gpu add float_status kernel

mindspore/ccsrc/kernel/gpu/cuda_impl/float_status_impl.cu

+/**
+ * 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);

mindspore/ccsrc/kernel/gpu/cuda_impl/float_status_impl.cuh

+/**
+ * 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_

mindspore/ccsrc/kernel/gpu/math/float_status_gpu_kernel.cc

+/**
+ * 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

mindspore/ccsrc/kernel/gpu/math/float_status_gpu_kernel.h

+/**
+ * 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

tests/st/ops/gpu/test_float_status_op.py

+# 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()