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import warnings
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
from accelerate import Accelerator, DistributedType
from accelerate.state import AcceleratorState
from decord import VideoReader, cpu
from torchvision.transforms.functional import to_pil_image
from tqdm import tqdm
from transformers import AutoConfig, AutoProcessor, MllamaForConditionalGeneration
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
warnings.filterwarnings("ignore")
from loguru import logger as eval_logger
DEFAULT_IMAGE_TOKEN = "<|image|>"
@register_model("llama_vision")
class LlamaVision(lmms):
def __init__(
self,
pretrained: str = "meta-llama/Llama-3.2-11B-Vision-Instruct",
revision: str = "main",
device: str = "cuda",
dtype: Optional[Union[str, torch.dtype]] = "auto",
batch_size: int = 1,
trust_remote_code: Optional[bool] = False,
attn_implementation: Optional[str] = None,
device_map: str = "",
max_frames_num: Optional[int] = 32,
**kwargs,
) -> None:
super().__init__()
# Do not use kwargs for now
assert kwargs == {}, f"Unexpected kwargs: {kwargs}"
accelerator = Accelerator()
if accelerator.num_processes > 1 and device_map == "":
self._device = torch.device(f"cuda:{accelerator.local_process_index}")
self.device_map = f"cuda:{accelerator.local_process_index}"
else:
self._device = torch.device(device)
self.device_map = device_map
if isinstance(dtype, str) and dtype != "auto":
dtype = getattr(torch, dtype)
self.max_frames_num = max_frames_num
self._model = MllamaForConditionalGeneration.from_pretrained(pretrained, revision=revision, torch_dtype=dtype, device_map=self.device_map, trust_remote_code=trust_remote_code, attn_implementation=attn_implementation)
self.model.eval()
self.processor = AutoProcessor.from_pretrained(pretrained)
if accelerator.num_processes > 1 and device_map == "":
assert accelerator.distributed_type in [DistributedType.FSDP, DistributedType.MULTI_GPU, DistributedType.DEEPSPEED], "Unsupported distributed type provided. Only DDP and FSDP are supported."
# If you want to use DistributedType.DEEPSPEED, you have to run accelerate config before using the model
# Also, you have to select zero stage 0 (equivalent to DDP) in order to make the prepare model works
# I tried to set different parameters in the kwargs to let default zero 2 stage works, but it didn't work.
if accelerator.distributed_type == DistributedType.DEEPSPEED:
kwargs = {
"train_micro_batch_size_per_gpu": self.batch_size_per_gpu,
"train_batch_size": self.batch_size_per_gpu * accelerator.num_processes,
}
AcceleratorState().deepspeed_plugin.deepspeed_config_process(must_match=True, **kwargs)
eval_logger.info("Detected that you are using DistributedType.DEEPSPEED. Make sure you run `accelerate config` and set zero stage to 0")
if accelerator.distributed_type == DistributedType.FSDP or accelerator.distributed_type == DistributedType.DEEPSPEED:
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
elif accelerator.num_processes == 1 and device_map == "auto":
eval_logger.info(f"Using {accelerator.num_processes} devices with pipeline parallelism")
self._rank = 0
self._world_size = 1
else:
eval_logger.info(f"Using single device: {self._device}")
self.model.to(self._device)
self._rank = 0
self._world_size = 1
self.accelerator = accelerator
@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):
# we use EOT because end of *text* is more accurate for what we're doing than end of *sentence*
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 tok_encode(self, string: str, left_truncate_len=None, add_special_tokens=None) -> List[int]:
""" """
add_special_tokens = False if add_special_tokens is None else add_special_tokens
encoding = self.tokenizer.encode(string, add_special_tokens=add_special_tokens)
# left-truncate the encoded context to be at most `left_truncate_len` tokens long
if left_truncate_len:
encoding = encoding[-left_truncate_len:]
return encoding
def tok_decode(self, tokens):
return self.tokenizer.decode(tokens)
def loglikelihood(self, requests: List[Instance]) -> List[Tuple[float, bool]]:
assert False, "Not implemented"
def flatten(self, input):
new_list = []
for i in input:
for j in i:
new_list.append(j)
return new_list
def load_video(self, video_path, max_frames_num):
if type(video_path) == str:
vr = VideoReader(video_path, ctx=cpu(0))
else:
vr = VideoReader(video_path[0], ctx=cpu(0))
total_frame_num = len(vr)
uniform_sampled_frames = np.linspace(0, total_frame_num - 1, max_frames_num, dtype=int)
frame_idx = uniform_sampled_frames.tolist()
spare_frames = vr.get_batch(frame_idx).asnumpy()
return spare_frames # (frames, height, width, channels)
def generate_until(self, requests: List[Instance]) -> List[str]:
res = []
pbar = tqdm(total=len(requests), disable=(self.rank != 0), desc="Model Responding")
for contexts, gen_kwargs, doc_to_visual, doc_id, task, split in [reg.args for reg in requests]:
visuals = [doc_to_visual(self.task_dict[task][split][doc_id])]
visuals = self.flatten(visuals)
messages = [{"role": "user", "content": []}]
images = []
for visual in visuals:
if isinstance(visual, str):
frames = self.load_video(visual, self.max_frames_num)
frames = torch.from_numpy(frames).permute(0, 3, 1, 2)
images.extend([to_pil_image(frame) for frame in frames])
elif isinstance(visual, PIL.Image.Image):
images.append(visual)
for _ in range(len(images)):
messages[-1]["content"].append({"type": "image"})
messages[-1]["content"].append({"type": "text", "text": contexts})
prompt = self.processor.apply_chat_template(messages, add_generation_prompt=True)
inputs = self.processor(images, prompt, add_special_tokens=False, return_tensors="pt").to(self.model.device)
if "max_new_tokens" not in gen_kwargs:
gen_kwargs["max_new_tokens"] = 1024
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
if "do_sample" not in gen_kwargs:
gen_kwargs["do_sample"] = False
with torch.no_grad():
output = self.model.generate(
**inputs,
max_new_tokens=gen_kwargs["max_new_tokens"],
temperature=gen_kwargs["temperature"],
do_sample=gen_kwargs["do_sample"],
)
output = output[:, inputs["input_ids"].shape[-1] :]
res.append(self.processor.decode(output[0], skip_special_tokens=True))
pbar.update(1)
pbar.close()
return res
def generate_until_multi_round(self, requests) -> List[str]:
raise NotImplementedError("TODO: Implement multi-round generation for LLaVAHF")
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