[NVIDIA] Add SM100 Flashinfer MoE per tensor scale fp8 backend (#21458)

Signed-off-by: Amir Klein <203507526+amirkl94@users.noreply.github.com>
Signed-off-by: mgoin <mgoin64@gmail.com>
Co-authored-by: mgoin <mgoin64@gmail.com>

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

@@ -0,0 +1,100 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from typing import Optional
+
+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)
+
+
+def apply_flashinfer_per_tensor_scale_fp8(
+    layer: torch.nn.Module,
+    hidden_states: torch.Tensor,
+    router_logits: torch.Tensor,
+    routing_bias: Optional[torch.Tensor],
+    top_k: int,
+    num_expert_group: Optional[int],
+    topk_group: Optional[int],
+    global_num_experts: int,
+    apply_router_weight_on_input: bool,
+) -> torch.Tensor:
+    from flashinfer.fused_moe import RoutingMethodType
+
+    from vllm.model_executor.models.llama4 import Llama4MoE
+    assert layer.custom_routing_function == Llama4MoE.custom_routing_function, \
+        "FusedMoE flashinfer kernels are only supported for Llama4"
+    return torch.ops.vllm.flashinfer_fused_moe_per_tensor_scale_fp8(
+        routing_logits=router_logits,
+        routing_bias=routing_bias,
+        hidden_states=hidden_states,
+        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,
+        routing_method_type=RoutingMethodType.Llama4,
+    )