CodeGeeX/codegeex/megatron/tools/pretrain_codegeex.py

import os
import subprocess
import torch
import logging

logging.getLogger("torch").setLevel(logging.WARNING)

import deepspeed
from deepspeed.runtime.utils import see_memory_usage
from functools import partial

from codegeex.megatron import get_args, print_rank_0, get_timers,get_tokenizer, mpu
from codegeex.megatron.data.prompt_dataset import build_train_valid_test_datasets
from codegeex.megatron.model import CodeGeeXModel  #, CodeGeeXModelPipe
from codegeex.megatron.training import pretrain
from codegeex.megatron.utils import get_ltor_masks_and_position_ids
from codegeex.megatron.utils import average_losses_across_data_parallel_group


def model_provider(pre_process=True, post_process=True):
    """Build the model."""

    print_rank_0("building GPT model ...")
    see_memory_usage(f"Before Building Model", force=True)

    args = get_args()
    with deepspeed.zero.Init(
        data_parallel_group=mpu.get_data_parallel_group(),
        remote_device=None if args.remote_device == "none" else args.remote_device,
        config_dict_or_path=args.deepspeed_config,
        enabled=args.zero_stage == 3,
        mpu=mpu,
    ):
        if args.deepspeed and not args.no_pipeline_parallel:
            model = CodeGeeXModelPipe(num_tokentypes=0, parallel_output=True)
            # This is a hack to give us a reference to get_batch_pipe from within training.py
            # We need to call model.set_batch_fn after deepspeed.initialize
            model._megatron_batch_fn = get_batch_pipe

            # Predompute the attention mask and store it in args. This avoids having to
            # pipeline it as an activation during training. The mask is constant, and thus
            # we can reuse it.
            attention_mask = torch.tril(
                torch.ones(
                    (1, args.seq_length, args.seq_length),
                    device=torch.cuda.current_device(),
                )
            ).view(1, 1, args.seq_length, args.seq_length)

            # Convert attention mask to binary:
            attention_mask = attention_mask < 0.5
            if args.fp16:
                attention_mask = attention_mask.half()
            elif args.bf16:
                attention_mask = attention_mask.bfloat16()

            # Attention mask must be bool.
            args.attn_mask = attention_mask.to(torch.bool)

        else:
            model = CodeGeeXModel(
                num_tokentypes=0,
                parallel_output=True,
            )
            
            if args.load_state is not None:
                timers = get_timers()
                print_rank_0("Loading warmstarting model states ...")
                timers("load-model-states").start()
                mp_rank = mpu.get_tensor_model_parallel_rank()
                if os.path.isdir(args.load_state):
                    model_path = os.path.join(
                        args.load_state, "mp_rank_{:02d}_model_states.pt".format(mp_rank)
                    )
                else:
                    model_path = args.load_state
                print_rank_0(f"Loading model from {model_path} ...")
                state_dict = torch.load(model_path, map_location="cpu")
                if "module" in state_dict:
                    state_dict = state_dict["module"]  # strip other client states
                model.load_state_dict(state_dict)
                timers("load-model-states").stop()
                timers.log(["load-model-states"])
    see_memory_usage(f"After Building Model", force=True)
    
    return model


def get_batch(data_iterator):
    """Generate a batch"""
    args = get_args()
    tokenizer = get_tokenizer()

    # Items and their type.
    keys = ["input_ids"]
    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["input_ids"].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,
        tokenizer.eod,
        args.reset_position_ids,
        args.reset_attention_mask,
        args.eod_mask_loss,
    )

    return tokens, labels, loss_mask, attention_mask, position_ids


def get_batch_pipe(data):
    """Modification of `get_batch` to work on `next(data_iterator)` instead of `data_iterator`"""
    args = get_args()
    tokenizer = get_tokenizer()

    # Items and their type.
    keys = ["input_ids"]
    datatype = torch.int64

    # Broadcast data.
    data_b = mpu.broadcast_data(keys, data, datatype)

    # Unpack.
    tokens_ = data_b["input_ids"].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,
        tokenizer.eod,
        args.reset_position_ids,
        args.reset_attention_mask,
        args.eod_mask_loss,
    )

    return (tokens, position_ids, attention_mask), (labels, loss_mask)


def loss_func(loss_mask, output_tensor):
    losses = output_tensor.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])

    return loss, {"lm loss": averaged_loss[0]}


def forward_step(data_iterator, model):
    """Forward step."""
    args = get_args()
    timers = get_timers()

    # Get the batch.
    timers("batch-generator").start()
    tokens, labels, loss_mask, attention_mask, position_ids = get_batch(data_iterator)
    timers("batch-generator").stop()

    output_tensor = model(tokens, position_ids, attention_mask, labels=labels)

    return output_tensor, partial(loss_func, loss_mask)


def train_valid_test_datasets_provider(train_val_test_num_samples):
    """Build train, valid, and test datasets."""
    args = get_args()

    print_rank_0("> building train, validation, and test datasets " "for GPT ...")
    train_ds, valid_ds, test_ds = build_train_valid_test_datasets(
        data_prefix=args.data_path,
        data_impl=args.data_impl,
        splits_string=args.split,
        train_valid_test_num_samples=train_val_test_num_samples,
        seq_length=args.seq_length,
        seed=args.seed,
        skip_warmup=(not args.mmap_warmup),
    )
    print_rank_0("> finished creating GPT datasets ...")

    return train_ds, valid_ds, test_ds


def command_exists(cmd):
    result = subprocess.Popen(f"type {cmd}", stdout=subprocess.PIPE, shell=True)
    return result.wait() == 0


if __name__ == "__main__":
    pretrain(
        train_valid_test_datasets_provider,
        model_provider,
        forward_step,
        args_defaults={"tokenizer_type": "GPT2BPETokenizer"},
    )