# Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import argparse import math from abc import ABC from functools import partial import torch import torch.nn.functional as F from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from torch.nn.parallel.distributed import DistributedDataParallel as torchDDP from ..optimizer import AcceleratedOptimizer from ..scheduler import AcceleratedScheduler from .imports import is_megatron_lm_available, is_transformers_available from .operations import recursively_apply, send_to_device if is_transformers_available(): from transformers.modeling_outputs import ( CausalLMOutputWithCrossAttentions, Seq2SeqLMOutput, SequenceClassifierOutput, ) if is_megatron_lm_available(): from megatron import ( get_args, get_num_microbatches, get_tensorboard_writer, get_timers, get_tokenizer, mpu, print_rank_0, print_rank_last, ) from megatron.arguments import _add_data_args, _add_validation_args, parse_args, validate_args from megatron.checkpointing import load_args_from_checkpoint, load_checkpoint, save_checkpoint from megatron.data.data_samplers import MegatronPretrainingRandomSampler, MegatronPretrainingSampler from megatron.global_vars import set_global_variables from megatron.initialize import ( _compile_dependencies, _init_autoresume, _set_random_seed, set_jit_fusion_options, write_args_to_tensorboard, ) from megatron.model import BertModel, Float16Module, GPTModel, ModelType, T5Model from megatron.model import DistributedDataParallel as LocalDDP from megatron.model.classification import Classification from megatron.optimizer import get_megatron_optimizer from megatron.schedules import get_forward_backward_func from megatron.text_generation.communication import broadcast_int_list, broadcast_tensor from megatron.text_generation.generation import ( beam_search_and_return_on_first_stage, generate_tokens_probs_and_return_on_first_stage, ) from megatron.tokenizer.tokenizer import _vocab_size_with_padding from megatron.training import get_model, get_optimizer_param_scheduler, training_log from megatron.utils import ( average_losses_across_data_parallel_group, calc_params_l2_norm, get_ltor_masks_and_position_ids, unwrap_model, ) # model utilities def model_provider_func(pre_process=True, post_process=True, add_encoder=True, add_decoder=True): """Build the model.""" args = get_args() mode = "pre-training" if args.pretraining_flag else "fine-tuning" if args.rank == 0: print(f"Building {args.model_type_name} model in the {mode} mode.") print( "The Megatron LM model weights are initialized at random in `accelerator.prepare`. " "Please use `accelerator.load_checkpoint` to load a pre-trained checkpoint matching the distributed setup." ) if args.model_type_name == "bert": if args.pretraining_flag: num_tokentypes = 2 if args.bert_binary_head else 0 model = BertModel( num_tokentypes=num_tokentypes, add_binary_head=args.bert_binary_head, parallel_output=True, pre_process=pre_process, post_process=post_process, ) else: model = Classification( num_classes=args.num_labels, num_tokentypes=2, pre_process=pre_process, post_process=post_process ) elif args.model_type_name == "gpt": model = GPTModel(num_tokentypes=0, parallel_output=True, pre_process=pre_process, post_process=post_process) elif args.model_type_name == "t5": model = T5Model( num_tokentypes=0, parallel_output=True, pre_process=pre_process, post_process=post_process, add_encoder=add_encoder, add_decoder=add_decoder, ) else: raise ValueError(f"Unsupported model type: {args.model_type_name}") return model def prepare_model(accelerator): accelerator.print("Preparing model") args = get_args() if accelerator.state.megatron_lm_plugin.custom_prepare_model_function is not None: if accelerator.state.megatron_lm_plugin.custom_model_provider_function is None: raise ValueError( "You must provide a `custom_model_provider_function` when using a `custom_prepare_model_function`." ) custom_model_provider_func = accelerator.state.megatron_lm_plugin.custom_model_provider_function model = accelerator.state.megatron_lm_plugin.custom_prepare_model_function(custom_model_provider_func) else: if args.model_type_name in ("bert", "gpt"): model_type = ModelType.encoder_or_decoder elif args.model_type_name == "t5": model_type = ModelType.encoder_and_decoder if args.pipeline_model_parallel_split_rank is None and args.pipeline_model_parallel_size > 1: args.pipeline_model_parallel_split_rank = args.pipeline_model_parallel_size // 2 model = get_model(model_provider_func, model_type) return model # dataloader utilities class MegatronLMDummyDataLoader: """ Dummy dataloader presents model parameters or param groups, this is primarily used to follow conventional training Args: **dataset_kwargs: Megatron data arguments. """ def __init__(self, **dataset_kwargs): parser = argparse.ArgumentParser() parser = _add_data_args(parser) parser = _add_validation_args(parser) data_args = parser.parse_known_args() self.dataset_args = vars(data_args[0]) self.dataset_args.update(dataset_kwargs) self.dataset_args["megatron_dataset_flag"] = True def set_megatron_data_args(self): args = get_args() for key, value in self.dataset_args.items(): setattr(args, key, value) def get_train_valid_test_datasets_provider(self): def train_valid_test_datasets_provider(train_val_test_num_samples): """Build train, valid, and test datasets.""" args = get_args() dataset_args = { "data_prefix": args.data_path, "data_impl": args.data_impl, "splits_string": args.split, "train_valid_test_num_samples": train_val_test_num_samples, "skip_warmup": (not args.mmap_warmup), "seed": args.seed, } if args.model_type_name == "bert": dataset_args.update( { "max_seq_length": args.seq_length, "masked_lm_prob": args.mask_prob, "short_seq_prob": args.short_seq_prob, "binary_head": args.bert_binary_head, } ) elif args.model_type_name == "gpt": dataset_args.update( { "seq_length": args.seq_length, } ) elif args.model_type_name == "t5": dataset_args.update( { "max_seq_length": args.encoder_seq_length, "max_seq_length_dec": args.decoder_seq_length, "masked_lm_prob": args.mask_prob, "short_seq_prob": args.short_seq_prob, "dataset_type": "t5", } ) else: raise ValueError(f"Unsupported model type: {args.model_type_name}") if args.model_type_name == "gpt": from megatron.data.gpt_dataset import build_train_valid_test_datasets else: from megatron.data.dataset_utils import build_train_valid_test_datasets train_ds, valid_ds, test_ds = build_train_valid_test_datasets(**dataset_args) return train_ds, valid_ds, test_ds return train_valid_test_datasets_provider def build_pretraining_data_loader(self, dataset, consumed_samples): if dataset is None: return None args = get_args() micro_batch_size = args.micro_batch_size * args.num_micro_batches # Megatron sampler if args.dataloader_type == "single": batch_sampler = MegatronPretrainingSampler( total_samples=len(dataset), consumed_samples=consumed_samples, micro_batch_size=micro_batch_size, data_parallel_rank=mpu.get_data_parallel_rank(), data_parallel_size=mpu.get_data_parallel_world_size(), ) elif args.dataloader_type == "cyclic": batch_sampler = MegatronPretrainingRandomSampler( dataset, total_samples=len(dataset), consumed_samples=consumed_samples, micro_batch_size=micro_batch_size, data_parallel_rank=mpu.get_data_parallel_rank(), data_parallel_size=mpu.get_data_parallel_world_size(), data_sharding=args.data_sharding, ) else: raise Exception("{} dataloader type is not supported.".format(args.dataloader_type)) # Torch dataloader. return torch.utils.data.DataLoader( dataset, batch_sampler=batch_sampler, num_workers=args.num_workers, pin_memory=True ) def build_train_valid_test_data_iterators(self): def cyclic_iter(iter): while True: for x in iter: yield x args = get_args() (train_dataloader, valid_dataloader, test_dataloader) = (None, None, None) print_rank_0("> building train, validation, and test datasets ...") # Backward compatibility, assume fixed batch size. if args.iteration > 0 and args.consumed_train_samples == 0: assert args.train_samples is None, "only backward compatiblity support for iteration-based training" args.consumed_train_samples = args.iteration * args.global_batch_size if args.iteration > 0 and args.consumed_valid_samples == 0: if args.train_samples is None: args.consumed_valid_samples = ( (args.iteration // args.eval_interval) * args.eval_iters * args.global_batch_size ) # Data loader only on rank 0 of each model parallel group. if mpu.get_tensor_model_parallel_rank() == 0: # Number of train/valid/test samples. if args.train_samples: train_samples = args.train_samples else: train_samples = args.train_iters * args.global_batch_size eval_iters = (args.train_iters // args.eval_interval + 1) * args.eval_iters test_iters = args.eval_iters train_val_test_num_samples = [ train_samples, eval_iters * args.global_batch_size, test_iters * args.global_batch_size, ] print_rank_0(" > datasets target sizes (minimum size):") print_rank_0(" train: {}".format(train_val_test_num_samples[0])) print_rank_0(" validation: {}".format(train_val_test_num_samples[1])) print_rank_0(" test: {}".format(train_val_test_num_samples[2])) # Build the datasets. train_valid_test_datasets_provider = self.get_train_valid_test_datasets_provider() train_ds, valid_ds, test_ds = train_valid_test_datasets_provider(train_val_test_num_samples) # Build dataloders. train_dataloader = self.build_pretraining_data_loader(train_ds, args.consumed_train_samples) valid_dataloader = self.build_pretraining_data_loader(valid_ds, args.consumed_valid_samples) test_dataloader = self.build_pretraining_data_loader(test_ds, 0) # Flags to know if we need to do training/validation/testing. do_train = train_dataloader is not None and args.train_iters > 0 do_valid = valid_dataloader is not None and args.eval_iters > 0 do_test = test_dataloader is not None and args.eval_iters > 0 # Need to broadcast num_tokens and num_type_tokens. flags = torch.cuda.LongTensor([int(do_train), int(do_valid), int(do_test)]) else: flags = torch.cuda.LongTensor([0, 0, 0]) # Broadcast num tokens. torch.distributed.broadcast( flags, mpu.get_tensor_model_parallel_src_rank(), group=mpu.get_tensor_model_parallel_group() ) args.do_train = flags[0].item() args.do_valid = flags[1].item() args.do_test = flags[2].item() # Build iterators. dl_type = args.dataloader_type assert dl_type in ["single", "cyclic"] if train_dataloader is not None: train_data_iterator = ( iter(train_dataloader) if dl_type == "single" else iter(cyclic_iter(train_dataloader)) ) else: train_data_iterator = None if valid_dataloader is not None: valid_data_iterator = ( iter(valid_dataloader) if dl_type == "single" else iter(cyclic_iter(valid_dataloader)) ) else: valid_data_iterator = None if test_dataloader is not None: test_data_iterator = iter(test_dataloader) if dl_type == "single" else iter(cyclic_iter(test_dataloader)) else: test_data_iterator = None return train_data_iterator, valid_data_iterator, test_data_iterator def prepare_data_loader(accelerator, dataloader): accelerator.print("Preparing dataloader") args = get_args() if not args.megatron_dataset_flag: from ..data_loader import _PYTORCH_DATALOADER_KWARGS, prepare_data_loader args = get_args() micro_batch_size = args.micro_batch_size * args.num_micro_batches kwargs = {k: getattr(dataloader, k, _PYTORCH_DATALOADER_KWARGS[k]) for k in _PYTORCH_DATALOADER_KWARGS} if kwargs["batch_size"] is None: if isinstance(kwargs["sampler"], torch.utils.data.BatchSampler): kwargs["sampler"].batch_size = micro_batch_size else: del kwargs["sampler"] del kwargs["shuffle"] del kwargs["batch_size"] kwargs["batch_sampler"].batch_size = micro_batch_size else: del kwargs["batch_sampler"] kwargs["batch_size"] = micro_batch_size dataloader = torch.utils.data.DataLoader(dataloader.dataset, **kwargs) return prepare_data_loader( dataloader, accelerator.device, num_processes=mpu.get_data_parallel_world_size(), process_index=mpu.get_data_parallel_rank(), split_batches=accelerator.split_batches, put_on_device=True, rng_types=accelerator.rng_types.copy(), dispatch_batches=accelerator.dispatch_batches, ) else: if args.consumed_samples is not None: ( args.consumed_train_samples, args.consumed_valid_samples, args.consumed_test_samples, ) = args.consumed_samples else: args.consumed_train_samples, args.consumed_valid_samples, args.consumed_test_samples = 0, 0, 0 ( train_data_iterator, valid_data_iterator, test_data_iterator, ) = dataloader.build_train_valid_test_data_iterators() return train_data_iterator, valid_data_iterator, test_data_iterator # optimizer utilities class MegatronLMOptimizerWrapper(AcceleratedOptimizer): def __init__(self, optimizer): super().__init__(optimizer, device_placement=False, scaler=None) def zero_grad(self, set_to_none=None): pass # `model(**batch)` is doing that automatically. Therefore, it's implementation is not needed def step(self): pass # `model(**batch)` is doing that automatically. Therefore, it's implementation is not needed @property def step_was_skipped(self): """Whether or not the optimizer step was done, or skipped because of gradient overflow.""" return self.optimizer.skipped_iter def prepare_optimizer(accelerator, model): accelerator.print("Preparing optimizer") args = get_args() optimizer = get_megatron_optimizer(model, args.no_wd_decay_cond, args.scale_lr_cond, args.lr_mult) return optimizer # scheduler utilities class MegatronLMDummyScheduler: """ Dummy scheduler presents model parameters or param groups, this is primarily used to follow conventional training loop when scheduler config is specified in the deepspeed config file. Args: optimizer (`torch.optim.optimizer.Optimizer`): The optimizer to wrap. total_num_steps (int): Total number of steps. warmup_num_steps (int): Number of steps for warmup. **kwargs: Other arguments. """ def __init__(self, optimizer, total_num_steps=None, warmup_num_steps=0, **kwargs): self.optimizer = optimizer self.total_num_steps = total_num_steps self.warmup_num_steps = warmup_num_steps self.kwargs = kwargs class MegatronLMSchedulerWrapper(AcceleratedScheduler): def __init__(self, scheduler, optimizers): super().__init__(scheduler, optimizers) def step(self, *args, **kwargs): return # `model(**batch)` is doing that automatically. Therefore, it's implementation is not needed def prepare_scheduler(accelerator, optimizer, scheduler): accelerator.print("Preparing scheduler") scheduler = get_optimizer_param_scheduler(optimizer) return scheduler class AbstractTrainStep(ABC): """Abstract class for batching, forward pass and loss handler.""" def __init__(self, name): super().__init__() self.name = name def get_batch_func(self): pass def get_forward_step_func(self): pass def get_loss_func(self): pass class BertTrainStep(AbstractTrainStep): """ Bert train step class. Args: args (`argparse.Namespace`): Megatron-LM arguments. """ def __init__(self, args): super().__init__("BertTrainStep") self.get_batch = self.get_batch_func(args.megatron_dataset_flag) self.loss_func = self.get_loss_func(args.pretraining_flag, args.num_labels) self.forward_step = self.get_forward_step_func(args.pretraining_flag, args.bert_binary_head) if not args.model_return_dict: self.model_output_class = None else: self.model_output_class = SequenceClassifierOutput def get_batch_func(self, megatron_dataset_flag): def get_batch_megatron(data_iterator): """Build the batch.""" # Items and their type. keys = ["text", "types", "labels", "is_random", "loss_mask", "padding_mask"] datatype = torch.int64 # Broadcast data. if data_iterator is not None: data = next(data_iterator) else: data = None data_b = mpu.broadcast_data(keys, data, datatype) # Unpack. tokens = data_b["text"].long() types = data_b["types"].long() sentence_order = data_b["is_random"].long() loss_mask = data_b["loss_mask"].float() lm_labels = data_b["labels"].long() padding_mask = data_b["padding_mask"].long() return tokens, types, sentence_order, loss_mask, lm_labels, padding_mask def get_batch_transformer(data_iterator): """Build the batch.""" data = next(data_iterator) data = send_to_device(data, torch.cuda.current_device()) # Unpack. tokens = data["input_ids"].long() padding_mask = data["attention_mask"].long() if "token_type_ids" in data: types = data["token_type_ids"].long() else: types = None if "labels" in data: lm_labels = data["labels"].long() loss_mask = (data["labels"] != -100).to(torch.float) else: lm_labels = None loss_mask = None if "next_sentence_label" in data: sentence_order = data["next_sentence_label"].long() else: sentence_order = None return tokens, types, sentence_order, loss_mask, lm_labels, padding_mask if megatron_dataset_flag: return get_batch_megatron else: return get_batch_transformer def get_loss_func(self, pretraining_flag, num_labels): def loss_func_pretrain(loss_mask, sentence_order, output_tensor): lm_loss_, sop_logits = output_tensor lm_loss_ = lm_loss_.float() loss_mask = loss_mask.float() lm_loss = torch.sum(lm_loss_.view(-1) * loss_mask.reshape(-1)) / loss_mask.sum() if sop_logits is not None: sop_loss = F.cross_entropy(sop_logits.view(-1, 2).float(), sentence_order.view(-1), ignore_index=-1) sop_loss = sop_loss.float() loss = lm_loss + sop_loss averaged_losses = average_losses_across_data_parallel_group([lm_loss, sop_loss]) return loss, {"lm loss": averaged_losses[0], "sop loss": averaged_losses[1]} else: loss = lm_loss averaged_losses = average_losses_across_data_parallel_group([lm_loss]) return loss, {"lm loss": averaged_losses[0]} def loss_func_finetune(labels, logits): if num_labels == 1: # We are doing regression loss_fct = MSELoss() loss = loss_fct(logits.view(-1), labels.view(-1)) elif self.num_labels > 1 and (labels.dtype in (torch.long, torch.int)): loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, num_labels), labels.view(-1)) else: loss_fct = BCEWithLogitsLoss() loss = loss_fct(logits, labels) averaged_losses = average_losses_across_data_parallel_group([loss]) return loss, {"loss": averaged_losses[0]} if pretraining_flag: return loss_func_pretrain else: return loss_func_finetune def get_forward_step_func(self, pretraining_flag, bert_binary_head): def forward_step(data_iterator, model): """Forward step.""" tokens, types, sentence_order, loss_mask, labels, padding_mask = self.get_batch(data_iterator) if not bert_binary_head: types = None # Forward pass through the model. if pretraining_flag: output_tensor = model(tokens, padding_mask, tokentype_ids=types, lm_labels=labels) return output_tensor, partial(self.loss_func, loss_mask, sentence_order) else: logits = model(tokens, padding_mask, tokentype_ids=types) return logits, partial(self.loss_func, labels) return forward_step class GPTTrainStep(AbstractTrainStep): """ GPT train step class. Args: args (`argparse.Namespace`): Megatron-LM arguments. """ def __init__(self, args): super().__init__("GPTTrainStep") self.get_batch = self.get_batch_func(args.megatron_dataset_flag) self.loss_func = self.get_loss_func() self.forward_step = self.get_forward_step_func() self.eod_token = args.padded_vocab_size - 1 if args.vocab_file is not None: tokenizer = get_tokenizer() self.eod_token = tokenizer.eod self.reset_position_ids = args.reset_position_ids self.reset_attention_mask = args.reset_attention_mask self.eod_mask_loss = args.eod_mask_loss if not args.model_return_dict: self.model_output_class = None else: self.model_output_class = CausalLMOutputWithCrossAttentions def get_batch_func(self, megatron_dataset_flag): def get_batch_megatron(data_iterator): """Generate a batch""" # Items and their type. keys = ["text"] datatype = torch.int64 # Broadcast data. if data_iterator is not None: data = next(data_iterator) else: data = None data_b = mpu.broadcast_data(keys, data, datatype) # Unpack. tokens_ = data_b["text"].long() labels = tokens_[:, 1:].contiguous() tokens = tokens_[:, :-1].contiguous() # Get the masks and postition ids. attention_mask, loss_mask, position_ids = get_ltor_masks_and_position_ids( tokens, self.eod_token, self.reset_position_ids, self.reset_attention_mask, self.eod_mask_loss ) return tokens, labels, loss_mask, attention_mask, position_ids def get_batch_transformer(data_iterator): data = next(data_iterator) data = {"input_ids": data["input_ids"]} data = send_to_device(data, torch.cuda.current_device()) tokens_ = data["input_ids"].long() padding = torch.zeros((tokens_.shape[0], 1), dtype=tokens_.dtype, device=tokens_.device) + self.eod_token tokens_ = torch.concat([tokens_, padding], dim=1) labels = tokens_[:, 1:].contiguous() tokens = tokens_[:, :-1].contiguous() # Get the masks and postition ids. attention_mask, loss_mask, position_ids = get_ltor_masks_and_position_ids( tokens, self.eod_token, self.reset_position_ids, self.reset_attention_mask, True ) return tokens, labels, loss_mask, attention_mask, position_ids if megatron_dataset_flag: return get_batch_megatron else: return get_batch_transformer def get_loss_func(self): args = get_args() def loss_func(loss_mask, output_tensor): if args.return_logits: losses, logits = output_tensor else: losses = output_tensor losses = losses.float() loss_mask = loss_mask.view(-1).float() loss = torch.sum(losses.view(-1) * loss_mask) / loss_mask.sum() # Reduce loss for logging. averaged_loss = average_losses_across_data_parallel_group([loss]) output_dict = {"lm loss": averaged_loss[0]} if args.return_logits: output_dict.update({"logits": logits}) return loss, output_dict return loss_func def get_forward_step_func(self): def forward_step(data_iterator, model): """Forward step.""" # Get the batch. tokens, labels, loss_mask, attention_mask, position_ids = self.get_batch(data_iterator) output_tensor = model(tokens, position_ids, attention_mask, labels=labels) return output_tensor, partial(self.loss_func, loss_mask) return forward_step class T5TrainStep(AbstractTrainStep): """ T5 train step class. Args: args (`argparse.Namespace`): Megatron-LM arguments. """ def __init__(self, args): super().__init__("T5TrainStep") self.get_batch = self.get_batch_func(args.megatron_dataset_flag) self.loss_func = self.get_loss_func() self.forward_step = self.get_forward_step_func() if not args.model_return_dict: self.model_output_class = None else: self.model_output_class = Seq2SeqLMOutput @staticmethod def attn_mask_postprocess(attention_mask): # We create a 3D attention mask from a 2D tensor mask. # [b, 1, s] attention_mask_b1s = attention_mask.unsqueeze(1) # [b, s, 1] attention_mask_bs1 = attention_mask.unsqueeze(2) # [b, s, s] attention_mask_bss = attention_mask_b1s * attention_mask_bs1 # Convert attention mask to binary: extended_attention_mask = attention_mask_bss < 0.5 return extended_attention_mask @staticmethod def get_decoder_mask(seq_length, device): attention_mask = torch.tril(torch.ones((1, seq_length, seq_length), device=device)) attention_mask = attention_mask < 0.5 return attention_mask @staticmethod def get_enc_dec_mask(attention_mask, dec_seq_length, device): batch_size, _ = attention_mask.shape # We create a 3D attention mask from a 2D tensor mask. # [b, 1, s] attention_mask_b1s = attention_mask.unsqueeze(1) # [b, s, 1] attention_mask_bs1 = torch.ones((batch_size, dec_seq_length, 1), device=device) attention_mask_bss = attention_mask_bs1 * attention_mask_b1s extended_attention_mask = attention_mask_bss < 0.5 return extended_attention_mask def get_batch_func(self, megatron_dataset_flag): def get_batch_megatron(data_iterator): """Build the batch.""" keys = ["text_enc", "text_dec", "labels", "loss_mask", "enc_mask", "dec_mask", "enc_dec_mask"] datatype = torch.int64 # Broadcast data. if data_iterator is not None: data = next(data_iterator) else: data = None data_b = mpu.broadcast_data(keys, data, datatype) # Unpack. tokens_enc = data_b["text_enc"].long() tokens_dec = data_b["text_dec"].long() labels = data_b["labels"].long() loss_mask = data_b["loss_mask"].float() enc_mask = data_b["enc_mask"] < 0.5 dec_mask = data_b["dec_mask"] < 0.5 enc_dec_mask = data_b["enc_dec_mask"] < 0.5 return tokens_enc, tokens_dec, loss_mask, labels, enc_mask, dec_mask, enc_dec_mask def get_batch_transformer(data_iterator): """Build the batch.""" data = next(data_iterator) data = send_to_device(data, torch.cuda.current_device()) tokens_enc = data["input_ids"].long() labels = data["labels"].long() loss_mask = (labels != -100).to(torch.float) if "decoder_input_ids" in data: tokens_dec = data["decoder_input_ids"].long() else: tokens_dec = labels.new_zeros(labels.shape, device=labels.device, dtype=torch.long) tokens_dec[..., 1:] = labels[..., :-1].clone() tokens_dec[..., 0] = 0 tokens_dec.masked_fill_(tokens_dec == -100, 0) enc_mask = T5TrainStep.attn_mask_postprocess(data["attention_mask"].long()) dec_mask = T5TrainStep.get_decoder_mask(tokens_dec.shape[1], tokens_dec.device) enc_dec_mask = T5TrainStep.get_enc_dec_mask( data["attention_mask"].long(), tokens_dec.shape[1], tokens_dec.device ) return tokens_enc, tokens_dec, loss_mask, labels, enc_mask, dec_mask, enc_dec_mask if megatron_dataset_flag: return get_batch_megatron else: return get_batch_transformer def get_loss_func(self): def loss_func(loss_mask, output_tensor): lm_loss_ = output_tensor.float() lm_loss = torch.sum(lm_loss_.view(-1) * loss_mask.reshape(-1)) / loss_mask.sum() loss = lm_loss averaged_losses = average_losses_across_data_parallel_group([lm_loss]) return loss, {"lm loss": averaged_losses[0]} return loss_func def get_forward_step_func(self): def forward_step(data_iterator, model): """Forward step.""" # Get the batch. tokens_enc, tokens_dec, loss_mask, lm_labels, enc_mask, dec_mask, enc_dec_mask = self.get_batch( data_iterator ) # Forward model lm_labels output_tensor = model( tokens_enc, tokens_dec, enc_mask, dec_mask, enc_dec_mask, tokentype_ids=None, lm_labels=lm_labels ) return output_tensor, partial(self.loss_func, loss_mask) return forward_step # intialize megatron setup def initialize(accelerator, extra_args_provider=None, args_defaults={}): accelerator.print("Initializing Megatron-LM") assert torch.cuda.is_available(), "Megatron requires CUDA." # Parse arguments args = parse_args(extra_args_provider, ignore_unknown_args=True) # Set defaults for key, value in args_defaults.items(): if getattr(args, key, None) is not None: if args.rank == 0: print( "WARNING: overriding default arguments for {key}:{v} \ with {key}:{v2}".format( key=key, v=getattr(args, key), v2=value ), flush=True, ) setattr(args, key, value) if args.use_checkpoint_args or args_defaults.get("use_checkpoint_args", False): assert args.load is not None, "--use-checkpoints-args requires --load argument" load_args_from_checkpoint(args) validate_args(args) # set global args, build tokenizer, and set adlr-autoresume, # tensorboard-writer, and timers. set_global_variables(args) # torch.distributed initialization def finish_mpu_init(): args = get_args() # Pytorch distributed. device_count = torch.cuda.device_count() args.rank = torch.distributed.get_rank() args.world_size = torch.distributed.get_world_size() if device_count > 0: device = args.rank % device_count if args.local_rank is not None: assert args.local_rank == device, "expected local-rank to be the same as rank % device-count." else: args.local_rank = device # Set the tensor model-parallel, pipeline model-parallel, and # data-parallel communicators. if mpu.model_parallel_is_initialized(): print("model parallel is already initialized") else: mpu.initialize_model_parallel( args.tensor_model_parallel_size, args.pipeline_model_parallel_size, args.virtual_pipeline_model_parallel_size, args.pipeline_model_parallel_split_rank, ) # Random seeds for reproducibility. if args.rank == 0: print("> setting random seeds to {} ...".format(args.seed)) _set_random_seed(args.seed, args.data_parallel_random_init) args = get_args() # Megatron's MPU is the master. Complete initialization right away. finish_mpu_init() # Autoresume. _init_autoresume() # Compile dependencies. _compile_dependencies() # Set pytorch JIT layer fusion options and warmup JIT functions. set_jit_fusion_options() args = get_args() args.padded_vocab_size = _vocab_size_with_padding(args.orig_vocab_size, args) if args.model_type_name == "bert" and args.pretraining_flag and args.num_labels == 2: args.bert_binary_head = True else: args.bert_binary_head = False args.iteration = 0 class MegatronEngine(torch.nn.Module): """ Megatron-LM model wrapper Args: accelerator (:class:`~accelerate.Accelerator`): The accelerator object to use. model: Megatron-LM model optimizer: Megatron-LM optimizer lr_scheduler: Megatron-LM lr scheduler """ def __init__(self, accelerator, model, optimizer, scheduler): super(MegatronEngine, self).__init__() self.module = model self.base_model = model[0] self.optimizer = optimizer self.scheduler = scheduler args = get_args() if accelerator.state.megatron_lm_plugin.custom_train_step_class is not None: self.train_step_handler = accelerator.state.megatron_lm_plugin.custom_train_step_class( args, **accelerator.state.megatron_lm_plugin.custom_train_step_kwargs ) elif args.model_type_name == "bert": self.train_step_handler = BertTrainStep(args) elif args.model_type_name == "gpt": self.train_step_handler = GPTTrainStep(args) elif args.model_type_name == "t5": self.train_step_handler = T5TrainStep(args) else: raise ValueError(f"Unsupported model type: {args.model_type_name}") self.optimizer.skipped_iter = False # Tracking loss. self.total_loss_dict = {} self.eval_total_loss_dict = {} self.iteration = 0 self.report_memory_flag = True if args.tensorboard_dir is not None: write_args_to_tensorboard() def train(self): for model_module in self.module: model_module.train() self.log_eval_results() def eval(self): for model_module in self.module: model_module.eval() def train_step(self, **batch_data): """ Training step for Megatron-LM Args: batch_data (:obj:`dict`): The batch data to train on. """ args = get_args() timers = get_timers() if len(batch_data) > 0: data_chunks = [] if args.num_micro_batches > 1: for i in range(0, args.num_micro_batches): data_chunks.append( { k: v[i * args.micro_batch_size : (i + 1) * args.micro_batch_size] for k, v in batch_data.items() } ) else: data_chunks = [batch_data] if len(self.module) > 1: batch_data_iterator = ( [iter(data_chunks) for _ in range(len(self.module))] if len(batch_data) > 0 else [None] * len(self.module) ) else: batch_data_iterator = iter(data_chunks) if len(batch_data) > 0 else None # Set grad to zero. if args.DDP_impl == "local" and args.use_contiguous_buffers_in_local_ddp: for partition in self.module: partition.zero_grad_buffer() self.optimizer.zero_grad() # Forward pass. forward_backward_func = get_forward_backward_func() losses_reduced = forward_backward_func( self.train_step_handler.forward_step, batch_data_iterator, self.module, self.optimizer, None, forward_only=False, ) # Empty unused memory. if args.empty_unused_memory_level >= 1: torch.cuda.empty_cache() # Reduce gradients. timers("backward-reduce-model-grads").start() self.optimizer.reduce_model_grads(args, timers) timers("backward-reduce-model-grads").stop() # Update parameters. timers("optimizer").start() update_successful, grad_norm, num_zeros_in_grad = self.optimizer.step(args, timers) timers("optimizer").stop() # Gather params. if update_successful: timers("backward-gather-model-params").start() self.optimizer.gather_model_params(args, timers) timers("backward-gather-model-params").stop() # Update learning rate. if update_successful: if self.scheduler is not None: increment = get_num_microbatches() * args.micro_batch_size * args.data_parallel_size self.scheduler.step(increment=increment) skipped_iter = 0 else: skipped_iter = 1 self.optimizer.skipped_iter = not update_successful # Empty unused memory. if args.empty_unused_memory_level >= 2: torch.cuda.empty_cache() args.consumed_train_samples += ( mpu.get_data_parallel_world_size() * args.micro_batch_size * get_num_microbatches() ) if mpu.is_pipeline_last_stage(ignore_virtual=True): # Average loss across microbatches. loss_reduced = {} for key in losses_reduced[0]: losses_reduced_for_key = [x[key] for x in losses_reduced] if len(losses_reduced_for_key[0].shape) == 0: loss_reduced[key] = sum(losses_reduced_for_key) / len(losses_reduced_for_key) else: loss_reduced[key] = torch.concat(losses_reduced_for_key) return loss_reduced, skipped_iter, grad_norm, num_zeros_in_grad return {}, skipped_iter, grad_norm, num_zeros_in_grad def eval_step(self, **batch_data): """ Evaluation step for Megatron-LM Args: batch_data (:obj:`dict`): The batch data to evaluate on. """ args = get_args() data_chunks = [] if args.num_micro_batches > 1: for i in range(0, args.num_micro_batches): data_chunks.append( {k: v[i * args.micro_batch_size : (i + 1) * args.micro_batch_size] for k, v in batch_data.items()} ) else: data_chunks = [batch_data] if len(self.module) > 1: batch_data_iterator = [iter(data_chunks) for _ in range(len(self.module))] else: batch_data_iterator = iter(data_chunks) forward_backward_func = get_forward_backward_func() loss_dicts = forward_backward_func( self.train_step_handler.forward_step, batch_data_iterator, self.module, optimizer=None, timers=None, forward_only=True, ) # Empty unused memory if args.empty_unused_memory_level >= 1: torch.cuda.empty_cache() args.consumed_valid_samples += ( mpu.get_data_parallel_world_size() * args.micro_batch_size * get_num_microbatches() ) if mpu.is_pipeline_last_stage(ignore_virtual=True): # Average loss across microbatches. loss_reduced = {} for key in loss_dicts[0]: losses_reduced_for_key = [x[key] for x in loss_dicts] if len(losses_reduced_for_key[0].shape) == 0: loss_reduced[key] = sum(losses_reduced_for_key) / len(losses_reduced_for_key) else: loss_reduced[key] = torch.concat(losses_reduced_for_key) return loss_reduced else: return {} def forward(self, **batch_data): # During training, we use train_step() # model(**batch_data) performs following operations by delegating it to `self.train_step`: # 1. Prepare **batch_data for Tendor, Pipeline and Model Parallelism # 2. Set grad to zero. # 3. forward pass and backward pass using Pipeline Parallelism # 4. Empty unused memory. # 5. Reduce gradients. # 6. Update parameters. # 7. Gather params when using Distributed Optimizer (Data Parallelism). # 8. Update learning rate if scheduler is specified. # 9. Empty unused memory. # 10. Average loss across microbatches and across DP ranks. # # During evaluation, we use eval_step() args = get_args() if self.module[0].training: loss_dict, skipped_iter, grad_norm, num_zeros_in_grad = self.train_step(**batch_data) self.iteration += 1 if args.tensorboard_dir is not None: # Logging. loss_scale = self.optimizer.get_loss_scale().item() params_norm = None if args.log_params_norm: params_norm = calc_params_l2_norm(self.model) self.report_memory_flag = training_log( loss_dict, self.total_loss_dict, self.optimizer.param_groups[0]["lr"], self.iteration, loss_scale, self.report_memory_flag, skipped_iter, grad_norm, params_norm, num_zeros_in_grad, ) else: loss_dict = self.eval_step(**batch_data) if args.tensorboard_dir is not None: for key in loss_dict: self.eval_total_loss_dict[key] = ( self.eval_total_loss_dict.get(key, torch.cuda.FloatTensor([0.0])) + loss_dict[key] ) self.eval_total_loss_dict[key + "_num_iters"] = self.eval_total_loss_dict.get( key + "_num_iters", torch.cuda.FloatTensor([0.0]) ) + torch.cuda.FloatTensor([1.0]) loss = torch.tensor(0.0, device=args.local_rank) for key in loss_dict: if len(loss_dict[key].shape) == 0: loss += loss_dict[key] logits = None if "logits" in loss_dict: logits = loss_dict["logits"] # loss = reduce(loss) if self.train_step_handler.model_output_class is not None: return self.train_step_handler.model_output_class(loss=loss, logits=logits) return loss def log_eval_results(self): args = get_args() if args.tensorboard_dir is None or self.iteration == 0: return args = get_args() writer = get_tensorboard_writer() string = f"validation loss at iteration {self.iteration} | " for key in self.eval_total_loss_dict: if key.endswith("_num_iters"): continue value = self.eval_total_loss_dict[key] / self.eval_total_loss_dict[key + "_num_iters"] string += f"{key} value: {value} | " ppl = math.exp(min(20, value.item())) if args.pretraining_flag: string += f"{key} PPL: {ppl} | " if writer: writer.add_scalar(f"{key} validation", value.item(), self.iteration) if args.pretraining_flag: writer.add_scalar(f"{key} validation ppl", ppl, self.iteration) length = len(string) + 1 print_rank_last("-" * length) print_rank_last(string) print_rank_last("-" * length) self.eval_total_loss_dict = {} def save_checkpoint(self, output_dir): self.log_eval_results() args = get_args() args.save = output_dir torch.distributed.barrier() save_checkpoint(self.iteration, self.module, self.optimizer, self.scheduler) torch.distributed.barrier() def load_checkpoint(self, input_dir): args = get_args() args.load = input_dir args.consumed_train_samples = 0 args.consumed_valid_samples = 0 torch.distributed.barrier() iteration = load_checkpoint(self.module, self.optimizer, self.scheduler) torch.distributed.barrier() self.iteration = iteration if args.fp16 and self.iteration == 0: self.optimizer.reload_model_params() def megatron_generate( self, inputs, attention_mask=None, max_length=None, max_new_tokens=None, num_beams=None, temperature=None, top_k=None, top_p=None, length_penalty=None, **kwargs, ): """ Generate method for GPT2 model. This method is used for inference. Supports both greedy and beam search along with sampling. Refer the Megatron-LM repo for more details Args: inputs (torch.Tensor): input ids attention_mask (torch.Tensor, optional): attention mask. Defaults to None. max_length (int, optional): max length of the generated sequence. Defaults to None. Either this or max_new_tokens should be provided. max_new_tokens (int, optional): max number of tokens to be generated. Defaults to None. Either this or max_length should be provided. num_beams (int, optional): number of beams to use for beam search. Defaults to None. temperature (float, optional): temperature for sampling. Defaults to 1.0. top_k (int, optional): top k tokens to consider for sampling. Defaults to 0.0. top_p (float, optional): tokens in top p probability are considered for sampling. Defaults to 0.0. length_penalty (float, optional): length penalty for beam search. Defaults to None. kwargs: additional key-value arguments """ # checking if required arguments are passed args = get_args() if args.model_type_name != "gpt": raise NotImplementedError("Generate method is not implemented for this model") if args.data_parallel_size > 1: raise ValueError("Generate method requires data parallelism to be 1") if args.sequence_parallel: raise ValueError("Generate method requires sequence parallelism to be False") if args.recompute_granularity is not None: raise ValueError("Checkpoint activations cannot be set for inference") if args.vocab_file is None: raise ValueError("Vocab file is required for inference") # Prepare inputs if max_length is None and max_new_tokens is None: raise ValueError("`max_length` or `max_new_tokens` are required for inference") if temperature is None: temperature = 1.0 elif not (0.0 < temperature <= 100.0): raise ValueError("temperature must be a positive number less than or equal to 100.0") if top_k is None: top_k = 0 elif not (0 <= top_k <= 1000): raise ValueError("top_k must be a positive number less than or equal to 1000") if top_p is None: top_p = 0.0 elif top_p > 0.0 and top_k > 0.0: raise ValueError("top_p and top_k sampling cannot be set together") else: if not (0.0 <= top_p <= 1.0): raise ValueError("top_p must be less than or equal to 1.0") top_p_decay = kwargs.get("top_p_decay", 0.0) if not (0.0 <= top_p_decay <= 1.0): raise ValueError("top_p_decay must be less than or equal to 1.0") top_p_bound = kwargs.get("top_p_bound", 0.0) if not (0.0 <= top_p_bound <= 1.0): raise ValueError("top_p_bound must be less than or equal to 1.0") add_BOS = kwargs.get("add_BOS", False) if not (isinstance(add_BOS, bool)): raise ValueError("add_BOS must be a boolean") beam_width = num_beams if beam_width is not None: if not isinstance(beam_width, int): raise ValueError("beam_width must be an integer") if beam_width < 1: raise ValueError("beam_width must be greater than 0") if inputs.shape[0] > 1: return "When doing beam_search, batch size must be 1" tokenizer = get_tokenizer() stop_token = kwargs.get("stop_token", tokenizer.eod) if stop_token is not None: if not isinstance(stop_token, int): raise ValueError("stop_token must be an integer") if length_penalty is None: length_penalty = 1.0 sizes_list = None prompts_tokens_tensor = None prompts_length_tensor = None if torch.distributed.get_rank() == 0: # Get the prompts length. if attention_mask is None: prompts_length_tensor = torch.cuda.LongTensor([inputs.shape[1]] * inputs.shape[0]) else: prompts_length_tensor = attention_mask.sum(axis=-1).cuda() if max_new_tokens is None: max_new_tokens = max_length - inputs.shape[1] if max_new_tokens <= 0: raise ValueError("max_new_tokens must be greater than 0") if add_BOS: max_length = max_new_tokens + inputs.shape[1] + 1 # making sure that `max_length` is a multiple of 4 to leverage fused kernels max_length = 4 * math.ceil(max_length / 4) max_new_tokens = max_length - (inputs.shape[1] + 1) padding = torch.cuda.LongTensor([[tokenizer.eod] * max_new_tokens] * inputs.shape[0]) prompts_tokens_tensor = torch.concat( [torch.unsqueeze(padding[:, 0], axis=-1), inputs.cuda(), padding], axis=-1 ) else: # making sure that `max_length` is a multiple of 4 to leverage fused kernels max_length = max_new_tokens + inputs.shape[1] max_length = 4 * math.ceil(max_length / 4) max_new_tokens = max_length - inputs.shape[1] padding = torch.cuda.LongTensor([[tokenizer.eod] * max_new_tokens] * inputs.shape[0]) prompts_tokens_tensor = torch.concat([inputs.cuda(), padding], axis=-1) # We need the sizes of these tensors for the boradcast sizes_list = [ prompts_tokens_tensor.size(0), # Batch size prompts_tokens_tensor.size(1), ] # Sequence lenght # First, broadcast the sizes. sizes_tensor = broadcast_int_list(2, int_list=sizes_list, rank=0) # Now that we have the sizes, we can boradcast the tokens # and length tensors. sizes = sizes_tensor.tolist() context_tokens_tensor = broadcast_tensor(sizes, torch.int64, tensor=prompts_tokens_tensor, rank=0) context_length_tensor = broadcast_tensor(sizes[0], torch.int64, tensor=prompts_length_tensor, rank=0) # Run the inference random_seed = kwargs.get("random_seed", 0) torch.random.manual_seed(random_seed) unwrapped_model = unwrap_model(self.base_model, (torchDDP, LocalDDP, Float16Module)) if beam_width is not None: tokens, _ = beam_search_and_return_on_first_stage( unwrapped_model, context_tokens_tensor, context_length_tensor, beam_width, stop_token=stop_token, num_return_gen=1, length_penalty=length_penalty, ) else: tokens, _, _ = generate_tokens_probs_and_return_on_first_stage( unwrapped_model, context_tokens_tensor, context_length_tensor, return_output_log_probs=False, top_k=top_k, top_p=top_p, top_p_decay=top_p_decay, top_p_bound=top_p_bound, temperature=temperature, use_eod_token_for_early_termination=True, ) return tokens # other utilities def avg_losses_across_data_parallel_group(losses): """ Average losses across data parallel group. Args: losses (List[Tensor]): List of losses to average across data parallel group. """ return average_losses_across_data_parallel_group(losses) def gather_across_data_parallel_groups(tensor): """ Recursively gather tensor in a nested list/tuple/dictionary of tensors from data parallel ranks. Args: tensor (nested list/tuple/dictionary of `torch.Tensor`): The data to gather across data parallel ranks. """ def _gpu_gather_one(tensor): if tensor.ndim == 0: tensor = tensor.clone()[None] output_tensors = [ torch.empty_like(tensor) for _ in range(torch.distributed.get_world_size(group=mpu.get_data_parallel_group())) ] torch.distributed.all_gather(output_tensors, tensor, group=mpu.get_data_parallel_group()) return torch.cat(output_tensors, dim=0) return recursively_apply(_gpu_gather_one, tensor, error_on_other_type=True)