mlcommons / mlcommons/chakra

Can't get rf_id in kineto trace!!!, trace_link.py can't find relation of cpu_op.

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Python
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Description

chakra_device_trace_loader.py:

kineto_rf_id_to_kineto_op_map = {op.rf_id: op for op in kineto_cpu_ops if op.rf_id is not None}


kineto.jason:
{
    "ph": "X", "cat": "cpu_op", "name": "MseLossBackward0", "pid": 1182, "tid": 1331,
    "ts": 1726134043823422, "dur": 158,
    "args": {
      "External id": 2562,"Sequence number": 4583, "Fwd thread id": 1, "Ev Idx": 1
    }
  },
  {
    "ph": "X", "cat": "cpu_op", "name": "aten::mse_loss_backward", "pid": 1182, "tid": 1331,
    "ts": 1726134043823444, "dur": 135,
    "args": {
      "External id": 2563,"Ev Idx": 2
    }
  },
  {
    "ph": "X", "cat": "cpu_op", "name": "aten::zeros_like", "pid": 1182, "tid": 1331,
    "ts": 1726134043823452, "dur": 90,
    "args": {
      "External id": 2564,"Ev Idx": 3

**There is no rf_id entry, that means trace_linker.py can't find connection of TE.json and kineto.json. **

Maybe pytorch version is key

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Research direction

Start by reading chakra_device_trace_loader.py and trace_link.py (or trace_linker.py) to follow how rf_id connects Kineto and TE traces. Reproduce the example with the reported PyTorch version and inspect whether the Kineto cpu_op events contain rf_id; done means identifying the version or parsing condition that prevents the relation, with a verified trace-linking result or a documented incompatibility.

Written by the indexing model from the issue text.

Assessment

Tech stack
python, pytorch
Domain
performance, tooling
Issue type
Bug
Difficulty
4/5
Estimated time
3-5 days
Activity status
Stale
Clarity
Needs clarification
Newbie friendliness
30/100

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