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133 lines
4.0 KiB
133 lines
4.0 KiB
# Convert a model checkpoint to a ggml compatible file
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#
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# Load the model using TensorFlow.
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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 tensorflow as tf
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# ref: https://github.com/openai/gpt-2/blob/master/src/encoder.py
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def bytes_to_unicode():
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"""
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Returns list of utf-8 byte and a corresponding list of unicode strings.
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The reversible bpe codes work on unicode strings.
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This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
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When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
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This is a signficant percentage of your normal, say, 32K bpe vocab.
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To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
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And avoids mapping to whitespace/control characters the bpe code barfs on.
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"""
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bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1))
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cs = bs[:]
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n = 0
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for b in range(2**8):
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if b not in bs:
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bs.append(b)
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cs.append(2**8+n)
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n += 1
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cs = [chr(n) for n in cs]
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return dict(zip(bs, cs))
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if len(sys.argv) < 2:
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print("Usage: convert-ckpt-to-ggml.py dir-model [use-f32]\n")
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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 + "/encoder.json", "r") as f:
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encoder = json.load(f)
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with open(dir_model + "/hparams.json", "r") as f:
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hparams = json.load(f)
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# use 16-bit or 32-bit floats
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use_f16 = True
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if len(sys.argv) > 2:
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use_f16 = False
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fname_out = sys.argv[1] + "/ggml-model-f32.bin"
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list_vars = tf.train.list_variables(dir_model)
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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["n_vocab"]))
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fout.write(struct.pack("i", hparams["n_ctx"]))
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fout.write(struct.pack("i", hparams["n_embd"]))
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fout.write(struct.pack("i", hparams["n_head"]))
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fout.write(struct.pack("i", hparams["n_layer"]))
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fout.write(struct.pack("i", use_f16))
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byte_encoder = bytes_to_unicode()
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byte_decoder = {v:k for k, v in byte_encoder.items()}
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fout.write(struct.pack("i", len(encoder)))
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for key in encoder:
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text = bytearray([byte_decoder[c] for c in key])
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fout.write(struct.pack("i", len(text)))
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fout.write(text)
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for name, shape in list_vars:
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print("Processing variable: " + name + " with shape: ", shape)
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data = tf.train.load_variable(dir_model, name).squeeze()
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n_dims = len(data.shape);
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# ftype == 0 -> float32, ftype == 1 -> float16
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ftype = 0;
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if use_f16:
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# match name:
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# "model/wte"
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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 == "model/wte" or name[-2:] == "/w":
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print(" Converting to float16")
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data = data.astype(np.float16)
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ftype = 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 = 0
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# for efficiency - transpose the projection matrices
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if name[-13:] == "/mlp/c_proj/w":
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print(" Transposing")
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data = data.transpose()
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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))
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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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