Prior to this patch, we are using `ConstantVariable.create` to create VT
for frozenset objects, and intended yet failed to predicate that on all
itmes being literals (see https://github.com/pytorch/pytorch/pull/140984#discussion_r1847393736).
The code was from https://github.com/pytorch/torchdynamo/commit/7c03434 and
the original goal was to help DBR quantization, but as the new test in
this patch shows, it could lead to silent incorrectness.
Upon a closer look, this exposes some subtleties in how Dynamo handles
`ConstantVariable` and `LOAD_CONST`, so this patch both fixes the
aforementioned issue and documents, enforces, and makes explicit the
invariants around `ConstantVariable` and `LOAD_CONST` -- only immutable
objects are supported.
Specifically, this patch:
1. refine the checks for wrapping a `frozenset` object, document why we
can't just wrap its items directly due to lack of `Sourcec` for set
items, and use a safe workaround (`SourcelessBuilder`) to ensure
soundness while keeping the DBR quantization support.
2. Adds more types to `common_constant_types`, thereby making
`ConstantVariable.is_base_literal` more lenient, and strictly checks
this property in the constructor of `ConstantVariable`.
3. Change relevant uses of `create_instruction("LOAD_CONST", ...)` to
`create_load_const` which checks `is_safe_constant`, and makes
developer overrides explicit by using `create_load_const_unchecked`
when needed.
4. In a few places, use more specific `VariableTracker`, e.g.,
`TypingVariable` rather than `ConstantVariable`, and
`FrozensetVariable` rather than `SetVariable`.
(2) and (3) are mainly to future-proof Dynamo against bugs like (1).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/141504
Approved by: https://github.com/jansel
reland of https://github.com/pytorch/pytorch/pull/133113
I have to create a new PR because the previous reverted PR could not either be rebased, or imported successfully :(
----
Moving DTensor to be in the public namespace, to formally add the documentation page that includes all the public APIs. This includes:
* many path renames and path import fixes
* a dedicated doc page without too much content yet (adding in the next PRs)
* To preserve the BC for users still using the torch.distributed._tensor, I added a shim script to redirect old path calls to the new module
The BC preserving is evidented by the fact that all DTensor tests are still working without changing the public imports. So it's safe to land the changes
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134203
Approved by: https://github.com/tianyu-l
Moving DTensor to be in the public namespace, to formally add the
documentation page that includes all the public APIs. This includes:
* many path renames and path import fixes
* a dedicated doc page without too much content yet (adding in the next
PRs)
* To preserve the BC for users still using the `torch.distributed._tensor`,
I added a shim script to redirect old path calls to the new module
The BC preserving is evidented by the fact that all DTensor tests are still
working without changing the public imports. So it's safe to land the
changes
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133113
Approved by: https://github.com/XilunWu
ghstack dependencies: #133305, #133306
Summary:
# context
* use FakeProcessGroup to mimic the multi-process tests
* can use `_test_compile_fake_pg_fn` as the single-process VB compile test
```
from torchrec.distributed.tests.test_pt2_multiprocess import _test_compile_fake_pg_fn
_test_compile_fake_pg_fn(
rank=0,
world_size=2,
)
```
reference: D59637444
Test Plan:
# run test
* run command and results: P1519228952, [tlparse](https://interncache-all.fbcdn.net/manifold/tlparse_reports/tree/logs/.tmpwMCK1E/index.html)
```
TORCH_TRACE=/var/tmp/tt TORCH_SHOW_CPP_STACKTRACES=1 TORCH_LOGS="+all" buck2 run fbcode//mode/opt fbcode//torchrec/distributed/tests:test_pt2_multiprocess
```
Differential Revision: D56124045
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133039
Approved by: https://github.com/ezyang
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
