### bc-breaking for existing users of the private API:
- Existing policy functions must now change their return value to be [CheckpointPolicy](c0b40ab42e/torch/utils/checkpoint.py (L1204-L1230)) Enum instead of bool.
- To restore previous behavior, return `PREFER_RECOMPUTE` instead of `False` and `{PREFER,MUST}_SAVE` instead of `True` depending whether you prefer the compiler to override your policy.
- Policy function now accepts a `ctx` object instead of `mode` for its first argument.
- To restore previous behavior, `mode = "recompute" if ctx.is_recompute else "forward"`.
- Existing calls to `_pt2_selective_checkpoint_context_fn_gen` must be renamed to `create_selective_checkpoint_contexts `. The way you use the API remains the same. It would've been nice to do something different (not make the user have to use functools.partial?), but this was the easiest to compile (idk if this should actually be a constraint).
Related doc: https://docs.google.com/document/d/1BKyizkZPdri9mHqdDOLAUpkI7SbbKfLHRFVVpK9ZWqo/edit
Memory considerations:
- As with the existing SAC, cached values are cleared upon first use.
- We error if the user wishes to backward a second time on a region forwarded with SAC enabled.
In-place:
- We use version counting to enforce that if any cached tensor has been mutated. In-place operations not mutating cached tensors are allowed.
- `allow_cache_entry_mutation=True` can be passed to disable this check (useful in the case of auto AC where the user is cleverly also saves the output of the in-place)
Randomness, views
- Currently in this PR, we don't do anything special for randomness or views, the author of the policy function is expected to handle them properly. (Would it would be beneficial to error? - we either want to save all or recompute all random tensors)
Tensor object preservation
- ~We guarantee that if a tensor does not requires grad, and it is saved, then what you get out is the same tensor object.~ UPDATE: We guarantee that if a tensor is of non-differentiable dtype AND it is not a view, and it is saved, then what you get out is the same tensor object. This is a nice guarantee for nested tensors which care about the object identity of of the offsets tensor.
Policy function
- Enum values are `{MUST,PREFER}_{SAVE,RECOMPUTE}` (bikeshed welcome). Alternatively there was `{SAVE,RECOMPUTE}_{NON_,}OVERRIDABLE`. The former was preferred bc it seemed clearer that two `MUST` clashing should error, versus it is ambiguous whether two `NON_OVERRIDABLE` being stacked should silently ignore or error.
- The usage of Enum today. There actually is NO API to stack SAC policies today. The only thing the Enum should matter for in the near term is the compiler. The stacking SAC policy would be useful if someone wants to implement something like simple FSDP, but it is not perfect because with a policy of `PREFER_SAVE` you are actually saving more than autograd would save normally (would be fixed with AC v3).
- The number of times we call the policy_fn is something that should be documented as part of public API. We call the policy function for all ops except ~~detach~~ UPDATE : metadata ops listed in `torch.utils.checkpoint.SAC_IGNORED_OPS`) because these ops may be called a different number of times by AC itself between forward and recompute.
- The policy function can be a stateful object (we do NOT make separate copies of this object for forward/recompute, the user is expected to handle that via is_recompute see below).
Tensors guaranteed to be the same tensor as-is
- Policy function signature takes ctx object as its first argument. The ctx function is an object encapsulating info that may be useful to the user, it currently only holds "is_recompute". Adding this indirection gives us flexibility to add more attrs later if necessary.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/125795
Approved by: https://github.com/Chillee, https://github.com/fmassa
Related doc: https://docs.google.com/document/d/1BKyizkZPdri9mHqdDOLAUpkI7SbbKfLHRFVVpK9ZWqo/edit
Memory considerations:
- As with the existing SAC, cached values are cleared upon first use.
- We error if the user wishes to backward a second time on a region forwarded with SAC enabled.
In-place:
- We use version counting to enforce that if any cached tensor has been mutated. In-place operations not mutating cached tensors are allowed.
- `allow_cache_entry_mutation=True` can be passed to disable this check (useful in the case of auto AC where the user is cleverly also saves the output of the in-place)
Randomness, views
- Currently in this PR, we don't do anything special for randomness or views, the author of the policy function is expected to handle them properly. (Would it would be beneficial to error? - we either want to save all or recompute all random tensors)
Tensor object preservation
- We guarantee that if a tensor does not requires grad, and it is saved, then what you get out is the same tensor object. If the tensor does require grad, we must detach to avoid creating a reference cycle. This is a nice guarantee for nested tensors which care about the object identity of of the offsets tensor.
Policy function
- Enum values are `{MUST,PREFER}_{SAVE,RECOMPUTE}` (bikeshed welcome). Alternatively there was `{SAVE,RECOMPUTE}_{NON_,}OVERRIDABLE`. The former was preferred bc it seemed clearer that two `MUST` clashing should error, versus it is ambiguous whether two `NON_OVERRIDABLE` being stacked should silently ignore or error.
- The usage of Enum today. There actually is NO API to stack SAC policies today. The only thing the Enum should matter for in the near term is the compiler. The stacking SAC policy would be useful if someone wants to implement something like simple FSDP, but it is not perfect because with a policy of `PREFER_SAVE` you are actually saving more than autograd would save normally (would be fixed with AC v3).
- The number of times we call the policy_fn is something documented part of public API. We call the policy function for all ops except detach because detach is itself called a different number of times by AC between forward and recompute.
- The policy function can be a stateful object (we do NOT make separate copies of this object for forward/recompute, the user is expected to handle that via is_recompute see below).
Tensors guaranteed to be the same tensor as-is
- Policy function signature takes ctx object as its first argument. The ctx function is an object encapsulating info that may be useful to the user, it currently only holds "is_recompute". Adding this indirection gives us flexibility to add more attrs later if necessary.
"bc-breaking" for existing users of the private API:
- Existing policy functions must now change their return value to use the Enum.
- Existing calls to `_pt2_selective_checkpoint_context_fn_gen` must be renamed to `gen_selective_checkpoint_context_fn`. The way you use the API remains the same. It would've been nice to do something different (not make the user have to use functools.partial?), but this was the easiest to compile (idk if this should actually be a constraint).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/125795
Approved by: https://github.com/Chillee, https://github.com/fmassa
as titled. I found that there're some issues in the eager mode SAC where
sometimes we would have recompute pop from storage of ops that are
missing, these ops are detach ops. So this PR refactors the two modes,
so that they would always recompute ignored ops
Pull Request resolved: https://github.com/pytorch/pytorch/pull/126751
Approved by: https://github.com/yf225
# Motivation
As discussed in [#124479](https://github.com/pytorch/pytorch/pull/124479), `torch.amp.autocast` can NOT be completely equivalent to `torch.cuda.amp.autocast` and `torch.cpu.amp.autocast` since `torch.amp.autocast` has NOT the default `dtype` for CPU (`torch.bfloat16` by default) and CUDA (`torch.float16` by default) respectively. We would like `torch.amp.autocast` to be more generic to help the developer/customer write the device-agnostic code. Because there are not enough reasons to add device-specific autocast `torch.xxx.amp.autocast` for each device backend.
# Solution
When `None` is passed to `dtype`, we should use `torch.get_autocast_dtype` to get the related dtype for each backend. Meanwhile, `torch.get_autocast_dtype` is necessary to be supported in JIT path for BC.
# Additional Context
With this PR, `torch.amp.autocast(device_type='cuda')` is equivalent to `torch.cuda.amp.autocast`.
Add two new UTs to cover this change in eager and jit path respectively.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/125103
Approved by: https://github.com/albanD, https://github.com/jgong5, https://github.com/gujinghui
Previously, we were checking `len(device_types)` where `device_types` is a `list`. This meant that if there were multiple inputs, we would see something like `device_types = ["cuda", "cuda"]` and a false positive warning. We should check `len(set(device_types))`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/122726
Approved by: https://github.com/soulitzer
`get_device_states` doesn't recursively look into nested lists/dicts to find tensors. As a result, activation checkpointing for such inputs results in silent incorrect results as `get_device_states` returns an empty result and no rng is saved as a result here: https://github.com/pytorch/pytorch/blob/main/torch/utils/checkpoint.py#L188 since `fwd_device_states` is empty.
Fixed this by using `tree_map` for both `get_device_states` and `_infer_device_type`. Also added appropriate unit tests.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/121462
Approved by: https://github.com/soulitzer
Fixes https://github.com/pytorch/pytorch/issues/118129
Suppressions automatically added with
```
import re
with open("error_file.txt", "r") as f:
errors = f.readlines()
error_lines = {}
for error in errors:
match = re.match(r"(.*):(\d+):\d+: error:.*\[(.*)\]", error)
if match:
file_path, line_number, error_type = match.groups()
if file_path not in error_lines:
error_lines[file_path] = {}
error_lines[file_path][int(line_number)] = error_type
for file_path, lines in error_lines.items():
with open(file_path, "r") as f:
code = f.readlines()
for line_number, error_type in sorted(lines.items(), key=lambda x: x[0], reverse=True):
code[line_number - 1] = code[line_number - 1].rstrip() + f" # type: ignore[{error_type}]\n"
with open(file_path, "w") as f:
f.writelines(code)
```
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Co-authored-by: Catherine Lee <csl@fb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118533
Approved by: https://github.com/Skylion007, https://github.com/zou3519
Fixes https://github.com/pytorch/pytorch/issues/118129
Suppressions automatically added with
```
import re
with open("error_file.txt", "r") as f:
errors = f.readlines()
error_lines = {}
for error in errors:
match = re.match(r"(.*):(\d+):\d+: error:.*\[(.*)\]", error)
if match:
file_path, line_number, error_type = match.groups()
if file_path not in error_lines:
error_lines[file_path] = {}
error_lines[file_path][int(line_number)] = error_type
for file_path, lines in error_lines.items():
with open(file_path, "r") as f:
code = f.readlines()
for line_number, error_type in sorted(lines.items(), key=lambda x: x[0], reverse=True):
code[line_number - 1] = code[line_number - 1].rstrip() + f" # type: ignore[{error_type}]\n"
with open(file_path, "w") as f:
f.writelines(code)
```
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118533
Approved by: https://github.com/Skylion007, https://github.com/zou3519
as titled, when using SAC + torch.compile, it currently only check for
functional tensor, but not checking any tensor subclasses, therefore SAC
under torch.compile would ignore the tensor types like tensor
subclasses. Fixed in this PR
Pull Request resolved: https://github.com/pytorch/pytorch/pull/115960
Approved by: https://github.com/bdhirsh
Fixes https://github.com/pytorch/pytorch/issues/113717.
When `preserve_rng_state=True`, we let AOTAutograd trace through `torch.random.fork_rng` op, and the tracing doesn't work under CUDA, hence the original error reported in the issue.
But since we are already doing RNG functionalization at Inductor level, we don't actually need to trace this `fork_rng` op. So we should just rewrite `preserve_rng_state` to False when we are using torch.compile (and let Inductor do its RNG functionalization which it's already been doing).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/113718
Approved by: https://github.com/wanchaol
The problem is that we have a subclass (FunctionalTensor) that overrides size/stride calls, causing them to go through __torch_dispatch__.
But when SAC is active, we have _CachingTorchDispatchMode.__torch_dispatch__ active, that intercepts those size/stride calls first, and does something different with them instead of letting FunctionalTensor.__torch_dispatch__ handle them.
This PR updates the SAC torch dispatch mode to know to not handle metadata calls, and let its tensor arguments handle them directly.
Right now, `FunctionalTensor` has a hardcoded list of metadata ops, but we should probably put them somewhere more general.
I'll add better testing before landing this PR.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/113380
Approved by: https://github.com/yf225, https://github.com/wanchaol
People access activation checkpoint through many layers of config and it is not always guaranteed that all the layers of wrapping around checkpoint properly propagate all the kwargs, e.g. debug mode. This context manager offers an alternative way to enable debug mode that bypasses the need for all layers to propagate kwargs.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110728
Approved by: https://github.com/albanD
ghstack dependencies: #110673, #110674, #110675, #110676
The first reland broke internal (failing diff: D49617462).
The major error looks like it's because there's an internal-only higher order op that needs a new functionalization rule. I'm going to land an internal diff for that and confirm tests pass before relanding this PR.
Also confirmed that the issue from https://github.com/pytorch/pytorch/issues/110121 is fixed, and added a test.
This reverts commit 1b90f07f5a.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110079
Approved by: https://github.com/ezyang
I'm pretty sure this is fixed but I'll run inductor and trunk CI. The failing test in trunk previously was that the selective activation checkpointing code that landed recently assumes that it can detect whether or not AOTAutograd is running by seeing if the inputs to SAC are C++ `FunctionalTensorWrapper`s
previous land broke some inductor trunk tests
This reverts commit 629a628cc8.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/109906
Approved by: https://github.com/ezyang
Pull Request resolved: https://github.com/pytorch/pytorch/pull/105489
NOTE: this PR is tagged "not user facing", because it's not ready to be announced externally yet.
This PR implements torch.compile + selective activation checkpoint (SAC) integration, by using `TagActivationCheckpoint` (same backend as torch.compile + full activation checkpoint integration).
TorchDispatchMode based implementation cannot support including inplace ops in the checkpointed region at the moment (the reason for this needs investigation), and there is also no way to ban them (because TorchDispatchMode now only sees "after-functionalization" ops, so can't detect if an op is in-place). Hence we hide torch.compile + SAC behind a flag (`torch._dynamo.config._experimental_support_context_fn_in_torch_utils_checkpoint`) and will only use it internally for cases that are known to not have in-place ops. This state won't last too long, because in-place op will at least be able to be detected after Brian's mode reordering and related functionalization changes.
So next steps after this PR:
1. Wait for Brian's mode reordering and related functionalization changes to land, and then try to enable the "inplace ops" unit test for torch.compile + selective activation checkpoint (if it doesn't work, investigate why).
2. Unify selective- and full-checkpoint under TorchDispatchMode based implementation.
Differential Revision: D47497145
Pull Request resolved: https://github.com/pytorch/pytorch/pull/105489
Approved by: https://github.com/anijain2305
This PR makes some improvements for debuggability of checkpointing:
- improved error messages that are more understandable
- errors are now `CheckpointError` which subclasses `RuntimeError` (only `CheckpointError` triggers debug message, see below)
- stricter error checking by default:
- shapes, dtypes, and device are compared
- we also now error when more tensors are being saved for backward during recompute
- NOTE: checks are relaxed if it is detected that you are doing backward within forward
- shapes, dtype, and device checking can be disabled by passing `determinism_check="none"`
- new debug flag: more helpful error message when `debug=True`
Note:
- cpp stack trace is only included for x86 linux machines
- the error message if cpp stack trace is included can be quite long. For a function checkpointed with 8 operators, the log was around 1300 lines! (should this be hidden behind a flag?)
[Error message when debug='True' (python stack trace only)](https://gist.github.com/soulitzer/3d5e19c7cceae8e22f9bdd625ec39dd4)
[Error message when debug='True' (with python and cpp stacktrace)](https://gist.github.com/soulitzer/ff8fd8c3ccbb2c90dfe3df6d7713b167)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/103859
Approved by: https://github.com/albanD
Fixes #ISSUE_NUMBER
1、add checkpoint support for custom device
2、add a device argument, I want to add a device="cuda" parameter to the func `forward` of `CheckpointFunction`, and I can specify the device type when using it, but the func `apply` of `torch.autograd.Function` does not support `kwargs`, so I added a variable named `_device`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/99626
Approved by: https://github.com/soulitzer
Now that we have updated all internal callsites, per https://fb.workplace.com/groups/pytorch.oss.dev/permalink/1635183750239493/ we should raise a warning when use_reentrant is not explicitly passed for 2.1
Deprecation note:
- Not passing in use_reentrant explicitly is now deprecated and will raise a warning. In the future the default value of use-reentrant will be False. To preserve the existing behavior you can pass in use_reentrant=True. It is recommended that you use use_reentrant=False.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/100551
Approved by: https://github.com/Skylion007
Why did I choose context manager instead of per-call? Early stopping is not part of the model definition, and depending on how a particular model is used, e.g., with PT2 or not we may or may not want to disable early stopping.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/96866
Approved by: https://github.com/albanD
Updates:
- ~recommend user to use non-reentrant, mention that reentrant will be deprecated in the future~
- merges all the warnings into a single list of non-reentrant improvements over reentrant
- adds an additional entry to the list about allowing backward inside checkpointed region
Pull Request resolved: https://github.com/pytorch/pytorch/pull/96862
Approved by: https://github.com/albanD
