train.py

# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.

import contextlib
import gc
import os
import time

from dataclasses import dataclass, field
from datetime import timedelta
from io import BytesIO
from timeit import default_timer as timer
from typing import Any, Dict, List

import numpy as np

import torch
import torch.nn.functional as F
from torch.distributed import destroy_process_group
from torch.distributed.checkpoint.stateful import Stateful
from torch.distributed.elastic.multiprocessing.errors import record
from torch.distributed.tensor.parallel import loss_parallel

from torchtitan.checkpoint import CheckpointManager
from torchtitan.config_manager import JobConfig
from torchtitan.datasets import build_hf_data_loader, create_tokenizer
from torchtitan.float8_linear import build_fp8_linear
from torchtitan.logging_utils import init_logger, logger
from torchtitan.lr_scheduling import get_lr_scheduler
from torchtitan.metrics import build_gpu_memory_monitor, build_metric_logger
from torchtitan.models import model_name_to_cls, model_name_to_tokenizer, models_config
from torchtitan.parallelisms import (
    models_parallelize_fns,
    models_pipelining_fns,
    ParallelDims,
)
from torchtitan.parallelisms.pipelining_utils import build_pipeline_schedule
from torchtitan.profiling import maybe_enable_profiling
from torchtitan.utils import (
    Color,
    dist_max,
    dist_mean,
    get_num_flop_per_token,
    get_num_params,
    get_peak_flops,
    init_distributed,
    NoColor,
    set_pg_timeouts,
)


@dataclass
class TrainState(Stateful):
    step: int = 0
    global_avg_losses: List[float] = field(default_factory=list)
    global_max_losses: List[float] = field(default_factory=list)
    log_steps: List[int] = field(default_factory=list)

    def state_dict(self) -> Dict[str, Any]:
        # Only checkpoint global_avg_losses and global_max_losses per log frequency
        # to avoid sync overhead in every iteration.
        global_avg_losses_bytes = BytesIO()
        torch.save(self.global_avg_losses, global_avg_losses_bytes)
        global_max_losses_bytes = BytesIO()
        torch.save(self.global_max_losses, global_max_losses_bytes)
        log_steps_bytes = BytesIO()
        torch.save(self.log_steps, log_steps_bytes)
        return {
            "step": torch.tensor(self.step, dtype=torch.int32),
            "global_avg_losses": global_avg_losses_bytes,
            "global_max_losses": global_max_losses_bytes,
            "log_steps": log_steps_bytes,
        }

    def load_state_dict(self, state_dict) -> None:
        self.step = state_dict["step"].item()
        state_dict["global_avg_losses"].seek(0)
        self.global_avg_losses = torch.load(
            state_dict["global_avg_losses"], weights_only=False
        )
        state_dict["global_max_losses"].seek(0)
        self.global_max_losses = torch.load(
            state_dict["global_max_losses"], weights_only=False
        )
        state_dict["log_steps"].seek(0)
        self.log_steps = torch.load(state_dict["log_steps"], weights_only=False)


def build_optimizer(model, job_config: JobConfig):
    # build optimizer
    name = job_config.optimizer.name
    lr = job_config.optimizer.lr
    if name == "Adam":
        # TODO: make the optimizer options configurable by toml/cmd args
        optimizer = torch.optim.Adam(
            model.parameters(), lr=lr, betas=(0.9, 0.95), weight_decay=0.1, foreach=True
        )
    elif name == "AdamW":
        optimizer = torch.optim.AdamW(
            model.parameters(), lr=lr, betas=(0.9, 0.95), weight_decay=0.1, foreach=True
        )
    else:
        raise NotImplementedError(f"Optimizer {name} not added.")

    return optimizer


# Enable debug tracing on failure: https://pytorch.org/docs/stable/elastic/errors.html
@record
def main(job_config: JobConfig):
    init_logger()
    logger.info(f"Starting job: {job_config.job.description}")

    # used for colorful printing
    color = Color if job_config.metrics.enable_color_printing else NoColor

    # take control of garbage collection to avoid stragglers
    _gc_freq = job_config.training.gc_freq
    gc.disable()
    gc.collect(1)

    # init world mesh
    world_size = int(os.environ["WORLD_SIZE"])
    parallel_dims = ParallelDims(
        dp=job_config.training.data_parallel_degree,
        tp=job_config.training.tensor_parallel_degree,
        pp=job_config.experimental.pipeline_parallel_degree,
        world_size=world_size,
        enable_loss_parallel=job_config.training.enable_loss_parallel,
    )
    device = torch.device(f"cuda:{int(os.environ['LOCAL_RANK'])}")
    torch.cuda.set_device(device)
    init_distributed(job_config)

    world_mesh = parallel_dims.build_mesh(device_type="cuda")

    model_name = job_config.model.name

    # build tokenizer
    tokenizer_type = model_name_to_tokenizer[model_name]
    tokenizer = create_tokenizer(tokenizer_type, job_config.model.tokenizer_path)

    # build dataloader
    if parallel_dims.dp_enabled:
        dp_mesh = world_mesh["dp"]
        dp_degree = dp_mesh.size()
        dp_rank = dp_mesh.get_local_rank()
    else:
        dp_degree, dp_rank = 1, 0

    if parallel_dims.pp_enabled:
        pp_mesh = world_mesh["pp"]

    data_loader = build_hf_data_loader(
        job_config.training.dataset,
        job_config.training.dataset_path,
        tokenizer,
        job_config.training.batch_size,
        job_config.training.seq_len,
        dp_degree,
        dp_rank,
    )

    # loss_parallel enables dispatching to efficient loss operators
    loss_parallel_ctx = (
        loss_parallel if parallel_dims.loss_parallel_enabled else contextlib.nullcontext
    )

    # loss fn can be shared by pipeline-parallel or non-pp execution
    def loss_fn(pred, labels):
        return F.cross_entropy(pred.flatten(0, 1), labels.flatten(0, 1))

    # build model (using meta init)
    model_cls = model_name_to_cls[model_name]
    model_config = models_config[model_name][job_config.model.flavor]
    # set the model configs from training inputs:
    # 1. norm type to decide which norm layer to use
    # 2. vocab size from tokenizer
    # 3. max_seq_len base on inputs
    model_config.norm_type = job_config.model.norm_type
    model_config.vocab_size = tokenizer.n_words
    model_config.max_seq_len = job_config.training.seq_len

    with torch.device("meta"):
        logger.info(
            f"Building {model_name} {job_config.model.flavor} with {model_config}"
        )
        model = model_cls.from_model_args(model_config)

    # apply fp8 linear module swap
    if job_config.training.fp8_linear:
        build_fp8_linear(model, job_config)

    # log model size
    model_param_count = get_num_params(model)
    num_flop_per_token = get_num_flop_per_token(
        get_num_params(model, exclude_embedding=True),
        model_config,
        job_config.training.seq_len,
    )
    logger.info(
        f"{color.blue}Model {model_name} {job_config.model.flavor} "
        f"{color.red}size: {model_param_count:,} total parameters{color.reset}"
    )

    # initialize GPU memory monitor before applying parallelisms to the model
    gpu_memory_monitor = build_gpu_memory_monitor()
    # obtain the peak flops of bf16 type for MFU calculation
    gpu_peak_flops = get_peak_flops(gpu_memory_monitor.device_name)

    if parallel_dims.pp_enabled:
        stage, model = models_pipelining_fns[model_name](
            model, world_mesh, parallel_dims, job_config, device, model_config
        )

    # apply PT-D DP/TP parallelisms and activation checkpointing
    model = models_parallelize_fns[model_name](
        model, world_mesh, parallel_dims, job_config
    )

    init_device = "cpu" if job_config.checkpoint.create_seed_checkpoint else "cuda"
    model.to_empty(device=init_device)

    if parallel_dims.pp_enabled:
        pp_schedule = build_pipeline_schedule(job_config, parallel_dims, stage, loss_fn)
    else:
        # If PP is enabled, we can't rely on init_weights, because some layers are missing.
        # In the future, we may make init_weights handle missing layers, but also have to consider RNG seed propagation.

        # allocate sharded model on GPU and initialize weights via DTensor
        model.init_weights()

    gpu_mem_stats = gpu_memory_monitor.get_peak_stats()
    logger.info(
        f"GPU memory usage for model: "
        f"{gpu_mem_stats.max_reserved_gib:.2f}GiB"
        f"({gpu_mem_stats.max_reserved_pct:.2f}%)"
    )

    # build optimizer after applying parallelisms to the model
    optimizer = build_optimizer(model, job_config)
    scheduler = get_lr_scheduler(optimizer, job_config)

    metric_logger = build_metric_logger(job_config)

    train_state = TrainState()

    # train loop
    model.train()

    checkpoint = CheckpointManager(
        model=model,
        optimizer=optimizer,
        lr_scheduler=scheduler,
        dataloader=data_loader,
        states={"train_state": train_state},
        job_config=job_config,
    )

    if job_config.checkpoint.create_seed_checkpoint:
        assert (
            world_size == 1
        ), "Must create seed-checkpoint using one gpu, to disable sharding"
        checkpoint.save(curr_step=0, force=True)
        logger.info("Created seed checkpoint")
        return

    checkpoint_loaded = checkpoint.load()

    if parallel_dims.pp_enabled and not checkpoint_loaded:
        raise RuntimeError(
            "Pipeline Parallelism requires meta-initialization and loading seed checkpoint. "
            "Please run `./create_seed_checkpoint.sh` and rerun training with `--checkpoint.enable_checkpoint`"
        )

    # plot losses loaded from checkpoint (if any) to TensorBoard
    # NOTE: Loss info after the last log step before checkpoint saving will not be ploted.
    #       This can be avoided by setting checkpoint.interval to be a multiple of metrics.log_freq
    if train_state.step > 0:
        for idx, step in enumerate(train_state.log_steps):
            metrics = {
                "loss_metrics/global_avg_loss": train_state.global_avg_losses[idx],
                "loss_metrics/global_max_loss": train_state.global_max_losses[idx],
            }
            metric_logger.log(metrics, step=step)

    data_iterator = iter(data_loader)

    logger.info(f"Training starts at step {train_state.step + 1}")
    with maybe_enable_profiling(
        job_config, global_step=train_state.step
    ) as torch_profiler:
        checkpoint.reset()

        # variables used to keep info for metrics logging
        losses_since_last_log: List[float] = []
        ntokens_since_last_log = 0
        data_loading_times: List[float] = []
        time_last_log = timer()
        gpu_memory_monitor.reset_peak_stats()

        while train_state.step < job_config.training.steps:
            train_state.step += 1
            if train_state.step > 1 and train_state.step % _gc_freq == 0:
                gc.collect(1)

            # get batch
            data_load_start = timer()
            batch = next(data_iterator)
            input_ids, labels = batch
            ntokens_since_last_log += labels.numel()
            data_loading_times.append(timer() - data_load_start)

            input_ids = input_ids.cuda()
            labels = labels.cuda()
            optimizer.zero_grad()

            if parallel_dims.pp_enabled:
                # pipeline parallel forward / backward inside step() call
                is_last_stage = pp_mesh.get_local_rank() == pp_mesh.size() - 1

                with loss_parallel_ctx():
                    if pp_mesh.get_local_rank() == 0:
                        pp_schedule.step(input_ids)
                    elif is_last_stage:
                        losses = []
                        pp_schedule.step(target=labels, losses=losses)
                    else:
                        pp_schedule.step()

                # accumulate losses across pipeline microbatches
                loss = (
                    torch.mean(torch.stack(losses))
                    if is_last_stage
                    else torch.Tensor([-1.0])
                )
            else:
                # Non-PP forward / backward
                with loss_parallel_ctx():
                    pred = model(input_ids)
                    loss = loss_fn(pred, labels)
                    loss.backward()

            # clip gradients
            torch.nn.utils.clip_grad_norm_(
                model.parameters(), job_config.training.max_norm, foreach=True
            )

            # optimizer step
            checkpoint.wait_for_staging()
            optimizer.step()
            scheduler.step()

            losses_since_last_log.append(loss)

            # log metrics
            if (
                train_state.step == 1
                or train_state.step % job_config.metrics.log_freq == 0
            ):
                losses = [loss.item() for loss in losses_since_last_log]
                avg_loss, max_loss = (
                    np.mean(losses),
                    np.max(losses),
                )
                if parallel_dims.dp_enabled:
                    global_avg_loss, global_max_loss = (
                        dist_mean(avg_loss, dp_mesh).item(),
                        dist_max(max_loss, dp_mesh).item(),
                    )
                else:
                    global_avg_loss, global_max_loss = avg_loss, max_loss

                train_state.log_steps.append(train_state.step)
                train_state.global_avg_losses.append(global_avg_loss)
                train_state.global_max_losses.append(global_max_loss)

                time_delta = timer() - time_last_log

                # tokens per second, abbr. as wps by convention
                wps = ntokens_since_last_log / (
                    time_delta * parallel_dims.model_parallel_size
                )
                # model FLOPS utilization
                # For its definition and calculation, please refer to the PaLM paper:
                # https://arxiv.org/abs/2204.02311
                mfu = 100 * num_flop_per_token * wps / gpu_peak_flops

                time_end_to_end = time_delta / job_config.metrics.log_freq
                time_data_loading = np.mean(data_loading_times)
                time_data_loading_pct = 100 * np.sum(data_loading_times) / time_delta

                gpu_mem_stats = gpu_memory_monitor.get_peak_stats()

                metrics = {
                    "loss_metrics/global_avg_loss": global_avg_loss,
                    "loss_metrics/global_max_loss": global_max_loss,
                    "wps": wps,
                    "mfu(%)": mfu,
                    "time_metrics/end_to_end(s)": time_end_to_end,
                    "time_metrics/data_loading(s)": time_data_loading,
                    "time_metrics/data_loading(%)": time_data_loading_pct,
                    "memory/max_active(GiB)": gpu_mem_stats.max_active_gib,
                    "memory/max_active(%)": gpu_mem_stats.max_active_pct,
                    "memory/max_reserved(GiB)": gpu_mem_stats.max_reserved_gib,
                    "memory/max_reserved(%)": gpu_mem_stats.max_reserved_pct,
                    "memory/num_alloc_retries": gpu_mem_stats.num_alloc_retries,
                    "memory/num_ooms": gpu_mem_stats.num_ooms,
                }
                metric_logger.log(metrics, step=train_state.step)

                logger.info(
                    f"{color.cyan}step: {train_state.step:2}  "
                    f"{color.green}loss: {global_avg_loss:7.4f}  "
                    f"{color.yellow}memory: {gpu_mem_stats.max_reserved_gib:5.2f}GiB"
                    f"({gpu_mem_stats.max_reserved_pct:.2f}%)  "
                    f"{color.blue}wps: {round(wps):,}  "
                    f"{color.magenta}mfu: {mfu:.2f}%{color.reset}"
                )

                losses_since_last_log.clear()
                ntokens_since_last_log = 0
                data_loading_times.clear()
                time_last_log = timer()
                gpu_memory_monitor.reset_peak_stats()

            checkpoint.save(
                train_state.step, force=(train_state.step == job_config.training.steps)
            )

            # signals the profiler that the next profiling step has started
            if torch_profiler:
                torch_profiler.step()

            # Reduce timeout after first train step for faster signal (assumes lazy init, compile are finished)
            if train_state.step == 1:
                set_pg_timeouts(
                    timeout=timedelta(seconds=job_config.comm.train_timeout_seconds),
                    world_mesh=world_mesh,
                )

    if torch.distributed.get_rank() == 0:
        logger.info("Sleeping 2 seconds for other ranks to complete")
        time.sleep(2)

    metric_logger.close()
    logger.info("Training completed")


if __name__ == "__main__":
    config = JobConfig()
    config.parse_args()
    main(config)
    destroy_process_group()