Merge pull request #12449 from NHZlX/add_tensorrt_elementwise_add
Add tensorrt elementwise addfea/anakin-support-x86
Zhaolong Xing
7 years ago
committed by
GitHub
commit
d7dd0868db
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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/framework/op_registry.h"
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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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class ElementwiseWeightOpConverter : public OpConverter {
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public:
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ElementwiseWeightOpConverter() {}
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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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// Here the two nullptr looks strange, that's because the
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// framework::OpDesc's constructor is strange.
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framework::OpDesc op_desc(op, nullptr);
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LOG(INFO) << "convert a fluid elementwise op to tensorrt IScaleLayer";
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PADDLE_ENFORCE_EQ(op_desc.Input("X").size(), 1);
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PADDLE_ENFORCE_EQ(op_desc.Input("Y").size(), 1); // Y is a weight
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PADDLE_ENFORCE_EQ(op_desc.Output("Out").size(), 1);
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auto* X = engine_->GetITensor(op_desc.Input("X").front());
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nvinfer1::Dims dims_x = X->getDimensions();
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PADDLE_ENFORCE(dims_x.nbDims >= 3);
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auto* Y_v = scope.FindVar(op_desc.Input("Y").front());
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PADDLE_ENFORCE_NOT_NULL(Y_v);
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auto* Y_t = Y_v->GetMutable<framework::LoDTensor>();
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auto* weight_data = Y_t->mutable_data<float>(platform::CPUPlace());
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auto scale_mode = nvinfer1::ScaleMode::kELEMENTWISE;
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std::vector<int> dims_y = framework::vectorize2int(Y_t->dims());
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if (static_cast<int>(dims_y.size()) == dims_x.nbDims + 1) {
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if (dims_y[0] == 1) dims_y.erase(dims_y.begin());
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}
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if (static_cast<int>(dims_y.size()) == 1 && dims_y[0] == dims_x.d[0]) {
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scale_mode = nvinfer1::ScaleMode::kCHANNEL;
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} else if (static_cast<int>(dims_y.size()) == dims_x.nbDims &&
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dims_y[0] == dims_x.d[0]) {
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scale_mode = nvinfer1::ScaleMode::kELEMENTWISE;
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for (int i = 1; i < dims_x.nbDims; i++) {
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if (dims_y[i] != dims_x.d[i]) {
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scale_mode = nvinfer1::ScaleMode::kCHANNEL;
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break;
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}
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}
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if (scale_mode == nvinfer1::ScaleMode::kCHANNEL) {
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for (int i = 1; i < dims_x.nbDims; i++) {
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if (dims_y[i] != 1)
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PADDLE_THROW(
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"TensorRT unsupported weight shape for Elementwise op!");
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}
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}
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} else {
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PADDLE_THROW("TensorRT unsupported weight Shape for Elementwise op!");
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}
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TensorRTEngine::Weight shift_weights{nvinfer1::DataType::kFLOAT,
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static_cast<void*>(weight_data),
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Y_t->memory_size() / sizeof(float)};
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TensorRTEngine::Weight scale_weights{nvinfer1::DataType::kFLOAT, nullptr,
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0};
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TensorRTEngine::Weight power_weights{nvinfer1::DataType::kFLOAT, nullptr,
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0};
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nvinfer1::IScaleLayer* layer = TRT_ENGINE_ADD_LAYER(
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engine_, Scale, *const_cast<nvinfer1::ITensor*>(X), scale_mode,
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shift_weights.get(), scale_weights.get(), power_weights.get());
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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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class ElementwiseTensorOpConverter : public OpConverter {
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public:
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ElementwiseTensorOpConverter() {}
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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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// Here the two nullptr looks strange, that's because the
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// framework::OpDesc's constructor is strange.
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framework::OpDesc op_desc(op, nullptr);
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LOG(INFO) << "convert a fluid elementwise op to tensorrt IScaleLayer";
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PADDLE_ENFORCE_EQ(op_desc.Input("X").size(), 1);
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PADDLE_ENFORCE_EQ(op_desc.Input("Y").size(), 1); // Y is a weight
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PADDLE_ENFORCE_EQ(op_desc.Output("Out").size(), 1);
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auto* X = engine_->GetITensor(op_desc.Input("X").front());
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auto* Y = engine_->GetITensor(op_desc.Input("Y").front());
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nvinfer1::Dims dims_x = X->getDimensions();
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nvinfer1::Dims dims_y = Y->getDimensions();
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// The two input tensor should have the same dims
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PADDLE_ENFORCE(dims_x.nbDims >= 3);
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if (dims_x.nbDims == dims_y.nbDims) {
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for (int i = 0; i < dims_x.nbDims; i++) {
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if (dims_x.d[i] != dims_y.d[i])
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PADDLE_THROW("TensorRT unsupported tensor shape for Elementwise op!");
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}
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} else {
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PADDLE_THROW("TensorRT unsupported tensor shape for Elementwise op!");
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}
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auto op_pair = ops.find(op_type_);
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if (op_pair == ops.end()) {
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PADDLE_THROW("Wrong elementwise op type!");
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}
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nvinfer1::IElementWiseLayer* layer = TRT_ENGINE_ADD_LAYER(
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engine_, ElementWise, *const_cast<nvinfer1::ITensor*>(X),
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*const_cast<nvinfer1::ITensor*>(Y), op_pair->second);
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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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protected:
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static const std::unordered_map<std::string, nvinfer1::ElementWiseOperation>
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ops;
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std::string op_type_;
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};
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const std::unordered_map<std::string, nvinfer1::ElementWiseOperation>
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ElementwiseTensorOpConverter::ops = {
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{"add", nvinfer1::ElementWiseOperation::kSUM},
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{"mul", nvinfer1::ElementWiseOperation::kPROD},
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{"sub", nvinfer1::ElementWiseOperation::kSUB},
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{"div", nvinfer1::ElementWiseOperation::kDIV},
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{"min", nvinfer1::ElementWiseOperation::kMIN},
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{"pow", nvinfer1::ElementWiseOperation::kPOW},
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{"max", nvinfer1::ElementWiseOperation::kMAX},
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};
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class ElementwiseTensorAddOpConverter : public ElementwiseTensorOpConverter {
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public:
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ElementwiseTensorAddOpConverter() { op_type_ = "add"; }
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};
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class ElementwiseTensorMulOpConverter : public ElementwiseTensorOpConverter {
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public:
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ElementwiseTensorMulOpConverter() { op_type_ = "mul"; }
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};
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class ElementwiseTensorSubOpConverter : public ElementwiseTensorOpConverter {
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public:
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ElementwiseTensorSubOpConverter() { op_type_ = "sub"; }
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};
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class ElementwiseTensorDivOpConverter : public ElementwiseTensorOpConverter {
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public:
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ElementwiseTensorDivOpConverter() { op_type_ = "div"; }
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};
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class ElementwiseTensorMinOpConverter : public ElementwiseTensorOpConverter {
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public:
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ElementwiseTensorMinOpConverter() { op_type_ = "min"; }
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};
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class ElementwiseTensorMaxOpConverter : public ElementwiseTensorOpConverter {
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public:
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ElementwiseTensorMaxOpConverter() { op_type_ = "max"; }
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};
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class ElementwiseTensorPowOpConverter : public ElementwiseTensorOpConverter {
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public:
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ElementwiseTensorPowOpConverter() { op_type_ = "pow"; }
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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(elementwise_add_weight, ElementwiseWeightOpConverter);
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REGISTER_TRT_OP_CONVERTER(elementwise_add_tensor,
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ElementwiseTensorAddOpConverter);
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REGISTER_TRT_OP_CONVERTER(elementwise_sub_tensor,
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ElementwiseTensorSubOpConverter);
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REGISTER_TRT_OP_CONVERTER(elementwise_div_tensor,
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ElementwiseTensorDivOpConverter);
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REGISTER_TRT_OP_CONVERTER(elementwise_mul_tensor,
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ElementwiseTensorMulOpConverter);
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REGISTER_TRT_OP_CONVERTER(elementwise_max_tensor,
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ElementwiseTensorMaxOpConverter);
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REGISTER_TRT_OP_CONVERTER(elementwise_min_tensor,
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ElementwiseTensorMinOpConverter);
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REGISTER_TRT_OP_CONVERTER(elementwise_pow_tensor,
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ElementwiseTensorPowOpConverter);
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@ -0,0 +1,73 @@
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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(elementwise_op, add_weight_test) {
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std::unordered_set<std::string> parameters({"elementwise_add-Y"});
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framework::Scope scope;
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TRTConvertValidation validator(10, parameters, scope, 1 << 15);
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validator.DeclInputVar("elementwise_add-X", nvinfer1::DimsCHW(10, 3, 3));
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validator.DeclParamVar("elementwise_add-Y", nvinfer1::Dims3(10, 1, 1));
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// validator.DeclParamVar("mul-Y", nvinfer1::Dims2(8, 2));
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validator.DeclOutputVar("elementwise_add-Out", nvinfer1::DimsCHW(10, 3, 3));
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// Prepare Op description
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framework::OpDesc desc;
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desc.SetType("elementwise_add");
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desc.SetInput("X", {"elementwise_add-X"});
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desc.SetInput("Y", {"elementwise_add-Y"});
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desc.SetOutput("Out", {"elementwise_add-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(8);
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}
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TEST(elementwise_op, add_tensor_test) {
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std::unordered_set<std::string> parameters;
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framework::Scope scope;
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TRTConvertValidation validator(8, parameters, scope, 1 << 15);
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validator.DeclInputVar("elementwise_add-X", nvinfer1::DimsCHW(10, 3, 3));
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validator.DeclInputVar("elementwise_add-Y", nvinfer1::Dims3(10, 3, 3));
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// validator.DeclParamVar("mul-Y", nvinfer1::Dims2(8, 2));
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validator.DeclOutputVar("elementwise_add-Out", nvinfer1::DimsCHW(10, 3, 3));
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// Prepare Op description
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framework::OpDesc desc;
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desc.SetType("elementwise_add");
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desc.SetInput("X", {"elementwise_add-X"});
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desc.SetInput("Y", {"elementwise_add-Y"});
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desc.SetOutput("Out", {"elementwise_add-Out"});
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// the defalut axis of elementwise op is -1
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validator.SetOp(*desc.Proto());
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validator.Execute(8);
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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(elementwise_add);
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