mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-28 12:24:35 +00:00
278 lines
9.3 KiB
Python
278 lines
9.3 KiB
Python
# Quick and dirty HF llama --> gguf conversion, GQA/70b wont work
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import gguf
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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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from typing import Any, List
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from pathlib import Path
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from transformers import AutoModelForCausalLM
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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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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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fname_out = sys.argv[1] + "/ggml-model.bin"
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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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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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model = AutoModelForCausalLM.from_pretrained(dir_model, low_cpu_mem_usage=True, trust_remote_code=True)
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list_vars = model.state_dict()
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gguf_writer = gguf.GGUFWriter.open(fname_out)
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print("gguf: add key-values, metadata")
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llm_arch = "llama"
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gguf_writer.add_name("llama2-7b")
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gguf_writer.add_description("gguf test model")
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gguf_writer.add_architecture(llm_arch)
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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_layer_count(llm_arch, hparams["num_hidden_layers"])
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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, hparams["num_attention_heads"])
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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: add key-values, tokenizer")
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tokens: List[str] = []
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scores: List[float] = []
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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: adding sentencepiece tokenizer vocab")
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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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if tokenizer.is_unknown(i):
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text = " \u2047 ".encode("utf-8")
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elif tokenizer.is_control(i):
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text = b""
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if tokenizer.is_byte(i):
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piece = tokenizer.id_to_piece(i)
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if len(piece) != 6:
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raise Exception(f"Invalid token: {piece}")
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byte_value = int(piece[3:-1], 16)
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text = struct.pack("B", byte_value)
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else:
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text = tokenizer.id_to_piece(i).replace("\u2581", " ").encode("utf-8")
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score: float = tokenizer.get_score(i)
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tokens.append(text)
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scores.append(score)
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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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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: adding 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 info
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print("gguf: add gguf tensor info")
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for name in list_vars.keys():
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data = list_vars[name].squeeze().numpy()
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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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# 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, hparams["num_attention_heads"])
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# chnage tensor name
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if name == "model.embed_tokens.weight":
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name = "tok_embeddings.weight"
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elif name == "model.norm.weight":
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name = "norm.weight"
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elif name == "lm_head.weight":
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name = "output.weight"
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else:
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for i in range(80): # maximum number of layers
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if name == "model.layers." + str(i) + ".input_layernorm.weight":
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name = "layers." + str(i) + ".attention_norm.weight"
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break
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if name == "model.layers." + str(i) + ".self_attn.q_proj.weight":
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name = "layers." + str(i) + ".attention.wq.weight"
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break
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if name == "model.layers." + str(i) + ".self_attn.k_proj.weight":
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name = "layers." + str(i) + ".attention.wk.weight"
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break
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if name == "model.layers." + str(i) + ".self_attn.v_proj.weight":
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name = "layers." + str(i) + ".attention.wv.weight"
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break
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if name == "model.layers." + str(i) + ".self_attn.o_proj.weight":
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name = "layers." + str(i) + ".attention.wo.weight"
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break
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if name == "model.layers." + str(i) + ".post_attention_layernorm.weight":
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name = "layers." + str(i) + ".ffn_norm.weight"
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break
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if name == "model.layers." + str(i) + ".mlp.gate_proj.weight":
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name = "layers." + str(i) + ".feed_forward.w1.weight"
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break
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if name == "model.layers." + str(i) + ".mlp.down_proj.weight":
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name = "layers." + str(i) + ".feed_forward.w2.weight"
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break
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if name == "model.layers." + str(i) + ".mlp.up_proj.weight":
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name = "layers." + str(i) + ".feed_forward.w3.weight"
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break
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n_dims = len(data.shape)
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# ftype == 0 -> float32, ftype == 1 -> float16
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ftype_cur = 0
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if ftype != 0:
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if name.endswith(".weight") and n_dims == 2:
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data = data.astype(np.float16)
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ftype_cur = 1
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else:
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data = data.astype(np.float32)
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ftype_cur = 0
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else:
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if data.dtype != np.float32:
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data = data.astype(np.float32)
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ftype_cur = 0
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gguf_writer.add_tensor_info(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 key-values")
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gguf_writer.write_kv_data_to_file()
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print("gguf: write tensor info")
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gguf_writer.write_ti_data_to_file()
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# tensor data
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print("gguf: write tensor data")
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for name in list_vars.keys():
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data = list_vars[name].squeeze().numpy()
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# print("Process tensor: " + name + " with shape: ", data.shape)
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# we don't need these
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if name.endswith(".rotary_emb.inv_freq"):
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# print(" Skip tensor: " + name)
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continue
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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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# print(" Permute tensor: " + name)
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data = permute(data, hparams["num_attention_heads"])
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n_dims = len(data.shape)
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# ftype == 0 -> float32, ftype == 1 -> float16
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ftype_cur = 0
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if ftype != 0:
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if name.endswith(".weight") and n_dims == 2:
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# print(" Converting to float16")
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data = data.astype(np.float16)
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ftype_cur = 1
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else:
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# print(" Converting to float32")
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data = data.astype(np.float32)
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ftype_cur = 0
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else:
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if data.dtype != np.float32:
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# print(" Converting to float32")
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data = data.astype(np.float32)
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ftype_cur = 0
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gguf_writer.write_tensor_to_file(data)
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gguf_writer.close()
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print("gguf: conversion done, output file: " + fname_out)
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print("")
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