Add expm1 op at GPU back-end

mindspore/ccsrc/backend/kernel_compiler/gpu/cuda_impl/unary_op_impl.cu

@@ -30,6 +30,13 @@ __global__ void ExponentialKernel(const half *input, half *output, const size_t
   return;
 }
 template <typename T>
+__global__ void Expm1Kernel(const T *input, T *output, const size_t count) {
+  for (size_t i = blockIdx.x * blockDim.x + threadIdx.x; i < (count); i += blockDim.x * gridDim.x) {
+    output[i] = static_cast<T>(expm1f(static_cast<float>(input[i])));
+  }
+  return;
+}
+template <typename T>
 __global__ void LogarithmKernel(const T *input, T *output, const size_t count) {
   for (size_t i = blockIdx.x * blockDim.x + threadIdx.x; i < (count); i += blockDim.x * gridDim.x) {
     output[i] = logf(input[i]);
@@ -46,21 +53,21 @@ __global__ void LogarithmKernel(const half *input, half *output, const size_t co
 template <typename T>
 __global__ void Log1pKernel(const T *input, T *output, const size_t count) {
   for (size_t i = blockIdx.x * blockDim.x + threadIdx.x; i < (count); i += blockDim.x * gridDim.x) {
-    output[i] = static_cast<T>(log1p(static_cast<double>(input[i])));
+    output[i] = static_cast<T>(log1pf(static_cast<float>(input[i])));
   }
   return;
 }
 template <typename T>
 __global__ void ErfKernel(const T *input, T *output, const size_t count) {
   for (size_t i = blockIdx.x * blockDim.x + threadIdx.x; i < (count); i += blockDim.x * gridDim.x) {
-    output[i] = static_cast<T>(erf(static_cast<float>(input[i])));
+    output[i] = static_cast<T>(erff(static_cast<float>(input[i])));
   }
   return;
 }
 template <typename T>
 __global__ void ErfcKernel(const T *input, T *output, const size_t count) {
   for (size_t i = blockIdx.x * blockDim.x + threadIdx.x; i < (count); i += blockDim.x * gridDim.x) {
-    output[i] = static_cast<T>(erfc(static_cast<float>(input[i])));
+    output[i] = static_cast<T>(erfcf(static_cast<float>(input[i])));
   }
   return;
 }
@@ -204,13 +211,13 @@ void Exponential(const T *input, T *output, const size_t count, cudaStream_t cud
   return;
 }
 template <typename T>
-void Logarithm(const T *input, T *output, const size_t count, cudaStream_t cuda_stream) {
+void Expm1(const T *input, T *output, const size_t count, cudaStream_t cuda_stream) {
-  LogarithmKernel<<<GET_BLOCKS(count), GET_THREADS, 0, cuda_stream>>>(input, output, count);
+  Expm1Kernel<<<GET_BLOCKS(count), GET_THREADS, 0, cuda_stream>>>(input, output, count);
   return;
 }
 template <typename T>
-void Negative(const T *input, T *output, const size_t count, cudaStream_t cuda_stream) {
+void Logarithm(const T *input, T *output, const size_t count, cudaStream_t cuda_stream) {
-  NegativeKernel<<<GET_BLOCKS(count), GET_THREADS, 0, cuda_stream>>>(input, output, count);
+  LogarithmKernel<<<GET_BLOCKS(count), GET_THREADS, 0, cuda_stream>>>(input, output, count);
   return;
 }
 template <typename T>
@@ -229,6 +236,11 @@ void Erfc(const T *input, T *output, const size_t count, cudaStream_t cuda_strea
   return;
 }
 template <typename T>
+void Negative(const T *input, T *output, const size_t count, cudaStream_t cuda_stream) {
+  NegativeKernel<<<GET_BLOCKS(count), GET_THREADS, 0, cuda_stream>>>(input, output, count);
+  return;
+}
+template <typename T>
 void Reciprocal(const T *input, T *output, const size_t count, cudaStream_t cuda_stream) {
   ReciprocalKernel<<<GET_BLOCKS(count), GET_THREADS, 0, cuda_stream>>>(input, output, count);
   return;
@@ -290,11 +302,12 @@ void Floor(const T *input, T *output, const size_t count, cudaStream_t cuda_stre
 }
 
 template void Exponential<float>(const float *input, float *output, const size_t count, cudaStream_t cuda_stream);
+template void Expm1<float>(const float *input, float *output, const size_t count, cudaStream_t cuda_stream);
 template void Logarithm<float>(const float *input, float *output, const size_t count, cudaStream_t cuda_stream);
-template void Negative<float>(const float *input, float *output, const size_t count, cudaStream_t cuda_stream);
 template void Log1p<float>(const float *input, float *output, const size_t count, cudaStream_t cuda_stream);
 template void Erf<float>(const float *input, float *output, const size_t count, cudaStream_t cuda_stream);
 template void Erfc<float>(const float *input, float *output, const size_t count, cudaStream_t cuda_stream);
+template void Negative<float>(const float *input, float *output, const size_t count, cudaStream_t cuda_stream);
 template void Reciprocal<float>(const float *input, float *output, const size_t count, cudaStream_t cuda_stream);
 template void Square<float>(const float *input, float *output, const size_t count, cudaStream_t cuda_stream);
 template void Sqrt<float>(const float *input, float *output, const size_t count, cudaStream_t cuda_stream);
@@ -307,11 +320,12 @@ template void Zeroslike<float>(float *output, const size_t count, cudaStream_t c
 template void Abs<float>(const float *input, float *output, const size_t count, cudaStream_t cuda_stream);
 template void Floor<float>(const float *input, float *output, const size_t count, cudaStream_t cuda_stream);
 template void Exponential<half>(const half *input, half *output, const size_t count, cudaStream_t cuda_stream);
+template void Expm1<half>(const half *input, half *output, const size_t count, cudaStream_t cuda_stream);
 template void Logarithm<half>(const half *input, half *output, const size_t count, cudaStream_t cuda_stream);
-template void Negative<half>(const half *input, half *output, const size_t count, cudaStream_t cuda_stream);
 template void Log1p<half>(const half *input, half *output, const size_t count, cudaStream_t cuda_stream);
 template void Erf<half>(const half *input, half *output, const size_t count, cudaStream_t cuda_stream);
 template void Erfc<half>(const half *input, half *output, const size_t count, cudaStream_t cuda_stream);
+template void Negative<half>(const half *input, half *output, const size_t count, cudaStream_t cuda_stream);
 template void Reciprocal<half>(const half *input, half *output, const size_t count, cudaStream_t cuda_stream);
 template void Square<half>(const half *input, half *output, const size_t count, cudaStream_t cuda_stream);
 template void Sqrt<half>(const half *input, half *output, const size_t count, cudaStream_t cuda_stream);

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

@@ -21,6 +21,8 @@
 template <typename T>
 void Exponential(const T *input, T *output, const size_t count, cudaStream_t cuda_stream);
 template <typename T>
+void Expm1(const T *input, T *output, const size_t count, cudaStream_t cuda_stream);
+template <typename T>
 void Logarithm(const T *input, T *output, const size_t count, cudaStream_t cuda_stream);
 template <typename T>
 void Log1p(const T *input, T *output, const size_t count, cudaStream_t cuda_stream);

mindspore/ccsrc/backend/kernel_compiler/gpu/math/unary_op_gpu_kernel.cc

@@ -22,13 +22,13 @@ MS_REG_GPU_KERNEL_ONE(Exp, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutp
                       UnaryOpGpuKernel, float)
 MS_REG_GPU_KERNEL_ONE(Exp, KernelAttr().AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),
                       UnaryOpGpuKernel, half)
-MS_REG_GPU_KERNEL_ONE(Log, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
+MS_REG_GPU_KERNEL_ONE(Expm1, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
                       UnaryOpGpuKernel, float)
-MS_REG_GPU_KERNEL_ONE(Log, KernelAttr().AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),
+MS_REG_GPU_KERNEL_ONE(Expm1, KernelAttr().AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),
                       UnaryOpGpuKernel, half)
-MS_REG_GPU_KERNEL_ONE(Neg, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
+MS_REG_GPU_KERNEL_ONE(Log, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
                       UnaryOpGpuKernel, float)
-MS_REG_GPU_KERNEL_ONE(Neg, KernelAttr().AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),
+MS_REG_GPU_KERNEL_ONE(Log, KernelAttr().AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),
                       UnaryOpGpuKernel, half)
 MS_REG_GPU_KERNEL_ONE(Log1p, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
                       UnaryOpGpuKernel, float)
@@ -42,6 +42,10 @@ MS_REG_GPU_KERNEL_ONE(Erfc, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOut
                       UnaryOpGpuKernel, float)
 MS_REG_GPU_KERNEL_ONE(Erfc, KernelAttr().AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),
                       UnaryOpGpuKernel, half)
+MS_REG_GPU_KERNEL_ONE(Neg, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
+                      UnaryOpGpuKernel, float)
+MS_REG_GPU_KERNEL_ONE(Neg, KernelAttr().AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),
+                      UnaryOpGpuKernel, half)
 MS_REG_GPU_KERNEL_ONE(Reciprocal, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
                       UnaryOpGpuKernel, float)
 MS_REG_GPU_KERNEL_ONE(Reciprocal, KernelAttr().AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),

mindspore/ccsrc/backend/kernel_compiler/gpu/math/unary_op_gpu_kernel.h

@@ -29,6 +29,7 @@ namespace mindspore {
 namespace kernel {
 enum UnaryOptype {
   UNARY_OP_EXP = 0,
+  UNARY_OP_EXPM1,
   UNARY_OP_LOG,
   UNARY_OP_LOG1P,
   UNARY_OP_ERF,
@@ -48,6 +49,7 @@ enum UnaryOptype {
   UNARY_OP_INVALID_TYPE = 255
 };
 static const std::map<std::string, UnaryOptype> kUnaryOpTypeMap = {{"Exp", UNARY_OP_EXP},
+                                                                   {"Expm1", UNARY_OP_EXPM1},
                                                                    {"Log", UNARY_OP_LOG},
                                                                    {"Log1p", UNARY_OP_LOG1P},
                                                                    {"Erf", UNARY_OP_ERF},
@@ -90,6 +92,10 @@ class UnaryOpGpuKernel : public GpuKernel {
         Exponential(input_addr, output_addr, inputs[0]->size / sizeof(T), reinterpret_cast<cudaStream_t>(stream_ptr));
         break;
       }
+      case UNARY_OP_EXPM1: {
+        Expm1(input_addr, output_addr, inputs[0]->size / sizeof(T), reinterpret_cast<cudaStream_t>(stream_ptr));
+        break;
+      }
       case UNARY_OP_LOG: {
         Logarithm(input_addr, output_addr, inputs[0]->size / sizeof(T), reinterpret_cast<cudaStream_t>(stream_ptr));
         break;

tests/st/ops/gpu/test_expm1_op.py

@@ -0,0 +1,56 @@
+# 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="GPU")
+
+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_gpu_training
+@pytest.mark.env_onecard
+def test_expm1_fp32():
+    expm1 = NetExpm1()
+    x = np.random.rand(3, 8).astype(np.float32)
+    output = expm1(Tensor(x, dtype=dtype.float32))
+    expect = np.expm1(x)
+    tol = 1e-6
+    assert (np.abs(output.asnumpy() - expect) < tol).all()
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_expm1_fp16():
+    expm1 = NetExpm1()
+    x = np.random.rand(3, 8).astype(np.float16)
+    output = expm1(Tensor(x, dtype=dtype.float16))
+    expect = np.expm1(x)
+    tol = 1e-3
+    assert (np.abs(output.asnumpy() - expect) < tol).all()