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97 lines
3.3 KiB
Python
97 lines
3.3 KiB
Python
# Copyright (c) 2025, NVIDIA CORPORATION. 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 torch.nn
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from omegaconf import DictConfig
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import nemo.core.optim.lr_scheduler
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from nemo.collections.speechlm2.parts.optim_setup import configure_optimizers, freeze_and_subset
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class DummyModel(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.linear = torch.nn.Linear(1, 1)
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self.conv = torch.nn.Conv1d(1, 1, 1)
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def forward(self, x):
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x = self.linear(x)
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x = self.conv(x)
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return x
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def test_freezing_params():
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model = DummyModel().train()
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assert model.linear.weight.requires_grad
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assert model.linear.bias.requires_grad
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assert model.conv.weight.requires_grad
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assert model.conv.bias.requires_grad
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params = freeze_and_subset(model.named_parameters(), exclude_patterns=[r"linear\..+"])
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list(params) # execute generator
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assert not model.linear.weight.requires_grad
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assert not model.linear.bias.requires_grad
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assert model.conv.weight.requires_grad
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assert model.conv.bias.requires_grad
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def test_keeping_unfrozen_params():
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model = DummyModel().train()
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assert model.linear.weight.requires_grad
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assert model.linear.bias.requires_grad
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assert model.conv.weight.requires_grad
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assert model.conv.bias.requires_grad
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params = freeze_and_subset(
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model.named_parameters(), exclude_patterns=[r"linear\..+"], keep_patterns=[r"linear.bias"]
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)
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list(params) # execute generator
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assert not model.linear.weight.requires_grad
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assert model.linear.bias.requires_grad
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assert model.conv.weight.requires_grad
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assert model.conv.bias.requires_grad
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def test_configure_optimizers():
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model = DummyModel()
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model.cfg = DictConfig(
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{
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"optimizer": {"_target_": "torch.optim.adamw.AdamW"},
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"freeze_params": [r"conv\..+"],
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}
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)
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ans = configure_optimizers(model)
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assert ans.keys() == {"optimizer"}
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assert isinstance(ans["optimizer"], torch.optim.AdamW)
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parameters = ans["optimizer"].param_groups[0]['params']
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assert len(parameters) == 2
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assert parameters[0] == model.linear.weight
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assert parameters[1] == model.linear.bias
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def test_configure_optimizers_with_lr_scheduler():
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model = DummyModel()
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model.cfg = DictConfig(
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{
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"optimizer": {"_target_": "torch.optim.adamw.AdamW"},
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"lr_scheduler": {
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"_target_": "nemo.core.optim.lr_scheduler.CosineAnnealing",
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"warmup_steps": 0,
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"min_lr": 1e-6,
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"max_steps": 100000,
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},
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}
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)
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ans = configure_optimizers(model)
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assert ans.keys() == {"optimizer", "lr_scheduler"}
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assert isinstance(ans["optimizer"], torch.optim.AdamW)
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assert isinstance(ans["lr_scheduler"]["scheduler"], nemo.core.optim.lr_scheduler.CosineAnnealing)
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