[Bugfix] Fix accuracy issue of TRTLLM FP8 MOE and improve logging (vllm-project#25895)

Signed-off-by: Pavani Majety <pmajety@nvidia.com>

Author: pavanimajety

vllm/model_executor/layers/quantization/fp8.py

@@ -434,14 +434,9 @@ def __init__(self, quant_config: Fp8Config, layer: torch.nn.Module):
         self.weight_block_size = self.quant_config.weight_block_size
         self.block_quant = self.weight_block_size is not None
 
-        self.flashinfer_moe_backend: Optional[FlashinferMoeBackend] = None
         self.fused_experts: Optional[
             mk.FusedMoEModularKernel] = None  # type: ignore
-        if envs.VLLM_USE_FLASHINFER_MOE_FP8 and has_flashinfer_moe():
-            self.flashinfer_moe_backend = get_flashinfer_moe_backend()
-            logger.info_once(
-                f"Using FlashInfer {self.flashinfer_moe_backend.value} kernels"
-            )
+
         # For GPUs that lack FP8 hardware support, we can leverage the Marlin
         # kernel for fast weight-only FP8 quantization
         self.use_marlin = (not current_platform.has_device_capability(89)
@@ -450,14 +445,27 @@ def __init__(self, quant_config: Fp8Config, layer: torch.nn.Module):
         if current_platform.is_rocm():
             self.use_marlin = False
 
+        # First check for Flashinfer MOE on Blackwell GPUs
+        self.flashinfer_moe_backend: Optional[FlashinferMoeBackend] = None
+        if (current_platform.is_cuda()
+                and current_platform.is_device_capability(100)
+                and envs.VLLM_USE_FLASHINFER_MOE_FP8 and has_flashinfer_moe()):
+            self.flashinfer_moe_backend = get_flashinfer_moe_backend()
+            logger.info_once(
+                f"Detected Blackwell GPUs, using FlashInfer "
+                f"{self.flashinfer_moe_backend.value} kernels for FP8 MOE.")
+
         # Check for DeepGemm support.
         self.allow_deep_gemm = False
         if envs.VLLM_USE_DEEP_GEMM:
             if not has_deep_gemm():
                 logger.warning_once("Failed to import DeepGemm kernels.")
             elif not self.block_quant:
-                logger.warning_once("Model is not block quantized. Not using "
-                                    "DeepGemm kernels")
+                logger.warning_once("Model is not block quantized. Not using"
+                                    " DeepGemm kernels")
+            elif self.flashinfer_moe_backend:
+                logger.info_once("DeepGemm disabled: FlashInfer MOE is"
+                                 " enabled.")
             elif (is_deep_gemm_supported()):
                 logger.info_once("Using DeepGemm kernels for Fp8MoEMethod.")
                 self.allow_deep_gemm = True
@@ -471,15 +479,12 @@ def __init__(self, quant_config: Fp8Config, layer: torch.nn.Module):
             logger.debug_once("Model is not block quantized. Not using "
                               "CutlassBlockScaledGroupedGemm kernels")
         elif (current_platform.is_cuda()
-              and current_platform.is_device_capability(100)):
+              and current_platform.is_device_capability(100)
+              and not self.flashinfer_moe_backend):
             logger.info_once(
-                "Using CutlassBlockScaledGroupedGemm kernels for Fp8MoEMethod."
-            )
+                "Using CutlassBlockScaledGroupedGemm kernels for Fp8 MOE "
+                "on SM100.")
             self.allow_cutlass_block_scaled_grouped_gemm = True
-        else:
-            logger.warning_once(
-                "CutlassBlockScaledGroupedGemm not supported on the current "
-                "platform.")
 
     def create_weights(self, layer: Module, num_experts: int, hidden_size: int,
                        intermediate_size_per_partition: int,
@@ -934,7 +939,9 @@ def apply(
                 import vllm.model_executor.layers.fused_moe.flashinfer_trtllm_moe  # noqa: E501, F401
                 assert (renormalize and use_grouped_topk
                         and custom_routing_function is None)
-                result = torch.ops.vllm.flashinfer_fused_moe_blockscale_fp8(
+                e_score_correction_bias = (e_score_correction_bias.to(
+                    x.dtype) if e_score_correction_bias is not None else None)
+                return torch.ops.vllm.flashinfer_fused_moe_blockscale_fp8(
                     routing_logits=router_logits.to(torch.float32),
                     routing_bias=e_score_correction_bias,
                     x=x,

vllm/utils/deep_gemm.py

@@ -27,7 +27,8 @@ def is_deep_gemm_supported() -> bool:
     is_supported_arch = current_platform.is_cuda() and (
         current_platform.is_device_capability(90)
         or current_platform.is_device_capability(100))
-    return envs.VLLM_USE_DEEP_GEMM and has_deep_gemm() and is_supported_arch
+    return (envs.VLLM_USE_DEEP_GEMM and has_deep_gemm() and is_supported_arch
+            and not envs.VLLM_USE_FLASHINFER_MOE_FP8)
 
 
 @functools.cache
@@ -46,6 +47,10 @@ def is_deep_gemm_e8m0_used() -> bool:
         logger.info_once("DeepGEMM E8M0 disabled: _fp8_gemm_nt_impl not found")
         return False
 
+    if envs.VLLM_USE_FLASHINFER_MOE_FP8:
+        logger.info_once("DeepGEMM E8M0 disabled: FlashInfer MOE is enabled.")
+        return False
+
     if current_platform.is_device_capability(100) and \
             envs.VLLM_USE_DEEP_GEMM_E8M0:
         logger.info_once("DeepGEMM E8M0 enabled on Blackwell GPU.")