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[MS][LITE] arm cpu fp32 op: move weight and bias initing to function Init

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pull/4692/head
yangruoqi713 5 years ago
parent 6ea1b21f25
commit 2bf61d2da1
12 changed files with 254 additions and 270 deletions
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111
mindspore/lite/src/runtime/kernel/arm/fp16/convolution_depthwise_fp16.cc
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@ -29,66 +29,67 @@ using mindspore::lite::RET_OK;
using mindspore::schema::PrimitiveType_DepthwiseConv2D;
namespace mindspore::kernel {
ConvolutionDepthwiseFp16CPUKernel::~ConvolutionDepthwiseFp16CPUKernel() { FreeTmpBuffer(); }
void ConvolutionDepthwiseFp16CPUKernel::FreeTmpBuffer() {
ConvolutionDepthwiseFp16CPUKernel::~ConvolutionDepthwiseFp16CPUKernel() {
if (sliding_ != nullptr) {
delete sliding_;
sliding_ = nullptr;
}
if (packed_weight_ != nullptr) {
delete packed_weight_;
packed_weight_ = nullptr;
}
if (packed_input_ != nullptr) {
delete packed_input_;
packed_input_ = nullptr;
}
if (packed_output_ != nullptr) {
delete packed_output_;
packed_output_ = nullptr;
FreeTmpBuffer();
}
void ConvolutionDepthwiseFp16CPUKernel::FreeTmpBuffer() {
if (need_align_) {
if (packed_input_ != nullptr) {
delete packed_input_;
packed_input_ = nullptr;
}
if (packed_output_ != nullptr) {
delete packed_output_;
packed_output_ = nullptr;
}
}
}
int ConvolutionDepthwiseFp16CPUKernel::InitBuffer() {
// malloc pack input buffer
int C8 = UP_DIV(conv_param_->input_channel_, C8NUM);
int pack_input_size = conv_param_->input_batch_ * conv_param_->input_h_ * conv_param_->input_w_ * C8NUM * C8;
packed_input_ = reinterpret_cast<float16_t *>(malloc(pack_input_size * sizeof(float16_t)));
if (packed_input_ == nullptr) {
MS_LOG(ERROR) << "Malloc buffer failed.";
return RET_ERROR;
}
memset(packed_input_, 0, pack_input_size * sizeof(float16_t));
if (conv_param_->input_channel_ % C4NUM != 0) {
need_align_ = true;
int C8 = UP_DIV(conv_param_->input_channel_, C8NUM);
int pack_input_size = conv_param_->input_batch_ * conv_param_->input_h_ * conv_param_->input_w_ * C8NUM * C8;
packed_input_ = reinterpret_cast<float16_t *>(malloc(pack_input_size * sizeof(float16_t)));
if (packed_input_ == nullptr) {
MS_LOG(ERROR) << "Malloc buffer failed.";
return RET_ERROR;
}
// malloc pack output buffer
int pack_output_size = conv_param_->output_batch_ * conv_param_->output_h_ * conv_param_->output_w_ * C8NUM * C8;
packed_output_ = reinterpret_cast<float16_t *>(malloc(pack_output_size * sizeof(float16_t)));
if (packed_output_ == nullptr) {
MS_LOG(ERROR) << "Malloc buffer failed.";
return RET_ERROR;
int pack_output_size = conv_param_->output_batch_ * conv_param_->output_h_ * conv_param_->output_w_ * C8NUM * C8;
packed_output_ = reinterpret_cast<float16_t *>(malloc(pack_output_size * sizeof(float16_t)));
if (packed_output_ == nullptr) {
MS_LOG(ERROR) << "Malloc buffer failed.";
return RET_ERROR;
}
}
return RET_OK;
}
int ConvolutionDepthwiseFp16CPUKernel::InitWeightBias() {
// init weight: o, h, w, i; o == group, i == 1
int OC8 = UP_DIV(conv_param_->output_channel_, C8NUM);
auto weight_tensor = in_tensors_[kWeightIndex];
int OC8 = UP_DIV(weight_tensor->Batch(), C8NUM);
auto origin_weight = reinterpret_cast<float *>(weight_tensor->Data());
int pack_weight_size = C8NUM * OC8 * conv_param_->kernel_h_ * conv_param_->kernel_w_;
int pack_weight_size = C8NUM * OC8 * weight_tensor->Height() * weight_tensor->Width();
packed_weight_ = reinterpret_cast<float16_t *>(malloc(pack_weight_size * sizeof(float16_t)));
if (packed_weight_ == nullptr) {
MS_LOG(ERROR) << "Malloc buffer failed.";
return RET_ERROR;
}
memset(packed_weight_, 0, pack_weight_size * sizeof(float16_t));
PackNCHWFp32ToNC8HW8Fp16(origin_weight, packed_weight_, 1, conv_param_->kernel_h_ * conv_param_->kernel_w_,
conv_param_->output_channel_);
PackNCHWFp32ToNC8HW8Fp16(origin_weight, packed_weight_, 1, weight_tensor->Height() * weight_tensor->Width(),
weight_tensor->Batch());
// init bias
bias_data_ = reinterpret_cast<float16_t *>(malloc(C8NUM * OC8 * sizeof(float16_t)));
if (bias_data_ == nullptr) {
MS_LOG(ERROR) << "Malloc buffer failed.";
@ -97,8 +98,9 @@ int ConvolutionDepthwiseFp16CPUKernel::InitWeightBias() {
memset(bias_data_, 0, C8NUM * OC8 * sizeof(float16_t));
auto bias_fp16 = reinterpret_cast<float16_t *>(bias_data_);
if (in_tensors_.size() == kInputSize2) {
auto ori_bias = reinterpret_cast<float *>(in_tensors_.at(kBiasIndex)->Data());
for (int i = 0; i < conv_param_->output_channel_; i++) {
auto bias_tensor = in_tensors_.at(kBiasIndex);
auto ori_bias = reinterpret_cast<float *>(bias_tensor->Data());
for (int i = 0; i < bias_tensor->ElementsNum(); i++) {
bias_fp16[i] = (float16_t)ori_bias[i];
}
}
@ -108,6 +110,18 @@ int ConvolutionDepthwiseFp16CPUKernel::InitWeightBias() {
}
int ConvolutionDepthwiseFp16CPUKernel::Init() {
sliding_ = new (std::nothrow) SlidingWindowParam;
if (sliding_ == nullptr) {
MS_LOG(ERROR) << "new sliding window param failed.";
return RET_ERROR;
}
auto ret = InitWeightBias();
if (ret != 0) {
MS_LOG(ERROR) << "Convolution depthwise fp16 InitWeightBias failed.";
return RET_ERROR;
}
if (!InferShapeDone()) {
return RET_OK;
}
@ -116,21 +130,12 @@ int ConvolutionDepthwiseFp16CPUKernel::Init() {
int ConvolutionDepthwiseFp16CPUKernel::ReSize() {
FreeTmpBuffer();
// conv base init
auto ret = ConvolutionBaseCPUKernel::Init();
if (ret != RET_OK) {
return ret;
}
// init sliding_ window param
sliding_ = new SlidingWindowParam;
InitSlidingParamConvDw(sliding_, conv_param_, C8NUM);
ret = InitWeightBias();
if (ret != 0) {
MS_LOG(ERROR) << "Convolution depthwise fp16 InitWeightBias failed.";
return RET_ERROR;
}
ret = InitBuffer();
if (ret != 0) {
MS_LOG(ERROR) << "Convolution depthwise fp16 InitBuffer failed.";
@ -171,19 +176,25 @@ int ConvolutionDepthwiseFp16CPUKernel::Run() {
MS_LOG(ERROR) << "Get Execute tensor failed.";
return ret;
}
// pack input: to nhwc8
PackNHWCToNHWC8Fp16(execute_input_, packed_input_, conv_param_->input_batch_,
conv_param_->input_h_ * conv_param_->input_w_, conv_param_->input_channel_);
if (need_align_) {
PackNHWCToNHWC8Fp16(execute_input_, packed_input_, conv_param_->input_batch_,
conv_param_->input_h_ * conv_param_->input_w_, conv_param_->input_channel_);
} else {
packed_input_ = execute_input_;
}
if (!need_align_) {
packed_output_ = execute_output_;
}
ret = LiteBackendParallelLaunch(ConvDwFp16Run, this, conv_param_->thread_num_);
if (ret != RET_OK) {
MS_LOG(ERROR) << "ConvDwFp16Run error: error_code[" << ret << "]";
return RET_ERROR;
}
PackNHWC8ToNHWCFp16(packed_output_, execute_output_, conv_param_->output_batch_,
conv_param_->output_h_ * conv_param_->output_w_, conv_param_->output_channel_);
if (need_align_) {
PackNHWC8ToNHWCFp16(packed_output_, execute_output_, conv_param_->output_batch_,
conv_param_->output_h_ * conv_param_->output_w_, conv_param_->output_channel_);
}
ConvolutionBaseFP16CPUKernel::IfCastOutput();
ConvolutionBaseFP16CPUKernel::FreeTmpBuffer();
return RET_OK;

1
mindspore/lite/src/runtime/kernel/arm/fp16/convolution_depthwise_fp16.h
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@ -56,6 +56,7 @@ class ConvolutionDepthwiseFp16CPUKernel : public ConvolutionBaseFP16CPUKernel {
float16_t *packed_weight_ = nullptr;
float16_t *packed_input_ = nullptr;
float16_t *packed_output_ = nullptr;
bool need_align_ = false;
};
} // namespace mindspore::kernel

87
mindspore/lite/src/runtime/kernel/arm/fp16/deconvolution_depthwise_fp16.cc
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@ -28,25 +28,28 @@ using mindspore::lite::RET_OK;
using mindspore::schema::PrimitiveType_DeDepthwiseConv2D;
namespace mindspore::kernel {
DeconvolutionDepthwiseFp16CPUKernel::~DeconvolutionDepthwiseFp16CPUKernel() { FreeTmpBuffer(); }
void DeconvolutionDepthwiseFp16CPUKernel::FreeTmpBuffer() {
DeconvolutionDepthwiseFp16CPUKernel::~DeconvolutionDepthwiseFp16CPUKernel() {
if (sliding_ != nullptr) {
delete sliding_;
sliding_ = nullptr;
}
if (packed_weight_ != nullptr) {
delete packed_weight_;
packed_weight_ = nullptr;
}
if (packed_input_ != nullptr) {
delete packed_input_;
packed_input_ = nullptr;
}
if (packed_output_ != nullptr) {
delete packed_output_;
packed_output_ = nullptr;
FreeTmpBuffer();
}
void DeconvolutionDepthwiseFp16CPUKernel::FreeTmpBuffer() {
if (need_align_) {
if (packed_input_ != nullptr) {
delete packed_input_;
packed_input_ = nullptr;
}
if (packed_output_ != nullptr) {
delete packed_output_;
packed_output_ = nullptr;
}
}
}
@ -59,14 +62,11 @@ int DeconvolutionDepthwiseFp16CPUKernel::InitSlideParam() {
conv_param_->output_h_ = in_tensors_.front()->shape().at(kNHWC_H);
conv_param_->output_w_ = in_tensors_.front()->shape().at(kNHWC_W);
conv_param_->output_channel_ = in_tensors_.front()->shape().at(kNHWC_C);
// init sliding_ window param
InitSlidingParamConvDw(sliding_, conv_param_, C8NUM);
return RET_OK;
}
int DeconvolutionDepthwiseFp16CPUKernel::InitBuffer() {
// malloc pack input buffer
int C8 = UP_DIV(conv_param_->input_channel_, C8NUM);
int pack_input_size = conv_param_->input_batch_ * conv_param_->input_h_ * conv_param_->input_w_ * C8NUM * C8;
packed_input_ = reinterpret_cast<float16_t *>(malloc(pack_input_size * sizeof(float16_t)));
@ -74,7 +74,6 @@ int DeconvolutionDepthwiseFp16CPUKernel::InitBuffer() {
MS_LOG(ERROR) << "Malloc buffer failed.";
return RET_ERROR;
}
memset(packed_input_, 0, pack_input_size * sizeof(float16_t));
int pack_output_size = conv_param_->output_batch_ * conv_param_->output_h_ * conv_param_->output_w_ * C8NUM * C8;
packed_output_ = reinterpret_cast<float16_t *>(malloc(pack_output_size * sizeof(float16_t)));
@ -88,21 +87,19 @@ int DeconvolutionDepthwiseFp16CPUKernel::InitBuffer() {
int DeconvolutionDepthwiseFp16CPUKernel::InitWeightBias() {
// init weight: o, h, w, i; o == group, i == 1
int OC8 = UP_DIV(conv_param_->output_channel_, C8NUM);
auto weight_tensor = in_tensors_[kWeightIndex];
int OC8 = UP_DIV(weight_tensor->Batch(), C8NUM);
auto origin_weight = reinterpret_cast<float *>(weight_tensor->Data());
int pack_weight_size = C8NUM * OC8 * conv_param_->kernel_h_ * conv_param_->kernel_w_;
int pack_weight_size = C8NUM * OC8 * weight_tensor->Height() * weight_tensor->Width();
packed_weight_ = reinterpret_cast<float16_t *>(malloc(pack_weight_size * sizeof(float16_t)));
if (packed_weight_ == nullptr) {
MS_LOG(ERROR) << "Malloc buffer failed.";
return RET_ERROR;
}
memset(packed_weight_, 0, pack_weight_size * sizeof(float16_t));
PackNCHWFp32ToNC8HW8Fp16(origin_weight, packed_weight_, 1, conv_param_->kernel_h_ * conv_param_->kernel_w_,
conv_param_->output_channel_);
PackNCHWFp32ToNC8HW8Fp16(origin_weight, packed_weight_, 1, weight_tensor->Height() * weight_tensor->Width(),
weight_tensor->Batch());
// init bias
bias_data_ = reinterpret_cast<float16_t *>(malloc(C8NUM * OC8 * sizeof(float16_t)));
if (bias_data_ == nullptr) {
MS_LOG(ERROR) << "Malloc buffer failed.";
@ -110,8 +107,9 @@ int DeconvolutionDepthwiseFp16CPUKernel::InitWeightBias() {
}
memset(bias_data_, 0, C8NUM * OC8 * sizeof(float16_t));
if (in_tensors_.size() == kInputSize2) {
auto ori_bias = reinterpret_cast<float *>(in_tensors_.at(kBiasIndex)->Data());
for (int i = 0; i < conv_param_->output_channel_; i++) {
auto bias_tensor = in_tensors_.at(kBiasIndex);
auto ori_bias = reinterpret_cast<float *>(bias_tensor->Data());
for (int i = 0; i < bias_tensor->ElementsNum(); i++) {
reinterpret_cast<float *>(bias_data_)[i] = (float16_t)ori_bias[i];
}
}
@ -121,6 +119,17 @@ int DeconvolutionDepthwiseFp16CPUKernel::InitWeightBias() {
}
int DeconvolutionDepthwiseFp16CPUKernel::Init() {
sliding_ = new (std::nothrow) SlidingWindowParam;
if (sliding_ == nullptr) {
MS_LOG(ERROR) << "new SlidingWindowParam fail!";
return RET_ERROR;
}
auto ret = InitWeightBias();
if (ret != 0) {
MS_LOG(ERROR) << "Deconvolution depthwise fp16 InitWeightBias failed.";
return RET_ERROR;
}
if (!InferShapeDone()) {
return RET_OK;
}
@ -129,25 +138,11 @@ int DeconvolutionDepthwiseFp16CPUKernel::Init() {
int DeconvolutionDepthwiseFp16CPUKernel::ReSize() {
FreeTmpBuffer();
sliding_ = new (std::nothrow) SlidingWindowParam;
if (sliding_ == nullptr) {
MS_LOG(ERROR) << "new SlidingWindowParam fail!";
return RET_ERROR;
}
InitSlideParam();
// conv base init
auto ret = ConvolutionBaseCPUKernel::Init();
if (ret != RET_OK) {
return ret;
}
ret = InitWeightBias();
if (ret != 0) {
MS_LOG(ERROR) << "Deconvolution depthwise fp16 InitWeightBias failed.";
return RET_ERROR;
}
ret = InitBuffer();
if (ret != 0) {
MS_LOG(ERROR) << "Deconvolution depthwise fp16 InitBuffer failed.";
@ -188,18 +183,26 @@ int DeconvolutionDepthwiseFp16CPUKernel::Run() {
MS_LOG(ERROR) << "Get Execute tensor failed.";
return ret;
}
// pack input: to nhwc8
PackNHWCToNHWC8Fp16(execute_input_, packed_input_, conv_param_->input_batch_,
conv_param_->input_h_ * conv_param_->input_w_, conv_param_->input_channel_);
if (need_align_) {
PackNHWCToNHWC8Fp16(execute_input_, packed_input_, conv_param_->input_batch_,
conv_param_->input_h_ * conv_param_->input_w_, conv_param_->input_channel_);
} else {
packed_input_ = execute_input_;
}
if (!need_align_) {
packed_output_ = execute_output_;
}
ret = LiteBackendParallelLaunch(DeconvDwFp16Run, this, conv_param_->thread_num_);
if (ret != RET_OK) {
MS_LOG(ERROR) << "DeconvDwFp16Run error: error_code[" << ret << "]";
return RET_ERROR;
}
PackNHWC8ToNHWCFp16(packed_output_, execute_output_, conv_param_->output_batch_,
conv_param_->output_h_ * conv_param_->output_w_, conv_param_->output_channel_);
if (need_align_) {
PackNHWC8ToNHWCFp16(packed_output_, execute_output_, conv_param_->output_batch_,
conv_param_->output_h_ * conv_param_->output_w_, conv_param_->output_channel_);
}
ConvolutionBaseFP16CPUKernel::IfCastOutput();
ConvolutionBaseFP16CPUKernel::FreeTmpBuffer();
return RET_OK;

1
mindspore/lite/src/runtime/kernel/arm/fp16/deconvolution_depthwise_fp16.h
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@ -57,6 +57,7 @@ class DeconvolutionDepthwiseFp16CPUKernel : public ConvolutionBaseFP16CPUKernel
float16_t *packed_weight_ = nullptr;
float16_t *packed_input_ = nullptr;
float16_t *packed_output_ = nullptr;
bool need_align_ = false;
};
} // namespace mindspore::kernel

50
mindspore/lite/src/runtime/kernel/arm/fp32/convolution_depthwise.cc
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@ -29,18 +29,19 @@ using mindspore::lite::RET_OK;
using mindspore::schema::PrimitiveType_DepthwiseConv2D;
namespace mindspore::kernel {
ConvolutionDepthwiseCPUKernel::~ConvolutionDepthwiseCPUKernel() { FreeTmpBuffer(); }
void ConvolutionDepthwiseCPUKernel::FreeTmpBuffer() {
ConvolutionDepthwiseCPUKernel::~ConvolutionDepthwiseCPUKernel() {
if (sliding_ != nullptr) {
delete sliding_;
sliding_ = nullptr;
}
if (packed_weight_ != nullptr) {
delete packed_weight_;
packed_weight_ = nullptr;
}
FreeTmpBuffer();
}
void ConvolutionDepthwiseCPUKernel::FreeTmpBuffer() {
if (need_align_) {
if (packed_input_ != nullptr) {
delete packed_input_;
@ -57,19 +58,17 @@ int ConvolutionDepthwiseCPUKernel::InitWeightBias() {
// init weight: o, h, w, i; o == group, i == 1
auto weight_tensor = in_tensors_[kWeightIndex];
auto origin_weight = reinterpret_cast<float *>(weight_tensor->Data());
int OC4 = UP_DIV(conv_param_->output_channel_, C4NUM);
int pack_weight_size = C4NUM * OC4 * conv_param_->kernel_h_ * conv_param_->kernel_w_;
int OC4 = UP_DIV(weight_tensor->Batch(), C4NUM);
int pack_weight_size = C4NUM * OC4 * weight_tensor->Height() * weight_tensor->Width();
packed_weight_ = reinterpret_cast<float *>(malloc(pack_weight_size * sizeof(float)));
if (packed_weight_ == nullptr) {
MS_LOG(ERROR) << "Malloc buffer failed.";
return RET_ERROR;
}
memset(packed_weight_, 0, pack_weight_size * sizeof(float));
PackNCHWToNC4HW4Fp32(origin_weight, packed_weight_, 1, conv_param_->kernel_h_ * conv_param_->kernel_w_,
conv_param_->output_channel_);
PackNCHWToNC4HW4Fp32(origin_weight, packed_weight_, 1, weight_tensor->Height() * weight_tensor->Width(),
weight_tensor->Batch());
// init bias
bias_data_ = reinterpret_cast<float *>(malloc(C4NUM * OC4 * sizeof(float)));
if (bias_data_ == nullptr) {
MS_LOG(ERROR) << "Malloc buffer failed.";
@ -78,16 +77,14 @@ int ConvolutionDepthwiseCPUKernel::InitWeightBias() {
memset(bias_data_, 0, C4NUM * OC4 * sizeof(float));
if (in_tensors_.size() == kInputSize2) {
auto ori_bias = reinterpret_cast<float *>(in_tensors_.at(kBiasIndex)->Data());
memcpy(bias_data_, ori_bias, conv_param_->output_channel_ * sizeof(float));
memcpy(bias_data_, ori_bias, in_tensors_.at(kBiasIndex)->ElementsNum() * sizeof(float));
}
// init threadNum;
conv_param_->thread_num_ = MSMIN(thread_count_, OC4);
return RET_OK;
}
int ConvolutionDepthwiseCPUKernel::InitBuffer() {
// malloc pack input and output buffer
if (conv_param_->input_channel_ % C4NUM != 0) {
need_align_ = true;
int IC4 = UP_DIV(conv_param_->input_channel_, C4NUM);
@ -97,7 +94,6 @@ int ConvolutionDepthwiseCPUKernel::InitBuffer() {
MS_LOG(ERROR) << "Malloc buffer failed.";
return RET_ERROR;
}
memset(packed_input_, 0, pack_input_size * sizeof(float));
int OC4 = UP_DIV(conv_param_->output_channel_, C4NUM);
int pack_output_size = conv_param_->output_batch_ * conv_param_->output_h_ * conv_param_->output_w_ * C4NUM * OC4;
@ -111,32 +107,29 @@ int ConvolutionDepthwiseCPUKernel::InitBuffer() {
}
int ConvolutionDepthwiseCPUKernel::Init() {
if (!InferShapeDone()) {
return RET_OK;
}
return ReSize();
}
int ConvolutionDepthwiseCPUKernel::ReSize() {
FreeTmpBuffer();
// conv base init
ConvolutionBaseCPUKernel::Init();
// init sliding window param
sliding_ = new (std::nothrow) SlidingWindowParam;
if (sliding_ == nullptr) {
MS_LOG(ERROR) << "new sliding window param failed.";
return RET_ERROR;
}
InitSlidingParamConvDw(sliding_, conv_param_, C4NUM);
auto ret = InitWeightBias();
if (ret != 0) {
MS_LOG(ERROR) << "Convolution depthwise fp32 InitWeightBias failed.";
return RET_ERROR;
}
if (!InferShapeDone()) {
return RET_OK;
}
return ReSize();
}
int ConvolutionDepthwiseCPUKernel::ReSize() {
FreeTmpBuffer();
ConvolutionBaseCPUKernel::Init();
InitSlidingParamConvDw(sliding_, conv_param_, C4NUM);
ret = InitBuffer();
auto ret = InitBuffer();
if (ret != 0) {
MS_LOG(ERROR) << "Convolution depthwise fp32 InitBuffer failed.";
return RET_ERROR;
@ -173,7 +166,6 @@ int ConvolutionDepthwiseCPUKernel::Run() {
auto input_tensor = in_tensors_.at(kInputIndex);
auto input_addr = reinterpret_cast<float *>(input_tensor->Data());
// pack input: to nhwc4
if (need_align_) {
PackNHWCToNHWC4Fp32(input_addr, packed_input_, conv_param_->input_batch_,
conv_param_->input_h_ * conv_param_->input_w_, conv_param_->input_channel_);

76
mindspore/lite/src/runtime/kernel/arm/fp32/convolution_depthwise_3x3.cc
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@ -27,12 +27,41 @@ using mindspore::lite::RET_OK;
using mindspore::schema::PrimitiveType_DepthwiseConv2D;
namespace mindspore::kernel {
ConvolutionDepthwise3x3CPUKernel::~ConvolutionDepthwise3x3CPUKernel() {
FreeTmpBufer();
if (block_buffer_ != nullptr) {
free(block_buffer_);
block_buffer_ = nullptr;
}
if (packed_weight_ != nullptr) {
free(packed_weight_);
packed_weight_ = nullptr;
}
}
void ConvolutionDepthwise3x3CPUKernel::FreeTmpBufer() {
if (need_align_) {
if (packed_input_ != nullptr) {
free(packed_input_);
packed_input_ = nullptr;
}
if (packed_output_ != nullptr) {
free(packed_output_);
packed_output_ = nullptr;
}
}
if (trans_buffer_ != nullptr) {
free(trans_buffer_);
trans_buffer_ = nullptr;
}
}
int ConvolutionDepthwise3x3CPUKernel::InitWeightBias() {
// init weight: o, h, w, i; o == group, i == 1
auto weight_tensor = in_tensors_[kWeightIndex];
auto origin_weight = reinterpret_cast<float *>(weight_tensor->Data());
// o h w 1 -> o/4 h w 1 4
int OC4 = UP_DIV(conv_param_->output_channel_, C4NUM);
int OC4 = UP_DIV(weight_tensor->Batch(), C4NUM);
int weight_c4_size = OC4 * C4NUM * 9;
auto tmp_weight = reinterpret_cast<float *>(malloc(weight_c4_size * sizeof(float)));
if (tmp_weight == nullptr) {
@ -40,8 +69,8 @@ int ConvolutionDepthwise3x3CPUKernel::InitWeightBias() {
return RET_ERROR;
}
memset(tmp_weight, 0, weight_c4_size * sizeof(float));
PackNCHWToNC4HW4Fp32(origin_weight, tmp_weight, 1, conv_param_->kernel_h_ * conv_param_->kernel_w_,
conv_param_->output_channel_);
PackNCHWToNC4HW4Fp32(origin_weight, tmp_weight, 1, weight_tensor->Height() * weight_tensor->Width(),
weight_tensor->Batch());
// weight transform
int packed_weight_size = OC4 * C4NUM * 16;
@ -62,8 +91,9 @@ int ConvolutionDepthwise3x3CPUKernel::InitWeightBias() {
memset(bias_data_, 0, C4NUM * OC4 * sizeof(float));
if (in_tensors_.size() == kInputSize2) {
auto ori_bias = reinterpret_cast<float *>(in_tensors_.at(kBiasIndex)->Data());
memcpy(bias_data_, ori_bias, conv_param_->output_channel_ * sizeof(float));
memcpy(bias_data_, ori_bias, in_tensors_.at(kBiasIndex)->ElementsNum() * sizeof(float));
}
conv_param_->thread_num_ = MSMIN(thread_count_, OC4);
return RET_OK;
}
@ -106,48 +136,22 @@ int ConvolutionDepthwise3x3CPUKernel::Init() {
MS_LOG(ERROR) << "malloc block buffer failed.";
return RET_ERROR;
}
auto ret = InitWeightBias();
if (ret != RET_OK) {
MS_LOG(ERROR) << "Depthwise3x3 fp32 initWeightBias error!ret: " << ret;
return ret;
}
if (!InferShapeDone()) {
return RET_OK;
}
return ReSize();
}
void ConvolutionDepthwise3x3CPUKernel::FreeTmpBufer() {
if (need_align_) {
if (packed_input_ != nullptr) {
free(packed_input_);
packed_input_ = nullptr;
}
if (packed_output_ != nullptr) {
free(packed_output_);
packed_output_ = nullptr;
}
}
if (trans_buffer_ != nullptr) {
free(trans_buffer_);
trans_buffer_ = nullptr;
}
if (packed_weight_ != nullptr) {
free(packed_weight_);
packed_weight_ = nullptr;
}
}
int ConvolutionDepthwise3x3CPUKernel::ReSize() {
FreeTmpBufer();
// conv base init
ConvolutionBaseCPUKernel::Init();
auto ret = InitWeightBias();
if (ret != RET_OK) {
MS_LOG(ERROR) << "Depthwise3x3 fp32 initWeightBias error!ret: " << ret;
return ret;
}
// init threadNum;
conv_param_->thread_num_ = MSMIN(thread_count_, UP_DIV(conv_param_->output_channel_, C4NUM));
ret = InitBuffer();
auto ret = InitBuffer();
if (ret != RET_OK) {
MS_LOG(ERROR) << "Depthwise3x3 fp32 initBuffer error!ret: " << ret;
return ret;

8
mindspore/lite/src/runtime/kernel/arm/fp32/convolution_depthwise_3x3.h
Unescape View File

@ -30,13 +30,7 @@ class ConvolutionDepthwise3x3CPUKernel : public ConvolutionBaseCPUKernel {
const mindspore::lite::PrimitiveC *primitive)
: ConvolutionBaseCPUKernel(parameter, inputs, outputs, ctx, primitive) {}
~ConvolutionDepthwise3x3CPUKernel() override {
FreeTmpBufer();
if (block_buffer_ != nullptr) {
free(block_buffer_);
block_buffer_ = nullptr;
}
};
~ConvolutionDepthwise3x3CPUKernel() override;
int Init() override;
int ReSize() override;

50
mindspore/lite/src/runtime/kernel/arm/fp32/deconvolution_depthwise.cc
Unescape View File

@ -27,18 +27,19 @@ using mindspore::lite::RET_OK;
using mindspore::schema::PrimitiveType_DeDepthwiseConv2D;
namespace mindspore::kernel {
DeconvolutionDepthwiseCPUKernel::~DeconvolutionDepthwiseCPUKernel() { FreeTmpBuffer(); }
void DeconvolutionDepthwiseCPUKernel::FreeTmpBuffer() {
DeconvolutionDepthwiseCPUKernel::~DeconvolutionDepthwiseCPUKernel() {
if (sliding_ != nullptr) {
delete sliding_;
sliding_ = nullptr;
}
if (packed_weight_ != nullptr) {
delete packed_weight_;
packed_weight_ = nullptr;
}
FreeTmpBuffer();
}
void DeconvolutionDepthwiseCPUKernel::FreeTmpBuffer() {
if (need_align_) {
if (packed_input_ != nullptr) {
delete packed_input_;
@ -60,9 +61,6 @@ int DeconvolutionDepthwiseCPUKernel::InitSlideParam() {
conv_param_->output_h_ = in_tensors_.front()->shape().at(kNHWC_H);
conv_param_->output_w_ = in_tensors_.front()->shape().at(kNHWC_W);
conv_param_->output_channel_ = in_tensors_.front()->shape().at(kNHWC_C);
// init sliding window param
sliding_ = new SlidingWindowParam;
InitSlidingParamConvDw(sliding_, conv_param_, C4NUM);
return RET_OK;
}
@ -71,19 +69,17 @@ int DeconvolutionDepthwiseCPUKernel::InitWeightBias() {
// init weight: o, h, w, i; o == group, i == 1
auto weight_tensor = in_tensors_[kWeightIndex];
auto origin_weight = reinterpret_cast<float *>(weight_tensor->Data());
int OC4 = UP_DIV(conv_param_->output_channel_, C4NUM);
int pack_weight_size = C4NUM * OC4 * conv_param_->kernel_h_ * conv_param_->kernel_w_;
int OC4 = UP_DIV(weight_tensor->Batch(), C4NUM);
int pack_weight_size = C4NUM * OC4 * weight_tensor->Height() * weight_tensor->Width();
packed_weight_ = reinterpret_cast<float *>(malloc(pack_weight_size * sizeof(float)));
if (packed_weight_ == nullptr) {
MS_LOG(ERROR) << "Malloc buffer failed.";
return RET_ERROR;
}
memset(packed_weight_, 0, pack_weight_size * sizeof(float));
PackNCHWToNC4HW4Fp32(origin_weight, packed_weight_, 1, conv_param_->kernel_h_ * conv_param_->kernel_w_,
conv_param_->output_channel_);
PackNCHWToNC4HW4Fp32(origin_weight, packed_weight_, 1, weight_tensor->Height() * weight_tensor->Width(),
weight_tensor->Batch());
// init bias
bias_data_ = reinterpret_cast<float *>(malloc(C4NUM * OC4 * sizeof(float)));
if (bias_data_ == nullptr) {
MS_LOG(ERROR) << "Malloc buffer failed.";
@ -92,16 +88,14 @@ int DeconvolutionDepthwiseCPUKernel::InitWeightBias() {
memset(bias_data_, 0, C4NUM * OC4 * sizeof(float));
if (in_tensors_.size() == kInputSize2) {
auto ori_bias = reinterpret_cast<float *>(in_tensors_.at(kBiasIndex)->Data());
memcpy(bias_data_, ori_bias, conv_param_->output_channel_ * sizeof(float));
memcpy(bias_data_, ori_bias, in_tensors_.at(kBiasIndex)->ElementsNum() * sizeof(float));
}
// init threadNum;
conv_param_->thread_num_ = MSMIN(conv_param_->thread_num_, OC4);
conv_param_->thread_num_ = MSMIN(thread_count_, OC4);
return RET_OK;
}
int DeconvolutionDepthwiseCPUKernel::InitBuffer() {
// malloc pack input and output buffer
if (conv_param_->input_channel_ % C4NUM != 0) {
need_align_ = true;
int IC4 = UP_DIV(conv_param_->input_channel_, C4NUM);
@ -111,7 +105,6 @@ int DeconvolutionDepthwiseCPUKernel::InitBuffer() {
MS_LOG(ERROR) << "Malloc buffer failed.";
return RET_ERROR;
}
memset(packed_input_, 0, pack_input_size * sizeof(float));
int OC4 = UP_DIV(conv_param_->output_channel_, C4NUM);
int pack_output_size = conv_param_->output_batch_ * conv_param_->output_h_ * conv_param_->output_w_ * C4NUM * OC4;
@ -126,6 +119,17 @@ int DeconvolutionDepthwiseCPUKernel::InitBuffer() {
}
int DeconvolutionDepthwiseCPUKernel::Init() {
sliding_ = new (std::nothrow) SlidingWindowParam;
if (sliding_ == nullptr) {
MS_LOG(ERROR) << "new sliding window param failed.";
return RET_ERROR;
}
auto ret = InitWeightBias();
if (ret != 0) {
MS_LOG(ERROR) << "Deconvolution depthwise fp32 InitWeightBias failed.ret: " << ret;
return ret;
}
if (!InferShapeDone()) {
return RET_OK;
}
@ -135,16 +139,9 @@ int DeconvolutionDepthwiseCPUKernel::Init() {
int DeconvolutionDepthwiseCPUKernel::ReSize() {
FreeTmpBuffer();
InitSlideParam();
// conv base init
ConvolutionBaseCPUKernel::Init();
auto ret = InitWeightBias();
if (ret != 0) {
MS_LOG(ERROR) << "Deconvolution depthwise fp32 InitWeightBias failed.ret: " << ret;
return ret;
}
ret = InitBuffer();
auto ret = InitBuffer();
if (ret != 0) {
MS_LOG(ERROR) << "Deconvolution depthwise fp32 InitBuffer failed.ret: " << ret;
return ret;
@ -181,7 +178,6 @@ int DeconvolutionDepthwiseCPUKernel::Run() {
auto input_tensor = in_tensors_.at(kInputIndex);
auto input_addr = reinterpret_cast<float *>(input_tensor->Data());
// pack input: to nhwc4
if (need_align_) {
PackNHWCToNHWC4Fp32(input_addr, packed_input_, conv_param_->input_batch_,
conv_param_->input_h_ * conv_param_->input_w_, conv_param_->input_channel_);

57
mindspore/lite/src/runtime/kernel/arm/int8/convolution_depthwise_int8.cc
Unescape View File

@ -29,15 +29,6 @@ using mindspore::schema::PrimitiveType_DepthwiseConv2D;
namespace mindspore::kernel {
void ConvolutionDepthwiseInt8CPUKernel::FreeTmpBuffer() {
if (sliding != nullptr) {
delete sliding;
sliding = nullptr;
}
if (packed_weight_ != nullptr) {
free(packed_weight_);
packed_weight_ = nullptr;
}
if (packed_input_ != nullptr) {
free(packed_input_);
packed_input_ = nullptr;
@ -51,6 +42,14 @@ void ConvolutionDepthwiseInt8CPUKernel::FreeTmpBuffer() {
}
ConvolutionDepthwiseInt8CPUKernel::~ConvolutionDepthwiseInt8CPUKernel() {
if (sliding != nullptr) {
delete sliding;
sliding = nullptr;
}
if (packed_weight_ != nullptr) {
free(packed_weight_);
packed_weight_ = nullptr;
}
FreeTmpBuffer();
FreeQuantParam();
}
@ -58,18 +57,18 @@ ConvolutionDepthwiseInt8CPUKernel::~ConvolutionDepthwiseInt8CPUKernel() {
int ConvolutionDepthwiseInt8CPUKernel::InitWeightBias() {
// init weight, int8 -> int16
// o, h, w, i -> o/8, h, w, i, 8; o == group, i == 1
auto origin_weight = reinterpret_cast<int8_t *>(in_tensors_[kWeightIndex]->Data());
int OC4 = UP_DIV(conv_param_->output_channel_, C4NUM);
int pack_weight_size = C4NUM * OC4 * conv_param_->kernel_h_ * conv_param_->kernel_w_;
auto weight_tensor = in_tensors_[kWeightIndex];
auto origin_weight = reinterpret_cast<int8_t *>(weight_tensor->Data());
int OC4 = UP_DIV(weight_tensor->Batch(), C4NUM);
int pack_weight_size = C4NUM * OC4 * weight_tensor->Height() * weight_tensor->Width();
packed_weight_ = reinterpret_cast<int16_t *>(malloc(pack_weight_size * sizeof(int16_t)));
if (packed_weight_ == nullptr) {
MS_LOG(ERROR) << "Malloc buffer failed.";
return RET_ERROR;
}
memset(packed_weight_, 0, pack_weight_size * sizeof(int16_t));
PackDepthwiseInt8Weight(origin_weight, packed_weight_, conv_param_);
PackDepthwiseInt8Weight(origin_weight, packed_weight_, weight_tensor->Height() * weight_tensor->Width(),
weight_tensor->Batch(), &(conv_param_->conv_quant_arg_));
// init bias, add output zp
bias_data_ = reinterpret_cast<int32_t *>(malloc(C4NUM * OC4 * sizeof(int32_t)));
if (bias_data_ == nullptr) {
MS_LOG(ERROR) << "Malloc buffer failed.";
@ -77,18 +76,19 @@ int ConvolutionDepthwiseInt8CPUKernel::InitWeightBias() {
}
memset(bias_data_, 0, C4NUM * OC4 * sizeof(int32_t));
if (in_tensors_.size() == kInputSize2) {
auto ori_bias = reinterpret_cast<int32_t *>(in_tensors_.at(kBiasIndex)->Data());
memcpy(bias_data_, ori_bias, conv_param_->output_channel_ * sizeof(int32_t));
auto bias_tensor = in_tensors_.at(kBiasIndex);
auto ori_bias = reinterpret_cast<int32_t *>(bias_tensor->Data());
memcpy(bias_data_, ori_bias, bias_tensor->ElementsNum() * sizeof(int32_t));
}
conv_param_->thread_num_ = MSMIN(thread_count_, OC4);
return RET_OK;
}
int ConvolutionDepthwiseInt8CPUKernel::InitBuffer() {
// malloc packed input buffer
int pack_input_size = conv_param_->input_batch_ * conv_param_->input_h_ * conv_param_->input_w_ * C4NUM *
UP_DIV(conv_param_->input_channel_, 4);
packed_input_ = reinterpret_cast<int16_t *>(malloc(pack_input_size * sizeof(int16_t)));
memset(packed_input_, 0, pack_input_size * sizeof(int16_t));
if (packed_input_ == nullptr) {
MS_LOG(ERROR) << "Malloc buffer failed.";
return RET_ERROR;
@ -108,6 +108,11 @@ int ConvolutionDepthwiseInt8CPUKernel::InitBuffer() {
}
int ConvolutionDepthwiseInt8CPUKernel::Init() {
sliding = new (std::nothrow) SlidingWindowParam;
if (sliding == nullptr) {
MS_LOG(ERROR) << "new sliding window param.";
return RET_ERROR;
}
if (!InferShapeDone()) {
return RET_OK;
}
@ -116,32 +121,19 @@ int ConvolutionDepthwiseInt8CPUKernel::Init() {
int ConvolutionDepthwiseInt8CPUKernel::ReSize() {
FreeTmpBuffer();
// conv base init
ConvolutionBaseCPUKernel::Init();
// init sliding window param
sliding = new (std::nothrow) SlidingWindowParam;
if (sliding == nullptr) {
MS_LOG(ERROR) << "new sliding window param.";
return RET_ERROR;
}
InitSlidingParamConvDw(sliding, conv_param_, C4NUM);
// init quant param
auto ret = ConvolutionBaseCPUKernel::SetQuantParam();
if (ret != RET_OK) {
MS_LOG(ERROR) << "Set quant param failed.";
return ret;
}
// init weight and bias
ret = InitWeightBias();
if (ret != RET_OK) {
MS_LOG(ERROR) << "Depthwise int8 InitWeightBias error!";
return ret;
}
ret = InitBuffer();
if (ret != RET_OK) {
MS_LOG(ERROR) << "Depthwise int8 ReSize error!";
@ -177,7 +169,6 @@ int ConvolutionDepthwiseInt8CPUKernel::Run() {
return RET_ERROR;
}
// pack input, assume input format: NHWC -> NHWC4
auto input_tensor = in_tensors_.at(kInputIndex);
auto input_addr = reinterpret_cast<int8_t *>(input_tensor->Data());
PackDepthwiseInt8Input(input_addr, packed_input_, conv_param_);

62
mindspore/lite/src/runtime/kernel/arm/int8/deconvolution_depthwise_int8.cc
Unescape View File

@ -29,11 +29,6 @@ using mindspore::schema::PrimitiveType_DeDepthwiseConv2D;
namespace mindspore::kernel {
DeconvolutionDepthwiseInt8CPUKernel::~DeconvolutionDepthwiseInt8CPUKernel() {
FreeTmpBuffer();
FreeQuantParam();
}
void DeconvolutionDepthwiseInt8CPUKernel::FreeTmpBuffer() {
if (sliding != nullptr) {
delete sliding;
sliding = nullptr;
@ -42,6 +37,11 @@ void DeconvolutionDepthwiseInt8CPUKernel::FreeTmpBuffer() {
delete packed_weight_;
packed_weight_ = nullptr;
}
FreeTmpBuffer();
FreeQuantParam();
}
void DeconvolutionDepthwiseInt8CPUKernel::FreeTmpBuffer() {
if (packed_input_ != nullptr) {
delete packed_input_;
packed_input_ = nullptr;
@ -61,18 +61,18 @@ void DeconvolutionDepthwiseInt8CPUKernel::FreeTmpBuffer() {
int DeconvolutionDepthwiseInt8CPUKernel::InitWeightBias() {
// init weight: int8 -> int16
// o, h, w, i -> o/8, h, w, i, 8; o == group, i == 1
auto origin_weight = reinterpret_cast<int8_t *>(in_tensors_[kWeightIndex]->Data());
int OC4 = UP_DIV(conv_param_->output_channel_, C4NUM);
int pack_weight_size = C4NUM * OC4 * conv_param_->kernel_h_ * conv_param_->kernel_w_;
auto weight_tensor = in_tensors_[kWeightIndex];
auto origin_weight = reinterpret_cast<int8_t *>(weight_tensor->Data());
int OC4 = UP_DIV(weight_tensor->Batch(), C4NUM);
int pack_weight_size = C4NUM * OC4 * weight_tensor->Height() * weight_tensor->Width();
packed_weight_ = reinterpret_cast<int16_t *>(malloc(pack_weight_size * sizeof(int16_t)));
if (packed_weight_ == nullptr) {
MS_LOG(ERROR) << "Malloc buffer failed.";
return RET_ERROR;
}
memset(packed_weight_, 0, pack_weight_size * sizeof(int16_t));
PackDepthwiseInt8Weight(origin_weight, packed_weight_, conv_param_);
PackDepthwiseInt8Weight(origin_weight, packed_weight_, weight_tensor->Height() * weight_tensor->Width(),
weight_tensor->Batch(), &(conv_param_->conv_quant_arg_));
// init bias, add output zp
bias_data_ = reinterpret_cast<int32_t *>(malloc(C4NUM * OC4 * sizeof(int32_t)));
if (bias_data_ == nullptr) {
MS_LOG(ERROR) << "Malloc buffer failed.";
@ -80,9 +80,11 @@ int DeconvolutionDepthwiseInt8CPUKernel::InitWeightBias() {
}
memset(bias_data_, 0, C4NUM * OC4 * sizeof(int32_t));
if (in_tensors_.size() == kInputSize2) {
auto ori_bias = reinterpret_cast<int32_t *>(in_tensors_.at(kBiasIndex)->Data());
memcpy(bias_data_, ori_bias, conv_param_->output_channel_ * sizeof(int32_t));
auto bias_tensor = in_tensors_.at(kBiasIndex);
auto ori_bias = reinterpret_cast<int32_t *>(bias_tensor->Data());
memcpy(bias_data_, ori_bias, bias_tensor->ElementsNum() * sizeof(int32_t));
}
conv_param_->thread_num_ = MSMIN(thread_count_, OC4);
return RET_OK;
}
@ -96,7 +98,6 @@ int DeconvolutionDepthwiseInt8CPUKernel::InitSlideParam() {
conv_param_->output_w_ = in_tensors_.front()->shape().at(kNHWC_W);
conv_param_->output_channel_ = in_tensors_.front()->shape().at(kNHWC_C);
// init sliding window param
InitSlidingParamConvDw(sliding, conv_param_, C4NUM);
sliding->in_h_step_ = conv_param_->input_w_ * C4NUM;
@ -108,11 +109,9 @@ int DeconvolutionDepthwiseInt8CPUKernel::InitSlideParam() {
}
int DeconvolutionDepthwiseInt8CPUKernel::InitBuffer() {
// malloc packed input buffer
int pack_input_size = conv_param_->input_batch_ * conv_param_->input_h_ * conv_param_->input_w_ * C4NUM *
UP_DIV(conv_param_->input_channel_, 4);
packed_input_ = reinterpret_cast<int16_t *>(malloc(pack_input_size * sizeof(int16_t)));
memset(packed_input_, 0, pack_input_size * sizeof(int16_t));
if (packed_input_ == nullptr) {
MS_LOG(ERROR) << "Malloc buffer failed.";
return RET_ERROR;
@ -130,7 +129,6 @@ int DeconvolutionDepthwiseInt8CPUKernel::InitBuffer() {
memset(packed_output_, 0, pack_output_size * sizeof(int8_t));
}
// malloc tmp buffer for int32 output
output_buffer_ =
reinterpret_cast<int32_t *>(malloc(conv_param_->output_h_ * conv_param_->output_w_ * C4NUM * sizeof(int32_t)));
if (output_buffer_ == nullptr) {
@ -145,41 +143,33 @@ int DeconvolutionDepthwiseInt8CPUKernel::InitBuffer() {
}
int DeconvolutionDepthwiseInt8CPUKernel::Init() {
if (!InferShapeDone()) {
return RET_OK;
}
return ReSize();
}
int DeconvolutionDepthwiseInt8CPUKernel::ReSize() {
FreeTmpBuffer();
sliding = new (std::nothrow) SlidingWindowParam;
if (sliding == nullptr) {
MS_LOG(ERROR) << "new SlidingWindowParam fail!";
return RET_ERROR;
}
InitSlideParam();
// conv base init
ConvolutionBaseCPUKernel::Init();
// init quant param
auto ret = ConvolutionBaseCPUKernel::SetQuantParam();
if (ret != RET_OK) {
MS_LOG(ERROR) << "Set quant param failed.";
return ret;
}
// init weight and bias
ret = InitWeightBias();
if (ret != RET_OK) {
MS_LOG(ERROR) << "Deconv Depthwise int8 InitWeightBias error!";
return ret;
}
if (!InferShapeDone()) {
return RET_OK;
}
return ReSize();
}
int DeconvolutionDepthwiseInt8CPUKernel::ReSize() {
FreeTmpBuffer();
InitSlideParam();
ConvolutionBaseCPUKernel::Init();
ret = InitBuffer();
auto ret = InitBuffer();
if (ret != RET_OK) {
MS_LOG(ERROR) << "Deconv Depthwise int8 InitBuffer error!";
return ret;

18
mindspore/lite/src/runtime/kernel/arm/nnacl/pack.c
Unescape View File

@ -1035,18 +1035,18 @@ void PackDepthwiseInt8Input(const int8_t *src, int16_t *dst, const ConvParameter
}
}
void PackDepthwiseInt8Weight(const int8_t *origin_weight, int16_t *packed_weight_, const ConvParameter *conv_param) {
int weight_zp = conv_param->conv_quant_arg_.filter_quant_args_[0].zp_;
int unit = conv_param->kernel_h_ * conv_param->kernel_w_;
for (int c = 0; c < conv_param->output_channel_; c++) {
if (conv_param->conv_quant_arg_.per_channel_ & FILTER_PER_CHANNEL) {
weight_zp = conv_param->conv_quant_arg_.filter_quant_args_[c].zp_;
void PackDepthwiseInt8Weight(const int8_t *origin_weight, int16_t *packed_weight_, int plane, int channel,
ConvQuantArg *quant_qrg) {
int weight_zp = quant_qrg->filter_quant_args_[0].zp_;
for (int c = 0; c < channel; c++) {
if (quant_qrg->per_channel_ & FILTER_PER_CHANNEL) {
weight_zp = quant_qrg->filter_quant_args_[c].zp_;
}
int c4_block_num = c / C4NUM;
int c4_block_rem = c % C4NUM;
const int8_t *src_c = origin_weight + c * unit;
int16_t *dst_c = packed_weight_ + c4_block_num * unit * C4NUM;
for (int k = 0; k < unit; k++) {
const int8_t *src_c = origin_weight + c * plane;
int16_t *dst_c = packed_weight_ + c4_block_num * plane * C4NUM;
for (int k = 0; k < plane; k++) {
const int8_t *src_kernel = src_c + k;
int16_t *dst_kernel = dst_c + C4NUM * k + c4_block_rem;
*dst_kernel = (int16_t)(src_kernel[0] - weight_zp);

3
mindspore/lite/src/runtime/kernel/arm/nnacl/pack.h
Unescape View File

@ -100,7 +100,8 @@ void PackNCHWToNHWCInt8(const void *src, void *dst, int batch, int plane, int ch
void PackDepthwiseInt8Input(const int8_t *src, int16_t *dst, const ConvParameter *conv_param);
void PackDepthwiseInt8Weight(const int8_t *src, int16_t *dst, const ConvParameter *conv_param);
void PackDepthwiseInt8Weight(const int8_t *origin_weight, int16_t *packed_weight_, int plane, int channel,
ConvQuantArg *quant_qrg);
#ifdef __cplusplus
}
#endif

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