feat: use DeepSpeed ZeRO-3 for optimal 2-GPU training

training/configs/llama2-7b-lora.yaml

@@ -46,12 +46,22 @@ train_params:
 # Precision
 mixed_precision: bf16
 
-# Distributed training (2x RTX 5090)
+# Distributed training (2x RTX 5090) - DeepSpeed ZeRO-3
-fsdp: full_shard
+plugin: deepspeed
-fsdp_config:
+huggingface_hub:
-  limit_all_gathers: true
+  token: null
-  offload_optimizer: true
+deeepspeed_config:
-  offload_model: false
+  zero_optimization:
+    stage: 3
+    offload_optimizer:
+      device: cpu
+      pin_memory: true
+    offload_param:
+      device: cpu
+      pin_memory: true
+  gradient_clipping: 1.0
+  train_batch_size: auto
+  train_micro_batch_size_per_gpu: auto
 
 # Evaluation
 eval_strategy: steps

training/scripts/train.py

@@ -34,16 +34,12 @@ def train(config_path):
     
     print(f"Loading model: {config['base_model']}")
     
-    # Load model with distributed training support
+    # Load model - let the model's own quantization config handle it
-    # Use FSDP for multi-GPU training
+    # (Ornith uses CompressedTensors, not BitsAndBytes)
-    from accelerate import Accelerator
-    accelerator = Accelerator()
-    
-    # Load model on CPU first, then distribute
     model = AutoModelForCausalLM.from_pretrained(
         config["base_model"],
         torch_dtype=torch.bfloat16,
-        device_map="cpu",  # Load on CPU first
+        device_map="auto",  # Let transformers distribute across GPUs
     )
     
     # Add LoRA
@@ -93,13 +89,9 @@ def train(config_path):
         gradient_checkpointing=config.get("gradient_checkpointing", True),
     )
     
-    # Use FSDP for multi-GPU training
+    # SFT Trainer (DeepSpeed handles distributed training via config)
     from trl import SFTTrainer
     
-    # Prepare model for FSDP
-    model = accelerator.prepare(model)
-    
-    # SFT Trainer
     trainer = SFTTrainer(
         model=model,
         tokenizer=tokenizer,
@@ -109,9 +101,6 @@ def train(config_path):
         max_seq_length=config["train_params"]["max_seq_length"],
     )
     
-    # Prepare trainer for distributed training
-    trainer = accelerator.prepare(trainer)
-    
     # Train
     print("Starting training...")
     trainer.train()