fix: w4afp8 accuracy problem and rebase (#8752)

Signed-off-by: yangsijia.614 <yangsijia.614@bytedance.com>
Co-authored-by: Jinwu <ayrnb@users.noreply.github.com>

python/sglang/srt/layers/moe/cutlass_w4a8_moe.py

@@ -11,7 +11,7 @@ from sgl_kernel import (
 )
 
 from sglang.srt.layers.moe.ep_moe.kernels import (
-    post_reorder_triton_kernel,
+    post_reorder_triton_kernel_for_cutlass_moe,
     pre_reorder_triton_kernel_for_cutlass_moe,
     run_cutlass_moe_ep_preproess,
 )
@@ -199,14 +199,13 @@ def cutlass_w4a8_moe(
     )
 
     output = torch.empty_like(a)
-    post_reorder_triton_kernel[(m,)](
+    post_reorder_triton_kernel_for_cutlass_moe[(m,)](
         c2,
         output,
         src2dst,
-        topk_ids_,
+        local_topk_ids,
         topk_weights,
-        start_expert_id,
-        end_expert_id,
+        num_experts,
         topk,
         k,
         0,

python/sglang/srt/layers/moe/ep_moe/kernels.py

@@ -581,6 +581,49 @@ def post_reorder_triton_kernel(
             )
 
 
+@triton.jit
+def post_reorder_triton_kernel_for_cutlass_moe(
+    down_output_ptr,
+    output_ptr,
+    src2dst_ptr,
+    topk_ids_ptr,
+    topk_weights_ptr,
+    num_experts,
+    topk,
+    hidden_size,
+    dst_start,
+    BLOCK_SIZE: tl.constexpr,
+):
+    InDtype = down_output_ptr.dtype.element_ty
+
+    src_idx_int32 = tl.program_id(0)
+    src_idx = src_idx_int32.to(tl.int64)
+    src2dst_ptr = src2dst_ptr + src_idx * topk
+    topk_ids_ptr = topk_ids_ptr + src_idx * topk
+    topk_weights_ptr = topk_weights_ptr + src_idx * topk
+
+    store_ptr = output_ptr + src_idx * hidden_size
+
+    vec = tl.arange(0, BLOCK_SIZE)
+
+    for start_offset in tl.range(0, hidden_size, BLOCK_SIZE):
+        offset = start_offset + vec
+        mask = offset < hidden_size
+
+        sum_vec = tl.zeros([BLOCK_SIZE], dtype=InDtype)
+        for idx in range(topk):
+            expert_id = tl.load(topk_ids_ptr + idx)
+            if expert_id != num_experts:
+                dst_idx_int32 = tl.load(src2dst_ptr + idx)
+                dst_idx = dst_idx_int32.to(tl.int64)
+                dst_idx = dst_idx - dst_start
+                weigh_scale = tl.load(topk_weights_ptr + idx).to(InDtype)
+                load_ptr = down_output_ptr + dst_idx * hidden_size
+                in_data = tl.load(load_ptr + offset, mask=mask)
+                sum_vec += in_data * weigh_scale
+        tl.store(store_ptr + offset, sum_vec, mask=mask)
+
+
 @triton.jit
 def compute_m_range(
     pid,

python/sglang/srt/layers/quantization/w4afp8.py

@@ -116,6 +116,8 @@ class W4AFp8MoEMethod(FusedMoEMethodBase):
         params_dtype: torch.dtype,
         **extra_weight_attrs,
     ):
+        from sglang.srt.layers.moe.fused_moe_triton import FusedMoeWeightScaleSupported
+
         assert "weight_loader" in extra_weight_attrs
 
         # Fused gate_up_proj (column parallel)
@@ -144,6 +146,9 @@ class W4AFp8MoEMethod(FusedMoEMethodBase):
         layer.register_parameter("w2_weight", w2_weight)
         set_weight_attrs(w2_weight, extra_weight_attrs)
 
+        extra_weight_attrs.update(
+            {"quant_method": FusedMoeWeightScaleSupported.GROUP.value}
+        )
         w13_weight_scale = torch.nn.Parameter(
             torch.zeros(
                 num_experts,
@@ -274,8 +279,11 @@ class W4AFp8MoEMethod(FusedMoEMethodBase):
     def apply(
         self,
         layer: EPMoE,
-        hidden_states: torch.Tensor,
+        x: torch.Tensor,
         topk_output: TopKOutput,
+        activation: str = "silu",
+        apply_router_weight_on_input: bool = False,
+        routed_scaling_factor: Optional[float] = None,
         **kwargs,
     ) -> torch.Tensor:
 
@@ -284,19 +292,17 @@ class W4AFp8MoEMethod(FusedMoEMethodBase):
 
         topk_weights, topk_ids, _ = topk_output
         local_topk_ids = topk_ids
-        if layer.expert_map is not None:
-            "Translate info from expert_map to topk_ids"
-            local_topk_ids = torch.where(
-                layer.expert_map[topk_ids] != layer.num_experts,
-                layer.expert_map[topk_ids],
-                layer.num_experts,
-            )
-
-        return cutlass_w4a8_moe(
+        local_topk_ids = torch.where(
+            topk_ids == -1,
+            layer.num_experts,
+            topk_ids,
+        )
+
+        output = cutlass_w4a8_moe(
             layer.start_expert_id,
             layer.end_expert_id,
             layer.num_experts,
-            hidden_states,
+            x,
             layer.w13_weight,
             layer.w2_weight,
             layer.w13_weight_scale_inv,
@@ -318,3 +324,6 @@ class W4AFp8MoEMethod(FusedMoEMethodBase):
             layer.w13_input_scale,
             layer.w2_input_scale,
         )
+        if routed_scaling_factor is not None:
+            output *= routed_scaling_factor
+        return output