mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2025-11-02 09:12:03 +00:00
327 lines
10 KiB
Python
327 lines
10 KiB
Python
# HF llama --> gguf conversion, GQA/70b not supported
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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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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 permute(weights: NDArray, n_head: int) -> NDArray:
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return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
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.swapaxes(1, 2)
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.reshape(weights.shape))
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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] != "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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gguf_writer = gguf.GGUFWriter.open(fname_out)
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print("gguf: get model metadata")
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llm_arch = "llama"
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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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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_source_hf_repo(hf_repo)
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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, hparams["hidden_size"] // hparams["num_attention_heads"])
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gguf_writer.add_head_count(llm_arch, head_count)
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gguf_writer.add_head_count_kv(llm_arch, head_count_kv)
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gguf_writer.add_layer_norm_rms_eps(llm_arch, hparams["rms_norm_eps"])
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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, scores and token types")
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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): toktype = 2
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if tokenizer.is_control(i): toktype = 3
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# TODO: How to determinate if a token is user defined?
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# ref: https://github.com/google/sentencepiece/blob/master/src/sentencepiece_model.proto
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# if tokenizer.is_user_defined(i): toktype = 4
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if tokenizer.is_unused(i): toktype = 5
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if tokenizer.is_byte(i): 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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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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if Path(dir_model + "/tokenizer.json").is_file():
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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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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 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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# 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(".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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# permute these
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if name.endswith(".q_proj.weight") or name.endswith(".k_proj.weight"):
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data = permute(data,head_count)
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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(".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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# permute these
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if name.endswith(".q_proj.weight") or name.endswith(".k_proj.weight"):
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data = permute(data, head_count)
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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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