forked from mindspore-Ecosystem/mindspore
1021 lines
32 KiB
Python
Executable File
1021 lines
32 KiB
Python
Executable File
# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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""" test math ops """
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import functools
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import numpy as np
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import mindspore as ms
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import mindspore.context as context
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import mindspore.nn as nn
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from mindspore import ops
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from mindspore import Tensor
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from mindspore.common import dtype as mstype
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from mindspore.ops import composite as C
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from mindspore.ops import operations as P
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from mindspore.ops import functional as F
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from mindspore.ops.operations._grad_ops import IgammaGradA
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from mindspore.ops import prim_attr_register, PrimitiveWithInfer
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from mindspore.ops.operations.math_ops import Zeta, Igamma, Igammac
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from mindspore.ops.operations.math_ops import MatrixTriangularSolve
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from mindspore.ops.operations.sparse_ops import DenseToDenseSetOperation
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from mindspore.ops.operations.sparse_ops import DenseToSparseSetOperation
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from mindspore.common.parameter import Parameter
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from mindspore.common.initializer import initializer
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from ..ut_filter import non_graph_engine
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from ....mindspore_test_framework.mindspore_test import mindspore_test
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from ....mindspore_test_framework.pipeline.forward.compile_forward \
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import pipeline_for_compile_forward_ge_graph_for_case_by_case_config
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from ....mindspore_test_framework.pipeline.forward.verify_exception \
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import pipeline_for_verify_exception_for_case_by_case_config
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context.set_context(mode=context.GRAPH_MODE)
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# pylint: disable=W0613
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# pylint: disable=W0231
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# W0613: unused-argument
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# W0231: super-init-not-called
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grad = C.GradOperation()
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def test_multiply():
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""" test_multiply """
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input_x = Tensor(np.array([[-0.1, 0.3, 3.6], [0.4, 0.5, -3.2]]))
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input_y = Tensor(np.array([[0.1, 0.3, -3.6], [0.4, 0.5, -3.2]]))
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mul = P.Mul()
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result = mul(input_x, input_y)
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expect = np.array([[-0.01, 0.09, -12.96], [0.16, 0.25, 10.24]])
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diff = result.asnumpy() - expect
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error = np.ones(shape=[2, 3]) * 1.0e-6
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assert np.all(diff < error)
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assert np.all(-diff < error)
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def test_sub():
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""" test_sub """
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input_x = Tensor(np.ones(shape=[3]))
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input_y = Tensor(np.zeros(shape=[3]))
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sub = P.Sub()
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result = sub(input_x, input_y)
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expect = np.ones(shape=[3])
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assert np.all(result.asnumpy() == expect)
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def test_square():
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""" test_square """
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input_tensor = Tensor(np.array([[1, 2, 3], [4, 5, 6]]))
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square = P.Square()
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result = square(input_tensor)
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expect = np.array([[1, 4, 9], [16, 25, 36]])
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assert np.all(result.asnumpy() == expect)
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def test_sqrt():
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""" test_sqrt """
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input_tensor = Tensor(np.array([[4, 4], [9, 9]]))
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sqrt = P.Sqrt()
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expect = np.array([[2, 2], [3, 3]])
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result = sqrt(input_tensor)
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assert np.all(result.asnumpy() == expect)
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class PowNet(nn.Cell):
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def __init__(self):
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super(PowNet, self).__init__()
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self.pow = P.Pow()
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def construct(self, x, y):
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return self.pow(x, y)
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def test_pow():
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""" test_pow """
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input_tensor = Tensor(np.array([[2, 2], [3, 3]]))
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power = Tensor(np.array(3.0, np.int64))
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power2 = Tensor(np.array(True, np.bool))
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testpow = P.Pow()
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expect = np.array([[8, 8], [27, 27]])
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result = testpow(input_tensor, power)
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assert np.all(result.asnumpy() == expect)
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net = PowNet()
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net(input_tensor, power2)
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def test_exp():
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""" test_exp """
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input_tensor = Tensor(np.array([[2, 2], [3, 3]]))
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testexp = P.Exp()
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result = testexp(input_tensor)
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expect = np.exp(np.array([[2, 2], [3, 3]]))
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assert np.all(result.asnumpy() == expect)
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def test_realdiv():
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""" test_realdiv """
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x = Tensor(2048.0)
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y = Tensor(128.0)
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div = P.RealDiv()
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result = div(x, y)
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x = x.asnumpy()
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y = y.asnumpy()
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expect = x / y
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assert np.all(result.asnumpy() == expect)
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def test_eye():
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""" test_eye """
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x = np.arange(3)
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expect = np.ones_like(x)
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expect = np.diag(expect)
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eye = P.Eye()
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eye_output = eye(3, 3, ms.float32)
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assert np.all(eye_output.asnumpy() == expect)
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class VirtualLossGrad(PrimitiveWithInfer):
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""" VirtualLossGrad definition """
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@prim_attr_register
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def __init__(self):
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"""init VirtualLossGrad"""
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def __call__(self, x, out, dout):
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raise NotImplementedError
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def infer_shape(self, x_shape, out_shape, dout_shape):
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return x_shape
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def infer_dtype(self, x_dtype, out_dtype, dout_dtype):
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return x_dtype
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class VirtualLoss(PrimitiveWithInfer):
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""" VirtualLoss definition """
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@prim_attr_register
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def __init__(self):
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"""init VirtualLoss"""
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def __call__(self, x):
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raise NotImplementedError
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def get_bprop(self):
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loss_grad = VirtualLossGrad()
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def bprop(x, out, dout):
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dx = loss_grad(x, out, dout)
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return (dx,)
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return bprop
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def infer_shape(self, x_shape):
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return [1]
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def infer_dtype(self, x_dtype):
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return x_dtype
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class NetWithLoss(nn.Cell):
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""" NetWithLoss definition """
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def __init__(self, network):
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super(NetWithLoss, self).__init__()
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self.loss = VirtualLoss()
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self.network = network
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def construct(self, x, y, b):
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predict = self.network(x, y, b)
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return self.loss(predict)
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class GradWrap(nn.Cell):
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""" GradWrap definition """
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def __init__(self, network):
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super(GradWrap, self).__init__()
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self.network = network
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def construct(self, x, y, b):
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return grad(self.network)(x, y, b)
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class MatMulNet(nn.Cell):
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""" MatMulNet definition """
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def __init__(self):
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super(MatMulNet, self).__init__()
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self.matmul = P.MatMul()
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self.biasAdd = P.BiasAdd()
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def construct(self, x, y, b):
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return self.biasAdd(self.matmul(x, y), b)
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class OrgqrFunc(nn.Cell):
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def __init__(self):
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super(OrgqrFunc, self).__init__()
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self.orgqr_ = ops.function.math_func.orgqr
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def construct(self, x, tau):
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y = self.orgqr_(x, tau)
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return y
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class NetWithLossSub(nn.Cell):
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""" NetWithLossSub definition """
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def __init__(self, network):
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super(NetWithLossSub, self).__init__()
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self.loss = VirtualLoss()
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self.network = network
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def construct(self, x, y):
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predict = self.network(x, y)
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return self.loss(predict)
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class GradWrapSub(nn.Cell):
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""" GradWrapSub definition """
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def __init__(self, network):
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super(GradWrapSub, self).__init__()
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self.network = network
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def construct(self, x, y):
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return grad(self.network)(x, y)
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class SubNet(nn.Cell):
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""" SubNet definition """
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def __init__(self):
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super(SubNet, self).__init__()
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self.sub = P.Sub()
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def construct(self, x, y):
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return self.sub(x, y)
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class NpuFloatNet(nn.Cell):
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""" NpuFloat definition """
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def __init__(self):
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super(NpuFloatNet, self).__init__()
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self.mul = P.Mul()
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self.alloc_status = P.NPUAllocFloatStatus()
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self.get_status = P.NPUGetFloatStatus()
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self.clear_status = P.NPUClearFloatStatus()
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self.fill = P.Fill()
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self.shape_op = P.Shape()
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self.select = P.Select()
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self.less = P.Less()
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self.cast = P.Cast()
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self.dtype = P.DType()
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self.reduce_sum = P.ReduceSum(keep_dims=True)
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self.sub = P.Sub()
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self.neg = P.Neg()
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def construct(self, x):
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init = self.alloc_status()
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clear_status = self.clear_status(init)
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x = F.depend(x, clear_status) # let x depend on clear_status
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res = self.sub(x, self.neg(x))
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init = F.depend(init, res) # let get_status depend on res
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get_status = self.get_status(init)
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init = F.depend(init, get_status) # let reduce_sum depend on get_statusk
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flag_sum = self.reduce_sum(init, (0,))
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base = self.cast(self.fill(self.dtype(res), self.shape_op(res), 0.0), self.dtype(flag_sum))
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cond = self.less(base, flag_sum)
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out = self.select(cond, self.cast(base, self.dtype(res)), res)
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return out
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class DiagNet(nn.Cell):
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""" DiagNet definition """
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def __init__(self):
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super(DiagNet, self).__init__()
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self.fill = P.Fill()
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self.diag = P.Diag()
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def construct(self, x):
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return x - self.diag(self.fill(mstype.float32, (3,), 1.0))
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class NetWithLossCumSum(nn.Cell):
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""" NetWithLossCumSum definition """
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def __init__(self, network):
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super(NetWithLossCumSum, self).__init__()
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self.loss = VirtualLoss()
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self.network = network
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def construct(self, input_):
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predict = self.network(input_)
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return self.loss(predict)
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class GradWrapCumSum(nn.Cell):
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""" GradWrap definition """
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def __init__(self, network):
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super(GradWrapCumSum, self).__init__()
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self.network = network
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def construct(self, input_):
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return grad(self.network)(input_)
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class NetCumSum(nn.Cell):
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""" NetCumSum definition """
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def __init__(self):
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super(NetCumSum, self).__init__()
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self.cumsum = P.CumSum()
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self.axis = 1
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def construct(self, input_):
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return self.cumsum(input_, self.axis)
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class SignNet(nn.Cell):
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def __init__(self):
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super(SignNet, self).__init__()
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self.sign = P.Sign()
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def construct(self, x):
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return self.sign(x)
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class AssignAdd(nn.Cell):
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def __init__(self):
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super().__init__()
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self.op = P.AssignAdd()
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self.inputdata = Parameter(initializer(1, [1], ms.float32), name="global_step")
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def construct(self, input_):
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self.inputdata = input_
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self.op(self.inputdata, input_)
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return self.inputdata
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class FloorNet(nn.Cell):
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def __init__(self):
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super(FloorNet, self).__init__()
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self.floor = P.Floor()
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def construct(self, x):
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return self.floor(x)
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class Log1pNet(nn.Cell):
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def __init__(self):
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super(Log1pNet, self).__init__()
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self.log1p = P.Log1p()
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def construct(self, x):
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return self.log1p(x)
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class ErfcNet(nn.Cell):
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def __init__(self):
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super(ErfcNet, self).__init__()
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self.erfc = P.Erfc()
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def construct(self, x):
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return self.erfc(x)
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class LdexpFunc(nn.Cell):
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def __init__(self):
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super(LdexpFunc, self).__init__()
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self.ldexp = ops.ldexp
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def construct(self, x, other):
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return self.ldexp(x, other)
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class AtLeast2DFunc(nn.Cell):
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def __init__(self):
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super(AtLeast2DFunc, self).__init__()
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self.atleast_2d = ops.atleast_2d
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def construct(self, x1, x2, x3):
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return self.atleast_2d([x1, x2, x3])
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class VstackFunc(nn.Cell):
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def __init__(self):
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super(VstackFunc, self).__init__()
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self.vstack = ops.vstack
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def construct(self, x1, x2):
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return self.vstack([x1, x2])
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class CopysignFunc(nn.Cell):
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def __init__(self):
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super(CopysignFunc, self).__init__()
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self.copysign = ops.copysign
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def construct(self, x, other):
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return self.copysign(x, other)
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class HypotFunc(nn.Cell):
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def __init__(self):
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super(HypotFunc, self).__init__()
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self.hypot_ = ops.function.hypot
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def construct(self, x1, x2):
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y = self.hypot_(x1, x2)
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return y
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class HeavisideFunc(nn.Cell):
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def __init__(self):
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super(HeavisideFunc, self).__init__()
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self.heaviside_ = ops.function.heaviside
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def construct(self, x, values):
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y = self.heaviside_(x, values)
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return y
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class LogAddExpFunc(nn.Cell):
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def __init__(self):
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super(LogAddExpFunc, self).__init__()
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self.logaddexp = ops.logaddexp
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def construct(self, x1, x2):
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y = self.logaddexp(x1, x2)
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return y
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class LogAddExp2Func(nn.Cell):
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def __init__(self):
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super(LogAddExp2Func, self).__init__()
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self.logaddexp2 = ops.logaddexp2
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def construct(self, x1, x2):
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y = self.logaddexp2(x1, x2)
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return y
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class AddmvFunc(nn.Cell):
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def __init__(self):
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super(AddmvFunc, self).__init__()
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self.addmv = ops.addmv
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def construct(self, x, mat, vec, beta=1, alpha=1):
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y = self.addmv(x, mat, vec, beta, alpha)
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return y
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class AddrFunc(nn.Cell):
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def __init__(self):
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super(AddrFunc, self).__init__()
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self.addr = ops.addr
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def construct(self, x, vec1, vec2, beta=1, alpha=1):
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y = self.addr(x, vec1, vec2, beta, alpha)
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return y
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class MvFunc(nn.Cell):
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def __init__(self):
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super(MvFunc, self).__init__()
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self.mv = ops.mv
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def construct(self, mat, vec):
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return self.mv(mat, vec)
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class OuterFunc(nn.Cell):
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def __init__(self):
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super(OuterFunc, self).__init__()
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self.outer = ops.outer
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def construct(self, x1, x2):
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return self.outer(x1, x2)
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class Exp2Func(nn.Cell):
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def __init__(self):
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super(Exp2Func, self).__init__()
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self.exp2 = ops.exp2
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def construct(self, x):
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y = self.exp2(x)
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return y
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class Deg2radNet(nn.Cell):
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def __init__(self):
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super(Deg2radNet, self).__init__()
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self.deg2rad = ops.deg2rad
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def construct(self, x):
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return self.deg2rad(x)
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class IsRealFunc(nn.Cell):
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def __init__(self):
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super(IsRealFunc, self).__init__()
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self.isreal = ops.isreal
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def construct(self, x):
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y = self.isreal(x)
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return y
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class LcmFunc(nn.Cell):
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def __init__(self):
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super(LcmFunc, self).__init__()
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self.lcm = ops.function.lcm
|
|
|
|
def construct(self, x1, x2):
|
|
return self.lcm(x1, x2)
|
|
|
|
|
|
class GcdFunc(nn.Cell):
|
|
def __init__(self):
|
|
super(GcdFunc, self).__init__()
|
|
self.gcd = ops.function.gcd
|
|
|
|
def construct(self, x1, x2):
|
|
return self.gcd(x1, x2)
|
|
|
|
|
|
class Rad2degNet(nn.Cell):
|
|
def __init__(self):
|
|
super(Rad2degNet, self).__init__()
|
|
self.rad2deg = ops.rad2deg
|
|
|
|
def construct(self, x):
|
|
return self.rad2deg(x)
|
|
|
|
|
|
class BaddbmmNet(nn.Cell):
|
|
def __init__(self, beta=1, alpha=1):
|
|
super(BaddbmmNet, self).__init__()
|
|
self.beta = beta
|
|
self.alpha = alpha
|
|
self.baddbmm = ops.baddbmm
|
|
|
|
def construct(self, x, batch1, batch2):
|
|
return self.baddbmm(x, batch1, batch2, self.beta, self.alpha)
|
|
|
|
|
|
class Log2Net(nn.Cell):
|
|
def __init__(self):
|
|
super(Log2Net, self).__init__()
|
|
self.log2 = ops.log2
|
|
|
|
def construct(self, x):
|
|
return self.log2(x)
|
|
|
|
|
|
class Log10Net(nn.Cell):
|
|
def __init__(self):
|
|
super(Log10Net, self).__init__()
|
|
self.log10 = ops.log10
|
|
|
|
def construct(self, x):
|
|
return self.log10(x)
|
|
|
|
|
|
class FracNet(nn.Cell):
|
|
def __init__(self):
|
|
super(FracNet, self).__init__()
|
|
self.frac = ops.frac
|
|
|
|
def construct(self, x):
|
|
return self.frac(x)
|
|
|
|
|
|
class KronFunc(nn.Cell):
|
|
def __init__(self):
|
|
super(KronFunc, self).__init__()
|
|
self.kron = ops.kron
|
|
|
|
def construct(self, x, y):
|
|
return self.kron(x, y)
|
|
|
|
|
|
class Rot90Func(nn.Cell):
|
|
def __init__(self):
|
|
super(Rot90Func, self).__init__()
|
|
self.rot90 = ops.rot90
|
|
self.k = 0
|
|
self.dims = (0, 1)
|
|
|
|
def construct(self, x):
|
|
return self.rot90(x, self.k, self.dims)
|
|
|
|
|
|
class RemainderNet(nn.Cell):
|
|
def __init__(self):
|
|
super(RemainderNet, self).__init__()
|
|
self.remainder = ops.remainder
|
|
|
|
def construct(self, x, y):
|
|
return self.remainder(x, y)
|
|
|
|
|
|
class TrapzFunc(nn.Cell):
|
|
def __init__(self):
|
|
super(TrapzFunc, self).__init__()
|
|
self.trapz = ops.trapz
|
|
|
|
def construct(self, y, x=None, dx=1.0, dim=-1):
|
|
out = self.trapz(y, x, dx, dim)
|
|
return out
|
|
|
|
|
|
test_case_math_ops = [
|
|
('MatMulGrad', {
|
|
'block': GradWrap(NetWithLoss(MatMulNet())),
|
|
'desc_inputs': [Tensor(np.ones([3, 3]).astype(np.int32)),
|
|
Tensor(np.ones([3, 3]).astype(np.int32)),
|
|
Tensor(np.ones([3]).astype(np.int32))],
|
|
'desc_bprop': [Tensor(np.ones([3, 3]).astype(np.int32)),
|
|
Tensor(np.ones([3, 3]).astype(np.int32)),
|
|
Tensor(np.ones([3]).astype(np.int32))],
|
|
'skip': ['backward']}),
|
|
('CumSumGrad', {
|
|
'block': GradWrapCumSum(NetWithLossCumSum(NetCumSum())),
|
|
'desc_inputs': [Tensor(np.array([[3, 4, 6, 10], [1, 6, 7, 9], [4, 3, 8, 7], [1, 3, 7, 9]]).astype(np.float16))],
|
|
'desc_bprop': [Tensor(np.array([[3, 4, 6, 10], [1, 6, 7, 9], [4, 3, 8, 7], [1, 3, 7, 9]]).astype(np.float16))],
|
|
'skip': ['backward']}),
|
|
('Diag', {
|
|
'block': DiagNet(),
|
|
'desc_inputs': [Tensor(np.array([[1, 1, 1], [2, 2, 2], [3, 3, 3]], np.float32))],
|
|
'desc_bprop': [Tensor(np.array([[1, 1, 1], [2, 2, 2], [3, 3, 3]], np.float32))],
|
|
'skip': ['backward']}),
|
|
('SubBroadcast', {
|
|
'block': GradWrapSub(NetWithLossSub(SubNet())),
|
|
'desc_inputs': [Tensor(np.ones([5, 3])), Tensor(np.ones([8, 5, 3]))],
|
|
'desc_bprop': [Tensor(np.array([[1, 1, 1], [2, 2, 2], [3, 3, 3]], np.float32))],
|
|
'skip': ['backward']}),
|
|
('NpuFloat_NotOverflow', {
|
|
'block': NpuFloatNet(),
|
|
'desc_inputs': [Tensor(np.full((8, 5, 3, 1), 655, dtype=np.float16), dtype=ms.float16)],
|
|
'desc_bprop': [Tensor(np.full((8, 5, 3, 1), 655, dtype=np.float16), dtype=ms.float16)],
|
|
'skip': ['backward']}),
|
|
('NpuFloat_Overflow', {
|
|
'block': NpuFloatNet(),
|
|
'desc_inputs': [Tensor(np.full((8, 5, 3, 1), 65504, dtype=np.float16), dtype=ms.float16)],
|
|
'desc_bprop': [Tensor(np.full((8, 5, 3, 1), 65504, dtype=np.float16), dtype=ms.float16)],
|
|
'skip': ['backward']}),
|
|
('Sign', {
|
|
'block': SignNet(),
|
|
'desc_inputs': [Tensor(np.array([[1., 0., -2.]], np.float32))],
|
|
'desc_bprop': [Tensor(np.array([[1., 0., -2.]], np.float32))],
|
|
'skip': ['backward']}),
|
|
('Floor', {
|
|
'block': FloorNet(),
|
|
'desc_inputs': [Tensor(np.array([[1., 0., -2.]], np.float32))],
|
|
'desc_bprop': [Tensor(np.array([[1., 0., -2.]], np.float32))],
|
|
'skip': ['backward']}),
|
|
('Log1p', {
|
|
'block': Log1pNet(),
|
|
'desc_inputs': [Tensor(np.array([[1.0, 2.0, 4.0]], np.float32))],
|
|
'desc_bprop': [Tensor(np.array([[1.0, 2.0, 4.0]], np.float32))],
|
|
'skip': ['backward']}),
|
|
('Erfc', {
|
|
'block': ErfcNet(),
|
|
'desc_inputs': [Tensor(np.array([[1.0, 2.0, 4.0]], np.float32))],
|
|
'desc_bprop': [Tensor(np.array([[1.0, 2.0, 4.0]], np.float32))],
|
|
}),
|
|
('Ldexp', {
|
|
'block': LdexpFunc(),
|
|
'desc_inputs': [Tensor(np.array([1.]), dtype=ms.float32),
|
|
Tensor(np.array([1, 2, 3, 4]), dtype=ms.int32)],
|
|
'skip': ['backward']
|
|
}),
|
|
('AtLeast2D', {
|
|
'block': AtLeast2DFunc(),
|
|
'desc_inputs': [Tensor(np.array([[1, 1, 1], [1, 1, 1]]), ms.float64),
|
|
Tensor(np.array(1), ms.float64),
|
|
Tensor(np.array([1, 1, 1, 1, 1]), ms.float64)]
|
|
}),
|
|
('Vstack', {
|
|
'block': VstackFunc(),
|
|
'desc_inputs': [Tensor(np.array([1, 2, 3]), ms.int32),
|
|
Tensor(np.array([4, 5, 6]), ms.int32)]
|
|
}),
|
|
('Copysign', {
|
|
'block': CopysignFunc(),
|
|
'desc_inputs': [Tensor(np.array([[0.3, -0.7], [0.5, 0.5]])),
|
|
Tensor(np.array([[-0.4, 0.6], [0.4, -0.6]]))]
|
|
}),
|
|
('LogAddExp2', {
|
|
'block': LogAddExp2Func(),
|
|
'desc_inputs': [Tensor(np.array([1.0, 2.0, 3.0], np.float16)), Tensor(np.array([2.0], np.float16))],
|
|
'desc_bprop': [Tensor(np.array([1.0, 2.0, 3.0], np.float16)), Tensor(np.array([2.0], np.float16))],
|
|
}),
|
|
('LogAddExp', {
|
|
'block': LogAddExpFunc(),
|
|
'desc_inputs': [Tensor(np.array([1.0, 2.0, 3.0], np.float16)), Tensor(np.array([2.0], np.float16))],
|
|
'desc_bprop': [Tensor(np.array([1.0, 2.0, 3.0], np.float16)), Tensor(np.array([2.0], np.float16))],
|
|
}),
|
|
('Mv', {
|
|
'block': MvFunc(),
|
|
'desc_inputs': [Tensor(np.array([[3., 4.], [1., 6.], [1., 3.]])),
|
|
Tensor(np.array([1., 2.]))],
|
|
'desc_bprop': [Tensor(np.array([[3., 4.], [1., 6.], [1., 3.]])),
|
|
Tensor(np.array([1., 2.]))],
|
|
}),
|
|
('Addr', {
|
|
'block': AddrFunc(),
|
|
'desc_inputs': [Tensor(np.array([[0., 0.], [0., 0.], [0., 0.]])),
|
|
Tensor(np.array([1., 2., 3.])),
|
|
Tensor(np.array([1., 2.]))],
|
|
'desc_bprop': [Tensor(np.array([[0., 0.], [0., 0.], [0., 0.]])),
|
|
Tensor(np.array([1., 2., 3.])),
|
|
Tensor(np.array([1., 2.]))],
|
|
}),
|
|
('Outer', {
|
|
'block': OuterFunc(),
|
|
'desc_inputs': [Tensor(np.array([1., 2., 3.])),
|
|
Tensor(np.array([1., 2., 3.]))],
|
|
'desc_bprop': [Tensor(np.array([1., 2., 3.])),
|
|
Tensor(np.array([1., 2., 3.]))],
|
|
'skip': ['backward']
|
|
}),
|
|
('Addmv', {
|
|
'block': AddmvFunc(),
|
|
'desc_inputs': [Tensor(np.array([1, 1])),
|
|
Tensor(np.array([[1, 2, 1], [1, 1, 1]])),
|
|
Tensor(np.array([1, 1, 1]))],
|
|
'desc_bprop': [Tensor(np.array([1, 1])),
|
|
Tensor(np.array([[1, 2, 1], [1, 1, 1]])),
|
|
Tensor(np.array([1, 1, 1]))],
|
|
}),
|
|
('Exp2', {
|
|
'block': Exp2Func(),
|
|
'desc_inputs': [Tensor(np.array([1.0, 2.0, 3.0], np.float16))],
|
|
}),
|
|
('Trapz', {
|
|
'block': TrapzFunc(),
|
|
'desc_inputs': [Tensor(np.array([[0, 1, 2], [3, 4, 5], [6, 7, 8]], np.float32))],
|
|
'desc_bprop': [Tensor(np.array([2, 8, 14], np.float32))],
|
|
}),
|
|
('DenseToDenseSetOperation', {
|
|
'block': DenseToDenseSetOperation(set_operation="a-b", validate_indices=True),
|
|
'desc_inputs': [Tensor(np.array([[1, 2, 4], [3, 4, 5]], np.int32)),
|
|
Tensor(np.array([[3, 2, 5], [1, 4, 7]], np.int32))],
|
|
'skip': ['backward']
|
|
}),
|
|
('DenseToSparseSetOperation', {
|
|
'block': DenseToSparseSetOperation(set_operation="a-b", validate_indices=True),
|
|
'desc_inputs': [Tensor(np.array([[1, 2, 4], [3, 4, 5]], np.int32)),
|
|
Tensor(np.array([[0, 1], [1, 0]], np.int64)),
|
|
Tensor(np.array([1, 6], np.int32)),
|
|
Tensor(np.array([2, 3], np.int64))],
|
|
'skip': ['backward']
|
|
}),
|
|
('Deg2rad', {
|
|
'block': Deg2radNet(),
|
|
'desc_inputs': [Tensor(np.array([[90.0, -90.0], [180.0, -180.0], [270.0, -270.0]], np.float32))],
|
|
'desc_bprop': [Tensor(np.array([[90.0, -90.0], [180.0, -180.0], [270.0, -270.0]], np.float32))],
|
|
}),
|
|
('IsReal', {
|
|
'block': IsRealFunc(),
|
|
'desc_inputs': [Tensor([1, 1+1j, 2+0j])],
|
|
}),
|
|
('Rad2deg', {
|
|
'block': Rad2degNet(),
|
|
'desc_inputs': [Tensor(np.array([[3.142, -3.142], [6.283, -6.283], [1.570, -1.570]], np.float32))],
|
|
'desc_bprop': [Tensor(np.array([[3.142, -3.142], [6.283, -6.283], [1.570, -1.570]], np.float32))],
|
|
}),
|
|
('Baddbmm', {
|
|
'block': BaddbmmNet(),
|
|
'desc_inputs': [Tensor(np.ones([1, 3, 3]).astype(np.float32)),
|
|
Tensor(np.ones([1, 3, 4]).astype(np.float32)),
|
|
Tensor(np.ones([1, 4, 3]).astype(np.float32))],
|
|
'skip': ['backward']
|
|
}),
|
|
('Log2', {
|
|
'block': Log2Net(),
|
|
'desc_inputs': [Tensor(np.array([[1.0, 2.0, 4.0]], np.float32))],
|
|
'desc_bprop': [Tensor(np.array([[1.0, 2.0, 4.0]], np.float32))]}),
|
|
('Log10', {
|
|
'block': Log10Net(),
|
|
'desc_inputs': [Tensor(np.array([[1.0, 2.0, 4.0]], np.float32))],
|
|
'desc_bprop': [Tensor(np.array([[1.0, 2.0, 4.0]], np.float32))]}),
|
|
('Frac', {
|
|
'block': FracNet(),
|
|
'desc_inputs': [Tensor(np.array([2, 4.2, -2.5], np.float32))],
|
|
'desc_bprop': [Tensor(np.array([2, 4.2, -2.5], np.float32))],
|
|
}),
|
|
('Kron', {
|
|
'block': KronFunc(),
|
|
'desc_inputs': [Tensor(np.array([[0, 1, 2], [3, 4, 5]]).astype(np.float32)),
|
|
Tensor(np.array([[-1, -2, -3], [-4, -6, -8]]).astype(np.float32))],
|
|
'skip': ['backward']}),
|
|
('Rot90', {
|
|
'block': Rot90Func(),
|
|
'desc_inputs': [Tensor(np.array([[0, 1], [2, 3]]).astype(np.float32))],
|
|
'skip': ['backward']}),
|
|
('Remainder', {
|
|
'block': RemainderNet(),
|
|
'desc_inputs': [Tensor(np.array([-1.0, 5.0, 6.0]), ms.float32), Tensor(np.array([3.0, 2.0, 3.0]), ms.float32)],
|
|
'skip': ['backward']}),
|
|
]
|
|
|
|
test_case_lists = [test_case_math_ops]
|
|
test_exec_case = functools.reduce(lambda x, y: x + y, test_case_lists)
|
|
# use -k to select certain testcast
|
|
# pytest tests/python/ops/test_ops.py::test_backward -k LayerNorm
|
|
|
|
|
|
@non_graph_engine
|
|
@mindspore_test(pipeline_for_compile_forward_ge_graph_for_case_by_case_config)
|
|
def test_exec():
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
return test_exec_case
|
|
|
|
|
|
raise_set = [
|
|
('Ldexp_Error1', {
|
|
'block': (LdexpFunc(), {'exception': ValueError}),
|
|
'desc_inputs': [Tensor(np.array([[1., 1.], [1., 2.], [1., 3.]]), dtype=ms.float32),
|
|
Tensor(np.array([1, 2, 3]), dtype=ms.int32)],
|
|
'skip': ['backward']}),
|
|
('Ldexp_Error2', {
|
|
'block': (LdexpFunc(), {'exception': ValueError}),
|
|
'desc_inputs': [Tensor(np.random.randn(5, 2), dtype=mstype.float16),
|
|
Tensor(np.random.randn(5), dtype=mstype.float16)],
|
|
'skip': ['backward']}),
|
|
('StridedSlice_1_Error', {
|
|
'block': (lambda x: P.StridedSlice(begin_mask="1"), {'exception': TypeError}),
|
|
'desc_inputs': [0]}),
|
|
('StridedSlice_2_Error', {
|
|
'block': (lambda x: P.StridedSlice(end_mask="1"), {'exception': TypeError}),
|
|
'desc_inputs': [0]}),
|
|
('StridedSlice_3_Error', {
|
|
'block': (lambda x: P.StridedSlice(ellipsis_mask=1.1), {'exception': TypeError}),
|
|
'desc_inputs': [0]}),
|
|
('StridedSlice_4_Error', {
|
|
'block': (lambda x: P.StridedSlice(new_axis_mask="1.1"), {'exception': TypeError}),
|
|
'desc_inputs': [0]}),
|
|
('AssignAdd_Error', {
|
|
'block': (P.AssignAdd(), {'exception': ValueError}),
|
|
'desc_inputs': [[1]]}),
|
|
('Hypot', {
|
|
'block': HypotFunc(),
|
|
'desc_inputs': [Tensor(np.array([3, 5, 7]).astype(np.float32)),
|
|
Tensor(np.array([4, 12, 24]).astype(np.float32))]}),
|
|
('Heaviside', {
|
|
'block': HeavisideFunc(),
|
|
'desc_inputs': [Tensor(np.array([4, 4, 12]).astype(np.float32)),
|
|
Tensor(np.array([4, 8, 12]).astype(np.float32))],
|
|
'skip': ['backward']}),
|
|
('Trunc', {
|
|
'block': P.Trunc(),
|
|
'desc_inputs': [Tensor(np.array([[1.1, 2.2, -4.1]], np.float32))],
|
|
'skip': ['backward']}),
|
|
('MatrixTriangularSolve', {
|
|
'block': MatrixTriangularSolve(adjoint=False, lower=True),
|
|
'desc_inputs': [Tensor(np.array([4, 4, 4]).astype(np.float32)),
|
|
Tensor(np.array([4, 4, 4]).astype(np.float32))],
|
|
'desc_bprop': [Tensor(np.array([4, 4, 4]).astype(np.float32))]}),
|
|
('Gcd', {
|
|
'block': GcdFunc(),
|
|
'desc_inputs': [Tensor(np.array([2, 5, 8]).astype(np.int32)),
|
|
Tensor(np.array([4, 3, 12]).astype(np.int32))],
|
|
'skip': ['backward']}),
|
|
('Zeta', {
|
|
'block': Zeta(),
|
|
'desc_inputs': [Tensor(np.array([1, 1, 1, 1], np.float32)),
|
|
Tensor([0.5, 0.5, 0.5, 0.5], mstype.float32)]}),
|
|
('Lcm', {
|
|
'block': LcmFunc(),
|
|
'desc_inputs': [Tensor(np.array([2, 5, 8]).astype(np.int32)),
|
|
Tensor(np.array([4, 3, 12]).astype(np.int32))],
|
|
'skip': ['backward']}),
|
|
('Igamma', {
|
|
'block': Igamma(),
|
|
'desc_inputs': [Tensor(np.array([1.1, 2.2, -4.1], np.float32)),
|
|
Tensor(np.array([0.2, 1.2, 2.1], np.float32))],
|
|
'desc_bprop': [Tensor(np.array([2, 3], np.float32)),
|
|
Tensor(np.array([2, 3], np.float32))],
|
|
'skip': ['backward']}),
|
|
('Igammac', {
|
|
'block': Igammac(),
|
|
'desc_inputs': [Tensor(np.array([1.1, 2.2, -4.1], np.float32)),
|
|
Tensor(np.array([0.2, 1.2, 2.1], np.float32))],
|
|
'desc_bprop': [Tensor(np.array([2, 3], np.float32)),
|
|
Tensor(np.array([2, 3], np.float32))],
|
|
'skip': ['backward']}),
|
|
('IgammaGradA', {
|
|
'block': IgammaGradA(),
|
|
'desc_inputs': [Tensor(np.array([1.1, 2.2, 8.1, 2.1], np.float32)),
|
|
Tensor(np.array([0.2, 1.2, 2.1, 3.4], np.float32))],
|
|
'skip': ['backward']}),
|
|
('Outer_Error', {
|
|
'block': (OuterFunc(), {'exception': ValueError}),
|
|
'desc_inputs': [Tensor(np.array([[1., 1.], [1., 2.], [1., 3.]]), dtype=ms.float32),
|
|
Tensor(np.array([1, 2, 3]), dtype=ms.int32)],
|
|
'skip': ['backward']}),
|
|
('Deg2rad_1_Error', {
|
|
'block': (lambda x: Deg2radNet(), {'exception': TypeError}),
|
|
'desc_inputs': [0]}),
|
|
('Deg2rad_2_Error', {
|
|
'block': (lambda x: Deg2radNet(), {'exception': TypeError}),
|
|
'desc_inputs': [Tensor(np.array([[90, -90], [180, -180], [270, -270]], np.int32))]}),
|
|
('Rad2deg_1_Error', {
|
|
'block': (lambda x: Rad2degNet(), {'exception': TypeError}),
|
|
'desc_inputs': [0]}),
|
|
('Rad2deg_2_Error', {
|
|
'block': (lambda x: Rad2degNet(), {'exception': TypeError}),
|
|
'desc_inputs': [Tensor(np.array([[3, -3], [6, -6], [1, -1]], np.int32))]}),
|
|
('Baddbmm_Error', {
|
|
'block': (BaddbmmNet(), {'exception': TypeError}),
|
|
'desc_inputs': [Tensor(np.ones([1, 3, 3]).astype(np.float32)),
|
|
Tensor(np.ones([1, 3, 4]).astype(np.float32)),
|
|
[1, 2]],
|
|
'skip': ['backward']}),
|
|
('Log2_Error_2', {
|
|
'block': (Log2Net(), {'exception;': TypeError}),
|
|
'desc_inputs': [Tensor(np.array([[1, 2, 4]], np.int32))],
|
|
'skip': ['backward']}),
|
|
('Log2_Error_1', {
|
|
'block': (Log2Net(), {'exception;': TypeError}),
|
|
'desc_inputs': [[1]],
|
|
'skip': ['backward']}),
|
|
('Log10_Error_1', {
|
|
'block': (Log10Net(), {'exception': TypeError}),
|
|
'desc_inputs': [[1]],
|
|
'skip': ['backward']}),
|
|
('Log10_Error_2', {
|
|
'block': (Log10Net(), {'exception': TypeError}),
|
|
'desc_inputs': [Tensor(np.array([[1, 2, 4]], np.int32))],
|
|
'skip': ['backward']}),
|
|
('Kron_1_Error', {
|
|
'block': (KronFunc(), {'exception': TypeError}),
|
|
'desc_inputs': [[-5, -3, -1, 1, 3, 5], [-5, -3, -1, 1, 3, 5]],
|
|
'skip': ['backward']}),
|
|
('Kron_2_Error', {
|
|
'block': (KronFunc(), {'exception': TypeError}),
|
|
'desc_inputs': [Tensor(np.random.randn(2, 5), dtype=mstype.float64),
|
|
Tensor(np.random.randn(2, 5), dtype=mstype.float64)],
|
|
'skip': ['backward']}),
|
|
('Kron_3_Error', {
|
|
'block': (KronFunc(), {'exception': RuntimeError}),
|
|
'desc_inputs': [Tensor(np.random.randn(2, 2, 3, 2, 3), dtype=mstype.float16),
|
|
Tensor(np.random.randn(3, 2), dtype=mstype.float16)],
|
|
'skip': ['backward']}),
|
|
('Rot90_1_Error', {
|
|
'block': (Rot90Func(), {'exception': TypeError}),
|
|
'desc_inputs': [[-5, -3, -1, 1, 3, 5]],
|
|
'skip': ['backward']}),
|
|
('Rot90_2_Error', {
|
|
'block': (Rot90Func(), {'exception': ValueError}),
|
|
'desc_inputs': [Tensor(np.array([0]), dtype=mstype.float16)],
|
|
'skip': ['backward']}),
|
|
('Orgqr', {
|
|
'block': OrgqrFunc(),
|
|
'desc_inputs': [Tensor(np.array([[-114.6, 10.9, 1.1],
|
|
[-0.304, 38.07, 69.38],
|
|
[-0.45, -0.17, 62.0]]).astype(np.float32)),
|
|
Tensor(np.array([1.55, 1.94, 0.0]).astype(np.float32))
|
|
],
|
|
'skip': ['backward']}),
|
|
('Remainder_Error_1', {
|
|
'block': (RemainderNet(), {'exception': TypeError}),
|
|
'desc_inputs': [Tensor(np.array([-4.0, 5.0, 6.0]), ms.float32),
|
|
[3.0, 2.0, 3.0]],
|
|
'skip': ['backward']}),
|
|
('Remainder_Error_2', {
|
|
'block': (RemainderNet(), {'exception': ValueError}),
|
|
'desc_inputs': [Tensor(np.array([-4.0, 5.0, 6.0]), ms.float32),
|
|
Tensor(np.array([3.0, 2.0]), ms.float32)],
|
|
'skip': ['backward']}),
|
|
]
|
|
|
|
|
|
@mindspore_test(pipeline_for_verify_exception_for_case_by_case_config)
|
|
def test_check_exception():
|
|
return raise_set
|