Define kv cache scaling factor as amax / 448 (#790)

## What does this PR do?

**Overview:** ?

Unified the FP8 and NVFP4 kv cache scaling factor definition so the same
checkpoint can be used for both FP8 and NVFP4 kv cache quantization
deployment

## Testing
Unit test

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## Summary by CodeRabbit

## Release Notes

* **Refactor**
* Fixed KV cache maximum bound to 448 for FP8 and NVFP4 quantization,
simplifying configuration logic.

* **Chores**
  * Removed internal constants from public exports.

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---------

Signed-off-by: Chenjie Luo <108829653+cjluo-nv@users.noreply.github.com>
Signed-off-by: Chenjie Luo <chenjiel@nvidia.com>

modelopt/torch/export/unified_export_hf.py

@@ -56,9 +56,6 @@ from .layer_utils import (
     set_expert_quantizer_amax,
 )
 from .model_config import (
-    KV_CACHE_FP8,
-    KV_CACHE_NVFP4,
-    KV_CACHE_NVFP4_AFFINE,
     QUANTIZATION_FP8,
     QUANTIZATION_FP8_PB_REAL,
     QUANTIZATION_FP8_PC_PT,
@@ -647,19 +644,6 @@ def _export_transformers_checkpoint(
 
     quant_config = get_quant_config(model, is_modelopt_qlora=is_modelopt_qlora)
 
-    kv_cache_max_bound = 0
-    kv_cache_format = quant_config["quantization"]["kv_cache_quant_algo"]
-
-    cache_bound_mapping = {
-        KV_CACHE_NVFP4: 6 * 448,
-        KV_CACHE_NVFP4_AFFINE: 6 * 448,
-        KV_CACHE_FP8: 448,
-    }
-
-    # Only update kv_cache_max_bound if a quantization is applied.
-    if kv_cache_format != QUANTIZATION_NONE:
-        kv_cache_max_bound = cache_bound_mapping.get(kv_cache_format)
-
     # Process all quantized modules and export weights
     _process_quantized_modules(model, dtype, is_modelopt_qlora)
 
@@ -669,6 +653,9 @@ def _export_transformers_checkpoint(
     else:
         quantized_state_dict = model.state_dict()
 
+    # We define kv cache scale as amax / 448 for both FP8 and NVFP4 KV cache quantization.
+    kv_cache_max_bound = 448
+    kv_cache_format = quant_config["quantization"]["kv_cache_quant_algo"]
     quantized_state_dict = postprocess_state_dict(
         quantized_state_dict, kv_cache_max_bound, kv_cache_format, is_modelopt_qlora
     )