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218 lines
8.0 KiB
218 lines
8.0 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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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#include <random>
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#include "paddle/fluid/framework/generator.h"
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#include "paddle/fluid/framework/op_registry.h"
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#include "paddle/fluid/framework/op_version_registry.h"
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#include "paddle/fluid/operators/fill_constant_op.h"
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#ifdef PADDLE_WITH_MKLDNN
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#include "paddle/fluid/platform/mkldnn_helper.h"
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#endif
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namespace paddle {
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namespace operators {
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using Tensor = framework::Tensor;
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template <typename T>
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class CPUGaussianRandomKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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float mean = context.Attr<float>("mean");
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float std = context.Attr<float>("std");
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auto* tensor = context.Output<framework::Tensor>("Out");
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std::normal_distribution<T> dist(mean, std);
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auto shape = GetShape(context);
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tensor->Resize(shape);
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int64_t size = tensor->numel();
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T* data = tensor->mutable_data<T>(context.GetPlace());
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unsigned int seed = static_cast<unsigned int>(context.Attr<int>("seed"));
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auto engine = framework::GetCPURandomEngine(seed);
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for (int64_t i = 0; i < size; ++i) {
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data[i] = dist(*engine);
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}
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}
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}; // namespace operators
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template <typename T>
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class CPUGaussianRandomBatchSizeLikeKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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float mean = context.Attr<float>("mean");
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float std = context.Attr<float>("std");
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auto* tensor = context.Output<framework::Tensor>("Out");
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T* data = tensor->mutable_data<T>(context.GetPlace());
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unsigned int seed = static_cast<unsigned int>(context.Attr<int>("seed"));
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std::minstd_rand engine;
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if (seed == 0) {
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seed = std::random_device()();
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}
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engine.seed(seed);
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std::normal_distribution<T> dist(mean, std);
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int64_t size = tensor->numel();
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for (int64_t i = 0; i < size; ++i) {
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data[i] = dist(engine);
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}
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}
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};
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class GaussianRandomOp : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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void InferShape(framework::InferShapeContext* ctx) const override {
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OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "GaussianRandom");
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auto shape = ctx->Attrs().Get<std::vector<int64_t>>("shape");
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std::vector<int64_t> temp;
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temp.reserve(shape.size());
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for (auto dim : shape) {
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temp.push_back(static_cast<int64_t>(dim));
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}
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if (shape.empty() && ctx->HasInput("ShapeTensor")) {
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auto shape_dims = ctx->GetInputDim("ShapeTensor");
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int num_ele = 1;
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for (int i = 0; i < shape_dims.size(); ++i) {
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num_ele *= shape_dims[i];
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}
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auto vec_dims = std::vector<int>(num_ele, -1);
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ctx->SetOutputDim("Out", framework::make_ddim(vec_dims));
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return;
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}
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if (!ctx->HasInput("ShapeTensor") && !ctx->HasInputs("ShapeTensorList")) {
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PADDLE_ENFORCE_GT(
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shape.size(), 0UL,
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platform::errors::InvalidArgument(
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"Attribute(shape) of GaussianRandomOp must be set "
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"and shape.size() > 0, but reveived shape.size() is %d",
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shape.size()));
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}
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ctx->SetOutputDim("Out", framework::make_ddim(temp));
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}
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protected:
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framework::OpKernelType GetExpectedKernelType(
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const framework::ExecutionContext& ctx) const override {
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framework::LibraryType library{framework::LibraryType::kPlain};
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framework::DataLayout layout{framework::DataLayout::kAnyLayout};
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auto data_type =
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static_cast<framework::proto::VarType::Type>(ctx.Attr<int>("dtype"));
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#ifdef PADDLE_WITH_MKLDNN
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if (library == framework::LibraryType::kPlain &&
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this->CanMKLDNNBeUsed(ctx, data_type)) {
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library = framework::LibraryType::kMKLDNN;
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layout = framework::DataLayout::kMKLDNN;
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}
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#endif
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return framework::OpKernelType(data_type, ctx.device_context(), layout,
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library);
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}
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framework::OpKernelType GetKernelTypeForVar(
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const std::string& var_name, const Tensor& tensor,
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const framework::OpKernelType& expected_kernel_type) const override {
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if (var_name == "ShapeTensor" || var_name == "ShapeTensorList") {
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return expected_kernel_type;
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}
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return framework::OpKernelType(expected_kernel_type.data_type_,
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tensor.place(), tensor.layout());
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}
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};
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class GaussianRandomOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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void Make() override {
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AddOutput("Out", "Output matrix of gaussian random op");
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AddAttr<std::vector<int64_t>>("shape",
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"(vector<int64_t>) "
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"The dimension of random tensor.")
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.SetDefault({});
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AddInput("ShapeTensor",
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"(Tensor<int>), optional). The shape of the output."
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"It has a higher priority than Attr(shape).")
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.AsDispensable();
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AddInput("ShapeTensorList",
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"(vector<Tensor<int>>, optional). The shape of the output. "
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"It has a higher priority than Attr(shape)."
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"The shape of the element in vector must be [1].")
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.AsDuplicable()
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.AsDispensable();
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AddAttr<float>("mean",
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"(float, default 0.0) "
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"mean of random tensor.")
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.SetDefault(.0f);
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AddAttr<float>("std",
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"(float, default 1.0) "
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"std of random tensor.")
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.SetDefault(1.0f);
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AddAttr<int>("seed",
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"(int, default 0) "
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"Random seed of generator."
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"0 means use system wide seed."
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"Note that if seed is not 0, this operator will always "
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"generate the same random numbers every time.")
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.SetDefault(0);
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AddAttr<int>("dtype",
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"(int, default 5(FP32)) "
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"Output data type.")
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.SetDefault(framework::proto::VarType::FP32);
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AddAttr<bool>("use_mkldnn",
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"(bool, default false) Only used in mkldnn kernel")
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.SetDefault(false);
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AddComment(R"DOC(
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GaussianRandom Operator.
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Used to initialize tensors with gaussian random generator.
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)DOC");
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}
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};
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} // namespace operators
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} // namespace paddle
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namespace ops = paddle::operators;
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REGISTER_OP_WITHOUT_GRADIENT(gaussian_random, ops::GaussianRandomOp,
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ops::GaussianRandomOpMaker);
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REGISTER_OP_CPU_KERNEL(gaussian_random, ops::CPUGaussianRandomKernel<float>,
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ops::CPUGaussianRandomKernel<double>);
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REGISTER_OP_CPU_KERNEL(gaussian_random_batch_size_like,
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ops::CPUGaussianRandomBatchSizeLikeKernel<float>,
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ops::CPUGaussianRandomBatchSizeLikeKernel<double>);
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REGISTER_OP_VERSION(gaussian_random)
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.AddCheckpoint(
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R"ROC(
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Upgrade gaussian_random add new inputs [ShapeTensor] and [ShapeTensorList]
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and modify the attribute of [shape])ROC",
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paddle::framework::compatible::OpVersionDesc()
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.NewInput("ShapeTensor",
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"The output shape supports Tensor type. ShapeTensor is "
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"dispensable.")
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.NewInput("ShapeTensorList",
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"The output shape supports list filled with Tensor. "
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"ShapeTensorList is dispensable.")
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.ModifyAttr("shape",
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"The arg 'default_value' of attr 'shape' is changed: "
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"from 'None' to '{}'.",
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std::vector<int64_t>{}));
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