wehub-resource-sync
122 lines
3.9 KiB
Python
122 lines
3.9 KiB
Python
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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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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import os
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import unittest
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from legacy_test.test_parallel_dygraph_dataparallel import (
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TestMultipleAccelerators,
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)
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import paddle
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class TestHybridPipeParallel(TestMultipleAccelerators):
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def test_hybrid_parallel_pp_layer(self):
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self.run_mnist_2accelerators(
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os.path.abspath('../../legacy_test/hybrid_parallel_pp_layer.py')
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)
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def test_hybrid_parallel_pp_tuple_inputs(self):
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self.run_mnist_2accelerators('hybrid_parallel_pp_embedding.py')
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def test_pipeline_parallel_amp(self):
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self.run_mnist_2accelerators('hybrid_parallel_pp_amp.py')
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def test_pipeline_parallel_fp16(self):
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self.run_mnist_2accelerators('hybrid_parallel_pp_fp16.py')
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def test_pipeline_parallel_bf16(self):
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self.run_mnist_2accelerators('hybrid_parallel_pp_bf16.py')
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def test_hybrid_parallel_transformer(self):
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self.run_mnist_2accelerators('hybrid_parallel_pp_transformer.py')
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def test_hybrid_parallel_save_load(self):
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self.run_mnist_2accelerators('hybrid_parallel_pp_save_load.py')
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def test_hybrid_parallel_recompute(self):
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self.run_mnist_2accelerators('hybrid_parallel_pp_recompute.py')
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def test_hybrid_parallel_pp_clip_grad(self):
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self.run_mnist_2accelerators('hybrid_parallel_pp_clip_grad.py')
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def test_hybrid_parallel_transformer_unbalanced_data(self):
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self.run_mnist_2accelerators(
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'hybrid_parallel_pp_transformer_unbalanced_data.py'
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)
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def test_hybrid_parallel_pp_return_micro_batch_loss(self):
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self.run_mnist_2accelerators(
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'hybrid_parallel_pp_return_micro_batch_loss.py'
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)
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def test_hybrid_parallel_pp_with_eager_connect(self):
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os.environ["FLAGS_eager_communication_connection"] = "1"
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self.run_mnist_2accelerators(
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'hybrid_parallel_pp_return_micro_batch_loss.py'
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)
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os.environ["FLAGS_eager_communication_connection"] = "0"
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class TestFakeMicroDataSet(unittest.TestCase):
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def test_fake_micro_data_set(self):
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import numpy as np
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from paddle.distributed.fleet.meta_parallel.pipeline_parallel import (
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FakeMicroDataset,
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)
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batch_size = 4
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micro_batch_size = 2
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acc_step = 2
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length = 4
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x_data = np.random.randint(0, batch_size, size=[batch_size, length])
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data1 = paddle.to_tensor(x_data)
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data1.stop_gradient = True
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data2 = [
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data1[
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(i * micro_batch_size) : ((i + 1) * micro_batch_size), :
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].detach()
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for i in range(acc_step)
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]
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data3 = None
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batch = [(data1, data2, data3), None]
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for micro_batch in FakeMicroDataset(
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batch, True, False, acc_step, micro_batch_size
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):
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x, y = micro_batch
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self.assertEqual(len(x), 3)
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for e in [x[0], x[1]]:
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self.assertEqual(e.shape[0], micro_batch_size)
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self.assertEqual(e.shape[1], length)
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self.assertTrue(x[2] is None)
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self.assertTrue(y is None)
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# not first stage or last stage
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micro_batches = FakeMicroDataset(
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batch, False, False, acc_step, micro_batch_size
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)
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x, y = micro_batches._load_micro_batch(0)
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self.assertTrue(x is None)
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self.assertTrue(y is None)
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if __name__ == "__main__":
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unittest.main()
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