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import os
import sys
import time
from concurrent.futures import ThreadPoolExecutor
from typing import List, Optional, Tuple, Union
from tqdm import tqdm
from transformers import AutoModel
from lmms_eval.api.instance import Instance
from lmms_eval.api.registry import register_model
from lmms_eval.models.model_utils.gen_metrics import log_metrics
from lmms_eval.models.simple.vllm import VLLM as VLLMSimple
from lmms_eval.protocol import ChatMessages
try:
from vllm import LLM, SamplingParams
except ImportError:
vllm = None
WORKERS = int(os.getenv("WORKERS", "32"))
@register_model("longvila")
class LongVila(VLLMSimple):
is_simple = False
def __init__(
self,
model="Efficient-Large-Model/LongVILA-R1-7B",
tensor_parallel_size=1,
data_parallel_size=1,
gpu_memory_utilization=0.5,
batch_size=1,
max_frame_num=32,
trust_remote_code=True,
chat_template=None,
max_pixels: int = 1605632,
min_image_pixels=28,
fps: Optional[int] = None,
device_map: Optional[str] = "cuda",
**kwargs,
):
# vLLM requires the path to the autoregressive llm weights under the model root
model_root = model
llm_path = os.path.join(model_root, "llm")
# Enable prompt embeddings so we can pass encoder-produced embeddings directly
kwargs["enable_prompt_embeds"] = True
self.fps = fps
self.max_pixels = max_pixels
# Set up imports from the model's remote_code directory
# The LongVILA repo provides preprocessing utilities we must call directly
try:
from remote_code.media import extract_media as _extract_media
from remote_code.mm_utils import process_images as _process_images
from remote_code.tokenizer_utils import (
tokenize_conversation as _tokenize_conversation,
)
except Exception as e:
raise ImportError(f"Failed to import LongVILA remote_code utilities from '{model_root}'. Ensure the model path contains remote_code. Original error: {e}")
self.extract_media = _extract_media
self.process_images = _process_images
self.tokenize_conversation = _tokenize_conversation
# Load the encoder that produces prompt embeddings for the LLM
# llm_only_need_embed reduces memory usage to only what's needed for embedding
self.model_encoder = AutoModel.from_pretrained(
model_root,
trust_remote_code=True,
device_map=device_map,
llm_only_need_embed=True,
)
super().__init__(llm_path, tensor_parallel_size, data_parallel_size, gpu_memory_utilization, batch_size, max_frame_num, trust_remote_code, chat_template, min_image_pixels, **kwargs)
def _to_remote_conversation(self, chat_messages: ChatMessages) -> list:
"""
Convert ChatMessages to LongVILA remote_code conversation format.
[{"from": "human"|"gpt", "value": [str | {"path": media_path}, ...]}, ...]
"""
role_map = {"user": "human", "assistant": "gpt", "system": "human"}
conversation = []
for msg in chat_messages.messages:
from_role = role_map.get(msg.role, "human")
value_parts = []
for content in msg.content:
# ChatTextContent
if getattr(content, "type", None) == "text":
value_parts.append(content.text)
# Images, Videos, Audios -> use path dicts as required by tokenizer_utils
elif getattr(content, "type", None) in ("image", "video", "audio"):
value_parts.append({"path": content.url})
if value_parts:
conversation.append({"from": from_role, "value": value_parts})
return conversation
def make_one_request(self, request: Instance) -> Tuple["object", dict]:
"""
Build prompt embeddings and per-request sampling params from an Instance.
Returns (inputs_embeds, params_dict). Does not mutate input.
"""
ctx, doc_to_messages, gen_kwargs, doc_id, task, split = request.arguments
raw_messages = doc_to_messages(self.task_dict[task][split][doc_id])
chat_messages = ChatMessages(messages=raw_messages)
# Copy to avoid side-effects across threads
_gen = dict(gen_kwargs or {})
_gen.setdefault("max_new_tokens", 4096)
_gen.setdefault("temperature", 0)
_gen.setdefault("top_p", 0.95)
params = {
"temperature": _gen["temperature"],
"max_tokens": _gen["max_new_tokens"],
"top_p": _gen["top_p"],
}
# Convert to LongVILA remote_code conversation format
conversation = self._to_remote_conversation(chat_messages)
# Extract and preprocess media
if self.fps:
self.model_encoder.config.fps = self.fps
else:
self.model_encoder.config.num_video_frames = self.max_frame_num
self.model_encoder.config.fps = 0
media = self.extract_media(conversation, self.model_encoder.config)
if "video" in media and media["video"] is not None:
media["video"] = [self.process_images(images, self.model_encoder.vision_tower.image_processor, self.model_encoder.config).half() for images in media["video"]]
# Tokenize conversation and move to CUDA for embedding
input_ids = self.tokenize_conversation(conversation, self.model_encoder.tokenizer, add_generation_prompt=True).unsqueeze(0).cuda()
# Create prompt embeddings using the model encoder
inputs_embeds, _, _ = self.model_encoder._embed(input_ids, media, {"video": {}}, None, None)
return inputs_embeds, params
def generate_until(self, requests) -> List[str]:
res = []
self.load_cache()
res, requests = self.get_response_from_cache(requests)
pbar = tqdm(total=len(requests), disable=(self.rank != 0), desc="Model Responding")
batch_size = self.batch_size_per_gpu
batched_requests = [requests[i : i + batch_size] for i in range(0, len(requests), batch_size)]
e2e_latency = 0
for batch_requests in batched_requests:
prompt_embeds_list = []
params_list = []
# Build embeddings sequentially to avoid GPU contention in the encoder
for req in tqdm(batch_requests, disable=(self.rank != 0), desc="Building embeddings"):
inputs_embeds, params = self.make_one_request(req)
prompt_embeds_list.append({"prompt_embeds": inputs_embeds.squeeze(0)})
params_list.append(params)
# For now, assume homogeneous sampling params within a batch
sampling_params = SamplingParams(**params_list[-1])
start_time = time.time()
response = self.client.generate(prompts=prompt_embeds_list, sampling_params=sampling_params)
end_time = time.time()
response_text = [o.outputs[0].text for o in response]
for req, text in zip(batch_requests, response_text):
self.add_request_response_to_cache(req, text)
# Calculate timing metrics for batch
e2e_latency += end_time - start_time
assert len(response_text) == len(batch_requests)
res.extend(response_text)
pbar.update(len(batch_requests))
pbar.close()
return res
def loglikelihood(self, requests: List[Instance]) -> List[Tuple[float, bool]]:
# TODO
assert False, "GPT4V not support"
def generate_until_multi_round(self, requests) -> List[str]:
raise NotImplementedError("TODO: Implement multi-round generation")
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