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Commit ad92d498 authored by haileyschoelkopf's avatar haileyschoelkopf
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remove llava model

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lm_eval/models/llava.py deleted 100644 → 0
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import logging
import warnings
from typing import List, Optional, Tuple, Union
import torch
from accelerate import Accelerator, DistributedType
from accelerate.state import AcceleratorState
from tqdm import tqdm
from lm_eval.api.instance import Instance
from lm_eval.api.model import LM
from lm_eval.api.registry import register_model
from lm_eval.models.utils import Collator
warnings.filterwarnings("ignore")
eval_logger = logging.getLogger("lm-eval")
try:
from llava.constants import (
DEFAULT_IMAGE_TOKEN,
IMAGE_TOKEN_INDEX,
)
from llava.conversation import conv_templates
from llava.mm_utils import (
get_model_name_from_path,
process_images,
tokenizer_image_token,
)
from llava.model.builder import load_pretrained_model
except ImportError:
eval_logger.error("LLaVA is not installed. Please install LLaVA to use this model.")
@register_model("llava")
class Llava(LM):
"""
Llava Model
"""
def __init__(
self,
pretrained: str = "liuhaotian/llava-v1.5-7b",
truncation: Optional[bool] = True,
device: Optional[str] = "cuda",
dtype: Optional[Union[str, torch.dtype]] = "auto",
batch_size: Optional[Union[int, str]] = 1,
trust_remote_code: Optional[bool] = False,
revision=None,
use_flash_attention_2=False,
conv_template="vicuna_v1",
use_cache=True,
truncate_context=False, # whether to truncate the context in generation, set it False for LLaVA-1.6
**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._tokenizer,
self._model,
self._image_processor,
self._max_length,
) = load_pretrained_model(
pretrained,
None,
get_model_name_from_path(pretrained),
device_map=self._device,
)
self._config = self._model.config
self.model.eval()
self.model.tie_weights()
self.truncation = truncation
self.batch_size_per_gpu = int(batch_size)
self.conv_template = conv_template
self.use_cache = use_cache
self.truncate_context = truncate_context
# assert self.batch_size_per_gpu == 1, "Llava currently does not support batched generation. See https://github.com/haotian-liu/LLaVA/issues/754. HF Llava also has this issue."
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._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):
# 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
def pad_sequence(self, input_ids, batch_first, padding_value):
if self.tokenizer.padding_side == "left":
input_ids = [torch.flip(_input_ids, [0]) for _input_ids in input_ids]
input_ids = torch.nn.utils.rnn.pad_sequence(
input_ids, batch_first=batch_first, padding_value=padding_value
)
if self.tokenizer.padding_side == "left":
input_ids = torch.flip(input_ids, [1])
return input_ids
@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]]:
# TODO
res = []
pbar = tqdm(
total=len(requests), disable=(self.rank != 0), desc="Model Responding"
)
for contexts, doc_to_target, doc_to_visual, doc, task in [
reg.args for reg in requests
]:
# encode, pad, and truncate contexts for this batch
if isinstance(doc_to_target, str):
continuation = doc_to_target
else:
continuation = doc_to_target(doc)
visuals = [doc_to_visual(doc)]
visuals = self.flatten(visuals)
if visuals:
image = process_images(visuals, self._image_processor, self._config)
if isinstance(image, list):
image = [
_image.to(dtype=torch.float16, device=self.device)
for _image in image
]
else:
image = image.to(dtype=torch.float16, device=self.device)
else:
image = None
prompts_input = contexts[0]
if (
image is not None
and len(image) != 0
and DEFAULT_IMAGE_TOKEN not in prompts_input
):
"""
Three senarios:
1. No image, and there for, no image token should be added.
2. image token is already specified in the context, so we don't need to add it.
3. image token is not specified in the context and there is image inputs, so we need to add it. In this case, we add the image token at the beginning of the context and add a new line.
"""
image_tokens = [DEFAULT_IMAGE_TOKEN] * len(visuals)
image_tokens = " ".join(image_tokens)
prompts_input = image_tokens + "\n" + contexts[0]
conv = conv_templates[self.conv_template].copy()
conv.append_message(conv.roles[0], prompts_input)
conv.append_message(conv.roles[1], None)
prompt = conv.get_prompt()
contxt_id = (
tokenizer_image_token(
prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt"
)
.unsqueeze(0)
.to(self.device)
)
# Add the answer of the second role
conv.messages[1][1] = continuation
prompt = conv.get_prompt()
input_ids = (
tokenizer_image_token(
prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt"
)
.unsqueeze(0)
.to(self.device)
)
labels = input_ids.clone()
# Context part no need to calculate for loss
labels[0, : contxt_id.shape[1]] = -100
with torch.inference_mode():
outputs = self.model(
input_ids=input_ids, labels=labels, images=image, use_cache=True
)
loss = outputs["loss"]
# loss = torch.exp(loss)
logits = outputs["logits"]
greedy_tokens = logits.argmax(dim=-1)
cont_toks = input_ids[:, contxt_id.shape[1] :] # [1, seq]
greedy_tokens = greedy_tokens[
:, contxt_id.shape[1] : 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 loglikelihood_rolling(
self, requests: List[Instance]
) -> List[Tuple[float, bool]]:
raise NotImplementedError()
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.tok_encode(x[0])
return -len(toks), 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 = Collator(
[reg.arguments for reg in requests],
sort_fn=_collate,
group_by="gen_kwargs",
group_fn=lambda x: x[1],
)
chunks = re_ords.get_batched(n=self.batch_size)
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, task = zip(*chunk)
task = task[0]
visuals = [doc_to_visual[0](doc[0])]
visuals = self.flatten(visuals)
# we assume all gen kwargs in the batch are the same
# this is safe to assume because the `grouper` object ensures it.
gen_kwargs = all_gen_kwargs[0]
# Set default values for until and max_new_tokens
until = [self.tok_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 (
"image_aspect_ratio" in gen_kwargs.keys()
and "image_aspect_ratio" not in self._config.__dict__
):
# here we should pop it out of gen_kwargs so that it doesn't get passed to the model for next step of generation
self._config.image_aspect_ratio = gen_kwargs.pop("image_aspect_ratio")
eval_logger.info(
f"Setting image aspect ratio: {self._config.image_aspect_ratio}"
)
# encode, pad, and truncate contexts for this batch
if visuals:
image_tensor = process_images(
visuals, self._image_processor, self._config
)
if isinstance(image_tensor, list):
image_tensor = [
_image.to(dtype=torch.float16, device=self.device)
for _image in image_tensor
]
else:
image_tensor = image_tensor.to(
dtype=torch.float16, device=self.device
)
else:
image_tensor = None
# prompts_input = contexts[0]
question_input = []
for visual, context in zip(visuals, contexts):
if (
image_tensor is not None
and len(image_tensor) != 0
and DEFAULT_IMAGE_TOKEN not in context
):
"""
Three senarios:
1. No image, and there for, no image token should be added.
2. image token is already specified in the context, so we don't need to add it.
3. image token is not specified in the context and there is image inputs, so we need to add it. In this case, we add the image token at the beginning of the context and add a new line.
"""
image_tokens = (
[DEFAULT_IMAGE_TOKEN] * len(visual)
if isinstance(visual, list)
else [DEFAULT_IMAGE_TOKEN]
)
image_tokens = " ".join(image_tokens)
question = image_tokens + "\n" + context
else:
question = context
conv = conv_templates[self.conv_template].copy()
conv.append_message(conv.roles[0], question)
conv.append_message(conv.roles[1], None)
prompt_question = conv.get_prompt()
question_input.append(prompt_question)
# The above for loop has bugs. When there is no visuals, e.g. pure text,
# there will be no for loop execute resulting in an empty question_input (because no visuals)
# Scenario 1 won't even be execute
if len(visuals) == 0:
for context in contexts:
question = context
conv = conv_templates[self.conv_template].copy()
conv.append_message(conv.roles[0], question)
conv.append_message(conv.roles[1], None)
prompt_question = conv.get_prompt()
question_input.append(prompt_question)
# input_ids = tokenizer_image_token(prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to(self.device)
# preconfigure gen_kwargs with defaults
gen_kwargs["image_sizes"] = [
visuals[idx].size for idx in range(len(visuals))
]
if "max_gen_toks" not in gen_kwargs:
gen_kwargs["max_gen_toks"] = 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
input_ids_list = [
tokenizer_image_token(
prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt"
)
for prompt in question_input
]
pad_token_ids = (
self.tokenizer.pad_token_id
if self.tokenizer.pad_token_id is not None
else self.tokenizer.eos_token_id
)
input_ids = self.pad_sequence(
input_ids_list, batch_first=True, padding_value=pad_token_ids
).to(self.device)
attention_masks = input_ids.ne(pad_token_ids).to(self.device)
# These steps are not in LLaVA's original code, but are necessary for generation to work
# TODO: pay attention to this major generation step...
try:
cont = self.model.generate(
input_ids,
attention_mask=attention_masks,
pad_token_id=pad_token_ids,
images=image_tensor,
image_sizes=gen_kwargs["image_sizes"],
do_sample=gen_kwargs["do_sample"],
temperature=gen_kwargs["temperature"],
top_p=gen_kwargs["top_p"],
num_beams=gen_kwargs["num_beams"],
max_new_tokens=gen_kwargs["max_gen_toks"],
use_cache=self.use_cache,
)
text_outputs = self.tokenizer.batch_decode(
cont, skip_special_tokens=True
)
except Exception as e:
eval_logger.error(f"Error {e} in generating")
cont = ""
text_outputs = [""]
# cont_toks_list = cont.tolist()
# for cont_toks, context in zip(cont_toks_list, contexts):
# discard context + left-padding toks if using causal decoder-only LMM
# if self.truncate_context:
# cont_toks = cont_toks[input_ids.shape[1] :]
# use secondary stop seqs to cut off should-have-been-stopped content post-hoc
# if self.truncate_context:
# for term in until:
# if len(term) > 0:
# # ignore '' separator,
# # for seq2seq case where self.tok_decode(self.eot_token_id) = ''
# text_outputs = text_outputs.split(term)[0]
res.extend(text_outputs)
self.cache_hook.add_partial(
"generate_until", (context, gen_kwargs), text_outputs
)
pbar.update(1)
# reorder this group of results back to original unsorted form
res = re_ords.get_original(res)
pbar.close()
return res
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