245 lines
8.5 KiB
245 lines
8.5 KiB
/* Copyright (c) 2017 PaddlePaddle Authors. All Rights Reserve.
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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/math/matrix_bit_code.h"
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#include <iostream>
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#include <map>
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namespace paddle {
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namespace operators {
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namespace math {
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template <typename T>
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void MatrixBitCodeFunctor<T>::Add(const framework::Tensor& vec,
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framework::Tensor* tmat) {
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size_t batch_size = tmat->dims()[0];
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size_t width = tmat->dims()[1];
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auto* tmat_data = tmat->data<T>();
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auto* vec_data = vec.data<T>();
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for (size_t i = 0; i < batch_size; ++i) {
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auto code = code_table_->get_code(i);
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int code_length = code->get_length();
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for (int j = 0; j < code_length; ++j) {
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size_t index = code->calc_index(j);
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tmat_data[i * width + j] += vec_data[index];
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}
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}
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}
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template <typename T>
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void MatrixBitCodeFunctor<T>::AddGrad(const framework::Tensor& tmat,
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framework::Tensor* vec) {
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size_t batch_size = tmat.dims()[0];
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size_t width = tmat.dims()[1];
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auto* vec_data = vec->data<T>();
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auto* tmat_data = tmat.data<T>();
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for (size_t i = 0; i < batch_size; ++i) {
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auto code = code_table_->get_code(i);
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int code_length = code->get_length();
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for (int j = 0; j < code_length; ++j) {
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size_t index = code->calc_index(j);
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vec_data[index] += tmat_data[i * width + j];
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}
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}
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}
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template <typename T>
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void MatrixBitCodeFunctor<T>::AddGrad(const framework::Tensor& tmat,
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framework::SelectedRows* vec) {
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size_t batch_size = tmat.dims()[0];
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size_t width = tmat.dims()[1];
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auto* vec_data = vec->mutable_value()->data<T>();
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auto* tmat_data = tmat.data<T>();
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for (size_t i = 0; i < batch_size; ++i) {
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auto code = code_table_->get_code(i);
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int code_length = code->get_length();
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for (int j = 0; j < code_length; ++j) {
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size_t index = code->calc_index(j);
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int64_t row_index = vec->GetIndexFromId(static_cast<int64_t>(index));
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vec_data[row_index] += tmat_data[i * width + j];
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}
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}
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}
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template <typename T>
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void MatrixBitCodeFunctor<T>::Sum(const framework::Tensor& tmat,
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framework::Tensor* sum, T scale_sum) {
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size_t num_samples = tmat.dims()[0];
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size_t o_width = tmat.dims()[1];
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auto* tmat_data = tmat.data<T>();
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auto* sum_data = sum->data<T>();
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for (size_t i = 0; i < num_samples; ++i) {
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T sm = static_cast<T>(0.0);
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auto code = code_table_->get_code(i);
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int code_length = code->get_length();
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for (int j = 0; j < code_length; ++j) {
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if (code->calc_bit(j)) {
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// calc_bit starts from right most bit, while data in tmat[i] is in the
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// reverse order.
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sm += tmat_data[i * o_width + j];
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}
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}
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sum_data[i] = scale_sum * sm;
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}
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}
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template <typename T>
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void MatrixBitCodeFunctor<T>::Mul(framework::Tensor* tmat,
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const framework::Tensor& weight,
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const framework::Tensor& input) {
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auto blas =
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GetBlas<platform::CPUDeviceContext, T>(platform::CPUDeviceContext());
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size_t num_samples = tmat->dims()[0];
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size_t tmat_width = tmat->dims()[1];
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size_t input_width = input.dims()[1];
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size_t weight_width = weight.dims()[1];
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auto tmat_value = tmat->data<T>();
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auto weight_value = weight.data<T>();
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auto input_value = input.data<T>();
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for (size_t i = 0; i < num_samples; ++i) {
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auto code = code_table_->get_code(i);
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int code_length = code->get_length();
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const T* input_row = input_value + input_width * i;
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for (int j = 0; j < code_length; ++j) {
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size_t index = code->calc_index(j);
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const T* weight_row = weight_value + weight_width * index;
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T sum = static_cast<T>(0.0);
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sum = blas.DOT(input_width, weight_row, input_row);
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tmat_value[i * tmat_width + j] += sum;
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}
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}
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}
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template <typename T>
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void MatrixBitCodeFunctor<T>::MulGradWeight(const framework::Tensor& tmat,
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framework::Tensor* weight,
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const framework::Tensor& input) {
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auto blas =
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GetBlas<platform::CPUDeviceContext, T>(platform::CPUDeviceContext());
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size_t num_samples = tmat.dims()[0];
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size_t input_width = input.dims()[1];
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size_t tmat_width = tmat.dims()[1];
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size_t weight_width = weight->dims()[1];
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auto tmat_value = tmat.data<T>();
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auto weight_value = weight->data<T>();
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auto input_value = input.data<T>();
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std::map<int, std::vector<std::pair<T, const T*>>> ops;
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for (size_t i = 0; i < num_samples; ++i) {
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auto code = code_table_->get_code(i);
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int code_length = code->get_length();
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const T* input_value_row = input_value + input_width * i;
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const T* tmat_row = tmat_value + i * tmat_width;
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for (int j = 0; j < code_length; ++j) {
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ops[code->calc_index(j)].emplace_back(tmat_row[j], input_value_row);
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}
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}
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for (auto& op : ops) {
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auto& op_in_row = op.second;
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for (auto& pair : op_in_row) {
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auto& scale = pair.first;
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auto* input_row = pair.second;
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T* weight_row = weight_value + op.first * weight_width;
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blas.AXPY(input_width, scale, input_row, weight_row);
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}
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}
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}
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template <typename T>
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void MatrixBitCodeFunctor<T>::MulGradWeight(const framework::Tensor& tmat,
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framework::SelectedRows* weight,
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const framework::Tensor& input) {
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auto blas =
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GetBlas<platform::CPUDeviceContext, T>(platform::CPUDeviceContext());
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size_t num_samples = tmat.dims()[0];
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size_t input_width = input.dims()[1];
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size_t tmat_width = tmat.dims()[1];
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size_t weight_width = weight->value().dims()[1];
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auto tmat_value = tmat.data<T>();
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auto weight_value = weight->mutable_value()->data<T>();
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auto input_value = input.data<T>();
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std::unordered_map<int, std::vector<std::pair<T, const T*>>> ops;
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ops.reserve(weight->rows().size());
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for (size_t i = 0; i < num_samples; ++i) {
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auto code = code_table_->get_code(i);
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int code_length = code->get_length();
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const T* input_value_row = input_value + input_width * i;
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const T* tmat_row = tmat_value + i * tmat_width;
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for (int j = 0; j < code_length; ++j) {
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ops[code->calc_index(j)].emplace_back(tmat_row[j], input_value_row);
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}
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}
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for (auto& row : weight->rows()) {
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auto& op_in_row = ops[row];
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for (auto& pair : op_in_row) {
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auto& scale = pair.first;
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auto* input_row = pair.second;
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blas.AXPY(input_width, scale, input_row, weight_value);
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}
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weight_value += weight_width;
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}
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}
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template <typename T>
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void MatrixBitCodeFunctor<T>::MulGradError(const framework::Tensor& tmat,
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const framework::Tensor& weight,
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framework::Tensor* input) {
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size_t num_samples = tmat.dims()[0];
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size_t tmat_width = tmat.dims()[1];
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size_t input_width = input->dims()[1];
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size_t weight_width = weight.dims()[1];
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auto tmat_value = tmat.data<T>();
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auto weight_value = weight.data<T>();
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auto input_value = input->data<T>();
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for (size_t i = 0; i < num_samples; ++i) {
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auto code = code_table_->get_code(i);
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int code_length = code->get_length();
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for (int j = 0; j < code_length; ++j) {
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size_t index = code->calc_index(j);
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for (size_t k = 0; k < input_width; ++k) {
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input_value[input_width * i + k] +=
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tmat_value[i * tmat_width + j] *
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weight_value[weight_width * index + k];
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}
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}
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}
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}
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template <typename T>
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void MatrixBitCodeFunctor<T>::Sub(framework::Tensor* tmat) {
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size_t num_samples = tmat->dims()[0];
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size_t o_width = tmat->dims()[1];
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auto* tmat_data = tmat->data<T>();
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for (size_t i = 0; i < num_samples; ++i) {
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auto code = code_table_->get_code(i);
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int code_length = code->get_length();
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for (int j = 0; j < code_length; ++j) {
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if (code->calc_bit(j)) {
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tmat_data[i * o_width + j] -= 1;
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}
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}
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}
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}
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template class MatrixBitCodeFunctor<float>;
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template class MatrixBitCodeFunctor<double>;
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} // namespace math
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} // namespace operators
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} // namespace paddle
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