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137 lines
3.7 KiB
137 lines
3.7 KiB
# Convert a LLaMA model checkpoint to a ggml compatible file
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#
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# Load the model using Torch
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# Iterate over all variables and write them to a binary file.
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#
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# For each variable, write the following:
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# - Number of dimensions (int)
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# - Name length (int)
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# - Dimensions (int[n_dims])
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# - Name (char[name_length])
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# - Data (float[n_dims])
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#
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# By default, the bigger matrices are converted to 16-bit floats.
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# This can be disabled by adding the "use-f32" CLI argument.
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#
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# At the start of the ggml file we write the model parameters
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# and vocabulary.
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#
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import sys
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import json
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import struct
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import numpy as np
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import torch
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from sentencepiece import SentencePieceProcessor
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if len(sys.argv) < 3:
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print("Usage: convert-ckpt-to-ggml.py dir-model ftype\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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fname_hparams = sys.argv[1] + "/params.json"
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fname_model = sys.argv[1] + "/consolidated.00.pth"
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fname_tokenizer = sys.argv[1] + "/../tokenizer.model"
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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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with open(fname_hparams, "r") as f:
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hparams = json.load(f)
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tokenizer = SentencePieceProcessor(fname_tokenizer)
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hparams.update({"vocab_size": tokenizer.vocab_size()})
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print(hparams)
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model = torch.load(fname_model, map_location="cpu")
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fout = open(fname_out, "wb")
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fout.write(struct.pack("i", 0x67676d6c)) # 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["dim"]))
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fout.write(struct.pack("i", hparams["multiple_of"]))
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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", 64)) # rot
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fout.write(struct.pack("i", ftype))
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# Is this correct??
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for i in range(32000):
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# TODO: this is probably wrong - not sure how this tokenizer works
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text = tokenizer.decode([29889, i]).encode('utf-8')
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# remove the first byte (it's always '.')
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text = text[1:]
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fout.write(struct.pack("i", len(text)))
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fout.write(text)
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for k, v in model.items():
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name = k
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shape = v.shape
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# skip layers.X.attention.inner_attention.rope.freqs
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if name[-5:] == "freqs":
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continue
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print("Processing variable: " + name + " with shape: ", shape, " and type: ", v.dtype)
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#data = tf.train.load_variable(dir_model, name).squeeze()
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data = v.numpy().squeeze()
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n_dims = len(data.shape);
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# for efficiency - transpose some matrices
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# "model/h.*/attn/c_attn/w"
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# "model/h.*/attn/c_proj/w"
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# "model/h.*/mlp/c_fc/w"
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# "model/h.*/mlp/c_proj/w"
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#if name[-14:] == "/attn/c_attn/w" or \
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# name[-14:] == "/attn/c_proj/w" or \
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# name[-11:] == "/mlp/c_fc/w" or \
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# name[-13:] == "/mlp/c_proj/w":
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# print(" Transposing")
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# data = data.transpose()
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dshape = data.shape
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# default type is fp16
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ftype_cur = 1
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if ftype == 0 or n_dims == 1:
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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", dshape[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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