forked from mindspore-Ecosystem/mindspore
193 lines
7.2 KiB
Python
193 lines
7.2 KiB
Python
# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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""" test_pynative_mixed_precision_cells """
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import pytest
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import numpy as np
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import mindspore as ms
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import mindspore.nn as nn
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import mindspore.ops.operations as P
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from mindspore import context
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from mindspore.nn import Cell
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from mindspore.nn import ReLU
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from mindspore.common.tensor import Tensor
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class MetaFactory:
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def __init__(self):
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self.device_target = context.get_context('device_target')
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self.rank_size = None
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self.device_id = None
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self.global_rank_id = None
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class ReluTanhSoftmax(Cell, MetaFactory):
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def __init__(self):
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super().__init__()
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MetaFactory.__init__(self)
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self.relu = ReLU()
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self.tanh = nn.Tanh()
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self.softmax = nn.Softmax()
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def construct(self, x):
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x = self.relu(x)
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y = self.tanh(x)
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z = self.softmax(x)
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return x, y, z
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class Add(Cell, MetaFactory):
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def __init__(self):
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super().__init__()
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MetaFactory.__init__(self)
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self.add = P.Add()
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def construct(self, x, y):
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return self.add(x, y)
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class ReluTanhAdd(Cell, MetaFactory):
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def __init__(self):
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super().__init__()
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MetaFactory.__init__(self)
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self.relu = ReLU()
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self.tanh = nn.Tanh()
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self.add = Add()
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def construct(self, x):
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x_1 = self.relu(x)
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y = self.tanh(x)
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x = self.add(x_1, y)
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return x
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def _count_unequal_element(data_expected, data_me, rtol, atol):
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assert data_expected.shape == data_me.shape
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total_count = len(data_expected.flatten())
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error = np.abs(data_expected - data_me)
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greater = np.greater(error, atol + np.abs(data_me)*rtol)
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loss_count = np.count_nonzero(greater)
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assert (loss_count/total_count) < rtol, \
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"\ndata_expected_std:{0}\ndata_me_error:{1}\nloss:{2}". \
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format(data_expected[greater], data_me[greater], error[greater])
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def allclose_nparray(data_expected, data_me, rtol, atol, equal_nan=True):
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if np.any(np.isnan(data_expected)):
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assert np.allclose(data_expected, data_me, rtol, atol, equal_nan=equal_nan)
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elif not np.allclose(data_expected, data_me, rtol, atol, equal_nan=equal_nan):
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_count_unequal_element(data_expected, data_me, rtol, atol)
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else:
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assert True
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def mixed_precision_multiple_cells_temp_01():
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np.random.seed(1)
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x = np.random.randn(1, 3, 28, 28).astype(np.float32)
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net = ReluTanhSoftmax()
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net.to_float(ms.float16)
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net.relu.to_float(ms.float32)
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net.softmax.to_float(ms.float16)
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out_me_relu_01, out_me_tanh_01, out_me_softmax_01 = net(Tensor(x))
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return out_me_relu_01, out_me_tanh_01, out_me_softmax_01
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def mixed_precision_multiple_cells_temp_02():
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np.random.seed(1)
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x = np.random.randn(1, 3, 28, 28).astype(np.float32)
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net = ReluTanhSoftmax()
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net.relu.to_float(ms.float32)
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net.softmax.to_float(ms.float16)
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net.to_float(ms.float16)
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out_me_relu_02, out_me_tanh_02, out_me_softmax_02 = net(Tensor(x))
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return out_me_relu_02, out_me_tanh_02, out_me_softmax_02
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def mixed_precision_multiple_cells_temp_03():
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np.random.seed(1)
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x = np.random.randn(1, 3, 28, 28).astype(np.float32)
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net = ReluTanhAdd()
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net.to_float(ms.float16)
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net.relu.to_float(ms.float32)
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net.add.to_float(ms.float32)
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out_me = net(Tensor(x))
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return out_me
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def mixed_precision_multiples_cell_01():
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context.set_context(mode=context.GRAPH_MODE, device_target=context.get_context('device_target'))
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graph_relu_01, graph_tanh_01, graph_softmax_01 = mixed_precision_multiple_cells_temp_01()
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context.set_context(mode=context.PYNATIVE_MODE, device_target=context.get_context('device_target'))
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pynative_relu_01, pynative_tanh_01, pynative_softmax_01 = mixed_precision_multiple_cells_temp_01()
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allclose_nparray(graph_relu_01.asnumpy(), pynative_relu_01.asnumpy(), 0.001, 0.001)
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allclose_nparray(graph_tanh_01.asnumpy(), pynative_tanh_01.asnumpy(), 0.001, 0.001)
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allclose_nparray(graph_softmax_01.asnumpy(), pynative_softmax_01.asnumpy(), 0.001, 0.001)
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def mixed_precision_multiples_cell_02():
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context.set_context(mode=context.GRAPH_MODE, device_target=context.get_context('device_target'))
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graph_relu_02, graph_tanh_02, graph_softmax_02 = mixed_precision_multiple_cells_temp_02()
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context.set_context(mode=context.PYNATIVE_MODE, device_target=context.get_context('device_target'))
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pynative_relu_02, pynative_tanh_02, pynative_softmax_02 = mixed_precision_multiple_cells_temp_02()
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allclose_nparray(graph_relu_02.asnumpy(), pynative_relu_02.asnumpy(), 0.001, 0.001)
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allclose_nparray(graph_tanh_02.asnumpy(), pynative_tanh_02.asnumpy(), 0.001, 0.001)
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allclose_nparray(graph_softmax_02.asnumpy(), pynative_softmax_02.asnumpy(), 0.001, 0.001)
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def mixed_precision_multiples_cell_03():
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context.set_context(mode=context.GRAPH_MODE, device_target=context.get_context('device_target'))
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graph_output_03 = mixed_precision_multiple_cells_temp_03()
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context.set_context(mode=context.PYNATIVE_MODE, device_target=context.get_context('device_target'))
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pynative_output_03 = mixed_precision_multiple_cells_temp_03()
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allclose_nparray(graph_output_03.asnumpy(), pynative_output_03.asnumpy(), 0.001, 0.001)
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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_ascend_training
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@pytest.mark.env_onecard
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def test_mixed_precision_multiples_cell_ascend_01():
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context.set_context(device_target="Ascend")
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mixed_precision_multiples_cell_01()
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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_mixed_precision_multiples_cell_gpu_01():
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context.set_context(device_target="GPU")
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mixed_precision_multiples_cell_01()
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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_ascend_training
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@pytest.mark.env_onecard
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def test_mixed_precision_multiples_cell_ascend_02():
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context.set_context(device_target="Ascend")
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mixed_precision_multiples_cell_02()
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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_mixed_precision_multiples_cell_gpu_02():
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context.set_context(device_target="GPU")
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mixed_precision_multiples_cell_02()
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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_ascend_training
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@pytest.mark.env_onecard
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def test_mixed_precision_multiples_cell_ascend_03():
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context.set_context(device_target="Ascend")
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mixed_precision_multiples_cell_03()
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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_mixed_precision_multiples_cell_gpu_03():
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context.set_context(device_target="GPU")
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mixed_precision_multiples_cell_03()
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