[Refactor] Refactor DeepEP dispatcher (#22822)

Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
Co-authored-by: Cheng Wan <54331508+ch-wan@users.noreply.github.com>

docs/advanced_features/server_arguments.md

@@ -320,6 +320,7 @@ Please consult the documentation below and [server_args.py](https://github.com/s
 | `--enable-flashinfer-allreduce-fusion` | Enable FlashInfer allreduce fusion with Residual RMSNorm. | `False` | bool flag (set to enable) |
 | `--enable-aiter-allreduce-fusion` | Enable aiter allreduce fusion with Residual RMSNorm. | `False` | bool flag (set to enable) |
 | `--deepep-mode` | Select the mode when enable DeepEP MoE, could be `normal`, `low_latency` or `auto`. Default is `auto`, which means `low_latency` for decode batch and `normal` for prefill batch. | `auto` | `normal`, `low_latency`, `auto` |
+|  `--deepep-dispatcher-output-dtype` | Select DeepEP dispather output dtype, could be `bf16`, `fp8`, `int8` (only Ascend A2/A3 NPU), `nvfp4` or `auto`. Default is `auto`, which follows a priority order (server argument → deprecated env var → input_global_scale check → dispatcher_output_dtype from quant_config → flashinfer/cutlass backend → NPU BF16 default → GPU FP8 default) | `auto` | `bf16`, `fp8`, `int8`, `nvfp4`, `auto` |
 | `--ep-num-redundant-experts` | Allocate this number of redundant experts in expert parallel. | `0` | Type: int |
 | `--ep-dispatch-algorithm` | The algorithm to choose ranks for redundant experts in expert parallel. | `None` | Type: str |
 | `--init-expert-location` | Initial location of EP experts. | `trivial` | Type: str |