import os
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import struct
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import sys
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import torch
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from transformers import AutoConfig, AutoTokenizer
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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 = (
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list(range(ord("!"), ord("~") + 1))
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+ list(range(ord("¡"), ord("¬") + 1))
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+ list(range(ord("®"), ord("ÿ") + 1))
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)
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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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def count_model_parts(dir_model: str) -> int:
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"""Returns the number of model parts in the model directory."""
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num_parts = 0
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for filename in os.listdir(dir_model):
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if filename.startswith("pytorch_model-"):
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num_parts += 1
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if num_parts > 0:
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print(f"Found {num_parts} model parts in {dir_model}")
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return num_parts
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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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# get number of model parts
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num_parts = count_model_parts(dir_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 = dir_model + "/ggml-model-" + ftype_str[ftype] + ".bin"
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tokenizer = AutoTokenizer.from_pretrained(dir_model, trust_remote_code=True)
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config = AutoConfig.from_pretrained(dir_model, trust_remote_code=True)
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hparams = config.to_dict()
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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["d_model"]))
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fout.write(struct.pack("i", hparams["max_seq_len"]))
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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", hparams["vocab_size"]))
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fout.write(struct.pack("f", hparams["attn_config"]["alibi_bias_max"]))
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fout.write(struct.pack("f", hparams["attn_config"]["clip_qkv"] or 0.0))
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fout.write(struct.pack("i", ftype))
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vocab_size = hparams["vocab_size"]
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encoder = tokenizer.vocab
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# Add added_tokens (special tokens) to the encoder
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encoder.update(tokenizer.get_added_vocab())
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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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counter = 0
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# sort by value
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for key in sorted(encoder, key=encoder.get):
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# workaround for key error when c not found
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text = ""
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for c in key:
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if c not in byte_decoder:
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text += c
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else:
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text += chr(byte_decoder[c])
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text = bytearray(text, encoding="utf-8")
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fout.write(struct.pack("i", len(text)))
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fout.write(text)
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counter += 1
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# Repeat last token until vocab_size
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while counter < vocab_size:
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fout.write(struct.pack("i", len(text)))
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fout.write(text)
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counter += 1
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if num_parts == 0:
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part_names = ("pytorch_model.bin",)
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else:
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part_names = (
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f"pytorch_model-{n:05}-of-{num_parts:05}.bin" for n in range(1, num_parts + 1)
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)
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for part_name in part_names:
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print(f"\n* Loading part: {part_name}")
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model_part = torch.load(f"{dir_model}/{part_name}", map_location="cpu")
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for name in model_part.keys():
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data = model_part[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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# default type is fp32
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ftype_cur = 0
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if ftype == 1 and name[-7:] == ".weight" and n_dims > 1:
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ftype_cur = 1
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data = data.to(dtype=torch.float16 if ftype_cur == 1 else torch.float32).numpy()
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print(
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"Processing variable: " + name + " with shape: ",
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data.shape,
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"->",
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data.dtype,
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)
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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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# release memory
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del model_part
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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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