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
In this PR, we deprecate _preserve_ops feature in run_decomposition API. We can't kill this API completely because Executorch team depends on it. As the syncing between two repos is non-trivial, I just leave this argument as deprecated for now. In the next PR, i will immediately remove it.
After this PR, run_decompositions will only decompose what's inside the decomp table and preserve the rest by default. Note that this feature is only rolled out to OSS for now. Old code path is protected under IS_FBCODE flag.
Differential Revision: [D62163161](https://our.internmc.facebook.com/intern/diff/D62163161/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/135080
Approved by: https://github.com/justinchuby, https://github.com/avikchaudhuri, https://github.com/bdhirsh
In joint-graph export we have a `copy.deepcopy(ep.graph_module)` call. This turns out to be an imperfect deepcopy, because deepcopy allows objects to overwrite their `__deepcopy__` methods. For fx.Graph, this ends up deferring to `Graph.create_node()`, which checks the graph namespace, and can avoiding copying the exact name in niche examples, like where the name is a Python keyword (e.g. `input` gets renamed to `input_1`).
Names like `input` happen because export's placeholder naming pass overwrites what the namespace creates, based on the model's `forward()` signature. So we can either 1) avoid overwriting such cases, which requires rewriting the naming pass logic, or 2) force another overwrite after deepcopying. This goes with 2).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133269
Approved by: https://github.com/zhxchen17, https://github.com/dvorjackz, https://github.com/ydwu4
Recently we decided to split export IR into two different IRs (training vs inference). In the inference IR, one major change we decided to introduce was we wanted to keep the composite ops that user specified in the IR. This PR does that by overriding the CompositeImplicitAutograd decomp in export inference path.
Differential Revision: [D58701607](https://our.internmc.facebook.com/intern/diff/D58701607)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/128077
Approved by: https://github.com/bdhirsh
Summary:
WARNING: This API is highly unstable and will be subject to change in the future.
Add a protoype to "decompose" an ExportedProgram into a joint graph form, so that we can compute the gradients on this graph.
Test Plan: buck test mode/opt caffe2/torch/fb/export:test_experimental
Differential Revision: D55657917
Pull Request resolved: https://github.com/pytorch/pytorch/pull/128847
Approved by: https://github.com/tugsbayasgalan
