support image_data input to /generate endpoint

lmdeploy/serve/openai/api_server.py

@@ -906,6 +906,25 @@ async def generate(request: GenerateReqInput, raw_request: Request = None):
         return error_check_ret
     if VariableInterface.async_engine.id2step.get(request.session_id, 0) != 0:
         return create_error_response(HTTPStatus.BAD_REQUEST, f'The session_id `{request.session_id}` is occupied.')
+    if (request.prompt is not None) ^ (request.input_ids is None):
+        return create_error_response(HTTPStatus.BAD_REQUEST, 'You must specify exactly one of prompt or input_ids')
+
+    prompt = request.prompt
+    input_ids = request.input_ids
+    image_data = request.image_data
+    if image_data is not None:
+        # convert to openai format
+        image_input = []
+        if not isinstance(image_data, List):
+            image_data = [image_data]
+        for img in image_data:
+            if isinstance(img, str):
+                image_input.append(dict(type='image_url', image_url=dict(url=img)))
+            else:
+                image_input.append(dict(type='image_url', image_url=img))
+        text_input = dict(type='text', text=prompt if prompt else input_ids)
+        prompt = [dict(role='user', content=[text_input] + image_input)]
+        input_ids = None
 
     gen_config = GenerationConfig(
         max_new_tokens=request.max_tokens,
@@ -925,9 +944,9 @@ async def generate(request: GenerateReqInput, raw_request: Request = None):
     )
 
     result_generator = VariableInterface.async_engine.generate(
-        messages=request.prompt,
+        messages=prompt,
         session_id=request.session_id,
-        input_ids=request.input_ids,
+        input_ids=input_ids,
         gen_config=gen_config,
         stream_response=True,  # always use stream to enable batching
         sequence_start=True,

lmdeploy/serve/openai/protocol.py

@@ -439,11 +439,17 @@ class UpdateParamsRequest(BaseModel):
     finished: bool = False
 
 
+# str for url/base64, base64 should be data:image/jpeg;base64, dict should be {'url': url/base64, 'options': ...}
+ImageDataInputItem = Union[str, Dict]
+ImageDataFormat = Union[ImageDataInputItem, List[ImageDataInputItem]]
+
+
 # /generate input
 class GenerateReqInput(BaseModel):
     session_id: Optional[int] = -1
     prompt: Optional[str] = None
     input_ids: Optional[List[int]] = None
+    image_data: Optional[ImageDataFormat] = None
     return_logprob: Optional[bool] = None
     max_tokens: int = 128
     stop: Optional[Union[str, List[str]]] = None

lmdeploy/vl/engine.py

@@ -86,12 +86,16 @@ async def wrap_for_pytorch(
                 ]
             )
         """
-        result = self.model.to_pytorch(messages,
-                                       chat_template,
-                                       tokenizer,
-                                       sequence_start,
-                                       tools=tools,
-                                       enable_thinking=enable_thinking)
+        has_input_ids = self.model.has_input_ids(messages)
+        if not has_input_ids:
+            result = self.model.to_pytorch(messages,
+                                           chat_template,
+                                           tokenizer,
+                                           sequence_start,
+                                           tools=tools,
+                                           enable_thinking=enable_thinking)
+        else:
+            result = self.model.to_pytorch_with_input_ids(messages)
         # clear data
         for i, message in enumerate(messages):
             if isinstance(message['content'], List):

lmdeploy/vl/model/base.py

@@ -1,5 +1,6 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 from abc import ABC, abstractmethod
+from itertools import groupby
 from typing import Dict, List, Union
 
 import numpy as np
@@ -104,6 +105,18 @@ def preprocess(self, messages: List[Dict]) -> List[Dict]:
         """  # noqa
         raise NotImplementedError()
 
+    def has_input_ids(self, messages: List[Dict]) -> bool:
+        """Check whether the messages contain input_ids directly.
+
+        Args:
+            messages (List[Dict]): a list of message, which is supposed to be
+                the output of `preprocess`
+        Returns:
+            bool: whether the messages contain input_ids directly
+        """
+        users = [x['content'] for x in messages if x['role'] == 'user']
+        return len(users) == 1 and isinstance(users[0], List) and isinstance(users[0][0].get('text', ''), List)
+
     def forward(self, messages: List[Dict], max_batch_size: int = 1) -> List[Dict]:
         """Extract image feature. ONLY implement it when the backend is
         turbomind engine.
@@ -168,6 +181,43 @@ def collect_images(messages):
             }) for x in content if x['type'] == 'image'])
         return images
 
+    def to_pytorch_with_input_ids(self, messages):
+        """Pack the preprocessing results in a format compatible with what is
+        required by pytorch engine when input_ids are provided directly.
+
+        Args:
+            messages(List[Dict]): the output of `preprocess`
+        """
+        # collect all preprocessing result from messages
+        preps = [x['content'] for x in messages if x['role'] == 'preprocess']
+        assert len(preps) == 1
+        preps = preps[0]
+
+        _input_ids = messages[0]['content'][0]['text']
+        segs = []
+        for k, g in groupby(_input_ids, lambda x: x == self.image_token_id):
+            if not k:
+                segs.append(list(g))
+            else:
+                segs.extend([[]] * (len(list(g)) - 1))
+        if _input_ids[0] == self.image_token_id:
+            segs = [[]] + segs
+        if _input_ids[-1] == self.image_token_id:
+            segs = segs + [[]]
+
+        assert self.image_token_id == preps[0]['image_token_id']
+        assert len(segs) == len(preps) + 1, (f'the number of image token id {self.image_token_id} is not equal '
+                                             f'to input images, {len(segs) - 1} vs {len(preps)}')
+        input_ids = []
+        for i, seg in enumerate(segs):
+            if i > 0 and i <= len(preps):
+                preps[i - 1].update(offset=len(input_ids))
+                image_tokens = preps[i - 1]['image_tokens']
+                input_ids.extend([self.image_token_id] * image_tokens)
+            input_ids.extend(seg)
+
+        return dict(prompt=None, input_ids=input_ids, multimodal=preps)
+
     def to_pytorch_aux(self, messages, prompt, IMAGE_TOKEN, tokenizer, sequence_start):
         """Auxiliary function to pack the preprocessing results in a format
         compatible with what is required by pytorch engine.