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Add Fp16 matmul & fc ops

tags/v1.0.0
zhanyuan 5 years ago
parent
01da7d2c1a
commit
9646c8503d
6 changed files with 623 additions and 0 deletions
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  1. +60
    -0
      mindspore/lite/nnacl/fp16/matmul_fp16.c
  2. +12
    -0
      mindspore/lite/nnacl/fp16/matmul_fp16.h
  3. +187
    -0
      mindspore/lite/src/runtime/kernel/arm/fp16/fullconnection_fp16.cc
  4. +56
    -0
      mindspore/lite/src/runtime/kernel/arm/fp16/fullconnection_fp16.h
  5. +250
    -0
      mindspore/lite/src/runtime/kernel/arm/fp16/matmul_fp16.cc
  6. +58
    -0
      mindspore/lite/src/runtime/kernel/arm/fp16/matmul_fp16.h

+ 60
- 0
mindspore/lite/nnacl/fp16/matmul_fp16.c View File

@@ -238,3 +238,63 @@ void RowMajor2Col16MajorFp16(float16_t *src_ptr, float16_t *dst_ptr, size_t row,
} }
return; return;
} }

void Fp32RowMajor2Fp16Col16Major(float *src, float16_t *dst, size_t row, size_t col) {
for (int r = 0; r < row; r++) {
for (int c = 0; c < col; c++) {
int r_div16 = r / 16;
int r_mod16 = r % 16;
dst[r_div16 * 16 * col + c * 16 + r_mod16] = (float16_t)(src[r * col + c]);
}
}
}

void Fp16RowMajor2Fp16Col16Major(float16_t *src, float16_t *dst, size_t row, size_t col) {
for (int r = 0; r < row; r++) {
for (int c = 0; c < col; c++) {
int r_div16 = r / 16;
int r_mod16 = r % 16;
dst[r_div16 * 16 * col + c * 16 + r_mod16] = src[r * col + c];
}
}
}

void Fp32RowMajor2Fp16Row16Major(float *src, float16_t *dst, size_t row, size_t col) {
for (int r = 0; r < row; r++) {
for (int c = 0; c < col; c++) {
int c_div16 = c / 16;
int c_mod16 = c % 16;
dst[c_div16 * 16 * row + r * 16 + c_mod16] = (float16_t)(src[r * col + c]);
}
}
}

void Fp16RowMajor2Fp16Row16Major(float16_t *src, float16_t *dst, size_t row, size_t col) {
for (int r = 0; r < row; r++) {
for (int c = 0; c < col; c++) {
int c_div16 = c / 16;
int c_mod16 = c % 16;
dst[c_div16 * 16 * row + r * 16 + c_mod16] = src[r * col + c];
}
}
}

void Fp32RowMajor2Fp16Row8Major(float *src, float16_t *dst, size_t row, size_t col) {
for (int r = 0; r < row; r++) {
for (int c = 0; c < col; c++) {
int c_div8 = c / 8;
int c_mod8 = c % 8;
dst[c_div8 * 8 * row + r * 8 + c_mod8] = (float16_t)src[r * col + c];
}
}
}

void Fp32RowMajor2Fp16Col8Major(float *src, float16_t *dst, size_t row, size_t col) {
for (int r = 0; r < row; r++) {
for (int c = 0; c < col; c++) {
int r_div8 = r / 8;
int r_mod8 = r % 8;
dst[r_div8 * 8 * col + c * 8 + r_mod8] = (float16_t)src[r * col + c];
}
}
}

+ 12
- 0
mindspore/lite/nnacl/fp16/matmul_fp16.h View File

@@ -39,6 +39,18 @@ void RowMajor2Col16MajorFp16(float16_t *src_ptr, float16_t *dst_ptr, size_t row,
void MatmulFp16Neon64(const float16_t *a, const float16_t *b, float16_t *c, const float16_t *bias, int act_type, void MatmulFp16Neon64(const float16_t *a, const float16_t *b, float16_t *c, const float16_t *bias, int act_type,
size_t depth, size_t row, size_t col, size_t stride, bool write_nhwc); size_t depth, size_t row, size_t col, size_t stride, bool write_nhwc);


void Fp32RowMajor2Fp16Col16Major(float *src, float16_t *dst, size_t row, size_t col);

void Fp16RowMajor2Fp16Col16Major(float16_t *src, float16_t *dst, size_t row, size_t col);

void Fp32RowMajor2Fp16Row16Major(float *src, float16_t *dst, size_t row, size_t col);

void Fp16RowMajor2Fp16Row16Major(float16_t *src, float16_t *dst, size_t row, size_t col);

void Fp32RowMajor2Fp16Row8Major(float *src, float16_t *dst, size_t row, size_t col);

void Fp32RowMajor2Fp16Col8Major(float *src, float16_t *dst, size_t row, size_t col);

#ifdef __cplusplus #ifdef __cplusplus
} }
#endif #endif


+ 187
- 0
mindspore/lite/src/runtime/kernel/arm/fp16/fullconnection_fp16.cc View File

@@ -0,0 +1,187 @@
/**
* Copyright 2020 Huawei Technologies Co., Ltd
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/

#include "src/runtime/kernel/arm/fp16/fullconnection_fp16.h"
#include "nnacl/fp16/matmul_fp16.h"
#include "nnacl/fp16/cast_fp16.h"
#include "src/runtime/runtime_api.h"
#include "include/errorcode.h"
#include "src/kernel_registry.h"

using mindspore::lite::KernelRegistrar;
using mindspore::lite::RET_ERROR;
using mindspore::lite::RET_INPUT_TENSOR_ERROR;
using mindspore::lite::RET_MEMORY_FAILED;
using mindspore::lite::RET_OK;
using mindspore::schema::PrimitiveType_FullConnection;

namespace mindspore::kernel {
FullconnectionFP16CPUKernel::~FullconnectionFP16CPUKernel() { FreeTmpBuffer(); }

void FullconnectionFP16CPUKernel::FreeTmpBuffer() {
if (a_pack_ptr_ != nullptr) {
ctx_->allocator->Free(a_pack_ptr_);
a_pack_ptr_ = nullptr;
}
if (b_pack_ptr_ != nullptr) {
ctx_->allocator->Free(b_pack_ptr_);
b_pack_ptr_ = nullptr;
}
if (bias_ptr_ != nullptr) {
ctx_->allocator->Free(bias_ptr_);
bias_ptr_ = nullptr;
}
if (output_fp16_ != nullptr) {
ctx_->allocator->Free(output_fp16_);
output_fp16_ = nullptr;
}
}

int FullconnectionFP16CPUKernel::ReSize() {
FreeTmpBuffer();
fc_param_->row_ = (in_tensors_[0]->shape())[0];
fc_param_->col_ = (in_tensors_[1]->shape())[0];
fc_param_->deep_ = (in_tensors_[1]->shape())[1];
fc_param_->row_16_ = UP_ROUND(fc_param_->row_, C16NUM);
fc_param_->col_8_ = UP_ROUND(fc_param_->col_, C8NUM);
thread_count_ = MSMIN(thread_count_, UP_DIV(fc_param_->col_, C8NUM));
thread_stride_ = UP_DIV(UP_DIV(fc_param_->col_, C8NUM), thread_count_) * C8NUM;

a_pack_ptr_ =
reinterpret_cast<float16_t *>(ctx_->allocator->Malloc(fc_param_->row_16_ * fc_param_->deep_ * sizeof(float16_t)));
if (a_pack_ptr_ == nullptr) {
FreeTmpBuffer();
return RET_MEMORY_FAILED;
}
memset(a_pack_ptr_, 0, fc_param_->row_16_ * fc_param_->deep_ * sizeof(float16_t));

b_pack_ptr_ =
reinterpret_cast<float16_t *>(ctx_->allocator->Malloc(fc_param_->col_8_ * fc_param_->deep_ * sizeof(float16_t)));
if (b_pack_ptr_ == nullptr) {
FreeTmpBuffer();
return RET_MEMORY_FAILED;
}
memset(b_pack_ptr_, 0, fc_param_->col_8_ * fc_param_->deep_ * sizeof(float16_t));

InitMatrixB(reinterpret_cast<float *>(in_tensors_[1]->Data()), b_pack_ptr_);
if (in_tensors_.size() == 3) {
bias_ptr_ = reinterpret_cast<float16_t *>(ctx_->allocator->Malloc(fc_param_->col_8_ * sizeof(float16_t)));
if (bias_ptr_ == nullptr) {
FreeTmpBuffer();
return RET_MEMORY_FAILED;
}
memset(bias_ptr_, 0, fc_param_->col_8_ * sizeof(float16_t));
Float32ToFloat16(reinterpret_cast<float *>(in_tensors_[2]->Data()), bias_ptr_, fc_param_->col_);
}

if (out_tensors_[0]->data_type() == kNumberTypeFloat32) {
output_fp16_ =
reinterpret_cast<float16_t *>(ctx_->allocator->Malloc(fc_param_->row_ * fc_param_->col_ * sizeof(float16_t)));
}
return RET_OK;
}

void FullconnectionFP16CPUKernel::InitMatrixA(float *a_ptr, float16_t *a_pack_ptr) {
Fp32RowMajor2Fp16Col16Major(a_ptr, a_pack_ptr, fc_param_->row_, fc_param_->deep_);
}

void FullconnectionFP16CPUKernel::InitMatrixA(float16_t *a_ptr, float16_t *a_pack_ptr) {
Fp16RowMajor2Fp16Col16Major(a_ptr, a_pack_ptr, fc_param_->row_, fc_param_->deep_);
}

void FullconnectionFP16CPUKernel::InitMatrixB(float *b_ptr, float16_t *b_pack_ptr) {
Fp32RowMajor2Fp16Col8Major(b_ptr, b_pack_ptr, fc_param_->col_, fc_param_->deep_);
}

int FullconnectionFP16CPUKernel::Init() {
if (!InferShapeDone()) {
return RET_OK;
}
return ReSize();
}

int FullconnectionFP16CPUKernel::RunImpl(int task_id) {
int cur_stride = fc_param_->col_ - task_id * thread_stride_;
int cur_oc = MSMIN(thread_stride_, cur_stride);
if (cur_oc <= 0) {
return RET_OK;
}
auto b = b_pack_ptr_ + task_id * thread_stride_ * fc_param_->deep_;
auto bias = (bias_ptr_ == nullptr) ? nullptr : bias_ptr_ + thread_stride_ * task_id;
auto c = output_ptr_ + task_id * thread_stride_;
MatMulFp16(a_pack_ptr_, b, c, bias, fc_param_->act_type_, fc_param_->deep_, fc_param_->row_, cur_oc, fc_param_->col_,
true);
return RET_OK;
}

int FcFP16Run(void *cdata, int task_id) {
auto op = reinterpret_cast<FullconnectionFP16CPUKernel *>(cdata);
auto error_code = op->RunImpl(task_id);
if (error_code != RET_OK) {
MS_LOG(ERROR) << "MatmulFp32Run error task_id[" << task_id << "] error_code[" << error_code << "]";
return RET_ERROR;
}
return RET_OK;
}

int FullconnectionFP16CPUKernel::Run() {
auto prepare_ret = Prepare();
if (prepare_ret != RET_OK) {
MS_LOG(ERROR) << "Prepare fail!ret: " << prepare_ret;
return prepare_ret;
}
auto out_tensor = out_tensors_[0];
if (out_tensor->data_type() == kNumberTypeFloat32) {
output_ptr_ = output_fp16_;
} else {
output_ptr_ = reinterpret_cast<float16_t *>(out_tensor->Data());
}
if (in_tensors_[0]->data_type() == kNumberTypeFloat32) {
InitMatrixA(reinterpret_cast<float *>(in_tensors_[0]->Data()), a_pack_ptr_);
} else {
InitMatrixA(reinterpret_cast<float16_t *>(in_tensors_[0]->Data()), a_pack_ptr_);
}
ParallelLaunch(THREAD_POOL_DEFAULT, FcFP16Run, this, thread_count_);
if (out_tensor->data_type() == kNumberTypeFloat32) {
auto size = out_tensor->ElementsNum();
auto out_tensor_data = reinterpret_cast<float *>(out_tensor->Data());
Float16ToFloat32(output_fp16_, out_tensor_data, size);
}
return RET_OK;
}

kernel::LiteKernel *CpuFullConnectionFp16KernelCreator(const std::vector<lite::tensor::Tensor *> &inputs,
const std::vector<lite::tensor::Tensor *> &outputs,
OpParameter *opParameter, const lite::Context *ctx,
const kernel::KernelKey &desc,
const mindspore::lite::PrimitiveC *primitive) {
auto *kernel = new (std::nothrow) FullconnectionFP16CPUKernel(opParameter, inputs, outputs, ctx, primitive);
if (kernel == nullptr) {
MS_LOG(ERROR) << "kernel is nullptr.";
return nullptr;
}
auto ret = kernel->Init();
if (ret != RET_OK) {
MS_LOG(ERROR) << "Init kernel failed, name: " << opParameter->name_ << ", type: "
<< schema::EnumNamePrimitiveType(static_cast<schema::PrimitiveType>(opParameter->type_));
delete kernel;
return nullptr;
}
return kernel;
}

REG_KERNEL(kCPU, kNumberTypeFloat16, PrimitiveType_FullConnection, CpuFullConnectionFp16KernelCreator)
} // namespace mindspore::kernel

+ 56
- 0
mindspore/lite/src/runtime/kernel/arm/fp16/fullconnection_fp16.h View File

@@ -0,0 +1,56 @@
/**
* Copyright 2020 Huawei Technologies Co., Ltd
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/

#ifndef MINDSPORE_LITE_SRC_RUNTIME_KERNEL_ARM_FP16_FULLCONNECTION_H_
#define MINDSPORE_LITE_SRC_RUNTIME_KERNEL_ARM_FP16_FULLCONNECTION_H_

#ifdef ENABLE_NEON
#include <arm_neon.h>
#endif
#include <vector>
#include "src/lite_kernel.h"
#include "nnacl/matmul_parameter.h"
#include "src/runtime/kernel/arm/base/fullconnection_base.h"

namespace mindspore::kernel {
class FullconnectionFP16CPUKernel : public FullconnectionBaseCPUKernel {
public:
explicit FullconnectionFP16CPUKernel(OpParameter *parameter, const std::vector<lite::tensor::Tensor *> &inputs,
const std::vector<lite::tensor::Tensor *> &outputs, const lite::Context *ctx,
const mindspore::lite::PrimitiveC *primitive)
: FullconnectionBaseCPUKernel(parameter, inputs, outputs, ctx, primitive) {}
~FullconnectionFP16CPUKernel() override;
int Init() override;
int ReSize() override;
int Run() override;
int RunImpl(int task_id);

private:
void InitMatrixA(float *a_ptr, float16_t *a_pack_ptr);
void InitMatrixA(float16_t *a_ptr, float16_t *a_pack_ptr);
void InitMatrixB(float *b_ptr, float16_t *b_pack_ptr);
void FreeTmpBuffer();

private:
float16_t *a_pack_ptr_ = nullptr;
float16_t *b_pack_ptr_ = nullptr;
float16_t *bias_ptr_ = nullptr;
float16_t *output_fp16_ = nullptr;
float16_t *output_ptr_ = nullptr;
};
} // namespace mindspore::kernel

#endif // MINDSPORE_LITE_SRC_RUNTIME_KERNEL_ARM_FP16_FULLCONNECTION_H_

+ 250
- 0
mindspore/lite/src/runtime/kernel/arm/fp16/matmul_fp16.cc View File

@@ -0,0 +1,250 @@
/**
* Copyright 2020 Huawei Technologies Co., Ltd
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/

#include "src/runtime/kernel/arm/fp16/matmul_fp16.h"
#include "nnacl/fp16/matmul_fp16.h"
#include "nnacl/fp16/cast_fp16.h"
#include "src/runtime/runtime_api.h"
#include "include/errorcode.h"
#include "src/kernel_registry.h"

using mindspore::lite::KernelRegistrar;
using mindspore::lite::RET_ERROR;
using mindspore::lite::RET_INPUT_TENSOR_ERROR;
using mindspore::lite::RET_MEMORY_FAILED;
using mindspore::lite::RET_OK;
using mindspore::schema::PrimitiveType_MatMul;

namespace mindspore::kernel {
MatmulFP16CPUKernel::~MatmulFP16CPUKernel() { FreeTmpBuffer(); }

void MatmulFP16CPUKernel::FreeTmpBuffer() {
if (a_pack_ptr_ != nullptr) {
ctx_->allocator->Free(a_pack_ptr_);
a_pack_ptr_ = nullptr;
}
if (b_pack_ptr_ != nullptr) {
ctx_->allocator->Free(b_pack_ptr_);
b_pack_ptr_ = nullptr;
}
if (bias_ptr_ != nullptr) {
ctx_->allocator->Free(bias_ptr_);
bias_ptr_ = nullptr;
}
if (output_ptr_ != nullptr) {
ctx_->allocator->Free(output_ptr_);
output_ptr_ = nullptr;
}
}

int MatmulFP16CPUKernel::ReSize() {
FreeTmpBuffer();
int batch = 1;
auto a_shape = in_tensors_[0]->shape();
auto c_shape = out_tensors_[0]->shape();
if (in_tensors_.size() == 3) {
auto bias_shape = in_tensors_[2]->shape();
if (bias_shape[bias_shape.size() - 1] != c_shape[c_shape.size() - 1]) {
MS_LOG(ERROR) << "The bias' dimension is not equal with column";
return RET_INPUT_TENSOR_ERROR;
}
}

for (size_t i = 0; i < a_shape.size() - 2; ++i) {
batch *= a_shape[i];
}
params_->batch = batch;
params_->row_ = c_shape[c_shape.size() - 2];
params_->col_ = c_shape[c_shape.size() - 1];
params_->deep_ = params_->a_transpose_ ? a_shape[a_shape.size() - 2] : a_shape[a_shape.size() - 1];
params_->row_16_ = UP_ROUND(params_->row_, C16NUM);
params_->col_8_ = UP_ROUND(params_->col_, C8NUM);
thread_count_ = MSMIN(thread_count_, UP_DIV(params_->col_, C8NUM));
thread_stride_ = UP_DIV(UP_DIV(params_->col_, C8NUM), thread_count_) * C8NUM;

a_pack_ptr_ = reinterpret_cast<float16_t *>(
ctx_->allocator->Malloc(params_->batch * params_->row_16_ * params_->deep_ * sizeof(float16_t)));
if (a_pack_ptr_ == nullptr) {
FreeTmpBuffer();
return RET_MEMORY_FAILED;
}
memset(a_pack_ptr_, 0, params_->batch * params_->row_16_ * params_->deep_ * sizeof(float16_t));

b_pack_ptr_ = reinterpret_cast<float16_t *>(
ctx_->allocator->Malloc(params_->batch * params_->col_8_ * params_->deep_ * sizeof(float16_t)));
if (b_pack_ptr_ == nullptr) {
FreeTmpBuffer();
return RET_MEMORY_FAILED;
}
memset(b_pack_ptr_, 0, params_->batch * params_->col_8_ * params_->deep_ * sizeof(float16_t));

params_->a_const_ = (in_tensors_[0]->Data() != nullptr);
params_->b_const_ = (in_tensors_[1]->Data() != nullptr);
if (params_->a_const_ == true) {
if (in_tensors_[0]->data_type() == kNumberTypeFloat32) {
InitMatrixA(reinterpret_cast<float *>(in_tensors_[0]->Data()), a_pack_ptr_);
} else {
InitMatrixA(reinterpret_cast<float16_t *>(in_tensors_[0]->Data()), a_pack_ptr_);
}
}
if (params_->b_const_ == true) {
InitMatrixB(reinterpret_cast<float *>(in_tensors_[1]->Data()), b_pack_ptr_);
}

if (in_tensors_.size() == 3) {
bias_ptr_ = reinterpret_cast<float16_t *>(ctx_->allocator->Malloc(params_->col_8_ * sizeof(float16_t)));
if (bias_ptr_ == nullptr) {
FreeTmpBuffer();
return RET_MEMORY_FAILED;
}
memset(bias_ptr_, 0, params_->col_8_ * sizeof(float16_t));
Float32ToFloat16(reinterpret_cast<float *>(in_tensors_[2]->Data()), bias_ptr_, params_->col_);
}

if (out_tensors_[0]->data_type() == kNumberTypeFloat32) {
output_ptr_ = reinterpret_cast<float16_t *>(
ctx_->allocator->Malloc(params_->batch * params_->row_ * params_->col_ * sizeof(float16_t)));
}
return RET_OK;
}

void MatmulFP16CPUKernel::InitMatrixA(float *a_ptr, float16_t *a_pack_ptr) {
for (int i = 0; i < params_->batch; i++) {
float *src = a_ptr + i * params_->deep_ * params_->row_;
float16_t *dst = a_pack_ptr + i * params_->deep_ * params_->row_16_;
if (params_->a_transpose_) {
Fp32RowMajor2Fp16Row16Major(src, dst, params_->deep_, params_->row_);
} else {
Fp32RowMajor2Fp16Col16Major(src, dst, params_->row_, params_->deep_);
}
}
}

void MatmulFP16CPUKernel::InitMatrixA(float16_t *a_ptr, float16_t *a_pack_ptr) {
for (int i = 0; i < params_->batch; i++) {
float16_t *src = a_ptr + i * params_->deep_ * params_->row_;
float16_t *dst = a_pack_ptr + i * params_->deep_ * params_->row_16_;
if (params_->a_transpose_) {
Fp16RowMajor2Fp16Row16Major(src, dst, params_->deep_, params_->row_);
} else {
Fp16RowMajor2Fp16Col16Major(src, dst, params_->row_, params_->deep_);
}
}
}

void MatmulFP16CPUKernel::InitMatrixB(float *b_ptr, float16_t *b_pack_ptr) {
for (int i = 0; i < params_->batch; i++) {
float *src = b_ptr + i * params_->deep_ * params_->col_;
float16_t *dst = b_pack_ptr + i * params_->deep_ * params_->col_8_;
if (params_->b_transpose_) {
Fp32RowMajor2Fp16Col8Major(src, dst, params_->col_, params_->deep_);
} else {
Fp32RowMajor2Fp16Row8Major(src, dst, params_->deep_, params_->col_);
}
}
}

int MatmulFP16CPUKernel::Init() {
if (!InferShapeDone()) {
return RET_OK;
}
return ReSize();
}

int MatmulFP16CPUKernel::RunImpl(int task_id) {
int cur_stride = params_->col_ - task_id * thread_stride_;
int cur_oc = MSMIN(thread_stride_, cur_stride);
if (cur_oc <= 0) {
return RET_OK;
}
auto b = current_b_ + task_id * thread_stride_ * params_->deep_;
auto bias = (bias_ptr_ == nullptr) ? nullptr : bias_ptr_ + thread_stride_ * task_id;
auto c = current_c_ + task_id * thread_stride_;
MatMulFp16(current_a_, b, c, bias, ActType_No, params_->deep_, params_->row_, cur_oc, params_->col_, true);

return RET_OK;
}

int MatmulFP16Run(void *cdata, int task_id) {
auto op = reinterpret_cast<MatmulFP16CPUKernel *>(cdata);
auto error_code = op->RunImpl(task_id);
if (error_code != RET_OK) {
MS_LOG(ERROR) << "MatmulFp32Run error task_id[" << task_id << "] error_code[" << error_code << "]";
return RET_ERROR;
}
return RET_OK;
}

int MatmulFP16CPUKernel::Run() {
auto prepare_ret = Prepare();
if (prepare_ret != RET_OK) {
MS_LOG(ERROR) << "Prepare fail!ret: " << prepare_ret;
return prepare_ret;
}
auto b = reinterpret_cast<float *>(in_tensors_[1]->Data());
auto out_tensor = out_tensors_[0];
float16_t *c_ptr;
if (out_tensor->data_type() == kNumberTypeFloat32) {
c_ptr = output_ptr_;
} else {
c_ptr = reinterpret_cast<float16_t *>(out_tensor->Data());
}
if (params_->a_const_ == false) {
if (in_tensors_[0]->data_type() == kNumberTypeFloat32) {
InitMatrixA(reinterpret_cast<float *>(in_tensors_[0]->Data()), a_pack_ptr_);
} else {
InitMatrixA(reinterpret_cast<float16_t *>(in_tensors_[0]->Data()), a_pack_ptr_);
}
}
if (params_->b_const_ == false) {
InitMatrixB(b, b_pack_ptr_);
}
for (int i = 0; i < params_->batch; ++i) {
current_a_ = a_pack_ptr_ + i * params_->row_16_ * params_->deep_;
current_b_ = b_pack_ptr_ + i * params_->deep_ * params_->col_8_;
current_c_ = c_ptr + i * params_->row_ * params_->col_;
ParallelLaunch(THREAD_POOL_DEFAULT, MatmulFP16Run, this, thread_count_);
}
if (out_tensor->data_type() == kNumberTypeFloat32) {
auto size = out_tensor->ElementsNum();
auto out_tensor_data = reinterpret_cast<float *>(out_tensor->Data());
Float16ToFloat32(output_ptr_, out_tensor_data, size);
}
return RET_OK;
}

kernel::LiteKernel *CpuMatmulFp16KernelCreator(const std::vector<lite::tensor::Tensor *> &inputs,
const std::vector<lite::tensor::Tensor *> &outputs,
OpParameter *opParameter, const lite::Context *ctx,
const kernel::KernelKey &desc,
const mindspore::lite::PrimitiveC *primitive) {
auto *kernel = new (std::nothrow) MatmulFP16CPUKernel(opParameter, inputs, outputs, ctx, primitive);
if (kernel == nullptr) {
MS_LOG(ERROR) << "kernel is nullptr.";
return nullptr;
}
auto ret = kernel->Init();
if (ret != RET_OK) {
MS_LOG(ERROR) << "Init kernel failed, name: " << opParameter->name_ << ", type: "
<< schema::EnumNamePrimitiveType(static_cast<schema::PrimitiveType>(opParameter->type_));
delete kernel;
return nullptr;
}
return kernel;
}

REG_KERNEL(kCPU, kNumberTypeFloat16, PrimitiveType_MatMul, CpuMatmulFp16KernelCreator)
} // namespace mindspore::kernel

+ 58
- 0
mindspore/lite/src/runtime/kernel/arm/fp16/matmul_fp16.h View File

@@ -0,0 +1,58 @@
/**
* Copyright 2020 Huawei Technologies Co., Ltd
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/

#ifndef MINDSPORE_LITE_SRC_RUNTIME_KERNEL_ARM_FP16_MATMUL_H_
#define MINDSPORE_LITE_SRC_RUNTIME_KERNEL_ARM_FP16_MATMUL_H_

#ifdef ENABLE_NEON
#include <arm_neon.h>
#endif
#include <vector>
#include "src/lite_kernel.h"
#include "nnacl/matmul_parameter.h"
#include "src/runtime/kernel/arm/base/matmul_base.h"

namespace mindspore::kernel {
class MatmulFP16CPUKernel : public MatmulBaseCPUKernel {
public:
explicit MatmulFP16CPUKernel(OpParameter *parameter, const std::vector<lite::tensor::Tensor *> &inputs,
const std::vector<lite::tensor::Tensor *> &outputs, const lite::Context *ctx,
const mindspore::lite::PrimitiveC *primitive)
: MatmulBaseCPUKernel(parameter, inputs, outputs, ctx, primitive) {}
~MatmulFP16CPUKernel() override;
int Init() override;
int ReSize() override;
int Run() override;
int RunImpl(int task_id);

private:
void InitMatrixA(float *a_ptr, float16_t *a_pack_ptr);
void InitMatrixA(float16_t *a_ptr, float16_t *a_pack_ptr);
void InitMatrixB(float *b_ptr, float16_t *b_pack_ptr);
void FreeTmpBuffer();

private:
float16_t *a_pack_ptr_ = nullptr;
float16_t *b_pack_ptr_ = nullptr;
float16_t *bias_ptr_ = nullptr;
float16_t *output_ptr_ = nullptr;
float16_t *current_a_ = nullptr;
float16_t *current_b_ = nullptr;
float16_t *current_c_ = nullptr;
};
} // namespace mindspore::kernel

#endif // MINDSPORE_LITE_SRC_RUNTIME_KERNEL_ARM_FP16_MATMUL_H_

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