forked from mindspore-Ecosystem/mindspore
104 lines
4.4 KiB
Python
104 lines
4.4 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 lstm """
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import pytest
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import mindspore.context as context
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from mindspore import nn
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from ..ut_filter import run_on_gpu
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from ....ops_common import convert
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class LstmTestNet(nn.Cell):
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""" LstmTestNet definition """
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def __init__(self, input_size, hidden_size, num_layers, has_bias, batch_first, bidirectional):
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super(LstmTestNet, self).__init__()
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self.lstm = nn.LSTM(input_size=input_size,
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hidden_size=hidden_size,
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num_layers=num_layers,
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has_bias=has_bias,
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batch_first=batch_first,
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bidirectional=bidirectional,
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dropout=0.0)
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def construct(self, inp, h0, c0):
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return self.lstm(inp, (h0, c0))
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test_case_cell_ops = [
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('lstm1_with_bias', {
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'cell': LstmTestNet(10, 12, 2, has_bias=True, batch_first=False, bidirectional=False),
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'input_shape': [[5, 3, 10], [2, 3, 12], [2, 3, 12]],
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'output_shape': [[5, 3, 12], [2, 3, 12], [2, 3, 12]]}),
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('lstm2_without_bias', {
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'cell': LstmTestNet(10, 12, 2, has_bias=False, batch_first=False, bidirectional=False),
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'input_shape': [[5, 3, 10], [2, 3, 12], [2, 3, 12]],
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'output_shape': [[5, 3, 12], [2, 3, 12], [2, 3, 12]]}),
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('lstm3_with_bias_bidirectional', {
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'cell': LstmTestNet(10, 12, 2, has_bias=True, batch_first=False, bidirectional=True),
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'input_shape': [[5, 3, 10], [4, 3, 12], [4, 3, 12]],
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'output_shape': [[5, 3, 24], [4, 3, 12], [4, 3, 12]]}),
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('lstm4_without_bias_bidirectional', {
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'cell': LstmTestNet(10, 12, 2, has_bias=False, batch_first=False, bidirectional=True),
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'input_shape': [[5, 3, 10], [4, 3, 12], [4, 3, 12]],
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'output_shape': [[5, 3, 24], [4, 3, 12], [4, 3, 12]]}),
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('lstm5_with_bias_batch_first', {
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'cell': LstmTestNet(10, 12, 2, has_bias=True, batch_first=True, bidirectional=False),
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'input_shape': [[3, 5, 10], [2, 3, 12], [2, 3, 12]],
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'output_shape': [[3, 5, 12], [2, 3, 12], [2, 3, 12]]}),
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('lstm6_without_bias_batch_first', {
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'cell': LstmTestNet(10, 12, 2, has_bias=False, batch_first=True, bidirectional=False),
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'input_shape': [[3, 5, 10], [2, 3, 12], [2, 3, 12]],
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'output_shape': [[3, 5, 12], [2, 3, 12], [2, 3, 12]]}),
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('lstm7_with_bias_bidirectional_batch_first', {
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'cell': LstmTestNet(10, 12, 2, has_bias=True, batch_first=True, bidirectional=True),
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'input_shape': [[3, 5, 10], [4, 3, 12], [4, 3, 12]],
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'output_shape': [[3, 5, 24], [4, 3, 12], [4, 3, 12]]}),
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('lstm8_without_bias_bidirectional_batch_first', {
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'cell': LstmTestNet(10, 12, 2, has_bias=False, batch_first=True, bidirectional=True),
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'input_shape': [[3, 5, 10], [4, 3, 12], [4, 3, 12]],
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'output_shape': [[3, 5, 24], [4, 3, 12], [4, 3, 12]]}),
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]
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# use -k to select certain testcast
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# pytest tests/python/ops/test_lstm.py::test_compile -k lstm_with_bias
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@pytest.mark.parametrize('args', test_case_cell_ops, ids=lambda x: x[0])
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def test_compile(args):
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config = args[1]
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shapes = config['input_shape']
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net = config['cell']
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net.set_train()
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inputs = [convert(shp) for shp in shapes]
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out = net(*inputs)
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print(f"out: {out}")
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@run_on_gpu
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@pytest.mark.parametrize('args', test_case_cell_ops, ids=lambda x: x[0])
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def test_execute(args):
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""" test_execute """
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config = args[1]
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shapes = config['input_shape']
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net = config['cell']
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net.set_train()
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inputs = [convert(shp) for shp in shapes]
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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# pylint: disable=unused-variable
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ret, (hn, cn) = net(*inputs)
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print(f'result: {shapes[0]} --> {ret.asnumpy().shape}, expected: {config["output_shape"][0]}')
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