Dont quantize the current token for attention

examples/models/llama2/source_transformation/quantized_kv_cache.py

@@ -188,6 +188,27 @@ def update(self, input_pos, k_val, v_val):
             self.quantized_cache_dtype,
             self.cache_fp_type,
         )
+
+        if self.is_transposed:
+            if self.enable_dynamic_shape:
+                start_pos = input_pos[0].item()
+                torch._check_is_size(start_pos)
+                dim_to_slice = 2 if self.is_transposed else 1
+                torch._check(start_pos < self.k_cache.size(dim_to_slice))
+                seq_length = k_val.size(dim_to_slice)
+                narrowed_k = k_out.narrow(dim_to_slice, start_pos, seq_length)
+                narrowed_k.copy_(k_val)
+                # pyre-ignore: Incompatible parameter type [6]
+                narrowed_v = v_out.narrow(dim_to_slice, start_pos, seq_length)
+                narrowed_v.copy_(v_val)
+            else:
+                k_out[:, :, input_pos] = k_val
+                v_out[:, :, input_pos] = v_val
+        else:
+            start_pos = input_pos[0].item()
+            _ = torch.ops.llama.update_quantized_cache(k_val, k_out, start_pos)
+            _ = torch.ops.llama.update_quantized_cache(v_val, v_out, start_pos)
+
         return k_out, v_out
 
     @classmethod