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import os
import pdb
import warnings
import time
import math
import json
from PIL import Image
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from torch.utils.tensorboard import SummaryWriter
import torchvision.transforms as transforms
from typing import List, Optional, Dict, Union, Any
import pandas as pd
import safetensors
import numpy as np
import torch
import torch.nn as nn
import datasets
from torch.utils.data import Dataset, DataLoader
from peft import PeftModel
from transformers import Qwen2VLForConditionalGeneration
from transformers import AutoConfig
from transformers.modeling_utils import PreTrainedModel
from transformers.trainer import TrainerCallback
from transformers.trainer import (
is_sagemaker_mp_enabled,
is_peft_available,
is_datasets_available,
WEIGHTS_NAME,
TRAINING_ARGS_NAME,
SAFE_WEIGHTS_NAME,
TRAINER_STATE_NAME,
PREFIX_CHECKPOINT_DIR,
logger,
speed_metrics,
deepspeed_init,
speed_metrics,
has_length,
EvalPrediction,
EvalLoopContainer,
PredictionOutput,
is_torch_xla_available,
denumpify_detensorize,
PredictionOutput,
EvalLoopOutput,
DistributedTensorGatherer,
SequentialDistributedSampler,
nested_concat,
)
from transformers.trainer_pt_utils import IterableDatasetShard
from transformers.trainer_callback import TrainerControl, TrainerState
from transformers.trainer_pt_utils import nested_detach, find_batch_size
from transformers.training_args import TrainingArguments
from trl import RewardTrainer
from hpsv3.utils.training_utils import get_peft_state_non_lora_maybe_zero_3
class Qwen2VLRewardModelBT(Qwen2VLForConditionalGeneration):
def __init__(
self,
config,
output_dim=4,
reward_token="last",
special_token_ids=None,
rm_head_type="default",
rm_head_kwargs=None,
):
super().__init__(config)
# pdb.set_trace()
self.output_dim = output_dim
if rm_head_type == "default":
self.rm_head = nn.Linear(config.hidden_size, output_dim, bias=False)
elif rm_head_type == "ranknet":
if rm_head_kwargs is not None:
for layer in range(rm_head_kwargs.get("num_layers", 3)):
if layer == 0:
self.rm_head = nn.Sequential(
nn.Linear(config.hidden_size, rm_head_kwargs["hidden_size"]),
nn.ReLU(),
nn.Dropout(rm_head_kwargs.get("dropout", 0.1)),
)
elif layer < rm_head_kwargs.get("num_layers", 3) - 1:
self.rm_head.add_module(
f"layer_{layer}",
nn.Sequential(
nn.Linear(rm_head_kwargs["hidden_size"], rm_head_kwargs["hidden_size"]),
nn.ReLU(),
nn.Dropout(rm_head_kwargs.get("dropout", 0.1)),
),
)
else:
self.rm_head.add_module(
f"output_layer",
nn.Linear(rm_head_kwargs["hidden_size"], output_dim, bias=rm_head_kwargs.get("bias", False)),
)
else:
self.rm_head = nn.Sequential(
nn.Linear(config.hidden_size, 1024),
nn.ReLU(),
nn.Dropout(0.05),
nn.Linear(1024, 16),
nn.ReLU(),
nn.Linear(16, output_dim),
)
self.rm_head.to(torch.float32)
self.reward_token = reward_token
self.special_token_ids = special_token_ids
if self.special_token_ids is not None:
self.reward_token = "special"
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
pixel_values: Optional[torch.Tensor] = None,
pixel_values_videos: Optional[torch.FloatTensor] = None,
image_grid_thw: Optional[torch.LongTensor] = None,
video_grid_thw: Optional[torch.LongTensor] = None,
rope_deltas: Optional[torch.LongTensor] = None,
):
## modified from the origin class Qwen2VLForConditionalGeneration
output_attentions = (
output_attentions
if output_attentions is not None
else self.config.output_attentions
)
output_hidden_states = (
output_hidden_states
if output_hidden_states is not None
else self.config.output_hidden_states
)
return_dict = (
return_dict if return_dict is not None else self.config.use_return_dict
)
# pdb.set_trace()
if inputs_embeds is None:
inputs_embeds = self.model.embed_tokens(input_ids)
if pixel_values is not None:
pixel_values = pixel_values.type(self.visual.get_dtype())
image_embeds = self.visual(pixel_values, grid_thw=image_grid_thw)
image_mask = (
(input_ids == self.config.image_token_id)
.unsqueeze(-1)
.expand_as(inputs_embeds)
)
image_embeds = image_embeds.to(
inputs_embeds.device, inputs_embeds.dtype
)
inputs_embeds = inputs_embeds.masked_scatter(image_mask, image_embeds)
if pixel_values_videos is not None:
pixel_values_videos = pixel_values_videos.type(self.visual.get_dtype())
video_embeds = self.visual(pixel_values_videos, grid_thw=video_grid_thw)
video_mask = (
(input_ids == self.config.video_token_id)
.unsqueeze(-1)
.expand_as(inputs_embeds)
)
video_embeds = video_embeds.to(
inputs_embeds.device, inputs_embeds.dtype
)
inputs_embeds = inputs_embeds.masked_scatter(video_mask, video_embeds)
if attention_mask is not None:
attention_mask = attention_mask.to(inputs_embeds.device)
outputs = self.model(
input_ids=None,
position_ids=position_ids,
attention_mask=attention_mask,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = outputs[0] # [B, L, D]
with torch.autocast(device_type='cuda', dtype=torch.float32):
logits = self.rm_head(hidden_states) # [B, L, N]
if input_ids is not None:
batch_size = input_ids.shape[0]
else:
batch_size = inputs_embeds.shape[0]
## get sequence length
if self.config.pad_token_id is None and batch_size != 1:
raise ValueError(
"Cannot handle batch sizes > 1 if no padding token is defined."
)
if self.config.pad_token_id is None:
sequence_lengths = -1
else:
if input_ids is not None:
# if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
sequence_lengths = (
torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
)
sequence_lengths = sequence_lengths % input_ids.shape[-1]
sequence_lengths = sequence_lengths.to(logits.device)
else:
sequence_lengths = -1
## get the last token's logits
if self.reward_token == "last":
pooled_logits = logits[
torch.arange(batch_size, device=logits.device), sequence_lengths
]
elif self.reward_token == "mean":
## get the mean of all valid tokens' logits
valid_lengths = torch.clamp(sequence_lengths, min=0, max=logits.size(1) - 1)
pooled_logits = torch.stack(
[logits[i, : valid_lengths[i]].mean(dim=0) for i in range(batch_size)]
)
elif self.reward_token == "special":
# special_token_ids = self.tokenizer.convert_tokens_to_ids(self.special_tokens)
# create a mask for special tokens
special_token_mask = torch.zeros_like(input_ids, dtype=torch.bool)
for special_token_id in self.special_token_ids:
special_token_mask = special_token_mask | (
input_ids == special_token_id
)
pooled_logits = logits[special_token_mask, ...]
pooled_logits = pooled_logits.view(
batch_size, 1, -1
) # [B, 3, N] assert 3 attributes
pooled_logits = pooled_logits.view(batch_size, -1)
# pdb.set_trace()
else:
raise ValueError("Invalid reward_token")
return {"logits": pooled_logits}
def _convert_A_B_to_chosen_rejected(
rewards_A,
rewards_B,
tied_threshold=None,
choice_dist=None,
):
"""
Inputs:
rewards_A: [B, 1]
rewards_B: [B, 1]
Outputs:
rewards_chosen: [B, 1]
rewards_rejected: [B, 1]
nontied_mask: [B, 1] (preference labels that is not tied)
"""
chosen_label = torch.ones_like(rewards_A, dtype=torch.int64).to(
rewards_A.device
) # [B, 1]
chosen_mask = chosen_label == 1
rejected_mask = chosen_label != 1
rewards_chosen = rewards_A
rewards_rejected = rewards_B
if tied_threshold is None:
nontied_mask = torch.ones_like(chosen_label, dtype=torch.float32).to(
rewards_A.device
)
else:
nontied_mask = (
torch.abs(
(choice_dist[:, 0] - choice_dist[:, 1]) / torch.sum(choice_dist, dim=-1)
)
> tied_threshold
)
print(nontied_mask)
return (
rewards_chosen,
rewards_rejected,
nontied_mask,
)
class PartialEmbeddingUpdateCallback(TrainerCallback):
"""
Callback to update the embedding of special tokens
Only the special tokens are updated, the rest of the embeddings are kept fixed
"""
def __init__(self, special_token_ids):
super().__init__()
self.special_token_ids = special_token_ids
self.orig_embeds_params = None
def on_train_begin(self, args, state, control, **kwargs):
model = kwargs.get("model")
self.orig_embeds_params = model.get_input_embeddings().weight.clone().detach()
def on_step_end(self, args, state, control, **kwargs):
# pdb.set_trace()
model = kwargs.get("model")
tokenizer = kwargs.get("tokenizer")
index_no_updates = torch.ones((len(tokenizer),), dtype=torch.bool)
index_no_updates[self.special_token_ids] = False
with torch.no_grad():
model.get_input_embeddings().weight[index_no_updates] = (
self.orig_embeds_params[index_no_updates]
)
class VLMRewardTrainer(RewardTrainer):
def __init__(self, loss_type="regular", loss_hyperparameters={}, tied_threshold=None,
visualization_steps=500, max_viz_samples=4, *args, **kwargs):
super(VLMRewardTrainer, self).__init__(*args, **kwargs)
self.loss_type = loss_type
self.tied_threshold = tied_threshold
self.rewards_chosen_accumulated = []
self.rewards_rejected_accumulated = []
self.loss_hyperparameters = loss_hyperparameters
self.visualization_steps = visualization_steps
self.max_viz_samples = max_viz_samples
def get_eval_dataloader(
self, eval_dataset: Optional[Union[str, Dataset]] = None
) -> DataLoader:
"""
Returns the evaluation [`~torch.utils.data.DataLoader`].
Subclass and override this method if you want to inject some custom behavior.
Args:
eval_dataset (`str` or `torch.utils.data.Dataset`, *optional*):
If a `str`, will use `self.eval_dataset[eval_dataset]` as the evaluation dataset. If a `Dataset`, will override `self.eval_dataset` and must implement `__len__`. If it is a [`~datasets.Dataset`], columns not accepted by the `model.forward()` method are automatically removed.
"""
if eval_dataset is None and self.eval_dataset is None:
raise ValueError("Trainer: evaluation requires an eval_dataset.")
# If we have persistent workers, don't do a fork bomb especially as eval datasets
# don't change during training
dataloader_key = eval_dataset if isinstance(eval_dataset, str) else "eval"
if (
hasattr(self, "_eval_dataloaders")
and dataloader_key in self._eval_dataloaders
and self.args.dataloader_persistent_workers
):
return self.accelerator.prepare(self._eval_dataloaders[dataloader_key])
eval_dataset = (
self.eval_dataset[eval_dataset]
if isinstance(eval_dataset, str)
else eval_dataset if eval_dataset is not None else self.eval_dataset
)
data_collator = self.data_collator
if is_datasets_available() and isinstance(eval_dataset, datasets.Dataset):
eval_dataset = self._remove_unused_columns(
eval_dataset, description="evaluation"
)
else:
data_collator = self._get_collator_with_removed_columns(
data_collator, description="evaluation"
)
dataloader_params = {
"batch_size": self.args.eval_batch_size,
"collate_fn": data_collator,
"num_workers": self.args.dataloader_num_workers,
"pin_memory": self.args.dataloader_pin_memory,
"persistent_workers": self.args.dataloader_persistent_workers,
}
if not isinstance(eval_dataset, torch.utils.data.IterableDataset):
dataloader_params["sampler"] = self._get_eval_sampler(eval_dataset)
dataloader_params["drop_last"] = self.args.dataloader_drop_last
dataloader_params["prefetch_factor"] = self.args.dataloader_prefetch_factor
# accelerator.free_memory() will destroy the references, so
# we need to store the non-prepared version
eval_dataloader = DataLoader(eval_dataset, **dataloader_params)
if self.args.dataloader_persistent_workers:
if hasattr(self, "_eval_dataloaders"):
self._eval_dataloaders[dataloader_key] = eval_dataloader
else:
self._eval_dataloaders = {dataloader_key: eval_dataloader}
return self.accelerator.prepare(eval_dataloader)
def create_optimizer(self):
"""
Setup the optimizer.
We provide a reasonable default that works well. If you want to use something else, you can pass a tuple in the
Trainer's init through `optimizers`, or subclass and override this method in a subclass.
"""
if is_sagemaker_mp_enabled():
return super().create_optimizer()
opt_model = self.model
if self.optimizer is None:
decay_parameters = self.get_decay_parameter_names(opt_model)
decay_parameters = [name for name in decay_parameters if "bias" not in name]
lr_mapper = {}
visual_parameters = []
merger_parameters = []
rm_head_parameters = []
if self.args.vision_lr is not None:
lr_mapper["visual"] = self.args.vision_lr
visual_parameters = [
name
for name, _ in opt_model.named_parameters()
if "visual" in name and "merger" not in name
]
if self.args.merger_lr is not None:
lr_mapper["merger"] = self.args.merger_lr
merger_parameters = [
name for name, _ in opt_model.named_parameters() if "merger" in name
]
if self.args.rm_head_lr is not None:
lr_mapper["rm_head"] = self.args.rm_head_lr
rm_head_parameters = [
name for name, _ in opt_model.named_parameters() if "rm_head" in name
]
if len(lr_mapper) > 0:
special_lr_parameters = merger_parameters + visual_parameters + rm_head_parameters
optimizer_grouped_parameters = [
{
"params": [
p
for n, p in opt_model.named_parameters()
if (
n in decay_parameters
and n not in special_lr_parameters
and p.requires_grad
)
],
"weight_decay": self.args.weight_decay,
},
{
"params": [
p
for n, p in opt_model.named_parameters()
if (
n not in decay_parameters
and n not in special_lr_parameters
and p.requires_grad
)
],
"weight_decay": 0.0,
},
]
if visual_parameters:
optimizer_grouped_parameters.extend(
[
{
"params": [
p
for n, p in opt_model.named_parameters()
if (
n in decay_parameters
and n in visual_parameters
and p.requires_grad
)
],
"weight_decay": self.args.weight_decay,
"lr": self.args.vision_lr,
},
{
"params": [
p
for n, p in opt_model.named_parameters()
if (
n not in decay_parameters
and n in visual_parameters
and p.requires_grad
)
],
"weight_decay": 0.0,
"lr": self.args.vision_lr,
},
]
)
if merger_parameters:
optimizer_grouped_parameters.extend(
[
{
"params": [
p
for n, p in opt_model.named_parameters()
if (
n in decay_parameters
and n in merger_parameters
and p.requires_grad
)
],
"weight_decay": self.args.weight_decay,
"lr": self.args.merger_lr,
},
{
"params": [
p
for n, p in opt_model.named_parameters()
if (
n not in decay_parameters
and n in merger_parameters
and p.requires_grad
)
],
"weight_decay": 0.0,
"lr": self.args.merger_lr,
},
]
)
if rm_head_parameters:
optimizer_grouped_parameters.extend(
[
{
"params": [
p
for n, p in opt_model.named_parameters()
if (
n in decay_parameters
and n in rm_head_parameters
and p.requires_grad
)
],
"weight_decay": self.args.weight_decay,
"lr": self.args.rm_head_lr,
},
{
"params": [
p
for n, p in opt_model.named_parameters()
if (
n not in decay_parameters
and n in rm_head_parameters
and p.requires_grad
)
],
"weight_decay": 0.0,
"lr": self.args.rm_head_lr,
},
]
)
else:
optimizer_grouped_parameters = [
{
"params": [
p
for n, p in opt_model.named_parameters()
if (n in decay_parameters and p.requires_grad)
],
"weight_decay": self.args.weight_decay,
},
{
"params": [
p
for n, p in opt_model.named_parameters()
if (n not in decay_parameters and p.requires_grad)
],
"weight_decay": 0.0,
},
]
if self.model.special_token_ids:
special_token_embeddings = opt_model.get_input_embeddings().weight
special_token_embeddings.requires_grad = True
optimizer_grouped_parameters.extend(
[
{
# "params": [p for n, p in opt_model.get_input_embeddings().named_parameters() if (p.requires_grad)],
"params": [special_token_embeddings],
"lr": self.args.special_token_lr,
"weight_decay": 0.0,
},
]
)
optimizer_cls, optimizer_kwargs = self.get_optimizer_cls_and_kwargs(
self.args, opt_model
)
self.optimizer = optimizer_cls(
optimizer_grouped_parameters, **optimizer_kwargs
)
return self.optimizer
def compute_loss(self, model, inputs, return_outputs=False, **kwargs):
rewards_A = model(return_dict=True, **inputs["batch_1"])["logits"]
rewards_B = model(return_dict=True, **inputs["batch_2"])["logits"]
# Log to TensorBoard for visualization
if (hasattr(self.state, 'global_step') and
self.state.global_step % self.visualization_steps == 0 and
self.state.global_step > 0):
# Pass the original inputs which should contain the text prompts
self._log_training_visualization(inputs, rewards_A, rewards_B)
# calculate loss, optionally modulate with margin
# get chosen and rejected rewards from the chosen label
(
rewards_chosen,
rewards_rejected,
nontied_mask,
) = _convert_A_B_to_chosen_rejected(
rewards_A,
rewards_B,
tied_threshold=self.tied_threshold,
choice_dist=inputs["choice_dist"],
)
loss_dict = {}
if self.loss_type == "bt":
# Bradley-Terry model
loss = -nn.functional.logsigmoid(rewards_chosen - rewards_rejected)
out_mask = nontied_mask
loss = loss * out_mask
loss = loss.mean()
elif self.loss_type == "likelihood_displacement":
# Bradley-Terry model
loss = -nn.functional.logsigmoid(rewards_chosen - self.loss_hyperparameters['tau'] * rewards_rejected)
out_mask = nontied_mask
loss = loss * out_mask
loss = loss.mean()
elif self.loss_type == "constant_margin":
# Bradley-Terry model with constant margin
loss = -nn.functional.logsigmoid(rewards_chosen - rewards_rejected - 0.57)
out_mask = nontied_mask
loss = loss * out_mask
loss = loss.mean()
elif self.loss_type == "btt":
# Bradley-Terry-With-Ties model
k = 5.0
log_k = math.log(k)
log_k2_sub_1 = math.log(k**2 - 1)
bt_loss = -nn.functional.logsigmoid(
rewards_chosen - rewards_rejected - log_k
)
same_loss = (
-nn.functional.logsigmoid(rewards_chosen - rewards_rejected - log_k)
- nn.functional.logsigmoid(rewards_rejected - rewards_chosen - log_k)
- log_k2_sub_1
)
loss = bt_loss * nontied_mask.float() + same_loss * (
1 - nontied_mask.float()
)
out_mask = torch.ones_like(nontied_mask, dtype=torch.float32).to(
rewards_A.device
) # [B, 1]
loss = loss * out_mask
loss = loss.mean()
elif self.loss_type == "hpsv2":
device = rewards_A.device
rewards = torch.nn.functional.softmax(
torch.cat([rewards_A, rewards_B], dim=-1), dim=-1
)
text_0_logits, text_1_logits = rewards[:, 0], rewards[:, 1]
label_0, label_1 = torch.ones_like(text_0_logits), torch.zeros_like(
text_0_logits
)
text_logits = torch.stack([text_0_logits, text_1_logits], dim=-1)
text_0_labels = torch.zeros(
text_logits.shape[0], device=device, dtype=torch.long
)
text_1_labels = text_0_labels + 1
text_0_loss = torch.nn.functional.cross_entropy(
text_logits, text_0_labels, reduction="none"
)
text_1_loss = torch.nn.functional.cross_entropy(
text_logits, text_1_labels, reduction="none"
)
loss = label_0 * text_0_loss + label_1 * text_1_loss
# absolute_example_weight = 1 / num_per_prompt
# denominator = absolute_example_weight.sum()
# weight_per_example = absolute_example_weight / denominator
# text_loss *= weight_per_example
loss = loss.sum()
elif self.loss_type == "uncertainty":
batch_size = rewards_A.shape[0]
mean_chosen = rewards_A[:, 0]
mean_rejected = rewards_B[:, 0]
sigma_chosen = torch.exp(rewards_A[:, 1])
sigma_rejected = torch.exp(rewards_B[:, 1])
mean_z = mean_chosen - mean_rejected
sigma_z = torch.sqrt(sigma_chosen**2 + sigma_rejected**2)
z_samples = torch.randn(batch_size, 1000).to(sigma_z.device).to(
torch.float16
) * sigma_z.unsqueeze(1).repeat(1, 1000) + mean_z.unsqueeze(1).repeat(
1, 1000
)
loss = -torch.nn.functional.logsigmoid(z_samples).mean()
else:
raise NotImplementedError(f"Loss type {self.loss_type} not implemented.")
loss_dict.update({"loss": loss.item()})
if return_outputs:
## return rewards_A/B instead of chosen/rejected
## easier to calculate metrics for multi-attribute
return loss, {
"rewards_A": rewards_A,
"rewards_B": rewards_B,
}
return loss
def prediction_step(
self,
model,
inputs,
prediction_loss_only,
ignore_keys=None,
):
model.eval()
inputs = self._prepare_inputs(inputs)
if ignore_keys is None:
if hasattr(self.model, "config"):
ignore_keys = getattr(
self.model.config, "keys_to_ignore_at_inference", []
)
else:
ignore_keys = []
with torch.no_grad():
loss, logits_dict = self.compute_loss(model, inputs, return_outputs=True)
if prediction_loss_only:
return (loss, None, None)
loss = loss.detach()
logits = tuple(v for k, v in logits_dict.items() if k not in ignore_keys)
logits = nested_detach(logits)
if self.loss_type != "uncertainty":
logits = torch.cat(logits, dim=1) # [B, 2]
else:
logits = torch.cat([p[:, [0]] for p in logits], dim=1)
labels = torch.ones((logits.shape[0], 1)).to(logits.device)
return loss, logits, labels
def _log_training_visualization(self, inputs, rewards_A, rewards_B):
"""Log training samples and predictions to TensorBoard"""
try:
# Get tensorboard writer from trainer
writer = None
if hasattr(self, 'log_metrics'):
# Try to get the writer from the logger
if hasattr(self.args, 'report_to') and 'tensorboard' in self.args.report_to:
from torch.utils.tensorboard import SummaryWriter
if not hasattr(self, '_tb_writer'):
self._tb_writer = SummaryWriter(log_dir=self.args.logging_dir)
writer = self._tb_writer
if writer is None:
return
step = self.state.global_step
batch_size = min(len(rewards_A), self.max_viz_samples)
# Log scalar metrics
for i in range(batch_size):
score_A = rewards_A[i].float().detach().cpu().numpy()
score_B = rewards_B[i].float().detach().cpu().numpy()
# Convert to float for logging
score_A_val = float(score_A.mean()) if score_A.ndim > 0 else float(score_A)
score_B_val = float(score_B.mean()) if score_B.ndim > 0 else float(score_B)
score_diff = score_A_val - score_B_val
writer.add_scalar(f'train_viz/sample_{i}/score_A', score_A_val, step)
writer.add_scalar(f'train_viz/sample_{i}/score_B', score_B_val, step)
writer.add_scalar(f'train_viz/sample_{i}/score_diff', score_diff, step)
try:
# Get image data from inputs
image_A = inputs['image_1'][i] if 'image_1' in inputs else None
image_B = inputs['image_2'][i] if 'image_2' in inputs else None
# Get prompt text from the original batch (now properly stored)
prompt_A = inputs.get('text_1', ['Unknown prompt'])[i] if 'text_1' in inputs else 'Unknown prompt'
fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(12, 8))
fig.text(0.05, 0.05, f'Prompt:\n{prompt_A[:200]}{"..." if len(prompt_A) > 200 else ""}',
ha='left', va='bottom', fontsize=8, wrap=True,
bbox=dict(boxstyle="round,pad=0.3", facecolor="lightblue", alpha=0.7))
img_A_np = np.array(image_A)
if img_A_np.ndim == 3 and img_A_np.shape[0] == 3: # CHW format
img_A_np = np.transpose(img_A_np, (1, 2, 0))
img_A_np = np.clip(img_A_np, 0, 1) # Ensure values are in [0,1]
axes[0].imshow(img_A_np)
axes[0].set_title(f'Image A - Score: {score_A_val:.3f}')
axes[0].axis('off')
img_B_np = np.array(image_B)
if img_B_np.ndim == 3 and img_B_np.shape[0] == 3: # CHW format
img_B_np = np.transpose(img_B_np, (1, 2, 0))
img_B_np = np.clip(img_B_np, 0, 1) # Ensure values are in [0,1]
axes[1].imshow(img_B_np)
axes[1].set_title(f'Image B - Score: {score_B_val:.3f}')
axes[1].axis('off')
# Add prediction info
winner = "A" if score_diff > 0 else "B"
plt.suptitle(f'Step {step} - Sample {i} | Predicted Winner: Image {winner} | Diff: {score_diff:.3f}', fontsize=14)
plt.tight_layout()
# Log figure to tensorboard
writer.add_figure(f'train_viz/sample_{i}_comparison', fig, step)
plt.close(fig)
except Exception as viz_error:
print(f"Warning: Could not extract images for visualization: {viz_error}")
continue
# Log aggregate statistics
all_scores_A = rewards_A.float().detach().cpu().numpy()
all_scores_B = rewards_B.float().detach().cpu().numpy()
writer.add_histogram('train_viz/all_scores_A', all_scores_A, step)
writer.add_histogram('train_viz/all_scores_B', all_scores_B, step)
writer.add_scalar('train_viz/mean_score_A', float(all_scores_A.mean()), step)
writer.add_scalar('train_viz/mean_score_B', float(all_scores_B.mean()), step)
writer.add_scalar('train_viz/mean_score_diff', float((all_scores_A - all_scores_B).mean()), step)
except Exception as e:
print(f"Error in training visualization: {e}")
def _save_checkpoint(self, model, trial, metrics=None):
if isinstance(self.model, PeftModel):
checkpoint_folder = f"{PREFIX_CHECKPOINT_DIR}-{self.state.global_step}"
if self.hp_search_backend is None and trial is None:
self.store_flos()
run_dir = self._get_output_dir(trial=trial)
output_dir = os.path.join(run_dir, checkpoint_folder)
os.makedirs(output_dir, exist_ok=True)
# TODO: Just Temp
self.save_model(output_dir, _internal_call=True)
# pdb.set_trace()
if not self.args.save_full_model:
non_lora_weights = get_peft_state_non_lora_maybe_zero_3(
self.model.named_parameters(), require_grad_only=True
)
torch.save(
non_lora_weights,
os.path.join(output_dir, "non_lora_state_dict.pth"),
)
# safetensors.torch.save(non_lora_weights, os.path.join(output_dir, "non_lora_model.safetensors"))
if not self.args.save_only_model:
# Save optimizer and scheduler
self._save_optimizer_and_scheduler(output_dir)
# Save RNG state
self._save_rng_state(output_dir)
else:
super(RewardTrainer, self)._save_checkpoint(model, trial, metrics)
def _save(self, output_dir: Optional[str] = None, state_dict=None):
# If we are executing this function, we are the process zero, so we don't check for that.
output_dir = output_dir if output_dir is not None else self.args.output_dir
os.makedirs(output_dir, exist_ok=True)
logger.info(f"Saving model checkpoint to {output_dir}")
# pdb.set_trace()
supported_classes = (
(PreTrainedModel,)
if not is_peft_available()
else (PreTrainedModel, PeftModel)
)
# Save a trained model and configuration using `save_pretrained()`.
# They can then be reloaded using `from_pretrained()`
if not isinstance(self.model, supported_classes):
if state_dict is None:
state_dict = self.model.state_dict()
if isinstance(self.accelerator.unwrap_model(self.model), supported_classes):
self.accelerator.unwrap_model(self.model).save_pretrained(
output_dir,
state_dict=state_dict,
safe_serialization=self.args.save_safetensors,
)
else:
logger.info(
"Trainer.model is not a `PreTrainedModel`, only saving its state dict."
)
if self.args.save_safetensors:
safetensors.torch.save_file(
state_dict,
os.path.join(output_dir, SAFE_WEIGHTS_NAME),
metadata={"format": "pt"},
)
else:
torch.save(state_dict, os.path.join(output_dir, WEIGHTS_NAME))
else:
if not self.args.save_full_model:
state_dict = {k: v for k, v in state_dict.items() if "wte" not in k}
self.model.save_pretrained(
output_dir,
state_dict=state_dict,
safe_serialization=self.args.save_safetensors,
)
else:
torch.save(state_dict, os.path.join(output_dir, "model.pth"))
if self.tokenizer is not None:
os.makedirs(os.path.join(output_dir, "tokenizer"), exist_ok=True)
self.tokenizer.save_pretrained(os.path.join(output_dir, "tokenizer"))
# Good practice: save your training arguments together with the trained model
torch.save(self.args, os.path.join(output_dir, TRAINING_ARGS_NAME))
# pdb.set_trace()
def compute_multi_attr_accuracy(eval_pred, metainfo_idxs=None) -> Dict[str, float]:
predictions, labels = eval_pred
metrics = {}
pred_curr = predictions
label_curr = labels.squeeze(1)
total_count = np.sum(label_curr != 0)
rewards_chosen = pred_curr[:, 0]
rewards_rejected = pred_curr[:, 1]
rewards_chosen_avg = np.sum(rewards_chosen) / total_count
rewards_rejected_avg = np.sum(rewards_rejected) / total_count
accuracy = np.sum(rewards_chosen > rewards_rejected) / total_count
metrics.update(
{
f"Acc": accuracy,
f"R_chosen_avg": rewards_chosen_avg,
f"R_rejected_avg": rewards_rejected_avg,
}
)
return metrics
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