Bugfix: Cutlass FP8 FusedMoE bad scaling factors (#27255)

Signed-off-by: Amir Klein <203507526+amirkl94@users.noreply.github.com>
Co-authored-by: Tyler Michael Smith <tyler@neuralmagic.com>
Co-authored-by: Michael Goin <mgoin64@gmail.com>

tests/kernels/moe/test_flashinfer.py

@@ -6,7 +6,10 @@ import pytest
 import torch
 
 from vllm.config import ParallelConfig, VllmConfig, set_current_vllm_config
-from vllm.model_executor.layers.fused_moe.config import fp8_w8a8_moe_quant_config
+from vllm.model_executor.layers.fused_moe.config import (
+    FusedMoEQuantConfig,
+    fp8_w8a8_moe_quant_config,
+)
 from vllm.model_executor.layers.fused_moe.fused_moe import fused_experts
 from vllm.model_executor.layers.fused_moe.layer import FusedMoE
 from vllm.model_executor.layers.quantization.utils.flashinfer_utils import (
@@ -22,10 +25,10 @@ from vllm.platforms import current_platform
 from vllm.utils.flashinfer import has_flashinfer_cutlass_fused_moe
 
 if not has_flashinfer_cutlass_fused_moe() or not current_platform.has_device_capability(
-    100
+    90
 ):
     pytest.skip(
-        "Requires flashinfer_cutlass_fused_moe and nvfp4 support",
+        "Supported for sm >= 90",
         allow_module_level=True,
     )
 
@@ -131,6 +134,8 @@ def test_flashinfer_per_tensor_moe_fp8_no_graph(
     topk: int,
     monkeypatch,
 ):
+    if not current_platform.has_device_capability(100):
+        pytest.skip("Test is only supported for sm >= 100")
     current_platform.seed_everything(7)
     monkeypatch.setenv("VLLM_FUSED_MOE_CHUNK_SIZE", "8192")
     with set_current_vllm_config(vllm_config):
@@ -184,9 +189,6 @@ def test_flashinfer_per_tensor_moe_fp8_no_graph(
         torch.testing.assert_close(output, flashinfer_output, atol=5.5e-2, rtol=1e-2)
 
 
-@pytest.mark.skip(
-    "Requires flashinfer version that contains https://github.com/flashinfer-ai/flashinfer/pull/1472"
-)
 @pytest.mark.parametrize("m,n,k", MNK_FACTORS)
 @pytest.mark.parametrize("e", NUM_EXPERTS)
 @pytest.mark.parametrize("topk", TOP_KS)
@@ -216,9 +218,13 @@ def test_flashinfer_cutlass_moe_fp8_no_graph(
 
         quant_config = fp8_w8a8_moe_quant_config(
             w1_scale=td.w13_weight_scale,
+            g1_alphas=(td.w13_weight_scale * td.a1_scale).squeeze(),
             w2_scale=td.w2_weight_scale,
+            g2_alphas=(td.w2_weight_scale * td.a2_scale).squeeze(),
             a1_scale=td.a1_scale,
+            a1_gscale=td.a1_scale,
             a2_scale=td.a2_scale,
+            a2_gscale=1.0 / td.a2_scale,
             per_act_token_quant=False,
         )
 
@@ -238,6 +244,12 @@ def test_flashinfer_cutlass_moe_fp8_no_graph(
 
         td.layer.dp_size = 1
 
+        def get_fused_moe_quant_config(n: torch.nn.Module) -> FusedMoEQuantConfig:
+            return quant_config
+
+        td.layer.get_fused_moe_quant_config = get_fused_moe_quant_config
+        td.layer.quant_method = td.layer
+
         flashinfer_cutlass_output = flashinfer_cutlass_moe_fp8(
             td.hidden_states,
             td.layer,

vllm/model_executor/layers/fused_moe/config.py

@@ -463,6 +463,10 @@ def fp8_w8a8_moe_quant_config(
     per_act_token_quant: bool = False,
     per_out_ch_quant: bool = False,
     block_shape: list[int] | None = None,
+    a1_gscale: torch.Tensor | None = None,
+    a2_gscale: torch.Tensor | None = None,
+    g1_alphas: torch.Tensor | None = None,
+    g2_alphas: torch.Tensor | None = None,
 ) -> FusedMoEQuantConfig:
     """
     Construct a quant config for fp8 activations and fp8 weights.
@@ -470,9 +474,13 @@ def fp8_w8a8_moe_quant_config(
     return FusedMoEQuantConfig.make(
         torch.float8_e4m3fn,
         w1_scale=w1_scale,
+        g1_alphas=g1_alphas,
         w2_scale=w2_scale,
+        g2_alphas=g2_alphas,
         a1_scale=a1_scale,
+        a1_gscale=a1_gscale,
         a2_scale=a2_scale,
+        a2_gscale=a2_gscale,
         per_act_token_quant=per_act_token_quant,
         per_out_ch_quant=per_out_ch_quant,
         block_shape=block_shape,

vllm/model_executor/layers/fused_moe/flashinfer_cutlass_prepare_finalize.py

@@ -170,7 +170,7 @@ class FlashInferAllGatherMoEPrepareAndFinalize(FlashInferCutlassMoEPrepareAndFin
         self._apply_router_weight_on_input(
             a1, topk_weights, topk_ids, apply_router_weight_on_input
         )
-        if not self.use_dp:
+        if not self.use_dp and quant_config.quant_dtype == "nvfp4":
             return a1, None, None, topk_ids, topk_weights
 
         a1q, a1q_scale = moe_kernel_quantize_input(
@@ -181,11 +181,13 @@ class FlashInferAllGatherMoEPrepareAndFinalize(FlashInferCutlassMoEPrepareAndFin
             quant_config.block_shape,
             is_fp4_scale_swizzled=not self.use_dp,
         )
-        topk_weights, topk_ids, a1q, a1q_scale = get_dp_group().all_gatherv(
-            [topk_weights, topk_ids, a1q, a1q_scale],
-            dim=0,
-            sizes=get_local_sizes(),
-        )
+
+        if self.use_dp:
+            topk_weights, topk_ids, a1q, a1q_scale = get_dp_group().all_gatherv(
+                [topk_weights, topk_ids, a1q, a1q_scale],
+                dim=0,
+                sizes=get_local_sizes(),
+            )
         if quant_config.quant_dtype == "nvfp4":
             a1q_scale = nvfp4_block_scale_interleave(a1q_scale)
 

vllm/model_executor/layers/quantization/modelopt.py

@@ -567,9 +567,13 @@ class ModelOptFp8MoEMethod(FusedMoEMethodBase):
 
         return fp8_w8a8_moe_quant_config(
             w1_scale=layer.w13_weight_scale,
+            g1_alphas=(layer.w13_weight_scale * layer.w13_input_scale).squeeze(),
             w2_scale=layer.w2_weight_scale,
+            g2_alphas=(layer.w2_weight_scale * layer.w2_input_scale).squeeze(),
             a1_scale=layer.w13_input_scale,
+            a1_gscale=layer.w13_input_scale,
             a2_scale=layer.w2_input_scale,
+            a2_gscale=1.0 / layer.w2_input_scale,
             per_act_token_quant=False,
         )
 
@@ -1138,8 +1142,8 @@ class ModelOptNvFp4FusedMoE(FusedMoEMethodBase):
         moe: FusedMoEConfig,
         layer: torch.nn.Module,
     ) -> None:
-        from vllm.model_executor.layers.quantization.utils.nvfp4_moe_support import (  # noqa: E501
-            detect_nvfp4_moe_support,
+        from vllm.model_executor.layers.quantization.utils.nvfp4_moe_support import (
+            detect_nvfp4_moe_support,  # noqa: E501
         )
 
         super().__init__(moe)