[MoE] Align Swiglu MXFP4 fused quant paths (#3123)

* [MoE] align Swiglu MXFP4 fused quant paths

Remove the GPT-OSS Swiglu layout env switch in favor of GateMode, align the CSV test filter with runtime dtype selection, and restore FlyDSL Swiglu _fp4 fused quant accuracy by matching the non-fused bf16 stage1 semantics.

Co-authored-by: Cursor <cursoragent@cursor.com>

* [MoE] keep Swiglu MXFP4 fused quant in fp32

---------

Co-authored-by: Cursor <cursoragent@cursor.com>

Author: XiaobingSuper

aiter/fused_moe.py

@@ -25,9 +25,6 @@
 # Default to Opus unless CK sorting is explicitly requested.
 _USE_CK_MOE_SORTING = os.environ.get("AITER_USE_CK_MOE_SORTING", "0") == "1"
 _ACT_TYPE_DISABLED_KEY = "__ignore__"
-_USE_GENERIC_SWIGLU_MXFP4_LAYOUT = (
-    os.environ.get("GPTOSS_USE_GENERIC_SWIGLU_MXFP4_LAYOUT", "0") == "1"
-)
 _SWIGLU_MXFP4_BF16_BOUND = int(os.environ.get("GPTOSS_SWIGLU_MXFP4_BF16_BOUND", "256"))
 
 
@@ -308,7 +305,7 @@ def fused_moe_(
         # a16wi4: bf16 activations, int4 weights with groupwise scale
         q_dtype_a = dtypes.bf16
     elif quant_type == QuantType.per_1x32:
-        if activation == ActivationType.Swiglu and _USE_GENERIC_SWIGLU_MXFP4_LAYOUT:
+        if activation == ActivationType.Swiglu and gate_mode == GateMode.SEPARATED:
             q_dtype_a = dtypes.bf16 if M < _SWIGLU_MXFP4_BF16_BOUND else dtypes.fp4x2
         elif activation == ActivationType.Swiglu or gate_mode == GateMode.INTERLEAVE:
             if get_gfx() != "gfx950" or M < bf16_fp8_bound:
@@ -334,6 +331,7 @@ def fused_moe_(
         hidden_pad,
         intermediate_pad,
         isShuffled,
+        gate_mode,
     )
 
     block_size_M = metadata.block_m if block_size_M is None else block_size_M
@@ -410,6 +408,7 @@ def fused_moe_(
             topk_weights=topk_weight,
             # only for flydsl dsv4
             swiglu_limit=swiglu_limit,
+            gate_mode=gate_mode,
         )
 
 
@@ -819,7 +818,9 @@ def get_2stage_cfgs(
     hidden_pad,
     intermediate_pad,
     is_shuffled=True,
+    gate_mode=GateMode.SEPARATED.value,
 ):
+    gate_mode = GateMode(gate_mode)
     _INDEX_COLS = [
         "cu_num",
         "token",
@@ -1160,7 +1161,7 @@ def get_block_m() -> int:
             stage2_has_bias=enable_bias and is_flydsl2,
         )
     if (
-        not _USE_GENERIC_SWIGLU_MXFP4_LAYOUT
+        gate_mode != GateMode.SEPARATED
         and dtype in [dtypes.bf16, dtypes.fp16]
         and q_type == QuantType.per_1x32
         and activation == ActivationType.Swiglu
@@ -1449,8 +1450,10 @@ def fused_moe_2stages(
     topk_ids=None,
     topk_weights=None,
     swiglu_limit=0.0,
+    gate_mode=GateMode.SEPARATED.value,
 ):
     quant_func = get_quant(quant_type)
+    gate_mode = GateMode(gate_mode)
     token_num, _ = hidden_states.shape
     E, model_dim, inter_dim = get_inter_dim(w1.shape, w2.shape)
     dtype = moe_out.dtype
@@ -1472,6 +1475,7 @@ def fused_moe_2stages(
         hidden_pad,
         intermediate_pad,
         is_shuffled,
+        gate_mode,
     )
     if (
         quant_type == QuantType.per_1x32

aiter/ops/flydsl/kernels/silu_and_mul_fq.py

@@ -297,10 +297,13 @@ def _f32_to_e2m1(qx_f32):
                             )
                         gate = g
                         linear = u
-
-                        t = g * neg_log2e
-
-                        if const_expr(swiglu_limit != 0 and act != "swiglu"):
+                        t = gate * neg_log2e
+                        if const_expr(act == "swiglu"):
+                            gate = arith.minimumf(gate, _limit)
+                            linear = arith.minimumf(linear, _limit)
+                            linear = arith.maximumf(linear, _neg_limit)
+                            t = gate * swiglu_neg_alpha_log2e
+                        elif const_expr(swiglu_limit != 0 and act != "swiglu"):
                             gate = arith.minimumf(gate, _limit)
                             linear = arith.minimumf(linear, _limit)
                             linear = arith.maximumf(linear, _neg_limit)
@@ -314,9 +317,10 @@ def _f32_to_e2m1(qx_f32):
                             f32, "llvm.amdgcn.rcp.f32", [den], [], []
                         )
                         if const_expr(act == "swiglu"):
-                            act_vals.append(gate * sig * (linear + c1_f32))
+                            act_v = gate * sig * (linear + c1_f32)
                         else:
-                            act_vals.append(gate * sig * linear)
+                            act_v = gate * sig * linear
+                        act_vals.append(act_v)
 
                     if const_expr(_need_quant):
                         local_max = c0_f32

op_tests/test_moe_2stage.py

@@ -22,6 +22,8 @@
 from aiter.fused_moe import (
     fused_topk,
     fused_moe,
+    get_2stage_cfgs,
+    get_padded_M,
     torch_moe_stage1,
     torch_moe_stage2,
 )
@@ -244,13 +246,47 @@ def weight_per_128x128_quant(weight, quant_dtype):
         w2_scale_aiter = fp4_utils.e8m0_shuffle(w2_scale)
 
     # # ######################## stage 1 start ###########
+    stage1_ref_dtype = dtype
+    if (
+        actType == aiter.ActivationType.Swiglu
+        and qType == aiter.QuantType.per_1x32
+        and WQDType == dtypes.fp4x2
+    ):
+        runtime_aq_dtype = _runtime_swiglu_mxfp4_q_dtype_a(
+            token, actType, gateMode, qType, AQDType, WQDType
+        )
+        if runtime_aq_dtype == dtypes.fp4x2:
+            metadata = get_2stage_cfgs(
+                get_padded_M(token),
+                model_dim,
+                inter_dim,
+                E,
+                topk,
+                dtype,
+                runtime_aq_dtype,
+                WQDType,
+                qType,
+                w1.shape[1] == (inter_dim * 2),
+                actType,
+                doweight_stage1,
+                hidden_pad,
+                intermediate_pad,
+                getattr(w1_qt_aiter, "is_shuffled", False)
+                or getattr(w2_qt_aiter, "is_shuffled", False),
+                gateMode,
+            )
+            if metadata.fuse_quant == "fp4":
+                # Fused Swiglu MXFP4 quantizes the f32 activation directly.
+                # Keep the torch reference at f32 until the quantization step.
+                stage1_ref_dtype = dtypes.fp32
+
     out1_ref = torch_moe_stage1(
         a1_qt,
         w1_qt,
         w2_qt,
         topk_weights,
         topk_ids,
-        dtype=dtype,
+        dtype=stage1_ref_dtype,
         activation=actType,
         quant_type=qType,
         a1_scale=a1_scale,
@@ -544,6 +580,8 @@ def _row_to_kwargs(row):
     aq_dtype = _str2dtype(row["q_dtype_a"])
     wq_dtype = _str2dtype(row["q_dtype_w"])
     act_type = _str2enum(row["act_type"], aiter.ActivationType)
+    # Tuned CSV rows do not carry gate mode explicitly. Infer the runtime mode
+    # from the selected activation/weight dtype layout used by fused_moe.
     gate_mode = _effective_gate_mode(aq_dtype, wq_dtype)
     return dict(
         dtype=_str2dtype(row["dtype"]),
@@ -594,6 +632,7 @@ def _iter_csv_cases():
         expected_aq_dtype = _runtime_swiglu_mxfp4_q_dtype_a(
             kwargs["token"],
             kwargs["actType"],
+            kwargs["gateMode"],
             kwargs["qType"],
             kwargs["AQDType"],
             kwargs["WQDType"],
@@ -634,7 +673,9 @@ def _effective_swiglu_limit(quant_type, aq_dtype, wq_dtype, swiglu_limit):
     return 0.0
 
 
-def _runtime_swiglu_mxfp4_q_dtype_a(token, act_type, q_type, aq_dtype, wq_dtype):
+def _runtime_swiglu_mxfp4_q_dtype_a(
+    token, act_type, gate_mode, q_type, aq_dtype, wq_dtype
+):
     """Return the q_dtype_a that fused_moe will select for Swiglu MXFP4."""
     if act_type != aiter.ActivationType.Swiglu:
         return None
@@ -643,11 +684,12 @@ def _runtime_swiglu_mxfp4_q_dtype_a(token, act_type, q_type, aq_dtype, wq_dtype)
     if aq_dtype not in [dtypes.bf16, dtypes.fp16, dtypes.fp8, dtypes.fp4x2]:
         return None
 
-    if os.environ.get("GPTOSS_USE_GENERIC_SWIGLU_MXFP4_LAYOUT", "0") == "1":
+    gate_mode = GateMode(gate_mode)
+    if gate_mode == GateMode.SEPARATED:
         bound = int(os.environ.get("GPTOSS_SWIGLU_MXFP4_BF16_BOUND", "256"))
         return dtypes.bf16 if token < bound else dtypes.fp4x2
 
-    bound = int(os.environ.get("AITER_BF16_FP8_BOUND", "512"))
+    bound = int(os.environ.get("AITER_BF16_FP8_MOE_BOUND", "256"))
     return dtypes.bf16 if get_gfx() != "gfx950" or token < bound else dtypes.fp8