Revert "fix some typos" (#6244)

test/srt/models/lora/test_lora_cuda_graph.py

@@ -47,8 +47,8 @@ TEST_CUDA_GRAPH_PADDING_PROMPTS = [
 class TestLoRACudaGraph(CustomTestCase):
 
     def _run_without_cuda_graph_on_model_cases(self, model_cases: List[LoRAModelCase]):
-        # Since we have already enabled CUDA graph by default in other LoRA tests,
-        # we only need to run LoRA tests without CUDA graph here.
+        # Since we have already enabled CUDA graph by default in other lora tests,
+        # we only need to run lora tests without CUDA graph here.
         for model_case in model_cases:
             # If skip_long_prompt is True, filter out prompts longer than 1000 characters
             prompts = (

test/srt/models/lora/utils.py

@@ -154,7 +154,7 @@ def run_lora_test_one_by_one(
         model_case (LoRAModelCase): The model case to test.
         torch_dtype (torch.dtype): The torch dtype to use.
         max_new_tokens (int): The maximum number of new tokens to generate.
-        backend (str): The LoRA backend to use.
+        backend (str): The lora backend to use.
         disable_cuda_graph (bool, optional): Whether to disable CUDA graph. Defaults to False.
         disable_radix_cache (bool, optional): Whether to disable radix cache. Defaults to True.
         mem_fraction_static (float, optional): The fraction of memory to use. Defaults to 0.88.
@@ -289,7 +289,7 @@ def run_lora_test_by_batch(
     test_tag: str = "",
 ):
     """
-    Run LoRA tests as a batch.
+    Run lora tests as a batch.
     For prompt0, prompt1, ..., promptN,
     we will use adaptor0, adaptor1, ..., adaptorN included in model case,
     We will then compare the outputs of HF and SRT with LoRA.
@@ -301,7 +301,7 @@ def run_lora_test_by_batch(
         model_case (LoRAModelCase): The model case to test.
         torch_dtype (torch.dtype): The torch dtype to use.
         max_new_tokens (int): The maximum number of new tokens to generate.
-        backend (str): The LoRA backend to use.
+        backend (str): The lora backend to use.
         disable_cuda_graph (bool, optional): Whether to disable CUDA graph. Defaults to False.
         disable_radix_cache (bool, optional): Whether to disable radix cache. Defaults to True.
         mem_fraction_static (float, optional): The fraction of memory to use. Defaults to 0.88.
@@ -372,8 +372,8 @@ def run_lora_test_by_batch(
         print("ROUGE-L score:", rouge_score)
         print("SRT output:", srt_output_str)
         print("HF output:", hf_output_str)
-        print("SRT no LoRA output:", srt_no_lora_outputs.output_strs[i].strip())
-        print("HF no LoRA output:", hf_no_lora_outputs.output_strs[i].strip())
+        print("SRT no lora output:", srt_no_lora_outputs.output_strs[i].strip())
+        print("HF no lora output:", hf_no_lora_outputs.output_strs[i].strip())
         assert srt_outputs.output_strs[i].strip(" ") == hf_outputs.output_strs[i].strip(
             " "
         ), (

test/srt/test_srt_engine_with_quant_args.py

@@ -8,7 +8,7 @@ class TestSRTEngineWithQuantArgs(CustomTestCase):
 
     def test_1_quantization_args(self):
 
-        # we only test fp8 because other methods are currently dependent on vLLM. We can add other methods back to test after vLLM dependency is resolved.
+        # we only test fp8 because other methods are currently dependent on vllm. We can add other methods back to test after vllm dependency is resolved.
         quantization_args_list = [
             # "awq",
             "fp8",
@@ -34,7 +34,7 @@ class TestSRTEngineWithQuantArgs(CustomTestCase):
 
     def test_2_torchao_args(self):
 
-        # we don't test int8dq because currently there is conflict between int8dq and capture CUDA graph
+        # we don't test int8dq because currently there is conflict between int8dq and capture cuda graph
         torchao_args_list = [
             # "int8dq",
             "int8wo",

test/srt/test_triton_attention_kernels.py

@@ -277,7 +277,7 @@ class TestTritonAttention(CustomTestCase):
 
     def test_decode_attention(self):
         # Here we just to ensure there is no error
-        # TODO: correctness test
+        # TODO: correctnesss test
 
         # Test configurations
         configs = [

test/srt/test_update_weights_from_distributed.py

@@ -189,7 +189,7 @@ def init_process_hf(
     print(f"[hf] {rank=} {broadcast_time=:.3f}s")
     param_queue.put(("broadcast_time", broadcast_time))
 
-    # Delete the HuggingFace models to free up memory.
+    # Delete the huggingface models to free up memory.
     del hf_instruct_model
     del hf_base_model
     gc.collect()