----
- First, we add a new expanded_def to FX, which will expand the
definitions of variables into multiple lines, one per variable
definition. This makes extremely long args/return lists much
more readable.
- Next, we extend this mechanism to also print out descriptors on
placeholders and return values, as comments, if available. This
is how we will test descriptors.
- We update tlparse for AOTAutograd to use this format.
- We update expect tests to use this format and update their formats,
so you can inspect what it can look at. There may be other tests
I should update, open to suggestions.
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/158708
Approved by: https://github.com/wconstab
ghstack dependencies: #158624
Collects some scattershot improvements made while attempting to enable training for AOTInductor. Non-typing changes are:
1. Swapping a few custom searches for the output node in an FX graph for calling `graph.output_node()`.
2. Removing two unused parameters from `torch.export._unlift._unlift`.
3. Switching handles to constants in `cpp_wrapper_cpu` to use C++ references for memory efficiency.
4. Cleaning out unused, unexported imports from `torch/export/__init__.py`, and adding one missing export to `__all__`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/158075
Approved by: https://github.com/Skylion007
Fixes https://github.com/pytorch/pytorch/issues/158382
```
renamed: torch/_functorch/_aot_autograd/dispatch_and_compile_graph.py -> torch/_functorch/_aot_autograd/graph_capture.py
renamed: torch/_functorch/_aot_autograd/traced_function_transforms.py -> torch/_functorch/_aot_autograd/graph_capture_wrappers.py
renamed: torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py -> torch/_functorch/_aot_autograd/graph_compile.py
```
Everything else is ONLY import changes. I did not rename any functions
even if we probably should have.
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/158449
Approved by: https://github.com/jamesjwu
Basically adds native _IntWrapper support to dynamo. Here's my process of trying to make symint input support work on dynamo, and how I ended up with this approach [(doc)](https://docs.google.com/document/d/1GvNRQd8BnxlMay_hrEVgEta6VUeUW_hcFeRuB7q1nDY/edit?tab=t.0).
What I did was, before passing inputs to dynamo.export, I first wrap them with a class, `_IntWrapper`. When processing dynamic shapes, I will then add the corresponding dynamic shape specification to the `dynamism` field stored on the `_IntWrapper`. If there is no dynamism specified, then this will get unwrapped back to an integer. When dynamo tracing, when we encounter an `_IntWrapper`, we will convert this to a symint if the dynamism was specified as `Dim.DYNAMIC/AUTO`. Dynamo will then trace a graph that contains symint inputs, which will get passed to AOTAutograd and so on.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/152677
Approved by: https://github.com/pianpwk
Change logging.error to logging.exception to log additional information when relevant. A few places have slipped in logging.errors in try except since I last did a clean up here and the rule is stabilized so I am enabling it codebase wide. I have NOQA'd much of our custom exception stack trace handling for RPC calls and distributed and tried to a fix a few errors based on whether we immediately reraised it or if we didn't print any exception handling where it could be useful.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/153473
Approved by: https://github.com/albanD, https://github.com/cyyever
We need to make function schema proxyable to trace a the auto_functionalized hop that takes function schema as inputs. The implementation basically follows how we support torchbind object:
1. upon seeing an untracked function schema arg, we creates a constant get_attr node
2. we track the function schema argument in export to support lift/unlift.
3. we need to support serde for functional schema. We'll add support for this in follow-up PRs.
However, compared with torchbind object:
1. we don't need a dynamo implementation, because the function schema is added when we auto_functionalize a hop to the argument of auto_functionalized. One potential use case is users re-traces an exported program with strict mode. Since non-strict is the default now, we don't see a use case yet.
2. we don't need an inductor implementation, because the function schema will go away after auto_functionalized re-inplacing pass.
edit: we greatly simplifies (and generalizes) the implementation following @zou3519 's suggestion of using pytree.register_constant
Pull Request resolved: https://github.com/pytorch/pytorch/pull/152073
Approved by: https://github.com/zou3519
ghstack dependencies: #152072
Summary:
as title
`export._trace._WrapperModule` is used to wrap functions into a Module so we can export the function.
We add `export._wrapper_utils` to `dynamo`'s `MOD_INLINELIST` so dynamo traces into `_WrapperModule`
Fixes https://github.com/pytorch/pytorch/issues/146867
Test Plan:
```
buck run fbcode//mode/dev-nosan //caffe2/test:test_export -- -r wrapper_module
```
Differential Revision: D72986826
Pull Request resolved: https://github.com/pytorch/pytorch/pull/151264
Approved by: https://github.com/angelayi
Summary:
as title
`export._trace._WrapperModule` is used to wrap functions into a Module so we can export the function.
We add `export._wrapper_utils` to `dynamo`'s `MOD_INLINELIST` so dynamo traces into `_WrapperModule`
Fixes https://github.com/pytorch/pytorch/issues/146867
Test Plan:
```
buck run fbcode//mode/dev-nosan //caffe2/test:test_export -- -r wrapper_module
```
Differential Revision: D69434316
Pull Request resolved: https://github.com/pytorch/pytorch/pull/146919
Approved by: https://github.com/angelayi
Changes decomposition behavior of `aten.to` to respect the aliasing/non-aliasing behavior in eager, and to specialize to the input/conversion dtype & device.
Before change: we always decompose `aten.to` into `_to_copy`, regardless of aliasing behavior. This leads us to ban mutations on the result of `_to_copy` when aliased, since we can't guarantee correct program semantics. This meant users had to explicitly call `.clone()` before mutating. In the special cases where we don’t ban mutations (e.g. dtype conversion), we add runtime assertions on the input & conversion dtype/devices in the decomposed program (see https://github.com/pytorch/pytorch/pull/142420).
After change: we decompose to the aliasing/non-aliasing behavior that matches eager, allowing mutations in all cases. We also add dtype/device assertions for all `aten.to` ops, starting in the pre-dispatch graph, basically specializing the program to the dtype/devices.
Differential Revision: D71229547
Pull Request resolved: https://github.com/pytorch/pytorch/pull/149235
Approved by: https://github.com/tugsbayasgalan
Summary:
Instead of explicitly specifying dynamic shapes, it is possible to infer them from additional example inputs. Together with the example inputs provided to export, we can basically make any varying dim dynamic and keep any fixed dim static. This should be useful for prod scenarios that have access to tests and/or profiling data, yet are somewhat removed from the model authoring process.
However this alone is not satisfactory: the exported program by design has only one graph, representing one path through the model, and we cannot necessarily guarantee that this graph works for the additional example inputs because different guards might have been created if we had exported with them instead (corresponding to different traced paths). However, checking that the additional example inputs satisfy the guards created by the original export should be sufficient for generalization.
Now, while we don't preserve all guards in the exported program, we do check a subset of them as part of input matching. So we add a verification step at the end of export when such additional example inputs are provided. This should be enough for now.
Test Plan: added test (positive and negative cases)
Differential Revision: D72001771
Pull Request resolved: https://github.com/pytorch/pytorch/pull/150144
Approved by: https://github.com/bobrenjc93
Lazos correctly pointed out this doesn't make sense for compile since
we graph break in compile. This results in tons of unwanted user log
spew. We do want this in export though since it's drastiaclly reduced
the support load for DDEs. This PR does the refactor to keep it in
export but remove it from compile
Pull Request resolved: https://github.com/pytorch/pytorch/pull/149831
Approved by: https://github.com/mlazos
WHen we create constraints, we look at the ordering of kwargs according to model signature. But when we trace, we use the ordering that is created based on how user passes in their kwargs. As a result, constraints and dynamic shapes end up having a different order causing issues when they have different dynamic tensor specs.
Differential Revision: [D71478578](https://our.internmc.facebook.com/intern/diff/D71478578)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/149528
Approved by: https://github.com/ydwu4
Minor refactor to trace.py
* Removed `_strict_export_lower_to_aten_ir` in favor of just `_strict_export` and `_non_strict_export`
* Matched the APIs of `_strict_export` and `_non_strict_export`
* Instead of a `lower_to_aten_callback` which is a callable, or `dispatch_tracing_mode`, both functions take in a `_to_aten_func` which can be either `_export_to_aten_ir_make_fx` or `_export_to_aten_ir`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/149240
Approved by: https://github.com/pianpwk
Plan: avoid the use of unbacked renamings, and introduce a pass run in `_produce_aten_artifact` that recomputes unbacked bindings. Decided to do this because in we don't serialize unbacked renamings (or any ShapeEnv state), so this used to compose poorly with de/serialization. This hopefully establishes the invariant that the unbacked binding keys are always in sync with the example values (i.e. same indices, and removed if the symbol is replaced / specialized).
For de/serialization, we don't stored unbacked bindings, and just rerun the pass.
Involved a refactor of compute_unbacked_bindings.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/147574
Approved by: https://github.com/avikchaudhuri
Summary:
Previously we were touching up unbacked bindings between Dynamo and AOTAutograd in strict export, but the logic had a bug: if an unbacked symint gets substituted by a backed symint, we would put the backed symint in the unbacked bindings (the check `is_symbol` was not enough here).
This PR fixes this logic, and moreover, moves it into the serializer instead, because we don't need this adjustment outside serde.
Test Plan: added test
D68880766
Pull Request resolved: https://github.com/pytorch/pytorch/pull/146115
Approved by: https://github.com/pianpwk
Previously, in non-strict path, we always error when trying to inplace update a constant tensor because those constant tensors are not actually wrapped by functional tensors. This is correct behaviour in torch.compile, because dynamo makes all constant tensors into buffers and AOTDispatcher just lifts them and wraps them in functional tensors. However, in non-strict, there is no such step that registers constants as buffers so AOTDispatcher panics when it sees these dangling constant tensors when functioanalizing.
Due to recent change in the IR, this is no longer an issue in non-strict path because we don't call AOTDispatcher at training IR level, but now it is a problem for both strict and non-strict when we lower to inference. (lowering to inference is very similar to non-strict tracing) As a result, we have at least one external (https://github.com/pytorch/pytorch/issues/141336) and internal issues reported due to this difference.
To fix this, there are two ways:
1. Make functionalization be aware of constant tensors and map them to functional tensors on the fly. This makes functionalization invariant uglier and could potentially open up a gate for more nasty bugs.
2. Special handle this in export. This seems more aligned with what dynamo does today so i think we should do it this way. I think the current state could benefit from more refactors to make the run_deocmpositions to be more similar to strict export (because both of them now handle this constant registerinig logic) but it is bit complicated to do it now because strict export version of this logic is also not complete because it doesn't take into account of export graph renaming pass etc). I will follow up with more refactors after this PR (T213466691) to unblock users faster.
For future reference:
Why are we not doing "turning constants into non-persistent buffers and never de-register"? The reason is because in some internal models, they rely on module.to to reliably work to move params/buffers to correct device. As a result, buffers are moved while constants are not. In composibility meeting, we agreed that export won't do device agnostic tracing going forward (it will provide a way to specify FakeTensor in CPU that can be configured to be run on GPU), so after that is done, we can always turn constants into non-persistent buffers which will simplify export's constant handling.
Differential Revision: [D68610739](https://our.internmc.facebook.com/intern/diff/D68610739)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/145593
Approved by: https://github.com/avikchaudhuri
Summary:
Previously we were touching up unbacked bindings between Dynamo and AOTAutograd in strict export, but the logic had a bug: if an unbacked symint gets substituted by a backed symint, we would put the backed symint in the unbacked bindings (the check `is_symbol` was not enough here).
This PR fixes this logic, and moreover, moves it into the serializer instead, because we don't need this adjustment outside serde.
Test Plan: added test
Differential Revision: D68880766
Pull Request resolved: https://github.com/pytorch/pytorch/pull/146115
Approved by: https://github.com/pianpwk
Adds unbacked bindings during deserialization. These are carried by a node's metadata, and map pending fresh unbacked symbols to paths to such symbols inside the corresponding example value carried by the node's metadata.
Since it is awkward to serialize paths, we only serialize the names of these symbols and reconstruct the paths on deserialization, using a shape env util. We also need to bump counters for unbacked symbols here, because the shape env util we use to create these symbols (when deserializing example values) don't do so, and not doing so makes later passes (like `run_decompositions`) crash because new unbacked symbols don't get new names.
This is enough for non-strict. For strict, the unbacked bindings and example values in node metadata can get out of sync, because of running AOTAutograd as an additional step after Dynamo. So we have to sync those back.
Differential Revision: [D68232274](https://our.internmc.facebook.com/intern/diff/D68232274/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/144894
Approved by: https://github.com/pianpwk
Summary:
Add experimental support for torch.nn.Module as input types.
Before this change, we don't support module inputs but recently we saw some interesting use cases like gpt-fast https://github.com/pytorch-labs/gpt-fast/blob/main/generate.py#L68 where we directly pass in a module input for different variants of the same models.
Since we don't really care about non-param or non-buffer states in non strict mode, we don't care about those either and pretend they are like plain constants during tracing. We treat any module input like a nested container of tensor, and each time we will automatically register a pytree handler for these module types to flatten its state dict into a group of tensors. We will just inline any module method call during tracing like we did for `self` module in export_for_training. This will make input modules' behavior very similar to the training module in typical case, except that we don't record the inputs as parameter or buffers but rather just plain user inputs.
Test Plan: buck run mode/opt caffe2/test:test_export -- -r test_module_input
Differential Revision: D67680827
Pull Request resolved: https://github.com/pytorch/pytorch/pull/143925
Approved by: https://github.com/tugsbayasgalan
Summary:
A reland of https://github.com/pytorch/pytorch/pull/142426.
Copying the description over here:
For torch.export (strict and non-strict), we don't do functional decomposition. Instead, we preserve the custom triton ops as custom ops. This is because we want the exported program to be high-level and serializable.
The alternative:
If we decompose the custom op to a functional hop and make it a node in exported program, we need to figure out ways of serializing the hop and its arguments, which can be triton.jited python functions and triton dtypes. This is undesireble because:
it can be tedious to maintain layer that serialize the jited function (e.g. with a string) and dtypes.
changes to triton or the serialization logic for triton arguments can be BC breaking
exported program will expose the implementation detail (i.e. triton source code) for a specific backend (GPU) to users, which mixes levels of abstraction.
Future plans:
After this PR, in the short term, we expect users to have a seperate aot_compile stage that compiles the exported program into a Cubin file on the same machine that users call export, which does autotuning and removes triton dependency and serve the model with Cubin. This guarantees that triton changes won't break BC.
In the long term, we may export multiple cubins for the triton op directly.
Test Plan: see new tests.
Differential Revision: D67879685
Pull Request resolved: https://github.com/pytorch/pytorch/pull/144284
Approved by: https://github.com/zou3519
