57 lines
1.9 KiB
Python
57 lines
1.9 KiB
Python
# Copyright The Lightning AI team.
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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
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from lightning.fabric import seed_everything
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from lightning.pytorch import Trainer
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from lightning.pytorch.demos.boring_classes import BoringModel
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from tests_pytorch.helpers.runif import RunIf
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class FusedOptimizerParityModel(BoringModel):
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def __init__(self, fused=False):
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super().__init__()
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self.fused = fused
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def configure_optimizers(self):
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assert isinstance(self.trainer.precision_plugin.scaler, torch.cuda.amp.GradScaler)
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return torch.optim.Adam(self.parameters(), lr=1.0, fused=self.fused)
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@RunIf(min_torch="1.13", min_cuda_gpus=1)
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def test_amp_fused_optimizer_parity(tmpdir):
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def run(fused=False):
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seed_everything(1234)
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model = FusedOptimizerParityModel(fused)
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trainer = Trainer(
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default_root_dir=tmpdir,
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accelerator="cuda",
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devices=1,
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precision="16-mixed",
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max_steps=5,
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logger=False,
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enable_checkpointing=False,
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enable_progress_bar=False,
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enable_model_summary=False,
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)
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trainer.fit(model)
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return model.parameters()
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params = run(fused=False)
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params_fused = run(fused=True)
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# Both the regular and the fused version of Adam produce the same losses and model weights
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for p, q in zip(params, params_fused):
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torch.testing.assert_close(p, q)
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