Spaces:
Sleeping
Sleeping
File size: 5,694 Bytes
234574a cf0a8ed 234574a | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 | """Token counting via real Qwen3 tokenizer - fixes BUG-001.
Replaces heuristic len(text.split()) // 4 * 3 with accurate tokenization.
Uses transformers AutoTokenizer for Qwen3-35B-A3B (or fallback).
"""
import asyncio
import logging
from functools import lru_cache
from typing import Optional
logger = logging.getLogger(__name__)
class TokenCounter:
"""
Accurate token counter using Qwen3 tokenizer.
Singleton pattern for lazy initialization.
Usage:
counter = TokenCounter.get()
token_count = counter.count("Hello world")
token_ids = counter.encode("Hello world")
kv_bytes = counter.compute_kv_vram_bytes(token_count)
"""
_instance: Optional["TokenCounter"] = None
def __init__(
self,
model_id: str = "Qwen/Qwen3-235B-A22B",
use_fast: bool = True,
):
self._model_id = model_id
self._use_fast = use_fast
self._tokenizer = None
self._initialized = False
self._use_fallback = False
@classmethod
def get(cls, model_id: str = "Qwen/Qwen3-235B-A22B") -> "TokenCounter":
"""Get or create singleton instance."""
if cls._instance is None:
cls._instance = cls(model_id)
return cls._instance
@classmethod
def reset(cls) -> None:
"""Reset singleton (for testing)."""
cls._instance = None
def _ensure_initialized(self) -> None:
"""Lazy initialization of tokenizer."""
if self._initialized:
return
try:
from transformers import AutoTokenizer
self._tokenizer = AutoTokenizer.from_pretrained(
self._model_id,
trust_remote_code=True,
use_fast=self._use_fast,
)
self._initialized = True
logger.info(f"TokenCounter initialized with {self._model_id}")
except Exception as e:
logger.warning(f"Failed to load {self._model_id}: {e}. Using fallback.")
self._use_fallback = True
self._initialized = True
def count(self, text: str) -> int:
"""
Count tokens in text (blocking - use count_async in hot path).
Args:
text: Input string
Returns:
Number of tokens
"""
self._ensure_initialized()
if self._use_fallback:
# Rough fallback: ~0.75 tokens per word
return max(1, int(len(text.split()) * 0.75))
return len(self._tokenizer.encode(text, add_special_tokens=False))
def encode(self, text: str) -> list[int]:
"""
Encode text to token IDs (blocking).
Args:
text: Input string
Returns:
List of token IDs
"""
self._ensure_initialized()
if self._use_fallback:
return [hash(w) % 50000 for w in text.split()]
return self._tokenizer.encode(text, add_special_tokens=False)
def decode(self, token_ids: list[int]) -> str:
"""Decode token IDs back to text."""
self._ensure_initialized()
if self._use_fallback:
return " ".join(str(t) for t in token_ids)
return self._tokenizer.decode(token_ids, skip_special_tokens=True)
async def count_async(self, text: str) -> int:
"""
Async token counting - non-blocking in hot path.
Args:
text: Input string
Returns:
Number of tokens
"""
loop = asyncio.get_event_loop()
return await loop.run_in_executor(None, self.count, text)
async def encode_async(self, text: str) -> list[int]:
"""
Async encoding - non-blocking in hot path.
Args:
text: Input string
Returns:
List of token IDs
"""
loop = asyncio.get_event_loop()
return await loop.run_in_executor(None, self.encode, text)
def compute_kv_vram_bytes(
self,
token_count: int,
n_layers: int = 64,
n_kv_heads: int = 8,
head_dim: int = 128,
dtype_bytes: int = 2, # fp16 = 2 bytes, bf16 = 2 bytes
) -> int:
"""
Compute VRAM bytes for KV cache given token count.
Formula: 2 (K+V) × layers × tokens × kv_heads × head_dim × dtype_bytes
Args:
token_count: Number of tokens in context
n_layers: Number of transformer layers (Qwen3-35B has 64)
n_kv_heads: Number of KV heads (Qwen3 uses GQA, typically 8)
head_dim: Dimension per head (typically 128 for Qwen)
dtype_bytes: Bytes per value (2 for fp16/bf16)
Returns:
VRAM bytes needed for KV cache
"""
return 2 * n_layers * token_count * n_kv_heads * head_dim * dtype_bytes
def compute_kv_vram_gb(
self,
token_count: int,
**kwargs
) -> float:
"""Compute VRAM in gigabytes."""
return self.compute_kv_vram_bytes(token_count, **kwargs) / (1024 ** 3)
# Convenience functions for use throughout codebase
def count_tokens(text: str) -> int:
"""Quick token count."""
return TokenCounter.get().count(text)
def encode_tokens(text: str) -> list[int]:
"""Quick token encode."""
return TokenCounter.get().encode(text)
def compute_kv_gb(token_count: int, **kwargs) -> float:
"""Quick KV VRAM compute in GB."""
return TokenCounter.get().compute_kv_vram_gb(token_count, **kwargs)
|