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feat: mpt convert from hf to ggml
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Zach Nussbaum
Adam Treat
parent
285e57ca68
commit
e3f17c8e82
176
convert_hf_to_ggml.py
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176
convert_hf_to_ggml.py
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# Convert Hugging Face fine-tuned bloom-like models to ggml format
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#
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# Usage:
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#
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# python3 models/convert-h5-to-ggml.py
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#
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# This script is similar to "convert-pt-to-ggml.py"
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#
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import io
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import os
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import sys
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import struct
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import json
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import code
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import torch
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import numpy as np
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from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig, BloomForCausalLM
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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 significant 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) < 3:
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print("Usage: python convert-hf-to-ggml.py model_name dir-output [use-f32]")
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print(" model_name: name of the model to convert. Example: 'bigscience/bloomz-560m'")
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print(" dir-output: directory where the output file will be written")
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print(" use-f32: if present, use float32 instead of float16")
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sys.exit(1)
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model_name = sys.argv[1]
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dir_out = sys.argv[2]
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# make sure the output directory exists
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os.makedirs(dir_out, exist_ok=True)
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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) > 3:
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ftype = 0
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tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
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config = AutoConfig.from_pretrained(model_name, trust_remote_code=True)
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hparams = config.to_dict()
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print("Loading model: ", model_name)
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model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True, config=config, torch_dtype=torch.float16 if ftype == 1 else torch.float32, low_cpu_mem_usage=True)
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print("Model loaded: ", model_name)
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fname_out = dir_out + f"/ggml-model-{model_name.split('/')[-1]}-{ftype_str[ftype]}.bin"
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fout = open(fname_out, "wb")
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hparams["multiple_of"] = 1
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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["seq_length"]))
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fout.write(struct.pack("i", hparams["d_model"]))
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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", ftype))
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# # Is this correct??
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# dot_token = tokenizer.encode(".")[0]
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# for i in range(hparams["vocab_size"]):
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# text = tokenizer.decode([i]).encode('utf-8')
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# fout.write(struct.pack("i", len(text)))
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# fout.write(text)
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list_vars = model.state_dict()
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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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# we don't need these
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if name.endswith("attn.masked_bias") or name.endswith(".attn.bias"):
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print(" Skipping variable: " + name)
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continue
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if "Wqkv.weight" in name:
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# chunk qkv
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query, key, value = np.split(data, 3, axis=0)
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new_name = name.split("Wqkv.weight")[0]
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for (data, name) in [(query, new_name + "q_proj_w"), (key, new_name + "k_proj_w"), (value, new_name + "v_proj_w")]:
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print(f"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.write(struct.pack("i", len(data)))
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else:
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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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