# 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 ast
import os
import copy
from dataclasses import dataclass, field
import json
import logging
import pathlib
from typing import Dict, Optional, Sequence, List
from PIL import Image, ImageFile
from packaging import version
import numpy as np
import time
import random
import yaml
import math
import re
import torch
import transformers
import tokenizers
import deepspeed
from transformers import AutoConfig
from torch.utils.data import Dataset
from llava.constants import IGNORE_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN, IMAGE_TOKEN_INDEX
from llava.train.llava_trainer import LLaVATrainer
from llava import conversation as conversation_lib
from llava.model import *
from llava.mm_utils import process_highres_image, process_anyres_image, process_highres_image_crop_split, tokenizer_image_token
from llava.utils import rank0_print, process_video_with_pyav, process_video_with_decord
torch.multiprocessing.set_sharing_strategy("file_system")
ImageFile.LOAD_TRUNCATED_IMAGES = True
local_rank = None
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")
model_class_name: Optional[str] = field(default=None, metadata={"help": "Used to init model class, format is XXXXForCausalLM. e.g. currently XXXX is chosen from LlavaLlama, LlavaMixtral, LlavaMistral, Llama"})
mm_tunable_parts: Optional[str] = field(
default=None, metadata={"help": 'Could be "mm_mlp_adapter", "mm_vision_resampler", "mm_vision_tower,mm_mlp_adapter,mm_language_model", "mm_vision_tower,mm_mlp_adapter,mm_language_model", "mm_mlp_adapter,mm_language_model"'}
)
# deciding which part of the multimodal model to tune, will overwrite other previous settings
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)
vision_tower_pretrained: Optional[str] = field(default=None) # default to the last layer
unfreeze_mm_vision_tower: bool = field(default=False)
unfreeze_language_model: 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_patch_merge_type: Optional[str] = field(default="flat")
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=0.0)
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)
mm_spatial_pool_stride: Optional[int] = field(default=None)
mm_spatial_pool_mode: str = field(default="bilinear")
mm_spatial_pool_out_channels: Optional[int] = field(default=None)
mm_perceiver_depth: Optional[int] = field(default=3)
mm_perceiver_latents: Optional[int] = field(default=32)
mm_perceiver_ff_mult: Optional[float] = field(default=4)
mm_perceiver_pretrained: Optional[str] = field(default=None)
mm_qformer_depth: Optional[int] = field(default=3)
mm_qformer_latents: Optional[int] = field(default=32)
mm_qformer_pretrained: Optional[str] = field(default=None)
rope_scaling_factor: Optional[float] = field(default=None)
rope_scaling_type: Optional[str] = field(default=None)
s2: Optional[bool] = field(default=False)
s2_scales: Optional[str] = field(default="336,672,1008")
use_pos_skipping: Optional[bool] = field(default=False)
pos_skipping_range: Optional[int] = field(default=4096)
mm_newline_position: Optional[str] = field(default="one_token")
@dataclass
class DataArguments:
data_path: str = field(default=None, metadata={"help": "Path to the training data, in llava's instruction.json format. Supporting multiple json files via /path/to/{a,b,c}.json"})
lazy_preprocess: bool = False
is_multimodal: bool = False
early_mix_text: bool = False
image_folder: Optional[str] = field(default=None)
image_aspect_ratio: str = "square"
image_grid_pinpoints: Optional[str] = field(default=None)
image_crop_resolution: Optional[int] = field(default=None)
image_split_resolution: Optional[int] = field(default=None)
video_folder: Optional[str] = field(default=None)
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=4096,
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)
auto_find_batch_size: bool = field(default=False)
gradient_checkpointing: bool = field(default=True)
verbose_logging: bool = field(default=False)
attn_implementation: str = field(default="flash_attention_2", metadata={"help": "Use transformers attention implementation."})
# @dataclass
# class EvaluationArguments:
# eval_num_processes: int = field(default=1)
# task_names: str = field(default=None)
# model: str = field(default="llava")
# model_args: Optional[str] = field(default=None)
# num_fewshot: Optional[int] = field(default=None)
# batch_size: int = field(default=1)
# device: Optional[str] = field(default=None)
# limit: Optional[int] = field(default=None)
# check_integrity: Optional[bool] = field(default=False)
# show_task_to_terminal: Optional[bool] = field(default=False)
# log_samples: Optional[bool] = field(default=True)
# gen_kwargs: Optional[str] = field(default="")
# log_samples_suffix: Optional[str] = field(default="")
# output_path: Optional[str] = field(default="./logs/")
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 hasattr(trainer.args, "tune_mm_mlp_adapter") and trainer.args.tune_mm_mlp_adapter:
check_only_save_mm_adapter_tunnable = True
# only has mm_mlp_adapter and mm_vision_resampler in the tuneable parts
elif hasattr(trainer.args, "mm_tunable_parts") and (len(trainer.args.mm_tunable_parts.split(",")) == 1 and ("mm_mlp_adapter" in trainer.args.mm_tunable_parts or "mm_vision_resampler" in trainer.args.mm_tunable_parts)):
check_only_save_mm_adapter_tunnable = True
else:
check_only_save_mm_adapter_tunnable = False
trainer.accelerator.wait_for_everyone()
torch.cuda.synchronize()
rank0_print(f"Only save projectors: {check_only_save_mm_adapter_tunnable}")
if check_only_save_mm_adapter_tunnable:
# 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:
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:
# TODO maybe this should be changed for interleaved data?
# if DEFAULT_IMAGE_TOKEN in sentence["value"] and not sentence["value"].startswith(DEFAULT_IMAGE_TOKEN):
# only check for num_im=1
num_im = len(re.findall(DEFAULT_IMAGE_TOKEN, sentence["value"]))
if num_im == 1 and 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)
# For videoInstruct-100k noisy_data. TODO: Ask Yuanhan to clean the data instead of leaving the noise code here.
sentence["value"] = sentence["value"].replace("QA_GT_caption_based_noisy", "")
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_gemma(sources: List[List[Dict[str, str]]], tokenizer: transformers.PreTrainedTokenizer, has_image: bool = False) -> Dict:
conv: conversation_lib.Conversation = conversation_lib.default_conversation.copy()
roles: Dict[str, str] = {"human": conv.roles[0], "gpt": conv.roles[1]}
# Apply prompt templates
conversations: List[str] = []
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: List[Dict[str, str]] = source[1:]
conv.messages = []
for j, sentence in enumerate(source):
role: str = 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.Tensor = torch.stack([tokenizer_image_token(prompt, tokenizer, return_tensors="pt") for prompt in conversations], dim=0)
else:
input_ids: torch.Tensor = tokenizer(
conversations,
return_tensors="pt",
padding="longest",
max_length=tokenizer.model_max_length,
truncation=True,
).input_ids
targets: torch.Tensor = input_ids.clone()
assert conv.sep_style == conversation_lib.SeparatorStyle.GEMMA
# Mask target
sep: str = conv.sep + conv.roles[1]
for conversation, target in zip(conversations, targets):
total_len: int = int(target.ne(tokenizer.pad_token_id).sum())
rounds: List[str] = conversation.split(conv.sep)
re_rounds = []
for conv_idx in range(0, len(rounds), 2):
re_rounds.append(conv.sep.join(rounds[conv_idx : conv_idx + 2]))
cur_len = 1 # Ignore
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 # Re-append sep because split on this
# Now "".join(parts)==rou
if has_image:
round_len = len(tokenizer_image_token(rou, tokenizer)) - 1 # Ignore
instruction_len = len(tokenizer_image_token(parts[0], tokenizer)) - 1 # Ignore
else:
round_len = len(tokenizer(rou).input_ids) - 1 # Ignore
instruction_len = len(tokenizer(parts[0]).input_ids) - 1 # Ignore
round_len += 2 # sep: \n takes 2 tokens
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_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"}
roles = {"human": "user", "gpt": "assistant"}
# Add image tokens to tokenizer as a special tokens
# Use a deepcopy of tokenizer so that we don't modify on the tokenizer
tokenizer = copy.deepcopy(tokenizer)
# When there is actually an image, we add the image tokens as a special token
if has_image:
tokenizer.add_tokens([""], special_tokens=True)
image_token_index = tokenizer.convert_tokens_to_ids("")
im_start, im_end = tokenizer.additional_special_tokens_ids
# unmask_tokens = ["<|im_start|>", "<|im_start|>", "\n"]
unmask_tokens_idx = [198, im_start, im_end]
nl_tokens = tokenizer("\n").input_ids
# Reset Qwen chat templates so that it won't include system message every time we apply
chat_template = "{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}"
tokenizer.chat_template = chat_template
# _system = tokenizer("system").input_ids + nl_tokens
# _user = tokenizer("user").input_ids + nl_tokens
# _assistant = tokenizer("assistant").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 = [], []
# New version, use apply chat template
# Build system message for each sentence
input_id += tokenizer.apply_chat_template([{"role" : "system", "content" : system_message}])
target += [IGNORE_INDEX] * len(input_id)
for conv in source:
# Make sure llava data can load
try:
role = conv["role"]
content = conv["content"]
except:
role = conv["from"]
content = conv["value"]
role = roles.get(role, role)
conv = [{"role" : role, "content" : content}]
encode_id = tokenizer.apply_chat_template(conv)
input_id += encode_id
if role in ["user", "system"]:
target += [IGNORE_INDEX] * len(encode_id)
else:
target += encode_id
assert len(input_id) == len(target), f"{len(input_id)} != {len(target)}"
for idx, encode_id in enumerate(input_id):
if encode_id in unmask_tokens_idx:
target[idx] = encode_id
if encode_id == image_token_index:
input_id[idx] = IMAGE_TOKEN_INDEX
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)
)
def preprocess_llama3(
sources,
tokenizer: transformers.PreTrainedTokenizer,
has_image: bool = False,
max_len=2048,
system_message: str = "You are a helpful language and vision assistant. You are able to understand the visual content that the user provides, and assist the user with a variety of tasks using natural language.",
) -> Dict:
# roles = {"human": "<|start_header_id|>user<|end_header_id|>", "gpt": "<|start_header_id|>assistant<|end_header_id|>"}
roles = {"human": "user", "gpt": "assistant"}
# Add image tokens to tokenizer as a special tokens
# Use a deepcopy of tokenizer so that we don't modify on the tokenizer
tokenizer = copy.deepcopy(tokenizer)
# When there is actually an image, we add the image tokens as a special token
if has_image:
tokenizer.add_tokens([""], special_tokens=True)
image_token_index = tokenizer.convert_tokens_to_ids("")
bos_token_id = tokenizer.convert_tokens_to_ids("<|begin_of_text|>")
start_header_id = tokenizer.convert_tokens_to_ids("<|start_header_id|>")
end_header_id = tokenizer.convert_tokens_to_ids("<|end_header_id|>")
eot_id = tokenizer.convert_tokens_to_ids("<|eot_id|>")
unmask_tokens = ["<|begin_of_text|>", "<|start_header_id|>", "<|end_header_id|>", "<|eot_id|>", "\n\n"]
unmask_tokens_idx = [tokenizer.convert_tokens_to_ids(tok) for tok in unmask_tokens]
# After update, calling tokenizer of llama3 will
# auto add bos id for the tokens. ヽ(｀⌒´)ﾉ
def safe_tokenizer_llama3(text):
input_ids = tokenizer(text).input_ids
if input_ids[0] == bos_token_id:
input_ids = input_ids[1:]
return input_ids
nl_tokens = tokenizer.convert_tokens_to_ids("\n\n")
# 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 = [], []
# New version, use apply chat template
# Build system message for each sentence
input_id += tokenizer.apply_chat_template([{"role" : "system", "content" : system_message}])
target += [IGNORE_INDEX] * len(input_id)
for conv in source:
# Make sure llava data can load
try:
role = conv["role"]
content = conv["content"]
except:
role = conv["from"]
content = conv["value"]
role = roles.get(role, role)
conv = [{"role" : role, "content" : content}]
# First is bos token we don't need here
encode_id = tokenizer.apply_chat_template(conv)[1:]
input_id += encode_id
if role in ["user", "system"]:
target += [IGNORE_INDEX] * len(encode_id)
else:
target += encode_id
assert len(input_id) == len(target), f"{len(input_id)} != {len(target)}"
for idx, encode_id in enumerate(input_id):
if encode_id in unmask_tokens_idx:
target[idx] = encode_id
if encode_id == image_token_index:
input_id[idx] = IMAGE_TOKEN_INDEX
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)
)
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 = 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(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 == "qwen":
return preprocess_qwen(sources, tokenizer, has_image=has_image)
if conversation_lib.default_conversation.version == "gemma":
return preprocess_gemma(sources, tokenizer, has_image=has_image)
if conversation_lib.default_conversation.version == "llama_v3":
return preprocess_llama3(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):
def __init__(self, data_path: str, tokenizer: transformers.PreTrainedTokenizer, data_args: DataArguments):
super(LazySupervisedDataset, self).__init__()
self.tokenizer = tokenizer
self.list_data_dict = []
# Handle multiple JSON files specified in the data_path
if "{" in data_path and "}" in data_path:
base_path, file_pattern = re.match(r"^(.*)\{(.*)\}\.json$", data_path).groups()
file_names = file_pattern.split(",")
rank0_print(f"Loading {file_names} from {base_path}")
data_args.dataset_paths = []
for file_name in file_names:
data_args.dataset_paths.append(f"{base_path}{file_name}.json")
full_path = f"{base_path}{file_name}.json"
rank0_print(f"Loading {full_path}")
with open(full_path, "r") as file:
cur_data_dict = json.load(file)
rank0_print(f"Loaded {len(cur_data_dict)} samples from {full_path}")
self.list_data_dict.extend(cur_data_dict)
elif data_path.endswith(".yaml"):
with open(data_path, "r") as file:
yaml_data = yaml.safe_load(file)
datasets = yaml_data.get("datasets")
# file should be in the format of:
# datasets:
# - json_path: xxxx1.json
# sampling_strategy: first:1000
# - json_path: xxxx2.json
# sampling_strategy: end:3000
# - json_path: xxxx3.json
# sampling_strategy: random:999
data_args.dataset_paths = [dataset.get("json_path") for dataset in datasets]
for dataset in datasets:
json_path = dataset.get("json_path")
sampling_strategy = dataset.get("sampling_strategy", "all")
sampling_number = None
rank0_print(f"Loading {json_path} with {sampling_strategy} sampling strategy")
if json_path.endswith(".jsonl"):
cur_data_dict = []
with open(json_path, "r") as json_file:
for line in json_file:
cur_data_dict.append(json.loads(line.strip()))
elif json_path.endswith(".json"):
with open(json_path, "r") as json_file:
cur_data_dict = json.load(json_file)
else:
raise ValueError(f"Unsupported file type: {json_path}")
if ":" in sampling_strategy:
sampling_strategy, sampling_number = sampling_strategy.split(":")
if "%" in sampling_number:
sampling_number = math.ceil(int(sampling_number.split("%")[0]) * len(cur_data_dict) / 100)
else:
sampling_number = int(sampling_number)
# Apply the sampling strategy
if sampling_strategy == "first" and sampling_number is not None:
cur_data_dict = cur_data_dict[:sampling_number]
elif sampling_strategy == "end" and sampling_number is not None:
cur_data_dict = cur_data_dict[-sampling_number:]
elif sampling_strategy == "random" and sampling_number is not None:
random.shuffle(cur_data_dict)
cur_data_dict = cur_data_dict[:sampling_number]
rank0_print(f"Loaded {len(cur_data_dict)} samples from {json_path}")
self.list_data_dict.extend(cur_data_dict)
else:
data_args.dataset_paths = [data_path]
rank0_print(f"Loading {data_path}")
with open(data_path, "r") as file:
cur_data_dict = json.load(file)
rank0_print(f"Loaded {len(cur_data_dict)} samples from {data_path}")
self.list_data_dict.extend(cur_data_dict)
rank0_print(f"Loaded {len(self.list_data_dict)} samples from {data_path}")
rank0_print("Formatting inputs...Skip in lazy mode")
self.tokenizer = tokenizer
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"])
assert cur_len > 0, f"Conversation length is 0 for {sample}"
if "image" in sample or "video" in sample or self.data_args.early_mix_text:
length_list.append(cur_len)
else:
length_list.append(-cur_len)
return length_list
def process_image(self, image_file, overwrite_image_aspect_ratio=None):
image_folder = self.data_args.image_folder
processor = self.data_args.image_processor
# print(f"\n\nInspecting the image path, folder = {image_folder}, image={image_file}\n\n")
try:
image = Image.open(os.path.join(image_folder, image_file)).convert("RGB")
except Exception as exn:
print(f"Failed to open image {image_file}. Exception:", exn)
raise exn
image_size = image.size
image_aspect_ratio = self.data_args.image_aspect_ratio
if overwrite_image_aspect_ratio is not None:
image_aspect_ratio = overwrite_image_aspect_ratio
if image_aspect_ratio == "highres":
image = process_highres_image(image, self.data_args.image_processor, self.data_args.image_grid_pinpoints)
elif image_aspect_ratio == "anyres" or "anyres_max" in image_aspect_ratio:
image = process_anyres_image(image, self.data_args.image_processor, self.data_args.image_grid_pinpoints)
elif image_aspect_ratio == "crop_split":
image = process_highres_image_crop_split(image, self.data_args)
elif 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 = processor.preprocess(image, return_tensors="pt")["pixel_values"][0]
else:
image = processor.preprocess(image, return_tensors="pt")["pixel_values"][0]
return image, image_size, "image"
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:
next_index = min(i + 1, len(self.list_data_dict) - 1)
# sample_idx = random.choice(range(len(self)))
sample = self._get_item(next_index)
return sample
except Exception as e:
# no need to sleep
print(f"[Try other #{attempt_idx}] Failed to fetch sample {next_index}. Exception:", e)
pass
try:
sample = self._get_item(i)
return sample
except Exception as e:
raise e
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]
# Handling multi images
# overwrite to process with simple pad
if len(image_file) > 1:
image = [self.process_image(f, "pad") for f in image_file]
image = [[im[0], im[1], "image"] for im in image]
else:
image = [self.process_image(image_file)]
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_folder = self.data_args.video_folder
video_file = os.path.join(video_folder, video_file)
suffix = video_file.split(".")[-1]
if not os.path.exists(video_file):
print("File {} not exist!".format(video_file))
try:
if "shareVideoGPTV" in video_file:
frame_files = [os.path.join(video_file, f) for f in os.listdir(video_file) if os.path.isfile(os.path.join(video_file, f))]
frame_files.sort() # Ensure the frames are sorted if they are named sequentially
# TODO: Hard CODE: Determine the indices for uniformly sampling 10 frames
num_frames_to_sample = 10
total_frames = len(frame_files)
sampled_indices = np.linspace(0, total_frames - 1, num_frames_to_sample, dtype=int)
# Read and store the sampled frames
video = []
for idx in sampled_indices:
frame_path = frame_files[idx]
try:
with Image.open(frame_path) as img:
frame = img.convert("RGB")
video.append(frame)
except IOError:
print(f"Failed to read frame at path: {frame_path}")
else:
video = process_video_with_decord(video_file, self.data_args)
processor = self.data_args.image_processor
image = processor.preprocess(video, return_tensors="pt")["pixel_values"]
image = [(image, video[0].size, "video")]
sources = preprocess_multimodal(copy.deepcopy([e["conversations"] for e in sources]), self.data_args)
except Exception as e:
print(f"Error: {e}")
print(f"Failed to read video file: {video_file}")
return self._get_item(i + 1)
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])
data_dict = preprocess(sources, self.tokenizer, has_image=has_image)
if "prompt" in data_dict:
prompt = data_dict["prompt"]
else:
prompt = None
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"] = image
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"]), (crop_size["width"], crop_size["height"]), "text"),
]
# prompt exist in the data
if prompt is not None:
data_dict["prompt"] = prompt
data_dict["id"] = self.list_data_dict[i].get("id", i)
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, ids = tuple([instance[key] for instance in instances] for key in ("input_ids", "labels", "id"))
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:
# self.tokenizer.pad_token_id = self.tokenizer.eos_token_id # FIXME: this could only be triggered for llama3 model.
self.tokenizer.pad_token_id = 0 # This gets the best result. Don't know why.
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.long() if labels.dtype == torch.int32 else labels, attention_mask=input_ids.ne(self.tokenizer.pad_token_id))
# batch = dict(input_ids=input_ids, labels=labels, attention_mask=input_ids.ne(self.tokenizer.pad_token_id), ids=ids)
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 = [im[0] for im_list in images for im in im_list]
# if all(x is not None and x.shape == images[0].shape for x in images):
# Image: (N, P, C, H, W)
# Video: (N, F, C, H, W)
# batch["images"] = torch.stack(images)
# else:
batch["images"] = images
if "prompt" in instances[0]:
batch["prompts"] = [instance["prompt"] for instance in instances]
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 get_model(model_args, training_args, bnb_model_from_pretrained_args):
assert training_args.attn_implementation
if training_args.attn_implementation == "sdpa" and torch.__version__ < "2.1.2":
raise ValueError("The 'sdpa' attention implementation requires torch version 2.1.2 or higher.")
customized_kwargs = dict()
customized_kwargs.update(bnb_model_from_pretrained_args)
cfg_pretrained = None
overwrite_config = {}
if any(
[
model_args.rope_scaling_factor is not None,
model_args.rope_scaling_type is not None,
model_args.mm_spatial_pool_stride is not None,
model_args.mm_spatial_pool_out_channels is not None,
model_args.mm_spatial_pool_mode is not None,
model_args.mm_resampler_type is not None,
]
):
cfg_pretrained = AutoConfig.from_pretrained(model_args.model_name_or_path)
if model_args.use_pos_skipping is not None and model_args.pos_skipping_range is not None:
overwrite_config["use_pos_skipping"] = model_args.use_pos_skipping
overwrite_config["pos_skipping_range"] = model_args.pos_skipping_range
if model_args.rope_scaling_factor is not None and model_args.rope_scaling_type is not None:
overwrite_config["rope_scaling"] = {
"factor": model_args.rope_scaling_factor,
"type": model_args.rope_scaling_type,
}
if training_args.model_max_length is None:
training_args.model_max_length = cfg_pretrained.max_position_embeddings * model_args.rope_scaling_factor
overwrite_config["max_sequence_length"] = training_args.model_max_length
assert training_args.model_max_length == int(cfg_pretrained.max_position_embeddings * model_args.rope_scaling_factor), print(
f"model_max_length: {training_args.model_max_length}, max_position_embeddings: {cfg_pretrained.max_position_embeddings}, rope_scaling_factor: {model_args.rope_scaling_factor}"
)
# overwrite_config["max_sequence_length"] = model_args.max_sequence_length
# overwrite_config["tokenizer_model_max_length"] = model_args.tokenizer_model_max_length
if model_args.mm_spatial_pool_stride is not None and model_args.mm_spatial_pool_out_channels is not None and model_args.mm_spatial_pool_mode is not None and model_args.mm_resampler_type is not None:
overwrite_config["mm_resampler_type"] = model_args.mm_resampler_type
overwrite_config["mm_spatial_pool_stride"] = model_args.mm_spatial_pool_stride
overwrite_config["mm_spatial_pool_out_channels"] = model_args.mm_spatial_pool_out_channels
overwrite_config["mm_spatial_pool_mode"] = model_args.mm_spatial_pool_mode
if model_args.mm_spatial_pool_mode is not None:
overwrite_config["mm_spatial_pool_mode"] = model_args.mm_spatial_pool_mode
if overwrite_config:
assert cfg_pretrained is not None, "cfg_pretrained is None"
rank0_print(f"Overwriting config with {overwrite_config}")
for k, v in overwrite_config.items():
setattr(cfg_pretrained, k, v)
customized_kwargs["config"] = cfg_pretrained
if model_args.model_class_name is not None:
actual_model_class_name = f"{model_args.model_class_name}ForCausalLM"
model_class = getattr(transformers, actual_model_class_name)
rank0_print(f"Using model class {model_class} from {model_args.model_class_name}")
model = model_class.from_pretrained(
model_args.model_name_or_path,
cache_dir=training_args.cache_dir,
attn_implementation=training_args.attn_implementation,
torch_dtype=(torch.bfloat16 if training_args.bf16 else None),
low_cpu_mem_usage=False,
**customized_kwargs,
)
elif model_args.vision_tower is not None:
if "mixtral" in model_args.model_name_or_path.lower():
model = LlavaMixtralForCausalLM.from_pretrained(
model_args.model_name_or_path,
cache_dir=training_args.cache_dir,
attn_implementation=training_args.attn_implementation,
torch_dtype=(torch.bfloat16 if training_args.bf16 else None),
low_cpu_mem_usage=False,
**customized_kwargs,
)
from transformers.models.mixtral.modeling_mixtral import MixtralSparseMoeBlock
deepspeed.utils.set_z3_leaf_modules(model, [MixtralSparseMoeBlock])
elif "mistral" in model_args.model_name_or_path.lower() or "zephyr" in model_args.model_name_or_path.lower():
model = LlavaMistralForCausalLM.from_pretrained(
model_args.model_name_or_path,
cache_dir=training_args.cache_dir,
attn_implementation=training_args.attn_implementation,
torch_dtype=(torch.bfloat16 if training_args.bf16 else None),
low_cpu_mem_usage=False,
**customized_kwargs,
)
elif (
"wizardlm-2" in model_args.model_name_or_path.lower()
or "vicuna" in model_args.model_name_or_path.lower()
or "llama" in model_args.model_name_or_path.lower()
or "yi" in model_args.model_name_or_path.lower()
or "nous-hermes" in model_args.model_name_or_path.lower()
and "wizard-2" in model_args.model_name_or_path.lower()
):
model = LlavaLlamaForCausalLM.from_pretrained(
model_args.model_name_or_path,
cache_dir=training_args.cache_dir,
attn_implementation=training_args.attn_implementation,
torch_dtype=(torch.bfloat16 if training_args.bf16 else None),
low_cpu_mem_usage=False,
**customized_kwargs,
)
elif "qwen" in model_args.model_name_or_path.lower():
if "moe" in model_args.model_name_or_path.lower() or "A14B" in model_args.model_name_or_path:
model = LlavaQwenMoeForCausalLM.from_pretrained(
model_args.model_name_or_path,
cache_dir=training_args.cache_dir,
attn_implementation=training_args.attn_implementation,
torch_dtype=(torch.bfloat16 if training_args.bf16 else None),
low_cpu_mem_usage=False,
**customized_kwargs,
)
from transformers.models.qwen2_moe.modeling_qwen2_moe import Qwen2MoeSparseMoeBlock
deepspeed.utils.set_z3_leaf_modules(model, [Qwen2MoeSparseMoeBlock])
else:
model = LlavaQwenForCausalLM.from_pretrained(
model_args.model_name_or_path,
cache_dir=training_args.cache_dir,
attn_implementation=training_args.attn_implementation,
torch_dtype=(torch.bfloat16 if training_args.bf16 else None),
low_cpu_mem_usage=False,
**customized_kwargs,
)
elif "gemma" in model_args.model_name_or_path.lower():
model = LlavaGemmaForCausalLM.from_pretrained(
model_args.model_name_or_path,
cache_dir=training_args.cache_dir,
attn_implementation=training_args.attn_implementation,
torch_dtype=(torch.bfloat16 if training_args.bf16 else None),
low_cpu_mem_usage=False,
**customized_kwargs,
)
else:
raise ValueError(f"Unknown model class {model_args}")
else:
model = transformers.LlamaForCausalLM.from_pretrained(
model_args.model_name_or_path,
cache_dir=training_args.cache_dir,
attn_implementation=training_args.attn_implementation,
torch_dtype=(torch.bfloat16 if training_args.bf16 else None),
low_cpu_mem_usage=False,
**customized_kwargs,
)
return model
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()
if training_args.verbose_logging:
rank0_print(f"Inspecting experiment hyperparameters:\n")
rank0_print(f"model_args = {vars(model_args)}\n\n")
rank0_print(f"data_args = {vars(data_args)}\n\n")
rank0_print(f"training_args = {vars(training_args)}\n\n")
# rank0_print(f"evaluation_args = {vars(evaluation_args)}\n\n")
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'}
),
)
)
model = get_model(model_args, training_args, bnb_model_from_pretrained_args)
model.config.use_cache = False
if model_args.rope_scaling_factor is not None and model_args.rope_scaling_type is not None:
model.config.rope_scaling = {
"factor": model_args.rope_scaling_factor,
"type": model_args.rope_scaling_type,
}
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 "mistral" in model_args.model_name_or_path.lower() or "mixtral" in model_args.model_name_or_path.lower() or "zephyr" 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="left")
elif "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")
elif (
"wizardlm-2" in model_args.model_name_or_path.lower()
or "vicuna" in model_args.model_name_or_path.lower()
or "llama" in model_args.model_name_or_path.lower()
or "yi" in model_args.model_name_or_path.lower()
or "nous-hermes" in model_args.model_name_or_path.lower()
and "wizard-2" 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",
use_fast=False,
)
rank0_print(f"Prompt version: {model_args.version}")
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.unk_token is not None:
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)
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_grid_pinpoints is not None:
if isinstance(data_args.image_grid_pinpoints, str) and "x" in data_args.image_grid_pinpoints:
try:
patch_size = data_args.image_processor.size[0]
except Exception as e:
patch_size = data_args.image_processor.size["shortest_edge"]
assert patch_size in [224, 336, 384, 448, 512], "patch_size should be in [224, 336, 384, 448, 512]"
# Use regex to extract the range from the input string
matches = re.findall(r"\((\d+)x(\d+)\)", data_args.image_grid_pinpoints)
range_start = tuple(map(int, matches[0]))
range_end = tuple(map(int, matches[-1]))
# Generate a matrix of tuples from (range_start[0], range_start[1]) to (range_end[0], range_end[1])
grid_pinpoints = [(i, j) for i in range(range_start[0], range_end[0] + 1) for j in range(range_start[1], range_end[1] + 1)]
# Multiply all elements by patch_size
data_args.image_grid_pinpoints = [[dim * patch_size for dim in pair] for pair in grid_pinpoints]
elif isinstance(data_args.image_grid_pinpoints, str):
data_args.image_grid_pinpoints = ast.literal_eval(data_args.image_grid_pinpoints)
model.config.image_grid_pinpoints = data_args.image_grid_pinpoints
model.config.image_crop_resolution = data_args.image_crop_resolution
model.config.image_split_resolution = data_args.image_split_resolution
model.config.tokenizer_padding_side = tokenizer.padding_side
model.config.tokenizer_model_max_length = tokenizer.model_max_length
model.config.mm_newline_position = model_args.mm_newline_position
### Deciding train which part of the model
if model_args.mm_tunable_parts is None: # traditional way of deciding which part to train
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)
else:
vision_tower.requires_grad_(False)
else:
rank0_print(f"Using mm_tunable_parts: {model_args.mm_tunable_parts}")
model.config.mm_tunable_parts = training_args.mm_tunable_parts = model_args.mm_tunable_parts
# Set the entire model to not require gradients by default
model.requires_grad_(False)
vision_tower.requires_grad_(False)
model.get_model().mm_projector.requires_grad_(False)
model.get_model().vision_resampler.requires_grad_(False)
# Parse the mm_tunable_parts to decide which parts to unfreeze
tunable_parts = model_args.mm_tunable_parts.split(",")
if "mm_mlp_adapter" in tunable_parts:
for p in model.get_model().mm_projector.parameters():
p.requires_grad = True
if "mm_vision_resampler" in tunable_parts:
for p in model.get_model().vision_resampler.parameters():
p.requires_grad = True
if "mm_vision_tower" in tunable_parts:
for name, param in model.named_parameters():
if "vision_tower" in name:
param.requires_grad_(True)
if "mm_language_model" in tunable_parts:
for name, param in model.named_parameters():
if "vision_tower" not in name and "mm_projector" not in name and "vision_resampler" not in name:
param.requires_grad_(True)
total_params = sum(p.ds_numel if hasattr(p, "ds_numel") else p.numel() for p in model.parameters())
trainable_params = sum(p.ds_numel if hasattr(p, "ds_numel") else p.numel() for p in model.parameters() if p.requires_grad)
rank0_print(f"Total parameters: ~{total_params/1e6:.2f} MB)")
rank0_print(f"Trainable parameters: ~{trainable_params/1e6:.2f} MB)")
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 = 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:
if hasattr(model, "config"):
model.config.save_pretrained(training_args.output_dir)
if hasattr(model, "generation_config"):
model.generation_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)
rank0_print(f"Model saved to {training_args.output_dir}")
if __name__ == "__main__":
train()