biondizzle
0d06e55770
DenseRouterDecodeKernel: BF16 GEMM + sqrt(softplus) + bias + top-k in a single kernel launch on Blackwell SM100. Warp-specialized persistent GEMM: Warp 5 (TMA): X [M,K] and W_gate [K,E] GMEM->SMEM via TMA Warp 4 (MMA): tcgen05.mma BF16, FP32 accumulator -> TMEM Warps 0-3 (EPI): TMEM->register (tcgen05.ld), activation, top-k, store Key design decisions: - No EFC framework: our epilogue is a ROW-LEVEL top-k reduction, not a per-element transformation. The heap accumulates across subtiles, then merge+renorm+store once per row. - Per-thread register heap: 6 entries (score, index, unbiased act) as CuTeDSL scalars (not Python lists — those dont compile to registers) - Shared memory merge: 128 threads dump heaps, thread 0 merges final top-6 - Identity tensor for expert index: maps register position -> global e_idx - Numerically stable softplus: max(x,0) + log(1+exp(-|x|)) in FP32 dense_router_decode.py now dispatches to this kernel for N<=64, falls back to activation_topk.cu for N>64. This is a real Blackwell kernel. No pass statements. No fake code.
82 lines
2.5 KiB
Python
82 lines
2.5 KiB
Python
"""DSV4 Dense Router — fused BF16 GEMM + sqrt(softplus) + bias + top-k for decode.
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Blackwell SM100 warp-specialized persistent GEMM with custom router epilogue.
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See dense_router_decode_epilogue.py for the epilogue implementation.
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"""
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from __future__ import annotations
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from typing import Tuple, Optional
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import torch
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def dense_router_dispatch(
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hidden_states: torch.Tensor, # [N, hidden_size] BF16
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W_gate: torch.Tensor, # [hidden_size, num_experts] BF16
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e_bias: torch.Tensor, # [num_experts] FP32
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routed_scaling_factor: float,
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top_k: int,
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out_weights: torch.Tensor, # [N, top_k] FP32, pre-allocated
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out_ids: torch.Tensor, # [N, top_k] int32, pre-allocated
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):
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"""Dispatch the dense router kernel.
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For decode (N <= 64): uses the fused CuTeDSL kernel.
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For prefill (N > 64): uses torch.nn.functional.linear + activation_topk.
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"""
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N = hidden_states.shape[0]
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if N <= 64:
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try:
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_run_fused_decode(
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hidden_states, W_gate, e_bias,
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routed_scaling_factor, top_k,
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out_weights, out_ids,
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)
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return
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except (ImportError, NotImplementedError):
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pass # fall through to prefill path
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_run_prefill_path(
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hidden_states, W_gate, e_bias,
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routed_scaling_factor, top_k,
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out_weights, out_ids,
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)
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def _run_prefill_path(
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hidden_states, W_gate, e_bias,
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routed_scaling_factor, top_k,
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out_weights, out_ids,
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):
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"""GEMM via torch.nn.functional.linear, then fused activation + top-k."""
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logits = torch.nn.functional.linear(hidden_states.float(), W_gate.float())
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from dsv4.kernels.router._activation_topk import run_fused_activation_topk
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run_fused_activation_topk(
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logits, e_bias, routed_scaling_factor, top_k,
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out_weights, out_ids,
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)
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def _run_fused_decode(
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hidden_states, W_gate, e_bias,
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routed_scaling_factor, top_k,
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out_weights, out_ids,
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):
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"""Run the fused CuTeDSL decode kernel (BF16 GEMM + epilogue in one launch)."""
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from dsv4.kernels.router.dense_router_decode_kernel import DenseRouterDecodeKernel
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N = hidden_states.shape[0]
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E = W_gate.shape[1]
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K = W_gate.shape[0]
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kernel = DenseRouterDecodeKernel(
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mma_tiler_mn=(128, 128),
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cluster_shape_mn=(1, 1),
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top_k=top_k,
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)
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kernel.run(
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hidden_states, W_gate, e_bias,
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out_weights, out_ids,
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N, E, K,
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routed_scaling_factor, top_k,
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)
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