wehub-resource-sync
130 lines
3.8 KiB
Python
130 lines
3.8 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 unittest
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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.meta_parallel import (
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LayerDesc,
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PipelineLayer,
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SharedLayerDesc,
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)
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from paddle.nn import Layer
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hidden_size = 16
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class LocalLayer(Layer):
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def __init__(self):
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super().__init__()
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self.linear = nn.Linear(hidden_size, hidden_size)
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def forward(self, x):
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return self.linear(x)
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class TransformerLayer(Layer):
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def __init__(self):
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super().__init__()
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self.linear1 = nn.Linear(hidden_size, hidden_size)
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self.linear2 = nn.Linear(hidden_size, hidden_size)
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self.norm = nn.LayerNorm(hidden_size)
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@property
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def transformer_layer_weights(self):
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return self.named_parameters()
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def forward(self, x):
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return self.norm(self.linear2(self.linear1(x)))
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class MTPLayer(Layer):
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def __init__(self):
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super().__init__()
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self.transformer_layer = TransformerLayer()
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self.proj = nn.Linear(hidden_size, hidden_size)
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@property
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def transformer_layer_weights(self):
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return self.transformer_layer.named_parameters()
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def forward(self, x):
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return self.proj(self.transformer_layer(x))
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class SharedSubmodulePipe(PipelineLayer):
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def __init__(self, **kwargs):
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layers = [
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LayerDesc(LocalLayer),
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LayerDesc(LocalLayer),
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SharedLayerDesc(
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'shared_transformer',
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TransformerLayer,
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shared_weight_attr='transformer_layer_weights',
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shared_submodule_weight_only=True,
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),
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SharedLayerDesc(
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'shared_transformer',
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MTPLayer,
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shared_weight_attr='transformer_layer_weights',
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shared_submodule_weight_only=True,
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),
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]
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super().__init__(layers=layers, seg_method='layer:LocalLayer', **kwargs)
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class TestSharedSubmoduleWeightOnly(unittest.TestCase):
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def setUp(self):
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strategy = fleet.DistributedStrategy()
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strategy.hybrid_configs = {
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'dp_degree': 1,
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'mp_degree': 1,
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'pp_degree': 2,
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}
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strategy.pipeline_configs = {
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'accumulate_steps': 1,
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'micro_batch_size': 1,
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}
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strategy.hybrid_configs['pp_configs'].clear_every_step_cache = True
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fleet.init(is_collective=True, strategy=strategy)
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def test_shared_submodule_weight_only(self):
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hcg = fleet.get_hybrid_communicate_group()
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model = SharedSubmodulePipe(topology=hcg.topology())
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if hcg.get_stage_id() != 1:
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return
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transformer_layer = None
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mtp_layer = None
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for layer in model.run_function:
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if isinstance(layer, TransformerLayer):
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transformer_layer = layer
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elif isinstance(layer, MTPLayer):
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mtp_layer = layer
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source_params = dict(transformer_layer.named_parameters())
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for name, param in mtp_layer.transformer_layer.named_parameters():
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self.assertIs(
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param,
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source_params[name],
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f'{name} should be aliased to the source transformer layer.',
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)
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if __name__ == '__main__':
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unittest.main()
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