refactor inference (modelscope#1245)

swift/llm/infer.py

@@ -243,7 +243,7 @@ def prepare_model_template(args: InferArguments,
 
 def read_media_file(infer_kwargs: Dict[str, Any], infer_media_type: Literal['none', 'round', 'dialogue']) -> None:
     text = 'Input a media path or URL <<< '
-    images = infer_kwargs.get('images', [])
+    images = infer_kwargs.get('images') or []
     if infer_media_type == 'none':
         return
     if infer_media_type == 'round' or len(images) == 0:

swift/llm/utils/argument.py

@@ -1299,7 +1299,7 @@ class DeployArguments(InferArguments):
     def __post_init__(self):
         super().__post_init__()
         model_info = MODEL_MAPPING[self.model_type]
-        tags = model_info.get('tags', [])
+        tags = model_info.get('tags') or []
         self.is_multimodal = 'multi-modal' in tags
 
 

swift/llm/utils/preprocess.py

@@ -197,7 +197,7 @@ def preprocess(self, d: Dict[str, Any]) -> Dict[str, Any]:
             response = conversations[-1][self.value_key]
             system = sys
             history = h
-            tools = d.get('tools', [])
+            tools = d.get('tools') or []
             row = {'system': system, 'history': history, 'history_roles': hr}
             row.update({
                 'query': query,

swift/llm/utils/utils.py

@@ -542,38 +542,22 @@ def __next__(self) -> List[int]:
             return value
 
 
-@torch.inference_mode()
-def inference_stream(model: PreTrainedModel,
-                     template: Template,
-                     query: str,
-                     history: Optional[History] = None,
-                     system: Optional[str] = None,
-                     images: Optional[List[str]] = None,
-                     *,
-                     generation_config: Optional[GenerationConfig] = None,
-                     stop_words: Optional[StopWords] = None,
-                     generation_info: Optional[Dict[str, int]] = None,
-                     adapter_names: Optional[List[str]] = None,
-                     **kwargs) -> Iterator[Tuple[str, History]]:
-    """
-    generation_config: Priority: generation_config > model.generation_config.
-    """
+def _prepare_inputs(model: PreTrainedModel,
+                    template: Template,
+                    query: str,
+                    history: History,
+                    system: Optional[str] = None,
+                    images: Optional[List[str]] = None,
+                    *,
+                    generation_config: Optional[GenerationConfig] = None,
+                    stop_words: Optional[StopWords] = None,
+                    adapter_names: Optional[List[str]] = None,
+                    **kwargs) -> Tuple[Dict[str, Any], Dict[str, Any], int]:
     if stop_words is None:
         stop_words = []
-    if history is None:
-        history = []
-    else:
-        history = deepcopy(history)
     if images is None:
         images = []
 
-    # agent support
-    is_observation = history[-1][-1].endswith('Observation:') if history and history[-1][-1] else False
-    if is_observation:
-        history[-1][-1] = history[-1][-1] + query
-        act_length = len(history[-1][-1])
-        query = None
-
     example = {
         'query': query,
         'history': history,
@@ -587,7 +571,7 @@ def inference_stream(model: PreTrainedModel,
     truncation_strategy = kwargs.pop('truncation_strategy', 'delete')
     if len(inputs) == 0 and truncation_strategy == 'delete':
         # input_ids exceeds `max_length`. Please increase the value of `max_length`.
-        return '', history
+        return {}, tokenizer_kwargs, 0
 
     inputs.pop('labels', None)
     tokenizer = template.tokenizer
@@ -606,11 +590,8 @@ def inference_stream(model: PreTrainedModel,
         inputs['token_type_ids'] = torch.tensor(inputs['token_type_ids'])[None]
     model.eval()
     if generation_config is None:
-        generation_config = getattr(model, 'generation_config', None)
+        generation_config = getattr(model, 'generation_config')
     generation_config = deepcopy(generation_config)
-    if generation_config.num_beams != 1:
-        error_msg = 'Streaming generation does not support beam search.'
-        raise ValueError(error_msg)
 
     if tokenizer.eos_token_id is not None:
         generation_config.eos_token_id = tokenizer.eos_token_id
@@ -627,21 +608,69 @@ def inference_stream(model: PreTrainedModel,
                 raise AssertionError('Current sentence length exceeds' f'the model max_length: {max_length}')
     if template.suffix[-1] not in stop_words:
         stop_words.append(template.suffix[-1])
-    stopping_criteria = StoppingCriteriaList([StopWordsCriteria(tokenizer, stop_words, **tokenizer_kwargs)])
     inputs = to_device(inputs, device)
-    if generation_info is not None:
-        generation_info['num_prompt_tokens'] = token_len
     if 'inputs_embeds' in inputs:
         inputs.pop('input_ids', None)
-    streamer = TokenListIteratorStreamer()
     if adapter_names is not None:
         inputs['adapter_names'] = adapter_names
-    generation_kwargs = {
-        'streamer': streamer,
-        'generation_config': generation_config,
-        'stopping_criteria': stopping_criteria,
-        **inputs
-    }
+
+    stopping_criteria = StoppingCriteriaList([StopWordsCriteria(tokenizer, stop_words, **tokenizer_kwargs)])
+    inputs['stopping_criteria'] = stopping_criteria
+    inputs['generation_config'] = generation_config
+    return inputs, tokenizer_kwargs, token_len
+
+
+@torch.inference_mode()
+def inference_stream(model: PreTrainedModel,
+                     template: Template,
+                     query: str,
+                     history: Optional[History] = None,
+                     system: Optional[str] = None,
+                     images: Optional[List[str]] = None,
+                     *,
+                     generation_config: Optional[GenerationConfig] = None,
+                     stop_words: Optional[StopWords] = None,
+                     generation_info: Optional[Dict[str, int]] = None,
+                     adapter_names: Optional[List[str]] = None,
+                     **kwargs) -> Iterator[Tuple[str, History]]:
+    """
+    generation_config: Priority: generation_config > model.generation_config.
+    """
+    if history is None:
+        history = []
+    else:
+        history = deepcopy(history)
+    inputs, tokenizer_kwargs, token_len = _prepare_inputs(
+        model,
+        template,
+        query,
+        history,
+        system,
+        images,
+        generation_config=generation_config,
+        stop_words=stop_words,
+        adapter_names=adapter_names,
+        **kwargs)
+    if len(inputs) == 0:
+        return '', history
+    if generation_info is None:
+        generation_info = {}
+    generation_info['num_prompt_tokens'] = token_len
+
+    # agent support
+    is_observation = history[-1][-1].endswith('Observation:') if history and history[-1][-1] else False
+    if is_observation:
+        history[-1][-1] = history[-1][-1] + query
+        act_length = len(history[-1][-1])
+        query = None
+
+    generation_config = inputs['generation_config']
+    if generation_config.num_beams != 1:
+        error_msg = 'Streaming generation does not support beam search.'
+        raise ValueError(error_msg)
+
+    streamer = TokenListIteratorStreamer()
+    generation_kwargs = {'streamer': streamer, **inputs}
     _model_generate = model.generate
     if is_torch_npu_available():
 
@@ -667,8 +696,7 @@ def _model_generate(*args, **kwargs):
         except StopIteration:
             is_finished = True
         generate_ids = template.get_generate_ids(torch.tensor(raw_generate_ids)[None], token_len)
-        if generation_info is not None:
-            generation_info['num_generated_tokens'] = len(generate_ids)
+        generation_info['num_generated_tokens'] = len(generate_ids)
         response = template.generate_ids_to_response(
             generate_ids,
             is_finished,
@@ -702,58 +730,38 @@ def inference(model: PreTrainedModel,
     """
     generation_config: Priority: generation_config > model.generation_config.
     """
-    if stop_words is None:
-        stop_words = []
     if history is None:
         history = []
     else:
         history = deepcopy(history)
-    if images is None:
-        images = []
+    inputs, tokenizer_kwargs, token_len = _prepare_inputs(
+        model,
+        template,
+        query,
+        history,
+        system,
+        images,
+        generation_config=generation_config,
+        stop_words=stop_words,
+        adapter_names=adapter_names,
+        **kwargs)
+    if len(inputs) == 0:
+        return '', history
+    if generation_info is None:
+        generation_info = {}
+    generation_info['num_prompt_tokens'] = token_len
 
+    # agent support
     is_observation = history[-1][-1].endswith('Observation:') if history and history[-1][-1] else False
     if is_observation:
         history[-1][-1] = history[-1][-1] + query
         query = None
 
-    example = {
-        'query': query,
-        'history': history,
-        'system': system,
-        'images': images,  # for vl. str.
-        'tools': kwargs.pop('tools', None)
-    }
-    template.model = model
-    inputs, tokenizer_kwargs = template.encode(example)
-
-    truncation_strategy = kwargs.pop('truncation_strategy', 'delete')
-    if len(inputs) == 0 and truncation_strategy == 'delete':
-        # input_ids exceeds `max_length`. Please increase the value of `max_length`.
-        return '', history
-
-    inputs.pop('labels', None)
-    tokenizer = template.tokenizer
-    device = next(model.parameters()).device
-    if 'input_ids' in inputs:
-        input_ids = torch.tensor(inputs['input_ids'])[None]
-        inputs['input_ids'] = input_ids
-        token_len = input_ids.shape[1]
-    if 'inputs_embeds' in inputs:
-        inputs_embeds = inputs['inputs_embeds'][None]
-        inputs['inputs_embeds'] = inputs_embeds
-        token_len = inputs_embeds.shape[1]
-
-    inputs['attention_mask'] = torch.ones(token_len)[None]
-    if 'token_type_ids' in inputs:
-        inputs['token_type_ids'] = torch.tensor(inputs['token_type_ids'])[None]
-    model.eval()
-    if generation_config is None:
-        generation_config = getattr(model, 'generation_config', None)
-    generation_config = deepcopy(generation_config)
     if stream and not verbose:
         logger.warning('Please set verbose to True to support TextStreamer, or use `inference_stream.`')
         stream = False
     streamer = None
+    tokenizer = template.tokenizer
     if stream:
         streamer = TextStreamer(tokenizer, skip_prompt=True)
     if verbose:
@@ -762,37 +770,12 @@ def inference(model: PreTrainedModel,
             print(
                 f'{prompt_prefix}{safe_tokenizer_decode(tokenizer, input_ids[0], **tokenizer_kwargs)}{output_prefix}',
                 end='')
-        elif 'query' in example:
-            query = example['query']
+        else:
             print(f'[QUERY]{query}\n{output_prefix}', end='')
-    if tokenizer.eos_token_id is not None:
-        generation_config.eos_token_id = tokenizer.eos_token_id
-    if tokenizer.pad_token_id is not None:
-        generation_config.pad_token_id = tokenizer.pad_token_id
-    if tokenizer.bos_token_id is not None:
-        generation_config.bos_token_id = tokenizer.bos_token_id
-    if generation_config.max_new_tokens is not None:
-        generation_config.max_length = 20  # fix max_length, max_new_tokens warning
-        max_length = get_max_model_len(model.config)
-        if max_length and token_len + generation_config.max_new_tokens > max_length:
-            generation_config.max_new_tokens = max_length - token_len
-            if generation_config.max_new_tokens <= 0:
-                raise AssertionError('Current sentence length exceeds' f'the model max_length: {max_length}')
-    if template.suffix[-1] not in stop_words:
-        stop_words.append(template.suffix[-1])
-    stopping_criteria = StoppingCriteriaList([StopWordsCriteria(tokenizer, stop_words, **tokenizer_kwargs)])
-    inputs = to_device(inputs, device)
-    if generation_info is not None:
-        generation_info['num_prompt_tokens'] = token_len
-    if 'inputs_embeds' in inputs:
-        inputs.pop('input_ids', None)
-    if adapter_names is not None:
-        inputs['adapter_names'] = adapter_names
-    generate_ids = model.generate(
-        streamer=streamer, generation_config=generation_config, stopping_criteria=stopping_criteria, **inputs)
+
+    generate_ids = model.generate(streamer=streamer, **inputs)
     generate_ids = template.get_generate_ids(generate_ids, token_len)
-    if generation_info is not None:
-        generation_info['num_generated_tokens'] = len(generate_ids)
+    generation_info['num_generated_tokens'] = len(generate_ids)
     if verbose and stream is False:
         response = tokenizer.decode(generate_ids, **tokenizer_kwargs)
         print(response)