Move llama in a cargo-examples directory.

1 files changed, 68 insertions, 0 deletions

diff --git a/candle-examples/examples/llama/convert_checkpoint.py b/candle-examples/examples/llama/convert_checkpoint.py
new file mode 100644
index 00000000..245c167c
--- /dev/null
+++ b/candle-examples/examples/llama/convert_checkpoint.py
@@ -0,0 +1,68 @@
+# Adapted from https://github.com/Lightning-AI/lit-llama/blob/main/scripts/convert_checkpoint.py
+import sys
+import torch
+import numpy as np
+from typing import Dict
+from pathlib import Path
+
+def tr(v):
+    return np.ascontiguousarray(np.transpose(v))
+
+def convert_state_dict(state_dict: Dict[str, torch.Tensor], dtype: torch.dtype = torch.float32) -> Dict[str, torch.Tensor]:
+    print("start conv")
+
+    def get_and_remove(key, transpose=False):
+        v = state_dict[key].to(dtype).numpy()
+        if transpose:
+            v = tr(v)
+        del state_dict[key]
+        return v
+
+    converted = {}
+    converted["transformer.wte.weight"] = get_and_remove("tok_embeddings.weight")
+    converted["lm_head.weight"] = get_and_remove("output.weight", transpose=True)
+    converted["transformer.ln_f.scale"] = get_and_remove("norm.weight")
+
+    for layer_idx in sorted(set([k.split(".")[1] for k in state_dict if k.startswith("layers")])):
+        print(layer_idx)
+
+        # attention
+        # the wq, wk, wv from the FB model are stacked in our model as c_attn
+        converted[f"transformer.h.{layer_idx}.attn.c_attn.weight"] = tr(np.concatenate(
+            (
+                get_and_remove(f"layers.{layer_idx}.attention.wq.weight"),
+                get_and_remove(f"layers.{layer_idx}.attention.wk.weight"),
+                get_and_remove(f"layers.{layer_idx}.attention.wv.weight"),
+            )
+        ))
+        converted[f"transformer.h.{layer_idx}.attn.c_proj.weight"] = tr(get_and_remove(
+            f"layers.{layer_idx}.attention.wo.weight"
+            ))
+        # mlp
+        converted[f"transformer.h.{layer_idx}.mlp.c_fc1.weight"] = get_and_remove(
+            f"layers.{layer_idx}.feed_forward.w1.weight", transpose=True,
+            )
+        converted[f"transformer.h.{layer_idx}.mlp.c_proj.weight"] = get_and_remove(
+            f"layers.{layer_idx}.feed_forward.w2.weight", transpose=True,
+            )
+        converted[f"transformer.h.{layer_idx}.mlp.c_fc2.weight"] = get_and_remove(
+            f"layers.{layer_idx}.feed_forward.w3.weight", transpose=True,
+            )
+        # rms norm
+        converted[f"transformer.h.{layer_idx}.rms_1.scale"] = get_and_remove(f"layers.{layer_idx}.attention_norm.weight")
+        converted[f"transformer.h.{layer_idx}.rms_2.scale"] = get_and_remove(f"layers.{layer_idx}.ffn_norm.weight")
+    return converted
+
+def convert_weights(llama_ckpt, *, output_npz: Path = Path("llama.npz"), dtype: str = "float32") -> None:
+    dt = getattr(torch, dtype, None)
+    if not isinstance(dt, torch.dtype):
+        raise ValueError(f"{dtype} is not a valid dtype.")
+    checkpoint = torch.load(llama_ckpt, map_location="cpu")
+    converted = convert_state_dict(checkpoint, dtype=dt)
+    del checkpoint
+    np.savez(output_npz, **converted)
+
+if __name__ == "__main__":
+    if len(sys.argv) != 2:
+        raise ValueError(f"usage: convert_checkpoint.py ..../LLaMA/7B/consolidated.00.pth")
+    convert_weights(sys.argv[1])