ROCm/AITER CK_MoE: update 2-stage kernels & support both Activations (#5228)

Author: HaiShaw

python/sglang/srt/layers/quantization/fp8.py

@@ -71,7 +71,8 @@ def prepare_fp8_layer_for_marlin(*args, **kwargs):
 _is_hip = is_hip()
 
 if _is_hip:
-    from aiter.fused_moe_bf16_asm import asm_moe
+    from aiter import ActivationType
+    from aiter.fused_moe_bf16_asm import asm_moe, ck_moe_2stages, ck_moe_2stages_win4
     from aiter.ops.shuffle import shuffle_weight
 
 _is_cuda = is_cuda()
@@ -487,7 +488,7 @@ def create_weights(
 
         if self.quant_config.is_checkpoint_fp8_serialized:
             params_dtype = (
-                torch.int32
+                torch.uint32
                 if get_bool_env_var("USE_INT4_WEIGHT")
                 else torch.float8_e4m3fn
             )
@@ -822,12 +823,14 @@ def process_weights_hip_int4(self, layer: Module):
         # INT4-FP8 (INT4 MoE Weight, FP8 Compute)
         # Weight Permutation
         layer.w13_weight = torch.nn.Parameter(
-            permute_weight(layer.w13_weight.data),
+            # permute_weight(layer.w13_weight.data),
+            shuffle_weight(layer.w13_weight.data, (16, 16)),
             requires_grad=False,
         )
         torch.cuda.empty_cache()
         layer.w2_weight = torch.nn.Parameter(
-            permute_weight(layer.w2_weight.data),
+            # permute_weight(layer.w2_weight.data),
+            shuffle_weight(layer.w2_weight.data, (16, 16)),
             requires_grad=False,
         )
         torch.cuda.empty_cache()
@@ -867,12 +870,14 @@ def process_weights_hip_scale_padding(self, layer: Module):
 
         if get_bool_env_var("CK_MOE"):
             layer.w13_weight = torch.nn.Parameter(
-                permute_weight(layer.w13_weight.data),
+                # permute_weight(layer.w13_weight.data),
+                shuffle_weight(layer.w13_weight.data, (16, 16)),
                 requires_grad=False,
             )
             torch.cuda.empty_cache()
             layer.w2_weight = torch.nn.Parameter(
-                permute_weight(layer.w2_weight.data),
+                # permute_weight(layer.w2_weight.data),
+                shuffle_weight(layer.w2_weight.data, (16, 16)),
                 requires_grad=False,
             )
             torch.cuda.empty_cache()
@@ -928,23 +933,25 @@ def apply(
         if _is_hip and get_bool_env_var("USE_INT4_WEIGHT"):
             # TODO: add triton kernel and add check get_bool_env_var("CK_MOE")
             assert not no_combine, f"{no_combine=} is not supported."
-            return asm_moe(
+            return ck_moe_2stages_win4(
                 x,
                 layer.w13_weight,
                 layer.w2_weight,
                 topk_weights,
                 topk_ids,
                 layer.w13_weight_scale1,
                 layer.w2_weight_scale1,
-                activation=activation,
+                activation=(
+                    ActivationType.Silu if activation == "silu" else ActivationType.Gelu
+                ),
             )
         if _is_hip and get_bool_env_var("CK_MOE"):
-            # TODO(CK_MOE): FP8 or FP8 block_quant only supports 'silu' for the time-being.
-            assert (
-                activation == "silu"
-            ), f"CK_MOE: FP8 and/or FP8 bloack_quant {activation=} will be supported later, unset CK_MOE"
             assert not no_combine, f"{no_combine=} is not supported."
             if self.block_quant:
+                # TODO(CK_MOE): FP8 block_quant only supports 'silu' for the time-being.
+                assert (
+                    activation == "silu"
+                ), f"CK_MOE: FP8 bloack_quant {activation=} will be supported later, unset CK_MOE"
                 return asm_moe(
                     x,
                     layer.w13_weight,
@@ -957,14 +964,19 @@ def apply(
                     expert_mask=None,
                 )
             else:
-                return asm_moe(
+                return ck_moe_2stages(
                     x,
                     layer.w13_weight,
                     layer.w2_weight,
                     topk_weights,
                     topk_ids,
                     layer.w13_weight_scale1,
                     layer.w2_weight_scale1,
+                    activation=(
+                        ActivationType.Silu
+                        if activation == "silu"
+                        else ActivationType.Gelu
+                    ),
                 )
         else:
             # Expert fusion with FP8 quantization