mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-25 10:54:36 +00:00
remove convert-llama-7b-pth-to-gguf.py and convert-llama-hf-to-gguf.py (#2906)
This commit is contained in:
slaren
GitHub
parent
aeefac4ff7
commit
528134dd02
@ -1,261 +0,0 @@
|
|||||||
#!/usr/bin/env python3
|
|
||||||
# 7b pth llama --> gguf conversion
|
|
||||||
# Only models with a single datafile are supported, like 7B
|
|
||||||
# HF files required in the model dir: config.json tokenizer_config.json tokenizer.json tokenizer.model
|
|
||||||
|
|
||||||
from __future__ import annotations
|
|
||||||
|
|
||||||
import argparse
|
|
||||||
import json
|
|
||||||
import os
|
|
||||||
import struct
|
|
||||||
import sys
|
|
||||||
from pathlib import Path
|
|
||||||
from typing import TYPE_CHECKING, Any
|
|
||||||
|
|
||||||
import gguf
|
|
||||||
import numpy as np
|
|
||||||
import torch
|
|
||||||
from sentencepiece import SentencePieceProcessor # type: ignore[import]
|
|
||||||
|
|
||||||
if TYPE_CHECKING:
|
|
||||||
from typing import TypeAlias
|
|
||||||
|
|
||||||
NDArray: TypeAlias = 'np.ndarray[Any, Any]'
|
|
||||||
|
|
||||||
|
|
||||||
def count_model_parts(dir_model: Path) -> int:
|
|
||||||
num_parts = 0
|
|
||||||
for filename in os.listdir(dir_model):
|
|
||||||
if filename.startswith("consolidated."):
|
|
||||||
num_parts += 1
|
|
||||||
|
|
||||||
if num_parts > 0:
|
|
||||||
print("gguf: found " + str(num_parts) + " model parts")
|
|
||||||
return num_parts
|
|
||||||
|
|
||||||
|
|
||||||
def parse_args() -> argparse.Namespace:
|
|
||||||
parser = argparse.ArgumentParser(description="Convert a PyTorch 7B LLaMA model to a GGML compatible file")
|
|
||||||
parser.add_argument("--vocab-only", action="store_true", help="extract only the vocab")
|
|
||||||
parser.add_argument("--outfile", type=Path, help="path to write to; default: based on input")
|
|
||||||
parser.add_argument("model", type=Path, help="directory containing model file, or model file itself (*.bin)")
|
|
||||||
parser.add_argument("ftype", type=int, choices=[0, 1], help="output format - use 0 for float32, 1 for float16", default = 1)
|
|
||||||
return parser.parse_args()
|
|
||||||
|
|
||||||
args = parse_args()
|
|
||||||
|
|
||||||
dir_model = args.model
|
|
||||||
ftype = args.ftype
|
|
||||||
if not dir_model.is_dir():
|
|
||||||
print(f'Error: {args.model} is not a directory', file = sys.stderr)
|
|
||||||
sys.exit(1)
|
|
||||||
|
|
||||||
# possible tensor data types
|
|
||||||
# ftype == 0 -> float32
|
|
||||||
# ftype == 1 -> float16
|
|
||||||
|
|
||||||
# map from ftype to string
|
|
||||||
ftype_str = ["f32", "f16"]
|
|
||||||
|
|
||||||
if args.outfile is not None:
|
|
||||||
fname_out = args.outfile
|
|
||||||
else:
|
|
||||||
# output in the same directory as the model by default
|
|
||||||
fname_out = dir_model / f'ggml-model-{ftype_str[ftype]}.gguf'
|
|
||||||
|
|
||||||
print("gguf: loading model "+dir_model.name)
|
|
||||||
|
|
||||||
with open(dir_model / "config.json", "r", encoding="utf-8") as f:
|
|
||||||
hparams = json.load(f)
|
|
||||||
|
|
||||||
if hparams["architectures"][0] != "LlamaForCausalLM":
|
|
||||||
print("Model architecture not supported: " + hparams["architectures"][0])
|
|
||||||
sys.exit()
|
|
||||||
|
|
||||||
# get number of model parts
|
|
||||||
num_parts = count_model_parts(dir_model)
|
|
||||||
|
|
||||||
if num_parts > 1:
|
|
||||||
print("gguf: Only models with a single datafile are supported.")
|
|
||||||
|
|
||||||
sys.exit()
|
|
||||||
|
|
||||||
ARCH=gguf.MODEL_ARCH.LLAMA
|
|
||||||
gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH])
|
|
||||||
|
|
||||||
|
|
||||||
print("gguf: get model metadata")
|
|
||||||
|
|
||||||
block_count = hparams["num_hidden_layers"]
|
|
||||||
head_count = hparams["num_attention_heads"]
|
|
||||||
|
|
||||||
if "num_key_value_heads" in hparams:
|
|
||||||
head_count_kv = hparams["num_key_value_heads"]
|
|
||||||
else:
|
|
||||||
head_count_kv = head_count
|
|
||||||
|
|
||||||
if "_name_or_path" in hparams:
|
|
||||||
hf_repo = hparams["_name_or_path"]
|
|
||||||
else:
|
|
||||||
hf_repo = ""
|
|
||||||
|
|
||||||
if "max_sequence_length" in hparams:
|
|
||||||
ctx_length = hparams["max_sequence_length"]
|
|
||||||
elif "max_position_embeddings" in hparams:
|
|
||||||
ctx_length = hparams["max_position_embeddings"]
|
|
||||||
else:
|
|
||||||
print("gguf: can not find ctx length parameter.")
|
|
||||||
|
|
||||||
sys.exit()
|
|
||||||
|
|
||||||
|
|
||||||
gguf_writer.add_name(dir_model.name)
|
|
||||||
gguf_writer.add_source_hf_repo(hf_repo)
|
|
||||||
gguf_writer.add_tensor_data_layout("Meta AI original pth")
|
|
||||||
gguf_writer.add_context_length(ctx_length)
|
|
||||||
gguf_writer.add_embedding_length(hparams["hidden_size"])
|
|
||||||
gguf_writer.add_block_count(block_count)
|
|
||||||
gguf_writer.add_feed_forward_length(hparams["intermediate_size"])
|
|
||||||
gguf_writer.add_rope_dimension_count(hparams["hidden_size"] // hparams["num_attention_heads"])
|
|
||||||
gguf_writer.add_head_count(head_count)
|
|
||||||
gguf_writer.add_head_count_kv(head_count_kv)
|
|
||||||
gguf_writer.add_layer_norm_rms_eps(hparams["rms_norm_eps"])
|
|
||||||
|
|
||||||
if "rope_scaling" in hparams and hparams["rope_scaling"] != None and "factor" in hparams["rope_scaling"]:
|
|
||||||
if "type" in hparams["rope_scaling"]:
|
|
||||||
if hparams["rope_scaling"]["type"] == "linear":
|
|
||||||
gguf_writer.add_rope_scale_linear(hparams["rope_scaling"]["factor"])
|
|
||||||
|
|
||||||
|
|
||||||
# TOKENIZATION
|
|
||||||
|
|
||||||
print("gguf: get tokenizer metadata")
|
|
||||||
|
|
||||||
tokens: list[bytes] = []
|
|
||||||
scores: list[float] = []
|
|
||||||
toktypes: list[int] = []
|
|
||||||
|
|
||||||
tokenizer_model_file = dir_model / 'tokenizer.model'
|
|
||||||
if not tokenizer_model_file.is_file():
|
|
||||||
print(f'Error: Missing {tokenizer_model_file}', file = sys.stderr)
|
|
||||||
sys.exit(1)
|
|
||||||
|
|
||||||
# vocab type sentencepiece
|
|
||||||
print("gguf: get sentencepiece tokenizer vocab and scores")
|
|
||||||
|
|
||||||
tokenizer = SentencePieceProcessor(str(tokenizer_model_file))
|
|
||||||
|
|
||||||
for i in range(tokenizer.vocab_size()):
|
|
||||||
text: bytes
|
|
||||||
score: float
|
|
||||||
|
|
||||||
piece = tokenizer.id_to_piece(i)
|
|
||||||
text = piece.encode("utf-8")
|
|
||||||
score = tokenizer.get_score(i)
|
|
||||||
|
|
||||||
toktype = 1 # defualt to normal token type
|
|
||||||
if tokenizer.is_unknown(i):
|
|
||||||
toktype = 2
|
|
||||||
if tokenizer.is_control(i):
|
|
||||||
toktype = 3
|
|
||||||
|
|
||||||
# toktype = 4 is user-defined = tokens from added_tokens.json
|
|
||||||
|
|
||||||
if tokenizer.is_unused(i):
|
|
||||||
toktype = 5
|
|
||||||
if tokenizer.is_byte(i):
|
|
||||||
toktype = 6
|
|
||||||
|
|
||||||
tokens.append(text)
|
|
||||||
scores.append(score)
|
|
||||||
toktypes.append(toktype)
|
|
||||||
|
|
||||||
added_tokens_file = dir_model / 'added_tokens.json'
|
|
||||||
if added_tokens_file.is_file():
|
|
||||||
with open(added_tokens_file, "r", encoding="utf-8") as f:
|
|
||||||
addtokens_json = json.load(f)
|
|
||||||
|
|
||||||
print("gguf: get added tokens")
|
|
||||||
|
|
||||||
for key in addtokens_json:
|
|
||||||
tokens.append( key.encode("utf-8") )
|
|
||||||
scores.append(-1000.0)
|
|
||||||
toktypes.append(4) # user-defined token type
|
|
||||||
|
|
||||||
gguf_writer.add_tokenizer_model("llama")
|
|
||||||
gguf_writer.add_token_list(tokens)
|
|
||||||
gguf_writer.add_token_scores(scores)
|
|
||||||
gguf_writer.add_token_types(toktypes)
|
|
||||||
|
|
||||||
special_vocab = gguf.SpecialVocab(dir_model)
|
|
||||||
special_vocab.add_to_gguf(gguf_writer)
|
|
||||||
|
|
||||||
# TENSORS
|
|
||||||
|
|
||||||
tensor_map = gguf.get_tensor_name_map(ARCH,block_count)
|
|
||||||
|
|
||||||
# tensor info
|
|
||||||
print("gguf: get tensor metadata")
|
|
||||||
|
|
||||||
part_names = (f"consolidated.{n:02}.pth" for n in range(0, num_parts))
|
|
||||||
|
|
||||||
for part_name in part_names:
|
|
||||||
if args.vocab_only:
|
|
||||||
break
|
|
||||||
print("gguf: loading model part '" + part_name + "'")
|
|
||||||
model_part = torch.load(f"{dir_model}/{part_name}", map_location="cpu")
|
|
||||||
|
|
||||||
for name in model_part.keys():
|
|
||||||
data = model_part[name]
|
|
||||||
|
|
||||||
# we don't need these
|
|
||||||
if name == "rope.freqs":
|
|
||||||
continue
|
|
||||||
|
|
||||||
old_dtype = data.dtype
|
|
||||||
|
|
||||||
# convert any unsupported data types to float32
|
|
||||||
if data.dtype != torch.float16 and data.dtype != torch.float32:
|
|
||||||
data = data.to(torch.float32)
|
|
||||||
|
|
||||||
data = data.squeeze().numpy()
|
|
||||||
|
|
||||||
# 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()
|
|
||||||
|
|
||||||
n_dims = len(data.shape)
|
|
||||||
data_dtype = data.dtype
|
|
||||||
|
|
||||||
# if f32 desired, convert any float16 to float32
|
|
||||||
if ftype == 0 and data_dtype == np.float16:
|
|
||||||
data = data.astype(np.float32)
|
|
||||||
|
|
||||||
# TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
|
|
||||||
if ftype == 1 and data_dtype == np.float16 and n_dims == 1:
|
|
||||||
data = data.astype(np.float32)
|
|
||||||
|
|
||||||
# if f16 desired, convert any float32 2-dim weight tensors to float16
|
|
||||||
if ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
|
|
||||||
data = data.astype(np.float16)
|
|
||||||
|
|
||||||
print(new_name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype))
|
|
||||||
|
|
||||||
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()
|
|
||||||
if not args.vocab_only:
|
|
||||||
print("gguf: write tensors")
|
|
||||||
gguf_writer.write_tensors_to_file()
|
|
||||||
|
|
||||||
gguf_writer.close()
|
|
||||||
|
|
||||||
print(f"gguf: model successfully exported to '{fname_out}'")
|
|
||||||
print("")
|
|
||||||
@ -1,280 +0,0 @@
|
|||||||
#!/usr/bin/env python3
|
|
||||||
# HF llama --> gguf conversion
|
|
||||||
|
|
||||||
from __future__ import annotations
|
|
||||||
|
|
||||||
import argparse
|
|
||||||
import json
|
|
||||||
import os
|
|
||||||
import struct
|
|
||||||
import sys
|
|
||||||
from pathlib import Path
|
|
||||||
from typing import TYPE_CHECKING, Any
|
|
||||||
|
|
||||||
import gguf
|
|
||||||
import numpy as np
|
|
||||||
import torch
|
|
||||||
from sentencepiece import SentencePieceProcessor # type: ignore[import]
|
|
||||||
|
|
||||||
if TYPE_CHECKING:
|
|
||||||
from typing import TypeAlias
|
|
||||||
|
|
||||||
NDArray: TypeAlias = 'np.ndarray[Any, Any]'
|
|
||||||
|
|
||||||
# reverse HF permute back to original pth layout
|
|
||||||
# https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/convert_llama_weights_to_hf.py
|
|
||||||
|
|
||||||
|
|
||||||
def reverse_hf_permute(weights: NDArray, n_head: int, n_kv_head: int | None = None) -> NDArray:
|
|
||||||
if n_kv_head is not None and n_head != n_kv_head:
|
|
||||||
n_head //= n_kv_head
|
|
||||||
|
|
||||||
return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
|
|
||||||
.swapaxes(1, 2)
|
|
||||||
.reshape(weights.shape))
|
|
||||||
|
|
||||||
|
|
||||||
def count_model_parts(dir_model: str) -> int:
|
|
||||||
num_parts = 0
|
|
||||||
|
|
||||||
for filename in os.listdir(dir_model):
|
|
||||||
if filename.startswith("pytorch_model-"):
|
|
||||||
num_parts += 1
|
|
||||||
|
|
||||||
if num_parts > 0:
|
|
||||||
print("gguf: found " + str(num_parts) + " model parts")
|
|
||||||
|
|
||||||
return num_parts
|
|
||||||
|
|
||||||
|
|
||||||
def parse_args() -> argparse.Namespace:
|
|
||||||
parser = argparse.ArgumentParser(description="Convert a HuggingFace LLaMA model to a GGML compatible file")
|
|
||||||
parser.add_argument("--vocab-only", action="store_true", help="extract only the vocab")
|
|
||||||
parser.add_argument("--outfile", type=Path, help="path to write to; default: based on input")
|
|
||||||
parser.add_argument("model", type=Path, help="directory containing model file, or model file itself (*.bin)")
|
|
||||||
parser.add_argument("ftype", type=int, choices=[0, 1], help="output format - use 0 for float32, 1 for float16", default = 1)
|
|
||||||
return parser.parse_args()
|
|
||||||
|
|
||||||
args = parse_args()
|
|
||||||
|
|
||||||
dir_model = args.model
|
|
||||||
ftype = args.ftype
|
|
||||||
if not dir_model.is_dir():
|
|
||||||
print(f'Error: {args.model} is not a directory', file = sys.stderr)
|
|
||||||
sys.exit(1)
|
|
||||||
|
|
||||||
# possible tensor data types
|
|
||||||
# ftype == 0 -> float32
|
|
||||||
# ftype == 1 -> float16
|
|
||||||
|
|
||||||
# map from ftype to string
|
|
||||||
ftype_str = ["f32", "f16"]
|
|
||||||
|
|
||||||
if args.outfile is not None:
|
|
||||||
fname_out = args.outfile
|
|
||||||
else:
|
|
||||||
# output in the same directory as the model by default
|
|
||||||
fname_out = dir_model / f'ggml-model-{ftype_str[ftype]}.gguf'
|
|
||||||
|
|
||||||
print("gguf: loading model "+dir_model.name)
|
|
||||||
|
|
||||||
with open(dir_model / "config.json", "r", encoding="utf-8") as f:
|
|
||||||
hparams = json.load(f)
|
|
||||||
|
|
||||||
if hparams["architectures"][0] != "LlamaForCausalLM":
|
|
||||||
print("Model architecture not supported: " + hparams["architectures"][0])
|
|
||||||
|
|
||||||
sys.exit()
|
|
||||||
|
|
||||||
# get number of model parts
|
|
||||||
num_parts = count_model_parts(dir_model)
|
|
||||||
|
|
||||||
ARCH=gguf.MODEL_ARCH.LLAMA
|
|
||||||
gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH])
|
|
||||||
|
|
||||||
print("gguf: get model metadata")
|
|
||||||
|
|
||||||
block_count = hparams["num_hidden_layers"]
|
|
||||||
head_count = hparams["num_attention_heads"]
|
|
||||||
|
|
||||||
if "num_key_value_heads" in hparams:
|
|
||||||
head_count_kv = hparams["num_key_value_heads"]
|
|
||||||
else:
|
|
||||||
head_count_kv = head_count
|
|
||||||
|
|
||||||
if "_name_or_path" in hparams:
|
|
||||||
hf_repo = hparams["_name_or_path"]
|
|
||||||
else:
|
|
||||||
hf_repo = ""
|
|
||||||
|
|
||||||
if "max_sequence_length" in hparams:
|
|
||||||
ctx_length = hparams["max_sequence_length"]
|
|
||||||
elif "max_position_embeddings" in hparams:
|
|
||||||
ctx_length = hparams["max_position_embeddings"]
|
|
||||||
else:
|
|
||||||
print("gguf: can not find ctx length parameter.")
|
|
||||||
|
|
||||||
sys.exit()
|
|
||||||
|
|
||||||
|
|
||||||
gguf_writer.add_name(dir_model.name)
|
|
||||||
gguf_writer.add_source_hf_repo(hf_repo)
|
|
||||||
gguf_writer.add_tensor_data_layout("Meta AI original pth")
|
|
||||||
gguf_writer.add_context_length(ctx_length)
|
|
||||||
gguf_writer.add_embedding_length(hparams["hidden_size"])
|
|
||||||
gguf_writer.add_block_count(block_count)
|
|
||||||
gguf_writer.add_feed_forward_length(hparams["intermediate_size"])
|
|
||||||
gguf_writer.add_rope_dimension_count(hparams["hidden_size"] // hparams["num_attention_heads"])
|
|
||||||
gguf_writer.add_head_count(head_count)
|
|
||||||
gguf_writer.add_head_count_kv(head_count_kv)
|
|
||||||
gguf_writer.add_layer_norm_rms_eps(hparams["rms_norm_eps"])
|
|
||||||
|
|
||||||
if "rope_scaling" in hparams and hparams["rope_scaling"] != None and "factor" in hparams["rope_scaling"]:
|
|
||||||
if "type" in hparams["rope_scaling"]:
|
|
||||||
if hparams["rope_scaling"]["type"] == "linear":
|
|
||||||
gguf_writer.add_rope_scale_linear(hparams["rope_scaling"]["factor"])
|
|
||||||
|
|
||||||
|
|
||||||
# TOKENIZATION
|
|
||||||
|
|
||||||
print("gguf: get tokenizer metadata")
|
|
||||||
|
|
||||||
tokens: list[bytes] = []
|
|
||||||
scores: list[float] = []
|
|
||||||
toktypes: list[int] = []
|
|
||||||
|
|
||||||
tokenizer_model_file = dir_model / 'tokenizer.model'
|
|
||||||
if not tokenizer_model_file.is_file():
|
|
||||||
print(f'Error: Missing {tokenizer_model_file}', file = sys.stderr)
|
|
||||||
sys.exit(1)
|
|
||||||
|
|
||||||
# vocab type sentencepiece
|
|
||||||
print("gguf: get sentencepiece tokenizer vocab, scores and token types")
|
|
||||||
|
|
||||||
tokenizer = SentencePieceProcessor(str(tokenizer_model_file))
|
|
||||||
|
|
||||||
for i in range(tokenizer.vocab_size()):
|
|
||||||
text: bytes
|
|
||||||
score: float
|
|
||||||
|
|
||||||
piece = tokenizer.id_to_piece(i)
|
|
||||||
text = piece.encode("utf-8")
|
|
||||||
score = tokenizer.get_score(i)
|
|
||||||
|
|
||||||
toktype = 1 # defualt to normal token type
|
|
||||||
if tokenizer.is_unknown(i):
|
|
||||||
toktype = 2
|
|
||||||
if tokenizer.is_control(i):
|
|
||||||
toktype = 3
|
|
||||||
|
|
||||||
# toktype = 4 is user-defined = tokens from added_tokens.json
|
|
||||||
|
|
||||||
if tokenizer.is_unused(i):
|
|
||||||
toktype = 5
|
|
||||||
if tokenizer.is_byte(i):
|
|
||||||
toktype = 6
|
|
||||||
|
|
||||||
tokens.append(text)
|
|
||||||
scores.append(score)
|
|
||||||
toktypes.append(toktype)
|
|
||||||
|
|
||||||
added_tokens_file = dir_model / 'added_tokens.json'
|
|
||||||
if added_tokens_file.is_file():
|
|
||||||
with open(added_tokens_file, "r", encoding="utf-8") as f:
|
|
||||||
addtokens_json = json.load(f)
|
|
||||||
|
|
||||||
print("gguf: get added tokens")
|
|
||||||
|
|
||||||
for key in addtokens_json:
|
|
||||||
tokens.append( key.encode("utf-8") )
|
|
||||||
scores.append(-1000.0)
|
|
||||||
toktypes.append(4) # user-defined token type
|
|
||||||
|
|
||||||
|
|
||||||
gguf_writer.add_tokenizer_model("llama")
|
|
||||||
gguf_writer.add_token_list(tokens)
|
|
||||||
gguf_writer.add_token_scores(scores)
|
|
||||||
gguf_writer.add_token_types(toktypes)
|
|
||||||
|
|
||||||
special_vocab = gguf.SpecialVocab(dir_model)
|
|
||||||
special_vocab.add_to_gguf(gguf_writer)
|
|
||||||
|
|
||||||
# TENSORS
|
|
||||||
|
|
||||||
tensor_map = gguf.get_tensor_name_map(ARCH,block_count)
|
|
||||||
|
|
||||||
# tensor info
|
|
||||||
print("gguf: get tensor metadata")
|
|
||||||
|
|
||||||
if num_parts == 0:
|
|
||||||
part_names = iter(("pytorch_model.bin",))
|
|
||||||
else:
|
|
||||||
part_names = (
|
|
||||||
f"pytorch_model-{n:05}-of-{num_parts:05}.bin" for n in range(1, num_parts + 1)
|
|
||||||
)
|
|
||||||
|
|
||||||
for part_name in part_names:
|
|
||||||
if args.vocab_only:
|
|
||||||
break
|
|
||||||
print("gguf: loading model part '" + part_name + "'")
|
|
||||||
model_part = torch.load(f"{dir_model}/{part_name}", map_location="cpu")
|
|
||||||
|
|
||||||
for name in model_part.keys():
|
|
||||||
data = model_part[name]
|
|
||||||
|
|
||||||
# we don't need these
|
|
||||||
if name.endswith(".rotary_emb.inv_freq"):
|
|
||||||
continue
|
|
||||||
|
|
||||||
old_dtype = data.dtype
|
|
||||||
|
|
||||||
# convert any unsupported data types to float32
|
|
||||||
if data.dtype != torch.float16 and data.dtype != torch.float32:
|
|
||||||
data = data.to(torch.float32)
|
|
||||||
|
|
||||||
data = data.squeeze().numpy()
|
|
||||||
|
|
||||||
# reverse permute these
|
|
||||||
if name.endswith(".q_proj.weight"):
|
|
||||||
data = reverse_hf_permute(data, head_count)
|
|
||||||
if name.endswith(".k_proj.weight"):
|
|
||||||
data = reverse_hf_permute(data, head_count, head_count_kv)
|
|
||||||
|
|
||||||
# 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()
|
|
||||||
|
|
||||||
n_dims = len(data.shape)
|
|
||||||
data_dtype = data.dtype
|
|
||||||
|
|
||||||
# if f32 desired, convert any float16 to float32
|
|
||||||
if ftype == 0 and data_dtype == np.float16:
|
|
||||||
data = data.astype(np.float32)
|
|
||||||
|
|
||||||
# TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
|
|
||||||
if ftype == 1 and data_dtype == np.float16 and n_dims == 1:
|
|
||||||
data = data.astype(np.float32)
|
|
||||||
|
|
||||||
# if f16 desired, convert any float32 2-dim weight tensors to float16
|
|
||||||
if ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
|
|
||||||
data = data.astype(np.float16)
|
|
||||||
|
|
||||||
print(new_name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype))
|
|
||||||
|
|
||||||
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()
|
|
||||||
if not args.vocab_only:
|
|
||||||
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