[Quantization]300I Duo support w8a8 quantization (#1560)

### What this PR does / why we need it?
This pr supports w8a8 on 300I Duo platform. The main change is to use
`npu_quant_grouped_matmul_dequant` to replace `npu_grouped_matmul`.

### Does this PR introduce _any_ user-facing change?
No.

### How was this patch tested?
offline inference on 310p runs normally.

---------

Signed-off-by: angazenn <zengyanjia@huawei.com>
Signed-off-by: tianyitang <tangtianyi4@huawei.com>
Co-authored-by: angazenn <zengyanjia@huawei.com>
Co-authored-by: tianyitang <tangtianyi4@huawei.com>

vllm_ascend/utils.py

@@ -31,6 +31,7 @@ from torch_npu.npu.streams import Event
 from vllm.logger import logger
 
 import vllm_ascend.envs as envs
+from vllm_ascend.ascend_config import get_ascend_config
 
 try:
     # Recent release of torchair has moved these ops to `.scope`.
@@ -175,6 +176,28 @@ def aligned_16(tensor: torch.Tensor):
     return new_tensor
 
 
+def maybe_converting_weight_acl_format(model, format=ACL_FORMAT_FRACTAL_NZ):
+    # currently, there are some operations which do not support ACL_FORMAT_FRACTAL_NZ
+    # in eager mode but support it in torchair graph mode. since ACL_FORMAT_FRACTAL_NZ
+    # is much more preferred than ACL_FORMAT_FRACTAL_ND on 300I Duo, we add this
+    # conversion when using torchair graph mode on 300I Duo platform.
+    # TODO: we will remove this conversion if npu_quant_grouped_matmul_dequant
+    # accepts weight format of ACL_FORMAT_FRACTAL_NZ in eager mode.
+    from vllm.model_executor.layers.fused_moe.layer import FusedMoE
+
+    use_torchair = get_ascend_config().torchair_graph_config.enabled
+    if not is_310p() or not use_torchair:
+        return
+    for module in model.modules():
+        if isinstance(module, FusedMoE):
+            if torch_npu.get_npu_format(module.w13_weight.data) == format:
+                return
+            module.w13_weight.data = torch_npu.npu_format_cast(
+                module.w13_weight.data, format)
+            module.w2_weight.data = torch_npu.npu_format_cast(
+                module.w2_weight.data, format)
+
+
 def try_register_lib(lib_name: str, lib_info: str = ""):
     import importlib
     import importlib.util