RFC-0051-Structured-call_hierarchy-metadata-for-FX-nodes#92
RFC-0051-Structured-call_hierarchy-metadata-for-FX-nodes#92andrewd-aws wants to merge 1 commit intopytorch:masterfrom
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| The implementation builds on existing Dynamo infrastructure. During proxy creation, `stack_trace` is already built by walking the InstructionTranslator (tx) parent chain. `call_hierarchy` is built during the same walk: at each frame, if `nn_module_stack` has new keys compared to the previous frame, the frame is a module entry; otherwise it is a function entry (filtered for torch-internal and non-meaningful frames). Because both module and function entries come from the same ordered traversal, the interleaving is correct by construction. This works regardless of how modules override `__call__` or `forward`. Module invocation counts come from `nn_module_stack`'s existing `@N` key suffix. Function invocation counts are tracked via a `function_call_counts` dict shared across the tx chain, incremented when `InliningInstructionTranslator` is created. | ||
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| We can gate the feature by `torch._dynamo.config.record_call_hierarchy` (default `False`). When disabled, zero additional work is performed. Or, if overhead is low enough have it on by default. |
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I think this is an interesting callout - from the prototyped implementation, do we have a feeling for the runtime overhead?
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We're actively working on getting some numbers on this
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| ## Alternatives | ||
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| - Parse `stack_trace` strings at consumption time. This is what consumers do today and it is fragile, lacks invocation counts, and cannot be correctly interleaved with `nn_module_stack`. |
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I'mm interested in why this is fragile. stack_trace gives you the exact line, which one can use for line-level profiling and etc. Can you give a little more detail on why the new proposal would be easier to use than stack_trace?
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Would be helpful if you can describe a concrete use-case for the new node meta.
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Sorry for the delay on this.
Yes, I agree stack_trace works well for line-level profiling and pointing developers to source locations.
A concrete use case for us where this breaks down is with profiling, where we want to display each instruction annotated with its origin in the model as a hierarchical path, so users can expand and collapse the model structure to identify bottlenecks across related ops at any level of granularity. Building this path requires knowing, for each FX node, the complete interleaved sequence of modules and functions that produced it, including invocation counts to distinguish repeated calls to shared modules or helpers. nn_module_stack provides the module nesting but is missing function calls entirely, and stack_trace includes functions but filters out module frames and has no invocation counts; neither alone nor together can one infer this interleaved path with invocation counts correctly.
So essentially the difficulty is in reconstructing the hierarchical scope tree from what we currently output w.r.t. op provenance. Module frames from torch/nn/modules/ are filtered out by is_co_filename_from_nn_modules(), so module boundaries don't appear in the trace. You could try to recover them by parsing the source file at the line number indicated in the trace (e.g., read line 8, see self.qkv(x), infer the module), but this requires source file access at consumption time and I see this breaking for dynamic attribute access, generated code, etc. Invocation counts for shared modules or functions called in a loop are also unrecoverable. The proposed call_hierarchy would provide this information directly.
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RFC for call hierarchy metadata