# Adopted from https://github.com/lm-sys/FastChat. Below is the original copyright:
# Adopted from tatsu-lab@stanford_alpaca. Below is the original copyright:
# Copyright 2023 Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import copy
import json
import logging
import os
import pathlib
from dataclasses import dataclass, field
from typing import Dict, List, Optional, Sequence
import tokenizers
import torch
import transformers
from packaging import version
from PIL import Image
from torch.utils.data import Dataset
from llava import conversation as conversation_lib
from llava.constants import (DEFAULT_IM_END_TOKEN, DEFAULT_IM_START_TOKEN,
DEFAULT_IMAGE_TOKEN, IGNORE_INDEX,
IMAGE_TOKEN_INDEX)
from llava.mm_utils import process_anyres_image, tokenizer_image_token
from llava.model import *
from llava.train.llava_trainer import LLaVATrainer
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)
vision_tower: Optional[str] = field(default=None)
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_patch_merge_type: Optional[str] = field(default='flat')
mm_vision_select_feature: Optional[str] = field(default="patch")
@dataclass
class DataArguments:
data_path: str = field(default=None,
metadata={"help": "Path to the training data."})
lazy_preprocess: bool = False
is_multimodal: bool = False
image_folder: Optional[str] = field(default=None)
image_aspect_ratio: str = 'square'
@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)
unfreeze_mm_vision_tower: 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"
lora_qv_proj_only: bool = False
mm_projector_lr: Optional[float] = None
mm_vision_tower_lr: Optional[float] = None
group_by_modality_length: 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 get_vision_tower_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, qv_proj_only=False):
if qv_proj_only:
rank0_print('Only add LoRA to QV proj')
return ['q_proj', 'v_proj']
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']
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 getattr(trainer.args, "unfreeze_mm_vision_tower", False):
if trainer.deepspeed:
torch.cuda.synchronize()
mm_vision_tower_folder = os.path.join(output_dir, 'vision_tower')
os.makedirs(mm_vision_tower_folder, exist_ok=True)
trainer.model.get_vision_tower().image_processor.save_pretrained(mm_vision_tower_folder)
trainer.model.get_vision_tower().vision_tower.vision_model.config.save_pretrained(
mm_vision_tower_folder)
weight_to_save = get_vision_tower_state_maybe_zero_3(
trainer.model.get_vision_tower().vision_tower.named_parameters())
if trainer.args.local_rank == 0 or trainer.args.local_rank == -1:
torch.save(weight_to_save, os.path.join(
mm_vision_tower_folder, 'pytorch_model.bin'))
if getattr(trainer.model.model, 'vision_tower', None) is not None:
del trainer.model.model.vision_tower
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']:
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_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_llama3(
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])]
for conv_idx in range(3, len(rounds), 2):
re_rounds.append(conv.sep.join(rounds[conv_idx:conv_idx + 2]))
cur_len = 0
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)) + 1
instruction_len = len(
tokenizer_image_token(parts[0], tokenizer))
else:
round_len = len(tokenizer(rou).input_ids) + 1
instruction_len = len(tokenizer(parts[0]).input_ids)
if i > 0:
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)"
)
return dict(
input_ids=input_ids,
labels=targets,
)
def preprocess_yi(
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])]
for conv_idx in range(3, len(rounds), 2):
re_rounds.append(conv.sep.join(rounds[conv_idx:conv_idx + 2]))
cur_len = 0
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
round_len = len(tokenizer_image_token(rou, tokenizer)) + len(tokenizer_image_token(conv.sep, tokenizer))
instruction_len = len(tokenizer_image_token(parts[0], tokenizer))
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_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()
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
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)"
)
return dict(
input_ids=input_ids,
labels=targets,
)
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 = 0
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
# not included <|im_end|>
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
# include <|im_end|> for all rounds
# if i != 0 and getattr(tokenizer, 'legacy', False) and IS_TOKENIZER_GREATER_THAN_0_14:
if 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" (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(
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.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 == "mpt":
return preprocess_mpt(sources, tokenizer, has_image=has_image)
if conversation_lib.default_conversation.version in ["llama3", "llava_llama_3"]:
return preprocess_llama3(sources, tokenizer, has_image=has_image)
if conversation_lib.default_conversation.version == "yi":
return preprocess_yi(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)
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
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:
cur_len = sum(len(conv['value'].split())
for conv in sample['conversations'])
cur_len = cur_len if 'image' in sample else -cur_len
length_list.append(cur_len)
return length_list
def __getitem__(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']
image_folder = self.data_args.image_folder
processor = self.data_args.image_processor
image = Image.open(os.path.join(
image_folder, image_file)).convert('RGB')
if self.data_args.image_aspect_ratio == 'pad':
def expand2square(pil_img, background_color):
width, height = pil_img.size
if width == height:
return pil_img
elif width > height:
result = Image.new(
pil_img.mode, (width, width), background_color)
result.paste(pil_img, (0, (width - height) // 2))
return result
else:
result = Image.new(
pil_img.mode, (height, height), background_color)
result.paste(pil_img, ((height - width) // 2, 0))
return result
image = expand2square(image, tuple(int(x * 255)
for x in processor.image_mean))
image_size = image.size
image = processor.preprocess(image, return_tensors='pt')[
'pixel_values'][0]
elif self.data_args.image_aspect_ratio == "anyres":
image_size = image.size
image = process_anyres_image(
image, processor, self.data_args.image_grid_pinpoints)
else:
image_size = image.size
image = processor.preprocess(image, return_tensors='pt')[
'pixel_values'][0]
sources = preprocess_multimodal(
copy.deepcopy([e["conversations"] for e in sources]),
self.data_args)
else:
sources = copy.deepcopy([e["conversations"] for e in sources])
data_dict = preprocess(
sources,
self.tokenizer,
has_image=('image' in self.list_data_dict[i]))
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
data_dict['image_size'] = image_size
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(
3, crop_size['height'], crop_size['width'])
data_dict['image_size'] = (crop_size['height'], crop_size['width'])
return data_dict
@dataclass
class DataCollatorForSupervisedDataset(object):
"""Collate examples for supervised fine-tuning."""
tokenizer: transformers.PreTrainedTokenizer
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 = torch.nn.utils.rnn.pad_sequence(
input_ids,
batch_first=True,
padding_value=self.tokenizer.pad_token_id)
labels = torch.nn.utils.rnn.pad_sequence(labels,
batch_first=True,
padding_value=IGNORE_INDEX)
input_ids = input_ids[:, :self.tokenizer.model_max_length]
labels = labels[:, :self.tokenizer.model_max_length]
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]
image_sizes = [instance['image_size'] for instance in instances]
if all(x is not None and x.shape == images[0].shape for x in images):
batch['images'] = torch.stack(images)
else:
batch['images'] = images
batch['image_sizes'] = image_sizes
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 unfreeze_vit(vision_tower):
for _, p in vision_tower.named_parameters():
p.requires_grad = True
def format_bytes(size):
billion = 10**9
million = 10**6
if size >= billion:
return f"{size / billion:.2f}B"
elif size >= million:
return f"{size / million:.2f}M"
else:
return f"{size} bytes"
def train(attn_implementation=None):
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_skip_modules=["mm_projector"],
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'}
)
))
model_max_length_args = {}
if 'llava-v1.6-8b' not in model_args.model_name_or_path:
config = transformers.AutoConfig.from_pretrained(
model_args.model_name_or_path, trust_remote_code=True)
if config.max_position_embeddings < training_args.model_max_length:
rank0_print(
f'Set the max_position_embeddings from {config.max_position_embeddings} to {training_args.model_max_length}')
model_max_length_args.update(
{'max_position_embeddings': training_args.model_max_length})
if model_args.vision_tower is not None:
if 'mpt' in model_args.model_name_or_path:
config = transformers.AutoConfig.from_pretrained(
model_args.model_name_or_path, trust_remote_code=True)
config.attn_config['attn_impl'] = training_args.mpt_attn_impl
model = LlavaMptForCausalLM.from_pretrained(
model_args.model_name_or_path,
config=config,
cache_dir=training_args.cache_dir,
**bnb_model_from_pretrained_args
)
else:
model = LlavaLlamaForCausalLM.from_pretrained(
model_args.model_name_or_path,
cache_dir=training_args.cache_dir,
attn_implementation=attn_implementation,
torch_dtype=(torch.bfloat16 if training_args.bf16 else None),
**bnb_model_from_pretrained_args,
**model_max_length_args
)
else:
model = transformers.LlamaForCausalLM.from_pretrained(
model_args.model_name_or_path,
cache_dir=training_args.cache_dir,
attn_implementation=attn_implementation,
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, training_args.lora_qv_proj_only),
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 'mpt' in model_args.model_name_or_path:
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
else:
if tokenizer.pad_token is None:
rank0_print("Adding pad token as ''")
smart_tokenizer_and_embedding_resize(
special_tokens_dict=dict(pad_token=""),
tokenizer=tokenizer,
model=model,
)
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)
data_args.image_processor = vision_tower.image_processor
data_args.is_multimodal = True
model.config.image_aspect_ratio = data_args.image_aspect_ratio
if data_args.image_aspect_ratio == 'anyres':
base_size = vision_tower.config.image_size
grids = [[1, 2], [2, 1], [2, 2], [3, 1], [1, 3]]
model.config.image_grid_pinpoints = data_args.image_grid_pinpoints = [
[g[0]*base_size, g[1]*base_size] for g in grids]
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
if model_args.tune_mm_mlp_adapter:
model.requires_grad_(False)
for p in model.get_model().mm_projector.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.unfreeze_mm_vision_tower = training_args.unfreeze_mm_vision_tower
if training_args.unfreeze_mm_vision_tower:
lr_of_vit = training_args.mm_vision_tower_lr if training_args.mm_vision_tower_lr is not None else training_args.learning_rate
lr_of_mlp = training_args.mm_projector_lr if training_args.mm_projector_lr is not None else training_args.learning_rate
training_args.mm_projector_lr = lr_of_mlp
unfreeze_vit(vision_tower)
rank0_print(
f'Tune the entire model! The LR of ViT is {lr_of_vit}. The LR of MLP is {lr_of_mlp}. The LR of LLM is {training_args.learning_rate}')
# Calculate total parameters and trainable parameters
total_params = sum(p.numel() for p in model.get_model().parameters())
trainable_params = sum(
p.numel() for p in model.get_model().parameters() if p.requires_grad)
rank0_print(f"Total parameters: {format_bytes(total_params)}")
rank0_print(f"Trainable parameters: {format_bytes(trainable_params)}")
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)
model.config.pad_token_id = tokenizer.pad_token_id
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 = LLaVATrainer(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'))
if training_args.unfreeze_mm_vision_tower:
if trainer.deepspeed:
torch.cuda.synchronize()
trainer.model.get_vision_tower().image_processor.save_pretrained(
os.path.join(training_args.output_dir, 'vision_tower'))
trainer.model.get_vision_tower().vision_tower.vision_model.config.save_pretrained(
os.path.join(training_args.output_dir, 'vision_tower'))
weight_to_save = get_vision_tower_state_maybe_zero_3(
trainer.model.get_vision_tower().vision_tower.named_parameters())
torch.save(weight_to_save, os.path.join(
training_args.output_dir, 'vision_tower/pytorch_model.bin'))
else:
safe_save_model_for_hf_trainer(trainer=trainer,
output_dir=training_args.output_dir)
if __name__ == "__main__":
train()