feat(profiling): add support for pytorch profiling

[sanchda]

PR does

• Patches torch.profiler.profile class by adding our own on_trace_ready handler
• Adds GPU time/flops/memory samples via libdatadog interface in on_trace_ready event handler
• Ensures that libdd exporter is enabled if pytorch is enabled
• Hides functionality behind a FF set to False by default
• changelog entry
• Is there a minimum python version?
  • the biggest requirement is that the current pytorch profiler API which we instrument was introduced in torch version 1.8.1 (https://pytorch.org/blog/introducing-pytorch-profiler-the-new-and-improved-performance-tool/), do we just want to document or we could disable the instrumentation if we detect an outdated version with torch.__version__
• Some documentation on needed user configuration, conflicting features, gotchas

Probably should make experimental/beta collectors not part of the ALL template (Is this blocking since we haven't done in the past??)

Testing Done

• Tested by running on ec2 GPU instance
• Tested by running prof-pytorch service in staging
• I'm not entirely sure if we need unit tests for this feature, or where they would live. Would we want the unit test suite to depend on torch? Maybe this is solved for tracing integrations, though

Checklist

• Change(s) are motivated and described in the PR description
• Testing strategy is described if automated tests are not included in the PR
• Risks are described (performance impact, potential for breakage, maintainability)
• Change is maintainable (easy to change, telemetry, documentation)
• Library release note guidelines are followed or label changelog/no-changelog is set
• Documentation is included (in-code, generated user docs, public corp docs)
• Backport labels are set (if applicable)
• If this PR changes the public interface, I've notified @DataDog/apm-tees.

Reviewer Checklist

• Title is accurate
• All changes are related to the pull request's stated goal
• Description motivates each change
• Avoids breaking API changes
• Testing strategy adequately addresses listed risks
• Change is maintainable (easy to change, telemetry, documentation)
• Release note makes sense to a user of the library
• Author has acknowledged and discussed the performance implications of this PR as reported in the benchmarks PR comment
• Backport labels are set in a manner that is consistent with the release branch maintenance policy

[pr-commenter]

Benchmarks

Benchmark execution time: 2024-12-13 22:37:43

Comparing candidate commit 9393216 in PR branch peterg17/pytorch_profiling_integration2 with baseline commit 1dd528c in branch main.

Found 0 performance improvements and 0 performance regressions! Performance is the same for 394 metrics, 2 unstable metrics.

[peterg17]

Thanks!

[datadog-dd-trace-py-rkomorn]

Datadog Report

Branch report: peterg17/pytorch_profiling_integration2
Commit report: 28ed224
Test service: dd-trace-py

✅ 0 Failed, 389 Passed, 1219 Skipped, 44m 19.55s Total duration (42m 52.03s time saved)

PTO