mirror of
https://github.com/nomic-ai/gpt4all.git
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103 lines
2.9 KiB
Python
103 lines
2.9 KiB
Python
import sys
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import struct
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import json
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import torch
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import numpy as np
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from transformers import AutoModel, AutoTokenizer
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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-model.bin"
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with open(dir_model + "/tokenizer.json", "r", encoding="utf-8") as f:
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encoder = json.load(f)
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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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with open(dir_model + "/vocab.txt", "r", encoding="utf-8") as f:
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vocab = f.readlines()
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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-model-" + ftype_str[ftype] + ".bin"
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tokenizer = AutoTokenizer.from_pretrained(dir_model)
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model = AutoModel.from_pretrained(dir_model, low_cpu_mem_usage=True)
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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", 0x62657274)) # magic: ggml 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_position_embeddings"]))
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fout.write(struct.pack("i", hparams["hidden_size"]))
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fout.write(struct.pack("i", hparams["intermediate_size"]))
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fout.write(struct.pack("i", hparams["num_attention_heads"]))
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fout.write(struct.pack("i", hparams["num_hidden_layers"]))
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fout.write(struct.pack("i", ftype))
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for i in range(hparams["vocab_size"]):
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text = vocab[i][:-1] # strips newline at the end
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#print(f"{i}:{text}")
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data = bytes(text, 'utf-8')
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fout.write(struct.pack("i", len(data)))
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fout.write(data)
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for name in list_vars.keys():
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data = list_vars[name].squeeze().numpy()
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if name in ['embeddings.position_ids', 'pooler.dense.weight', 'pooler.dense.bias']:
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continue
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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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if ftype == 1 and 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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l_type = 1
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else:
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l_type = 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), l_type))
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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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