FSDP Training for GLM-4.5-Air MoE

Distributed FSDP training with LoRA fine-tuning for the GLM-4.5-Air Mixture-of-Experts model (106B parameters) on 8x Nvidia H200 GPUs.

🎯 Status: Production Ready ✅

• ✅ Stable training (87GB/141GB per GPU)
• ✅ CPU offload checkpointing (saves in ~3 min)
• ✅ Checkpoint resume verified
• ✅ Batch size 4 (4x throughput vs initial)

Quick Start

Training from Scratch

cd "/workspace/MoE Training/fsdp"
source /workspace/MoE\ Training/moe_env/bin/activate

PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True \
NCCL_TIMEOUT=7200 \
accelerate launch --config_file accelerate_config.yaml \
  train_fsdp_lora.py --config config.yaml

Resume from Checkpoint

PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True \
NCCL_TIMEOUT=7200 \
accelerate launch --config_file accelerate_config.yaml \
  train_fsdp_lora.py --config config.yaml \
  --resume_from_checkpoint glm_output/checkpoint-10

Key Configuration

Parameter	Value	Notes
GPUs	8x H200 (141GB each)	87GB used per GPU
Batch Size	4 per GPU	32 global batch
Sequence Length	1024 tokens	Reduced from 4096
LoRA Rank	16	32M trainable params
FSDP Policy	SIZE_BASED_WRAP	500M param threshold
Checkpoint Freq	Every 10 steps	~3 min save time

Performance

• Training Speed: ~0.07 it/s (7 iterations/min)
• Memory Usage: 87GB/141GB per GPU (62% utilization)
• Checkpoint Size: 210GB (sharded) + 121MB (LoRA adapters)
• Stability: No OOM, no NCCL timeouts, no FSDP errors

Files

• train_fsdp_lora.py: Main training script with CPU offload checkpointing
• config.yaml: Training hyperparameters and data settings
• accelerate_config.yaml: FSDP and distributed configuration
• DOCUMENTATION.md: Complete technical documentation ⭐
• GLM_TRAINING_GUIDE.md: Original training guide

Features

✅ Working

• Stable distributed training across 8 GPUs
• CPU offload checkpoint saving (no OOM/timeout)
• Checkpoint resume with full state restoration
• Gradient checkpointing for memory efficiency
• Expert usage logging for MoE layers
• JSONL training logs

⚠️ Known Limitations

• Gradient accumulation > 1 causes FSDP state error (kept at 1)
• Evaluation disabled (needs synchronization fixes)
• Flash Attention 2 not installed (using SDPA fallback)
• Sequence length limited to 1024 (memory constraints)

Architecture

Model: GLM-4.5-Air MoE

• Total Parameters: 106.9B
• Trainable (LoRA): 31.6M (0.03%)
• Experts per Layer: 96
• Top-K Routing: 2

LoRA Configuration:

• Rank: 16
• Alpha: 32
• Target Modules: query_key_value, dense
• Dropout: 0.05

Critical Implementation Details

CPU Offload Checkpointing ⭐

Problem: Standard checkpointing methods failed:

• accelerator.get_state_dict(): 10+ min timeout (gathering 212GB)
• get_peft_state_dict(): GPU OOM (tries to clone on GPU)

Solution: Dual checkpoint strategy

1. Sharded state via accelerator.save_state() (fast, parallel)
2. LoRA adapters via CPU offload (avoids GPU OOM)

# Key code pattern
save_policy = FullStateDictConfig(offload_to_cpu=True, rank0_only=True)
with FSDP.state_dict_type(model, StateDictType.FULL_STATE_DICT, save_policy):
    cpu_state_dict = model.state_dict()  # Gathers to CPU RAM (1TB+)
    # Filter and save LoRA params...

Why it works: Uses abundant CPU RAM (1TB+) instead of limited GPU VRAM (141GB).

FSDP Wrapping

Critical: Use SIZE_BASED_WRAP with fsdp_min_num_params: 500000000

• ❌ TRANSFORMER_BASED_WRAP: Causes severe OOM
• ✅ SIZE_BASED_WRAP: Balanced sharding across GPUs

Gradient Flow Fix

Required for LoRA + gradient checkpointing:

model = get_peft_model(model, peft_config)
model.enable_input_require_grads()  # Critical!
model = model.to(torch.bfloat16)

Troubleshooting

OOM Errors

1. Check FSDP policy is SIZE_BASED_WRAP
2. Verify block_size: 1024 in config.yaml
3. Confirm batch_size: 4 or lower
4. Ensure use_cache: false in model config

NCCL Timeout

1. Set NCCL_TIMEOUT=7200 environment variable
2. Verify fsdp_ddp_timeout: 7200 in accelerate_config.yaml
3. Check network connectivity between GPUs

Checkpoint Hangs

1. Ensure using latest train_fsdp_lora.py with CPU offload
2. Verify fsdp_state_dict_type: SHARDED_STATE_DICT
3. Check accelerator.wait_for_everyone() calls present

Documentation

📖 For complete details, see DOCUMENTATION.md which includes:

• All problems encountered and solutions
• Detailed observations and findings
• Performance analysis
• Configuration explanations
• Best practices and lessons learned

Requirements

pip install torch transformers accelerate peft datasets pyyaml

See requirements.txt for specific versions.

Monitoring

# Watch training logs
tail -f training_output_new.log

# Monitor GPU usage
watch -n 1 nvidia-smi

# Check checkpoints
ls -lh glm_output/checkpoint-*
du -sh glm_output/checkpoint-*

Using Trained Adapters

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

# Load base model
model = AutoModelForCausalLM.from_pretrained(
    "THUDM/glm-4-9b-chat",
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Load LoRA adapters
model = PeftModel.from_pretrained(model, "glm_output/checkpoint-50")
tokenizer = AutoTokenizer.from_pretrained("glm_output/checkpoint-50")

# Inference
model.eval()
# ... use for generation

Environment

• Python: 3.12.3
• PyTorch: 2.9.1+cu128
• Accelerate: 1.11.0
• CUDA: 12.8
• Hardware: 8x Nvidia H200 (141GB VRAM)

License

Same as GLM-4.5-Air model license.

Acknowledgments

• Implements CPU offload checkpointing strategy for FSDP+LoRA
• Based on HuggingFace Accelerate and PEFT libraries
• Optimized for Nvidia H200 GPUs

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Last Updated: 2025-11-21
Status: Production Ready ✅