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add complex functions for cpu backend.

This commit is contained in:
z00512249 2022-01-21 14:40:13 +08:00
parent 9d20bce3b4
commit 7257cf129a
4 changed files with 353 additions and 13 deletions
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96
mindspore/ccsrc/backend/kernel_compiler/cpu/unary_op_cpu_kernel.cc Normal file
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@ -0,0 +1,96 @@
/**
* Copyright 2022 Huawei Technologies Co., Ltd
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "backend/kernel_compiler/cpu/unary_op_cpu_kernel.h"
#include <map>
#include <string>
#include <vector>
namespace mindspore {
namespace kernel {
template <typename T, typename S>
void Real(const T *input, S *output, size_t start, size_t end) {
if (!std::is_same<S, complex64>::value && !std::is_same<S, complex128>::value) {
for (size_t i = start; i < end; ++i) {
output[i] = static_cast<S>(std::real(input[i]));
}
} else {
MS_LOG(EXCEPTION) << "For Real, it's output data type only support these types: float or double";
}
}
template <typename T, typename S>
void Imag(const T *input, S *output, size_t start, size_t end) {
if constexpr (!std::is_same<S, std::complex<float>>::value && !std::is_same<S, std::complex<double>>::value) {
for (size_t i = start; i < end; ++i) {
output[i] = static_cast<S>(std::imag(input[i]));
}
} else {
MS_LOG(EXCEPTION) << "For Imag, it's output data type only support these types: float or double";
}
}
template <typename T, typename S>
void Conj(const T *input, S *output, size_t start, size_t end) {
if constexpr (std::is_same<T, S>::value &&
(std::is_same<T, complex64>::value || std::is_same<T, complex128>::value)) {
for (size_t i = start; i < end; ++i) {
output[i] = static_cast<S>(std::conj(input[i]));
}
} else {
MS_LOG(EXCEPTION) << "For Conj, it's output data type only support these types: complex<float> or complex<double>";
}
}
template <typename T, typename S>
void UnaryOpCPUKernel<T, S>::GetUnaryOpFunc() {
if constexpr (std::is_same<T, complex64>::value || std::is_same<T, complex128>::value) {
static std::map<std::string, UnaryOpFunc> kComplexSupportedTypeMap = {{prim::kPrimReal->name(), &Real<T, S>},
{prim::kPrimImag->name(), &Imag<T, S>},
{prim::kPrimConj->name(), &Conj<T, S>}};
auto iter = kComplexSupportedTypeMap.find(kernel_name_);
if (iter != kComplexSupportedTypeMap.end()) {
unary_op_func_ = iter->second;
return;
}
MS_LOG(EXCEPTION) << "For '" << kernel_name_ << ", only support these types: Real, Imag, Conj currently, but got "
<< kernel_name_;
}
}
template <typename T, typename S>
void UnaryOpCPUKernel<T, S>::InitKernel(const CNodePtr &kernel_node) {
kernel_name_ = AnfAlgo::GetCNodeName(kernel_node);
GetUnaryOpFunc();
}
template <typename T, typename S>
bool UnaryOpCPUKernel<T, S>::Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
const std::vector<AddressPtr> &outputs) {
auto input = inputs.front();
auto output = outputs.front();
const auto input_addr = reinterpret_cast<T *>(input->addr);
auto output_addr = reinterpret_cast<S *>(output->addr);
if (unary_op_func_ != nullptr) {
ParallelLaunchAutoSearch(
std::bind(unary_op_func_, input_addr, output_addr, std::placeholders::_1, std::placeholders::_2),
output->size / sizeof(S), this, &parallel_search_info_);
} else {
(void)memcpy_s(output_addr, output->size, input_addr, input->size);
}
return true;
}
} // namespace kernel
} // namespace mindspore

120
mindspore/ccsrc/backend/kernel_compiler/cpu/unary_op_cpu_kernel.h Normal file
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@ -0,0 +1,120 @@
/**
* Copyright 2022 Huawei Technologies Co., Ltd
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef MINDSPORE_MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_UNARY_OP_CPU_KERNEL_H_
#define MINDSPORE_MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_UNARY_OP_CPU_KERNEL_H_
#include <complex>
#include <vector>
#include "backend/kernel_compiler/cpu/cpu_kernel.h"
#include "backend/kernel_compiler/cpu/cpu_kernel_factory.h"
using complex64 = std::complex<float>;
using complex128 = std::complex<double>;
namespace mindspore {
namespace kernel {
template <typename T, typename S>
class UnaryOpCPUKernel : public CPUKernel {
public:
UnaryOpCPUKernel() = default;
~UnaryOpCPUKernel() override = default;
using UnaryOpFunc = std::function<void(const T *, S *, size_t, size_t)>;
void InitKernel(const CNodePtr &kernel_node) override;
bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
const std::vector<AddressPtr> &outputs) override;
private:
void GetUnaryOpFunc();
UnaryOpFunc unary_op_func_{nullptr};
};
MS_REG_CPU_KERNEL_T_S(Real, KernelAttr().AddInputAttr(kNumberTypeComplex128).AddOutputAttr(kNumberTypeFloat64),
UnaryOpCPUKernel, complex128, double)
MS_REG_CPU_KERNEL_T_S(Real, KernelAttr().AddInputAttr(kNumberTypeComplex64).AddOutputAttr(kNumberTypeFloat32),
UnaryOpCPUKernel, complex64, float)
MS_REG_CPU_KERNEL_T_S(Real, KernelAttr().AddInputAttr(kNumberTypeInt8).AddOutputAttr(kNumberTypeInt8), UnaryOpCPUKernel,
char, char)
MS_REG_CPU_KERNEL_T_S(Real, KernelAttr().AddInputAttr(kNumberTypeInt16).AddOutputAttr(kNumberTypeInt16),
UnaryOpCPUKernel, int16_t, int16_t)
MS_REG_CPU_KERNEL_T_S(Real, KernelAttr().AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
UnaryOpCPUKernel, int32_t, int32_t)
MS_REG_CPU_KERNEL_T_S(Real, KernelAttr().AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeInt64),
UnaryOpCPUKernel, int64_t, int64_t)
MS_REG_CPU_KERNEL_T_S(Real, KernelAttr().AddInputAttr(kNumberTypeUInt16).AddOutputAttr(kNumberTypeUInt16),
UnaryOpCPUKernel, uint16_t, uint16_t)
MS_REG_CPU_KERNEL_T_S(Real, KernelAttr().AddInputAttr(kNumberTypeUInt32).AddOutputAttr(kNumberTypeUInt32),
UnaryOpCPUKernel, uint32_t, uint32_t)
MS_REG_CPU_KERNEL_T_S(Real, KernelAttr().AddInputAttr(kNumberTypeUInt64).AddOutputAttr(kNumberTypeUInt64),
UnaryOpCPUKernel, uint64_t, uint64_t)
MS_REG_CPU_KERNEL_T_S(Real, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
UnaryOpCPUKernel, float, float)
MS_REG_CPU_KERNEL_T_S(Real, KernelAttr().AddInputAttr(kNumberTypeFloat64).AddOutputAttr(kNumberTypeFloat64),
UnaryOpCPUKernel, double, double)
MS_REG_CPU_KERNEL_T_S(Real, KernelAttr().AddInputAttr(kNumberTypeBool).AddOutputAttr(kNumberTypeBool), UnaryOpCPUKernel,
bool, bool)
MS_REG_CPU_KERNEL_T_S(Imag, KernelAttr().AddInputAttr(kNumberTypeComplex128).AddOutputAttr(kNumberTypeFloat64),
UnaryOpCPUKernel, complex128, double)
MS_REG_CPU_KERNEL_T_S(Imag, KernelAttr().AddInputAttr(kNumberTypeComplex64).AddOutputAttr(kNumberTypeFloat32),
UnaryOpCPUKernel, complex64, float)
MS_REG_CPU_KERNEL_T_S(Imag, KernelAttr().AddInputAttr(kNumberTypeInt8).AddOutputAttr(kNumberTypeInt8), UnaryOpCPUKernel,
char, char)
MS_REG_CPU_KERNEL_T_S(Imag, KernelAttr().AddInputAttr(kNumberTypeInt16).AddOutputAttr(kNumberTypeInt16),
UnaryOpCPUKernel, int16_t, int16_t)
MS_REG_CPU_KERNEL_T_S(Imag, KernelAttr().AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
UnaryOpCPUKernel, int32_t, int32_t)
MS_REG_CPU_KERNEL_T_S(Imag, KernelAttr().AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeInt64),
UnaryOpCPUKernel, int64_t, int64_t)
MS_REG_CPU_KERNEL_T_S(Imag, KernelAttr().AddInputAttr(kNumberTypeUInt16).AddOutputAttr(kNumberTypeUInt16),
UnaryOpCPUKernel, uint16_t, uint16_t)
MS_REG_CPU_KERNEL_T_S(Imag, KernelAttr().AddInputAttr(kNumberTypeUInt32).AddOutputAttr(kNumberTypeUInt32),
UnaryOpCPUKernel, uint32_t, uint32_t)
MS_REG_CPU_KERNEL_T_S(Imag, KernelAttr().AddInputAttr(kNumberTypeUInt64).AddOutputAttr(kNumberTypeUInt64),
UnaryOpCPUKernel, uint64_t, uint64_t)
MS_REG_CPU_KERNEL_T_S(Imag, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
UnaryOpCPUKernel, float, float)
MS_REG_CPU_KERNEL_T_S(Imag, KernelAttr().AddInputAttr(kNumberTypeFloat64).AddOutputAttr(kNumberTypeFloat64),
UnaryOpCPUKernel, double, double)
MS_REG_CPU_KERNEL_T_S(Imag, KernelAttr().AddInputAttr(kNumberTypeBool).AddOutputAttr(kNumberTypeBool), UnaryOpCPUKernel,
bool, bool)
MS_REG_CPU_KERNEL_T_S(Conj, KernelAttr().AddInputAttr(kNumberTypeComplex128).AddOutputAttr(kNumberTypeComplex128),
UnaryOpCPUKernel, complex128, complex128)
MS_REG_CPU_KERNEL_T_S(Conj, KernelAttr().AddInputAttr(kNumberTypeComplex64).AddOutputAttr(kNumberTypeComplex64),
UnaryOpCPUKernel, complex64, complex64)
MS_REG_CPU_KERNEL_T_S(Conj, KernelAttr().AddInputAttr(kNumberTypeInt8).AddOutputAttr(kNumberTypeInt8), UnaryOpCPUKernel,
char, char)
MS_REG_CPU_KERNEL_T_S(Conj, KernelAttr().AddInputAttr(kNumberTypeInt16).AddOutputAttr(kNumberTypeInt16),
UnaryOpCPUKernel, int16_t, int16_t)
MS_REG_CPU_KERNEL_T_S(Conj, KernelAttr().AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
UnaryOpCPUKernel, int32_t, int32_t)
MS_REG_CPU_KERNEL_T_S(Conj, KernelAttr().AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeInt64),
UnaryOpCPUKernel, int64_t, int64_t)
MS_REG_CPU_KERNEL_T_S(Conj, KernelAttr().AddInputAttr(kNumberTypeUInt16).AddOutputAttr(kNumberTypeUInt16),
UnaryOpCPUKernel, uint16_t, uint16_t)
MS_REG_CPU_KERNEL_T_S(Conj, KernelAttr().AddInputAttr(kNumberTypeUInt32).AddOutputAttr(kNumberTypeUInt32),
UnaryOpCPUKernel, uint32_t, uint32_t)
MS_REG_CPU_KERNEL_T_S(Conj, KernelAttr().AddInputAttr(kNumberTypeUInt64).AddOutputAttr(kNumberTypeUInt64),
UnaryOpCPUKernel, uint64_t, uint64_t)
MS_REG_CPU_KERNEL_T_S(Conj, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
UnaryOpCPUKernel, float, float)
MS_REG_CPU_KERNEL_T_S(Conj, KernelAttr().AddInputAttr(kNumberTypeFloat64).AddOutputAttr(kNumberTypeFloat64),
UnaryOpCPUKernel, double, double)
MS_REG_CPU_KERNEL_T_S(Conj, KernelAttr().AddInputAttr(kNumberTypeBool).AddOutputAttr(kNumberTypeBool), UnaryOpCPUKernel,
bool, bool)
} // namespace kernel
} // namespace mindspore
#endif // MINDSPORE_MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_UNARY_OP_CPU_KERNEL_H_

27
mindspore/python/mindspore/ops/operations/math_ops.py
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@ -2278,6 +2278,7 @@ class Exp(PrimitiveWithInfer):
return Tensor(out) return Tensor(out)
return None return None
class Einsum(Primitive): class Einsum(Primitive):
""" """
This operator uses equation to represent a tuple of tensors operations, This operator uses equation to represent a tuple of tensors operations,
@ -2360,6 +2361,7 @@ class Einsum(Primitive):
>>> print(output) >>> print(output)
[[2., 4., 1.], [4., 8., 2.], [6., 12., 3.]] [[2., 4., 1.], [4., 8., 2.], [6., 12., 3.]]
""" """
@prim_attr_register @prim_attr_register
def __init__(self, equation): def __init__(self, equation):
if not isinstance(equation, str): if not isinstance(equation, str):
@ -2370,6 +2372,7 @@ class Einsum(Primitive):
self.add_prim_attr('equation', equation) self.add_prim_attr('equation', equation)
self.init_prim_io_names(inputs=['inputs'], outputs=['output']) self.init_prim_io_names(inputs=['inputs'], outputs=['output'])
class Expm1(Primitive): class Expm1(Primitive):
r""" r"""
Returns exponential then minus 1 of a tensor element-wise. Returns exponential then minus 1 of a tensor element-wise.
@ -2406,7 +2409,6 @@ class Expm1(Primitive):
self.init_prim_io_names(inputs=['x'], outputs=['y']) self.init_prim_io_names(inputs=['x'], outputs=['y'])
class HistogramFixedWidth(PrimitiveWithInfer): class HistogramFixedWidth(PrimitiveWithInfer):
""" """
Returns a rank 1 histogram counting the number of entries in values that fall into every bin. The bins are equal Returns a rank 1 histogram counting the number of entries in values that fall into every bin. The bins are equal
@ -2626,6 +2628,7 @@ class Erfc(Primitive):
"""Initialize Erfc""" """Initialize Erfc"""
self.init_prim_io_names(inputs=['x'], outputs=['y']) self.init_prim_io_names(inputs=['x'], outputs=['y'])
class Minimum(_MathBinaryOp): class Minimum(_MathBinaryOp):
r""" r"""
Computes the minimum of input tensors element-wise. Computes the minimum of input tensors element-wise.
@ -2899,7 +2902,6 @@ class DivNoNan(_MathBinaryOp):
self.init_prim_io_names(inputs=['x', 'y'], outputs=['output']) self.init_prim_io_names(inputs=['x', 'y'], outputs=['output'])
class MulNoNan(_MathBinaryOp): class MulNoNan(_MathBinaryOp):
r""" r"""
Computes `x` * `y` element-wise. If `y` is zero, no matter what `x` is, it will return 0. Computes `x` * `y` element-wise. If `y` is zero, no matter what `x` is, it will return 0.
@ -3017,6 +3019,7 @@ class FloorDiv(Primitive):
"""Initialize FloorDiv.""" """Initialize FloorDiv."""
self.init_prim_io_names(inputs=['x', 'y'], outputs=['output']) self.init_prim_io_names(inputs=['x', 'y'], outputs=['output'])
class TruncateDiv(Primitive): class TruncateDiv(Primitive):
""" """
Divides the first input tensor by the second input tensor element-wise for integer types, negative numbers will Divides the first input tensor by the second input tensor element-wise for integer types, negative numbers will
@ -3116,6 +3119,7 @@ class TruncateMod(Primitive):
"""Initialize TruncateMod.""" """Initialize TruncateMod."""
self.init_prim_io_names(inputs=['x', 'y'], outputs=['output']) self.init_prim_io_names(inputs=['x', 'y'], outputs=['output'])
class Mod(_MathBinaryOp): class Mod(_MathBinaryOp):
r""" r"""
Computes the remainder of dividing the first input tensor by the second input tensor element-wise. Computes the remainder of dividing the first input tensor by the second input tensor element-wise.
@ -3339,6 +3343,7 @@ class Xdivy(Primitive):
"""Initialize Xdivy.""" """Initialize Xdivy."""
self.init_prim_io_names(inputs=['x', 'y'], outputs=['output']) self.init_prim_io_names(inputs=['x', 'y'], outputs=['output'])
class Xlogy(Primitive): class Xlogy(Primitive):
r""" r"""
Computes the first input tensor multiplied by the logarithm of second input tensor element-wise. Computes the first input tensor multiplied by the logarithm of second input tensor element-wise.
@ -3431,6 +3436,7 @@ class Acosh(Primitive):
"""Initialize Acosh""" """Initialize Acosh"""
self.init_prim_io_names(inputs=['x'], outputs=['y']) self.init_prim_io_names(inputs=['x'], outputs=['y'])
class Cosh(Primitive): class Cosh(Primitive):
r""" r"""
Computes hyperbolic cosine of input element-wise. Computes hyperbolic cosine of input element-wise.
@ -3534,7 +3540,6 @@ class Sinh(Primitive):
"""Initialize Sinh""" """Initialize Sinh"""
class _LogicBinaryOp(_BinaryOp): class _LogicBinaryOp(_BinaryOp):
""" """
Define logic binary operators. Define logic binary operators.
@ -4044,7 +4049,6 @@ class LogicalNot(Primitive):
self.init_prim_io_names(inputs=['x'], outputs=['output']) self.init_prim_io_names(inputs=['x'], outputs=['output'])
class LogicalAnd(_LogicBinaryOp): class LogicalAnd(_LogicBinaryOp):
r""" r"""
Computes the "logical AND" of two tensors element-wise. Computes the "logical AND" of two tensors element-wise.
@ -4129,7 +4133,6 @@ class LogicalOr(_LogicBinaryOp):
""" """
class IsNan(Primitive): class IsNan(Primitive):
r""" r"""
Determines which elements are NaN for each position. Determines which elements are NaN for each position.
@ -4585,7 +4588,6 @@ class Sin(Primitive):
"""Initialize Sin.""" """Initialize Sin."""
class Asin(Primitive): class Asin(Primitive):
r""" r"""
Computes arcsine of input tensors element-wise. Computes arcsine of input tensors element-wise.
@ -4838,7 +4840,6 @@ class Tan(Primitive):
"""Initialize Tan""" """Initialize Tan"""
class Atan(Primitive): class Atan(Primitive):
r""" r"""
Computes the trigonometric inverse tangent of the input element-wise. Computes the trigonometric inverse tangent of the input element-wise.
@ -4913,7 +4914,6 @@ class Atanh(Primitive):
self.init_prim_io_names(inputs=['x'], outputs=['output']) self.init_prim_io_names(inputs=['x'], outputs=['output'])
class Atan2(_MathBinaryOp): class Atan2(_MathBinaryOp):
r""" r"""
Returns arctangent of x/y element-wise. Returns arctangent of x/y element-wise.
@ -5262,7 +5262,6 @@ class BesselI1e(Primitive):
self.init_prim_io_names(inputs=['x'], outputs='output') self.init_prim_io_names(inputs=['x'], outputs='output')
class Inv(Primitive): class Inv(Primitive):
r""" r"""
Computes Reciprocal of input tensor element-wise. Computes Reciprocal of input tensor element-wise.
@ -5297,7 +5296,6 @@ class Inv(Primitive):
pass pass
class Invert(Primitive): class Invert(Primitive):
r""" r"""
Flips all bits of input tensor element-wise. Flips all bits of input tensor element-wise.
@ -5680,7 +5678,7 @@ class Conj(PrimitiveWithInfer):
TypeError: If the input is not a Tensor. TypeError: If the input is not a Tensor.
Supported Platforms: Supported Platforms:
``GPU`` ``CPU`` ``GPU``
Examples: Examples:
>>> x = Tensor(np.asarray(np.complex(1.3+0.4j)), mindspore.complex64) >>> x = Tensor(np.asarray(np.complex(1.3+0.4j)), mindspore.complex64)
@ -5711,7 +5709,7 @@ class Real(PrimitiveWithInfer):
TypeError: If the input is not a Tensor. TypeError: If the input is not a Tensor.
Supported Platforms: Supported Platforms:
``GPU`` ``CPU`` ``GPU``
Examples: Examples:
>>> x = Tensor(np.asarray(np.complex(1.3+0.4j)), mindspore.complex64) >>> x = Tensor(np.asarray(np.complex(1.3+0.4j)), mindspore.complex64)
@ -5775,7 +5773,7 @@ class Imag(PrimitiveWithInfer):
TypeError: If the input is not a Tensor. TypeError: If the input is not a Tensor.
Supported Platforms: Supported Platforms:
``GPU`` ``CPU`` ``GPU``
Examples: Examples:
>>> x = Tensor(np.asarray(np.complex(1.3+0.4j)), mindspore.complex64) >>> x = Tensor(np.asarray(np.complex(1.3+0.4j)), mindspore.complex64)
@ -5865,6 +5863,7 @@ class IsClose(Primitive):
>>> print(output) >>> print(output)
[true false false false true] [true false false false true]
""" """
@prim_attr_register @prim_attr_register
def __init__(self, rtol=1e-05, atol=1e-08, equal_nan=True): def __init__(self, rtol=1e-05, atol=1e-08, equal_nan=True):
"""Initialize IsClose""" """Initialize IsClose"""
@ -5924,6 +5923,7 @@ class LuSolve(Primitive):
[-1.4000001] [-1.4000001]
[ 0.6 ]] [ 0.6 ]]
""" """
@prim_attr_register @prim_attr_register
def __init__(self): def __init__(self):
pass pass
@ -5972,6 +5972,7 @@ class CholeskyInverse(Primitive):
[-2.625 1.25 -0.25 ] [-2.625 1.25 -0.25 ]
[ 0.625 -0.25 0.25 ]] [ 0.625 -0.25 0.25 ]]
""" """
@prim_attr_register @prim_attr_register
def __init__(self, upper=False): def __init__(self, upper=False):
"""Initialize CholeskyInverse""" """Initialize CholeskyInverse"""

123
tests/st/ops/cpu/test_unary_op.py Normal file
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@ -0,0 +1,123 @@
# Copyright 2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
import numpy as np
import pytest
from mindspore import Tensor
from mindspore import context
from mindspore.ops import operations as P
from mindspore import dtype as mstype
@pytest.mark.level0
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_graph_conj():
"""
Feature: ALL TO ALL
Description: test cases for conj in graph mode cpu backend.
Expectation: the result match numpy conj
"""
context.set_context(mode=context.GRAPH_MODE)
x = np.asarray(np.complex(1.3 + 0.4j), dtype=np.complex64)
ms_x = Tensor(x, mstype.complex64)
output = P.Conj()(ms_x)
expect = np.conj(x)
assert np.allclose(output.asnumpy(), expect)
@pytest.mark.level0
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_pynative_conj():
"""
Feature: ALL TO ALL
Description: test cases for conj in pynative mode cpu backend.
Expectation: the result match numpy conj
"""
context.set_context(mode=context.PYNATIVE_MODE)
x = np.asarray(np.complex(1.3 + 0.4j), dtype=np.complex64)
ms_x = Tensor(x, mstype.complex64)
output = P.Conj()(ms_x)
expect = np.conj(x)
assert np.allclose(output.asnumpy(), expect)
@pytest.mark.level0
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_graph_real():
"""
Feature: ALL TO ALL
Description: test cases for real in graph mode cpu backend.
Expectation: the result match numpy real
"""
context.set_context(mode=context.GRAPH_MODE)
x = np.asarray(np.complex(1.3 + 0.4j), dtype=np.complex64)
ms_x = Tensor(x, mstype.complex64)
output = P.Real()(ms_x)
expect = np.real(x)
assert np.allclose(output.asnumpy(), expect)
@pytest.mark.level0
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_pynative_real():
"""
Feature: ALL TO ALL
Description: test cases for real in pynative mode cpu backend.
Expectation: the result match numpy real
"""
context.set_context(mode=context.GRAPH_MODE)
x = np.asarray(np.complex(1.3 + 0.4j), dtype=np.complex64)
ms_x = Tensor(x, mstype.complex64)
output = P.Real()(ms_x)
expect = np.real(x)
assert np.allclose(output.asnumpy(), expect)
@pytest.mark.level0
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_graph_imag():
"""
Feature: ALL TO ALL
Description: test cases for image in graph mode cpu backend.
Expectation: the result match numpy conj
"""
context.set_context(mode=context.GRAPH_MODE)
x = np.asarray(np.complex(1.3 + 0.4j), dtype=np.complex64)
ms_x = Tensor(x, mstype.complex64)
output = P.Imag()(ms_x)
expect = np.imag(x)
assert np.allclose(output.asnumpy(), expect)
@pytest.mark.level0
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_pynative_imag():
"""
Feature: ALL TO ALL
Description: test cases for image in pynative mode cpu backend.
Expectation: the result match numpy image
"""
context.set_context(mode=context.GRAPH_MODE)
x = np.asarray(np.complex(1.3 + 0.4j), dtype=np.complex64)
ms_x = Tensor(x, mstype.complex64)
output = P.Imag()(ms_x)
expect = np.imag(x)
assert np.allclose(output.asnumpy(), expect)