mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2025-11-02 09:12:03 +00:00
308 lines
9.7 KiB
Python
308 lines
9.7 KiB
Python
# 7b pth llama --> gguf conversion
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# Only models with a single datafile are supported, like 7B
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# HF files required in the model dir: config.json tokenizer_config.json tokenizer.json tokenizer.model
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import gguf
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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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import torch
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from typing import Any, List
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from pathlib import Path
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from sentencepiece import SentencePieceProcessor
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#NDArray = np.ndarray[Any, Any]
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# compatible with python < 3.9
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NDArray: 'TypeAlias' = 'np.ndarray[Any, Any]'
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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("consolidated."):
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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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# 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] != "LlamaForCausalLM":
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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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if num_parts > 1:
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print("gguf: Only models with a single datafile are supported.")
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sys.exit()
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ARCH=gguf.MODEL_ARCH.LLAMA
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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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block_count = hparams["num_hidden_layers"]
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head_count = hparams["num_attention_heads"]
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if "num_key_value_heads" in hparams:
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head_count_kv = hparams["num_key_value_heads"]
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else:
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head_count_kv = head_count
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if "_name_or_path" in hparams:
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hf_repo = hparams["_name_or_path"]
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else:
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hf_repo = ""
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if "max_sequence_length" in hparams:
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ctx_length = hparams["max_sequence_length"]
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elif "max_position_embeddings" in hparams:
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ctx_length = hparams["max_position_embeddings"]
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else:
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print("gguf: can not find ctx length parameter.")
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sys.exit()
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gguf_writer.add_name(last_dir)
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gguf_writer.add_source_hf_repo(hf_repo)
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gguf_writer.add_tensor_data_layout("Meta AI original pth")
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gguf_writer.add_context_length(ctx_length)
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gguf_writer.add_embedding_length(hparams["hidden_size"])
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gguf_writer.add_block_count(block_count)
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gguf_writer.add_feed_forward_length(hparams["intermediate_size"])
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gguf_writer.add_rope_dimension_count(hparams["hidden_size"] // hparams["num_attention_heads"])
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gguf_writer.add_head_count(head_count)
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gguf_writer.add_head_count_kv(head_count_kv)
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gguf_writer.add_layer_norm_rms_eps(hparams["rms_norm_eps"])
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if "rope_scaling" in hparams and hparams["rope_scaling"] != None and "factor" in hparams["rope_scaling"]:
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if "type" in hparams["rope_scaling"]:
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if hparams["rope_scaling"]["type"] == "linear":
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gguf_writer.add_rope_scale_linear(hparams["rope_scaling"]["factor"])
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# TOKENIZATION
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print("gguf: get tokenizer metadata")
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tokens: List[bytes] = []
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scores: List[float] = []
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toktypes: List[int] = []
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if Path(dir_model + "/tokenizer.model").is_file():
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# vocab type sentencepiece
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print("gguf: get sentencepiece tokenizer vocab and scores")
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tokenizer = SentencePieceProcessor(dir_model + "/tokenizer.model")
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for i in range(tokenizer.vocab_size()):
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text: bytes
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score: float
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piece = tokenizer.id_to_piece(i)
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text = piece.encode("utf-8")
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score = tokenizer.get_score(i)
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toktype = 1 # defualt to normal token type
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if tokenizer.is_unknown(i):
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toktype = 2
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if tokenizer.is_control(i):
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toktype = 3
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# toktype = 4 is user-defined = tokens from added_tokens.json
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if tokenizer.is_unused(i):
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toktype = 5
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if tokenizer.is_byte(i):
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toktype = 6
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tokens.append(text)
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scores.append(score)
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toktypes.append(toktype)
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if Path(dir_model + "/added_tokens.json").is_file():
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with open(dir_model + "/added_tokens.json", "r", encoding="utf-8") as f:
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addtokens_json = json.load(f)
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print("gguf: get added tokens")
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for key in addtokens_json:
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tokens.append( key.encode("utf-8") )
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scores.append(-1000.0)
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toktypes.append(4) # user-defined token type
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gguf_writer.add_tokenizer_model("llama")
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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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print("gguf: get special token ids")
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if Path(dir_model + "/tokenizer.json").is_file():
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# Look for special tokens in tokenizer.json if it exists
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with open(dir_model + "/tokenizer.json", "r", encoding="utf-8") as f:
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tokenizer = json.load(f)
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if "added_tokens" in tokenizer and Path(dir_model + "/tokenizer_config.json").is_file():
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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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if "bos_token" in tokenizer_config and tokenizer_config["bos_token"] != None:
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for key in tokenizer["added_tokens"]:
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if key["content"] == tokenizer_config["bos_token"]["content"]:
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gguf_writer.add_bos_token_id(key["id"])
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if "eos_token" in tokenizer_config and tokenizer_config["eos_token"] != None:
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for key in tokenizer["added_tokens"]:
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if key["content"] == tokenizer_config["eos_token"]["content"]:
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gguf_writer.add_eos_token_id(key["id"])
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if "unk_token" in tokenizer_config and tokenizer_config["unk_token"] != None:
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for key in tokenizer["added_tokens"]:
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if key["content"] == tokenizer_config["unk_token"]["content"]:
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gguf_writer.add_unk_token_id(key["id"])
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if "sep_token" in tokenizer_config and tokenizer_config["sep_token"] != None:
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for key in tokenizer["added_tokens"]:
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if key["content"] == tokenizer_config["sep_token"]["content"]:
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gguf_writer.add_sep_token_id(key["id"])
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if "pad_token" in tokenizer_config and tokenizer_config["pad_token"] != None:
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for key in tokenizer["added_tokens"]:
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if key["content"] == tokenizer_config["pad_token"]["content"]:
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gguf_writer.add_pad_token_id(key["id"])
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else:
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# If no tokenizer.json: Look for special tokens in config.json
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if "bos_token_id" in hparams and hparams["bos_token_id"] != None:
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gguf_writer.add_bos_token_id(hparams["bos_token_id"])
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if "eos_token_id" in hparams and hparams["eos_token_id"] != None:
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gguf_writer.add_eos_token_id(hparams["eos_token_id"])
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if "unk_token_id" in hparams and hparams["unk_token_id"] != None:
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gguf_writer.add_unk_token_id(hparams["unk_token_id"])
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if "sep_token_id" in hparams and hparams["sep_token_id"] != None:
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gguf_writer.add_sep_token_id(hparams["sep_token_id"])
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if "pad_token_id" in hparams and hparams["pad_token_id"] != None:
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gguf_writer.add_pad_token_id(hparams["pad_token_id"])
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# TENSORS
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tensor_map = gguf.get_tensor_name_map(ARCH,block_count)
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# tensor info
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print("gguf: get tensor metadata")
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part_names = (f"consolidated.{n:02}.pth" for n in range(0, num_parts))
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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 == "rope.freqs":
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continue
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old_dtype = data.dtype
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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 + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype))
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gguf_writer.add_tensor(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("gguf: model successfully exported to '" + fname_out + "'")
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print("")
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