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https://github.com/nomic-ai/gpt4all.git
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backend: port MPT to GGUF
This commit is contained in:
Cebtenzzre
Adam Treat
@@ -1,145 +0,0 @@
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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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vocab = tokenizer.vocab
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hparams["multiple_of"] = 1
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fout.write(struct.pack("I", 0x67676d6d)) # magic: ggml in hex
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fout.write(struct.pack("I", model.config.vocab_size))
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fout.write(struct.pack("I", model.config.max_seq_len))
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fout.write(struct.pack("I", model.config.n_layers))
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fout.write(struct.pack("I", model.config.n_heads))
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fout.write(struct.pack("I", model.config.d_model))
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fout.write(struct.pack("f", model.config.attn_config['alibi_bias_max']))
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clip_qkv = model.config.attn_config['clip_qkv']
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fout.write(struct.pack("f", clip_qkv if clip_qkv is not None else 0))
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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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# write tokens to ggml file
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dot_token = tokenizer.encode('.')[0]
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fout.write(struct.pack("I", model.config.vocab_size))
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for i in range(model.config.vocab_size):
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text = tokenizer.decode([dot_token, 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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enclen = len(text)
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if i in tokenizer.all_special_ids:
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print(f"special token: {text}")
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enclen = enclen | 1<<31
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fout.write(struct.pack("I", enclen))
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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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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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# Keep token embeddings in fp32
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if name[-7:] == ".weight" and n_dims == 2 and ".wte" not in name:
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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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172
gpt4all-backend/scripts/convert_mpt_hf_to_gguf.py
Normal file
172
gpt4all-backend/scripts/convert_mpt_hf_to_gguf.py
Normal file
@@ -0,0 +1,172 @@
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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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from __future__ import annotations
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import json
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import os
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import struct
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import sys
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from pathlib import Path
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import gguf
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import numpy as np
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import torch
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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 not 3 <= len(sys.argv) < 5:
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print("Usage: python {} model-name dir-output [ftype]".format(Path(__file__).name))
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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(" ftype == 0 -> float32")
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print(" ftype == 1 -> float16")
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sys.exit(1)
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model_name = sys.argv[1]
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dir_out = Path(sys.argv[2])
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# make sure the output directory exists
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dir_out.mkdir(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 = int(sys.argv[3])
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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_out / f"ggml-model-{Path(model_name).name}-{ftype_str[ftype]}.gguf"
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ARCH = gguf.MODEL_ARCH.MPT
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gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH])
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print("gguf: get model metadata")
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config = AutoConfig.from_pretrained(model_name)
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print("Loading model:", model_name)
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model = AutoModelForCausalLM.from_pretrained(
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model_name, config=config, torch_dtype=torch.float16 if ftype == 1 else torch.float32, low_cpu_mem_usage=True,
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)
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config = model.config
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print("Model loaded:", model_name)
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block_count = config.n_layers
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gguf_writer.add_name("MPT")
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gguf_writer.add_context_length(config.max_seq_len)
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gguf_writer.add_embedding_length(config.d_model)
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gguf_writer.add_block_count(block_count)
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gguf_writer.add_head_count(config.n_heads)
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gguf_writer.add_max_alibi_bias(config.attn_config.alibi_bias_max)
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gguf_writer.add_layer_norm_eps(config.layer_norm_epsilon)
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gguf_writer.add_file_type(ftype)
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clip_qkv = config.attn_config.clip_qkv
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if clip_qkv is not None:
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gguf_writer.add_clamp_kqv(clip_qkv)
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print("gguf: get gpt2 tokenizer vocab")
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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special_ids = tokenizer.all_special_ids
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tokens: list[bytearray] = []
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toktypes: list[gguf.TokenType] = []
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# TODO(cebtenzzre): this is probably wrong, but I don't know what else to put here
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dot_token = tokenizer.encode('.')[0]
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# The number of tokens in tokenizer.json can differ from the expected vocab size.
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# This causes downstream issues with mismatched tensor sizes when running the inference
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for i in range(config.vocab_size):
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text = tokenizer.decode([dot_token, i]).encode('utf-8')
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text = text[1:] # remove the first byte (it's always '.')
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tokens.append(text)
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# TODO(cebtenzzre): is there a better way to do this?
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toktypes.append(gguf.TokenType.CONTROL if i in special_ids else gguf.TokenType.NORMAL)
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gguf_writer.add_tokenizer_model("gpt2")
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gguf_writer.add_token_list(tokens)
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gguf_writer.add_token_types(toktypes)
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print("gguf: get tensor metadata")
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tensor_map = gguf.get_tensor_name_map(ARCH, block_count)
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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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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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# Keep token embeddings in fp32
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if ftype == 1 and name[-7:] == ".weight" and n_dims == 2 and ".wte" not in name:
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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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elif ftype == 1 or 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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# map tensor names
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new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
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if new_name is None:
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print("Can not map tensor '" + name + "'")
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sys.exit()
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gguf_writer.add_tensor(new_name, data)
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print("gguf: write header")
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gguf_writer.write_header_to_file()
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print("gguf: write metadata")
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gguf_writer.write_kv_data_to_file()
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print("gguf: write tensors")
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gguf_writer.write_tensors_to_file()
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gguf_writer.close()
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print(f"gguf: model successfully exported to '{fname_out}'")
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print()
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