Quantized KV cache: update quanto (huggingface#31052)

* quanto latest version was refactored

* add error msg

* incorrect compare sign

* Update src/transformers/cache_utils.py

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

---------

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

src/transformers/cache_utils.py

@@ -1,17 +1,21 @@
 import copy
+import importlib.metadata
 import json
 import os
 from dataclasses import dataclass
 from typing import Any, Dict, List, Optional, Tuple, Union
 
 import torch
+from packaging import version
 
 from .configuration_utils import PretrainedConfig
 from .utils import is_hqq_available, is_quanto_available, logging
 
 
 if is_quanto_available():
-    from quanto import QBitsTensor, qint2, qint4
+    quanto_version = version.parse(importlib.metadata.version("quanto"))
+    if quanto_version >= version.parse("0.2.0"):
+        from quanto import AffineQuantizer, MaxOptimizer, qint2, qint4
 
 if is_hqq_available():
     from hqq.core.quantize import Quantizer as HQQQuantizer
@@ -488,6 +492,13 @@ class QuantoQuantizedCache(QuantizedCache):
 
     def __init__(self, cache_config: CacheConfig) -> None:
         super().__init__(cache_config)
+        quanto_version = version.parse(importlib.metadata.version("quanto"))
+        if quanto_version < version.parse("0.2.0"):
+            raise ImportError(
+                f"You need quanto package version to be greater or equal than 0.2.0 to use `QuantoQuantizedCache`. Detected version {quanto_version}. "
+                f"Please upgrade quanto with `pip install -U quanto`"
+            )
+
         if self.nbits not in [2, 4]:
             raise ValueError(f"`nbits` for `quanto` backend has to be one of [`2`, `4`] but got {self.nbits}")
 
@@ -500,9 +511,11 @@ def __init__(self, cache_config: CacheConfig) -> None:
             )
 
         self.qtype = qint4 if self.nbits == 4 else qint2
+        self.optimizer = MaxOptimizer()  # hardcode as it's the only one for per-channel quantization
 
     def _quantize(self, tensor, axis):
-        qtensor = QBitsTensor.quantize(tensor, axis=axis, qtype=self.qtype, group_size=self.q_group_size)
+        scale, zeropoint = self.optimizer(tensor, self.qtype.bits, axis, self.q_group_size)
+        qtensor = AffineQuantizer.apply(tensor, self.qtype, axis, self.q_group_size, scale, zeropoint)
         return qtensor
 
     def _dequantize(self, qtensor):