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!16988 upload unify cpu ops info library code 

From: @chengxb7532
Reviewed-by: @linqingke,@guoqi1024
Signed-off-by: @guoqi1024
This commit is contained in:
mindspore-ci-bot 2021-05-27 16:58:24 +08:00 committed by Gitee
parent 7b2525fbdb 0c559f6d57
commit 5ed742a893
100 changed files with 2303 additions and 1173 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

17
mindspore/ccsrc/backend/kernel_compiler/cpu/adam_cpu_kernel.h
View File

@ -40,22 +40,7 @@ class AdamCPUKernel : public CPUKernel {
TypeId dtype_{kTypeUnknown};
};
MS_REG_CPU_KERNEL(Adam,
KernelAttr()
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32),
AdamCPUKernel)
MS_REG_CPU_KERNEL(Adam, KernelAttr(), AdamCPUKernel)
} // namespace kernel
} // namespace mindspore

6
mindspore/ccsrc/backend/kernel_compiler/cpu/argmax_cpu_kernel.h
View File

@ -40,10 +40,8 @@ class ArgmaxCPUKernel : public CPUKernel {
size_t dim_axis_;
};
MS_REG_CPU_KERNEL_T(Argmax, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeInt32),
ArgmaxCPUKernel, float);
MS_REG_CPU_KERNEL_T(Argmax, KernelAttr().AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeInt32),
ArgmaxCPUKernel, float16);
MS_REG_CPU_KERNEL_T(Argmax, KernelAttr(), ArgmaxCPUKernel, float);
MS_REG_CPU_KERNEL_T(Argmax, KernelAttr(), ArgmaxCPUKernel, float16);
} // namespace kernel
} // namespace mindspore

10
mindspore/ccsrc/backend/kernel_compiler/cpu/argmin_with_value_cpu_kernel.h
View File

@ -42,14 +42,8 @@ class ArgMinWithValueCPUKernel : public CPUKernel {
size_t dim_axis_;
};
MS_REG_CPU_KERNEL_T(
ArgMinWithValue,
KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeFloat32),
ArgMinWithValueCPUKernel, float);
MS_REG_CPU_KERNEL_T(
ArgMinWithValue,
KernelAttr().AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeFloat16),
ArgMinWithValueCPUKernel, float16);
MS_REG_CPU_KERNEL_T(ArgMinWithValue, KernelAttr(), ArgMinWithValueCPUKernel, float);
MS_REG_CPU_KERNEL_T(ArgMinWithValue, KernelAttr(), ArgMinWithValueCPUKernel, float16);
} // namespace kernel
} // namespace mindspore

52
mindspore/ccsrc/backend/kernel_compiler/cpu/arithmetic_cpu_kernel.h
View File

@ -63,43 +63,18 @@ class ArithmeticCPUKernel : public CPUKernel {
TypeId target_dtype_{kTypeUnknown};
};
MS_REG_CPU_KERNEL_T(
Sub, KernelAttr().AddInputAttr(kNumberTypeInt32).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
ArithmeticCPUKernel, int);
MS_REG_CPU_KERNEL_T(
Sub, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
ArithmeticCPUKernel, float);
MS_REG_CPU_KERNEL_T(
Sub, KernelAttr().AddInputAttr(kNumberTypeInt64).AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeInt64),
ArithmeticCPUKernel, int64_t);
MS_REG_CPU_KERNEL_T(
Pow, KernelAttr().AddInputAttr(kNumberTypeInt32).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
ArithmeticCPUKernel, int);
MS_REG_CPU_KERNEL_T(
Pow, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
ArithmeticCPUKernel, float);
MS_REG_CPU_KERNEL_T(
Pow, KernelAttr().AddInputAttr(kNumberTypeInt64).AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeInt64),
ArithmeticCPUKernel, int64_t);
MS_REG_CPU_KERNEL_T(
RealDiv, KernelAttr().AddInputAttr(kNumberTypeInt32).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
ArithmeticCPUKernel, int);
MS_REG_CPU_KERNEL_T(
RealDiv,
KernelAttr().AddInputAttr(kNumberTypeFloat32).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
ArithmeticCPUKernel, float);
MS_REG_CPU_KERNEL_T(
RealDiv, KernelAttr().AddInputAttr(kNumberTypeInt64).AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeInt64),
ArithmeticCPUKernel, int64_t);
MS_REG_CPU_KERNEL_T(
Div, KernelAttr().AddInputAttr(kNumberTypeInt64).AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeInt64),
ArithmeticCPUKernel, int64_t);
MS_REG_CPU_KERNEL_T(
Div, KernelAttr().AddInputAttr(kNumberTypeInt32).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
ArithmeticCPUKernel, int);
MS_REG_CPU_KERNEL_T(
Div, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
ArithmeticCPUKernel, float);
MS_REG_CPU_KERNEL_T(Sub, KernelAttr(), ArithmeticCPUKernel, int32_t);
MS_REG_CPU_KERNEL_T(Sub, KernelAttr(), ArithmeticCPUKernel, float);
MS_REG_CPU_KERNEL_T(Sub, KernelAttr(), ArithmeticCPUKernel, int64_t);
MS_REG_CPU_KERNEL_T(Pow, KernelAttr(), ArithmeticCPUKernel, int32_t);
MS_REG_CPU_KERNEL_T(Pow, KernelAttr(), ArithmeticCPUKernel, float);
MS_REG_CPU_KERNEL_T(Pow, KernelAttr(), ArithmeticCPUKernel, int64_t);
MS_REG_CPU_KERNEL_T(RealDiv, KernelAttr(), ArithmeticCPUKernel, int32_t);
MS_REG_CPU_KERNEL_T(RealDiv, KernelAttr(), ArithmeticCPUKernel, float);
MS_REG_CPU_KERNEL_T(RealDiv, KernelAttr(), ArithmeticCPUKernel, int64_t);
MS_REG_CPU_KERNEL_T(Div, KernelAttr(), ArithmeticCPUKernel, int32_t);
MS_REG_CPU_KERNEL_T(Div, KernelAttr(), ArithmeticCPUKernel, float);
MS_REG_CPU_KERNEL_T(Div, KernelAttr(), ArithmeticCPUKernel, int64_t);
MS_REG_CPU_KERNEL_T(
FloorDiv, KernelAttr().AddInputAttr(kNumberTypeInt64).AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeInt64),
ArithmeticCPUKernel, int64_t);
@ -139,9 +114,6 @@ MS_REG_CPU_KERNEL_T(
MS_REG_CPU_KERNEL_T(
AssignAdd, KernelAttr().AddInputAttr(kNumberTypeInt64).AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeInt64),
ArithmeticCPUKernel, int64_t);
MS_REG_CPU_KERNEL_T(
Mul, KernelAttr().AddInputAttr(kNumberTypeInt32).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
ArithmeticCPUKernel, int);
MS_REG_CPU_KERNEL_T(
SquaredDifference,
KernelAttr().AddInputAttr(kNumberTypeInt32).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),

5
mindspore/ccsrc/backend/kernel_compiler/cpu/bias_add_cpu_kernel.h
View File

@ -37,10 +37,7 @@ class BiasAddCPUKernel : public CPUKernel {
std::vector<size_t> input_shape_;
std::vector<size_t> bias_shape_;
};
MS_REG_CPU_KERNEL(
BiasAdd,
KernelAttr().AddInputAttr(kNumberTypeFloat32).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
BiasAddCPUKernel);
MS_REG_CPU_KERNEL(BiasAdd, KernelAttr(), BiasAddCPUKernel);
} // namespace kernel
} // namespace mindspore
#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_BIAS_ADD_CPU_KERNEL_H_

3
mindspore/ccsrc/backend/kernel_compiler/cpu/bias_add_grad_cpu_kernel.h
View File

@ -36,8 +36,7 @@ class BiasAddGradCPUKernel : public CPUKernel {
private:
std::vector<size_t> input_shape_;
};
MS_REG_CPU_KERNEL(BiasAddGrad, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
BiasAddGradCPUKernel);
MS_REG_CPU_KERNEL(BiasAddGrad, KernelAttr(), BiasAddGradCPUKernel);
} // namespace kernel
} // namespace mindspore
#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_BIASADDGRADCPUKERNEL_H_

4
mindspore/ccsrc/backend/kernel_compiler/cpu/cast_cpu_kernel.cc
View File

@ -34,8 +34,8 @@ void Cast(const S *in, T *out, size_t size) {
template <typename S, typename T>
void CastCPUKernel<S, T>::InitKernel(const CNodePtr &kernel_node) {
MS_EXCEPTION_IF_NULL(kernel_node);
source_dtype = AnfAlgo::GetInputDeviceDataType(kernel_node, 0);
target_dtype = AnfAlgo::GetOutputDeviceDataType(kernel_node, 0);
source_dtype_ = AnfAlgo::GetInputDeviceDataType(kernel_node, 0);
target_dtype_ = AnfAlgo::GetOutputDeviceDataType(kernel_node, 0);
}
template <typename S, typename T>

437
mindspore/ccsrc/backend/kernel_compiler/cpu/cast_cpu_kernel.h
View File

@ -35,309 +35,166 @@ class CastCPUKernel : public CPUKernel {
const std::vector<AddressPtr> &outputs) override;
private:
TypeId source_dtype{kTypeUnknown};
TypeId target_dtype{kTypeUnknown};
TypeId source_dtype_{kTypeUnknown};
TypeId target_dtype_{kTypeUnknown};
};
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeBool).AddOutputAttr(kNumberTypeFloat16), CastCPUKernel,
bool, float16);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeBool).AddOutputAttr(kNumberTypeFloat32), CastCPUKernel,
bool, float);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeBool).AddOutputAttr(kNumberTypeFloat64), CastCPUKernel,
bool, double);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeBool).AddOutputAttr(kNumberTypeInt8), CastCPUKernel,
bool, int8_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeBool).AddOutputAttr(kNumberTypeInt16), CastCPUKernel,
bool, int16_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeBool).AddOutputAttr(kNumberTypeInt32), CastCPUKernel,
bool, int32_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeBool).AddOutputAttr(kNumberTypeInt64), CastCPUKernel,
bool, int64_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeBool).AddOutputAttr(kNumberTypeUInt8), CastCPUKernel,
bool, uint8_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeBool).AddOutputAttr(kNumberTypeUInt16), CastCPUKernel,
bool, uint16_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeBool).AddOutputAttr(kNumberTypeUInt32), CastCPUKernel,
bool, uint32_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeBool).AddOutputAttr(kNumberTypeUInt64), CastCPUKernel,
bool, uint64_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeBool).AddOutputAttr(kNumberTypeBool), CastCPUKernel,
bool, bool);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, uint8_t, uint8_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, uint8_t, uint16_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, uint8_t, uint32_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, uint8_t, uint64_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, uint8_t, int8_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, uint8_t, int16_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, uint8_t, int32_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, uint8_t, int64_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, uint8_t, float16);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, uint8_t, float);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, uint8_t, double);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, uint8_t, bool);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),
CastCPUKernel, float16, float16);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat32),
CastCPUKernel, float16, float);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat64),
CastCPUKernel, float16, double);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeInt8), CastCPUKernel,
float16, int8_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeInt16),
CastCPUKernel, float16, int16_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeInt32),
CastCPUKernel, float16, int32_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeInt64),
CastCPUKernel, float16, int64_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeUInt8),
CastCPUKernel, float16, uint8_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeUInt16),
CastCPUKernel, float16, uint16_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeUInt32),
CastCPUKernel, float16, uint32_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeUInt64),
CastCPUKernel, float16, uint64_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeBool), CastCPUKernel,
float16, bool);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, uint16_t, uint8_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, uint16_t, uint16_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, uint16_t, uint32_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, uint16_t, uint64_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, uint16_t, int8_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, uint16_t, int16_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, uint16_t, int32_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, uint16_t, int64_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, uint16_t, float16);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, uint16_t, float);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, uint16_t, double);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, uint16_t, bool);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat16),
CastCPUKernel, float, float16);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
CastCPUKernel, float, float);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat64),
CastCPUKernel, float, double);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeInt8), CastCPUKernel,
float, int8_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeInt16),
CastCPUKernel, float, int16_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeInt32),
CastCPUKernel, float, int32_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeInt64),
CastCPUKernel, float, int64_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeUInt8),
CastCPUKernel, float, uint8_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeUInt16),
CastCPUKernel, float, uint16_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeUInt32),
CastCPUKernel, float, uint32_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeUInt64),
CastCPUKernel, float, uint64_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeBool), CastCPUKernel,
float, bool);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, uint32_t, uint8_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, uint32_t, uint16_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, uint32_t, uint32_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, uint32_t, uint64_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, uint32_t, int8_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, uint32_t, int16_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, uint32_t, int32_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, uint32_t, int64_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, uint32_t, float16);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, uint32_t, float);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, uint32_t, double);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, uint32_t, bool);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeFloat64).AddOutputAttr(kNumberTypeFloat16),
CastCPUKernel, double, float16);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeFloat64).AddOutputAttr(kNumberTypeFloat32),
CastCPUKernel, double, float);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeFloat64).AddOutputAttr(kNumberTypeFloat64),
CastCPUKernel, double, double);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeFloat64).AddOutputAttr(kNumberTypeInt8), CastCPUKernel,
double, int8_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeFloat64).AddOutputAttr(kNumberTypeInt16),
CastCPUKernel, double, int16_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeFloat64).AddOutputAttr(kNumberTypeInt32),
CastCPUKernel, double, int32_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeFloat64).AddOutputAttr(kNumberTypeInt64),
CastCPUKernel, double, int64_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeFloat64).AddOutputAttr(kNumberTypeUInt8),
CastCPUKernel, double, uint8_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeFloat64).AddOutputAttr(kNumberTypeUInt16),
CastCPUKernel, double, uint16_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeFloat64).AddOutputAttr(kNumberTypeUInt32),
CastCPUKernel, double, uint32_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeFloat64).AddOutputAttr(kNumberTypeUInt64),
CastCPUKernel, double, uint64_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeFloat64).AddOutputAttr(kNumberTypeBool), CastCPUKernel,
double, bool);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, uint64_t, uint8_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, uint64_t, uint16_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, uint64_t, uint32_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, uint64_t, uint64_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, uint64_t, int8_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, uint64_t, int16_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, uint64_t, int32_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, uint64_t, int64_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, uint64_t, float16);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, uint64_t, float);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, uint64_t, double);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, uint64_t, bool);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeInt8).AddOutputAttr(kNumberTypeFloat16), CastCPUKernel,
int8_t, float16);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeInt8).AddOutputAttr(kNumberTypeFloat32), CastCPUKernel,
int8_t, float);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeInt8).AddOutputAttr(kNumberTypeFloat64), CastCPUKernel,
int8_t, double);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeInt8).AddOutputAttr(kNumberTypeInt8), CastCPUKernel,
int8_t, int8_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeInt8).AddOutputAttr(kNumberTypeInt16), CastCPUKernel,
int8_t, int16_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeInt8).AddOutputAttr(kNumberTypeInt32), CastCPUKernel,
int8_t, int32_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeInt8).AddOutputAttr(kNumberTypeInt64), CastCPUKernel,
int8_t, int64_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeInt8).AddOutputAttr(kNumberTypeUInt8), CastCPUKernel,
int8_t, uint8_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeInt8).AddOutputAttr(kNumberTypeUInt16), CastCPUKernel,
int8_t, uint16_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeInt8).AddOutputAttr(kNumberTypeUInt32), CastCPUKernel,
int8_t, uint32_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeInt8).AddOutputAttr(kNumberTypeUInt64), CastCPUKernel,
int8_t, uint64_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeInt8).AddOutputAttr(kNumberTypeBool), CastCPUKernel,
int8_t, bool);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, int8_t, uint8_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, int8_t, uint16_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, int8_t, uint32_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, int8_t, uint64_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, int8_t, int8_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, int8_t, int16_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, int8_t, int32_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, int8_t, int64_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, int8_t, float16);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, int8_t, float);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, int8_t, double);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, int8_t, bool);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeInt16).AddOutputAttr(kNumberTypeFloat16),
CastCPUKernel, int16_t, float16);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeInt16).AddOutputAttr(kNumberTypeFloat32),
CastCPUKernel, int16_t, float);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeInt16).AddOutputAttr(kNumberTypeFloat64),
CastCPUKernel, int16_t, double);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeInt16).AddOutputAttr(kNumberTypeInt8), CastCPUKernel,
int16_t, int8_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeInt16).AddOutputAttr(kNumberTypeInt16), CastCPUKernel,
int16_t, int16_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeInt16).AddOutputAttr(kNumberTypeInt32), CastCPUKernel,
int16_t, int32_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeInt16).AddOutputAttr(kNumberTypeInt64), CastCPUKernel,
int16_t, int64_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeInt16).AddOutputAttr(kNumberTypeUInt8), CastCPUKernel,
int16_t, uint8_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeInt16).AddOutputAttr(kNumberTypeUInt16), CastCPUKernel,
int16_t, uint16_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeInt16).AddOutputAttr(kNumberTypeUInt32), CastCPUKernel,
int16_t, uint32_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeInt16).AddOutputAttr(kNumberTypeUInt64), CastCPUKernel,
int16_t, uint64_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeInt16).AddOutputAttr(kNumberTypeBool), CastCPUKernel,
int16_t, bool);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, int16_t, uint8_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, int16_t, uint16_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, int16_t, uint32_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, int16_t, uint64_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, int16_t, int8_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, int16_t, int16_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, int16_t, int32_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, int16_t, int64_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, int16_t, float16);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, int16_t, float);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, int16_t, double);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, int16_t, bool);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeFloat16),
CastCPUKernel, int32_t, float16);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeFloat32),
CastCPUKernel, int32_t, float);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeFloat64),
CastCPUKernel, int32_t, double);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt8), CastCPUKernel,
int32_t, int8_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt16), CastCPUKernel,
int32_t, int16_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32), CastCPUKernel,
int32_t, int32_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt64), CastCPUKernel,
int32_t, int64_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeUInt8), CastCPUKernel,
int32_t, uint8_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeUInt16), CastCPUKernel,
int32_t, uint16_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeUInt32), CastCPUKernel,
int32_t, uint32_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeUInt64), CastCPUKernel,
int32_t, uint64_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeBool), CastCPUKernel,
int32_t, bool);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, int32_t, uint8_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, int32_t, uint16_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, int32_t, uint32_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, int32_t, uint64_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, int32_t, int8_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, int32_t, int16_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, int32_t, int32_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, int32_t, int64_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, int32_t, float16);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, int32_t, float);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, int32_t, double);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, int32_t, bool);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeFloat16),
CastCPUKernel, int64_t, float16);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeFloat32),
CastCPUKernel, int64_t, float);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeFloat64),
CastCPUKernel, int64_t, double);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeInt8), CastCPUKernel,
int64_t, int8_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeInt16), CastCPUKernel,
int64_t, int16_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeInt32), CastCPUKernel,
int64_t, int32_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeInt64), CastCPUKernel,
int64_t, int64_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeUInt8), CastCPUKernel,
int64_t, uint8_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeUInt16), CastCPUKernel,
int64_t, uint16_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeUInt32), CastCPUKernel,
int64_t, uint32_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeUInt64), CastCPUKernel,
int64_t, uint64_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeBool), CastCPUKernel,
int64_t, bool);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, int64_t, uint8_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, int64_t, uint16_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, int64_t, uint32_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, int64_t, uint64_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, int64_t, int8_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, int64_t, int16_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, int64_t, int32_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, int64_t, int64_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, int64_t, float16);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, int64_t, float);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, int64_t, double);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, int64_t, bool);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeUInt8).AddOutputAttr(kNumberTypeFloat16),
CastCPUKernel, uint8_t, float16);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeUInt8).AddOutputAttr(kNumberTypeFloat32),
CastCPUKernel, uint8_t, float);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeUInt8).AddOutputAttr(kNumberTypeFloat64),
CastCPUKernel, uint8_t, double);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeUInt8).AddOutputAttr(kNumberTypeInt8), CastCPUKernel,
uint8_t, int8_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeUInt8).AddOutputAttr(kNumberTypeInt16), CastCPUKernel,
uint8_t, int16_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeUInt8).AddOutputAttr(kNumberTypeInt32), CastCPUKernel,
uint8_t, int32_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeUInt8).AddOutputAttr(kNumberTypeInt64), CastCPUKernel,
uint8_t, int64_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeUInt8).AddOutputAttr(kNumberTypeUInt8), CastCPUKernel,
uint8_t, uint8_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeUInt8).AddOutputAttr(kNumberTypeUInt16), CastCPUKernel,
uint8_t, uint16_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeUInt8).AddOutputAttr(kNumberTypeUInt32), CastCPUKernel,
uint8_t, uint32_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeUInt8).AddOutputAttr(kNumberTypeUInt64), CastCPUKernel,
uint8_t, uint64_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeUInt8).AddOutputAttr(kNumberTypeBool), CastCPUKernel,
uint8_t, bool);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, float16, uint8_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, float16, uint16_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, float16, uint32_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, float16, uint64_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, float16, int8_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, float16, int16_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, float16, int32_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, float16, int64_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, float16, float16);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, float16, float);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, float16, double);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, float16, bool);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeUInt16).AddOutputAttr(kNumberTypeFloat16),
CastCPUKernel, uint16_t, float16);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeUInt16).AddOutputAttr(kNumberTypeFloat32),
CastCPUKernel, uint16_t, float);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeUInt16).AddOutputAttr(kNumberTypeFloat64),
CastCPUKernel, uint16_t, double);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeUInt16).AddOutputAttr(kNumberTypeInt8), CastCPUKernel,
uint16_t, int8_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeUInt16).AddOutputAttr(kNumberTypeInt16), CastCPUKernel,
uint16_t, int16_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeUInt16).AddOutputAttr(kNumberTypeInt32), CastCPUKernel,
uint16_t, int32_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeUInt16).AddOutputAttr(kNumberTypeInt64), CastCPUKernel,
uint16_t, int64_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeUInt16).AddOutputAttr(kNumberTypeUInt8), CastCPUKernel,
uint16_t, uint8_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeUInt16).AddOutputAttr(kNumberTypeUInt16),
CastCPUKernel, uint16_t, uint16_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeUInt16).AddOutputAttr(kNumberTypeUInt32),
CastCPUKernel, uint16_t, uint32_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeUInt16).AddOutputAttr(kNumberTypeUInt64),
CastCPUKernel, uint16_t, uint64_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeUInt16).AddOutputAttr(kNumberTypeBool), CastCPUKernel,
uint16_t, bool);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, float, uint8_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, float, uint16_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, float, uint32_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, float, uint64_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, float, int8_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, float, int16_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, float, int32_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, float, int64_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, float, float16);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, float, float);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, float, double);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, float, bool);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeUInt32).AddOutputAttr(kNumberTypeFloat16),
CastCPUKernel, uint32_t, float16);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeUInt32).AddOutputAttr(kNumberTypeFloat32),
CastCPUKernel, uint32_t, float);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeUInt32).AddOutputAttr(kNumberTypeFloat64),
CastCPUKernel, uint32_t, double);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeUInt32).AddOutputAttr(kNumberTypeInt8), CastCPUKernel,
uint32_t, int8_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeUInt32).AddOutputAttr(kNumberTypeInt16), CastCPUKernel,
uint32_t, int16_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeUInt32).AddOutputAttr(kNumberTypeInt32), CastCPUKernel,
uint32_t, int32_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeUInt32).AddOutputAttr(kNumberTypeInt64), CastCPUKernel,
uint32_t, int64_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeUInt32).AddOutputAttr(kNumberTypeUInt8), CastCPUKernel,
uint32_t, uint8_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeUInt32).AddOutputAttr(kNumberTypeUInt16),
CastCPUKernel, uint32_t, uint16_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeUInt32).AddOutputAttr(kNumberTypeUInt32),
CastCPUKernel, uint32_t, uint32_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeUInt32).AddOutputAttr(kNumberTypeUInt64),
CastCPUKernel, uint32_t, uint64_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeUInt32).AddOutputAttr(kNumberTypeBool), CastCPUKernel,
uint32_t, bool);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, double, uint8_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, double, uint16_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, double, uint32_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, double, uint64_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, double, int8_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, double, int16_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, double, int32_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, double, int64_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, double, float16);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, double, float);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, double, double);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, double, bool);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, bool, uint8_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, bool, uint16_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, bool, uint32_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, bool, uint64_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, bool, int8_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, bool, int16_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, bool, int32_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, bool, int64_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, bool, float16);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, bool, float);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, bool, double);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr(), CastCPUKernel, bool, bool);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeUInt64).AddOutputAttr(kNumberTypeFloat16),
CastCPUKernel, uint64_t, float16);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeUInt64).AddOutputAttr(kNumberTypeFloat32),
CastCPUKernel, uint64_t, float);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeUInt64).AddOutputAttr(kNumberTypeFloat64),
CastCPUKernel, uint64_t, double);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeUInt64).AddOutputAttr(kNumberTypeInt8), CastCPUKernel,
uint64_t, int8_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeUInt64).AddOutputAttr(kNumberTypeInt16), CastCPUKernel,
uint64_t, int16_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeUInt64).AddOutputAttr(kNumberTypeInt32), CastCPUKernel,
uint64_t, int32_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeUInt64).AddOutputAttr(kNumberTypeInt64), CastCPUKernel,
uint64_t, int64_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeUInt64).AddOutputAttr(kNumberTypeUInt8), CastCPUKernel,
uint64_t, uint8_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeUInt64).AddOutputAttr(kNumberTypeUInt16),
CastCPUKernel, uint64_t, uint16_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeUInt64).AddOutputAttr(kNumberTypeUInt32),
CastCPUKernel, uint64_t, uint32_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeUInt64).AddOutputAttr(kNumberTypeUInt64),
CastCPUKernel, uint64_t, uint64_t);
MS_REG_CPU_KERNEL_T_S(Cast, KernelAttr().AddInputAttr(kNumberTypeUInt64).AddOutputAttr(kNumberTypeBool), CastCPUKernel,
uint64_t, bool);
} // namespace kernel
} // namespace mindspore

40
mindspore/ccsrc/backend/kernel_compiler/cpu/concat_cpu_kernel.h
View File

@ -39,36 +39,16 @@ class ConcatCPUKernel : public CPUKernel {
CNodeWeakPtr node_wpt_;
};
MS_REG_CPU_KERNEL_T(
Concat, KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
ConcatCPUKernel, float);
MS_REG_CPU_KERNEL_T(Concat,
KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeInt8).AddOutputAttr(kNumberTypeInt8),
ConcatCPUKernel, int8_t)
MS_REG_CPU_KERNEL_T(Concat,
KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeInt16).AddOutputAttr(kNumberTypeInt16),
ConcatCPUKernel, int16_t)
MS_REG_CPU_KERNEL_T(Concat,
KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
ConcatCPUKernel, int)
MS_REG_CPU_KERNEL_T(Concat,
KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeInt64),
ConcatCPUKernel, int64_t)
MS_REG_CPU_KERNEL_T(Concat,
KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeUInt8).AddOutputAttr(kNumberTypeUInt8),
ConcatCPUKernel, uint8_t)
MS_REG_CPU_KERNEL_T(Concat,
KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeUInt16).AddOutputAttr(kNumberTypeUInt16),
ConcatCPUKernel, uint16_t)
MS_REG_CPU_KERNEL_T(Concat,
KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeUInt32).AddOutputAttr(kNumberTypeUInt32),
ConcatCPUKernel, uint32_t)
MS_REG_CPU_KERNEL_T(Concat,
KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeUInt64).AddOutputAttr(kNumberTypeUInt64),
ConcatCPUKernel, uint64_t)
MS_REG_CPU_KERNEL_T(Concat,
KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeBool).AddOutputAttr(kNumberTypeBool),
ConcatCPUKernel, bool)
MS_REG_CPU_KERNEL_T(Concat, KernelAttr(), ConcatCPUKernel, float);
MS_REG_CPU_KERNEL_T(Concat, KernelAttr(), ConcatCPUKernel, int8_t)
MS_REG_CPU_KERNEL_T(Concat, KernelAttr(), ConcatCPUKernel, int16_t)
MS_REG_CPU_KERNEL_T(Concat, KernelAttr(), ConcatCPUKernel, int32_t)
MS_REG_CPU_KERNEL_T(Concat, KernelAttr(), ConcatCPUKernel, int64_t)
MS_REG_CPU_KERNEL_T(Concat, KernelAttr(), ConcatCPUKernel, uint8_t)
MS_REG_CPU_KERNEL_T(Concat, KernelAttr(), ConcatCPUKernel, uint16_t)
MS_REG_CPU_KERNEL_T(Concat, KernelAttr(), ConcatCPUKernel, uint32_t)
MS_REG_CPU_KERNEL_T(Concat, KernelAttr(), ConcatCPUKernel, uint64_t)
MS_REG_CPU_KERNEL_T(Concat, KernelAttr(), ConcatCPUKernel, bool)
} // namespace kernel
} // namespace mindspore

1
mindspore/ccsrc/backend/kernel_compiler/cpu/cpu_kernel.h
View File

@ -23,6 +23,7 @@
#include <vector>
#include "backend/kernel_compiler/kernel.h"
#include "backend/session/anf_runtime_algorithm.h"
#include "backend/kernel_compiler/common_utils.h"
#include "ir/anf.h"
using mindspore::kernel::Address;

70
mindspore/ccsrc/backend/kernel_compiler/cpu/cpu_kernel_factory.cc
View File

@ -17,12 +17,14 @@
#include "backend/kernel_compiler/cpu/cpu_kernel_factory.h"
#include <memory>
#include <set>
#include <string>
#include "runtime/device/kernel_info.h"
namespace mindspore {
namespace kernel {
const std::set<std::string> same_op_name = {"Concat", "Pack", "Stack", "Split", "Transpose", "Unpack", "AddN"};
CPUKernelFactory &CPUKernelFactory::GetInstance() {
static CPUKernelFactory instance;
return instance;
@ -48,6 +50,58 @@ std::shared_ptr<CPUKernel> CPUKernelFactory::Create(const std::string &kernel_na
return nullptr;
}
void CPUKernelFactory::SetKernelAttrs(const std::shared_ptr<kernel::OpInfo> op_info,
std::vector<KernelAttr> *kernel_attrs) {
auto inputs_ptr = op_info->inputs_ptr();
auto outputs_ptr = op_info->outputs_ptr();
auto first_input_dtypes = inputs_ptr[0]->dtypes();
auto input_formats = inputs_ptr[0]->formats();
for (size_t i = 0; i < first_input_dtypes.size(); i++) {
KernelAttr kernel_attr;
kernel_attr.AddInputAttr(kernel::DtypeToTypeId(first_input_dtypes[i]), input_formats[i]);
for (size_t j = 1; j < inputs_ptr.size(); j++) {
auto input_dtypes = inputs_ptr[j]->dtypes();
input_formats = inputs_ptr[j]->formats();
kernel_attr.AddInputAttr(kernel::DtypeToTypeId(input_dtypes[i]), input_formats[i]);
}
for (size_t j = 0; j < outputs_ptr.size(); j++) {
auto output_dtypes = outputs_ptr[j]->dtypes();
auto output_formats = outputs_ptr[j]->formats();
kernel_attr.AddOutputAttr(kernel::DtypeToTypeId(output_dtypes[i]), output_formats[i]);
}
if (same_op_name.count(op_info->op_name()) != 0) {
kernel_attr.SetAllSameAttr(true);
}
kernel_attrs->emplace_back(kernel_attr);
}
}
void CPUKernelFactory::UpdateKernelAttrs(const std::string &kernel_name, const std::vector<KernelAttr> &kernel_attrs) {
size_t attr_size = kernel_attrs.size();
std::vector<std::pair<KernelAttr, CPUKernelCreator>> attr_creators(attr_size);
auto iter = name_to_attr_creator_.find(kernel_name);
if (iter == name_to_attr_creator_.end()) {
MS_LOG(ERROR) << "CPUKernelFactory has not registered operator: " << kernel_name;
return;
}
if (attr_size <= iter->second.size()) {
for (size_t i = 0; i < attr_size; i++) {
auto creator = name_to_attr_creator_.find(kernel_name)->second[i].second;
attr_creators[i] = std::make_pair(kernel_attrs[i], creator);
}
} else {
MS_LOG(INFO) << "attr size is not equal creators size " << kernel_name << " attr_size = " << attr_size
<< " creator_size = " << iter->second.size();
auto single_creator = name_to_attr_creator_.find(kernel_name)->second[0].second;
for (size_t i = 0; i < attr_size; i++) {
attr_creators[i] = std::make_pair(kernel_attrs[i], single_creator);
}
}
name_to_attr_creator_[kernel_name] = attr_creators;
}
std::pair<bool, size_t> CPUKernelFactory::CPUKernelAttrCheck(const std::string &kernel_name,
const KernelBuildInfo &kernel_info) {
auto iter = name_to_attr_creator_.find(kernel_name);
@ -55,10 +109,18 @@ std::pair<bool, size_t> CPUKernelFactory::CPUKernelAttrCheck(const std::string &
MS_LOG(INFO) << "Not registered CPU kernel: op[" << kernel_name << "]!";
return std::make_pair(false, 0);
}
auto creators = iter->second;
for (size_t index = 0; index < creators.size(); ++index) {
auto attr_creator = creators[index];
if (CPUKernelSingleAttrCheck(attr_creator.first, kernel_info)) {
auto kernel_attrs = GetSupportedKernelAttrList(kernel_name);
if (kernel_attrs[0].GetInputSize() == 0 && kernel_attrs[0].GetOutputSize() == 0) {
auto op_info_ptr = mindspore::kernel::OpLib::FindOp(kernel_name, kernel::OpImplyType::kCPU);
if (op_info_ptr == nullptr) {
MS_LOG(ERROR) << "Not find op[" << kernel_name << "] in cpu";
}
kernel_attrs.clear();
SetKernelAttrs(op_info_ptr, &kernel_attrs);
kernel::CPUKernelFactory::GetInstance().UpdateKernelAttrs(kernel_name, kernel_attrs);
}
for (size_t index = 0; index < kernel_attrs.size(); ++index) {
if (CPUKernelSingleAttrCheck(kernel_attrs[index], kernel_info)) {
return std::make_pair(true, index);
}
}

3
mindspore/ccsrc/backend/kernel_compiler/cpu/cpu_kernel_factory.h
View File

@ -25,6 +25,7 @@
#include "utils/ms_utils.h"
#include "backend/kernel_compiler/cpu/cpu_kernel.h"
#include "backend/kernel_compiler/oplib/oplib.h"
#include "runtime/device/cpu/kernel_select_cpu.h"
namespace mindspore {
@ -36,6 +37,8 @@ class CPUKernelFactory {
static CPUKernelFactory &GetInstance();
void Register(const std::string &kernel_name, const KernelAttr &kernel_attr, CPUKernelCreator &&kernel_creator);
std::shared_ptr<CPUKernel> Create(const std::string &kernel_name, const CNodePtr &apply_kernel);
void SetKernelAttrs(const std::shared_ptr<kernel::OpInfo> op_info, std::vector<KernelAttr> *kernel_attrs);
void UpdateKernelAttrs(const std::string &kernel_name, const std::vector<KernelAttr> &kernel_attrs);
std::vector<KernelAttr> GetSupportedKernelAttrList(const std::string &kernel_name);
private:

9
mindspore/ccsrc/backend/kernel_compiler/cpu/dropout_cpu_kernel.h
View File

@ -46,14 +46,7 @@ class DropoutCPUKernel : public CPUKernel {
uint64_t tensor_size_ = 1;
};
MS_REG_CPU_KERNEL(
Dropout,
KernelAttr().AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),
DropoutCPUKernel);
MS_REG_CPU_KERNEL(
Dropout,
KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
DropoutCPUKernel);
MS_REG_CPU_KERNEL(Dropout, KernelAttr(), DropoutCPUKernel);
} // namespace kernel
} // namespace mindspore
#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_DROPOUT_CPU_KERNEL_H_

9
mindspore/ccsrc/backend/kernel_compiler/cpu/dropout_grad_kernel.h
View File

@ -43,14 +43,7 @@ class DropoutGradCpuBwdKernel : public CPUKernel {
size_t num_count, float keep_prob);
};
MS_REG_CPU_KERNEL(
DropoutGrad,
KernelAttr().AddInputAttr(kNumberTypeFloat32).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
DropoutGradCpuBwdKernel);
MS_REG_CPU_KERNEL(
DropoutGrad,
KernelAttr().AddInputAttr(kNumberTypeFloat16).AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),
DropoutGradCpuBwdKernel);
MS_REG_CPU_KERNEL(DropoutGrad, KernelAttr(), DropoutGradCpuBwdKernel);
} // namespace kernel
} // namespace mindspore

5
mindspore/ccsrc/backend/kernel_compiler/cpu/equal_count_cpu_kernel.h
View File

@ -33,10 +33,7 @@ class EqualCountCPUKernel : public CPUKernel {
const std::vector<AddressPtr> &outputs) override;
};
MS_REG_CPU_KERNEL(
EqualCount,
KernelAttr().AddInputAttr(kNumberTypeInt32).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
EqualCountCPUKernel);
MS_REG_CPU_KERNEL(EqualCount, KernelAttr(), EqualCountCPUKernel);
} // namespace kernel
} // namespace mindspore

46
mindspore/ccsrc/backend/kernel_compiler/cpu/gather_cpu_kernel.h
View File

@ -44,41 +44,17 @@ class GatherV2CPUKernel : public CPUKernel {
int64_t axis_{0};
};
MS_REG_CPU_KERNEL_T(
Gather, KernelAttr().AddInputAttr(kNumberTypeBool).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeBool),
GatherV2CPUKernel, bool);
MS_REG_CPU_KERNEL_T(
Gather,
KernelAttr().AddInputAttr(kNumberTypeFloat32).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeFloat32),
GatherV2CPUKernel, float);
MS_REG_CPU_KERNEL_T(
Gather,
KernelAttr().AddInputAttr(kNumberTypeFloat64).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeFloat64),
GatherV2CPUKernel, double);
MS_REG_CPU_KERNEL_T(
Gather, KernelAttr().AddInputAttr(kNumberTypeInt8).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt8),
GatherV2CPUKernel, int8_t);
MS_REG_CPU_KERNEL_T(
Gather, KernelAttr().AddInputAttr(kNumberTypeInt16).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt16),
GatherV2CPUKernel, int16_t);
MS_REG_CPU_KERNEL_T(
Gather, KernelAttr().AddInputAttr(kNumberTypeInt32).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
GatherV2CPUKernel, int32_t);
MS_REG_CPU_KERNEL_T(
Gather, KernelAttr().AddInputAttr(kNumberTypeInt64).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt64),
GatherV2CPUKernel, int64_t);
MS_REG_CPU_KERNEL_T(
Gather, KernelAttr().AddInputAttr(kNumberTypeUInt8).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeUInt8),
GatherV2CPUKernel, uint8_t);
MS_REG_CPU_KERNEL_T(
Gather, KernelAttr().AddInputAttr(kNumberTypeUInt16).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeUInt16),
GatherV2CPUKernel, uint16_t);
MS_REG_CPU_KERNEL_T(
Gather, KernelAttr().AddInputAttr(kNumberTypeUInt32).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeUInt32),
GatherV2CPUKernel, uint32_t);
MS_REG_CPU_KERNEL_T(
Gather, KernelAttr().AddInputAttr(kNumberTypeUInt64).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeUInt64),
GatherV2CPUKernel, uint64_t);
MS_REG_CPU_KERNEL_T(Gather, KernelAttr(), GatherV2CPUKernel, uint8_t);
MS_REG_CPU_KERNEL_T(Gather, KernelAttr(), GatherV2CPUKernel, uint16_t);
MS_REG_CPU_KERNEL_T(Gather, KernelAttr(), GatherV2CPUKernel, uint32_t);
MS_REG_CPU_KERNEL_T(Gather, KernelAttr(), GatherV2CPUKernel, uint64_t);
MS_REG_CPU_KERNEL_T(Gather, KernelAttr(), GatherV2CPUKernel, int8_t);
MS_REG_CPU_KERNEL_T(Gather, KernelAttr(), GatherV2CPUKernel, int16_t);
MS_REG_CPU_KERNEL_T(Gather, KernelAttr(), GatherV2CPUKernel, int32_t);
MS_REG_CPU_KERNEL_T(Gather, KernelAttr(), GatherV2CPUKernel, int64_t);
MS_REG_CPU_KERNEL_T(Gather, KernelAttr(), GatherV2CPUKernel, float);
MS_REG_CPU_KERNEL_T(Gather, KernelAttr(), GatherV2CPUKernel, double);
MS_REG_CPU_KERNEL_T(Gather, KernelAttr(), GatherV2CPUKernel, bool);
} // namespace kernel
} // namespace mindspore

80
mindspore/ccsrc/backend/kernel_compiler/cpu/gather_d_cpu_kernel.h
View File

@ -39,76 +39,16 @@ class GatherDCPUKernel : public CPUKernel {
std::vector<size_t> output_shape_;
};
MS_REG_CPU_KERNEL_T_S(GatherD,
KernelAttr()
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeInt32)
.AddInputAttr(kNumberTypeInt32)
.AddOutputAttr(kNumberTypeFloat32),
GatherDCPUKernel, float, int32_t);
MS_REG_CPU_KERNEL_T_S(GatherD,
KernelAttr()
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeInt32)
.AddInputAttr(kNumberTypeInt64)
.AddOutputAttr(kNumberTypeFloat32),
GatherDCPUKernel, float, int64_t);
MS_REG_CPU_KERNEL_T_S(GatherD,
KernelAttr()
.AddInputAttr(kNumberTypeFloat16)
.AddInputAttr(kNumberTypeInt32)
.AddInputAttr(kNumberTypeInt32)
.AddOutputAttr(kNumberTypeFloat16),
GatherDCPUKernel, float16, int32_t);
MS_REG_CPU_KERNEL_T_S(GatherD,
KernelAttr()
.AddInputAttr(kNumberTypeFloat16)
.AddInputAttr(kNumberTypeInt32)
.AddInputAttr(kNumberTypeInt64)
.AddOutputAttr(kNumberTypeFloat16),
GatherDCPUKernel, float16, int64_t);
MS_REG_CPU_KERNEL_T_S(GatherD,
KernelAttr()
.AddInputAttr(kNumberTypeInt32)
.AddInputAttr(kNumberTypeInt32)
.AddInputAttr(kNumberTypeInt32)
.AddOutputAttr(kNumberTypeInt32),
GatherDCPUKernel, int32_t, int32_t);
MS_REG_CPU_KERNEL_T_S(GatherD,
KernelAttr()
.AddInputAttr(kNumberTypeInt32)
.AddInputAttr(kNumberTypeInt32)
.AddInputAttr(kNumberTypeInt64)
.AddOutputAttr(kNumberTypeInt32),
GatherDCPUKernel, int32_t, int64_t);
MS_REG_CPU_KERNEL_T_S(GatherD,
KernelAttr()
.AddInputAttr(kNumberTypeInt64)
.AddInputAttr(kNumberTypeInt32)
.AddInputAttr(kNumberTypeInt32)
.AddOutputAttr(kNumberTypeInt64),
GatherDCPUKernel, int64_t, int32_t);
MS_REG_CPU_KERNEL_T_S(GatherD,
KernelAttr()
.AddInputAttr(kNumberTypeInt64)
.AddInputAttr(kNumberTypeInt32)
.AddInputAttr(kNumberTypeInt64)
.AddOutputAttr(kNumberTypeInt64),
GatherDCPUKernel, int64_t, int64_t);
MS_REG_CPU_KERNEL_T_S(GatherD,
KernelAttr()
.AddInputAttr(kNumberTypeBool)
.AddInputAttr(kNumberTypeInt32)
.AddInputAttr(kNumberTypeInt32)
.AddOutputAttr(kNumberTypeBool),
GatherDCPUKernel, bool, int32_t);
MS_REG_CPU_KERNEL_T_S(GatherD,
KernelAttr()
.AddInputAttr(kNumberTypeBool)
.AddInputAttr(kNumberTypeInt32)
.AddInputAttr(kNumberTypeInt64)
.AddOutputAttr(kNumberTypeBool),
GatherDCPUKernel, bool, int64_t);
MS_REG_CPU_KERNEL_T_S(GatherD, KernelAttr(), GatherDCPUKernel, float, int32_t);
MS_REG_CPU_KERNEL_T_S(GatherD, KernelAttr(), GatherDCPUKernel, float, int64_t);
MS_REG_CPU_KERNEL_T_S(GatherD, KernelAttr(), GatherDCPUKernel, float16, int32_t);
MS_REG_CPU_KERNEL_T_S(GatherD, KernelAttr(), GatherDCPUKernel, float16, int64_t);
MS_REG_CPU_KERNEL_T_S(GatherD, KernelAttr(), GatherDCPUKernel, int32_t, int32_t);
MS_REG_CPU_KERNEL_T_S(GatherD, KernelAttr(), GatherDCPUKernel, int32_t, int64_t);
MS_REG_CPU_KERNEL_T_S(GatherD, KernelAttr(), GatherDCPUKernel, int64_t, int32_t);
MS_REG_CPU_KERNEL_T_S(GatherD, KernelAttr(), GatherDCPUKernel, int64_t, int64_t);
MS_REG_CPU_KERNEL_T_S(GatherD, KernelAttr(), GatherDCPUKernel, bool, int32_t);
MS_REG_CPU_KERNEL_T_S(GatherD, KernelAttr(), GatherDCPUKernel, bool, int64_t);
} // namespace kernel
} // namespace mindspore

48
mindspore/ccsrc/backend/kernel_compiler/cpu/gather_d_grad_cpu_kernel.h
View File

@ -40,44 +40,16 @@ class GatherDGradCPUKernel : public CPUKernel {
int32_t axis_;
};
MS_REG_CPU_KERNEL_T_S(
GatherDGrad,
KernelAttr().AddInputAttr(kNumberTypeInt32).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
GatherDGradCPUKernel, int32_t, int32_t);
MS_REG_CPU_KERNEL_T_S(
GatherDGrad,
KernelAttr().AddInputAttr(kNumberTypeInt32).AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeInt64),
GatherDGradCPUKernel, int32_t, int64_t);
MS_REG_CPU_KERNEL_T_S(
GatherDGrad,
KernelAttr().AddInputAttr(kNumberTypeInt32).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
GatherDGradCPUKernel, int32_t, float);
MS_REG_CPU_KERNEL_T_S(
GatherDGrad,
KernelAttr().AddInputAttr(kNumberTypeInt32).AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),
GatherDGradCPUKernel, int32_t, float16);
MS_REG_CPU_KERNEL_T_S(
GatherDGrad, KernelAttr().AddInputAttr(kNumberTypeInt32).AddInputAttr(kNumberTypeBool).AddOutputAttr(kNumberTypeBool),
GatherDGradCPUKernel, int32_t, bool);
MS_REG_CPU_KERNEL_T_S(
GatherDGrad,
KernelAttr().AddInputAttr(kNumberTypeInt64).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
GatherDGradCPUKernel, int64_t, int32_t);
MS_REG_CPU_KERNEL_T_S(
GatherDGrad,
KernelAttr().AddInputAttr(kNumberTypeInt64).AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeInt64),
GatherDGradCPUKernel, int64_t, int64_t);
MS_REG_CPU_KERNEL_T_S(
GatherDGrad,
KernelAttr().AddInputAttr(kNumberTypeInt64).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
GatherDGradCPUKernel, int64_t, float);
MS_REG_CPU_KERNEL_T_S(
GatherDGrad,
KernelAttr().AddInputAttr(kNumberTypeInt64).AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),
GatherDGradCPUKernel, int64_t, float16);
MS_REG_CPU_KERNEL_T_S(
GatherDGrad, KernelAttr().AddInputAttr(kNumberTypeInt64).AddInputAttr(kNumberTypeBool).AddOutputAttr(kNumberTypeBool),
GatherDGradCPUKernel, int64_t, bool);
MS_REG_CPU_KERNEL_T_S(GatherDGrad, KernelAttr(), GatherDGradCPUKernel, int32_t, int32_t);
MS_REG_CPU_KERNEL_T_S(GatherDGrad, KernelAttr(), GatherDGradCPUKernel, int32_t, int64_t);
MS_REG_CPU_KERNEL_T_S(GatherDGrad, KernelAttr(), GatherDGradCPUKernel, int32_t, float);
MS_REG_CPU_KERNEL_T_S(GatherDGrad, KernelAttr(), GatherDGradCPUKernel, int32_t, float16);
MS_REG_CPU_KERNEL_T_S(GatherDGrad, KernelAttr(), GatherDGradCPUKernel, int32_t, bool);
MS_REG_CPU_KERNEL_T_S(GatherDGrad, KernelAttr(), GatherDGradCPUKernel, int64_t, int32_t);
MS_REG_CPU_KERNEL_T_S(GatherDGrad, KernelAttr(), GatherDGradCPUKernel, int64_t, int64_t);
MS_REG_CPU_KERNEL_T_S(GatherDGrad, KernelAttr(), GatherDGradCPUKernel, int64_t, float);
MS_REG_CPU_KERNEL_T_S(GatherDGrad, KernelAttr(), GatherDGradCPUKernel, int64_t, float16);
MS_REG_CPU_KERNEL_T_S(GatherDGrad, KernelAttr(), GatherDGradCPUKernel, int64_t, bool);
} // namespace kernel
} // namespace mindspore

15
mindspore/ccsrc/backend/kernel_compiler/cpu/gathernd_cpu_kernel.h
View File

@ -47,20 +47,7 @@ class GatherNdCPUKernel : public CPUKernel {
TypeId dtype_{kTypeUnknown};
};
MS_REG_CPU_KERNEL(
GatherNd, KernelAttr().AddInputAttr(kNumberTypeInt32).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
GatherNdCPUKernel);
MS_REG_CPU_KERNEL(
GatherNd, KernelAttr().AddInputAttr(kNumberTypeInt64).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt64),
GatherNdCPUKernel);
MS_REG_CPU_KERNEL(
GatherNd,
KernelAttr().AddInputAttr(kNumberTypeFloat32).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeFloat32),
GatherNdCPUKernel);
MS_REG_CPU_KERNEL(
GatherNd,
KernelAttr().AddInputAttr(kNumberTypeFloat64).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeFloat64),
GatherNdCPUKernel);
MS_REG_CPU_KERNEL(GatherNd, KernelAttr(), GatherNdCPUKernel);
} // namespace kernel
} // namespace mindspore

15
mindspore/ccsrc/backend/kernel_compiler/cpu/hsigmoid_cpu_kernel.h
View File

@ -41,20 +41,15 @@ class HSigmoidCPUKernel : public CPUKernel {
uint64_t tensor_size_ = 1;
};
MS_REG_CPU_KERNEL_T(HSigmoid, KernelAttr().AddInputAttr(kNumberTypeInt8).AddOutputAttr(kNumberTypeInt8),
HSigmoidCPUKernel, int8_t);
MS_REG_CPU_KERNEL_T(HSigmoid, KernelAttr(), HSigmoidCPUKernel, int8_t);
MS_REG_CPU_KERNEL_T(HSigmoid, KernelAttr().AddInputAttr(kNumberTypeInt16).AddOutputAttr(kNumberTypeInt16),
HSigmoidCPUKernel, int16_t);
MS_REG_CPU_KERNEL_T(HSigmoid, KernelAttr(), HSigmoidCPUKernel, int16_t);
MS_REG_CPU_KERNEL_T(HSigmoid, KernelAttr().AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
HSigmoidCPUKernel, int);
MS_REG_CPU_KERNEL_T(HSigmoid, KernelAttr(), HSigmoidCPUKernel, int32_t);
MS_REG_CPU_KERNEL_T(HSigmoid, KernelAttr().AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeInt64),
HSigmoidCPUKernel, int64_t);
MS_REG_CPU_KERNEL_T(HSigmoid, KernelAttr(), HSigmoidCPUKernel, int64_t);
MS_REG_CPU_KERNEL_T(HSigmoid, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
HSigmoidCPUKernel, float);
MS_REG_CPU_KERNEL_T(HSigmoid, KernelAttr(), HSigmoidCPUKernel, float);
} // namespace kernel
} // namespace mindspore
#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_TILE_CPU_KERNEL_H_

28
mindspore/ccsrc/backend/kernel_compiler/cpu/hsigmoid_grad_cpu_kernel.h
View File

@ -41,29 +41,11 @@ class HSigmoidGradCPUKernel : public CPUKernel {
uint64_t tensor_size_ = 1;
};
MS_REG_CPU_KERNEL_T(
HSigmoidGrad, KernelAttr().AddInputAttr(kNumberTypeInt8).AddInputAttr(kNumberTypeInt8).AddOutputAttr(kNumberTypeInt8),
HSigmoidGradCPUKernel, int8_t);
MS_REG_CPU_KERNEL_T(
HSigmoidGrad,
KernelAttr().AddInputAttr(kNumberTypeInt16).AddInputAttr(kNumberTypeInt16).AddOutputAttr(kNumberTypeInt16),
HSigmoidGradCPUKernel, int16_t);
MS_REG_CPU_KERNEL_T(
HSigmoidGrad,
KernelAttr().AddInputAttr(kNumberTypeInt32).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
HSigmoidGradCPUKernel, int);
MS_REG_CPU_KERNEL_T(
HSigmoidGrad,
KernelAttr().AddInputAttr(kNumberTypeInt64).AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeInt64),
HSigmoidGradCPUKernel, int64_t);
MS_REG_CPU_KERNEL_T(
HSigmoidGrad,
KernelAttr().AddInputAttr(kNumberTypeFloat32).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
HSigmoidGradCPUKernel, float);
MS_REG_CPU_KERNEL_T(HSigmoidGrad, KernelAttr(), HSigmoidGradCPUKernel, int8_t);
MS_REG_CPU_KERNEL_T(HSigmoidGrad, KernelAttr(), HSigmoidGradCPUKernel, int16_t);
MS_REG_CPU_KERNEL_T(HSigmoidGrad, KernelAttr(), HSigmoidGradCPUKernel, int32_t);
MS_REG_CPU_KERNEL_T(HSigmoidGrad, KernelAttr(), HSigmoidGradCPUKernel, int64_t);
MS_REG_CPU_KERNEL_T(HSigmoidGrad, KernelAttr(), HSigmoidGradCPUKernel, float);
} // namespace kernel
} // namespace mindspore
#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_TILE_CPU_KERNEL_H_

19
mindspore/ccsrc/backend/kernel_compiler/cpu/hswish_cpu_kernel.h
View File

@ -41,20 +41,11 @@ class HSwishCPUKernel : public CPUKernel {
uint64_t tensor_size_ = 1;
};
MS_REG_CPU_KERNEL_T(HSwish, KernelAttr().AddInputAttr(kNumberTypeInt8).AddOutputAttr(kNumberTypeInt8), HSwishCPUKernel,
int8_t);
MS_REG_CPU_KERNEL_T(HSwish, KernelAttr().AddInputAttr(kNumberTypeInt16).AddOutputAttr(kNumberTypeInt16),
HSwishCPUKernel, int16_t);
MS_REG_CPU_KERNEL_T(HSwish, KernelAttr().AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
HSwishCPUKernel, int);
MS_REG_CPU_KERNEL_T(HSwish, KernelAttr().AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeInt64),
HSwishCPUKernel, int64_t);
MS_REG_CPU_KERNEL_T(HSwish, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
HSwishCPUKernel, float);
MS_REG_CPU_KERNEL_T(HSwish, KernelAttr(), HSwishCPUKernel, int8_t);
MS_REG_CPU_KERNEL_T(HSwish, KernelAttr(), HSwishCPUKernel, int16_t);
MS_REG_CPU_KERNEL_T(HSwish, KernelAttr(), HSwishCPUKernel, int32_t);
MS_REG_CPU_KERNEL_T(HSwish, KernelAttr(), HSwishCPUKernel, int64_t);
MS_REG_CPU_KERNEL_T(HSwish, KernelAttr(), HSwishCPUKernel, float);
} // namespace kernel
} // namespace mindspore
#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_TILE_CPU_KERNEL_H_

28
mindspore/ccsrc/backend/kernel_compiler/cpu/hswish_grad_cpu_kernel.h
View File

@ -41,29 +41,11 @@ class HSwishGradCPUKernel : public CPUKernel {
uint64_t tensor_size_ = 1;
};
MS_REG_CPU_KERNEL_T(
HSwishGrad, KernelAttr().AddInputAttr(kNumberTypeInt8).AddInputAttr(kNumberTypeInt8).AddOutputAttr(kNumberTypeInt8),
HSwishGradCPUKernel, int8_t);
MS_REG_CPU_KERNEL_T(
HSwishGrad,
KernelAttr().AddInputAttr(kNumberTypeInt16).AddInputAttr(kNumberTypeInt16).AddOutputAttr(kNumberTypeInt16),
HSwishGradCPUKernel, int16_t);
MS_REG_CPU_KERNEL_T(
HSwishGrad,
KernelAttr().AddInputAttr(kNumberTypeInt32).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
HSwishGradCPUKernel, int);
MS_REG_CPU_KERNEL_T(
HSwishGrad,
KernelAttr().AddInputAttr(kNumberTypeInt64).AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeInt64),
HSwishGradCPUKernel, int64_t);
MS_REG_CPU_KERNEL_T(
HSwishGrad,
KernelAttr().AddInputAttr(kNumberTypeFloat32).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
HSwishGradCPUKernel, float);
MS_REG_CPU_KERNEL_T(HSwishGrad, KernelAttr(), HSwishGradCPUKernel, int8_t);
MS_REG_CPU_KERNEL_T(HSwishGrad, KernelAttr(), HSwishGradCPUKernel, int16_t);
MS_REG_CPU_KERNEL_T(HSwishGrad, KernelAttr(), HSwishGradCPUKernel, int32_t);
MS_REG_CPU_KERNEL_T(HSwishGrad, KernelAttr(), HSwishGradCPUKernel, int64_t);
MS_REG_CPU_KERNEL_T(HSwishGrad, KernelAttr(), HSwishGradCPUKernel, float);
} // namespace kernel
} // namespace mindspore
#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_TILE_CPU_KERNEL_H_

36
mindspore/ccsrc/backend/kernel_compiler/cpu/isfinite_cpu_kernel.h
View File

@ -51,41 +51,7 @@ class IsFiniteCPUKernel : public CPUKernel {
TypeId input_dtype_{kTypeUnknown};
};
MS_REG_CPU_KERNEL(IsFinite, KernelAttr().AddInputAttr(kNumberTypeBool).AddOutputAttr(kNumberTypeBool),
IsFiniteCPUKernel);
MS_REG_CPU_KERNEL(IsFinite, KernelAttr().AddInputAttr(kNumberTypeInt8).AddOutputAttr(kNumberTypeBool),
IsFiniteCPUKernel);
MS_REG_CPU_KERNEL(IsFinite, KernelAttr().AddInputAttr(kNumberTypeInt16).AddOutputAttr(kNumberTypeBool),
IsFiniteCPUKernel);
MS_REG_CPU_KERNEL(IsFinite, KernelAttr().AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeBool),
IsFiniteCPUKernel);
MS_REG_CPU_KERNEL(IsFinite, KernelAttr().AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeBool),
IsFiniteCPUKernel);
MS_REG_CPU_KERNEL(IsFinite, KernelAttr().AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeBool),
IsFiniteCPUKernel);
MS_REG_CPU_KERNEL(IsFinite, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeBool),
IsFiniteCPUKernel);
MS_REG_CPU_KERNEL(IsFinite, KernelAttr().AddInputAttr(kNumberTypeFloat64).AddOutputAttr(kNumberTypeBool),
IsFiniteCPUKernel);
MS_REG_CPU_KERNEL(IsFinite, KernelAttr().AddInputAttr(kNumberTypeUInt8).AddOutputAttr(kNumberTypeBool),
IsFiniteCPUKernel);
MS_REG_CPU_KERNEL(IsFinite, KernelAttr().AddInputAttr(kNumberTypeUInt16).AddOutputAttr(kNumberTypeBool),
IsFiniteCPUKernel);
MS_REG_CPU_KERNEL(IsFinite, KernelAttr().AddInputAttr(kNumberTypeUInt32).AddOutputAttr(kNumberTypeBool),
IsFiniteCPUKernel);
MS_REG_CPU_KERNEL(IsFinite, KernelAttr().AddInputAttr(kNumberTypeUInt64).AddOutputAttr(kNumberTypeBool),
IsFiniteCPUKernel);
MS_REG_CPU_KERNEL(IsFinite, KernelAttr(), IsFiniteCPUKernel);
} // namespace kernel
} // namespace mindspore

20
mindspore/ccsrc/backend/kernel_compiler/cpu/layer_norm_cpu_kernel.h
View File

@ -46,25 +46,7 @@ class LayerNormCPUKernel : public CPUKernel {
size_t param_num_{1};
};
MS_REG_CPU_KERNEL(LayerNorm,
KernelAttr()
.AddInputAttr(kNumberTypeFloat16)
.AddInputAttr(kNumberTypeFloat16)
.AddInputAttr(kNumberTypeFloat16)
.AddOutputAttr(kNumberTypeFloat16)
.AddOutputAttr(kNumberTypeFloat16)
.AddOutputAttr(kNumberTypeFloat16),
LayerNormCPUKernel);
MS_REG_CPU_KERNEL(LayerNorm,
KernelAttr()
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32),
LayerNormCPUKernel);
MS_REG_CPU_KERNEL(LayerNorm, KernelAttr(), LayerNormCPUKernel);
} // namespace kernel
} // namespace mindspore
#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_LAYER_NORM_CPU_KERNEL_H_

24
mindspore/ccsrc/backend/kernel_compiler/cpu/layer_norm_grad_cpu_kernel.h
View File

@ -48,29 +48,7 @@ class LayerNormGradCPUKernel : public CPUKernel {
size_t param_size_{1};
};
MS_REG_CPU_KERNEL(LayerNormGrad,
KernelAttr()
.AddInputAttr(kNumberTypeFloat16)
.AddInputAttr(kNumberTypeFloat16)
.AddInputAttr(kNumberTypeFloat16)
.AddInputAttr(kNumberTypeFloat16)
.AddInputAttr(kNumberTypeFloat16)
.AddOutputAttr(kNumberTypeFloat16)
.AddOutputAttr(kNumberTypeFloat16)
.AddOutputAttr(kNumberTypeFloat16),
LayerNormGradCPUKernel);
MS_REG_CPU_KERNEL(LayerNormGrad,
KernelAttr()
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32),
LayerNormGradCPUKernel);
MS_REG_CPU_KERNEL(LayerNormGrad, KernelAttr(), LayerNormGradCPUKernel);
} // namespace kernel
} // namespace mindspore
#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_LAYER_NORM_GRAD_CPU_KERNEL_H_

33
mindspore/ccsrc/backend/kernel_compiler/cpu/maximum_cpu_kernel.h
View File

@ -75,33 +75,12 @@ class MaximumCPUKernel : public CPUKernel {
const size_t max_dims{7};
};
MS_REG_CPU_KERNEL_T(
Maximum, KernelAttr().AddInputAttr(kNumberTypeInt32).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
MaximumCPUKernel, int32_t);
MS_REG_CPU_KERNEL_T(
Maximum,
KernelAttr().AddInputAttr(kNumberTypeUInt32).AddInputAttr(kNumberTypeUInt32).AddOutputAttr(kNumberTypeUInt32),
MaximumCPUKernel, uint32_t);
MS_REG_CPU_KERNEL_T(
Maximum,
KernelAttr().AddInputAttr(kNumberTypeFloat32).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
MaximumCPUKernel, float);
MS_REG_CPU_KERNEL_T(
Maximum, KernelAttr().AddInputAttr(kNumberTypeInt64).AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeInt64),
MaximumCPUKernel, int64_t);
MS_REG_CPU_KERNEL_T(
Maximum,
KernelAttr().AddInputAttr(kNumberTypeUInt64).AddInputAttr(kNumberTypeUInt64).AddOutputAttr(kNumberTypeUInt64),
MaximumCPUKernel, uint64_t);
MS_REG_CPU_KERNEL_T(
Maximum,
KernelAttr().AddInputAttr(kNumberTypeFloat64).AddInputAttr(kNumberTypeFloat64).AddOutputAttr(kNumberTypeFloat64),
MaximumCPUKernel, double);
MS_REG_CPU_KERNEL_T(Maximum, KernelAttr(), MaximumCPUKernel, int32_t);
MS_REG_CPU_KERNEL_T(Maximum, KernelAttr(), MaximumCPUKernel, int64_t);
MS_REG_CPU_KERNEL_T(Maximum, KernelAttr(), MaximumCPUKernel, uint32_t);
MS_REG_CPU_KERNEL_T(Maximum, KernelAttr(), MaximumCPUKernel, uint64_t);
MS_REG_CPU_KERNEL_T(Maximum, KernelAttr(), MaximumCPUKernel, float);
MS_REG_CPU_KERNEL_T(Maximum, KernelAttr(), MaximumCPUKernel, double);
} // namespace kernel
} // namespace mindspore

54
mindspore/ccsrc/backend/kernel_compiler/cpu/maximum_grad_cpu_kernel.h
View File

@ -47,59 +47,7 @@ class MaximumGradCPUKernel : public CPUKernel {
TypeId dtype_{kTypeUnknown};
};
MS_REG_CPU_KERNEL(MaximumGrad,
KernelAttr()
.AddInputAttr(kNumberTypeInt32)
.AddInputAttr(kNumberTypeInt32)
.AddInputAttr(kNumberTypeInt32)
.AddOutputAttr(kNumberTypeInt32)
.AddOutputAttr(kNumberTypeInt32),
MaximumGradCPUKernel);
MS_REG_CPU_KERNEL(MaximumGrad,
KernelAttr()
.AddInputAttr(kNumberTypeUInt32)
.AddInputAttr(kNumberTypeUInt32)
.AddInputAttr(kNumberTypeUInt32)
.AddOutputAttr(kNumberTypeUInt32)
.AddOutputAttr(kNumberTypeUInt32),
MaximumGradCPUKernel);
MS_REG_CPU_KERNEL(MaximumGrad,
KernelAttr()
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32),
MaximumGradCPUKernel);
MS_REG_CPU_KERNEL(MaximumGrad,
KernelAttr()
.AddInputAttr(kNumberTypeInt64)
.AddInputAttr(kNumberTypeInt64)
.AddInputAttr(kNumberTypeInt64)
.AddOutputAttr(kNumberTypeInt64)
.AddOutputAttr(kNumberTypeInt64),
MaximumGradCPUKernel);
MS_REG_CPU_KERNEL(MaximumGrad,
KernelAttr()
.AddInputAttr(kNumberTypeUInt64)
.AddInputAttr(kNumberTypeUInt64)
.AddInputAttr(kNumberTypeUInt64)
.AddOutputAttr(kNumberTypeUInt64)
.AddOutputAttr(kNumberTypeUInt64),
MaximumGradCPUKernel);
MS_REG_CPU_KERNEL(MaximumGrad,
KernelAttr()
.AddInputAttr(kNumberTypeFloat64)
.AddInputAttr(kNumberTypeFloat64)
.AddInputAttr(kNumberTypeFloat64)
.AddOutputAttr(kNumberTypeFloat64)
.AddOutputAttr(kNumberTypeFloat64),
MaximumGradCPUKernel);
MS_REG_CPU_KERNEL(MaximumGrad, KernelAttr(), MaximumGradCPUKernel);
} // namespace kernel
} // namespace mindspore
#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_MaximumGrad_CPU_KERNEL_H_

33
mindspore/ccsrc/backend/kernel_compiler/cpu/minimum_cpu_kernel.h
View File

@ -75,33 +75,12 @@ class MinimumCPUKernel : public CPUKernel {
const size_t max_dims{7};
};
MS_REG_CPU_KERNEL_T(
Minimum, KernelAttr().AddInputAttr(kNumberTypeInt32).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
MinimumCPUKernel, int32_t);
MS_REG_CPU_KERNEL_T(
Minimum,
KernelAttr().AddInputAttr(kNumberTypeUInt32).AddInputAttr(kNumberTypeUInt32).AddOutputAttr(kNumberTypeUInt32),
MinimumCPUKernel, uint32_t);
MS_REG_CPU_KERNEL_T(
Minimum,
KernelAttr().AddInputAttr(kNumberTypeFloat32).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
MinimumCPUKernel, float);
MS_REG_CPU_KERNEL_T(
Minimum, KernelAttr().AddInputAttr(kNumberTypeInt64).AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeInt64),
MinimumCPUKernel, int64_t);
MS_REG_CPU_KERNEL_T(
Minimum,
KernelAttr().AddInputAttr(kNumberTypeUInt64).AddInputAttr(kNumberTypeUInt64).AddOutputAttr(kNumberTypeUInt64),
MinimumCPUKernel, uint64_t);
MS_REG_CPU_KERNEL_T(
Minimum,
KernelAttr().AddInputAttr(kNumberTypeFloat64).AddInputAttr(kNumberTypeFloat64).AddOutputAttr(kNumberTypeFloat64),
MinimumCPUKernel, double);
MS_REG_CPU_KERNEL_T(Minimum, KernelAttr(), MinimumCPUKernel, int32_t);
MS_REG_CPU_KERNEL_T(Minimum, KernelAttr(), MinimumCPUKernel, uint32_t);
MS_REG_CPU_KERNEL_T(Minimum, KernelAttr(), MinimumCPUKernel, float);
MS_REG_CPU_KERNEL_T(Minimum, KernelAttr(), MinimumCPUKernel, int64_t);
MS_REG_CPU_KERNEL_T(Minimum, KernelAttr(), MinimumCPUKernel, uint64_t);
MS_REG_CPU_KERNEL_T(Minimum, KernelAttr(), MinimumCPUKernel, double);
} // namespace kernel
} // namespace mindspore

54
mindspore/ccsrc/backend/kernel_compiler/cpu/minimum_grad_cpu_kernel.h
View File

@ -47,59 +47,7 @@ class MinimumGradCPUKernel : public CPUKernel {
TypeId dtype_{kTypeUnknown};
};
MS_REG_CPU_KERNEL(MinimumGrad,
KernelAttr()
.AddInputAttr(kNumberTypeInt32)
.AddInputAttr(kNumberTypeInt32)
.AddInputAttr(kNumberTypeInt32)
.AddOutputAttr(kNumberTypeInt32)
.AddOutputAttr(kNumberTypeInt32),
MinimumGradCPUKernel);
MS_REG_CPU_KERNEL(MinimumGrad,
KernelAttr()
.AddInputAttr(kNumberTypeUInt32)
.AddInputAttr(kNumberTypeUInt32)
.AddInputAttr(kNumberTypeUInt32)
.AddOutputAttr(kNumberTypeUInt32)
.AddOutputAttr(kNumberTypeUInt32),
MinimumGradCPUKernel);
MS_REG_CPU_KERNEL(MinimumGrad,
KernelAttr()
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32),
MinimumGradCPUKernel);
MS_REG_CPU_KERNEL(MinimumGrad,
KernelAttr()
.AddInputAttr(kNumberTypeInt64)
.AddInputAttr(kNumberTypeInt64)
.AddInputAttr(kNumberTypeInt64)
.AddOutputAttr(kNumberTypeInt64)
.AddOutputAttr(kNumberTypeInt64),
MinimumGradCPUKernel);
MS_REG_CPU_KERNEL(MinimumGrad,
KernelAttr()
.AddInputAttr(kNumberTypeUInt64)
.AddInputAttr(kNumberTypeUInt64)
.AddInputAttr(kNumberTypeUInt64)
.AddOutputAttr(kNumberTypeUInt64)
.AddOutputAttr(kNumberTypeUInt64),
MinimumGradCPUKernel);
MS_REG_CPU_KERNEL(MinimumGrad,
KernelAttr()
.AddInputAttr(kNumberTypeFloat64)
.AddInputAttr(kNumberTypeFloat64)
.AddInputAttr(kNumberTypeFloat64)
.AddOutputAttr(kNumberTypeFloat64)
.AddOutputAttr(kNumberTypeFloat64),
MinimumGradCPUKernel);
MS_REG_CPU_KERNEL(MinimumGrad, KernelAttr(), MinimumGradCPUKernel);
} // namespace kernel
} // namespace mindspore
#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_MinimumGrad_CPU_KERNEL_H_

14
mindspore/ccsrc/backend/kernel_compiler/cpu/mirror_pad_cpu_kernel.h
View File

@ -64,19 +64,7 @@ class MirrorPadCPUKernel : public CPUKernel {
int num_paddings_;
};
MS_REG_CPU_KERNEL(
MirrorPad,
KernelAttr().AddInputAttr(kNumberTypeFloat16).AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeFloat16),
MirrorPadCPUKernel);
MS_REG_CPU_KERNEL(
MirrorPad,
KernelAttr().AddInputAttr(kNumberTypeFloat32).AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeFloat32),
MirrorPadCPUKernel);
MS_REG_CPU_KERNEL(
MirrorPad, KernelAttr().AddInputAttr(kNumberTypeInt32).AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeInt32),
MirrorPadCPUKernel);
MS_REG_CPU_KERNEL(MirrorPad, KernelAttr(), MirrorPadCPUKernel);
} // namespace kernel
} // namespace mindspore
#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_MIRROR_PAD_CPU_KERNEL_H_

15
mindspore/ccsrc/backend/kernel_compiler/cpu/mirror_pad_grad_cpu_kernel.h
View File

@ -80,20 +80,7 @@ class MirrorPadGradCPUKernel : public CPUKernel {
int num_paddings_;
};
MS_REG_CPU_KERNEL(
MirrorPadGrad,
KernelAttr().AddInputAttr(kNumberTypeFloat16).AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeFloat16),
MirrorPadGradCPUKernel);
MS_REG_CPU_KERNEL(
MirrorPadGrad,
KernelAttr().AddInputAttr(kNumberTypeFloat32).AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeFloat32),
MirrorPadGradCPUKernel);
MS_REG_CPU_KERNEL(
MirrorPadGrad,
KernelAttr().AddInputAttr(kNumberTypeInt32).AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeInt32),
MirrorPadGradCPUKernel);
MS_REG_CPU_KERNEL(MirrorPadGrad, KernelAttr(), MirrorPadGradCPUKernel);
} // namespace kernel
} // namespace mindspore
#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_MIRROR_PAD_CPU_KERNEL_H_

5
mindspore/ccsrc/backend/kernel_compiler/cpu/mkldnn/conv_cpu_kernel.h
View File

@ -33,10 +33,7 @@ class ConvCPUKernel : public MKLCPUKernel {
const std::vector<AddressPtr> &outputs) override;
};
MS_REG_CPU_KERNEL(
Conv2D,
KernelAttr().AddInputAttr(kNumberTypeFloat32).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
ConvCPUKernel);
MS_REG_CPU_KERNEL(Conv2D, KernelAttr(), ConvCPUKernel);
MS_REG_CPU_KERNEL(
Conv3D,
KernelAttr().AddInputAttr(kNumberTypeFloat32).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),

6
mindspore/ccsrc/backend/kernel_compiler/cpu/mkldnn/mul_cpu_kernel.h
View File

@ -18,6 +18,7 @@
#include <vector>
#include <memory>
#include "backend/kernel_compiler/cpu/arithmetic_cpu_kernel.h"
#include "backend/kernel_compiler/cpu/mkldnn/mkl_cpu_kernel.h"
namespace mindspore {
@ -36,9 +37,8 @@ class MulCPUKernel : public MKLCPUKernel {
bool need_swap_{false};
};
MS_REG_CPU_KERNEL(
Mul, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
MulCPUKernel);
MS_REG_CPU_KERNEL(Mul, KernelAttr(), MulCPUKernel);
MS_REG_CPU_KERNEL_T(Mul, KernelAttr(), ArithmeticCPUKernel, int32_t);
} // namespace kernel
} // namespace mindspore

8
mindspore/ccsrc/backend/kernel_compiler/cpu/one_hot_cpu_kernel.h
View File

@ -38,13 +38,7 @@ class OneHotCPUKernel : public CPUKernel {
size_t axis_;
};
MS_REG_CPU_KERNEL(OneHot,
KernelAttr()
.AddInputAttr(kNumberTypeInt32)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32),
OneHotCPUKernel);
MS_REG_CPU_KERNEL(OneHot, KernelAttr(), OneHotCPUKernel);
} // namespace kernel
} // namespace mindspore

44
mindspore/ccsrc/backend/kernel_compiler/cpu/pack_cpu_kernel.h
View File

@ -44,39 +44,17 @@ class PackCpuFwdKernel : public CPUKernel {
std::unique_ptr<T *[]> inputs_host_;
};
MS_REG_CPU_KERNEL_T(Stack,
KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeInt8).AddOutputAttr(kNumberTypeInt8),
PackCpuFwdKernel, int8_t)
MS_REG_CPU_KERNEL_T(Stack,
KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeInt16).AddOutputAttr(kNumberTypeInt16),
PackCpuFwdKernel, int16_t)
MS_REG_CPU_KERNEL_T(Stack,
KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
PackCpuFwdKernel, int32_t)
MS_REG_CPU_KERNEL_T(Stack,
KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeInt64),
PackCpuFwdKernel, int64_t)
MS_REG_CPU_KERNEL_T(Stack,
KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeUInt8).AddOutputAttr(kNumberTypeUInt8),
PackCpuFwdKernel, uint8_t)
MS_REG_CPU_KERNEL_T(Stack,
KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeBool).AddOutputAttr(kNumberTypeBool),
PackCpuFwdKernel, bool)
MS_REG_CPU_KERNEL_T(Stack,
KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeUInt16).AddOutputAttr(kNumberTypeUInt16),
PackCpuFwdKernel, uint16_t)
MS_REG_CPU_KERNEL_T(Stack,
KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeUInt32).AddOutputAttr(kNumberTypeUInt32),
PackCpuFwdKernel, uint32_t)
MS_REG_CPU_KERNEL_T(Stack,
KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeUInt64).AddOutputAttr(kNumberTypeUInt64),
PackCpuFwdKernel, uint64_t)
MS_REG_CPU_KERNEL_T(
Stack, KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),
PackCpuFwdKernel, float16)
MS_REG_CPU_KERNEL_T(
Stack, KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
PackCpuFwdKernel, float)
MS_REG_CPU_KERNEL_T(Stack, KernelAttr(), PackCpuFwdKernel, int8_t)
MS_REG_CPU_KERNEL_T(Stack, KernelAttr(), PackCpuFwdKernel, int16_t)
MS_REG_CPU_KERNEL_T(Stack, KernelAttr(), PackCpuFwdKernel, int32_t)
MS_REG_CPU_KERNEL_T(Stack, KernelAttr(), PackCpuFwdKernel, int64_t)
MS_REG_CPU_KERNEL_T(Stack, KernelAttr(), PackCpuFwdKernel, uint8_t)
MS_REG_CPU_KERNEL_T(Stack, KernelAttr(), PackCpuFwdKernel, uint16_t)
MS_REG_CPU_KERNEL_T(Stack, KernelAttr(), PackCpuFwdKernel, uint32_t)
MS_REG_CPU_KERNEL_T(Stack, KernelAttr(), PackCpuFwdKernel, uint64_t)
MS_REG_CPU_KERNEL_T(Stack, KernelAttr(), PackCpuFwdKernel, float16)
MS_REG_CPU_KERNEL_T(Stack, KernelAttr(), PackCpuFwdKernel, float)
MS_REG_CPU_KERNEL_T(Stack, KernelAttr(), PackCpuFwdKernel, bool)
} // namespace kernel
} // namespace mindspore
#endif // MINDSPORE_PACK_CPU_KERNEL_H

6
mindspore/ccsrc/backend/kernel_compiler/cpu/pad_cpu_kernel.h
View File

@ -48,11 +48,7 @@ class PadCPUKernel : public CPUKernel {
std::vector<size_t> output_shape_;
};
MS_REG_CPU_KERNEL(Pad, KernelAttr().AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16), PadCPUKernel);
MS_REG_CPU_KERNEL(Pad, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32), PadCPUKernel);
MS_REG_CPU_KERNEL(Pad, KernelAttr().AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32), PadCPUKernel);
MS_REG_CPU_KERNEL(Pad, KernelAttr(), PadCPUKernel);
} // namespace kernel
} // namespace mindspore
#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_PAD_CPU_KERNEL_H_

7
mindspore/ccsrc/backend/kernel_compiler/cpu/range_cpu_kernel.h
View File

@ -41,12 +41,7 @@ class RangeCPUKernel : public CPUKernel {
int64_t delta_;
};
MS_REG_CPU_KERNEL(Range, KernelAttr().AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32), RangeCPUKernel);
MS_REG_CPU_KERNEL(Range, KernelAttr().AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeInt64), RangeCPUKernel);
MS_REG_CPU_KERNEL(Range, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
RangeCPUKernel);
MS_REG_CPU_KERNEL(Range, KernelAttr().AddInputAttr(kNumberTypeFloat64).AddOutputAttr(kNumberTypeFloat64),
RangeCPUKernel);
MS_REG_CPU_KERNEL(Range, KernelAttr(), RangeCPUKernel);
} // namespace kernel
} // namespace mindspore

54
mindspore/ccsrc/backend/kernel_compiler/cpu/reduce_cpu_kernel.h
View File

@ -41,47 +41,29 @@ class ReduceCPUKernel : public CPUKernel {
std::function<void(const T *, size_t, T *)> reduce_func_;
};
MS_REG_CPU_KERNEL_T(ReduceMean, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
ReduceCPUKernel, float);
MS_REG_CPU_KERNEL_T(ReduceMean, KernelAttr().AddInputAttr(kNumberTypeFloat64).AddOutputAttr(kNumberTypeFloat64),
ReduceCPUKernel, double);
MS_REG_CPU_KERNEL_T(ReduceMean, KernelAttr().AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
ReduceCPUKernel, int32_t);
MS_REG_CPU_KERNEL_T(ReduceMean, KernelAttr().AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeInt64),
ReduceCPUKernel, int64_t);
MS_REG_CPU_KERNEL_T(ReduceMean, KernelAttr(), ReduceCPUKernel, float);
MS_REG_CPU_KERNEL_T(ReduceMean, KernelAttr(), ReduceCPUKernel, double);
MS_REG_CPU_KERNEL_T(ReduceMean, KernelAttr(), ReduceCPUKernel, int32_t);
MS_REG_CPU_KERNEL_T(ReduceMean, KernelAttr(), ReduceCPUKernel, int64_t);
MS_REG_CPU_KERNEL_T(ReduceMax, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
ReduceCPUKernel, float);
MS_REG_CPU_KERNEL_T(ReduceMax, KernelAttr().AddInputAttr(kNumberTypeFloat64).AddOutputAttr(kNumberTypeFloat64),
ReduceCPUKernel, double);
MS_REG_CPU_KERNEL_T(ReduceMax, KernelAttr().AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
ReduceCPUKernel, int32_t);
MS_REG_CPU_KERNEL_T(ReduceMax, KernelAttr().AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeInt64),
ReduceCPUKernel, int64_t);
MS_REG_CPU_KERNEL_T(ReduceMax, KernelAttr(), ReduceCPUKernel, float);
MS_REG_CPU_KERNEL_T(ReduceMax, KernelAttr(), ReduceCPUKernel, double);
MS_REG_CPU_KERNEL_T(ReduceMax, KernelAttr(), ReduceCPUKernel, int32_t);
MS_REG_CPU_KERNEL_T(ReduceMax, KernelAttr(), ReduceCPUKernel, int64_t);
MS_REG_CPU_KERNEL_T(ReduceSum, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
ReduceCPUKernel, float);
MS_REG_CPU_KERNEL_T(ReduceSum, KernelAttr().AddInputAttr(kNumberTypeFloat64).AddOutputAttr(kNumberTypeFloat64),
ReduceCPUKernel, double);
MS_REG_CPU_KERNEL_T(ReduceSum, KernelAttr().AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
ReduceCPUKernel, int32_t);
MS_REG_CPU_KERNEL_T(ReduceSum, KernelAttr().AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeInt64),
ReduceCPUKernel, int64_t);
MS_REG_CPU_KERNEL_T(ReduceSum, KernelAttr(), ReduceCPUKernel, float);
MS_REG_CPU_KERNEL_T(ReduceSum, KernelAttr(), ReduceCPUKernel, double);
MS_REG_CPU_KERNEL_T(ReduceSum, KernelAttr(), ReduceCPUKernel, int32_t);
MS_REG_CPU_KERNEL_T(ReduceSum, KernelAttr(), ReduceCPUKernel, int64_t);
MS_REG_CPU_KERNEL_T(ReduceMin, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
ReduceCPUKernel, float);
MS_REG_CPU_KERNEL_T(ReduceMin, KernelAttr().AddInputAttr(kNumberTypeFloat64).AddOutputAttr(kNumberTypeFloat64),
ReduceCPUKernel, double);
MS_REG_CPU_KERNEL_T(ReduceMin, KernelAttr().AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
ReduceCPUKernel, int32_t);
MS_REG_CPU_KERNEL_T(ReduceMin, KernelAttr().AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeInt64),
ReduceCPUKernel, int64_t);
MS_REG_CPU_KERNEL_T(ReduceMin, KernelAttr(), ReduceCPUKernel, float);
MS_REG_CPU_KERNEL_T(ReduceMin, KernelAttr(), ReduceCPUKernel, double);
MS_REG_CPU_KERNEL_T(ReduceMin, KernelAttr(), ReduceCPUKernel, int32_t);
MS_REG_CPU_KERNEL_T(ReduceMin, KernelAttr(), ReduceCPUKernel, int64_t);
MS_REG_CPU_KERNEL_T(ReduceAll, KernelAttr().AddInputAttr(kNumberTypeBool).AddOutputAttr(kNumberTypeBool),
ReduceCPUKernel, bool);
MS_REG_CPU_KERNEL_T(ReduceAll, KernelAttr(), ReduceCPUKernel, bool);
MS_REG_CPU_KERNEL_T(ReduceAny, KernelAttr().AddInputAttr(kNumberTypeBool).AddOutputAttr(kNumberTypeBool),
ReduceCPUKernel, bool);
MS_REG_CPU_KERNEL_T(ReduceAny, KernelAttr(), ReduceCPUKernel, bool);
} // namespace kernel
} // namespace mindspore
#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_REDUCE_CPU_KERNEL_H_

24
mindspore/ccsrc/backend/kernel_compiler/cpu/split_cpu_kernel.h
View File

@ -57,24 +57,12 @@ class SplitCPUKernel : public CPUKernel {
TypeId dtype_{kTypeUnknown};
};
MS_REG_CPU_KERNEL_T(
Split, KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
SplitCPUKernel, float);
MS_REG_CPU_KERNEL_T(
Split, KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),
SplitCPUKernel, float16);
MS_REG_CPU_KERNEL_T(
Split, KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeFloat64).AddOutputAttr(kNumberTypeFloat64),
SplitCPUKernel, double);
MS_REG_CPU_KERNEL_T(Split,
KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
SplitCPUKernel, int32_t);
MS_REG_CPU_KERNEL_T(Split,
KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeUInt32).AddOutputAttr(kNumberTypeUInt32),
SplitCPUKernel, uint32_t);
MS_REG_CPU_KERNEL_T(Split,
KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeInt64),
SplitCPUKernel, int64_t);
MS_REG_CPU_KERNEL_T(Split, KernelAttr(), SplitCPUKernel, float);
MS_REG_CPU_KERNEL_T(Split, KernelAttr(), SplitCPUKernel, float16);
MS_REG_CPU_KERNEL_T(Split, KernelAttr(), SplitCPUKernel, double);
MS_REG_CPU_KERNEL_T(Split, KernelAttr(), SplitCPUKernel, int32_t);
MS_REG_CPU_KERNEL_T(Split, KernelAttr(), SplitCPUKernel, uint32_t);
MS_REG_CPU_KERNEL_T(Split, KernelAttr(), SplitCPUKernel, int64_t);
} // namespace kernel
} // namespace mindspore

12
mindspore/ccsrc/backend/kernel_compiler/cpu/tensoradd_cpu_kernel.h
View File

@ -40,15 +40,9 @@ class TensorAddCPUKernel : public CPUKernel {
std::vector<size_t> output_shape_;
};
MS_REG_CPU_KERNEL_T(
Add, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
TensorAddCPUKernel, float);
MS_REG_CPU_KERNEL_T(
Add, KernelAttr().AddInputAttr(kNumberTypeInt32).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
TensorAddCPUKernel, int);
MS_REG_CPU_KERNEL_T(
Add, KernelAttr().AddInputAttr(kNumberTypeUInt32).AddInputAttr(kNumberTypeUInt32).AddOutputAttr(kNumberTypeUInt32),
TensorAddCPUKernel, uint32_t);
MS_REG_CPU_KERNEL_T(Add, KernelAttr(), TensorAddCPUKernel, int32_t);
MS_REG_CPU_KERNEL_T(Add, KernelAttr(), TensorAddCPUKernel, uint32_t);
MS_REG_CPU_KERNEL_T(Add, KernelAttr(), TensorAddCPUKernel, float);
} // namespace kernel
} // namespace mindspore

20
mindspore/ccsrc/backend/kernel_compiler/cpu/tile_cpu_kernel.h
View File

@ -57,25 +57,7 @@ class TileCPUKernel : public CPUKernel {
size_t input_size_;
};
MS_REG_CPU_KERNEL(Tile, KernelAttr().AddInputAttr(kNumberTypeInt8).AddOutputAttr(kNumberTypeInt8), TileCPUKernel);
MS_REG_CPU_KERNEL(Tile, KernelAttr().AddInputAttr(kNumberTypeInt16).AddOutputAttr(kNumberTypeInt16), TileCPUKernel);
MS_REG_CPU_KERNEL(Tile, KernelAttr().AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32), TileCPUKernel);
MS_REG_CPU_KERNEL(Tile, KernelAttr().AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeInt64), TileCPUKernel);
MS_REG_CPU_KERNEL(Tile, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32), TileCPUKernel);
MS_REG_CPU_KERNEL(Tile, KernelAttr().AddInputAttr(kNumberTypeUInt8).AddOutputAttr(kNumberTypeUInt8), TileCPUKernel);
MS_REG_CPU_KERNEL(Tile, KernelAttr().AddInputAttr(kNumberTypeUInt16).AddOutputAttr(kNumberTypeUInt16), TileCPUKernel);
MS_REG_CPU_KERNEL(Tile, KernelAttr().AddInputAttr(kNumberTypeUInt32).AddOutputAttr(kNumberTypeUInt32), TileCPUKernel);
MS_REG_CPU_KERNEL(Tile, KernelAttr().AddInputAttr(kNumberTypeUInt64).AddOutputAttr(kNumberTypeUInt64), TileCPUKernel);
MS_REG_CPU_KERNEL(Tile, KernelAttr().AddInputAttr(kNumberTypeBool).AddOutputAttr(kNumberTypeBool), TileCPUKernel);
MS_REG_CPU_KERNEL(Tile, KernelAttr(), TileCPUKernel);
} // namespace kernel
} // namespace mindspore
#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_TILE_CPU_KERNEL_H_

15
mindspore/ccsrc/backend/kernel_compiler/cpu/topk_cpu_kernel.h
View File

@ -40,20 +40,7 @@ class TopKCPUKernel : public CPUKernel {
TypeId dtype_{kTypeUnknown};
};
MS_REG_CPU_KERNEL(TopK,
KernelAttr()
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeInt32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeInt32),
TopKCPUKernel)
MS_REG_CPU_KERNEL(TopK,
KernelAttr()
.AddInputAttr(kNumberTypeFloat16)
.AddInputAttr(kNumberTypeInt32)
.AddOutputAttr(kNumberTypeFloat16)
.AddOutputAttr(kNumberTypeInt32),
TopKCPUKernel)
MS_REG_CPU_KERNEL(TopK, KernelAttr(), TopKCPUKernel)
} // namespace kernel
} // namespace mindspore
#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_TOPK_CPU_KERNEL_H_

31
mindspore/ccsrc/backend/kernel_compiler/cpu/transpose_cpu_kernel.h
View File

@ -52,36 +52,7 @@ class TransposeCPUFwdKernel : public CPUKernel {
std::unordered_map<TypeId, TypeKernel> launch_map_;
TypeKernel launch_func_;
};
MS_REG_CPU_KERNEL(Transpose,
KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
TransposeCPUFwdKernel);
MS_REG_CPU_KERNEL(Transpose,
KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeInt8).AddOutputAttr(kNumberTypeInt8),
TransposeCPUFwdKernel);
MS_REG_CPU_KERNEL(Transpose,
KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeInt16).AddOutputAttr(kNumberTypeInt16),
TransposeCPUFwdKernel);
MS_REG_CPU_KERNEL(Transpose,
KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
TransposeCPUFwdKernel);
MS_REG_CPU_KERNEL(Transpose,
KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeInt64),
TransposeCPUFwdKernel);
MS_REG_CPU_KERNEL(Transpose,
KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeUInt8).AddOutputAttr(kNumberTypeUInt8),
TransposeCPUFwdKernel);
MS_REG_CPU_KERNEL(Transpose,
KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeUInt16).AddOutputAttr(kNumberTypeUInt16),
TransposeCPUFwdKernel);
MS_REG_CPU_KERNEL(Transpose,
KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeUInt32).AddOutputAttr(kNumberTypeUInt32),
TransposeCPUFwdKernel);
MS_REG_CPU_KERNEL(Transpose,
KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeUInt64).AddOutputAttr(kNumberTypeUInt64),
TransposeCPUFwdKernel);
MS_REG_CPU_KERNEL(Transpose,
KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeBool).AddOutputAttr(kNumberTypeBool),
TransposeCPUFwdKernel);
MS_REG_CPU_KERNEL(Transpose, KernelAttr(), TransposeCPUFwdKernel);
} // namespace kernel
} // namespace mindspore
#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_TRANSPOSE_CPU_KERNEL_H_

3
mindspore/ccsrc/backend/kernel_compiler/kernel.h
View File

@ -35,7 +35,8 @@ enum KernelType : int {
RT_KERNEL,
HCCL_KERNEL,
TBE_KERNEL,
HOST_KERNEL
HOST_KERNEL,
CPU_KERNEL,
};
namespace kernel {

2
mindspore/ccsrc/backend/kernel_compiler/oplib/opinfo.h
View File

@ -25,7 +25,7 @@
namespace mindspore {
namespace kernel {
enum OpImplyType { kAKG = 0, kTBE = 1, kAICPU };
enum OpImplyType { kAKG = 0, kTBE = 1, kAICPU = 2, kCPU };
enum OpIOType { kInput = 0, kOutput };
class OpAttr {

4
mindspore/ccsrc/backend/kernel_compiler/oplib/oplib.cc
View File

@ -50,6 +50,7 @@ constexpr auto kAiCore = "AiCore";
constexpr auto kCUDA = "CUDA";
constexpr auto kTbe = "TBE";
constexpr auto kAkg = "AKG";
constexpr auto kCpu = "CPU";
constexpr auto kName = "name";
constexpr auto kParamType = "param_type";
constexpr auto kDtype = "dtype";
@ -90,6 +91,9 @@ bool OpLib::RegOp(const std::string &json_string, const std::string &impl_path)
} else if (imply_type_string == kAiCPU) {
OpImplyType imply_type = kAICPU;
ret = DecodeOpInfo(op_json, imply_type, impl_path);
} else if (imply_type_string == kCpu) {
OpImplyType imply_type = kCPU;
ret = DecodeOpInfo(op_json, imply_type, impl_path);
} else {
MS_LOG(ERROR) << "Not support imply_type";
}

121
mindspore/ccsrc/runtime/device/cpu/kernel_select_cpu.cc
View File

@ -18,7 +18,11 @@
#include <string>
#include <memory>
#include <algorithm>
#include "backend/kernel_compiler/common_utils.h"
#include "backend/kernel_compiler/cpu/cpu_kernel_factory.h"
#include "backend/kernel_compiler/kernel_build_info.h"
#include "backend/kernel_compiler/oplib/opinfo.h"
#include "backend/kernel_compiler/oplib/oplib.h"
#include "utils/trace_base.h"
namespace mindspore {
namespace device {
@ -35,19 +39,17 @@ bool IsInputNotCNode(const CNodePtr &kernel_node, size_t input_index) {
return false;
}
void GetOutputInferFormatsAndDtypes(const CNodePtr &kernel_node, std::vector<std::string> *output_formats,
std::vector<TypeId> *output_types) {
void GetOutputDtypes(const CNodePtr &kernel_node, std::vector<TypeId> *output_types) {
size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node);
for (size_t output_index = 0; output_index < output_num; ++output_index) {
TypeId dtype = kTypeUnknown;
dtype = AnfAlgo::GetOutputInferDataType(kernel_node, output_index);
output_formats->emplace_back(kOpFormat_DEFAULT);
output_types->emplace_back(dtype);
}
}
void GetInputFormatsAndDtypes(const CNodePtr &kernel_node, std::vector<std::string> *input_formats,
std::vector<TypeId> *input_types, std::vector<size_t> *input_no_cnode_indexes) {
void GetInputDtypes(const CNodePtr &kernel_node, std::vector<TypeId> *input_types,
std::vector<size_t> *input_no_cnode_indexes) {
size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
for (size_t input_index = 0; input_index < input_num; ++input_index) {
TypeId dtype = kTypeUnknown;
@ -57,7 +59,6 @@ void GetInputFormatsAndDtypes(const CNodePtr &kernel_node, std::vector<std::stri
} else {
dtype = AnfAlgo::GetPrevNodeOutputDeviceDataType(kernel_node, input_index);
}
input_formats->emplace_back(kOpFormat_DEFAULT);
input_types->emplace_back(dtype);
}
}
@ -86,16 +87,13 @@ bool InputDtypeMatch(TypeId InputAttr, TypeId input_type, bool strict) {
return false;
}
std::pair<int, int> GetOutputDtypeFormatMatchedNum(const KernelAttr &kernel_attr,
const std::vector<std::string> &output_formats,
const std::vector<TypeId> &output_types) {
int GetOutputDtypeMatchedNum(const KernelAttr &kernel_attr, const std::vector<TypeId> &output_types) {
if (kernel_attr.GetOutputSize() != output_types.size()) {
MS_LOG(DEBUG) << "required output num:" << kernel_attr.GetInputSize()
<< ", actual output num:" << output_types.size();
return std::make_pair(0, 0);
return 0;
}
int data_type_matched_num = 0;
int format_matched_num = 0;
auto output_num = output_types.size();
for (size_t i = 0; i < output_num; ++i) {
if (kernel_attr.GetOutputAttr(i).first != output_types[i]) {
@ -104,27 +102,17 @@ std::pair<int, int> GetOutputDtypeFormatMatchedNum(const KernelAttr &kernel_attr
} else {
data_type_matched_num++;
}
if (kernel_attr.GetOutputAttr(i).second != output_formats[i]) {
MS_LOG(DEBUG) << "required format:" << kernel_attr.GetOutputAttr(i).second
<< ", actual output format:" << output_formats[i];
} else {
format_matched_num++;
}
}
return std::make_pair(data_type_matched_num, format_matched_num);
return data_type_matched_num;
}
std::pair<int, int> GetInputDtypeFormatMatchedNum(const KernelAttr &kernel_attr,
const std::vector<std::string> &input_formats,
const std::vector<TypeId> &input_types,
const std::vector<size_t> &input_not_cnode_indexes, bool strict) {
int GetInputDtypeFormatMatchedNum(const KernelAttr &kernel_attr, const std::vector<TypeId> &input_types,
const std::vector<size_t> &input_not_cnode_indexes, bool strict) {
if (kernel_attr.GetInputSize() != input_types.size()) {
MS_LOG(DEBUG) << "required input num:" << kernel_attr.GetInputSize() << ", actual input num:" << input_types.size();
return std::make_pair(0, 0);
return 0;
}
int data_type_matched_num = 0;
int format_matched_num = 0;
auto input_num = input_types.size();
for (size_t i = 0; i < input_num; ++i) {
if (!InputDtypeMatch(kernel_attr.GetInputAttr(i).first, input_types[i], strict)) {
@ -132,10 +120,9 @@ std::pair<int, int> GetInputDtypeFormatMatchedNum(const KernelAttr &kernel_attr,
<< ", actual input dtype:" << input_types[i];
} else {
data_type_matched_num++;
format_matched_num++;
}
}
return std::make_pair(data_type_matched_num, format_matched_num);
return data_type_matched_num;
}
void ExpandKernelAttr(const CNodePtr &kernel_node, KernelAttr *kernel_attr) {
@ -197,12 +184,9 @@ void KernelNotSupportException(const AnfNodePtr &kernel_node, const std::vector<
}
} // namespace
bool SelectKernel(const CNodePtr &kernel_node, KernelAttr *selected_kernel_attr,
const std::vector<KernelAttr> &kernel_attrs, const std::vector<std::string> &input_formats,
const std::vector<TypeId> &input_types, const std::vector<size_t> &input_not_cnode_indexes,
const std::vector<std::string> &infer_output_formats, const std::vector<TypeId> &infer_output_types,
const std::vector<KernelAttr> &kernel_attrs, const std::vector<TypeId> &input_types,
const std::vector<size_t> &input_not_cnode_indexes, const std::vector<TypeId> &output_types,
std::pair<bool, bool> *matched, bool strict) {
int max_type_matched_num = -1;
int max_format_matched_num = -1;
for (auto kernel_attr : kernel_attrs) {
if (kernel_attr.GetAllSame()) {
ExpandKernelAttr(kernel_node, &kernel_attr);
@ -212,32 +196,14 @@ bool SelectKernel(const CNodePtr &kernel_node, KernelAttr *selected_kernel_attr,
MS_LOG(DEBUG) << "Output num is not equal!";
continue;
}
std::pair<int, int> input_type_format_matched_num =
GetInputDtypeFormatMatchedNum(kernel_attr, input_formats, input_types, input_not_cnode_indexes, strict);
std::pair<int, int> output_type_format_matched_num =
GetOutputDtypeFormatMatchedNum(kernel_attr, infer_output_formats, infer_output_types);
// Data type first
if (input_type_format_matched_num.first > max_type_matched_num) {
max_type_matched_num = input_type_format_matched_num.first;
max_format_matched_num = input_type_format_matched_num.second;
*selected_kernel_attr = kernel_attr;
} else if (input_type_format_matched_num.first == max_type_matched_num &&
input_type_format_matched_num.second > max_format_matched_num) {
max_format_matched_num = input_type_format_matched_num.second;
*selected_kernel_attr = kernel_attr;
} else if (input_type_format_matched_num.first == max_type_matched_num &&
input_type_format_matched_num.second == max_format_matched_num) {
if (output_type_format_matched_num.first == SizeToInt(infer_output_types.size()) &&
output_type_format_matched_num.second == SizeToInt(infer_output_types.size())) {
*selected_kernel_attr = kernel_attr;
}
}
int input_dtype_matched_num =
GetInputDtypeFormatMatchedNum(kernel_attr, input_types, input_not_cnode_indexes, strict);
int output_dtype_matched_num = GetOutputDtypeMatchedNum(kernel_attr, output_types);
// All formats and data types matched
if (input_type_format_matched_num.first == SizeToInt(input_types.size()) &&
input_type_format_matched_num.second == SizeToInt(input_types.size())) {
if (input_dtype_matched_num == SizeToInt(input_types.size())) {
*selected_kernel_attr = kernel_attr;
matched->first = true;
if (output_type_format_matched_num.first == SizeToInt(infer_output_types.size()) &&
output_type_format_matched_num.second == SizeToInt(infer_output_types.size())) {
if (output_dtype_matched_num == SizeToInt(output_types.size())) {
matched->second = true;
return true;
}
@ -249,43 +215,50 @@ void SetKernelInfo(const CNodePtr &kernel_node) {
std::vector<std::string> input_formats;
std::vector<TypeId> input_types;
std::vector<size_t> input_not_cnode_indexes;
std::vector<std::string> output_formats;
std::vector<std::string> selected_output_formats;
std::vector<TypeId> output_types;
std::vector<std::string> infer_output_formats;
std::vector<TypeId> infer_output_types;
std::vector<TypeId> selected_output_types;
MS_LOG(INFO) << "SetKernelInfo, CNode Name: " << AnfAlgo::GetCNodeName(kernel_node);
auto kernel_attrs =
kernel::CPUKernelFactory::GetInstance().GetSupportedKernelAttrList(AnfAlgo::GetCNodeName(kernel_node));
if (kernel_attrs.empty()) {
MS_LOG(EXCEPTION) << "Operator[" << AnfAlgo::GetCNodeName(kernel_node)
<< "] is not support. Trace: " << trace::DumpSourceLines(kernel_node);
if (kernel_attrs.empty() || (kernel_attrs[0].GetInputSize() == 0 && kernel_attrs[0].GetOutputSize() == 0)) {
MS_LOG(DEBUG) << "Operator[" << AnfAlgo::GetCNodeName(kernel_node) << "] will get ops attr info.";
std::string op_name = AnfAlgo::GetCNodeName(kernel_node);
auto op_info_ptr = mindspore::kernel::OpLib::FindOp(op_name, kernel::OpImplyType::kCPU);
if (op_info_ptr == nullptr) {
MS_LOG(ERROR) << "Not find op[" << op_name << "] in cpu";
}
kernel_attrs.clear();
kernel::CPUKernelFactory::GetInstance().SetKernelAttrs(op_info_ptr, &kernel_attrs);
kernel::CPUKernelFactory::GetInstance().UpdateKernelAttrs(op_name, kernel_attrs);
}
GetInputFormatsAndDtypes(kernel_node, &input_formats, &input_types, &input_not_cnode_indexes);
GetOutputInferFormatsAndDtypes(kernel_node, &infer_output_formats, &infer_output_types);
GetInputDtypes(kernel_node, &input_types, &input_not_cnode_indexes);
GetOutputDtypes(kernel_node, &output_types);
KernelAttr selected_kernel_attr;
std::pair<bool, bool> matched = std::make_pair(false, false);
if (!SelectKernel(kernel_node, &selected_kernel_attr, kernel_attrs, input_formats, input_types,
input_not_cnode_indexes, infer_output_formats, infer_output_types, &matched, true)) {
if (!SelectKernel(kernel_node, &selected_kernel_attr, kernel_attrs, input_types, input_not_cnode_indexes,
output_types, &matched, true)) {
if (AnfAlgo::GetCNodeName(kernel_node) == "Cast") {
KernelNotSupportException(kernel_node, input_types, infer_output_types);
KernelNotSupportException(kernel_node, input_types, output_types);
}
matched = std::make_pair(false, false);
SelectKernel(kernel_node, &selected_kernel_attr, kernel_attrs, input_formats, input_types, input_not_cnode_indexes,
infer_output_formats, infer_output_types, &matched, false);
SelectKernel(kernel_node, &selected_kernel_attr, kernel_attrs, input_types, input_not_cnode_indexes, output_types,
&matched, false);
if (!matched.first) {
KernelNotSupportException(kernel_node, input_types, infer_output_types);
KernelNotSupportException(kernel_node, input_types, output_types);
}
}
if (selected_kernel_attr.GetInputSize() > 0 &&
(matched.first || input_types.size() == input_not_cnode_indexes.size())) {
MS_LOG(INFO) << "Input format and dtype is matched";
GetOutputFormatsAndDtypes(kernel_node, selected_kernel_attr, &output_formats, &output_types);
for (size_t i = 0; i < selected_kernel_attr.GetInputSize(); ++i) {
input_types[SizeToInt(i)] = selected_kernel_attr.GetInputAttr(i).first;
GetOutputFormatsAndDtypes(kernel_node, selected_kernel_attr, &selected_output_formats, &selected_output_types);
for (size_t index = 0; index < selected_kernel_attr.GetInputSize(); index++) {
input_types[index] = selected_kernel_attr.GetInputAttr(index).first;
input_formats.emplace_back(selected_kernel_attr.GetInputAttr(index).second);
}
}
SetKernelBuildInfo(input_formats, input_types, output_formats, output_types, kernel_node.get());
SetKernelBuildInfo(input_formats, input_types, selected_output_formats, selected_output_types, kernel_node.get());
}
} // namespace cpu
} // namespace device

4
mindspore/ops/__init__.py
View File

@ -23,7 +23,7 @@ Examples:
from .primitive import Primitive, PrimitiveWithInfer, PrimitiveWithCheck, prim_attr_register
from .vm_impl_registry import get_vm_impl_fn, vm_impl_registry
from .op_info_register import op_info_register, AkgGpuRegOp, AkgAscendRegOp, AiCPURegOp, TBERegOp, DataType
from .op_info_register import op_info_register, AkgGpuRegOp, AkgAscendRegOp, AiCPURegOp, TBERegOp, CpuRegOp, DataType
from .primitive import constexpr
from . import composite, operations, functional
from . import signature
@ -36,7 +36,7 @@ __primitive__ = [
]
__all__ = ["get_vm_impl_fn", "vm_impl_registry",
"op_info_register", "AkgGpuRegOp", "AkgAscendRegOp", "AiCPURegOp", "TBERegOp", "DataType",
"op_info_register", "AkgGpuRegOp", "AkgAscendRegOp", "AiCPURegOp", "TBERegOp", "CpuRegOp", "DataType",
"constexpr"]
__all__.extend(__primitive__)
__all__.extend(composite.__all__)

1
mindspore/ops/_op_impl/__init__.py
View File

@ -16,6 +16,7 @@
import platform
from .aicpu import *
from .cpu import *
if "Windows" not in platform.system():
from .akg import *
from .tbe import *

63
mindspore/ops/_op_impl/cpu/__init__.py Normal file
View File

@ -0,0 +1,63 @@
# Copyright 2021 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.
"""cpu ops"""
from .cast import _cast_cpu
from .mul import _mul_cpu
from .sub import _sub_cpu
from .pow import _pow_cpu
from .real_div import _real_div_cpu
from .div import _div_cpu
from .concat import _concat_cpu
from .split import _split_cpu
from .adam import _adam_cpu
from .arg_max import _arg_max_cpu
from .arg_min_with_value import _arg_min_with_value_cpu
from .bias_add import _bias_add_cpu
from .bias_add_grad import _bias_add_grad_cpu
from .dropout import _dropout_cpu
from .dropout_grad import _dropout_grad_cpu
from .gather_d import _gather_cpu
from .gather_d_grad import _gather_d_grad_cpu
from .gather_v2 import _gather_v2_cpu
from .gather_nd import _gather_nd_cpu
from .maximum import _maximum_cpu
from .maximum_grad import _maximum_grad_cpu
from .conv2d import _conv2d_cpu
from .hsigmoid import _hsigmoid_cpu
from .hsigmoid_grad import _hsigmoid_grad_cpu
from .hswish import _hswish_cpu
from .hswish_grad import _hswish_grad_cpu
from .is_finite import _is_finite_cpu
from .layer_norm import _layer_norm_cpu
from .layer_norm_grad import _layer_norm_grad_cpu
from .minimum import _minimum_cpu
from .minimum_grad import _minimum_grad_cpu
from .equal_count import _equal_count_cpu
from .mirror_pad import _mirror_pad_cpu
from .mirror_pad_grad import _mirror_pad_grad_cpu
from .stack import _stack_cpu
from .reduce_mean import _reduce_mean_cpu
from .reduce_max import _reduce_max_cpu
from .reduce_sum import _reduce_sum_cpu
from .reduce_min import _reduce_min_cpu
from .reduce_all import _reduce_all_cpu
from .reduce_any import _reduce_any_cpu
from .transpose import _transpose_cpu
from .tile import _tile_cpu
from .top_k import _top_k_cpu
from .add import _add_cpu
from .one_hot import _one_hot_cpu
from .pad import _pad_cpu
from .range import _range_cpu

44
mindspore/ops/_op_impl/cpu/adam.py Normal file
View File

@ -0,0 +1,44 @@
# 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.
# ============================================================================
"""Concat op"""
from mindspore.ops.op_info_register import op_info_register, CpuRegOp, DataType
adam_op_info = CpuRegOp("Adam") \
.input(0, "var", "required") \
.input(1, "m", "required") \
.input(2, "v", "required") \
.input(3, "beta1_power", "required") \
.input(4, "beta2_power", "required") \
.input(5, "lr", "required") \
.input(6, "beta1", "required") \
.input(7, "beta2", "required") \
.input(8, "epsilon", "required") \
.input(9, "gradient", "required") \
.output(0, "output0", "required") \
.output(1, "output0", "required") \
.output(2, "output0", "required") \
.dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
DataType.F32_Default) \
.get_op_info()
@op_info_register(adam_op_info)
def _adam_cpu():
"""Adam cpu register"""
return

32
mindspore/ops/_op_impl/cpu/add.py Normal file
View File

@ -0,0 +1,32 @@
# Copyright 2021 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.
# ============================================================================
"""Add op"""
from mindspore.ops.op_info_register import op_info_register, CpuRegOp, DataType
add_op_info = CpuRegOp("Add") \
.input(0, "x1", "required") \
.input(1, "x2", "required") \
.output(0, "y", "required") \
.dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
.dtype_format(DataType.U32_Default, DataType.U32_Default, DataType.U32_Default) \
.dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
.get_op_info()
@op_info_register(add_op_info)
def _add_cpu():
"""Add cpu register"""
return

30
mindspore/ops/_op_impl/cpu/arg_max.py Normal file
View File

@ -0,0 +1,30 @@
# Copyright 2021 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.
# ============================================================================
"""Argmax op"""
from mindspore.ops.op_info_register import op_info_register, CpuRegOp, DataType
arg_max_op_info = CpuRegOp("Argmax") \
.input(0, "x", "required") \
.output(0, "y", "required") \
.dtype_format(DataType.F32_Default, DataType.I32_Default) \
.dtype_format(DataType.F16_Default, DataType.I32_Default) \
.get_op_info()
@op_info_register(arg_max_op_info)
def _arg_max_cpu():
"""Argmax cpu register"""
return

31
mindspore/ops/_op_impl/cpu/arg_min_with_value.py Normal file
View File

@ -0,0 +1,31 @@
# 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.
# ============================================================================
"""ArgMinWithValue op"""
from mindspore.ops.op_info_register import op_info_register, CpuRegOp, DataType
arg_min_with_value_op_info = CpuRegOp("ArgMinWithValue") \
.input(0, "x", "required") \
.output(0, "indice", "required") \
.output(1, "values", "required") \
.dtype_format(DataType.F32_Default, DataType.I32_Default, DataType.F32_Default) \
.dtype_format(DataType.F16_Default, DataType.I32_Default, DataType.F16_Default) \
.get_op_info()
@op_info_register(arg_min_with_value_op_info)
def _arg_min_with_value_cpu():
"""ArgMinWithValue cpu register"""
return

30
mindspore/ops/_op_impl/cpu/bias_add.py Normal file
View File

@ -0,0 +1,30 @@
# Copyright 2021 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.
# ============================================================================
"""BiasAdd op"""
from mindspore.ops.op_info_register import op_info_register, CpuRegOp, DataType
bias_add_op_info = CpuRegOp("BiasAdd") \
.input(0, "x", "required") \
.input(1, "bias", "required") \
.output(0, "y", "required") \
.dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
.get_op_info()
@op_info_register(bias_add_op_info)
def _bias_add_cpu():
"""BiasAdd cpu register"""
return

29
mindspore/ops/_op_impl/cpu/bias_add_grad.py Normal file
View File

@ -0,0 +1,29 @@
# Copyright 2021 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.
# ============================================================================
"""BiasAddGrad op"""
from mindspore.ops.op_info_register import op_info_register, CpuRegOp, DataType
bias_add_grad_op_info = CpuRegOp("BiasAddGrad") \
.input(0, "x", "required") \
.output(0, "y", "required") \
.dtype_format(DataType.F32_Default, DataType.F32_Default) \
.get_op_info()
@op_info_register(bias_add_grad_op_info)
def _bias_add_grad_cpu():
"""BiasAddGrad cpu register"""
return

171
mindspore/ops/_op_impl/cpu/cast.py Normal file
View File

@ -0,0 +1,171 @@
# Copyright 2021 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.
# ============================================================================
"""Cast op"""
from mindspore.ops.op_info_register import op_info_register, CpuRegOp, DataType
cast_op_info = CpuRegOp("Cast") \
.input(0, "x", "required") \
.output(0, "y", "required") \
.dtype_format(DataType.U8_Default, DataType.U8_Default) \
.dtype_format(DataType.U8_Default, DataType.U16_Default) \
.dtype_format(DataType.U8_Default, DataType.U32_Default) \
.dtype_format(DataType.U8_Default, DataType.U64_Default) \
.dtype_format(DataType.U8_Default, DataType.I8_Default) \
.dtype_format(DataType.U8_Default, DataType.I16_Default) \
.dtype_format(DataType.U8_Default, DataType.I32_Default) \
.dtype_format(DataType.U8_Default, DataType.I64_Default) \
.dtype_format(DataType.U8_Default, DataType.F16_Default) \
.dtype_format(DataType.U8_Default, DataType.F32_Default) \
.dtype_format(DataType.U8_Default, DataType.F64_Default) \
.dtype_format(DataType.U8_Default, DataType.BOOL_Default) \
.dtype_format(DataType.U16_Default, DataType.U8_Default) \
.dtype_format(DataType.U16_Default, DataType.U16_Default) \
.dtype_format(DataType.U16_Default, DataType.U32_Default) \
.dtype_format(DataType.U16_Default, DataType.U64_Default) \
.dtype_format(DataType.U16_Default, DataType.I8_Default) \
.dtype_format(DataType.U16_Default, DataType.I16_Default) \
.dtype_format(DataType.U16_Default, DataType.I32_Default) \
.dtype_format(DataType.U16_Default, DataType.I64_Default) \
.dtype_format(DataType.U16_Default, DataType.F16_Default) \
.dtype_format(DataType.U16_Default, DataType.F32_Default) \
.dtype_format(DataType.U16_Default, DataType.F64_Default) \
.dtype_format(DataType.U16_Default, DataType.BOOL_Default) \
.dtype_format(DataType.U32_Default, DataType.U8_Default) \
.dtype_format(DataType.U32_Default, DataType.U16_Default) \
.dtype_format(DataType.U32_Default, DataType.U32_Default) \
.dtype_format(DataType.U32_Default, DataType.U64_Default) \
.dtype_format(DataType.U32_Default, DataType.I8_Default) \
.dtype_format(DataType.U32_Default, DataType.I16_Default) \
.dtype_format(DataType.U32_Default, DataType.I32_Default) \
.dtype_format(DataType.U32_Default, DataType.I64_Default) \
.dtype_format(DataType.U32_Default, DataType.F16_Default) \
.dtype_format(DataType.U32_Default, DataType.F32_Default) \
.dtype_format(DataType.U32_Default, DataType.F64_Default) \
.dtype_format(DataType.U32_Default, DataType.BOOL_Default) \
.dtype_format(DataType.U64_Default, DataType.U8_Default) \
.dtype_format(DataType.U64_Default, DataType.U16_Default) \
.dtype_format(DataType.U64_Default, DataType.U32_Default) \
.dtype_format(DataType.U64_Default, DataType.U64_Default) \
.dtype_format(DataType.U64_Default, DataType.I8_Default) \
.dtype_format(DataType.U64_Default, DataType.I16_Default) \
.dtype_format(DataType.U64_Default, DataType.I32_Default) \
.dtype_format(DataType.U64_Default, DataType.I64_Default) \
.dtype_format(DataType.U64_Default, DataType.F16_Default) \
.dtype_format(DataType.U64_Default, DataType.F32_Default) \
.dtype_format(DataType.U64_Default, DataType.F64_Default) \
.dtype_format(DataType.U64_Default, DataType.BOOL_Default) \
.dtype_format(DataType.I8_Default, DataType.U8_Default) \
.dtype_format(DataType.I8_Default, DataType.U16_Default) \
.dtype_format(DataType.I8_Default, DataType.U32_Default) \
.dtype_format(DataType.I8_Default, DataType.U64_Default) \
.dtype_format(DataType.I8_Default, DataType.I8_Default) \
.dtype_format(DataType.I8_Default, DataType.I16_Default) \
.dtype_format(DataType.I8_Default, DataType.I32_Default) \
.dtype_format(DataType.I8_Default, DataType.I64_Default) \
.dtype_format(DataType.I8_Default, DataType.F16_Default) \
.dtype_format(DataType.I8_Default, DataType.F32_Default) \
.dtype_format(DataType.I8_Default, DataType.F64_Default) \
.dtype_format(DataType.I8_Default, DataType.BOOL_Default) \
.dtype_format(DataType.I16_Default, DataType.U8_Default) \
.dtype_format(DataType.I16_Default, DataType.U16_Default) \
.dtype_format(DataType.I16_Default, DataType.U32_Default) \
.dtype_format(DataType.I16_Default, DataType.U64_Default) \
.dtype_format(DataType.I16_Default, DataType.I8_Default) \
.dtype_format(DataType.I16_Default, DataType.I16_Default) \
.dtype_format(DataType.I16_Default, DataType.I32_Default) \
.dtype_format(DataType.I16_Default, DataType.I64_Default) \
.dtype_format(DataType.I16_Default, DataType.F16_Default) \
.dtype_format(DataType.I16_Default, DataType.F32_Default) \
.dtype_format(DataType.I16_Default, DataType.F64_Default) \
.dtype_format(DataType.I16_Default, DataType.BOOL_Default) \
.dtype_format(DataType.I32_Default, DataType.U8_Default) \
.dtype_format(DataType.I32_Default, DataType.U16_Default) \
.dtype_format(DataType.I32_Default, DataType.U32_Default) \
.dtype_format(DataType.I32_Default, DataType.U64_Default) \
.dtype_format(DataType.I32_Default, DataType.I8_Default) \
.dtype_format(DataType.I32_Default, DataType.I16_Default) \
.dtype_format(DataType.I32_Default, DataType.I32_Default) \
.dtype_format(DataType.I32_Default, DataType.I64_Default) \
.dtype_format(DataType.I32_Default, DataType.F16_Default) \
.dtype_format(DataType.I32_Default, DataType.F32_Default) \
.dtype_format(DataType.I32_Default, DataType.F64_Default) \
.dtype_format(DataType.I32_Default, DataType.BOOL_Default) \
.dtype_format(DataType.I64_Default, DataType.U8_Default) \
.dtype_format(DataType.I64_Default, DataType.U16_Default) \
.dtype_format(DataType.I64_Default, DataType.U32_Default) \
.dtype_format(DataType.I64_Default, DataType.U64_Default) \
.dtype_format(DataType.I64_Default, DataType.I8_Default) \
.dtype_format(DataType.I64_Default, DataType.I16_Default) \
.dtype_format(DataType.I64_Default, DataType.I32_Default) \
.dtype_format(DataType.I64_Default, DataType.I64_Default) \
.dtype_format(DataType.I64_Default, DataType.F16_Default) \
.dtype_format(DataType.I64_Default, DataType.F32_Default) \
.dtype_format(DataType.I64_Default, DataType.F64_Default) \
.dtype_format(DataType.I64_Default, DataType.BOOL_Default) \
.dtype_format(DataType.F16_Default, DataType.U8_Default) \
.dtype_format(DataType.F16_Default, DataType.U16_Default) \
.dtype_format(DataType.F16_Default, DataType.U32_Default) \
.dtype_format(DataType.F16_Default, DataType.U64_Default) \
.dtype_format(DataType.F16_Default, DataType.I8_Default) \
.dtype_format(DataType.F16_Default, DataType.I16_Default) \
.dtype_format(DataType.F16_Default, DataType.I32_Default) \
.dtype_format(DataType.F16_Default, DataType.I64_Default) \
.dtype_format(DataType.F16_Default, DataType.F16_Default) \
.dtype_format(DataType.F16_Default, DataType.F32_Default) \
.dtype_format(DataType.F16_Default, DataType.F64_Default) \
.dtype_format(DataType.F16_Default, DataType.BOOL_Default) \
.dtype_format(DataType.F32_Default, DataType.U8_Default) \
.dtype_format(DataType.F32_Default, DataType.U16_Default) \
.dtype_format(DataType.F32_Default, DataType.U32_Default) \
.dtype_format(DataType.F32_Default, DataType.U64_Default) \
.dtype_format(DataType.F32_Default, DataType.I8_Default) \
.dtype_format(DataType.F32_Default, DataType.I16_Default) \
.dtype_format(DataType.F32_Default, DataType.I32_Default) \
.dtype_format(DataType.F32_Default, DataType.I64_Default) \
.dtype_format(DataType.F32_Default, DataType.F16_Default) \
.dtype_format(DataType.F32_Default, DataType.F32_Default) \
.dtype_format(DataType.F32_Default, DataType.F64_Default) \
.dtype_format(DataType.F32_Default, DataType.BOOL_Default) \
.dtype_format(DataType.F64_Default, DataType.U8_Default) \
.dtype_format(DataType.F64_Default, DataType.U16_Default) \
.dtype_format(DataType.F64_Default, DataType.U32_Default) \
.dtype_format(DataType.F64_Default, DataType.U64_Default) \
.dtype_format(DataType.F64_Default, DataType.I8_Default) \
.dtype_format(DataType.F64_Default, DataType.I16_Default) \
.dtype_format(DataType.F64_Default, DataType.I32_Default) \
.dtype_format(DataType.F64_Default, DataType.I64_Default) \
.dtype_format(DataType.F64_Default, DataType.F16_Default) \
.dtype_format(DataType.F64_Default, DataType.F32_Default) \
.dtype_format(DataType.F64_Default, DataType.F64_Default) \
.dtype_format(DataType.F64_Default, DataType.BOOL_Default) \
.dtype_format(DataType.BOOL_Default, DataType.U8_Default) \
.dtype_format(DataType.BOOL_Default, DataType.U16_Default) \
.dtype_format(DataType.BOOL_Default, DataType.U32_Default) \
.dtype_format(DataType.BOOL_Default, DataType.U64_Default) \
.dtype_format(DataType.BOOL_Default, DataType.I8_Default) \
.dtype_format(DataType.BOOL_Default, DataType.I16_Default) \
.dtype_format(DataType.BOOL_Default, DataType.I32_Default) \
.dtype_format(DataType.BOOL_Default, DataType.I64_Default) \
.dtype_format(DataType.BOOL_Default, DataType.F16_Default) \
.dtype_format(DataType.BOOL_Default, DataType.F32_Default) \
.dtype_format(DataType.BOOL_Default, DataType.F64_Default) \
.dtype_format(DataType.BOOL_Default, DataType.BOOL_Default) \
.get_op_info()
@op_info_register(cast_op_info)
def _cast_cpu():
"""Cast Cpu register"""
return

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mindspore/ops/_op_impl/cpu/concat.py Normal file
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# 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.
# ============================================================================
"""Concat op"""
from mindspore.ops.op_info_register import op_info_register, CpuRegOp, DataType
concat_op_info = CpuRegOp("Concat") \
.input(0, "x", "required") \
.output(0, "y", "required") \
.dtype_format(DataType.F32_Default, DataType.F32_Default) \
.dtype_format(DataType.I8_Default, DataType.I8_Default) \
.dtype_format(DataType.I16_Default, DataType.I16_Default) \
.dtype_format(DataType.I32_Default, DataType.I32_Default) \
.dtype_format(DataType.I64_Default, DataType.I64_Default) \
.dtype_format(DataType.U8_Default, DataType.U8_Default) \
.dtype_format(DataType.U16_Default, DataType.U16_Default) \
.dtype_format(DataType.U32_Default, DataType.U32_Default) \
.dtype_format(DataType.U64_Default, DataType.U64_Default) \
.dtype_format(DataType.BOOL_Default, DataType.BOOL_Default) \
.get_op_info()
@op_info_register(concat_op_info)
def _concat_cpu():
"""Concat cpu register"""
return

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mindspore/ops/_op_impl/cpu/conv2d.py Normal file
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# Copyright 2021 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.
# ============================================================================
"""Conv2D op"""
from mindspore.ops.op_info_register import op_info_register, CpuRegOp, DataType
conv2d_op_info = CpuRegOp("Conv2D") \
.input(0, "x", "required") \
.input(1, "filter", "required") \
.output(0, "y", "required") \
.dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
.get_op_info()
@op_info_register(conv2d_op_info)
def _conv2d_cpu():
"""Conv2D cpu register"""
return

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mindspore/ops/_op_impl/cpu/div.py Normal file
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# Copyright 2021 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.
"""Div op"""
from mindspore.ops.op_info_register import op_info_register, CpuRegOp, DataType
div_op_info = CpuRegOp("Div") \
.input(0, "x", "required") \
.input(1, "y", "required") \
.output(0, "output", "required") \
.dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
.dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
.dtype_format(DataType.I64_Default, DataType.I64_Default, DataType.I64_Default) \
.get_op_info()
@op_info_register(div_op_info)
def _div_cpu():
"""Div cpu register"""
return

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mindspore/ops/_op_impl/cpu/dropout.py Normal file
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# Copyright 2021 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.
"""Dropout op"""
from mindspore.ops.op_info_register import op_info_register, CpuRegOp, DataType
dropout_op_info = CpuRegOp("Dropout") \
.input(0, "x", "required") \
.output(0, "output0", "required") \
.output(1, "output1", "required") \
.dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
.dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
.get_op_info()
@op_info_register(dropout_op_info)
def _dropout_cpu():
"""Dropout cpu register"""
return

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mindspore/ops/_op_impl/cpu/dropout_grad.py Normal file
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# Copyright 2021 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.
"""DropoutGrad op"""
from mindspore.ops.op_info_register import op_info_register, CpuRegOp, DataType
dropout_grad_op_info = CpuRegOp("DropoutGrad") \
.input(0, "x", "required") \
.input(1, "y", "required") \
.output(0, "output", "required") \
.dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
.dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
.get_op_info()
@op_info_register(dropout_grad_op_info)
def _dropout_grad_cpu():
"""DropoutGrad cpu register"""
return

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mindspore/ops/_op_impl/cpu/equal_count.py Normal file
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# Copyright 2021 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.
# ============================================================================
"""EqualCount op"""
from mindspore.ops.op_info_register import op_info_register, CpuRegOp, DataType
equal_count_op_info = CpuRegOp("EqualCount") \
.input(0, "x1", "required") \
.input(1, "x2", "required") \
.output(0, "y", "required") \
.dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
.get_op_info()
@op_info_register(equal_count_op_info)
def _equal_count_cpu():
"""EqualCount cpu register"""
return

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mindspore/ops/_op_impl/cpu/gather_d.py Normal file
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# Copyright 2021 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.
# ============================================================================
"""GatherD op"""
from mindspore.ops.op_info_register import op_info_register, CpuRegOp, DataType
gather_d_op_info = CpuRegOp("GatherD") \
.input(0, "input", "required") \
.input(1, "dim", "required") \
.input(2, "index", "required") \
.output(0, "output", "required") \
.dtype_format(DataType.F32_Default, DataType.I32_Default, \
DataType.I32_Default, DataType.F32_Default) \
.dtype_format(DataType.F32_Default, DataType.I32_Default, \
DataType.I64_Default, DataType.F32_Default) \
.dtype_format(DataType.F16_Default, DataType.I32_Default, \
DataType.I32_Default, DataType.F16_Default) \
.dtype_format(DataType.F16_Default, DataType.I32_Default, \
DataType.I64_Default, DataType.F16_Default) \
.dtype_format(DataType.I32_Default, DataType.I32_Default, \
DataType.I32_Default, DataType.I32_Default) \
.dtype_format(DataType.I32_Default, DataType.I32_Default, \
DataType.I64_Default, DataType.I32_Default) \
.dtype_format(DataType.I64_Default, DataType.I32_Default, \
DataType.I32_Default, DataType.I64_Default) \
.dtype_format(DataType.I64_Default, DataType.I32_Default, \
DataType.I64_Default, DataType.I64_Default) \
.dtype_format(DataType.BOOL_Default, DataType.I32_Default, \
DataType.I32_Default, DataType.BOOL_Default) \
.dtype_format(DataType.BOOL_Default, DataType.I32_Default, \
DataType.I64_Default, DataType.BOOL_Default) \
.get_op_info()
@op_info_register(gather_d_op_info)
def _gather_cpu():
"""GatherD cpu register"""
return

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mindspore/ops/_op_impl/cpu/gather_d_grad.py Normal file
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# Copyright 2021 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.
# ============================================================================
"""GatherDGrad op"""
from mindspore.ops.op_info_register import op_info_register, CpuRegOp, DataType
gather_d_grad_op_info = CpuRegOp("GatherDGrad") \
.input(0, "index", "required") \
.input(1, "src", "required") \
.output(0, "output", "required") \
.dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
.dtype_format(DataType.I32_Default, DataType.I64_Default, DataType.I64_Default) \
.dtype_format(DataType.I32_Default, DataType.F32_Default, DataType.F32_Default) \
.dtype_format(DataType.I32_Default, DataType.F16_Default, DataType.F16_Default) \
.dtype_format(DataType.I32_Default, DataType.BOOL_Default, DataType.BOOL_Default) \
.dtype_format(DataType.I64_Default, DataType.I32_Default, DataType.I32_Default) \
.dtype_format(DataType.I64_Default, DataType.I64_Default, DataType.I64_Default) \
.dtype_format(DataType.I64_Default, DataType.F32_Default, DataType.F32_Default) \
.dtype_format(DataType.I64_Default, DataType.F16_Default, DataType.F16_Default) \
.dtype_format(DataType.I64_Default, DataType.BOOL_Default, DataType.BOOL_Default) \
.get_op_info()
@op_info_register(gather_d_grad_op_info)
def _gather_d_grad_cpu():
"""GatherDGrad cpu register"""
return

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mindspore/ops/_op_impl/cpu/gather_nd.py Normal file
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# Copyright 2021 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.
# ============================================================================
"""GatherNd op"""
from mindspore.ops.op_info_register import op_info_register, CpuRegOp, DataType
gather_nd_op_info = CpuRegOp("GatherNd") \
.input(0, "x1", "required") \
.input(1, "x2", "required") \
.output(0, "y", "required") \
.dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
.dtype_format(DataType.I64_Default, DataType.I32_Default, DataType.I64_Default) \
.dtype_format(DataType.F32_Default, DataType.I32_Default, DataType.F32_Default) \
.dtype_format(DataType.F64_Default, DataType.I32_Default, DataType.F64_Default) \
.get_op_info()
@op_info_register(gather_nd_op_info)
def _gather_nd_cpu():
"""GatherNd cpu register"""
return

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mindspore/ops/_op_impl/cpu/gather_v2.py Normal file
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# Copyright 2021 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.
# ============================================================================
"""GatherV2 op"""
from mindspore.ops.op_info_register import op_info_register, CpuRegOp, DataType
gather_v2_op_info = CpuRegOp("Gather") \
.input(0, "x", "required") \
.input(1, "indices", "required") \
.output(0, "y", "required") \
.dtype_format(DataType.U8_Default, DataType.I32_Default, DataType.U8_Default) \
.dtype_format(DataType.U16_Default, DataType.I32_Default, DataType.U16_Default) \
.dtype_format(DataType.U32_Default, DataType.I32_Default, DataType.U32_Default) \
.dtype_format(DataType.U64_Default, DataType.I32_Default, DataType.U64_Default) \
.dtype_format(DataType.I8_Default, DataType.I32_Default, DataType.I8_Default) \
.dtype_format(DataType.I16_Default, DataType.I32_Default, DataType.I16_Default) \
.dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
.dtype_format(DataType.I64_Default, DataType.I32_Default, DataType.I64_Default) \
.dtype_format(DataType.F32_Default, DataType.I32_Default, DataType.F32_Default) \
.dtype_format(DataType.F64_Default, DataType.I32_Default, DataType.F64_Default) \
.dtype_format(DataType.BOOL_Default, DataType.I32_Default, DataType.BOOL_Default) \
.get_op_info()
@op_info_register(gather_v2_op_info)
def _gather_v2_cpu():
"""GatherV2 cpu register"""
return

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mindspore/ops/_op_impl/cpu/hsigmoid.py Normal file
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# Copyright 2021 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.
"""HSigmoid op"""
from mindspore.ops.op_info_register import op_info_register, CpuRegOp, DataType
hsigmoid_op_info = CpuRegOp("HSigmoid") \
.input(0, "x") \
.output(0, "output") \
.dtype_format(DataType.I8_Default, DataType.I8_Default) \
.dtype_format(DataType.I16_Default, DataType.I16_Default) \
.dtype_format(DataType.I32_Default, DataType.I32_Default) \
.dtype_format(DataType.I64_Default, DataType.I64_Default) \
.dtype_format(DataType.F32_Default, DataType.F32_Default) \
.get_op_info()
@op_info_register(hsigmoid_op_info)
def _hsigmoid_cpu():
"""HSigmoid cpu register"""
return

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mindspore/ops/_op_impl/cpu/hsigmoid_grad.py Normal file
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# Copyright 2021 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.
"""HSigmoidGrad op"""
from mindspore.ops.op_info_register import op_info_register, CpuRegOp, DataType
hsigmoidgrad_op_info = CpuRegOp("HSigmoidGrad") \
.input(0, "y_grad") \
.input(1, "x") \
.output(0, "output") \
.dtype_format(DataType.I8_Default, DataType.I8_Default, DataType.I8_Default) \
.dtype_format(DataType.I16_Default, DataType.I16_Default, DataType.I16_Default) \
.dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
.dtype_format(DataType.I64_Default, DataType.I64_Default, DataType.I64_Default) \
.dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
.get_op_info()
@op_info_register(hsigmoidgrad_op_info)
def _hsigmoid_grad_cpu():
"""HSigmoidGrad cpu register"""
return

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mindspore/ops/_op_impl/cpu/hswish.py Normal file
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# Copyright 2021 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.
"""HSwish op"""
from mindspore.ops.op_info_register import op_info_register, CpuRegOp, DataType
hswish_op_info = CpuRegOp("HSwish") \
.input(0, "x") \
.output(0, "output") \
.dtype_format(DataType.I8_Default, DataType.I8_Default) \
.dtype_format(DataType.I16_Default, DataType.I16_Default) \
.dtype_format(DataType.I32_Default, DataType.I32_Default) \
.dtype_format(DataType.I64_Default, DataType.I64_Default) \
.dtype_format(DataType.F32_Default, DataType.F32_Default) \
.get_op_info()
@op_info_register(hswish_op_info)
def _hswish_cpu():
"""HSwish cpu register"""
return

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mindspore/ops/_op_impl/cpu/hswish_grad.py Normal file
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# Copyright 2021 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.
"""HSwishGrad op"""
from mindspore.ops.op_info_register import op_info_register, CpuRegOp, DataType
hswish_grad_op_info = CpuRegOp("HSwishGrad") \
.input(0, "y_grad") \
.input(1, "x") \
.output(0, "output") \
.dtype_format(DataType.I8_Default, DataType.I8_Default, DataType.I8_Default) \
.dtype_format(DataType.I16_Default, DataType.I16_Default, DataType.I16_Default) \
.dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
.dtype_format(DataType.I64_Default, DataType.I64_Default, DataType.I64_Default) \
.dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
.get_op_info()
@op_info_register(hswish_grad_op_info)
def _hswish_grad_cpu():
"""HSwishGrad cpu register"""
return

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mindspore/ops/_op_impl/cpu/is_finite.py Normal file
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# Copyright 2021 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.
# ============================================================================
"""IsFinite op"""
from mindspore.ops.op_info_register import op_info_register, CpuRegOp, DataType
is_finite_op_info = CpuRegOp("IsFinite") \
.input(0, "x", "required") \
.output(0, "y", "required") \
.dtype_format(DataType.BOOL_Default, DataType.BOOL_Default) \
.dtype_format(DataType.I8_Default, DataType.BOOL_Default) \
.dtype_format(DataType.I16_Default, DataType.BOOL_Default) \
.dtype_format(DataType.I32_Default, DataType.BOOL_Default) \
.dtype_format(DataType.I64_Default, DataType.BOOL_Default) \
.dtype_format(DataType.U8_Default, DataType.BOOL_Default) \
.dtype_format(DataType.U16_Default, DataType.BOOL_Default) \
.dtype_format(DataType.U32_Default, DataType.BOOL_Default) \
.dtype_format(DataType.U64_Default, DataType.BOOL_Default) \
.dtype_format(DataType.F16_Default, DataType.BOOL_Default) \
.dtype_format(DataType.F32_Default, DataType.BOOL_Default) \
.dtype_format(DataType.F64_Default, DataType.BOOL_Default) \
.get_op_info()
@op_info_register(is_finite_op_info)
def _is_finite_cpu():
"""IsFinite cpu register"""
return

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mindspore/ops/_op_impl/cpu/layer_norm.py Normal file
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# Copyright 2021 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.
# ============================================================================
"""LayerNorm op"""
from mindspore.ops.op_info_register import op_info_register, CpuRegOp, DataType
layer_norm_op_info = CpuRegOp("LayerNorm") \
.input(0, "x", "required") \
.input(1, "gamma", "required") \
.input(2, "beta", "required") \
.output(0, "y", "required") \
.output(1, "mean", "required") \
.output(2, "variance", "required") \
.dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, \
DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
.dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, \
DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
.get_op_info()
@op_info_register(layer_norm_op_info)
def _layer_norm_cpu():
"""LayerNorm cpu register"""
return

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mindspore/ops/_op_impl/cpu/layer_norm_grad.py Normal file
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# 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.
# ============================================================================
"""LayerNormGrad op"""
from mindspore.ops.op_info_register import op_info_register, CpuRegOp, DataType
layer_norm_grad_op_info = CpuRegOp("LayerNormGrad") \
.input(0, "dy", "required") \
.input(1, "x", "required") \
.input(2, "variance", "required") \
.input(3, "mean", "required") \
.input(4, "gamma", "required") \
.output(0, "pd_x", "required") \
.output(1, "pd_gamma", "required") \
.output(2, "pd_beta", "required") \
.dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default,
DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
.dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
.get_op_info()
@op_info_register(layer_norm_grad_op_info)
def _layer_norm_grad_cpu():
"""LayerNormGrad TBE register"""
return

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mindspore/ops/_op_impl/cpu/maximum.py Normal file
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# Copyright 2021 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.
# ============================================================================
"""Maximum op"""
from mindspore.ops.op_info_register import op_info_register, CpuRegOp, DataType
maximum_op_info = CpuRegOp("Maximum") \
.input(0, "x1", "required") \
.input(1, "x2", "required") \
.output(0, "y", "required") \
.dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
.dtype_format(DataType.I64_Default, DataType.I64_Default, DataType.I64_Default) \
.dtype_format(DataType.U32_Default, DataType.U32_Default, DataType.U32_Default) \
.dtype_format(DataType.U64_Default, DataType.U64_Default, DataType.U64_Default) \
.dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
.dtype_format(DataType.F64_Default, DataType.F64_Default, DataType.F64_Default) \
.get_op_info()
@op_info_register(maximum_op_info)
def _maximum_cpu():
"""Maximum cpu register"""
return

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mindspore/ops/_op_impl/cpu/maximum_grad.py Normal file
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# Copyright 2021 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.
# ============================================================================
"""MaximumGrad op"""
from mindspore.ops.op_info_register import op_info_register, CpuRegOp, DataType
maximum_grad_op_info = CpuRegOp("MaximumGrad") \
.input(0, "grads", "required") \
.input(1, "x1", "required") \
.input(2, "x2", "required") \
.output(0, "y1", "required") \
.output(1, "y2", "required") \
.dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default,
DataType.I32_Default, DataType.I32_Default) \
.dtype_format(DataType.I64_Default, DataType.I64_Default, DataType.I64_Default,
DataType.I64_Default, DataType.I64_Default) \
.dtype_format(DataType.U32_Default, DataType.U32_Default, DataType.U32_Default,
DataType.U32_Default, DataType.U32_Default) \
.dtype_format(DataType.U64_Default, DataType.U64_Default, DataType.U64_Default,
DataType.U64_Default, DataType.U64_Default) \
.dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
DataType.F32_Default, DataType.F32_Default) \
.dtype_format(DataType.F64_Default, DataType.F64_Default, DataType.F64_Default,
DataType.F64_Default, DataType.F64_Default) \
.get_op_info()
@op_info_register(maximum_grad_op_info)
def _maximum_grad_cpu():
"""MaximumGrad cpu register"""
return

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mindspore/ops/_op_impl/cpu/minimum.py Normal file
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# Copyright 2021 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.
# ============================================================================
"""Minimum op"""
from mindspore.ops.op_info_register import op_info_register, CpuRegOp, DataType
minimum_op_info = CpuRegOp("Minimum") \
.input(0, "x1", "required") \
.input(1, "x2", "required") \
.output(0, "y", "required") \
.dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
.dtype_format(DataType.U32_Default, DataType.U32_Default, DataType.U32_Default) \
.dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
.dtype_format(DataType.I64_Default, DataType.I64_Default, DataType.I64_Default) \
.dtype_format(DataType.U64_Default, DataType.U64_Default, DataType.U64_Default) \
.dtype_format(DataType.F64_Default, DataType.F64_Default, DataType.F64_Default) \
.get_op_info()
@op_info_register(minimum_op_info)
def _minimum_cpu():
"""Minimum cpu register"""
return

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# Copyright 2021 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.
# ============================================================================
"""MinimumGrad op"""
from mindspore.ops.op_info_register import op_info_register, CpuRegOp, DataType
minimum_grad_op_info = CpuRegOp("MinimumGrad") \
.input(0, "grads", "required") \
.input(1, "x1", "required") \
.input(2, "x2", "required") \
.output(0, "y1", "required") \
.output(1, "y2", "required") \
.dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default, \
DataType.I32_Default, DataType.I32_Default) \
.dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, \
DataType.F16_Default, DataType.F16_Default) \
.dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, \
DataType.F32_Default, DataType.F32_Default) \
.get_op_info()
@op_info_register(minimum_grad_op_info)
def _minimum_grad_cpu():
"""MinimumGrad cpu register"""
return

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mindspore/ops/_op_impl/cpu/mirror_pad.py Normal file
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# Copyright 2021 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.
# ============================================================================
"""MirrorPad op"""
from mindspore.ops.op_info_register import op_info_register, CpuRegOp, DataType
mirror_pad_op_info = CpuRegOp("MirrorPad") \
.input(0, "x", "required") \
.input(1, "paddings", "required") \
.output(0, "y", "required") \
.dtype_format(DataType.F16_Default, DataType.I64_Default, DataType.F16_Default) \
.dtype_format(DataType.F32_Default, DataType.I64_Default, DataType.F32_Default) \
.dtype_format(DataType.I32_Default, DataType.I64_Default, DataType.I32_Default) \
.get_op_info()
@op_info_register(mirror_pad_op_info)
def _mirror_pad_cpu():
"""MirrorPad cpu register"""
return

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mindspore/ops/_op_impl/cpu/mirror_pad_grad.py Normal file
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# Copyright 2021 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.
# ============================================================================
"""MirrorPadGrad op"""
from mindspore.ops.op_info_register import op_info_register, CpuRegOp, DataType
mirror_pad_grad_op_info = CpuRegOp("MirrorPadGrad") \
.input(0, "x", "required") \
.input(1, "paddings", "required") \
.output(0, "y", "required") \
.dtype_format(DataType.F16_Default, DataType.I64_Default, DataType.F16_Default) \
.dtype_format(DataType.F32_Default, DataType.I64_Default, DataType.F32_Default) \
.dtype_format(DataType.I32_Default, DataType.I64_Default, DataType.I32_Default) \
.get_op_info()
@op_info_register(mirror_pad_grad_op_info)
def _mirror_pad_grad_cpu():
"""MirrorPadGrad cpu register"""
return

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mindspore/ops/_op_impl/cpu/mul.py Normal file
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# Copyright 2021 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.
"""Mul op"""
from mindspore.ops.op_info_register import op_info_register, CpuRegOp, DataType
mul_op_info = CpuRegOp("Mul") \
.input(0, "x", "required") \
.input(1, "y", "required") \
.output(0, "output", "required") \
.dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
.dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
.get_op_info()
@op_info_register(mul_op_info)
def _mul_cpu():
"""Mul cpu register"""
return

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mindspore/ops/_op_impl/cpu/one_hot.py Normal file
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# Copyright 2021 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.
# ============================================================================
"""OneHot op"""
from mindspore.ops.op_info_register import op_info_register, CpuRegOp, DataType
one_hot_op_info = CpuRegOp("OneHot") \
.input(0, "x", "required") \
.input(1, "on_value", "required") \
.input(2, "off_value", "required") \
.output(0, "y", "required") \
.dtype_format(DataType.I32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
.get_op_info()
@op_info_register(one_hot_op_info)
def _one_hot_cpu():
"""OneHot cpu register"""
return

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mindspore/ops/_op_impl/cpu/pad.py Normal file
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# Copyright 2021 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.
# ============================================================================
"""Pad op"""
from mindspore.ops.op_info_register import op_info_register, CpuRegOp, DataType
pad_op_info = CpuRegOp("Pad") \
.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.I32_Default, DataType.I32_Default) \
.get_op_info()
@op_info_register(pad_op_info)
def _pad_cpu():
"""Pad cpu register"""
return

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mindspore/ops/_op_impl/cpu/pow.py Normal file
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# Copyright 2021 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.
"""Power op"""
from mindspore.ops.op_info_register import op_info_register, CpuRegOp, DataType
pow_op_info = CpuRegOp("Pow") \
.input(0, "x", "required") \
.input(1, "y", "required") \
.output(0, "output", "required") \
.dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
.dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
.dtype_format(DataType.I64_Default, DataType.I64_Default, DataType.I64_Default) \
.get_op_info()
@op_info_register(pow_op_info)
def _pow_cpu():
"""Pow cpu register"""
return

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mindspore/ops/_op_impl/cpu/range.py Normal file
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# Copyright 2021 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.
# ============================================================================
"""Range op"""
from mindspore.ops.op_info_register import op_info_register, CpuRegOp, DataType
range_op_info = CpuRegOp("Range") \
.input(0, "x", "required") \
.output(0, "y", "required") \
.dtype_format(DataType.I32_Default, DataType.I32_Default) \
.dtype_format(DataType.I64_Default, DataType.I64_Default) \
.dtype_format(DataType.F32_Default, DataType.F32_Default) \
.dtype_format(DataType.F64_Default, DataType.F64_Default) \
.get_op_info()
@op_info_register(range_op_info)
def _range_cpu():
"""Range cpu register"""
return

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mindspore/ops/_op_impl/cpu/real_div.py Normal file
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# Copyright 2021 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.
"""RealDiv op"""
from mindspore.ops.op_info_register import op_info_register, CpuRegOp, DataType
real_div_op_info = CpuRegOp("RealDiv") \
.input(0, "x", "required") \
.input(1, "y", "required") \
.output(0, "output", "required") \
.dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
.dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
.dtype_format(DataType.I64_Default, DataType.I64_Default, DataType.I64_Default) \
.get_op_info()
@op_info_register(real_div_op_info)
def _real_div_cpu():
"""RealDiv cpu register"""
return

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mindspore/ops/_op_impl/cpu/reduce_all.py Normal file
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# Copyright 2021 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.
# ============================================================================
"""ReduceAll op"""
from mindspore.ops.op_info_register import op_info_register, CpuRegOp, DataType
reduce_all_op_info = CpuRegOp("ReduceAll") \
.input(0, "x", "required") \
.output(0, "y", "required") \
.dtype_format(DataType.BOOL_Default, DataType.BOOL_Default) \
.get_op_info()
@op_info_register(reduce_all_op_info)
def _reduce_all_cpu():
"""ReduceAll cpu register"""
return

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mindspore/ops/_op_impl/cpu/reduce_any.py Normal file
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# Copyright 2021 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.
# ============================================================================
"""ReduceAny op"""
from mindspore.ops.op_info_register import op_info_register, CpuRegOp, DataType
reduce_any_op_info = CpuRegOp("ReduceAny") \
.input(0, "x", "required") \
.output(0, "y", "required") \
.dtype_format(DataType.BOOL_Default, DataType.BOOL_Default) \
.get_op_info()
@op_info_register(reduce_any_op_info)
def _reduce_any_cpu():
"""ReduceAny cpu register"""
return

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mindspore/ops/_op_impl/cpu/reduce_max.py Normal file
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# Copyright 2021 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.
# ============================================================================
"""ReduceMax op"""
from mindspore.ops.op_info_register import op_info_register, CpuRegOp, DataType
reduce_max_op_info = CpuRegOp("ReduceMax") \
.input(0, "x", "required") \
.output(0, "y", "required") \
.dtype_format(DataType.F32_Default, DataType.F32_Default) \
.dtype_format(DataType.F64_Default, DataType.F64_Default) \
.dtype_format(DataType.I32_Default, DataType.I32_Default) \
.dtype_format(DataType.I64_Default, DataType.I64_Default) \
.get_op_info()
@op_info_register(reduce_max_op_info)
def _reduce_max_cpu():
"""ReduceMax cpu register"""
return

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mindspore/ops/_op_impl/cpu/reduce_mean.py Normal file
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# Copyright 2021 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.
# ============================================================================
"""ReduceMean op"""
from mindspore.ops.op_info_register import op_info_register, CpuRegOp, DataType
reduce_mean_op_info = CpuRegOp("ReduceMean") \
.input(0, "x", "required") \
.output(0, "y", "required") \
.dtype_format(DataType.F32_Default, DataType.F32_Default) \
.dtype_format(DataType.F64_Default, DataType.F64_Default) \
.dtype_format(DataType.I32_Default, DataType.I32_Default) \
.dtype_format(DataType.I64_Default, DataType.I64_Default) \
.get_op_info()
@op_info_register(reduce_mean_op_info)
def _reduce_mean_cpu():
"""ReduceMean cpu register"""
return

32
mindspore/ops/_op_impl/cpu/reduce_min.py Normal file
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@ -0,0 +1,32 @@
# Copyright 2021 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.
# ============================================================================
"""ReduceMin op"""
from mindspore.ops.op_info_register import op_info_register, CpuRegOp, DataType
reduce_min_op_info = CpuRegOp("ReduceMin") \
.input(0, "x", "required") \
.output(0, "y", "required") \
.dtype_format(DataType.F32_Default, DataType.F32_Default) \
.dtype_format(DataType.F64_Default, DataType.F64_Default) \
.dtype_format(DataType.I32_Default, DataType.I32_Default) \
.dtype_format(DataType.I64_Default, DataType.I64_Default) \
.get_op_info()
@op_info_register(reduce_min_op_info)
def _reduce_min_cpu():
"""ReduceMin cpu register"""
return

32
mindspore/ops/_op_impl/cpu/reduce_sum.py Normal file
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@ -0,0 +1,32 @@
# Copyright 2021 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.
# ============================================================================
"""ReduceSum op"""
from mindspore.ops.op_info_register import op_info_register, CpuRegOp, DataType
reduce_sum_op_info = CpuRegOp("ReduceSum") \
.input(0, "x", "required") \
.output(0, "y", "required") \
.dtype_format(DataType.F32_Default, DataType.F32_Default) \
.dtype_format(DataType.F64_Default, DataType.F64_Default) \
.dtype_format(DataType.I32_Default, DataType.I32_Default) \
.dtype_format(DataType.I64_Default, DataType.I64_Default) \
.get_op_info()
@op_info_register(reduce_sum_op_info)
def _reduce_sum_cpu():
"""ReduceSum cpu register"""
return

34
mindspore/ops/_op_impl/cpu/split.py Normal file
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@ -0,0 +1,34 @@
# 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.
# ============================================================================
"""Split op"""
from mindspore.ops.op_info_register import op_info_register, CpuRegOp, DataType
split_op_info = CpuRegOp("Split") \
.input(0, "x", "required") \
.output(0, "y", "required") \
.dtype_format(DataType.F32_Default, DataType.F32_Default) \
.dtype_format(DataType.F16_Default, DataType.F16_Default) \
.dtype_format(DataType.F64_Default, DataType.F64_Default) \
.dtype_format(DataType.I32_Default, DataType.I32_Default) \
.dtype_format(DataType.U32_Default, DataType.U32_Default) \
.dtype_format(DataType.I64_Default, DataType.I64_Default) \
.get_op_info()
@op_info_register(split_op_info)
def _split_cpu():
"""Split cpu register"""
return

38
mindspore/ops/_op_impl/cpu/stack.py Normal file
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@ -0,0 +1,38 @@
# Copyright 2021 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.
# ============================================================================
"""Stack op"""
from mindspore.ops.op_info_register import op_info_register, CpuRegOp, DataType
stack_op_info = CpuRegOp("Stack") \
.input(0, "x", "required") \
.output(0, "y", "required") \
.dtype_format(DataType.I8_Default, DataType.I8_Default) \
.dtype_format(DataType.I16_Default, DataType.I16_Default) \
.dtype_format(DataType.I32_Default, DataType.I32_Default) \
.dtype_format(DataType.I64_Default, DataType.I64_Default) \
.dtype_format(DataType.U8_Default, DataType.U8_Default) \
.dtype_format(DataType.U16_Default, DataType.U16_Default) \
.dtype_format(DataType.U32_Default, DataType.U32_Default) \
.dtype_format(DataType.U64_Default, DataType.U64_Default) \
.dtype_format(DataType.F16_Default, DataType.F16_Default) \
.dtype_format(DataType.F32_Default, DataType.F32_Default) \
.dtype_format(DataType.BOOL_Default, DataType.BOOL_Default) \
.get_op_info()
@op_info_register(stack_op_info)
def _stack_cpu():
"""Stack cpu register"""
return

31
mindspore/ops/_op_impl/cpu/sub.py Normal file
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@ -0,0 +1,31 @@
# Copyright 2021 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.
"""Sub op"""
from mindspore.ops.op_info_register import op_info_register, CpuRegOp, DataType
sub_op_info = CpuRegOp("Sub") \
.input(0, "x", "required") \
.input(1, "y", "required") \
.output(0, "output", "required") \
.dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
.dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
.dtype_format(DataType.I64_Default, DataType.I64_Default, DataType.I64_Default) \
.get_op_info()
@op_info_register(sub_op_info)
def _sub_cpu():
"""Sub cpu register"""
return

38
mindspore/ops/_op_impl/cpu/tile.py Normal file
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@ -0,0 +1,38 @@
# Copyright 2021 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.
# ============================================================================
"""Tile op"""
from mindspore.ops.op_info_register import op_info_register, CpuRegOp, DataType
tile_op_info = CpuRegOp("Tile") \
.input(0, "x1", "required") \
.output(0, "y", "required") \
.dtype_format(DataType.I8_Default, DataType.I8_Default) \
.dtype_format(DataType.I16_Default, DataType.I16_Default) \
.dtype_format(DataType.I32_Default, DataType.I32_Default) \
.dtype_format(DataType.I64_Default, DataType.I64_Default) \
.dtype_format(DataType.U8_Default, DataType.U8_Default) \
.dtype_format(DataType.U16_Default, DataType.U16_Default) \
.dtype_format(DataType.U32_Default, DataType.U32_Default) \
.dtype_format(DataType.U64_Default, DataType.U64_Default) \
.dtype_format(DataType.F32_Default, DataType.F32_Default) \
.dtype_format(DataType.BOOL_Default, DataType.BOOL_Default) \
.get_op_info()
@op_info_register(tile_op_info)
def _tile_cpu():
"""Tile cpu register"""
return

31
mindspore/ops/_op_impl/cpu/top_k.py Normal file
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@ -0,0 +1,31 @@
# Copyright 2021 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.
# ============================================================================
"""TopK op"""
from mindspore.ops.op_info_register import op_info_register, CpuRegOp, DataType
top_k_op_info = CpuRegOp("TopK") \
.input(0, "input", "required") \
.input(1, "k", "required") \
.output(0, "values", "required") \
.output(1, "indices", "required") \
.dtype_format(DataType.F16_Default, DataType.I32_Default, DataType.F16_Default, DataType.I32_Default) \
.dtype_format(DataType.F32_Default, DataType.I32_Default, DataType.F32_Default, DataType.I32_Default) \
.get_op_info()
@op_info_register(top_k_op_info)
def _top_k_cpu():
"""TopK cpu register"""
return

38
mindspore/ops/_op_impl/cpu/transpose.py Normal file
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@ -0,0 +1,38 @@
# Copyright 2021 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.
# ============================================================================
"""Transpose op"""
from mindspore.ops.op_info_register import op_info_register, CpuRegOp, DataType
transpose_op_info = CpuRegOp("Transpose") \
.input(0, "x", "required") \
.output(0, "y", "required") \
.dtype_format(DataType.I8_Default, DataType.I8_Default) \
.dtype_format(DataType.I16_Default, DataType.I16_Default) \
.dtype_format(DataType.I32_Default, DataType.I32_Default) \
.dtype_format(DataType.I64_Default, DataType.I64_Default) \
.dtype_format(DataType.U8_Default, DataType.U8_Default) \
.dtype_format(DataType.U16_Default, DataType.U16_Default) \
.dtype_format(DataType.U32_Default, DataType.U32_Default) \
.dtype_format(DataType.U64_Default, DataType.U64_Default) \
.dtype_format(DataType.F32_Default, DataType.F32_Default) \
.dtype_format(DataType.BOOL_Default, DataType.BOOL_Default) \
.get_op_info()
@op_info_register(transpose_op_info)
def _transpose_cpu():
"""TransposeD cpu register"""
return

59
mindspore/ops/op_info_register.py
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@ -213,6 +213,65 @@ class RegOp:
return op_info
class CpuRegOp(RegOp):
"""Class for Cpu op info register"""
def __init__(self, op_name):
super(CpuRegOp, self).__init__(op_name)
self.imply_type = "CPU"
def input(self, index=None, name=None, param_type=None, **kwargs):
"""
Register Cpu op input information.
Args:
index (int): Order of the input. Default: None.
name (str): Name of the input. Default: None.
param_type (str): Param type of the input. Default: None.
kwargs (dict): Other information of the input.
"""
param_list = [index, name, param_type]
key_list = ["index", "name", "param_type"]
fn_list = [self._is_int, self._is_string, self._is_string]
input_dict = self._check_param(param_list, key_list, fn_list, kwargs)
self.inputs.append(input_dict)
return self
def output(self, index=None, name=None, param_type=None, **kwargs):
"""
Register AiCPU op output information.
Args:
index (int): Order of the output. Default: None.
name (str): Name of the output. Default: None.
param_type (str): Param type of the output. Default: None.
kwargs (dict): Other information of the output.
"""
param_list = [index, name, param_type]
key_list = ["index", "name", "param_type"]
fn_list = [self._is_int, self._is_string, self._is_string]
output_dict = self._check_param(param_list, key_list, fn_list, kwargs)
self.outputs.append(output_dict)
return self
def attr(self, name=None, value_type=None, value=None, **kwargs):
"""
Register AiCPU op attribute information.
Args:
name (str): Name of the attribute. Default: None.
value_type (str): Value type of the attribute. Default: None.
value (str): Value of the attribute. Default: None.
kwargs (dict): Other information of the attribute.
"""
param_list = [name, value_type, value]
key_list = ["name", "type", "value"]
fn_list = [self._is_string]
attr_dict = self._check_param(param_list, key_list, fn_list, kwargs)
self.attr_.append(attr_dict)
return self
class AkgRegOp(RegOp):
"""Class for Akg op info register."""