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!12015 cpu support scalar tensor 

From: @huaweib
Reviewed-by: @kisnwang,@zhoufeng54
Signed-off-by: @zhoufeng54
This commit is contained in:
mindspore-ci-bot 2021-02-04 21:36:16 +08:00 committed by Gitee
parent b6183f718f a0b4175940
commit c7494512eb
3 changed files with 10 additions and 2 deletions
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4
mindspore/ccsrc/backend/kernel_compiler/cpu/mkldnn/assignadd_cpu_kernel.cc
View File

@ -24,6 +24,10 @@ void AssignAddCPUKernel::InitKernel(const CNodePtr &kernel_node) {
MS_EXCEPTION_IF_NULL(kernel_node);
std::vector<size_t> src0_shape = AnfAlgo::GetInputDeviceShape(kernel_node, 0);
std::vector<size_t> src1_shape = AnfAlgo::GetInputDeviceShape(kernel_node, 1);
if (src1_shape.size() == 0 && src0_shape.size() == 0) {
src0_shape.insert(src0_shape.begin(), 1);
src1_shape.insert(src1_shape.begin(), 1);
}
if (src0_shape.size() != src1_shape.size() && src1_shape.size() > 1) {
MS_LOG(EXCEPTION) << "AssignAdd only support same dim input or tensor * scalar " << src0_shape.size() << " vs "
<< src1_shape.size();

6
mindspore/ccsrc/backend/kernel_compiler/cpu/mkldnn/mkl_cpu_kernel.cc
View File

@ -130,7 +130,11 @@ dnnl::memory::format_tag MKLCPUKernel::GetDefaultFormatTag(const dnnl::memory::d
dnnl::memory::desc MKLCPUKernel::GetDefaultMemDesc(const std::vector<size_t> &shape) {
dnnl::memory::dims dims;
dims.insert(dims.end(), shape.begin(), shape.end());
if (shape.size() == 0) {
dims.insert(dims.end(), 1);
} else {
dims.insert(dims.end(), shape.begin(), shape.end());
}
dnnl::memory::format_tag mem_tag = GetDefaultFormatTag(dims);
dnnl::memory::desc mem_desc(dims, dnnl::memory::data_type::f32, mem_tag);
return mem_desc;

2
mindspore/ccsrc/backend/kernel_compiler/gpu/nn/ctcloss_gpu_kernel.h
View File

@ -151,7 +151,7 @@ class CtcLossGpuKernel : public GpuKernel {
void LaunchSecondHalf(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
const std::vector<AddressPtr> &outputs, void *stream_ptr) {
cudaStream_t stream = reinterpret_cast<cudaStream_t>(stream_ptr);
int SOffSet = 2 * max_labels_length_host + 1;
const int SOffSet = 2 * max_labels_length_host + 1;
int log_prob_size = batch * SOffSet * max_time;
if (!ignore_longer_outputs_than_inputs_ && max_labels_length_host > max_time) {