Yibing Liu
7 years ago
commit
13509da69e
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/* Copyright (c) 2018 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/inference/tensorrt/convert/op_converter.h"
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namespace paddle {
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namespace inference {
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namespace tensorrt {
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/*
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* MulOp, IMatrixMultiplyLayer in TRT. This Layer doesn't has weights.
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*/
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class ConcatOpConverter : public OpConverter {
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public:
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void operator()(const framework::proto::OpDesc& op,
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const framework::Scope& scope, bool test_mode) override {
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VLOG(4) << "convert a fluid mul op to tensorrt mul layer without bias";
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framework::OpDesc op_desc(op, nullptr);
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// Declare inputs
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std::vector<nvinfer1::ITensor*> itensors;
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for (auto& input_name : op_desc.Input("X")) {
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itensors.push_back(engine_->GetITensor(input_name));
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}
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int axis = boost::get<int>(op_desc.GetAttr("axis"));
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PADDLE_ENFORCE(axis > 0,
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"The axis attr of Concat op should be large than 0 for trt");
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auto* layer = TRT_ENGINE_ADD_LAYER(engine_, Concatenation, itensors.data(),
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itensors.size());
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axis = axis - 1; // Remove batch dim
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layer->setAxis(axis);
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auto output_name = op_desc.Output("Out")[0];
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engine_->SetITensor(output_name, layer->getOutput(0));
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if (test_mode) { // the test framework can not determine which is the
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// output, so place the declaration inside.
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engine_->DeclareOutput(output_name);
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}
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}
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};
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} // namespace tensorrt
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} // namespace inference
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} // namespace paddle
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REGISTER_TRT_OP_CONVERTER(concat, ConcatOpConverter);
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/* Copyright (c) 2018 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 <gtest/gtest.h>
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#include "paddle/fluid/inference/tensorrt/convert/op_converter.h"
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#include "paddle/fluid/inference/tensorrt/convert/ut_helper.h"
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namespace paddle {
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namespace inference {
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namespace tensorrt {
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TEST(concat_op, test) {
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std::unordered_set<std::string> parameters({""});
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framework::Scope scope;
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TRTConvertValidation validator(10, parameters, scope, 1000);
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validator.DeclInputVar("concat_x1", nvinfer1::DimsCHW(10, 3, 1));
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validator.DeclInputVar("concat_x2", nvinfer1::DimsCHW(3, 3, 1));
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validator.DeclInputVar("concat_x3", nvinfer1::DimsCHW(7, 3, 1));
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validator.DeclOutputVar("concat_out", nvinfer1::DimsCHW(20, 3, 1));
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// Prepare Op description
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framework::OpDesc desc;
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desc.SetType("concat");
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desc.SetInput("X", {"concat_x1", "concat_x2", "concat_x3"});
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desc.SetOutput("Out", {"concat_out"});
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int axis = 1;
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desc.SetAttr("axis", axis);
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validator.SetOp(*desc.Proto());
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validator.Execute(5);
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}
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} // namespace tensorrt
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} // namespace inference
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} // namespace paddle
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USE_OP(concat);
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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/fusion_seqexpand_concat_fc_op.h"
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#include <string>
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#include "paddle/fluid/operators/math/blas.h"
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#include "paddle/fluid/operators/math/cpu_vec.h"
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#include "paddle/fluid/operators/math/fc_compute.h"
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#include "paddle/fluid/platform/cpu_info.h"
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namespace paddle {
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namespace operators {
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void FusionSeqExpandConcatFCOp::InferShape(
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framework::InferShapeContext* ctx) const {
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PADDLE_ENFORCE_GT(
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ctx->Inputs("X").size(), 1UL,
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"Inputs(X) of FusionSeqExpandConcatFCOp should larger than 1.");
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PADDLE_ENFORCE(
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ctx->HasInput("FCWeight"),
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"Input(FCWeight) of FusionSeqExpandConcatFCOp should not be null.");
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PADDLE_ENFORCE(
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ctx->HasOutput("Out"),
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"Output(Out) of FusionSeqExpandConcatFCOp should not be null.");
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PADDLE_ENFORCE(
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ctx->HasOutput("FCOut"),
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"Output(FCOut) of FusionSeqExpandConcatFCOp should not be null.");
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auto ins_dims = ctx->GetInputsDim("X");
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auto w_dims = ctx->GetInputDim("FCWeight"); // (M0+M1+M2+..) x D
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PADDLE_ENFORCE_EQ(w_dims.size(), 2UL, "Input(FCWeight)'s rank must be 2.");
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const int D = w_dims[1];
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int sum = ins_dims[0][1];
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for (size_t i = 1; i < ins_dims.size(); ++i) {
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sum += ins_dims[i][1];
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}
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PADDLE_ENFORCE_EQ(sum, w_dims[0],
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"FC height should be sum of all inputs width.");
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if (ctx->HasInput("FCBias")) {
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auto b_dims = ctx->GetInputDim("FCBias");
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PADDLE_ENFORCE_EQ(b_dims.size(), 2, "Input(FCBias)'s rank must be 2.");
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PADDLE_ENFORCE_EQ(b_dims[0], 1, "FCBias shapes must be 1 * %d.", D);
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PADDLE_ENFORCE_EQ(b_dims[1], D, "FCBias shapes must be 1 * %d.", D);
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}
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ctx->SetOutputDim("Out", {ins_dims[0][0], D});
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// fcout should be reshape when run since can not get lod in infershape
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// explicit share the ref lod
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ctx->ShareLoD("X", "Out", 0);
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}
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framework::OpKernelType FusionSeqExpandConcatFCOp::GetExpectedKernelType(
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const framework::ExecutionContext& ctx) const {
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return framework::OpKernelType(
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framework::ToDataType(ctx.MultiInput<LoDTensor>("X")[0]->type()),
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ctx.device_context());
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}
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void FusionSeqExpandConcatFCOpMaker::Make() {
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AddInput("X",
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"(LoDTensor) input LodDTensors, the first one must be have ref lod "
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"for sequence expand, and the rest input should have same lod.")
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.AsDuplicable();
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AddInput("FCWeight", "(Tensor) the weights of fc.");
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AddInput("FCBias", "(Tensor, optional) the bias of fc.").AsDispensable();
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AddOutput("Out", "(LoDTensor) Output LodTensor.");
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AddOutput(
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"FCOut",
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"(Tensor) the intermediate tensor to keep the result of fc."
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"Shape is (N x D), where N is the batch size, D is the output dim of fc")
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.AsIntermediate();
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AddAttr<std::string>("fc_activation",
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"(string, default: identity)"
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"The activation for the result of fc."
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"`identity` by default.")
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.SetDefault("identity")
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.InEnum({"sigmoid", "tanh", "relu", "identity"});
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AddComment(R"DOC(
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Fusion Sequence expand + concat + fc Operator.
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All below conditions should be meet:
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The ref_level of seq_expand should be 0.
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The ref lod of seq_expand level is the first input of concat.
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The other inputs should have same lod and same batch size of ref lod.
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The seq len of other inputs should be 1.
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The concat axis should be 1.
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)DOC");
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}
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template <typename T>
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class FusionSeqExpandConcatFCOpKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const override {
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using DeviceContext = paddle::platform::CPUDeviceContext;
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auto ins = ctx.MultiInput<LoDTensor>("X");
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auto* w = ctx.Input<Tensor>("FCWeight");
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auto* b = ctx.Input<Tensor>("FCBias");
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auto* out = ctx.Output<LoDTensor>("Out");
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auto* fc_out = ctx.Output<Tensor>("FCOut");
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auto* ref_in = ins[0];
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auto ref_lod = ref_in->lod();
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auto in1_lod = ins[1]->lod();
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auto ref_dims = ref_in->dims(); // T x M0
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auto in1_dims = ins[1]->dims(); // N x M1
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auto w_dims = w->dims();
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const int N = ref_lod[0].size() - 1;
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const int total_T = ref_dims[0];
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const int M0 = ref_dims[1];
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const int M1 = in1_dims[1];
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const int D = w_dims[1];
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// some check and fcout should be reshape here
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// since infershape can not get lod info
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PADDLE_ENFORCE_EQ(ref_lod.size(), 1UL, "Only support input lod size is 1.");
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PADDLE_ENFORCE_EQ(in1_lod.size(), 1UL, "Only support input lod size is 1.");
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PADDLE_ENFORCE_EQ(in1_lod[0].size() - 1, N,
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"Batch size of all inputs should be equal.");
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PADDLE_ENFORCE_EQ(in1_lod[0][N], N,
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"Seq_length of other inputs should be 1.");
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PADDLE_ENFORCE_EQ(in1_dims[0], N, "input height should be batch size.");
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for (size_t i = 2; i < ins.size(); ++i) {
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PADDLE_ENFORCE_EQ(ins[i]->dims()[0], N,
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"All other inputs height should be equal");
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PADDLE_ENFORCE_EQ(ins[i]->lod(), in1_lod,
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"All other inputs should have same lod");
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}
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fc_out->Resize({N, D});
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std::function<void(const int, const T*, T*)> fc_act;
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auto& fc_act_str = ctx.Attr<std::string>("fc_activation");
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if (platform::jit::MayIUse(platform::jit::avx)) {
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math::VecActivations<T, platform::jit::avx> act_functor;
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fc_act = act_functor(fc_act_str);
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} else {
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math::VecActivations<T, platform::jit::isa_any> act_functor;
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fc_act = act_functor(fc_act_str);
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}
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const T* ref_in_data = ref_in->data<T>();
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const T* in1_data = ins[1]->data<T>();
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const T* w_data = w->data<T>();
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T* out_data = out->mutable_data<T>(ctx.GetPlace());
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T* fc_out_data = fc_out->mutable_data<T>(ctx.GetPlace());
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auto blas = math::GetBlas<DeviceContext, T>(ctx);
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math::FCCompute<DeviceContext, T>(blas, total_T, D, M0, ref_in_data, w_data,
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out_data, b ? b->data<T>() : NULL);
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w_data = w_data + M0 * D;
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// first write on
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blas.MatMul(N, D, M1, in1_data, w_data, fc_out_data);
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w_data = w_data + M1 * D;
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for (size_t i = 2; i < ins.size(); ++i) {
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// add on
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const T* in_data = ins[i]->data<T>();
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const int K = ins[i]->dims()[1];
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blas.GEMM(CblasNoTrans, CblasNoTrans, N, D, K, static_cast<T>(1), in_data,
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K, w_data, D, static_cast<T>(1), fc_out_data, D);
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w_data = w_data + K * D;
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}
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T* cur_out_data = out_data;
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for (int i = 0; i < N; ++i) {
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int seq_len = ref_lod[0][i + 1] - ref_lod[0][i];
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T* src = fc_out_data + i * D;
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for (int step = 0; step < seq_len; ++step) {
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blas.VADD(D, cur_out_data, src, cur_out_data);
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cur_out_data = cur_out_data + D;
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}
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}
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fc_act(total_T * D, out_data, out_data);
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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_OPERATOR(fusion_seqexpand_concat_fc, ops::FusionSeqExpandConcatFCOp,
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ops::FusionSeqExpandConcatFCOpMaker,
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paddle::framework::DefaultGradOpDescMaker<true>);
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REGISTER_OP_CPU_KERNEL(fusion_seqexpand_concat_fc,
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ops::FusionSeqExpandConcatFCOpKernel<float>,
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ops::FusionSeqExpandConcatFCOpKernel<double>);
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