This is follow-up of #165037. It generally recommended to use `is/is not` to compare types. Therefore this series of changes apply this suggestion in the code base, and it aims to finally enabling related linter checks.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165142
Approved by: https://github.com/albanD
It generally recommended to use `is/is not` to compare types. Therefore this series of changes apply this suggestion in the code base, and it aims to finally enabling related linter checks.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165037
Approved by: https://github.com/mlazos
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
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
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
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
Summary: after converting nn.multihead attention we weren't deleting the
old in_proj_weight and in_proj_bias despite not (really) using them.
Test Plan: python test/test_quantization.py -k
"test_custom_module_multi_head_attention"
Reviewers:
Subscribers:
Tasks:
Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110407
Approved by: https://github.com/jerryzh168
Summary: https://github.com/pytorch/pytorch/issues/100654 noticed prelu
was not running its observers when the quantization flow was being run,
this was a bug which is now fixed and the relevant prelu tests also now
check for this. Also added a corrected observer for PReLU to
qconfig_mapping
Test Plan: python test/test_quantization.py TestStaticQuantizedModule.test_prelu
Reviewers:
Subscribers:
Tasks:
Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/103455
Approved by: https://github.com/jerryzh168
