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Merge pull request #22 from songyuwen0808/paddlebox
conv相关代码合入
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paddle/fluid/operators/fused/fused_seqpool_cvm_with_conv_op.cc
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| /* Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. | ||
| Licensed under the Apache License, Version 2.0 (the "License"); | ||
| you may not use this file except in compliance with the License. | ||
| You may obtain a copy of the License at | ||
| http://www.apache.org/licenses/LICENSE-2.0 | ||
| Unless required by applicable law or agreed to in writing, software | ||
| distributed under the License is distributed on an "AS IS" BASIS, | ||
| WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| See the License for the specific language governing permissions and | ||
| limitations under the License. */ | ||
|
|
||
| #include "paddle/fluid/operators/fused/fused_seqpool_cvm_with_conv_op.h" | ||
| #include <string> | ||
| namespace paddle { | ||
| namespace operators { | ||
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|
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| class FusedSeqpoolCVMOpWithConv : public framework::OperatorWithKernel { | ||
| public: | ||
| using framework::OperatorWithKernel::OperatorWithKernel; | ||
| void InferShape(framework::InferShapeContext* ctx) const override { | ||
| PADDLE_ENFORCE_GE(ctx->Inputs("X").size(), 1UL, "Inputs(X) of FusedSeqpoolCVMOpWithConv should not be empty."); | ||
| PADDLE_ENFORCE_GE(ctx->Outputs("Out").size(), 1UL, "Outputs(Out) of FusedSeqpoolCVMOpWithConv should not be empty."); | ||
|
|
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| auto cvm_dims = ctx->GetInputDim("CVM"); | ||
| PADDLE_ENFORCE_EQ(cvm_dims.size(), 2UL, platform::errors::InvalidArgument("Input(CVM)'s rank should be 2.")); | ||
| PADDLE_ENFORCE_EQ(cvm_dims[1], 3UL, | ||
| platform::errors::InvalidArgument("The 2nd dimension of Input(CVM) should be 3.")); | ||
|
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| auto ins_dims = ctx->GetInputsDim("X"); | ||
| const int cvm_offset = ctx->Attrs().Get<int>("cvm_offset"); | ||
| const size_t num_inputs = ins_dims.size(); | ||
| std::vector<framework::DDim> outs_dims; | ||
| outs_dims.resize(num_inputs); | ||
| bool use_cvm = ctx->Attrs().Get<bool>("use_cvm"); | ||
| bool show_filter = ctx->Attrs().Get<bool>("show_filter"); | ||
|
|
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| PADDLE_ENFORCE_GT(num_inputs, 0UL, | ||
| platform::errors::InvalidArgument( | ||
| "Input tensors count should be greater than 0, " | ||
| "but received value is %d.", | ||
| num_inputs)); | ||
|
|
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| // The output height should be confirmed in Compute, | ||
| // since input lod is not accessible here. | ||
| PADDLE_ENFORCE_EQ(ins_dims[0].size(), 2, | ||
| platform::errors::InvalidArgument( | ||
| "The dims size of first input should be equal to 2, " | ||
| "but received value is %d.", | ||
| ins_dims[0].size())); | ||
|
|
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| for (size_t i = 0; i < num_inputs; ++i) { | ||
| const auto dims = ins_dims[i]; | ||
| int rank = dims.size(); | ||
| if (use_cvm) { | ||
| PADDLE_ENFORCE_GT( | ||
| dims[rank - 1], 2, | ||
| "Shape error in %lu id, the last dimension(embedding) of the " | ||
| "'X' tensor must be larger than 2.", | ||
| i); | ||
| } | ||
| // input lod is not accessible here | ||
| std::vector<int64_t> out_dim; | ||
| if (use_cvm) { | ||
| if (show_filter) { | ||
| out_dim = {-1, dims[rank - 1] - 1}; | ||
| } else { | ||
| out_dim = {-1, dims[rank - 1]}; | ||
| } | ||
| } else { | ||
| out_dim = {-1, dims[rank - 1] - cvm_offset}; | ||
| } | ||
| outs_dims[i] = framework::make_ddim(out_dim); | ||
| } | ||
| ctx->SetOutputsDim("Out", outs_dims); | ||
| } | ||
|
|
||
| protected: | ||
| framework::OpKernelType GetExpectedKernelType( | ||
| const framework::ExecutionContext& ctx) const override { | ||
| return framework::OpKernelType(framework::proto::VarType::FP32, | ||
| ctx.device_context()); | ||
| } | ||
| }; | ||
|
|
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| class FusedSeqpoolCVMOpWithConvMaker : public framework::OpProtoAndCheckerMaker { | ||
| public: | ||
| void Make() override { | ||
| AddInput("X", | ||
| "(vector<LoDTensor>) The input tensors of" | ||
| " operator.") | ||
| .AsDuplicable(); | ||
| AddInput("CVM", | ||
| "(Tensor), a 2-D Tensor with shape [N x 2], where N is the batch " | ||
| "size, 2 is show and click."); | ||
| AddOutput("Out", | ||
| "(vector<Tensor>) The output of Op does not contain LoD " | ||
| "information.") | ||
| .AsDuplicable(); | ||
| AddAttr<std::string>("pooltype", | ||
| "(string, default 'SUM') the pooling pooltype of " | ||
| "SequencePoolOp, only support SUM now.") | ||
| .SetDefault("SUM") | ||
| .InEnum({"SUM"}); | ||
| AddAttr<float>("pad_value", | ||
| "(float, default 0.0) The value to pad for empty sequence.") | ||
| .SetDefault(0.0); | ||
| AddAttr<bool>("use_cvm", "bool, use cvm or not").SetDefault(true); | ||
| AddAttr<int>("cvm_offset", "(int, default 3)").SetDefault(3); | ||
| AddAttr<bool>("show_filter", "(bool, default false)").SetDefault(false); | ||
|
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||
| AddComment(R"DOC( | ||
| Fuse multiple pairs of Sequence Pool and CVM Operator. | ||
| )DOC"); | ||
| } | ||
| }; | ||
|
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| class FusedSeqpoolCVMGradOpWithConv : public framework::OperatorWithKernel { | ||
| public: | ||
| using framework::OperatorWithKernel::OperatorWithKernel; | ||
|
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| void InferShape(framework::InferShapeContext* ctx) const override { | ||
| auto og_dims = ctx->GetInputsDim(framework::GradVarName("Out")); | ||
| auto x_dims = ctx->GetInputsDim("X"); | ||
| auto cvm_dims = ctx->GetInputDim("CVM"); | ||
| const int cvm_offset = ctx->Attrs().Get<int>("cvm_offset"); | ||
| bool use_cvm = ctx->Attrs().Get<bool>("use_cvm"); | ||
| bool show_filter = ctx->Attrs().Get<bool>("show_filter"); | ||
|
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| PADDLE_ENFORCE_EQ( | ||
| cvm_dims.size(), 2, | ||
| platform::errors::InvalidArgument("Input(CVM)'s rank should be 2.")); | ||
|
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| for (size_t i = 0; i < og_dims.size(); i++) { | ||
| PADDLE_ENFORCE_EQ( | ||
| og_dims[i].size(), x_dims[i].size(), | ||
| platform::errors::InvalidArgument( | ||
| "The rank of output grad must equal to Input(X). But " | ||
| "received: input rank %u, input shape [%s].", | ||
| og_dims[i].size(), og_dims[i])); | ||
| if (use_cvm) { | ||
| auto o_dim = og_dims[i][og_dims[i].size() - 1]; | ||
| if (show_filter) { | ||
| o_dim += 1; | ||
| } | ||
| PADDLE_ENFORCE_EQ( | ||
| o_dim, x_dims[i][og_dims[i].size() - 1], | ||
| platform::errors::InvalidArgument( | ||
| "The dimension mismatch between Input(OUT@GRAD) and " | ||
| "Input(X). Received Input(OUT@GRAD): input rank %u, " | ||
| "input shape [%s]; received Input(X): input rank %u, " | ||
| "input shape [%s].", | ||
| og_dims[i].size(), og_dims[i], x_dims[i].size(), x_dims[i])); | ||
| } else { | ||
| PADDLE_ENFORCE_EQ( | ||
| og_dims[i][og_dims[i].size() - 1], | ||
| x_dims[i][og_dims[i].size() - 1] - cvm_offset, | ||
| platform::errors::InvalidArgument( | ||
| "The dimension mismatch between Input(OUT@GRAD) and " | ||
| "Input(X). Received Input(OUT@GRAD): input rank %u, " | ||
| "input shape [%s]; received Input(X): input rank %u, " | ||
| "input shape [%s].", | ||
| og_dims[i].size(), og_dims[i], x_dims[i].size(), x_dims[i])); | ||
| } | ||
| } | ||
| for (size_t i = 0; i < x_dims.size(); ++i) { | ||
| ctx->ShareLoD("X", framework::GradVarName("X"), i, i); | ||
| ctx->ShareDim("X", framework::GradVarName("X"), i, i); | ||
| } | ||
| } | ||
|
|
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| protected: | ||
| framework::OpKernelType GetExpectedKernelType( | ||
| const framework::ExecutionContext& ctx) const override { | ||
| return framework::OpKernelType(OperatorWithKernel::IndicateVarDataType( | ||
| ctx, framework::GradVarName("Out")), | ||
| ctx.device_context()); | ||
| } | ||
| }; | ||
|
|
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| template <typename T> | ||
| class FusedSeqpoolCVMGradOpWithConvMaker : public framework::SingleGradOpMaker<T> { | ||
| public: | ||
| using framework::SingleGradOpMaker<T>::SingleGradOpMaker; | ||
|
|
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| protected: | ||
| void Apply(GradOpPtr<T> op_desc_ptr) const override { | ||
| op_desc_ptr->SetType("fused_seqpool_cvm_with_conv_grad"); | ||
| op_desc_ptr->SetInput("X", this->Input("X")); | ||
| op_desc_ptr->SetInput("CVM", this->Input("CVM")); | ||
|
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| op_desc_ptr->SetInput(framework::GradVarName("Out"), | ||
| this->OutputGrad("Out")); | ||
| op_desc_ptr->SetOutput(framework::GradVarName("X"), | ||
| this->InputGrad("X", false)); | ||
| op_desc_ptr->SetOutput(framework::GradVarName("CVM"), | ||
| this->InputGrad("CVM")); | ||
| op_desc_ptr->SetAttrMap(this->Attrs()); | ||
| } | ||
| }; | ||
|
|
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| } // namespace operators | ||
| } // namespace paddle | ||
|
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| namespace ops = paddle::operators; | ||
| namespace plat = paddle::platform; | ||
|
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| REGISTER_OPERATOR(fused_seqpool_cvm_with_conv, ops::FusedSeqpoolCVMOpWithConv, | ||
| ops::FusedSeqpoolCVMOpWithConvMaker, | ||
| ops::FusedSeqpoolCVMGradOpWithConvMaker<paddle::framework::OpDesc>, | ||
| ops::FusedSeqpoolCVMGradOpWithConvMaker<paddle::imperative::OpBase>); | ||
| REGISTER_OPERATOR(fused_seqpool_cvm_with_conv_grad, ops::FusedSeqpoolCVMGradOpWithConv) | ||
|
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| REGISTER_OP_CPU_KERNEL(fused_seqpool_cvm_with_conv, | ||
| ops::FusedSeqpoolCVMOpWithConvCPUKernel<float>) | ||
| REGISTER_OP_CPU_KERNEL(fused_seqpool_cvm_with_conv_grad, | ||
| ops::FusedSeqpoolCVMGradOpWithConvCPUKernel<float>) |
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