253 lines
8.5 KiB
Python
253 lines
8.5 KiB
Python
# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
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"""Pretrain GPT."""
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import os
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import torch
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from functools import partial
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from typing import Union
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from megatron.training import get_args
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from megatron.training import print_rank_0
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from megatron.training import get_timers
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from megatron.training import get_tokenizer
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from megatron.core import mpu
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from megatron.core.enums import ModelType
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from megatron.core.datasets.blended_megatron_dataset_builder import BlendedMegatronDatasetBuilder
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from megatron.core.datasets.utils import get_blend_from_list
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from megatron.core.datasets.gpt_dataset import GPTDatasetConfig
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from megatron.core.datasets.gpt_dataset import MockGPTDataset, GPTDataset
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import megatron.legacy.model
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from megatron.core.models.gpt import GPTModel
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from megatron.training import pretrain
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from megatron.core.utils import StragglerDetector
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from megatron.core.transformer.spec_utils import import_module
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from megatron.training.utils import (
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get_batch_on_this_cp_rank,
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get_batch_on_this_tp_rank,
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)
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from megatron.training.arguments import core_transformer_config_from_args
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from megatron.training.yaml_arguments import core_transformer_config_from_yaml
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from megatron.core.models.gpt.gpt_layer_specs import (
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get_gpt_layer_local_spec,
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get_gpt_layer_with_transformer_engine_spec,
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)
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stimer = StragglerDetector()
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def model_provider(pre_process=True, post_process=True) -> Union[GPTModel, megatron.legacy.model.GPTModel]:
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"""Builds the model.
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If you set the use_legacy_models to True, it will return the legacy GPT model and if not the mcore GPT model.
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Args:
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pre_process (bool, optional): Set to true if you need to compute embedings. Defaults to True.
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post_process (bool, optional): Set to true if you need to want to compute output logits/loss. Defaults to True.
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Returns:
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Union[GPTModel, megatron.legacy.model.GPTModel]: The returned model
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"""
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args = get_args()
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use_te = args.transformer_impl == "transformer_engine"
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print_rank_0('building GPT model ...')
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# Experimental loading arguments from yaml
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if args.yaml_cfg is not None:
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config = core_transformer_config_from_yaml(args, "language_model")
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else:
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config = core_transformer_config_from_args(args)
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if args.use_legacy_models:
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model = megatron.legacy.model.GPTModel(
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config,
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num_tokentypes=0,
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parallel_output=True,
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pre_process=pre_process,
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post_process=post_process,
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)
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else: # using core models
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if args.spec is not None:
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transformer_layer_spec = import_module(args.spec)
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else:
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if use_te:
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transformer_layer_spec = get_gpt_layer_with_transformer_engine_spec(args.num_experts, args.moe_grouped_gemm, args.qk_layernorm)
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else:
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transformer_layer_spec = get_gpt_layer_local_spec(args.num_experts, args.moe_grouped_gemm, args.qk_layernorm)
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model = GPTModel(
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config=config,
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transformer_layer_spec=transformer_layer_spec,
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vocab_size=args.padded_vocab_size,
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max_sequence_length=args.max_position_embeddings,
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pre_process=pre_process,
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post_process=post_process,
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fp16_lm_cross_entropy=args.fp16_lm_cross_entropy,
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parallel_output=True,
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share_embeddings_and_output_weights=not args.untie_embeddings_and_output_weights,
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position_embedding_type=args.position_embedding_type,
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rotary_percent=args.rotary_percent,
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rotary_base=args.rotary_base
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)
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return model
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def get_batch(data_iterator):
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"""Generate a batch."""
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# TODO: this is pretty hacky, find a better way
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if (not mpu.is_pipeline_first_stage()) and (not mpu.is_pipeline_last_stage()):
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return None, None, None, None, None
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# get batches based on the TP rank you are on
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batch = get_batch_on_this_tp_rank(data_iterator)
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# slice batch along sequence dimension for context parallelism
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batch = get_batch_on_this_cp_rank(batch)
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return batch.values()
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def loss_func(loss_mask: torch.Tensor, output_tensor: torch.Tensor):
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"""Loss function.
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Args:
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loss_mask (torch.Tensor): Used to mask out some portions of the loss
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output_tensor (torch.Tensor): The tensor with the losses
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Returns:
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the loss scalar for this micro-batch
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the number of non-padded tokens in this microbatch
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a dict containing reporting metrics on the loss and number of tokens across
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the data parallel ranks
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"""
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args = get_args()
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losses = output_tensor.float()
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loss_mask = loss_mask.view(-1).float()
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total_tokens = loss_mask.sum()
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loss = torch.cat([torch.sum(losses.view(-1) * loss_mask).view(1), total_tokens.view(1)])
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if args.context_parallel_size > 1:
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torch.distributed.all_reduce(loss, group=mpu.get_context_parallel_group())
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# Check individual rank losses are not NaN prior to DP all-reduce.
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if args.check_for_nan_in_loss_and_grad:
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global_rank = torch.distributed.get_rank()
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assert not loss[0].isnan(), (
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f'Rank {global_rank}: found NaN in local forward loss calculation. '
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f'Device: {torch.cuda.current_device()}, node: {os.uname()[1]}'
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)
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# Reduce loss for logging.
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reporting_loss = loss.clone().detach()
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if args.num_micro_batches_per_dp is None:
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torch.distributed.all_reduce(reporting_loss, group=mpu.get_data_parallel_group())
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local_num_tokens = loss[1].clone().detach().to(torch.int)
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return (
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loss[0] * args.context_parallel_size,
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local_num_tokens,
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{'lm loss': (reporting_loss[0], reporting_loss[1])},
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)
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def forward_step(data_iterator, model: GPTModel):
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"""Forward training step.
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Args:
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data_iterator : Input data iterator
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model (GPTModel): The GPT Model
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"""
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args = get_args()
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timers = get_timers()
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# Get the batch.
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timers('batch-generator', log_level=2).start()
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global stimer
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with stimer(bdata=True):
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tokens, labels, loss_mask, attention_mask, position_ids = get_batch(
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data_iterator)
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timers('batch-generator').stop()
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with stimer:
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output_tensor = model(tokens, position_ids, attention_mask,
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labels=labels)
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return output_tensor, partial(loss_func, loss_mask)
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def is_dataset_built_on_rank():
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return (
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mpu.is_pipeline_first_stage() or mpu.is_pipeline_last_stage()
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) and mpu.get_tensor_model_parallel_rank() == 0
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def core_gpt_dataset_config_from_args(args):
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tokenizer = get_tokenizer()
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return GPTDatasetConfig(
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random_seed=args.seed,
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sequence_length=args.seq_length,
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blend=get_blend_from_list(args.data_path),
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blend_per_split=[
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get_blend_from_list(args.train_data_path),
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get_blend_from_list(args.valid_data_path),
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get_blend_from_list(args.test_data_path)
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],
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split=args.split,
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num_dataset_builder_threads=args.num_dataset_builder_threads,
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path_to_cache=args.data_cache_path,
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mmap_bin_files=args.mmap_bin_files,
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tokenizer=tokenizer,
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reset_position_ids=args.reset_position_ids,
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reset_attention_mask=args.reset_attention_mask,
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eod_mask_loss=args.eod_mask_loss,
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create_attention_mask=args.create_attention_mask_in_dataloader,
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s3_cache_path = args.s3_cache_path
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)
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def train_valid_test_datasets_provider(train_val_test_num_samples):
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"""Build the train test and validation datasets.
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Args:
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train_val_test_num_samples : A list containing the number of samples in train test and validation.
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"""
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args = get_args()
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config = core_gpt_dataset_config_from_args(args)
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if args.mock_data:
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dataset_type = MockGPTDataset
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else:
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dataset_type = GPTDataset
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print_rank_0("> building train, validation, and test datasets for GPT ...")
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train_ds, valid_ds, test_ds = BlendedMegatronDatasetBuilder(
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dataset_type,
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train_val_test_num_samples,
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is_dataset_built_on_rank,
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config
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).build()
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print_rank_0("> finished creating GPT datasets ...")
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return train_ds, valid_ds, test_ds
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if __name__ == "__main__":
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# Temporary for transition to core datasets
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train_valid_test_datasets_provider.is_distributed = True
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pretrain(
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train_valid_test_datasets_provider,
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model_provider,
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ModelType.encoder_or_decoder,
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forward_step,
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args_defaults={'tokenizer_type': 'GPT2BPETokenizer'},
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)
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