wehub-resource-sync
118 lines
3.5 KiB
Python
118 lines
3.5 KiB
Python
# Copyright (c) 2025 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 numpy as np
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import paddle
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from paddle import nn
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from paddle.distributed import fleet
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from paddle.distributed.fleet.utils import mix_precision_utils
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from paddle.distributed.fsdp.fully_shard import fully_shard
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from paddle.distributed.sharding import group_sharded_parallel
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class Model(nn.Layer):
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def __init__(self):
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super().__init__()
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self.first_stage = nn.Linear(4096, 4096, bias_attr=False)
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self.center_stage = nn.Linear(4096, 4096)
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self.center_stage.weight.stop_gradient = True
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self.center_stage.bias.stop_gradient = True
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self.final_stage = nn.Linear(4096, 2, bias_attr=False)
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paddle.distributed.init_parallel_env()
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dist_strategy = fleet.DistributedStrategy()
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dist_strategy.hybrid_configs = {
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"sharding_degree": 2,
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"dp_degree": 1,
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"mp_degree": 1,
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"pp_degree": 1,
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}
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fleet.init(is_collective=True, strategy=dist_strategy)
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def forward(self, x):
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x = self.first_stage(x)
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x = self.center_stage(x)
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x = self.final_stage(x)
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return x
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def train_mlp(
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model,
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use_fsdp=True,
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data=None,
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use_pure_bf16=True,
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):
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model = mix_precision_utils.MixPrecisionLayer(model, dtype="bfloat16")
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clip = paddle.nn.ClipGradByGlobalNorm(0.5)
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optimizer = optimizer = paddle.optimizer.AdamW(
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learning_rate=0.001,
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parameters=model.parameters(),
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grad_clip=clip,
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multi_precision=use_pure_bf16,
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)
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optimizer = mix_precision_utils.MixPrecisionOptimizer(optimizer)
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if use_fsdp:
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model = fully_shard(model)
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else:
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model, optimizer, _ = group_sharded_parallel(
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model=model,
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optimizer=optimizer,
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level="p_g_os",
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sync_buffers=False,
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)
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losses = []
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for i in range(20):
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model.train()
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img = data[i]
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with paddle.amp.auto_cast(level='O1'):
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out = model(img)
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loss = out.mean()
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losses.append(loss.item())
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loss.backward()
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optimizer.step()
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optimizer.clear_grad()
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return losses
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def test_fsdp_api():
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# test sharding with fsdp api
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paddle.seed(2025)
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np.random.seed(2025)
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data = [paddle.randn([8, 4096]) for i in range(20)]
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model = Model()
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loss_fsdp = train_mlp(model, use_fsdp=True, data=data)
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# test sharding with group_sharded_parallel
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paddle.seed(2025)
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np.random.seed(2025)
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data = [paddle.randn([8, 4096]) for i in range(20)]
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model = Model()
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loss = train_mlp(model, use_fsdp=False, data=data)
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assert loss == loss_fsdp
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# test sharding with fsdp api with fp32
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paddle.seed(2025)
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np.random.seed(2025)
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data = [paddle.randn([8, 4096]) for i in range(20)]
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model = Model()
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loss = train_mlp(model, use_fsdp=True, data=data, use_pure_bf16=False)
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if __name__ == '__main__':
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test_fsdp_api()
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