import os
import copy
from dataclasses import dataclass, field
import json
import logging
import pathlib
from typing import Dict, Optional, Sequence, List
import ast
import torch
import time
import random
import cv2
import transformers
import tokenizers
from oryx.constants import IGNORE_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN, IMAGE_TOKEN_INDEX
from torch.utils.data import Dataset
from oryx.train.oryx_trainer import OryxTrainer
from oryx import conversation as conversation_lib
from oryx.model import *
from oryx.mm_utils import tokenizer_image_token, process_anyres_highres_image_genli, process_anyres_video_genli, process_anyres_video_genli_long
from PIL import Image
import io
import base64
from packaging import version
import numpy as np
from transformers import AutoConfig
import math
import copy
local_rank = None
def rank0_print(*args):
if local_rank == 0:
print(*args)
IS_TOKENIZER_GREATER_THAN_0_14 = version.parse(tokenizers.__version__) >= version.parse('0.14')
@dataclass
class ModelArguments:
model_name_or_path: Optional[str] = field(default="facebook/opt-125m")
version: Optional[str] = field(default="v0")
freeze_backbone: bool = field(default=False)
tune_mm_mlp_adapter: bool = field(default=False)
tune_mm_vision_resampler: bool = field(default=False)
vision_tower: Optional[str] = field(default=None)
image_processor: Optional[str] = field(default=None)
unfreeze_mm_vision_tower: bool = field(default=False)
mm_vision_select_layer: Optional[int] = field(default=-1) # default to the last layer
pretrain_mm_mlp_adapter: Optional[str] = field(default=None)
mm_projector_type: Optional[str] = field(default='linear')
mm_use_im_start_end: bool = field(default=False)
mm_use_im_patch_token: bool = field(default=True)
mm_vision_select_feature: Optional[str] = field(default="patch")
mm_resampler_type: Optional[str] = field(default=None)
mm_mask_drop_mode: str = field(default="fixed")
mm_mask_drop_skip_percentage: float = field(default=0.)
mm_mask_drop_ratio: float = field(default=0.25)
mm_mask_drop_ratio_upper: Optional[float] = field(default=None)
mm_mask_drop_ratio_lower: Optional[float] = field(default=None)
@dataclass
class DataArguments:
data_path: str = field(default=None,
metadata={"help": "Path to the training data."})
lazy_preprocess: bool = False
is_multimodal: bool = False
video_fps: Optional[int] = field(default=1)
frames_upbound: Optional[int] = field(default=0)
@dataclass
class TrainingArguments(transformers.TrainingArguments):
cache_dir: Optional[str] = field(default=None)
optim: str = field(default="adamw_torch")
remove_unused_columns: bool = field(default=False)
freeze_mm_mlp_adapter: bool = field(default=False)
freeze_mm_vision_resampler: bool = field(default=False)
mpt_attn_impl: Optional[str] = field(default="triton")
model_max_length: int = field(
default=512,
metadata={
"help":
"Maximum sequence length. Sequences will be right padded (and possibly truncated)."
},
)
double_quant: bool = field(
default=True,
metadata={"help": "Compress the quantization statistics through double quantization."}
)
quant_type: str = field(
default="nf4",
metadata={"help": "Quantization data type to use. Should be one of `fp4` or `nf4`."}
)
bits: int = field(
default=16,
metadata={"help": "How many bits to use."}
)
lora_enable: bool = False
lora_r: int = 64
lora_alpha: int = 16
lora_dropout: float = 0.05
lora_weight_path: str = ""
lora_bias: str = "none"
mm_projector_lr: Optional[float] = None
mm_vision_tower_lr: Optional[float] = None
group_by_varlen: bool = field(default=False)
group_by_modality_length: bool = field(default=False)
group_by_modality_length_auto: bool = field(default=False)
do_resize: bool = field(default=False)
do_center_crop: bool = field(default=False)
def maybe_zero_3(param, ignore_status=False, name=None):
from deepspeed import zero
from deepspeed.runtime.zero.partition_parameters import ZeroParamStatus
if hasattr(param, "ds_id"):
if param.ds_status == ZeroParamStatus.NOT_AVAILABLE:
if not ignore_status:
logging.warning(f"{name}: param.ds_status != ZeroParamStatus.NOT_AVAILABLE: {param.ds_status}")
with zero.GatheredParameters([param]):
param = param.data.detach().cpu().clone()
else:
param = param.detach().cpu().clone()
return param
# Borrowed from peft.utils.get_peft_model_state_dict
def get_peft_state_maybe_zero_3(named_params, bias):
if bias == "none":
to_return = {k: t for k, t in named_params if "lora_" in k}
elif bias == "all":
to_return = {k: t for k, t in named_params if "lora_" in k or "bias" in k}
elif bias == "lora_only":
to_return = {}
maybe_lora_bias = {}
lora_bias_names = set()
for k, t in named_params:
if "lora_" in k:
to_return[k] = t
bias_name = k.split("lora_")[0] + "bias"
lora_bias_names.add(bias_name)
elif "bias" in k:
maybe_lora_bias[k] = t
for k, t in maybe_lora_bias:
if bias_name in lora_bias_names:
to_return[bias_name] = t
else:
raise NotImplementedError
to_return = {k: maybe_zero_3(v, ignore_status=True) for k, v in to_return.items()}
return to_return
def get_peft_state_non_lora_maybe_zero_3(named_params, require_grad_only=True):
to_return = {k: t for k, t in named_params if "lora_" not in k}
if require_grad_only:
to_return = {k: t for k, t in to_return.items() if t.requires_grad}
to_return = {k: maybe_zero_3(v, ignore_status=True).cpu() for k, v in to_return.items()}
return to_return
def get_mm_adapter_state_maybe_zero_3(named_params, keys_to_match):
to_return = {k: t for k, t in named_params if any(key_match in k for key_match in keys_to_match)}
to_return = {k: maybe_zero_3(v, ignore_status=True).cpu() for k, v in to_return.items()}
return to_return
def find_all_linear_names(model):
cls = torch.nn.Linear
lora_module_names = set()
multimodal_keywords = ['mm_projector', 'vision_tower', 'vision_resampler']
for name, module in model.named_modules():
if any(mm_keyword in name for mm_keyword in multimodal_keywords):
continue
if isinstance(module, cls):
names = name.split('.')
lora_module_names.add(names[0] if len(names) == 1 else names[-1])
if 'lm_head' in lora_module_names: # needed for 16-bit
lora_module_names.remove('lm_head')
return list(lora_module_names)
def safe_save_model_for_hf_trainer(trainer: transformers.Trainer,
output_dir: str):
"""Collects the state dict and dump to disk."""
if getattr(trainer.args, "tune_mm_mlp_adapter", False):
# Only save Adapter
keys_to_match = ['mm_projector', 'vision_resampler']
if getattr(trainer.args, "use_im_start_end", False):
keys_to_match.extend(['embed_tokens', 'embed_in'])
weight_to_save = get_mm_adapter_state_maybe_zero_3(trainer.model.named_parameters(), keys_to_match)
trainer.model.config.save_pretrained(output_dir)
current_folder = output_dir.split('/')[-1]
parent_folder = os.path.dirname(output_dir)
if trainer.args.local_rank == 0 or trainer.args.local_rank == -1:
if current_folder.startswith('checkpoint-'):
mm_projector_folder = os.path.join(parent_folder, "mm_projector")
os.makedirs(mm_projector_folder, exist_ok=True)
torch.save(weight_to_save, os.path.join(mm_projector_folder, f'{current_folder}.bin'))
else:
torch.save(weight_to_save, os.path.join(output_dir, f'mm_projector.bin'))
return
if trainer.deepspeed:
torch.cuda.synchronize()
trainer.save_model(output_dir)
return
state_dict = trainer.model.state_dict()
if trainer.args.should_save:
cpu_state_dict = {
key: value.cpu()
for key, value in state_dict.items()
}
del state_dict
trainer._save(output_dir, state_dict=cpu_state_dict) # noqa
def smart_tokenizer_and_embedding_resize(
special_tokens_dict: Dict,
tokenizer: transformers.PreTrainedTokenizer,
model: transformers.PreTrainedModel,
):
"""Resize tokenizer and embedding.
Note: This is the unoptimized version that may make your embedding size not be divisible by 64.
"""
num_new_tokens = tokenizer.add_special_tokens(special_tokens_dict)
model.resize_token_embeddings(len(tokenizer))
if num_new_tokens > 0:
input_embeddings = model.get_input_embeddings().weight.data
output_embeddings = model.get_output_embeddings().weight.data
input_embeddings_avg = input_embeddings[:-num_new_tokens].mean(
dim=0, keepdim=True)
output_embeddings_avg = output_embeddings[:-num_new_tokens].mean(
dim=0, keepdim=True)
input_embeddings[-num_new_tokens:] = input_embeddings_avg
output_embeddings[-num_new_tokens:] = output_embeddings_avg
def _tokenize_fn(strings: Sequence[str],
tokenizer: transformers.PreTrainedTokenizer) -> Dict:
"""Tokenize a list of strings."""
tokenized_list = [
tokenizer(
text,
return_tensors="pt",
padding="longest",
max_length=tokenizer.model_max_length,
truncation=True,
) for text in strings
]
input_ids = labels = [
tokenized.input_ids[0] for tokenized in tokenized_list
]
input_ids_lens = labels_lens = [
tokenized.input_ids.ne(tokenizer.pad_token_id).sum().item()
for tokenized in tokenized_list
]
return dict(
input_ids=input_ids,
labels=labels,
input_ids_lens=input_ids_lens,
labels_lens=labels_lens,
)
def _mask_targets(target, tokenized_lens, speakers):
# cur_idx = 0
cur_idx = tokenized_lens[0]
tokenized_lens = tokenized_lens[1:]
target[:cur_idx] = IGNORE_INDEX
for tokenized_len, speaker in zip(tokenized_lens, speakers):
if speaker == "human":
target[cur_idx+2:cur_idx + tokenized_len] = IGNORE_INDEX
cur_idx += tokenized_len
def _add_speaker_and_signal(header, source, get_conversation=True):
"""Add speaker and start/end signal on each round."""
BEGIN_SIGNAL = "### "
END_SIGNAL = "\n"
conversation = header
for sentence in source:
from_str = sentence["from"]
if from_str.lower() == "human":
from_str = conversation_lib.default_conversation.roles[0]
elif from_str.lower() == "gpt":
from_str = conversation_lib.default_conversation.roles[1]
else:
from_str = 'unknown'
sentence["value"] = (BEGIN_SIGNAL + from_str + ": " +
sentence["value"] + END_SIGNAL)
if get_conversation:
conversation += sentence["value"]
conversation += BEGIN_SIGNAL
return conversation
def preprocess_multimodal(
sources: Sequence[str],
data_args: DataArguments,
) -> Dict:
is_multimodal = data_args.is_multimodal
if not is_multimodal:
return sources
for source in sources:
for sentence in source:
if DEFAULT_IMAGE_TOKEN in sentence['value'] and not sentence['value'].startswith(DEFAULT_IMAGE_TOKEN):
sentence['value'] = sentence['value'].replace(DEFAULT_IMAGE_TOKEN, '').strip()
sentence['value'] = DEFAULT_IMAGE_TOKEN + '\n' + sentence['value']
sentence['value'] = sentence['value'].strip()
if "mmtag" in conversation_lib.default_conversation.version:
sentence['value'] = sentence['value'].replace(DEFAULT_IMAGE_TOKEN, '' + DEFAULT_IMAGE_TOKEN + '')
replace_token = DEFAULT_IMAGE_TOKEN
if data_args.mm_use_im_start_end:
replace_token = DEFAULT_IM_START_TOKEN + replace_token + DEFAULT_IM_END_TOKEN
sentence["value"] = sentence["value"].replace(DEFAULT_IMAGE_TOKEN, replace_token)
return sources
def preprocess_multimodal_movie(
sources: Sequence[str],
data_args: DataArguments,
video_inputs: str
) -> Dict:
is_multimodal = data_args.is_multimodal
if not is_multimodal:
return sources
for source in sources:
for sentence in source:
if DEFAULT_IMAGE_TOKEN in sentence['value']:
prompt = sentence['value'].replace(DEFAULT_IMAGE_TOKEN, '').strip()
replace_token = video_inputs
if data_args.mm_use_im_start_end:
replace_token = DEFAULT_IM_START_TOKEN + replace_token + DEFAULT_IM_END_TOKEN
sentence["value"] = sentence["value"].replace(DEFAULT_IMAGE_TOKEN, replace_token)
return sources, prompt
def preprocess_qwen(sources, tokenizer: transformers.PreTrainedTokenizer, has_image: bool = False, max_len=2048, system_message: str = "You are a helpful assistant.") -> Dict:
roles = {"human": "<|im_start|>user", "gpt": "<|im_start|>assistant"}
# im_start, im_end = tokenizer.additional_special_tokens_ids
im_start = tokenizer("<|im_start|>").input_ids[0]
im_end = tokenizer("<|im_end|>").input_ids[0]
nl_tokens = tokenizer("\n").input_ids
_system = tokenizer("system").input_ids + nl_tokens
# Apply prompt templates
input_ids, targets = [], []
for i, source in enumerate(sources):
if roles[source[0]["from"]] != roles["human"]:
source = source[1:]
input_id, target = [], []
system = [im_start] + _system + tokenizer(system_message).input_ids + [im_end] + nl_tokens
input_id += system
target += [im_start] + [IGNORE_INDEX] * (len(system) - 3) + [im_end] + nl_tokens
assert len(input_id) == len(target)
for j, sentence in enumerate(source):
role = roles[sentence["from"]]
if has_image and "" in sentence["value"]:
# assert sentence["value"].startswith(""), print(sentence["value"])
if sentence["value"].startswith(""):
_input_id = tokenizer(role).input_ids + nl_tokens + [IMAGE_TOKEN_INDEX] + nl_tokens + tokenizer(sentence["value"][len("") :]).input_ids + [im_end] + nl_tokens
else:
_input_id = []
split_value = sentence["value"].split('\n')
_input_id += tokenizer(role).input_ids + nl_tokens
for idx, cur_value in enumerate(split_value):
if idx == len(split_value) - 1:
_input_id = _input_id + tokenizer(cur_value).input_ids + [im_end] + nl_tokens
else:
_input_id = _input_id + tokenizer(cur_value).input_ids + [IMAGE_TOKEN_INDEX] + nl_tokens
# # add end of text token
# if PACK_SEQ > 0:
# if j > 0:
# _input_id = _end_of_text + _input_id
else:
_input_id = tokenizer(role).input_ids + nl_tokens + tokenizer(sentence["value"]).input_ids + [im_end] + nl_tokens
# # add end of text token for pure text data
# if PACK_SEQ > 0:
# if sentence['from'] == 'human' and j > 0:
# _input_id = _end_of_text + _input_id
input_id += _input_id
if role == "<|im_start|>user":
_target = [im_start] + [IGNORE_INDEX] * (len(_input_id) - 3) + [im_end] + nl_tokens
elif role == "<|im_start|>assistant":
_target = [im_start] + [IGNORE_INDEX] * len(tokenizer(role).input_ids) + _input_id[len(tokenizer(role).input_ids) + 1 : -2] + [im_end] + nl_tokens
else:
raise NotImplementedError
target += _target
assert len(input_id) == len(target)
# input_id += [tokenizer.pad_token_id] * (max_len - len(input_id))
# target += [IGNORE_INDEX] * (max_len - len(target))
input_ids.append(input_id)
targets.append(target)
input_ids = torch.tensor(input_ids, dtype=torch.long)
targets = torch.tensor(targets, dtype=torch.long)
return dict(
input_ids=input_ids, # tensor(bs x seq_len)
labels=targets, # tensor(bs x seq_len)
# attention_mask=input_ids.ne(tokenizer.pad_token_id), # tensor(bs x seq_len)
)
def preprocess_llama_2(
sources,
tokenizer: transformers.PreTrainedTokenizer,
has_image: bool = False
) -> Dict:
conv = conversation_lib.default_conversation.copy()
roles = {"human": conv.roles[0], "gpt": conv.roles[1]}
# Apply prompt templates
conversations = []
for i, source in enumerate(sources):
if roles[source[0]["from"]] != conv.roles[0]:
# Skip the first one if it is not from human
source = source[1:]
conv.messages = []
for j, sentence in enumerate(source):
role = roles[sentence["from"]]
assert role == conv.roles[j % 2], f"{i}"
conv.append_message(role, sentence["value"])
conversations.append(conv.get_prompt())
# Tokenize conversations
if has_image:
input_ids = torch.stack([tokenizer_image_token(prompt, tokenizer, return_tensors='pt') for prompt in conversations], dim=0)
else:
input_ids = tokenizer(
conversations,
return_tensors="pt",
padding="longest",
max_length=tokenizer.model_max_length,
truncation=True,
).input_ids
targets = input_ids.clone()
assert conv.sep_style == conversation_lib.SeparatorStyle.LLAMA_2
# Mask targets
sep = "[/INST] "
for conversation, target in zip(conversations, targets):
total_len = int(target.ne(tokenizer.pad_token_id).sum())
rounds = conversation.split(conv.sep2)
cur_len = 1
target[:cur_len] = IGNORE_INDEX
for i, rou in enumerate(rounds):
if rou == "":
break
parts = rou.split(sep)
if len(parts) != 2:
break
parts[0] += sep
if has_image:
round_len = len(tokenizer_image_token(rou, tokenizer))
instruction_len = len(tokenizer_image_token(parts[0], tokenizer)) - 2
else:
round_len = len(tokenizer(rou).input_ids)
instruction_len = len(tokenizer(parts[0]).input_ids) - 2
target[cur_len : cur_len + instruction_len] = IGNORE_INDEX
cur_len += round_len
target[cur_len:] = IGNORE_INDEX
if cur_len < tokenizer.model_max_length:
if cur_len != total_len:
target[:] = IGNORE_INDEX
print(
f"WARNING: tokenization mismatch: {cur_len} vs. {total_len}."
f" (ignored)"
)
return dict(
input_ids=input_ids,
labels=targets,
)
def preprocess_llama_3(
sources,
tokenizer: transformers.PreTrainedTokenizer,
has_image: bool = False
) -> Dict:
conv = copy.deepcopy(conversation_lib.conv_llava_llama_3)
roles = {"human": conv.roles[0], "gpt": conv.roles[1]}
# Apply prompt templates
conversations = []
for i, source in enumerate(sources):
if roles[source[0]["from"]] != conv.roles[0]:
# Skip the first one if it is not from human
source = source[1:]
conv.messages = []
for j, sentence in enumerate(source):
role = roles[sentence["from"]]
assert role == conv.roles[j % 2], f"{i}"
conv.append_message(role, sentence["value"])
conversations.append(conv.get_prompt())
# Tokenize conversations
if has_image:
input_ids = torch.stack([tokenizer_image_token(prompt, tokenizer, return_tensors='pt') for prompt in conversations], dim=0)
else:
input_ids = tokenizer(
conversations,
return_tensors="pt",
padding="longest",
max_length=tokenizer.model_max_length,
truncation=True,
).input_ids
targets = input_ids.clone()
offset = 0 if input_ids[0][0] != tokenizer.bos_token_id else 1
assert conv.sep_style == conversation_lib.SeparatorStyle.LLAMA_3
# Mask targets
# sep = conv.sep + conv.roles[1] + ":"
sep = '<|start_header_id|>assistant<|end_header_id|>\n\n'
sep2 = '<|start_header_id|>user<|end_header_id|>\n\n'
# Llama3 tokenizer has the token for whitespace
# Typically, the token after whitespace will be naturally encoded as one token with whitespace
# some special cases like ": 3" will be encoded as :, whitespace, 3; 3 tokens. Only in this case, the loss on whitespace will be calculated
for conversation, target in zip(conversations, targets):
total_len = int(target.ne(tokenizer.pad_token_id).sum())
rounds = conversation.split(sep2)
cur_len = 1
target[:cur_len] = IGNORE_INDEX
# process system prompt
try:
rounds[1] = rounds[0] + sep2 + rounds[1]
del rounds[0]
except:
print('no user found')
raise ValueError
# add user
for i, rou in enumerate(rounds):
if i != 0:
rounds[i] = sep2 + rou
for i, rou in enumerate(rounds):
if rou == "":
break
parts = rou.split(sep)
if len(parts) != 2:
break
# parts[0] += sep
# supervise assistant: from pp's report
parts[1] = sep + parts[1]
# parts[0] = parts[0] + sep
if has_image:
round_len = len(tokenizer_image_token(rou, tokenizer)) - offset
instruction_len = len(tokenizer_image_token(parts[0], tokenizer))
else:
round_len = len(tokenizer(rou).input_ids) - offset
instruction_len = len(tokenizer(parts[0]).input_ids)
target[cur_len : cur_len + instruction_len] = IGNORE_INDEX
cur_len += round_len + (1 - offset) #starting from index 0, then cur_len will not cover eos token
if cur_len < tokenizer.model_max_length:
if cur_len != total_len:
target[:] = IGNORE_INDEX
print(
f"WARNING: tokenization mismatch: {cur_len} vs. {total_len}."
f" (ignored)"
)
if input_ids[0][0] != tokenizer.bos_token_id:
input_ids = [torch.cat([torch.LongTensor([tokenizer.bos_token_id]), i]) for i in input_ids]
targets = [torch.cat([torch.LongTensor([IGNORE_INDEX]), i]) for i in targets]
return dict(
input_ids=input_ids,
labels=targets,
)
def preprocess_v1(
sources,
tokenizer: transformers.PreTrainedTokenizer,
has_image: bool = False
) -> Dict:
conv = conversation_lib.default_conversation.copy()
roles = {"human": conv.roles[0], "gpt": conv.roles[1]}
# Apply prompt templates
conversations = []
for i, source in enumerate(sources):
if roles[source[0]["from"]] != conv.roles[0]:
# Skip the first one if it is not from human
source = source[1:]
conv.messages = []
for j, sentence in enumerate(source):
role = roles[sentence["from"]]
assert role == conv.roles[j % 2], f"{i}"
conv.append_message(role, sentence["value"])
conversations.append(conv.get_prompt())
# Tokenize conversations
if has_image:
input_ids = torch.stack([tokenizer_image_token(prompt, tokenizer, return_tensors='pt') for prompt in conversations], dim=0)
else:
input_ids = tokenizer(
conversations,
return_tensors="pt",
padding="longest",
max_length=tokenizer.model_max_length,
truncation=True,
).input_ids
targets = input_ids.clone()
if conv.sep_style == conversation_lib.SeparatorStyle.TWO:
# Mask targets
sep = conv.sep + conv.roles[1] + ": "
for conversation, target in zip(conversations, targets):
total_len = int(target.ne(tokenizer.pad_token_id).sum())
rounds = conversation.split(conv.sep2)
cur_len = 1
target[:cur_len] = IGNORE_INDEX
for i, rou in enumerate(rounds):
if rou == "":
break
parts = rou.split(sep)
if len(parts) != 2:
break
parts[0] += sep
if has_image:
round_len = len(tokenizer_image_token(rou, tokenizer))
instruction_len = len(tokenizer_image_token(parts[0], tokenizer)) - 2
else:
round_len = len(tokenizer(rou).input_ids)
instruction_len = len(tokenizer(parts[0]).input_ids) - 2
if i != 0 and not tokenizer.legacy and IS_TOKENIZER_GREATER_THAN_0_14:
round_len -= 1
instruction_len -= 1
target[cur_len : cur_len + instruction_len] = IGNORE_INDEX
cur_len += round_len
target[cur_len:] = IGNORE_INDEX
if cur_len < tokenizer.model_max_length:
if cur_len != total_len:
target[:] = IGNORE_INDEX
print(
f"WARNING: tokenization mismatch: {cur_len} vs. {total_len}."
f" (ignored)"
)
elif conv.sep_style == conversation_lib.SeparatorStyle.QWEN2:
# Mask targets
sep = '<|im_start|>assistant\n'
for conversation, target in zip(conversations, targets):
total_len = int(target.ne(tokenizer.pad_token_id).sum())
raw_rounds = conversation.split('<|im_end|>\n')
cur_len = 0
rounds = []
now_str = ''
for rou in raw_rounds:
if len(rou) > 0:
rou = rou + '<|im_end|>\n'
if rou.startswith('<|endoftext|>'):
rounds[-1] = rounds[-1] + '<|endoftext|>'
rou = rou.replace('<|endoftext|>', '')
if len(rou.strip()) == 0:
continue
if '<|im_start|>assistant\n' in rou:
now_str += rou
rounds.append(now_str)
now_str = ''
else:
now_str += rou
for i, rou in enumerate(rounds):
if rou == "":
break
parts = rou.split(sep)
if len(parts) != 2:
break
parts[0] += sep
if has_image:
round_len = len(tokenizer_image_token(rou, tokenizer))
instruction_len = len(tokenizer_image_token(parts[0], tokenizer)) - 2
else:
round_len = len(tokenizer(rou).input_ids)
instruction_len = len(tokenizer(parts[0]).input_ids) - 2
try:
is_legacy = tokenizer.legacy
except:
is_legacy = True
if i != 0 and not is_legacy and IS_TOKENIZER_GREATER_THAN_0_14:
round_len -= 1
instruction_len -= 1
target[cur_len : cur_len + instruction_len] = IGNORE_INDEX
cur_len += round_len
target[cur_len:] = IGNORE_INDEX
if cur_len < tokenizer.model_max_length:
if cur_len != total_len:
target[:] = IGNORE_INDEX
print(
f"WARNING: tokenization mismatch for QWEN2: {cur_len} vs. {total_len}."
f" (ignored)"
)
return dict(
input_ids=input_ids,
labels=targets,
)
def preprocess_imgsp_v1(
sources,
tokenizer: transformers.PreTrainedTokenizer,
has_image: bool = False,
img_token: str = '',
refine_prompt: bool = False,
) -> Dict:
conv = conversation_lib.default_conversation.copy()
roles = {"human": conv.roles[0], "gpt": conv.roles[1]}
# Apply prompt templates
conversations = []
guided_prompt = []
for i, source in enumerate(sources):
if roles[source[0]["from"]] != conv.roles[0]:
# Skip the first one if it is not from human
source = source[1:]
conv.messages = []
img_in_text = False
for j, sentence in enumerate(source):
role = roles[sentence["from"]]
assert role == conv.roles[j % 2], f"{i}"
# add guided prompt
if role==conv.roles[0]:
guided_sent = sentence["value"].replace(DEFAULT_IMAGE_TOKEN, '').replace('\n', '')
if refine_prompt:
# only keep the useful part of the prompt
if '\n' in guided_sent:
for _sent in guided_sent.split('\n'):
if '?' in _sent:
guided_sent = _sent
break
guided_prompt.append(guided_sent)
# check if image token in text
if img_token in sentence["value"]:
img_in_text = True
# add image token to all sentence if multimoal input
if role==conv.roles[0] and img_in_text and img_token not in sentence["value"]:
# randomly add image token to the beginning or end of the sentence
if random.randint(0,1)==0:
img_conv = img_token + '\n' + sentence["value"]
else:
img_conv = sentence["value"] + '\n' + img_token
conv.append_message(role, img_conv)
else:
conv.append_message(role, sentence["value"])
conversations.append(conv.get_prompt())
# Tokenize conversations
if has_image:
input_ids = torch.stack([tokenizer_image_token(prompt, tokenizer, return_tensors='pt') for prompt in conversations], dim=0)
else:
input_ids = tokenizer(
conversations,
return_tensors="pt",
padding="longest",
max_length=tokenizer.model_max_length,
truncation=True,
).input_ids
targets = input_ids.clone()
assert conv.sep_style == conversation_lib.SeparatorStyle.TWO
# Mask targets
sep = conv.sep + conv.roles[1] + ": "
for conversation, target in zip(conversations, targets):
total_len = int(target.ne(tokenizer.pad_token_id).sum())
rounds = conversation.split(conv.sep2)
cur_len = 1
target[:cur_len] = IGNORE_INDEX
for i, rou in enumerate(rounds):
if rou == "":
break
parts = rou.split(sep)
if len(parts) != 2:
break
parts[0] += sep
if has_image:
round_len = len(tokenizer_image_token(rou, tokenizer))
instruction_len = len(tokenizer_image_token(parts[0], tokenizer)) - 2
else:
round_len = len(tokenizer(rou).input_ids)
instruction_len = len(tokenizer(parts[0]).input_ids) - 2
target[cur_len : cur_len + instruction_len] = IGNORE_INDEX
cur_len += round_len
target[cur_len:] = IGNORE_INDEX
if cur_len < tokenizer.model_max_length:
if cur_len != total_len:
target[:] = IGNORE_INDEX
print(
f"WARNING: tokenization mismatch: {cur_len} vs. {total_len}."
f" (ignored)"
)
return dict(
input_ids=input_ids,
labels=targets,
prompt=guided_prompt,
)
def preprocess_mpt(
sources,
tokenizer: transformers.PreTrainedTokenizer,
has_image: bool = False
) -> Dict:
conv = conversation_lib.default_conversation.copy()
roles = {"human": conv.roles[0], "gpt": conv.roles[1]}
# Apply prompt templates
conversations = []
for i, source in enumerate(sources):
if roles[source[0]["from"]] != conv.roles[0]:
# Skip the first one if it is not from human
source = source[1:]
conv.messages = []
for j, sentence in enumerate(source):
role = roles[sentence["from"]]
assert role == conv.roles[j % 2], f"{i}"
conv.append_message(role, sentence["value"])
conversations.append(conv.get_prompt())
# Tokenize conversations
if has_image:
input_ids = torch.stack([tokenizer_image_token(prompt, tokenizer, return_tensors='pt') for prompt in conversations], dim=0)
else:
input_ids = tokenizer(
conversations,
return_tensors="pt",
padding="longest",
max_length=tokenizer.model_max_length,
truncation=True,
).input_ids
targets = input_ids.clone()
assert conv.sep_style == conversation_lib.SeparatorStyle.MPT
# Mask targets
sep = conv.sep + conv.roles[1]
for conversation, target in zip(conversations, targets):
total_len = int(target.ne(tokenizer.pad_token_id).sum())
rounds = conversation.split(conv.sep)
re_rounds = [conv.sep.join(rounds[:3])] # system + user + gpt
for conv_idx in range(3, len(rounds), 2):
re_rounds.append(conv.sep.join(rounds[conv_idx:conv_idx+2])) # user + gpt
cur_len = 1
target[:cur_len] = IGNORE_INDEX
for i, rou in enumerate(re_rounds):
if rou == "":
break
parts = rou.split(sep)
if len(parts) != 2:
break
parts[0] += sep
if has_image:
round_len = len(tokenizer_image_token(rou, tokenizer))
instruction_len = len(tokenizer_image_token(parts[0], tokenizer)) - 1
else:
round_len = len(tokenizer(rou).input_ids)
instruction_len = len(tokenizer(parts[0]).input_ids) - 1
if i != 0 and getattr(tokenizer, 'legacy', False) and IS_TOKENIZER_GREATER_THAN_0_14:
round_len += 1
instruction_len += 1
target[cur_len : cur_len + instruction_len] = IGNORE_INDEX
cur_len += round_len
target[cur_len:] = IGNORE_INDEX
if cur_len < tokenizer.model_max_length:
if cur_len != total_len:
target[:] = IGNORE_INDEX
print(
f"WARNING: tokenization mismatch: {cur_len} vs. {total_len}."
f"(#turns={len(re_rounds)} ignored)"
)
return dict(
input_ids=input_ids,
labels=targets,
)
def preprocess_plain(
sources: Sequence[str],
tokenizer: transformers.PreTrainedTokenizer,
) -> Dict:
# add end signal and concatenate together
conversations = []
for source in sources:
assert len(source) == 2
assert DEFAULT_IMAGE_TOKEN in source[0]['value']
source[0]['value'] = DEFAULT_IMAGE_TOKEN
conversation = source[0]['value'] + source[1]['value'] + conversation_lib.default_conversation.sep
conversations.append(conversation)
# tokenize conversations
input_ids = [tokenizer_image_token(prompt, tokenizer, return_tensors='pt') for prompt in conversations]
targets = copy.deepcopy(input_ids)
for target, source in zip(targets, sources):
tokenized_len = len(tokenizer_image_token(source[0]['value'], tokenizer))
target[:tokenized_len] = IGNORE_INDEX
return dict(input_ids=input_ids, labels=targets)
def preprocess_plain_guided(
sources: Sequence[str],
tokenizer: transformers.PreTrainedTokenizer,
prompt: str = None,
) -> Dict:
# add end signal and concatenate together
guided_prompt = []
conversations = []
for source in sources:
assert len(source) == 2
assert DEFAULT_IMAGE_TOKEN in source[0]['value']
guided_prompt.append(source[0]['value'].replace(DEFAULT_IMAGE_TOKEN, '').replace('\n', ''))
source[0]['value'] = DEFAULT_IMAGE_TOKEN
conversation = source[0]['value'] + source[1]['value'] + conversation_lib.default_conversation.sep
conversations.append(conversation)
# tokenize conversations
input_ids = [tokenizer_image_token(prompt, tokenizer, return_tensors='pt') for prompt in conversations]
targets = copy.deepcopy(input_ids)
for target, source in zip(targets, sources):
tokenized_len = len(tokenizer_image_token(source[0]['value'], tokenizer))
target[:tokenized_len] = IGNORE_INDEX
def preprocess(
sources: Sequence[str],
tokenizer: transformers.PreTrainedTokenizer,
has_image: bool = False,
) -> Dict:
"""
Given a list of sources, each is a conversation list. This transform:
1. Add signal '### ' at the beginning each sentence, with end signal '\n';
2. Concatenate conversations together;
3. Tokenize the concatenated conversation;
4. Make a deepcopy as the target. Mask human words with IGNORE_INDEX.
"""
if conversation_lib.default_conversation.version.startswith("plain_guided"):
return preprocess_plain_guided(sources, tokenizer)
elif conversation_lib.default_conversation.sep_style == conversation_lib.SeparatorStyle.PLAIN:
return preprocess_plain(sources, tokenizer)
if conversation_lib.default_conversation.sep_style == conversation_lib.SeparatorStyle.LLAMA_2:
return preprocess_llama_2(sources, tokenizer, has_image=has_image)
if conversation_lib.default_conversation.version.startswith("v1"):
return preprocess_v1(sources, tokenizer, has_image=has_image)
if conversation_lib.default_conversation.version.startswith("llama_v3"): # for llama 3 tokenizer
return preprocess_llama_3(sources, tokenizer, has_image=has_image)
if conversation_lib.default_conversation.version == "qwen":
return preprocess_qwen(sources, tokenizer, has_image=has_image)
elif conversation_lib.default_conversation.version.startswith("imgsp"):
return preprocess_imgsp_v1(sources, tokenizer, has_image=has_image)
if conversation_lib.default_conversation.version == "mpt":
return preprocess_mpt(sources, tokenizer, has_image=has_image)
# add end signal and concatenate together
conversations = []
for source in sources:
header = f"{conversation_lib.default_conversation.system}\n\n"
conversation = _add_speaker_and_signal(header, source)
conversations.append(conversation)
# tokenize conversations
def get_tokenize_len(prompts):
return [len(tokenizer_image_token(prompt, tokenizer)) for prompt in prompts]
if has_image:
input_ids = [tokenizer_image_token(prompt, tokenizer, return_tensors='pt') for prompt in conversations]
else:
conversations_tokenized = _tokenize_fn(conversations, tokenizer)
input_ids = conversations_tokenized["input_ids"]
targets = copy.deepcopy(input_ids)
for target, source in zip(targets, sources):
if has_image:
tokenized_lens = get_tokenize_len([header] + [s["value"] for s in source])
else:
tokenized_lens = _tokenize_fn([header] + [s["value"] for s in source], tokenizer)["input_ids_lens"]
speakers = [sentence["from"] for sentence in source]
_mask_targets(target, tokenized_lens, speakers)
return dict(input_ids=input_ids, labels=targets)
def read_image_patch(patch_info):
if 'img_path' in patch_info.keys():
image = Image.open(patch_info['img_path']).convert('RGB')
else:
image_file_name = patch_info['patch']
start_bytes = int(patch_info['start_num'])
file_size = int(patch_info['size'])
with open(image_file_name, 'rb') as f:
f.seek(start_bytes)
if 'image_encoding' in patch_info.keys() and patch_info['image_encoding'] == 'base64':
image = Image.open(io.BytesIO(base64.b64decode(f.read(file_size).decode()))).convert("RGB")
else:
image = Image.open(io.BytesIO(f.read(file_size))).convert("RGB")
return image
def read_video_patch(patch_info):
if 'img_path' in patch_info.keys():
image = Image.open(patch_info['img_path']).convert('RGB')
else:
image_file_name = patch_info['patch']
start_bytes = int(patch_info['start_num'])
file_size = patch_info['size'] # list of int
total_file_size = 0
images_all = []
with open(image_file_name, 'rb') as f:
for idx in range(len(file_size)):
f.seek(start_bytes + total_file_size)
if 'image_encoding' in patch_info.keys() and patch_info['image_encoding'] == 'base64':
image = Image.open(io.BytesIO(base64.b64decode(f.read(int(file_size[idx])).decode()))).convert("RGB")
else:
if 'sharegpt4o' in image_file_name or 'ShareGPT4Video/new_patch' in image_file_name or 'cinepile' in image_file_name or 'nextqa' in image_file_name or 'perceptiontest' in image_file_name:
byte_str = io.BytesIO(f.read(int(file_size[idx])))
array = np.frombuffer(byte_str.getvalue(), dtype=np.uint8)
image = cv2.imdecode(array, cv2.IMREAD_COLOR)
image = Image.fromarray(image)
else:
image = Image.open(io.BytesIO(f.read(int(file_size[idx])))).convert("RGB")
images_all.append(image)
total_file_size += int(file_size[idx])
return images_all
class LazySupervisedDataset(Dataset):
"""Dataset for supervised fine-tuning."""
def __init__(self, data_path: str,
tokenizer: transformers.PreTrainedTokenizer,
data_args: DataArguments):
super(LazySupervisedDataset, self).__init__()
list_data_dict = json.load(open(data_path, "r"))
rank0_print("Formatting inputs...Skip in lazy mode")
self.tokenizer = tokenizer
self.list_data_dict = list_data_dict
self.data_args = data_args
# if PRETRAIN:
self.mapping_dict = json.load(open('/apdcephfs_jn/share_302244400/peterrao/nj3/data/llava/videodata/MovieNet/movienet_mapping.json', "r"))
print('loadding mapping dict')
def __len__(self):
return len(self.list_data_dict)
@property
def lengths(self):
length_list = []
for sample in self.list_data_dict:
img_tokens = 128 if 'image' in sample else 0
length_list.append(sum(len(conv['value'].split()) for conv in sample['conversations']) + img_tokens)
return length_list
@property
def modality_lengths(self):
length_list = []
for sample in self.list_data_dict:
try:
cur_len = sum(len(conv['value'].split()) for conv in sample['conversations'])
except:
cur_len = 1
cur_len = cur_len if ('image' in sample) or ('video' in sample) or ('video_long' in sample) else -cur_len
length_list.append(cur_len)
return length_list
def process_image(self, image_file):
if type(image_file) is str:
image = Image.open(image_file).convert('RGB')
elif type(image_file) is dict:
image = read_image_patch(image_file)
else:
raise ValueError(f"Unknown image file type: {type(image_file)}, {image_file}")
image_size = image.size
image, image_padded = process_anyres_highres_image_genli(image, self.data_args.image_processor)
return (image, image_padded), image_size, "image"
def process_video(self, video_file):
video = read_video_patch(video_file)
video_processed = []
cur_frames_upbound = self.data_args.frames_upbound
if cur_frames_upbound > 0:
if len(video) > cur_frames_upbound:
uniform_sampled_frames = np.linspace(0, len(video) - 1, cur_frames_upbound, dtype=int)
frame_idx = uniform_sampled_frames.tolist()
else:
frame_idx = None
for idx, frame in enumerate(video):
frame = process_anyres_video_genli(frame, self.data_args.image_processor)
if frame_idx is not None and idx in frame_idx:
video_processed.append(frame.unsqueeze(0))
elif frame_idx is None:
video_processed.append(frame.unsqueeze(0))
if frame_idx is None:
frame_idx = np.arange(0, len(video_processed), dtype=int).tolist()
video_processed = torch.cat(video_processed, dim=0)
video_processed = (video_processed, video_processed)
return (video_processed, (384, 384), "video"), frame_idx
def process_video_pretrain(self, video_file, target_idx):
video = read_video_patch(video_file)
cur_frames_upbound = random.randint(self.data_args.frames_upbound * 3, self.data_args.frames_upbound * 4)
video_processed = []
if cur_frames_upbound > 0:
if len(video) > cur_frames_upbound:
uniform_sampled_frames = np.linspace(0, len(video) - 1, cur_frames_upbound, dtype=int)
frame_idx = uniform_sampled_frames.tolist()
# process longer case
target_idx_new = []
target_frame = []
if len(target_idx) == 1:
target_idx_new.append(np.random.randint(0, len(uniform_sampled_frames)))
target_frame.append(video[target_idx[0]])
elif len(target_idx) == 2:
num1 = np.random.randint(0, len(uniform_sampled_frames) // 2)
num2 = np.random.randint(num1 + 1, len(uniform_sampled_frames))
target_idx_new.append(num1)
target_idx_new.append(num2)
target_frame.append(video[target_idx[0]])
target_frame.append(video[target_idx[1]])
else:
frame_idx = None
target_idx_new = target_idx
target_frame = None
for idx, frame in enumerate(video):
frame = process_anyres_video_genli_long(frame, self.data_args.image_processor)
if frame_idx is not None and idx in frame_idx:
video_processed.append(frame.unsqueeze(0))
elif frame_idx is None:
video_processed.append(frame.unsqueeze(0))
# process longer case
if target_frame is not None:
for idx in target_idx_new:
frame = target_frame.pop(0)
frame = process_anyres_video_genli_long(frame, self.data_args.image_processor)
video_processed[idx] = frame.unsqueeze(0)
if frame_idx is None:
frame_idx = np.arange(0, len(video_processed), dtype=int).tolist()
video_processed = torch.cat(video_processed, dim=0)
video_processed = (video_processed, video_processed)
return (video_processed, (384, 384), "video_long"), target_idx_new
def __getitem__(self, i) -> Dict[str, torch.Tensor]:
# TODO: define number of retries somewhere else
num_base_retries = 3
num_final_retries = 300
# try the current sample first
for attempt_idx in range(num_base_retries):
try:
sample = self._get_item(i)
return sample
except Exception as e:
# sleep 1s in case it is a cloud disk issue
print(f'[try #{attempt_idx}] Failed to fetch sample {i}. Exception:', e)
time.sleep(1)
# try other samples, in case it is file corruption issue
for attempt_idx in range(num_base_retries):
try:
sample_idx = random.choice(range(len(self)))
sample = self._get_item(sample_idx)
return sample
except Exception as e:
# no need to sleep
print(f'[try other #{attempt_idx}] Failed to fetch sample {sample_idx}. Exception:', e)
pass
# still fail, most likely to be path issue or cloud disk issue, retry the same sample for longer
for attempt_idx in range(num_final_retries):
try:
sample = self._get_item(i)
return sample
except Exception as e:
# sleep 1s in case it is a cloud disk issue
print(f'[final try #{attempt_idx}] Failed to fetch sample {i}. Exception:', e)
time.sleep(1)
# Finally raise exception on failing.
assert False, "Failed to fetch sample."
def _get_item(self, i) -> Dict[str, torch.Tensor]:
sources = self.list_data_dict[i]
if isinstance(i, int):
sources = [sources]
assert len(sources) == 1, "Don't know why it is wrapped to a list" # FIXME
if 'image' in sources[0]:
image_file = self.list_data_dict[i]['image']
if type(image_file) is list:
image = [self.process_image(f) for f in image_file]
else:
image = [self.process_image(image_file)]
num_frames = 0
sources = preprocess_multimodal(
copy.deepcopy([e["conversations"] for e in sources]),
self.data_args
)
elif 'video' in sources[0]:
video_file = self.list_data_dict[i]['video']
video, _ = self.process_video(video_file)
video = [video]
num_frames = len(video[0][0])
sources = preprocess_multimodal(
copy.deepcopy([e["conversations"] for e in sources]),
self.data_args)
elif 'video_long' in sources[0]:
video_file = self.mapping_dict[self.list_data_dict[i]['video_long']]['video']
video, target_idx = self.process_video_pretrain(video_file, self.list_data_dict[i]['idx'])
video = [video]
num_frames = len(video[0][0][0])
question = sources[0]['question']
answer = sources[0]['answer']
if sources[0]['type'] == 'diff':
question = question.replace('', str(target_idx[0]))
question = question.replace('', str(target_idx[1]))
elif sources[0]['type'] == 'caption':
question = question.replace('', str(target_idx[0]))
else:
raise NotImplementedError
sources[0]['conversations'] = [{'from': 'human', 'value': f'\nThis is a extremely long video with a total of {num_frames} frames sampled from the video. Please carefully read every given frame in this video, identifying the detailed contents in every frame. '+ question},
{'from': 'gpt', 'value': answer}]
sources = preprocess_multimodal(
copy.deepcopy([e["conversations"] for e in sources]),
self.data_args)
else:
sources = copy.deepcopy([e["conversations"] for e in sources])
has_image = ('image' in self.list_data_dict[i]) or ('video' in self.list_data_dict[i]) or ('video_long' in self.list_data_dict[i])
data_dict = preprocess(
sources,
self.tokenizer,
has_image=has_image)
if isinstance(i, int):
data_dict = dict(input_ids=data_dict["input_ids"][0],
labels=data_dict["labels"][0])
# image exist in the data
if 'image' in self.list_data_dict[i]:
data_dict['image'] = image
elif 'video' in self.list_data_dict[i]:
data_dict['image'] = video
elif 'video_long' in self.list_data_dict[i]:
data_dict['image'] = video
elif self.data_args.is_multimodal:
# image does not exist in the data, but the model is multimodal
crop_size = self.data_args.image_processor.crop_size
data_dict['image'] = [
(
(torch.zeros(1, 3, crop_size['height'], crop_size['width']), torch.zeros(1, 3, crop_size['height'], crop_size['width'])),
(crop_size['width'], crop_size['height']),
"text"
),
]
return data_dict
@dataclass
class DataCollatorForSupervisedDataset(object):
"""Collate examples for supervised fine-tuning."""
tokenizer: transformers.PreTrainedTokenizer
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
def __call__(self, instances: Sequence[Dict]) -> Dict[str, torch.Tensor]:
# input_ids, labels = tuple([instance[key] for instance in instances]
# for key in ("input_ids", "labels"))
input_ids, labels = tuple([instance[key] for instance in instances]
for key in ("input_ids", "labels"))
input_ids = [_input_ids[:self.tokenizer.model_max_length] for _input_ids in input_ids]
labels = [_labels[:self.tokenizer.model_max_length] for _labels in labels]
if self.tokenizer.pad_token_id is None:
if "qwen" in self.tokenizer.name_or_path.lower():
print("Setting pad token to bos token for qwen model.")
self.tokenizer.pad_token_id = 151643
else:
self.tokenizer.pad_token_id = self.tokenizer.eos_token_id # FIXME: this could only be triggered for llama3 model.
input_ids = self.pad_sequence(
input_ids,
batch_first=True,
padding_value=self.tokenizer.pad_token_id)
labels = self.pad_sequence(labels,
batch_first=True,
padding_value=IGNORE_INDEX)
batch = dict(
input_ids=input_ids,
labels=labels,
attention_mask=input_ids.ne(self.tokenizer.pad_token_id)
)
if 'image' in instances[0]:
images = [instance['image'] for instance in instances]
batch['image_sizes'] = [im[1] for im_list in images for im in im_list]
batch['modalities'] = [im[2] for im_list in images for im in im_list]
images_lowres = [im[0][0] for im_list in images for im in im_list]
images_highres = [im[0][1] for im_list in images for im in im_list]
batch['images_highres'] = images_highres
if all(x is not None and x.shape == images_lowres[0].shape for x in images_lowres):
batch['images'] = torch.stack(images_lowres)
else:
batch['images'] = images_lowres
return batch
def make_supervised_data_module(tokenizer: transformers.PreTrainedTokenizer,
data_args) -> Dict:
"""Make dataset and collator for supervised fine-tuning."""
train_dataset = LazySupervisedDataset(tokenizer=tokenizer,
data_path=data_args.data_path,
data_args=data_args)
data_collator = DataCollatorForSupervisedDataset(tokenizer=tokenizer)
return dict(train_dataset=train_dataset,
eval_dataset=None,
data_collator=data_collator)
def train():
global local_rank
parser = transformers.HfArgumentParser(
(ModelArguments, DataArguments, TrainingArguments))
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
local_rank = training_args.local_rank
compute_dtype = (torch.float16 if training_args.fp16 else (torch.bfloat16 if training_args.bf16 else torch.float32))
bnb_model_from_pretrained_args = {}
if training_args.bits in [4, 8]:
from transformers import BitsAndBytesConfig
bnb_model_from_pretrained_args.update(dict(
device_map={"": training_args.device},
load_in_4bit=training_args.bits == 4,
load_in_8bit=training_args.bits == 8,
quantization_config=BitsAndBytesConfig(
load_in_4bit=training_args.bits == 4,
load_in_8bit=training_args.bits == 8,
llm_int8_threshold=6.0,
llm_int8_has_fp16_weight=False,
bnb_4bit_compute_dtype=compute_dtype,
bnb_4bit_use_double_quant=training_args.double_quant,
bnb_4bit_quant_type=training_args.quant_type # {'fp4', 'nf4'}
)
))
if model_args.vision_tower is not None:
print(model_args.vision_tower)
if 'qwen' in model_args.model_name_or_path.lower():
if not model_args.pretrain_mm_mlp_adapter:
cfg_pretrained = AutoConfig.from_pretrained(model_args.model_name_or_path)
overwrite_config = {}
overwrite_config["mm_resampler_type"] = model_args.mm_resampler_type
print(f"Overwriting config with {overwrite_config}")
for k, v in overwrite_config.items():
setattr(cfg_pretrained, k, v)
model = OryxQwenForCausalLM.from_pretrained(
model_args.model_name_or_path,
config=cfg_pretrained,
cache_dir=training_args.cache_dir,
attn_implementation="flash_attention_2",
torch_dtype=(torch.bfloat16 if training_args.bf16 else None),
**bnb_model_from_pretrained_args
)
else:
model = OryxQwenForCausalLM.from_pretrained(
model_args.model_name_or_path,
cache_dir=training_args.cache_dir,
attn_implementation="flash_attention_2",
torch_dtype=(torch.bfloat16 if training_args.bf16 else None),
**bnb_model_from_pretrained_args
)
else:
# finetune from a image trained model
# if not model_args.pretrain_mm_mlp_adapter:
cfg_pretrained = AutoConfig.from_pretrained(model_args.model_name_or_path)
overwrite_config = {}
overwrite_config["mm_resampler_type"] = model_args.mm_resampler_type
print(f"Overwriting config with {overwrite_config}")
for k, v in overwrite_config.items():
setattr(cfg_pretrained, k, v)
model = OryxLlamaForCausalLM.from_pretrained(
model_args.model_name_or_path,
config=cfg_pretrained,
cache_dir=training_args.cache_dir,
attn_implementation="flash_attention_2",
torch_dtype=(torch.bfloat16 if training_args.bf16 else None),
**bnb_model_from_pretrained_args
)
else:
model = transformers.LlamaForCausalLM.from_pretrained(
model_args.model_name_or_path,
cache_dir=training_args.cache_dir,
attn_implementation="flash_attention_2",
torch_dtype=(torch.bfloat16 if training_args.bf16 else None),
**bnb_model_from_pretrained_args
)
model.config.use_cache = False
if model_args.freeze_backbone:
model.model.requires_grad_(False)
if training_args.bits in [4, 8]:
from peft import prepare_model_for_kbit_training
model.config.torch_dtype=(torch.float32 if training_args.fp16 else (torch.bfloat16 if training_args.bf16 else torch.float32))
model = prepare_model_for_kbit_training(model, use_gradient_checkpointing=training_args.gradient_checkpointing)
if training_args.gradient_checkpointing:
if hasattr(model, "enable_input_require_grads"):
model.enable_input_require_grads()
else:
def make_inputs_require_grad(module, input, output):
output.requires_grad_(True)
model.get_input_embeddings().register_forward_hook(make_inputs_require_grad)
if training_args.lora_enable:
from peft import LoraConfig, get_peft_model
lora_config = LoraConfig(
r=training_args.lora_r,
lora_alpha=training_args.lora_alpha,
target_modules=find_all_linear_names(model),
lora_dropout=training_args.lora_dropout,
bias=training_args.lora_bias,
task_type="CAUSAL_LM",
)
if training_args.bits == 16:
if training_args.bf16:
model.to(torch.bfloat16)
if training_args.fp16:
model.to(torch.float16)
rank0_print("Adding LoRA adapters...")
model = get_peft_model(model, lora_config)
if "qwen" in model_args.model_name_or_path.lower():
tokenizer = transformers.AutoTokenizer.from_pretrained(
model_args.model_name_or_path,
cache_dir=training_args.cache_dir,
model_max_length=training_args.model_max_length,
padding_side="right")
else:
tokenizer = transformers.AutoTokenizer.from_pretrained(
model_args.model_name_or_path,
cache_dir=training_args.cache_dir,
model_max_length=training_args.model_max_length,
padding_side="right",
use_fast=False,
)
if model_args.version == "v0":
if tokenizer.pad_token is None:
smart_tokenizer_and_embedding_resize(
special_tokens_dict=dict(pad_token="[PAD]"),
tokenizer=tokenizer,
model=model,
)
elif model_args.version == "v0.5":
tokenizer.pad_token = tokenizer.unk_token
elif model_args.version == "llava_llama_3":
tokenizer.pad_token = "<|reserved_special_token_0|>" # only for llama3
conversation_lib.default_conversation = conversation_lib.conv_templates["llava_llama_3"]
else:
if 'llama-3' in model_args.model_name_or_path.lower():
tokenizer.pad_token = "<|reserved_special_token_0|>"
else:
tokenizer.pad_token = tokenizer.unk_token
if model_args.version in conversation_lib.conv_templates:
conversation_lib.default_conversation = conversation_lib.conv_templates[model_args.version]
else:
conversation_lib.default_conversation = conversation_lib.conv_templates["vicuna_v1"]
if model_args.vision_tower is not None:
model.get_model().initialize_vision_modules(
model_args=model_args,
fsdp=training_args.fsdp
)
vision_tower = model.get_vision_tower()
vision_tower.to(dtype=torch.bfloat16 if training_args.bf16 else torch.float16, device=training_args.device)
vision_tower.image_processor.do_resize = training_args.do_resize
vision_tower.image_processor.do_center_crop = training_args.do_center_crop
data_args.image_processor = vision_tower.image_processor
data_args.is_multimodal = True
model.config.tokenizer_padding_side = tokenizer.padding_side
model.config.tokenizer_model_max_length = tokenizer.model_max_length
model.config.tune_mm_mlp_adapter = training_args.tune_mm_mlp_adapter = model_args.tune_mm_mlp_adapter
model.config.tune_mm_vision_resampler = training_args.tune_mm_vision_resampler = model_args.tune_mm_vision_resampler
if model_args.tune_mm_mlp_adapter or model_args.tune_mm_vision_resampler:
model.requires_grad_(False)
if model_args.tune_mm_mlp_adapter:
for p in model.get_model().mm_projector.parameters():
p.requires_grad = True
if model_args.tune_mm_vision_resampler:
for p in model.get_model().vision_resampler.parameters():
p.requires_grad = True
model.config.freeze_mm_mlp_adapter = training_args.freeze_mm_mlp_adapter
if training_args.freeze_mm_mlp_adapter:
for p in model.get_model().mm_projector.parameters():
p.requires_grad = False
model.config.freeze_mm_vision_resampler = training_args.freeze_mm_vision_resampler
if training_args.freeze_mm_vision_resampler:
for p in model.get_model().vision_resampler.parameters():
p.requires_grad = False
model.config.unfreeze_mm_vision_tower = model_args.unfreeze_mm_vision_tower
if model_args.unfreeze_mm_vision_tower:
vision_tower.requires_grad_(True)
if training_args.bits in [4, 8]:
model.get_model().mm_projector.to(dtype=compute_dtype, device=training_args.device)
model.config.mm_use_im_start_end = data_args.mm_use_im_start_end = model_args.mm_use_im_start_end
model.config.mm_projector_lr = training_args.mm_projector_lr
model.config.mm_vision_tower_lr = training_args.mm_vision_tower_lr
training_args.use_im_start_end = model_args.mm_use_im_start_end
model.config.mm_use_im_patch_token = model_args.mm_use_im_patch_token
model.initialize_vision_tokenizer(model_args, tokenizer=tokenizer)
if training_args.bits in [4, 8]:
from peft.tuners.lora import LoraLayer
for name, module in model.named_modules():
if isinstance(module, LoraLayer):
if training_args.bf16:
module = module.to(torch.bfloat16)
if 'norm' in name:
module = module.to(torch.float32)
if 'lm_head' in name or 'embed_tokens' in name:
if hasattr(module, 'weight'):
if training_args.bf16 and module.weight.dtype == torch.float32:
module = module.to(torch.bfloat16)
data_module = make_supervised_data_module(tokenizer=tokenizer,
data_args=data_args)
trainer = OryxTrainer(model=model,
tokenizer=tokenizer,
args=training_args,
**data_module)
if list(pathlib.Path(training_args.output_dir).glob("checkpoint-*")):
trainer.train(resume_from_checkpoint=True)
else:
trainer.train()
trainer.save_state()
model.config.use_cache = True
if training_args.lora_enable:
state_dict = get_peft_state_maybe_zero_3(
model.named_parameters(), training_args.lora_bias
)
non_lora_state_dict = get_peft_state_non_lora_maybe_zero_3(
model.named_parameters()
)
if training_args.local_rank == 0 or training_args.local_rank == -1:
model.config.save_pretrained(training_args.output_dir)
model.save_pretrained(training_args.output_dir, state_dict=state_dict)
torch.save(non_lora_state_dict, os.path.join(training_args.output_dir, 'non_lora_trainables.bin'))
else:
safe_save_model_for_hf_trainer(trainer=trainer,
output_dir=training_args.output_dir)
if __name__ == "__main__":
train()