此笔记本演示使用 Databricks 无服务器 GPU 计算在 8 H100 GPU 上对大型 120B 参数 GPT-OSS 模型进行监督微调（SFT）。 训练课程中利用了：
- FSDP（完全分片数据并行）：将模型参数、梯度和优化器状态分片到多个 GPU 上，以训练那些无法在单个 GPU 上运行的大型模型。
- DDP （分布式数据并行）：跨多个 GPU 分配训练，以加快训练速度。
- LoRA （低秩适应）：通过添加小型适配器层来减少可训练参数的数量，从而使微调更高效。
- 变压器强化学习（TRL）：提供 SFTTrainer 用于进行监督微调。
通过设置 remote=False 和指定 16 个 GPU，这可以扩展到跨 16 个 GPU 的多节点训练。
安装所需程序包
安装用于分布式训练和模型微调的必要库：
-
trl：用于 SFT 训练的转换器强化学习库 -
peft：参数高效微调的 LoRA 适配器 -
transformers：Hugging Face Transformers库 -
datasets：用于加载训练数据集 -
accelerate：用于分布式训练协调 -
hf_transfer：提高从 Hugging Face 下载模型的速度
%pip install "trl==1.1.0"
%pip install "peft==0.19.1"
%pip install "transformers==5.5.4"
%pip install "fsspec==2024.9.0"
%pip install "huggingface_hub==1.11.0"
%pip install "datasets==3.2.0"
%pip install "accelerate==1.13.0"
%restart_python
使用 FSDP 定义分布式训练函数
此单元格定义训练函数，该函数将使用修饰器在 8 个 H100 GPU @distributed 上运行。 该函数包括：
- 模型加载：以 bfloat16 精度加载 120B 参数 GPT-OSS 模型
- LoRA 配置：应用具有秩为 16 的低秩适应方法，以减少可训练参数的数量。
- FSDP 设置：配置全分片数据并行处理，并自动进行层包装和激活检查点设定
- 训练配置：设置批大小、学习速率、渐变累积和其他超参数
- 数据集：使用 HuggingFaceH4/Multilingual-Thinking 数据集进行微调
该函数自动检测 FSDP 包装的转换器块类，并处理所有 GPU 之间的分布式训练协调。
dbutils.widgets.text("uc_catalog", "main")
dbutils.widgets.text("uc_schema", "default")
dbutils.widgets.text("uc_model_name", "gpt-oss-120b-peft")
dbutils.widgets.text("uc_volume", "checkpoints")
dbutils.widgets.text("model", "openai/gpt-oss-120b")
dbutils.widgets.text("dataset_path", "HuggingFaceH4/Multilingual-Thinking")
UC_CATALOG = dbutils.widgets.get("uc_catalog")
UC_SCHEMA = dbutils.widgets.get("uc_schema")
UC_MODEL_NAME = dbutils.widgets.get("uc_model_name")
UC_VOLUME = dbutils.widgets.get("uc_volume")
HF_MODEL_NAME = dbutils.widgets.get("model")
DATASET_PATH = dbutils.widgets.get("dataset_path")
print(f"UC_CATALOG: {UC_CATALOG}")
print(f"UC_SCHEMA: {UC_SCHEMA}")
print(f"UC_MODEL_NAME: {UC_MODEL_NAME}")
print(f"UC_VOLUME: {UC_VOLUME}")
print(f"HF_MODEL_NAME: {HF_MODEL_NAME}")
print(f"DATASET_PATH: {DATASET_PATH}")
OUTPUT_DIR = f"/Volumes/{UC_CATALOG}/{UC_SCHEMA}/{UC_VOLUME}/{UC_MODEL_NAME}"
print(f"OUTPUT_DIR: {OUTPUT_DIR}")
from serverless_gpu import distributed
@distributed(gpus=8, gpu_type='H100')
def train_gpt_oss_fsdp_120b():
"""
Fine-tune a 120B-class model with TRL SFTTrainer + FSDP2 on H100s.
Uses LoRA + activation ckpt + full_shard auto_wrap.
"""
# --- imports inside for pickle safety ---
import os, torch, torch.distributed as dist
from transformers import AutoModelForCausalLM, AutoTokenizer, Mxfp4Config
from trl import SFTTrainer, SFTConfig
from datasets import load_dataset
from peft import LoraConfig, get_peft_model
# ---------- DDP / CUDA binding ----------
local_rank = int(os.environ.get("LOCAL_RANK", "0"))
torch.cuda.set_device(local_rank)
os.environ.setdefault("TOKENIZERS_PARALLELISM", "false")
os.environ.setdefault("NCCL_DEBUG", "WARN")
os.environ.setdefault("CUDA_LAUNCH_BLOCKING", "0")
os.environ.setdefault("TORCH_NCCL_ASYNC_ERROR_HANDLING", "1") # replaces NCCL_ASYNC_ERROR_HANDLING
# ---------- Config ----------
MAX_LENGTH = 2048
PER_DEVICE_BATCH = 1 # start conservative for 120B
GRAD_ACCUM = 4 # tune for throughput
LR = 1.5e-4
EPOCHS = 1
is_main = int(os.environ.get("RANK", "0")) == 0
world_size = int(os.environ.get("WORLD_SIZE", "1"))
if is_main:
print("=" * 60)
print("FSDP (full_shard) launch for 120B")
print(f"WORLD_SIZE={world_size} | LOCAL_RANK={local_rank}")
print("=" * 60)
# ---------- Tokenizer ----------
tokenizer = AutoTokenizer.from_pretrained(HF_MODEL_NAME)
if tokenizer.pad_token_id is None and tokenizer.eos_token_id is not None:
tokenizer.pad_token = tokenizer.eos_token
tokenizer.model_max_length = MAX_LENGTH
tokenizer.truncation_side = "right"
# ---------- Model ----------
# IMPORTANT: no device_map, no .to(device) — let Trainer/Accelerate+FSDP handle placement
# low_cpu_mem_usage helps with massive checkpoints (still needs decent host RAM)
quantization_config = Mxfp4Config(dequantize=True)
model = AutoModelForCausalLM.from_pretrained(
HF_MODEL_NAME,
dtype=torch.bfloat16,
attn_implementation="eager",
quantization_config=quantization_config,
use_cache=False, # needed for grad ckpt
low_cpu_mem_usage=True,
)
# ---------- LoRA ----------
# the following config works
# include MoE layers as well.
peft_config = LoraConfig(
r=32,
lora_alpha=32,
target_modules="all-linear",
rank_pattern={
"mlp.experts.gate_up_proj": 8,
"mlp.experts.down_proj": 8
},
target_parameters=["mlp.experts.gate_up_proj", "mlp.experts.down_proj"],
lora_dropout=0.0,
bias="none",
task_type="CAUSAL_LM",
)
model = get_peft_model(model, peft_config)
# Cast all parameters to bfloat16 so FSDP sees a uniform dtype
# (LoRA adapters are initialized in float32 by default)
model = model.to(torch.bfloat16)
if is_main:
model.print_trainable_parameters()
# ---------- Data ----------
dataset = load_dataset("HuggingFaceH4/Multilingual-Thinking", split="train")
if is_main:
print(f"Dataset size: {len(dataset)}")
# ---------- FSDP settings ----------
def infer_transformer_blocks_for_fsdp(model):
COMMON = {
"LlamaDecoderLayer", "MistralDecoderLayer", "MixtralDecoderLayer",
"Qwen2DecoderLayer", "Gemma2DecoderLayer", "Phi3DecoderLayer",
"GPTNeoXLayer", "MPTBlock", "BloomBlock", "FalconDecoderLayer",
"DecoderLayer", "GPTJBlock", "OPTDecoderLayer"
}
hits = set()
for _, m in model.named_modules():
name = m.__class__.__name__
if name in COMMON:
hits.add(name)
# Fallback: grab anything that *looks* like a decoder block
if not hits:
for _, m in model.named_modules():
name = m.__class__.__name__
if any(s in name for s in ["Block", "DecoderLayer", "Layer"]) and "Embedding" not in name:
hits.add(name)
return sorted(hits)
fsdp_wrap_classes = infer_transformer_blocks_for_fsdp(model)
if not fsdp_wrap_classes:
raise RuntimeError("Could not infer transformer block classes for FSDP wrapping; "
"print(model) and add the block class explicitly.")
training_args = SFTConfig(
output_dir=OUTPUT_DIR,
num_train_epochs=EPOCHS,
per_device_train_batch_size=PER_DEVICE_BATCH,
gradient_accumulation_steps=GRAD_ACCUM,
learning_rate=LR,
warmup_ratio=0.03,
lr_scheduler_type="cosine",
bf16=True,
logging_steps=5,
logging_strategy="steps",
save_strategy="no",
report_to="none",
ddp_find_unused_parameters=False,
dataloader_pin_memory=True,
max_length=MAX_LENGTH,
gradient_checkpointing=False,
# ---- FSDP2 knobs ----
fsdp="full_shard auto_wrap",
fsdp_config={
"version": 2,
"fsdp_transformer_layer_cls_to_wrap": fsdp_wrap_classes,
"reshard_after_forward": True,
"activation_checkpointing": True, # <- use activation ckpt (not gradient)
"xla": False,
"limit_all_gathers": True,
},
)
# ---------- Trainer ----------
trainer = SFTTrainer(
model=model,
args=training_args,
train_dataset=dataset,
processing_class=tokenizer,
)
# verify distributed init & FSDP
rank = int(os.getenv("RANK", "0"))
print(f"[rank {rank}] dist.is_initialized() -> {dist.is_initialized()}")
acc = getattr(trainer, "accelerator", None)
print(f"[rank {rank}] accelerator.distributed_type = {getattr(getattr(acc,'state',None),'distributed_type','n/a')}")
print(f"[rank {rank}] accelerator.num_processes = {getattr(acc, 'num_processes', 'n/a')}")
# ---------- Train ----------
result = trainer.train()
if is_main:
print("\nTraining complete (FSDP).")
print(result.metrics)
运行分布式训练任务
在 8 H100 GPU 上执行训练函数。
@distributed修饰器负责协调在所有 GPU 上启动训练，并保证适当的分布式设置。
train_gpt_oss_fsdp_120b.distributed()