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Add the memory timeline profiling support through the PyTorch profile…
…r. (#2771) * v1 * fix issues * add logs * change names * comment * add device * uncomment original trace * add custome plot * fix pyright * Update composer/profiler/torch_profiler.py Co-authored-by: Charles Tang <j316chuck@users.noreply.github.com> * address comments * fix code check * fix formatting * address comments * add unit test * fix check * fix check * fix check * fix check * fix print * add test comment * add test comment --------- Co-authored-by: Mihir Patel <mihir.v.patel7@gmail.com> Co-authored-by: Charles Tang <j316chuck@users.noreply.github.com>
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,97 @@ | ||
| # Copyright 2022 MosaicML Composer authors | ||
| # SPDX-License-Identifier: Apache-2.0 | ||
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| """Utility functions for torch profiler.""" | ||
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| import importlib.util | ||
| import logging | ||
| from base64 import b64encode | ||
| from os import remove | ||
| from tempfile import NamedTemporaryFile | ||
| from typing import Any, Optional, Union | ||
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| import numpy as np | ||
| import torch | ||
| import torch.cuda | ||
| from packaging import version | ||
| from torch.profiler.profiler import profile as TorchProfile | ||
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| log = logging.getLogger(__name__) | ||
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| def export_memory_timeline_html(prof: TorchProfile, | ||
| path: str, | ||
| device: Optional[str] = None, | ||
| figsize=(20, 12), | ||
| title=None, | ||
| yxis_step_size: float = 1.0, | ||
| return_fig: bool = False) -> Optional[Union[None, Any]]: | ||
| """Exports a memory timeline to an HTML file. Similar to the PyTorch plotting function, but with adjusted axis tickers and grids.""" | ||
| if version.parse(torch.__version__) <= version.parse('2.1.0.dev'): | ||
| log.warning('export_memory_timeline_html failed because memory timeline is supported after PyTorch 2.1.0.') | ||
| return | ||
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| from torch.profiler._memory_profiler import _CATEGORY_TO_COLORS, _CATEGORY_TO_INDEX, MemoryProfileTimeline | ||
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| # Default to device 0, if unset. Fallback on cpu. | ||
| if device is None and prof.use_device and prof.use_device != 'cuda': | ||
| device = prof.use_device + ':0' | ||
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| if device is None: | ||
| device = 'cuda:0' if torch.cuda.is_available() else 'cpu' | ||
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| # Construct the memory timeline plot data | ||
| mem_tl = MemoryProfileTimeline(prof._memory_profile()) | ||
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| # Check if user has matplotlib installed, return gracefully if not. | ||
| matplotlib_spec = importlib.util.find_spec('matplotlib') | ||
| if matplotlib_spec is None: | ||
| log.warning('export_memory_timeline_html failed because matplotlib was not found.') | ||
| return | ||
| import matplotlib.pyplot as plt | ||
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| mt = mem_tl._coalesce_timeline(device) | ||
| times, sizes = np.array(mt[0]), np.array(mt[1]) | ||
| stacked = np.cumsum(sizes, axis=1) / 1024**3 | ||
| max_memory_allocated = torch.cuda.max_memory_allocated() | ||
| max_memory_reserved = torch.cuda.max_memory_reserved() | ||
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| # Plot memory timeline as stacked data | ||
| fig = plt.figure(figsize=figsize, dpi=80) | ||
| axes = fig.gca() | ||
| for category, color in _CATEGORY_TO_COLORS.items(): | ||
| i = _CATEGORY_TO_INDEX[category] | ||
| axes.fill_between(times / 1e3, stacked[:, i], stacked[:, i + 1], color=color, alpha=0.7) | ||
| fig.legend(['Unknown' if i is None else i.name for i in _CATEGORY_TO_COLORS]) | ||
| axes.set_xlabel('Time (us)') | ||
| axes.set_ylabel('Memory (GB)') | ||
| _, end = axes.get_ylim() | ||
| axes.grid(True) | ||
| axes.set_yticks(np.arange(0, end, yxis_step_size)) | ||
| title = '\n\n'.join(([title] if title else []) + [ | ||
| f'Max memory allocated: {max_memory_allocated/(10**9):.2f} GB \n' | ||
| f'Max memory reserved: {max_memory_reserved/(10**9):.2f} GB' | ||
| ]) | ||
| axes.set_title(title) | ||
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| if return_fig: | ||
| return fig | ||
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| # Embed the memory timeline image into the HTML file | ||
| tmpfile = NamedTemporaryFile('wb', suffix='.png', delete=False) | ||
| tmpfile.close() | ||
| fig.savefig(tmpfile.name, format='png') | ||
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| with open(tmpfile.name, 'rb') as tmp: | ||
| encoded = b64encode(tmp.read()).decode('utf-8') | ||
| html = f"""<html> | ||
| <head><meta charset="utf-8" /><title>GPU Memory Timeline HTML</title></head> | ||
| <body> | ||
| <img src='data:image/png;base64,{encoded}'> | ||
| </body> | ||
| </html>""" | ||
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| with open(path, 'w') as f: | ||
| f.write(html) | ||
| log.debug('Memory timeline exported to', path, '.') | ||
| remove(tmpfile.name) |
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,52 @@ | ||
| # Copyright 2022 MosaicML Composer authors | ||
| # SPDX-License-Identifier: Apache-2.0 | ||
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| import os | ||
| import pathlib | ||
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| import pytest | ||
| import torch | ||
| from packaging import version | ||
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| from composer.profiler.utils import export_memory_timeline_html | ||
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| @pytest.mark.gpu | ||
| def test_memory_timeline(tmp_path: pathlib.Path) -> None: | ||
| if version.parse(torch.__version__) <= version.parse('2.1.0.dev'): | ||
| # memory timeline is supported after PyTorch 2.1.0. | ||
| return | ||
| import torch.profiler._memory_profiler as _memory_profiler | ||
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| model = torch.nn.Sequential( | ||
| torch.nn.Linear(1024, 1024, bias=True), | ||
| torch.nn.ReLU(), | ||
| torch.nn.Linear(1024, 1024, bias=False), | ||
| torch.nn.Softmax(dim=1), | ||
| ).to('cuda') | ||
| optimizer = torch.optim.Adam(model.parameters(), lr=0.1) | ||
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| x = torch.ones((1024, 1024), device='cuda') | ||
| targets = torch.ones((1024, 1024), device='cuda') | ||
| with torch.profiler.profile(record_shapes=True, with_stack=True, profile_memory=True) as prof: | ||
| y = model(x) | ||
| loss = torch.nn.functional.mse_loss(y, targets) | ||
| loss.backward() | ||
| optimizer.step() | ||
| optimizer.zero_grad() | ||
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| memory_profile = prof._memory_profile() | ||
| timeline = memory_profile.timeline | ||
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| # this checks the default memory timeline event value (t == -1) for preexisting tensors | ||
| assert all((t == -1) if action == _memory_profiler.Action.PREEXISTING else (t > 0) for t, action, _, _ in timeline) | ||
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| fig = export_memory_timeline_html( | ||
| prof, | ||
| os.path.join(tmp_path, 'test_memory_timeline.html'), | ||
| yxis_step_size=0.01, | ||
| return_fig=True, | ||
| ) | ||
| assert fig is not None, 'export_memory_timeline_html should return a figure when return_fig=True' | ||
| _, end = fig.gca().get_ylim() | ||
| assert round(end, 2) == 0.06 |