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backend: port Replit to GGUF
This commit is contained in:
Cebtenzzre
Adam Treat
@@ -1,113 +0,0 @@
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from pathlib import Path
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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 transformers import AutoModelForCausalLM, AutoTokenizer
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import sentencepiece.sentencepiece_model_pb2 as model
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if len(sys.argv) < 3:
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print("Usage: convert-h5-to-ggml.py dir-model [use-f32]\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-replit-code-v1-3b.bin"
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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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sp_proto = model.ModelProto()
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sp_proto.ParseFromString(open(Path(sys.argv[1]) / "spiece.model", "rb").read())
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# possible 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-replit-code-v1-3b-" + ftype_str[ftype] + ".bin"
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tokenizer = AutoTokenizer.from_pretrained(dir_model, trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(
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dir_model, low_cpu_mem_usage=True, trust_remote_code=True
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)
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# print (model)
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# print(tokenizer.encode('I believe the meaning of life is'))
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list_vars = model.state_dict()
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for name in list_vars.keys():
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print(name, list_vars[name].shape, list_vars[name].dtype)
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fout = open(fname_out, "wb")
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print(hparams)
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fout.write(struct.pack("i", 0x7265706c)) # magic: repl in hex
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fout.write(struct.pack("i", hparams["vocab_size"]))
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fout.write(struct.pack("i", hparams["max_seq_len"]))
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fout.write(struct.pack("i", hparams["d_model"]))
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fout.write(struct.pack("i", hparams["n_heads"]))
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fout.write(struct.pack("i", hparams["n_layers"]))
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fout.write(struct.pack("i", ftype))
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# TODO: temporary hack to not deal with implementing the tokenizer
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for piece in sp_proto.pieces:
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encoded_piece = piece.piece.encode("utf-8")
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fout.write(struct.pack("i", len(encoded_piece)))
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fout.write(encoded_piece)
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fout.write(struct.pack("f", piece.score))
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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("Processing variable: " + name + " with shape: ", data.shape)
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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[-7:] == ".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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# header
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str = name.encode("utf-8")
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fout.write(struct.pack("iii", n_dims, len(str), ftype_cur))
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for i in range(n_dims):
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fout.write(struct.pack("i", data.shape[n_dims - 1 - i]))
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fout.write(str)
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# data
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data.tofile(fout)
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fout.close()
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print("Done. Output file: " + fname_out)
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print("")
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142
gpt4all-backend/scripts/convert_replit_hf_to_gguf.py
Normal file
142
gpt4all-backend/scripts/convert_replit_hf_to_gguf.py
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@@ -0,0 +1,142 @@
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from __future__ import annotations
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import json
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import os
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import struct
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import sys
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from pathlib import Path
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import gguf
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import numpy as np
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from sentencepiece import SentencePieceProcessor
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from transformers import AutoModelForCausalLM, AutoTokenizer
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if not 2 <= len(sys.argv) < 4:
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print("Usage: {} dir-model [ftype]\n".format(os.path.basename(__file__)))
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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 = Path(sys.argv[1])
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# possible 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 = dir_model / ("ggml-replit-code-v1-3b-" + ftype_str[ftype] + ".gguf")
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ARCH = gguf.MODEL_ARCH.MPT
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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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model = AutoModelForCausalLM.from_pretrained(dir_model, low_cpu_mem_usage=True)
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config = model.config
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#print(model)
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block_count = config.n_layers
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gguf_writer.add_name("Replit")
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gguf_writer.add_context_length(config.max_seq_len)
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gguf_writer.add_embedding_length(config.d_model)
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gguf_writer.add_block_count(block_count)
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gguf_writer.add_head_count(config.n_heads)
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gguf_writer.add_max_alibi_bias(config.attn_config.alibi_bias_max)
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gguf_writer.add_layer_norm_eps(config.layer_norm_epsilon)
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gguf_writer.add_file_type(ftype)
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clip_qkv = config.attn_config.clip_qkv
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if clip_qkv is not None:
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gguf_writer.add_clamp_kqv(clip_qkv)
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print("gguf: get sentencepiece tokenizer vocab")
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tokenizer = SentencePieceProcessor(str(dir_model / "spiece.model"))
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#print(tokenizer.encode('I believe the meaning of life is'))
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tokens: list[bytearray] = []
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scores: list[float] = []
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toktypes: list[int] = []
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for i in range(tokenizer.vocab_size()):
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tokens.append(tokenizer.id_to_piece(i).encode('utf-8'))
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scores.append(tokenizer.get_score(i))
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toktype = gguf.TokenType.NORMAL
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if tokenizer.is_unknown(i):
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toktype = gguf.TokenType.UNKNOWN
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elif tokenizer.is_control(i):
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toktype = gguf.TokenType.CONTROL
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elif tokenizer.is_unused(i):
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toktype = gguf.TokenType.UNUSED
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elif tokenizer.is_byte(i):
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toktype = gguf.TokenType.BYTE
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toktypes.append(toktype)
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gguf_writer.add_tokenizer_model("llama") # sentencepiece
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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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special_vocab = gguf.SpecialVocab(dir_model, load_merges=True)
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special_vocab.add_to_gguf(gguf_writer)
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print("gguf: get tensor metadata")
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tensor_map = gguf.get_tensor_name_map(ARCH, block_count)
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list_vars = model.state_dict()
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for name in list_vars.keys():
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print(name, list_vars[name].shape, list_vars[name].dtype)
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print(config)
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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("Processing variable:", name, "with shape:", data.shape)
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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 == 1 and name[-7:] == ".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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elif ftype == 1 or 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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# map tensor names
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new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
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if new_name is None:
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print("Can not map tensor '" + name + "'")
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sys.exit()
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gguf_writer.add_tensor(new_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(f"gguf: model successfully exported to '{fname_out}'")
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print()
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