"""Module containing the Trainer class and related functions""" import importlib import logging import math import os import sys from contextlib import contextmanager from dataclasses import dataclass, field from functools import partial from pathlib import Path from typing import Optional, Union import bitsandbytes as bnb import numpy as np import torch.cuda import transformers from datasets import Dataset, set_caching_enabled from torch import nn from torch.optim.lr_scheduler import OneCycleLR from torch.utils.data import DataLoader, DistributedSampler, RandomSampler from transformers import EarlyStoppingCallback, Trainer, TrainingArguments from transformers.trainer_pt_utils import get_parameter_names from axolotl.utils.callbacks import ( GPUStatsCallback, SaveBetterTransformerModelCallback, SavePeftModelCallback, ) from axolotl.utils.collators import DataCollatorForSeq2Seq from axolotl.utils.dataloader import MultipackDistributedDataloader from axolotl.utils.schedulers import ( InterpolatingLogScheduler, get_cosine_schedule_with_quadratic_warmup, ) LOG = logging.getLogger("axolotl") @torch.jit.script def weighted_cross_entropy( logits: torch.Tensor, labels: torch.Tensor, weights: torch.Tensor ): # Flatten the logits, labels, and weights tensors logits = logits.view( -1, logits.size(-1) ) # logits becomes of shape [batch_size*sequence_length, vocab_size] labels = labels.view(-1) # labels becomes of shape [batch_size*sequence_length] weights = weights.view(-1) # weights becomes of shape [batch_size*sequence_length] # Compute the unweighted cross entropy loss losses = torch.nn.functional.cross_entropy(logits, labels, reduction="none") # Apply the weights to the losses and compute their sum return (weights * losses).sum() @torch.jit.script def create_weighted_mask(labels: torch.Tensor): # Check if the tensor is 2D. If not, unsqueeze it to make it 2D if len(labels.shape) == 1: labels = labels.unsqueeze(0) weights = torch.zeros_like(labels).float() for i in range(labels.shape[0]): mask = labels[i] != -100 # Create a tensor to track group ids group_ids = torch.zeros_like(labels[i]).int() curr_group_id = 0 for j in range(1, len(labels[i])): if mask[j] and not mask[j - 1]: # switch from masked to unmasked label curr_group_id += 1 # start new group group_ids[j] = ( curr_group_id if mask[j] else 0 ) # assign group id if unmasked label # Count only unmasked labels in each group group_counts = torch.bincount(group_ids[mask]) mask_weights = torch.zeros_like(labels[i]).float() mask_weights[mask] = 1.0 / group_counts[group_ids[mask]] weights[i] = mask_weights return weights.squeeze() # squeeze the output to match the input dimension def trainer_weighted_loss(model_output, labels, shift_labels=True): logits = ( model_output["logits"] if isinstance(model_output, dict) else model_output[0] ) if shift_labels: logits = logits[..., :-1, :].contiguous() labels = labels[..., 1:].contiguous() weights = create_weighted_mask(labels) return weighted_cross_entropy(logits, labels, weights) @dataclass class AxolotlTrainingArguments(TrainingArguments): """ Extend the base TrainingArguments for axolotl helpers """ lr_quadratic_warmup: bool = field( default=False, metadata={"help": "Use quadratic warmup for cosine scheduling."}, ) sample_packing: bool = field( default=False, metadata={"help": "Use sample packing for efficient training."}, ) sample_packing_efficiency: float = field( default=1.0, metadata={"help": "Sample packing efficiency for calculating batch length."}, ) max_seq_length: int = field( default=2048, metadata={"help": "The maximum sequence length the model can handle"}, ) sample_packing_seq_len_multiplier: int = field( default=1, metadata={"help": "the multiplier for the max len for packed sequences"}, ) class AxolotlTrainer(Trainer): """ Extend the base Trainer for axolotl helpers """ args = None # type: AxolotlTrainingArguments def create_scheduler( self, num_training_steps: int, optimizer: torch.optim.Optimizer = None ): """ Setup the scheduler. The optimizer of the trainer must have been set up either before this method is called or passed as an argument. Args: num_training_steps (int): The number of training steps to do. optimizer (torch.optim.Optimizer): The training optimizer """ # fmt: off if self.lr_scheduler is None: # type: ignore # pylint: disable=access-member-before-definition # fmt: on if ( self.args.lr_scheduler_type == "cosine" and self.args.lr_quadratic_warmup is True ): self.lr_scheduler = get_cosine_schedule_with_quadratic_warmup( # pylint: disable=attribute-defined-outside-init optimizer, num_warmup_steps=self.args.get_warmup_steps(num_training_steps), num_training_steps=num_training_steps, ) else: return super().create_scheduler(num_training_steps, optimizer) return self.lr_scheduler def _get_train_sampler(self) -> Optional[torch.utils.data.Sampler]: if self.args.world_size > 1 and self.args.sample_packing: return DistributedSampler( self.train_dataset, num_replicas=self.args.world_size, rank=self.args.process_index, seed=self.args.seed, ) return super()._get_train_sampler() def get_train_dataloader(self) -> Union[DataLoader, MultipackDistributedDataloader]: if self.args.sample_packing: train_sampler = self._get_train_sampler() return self.accelerator.prepare( MultipackDistributedDataloader( self.train_dataset, batch_size=self._train_batch_size, seq_max_length=self.args.max_seq_length, collate_fn=self.data_collator, sampler=train_sampler, packing_efficiency_estimate=self.args.sample_packing_efficiency, sample_packing_seq_len_multiplier=self.args.sample_packing_seq_len_multiplier, device_count=int(os.environ.get("WORLD_SIZE", 1)), ) ) return super().get_train_dataloader() def get_eval_dataloader( self, eval_dataset: Optional[Dataset] = None ) -> Union[DataLoader, MultipackDistributedDataloader]: if self.args.sample_packing: eval_dataset = ( eval_dataset if eval_dataset is not None else self.eval_dataset ) eval_sampler = self._get_eval_sampler(eval_dataset) return self.accelerator.prepare( MultipackDistributedDataloader( eval_dataset, batch_size=self.args.eval_batch_size, seq_max_length=self.args.max_seq_length, collate_fn=self.data_collator, sampler=eval_sampler, packing_efficiency_estimate=self.args.sample_packing_efficiency, sample_packing_seq_len_multiplier=self.args.eval_batch_size, device_count=int(os.environ.get("WORLD_SIZE", 1)), ) ) return super().get_eval_dataloader(eval_dataset) def compute_loss(self, model, inputs, return_outputs=False): # use one's weighted cross entropy loss calc # if self.args.sample_packing: # labels = inputs.pop("labels") # outputs = model(**inputs) # loss = trainer_weighted_loss(outputs, labels, shift_labels=True) # return (loss, outputs) if return_outputs else loss return super().compute_loss(model, inputs, return_outputs=return_outputs) class OneCycleLRSchedulerTrainer(AxolotlTrainer): """ Trainer subclass that uses the OneCycleLR scheduler """ def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.lr_scheduler = None def create_scheduler( self, num_training_steps: int, optimizer: Optional[torch.optim.Optimizer] = None, ): optimizer = self.optimizer if optimizer is None else optimizer num_warmup_steps = self.args.get_warmup_steps(num_training_steps) pct_start = num_warmup_steps / num_training_steps self.lr_scheduler = OneCycleLR( optimizer, max_lr=self.args.learning_rate, total_steps=num_training_steps, pct_start=pct_start, div_factor=6, ) return self.lr_scheduler def add_position_ids(sample): sample["position_ids"] = torch.arange(len(sample["input_ids"])) return sample def drop_long_seq(sample, sequence_len=2048): return len(sample["input_ids"]) <= sequence_len @contextmanager def disable_datasets_caching(): try: set_caching_enabled(False) yield finally: set_caching_enabled(True) def process_datasets_for_packing(cfg, train_dataset, eval_dataset): if cfg.sample_packing: drop_long = partial(drop_long_seq, sequence_len=cfg.sequence_len) train_dataset = train_dataset.filter(drop_long, num_proc=os.cpu_count()).map( add_position_ids, num_proc=os.cpu_count() ) if eval_dataset: eval_dataset = eval_dataset.filter(drop_long, num_proc=os.cpu_count()).map( add_position_ids, num_proc=os.cpu_count() ) return train_dataset, eval_dataset def calculate_total_num_steps(cfg, train_dataset, tokenizer): if cfg.sample_packing: # we have to drop anything longer then sequence len otherwise # flash attention with position ids fails if not cfg.total_num_tokens: LOG.info("calculating total_num_tokens") total_num_tokens = np.sum( train_dataset.data.column("input_ids") .to_pandas() .apply(lambda x: len(x)) # pylint: disable=unnecessary-lambda .values ) LOG.info(f"📝 UPDATE CONFIG WITH: `total_num_tokens: {total_num_tokens}`") cfg.total_num_tokens = total_num_tokens if cfg.sample_packing_eff_est: total_num_steps = ( # match count to len est in dataloader ( math.floor( 0.99 * cfg.total_num_tokens / cfg.sample_packing_eff_est / cfg.sequence_len // cfg.batch_size // int(os.environ.get("WORLD_SIZE", 1)) ) - 1 ) * cfg.num_epochs ) LOG.info( f"total_num_tokens: {cfg.total_num_tokens}, total_num_steps: {total_num_steps}" ) else: sampler = RandomSampler(train_dataset) data_loader = MultipackDistributedDataloader( train_dataset, batch_size=cfg.micro_batch_size, seq_max_length=cfg.max_packed_sequence_len or cfg.sequence_len, collate_fn=DataCollatorForSeq2Seq( tokenizer, return_tensors="pt", padding="longest", ), sampler=sampler, packing_efficiency_estimate=cfg.sample_packing_eff_est, sample_packing_seq_len_multiplier=cfg.micro_batch_size, device_count=int(os.environ.get("WORLD_SIZE", 1)), ) data_loader_len = data_loader.len_w_stats() actual_eff = data_loader.efficiency() LOG.info(f"data_loader_len: {data_loader_len}") total_num_steps = int( math.floor( data_loader_len * cfg.micro_batch_size * cfg.num_epochs // cfg.batch_size ) ) LOG.info( f"📝 UPDATE CONFIG WITH: `sample_packing_eff_est: {math.ceil(actual_eff * 100.0) / 100.0}`" ) cfg.sample_packing_eff_est = math.ceil(actual_eff * 100.0) / 100.0 else: total_num_steps = int( math.ceil(len(train_dataset) * cfg.num_epochs / cfg.batch_size) ) LOG.info(f"total_num_steps: {total_num_steps}") return total_num_steps def setup_fsdp_envs(cfg): os.environ["ACCELERATE_USE_FSDP"] = "true" if cfg.fsdp_config.fsdp_sync_module_states: os.environ["FSDP_SYNC_MODULE_STATES"] = "true" if cfg.fsdp_config.fsdp_state_dict_type: os.environ["FSDP_STATE_DICT_TYPE"] = cfg.fsdp_config.fsdp_state_dict_type def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer, total_num_steps): if cfg.fsdp: setup_fsdp_envs(cfg) warmup_steps = ( cfg.warmup_steps if cfg.warmup_steps is not None else min(int(0.03 * total_num_steps), 100) ) logging_steps = ( cfg.logging_steps if cfg.logging_steps is not None else max(min(int(0.005 * total_num_steps), 10), 1) ) training_arguments_kwargs = {} if cfg.bf16 == "full": training_arguments_kwargs["bf16_full_eval"] = True else: training_arguments_kwargs["bf16"] = cfg.bf16 training_arguments_kwargs["fp16"] = (cfg.fp16 and not cfg.bf16) or False training_arguments_kwargs["tf32"] = cfg.tf32 training_arguments_kwargs["warmup_steps"] = warmup_steps training_arguments_kwargs["logging_steps"] = logging_steps if cfg.seed: training_arguments_kwargs["seed"] = cfg.seed if cfg.gradient_checkpointing: if cfg.gptq: from alpaca_lora_4bit.gradient_checkpointing import ( apply_gradient_checkpointing, ) gradient_checkpointing_ratio = ( cfg.gradient_checkpointing_ratio if cfg.gradient_checkpointing_ratio else 1.0 ) apply_gradient_checkpointing( model, checkpoint_ratio=gradient_checkpointing_ratio ) else: training_arguments_kwargs[ "gradient_checkpointing" ] = cfg.gradient_checkpointing if cfg.fsdp: training_arguments_kwargs["fsdp"] = cfg.fsdp if cfg.fsdp_config: training_arguments_kwargs["fsdp_config"] = dict(cfg.fsdp_config) if cfg.lr_quadratic_warmup is not None: training_arguments_kwargs["lr_quadratic_warmup"] = cfg.lr_quadratic_warmup # deepspeed if ( os.environ.get("ACCELERATE_USE_DEEPSPEED") == "true" and torch.cuda.device_count() > 1 ): if cfg.deepspeed: training_arguments_kwargs["deepspeed"] = cfg.deepspeed else: # make a guess here # TODO search Path("./") for one training_arguments_kwargs["deepspeed"] = "./ds_config.json" if cfg.adam_beta1: training_arguments_kwargs["adam_beta1"] = cfg.adam_beta1 if cfg.adam_beta2: training_arguments_kwargs["adam_beta2"] = cfg.adam_beta2 if cfg.adam_epsilon: training_arguments_kwargs["adam_epsilon"] = cfg.adam_epsilon if cfg.max_grad_norm: training_arguments_kwargs["max_grad_norm"] = cfg.max_grad_norm if cfg.hub_model_id: training_arguments_kwargs["hub_model_id"] = cfg.hub_model_id training_arguments_kwargs["push_to_hub"] = True training_arguments_kwargs["hub_private_repo"] = True if cfg.hub_strategy: training_arguments_kwargs["hub_strategy"] = cfg.hub_strategy if cfg.save_safetensors: training_arguments_kwargs["save_safetensors"] = cfg.save_safetensors if cfg.sample_packing_eff_est: training_arguments_kwargs[ "sample_packing_efficiency" ] = cfg.sample_packing_eff_est if cfg.val_set_size == 0: training_arguments_kwargs["evaluation_strategy"] = "no" elif cfg.eval_steps: training_arguments_kwargs["evaluation_strategy"] = "steps" training_arguments_kwargs["eval_steps"] = cfg.eval_steps else: # we have an eval set, but no steps defined, use epoch training_arguments_kwargs["evaluation_strategy"] = "epoch" training_args = AxolotlTrainingArguments( # pylint: disable=unexpected-keyword-arg max_steps=total_num_steps if cfg.max_steps else -1, max_seq_length=cfg.sequence_len, per_device_train_batch_size=cfg.micro_batch_size, per_device_eval_batch_size=cfg.eval_batch_size if cfg.eval_batch_size is not None else cfg.micro_batch_size, gradient_accumulation_steps=cfg.gradient_accumulation_steps, eval_accumulation_steps=cfg.gradient_accumulation_steps, num_train_epochs=cfg.num_epochs, learning_rate=cfg.learning_rate, save_strategy="steps" if cfg.save_steps else "epoch", save_steps=cfg.save_steps, output_dir=cfg.output_dir, save_total_limit=cfg.save_total_limit if cfg.save_total_limit else 4, load_best_model_at_end=( cfg.load_best_model_at_end is not False and cfg.val_set_size > 0 and cfg.save_steps and cfg.save_steps % cfg.eval_steps == 0 and cfg.load_in_8bit is not True ) or False, ddp_find_unused_parameters=False if cfg.ddp else None, group_by_length=cfg.group_by_length, report_to="wandb" if cfg.use_wandb else None, run_name=cfg.wandb_run_id if cfg.use_wandb else None, optim=cfg.optimizer if cfg.optimizer else "adamw_hf", lr_scheduler_type=cfg.lr_scheduler if cfg.lr_scheduler and cfg.lr_scheduler not in ("one_cycle", "log_sweep") else "cosine", weight_decay=cfg.weight_decay if cfg.weight_decay is not None else 0.0, sample_packing=cfg.sample_packing if cfg.sample_packing else False, sample_packing_seq_len_multiplier=cfg.micro_batch_size, **training_arguments_kwargs, ) trainer_kwargs = {} if cfg.optimizer == "adamw_anyprecision": if Path(cfg.torchdistx_path).exists(): sys.path.append(cfg.torchdistx_path) importlib.import_module("torchdistx") if ( cfg.optimizer == "adamw_bnb_8bit" and not cfg.gptq and "deepspeed" not in training_arguments_kwargs and not cfg.fsdp ): decay_parameters = get_parameter_names(model, [nn.LayerNorm]) decay_parameters = [name for name in decay_parameters if "bias" not in name] optimizer_grouped_parameters = [ { "params": [ p for n, p in model.named_parameters() if (n in decay_parameters and p.requires_grad) ], "weight_decay": training_args.weight_decay, }, { "params": [ p for n, p in model.named_parameters() if (n not in decay_parameters and p.requires_grad) ], "weight_decay": 0.0, }, ] optimizer = bnb.optim.Adam8bit( optimizer_grouped_parameters, betas=(training_args.adam_beta1, training_args.adam_beta2), eps=training_args.adam_epsilon, lr=training_args.learning_rate, ) if cfg.lr_scheduler == "one_cycle": lr_scheduler_kwargs = ( cfg.lr_scheduler_kwargs if cfg.lr_scheduler_kwargs else {} ) lr_scheduler = OneCycleLR( optimizer, cfg.learning_rate, total_steps=total_num_steps, epochs=cfg.num_epochs, div_factor=cfg.lr_div_factor if cfg.lr_div_factor else 6, **lr_scheduler_kwargs, ) elif cfg.lr_scheduler == "log_sweep": lr_scheduler = InterpolatingLogScheduler( optimizer, cfg.warmup_steps, cfg.log_sweep_min_lr if cfg.log_sweep_min_lr else 1e-10, cfg.log_sweep_max_lr if cfg.log_sweep_max_lr else 10, ) else: lr_scheduler = transformers.get_cosine_schedule_with_warmup( optimizer, training_args.warmup_steps, total_num_steps, ) trainer_kwargs["optimizers"] = (optimizer, lr_scheduler) callbacks = [] callbacks.append(GPUStatsCallback(cfg)) # TODO on_save callback to sync checkpoints to GCP/AWS in background if cfg.early_stopping_patience: early_stop_cb = EarlyStoppingCallback( cfg.early_stopping_patience, ) callbacks.append(early_stop_cb) if cfg.local_rank == 0 and cfg.adapter in [ "lora", "qlora", ]: # only save in rank 0 callbacks.append(SavePeftModelCallback) if hasattr(model, "use_bettertransformer") and model.use_bettertransformer is True: callbacks.append(SaveBetterTransformerModelCallback) data_collator_kwargs = { "padding": True, } if cfg.collator_pad_to_longest: data_collator_kwargs["padding"] = "longest" else: # A100 is best at 64, while others at 8. Let's use the larger so we don't have to check # https://docs.nvidia.com/deeplearning/performance/dl-performance-matrix-multiplication/index.html data_collator_kwargs["pad_to_multiple_of"] = 64 if cfg.is_llama_derived_model and cfg.landmark_attention: from axolotl.monkeypatch.llama_landmark_attn import ( add_mem_tokens, get_mem_id, set_model_mem_id, ) set_model_mem_id(model, tokenizer) LOG.info("Adding landmark attention tokens to dataset") for dataset in [train_dataset, eval_dataset]: dataset = dataset.map( partial(add_mem_tokens, mem_freq=50, mem_id=get_mem_id(tokenizer)), batched=False, num_proc=32, ) trainer_cls = ( OneCycleLRSchedulerTrainer if cfg.lr_scheduler == "one_cycle" and (cfg.fsdp or cfg.adapter == "qlora") else AxolotlTrainer ) trainer = trainer_cls( model=model, train_dataset=train_dataset, eval_dataset=eval_dataset, args=training_args, data_collator=DataCollatorForSeq2Seq( tokenizer, return_tensors="pt", **data_collator_kwargs, ), callbacks=callbacks, **trainer_kwargs, ) return trainer