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import warnings
warnings.simplefilter("ignore", category=DeprecationWarning)
warnings.filterwarnings("ignore")
from typing import List, Optional, Tuple, Union
import torch
from accelerate import Accelerator, DistributedType
from accelerate.state import AcceleratorState
from loguru import logger as eval_logger
from PIL import Image
from tqdm import tqdm
from transformers import (
AutoTokenizer,
FuyuForCausalLM,
FuyuImageProcessor,
FuyuProcessor,
)
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
@register_model("fuyu")
class Fuyu(lmms):
"""
Fuyu Model
"""
def __init__(
self,
pretrained: str = "adept/fuyu-8b",
device: Optional[str] = "cuda",
max_new_tokens: int = 256,
batch_size: Optional[Union[int, str]] = 1,
**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}")
else:
self._device = device
self._model = FuyuForCausalLM.from_pretrained(pretrained, torch_dtype=torch.bfloat16, device_map=self.device)
self.model.eval()
self.model.tie_weights()
self._tokenizer = AutoTokenizer.from_pretrained(pretrained)
self._config = self.model.config
self.image_processor = FuyuImageProcessor()
self.processor = FuyuProcessor(image_processor=self.image_processor, tokenizer=self.tokenizer)
self.max_new_tokens = max_new_tokens
self.batch_size_per_gpu = int(batch_size)
accelerator = Accelerator()
if accelerator.num_processes > 1:
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
else:
self.model.to(self._device)
self._rank = 0
self._world_size = 1
"""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"""
@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):
# Assuming max_length is the sum of max context tokens and max new tokens
return self.tokenizer.model_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 flatten(self, input, only_get_first=False):
new_list = []
for i in input:
for j in i:
new_list.append(j)
if only_get_first:
break
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.tok_encode(x[0])
return -len(toks), x[0]
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)
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")
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, only_get_first=True)
gen_kwargs = all_gen_kwargs[0]
# if isinstance(visuals[0], list):
# visuals = [visuals[idx][0] for idx in range(len(visuals))] # get the first image in multi-image scenarios.
# assert len(contexts) == self.batch_size_per_gpu, f"Expected contexts batch size {self.batch_size_per_gpu}, got {len(contexts)}"
# assert len(visuals) == self.batch_size_per_gpu, f"Expected visuals batch size {self.batch_size_per_gpu}, got {len(visuals)}"
formatted_contexts = [f"{context}\n" for context in contexts]
model_inputs = self.processor(text=formatted_contexts, images=visuals, device=self.device)
for k, v in model_inputs.items():
model_inputs[k] = v.to(self.device, non_blocking=True) if isinstance(v, torch.Tensor) else [vv.to(self.device, non_blocking=True) for vv in v]
for index in range(len(model_inputs["image_patches"])):
model_inputs["image_patches"][index] = model_inputs["image_patches"][index].to(dtype=next(self.model.parameters()).dtype)
# preconfigure gen_kwargs with defaults
gen_kwargs["image_sizes"] = [visuals[idx].size for idx in range(len(visuals))]
if "max_new_tokens" not in gen_kwargs:
gen_kwargs["max_new_tokens"] = 256
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
# generation_output = self.model.generate(
# **model_inputs, temperature=gen_kwargs["temperature"], max_new_tokens=gen_kwargs["max_new_tokens"], top_p=gen_kwargs["top_p"], num_beams=gen_kwargs["num_beams"], pad_token_id=self.tokenizer.eos_token_id
# )
generation_output = self.model.generate(**model_inputs, max_new_tokens=gen_kwargs["max_new_tokens"], pad_token_id=self.tokenizer.eos_token_id)
generation_texts = self.processor.batch_decode(generation_output, skip_special_tokens=True)
response = [gen_text.split("\x04")[1].strip(" ").strip("\n") for gen_text in generation_texts]
res.extend(response)
pbar.update(1)
pbar.close()
return res
def loglikelihood(self, requests: List[Instance]) -> List[Tuple[float, bool]]:
# TODO
res = []
pbar = tqdm(total=len(requests), disable=(self.rank != 0), desc="Model Responding")
for contexts, doc_to_target, doc_to_visual, doc_id, task, split in [reg.args for reg in requests]:
# encode, pad, and truncate contexts for this batch
if type(doc_to_target) == str:
continuation = doc_to_target
else:
continuation = doc_to_target(self.task_dict[task][split][doc_id])
visuals = [doc_to_visual(self.task_dict[task][split][doc_id])]
visuals = self.flatten(visuals)
formatted_contexts = [f"{contexts}\n"]
formatted_continuation = [f"{contexts}\n{continuation}"]
model_inputs = self.processor(text=formatted_continuation, images=visuals, device=self.device)
for k, v in model_inputs.items():
model_inputs[k] = v.to(self.device, non_blocking=True) if isinstance(v, torch.Tensor) else [vv.to(self.device, non_blocking=True) for vv in v]
for index in range(len(model_inputs["image_patches"])):
model_inputs["image_patches"][index] = model_inputs["image_patches"][index].to(dtype=next(self.model.parameters()).dtype)
labels = model_inputs["input_ids"].clone()
contxt_id = self.processor(text=formatted_contexts, return_tensors="pt")["input_ids"]
labels[: len(contxt_id)] = -100
with torch.inference_mode():
outputs = self.model(**model_inputs, labels=labels)
loss = outputs["loss"]
# loss = torch.exp(loss)
logits = outputs["logits"]
greedy_tokens = logits.argmax(dim=-1)
cont_toks = model_inputs["input_ids"][:, contxt_id.shape[1] :] # [1, seq]
greedy_tokens = greedy_tokens[:, contxt_id.shape[1] : model_inputs["input_ids"].shape[1]] # [1, seq]
max_equal = (greedy_tokens == cont_toks).all()
res.append((float(loss.item()), bool(max_equal)))
pbar.update(1)
pbar.close()
return res
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 generate_until_multi_round(self, requests) -> List[str]:
raise NotImplementedError("TODO: Implement multi-round generation for Fuyu")
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