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import time
from typing import List, Optional, Tuple, Union
import numpy as np
from loguru import logger as eval_logger
from PIL import Image
from tqdm import tqdm
from lmms_eval import utils
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.model_utils.reasoning_model_utils import (
parse_reasoning_model_answer,
)
from lmms_eval.models.simple.qwen2_5_vl import Qwen2_5_VL as Qwen2_5_VLSimple
from lmms_eval.protocol import ChatMessages
try:
from qwen_vl_utils import process_vision_info
except ImportError:
eval_logger.warning("Failed to import qwen_vl_utils; Please install it via `pip install qwen-vl-utils`")
@register_model("qwen2_5_vl_chat")
class Qwen2_5_VL(Qwen2_5_VLSimple):
is_simple = False
def generate_until(self, requests: List[Instance]) -> List[str]:
res = []
# A dummy collate here to sort by doc id
def _collate(x):
return x[0], x[0]
# we group requests by their generation_kwargs,
# so that we don't try to execute e.g. greedy sampling and temp=0.8 sampling
# in the same batch.
re_ords = utils.Collator([reg.args for reg in requests], _collate, group_fn=lambda x: x[2], grouping=True)
chunks = re_ords.get_batched(n=self.batch_size, batch_fn=None)
num_iters = len(requests) // self.batch_size if len(requests) % self.batch_size == 0 else len(requests) // self.batch_size + 1
pbar = tqdm(total=num_iters, disable=(self.rank != 0), desc="Model Responding")
e2e_latency = 0
total_tokens = 0
for chunk in chunks:
ctx, doc_to_messages, all_gen_kwargs, doc_id, task, split = zip(*chunk)
chat_messages = [doc_to_messages[idx](self.task_dict[task][split][ids]) for idx, (ids, task, split) in enumerate(zip(doc_id, task, split))]
chat_messages: List[ChatMessages] = [ChatMessages(**{"messages": message}) for message in chat_messages]
visuals = []
videos = []
for messages in chat_messages:
visual, video, _ = messages.extract_media()
visuals.append(visual)
videos.append(video)
visuals = self.flatten(visuals)
videos = self.flatten(videos)
gen_kwargs = all_gen_kwargs[0]
# Apply chat template
video_kwargs = {
"max_pixels": self.max_pixels,
"min_pixels": self.min_pixels,
}
if self.fps is not None:
video_kwargs["fps"] = self.fps
else:
video_kwargs["nframes"] = self.max_num_frames
batched_messages = [chat_message.to_hf_messages(video_kwargs=video_kwargs) for chat_message in chat_messages]
texts = [self.processor.apply_chat_template(msg, tokenize=False, add_generation_prompt=True) for msg in batched_messages]
image_inputs, video_inputs = process_vision_info(batched_messages)
if video_inputs is not None:
total_frames = video_inputs[0].shape[0]
indices = np.linspace(0, total_frames - 1, self.max_num_frames, dtype=int)
# Append the last frame index if not already included
if total_frames - 1 not in indices:
indices = np.append(indices, total_frames - 1)
video_inputs[0] = video_inputs[0][indices]
inputs = self.processor(text=texts, images=image_inputs, videos=video_inputs, padding=True, return_tensors="pt")
if self.device_map == "auto":
inputs = inputs.to("cuda")
else:
inputs = inputs.to(self.device)
# Set default generation kwargs
default_gen_kwargs = {
"max_new_tokens": 128,
"temperature": 0.0, # Set to 0 for greedy default
"top_p": None,
"num_beams": 1,
}
# Update with provided kwargs
current_gen_kwargs = {**default_gen_kwargs, **gen_kwargs}
pad_token_id = self.tokenizer.pad_token_id
if current_gen_kwargs["temperature"] > 0:
current_gen_kwargs["do_sample"] = True
else:
current_gen_kwargs["do_sample"] = False
current_gen_kwargs["temperature"] = None
current_gen_kwargs["top_p"] = None
current_gen_kwargs["top_k"] = None
start_time = time.time()
cont = self.model.generate(
**inputs,
eos_token_id=self.tokenizer.eos_token_id,
pad_token_id=pad_token_id,
do_sample=current_gen_kwargs["do_sample"],
temperature=current_gen_kwargs["temperature"],
top_p=current_gen_kwargs["top_p"],
num_beams=current_gen_kwargs["num_beams"],
max_new_tokens=current_gen_kwargs["max_new_tokens"],
top_k=current_gen_kwargs.get("top_k", None),
use_cache=self.use_cache,
)
end_time = time.time()
generated_ids_trimmed = [out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, cont)]
answers = self.processor.batch_decode(generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False)
# Calculate timing metrics for batch
e2e_latency += end_time - start_time
total_tokens += sum(len(ids) for ids in generated_ids_trimmed)
for ans, context in zip(answers, texts):
clean_ans = parse_reasoning_model_answer(ans)
res.append(clean_ans)
self.cache_hook.add_partial("generate_until", (context, gen_kwargs), clean_ans)
pbar.update(1)
eval_logger.debug(f"Question: {context}")
eval_logger.debug(f"Model Raw Response: {ans}")
eval_logger.debug(f"Model Clean Response: {clean_ans}")
# reorder this group of results back to original unsorted form
res = re_ords.get_original(res)
# Calculate average speed
avg_speed = total_tokens / e2e_latency if e2e_latency > 0 else 0
# Log metrics
metric_dict = {
"total_tokens": total_tokens,
"e2e_latency": e2e_latency,
"avg_speed": avg_speed,
"additional_metrics": {
"rank": self.rank,
},
}
log_metrics(**metric_dict)
pbar.close()
return res
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