[pre-commit.ci] pre-commit autoupdate

.azure-pipelines/scripts/codeScan/codespell/inc_dict.txt

@@ -1,5 +1,6 @@
 activ
 ans
+assertin
 datas
 ende
 lates

.pre-commit-config.yaml

@@ -4,7 +4,7 @@ ci:
 
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.6.0
+    rev: v5.0.0
     hooks:
       - id: end-of-file-fixer
         files: (.*\.(py|md|rst|yaml|yml))$
@@ -101,7 +101,7 @@ repos:
           )$
 
   - repo: https://github.com/psf/black.git
-    rev: 24.3.0
+    rev: 24.10.0
     hooks:
       - id: black
         files: (.*\.py)$
@@ -115,12 +115,12 @@ repos:
           )$
 
   - repo: https://github.com/asottile/blacken-docs
-    rev: 1.16.0
+    rev: 1.19.1
     hooks:
       - id: blacken-docs
         args: [--line-length=120, --skip-errors]
         additional_dependencies:
-          - black==24.3.0
+          - black==24.10.0
         exclude: |
           (?x)^(
               examples/.+|
@@ -130,7 +130,7 @@ repos:
           )$
 
   - repo: https://github.com/codespell-project/codespell
-    rev: v2.2.6
+    rev: v2.3.0
     hooks:
       - id: codespell
         args: [-w]
@@ -149,7 +149,7 @@ repos:
           )$
 
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.3.5
+    rev: v0.8.6
     hooks:
       - id: ruff
         args: [--fix, --exit-non-zero-on-fix, --no-cache]

docs/source/CODE_OF_CONDUCT.md

@@ -14,7 +14,7 @@ In the interest of fostering an open and welcoming environment, we as
 contributors and maintainers pledge to making participation in our project and
 our community a harassment-free experience for everyone, regardless of age, body
 size, disability, ethnicity, sex characteristics, gender identity and expression,
-level of experience, education, socio-economic status, nationality, personal
+level of experience, education, socioeconomic status, nationality, personal
 appearance, race, religion, or sexual identity and orientation.
 
 ## Our Standards

examples/3.x_api/pytorch/recommendation/dlrm/static_quant/ipex/data_utils.py

@@ -1241,7 +1241,7 @@ def loadDataset(
             data_ready = False
 
     # pre-process data if needed
-    # WARNNING: when memory mapping is used we get a collection of files
+    # WARNING: when memory mapping is used we get a collection of files
     if data_ready:
         print("Reading pre-processed data=%s" % (str(pro_data)))
         file = str(pro_data)

examples/3.x_api/pytorch/recommendation/dlrm/static_quant/ipex/dlrm_data_pytorch.py

@@ -111,7 +111,7 @@ def __init__(
                 data_ready = False
 
         # pre-process data if needed
-        # WARNNING: when memory mapping is used we get a collection of files
+        # WARNING: when memory mapping is used we get a collection of files
         if data_ready:
             print("Reading pre-processed data=%s" % (str(pro_data)))
             file = str(pro_data)

examples/notebook/dynas/BERT_SST2_Supernet_NAS.ipynb

@@ -189,7 +189,7 @@
     "# Perform Search\n",
     "\n",
     "After the DyNAS configuration parameters are set, the search process can be started. Depending on how many evaluations `config.dynas.num_evals` were defined, the search time can vary from hours to days. \n",
-    "The search process will populate the `config.dynas.results_csv_path` file and will also return a list of the final iteration's best sub-network population recommondation. \n",
+    "The search process will populate the `config.dynas.results_csv_path` file and will also return a list of the final iteration's best sub-network population recommendation. \n",
     "\n",
     "Note: example search results are provided for the plotting section if you wish to skip this step for now. "
    ]

examples/notebook/dynas/MobileNetV3_Supernet_NAS.ipynb

@@ -191,7 +191,7 @@
     "# Perform Search\n",
     "\n",
     "After the DyNAS configuration parameters are set, the search process can be started. Depending on how many evaluations `config.dynas.num_evals` were defined, the search time can vary from hours to days. \n",
-    "The search process will populate the `config.dynas.results_csv_path` file and will also return a list of the final iteration's best sub-network population recommondation. \n",
+    "The search process will populate the `config.dynas.results_csv_path` file and will also return a list of the final iteration's best sub-network population recommendation. \n",
     "\n",
     "Note: example search results are provided for the plotting section if you wish to skip this step for now. "
    ]

examples/pytorch/nlp/huggingface_models/question-answering/distillation/eager/utils_qa.py

@@ -288,7 +288,7 @@ def postprocess_qa_predictions_with_beam_search(
 
     assert len(predictions[0]) == len(
         features
-    ), f"Got {len(predictions[0])} predicitions and {len(features)} features."
+    ), f"Got {len(predictions[0])} predictions and {len(features)} features."
 
     # Build a map example to its corresponding features.
     example_id_to_index = {k: i for i, k in enumerate(examples["id"])}

examples/pytorch/nlp/huggingface_models/question-answering/optimization_pipeline/prune_once_for_all/fx/utils_qa.py

@@ -288,7 +288,7 @@ def postprocess_qa_predictions_with_beam_search(
 
     assert len(predictions[0]) == len(
         features
-    ), f"Got {len(predictions[0])} predicitions and {len(features)} features."
+    ), f"Got {len(predictions[0])} predictions and {len(features)} features."
 
     # Build a map example to its corresponding features.
     example_id_to_index = {k: i for i, k in enumerate(examples["id"])}

examples/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/python/models/ssd_r34.py

@@ -262,7 +262,7 @@ def __init__(self, label_num=81, backbone='resnet34', model_path="./resnet34-333
             self.size=(1200,1200)
             dboxes = dboxes_R34_coco(list(self.size),[3,3,2,2,2,2])
             self.encoder = Encoder(dboxes)
-        # intitalize all weights
+        # initialize all weights
         self._init_weights()
         self.device = 1
     def _build_additional_features(self, input_channels):

examples/pytorch/object_detection/ssd_resnet34/quantization/ptq/ipex/ssd300.py

@@ -41,7 +41,7 @@ def __init__(self, label_num, backbone='resnet34', model_path="./resnet34-333f7e
 
         self.loc = nn.ModuleList(self.loc)
         self.conf = nn.ModuleList(self.conf)
-        # intitalize all weights
+        # initialize all weights
         self._init_weights()
 
     def _build_additional_features(self, input_size, input_channels):

examples/pytorch/object_detection/ssd_resnet34/quantization/ptq/ipex/ssd_r34.py

@@ -65,7 +65,7 @@ def __init__(self, label_num, backbone='resnet34', model_path="./resnet34-333f7e
         self.loc = nn.ModuleList(self.loc)
         self.conf = nn.ModuleList(self.conf)
 
-        # intitalize all weights
+        # initialize all weights
         self._init_weights()
 
     def _build_additional_features(self, input_channels):

examples/pytorch/object_detection/ssd_resnet34/quantization/qat/fx/ssd/ssd300.py

@@ -40,7 +40,7 @@ def __init__(self, label_num, backbone='resnet34', model_path=None):
 
         self.loc = nn.ModuleList(self.loc)
         self.conf = nn.ModuleList(self.conf)
-        # intitalize all weights
+        # initialize all weights
         self._init_weights()
 
     def _build_additional_features(self, input_size, input_channels):

examples/pytorch/recommendation/dlrm/quantization/ptq/fx/data_utils.py

@@ -1160,7 +1160,7 @@ def loadDataset(
             data_ready = False
 
     # pre-process data if needed
-    # WARNNING: when memory mapping is used we get a collection of files
+    # WARNING: when memory mapping is used we get a collection of files
     if data_ready:
         print("Reading pre-processed data=%s" % (str(pro_data)))
         file = str(pro_data)

examples/pytorch/recommendation/dlrm/quantization/ptq/fx/dlrm_data_pytorch.py

@@ -97,7 +97,7 @@ def __init__(
                 data_ready = False
 
         # pre-process data if needed
-        # WARNNING: when memory mapping is used we get a collection of files
+        # WARNING: when memory mapping is used we get a collection of files
         if data_ready:
             print("Reading pre-processed data=%s" % (str(pro_data)))
             file = str(pro_data)

examples/pytorch/recommendation/dlrm/quantization/ptq/ipex/data_utils.py

@@ -1241,7 +1241,7 @@ def loadDataset(
             data_ready = False
 
     # pre-process data if needed
-    # WARNNING: when memory mapping is used we get a collection of files
+    # WARNING: when memory mapping is used we get a collection of files
     if data_ready:
         print("Reading pre-processed data=%s" % (str(pro_data)))
         file = str(pro_data)

examples/pytorch/recommendation/dlrm/quantization/ptq/ipex/dlrm_data_pytorch.py

@@ -111,7 +111,7 @@ def __init__(
                 data_ready = False
 
         # pre-process data if needed
-        # WARNNING: when memory mapping is used we get a collection of files
+        # WARNING: when memory mapping is used we get a collection of files
         if data_ready:
             print("Reading pre-processed data=%s" % (str(pro_data)))
             file = str(pro_data)

neural_compressor/adaptor/adaptor.py

@@ -49,7 +49,7 @@ def __init__(self, framework_specific_info):
 
     @abstractmethod
     def quantize(self, tune_cfg, model, dataloader, q_func=None):
-        """The function is used to do calibration and quanitization in post-training quantization.
+        """The function is used to do calibration and quantization in post-training quantization.
 
         Args:
             tune_cfg(dict): The chosen tuning configuration.

neural_compressor/adaptor/mxnet.py

@@ -59,7 +59,7 @@ def __init__(self, framework_specific_info):
 
     @dump_elapsed_time("Pass quantize model")
     def quantize(self, tune_cfg, nc_model, dataloader, q_func=None):
-        """The function is used to do MXNet calibration and quanitization in post-training
+        """The function is used to do MXNet calibration and quantization in post-training
            quantization.
 
         Args:

neural_compressor/adaptor/onnxrt.py

@@ -252,7 +252,7 @@ def _need_smooth_quant(self, tune_cfg) -> bool:
 
     @dump_elapsed_time("Pass quantize model")
     def quantize(self, tune_cfg, model, data_loader, q_func=None):
-        """The function is used to do calibration and quanitization in post-training
+        """The function is used to do calibration and quantization in post-training
            quantization.
 
         Args:
@@ -1853,7 +1853,7 @@ def __init__(self, framework_specific_info):
 
     @dump_elapsed_time("Pass quantize model")
     def quantize(self, tune_cfg, model, data_loader, q_func=None):
-        """The function is used to do calibration and quanitization in post-training
+        """The function is used to do calibration and quantization in post-training
            quantization.
 
         Args:

neural_compressor/adaptor/tf_utils/graph_util.py

@@ -212,7 +212,7 @@ def query_fusion_pattern_nodes(self, patterns=None):
             return self._search_patterns(patterns)
 
     def _search_patterns(self, input_pattern):
-        """Search user specified patterns on internal grpah structure.
+        """Search user specified patterns on internal graph structure.
 
         Args:
             input_pattern (list): The element of the pattern list could be string/list/tuple.

neural_compressor/adaptor/torch_utils/hawq_metric.py

@@ -23,14 +23,11 @@
 import logging
 
 import numpy as np
-import torch.nn
-import torch.nn as nn
 from torch.quantization.quantize_fx import fuse_fx
 
 logger = logging.getLogger(__name__)
 from typing import Any, Callable, Dict, List, Optional, Set, Union
 
-import torch
 import tqdm
 
 

neural_compressor/data/datasets/dummy_dataset_v2.py

@@ -236,7 +236,7 @@ def __init__(
                 self.label_shape = len(self.dense_shape) * self.label_shape
             assert len(self.label_shape) == len(
                 self.dense_shape
-            ), "length of dense_shape should be euqal to length of label_shape"
+            ), "length of dense_shape should be equal to length of label_shape"
             self.label_dim = len(self.label_shape)
 
         self.input_dim = 1 if isinstance(dense_shape, tuple) else len(dense_shape)

neural_compressor/tensorflow/quantization/utils/graph_util.py

@@ -212,7 +212,7 @@ def query_fusion_pattern_nodes(self, patterns=None):
             return self._search_patterns(patterns)
 
     def _search_patterns(self, input_pattern):
-        """Search user specified patterns on internal grpah structure.
+        """Search user specified patterns on internal graph structure.
 
         Args:
             input_pattern (list): The element of the pattern list could be string/list/tuple.