forked from mindspore-Ecosystem/mindspore
1614 lines
52 KiB
Python
1614 lines
52 KiB
Python
# Copyright 2020-2021 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 ops """
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import numpy as np
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import pytest
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from mindspore import dtype as ms
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from mindspore import Tensor
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from mindspore import context
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from mindspore import nn
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from mindspore.common.parameter import Parameter, ParameterTuple
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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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grad_by_list = C.GradOperation(get_by_list=True)
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grad_all = C.GradOperation(get_all=True)
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_while_grad():
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class MyWhileNet(nn.Cell):
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def __init__(self):
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super().__init__()
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self.max = P.ReduceMax()
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def construct(self, idx, end, x):
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while idx < end:
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part = x[idx, :, :]
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max_num = self.max(part)
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x[idx, :, 0:2] = max_num
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idx = idx + 1
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return x
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class GradNet(nn.Cell):
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def __init__(self, net):
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super(GradNet, self).__init__()
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self.net = net
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def construct(self, *inputs):
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return grad_all(self.net)(*inputs)
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idx = Tensor(np.array(0), dtype=ms.int32)
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end = Tensor(np.array(2), dtype=ms.int32)
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input_x = np.array([[[4, 0], [0, 0]],
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[[0, 4], [0, 0]]]).astype(np.float32)
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x = Tensor(input_x, dtype=ms.float32)
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# graph mode
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context.set_context(mode=context.GRAPH_MODE)
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while_net = MyWhileNet()
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net = GradNet(while_net)
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graph_output = net(idx, end, x)
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expect_zero = np.array([0], dtype=np.float32)
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expect_two = input_x
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assert np.allclose(graph_output[0].asnumpy(), expect_zero, 0.0001, 0.0001)
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assert np.allclose(graph_output[1].asnumpy(), expect_zero, 0.0001, 0.0001)
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assert np.allclose(graph_output[2].asnumpy(), expect_two, 0.0001, 0.0001)
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@pytest.mark.level1
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_while_with_const_param_grad():
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class MyWhileNet(nn.Cell):
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def __init__(self):
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super().__init__()
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self.mul = P.Mul()
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self.add = P.Add()
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def construct(self, x, y):
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while x < y:
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z = self.mul(x, x)
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x = self.add(z, 1)
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return x
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class GradNet(nn.Cell):
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def __init__(self, net):
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super(GradNet, self).__init__()
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self.net = net
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def construct(self, *inputs):
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return grad_all(self.net)(*inputs)
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context.set_context(mode=context.GRAPH_MODE)
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while_net = MyWhileNet()
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net = GradNet(while_net)
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idx = Tensor([1.1], dtype=ms.float32)
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end = Tensor([8.0], dtype=ms.float32)
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graph_output = net(idx, end)
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expect_one = np.array([1.14433983e+02], dtype=np.float32)
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expect_two = np.array([0], dtype=np.float32)
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assert np.allclose(graph_output[0].asnumpy(), expect_one, 0.0001, 0.0001)
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assert np.allclose(graph_output[1].asnumpy(), expect_two, 0.0001, 0.0001)
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@pytest.mark.level1
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_while_with_variable_grad():
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class MyWhileNet(nn.Cell):
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def __init__(self):
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super().__init__()
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self.mul = P.Mul()
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self.add = P.Add()
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def construct(self, x, y):
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while x < y:
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z = self.mul(x, x)
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x = self.add(z, y)
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return x
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class GradNet(nn.Cell):
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def __init__(self, net):
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super(GradNet, self).__init__()
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self.net = net
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def construct(self, *inputs):
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return grad_all(self.net)(*inputs)
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context.set_context(mode=context.GRAPH_MODE)
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while_net = MyWhileNet()
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net = GradNet(while_net)
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idx = Tensor([1.1], dtype=ms.float32)
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end = Tensor([8.0], dtype=ms.float32)
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graph_output = net(idx, end)
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expect_one = np.array([2.20000005e+00], dtype=np.float32)
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expect_two = np.array([1.00000000e+00], dtype=np.float32)
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assert np.allclose(graph_output[0].asnumpy(), expect_one, 0.0001, 0.0001)
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assert np.allclose(graph_output[1].asnumpy(), expect_two, 0.0001, 0.0001)
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@pytest.mark.level1
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_while_with_param_forward():
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class MyWhileNet(nn.Cell):
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def __init__(self):
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super().__init__()
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self.max = P.ReduceMax()
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self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
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self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)
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def construct(self, idx, end, x):
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out = self.zero
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while idx < end:
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part = x[idx, :, :]
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max_num = self.max(part)
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x[idx, :, 0:2] = max_num
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out = out + x + self.param
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idx = idx + 1
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return out
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# graph mode
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context.set_context(mode=context.GRAPH_MODE)
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net = MyWhileNet()
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idx = Tensor(np.array(0), dtype=ms.int32)
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end = Tensor(np.array(2), dtype=ms.int32)
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x = Tensor(np.arange(8).reshape(2, 2, 2).astype(np.float32), dtype=ms.float32)
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graph_output = net(idx, end, x)
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expect = np.array([[[6, 8], [10, 12]], [[19, 22], [25, 28]]], dtype=np.int32)
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assert np.allclose(graph_output.asnumpy(), expect, 0.0001, 0.0001)
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@pytest.mark.level1
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_while_endless_case():
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"""endless case when optimization"""
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class MyWhileNet(nn.Cell):
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def __init__(self):
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super().__init__()
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self.max = P.ReduceMax()
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self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
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self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)
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def construct(self, idx, end, x):
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out = self.zero
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while idx < end:
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part = x[idx, :, :]
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out = out + part
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idx = idx + 1
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return out
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idx = Tensor(np.array(0), dtype=ms.int32)
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end = Tensor(np.array(2), dtype=ms.int32)
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x = Tensor(np.arange(8).reshape(2, 2, 2).astype(np.float32), dtype=ms.float32)
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# graph mode
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context.set_context(mode=context.GRAPH_MODE)
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net = MyWhileNet()
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graph_output = net(idx, end, x)
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expect = np.array([[[4, 6], [8, 10]],
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[[4, 6], [8, 10]]]).astype(np.float32)
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assert np.allclose(graph_output.asnumpy(), expect, 0.0001, 0.0001)
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@pytest.mark.level1
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_while_with_param_grad():
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class MyWhileNet(nn.Cell):
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def __init__(self):
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super().__init__()
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self.max = P.ReduceMax()
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self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
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self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)
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def construct(self, idx, end, x):
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out = self.zero
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while idx < end:
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part = x[idx, :, :]
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max_num = self.max(part)
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x[idx, :, 0:2] = max_num
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out = out + x + self.param
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idx = idx + 1
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return out
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class GradNet(nn.Cell):
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def __init__(self, net):
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super(GradNet, self).__init__()
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self.net = net
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self.weights = ParameterTuple(net.trainable_params())
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def construct(self, a, b, c):
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return grad_by_list(self.net, self.weights)(a, b, c)
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context.set_context(mode=context.GRAPH_MODE)
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while_net = MyWhileNet()
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net = GradNet(while_net)
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idx = Tensor(np.array(0), dtype=ms.int32)
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end = Tensor(np.array(2), dtype=ms.int32)
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x = Tensor(np.arange(8).reshape(2, 2, 2).astype(np.float32), dtype=ms.float32)
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graph_output = net(idx, end, x)
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expect = np.array([[[2, 2], [2, 2]], [[2, 2], [2, 2]]], dtype=np.int32)
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assert np.allclose(graph_output[0].asnumpy(), expect, 0.0001, 0.0001)
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@pytest.mark.level1
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_while_with_param_forward_with_const_branch():
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class MyWhileNet(nn.Cell):
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def __init__(self):
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super().__init__()
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self.max = P.ReduceMax()
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self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
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self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)
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self.reduce = P.ReduceSum()
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def construct(self, idx, end, x):
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out = self.zero
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while idx < end:
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if 2 > 1:
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out = out + self.param
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else:
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out = out + idx + self.param
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idx = idx + 1
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return out
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idx = Tensor(np.array(0), dtype=ms.int32)
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end = Tensor(np.array(4), dtype=ms.int32)
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x = Tensor(np.arange(8).reshape(2, 2, 2).astype(np.float32), dtype=ms.float32)
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# graph mode
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context.set_context(mode=context.GRAPH_MODE)
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while_net = MyWhileNet()
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net = while_net
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graph_output = net(idx, end, x)
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expect = np.array([[[0, 4], [8, 12]],
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[[16, 20], [24, 28]]]).astype(np.float32)
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assert np.allclose(graph_output.asnumpy(), expect, 0.0001, 0.0001)
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@pytest.mark.level1
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_while_opt_endless():
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"""endless during optimization case"""
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class MyWhileNet(nn.Cell):
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def __init__(self):
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super().__init__()
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self.max = P.ReduceMax()
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self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
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self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)
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self.reduce = P.ReduceSum()
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self.addn = P.AddN()
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def construct(self, idx, end, x):
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addn1 = self.addn((x, x, x))
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out = addn1
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while idx < end:
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out = self.addn((out, addn1))
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idx = idx + 1
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out = self.addn((out, x))
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return out
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class GradNet(nn.Cell):
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def __init__(self, net):
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super(GradNet, self).__init__()
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self.net = net
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def construct(self, *inputs):
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return grad_all(self.net)(*inputs)
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idx = Tensor(np.array(0), dtype=ms.int32)
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end = Tensor(np.array(4), dtype=ms.int32)
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x = Tensor(np.ones([2, 2, 2]).astype(np.float32) * 3, dtype=ms.float32)
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# graph mode
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context.set_context(mode=context.GRAPH_MODE)
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while_net = MyWhileNet()
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net = GradNet(while_net)
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graph_output = net(idx, end, x)
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expect1 = 0
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expect2 = 0
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expect3 = np.array([[[16, 16], [16, 16]],
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[[16, 16], [16, 16]]]).astype(np.float32)
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assert np.allclose(graph_output[0].asnumpy(), expect1, 0.0001, 0.0001)
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assert np.allclose(graph_output[1].asnumpy(), expect2, 0.0001, 0.0001)
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assert np.allclose(graph_output[2].asnumpy(), expect3, 0.0001, 0.0001)
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@pytest.mark.level1
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_no_while_call():
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class MyWhileNet(nn.Cell):
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def __init__(self):
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super().__init__()
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self.max = P.ReduceMax()
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self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
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self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)
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self.reduce = P.ReduceSum()
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def construct(self, idx, end, x):
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out = self.zero
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if 2 > 1:
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out = out + self.param
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else:
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out = out + idx + self.param
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return out
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idx = Tensor(np.array(0), dtype=ms.int32)
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end = Tensor(np.array(4), dtype=ms.int32)
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x = Tensor(np.arange(8).reshape(2, 2, 2).astype(np.float32), dtype=ms.float32)
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# graph mode
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context.set_context(mode=context.GRAPH_MODE)
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while_net = MyWhileNet()
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net = while_net
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graph_output = net(idx, end, x)
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expect = np.array([[[0, 1], [2, 3]],
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[[4, 5], [6, 7]]]).astype(np.float32)
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assert np.allclose(graph_output.asnumpy(), expect, 0.0001, 0.0001)
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@pytest.mark.level1
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_while_with_param_grad_with_const_branch():
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class MyWhileNet(nn.Cell):
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def __init__(self):
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super().__init__()
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self.max = P.ReduceMax()
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self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
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self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)
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self.reduce = P.ReduceSum()
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def construct(self, idx, end, x):
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out = self.zero
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while idx < end:
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if 2 > 1:
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out = out + self.param
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else:
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out = out + idx + self.param
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idx = idx + 1
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return out
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class GradNet(nn.Cell):
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def __init__(self, net):
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super(GradNet, self).__init__()
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self.net = net
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self.weights = ParameterTuple(net.trainable_params())
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def construct(self, a, b, c):
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return grad_by_list(self.net, self.weights)(a, b, c)
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idx = Tensor(np.array(0), dtype=ms.int32)
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end = Tensor(np.array(4), dtype=ms.int32)
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x = Tensor(np.arange(8).reshape(2, 2, 2).astype(np.float32), dtype=ms.float32)
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# graph mode
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context.set_context(mode=context.GRAPH_MODE)
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while_net = MyWhileNet()
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net = GradNet(while_net)
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graph_output = net(idx, end, x)
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expect = np.array([[[4, 4], [4, 4]],
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[[4, 4], [4, 4]]]).astype(np.float32)
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assert np.allclose(graph_output[0].asnumpy(), expect, 0.0001, 0.0001)
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@pytest.mark.level1
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_for_while_with_param_grad_with_const_branch():
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class MyWhileNet(nn.Cell):
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def __init__(self):
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super().__init__()
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self.max = P.ReduceMax()
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self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
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self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)
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self.reduce = P.ReduceSum()
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self.start = Tensor(np.array(0), dtype=ms.int32)
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def construct(self, idx, end, x):
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out = self.zero
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for _ in range(0, 2):
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idx = self.start
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while idx < end:
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if 2 > 1:
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out = out + self.param
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else:
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out = out + idx + self.param
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idx = idx + 1
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return out
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class GradNet(nn.Cell):
|
|
def __init__(self, net):
|
|
super(GradNet, self).__init__()
|
|
self.net = net
|
|
self.weights = ParameterTuple(net.trainable_params())
|
|
|
|
def construct(self, a, b, c):
|
|
return grad_by_list(self.net, self.weights)(a, b, c)
|
|
|
|
idx = Tensor(np.array(0), dtype=ms.int32)
|
|
end = Tensor(np.array(4), dtype=ms.int32)
|
|
x = Tensor(np.arange(8).reshape(2, 2, 2).astype(np.float32), dtype=ms.float32)
|
|
# graph mode
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
while_net = MyWhileNet()
|
|
net = GradNet(while_net)
|
|
graph_output = net(idx, end, x)
|
|
|
|
expect = np.array([[[8, 8], [8, 8]],
|
|
[[8, 8], [8, 8]]]).astype(np.float32)
|
|
assert np.allclose(graph_output[0].asnumpy(), expect, 0.0001, 0.0001)
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_for_while_with_param_grad_basic():
|
|
class MyWhileNet(nn.Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.max = P.ReduceMax()
|
|
self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
|
|
self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)
|
|
self.reduce = P.ReduceSum()
|
|
self.start = Tensor(np.array(0), dtype=ms.int32)
|
|
|
|
def construct(self, idx, end, x):
|
|
out = self.zero
|
|
for _ in range(0, 2):
|
|
idx = self.start
|
|
while idx < end:
|
|
out = out + self.param
|
|
idx = idx + 1
|
|
return out
|
|
|
|
class GradNet(nn.Cell):
|
|
def __init__(self, net):
|
|
super(GradNet, self).__init__()
|
|
self.net = net
|
|
self.weights = ParameterTuple(net.trainable_params())
|
|
|
|
def construct(self, a, b, c):
|
|
return grad_by_list(self.net, self.weights)(a, b, c)
|
|
|
|
idx = Tensor(np.array(0), dtype=ms.int32)
|
|
end = Tensor(np.array(4), dtype=ms.int32)
|
|
x = Tensor(np.arange(8).reshape(2, 2, 2).astype(np.float32), dtype=ms.float32)
|
|
# graph mode
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
while_net = MyWhileNet()
|
|
net = GradNet(while_net)
|
|
graph_output = net(idx, end, x)
|
|
expect = np.array([[[8, 8], [8, 8]],
|
|
[[8, 8], [8, 8]]]).astype(np.float32)
|
|
assert np.allclose(graph_output[0].asnumpy(), expect, 0.0001, 0.0001)
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_for_while_with_param_grad_normal():
|
|
class MyWhileNet(nn.Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.max = P.ReduceMax()
|
|
self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
|
|
self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)
|
|
self.reduce = P.ReduceSum()
|
|
self.start = Tensor(np.array(0), dtype=ms.int32)
|
|
|
|
def construct(self, idx, end, x):
|
|
out = x
|
|
for _ in range(0, 2):
|
|
idx = self.start
|
|
while idx < end:
|
|
out = out + self.param
|
|
idx = idx + 1
|
|
return out
|
|
|
|
class GradNet(nn.Cell):
|
|
def __init__(self, net):
|
|
super(GradNet, self).__init__()
|
|
self.net = net
|
|
self.weights = ParameterTuple(net.trainable_params())
|
|
|
|
def construct(self, a, b, c):
|
|
return grad_by_list(self.net, self.weights)(a, b, c)
|
|
|
|
idx = Tensor(np.array(0), dtype=ms.int32)
|
|
end = Tensor(np.array(4), dtype=ms.int32)
|
|
x = Tensor(np.arange(8).reshape(2, 2, 2).astype(np.float32), dtype=ms.float32)
|
|
# graph mode
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
while_net = MyWhileNet()
|
|
net = GradNet(while_net)
|
|
graph_output = net(idx, end, x)
|
|
expect = np.array([[[8, 8], [8, 8]],
|
|
[[8, 8], [8, 8]]]).astype(np.float32)
|
|
assert np.allclose(graph_output[0].asnumpy(), expect, 0.0001, 0.0001)
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_while_with_param_basic_grad():
|
|
class MyWhileNet(nn.Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.max = P.ReduceMax()
|
|
self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
|
|
self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)
|
|
self.t2 = Tensor(np.array(2), dtype=ms.float32)
|
|
|
|
def construct(self, idx, end, x):
|
|
out = self.zero
|
|
while idx < end:
|
|
out = out + self.param
|
|
idx = idx + 1
|
|
return out + self.param
|
|
|
|
class GradNet(nn.Cell):
|
|
def __init__(self, net):
|
|
super(GradNet, self).__init__()
|
|
self.net = net
|
|
self.weights = ParameterTuple(net.trainable_params())
|
|
|
|
def construct(self, a, b, c):
|
|
return grad_by_list(self.net, self.weights)(a, b, c)
|
|
|
|
idx = Tensor(np.array(0), dtype=ms.int32)
|
|
end = Tensor(np.array(3), dtype=ms.int32)
|
|
x = Tensor(np.arange(8).reshape(2, 2, 2).astype(np.float32), dtype=ms.float32)
|
|
# graph mode
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
while_net = MyWhileNet()
|
|
net = GradNet(while_net)
|
|
graph_output = net(idx, end, x)
|
|
expect = np.array([[[4, 4], [4, 4]],
|
|
[[4, 4], [4, 4]]]).astype(np.float32)
|
|
assert np.allclose(graph_output[0].asnumpy(), expect, 0.0001, 0.0001)
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_while_with_param_basic_grad_mul():
|
|
class MyWhileNet(nn.Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.max = P.ReduceMax()
|
|
self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
|
|
self.zero = Tensor(np.ones(([2, 2, 2])), ms.float32)
|
|
self.t2 = Tensor(np.array(2), dtype=ms.float32)
|
|
|
|
def construct(self, idx, end, x):
|
|
out = self.zero
|
|
while idx < end:
|
|
out = out * self.param
|
|
idx = idx + 1
|
|
return out + self.param
|
|
|
|
class GradNet(nn.Cell):
|
|
def __init__(self, net):
|
|
super(GradNet, self).__init__()
|
|
self.net = net
|
|
self.weights = ParameterTuple(net.trainable_params())
|
|
|
|
def construct(self, a, b, c):
|
|
return grad_by_list(self.net, self.weights)(a, b, c)
|
|
|
|
idx = Tensor(np.array(0), dtype=ms.int32)
|
|
end = Tensor(np.array(3), dtype=ms.int32)
|
|
x = Tensor(np.arange(8).reshape(2, 2, 2).astype(np.float32), dtype=ms.float32)
|
|
# graph mode
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
while_net = MyWhileNet()
|
|
net = GradNet(while_net)
|
|
graph_output = net(idx, end, x)
|
|
expect = np.array([[[1, 4], [13, 28]],
|
|
[[49, 76], [109, 148]]]).astype(np.float32)
|
|
assert np.allclose(graph_output[0].asnumpy(), expect, 0.0001, 0.0001)
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_while_with_param_basic_grad_two():
|
|
class MyWhileNet(nn.Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.max = P.ReduceMax()
|
|
self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
|
|
self.weight = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="loss")
|
|
self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)
|
|
self.t2 = Tensor(np.array(2), dtype=ms.float32)
|
|
|
|
def construct(self, idx, end, x):
|
|
out = self.zero
|
|
while idx < end:
|
|
out = out + self.param + self.weight
|
|
idx = idx + 1
|
|
return out + self.param
|
|
|
|
class GradNet(nn.Cell):
|
|
def __init__(self, net):
|
|
super(GradNet, self).__init__()
|
|
self.net = net
|
|
self.weights = ParameterTuple(net.trainable_params())
|
|
|
|
def construct(self, a, b, c):
|
|
return grad_by_list(self.net, self.weights)(a, b, c)
|
|
|
|
idx = Tensor(np.array(0), dtype=ms.int32)
|
|
end = Tensor(np.array(3), dtype=ms.int32)
|
|
x = Tensor(np.arange(8).reshape(2, 2, 2).astype(np.float32), dtype=ms.float32)
|
|
# graph mode
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
while_net = MyWhileNet()
|
|
net = GradNet(while_net)
|
|
graph_output = net(idx, end, x)
|
|
|
|
expect1 = np.array([[[4, 4], [4, 4]],
|
|
[[4, 4], [4, 4]]]).astype(np.float32)
|
|
expect2 = np.array([[[3, 3], [3, 3]],
|
|
[[3, 3], [3, 3]]]).astype(np.float32)
|
|
assert np.allclose(graph_output[0].asnumpy(), expect1, 0.0001, 0.0001)
|
|
assert np.allclose(graph_output[1].asnumpy(), expect2, 0.0001, 0.0001)
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_while_with_param_basic_grad_three():
|
|
class MyWhileNet(nn.Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.max = P.ReduceMax()
|
|
self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
|
|
self.weight = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="loss")
|
|
self.key = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="key")
|
|
self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)
|
|
self.t2 = Tensor(np.array(2), dtype=ms.float32)
|
|
|
|
def construct(self, idx, end, x):
|
|
out = self.zero
|
|
while idx < end:
|
|
out = out + self.param + self.weight + self.key
|
|
idx = idx + 1
|
|
return out + self.param
|
|
|
|
class GradNet(nn.Cell):
|
|
def __init__(self, net):
|
|
super(GradNet, self).__init__()
|
|
self.net = net
|
|
self.weights = ParameterTuple(net.trainable_params())
|
|
|
|
def construct(self, a, b, c):
|
|
return grad_by_list(self.net, self.weights)(a, b, c)
|
|
|
|
idx = Tensor(np.array(0), dtype=ms.int32)
|
|
end = Tensor(np.array(3), dtype=ms.int32)
|
|
x = Tensor(np.arange(8).reshape(2, 2, 2).astype(np.float32), dtype=ms.float32)
|
|
# graph mode
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
while_net = MyWhileNet()
|
|
net = GradNet(while_net)
|
|
graph_output = net(idx, end, x)
|
|
expect1 = np.array([[[4, 4], [4, 4]],
|
|
[[4, 4], [4, 4]]]).astype(np.float32)
|
|
expect2 = np.array([[[3, 3], [3, 3]],
|
|
[[3, 3], [3, 3]]]).astype(np.float32)
|
|
expect3 = np.array([[[3, 3], [3, 3]],
|
|
[[3, 3], [3, 3]]]).astype(np.float32)
|
|
assert np.allclose(graph_output[0].asnumpy(), expect1, 0.0001, 0.0001)
|
|
assert np.allclose(graph_output[1].asnumpy(), expect2, 0.0001, 0.0001)
|
|
assert np.allclose(graph_output[2].asnumpy(), expect3, 0.0001, 0.0001)
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_while_if_with_param_grad():
|
|
class MyWhileNet(nn.Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.max = P.ReduceMax()
|
|
self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
|
|
self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)
|
|
self.t2 = Tensor(np.array(2), dtype=ms.float32)
|
|
|
|
def construct(self, idx, end, x):
|
|
out = self.zero
|
|
while idx < end:
|
|
if self.max(out) < self.max(x):
|
|
out = out + self.param * 2
|
|
else:
|
|
out = out + self.param
|
|
idx = idx + 1
|
|
return out + self.param
|
|
|
|
class GradNet(nn.Cell):
|
|
def __init__(self, net):
|
|
super(GradNet, self).__init__()
|
|
self.net = net
|
|
self.weights = ParameterTuple(net.trainable_params())
|
|
|
|
def construct(self, a, b, c):
|
|
return grad_by_list(self.net, self.weights)(a, b, c)
|
|
|
|
idx = Tensor(np.array(0), dtype=ms.int32)
|
|
end = Tensor(np.array(3), dtype=ms.int32)
|
|
x = Tensor(np.ones([2, 2, 2]).astype(np.float32), dtype=ms.float32)
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
while_net = MyWhileNet()
|
|
net = GradNet(while_net)
|
|
graph_output = net(idx, end, x)
|
|
expect = np.array([[[5, 5], [5, 5]],
|
|
[[5, 5], [5, 5]]]).astype(np.float32)
|
|
assert np.allclose(graph_output[0].asnumpy(), expect, 0.0001, 0.0001)
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_while_with_param_grad_not_enter_while():
|
|
class MyWhileNet(nn.Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.max = P.ReduceMax()
|
|
self.param = Parameter(Tensor(2, ms.float32), name="weight")
|
|
self.zero = Tensor(0, ms.float32)
|
|
|
|
def construct(self, idx, end, x):
|
|
out = self.zero
|
|
while idx < end:
|
|
out = out + self.param * 3
|
|
idx = idx + 1
|
|
return out + self.param
|
|
|
|
class GradNet(nn.Cell):
|
|
def __init__(self, net):
|
|
super(GradNet, self).__init__()
|
|
self.net = net
|
|
self.weights = ParameterTuple(net.trainable_params())
|
|
|
|
def construct(self, a, b, c):
|
|
return grad_by_list(self.net, self.weights)(a, b, c)
|
|
|
|
idx = Tensor(np.array(3), dtype=ms.int32)
|
|
end = Tensor(np.array(0), dtype=ms.int32)
|
|
x = Tensor(2, dtype=ms.float32)
|
|
# graph mode
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
while_net = MyWhileNet()
|
|
net = GradNet(while_net)
|
|
graph_output = net(idx, end, x)
|
|
|
|
assert np.allclose(graph_output[0].asnumpy(), 1, 0.0001, 0.0001)
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_with_param_if_by_if_forward():
|
|
class MyIfByIfNet(nn.Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.max = P.ReduceMax()
|
|
self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
|
|
self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)
|
|
|
|
def construct(self, a, b, x):
|
|
out = self.zero
|
|
if a < b:
|
|
out = out + x + self.param
|
|
else:
|
|
out = out + x
|
|
if a == b:
|
|
out = out + x * 3 + self.param
|
|
else:
|
|
out = out + x * 2
|
|
return out
|
|
|
|
idx = Tensor(np.array(0), dtype=ms.int32)
|
|
end = Tensor(np.array(4), dtype=ms.int32)
|
|
x = Tensor(np.ones([2, 2, 2]).astype(np.float32), dtype=ms.float32)
|
|
# graph mode
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
if_net = MyIfByIfNet()
|
|
net = if_net
|
|
graph_output = net(idx, end, x)
|
|
expect = np.array([[[3, 4], [5, 6]],
|
|
[[7, 8], [9, 10]]]).astype(np.float32)
|
|
assert np.allclose(graph_output.asnumpy(), expect, 0.0001, 0.0001)
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_with_param_if_by_if_grad_inputs():
|
|
class MyIfByIfNet(nn.Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.max = P.ReduceMax()
|
|
self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
|
|
self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)
|
|
|
|
def construct(self, a, b, x):
|
|
out = self.zero
|
|
if a < b:
|
|
out = out + x + self.param * 4
|
|
if a == b:
|
|
out = out + x * 3 + self.param * 3
|
|
return out
|
|
|
|
class GradNet(nn.Cell):
|
|
def __init__(self, net):
|
|
super(GradNet, self).__init__()
|
|
self.net = net
|
|
|
|
def construct(self, *inputs):
|
|
return grad_all(self.net)(*inputs)
|
|
|
|
idx = Tensor(np.array(0), dtype=ms.int32)
|
|
end = Tensor(np.array(0), dtype=ms.int32)
|
|
x = Tensor(np.arange(8).reshape(2, 2, 2).astype(np.float32), dtype=ms.float32)
|
|
# graph mode
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
if_net = MyIfByIfNet()
|
|
net = GradNet(if_net)
|
|
graph_output = net(idx, end, x)
|
|
expect1 = Tensor(np.array(0), dtype=ms.int32)
|
|
expect2 = Tensor(np.array(0), dtype=ms.int32)
|
|
expect3 = np.array([[[3, 3], [3, 3]],
|
|
[[3, 3], [3, 3]]]).astype(np.float32)
|
|
assert np.allclose(graph_output[0].asnumpy(), expect1.asnumpy(), 0.0001, 0.0001)
|
|
assert np.allclose(graph_output[1].asnumpy(), expect2.asnumpy(), 0.0001, 0.0001)
|
|
assert np.allclose(graph_output[2].asnumpy(), expect3, 0.0001, 0.0001)
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_with_param_if_by_if_grad_parameter():
|
|
class MyIfByIfNet(nn.Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.max = P.ReduceMax()
|
|
self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
|
|
self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)
|
|
|
|
def construct(self, a, b, x):
|
|
out = self.zero
|
|
if a < b:
|
|
out = out + x + self.param * 2
|
|
if a == b:
|
|
out = out + x * 3 + self.param
|
|
return out
|
|
|
|
class GradNet(nn.Cell):
|
|
def __init__(self, net):
|
|
super(GradNet, self).__init__()
|
|
self.net = net
|
|
self.weights = ParameterTuple(net.trainable_params())
|
|
|
|
def construct(self, *inputs):
|
|
return grad_by_list(self.net, self.weights)(*inputs)
|
|
|
|
idx = Tensor(np.array(0), dtype=ms.int32)
|
|
end = Tensor(np.array(2), dtype=ms.int32)
|
|
x = Tensor(np.arange(8).reshape(2, 2, 2).astype(np.float32), dtype=ms.float32)
|
|
# graph mode
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
if_net = MyIfByIfNet()
|
|
net = GradNet(if_net)
|
|
graph_output = net(idx, end, x)
|
|
|
|
expect = np.array([[[2, 2], [2, 2]],
|
|
[[2, 2], [2, 2]]]).astype(np.float32)
|
|
assert np.allclose(graph_output[0].asnumpy(), expect, 0.0001, 0.0001)
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_with_param_if_by_if_grad_param_excute_null():
|
|
class MyIfByIfNet(nn.Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.max = P.ReduceMax()
|
|
self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
|
|
self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)
|
|
|
|
def construct(self, a, b, x):
|
|
out = self.zero
|
|
if a < b:
|
|
out = out + x + self.param * 2
|
|
return out
|
|
|
|
class GradNet(nn.Cell):
|
|
def __init__(self, net):
|
|
super(GradNet, self).__init__()
|
|
self.net = net
|
|
self.weights = ParameterTuple(net.trainable_params())
|
|
|
|
def construct(self, *inputs):
|
|
return grad_by_list(self.net, self.weights)(*inputs)
|
|
|
|
idx = Tensor(np.array(4), dtype=ms.int32)
|
|
end = Tensor(np.array(0), dtype=ms.int32)
|
|
x = Tensor(np.arange(8).reshape(2, 2, 2).astype(np.float32), dtype=ms.float32)
|
|
# graph mode
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
if_net = MyIfByIfNet()
|
|
net = GradNet(if_net)
|
|
graph_output = net(idx, end, x)
|
|
|
|
expect = np.array([[[0, 0], [0, 0]],
|
|
[[0, 0], [0, 0]]]).astype(np.float32)
|
|
assert np.allclose(graph_output[0].asnumpy(), expect, 0.0001, 0.0001)
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_if_by_if_return_inside_grad():
|
|
class MyIfByIfNet(nn.Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.max = P.ReduceMax()
|
|
self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
|
|
self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)
|
|
|
|
def construct(self, a, b, x):
|
|
out = self.zero
|
|
if a < b:
|
|
return out + x + self.param
|
|
if a == b:
|
|
return out + self.param * 2
|
|
return out + self.param * 3
|
|
|
|
class GradNet(nn.Cell):
|
|
def __init__(self, net):
|
|
super(GradNet, self).__init__()
|
|
self.net = net
|
|
self.weights = ParameterTuple(net.trainable_params())
|
|
|
|
def construct(self, *inputs):
|
|
return grad_by_list(self.net, self.weights)(*inputs)
|
|
|
|
idx = Tensor(np.array(1), dtype=ms.int32)
|
|
end = Tensor(np.array(0), dtype=ms.int32)
|
|
x = Tensor(np.arange(8).reshape(2, 2, 2).astype(np.float32), dtype=ms.float32)
|
|
# graph mode
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
if_net = MyIfByIfNet()
|
|
net = GradNet(if_net)
|
|
graph_output = net(idx, end, x)
|
|
|
|
expect = np.array([[[3, 3], [3, 3]],
|
|
[[3, 3], [3, 3]]]).astype(np.float32)
|
|
assert np.allclose(graph_output[0].asnumpy(), expect, 0.0001, 0.0001)
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_if_by_if_forward():
|
|
class MyIfByIfNet(nn.Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.add = P.Add()
|
|
self.sub = P.Sub()
|
|
self.mul = P.Mul()
|
|
self.div = P.RealDiv()
|
|
|
|
def construct(self, a, b, x):
|
|
if a < b:
|
|
a = self.add(a, b)
|
|
else:
|
|
a = self.sub(a, b)
|
|
if a == x:
|
|
a = self.mul(a, b)
|
|
else:
|
|
a = self.div(a, b)
|
|
if b == x:
|
|
b = self.add(a, b)
|
|
else:
|
|
b = self.add(a, x)
|
|
a = a * b
|
|
out = a + b + x
|
|
return out
|
|
|
|
idx = Tensor(np.array(2), dtype=ms.float32)
|
|
end = Tensor(np.array(3), dtype=ms.float32)
|
|
x = Tensor(np.array(4), dtype=ms.float32)
|
|
# graph mode
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
if_net = MyIfByIfNet()
|
|
net = if_net
|
|
graph_output = net(idx, end, x)
|
|
expect = 19.11111
|
|
assert np.allclose(graph_output.asnumpy(), expect, 0.0001, 0.0001)
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_if_by_if_forward_control_tuple_switch():
|
|
"""tuple_get from switch op will generate new switch inside to eliminate tuple_get"""
|
|
|
|
class Branch3Net(nn.Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.add = P.Add()
|
|
self.sub = P.Sub()
|
|
self.mul = P.Mul()
|
|
self.div = P.RealDiv()
|
|
|
|
def construct(self, a, b, x):
|
|
if b == x:
|
|
b = self.add(a, b)
|
|
else:
|
|
b = self.add(a, x)
|
|
return a, b, x
|
|
|
|
class Branch2Net(nn.Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.add = P.Add()
|
|
self.sub = P.Sub()
|
|
self.mul = P.Mul()
|
|
self.div = P.RealDiv()
|
|
self.net = Branch3Net()
|
|
|
|
def construct(self, a, b, x):
|
|
if a == x:
|
|
a = self.mul(a, b)
|
|
else:
|
|
a = self.div(a, b)
|
|
return self.net(a, b, x)
|
|
|
|
class MyIfByIfNet(nn.Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.add = P.Add()
|
|
self.sub = P.Sub()
|
|
self.mul = P.Mul()
|
|
self.div = P.RealDiv()
|
|
self.net = Branch2Net()
|
|
|
|
def construct(self, a, b, x):
|
|
if a < b:
|
|
a = self.add(a, b)
|
|
else:
|
|
a = self.sub(a, b)
|
|
a, b, x = self.net(a, b, x)
|
|
a = a * b
|
|
out = a + b + x
|
|
return out
|
|
|
|
idx = Tensor(np.array(2), dtype=ms.float32)
|
|
end = Tensor(np.array(3), dtype=ms.float32)
|
|
x = Tensor(np.array(0), dtype=ms.float32)
|
|
# graph mode
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
if_net = MyIfByIfNet()
|
|
net = if_net
|
|
graph_output = net(idx, end, x)
|
|
expect = 4.444444
|
|
assert np.allclose(graph_output.asnumpy(), expect, 0.0001, 0.0001)
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_if_by_if_forward_control_inside_net():
|
|
class Branch3Net(nn.Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.add = P.Add()
|
|
self.sub = P.Sub()
|
|
self.mul = P.Mul()
|
|
self.div = P.RealDiv()
|
|
|
|
def construct(self, a, b, x):
|
|
if b == x:
|
|
b = self.add(a, b)
|
|
else:
|
|
b = self.add(a, x)
|
|
a = a * b
|
|
out = a + b + x
|
|
return out
|
|
|
|
class Branch2Net(nn.Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.add = P.Add()
|
|
self.sub = P.Sub()
|
|
self.mul = P.Mul()
|
|
self.div = P.RealDiv()
|
|
self.net = Branch3Net()
|
|
|
|
def construct(self, a, b, x):
|
|
if a == x:
|
|
a = self.mul(a, b)
|
|
else:
|
|
a = self.div(a, b)
|
|
return self.net(a, b, x)
|
|
|
|
class MyIfByIfNet(nn.Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.add = P.Add()
|
|
self.sub = P.Sub()
|
|
self.mul = P.Mul()
|
|
self.div = P.RealDiv()
|
|
self.net = Branch2Net()
|
|
|
|
def construct(self, a, b, x):
|
|
if a < b:
|
|
a = self.add(a, b)
|
|
else:
|
|
a = self.sub(a, b)
|
|
out = self.net(a, b, x)
|
|
return out
|
|
|
|
idx = Tensor(np.array(2), dtype=ms.float32)
|
|
end = Tensor(np.array(3), dtype=ms.float32)
|
|
x = Tensor(np.array(0), dtype=ms.float32)
|
|
# graph mode
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
if_net = MyIfByIfNet()
|
|
net = if_net
|
|
graph_output = net(idx, end, x)
|
|
expect = 4.444444
|
|
assert np.allclose(graph_output.asnumpy(), expect, 0.0001, 0.0001)
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_if_by_if_forward_use_namespace():
|
|
class MyIfByIfNet(nn.Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.add = P.Add()
|
|
self.sub = P.Sub()
|
|
self.mul = P.Mul()
|
|
self.div = P.RealDiv()
|
|
|
|
def construct(self, a, b, x):
|
|
if a < b:
|
|
a = P.Add()(a, b)
|
|
else:
|
|
a = P.Sub()(a, b)
|
|
if a == x:
|
|
a = P.Mul()(a, b)
|
|
else:
|
|
a = P.RealDiv()(a, b)
|
|
if b == x:
|
|
b = P.Add()(a, b)
|
|
else:
|
|
b = P.Add()(a, x)
|
|
a = a * b
|
|
out = a + b + x
|
|
return out
|
|
|
|
idx = Tensor(np.array(2), dtype=ms.float32)
|
|
end = Tensor(np.array(3), dtype=ms.float32)
|
|
x = Tensor(np.array(0), dtype=ms.float32)
|
|
# graph mode
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
if_net = MyIfByIfNet()
|
|
net = if_net
|
|
graph_output = net(idx, end, x)
|
|
expect = 4.444444
|
|
assert np.allclose(graph_output.asnumpy(), expect, 0.0001, 0.0001)
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_if_by_if_forward_use_global_op():
|
|
class MyIfByIfNet(nn.Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.add = P.Add()
|
|
self.sub = P.Sub()
|
|
self.mul = P.Mul()
|
|
self.div = P.RealDiv()
|
|
|
|
def construct(self, a, b, x):
|
|
add = P.Add()
|
|
sub = P.Sub()
|
|
mul = P.Mul()
|
|
div = P.RealDiv()
|
|
if a < b:
|
|
a = add(a, b)
|
|
else:
|
|
a = sub(a, b)
|
|
if a == x:
|
|
a = mul(a, b)
|
|
else:
|
|
a = div(a, b)
|
|
if b == x:
|
|
b = add(a, b)
|
|
else:
|
|
b = add(a, x)
|
|
a = a * b
|
|
out = a + b + x
|
|
return out
|
|
|
|
idx = Tensor(np.array(2), dtype=ms.float32)
|
|
end = Tensor(np.array(3), dtype=ms.float32)
|
|
x = Tensor(np.array(0), dtype=ms.float32)
|
|
# graph mode
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
if_net = MyIfByIfNet()
|
|
net = if_net
|
|
graph_output = net(idx, end, x)
|
|
|
|
expect = 4.444444
|
|
assert np.allclose(graph_output.asnumpy(), expect, 0.0001, 0.0001)
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_for_with_if_by_if_forward():
|
|
class MyIfByIfNet(nn.Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.add = P.Add()
|
|
self.sub = P.Sub()
|
|
|
|
def construct(self, a, b, x):
|
|
for _ in range(0, 4):
|
|
if a < b:
|
|
a = self.add(a, b)
|
|
else:
|
|
b = self.sub(b, x)
|
|
a = a * b
|
|
out = a + b + x
|
|
return out
|
|
|
|
idx = Tensor(np.array(2), dtype=ms.float32)
|
|
end = Tensor(np.array(3), dtype=ms.float32)
|
|
x = Tensor(np.array(0), dtype=ms.float32)
|
|
# graph mode
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
if_net = MyIfByIfNet()
|
|
net = if_net
|
|
graph_output = net(idx, end, x)
|
|
|
|
expect = 18.0
|
|
assert np.allclose(graph_output.asnumpy(), expect, 0.0001, 0.0001)
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_for_with_if_by_if_forward_namespace():
|
|
class MyIfByIfNet(nn.Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.add = P.Add()
|
|
self.sub = P.Sub()
|
|
self.mul = P.Mul()
|
|
self.div = P.RealDiv()
|
|
|
|
def construct(self, a, b, x):
|
|
for _ in range(0, 6):
|
|
if a < b:
|
|
a = P.Add()(a, b)
|
|
else:
|
|
b = P.Sub()(b, x)
|
|
a = a * b
|
|
out = a + b + x
|
|
return out
|
|
|
|
idx = Tensor(np.array(2), dtype=ms.float32)
|
|
end = Tensor(np.array(3), dtype=ms.float32)
|
|
x = Tensor(np.array(0), dtype=ms.float32)
|
|
# graph mode
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
if_net = MyIfByIfNet()
|
|
net = if_net
|
|
graph_output = net(idx, end, x)
|
|
|
|
expect = 18.0
|
|
assert np.allclose(graph_output.asnumpy(), expect, 0.0001, 0.0001)
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_if_by_if_forward_const_branch_inner():
|
|
class MyIfByIfNet(nn.Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.add = P.Add()
|
|
self.sub = P.Sub()
|
|
self.mul = P.Mul()
|
|
self.div = P.RealDiv()
|
|
|
|
def construct(self, a, b, x):
|
|
add = P.Add()
|
|
sub = P.Sub()
|
|
mul = P.Mul()
|
|
div = P.RealDiv()
|
|
if a < b:
|
|
a = add(a, b)
|
|
else:
|
|
a = sub(a, b)
|
|
if 2 > 1:
|
|
a = mul(a, b)
|
|
else:
|
|
a = div(a, b)
|
|
if b == x:
|
|
b = add(a, b)
|
|
else:
|
|
b = add(a, x)
|
|
a = a * b
|
|
out = a + b + x
|
|
return out
|
|
|
|
idx = Tensor(np.array(2), dtype=ms.float32)
|
|
end = Tensor(np.array(3), dtype=ms.float32)
|
|
x = Tensor(np.array(0), dtype=ms.float32)
|
|
# graph mode
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
if_net = MyIfByIfNet()
|
|
net = if_net
|
|
graph_output = net(idx, end, x)
|
|
|
|
expect = 240.0
|
|
assert np.allclose(graph_output.asnumpy(), expect, 0.0001, 0.0001)
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_if_by_if_forward_all_const_branch():
|
|
class MyIfByIfNet(nn.Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.add = P.Add()
|
|
self.sub = P.Sub()
|
|
self.mul = P.Mul()
|
|
self.div = P.RealDiv()
|
|
|
|
def construct(self, a, b, x):
|
|
add = P.Add()
|
|
sub = P.Sub()
|
|
mul = P.Mul()
|
|
div = P.RealDiv()
|
|
if 2 < 12:
|
|
a = add(a, b)
|
|
else:
|
|
a = sub(a, b)
|
|
if 2 > 1:
|
|
a = mul(a, b)
|
|
else:
|
|
a = div(a, b)
|
|
if 2 == 1:
|
|
b = add(a, b)
|
|
else:
|
|
b = add(a, x)
|
|
a = a * b
|
|
out = a + b + x
|
|
return out
|
|
|
|
idx = Tensor(np.array(2), dtype=ms.float32)
|
|
end = Tensor(np.array(3), dtype=ms.float32)
|
|
x = Tensor(np.array(0), dtype=ms.float32)
|
|
# graph mode
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
if_net = MyIfByIfNet()
|
|
net = if_net
|
|
graph_output = net(idx, end, x)
|
|
|
|
expect = 240.0
|
|
assert np.allclose(graph_output.asnumpy(), expect, 0.0001, 0.0001)
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_if_const_grad():
|
|
class MyNet(nn.Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.add = P.Add()
|
|
|
|
def construct(self, *inputs):
|
|
out = self.add(*inputs)
|
|
return out
|
|
|
|
class GradNet(nn.Cell):
|
|
def __init__(self, net):
|
|
super(GradNet, self).__init__()
|
|
self.net = net
|
|
self.weights = ParameterTuple(net.trainable_params())
|
|
|
|
def construct(self, *inputs):
|
|
a = 1
|
|
b = 2
|
|
if a > 0:
|
|
b = 1
|
|
a += b
|
|
return grad_by_list(self.net, self.weights)(*inputs)
|
|
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
my_net = MyNet()
|
|
net = GradNet(my_net)
|
|
a = Tensor(np.array(0), dtype=ms.int32)
|
|
b = Tensor(np.array(1), dtype=ms.int32)
|
|
net(a, b)
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_if_by_if_const_grad():
|
|
class MyNet(nn.Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.add = P.Add()
|
|
|
|
def construct(self, *inputs):
|
|
out = self.add(*inputs)
|
|
return out
|
|
|
|
class GradNet(nn.Cell):
|
|
def __init__(self, net):
|
|
super(GradNet, self).__init__()
|
|
self.net = net
|
|
self.weights = ParameterTuple(net.trainable_params())
|
|
|
|
def construct(self, *inputs):
|
|
a = 1
|
|
b = 2
|
|
if a > 0:
|
|
b = 1
|
|
if a < 0:
|
|
b = 0
|
|
if a == 0:
|
|
b = 3
|
|
a += b
|
|
return grad_by_list(self.net, self.weights)(*inputs)
|
|
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
my_net = MyNet()
|
|
net = GradNet(my_net)
|
|
a = Tensor(np.array(0), dtype=ms.int32)
|
|
b = Tensor(np.array(1), dtype=ms.int32)
|
|
net(a, b)
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_while_const_grad():
|
|
class MyNet(nn.Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.add = P.Add()
|
|
|
|
def construct(self, *inputs):
|
|
out = self.add(*inputs)
|
|
return out
|
|
|
|
class GradNet(nn.Cell):
|
|
def __init__(self, net):
|
|
super(GradNet, self).__init__()
|
|
self.net = net
|
|
self.weights = ParameterTuple(net.trainable_params())
|
|
|
|
def construct(self, *inputs):
|
|
a = 1
|
|
while a > 1:
|
|
a = a - 1
|
|
return grad_by_list(self.net, self.weights)(*inputs)
|
|
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
my_net = MyNet()
|
|
net = GradNet(my_net)
|
|
a = Tensor(np.array(0), dtype=ms.int32)
|
|
b = Tensor(np.array(1), dtype=ms.int32)
|
|
net(a, b)
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_if_by_while_const_grad():
|
|
class MyNet(nn.Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.add = P.Add()
|
|
|
|
def construct(self, *inputs):
|
|
out = self.add(*inputs)
|
|
return out
|
|
|
|
class GradNet(nn.Cell):
|
|
def __init__(self, net):
|
|
super(GradNet, self).__init__()
|
|
self.net = net
|
|
self.weights = ParameterTuple(net.trainable_params())
|
|
|
|
def construct(self, *inputs):
|
|
a = 1
|
|
b = 2
|
|
if a > 0:
|
|
b = 0
|
|
while a > 1:
|
|
a = a - 1
|
|
a += b
|
|
return grad_by_list(self.net, self.weights)(*inputs)
|
|
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
my_net = MyNet()
|
|
net = GradNet(my_net)
|
|
a = Tensor(np.array(0), dtype=ms.int32)
|
|
b = Tensor(np.array(1), dtype=ms.int32)
|
|
net(a, b)
|