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[Profiler] Improve the docstring for export_memory_timeline (#110949)

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Summary: Add more details about the export_memory_timeline API, as we've landed new representations of the memory timeline data.

Test Plan: CI, should be no functional change, as we only changed comments.

Differential Revision: D50123450

Pull Request resolved: https://github.com/pytorch/pytorch/pull/110949
Approved by: https://github.com/davidberard98
This commit is contained in:
Aaron Shi
2023-10-10 17:53:56 +00:00
committed by PyTorch MergeBot
parent 31611b40b9
commit 52b1470935
1 changed files with 21 additions and 8 deletions
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29
torch/profiler/profiler.py
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@ -54,7 +54,8 @@ class _KinetoProfile:
``torch.profiler.ProfilerActivity.CPU``, ``torch.profiler.ProfilerActivity.CUDA``. ``torch.profiler.ProfilerActivity.CPU``, ``torch.profiler.ProfilerActivity.CUDA``.
Default value: ProfilerActivity.CPU and (when available) ProfilerActivity.CUDA. Default value: ProfilerActivity.CPU and (when available) ProfilerActivity.CUDA.
record_shapes (bool): save information about operator's input shapes. record_shapes (bool): save information about operator's input shapes.
profile_memory (bool): track tensor memory allocation/deallocation. profile_memory (bool): track tensor memory allocation/deallocation (see ``export_memory_timeline``
for more details).
with_stack (bool): record source information (file and line number) for the ops. with_stack (bool): record source information (file and line number) for the ops.
with_flops (bool): use formula to estimate the FLOPS of specific operators with_flops (bool): use formula to estimate the FLOPS of specific operators
(matrix multiplication and 2D convolution). (matrix multiplication and 2D convolution).
@ -251,14 +252,26 @@ class _KinetoProfile:
return MemoryProfile(self.profiler.kineto_results) return MemoryProfile(self.profiler.kineto_results)
def export_memory_timeline(self, path: str, device: Optional[str] = None) -> None: def export_memory_timeline(self, path: str, device: Optional[str] = None) -> None:
"""Extract the memory information from the memory profile collected """Export memory event information from the profiler collected
tree for a given device, and export a timeline plot consisting of tree for a given device, and export a timeline plot. There are 3
[times, [sizes by category]], where times are timestamps and sizes exportable files using ``export_memory_timeline``, each controlled by the
are memory usage for each category. The memory timeline plot will ``path``'s suffix.
be saved a JSON (by default) or gzipped JSON.
Input: (path of file, device) - For an HTML compatible plot, use the suffix ``.html``, and a memory timeline
Output: File written as JSON or gzipped JSON plot will be embedded as a PNG file in the HTML file.
- For plot points consisting of ``[times, [sizes by category]]``, where
``times`` are timestamps and ``sizes`` are memory usage for each category.
The memory timeline plot will be saved a JSON (``.json``) or gzipped JSON
(``.json.gz``) depending on the suffix.
- For raw memory points, use the suffix ``.raw.json.gz``. Each raw memory
event will consist of ``(timestamp, action, numbytes, category)``, where
``action`` is one of ``[PREEXISTING, CREATE, INCREMENT_VERSION, DESTROY]``,
and ``category`` is one of the enums from
``torch.profiler._memory_profiler.Category``.
Output: Memory timeline written as gzipped JSON, JSON, or HTML.
""" """
# Default to device 0, if unset. Fallback on cpu. # Default to device 0, if unset. Fallback on cpu.
if device is None and self.use_device and self.use_device != "cuda": if device is None and self.use_device and self.use_device != "cuda":