+++ /dev/null
-#!/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("")
+++ /dev/null
-#!/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("")
overview / pkg / ggml / sources / llama.cpp / commitdiff