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@ -29,6 +29,7 @@ using mindspore::lite::RET_OK;
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using mindspore::schema::PrimitiveType_Add;
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using mindspore::schema::PrimitiveType_Div;
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using mindspore::schema::PrimitiveType_Eltwise;
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using mindspore::schema::PrimitiveType_Equal;
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using mindspore::schema::PrimitiveType_FloorDiv;
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using mindspore::schema::PrimitiveType_FloorMod;
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@ -172,8 +173,6 @@ int ArithmeticFP16CPUKernel::ReSize() {
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MS_LOG(ERROR) << "malloc data fail!";
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return RET_ERROR;
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}
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Float32ToFloat16(reinterpret_cast<float *>(in_tensors_[0]->Data()), input0_fp16_,
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arithmeticParameter_->in_elements_num0_);
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}
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if (in_tensors_[1]->data_type() == kNumberTypeFloat32 || in_tensors_[1]->data_type() == kNumberTypeFloat) {
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input1_fp16_ = reinterpret_cast<float16_t *>(
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@ -182,8 +181,6 @@ int ArithmeticFP16CPUKernel::ReSize() {
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MS_LOG(ERROR) << "malloc data fail!";
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return RET_ERROR;
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}
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Float32ToFloat16(reinterpret_cast<float *>(in_tensors_[1]->Data()), input1_fp16_,
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arithmeticParameter_->in_elements_num1_);
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}
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if (out_tensors_[0]->data_type() == kNumberTypeFloat32 || out_tensors_[0]->data_type() == kNumberTypeFloat) {
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output_fp16_ = reinterpret_cast<float16_t *>(
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@ -297,15 +294,33 @@ int ArithmeticFP16CPUKernel::ReSize() {
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}
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if (arithmeticParameter_->broadcasting_) {
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auto tile_size = arithmeticParameter_->out_elements_num_ * sizeof(float16_t);
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tile_data0_ = reinterpret_cast<float16_t *>(malloc(tile_size));
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tile_data1_ = reinterpret_cast<float16_t *>(malloc(tile_size));
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if (tile_data0_ == nullptr || tile_data1_ == nullptr) {
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MS_LOG(ERROR) << "malloc tile data fail!";
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return RET_ERROR;
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outside_ = 1;
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for (int i = arithmeticParameter_->ndim_ - 1; i >= 0; --i) {
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if (arithmeticParameter_->in_shape0_[i] != arithmeticParameter_->in_shape1_[i]) {
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break_pos_ = i;
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break;
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}
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outside_ *= arithmeticParameter_->out_shape_[i];
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}
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ComputeStrides(arithmeticParameter_->in_shape0_, arithmeticParameter_->in_strides0_, arithmeticParameter_->ndim_);
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ComputeStrides(arithmeticParameter_->in_shape1_, arithmeticParameter_->in_strides1_, arithmeticParameter_->ndim_);
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ComputeStrides(arithmeticParameter_->out_shape_, arithmeticParameter_->out_strides_, arithmeticParameter_->ndim_);
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}
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return RET_OK;
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}
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int ArithmeticFP16CPUKernel::broadcast_run_(float16_t *input0, float16_t *input1, float16_t *output, int dim) {
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if (dim > break_pos_) {
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return arithmetic_run_(input0 + out_thread_stride_, input1 + out_thread_stride_, output + out_thread_stride_,
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out_count_);
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}
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for (int i = 0; i < arithmeticParameter_->out_shape_[dim]; ++i) {
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int pos0_ = arithmeticParameter_->in_shape0_[0] == 1 ? 0 : i;
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int pos1_ = arithmeticParameter_->in_shape1_[0] == 1 ? 0 : i;
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return broadcast_run_(input0 + pos0_ * arithmeticParameter_->in_strides0_[dim],
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input1 + pos1_ * arithmeticParameter_->in_strides1_[dim],
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output + i * arithmeticParameter_->out_strides_[dim], dim + 1);
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}
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return RET_OK;
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}
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@ -329,8 +344,10 @@ int ArithmeticFP16CPUKernel::DoArithmetic(int task_id) {
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int error_code = RET_OK;
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if (arithmeticParameter_->broadcasting_) {
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error_code =
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arithmetic_run_(tile_data0_ + thread_stride, tile_data1_ + thread_stride, output_data + thread_stride, count);
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stride = UP_DIV(outside_, context_->thread_num_);
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out_count_ = MSMIN(stride, outside_ - stride * task_id);
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out_thread_stride_ = stride * task_id;
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error_code = broadcast_run_(input0_data, input1_data1, output_data, 0);
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} else if (arithmetic_opt_run_ != nullptr) {
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if (arithmeticParameter_->in_elements_num0_ == 1) {
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error_code = arithmetic_opt_run_(input0_data, input1_data1 + thread_stride, output_data + thread_stride, count,
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@ -373,13 +390,15 @@ int ArithmeticFP16CPUKernel::Run() {
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return ret;
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}
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if (arithmeticParameter_->broadcasting_) {
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auto input_data0 = reinterpret_cast<float16_t *>(in_tensors_[0]->Data());
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auto input_data1 = reinterpret_cast<float16_t *>(in_tensors_[1]->Data());
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float16_t *input0 = input0_fp16_ == nullptr ? input_data0 : input0_fp16_;
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float16_t *input1 = input1_fp16_ == nullptr ? input_data1 : input1_fp16_;
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TileDimensionsFp16(input0, input1, tile_data0_, tile_data1_, arithmeticParameter_);
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if (in_tensors_[0]->data_type() == kNumberTypeFloat32 || in_tensors_[0]->data_type() == kNumberTypeFloat) {
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Float32ToFloat16(reinterpret_cast<float *>(in_tensors_[0]->Data()), input0_fp16_,
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arithmeticParameter_->in_elements_num0_);
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}
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if (in_tensors_[1]->data_type() == kNumberTypeFloat32 || in_tensors_[1]->data_type() == kNumberTypeFloat) {
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Float32ToFloat16(reinterpret_cast<float *>(in_tensors_[1]->Data()), input1_fp16_,
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arithmeticParameter_->in_elements_num1_);
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}
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ret = LiteBackendParallelLaunch(ArithmeticsRun, this, context_->thread_num_);
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if (ret != RET_OK) {
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MS_LOG(ERROR) << "Arithmetic function fail!ret: " << ret;
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@ -428,4 +447,5 @@ REG_KERNEL(kCPU, kNumberTypeFloat16, PrimitiveType_Less, CpuArithmeticFp16Kernel
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REG_KERNEL(kCPU, kNumberTypeFloat16, PrimitiveType_LessEqual, CpuArithmeticFp16KernelCreator)
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REG_KERNEL(kCPU, kNumberTypeFloat16, PrimitiveType_Greater, CpuArithmeticFp16KernelCreator)
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REG_KERNEL(kCPU, kNumberTypeFloat16, PrimitiveType_GreaterEqual, CpuArithmeticFp16KernelCreator)
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REG_KERNEL(kCPU, kNumberTypeFloat16, PrimitiveType_Eltwise, CpuArithmeticFp16KernelCreator)
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} // namespace mindspore::kernel
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