S0, S1, S2, S3). The block_row_indices and block_col_indices are the CSR representation of block mask. The block_col_indices might contain paddings at the right side when different layout has different number of non-zeros in block mask. An example of block mask with 2 layouts where each layout is 4 x 4 blocks: [[[1, 0, 0, 0], [1, 1, 0, 0], [0, 1, 1, 0], [0, 1, 1, 1]], [[1, 0, 0, 0], [1, 1, 0, 0], [1, 1, 1, 0], [1, 0, 1, 1]]] The corresponding CSR format: block_col_indices = [[0, 0, 1, 1, 2, 1, 2, 3, -1], [0, 0, 1, 0, 1, 2, 0, 2, 3]] block_row_indices = [[0, 1, 3, 5, 8], [0, 1, 3, 6, 9]] When do_rotary is True, cos_cache and sin_cache are required. Note that the maximum sequence length supported by cos or sin cache can be different from the maximum sequence length used by kv cache. Only supports unidirectional attention with cache of past key and value in linear buffers. For performance, past_key and present_key share same memory buffer, and past_value and present_value too. Number of attention heads for queryNumber of attention heads for key and valueScaling factor applied prior to softmax. The default value is 1/sqrt(head_size)Number of tokens per sparse block. Choices: 16, 32, 64, 128Rotary use interleaved pattern or not. Default value is 0.Query with shape (batch_size, sequence_length, num_heads * head_size), or packed QKV with shape is(batch_size, sequence_length, d) where d is (num_heads + 2 * kv_num_heads) * head_size.Key with shape (batch_size, sequence_length, kv_num_heads * head_size)Value with shape (batch_size, sequence_length, kv_num_heads * head_size)Key cache with shape (batch_size, kv_num_heads, max_cache_sequence_length, head_size)Value cache with shape (batch_size, kv_num_heads, max_cache_sequence_length, head_size)The row indices of CSR format of block mask with shape (num_layout, max_blocks + 1).The num_heads is divisible by num_layout, and max_blocks is max_sequence_length / sparse_block_size.block_row_indicesThe col indices of CSR format of block mask with shape (num_layout, max_nnz_blocks).The max_nnz_blocks is the maximum number of non-zeros per layout in block mask.block_col_indicesScalar tensor of maximum total sequence length (past_sequence_length + sequence_length) among keys.1D tensor with shape (batch_size) where each value is total sequence length of key excluding paddings.key_total_sequence_lengthsCos cache of rotary with shape (max_rotary_sequence_length, head_size / 2).The NGram size.2D input tensor with shape (batch_size, sequence_length)2D input tensor with shape (batch_size, vocab_size)2D output tensor with shape (batch_size, vocab_size)scores_outConstrain scores input and output types to float tensors. Component for aggressive decoding. Find the bifurcation index of predicted tokens, between source tokens, starting from previous suffix match index, and predicted tokens. Concat predicted tokens, starting from bifurcation index, to the back of current tokens. This forms the output tokens. Detect suffix match index in source tokens, between source tokens and output tokens. Detection is based on finding the appearances of last n-gram in output tokens in source tokens. A match is considered found if source tokens contain a single matching n-gram. Return the index of the start of the n-gram in source tokens. No matching if found if src tokens contain multiple or zero matching n-grams. Return -1. The minimum NGram size for suffix matching.The maximum NGram size for suffix matching.Encoder input ids.src_tokensDecoder input ids.cur_tokensPrevious suffix match indexprev_suffix_match_idxPredicted token ids from aggressive decodingpred_tokensDecoder input ids after merging predicted tokenstokensnew suffix match indexsuffix_match_idx It's a fusion of MatMul and FastGelu.GemmFastGelu Compress transformer input by removing paddings. It assumes padding is on the right side of sequence. The input has padding with shape (batch_size, sequence_length, hidden_size). This will generate two outputs: output has shape (total_tokens, hidden_size); token_offset with shape (batch_size, sequence_length). token_offset has offsets of all non-padding tokens first, then offset of all padding tokens. It is a list of batch_size * sequence_length elements, which is reshaped to 2D for convenience of shape inference. Input tensor with shape (batch_size, sequence_length, hidden_size)Number of non-padding tokens in each sequence with shape (batch_size).sequence_token_countoutput tensor with shape (total_tokens, hidden_size)Offset of non-padding tokens, and those of padding tokens. Its shape is (batch_size, sequence_length)Cumulated sequence lengths. Its shape is (batch_size + 1)cumulated_seq_lenMax sequence length without padding. Its shape is (1)max_seq_lenConstrain sequence_token_count and token_offset to integer typesRemovePadding Restore paddings and fill padding with zeros. The input has padding with shape (total_tokens, hidden_size) and token_offset with shape (batch_size, sequence_length). The output has shape (batch_size, sequence_length, hidden_size). Input tensor with shape (total_tokens, hidden_size)Offset of non-padding tokens and paddings. Its shape is (batch_size, sequence_length)output tensor with shape (batch_size, sequence_length, hidden_size)Constrain token_offset to integer typesRestorePadding query_layer = (query_layer + query_bias).reshape(batch_size, seq_len, num_heads, head_size).transpose(1, 2) gate_u, gate_r = torch.sigmoid( self.gate_ur_linear(query_layer).view(batch_size, num_head, seq_len, 2, D/2).sum(-1, keepdim=False) ).chunk(2, dim=-1) gate_u_1 = gate_u * (gate_r * self.eco_a - 1.0) + 2.0 rel_pos_bias = gate_u_1 * rel_pos tensor with shape (batch_size, seq_len, num_heads x head_size) or (token_count, num_heads x head_size)query_layer1-d tensor with shape (num_heads x head_size)query_biastensor with shape (1, num_head, seq_len, seq_len)rel_posgemm weight for the gated_ur_linear, shape (head_size, D), D is divisible by 2bias for the gated_ur_linear, shape (D)tensor of shape (1, num_heads, 1, 1)eco_aoffset of each token with shape (batch_size, seq_len)output tensor with shape (batch_size, num_heads, seq_len, seq_len)GatedRelativePositionBias4D tensor with shape (batch_size, num_heads, seq_len, dim)output1output2Constrain input and output types to float16 tensors.Constrain input 0 type to float tensorsGemmaRotaryEmbedding EmbedLayerNormalization is the fusion of embedding layer in BERT model, with optional mask processing. The embedding layer takes input_ids (word IDs) and segment_ids (sentence IDs) to look up word_embedding, position_embedding, and segment_emedding; the embeddings are added then applied layer normalization using gamma and beta tensors. The last input mask is optional. If mask is provided, mask index (that is position of first 0 in mask, or number of words) will be calculated.The mask index tensor type for shape inference (0: None, 1: 1D mask_index)mask_index_type2D words IDs with shape (batch_size, sequence_length)2D segment IDs with shape (batch_size, sequence_length)segment_ids2D with shape (,hidden_size)word_embedding2D with shape (, hidden_size)position_embeddingsegment_embedding1D gamma tensor for layer normalization with shape (hidden_size)1D beta tensor for layer normalization with shape (hidden_size)2D attention mask with shape (batch_size, sequence_length)2D position ids with shape (batch_size, sequence_length) or (1, sequence_length)1D mask_index tensor with shape (batch_size)sum of word_embedding and position_embedding without layer normalizationembedding_sumConstrain input and output integer tensors typesConstrain input and output float tensors types. GELU (Gaussian Error Linear Unit) approximation: Y=0.5*X*(1+tanh(0.797885*X+0.035677*X*X*X)) with an optional input of bias that will be added to X before GELU.abconeX_bias = Add (X, bias)X_bias = Identity (X) T1 = Mul (X_bias, X_bias) T2 = Mul (c, T1) T3 = Add (b, T2) T4 = Mul (X_bias, T3) T5 = Tanh (T4) T6 = Add (one, T5) T7 = Mul (X_bias, T6) Y = Mul (a, T7) bias tensorCompute binned relative position bias for T5 model. ref: https://arxiv.org/abs/1803.02155v2Max distancemax_distanceDefault value is 0.is_bidirectional2D input tensor with shape (num_buckets, num_heads), COL-major(See UT for example)bias_tableThe length of query. Self Attention requires query_length = key_lengthquery_lengthThe length of key.key_length4D output tensor with shape (1, num_heads, sequence_length, sequence_length)Constrain sequence_length to int tensors.RelativePositionBiasSkip and Layer Normalization Fusion3D input tensor with shape (batch_size, sequence_length, hidden_size)3D skip tensor with shape (batch_size, sequence_length, hidden_size) or (1, sequence_length, hidden_size) or (sequence_length, hidden_size)skip1D input tensor with shape (hidden_size)1D skip tensor with shape (hidden_size1D bias tensor with shape (hidden_sizeSum of the input and skip inputs (and bias if it exists) with shape (batch_size, sequence_length, hidden_size).input_skip_bias_sumConstrain mean and inv_std_var to float tensors.Skip and Root Mean Square Layer Normalization3D input tensor with shape (batch_size, sequence_length, hidden_size)Or 2D input tensor with shape (token_count, hidden_size)3D output tensor with shape (batch_size, sequence_length, hidden_size)Or 2D output tensor with shape (token_count, hidden_size)Sum of the input and skip inputs (and bias if it exists)with shape (batch_size, sequence_length, hidden_size) or (token_count, hidden_size). Enforce no repetition of n-grams. Scores are set to `-inf` for tokens that form a repeated n-gram if added to the back of the input_ids. ̼+̼+ Applies Group Normalization over a mini-batch of inputs as described in the paper Group Normalization (https://arxiv.org/abs/1803.08494). This operator transforms input according to y = gamma * (x - mean) / sqrt(variance + epsilon) + beta The input channels are separated into num_groups groups, each containing num_channels / num_groups channels. num_channels must be divisible by num_groups. The mean and standard-deviation are calculated separately over the each group. The weight and bias are per-channel affine transform parameter vectors of size num_channels. The activation attribute can be used to enable activation after group normalization. The epsilon value to use to avoid division by zeroThe number of groups of channels. It should be a divisor of the number of channels CActivation after group normalization: 0 for None, 1 for SiLU1 if the input and output are in the NHWC layout, 0 if it is in the NCHW layout. Defaults to 1.Input data tensor. Dimensions are (N x H x W x C) when channels_last is 1 or (N x C x H x W) otherwise, where N is the batch size, C is the number of channels, and H and W are the height and width of the data1D gamma tensor for normalization with shape (C), where C is number of channels1D beta tensor for normalization with shape (C), where C is number of channelsThe output tensor of the same shape as XConstrain input X and output Y types to float tensors.Constrain gamma and beta to float tensors.D:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\diffusion_defs.cc This operator element-wise adds x, skip and bias, then apply group normalization and optional activation. This operator transforms input according to s = x + skip + bias y = gamma * (s - mean) / sqrt(variance + epsilon) + beta The input channels are separated into num_groups groups, each containing num_channels / num_groups channels. The num_channels must be divisible by num_groups. The mean and standard-deviation of s are calculated separately over the each group. The weight and bias are per-channel affine transform parameter vectors of size num_channels. The activation attribute can be used to enable activation after group normalization. Input data tensor. Dimensions are (N x H x W x C) when channels_last is 1 or (N x C x H x W) otherwise, where N is the batch size, C is the number of channels, and H and W are the height and width of the data1D beta tensor for normalization with shape (C), where C is number of channels4D or 2D skip tensor. The shape can be (N x H x W x C) or (N x 1 x 1 x C) or (N x C)1D bias tensor. Dimensions are (C), where C is number of channelsThe element-wise sum of input x, skip and bias tensors. It has the same shape as XConstrain input X, skip, bias and output Y, S types to float tensors.SkipGroupNorm A fusion used in diffusion model that after adding bias, hidden state is sliced into two tensors of same size, then left tensor multiplies the Gelu activation result of right tensor. bias shall be 1 dimensionInput tensor. Dimensions are (N, S, D), where N is the batch size, S are image size, and D is hidden dimensionBias tensor. Dimensions are (D), where D is the same hidden dimension as input tensorThe output tensor with dimensions (N, S, D/2) Add input with bias, then add residual inputs. Input tensor. Dimensions are (N, S, C), where N is the batch size, S is image size H*W, and C is number of channelsBias tensor. Dimensions are (C)Residual tensor. Dimensions are (N, S, C)The output tensor with dimensions (N, S, C) Dimension=Can't merge shape info. Both inferred and declared dimension have values but they differ. Inferred= or UNDEFINED. Got: expected to have: 1-D, contiguous, raw, BFP data to be de-quantized.shape of the original tensor.strides of the original tensor.de-quantized tensor.Constrain the input to uint8.Constrain y to float and bfloat16.DequantizeBFP Computes the sum of the low-precision input tensor's element along the provided axes. The resulting tensor has the same rank as the input if keepdims equal 1. If keepdims equal 0, then the resulting tensor have the reduced dimension pruned. The above behavior is similar to numpy, with the exception that numpy default keepdims to False instead of True.An input tensor.Reduced output tensor.reducedConstrain input type to 8-bit integer tensor.Constrain output data type to 32-bit integer tensor.T2 must be tensor(uint32) when T1 is tensor(uint8),or must be tensor(int32) when T1 is tensor(int8).A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor.Keep the reduced dimension or not, default 1 mean keep reduced dimension.ReduceSumIntegerPerforms element-wise binary quantized multiplication (with Numpy-style broadcasting support). "This operator supports **multidirectional (i.e., Numpy-style) broadcasting**" The output of this op is the int32 accumulated result of the mul operation ``` C (int32) = (A - A_zero_point) * (B - B_zero_point) ``` Constrain output to 32 bit tensorConstrain input types to 8 bit signed and unsigned tensors.Constrain output types to 32 bit tensors.MulIntegerScale of quantized input 'B'. It could be a scalar or a 1-D tensor, which means a per-tensor or per-column quantization. If it's a 1-D tensor, its number of elements should be equal to the number of columns of input 'B'.b_scaleZero point tensor for input 'B'. It's optional and default value is 0. It could be a scalar or a 1-D tensor, which means a per-tensor or per-column quantization. If it's a 1-D tensor, its number of elements should be equal to the number of columns of input 'B'.b_zero_point1D input tensor, whose dimension is same as B's last dimensionConstrain input A, b_scale and output Y data type as float tensor.Constrain input B data type to 8-bit integer tensor.Scale of quantized input 'A'. It could be a scalar or a 1-D tensor, which means a per-tensor or per-column quantization. If it's a 1-D tensor, its number of elements should be equal to the number of columns of input 'A'.a_scaleZero point tensor for input 'A'. It's optional and default value is 0. It could be a scalar or a 1-D tensor, which means a per-tensor or per-column quantization. If it's a 1-D tensor, its number of elements should be equal to the number of columns of input 'A'.a_zero_pointConstrain input A data type to 8-bit integer tensor.Constrain input a_scale, b_scale and output Y data type as float tensor.C = (A_scale * (A - A_zero_point) + B_scale * (B - B_zero_point))/C_scale + C_zero_pointadditionC = ((A - A_zero_point) * (B - B_zero_point)) * (A_scale * B_scale)/C_scale + C_zero_pointmultiplicationaxis must be in [-rank, rank-1]. input rank was Computes the mean of the low-precision input tensor's element along the provided axes. The resulting tensor has the same rank as the input if keepdims equal 1. If keepdims equal 0, then the resulting tensor have the reduced dimension pruned. The above behavior is similar to numpy, with the exception that numpy default keepdims to False instead of True. Input and Output scales and zero points are used to requantize the output in a new range. This helps to improve accuracy as after ReduceMean operation the range of the output is expected to decrease. ``` "Output = Dequantize(Input) -> ReduceMean on fp32 data -> Quantize(output)", ``` Input scale. It's a scalar, which means a per-tensor/layer quantization.data_scaleInput zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.data_zero_pointreduced_scalereduced_zero_point QLinearLeakyRelu takes quantized input data (Tensor), an argument alpha, and quantize parameter for output, and produces one output data (Tensor) where the function `f(x) = quantize(alpha * dequantize(x)) for dequantize(x) < 0`, `f(x) = quantize(dequantize(x)) for dequantize(x) >= 0`, is applied to the data tensor elementwise. Coefficient of leakage.Input X's scale. It's a scalar, which means a per-tensor/layer quantization.X_scaleInput X's zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.X_zero_pointOutput Y's scale. It's a scalar, which means a per-tensor/layer quantization.Y_scaleOutput Y's zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.Y_zero_pointConstrain input and output types to 8 bit tensors. QLinearSigmoid takes quantized input data (Tensor), and quantize parameter for output, and produces one output data (Tensor) where the function `f(x) = quantize(Sigmoid(dequantize(x)))`, is applied to the data tensor elementwise. Wwhere the function `Sigmoid(x) = 1 / (1 + exp(-x))` ]. Its actual value is: , 'axis' must be in [ QLinearSoftmax computes the normalized exponential values for the given input: Softmax(input, axis) = Exp(input) / ReduceSum(Exp(input), axis=axis, keepdims=1) The input does not need to explicitly be a 2D vector. The "axis" attribute indicates the dimension along which QLinearSoftmax will be performed for onnx v.13+. or the dimension coerced to NxD Matrix for onnx v.12-. The output tensor has the same shape. apply softmax to elements for dimensions axis,or all dims along with axis according to op-versionopset version of corresponding SoftMax.Input data type does not match the expected data typeInput data type does not match the expected data type. Current data type is Scale and Zero-point must be a scalarScale and Zero-point must be of rank 1Scale and Zero-point must be of rank 1 and the number of elements should be equal to the number of rows of the corresponding input.inputs are expected to have tensor type. Performs element-wise binary {name} on 8 bit data types (with Numpy-style broadcasting support). {additionalDocumentation} {name}{additionalDocumentation}First operand.Input A's scale. It's a scalar, which means a per-tensor/layer quantization.A_scaleInput A zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.A_zero_pointSecond operand.Input B's scale. It's a scalar, which means a per-tensor/layer quantization.B_scaleInput B zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.B_zero_pointOutput scale. It's a scalar, which means a per-tensor/layer quantization.C_scaleOutput zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.C_zero_pointResult, has same element type as two inputsConstrain input and output types to 8 bit signed and unsigned tensors. The linear quantization operator. It consumes a full precision data, a scale, a zero point to compute the low precision / quantized tensor. The quantization formula is y = saturate ((x / y_scale) + y_zero_point). For saturation, it saturates to [0, 255] if it's uint8, [-128, 127] if it's int8, [0, 65,535] if it's uint16, and [-32,768, 32,767] if it's int16. For (x / y_scale), it's rounding to nearest ties to even. Refer to https://en.wikipedia.org/wiki/Rounding for details. Scale and zero point must have same shape. They must be either scalar (per tensor) or 1-D tensor (per 'axis').The axis along which same quantization parameters are applied. It's optional.If it's not specified, it means per-tensor quantization and input 'x_scale' and 'x_zero_point' must be scalars.If it's specified, it means per 'axis' quantization and input 'x_scale' and 'x_zero_point' must be 1-D tensors.N-D full precision Input tensor to be quantized.Scale for doing quantization to get 'y'. It can be a scalar, which means per-tensor/layer quantization, or a 1-D tensor for per-axis quantization.y_scaleZero point for doing quantization to get 'y'. Shape must match y_scale. Default is uint8 with zero point of 0 if it's not specified.y_zero_pointN-D quantized output tensor. It has same shape as input 'x'.Constrain 'x', 'y_scale' to float tensors.Constrain 'y_zero_point' and 'y' to 8-bit and 16-bit integer tensors.D:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\quantization_defs.cc The linear dequantization operator. It consumes a quantized data, a scale, a zero point and computes the full precision data. The dequantization formula is y = (x - x_zero_point) * x_scale. Scale and zero point must have same shape. They must be either scalar (per tensor) or 1-D tensor (per 'axis').N-D quantized Input tensor to be de-quantized.Scale for input 'x'. It can be a scalar, which means a per-tensor/layer dequantization, or a 1-D tensor for per-axis dequantization.x_scaleZero point for input 'x'. Shape must match x_scale. It's optional. Zero point is 0 when it's not specified.x_zero_pointN-D full precision output tensor. It has same shape as input 'x'.Constrain 'x' and 'x_zero_point' to 8-bit integer tensors, 16-bit integer tensors, or 32-bit signed integer tensors.Constrain 'y', 'x_scale' to float tensors. The BFP quantization operator. It consumes a full precision tensor and computes an BFP tensor. More documentation on the BFP format can be found in this paper: https://www.microsoft.com/en-us/research/publication/pushing-the-limits-of-narrow-precision-inferencing-at-cloud-scale-with-microsoft-floating-point/The type of BFP - must match with the BFPType enumbfp_typeEach bounding box spans this dimension.Typically, the block dimension corresponds to the reduction dimension of the matrix multipication that consumes the output of this operator.For example, for a 2D matrix multiplication A@W, QuantizeBFP(A) would use block_dim 1 and QuantizeBFP(W) would use block_dim 0.The default is the last dimension.block_dimN-D full precision input tensor to be quantized.1-D, contiguous BFP dataShape of xStrides of xConstrain the input to float and bfloat.Constrain y to uint8.Constrain shape and strides to uint64.QuantizeBFP The BFP dequantization operator. It consumes the raw BFP data and some metadata such as the shape and strides of the original tensor and computes the dequantized tensor. More documentation on the BFP format can be found in this paper: https://www.microsoft.com/en-us/research/publication/pushing-the-limits-of-narrow-precision-inferencing-at-cloud-scale-with-microsoft-floating-point/Shape of quantized tensor must be 1D.The datatype to dequantize to.Constrain scale types to float tensors.Constrain input A and its zero point types to 8 bit tensors.Constrain input B and its zero point types to 8 bit tensors.Constrain input C to 32 bit integer tensors.Constrain output zero point types to 8 bit tensors.Constrain output type to float32 or 8 bit tensors.Quantization of Multi-Head Self Attention.Corresponding past and present are same tensor, its shape is (2, batch_size, num_heads, max_sequence_length, head_size)3D input tensor with shape (batch_size, sequence_length, input_hidden_size)2D input tensor with shape (input_hidden_size, 3 * hidden_size), hidden_size = num_heads * head_sizescale of quantized input tensor. It's a scalar, which means a per-tensor/layer quantization.input_scalescale of weight scale. It's a scalar or a 1D tensor, which means a per-tensor/per-column quantization.Its size should be 3 * hidden_size if it is per-column quantizationweight_scaleAttention mask index with shape (batch_size)zero point of quantized input tensor.It's a scalar, which means a per-tensor/layer quantization.input_zero_pointzero point of quantized weight tensor. It's a scalar or a 1D tensor, which means a per-tensor/per-column quantization.Its size should be 3 * hidden_size if it is per-column quantizationweight_zero_pointpast state for key and value with shape (2, batch_size, num_heads, past_sequence_length, head_size).present state for key and value with shape (2, batch_size, num_heads, past_sequence_length + sequence_length, head_size)Constrain input and output types to int8 tensors. QEmbedLayerNormalization is the quantized fusion of embedding layer in BERT model, with optional mask processing. The embedding layer takes input_ids (word IDs) and segment_ids (sentence IDs) to look up word_embedding, position_embedding, and segment_emedding; the embeddings are added then applied layer normalization using gamma and beta tensors. The input_ids and segment_ids remain int32. All embeddings, gamma, and beta tensors are converted to int8/uint8. The last input mask is optional. If mask is provided, mask index (that is position of first 0 in mask, or number of words will be calculated.word_embedding_quantposition_embedding_quantgamma_quantbeta_quantMaskScale for word embeddingsword_embedding_scaleScale for position embeddingsposition_embedding_scaleScale for segment embeddingssegment_embedding_scaleScale for 1D gamma tensorgamma_scaleScale for 1D beta tensorbeta_scaleZero point for word embeddingsword_embedding_zero_pointZero point for position embeddingsposition_embedding_zero_pointZero Point for segment embeddingssegment_embedding_zero_pointZero Point for 1D gamma tensorgamma_zero_pointZero Point for 1D beta tensorbeta_zero_pointLayerNorm Outputlayernorm_outMask Index Outputmask_index_outConstrain input and output types to float32 tensors.Quantize input matrix to specific layout used in cublaslt.cublasLt order of input matrix. ORDER_COL = 0, ORDER_ROW = 1, ORDER_COL32 = 2, ORDER_COL4_4R2_8C = 3, ORDER_COL32_2R_4R4 = 4. Please refer https://docs.nvidia.com/cuda/cublas/index.html#cublasLtOrder_t for their meaning.order_inputcublasLt order of output matrix.order_outputTODO: input tensor of (ROWS, COLS). if less than 2d, will broadcast to (1, X). If 3d, it is treated as (B, ROWS, COS)scale of the inputscale_inputQConstrain to float typesConstrain Scale to float32 typesThe input tensorScale of quantized input 'X'. It must be a scalar.Zero point tensor for input 'X'.It must be a scalar.Scale of quantized output 'Y'. It must be a scalar.Zero point tensor for output 'Y'. It must be a scalar.Output data tensor from pooling across the input tensor. The output tensor has the same rank as the input. Constrain input and output types to signed/unsigned int8 tensors.Specify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional.Number of neurons in the hidden layerOptional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.For example with LeakyRelu, the default alpha is 0.01.Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.Cell clip threshold. Clipping bounds the elements of a tensor in the range of [-threshold, +threshold] and is applied to the input of activations. No clip if not specified.A list of 3 (or 6 if bidirectional) activation functions for input, output, forget, cell, and hidden. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified.Couple the input and forget gates if 1.The input sequences packed (and potentially padded) into one 3-D tensor with the shape of `[seq_length, batch_size, input_size]`.The weight tensor for the gates. Concatenation of `W[iofc]` and `WB[iofc]` (if bidirectional) along dimension 0. The tensor has shape `[num_directions, input_size, 4*hidden_size]`.The recurrence weight tensor. Concatenation of `R[iofc]` and `RB[iofc]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, hidden_size, 4*hidden_size]`.RThe bias tensor for input gate. Concatenation of `[Wb[iofc], Rb[iofc]]`, and `[WBb[iofc], RBb[iofc]]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 8*hidden_size]`. Optional: If not specified - assumed to be 0.Optional tensor specifying lengths of the sequences in a batch. If not specified - assumed all sequences in the batch to have length `seq_length`. It has shape `[batch_size]`.sequence_lensOptional initial value of the hidden. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.initial_hOptional initial value of the cell. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.initial_cThe weight tensor for peepholes. Concatenation of `P[iof]` and `PB[iof]` (if bidirectional) along dimension 0. It has shape `[num_directions, 3*hidde_size]`. Optional: If not specified - assumed to be 0.W's scale. Its size is [num_directions] for per-tensor/layer quantization, or [num_directions, 4*hidden_size] for per-channel quantization on the axis input_size.W's zero point. Its size is [num_directions] for per-tensor/layer quantization, or [num_directions, 4*hidden_size] for per-channel quantization on the axis input_size.R's scale. Its size is [num_directions] for per-tensor/layer quantization, or [num_directions, 4*hidden_size] for per-channel quantization on the axis input_size.R's zero point. Its size is [num_directions] for per-tensor/layer quantization, or [num_directions, 4*hidden_size] for per-channel quantization on the axis input_size.A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`. The last output value of the hidden. It has shape `[num_directions, batch_size, hidden_size]`.Y_hThe last output value of the cell. It has shape `[num_directions, batch_size, hidden_size]`.Y_cConstrain seq_lens to integer tensor.Constrain weights types to 8 bit tensors.Required attribute axis is missingaxis must be in [-rank, rank)All inputs to Concat must have same rankWhich axis to concat onConcatenate a list of tensors into a single tensor.All input tensors must have the same shape, except for the dimension size of the axis to concatenate on.Y's scale.Y's zero point.List of tensors/scale/zero_point for concatenationConcatenated tensorConstrain scale types to any float tensor type.Sequence of (Tensor, Scale, ZeroPoint) tuples. The type is sequence of (T8, TF, T8).Return elements, either from X or Y, depending on condition. When True (nonzero), yield x, otherwise yield yconditionX's scale.X's zero point.Z's scale.z_scaleZ's zero point.z_zero_pointTensor of shape equal to the broadcasted shape of condition, X, and YQuantized GemmScale of quantized input 'A'. It is a scalar,which means a per-tensor quantization.Zero point tensor for input 'A'. It is a scalar.Optional input tensor C. If not specified, the computation is done as if C is a scalar 0. The shape of C should be unidirectional broadcastable to (M, N). Its type is int32_t and must be quantized with zero_point = 0 and scale = alpha / beta * a_scale * b_scale.Scale of output 'Y'. It is a scalar, which means a per-tensor quantization. It is optional. The output is full precision(float32) if it is not provided. Or the output is quantized.Zero point tensor for output 'Y'. It is a scalar, which means a per-tensor quantization. It is optional. The output is full precision(float32) if it is not provided. Or the output is quantized.quantization scale must be float tensors.quantization tensor must be int8 tensors.Ordered Quantize Gelu.cublasLt order of input X. Optional. See the schema of QuantizeWithOrder for order definition.cublasLt order of matrix Y, must be same as order_X if specified together. Optional.N-dimensional input Ascale of the input Ascale of the output YOutput of the GeluConstrain scales to float32QOrderedGeluQuantized version of Longformer Self Attention (using int8 with specific matrix Layout).order_global_weightscale of the weightscale_weight1D input tensor with shape (3 * hidden_size), fp32 only currently.reserved. (not used as add bias need float value in cublasLt for normal order.)scale_biasscale of the output for fused kqv gemmscale_qkv_gemmscale of the global_weightscale_global_weightscale of the global_qkv_gemmscale_global_gemmscale of the outputscale_outputBe compatible with float version.QOrderedLongformerAttentioninput and zero_point pair is expected to have be same type.weight and zero_point pair is expected to have same type. Similar to ConvTranspose in onnx, but with quantization. The convolution transpose operator consumes an input tensor and a filter, and computes the output. If the pads parameter is provided the shape of the output is calculated via the following equation: output_shape[i] = stride[i] * (input_size[i] - 1) + output_padding[i] + ((kernel_shape[i] - 1) * dilations[i] + 1) - pads[start_i] - pads[end_i] output_shape can also be explicitly specified in which case pads values are auto generated using these equations: total_padding[i] = stride[i] * (input_size[i] - 1) + output_padding[i] + ((kernel_shape[i] - 1) * dilations[i] + 1) - output_shape[i] If (auto_pads == SAME_UPPER): pads[start_i] = total_padding[i]/2; pads[end_i] = total_padding[i] - (total_padding[i]/2) Else: pads[start_i] = total_padding[i] - (total_padding[i]/2); pads[end_i] = (total_padding[i]/2). Input data tensor from previous layer; has size (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and width. Note that this is for the 2D image. Otherwise the size is (N x C x D1 x D2 ... x Dn)Scale tensor for input 'x'. It's a scalar, which means a per-tensor/layer quantization.Zero point tensor for input 'x'. It's a scalar, which means a per-tensor/layer quantization.The weight tensor that will be used in the convolutions; has size (C x M/group x kH x kW), where C is the number of channels, and kH and kW are the height and width of the kernel, and M is the number of feature maps. wScale tensor for input 'w'. It could be a scalar or a 1-D tensor, which means a per-tensor/layer or per output channel quantization. If it's a 1-D tensor, its number of elements should be equal to the number of output channels (M).w_scaleZero point tensor for input 'w'. It could be a scalar or a 1-D tensor, which means a per-tensor/layer or per output channel quantization. If it's a 1-D tensor, its number of elements should be equal to the number of output channels (M).w_zero_pointScale tensor for output 'y'. It's a scalar, which means a per-tensor/layer quantization.Zero point tensor for output 'y'. It's a scalar, which means a per-tensor/layer quantization.Optional 1D bias to be added to the convolution, has size of M. Bias must be quantized using scale = x_scale * w_scale and zero_point = 0Output data tensor that contains the result of the convolution. The output dimensions are functions of the kernel size, stride size, and pad lengths.Constrain filter type to 8-bit integer tensor.Constrain output type to 8-bit integer tensor.Constrain bias type to 32-bit integer tensor.The shape of the convolution kernel. If not present, should be inferred from input W.The shape of the output can be explicitly set which will cause pads values to be auto generated. If output_shape is specified pads values are ignored. See doc for details for equations to generate padsAdditional elements added to the side with higher coordinate indices in the output. Each padding value in "output_padding" must be less than the corresponding stride/dilation dimension. By default, this attribute is a zero vector. Note that this attribute doesn't directly affect the computed output values. It only controls the selection of the computed values, so changing this attribute only adds or removes output elements. If "output_shape" is explicitly provided, "output_padding" does not contribute additional size to "output_shape" but participates in the computation of the needed padding amount. This is also called adjs or adjustment in some frameworks.dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each spatial axis.Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSETPadding for the beginning and ending along each spatial axisnumber of groups input channels and output channels are divided into.QLinearConvTransposeQuantizeWithOrderDequantize input matrix to specific layout used in cublaslt. attr to specify output type, float16 or float32cublasLt order of input matrix. See the schema of QuantizeWithOrder for order definition.cublasLt order of output matrixThe output data type, only support TensorProto_DataType_FLOAT (1) and TensorProto_DataType_FLOAT16 (10)DequantizeWithOrder Quantize (Int8) MatMul with order. Implement Y = alpha * A * B + bias + beta * C. Matrix A, B, C, Y are all int8 matrix. Two type of order combination supported: *) When order_B is ORDER_COL, order_A must be ORDER_ROW. bias is vector of {#cols of Y} of float32, C should be batch 1/batch_A. B could be of batch 1 or batch_A. Note B is reorder to ORDER_COL, or Transposed. Not Transposed first and then Reordered here. *) When order_B is specify ORDER_COL4_4R2_8C or ORDER_COL32_2R_4R4, orderA must be ORDER_COL32. MatMul will be implemented using alpha(A * B) + beta * C => Y. bias is not supported here. B in fact is transposed first then reordered into ORDER_COL4_4R2_8C or ORDER_COL32_2R_4R4 here. order_Y and order_C will be same as order_A. Support per column quantized weight, ie, scale_B is 1-D vector of size [#cols of matrix B]. cublasLt order of matrix A. See the schema of QuantizeWithOrder for order definition.order_AcublasLt order of matrix Border_BcublasLt order of matrix Y and optional matrix Corder_Y3-dimensional matrix Ascale of the input A.scale_A2-dimensional matrix B. Transposed if order_B is ORDER_COL.scale of the input B. Scalar or 1-D float32.scale_Bscale of the output Y.scale_Y1d bias, not scaled with scale_Y.3d or 2d matrix C. if 2d expand to 3d first. Shape[0] should be 1 or same as A.shape[0] scale_CConstrain bias and scales to float32QOrderedMatMul Quantized version of simplified Multi-Head Self Attention(using int8 with specific matrix Layout). Multi-Head Self Attention that can be either unidirectional (like GPT-2) or bidirectional (like BERT). The mask_index input is optional. Besides raw attention mask with shape (batch_size, past_sequence_length + sequence_length) or (batch_size, sequence_length, past_sequence_length + sequence_length) with value 0 for masked and 1 otherwise, we also support other two formats: When input has right-side padding, mask_index is one dimension with shape (batch_size), where value of each element is the end position, or valid length of actual sequence excluding padding. When input has left-side padding, mask_index has shape (2 * batch_size), where the values are the exclusive end positions followed by the inclusive start positions. When unidirectional is 1, and each token only attend to previous tokens. For GPT-2, both past and present state are optional. Present state could appear in output even when past state is not in input. Current version does not support past/present, attention_bias and qkv_hidden_sizes. TODO: Support them if needed in the future. Hidden layer sizes of Q, K, V paths in AttentioncublasLt order of weight matrixorder_weightcublasLt order of global biasscale of the input, scalar value (per tensor) currently.scale of the gemm - scalar (per-tensor quantization)scale_Q_gemmscale_K_gemmscale_V_gemm2D input tensor with shape (input_hidden_size, hidden_size), where hidden_size = num_heads * head_sizeQ_weightK_weightV_weightscale of the weight (scalar for per-tensor quantization or 1-D of dims [hidden_size] for per-channel quantization)scale_Q_weightscale_K_weightscale_V_weightQ_biasK_biasV_biasscale_QKT_gemmscale of the softmax result - scalar (per-tensor quantization)scale_QKT_softmaxscale of the gemm - scalar (per-tensor quantization). Also this is the output scale for the operator.scale_values_gemmAttention mask with shape (batch_size, 1, max_sequence_length, max_sequence_length), (batch_size, past_sequence_length + sequence_length)or (batch_size, sequence_length, past_sequence_length + sequence_length), or index with shape (batch_size) or (2 * batch_size).additional add to QxK' with shape (batch_size or 1, num_heads or 1, sequence_length, total_sequence_length).Constrain scales to float32 tensors.QOrderedAttentionQOrderedLayerNormalizationcublasLt order of input X. Default is ROW MAJOR. See the schema of QuantizeWithOrder for order definition.order_XcublasLt order of matrix Y, must be same as order_X. Default is ROW MAJOR.scale of the quantized Xscale_XScale tensor, i.e., gamma vector.Constrain input gamma and bias could be float16/float tensors. float may get better precision, float16 runs faster.̼+D:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\nhwc_schema_defs.cc QLinearGlobalAveragePool consumes an input tensor X and applies Average pooling across the values in the same channel. This is equivalent to AveragePool with kernel size equal to the spatial dimension of input tensor. Input is of type uint8_t or int8_t. Input data tensor from the previous operator; According to channels_last, dimensions for image case are (N x C x H x W), or (N x H x W x C) where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), or (N x D1 X D2 ... Dn x C) where N is the batch size.Zero point tensor for input 'X'. It must be a scalar.Output data tensor from pooling across the input tensor. The output tensor has the same rank as the input. with the N and C value keep it value, while the otherdimensions are all 1. QLinearAveragePool consumes an input tensor X and applies average pooling across the tensor according to kernel sizes, stride sizes, and pad lengths. average pooling consisting of computing the average on all values of a subset of the input tensor according to the kernel size and downsampling the data into the output tensor Y for further processing. The output spatial shape will be following: ``` output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - kernel_spatial_shape[i]) / strides_spatial_shape[i] + 1) ``` or ``` output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - kernel_spatial_shape[i]) / strides_spatial_shape[i] + 1) ``` if ceil_mode is enabled ``` * pad_shape[i] is sum of pads along axis i ``` `auto_pad` is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following: ``` VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i] - kernel_spatial_shape[i] + 1) / strides_spatial_shape[i]) SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides_spatial_shape[i]) ``` And pad shape will be following if `SAME_UPPER` or `SAME_LOWER`: ``` pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial_shape[i] + kernel_spatial_shape[i] - input_spatial_shape[i] ``` The output of each pooling window is divided by the number of elements (exclude pad when attribute count_include_pad is zero). Input and output scales and zero points are used to convert the output to a new quantization range. Output = Dequantize(Input) -> AveragePool on fp32 data -> Quantize(output) Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output spatial size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding.Whether include pad pixels when calculating values for the edges. Default is 0, doesn't count include pad.The size of the kernel along each axis.Whether to use ceil or floor (default) to compute the output shape.Works on NHWC layout or not? Default not.Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].Output data tensor from average or max pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes. Floor value of the dimension is usedInput data tensor from previous layer; has size (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and width. Note that this is for the 2D image. Otherwise the size is (N x C x D1 x D2 ... x Dn). Optionally, if dimension denotation is in effect, the operation expects input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].The weight tensor that will be used in the convolutions; has size (M x C/group x kH x kW), where C is the number of channels, and kH and kW are the height and width of the kernel, and M is the number of feature maps. For more than 2 dimensions, the kernel shape will be (M x C/group x k1 x k2 x ... x kn), where (k1 x k2 x ... kn) is the dimension of the kernel. Optionally, if dimension denotation is in effect, the operation expects the weight tensor to arrive with the dimension denotation of [FILTER_OUT_CHANNEL, FILTER_IN_CHANNEL, FILTER_SPATIAL, FILTER_SPATIAL ...]. Assuming zero based indices for the shape array, X.shape[1] == (W.shape[1] * group) == C and W.shape[0] mod G == 0. Or in other words FILTER_IN_CHANNEL multiplied by the number of groups should be equal to DATA_CHANNEL and the number of feature maps M should be a multiple of the number of groups G.Optional 1D bias to be added to the convolution, has size of M.dilation value along each spatial axis of the filter. If not present, the dilation defaults is 1 along each spatial axis.Stride along each spatial axis. If not present, the stride defaults is 1 along each spatial axis. NhwcFusedConv is a Conv operator with optional activation and add operators fused in. Only has fp16 implementation as of 2023/04/15. Tensor to be added to the output, must be the same shape and format as the output tensor. cannot be safely updated to Implicit input name onnxruntime::graph_utils::CanUpdateImplicitInputNameInSubgraphsNode must only have one used outputstd::all_of(output_edges.cbegin(), output_edges.cend(), [&src_idx](const GraphEdge& edge) { return edge.src_arg_index == src_idx; })onnxruntime::graph_utils::RemoveNodeWithSingleNodeInSingleUsedOutputInitializer with same name exists. Name:!graph.GetInitializedTensor(new_initializer.name(), existing)onnxruntime::graph_utils::AddInitializerAttempting to get an input that does not exist.index >= 0 && static_cast(index) < inputs.size()onnxruntime::graph_utils::GetNodeInputNameAttempting to get an output that does not exist.index >= 0 && static_cast(index) < outputs.size()onnxruntime::graph_utils::GetNodeOutputNameShould be unreachable if CanRemoveNodeAndMergeEdges is in sync with the logic here.onnxruntime::graph_utils::RemoveNode ImplicitInputs: ExplicitInputs:. Index:Invalid input index for node onnxruntime::graph_utils::ReplaceNodeInputCan only add a new input at the end of the current ones.num_explicit_inputs == static_cast(target_input_idx)onnxruntime::graph_utils::AddNodeInputFailed since multiple edges matched:onnxruntime::graph_utils::FindPathfor node: Attempting to get index by a name which does not exist:itr != node_args.end()onnxruntime::graph_utils::GetIndexFromNameD:\a\_work\1\s\onnxruntime\core\graph\graph_utils.ccstd::count_if(subgraph_node.InputEdgesBegin(), subgraph_node.InputEdgesEnd(), [input_slot_index](const Node::EdgeEnd& entry) { return entry.GetDstArgIndex() == input_slot_index; }) == 0onnxruntime::graph_utils::UpdateImplicitInputNameInSubgraph in one of the subgraphs.AttributeProto must have a name.utils::HasName(attribute)onnxruntime::utils::SetNodeAttributeD:\a\_work\1\s\onnxruntime\core\graph\node_attr_utils.cconnxruntime::SaveRuntimeOptimizationRecordToOrtFormatD:\a\_work\1\s\onnxruntime\core\graph\runtime_optimization_record_container.cconnxruntime::RuntimeOptimizationRecordContainer::SaveToOrtFormat is null. runtime optimization record produced op idonnxruntime::LoadRuntimeOptimizationRecordFromOrtFormatonnxruntime::RuntimeOptimizationRecordContainer::LoadFromOrtFormatAttempting to load runtime optimization records for a previously loaded optimizer: onnxruntime::fbs::utils::LoadOpIdentifierOrtFormatD:\a\_work\1\s\onnxruntime\core\graph\op_identifier_utils.ccInvalid OpIdentifier string: onnxruntime::BasicOpIdentifier,class std::allocator > >::LoadFromStringD:\a\_work\1\s\onnxruntime\core/graph/op_identifier.hFailed to parse since_version from TENSORUNDEFINEDFLOATINTSTRINGGRAPHFLOATSINTSSTRINGSonnxruntime::fbs::utils::SaveInitializerOrtFormatD:\a\_work\1\s\onnxruntime\core\graph\graph_flatbuffers_utils.cconnxruntime::fbs::utils::SaveSparseInitializerOrtFormatonnxruntime::fbs::utils::SaveAttributeOrtFormatGraph attribute value was null. Invalid ORT format model.SaveAttributeOrtFormat: Unsupported attribute type: String data type is not supported for on-device trainingonnxruntime::fbs::utils::GetSizeInBytesFromFbsTensorUnsupported tensor data type for tensor Missing dimensions for initializer. Invalid ORT format model.onnxruntime::fbs::utils::LoadInitializerOrtFormatMissing string data for initializer. Invalid ORT format model.Missing raw data for initializer. Invalid ORT format model.Tensor has external data but a data reader was not provided.Missing values for sparse initializer. Invalid ORT format model.onnxruntime::fbs::utils::LoadSparseInitializerOrtFormatMissing name for SparseTensor initializer. Invalid ORT format model.Missing indicies for sparse initializer: Missing dims for sparse initializer: Null string attribute. Invalid ORT format model.onnxruntime::fbs::utils::LoadAttributeOrtFormatNull tensor attribute. Invalid ORT format model.Null graph attribute. Invalid ORT format model.Empty graph proto from deserialization of ORT format modelNull floats attribute. Invalid ORT format model.Null ints attribute. Invalid ORT format model.Null strings attribute. Invalid ORT format model.Null string in strings attribute. Invalid ORT format model.Null tensors attribute. Invalid ORT format model.Null tensor in tensors attribute. Invalid ORT format model.TENSORSGRAPHSSPARSE_TENSORSPARSE_TENSORSP8&D&L&P&0&X&`&h&p&- --$-D:\a\_work\1\s\onnxruntime\core\graph\function_utils.ccall_tensor_types_ir4TAggregatedTypesinput_arg->Type() != nullptronnxruntime::function_utils::CreateSchema in function opset imports.No opset registered for domain it != opset_imports.end()onnxruntime::function_utils::IOTypeConstraintHelper::::operator ()domain_version != -1Too many inputs for op static_cast(i) < node_op_schema->inputs().size()inTinoutTout is not found in model local functions, domain: The given function name: onnx_func_protoNumber of actual parameters cannot exceed number of formal parametersactuals.size() <= formals.size()onnxruntime::function_utils::Inliner::bind is not found or is not constant initializer.initializer != nullptronnxruntime::FunctionImpl::FunctionImplD:\a\_work\1\s\onnxruntime\core\graph\function.cc Creates a sequence of numbers that begins at `start` and extends by increments of `delta` up to but not including `limit`. Can not get shape initializer data!Unsupported type:Tensor(scalar, or dims=[1]). First entry in the range.Tensor(scalar, or dims=[1]). Upper limit of sequence, exclusive.limitTensor(scalar, or dims=[1]). Number that increments start. Defaults to 1.delta1-D Tensor of the range.Unsupported non-raw-data data type!tensor rank too smallinvalid scales valueinvalid scales dimensionD:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\nchwc_schema_defs.ccCouple the input and forget gates if 1, default 0. Computes an one-layer RNN where its RNN Cell is an AttentionWrapper wrapped a LSTM Cell. The RNN layer contains following basic component: LSTM Cell, Bahdanau Attention Mechanism, AttentionWrapp. Activation functions: Relu(x) - max(0, x) Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x}) Sigmoid(x) - 1/(1 + e^{-x}) (NOTE: Below are optional) Affine(x) - alpha*x + beta LeakyRelu(x) - x if x >= 0 else alpha * x ThresholdedRelu(x) - x if x >= alpha else 0 ScaledTanh(x) - alpha*Tanh(beta*x) HardSigmoid(x) - min(max(alpha*x + beta, 0), 1) Elu(x) - x if x >= 0 else alpha*(e^x - 1) Softsign(x) - x/(1 + |x|) Softplus(x) - log(1 + e^x) Softmax(x) - exp(x) / sum(exp(x)) Bahdanau Attention Mechanism: `M` - Memory tensor. `VALUES` - masked Memory by its real sequence length. `MW` - Memory layer weight. `KEYS` - Processed memory tensor by the memory layer. KEYS = M * MW `Query` - Query tensor, normally at specific time step in sequence. `QW` - Query layer weight in the attention mechanism `PQ` - processed query, = `Query` * `QW` `V' - attention vector `ALIGN` - calculated alignment based on Query and KEYS ALIGN = softmax(reduce_sum(`V` * Tanh(`KEYS` + `PQ`))) `CONTEXT` - context based on `ALIGN` and `VALUES` CONTEXT = `ALIGN` * `VALUES` LSTM Cell: `X` - input tensor concat with attention state in the attention wrapper `i` - input gate `o` - output gate `f` - forget gate `c` - cell gate `t` - time step (t-1 means previous time step) `W[iofc]` - W parameter weight matrix for input, output, forget, and cell gates `R[iofc]` - R recurrence weight matrix for input, output, forget, and cell gates `Wb[iofc]` - W bias vectors for input, output, forget, and cell gates `Rb[iofc]` - R bias vectors for input, output, forget, and cell gates `P[iof]` - P peephole weight vector for input, output, and forget gates `WB[iofc]` - W parameter weight matrix for backward input, output, forget, and cell gates `RB[iofc]` - R recurrence weight matrix for backward input, output, forget, and cell gates `WBb[iofc]` - W bias vectors for backward input, output, forget, and cell gates `RBb[iofc]` - R bias vectors for backward input, output, forget, and cell gates `PB[iof]` - P peephole weight vector for backward input, output, and forget gates `H` - Hidden state `num_directions` - 2 if direction == bidirectional else 1 Equations (Default: f=Sigmoid, g=Tanh, h=Tanh): - it = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Pi (.) Ct-1 + Wbi + Rbi) - ft = f(Xt*(Wf^T) + Ht-1*(Rf^T) + Pf (.) Ct-1 + Wbf + Rbf) - ct = g(Xt*(Wc^T) + Ht-1*(Rc^T) + Wbc + Rbc) - Ct = ft (.) Ct-1 + it (.) ct - ot = f(Xt*(Wo^T) + Ht-1*(Ro^T) + Po (.) Ct + Wbo + Rbo) - Ht = ot (.) h(Ct) AttentionWrapp Notations: `lstm()' - wrapped inner cell. Ht, Ct = lstm(concat(Xt, ATTNt-1), Ct-1) `am()` - attention mechanism the wrapper used. CONTEXTt, ALIGNt = am(Ht, ALIGNt-1) `AW` - attention layer weights, optional. `ATTN` - attention state, initial is zero. If `AW` provided, it is the output of the attention layer, ATTNt = concat(Ht, CONTEXTt) * AW otherwise, ATTNt = CONTEXTt RNN layer output: `Y` - if needed is the sequence of Ht from lstm cell. `Y_h` - is the last valid H from lstm cell. `Y_c` - is the last valid C from lstm cell. Constrain seq_lens to integral tensors.Number of neurons in the hidden layer.The weight tensor for the gates. Concatenation of `W[iofc]` and `WB[iofc]` (if bidirectional) along dimension 0. The tensor has shape `[num_directions, 4*hidden_size, input_size]`.The input sequences packed (and potentially padded) into one 3-D tensor with the shape of `[seq_length, batch_size, input_size]`Optional tensor specifying lengths of the sequences in a batch. If not specified - assumed all sequences in the batch to have length `seq_length`. It has shape `[batch_size]` The recurrence weight tensor. Concatenation of `R[iofc]` and `RB[iofc]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 4*hidden_size, hidden_size]`.QWThe weight tensor of the query layer in the attention mechanism. Should be of shape `[num_directions, am_query_depth(hidden_size of lstm), am_attn_size]` MWThe weight tensor of the memory layer in the attention mechanism. Should be of shape `[num_directions, memory_depth, am_attn_size]` The sequence of the memory (input) for attention mechanism. Should be of `[batch_size, max_memory_step, memory_depth]` The attention_v tensor in the attention mechanism. Should be of shape `[num_directions, am_attn_size]` memory_seq_lensThe sequence length of the input memory for the attention mechanism. Should be of `[batch_size]` AWThe weights of attention layer in the attention wrapper. If exists, should be of shape `[num_directions, memory_depth+hidden_size, aw_attn_size]. Please note that attention mechanism context depth is also memory_depth in the attention mechanism.` The last output value of the hidden. It has shape `[num_directions, batch_size, hidden_size]`. A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`input_ids shall be 2 dimensionsword_embedding should have 2 dimensions and dimension size is known.segment_ids input shall be 2 dimensionssegment_embedding should have 2 dimensions, dimension size known, and same hidden size as word_embedding.position_embedding should have 2 dimensions, dimension size known, and same hidden size as word_embedding.beta should have 1 dimension, dimension size known, and same hidden size as word_embedding.gamma should have 2 dimension, dimension size known, and same hidden size as word_embedding.The past input shall be 5 dimensionsInputs 0 shall be 3 dimensions thread_pool_size: OrtThreadPoolParams { allow_spinning: auto_set_affinity: stack_size: dynamic_block_base_: set_denormal_as_zero: affinity_str: std::all_of(processor_interval[0].begin(), processor_interval[0].end(), ::isdigit) && std::all_of(processor_interval[1].begin(), processor_interval[1].end(), ::isdigit)Processor id must consist of only digits: D:\a\_work\1\s\onnxruntime\core\util\thread_utils.cconnxruntime::concurrency::ReadThreadAffinityConfigprocessor_from > 0 && processor_to > 0Processor id must start from 1: processor_from <= processor_toInvalid processor interval: processor_id > 0std::all_of(processor_str.begin(), processor_str.end(), ::isdigit)Found invalid processor id in affinity string: , skip affinity settingFailed to read affinities from affinity stringFound out-of-range processor id in affinity string: Number of affinities does not equal to thread_pool_size minus one, affinities: , thread_pool_size: onnxruntime::concurrency::CreateThreadPoolHelperactual_num_affinities == static_cast(options.thread_pool_size) - 1Received null OrtThreadingOptionscustom join thread function not setReceived null affinity_stringReceived invalid value for allow_spinning. Valid values are 0 or 1Size of affinity string must be between 1 and onnxruntime::math::GemmCblasNoTrans Unexpected CBLAS_TRANSPOSE for TransB of CblasTrans Unexpected CBLAS_TRANSPOSE for TransB of D:\a\_work\1\s\onnxruntime\core\util\math_cpu.ccdims[d_i] < d_maxUnexpected CBLAS_TRANSPOSE for TransA of onnxruntime::math::NextPositionD:\a\_work\1\s\onnxruntime\core\mlas\lib\q4_dq.cppUnsigned quant types without zero points must allocate zero points with value 0.src_zero_points || signed_quant || dst_zero_pointsBlockwiseQDQQuantizer::TransposeColumnWiseQuantizedBlockwiseQDQQuantizer::TransposeColumnWiseQuantizedColumns must be multiple of 2BlockwiseQDQQuantizer::TransposeColumnWiseQuantizedPackAlignedcolumns % 2 == 0BlockwiseQDQQuantizer::TransposeColumnWiseQuantizedPackAlignedBlockwiseQDQQuantizer::TransposeColumnWiseQuantizedPackAlignedBlockwiseQDQQuantizer::TransposeColumnWiseQuantizedPackAlignedbad dimensions?@@@Softcap_Fp16 is not supported.Lacks kernels for fp16 softmax.bad mlas activation kind) is not supported on this deviceQuant GEMM format: AIsSigned(), BIsSigned(hgemm does not have A x B kernelshgemm currently only support A x Transpoe(B) or A x Bhgemm does not have A x Transposed(B) kernelsAdd_Fp16 is not supported.QLinearGlobalAveragePool parameter out of computation range!ElementSize must be power of 2 and less or equal than 16!QLinearGlobalAveragePool ImageSize too large!!points_.empty()onnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat::LogEnd{"main_thread": {LogStart must pair with LogEndonnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat::Reset", "block_size": [onnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat::LogEndAndStartpoints_.empty()DistributionDistributionEnqueue"thread_id": "], "core": WaitRevokeUnknownEventRunWait": {Nested parallelism not supported"core": "num_run": !ps_n >= 0!current_parallel_section.has_value()onnxruntime::concurrency::ThreadPool::ParallelSection::ParallelSectionn <= num_threads_ + 1onnxruntime::concurrency::ThreadPoolTempl::RunInParallelonnxruntime::concurrency::ThreadPool::ParallelForMore work items than threadsD:\a\_work\1\s\include\onnxruntime\core/platform/EigenNonBlockingThreadPool.honnxruntime::concurrency::ThreadPoolTempl::RunInParallelSectionunnamed_thread_poolonnxruntime::concurrency::ThreadPoolProfiler::StopD:\a\_work\1\s\onnxruntime\core\common\threadpool.ccProfiler not started yetenabled_", "thread_pool_name": "}}}, "sub_threads": {GenuineAMDQualcommGenuineIntelNVinvalid stoul argumentstoul argument out of rangeVEN_DEV_DxgiVideoMemory MBFailed to create DXGI factory. 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P `    9 X x       0 `0pR6P*25;GKPW]fp]P`0  7;PP 8Pp :<?P`gj>$p$$/0@4Dh@j`jnjjjjkPk[kck}k@noOoooop2PUdpp0P){`V ;@FJR%#&00@NP^q!$')49;BGIKMQTWY[]_adglty~0@P`{P``p@ P_ &270;e;1 J             3  : H$'$ y)!9C VY ^  Y+F *, >A M/t<<+ +&5 / !!  ]-8~Y ?Vg+^T/mr7\; 8F tN !7 g<~ +1nj}6*3 {-X .1$+% $$(4  7* ,H !82 @' 6I33. *+*N$> 5'`/ ;  \H 6&E : <S . HSI - , % )2oc0G9 Q F [K9 #k .t'JpFWUG AT$>- :*L D=  ,d%,  w_  8 (T . 7 :1K`tensor_typesequence_typemap_typeonnxruntime::fbs::utils::SaveMapTypeOrtFormatonnxruntime::fbs::utils::SaveTensorTypeAndShapeOrtFormatonnxruntime::fbs::utils::SaveSequenceTypeOrtFormatD:\a\_work\1\s\onnxruntime\core\flatbuffers\flatbuffers_utils.ccWe do not support type [onnxruntime::fbs::utils::SaveValueInfoOrtFormatonnxruntime::fbs::utils::SaveTypeInfoOrtFormat] for nowdim_param value with no name. Invalid ORT format model.onnxruntime::fbs::utils::LoadTensorDimensionOrtFormat is missing type info.SaveValueInfoOrtFormat: value_info_proto for onnxruntime::fbs::utils::LoadTensorTypeAndShapeOrtFormatNull value type info in fbs::SequenceType. Invalid ORT format model.Null entry in dimensions. Invalid ORT format model.onnxruntime::fbs::utils::LoadTensorShapeOrtFormatonnxruntime::fbs::utils::LoadMapTypeOrtFormatNull tensor type info. Invalid ORT format model.onnxruntime::fbs::utils::LoadSequenceTypeOrtFormatNull value type info in fbs::MapType. Invalid ORT format model.Null map type info. Invalid ORT format model. is not supported currentlyonnxruntime::fbs::utils::LoadTypeInfoOrtFormatNull sequence type info. Invalid ORT format model.Null type info for onnxruntime::fbs::utils::LoadValueInfoOrtFormatType:. Invalid ORT format model.opset id is null. Invalid ORT format model.opset import domain is null. Invalid ORT format model.Model must have opset imports. Invalid ORT format model.onnxruntime::fbs::utils::LoadOpsetImportOrtFormat$4<%%~3a*++̼+Q5@.7./{1w+2o2A\33N34,W55Ӡ556'7JCy77c7;A888֕8m9f9A : ':mL:?:;f;~;Y;Y;; ף;#B ;e9 =u>>< >*>*>yE>L>2?~>>p>> >>"I>>?d! ?-C6?P"?r1?5?*BL?fff?l?4?~???{Gz?_}??&TpxAD?r???;??}-?#??333333?F????9B.?@@@@-DT! @@@@@-DT!@ @.@9@>@@@I@N@P@_@o@p@t@33{@@@z@@F@@@@@@@@@j@V @AA A0ApAAAAAAAABļBBCCCCD,FFFGGJ@KOO__ֳ@-DT!@`pV  @ktsr h, l d  hp l D (  \ $T(com.microsoft:QuantizeLinear:1`d N p$8\:QuantizeLinear:10  P P l<4 :Gather:13d nhX, com.microsoft:DequantizeLinear:1 < \0 :Identity:16X F@|4 :Gather:1P 8 :Gather:11  0TindX :Squeeze:1| jd :Identity:13H L \:QuantizeLinear:13 xHP$`|4 :Squeeze:13P H:QuantizeLinear:19l<\$`8 :Squeeze:21vp :Unsqueeze:1 $D:DequantizeLinear:19HP0lXZDd :DequantizeLinear:13(8 `X( :Transpose:21P>8t$com.microsoft:NhwcMaxPool:1h$D:DequantizeLinear:21P$|LH:QuantizeLinear:21|Hj< :Unsqueeze:21Td4 :Identity:14\JD :Identity:1l :Unsqueeze:11 < :Unsqueeze:13@.(daxes 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