# 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 os
import copy
from dataclasses import dataclass, field
import json
import logging
import pathlib
from typing import Dict, Optional, Sequence, List
import numpy as np
import torch
import transformers
import tokenizers
from ola_vlm.constants import IGNORE_INDEX, IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
from torch.utils.data import Dataset
from ola_vlm.train.llava_trainer import LLaVATrainer
from ola_vlm import conversation as conversation_lib
from ola_vlm.model import *
from ola_vlm.mm_utils import tokenizer_image_token
from PIL import Image, ImageFile
from transformers import set_seed
set_seed(42)
# Enable loading of truncated images
ImageFile.LOAD_TRUNCATED_IMAGES = True
local_rank = None
def rank0_print(*args):
if local_rank == 0:
print(*args)
from packaging import version
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)
unfreeze_mm_vision_tower: bool = field(default=False)
unfreeze_whole_model: bool = field(default=False)
use_s2: bool = field(default=False)
s2_scales: Optional[str] = field(default="336,1008")
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")
attn_mask_type: Optional[str] = field(default="causal")
contrastive_loss_weight: Optional[float] = field(default=0.1)
# visual interpretors
image_generator: Optional[str] = field(default="stabilityai/stable-diffusion-2-1-unclip")
image_segmentor: Optional[str] = field(default="shi-labs/oneformer_coco_swin_large") # sam_vit_l_0b3195.pth
depth_estimator: Optional[str] = field(default="depth_anything_v2_vitl.pth")
mode: Optional[str] = field(default="depth-seg-gen")
num_task_tokens: Optional[int] = 0
task_token_format: Optional[str] = "expand_emb"
sample_tokens: Optional[bool] = False
pass_text_to_aux: Optional[bool] = False
# dinov2
use_dinov2: Optional[bool] = False
dinov2_model: Optional[str] = "/mnt/projects4jw/jiteshjain_sherlock/dinov2-large-res336"
dinov2_dim: Optional[str] = 1024
dinov2_layers: Optional[str] = "8-12"
dinov2_loss_weight: Optional[float] = 0.25
use_contrastive: Optional[bool] = True
use_ce: Optional[bool] = False
layer_indices: Optional[str] = "d8-14_s10-16_g12-18"
loss_weights: Optional[str] = "d0.5_s0.5_g0.5"
# gen
img_head_depth: Optional[int] = 1
img_head_dim_head: Optional[int] = 32
img_head_num_heads: Optional[int] = 4
img_head_num_tokens: Optional[int] = 1
img_head_output_dim: Optional[int] = 1024
img_head_ff_mult: Optional[int] = 1
# seg
seg_head_depth: Optional[int] = 1
seg_head_dim_head: Optional[int] = 32
seg_head_num_heads: Optional[int] = 4
seg_head_num_tokens: Optional[int] = 576
seg_head_output_dim: Optional[int] = 1536 # 256
seg_head_ff_mult: Optional[int] = 1
seg_teacher: Optional[str] = "oneformer" # "sam"
# depth
depth_head_depth: Optional[int] = 1
depth_head_dim_head: Optional[int] = 32
depth_head_num_heads: Optional[int] = 4
depth_head_num_tokens: Optional[int] = 576
depth_head_output_dim: Optional[int] = 1024
depth_head_ff_mult: Optional[int] = 1
use_intermediate_depth: Optional[bool] = False
freeze_task_token: Optional[bool] = field(default=False)
freeze_aux_heads: Optional[bool] = field(default=False)
use_reference_model: Optional[bool] = field(default=False)
@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)
depth_folder: Optional[str] = field(default=None)
unclip_folder: Optional[str] = field(default=None)
seg_folder: Optional[str] = field(default=None)
image_aspect_ratio: str = 'square'
use_cost: bool = False
@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)
mpt_attn_impl: Optional[str] = field(default="triton")
model_max_length: int = field(
default=512,
metadata={
"help":
"Maximum sequence length. Sequences will be right padded (and possibly truncated)."
},
)
double_quant: bool = field(
default=True,
metadata={"help": "Compress the quantization statistics through double quantization."}
)
quant_type: str = field(
default="nf4",
metadata={"help": "Quantization data type to use. Should be one of `fp4` or `nf4`."}
)
bits: int = field(
default=16,
metadata={"help": "How many bits to use."}
)
lora_enable: bool = False
lora_r: int = 64
lora_alpha: int = 16
lora_dropout: float = 0.05
lora_weight_path: str = ""
lora_bias: str = "none"
mm_projector_lr: Optional[float] = None
mm_vision_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 find_all_linear_names(model):
cls = torch.nn.Linear
lora_module_names = set()
multimodal_keywords = ['mm_projector', 'vision_tower', 'vision_resampler']
for name, module in model.named_modules():
if any(mm_keyword in name for mm_keyword in multimodal_keywords):
continue
if isinstance(module, cls):
names = name.split('.')
lora_module_names.add(names[0] if len(names) == 1 else names[-1])
if 'lm_head' in lora_module_names: # needed for 16-bit
lora_module_names.remove('lm_head')
return list(lora_module_names)
def safe_save_model_for_hf_trainer(trainer: transformers.Trainer,
output_dir: str):
"""Collects the state dict and dump to disk."""
if getattr(trainer.args, "tune_mm_mlp_adapter", False):
# Only save Adapter
keys_to_match = ['mm_projector']
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'))
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_phi_3(
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)) - 2
else:
round_len = len(tokenizer(rou).input_ids)
instruction_len = len(tokenizer(parts[0]).input_ids) - 2
if i > 0:
round_len -= 2
instruction_len -= 2
target[cur_len : cur_len + instruction_len] = IGNORE_INDEX
cur_len += round_len
target[cur_len:] = IGNORE_INDEX
if cur_len < tokenizer.model_max_length:
if cur_len != total_len:
target[:] = IGNORE_INDEX
print(
f"WARNING: tokenization mismatch: {cur_len} vs. {total_len}."
f" (ignored)"
)
return dict(
input_ids=input_ids,
labels=targets,
)
def preprocess_llama_3(
sources,
tokenizer: transformers.PreTrainedTokenizer,
has_image: bool = False
) -> Dict:
conv = 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 = 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)) - 2
else:
round_len = len(tokenizer(rou).input_ids)
instruction_len = len(tokenizer(parts[0]).input_ids) - 2
# 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_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_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_qwen(
sources,
tokenizer: transformers.PreTrainedTokenizer,
has_image: bool = False,
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(
sources: Sequence[str],
tokenizer: transformers.PreTrainedTokenizer,
has_image: bool = False
) -> Dict:
"""
Given a list of sources, each is a conversation list. This transform:
1. Add signal '### ' at the beginning each sentence, with end signal '\n';
2. Concatenate conversations together;
3. Tokenize the concatenated conversation;
4. Make a deepcopy as the target. Mask human words with IGNORE_INDEX.
"""
if conversation_lib.default_conversation.version == "llama3":
return preprocess_llama_3(sources, tokenizer, has_image=has_image)
if conversation_lib.default_conversation.version == "phi3":
return preprocess_phi_3(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.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)
# add end signal and concatenate together
conversations = []
for source in sources:
header = f"{conversation_lib.default_conversation.system}\n\n"
conversation = _add_speaker_and_signal(header, source)
conversations.append(conversation)
# tokenize conversations
def get_tokenize_len(prompts):
return [len(tokenizer_image_token(prompt, tokenizer)) for prompt in prompts]
if has_image:
input_ids = [tokenizer_image_token(prompt, tokenizer, return_tensors='pt') for prompt in conversations]
else:
conversations_tokenized = _tokenize_fn(conversations, tokenizer)
input_ids = conversations_tokenized["input_ids"]
targets = copy.deepcopy(input_ids)
for target, source in zip(targets, sources):
if has_image:
tokenized_lens = get_tokenize_len([header] + [s["value"] for s in source])
else:
tokenized_lens = _tokenize_fn([header] + [s["value"] for s in source], tokenizer)["input_ids_lens"]
speakers = [sentence["from"] for sentence in source]
_mask_targets(target, tokenized_lens, speakers)
return dict(input_ids=input_ids, labels=targets)
def read_jsonl(path):
list_data_dict = []
with open(path, "r") as file:
for line in file:
d = json.loads(line)
list_data_dict.append(d)
return list_data_dict
def _obtain_seg_texts(file_path):
def _remove_specific_word(text, word_to_remove):
import re
tokens = re.findall(r'\b\w+\b|[,.]', text)
result_tokens = []
word_found = False
for i, token in enumerate(tokens):
if token == word_to_remove:
if not word_found:
# Keep the first occurrence and mark it as found
result_tokens.append(token)
word_found = True
else:
# Remove any preceding punctuation if it's just before this word
if i > 0 and tokens[i-1] in {',', '.'}:
result_tokens.pop()
else:
result_tokens.append(token)
# Join tokens and clean up spaces before punctuation
result_text = ' '.join(result_tokens)
result_text = re.sub(r'\s([,.](?:\s|$))', r'\1', result_text)
return result_text
with open(file_path) as f:
lines = f.readlines()
seg_labels = {}
for line in lines:
key = line.split("![]()
")[1].strip("\n")
label = line.split("![]()
")[2].strip("\n")
label = _remove_specific_word(label, "wall")
label = _remove_specific_word(label, "window")
seg_labels[key] = label
return seg_labels
from ola_vlm.ola_utils import PANOPTIC_QUESTIONS, SEMANTIC_QUESTIONS, INSTANCE_QUESTIONS
import random
def get_object_data_split(data_args):
list_data_dict = []
for bucket in ["train"]:
panoptic_labels = _obtain_seg_texts(os.path.join(data_args.image_folder, "coco", "panoptic.txt"))
semantic_labels = _obtain_seg_texts(os.path.join(data_args.image_folder, "coco", "semantic.txt"))
instance_labels = _obtain_seg_texts(os.path.join(data_args.image_folder, "coco", "instance.txt"))
for key in panoptic_labels.keys():
assert key in semantic_labels.keys() and key in instance_labels.keys(), "Instance, semantic, and panoptic labels should have the same keys."
prob_task = np.random.uniform(0,1.)
question_prob = np.random.uniform(0,1.)
if prob_task < 0.33:
answer = semantic_labels[key]
if question_prob > 0.90:
question = "What objects can be seen in the image?"
else:
question = random.choice(SEMANTIC_QUESTIONS)
elif prob_task < 0.66:
answer = instance_labels[key]
if question_prob > 0.90:
question = "What objects can be seen in the image?"
else:
question = random.choice(INSTANCE_QUESTIONS)
else:
answer = panoptic_labels[key]
if question_prob > 0.90:
question = "What objects can be seen in the image?"
else:
question = random.choice(PANOPTIC_QUESTIONS)
question += "\n"
conversations = [
{
"from": "human",
"value": question
},
{
"from": "gpt",
"value": answer
},
]
list_data_dict.append(
{
"conversations": conversations,
"image": "coco/" + bucket + "2017/" + key,
}
)
random.shuffle(list_data_dict)
return list_data_dict
class LazySupervisedDataset(Dataset):
"""Dataset for supervised fine-tuning."""
def __init__(self, data_path: str,
tokenizer: transformers.PreTrainedTokenizer,
data_args: DataArguments):
super(LazySupervisedDataset, self).__init__()
if "jsonl" in data_path:
list_data_dict = read_jsonl(data_path)
else:
list_data_dict = json.load(open(data_path, "r"))
rank0_print("Formatting inputs...Skip in lazy mode")
self.tokenizer = tokenizer
self.list_data_dict = list_data_dict
self.data_args = data_args
if data_args.use_cost:
cost_list_data = get_object_data_split(data_args)
self.list_data_dict.extend(cost_list_data)
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
try:
crop_size = self.data_args.image_processor.crop_size
except:
crop_size = self.data_args.image_processor.size
try:
image = Image.open(os.path.join(image_folder, image_file)).convert('RGB')
pil_image = Image.open(os.path.join(image_folder, image_file)).convert('RGB')
except Exception as e:
from icecream import ic
ic("----------------------------------")
ic("OS ERROROROROROROROROROROOR")
ic("OS ERROROROROROROROROROROOR")
ic(image_file)
ic(e)
ic("OS ERROROROROROROROROROROOR")
ic("OS ERROROROROROROROROROROOR")
ic("===================================")
return self.__getitem__(0)
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 = processor.preprocess(image, return_tensors='pt')['pixel_values'][0]
else:
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['pil_image'] = pil_image
data_dict['seg_mask'] = 1
data_dict['depth_mask'] = 1
data_dict['gen_mask'] = 1
elif self.data_args.is_multimodal:
try:
crop_size = self.data_args.image_processor.crop_size
except:
crop_size = self.data_args.image_processor.size
data_dict['image'] = torch.zeros(3, crop_size['height'], crop_size['width'])
data_dict['pil_image'] = Image.new('RGB', (crop_size['width'], crop_size['height']), color='black')
data_dict['seg_mask'] = 0
data_dict['depth_mask'] = 0
data_dict['gen_mask'] = 0
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]
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
if 'pil_image' in instances[0]:
pil_images = [instance['pil_image'] for instance in instances]
batch['pil_images'] = pil_images
seg_mask = [instance['seg_mask'] for instance in instances]
batch['seg_mask'] = torch.tensor(seg_mask)
depth_mask = [instance['depth_mask'] for instance in instances]
batch['depth_mask'] = torch.tensor(depth_mask)
gen_mask = [instance['gen_mask'] for instance in instances]
batch['gen_mask'] = torch.tensor(gen_mask)
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 add_special_tokens(
special_tokens: List,
tokenizer: transformers.PreTrainedTokenizer,
model: transformers.PreTrainedModel,
):
"""Resize tokenizer and embedding.
Initialize new token embeddings to follow the distribution of existing embeddings.
"""
# Add special tokens to tokenizer
num_new_tokens = tokenizer.add_tokens(special_tokens, special_tokens=True)
# Resize the token embeddings in the model
model.resize_token_embeddings(len(tokenizer))
if num_new_tokens > 0:
# Get input embeddings and compute global mean and std over all dimensions
input_embeddings = model.get_input_embeddings().weight.data
input_mean, input_std = input_embeddings.mean(), input_embeddings.std()
# Initialize new input embeddings with the same distribution as existing ones
input_embeddings[-num_new_tokens:] = torch.nn.init.normal_(
torch.empty(num_new_tokens, input_embeddings.size(1)),
mean=input_mean.item(),
std=input_std.item()
)
# Check if model has output embeddings and initialize them similarly
if model.get_output_embeddings() is not None:
output_embeddings = model.get_output_embeddings().weight.data
output_mean, output_std = output_embeddings.mean(), output_embeddings.std()
# Initialize new output embeddings with the same distribution as existing ones
output_embeddings[-num_new_tokens:] = torch.nn.init.normal_(
torch.empty(num_new_tokens, output_embeddings.size(1)),
mean=output_mean.item(),
std=output_std.item()
)
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'}
)
))
if model_args.vision_tower is not None:
if 'phi' in model_args.model_name_or_path.lower():
model = OlaLlavaPhi3ForCausalLM.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
)
elif 'qwen' in model_args.model_name_or_path.lower():
model = OlaLlavaQwenForCausalLM.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
)
else:
model = OlaLlavaLlamaForCausalLM.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
)
else:
if 'phi' in model_args.model_name_or_path.lower():
model = transformers.Phi3ForCausalLM.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
)
elif 'qwen2' in model_args.model_name_or_path.lower():
model = transformers.Qwen2ForCausalLM.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
)
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),
lora_dropout=training_args.lora_dropout,
bias=training_args.lora_bias,
task_type="CAUSAL_LM",
)
if training_args.bits == 16:
if training_args.bf16:
model.to(torch.bfloat16)
if training_args.fp16:
model.to(torch.float16)
rank0_print("Adding LoRA adapters...")
model = get_peft_model(model, lora_config)
if "qwen" in model_args.model_name_or_path.lower():
tokenizer = transformers.AutoTokenizer.from_pretrained(model_args.model_name_or_path, cache_dir=training_args.cache_dir, model_max_length=training_args.model_max_length, padding_side="right")
else:
tokenizer = transformers.AutoTokenizer.from_pretrained(
model_args.model_name_or_path,
cache_dir=training_args.cache_dir,
model_max_length=training_args.model_max_length,
padding_side="right",
use_fast=False,
)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.unk_token
if tokenizer.pad_token_id is None:
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["llava_phi_3"]
if "sherlock" in model_args.model_name_or_path:
vision_tower = model.get_vision_tower()
if vision_tower is None:
model.get_model().initialize_vision_modules(
model_args=model_args,
fsdp=training_args.fsdp
)
vision_tower = model.get_vision_tower()
if not vision_tower.is_loaded:
vision_tower.load_model()
vision_tower.to(dtype=torch.bfloat16 if training_args.bf16 else torch.float16, device=training_args.device)
elif 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_grid_pinpoints = [[336,672], [672,336], [672,672], [1008,336], [336,1008]]
model.config.image_aspect_ratio = data_args.image_aspect_ratio
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
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
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 model_args.unfreeze_mm_vision_tower:
model.requires_grad_(False)
for p in model.get_model().mm_projector.parameters():
p.requires_grad = True
model.get_model().vision_tower.requires_grad_(True)
else:
model.get_model().vision_tower.requires_grad_(False)
model.config.use_s2 = model_args.use_s2
model.config.s2_scales = model_args.s2_scales
if "sherlock" not in model_args.model_name_or_path.split("/")[-1]:
aux_mode = model_args.mode
model.config.aux_mode = model_args.mode
model.config.contrastive_loss_weight = model_args.contrastive_loss_weight
model.config.num_task_tokens = model_args.num_task_tokens
model.config.task_token_format = model_args.task_token_format
model.config.pass_text_to_aux = model_args.pass_text_to_aux
model.config.use_contrastive = model_args.use_contrastive
model.config.use_ce = model_args.use_ce
layer_indices = model_args.layer_indices
pattern = r'[a-zA-Z]\d+(?:-\d+)?'
import re
# Extract matching substrings from each string
matches = re.findall(pattern, layer_indices)
depth_layer_indices = "0"
seg_layer_indices = "0"
img_layer_indices = "0"
for match in matches:
if match.startswith('d'):
depth_layer_indices = match[1:]
elif match.startswith('s'):
seg_layer_indices = match[1:]
elif match.startswith('g'):
img_layer_indices = match[1:]
loss_weights = model_args.loss_weights
pattern = r'[a-zA-Z]\d+\.\d+'
matches = re.findall(pattern, loss_weights)
img_loss_weight = 0.5
seg_loss_weight = 0.5
depth_loss_weight = 0.5
for match in matches:
if match.startswith('d'):
depth_loss_weight = float(match[1:])
elif match.startswith('s'):
seg_loss_weight = float(match[1:])
elif match.startswith('g'):
img_loss_weight = float(match[1:])
model.config.image_gen = {
"depth": model_args.img_head_depth,
"dim_head": model_args.img_head_dim_head,
"num_heads": model_args.img_head_num_heads,
"num_tokens": model_args.img_head_num_tokens,
"output_dim": model_args.img_head_output_dim,
"ff_mult": model_args.img_head_ff_mult,
"img_layer_indices": img_layer_indices,
"img_loss_weight": img_loss_weight,
}
model.config.image_generator = model_args.image_generator
model.config.image_seg = {
"depth": model_args.seg_head_depth,
"dim_head": model_args.seg_head_dim_head,
"num_heads": model_args.seg_head_num_heads,
"num_tokens": model_args.seg_head_num_tokens,
"output_dim": model_args.seg_head_output_dim,
"ff_mult": model_args.seg_head_ff_mult,
"seg_layer_indices": seg_layer_indices,
"seg_loss_weight": seg_loss_weight,
"seg_teacher": model_args.seg_teacher,
}
model.config.image_segmentor = model_args.image_segmentor
model.config.image_depth = {
"depth": model_args.depth_head_depth,
"dim_head": model_args.depth_head_dim_head,
"num_heads": model_args.depth_head_num_heads,
"num_tokens": model_args.depth_head_num_tokens,
"output_dim": model_args.depth_head_output_dim,
"ff_mult": model_args.depth_head_ff_mult,
"depth_layer_indices": depth_layer_indices,
"depth_loss_weight": depth_loss_weight,
"use_intermediate_depth": model_args.use_intermediate_depth,
}
model.config.depth_estimator = model_args.depth_estimator
model.config.sample_tokens = model_args.sample_tokens
num_task_tokens = model_args.num_task_tokens
if model_args.use_dinov2:
model.config.dinov2_feats = {
"model": model_args.dinov2_model,
"dinov2_layer_indices": model_args.dinov2_layers,
"dim": model_args.dinov2_dim,
"dinov2_loss_weight": model_args.dinov2_loss_weight,
}
model.config.num_task_tokens = model_args.num_task_tokens
model.config.task_token_format = model_args.task_token_format
if model_args.num_task_tokens > 0:
if model_args.task_token_format == "text":
if "depth" in aux_mode:
special_depth_tokens = [f"" for i in range(num_task_tokens)]
special_depth_tokens_str = "".join(special_depth_tokens)
add_special_tokens(
special_tokens=special_depth_tokens,
tokenizer=tokenizer,
model=model,
)
model.config.depth_tokens = tokenizer(special_depth_tokens_str).input_ids[1:]
if "seg" in aux_mode:
special_seg_tokens = [f"" for i in range(num_task_tokens)]
special_seg_tokens_str = "".join(special_seg_tokens)
add_special_tokens(
special_tokens=special_seg_tokens,
tokenizer=tokenizer,
model=model,
)
model.config.seg_tokens = tokenizer(special_seg_tokens_str).input_ids[1:]
if "gen" in aux_mode:
special_gen_tokens = [f"" for i in range(num_task_tokens)]
special_gen_tokens_str = "".join(special_gen_tokens)
add_special_tokens(
special_tokens=special_gen_tokens,
tokenizer=tokenizer,
model=model,
)
model.config.gen_tokens = tokenizer(special_gen_tokens_str).input_ids[1:]
model.get_model().initialize_special_tokens(model.config)
model.init_heads(model.config)
model.init_target_models(model.config)
elif model_args.unfreeze_whole_model:
model.requires_grad_(True)
elif model_args.unfreeze_mm_vision_tower:
if "depth" in model_args.mode:
for p in model.image_depth_heads.parameters():
p.requires_grad = True
if "gen" in model_args.mode:
for p in model.image_gen_heads.parameters():
p.requires_grad = True
if "seg" in model_args.mode:
for p in model.image_seg_heads.parameters():
p.requires_grad = True
if "emb" in model.config.task_token_format and model.config.num_task_tokens > 0:
if "gen" in aux_mode:
model.get_model().special_gen_tokens.requires_grad_(True)
if "seg" in aux_mode:
model.get_model().special_seg_tokens.requires_grad_(True)
if "depth" in aux_mode:
model.get_model().special_depth_tokens.requires_grad_(True)
elif not model_args.tune_mm_mlp_adapter:
if "emb" in model.config.task_token_format and model.config.num_task_tokens > 0:
if "gen" in model.config.aux_mode:
model.get_model().special_gen_tokens.requires_grad_(False)
if "seg" in model.config.aux_mode:
model.get_model().special_seg_tokens.requires_grad_(False)
if "depth" in model.config.aux_mode:
model.get_model().special_depth_tokens.requires_grad_(False)
loss_weights = model_args.loss_weights
import re
pattern = r'[a-zA-Z]\d+\.\d+'
matches = re.findall(pattern, loss_weights)
img_loss_weight = 0.5
seg_loss_weight = 0.5
depth_loss_weight = 0.5
for match in matches:
if match.startswith('d'):
depth_loss_weight = float(match[1:])
elif match.startswith('s'):
seg_loss_weight = float(match[1:])
elif match.startswith('g'):
img_loss_weight = float(match[1:])
model.config.image_seg["seg_loss_weight"] = seg_loss_weight
model.config.image_gen["img_loss_weight"] = img_loss_weight
model.config.image_depth["depth_loss_weight"] = depth_loss_weight
if model_args.use_reference_model:
model.init_reference_model()
for name, p in model.named_parameters():
if "sam." in name or "da_v2_head." in name or "dinov2_model." in name or "gen_encoder." in name or "dav2_backbone." in name or "oneformer." in name:
p.requires_grad = False
model.img_gen_loss_weight = img_loss_weight
model.img_seg_loss_weight = seg_loss_weight
model.img_depth_loss_weight = depth_loss_weight
if model_args.num_task_tokens > 0:
if "emb" in model.config.task_token_format and model_args.freeze_task_token:
if "gen" in model.config.aux_mode:
model.get_model().special_gen_tokens.requires_grad_(False)
if "seg" in model.config.aux_mode:
model.get_model().special_seg_tokens.requires_grad_(False)
if "depth" in model.config.aux_mode:
model.get_model().special_depth_tokens.requires_grad_(False)
else:
if "gen" in model.config.aux_mode:
model.get_model().special_gen_tokens.requires_grad_(True)
if "seg" in model.config.aux_mode:
model.get_model().special_seg_tokens.requires_grad_(True)
if "depth" in model.config.aux_mode:
model.get_model().special_depth_tokens.requires_grad_(True)
if model_args.freeze_aux_heads:
model.get_model().vision_tower.requires_grad_(False)
if "depth" in model.config.aux_mode:
for p in model.image_depth_heads.parameters():
p.requires_grad = False
model.depth_logit_scale.requires_grad_(False)
if "gen" in model.config.aux_mode:
for p in model.image_gen_heads.parameters():
p.requires_grad = False
model.gen_logit_scale.requires_grad_(False)
if "seg" in model.config.aux_mode:
for p in model.image_seg_heads.parameters():
p.requires_grad = False
model.seg_logit_scale.requires_grad_(False)
import torch.distributed as dist
from icecream import ic
if dist.get_rank() == 0:
gen_heads = 0
depth_heads = 0
seg_heads = 0
for n, p in model.named_parameters():
if p.requires_grad:
if "gen_head" in n:
gen_heads += p.numel()
elif "depth_head" in n:
depth_heads += p.numel()
elif "seg_head" in n:
seg_heads += p.numel()
ic(n)
ic(depth_heads, gen_heads, seg_heads)
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)
print('starting training...', local_rank)
if list(pathlib.Path(training_args.output_dir).glob("checkpoint-*")):
trainer.train(resume_from_checkpoint=True)
else:
trainer.train()
trainer.save_state()
model.config.use_cache = True
if training_args.lora_enable:
state_dict = get_peft_state_maybe_zero_3(
model.named_parameters(), training_args.lora_bias
)
non_lora_state_dict = get_peft_state_non_lora_maybe_zero_3(
model.named_parameters()
)
if training_args.local_rank == 0 or training_args.local_rank == -1:
model.config.save_pretrained(training_args.output_dir)
model.save_pretrained(training_args.output_dir, state_dict=state_dict)
torch.save(non_lora_state_dict, os.path.join(training_args.output_dir, 'non_lora_trainables.bin'))
else:
safe_save_model_for_hf_trainer(trainer=trainer,
output_dir=training_args.output_dir)
if __name__ == "__main__":
train()