73 lines
2.1 KiB
Python
73 lines
2.1 KiB
Python
# Copyright The PyTorch Lightning 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 os
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from multiprocessing import Event, Process
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import pytest
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from pytorch_lightning import Trainer
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from pytorch_lightning.profiler import XLAProfiler
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from pytorch_lightning.utilities import _TPU_AVAILABLE
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from tests.helpers import BoringModel
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from tests.helpers.runif import RunIf
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if _TPU_AVAILABLE:
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import torch_xla.debug.profiler as xp
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import torch_xla.utils.utils as xu
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@RunIf(tpu=True)
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def test_xla_profiler_instance(tmpdir):
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model = BoringModel()
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trainer = Trainer(
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default_root_dir=tmpdir,
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fast_dev_run=True,
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profiler="xla",
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tpu_cores=8,
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)
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assert isinstance(trainer.profiler, XLAProfiler)
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trainer.fit(model)
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assert trainer.state.finished, f"Training failed with {trainer.state}"
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@pytest.mark.skipif(True, reason="XLA Profiler doesn't support Prog. capture yet")
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def test_xla_profiler_prog_capture(tmpdir):
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port = xu.get_free_tcp_ports()[0]
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training_started = Event()
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def train_worker():
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model = BoringModel()
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trainer = Trainer(
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default_root_dir=tmpdir,
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max_epochs=4,
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profiler="xla",
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tpu_cores=8,
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)
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trainer.fit(model)
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p = Process(target=train_worker, daemon=True)
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p.start()
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training_started.wait(120)
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logdir = str(tmpdir)
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xp.trace(f'localhost:{port}', logdir, duration_ms=2000, num_tracing_attempts=5, delay_ms=1000)
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p.terminate()
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assert os.isfile(os.path.join(logdir, 'plugins', 'profile', '*', '*.xplane.pb'))
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