forked from mindspore-Ecosystem/mindspore
182 lines
6.6 KiB
Python
182 lines
6.6 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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import numpy as np
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import pytest
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import mindspore.context as context
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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore.ops.operations import _grad_ops as G
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from mindspore.ops import functional as F
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from mindspore.ops.operations import _inner_ops as inner
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class NetInvGrad(nn.Cell):
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def __init__(self):
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super(NetInvGrad, self).__init__()
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self.grad = G.InvGrad()
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def construct(self, y, dy):
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return self.grad(y, dy)
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class InvGradDynamicShapeNet(nn.Cell):
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def __init__(self):
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super(InvGradDynamicShapeNet, self).__init__()
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self.grad = G.InvGrad()
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self.test_dynamic = inner.GpuConvertToDynamicShape()
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def construct(self, y, dy):
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y = self.test_dynamic(y)
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dy = self.test_dynamic(dy)
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return self.grad(y, dy)
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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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@pytest.mark.parametrize('mode', [context.GRAPH_MODE, context.PYNATIVE_MODE])
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def test_inv_grad_float32(mode):
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"""
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Feature: ALL To ALL
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Description: test cases for InvGrad for float32
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Expectation: the result match to numpy
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"""
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context.set_context(mode=mode, device_target="GPU")
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y = Tensor(np.array([[[[-1, 1, 12],
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[5, 34, 6],
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[10, 2, -1]]]]).astype(np.float32))
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dy = Tensor(np.array([[[[29, 1, 55],
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[2.2, 63, 2],
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[3, 3, 12]]]]).astype(np.float32))
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expect = np.array([[[[-29, -1, -7920],
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[-55, -72828, -72],
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[-300, -12, -12]]]]).astype(np.float32)
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net = NetInvGrad()
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output = net(y, dy)
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np.testing.assert_array_almost_equal(output.asnumpy(), expect)
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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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@pytest.mark.parametrize('mode', [context.GRAPH_MODE, context.PYNATIVE_MODE])
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def test_inv_grad_float16(mode):
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"""
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Feature: ALL To ALL
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Description: test cases for InvGrad for float16
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Expectation: the result match to numpy
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"""
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context.set_context(mode=mode, device_target="GPU")
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y = Tensor(np.array([[0.01, 0.2, 0.22],
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[10.002, 2, -1]]).astype(np.float16))
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dy = Tensor(np.array([[34, 1, 55],
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[3, 3, 63]]).astype(np.float16))
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expect = np.array([[-0.0034, -0.03998, -2.662],
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[-300, -12, -63]]).astype(np.float16)
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net = NetInvGrad()
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output = net(y, dy)
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np.testing.assert_array_almost_equal(output.asnumpy(), expect)
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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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@pytest.mark.parametrize('mode', [context.GRAPH_MODE, context.PYNATIVE_MODE])
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@pytest.mark.parametrize('dtype', [np.int8, np.int32])
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def test_inv_grad_int(mode, dtype):
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"""
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Feature: ALL To ALL
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Description: test cases for InvGrad for int
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Expectation: the result match to numpy
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"""
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context.set_context(mode=mode, device_target="GPU")
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y = Tensor(np.array([[-1, 1, 5],
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[5, 3, 6],
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[3, 2, -1]]).astype(dtype))
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dy = Tensor(np.array([[29, 1, -2],
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[2, -1, 2],
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[3, 1, 12]]).astype(dtype))
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expect = np.array([[-29, -1, 50],
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[-50, 9, -72],
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[-27, -4, -12]]).astype(dtype)
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net = NetInvGrad()
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output = net(y, dy)
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np.testing.assert_array_almost_equal(output.asnumpy(), expect)
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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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@pytest.mark.parametrize('mode', [context.GRAPH_MODE, context.PYNATIVE_MODE])
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def test_inv_grad_vmap(mode):
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"""
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Feature: test inv_grad vmap feature.
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Description: test inv_grad vmap feature.
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Expectation: Success.
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"""
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context.set_context(mode=mode, device_target="GPU")
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y = Tensor(np.array([[-1, 1, 12],
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[5, 34, 6],
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[10, 2, -1]]).astype(np.float32))
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dout = Tensor(np.array([[29, 1, 55],
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[2.2, 63, 2],
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[3, 3, 12]]).astype(np.float32))
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# Case 1
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output = F.vmap(NetInvGrad(), (0, 0), 0)(y, dout)
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expect_output = np.array([[-29, -1, -7920],
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[-55, -72828, -72],
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[-300, -12, -12]]).astype(np.float32)
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np.testing.assert_almost_equal(output.asnumpy(), expect_output)
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# Case 2
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output = F.vmap(NetInvGrad(), (0, 1), 0)(y, dout)
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expect_output = np.array([[-29, -2.2, -432],
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[-25, -72828, -108],
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[-5500, -8, -12]]).astype(np.float32)
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np.testing.assert_almost_equal(output.asnumpy(), expect_output)
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# Case 3
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output = F.vmap(NetInvGrad(), (0, 0), 1)(y, dout)
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expect_output = np.array([[-29, -55, -300],
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[-1, -72828, -12],
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[-7920, -72, -12]]).astype(np.float32)
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np.testing.assert_almost_equal(output.asnumpy(), expect_output)
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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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@pytest.mark.parametrize('mode', [context.GRAPH_MODE, context.PYNATIVE_MODE])
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def test_inv_grad_dynamic_shape(mode):
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"""
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Feature: test inv_grad dynamic_shape feature.
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Description: test inv_grad dynamic_shape feature.
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Expectation: Success.
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"""
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context.set_context(mode=mode, device_target="GPU")
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y = Tensor(np.array([[-1, 1, 12],
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[5, 34, 6],
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[10, 2, -1]]).astype(np.float32))
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dout = Tensor(np.array([[29, 1, 55],
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[2.2, 63, 2],
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[3, 3, 12]]).astype(np.float32))
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output = InvGradDynamicShapeNet()(y, dout)
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expect_output = np.array([[-29, -1, -7920],
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[-55, -72828, -72],
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[-300, -12, -12]]).astype(np.float32)
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np.testing.assert_almost_equal(output.asnumpy(), expect_output)
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