Fix typos (#3123)

swift/llm/argument/base_args/quant_args.py

@@ -21,7 +21,7 @@ class QuantizeArguments:
             The compute dtype for bnb 4-bit quantization.
         bnb_4bit_quant_type (Literal['fp4', 'nf4']): The quantization type for bnb 4-bit quantization.
         bnb_4bit_use_double_quant (bool): Whether to use double quantization for bnb 4-bit quantization.
-        bnb_4bit_quant_storage (Optional[str]): This sets the storage type to pack the quanitzed 4-bit prarams.
+        bnb_4bit_quant_storage (Optional[str]): This sets the storage type to pack the quantized 4-bit params.
     """
     # awq, gptq, and aqlm need to be pre-quantized models.
     #   It can be detected automatically, without the need to pass in.

swift/llm/dataset/dataset/mllm.py

@@ -586,7 +586,7 @@ def preprocess_row(row: Dict[str, Any]) -> Dict[str, Any]:
             'Input some text into a web element like <input> or <textbox>',
             'parameter': [{
                 'element': 'string, the element in the web page to input to',
-                'content': 'string, what content to input into the textbox elment'
+                'content': 'string, what content to input into the textbox element'
             }]
         }
     }, {

swift/llm/infer/infer_engine/infer_client.py

@@ -105,9 +105,9 @@ def infer(
     def _prepare_request_data(model: str, infer_request: InferRequest, request_config: RequestConfig) -> Dict[str, Any]:
         res = asdict(ChatCompletionRequest(model, **asdict(infer_request), **asdict(request_config)))
         # ignore empty
-        empty_requset = ChatCompletionRequest('', [])
+        empty_request = ChatCompletionRequest('', [])
         for k in list(res.keys()):
-            if res[k] == getattr(empty_requset, k):
+            if res[k] == getattr(empty_request, k):
                 res.pop(k)
         return res
 

swift/plugin/tools.py

@@ -77,7 +77,7 @@ def format_toolbench(tool_names, tool_descs):
 
 After the call, you will get the call result, and you are now in a new state.
 Then you will analyze your status now, then decide what to do next...
-After many (Thought-call) pairs, you finally perform the task, then you can give your finial answer.
+After many (Thought-call) pairs, you finally perform the task, then you can give your final answer.
 Remember:
 1.the state change is irreversible, you can't go back to one of the former state, if you want to restart the task, \
 say \"I give up and restart\".

swift/tuners/base.py

@@ -383,13 +383,13 @@ def _prepare_model(
         assert (hasattr(config, SWIFT_TYPE_KEY))
         from .mapping import SWIFT_MAPPING
 
-        adatper_cls = SWIFT_MAPPING[config.swift_type][1]
-        if adatper_cls.has_additional_modules() and not getattr(model, 'model_frozen', False):
+        adapter_cls = SWIFT_MAPPING[config.swift_type][1]
+        if adapter_cls.has_additional_modules() and not getattr(model, 'model_frozen', False):
             for _, p in model.named_parameters():
                 p.requires_grad = False
             model.model_frozen = True
-        config.has_additional_modules = adatper_cls.has_additional_modules()
-        return adatper_cls.prepare_model(model, config, adapter_name)
+        config.has_additional_modules = adapter_cls.has_additional_modules()
+        return adapter_cls.prepare_model(model, config, adapter_name)
 
     def create_or_update_model_card(self, output_dir: str):
         """

swift/ui/base.py

@@ -103,7 +103,7 @@ class BaseUI:
         'local_dir_alert': {
             'value': {
                 'zh': '无法识别model_type和template,请手动选择',
-                'en': 'Cannot recognize the model_type and template, please choose manully'
+                'en': 'Cannot recognize the model_type and template, please choose manually'
             }
         },
     }

swift/utils/torchacc_utils.py

@@ -53,16 +53,16 @@ def _get_closet_bucket(bucket_sizes, data_length):
     """Select the one from bucket_sizes that is closest in distance to
     data_length. This is required for TorchAcc.
     """
-    cloest_length = sys.maxsize
+    closest_length = sys.maxsize
     for b in bucket_sizes:
-        if b == data_length or ((b < cloest_length) and (b > data_length)):
-            cloest_length = b
+        if b == data_length or ((b < closest_length) and (b > data_length)):
+            closest_length = b
 
-    if cloest_length == sys.maxsize:
+    if closest_length == sys.maxsize:
         bucket_sizes.append(data_length)
-        cloest_length = data_length
+        closest_length = data_length
 
-    return cloest_length
+    return closest_length
 
 
 def pad_and_split_batch(padding_to, input_ids, attention_mask, labels, loss_scale, max_length, tokenizer, rank,
@@ -672,7 +672,7 @@ def qwen2_attn_forward(
             key_states = key_states.to(target_dtype)
             value_states = value_states.to(target_dtype)
 
-        # Reashape to the expected shape for Flash Attention
+        # Reshape to the expected shape for Flash Attention
         query_states = query_states.transpose(1, 2)
         key_states = key_states.transpose(1, 2)
         value_states = value_states.transpose(1, 2)

tests/deploy/test_dataset.py

@@ -43,7 +43,7 @@ def test_pt():
     _test('pt')
 
 
-def test_vllm_orgin():
+def test_vllm_origin():
     import subprocess
     import sys
     from modelscope import snapshot_download
@@ -55,7 +55,7 @@ def test_vllm_orgin():
 
 
 if __name__ == '__main__':
-    # test_vllm_orgin()
+    # test_vllm_origin()
     # test_vllm()
     test_lmdeploy()
     # test_pt()

tests/deploy/test_logprobs.py

@@ -94,7 +94,7 @@ def test_pt():
     _test('pt')
 
 
-def test_vllm_orgin():
+def test_vllm_origin():
     import os
     import subprocess
     import sys
@@ -107,7 +107,7 @@ def test_vllm_orgin():
 
 
 if __name__ == '__main__':
-    # test_vllm_orgin()
+    # test_vllm_origin()
     # test_vllm()
     test_vllm_vlm()
     # test_lmdeploy()

tests/llm/test_template.py

@@ -85,7 +85,7 @@ def test_tool_message_join(self):
             StdTemplateInputs.messages_join_observation(test_messages, tools_prompt=tool_prompt)
 
             # multi-round tool calling should be joined that only one assistant message left.
-            assert len(test_messages) == 2, f'Tool prompot {tool_prompt} join failed, {messages}'
+            assert len(test_messages) == 2, f'Tool prompt {tool_prompt} join failed, {messages}'
             assert test_messages[1]['content'] == f"""{obs_word}first_round_result\n{obs_word}second_round_result\n"""
 
 

tests/run.py

@@ -383,7 +383,7 @@ def get_selected_cases():
 
 
 def run_in_subprocess(args):
-    # only case args.isolated_cases run in subporcess, all other run in a subprocess
+    # only case args.isolated_cases run in subprocess, all other run in a subprocess
     if not args.no_diff:  # run based on git diff
         try:
             test_suite_files = get_selected_cases()

tests/run_config.yaml

@@ -1,5 +1,5 @@
 # isolate cases in env, we can install different dependencies in each env.
-isolated:  # test cases that may require excessive anmount of GPU memory or run long time, which will be executed in dedicagted process.
+isolated:  # test cases that may require excessive amount of GPU memory or run long time, which will be executed in dedicated process.
 
 envs:
   default: # default env, case not in other env will in default, pytorch.