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131 lines
6.0 KiB
131 lines
6.0 KiB
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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//
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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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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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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/anakin/convert/conv2d_fusion.h"
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#include <algorithm>
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#include <memory>
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#include <vector>
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#include "paddle/fluid/inference/anakin/convert/helper.h"
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using anakin::PTuple;
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namespace paddle {
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namespace inference {
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namespace anakin {
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template <typename TargetT, ::anakin::Precision PrecisionT>
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void Conv2dFusionOpConverter<TargetT, PrecisionT>::operator()(
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const framework::proto::OpDesc &op, const framework::BlockDesc &block_desc,
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const framework::Scope &scope, bool test_mode) {
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framework::OpDesc op_desc(op, nullptr);
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PADDLE_ENFORCE_EQ(op_desc.Input("Input").size(), 1UL);
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PADDLE_ENFORCE_EQ(op_desc.Input("Filter").size(), 1UL);
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PADDLE_ENFORCE_EQ(op_desc.Input("Bias").size(), 1UL);
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PADDLE_ENFORCE_EQ(op_desc.Output("Output").size(), 1UL);
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auto input_name = op_desc.Input("Input").front();
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auto output_name = op_desc.Output("Output").front();
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auto op_name = op_desc.Type() + ":" + op_desc.Output("Output").front();
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this->engine_->AddOp(op_name, "Convolution", {input_name}, {output_name});
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auto *filter_v = scope.FindVar(op_desc.Input("Filter").front());
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PADDLE_ENFORCE_NOT_NULL(filter_v);
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auto weight_tensor = tensor_from_var(*filter_v, platform::CPUPlace());
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auto weight_shape = framework::vectorize2int(weight_tensor->dims());
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auto *b_v = scope.FindVar(op_desc.Input("Bias").front());
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PADDLE_ENFORCE_NOT_NULL(b_v);
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PADDLE_ENFORCE_EQ(weight_tensor->dims().size(), 4UL);
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const int filter_h = weight_tensor->dims()[2];
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const int filter_w = weight_tensor->dims()[3];
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auto filter_num = weight_tensor->dims()[0];
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this->engine_->template AddOpAttr<int>(op_name, "filter_num", filter_num);
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this->engine_->template AddOpAttr<PTuple<int>>(op_name, "kernel_size",
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{filter_h, filter_w});
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auto strides = boost::get<std::vector<int>>(op_desc.GetAttr("strides"));
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this->engine_->template AddOpAttr<PTuple<int>>(op_name, "strides", strides);
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auto paddings = boost::get<std::vector<int>>(op_desc.GetAttr("paddings"));
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this->engine_->template AddOpAttr<PTuple<int>>(op_name, "padding", paddings);
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auto dilations = boost::get<std::vector<int>>(op_desc.GetAttr("dilations"));
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this->engine_->template AddOpAttr<PTuple<int>>(op_name, "dilation_rate",
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dilations);
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const int groups = boost::get<int>(op_desc.GetAttr("groups"));
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this->engine_->AddOpAttr(op_name, "group", groups);
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this->engine_->AddOpAttr(op_name, "axis", 1);
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this->engine_->AddOpAttr(op_name, "bias_term", true);
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::anakin::saber::Shape anakin_shape(weight_shape);
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bool enable_int8 = boost::get<bool>(op_desc.HasAttr("enable_int8"));
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if (enable_int8) {
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const float int8_range = 127.;
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float in_scale = boost::get<float>(op_desc.GetAttr("input_scale"));
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float weight_scale = boost::get<float>(op_desc.GetAttr("weight_scale"));
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auto *weight1 = ::anakin::graph::GraphGlobalMem<TargetT>::Global()
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.template new_block<::anakin::AK_INT8>(anakin_shape);
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float *weight_data = weight_tensor->data<float>();
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std::vector<char> weight_int8;
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int weight_num = weight_tensor->numel();
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for (int i = 0; i < weight_tensor->numel(); i++) {
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bool is_valid_int8 =
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((weight_data[i] >= -128) && (weight_data[i] <= 127));
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PADDLE_ENFORCE(is_valid_int8,
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"We are in anakin subgraph int8 mode, the weight of conv "
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"should be in range [-128, 127]");
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weight_int8.push_back(static_cast<char>(weight_data[i]));
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}
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memcpy(static_cast<void *>(weight1->h_tensor().mutable_data()),
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static_cast<void *>(weight_int8.data()), sizeof(char) * weight_num);
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weight1->d_tensor().set_shape(anakin_shape);
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weight1->d_tensor().copy_from(weight1->h_tensor());
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this->engine_->AddOpAttr(op_name, "weight_1", *weight1);
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this->engine_->Graph()->SetOpPrec(op_name, ::anakin::AK_INT8);
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this->engine_->Graph()->SetWeightsScale(op_name,
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{weight_scale / int8_range}, false);
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this->engine_->AddTensorScale(input_name, in_scale / int8_range);
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} else {
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auto weight_tensor = tensor_from_var(*filter_v, platform::CPUPlace());
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auto weight_shape = framework::vectorize2int(weight_tensor->dims());
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auto *weight1 = pblock_from_tensor<TargetT>(*weight_tensor, weight_shape);
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this->engine_->AddOpAttr(op_name, "weight_1", *weight1);
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auto weight2 = pblock_from_var<TargetT>(*b_v);
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this->engine_->AddOpAttr(op_name, "weight_2", *weight2);
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}
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}
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} // namespace anakin
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} // namespace inference
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} // namespace paddle
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#ifdef PADDLE_WITH_CUDA
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using conv2d_fusion_nv_fp32 =
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::paddle::inference::anakin::Conv2dFusionOpConverter<
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::anakin::saber::NV, ::anakin::Precision::FP32>;
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using conv2d_fusion_nv_int8 =
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::paddle::inference::anakin::Conv2dFusionOpConverter<
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::anakin::saber::NV, ::anakin::Precision::INT8>;
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REGISTER_CUDA_ANAKIN_OP_CONVERTER(conv2d_fusion, conv2d_fusion_nv_fp32);
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REGISTER_CUDA_INT8_ANAKIN_OP_CONVERTER(conv2d_fusion, conv2d_fusion_nv_int8);
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#endif
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using conv2d_fusion_cpu_fp32 =
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::paddle::inference::anakin::Conv2dFusionOpConverter<
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::anakin::saber::X86, ::anakin::Precision::FP32>;
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using conv2d_fusion_cpu_int8 =
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::paddle::inference::anakin::Conv2dFusionOpConverter<
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::anakin::saber::X86, ::anakin::Precision::INT8>;
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REGISTER_CPU_ANAKIN_OP_CONVERTER(conv2d_fusion, conv2d_fusion_cpu_fp32);
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REGISTER_CPU_INT8_ANAKIN_OP_CONVERTER(conv2d_fusion, conv2d_fusion_cpu_int8);
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