forked from mindspore-Ecosystem/mindspore
281 lines
8.6 KiB
Python
281 lines
8.6 KiB
Python
# Copyright 2022 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_control_flow_specialize """
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import os
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import pytest
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import numpy as np
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from mindspore.nn import Cell
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from mindspore.common import Tensor, dtype, Parameter
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from mindspore.ops import operations as P
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from mindspore import jit
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import mindspore.ops.functional as F
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_renormalization_after_cconv_poly_node():
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"""
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Feature: control flow
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Description: In the renormalization after cconv, there should be no poly node error.
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Expectation: No exception.
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"""
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class Net(Cell):
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def __init__(self):
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super().__init__()
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self.w = Parameter(Tensor([(- 1)], dtype.float32), name='w')
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self.b = Parameter(Tensor([(- 1)], dtype.float32), name='b')
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def construct(self, x, y):
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def inner(x):
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if x >= 5:
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return x
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return x
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def outer(x):
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if x >= inner(x):
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return x
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return x
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while self.b == 0:
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if outer(self.b) <= self.b:
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y = self.w + outer(self.w)
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if y > inner(self.b):
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break
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return x + y
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x = np.array([5], np.float32)
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y = np.array([3], np.float32)
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net1 = Net()
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grad_net = F.grad(net1, grad_position=(0, 1))
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expected = np.array([1], np.float32)
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output = grad_net(Tensor(x), Tensor(y))
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assert np.allclose(expected, output[0].asnumpy(), 0.0001)
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assert np.allclose(expected, output[1].asnumpy(), 0.0001)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_poly_delay_specialize():
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"""
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Feature: Specialize.
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Description: If a poly node's parent are not specialized, poly node should be delay specialized.
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Expectation: graph can be executed and no exception raised.
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"""
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pow_ops = P.Pow()
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@jit
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def poly_node_network(x, y):
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def function_h():
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pow_res = pow_ops(x, x)
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def function_g(param_x):
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return pow(pow_res, param_x)
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return F.make_tuple(pow_res, function_g)
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def function_f():
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h_out = function_h()
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h_forward_out = F.tuple_getitem(h_out, 0)
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g = F.tuple_getitem(h_out, 1)
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def function_k():
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kout1 = g(x)
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kout2 = g(y)
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kout = F.depend(kout1, kout2)
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return kout
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return F.make_tuple(h_forward_out, function_k)
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out = function_f()
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forward_out = F.tuple_getitem(out, 0)
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closure_out = F.tuple_getitem(out, 1)
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closure_out_tensor = closure_out()
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return F.add(forward_out, closure_out_tensor)
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x = Tensor([1], dtype.int32)
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y = Tensor([1, 2], dtype.int32)
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poly_node_network(x, y)
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def test_renormalization_cannot_find_specialized_abstract():
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"""
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Feature: control flow
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Description: after renormalization, funcgraph with different args in different abstracts are broadened
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to use the same funcgraph, so all these abstracts have to map the same abstract, then
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the backend can find that specialized func_graph.
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Expectation: No exception.
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"""
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def foo(x, y):
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for e in range(2):
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x = e * y
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if x >= 2:
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break
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if y >= x:
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x = x * y
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return x + y
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x = np.array([5], np.int32)
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y = np.array([3], np.int32)
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grad_foo = F.grad(foo, grad_position=(0, 1))
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output = grad_foo(Tensor(x), Tensor(y))
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assert output[0].asnumpy() == np.array([0], np.int32)
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assert output[1].asnumpy() == np.array([7], np.int32)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_renormalization_cannot_find_specialized_abstract_2():
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"""
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Feature: control flow
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Description: after renormalization, funcgraph with different args in different abstracts are broadened
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to use the same funcgraph, so all these abstracts have to map the same abstract, then
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the backend can find that specialized func_graph.
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Expectation: No exception.
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"""
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def foo(x, y):
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for e in range(2):
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x = e * y
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if x >= 2:
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break
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if y >= 4:
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x = x * y
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elif y >= x:
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x = x * y
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return x + y
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x = np.array([5], np.int32)
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y = np.array([3], np.int32)
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grad_foo = F.grad(foo, grad_position=(0, 1))
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output = grad_foo(Tensor(x), Tensor(y))
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assert output[0].asnumpy() == np.array([0], np.int32)
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assert output[1].asnumpy() == np.array([7], np.int32)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_renormalization_cannot_find_specialized_abstract_2nd_grad():
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"""
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Feature: control flow
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Description: after renormalization, funcgraph with different args in different abstracts are broadened
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to use the same funcgraph, so all these abstracts have to map the same abstract, then
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the backend can find that specialized func_graph.
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Expectation: No exception.
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"""
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def foo(x):
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out = x
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for e in range(2):
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x = e * out
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if x >= 2:
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break
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if out >= x:
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out = x * out
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return x + out
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x = np.array([5], np.int32)
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grad_foo = F.grad(foo, grad_position=(0,))
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grad_foo_2nd = F.grad(grad_foo, grad_position=(0,))
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output = grad_foo_2nd(Tensor(x))
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assert output[0].asnumpy() == np.array([2], np.int32)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_renormalization_a_dead_node_in_second_grad():
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"""
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Feature: control flow
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Description: after renormalization of second grad, a dead node should not be generated.
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Expectation: No exception.
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"""
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def foo(x, y, b, w):
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while b > w:
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for i in range(2):
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b = x + i
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x = i * w
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if b > 3:
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break
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return x + y
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x = np.array([5], np.int32)
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y = np.array([5], np.int32)
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b = np.array([5], np.int32)
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w = np.array([5], np.int32)
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grad_foo = F.grad(foo, grad_position=(0, 1))
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grad_foo_2nd = F.grad(grad_foo)
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output = grad_foo_2nd(Tensor(x), Tensor(y), Tensor(b), Tensor(w))
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assert output[0].asnumpy() == np.array([0], np.int32)
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def renorm_join_fail(x, y):
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"""
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Description: control flow test case simplified from test_dde_err_log.
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"""
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if x != y:
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x = y - 3
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elif x == 4:
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for _ in range(2):
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if x > 2:
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y = x * x
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elif y >= x:
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x = x * x
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return x + y
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_renormalization_join_fail_in_second_grad_non_recur_eval():
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"""
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Feature: control flow
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Description: after renormalization of second grad, join failure should not be generated.
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Expectation: No exception.
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"""
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x = np.array([5], np.int32)
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y = np.array([5], np.int32)
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grad_foo = F.grad(renorm_join_fail, grad_position=(0, 1))
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grad_foo_2nd = F.grad(grad_foo)
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output = grad_foo_2nd(Tensor(x), Tensor(y))
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assert output[0].asnumpy() == np.array([0], np.int32)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_renormalization_join_fail_in_second_grad_recur_eval():
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"""
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Feature: control flow
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Description: In recursive eval, after renormalization of second grad, join failure should not be generated.
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Expectation: No exception.
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"""
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x = np.array([5], np.int32)
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y = np.array([5], np.int32)
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grad_foo = F.grad(renorm_join_fail, grad_position=(0, 1))
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grad_foo_2nd = F.grad(grad_foo)
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os.environ['MS_DEV_RECURSIVE_EVAL'] = '1'
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output = grad_foo_2nd(Tensor(x), Tensor(y))
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assert output[0].asnumpy() == np.array([0], np.int32)
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os.environ['MS_DEV_RECURSIVE_EVAL'] = ''
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