Adapt weight conversion script

examples/llama/convert_nanotron_to_hf.py

@@ -2,8 +2,11 @@
 Converts a nanotron model to HF format
 Command:
     torchrun --nproc_per_node=1 convert_nanotron_to_hf.py --checkpoint_path=nanotron-path --save_path=hf-path
+
+Modified for Petagraph usage.
 """
 
+import os
 import json
 from argparse import ArgumentParser
 from pathlib import Path
@@ -128,25 +131,55 @@ def check_converted_model_generation(save_path: Path):
     """Loads a huggingface model and tokenizer from `save_path` and
     performs a dummy text generation."""
 
-    tokenizer = AutoTokenizer.from_pretrained(save_path)
-    input_ids = tokenizer(TEST_PROMPT, return_tensors="pt")["input_ids"].cuda()
-    print("Inputs:", tokenizer.batch_decode(input_ids))
+    # tokenizer = AutoTokenizer.from_pretrained(save_path)
+    # input_ids = tokenizer(TEST_PROMPT, return_tensors="pt")["input_ids"].cuda()
+    # print("Inputs:", tokenizer.batch_decode(input_ids))
+
+    # Vocabulary is defined in the run_train.py right now
+    # We need to write a proper tokenizer when we're getting serious
+    VOCABULARY = {
+            "BOS": 0, "EOS": 1, "PAD": 2, "UNK": 3,
+            "A": 4, "C": 5, "G": 6, "T": 7
+        }
+    INV_VOCABULARY = {v: k for k, v in VOCABULARY.items()}
+
+    TEST_SEQUENCE = "ACGTACGT"
+    test_sequence = [VOCABULARY["BOS"]] + [VOCABULARY[char] for char in TEST_SEQUENCE]
+    input_ids = torch.tensor(test_sequence).unsqueeze(0).cuda()
+    print(f"Test sequence: {TEST_SEQUENCE}")
+    print(f"Test sequence (converted): {input_ids}")
 
     model = LlamaForCausalLM.from_pretrained(save_path).cuda().bfloat16()
     out = model.generate(input_ids, max_new_tokens=100)
-    print("Generation (converted): ", tokenizer.batch_decode(out))
+    decoded = [INV_VOCABULARY[token] for token in out[0].tolist()]
+    print("Generation: ", out)
+    print("Generation (decoded): ", decoded)
+
+    # print("Generation (converted): ", tokenizer.batch_decode(out))
+
 
 
 if __name__ == "__main__":
     parser = ArgumentParser(description="Convert Nanotron weights to HF format")
     parser.add_argument("--checkpoint_path", type=Path, default="llama-7b", help="Path to the checkpoint")
     parser.add_argument("--save_path", type=Path, default="llama-7b-hf", help="Path to save the HF model")
     parser.add_argument("--tokenizer_name", type=str, default="meta-llama/Llama-2-7b-chat-hf")
+    parser.add_argument("--set_single_gpu_env", action="store_true", help="Set the environment variables for single gpu", default=False)
     args = parser.parse_args()
 
+    print(f"Converting Nanotron model in {args.checkpoint_path} to HF format and saving to {args.save_path}")
+    if args.set_single_gpu_env:
+        print("Setting environment variables for single GPU")
+        torch.cuda.set_device(0)
+
+        os.environ["CUDA_VISIBLE_DEVICES"] = "0"
+        os.environ["MASTER_ADDR"] = "localhost"
+        os.environ["MASTER_PORT"] = "12355"
+        os.environ["WORLD_SIZE"] = "1"
+
     # Convert Nanotron model to HF format.
     convert_checkpoint_and_save(
-        checkpoint_path=args.checkpoint_path, save_path=args.save_path, tokenizer_name=args.tokenizer_name
+        checkpoint_path=args.checkpoint_path, save_path=args.save_path, tokenizer_name=None
     )
 
     # Check if the conversion was successful by generating some text.