This is a reland of https://github.com/pytorch/pytorch/pull/100007 with a build fix for Windows debug builds.
`at::native::ParamsHash` only works on structs with standard layout, but `std::string` isn't one in Visual C++ debug builds, which one can easily verified by running something like:
```cpp
#define _DEBUG
#include <type_traits>
#include <string>
static_assert(std::is_standard_layout_v<std::string>, "Oh noes");
```
If above conditon is not met, instead of printing a static_assert output, VC++ raises a very cryptic compilation errors, see https://github.com/pytorch/pytorch/pull/100007#discussion_r1227116292 for more detail.
Also, using `std::hash` for string should result in a faster hash function.
(cherry picked from commit 74b7a6c75e)
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### <samp>🤖 Generated by Copilot at 5914771</samp>
This pull request introduces a new function `_group_tensors_by_device_and_dtype` that can group tensors by their device and dtype, and updates the `foreach` utilities and several optimizers to use this function. The goal is to improve the performance, readability, and compatibility of the code that handles tensors with different properties. The pull request also adds a test case and type annotations for the new function, and some error checks for the `fused` argument in Adam and AdamW.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/103912
Approved by: https://github.com/janeyx99
Big OOP correction continued. Also added a test this time to verify the defaulting was as expected.
The key here is realizing that the grouping for foreach already assumes that the non-param tensorlists follow suit in dtype and device, so it is too narrow to check that _all_ tensors were on CUDA. The main leeway this allowed was state_steps, which are sometimes cpu tensors. Since foreach _can_ handle cpu tensors, this should not introduce breakage.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/95820
Approved by: https://github.com/albanD
Rolling back the default change for Adam and rectifying the docs to reflect that AdamW never defaulted to fused.
Since our fused implementations are relatively newer, let's give them a longer bake-in time before flipping the switch for every user.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/95241
Approved by: https://github.com/ngimel
This allows it so that ONLY when the users don't set anything for foreach or fused do we switch the default and cascades adam so that we default to fused, then foreach, then single-tensor.
To clarify:
* if the user puts True in foreach _only_, it will run the foreach implementation.
* if the user puts True in fused _only_, it will run the fused implementation.
* if the user puts True in foreach AND for fused, it will run the fused implementation.
And:
* if the user puts False in foreach _only_, it will run the single tensor implementation.
* if the user puts False in fused _only_, it will still run the single tensor implementation.
* if the user puts False in foreach AND for fused, it will run the single tensor implementation.
I also didn't trust myself that much with the helper function, so I ran some local asserts on _default_to_fused_or_foreach. The only point left to really test is the type(p) -- torch.Tensor but I think the distributed tests will catch that in CI.
```
cuda_only_fp_list = [
torch.rand((1, 2), device="cuda", dtype=torch.float32),
torch.rand((1, 2), device="cuda", dtype=torch.float64),
torch.rand((1, 2), device="cuda", dtype=torch.float16),
torch.rand((1, 2), device="cuda", dtype=torch.bfloat16),
]
cuda_only_int_list = [
torch.randint(1024, (1, 2), device="cuda", dtype=torch.int64),
]
cpu_list = [
torch.rand((1, 2), device="cpu", dtype=torch.float32),
torch.rand((1, 2), device="cpu", dtype=torch.float64),
torch.rand((1, 2), device="cpu", dtype=torch.float16),
]
none_list = [None]
# differentiable should always make it return false for both
assert _default_to_fused_or_foreach([cuda_only_fp_list], True, True) == (False, False)
assert _default_to_fused_or_foreach([cuda_only_fp_list], True, False) == (False, False)
# cpu lists should always make it return false for both
assert _default_to_fused_or_foreach([cuda_only_fp_list, cpu_list], False, True) == (False, False)
assert _default_to_fused_or_foreach([cpu_list], False, True) == (False, False)
assert _default_to_fused_or_foreach([cuda_only_fp_list, cpu_list], False, False) == (False, False)
assert _default_to_fused_or_foreach([cpu_list], False, False) == (False, False)
# has fused triggers correctly
assert _default_to_fused_or_foreach([cuda_only_fp_list], False, True) == (True, False)
assert _default_to_fused_or_foreach([cuda_only_fp_list], False, False) == (False, True)
# ints always goes to foreach
assert _default_to_fused_or_foreach([cuda_only_fp_list, cuda_only_int_list], False, True) == (False, True)
assert _default_to_fused_or_foreach([cuda_only_fp_list, cuda_only_int_list], False, False) == (False, True)
# Nones don't error
assert _default_to_fused_or_foreach([cuda_only_fp_list, none_list], False, True) == (True, False)
assert _default_to_fused_or_foreach([cuda_only_fp_list, cuda_only_int_list, none_list], False, True) == (False, True)
assert _default_to_fused_or_foreach([none_list], False, True) == (True, False)
assert _default_to_fused_or_foreach([none_list], False, False) == (False, True)
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/93184
Approved by: https://github.com/albanD
@mlazos: skips `item()` calls if compiling with dynamo, by defining a helper function `_get_value` which either returns the result of `.item()` or the scalar cpu tensor if compiling with dynamo. This was done because removing `item()` calls significantly regresses eager perf. Additionally, `_dispatch_sqrt` calls the appropriate sqrt function (math.sqrt, or torch.sqrt).
Fixes https://github.com/pytorch/torchdynamo/issues/1083
This PR will no longer be needed once symint support is default.
This PR closes all remaining graph breaks in the optimizers (!!)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/88173
Approved by: https://github.com/albanD
### Description
Across PyTorch's docstrings, both `callable` and `Callable` for variable types. The Callable should be capitalized as we are referring to the `Callable` type, and not the Python `callable()` function.
### Testing
There shouldn't be any testing required.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/82487
Approved by: https://github.com/albanD
Generator comprehensions with any/all are less verbose and potentially help to save memory/CPU : https://eklitzke.org/generator-comprehensions-and-using-any-and-all-in-python
To make JIT work with this change, I added code to convert GeneratorExp to ListComp. So the whole PR is basically NoOp for JIT, but potentially memory and speed improvement for eager mode.
Also I removed a test from test/jit/test_parametrization.py. The test was bad and had a TODO to actually implement and just tested that UnsupportedNodeError is thrown, and with GeneratorExp support a different error would be thrown.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78142
Approved by: https://github.com/malfet, https://github.com/albanD
Summary:
Fixes : https://github.com/pytorch/pytorch/issues/5804
In the paper : https://openreview.net/forum?id=OM0jvwB8jIp57ZJjtNEZ Timothy Dozat suggested a new optimization algorithm with an essence of combination of NAG and Adam algorithms.
It is known that the idea of momentum can be improved with the Nesterov acceleration in optimization algorithms, and Dozat is investigating to apply this idea to momentum component of Adam algorithm. Author provided experiment evidence in their work to show excellence of the idea.
In this PR we are implementing the proposed algorithm NAdam in the mentioned paper. Author has a preliminary work http://cs229.stanford.edu/proj2015/054_report.pdf where he shows the decay base constant should be taken as 0.96 which we also followed the same phenomenon here in this implementation similar to Keras. Moreover, implementation / coding practice have been followed similar to Keras in some other places as well:
f9d3868495/tensorflow/python/keras/optimizer_v2/nadam.py
Pull Request resolved: https://github.com/pytorch/pytorch/pull/59009
Reviewed By: gchanan, vincentqb
Differential Revision: D29220375
Pulled By: iramazanli
fbshipit-source-id: 4b4bb4b15f7e16f7527f368bbf4207ed345751aa
