Summary:
- When a user specify `TORCHINDUCTOR_MAX_AUTOTUNE=1` env variable, we add `config.max_autotune=True` to the generated minifier_launcher
- We should do this to other inductor configs as well in a followup Diff
Currently in dynamo and aoti minifier, if a config is overwritten by an env variable, the config will not show up in the config list in the minifier_launcher.py file. As a result, when running the minifier_launcher, they need to re-apply the same env variable.
This is:
1) not convenient for the users
2) if they copy-paste the minifier_launcher.py to us without including the env variable, we could be confused and not able to reproduce the error.
Underlying implementation change:
- Add `env_default` parameter to `codegen_config()`. If set, configs overriden by the env are not considered default.
Test Plan:
```
buck2 run 'fbcode//mode/dev-nosan' fbcode//caffe2/test:utils -- -r test_codegen_config
```
Differential Revision: D67299312
Pull Request resolved: https://github.com/pytorch/pytorch/pull/143330
Approved by: https://github.com/jansel, https://github.com/eellison
Fixes#114844
In the linked issue we have
```
compiled_module = torch.compile(module)
compiled_module.x = ...
compiled_module(...) # Mutates self.x
```
Where since the module mutates `self.x` you would expect `compiled_module.x`
to be updated but actually `compiled_module.x = ...` sets an attribute "x"
on the `OptimizedModule` object while the forward method of the module mutates
`module.x`.
This gives the expected behavior by forwarding `compiled_module.__setattr__`
down to `module.__setattr__`. There is already a corresponding `__getattr__`
so now `compiled_module.x` becomes an alias for `module.x`.
Co-authored-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/122098
Approved by: https://github.com/ezyang, https://github.com/lezcano
Fixes#114844
In the linked issue we have
```
compiled_module = torch.compile(module)
compiled_module.x = ...
compiled_module(...) # Mutates self.x
```
Where since the module mutates `self.x` you would expect `compiled_module.x`
to be updated but actually `compiled_module.x = ...` sets an attribute "x"
on the `OptimizedModule` object while the forward method of the module mutates
`module.x`.
This gives the expected behavior by forwarding `compiled_module.__setattr__`
down to `module.__setattr__`. There is already a corresponding `__getattr__`
so now `compiled_module.x` becomes an alias for `module.x`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/122098
Approved by: https://github.com/ezyang, https://github.com/lezcano
Fixes#114844
In the linked issue we have
```
compiled_module = torch.compile(module)
compiled_module.x = ...
compiled_module(...) # Mutates self.x
```
Where since the module mutates `self.x` you would expect `compiled_module.x`
to be updated but actually `compiled_module.x = ...` sets an attribute "x"
on the `OptimizedModule` object while the forward method of the module mutates
`module.x`.
This gives the expected behavior by forwarding `compiled_module.__setattr__`
down to `module.__setattr__`. There is already a corresponding `__getattr__`
so now `compiled_module.x` becomes an alias for `module.x`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/122098
Approved by: https://github.com/ezyang, https://github.com/lezcano
```
Takes in a function which has been printed with print_readable() and constructs kwargs to run it.
Currently only handles Tensor inputs and a graph module which might have tensor constants.
Example:
Consider a function `forward` defined as follows:
>>> def forward(self, primals_1: "f32[1001, 6]"):
... _tensor_constant0: "i64[4190]" = self._tensor_constant0
... # Further implementation
>>> kwargs = aot_graph_input_parser(forward)
>>> forward(**kwargs)
"""
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/119409
Approved by: https://github.com/shunting314
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
Notes:
* `debug_insert_nops` in testing.py was passing `None` to the compiler_fn
parameter of `OutputGraph`, hence the modifications there.
* I added `disable-error-code="method-assign"` to debug_utils.py as it
does several such assignments. I guess mypy doesn't like it because it
makes code near-impossible to safely typecheck.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/113519
Approved by: https://github.com/Skylion007
ghstack dependencies: #113413, #113518
Did some easy fixes from enabling TRY200. Most of these seem like oversights instead of intentional. The proper way to silence intentional errors is with `from None` to note that you thought about whether it should contain the cause and decided against it.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/111496
Approved by: https://github.com/malfet
Generating reference outputs somtimes fails because of type mismatches in the graph,
an issue which was noticed previously for `prims.convert_element_type` and fixed in #92036
but the same issue happens with other functions such as tensor constructors.
This expands the fix from #92036 to all dtype keyword arguments.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110232
Approved by: https://github.com/ezyang
Minor QOL change. This log message is pushed into my history by the
backtrace, which is a pain because if I tab up in tmux I can no longer
paste it without line breaks. This makes it more convenient to use tmux
copy mode to get only the file (as I get the entire line this way.)
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/103083
Approved by: https://github.com/albanD
The changes:
* Add config knob `same_two_models_use_fp64` for toggling whether or not to use fp64
* Add a test showing that RMSE is superior to atol/rtol
* Add `--strict-accuracy` options, which allows for testing against integral/boolean accuracy. Regular accuracy by default now ONLY. There's a test which exercises this, it's a little delicate but I had trouble thinking of a good test otherwise.
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/100447
Approved by: https://github.com/voznesenskym
The basic idea is that if we're trying to match for an accuracy
error, we don't want to switch to a compile/runtime error, because
that's probably us breaking things in a different way.
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/100324
Approved by: https://github.com/voznesenskym
