[feat][assistant][I48O73] add new operator log1p

mindspore/ccsrc/plugin/device/cpu/kernel/log1p_cpu_kernel.cc

@@ -0,0 +1,75 @@
+/**
+ * Copyright 2022 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 "plugin/device/cpu/kernel/log1p_cpu_kernel.h"
+#include <cmath>
+#include "plugin/device/cpu/hal/device/cpu_device_address.h"
+
+namespace mindspore {
+namespace kernel {
+namespace {
+constexpr size_t kLog1pInputsNum = 1;
+constexpr size_t kLog1pOutputsNum = 1;
+}  // namespace
+
+void Log1pCpuKernelMod::InitKernel(const CNodePtr &kernel_node) {
+  MS_EXCEPTION_IF_NULL(kernel_node);
+  kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node);
+  input_dtype_ = AnfAlgo::GetInputDeviceDataType(kernel_node, 0);
+  if (input_dtype_ != kNumberTypeFloat16 && input_dtype_ != kNumberTypeFloat32 && input_dtype_ != kNumberTypeFloat64 &&
+      input_dtype_ != kNumberTypeComplex64 && input_dtype_ != kNumberTypeComplex128) {
+    MS_LOG(EXCEPTION) << "For '" << kernel_name_
+                      << "', the dtype of input should be Float16, Float32, Float64, Complex64 or Complex128, but got: "
+                      << input_dtype_;
+  }
+}
+
+bool Log1pCpuKernelMod::Launch(const std::vector<kernel::AddressPtr> &inputs, const std::vector<kernel::AddressPtr> &,
+                               const std::vector<kernel::AddressPtr> &outputs) {
+  CHECK_KERNEL_INPUTS_NUM(inputs.size(), kLog1pInputsNum, kernel_name_);
+  CHECK_KERNEL_OUTPUTS_NUM(outputs.size(), kLog1pOutputsNum, kernel_name_);
+  if (input_dtype_ == kNumberTypeFloat16) {
+    LaunchKernel<float16>(inputs, outputs);
+  } else if (input_dtype_ == kNumberTypeFloat32) {
+    LaunchKernel<float>(inputs, outputs);
+  } else if (input_dtype_ == kNumberTypeFloat64) {
+    LaunchKernel<double>(inputs, outputs);
+  } else if (input_dtype_ == kNumberTypeComplex64) {
+    LaunchKernel<std::complex<float>>(inputs, outputs);
+  } else if (input_dtype_ == kNumberTypeComplex128) {
+    LaunchKernel<std::complex<double>>(inputs, outputs);
+  } else {
+    MS_LOG(EXCEPTION) << "For '" << kernel_name_
+                      << "', the dtype of input should be Float16, Float32, Float64, Complex64 or Complex128, but got: "
+                      << TypeIdLabel(input_dtype_);
+  }
+  return true;
+}
+
+template <typename T>
+void Log1pCpuKernelMod::LaunchKernel(const std::vector<AddressPtr> &inputs,
+                                     const std::vector<kernel::AddressPtr> &outputs) {
+  const auto *input = reinterpret_cast<T *>(inputs[0]->addr);
+  auto *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] = log(input[i] + T(1));
+  }
+}
+
+MS_KERNEL_FACTORY_REG(NativeCpuKernelMod, Log1p, Log1pCpuKernelMod);
+}  // namespace kernel
+}  // namespace mindspore

mindspore/ccsrc/plugin/device/cpu/kernel/log1p_cpu_kernel.h

@@ -0,0 +1,61 @@
+/**
+ * Copyright 2022 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_LOG1P_CPU_KERNEL_H_
+#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_LOG1P_CPU_KERNEL_H_
+
+#include <complex>
+#include <vector>
+#include "plugin/device/cpu/kernel/cpu_kernel.h"
+#include "plugin/factory/ms_factory.h"
+
+using complex64 = std::complex<float>;
+using complex128 = std::complex<double>;
+
+namespace mindspore {
+namespace kernel {
+class Log1pCpuKernelMod : public DeprecatedNativeCpuKernelMod {
+ public:
+  Log1pCpuKernelMod() = default;
+  ~Log1pCpuKernelMod() 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;
+
+ protected:
+  std::vector<KernelAttr> GetOpSupport() override {
+    static std::vector<KernelAttr> support_list = {
+      KernelAttr().AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),
+      KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
+      KernelAttr().AddInputAttr(kNumberTypeFloat64).AddOutputAttr(kNumberTypeFloat64),
+      KernelAttr().AddInputAttr(kNumberTypeComplex64).AddOutputAttr(kNumberTypeComplex64),
+      KernelAttr().AddInputAttr(kNumberTypeComplex128).AddOutputAttr(kNumberTypeComplex128)};
+    return support_list;
+  }
+
+ private:
+  template <typename T>
+  void LaunchKernel(const std::vector<AddressPtr> &inputs, const std::vector<kernel::AddressPtr> &outputs);
+
+ private:
+  TypeId input_dtype_{kTypeUnknown};
+};
+}  // namespace kernel
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_LOG1P_CPU_KERNEL_H_

mindspore/core/ops/log1p.cc

@@ -40,7 +40,7 @@ TypePtr Log1pInferType(const PrimitivePtr &prim, const std::vector<AbstractBaseP
   MS_EXCEPTION_IF_NULL(prim);
   auto prim_name = prim->name();
   // check
-  std::set<TypePtr> valid_index_types = {kFloat16, kFloat32};
+  std::set<TypePtr> valid_index_types = {kFloat16, kFloat32, kFloat64, kComplex64, kComplex128};
   auto x_type = input_args[0]->BuildType();
   (void)CheckAndConvertUtils::CheckTensorTypeValid("x", input_args[0]->BuildType(), valid_index_types, prim_name);
   return x_type;

mindspore/python/mindspore/ops/_op_impl/aicpu/__init__.py

@@ -41,6 +41,7 @@ from .get_next import _get_next_aicpu
 from .print_tensor import _print_aicpu
 from .topk import _top_k_aicpu
 from .logical_xor import _logical_xor_aicpu
+from .log1p import _log1p_aicpu
 from .asin import _asin_aicpu
 from .asin_grad import _asin_grad_aicpu
 from .is_finite import _is_finite_aicpu

mindspore/python/mindspore/ops/_op_impl/aicpu/log1p.py

@@ -0,0 +1,34 @@
+# Copyright 2022 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.
+# ============================================================================
+
+"""Log1p op"""
+from mindspore.ops.op_info_register import op_info_register, AiCPURegOp, DataType
+
+log1p_op_info = AiCPURegOp("Log1p") \
+    .fusion_type("OPAQUE") \
+    .input(0, "x", "required") \
+    .output(0, "y", "required") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F64_Default, DataType.F64_Default) \
+    .dtype_format(DataType.C64_Default, DataType.C64_Default) \
+    .dtype_format(DataType.C128_Default, DataType.C128_Default) \
+    .get_op_info()
+
+
+@op_info_register(log1p_op_info)
+def _log1p_aicpu():
+    """Log1p AiCPU register"""
+    return

mindspore/python/mindspore/ops/operations/math_ops.py

@@ -2622,7 +2622,7 @@ class Log1p(Primitive):
         TypeError: If dtype of `x` is neither float16 nor float32.
 
     Supported Platforms:
-        ``Ascend`` ``GPU``
+        ``Ascend`` ``GPU`` ``CPU``
 
     Examples:
         >>> x = Tensor(np.array([1.0, 2.0, 4.0]), mindspore.float32)

tests/ut/python/ops/test_ops.py

@@ -1266,6 +1266,10 @@ test_case_math_ops = [
         'block': P.Expm1(),
         'desc_inputs': [[2, 3]],
         'desc_bprop': [[2, 3]]}),
+    ('Log1p', {
+        'block': P.Log1p(),
+        'desc_inputs': [[1, 2, 3]],
+        'desc_bprop': [[1, 2, 3]]}),
     ('Erf', {
         'block': P.Erf(),
         'desc_inputs': [Tensor(np.array([-2, -1, 0, 1, 2]).astype(np.float16))],