Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into fix_ut
minqiyang
7 years ago
commit
7a75cefea1
@ -1,26 +0,0 @@
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/* 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 "paddle/fluid/operators/reshape_op.h"
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using CUDA = paddle::platform::CUDADeviceContext;
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REGISTER_OP_CUDA_KERNEL(reshape, paddle::operators::ReshapeKernel<CUDA, float>,
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paddle::operators::ReshapeKernel<CUDA, double>,
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paddle::operators::ReshapeKernel<CUDA, int>,
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paddle::operators::ReshapeKernel<CUDA, int64_t>);
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REGISTER_OP_CUDA_KERNEL(reshape_grad,
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paddle::operators::ReshapeGradKernel<CUDA, float>,
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paddle::operators::ReshapeGradKernel<CUDA, double>,
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paddle::operators::ReshapeGradKernel<CUDA, int>,
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paddle::operators::ReshapeGradKernel<CUDA, int64_t>);
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@ -1,189 +0,0 @@
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/* 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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#pragma once
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#include <string>
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#include <vector>
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#include "paddle/fluid/framework/eigen.h"
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#include "paddle/fluid/framework/op_registry.h"
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namespace paddle {
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namespace operators {
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class ReshapeOp : public framework::OperatorWithKernel {
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public:
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ReshapeOp(const std::string &type, const framework::VariableNameMap &inputs,
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const framework::VariableNameMap &outputs,
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const framework::AttributeMap &attrs)
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: OperatorWithKernel(type, inputs, outputs, attrs) {}
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void InferShape(framework::InferShapeContext *ctx) const override {
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PADDLE_ENFORCE(ctx->HasInput("X"),
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"Input(X) of ReshapeOp should not be null.");
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PADDLE_ENFORCE(ctx->HasOutput("Out"),
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"Output(Out) of ReshapeOp should not be null.");
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const std::vector<int> &shape = ctx->Attrs().Get<std::vector<int>>("shape");
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PADDLE_ENFORCE(!shape.empty(),
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"The shape information must be set by Attr(shape).");
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if (ctx->HasInput("Shape") && ctx->IsRuntime()) {
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// If true, set the shape of Output(Out) according to Input(Shape) in
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// ReshapeKernel with ExecutionContext. Also check LoD in ReshapeKernel.
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ctx->ShareLoD("X", /*->*/ "Out");
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return;
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}
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auto x_dims = ctx->GetInputDim("X");
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auto out_dims = ValidateShape(shape, x_dims);
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ctx->SetOutputDim("Out", out_dims);
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if (x_dims[0] == out_dims[0]) {
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// Only pass LoD when the first dimension of output and Input(X)
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// are the same.
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ctx->ShareLoD("X", /*->*/ "Out");
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}
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}
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static framework::DDim ValidateShape(const std::vector<int> shape,
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const framework::DDim &in_dims) {
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const int64_t in_size = framework::product(in_dims);
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// only one dimension can be set to -1, whose size will be automatically
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// infered.
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const int64_t unk_dim_val = -1;
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const int64_t copy_dim_val = 0;
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std::vector<int64_t> output_shape(shape.size(), 0);
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int64_t capacity = 1;
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int unk_dim_idx = -1;
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for (size_t i = 0; i < shape.size(); ++i) {
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if (shape[i] == unk_dim_val) {
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PADDLE_ENFORCE(
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unk_dim_idx == -1,
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"Only one input dimension of Attr(shape) can be unknown.");
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unk_dim_idx = i;
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} else if (shape[i] == copy_dim_val) {
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PADDLE_ENFORCE(
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static_cast<int>(i) < in_dims.size(),
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"The index of dimension to copy from input shape must be less "
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"than the size of input shape.");
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} else {
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PADDLE_ENFORCE(
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shape[i] > 0,
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"Each input dimension of Attr(shape) must not be negtive except "
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"one unknown dimension.");
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}
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capacity *= (shape[i] ? shape[i] : in_dims[i]);
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output_shape[i] =
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(shape[i] ? static_cast<int64_t>(shape[i]) : in_dims[i]);
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}
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if (unk_dim_idx != -1) {
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if (in_size > 0) {
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// in_size < 0 and is un-determinate in compile time, skip the check,
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// for example, in_dims = [-1, 8, 1, 1], shape = [-1, 3, 8],
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// capacity = -24, in_size = -8, output_shape[0] = 0
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// the following check will fail.
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output_shape[unk_dim_idx] = -in_size / capacity;
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PADDLE_ENFORCE_EQ(output_shape[unk_dim_idx] * capacity, -in_size,
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"Invalid shape is given.");
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} else {
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output_shape[unk_dim_idx] = -1;
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}
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} else {
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PADDLE_ENFORCE_EQ(capacity, in_size, "Invalid shape is given.");
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}
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return framework::make_ddim(output_shape);
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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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return framework::OpKernelType(
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framework::ToDataType(ctx.Input<framework::LoDTensor>("X")->type()),
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ctx.device_context());
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}
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};
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template <typename DeviceContext, typename T>
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class ReshapeKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext &ctx) const {
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auto *out = ctx.Output<framework::LoDTensor>("Out");
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auto *in = ctx.Input<framework::LoDTensor>("X");
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auto *shape_tensor = ctx.HasInput("Shape")
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? ctx.Input<framework::LoDTensor>("Shape")
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: nullptr;
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framework::DDim out_dims = out->dims();
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if (shape_tensor) {
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auto *shape_data = shape_tensor->data<int>();
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framework::Tensor cpu_shape_tensor;
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if (platform::is_gpu_place(ctx.GetPlace())) {
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TensorCopySync(*shape_tensor, platform::CPUPlace(), &cpu_shape_tensor);
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shape_data = cpu_shape_tensor.data<int>();
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}
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auto shape =
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std::vector<int>(shape_data, shape_data + shape_tensor->numel());
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out_dims = ReshapeOp::ValidateShape(shape, in->dims());
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}
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if (!in->lod().empty()) {
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PADDLE_ENFORCE_EQ(
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out_dims[0], in->dims()[0],
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"Reshape operator cannot reshape an input sequence batch "
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"into an output sequence batch that has a different "
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"number of time steps. Please consider using "
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"sequence_reshape op.");
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}
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bool inplace = ctx.Attr<bool>("inplace");
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out->Resize(out_dims);
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if (!inplace) {
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out->mutable_data<T>(ctx.GetPlace());
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framework::TensorCopySync(*in, ctx.GetPlace(), out);
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out->Resize(out_dims);
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} else {
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out->ShareDataWith(*in);
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out->Resize(out_dims);
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}
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}
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};
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template <typename DeviceContext, typename T>
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class ReshapeGradKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext &ctx) const {
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auto *d_out = ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
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auto *d_x = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
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d_x->mutable_data<T>(ctx.GetPlace());
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bool inplace = ctx.Attr<bool>("inplace");
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auto in_dims = d_x->dims();
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if (!inplace) {
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framework::TensorCopy(*d_out, ctx.GetPlace(), ctx.device_context(), d_x);
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ctx.device_context().Wait();
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d_x->Resize(in_dims);
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} else {
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d_x->ShareDataWith(*d_out);
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d_x->Resize(in_dims);
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}
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}
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};
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} // namespace operators
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} // namespace paddle
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