Use KV cache constant names provided by compressed tensors (#1200)

## Purpose ##
* Harden code related to the kv_cache parameter names

---------

Signed-off-by: Kyle Sayers <kylesayrs@gmail.com>

Author: kylesayrs

src/llmcompressor/modifiers/quantization/calibration.py

@@ -1,7 +1,11 @@
 from typing import Any, Dict, Optional, Tuple
 
 import torch
-from compressed_tensors.quantization import QuantizationStatus, is_attention_module
+from compressed_tensors.quantization import (
+    KVCacheScaleType,
+    QuantizationStatus,
+    is_attention_module,
+)
 from compressed_tensors.quantization.lifecycle.forward import forward_quantize
 from compressed_tensors.quantization.utils import is_kv_cache_quant_scheme
 from compressed_tensors.utils.offload import is_module_offloaded, update_parameter_data
@@ -194,8 +198,10 @@ def calibrate_kv_cache_output_hook(module: Module, _args: Any, _output: torch.Te
     Hook to update k_scale and v_scale parameters when running kv_cache quantization.
     """
     kv_cache = getattr(module, "kv_cache")
-    update_parameter_data(module, kv_cache.k_scales[module.layer_idx], "k_scale")
-    update_parameter_data(module, kv_cache.v_scales[module.layer_idx], "v_scale")
+    k_scale = kv_cache.k_scales[module.layer_idx]
+    v_scale = kv_cache.v_scales[module.layer_idx]
+    update_parameter_data(module, k_scale, KVCacheScaleType.KEY.value)
+    update_parameter_data(module, v_scale, KVCacheScaleType.VALUE.value)
 
 
 def set_unset_kv_cache(module: Module):