mirror of
https://github.com/nomic-ai/gpt4all.git
synced 2024-10-01 01:06:10 -04:00
166 lines
4.7 KiB
Python
Executable File
166 lines
4.7 KiB
Python
Executable File
#!/usr/bin/env python3
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# Convert GPT-J-6B h5 transformer model to ggml format
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#
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# Load the model using GPTJForCausalLM.
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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 "ftype" 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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from __future__ import annotations
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import sys
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import struct
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import json
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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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from transformers import AutoTokenizer, GPTJConfig, GPTJForCausalLM
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from transformers.models.gpt2 import tokenization_gpt2
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if not 2 <= len(sys.argv) < 4:
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print("Usage: python {} dir-model [ftype]\n".format(Path(__file__).name))
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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 = Path(sys.argv[1])
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fname_out = dir_model / "ggml-model.gguf"
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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] + ".gguf")
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ARCH = gguf.MODEL_ARCH.GPTJ
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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 = GPTJConfig(dir_model)
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block_count = config.n_layer
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gguf_writer.add_name("GPT-J")
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gguf_writer.add_context_length(config.n_positions)
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gguf_writer.add_embedding_length(config.n_embd)
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gguf_writer.add_block_count(block_count)
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gguf_writer.add_feed_forward_length(4 * config.n_embd)
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gguf_writer.add_head_count(config.n_head)
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gguf_writer.add_rope_dimension_count(config.rotary_dim)
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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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print("gguf: get gpt2 tokenizer vocab")
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tokenizer = AutoTokenizer.from_pretrained(dir_model)
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reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()}
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byte_encoder = tokenization_gpt2.bytes_to_unicode()
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byte_decoder = {v: k for k, v in byte_encoder.items()}
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tokens: list[bytearray] = []
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for i in range(config.vocab_size):
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if i in reverse_vocab:
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try:
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text = bytearray([byte_decoder[c] for c in reverse_vocab[i]])
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except KeyError:
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text = bytearray()
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for c in reverse_vocab[i]:
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if ord(c) < 256: # single byte character
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text.append(byte_decoder[c])
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else: # multibyte special token character
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text.extend(c.encode('utf-8'))
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else:
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print(f"Key {i} not in tokenizer vocabulary. Padding with an arbitrary token.")
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pad_token = f"[PAD{i}]".encode("utf8")
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text = bytearray(pad_token)
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tokens.append(text)
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gguf_writer.add_tokenizer_model("gpt2")
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gguf_writer.add_token_list(tokens)
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special_vocab = gguf.SpecialVocab(dir_model, load_merges=True)
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special_vocab.add_to_gguf(gguf_writer)
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print("gguf: get tensor metadata")
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model = GPTJForCausalLM.from_pretrained(dir_model, config=config, low_cpu_mem_usage=True)
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#print (model)
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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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#print (list_vars)
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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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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 == 1 and 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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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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