File size: 5,770 Bytes
d376ead | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 | import os
import json
import torch
import numpy as np
import gguf
from safetensors.torch import load_file
import sentencepiece as spm
from pathlib import Path
def permute_rope(w, n_heads=8, head_dim=48):
# Permute weights from HF half-half RoPE layout to llama.cpp interleaved RoPE layout
return w.view(n_heads, 2, head_dim // 2, -1).transpose(1, 2).reshape(w.shape)
def export_gguf(weights_path="exported_model/model.safetensors", tokenizer_path="tokenizer/tokenizer.model", quant_type=gguf.GGMLQuantizationType.F16, output_file="webgpu_space/model_f16.gguf"):
print(f"Exporting permuted GGUF model: {output_file} with quant type: {quant_type.name}...")
weights = load_file(weights_path)
if "tok_embeddings.weight" not in weights and "output.weight" in weights:
weights["tok_embeddings.weight"] = weights["output.weight"]
sp = spm.SentencePieceProcessor(model_file=tokenizer_path)
vocab_size = sp.get_piece_size()
tokens = []
scores = []
tok_types = []
for i in range(vocab_size):
tokens.append(sp.id_to_piece(i))
scores.append(sp.get_score(i))
if sp.is_unknown(i):
tok_types.append(gguf.TokenType.UNKNOWN)
elif sp.is_control(i):
tok_types.append(gguf.TokenType.CONTROL)
elif sp.is_byte(i):
tok_types.append(gguf.TokenType.BYTE)
else:
tok_types.append(gguf.TokenType.NORMAL)
writer = gguf.GGUFWriter(output_file, "llama")
writer.add_name("Simple-Stories-Hindi-20M")
writer.add_context_length(512)
writer.add_embedding_length(384)
writer.add_feed_forward_length(1024)
writer.add_block_count(10)
writer.add_head_count(8)
writer.add_head_count_kv(8)
writer.add_rope_dimension_count(48)
writer.add_rope_freq_base(10000.0)
writer.add_layer_norm_rms_eps(1e-5)
writer.add_vocab_size(vocab_size)
writer.add_tokenizer_model("llama")
writer.add_token_list(tokens)
writer.add_token_scores(scores)
writer.add_token_types(tok_types)
writer.add_bos_token_id(2)
writer.add_eos_token_id(3)
writer.add_pad_token_id(0)
writer.add_unk_token_id(1)
mapping = {
"tok_embeddings.weight": "token_embd.weight",
"norm.weight": "output_norm.weight",
"output.weight": "output.weight",
}
for i in range(10):
mapping[f"layers.{i}.attention.wq.weight"] = f"blk.{i}.attn_q.weight"
mapping[f"layers.{i}.attention.wk.weight"] = f"blk.{i}.attn_k.weight"
mapping[f"layers.{i}.attention.wv.weight"] = f"blk.{i}.attn_v.weight"
mapping[f"layers.{i}.attention.wo.weight"] = f"blk.{i}.attn_output.weight"
mapping[f"layers.{i}.attention_norm.weight"] = f"blk.{i}.attn_norm.weight"
mapping[f"layers.{i}.feed_forward.w1.weight"] = f"blk.{i}.ffn_gate.weight"
mapping[f"layers.{i}.feed_forward.w2.weight"] = f"blk.{i}.ffn_up.weight"
mapping[f"layers.{i}.feed_forward.w3.weight"] = f"blk.{i}.ffn_down.weight"
mapping[f"layers.{i}.ffn_norm.weight"] = f"blk.{i}.ffn_norm.weight"
for orig_name, gguf_name in mapping.items():
tensor = weights[orig_name]
# Permute WQ and WK weights for llama.cpp RoPE layout!
if "attn_q.weight" in gguf_name or "attn_k.weight" in gguf_name:
tensor = permute_rope(tensor, n_heads=8, head_dim=48)
tensor_np = tensor.numpy().astype(np.float32)
if tensor_np.ndim == 2 and quant_type != gguf.GGMLQuantizationType.F16:
quant_data = gguf.quantize(tensor_np, quant_type)
writer.add_tensor(gguf_name, quant_data, raw_dtype=quant_type)
else:
if quant_type == gguf.GGMLQuantizationType.F16 and tensor_np.ndim == 2:
writer.add_tensor(gguf_name, tensor_np.astype(np.float16))
else:
writer.add_tensor(gguf_name, tensor_np)
writer.write_header_to_file()
writer.write_kv_data_to_file()
writer.write_tensors_to_file()
writer.close()
file_size_mb = os.path.getsize(output_file) / (1024 * 1024)
print(f"Successfully generated permuted {output_file} ({file_size_mb:.2f} MB)!")
def generate_tokenizer_json(tokenizer_path="tokenizer/tokenizer.model", output_file="webgpu_space/tokenizer.json"):
sp = spm.SentencePieceProcessor(model_file=tokenizer_path)
tokenizer_json = {
"version": "1.0",
"truncation": None,
"padding": None,
"added_tokens": [
{"id": 0, "special": True, "content": "<pad>", "single_word": False, "lstrip": False, "rstrip": False, "normalized": False},
{"id": 1, "special": True, "content": "<unk>", "single_word": False, "lstrip": False, "rstrip": False, "normalized": False},
{"id": 2, "special": True, "content": "<s>", "single_word": False, "lstrip": False, "rstrip": False, "normalized": False},
{"id": 3, "special": True, "content": "</s>", "single_word": False, "lstrip": False, "rstrip": False, "normalized": False}
],
"normalizer": None,
"pre_tokenizer": None,
"post_processor": None,
"decoder": None,
"model": {
"type": "Unigram",
"vocab": [[sp.id_to_piece(i), sp.get_score(i)] for i in range(sp.get_piece_size())]
}
}
with open(output_file, "w", encoding="utf-8") as f:
json.dump(tokenizer_json, f, ensure_ascii=False, indent=2)
print(f"Successfully generated {output_file}!")
if __name__ == "__main__":
export_gguf(quant_type=gguf.GGMLQuantizationType.F16, output_file="webgpu_space/model_f16.gguf")
export_gguf(quant_type=gguf.GGMLQuantizationType.Q8_0, output_file="webgpu_space/model_q8_0.gguf")
generate_tokenizer_json()
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