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[HybridParallel]Support fp16 in dygraph hybrid parallel (#36420)
* [HybridParallel]Support fp16 in dygraph hybrid parallel * update * update * update for recompute * add unittest of pp+fp16 * add unittest of recompute+fp16 * update * modify ut
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python/paddle/fluid/tests/unittests/hybrid_parallel_pp_fp16.py
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| # Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
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| from __future__ import division | ||
| from __future__ import print_function | ||
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| import unittest | ||
| import paddle | ||
| import numpy as np | ||
| import random | ||
| import paddle | ||
| import paddle.distributed as dist | ||
| import paddle.distributed.fleet as fleet | ||
| from hybrid_parallel_pp_layer import AlexNetPipeDesc, AlexNet | ||
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| def set_random_seed(seed, dp_id, rank_id): | ||
| """Set random seed for reproducability.""" | ||
| random.seed(seed) | ||
| np.random.seed(seed + dp_id) | ||
| paddle.seed(seed + dp_id) | ||
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| batch_size = 4 | ||
| micro_batch_size = 2 | ||
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| class TestDistPPTraning(unittest.TestCase): | ||
| def setUp(self): | ||
| strategy = fleet.DistributedStrategy() | ||
| self.model_parallel_size = 1 | ||
| self.data_parallel_size = 1 | ||
| self.pipeline_parallel_size = 2 | ||
| strategy.hybrid_configs = { | ||
| "dp_degree": self.data_parallel_size, | ||
| "mp_degree": self.model_parallel_size, | ||
| "pp_degree": self.pipeline_parallel_size, | ||
| } | ||
| strategy.pipeline_configs = { | ||
| "accumulate_steps": batch_size // micro_batch_size, | ||
| "micro_batch_size": micro_batch_size | ||
| } | ||
| fleet.init(is_collective=True, strategy=strategy) | ||
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| def test_pp_model(self): | ||
| hcg = fleet.get_hybrid_communicate_group() | ||
| word_size = hcg.get_model_parallel_world_size() | ||
| dp_id = hcg.get_data_parallel_rank() | ||
| pp_id = hcg.get_stage_id() | ||
| rank_id = dist.get_rank() | ||
| set_random_seed(1024, dp_id, rank_id) | ||
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| #construct model a | ||
| model_a = AlexNet(10) | ||
| scheduler_a = paddle.optimizer.lr.PiecewiseDecay( | ||
| boundaries=[2], values=[0.001, 0.002], verbose=True) | ||
| optimizer_a = paddle.optimizer.SGD(learning_rate=scheduler_a, | ||
| parameters=model_a.parameters()) | ||
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| scaler_a = paddle.amp.GradScaler(init_loss_scaling=2**5) | ||
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| # construct model b | ||
| model_b = AlexNetPipeDesc(num_stages=self.pipeline_parallel_size) | ||
| scheduler_b = paddle.optimizer.lr.PiecewiseDecay( | ||
| boundaries=[2], values=[0.001, 0.002], verbose=True) | ||
| optimizer_b = paddle.optimizer.SGD(learning_rate=scheduler_b, | ||
| parameters=model_b.parameters()) | ||
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| param_len = len(model_a.parameters()) | ||
| parameters = [] | ||
| for param in model_a.parameters(): | ||
| parameters.append(param.numpy()) | ||
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| for idx, param in enumerate(model_b.parameters()): | ||
| param.set_value(parameters[idx + pp_id * (param_len // 2)]) | ||
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| model_a, optimizer_a = paddle.amp.decorate( | ||
| models=model_a, | ||
| optimizers=optimizer_a, | ||
| level='O2', | ||
| save_dtype='float32') | ||
| model_b, optimizer_b = paddle.amp.decorate( | ||
| models=model_b, | ||
| optimizers=optimizer_b, | ||
| level='O2', | ||
| save_dtype='float32') | ||
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| model_b = fleet.distributed_model(model_b) | ||
| optimizer_b = fleet.distributed_optimizer(optimizer_b) | ||
| scaler_b = paddle.amp.GradScaler(init_loss_scaling=2**5) | ||
| scaler_b = fleet.distributed_scaler(scaler_b) | ||
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| # construct reader | ||
| train_reader = paddle.batch( | ||
| paddle.dataset.mnist.train(), batch_size=batch_size, drop_last=True) | ||
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| for step_id, data in enumerate(train_reader()): | ||
| x_data = np.array([x[0] for x in data]).astype('float32').reshape( | ||
| batch_size, 1, 28, 28) | ||
| y_data = np.array([x[1] for x in data]).astype('int64').reshape( | ||
| batch_size, 1) | ||
| img = paddle.to_tensor(x_data) | ||
| label = paddle.to_tensor(y_data) | ||
| img.stop_gradient = True | ||
| label.stop_gradient = True | ||
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| if step_id >= 5: | ||
| return True | ||
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| with paddle.amp.auto_cast(enable=True, level='O2'): | ||
| loss_a = model_a(img, label) | ||
| scaler_a.scale(loss_a).backward() | ||
| with paddle.amp.auto_cast(enable=False): | ||
| scaler_a.minimize(optimizer_a, loss_a) | ||
| optimizer_a.clear_grad() | ||
| scheduler_a.step() | ||
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| loss_b = model_b.train_batch( | ||
| [img, label], optimizer_b, scheduler_b, scaler=scaler_b) | ||
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| print("loss: ", loss_a.numpy(), loss_b.numpy()) | ||
| np.testing.assert_allclose( | ||
| loss_a.numpy(), loss_b.numpy(), rtol=5e-3) | ||
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| if __name__ == "__main__": | ||
| unittest.main() |
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