init

vllm/model_executor/layers/fused_moe/trtllm_moe.py

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+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from typing import Optional
+
+import torch
+
+import vllm.model_executor.layers.fused_moe.modular_kernel as mk
+from vllm.model_executor.layers.fused_moe.config import (FusedMoEConfig,
+                                                         FusedMoEQuantConfig)
+from vllm.model_executor.layers.fused_moe.topk_weight_and_reduce import (
+    TopKWeightAndReduceNoOP)
+from vllm.utils import next_power_of_2
+
+
+class TrtLlmGenExperts(mk.FusedMoEPermuteExpertsUnpermute):
+
+    def __init__(
+        self,
+        moe: FusedMoEConfig,
+        quant_config: FusedMoEQuantConfig,
+        gemm1_alpha,
+        gemm1_beta,
+        gemm1_clamp_limit,
+        max_capture_size,
+    ):
+        super().__init__(quant_config)
+        self.moe = moe
+        self.gemm1_alpha = gemm1_alpha
+        self.gemm1_beta = gemm1_beta
+        self.gemm1_clamp_limit = gemm1_clamp_limit
+        self.max_capture_size = max_capture_size
+
+    @property
+    def activation_formats(
+        self
+    ) -> tuple[mk.FusedMoEActivationFormat, mk.FusedMoEActivationFormat]:
+        return (mk.FusedMoEActivationFormat.Standard,
+                mk.FusedMoEActivationFormat.Standard)
+
+    def supports_chunking(self) -> bool:
+        return True
+
+    def supports_expert_map(self) -> bool:
+        return True
+
+    def finalize_weight_and_reduce_impl(self) -> mk.TopKWeightAndReduce:
+        return TopKWeightAndReduceNoOP()
+
+    def workspace_shapes(
+        self,
+        a: torch.Tensor,
+        aq: torch.Tensor,
+        M: int,
+        N: int,
+        K: int,
+        topk: int,
+        global_num_experts: int,
+        local_num_experts: int,
+        expert_tokens_meta: Optional[mk.ExpertTokensMetadata],
+    ) -> tuple[tuple[int, ...], tuple[int, ...], tuple[int, ...], torch.dtype]:
+        # The workspaces for this implementation are managed by flashinfer.
+        # TODO(varun) : workspace1 is could be used as the output tensor. This
+        # is error-prone. Allow the `workspace_shapes` to return None workspaces
+        workspace1 = (M, K)
+        workspace2 = (0, 0)
+        output = (M, K)
+        return (workspace1, workspace2, output, a.dtype)
+
+    def _get_tile_tokens_dim(self, x: torch.Tensor, top_k: int,
+                             local_num_experts: int):
+        # Number of tokens in the input tensor.
+        num_tokens = x.shape[0]
+        # Factor to account for the imbalance of the experts.
+        # factor equals to the
+        # max_real_num_tokens_per_expert / perfect_num_tokens_per_expert
+        # 1.0 means perfect expert distribution.
+        # > 1.0 means some experts have more tokens than the perfect
+        # distribution.
+        # < 1.0 does not make sense.
+        imbalance_factor = 1.3
+        # Calculate the number of tokens per expert assuming perfect
+        # distribution.
+        num_tokens_per_expert = (num_tokens * top_k) // local_num_experts
+        # Apply the imbalance factor.
+        num_tokens_per_expert = int(num_tokens_per_expert * imbalance_factor)
+        # And pad the number to the next power of 2.
+        tile_tokens_dim = next_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 apply(
+        self,
+        output: torch.Tensor,
+        hidden_states: torch.Tensor,
+        w1: torch.Tensor,
+        w2: torch.Tensor,
+        topk_weights: torch.Tensor,
+        topk_ids: torch.Tensor,
+        activation: str,
+        global_num_experts: int,
+        expert_map: Optional[torch.Tensor],
+        a1q_scale: Optional[torch.Tensor],
+        a2_scale: Optional[torch.Tensor],
+        workspace13: torch.Tensor,
+        workspace2: torch.Tensor,
+        expert_tokens_meta: Optional[mk.ExpertTokensMetadata],
+        apply_router_weight_on_input: bool,
+    ):
+        topk = topk_ids.size(-1)
+        local_num_experts = w1.size(0)
+        intermediate_size = w2.size(1)
+        local_expert_offset = self.moe.ep_rank * local_num_experts
+
+        x_quant = hidden_states
+        x_scale = a1q_scale
+        if x_scale is not None:
+            x_scale = x_scale.view(torch.float8_e4m3fn).reshape(
+                *x_quant.shape[:-1], -1)
+
+        packed_tensor = (topk_ids.to(torch.int32) << 16) | topk_weights.to(
+            torch.bfloat16).view(torch.int16)
+
+        assert self.w1_scale is not None
+        assert self.w2_scale is not None
+        kwargs = {
+            "topk_ids":
+            packed_tensor,
+            "routing_bias":
+            None,
+            "hidden_states":
+            x_quant,
+            "hidden_states_scale":
+            x_scale,
+            "gemm1_weights":
+            w1,
+            "gemm1_weights_scale":
+            self.w1_scale,
+            "gemm1_bias":
+            self.w1_bias,
+            "gemm1_alpha":
+            self.gemm1_alpha,
+            "gemm1_beta":
+            self.gemm1_beta,
+            "gemm1_clamp_limit":
+            self.gemm1_clamp_limit,
+            "gemm2_weights":
+            w2,
+            "gemm2_weights_scale":
+            self.w2_scale,
+            "gemm2_bias":
+            self.w2_bias,
+            "output1_scale_scalar":
+            None,
+            "output1_scale_gate_scalar":
+            None,
+            "output2_scale_scalar":
+            None,
+            "num_experts":
+            global_num_experts,
+            "top_k":
+            topk,
+            "n_group":
+            None,
+            "topk_group":
+            None,
+            "intermediate_size":
+            intermediate_size,
+            "local_expert_offset":
+            local_expert_offset,
+            "local_num_experts":
+            local_num_experts,
+            "routed_scaling_factor":
+            None,
+            "tile_tokens_dim":
+            self._get_tile_tokens_dim(x_quant, topk, local_num_experts),
+            "routing_method_type":
+            1,
+            "do_finalize":
+            True,
+            "output":
+            output,
+            "tune_max_num_tokens":
+            self.max_capture_size,
+        }
+
+        from flashinfer import trtllm_fp4_block_scale_routed_moe
+        trtllm_fp4_block_scale_routed_moe(**kwargs)
+        return output