Use `typing_extensions.deprecated` for deprecation annotation if possible. Otherwise, add `category=FutureWarning` to `warnings.warn("message")` if the category is missing.
Note that only warnings that their messages contain `[Dd]eprecat(ed|ion)` are updated in this PR.
Resolves#126888
- #126888
This PR is split from PR #126898.
- #126898
------
Pull Request resolved: https://github.com/pytorch/pytorch/pull/127689
Approved by: https://github.com/Skylion007
Use `typing_extensions.deprecated` for deprecation annotation if possible. Otherwise, add `category=FutureWarning` to `warnings.warn("message")` if the category is missing.
Note that only warnings that their messages contain `[Dd]eprecat(ed|ion)` are updated in this PR.
UPDATE: Use `FutureWarning` instead of `DeprecationWarning`.
Resolves#126888
- #126888
Pull Request resolved: https://github.com/pytorch/pytorch/pull/126898
Approved by: https://github.com/albanD
# Motivation
## for `torch.amp.GradScaler`,
- `torch.cpu.amp.GradScaler(args...)` is completely equivalent to `torch. amp.GradScaler("cpu", args...)`.
- `torch.cuda.amp.GradScaler(args...)` is completely equivalent to `torch.amp.GradScaler("cuda", args...)`.
So, we intend to depreate them and **strongly recommend** developer to use `torch.amp.GradScaler`.
## for `custom_fwd` and `custom_bwd`,
this is a good solution to make the custom function run with or without effect even in an autocast-enabled region and can be shared by other backends, like CPU and XPU.
So we generalize it to be device-agnostic and put them int `torch/amp/autocast_mode.py` and re-expose to `torch.amp.custom_fwd` and `torch.amp.custom_bwd`. Meanwhile, we deprecate `torch.cuda.amp.custom_fwd` and `torch.cuda.amp.custom_bwd`.
# Additional Context
Add UT to cover the deprecated warning.
No need for more UTs to cover the functionality of `torch.amp.custom_f/bwd`, the existing UTs that previously covered the functionality of `torch.cuda.amp.custom_f/bwd` can cover them.
To facilitate the review, we separate these code changes to two PRs. The first PR cover `torch.amp.GradScaler`. The follow-up covers `custom_fwd` and `custom_bwd`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/126527
Approved by: https://github.com/jgong5, https://github.com/gujinghui, https://github.com/janeyx99, https://github.com/EikanWang
The current call passes in `['/actual/path']` to os.walk which is a string pointing to no path and thus silently leads to and empty traversal.
There is an unused function just above that handles that, so I guess this is what was supposed to be called.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/126103
Approved by: https://github.com/suo
Fixes#112592
1) **File: torch/cuda/random.py**
```
Before:
/content/pytorch/torch/cuda/random.py:1 at module level:
D100: Missing docstring in public module
/content/pytorch/torch/cuda/random.py:21 in public function `get_rng_state`:
D401: First line should be in imperative mood (perhaps 'Return', not 'Returns')
/content/pytorch/torch/cuda/random.py:43 in public function `get_rng_state_all`:
D202: No blank lines allowed after function docstring (found 1)
/content/pytorch/torch/cuda/random.py:43 in public function `get_rng_state_all`:
D401: First line should be in imperative mood (perhaps 'Return', not 'Returns')
/content/pytorch/torch/cuda/random.py:54 in public function `set_rng_state`:
D401: First line should be in imperative mood (perhaps 'Set', not 'Sets')
/content/pytorch/torch/cuda/random.py:79 in public function `set_rng_state_all`:
D208: Docstring is over-indented
/content/pytorch/torch/cuda/random.py:79 in public function `set_rng_state_all`:
D209: Multi-line docstring closing quotes should be on a separate line
/content/pytorch/torch/cuda/random.py:79 in public function `set_rng_state_all`:
D401: First line should be in imperative mood (perhaps 'Set', not 'Sets')
/content/pytorch/torch/cuda/random.py:79 in public function `set_rng_state_all`:
D414: Section has no content ('Args')
/content/pytorch/torch/cuda/random.py:88 in public function `manual_seed`:
D205: 1 blank line required between summary line and description (found 0)
/content/pytorch/torch/cuda/random.py:88 in public function `manual_seed`:
D401: First line should be in imperative mood (perhaps 'Set', not 'Sets')
/content/pytorch/torch/cuda/random.py:110 in public function `manual_seed_all`:
D205: 1 blank line required between summary line and description (found 0)
/content/pytorch/torch/cuda/random.py:110 in public function `manual_seed_all`:
D401: First line should be in imperative mood (perhaps 'Set', not 'Sets')
/content/pytorch/torch/cuda/random.py:128 in public function `seed`:
D205: 1 blank line required between summary line and description (found 0)
/content/pytorch/torch/cuda/random.py:128 in public function `seed`:
D401: First line should be in imperative mood (perhaps 'Set', not 'Sets')
/content/pytorch/torch/cuda/random.py:146 in public function `seed_all`:
D205: 1 blank line required between summary line and description (found 0)
/content/pytorch/torch/cuda/random.py:146 in public function `seed_all`:
D401: First line should be in imperative mood (perhaps 'Set', not 'Sets')
/content/pytorch/torch/cuda/random.py:167 in public function `initial_seed`:
D401: First line should be in imperative mood (perhaps 'Return', not 'Returns')
18
```
```
After:
/content/pytorch/torch/cuda/random.py:1 at module level:
D100: Missing docstring in public module
1
```
2) **File: torch/cuda/amp/autocast_mode.py**
```
Before: /content/pytorch/torch/cuda/amp/autocast_mode.py:1 at module level:
D100: Missing docstring in public module
/content/pytorch/torch/cuda/amp/autocast_mode.py:18 in public class `autocast`:
D205: 1 blank line required between summary line and description (found 0)
/content/pytorch/torch/cuda/amp/autocast_mode.py:23 in public method `__init__`:
D107: Missing docstring in __init__
/content/pytorch/torch/cuda/amp/autocast_mode.py:38 in public method `__enter__`:
D105: Missing docstring in magic method
/content/pytorch/torch/cuda/amp/autocast_mode.py:44 in public method `__exit__`:
D105: Missing docstring in magic method
/content/pytorch/torch/cuda/amp/autocast_mode.py:49 in public method `__call__`:
D102: Missing docstring in public method
/content/pytorch/torch/cuda/amp/autocast_mode.py:90 in public function `custom_fwd`:
D205: 1 blank line required between summary line and description (found 0)
/content/pytorch/torch/cuda/amp/autocast_mode.py:90 in public function `custom_fwd`:
D400: First line should end with a period (not 'f')
/content/pytorch/torch/cuda/amp/autocast_mode.py:90 in public function `custom_fwd`:
D401: First line should be in imperative mood; try rephrasing (found 'Helper')
/content/pytorch/torch/cuda/amp/autocast_mode.py:130 in public function `custom_bwd`:
D205: 1 blank line required between summary line and description (found 0)
/content/pytorch/torch/cuda/amp/autocast_mode.py:130 in public function `custom_bwd`:
D400: First line should end with a period (not 'f')
/content/pytorch/torch/cuda/amp/autocast_mode.py:130 in public function `custom_bwd`:
D401: First line should be in imperative mood; try rephrasing (found 'Helper')
12
```
```
After:
/content/pytorch/torch/cuda/amp/autocast_mode.py:1 at module level:
D100: Missing docstring in public module
/content/pytorch/torch/cuda/amp/autocast_mode.py:23 in public method `__init__`:
D107: Missing docstring in __init__
/content/pytorch/torch/cuda/amp/autocast_mode.py:38 in public method `__enter__`:
D105: Missing docstring in magic method
/content/pytorch/torch/cuda/amp/autocast_mode.py:44 in public method `__exit__`:
D105: Missing docstring in magic method
/content/pytorch/torch/cuda/amp/autocast_mode.py:49 in public method `__call__`:
D102: Missing docstring in public method
5
```
3) **File: torch/cuda/amp/grad_scaler.py**
```
Before: /content/pytorch/torch/cuda/amp/grad_scaler.py:1 at module level:
D100: Missing docstring in public module
/content/pytorch/torch/cuda/amp/grad_scaler.py:17 in private class `_MultiDeviceReplicator`:
D200: One-line docstring should fit on one line with quotes (found 3)
/content/pytorch/torch/cuda/amp/grad_scaler.py:39 in public class `OptState`:
D101: Missing docstring in public class
/content/pytorch/torch/cuda/amp/grad_scaler.py:50 in public class `GradScaler`:
D205: 1 blank line required between summary line and description (found 0)
/content/pytorch/torch/cuda/amp/grad_scaler.py:50 in public class `GradScaler`:
D400: First line should end with a period (not 'g')
/content/pytorch/torch/cuda/amp/grad_scaler.py:115 in public method `__init__`:
D107: Missing docstring in __init__
/content/pytorch/torch/cuda/amp/grad_scaler.py:354 in public method `step`:
D400: First line should end with a period (not ':')
/content/pytorch/torch/cuda/amp/grad_scaler.py:456 in public method `update`:
D401: First line should be in imperative mood (perhaps 'Update', not 'Updates')
/content/pytorch/torch/cuda/amp/grad_scaler.py:529 in public method `get_scale`:
D401: First line should be in imperative mood (perhaps 'Return', not 'Returns')
/content/pytorch/torch/cuda/amp/grad_scaler.py:544 in public method `get_growth_factor`:
D200: One-line docstring should fit on one line with quotes (found 3)
/content/pytorch/torch/cuda/amp/grad_scaler.py:544 in public method `get_growth_factor`:
D401: First line should be in imperative mood (perhaps 'Return', not 'Returns')
/content/pytorch/torch/cuda/amp/grad_scaler.py:550 in public method `set_growth_factor`:
D205: 1 blank line required between summary line and description (found 0)
/content/pytorch/torch/cuda/amp/grad_scaler.py:550 in public method `set_growth_factor`:
D400: First line should end with a period (not ':')
/content/pytorch/torch/cuda/amp/grad_scaler.py:557 in public method `get_backoff_factor`:
D200: One-line docstring should fit on one line with quotes (found 3)
/content/pytorch/torch/cuda/amp/grad_scaler.py:557 in public method `get_backoff_factor`:
D401: First line should be in imperative mood (perhaps 'Return', not 'Returns')
/content/pytorch/torch/cuda/amp/grad_scaler.py:563 in public method `set_backoff_factor`:
D205: 1 blank line required between summary line and description (found 0)
/content/pytorch/torch/cuda/amp/grad_scaler.py:563 in public method `set_backoff_factor`:
D400: First line should end with a period (not ':')
/content/pytorch/torch/cuda/amp/grad_scaler.py:570 in public method `get_growth_interval`:
D200: One-line docstring should fit on one line with quotes (found 3)
/content/pytorch/torch/cuda/amp/grad_scaler.py:570 in public method `get_growth_interval`:
D401: First line should be in imperative mood (perhaps 'Return', not 'Returns')
/content/pytorch/torch/cuda/amp/grad_scaler.py:576 in public method `set_growth_interval`:
D205: 1 blank line required between summary line and description (found 0)
/content/pytorch/torch/cuda/amp/grad_scaler.py:576 in public method `set_growth_interval`:
D400: First line should end with a period (not ':')
/content/pytorch/torch/cuda/amp/grad_scaler.py:592 in public method `is_enabled`:
D200: One-line docstring should fit on one line with quotes (found 3)
/content/pytorch/torch/cuda/amp/grad_scaler.py:592 in public method `is_enabled`:
D401: First line should be in imperative mood (perhaps 'Return', not 'Returns')
/content/pytorch/torch/cuda/amp/grad_scaler.py:598 in public method `state_dict`:
D400: First line should end with a period (not ':')
/content/pytorch/torch/cuda/amp/grad_scaler.py:598 in public method `state_dict`:
D401: First line should be in imperative mood (perhaps 'Return', not 'Returns')
/content/pytorch/torch/cuda/amp/grad_scaler.py:624 in public method `load_state_dict`:
D401: First line should be in imperative mood (perhaps 'Load', not 'Loads')
/content/pytorch/torch/cuda/amp/grad_scaler.py:649 in public method `__getstate__`:
D105: Missing docstring in magic method
/content/pytorch/torch/cuda/amp/grad_scaler.py:665 in public method `__setstate__`:
D105: Missing docstring in magic method
28
```
```
After:
/content/pytorch/torch/cuda/amp/grad_scaler.py:1 at module level:
D100: Missing docstring in public module
/content/pytorch/torch/cuda/amp/grad_scaler.py:40 in public class `OptState`:
D101: Missing docstring in public class
/content/pytorch/torch/cuda/amp/grad_scaler.py:117 in public method `__init__`:
D107: Missing docstring in __init__
/content/pytorch/torch/cuda/amp/grad_scaler.py:647 in public method `__getstate__`:
D105: Missing docstring in magic method
/content/pytorch/torch/cuda/amp/grad_scaler.py:663 in public method `__setstate__`:
D105: Missing docstring in magic method
5
```
4) **File: torch/optim/_functional.py**
```
Before:
/content/pytorch/torch/optim/_functional.py:1 at module level:
D400: First line should end with a period (not 'e')
1
```
```
After:
0
```
5) **File: torch/optim/__init__.py**
```
Before:
/content/pytorch/torch/optim/__init__.py:1 at module level:
D205: 1 blank line required between summary line and description (found 0)
1
```
```
After:
0
```
6) **File: torch/optim/lbfgs.py**
```
Before:
/content/pytorch/torch/optim/lbfgs.py:1 at module level:
D100: Missing docstring in public module
/content/pytorch/torch/optim/lbfgs.py:185 in public class `LBFGS`:
D205: 1 blank line required between summary line and description (found 0)
/content/pytorch/torch/optim/lbfgs.py:185 in public class `LBFGS`:
D400: First line should end with a period (not 'c')
/content/pytorch/torch/optim/lbfgs.py:215 in public method `__init__`:
D107: Missing docstring in __init__
/content/pytorch/torch/optim/lbfgs.py:285 in public method `step`:
D401: First line should be in imperative mood (perhaps 'Perform', not 'Performs')
5
```
```
After:
/content/pytorch/torch/optim/lbfgs.py:1 at module level:
D100: Missing docstring in public module
/content/pytorch/torch/optim/lbfgs.py:217 in public method `__init__`:
D107: Missing docstring in __init__
2
```
7)**File: torch/optim/sparse_adam.py**
```
Before: /content/pytorch/torch/optim/sparse_adam.py:1 at module level:
D100: Missing docstring in public module
/content/pytorch/torch/optim/sparse_adam.py:7 in public class `SparseAdam`:
D101: Missing docstring in public class
/content/pytorch/torch/optim/sparse_adam.py:8 in public method `__init__`:
D107: Missing docstring in __init__
/content/pytorch/torch/optim/sparse_adam.py:40 in public method `step`:
D401: First line should be in imperative mood (perhaps 'Perform', not 'Performs')
4
```
```
After:
/content/pytorch/torch/optim/sparse_adam.py:1 at module level:
D100: Missing docstring in public module
/content/pytorch/torch/optim/sparse_adam.py:7 in public class `SparseAdam`:
D101: Missing docstring in public class
/content/pytorch/torch/optim/sparse_adam.py:8 in public method `__init__`:
D107: Missing docstring in __init__
3
```
8) **File:torch/optim/adadelta.py**
```
Before:
/content/pytorch/torch/optim/adadelta.py:1 at module level:
D100: Missing docstring in public module
/content/pytorch/torch/optim/adadelta.py:11 in public class `Adadelta`:
D101: Missing docstring in public class
/content/pytorch/torch/optim/adadelta.py:12 in public method `__init__`:
D107: Missing docstring in __init__
/content/pytorch/torch/optim/adadelta.py:44 in public method `__setstate__`:
D105: Missing docstring in magic method
/content/pytorch/torch/optim/adadelta.py:82 in public method `step`:
D401: First line should be in imperative mood (perhaps 'Perform', not 'Performs')
/content/pytorch/torch/optim/adadelta.py:193 in public function `adadelta`:
D202: No blank lines allowed after function docstring (found 1)
6
```
```
After:
/content/pytorch/torch/optim/adadelta.py:1 at module level:
D100: Missing docstring in public module
/content/pytorch/torch/optim/adadelta.py:11 in public class `Adadelta`:
D101: Missing docstring in public class
/content/pytorch/torch/optim/adadelta.py:12 in public method `__init__`:
D107: Missing docstring in __init__
/content/pytorch/torch/optim/adadelta.py:44 in public method `__setstate__`:
D105: Missing docstring in magic method
4
```
9) **File: torch/optim/adagrad.py**
```
Before:
/content/pytorch/torch/optim/adagrad.py:1 at module level:
D100: Missing docstring in public module
/content/pytorch/torch/optim/adagrad.py:11 in public class `Adagrad`:
D101: Missing docstring in public class
/content/pytorch/torch/optim/adagrad.py:12 in public method `__init__`:
D107: Missing docstring in __init__
/content/pytorch/torch/optim/adagrad.py:63 in public method `__setstate__`:
D105: Missing docstring in magic method
/content/pytorch/torch/optim/adagrad.py:78 in public method `share_memory`:
D102: Missing docstring in public method
/content/pytorch/torch/optim/adagrad.py:100 in public method `step`:
D401: First line should be in imperative mood (perhaps 'Perform', not 'Performs')
/content/pytorch/torch/optim/adagrad.py:201 in public function `adagrad`:
D202: No blank lines allowed after function docstring (found 1)
7
```
```
After:
/content/pytorch/torch/optim/adagrad.py:1 at module level:
D100: Missing docstring in public module
/content/pytorch/torch/optim/adagrad.py:11 in public class `Adagrad`:
D101: Missing docstring in public class
/content/pytorch/torch/optim/adagrad.py:12 in public method `__init__`:
D107: Missing docstring in __init__
/content/pytorch/torch/optim/adagrad.py:63 in public method `__setstate__`:
D105: Missing docstring in magic method
/content/pytorch/torch/optim/adagrad.py:78 in public method `share_memory`:
D102: Missing docstring in public method
5
```
10) **File: torch/optim/adam.py**
```
Before:
/content/pytorch/torch/optim/adam.py:1 at module level:
D100: Missing docstring in public module
/content/pytorch/torch/optim/adam.py:14 in public class `Adam`:
D101: Missing docstring in public class
/content/pytorch/torch/optim/adam.py:15 in public method `__init__`:
D107: Missing docstring in __init__
/content/pytorch/torch/optim/adam.py:65 in public method `__setstate__`:
D105: Missing docstring in magic method
/content/pytorch/torch/optim/adam.py:135 in public method `step`:
D401: First line should be in imperative mood (perhaps 'Perform', not 'Performs')
/content/pytorch/torch/optim/adam.py:281 in public function `adam`:
D202: No blank lines allowed after function docstring (found 1)
/content/pytorch/torch/optim/adam.py:281 in public function `adam`:
D205: 1 blank line required between summary line and description (found 0)
7
```
```
After:
/content/pytorch/torch/optim/adam.py:1 at module level:
D100: Missing docstring in public module
/content/pytorch/torch/optim/adam.py:14 in public class `Adam`:
D101: Missing docstring in public class
/content/pytorch/torch/optim/adam.py:15 in public method `__init__`:
D107: Missing docstring in __init__
/content/pytorch/torch/optim/adam.py:65 in public method `__setstate__`:
D105: Missing docstring in magic method
4
```
11) **File: torch/optim/adamax.py**
```
Before:
/content/pytorch/torch/optim/adamax.py:1 at module level:
D100: Missing docstring in public module
/content/pytorch/torch/optim/adamax.py:12 in public class `Adamax`:
D101: Missing docstring in public class
/content/pytorch/torch/optim/adamax.py:13 in public method `__init__`:
D107: Missing docstring in __init__
/content/pytorch/torch/optim/adamax.py:47 in public method `__setstate__`:
D105: Missing docstring in magic method
/content/pytorch/torch/optim/adamax.py:91 in public method `step`:
D401: First line should be in imperative mood (perhaps 'Perform', not 'Performs')
/content/pytorch/torch/optim/adamax.py:203 in public function `adamax`:
D202: No blank lines allowed after function docstring (found 1)
6
```
```
After:
/content/pytorch/torch/optim/adamax.py:1 at module level:
D100: Missing docstring in public module
/content/pytorch/torch/optim/adamax.py:12 in public class `Adamax`:
D101: Missing docstring in public class
/content/pytorch/torch/optim/adamax.py:13 in public method `__init__`:
D107: Missing docstring in __init__
/content/pytorch/torch/optim/adamax.py:47 in public method `__setstate__`:
D105: Missing docstring in magic method
4
```
12) **File: torch/optim/adamw.py**
```
Before:
/content/pytorch/torch/optim/adamw.py:1 at module level:
D100: Missing docstring in public module
/content/pytorch/torch/optim/adamw.py:12 in public class `AdamW`:
D101: Missing docstring in public class
/content/pytorch/torch/optim/adamw.py:13 in public method `__init__`:
D107: Missing docstring in __init__
/content/pytorch/torch/optim/adamw.py:73 in public method `__setstate__`:
D105: Missing docstring in magic method
/content/pytorch/torch/optim/adamw.py:153 in public method `step`:
D401: First line should be in imperative mood (perhaps 'Perform', not 'Performs')
/content/pytorch/torch/optim/adamw.py:304 in public function `adamw`:
D202: No blank lines allowed after function docstring (found 1)
6
```
```
After:
/content/pytorch/torch/optim/adamw.py:1 at module level:
D100: Missing docstring in public module
/content/pytorch/torch/optim/adamw.py:12 in public class `AdamW`:
D101: Missing docstring in public class
/content/pytorch/torch/optim/adamw.py:13 in public method `__init__`:
D107: Missing docstring in __init__
/content/pytorch/torch/optim/adamw.py:73 in public method `__setstate__`:
D105: Missing docstring in magic method
4
```
13) **File: torch/optim/asgd.py**
```
Before:
/content/pytorch/torch/optim/asgd.py:1 at module level:
D100: Missing docstring in public module
/content/pytorch/torch/optim/asgd.py:17 in public class `ASGD`:
D101: Missing docstring in public class
/content/pytorch/torch/optim/asgd.py:18 in public method `__init__`:
D107: Missing docstring in __init__
/content/pytorch/torch/optim/asgd.py:52 in public method `__setstate__`:
D105: Missing docstring in magic method
/content/pytorch/torch/optim/asgd.py:107 in public method `step`:
D401: First line should be in imperative mood (perhaps 'Perform', not 'Performs')
/content/pytorch/torch/optim/asgd.py:195 in public function `asgd`:
D202: No blank lines allowed after function docstring (found 1)
6
```
```
After:
/content/pytorch/torch/optim/asgd.py:1 at module level:
D100: Missing docstring in public module
/content/pytorch/torch/optim/asgd.py:17 in public class `ASGD`:
D101: Missing docstring in public class
/content/pytorch/torch/optim/asgd.py:18 in public method `__init__`:
D107: Missing docstring in __init__
/content/pytorch/torch/optim/asgd.py:52 in public method `__setstate__`:
D105: Missing docstring in magic method
4
```
Resolved docstring errors as listed. I initially changed in the main branch of forked repo which caused changes to appear in my PR to other issue. I have fixed that and hope this PR won't have any conflicts.
Kindly review @svekars @jbschlosser.
In case of any other issues please let me know. Thanks!
Pull Request resolved: https://github.com/pytorch/pytorch/pull/112964
Approved by: https://github.com/kit1980
Summary:
I'd like the following pattern (a natural composition of Amp with full fwd+bwd capture) to work:
```python
# Create "static_input" with dummy data, run warmup iterations,
# call optimizer.zero_grad(set_to_none=True), then
g = torch.cuda._Graph()
s.wait_stream(torch.cuda.current_stream())
with torch.cuda.stream(s):
optimizer.zero_grad(set_to_none=True)
g.capture_begin()
with autocast():
out = model(static_input)
loss = loss_fn(out)
scaler.scale(loss).backward()
g.capture_end()
torch.cuda.current_stream().wait_stream(s)
# Training loop:
for b in data:
# optimizer.zero_grad() deliberately omitted, replay()'s baked-in backward will refill statically held .grads
static_input.copy_(b)
g.replay()
scaler.step(optimizer)
scaler.update()
```
Right now `GradScaler` can't work with this pattern because `update()` creates the scale tensor for the next iteration out of place. This PR changes `update()` to act in place on a long-lived scale tensor that stays static across iterations.
I'm not sure how this change affects XLA (see https://github.com/pytorch/pytorch/pull/48570), so we shouldn't merge without approval from ailzhang yaochengji.
Tagged bc-breaking because it's a change to the amp update utility function in native_functions.yaml. The function was never meant to be user-facing though.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/55562
Reviewed By: zou3519
Differential Revision: D28046159
Pulled By: ngimel
fbshipit-source-id: 02018c221609974546c562f691e20ab6ac611910
Summary:
Amp gradient unscaling is a great use case for multi tensor apply (in fact it's the first case I wrote it for). This PR adds an MTA unscale+infcheck functor. Really excited to have it for `torch.cuda.amp`. izdeby your interface was clean and straightforward to use, great work!
Labeled as bc-breaking because the native_functions.yaml exposure of unscale+infcheck changes from [`_amp_non_finite_check_and_unscale_` to `_amp_foreach_non_finite_check_and_unscale_`]( https://github.com/pytorch/pytorch/pull/44778/files#diff-f1e4b2c15de770d978d0eb77b53a4077L6289-L6293).
The PR also modifies Unary/Binary/Pointwise Functors to
- do ops' internal math in FP32 for FP16 or bfloat16 inputs, which improves precision ([and throughput, on some architectures!](https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#arithmetic-instructions)) and has no downside for the ops we care about.
- accept an instantiated op functor rather than an op functor template (`template<class> class Op`). This allows calling code to pass lambdas.
Open question: As written now, the PR has MTA Functors take care of pre- and post-casting FP16/bfloat16 inputs to FP32 before running the ops. However, alternatively, the pre- and post-math casting could be deferred/written into the ops themselves, which gives them a bit more control. I can easily rewrite it that way if you prefer.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44778
Reviewed By: gchanan
Differential Revision: D23944102
Pulled By: izdeby
fbshipit-source-id: 22b25ccad5f69b413c77afe8733fa9cacc8e766d
Summary:
Fix `torch._C._autocast_*_nesting` declarations in __init__.pyi
Fix iterable constructor logic: not every iterable can be constructed using `type(val)(val)` trick, for example it would not work for `val=range(10)` although `isinstance(val, Iterable)` is True
Change optional resolution logic to meet mypy expectations
Fixes https://github.com/pytorch/pytorch/issues/45436
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45480
Reviewed By: walterddr
Differential Revision: D23982822
Pulled By: malfet
fbshipit-source-id: 6418a28d04ece1b2427dcde4b71effb67856a872
Summary:
Should close https://github.com/pytorch/pytorch/issues/35810.
I decided to keep sparse handling on the Python side for clarity, although it could be moved to the C++ side (into `_amp_non_finite_check_and_unscale_`) without much trouble.
For non-fp16 sparse grads the logic is simple (call `_amp_non_finite_check_and_unscale_` on `grad._values()`) instead of `grad` itself. At least I hope it's that easy.
For fp16 sparse grads, it's tricker. Sparse tensors can be uncoalesced. From the [Note](https://pytorch.org/docs/master/sparse.html#torch.sparse.FloatTensor):
> Our sparse tensor format permits uncoalesced sparse tensors, where there may be duplicate coordinates in the indices; in this case, the interpretation is that the value at that index is the sum of all duplicate value entries.
An uncoalesced scaled fp16 grad may have values at duplicate coordinates that are all finite but large, such that adding them to make the coalesced version WOULD cause overflows.** If I checked `_values()` on the uncoalesced version, it might not report overflows, but I think it should.
So, if the grad is sparse, fp16, and uncoalesced, I still call `_amp_non_finite_check_and_unscale_` to unscale `grad._values()` in-place, but I also double-check the coalesced version by calling a second `_amp_non_finite_check_and_unscale_` on `grad.coalesce()._values()`. `coalesce()` is out-of-place, so this call doesn't redundantly affect `grad._values()`, but it does have the power to populate the same `found_inf` tensor. The `is_coalesced()` check and `coalesce()` probably aren't great for performance, but if someone needs a giant embedding table in FP16, they're better than nothing and memorywise, they'll only create a copy of nnz gradient values+indices, which is still way better than changing the whole table to FP32.
An `unscale` variant with liberty to create unscaled grads out-of-place, and replace `param.grad` instead of writing through it, could get away with just one `_amp_non_finite_check_and_unscale_`. It could say `coalesced = grad.coalesced()`, do only the stronger `_amp_non_finite_check_and_unscale_` on `coalesced._values()`, and set `param.grad = coalesced`. I could even avoid replacing `param.grad` itself by going one level deeper and setting `param.grad`'s indices and values to `coalesced`'s, but that seems brittle and still isn't truly "in place".
** you could whiteboard an uncoalesced fp32 grad with the same property, but fp32's range is big enough that I don't think it's realistic.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/36786
Reviewed By: ezyang
Differential Revision: D22202832
Pulled By: ngimel
fbshipit-source-id: b70961a4b6fc3a4c1882f65e7f34874066435735
Summary:
Several people have asked me about proper Amp usage with gradient accumulation. In particular, it's [unclear to people](https://github.com/NVIDIA/apex/issues/439#issuecomment-610351482) that you should only call `scaler.unscale_()` (if desired) and `scaler.update()` in iterations where you actually plan to step. This PR adds a minimal accumulation example.
I built the docs locally and it looks free from sphinx errors, at least.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/36601
Differential Revision: D21082295
Pulled By: ngimel
fbshipit-source-id: b2faa6c02b9f7e1972618a0f1d5360a03f0450ac
Summary:
Initial integration of eager autocasting, supporting out-of-place ops only for easier review.
Relevant issue/RFC: https://github.com/pytorch/pytorch/issues/25081
In-place ops and ops with user-supplied `out=...` can certainly be supported as well (my initial WIP https://github.com/pytorch/pytorch/pull/29552 handled many) but require substantially more complex special casing in the autocasting backend and tests. Support for these ops (much of which has already been written) will be broken into later PRs.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/32140
Differential Revision: D20346700
Pulled By: ezyang
fbshipit-source-id: 12d77b3917310186fbddf11c59b2794dc859131f
Summary:
hard to get right locally...I can build the docs but never quite match what it looks like live. the bullet point indentation was just an oversight.
Removing `Returns:` formatting tabs because they take up a lot of space when rendered and add no clarity. Some functions in Pytorch [do use them](https://pytorch.org/docs/master/torch.html#torch.eye), but [many don't bother](https://pytorch.org/docs/master/torch.html#torch.is_tensor), so apparently some people shared my feelings (Not using them is in line with existing practice).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33832
Differential Revision: D20135581
Pulled By: ngimel
fbshipit-source-id: bc788a7e57b142f95c4fa5baf3fe01f94c45abd8
Summary:
Also, windows memory failures responsible for the earlier reversion have been fixed.
This PR (initially) contains 2 commits:
* a revert of the revert
* all changes to implement the original Apex scale update heuristic, squashed into a single commit for easier diff review
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33366
Differential Revision: D20099026
Pulled By: ngimel
fbshipit-source-id: 339b9b6bd5134bf055057492cd1eedb7e4461529
Summary:
This PR implements the gradient scaling API that mruberry, jjsjann123, ngimel, zdevito, gchanan and I have been discussing. Relevant issue/RFC: https://github.com/pytorch/pytorch/issues/25081.
Volume-wise, this PR is mostly documentation and tests. The Python API (found entirely in `torch/cuda/amp/amp_scaler.py`) is lightweight . The exposed functions are intended to make the implementation and control flow of gradient scaling convenient, intuitive, and performant.
The API is probably easiest to digest by looking at the documentation and examples. `docs/source/amp.rst` is the homepage for the Automatic Mixed Precision package. `docs/source/notes/amp_examples.rst` includes several examples demonstrating common but not-immediately-obvious use cases. Examples are backed by tests in `test_cuda.py` (and thankfully the tests pass :P).
Two small utility kernels have been added in `native/cuda/AmpKernels.cu` to improve performance and avoid host-device synchronizations wherever possible.
Existing optimizers, both in the wild and in Pytorch core, do not need to change to use the scaling API.
However, the API was also designed to establish a contract between user scripts and optimizers such that writers of _new_ custom optimizers have the control points they need to implement fast, optionally sync-free updates. User scripts that obey the scaling API can drop such custom optimizers in and reap performance benefits without having to change anything aside from the optimizer constructor itself. [I know what the contract with custom optimizers should be](35829f24ef/torch/cuda/amp/amp_scaler.py (L179-L184)), but I'm waiting for review on the rest of the API before I go about documenting it (it will be given a dedicated section in `docs/source/notes/amp_examples.rst`.
Currently, the gradient scaling examples do not include the auto-casting API as discussed in https://github.com/pytorch/pytorch/issues/25081. The gradient scaling API is intended to be orthogonal/modular relative to autocasting. Without auto-casting the gradient scaling API is fully use-_able_, but not terribly use-_ful_, so it's up to you guys whether you want to wait until auto-casting is ready before merging the scaling API as well.
### Todo
- [ ] How do I get c10 registered status for my two custom kernels? They're very simple.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26512
Differential Revision: D19859905
Pulled By: mruberry
fbshipit-source-id: bb8ae6966214718dfee11345db824389e4286923
