Summary:
The reference quantized modules for linear / conv / etc fail to torchscript due to two issues
(1) The type of torch.qscheme doesn't script
(2) The "_DTYPE_TO_QVALUE_BOUNDS" values were resolving to union[float, int] instead of just int. We fix that with a hard cast.
See: <internal post> + comments for more context
Test Plan: unit tests + fixing this NB N6923590
Differential Revision: D72652616
Pull Request resolved: https://github.com/pytorch/pytorch/pull/150870
Approved by: https://github.com/jerryzh168
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
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
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
Summary:
Fixed quant_min/quant_max for per channel quantized weight for reference quantized module in decomposed mode,
this bug is triggered while onboard an internal model
Test Plan:
python test/test_quantization.py TestQuantizeFx.test__convert_to_reference_decomposed_fx_per_channel_quant_module
Reviewers:
Subscribers:
Tasks:
Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/98903
Approved by: https://github.com/andrewor14
Summary:
Fixed quant_min/quant_max for per channel quantized weight for reference quantized module in decomposed mode,
this bug is triggered while onboard an internal model
Test Plan:
python test/test_quantization.py TestQuantizeFx.test__convert_to_reference_decomposed_fx_per_channel_quant_module
Reviewers:
Subscribers:
Tasks:
Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/98903
Approved by: https://github.com/andrewor14
Summary:
Makes the `nnqr.Linear` module respect the qmin/qmax attributes of weight observer. This is to unblock some customer teams who are depending on non-default values of these attributes.
Test plan:
```
python test/test_quantization.py -k TestReferenceQuantizedModule.test_linear_decomposed
```
Fixes #ISSUE_NUMBER
Pull Request resolved: https://github.com/pytorch/pytorch/pull/96232
Approved by: https://github.com/andrewor14
Changes:
- #95200
1. Recognize `.py.in` and `.pyi.in` files as Python in VS Code for a better development experience.
2. Fix deep setting merge in `tools/vscode_settings.py`.
- => this PR: #95267
3. Use `Namedtuple` rather than `namedtuple + __annotations__` for `torch.nn.utils.rnn.PackedSequence_`:
`namedtuple + __annotations__`:
```python
PackedSequence_ = namedtuple('PackedSequence_',
['data', 'batch_sizes', 'sorted_indices', 'unsorted_indices'])
# type annotation for PackedSequence_ to make it compatible with TorchScript
PackedSequence_.__annotations__ = {'data': torch.Tensor, 'batch_sizes': torch.Tensor,
'sorted_indices': Optional[torch.Tensor],
'unsorted_indices': Optional[torch.Tensor]}
```
`Namedtuple`: Python 3.6+
```python
class PackedSequence_(NamedTuple):
data: torch.Tensor
batch_sizes: torch.Tensor
sorted_indices: Optional[torch.Tensor]
unsorted_indices: Optional[torch.Tensor]
```
- #95268
4. Sort import statements and remove unnecessary imports in `.pyi`, `.pyi.in` files.
5. Format `.pyi`, `.pyi.in` files and remove unnecessary ellipsis `...` in type stubs.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/95267
Approved by: https://github.com/janeyx99
