Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into pad_op
wanghaoshuang
8 years ago
commit
d89598054e
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/*
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Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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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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*/
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#include <cuda.h>
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#include <cuda_runtime.h>
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#include "paddle/framework/lod_tensor.h"
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#include "paddle/platform/assert.h"
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#include <gtest/gtest.h>
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__global__ void test(size_t* a, int size) {
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for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < size;
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i += blockDim.x * gridDim.x) {
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a[i] *= 2;
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}
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}
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TEST(LoDTensor, LoDInGPU) {
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paddle::framework::Tensor tensor;
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paddle::framework::LoDTensor lod_tensor;
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paddle::platform::GPUPlace place(0);
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paddle::framework::LoD src_lod;
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src_lod.push_back(std::vector<size_t>{0, 2, 4, 6, 8, 10, 12, 14});
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tensor.Resize({14, 16});
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tensor.mutable_data<float>(place);
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lod_tensor.set_lod(src_lod);
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lod_tensor.set_tensor(&tensor);
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CHECK_EQ(lod_tensor.lod_element(0, 2), 4);
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CHECK_EQ(lod_tensor.lod_element(0, 4), 8);
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auto lod = lod_tensor.lod();
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test<<<1, 8>>>(lod[0].data(), lod[0].size());
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cudaDeviceSynchronize();
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for (size_t i = 0; i < src_lod[0].size(); ++i) {
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CHECK_EQ(lod[0].data()[i], src_lod[0].data()[i] * 2);
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}
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}
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## Operator's Parameter Name Convention
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To make the operator document itself more clear, we recommend operator names obey the listing conventions.
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### OpProtoMaker names
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When defining an operator in Paddle, a corresponding [OpProtoMaker](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/operator.h#L170) (TODO: OpProtoMaker Doc)need to be defined. All the Input/Output and Attributes will write into the [OpProto](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/framework.proto#L61) , and will be used in client language to create operator.
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- Input/Output.
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- Input/Output names follow the **CamelCase**. e.g. `X`, `Y`, `Matrix`, `LastAxisInMatrix`. Input/Output much more like Variables, we prefer to meaningful English words.
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- If an operator's Input/Output are tensors in math, not match to any meaningful words, input name should starts from `X`. e.g. `X`, `Y`, and output name should starts from `Out`. e.g. `Out`. This rule intends making operators which have few inputs/outputs unified.
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- Attribute.
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- Attribute name follows the **camelCase**. e.g. `x`, `y`, `axis`, `rowwiseMatrix`. Also, attribute name prefers to meaningful English words.
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- Comments.
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- Input/Output/Attr comment follow the format of **(type,default value) usage**, corresponding to which type it can be and how it will be used in the operator. e.g. Attribute in Accumulator`"gamma" `,`(float, default 1.0) Accumulation multiplier`.
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- Operator comment format of` R"DOC(your comment here)DOC"`. You should explain the input/output of the operator first. If there is math calculation in this operator, you should write the equation in the comment. e.g. `Out = X + Y`.
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- Order.
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- Follow the order of Input/Output, then Attribute, then Comments. See the example in best practice.
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### Best Practice
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Here we give some examples to show how these rules will be used.
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- The operator has one input, one output. e.g.`relu`, inputs: `X`, outputs: `Out`.
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- The operator has two input, one output. e.g. `rowwise_add`, inputs : `X`, `Y`, outputs : `Out`.
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- The operator contains attribute. e.g. `cosine`, inputs : `X`, `axis`, outputs : `Out`.
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We give a full example of Accumulator Operator.
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```c++
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class AccumulateOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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AccumulateOpMaker(framework::OpProto *proto,
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framework::OpAttrChecker *op_checker)
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: OpProtoAndCheckerMaker(proto, op_checker) {
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AddInput("X", "(Tensor) The input tensor that has to be accumulated to the output tensor. If the output size is not the same as input size, the output tensor is first reshaped and initialized to zero, and only then, accumulation is done.");
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AddOutput("Out", "(Tensor) Accumulated output tensor");
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AddAttr<float>("gamma", "(float, default 1.0) Accumulation multiplier");
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AddComment(R"DOC(
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Accumulate operator accumulates the input tensor to the output tensor. If the
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output tensor already has the right size, we add to it; otherwise, we first
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initialize the output tensor to all zeros, and then do accumulation. Any
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further calls to the operator, given that no one else fiddles with the output
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in the interim, will do simple accumulations.
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Accumulation is done as shown:
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Out = 1*X + gamma*Out
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where X is the input tensor, Y is the output tensor and gamma is the multiplier
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argument.
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)DOC");
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}
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};
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```
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@ -0,0 +1,107 @@
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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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/operators/reshape_op.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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protected:
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void InferShape(const framework::InferShapeContext &ctx) const override {
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// input check
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PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) shouldn't be null");
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auto shape = ctx.Attr<std::vector<int>>("shape");
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PADDLE_ENFORCE(shape.size() > 0, "Attr(shape) shouldn't be empty.");
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for (auto dim : shape) {
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PADDLE_ENFORCE(dim > 0, "Each dimension of shape must be positive.");
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}
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// capacity check
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int64_t capacity =
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std::accumulate(shape.begin(), shape.end(), 1, std::multiplies<int>());
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auto *in = ctx.Input<framework::Tensor>("X");
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int64_t in_size = framework::product(in->dims());
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PADDLE_ENFORCE_EQ(capacity, in_size,
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"The size of Input(X) mismatches with Attr(shape).");
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// resize output
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std::vector<int64_t> shape_int64(shape.size(), 0);
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std::transform(shape.begin(), shape.end(), shape_int64.begin(),
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[](int a) { return static_cast<int64_t>(a); });
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auto out_dims = framework::make_ddim(shape_int64);
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ctx.Output<framework::Tensor>("Out")->Resize(out_dims);
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}
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};
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class ReshapeOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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ReshapeOpMaker(framework::OpProto *proto,
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framework::OpAttrChecker *op_checker)
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: OpProtoAndCheckerMaker(proto, op_checker) {
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AddInput("X", "The input tensor of reshape operator.");
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AddOutput("Out", "The output tensor of reshape operator.");
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AddAttr<std::vector<int>>("shape", "Target shape of reshape operator.");
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AddComment(R"DOC(Reshape operator
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Reshape Input(X) into the shape specified by Attr(shape).
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An example:
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Given a 2-D tensor X with 2 rows and 2 columns
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[[1, 2], [3, 4]]
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with target shape = [1, 4], the reshape operator will transform
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the tensor X into a 1-D tensor:
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[1, 2, 3, 4]
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)DOC");
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}
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};
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class ReshapeGradOp : public framework::OperatorWithKernel {
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public:
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ReshapeGradOp(const std::string &type,
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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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protected:
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void InferShape(const framework::InferShapeContext &ctx) const override {
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PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) shouldn't be null.");
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PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
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"Input(Out@GRAD) shouldn't be null.");
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auto dims = ctx.Input<framework::Tensor>("X")->dims();
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auto *d_in = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
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d_in->Resize(dims);
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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(reshape, ops::ReshapeOp, ops::ReshapeOpMaker, reshape_grad,
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ops::ReshapeGradOp);
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REGISTER_OP_CPU_KERNEL(reshape,
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ops::ReshapeKernel<paddle::platform::CPUPlace, float>);
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REGISTER_OP_CPU_KERNEL(
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reshape_grad, ops::ReshapeGradKernel<paddle::platform::CPUPlace, float>);
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@ -0,0 +1,22 @@
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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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/operators/reshape_op.h"
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REGISTER_OP_GPU_KERNEL(
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reshape,
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paddle::operators::ReshapeKernel<paddle::platform::GPUPlace, float>);
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REGISTER_OP_GPU_KERNEL(
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reshape_grad,
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paddle::operators::ReshapeGradKernel<paddle::platform::GPUPlace, float>);
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@ -0,0 +1,55 @@
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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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 "paddle/framework/eigen.h"
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#include "paddle/framework/op_registry.h"
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namespace paddle {
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namespace operators {
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template <typename Place, typename T>
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class ReshapeKernel : public framework::OpKernel {
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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::Tensor>("Out");
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auto* in = ctx.Input<framework::Tensor>("X");
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out->mutable_data<T>(ctx.GetPlace());
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auto shape = ctx.Attr<std::vector<int>>("shape");
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std::vector<int64_t> shape_int64(shape.size(), 0);
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std::transform(shape.begin(), shape.end(), shape_int64.begin(),
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[](int a) { return static_cast<int64_t>(a); });
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auto out_dims = framework::make_ddim(shape_int64);
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out->CopyFrom<T>(*in, ctx.GetPlace());
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out->Resize(out_dims);
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}
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};
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template <typename Place, typename T>
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class ReshapeGradKernel : public framework::OpKernel {
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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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auto in_dims = d_x->dims();
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d_x->CopyFrom<T>(*d_out, ctx.GetPlace());
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d_x->Resize(in_dims);
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}
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};
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} // namespace operators
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} // namespace paddle
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