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feat(jit): add explicit module registration - #43
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Review comments at @aitune/torch/jit/patcher.py:
- Around line 392-417: Update Patcher._unpatch() to remove each wrapper from
Patcher._patched_modules even when _restore_original_forward() raises,
preserving the cleanup error while ensuring the registry cannot retain stale
wrappers. Keep jit_reset()’s reset state consistent with the emptied registry.
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CHANGELOG.mdREADME.mdaitune/torch/__init__.pyaitune/torch/jit/patched_module.pyaitune/torch/jit/patcher.pyaitune/torch/jit/registration.pyconftest.pydocs/guides/jit_tuning.mddocs/learn/core_functionalities.mddocs/learn/overview.mddocs/learn/quick_start.mdtests/unit/torch/jit/test_patcher.pytests/unit/torch/jit/test_registration.py
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- 🪄 Fix CodeRabbit comments on this PR
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Review comments at @aitune/torch/jit/patched_module.py:
- Around line 359-360: Update _forward_router to route DETACHED wrapper calls
directly to _original_forward before checking session-mode mismatches. Preserve
the existing mode check and error behavior for active wrappers.
Review comments at @aitune/torch/jit/patcher.py:
- Around line 405-406: Update the session report handling around _ensure_session
and jit_reset to track whether the JIT session started the tuning report. In
jit_reset, call report_tune_run_end only when this session owns the active
report, preserving reports started by other runs.
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Review comments at @aitune/torch/jit/patcher.py:
- Around line 172-180: In PatchedModule.__init__, assign _forward_hooks before
replacing the wrapped module’s forward with the proxy, so an assignment failure
leaves the original forward intact.
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aitune/torch/jit/patched_module.pyaitune/torch/jit/patcher.pydocs/guides/jit_tuning.mdtests/unit/torch/jit/test_patcher.pytests/unit/torch/jit/test_registration.pytests/unit/torch/jit/test_session_lifecycle.py
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Signed-off-by: Kacper Kleczewski <kkleczewski@nvidia.com>
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| module_counter: ClassVar[int] = 0 | ||
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| def __init__(self, module: torch.nn.Module): | ||
| def __init__(self, module: torch.nn.Module, *, explicit_head: bool = False): |
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What if we could have PassthroughPatchedModule that just useses _original_forward ?
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Would you need all this variables?
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Overrides for forward_router and unpatch ?
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| ### Registering existing modules | ||
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| If another library or runtime has already constructed the modules you want to tune, register those final instances |
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q: What do you mean by constructed?
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This means modules already existing (and for example already managed by Flex Tensor) before AiTune import.
For example:
model = from_pretrained("model")
# import aitune...
# register existing modules that wre created earlier.
registration = register_for_jit_tuning(model.blocks)
I will clarify this description. I also should clarify that 'register' feature, instead of wrapping at construction time of the model/pipeline, it is wrapping explicitly passed modules.
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| ## Overview | ||
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| Just-in-time tuning provides a zero-code-change approach to model tuning: |
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"a zero-code-change" was a marketing catchphrase. IMO it should stay.
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Agree, I will revert this
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Please add one functional test with the "constructed" module - managed externally.
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Or point me to the use case.
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First part of the review. Will be continued.
| - **Automatic Discovery**: Automatically detects PyTorch modules during execution | ||
| - **Zero Code Changes**: No need to modify your existing scripts | ||
| - **Hierarchical Tuning**: Recursively tunes modules from top to bottom | ||
| - **Explicit Registration**: Select already constructed module instances without enabling global interception | ||
| - **Hierarchical Tuning**: Recursively tunes automatically discovered modules from top to bottom | ||
| - **Configurable**: Fine-tune behavior through environment variables or configuration |
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Automatic and manual registration points are mixed together. IMO it would be better to leave original introduction and add below it that for cases where the model was already created or augmented like FT, there is explicit registration.
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I will separate both descriptions.
| Cached references to a registered module's `forward` remain callable after reset and run the original forward without | ||
| starting another tuning session. Reset finalizes only the tuning report created by JIT; reports owned by other tuning | ||
| runs remain active. If module restoration fails, reset retains the failed wrappers for cleanup retry and rejects new | ||
| registrations until a subsequent `jit_reset()` succeeds. |
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This is a bit unclear or complicated:
- run original forward - question: this shouldn't be possible for aot backends as they drop module?
- what does mean reset finalizes tuning report?
- reports owned by other tuning runs remain active - what are other runs and why are they active?
Perhaps I'd understand this by looking at the code, but from a user's perspective this doc is not clear.
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Thanks for catching this AOT. I agree that reseting AOT backend doesn't make sense. I will work on removing/refactoring this reset part as it seems over-engineered.
| @classmethod | ||
| def _raise_if_cleanup_pending(cls) -> None: | ||
| """Prevent a new tuning session from reusing partially cleaned wrappers.""" | ||
| if cls._cleanup_pending: |
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question: is it really doable to jit_reset() and register again? Perhaps the whole solution would be simpler if we would not allow it (e.g. there was exception during unpatching a module.)
| cls._raise_if_cleanup_pending() | ||
| if cls._explicit_registrations or any( | ||
| isinstance(module, PatchedModule) and module.explicit_head for module in cls._patched_modules | ||
| ): | ||
| raise RuntimeError( | ||
| "Global JIT constructor interception cannot run while explicit JIT registrations are active" | ||
| ) | ||
| cls._raise_if_session_mode_changed() | ||
| cls._ensure_exit_handler() | ||
| if cls._torch_patched: | ||
| return |
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Seems there is a lot of code protecting user for harming itself. Perhaps this all could be avoided by simply stating you can't mix automatic and explicit registartion and you cannot start twice the process.
| if not cls._cleanup_pending: | ||
| PatchedModule.module_counter = previous_module_counter | ||
| try: | ||
| cls._rollback_explicit_registration_session( |
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question: what is the use case for rollback? Seems this complicated the design a bit and in the end we raise exception.
| @classmethod | ||
| def _validate_registration_targets(cls, modules: Sequence[torch.nn.Module]) -> list[set[int]]: | ||
| """Validate exclusions and disjoint ownership without changing module state.""" | ||
| trees = [cls._module_tree_ids(module) for module in modules] |
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question: why don't we allow overlapping modules? I think torch inductor allows that.
I think the code could be simplified by just comparing duplicates len(trees) != len(set(trees))
| def _unique_modules(modules: list[torch.nn.Module]) -> list[torch.nn.Module]: | ||
| """Deduplicate targets by identity while retaining input order.""" | ||
| unique: list[torch.nn.Module] = [] | ||
| seen_module_ids: set[int] = set() | ||
| for module in modules: | ||
| if id(module) not in seen_module_ids: | ||
| seen_module_ids.add(id(module)) | ||
| unique.append(module) | ||
| return unique |
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question: what is the reason for dedup? Does a real model has duplicated modules?
nipick: this can be simplified to list({id(module): module for module in modules}.values())
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| def _materialize_modules(modules: Iterable[torch.nn.Module]) -> list[torch.nn.Module]: | ||
| """Materialize and validate an explicit module iterable without mutating JIT state.""" |
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nitpick: do we have to check list types if we have type info in the call chain? This function actually could be replaced with list(modules). materialize is a bit confusing IMO.
| self._name += f" 📊{formatted_params}" | ||
| self._call_count = 1 | ||
| if PatchedModule.stack: | ||
| if self._explicit_head: |
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question: do we need explicit head here? Would empty stack detect this as a head?
Signed-off-by: Kacper Kleczewski <kkleczewski@nvidia.com>
Signed-off-by: Kacper Kleczewski <kkleczewski@nvidia.com>
Signed-off-by: Kacper Kleczewski <kkleczewski@nvidia.com>
Signed-off-by: Kacper Kleczewski <kkleczewski@nvidia.com>
Signed-off-by: Kacper Kleczewski <kkleczewski@nvidia.com>
Signed-off-by: Kacper Kleczewski <kkleczewski@nvidia.com>
Signed-off-by: Kacper Kleczewski <kkleczewski@nvidia.com>
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Signed-off-by: Kacper Kleczewski <kkleczewski@nvidia.com>
Signed-off-by: Kacper Kleczewski <kkleczewski@nvidia.com>
Signed-off-by: Kacper Kleczewski <kkleczewski@nvidia.com>
Signed-off-by: Kacper Kleczewski <kkleczewski@nvidia.com>
Note
PR 1 of 3 in the dependency chain: this PR → PR #45 → PR #89. PR #44 is outside this chain.
Summary
Add
register_for_jit_tuning()andJITRegistrationto the publicaitune.torchAPI. Applications can register already-constructed and prepared PyTorch modules as independent JIT tuning targets.Behavior
[module]for one target. Passingnn.ModuleListdirectly registers its elements. Duplicate instances are deduplicated in input order.deferred()fromaitune.torch.jit.tuneand tunes on a subsequent forward call.modules, freshreports,state_countsandall_tuned. The tuned state does not guarantee acceleration; eager backends and successful dry runs can also reach it.Worker lifetime
Choose automatic discovery or explicit registration once per worker. Explicit registration takes one complete batch in an unused worker; repeat registration and later extension are unsupported. The JIT tuning mode is fixed after the first wrapper is installed, and explicit registration does not support inspection mode.
Installation is not transactional. An installation or finalization error propagates and requires a fresh worker; an installation failure can leave earlier targets patched. Ordinary backend-candidate failures retain the strategy's fallback behavior.
For Dynamo-based compilation, the caller configures finite compilation limits for the worker's input routes and shared forward frames. The caller warms the routes needed for serving. AITune does not reserve or restore compiler capacity.
Downstream FlexTensor integration
The optional FlexTensor shell/compute boundary and profiling-phase coordination belong to PR #89, via #45's external-placement API. The current integration fix does not change this PR's branch, registration contract or ordinary per-module tuning semantics, and introduces no FlexTensor dependency here. Native FT and LTX results are local validation of the cumulative downstream branch, not separate qualification of this PR's head.
The additive
prepare_flextensor_units(..., target=...)convenience setup is being added only in #89. It preserves manual setup and explicit startup/registration/tuning/replanning; it adds no commit, dependency or core behavior change to this branch. Its new validation is local to the cumulative integration worktree, not CI evidence for this head.Validation
At
cef4deb, the push CI run passes the CPU/GPU test jobs, Python 3.11–3.13 matrix, lint, pytype, documentation, license/header checks and package build. DCO and CodeQL also pass on that tested commit.Current branch head is
e9659c6; its CodeQL run passed. The full-CI evidence above refers to the explicitly named historical commit, not a newly verified full-suite run on this head.Regression coverage includes registration validation, prepared forwards/hooks, independent and nested targets, cached-forward fallback, finalization errors, reporting and fresh-process pytest isolation. Separate GPU functional workflows and provider integrations are not qualified by these CI results.