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https://github.com/saymrwulf/onnxruntime.git
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Migrate ReverseSequence from contrib op to ONNX opset 10 (#896)
* Update ONNX to 70c9026ca11b0af0050f8186bea6cab94636947f to pickup ReverseSequence op. Copy ReverseSequence from contrib ops to ONNX (keep contrib op in this commit), and update to use int64_t for sequence_lens input. * Copy ReverseSequence from contrib to ONNX and update to use int64_t for sequence_lens. Maintain contrib op in this commit. * Remove contrib op as it was temporary and only used internally. * Remove contrib op schema defs. * Cleanup contrib_defs.cc
This commit is contained in:
Scott McKay
GitHub
parent
8ed3eed7b5
commit
b8eaa88bd4
8 changed files with 29 additions and 169 deletions
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@ -18,7 +18,6 @@ class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, WordC
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class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, GatherND);
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class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, MurmurHash3);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, float, MaxpoolWithMask);
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class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, ReverseSequence);
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// This section includes all opkernel declarations for former experimental ops which have now been removed from onnx.
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// To maintain backward compatibility these are added as contrib ops.
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@ -61,7 +60,6 @@ void RegisterContribKernels(KernelRegistry& kernel_registry) {
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BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, GatherND)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, MurmurHash3)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, float, MaxpoolWithMask)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, ReverseSequence)>,
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// These ops were experimental ops in onnx domain which have been removed now. We add them here as
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// contrib ops to main backward compatibility
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@ -5,7 +5,6 @@
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#include "core/graph/contrib_ops/attn_lstm_schema_defs.h"
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#include "core/graph/contrib_ops/contrib_defs.h"
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#include "core/graph/contrib_ops/range_schema_defs.h"
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#include "core/graph/contrib_ops/reverse_sequence_schema_defs.h"
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#include "core/graph/op.h"
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#include "onnx/defs/schema.h"
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#include "onnx/defs/shape_inference.h"
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@ -729,7 +728,6 @@ activation and leaky_relu_alpha.)DOC")
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ONNX_CONTRIB_OPERATOR_SCHEMA_ELSEWHERE(AttnLSTM, RegisterAttnLSTMContribOpSchema);
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ONNX_CONTRIB_OPERATOR_SCHEMA_ELSEWHERE(Range, RegisterRangeOpSchema);
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ONNX_CONTRIB_OPERATOR_SCHEMA_ELSEWHERE(ReverseSequence, RegisterReverseSequenceOpSchema);
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static const char* Tokenizer_ver1_doc = R"DOC(
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Tokenizer divides each string in X into a vector of strings along the last axis. Allowed input shapes are [C] and [N, C].
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@ -1,110 +0,0 @@
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// Copyright (c) Microsoft Corporation. All rights reserved.
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// Licensed under the MIT License.
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#include "reverse_sequence_schema_defs.h"
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#include "core/graph/op.h"
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namespace onnxruntime {
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namespace contrib {
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using ONNX_NAMESPACE::AttributeProto;
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using ONNX_NAMESPACE::InferenceContext;
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using ONNX_NAMESPACE::OpSchema;
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using ONNX_NAMESPACE::OPTIONAL;
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using ONNX_NAMESPACE::TensorProto;
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using ONNX_NAMESPACE::TensorProto_DataType;
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using ONNX_NAMESPACE::TensorShapeProto;
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static const char* ReverseSequence_ver1_doc = R"DOC(
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Reverse batch of sequences having different lengths specified by `sequence_lens`.
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For each slice i iterating on batch axis, the operator reverses the first sequence_lens[i] elements on time axis,
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and copies elements whose index's beyond sequence_lens[i] to the output. So the output slice i contains reversed
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sequences on the first sequence_lens[i] elements, then have original values copied for the other elements.
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Example 1:
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input = [[0.0, 4.0, 8.0, 12.0],
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[1.0, 5.0, 9.0, 13.0],
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[2.0, 6.0, 10.0, 14.0],
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[3.0, 7.0, 11.0, 15.0]]
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sequence_lens = [4, 3, 2, 1]
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time_axis = 0
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batch_axis = 1
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output = [[3.0, 6.0, 9.0, 12.0],
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[2.0, 5.0, 8.0, 13.0],
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[1.0, 4.0, 10.0, 14.0],
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[0.0, 7.0, 11.0, 15.0]]
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Example 2:
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input = [[0.0, 1.0, 2.0, 3.0 ],
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[4.0, 5.0, 6.0, 7.0 ],
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[8.0, 9.0, 10.0, 11.0],
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[12.0, 13.0, 14.0, 15.0]]
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sequence_lens = [1, 2, 3, 4]
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time_axis = 1
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batch_axis = 0
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output = [[0.0, 1.0, 2.0, 3.0 ],
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[5.0, 4.0, 6.0, 7.0 ],
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[10.0, 9.0, 8.0, 11.0],
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[15.0, 14.0, 13.0, 12.0]]
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)DOC";
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void ReverseSequenceShapeInference(InferenceContext& ctx) {
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propagateElemTypeFromInputToOutput(ctx, 0, 0);
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if (!hasNInputShapes(ctx, 2)) {
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return;
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}
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auto& first_input_shape = getInputShape(ctx, 0);
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if (first_input_shape.dim_size() < 2) {
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fail_shape_inference("'input' must have rank >= 2");
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}
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auto& seq_len_input_shape = getInputShape(ctx, 1);
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if (seq_len_input_shape.dim_size() != 1) {
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fail_shape_inference("'sequence_lens' must have rank of 1");
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}
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propagateShapeFromInputToOutput(ctx, 0, 0);
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}
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OpSchema& RegisterReverseSequenceOpSchema(OpSchema&& op_schema) {
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return op_schema
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.SetDomain(kMSDomain)
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.SinceVersion(1)
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.TypeConstraint(
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"T",
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OpSchema::all_tensor_types(),
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"Input and output types can be of any tensor type.")
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.Attr(
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"time_axis",
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"(Optional) Specify which axis is time axis. Must be one of 0 (default), or 1.",
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AttributeProto::INT,
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static_cast<int64_t>(0))
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.Attr(
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"batch_axis",
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"(Optional) Specify which axis is batch axis. Must be one of 1 (default), or 0.",
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AttributeProto::INT,
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static_cast<int64_t>(1))
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.Input(
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0,
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"input",
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"Tensor of rank r >= 2.",
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"T")
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.Input(
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1,
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"sequence_lens",
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"Tensor specifying lengths of the sequences in a batch. It has shape `[batch_size]`.",
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"tensor(int32)")
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.Output(
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0,
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"Y",
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"Tensor with same shape of input.",
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"T")
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.SetDoc(ReverseSequence_ver1_doc)
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.TypeAndShapeInferenceFunction(ReverseSequenceShapeInference);
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}
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} // namespace contrib
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} // namespace onnxruntime
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@ -1,22 +0,0 @@
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// Copyright (c) Microsoft Corporation. All rights reserved.
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// Licensed under the MIT License.
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#pragma once
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#ifdef __GNUC__
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#pragma GCC diagnostic push
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#pragma GCC diagnostic ignored "-Wignored-qualifiers"
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#pragma GCC diagnostic ignored "-Wunused-parameter"
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#endif
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#include "onnx/defs/schema.h"
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#ifdef __GNUC__
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#pragma GCC diagnostic pop
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#endif
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namespace onnxruntime {
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namespace contrib {
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ONNX_NAMESPACE::OpSchema& RegisterReverseSequenceOpSchema(ONNX_NAMESPACE::OpSchema&& op_schema);
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} // namespace contrib
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} // namespace onnxruntime
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@ -270,6 +270,7 @@ class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, No
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class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, IsInf);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, float, RoiAlign);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, double, RoiAlign);
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class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, ReverseSequence);
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void RegisterOnnxOperatorKernels(KernelRegistry& kernel_registry) {
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static const BuildKernelCreateInfoFn function_table[] = {
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@ -529,6 +530,7 @@ void RegisterOnnxOperatorKernels(KernelRegistry& kernel_registry) {
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BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, IsInf)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, float, RoiAlign)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, double, RoiAlign)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, ReverseSequence)>,
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};
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for (auto& function_table_entry : function_table) {
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@ -23,20 +23,17 @@
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#include "core/framework/tensor_shape.h"
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namespace onnxruntime {
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namespace contrib {
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ONNX_OPERATOR_KERNEL_EX(ReverseSequence,
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kMSDomain,
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1,
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kOnnxDomain,
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10,
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kCpuExecutionProvider,
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KernelDefBuilder()
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.TypeConstraint("T", DataTypeImpl::AllTensorTypes())
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.TypeConstraint("T1", DataTypeImpl::GetTensorType<int32_t>()),
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KernelDefBuilder().TypeConstraint("T", DataTypeImpl::AllTensorTypes()),
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ReverseSequenceOp);
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template <typename T>
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static void ReverseSequenceImpl(const Tensor& X, Tensor& Y,
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gsl::span<const int> sequence_lengths,
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gsl::span<const int64_t> sequence_lengths,
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const int64_t max_seq_len,
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const int64_t batch_size,
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const int64_t input_size,
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@ -63,7 +60,7 @@ Status ReverseSequenceOp::Compute(OpKernelContext* context) const {
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auto& Y = *context->Output(0, dims);
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DispatchOnTensorType(data_type, ReverseSequenceImpl, X, Y, seq_lengths.DataAsSpan<int>(),
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DispatchOnTensorType(data_type, ReverseSequenceImpl, X, Y, seq_lengths.DataAsSpan<int64_t>(),
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max_seq_len, batch_size, input_size, time_major_);
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return status;
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@ -110,7 +107,7 @@ static int64_t BatchMajorOutputOffset(const int64_t max_seq_len,
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template <typename T>
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static void ReverseSequenceImpl(const Tensor& X,
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Tensor& Y,
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gsl::span<const int> sequence_lengths,
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gsl::span<const int64_t> sequence_lengths,
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const int64_t max_seq_len,
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const int64_t batch_size,
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const int64_t input_size,
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@ -123,7 +120,7 @@ static void ReverseSequenceImpl(const Tensor& X,
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auto reversed_output_offset = time_major ? TimeMajorOutputOffset : BatchMajorOutputOffset;
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for (int i = 0; i < batch_size; i++) {
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int seq_len = sequence_lengths[i];
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int64_t seq_len = sequence_lengths[i];
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if (seq_len == 0)
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continue;
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@ -132,7 +129,7 @@ static void ReverseSequenceImpl(const Tensor& X,
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// Parallel execute the loop.
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#pragma omp parallel for
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#endif
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for (int j = 0; j < seq_len; j++) {
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for (int64_t j = 0; j < seq_len; j++) {
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gsl::span<const T> src = inputs.subspan(input_offset(max_seq_len, batch_size, input_size, i, j), input_size);
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gsl::span<T> dest = inputs_reverse.subspan(
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reversed_output_offset(max_seq_len, batch_size, input_size, i, j, seq_len), input_size);
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@ -145,7 +142,7 @@ static void ReverseSequenceImpl(const Tensor& X,
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// Parallel execute the loop.
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#pragma omp parallel for
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#endif
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for (int j = seq_len; j < max_seq_len; j++) {
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for (int64_t j = seq_len; j < max_seq_len; j++) {
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const auto offset = input_offset(max_seq_len, batch_size, input_size, i, j);
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gsl::span<const T> src = inputs.subspan(offset, input_size);
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gsl::span<T> dest = inputs_reverse.subspan(offset, input_size);
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@ -156,5 +153,4 @@ static void ReverseSequenceImpl(const Tensor& X,
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}
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}
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} // namespace contrib
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} // namespace onnxruntime
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@ -8,7 +8,6 @@
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#include "core/framework/tensor.h"
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namespace onnxruntime {
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namespace contrib {
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class ReverseSequenceOp : public OpKernel {
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public:
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@ -32,5 +31,4 @@ class ReverseSequenceOp : public OpKernel {
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bool time_major_;
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};
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} // namespace contrib
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} // namespace onnxruntime
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@ -8,10 +8,10 @@ namespace onnxruntime {
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namespace test {
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TEST(ReverseSequenceTest, BatchMajor) {
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OpTester test("ReverseSequence", 1, onnxruntime::kMSDomain);
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OpTester test("ReverseSequence", 10);
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std::vector<int64_t> input = {0, 1, 2, 3,
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4, 5, 6, 7};
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std::vector<int32_t> sequence_lens = {4, 3};
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std::vector<int64_t> sequence_lens = {4, 3};
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std::vector<int64_t> expected_output = {3, 2, 1, 0,
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6, 5, 4, 7};
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@ -19,19 +19,19 @@ TEST(ReverseSequenceTest, BatchMajor) {
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test.AddAttribute("time_axis", int64_t(1));
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test.AddInput<int64_t>("input", {2, 4, 1}, input);
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test.AddInput<int32_t>("sequence_lens", {2}, sequence_lens);
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test.AddInput<int64_t>("sequence_lens", {2}, sequence_lens);
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test.AddOutput<int64_t>("Y", {2, 4, 1}, expected_output);
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test.Run();
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}
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TEST(ReverseSequenceTest, TimeMajor) {
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OpTester test("ReverseSequence", 1, onnxruntime::kMSDomain);
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OpTester test("ReverseSequence", 10);
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std::vector<int64_t> input = {0, 4,
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1, 5,
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2, 6,
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3, 7};
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std::vector<int32_t> sequence_lens = {4, 3};
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std::vector<int64_t> sequence_lens = {4, 3};
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std::vector<int64_t> expected_output = {3, 6,
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2, 5,
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1, 4,
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@ -41,13 +41,13 @@ TEST(ReverseSequenceTest, TimeMajor) {
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test.AddAttribute("time_axis", int64_t(0));
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test.AddInput<int64_t>("input", {4, 2, 1}, input);
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test.AddInput<int32_t>("sequence_lens", {2}, sequence_lens);
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test.AddInput<int64_t>("sequence_lens", {2}, sequence_lens);
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test.AddOutput<int64_t>("Y", {4, 2, 1}, expected_output);
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test.Run();
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}
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TEST(ReverseSequenceTest, LargerDim2) {
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OpTester test("ReverseSequence", 1, onnxruntime::kMSDomain);
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OpTester test("ReverseSequence", 10);
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std::vector<float> input = {0.f, 1.f,
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2.f, 3.f,
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4.f, 5.f,
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@ -55,7 +55,7 @@ TEST(ReverseSequenceTest, LargerDim2) {
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6.f, 7.f,
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8.f, 9.f,
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10.f, 11.f};
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std::vector<int32_t> sequence_lens = {2, 3};
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std::vector<int64_t> sequence_lens = {2, 3};
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std::vector<float> expected_output = {2.f, 3.f,
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0.f, 1.f,
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4.f, 5.f,
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@ -68,19 +68,19 @@ TEST(ReverseSequenceTest, LargerDim2) {
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test.AddAttribute("time_axis", int64_t(1));
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test.AddInput<float>("input", {2, 3, 2}, input);
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test.AddInput<int32_t>("sequence_lens", {2}, sequence_lens);
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test.AddInput<int64_t>("sequence_lens", {2}, sequence_lens);
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test.AddOutput<float>("Y", {2, 3, 2}, expected_output);
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test.Run();
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}
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TEST(ReverseSequenceTest, Strings) {
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OpTester test("ReverseSequence", 1, onnxruntime::kMSDomain);
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OpTester test("ReverseSequence", 10);
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std::vector<std::string> input = {"0", "4 string longer than 16 chars that requires its own buffer",
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"1", "5",
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"2", "6",
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"3", "7"};
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std::vector<int32_t> sequence_lens = {4, 3};
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std::vector<int64_t> sequence_lens = {4, 3};
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std::vector<std::string> expected_output = {"3", "6",
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"2", "5",
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"1", "4 string longer than 16 chars that requires its own buffer",
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@ -90,7 +90,7 @@ TEST(ReverseSequenceTest, Strings) {
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test.AddAttribute("time_axis", int64_t(0));
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test.AddInput<std::string>("input", {4, 2, 1}, input);
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test.AddInput<int32_t>("sequence_lens", {2}, sequence_lens);
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test.AddInput<int64_t>("sequence_lens", {2}, sequence_lens);
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test.AddOutput<std::string>("Y", {4, 2, 1}, expected_output);
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test.Run();
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}
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@ -103,16 +103,16 @@ TEST(ReverseSequenceTest, InvalidInput) {
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auto check_bad_axis = [&](int64_t batch_dim, int64_t seq_dim,
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const std::vector<int64_t>& input_shape,
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const std::string err_msg) {
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OpTester test("ReverseSequence", 1, onnxruntime::kMSDomain);
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OpTester test("ReverseSequence", 10);
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std::vector<int64_t> input(batch_size * seq_size, 0);
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std::vector<int32_t> sequence_lens(batch_size, 1);
|
||||
std::vector<int64_t> sequence_lens(batch_size, 1);
|
||||
std::vector<int64_t> expected_output = input;
|
||||
|
||||
test.AddAttribute("batch_axis", batch_dim);
|
||||
test.AddAttribute("time_axis", seq_dim);
|
||||
|
||||
test.AddInput<int64_t>("input", input_shape, input);
|
||||
test.AddInput<int32_t>("sequence_lens", {batch_size}, sequence_lens);
|
||||
test.AddInput<int64_t>("sequence_lens", {batch_size}, sequence_lens);
|
||||
test.AddOutput<int64_t>("Y", input_shape, expected_output);
|
||||
test.Run(test::OpTester::ExpectResult::kExpectFailure, err_msg);
|
||||
};
|
||||
|
|
@ -124,12 +124,12 @@ TEST(ReverseSequenceTest, InvalidInput) {
|
|||
|
||||
// invalid sequence_lens size
|
||||
{
|
||||
OpTester test("ReverseSequence", 1, onnxruntime::kMSDomain);
|
||||
OpTester test("ReverseSequence", 10);
|
||||
|
||||
// Bad data_format value
|
||||
std::vector<int64_t> input = {0, 1, 2, 3,
|
||||
4, 5, 6, 7};
|
||||
std::vector<int32_t> sequence_lens = {4, 3, 4};
|
||||
std::vector<int64_t> sequence_lens = {4, 3, 4};
|
||||
std::vector<int64_t> expected_output = {3, 2, 1, 0,
|
||||
6, 5, 4, 7};
|
||||
|
||||
|
|
@ -137,7 +137,7 @@ TEST(ReverseSequenceTest, InvalidInput) {
|
|||
test.AddAttribute("time_axis", int64_t(1));
|
||||
|
||||
test.AddInput<int64_t>("input", {2, 4, 1}, input);
|
||||
test.AddInput<int32_t>("sequence_lens", {3}, sequence_lens);
|
||||
test.AddInput<int64_t>("sequence_lens", {3}, sequence_lens);
|
||||
test.AddOutput<int64_t>("Y", {2, 4, 1}, expected_output);
|
||||
test.Run(test::OpTester::ExpectResult::kExpectFailure,
|
||||
"sequence_lens shape must be {batch_size}. Got:{3}. batch_size=2");
|
||||
Loading…
Reference in a new issue