optimized

added parallel file processing, add faster tensor ops

inference/convert.py

@@ -1,12 +1,14 @@
 import os
 import shutil
+import mmap
+import threading
 from argparse import ArgumentParser
-from glob import glob
+from pathlib import Path
 from tqdm import tqdm, trange
-
+from concurrent.futures import ThreadPoolExecutor, as_completed
 import torch
 from safetensors.torch import safe_open, save_file
-
+from collections import defaultdict
 
 mapping = {
     "embed_tokens": ("embed", 0),
@@ -29,40 +31,35 @@ mapping = {
     "scale": ("scale", None),
 }
 
+# Thread Lock for Safe Dictionary Access
+state_lock = threading.Lock()
 
-def main(hf_ckpt_path, save_path, n_experts, mp):
-    """
-    Converts and saves model checkpoint files into a specified format.
+def fast_copy(src: Path, dst: Path):
+    """Efficiently copies large files using shutil for optimal memory usage"""
+    if dst.exists():
+        dst.unlink()  # Remove file if it already exists
+    if src.stat().st_size < 10 * 1024 * 1024:  # If file < 10MB, use shutil
+        shutil.copyfile(src, dst)
+    else:
+        with open(src, "rb") as f_src, open(dst, "wb") as f_dst:
+            shutil.copyfileobj(f_src, f_dst, length=16*1024*1024)
 
-    Args:
-        hf_ckpt_path (str): Path to the directory containing the input checkpoint files.
-        save_path (str): Path to the directory where the converted checkpoint files will be saved.
-        n_experts (int): Total number of experts in the model.
-        mp (int): Model parallelism factor.
+def copy_token_file(file_path, save_path):
+    """Helper function for parallel copying of token files"""
+    fast_copy(file_path, Path(save_path) / file_path.name)
 
-    Returns:
-        None
-    """
-    torch.set_num_threads(8)
-    n_local_experts = n_experts // mp
-    state_dicts = [{} for _ in range(mp)]
-
-    for file_path in tqdm(glob(os.path.join(hf_ckpt_path, "*.safetensors"))):
-        with safe_open(file_path, framework="pt", device="cpu") as f:
-            for name in f.keys():
-                if "model.layers.61" in name:
-                    continue
+def inner_safe_open(name: str, f, mp, state_dicts, n_local_experts): 
+    """Processes tensor files and maps keys correctly"""
+    with torch.no_grad():
         param: torch.Tensor = f.get_tensor(name)
-                if name.startswith("model."):
-                    name = name[len("model."):]
-                name = name.replace("self_attn", "attn")
-                name = name.replace("mlp", "ffn")
-                name = name.replace("weight_scale_inv", "scale")
-                name = name.replace("e_score_correction_bias", "bias")
+        name = name[len("model."):] if name.startswith("model.") else name
+        name = name.replace("self_attn", "attn").replace("mlp", "ffn")
+        name = name.replace("weight_scale_inv", "scale").replace("e_score_correction_bias", "bias")
         key = name.split(".")[-2]
         assert key in mapping
         new_key, dim = mapping[key]
         name = name.replace(key, new_key)
+
         for i in range(mp):
             new_param = param
             if "experts" in name and "shared_experts" not in name:
@@ -70,20 +67,53 @@ def main(hf_ckpt_path, save_path, n_experts, mp):
                 if idx < i * n_local_experts or idx >= (i + 1) * n_local_experts:
                     continue
             elif dim is not None:
-                        assert param.size(dim) % mp == 0
                 shard_size = param.size(dim) // mp
                 new_param = param.narrow(dim, i * shard_size, shard_size).contiguous()
+
+            # Lock to avoid race conditions
+            with state_lock:
                 state_dicts[i][name] = new_param
 
-    os.makedirs(save_path, exist_ok=True)
+def process_file(file_path, mp, state_dicts, n_local_experts):
+    """Processes a single safetensor file"""
+    with safe_open(file_path, framework="pt", device="cpu") as f:
+        for name in f.keys():
+            if "model.layers.61" not in name:
+                inner_safe_open(name, f, mp, state_dicts, n_local_experts)
 
-    for i in trange(mp):
+def main(hf_ckpt_path, save_path, n_experts, mp):
+    """Converts and saves model checkpoint files into a specified format."""
+    n_local_experts = n_experts // mp
+
+    # Use defaultdict to prevent key errors in multi-threading
+    state_dicts = [defaultdict(dict) for _ in range(mp)]
+    
+    file_list = list(Path(hf_ckpt_path).glob("*.safetensors"))
+    token_files = list(Path(hf_ckpt_path).glob("*token*"))
+
+    Path(save_path).mkdir(parents=True, exist_ok=True)
+
+    # Parallel Processing with ThreadPoolExecutor
+    with ThreadPoolExecutor() as executor:
+        futures = {
+            executor.submit(process_file, file, mp, state_dicts, n_local_experts): file
+            for file in file_list
+        }
+        for future in tqdm(as_completed(futures), desc="Processing safetensors", total=len(file_list)):
+            future.result()  # Ensure exceptions are raised
+
+    # Save processed model shards
+    for i in trange(mp, desc="Saving model shards"):
         save_file(state_dicts[i], os.path.join(save_path, f"model{i}-mp{mp}.safetensors"))
 
-    for file_path in glob(os.path.join(hf_ckpt_path, "*token*")):
-        new_file_path = os.path.join(save_path, os.path.basename(file_path))
-        shutil.copyfile(file_path, new_file_path)
-
+    # Parallel Token File Copying
+    with ThreadPoolExecutor() as executor:
+        futures = {
+            executor.submit(copy_token_file, file, save_path): file
+            for file in token_files
+        }
+        for future in tqdm(as_completed(futures), desc="Copying token files", total=len(token_files)):
+            future.result()  # Ensure exceptions are raised
 
 if __name__ == "__main__":
     parser = ArgumentParser()
@@ -92,5 +122,6 @@ if __name__ == "__main__":
     parser.add_argument("--n-experts", type=int, required=True)
     parser.add_argument("--model-parallel", type=int, required=True)
     args = parser.parse_args()
-    assert args.n_experts % args.model_parallel == 0
+
+    assert args.n_experts % args.model_parallel == 0, "n_experts must be divisible by model_parallel"
     main(args.hf_ckpt_path, args.save_path, args.n_experts, args.model_parallel)