forked from mindspore-Ecosystem/mindspore
gpu add float_status kernel
This commit is contained in:
VectorSL
parent
39b9e831cb
commit
c000fb2f34
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/**
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* Copyright 2020 Huawei Technologies Co., Ltd
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#include "include/cuda_runtime.h"
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#include "kernel/gpu/cuda_impl/float_status_impl.cuh"
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template <typename T>
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__global__ void IsNan(const size_t size, const T* input, bool* out) {
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for (size_t pos = blockIdx.x * blockDim.x + threadIdx.x; pos < (size); pos += blockDim.x * gridDim.x) {
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if (isnan(input[pos])) {
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out[pos] = true;
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} else {
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out[pos] = false;
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}
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}
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return;
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}
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template <>
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__global__ void IsNan(const size_t size, const half* input, bool* out) {
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for (size_t pos = blockIdx.x * blockDim.x + threadIdx.x; pos < (size); pos += blockDim.x * gridDim.x) {
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if (__hisnan(input[pos])) {
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out[pos] = true;
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} else {
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out[pos] = false;
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}
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}
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return;
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}
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template <typename T>
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__global__ void IsInf(const size_t size, const T* input, bool* out) {
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for (size_t pos = blockIdx.x * blockDim.x + threadIdx.x; pos < (size); pos += blockDim.x * gridDim.x) {
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if (isinf(input[pos]) != 0) {
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out[pos] = true;
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} else {
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out[pos] = false;
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}
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}
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return;
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}
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template <>
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__global__ void IsInf(const size_t size, const half* input, bool* out) {
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for (size_t pos = blockIdx.x * blockDim.x + threadIdx.x; pos < (size); pos += blockDim.x * gridDim.x) {
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if (__hisinf(input[pos]) != 0) {
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out[pos] = true;
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} else {
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out[pos] = false;
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}
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}
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return;
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}
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template <typename T>
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__global__ void IsFinite(const size_t size, const T* input, bool* out) {
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for (size_t pos = blockIdx.x * blockDim.x + threadIdx.x; pos < (size); pos += blockDim.x * gridDim.x) {
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if (isinf(input[pos]) == 0 && !isnan(input[pos])) {
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out[pos] = true;
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} else {
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out[pos] = false;
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}
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}
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return;
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}
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template <>
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__global__ void IsFinite(const size_t size, const half* input, bool* out) {
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for (size_t pos = blockIdx.x * blockDim.x + threadIdx.x; pos < (size); pos += blockDim.x * gridDim.x) {
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if (__hisinf(input[pos]) == 0 && !__hisnan(input[pos])) {
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out[pos] = true;
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} else {
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out[pos] = false;
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}
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}
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return;
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}
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template <typename T>
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__global__ void FloatStatus(const size_t size, const T* input, T* out) {
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out[0] = 0;
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for (size_t pos = blockIdx.x * blockDim.x + threadIdx.x; pos < (size); pos += blockDim.x * gridDim.x) {
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if (isinf(input[pos]) != 0 || isnan(input[pos])) {
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out[0] = 1;
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}
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}
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return;
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}
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template <>
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__global__ void FloatStatus(const size_t size, const half* input, half* out) {
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out[0] = 0;
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for (size_t pos = blockIdx.x * blockDim.x + threadIdx.x; pos < (size); pos += blockDim.x * gridDim.x) {
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if (__hisinf(input[pos]) != 0 || __hisnan(input[pos])) {
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out[0] = 1;
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}
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}
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return;
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}
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template <typename T>
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void CalFloatStatus(const size_t size, const T* input, T* output, cudaStream_t cuda_stream) {
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FloatStatus<<<GET_BLOCKS(size), GET_THREADS, 0, cuda_stream>>>(size, input, output);
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return;
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}
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template <typename T>
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void CalIsNan(const size_t size, const T* input, bool* output, cudaStream_t cuda_stream) {
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IsNan<<<GET_BLOCKS(size), GET_THREADS, 0, cuda_stream>>>(size, input, output);
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return;
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}
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template <typename T>
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void CalIsInf(const size_t size, const T* input, bool* output, cudaStream_t cuda_stream) {
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IsInf<<<GET_BLOCKS(size), GET_THREADS, 0, cuda_stream>>>(size, input, output);
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return;
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}
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template <typename T>
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void CalIsFinite(const size_t size, const T* input, bool* output, cudaStream_t cuda_stream) {
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IsFinite<<<GET_BLOCKS(size), GET_THREADS, 0, cuda_stream>>>(size, input, output);
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return;
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}
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template void CalFloatStatus<float>(const size_t size, const float* input, float* output, cudaStream_t cuda_stream);
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template void CalFloatStatus<half>(const size_t size, const half* input, half* output, cudaStream_t cuda_stream);
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template void CalIsInf<float>(const size_t size, const float* input, bool* output, cudaStream_t cuda_stream);
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template void CalIsInf<half>(const size_t size, const half* input, bool* output, cudaStream_t cuda_stream);
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template void CalIsNan<float>(const size_t size, const float* input, bool* output, cudaStream_t cuda_stream);
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template void CalIsNan<half>(const size_t size, const half* input, bool* output, cudaStream_t cuda_stream);
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template void CalIsFinite<float>(const size_t size, const float* input, bool* output, cudaStream_t cuda_stream);
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template void CalIsFinite<half>(const size_t size, const half* input, bool* output, cudaStream_t cuda_stream);
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@ -0,0 +1,28 @@
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/**
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* Copyright 2020 Huawei Technologies Co., Ltd
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_FLOATSTATUS_H_
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#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_FLOATSTATUS_H_
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#include "device/gpu/cuda_common.h"
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template <typename T>
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void CalFloatStatus(const size_t size, const T *input, T *output, cudaStream_t stream);
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template <typename T>
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void CalIsNan(const size_t size, const T *input, bool *output, cudaStream_t stream);
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template <typename T>
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void CalIsInf(const size_t size, const T *input, bool *output, cudaStream_t stream);
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template <typename T>
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void CalIsFinite(const size_t size, const T *input, bool *output, cudaStream_t stream);
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#endif // MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_FLOATSTATUS_H_
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@ -0,0 +1,38 @@
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/**
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* Copyright 2020 Huawei Technologies Co., Ltd
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#include "kernel/gpu/math/float_status_gpu_kernel.h"
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namespace mindspore {
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namespace kernel {
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MS_REG_GPU_KERNEL_ONE(FloatStatus, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
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FloatStatusGpuKernel, float)
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MS_REG_GPU_KERNEL_ONE(FloatStatus, KernelAttr().AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),
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FloatStatusGpuKernel, half)
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MS_REG_GPU_KERNEL_ONE(IsInf, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeBool),
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FloatStatusGpuKernel, float)
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MS_REG_GPU_KERNEL_ONE(IsInf, KernelAttr().AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeBool),
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FloatStatusGpuKernel, half)
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MS_REG_GPU_KERNEL_ONE(IsNan, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeBool),
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FloatStatusGpuKernel, float)
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MS_REG_GPU_KERNEL_ONE(IsNan, KernelAttr().AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeBool),
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FloatStatusGpuKernel, half)
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MS_REG_GPU_KERNEL_ONE(IsFinite, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeBool),
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FloatStatusGpuKernel, float)
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MS_REG_GPU_KERNEL_ONE(IsFinite, KernelAttr().AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeBool),
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FloatStatusGpuKernel, half)
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} // namespace kernel
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} // namespace mindspore
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@ -0,0 +1,130 @@
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/**
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* Copyright 2020 Huawei Technologies Co., Ltd
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#ifndef MINDSPORE_CCSRC_KERNEL_GPU_FLOAT_STATUS_GPU_KERNEL_H
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#define MINDSPORE_CCSRC_KERNEL_GPU_FLOAT_STATUS_GPU_KERNEL_H
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#include <memory>
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#include <vector>
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#include <map>
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#include <string>
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#include "kernel/gpu/gpu_kernel.h"
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#include "kernel/gpu/gpu_kernel_factory.h"
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#include "kernel/gpu/cuda_impl/float_status_impl.cuh"
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namespace mindspore {
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namespace kernel {
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enum Optype { OP_STATUS = 0, OP_INF, OP_NAN, OP_FINITE, OP_INVALID = 255 };
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static const std::map<std::string, Optype> kOpTypeMap = {
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{"FloatStatus", OP_STATUS}, {"IsInf", OP_INF}, {"IsNan", OP_NAN}, {"IsFinite", OP_FINITE}};
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template <typename T>
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class FloatStatusGpuKernel : public GpuKernel {
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public:
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FloatStatusGpuKernel() : kernel_name_(OP_INVALID), input_size_(0), output_size_(0) {}
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~FloatStatusGpuKernel() override = default;
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const std::vector<size_t> &GetInputSizeList() const override { return input_size_list_; }
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const std::vector<size_t> &GetOutputSizeList() const override { return output_size_list_; }
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const std::vector<size_t> &GetWorkspaceSizeList() const override { return workspace_size_list_; }
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bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &,
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const std::vector<AddressPtr> &outputs, uintptr_t stream_ptr) override {
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T *input = GetDeviceAddress<T>(inputs, 0);
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switch (kernel_name_) {
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case OP_STATUS: {
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T *output = GetDeviceAddress<T>(outputs, 0);
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CalFloatStatus(input_size_ / sizeof(T), input, output, reinterpret_cast<cudaStream_t>(stream_ptr));
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break;
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}
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case OP_INF: {
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bool *output = GetDeviceAddress<bool>(outputs, 0);
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CalIsInf(input_size_ / sizeof(T), input, output, reinterpret_cast<cudaStream_t>(stream_ptr));
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break;
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}
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case OP_NAN: {
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bool *output = GetDeviceAddress<bool>(outputs, 0);
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CalIsNan(input_size_ / sizeof(T), input, output, reinterpret_cast<cudaStream_t>(stream_ptr));
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break;
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}
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case OP_FINITE: {
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bool *output = GetDeviceAddress<bool>(outputs, 0);
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CalIsFinite(input_size_ / sizeof(T), input, output, reinterpret_cast<cudaStream_t>(stream_ptr));
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break;
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}
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default: {
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MS_LOG(EXCEPTION) << "FloatStatus type " << kernel_name_ << " is not supported.";
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}
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}
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return true;
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}
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bool Init(const CNodePtr &kernel_node) override {
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if (!CheckParam(kernel_node)) {
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return false;
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}
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auto shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
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input_size_ = sizeof(T);
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for (size_t x : shape) {
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input_size_ = input_size_ * x;
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}
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auto kernel_name = AnfAlgo::GetCNodeName(kernel_node);
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auto iter = kOpTypeMap.find(kernel_name);
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if (iter == kOpTypeMap.end()) {
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MS_LOG(EXCEPTION) << "FloatStatus kernel " << kernel_name << " is not supported.";
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} else {
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kernel_name_ = iter->second;
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}
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if (kernel_name_ == OP_STATUS) {
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output_size_ = sizeof(T);
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} else {
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output_size_ = input_size_ / sizeof(T) * sizeof(bool);
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}
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InitSizeLists();
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return true;
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}
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protected:
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void InitSizeLists() override {
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input_size_list_.push_back(input_size_);
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output_size_list_.push_back(output_size_);
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}
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private:
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bool CheckParam(const CNodePtr &kernel_node) {
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size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
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if (input_num != 1) {
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MS_LOG(ERROR) << "Input number is " << input_num << ", but FloatStatusGpuKernel needs 1 output.";
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return false;
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}
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size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node);
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if (output_num != 1) {
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MS_LOG(ERROR) << "Output number is " << output_num << ", but FloatStatusGpuKernel needs 1 output.";
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return false;
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}
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return true;
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}
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std::vector<size_t> input_size_list_;
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std::vector<size_t> output_size_list_;
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std::vector<size_t> workspace_size_list_;
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Optype kernel_name_;
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size_t input_size_;
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size_t output_size_;
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};
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} // namespace kernel
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} // namespace mindspore
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#endif // MINDSPORE_CCSRC_KERNEL_GPU_FLOAT_STATUS_GPU_KERNEL_H
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@ -0,0 +1,118 @@
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|
# Copyright 2020 Huawei Technologies Co., Ltd
|
||||||
|
#
|
||||||
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||||
|
# you may not use this file except in compliance with the License.
|
||||||
|
# You may obtain a copy of the License at
|
||||||
|
#
|
||||||
|
# http://www.apache.org/licenses/LICENSE-2.0
|
||||||
|
#
|
||||||
|
# Unless required by applicable law or agreed to in writing, software
|
||||||
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||||
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||||
|
# See the License for the specific language governing permissions and
|
||||||
|
# limitations under the License.
|
||||||
|
# ============================================================================
|
||||||
|
|
||||||
|
import pytest
|
||||||
|
from mindspore import Tensor
|
||||||
|
from mindspore.ops import operations as P
|
||||||
|
import mindspore.nn as nn
|
||||||
|
import numpy as np
|
||||||
|
import mindspore.context as context
|
||||||
|
|
||||||
|
class Net(nn.Cell):
|
||||||
|
def __init__(self):
|
||||||
|
super(Net, self).__init__()
|
||||||
|
self.status = P.FloatStatus()
|
||||||
|
|
||||||
|
def construct(self, x):
|
||||||
|
return self.status(x)
|
||||||
|
|
||||||
|
class Netnan(nn.Cell):
|
||||||
|
def __init__(self):
|
||||||
|
super(Netnan, self).__init__()
|
||||||
|
self.isnan = P.IsNan()
|
||||||
|
|
||||||
|
def construct(self, x):
|
||||||
|
return self.isnan(x)
|
||||||
|
|
||||||
|
class Netinf(nn.Cell):
|
||||||
|
def __init__(self):
|
||||||
|
super(Netinf, self).__init__()
|
||||||
|
self.isinf = P.IsInf()
|
||||||
|
|
||||||
|
def construct(self, x):
|
||||||
|
return self.isinf(x)
|
||||||
|
|
||||||
|
class Netfinite(nn.Cell):
|
||||||
|
def __init__(self):
|
||||||
|
super(Netfinite, self).__init__()
|
||||||
|
self.isfinite = P.IsFinite()
|
||||||
|
|
||||||
|
def construct(self, x):
|
||||||
|
return self.isfinite(x)
|
||||||
|
|
||||||
|
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
|
||||||
|
x1 = np.array([[1.2, 2, np.nan, 88]]).astype(np.float32)
|
||||||
|
x2 = np.array([[np.inf, 1, 88.0, 0]]).astype(np.float32)
|
||||||
|
x3 = np.array([[1, 2], [3, 4], [5.0, 88.0]]).astype(np.float32)
|
||||||
|
|
||||||
|
@pytest.mark.level0
|
||||||
|
@pytest.mark.platform_x86_gpu_training
|
||||||
|
@pytest.mark.env_onecard
|
||||||
|
def test_status():
|
||||||
|
ms_status = Net();
|
||||||
|
output1 = ms_status(Tensor(x1))
|
||||||
|
output2 = ms_status(Tensor(x2))
|
||||||
|
output3 = ms_status(Tensor(x3))
|
||||||
|
expect1 = 1
|
||||||
|
expect2 = 1
|
||||||
|
expect3 = 0
|
||||||
|
assert output1.asnumpy()[0] == expect1
|
||||||
|
assert output2.asnumpy()[0] == expect2
|
||||||
|
assert output3.asnumpy()[0] == expect3
|
||||||
|
|
||||||
|
@pytest.mark.level0
|
||||||
|
@pytest.mark.platform_x86_gpu_training
|
||||||
|
@pytest.mark.env_onecard
|
||||||
|
def test_nan():
|
||||||
|
ms_isnan = Netnan();
|
||||||
|
output1 = ms_isnan(Tensor(x1))
|
||||||
|
output2 = ms_isnan(Tensor(x2))
|
||||||
|
output3 = ms_isnan(Tensor(x3))
|
||||||
|
expect1 = [[False, False, True, False]]
|
||||||
|
expect2 = [[False, False, False, False]]
|
||||||
|
expect3 = [[False, False], [False, False], [False, False]]
|
||||||
|
assert (output1.asnumpy() == expect1).all()
|
||||||
|
assert (output2.asnumpy() == expect2).all()
|
||||||
|
assert (output3.asnumpy() == expect3).all()
|
||||||
|
|
||||||
|
@pytest.mark.level0
|
||||||
|
@pytest.mark.platform_x86_gpu_training
|
||||||
|
@pytest.mark.env_onecard
|
||||||
|
def test_inf():
|
||||||
|
ms_isinf = Netinf();
|
||||||
|
output1 = ms_isinf(Tensor(x1))
|
||||||
|
output2 = ms_isinf(Tensor(x2))
|
||||||
|
output3 = ms_isinf(Tensor(x3))
|
||||||
|
expect1 = [[False, False, False, False]]
|
||||||
|
expect2 = [[True, False, False, False]]
|
||||||
|
expect3 = [[False, False], [False, False], [False, False]]
|
||||||
|
assert (output1.asnumpy() == expect1).all()
|
||||||
|
assert (output2.asnumpy() == expect2).all()
|
||||||
|
assert (output3.asnumpy() == expect3).all()
|
||||||
|
|
||||||
|
@pytest.mark.level0
|
||||||
|
@pytest.mark.platform_x86_gpu_training
|
||||||
|
@pytest.mark.env_onecard
|
||||||
|
def test_finite():
|
||||||
|
ms_isfinite = Netfinite();
|
||||||
|
output1 = ms_isfinite(Tensor(x1))
|
||||||
|
output2 = ms_isfinite(Tensor(x2))
|
||||||
|
output3 = ms_isfinite(Tensor(x3))
|
||||||
|
expect1 = [[True, True, False, True]]
|
||||||
|
expect2 = [[False, True, True, True]]
|
||||||
|
expect3 = [[True, True], [True, True], [True, True]]
|
||||||
|
assert (output1.asnumpy() == expect1).all()
|
||||||
|
assert (output2.asnumpy() == expect2).all()
|
||||||
|
assert (output3.asnumpy() == expect3).all()
|
||||||
Loading…
Reference in New Issue