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[MSLITE][Develop] fix bug of arm cpu fp16 op cast

This commit is contained in:
yangruoqi713 2020-11-04 16:12:01 +08:00
parent a3f9be98c0
commit 27cc6d6c17
5 changed files with 70 additions and 23 deletions
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12
mindspore/lite/nnacl/fp16/cast_fp16.c
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@ -27,6 +27,18 @@ void Uint8ToFloat16(const uint8_t *input, float16_t *output, int number) {
}
}
void Float16ToInt32(const float16_t *input, int32_t *output, int number) {
for (int i = 0; i < number; ++i) {
output[i] = (int32_t)input[i];
}
}
void Float16ToInt64(const float16_t *input, int64_t *output, int number) {
for (int i = 0; i < number; ++i) {
output[i] = (int64_t)input[i];
}
}
#ifndef ENABLE_ARM64
void Float32ToFloat16(const float *input, float16_t *output, int number) {
for (int i = 0; i < number; ++i) {

2
mindspore/lite/nnacl/fp16/cast_fp16.h
View File

@ -24,6 +24,8 @@ extern "C" {
#endif
void BoolToFloat16(const bool *input, float16_t *output, int number);
void Uint8ToFloat16(const uint8_t *input, float16_t *output, int number);
void Float16ToInt32(const float16_t *input, int32_t *output, int number);
void Float16ToInt64(const float16_t *input, int64_t *output, int number);
void Float32ToFloat16(const float *input, float16_t *output, int number);
void Float16ToFloat32(const float16_t *input, float *output, int number);
#ifdef __cplusplus

71
mindspore/lite/src/runtime/kernel/arm/fp16/cast_fp16.cc
View File

@ -65,25 +65,58 @@ int CastFp16CPUKernel::DoCast(int thread_id) {
}
auto offset = thread_id * stride_;
auto output_data = out_tensors_.at(0)->MutableData();
switch (input->data_type()) {
case kNumberTypeBool:
BoolToFloat16(reinterpret_cast<bool *>(input->MutableData()) + offset,
reinterpret_cast<float16_t *>(output_data) + offset, data_num);
case kNumberTypeUInt8:
Uint8ToFloat16(reinterpret_cast<uint8_t *>(input->MutableData()) + offset,
reinterpret_cast<float16_t *>(output_data) + offset, data_num);
case kNumberTypeFloat32:
Float32ToFloat16(reinterpret_cast<float *>(input->MutableData()) + offset,
reinterpret_cast<float16_t *>(output_data) + offset, data_num);
break;
case kNumberTypeFloat16:
Float16ToFloat32(reinterpret_cast<float16_t *>(input->MutableData()) + offset,
reinterpret_cast<float *>(output_data) + offset, data_num);
break;
default:
MS_LOG(ERROR) << "Unsupported input data type " << input->data_type();
return RET_ERROR;
auto output = out_tensors_.at(0);
auto output_data = output->data_c();
auto input_data_type = input->data_type();
auto output_data_type = output->data_type();
if (input_data_type == kNumberTypeFloat16) {
switch (output_data_type) {
case kNumberTypeInt64:
Float16ToInt64(reinterpret_cast<float16_t *>(input->data_c()) + offset,
reinterpret_cast<int64_t *>(output_data) + offset, data_num);
break;
case kNumberTypeInt32:
Float16ToInt32(reinterpret_cast<float16_t *>(input->data_c()) + offset,
reinterpret_cast<int32_t *>(output_data) + offset, data_num);
break;
case kNumberTypeFloat32:
Float16ToFloat32(reinterpret_cast<float16_t *>(input->MutableData()) + offset,
reinterpret_cast<float *>(output_data) + offset, data_num);
break;
case kNumberTypeFloat16:
memcpy(reinterpret_cast<float16_t *>(output_data) + offset,
reinterpret_cast<float16_t *>(input->data_c()) + offset, data_num * sizeof(float16_t));
break;
default:
MS_LOG(ERROR) << "Unsupported output data type " << output_data_type;
return RET_ERROR;
}
} else if (input_data_type == kNumberTypeFloat32) {
switch (output_data_type) {
case kNumberTypeInt64:
Float32ToInt64(reinterpret_cast<float *>(input->data_c()) + offset,
reinterpret_cast<int64_t *>(output_data) + offset, data_num);
break;
case kNumberTypeInt32:
Float32ToInt32(reinterpret_cast<float *>(input->data_c()) + offset,
reinterpret_cast<int32_t *>(output_data) + offset, data_num);
break;
case kNumberTypeFloat32:
memcpy(reinterpret_cast<float *>(output_data) + offset, reinterpret_cast<float *>(input->data_c()) + offset,
data_num * sizeof(float));
break;
case kNumberTypeFloat16:
Float32ToFloat16(reinterpret_cast<float *>(input->MutableData()) + offset,
reinterpret_cast<float16_t *>(output_data) + offset, data_num);
break;
default:
MS_LOG(ERROR) << "Unsupported output data type " << output_data_type;
return RET_ERROR;
}
} else {
MS_LOG(ERROR) << "Unsupported input data type " << input_data_type;
return RET_ERROR;
}
return RET_OK;
}

2
mindspore/lite/src/runtime/kernel/arm/fp16/pad_fp16.cc
View File

@ -94,6 +94,4 @@ kernel::LiteKernel *CpuPadFp16KernelCreator(const std::vector<lite::Tensor *> &i
}
return kernel;
}
REG_KERNEL(kCPU, kNumberTypeFloat16, PrimitiveType_Pad, CpuPadFp16KernelCreator)
} // namespace mindspore::kernel

6
mindspore/lite/src/scheduler.cc
View File

@ -304,7 +304,8 @@ TypeId Scheduler::GetFirstFp32Fp16OrInt8Type(const std::vector<Tensor *> &in_ten
return dtype;
}
}
return kNumberTypeFloat32;
MS_ASSERT(in_tensors.size() > 0);
return in_tensors[0]->data_type();
}
void Scheduler::SetKernelTensorDataType(kernel::LiteKernel *kernel) {
@ -346,7 +347,8 @@ kernel::SubGraphType Scheduler::GetKernelSubGraphType(kernel::LiteKernel *kernel
if (desc.data_type == kNumberTypeFloat16) {
return kernel::kCpuFP16SubGraph;
} else if (desc.data_type == kNumberTypeFloat32 || desc.data_type == kNumberTypeInt8 ||
desc.data_type == kNumberTypeInt32 || desc.data_type == kNumberTypeBool) {
desc.data_type == kNumberTypeInt32 || desc.data_type == kNumberTypeBool ||
desc.data_type == kNumberTypeUInt8) {
return kernel::kCpuFP32SubGraph;
}
}