Add similar semantics for creating a buffer object similar to creating a parameter. This is done by introducing a new Buffer class that can be used for type disambiguation. The underlying functionality of registering a buffer remains the same as the register_buffer method has not been changed. The persistent parameter in the Buffer type is to indicate whether a buffer object should be persistent or not. Other non-test changes have to do with getting the new Buffer type recognized by inductor and dynamo. Remaining changes are test changes to make sure that the Buffer type can be used as a drop in replacement for register_buffer as it just leads to register_buffer being called. The addition of this new functionality still allows for normal tensors to be used as buffers so these changes are intended to be backwards compatible.
Fixes#35735
Co-authored-by: Mikayla Gawarecki <mikaylagawarecki@gmail.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/125971
Approved by: https://github.com/albanD, https://github.com/anijain2305, https://github.com/mlazos
Fixes#68972
Relands #107246
To avoid causing Meta-internal CI failures, this PR avoids always asserting that the default dtype is float in the `TestCase.setUp/tearDown` methods. Instead, the assert is only done if `TestCase._default_dtype_check_enabled == True`. `_default_dtype_check_enabled` is set to True in the `if __name__ == "__main__":` blocks of all the relevant test files that have required changes for this issue
Pull Request resolved: https://github.com/pytorch/pytorch/pull/108088
Approved by: https://github.com/ezyang
Add similar semantics for creating a buffer object similar to creating a parameter. This is done by introducing a new `Buffer` class that can be used for type disambiguation. The underlying functionality of registering a buffer remains the same as the `register_buffer` method has not been changed. The `persistent` parameter in the `Buffer` type is to indicate whether a buffer object should be persistent or not. Other non-test changes have to do with getting the new `Buffer` type recognized by inductor and dynamo. Remaining changes are test changes to make sure that the `Buffer` type can be used as a drop in replacement for `register_buffer` as it just leads to `register_buffer` being called. The addition of this new functionality still allows for normal tensors to be used as buffers so these changes are intended to be backwards compatible.
Fixes#35735
Pull Request resolved: https://github.com/pytorch/pytorch/pull/104069
Approved by: https://github.com/mikaylagawarecki
Summary:
The only difference with plain list/dict now is that nn.Parameters are
handled specially and registered as parameters properly.
test_nn and parametrization works locally.
Will see in CI if DP is fixed as well.
Tentative fix for https://github.com/pytorch/pytorch/issues/36035
Pull Request resolved: https://github.com/pytorch/pytorch/pull/70499
Reviewed By: jbschlosser, alexeib
Differential Revision: D34005332
Pulled By: albanD
fbshipit-source-id: 7e76b0873d0fec345cb537e2a6ecba0258e662b9
(cherry picked from commit dc1e6f8d86)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/61887
1) Introduced a `sandcastle_skip_if` decorator that ensures these
tests just get passed on sandcastle.
2) Fixed all test files under `test/distributed` to not use `unittest.skip`
Overall goal is to avoid using skips since sandcastle tags these tests as
continuously skipping.
ghstack-source-id: 134382237
Test Plan: waitforbuildbot
Reviewed By: SciPioneer
Differential Revision: D29784152
fbshipit-source-id: 17b4df6c5a55ff1d1e8e1de128fa679c3dfbcb7d
Summary:
Enabling the test cases because they are passing for ROCm.
Signed-off-by: Kyle Chen <kylechen@amd.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/52708
Reviewed By: albanD
Differential Revision: D26994458
Pulled By: mrshenli
fbshipit-source-id: f0b3797c7889287a0154b1d5397df715ffb1c605
Summary:
Fixes https://github.com/pytorch/pytorch/issues/47330
Add support for DataParallel complex tensors by handling them as `torch.view_as_real` for `broadcast_coalesced`, `scatter` and `gather`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/48686
Reviewed By: osalpekar
Differential Revision: D25261533
Pulled By: sidneyfletcher
fbshipit-source-id: 3a25e05deee43e053f40d1068fc5c7867cfa9686
Summary:
Added a convenience function that allows users to load models without DP/DDP from a DP/DDP state dict.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45643
Reviewed By: rohan-varma
Differential Revision: D24574649
fbshipit-source-id: 17d29ab16ae24a30890168fa84da6c63650e61e9
Summary:
Retake on https://github.com/pytorch/pytorch/issues/40493 after all the feedback from albanD
This PR implements the generic Lazy mechanism and a sample `LazyLinear` layer with the `UninitializedParameter`.
The main differences with the previous PR are two;
Now `torch.nn.Module` remains untouched.
We don't require an explicit initialization or a dummy forward pass before starting the training or inference of the actual module. Making this much simpler to use from the user side.
As we discussed offline, there was the suggestion of not using a mixin, but changing the `__class__` attribute of `LazyLinear` to become `Linear` once it's completely initialized. While this can be useful, by the time being we need `LazyLinear` to be a `torch.nn.Module` subclass since there are many checks that rely on the modules being instances of `torch.nn.Module`.
This can cause problems when we create complex modules such as
```
class MyNetwork(torch.nn.Module):
def __init__(self):
super(MyNetwork, self).__init__()
self.conv = torch.nn.Conv2d(20, 4, 2)
self.linear = torch.nn.LazyLinear(10)
def forward(self, x):
y = self.conv(x).clamp(min=0)
return self.linear(y)
```
Here, when the __setattr__ function is called at the time LazyLinear is registered, it won't be added to the child modules of `MyNetwork`, so we have to manually do it later, but currently there is no way to do such thing as we can't access the parent module from LazyLinear once it becomes the Linear module. (We can add a workaround to this if needed).
TODO:
Add convolutions once the design is OK
Fix docstrings
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44538
Reviewed By: ngimel
Differential Revision: D24162854
Pulled By: albanD
fbshipit-source-id: 6d58dfe5d43bfb05b6ee506e266db3cf4b885f0c
Summary:
Take care of the state of autocast in `parallel_apply`, so there is no need to decorate model implementations.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43102
Reviewed By: ngimel
Differential Revision: D23294610
Pulled By: mrshenli
fbshipit-source-id: 0fbe0c79de976c88cadf2ceb3f2de99d9342d762
Summary:
https://github.com/pytorch/pytorch/pull/40129 fixed the error responsible for the first revert, but exposed another error in the same test.
This PR is intended as the "master copy" for merge, and it runs on full CI.
Two other PRs (restricted to run on a small subset of CI) supporting debugging DDP failures/hangs with multiple devices per process (`test_c10d.py:DistributedDataParallelTest.test_grad_layout_1devicemodule_2replicaperprocess`).
- https://github.com/pytorch/pytorch/pull/40290 tries the test with purely rowmajor contiguous params on an untouched master. In other words https://github.com/pytorch/pytorch/pull/40290 contains none of this PR's diffs aside from the test itself.
- https://github.com/pytorch/pytorch/pull/40178, for comparison, tries the test with this PR's diffs.
Both fail the same way, indicating failure is unrelated to this PR's other diffs.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/40358
Differential Revision: D22165785
Pulled By: albanD
fbshipit-source-id: ac7cdd79af5c080ab74341671392dca8e717554e
Summary:
Currently, whether `AccumulateGrad` [steals](67cb018462/torch/csrc/autograd/functions/accumulate_grad.h (L42)) or [clones](67cb018462/torch/csrc/autograd/functions/accumulate_grad.h (L80)) an incoming gradient, the gradient ends up rowmajor contiguous, regardless of its param's layout. If the param's layout is channels last, or otherwise not rowmajor contigous, later kernels that apply gradients to params are forced into an uncoalesced memory access pattern for either the param or the gradient. This may not sound like a big deal but for any binary op on large tensors it's a >3X increase in gmem traffic => 3X slowdown.
The present PR changes `AccumulateGrad` to prefer, where possible, stashing gradients that match their params' layouts (["Gradient Layout Contract"](https://github.com/pytorch/pytorch/pull/34904/files#diff-ef1a56d24f66b280dcdb401502d6a796R29-R38)).
Allowing `AccumulateGrad` to stash non-rowmajor-contiguous grads means DDP allreduces and DP reduces must allow non-rowmajor-contiguous grads. This PR extends DDP and DP to allow gradients with non-rowmajor-contiguous strides as long as their layout is nonoverlapping and dense.
For good measure, I include changes that allow all five nccl primitives (allreduce, reduce, broadcast, allgather, reducescatter) to act on non-rowmajor-contiguous tensors (again as long as each input's layout is nonoverlapping and dense, and as long as all tensors participating in a given collective have the same layout). The primitive comm changes aren't necessary to enable the DDP changes, but I wasn't sure this would end up true until I had written both sets of changes. I think primitive comm enablement is reasonable to keep in the PR, especially since the code for it is simple.
Channels last params will be a major beneficiary of this PR, but I don't see it as channels-last-specific fix. The spirit is layout matching in general:
- Grads should be stashed with memory layouts matching their params.
- Src and dst tensors on opposite ends of collectives should have matching dense layouts.
This PR also updates autograd docs to describe potential BC-breaking changes below.
## BC notes
ngimel albanD gchanan
#### BC-breaking
In the common case where the user lets AccumulateGrad decide grad layouts, strides for grads of dense but non-rowmajor-contiguous params will change. Any user code that was accustomed to `view(-1)`ing these grads will break.
Also, the circumstances under which a grad can be stolen directly from the backward function that created it, as opposed to deep-copied by AccumulateGrad, have changed. In most cases we expect silent performance improvement, because we expect channels-last-aware backward kernels will create channels last gradients for channels last params. Now those can be stolen, whereas before this PR they were cloned and made rowmajor contiguous. IMO this is a mild BC breakage. Param backward hooks still see grads come in with whatever format the backward kernel gave them. The only BC breakage potential I see is if user code relies somehow on a grad in a hook having or not having the same deep memory as the eventual `param.grad`. Any such users hopefully know they're off the edge of the map and understand how to update their expectations.
#### BC escape hatches
At alband's recommendation, this PR's changes to AccumulateGrad do not alter the pre-PR code's decisions about whether grad is accumulated in or out of place. Accumulations of new grads onto an existing `.grad` attribute were (usually) in-place before this PR and remain in-place after this PR, keeping the existing `.grad`'s layout. After this PR, if the user wants to force accumulation into a grad with a particular layout, they can preset `param.grad` to a zeroed tensor with the desired strides or call `grad.contiguous(desired format)`. This likely won't be as performant as letting AccumulateGrad establish grad layouts by cloning or stealing grads with contract-compliant strides, but at least users have a control point.
One limitation (present before this PR and unchanged by this PR): Presetting `param.grad` does not ensure in-place accumulation all the time. For example, if `create_graph=True`, or if incoming `new_grad` is dense and existing `variable_grad` is sparse, accumulation occurs out of place, and the out-of-place result may not match the existing grad's strides.
----------------------------
I also noticed some potential DDP improvements that I considered out of scope but want to mention for visibility:
1. make sure Reducer's ops sync with AccumulateGrad streams
2. ~to reduce CPU overhead and incur fewer kernel launches, lazily create flat `contents` tensors by a single `cat` kernel only when a bucket is full, instead of `copy_`ing grads into `contents` individually as soon as they are received.~ PR includes a [minor change](https://github.com/pytorch/pytorch/pull/34904/files#diff-c269190a925a4b0df49eda8a8f6c5bd3R312-R315) to divide grads while copying them into flat buffers, instead of copying them in, then dividing separately. Without cat+div fusion, div-while-copying is the best we can do.
3. https://github.com/pytorch/pytorch/issues/38942
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34904
Differential Revision: D20496044
Pulled By: albanD
fbshipit-source-id: 248d680f4b1bf77b0a986451844ec6e254469217
Summary:
This updates assertEqual and assertEqual-like functions to either require both or neither of atol and rtol be specified. This should improve clarity around handling precision in the test suite, and it allows us to remove the legacy positional atol argument from assertEqual. In addition, the "message" kwarg is replace with a kwarg-only "msg" argument whose name is consistent with unittest's assertEqual argument.
In the future we could make "msg" an optional third positional argument to be more consistent with unittest's assertEqual, but requiring it be specified should be clear, and we can easily update the signature to make "msg" an optional positional argument in the future, too.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/38872
Differential Revision: D21740237
Pulled By: mruberry
fbshipit-source-id: acbc027aa1d7877a49664d94db9a5fff91a07042
Summary:
This updates assertEqual and assertEqual-like functions to either require both or neither of atol and rtol be specified. This should improve clarity around handling precision in the test suite, and it allows us to remove the legacy positional atol argument from assertEqual. In addition, the "message" kwarg is replace with a kwarg-only "msg" argument whose name is consistent with unittest's assertEqual argument.
In the future we could make "msg" an optional third positional argument to be more consistent with unittest's assertEqual, but requiring it be specified should be clear, and we can easily update the signature to make "msg" an optional positional argument in the future, too.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/38872
Differential Revision: D21717199
Pulled By: mruberry
fbshipit-source-id: 9feb856f94eee911b44f6c7140a1d07c1b026d3a
Summary:
Fixes https://github.com/pytorch/pytorch/issues/37182
The `zero_grad` wrapper from `_replicate_for_data_parallel` can't be pickled. So instead, I set an attribute `_is_replica = True` and check for this in `Module.zero_grad`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/37307
Differential Revision: D21246119
Pulled By: mrshenli
fbshipit-source-id: 4755786d48a20bc247570ba672de9dd526914ce1
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35615
Python 2 has reached end-of-life and is no longer supported by PyTorch.
Now we can clean up a lot of cruft that we put in place to support it.
These changes were all done manually, and I skipped anything that seemed
like it would take more than a few seconds, so I think it makes sense to
review it manually as well (though using side-by-side view and ignoring
whitespace change might be helpful).
Test Plan: CI
Differential Revision: D20842886
Pulled By: dreiss
fbshipit-source-id: 8cad4e87c45895e7ce3938a88e61157a79504aed
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/36411
This PR remove pytorch specific defined assertwarns and use the unit
test one, also format some tests
Test Plan: Imported from OSS
Differential Revision: D20998159
Pulled By: wanchaol
fbshipit-source-id: 1280ecff2dd293b95a639d13cc7417fc819c2201
