expm1 operator

mindspore/ccsrc/backend/kernel_compiler/cpu/expm1_cpu_kernel.cc

@@ -0,0 +1,66 @@
+/**
+ * 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/cpu/expm1_cpu_kernel.h"
+#include <cmath>
+#include "runtime/device/cpu/cpu_device_address.h"
+
+namespace mindspore {
+namespace kernel {
+void Expm1CPUKernel::InitKernel(const CNodePtr &kernel_node) {
+  MS_EXCEPTION_IF_NULL(kernel_node);
+  size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
+  if (input_num != 1) {
+    MS_LOG(EXCEPTION) << "Input number is " << input_num << ", but Expm1CPUKernel needs 1 inputs.";
+  }
+  size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node);
+  if (output_num != 1) {
+    MS_LOG(EXCEPTION) << "Output number is " << output_num << ", but Expm1CPUKernel needs 1 output.";
+  }
+
+  input_dtype_ = AnfAlgo::GetPrevNodeOutputInferDataType(kernel_node, 0);
+  if (input_dtype_ != kNumberTypeFloat16 && input_dtype_ != kNumberTypeFloat32 && input_dtype_ != kNumberTypeFloat) {
+    MS_LOG(EXCEPTION) << "Unsupported input type found.";
+  }
+}
+
+bool Expm1CPUKernel::Launch(const std::vector<kernel::AddressPtr> &inputs,
+                            const std::vector<kernel::AddressPtr> & /*workspace*/,
+                            const std::vector<kernel::AddressPtr> &outputs) {
+  if (input_dtype_ == kNumberTypeFloat16) {
+    LaunchKernel<float16>(inputs, outputs);
+  } else if (input_dtype_ == kNumberTypeFloat32 || input_dtype_ == kNumberTypeFloat) {
+    LaunchKernel<float>(inputs, outputs);
+  } else {
+    MS_LOG(EXCEPTION) << "Only support float, half, but actual data type is " << TypeIdLabel(input_dtype_);
+  }
+  return true;
+}
+
+template <typename T>
+void Expm1CPUKernel::LaunchKernel(const std::vector<AddressPtr> &inputs,
+                                  const std::vector<kernel::AddressPtr> &outputs) {
+  T *input = reinterpret_cast<T *>(inputs[0]->addr);
+  T *output = reinterpret_cast<T *>(outputs[0]->addr);
+
+  size_t elem_num = inputs[0]->size / sizeof(T);
+
+  for (size_t i = 0; i < elem_num; i++) {
+    output[i] = exp(input[i]) - T(1);
+  }
+}
+}  // namespace kernel
+}  // namespace mindspore

mindspore/ccsrc/backend/kernel_compiler/cpu/expm1_cpu_kernel.h

@@ -0,0 +1,53 @@
+/**
+ * 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_CPU_EXPM1_CPU_KERNEL_H_
+#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_EXPM1_CPU_KERNEL_H_
+
+#include <vector>
+#include "backend/kernel_compiler/cpu/cpu_kernel.h"
+#include "backend/kernel_compiler/cpu/cpu_kernel_factory.h"
+
+namespace mindspore {
+namespace kernel {
+class Expm1CPUKernel : public CPUKernel {
+ public:
+  Expm1CPUKernel() = default;
+  ~Expm1CPUKernel() override = default;
+
+  void InitKernel(const CNodePtr &kernelNode) override;
+
+  bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
+              const std::vector<AddressPtr> &outputs) override;
+
+ private:
+  template <typename T>
+  void LaunchKernel(const std::vector<AddressPtr> &inputs, const std::vector<kernel::AddressPtr> &outputs);
+
+ private:
+  TypeId input_dtype_{kTypeUnknown};
+};
+
+MS_REG_CPU_KERNEL(Expm1, KernelAttr().AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),
+                  Expm1CPUKernel);
+
+MS_REG_CPU_KERNEL(Expm1, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
+                  Expm1CPUKernel);
+
+MS_REG_CPU_KERNEL(Expm1, KernelAttr().AddInputAttr(kNumberTypeFloat).AddOutputAttr(kNumberTypeFloat32), Expm1CPUKernel);
+}  // namespace kernel
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_EXPM1_CPU_KERNEL_H_

mindspore/ops/operations/math_ops.py

@@ -1588,7 +1588,7 @@ class Expm1(PrimitiveWithInfer):
         Tensor, has the same shape as the `input_x`.
 
     Supported Platforms:
-        ``Ascend``
+        ``Ascend`` ``GPU`` ``CPU``
 
     Examples:
         >>> input_x = Tensor(np.array([0.0, 1.0, 2.0, 4.0]), mindspore.float32)

tests/st/ops/cpu/test_expm1_op.py

@@ -0,0 +1,53 @@
+# 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
+from mindspore import dtype
+
+context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
+
+
+class NetExpm1(nn.Cell):
+    def __init__(self):
+        super(NetExpm1, self).__init__()
+        self.expm1 = P.Expm1()
+
+    def construct(self, x):
+        return self.expm1(x)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_expm1_op():
+    x = np.random.rand(3, 8).astype(np.float32)
+    y = np.random.rand(3, 8).astype(np.float16)
+
+    expm1 = NetExpm1()
+    output_x = expm1(Tensor(x, dtype=dtype.float32))
+    expect_x = np.expm1(x)
+    tol_x = 1e-6
+    assert (np.abs(output_x.asnumpy() - expect_x) < tol_x).all()
+
+    output_y = expm1(Tensor(y, dtype=dtype.float16))
+    expect_y = np.expm1(y)
+    tol_y = 1e-3
+    assert (np.abs(output_y.asnumpy() - expect_y) < tol_y).all()