add GPU support to RandomChoiceWithMask

mindspore/ccsrc/backend/kernel_compiler/gpu/cuda_impl/random_choice_with_mask_impl.cuh

@@ -0,0 +1,34 @@
+/**
+ * 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_BACKEND_KERNEL_COMPILER_GPU_CUDA_IMPL_RANDOM_CHOICE_WITH_MASK_IMPL_CUH_
+#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_CUDA_IMPL_RANDOM_CHOICE_WITH_MASK_IMPL_CUH_
+
+#include <cuda_runtime.h>
+#include <curand_kernel.h>
+#include "runtime/device/gpu/cuda_common.h"
+#define BLOCKSIZE 256
+#define MAX_DIMENSION 5
+
+template <typename T, typename S>
+void CalRandomChoiceWithMask(const int &input_size, const int &input_shape_size, const int &d1, const int &d2,
+                             const int &d3, const int &d4, const int &d5, const int &seedc, const int &count,
+                             const T *input, S *output_index, T *output_mask, S *index_buff, S *mask_buff, S *rank_buff,
+                             S *Tnum_buff, S *tmp_buff, curandState *globalState, cudaStream_t stream);
+
+int RcwmRoundUpPower2(int v);
+
+#endif  // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_CUDA_IMPL_RANDOM_CHOICE_WITH_MASK_IMPL_CUH_

mindspore/ccsrc/backend/kernel_compiler/gpu/random/random_choice_with_mask_gpu_kernel.cc

@@ -0,0 +1,26 @@
+/**
+ * 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 "backend/kernel_compiler/gpu/random/random_choice_with_mask_gpu_kernel.h"
+
+namespace mindspore {
+namespace kernel {
+MS_REG_GPU_KERNEL_TWO(
+  RandomChoiceWithMask,
+  KernelAttr().AddInputAttr(kNumberTypeBool).AddOutputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeBool),
+  RandomChoiceWithMaskGpuKernel, bool, int)
+}
+}  // namespace mindspore

mindspore/ccsrc/backend/kernel_compiler/gpu/random/random_choice_with_mask_gpu_kernel.h

@@ -0,0 +1,129 @@
+/**
+ * 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_BACKEND_KERNEL_COMPILER_GPU_RANDOM_RANDOM_CHOICE_WITH_MASK_GPU_KERNEL_H_
+#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_RANDOM_RANDOM_CHOICE_WITH_MASK_GPU_KERNEL_H_
+
+#include <vector>
+#include "backend/kernel_compiler/gpu/gpu_kernel.h"
+#include "backend/kernel_compiler/gpu/gpu_kernel_factory.h"
+#include "backend/kernel_compiler/gpu/cuda_impl/random_choice_with_mask_impl.cuh"
+
+namespace mindspore {
+namespace kernel {
+template <typename T, typename S>
+class RandomChoiceWithMaskGpuKernel : public GpuKernel {
+ public:
+  RandomChoiceWithMaskGpuKernel() : input_shape_size_(0), seedc_(0), input_size_(1), count_(0), ceil_power2_(0) {}
+  ~RandomChoiceWithMaskGpuKernel() 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> &workspaces,
+              const std::vector<AddressPtr> &outputs, void *stream_ptr) override {
+    T *input = GetDeviceAddress<T>(inputs, 0);
+    S *output_index = GetDeviceAddress<S>(outputs, 0);
+    T *output_mask = GetDeviceAddress<T>(outputs, 1);
+    S *index_buff = GetDeviceAddress<S>(workspaces, 0);
+    S *mask_buff = GetDeviceAddress<S>(workspaces, 1);
+    S *rank_buff = GetDeviceAddress<S>(workspaces, 2);
+    S *Tnum_buff = GetDeviceAddress<S>(workspaces, 3);
+    S *tmp_buff = GetDeviceAddress<S>(workspaces, 4);
+    void *States = GetDeviceAddress<void *>(workspaces, 5);
+    curandState *devStates = reinterpret_cast<curandState *>(States);
+    CalRandomChoiceWithMask(input_size_, input_shape_size_, input_shape_5D_[0], input_shape_5D_[1], input_shape_5D_[2],
+                            input_shape_5D_[3], input_shape_5D_[4], seedc_, count_, input, output_index, output_mask,
+                            index_buff, mask_buff, rank_buff, Tnum_buff, tmp_buff, devStates,
+                            reinterpret_cast<cudaStream_t>(stream_ptr));
+    return true;
+  }
+
+  bool Init(const CNodePtr &kernel_node) override {
+    size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
+    if (input_num != 1) {
+      MS_LOG(ERROR) << "Input number is " << input_num << ", but RandomChoiceWithMask needs 1 input.";
+      return false;
+    }
+    size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node);
+    if (output_num != 2) {
+      MS_LOG(ERROR) << "Output number is " << output_num << ", but RandomChoiceWithMask has 2 outputs.";
+      return false;
+    }
+    auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
+    input_shape_size_ = input_shape.size();
+    if (input_shape_size_ < 1 || input_shape_size_ > MAX_DIMENSION) {
+      MS_LOG(ERROR) << "Input is " << input_shape_size_
+                    << "-D, but RandomChoiceWithMask supports only 1-D to 5-D inputs.";
+      return false;
+    }
+    // convert size_t to int
+    for (auto i = 0; i < input_shape_size_; i++) {
+      input_shape_5D_.push_back(input_shape[i]);
+    }
+    // convert shape to 5D
+    while (input_shape_5D_.size() != MAX_DIMENSION) {
+      input_shape_5D_.insert(input_shape_5D_.begin(), 1);
+    }
+    // init seedc_
+    int seed = GetAttr<int>(kernel_node, "seed");
+    int seed2 = GetAttr<int>(kernel_node, "seed2");
+    if (seed2 != 0)
+      seedc_ = seed2;
+    else if (seed != 0)
+      seedc_ = seed;
+    else
+      seedc_ = time(NULL);
+    // init memory
+    for (size_t i = 0; i < input_shape.size(); i++) {
+      input_size_ *= input_shape[i];
+    }
+    count_ = GetAttr<int>(kernel_node, "count");
+    // upper ceiling for input for ceil_power2
+    ceil_power2_ = RcwmRoundUpPower2(input_size_);
+    InitSizeLists();
+    return true;
+  }
+
+ protected:
+  void InitSizeLists() override {
+    input_size_list_.push_back(input_size_ * sizeof(T));
+    output_size_list_.push_back(count_ * input_shape_size_ * sizeof(S));
+    output_size_list_.push_back(count_ * sizeof(T));
+    workspace_size_list_.push_back(input_size_ * input_shape_size_ * sizeof(S));
+    workspace_size_list_.push_back(ceil_power2_ * sizeof(S));
+    workspace_size_list_.push_back(ceil_power2_ * sizeof(S));
+    int blocknum = std::ceil(static_cast<float>(ceil_power2_) / BLOCKSIZE);
+    workspace_size_list_.push_back(blocknum * sizeof(S));
+    workspace_size_list_.push_back(ceil_power2_ * sizeof(S));
+    workspace_size_list_.push_back(ceil_power2_ * sizeof(curandState));
+  }
+
+ private:
+  int input_shape_size_;
+  int seedc_;
+  int input_size_;
+  int count_;
+  int ceil_power2_;
+  std::vector<int> input_shape_5D_;
+  std::vector<size_t> input_size_list_;
+  std::vector<size_t> output_size_list_;
+  std::vector<size_t> workspace_size_list_;
+};
+}  // namespace kernel
+}  // namespace mindspore
+#endif  // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_RANDOM_RANDOM_CHOICE_WITH_MASK_GPU_KERNEL_H_

mindspore/ops/operations/random_ops.py

@@ -348,13 +348,13 @@ class RandomChoiceWithMask(PrimitiveWithInfer):
         seed2 (int): Random seed2. Default: 0.
 
     Inputs:
-        - **input_x** (Tensor[bool]) - The input tensor.
+        - **input_x** (Tensor[bool]) - The input tensor. The input tensor rank should be >= 1 and <= 5.
 
     Outputs:
         Two tensors, the first one is the index tensor and the other one is the mask tensor.
 
-        - **index** (Tensor) - The output has shape between 2-D and 5-D.
+        - **index** (Tensor) - The output shape is 2-D.
-        - **mask** (Tensor) - The output has shape 1-D.
+        - **mask** (Tensor) - The output shape is 1-D.
 
     Examples:
         >>> rnd_choice_mask = P.RandomChoiceWithMask()
@@ -372,6 +372,7 @@ class RandomChoiceWithMask(PrimitiveWithInfer):
 
     def infer_shape(self, x_shape):
         validator.check_integer("input_x rank", len(x_shape), 1, Rel.GE, self.name)
+        validator.check_integer("input_x rank", len(x_shape), 5, Rel.LE, self.name)
         return ([self.count, len(x_shape)], [self.count])
 
     def infer_dtype(self, x_dtype):

tests/st/ops/gpu/test_random_choice_with_mask.py

@@ -0,0 +1,86 @@
+# 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 numpy as np
+import pytest
+
+import mindspore.context as context
+import mindspore.nn as nn
+from mindspore import Tensor
+from mindspore.ops import operations as P
+
+class RCWM_count_in(nn.Cell):
+    def __init__(self):
+        super(RCWM_count_in, self).__init__()
+        self.RCWM_count_in = P.RandomChoiceWithMask(count=4, seed=1)
+
+    def construct(self, x):
+        return self.RCWM_count_in(x)
+
+class RCWM_count_out(nn.Cell):
+    def __init__(self):
+        super(RCWM_count_out, self).__init__()
+        self.RCWM_count_out = P.RandomChoiceWithMask(count=10, seed=1)
+
+    def construct(self, x):
+        return self.RCWM_count_out(x)
+
+class RCWM_3D(nn.Cell):
+    def __init__(self):
+        super(RCWM_3D, self).__init__()
+        self.RCWM_3D = P.RandomChoiceWithMask(count=10, seed=1)
+
+    def construct(self, x):
+        return self.RCWM_3D(x)
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_RCWM_3D():
+    context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
+    input_tensor = Tensor(np.ones([3, 4, 5]).astype(np.bool))
+    expect1 = [[0, 1, 1], [0, 2, 1], [0, 2, 2], [1, 0, 1], [0, 1, 3], [0, 3, 0], [1, 3, 2], \
+    [0, 0, 0], [1, 1, 2], [1, 3, 4]]
+    expect2 = [True, True, True, True, True, True, True, True, True, True]
+    rcwm = RCWM_3D()
+    output1, output2 = rcwm(input_tensor)
+    assert np.all(output1.asnumpy() == np.array(expect1)), "output: {}, expect: {}".format(output1, expect1)
+    assert np.all(output2.asnumpy() == np.array(expect2)), "output: {}, expect: {}".format(output2, expect2)
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_RCWM_count_out():
+    context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
+    input_tensor = Tensor(np.array([[1, 0, 1, 0], [0, 0, 0, 1], [1, 1, 1, 1], [0, 0, 0, 1]]).astype(np.bool))
+    expect1 = [[0, 2], [2, 2], [2, 1], [2, 0], [0, 0], [3, 3], [2, 3], [1, 3], [0, 0], [0, 0]]
+    expect2 = [True, True, True, True, True, True, True, True, False, False]
+    rcwm = RCWM_count_out()
+    output1, output2 = rcwm(input_tensor)
+    assert np.all(output1.asnumpy() == np.array(expect1)), "output: {}, expect: {}".format(output1, expect1)
+    assert np.all(output2.asnumpy() == np.array(expect2)), "output: {}, expect: {}".format(output2, expect2)
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_RCWM_count_in():
+    context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
+    input_tensor = Tensor(np.array([[1, 0, 1, 0], [0, 0, 0, 1], [1, 1, 1, 1], [0, 0, 0, 1]]).astype(np.bool))
+    expect1 = [[0, 2], [2, 2], [2, 1], [2, 0]]
+    expect2 = [True, True, True, True]
+    rcwm = RCWM_count_in()
+    output1, output2 = rcwm(input_tensor)
+    assert np.all(output1.asnumpy() == np.array(expect1)), "output: {}, expect: {}".format(output1, expect1)
+    assert np.all(output2.asnumpy() == np.array(expect2)), "output: {}, expect: {}".format(output2, expect2)