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estimation.py
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estimation.py
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# 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 torch
import torch.nn.functional as F
from torch._guards import active_fake_mode
from torch._subclasses.fake_tensor import FakeTensorMode
from torch.distributed import destroy_process_group
from torch.distributed._tools.fsdp2_mem_tracker import FSDPMemTracker
from torch.distributed.tensor.parallel import loss_parallel
from torch.testing._internal.distributed.fake_pg import FakeStore
from torchtitan.config_manager import JobConfig
from torchtitan.datasets import 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_schedulers
from torchtitan.models import model_name_to_cls, model_name_to_tokenizer, models_config
from torchtitan.parallelisms import models_parallelize_fns, ParallelDims
from train import build_optimizers
def estimate_memory(job_config: JobConfig):
init_logger()
logger.info("Estimating memory usage...")
gc.disable()
gc.collect(1)
# Get the world size
world_size = int(os.environ["WORLD_SIZE"])
# if tp > or pp > 1, we exit
if (
job_config.training.tensor_parallel_degree > 1
or job_config.experimental.pipeline_parallel_degree > 1
):
logger.info(
"Tensor parallelism and pipeline parallelism are not supported yet."
)
return
# fake tensor doesn't work with fused rmsnorm
if (
job_config.model.norm_type == "fused_rmsnorm"
and not job_config.memory_estimation.disable_fake_mode
):
logger.info(
"Fused RMSNorm is not supported yet under fake estimation mode. "
"Switching to rmsnorm."
)
job_config.model.norm_type = "rmsnorm"
if job_config.model.norm_type == "compiled_rmsnorm":
logger.info("Compiled RMSNorm is not supported yet. Switching to RMSNorm.")
job_config.model.norm_type = "rmsnorm"
if job_config.training.compile:
logger.info("Compile mode is not supported yet. Switching to eager mode.")
job_config.training.compile = False
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,
dp_type=job_config.training.data_parallel_type,
)
device = torch.device(f"cuda:{int(os.environ['LOCAL_RANK'])}")
torch.cuda.set_device(device)
# init fake pg
store = FakeStore()
torch.distributed.init_process_group(
"fake", rank=int(os.environ["LOCAL_RANK"]), world_size=world_size, store=store
)
# build meshes
world_mesh = parallel_dims.build_mesh(device_type="cuda")
if not parallel_dims.dp_enabled:
logger.info("Data parallelism is not enabled. Skipping memory estimation.")
return
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)
# 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 FakeTensorMode() if not job_config.memory_estimation.disable_fake_mode else contextlib.nullcontext():
logger.info(
f"Building {model_name} {job_config.model.flavor} with {model_config}"
)
with torch.device("meta"):
whole_model = model_cls.from_model_args(model_config)
# apply fp8 linear module swap
if job_config.training.enable_fp8_linear:
build_fp8_linear(whole_model, job_config, parallel_dims.dp_enabled)
# apply PT-D DP/TP parallelisms and activation checkpointing
model_parts = [whole_model]
model_parts = [
models_parallelize_fns[model_name](m, world_mesh, parallel_dims, job_config)
for m in model_parts
]
init_device = "cuda"
for model in model_parts:
model.to_empty(device=init_device)
if not active_fake_mode():
whole_model.init_weights()
# build optimizer after applying parallelisms to the model
optimizers = build_optimizers(model_parts, job_config)
lr_schedulers = get_lr_schedulers(optimizers.optimizers, job_config)
for model in model_parts:
model.train()
logger.info(f"Vocab size: {model_config.vocab_size}")
# Create a dummy batch instead of loading from a dataset
batch = (
torch.randint(
0,
model_config.vocab_size,
(job_config.training.batch_size, model_config.max_seq_len),
device="cuda",
),
torch.randint(
0,
model_config.vocab_size,
(job_config.training.batch_size, model_config.max_seq_len),
device="cuda",
),
)
fsdp_memtracker = FSDPMemTracker(mod=whole_model, optm=optimizers.optimizers[0])
fsdp_memtracker.track_inputs(batch)
with fsdp_memtracker:
for iter_idx in range(2):
input_ids, labels = batch
# train step
with loss_parallel_ctx():
pred = whole_model(input_ids)
loss = loss_fn(pred, labels)
del pred
loss.backward()
# clip gradients
for model in model_parts:
torch.nn.utils.clip_grad_norm_(
model.parameters(), job_config.training.max_norm, foreach=True
)
# optimizer step
optimizers.step()
lr_schedulers.step()
optimizers.zero_grad()
print(f"Peak Memory at iter: {iter_idx}")
fsdp_memtracker.display_snapshot("peak", units="MiB", tabulate=True)
if iter_idx == 0:
fsdp_memtracker.reset_mod_stats() # iter 0 does not have optimizer state
gc.collect(1)
fsdp_memtracker.display_modulewise_snapshots(
depth=3, units="MiB", tabulate=True
)
mem_stats = torch.cuda.memory_stats()
peak_active = mem_stats["active_bytes.all.peak"]
peak_reserved = mem_stats["reserved_bytes.all.peak"]
num_retries = mem_stats["num_alloc_retries"]
dev = torch.device(torch.cuda.current_device())
tracker_peak = fsdp_memtracker.get_tracker_snapshot("peak")[dev]["Total"]
gib = 1024**3
print(
f"peak active: {peak_active / gib} GiB | peak reserved:"
f" {peak_reserved / gib} GiB | num_retries: {num_retries}"
)
print(f"Tracker Max: {tracker_peak / gib} GiB")
if job_config.memory_estimation.disable_fake_mode and peak_active > 0:
print(f"Tracker Accuracy: {tracker_peak/peak_active}")
gc.enable()
if __name__ == "__main__":
config = JobConfig()
config.parse_args()
try:
estimate_memory(config)
finally:
destroy_process_group()