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 shutil
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import tempfile
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import unittest
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import numpy as np
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from hybrid_parallel_pp_transformer import ModelPipe, set_random_seed
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import paddle
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import paddle.distributed as dist
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from paddle.distributed import fleet
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batch_size = 8
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length = 8
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micro_batch_size = 2
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vocab_size = 128
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class TestDistPPSaveLoadTraining(unittest.TestCase):
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def setUp(self):
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strategy = fleet.DistributedStrategy()
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self.model_parallel_size = 1
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self.data_parallel_size = 1
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self.pipeline_parallel_size = 2
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strategy.hybrid_configs = {
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"dp_degree": self.data_parallel_size,
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"mp_degree": self.model_parallel_size,
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"pp_degree": self.pipeline_parallel_size,
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}
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strategy.pipeline_configs = {
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"accumulate_steps": batch_size // micro_batch_size,
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"micro_batch_size": micro_batch_size,
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}
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fleet.init(is_collective=True, strategy=strategy)
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def test_pp_model(self):
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hcg = fleet.get_hybrid_communicate_group()
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word_size = hcg.get_model_parallel_world_size()
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dp_id = hcg.get_data_parallel_rank()
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pp_id = hcg.get_stage_id()
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rank_id = dist.get_rank()
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topology = hcg.topology()
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set_random_seed(1024, dp_id, rank_id)
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model = ModelPipe(topology)
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scheduler = paddle.optimizer.lr.PiecewiseDecay(
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boundaries=[2], values=[0.001, 0.002], verbose=True
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)
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optimizer = paddle.optimizer.SGD(
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learning_rate=scheduler, parameters=model.parameters()
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)
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model = fleet.distributed_model(model)
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optimizer = fleet.distributed_optimizer(optimizer)
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output_dir = tempfile.mkdtemp()
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# warmup step
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for step_id in range(2):
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x_data = np.random.randint(0, vocab_size, size=[batch_size, length])
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x = paddle.to_tensor(x_data)
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x.stop_gradient = True
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loss = model.train_batch([x, x], optimizer, scheduler)
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model._layers.save_state_dict(output_dir)
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paddle.save(
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optimizer.state_dict(),
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os.path.join(output_dir, "model_state.pdopt"),
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)
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# construct data
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test_steps = 5
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np_data = np.random.randint(
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0, vocab_size, size=[test_steps, batch_size, length]
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)
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origin_loss = []
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for step_id in range(5):
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x_data = np_data[step_id, :]
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x = paddle.to_tensor(x_data)
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x.stop_gradient = True
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loss = model.train_batch([x, x], optimizer, scheduler)
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origin_loss.append(loss.numpy())
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# test step
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model._layers.set_state_dir(output_dir)
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opt_dict = paddle.load(os.path.join(output_dir, "model_state.pdopt"))
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optimizer.set_state_dict(opt_dict)
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for step_id in range(5):
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x_data = np_data[step_id, :]
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x = paddle.to_tensor(x_data)
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x.stop_gradient = True
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loss = model.train_batch([x, x], optimizer, scheduler)
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print(
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"origin loss: ",
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origin_loss[step_id],
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"current loss: ",
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loss.numpy(),
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)
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np.testing.assert_allclose(loss.numpy(), origin_loss[step_id])
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# finally, remove the model/optimizer path
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shutil.rmtree(output_dir)
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if __name__ == "__main__":
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unittest.main()
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