Over time, a large number of the existing type ignores have become irrelevant/unused/dead as a result of improvements in annotations and type checking.
Having these `# type: ignore` linger around is not ideal for two reasons:
- They are verbose/ugly syntatically.
- They could hide genuine bugs in the future, if a refactoring would actually introduce a bug but it gets hidden by the ignore.
I'm counting over 1500 unused ignores already. This is a first PR that removes some of them. Note that I haven't touched type ignores that looked "conditional" like the import challenge mentioned in https://github.com/pytorch/pytorch/pull/60006#issuecomment-2480604728. I will address these at a later point, and eventually would enable `warn_unused_ignores = True` in the mypy configuration as discussed in that comment to prevent accumulating more dead ignores going forward.
This PR should have no effect on runtime at all.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/142325
Approved by: https://github.com/Skylion007, https://github.com/janeyx99
Add option to split Linear gates for Quantizable LSTM into separate ops (#141366)
Summary:
Reattempt to land D65283170, adding pyre-fixmes / mypy ignores following D52890934
For LSTM, the input and hidden state are projected with Linear layers to construct the 4 gates. This is typically performed together as a single Linear (for each state) with output channel count `4 * hidden_dim` for efficiency.
https://www.internalfb.com/code/fbsource/[ebef7c4238aa55948b2b444044f2c8ed2040de55]/fbcode/caffe2/torch/ao/nn/quantizable/modules/rnn.py?lines=52-58
The output is then ultimately split into 4:
https://www.internalfb.com/code/fbsource/[ebef7c4238aa55948b2b444044f2c8ed2040de55]/fbcode/caffe2/torch/ao/nn/quantizable/modules/rnn.py?lines=83-87
For on-device latency (and possibly memory) considerations, we want to avoid constructing the intermediate `gates` tensor (which can be relatively large), by splitting `igates` and `hgates` first (as 4x `Linear(hidden_dim, hidden_dim)` each), applying add separately, then proceeding as usual.
This functionality can be enabled by specifying `split_gates=True` (default False is original behavior) at any entry point (directly with `torch.ao.nn.quantizable.LSTM` or via `_get_lstm_with_individually_observed_parts`).
Test Plan:
piggy back on existing test to check for correct swap handling, numerics, and jit.script during prepare/convert
```
buck2 test 'fbcode//mode/opt' fbcode//caffe2/test/quantization:test_quantization -- --exact 'caffe2/test/quantization:test_quantization - test_custom_module_lstm (caffe2.test.quantization.core.test_quantized_op.TestQuantizedOps)'
```
https://www.internalfb.com/intern/testinfra/testrun/4503599884152725
This test is quite long running now (more than double original).
---
shorter test to confirm original `LSTMCell` passes
```
buck2 test 'fbcode//mode/opt' fbcode//caffe2/test:quantization_fx -- --exact 'caffe2/test:quantization_fx - test_static_lstm_with_custom_fixed_qparams (quantization.fx.test_quantize_fx.TestQuantizeFx)'
```
https://www.internalfb.com/intern/testinfra/testrun/11258999127933996
Reviewed By: Ninja91
Differential Revision: D66380336
* Automatically applies ruff rule 401. Turns loops into equivalent list comprehensions which are faster and do not leak the scope of the loop variables.
* list comprehensions not only often have better typing, but are 50+% faster than for loops on overhead. They also preserve length information etc and are better for the interpreter to optimize.
* Manually went back and made mypy happy after the change.
* Also fixed style lints in files covered by flake8 but not by pyfmt
Pull Request resolved: https://github.com/pytorch/pytorch/pull/140980
Approved by: https://github.com/justinchuby, https://github.com/malfet
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
Summary:
After QAT is completed or given pre-tuned weight observer via tunable PTQ algorithm, it should not over-write again with a given weight, at least for static QAT never.
Dynamic QAT also does not require to re-run weight observer again by design.
This is a fix
Test Plan: Signals
Differential Revision: D57747749
Pull Request resolved: https://github.com/pytorch/pytorch/pull/127309
Approved by: https://github.com/jerryzh168
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
Adds a ruff lint rule to ban raising raw exceptions. Most of these should at the very least be runtime exception, value errors, type errors or some other errors. There are hundreds of instance of these bad exception types already in the codebase, so I have noqa'd most of them. Hopefully this error code will get commiters to rethink what exception type they should raise when they submit a PR.
I also encourage people to gradually go and fix all the existing noqas that have been added so they can be removed overtime and our exception typing can be improved.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/124570
Approved by: https://github.com/ezyang
Update ruff to 0.4.1 .
This version fixes a lot false negatives/false positives, is 20-40% faster, and has various other bug fixes.
Below is a before and after table showing the execution time of ruff lint and ruff format in milliseconds courtesy of https://astral.sh/blog/ruff-v0.4.0
| Repository | Linter (v0.3) | Linter (v0.4) | Formatter (v0.3) | Formatter (v0.4) |
|----------------------------------------------------|---------------|---------------|------------------|------------------|
| [pytorch/pytorch](https://github.com/pytorch/pytorch) | 328.7 | 251.8 | 351.1 | 274.9 |
Pull Request resolved: https://github.com/pytorch/pytorch/pull/124549
Approved by: https://github.com/ezyang
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
dmypy silently ignores follow_imports = skip, so to get parity between
dmypy and mypy we have to suck it up and type: ignore all of the sympy
typing problems.
The suppressions were added automatically with the following script generated by GPT-4:
```
import re
# Read the error file
with open("error_file.txt", "r") as f:
errors = f.readlines()
# Parse the lines with errors and error types
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
# Insert ignore comments in the source files
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/118469
Approved by: https://github.com/Skylion007
ghstack dependencies: #118414, #118418, #118432, #118467, #118468
Updates flake8 to v6.1.0 and fixes a few lints using sed and some ruff tooling.
- Replace `assert(0)` with `raise AssertionError()`
- Remove extraneous parenthesis i.e.
- `assert(a == b)` -> `assert a == b`
- `if(x > y or y < z):`->`if x > y or y < z:`
- And `return('...')` -> `return '...'`
Co-authored-by: Nikita Shulga <2453524+malfet@users.noreply.github.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/116591
Approved by: https://github.com/albanD, https://github.com/malfet
This pull request addresses an inconsistency in the representation of the Hadamard product across PyTorch documentation. Currently, the notation varies among different modules:
- In `torch.nn.LSTM` documentation the Hadamard product is represented with $\odot$
- In `torch.nn.GRU` documentation the Hadamard product is represented with $*$
- In `torch.nn.LSTMCell` documentation the Hadamard product is represented with $*$
- In `torch.nn.GRUCell` documentation the Hadamard product is represented with $*$
- In `torch.ao.nn.quantized.dynamic.GRU` documentation the Hadamard product is represented with $*$
This PR proposes consistently representing the Hadamard product throughout the documentation to enhance clarity and align with established standards.
The notation $\odot$ will be uniformly adopted, following the convention in the [Deep Learning Book](https://www.deeplearningbook.org/contents/linear_algebra.html).
**Changes Made:**
- Modified `torch.nn.GRU` documentation to represent the Hadamard product with $\odot$
- Modified `torch.nn.LSTMCell` documentation to represent the Hadamard product with $\odot$
- Modified `torch.nn.GRUCell` documentation to represent the Hadamard product with $\odot$
- Modified `torch.ao.nn.quantized.dynamic.GRU` documentation to represent the Hadamard product with $\odot$
Pull Request resolved: https://github.com/pytorch/pytorch/pull/111763
Approved by: https://github.com/albanD
