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The `handle_kineto_segmentation` function is intended to support kineto traces cross multiple iterations by splitting a trace into several segments according to the provided annotations. Unfortunately, this function is not operating as expected, leading to errors. It is advisable to remove it.
The multi-iteration support feature for PyTorch execution traces is designed to facilitate the handling of traces over multiple iterations. Unfortunately, this feature is not functioning as expected and is leading to errors. It is advisable to remove it.
This commit introduces support for inter-thread dependencies within the Chakra framework. By examining Kineto traces via chrome://tracing, one can observe multiple CPU threads and their implicit dependencies. This update explicitly encodes these dependencies in the output trace, enabling accurate handling by subsequent tools.
This commit adds stream ID encoding to GPU operators. This ensures that all operators within the same stream are executed in the correct order, supporting intra-stream dependencies.
Introduced exclusive duration calculation for Kineto operators in the TraceLinker class. This update differentiates between inclusive and exclusive durations, providing a clearer distinction in the profiling data. Exclusive durations are now calculated to identify the actual time spent in individual operations, excluding overlaps with child operators.
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srinivas212
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May 9, 2024
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Summary
Import and refactor trace_link.py
Test Plan
1. Run trace_link
2. Run et_converter
3. Results
