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2 changed files with 57 additions and 27 deletions
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@ -1,5 +1,5 @@
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#!/usr/bin/env python3
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# HF falcon--> gguf conversion
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# HF refact--> gguf conversion
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from __future__ import annotations
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@ -15,8 +15,8 @@ import numpy as np
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import torch
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from transformers import AutoTokenizer # type: ignore[import]
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if 'NO_LOCAL_GGUF' not in os.environ:
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sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf'))
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if "NO_LOCAL_GGUF" not in os.environ:
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sys.path.insert(1, str(Path(__file__).parent / "gguf-py" / "gguf"))
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import gguf
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@ -31,13 +31,17 @@ def bytes_to_unicode():
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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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bs = (
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list(range(ord("!"), ord("~") + 1))
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+ list(range(ord("¡"), ord("¬") + 1))
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+ list(range(ord("®"), ord("ÿ") + 1))
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)
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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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cs.append(2**8 + n)
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n += 1
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return dict(zip(bs, (chr(n) for n in cs)))
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@ -54,32 +58,41 @@ def count_model_parts(dir_model: Path) -> int:
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def parse_args() -> argparse.Namespace:
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parser = argparse.ArgumentParser(description="Convert a Refact model to a GGML compatible file")
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parser = argparse.ArgumentParser(
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description="Convert a Refact model to a GGML compatible file"
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)
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parser.add_argument(
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"--vocab-only", action="store_true",
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"--vocab-only",
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action="store_true",
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help="extract only the vocab",
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)
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parser.add_argument(
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"--outfile", type=Path,
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"--outfile",
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type=Path,
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help="path to write to; default: based on input",
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)
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parser.add_argument(
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"model", type=Path,
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"model",
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type=Path,
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help="directory containing model file, or model file itself (*.bin)",
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)
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parser.add_argument(
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"ftype", type=int, choices=[0, 1], default=1, nargs='?',
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"ftype",
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type=int,
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choices=[0, 1],
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default=1,
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nargs="?",
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help="output format - use 0 for float32, 1 for float16",
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)
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return parser.parse_args()
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args = parse_args()
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dir_model = args.model
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ftype = args.ftype
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if not dir_model.is_dir():
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print(f'Error: {args.model} is not a directory', file = sys.stderr)
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print(f"Error: {args.model} is not a directory", file=sys.stderr)
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sys.exit(1)
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# possible tensor data types
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@ -93,9 +106,9 @@ if args.outfile is not None:
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fname_out = args.outfile
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else:
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# output in the same directory as the model by default
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fname_out = dir_model / f'ggml-model-{ftype_str[ftype]}.gguf'
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fname_out = dir_model / f"ggml-model-{ftype_str[ftype]}.gguf"
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print("gguf: loading model "+dir_model.name)
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print("gguf: loading model " + dir_model.name)
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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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@ -108,7 +121,7 @@ if hparams["architectures"][0] != "GPTRefactForCausalLM":
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# get number of model parts
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num_parts = count_model_parts(dir_model)
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ARCH=gguf.MODEL_ARCH.REFACT
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ARCH = gguf.MODEL_ARCH.REFACT
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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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@ -142,9 +155,9 @@ tokens: list[bytearray] = []
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scores: list[float] = []
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toktypes: list[int] = []
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tokenizer_json_file = dir_model / 'tokenizer.json'
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tokenizer_json_file = dir_model / "tokenizer.json"
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if not tokenizer_json_file.is_file():
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print(f'Error: Missing {tokenizer_json_file}', file = sys.stderr)
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print(f"Error: Missing {tokenizer_json_file}", file=sys.stderr)
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sys.exit(1)
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# gpt2 tokenizer
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@ -157,7 +170,11 @@ print("gguf: get gpt2 tokenizer vocab")
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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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vocab_size = hparams["vocab_size"] if "vocab_size" in hparams else len(tokenizer_json["model"]["vocab"])
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vocab_size = (
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hparams["vocab_size"]
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if "vocab_size" in hparams
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else len(tokenizer_json["model"]["vocab"])
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)
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tokenizer = AutoTokenizer.from_pretrained(dir_model, trust_remote_code=True)
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@ -176,29 +193,29 @@ for i in range(vocab_size):
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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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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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scores.append(0.0) # dymmy
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scores.append(0.0) # dymmy
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toktypes.append(gguf.TokenType.NORMAL) # dummy
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gguf_writer.add_token_list(tokens)
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gguf_writer.add_token_scores(scores)
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gguf_writer.add_token_types(toktypes)
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special_vocab = gguf.SpecialVocab(dir_model, load_merges = True)
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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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# TENSORS
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tensor_map = gguf.get_tensor_name_map(ARCH,block_count)
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tensor_map = gguf.get_tensor_name_map(ARCH, block_count)
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# params for qkv transform
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n_head = hparams["n_head"]
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n_head = hparams["n_head"]
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n_head_kv = 1
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head_dim = hparams["n_embd"] // n_head
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@ -230,7 +247,7 @@ for part_name in part_names:
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data = data.squeeze().numpy()
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# map tensor names
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new_name = tensor_map.get_name(name, try_suffixes = (".weight", ))
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new_name = tensor_map.get_name(name, try_suffixes=(".weight",))
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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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@ -247,10 +264,23 @@ for part_name in part_names:
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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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if (
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ftype == 1
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and data_dtype == np.float32
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and name.endswith(".weight")
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and n_dims == 2
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):
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data = data.astype(np.float16)
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print(new_name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype))
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print(
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new_name
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+ ", n_dims = "
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+ str(n_dims)
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+ ", "
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+ str(old_dtype)
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+ " --> "
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+ str(data.dtype)
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)
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gguf_writer.add_tensor(new_name, data)
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@ -4473,7 +4473,7 @@ static int llama_decode_internal(
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// If all tensors can be run on the GPU then using more than 1 thread is detrimental.
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const bool full_offload_supported = model.arch == LLM_ARCH_LLAMA ||
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model.arch == LLM_ARCH_BAICHUAN ||
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model.arch == LLM_ARCH_FALCON ||
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model.arch == LLM_ARCH_FALCON ||
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model.arch == LLM_ARCH_REFACT;
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const bool fully_offloaded = model.n_gpu_layers >= (int) hparams.n_layer + 3;
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if (ggml_cpu_has_cublas() && full_offload_supported && fully_offloaded) {
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