Adding flashinfer fp8 FusedMoE

Signed-off-by: Amir Klein <[email protected]>

Author: amirkl94

vllm/model_executor/layers/fused_moe/fused_moe.py

@@ -29,6 +29,8 @@
     TopKWeightAndReduceNoOP)
 from vllm.model_executor.layers.fused_moe.utils import (
     _resize_cache, moe_kernel_quantize_input, per_token_group_quant_fp8)
+from vllm.model_executor.layers.quantization.utils.flashinfer_utils import (
+    calculate_tile_tokens_dim)
 from vllm.model_executor.layers.quantization.utils.mxfp4_utils import (
     dequant_mxfp4)
 from vllm.platforms import current_platform
@@ -1061,22 +1063,6 @@ def inplace_fused_experts_fake(
 )
 
 
-def next_positive_power_of_2(x: int) -> int:
-    if x < 1:
-        return 1
-    return 1 << (x - 1).bit_length()
-
-
-def _get_tile_tokens_dim(num_tokens, top_k, num_experts):
-    # Guess tokens per expert assuming perfect expert distribution first.
-    num_tokens_per_expert = (num_tokens * top_k) // num_experts
-    # And pad the number to the next power of 2.
-    tile_tokens_dim = next_positive_power_of_2(num_tokens_per_expert)
-    # Cap to 8-64 tokens per CTA tile as it's the range supported by the kernel.
-    tile_tokens_dim = min(max(tile_tokens_dim, 8), 64)
-    return tile_tokens_dim
-
-
 def flashinfer_fused_moe_blockscale_fp8(
         routing_logits: torch.Tensor,
         routing_bias: torch.Tensor,
@@ -1124,8 +1110,8 @@ def flashinfer_fused_moe_blockscale_fp8(
         local_expert_offset=expert_offset,
         local_num_experts=local_num_experts,
         routed_scaling_factor=routed_scaling,
-        tile_tokens_dim=_get_tile_tokens_dim(x.shape[0], top_k,
-                                             global_num_experts),
+        tile_tokens_dim=calculate_tile_tokens_dim(x.shape[0], top_k,
+                                                  global_num_experts),
         routing_method_type=2,  # DeepSeek-styled routing method
         use_shuffled_weight=False,
     )
@@ -1160,6 +1146,101 @@ def flashinfer_fused_moe_blockscale_fp8_fake(
 )
 
 
+def flashinfer_fused_moe_per_tensor_scale_fp8(
+    routing_logits: torch.Tensor,
+    routing_bias: Optional[torch.Tensor],
+    hidden_states: torch.Tensor,
+    input_scale: torch.Tensor,
+    gemm1_weights: torch.Tensor,
+    gemm1_weights_scale: torch.Tensor,
+    activation_scale: torch.Tensor,
+    gemm2_weights: torch.Tensor,
+    gemm2_weights_scale: torch.Tensor,
+    num_experts: int,
+    top_k: int,
+    num_expert_group: Optional[int],
+    topk_group: Optional[int],
+    intermediate_size: int,
+    local_expert_offset: int,
+    local_num_experts: int,
+    use_routing_scales_on_input: bool,
+    routed_scaling_factor: float = 1.0,
+    routing_method_type: int = 3  # Llama4-styled routing method
+) -> torch.Tensor:
+    if routing_bias is None:
+        routing_bias = torch.zeros(num_experts,
+                                   dtype=torch.bfloat16,
+                                   device=hidden_states.device)
+    num_expert_group = num_expert_group if num_expert_group is not None else 1
+    topk_group = topk_group if topk_group is not None else 0
+
+    quant_hidden_states, input_scale = moe_kernel_quantize_input(
+        hidden_states,
+        input_scale,
+        quant_dtype=torch.float8_e4m3fn,
+        per_act_token_quant=False)
+
+    output1_scales_scalar = gemm1_weights_scale * input_scale * (
+        1.0 / activation_scale)
+    output1_scales_gate_scalar = gemm1_weights_scale * input_scale
+    output2_scales_scalar = activation_scale * gemm2_weights_scale
+
+    from vllm.utils.flashinfer import (
+        flashinfer_trtllm_fp8_per_tensor_scale_moe)
+    return flashinfer_trtllm_fp8_per_tensor_scale_moe(
+        routing_logits=routing_logits,
+        routing_bias=routing_bias,
+        hidden_states=quant_hidden_states,
+        gemm1_weights=gemm1_weights,
+        output1_scales_scalar=output1_scales_scalar,
+        output1_scales_gate_scalar=output1_scales_gate_scalar,
+        gemm2_weights=gemm2_weights,
+        output2_scales_scalar=output2_scales_scalar,
+        num_experts=num_experts,
+        top_k=top_k,
+        n_group=num_expert_group,
+        topk_group=topk_group,
+        intermediate_size=intermediate_size,
+        local_expert_offset=local_expert_offset,
+        local_num_experts=local_num_experts,
+        routed_scaling_factor=routed_scaling_factor,
+        use_routing_scales_on_input=use_routing_scales_on_input,
+        tile_tokens_dim=8,
+        routing_method_type=routing_method_type)
+
+
+def flashinfer_fused_moe_per_tensor_scale_fp8_fake(
+        routing_logits: torch.Tensor,
+        routing_bias: torch.Tensor,
+        hidden_states: torch.Tensor,
+        gemm1_weights: torch.Tensor,
+        output1_scales_scalar: torch.Tensor,
+        output1_scales_gate_scalar: torch.Tensor,
+        gemm2_weights: torch.Tensor,
+        output2_scales_scalar: torch.Tensor,
+        num_experts: int,
+        top_k: int,
+        num_expert_group: int,
+        topk_group: int,
+        intermediate_size: int,
+        local_expert_offset: int,
+        local_num_experts: int,
+        routed_scaling_factor: float = 1.0,
+        use_routing_scales_on_input: bool = False,
+        tile_tokens_dim: int = 8,
+        routing_method_type: int = 0) -> torch.Tensor:
+    pass
+
+
+direct_register_custom_op(
+    op_name="flashinfer_fused_moe_per_tensor_scale_fp8",
+    op_func=flashinfer_fused_moe_per_tensor_scale_fp8,
+    mutates_args=["hidden_states"],
+    fake_impl=flashinfer_fused_moe_per_tensor_scale_fp8_fake,
+    tags=(torch.Tag.needs_fixed_stride_order, ),
+)
+
+
 def outplace_fused_experts(
         hidden_states: torch.Tensor,
         w1: torch.Tensor,

vllm/model_executor/layers/quantization/fp8.py

@@ -23,6 +23,8 @@
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig, QuantizeMethodBase)
 from vllm.model_executor.layers.quantization.kv_cache import BaseKVCacheMethod
+from vllm.model_executor.layers.quantization.utils.flashinfer_utils import (
+    rotate_flashinfer_fp8_moe_weights, swap_w13_to_w31)
 from vllm.model_executor.layers.quantization.utils.fp8_utils import (
     get_col_major_tma_aligned_tensor, requant_weight_ue8m0_inplace)
 from vllm.model_executor.layers.quantization.utils.marlin_utils_fp8 import (
@@ -53,11 +55,6 @@
 logger = init_logger(__name__)
 
 
-def _swap_w13_to_w31(x: torch.Tensor) -> torch.Tensor:
-    return x.reshape(-1, 2, x.shape[-2] // 2,
-                     x.shape[-1]).flip(dims=[1]).reshape(x.shape)
-
-
 def _is_col_major(x: torch.Tensor) -> bool:
     assert x.dim() == 3
     b, m, n = x.shape
@@ -695,11 +692,13 @@ def process_weights_after_loading(self, layer: Module) -> None:
             elif self.flashinfer_moe_enabled:
                 # NOTE: weights have to be swapped since the activation is
                 # applied on different half for flashinfer vs vllm
-                w13_weight = _swap_w13_to_w31(layer.w13_weight.data)
-                w13_weight_scale_inv = _swap_w13_to_w31(
+                w13_weight = swap_w13_to_w31(layer.w13_weight.data)
+                w13_weight_scale_inv = swap_w13_to_w31(
                     layer.w13_weight_scale_inv.data)
                 w2_weight = layer.w2_weight.data
                 w2_weight_scale_inv = layer.w2_weight_scale_inv.data
+                if not self.block_quant:
+                    rotate_flashinfer_fp8_moe_weights(w13_weight, w2_weight)
             else:
                 w13_weight = layer.w13_weight.data
                 w13_weight_scale_inv = layer.w13_weight_scale_inv.data
@@ -998,30 +997,53 @@ def apply(
                 global_num_experts=global_num_experts,
                 expert_map=expert_map)
         elif self.flashinfer_moe_enabled:
-            # Currently only work with DS models
-            assert self.block_quant
-            assert (renormalize and use_grouped_topk
-                    and scoring_func == 'sigmoid'
-                    and custom_routing_function is None)
-            assert activation == "silu"
-            return torch.ops.vllm.flashinfer_fused_moe_blockscale_fp8(
-                routing_logits=router_logits.to(torch.float32),
-                routing_bias=e_score_correction_bias,
-                x=x,
-                w13_weight=layer.w13_weight,
-                w13_weight_scale_inv=layer.w13_weight_scale_inv,
-                w2_weight=layer.w2_weight,
-                w2_weight_scale_inv=layer.w2_weight_scale_inv,
-                global_num_experts=global_num_experts,
-                top_k=top_k,
-                num_expert_group=num_expert_group,
-                topk_group=topk_group,
-                intermediate_size=layer.intermediate_size_per_partition,
-                expert_offset=layer.ep_rank * layer.local_num_experts,
-                local_num_experts=layer.local_num_experts,
-                block_shape=self.quant_config.weight_block_size,
-                routed_scaling=1.0,
-            )
+            assert activation == 'silu'
+            assert scoring_func == 'sigmoid'
+            if self.block_quant:
+                assert (renormalize and use_grouped_topk
+                        and custom_routing_function is 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,
+                    w13_weight=layer.w13_weight,
+                    w13_weight_scale_inv=layer.w13_weight_scale_inv,
+                    w2_weight=layer.w2_weight,
+                    w2_weight_scale_inv=layer.w2_weight_scale_inv,
+                    global_num_experts=global_num_experts,
+                    top_k=top_k,
+                    num_expert_group=num_expert_group,
+                    topk_group=topk_group,
+                    intermediate_size=layer.intermediate_size_per_partition,
+                    expert_offset=layer.ep_rank * layer.local_num_experts,
+                    local_num_experts=layer.local_num_experts,
+                    block_shape=self.quant_config.weight_block_size,
+                    routed_scaling=1.0,
+                )
+            else:
+                assert (not renormalize
+                        and custom_routing_function is not None)
+                return torch.ops.vllm.flashinfer_fused_moe_per_tensor_scale_fp8(
+                    routing_logits=router_logits,
+                    routing_bias=e_score_correction_bias,
+                    hidden_states=x,
+                    input_scale=layer.w13_input_scale,
+                    gemm1_weights=layer.w13_weight,
+                    gemm1_weights_scale=layer.w13_weight_scale,
+                    gemm2_weights=layer.w2_weight,
+                    gemm2_weights_scale=layer.w2_weight_scale,
+                    activation_scale=layer.w2_input_scale,
+                    num_experts=global_num_experts,
+                    top_k=top_k,
+                    num_expert_group=num_expert_group,
+                    topk_group=topk_group,
+                    intermediate_size=layer.intermediate_size_per_partition,
+                    local_expert_offset=layer.ep_rank *
+                    layer.local_num_experts,
+                    local_num_experts=layer.local_num_experts,
+                    use_routing_scales_on_input=apply_router_weight_on_input,
+                )
         else:
             return self.fused_experts(
                 hidden_states=x,

vllm/model_executor/layers/quantization/modelopt.py

@@ -24,6 +24,8 @@
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig, QuantizeMethodBase)
 from vllm.model_executor.layers.quantization.kv_cache import BaseKVCacheMethod
+from vllm.model_executor.layers.quantization.utils.flashinfer_utils import (
+    rotate_flashinfer_fp8_moe_weights, swap_w13_to_w31)
 from vllm.model_executor.layers.quantization.utils.marlin_utils_fp4 import (
     apply_fp4_marlin_linear, is_fp4_marlin_supported,
     prepare_fp4_layer_for_marlin, prepare_moe_fp4_layer_for_marlin)
@@ -35,6 +37,7 @@
                                            PerTensorScaleParameter)
 from vllm.platforms import current_platform
 from vllm.scalar_type import scalar_types
+from vllm.utils.flashinfer import has_flashinfer_moe
 
 logger = init_logger(__name__)
 
@@ -268,6 +271,11 @@ def __init__(self, quant_config: ModelOptFp8Config):
         from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
             cutlass_fp8_supported)
         self.cutlass_fp8_supported = cutlass_fp8_supported()
+        self.flashinfer_moe_enabled = False
+        if envs.VLLM_USE_FLASHINFER_MOE_FP8 and has_flashinfer_moe():
+            logger.info_once(
+                "Using FlashInfer MoE FP8 kernels for ModelOptFp8MoEMethod.")
+            self.flashinfer_moe_enabled = True
 
     def create_weights(
         self,
@@ -411,6 +419,11 @@ def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
             layer.w2_input_scale = Parameter(layer.w2_input_scale.max(),
                                              requires_grad=False)
 
+        if self.flashinfer_moe_enabled:
+            layer.w13_weight.data = swap_w13_to_w31(layer.w13_weight.data)
+            rotate_flashinfer_fp8_moe_weights(layer.w13_weight,
+                                              layer.w2_weight)
+
     def apply(
         self,
         layer: torch.nn.Module,
@@ -437,6 +450,29 @@ def apply(
             raise NotImplementedError(
                 "EPLB not supported for `ModelOptFp8MoEMethod` yet.")
 
+        if self.flashinfer_moe_enabled:
+            assert activation == 'silu'
+            assert not renormalize
+            return torch.ops.vllm.flashinfer_fused_moe_per_tensor_scale_fp8(
+                routing_logits=router_logits,
+                routing_bias=e_score_correction_bias,
+                hidden_states=x,
+                input_scale=layer.w13_input_scale,
+                gemm1_weights=layer.w13_weight,
+                gemm1_weights_scale=layer.w13_weight_scale,
+                gemm2_weights=layer.w2_weight,
+                gemm2_weights_scale=layer.w2_weight_scale,
+                activation_scale=layer.w2_input_scale,
+                num_experts=global_num_experts,
+                top_k=top_k,
+                num_expert_group=num_expert_group,
+                topk_group=topk_group,
+                intermediate_size=layer.intermediate_size_per_partition,
+                local_expert_offset=layer.ep_rank * layer.local_num_experts,
+                local_num_experts=layer.local_num_experts,
+                use_routing_scales_on_input=apply_router_weight_on_input,
+            )
+
         # Expert selection
         topk_weights, topk_ids = FusedMoE.select_experts(
             hidden_states=x,

vllm/model_executor/layers/quantization/utils/flashinfer_utils.py

@@ -0,0 +1,60 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import torch
+
+
+def calculate_tile_tokens_dim(num_tokens, top_k, num_experts):
+    from flashinfer import next_positive_power_of_2
+
+    # Guess tokens per expert assuming perfect expert distribution first.
+    num_tokens_per_expert = (num_tokens * top_k) // num_experts
+    # And pad the number to the next power of 2.
+    tile_tokens_dim = next_positive_power_of_2(num_tokens_per_expert)
+    # Cap to 8-64 tokens per CTA tile as it's the range supported by the kernel.
+    tile_tokens_dim = min(max(tile_tokens_dim, 8), 64)
+    return tile_tokens_dim
+
+
+def swap_w13_to_w31(x: torch.Tensor) -> torch.Tensor:
+    return x.reshape(-1, 2, x.shape[-2] // 2,
+                     x.shape[-1]).flip(dims=[1]).reshape(x.shape)
+
+
+def rotate_flashinfer_fp8_moe_weights(gemm1_weights: torch.Tensor,
+                                      gemm2_weights: torch.Tensor):
+    from flashinfer import reorder_rows_for_gated_act_gemm, shuffle_matrix_a
+    epilogue_tile_m = 128
+    num_experts = gemm1_weights.shape[0]
+    hidden_size = gemm1_weights.shape[-1]
+    intermediate_size = gemm1_weights.shape[1] // 2
+
+    # Reorder rows of W1 for fused gated activation
+    gemm1_weights_fp8_interleaved = []
+    for i in range(num_experts):
+        gemm1_weights_fp8_interleaved.append(
+            reorder_rows_for_gated_act_gemm(gemm1_weights[i]))
+
+    # Stack weights and scales for all experts
+    gemm1_weights_fp8_interleaved = torch.stack(
+        gemm1_weights_fp8_interleaved).reshape(num_experts,
+                                               2 * intermediate_size,
+                                               hidden_size)
+
+    # Shuffle weights and scaling factors for transposed mma output
+    gemm1_weights_fp8_shuffled = []
+    gemm2_weights_fp8_shuffled = []
+    for i in range(num_experts):
+        gemm1_weights_fp8_shuffled.append(
+            shuffle_matrix_a(
+                gemm1_weights_fp8_interleaved[i].view(torch.uint8),
+                epilogue_tile_m))
+
+        gemm2_weights_fp8_shuffled.append(
+            shuffle_matrix_a(gemm2_weights[i].view(torch.uint8),
+                             epilogue_tile_m))
+
+    # Stack weights for all experts
+    gemm1_weights.data = torch.stack(gemm1_weights_fp8_shuffled).view(
+        torch.float8_e4m3fn)
+    gemm2_weights.data = torch.stack(gemm2_weights_fp8_shuffled).view(
+        torch.float8_e4m3fn)