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https://github.com/ggml-org/llama.cpp.git
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318 lines
11 KiB
Python
318 lines
11 KiB
Python
# HF gptneox--> gguf conversion
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import gguf
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import gguf_namemap as tmap
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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 numpy as np
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from typing import Any, List
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from pathlib import Path
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import torch
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from transformers import 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 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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def count_model_parts(dir_model: str) -> int:
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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("gguf: found " + str(num_parts) + " model parts")
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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 ftype\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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last_dir = os.path.basename(os.path.normpath(dir_model))
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# possible tensor 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 = sys.argv[1] + "/ggml-model-" + ftype_str[ftype] + ".gguf"
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print("gguf: loading model "+last_dir)
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with open(dir_model + "/config.json", "r", encoding="utf-8") as f:
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hparams = json.load(f)
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if hparams["architectures"][0] != "GPTNeoXForCausalLM":
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print("Model architecture not supported: " + hparams["architectures"][0] )
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sys.exit()
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# get number of model parts
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num_parts = count_model_parts(dir_model)
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gguf_writer = gguf.GGUFWriter.open(fname_out)
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print("gguf: get model metadata")
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llm_arch = "gptneox"
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block_count = hparams["num_hidden_layers"]
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gguf_writer.add_architecture(llm_arch)
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gguf_writer.add_name(last_dir)
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gguf_writer.add_file_type( "All tensors F32" if ftype == 0 else "Most tensors F16, some F32")
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gguf_writer.add_context_length(llm_arch, hparams["max_position_embeddings"])
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gguf_writer.add_embedding_length(llm_arch, hparams["hidden_size"])
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gguf_writer.add_block_count(llm_arch, block_count)
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gguf_writer.add_feed_forward_length(llm_arch, hparams["intermediate_size"])
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gguf_writer.add_rope_dimension_count(llm_arch, int( hparams["rotary_pct"]*(hparams["hidden_size"]//hparams["num_attention_heads"])) )
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gguf_writer.add_head_count(llm_arch, hparams["num_attention_heads"])
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gguf_writer.add_parallel_residual(llm_arch, hparams["use_parallel_residual"] if "use_parallel_residual" in hparams else True)
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gguf_writer.add_layer_norm_eps(llm_arch, hparams["layer_norm_eps"])
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# TOKENIZATION
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print("gguf: get tokenizer metadata")
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tokens: List[str] = []
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merges: List[str] = []
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if Path(dir_model + "/tokenizer.json").is_file():
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# gpt2 tokenizer
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gguf_writer.add_tokenizer_model("gpt2")
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print("gguf: get gpt2 tokenizer merges")
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with open(dir_model + "/tokenizer.json", "r", encoding="utf-8") as f:
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tokenizer_json = json.load(f)
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merges = tokenizer_json["model"]["merges"]
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gguf_writer.add_token_merges(merges)
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print("gguf: get gpt2 tokenizer vocab")
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vocab_size = len( tokenizer_json["model"]["vocab"] )
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# ref: https://github.com/cmp-nct/ggllm.cpp/blob/master/falcon_convert.py
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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 = bytes_to_unicode()
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byte_decoder = {v:k for k, v in byte_encoder.items()}
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for i in range(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[ord(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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padding_token = f"[PAD{i}]".encode("utf8")
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text = bytearray(padding_token)
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tokens.append(text)
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gguf_writer.add_token_list(tokens)
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if "added_tokens" in tokenizer_json and Path(dir_model + "/tokenizer_config.json").is_file():
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print("gguf: get special token ids")
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with open(dir_model + "/tokenizer_config.json", "r", encoding="utf-8") as f:
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tokenizer_config = json.load(f)
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# find special token ids
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if "bos_token" in tokenizer_config:
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for key in tokenizer_json["added_tokens"]:
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if key["content"] == tokenizer_config["bos_token"]:
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gguf_writer.add_bos_token_id(key["id"])
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if "eos_token" in tokenizer_config:
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for key in tokenizer_json["added_tokens"]:
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if key["content"] == tokenizer_config["eos_token"]:
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gguf_writer.add_eos_token_id(key["id"])
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if "unk_token" in tokenizer_config:
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for key in tokenizer_json["added_tokens"]:
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if key["content"] == tokenizer_config["unk_token"]:
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gguf_writer.add_unk_token_id(key["id"])
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if "sep_token" in tokenizer_config:
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for key in tokenizer_json["added_tokens"]:
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if key["content"] == tokenizer_config["sep_token"]:
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gguf_writer.add_sep_token_id(key["id"])
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if "pad_token" in tokenizer_config:
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for key in tokenizer_json["added_tokens"]:
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if key["content"] == tokenizer_config["pad_token"]:
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gguf_writer.add_pad_token_id(key["id"])
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# TENSORS
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tensor_map = tmap.get_tensor_namemap(block_count)
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# tensor info
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print("gguf: get tensor metadata")
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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("gguf: loading model 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]
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# we don't need these
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if name.endswith(".attention.masked_bias") or name.endswith(".attention.bias") or name.endswith(".attention.rotary_emb.inv_freq"):
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continue
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# convert any unsupported data types to float32
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if data.dtype != torch.float16 and data.dtype != torch.float32:
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data = data.to(torch.float32)
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data = data.squeeze().numpy()
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# map tensor names
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if name.endswith(".weight") and name[:-7] in tensor_map:
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name = tensor_map[name[:-7]] + ".weight"
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elif name.endswith(".bias") and name[:-5] in tensor_map:
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name = tensor_map[name[:-5]] + ".bias"
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else:
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print( "Can not map tensor '" + name + "'" )
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sys.exit()
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n_dims = len(data.shape)
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data_dtype = data.dtype
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# if f32 desired, convert any float16 to float32
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if ftype == 0 and data.dtype == np.float16:
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data_dtype = np.float32
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# TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
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if ftype == 1 and data.dtype == np.float16 and n_dims == 1:
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data_dtype = np.float32
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# if f16 desired, convert any float32 2-dim weight tensors to float16
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if ftype == 1 and data.dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
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data_dtype = np.float16
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data_nbytes = data.size * 2 if data_dtype == np.float16 else data.size * 4
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gguf_writer.add_tensor_info(name, data.shape, data_dtype, data_nbytes)
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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 tensor metadata")
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gguf_writer.write_ti_data_to_file()
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# tensor data
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print("gguf: convert and write tensor data")
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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("gguf: loading model 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]
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old_dtype = data.dtype
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# we don't need these
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if name.endswith(".attention.masked_bias") or name.endswith(".attention.bias") or name.endswith(".attention.rotary_emb.inv_freq"):
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continue
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# convert any unsupported data types to float32
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if data.dtype != torch.float16 and data.dtype != torch.float32:
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data = data.to(torch.float32)
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data = data.squeeze().numpy()
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# map tensor names
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if name.endswith(".weight") and name[:-7] in tensor_map:
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name = tensor_map[name[:-7]] + ".weight"
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elif name.endswith(".bias") and name[:-5] in tensor_map:
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name = tensor_map[name[:-5]] + ".bias"
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else:
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print( "Can not map tensor '" + name + "'" )
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sys.exit()
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n_dims = len(data.shape)
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data_dtype = data.dtype
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# if f32 desired, convert any float16 to float32
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if ftype == 0 and data.dtype == np.float16:
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data = data.astype(np.float32)
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# TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
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if ftype == 1 and data_dtype == np.float16 and n_dims == 1:
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data = data.astype(np.float32)
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# if f16 desired, convert any float32 2-dim weight tensors to float16
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if ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
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data = data.astype(np.float16)
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print( name + ", shape " + str(len(data.shape)) + ", " + str(old_dtype) + " --> " + str(data.dtype))
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gguf_writer.write_tensor_to_file(data)
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gguf_writer.close()
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print("gguf: model successfully exported to '" + fname_out + "'" )
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print("")
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