[feat][assistant][I5EWNX][I5EWO5]Add datatype for Mod and Erf

docs/api/api_python/ops/mindspore.ops.func_erf.rst

@@ -10,11 +10,11 @@ mindspore.ops.erf
         erf(x)=\frac{2} {\sqrt{\pi}} \int\limits_0^{x} e^{-t^{2}} dt
 
     参数：
-        - **x** (Tensor) - 高斯误差函数的输入Tensor。维度必须小于8，数据类型必须为float16或float32。
+        - **x** (Tensor) - 高斯误差函数的输入Tensor。维度必须小于8，数据类型必须为float16,float32或float64。
 
     返回：
         Tensor，具有与 `x` 相同的数据类型和shape。
 
     异常：
         - **TypeError** - `x` 不是Tensor。
-        - **TypeError** - `x` 的数据类型既不是float16也不是float32。
+        - **TypeError** - `x` 的数据类型既不是float16,float32也不是float64。

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

@@ -1020,12 +1020,20 @@ static std::map<std::string, std::vector<std::pair<KernelAttr, ArithmeticCpuFunc
     {KernelAttr().AddInputAttr(kNumberTypeFloat64).AddInputAttr(kNumberTypeFloat64).AddOutputAttr(kNumberTypeFloat64),
      SpecializeArithFunc<double>}}},
   {kMod,
-   {{KernelAttr().AddInputAttr(kNumberTypeInt32).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
+   {{KernelAttr().AddInputAttr(kNumberTypeInt64).AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeInt64),
+     SpecializeArithFunc<int64_t>},
+    {KernelAttr().AddInputAttr(kNumberTypeInt32).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
      SpecializeArithFunc<int>},
+    {KernelAttr().AddInputAttr(kNumberTypeInt8).AddInputAttr(kNumberTypeInt8).AddOutputAttr(kNumberTypeInt8),
+     SpecializeArithFunc<int8_t>},
+    {KernelAttr().AddInputAttr(kNumberTypeUInt8).AddInputAttr(kNumberTypeUInt8).AddOutputAttr(kNumberTypeUInt8),
+     SpecializeArithFunc<uint8_t>},
     {KernelAttr().AddInputAttr(kNumberTypeFloat32).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
      SpecializeArithFunc<float>},
-    {KernelAttr().AddInputAttr(kNumberTypeInt64).AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeInt64),
-     SpecializeArithFunc<int64_t>}}},
+    {KernelAttr().AddInputAttr(kNumberTypeFloat16).AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),
+     SpecializeArithFunc<float16>},
+    {KernelAttr().AddInputAttr(kNumberTypeFloat64).AddInputAttr(kNumberTypeFloat64).AddOutputAttr(kNumberTypeFloat64),
+     SpecializeArithFunc<double>}}},
   {kFloorMod,
    {{KernelAttr().AddInputAttr(kNumberTypeInt64).AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeInt64),
      SpecializeArithFunc<int64_t>},

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

@@ -611,14 +611,14 @@ bool ArithmeticSelfCpuKernelFunc::RunFunc(const std::vector<kernel::AddressPtr>
   CHECK_KERNEL_OUTPUTS_NUM(outputs.size(), kOutputsNum, kernel_name_);
   if (dtype_ == kNumberTypeFloat32) {
     LaunchKernel<float>(inputs, outputs);
+  } else if (dtype_ == kNumberTypeFloat16) {
+    LaunchKernelFloat16(inputs, outputs);
   } else if (dtype_ == kNumberTypeFloat64) {
     LaunchKernel<double>(inputs, outputs);
   } else if (dtype_ == kNumberTypeComplex64) {
     LaunchKernelComplex<std::complex<float>>(inputs, outputs);
   } else if (dtype_ == kNumberTypeComplex128) {
     LaunchKernelComplex<std::complex<double>>(inputs, outputs);
-  } else if (dtype_ == kNumberTypeFloat16) {
-    LaunchKernelFloat16(inputs, outputs);
   } else if (dtype_ == kNumberTypeInt8) {
     LaunchKernel<int8_t>(inputs, outputs);
   } else if (dtype_ == kNumberTypeInt16) {
@@ -751,7 +751,7 @@ void ArithmeticSelfCpuKernelFunc::LaunchKernelFloat16(const std::vector<AddressP
                           {prim::kPrimAsin->name(), Asin<float16>},   {prim::kPrimACos->name(), ACos<float16>},
                           {prim::kPrimSinh->name(), Sinh<float16>},   {prim::kPrimCosh->name(), Cosh<float16>},
                           {prim::kPrimAsinh->name(), Asinh<float16>}, {prim::kPrimErfc->name(), Erfc<float16>},
-                          {prim::kPrimRsqrt->name(), Rsqrt<float16>}};
+                          {prim::kPrimRsqrt->name(), Rsqrt<float16>}, {prim::kPrimErf->name(), Erf<float16>}};
 
   const auto func_pair = arithmeticSelfFuncMap.find(kernel_name_);
   if (arithmeticSelfFuncMap.find(kernel_name_) == arithmeticSelfFuncMap.end()) {
@@ -1020,7 +1020,8 @@ static std::map<std::string, std::vector<std::pair<KernelAttr, ArithFuncCreator>
     {KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
      []() { return std::make_shared<LogMKLKernelFunc>(); }}}},
   {kErf,
-   {{KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32), CreateArithSelfFunc},
+   {{KernelAttr().AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16), CreateArithSelfFunc},
+    {KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32), CreateArithSelfFunc},
     {KernelAttr().AddInputAttr(kNumberTypeFloat64).AddOutputAttr(kNumberTypeFloat64), CreateArithSelfFunc}}},
   {kErfc,
    {{KernelAttr().AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16), CreateArithSelfFunc},

mindspore/ccsrc/plugin/device/gpu/kernel/math/unary_op_gpu_kernel.cc

@@ -88,7 +88,9 @@ std::map<std::string, std::vector<std::pair<KernelAttr, UnaryOpGpuKernelMod::Una
       {KernelAttr().AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),
        &UnaryOpGpuKernelMod::LaunchKernel<half>}}},
     {kErf,
-     {{KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
+     {{KernelAttr().AddInputAttr(kNumberTypeFloat64).AddOutputAttr(kNumberTypeFloat64),
+       &UnaryOpGpuKernelMod::LaunchKernel<double>},
+      {KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
        &UnaryOpGpuKernelMod::LaunchKernel<float>},
       {KernelAttr().AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),
        &UnaryOpGpuKernelMod::LaunchKernel<half>}}},

mindspore/core/ops/erf.cc

@@ -51,7 +51,7 @@ TypePtr ErfInferType(const PrimitivePtr &primitive, const std::vector<AbstractBa
   for (const auto &item : input_args) {
     MS_EXCEPTION_IF_NULL(item);
   }
-  const std::set<TypePtr> valid_types = {kFloat16, kFloat32};
+  const std::set<TypePtr> valid_types = {kFloat16, kFloat32, kFloat64};
   auto infer_type = input_args[0]->BuildType();
   MS_EXCEPTION_IF_NULL(infer_type);
   (void)CheckAndConvertUtils::CheckTensorTypeValid("x", infer_type, valid_types, prim_name);

mindspore/python/mindspore/ops/function/math_func.py

@@ -1864,14 +1864,14 @@ def erf(x):
 
     Args:
         x (Tensor): The input tensor of Gaussian error function. Its rank must be in [0, 7] inclusive
-            and data type must be float16 or float32.
+            and data type must be float16 float32 or float64.
 
     Returns:
         Tensor, has the same shape and dtype as the `x`.
 
     Raises:
         TypeError: If `x` is not a Tensor.
-        TypeError: If dtype of `x` is neither float16 nor float32.
+        TypeError: If dtype of `x` is neither float16 float32 or float64.
 
     Supported Platforms:
         ``Ascend`` ``GPU`` ``CPU``