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FEAT: support qwen2-vl-instruct (#2205)
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| Original file line number | Diff line number | Diff line change |
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@@ -64,6 +64,7 @@ | |
| "MiniCPM-Llama3-V-2_5", | ||
| "MiniCPM-V-2.6", | ||
| "glm-4v", | ||
| "qwen2-vl-instruct", | ||
| ] | ||
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,208 @@ | ||
| # Copyright 2022-2023 XProbe Inc. | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
| import logging | ||
| import uuid | ||
| from typing import Iterator, List, Optional, Union | ||
|
|
||
| from ....model.utils import select_device | ||
| from ....types import ( | ||
| ChatCompletion, | ||
| ChatCompletionChunk, | ||
| ChatCompletionMessage, | ||
| CompletionChunk, | ||
| ) | ||
| from ..llm_family import LLMFamilyV1, LLMSpecV1 | ||
| from ..utils import generate_chat_completion, generate_completion_chunk | ||
| from .core import PytorchChatModel, PytorchGenerateConfig | ||
|
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| logger = logging.getLogger(__name__) | ||
|
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|
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| class Qwen2VLChatModel(PytorchChatModel): | ||
| def __init__(self, *args, **kwargs): | ||
| super().__init__(*args, **kwargs) | ||
| self._tokenizer = None | ||
| self._model = None | ||
| self._device = None | ||
| self._processor = None | ||
|
|
||
| @classmethod | ||
| def match( | ||
| cls, model_family: "LLMFamilyV1", model_spec: "LLMSpecV1", quantization: str | ||
| ) -> bool: | ||
| llm_family = model_family.model_family or model_family.model_name | ||
| if "qwen2-vl-instruct".lower() in llm_family.lower(): | ||
| return True | ||
| return False | ||
|
|
||
| def load(self): | ||
| from transformers import AutoProcessor, Qwen2VLForConditionalGeneration | ||
|
|
||
| device = self._pytorch_model_config.get("device", "auto") | ||
| device = select_device(device) | ||
| self._device = device | ||
| # for multiple GPU, set back to auto to make multiple devices work | ||
| device = "auto" if device == "cuda" else device | ||
|
|
||
| self._processor = AutoProcessor.from_pretrained( | ||
| self.model_path, trust_remote_code=True | ||
| ) | ||
| self._tokenizer = self._processor.tokenizer | ||
| self._model = Qwen2VLForConditionalGeneration.from_pretrained( | ||
| self.model_path, device_map=device, trust_remote_code=True | ||
| ).eval() | ||
|
|
||
| def _transform_messages( | ||
| self, | ||
| messages: List[ChatCompletionMessage], | ||
| ): | ||
| transformed_messages = [] | ||
| for msg in messages: | ||
| new_content = [] | ||
| role = msg["role"] | ||
| content = msg["content"] | ||
| if isinstance(content, str): | ||
| new_content.append({"type": "text", "text": content}) | ||
| elif isinstance(content, List): | ||
| for item in content: # type: ignore | ||
| if "text" in item: | ||
| new_content.append({"type": "text", "text": item["text"]}) | ||
| elif "image_url" in item: | ||
| new_content.append( | ||
| {"type": "image", "image": item["image_url"]["url"]} | ||
| ) | ||
| elif "video_url" in item: | ||
| new_content.append( | ||
| {"type": "video", "video": item["video_url"]["url"]} | ||
| ) | ||
| new_message = {"role": role, "content": new_content} | ||
| transformed_messages.append(new_message) | ||
|
|
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| return transformed_messages | ||
|
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||
| def chat( | ||
| self, | ||
| messages: List[ChatCompletionMessage], # type: ignore | ||
| generate_config: Optional[PytorchGenerateConfig] = None, | ||
| ) -> Union[ChatCompletion, Iterator[ChatCompletionChunk]]: | ||
| messages = self._transform_messages(messages) | ||
|
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| generate_config = generate_config if generate_config else {} | ||
|
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| stream = generate_config.get("stream", False) if generate_config else False | ||
|
|
||
| if stream: | ||
| it = self._generate_stream(messages, generate_config) | ||
| return self._to_chat_completion_chunks(it) | ||
| else: | ||
| c = self._generate(messages, generate_config) | ||
| return c | ||
|
|
||
| def _generate( | ||
| self, messages: List, config: PytorchGenerateConfig = {} | ||
| ) -> ChatCompletion: | ||
| from qwen_vl_utils import process_vision_info | ||
|
|
||
| # Preparation for inference | ||
| text = self._processor.apply_chat_template( | ||
| messages, tokenize=False, add_generation_prompt=True | ||
| ) | ||
| image_inputs, video_inputs = process_vision_info(messages) | ||
| inputs = self._processor( | ||
| text=[text], | ||
| images=image_inputs, | ||
| videos=video_inputs, | ||
| padding=True, | ||
| return_tensors="pt", | ||
| ) | ||
| inputs = inputs.to("cuda") | ||
|
|
||
| # Inference: Generation of the output | ||
| generated_ids = self._model.generate( | ||
| **inputs, | ||
| max_new_tokens=config.get("max_tokens", 512), | ||
| temperature=config.get("temperature", 1), | ||
| ) | ||
| generated_ids_trimmed = [ | ||
| out_ids[len(in_ids) :] | ||
| for in_ids, out_ids in zip(inputs.input_ids, generated_ids) | ||
| ] | ||
| output_text = self._processor.batch_decode( | ||
| generated_ids_trimmed, | ||
| skip_special_tokens=True, | ||
| clean_up_tokenization_spaces=False, | ||
| )[0] | ||
| return generate_chat_completion(self.model_uid, output_text) | ||
|
|
||
| def _generate_stream( | ||
| self, messages: List, config: PytorchGenerateConfig = {} | ||
| ) -> Iterator[CompletionChunk]: | ||
| from threading import Thread | ||
|
|
||
| from qwen_vl_utils import process_vision_info | ||
| from transformers import TextIteratorStreamer | ||
|
|
||
| text = self._processor.apply_chat_template( | ||
| messages, tokenize=False, add_generation_prompt=True | ||
| ) | ||
| image_inputs, video_inputs = process_vision_info(messages) | ||
| inputs = self._processor( | ||
| text=[text], | ||
| images=image_inputs, | ||
| videos=video_inputs, | ||
| padding=True, | ||
| return_tensors="pt", | ||
| ) | ||
| inputs = inputs.to(self._model.device) | ||
|
|
||
| tokenizer = self._tokenizer | ||
| streamer = TextIteratorStreamer( | ||
| tokenizer, timeout=60.0, skip_prompt=True, skip_special_tokens=True | ||
| ) | ||
|
|
||
| gen_kwargs = { | ||
| "max_new_tokens": config.get("max_tokens", 512), | ||
| "temperature": config.get("temperature", 1), | ||
| "streamer": streamer, | ||
| **inputs, | ||
| } | ||
|
|
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| thread = Thread(target=self._model.generate, kwargs=gen_kwargs) | ||
| thread.start() | ||
|
|
||
| completion_id = str(uuid.uuid1()) | ||
| for new_text in streamer: | ||
| yield generate_completion_chunk( | ||
| chunk_text=new_text, | ||
| finish_reason=None, | ||
| chunk_id=completion_id, | ||
| model_uid=self.model_uid, | ||
| prompt_tokens=-1, | ||
| completion_tokens=-1, | ||
| total_tokens=-1, | ||
| has_choice=True, | ||
| has_content=True, | ||
| ) | ||
|
|
||
| yield generate_completion_chunk( | ||
| chunk_text=None, | ||
| finish_reason="stop", | ||
| chunk_id=completion_id, | ||
| model_uid=self.model_uid, | ||
| prompt_tokens=-1, | ||
| completion_tokens=-1, | ||
| total_tokens=-1, | ||
| has_choice=True, | ||
| has_content=False, | ||
| ) |
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