import collections import contextlib import functools import shutil import sys import time from datetime import timedelta from packaging import version from accelerate import skip_first_batches, DistributedType, InitProcessGroupKwargs from transformers import PretrainedConfig from transformers.trainer import Trainer, TRAINING_ARGS_NAME, TRAINER_STATE_NAME import torch.distributed as dist from typing import Optional import os import torch import math from src.data.collator.train_collator import split_vlm_inputs, get_dense_rep, split_and_process_vlm_inputs from src.model.model_multi_layer_AOP import MMEBModel from src.loss_multi_layer import SimpleContrastiveLoss, DistributedContrastiveLoss from src.grad_cache.grad_cache import GradCache from torch.utils.data import DataLoader, Dataset, IterableDataset, RandomSampler, SequentialSampler from transformers.training_args import OptimizerNames, ParallelMode, TrainingArguments from transformers.trainer_callback import ( ExportableState, TrainerState, ) from transformers.trainer_utils import ( TrainOutput, has_length, speed_metrics, seed_worker, ) from transformers.trainer_pt_utils import ( get_model_param_count, ) from transformers.trainer import FSDP_MODEL_NAME from transformers.utils import ( XLA_FSDPV2_MIN_VERSION, is_accelerate_available, is_apex_available, is_torch_xla_available, logging, is_sagemaker_mp_enabled, CONFIG_NAME, WEIGHTS_NAME, SAFE_WEIGHTS_NAME, ADAPTER_WEIGHTS_NAME, ADAPTER_SAFE_WEIGHTS_NAME ) from src.utils import batch_to_device from src.utils import print_master, print_rank if is_apex_available(): from apex import amp if is_torch_xla_available(): import torch_xla.core.xla_model as xm from torch_xla import __version__ as XLA_VERSION IS_XLA_FSDPV2_POST_2_2 = version.parse(XLA_VERSION) >= version.parse(XLA_FSDPV2_MIN_VERSION) if IS_XLA_FSDPV2_POST_2_2: pass else: IS_XLA_FSDPV2_POST_2_2 = False logger = logging.get_logger(__name__) class MMEBTrainer(Trainer): def __init__(self, *args, **kwargs): super(MMEBTrainer, self).__init__(*args, **kwargs) self.is_ddp = dist.is_initialized() self.processor = self.processing_class self._dist_loss_scale_factor = dist.get_world_size() if self.is_ddp else 1 def get_batch_samples(self, epoch_iterator, num_batches): batch_samples = [] num_items_in_batch = None for _ in range(num_batches): try: batch_samples += [next(epoch_iterator)] except StopIteration: break if len(batch_samples) > 0 and "labels" in batch_samples[0]: # For now we don't support object detection try: num_items_in_batch = sum([(batch["labels"].ne(-100)).sum() for batch in batch_samples]) except (TypeError, AttributeError): pass if self.args.average_tokens_across_devices and num_items_in_batch is not None: num_items_in_batch = self.accelerator.gather(num_items_in_batch).sum().item() if torch.is_tensor(num_items_in_batch): num_items_in_batch = num_items_in_batch.item() return batch_samples, num_items_in_batch def compute_loss(self, model, inputs, *args, **kwargs): qry_inputs, tgt_inputs = inputs return model(qry=qry_inputs, tgt=tgt_inputs) def _save(self, output_dir: Optional[str] = None, state_dict=None): os.makedirs(output_dir, exist_ok=True) if state_dict is None: state_dict = self.model.state_dict() prefix = 'encoder.' assert all(k.startswith(prefix) for k in state_dict.keys()), list(state_dict.keys()) state_dict = {k[len(prefix):]: v for k, v in state_dict.items()} self.model.encoder.save_pretrained( output_dir, state_dict=state_dict, safe_serialization=self.args.save_safetensors ) if self.tokenizer is not None: self.tokenizer.save_pretrained(output_dir) torch.save(self.args, os.path.join(output_dir, TRAINING_ARGS_NAME)) def _get_train_sampler(self) -> Optional[torch.utils.data.Sampler]: # override original trainer's method if self.train_dataset is None or not has_length(self.train_dataset): return None return RandomSampler(self.train_dataset) def get_train_dataloader(self) -> DataLoader: """ override original trainer's method to disable self.accelerator.prepare since it will wrap DataLoaderDispatcher and lead to (1) `RuntimeError: You can't use batches of different size with `dispatch_batches=True` or when using an `IterableDataset`.` (2) all outputs of dataloader must be tensors """ if self.train_dataset is None: raise ValueError("Trainer: training requires a train_dataset.") train_dataset = self.train_dataset data_collator = self.data_collator train_dataset = self._remove_unused_columns(train_dataset, description="training") dataloader_params = { "batch_size": self._train_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(train_dataset, torch.utils.data.IterableDataset): dataloader_params["sampler"] = self._get_train_sampler() dataloader_params["drop_last"] = self.args.dataloader_drop_last dataloader_params["worker_init_fn"] = seed_worker dataloader_params["prefetch_factor"] = self.args.dataloader_prefetch_factor else: dataloader_params["sampler"] = None dataloader_params["shuffle"] = False dataloader_params["drop_last"] = True dataloader_params["prefetch_factor"] = None # # tune on both prefetch_factor and persistent_workers will cause hang at epoch2 return DataLoader(train_dataset, **dataloader_params) def _load_from_checkpoint(self, resume_from_checkpoint, model=None): self.model_args.checkpoint_path = resume_from_checkpoint logger.info(f"Loading checkpoint from {resume_from_checkpoint}") self.model = MMEBModel.load(self.model_args) self.model_wrapped = self.model def _inner_training_loop( self, batch_size=None, args=None, resume_from_checkpoint=None, trial=None, ignore_keys_for_eval=None ): self.accelerator.free_memory() self._train_batch_size = batch_size if self.args.auto_find_batch_size: if self.state.train_batch_size != self._train_batch_size: from accelerate.utils import release_memory (self.model_wrapped,) = release_memory(self.model_wrapped) self.model_wrapped = self.model # Check for DeepSpeed *after* the intial pass and modify the config if self.is_deepspeed_enabled: # Temporarily unset `self.args.train_batch_size` original_bs = self.args.per_device_train_batch_size self.args.per_device_train_batch_size = self._train_batch_size // max(1, self.args.n_gpu) self.propagate_args_to_deepspeed(True) self.args.per_device_train_batch_size = original_bs self.state.train_batch_size = self._train_batch_size logger.debug(f"Currently training with a batch size of: {self._train_batch_size}") # Data loader and number of training steps train_dataloader = self.get_train_dataloader() # Setting up training control variables: # number of training epochs: num_train_epochs # number of training steps per epoch: num_update_steps_per_epoch # total number of training steps to execute: max_steps total_train_batch_size = self._train_batch_size * args.gradient_accumulation_steps * args.world_size len_dataloader = None num_train_tokens = None if has_length(train_dataloader): len_dataloader = len(train_dataloader) num_update_steps_per_epoch = len_dataloader // args.gradient_accumulation_steps num_update_steps_per_epoch = max(num_update_steps_per_epoch, 1) num_examples = self.num_examples(train_dataloader) if args.max_steps > 0: max_steps = args.max_steps num_train_epochs = args.max_steps // num_update_steps_per_epoch + int( args.max_steps % num_update_steps_per_epoch > 0 ) # May be slightly incorrect if the last batch in the training dataloader has a smaller size but it's # the best we can do. num_train_samples = args.max_steps * total_train_batch_size if args.include_tokens_per_second: num_train_tokens = ( self.num_tokens(train_dataloader, args.max_steps) * args.gradient_accumulation_steps ) else: max_steps = math.ceil(args.num_train_epochs * num_update_steps_per_epoch) num_train_epochs = math.ceil(args.num_train_epochs) num_train_samples = self.num_examples(train_dataloader) * args.num_train_epochs if args.include_tokens_per_second: num_train_tokens = self.num_tokens(train_dataloader) * args.num_train_epochs elif args.max_steps > 0: # Rely on max_steps when dataloader does not have a working size max_steps = args.max_steps # Setting a very large number of epochs so we go as many times as necessary over the iterator. num_train_epochs = sys.maxsize num_update_steps_per_epoch = max_steps num_examples = total_train_batch_size * args.max_steps num_train_samples = args.max_steps * total_train_batch_size if args.include_tokens_per_second: num_train_tokens = self.num_tokens(train_dataloader, args.max_steps) * args.gradient_accumulation_steps else: raise ValueError( "args.max_steps must be set to a positive value if dataloader does not have a length, was" f" {args.max_steps}" ) delay_optimizer_creation = is_sagemaker_mp_enabled() or self.is_fsdp_xla_enabled or self.is_fsdp_enabled # We need to reset the scheduler, as its parameters may be different on subsequent calls if self._created_lr_scheduler: self.lr_scheduler = None self._created_lr_scheduler = False self.create_optimizer_and_scheduler(num_training_steps=max_steps) self.state = TrainerState( stateful_callbacks=[ cb for cb in self.callback_handler.callbacks + [self.control] if isinstance(cb, ExportableState) ] ) self.state.is_hyper_param_search = trial is not None self.state.train_batch_size = self._train_batch_size # Compute absolute values for logging, eval, and save if given as ratio if args.logging_steps is not None: if args.logging_steps < 1: self.state.logging_steps = math.ceil(max_steps * args.logging_steps) else: self.state.logging_steps = args.logging_steps if args.eval_steps is not None: if args.eval_steps < 1: self.state.eval_steps = math.ceil(max_steps * args.eval_steps) else: self.state.eval_steps = args.eval_steps if args.save_steps is not None: if args.save_steps < 1: self.state.save_steps = math.ceil(max_steps * args.save_steps) else: self.state.save_steps = args.save_steps # Activate gradient checkpointing if needed if args.gradient_checkpointing: self.model.gradient_checkpointing_enable(gradient_checkpointing_kwargs=args.gradient_checkpointing_kwargs) model = self._wrap_model(self.model_wrapped) # as the model is wrapped, don't use `accelerator.prepare` # this is for unhandled cases such as # FSDP-XLA, SageMaker MP/DP, DataParallel, IPEX use_accelerator_prepare = True if model is self.model else False if delay_optimizer_creation: if use_accelerator_prepare: self._fsdp_qlora_plugin_updates() self.model = self.accelerator.prepare(self.model) self.create_optimizer_and_scheduler(num_training_steps=max_steps) # prepare using `accelerator` prepare if use_accelerator_prepare: self.model.train() if hasattr(self.lr_scheduler, "step"): if self.use_apex: model = self.accelerator.prepare(self.model) else: model, self.optimizer = self.accelerator.prepare(self.model, self.optimizer) else: # to handle cases wherein we pass "DummyScheduler" such as when it is specified in DeepSpeed config. model, self.optimizer, self.lr_scheduler = self.accelerator.prepare( self.model, self.optimizer, self.lr_scheduler ) elif self.args.optim in [OptimizerNames.LOMO, OptimizerNames.ADALOMO]: # In this case we are in DDP + LOMO, which should be supported self.optimizer = self.accelerator.prepare(self.optimizer) if self.is_fsdp_enabled: self.model = self.model_wrapped = model # for the rest of this function `model` is the outside model, whether it was wrapped or not if model is not self.model: self.model_wrapped = model # backward compatibility if self.is_deepspeed_enabled: self.deepspeed = self.model_wrapped # Check if saved optimizer or scheduler states exist self._load_optimizer_and_scheduler(resume_from_checkpoint) # important: at this point: # self.model is the Transformers Model # self.model_wrapped is DDP(Transformers Model), Deepspeed(Transformers Model), # FSDP(Transformers Model), Dynamo Optimized Module(Transformers Model) etc. # Train! logger.info("***** Running training *****") logger.info(f" Num examples = {num_examples:,}") logger.info(f" Num Epochs = {num_train_epochs:,}") logger.info(f" Instantaneous batch size per device = {self.args.per_device_train_batch_size:,}") if self.args.per_device_train_batch_size != self._train_batch_size: logger.info(f" Training with DataParallel so batch size has been adjusted to: {self._train_batch_size:,}") logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_train_batch_size:,}") logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}") logger.info(f" Total optimization steps = {max_steps:,}") logger.info(f" Number of trainable parameters = {get_model_param_count(model, trainable_only=True):,}") self.state.epoch = 0 start_time = time.time() epochs_trained = 0 steps_trained_in_current_epoch = 0 steps_trained_progress_bar = None # @ruimeng use steps_trained_in_current_epoch to skip batches for finding buggy data # steps_trained_in_current_epoch = 42 # Check if continuing training from a checkpoint if resume_from_checkpoint is not None and os.path.isfile( os.path.join(resume_from_checkpoint, TRAINER_STATE_NAME) ): self.state = TrainerState.load_from_json(os.path.join(resume_from_checkpoint, TRAINER_STATE_NAME)) self.compare_trainer_and_checkpoint_args(self.args, self.state) self._load_callback_state() epochs_trained = int(self.state.global_step // num_update_steps_per_epoch) if not args.ignore_data_skip: steps_trained_in_current_epoch = self.state.global_step % (num_update_steps_per_epoch) steps_trained_in_current_epoch *= args.gradient_accumulation_steps else: steps_trained_in_current_epoch = 0 logger.info(" Continuing training from checkpoint, will skip to saved global_step") logger.info(f" Continuing training from epoch {epochs_trained}") logger.info(f" Continuing training from global step {self.state.global_step}") if not args.ignore_data_skip: logger.info( f" Will skip the first {epochs_trained} epochs then the first" f" {steps_trained_in_current_epoch} batches in the first epoch." ) # Update the references self.callback_handler.model = self.model self.callback_handler.optimizer = self.optimizer self.callback_handler.lr_scheduler = self.lr_scheduler self.callback_handler.train_dataloader = train_dataloader # This should be the same if the state has been saved but in case the training arguments changed, it's safer # to set this after the load. self.state.max_steps = max_steps self.state.num_train_epochs = num_train_epochs self.state.is_local_process_zero = self.is_local_process_zero() self.state.is_world_process_zero = self.is_world_process_zero() # tr_loss is a tensor to avoid synchronization of TPUs through .item() tr_loss = torch.tensor(0.0).to(args.device) # _total_loss_scalar is updated everytime .item() has to be called on tr_loss and stores the sum of all losses self._total_loss_scalar = 0.0 self._globalstep_last_logged = self.state.global_step model.zero_grad() grad_norm: Optional[float] = None self.control = self.callback_handler.on_train_begin(args, self.state, self.control) if args.eval_on_start: self._evaluate(trial, ignore_keys_for_eval, skip_scheduler=True) total_batched_samples = 0 for epoch in range(epochs_trained, num_train_epochs): epoch_dataloader = train_dataloader if hasattr(epoch_dataloader.dataset, "set_epoch"): # print(f'\t\tSetting new epoch={epoch}') epoch_dataloader.dataset.set_epoch(epoch) # Reset the past mems state at the beginning of each epoch if necessary. if args.past_index >= 0: self._past = None steps_in_epoch = ( len(epoch_dataloader) if len_dataloader is not None else args.max_steps * args.gradient_accumulation_steps ) self.control = self.callback_handler.on_epoch_begin(args, self.state, self.control) if epoch == epochs_trained and resume_from_checkpoint is not None and steps_trained_in_current_epoch == 0: self._load_rng_state(resume_from_checkpoint) rng_to_sync = False steps_skipped = 0 if steps_trained_in_current_epoch > 0: epoch_dataloader = skip_first_batches(epoch_dataloader, steps_trained_in_current_epoch) steps_skipped = steps_trained_in_current_epoch steps_trained_in_current_epoch = 0 rng_to_sync = True step = -1 epoch_iterator = iter(epoch_dataloader) # We chunkify the epoch iterator into gradient accumulation steps `n` batches remainder = num_examples % args.gradient_accumulation_steps num_items_in_batch = None if remainder == 0: remainder = args.gradient_accumulation_steps update_step = -1 total_updates = steps_in_epoch // args.gradient_accumulation_steps + 1 for _ in range(total_updates): update_step += 1 num_batches = args.gradient_accumulation_steps if update_step != (total_updates - 1) else remainder batch_samples, num_items_in_batch = self.get_batch_samples(epoch_iterator, num_batches) for i, inputs in enumerate(batch_samples): step += 1 total_batched_samples += 1 dataset_stat = collections.Counter(inputs[0]['global_dataset_name']) # print_rank(f"dataset name: {str(set(inputs[0]['global_dataset_name']))}") # for dname, count in sorted(dataset_stat.items(), key=lambda t:t[1], reverse=True): # print_rank(f"\t\tdataset_name={dname}, count={count}") is_last_step_and_steps_less_than_grad_acc = ( steps_in_epoch <= args.gradient_accumulation_steps and (step + 1) == steps_in_epoch ) do_sync_step = is_last_step_and_steps_less_than_grad_acc or ( total_batched_samples % args.gradient_accumulation_steps == 0 ) # Since we perform prefetching, we need to manually set sync_gradients if not do_sync_step: self.accelerator.gradient_state._set_sync_gradients(False) else: self.accelerator.gradient_state._set_sync_gradients(True) if self.args.include_num_input_tokens_seen: main_input_name = getattr(self.model, "main_input_name", "input_ids") if main_input_name not in inputs: logger.warning( "Tried to track the number of tokens seen, however the current model is " "not configured properly to know what item is the input. To fix this, add " "a `main_input_name` attribute to the model class you are using." ) else: input_tokens = inputs[main_input_name].numel() input_tokens = torch.tensor(input_tokens, device=self.args.device, dtype=torch.int64) self.state.num_input_tokens_seen += self.accelerator.gather(input_tokens).cpu().item() if rng_to_sync: self._load_rng_state(resume_from_checkpoint) rng_to_sync = False # Skip past any already trained steps if resuming training if steps_trained_in_current_epoch > 0: steps_trained_in_current_epoch -= 1 if steps_trained_progress_bar is not None: steps_trained_progress_bar.update(1) if steps_trained_in_current_epoch == 0: self._load_rng_state(resume_from_checkpoint) continue elif steps_trained_progress_bar is not None: steps_trained_progress_bar.close() steps_trained_progress_bar = None if step % args.gradient_accumulation_steps == 0: self.control = self.callback_handler.on_step_begin(args, self.state, self.control) # We explicitly want to avoid relying on `accelerator.accumulate` for generation training context = ( functools.partial(self.accelerator.no_sync, model=model) if i != len(batch_samples) - 1 else contextlib.nullcontext ) with context(): tr_loss_step = self.training_step(model, inputs, num_items_in_batch) if ( args.logging_nan_inf_filter and not is_torch_xla_available() and (torch.isnan(tr_loss_step) or torch.isinf(tr_loss_step)) ): # if loss is nan or inf simply add the average of previous logged losses tr_loss = tr_loss + tr_loss / (1 + self.state.global_step - self._globalstep_last_logged) else: if tr_loss.device != tr_loss_step.device: raise ValueError( f"Calculated loss must be on the original device: {tr_loss.device} but device in use is {tr_loss_step.device}" ) tr_loss = tr_loss + tr_loss_step self.current_flos += float(self.floating_point_ops(inputs)) if do_sync_step: # Since we perform prefetching, we need to manually set sync_gradients to True self.accelerator.gradient_state._set_sync_gradients(True) # Gradient clipping if args.max_grad_norm is not None and args.max_grad_norm > 0: # deepspeed does its own clipping if self.use_apex: # Revert to normal clipping otherwise, handling Apex or full precision _grad_norm = torch.nn.utils.clip_grad_norm_( amp.master_params(self.optimizer), args.max_grad_norm, ) else: _grad_norm = self.accelerator.clip_grad_norm_( model.parameters(), args.max_grad_norm, ) if ( is_accelerate_available() and self.accelerator.distributed_type == DistributedType.DEEPSPEED ): grad_norm = model.get_global_grad_norm() # In some cases the grad norm may not return a float if hasattr(grad_norm, "item"): grad_norm = grad_norm.item() else: grad_norm = _grad_norm self.control = self.callback_handler.on_pre_optimizer_step(args, self.state, self.control) self.optimizer.step() self.control = self.callback_handler.on_optimizer_step(args, self.state, self.control) optimizer_was_run = not self.accelerator.optimizer_step_was_skipped if optimizer_was_run: # Delay optimizer scheduling until metrics are generated if not isinstance(self.lr_scheduler, torch.optim.lr_scheduler.ReduceLROnPlateau): self.lr_scheduler.step() model.zero_grad() self.state.global_step += 1 self.state.epoch = epoch + (step + 1 + steps_skipped) / steps_in_epoch self.control = self.callback_handler.on_step_end(args, self.state, self.control) self._maybe_log_save_evaluate(tr_loss, grad_norm, model, trial, epoch, ignore_keys_for_eval, time.time()) else: self.control = self.callback_handler.on_substep_end(args, self.state, self.control) # PyTorch/XLA relies on the data loader to insert the mark_step for # each step. Since we are breaking the loop early, we need to manually # insert the mark_step here. if self.control.should_epoch_stop or self.control.should_training_stop: if is_torch_xla_available(): xm.mark_step() break # We also need to break out of the nested loop if self.control.should_epoch_stop or self.control.should_training_stop: if is_torch_xla_available(): xm.mark_step() break if step < 0: logger.warning( "There seems not to be a single sample in your epoch_iterator, stopping training at step" f" {self.state.global_step}! This is expected if you're using an IterableDataset and set" f" num_steps ({max_steps}) higher than the number of available samples." ) self.control.should_training_stop = True self.control = self.callback_handler.on_epoch_end(args, self.state, self.control) self._maybe_log_save_evaluate(tr_loss, grad_norm, model, trial, epoch, ignore_keys_for_eval, time.time()) if self.control.should_training_stop: break if args.past_index and hasattr(self, "_past"): # Clean the state at the end of training delattr(self, "_past") logger.info("\n\nTraining completed. Do not forget to share your model on huggingface.co/models =)\n\n") if args.load_best_model_at_end and self.state.best_model_checkpoint is not None: # Wait for everyone to get here so we are sure the model has been saved by process 0. if is_torch_xla_available(): xm.rendezvous("load_best_model_at_end") elif args.parallel_mode == ParallelMode.DISTRIBUTED: dist.barrier() self._load_best_model() # add remaining tr_loss self._total_loss_scalar += tr_loss.item() effective_global_step = max(self.state.global_step, 0.001) # Avoid ZeroDivisionError train_loss = self._total_loss_scalar / effective_global_step metrics = speed_metrics( "train", start_time, num_samples=num_train_samples, num_steps=self.state.max_steps, num_tokens=num_train_tokens, ) self.store_flos() metrics["total_flos"] = self.state.total_flos metrics["train_loss"] = train_loss self.is_in_train = False self._memory_tracker.stop_and_update_metrics(metrics) self.log(metrics) run_dir = self._get_output_dir(trial) checkpoints_sorted = self._sorted_checkpoints(use_mtime=False, output_dir=run_dir) # Delete the last checkpoint when save_total_limit=1 if it's different from the best checkpoint and process allowed to save. if self.args.should_save and self.state.best_model_checkpoint is not None and self.args.save_total_limit == 1: for checkpoint in checkpoints_sorted: if not os.path.samefile(checkpoint, self.state.best_model_checkpoint): logger.info(f"Deleting older checkpoint [{checkpoint}] due to args.save_total_limit") shutil.rmtree(checkpoint, ignore_errors=True) self.control = self.callback_handler.on_train_end(args, self.state, self.control) # Wait for the checkpoint to be uploaded. self._finish_current_push() # After training we make sure to retrieve back the original forward pass method # for the embedding layer by removing the forward post hook. if self.neftune_noise_alpha is not None: self._deactivate_neftune(self.model) return TrainOutput(self.state.global_step, train_loss, metrics) class GradCacheLateProcessTrainer(MMEBTrainer): """ Adapted from gradcache repo. """ def __init__(self, *args, **kwargs): self.max_length = kwargs.get("max_length", 512) if "max_length" in kwargs: del kwargs["max_length"] self.model_args = kwargs.get("model_args", None) if "model_args" in kwargs: del kwargs["model_args"] super(GradCacheLateProcessTrainer, self).__init__(*args, **kwargs) self.is_ddp = dist.is_initialized() self._dist_loss_scale_factor = dist.get_world_size() if self.is_ddp else 1 loss_fn_cls = DistributedContrastiveLoss if self.is_ddp else SimpleContrastiveLoss loss_fn = loss_fn_cls( temperature=self.model.temperature, alpha=getattr(self.model, "dual_alpha", 0.15), # 兼容旧参数 weights=getattr(self.model, "supervise_weights", None) # 新增：传入多层权重 ) self.gc = GradCache( models=[self.model, self.model], chunk_sizes=[self.args.gc_q_chunk_size, self.args.gc_p_chunk_size], loss_fn=loss_fn, split_input_fn=split_and_process_vlm_inputs, get_rep_fn=get_dense_rep, fp16=self.args.fp16, scaler=self.scaler if self.args.fp16 else None ) def _maybe_sample_keep_ratios(self, model): """ 每步按配置在 [min, max] 范围内随机采样 keep_ratio_text/vision，并覆盖到 encoder.aop_prune_config。 仅在 mode=ratio 且 enabled 时生效。优先使用各自 range；否则使用通用 range；否则不改。 同时把本次采样值写入 cfg['_last_sampled_keep_ratio_*'] 供底模 monitor 打印。 """ import torch enc = model.module.encoder if hasattr(model, "module") and hasattr(model.module, "encoder") else getattr(model, "encoder", None) if enc is None: return None cfg = getattr(enc, "aop_prune_config", None) if not isinstance(cfg, dict) or not cfg.get("enabled", False): return None if str(cfg.get("mode", "ratio")).lower() != "ratio": return None sampling = str(cfg.get("keep_ratio_sampling", "off")).lower() if sampling not in {"step", "batch"}: return None device = getattr(model, "device", torch.device("cpu")) def _sample_from_range(rng, fallback): if rng is None: return fallback try: lo, hi = float(rng[0]), float(rng[1]) lo = max(0.0, min(1.0, lo)); hi = max(0.0, min(1.0, hi)) if hi < lo: lo, hi = hi, lo val = lo + (hi - lo) * torch.rand((), device=device).item() return float(val) except Exception: return fallback r_all = cfg.get("keep_ratio_range", None) r_t = cfg.get("keep_ratio_text_range", r_all) r_v = cfg.get("keep_ratio_vision_range", r_all) kr_t = _sample_from_range(r_t, cfg.get("keep_ratio_text", cfg.get("keep_ratio", None))) kr_v = _sample_from_range(r_v, cfg.get("keep_ratio_vision", cfg.get("keep_ratio", None))) if kr_t is not None: cfg["keep_ratio_text"] = float(kr_t) if kr_v is not None: cfg["keep_ratio_vision"] = float(kr_v) # 记录最近一次采样值，供底模 monitor 打印 cfg["_last_sampled_keep_ratio_text"] = float(kr_t) if kr_t is not None else None cfg["_last_sampled_keep_ratio_vision"] = float(kr_v) if kr_v is not None else None setattr(enc, "aop_prune_config", cfg) # 可选控制台回显采样值（仅前几步打印，避免刷屏） if os.getenv("AOP_MONITOR", "0") == "1": step = getattr(self.state, "global_step", None) if hasattr(self, "state") else None tag = f"step={step}" if step is not None else "" if not hasattr(self, "_aop_sample_prints"): self._aop_sample_prints = 0 if self._aop_sample_prints < 5: print(f"[AOP][sample] {tag} keep_ratio_text={cfg.get('keep_ratio_text')} keep_ratio_vision={cfg.get('keep_ratio_vision')}") self._aop_sample_prints += 1 return (kr_t, kr_v) def training_step(self, model, inputs, *args, **kwargs) -> torch.Tensor: """ 单卡/非DDP：两阶段 stop-grad 分块训练，按 dual_alpha 设置权重并做分块均值缩放，保证数值尺度与原始实现一致； 多卡DDP：沿用 GradCache。 """ model.train() # === 每步随机采样 keep_ratio（若启用） === self._maybe_sample_keep_ratios(model) queries_raw, targets_raw = inputs queries_raw = batch_to_device(queries_raw, model.device) targets_raw = batch_to_device(targets_raw, model.device) # 多卡 DDP：使用 GradCache is_ddp_wrapped = isinstance(model, torch.nn.parallel.DistributedDataParallel) ws = dist.get_world_size() if dist.is_initialized() else 1 use_ddp = (is_ddp_wrapped and ws > 1) if use_ddp: queries, targets = {'qry': queries_raw}, {'tgt': targets_raw} self.gc.models = [model, model] loss = self.gc(queries, targets, no_sync_except_last=True) return loss / self._dist_loss_scale_factor # 单卡/非DDP：两阶段 stop-grad + 分块 def _batch_size(d): for v in d.values(): if isinstance(v, torch.Tensor): return v.size(0) if isinstance(v, (list, tuple)): return len(v) raise ValueError("Cannot infer batch size from inputs") def _slice_dict(d, s, e): out = {} for k, v in d.items(): if isinstance(v, torch.Tensor): out[k] = v[s:e] elif isinstance(v, list): out[k] = v[s:e] elif isinstance(v, tuple): out[k] = v[s:e] else: out[k] = v return out B = _batch_size(queries_raw) assert B == _batch_size(targets_raw), "Batch mismatch between queries and targets" q_chunk = max(1, int(getattr(self.args, "gc_q_chunk_size", 8))) p_chunk = max(1, int(getattr(self.args, "gc_p_chunk_size", 8))) Nq = (B + q_chunk - 1) // q_chunk Np = (B + p_chunk - 1) // p_chunk temp = float(getattr(self.model, "temperature", 0.02)) alpha = float(getattr(self.model, "dual_alpha", 0.15)) # 与原始损失一致： (1-alpha)*CE(q->p) + alpha*CE(p->q) # 先构建 p_bank（无梯度） with torch.no_grad(): p_chunks = [] for s in range(0, B, p_chunk): e = min(s + p_chunk, B) out_p = model(tgt=_slice_dict(targets_raw, s, e)) p_chunks.append(out_p["tgt_reps"].detach()) # [b,K,D] p_bank = torch.cat(p_chunks, dim=0) # [B,K,D] K = p_bank.size(1) weights = getattr(self.model, "supervise_weights", None) if weights is None or len(weights) != K: weights = [1.0] * K w = torch.tensor(weights, dtype=p_bank.dtype, device=p_bank.device) w = torch.clamp(w, min=0) w = w / max(w.sum().item(), 1e-8) # 统计用的“全批平均”日志损失 Lq_sum_weighted = 0.0 Lp_sum_weighted = 0.0 # 第一阶段：仅对 q 侧反传（p_bank stop-grad），按 Nq 做均值缩放 for s in range(0, B, q_chunk): e = min(s + q_chunk, B) out_q = model(qry=_slice_dict(queries_raw, s, e)) q_rep = out_q["qry_reps"] # [b,K,D] b = q_rep.size(0) loss_chunk = 0.0 for k in range(K): logits = torch.matmul(q_rep[:, k, :], p_bank[:, k, :].transpose(0, 1)) / temp # [b,B] tgt = torch.arange(s, e, device=logits.device, dtype=torch.long) loss_k = self.model.cross_entropy(logits, tgt) loss_chunk = loss_chunk + w[k] * loss_k # 记录全批平均用：按样本数加权 Lq_sum_weighted += float(loss_chunk.detach().item()) * b # 反传：分块均值缩放，匹配全批平均 scale_q = (1.0 - alpha) / float(Nq) self.accelerator.backward((loss_chunk * scale_q) / self._dist_loss_scale_factor) # 第二阶段：仅对 p 侧反传（q_bank stop-grad），按 Np 做均值缩放 with torch.no_grad(): q_chunks = [] for s in range(0, B, q_chunk): e = min(s + q_chunk, B) out_q = model(qry=_slice_dict(queries_raw, s, e)) q_chunks.append(out_q["qry_reps"].detach()) q_bank = torch.cat(q_chunks, dim=0) # [B,K,D] for s in range(0, B, p_chunk): e = min(s + p_chunk, B) out_p = model(tgt=_slice_dict(targets_raw, s, e)) p_rep = out_p["tgt_reps"] # [b,K,D] b = p_rep.size(0) loss_chunk = 0.0 for k in range(K): logits = torch.matmul(p_rep[:, k, :], q_bank[:, k, :].transpose(0, 1)) / temp # [b,B] tgt = torch.arange(s, e, device=logits.device, dtype=torch.long) loss_k = self.model.cross_entropy(logits, tgt) loss_chunk = loss_chunk + w[k] * loss_k # 记录全批平均用：按样本数加权 Lp_sum_weighted += float(loss_chunk.detach().item()) * b # 反传：分块均值缩放 scale_p = alpha / float(Np) self.accelerator.backward((loss_chunk * scale_p) / self._dist_loss_scale_factor) # 返回用于日志的“全批平均”损失：(1-alpha)*mean_q + alpha*mean_p Lq_avg = Lq_sum_weighted / float(B) Lp_avg = Lp_sum_weighted / float(B) loss_log_val = (1.0 - alpha) * Lq_avg + alpha * Lp_avg ref = next(model.parameters()) loss_log = torch.tensor(loss_log_val, device=ref.device, dtype=ref.dtype) dummy_zero = ref.sum() * 0.0 # 不再触发额外反传 return dummy_zero + loss_log def _save(self, output_dir: Optional[str] = None, state_dict=None): print_master(f"Saving model to {output_dir}") os.makedirs(output_dir, exist_ok=True) if state_dict is None: state_dict = self.model.state_dict() prefix = 'encoder.' assert all(k.startswith(prefix) for k in state_dict.keys()), list(state_dict.keys()) state_dict = {k[len(prefix):]: v for k, v in state_dict.items()} self.model.encoder.save_pretrained( output_dir, state_dict=state_dict, safe_serialization=self.args.save_safetensors ) if self.tokenizer is not None: self.tokenizer.save_pretrained(output_dir) torch.save(self.args, os.path.join(output_dir, TRAINING_ARGS_NAME)) self.model.encoder.config.to_json_file(os.path.join(output_dir, 'config.json'))