modularize

convert-pth-to-ggml.py

@@ -16,7 +16,6 @@
 # At the start of the ggml file we write the model parameters
 # and vocabulary.
 #
-
 import argparse
 import sys
 import json
@@ -25,108 +24,88 @@ import numpy as np
 import torch
 from sentencepiece import SentencePieceProcessor
 
+def parse_args():
+
     parser = argparse.ArgumentParser(description='Convert a LLaMA model checkpoint to a ggml compatible file')
     parser.add_argument('dir_model', help='directory containing the model checkpoint')
     parser.add_argument('ftype', type=int, choices=[0, 1], default=1, help='file type (0: float32, 1: float16)')
-args = parser.parse_args()
+    return parser.parse_args()
-
-# output in the same directory as the model
-dir_model = args.dir_model
-ftype = args.ftype
-
-fname_hparams   = f"{dir_model}/params.json"
-fname_tokenizer = f"{dir_model}/../tokenizer.model"
 
 def get_n_parts(dim):
+    
     mappings = {4096: 1, 5120: 2, 6656: 4, 8192: 8}
     n_parts = mappings.get(dim)
     if n_parts is None:
         print(f"Invalid dim: {dim}")
         sys.exit(1)
+
+    print(f"n_parts = {n_parts}\n")
     return n_parts
 
-# map from ftype to string
+def load_hparams_and_tokenizer(dir_model):
-ftype_str = ["f32", "f16"]
+    
+    fname_hparams = f"{dir_model}/params.json"
+    fname_tokenizer = f"{dir_model}/../tokenizer.model"
 
     with open(fname_hparams, "r") as f:
         hparams = json.load(f)
+        print(hparams)
 
     tokenizer = SentencePieceProcessor(fname_tokenizer)
-
     hparams.update({"vocab_size": tokenizer.vocab_size()})
 
-n_parts = get_n_parts(hparams["dim"])
+    return hparams, tokenizer
 
-print(hparams)
+def write_header(fout, hparams, ftype):
-print(f"n_parts = {n_parts}\n")
     
-for p in range(n_parts):
+    keys = ["vocab_size", "dim", "multiple_of", "n_heads", "n_layers"]
-    print(f"Processing part {p}\n")
+    values = [
+        0x67676d6c,  # magic: ggml in hex
+        *[hparams[key] for key in keys],
+        hparams["dim"] // hparams["n_heads"],  # rot (obsolete)
+        ftype
+    ]
+    fout.write(struct.pack("i" * len(values), *values))
 
-    #fname_model = args[1] + "/consolidated.00.pth"
+def write_tokens(fout, tokenizer):
-    fname_model = f"{dir_model}/consolidated.0{p}.pth"
-    fname_out = f"{dir_model}/ggml-model-{ftype_str[ftype]}.bin{'' if p == 0 else '.' + str(p)}"
 
-    model = torch.load(fname_model, map_location="cpu")
-
-    with open(fname_out, "wb") as fout:
-
-        fout.write(struct.pack("i", 0x67676d6c)) # magic: ggml in hex
-        fout.write(struct.pack("i", hparams["vocab_size"]))
-        fout.write(struct.pack("i", hparams["dim"]))
-        fout.write(struct.pack("i", hparams["multiple_of"]))
-        fout.write(struct.pack("i", hparams["n_heads"]))
-        fout.write(struct.pack("i", hparams["n_layers"]))
-        fout.write(struct.pack("i", hparams["dim"] // hparams["n_heads"])) # rot (obsolete)
-        fout.write(struct.pack("i", ftype))
-
-        # Is this correct??
     for i in range(32000):
         if tokenizer.is_unknown(i):
-                # "<unk>" token (translated as ??)
             text = " \u2047 ".encode("utf-8")
-                fout.write(struct.pack("i", len(text)))
-                fout.write(text)
         elif tokenizer.is_control(i):
-                # "<s>"/"</s>" tokens
+            text = b""
-                fout.write(struct.pack("i", 0))
         elif tokenizer.is_byte(i):
-                # "<U+XX>" tokens (which may be invalid UTF-8)
             piece = tokenizer.id_to_piece(i)
             if len(piece) != 6:
                 print(f"Invalid token: {piece}")
                 sys.exit(1)
             byte_value = int(piece[3:-1], 16)
-                fout.write(struct.pack("i", 1))
+            text = struct.pack("B", byte_value)
-                fout.write(struct.pack("B", byte_value))
         else:
-                # normal token. Uses U+2581 (LOWER ONE EIGHTH BLOCK) to represent spaces.
             text = tokenizer.id_to_piece(i).replace("\u2581", " ").encode("utf-8")
         fout.write(struct.pack("i", len(text)))
         fout.write(text)
 
-        for name, data in model.items():
+def process_and_write_variables(fout, model, ftype):
-            shape = data.shape
+
+    for name, data in model.items():
     
-            # skip layers.X.attention.inner_attention.rope.freqs
         if name.endswith("freqs"):
             continue
         
+        shape = data.shape
+        
         print(f"Processing variable: {name} with shape: {shape} and type: {data.dtype}\n")
         
-            #data = tf.train.load_variable(dir_model, name).squeeze()
         data = np.squeeze(data)
-            n_dims = len(data.shape)
+        n_dims = len(shape)
 
         # for efficiency - transpose some matrices
         # "model/h.*/attn/c_attn/w"
         # "model/h.*/attn/c_proj/w"
         # "model/h.*/mlp/c_fc/w"
         # "model/h.*/mlp/c_proj/w"
-            #if name[-14:] == "/attn/c_attn/w" or \
+        #if name.endswith(("/attn/c_attn/w", "/attn/c_proj/w", "/mlp/c_fc/w", "/mlp/c_proj/w")):
-            #   name[-14:] == "/attn/c_proj/w" or \
-            #   name[-11:] == "/mlp/c_fc/w" or \
-            #   name[-13:] == "/mlp/c_proj/w":
         #    print("Transposing")
         #    data = data.transpose()
 
@@ -139,15 +118,40 @@ for p in range(n_parts):
 
         # header
         sname = name.encode('utf-8')
-            fout.write(struct.pack("iii", n_dims, len(sname), ftype_cur))
+        fout.write(struct.pack("iii", len(data.shape), len(sname), ftype_cur))
         for dim in reversed(data.shape):
             fout.write(struct.pack("i", dim))
-
         fout.write(sname)
         
         # data
         data.tofile(fout)
 
-        del model
+def main():
 
+    args = parse_args()
+    dir_model = args.dir_model
+    ftype = args.ftype
+    ftype_str = ["f32", "f16"]
+
+    hparams, tokenizer = load_hparams_and_tokenizer(dir_model)
+    n_parts = get_n_parts(hparams["dim"])
+
+    for p in range(n_parts):
+
+        print(f"Processing part {p}\n")
+        
+        fname_model = f"{dir_model}/consolidated.0{p}.pth"
+        fname_out = f"{dir_model}/ggml-model-{ftype_str[ftype]}.bin{'' if p == 0 else '.' + str(p)}"
+
+        model = torch.load(fname_model, map_location="cpu")
+
+        with open(fname_out, "wb") as fout:
+            write_header(fout, hparams, ftype)
+            write_tokens(fout, tokenizer)
+            process_and_write_variables(fout, model, ftype)
+
+        del model
         print(f"Done. Output file: {fname_out}, (part {p})\n")
+
+if __name__ == "__main__":
+    main()