add cpu transpose

mindspore/ccsrc/kernel/cpu/transpose_cpu_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/cpu/transpose_cpu_kernel.h"
+#include "device/cpu/cpu_device_address.h"
+namespace mindspore {
+namespace kernel {
+const size_t kMaxDim = 100;
+void TransposeCPUFwdKernel::InitKernel(const CNodePtr &kernel_node) {
+  MS_EXCEPTION_IF_NULL(kernel_node);
+  shape_ = AnfAlgo::GetInputDeviceShape(kernel_node, 0);
+  axis_ = AnfAlgo::GetNodeAttr<std::vector<int>>(kernel_node, "perm");
+  if (shape_.size() != axis_.size()) {
+    MS_LOG(EXCEPTION) << "The size of input shape and transpose axis shape must be equal.";
+  }
+}
+bool TransposeCPUFwdKernel::Launch(const std::vector<kernel::AddressPtr> &inputs,
+                                   const std::vector<kernel::AddressPtr> & /*workspace*/,
+                                   const std::vector<kernel::AddressPtr> &outputs) {
+  auto input = reinterpret_cast<float *>(inputs[0]->addr);
+  auto output = reinterpret_cast<float *>(outputs[0]->addr);
+  size_t size = IntToSize(inputs[0]->size / sizeof(float));
+  size_t shape_size = IntToSize(shape_.size());
+  if (shape_size > kMaxDim) {
+    MS_LOG(EXCEPTION) << "Input is " << shape_size << "-D, but transpose supports max " << kMaxDim << "-D inputs.";
+  }
+  size_t pos_array[kMaxDim];
+  size_t size_offset[kMaxDim];
+  size_offset[0] = size / shape_[0];
+  for (size_t i = 1; i < shape_size; i++) {
+    size_offset[i] = size_offset[SizeToInt(i) - 1] / shape_[i];
+  }
+  for (size_t position = 0; position < size; position += 1) {
+    size_t temp_position = position;
+    pos_array[0] = temp_position / size_offset[0];
+    for (size_t i = 1; i < shape_size; i++) {
+      temp_position -= pos_array[SizeToInt(i) - 1] * size_offset[i - 1];
+      pos_array[i] = temp_position / size_offset[i];
+    }
+    size_t new_position = pos_array[axis_[SizeToInt(shape_size) - 1]];
+    size_t new_position_size = 1;
+    for (int j = shape_size - 2; j >= 0; j--) {
+      new_position_size *= shape_[axis_[j + 1]];
+      new_position += pos_array[axis_[j]] * new_position_size;
+    }
+    output[new_position] = input[position];
+  }
+  return true;
+}
+}  // namespace kernel
+}  // namespace mindspore

mindspore/ccsrc/kernel/cpu/transpose_cpu_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_CPU_TRANSPOSE_CPU_KERNEL_H_
+#define MINDSPORE_CCSRC_KERNEL_CPU_TRANSPOSE_CPU_KERNEL_H_
+#include <vector>
+#include <memory>
+#include <string>
+#include "kernel/cpu/cpu_kernel.h"
+#include "kernel/cpu/cpu_kernel_factory.h"
+namespace mindspore {
+namespace kernel {
+class TransposeCPUFwdKernel : public CPUKernel {
+ public:
+  TransposeCPUFwdKernel() = default;
+  ~TransposeCPUFwdKernel() override = default;
+
+  void InitKernel(const CNodePtr &kernel_node) override;
+
+  bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
+              const std::vector<AddressPtr> &outputs) override;
+
+ private:
+  std::vector<size_t> shape_;
+  std::vector<int> axis_;
+};
+
+MS_REG_CPU_KERNEL(Transpose, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
+                  TransposeCPUFwdKernel);
+}  // namespace kernel
+}  // namespace mindspore
+#endif  // MINDSPORE_CCSRC_KERNEL_CPU_TRANSPOSE_CPU_KERNEL_H_

tests/st/ops/cpu/test_transpose_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
+import numpy as np
+from mindspore import Tensor
+from mindspore.ops import operations as P
+from mindspore.common.api import ms_function
+from mindspore.common.initializer import initializer
+from mindspore.common.parameter import Parameter
+import mindspore.nn as nn
+import mindspore.context as context
+
+context.set_context(device_target='CPU')
+
+
+class Transpose(nn.Cell):
+    def __init__(self):
+        super(Transpose, self).__init__()
+        self.transpose = P.Transpose()
+
+        self.x_2D = Parameter(initializer(Tensor(np.arange(5 * 6).reshape(5, 6).astype(np.float32)), [5, 6]),
+                              name='x_2D')
+        self.perm_2D = (1, 0)
+
+        self.x_3D = Parameter(initializer(Tensor(np.arange(2 * 2 * 4).reshape(2, 2, 4).astype(np.float32)), [2, 2, 4]),
+                              name='x_3D')
+        self.perm_3D = (1, 0, 2)
+
+        self.x_4D = Parameter(
+            initializer(Tensor(np.arange(2 * 3 * 4 * 5).reshape(2, 3, 4, 5).astype(np.float32)), [2, 3, 4, 5]),
+            name='x_4D')
+        self.perm_4D = (0, 1, 2, 3)
+
+        self.x_5D = Parameter(
+            initializer(Tensor(np.arange(1 * 2 * 3 * 4 * 5).reshape(1, 2, 3, 4, 5).astype(np.float32)),
+                        [1, 2, 3, 4, 5]), name='x_5D')
+        self.perm_5D = (1, 0, 3, 4, 2)
+
+    @ms_function
+    def construct(self):
+        return (self.transpose(self.x_2D, self.perm_2D), self.transpose(self.x_3D, self.perm_3D),
+                self.transpose(self.x_4D, self.perm_4D), self.transpose(self.x_5D, self.perm_5D))
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_transpose():
+    transpose = Transpose()
+    output = transpose()
+
+    expect0 = np.array([[[0, 6, 12, 18, 24],
+                         [1, 7, 13, 19, 25],
+                         [2, 8, 14, 20, 26],
+                         [3, 9, 15, 21, 27],
+                         [4, 10, 16, 22, 28],
+                         [5, 11, 17, 23, 29]]]).astype(np.float32)
+    expect1 = np.array([[[[0, 1, 2, 3],
+                          [8, 9, 10, 11]],
+                         [[4, 5, 6, 7],
+                          [12, 13, 14, 15]]]]).astype(np.float32)
+    expect2 = np.array([[[[[0, 1, 2, 3, 4],
+                           [5, 6, 7, 8, 9],
+                           [10, 11, 12, 13, 14],
+                           [15, 16, 17, 18, 19]],
+                          [[20, 21, 22, 23, 24],
+                           [25, 26, 27, 28, 29],
+                           [30, 31, 32, 33, 34],
+                           [35, 36, 37, 38, 39]],
+                          [[40, 41, 42, 43, 44],
+                           [45, 46, 47, 48, 49],
+                           [50, 51, 52, 53, 54],
+                           [55, 56, 57, 58, 59]]],
+
+                         [[[60, 61, 62, 63, 64],
+                           [65, 66, 67, 68, 69],
+                           [70, 71, 72, 73, 74],
+                           [75, 76, 77, 78, 79]],
+                          [[80, 81, 82, 83, 84],
+                           [85, 86, 87, 88, 89],
+                           [90, 91, 92, 93, 94],
+                           [95, 96, 97, 98, 99]],
+                          [[100, 101, 102, 103, 104],
+                           [105, 106, 107, 108, 109],
+                           [110, 111, 112, 113, 114],
+                           [115, 116, 117, 118, 119]]]]]).astype(np.float32)
+    expect3 = np.array([[[[[[0, 20, 40],
+                            [1, 21, 41],
+                            [2, 22, 42],
+                            [3, 23, 43],
+                            [4, 24, 44]],
+                           [[5, 25, 45],
+                            [6, 26, 46],
+                            [7, 27, 47],
+                            [8, 28, 48],
+                            [9, 29, 49]],
+                           [[10, 30, 50],
+                            [11, 31, 51],
+                            [12, 32, 52],
+                            [13, 33, 53],
+                            [14, 34, 54]],
+                           [[15, 35, 55],
+                            [16, 36, 56],
+                            [17, 37, 57],
+                            [18, 38, 58],
+                            [19, 39, 59]]]],
+
+                         [[[[60, 80, 100],
+                            [61, 81, 101],
+                            [62, 82, 102],
+                            [63, 83, 103],
+                            [64, 84, 104]],
+                           [[65, 85, 105],
+                            [66, 86, 106],
+                            [67, 87, 107],
+                            [68, 88, 108],
+                            [69, 89, 109]],
+                           [[70, 90, 110],
+                            [71, 91, 111],
+                            [72, 92, 112],
+                            [73, 93, 113],
+                            [74, 94, 114]],
+                           [[75, 95, 115],
+                            [76, 96, 116],
+                            [77, 97, 117],
+                            [78, 98, 118],
+                            [79, 99, 119]]]]]]).astype(np.float32)
+    assert (output[0].asnumpy() == expect0).all()
+    assert (output[1].asnumpy() == expect1).all()
+    assert (output[2].asnumpy() == expect2).all()
+    assert (output[3].asnumpy() == expect3).all()
+test_transpose()
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