Check TF ops for ONNX compliance (#10025)
* Add check-ops script * Finish to implement check_tf_ops and start the test * Make the test mandatory only for BERT * Update tf_ops folder * Remove useless classes * Add the ONNX test for GPT2 and BART * Add a onnxruntime slow test + better opset flexibility * Fix test + apply style * fix tests * Switch min opset from 12 to 10 * Update src/transformers/file_utils.py Co-authored-by: Lysandre Debut <lysandre@huggingface.co> * Fix GPT2 * Remove extra shape_list usage * Fix GPT2 * Address Morgan's comments Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
This commit is contained in:
Julien Plu
101
utils/check_tf_ops.py
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101
utils/check_tf_ops.py
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# coding=utf-8
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# Copyright 2020 The HuggingFace Inc. team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import argparse
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import json
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import os
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from tensorflow.core.protobuf.saved_model_pb2 import SavedModel
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# All paths are set with the intent you should run this script from the root of the repo with the command
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# python utils/check_copies.py
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REPO_PATH = "."
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# Internal TensorFlow ops that can be safely ignored (mostly specific to a saved model)
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INTERNAL_OPS = [
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"Assert",
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"AssignVariableOp",
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"EmptyTensorList",
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"MergeV2Checkpoints",
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"ReadVariableOp",
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"ResourceGather",
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"RestoreV2",
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"SaveV2",
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"ShardedFilename",
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"StatefulPartitionedCall",
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"StaticRegexFullMatch",
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"VarHandleOp",
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]
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def onnx_compliancy(saved_model_path, strict, opset):
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saved_model = SavedModel()
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onnx_ops = []
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with open(os.path.join(REPO_PATH, "utils", "tf_ops", "onnx.json")) as f:
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onnx_opsets = json.load(f)["opsets"]
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for i in range(1, opset + 1):
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onnx_ops.extend(onnx_opsets[str(i)])
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with open(saved_model_path, "rb") as f:
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saved_model.ParseFromString(f.read())
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model_op_names = set()
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# Iterate over every metagraph in case there is more than one (a saved model can contain multiple graphs)
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for meta_graph in saved_model.meta_graphs:
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# Add operations in the graph definition
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model_op_names.update(node.op for node in meta_graph.graph_def.node)
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# Go through the functions in the graph definition
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for func in meta_graph.graph_def.library.function:
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# Add operations in each function
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model_op_names.update(node.op for node in func.node_def)
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# Convert to list, sorted if you want
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model_op_names = sorted(model_op_names)
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incompatible_ops = []
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for op in model_op_names:
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if op not in onnx_ops and op not in INTERNAL_OPS:
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incompatible_ops.append(op)
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if strict and len(incompatible_ops) > 0:
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raise Exception(f"Found the following incompatible ops for the opset {opset}:\n" + incompatible_ops)
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elif len(incompatible_ops) > 0:
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print(f"Found the following incompatible ops for the opset {opset}:")
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print(*incompatible_ops, sep="\n")
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else:
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print(f"The saved model {saved_model_path} can properly be converted with ONNX.")
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--saved_model_path", help="Path of the saved model to check (the .pb file).")
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parser.add_argument(
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"--opset", default=12, type=int, help="The ONNX opset against which the model has to be tested."
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)
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parser.add_argument(
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"--framework", choices=["onnx"], default="onnx", help="Frameworks against which to test the saved model."
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)
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parser.add_argument(
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"--strict", action="store_true", help="Whether make the checking strict (raise errors) or not (raise warnings)"
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)
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args = parser.parse_args()
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if args.framework == "onnx":
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onnx_compliancy(args.saved_model_path, args.strict, args.opset)
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245
utils/tf_ops/onnx.json
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245
utils/tf_ops/onnx.json
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{
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"opsets": {
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"1": [
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"Abs",
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"Add",
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"AddV2",
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"ArgMax",
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"ArgMin",
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"AvgPool",
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"AvgPool3D",
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"BatchMatMul",
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"BatchMatMulV2",
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"BatchToSpaceND",
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"BiasAdd",
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"BiasAddV1",
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"Cast",
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"Ceil",
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"CheckNumerics",
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"ComplexAbs",
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"Concat",
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"ConcatV2",
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"Const",
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"ConstV2",
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"Conv1D",
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"Conv2D",
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"Conv2DBackpropInput",
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"Conv3D",
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"Conv3DBackpropInputV2",
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"DepthToSpace",
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"DepthwiseConv2d",
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"DepthwiseConv2dNative",
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"Div",
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"Dropout",
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"Elu",
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"Equal",
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"Erf",
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"Exp",
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"ExpandDims",
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"Flatten",
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"Floor",
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"Gather",
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"GatherNd",
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"GatherV2",
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"Greater",
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"Identity",
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"IdentityN",
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"If",
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"LRN",
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"LSTMBlockCell",
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"LeakyRelu",
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"Less",
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"Log",
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"LogSoftmax",
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"LogicalAnd",
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"LogicalNot",
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"LogicalOr",
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"LookupTableSizeV2",
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"MatMul",
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"Max",
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"MaxPool",
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"MaxPool3D",
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"MaxPoolV2",
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"Maximum",
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"Mean",
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"Min",
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"Minimum",
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"MirrorPad",
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"Mul",
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"Neg",
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"NoOp",
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"NotEqual",
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"OneHot",
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"Pack",
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"Pad",
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"PadV2",
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"Placeholder",
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"PlaceholderV2",
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"PlaceholderWithDefault",
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"Pow",
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"Prod",
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"RFFT",
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"RandomNormal",
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"RandomNormalLike",
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"RandomUniform",
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"RandomUniformLike",
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"RealDiv",
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"Reciprocal",
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"Relu",
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"Relu6",
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"Reshape",
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"Rsqrt",
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"Selu",
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"Shape",
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"Sigmoid",
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"Sign",
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"Size",
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"Slice",
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"Softmax",
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"Softplus",
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"Softsign",
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"SpaceToBatchND",
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"SpaceToDepth",
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"Split",
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"SplitV",
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"Sqrt",
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"Square",
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"SquaredDifference",
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"Squeeze",
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"StatelessIf",
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"StopGradient",
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"StridedSlice",
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"StringJoin",
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"Sub",
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"Sum",
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"Tanh",
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"Tile",
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"TopKV2",
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"Transpose",
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"TruncateDiv",
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"Unpack",
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"ZerosLike"
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],
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"2": [],
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"3": [],
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"4": [],
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"5": [],
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"6": [
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"AddN",
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"All",
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"Any",
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"FloorDiv",
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"FusedBatchNorm",
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"FusedBatchNormV2",
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"FusedBatchNormV3"
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],
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"7": [
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"Acos",
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"Asin",
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"Atan",
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"Cos",
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"Fill",
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"FloorMod",
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"GreaterEqual",
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"LessEqual",
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"Loop",
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"MatrixBandPart",
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"Multinomial",
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"Range",
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"ResizeBilinear",
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"ResizeNearestNeighbor",
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"Scan",
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"Select",
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"SelectV2",
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"Sin",
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"SoftmaxCrossEntropyWithLogits",
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"SparseSoftmaxCrossEntropyWithLogits",
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"StatelessWhile",
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"Tan",
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"TensorListFromTensor",
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"TensorListGetItem",
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"TensorListLength",
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"TensorListReserve",
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"TensorListResize",
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"TensorListSetItem",
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"TensorListStack",
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"While"
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],
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"8": [
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"BroadcastTo",
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"ClipByValue",
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"FIFOQueueV2",
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"HashTableV2",
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"IteratorGetNext",
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"IteratorV2",
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"LookupTableFindV2",
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"MaxPoolWithArgmax",
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"QueueDequeueManyV2",
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"QueueDequeueUpToV2",
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"QueueDequeueV2",
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"ReverseSequence"
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],
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"9": [
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"SegmentMax",
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"SegmentMean",
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"SegmentMin",
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"SegmentProd",
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"SegmentSum",
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"Sinh",
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"SparseSegmentMean",
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"SparseSegmentMeanWithNumSegments",
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"SparseSegmentSqrtN",
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"SparseSegmentSqrtNWithNumSegments",
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"SparseSegmentSum",
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"SparseSegmentSumWithNumSegments",
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"UnsortedSegmentMax",
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"UnsortedSegmentMin",
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"UnsortedSegmentProd",
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"UnsortedSegmentSum",
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"Where"
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],
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"10": [
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"CropAndResize",
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"CudnnRNN",
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"DynamicStitch",
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"FakeQuantWithMinMaxArgs",
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"IsFinite",
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"IsInf",
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"NonMaxSuppressionV2",
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"NonMaxSuppressionV3",
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"NonMaxSuppressionV4",
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"NonMaxSuppressionV5",
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"ParallelDynamicStitch",
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"ReverseV2",
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"Roll"
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],
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"11": [
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"Bincount",
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"Cumsum",
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"InvertPermutation",
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"LeftShift",
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"MatrixDeterminant",
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"MatrixDiagPart",
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"MatrixDiagPartV2",
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"MatrixDiagPartV3",
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"RaggedRange",
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"RightShift",
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"Round",
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"ScatterNd",
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"SparseFillEmptyRows",
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"SparseReshape",
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"SparseToDense",
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"TensorScatterUpdate",
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"Unique"
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],
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"12": [
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"Einsum",
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"MatrixDiag",
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"MatrixDiagV2",
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"MatrixDiagV3",
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"MatrixSetDiagV3",
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"SquaredDistance"
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],
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"13": []
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}
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}
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