Fix typos in strings and comments (#37799)

tests/quantization/autoround/test_auto_round.py

@@ -152,7 +152,7 @@ class AutoRoundTest(unittest.TestCase):
 
     def test_convert_from_gptq(self):
         """
-        Simple test that checks if auto-round work properly wth gptq format
+        Simple test that checks if auto-round work properly with gptq format
         """
         model_name = "ybelkada/opt-125m-gptq-4bit"
 
@@ -170,7 +170,7 @@ class AutoRoundTest(unittest.TestCase):
     @require_intel_extension_for_pytorch
     def test_convert_from_awq_cpu(self):
         """
-        Simple test that checks if auto-round work properly wth awq format
+        Simple test that checks if auto-round work properly with awq format
         """
         model_name = "casperhansen/opt-125m-awq"
 
@@ -187,7 +187,7 @@ class AutoRoundTest(unittest.TestCase):
 
     def test_mixed_bits(self):
         """
-        Simple test that checks if auto-round work properly wth mixed bits
+        Simple test that checks if auto-round work properly with mixed bits
         """
         model_name = "facebook/opt-125m"
         model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto")

tests/quantization/bnb/README.md

@@ -67,7 +67,7 @@ Same solution as above.
 
 ### `RuntimeError: CUDA error: an illegal memory access was encountered ... consider passing CUDA_LAUNCH_BLOCKING=1`
 
-Run your script by pre-pending `CUDA_LAUNCH_BLOCKING=1` and you should observe an error as described in the next section.
+Run your script by prepending `CUDA_LAUNCH_BLOCKING=1` and you should observe an error as described in the next section.
 
 ### `CUDA illegal memory error: an illegal memory access at line...`:
 

tests/quantization/quark_integration/test_quark.py

@@ -97,7 +97,7 @@ class QuarkTest(unittest.TestCase):
 
     def test_original_dtype(self):
         r"""
-        A simple test to check if the model succesfully stores the original dtype
+        A simple test to check if the model successfully stores the original dtype
         """
         self.assertTrue(hasattr(self.quantized_model.config, "_pre_quantization_dtype"))
         self.assertFalse(hasattr(self.model_fp16.config, "_pre_quantization_dtype"))