Some Trouble when eproducing the 7B model on the VideoMME Benchmark
#3
by
tyewang
- opened
Thank you for your work. When reproducing the 7B model on the VideoMME Benchmark (w/o subwords task), I encountered an issue where the performance could not be replicated. Using the lmms-eval framework, I tried several different max_frames settings, with the fps consistently set to 1. I tested different max_frames values, and the results are as follows:
- 32 frames: 60.8148
- 64 frames: 61.2963
- 180 frames: 62.7778
Based on the code below, which mainly references the implementation from Hugging Face, could you please let me know if there are any obvious issues?
from typing import List, Optional, Tuple, Union
import torch
from PIL import Image
from accelerate import Accelerator, DistributedType
from lmms_eval import utils
from lmms_eval.api.instance import Instance
from lmms_eval.api.model import lmms
from lmms_eval.api.registry import register_model
from loguru import logger as eval_logger
from tqdm import tqdm
from transformers import AutoModelForCausalLM, AutoProcessor, AutoModel, AutoTokenizer
@register_model("video_llama3_repo")
class VideoLLama3_Repo(lmms):
def __init__(
self,
pretrained: str = "DAMO-NLP-SG/VideoLLaMA3-7B",
device: Optional[str] = "cuda",
device_map: Optional[str] = "cuda",
batch_size: Optional[Union[int, str]] = 1,
use_cache=True,
use_flash_attention_2: Optional[bool] = False,
nframes: int = None,
fps: int = None,
**kwargs,
) -> None:
super().__init__()
# Do not use kwargs for now
assert kwargs == {}, f"Unexpected kwargs: {kwargs}"
accelerator = Accelerator()
if accelerator.num_processes > 1:
self._device = torch.device(f"cuda:{accelerator.local_process_index}")
self.device_map = f"cuda:{accelerator.local_process_index}"
elif accelerator.num_processes == 1 and device_map == "auto":
self._device = torch.device(device)
self.device_map = device_map
else:
self._device = torch.device(f"cuda:{accelerator.local_process_index}")
self.device_map = f"cuda:{accelerator.local_process_index}"
if use_flash_attention_2:
self._model = AutoModelForCausalLM.from_pretrained(
pretrained,
torch_dtype=torch.bfloat16,
device_map=self.device_map,
attn_implementation="flash_attention_2",
trust_remote_code=True
).eval()
else:
self._model = AutoModel.from_pretrained(
pretrained, torch_dtype="auto", device_map=self.device_map,
attn_implementation="sdpa",
trust_remote_code=True
).eval()
self.processor = AutoProcessor.from_pretrained(pretrained, trust_remote_code=True)
self.nframes = nframes
self.fps = fps
self._tokenizer = AutoTokenizer.from_pretrained(pretrained)
self._config = self.model.config
self.batch_size_per_gpu = int(batch_size)
self.use_cache = use_cache
if accelerator.num_processes > 1:
assert accelerator.distributed_type in [
DistributedType.FSDP,
DistributedType.MULTI_GPU,
], "Unsupported distributed type provided. Only DDP and FSDP are supported."
if accelerator.distributed_type == DistributedType.FSDP:
self._model = accelerator.prepare(self.model)
else:
self._model = accelerator.prepare_model(self.model, evaluation_mode=True)
self.accelerator = accelerator
if self.accelerator.is_local_main_process:
eval_logger.info(f"Using {accelerator.num_processes} devices with data parallelism")
self._rank = self.accelerator.local_process_index
self._world_size = self.accelerator.num_processes
else:
self._rank = 0
self._word_size = 1
@property
def config(self):
# return the associated transformers.AutoConfig for the given pretrained model.
return self._config
@property
def tokenizer(self):
return self._tokenizer
@property
def model(self):
# returns the model, unwrapping it if using Accelerate
if hasattr(self, "accelerator"):
return self.accelerator.unwrap_model(self._model)
else:
return self._model
@property
def eot_token_id(self):
return self.tokenizer.eos_token_id
@property
def max_length(self):
return self._max_length
@property
def batch_size(self):
return self.batch_size_per_gpu
@property
def device(self):
return self._device
@property
def rank(self):
return self._rank
@property
def world_size(self):
return self._world_size
def loglikelihood(self, requests: List[Instance]) -> List[Tuple[float, bool]]:
raise NotImplementedError("Loglikelihood is not implemented")
def flatten(self, input):
new_list = []
for i in input:
for j in i:
new_list.append(j)
return new_list
def generate_until(self, requests: List[Instance]) -> List[str]:
res = []
def _collate(x):
# the negative sign on len(toks) sorts descending - this has a few advantages:
# - time estimates will always be over not underestimates, which is more useful for planning
# - to know the size of a batch when going through the list, you know the first one is always the batch
# padded context length. this is useful to simplify the batching logic and more importantly to make
# automatic adaptive batches much much easier to implement
# - any OOMs will happen right away rather than near the end
toks = self.tokenizer.encode(x[0])
return -len(toks), x[0]
pbar = tqdm(total=len(requests), disable=(self.rank != 0), desc="Model Responding")
# 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, grouping=True)
chunks = re_ords.get_batched(n=self.batch_size, batch_fn=None)
for chunk in chunks:
contexts, all_gen_kwargs, doc_to_visual, doc_id, task, split = zip(*chunk)
task = task[0]
split = split[0]
visuals = [doc_to_visual[0](self.task_dict[task][split][ids]) for ids in doc_id]
visuals = self.flatten(visuals)
gen_kwargs = all_gen_kwargs[0]
# Set default values for until and max_new_tokens
until = [self.tokenizer.decode(self.eot_token_id)]
# Update values from gen_kwargs if present
if "until" in gen_kwargs:
until = gen_kwargs.pop("until")
if isinstance(until, str):
until = [until]
elif not isinstance(until, list):
raise ValueError(f"Expected `gen_kwargs['until']` to be of type Union[str,list] but got {type(until)}")
if isinstance(contexts, tuple):
contexts = list(contexts)
for i in range(len(contexts)):
if "<image>" in contexts[i]:
contexts[i] = contexts[i].replace("<image>", "")
answers = []
for i, context in enumerate(contexts):
if "<image>" in context:
context = context.replace("<image>", "")
message = [{"role": "system", "content": "You are a helpful assistant."}]
if len(visuals) > 0:
visual = visuals[i] if i < len(visuals) else None
if isinstance(visual, str) and visual.endswith((".mp4", ".avi", ".mov")): # Video file
message.append(
{
"role": "user",
"content": [
{"type": "video", "video": {"video_path": visual, "fps": self.fps,
"max_frames": self.nframes}},
{"type": "text", "text": context}
]
},
)
elif isinstance(visual, Image.Image): # Single image
msg = [{"type": "image", "image": visual}, {"type": "text", "text": context}]
message.append({"role": "user", "content": msg})
elif isinstance(visual, (list, tuple)) and all(isinstance(v, Image.Image) for v in visual): # Multiple images
msg = [{"type": "image", "image": visual}, {"type": "text", "text": context}]
message.append({"role": "user", "content": msg})
else:
message.append({"role": "user", "content": [{"type": "text", "text": context}]})
else:
message.append({"role": "user", "content": [{"type": "text", "text": context}]})
inputs = self.processor(conversation=message, return_tensors="pt")
if "pixel_values" in inputs:
inputs["pixel_values"] = inputs["pixel_values"].to(torch.bfloat16)
if self.device_map == "auto":
inputs = {k: v.cuda() if isinstance(v, torch.Tensor) else v for k, v in inputs.items()}
else:
inputs = {k: v.cuda(self.device) if isinstance(v, torch.Tensor) else v for k, v in inputs.items()}
if "max_new_tokens" not in gen_kwargs:
gen_kwargs["max_new_tokens"] = 128
if "temperature" not in gen_kwargs:
gen_kwargs["temperature"] = 0
if "top_p" not in gen_kwargs:
gen_kwargs["top_p"] = None
if "num_beams" not in gen_kwargs:
gen_kwargs["num_beams"] = 1
cont = self.model.generate(
**inputs,
eos_token_id=self.tokenizer.eos_token_id,
pad_token_id=self.tokenizer.pad_token_id,
do_sample=True if gen_kwargs["temperature"] > 0 else False,
temperature=gen_kwargs["temperature"],
top_p=gen_kwargs["top_p"],
num_beams=gen_kwargs["num_beams"],
max_new_tokens=gen_kwargs["max_new_tokens"],
use_cache=self.use_cache,
)
sample_answer = self.processor.batch_decode(cont, skip_special_tokens=True)[0].strip()
answers.append(sample_answer)
for i, ans in enumerate(answers):
for term in until:
if len(term) > 0:
ans = ans.split(term)[0]
answers[i] = ans
for ans, context in zip(answers, contexts):
res.append(ans)
self.cache_hook.add_partial("generate_until", (context, gen_kwargs), ans)
pbar.update(1)
# reorder this group of results back to original unsorted form
res = re_ords.get_original(res)
pbar.close()
return res
def generate_until_multi_round(self, requests) -> List[str]:
raise NotImplementedError("TODO: Implement multi-round generation")