@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
This is causing issues if the user has the step on cuda for a good reason.
These assert prevents code that used to run just fine to fail.
Note that this is a pretty bad thing to do for performance though so it is ok to try and push users away from doing it.
For the 1.12.1 milestone: this is not asking for a dot release to fix this (as this is bad practice anyways). But it would be a great thing to add if we do one: it is very low risk and will prevent breakage for users.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/80222
Approved by: https://github.com/jbschlosser, https://github.com/ngimel
Near term fix for https://github.com/pytorch/pytorch/issues/76368.
Q. Why does the user need to request `capturable=True` in the optimizer constructor? Why can't capture safety be completely automatic?
A. We need to set up capture-safe (device-side) state variables before capture. If we don't, and step() internally detects capture is underway, it's too late: the best we could do is create a device state variable and copy the current CPU value into it, which is not something we want baked into the graph.
Q. Ok, why not just do the capture-safe approach with device-side state variables all the time?
A. It incurs several more kernel launches per parameter, which could really add up and regress cpu overhead for ungraphed step()s. If the optimizer won't be captured, we should allow step() to stick with its current cpu-side state handling.
Q. But cuda RNG is a stateful thing that maintains its state on the cpu outside of capture and replay, and we capture it automatically. Why can't we do the same thing here?
A. The graph object can handle RNG generator increments because its capture_begin, capture_end, and replay() methods can see and access generator object. But the graph object has no explicit knowledge of or access to optimizer steps in its capture scope. We could let the user tell the graph object what optimizers will be stepped in its scope, ie something like
```python
graph.will_use_optimizer(opt)
graph.capture_begin()
...
```
but that seems clunkier than an optimizer constructor arg.
I'm open to other ideas, but right now I think constructor arg is necessary and the least bad approach.
Long term, https://github.com/pytorch/pytorch/issues/71274 is a better fix.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/77862
Approved by: https://github.com/ezyang
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/71333
Updated
- Adagrad
- Adamax
- Adam
- AdamW
- RAdam
make multi_tensor functionals take `state_steps: List[Tensor]` instead of taking `states: List[Dict]`
make `state_steps: List[int]s -> state_steps:List[Tensor]` where each is a Singleton tensor so step can be updated within the functional
(NAdam and ASGD) were updated in separate diffs to fold their handling of state into the functionals
Test Plan: Imported from OSS
Reviewed By: anjali411
Differential Revision: D33767872
Pulled By: mikaylagawarecki
fbshipit-source-id: 9baa7cafb6375eab839917df9287c65a437891f2
(cherry picked from commit 831c02b3d0)
Summary:
Solves the next most important use case in https://github.com/pytorch/pytorch/issues/68052.
I have kept the style as close to that in SGD as seemed reasonable, given the slight differences in their internal implementations.
All feedback welcome!
cc pietern mrshenli pritamdamania87 zhaojuanmao satgera rohan-varma gqchen aazzolini osalpekar jiayisuse SciPioneer H-Huang
Pull Request resolved: https://github.com/pytorch/pytorch/pull/68164
Reviewed By: VitalyFedyunin
Differential Revision: D32994129
Pulled By: albanD
fbshipit-source-id: 65c57c3f3dbbd3e3e5338d51def54482503e8850
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/52944
This fix the bug introduced during refactoring optimizers https://github.com/pytorch/pytorch/pull/50411. When all parameters have no grads, we should still allows `beta` like hyper params to be defined.
Reviewed By: ngimel
Differential Revision: D26699827
fbshipit-source-id: 8a7074127704c7a4a1fbc17d48a81e23a649f280
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/51316
Make optim functional API be private until we release with beta
Test Plan: Imported from OSS
Reviewed By: albanD
Differential Revision: D26213469
fbshipit-source-id: b0fd001a8362ec1c152250bcd57c7205ed893107
Summary:
Adam and AdamW are missing parameter validation for weight_decay. Other optimisers have this check present.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33126
Differential Revision: D19860366
Pulled By: vincentqb
fbshipit-source-id: 286d7dc90e2f4ccf6540638286d2fe17939648fc
Summary:
Apply weight decay for Adam in-place instead of via copy.
Synced offline with soumith , who mentioned that it should be OK. This is also consistent with other optimizers, e.g. eee01731a5/torch/optim/sgd.py (L93)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12107
Reviewed By: soumith
Differential Revision: D10071787
Pulled By: jma127
fbshipit-source-id: 5fd7939c79039693b225c44c4c80450923b8d673
Summary:
Minor addition to the docstring of `torch.nn.optim.Adam`, adding the default argument description for the `amsgrad` argument to the docstring for concistency.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9971
Differential Revision: D9040820
Pulled By: soumith
fbshipit-source-id: 168744a6bb0d1422331beffd7e694b9d6f61900c
