Make `torch.__future__.get_swap_module_params_on_conversion() == True` account for `assign` argument to `nn.Module.load_state_dict`
Similar to when `torch.__future__.set_swap_module_params_on_conversion()` is `False`, `assign=True` means that we do not incur a `self.copy_(other)` and the properties of `other` will be preserved
Pull Request resolved: https://github.com/pytorch/pytorch/pull/121158
Approved by: https://github.com/albanD
ghstack dependencies: #121157
Always preserve requires_grad of param in module. Documentation fixed in PR stacked above.
Also fix test case to test load a state_dict generated with `keep_vars=False` (the default)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/121157
Approved by: https://github.com/albanD
This updates the nesting of if statements in `nn.Module._apply` such that if
`torch.__future__.set_swap_module_params_on_conversion(True)`, we always try to swap regardless of whether
- `torch._has_compatible_shallow_copy_type(param, fn(param)`
- `torch.__future__.set_overwrite_module_params_on_conversion` is set
This means that `meta_module.to_empty('device')` can now use the swap_tensors path cc @awgu
Pull Request resolved: https://github.com/pytorch/pytorch/pull/120659
Approved by: https://github.com/albanD
Implicitly, the return type of `set_extra_state` is `NoReturn` since it always raises an error, and pyright will complain about mismatched return types if you override it with an implementation that doesn't also always raise an error. If we explicitly hint the return type as `None` (how we expect people to override it), we can avoid this error message.
```
Method "set_extra_state" overrides class "Module" in an incompatible manner
Return type mismatch: base method returns type "NoReturn", override returns type "None"
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/120161
Approved by: https://github.com/mikaylagawarecki
Added a `torch.Tensor` method that defines how to transform `other`, a value in the state dictionary, to be loaded into `self`, a param/buffer in an `nn.Module` before swapping via `torch.utils.swap_tensors`
* `param.module_load(sd[key])`
This method can be overridden using `__torch_function__`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/117913
Approved by: https://github.com/albanD
Added `torch.__future__.{get/set}_swap_module_params_on_conversion` that defaults to `False` for now, but we probably want to modify to override this and default to `True` in `nn.Module._apply` if input is a tensor subclass.
From offline discussion, for now we are **not** allowing `swap_tensor` after the first module forward has been run*** if the autograd graph is still alive. The reason being that `torch.utils.swap_tensors(t1, t2)` requires the `use_count` of both `TensorImpl`s associated with `t1` and `t2` to be 1. The first forward pass will install `AccumulateGrad` nodes on each param, which [bump the refcount of the associated TensorImpl](6cf1fc66e3/torch/csrc/autograd/variable.cpp (L307)). **Future work might be to swap the refs that the `AccumulateGrad` nodes hold if it is necessary.**
***From this, it might seem like we don't need to handle gradients. However, I still handle the grads for the edge case that the grads are set via `p.grad = grad` OR the autograd graph is no longer alive because the output has been garbage collected.
If any `swap_tensors` fails on any of the parameters in the `nn.Module` we raise an error.
**`RNNBase` overrides `nn.Module._apply()` and installs weakrefs on some parameters. As a result, all modules that inherit from `RNNBase` (`RNN`, `GRU` and `LSTM`) cannot use the`swap_tensors` path as of now**
Pull Request resolved: https://github.com/pytorch/pytorch/pull/117167
Approved by: https://github.com/albanD
ghstack dependencies: #118028
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
Using mypy in code that depends on pytorch, I noticed that the type annotation doesn't allow a device ordinal.
`error: Argument "device" to "to_empty" of "Module" has incompatible type "int"; expected "str | device" [arg-type]`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/113647
Approved by: https://github.com/albanD
Fixes #112601
```
pydocstyle torch/nn/modules/module.py --count
```
On master:
115
After my changes on this PR:
8
The remaining 8 are due to missing docstrings in the magic methods:
```
torch/nn/modules/module.py:1 at module level:
D100: Missing docstring in public module
torch/nn/modules/module.py:1635 in public method `__getstate__`:
D105: Missing docstring in magic method
torch/nn/modules/module.py:1640 in public method `__setstate__`:
D105: Missing docstring in magic method
torch/nn/modules/module.py:1674 in public method `__getattr__`:
D105: Missing docstring in magic method
torch/nn/modules/module.py:1689 in public method `__setattr__`:
D105: Missing docstring in magic method
torch/nn/modules/module.py:1748 in public method `__delattr__`:
D105: Missing docstring in magic method
torch/nn/modules/module.py:2480 in public method `__repr__`:
D105: Missing docstring in magic method
torch/nn/modules/module.py:2505 in public method `__dir__`:
D105: Missing docstring in magic method
```
Should I add them too? Happy to do it, I just wasn't sure if you wanted these documented. Please let me know.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/112674
Approved by: https://github.com/mikaylagawarecki
This updates ruff to 0.285 which is faster, better, and have fixes a bunch of false negatives with regards to fstrings.
I also enabled RUF017 which looks for accidental quadratic list summation. Luckily, seems like there are no instances of it in our codebase, so enabling it so that it stays like that. :)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/107519
Approved by: https://github.com/ezyang
This updates ruff to 0.285 which is faster, better, and have fixes a bunch of false negatives with regards to fstrings.
I also enabled RUF017 which looks for accidental quadratic list summation. Luckily, seems like there are no instances of it in our codebase, so enabling it so that it stays like that. :)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/107519
Approved by: https://github.com/ezyang
Add similar semantics for creating a buffer object similar to creating a parameter. This is done by introducing a new `Buffer` class that can be used for type disambiguation. The underlying functionality of registering a buffer remains the same as the `register_buffer` method has not been changed. The `persistent` parameter in the `Buffer` type is to indicate whether a buffer object should be persistent or not. Other non-test changes have to do with getting the new `Buffer` type recognized by inductor and dynamo. Remaining changes are test changes to make sure that the `Buffer` type can be used as a drop in replacement for `register_buffer` as it just leads to `register_buffer` being called. The addition of this new functionality still allows for normal tensors to be used as buffers so these changes are intended to be backwards compatible.
Fixes#35735
Pull Request resolved: https://github.com/pytorch/pytorch/pull/104069
Approved by: https://github.com/mikaylagawarecki
When working with a highly-dynamic python code it's not always possible to express the static types. However if we consider the end-user experience for somebody who uses both pytorch and a static type checker (mypy, pyright), we should error on the side of being ergonomic and not technically correct.
The `nn.Module.__getattr__` is one of the such examples: on paper the return type is correct. In practice the community would benefit from having `Any` as a return type because it would avoid littering the idiomatic pytorch code with `cast`, `# type: ignore`, `assert`, `isinstance`, etc.
Some evidences:
- linked in the comment thread on pyright bug tracker https://github.com/microsoft/pyright/issues/4213
- `pyre` type checker steps outside of the normal type checking practices and special-cases `registrer_buffer()` in part to avoid this problem. https://pyre-check.org/docs/features/ This is not a very scalable solution since type-checkers generally aim at adhering to the spec (various typing PEPs).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/104321
Approved by: https://github.com/kit1980, https://github.com/albanD
Fixes#64601 and #98906
Adds an `assign` argument to `load_state_dict` that loads params/buffers by assignment instead of doing `param.copy_(param_from_state_dict)`.
Primarily intended to remove the need for the `.to_empty()` in
```
with torch.device('meta'):
m = SomeModule()
m.to_empty()
state_dict = torch.load('...pth')
m.load_state_dict(state_dict)
```
so we can instead do
```
with torch.device('meta'):
m = SomeModule()
state_dict = torch.load('...pth')
m.load_state_dict(state_dict, assign=True)
```
**A problem with this PR for the case where the model is initialized on meta is what happens to nonpersistent buffers/params corresponding to keys missing from the state dict?**
What happens in the case where `load_state_dict(state_dict, strict=False, assign=True)` and the state_dict is missing some keys? The corresponding params missing from the `state_dict` and nonpersistent buffers would still be on `meta` and need to be manually initialized. However, I don't think we offer an API that would initialize these.
One solution would be to make these empty tensors but it might not be semantically correct...
Pull Request resolved: https://github.com/pytorch/pytorch/pull/102212
Approved by: https://github.com/albanD
Opening this so I can discuss with @albanD
I built a proof of concept of an in place API for an nn.Module that allows us to save and load a torch.compiled model with no issues https://github.com/msaroufim/mlsys-experiments/blob/main/save-compiled-model.py
So users can run` model.compile()` and then run `torch.save(model, "model.pt")` and `torch.load(model, "model.pt)` with no issues unlike the rather strange current suggestion we give to users which is `opt_mod = torch.compile(mod); torch.save(mod, "model.pt")`
Right now I'm trying to extend this to work for nn.modules more generally
TODO: Failing tests
* [x] torch.jit.load -> issue was because of aliasing `__call__` to `_call_impl`, _call_impl used to be skipped when now it lo longer is so expanded the skip check. I added an explicit `torch.jit.load()` test now which @davidberard98 suggested
* [x] functorch seems to be a flake - ran locally and it worked `pytest functorch/test_eager_transforms.py`
* [x] a test infra flake - `test_testing.py::TestImports::test_no_mutate_global_logging_on_import_path_functorch`
* [x] It seems like I broke inlining in dynamo though `python -m pytest test/dynamo/test_dynamic_shapes.py -k test_issue175` chatting with Voz about it but still not entirely sure how to fix - found a workaround after chatting with @yanboliang
* [x] `pytest test/dynamo/test_modules.py` and `test/dynamo/test_dynamic_shapes` `test/dynamo/test_misc.py` seem to be failing in CI but trying it out locally they all pass tests passed with 0 failures
* [x] `pytest test/profiler/test_profiler_tree.py ` these tests have ProfilerTrees explicitly printed and will now break if __call__ is not in tree - ran with `EXPECT_ACCEPT=1`
* [x] `pytest test/test_torch.py::TestTorch::test_typed_storage_deprecation_warning` a flake, ran this locally and it works fine
* [x] I reverted my changes to `_dynamo/nn_module.py` since it looks like @wconstab is now directly handling `_call_impl` there but this is triggering an infinite inlining which is crashing
* [x] Tried out to instead override `__call__`, python doesnt like this though https://github.com/pytorch/pytorch/pull/97565#issuecomment-1524570439
Pull Request resolved: https://github.com/pytorch/pytorch/pull/97565
Approved by: https://github.com/aaronenyeshi, https://github.com/albanD, https://github.com/voznesenskym
Opening this so I can discuss with @albanD
I built a proof of concept of an in place API for an nn.Module that allows us to save and load a torch.compiled model with no issues https://github.com/msaroufim/mlsys-experiments/blob/main/save-compiled-model.py
So users can run` model.compile()` and then run `torch.save(model, "model.pt")` and `torch.load(model, "model.pt)` with no issues unlike the rather strange current suggestion we give to users which is `opt_mod = torch.compile(mod); torch.save(mod, "model.pt")`
Right now I'm trying to extend this to work for nn.modules more generally
TODO: Failing tests
* [x] torch.jit.load -> issue was because of aliasing `__call__` to `_call_impl`, _call_impl used to be skipped when now it lo longer is so expanded the skip check. I added an explicit `torch.jit.load()` test now which @davidberard98 suggested
* [x] functorch seems to be a flake - ran locally and it worked `pytest functorch/test_eager_transforms.py`
* [x] a test infra flake - `test_testing.py::TestImports::test_no_mutate_global_logging_on_import_path_functorch`
* [x] It seems like I broke inlining in dynamo though `python -m pytest test/dynamo/test_dynamic_shapes.py -k test_issue175` chatting with Voz about it but still not entirely sure how to fix - found a workaround after chatting with @yanboliang
* [x] `pytest test/dynamo/test_modules.py` and `test/dynamo/test_dynamic_shapes` `test/dynamo/test_misc.py` seem to be failing in CI but trying it out locally they all pass tests passed with 0 failures
* [x] `pytest test/profiler/test_profiler_tree.py ` these tests have ProfilerTrees explicitly printed and will now break if __call__ is not in tree - ran with `EXPECT_ACCEPT=1`
* [x] `pytest test/test_torch.py::TestTorch::test_typed_storage_deprecation_warning` a flake, ran this locally and it works fine
* [x] I reverted my changes to `_dynamo/nn_module.py` since it looks like @wconstab is now directly handling `_call_impl` there but this is triggering an infinite inlining which is crashing
* [x] Tried out to instead override `__call__`, python doesnt like this though https://github.com/pytorch/pytorch/pull/97565#issuecomment-1524570439
Pull Request resolved: https://github.com/pytorch/pytorch/pull/97565
Approved by: https://github.com/aaronenyeshi, https://github.com/albanD
Some modules like lazyModule may override '_save_to_state_dict()', in this case, pre_state_dict hook will not be called. So move the pre_state_dict hook out from '_save_to_state_dict()' to make sure the pre hook could be called
Pull Request resolved: https://github.com/pytorch/pytorch/pull/98964
Approved by: https://github.com/albanD
This PR optimizes the guards overhead introduced by dynamo tracing module forward hooks.
It can and maybe should be followed by a wider change proposed by @voznesenskym to optimize specialized nnmodules by 'observing' any user mutations and directly invalidating the root guard, obviating the need to install other nnmodule guards. (But this observer change seems more involved...)
Idea: maintain a flag, and keep it up to date whenever adding or
removing hooks. Use the flag rather than dict checks to enter the call fast path.
- need to extend RemovableHandle to keep a ref to nnModule so it can update the flag on removal.
- also need to handle the flag in ScriptModule which still uses the python call impl when called from python.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/95931
Approved by: https://github.com/ezyang, https://github.com/voznesenskym
Applies some more harmless pyupgrades. This one gets rid of deprecated aliases in unit_tests and more upgrades yield for loops into yield from generators which are more performance and propagates more information / exceptions from original generator. This is the modern recommended way of forwarding generators.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/94309
Approved by: https://github.com/albanD
Attempts to fix#92656
BC-breaking! This changes the default of zero_grad in optim and in nn to default set grads to None instead of zero tensors. We are changing the default because there are proven perf wins and existing code has typically not regressed due to this change. (will probably have to flesh out this note more).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/92731
Approved by: https://github.com/ngimel
