Summary:
Add optional user-passed `alpha` argument to
`at::cuda::blas::scaled_gemm`, necessary for two-level-scaled NVFP4 gemm
calls (where the global de-scales are folded into the `alpha` argument.
Global de-scales are naturally device tensors, but using cublas'
device-pointer mode for `alpha`/`beta` has an interesting lifetime
implication - the `alpha` tensor must be valid & correct until the end
of the matmul call, *not* just the launch (as for host values). To
enable this, I added device-constant memory for `one` and `zero`, along
with a statically-held single-fp32-value tensor, which is valid from the
first passed-`alpha` invocation of `scaled_gemm` to the end of the
program. User-passed values are copied into this perpetual buffer to
ensure lifetime requirements are met.
Test Plan:
Reviewers:
Subscribers:
Tasks:
Tags:
Signed-off-by: Simon Layton <simonlayton@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165563
Approved by: https://github.com/drisspg, https://github.com/eqy
I'm cleaning this PR up as a proper way of disabling functionalization via config in AOTDispatcher. I removed the non-functionalization related changes from the original version:
(1) preventing proxy mode (and functionalization) from incorrectly decomposing CIA ops (Ed has a PR for it here: https://github.com/pytorch/pytorch/pull/164939)
(2) preventing python-dispatcher-based decomps above autograd from running. I'm not doing this for now, will likely do it in a followup
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164577
Approved by: https://github.com/ezyang
ghstack dependencies: #165372
A LocalTensor is a tensor subclass which simulates a tensor that is
distributed across SPMD ranks. A LocalTensor might be size N, but in fact
there are world_size shards/replicas of it stored internally. When you do a
plain PyTorch operation on it, we apply the operation to each shard; when you
do a collective, we do the mathematically equivalent operation on the local
shards. A LocalTensor is associated with a list of ranks which specify
which ranks it holds local tensors for.
NB, this is NOT a DataParallel like abstraction where you can run operations
on multiple different GPUs. It is intended purely for *debugging* purposes,
the overhead is almost certainly too high to keep eight GPUs (even the C++
autograd needs multithreading to keep up!) (It might potentially be possible
to trace through this with torch.compile and then compile it with CUDA graphs
but this is currently a non-goal.)
In order to handle MPMD, we provide a helper decorator that allows you to
run a function with no side effects for each LocalTensor shard and combine
results back into LocalTensor or LocalIntNode.
Note: This PR convert all DTensor ops and some DTensor tests to illustrate
intended usage and ensure conrrectness. In subsequent PR more tests will be
converted. DUring test conversion we aim to share as much as possible of
test logic between multi-process / multi-threaded and local tensor tests.
We would like to developers to be able to run both flavors of the tests.
Note: This work is based on the original proposal
by @ezyang (WIP PR https://github.com/pytorch/pytorch/pull/162753).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164537
Approved by: https://github.com/ezyang
This fixes AOTAutograd rms_norm not being bitwise equivalent to
eager, because it avoids a decomposition. You can force the
decomposition by having the decomposition in the dispatch table,
but if eager mode wouldn't have decomposed (because it went to the fused
one), we now default to preserving the fused call by default.
This largely reverts https://github.com/pytorch/pytorch/pull/103275/ for view ops. This means that in inference mode we could hit the wrong C++ kernel; if this occurs we should just SymInt'ify the C++ kernel.
Another neat side effect of this change is that Inductor's generated kernels for rms_norm now have rms_norm in their name.
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164939
Approved by: https://github.com/bdhirsh
DebugMode reports tensor type, it shapes and placements while active. This change augments reporting to tensor attributes from configured set. This feature is intended to be used to ease understanding debug string when dealing with larger outputs. For example, before running forward pass of a model we can annotate each of parameters and buffers with their fully qualified names, so that we can see which ops are being executed against specific tensors.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165109
Approved by: https://github.com/ezyang, https://github.com/pianpwk
This is follow-up of #165037. It generally recommended to use `is/is not` to compare types. Therefore this series of changes apply this suggestion in the code base, and it aims to finally enabling related linter checks.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165142
Approved by: https://github.com/albanD
It generally recommended to use `is/is not` to compare types. Therefore this series of changes apply this suggestion in the code base, and it aims to finally enabling related linter checks.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165037
Approved by: https://github.com/mlazos
This fixes AOTAutograd rms_norm not being bitwise equivalent to
eager, because it avoids a decomposition. You can force the
decomposition by having the decomposition in the dispatch table,
but if eager mode wouldn't have decomposed (because it went to the fused
one), we now default to preserving the fused call by default.
This largely reverts https://github.com/pytorch/pytorch/pull/103275/ for view ops. This means that in inference mode we could hit the wrong C++ kernel; if this occurs we should just SymInt'ify the C++ kernel.
Another neat side effect of this change is that Inductor's generated kernels for rms_norm now have rms_norm in their name.
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164939
Approved by: https://github.com/bdhirsh
This fixes AOTAutograd rms_norm not being bitwise equivalent to
eager, because it avoids a decomposition. You can force the
decomposition by having the decomposition in the dispatch table,
but if eager mode wouldn't have decomposed (because it went to the fused
one), we now default to preserving the fused call by default.
This largely reverts https://github.com/pytorch/pytorch/pull/103275/ for view ops. This means that in inference mode we could hit the wrong C++ kernel; if this occurs we should just SymInt'ify the C++ kernel.
Another neat side effect of this change is that Inductor's generated kernels for rms_norm now have rms_norm in their name.
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164939
Approved by: https://github.com/bdhirsh
ghstack dependencies: #164573
Fixes#156052 and #156444.
This PR setup the privateuseone key in Python to be used as a python backend for pytorch.
Meaning that, after calling `setup_privateuseone_for_python_backend('npy')`, one can use a subclass to with that device to hold arbitrary python data as "device data" and use `torch.library` to register ops that takes that Tensor.
Changes done in this PR:
1. Register an vanilla Device Guard: I extended NoOpDeviceGuard to have allow device index of 0 and to not raise errors when event related functions are accessed. If I don't do those, when calling backward I would get errors. (CPU backend uses NoOpDeviceGuard just fine, although there seems to be special treatment of CPU in the autograd engine.
2. Tensor subclass allows not having `__torch_dispatch__` if the device is not CUDA or CPU. The comment of the check suggests it was to avoid segfault when calling into ops that expects a storage. Here we have a different device so will not call into those ops.
3. python function that invokes the other incantations to setup the privateusekey backend.
This took inspiration of https://github.com/bdhirsh/pytorch_open_registration_example and https://github.com/tinygrad/tinygrad/blob/master/extra/torch_backend/wrapped_tensor.cpp; great thanks to @bdhirsh and @geohot.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/157859
Approved by: https://github.com/albanD
To run this, you need to install `mingw64-gcc-c++` and download windows cuda library toolkit.
See design doc and demo instructions in https://docs.google.com/document/d/1iDaChqA5nNKkBFTzsdkmoomvQlXHbnlb1Z4yEp7xaJA/edit?tab=t.0
If cross_platform_target is windows, we do the following:
- do not link to `sleef`. This can be improved in the future if we need it. Currently I avoid it because that requires extra setup on the linux side
- Use `mingw64-gcc-c++` to compile
- Use `WINDOWS_CUDA_HOME` instead of `CUDA_HOME` when linking to cuda
```
python test/inductor/test_aot_inductor_windows.py -k so
```
Other changes:
- de-couples compile_standalone config and dynamic link flag
- create a new aot_inductor_mode config module, which is used to control configs in aot_inductor.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/163188
Approved by: https://github.com/desertfire
Fixes#156052 and #156444.
This PR setup the privateuseone key in Python to be used as a python backend for pytorch.
Meaning that, after calling `setup_privateuseone_for_python_backend('npy')`, one can use a subclass to with that device to hold arbitrary python data as "device data" and use `torch.library` to register ops that takes that Tensor.
Changes done in this PR:
1. Register an vanilla Device Guard: I extended NoOpDeviceGuard to have allow device index of 0 and to not raise errors when event related functions are accessed. If I don't do those, when calling backward I would get errors. (CPU backend uses NoOpDeviceGuard just fine, although there seems to be special treatment of CPU in the autograd engine.
2. Tensor subclass allows not having `__torch_dispatch__` if the device is not CUDA or CPU. The comment of the check suggests it was to avoid segfault when calling into ops that expects a storage. Here we have a different device so will not call into those ops.
3. python function that invokes the other incantations to setup the privateusekey backend.
This took inspiration of https://github.com/bdhirsh/pytorch_open_registration_example and https://github.com/tinygrad/tinygrad/blob/master/extra/torch_backend/wrapped_tensor.cpp; great thanks to @bdhirsh and @geohot.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/157859
Approved by: https://github.com/albanD
