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!10749 [MSLITE][DEVELOP] debug npu model, fix bug of npu

Browse Source 
From: @yangruoqi713
Reviewed-by: @zhang_xue_tong,@hangangqiang
Signed-off-by: @zhang_xue_tong
pull/10749/MERGE
mindspore-ci-bot 4 years ago committed by Gitee
parent 0525e98953 601697439c
commit 0fe251ec7c
8 changed files with 58 additions and 48 deletions
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2
mindspore/lite/src/lite_kernel.h
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@ -94,7 +94,7 @@ class LiteKernel {
virtual int Init() { return mindspore::lite::RET_ERROR; }
OpParameter *op_parameter() { return op_parameter_; }
OpParameter *op_parameter() const { return op_parameter_; }
std::string name() const { return this->name_; }

41
mindspore/lite/src/runtime/agent/npu/npu_executor.cc
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@ -37,23 +37,42 @@ int NPUExecutor::Prepare(const std::vector<kernel::LiteKernel *> &kernels) {
return RET_OK;
}
bool IsSameShapeTensor(Tensor *tensor, std::shared_ptr<hiai::AiTensor> npu_tensor) {
return tensor->Batch() == npu_tensor->GetTensorDimension().GetNumber() &&
tensor->Channel() == npu_tensor->GetTensorDimension().GetChannel() &&
tensor->Height() == npu_tensor->GetTensorDimension().GetHeight() &&
tensor->Width() == npu_tensor->GetTensorDimension().GetWidth();
}
int NPUExecutor::Run(const std::vector<Tensor *> &in_tensors, const std::vector<Tensor *> &out_tensors,
const std::vector<kernel::LiteKernel *> &out_kernels,
const std::vector<kernel::LiteKernel *> &kernels, Allocator *allocator,
const KernelCallBack &before, const KernelCallBack &after) {
hiai::AiContext context;
for (int i = 0; i < npu_input_tensors_.size(); ++i) {
void *data = in_tensors[i]->data_c();
if (data == nullptr) {
MS_LOG(ERROR) << model_name_ << " inputs data is nullptr";
return RET_ERROR;
}
memcpy(npu_input_tensors_[i]->GetBuffer(), data, in_tensors[i]->Size());
in_tensors[i]->set_ref_count(in_tensors[i]->ref_count() - 1);
if (in_tensors[i]->ref_count() <= 0) {
auto ret = in_tensors[i]->FreeData();
if (ret != RET_OK) {
MS_LOG(ERROR) << "Free tensor data failed";
int index = 0;
for (; index < in_tensors.size(); index++) {
if (IsSameShapeTensor(in_tensors[index], npu_input_tensors_[i])) {
void *data = in_tensors[index]->data_c();
if (data == nullptr) {
MS_LOG(ERROR) << model_name_ << " Inputs data is nullptr";
return RET_ERROR;
}
memcpy(npu_input_tensors_[i]->GetBuffer(), data, in_tensors[index]->Size());
in_tensors[index]->set_ref_count(in_tensors[index]->ref_count() - 1);
if (in_tensors[index]->ref_count() <= 0) {
auto ret = in_tensors[index]->FreeData();
if (ret != RET_OK) {
MS_LOG(ERROR) << "Free tensor data failed";
return RET_ERROR;
}
}
break;
}
if (index == in_tensors.size()) {
MS_LOG(ERROR) << "Can't find corresponding ms lite tensor of " << i << " input tensor for npu executor "
<< model_name_;
return RET_ERROR;
}
}

16
mindspore/lite/src/runtime/agent/npu/optimizer/npu_fusion_pass.cc
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@ -23,15 +23,14 @@ namespace mindspore::lite {
bool CheckFusion(kernel::LiteKernel *kernel) {
auto pre_flag =
std::all_of(kernel->in_kernels().begin(), kernel->in_kernels().end(), [](const kernel::LiteKernel *in_kernel) {
return NPUPassUtils::IsNchw2Nhwc(const_cast<kernel::LiteKernel *>(in_kernel)) &&
in_kernel->out_kernels().size() == 1;
return NPUPassUtils::IsNchw2Nhwc(in_kernel) && in_kernel->out_kernels().size() == 1;
});
if (!pre_flag) {
return false;
}
auto post_flag =
std::all_of(kernel->out_kernels().begin(), kernel->out_kernels().end(), [](const kernel::LiteKernel *out_kernel) {
return NPUPassUtils::IsNhwc2Nchw(const_cast<kernel::LiteKernel *>(out_kernel));
return NPUPassUtils::IsNhwc2Nchw(out_kernel) && (!out_kernel->out_kernels().empty());
});
return post_flag;
}
@ -40,16 +39,16 @@ bool CheckFormatFusion(kernel::LiteKernel *kernel) {
if (kernel->out_kernels().empty()) {
return false;
}
if (NPUPassUtils::IsNhwc2Nchw(const_cast<kernel::LiteKernel *>(kernel))) {
if (NPUPassUtils::IsNhwc2Nchw(kernel)) {
return std::all_of(kernel->out_kernels().begin(), kernel->out_kernels().end(),
[](const kernel::LiteKernel *kernel) {
return NPUPassUtils::IsNchw2Nhwc(const_cast<kernel::LiteKernel *>(kernel));
return NPUPassUtils::IsNchw2Nhwc(kernel) && (!kernel->out_kernels().empty());
});
}
if (NPUPassUtils::IsNchw2Nhwc(const_cast<kernel::LiteKernel *>(kernel))) {
if (NPUPassUtils::IsNchw2Nhwc(kernel)) {
return std::all_of(kernel->out_kernels().begin(), kernel->out_kernels().end(),
[](const kernel::LiteKernel *kernel) {
return NPUPassUtils::IsNhwc2Nchw(const_cast<kernel::LiteKernel *>(kernel));
return NPUPassUtils::IsNhwc2Nchw(kernel) && (!kernel->out_kernels().empty());
});
}
return false;
@ -230,8 +229,7 @@ int NPUFusionPass::FormatFusion(kernel::LiteKernel *kernel) {
int NPUFusionPass::Run() {
for (size_t i = 0; i < kernels->size(); i++) {
auto kernel = (*kernels)[i];
if (NPUPassUtils::IsNchw2Nhwc(const_cast<kernel::LiteKernel *>(kernel)) ||
NPUPassUtils::IsNhwc2Nchw(const_cast<kernel::LiteKernel *>(kernel))) {
if (NPUPassUtils::IsNchw2Nhwc(kernel) || NPUPassUtils::IsNhwc2Nchw(kernel)) {
if (CheckFormatFusion(kernel)) {
i--;
FormatFusion(kernel);

16
mindspore/lite/src/runtime/agent/npu/optimizer/npu_insert_transform_pass.cc
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@ -29,14 +29,10 @@ int GetInsertState(kernel::LiteKernel *kernel) {
if (npu_insert_nodes.find(kernel->Type()) == npu_insert_nodes.end()) {
return InsertNone;
}
auto pre_flag =
std::all_of(kernel->in_kernels().begin(), kernel->in_kernels().end(), [](const kernel::LiteKernel *kernel) {
return NPUPassUtils::IsNchw2Nhwc(const_cast<kernel::LiteKernel *>(kernel));
});
auto post_flag =
std::all_of(kernel->out_kernels().begin(), kernel->out_kernels().end(), [](const kernel::LiteKernel *kernel) {
return NPUPassUtils::IsNhwc2Nchw(const_cast<kernel::LiteKernel *>(kernel));
});
auto pre_flag = std::all_of(kernel->in_kernels().begin(), kernel->in_kernels().end(),
[](const kernel::LiteKernel *kernel) { return NPUPassUtils::IsNchw2Nhwc(kernel); });
auto post_flag = std::all_of(kernel->out_kernels().begin(), kernel->out_kernels().end(),
[](const kernel::LiteKernel *kernel) { return NPUPassUtils::IsNhwc2Nchw(kernel); });
if (pre_flag && !post_flag) {
return PostInsert;
}
@ -50,7 +46,7 @@ int NPUInsertTransformPass::InsertPreNode(const InnerContext *context, kernel::L
std::vector<kernel::LiteKernel *> *trans_kernels,
std::vector<Tensor *> *all_tensors) {
for (auto in_kernel : kernel->in_kernels()) {
if (NPUPassUtils::IsNchw2Nhwc(const_cast<kernel::LiteKernel *>(in_kernel))) {
if (NPUPassUtils::IsNchw2Nhwc(in_kernel)) {
continue;
}
auto nhwc_shape = in_kernel->out_tensors()[0]->shape();
@ -88,7 +84,7 @@ int NPUInsertTransformPass::InsertPostNode(const InnerContext *context, kernel::
std::vector<kernel::LiteKernel *> *trans_kernels,
std::vector<Tensor *> *all_tensors) {
for (auto out_kernel : kernel->out_kernels()) {
if (NPUPassUtils::IsNhwc2Nchw(const_cast<kernel::LiteKernel *>(out_kernel))) {
if (NPUPassUtils::IsNhwc2Nchw(out_kernel)) {
continue;
}
auto nhwc_shape = kernel->out_tensors()[0]->shape();

4
mindspore/lite/src/runtime/agent/npu/optimizer/npu_pass_utils.cc
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@ -192,7 +192,7 @@ void NPUPassUtils::UpdateNC2NHTransNodeAfterKernel(kernel::LiteKernel *kernel, k
post_kernel->set_in_tensors({post_in_tensors});
}
bool NPUPassUtils::IsNhwc2Nchw(kernel::LiteKernel *kernel) {
bool NPUPassUtils::IsNhwc2Nchw(const kernel::LiteKernel *kernel) {
if (kernel->Type() != schema::PrimitiveType_Transpose) {
return false;
}
@ -209,7 +209,7 @@ bool NPUPassUtils::IsNhwc2Nchw(kernel::LiteKernel *kernel) {
return false;
}
bool NPUPassUtils::IsNchw2Nhwc(kernel::LiteKernel *kernel) {
bool NPUPassUtils::IsNchw2Nhwc(const kernel::LiteKernel *kernel) {
if (kernel->Type() != schema::PrimitiveType_Transpose) {
return false;
}

4
mindspore/lite/src/runtime/agent/npu/optimizer/npu_pass_utils.h
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@ -47,9 +47,9 @@ class NPUPassUtils {
static void UpdateNC2NHTransNodeAfterKernel(kernel::LiteKernel *kernel, kernel::LiteKernel *trans_kernel,
kernel::LiteKernel *post_kernel);
static bool IsNhwc2Nchw(kernel::LiteKernel *kernel);
static bool IsNhwc2Nchw(const kernel::LiteKernel *kernel);
static bool IsNchw2Nhwc(kernel::LiteKernel *kernel);
static bool IsNchw2Nhwc(const kernel::LiteKernel *kernel);
private:
static PrimitiveC *CreateTransposePrimitive();

17
mindspore/lite/src/runtime/kernel/arm/fp32/transpose_fp32.cc
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@ -93,25 +93,26 @@ int TransposeCPUKernel::Run() {
memcpy(out_data_, in_data_, in_tensor->ElementsNum() * sizeof(float));
return RET_OK;
}
auto out_shape = out_tensor->shape();
if (in_tensor->shape().size() == 4 && param->perm_[0] == 0 && param->perm_[1] == 2 && param->perm_[2] == 3 &&
param->perm_[3] == 1) {
if (in_tensor->data_type() == kNumberTypeFloat32) {
PackNCHWToNHWCFp32(in_tensor->MutableData(), out_tensor->MutableData(), out_tensor->Batch(),
out_tensor->Height() * out_tensor->Width(), out_tensor->Channel());
PackNCHWToNHWCFp32(in_tensor->MutableData(), out_tensor->MutableData(), out_shape[0], out_shape[1] * out_shape[2],
out_shape[3]);
} else if (in_tensor->data_type() == kNumberTypeInt8) {
PackNCHWToNHWCInt8(in_tensor->MutableData(), out_tensor->MutableData(), out_tensor->Batch(),
out_tensor->Height() * out_tensor->Width(), out_tensor->Channel());
PackNCHWToNHWCInt8(in_tensor->MutableData(), out_tensor->MutableData(), out_shape[0], out_shape[1] * out_shape[2],
out_shape[3]);
}
return RET_OK;
}
if (in_tensor->shape().size() == 4 && param->perm_[0] == 0 && param->perm_[1] == 3 && param->perm_[2] == 1 &&
param->perm_[3] == 2) {
if (in_tensor->data_type() == kNumberTypeFloat32) {
PackNHWCToNCHWFp32(in_tensor->MutableData(), out_tensor->MutableData(), out_tensor->Batch(),
out_tensor->Height() * out_tensor->Width(), out_tensor->Channel());
PackNHWCToNCHWFp32(in_tensor->MutableData(), out_tensor->MutableData(), out_shape[0], out_shape[2] * out_shape[3],
out_shape[1]);
} else if (in_tensor->data_type() == kNumberTypeInt8) {
PackNHWCToNCHWInt8(in_tensor->MutableData(), out_tensor->MutableData(), out_tensor->Batch(),
out_tensor->Height() * out_tensor->Width(), out_tensor->Channel());
PackNHWCToNCHWInt8(in_tensor->MutableData(), out_tensor->MutableData(), out_shape[0], out_shape[2] * out_shape[3],
out_shape[1]);
}
return RET_OK;
}

6
mindspore/lite/src/runtime/kernel/npu/deconvolution_npu.cc
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@ -24,17 +24,13 @@ using mindspore::schema::PrimitiveType_DeConv2D;
namespace mindspore::kernel {
int DeconvolutionNPUKernel::IsSupport(const std::vector<lite::Tensor *> &inputs,
const std::vector<lite::Tensor *> &outputs, OpParameter *opParameter) {
if (conv_param_->group_ != 1) {
MS_LOG(WARNING) << "Only support group equals 1 for npu deconvolution op";
return RET_ERROR;
}
return RET_OK;
}
int DeconvolutionNPUKernel::SetConvParam() {
deconv_->set_attr_strides(ge::AttrValue::LIST_INT({conv_param_->stride_h_, conv_param_->stride_w_}));
deconv_->set_attr_dilations(ge::AttrValue::LIST_INT({conv_param_->dilation_h_, conv_param_->dilation_w_}));
deconv_->set_attr_groups(conv_param_->group_);
deconv_->set_attr_groups(1);
if (conv_param_->pad_mode_ == Pad_Same) {
deconv_->set_attr_pad_mode(ge::AttrValue::STR{"SAME"});

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