Summary:
When we convert `dynamic_shapes` to `constraints` and pass them to `_dynamo.export`, we shouldn't give a deprecation warning. Such conversion happens when calling `torch.export.export`, e.g. But it can also happen when calling `capture_pre_autograd_graph` (which itself has this deprecation warning when `constraints` are passed directly as well).
Since `_log_export_usage` is an indicator of a top-level call (it is `True` by default but set to `False`, or at least passed through, by callers), we can (ab)use it to indicate when to give this deprecation warning.
Test Plan: none
Differential Revision: D54350172
Pull Request resolved: https://github.com/pytorch/pytorch/pull/120896
Approved by: https://github.com/BoyuanFeng, https://github.com/zhxchen17
Summary:
as title.
The following APIs are logged:
- capture_preautograd_graph
- torch._export.aot_compile
- external usage of _export_to_torch_ir (AOTInductor, Pippy)
- constraints API
- public use of torch._dynamo.export
Test Plan: CI
Differential Revision: D53735599
Pull Request resolved: https://github.com/pytorch/pytorch/pull/119848
Approved by: https://github.com/suo
This is going to fix a legacy issue like:
```
torch._dynamo.export(torch.ops.aten.scaled_dot_product_attention, ...)(*inputs,)
```
This is not supported any more, now the top level ```torch.export``` only support ```nn.Module```, but there are still some tests using the internal APIs and caused the ```trace_rules.check``` assertion error. This PR is going to mitigate such cases.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/119528
Approved by: https://github.com/ydwu4
Attempt #2 for https://github.com/pytorch/pytorch/pull/117875 to fix https://github.com/pytorch/pytorch/issues/112090.
Summary of changes:
- ~Changed CacheEntry linked list into a doubly-linked list structure to support deletion.~ (done by C++ refactor)
- Added CacheEntry and ExtraState borrowed references to GuardFn so that GuardFn can tell ExtraState to delete CacheEntry when the GuardFn is invalidated.
- ~Added ExtraState raw reference to CacheEntry so that we can get ExtraState to correctly point to the first CacheEntry if it gets deleted.~ (done by C++ refactor)
- CacheEntry destructor needs to reset GuardFn refs to ExtraState/CacheEntry in order to prevent use-after-free.
- code_context values that are nn.GraphModules need to be weakrefs in order to prevent circular references.
- Added tests that check for memory leaks and cache deletion operations.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/119107
Approved by: https://github.com/jansel
With this change, we now have the invariant that eval_frame only
contains "hot" functions that are called at runtime, as opposed to
cold functions which are only called at compile time.
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/119253
Approved by: https://github.com/yanboliang
ghstack dependencies: #119251
Fixes https://github.com/pytorch/pytorch/issues/119238
Here's what it looks like now:
```
$ TORCH_LOGS=+torch._dynamo.convert_frame python a.py
[2024-02-05 18:52:07,248] [0/0] torch._dynamo.convert_frame: [DEBUG] torchdynamo start compiling f /data/users/ezyang/b/pytorch/a.py:3, stack (elided 5 frames):
[2024-02-05 18:52:07,248] [0/0] torch._dynamo.convert_frame: [DEBUG] File "/data/users/ezyang/b/pytorch/a.py", line 7, in <module>
[2024-02-05 18:52:07,248] [0/0] torch._dynamo.convert_frame: [DEBUG] f(torch.randn(2))
[2024-02-05 18:52:07,248] [0/0] torch._dynamo.convert_frame: [DEBUG] File "/data/users/ezyang/b/pytorch/torch/_dynamo/eval_frame.py", line 453, in _fn
[2024-02-05 18:52:07,248] [0/0] torch._dynamo.convert_frame: [DEBUG] return fn(*args, **kwargs)
[2024-02-05 18:52:07,248] [0/0] torch._dynamo.convert_frame: [DEBUG]
$ cat a.py
import torch
@torch.compile
def f(x):
return x * 2
f(torch.randn(2))
```
The eval_frame frame is intentionally present, since what happens is you run the torch.compile wrapper, and then you actually hit the user frame to be compiled.
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/119251
Approved by: https://github.com/yanboliang, https://github.com/mlazos
This PR fixes several bugs, listed in priority:
1. `load_state_dict` with a nontensor step was incorrect for capturable and fused implementations since we don't create the tensors on the right device in `__setstate__`. This has been fixed.
2. The most recently added capturable implementations forgot the check that all tensors should be on CUDA for eager. We've now added those checks
3. The most recent change in Adamax only adds capturable for foreach but will silently be incorrect for forloop/single-tensor. I've added erroring and modified testing with many many many skips for that. Honestly my preference after this PR has only been further cemented that we should just do the single tensor and multi tensor capturable implementations together in the future. @mlazos
4. The conditional for adding cuda-supported configs for the optimizer infos was incorrect! So we hadn't been testing capturable! This also stands rectified and was the trigger for this PR in the first place.
5. In a similar way, the conditional for `_get_optim_inputs_including_global_cliquey_kwargs` was incorrect sometimes as well. This has also been corrected.
The following is not a bug, but is just something to make life simpler by not needing to handle Nones: `optim_input_funcs` must now mandatorily take in a `device`, which could be a string or a torch.device.
Details for posterity:
4. Running the test_foreach_matches_forloop test and printing the configs that get printed yields capturable getting included, which is correct.
```
(pytorch-3.10) [janeyx@devgpu023.odn1 ~/local/pytorch (5d50138f)]$ python test/test_optim.py -k test_foreach_matches_forloop_AdamW_cuda
/home/janeyx/.conda/envs/pytorch-3.10/lib/python3.10/site-packages/transformers/utils/generic.py:441: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
_torch_pytree._register_pytree_node(
/home/janeyx/.conda/envs/pytorch-3.10/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.17.3 and <1.25.0 is required for this version of SciPy (detected version 1.26.0
warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}"
params=None, kwargs={}, desc=default
params=None, kwargs={'lr': 0.01}, desc=non-default lr
params=None, kwargs={'weight_decay': 0.1}, desc=nonzero weight_decay
params=None, kwargs={'weight_decay': 0.1, 'maximize': True}, desc=maximize
params=None, kwargs={'weight_decay': 0.1, 'amsgrad': True}, desc=amsgrad
params=None, kwargs={'capturable': True}, desc=capturable
params=None, kwargs={'weight_decay': 0.1, 'amsgrad': True, 'capturable': True}, desc=capturable, amsgrad
params=None, kwargs={'lr': tensor(0.0010), 'amsgrad': True, 'capturable': True}, desc=Tensor lr with capturable and amsgrad
.
----------------------------------------------------------------------
Ran 1 test in 19.229s
OK
```
5. Running the test_optimizer_can_be_printed test (which calls `_get_optim_inputs_including_global_cliquey_kwargs`) and printing what gets run is also now correct.
```
/home/janeyx/.conda/envs/pytorch-3.10/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.17.3 and <1.25.0 is required for this version of SciPy (detected version 1.26.0
warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}"
params=None, kwargs={'differentiable': False}, desc=default
params=None, kwargs={'differentiable': True}, desc=default & differentiable
params=None, kwargs={'lr': 0.01, 'differentiable': False}, desc=non-default lr
params=None, kwargs={'lr': 0.01, 'differentiable': True}, desc=non-default lr & differentiable
params=None, kwargs={'weight_decay': 0.1, 'differentiable': False}, desc=nonzero weight_decay
params=None, kwargs={'weight_decay': 0.1, 'differentiable': True}, desc=nonzero weight_decay & differentiable
params=None, kwargs={'weight_decay': 0.1, 'maximize': True, 'differentiable': False}, desc=maximize
params=None, kwargs={'weight_decay': 0.1, 'maximize': True, 'differentiable': True}, desc=maximize & differentiable
params=None, kwargs={'weight_decay': 0.1, 'amsgrad': True, 'differentiable': False}, desc=amsgrad
params=None, kwargs={'weight_decay': 0.1, 'amsgrad': True, 'differentiable': True}, desc=amsgrad & differentiable
.params=None, kwargs={'foreach': False, 'differentiable': False, 'fused': False}, desc=default
params=None, kwargs={'foreach': True, 'differentiable': False, 'fused': False}, desc=default & foreach
params=None, kwargs={'foreach': False, 'differentiable': True, 'fused': False}, desc=default & differentiable
params=None, kwargs={'foreach': False, 'differentiable': False, 'fused': True}, desc=default & fused
params=None, kwargs={'lr': 0.01, 'foreach': False, 'differentiable': False, 'fused': False}, desc=non-default lr
params=None, kwargs={'lr': 0.01, 'foreach': True, 'differentiable': False, 'fused': False}, desc=non-default lr & foreach
params=None, kwargs={'lr': 0.01, 'foreach': False, 'differentiable': True, 'fused': False}, desc=non-default lr & differentiable
params=None, kwargs={'lr': 0.01, 'foreach': False, 'differentiable': False, 'fused': True}, desc=non-default lr & fused
params=None, kwargs={'weight_decay': 0.1, 'foreach': False, 'differentiable': False, 'fused': False}, desc=nonzero weight_decay
params=None, kwargs={'weight_decay': 0.1, 'foreach': True, 'differentiable': False, 'fused': False}, desc=nonzero weight_decay & foreach
params=None, kwargs={'weight_decay': 0.1, 'foreach': False, 'differentiable': True, 'fused': False}, desc=nonzero weight_decay & differentiable
params=None, kwargs={'weight_decay': 0.1, 'foreach': False, 'differentiable': False, 'fused': True}, desc=nonzero weight_decay & fused
params=None, kwargs={'weight_decay': 0.1, 'maximize': True, 'foreach': False, 'differentiable': False, 'fused': False}, desc=maximize
params=None, kwargs={'weight_decay': 0.1, 'maximize': True, 'foreach': True, 'differentiable': False, 'fused': False}, desc=maximize & foreach
params=None, kwargs={'weight_decay': 0.1, 'maximize': True, 'foreach': False, 'differentiable': True, 'fused': False}, desc=maximize & differentiable
params=None, kwargs={'weight_decay': 0.1, 'maximize': True, 'foreach': False, 'differentiable': False, 'fused': True}, desc=maximize & fused
params=None, kwargs={'weight_decay': 0.1, 'amsgrad': True, 'foreach': False, 'differentiable': False, 'fused': False}, desc=amsgrad
params=None, kwargs={'weight_decay': 0.1, 'amsgrad': True, 'foreach': True, 'differentiable': False, 'fused': False}, desc=amsgrad & foreach
params=None, kwargs={'weight_decay': 0.1, 'amsgrad': True, 'foreach': False, 'differentiable': True, 'fused': False}, desc=amsgrad & differentiable
params=None, kwargs={'weight_decay': 0.1, 'amsgrad': True, 'foreach': False, 'differentiable': False, 'fused': True}, desc=amsgrad & fused
params=None, kwargs={'capturable': True, 'foreach': False, 'differentiable': False, 'fused': False}, desc=capturable
params=None, kwargs={'capturable': True, 'foreach': True, 'differentiable': False, 'fused': False}, desc=capturable & foreach
params=None, kwargs={'capturable': True, 'foreach': False, 'differentiable': True, 'fused': False}, desc=capturable & differentiable
params=None, kwargs={'capturable': True, 'foreach': False, 'differentiable': False, 'fused': True}, desc=capturable & fused
params=None, kwargs={'weight_decay': 0.1, 'amsgrad': True, 'capturable': True, 'foreach': False, 'differentiable': False, 'fused': False}, desc=capturable, amsgrad
params=None, kwargs={'weight_decay': 0.1, 'amsgrad': True, 'capturable': True, 'foreach': True, 'differentiable': False, 'fused': False}, desc=capturable, amsgrad & foreach
params=None, kwargs={'weight_decay': 0.1, 'amsgrad': True, 'capturable': True, 'foreach': False, 'differentiable': True, 'fused': False}, desc=capturable, amsgrad & differentiable
params=None, kwargs={'weight_decay': 0.1, 'amsgrad': True, 'capturable': True, 'foreach': False, 'differentiable': False, 'fused': True}, desc=capturable, amsgrad & fused
params=None, kwargs={'lr': tensor(0.0010), 'amsgrad': True, 'capturable': True, 'foreach': False, 'differentiable': False, 'fused': False}, desc=Tensor lr with capturable and amsgrad
params=None, kwargs={'lr': tensor(0.0010), 'amsgrad': True, 'capturable': True, 'foreach': True, 'differentiable': False, 'fused': False}, desc=Tensor lr with capturable and amsgrad & foreach
params=None, kwargs={'lr': tensor(0.0010), 'amsgrad': True, 'capturable': True, 'foreach': False, 'differentiable': True, 'fused': False}, desc=Tensor lr with capturable and amsgrad & differentiable
params=None, kwargs={'lr': tensor(0.0010), 'amsgrad': True, 'capturable': True, 'foreach': False, 'differentiable': False, 'fused': True}, desc=Tensor lr with capturable and amsgrad & fused
.
----------------------------------------------------------------------
Ran 2 tests in 11.112s
OK
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118326
Approved by: https://github.com/mlazos
This PR fixes several bugs, listed in priority:
1. `load_state_dict` with a nontensor step was incorrect for capturable and fused implementations since we don't create the tensors on the right device in `__setstate__`. This has been fixed.
2. The most recently added capturable implementations forgot the check that all tensors should be on CUDA for eager. We've now added those checks
3. The most recent change in Adamax only adds capturable for foreach but will silently be incorrect for forloop/single-tensor. I've added erroring and modified testing with many many many skips for that. Honestly my preference after this PR has only been further cemented that we should just do the single tensor and multi tensor capturable implementations together in the future. @mlazos
4. The conditional for adding cuda-supported configs for the optimizer infos was incorrect! So we hadn't been testing capturable! This also stands rectified and was the trigger for this PR in the first place.
5. In a similar way, the conditional for `_get_optim_inputs_including_global_cliquey_kwargs` was incorrect sometimes as well. This has also been corrected.
The following is not a bug, but is just something to make life simpler by not needing to handle Nones: `optim_input_funcs` must now mandatorily take in a `device`, which could be a string or a torch.device.
Details for posterity:
4. Running the test_foreach_matches_forloop test and printing the configs that get printed yields capturable getting included, which is correct.
```
(pytorch-3.10) [janeyx@devgpu023.odn1 ~/local/pytorch (5d50138f)]$ python test/test_optim.py -k test_foreach_matches_forloop_AdamW_cuda
/home/janeyx/.conda/envs/pytorch-3.10/lib/python3.10/site-packages/transformers/utils/generic.py:441: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
_torch_pytree._register_pytree_node(
/home/janeyx/.conda/envs/pytorch-3.10/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.17.3 and <1.25.0 is required for this version of SciPy (detected version 1.26.0
warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}"
params=None, kwargs={}, desc=default
params=None, kwargs={'lr': 0.01}, desc=non-default lr
params=None, kwargs={'weight_decay': 0.1}, desc=nonzero weight_decay
params=None, kwargs={'weight_decay': 0.1, 'maximize': True}, desc=maximize
params=None, kwargs={'weight_decay': 0.1, 'amsgrad': True}, desc=amsgrad
params=None, kwargs={'capturable': True}, desc=capturable
params=None, kwargs={'weight_decay': 0.1, 'amsgrad': True, 'capturable': True}, desc=capturable, amsgrad
params=None, kwargs={'lr': tensor(0.0010), 'amsgrad': True, 'capturable': True}, desc=Tensor lr with capturable and amsgrad
.
----------------------------------------------------------------------
Ran 1 test in 19.229s
OK
```
5. Running the test_optimizer_can_be_printed test (which calls `_get_optim_inputs_including_global_cliquey_kwargs`) and printing what gets run is also now correct.
```
/home/janeyx/.conda/envs/pytorch-3.10/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.17.3 and <1.25.0 is required for this version of SciPy (detected version 1.26.0
warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}"
params=None, kwargs={'differentiable': False}, desc=default
params=None, kwargs={'differentiable': True}, desc=default & differentiable
params=None, kwargs={'lr': 0.01, 'differentiable': False}, desc=non-default lr
params=None, kwargs={'lr': 0.01, 'differentiable': True}, desc=non-default lr & differentiable
params=None, kwargs={'weight_decay': 0.1, 'differentiable': False}, desc=nonzero weight_decay
params=None, kwargs={'weight_decay': 0.1, 'differentiable': True}, desc=nonzero weight_decay & differentiable
params=None, kwargs={'weight_decay': 0.1, 'maximize': True, 'differentiable': False}, desc=maximize
params=None, kwargs={'weight_decay': 0.1, 'maximize': True, 'differentiable': True}, desc=maximize & differentiable
params=None, kwargs={'weight_decay': 0.1, 'amsgrad': True, 'differentiable': False}, desc=amsgrad
params=None, kwargs={'weight_decay': 0.1, 'amsgrad': True, 'differentiable': True}, desc=amsgrad & differentiable
.params=None, kwargs={'foreach': False, 'differentiable': False, 'fused': False}, desc=default
params=None, kwargs={'foreach': True, 'differentiable': False, 'fused': False}, desc=default & foreach
params=None, kwargs={'foreach': False, 'differentiable': True, 'fused': False}, desc=default & differentiable
params=None, kwargs={'foreach': False, 'differentiable': False, 'fused': True}, desc=default & fused
params=None, kwargs={'lr': 0.01, 'foreach': False, 'differentiable': False, 'fused': False}, desc=non-default lr
params=None, kwargs={'lr': 0.01, 'foreach': True, 'differentiable': False, 'fused': False}, desc=non-default lr & foreach
params=None, kwargs={'lr': 0.01, 'foreach': False, 'differentiable': True, 'fused': False}, desc=non-default lr & differentiable
params=None, kwargs={'lr': 0.01, 'foreach': False, 'differentiable': False, 'fused': True}, desc=non-default lr & fused
params=None, kwargs={'weight_decay': 0.1, 'foreach': False, 'differentiable': False, 'fused': False}, desc=nonzero weight_decay
params=None, kwargs={'weight_decay': 0.1, 'foreach': True, 'differentiable': False, 'fused': False}, desc=nonzero weight_decay & foreach
params=None, kwargs={'weight_decay': 0.1, 'foreach': False, 'differentiable': True, 'fused': False}, desc=nonzero weight_decay & differentiable
params=None, kwargs={'weight_decay': 0.1, 'foreach': False, 'differentiable': False, 'fused': True}, desc=nonzero weight_decay & fused
params=None, kwargs={'weight_decay': 0.1, 'maximize': True, 'foreach': False, 'differentiable': False, 'fused': False}, desc=maximize
params=None, kwargs={'weight_decay': 0.1, 'maximize': True, 'foreach': True, 'differentiable': False, 'fused': False}, desc=maximize & foreach
params=None, kwargs={'weight_decay': 0.1, 'maximize': True, 'foreach': False, 'differentiable': True, 'fused': False}, desc=maximize & differentiable
params=None, kwargs={'weight_decay': 0.1, 'maximize': True, 'foreach': False, 'differentiable': False, 'fused': True}, desc=maximize & fused
params=None, kwargs={'weight_decay': 0.1, 'amsgrad': True, 'foreach': False, 'differentiable': False, 'fused': False}, desc=amsgrad
params=None, kwargs={'weight_decay': 0.1, 'amsgrad': True, 'foreach': True, 'differentiable': False, 'fused': False}, desc=amsgrad & foreach
params=None, kwargs={'weight_decay': 0.1, 'amsgrad': True, 'foreach': False, 'differentiable': True, 'fused': False}, desc=amsgrad & differentiable
params=None, kwargs={'weight_decay': 0.1, 'amsgrad': True, 'foreach': False, 'differentiable': False, 'fused': True}, desc=amsgrad & fused
params=None, kwargs={'capturable': True, 'foreach': False, 'differentiable': False, 'fused': False}, desc=capturable
params=None, kwargs={'capturable': True, 'foreach': True, 'differentiable': False, 'fused': False}, desc=capturable & foreach
params=None, kwargs={'capturable': True, 'foreach': False, 'differentiable': True, 'fused': False}, desc=capturable & differentiable
params=None, kwargs={'capturable': True, 'foreach': False, 'differentiable': False, 'fused': True}, desc=capturable & fused
params=None, kwargs={'weight_decay': 0.1, 'amsgrad': True, 'capturable': True, 'foreach': False, 'differentiable': False, 'fused': False}, desc=capturable, amsgrad
params=None, kwargs={'weight_decay': 0.1, 'amsgrad': True, 'capturable': True, 'foreach': True, 'differentiable': False, 'fused': False}, desc=capturable, amsgrad & foreach
params=None, kwargs={'weight_decay': 0.1, 'amsgrad': True, 'capturable': True, 'foreach': False, 'differentiable': True, 'fused': False}, desc=capturable, amsgrad & differentiable
params=None, kwargs={'weight_decay': 0.1, 'amsgrad': True, 'capturable': True, 'foreach': False, 'differentiable': False, 'fused': True}, desc=capturable, amsgrad & fused
params=None, kwargs={'lr': tensor(0.0010), 'amsgrad': True, 'capturable': True, 'foreach': False, 'differentiable': False, 'fused': False}, desc=Tensor lr with capturable and amsgrad
params=None, kwargs={'lr': tensor(0.0010), 'amsgrad': True, 'capturable': True, 'foreach': True, 'differentiable': False, 'fused': False}, desc=Tensor lr with capturable and amsgrad & foreach
params=None, kwargs={'lr': tensor(0.0010), 'amsgrad': True, 'capturable': True, 'foreach': False, 'differentiable': True, 'fused': False}, desc=Tensor lr with capturable and amsgrad & differentiable
params=None, kwargs={'lr': tensor(0.0010), 'amsgrad': True, 'capturable': True, 'foreach': False, 'differentiable': False, 'fused': True}, desc=Tensor lr with capturable and amsgrad & fused
.
----------------------------------------------------------------------
Ran 2 tests in 11.112s
OK
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118326
Approved by: https://github.com/mlazos
Summary: Exposes `dynamic_shapes` api at multiple levels so it's easier to replace the old API `dynamic_dim()` with the new API `Dim()`.
Test Plan: CI
Differential Revision: D53246409
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118695
Approved by: https://github.com/ydwu4
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
The original motivation for MYPYINDUCTOR was a faster type checking configuration that only checked a subset of files. With the removal of `follow_imports = ignore`, we are now able to use dmypy to do fast incremental typechecking, eliminating the need for this.
Perhaps erroneously, when I tee'ed up this PR I elected to delete the `follow_imports = skip` designations in the mypy-inductor.ini. This lead to a number of extra type error suppressions that I manually edited. You will need to review.
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118432
Approved by: https://github.com/Skylion007
ghstack dependencies: #118414, #118418
I feel it's easier to open a new PR rather than iterating on the previous PR (https://github.com/pytorch/pytorch/pull/105257 ) since this is more like a rewrite.
In this PR, instead of changing GraphModule directly which can easily causes BC issue, I create a LazyGraphModule class as Zachary & Jason suggested in comments from the previous PR.
The difference between LazyGraphModule and GraphModule is mainly about how re-compile for the graph module happens. In GraphModule the recompilation happens 'eagerly': constructing a GraphModule will cause the recompilation. While in LazyGraphModule, we just mark the module as needing recompilation. The real recompilation only happens when absolutely required (e.g. call forward method, access the code property etc.). In a lot of cases in torch.compile, the real recompilation eventually is not triggered at all. This can save a few seconds of compilation time.
By default, GraphModule rather than LazyGraphModule is used. `use_lazy_graph_module(True)` context manager can be used to pick LazyGraphModule instead. This has been applied to the torch.compile stack.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/117911
Approved by: https://github.com/jansel
Added support for constant outputs. We will just embed the constant directly into the output, like `return (x, 1)`.
Also adds support for None input/outputs. For None inputs we address it the same way we do to constants, which is that a placeholder with no users will be inserted into the graph, and the None will be embedded into whatever operator is using the None. For None outputs, we will also address the same way we do constants, which is that we embed it into the output, like `return (x, None)`.
Differential Revision: D52881070
Pull Request resolved: https://github.com/pytorch/pytorch/pull/117894
Approved by: https://github.com/zhxchen17
1. I'd like to remove the patching that avoids the profiler hook, but it adds an additional graph break due to nested wrappers. #117767 if interested, see (internal only) paste for [before](P996529232) and [after](P997507449) this PR.
```
I've locally run perf benchmarks for yolov3: Before the speedup is 4.183x, and after it is 4.208x.
I've also run it for resnet50: before, speedup is 3.706x and now it is 3.924x.
```
2. @mlazos I now unwrap twice in the dynamo and inductor tests. This feels like we're testing deficiently--should we add tests to test that tracing through the profiler hook and the use_grad hook are functioning according to expectations (I know there's at least one graph break in one).
3. There's a strange memory thing going on...what is happening? This has been resolved with @voznesenskym's [change](https://github.com/pytorch/pytorch/pull/116169). (for details see below)
<details>
This PR will fail the test_static_address_finalizer test due to a mysterious thing that is happening (idk what, but maybe the dynamo cache or a frame _expecting_ the patching to have been done).
There is no Python refcycle, as the backrefs for `p_ref()` look like:
(so 5 backrefs but none of them python)
And the refs:
</details>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/115772
Approved by: https://github.com/jansel, https://github.com/mlazos
Attempts to make the input/output mismatch error better by first checking if the inputs/outputs are able to be pytree flattened into supporting types (tensors, symints, ...). So if user passes in some datastructure which does not have a pytree flatten registration, this will error with the message "It looks like one of the inputs is with type CustomType is not supported or pytree flatten-able.... please register a pytree flatten/unflatten function using the pytree.register_pytree_node API".
The check inside of produce_matching should now only error if something unexpected happens (dynamo accidentally adds an input or removes an output), and should be considered an internal error.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/117598
Approved by: https://github.com/avikchaudhuri, https://github.com/BowenBao
Attempts to make the input/output mismatch error better by first checking if the inputs/outputs are able to be pytree flattened into supporting types (tensors, symints, ...). So if user passes in some datastructure which does not have a pytree flatten registration, this will error with the message "It looks like one of the inputs is with type CustomType is not supported or pytree flatten-able.... please register a pytree flatten/unflatten function using the pytree.register_pytree_node API".
The check inside of produce_matching should now only error if something unexpected happens (dynamo accidentally adds an input or removes an output), and should be considered an internal error.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/117598
Approved by: https://github.com/avikchaudhuri, https://github.com/BowenBao
Attempts to make the input/output mismatch error better by first checking if the inputs/outputs are able to be pytree flattened into supporting types (tensors, symints, ...). So if user passes in some datastructure which does not have a pytree flatten registration, this will error with the message "It looks like one of the inputs is with type CustomType is not supported or pytree flatten-able.... please register a pytree flatten/unflatten function using the pytree.register_pytree_node API".
The check inside of produce_matching should now only error if something unexpected happens (dynamo accidentally adds an input or removes an output), and should be considered an internal error.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/117598
Approved by: https://github.com/avikchaudhuri
Summary:
Original commit changeset: 2a9588cfd51b
Original Phabricator Diff: D52062368
Test Plan: In investigating S386328 and S382826, we found checkpoint loading succeed after backout D52062368: S386328_backout_1220_193648
Differential Revision: D52356011
Pull Request resolved: https://github.com/pytorch/pytorch/pull/116243
Approved by: https://github.com/voznesenskym
Fixes#113422Fixes#94575
This is now possible:
```py
model = Model()
compiled_model = torch.compile(model)
model.load_state_dict(compiled_model.state_dict()) # previously key mismatch!
```
This also makes it much easier to checkpoint and load models that were wrapped like so:
```py
FSDP(torch.compile(model))
# or
DDP(torch.compile(model))
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/113423
Approved by: https://github.com/msaroufim
