Add HPU SFT example

examples/scripts/sft_hpu_example.py

@@ -0,0 +1,127 @@
+#!/usr/bin/env python3
+sample_name="SFT Training Example for HPU"
+"""
+This script demonstrates how to do SFT training on HPU
+using a single-node, multi-GPU setup with training_hub.
+
+Example usage:
+    python sft_hpu_example.py \\
+        --data-path /path/to/data.jsonl \\
+        --ckpt-output-dir /path/to/checkpoints
+"""
+
+import os
+import sys
+import time
+from datetime import datetime
+import argparse
+
+from training_hub import sft
+
+
+def main():
+    #disable HPU backend autoloading
+    os.environ['PT_HPU_AUTOLOAD'] = '0'
+
+    parser = argparse.ArgumentParser(description=sample_name)
+
+    # Required parameters
+    parser.add_argument('--data-path', required=True,
+                       help='Path to training data (JSONL format)')
+    parser.add_argument('--ckpt-output-dir', required=True,
+                       help='Directory to save checkpoints')
+    parser.add_argument('--model-path', required=True,
+                       help='Model path or HuggingFace name')
+
+    # Optional overrides
+    parser.add_argument('--num-epochs', type=int, default=3,
+                       help='Number of epochs (default: 3)')
+    parser.add_argument('--effective-batch-size', type=int, default=128,
+                       help='effective batch size')
+    parser.add_argument('--max-seq-len', type=int, default=16384,
+                       help='Maximum sequence length')
+    parser.add_argument('--checkpoint-at-epoch', action='store_true',
+                       help='Store checkpoint after each epoch')
+    parser.add_argument('--max-tokens-per-gpu', type=int, default=32768,
+                       help='Max tokens per GPU')
+    parser.add_argument('--nproc-per-node', type=int, default=8,
+                       help='Number of GPUs')
+    parser.add_argument('--torch-compile', action='store_true', default=False,
+                       help='Enable torch.compile, hpu only')    
+    parser.add_argument('--num-chunks', type=int, default=1,
+                       help='Number of chunks to split dataset into for sequential training')
+
+    args = parser.parse_args()
+
+    # sample configuration
+    print(f"🚀 {sample_name}")
+    print("=" * 50)
+    print(f"Model: {args.model_path}")
+    print(f"Data: {args.data_path}")
+    print(f"Output: {args.ckpt_output_dir}")
+    print(f"GPUs: {args.nproc_per_node}")
+    print(f"Max tokens per GPU: {args.max_tokens_per_gpu:,}")
+    print()
+
+    # Training configuration optimized for Llama 3.1 8B Instruct
+    start_time = time.time()
+
+    try:
+        result = sft(
+            # Model and data
+            model_path=args.model_path,
+            data_path=args.data_path,
+            ckpt_output_dir=args.ckpt_output_dir,
+
+            # Training parameters
+            num_epochs=args.num_epochs,
+            effective_batch_size=args.effective_batch_size,
+            learning_rate=1e-5,                # Lower LR for instruct model
+            max_seq_len=args.max_seq_len,
+            max_tokens_per_gpu=args.max_tokens_per_gpu,
+
+            # Data processing
+            data_output_dir="/dev/shm",        # Use RAM disk for speed
+            warmup_steps=100,
+            save_samples=0,                    # 0 disables sample-based checkpointing, use epoch-based only
+
+            # Checkpointing
+            checkpoint_at_epoch=args.checkpoint_at_epoch,
+            accelerate_full_state_at_epoch=False, # Disable for smaller checkpoints (no auto-resumption)
+
+            # Single-node multi-GPU setup
+            nproc_per_node=args.nproc_per_node,
+            nnodes=1,
+            node_rank=0,
+            rdzv_id=101,
+            rdzv_endpoint="127.0.0.1:29500",
+
+            # HPU specific arguments
+            disable_flash_attn = True,
+            device = 'hpu',
+            torch_compile = args.torch_compile,
+            num_chunks = args.num_chunks,
+        )
+
+        end_time = time.time()
+        duration = end_time - start_time
+
+        print("=" * 50)
+        print("✅ Training completed successfully!")
+        print(f"⏱️  Duration: {duration/3600:.2f} hours")
+        print(f"📁 Checkpoints: {args.ckpt_output_dir}/hf_format/")
+
+    except Exception as e:
+        end_time = time.time()
+        duration = end_time - start_time
+
+        print("=" * 50)
+        print(f"❌ Training failed after {duration/60:.1f} minutes")
+        print(f"Error: {e}")
+        print()
+        print("💡 Try reducing --max-tokens-per-gpu if you see OOM errors")
+        sys.exit(1)
+
+
+if __name__ == "__main__":
+    main()