[pre-commit.ci] pre-commit autoupdate (#1297)

pre-commit-ci[bot] · mengniwang95 · commit 705d7449d4dd · 2023-11-10T16:58:04.000+08:00
Signed-off-by: Sun, Xuehao &lt;xuehao.sun@intel.com&gt;
Co-authored-by: pre-commit-ci[bot] &lt;66853113+pre-commit-ci[bot]@users.noreply.github.com&gt;
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
@@ -128,7 +128,7 @@ repos:
           )$
 
   - repo: https://github.com/codespell-project/codespell
-    rev: v2.2.5
+    rev: v2.2.6
     hooks:
       - id: codespell
         additional_dependencies:
@@ -140,7 +140,7 @@ repos:
           )$
 
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.0.287
+    rev: v0.0.292
     hooks:
       - id: ruff
         args: [--fix, --exit-non-zero-on-fix, --no-cache]
diff --git a/examples/pytorch/diffusion_model/diffusers/textual_inversion/README.md b/examples/pytorch/diffusion_model/diffusers/textual_inversion/README.md
@@ -125,7 +125,7 @@ pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float
 # use gpu
 #pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda")
 
-prompt = "a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings."
+prompt = "a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brightly buildings."
 
 image = pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]
 
diff --git a/examples/pytorch/diffusion_model/diffusers/textual_inversion/distillation_for_quantization/README.md b/examples/pytorch/diffusion_model/diffusers/textual_inversion/distillation_for_quantization/README.md
@@ -44,7 +44,7 @@ Once you have trained a INT8 model with the above command, the inference can be
 ```bash
 python text2image.py \
   --pretrained_model_name_or_path=$INT8_MODEL_NAME \
-  --caption "a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings." \
+  --caption "a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brightly buildings." \
   --images_num 4
 ```
 
diff --git a/examples/tensorflow/nlp/bert_large_squad/quantization/ptq/freeze_estimator_to_pb.py b/examples/tensorflow/nlp/bert_large_squad/quantization/ptq/freeze_estimator_to_pb.py
@@ -312,7 +312,7 @@ def main(_):
       drop_remainder=False)
 
   from neural_compressor.adaptor.tf_utils.util import is_ckpt_format
-  assert is_ckpt_format(FLAGS.input_model), 'invalid chekpoint path....'
+  assert is_ckpt_format(FLAGS.input_model), 'invalid checkpoint path....'
   ckpt_model = [os.path.splitext(i)[0] for i in os.listdir(FLAGS.input_model) \
       if i.endswith('.meta')][0]
   model_fn = model_fn_builder(
diff --git a/examples/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/freeze_estimator_to_pb.py b/examples/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/freeze_estimator_to_pb.py
@@ -312,7 +312,7 @@ def main(_):
       drop_remainder=False)
 
   from neural_compressor.adaptor.tf_utils.util import is_ckpt_format
-  assert is_ckpt_format(FLAGS.input_model), 'invalid chekpoint path....'
+  assert is_ckpt_format(FLAGS.input_model), 'invalid checkpoint path....'
   ckpt_model = [os.path.splitext(i)[0] for i in os.listdir(FLAGS.input_model) \
       if i.endswith('.meta')][0]
   model_fn = model_fn_builder(
diff --git a/neural_coder/graphers/function.py b/neural_coder/graphers/function.py
@@ -56,7 +56,7 @@ def c():
             if is_in_function and line_idx == func_end_line_idx:
                 is_in_function = False
 
-            # handle function's defnition line, to initiate a function
+            # handle function's definition line, to initiate a function
             if not is_in_function and "def " in line:  # only deal with outermost def
                 function_name = line[line.find("def") + 4 : line.find("(")]
 
diff --git a/neural_coder/utils/numa_launcher.py b/neural_coder/utils/numa_launcher.py
@@ -34,7 +34,7 @@
 
 
 class CPUinfo:
-    """Get CPU inforamation, such as cores list and NUMA information."""
+    """Get CPU information, such as cores list and NUMA information."""
 
     def __init__(self):
         self.cpuinfo = []
@@ -460,7 +460,7 @@ def launch(self, args):
 
 
 class DistributedTrainingLauncher(Launcher):
-    r"""Launcher for distributed traning with MPI launcher."""
+    r"""Launcher for distributed training with MPI launcher."""
 
     def get_mpi_pin_domain(self, nproc_per_node, ccl_worker_count, total_cores):
         """I_MPI_PIN_DOMAIN specify the cores used for every MPI process.
diff --git a/neural_compressor/adaptor/keras.py b/neural_compressor/adaptor/keras.py
@@ -867,7 +867,7 @@ def train(self, model, dataloader, optimizer_tuple, criterion_tuple, hooks, post
                     for i in range(len(list_len_dataloader) - 1):
                         if list_len_dataloader[i] != list_len_dataloader[i + 1]:
                             raise AttributeError(
-                                "The traning dataloader's iteration is"
+                                "The training dataloader's iteration is"
                                 "different between processes, please reset dataloader's batch_size."
                             )
 
diff --git a/neural_compressor/adaptor/onnxrt.py b/neural_compressor/adaptor/onnxrt.py
@@ -1094,7 +1094,7 @@ def query_fw_capability(self, model):
 
         ffn_matmul = []
         attention_matmul_optype = [node.op_type for node in attention_matmul]
-        # find matmul ops in feed forward network (FFN) structure which mainly in transfomers based NLP models
+        # find matmul ops in feed forward network (FFN) structure which mainly in transformers based NLP models
         if len(attention_matmul) > 0 and "Attention" in attention_matmul_optype:
             # model is optimized and Attention is fused,
             # index of Attention is used as split to find FFN MatMul
diff --git a/neural_compressor/adaptor/ox_utils/util.py b/neural_compressor/adaptor/ox_utils/util.py
@@ -363,7 +363,7 @@ def quantize_data_per_channel(data, axis, quantize_range, qType, scheme):
 
 
 def dequantize_data_with_scale_zero(tensor_value, scale_value, zo_value):  # pragma: no cover
-    """Dequantize tensor with sacale and zero point."""
+    """Dequantize tensor with scale and zero point."""
     return (tensor_value.astype(np.float32) - zo_value.astype(np.float32)) * scale_value
 
 
diff --git a/neural_compressor/adaptor/pytorch.py b/neural_compressor/adaptor/pytorch.py
@@ -4176,7 +4176,7 @@ def prepare_sub_graph(
             model (dir): input model which is PyTorch model.
             prefix (string): prefix of op name
             is_qat (bool): whether it is a qat quantization
-            example_inputs (tensor / tupe of tensor): example inputs
+            example_inputs (tensor / tuple of tensor): example inputs
             custom_config (dict): custom non traceable module dict
 
         Returns:
diff --git a/neural_compressor/adaptor/tensorflow.py b/neural_compressor/adaptor/tensorflow.py
@@ -214,7 +214,7 @@ def train(self, model, dataloader, optimizer_tuple, criterion_tuple, hooks, post
                     for i in range(len(list_len_dataloader) - 1):
                         if list_len_dataloader[i] != list_len_dataloader[i + 1]:
                             raise AttributeError(
-                                "The traning dataloader's iteration is"
+                                "The training dataloader's iteration is"
                                 "different between processes, please reset dataloader's batch_size."
                             )
 
@@ -373,7 +373,7 @@ def evaluate(
                 import shutil
 
                 shutil.rmtree(temp_dir, ignore_errors=True)
-            # Create the writer using TF2.x APIs to handle eager excutions
+            # Create the writer using TF2.x APIs to handle eager executions
             writer = tf.summary.create_file_writer(temp_dir)  # pylint: disable=no-member
             with writer.as_default():
                 tf.summary.graph(model.graph)  # pylint: disable=no-member
diff --git a/neural_compressor/adaptor/tf_utils/graph_rewriter/generic/fuse_pad_with_conv.py b/neural_compressor/adaptor/tf_utils/graph_rewriter/generic/fuse_pad_with_conv.py
@@ -30,7 +30,7 @@ class FusePadWithConv2DOptimizer(GraphRewriterBase):
     """Fuse Pad op into Conv2D/DepthwiseConv2dNative/Conv3D."""
 
     def __init__(self, model, excluded_op_names, inputs, cfg, new_api, itex_qdq_mode=False):
-        """Intilization."""
+        """Initialization."""
         super().__init__(model)
         self.excluded_conv = excluded_op_names
         self.inputs = inputs
diff --git a/neural_compressor/adaptor/tf_utils/graph_rewriter/generic/insert_print_node.py b/neural_compressor/adaptor/tf_utils/graph_rewriter/generic/insert_print_node.py
@@ -32,7 +32,7 @@ class InsertPrintMinMaxNode(GraphRewriterBase):
     """InsertPrintMinMaxNode Pass for tensorflow sampling."""
 
     def __init__(self, model, pre_node_name, post_node_name, new_api):
-        """Intilization."""
+        """Initialization."""
         super().__init__(model)
         self.pre_node_name = pre_node_name
         self.post_node_name = post_node_name
diff --git a/neural_compressor/adaptor/torch_utils/smooth_quant.py b/neural_compressor/adaptor/torch_utils/smooth_quant.py
@@ -529,7 +529,7 @@ def _absorb_scales(self, layer_name, scale):  ##output channel
                 layer.bias *= scale
 
     def _cal_scales(self, absorb_to_layer, input_maxes, alpha=0.5, tuning=False):
-        """Cal the adjsut scales
+        """Cal the adjust scales
         :param absorb_to_layer: A dict mapping absorb layer to smooth quantized layer
         :param input_maxes: The channel-wise input max info for layers
         :param alpha: Alpha value to balance the quantization difficulty of activation and weight, a float of a dict
diff --git a/neural_compressor/adaptor/torch_utils/weight_only.py b/neural_compressor/adaptor/torch_utils/weight_only.py
@@ -366,7 +366,7 @@ def rtn_quantize(
         scheme (str, optional): sym or asym. Defaults to "asym".
         quantile (float, optional): percentile of clip. Defaults to 1.0.
         data_type (str, optional): select from int, nf4, fp4. Defaults to int.
-        weight_config (dict, optional): specific layer wise configirations. Defaults to {}.
+        weight_config (dict, optional): specific layer wise configurations. Defaults to {}.
             For example,
                 weight_config={
                     'fc2':
diff --git a/neural_compressor/compression/pruner/__init__.py b/neural_compressor/compression/pruner/__init__.py
@@ -62,7 +62,7 @@ def _rewrite_optimizer_step(opt):
     """
 
     def new_step(self, closure=None):
-        if hasattr(self, "prunings"):  ## in case user save the whole optimzer
+        if hasattr(self, "prunings"):  ## in case user save the whole optimizer
             for pruning in self.prunings:
                 pruning.on_before_optimizer_step()
 
diff --git a/neural_compressor/experimental/pruning.py b/neural_compressor/experimental/pruning.py
@@ -97,7 +97,7 @@ def _on_before_optimizer_step(self):
             pruner.on_before_optimizer_step()
 
     def _on_after_optimizer_step(self):
-        """Functions called after optimzier step."""
+        """Functions called after optimizer step."""
         for pruner in self.pruners:
             pruner.on_after_optimizer_step()
 
diff --git a/neural_compressor/experimental/pruning_v2.py b/neural_compressor/experimental/pruning_v2.py
@@ -167,7 +167,7 @@ def _on_before_optimizer_step(self):
             pruner.on_before_optimizer_step()
 
     def _on_after_optimizer_step(self):
-        """Functions called after optimzier step."""
+        """Functions called after optimizer step."""
         for pruner in self.pruners:
             pruner.on_after_optimizer_step()
 
diff --git a/neural_compressor/utils/utility.py b/neural_compressor/utils/utility.py
@@ -119,7 +119,7 @@ def __call__(self, *args, **kwargs):
 def singleton(cls):
     """Not displayed in API Docs.
 
-    Singleton decorater.
+    Singleton decorator.
     """
     instances = {}
 

Original file line number	Diff line number	Diff line change
`@@ -867,7 +867,7 @@ def train(self, model, dataloader, optimizer_tuple, criterion_tuple, hooks, post`
`867`	`867`	`for i in range(len(list_len_dataloader) - 1):`
`868`	`868`	`if list_len_dataloader[i] != list_len_dataloader[i + 1]:`
`869`	`869`	`raise AttributeError(`
`870`		`- "The traning dataloader's iteration is"`
	`870`	`+ "The training dataloader's iteration is"`
`871`	`871`	`"different between processes, please reset dataloader's batch_size."`
`872`	`872`	`)`
`873`	`873`