forked from mindspore-Ecosystem/mindspore
464 lines
13 KiB
Python
Executable File
464 lines
13 KiB
Python
Executable File
# 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.
|
|
# ============================================================================
|
|
""" test math ops """
|
|
import functools
|
|
import numpy as np
|
|
import mindspore as ms
|
|
import mindspore.nn as nn
|
|
from mindspore.common.api import _executor
|
|
from mindspore.common import dtype as mstype
|
|
from mindspore.ops import prim_attr_register, PrimitiveWithInfer
|
|
from mindspore import Tensor
|
|
from mindspore.ops import composite as C
|
|
from mindspore.ops import operations as P
|
|
from mindspore.ops import functional as F
|
|
import mindspore.context as context
|
|
from ..ut_filter import non_graph_engine
|
|
from ....mindspore_test_framework.mindspore_test import mindspore_test
|
|
from ....mindspore_test_framework.pipeline.forward.compile_forward \
|
|
import pipeline_for_compile_forward_ge_graph_for_case_by_case_config
|
|
from ....mindspore_test_framework.pipeline.forward.verify_exception \
|
|
import pipeline_for_verify_exception_for_case_by_case_config
|
|
import pytest
|
|
|
|
|
|
# pylint: disable=W0613
|
|
# pylint: disable=W0231
|
|
# W0613: unused-argument
|
|
# W0231: super-init-not-called
|
|
|
|
def test_multiply():
|
|
""" test_multiply """
|
|
input_x = Tensor(np.array([[-0.1, 0.3, 3.6], [0.4, 0.5, -3.2]]))
|
|
input_y = Tensor(np.array([[0.1, 0.3, -3.6], [0.4, 0.5, -3.2]]))
|
|
|
|
mul = P.Mul()
|
|
result = mul(input_x, input_y)
|
|
expect = np.array([[-0.01, 0.09, -12.96], [0.16, 0.25, 10.24]])
|
|
diff = result.asnumpy() - expect
|
|
error = np.ones(shape=[2, 3]) * 1.0e-6
|
|
assert np.all(diff < error)
|
|
assert np.all(-diff < error)
|
|
|
|
|
|
def test_sub():
|
|
""" test_sub """
|
|
input_x = Tensor(np.ones(shape=[3]))
|
|
input_y = Tensor(np.zeros(shape=[3]))
|
|
|
|
sub = P.Sub()
|
|
result = sub(input_x, input_y)
|
|
expect = np.ones(shape=[3])
|
|
assert np.all(result.asnumpy() == expect)
|
|
|
|
|
|
def test_square():
|
|
""" test_square """
|
|
input_tensor = Tensor(np.array([[1, 2, 3], [4, 5, 6]]))
|
|
square = P.Square()
|
|
result = square(input_tensor)
|
|
expect = np.array([[1, 4, 9], [16, 25, 36]])
|
|
assert np.all(result.asnumpy() == expect)
|
|
|
|
|
|
def test_sqrt():
|
|
""" test_sqrt """
|
|
input_tensor = Tensor(np.array([[4, 4], [9, 9]]))
|
|
|
|
sqrt = P.Sqrt()
|
|
expect = np.array([[2, 2], [3, 3]])
|
|
result = sqrt(input_tensor)
|
|
assert np.all(result.asnumpy() == expect)
|
|
|
|
|
|
class PowNet(nn.Cell):
|
|
def __init__(self):
|
|
super(PowNet, self).__init__()
|
|
self.pow = P.Pow()
|
|
|
|
def construct(self, x, y):
|
|
return self.pow(x, y)
|
|
|
|
|
|
def test_pow():
|
|
""" test_pow """
|
|
input_tensor = Tensor(np.array([[2, 2], [3, 3]]))
|
|
power = Tensor(np.array(3.0, np.int64))
|
|
power2 = Tensor(np.array(True, np.bool))
|
|
testpow = P.Pow()
|
|
expect = np.array([[8, 8], [27, 27]])
|
|
result = testpow(input_tensor, power)
|
|
assert np.all(result.asnumpy() == expect)
|
|
net = PowNet()
|
|
with pytest.raises(TypeError):
|
|
net(input_tensor, True)
|
|
with pytest.raises(TypeError):
|
|
net(input_tensor, power2)
|
|
|
|
|
|
def test_exp():
|
|
""" test_exp """
|
|
input_tensor = Tensor(np.array([[2, 2], [3, 3]]))
|
|
testexp = P.Exp()
|
|
result = testexp(input_tensor)
|
|
expect = np.exp(np.array([[2, 2], [3, 3]]))
|
|
assert np.all(result.asnumpy() == expect)
|
|
|
|
|
|
def test_realdiv():
|
|
""" test_realdiv """
|
|
x = Tensor(2048.0)
|
|
y = Tensor(128.0)
|
|
div = P.RealDiv()
|
|
result = div(x, y)
|
|
x = x.asnumpy()
|
|
y = y.asnumpy()
|
|
expect = x / y
|
|
assert np.all(result.asnumpy() == expect)
|
|
|
|
|
|
def test_eye():
|
|
""" test_eye """
|
|
x = np.arange(3)
|
|
expect = np.ones_like(x)
|
|
expect = np.diag(expect)
|
|
eye = P.Eye()
|
|
eye_output = eye(3, 3, ms.float32)
|
|
assert np.all(eye_output.asnumpy() == expect)
|
|
|
|
|
|
class VirtualLossGrad(PrimitiveWithInfer):
|
|
""" VirtualLossGrad definition """
|
|
|
|
@prim_attr_register
|
|
def __init__(self):
|
|
"""init VirtualLossGrad"""
|
|
|
|
def __call__(self, x, out, dout):
|
|
raise NotImplementedError
|
|
|
|
def infer_shape(self, x_shape, out_shape, dout_shape):
|
|
return x_shape
|
|
|
|
def infer_dtype(self, x_dtype, out_dtype, dout_dtype):
|
|
return x_dtype
|
|
|
|
|
|
class VirtualLoss(PrimitiveWithInfer):
|
|
""" VirtualLoss definition """
|
|
|
|
@prim_attr_register
|
|
def __init__(self):
|
|
"""init VirtualLoss"""
|
|
|
|
def __call__(self, x):
|
|
raise NotImplementedError
|
|
|
|
def get_bprop(self):
|
|
loss_grad = VirtualLossGrad()
|
|
|
|
def bprop(x, out, dout):
|
|
dx = loss_grad(x, out, dout)
|
|
return (dx,)
|
|
|
|
return bprop
|
|
|
|
def infer_shape(self, x_shape):
|
|
return [1]
|
|
|
|
def infer_dtype(self, x_dtype):
|
|
return x_dtype
|
|
|
|
|
|
class NetWithLoss(nn.Cell):
|
|
""" NetWithLoss definition """
|
|
|
|
def __init__(self, network):
|
|
super(NetWithLoss, self).__init__()
|
|
self.loss = VirtualLoss()
|
|
self.network = network
|
|
|
|
def construct(self, x, y, b):
|
|
predict = self.network(x, y, b)
|
|
return self.loss(predict)
|
|
|
|
|
|
class GradWrap(nn.Cell):
|
|
""" GradWrap definition """
|
|
|
|
def __init__(self, network):
|
|
super(GradWrap, self).__init__()
|
|
self.network = network
|
|
|
|
def construct(self, x, y, b):
|
|
return C.grad(self.network)(x, y, b)
|
|
|
|
|
|
class MatMulNet(nn.Cell):
|
|
""" MatMulNet definition """
|
|
|
|
def __init__(self):
|
|
super(MatMulNet, self).__init__()
|
|
self.matmul = P.MatMul()
|
|
self.biasAdd = P.BiasAdd()
|
|
|
|
def construct(self, x, y, b):
|
|
return self.biasAdd(self.matmul(x, y), b)
|
|
|
|
|
|
class NetWithLossSub(nn.Cell):
|
|
""" NetWithLossSub definition """
|
|
|
|
def __init__(self, network):
|
|
super(NetWithLossSub, self).__init__()
|
|
self.loss = VirtualLoss()
|
|
self.network = network
|
|
|
|
def construct(self, x, y):
|
|
predict = self.network(x, y)
|
|
return self.loss(predict)
|
|
|
|
|
|
class GradWrapSub(nn.Cell):
|
|
""" GradWrapSub definition """
|
|
|
|
def __init__(self, network):
|
|
super(GradWrapSub, self).__init__()
|
|
self.network = network
|
|
|
|
def construct(self, x, y):
|
|
return C.grad(self.network)(x, y)
|
|
|
|
|
|
class SubNet(nn.Cell):
|
|
""" SubNet definition """
|
|
|
|
def __init__(self):
|
|
super(SubNet, self).__init__()
|
|
self.sub = P.Sub()
|
|
|
|
def construct(self, x, y):
|
|
return self.sub(x, y)
|
|
|
|
|
|
class NpuFloatNet(nn.Cell):
|
|
""" NpuFloat definition """
|
|
|
|
def __init__(self):
|
|
super(NpuFloatNet, self).__init__()
|
|
self.mul = P.Mul()
|
|
self.alloc_status = P.NPUAllocFloatStatus()
|
|
self.get_status = P.NPUGetFloatStatus()
|
|
self.clear_status = P.NPUClearFloatStatus()
|
|
self.fill = P.Fill()
|
|
self.shape_op = P.Shape()
|
|
self.select = P.Select()
|
|
self.less = P.Less()
|
|
self.cast = P.Cast()
|
|
self.dtype = P.DType()
|
|
self.reduce_sum = P.ReduceSum(keep_dims=True)
|
|
self.sub = P.Sub()
|
|
self.neg = P.Neg()
|
|
self.add_flags(has_effect=True)
|
|
|
|
def construct(self, x):
|
|
init = self.alloc_status()
|
|
self.clear_status(init)
|
|
res = self.sub(x, self.neg(x))
|
|
self.get_status(init)
|
|
flag_sum = self.reduce_sum(init, (0,))
|
|
base = self.cast(self.fill(self.dtype(res), self.shape_op(res), 0.0), self.dtype(flag_sum))
|
|
cond = self.less(base, flag_sum)
|
|
out = self.select(cond, self.cast(base, self.dtype(res)), res)
|
|
return out
|
|
|
|
|
|
class DiagNet(nn.Cell):
|
|
""" DiagNet definition """
|
|
|
|
def __init__(self):
|
|
super(DiagNet, self).__init__()
|
|
self.fill = P.Fill()
|
|
self.diag = P.Diag()
|
|
|
|
def construct(self, x):
|
|
return x - self.diag(self.fill(mstype.float32, (3,), 1.0))
|
|
|
|
|
|
class NetWithLossCumSum(nn.Cell):
|
|
""" NetWithLossCumSum definition """
|
|
|
|
def __init__(self, network):
|
|
super(NetWithLossCumSum, self).__init__()
|
|
self.loss = VirtualLoss()
|
|
self.network = network
|
|
|
|
def construct(self, input):
|
|
predict = self.network(input)
|
|
return self.loss(predict)
|
|
|
|
|
|
class GradWrapCumSum(nn.Cell):
|
|
""" GradWrap definition """
|
|
|
|
def __init__(self, network):
|
|
super(GradWrapCumSum, self).__init__()
|
|
self.network = network
|
|
|
|
def construct(self, input):
|
|
return C.grad(self.network)(input)
|
|
|
|
|
|
class NetCumSum(nn.Cell):
|
|
""" NetCumSum definition """
|
|
|
|
def __init__(self):
|
|
super(NetCumSum, self).__init__()
|
|
self.cumsum = P.CumSum()
|
|
self.axis = 1
|
|
|
|
def construct(self, input):
|
|
return self.cumsum(input, self.axis)
|
|
|
|
|
|
class SignNet(nn.Cell):
|
|
def __init__(self):
|
|
super(SignNet, self).__init__()
|
|
self.sign = P.Sign()
|
|
|
|
def construct(self, x):
|
|
return self.sign(x)
|
|
|
|
|
|
class AssignAdd(nn.Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.op = P.AssignAdd()
|
|
self.inputdata = Parameter(initializer(1, [1], ms.float32), name="global_step")
|
|
|
|
def construct(self, input_):
|
|
self.inputdata = input_
|
|
return self.op(self.inputdata, input_)
|
|
|
|
|
|
class FloorNet(nn.Cell):
|
|
def __init__(self):
|
|
super(FloorNet, self).__init__()
|
|
self.floor = P.Floor()
|
|
|
|
def construct(self, x):
|
|
return self.floor(x)
|
|
|
|
|
|
class Log1pNet(nn.Cell):
|
|
def __init__(self):
|
|
super(Log1pNet, self).__init__()
|
|
self.log1p = P.Log1p()
|
|
|
|
def construct(self, x):
|
|
return self.log1p(x)
|
|
|
|
|
|
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']}),
|
|
]
|
|
|
|
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
|
|
|
|
|
|
import mindspore.context as context
|
|
|
|
|
|
@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 = [
|
|
('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': TypeError}),
|
|
'desc_inputs': [[1]]}),
|
|
]
|
|
|
|
|
|
@mindspore_test(pipeline_for_verify_exception_for_case_by_case_config)
|
|
def test_check_exception():
|
|
return raise_set
|