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train.py
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1270 lines (1031 loc) · 51.4 KB
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"""
Trainer adapted from https://github.com/aimagelab/DiCO/blob/master/trainers/dico_trainer.py
"""
# -------------------- PATCHES
def patch_trainer_save_checkpoint():
"""
Patch LLaVATrainer to save correctly mm_adapter weights and peft config in intermediate checkpoints.
See:
- https://github.com/haotian-liu/LLaVA/issues/844
- https://github.com/haotian-liu/LLaVA/issues/729
"""
import os
import torch
from llava.train.llava_trainer import get_mm_adapter_state_maybe_zero_3
from llava.train.train import get_peft_state_maybe_zero_3, get_peft_state_non_lora_maybe_zero_3
import llava
def _save_checkpoint(self, model, trial, metrics=None):
if getattr(self.args, 'tune_mm_mlp_adapter', False):
from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR
checkpoint_folder = f"{PREFIX_CHECKPOINT_DIR}-{self.state.global_step}"
run_dir = self._get_output_dir(trial=trial)
output_dir = os.path.join(run_dir, checkpoint_folder)
# Only save Adapter
keys_to_match = ['mm_projector', 'vision_resampler']
if getattr(self.args, "use_im_start_end", False):
keys_to_match.extend(['embed_tokens', 'embed_in'])
weight_to_save = get_mm_adapter_state_maybe_zero_3(self.model.named_parameters(), keys_to_match)
if self.args.local_rank == 0 or self.args.local_rank == -1:
self.model.config.save_pretrained(output_dir)
torch.save(weight_to_save, os.path.join(output_dir, f'mm_projector.bin'))
else:
if self.args.lora_enable:
from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR
checkpoint_folder = f"{PREFIX_CHECKPOINT_DIR}-{self.state.global_step}"
run_dir = self._get_output_dir(trial=trial)
output_dir = os.path.join(self.args.output_dir, checkpoint_folder)
os.makedirs(output_dir, exist_ok=True)
state_dict = get_peft_state_maybe_zero_3(
self.model.named_parameters(), self.args.lora_bias
)
non_lora_state_dict = get_peft_state_non_lora_maybe_zero_3(
self.model.named_parameters()
)
if self.is_world_process_zero():
print(f"save models to {output_dir} ")
self.model.config.save_pretrained(output_dir)
self.model.save_pretrained(output_dir, state_dict=state_dict)
torch.save(non_lora_state_dict, os.path.join(output_dir, 'non_lora_trainables.bin'))
else:
super()._save_checkpoint(model, trial, metrics)
llava.train.llava_trainer.LLaVATrainer._save_checkpoint = _save_checkpoint
print("### LLaVATrainer._save_checkpoint patched successfully! ###")
patch_trainer_save_checkpoint()
# -------------------- IMPORTS
import copy
import json
import os
import pathlib
import random
from argparse import Namespace
from collections import defaultdict
from contextlib import nullcontext
from dataclasses import dataclass, field
from typing import Dict, Optional, Sequence
import torch
import torch.nn.functional as F
import transformers
from PIL import Image
from transformers import (
Seq2SeqTrainer,
Seq2SeqTrainingArguments,
logging,
set_seed,
)
from llava import conversation as conversation_lib
from llava.constants import IGNORE_INDEX
from llava.mm_utils import process_anyres_image, tokenizer_image_token
from llava.model.language_model.llava_llama import LlavaLlamaForCausalLM
from llava.train.llava_trainer import LLaVATrainer
from llava.train.train import (
LazySupervisedDataset,
ModelArguments,
TrainingArguments,
find_all_linear_names,
get_peft_state_maybe_zero_3,
get_peft_state_non_lora_maybe_zero_3,
preprocess_multimodal,
rank0_print,
safe_save_model_for_hf_trainer,
smart_tokenizer_and_embedding_resize,
)
from chair_modeling import ChairRewardModel
logger = logging.get_logger(__name__)
SEED = 42
local_rank = None
# -------------------- DEBUG
class CUDATimer:
def __init__(self):
self.start = torch.cuda.Event(enable_timing=True)
self.end = torch.cuda.Event(enable_timing=True)
self.elapsed_time = None
def __enter__(self):
self.start.record()
return self
def __exit__(self, exc_type, exc_val, exc_tb):
self.end.record()
torch.cuda.synchronize()
self.elapsed_time = self.start.elapsed_time(self.end)
# -------------------- PARSER
@dataclass
class DataArguments:
train_split_path: str
eval_split_path: str
detections_path: str
lazy_preprocess: bool = False
is_multimodal: bool = False
image_folder: Optional[str] = field(default=None)
image_aspect_ratio: str = "pad"
mini_eval: bool = field(default=True)
@dataclass
class CustomArguments:
dpo_beta: float = field(default=0.2)
dico_tau: float = field(default=300)
dpo_num_beams: int = field(default=2)
dpo_max_new_tokens: int = field(default=200)
online_generations: bool = field(default=False)
dpo_xe_weight: float = field(default=0.05)
dpo_chair_weight: float = field(default=1.0)
dpo_recall_weight: float = field(default=0.0)
@dataclass
class LlavaSeq2SeqTrainingArguments(TrainingArguments, Seq2SeqTrainingArguments):
pass
# -------------------- DATA PREPROCESSING - OFFLINE
def preprocess_offlinedpo(
sources,
completions,
targets,
tokenizer: transformers.PreTrainedTokenizer,
has_image: bool = False
) -> Dict:
if conversation_lib.default_conversation.sep_style == conversation_lib.SeparatorStyle.LLAMA_3_1:
return preprocess_offlinedpo_llama_3_1(sources=sources, completions=completions, targets=targets, tokenizer=tokenizer, has_image=has_image)
elif conversation_lib.default_conversation.version.startswith("v1"):
return preprocess_offlinedpo_v1(sources=sources, completions=completions, targets=targets, tokenizer=tokenizer, has_image=has_image)
else:
raise NotImplementedError("preprocess only supports models based on 'llama_3_1' and 'v1'")
def preprocess_offlinedpo_llama_3_1(
sources,
completions,
targets,
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"])
conv.append_message(roles["gpt"], None)
conversations.append(conv.get_prompt())
# Tokenize prompts
if has_image:
prompt_ids = torch.stack([tokenizer_image_token(prompt, tokenizer, return_tensors='pt') for prompt in conversations], dim=0)
else:
prompt_ids = tokenizer(
conversations,
return_tensors="pt",
padding="longest",
max_length=tokenizer.model_max_length,
truncation=True,
).input_ids
# remove the first bos token
if prompt_ids[0][0] == prompt_ids[0][1] == tokenizer.bos_token_id:
prompt_ids = prompt_ids[:, 1:]
assert conv.sep_style == conversation_lib.SeparatorStyle.LLAMA_3_1
# Tokenize completions
completions = [completion + tokenizer.eos_token for completion in completions] # add EOS
completion_ids = tokenizer(
completions,
return_tensors="pt",
padding="longest",
max_length=tokenizer.model_max_length,
truncation=True,
).input_ids
completion_ids = completion_ids[:, 1:] # remove BOS
# Tokenize targets
targets = [target + tokenizer.eos_token for target in targets] # add EOS
target_ids = tokenizer(
targets,
return_tensors="pt",
padding="longest",
max_length=tokenizer.model_max_length,
truncation=True,
).input_ids
target_ids = target_ids[:, 1:] # remove BOS
return dict(
prompt_ids=prompt_ids,
completion_ids=completion_ids,
target_ids=target_ids
)
def preprocess_offlinedpo_v1(
sources: Sequence[str],
completions,
targets,
tokenizer: transformers.PreTrainedTokenizer,
has_image: bool = False
) -> Dict:
assert conversation_lib.default_conversation.version.startswith("v1")
conv = conversation_lib.default_conversation.copy()
roles = {"human": conv.roles[0], "gpt": conv.roles[1]}
# Apply prompt templates
conversations = [] # ["q1 a1 q2 a2 ... qn",]
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"])
conv.append_message(roles["gpt"], None)
conversations.append(conv.get_prompt())
# Tokenize prompts
if has_image:
prompt_ids = torch.stack([tokenizer_image_token(prompt, tokenizer, return_tensors='pt') for prompt in conversations], dim=0)
else:
prompt_ids = tokenizer(
conversations,
return_tensors="pt",
padding="longest",
max_length=tokenizer.model_max_length,
truncation=True,
).input_ids
# Tokenize completions
completions = [completion + "</s>" for completion in completions] # add EOS
completion_ids = tokenizer(
completions,
return_tensors="pt",
padding="longest",
max_length=tokenizer.model_max_length,
truncation=True,
).input_ids
completion_ids = completion_ids[:, 1:] # remove BOS
# Tokenize targets
targets = [target + "</s>" for target in targets] # add EOS
target_ids = tokenizer(
targets,
return_tensors="pt",
padding="longest",
max_length=tokenizer.model_max_length,
truncation=True,
).input_ids
target_ids = target_ids[:, 1:] # remove BOS
return dict(
prompt_ids=prompt_ids,
completion_ids=completion_ids,
target_ids=target_ids
)
class OfflineDPODataset(LazySupervisedDataset):
MINI_EVAL_SIZE = 500
def __init__(
self,
data_path: str,
tokenizer: transformers.PreTrainedTokenizer,
data_args: DataArguments,
train_eval,
model_args
):
super(OfflineDPODataset, self).__init__(data_path, tokenizer, data_args, model_args)
self.train_eval = train_eval
if data_args.mini_eval and train_eval == "eval":
self.list_data_dict = self.list_data_dict[:self.MINI_EVAL_SIZE]
logger.warning("Using mini eval")
def __getitem__(self, i) -> Dict[str, torch.Tensor]:
sources = self.list_data_dict[i]
if isinstance(i, int):
sources = [sources]
assert len(sources) == 1, "Don't know why it is wrapped to a list" # FIXME
if 'image' in sources[0]:
image_file = self.list_data_dict[i]['image']
image_folder = self.data_args.image_folder
processor = self.data_args.image_processor
image = Image.open(os.path.join(image_folder, image_file)).convert('RGB')
if self.data_args.image_aspect_ratio == 'pad':
def expand2square(pil_img, background_color):
width, height = pil_img.size
if width == height:
return pil_img
elif width > height:
result = Image.new(pil_img.mode, (width, width), background_color)
result.paste(pil_img, (0, (width - height) // 2))
return result
else:
result = Image.new(pil_img.mode, (height, height), background_color)
result.paste(pil_img, ((height - width) // 2, 0))
return result
image = expand2square(image, tuple(int(x*255) for x in processor.image_mean))
image_size = image.size
image = processor.preprocess(image, return_tensors='pt')['pixel_values'][0]
elif self.data_args.image_aspect_ratio == "anyres":
image_size = image.size
image = process_anyres_image(image, processor, self.data_args.image_grid_pinpoints, self.siglip) # torch.Size([5, 3, 336, 336])
else:
image_size = image.size
image = processor.preprocess(image, return_tensors='pt')['pixel_values'][0]
sources = preprocess_multimodal(
copy.deepcopy([e["conversations"] for e in sources]),
self.data_args)
else:
sources = copy.deepcopy([e["conversations"] for e in sources])
assert isinstance(i, int), "expected an int i"
completions = [self.list_data_dict[i]["answer_1"], self.list_data_dict[i]["answer_2"]]
targets = [self.list_data_dict[i]["target"]] # list containing a single str
data_dict = preprocess_offlinedpo(
sources=sources,
completions=completions,
targets=targets,
tokenizer=self.tokenizer,
has_image=('image' in self.list_data_dict[i])
)
if isinstance(i, int):
data_dict = dict(
prompt_ids=data_dict["prompt_ids"][0],
completion_ids=data_dict["completion_ids"],
target_ids=data_dict["target_ids"][0]
)
# store sample ids to retrieve detections
assert isinstance(i, int)
data_dict["sample_id"] = self.list_data_dict[i]["id"]
# image exist in the data
if 'image' in self.list_data_dict[i]:
data_dict['image'] = image
data_dict['image_size'] = image_size
elif self.data_args.is_multimodal:
# image does not exist in the data, but the model is multimodal
crop_size = self.data_args.image_processor.crop_size
data_dict['image'] = torch.zeros(3, crop_size['height'], crop_size['width'])
data_dict['image_size'] = (crop_size['height'], crop_size['width'])
return data_dict
@dataclass
class OfflineDPODataCollator:
"""Collate examples for supervised fine-tuning."""
tokenizer: transformers.PreTrainedTokenizer
def __call__(self, instances: Sequence[Dict]) -> Dict[str, torch.Tensor]:
prompt_ids, completion_ids, target_ids = tuple(
[instance[key] for instance in instances]
for key in ("prompt_ids", "completion_ids", "target_ids")
)
prompt_ids = torch.nn.utils.rnn.pad_sequence(
prompt_ids,
batch_first=True,
padding_value=self.tokenizer.pad_token_id)
completion_ids = [c for pair in completion_ids for c in pair]
completion_ids = torch.nn.utils.rnn.pad_sequence(
completion_ids,
batch_first=True,
padding_value=self.tokenizer.pad_token_id)
target_ids = torch.nn.utils.rnn.pad_sequence(
target_ids,
batch_first=True,
padding_value=IGNORE_INDEX
)
prompt_ids = prompt_ids[:, :self.tokenizer.model_max_length]
completion_ids = completion_ids[:, :self.tokenizer.model_max_length]
target_ids = target_ids[:, :self.tokenizer.model_max_length]
batch = dict(
prompt_ids=prompt_ids,
attention_mask=prompt_ids.ne(self.tokenizer.pad_token_id),
completion_ids=completion_ids,
target_ids=target_ids,
)
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
# add sample ids
batch["sample_ids"] = [instance["sample_id"] for instance in instances]
return batch
def make_offlinedpo_data_module(
tokenizer: transformers.PreTrainedTokenizer,
data_args,
model_args
) -> Dict:
train_dataset = OfflineDPODataset(
tokenizer=tokenizer,
data_path=data_args.train_split_path,
data_args=data_args,
train_eval="train",
model_args=model_args
)
eval_dataset = OfflineDPODataset(
tokenizer=tokenizer,
data_path=data_args.eval_split_path,
data_args=data_args,
train_eval="eval",
model_args=model_args
)
data_collator = OfflineDPODataCollator(tokenizer=tokenizer)
return dict(train_dataset=train_dataset,
eval_dataset=eval_dataset,
data_collator=data_collator)
# -------------------- TRAINER
def _get_batch_xe(logits, labels):
"""
For each batch and for each completion computes XE with the corresponding target.
Args:
logits: (B * num_completions, prompt_completion_len, vocab_size)
labels: (B * num_completions, prompt_completion_len)
Returns:
xe: (B * num_completions,)
"""
assert logits.shape[:-1] == labels.shape
B_num_completions, _, vocab_size = logits.shape
# Shift so that tokens < n predict n
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
# Flatten the tokens
shift_logits = shift_logits.view(-1, vocab_size)
shift_labels = shift_labels.view(-1)
xe = F.cross_entropy(shift_logits, shift_labels, reduction="none") # (B * num_completions * (prompt_completion_len-1),)
mask = (shift_labels != IGNORE_INDEX).float() # 1s where loss is active
xe = xe.view(B_num_completions, -1) # (B * num_completions, prompt_completion_len-1)
xe = (xe * mask.view(B_num_completions, -1)).sum(dim=-1)
count = mask.view(B_num_completions, -1).sum(dim=-1) # (B * num_completions,)
xe = xe / (count + 1e-8)
return xe
def _get_batch_logps(logits: torch.FloatTensor, labels: torch.LongTensor, average_log_prob: bool = False) -> torch.FloatTensor:
"""Compute the log probabilities of the given labels under the given logits.
Args:
logits: Logits of the model (unnormalized). Shape: (batch_size, sequence_length, vocab_size)
labels: Labels for which to compute the log probabilities. Label tokens with a value of -100 are ignored. Shape: (batch_size, sequence_length)
average_log_prob: If True, return the average log probability per (non-masked) token. Otherwise, return the sum of the log probabilities of the (non-masked) tokens.
Returns:
A tensor of shape (batch_size,) containing the average/sum log probabilities of the given labels under the given logits.
"""
assert logits.shape[:-1] == labels.shape
labels = labels[:, 1:].clone()
logits = logits[:, :-1, :]
loss_mask = (labels != IGNORE_INDEX)
# dummy token; we'll ignore the losses on these tokens later
labels[labels == IGNORE_INDEX] = 0
per_token_logps = torch.gather(logits.log_softmax(-1), dim=2, index=labels.unsqueeze(2)).squeeze(2)
if average_log_prob:
return (per_token_logps * loss_mask).sum(-1) / loss_mask.sum(-1)
else:
return (per_token_logps * loss_mask).sum(-1)
class CHAIRDPOTrainer(LLaVATrainer, Seq2SeqTrainer):
def __init__(self, *, reward_model: ChairRewardModel, tokenizer_rm = None, custom_args: Optional[Namespace] = None, **kwargs):
super().__init__(**kwargs)
self.custom_args = custom_args
assert reward_model is not None
self.reward_model = reward_model
self.tokenizer_rm = tokenizer_rm # TODO: remove tokenizer_rm arg
# --- generation args
self.eval_gen_kwargs = dict(
max_new_tokens=self.custom_args.dpo_max_new_tokens,
do_sample=True,
temperature=0.7,
pad_token_id=self.tokenizer.pad_token_id,
return_dict_in_generate=True,
output_scores=False,
)
if self.args.per_device_eval_batch_size > 1:
logger.warning("WARNING: using per_device_eval_batch_size > 1 needs batched generation with right pad, this could produce inaccurate completions")
# --- metrics
self._stored_metrics = defaultdict(lambda: defaultdict(list))
def compute_rank(self, rewards, num_beams):
rewards_batch = rewards.reshape(-1, num_beams)
positive_indexes = torch.argmin(rewards_batch, dim=-1, keepdim=True) # low chair --> few hallucinations
positive_mask = torch.zeros_like(rewards_batch, dtype=torch.bool)
return torch.scatter(
positive_mask,
dim=1,
index=positive_indexes,
src=torch.ones_like(positive_indexes, dtype=torch.bool)
)
def weight_quality_distances(self, rewards, policy_rejected_logps, reference_rejected_logps, chosen_mask, rejected_mask, tau):
max_rewards = rewards.view_as(chosen_mask)[chosen_mask].unsqueeze(1)
num_negatives = chosen_mask.shape[1]
diff_measure = (max_rewards - rewards.reshape(-1, num_negatives))[rejected_mask] * tau
gamma = torch.nn.functional.softmax(diff_measure.reshape(-1, num_negatives-1).cuda(), dim=-1).reshape(-1)
policy_rejected_logps *= gamma
reference_rejected_logps *= gamma
return policy_rejected_logps, reference_rejected_logps
def dpo_loss(self, candidate_policy, ref_policy, chosen_mask, rejected_mask, rewards, xe, metrics_prefix):
bsz = chosen_mask.shape[0]
reference_chosen_logps = ref_policy[chosen_mask]
reference_rejected_logps = ref_policy[rejected_mask]
policy_chosen_logps = candidate_policy[chosen_mask]
policy_rejected_logps = candidate_policy[rejected_mask]
policy_rejected_logps, reference_rejected_logps = self.weight_quality_distances(rewards, policy_rejected_logps, reference_rejected_logps, chosen_mask, rejected_mask, self.custom_args.dico_tau)
policy_rejected_logps = policy_rejected_logps.view(bsz, -1).sum(-1)
reference_rejected_logps = reference_rejected_logps.view(bsz, -1).sum(-1)
pi_logratios = policy_chosen_logps - policy_rejected_logps
ref_logratios = reference_chosen_logps - reference_rejected_logps
logits = pi_logratios - ref_logratios
dpo_term = -F.logsigmoid(self.custom_args.dpo_beta * logits)
xe_term = self.custom_args.dpo_xe_weight * xe
loss = dpo_term + xe_term
chosen_rewards = self.custom_args.dpo_beta * (policy_chosen_logps - reference_chosen_logps).detach()
rejected_rewards = self.custom_args.dpo_beta * (policy_rejected_logps - reference_rejected_logps).detach()
loss = loss.mean()
# --- metrics
metrics = {}
gathered_chosen_rewards = self.accelerator.gather(chosen_rewards)
metrics[f"{metrics_prefix}_rewards/chosen"] = gathered_chosen_rewards.mean().item()
gathered_rejected_rewards = self.accelerator.gather(rejected_rewards)
metrics[f"{metrics_prefix}_rewards/rejected"] = gathered_rejected_rewards.mean().item()
margin = gathered_chosen_rewards - gathered_rejected_rewards
metrics[f"{metrics_prefix}_rewards/margins"] = margin.mean().item()
accuracy = margin > 0
metrics[f"{metrics_prefix}_rewards/accuracies"] = accuracy.float().mean().item()
# halscore is weight_chair * chair - weight_recall * recall, lower is better
gathered_halscore_chosen = self.accelerator.gather(rewards.view_as(chosen_mask)[chosen_mask])
gathered_halscore_rejected = self.accelerator.gather(rewards.view_as(chosen_mask)[rejected_mask])
metrics[f"{metrics_prefix}_halscore/chosen"] = gathered_halscore_chosen.mean().item()
metrics[f"{metrics_prefix}_halscore/rejected"] = gathered_halscore_rejected.mean().item()
gathered_xe = self.accelerator.gather(xe.detach())
metrics[f"{metrics_prefix}/dataset_completion_mean_xe"] = gathered_xe.mean().item()
# loss components
gathered_dpo_term = self.accelerator.gather(dpo_term.detach())
metrics[f"{metrics_prefix}/dpo_term"] = gathered_dpo_term.mean().item()
gathered_xe_term = self.accelerator.gather(xe_term.detach())
metrics[f"{metrics_prefix}/xe_term"] = gathered_xe_term.mean().item()
return loss, metrics
def get_batch_loss_metrics_offline(self, model, inputs, train_eval):
# | train | eval |
# model | DeepSpeedEngine | PeftModelForCausalLM |
# self.model | PeftModelForCausalLM | PeftModelForCausalLM |
# self.model_wrapped | DeepSpeedEngine | DeepSpeedEngine |
# self.deepspeed | DeepSpeedEngine | DeepSpeedEngine |
prompt_ids = inputs.pop("prompt_ids") # (B, prompt_len)
prompt_mask = inputs.pop("attention_mask") # (B, prompt_len)
images = inputs.pop("images") # (B, C, H, W)
completion_ids = inputs.pop("completion_ids") # (B * num_completions, completion_len)
num_completions = self.custom_args.dpo_num_beams # completions per prompt
# 1. Rank completions with reward model
completion_texts = self.tokenizer.batch_decode(completion_ids, skip_special_tokens=True)
sample_ids = inputs.pop("sample_ids")
sample_ids = [sample_id for sample_id in sample_ids for _ in range(num_completions)]
rewards = self.reward_model(
caption_texts=completion_texts,
sample_ids=sample_ids
)
chair = torch.tensor([r["chair_i"] for r in rewards], device=completion_ids.device) # (B * num_completions,)
recall = torch.tensor([r["recall"] for r in rewards], device=completion_ids.device) # (B * num_completions,)
rewards = self.custom_args.dpo_chair_weight * chair - self.custom_args.dpo_recall_weight * recall # (B * num_completions,)
chosen_mask = self.compute_rank(rewards=rewards, num_beams=num_completions) # (B, num_completions)
rejected_mask = ~chosen_mask
# 2. Build inputs by concatenating prompts and completions
interleaved_prompt_lengths = prompt_mask.sum(dim=-1).repeat_interleave(num_completions, dim=0)
completion_lengths = (completion_ids != self.tokenizer.pad_token_id).sum(dim=-1)
max_length = torch.max(interleaved_prompt_lengths + completion_lengths).item()
prompt_completion_ids = torch.full(
(prompt_ids.size(0) * num_completions, max_length),
fill_value=self.tokenizer.pad_token_id,
device=prompt_ids.device,
dtype=prompt_ids.dtype
)
ref_labels = torch.full_like(prompt_completion_ids, fill_value=IGNORE_INDEX) # contains completion_ids, will be used as label to compute logprobs
for i, (plen, clen) in enumerate(zip(interleaved_prompt_lengths, completion_lengths)):
prompt_completion_ids[i, :plen] = prompt_ids[i // num_completions, :plen]
prompt_completion_ids[i, plen:plen+clen] = completion_ids[i, :clen]
ref_labels[i, plen:plen+clen] = completion_ids[i, :clen]
if prompt_completion_ids.size(-1) > self.tokenizer.model_max_length:
logger.warning("prompt_completion_ids exceeds model_max_length by {}".format(prompt_completion_ids.size(-1)-self.tokenizer.model_max_length))
prompt_completion_ids = prompt_completion_ids[:, :self.tokenizer.model_max_length]
ref_labels = ref_labels[:, :self.tokenizer.model_max_length]
prompt_completion_mask = prompt_completion_ids != self.tokenizer.pad_token_id
interleaved_images = images.repeat_interleave(num_completions, dim=0)
del images, prompt_ids, prompt_mask, completion_ids
# 3. Compute logprob of the completion for the ref model given the prompt and the image
with torch.no_grad():
with self.model.disable_adapter():
(
_,
_,
embeds_attention_mask,
_,
inputs_embeds,
ref_labels # refined to take into account image token embeds
) = self.model.prepare_inputs_labels_for_multimodal(
input_ids=prompt_completion_ids,
position_ids=None,
attention_mask=prompt_completion_mask,
past_key_values=None,
labels=ref_labels,
images=interleaved_images,
image_sizes=None
)
ref_logits = self.model(
input_ids=None,
attention_mask=embeds_attention_mask,
position_ids=None,
past_key_values=None,
inputs_embeds=inputs_embeds,
labels=None,
use_cache=None,
output_attentions=None,
output_hidden_states=None,
images=None,
image_sizes=None,
return_dict=None,
).logits # (B * num_completions, prompt_completion_len, vocab_size)
del embeds_attention_mask, inputs_embeds
ref_policy = _get_batch_logps(
logits=ref_logits,
labels=ref_labels,
average_log_prob=False
).view(chosen_mask.shape) # (B, num_completions)
# 4. Compute logprob of the completion for the candidate model given the prompt and the image
target_ids = inputs.pop("target_ids") # (B, target_len)
target_ids = target_ids.repeat_interleave(num_completions, dim=0) # (B * num_completions, target_len)
target_labels = torch.full_like(prompt_completion_ids, fill_value=IGNORE_INDEX) # contains target_ids, will be used as label to compute XE
target_lengths = (target_ids != IGNORE_INDEX).sum(dim=-1)
# NOTE to use prepare_inputs_labels_for_multimodal we need target_labels.shape == prompt_completion_ids.shape
# if the unpadded length of prompt_i + unpadded length of target_i < prompt_completion_ids.size(-1) we right pad target_i with IGNORE_INDEX
# if the unpadded length of prompt_i + unpadded length of target_i > prompt_completion_ids.size(-1) we truncate right target_i
for i, (plen, tlen) in enumerate(zip(interleaved_prompt_lengths, target_lengths, strict=True)):
if plen+tlen > prompt_completion_ids.size(-1):
tlen = prompt_completion_ids.size(-1) - plen
target_labels[i, plen:plen+tlen] = target_ids[i, :tlen]
(
_,
_,
embeds_attention_mask,
_,
inputs_embeds,
target_labels # refined to take into account image token embeds
) = self.model.prepare_inputs_labels_for_multimodal(
input_ids=prompt_completion_ids,
position_ids=None,
attention_mask=prompt_completion_mask,
past_key_values=None,
labels=target_labels,
images=interleaved_images,
image_sizes=None
)
candidate_logits = self.model(
input_ids=None,
attention_mask=embeds_attention_mask,
position_ids=None,
past_key_values=None,
inputs_embeds=inputs_embeds,
labels=None,
use_cache=None,
output_attentions=None,
output_hidden_states=None,
images=None,
image_sizes=None,
return_dict=None,
).logits # (B * num_completions, prompt_completion_len, vocab_size)
# compute for each batch the mean XE of completions wrt targets
xe = _get_batch_xe(candidate_logits, target_labels) # (B * num_completions,)
xe = xe.view(chosen_mask.shape) # (B, num_completions)
xe = xe.mean(axis=-1) # (B,)
del embeds_attention_mask, inputs_embeds, prompt_completion_ids
candidate_policy = _get_batch_logps(
logits=candidate_logits,
labels=ref_labels,
average_log_prob=False
).view(chosen_mask.shape) # (B, num_completions)
# 5. Compute DPO loss
loss, metrics = self.dpo_loss(
candidate_policy=candidate_policy,
ref_policy=ref_policy,
chosen_mask=chosen_mask,
rejected_mask=rejected_mask,
rewards=rewards,
xe=xe,
metrics_prefix=train_eval
)
# Update metrics with chair and recall
gathered_chair_chosen = self.accelerator.gather(chair.view_as(chosen_mask)[chosen_mask])
gathered_chair_rejected = self.accelerator.gather(chair.view_as(chosen_mask)[rejected_mask])
gathered_chair = self.accelerator.gather(chair)
metrics[f"{train_eval}_chair/chosen"] = gathered_chair_chosen.mean().item()
metrics[f"{train_eval}_chair/rejected"] = gathered_chair_rejected.mean().item()
metrics[f"{train_eval}_chair/mean"] = gathered_chair.mean().item()
gathered_recall_chosen = self.accelerator.gather(recall.view_as(chosen_mask)[chosen_mask])
gathered_recall_rejected = self.accelerator.gather(recall.view_as(chosen_mask)[rejected_mask])
gathered_recall = self.accelerator.gather(recall)
metrics[f"{train_eval}_recall/chosen"] = gathered_recall_chosen.mean().item()
metrics[f"{train_eval}_recall/rejected"] = gathered_recall_rejected.mean().item()
metrics[f"{train_eval}_recall/mean"] = gathered_recall.mean().item()
return loss, metrics
def store_metrics(self, metrics, train_eval):
for key, value in metrics.items():
self._stored_metrics[train_eval][key].append(value)
def compute_loss(self, model, inputs, return_outputs=False):
do_profiling = False # TODO: parser arg
ctx = CUDATimer if do_profiling else nullcontext
with ctx() as timer:
loss, metrics = self.get_batch_loss_metrics_offline(model, inputs, train_eval="train")
if do_profiling and self.is_world_process_zero():
print("forward elapsed ms:", timer.elapsed_time)
metrics["forward_elapsed_ms"] = timer.elapsed_time
if self.is_world_process_zero():
self.store_metrics(metrics, train_eval="train")
if return_outputs:
return loss, metrics
return loss
def evaluate(self, eval_dataset=None, ignore_keys=None, metric_key_prefix: str = "eval"):
# reset counters for overall chair-i and overall recall
self.hallucinated_word_count = torch.tensor(0.0, device=self.accelerator.device)
self.coco_word_count = torch.tensor(0.0, device=self.accelerator.device)
self.num_recall_gt_objects = torch.tensor(0.0, device=self.accelerator.device)
self.num_gt_objects = torch.tensor(0.0, device=self.accelerator.device)
# reset containers for per sample chair-i and per sample recall
self.sample_chairi_list = []
self.sample_recall_list = []
# reset container for XE
self.xe_list = []
# reset container for completions
self.prompt_completion_list = []
# start eval loop
output = super().evaluate(eval_dataset, ignore_keys=ignore_keys, metric_key_prefix=metric_key_prefix)
# gather and log all metrics
logs = {}
gathered_hallucinated_word_count = self.accelerator.gather(self.hallucinated_word_count)
gathered_coco_word_count = self.accelerator.gather(self.coco_word_count)
total_hallucinated_word_count = gathered_hallucinated_word_count.sum().item()
total_coco_word_count = gathered_coco_word_count.sum().item()
chair_i_overall = 0.0
if total_coco_word_count > 0.0:
chair_i_overall = total_hallucinated_word_count / total_coco_word_count
logs[f"{metric_key_prefix}_chair_i_overall"] = chair_i_overall
gathered_num_recall_gt_objects = self.accelerator.gather(self.num_recall_gt_objects)
gathered_num_gt_objects = self.accelerator.gather(self.num_gt_objects)
total_num_recall_gt_objects = gathered_num_recall_gt_objects.sum().item()
total_num_gt_objects = gathered_num_gt_objects.sum().item()
recall_overall = 0.0
if total_num_gt_objects > 0.0:
recall_overall = total_num_recall_gt_objects / total_num_gt_objects
logs[f"{metric_key_prefix}_recall_overall"] = recall_overall
# gather per sample containers
gathered_sample_chairi_list = self.accelerator.gather(torch.tensor(self.sample_chairi_list, device=self.accelerator.device))
gathered_sample_recall_list = self.accelerator.gather(torch.tensor(self.sample_recall_list, device=self.accelerator.device))
mean_sample_chairi = gathered_sample_chairi_list.mean().item()
mean_sample_recall = gathered_sample_recall_list.mean().item()
logs[f"{metric_key_prefix}_mean_sample_chairi"] = mean_sample_chairi
logs[f"{metric_key_prefix}_mean_sample_recall"] = mean_sample_recall
# gather XE
gathered_xe_list = self.accelerator.gather(torch.tensor(self.xe_list, device=self.accelerator.device))
mean_xe = gathered_xe_list.mean().item()
logs[f"{metric_key_prefix}_generated_completion_mean_xe"] = mean_xe
# save prompt_completion_list to file
# we do not gather on purpose
if self.is_world_process_zero():
print("Saving prompt completion data...", end=" ")
with open(os.path.join(self.args.output_dir, "prompt_completion.jsonl"), mode="a") as f:
f.write(json.dumps(self.prompt_completion_list) + "\n")
print("Done.")
# only the main process logs to wandb
if self.is_world_process_zero():
super().log(logs)
return output
def update_chair_xe(self, model, inputs):
# 1. Compute chair related quantities
prompt_ids = inputs["prompt_ids"]
prompt_mask = inputs["attention_mask"]
images = inputs["images"]
sample_ids = inputs["sample_ids"]
with torch.no_grad():
output = model.generate(
inputs=prompt_ids,
attention_mask=prompt_mask,
images=images,
**self.eval_gen_kwargs
)
#completion_ids = output.sequences[:, 1:] # skip BOS # not needed in transformers 4.43
completion_ids = output.sequences
completion_texts = self.tokenizer.batch_decode(completion_ids, skip_special_tokens=True)
if len(self.prompt_completion_list) < 10:
placeholder_tok_id = self.tokenizer("#").input_ids[-1]
temp = torch.where(prompt_ids < 0, torch.ones_like(prompt_ids)*placeholder_tok_id, prompt_ids) # HACK to decode image token as #
temp = self.tokenizer.batch_decode(temp, skip_special_tokens=True)
self.prompt_completion_list.append(
dict(step=self.state.global_step,
prompt=temp,
completion=completion_texts)
)
chair_dicts = [
self.reward_model.chair.compute_chairi_sample(completion_text, sample_id)
for completion_text, sample_id in zip(completion_texts, sample_ids, strict=True)
]
for chair_dict in chair_dicts:
self.hallucinated_word_count += torch.tensor(float(chair_dict["partial_hallucinated_word_count"]), device=self.accelerator.device)
self.coco_word_count += torch.tensor(float(chair_dict["partial_coco_word_count"]), device=self.accelerator.device)
self.num_recall_gt_objects += torch.tensor(float(chair_dict["partial_num_recall_gt_objects"]), device=self.accelerator.device)
self.num_gt_objects += torch.tensor(float(chair_dict["partial_num_gt_objects"]), device=self.accelerator.device)
self.sample_chairi_list.append(chair_dict["chair_i"])
self.sample_recall_list.append(chair_dict["recall"])
# 2. Compute cross entropy between generated completions and targets
prompt_lenghts = prompt_mask.sum(dim=-1)
completion_lengths = (completion_ids != self.tokenizer.pad_token_id).sum(dim=-1)
max_length = torch.max(prompt_lenghts + completion_lengths).item()