Fixes https://github.com/pytorch/pytorch/issues/122404
Previously, when rewriting c10d collectives, if the group argument is
unspecified or None, we create a world pg variable out of thin air and
pass it to the rewrite target. The approach was problematic, as it
assumes the symbol `torch` is available in the scope (see #122404).
After #120560, dynamo can now trace dist.group.WORLD. If the group
argument is unspecified, we can just set it with dist.group.WORLD in the
rewrite target.
Testing
pytest test/distributed/test_inductor_collectives.py -k test_dynamo_rewrite_dist_allreduce
Also verified with the repro provided in #122404
Pull Request resolved: https://github.com/pytorch/pytorch/pull/122561
Approved by: https://github.com/wconstab
ghstack dependencies: #120560
Additional changes: tests in test_functional_api.py uses multi-threaded pg which is implemented in Python. For the native ops to call into the Python pg implementation, glue code in PyProcessGroup is required for each collective. This PR also adds a few pieces of previously missing glue code, which are necessary for running test_functional_api.py with native funcol.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/119982
Approved by: https://github.com/wanchaol
The original motivation for MYPYINDUCTOR was a faster type checking configuration that only checked a subset of files. With the removal of `follow_imports = ignore`, we are now able to use dmypy to do fast incremental typechecking, eliminating the need for this.
Perhaps erroneously, when I tee'ed up this PR I elected to delete the `follow_imports = skip` designations in the mypy-inductor.ini. This lead to a number of extra type error suppressions that I manually edited. You will need to review.
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118432
Approved by: https://github.com/Skylion007
ghstack dependencies: #118414, #118418
This PR refactors the distributed related variables to use
DistributedVariable for common methods, so that things like
`python_type` works for all distributed variables.
Maybe we can add `as_python_constant` to the DistributedVariable too? I
didn't add in this PR but if that make sense I can update.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/117590
Approved by: https://github.com/voznesenskym
Summary:
Rename _device_mesh.py to device_mesh.py, update all callsites, add documentation.
We created stubs for public class and methods in torch.distributed.device_mesh so that torch.distributed.device_mesh can be imported with or without distributed is available().
Original diff reverted: D51629761
Original PR reverted: https://github.com/pytorch/pytorch/pull/115099
Prior to landing, CI signals are all passed. Shipit added the "ci/trunk" label to the PR and DID NOT wait for it and went ahead committing. More context can be found in the reverted PR above.
Test Plan: CI.
Differential Revision: D51861018
Pull Request resolved: https://github.com/pytorch/pytorch/pull/115193
Approved by: https://github.com/fegin
Summary:
Rename _device_mesh.py to device_mesh.py, update all callsites, adds documentation.
Original diff reverted: D51629761
Original PR reverted: https://github.com/pytorch/pytorch/pull/114991
It was failing because failing a public module binding tests in MacOS, and this is due to the change in import order for torch/distributed/fsdp/_common_utils.py. Since this original import would still work, we remove the changes in this file.
Test Plan: CI.
Differential Revision: D51825114
Pull Request resolved: https://github.com/pytorch/pytorch/pull/115099
Approved by: https://github.com/wanchaol, https://github.com/fegin
This PR mimics what we have done to trace ProcessGroup. This allows use to use FakeProcessGroup with torch.compile. FakeProcessGroup allows us to use world_size > 1 without creating multiple processes thus enabling the usage of PDB to debug bucketing DDP allreduce in the Inductor. We can theoretically use GLOO with world_size==1 to achieve the same goal. However, the `wait()` seems to be optimized away when the world_size is 1.
Differential Revision: [D51136463](https://our.internmc.facebook.com/intern/diff/D51136463/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/113314
Approved by: https://github.com/wanchaol
This PR adds initial dynamo support for DTensor, in particular, it:
- allows DTensor be passed into a compiled function, and allow fakify
DTensor during dynamo tracing by turning the inner local tensor to meta
tensor.
- We use `allow_in_graph` to include `DTensor` and `DTensor.from_local` to be represented as `TorchVariable`
- The dtensor created becomes a normal `TensorVariable` and it would insert any tensor operations to the output graph just like torch.Tensor
- note that dtensor have a new instance method `redistribute` compare to plain tensor, and we currently special handle it in `TensorVariable`
`from_local` and `redistribute` both accepts some non-trival metadata as arguments (i.e. DeviceMesh, Placement) which fx.Graph does not support. In order to let these two APIs appear in the dynamo captured graph, we encoded the metadata into a new_function (like `functools.partial`) and the new function only accepts prim args (i.e. tensor), then we put `call_function` with this new_function to the graph. This is suggested by @ezyang. The underlying rationale here is that the metadata will not change across the graph invocations so it's safe to encode them.
Captured graph:
```
def forward(self, L_x_ : torch.Tensor):
l_x_ = L_x_
# File: /scratch/wanchaol/work/pytorch/test/distributed/_tensor/test_dtensor.py:685, code: dt = DTensor.from_local(x, mesh, [Shard(0)], run_check=False)
prim_from_local = torch__dynamo_variables_torch_prim_from_local(l_x_, run_check = False); l_x_ = None
# File: /scratch/wanchaol/work/pytorch/test/distributed/_tensor/test_dtensor.py:686, code: return dt.redistribute(mesh, [Replicate()]).to_local() + 2
prim_redistribute = torch__dynamo_variables_tensor_prim_redistribute(prim_from_local); prim_from_local = None
to_local = prim_redistribute.to_local(); prim_redistribute = None
add = to_local + 2; to_local = None
return (add,)
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/103146
Approved by: https://github.com/voznesenskym
