File size: 9,153 Bytes
88e6849 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 | # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
import os
import torch
from torch.optim import Adam
from torch.utils.data import DataLoader
from functools import partial
from pathlib import Path
from typing import Any, Callable, Dict, Tuple, Iterator
from megatron.core import parallel_state
from megatron.core import dist_checkpointing
from megatron.core.pipeline_parallel.schedules import get_forward_backward_func
from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed
from megatron.core.transformer.transformer_config import TransformerConfig
from megatron.core.models.gpt.gpt_model import GPTModel
from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_local_spec
from megatron.core.datasets.utils import compile_helpers
from megatron.core.datasets.blended_megatron_dataset_builder import (
BlendedMegatronDatasetBuilder,
)
from megatron.core.datasets.gpt_dataset import GPTDatasetConfig, MockGPTDataset
from megatron.training.tokenizer.tokenizer import _NullTokenizer
from megatron.core.distributed import DistributedDataParallel
from megatron.core.distributed import DistributedDataParallelConfig
from megatron.core.distributed.finalize_model_grads import finalize_model_grads
_SEQUENCE_LENGTH: int = 64
def initialize_distributed(
tensor_model_parallel_size: int = 1, pipeline_model_parallel_size: int = 1
) -> None:
"""
Initialize torch.distributed and Megatron-Core model parallel groups.
Args:
tensor_model_parallel_size: Number of GPUs for tensor model parallelism.
pipeline_model_parallel_size: Number of GPUs for pipeline model parallelism.
"""
parallel_state.destroy_model_parallel()
# Torch setup for distributed training
rank: int = int(os.environ["RANK"])
world_size: int = int(os.environ["WORLD_SIZE"])
local_rank: int = int(os.environ["LOCAL_RANK"])
torch.cuda.set_device(local_rank)
torch.distributed.init_process_group(
backend="nccl", rank=rank, world_size=world_size
)
# Megatron core distributed training initialization
parallel_state.initialize_model_parallel(
tensor_model_parallel_size, pipeline_model_parallel_size
)
def model_provider() -> GPTModel:
"""
Build and return a simple GPT model for demonstration.
Returns:
GPTModel: A small GPT model with 2 layers for testing.
"""
transformer_config: TransformerConfig = TransformerConfig(
num_layers=2,
hidden_size=12,
num_attention_heads=4,
use_cpu_initialization=True,
pipeline_dtype=torch.float32,
)
gpt_model: GPTModel = GPTModel(
config=transformer_config,
transformer_layer_spec=get_gpt_layer_local_spec(),
vocab_size=100,
max_sequence_length=_SEQUENCE_LENGTH,
)
return gpt_model
def get_train_data_iterator() -> Iterator:
"""
Create a mock dataset and return a data iterator.
Returns:
Iterator: Data iterator for training batches.
"""
if torch.distributed.is_available() and torch.distributed.is_initialized():
if torch.distributed.get_rank() == 0:
compile_helpers()
torch.distributed.barrier()
else:
compile_helpers()
config: GPTDatasetConfig = GPTDatasetConfig(
random_seed=0,
sequence_length=_SEQUENCE_LENGTH,
reset_position_ids=False,
reset_attention_mask=False,
eod_mask_loss=False,
tokenizer=_NullTokenizer(vocab_size=_SEQUENCE_LENGTH),
mid_level_dataset_surplus=0.005,
)
datasets = BlendedMegatronDatasetBuilder(
MockGPTDataset, [1000, None, None], lambda: True, config
).build()
train_dataloader: DataLoader = DataLoader(datasets[0], batch_size=8, shuffle=True)
train_iterator: Iterator = iter(train_dataloader)
return train_iterator
def forward_step_func(
data_iterator: Iterator, model: torch.nn.Module
) -> Tuple[torch.Tensor, Callable]:
"""
Forward step function that computes model output and returns loss function.
Args:
data_iterator: Iterator providing training batches.
model: The GPT model to train.
Returns:
Tuple of (output_tensor, loss_function) where loss_function is a partial
function that will compute the final loss when called.
"""
def loss_func(
loss_mask: torch.Tensor, output_tensor: torch.Tensor
) -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
losses: torch.Tensor = output_tensor.float()
loss_mask = loss_mask.view(-1).float()
loss: torch.Tensor = torch.sum(losses.view(-1) * loss_mask) / loss_mask.sum()
# If you have data parallel reduce loss across data parallel groups.
# If pipeline parallel, loss computation is done only in last stage.
return loss, {"lm loss": loss}
data: Dict[str, torch.Tensor] = next(data_iterator)
tokens: torch.Tensor = data["tokens"].to(device)
attention_mask: torch.Tensor = data["attention_mask"].to(device)
position_ids: torch.Tensor = data["position_ids"].to(device)
labels: torch.Tensor = data["labels"].to(device)
loss_mask: torch.Tensor = data["loss_mask"].to(device)
output_tensor: torch.Tensor = model(
tokens, position_ids, attention_mask, labels=labels
)
return output_tensor, partial(loss_func, loss_mask)
def save_distributed_checkpoint(
checkpoint_path: str, gpt_model: torch.nn.Module
) -> None:
"""
Save model checkpoint using Megatron-Core distributed checkpointing.
Args:
checkpoint_path: Directory path to save checkpoint.
gpt_model: The model to checkpoint (may be wrapped with DDP).
"""
# Access underlying model if wrapped with DDP
model: torch.nn.Module = (
gpt_model.module if hasattr(gpt_model, "module") else gpt_model
)
sharded_state_dict: Dict = model.sharded_state_dict(prefix="")
dist_checkpointing.save(
sharded_state_dict=sharded_state_dict, checkpoint_dir=checkpoint_path
)
def load_distributed_checkpoint(
checkpoint_path: str, gpt_model: torch.nn.Module
) -> torch.nn.Module:
"""
Load model checkpoint using Megatron-Core distributed checkpointing.
Args:
checkpoint_path: Directory path to load checkpoint from.
gpt_model: The model to load into (may be wrapped with DDP).
Returns:
The model with loaded checkpoint weights.
"""
# Access underlying model if wrapped with DDP
model: torch.nn.Module = (
gpt_model.module if hasattr(gpt_model, "module") else gpt_model
)
sharded_state_dict: Dict = model.sharded_state_dict(prefix="")
checkpoint: Dict = dist_checkpointing.load(
sharded_state_dict=sharded_state_dict, checkpoint_dir=checkpoint_path
)
model.load_state_dict(checkpoint)
return gpt_model
if __name__ == "__main__":
initialize_distributed(tensor_model_parallel_size=2, pipeline_model_parallel_size=1)
model_parallel_cuda_manual_seed(123)
gpt_model: GPTModel = model_provider()
device: torch.device = torch.device("cuda")
gpt_model.to(device)
# Wrap model with DistributedDataParallel for proper gradient synchronization.
# This provides the finish_grad_sync() method required by finalize_model_grads().
config: TransformerConfig = gpt_model.config
ddp_config: DistributedDataParallelConfig = DistributedDataParallelConfig(
grad_reduce_in_fp32=False,
overlap_grad_reduce=False,
use_distributed_optimizer=False,
)
gpt_model = DistributedDataParallel(
config=config,
ddp_config=ddp_config,
module=gpt_model,
)
optim: Adam = Adam(gpt_model.parameters())
train_iterator: Iterator = get_train_data_iterator()
forward_backward_func: Callable[..., Dict[str, Any]] = get_forward_backward_func()
# Running the model for 5 iterations
for iteration in range(5):
optim.zero_grad()
losses_reduced: Dict[str, Any] = forward_backward_func(
forward_step_func=forward_step_func,
data_iterator=train_iterator,
model=gpt_model,
num_microbatches=1,
seq_length=_SEQUENCE_LENGTH,
micro_batch_size=8,
decoder_seq_length=_SEQUENCE_LENGTH,
forward_only=False,
)
# Finalize model gradients: all-reduce across DP and TP groups.
# This synchronizes gradients for non-tensor-parallel parameters (e.g., LayerNorm)
# across tensor parallel ranks and all gradients across data parallel ranks.
finalize_model_grads([gpt_model])
optim.step()
print(f"Iteration {iteration}: Losses reduced: {losses_reduced}")
# Saving the model
ckpt_path: str = os.getcwd() + "/ckpt"
Path(ckpt_path).mkdir(exist_ok=True)
save_distributed_checkpoint(gpt_model=gpt_model, checkpoint_path=ckpt_path)
# Loading the model
gpt_model = load_distributed_checkpoint(
gpt_model=gpt_model, checkpoint_path=ckpt_path
)
gpt_model.to(device)
print("Successfully loaded the model")
|