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114 lines
3.1 KiB
Python
114 lines
3.1 KiB
Python
from pathlib import Path
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import sys
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import struct
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import json
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import numpy as np
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import sentencepiece.sentencepiece_model_pb2 as model
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if len(sys.argv) < 3:
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print("Usage: convert-h5-to-ggml.py dir-model [use-f32]\n")
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print(" ftype == 0 -> float32")
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print(" ftype == 1 -> float16")
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sys.exit(1)
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# output in the same directory as the model
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dir_model = sys.argv[1]
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fname_out = sys.argv[1] + "/ggml-replit-code-v1-3b.bin"
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with open(dir_model + "/config.json", "r", encoding="utf-8") as f:
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hparams = json.load(f)
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sp_proto = model.ModelProto()
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sp_proto.ParseFromString(open(Path(sys.argv[1]) / "spiece.model", "rb").read())
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# possible data types
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# ftype == 0 -> float32
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# ftype == 1 -> float16
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#
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# map from ftype to string
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ftype_str = ["f32", "f16"]
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ftype = 1
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if len(sys.argv) > 2:
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ftype = int(sys.argv[2])
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if ftype < 0 or ftype > 1:
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print("Invalid ftype: " + str(ftype))
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sys.exit(1)
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fname_out = sys.argv[1] + "/ggml-replit-code-v1-3b-" + ftype_str[ftype] + ".bin"
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tokenizer = AutoTokenizer.from_pretrained(dir_model, trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(
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dir_model, low_cpu_mem_usage=True, trust_remote_code=True
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)
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# print (model)
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# print(tokenizer.encode('I believe the meaning of life is'))
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list_vars = model.state_dict()
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for name in list_vars.keys():
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print(name, list_vars[name].shape, list_vars[name].dtype)
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fout = open(fname_out, "wb")
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print(hparams)
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fout.write(struct.pack("i", 0x7265706c)) # magic: repl in hex
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fout.write(struct.pack("i", hparams["vocab_size"]))
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fout.write(struct.pack("i", hparams["max_seq_len"]))
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fout.write(struct.pack("i", hparams["d_model"]))
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fout.write(struct.pack("i", hparams["n_heads"]))
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fout.write(struct.pack("i", hparams["n_layers"]))
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fout.write(struct.pack("i", ftype))
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# TODO: temporary hack to not deal with implementing the tokenizer
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for piece in sp_proto.pieces:
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encoded_piece = piece.piece.encode("utf-8")
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fout.write(struct.pack("i", len(encoded_piece)))
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fout.write(encoded_piece)
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fout.write(struct.pack("f", piece.score))
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for name in list_vars.keys():
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data = list_vars[name].squeeze().numpy()
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print("Processing variable: " + name + " with shape: ", data.shape)
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n_dims = len(data.shape)
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# ftype == 0 -> float32, ftype == 1 -> float16
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ftype_cur = 0
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if ftype != 0:
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if name[-7:] == ".weight" and n_dims == 2:
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print(" Converting to float16")
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data = data.astype(np.float16)
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ftype_cur = 1
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else:
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print(" Converting to float32")
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data = data.astype(np.float32)
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ftype_cur = 0
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else:
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if data.dtype != np.float32:
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print(" Converting to float32")
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data = data.astype(np.float32)
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ftype_cur = 0
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# header
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str = name.encode("utf-8")
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fout.write(struct.pack("iii", n_dims, len(str), ftype_cur))
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for i in range(n_dims):
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fout.write(struct.pack("i", data.shape[n_dims - 1 - i]))
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fout.write(str)
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# data
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data.tofile(fout)
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fout.close()
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print("Done. Output file: " + fname_out)
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print("")
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