-
Notifications
You must be signed in to change notification settings - Fork 5.6k
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
[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
- Loading branch information
1 parent
bdac9ff
commit 10f0a0f
Showing
8 changed files
with
257 additions
and
31 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
138 changes: 138 additions & 0 deletions
138
python/paddle/fluid/tests/unittests/hybrid_parallel_pp_fp16.py
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,138 @@ | ||
| # 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. | ||
|
|
||
| from __future__ import division | ||
| from __future__ import print_function | ||
|
|
||
| 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 | ||
|
|
||
|
|
||
| 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) | ||
|
|
||
|
|
||
| batch_size = 4 | ||
| micro_batch_size = 2 | ||
|
|
||
|
|
||
| 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) | ||
|
|
||
| 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) | ||
|
|
||
| #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()) | ||
|
|
||
| scaler_a = paddle.amp.GradScaler(init_loss_scaling=2**5) | ||
|
|
||
| # 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()) | ||
|
|
||
| param_len = len(model_a.parameters()) | ||
| parameters = [] | ||
| for param in model_a.parameters(): | ||
| parameters.append(param.numpy()) | ||
|
|
||
| for idx, param in enumerate(model_b.parameters()): | ||
| param.set_value(parameters[idx + pp_id * (param_len // 2)]) | ||
|
|
||
| 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') | ||
|
|
||
| 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) | ||
|
|
||
| # construct reader | ||
| train_reader = paddle.batch( | ||
| paddle.dataset.mnist.train(), batch_size=batch_size, drop_last=True) | ||
|
|
||
| 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 | ||
|
|
||
| if step_id >= 5: | ||
| return True | ||
|
|
||
| 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() | ||
|
|
||
| loss_b = model_b.train_batch( | ||
| [img, label], optimizer_b, scheduler_b, scaler=scaler_b) | ||
|
|
||
| print("loss: ", loss_a.numpy(), loss_b.numpy()) | ||
| np.testing.assert_allclose( | ||
| loss_a.numpy(), loss_b.numpy(), rtol=5e-3) | ||
|
|
||
|
|
||
| if __name__ == "__main__": | ||
| unittest.main() |
Oops, something went wrong.