flashinfer.fused_moe.prims_ts_bf16_routed_moe

flashinfer.fused_moe.prims_ts_bf16_routed_moe(topk_ids: Tensor | tuple[Tensor, Tensor], hidden_states: Tensor, gemm1_weights: Tensor, gemm2_weights: Tensor, num_experts: int, top_k: int, n_group: int | None, topk_group: int | None, intermediate_size: int, local_expert_offset: int, local_num_experts: int, routed_scaling_factor: float | None = None, routing_method_type: int = 0, use_shuffled_weight: bool = True, weight_layout: int = WeightLayout.MajorK, do_finalize: bool = True, enable_pdl: bool = True, gemm1_lora_delta: Tensor | None = None, tune_max_num_tokens: int = 8192, activation_type: int = 3, routing_replay_out: Tensor | None = None, gemm1_alpha: Tensor | None = None, gemm1_beta: Tensor | None = None, gemm1_clamp_limit: Tensor | None = None, output: Tensor | None = None) → List[Tensor] | Tensor

BF16 Prims-TS MoE with precomputed packed or unpacked routing.

Same arguments and return value as trtllm_bf16_routed_moe().

Parameters:

  • topk_ids (torch.Tensor or tuple[torch.Tensor, torch.Tensor]) – Packed (expert_id, weight) tensor or unpacked (topk_ids, topk_weights) pair.

  • hidden_states (torch.Tensor) – [seq_len, hidden_size] BF16 activations.

  • gemm1_weights (torch.Tensor) – First-layer expert weights.

  • gemm2_weights (torch.Tensor) – Second-layer expert weights.

  • num_experts (int) – Total number of experts.

  • top_k (int) – Experts selected per token.

  • n_group (Optional[int]) – Number of expert groups.

  • topk_group (Optional[int]) – Groups considered for top-k routing.

  • intermediate_size (int) – Intermediate (FFN) width.

  • local_expert_offset (int) – Global offset of the first local expert.

  • local_num_experts (int) – Number of experts resident on this device.

  • routed_scaling_factor (Optional[float]) – Optional routing scale.

  • routing_method_type (int) – Routing method selector (default 0).

  • use_shuffled_weight (bool) – Whether weights use the shuffled MajorK layout (default True).

  • weight_layout (int) – Weight layout enum value (default MajorK).

  • do_finalize (bool) – If True, return the finalized MoE output.

  • enable_pdl (bool) – Enable Programmatic Dependent Launch when supported.

  • gemm1_lora_delta (Optional[torch.Tensor]) – Optional MoE LoRA delta applied before the gated activation.

  • tune_max_num_tokens (int) – Autotune token-bucket upper bound (default 8192).

  • activation_type (int) – Activation enum value (default Swiglu).

  • routing_replay_out (Optional[torch.Tensor]) – Optional buffer that captures selected expert IDs.

  • gemm1_alpha (Optional[torch.Tensor]) – Optional per-expert SwiGLU alpha.

  • gemm1_beta (Optional[torch.Tensor]) – Optional per-expert SwiGLU beta.

  • gemm1_clamp_limit (Optional[torch.Tensor]) – Optional per-expert clamp limit.

  • output (Optional[torch.Tensor]) – Optional in-place [seq_len, hidden_size] output.

Returns:

Same return contract as trtllm_bf16_routed_moe().

Return type:

torch.Tensor or List[torch.Tensor]