wehub-resource-sync
233 lines
8.4 KiB
C++
233 lines
8.4 KiB
C++
/* Copyright (c) 2021 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 <NvInfer.h>
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#include <sys/types.h>
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#include <cstddef>
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#include <cstdint>
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#include <vector>
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#include "paddle/fluid/inference/tensorrt/convert/op_converter.h"
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namespace paddle::inference::tensorrt {
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class ReduceOpConverter : 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,
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bool test_mode) override {
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VLOG(4) << "Convert " << op_type << " op to tensorrt reduce layer";
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framework::OpDesc op_desc(op, nullptr);
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auto reduce_type = ops_.find(op_type);
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auto* x = engine_->GetITensor(op_desc.Input("X").front());
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nvinfer1::Dims input_shape = x->getDimensions();
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int input_dims = input_shape.nbDims;
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bool keep_dim = PADDLE_GET_CONST(bool, op_desc.GetAttr("keep_dim"));
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std::vector<int32_t> dim;
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if (op_desc.GetProtoAttr("dim").type() ==
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framework::proto::AttrType::INTS) {
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dim = PADDLE_GET_CONST(std::vector<int32_t>, op_desc.GetAttr("dim"));
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} else if (op_desc.GetProtoAttr("dim").type() ==
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framework::proto::AttrType::LONGS) {
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std::vector<int64_t> tem_dim =
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PADDLE_GET_CONST(std::vector<int64_t>, op_desc.GetAttr("dim"));
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for (size_t i = 0; i < tem_dim.size(); i++) {
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dim.push_back(static_cast<int32_t>(tem_dim[i]));
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}
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}
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bool reduce_all = PADDLE_GET_CONST(bool, op_desc.GetAttr("reduce_all"));
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if (dim.size() == 0) {
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reduce_all = true;
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}
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nvinfer1::IReduceLayer* layer = nullptr;
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if (reduce_all) {
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uint32_t reduce_dim = 0;
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for (int i = 0; i < input_dims; ++i) {
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reduce_dim |= 1 << i;
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}
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layer = TRT_ENGINE_ADD_LAYER(engine_,
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Reduce,
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*x,
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reduce_type->second.front(),
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reduce_dim,
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keep_dim);
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} else {
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auto CvtToBitMask = [&](const std::vector<int32_t>& dims) -> uint32_t {
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uint32_t res = 0;
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for (auto x : dims) {
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if (x < 0) {
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res |= 1 << (x + input_dims);
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} else {
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res |= 1 << x;
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}
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}
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return res;
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};
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layer = TRT_ENGINE_ADD_LAYER(engine_,
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Reduce,
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*x,
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reduce_type->second.front(),
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CvtToBitMask(dim),
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keep_dim);
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}
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auto output_name = op_desc.Output("Out")[0];
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// Ensure that the output type and input type are consistent.
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layer->getOutput(0)->setType(layer->getInput(0)->getType());
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ReplenishLayerAndOutput(layer, op_type, {output_name}, test_mode);
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}
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protected:
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std::string op_type;
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static const std::unordered_map<std::string,
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std::vector<nvinfer1::ReduceOperation>>
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ops_;
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};
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const std::unordered_map<std::string, std::vector<nvinfer1::ReduceOperation>>
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ReduceOpConverter::ops_ = {
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{"reduce_mean", {nvinfer1::ReduceOperation::kAVG}},
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{"reduce_sum", {nvinfer1::ReduceOperation::kSUM}},
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{"reduce_max", {nvinfer1::ReduceOperation::kMAX}},
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{"reduce_min", {nvinfer1::ReduceOperation::kMIN}},
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{"reduce_prod", {nvinfer1::ReduceOperation::kPROD}},
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{"reduce_any", {nvinfer1::ReduceOperation::kMAX}},
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{"reduce_all", {nvinfer1::ReduceOperation::kMIN}},
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};
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class ReduceSumOpConverter : public ReduceOpConverter {
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public:
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ReduceSumOpConverter() { op_type = "reduce_sum"; }
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};
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class ReduceMeanOpConverter : public ReduceOpConverter {
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public:
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ReduceMeanOpConverter() { op_type = "reduce_mean"; }
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};
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class ReduceMaxOpConverter : public ReduceOpConverter {
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public:
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ReduceMaxOpConverter() { op_type = "reduce_max"; }
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};
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class ReduceMinOpConverter : public ReduceOpConverter {
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public:
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ReduceMinOpConverter() { op_type = "reduce_min"; }
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};
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class ReduceProdOpConverter : public ReduceOpConverter {
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public:
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ReduceProdOpConverter() { op_type = "reduce_prod"; }
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};
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class ReduceAnyOpConverter : public ReduceOpConverter {
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public:
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ReduceAnyOpConverter() { op_type = "reduce_any"; }
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void operator()(const framework::proto::OpDesc& op,
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const framework::Scope& scope,
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bool test_mode) override {
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VLOG(4) << "convert a paddle " << op_type << " op to tensorrt reduce layer";
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framework::OpDesc op_desc(op, nullptr);
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auto reduce_type = ops_.find(op_type);
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auto* x = engine_->GetITensor(op_desc.Input("X").front());
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// Cast the DataType to float
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nvinfer1::IReduceLayer* reduce_layer = nullptr;
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auto* cast_layer = TRT_ENGINE_ADD_LAYER(engine_, Identity, *x);
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cast_layer->setOutputType(0, nvinfer1::DataType::kINT32);
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cast_layer->getOutput(0)->setType(nvinfer1::DataType::kINT32);
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nvinfer1::Dims input_shape = x->getDimensions();
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int input_dims = input_shape.nbDims;
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// Discriminate DataType between int and bool.
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bool keep_dim = PADDLE_GET_CONST(bool, op_desc.GetAttr("keep_dim"));
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std::vector<int32_t> dim;
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if (op_desc.GetProtoAttr("dim").type() ==
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framework::proto::AttrType::INTS) {
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dim = PADDLE_GET_CONST(std::vector<int32_t>, op_desc.GetAttr("dim"));
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} else if (op_desc.GetProtoAttr("dim").type() ==
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framework::proto::AttrType::LONGS) {
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std::vector<int64_t> tem_dim =
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PADDLE_GET_CONST(std::vector<int64_t>, op_desc.GetAttr("dim"));
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for (size_t i = 0; i < tem_dim.size(); i++) {
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dim.push_back(static_cast<int32_t>(tem_dim[i]));
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}
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}
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bool reduce_all = PADDLE_GET_CONST(bool, op_desc.GetAttr("reduce_all"));
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if (reduce_all) {
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uint32_t reduce_dim = 0;
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for (int i = 0; i < input_dims; ++i) {
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reduce_dim |= 1 << i;
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}
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reduce_layer = TRT_ENGINE_ADD_LAYER(engine_,
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Reduce,
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*cast_layer->getOutput(0),
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reduce_type->second.front(),
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reduce_dim,
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keep_dim);
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} else {
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auto CvtToBitMask = [&](const std::vector<int32_t>& dims) -> uint32_t {
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uint32_t res = 0;
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for (auto x : dims) {
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if (x < 0) {
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res |= 1 << (x + input_dims);
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} else {
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res |= 1 << x;
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}
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}
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return res;
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};
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reduce_layer = TRT_ENGINE_ADD_LAYER(engine_,
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Reduce,
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*cast_layer->getOutput(0),
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reduce_type->second.front(),
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CvtToBitMask(dim),
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keep_dim);
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}
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auto output_name = op_desc.Output("Out")[0];
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auto* layer =
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TRT_ENGINE_ADD_LAYER(engine_, Identity, *reduce_layer->getOutput(0));
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layer->setOutputType(0, nvinfer1::DataType::kBOOL);
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layer->getOutput(0)->setType(nvinfer1::DataType::kBOOL);
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// Ensure that the output type and input type are consistent.
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layer->getOutput(0)->setType(cast_layer->getInput(0)->getType());
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ReplenishLayerAndOutput(layer, op_type, {output_name}, test_mode);
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};
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};
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class ReduceAllOpConverter : public ReduceAnyOpConverter {
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public:
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ReduceAllOpConverter() { op_type = "reduce_all"; }
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};
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} // namespace paddle::inference::tensorrt
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REGISTER_TRT_OP_CONVERTER(reduce_sum, ReduceSumOpConverter);
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REGISTER_TRT_OP_CONVERTER(reduce_mean, ReduceMeanOpConverter);
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REGISTER_TRT_OP_CONVERTER(reduce_max, ReduceMaxOpConverter);
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REGISTER_TRT_OP_CONVERTER(reduce_min, ReduceMinOpConverter);
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REGISTER_TRT_OP_CONVERTER(reduce_prod, ReduceProdOpConverter);
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REGISTER_TRT_OP_CONVERTER(reduce_any, ReduceAnyOpConverter);
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REGISTER_TRT_OP_CONVERTER(reduce_all, ReduceAllOpConverter);
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