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Support bias for matmul op

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pull/4508/head
zhanyuan 5 years ago
parent dbee1de2e5
commit bb8ff96313
4 changed files with 87 additions and 5 deletions
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4
mindspore/lite/src/ops/matmul.cc
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@ -22,10 +22,6 @@
namespace mindspore::lite {
int MatMul::InferShape(std::vector<tensor::Tensor *> inputs_, std::vector<tensor::Tensor *> outputs_) {
MS_ASSERT(this->primitive != nullptr);
if (inputs_.size() != kDoubleNum) {
MS_LOG(ERROR) << "OpMatMul inputs size: " << inputs_.size();
return RET_INPUT_TENSOR_ERROR;
}
auto input0 = inputs_.front();
MS_ASSERT(input0 != nullptr);
auto input1 = inputs_.at(1);

26
mindspore/lite/src/runtime/kernel/arm/fp32/matmul.cc
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@ -20,6 +20,7 @@
#include "include/errorcode.h"
using mindspore::lite::RET_ERROR;
using mindspore::lite::RET_INPUT_TENSOR_ERROR;
using mindspore::lite::RET_MEMORY_FAILED;
using mindspore::lite::RET_OK;
@ -28,6 +29,7 @@ MatmulCPUKernel::~MatmulCPUKernel() {
ctx_->allocator->Free(a_c8_ptr_);
ctx_->allocator->Free(b_r8_ptr_);
ctx_->allocator->Free(c_r8x8_ptr_);
ctx_->allocator->Free(bias_ptr_);
}
int MatmulCPUKernel::ReSize() { return RET_OK; }
@ -40,6 +42,14 @@ int MatmulCPUKernel::Init() {
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 (int i = 0; i < a_shape.size() - 2; ++i) {
batch *= a_shape[i];
}
@ -67,6 +77,15 @@ int MatmulCPUKernel::Init() {
return RET_MEMORY_FAILED;
}
memset(c_r8x8_ptr_, 0, params_->row_8_ * params_->col_8_ * sizeof(float));
if (in_tensors_.size() == 3) {
bias_ptr_ = reinterpret_cast<float *>(malloc(params_->col_8_ * sizeof(float)));
memset(bias_ptr_, 0, params_->col_8_ * sizeof(float));
memcpy(bias_ptr_, in_tensors_[2]->Data(), params_->col_ * sizeof(float));
} else {
bias_ptr_ = nullptr;
}
return RET_OK;
}
@ -77,7 +96,12 @@ int MatmulCPUKernel::RunImpl(int task_id) {
}
auto cur_b = b_r8_ptr_ + task_id * thread_stride_ * C8NUM * params_->deep_;
auto cur_c = c_r8x8_ptr_ + task_id * thread_stride_ * C8NUM * params_->row_8_;
MatMul(a_c8_ptr_, cur_b, cur_c, NULL, ActType_No, params_->deep_, params_->row_8_, cur_oc * 8, 0, false);
if (bias_ptr_) {
auto cur_bias = bias_ptr_ + task_id * thread_stride_ * C8NUM;
MatMul(a_c8_ptr_, cur_b, cur_c, cur_bias, ActType_No, params_->deep_, params_->row_8_, cur_oc * 8, 0, false);
} else {
MatMul(a_c8_ptr_, cur_b, cur_c, NULL, ActType_No, params_->deep_, params_->row_8_, cur_oc * 8, 0, false);
}
return RET_OK;
}

1
mindspore/lite/src/runtime/kernel/arm/fp32/matmul.h
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@ -39,6 +39,7 @@ class MatmulCPUKernel : public MatmulBaseCPUKernel {
float *a_c8_ptr_;
float *b_r8_ptr_;
float *c_r8x8_ptr_;
float *bias_ptr_;
};
} // namespace mindspore::kernel

61
mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/matmul_fp32_tests.cc
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@ -202,6 +202,35 @@ int MMTestInit(std::vector<lite::tensor::Tensor *> *inputs_, std::vector<lite::t
return out_t->ElementsNum();
}
int MMTestInit2(std::vector<lite::tensor::Tensor *> *inputs_, std::vector<lite::tensor::Tensor *> *outputs_,
float *a_ptr, float *b_ptr, float *bias_ptr, std::vector<int> a_shape, std::vector<int> b_shape,
std::vector<int> bias_shape, std::vector<int> c_shape) {
auto in_t =
new lite::tensor::Tensor(kNumberTypeFloat, a_shape, schema::Format_NHWC, static_cast<schema::NodeType>(1));
in_t->MallocData();
memcpy(in_t->Data(), a_ptr, sizeof(float) * in_t->ElementsNum());
inputs_->push_back(in_t);
auto weight_t =
new lite::tensor::Tensor(kNumberTypeFloat, b_shape, schema::Format_NHWC, static_cast<schema::NodeType>(1));
weight_t->MallocData();
memcpy(weight_t->Data(), b_ptr, sizeof(float) * weight_t->ElementsNum());
inputs_->push_back(weight_t);
auto bias_t =
new lite::tensor::Tensor(kNumberTypeFloat, bias_shape, schema::Format_NHWC, static_cast<schema::NodeType>(1));
bias_t->MallocData();
memcpy(bias_t->Data(), bias_ptr, sizeof(float) * bias_t->ElementsNum());
inputs_->push_back(bias_t);
auto out_t =
new lite::tensor::Tensor(kNumberTypeFloat, c_shape, schema::Format_NHWC, static_cast<schema::NodeType>(1));
out_t->MallocData();
outputs_->push_back(out_t);
return out_t->ElementsNum();
}
TEST_F(TestMatMulFp32, simple) {
std::vector<lite::tensor::Tensor *> inputs_;
std::vector<lite::tensor::Tensor *> outputs_;
@ -232,6 +261,38 @@ TEST_F(TestMatMulFp32, simple) {
for (auto t : outputs_) delete t;
}
TEST_F(TestMatMulFp32, simple_bias) {
std::vector<lite::tensor::Tensor *> inputs_;
std::vector<lite::tensor::Tensor *> outputs_;
auto matmul_param = new MatMulParameter();
matmul_param->a_transpose_ = false;
matmul_param->b_transpose_ = false;
matmul_param->has_bias_ = false;
float a[] = {-3.2366564, -4.7733846, -7.8329225, 16.146885, 5.060793, -6.1471, -1.7680453, -6.5721383,
17.87506, -5.1192183, 10.742863, 1.4536934, 19.693445, 19.45783, 5.063163, 0.5234792};
float b[] = {-0.0024438887, 0.0006738146, -0.008169129, 0.0021510671, -0.012470592, -0.0053063435,
0.006050155, 0.008656233, 0.012911413, -0.0028635843, -0.00034080597, -0.0010622552,
-0.012254699, -0.01312836, 0.0025241964, -0.004706142, 0.002451482, -0.009558459,
0.004481974, 0.0033251503, -0.011705584, -0.001720293, -0.0039410214, -0.0073637343};
float bias[] = {1, 2, 3};
std::vector<int> a_shape = {2, 8};
std::vector<int> b_shape = {8, 3};
std::vector<int> bias_shape = {1, 3};
std::vector<int> c_shape = {2, 3};
int total_size = MMTestInit2(&inputs_, &outputs_, a, b, bias, a_shape, b_shape, bias_shape, c_shape);
auto ctx = new lite::Context;
ctx->thread_num_ = 1;
auto mm = new kernel::MatmulCPUKernel(reinterpret_cast<OpParameter *>(matmul_param), inputs_, outputs_, ctx, nullptr);
mm->Init();
mm->Run();
float correct[] = {-0.1256939023733139 + 1, -0.07744802534580231 + 2, 0.07410638779401779 + 3,
-0.3049793541431427 + 1, -0.027687929570674896 + 2, -0.18109679222106934 + 3};
CompareOutputData(reinterpret_cast<float *>(outputs_[0]->Data()), correct, total_size, 0.0001);
delete mm;
for (auto t : inputs_) delete t;
for (auto t : outputs_) delete t;
}
TEST_F(TestMatMulFp32, simple2) {
std::vector<lite::tensor::Tensor *> inputs_;
std::vector<lite::tensor::Tensor *> outputs_;

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