llama v2 with sharded weights

llama/convert.py

@@ -1,53 +1,59 @@
 # Copyright © 2023 Apple Inc.
 
 import argparse
-from itertools import starmap
+import collections
+import glob
+from pathlib import Path
 
 import numpy as np
 import torch
 
+SHARD_FIRST = ["wv", "wq", "wk", "w1", "w3", "output"]
+SHARD_SECOND = ["tok_embeddings", "wo", "w2"]
+SHARD_WEIGHTS = set(SHARD_FIRST + SHARD_SECOND)
 
-def map_torch_to_mlx(key, value):
-    if "tok_embedding" in key:
-        key = "embedding.weight"
 
-    elif "norm" in key:
-        key = key.replace("attention_norm", "norm1").replace("ffn_norm", "norm2")
+def shard_key(k):
+    keys = k.split(".")
+    if len(keys) < 2:
+        return None
+    return keys[-2]
 
-    elif "wq" in key or "wk" in key or "wv" in key or "wo" in key:
-        key = key.replace("wq", "query_proj")
-        key = key.replace("wk", "key_proj")
-        key = key.replace("wv", "value_proj")
-        key = key.replace("wo", "out_proj")
 
-    elif "w1" in key or "w2" in key or "w3" in key:
-        # The FFN is a separate submodule in PyTorch
-        key = key.replace("feed_forward.w1", "linear1")
-        key = key.replace("feed_forward.w3", "linear2")
-        key = key.replace("feed_forward.w2", "linear3")
-
-    elif "output" in key:
-        key = key.replace("output", "out_proj")
-
-    elif "rope" in key:
-        return None, None
-
-    return (
-        key,
-        value.numpy()
-        if value.dtype != torch.bfloat16
-        else value.to(torch.float32).numpy(),
-    )
+def unshard(k, v):
+    wn = shard_key(k)
+    if wn not in SHARD_WEIGHTS:
+        return v
+    elif wn in SHARD_FIRST:
+        axis = 0
+    elif wn in SHARD_SECOND:
+        axis = 1
+    else:
+        raise ValueError("Invalid weight name")
+    return np.concatenate(v, axis=axis)
 
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(description="Convert Llama weights to MLX")
-    parser.add_argument("torch_weights")
-    parser.add_argument("output_file")
+    parser.add_argument(
+        "--model_path",
+        help="Path to the Torch model. The MLX weights will also be saved there.",
+    )
     args = parser.parse_args()
 
-    state = torch.load(args.torch_weights, map_location=torch.device('cpu'))
-    np.savez(
-        args.output_file,
-        **{k: v for k, v in starmap(map_torch_to_mlx, state.items()) if k is not None}
-    )
+    model_path = Path(args.model_path)
+    torch_files = glob.glob(str(model_path / "consolidated.*.pth"))
+    weights = collections.defaultdict(list)
+    for wf in torch_files:
+        state = torch.load(wf, map_location=torch.device("cpu"))
+        for k, v in state.items():
+            v = v.to(torch.float16).numpy()
+            if shard_key(k) in SHARD_WEIGHTS:
+                weights[k].append(v)
+            else:
+                weights[k] = v
+
+    out_file = str(model_path / "weights.npz")
+    for k, v in weights.items():
+        weights[k] = unshard(k, v)
+    np.savez(out_file, **weights)