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convert-gptneox-h5-to-gguf.py : map tensor names

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klosax 2023-08-09 00:50:11 +02:00 committed by GitHub
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1 changed files with 25 additions and 11 deletions
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convert-gptneox-h5-to-gguf.py
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@ -1,6 +1,7 @@
# Quick and dirty HF gptneox--> gguf conversion
import gguf
import gguf_tensor_map as tmap
import os
import sys
import struct
@ -32,6 +33,7 @@ def bytes_to_unicode():
cs = [chr(n) for n in cs]
return dict(zip(bs, cs))
if len(sys.argv) < 3:
print("Usage: convert-h5-to-ggml.py dir-model ftype\n")
print(" ftype == 0 -> float32")
@ -74,16 +76,17 @@ list_vars = model.state_dict()
gguf_writer = gguf.GGUFWriter.open(fname_out)
print("gguf: add metadata")
print("gguf: get model metadata")
llm_arch = "gptneox"
llm_arch = "gptneox"
block_count = hparams["num_hidden_layers"]
gguf_writer.add_name(last_dir)
gguf_writer.add_description("gguf test model")
gguf_writer.add_architecture(llm_arch)
gguf_writer.add_context_length(llm_arch, hparams["max_position_embeddings"])
gguf_writer.add_embedding_length(llm_arch, hparams["hidden_size"])
gguf_writer.add_layer_count(llm_arch, hparams["num_hidden_layers"])
gguf_writer.add_layer_count(llm_arch, block_count)
gguf_writer.add_feed_forward_length(llm_arch, hparams["intermediate_size"])
gguf_writer.add_rope_dimension_count(llm_arch, int( hparams["rotary_pct"]*(hparams["hidden_size"]//hparams["num_attention_heads"])) )
gguf_writer.add_head_count(llm_arch, hparams["num_attention_heads"])
@ -92,7 +95,7 @@ gguf_writer.add_layer_norm_eps(llm_arch, hparams["layer_norm_eps"])
# TOKENIZATION
print("gguf: add tokenizer")
print("gguf: get tokenizer metadata")
tokens: List[str] = []
merges: List[str] = []
@ -102,7 +105,7 @@ if Path(dir_model + "/tokenizer.json").is_file():
# gpt2 tokenizer
gguf_writer.add_tokenizer_model("gpt2")
print("gguf: adding gpt2 tokenizer merges")
print("gguf: get gpt2 tokenizer merges")
with open(dir_model + "/tokenizer.json", "r", encoding="utf-8") as f:
tokenizer_json = json.load(f)
@ -110,7 +113,7 @@ if Path(dir_model + "/tokenizer.json").is_file():
gguf_writer.add_token_merges(merges)
print("gguf: adding gpt2 tokenizer vocab")
print("gguf: get gpt2 tokenizer vocab")
vocab_size = len( tokenizer_json["model"]["vocab"] )
@ -141,7 +144,7 @@ if Path(dir_model + "/tokenizer.json").is_file():
gguf_writer.add_token_list(tokens)
if "added_tokens" in tokenizer_json and Path(dir_model + "/tokenizer_config.json").is_file():
print("gguf: adding special token ids")
print("gguf: get special token ids")
with open(dir_model + "/tokenizer_config.json", "r", encoding="utf-8") as f:
tokenizer_config = json.load(f)
@ -176,8 +179,10 @@ if Path(dir_model + "/tokenizer.json").is_file():
# TENSORS
tensor_map = tmap.get_tensor_map(block_count)
# tensor info
print("gguf: add gguf tensor info")
print("gguf: get tensor metadata")
for name in list_vars.keys():
data = list_vars[name].squeeze().numpy()
@ -186,6 +191,15 @@ for name in list_vars.keys():
if name.endswith(".attention.masked_bias") or name.endswith(".attention.bias") or name.endswith(".attention.rotary_emb.inv_freq"):
continue
# map tensor names
if name.endswith(".weight") and name[:-7] in tensor_map:
name = tensor_map[name[:-7]] + ".weight"
elif name.endswith(".bias") and name[:-5] in tensor_map:
name = tensor_map[name[:-5]] + ".bias"
else:
print( "Can not map tensor '" + name + "'" )
sys.exit()
n_dims = len(data.shape)
# ftype == 0 -> float32, ftype == 1 -> float16
@ -206,9 +220,9 @@ for name in list_vars.keys():
print("gguf: write header")
gguf_writer.write_header_to_file()
print("gguf: write key-values")
print("gguf: write metadata")
gguf_writer.write_kv_data_to_file()
print("gguf: write tensor info")
print("gguf: write tensor metadata")
gguf_writer.write_ti_data_to_file()
# tensor data
@ -242,5 +256,5 @@ for name in list_vars.keys():
gguf_writer.close()
print("gguf: conversion done, output file: " + fname_out)
print("gguf: model successfully exported to '" + fname_out + "'" )
print("")