venv/lib/python3.10/site-packages/benchmarks/fsdp.py

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

import argparse
from collections import defaultdict
from functools import reduce
import gc
import logging
import math
import operator
import time

from datasets.wikitext2_data import get_real_dataloaders as get_real_wikitext2_dataloaders
from datasets.wikitext2_data import get_synthetic_dataloaders as get_synthetic_wikitext2_dataloaders
from models import transformer_lm
import numpy as np
import torch
import torch.distributed as dist
import torch.multiprocessing as mp
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.optim import Adam

from benchmarks.golden_configs.lm_wikitext2 import FSDP as lm_wikitext2
from fairscale.nn import auto_wrap, default_auto_wrap_policy, enable_wrap
from fairscale.nn.data_parallel import FullyShardedDataParallel as FSDP

RPC_PORT = 29501


def verify_peak_memory(rank, golden_config, std_dev):
    logging.debug(
        "Peak allocated bytes on cuda:0: {:1d}".format(torch.cuda.memory_stats(rank)["allocated_bytes.all.peak"])
    )
    current_device_usage = torch.cuda.memory_stats(rank)["allocated_bytes.all.peak"]
    golden_ref = golden_config["peak_mem_usage"][rank]
    if not current_device_usage < golden_ref * std_dev:
        raise RuntimeError(
            "Peak memory usage for cuda device {:d} is {:d} which"
            "is less than golden reference value of {:d}".format(rank, current_device_usage, golden_ref)
        )


def verify_lm_run(wps, golden_config, args):
    """Verify that words per second for a given benchmark run matches the golden data."""

    if torch.distributed.get_rank() == 0:
        # Assert that words per second is within 3 standard deviations of the average
        # of five golden runs
        logging.info("Throughput(wps) is {:.2f}.".format(wps))
        if not wps > (golden_config["avg_wps"] - (3 * golden_config["std_dev_wps"])):
            raise RuntimeError(
                "Throughput(wps):{:.2f} is below the golden threshold of an "
                "average value of {:.2f} and standard dev of {:.2f}.".format(
                    wps, golden_config["avg_wps"], golden_config["std_dev_wps"]
                )
            )

    for i in range(torch.cuda.device_count()):
        verify_peak_memory(i, golden_config, 1.1)


def init_random_seed(seed: int):

    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)
    np.random.seed(seed)


def get_model_and_optimizer(args, device, benchmark_config, model_config):
    """Return instantiated model and optimizer function."""

    if args.model_name == "lm":
        model = get_lm_model(args, device, model_config)

    lr = benchmark_config["lr"]

    def make_adam(params):
        return Adam(params, lr=lr)

    optimizer = make_adam
    return model, optimizer


def get_lm_model(args, device, config):
    """Get language model(based on GPT-2) used for sequence prediction."""

    ninp = config["ninp"]
    nhead = config["nhead"]
    initrange = config["initrange"]
    dropout = config["dropout"]
    vocab_size = config["vocab_size"]
    nhid = config["nhid"]
    ndecoder = config["num_decoder_layers"]

    return transformer_lm.TransformerLM(vocab_size, ninp, nhead, nhid, dropout, initrange, ndecoder).to(device)


def get_tensors_by_size_bucket():

    size_buckets = defaultdict(int)
    for obj in gc.get_objects():
        if not isinstance(obj, torch.Tensor):
            continue
        if obj.device.type == "cuda":
            size_buckets[(*obj.size(),) + (obj.element_size(),)] += 1

    return size_buckets


def log_number_of_parameters(model):

    num_params = reduce(operator.add, (reduce(operator.mul, x.size()) for x in model.parameters()))
    if hasattr(model, "group"):
        total = torch.Tensor([num_params])
        if torch.cuda.is_available():
            total = total.cuda()
        torch.distributed.all_reduce(total, group=model.group)
        print(
            f"training model, #params = {num_params/10**6}M, group: {model.group.rank()}, grank:"
            f" {torch.distributed.get_rank()}, sizes {model.group.size()}"
        )
        torch.distributed.barrier()
        if model.group.rank() == 0:
            print(f"total #prams = {total.item()}")
    else:
        print(f"training model, #params = {num_params/10**6}M")


def get_device(model, index):
    if isinstance(model, DDP):
        model = model.module

    if not torch.cuda.is_available():
        return torch.device("cpu")
    if hasattr(model, "devices"):
        return model.devices[index]
    else:
        return torch.cuda.current_device()


def get_fake_dataloader(lm_dataloader_len, args):
    fake_input = {"input": torch.zeros(args.batch_size)}

    class FakeDataset:
        def __getitem__(self, index):
            return fake_input

        def __len__(self):
            return lm_dataloader_len

    return FakeDataset()


def train(model_config, model, benchmark_config, model_specs, args):
    lm_dataloader, _, _ = model_config["data"]
    criterion = benchmark_config["criterion"]
    vocab_size = model_specs["vocab_size"]
    optimizer = model_config["optimizer"]

    if not args.benchmark_eval:
        model.train()
    log_number_of_parameters(model)

    total_loss = 0.0
    word_counter = 0

    optimizer = optimizer(model.parameters())

    device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")

    total_tokens = 0
    total_tokens_per_log_interval = 0
    bptt = 2
    start_time = time.time()
    epoch_start_time = 0.0

    def get_batch(source):
        seq_len = len(source) - 1
        data = source[0:seq_len]
        target = source[1 : 1 + seq_len]
        return data, target

    for i, batch in enumerate(lm_dataloader):
        if i == 1:
            epoch_start_time = time.time()

        source, target = get_batch(batch)
        if args.full_fp16:
            # source = source.half()
            target = target.half()
        if args.max_batch and i > args.max_batch:
            break

        if i > 0:
            total_tokens += source.numel()

        if args.benchmark_eval:
            input = source.cuda()
            target = target.cuda()
            output = model(input)
            print(f"output.dtype {output.dtype}, target.dtype {target.dtype}")
            loss = torch.nn.CrossEntropyLoss()(output.view(-1, vocab_size), target.view(-1))
        else:
            optimizer.zero_grad()
            input = source.cuda()
            target = target.cuda()
            output = model(input)

            loss = criterion(output.view(-1, vocab_size), target.view(-1))
            loss.backward()

            torch.nn.utils.clip_grad_value_(model.parameters(), model_specs["clip_value"])
            optimizer.step()

        total_loss += loss.item()

        log_interval = 1
        total_tokens_per_log_interval += source.numel()
        if i % log_interval == 0 and i > 0:
            cur_loss = total_loss / log_interval
            elapsed = time.time() - start_time
            if dist.get_rank() == 0:
                print(
                    "| batch {:5d} | wps {:5.2f} | loss {:5.2f} | ppl {:8.2f}".format(
                        i, total_tokens_per_log_interval / elapsed, cur_loss, math.exp(cur_loss)
                    )
                )
            total_tokens_per_log_interval = 0
            total_loss = 0
            start_time = time.time()

    if epoch_start_time != 0:
        torch.cuda.synchronize()
        wps = total_tokens / (time.time() - epoch_start_time)
    else:
        raise RuntimeError(
            "Unable to benchmark on a single batch. Increase the size " " of the dataset and rerun the benchmark."
        )
    return wps, loss.item()


def get_number_of_words(data):
    return data.size()[0] * data.size()[1]


def benchmark_language_model(model_config, model, benchmark_config, model_specs, args):
    golden_config = get_golden_config(args.model_name, args)
    epoch = benchmark_config["epochs"]
    start_time = time.time()
    if dist.get_rank() == 0:
        print("-" * 110)
        print("| start of epoch {:1d}".format(epoch))
        print("-" * 110)
    wps, loss = train(model_config, model, benchmark_config, model_specs, args)
    elapsed_time = time.time() - start_time
    if dist.get_rank() == 0:
        print("-" * 110)
        print("| end of epoch {:1d} | time: {:5.2f}s | train loss {:5.2f} ".format(epoch, elapsed_time, loss))
        print("-" * 110)
        print("Throughput(wps) is {:.2f}.".format(wps))
    print(
        "Peak allocated bytes on cuda:{}: {:4f}GB".format(
            dist.get_rank(), torch.cuda.memory_stats(dist.get_rank())["allocated_bytes.all.peak"] / 2**30
        )
    )

    verify_lm_run(wps, golden_config, args)


def get_synthetic_dataloaders(args, device, benchmark_config, model_specs):
    """Returns dataloader for synthetic data."""

    if args.model_name == "lm":
        return get_synthetic_wikitext2_dataloaders(args, benchmark_config, model_specs)
    else:
        raise RuntimeError("Unrecognized args.model_mame " % args.model_name)


def get_real_dataloaders(args, device, benchmark_config, model_specs):
    """Returns dataloaders for real data."""

    if args.model_name == "lm":
        data = get_real_wikitext2_dataloaders(args, benchmark_config, model_specs)
        ntokens, train_dataloader, valid_dataloader, test_dataloader = data
        model_specs["vocab_size"] = ntokens
        return train_dataloader, valid_dataloader, test_dataloader
    else:
        raise RuntimeError("Unrecognized args.model_mame " % args.model_name)


def create_model_config(args, benchmark_config=None, model_specs=None):
    """Return a dict with the given model, dataset and optimizer."""

    device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")

    if args.use_synthetic_data:
        dataloader_fn = get_synthetic_dataloaders
    else:
        dataloader_fn = get_real_dataloaders

    data = dataloader_fn(args, device, benchmark_config, model_specs)
    model, optimizer = get_model_and_optimizer(args, device, benchmark_config, model_specs)
    return {
        "model": model,
        "optimizer": optimizer,
        "data": data,
    }


def create_benchmark_config(model_name):
    """Return a dict with configurations required for benchmarking `model_name` model."""

    if model_name == "lm":
        return lm_wikitext2.get_benchmark_config()
    else:
        raise RuntimeError("Unrecognized args.model_mame " % args.model_name)


def get_model_specs(model_name):
    """Return a dict with configurations required for configuring `model_name` model."""

    if model_name == "lm":
        return lm_wikitext2.get_model_config()
    else:
        raise RuntimeError("Unrecognized args.model_mame " % args.model_name)


def get_golden_config(model_name, args):
    """Return a dict with the golden data for throughput and memory usage."""

    if model_name == "lm":
        return lm_wikitext2.get_golden_synthetic_stats()
    else:
        raise RuntimeError("Unrecognized args.model_mame " % args.model_name)


def benchmark_fsdp(rank, args, world_size):
    """Benchmark a given model using a single process and multiple devices."""

    init_method_pgroup = "tcp://localhost:{}".format(RPC_PORT)
    torch.distributed.init_process_group(
        backend="nccl", rank=rank, world_size=world_size, init_method=init_method_pgroup
    )

    torch.cuda.set_device(rank)
    init_random_seed(0)
    logging.basicConfig(level=logging.DEBUG)

    benchmark_config = create_benchmark_config(args.model_name)
    model_specs = get_model_specs(args.model_name)
    model_config = create_model_config(args, benchmark_config=benchmark_config, model_specs=model_specs)
    model = model_config["model"]
    config = {}

    if args.full_fp16:
        config["compute_dtype"] = torch.float16
        config["mixed_precision"] = False

    if args.enable_auto_wrap:
        with enable_wrap(wrapper_cls=FSDP, **config):
            fsdp_model = auto_wrap(model, auto_wrap_policy=default_auto_wrap_policy)
            fsdp_model = FSDP(fsdp_model, **config)
    else:
        fsdp_model = FSDP(model, **config)

    if args.full_fp16:
        fsdp_model = fsdp_model.half()
    print(f"param dtype {[p.dtype for p in fsdp_model.parameters()]}")
    if args.dry_run:
        train(model_config, fsdp_model, benchmark_config, model_specs, args)
    else:
        benchmark_language_model(model_config, fsdp_model, benchmark_config, model_specs, args)


parser = argparse.ArgumentParser(description="benchmark")
parser.add_argument("--max_batch", type=int, default=4, help="Max number of batches")
parser.add_argument("--use_synthetic_data", action="store_true", help="Uses synthetic data for running benchmarks.")
parser.add_argument("--dry_run", action="store_true", help="Run a sample training run without regression testing.")
parser.add_argument(
    "--model_name",
    default="lm",
    help="Language Model(LM) used to benchmark FSDP.",
)
parser.add_argument("--debug", action="store_true", default=False, help="Display additional debug information")
parser.add_argument("--enable_auto_wrap", action="store_true", default=False, help="Use auto_wrap with FSDP")
parser.add_argument("--benchmark_eval", action="store_true", default=False, help="Benchmark evaluation workflow.")
parser.add_argument("--full_fp16", action="store_true", default=False, help="Benchmark in full fp16 mode.")

if __name__ == "__main__":
    args = parser.parse_args()
    logging.basicConfig(level=logging.DEBUG)

    print(f"Running FSDP benchmark with args: {args}")
    num_devices = torch.cuda.device_count() if torch.cuda.is_available() else 1
    assert num_devices > 0

    mp.spawn(
        benchmark_fsdp,
        args=(args, num_devices),
        nprocs=num_devices,
        join=True,
    )