fuzhiye
4 years ago
parent
c78683a411
commit
ca87533cd7
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/**
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* Copyright 2020 Huawei Technologies Co., Ltd
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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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*/
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#include "nnacl/fp32/adder_fp32.h"
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#include <string.h>
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#include <math.h>
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#include "nnacl/fp32/common_func_fp32.h"
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#include "nnacl/fp32/matmul_fp32.h"
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void Adder12x4(const float *a, const float *b, float *dst, const float *bias, ActType act_type, int deep, int row,
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int col, int stride) {
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for (int r = 0; r < row; r++) {
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for (int c = 0; c < col; c++) {
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int r12div = r / 12, r12mod = r % 12;
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int c4div = c / 4, c4mod = c % 4;
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size_t ci = r * stride + c;
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float value = 0;
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for (int d = 0; d < deep; d++) {
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size_t ai = r12div * deep * 12 + d * 12 + r12mod;
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size_t bi = c4div * deep * 4 + d * 4 + c4mod;
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value += fabsf(a[ai] - b[bi]);
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}
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value = -value;
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if (bias != NULL) value += bias[c];
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if (act_type == ActType_Relu6) value = MSMIN(6.0f, value);
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if (act_type != ActType_No) value = MSMAX(0.0f, value);
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dst[ci] = value;
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}
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}
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}
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void AdderOpt(const float *a, const float *b, float *c, const float *bias, ActType act_type, int deep, int row, int col,
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size_t stride) {
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#ifdef ENABLE_ARM64
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AdderFloatNeon64(a, b, c, bias, (int)act_type, deep, row, col, stride);
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#else
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Adder12x4(a, b, c, bias, act_type, deep, row, col, stride);
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#endif
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}
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void AdderFp32(const float *input_data, float *packed_input, const float *packed_weight, const float *bias_data,
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float *col_major_input, float *output_data, int task_id, ConvParameter *conv_param) {
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int out_channel = conv_param->output_channel_;
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int deep = conv_param->kernel_h_ * conv_param->kernel_w_ * conv_param->input_channel_;
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int output_count = conv_param->output_h_ * conv_param->output_w_;
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#if defined(ENABLE_ARM32) || defined(ENABLE_X86_64_SSE)
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const int cal_num = C4NUM;
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#else
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const int cal_num = C12NUM;
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#endif
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int output_tile_count = UP_DIV(output_count, cal_num);
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for (int b = 0; b < conv_param->input_batch_; b++) {
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int in_batch_offset = b * conv_param->input_channel_ * conv_param->input_h_ * conv_param->input_w_;
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int out_batch_offset = b * out_channel * output_count;
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for (int thread_id = task_id; thread_id < output_tile_count; thread_id += conv_param->thread_num_) {
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int start_index = thread_id * cal_num;
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int real_cal_num = (output_count - start_index) < cal_num ? (output_count - start_index) : cal_num;
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float *gemm_input = packed_input + task_id * deep * cal_num;
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float *col_major_gemm_input = col_major_input + task_id * deep * cal_num;
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size_t packed_input_size = deep * cal_num * sizeof(float);
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memset(gemm_input, 0, packed_input_size);
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memset(col_major_gemm_input, 0, packed_input_size);
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Im2ColPackUnitFp32(input_data + in_batch_offset, conv_param, gemm_input, real_cal_num, start_index);
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int out_offset = thread_id * cal_num * out_channel + out_batch_offset;
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float *gemm_output = output_data + out_offset;
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#if defined(ENABLE_ARM32) || defined(ENABLE_X86_64_SSE)
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RowMajor2Col4Major(gemm_input, col_major_gemm_input, cal_num, deep);
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#else
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RowMajor2Col12Major(gemm_input, col_major_gemm_input, cal_num, deep);
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#endif
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AdderOpt(col_major_gemm_input, packed_weight, gemm_output, bias_data, conv_param->act_type_, deep, real_cal_num,
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out_channel, out_channel);
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}
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}
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}
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/**
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* Copyright 2020 Huawei Technologies Co., Ltd
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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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*/
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#ifndef MINDSPORE_LITE_NNACL_FP32_ADDER_H_
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#define MINDSPORE_LITE_NNACL_FP32_ADDER_H_
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#ifdef ENABLE_NEON
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#include <arm_neon.h>
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#endif
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#include "nnacl/pack.h"
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#include "nnacl/op_base.h"
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#include "nnacl/common_func.h"
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#include "nnacl/conv_parameter.h"
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#ifdef __cplusplus
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extern "C" {
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#endif
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#ifdef ENABLE_ARM64
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void AdderFloatNeon64(const float *a, const float *b, float *c, const float *bias, int act_type, int depth, int row,
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int col, size_t stride);
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#endif
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void AdderOpt(const float *a, const float *b, float *c, const float *bias, ActType act_type, int deep, int row, int col,
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size_t stride);
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void AdderFp32(const float *input_data, float *packed_input, const float *packed_weight, const float *bias_data,
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float *col_major_input, float *output_data, int task_id, ConvParameter *conv_param);
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#ifdef __cplusplus
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}
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#endif
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#endif // MINDSPORE_LITE_NNACL_FP32_ADDER_H_
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/**
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* Copyright 2020 Huawei Technologies Co., Ltd
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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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*/
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#include "src/runtime/kernel/arm/fp32/adder_fp32.h"
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#include "src/kernel_registry.h"
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#include "src/runtime/runtime_api.h"
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#include "include/errorcode.h"
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#include "schema/model_generated.h"
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#include "nnacl/fp32/adder_fp32.h"
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#include "nnacl/fp32/matmul_fp32.h"
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using mindspore::kernel::KERNEL_ARCH::kCPU;
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using mindspore::lite::KernelRegistrar;
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using mindspore::lite::RET_ERROR;
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using mindspore::lite::RET_INFER_INVALID;
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using mindspore::lite::RET_OK;
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using mindspore::schema::PrimitiveType_Adder;
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using mindspore::schema::Format::Format_NHWC;
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namespace mindspore::kernel {
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int AdderCPUKernel::InitWeightBias() {
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auto filter_tensor = in_tensors_.at(kWeightIndex);
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int kernel_h = filter_tensor->Height();
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int kernel_w = filter_tensor->Width();
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int in_channel = filter_tensor->Channel();
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int out_channel = filter_tensor->Batch();
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conv_param_->input_channel_ = in_channel;
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conv_param_->output_channel_ = out_channel;
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int kernel_plane = kernel_h * kernel_w;
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const int oc_block = C4NUM;
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int oc_block_num = UP_DIV(out_channel, C4NUM);
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int pack_weight_size = oc_block_num * oc_block * in_channel * kernel_plane;
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auto origin_weight = reinterpret_cast<float *>(filter_tensor->MutableData());
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packed_weight_ = reinterpret_cast<float *>(malloc(pack_weight_size * sizeof(float)));
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if (packed_weight_ == nullptr) {
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MS_LOG(ERROR) << "malloc packed weight failed.";
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return RET_ERROR;
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}
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memset(packed_weight_, 0, pack_weight_size * sizeof(float));
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RowMajor2Col4Major(origin_weight, packed_weight_, out_channel, in_channel * kernel_plane);
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bias_data_ = reinterpret_cast<float *>(malloc(oc_block_num * oc_block * sizeof(float)));
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if (bias_data_ == nullptr) {
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MS_LOG(ERROR) << "malloc bias failed.";
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return RET_ERROR;
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}
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memset(bias_data_, 0, oc_block_num * oc_block * sizeof(float));
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if (in_tensors_.size() == kInputSize2) {
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auto ori_bias = reinterpret_cast<float *>(in_tensors_.at(kBiasIndex)->MutableData());
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memcpy(bias_data_, ori_bias, out_channel * sizeof(float));
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} else {
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MS_ASSERT(in_tensors_.size() == kInputSize1);
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}
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return RET_OK;
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}
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int AdderCPUKernel::RunImpl(int task_id) {
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auto input_tensor = in_tensors_.at(kInputIndex);
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auto ori_input_data = reinterpret_cast<float *>(input_tensor->data_c());
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auto output_addr = reinterpret_cast<float *>(out_tensors_.at(kOutputIndex)->data_c());
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AdderFp32(ori_input_data, packed_input_, packed_weight_, reinterpret_cast<float *>(bias_data_), col_major_input_,
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output_addr, task_id, conv_param_);
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return RET_OK;
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}
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int AdderImpl(void *cdata, int task_id) {
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auto adder = reinterpret_cast<AdderCPUKernel *>(cdata);
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auto error_code = adder->RunImpl(task_id);
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if (error_code != RET_OK) {
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MS_LOG(ERROR) << "Adder Run error task_id[" << task_id << "] error_code[" << error_code << "]";
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return RET_ERROR;
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}
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return RET_OK;
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}
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int AdderCPUKernel::Run() {
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auto ret = InitTmpBuffer();
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if (ret != RET_OK) {
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MS_LOG(ERROR) << "Init tmp buffer failed.";
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return RET_ERROR;
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}
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int error_code = ParallelLaunch(this->context_->thread_pool_, AdderImpl, this, thread_count_);
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if (error_code != RET_OK) {
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MS_LOG(ERROR) << "adder error error_code[" << error_code << "]";
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FreeTmpBuffer();
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return RET_ERROR;
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}
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FreeTmpBuffer();
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return RET_OK;
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}
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kernel::LiteKernel *CpuAdderFp32KernelCreator(const std::vector<lite::Tensor *> &inputs,
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const std::vector<lite::Tensor *> &outputs, OpParameter *op_parameter,
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const InnerContext *ctx, const kernel::KernelKey &desc,
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const mindspore::lite::PrimitiveC *primitive) {
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MS_ASSERT(op_parameter != nullptr);
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MS_ASSERT(desc.type == schema::PrimitiveType_Adder);
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MS_ASSERT(desc.data_type == kNumberTypeFloat32);
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kernel::LiteKernel *kernel = new (std::nothrow) kernel::AdderCPUKernel(op_parameter, inputs, outputs, ctx, primitive);
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if (kernel == nullptr) {
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MS_LOG(ERROR) << "kernel is nullptr.";
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free(op_parameter);
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return nullptr;
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}
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auto ret = kernel->Init();
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if (ret != RET_OK && ret != RET_INFER_INVALID) {
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delete kernel;
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MS_LOG(ERROR) << "Init kernel failed, name: " << op_parameter->name_ << ", type: "
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<< schema::EnumNamePrimitiveType(static_cast<schema::PrimitiveType>(op_parameter->type_));
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return nullptr;
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}
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return kernel;
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}
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REG_KERNEL(kCPU, kNumberTypeFloat32, PrimitiveType_Adder, CpuAdderFp32KernelCreator)
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} // namespace mindspore::kernel
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/**
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* Copyright 2020 Huawei Technologies Co., Ltd
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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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*/
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#ifndef MINDSPORE_LITE_SRC_RUNTIME_KERNEL_ARM_FP32_ADDER_H_
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#define MINDSPORE_LITE_SRC_RUNTIME_KERNEL_ARM_FP32_ADDER_H_
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#include <vector>
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#include "src/lite_kernel.h"
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#include "nnacl/op_base.h"
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#include "src/runtime/kernel/arm/fp32/convolution_fp32.h"
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#include "nnacl/fp32/conv_fp32.h"
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namespace mindspore::kernel {
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class AdderCPUKernel : public ConvolutionCPUKernel {
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public:
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AdderCPUKernel(OpParameter *parameter, const std::vector<lite::Tensor *> &inputs,
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const std::vector<lite::Tensor *> &outputs, const lite::InnerContext *ctx,
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const mindspore::lite::PrimitiveC *primitive)
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: ConvolutionCPUKernel(parameter, inputs, outputs, ctx, primitive) {}
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~AdderCPUKernel() override = default;
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int InitWeightBias() override;
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int Run() override;
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int RunImpl(int task_id) override;
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};
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} // namespace mindspore::kernel
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#endif // MINDSPORE_LITE_SRC_RUNTIME_KERNEL_ARM_FP32_ADDER_H_
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