[pre-commit.ci] pre-commit autoupdate

.pre-commit-config.yaml

@@ -72,7 +72,7 @@ repos:
         name: Unused noqa
 
   - repo: https://github.com/pycqa/isort
-    rev: 5.13.2
+    rev: 6.0.1
     hooks:
       - id: isort
         exclude: |
@@ -101,7 +101,7 @@ repos:
           )$
 
   - repo: https://github.com/psf/black.git
-    rev: 24.10.0
+    rev: 25.1.0
     hooks:
       - id: black
         files: (.*\.py)$
@@ -130,7 +130,7 @@ repos:
           )$
 
   - repo: https://github.com/codespell-project/codespell
-    rev: v2.3.0
+    rev: v2.4.1
     hooks:
       - id: codespell
         args: [-w]
@@ -149,7 +149,7 @@ repos:
           )$
 
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.8.6
+    rev: v0.11.4
     hooks:
       - id: ruff
         args: [--fix, --exit-non-zero-on-fix, --no-cache]

examples/pytorch/nlp/huggingface_models/text-classification/quantization/ptq_dynamic/fx/README.md

@@ -105,7 +105,7 @@ We also upstreamed several INT8 models into HuggingFace [model hub](https://hugg
 2. User specifies fp32 'model', calibration dataset 'q_dataloader' and a custom "eval_func" which encapsulates the evaluation dataset and metrics by itself.
 
 ## 2. Code Prepare
-We update `run_glue.py` like belows:
+We update `run_glue.py` like below:
 
 ```python
 trainer = QuestionAnsweringTrainer(

neural_compressor/adaptor/torch_utils/layer_wise_quant/modified_pickle.py

@@ -483,7 +483,7 @@ def clear_memo(self):
         The memo is the data structure that remembers which objects the
         pickler has already seen, so that shared or recursive objects
         are pickled by reference and not by value.  This method is
-        useful when re-using picklers.
+        useful when reusing picklers.
         """
         self.memo.clear()
 

neural_compressor/common/utils/utility.py

@@ -108,13 +108,13 @@ def __init__(self):
             max_extension_support = cpuid.get_max_extension_support()
             if max_extension_support >= 7:
                 ecx = cpuid._run_asm(
-                    b"\x31\xC9",  # xor ecx, ecx
-                    b"\xB8\x07\x00\x00\x00" b"\x0f\xa2" b"\x89\xC8" b"\xC3",  # mov eax, 7  # cpuid  # mov ax, cx  # ret
+                    b"\x31\xc9",  # xor ecx, ecx
+                    b"\xb8\x07\x00\x00\x00" b"\x0f\xa2" b"\x89\xc8" b"\xc3",  # mov eax, 7  # cpuid  # mov ax, cx  # ret
                 )
                 self._vnni = bool(ecx & (1 << 11))
                 eax = cpuid._run_asm(
-                    b"\xB9\x01\x00\x00\x00",  # mov ecx, 1
-                    b"\xB8\x07\x00\x00\x00" b"\x0f\xa2" b"\xC3",  # mov eax, 7  # cpuid  # ret
+                    b"\xb9\x01\x00\x00\x00",  # mov ecx, 1
+                    b"\xb8\x07\x00\x00\x00" b"\x0f\xa2" b"\xc3",  # mov eax, 7  # cpuid  # ret
                 )
                 self._bf16 = bool(eax & (1 << 5))
         self._info = info

neural_compressor/tensorflow/utils/utility.py

@@ -220,13 +220,13 @@ def __init__(self):
             max_extension_support = cpuid.get_max_extension_support()
             if max_extension_support >= 7:
                 ecx = cpuid._run_asm(
-                    b"\x31\xC9",  # xor ecx, ecx
-                    b"\xB8\x07\x00\x00\x00" b"\x0f\xa2" b"\x89\xC8" b"\xC3",  # mov eax, 7  # cpuid  # mov ax, cx  # ret
+                    b"\x31\xc9",  # xor ecx, ecx
+                    b"\xb8\x07\x00\x00\x00" b"\x0f\xa2" b"\x89\xc8" b"\xc3",  # mov eax, 7  # cpuid  # mov ax, cx  # ret
                 )
                 self._vnni = bool(ecx & (1 << 11))
                 eax = cpuid._run_asm(
-                    b"\xB9\x01\x00\x00\x00",  # mov ecx, 1
-                    b"\xB8\x07\x00\x00\x00" b"\x0f\xa2" b"\xC3",  # mov eax, 7  # cpuid  # ret
+                    b"\xb9\x01\x00\x00\x00",  # mov ecx, 1
+                    b"\xb8\x07\x00\x00\x00" b"\x0f\xa2" b"\xc3",  # mov eax, 7  # cpuid  # ret
                 )
                 self._bf16 = bool(eax & (1 << 5))
         if "arch" in info and "ARM" in info["arch"]:  # pragma: no cover

neural_compressor/torch/algorithms/layer_wise/modified_pickle.py

@@ -482,7 +482,7 @@ def clear_memo(self):
         The memo is the data structure that remembers which objects the
         pickler has already seen, so that shared or recursive objects
         are pickled by reference and not by value.  This method is
-        useful when re-using picklers.
+        useful when reusing picklers.
         """
         self.memo.clear()
 

neural_compressor/utils/utility.py

@@ -239,13 +239,13 @@ def __init__(self):
             max_extension_support = cpuid.get_max_extension_support()
             if max_extension_support >= 7:
                 ecx = cpuid._run_asm(
-                    b"\x31\xC9",  # xor ecx, ecx
-                    b"\xB8\x07\x00\x00\x00" b"\x0f\xa2" b"\x89\xC8" b"\xC3",  # mov eax, 7  # cpuid  # mov ax, cx  # ret
+                    b"\x31\xc9",  # xor ecx, ecx
+                    b"\xb8\x07\x00\x00\x00" b"\x0f\xa2" b"\x89\xc8" b"\xc3",  # mov eax, 7  # cpuid  # mov ax, cx  # ret
                 )
                 self._vnni = bool(ecx & (1 << 11))
                 eax = cpuid._run_asm(
-                    b"\xB9\x01\x00\x00\x00",  # mov ecx, 1
-                    b"\xB8\x07\x00\x00\x00" b"\x0f\xa2" b"\xC3",  # mov eax, 7  # cpuid  # ret
+                    b"\xb9\x01\x00\x00\x00",  # mov ecx, 1
+                    b"\xb8\x07\x00\x00\x00" b"\x0f\xa2" b"\xc3",  # mov eax, 7  # cpuid  # ret
                 )
                 self._bf16 = bool(eax & (1 << 5))
         self._info = info

test/adaptor/onnxrt_adaptor/test_onnxrt_operators.py

@@ -1965,7 +1965,7 @@ def test_fp16(self):
             weights = [["input2", TensorProto.FLOAT, [2], np.random.random((2))]]
             node_infos = [["test", ["input1", "input2"], ["output"], optype, "com.microsoft"]]
             model = self.build_model(inps, outs, weights, node_infos)
-            input_data = self.build_test_data(["input1"], [(2)], ["float32"])
+            input_data = self.build_test_data(["input1"], [2], ["float32"])
             convert_model = self.get_fp16_mixed_precision_model(model)
             self.assertTrue("Cast" in set([i.op_type for i in convert_model.nodes()]))
             self.assertTrue(10 in set([i.attribute[0].i for i in convert_model.nodes() if i.op_type == "Cast"]))
@@ -2190,7 +2190,7 @@ def test_bf16(self):
             weights = [["input2", TensorProto.FLOAT, [2], np.random.random((2))]]
             node_infos = [["test", ["input1", "input2"], ["output"], optype, "com.microsoft"]]
             model = self.build_model(inps, outs, weights, node_infos)
-            input_data = self.build_test_data(["input1"], [(2)], ["float32"])
+            input_data = self.build_test_data(["input1"], [2], ["float32"])
             convert_model = self.get_bf16_mixed_precision_model(model)
             self.assertTrue("Cast" in set([i.op_type for i in convert_model.nodes()]))
             self.assertTrue(16 in set([i.attribute[0].i for i in convert_model.nodes() if i.op_type == "Cast"]))