forked from mindspore-Ecosystem/mindspore
!313 GPU add akg kernel float_status
Merge pull request !313 from VectorSL/float_status
This commit is contained in:
mindspore-ci-bot
Gitee
commit
728801301c
|
|
@ -0,0 +1,138 @@
|
|||
/**
|
||||
* 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.
|
||||
*/
|
||||
|
||||
#include "include/cuda_runtime.h"
|
||||
#include "kernel/gpu/cuda_impl/float_status_impl.cuh"
|
||||
|
||||
template <typename T>
|
||||
__global__ void IsNan(const size_t size, const T* input, bool* out) {
|
||||
for (size_t pos = blockIdx.x * blockDim.x + threadIdx.x; pos < (size); pos += blockDim.x * gridDim.x) {
|
||||
if (isnan(input[pos])) {
|
||||
out[pos] = true;
|
||||
} else {
|
||||
out[pos] = false;
|
||||
}
|
||||
}
|
||||
return;
|
||||
}
|
||||
template <>
|
||||
__global__ void IsNan(const size_t size, const half* input, bool* out) {
|
||||
for (size_t pos = blockIdx.x * blockDim.x + threadIdx.x; pos < (size); pos += blockDim.x * gridDim.x) {
|
||||
if (__hisnan(input[pos])) {
|
||||
out[pos] = true;
|
||||
} else {
|
||||
out[pos] = false;
|
||||
}
|
||||
}
|
||||
return;
|
||||
}
|
||||
|
||||
template <typename T>
|
||||
__global__ void IsInf(const size_t size, const T* input, bool* out) {
|
||||
for (size_t pos = blockIdx.x * blockDim.x + threadIdx.x; pos < (size); pos += blockDim.x * gridDim.x) {
|
||||
if (isinf(input[pos]) != 0) {
|
||||
out[pos] = true;
|
||||
} else {
|
||||
out[pos] = false;
|
||||
}
|
||||
}
|
||||
return;
|
||||
}
|
||||
template <>
|
||||
__global__ void IsInf(const size_t size, const half* input, bool* out) {
|
||||
for (size_t pos = blockIdx.x * blockDim.x + threadIdx.x; pos < (size); pos += blockDim.x * gridDim.x) {
|
||||
if (__hisinf(input[pos]) != 0) {
|
||||
out[pos] = true;
|
||||
} else {
|
||||
out[pos] = false;
|
||||
}
|
||||
}
|
||||
return;
|
||||
}
|
||||
|
||||
template <typename T>
|
||||
__global__ void IsFinite(const size_t size, const T* input, bool* out) {
|
||||
for (size_t pos = blockIdx.x * blockDim.x + threadIdx.x; pos < (size); pos += blockDim.x * gridDim.x) {
|
||||
if (isinf(input[pos]) == 0 && !isnan(input[pos])) {
|
||||
out[pos] = true;
|
||||
} else {
|
||||
out[pos] = false;
|
||||
}
|
||||
}
|
||||
return;
|
||||
}
|
||||
template <>
|
||||
__global__ void IsFinite(const size_t size, const half* input, bool* out) {
|
||||
for (size_t pos = blockIdx.x * blockDim.x + threadIdx.x; pos < (size); pos += blockDim.x * gridDim.x) {
|
||||
if (__hisinf(input[pos]) == 0 && !__hisnan(input[pos])) {
|
||||
out[pos] = true;
|
||||
} else {
|
||||
out[pos] = false;
|
||||
}
|
||||
}
|
||||
return;
|
||||
}
|
||||
|
||||
template <typename T>
|
||||
__global__ void FloatStatus(const size_t size, const T* input, T* out) {
|
||||
out[0] = 0;
|
||||
for (size_t pos = blockIdx.x * blockDim.x + threadIdx.x; pos < (size); pos += blockDim.x * gridDim.x) {
|
||||
if (isinf(input[pos]) != 0 || isnan(input[pos])) {
|
||||
out[0] = 1;
|
||||
}
|
||||
}
|
||||
return;
|
||||
}
|
||||
template <>
|
||||
__global__ void FloatStatus(const size_t size, const half* input, half* out) {
|
||||
out[0] = 0;
|
||||
for (size_t pos = blockIdx.x * blockDim.x + threadIdx.x; pos < (size); pos += blockDim.x * gridDim.x) {
|
||||
if (__hisinf(input[pos]) != 0 || __hisnan(input[pos])) {
|
||||
out[0] = 1;
|
||||
}
|
||||
}
|
||||
return;
|
||||
}
|
||||
|
||||
template <typename T>
|
||||
void CalFloatStatus(const size_t size, const T* input, T* output, cudaStream_t cuda_stream) {
|
||||
FloatStatus<<<GET_BLOCKS(size), GET_THREADS, 0, cuda_stream>>>(size, input, output);
|
||||
return;
|
||||
}
|
||||
template <typename T>
|
||||
void CalIsNan(const size_t size, const T* input, bool* output, cudaStream_t cuda_stream) {
|
||||
IsNan<<<GET_BLOCKS(size), GET_THREADS, 0, cuda_stream>>>(size, input, output);
|
||||
return;
|
||||
}
|
||||
template <typename T>
|
||||
void CalIsInf(const size_t size, const T* input, bool* output, cudaStream_t cuda_stream) {
|
||||
IsInf<<<GET_BLOCKS(size), GET_THREADS, 0, cuda_stream>>>(size, input, output);
|
||||
return;
|
||||
}
|
||||
template <typename T>
|
||||
void CalIsFinite(const size_t size, const T* input, bool* output, cudaStream_t cuda_stream) {
|
||||
IsFinite<<<GET_BLOCKS(size), GET_THREADS, 0, cuda_stream>>>(size, input, output);
|
||||
return;
|
||||
}
|
||||
|
||||
template void CalFloatStatus<float>(const size_t size, const float* input, float* output, cudaStream_t cuda_stream);
|
||||
template void CalFloatStatus<half>(const size_t size, const half* input, half* output, cudaStream_t cuda_stream);
|
||||
template void CalIsInf<float>(const size_t size, const float* input, bool* output, cudaStream_t cuda_stream);
|
||||
template void CalIsInf<half>(const size_t size, const half* input, bool* output, cudaStream_t cuda_stream);
|
||||
template void CalIsNan<float>(const size_t size, const float* input, bool* output, cudaStream_t cuda_stream);
|
||||
template void CalIsNan<half>(const size_t size, const half* input, bool* output, cudaStream_t cuda_stream);
|
||||
template void CalIsFinite<float>(const size_t size, const float* input, bool* output, cudaStream_t cuda_stream);
|
||||
template void CalIsFinite<half>(const size_t size, const half* input, bool* output, cudaStream_t cuda_stream);
|
||||
|
|
@ -0,0 +1,28 @@
|
|||
/**
|
||||
* 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.
|
||||
*/
|
||||
|
||||
#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_FLOATSTATUS_H_
|
||||
#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_FLOATSTATUS_H_
|
||||
#include "device/gpu/cuda_common.h"
|
||||
template <typename T>
|
||||
void CalFloatStatus(const size_t size, const T *input, T *output, cudaStream_t stream);
|
||||
template <typename T>
|
||||
void CalIsNan(const size_t size, const T *input, bool *output, cudaStream_t stream);
|
||||
template <typename T>
|
||||
void CalIsInf(const size_t size, const T *input, bool *output, cudaStream_t stream);
|
||||
template <typename T>
|
||||
void CalIsFinite(const size_t size, const T *input, bool *output, cudaStream_t stream);
|
||||
#endif // MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_FLOATSTATUS_H_
|
||||
|
|
@ -0,0 +1,38 @@
|
|||
/**
|
||||
* 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.
|
||||
*/
|
||||
|
||||
#include "kernel/gpu/math/float_status_gpu_kernel.h"
|
||||
|
||||
namespace mindspore {
|
||||
namespace kernel {
|
||||
MS_REG_GPU_KERNEL_ONE(FloatStatus, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
|
||||
FloatStatusGpuKernel, float)
|
||||
MS_REG_GPU_KERNEL_ONE(FloatStatus, KernelAttr().AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),
|
||||
FloatStatusGpuKernel, half)
|
||||
MS_REG_GPU_KERNEL_ONE(IsInf, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeBool),
|
||||
FloatStatusGpuKernel, float)
|
||||
MS_REG_GPU_KERNEL_ONE(IsInf, KernelAttr().AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeBool),
|
||||
FloatStatusGpuKernel, half)
|
||||
MS_REG_GPU_KERNEL_ONE(IsNan, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeBool),
|
||||
FloatStatusGpuKernel, float)
|
||||
MS_REG_GPU_KERNEL_ONE(IsNan, KernelAttr().AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeBool),
|
||||
FloatStatusGpuKernel, half)
|
||||
MS_REG_GPU_KERNEL_ONE(IsFinite, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeBool),
|
||||
FloatStatusGpuKernel, float)
|
||||
MS_REG_GPU_KERNEL_ONE(IsFinite, KernelAttr().AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeBool),
|
||||
FloatStatusGpuKernel, half)
|
||||
} // namespace kernel
|
||||
} // namespace mindspore
|
||||
|
|
@ -0,0 +1,130 @@
|
|||
/**
|
||||
* 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.
|
||||
*/
|
||||
|
||||
#ifndef MINDSPORE_CCSRC_KERNEL_GPU_FLOAT_STATUS_GPU_KERNEL_H
|
||||
#define MINDSPORE_CCSRC_KERNEL_GPU_FLOAT_STATUS_GPU_KERNEL_H
|
||||
|
||||
#include <memory>
|
||||
#include <vector>
|
||||
#include <map>
|
||||
#include <string>
|
||||
#include "kernel/gpu/gpu_kernel.h"
|
||||
#include "kernel/gpu/gpu_kernel_factory.h"
|
||||
#include "kernel/gpu/cuda_impl/float_status_impl.cuh"
|
||||
|
||||
namespace mindspore {
|
||||
namespace kernel {
|
||||
enum Optype { OP_STATUS = 0, OP_INF, OP_NAN, OP_FINITE, OP_INVALID = 255 };
|
||||
static const std::map<std::string, Optype> kOpTypeMap = {
|
||||
{"FloatStatus", OP_STATUS}, {"IsInf", OP_INF}, {"IsNan", OP_NAN}, {"IsFinite", OP_FINITE}};
|
||||
template <typename T>
|
||||
class FloatStatusGpuKernel : public GpuKernel {
|
||||
public:
|
||||
FloatStatusGpuKernel() : kernel_name_(OP_INVALID), input_size_(0), output_size_(0) {}
|
||||
~FloatStatusGpuKernel() override = default;
|
||||
const std::vector<size_t> &GetInputSizeList() const override { return input_size_list_; }
|
||||
const std::vector<size_t> &GetOutputSizeList() const override { return output_size_list_; }
|
||||
const std::vector<size_t> &GetWorkspaceSizeList() const override { return workspace_size_list_; }
|
||||
|
||||
bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &,
|
||||
const std::vector<AddressPtr> &outputs, uintptr_t stream_ptr) override {
|
||||
T *input = GetDeviceAddress<T>(inputs, 0);
|
||||
|
||||
switch (kernel_name_) {
|
||||
case OP_STATUS: {
|
||||
T *output = GetDeviceAddress<T>(outputs, 0);
|
||||
CalFloatStatus(input_size_ / sizeof(T), input, output, reinterpret_cast<cudaStream_t>(stream_ptr));
|
||||
break;
|
||||
}
|
||||
case OP_INF: {
|
||||
bool *output = GetDeviceAddress<bool>(outputs, 0);
|
||||
CalIsInf(input_size_ / sizeof(T), input, output, reinterpret_cast<cudaStream_t>(stream_ptr));
|
||||
break;
|
||||
}
|
||||
case OP_NAN: {
|
||||
bool *output = GetDeviceAddress<bool>(outputs, 0);
|
||||
CalIsNan(input_size_ / sizeof(T), input, output, reinterpret_cast<cudaStream_t>(stream_ptr));
|
||||
break;
|
||||
}
|
||||
case OP_FINITE: {
|
||||
bool *output = GetDeviceAddress<bool>(outputs, 0);
|
||||
CalIsFinite(input_size_ / sizeof(T), input, output, reinterpret_cast<cudaStream_t>(stream_ptr));
|
||||
break;
|
||||
}
|
||||
default: {
|
||||
MS_LOG(EXCEPTION) << "FloatStatus type " << kernel_name_ << " is not supported.";
|
||||
}
|
||||
}
|
||||
return true;
|
||||
}
|
||||
|
||||
bool Init(const CNodePtr &kernel_node) override {
|
||||
if (!CheckParam(kernel_node)) {
|
||||
return false;
|
||||
}
|
||||
auto shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
|
||||
input_size_ = sizeof(T);
|
||||
for (size_t x : shape) {
|
||||
input_size_ = input_size_ * x;
|
||||
}
|
||||
auto kernel_name = AnfAlgo::GetCNodeName(kernel_node);
|
||||
auto iter = kOpTypeMap.find(kernel_name);
|
||||
if (iter == kOpTypeMap.end()) {
|
||||
MS_LOG(EXCEPTION) << "FloatStatus kernel " << kernel_name << " is not supported.";
|
||||
} else {
|
||||
kernel_name_ = iter->second;
|
||||
}
|
||||
if (kernel_name_ == OP_STATUS) {
|
||||
output_size_ = sizeof(T);
|
||||
} else {
|
||||
output_size_ = input_size_ / sizeof(T) * sizeof(bool);
|
||||
}
|
||||
InitSizeLists();
|
||||
return true;
|
||||
}
|
||||
|
||||
protected:
|
||||
void InitSizeLists() override {
|
||||
input_size_list_.push_back(input_size_);
|
||||
output_size_list_.push_back(output_size_);
|
||||
}
|
||||
|
||||
private:
|
||||
bool CheckParam(const CNodePtr &kernel_node) {
|
||||
size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
|
||||
if (input_num != 1) {
|
||||
MS_LOG(ERROR) << "Input number is " << input_num << ", but FloatStatusGpuKernel needs 1 output.";
|
||||
return false;
|
||||
}
|
||||
size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node);
|
||||
if (output_num != 1) {
|
||||
MS_LOG(ERROR) << "Output number is " << output_num << ", but FloatStatusGpuKernel needs 1 output.";
|
||||
return false;
|
||||
}
|
||||
return true;
|
||||
}
|
||||
|
||||
std::vector<size_t> input_size_list_;
|
||||
std::vector<size_t> output_size_list_;
|
||||
std::vector<size_t> workspace_size_list_;
|
||||
Optype kernel_name_;
|
||||
size_t input_size_;
|
||||
size_t output_size_;
|
||||
};
|
||||
} // namespace kernel
|
||||
} // namespace mindspore
|
||||
|
||||
#endif // MINDSPORE_CCSRC_KERNEL_GPU_FLOAT_STATUS_GPU_KERNEL_H
|
||||
|
|
@ -0,0 +1,118 @@
|
|||
# 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