"""Module containing the Trainer class and related functions""" import math import os from contextlib import contextmanager from functools import partial from typing import List import numpy as np import torch import torch.cuda from accelerate.logging import get_logger from datasets import set_caching_enabled from torch.utils.data import DataLoader, RandomSampler from axolotl.core.trainer_builder import HFCausalTrainerBuilder, HFDPOTrainerBuilder from axolotl.utils.distributed import is_main_process, reduce_and_broadcast, zero_first from axolotl.utils.samplers import MultipackBatchSampler, get_dataset_lengths LOG = get_logger("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) @contextmanager def disable_datasets_caching(): try: set_caching_enabled(False) yield finally: set_caching_enabled(True) def add_position_ids(sample): sample_len = len(sample["input_ids"]) sample["position_ids"] = torch.arange(len(sample["input_ids"])) sample["length"] = sample_len return sample def add_length(sample): sample["length"] = len(sample["input_ids"]) return sample def drop_long_seq(sample, sequence_len=2048): return len(sample["input_ids"]) <= sequence_len and len(sample["input_ids"]) > 0 def process_datasets_for_packing(cfg, train_dataset, eval_dataset): drop_long = partial(drop_long_seq, sequence_len=cfg.sequence_len) with zero_first(is_main_process()): if cfg.is_preprocess: min_input_len = np.min(get_dataset_lengths(train_dataset)) LOG.debug(f"min_input_len: {min_input_len}", main_process_only=True) max_input_len = np.max(get_dataset_lengths(train_dataset)) LOG.debug(f"max_input_len: {max_input_len}", main_process_only=True) if ( cfg.is_mistral_derived_model and cfg.flash_attention ) or cfg.model_config_type == "mamba": LOG.info("dropping attention_mask column") train_dataset = train_dataset.remove_columns("attention_mask") if eval_dataset: eval_dataset = eval_dataset.remove_columns("attention_mask") if cfg.model_config_type == "falcon": LOG.info("dropping token_type_ids column") train_dataset = train_dataset.remove_columns("token_type_ids") if eval_dataset: eval_dataset = eval_dataset.remove_columns("token_type_ids") train_dataset = train_dataset.filter( drop_long, num_proc=cfg.dataset_processes, load_from_cache_file=not cfg.is_preprocess, desc="Dropping Long Sequences", ) if eval_dataset: eval_dataset = eval_dataset.filter( drop_long, num_proc=cfg.dataset_processes, load_from_cache_file=not cfg.is_preprocess, desc="Dropping Long Sequences", ) if cfg.group_by_length: train_dataset = train_dataset.map( add_length, num_proc=cfg.dataset_processes, load_from_cache_file=not cfg.is_preprocess, desc="Group By Length", ) if cfg.sample_packing: train_dataset = train_dataset.map( add_position_ids, num_proc=cfg.dataset_processes, load_from_cache_file=not cfg.is_preprocess, desc="Add position_id column (Sample Packing)", ) if cfg.eval_sample_packing is not False: if eval_dataset: eval_dataset = eval_dataset.map( add_position_ids, num_proc=cfg.dataset_processes, load_from_cache_file=not cfg.is_preprocess, desc="Add position_id column (Sample Packing)", ) return train_dataset, eval_dataset def process_pretraining_datasets_for_packing(train_dataset, sequence_len): drop_long = partial(drop_long_seq, sequence_len=sequence_len) train_dataset = train_dataset.filter( drop_long, desc="Dropping Long Sequences", ) train_dataset = train_dataset.map( add_position_ids, desc="Add position_id column (Pretraining Sample Packing)", ) return train_dataset def calculate_total_num_steps(cfg, train_dataset, update=True): if not cfg.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.debug(f"total_num_tokens: {total_num_tokens}", main_process_only=True) if update: cfg.total_num_tokens = total_num_tokens skip_estimates = cfg.model_config_type == "mamba" if not skip_estimates and not cfg.total_supervised_tokens: total_supervised_tokens = ( train_dataset.data.column("labels") .to_pandas() .apply(lambda x: np.sum(np.array(x) != -100)) .sum() ) LOG.debug( f"`total_supervised_tokens: {total_supervised_tokens}`", main_process_only=True, ) if update: cfg.total_supervised_tokens = total_supervised_tokens if not skip_estimates and cfg.sample_packing: # we have to drop anything longer then sequence len otherwise # flash attention with position ids fails 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.debug( f"total_num_tokens: {cfg.total_num_tokens}, total_num_steps: {total_num_steps}", main_process_only=True, ) else: if cfg.flash_attention: batch_size = 1 batch_max_len = cfg.micro_batch_size * cfg.sequence_len else: batch_size = cfg.micro_batch_size batch_max_len = cfg.sequence_len sampler = MultipackBatchSampler( sampler=RandomSampler(train_dataset), batch_size=batch_size, drop_last=True, batch_max_len=batch_max_len, lengths=get_dataset_lengths(train_dataset), ) data_loader = DataLoader( train_dataset.remove_columns(["length"]), batch_sampler=sampler, ) data_loader_len = len(data_loader) // batch_size actual_eff = sampler.efficiency() LOG.debug(f"data_loader_len: {data_loader_len}", main_process_only=True) # FIXME: is there a bug here somewhere? the total num steps depends # on the agreed on value for sample_packing_eff_est total_num_steps = int( math.floor( data_loader_len * cfg.num_epochs / int(os.environ.get("WORLD_SIZE", 1)) ) ) def calc_sample_packing_eff_est(estimates: List[float]): LOG.info(f"sample_packing_eff_est across ranks: {repr(estimates)}") return max(estimates) sample_packing_actual_eff_all = reduce_and_broadcast( lambda: actual_eff, calc_sample_packing_eff_est, ) sample_packing_eff_est = ( math.ceil(sample_packing_actual_eff_all * 100.0) / 100.0 ) if update: cfg.sample_packing_eff_est = sample_packing_eff_est LOG.debug( f"sample_packing_eff_est: {cfg.sample_packing_eff_est}", main_process_only=True, ) else: total_num_steps = int( math.ceil( len(train_dataset) * cfg.num_epochs / int(os.environ.get("WORLD_SIZE", 1)) / cfg.batch_size ) ) LOG.debug(f"total_num_steps: {total_num_steps}", main_process_only=True) return total_num_steps def setup_fsdp_envs(cfg): os.environ["ACCELERATE_USE_FSDP"] = "true" if cfg.fsdp_config.fsdp_offload_params: os.environ["FSDP_OFFLOAD_PARAMS"] = "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 if cfg.fsdp_config.fsdp_transformer_layer_cls_to_wrap: os.environ[ "FSDP_TRANSFORMER_CLS_TO_WRAP" ] = cfg.fsdp_config.fsdp_transformer_layer_cls_to_wrap def prepare_optim_env(cfg): if cfg.fsdp: setup_fsdp_envs(cfg) elif cfg.deepspeed: os.environ["ACCELERATE_USE_DEEPSPEED"] = "true" os.environ["ACCELERATE_DEEPSPEED_CONFIG_FILE"] = cfg.deepspeed def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer, total_num_steps): if cfg.rl in ["dpo", "ipo", "kto_pair"]: trainer_builder = HFDPOTrainerBuilder(cfg, model[0], tokenizer) trainer_builder.model_ref = model[1] trainer_builder.peft_config = model[2] else: trainer_builder = HFCausalTrainerBuilder(cfg, model[0], tokenizer) trainer_builder.train_dataset = train_dataset trainer_builder.eval_dataset = eval_dataset return trainer_builder.build(total_num_steps)