forked from mindspore-Ecosystem/mindspore
fix reduce ops buffer_sizes_ and data_buffers_ size
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ca0bbd3d69
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@ -146,7 +146,8 @@ void ReduceBaseCPUKernel::CalculateInnerOuterSize() {
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void ReduceBaseCPUKernel::CalculateTmpBufferSize() {
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buffer_sizes_.clear();
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auto input_shape = in_tensors_.at(0)->shape();
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for (auto i = 0; i < num_axes_; i++) {
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// calculate size of buffer to malloc for each reducing axis
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for (auto i = 0; i < num_axes_ - 1; i++) {
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int axis = axes_[i];
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size_t size = 1;
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for (size_t j = 0; j < input_shape.size(); j++) {
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@ -82,14 +82,7 @@ int ReduceFp16CPUKernel::Run() {
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}
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auto in_tensor = in_tensors_.at(0);
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if (in_tensor->data_type() == kNumberTypeFloat32 || in_tensor->data_type() == kNumberTypeFloat) {
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auto input_data = reinterpret_cast<float *>(in_tensor->MutableData());
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Float32ToFloat16(input_data, fp16_input_, in_tensor->ElementsNum());
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} else {
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fp16_input_ = reinterpret_cast<float16_t *>(in_tensor->MutableData());
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}
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fp16_src_data_ = fp16_input_;
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fp16_src_data_ = reinterpret_cast<float16_t *>(in_tensor->MutableData());
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for (size_t i = 0; i < data_buffers_.size(); ++i) {
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fp16_dst_data_ = data_buffers_.at(i);
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outer_size_ = outer_sizes_.at(i);
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@ -105,11 +98,16 @@ int ReduceFp16CPUKernel::Run() {
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}
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auto out_tensor = out_tensors_.at(0);
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if (out_tensor->data_type() == kNumberTypeFloat32 || out_tensor->data_type() == kNumberTypeFloat) {
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dst_data_ = reinterpret_cast<float *>(out_tensor->MutableData());
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Float16ToFloat32(fp16_dst_data_, dst_data_, out_tensor->ElementsNum());
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} else {
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memcpy(out_tensor->MutableData(), fp16_dst_data_, out_tensor->ElementsNum() * sizeof(float16_t));
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fp16_dst_data_ = reinterpret_cast<float16_t *>(out_tensor->data_c());
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MS_ASSERT(fp16_dst_data_ != nullptr);
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outer_size_ = outer_sizes_.back();
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inner_size_ = inner_sizes_.back();
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axis_size_ = axis_sizes_.back();
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auto error_code = ParallelLaunch(this->context_->thread_pool_, ReduceFp16Impl, this, context_->thread_num_);
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if (error_code != RET_OK) {
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FreeTmpBuffer();
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MS_LOG(ERROR) << "Reduce run error, error_code[" << error_code << "]";
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return RET_ERROR;
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}
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FreeTmpBuffer();
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@ -124,14 +122,6 @@ void ReduceFp16CPUKernel::FreeTmpBuffer() {
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}
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}
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data_buffers_.clear();
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auto in_tensor = in_tensors_.at(0);
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if (in_tensor->data_type() == kNumberTypeFloat32 || in_tensor->data_type() == kNumberTypeFloat) {
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if (fp16_input_ != nullptr) {
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context_->allocator->Free(fp16_input_);
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fp16_input_ = nullptr;
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}
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}
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}
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int ReduceFp16CPUKernel::MallocTmpBuffer() {
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@ -144,16 +134,6 @@ int ReduceFp16CPUKernel::MallocTmpBuffer() {
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}
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data_buffers_.emplace_back(buffer);
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}
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auto in_tensor = in_tensors_.front();
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if (in_tensor->data_type() == kNumberTypeFloat32 || in_tensor->data_type() == kNumberTypeFloat) {
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fp16_input_ =
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reinterpret_cast<float16_t *>(context_->allocator->Malloc(in_tensor->ElementsNum() * sizeof(float16_t)));
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if (fp16_input_ == nullptr) {
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MS_LOG(ERROR) << "Malloc data failed";
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return RET_ERROR;
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}
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}
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return RET_OK;
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}
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@ -44,8 +44,6 @@ class ReduceFp16CPUKernel : public ReduceBaseCPUKernel {
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private:
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Reducer reducer_ = nullptr;
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std::vector<float16_t *> data_buffers_;
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float *dst_data_ = nullptr;
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float16_t *fp16_input_ = nullptr;
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const float16_t *fp16_src_data_ = nullptr;
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float16_t *fp16_dst_data_ = nullptr;
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@ -315,6 +315,7 @@ int ReduceInt8CPUKernel::CalculateQuantArgs() {
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int ReduceInt8CPUKernel::MallocTmpBuffer() {
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data_buffers_.clear();
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MS_ASSERT(static_cast<int>(buffer_sizes_.size()) == num_axes_ - 1);
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// malloc num_axes_-1 buffers, since reduce on last axis will generate result to out_tensor, no need for buffer.
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for (auto buffer_size : buffer_sizes_) {
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int32_t *buffer = reinterpret_cast<int32_t *>(context_->allocator->Malloc(buffer_size * sizeof(int32_t)));
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if (buffer == nullptr) {
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@ -488,7 +489,7 @@ int ReduceInt8CPUKernel::Run() {
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begin_src_data_[i] = static_cast<int32_t>(input_data[i]);
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}
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src_data_ = begin_src_data_;
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for (size_t i = 0; i < data_buffers_.size() - 1; ++i) {
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for (size_t i = 0; i < data_buffers_.size(); ++i) {
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GetQuantArgs(i);
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dst_data_ = data_buffers_[i];
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outer_size_ = outer_sizes_[i];
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