[Distributed] Accept model names

dist_run.py

@@ -4,10 +4,11 @@
 # This source code is licensed under the license found in the
 # LICENSE file in the root directory of this source tree.
 
+import argparse
 import os
 from pathlib import Path
 from types import SimpleNamespace
-from typing import Any, Dict
+from typing import Any, Dict, Optional
 
 # Run command:
 # torchrun --nproc-per-node 4 dist_run.py
@@ -52,10 +53,12 @@
 
 logger = SingletonLogger.get_logger()
 
-MODEL_NAME = "Transformer-2-7b-chat-hf"
-NAME_TO_HF_MODEL_ID_AND_DTYPE = {
-    "Transformer-2-7b-chat-hf": ("meta-llama/Llama-2-7b-chat-hf", torch.float16),
-    "Meta-Llama-3-8B": ("meta-llama/Meta-Llama-3-8B-Instruct", torch.bfloat16),
+# Using model name to identify the model to load, for example "llama2-7b-chat".
+# You can change it to other values listed below.
+# For details on the name-to-distribution mapping, see README.md or models.json.
+NAME_TO_DISTRIBUTION_AND_DTYPE = {
+    "llama2-7b-chat": ("meta-llama/Llama-2-7b-chat-hf", torch.float16),
+    "llama3": ("meta-llama/Meta-Llama-3-8B-Instruct", torch.bfloat16),
 }
 CACHE_PRECISION = torch.bfloat16
 
@@ -78,8 +81,19 @@ def dict_to_args(dictionary: Dict[str, Any]) -> SimpleNamespace:
 
 
 def _build_chat_tokenizer(
-    model_base_name: str = "llama3",
+    model_name: str,
+    model_base_name: Optional[str] = None,
 ) -> SentencePieceProcessor | TiktokenTokenizer:
+    """Builds a tokenizer for the given model name."""
+    # Try to infer the model base name from the model name:
+    # e.g. "llama2-7b-chat" -> "llama2"
+    if model_base_name is None:
+        model_base_name = model_name.split("-")[0]
+        logger.info(
+            f"Using model base name '{model_base_name}' to build tokenizer. "
+            "If not found, please specify it using the `model_base_name` argument."
+        )
+
     # Create base args for tokenizer
     default_model_dir = Path(
         os.getenv("TORCHCHAT_MODELDIR", "~/.torchchat/model-cache")
@@ -100,12 +114,12 @@ def _build_chat_tokenizer(
     return tokenizer
 
 
-def _load_model_weights(stage_module, hf_model_name, device, model_config):
+def _load_model_weights(stage_module, distribution, device, model_config):
     """Load the weights from the safetensor file(s) into the model stage.
     Model config is needed b/c we permute wq and wk weights based on attn heads.
     """
 
-    weight_map, weight_path, key_map = get_hf_weight_map_and_path(hf_model_name)
+    weight_map, weight_path, key_map = get_hf_weight_map_and_path(distribution)
 
     num_loaded_weights, num_missing_weights = load_safetensor_weights(
         stage_module,
@@ -127,32 +141,31 @@ def _cleanup():
     dist.destroy_process_group()
 
 
-def main():
+def main(args):
+    model_name = args.model_name
+    pp_degree = args.pp
+
     rank, world_size = _init_distributed()
 
     gpu_memory_monitor = GPUMemoryMonitor("cuda")
     logger.info(f"{color.yellow} {gpu_memory_monitor.get_device_info()}{color.reset}")
 
-    config = ModelArgs.from_name(MODEL_NAME).transformer_args['text']
-    logger.info(f"Chat Model Config: {config}")
+    distribution, model_dtype = NAME_TO_DISTRIBUTION_AND_DTYPE[model_name]
+    logger.info(f"Using HF model weights from {distribution} and dtype {model_dtype}")
 
-    tokenizer = _build_chat_tokenizer()
-    logger.info(f"built tokenizer {tokenizer=}")
+    config = ModelArgs.from_name(distribution).transformer_args['text']
+    logger.info(f"Chat Model Config: {config}")
 
-    hf_model_name, model_dtype = NAME_TO_HF_MODEL_ID_AND_DTYPE[MODEL_NAME]
-    logger.info(f"Using HF model weights from {hf_model_name} and dtype {model_dtype}")
+    tokenizer = _build_chat_tokenizer(model_name)
 
     set_precision(CACHE_PRECISION)
     logger.info(f"Using cache precision {CACHE_PRECISION}")
 
-    hf_config = get_hf_config_file(hf_model_name)
+    hf_config = get_hf_config_file(distribution)
     if hf_config is None:
-        raise ValueError(f"Config file not found for model id {hf_model_name}")
-    logger.info(f"Using HF model weights from {hf_model_name}")
+        raise ValueError(f"Config file not found for model id {distribution}")
 
-    # Assuming 2 pipeline stages, feel free to change this as long as the
-    # asserts are satisfied
-    pp_degree = 2
+    # Validate pipeline degree
     assert world_size % pp_degree == 0
     assert config.n_layers % pp_degree == 0
 
@@ -182,7 +195,8 @@ def main():
 
     # Distribute model on TP mesh
     model.distribute(tp_mesh)
-    logger.info(f"Model: {model}")
+    if rank == 0:
+        logger.info(f"Model: {model}")
 
     mbs = 2  # number of micro-batches
     mb_size = 1  # micro-batch size
@@ -200,7 +214,7 @@ def main():
     # Load weights
     logger.info(f"Loading weights for {pp_rank=} on {device=}")
     with TrackTime("cuda") as timer:
-        _load_model_weights(model, hf_model_name, device=device, model_config=config)
+        _load_model_weights(model, distribution, device=device, model_config=config)
     logger.info(
         f"{color.green}Total weight loading time: {timer.get_time()} {timer.unit} for stage {rank}{color.reset}"
     )
@@ -253,7 +267,7 @@ def main():
 
     with torch.no_grad():  # .inference_mode():
         if pp_rank == 0:
-            schedule.step(input_ids)
+            output = schedule.step(input_ids)
         else:
             output = schedule.step()
 
@@ -274,4 +288,9 @@ def main():
 
 
 if __name__ == "__main__":
-    main()
+    parser = argparse.ArgumentParser()
+    parser.add_argument("model_name", type=str, help="Name of the model to load", choices=NAME_TO_DISTRIBUTION_AND_DTYPE.keys())
+    parser.add_argument("--pp", type=int, default=1, help="Pipeline parallel degree")
+    args = parser.parse_args()
+
+    main(args)