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# 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])
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