mirror of
https://github.com/nomic-ai/gpt4all.git
synced 2024-10-01 01:06:10 -04:00
backend: remove obsolete conversion scripts
All supported models are now converted using llama.cpp's conversion scripts. Signed-off-by: Jared Van Bortel <jared@nomic.ai>
This commit is contained in:
Jared Van Bortel
parent
f0ba8811e4
commit
d589e2929b
@ -1,140 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import struct
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
import gguf
|
||||
import numpy as np
|
||||
from transformers import AutoConfig, AutoModel, AutoTokenizer
|
||||
|
||||
|
||||
if not 2 <= len(sys.argv) < 4:
|
||||
print("Usage: {} dir-model [ftype]\n".format(Path(__file__).name))
|
||||
print(" ftype == 0 -> float32")
|
||||
print(" ftype == 1 -> float16")
|
||||
sys.exit(1)
|
||||
|
||||
# output in the same directory as the model
|
||||
dir_model = Path(sys.argv[1])
|
||||
|
||||
with open(dir_model / "vocab.txt", encoding="utf-8") as f:
|
||||
vocab = f.readlines()
|
||||
|
||||
# possible data types
|
||||
# ftype == 0 -> float32
|
||||
# ftype == 1 -> float16
|
||||
#
|
||||
# map from ftype to string
|
||||
ftype_str = ["f32", "f16"]
|
||||
|
||||
ftype = 1
|
||||
if len(sys.argv) > 2:
|
||||
ftype = int(sys.argv[2])
|
||||
if ftype < 0 or ftype > 1:
|
||||
print("Invalid ftype: " + str(ftype))
|
||||
sys.exit(1)
|
||||
|
||||
fname_out = dir_model / ("ggml-model-" + ftype_str[ftype] + ".gguf")
|
||||
|
||||
|
||||
ARCH = gguf.MODEL_ARCH.BERT
|
||||
gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH])
|
||||
|
||||
print("gguf: get model metadata")
|
||||
|
||||
config = AutoConfig.from_pretrained(dir_model)
|
||||
|
||||
block_count = config.num_hidden_layers
|
||||
gguf_writer.add_name("BERT")
|
||||
gguf_writer.add_context_length(config.max_position_embeddings)
|
||||
gguf_writer.add_embedding_length(config.hidden_size)
|
||||
gguf_writer.add_feed_forward_length(config.intermediate_size)
|
||||
gguf_writer.add_block_count(block_count)
|
||||
gguf_writer.add_head_count(config.num_attention_heads)
|
||||
gguf_writer.add_file_type(ftype)
|
||||
|
||||
print("gguf: get tokenizer metadata")
|
||||
|
||||
try:
|
||||
with open(dir_model / "tokenizer.json", encoding="utf-8") as f:
|
||||
tokenizer_json = json.load(f)
|
||||
except FileNotFoundError as e:
|
||||
print(f'Error: Missing {e.filename!r}', file=sys.stderr)
|
||||
sys.exit(1)
|
||||
|
||||
print("gguf: get wordpiece tokenizer vocab")
|
||||
|
||||
tokenizer = AutoTokenizer.from_pretrained(dir_model)
|
||||
print(tokenizer.encode('I believe the meaning of life is'))
|
||||
|
||||
tokens: list[bytearray] = []
|
||||
reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()}
|
||||
|
||||
# The number of tokens in tokenizer.json can differ from the expected vocab size.
|
||||
# This causes downstream issues with mismatched tensor sizes when running the inference
|
||||
for i in range(config.vocab_size):
|
||||
try:
|
||||
text = reverse_vocab[i]
|
||||
except KeyError:
|
||||
print(f"Key {i} not in tokenizer vocabulary. Padding with an arbitrary token.")
|
||||
pad_token = f"[PAD{i}]".encode("utf8")
|
||||
text = bytearray(pad_token)
|
||||
|
||||
tokens.append(text)
|
||||
|
||||
gguf_writer.add_tokenizer_model("bert") # wordpiece
|
||||
gguf_writer.add_token_list(tokens)
|
||||
|
||||
special_vocab = gguf.SpecialVocab(dir_model, load_merges=True)
|
||||
special_vocab.add_to_gguf(gguf_writer)
|
||||
|
||||
print("gguf: get tensor metadata")
|
||||
|
||||
model = AutoModel.from_pretrained(dir_model, config=config, low_cpu_mem_usage=True)
|
||||
print(model)
|
||||
|
||||
tensor_map = gguf.get_tensor_name_map(ARCH, block_count)
|
||||
|
||||
list_vars = model.state_dict()
|
||||
for name in list_vars.keys():
|
||||
print(name, list_vars[name].shape, list_vars[name].dtype)
|
||||
|
||||
for name in list_vars.keys():
|
||||
data = list_vars[name].squeeze().numpy()
|
||||
if name in ['embeddings.position_ids', 'pooler.dense.weight', 'pooler.dense.bias']:
|
||||
continue
|
||||
print("Processing variable:", name, "with shape:", data.shape)
|
||||
|
||||
n_dims = len(data.shape)
|
||||
|
||||
# ftype == 0 -> float32, ftype == 1 -> float16
|
||||
if ftype == 1 and name[-7:] == ".weight" and n_dims == 2:
|
||||
print(" Converting to float16")
|
||||
data = data.astype(np.float16)
|
||||
l_type = 1
|
||||
else:
|
||||
l_type = 0
|
||||
|
||||
# map tensor names
|
||||
new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
|
||||
if new_name is None:
|
||||
print("Can not map tensor '" + name + "'")
|
||||
sys.exit()
|
||||
|
||||
gguf_writer.add_tensor(new_name, data)
|
||||
|
||||
|
||||
print("gguf: write header")
|
||||
gguf_writer.write_header_to_file()
|
||||
print("gguf: write metadata")
|
||||
gguf_writer.write_kv_data_to_file()
|
||||
print("gguf: write tensors")
|
||||
gguf_writer.write_tensors_to_file()
|
||||
|
||||
gguf_writer.close()
|
||||
|
||||
print(f"gguf: model successfully exported to '{fname_out}'")
|
||||
print()
|
||||
@ -1,165 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
# Convert GPT-J-6B h5 transformer model to ggml format
|
||||
#
|
||||
# Load the model using GPTJForCausalLM.
|
||||
# Iterate over all variables and write them to a binary file.
|
||||
#
|
||||
# For each variable, write the following:
|
||||
# - Number of dimensions (int)
|
||||
# - Name length (int)
|
||||
# - Dimensions (int[n_dims])
|
||||
# - Name (char[name_length])
|
||||
# - Data (float[n_dims])
|
||||
#
|
||||
# By default, the bigger matrices are converted to 16-bit floats.
|
||||
# This can be disabled by adding the "ftype" CLI argument.
|
||||
#
|
||||
# At the start of the ggml file we write the model parameters
|
||||
# and vocabulary.
|
||||
#
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import sys
|
||||
import struct
|
||||
import json
|
||||
from pathlib import Path
|
||||
|
||||
import gguf
|
||||
import numpy as np
|
||||
from transformers import AutoConfig, AutoTokenizer, GPTJForCausalLM
|
||||
from transformers.models.gpt2 import tokenization_gpt2
|
||||
|
||||
|
||||
if not 2 <= len(sys.argv) < 4:
|
||||
print("Usage: python {} dir-model [ftype]\n".format(Path(__file__).name))
|
||||
print(" ftype == 0 -> float32")
|
||||
print(" ftype == 1 -> float16")
|
||||
sys.exit(1)
|
||||
|
||||
# output in the same directory as the model
|
||||
dir_model = Path(sys.argv[1])
|
||||
fname_out = dir_model / "ggml-model.gguf"
|
||||
|
||||
# possible data types
|
||||
# ftype == 0 -> float32
|
||||
# ftype == 1 -> float16
|
||||
#
|
||||
# map from ftype to string
|
||||
ftype_str = ["f32", "f16"]
|
||||
|
||||
ftype = 1
|
||||
if len(sys.argv) > 2:
|
||||
ftype = int(sys.argv[2])
|
||||
if ftype < 0 or ftype > 1:
|
||||
print("Invalid ftype: " + str(ftype))
|
||||
sys.exit(1)
|
||||
|
||||
fname_out = dir_model / ("ggml-model-" + ftype_str[ftype] + ".gguf")
|
||||
|
||||
|
||||
ARCH = gguf.MODEL_ARCH.GPTJ
|
||||
gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH])
|
||||
|
||||
print("gguf: get model metadata")
|
||||
|
||||
config = AutoConfig.from_pretrained(dir_model)
|
||||
|
||||
block_count = config.n_layer
|
||||
gguf_writer.add_name("GPT-J")
|
||||
gguf_writer.add_context_length(config.n_positions)
|
||||
gguf_writer.add_embedding_length(config.n_embd)
|
||||
gguf_writer.add_block_count(block_count)
|
||||
gguf_writer.add_feed_forward_length(4 * config.n_embd)
|
||||
gguf_writer.add_head_count(config.n_head)
|
||||
gguf_writer.add_rope_dimension_count(config.rotary_dim)
|
||||
gguf_writer.add_layer_norm_eps(config.layer_norm_epsilon)
|
||||
gguf_writer.add_file_type(ftype)
|
||||
|
||||
print("gguf: get gpt2 tokenizer vocab")
|
||||
|
||||
tokenizer = AutoTokenizer.from_pretrained(dir_model)
|
||||
|
||||
reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()}
|
||||
byte_encoder = tokenization_gpt2.bytes_to_unicode()
|
||||
byte_decoder = {v: k for k, v in byte_encoder.items()}
|
||||
|
||||
tokens: list[bytearray] = []
|
||||
|
||||
for i in range(config.vocab_size):
|
||||
if i in reverse_vocab:
|
||||
try:
|
||||
text = bytearray([byte_decoder[c] for c in reverse_vocab[i]])
|
||||
except KeyError:
|
||||
text = bytearray()
|
||||
for c in reverse_vocab[i]:
|
||||
if ord(c) < 256: # single byte character
|
||||
text.append(byte_decoder[c])
|
||||
else: # multibyte special token character
|
||||
text.extend(c.encode('utf-8'))
|
||||
else:
|
||||
print(f"Key {i} not in tokenizer vocabulary. Padding with an arbitrary token.")
|
||||
pad_token = f"[PAD{i}]".encode("utf8")
|
||||
text = bytearray(pad_token)
|
||||
|
||||
tokens.append(text)
|
||||
|
||||
|
||||
gguf_writer.add_tokenizer_model("gpt2")
|
||||
gguf_writer.add_token_list(tokens)
|
||||
|
||||
special_vocab = gguf.SpecialVocab(dir_model, load_merges=True)
|
||||
special_vocab.add_to_gguf(gguf_writer)
|
||||
|
||||
print("gguf: get tensor metadata")
|
||||
|
||||
model = GPTJForCausalLM.from_pretrained(dir_model, config=config, low_cpu_mem_usage=True)
|
||||
#print (model)
|
||||
|
||||
tensor_map = gguf.get_tensor_name_map(ARCH, block_count)
|
||||
|
||||
list_vars = model.state_dict()
|
||||
#print (list_vars)
|
||||
|
||||
for name in list_vars.keys():
|
||||
data = list_vars[name].squeeze().numpy()
|
||||
print("Processing variable:", name, "with shape:", data.shape)
|
||||
|
||||
# we don't need these
|
||||
if name.endswith("attn.masked_bias") or name.endswith(".attn.bias"):
|
||||
print(" Skipping variable:", name)
|
||||
continue
|
||||
|
||||
n_dims = len(data.shape)
|
||||
|
||||
# ftype == 0 -> float32, ftype == 1 -> float16
|
||||
ftype_cur = 0
|
||||
if ftype == 1 and name[-7:] == ".weight" and n_dims == 2:
|
||||
print(" Converting to float16")
|
||||
data = data.astype(np.float16)
|
||||
ftype_cur = 1
|
||||
elif ftype == 1 or data.dtype != np.float32:
|
||||
print(" Converting to float32")
|
||||
data = data.astype(np.float32)
|
||||
ftype_cur = 0
|
||||
|
||||
# map tensor names
|
||||
new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
|
||||
if new_name is None:
|
||||
print("Can not map tensor '" + name + "'")
|
||||
sys.exit()
|
||||
|
||||
gguf_writer.add_tensor(new_name, data)
|
||||
|
||||
|
||||
print("gguf: write header")
|
||||
gguf_writer.write_header_to_file()
|
||||
print("gguf: write metadata")
|
||||
gguf_writer.write_kv_data_to_file()
|
||||
print("gguf: write tensors")
|
||||
gguf_writer.write_tensors_to_file()
|
||||
|
||||
gguf_writer.close()
|
||||
|
||||
print(f"gguf: model successfully exported to '{fname_out}'")
|
||||
print()
|
||||
Loading…
Reference in New Issue
Block a user