gpu add assigin

mindspore/ccsrc/kernel/gpu/other/assign_gpu_kernel.cc

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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.
+ */
+
+#include "kernel/gpu/other/assign_gpu_kernel.h"
+
+namespace mindspore {
+namespace kernel {
+MS_REG_GPU_KERNEL_ONE(
+  Assign,
+  KernelAttr().AddInputAttr(kNumberTypeFloat32).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
+  AssignGpuKernel, float)
+MS_REG_GPU_KERNEL_ONE(
+  Assign,
+  KernelAttr().AddInputAttr(kNumberTypeFloat16).AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),
+  AssignGpuKernel, half)
+MS_REG_GPU_KERNEL_ONE(
+  Assign, KernelAttr().AddInputAttr(kNumberTypeInt32).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
+  AssignGpuKernel, int)
+}  // namespace kernel
+}  // namespace mindspore

mindspore/ccsrc/kernel/gpu/other/assign_gpu_kernel.h

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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.
+ */
+
+#ifndef MINDSPORE_CCSRC_KERNEL_GPU_ASSIGN_GPU_KERNEL_H
+#define MINDSPORE_CCSRC_KERNEL_GPU_ASSIGN_GPU_KERNEL_H
+
+#include <vector>
+#include "kernel/gpu/gpu_kernel.h"
+#include "kernel/gpu/gpu_kernel_factory.h"
+
+namespace mindspore {
+namespace kernel {
+template <typename T>
+class AssignGpuKernel : public GpuKernel {
+ public:
+  AssignGpuKernel() : input_size_(0) {}
+  ~AssignGpuKernel() 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 *var = GetDeviceAddress<T>(inputs, 0);
+    T *value = GetDeviceAddress<T>(inputs, 1);
+    T *output = GetDeviceAddress<T>(outputs, 0);
+    CHECK_CUDA_RET_WITH_EXCEPT(
+      cudaMemcpyAsync(var, value, input_size_, cudaMemcpyDeviceToDevice, reinterpret_cast<cudaStream_t>(stream_ptr)),
+      "cudaMemxcpyAsync failed.");
+    CHECK_CUDA_RET_WITH_EXCEPT(
+      cudaMemcpyAsync(output, value, input_size_, cudaMemcpyDeviceToDevice, reinterpret_cast<cudaStream_t>(stream_ptr)),
+      "cudaMemxcpyAsync failed.");
+    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;
+    }
+    InitSizeLists();
+    return true;
+  }
+
+ protected:
+  void InitSizeLists() override {
+    input_size_list_.push_back(input_size_);
+    input_size_list_.push_back(input_size_);
+    output_size_list_.push_back(input_size_);
+  }
+
+ private:
+  bool CheckParam(const CNodePtr &kernel_node) {
+    size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
+    if (input_num != 2) {
+      MS_LOG(ERROR) << "Input number is " << input_num << ", but AssignGpuKernel needs 2 output.";
+      return false;
+    }
+    size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node);
+    if (output_num != 1) {
+      MS_LOG(ERROR) << "Output number is " << output_num << ", but AssignGpuKernel 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_;
+
+  size_t input_size_;
+};
+}  // namespace kernel
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CCSRC_KERNEL_GPU_ASSIGN_GPU_KERNEL_H

tests/st/ops/gpu/test_assign_op.py

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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.assign = P.Assign()
+
+    def construct(self, var, value):
+        return self.assign(var, value)
+
+x = np.array([[1.2, 1], [1, 0]]).astype(np.float32)
+value = np.array([[1, 2], [3, 4.0]]).astype(np.float32)
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_assign():
+    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
+    assign = Net()
+    var = Tensor(x)
+    output = assign(var, Tensor(value))
+
+    error = np.ones(shape=[2, 2]) * 1.0e-6
+    diff1 = output.asnumpy() - value
+    diff2 = var.asnumpy() - value
+    assert np.all(diff1 < error)
+    assert np.all(-diff1 < error)
+    assert np.all(diff2 < error)
+    assert np.all(-diff2 < error)