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123 lines
4.6 KiB
123 lines
4.6 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/fc.h"
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#include <algorithm>
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#include <string>
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#include <vector>
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#include "paddle/fluid/inference/anakin/convert/helper.h"
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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 FcBaseOpConverter<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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auto input_names = op_desc.InputNames();
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bool with_bias = input_names.size() >= 3;
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std::string w_name = "Y";
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std::string i_name = "X";
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if (with_bias) {
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w_name = "W";
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i_name = "Input";
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}
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auto op_name = op_desc.Type() + ":" + op_desc.Output("Out").front();
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// get weights
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auto *y_v = scope.FindVar(op_desc.Input(w_name).front());
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PADDLE_ENFORCE_NOT_NULL(y_v);
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auto weight_tensor = tensor_from_var(*y_v, platform::CPUPlace());
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auto weight_shape = framework::vectorize<int>(weight_tensor->dims());
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int out_dim = weight_shape[1];
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const int w_m = weight_shape[0];
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const int w_k = weight_shape[1];
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auto input_name = op_desc.Input(i_name).front();
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auto output_name = op_desc.Output("Out").front();
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this->engine_->AddOp(op_name, "Dense", {input_name}, {output_name});
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this->engine_->AddOpAttr(op_name, "bias_term", with_bias);
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this->engine_->AddOpAttr(op_name, "axis", 1);
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this->engine_->AddOpAttr(op_name, "out_dim", out_dim);
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auto *weight_data = weight_tensor->data<float>();
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PADDLE_ENFORCE(w_m * w_k == weight_tensor->numel());
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std::vector<float> trans_weight_data(weight_tensor->numel());
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for (int i = 0; i < w_m; i++) {
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for (int j = 0; j < w_k; j++) {
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trans_weight_data[i + j * w_m] = weight_data[i * w_k + j];
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}
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}
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int weight_num = weight_tensor->numel();
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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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if (weight_shape.size() < 4UL) {
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weight_shape.insert(weight_shape.begin(), 4UL - weight_shape.size(), 1);
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}
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::anakin::saber::Shape anakin_shape(weight_shape);
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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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auto weight_scale =
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boost::get<std::vector<float>>(op_desc.GetAttr("weight_scale"));
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PBlock<TargetT> *weight1 =
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new PBlock<TargetT>(anakin_shape, ::anakin::AK_INT8);
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this->engine_->RegistBlock(weight1);
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std::vector<char> weight_int8;
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for (int i = 0; i < weight_num; i++) {
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bool is_valid_int8 =
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((trans_weight_data[i] >= -128) && (trans_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 fc "
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"should be in range [-128, 127]");
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weight_int8.push_back(static_cast<char>(trans_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(
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op_name, {weight_scale[0] / 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 *weight1 = pblock_from_vector<TargetT, PrecisionT>(trans_weight_data,
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this->engine_);
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this->engine_->AddOpAttr(op_name, "weight_1", *weight1);
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}
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// get bias
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if (with_bias) {
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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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auto weight2 = pblock_from_var<TargetT, PrecisionT>(*b_v, this->engine_);
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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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REGISTER_ANAKIN_OP_CONVERTER(mul, MulOpConverter);
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REGISTER_ANAKIN_OP_CONVERTER(fc, FcOpConverter);
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