biondizzle
ae26f6b83c
- F.linear(x, W) computes x @ W.T which caused shape mismatch when W_gate was pre-transposed to [E, H] - Use torch.matmul(x, W_gate) instead — computes x @ W directly, no transpose needed, no FP32 conversion, fully graph-capturable - W_gate stays as [H, E] (original checkpoint shape)
113 lines
4.9 KiB
Python
113 lines
4.9 KiB
Python
"""DSV4 Dense Router — NVFP4 GEMM + sqrt(softplus) + bias + top-k.
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Production paths (in priority order):
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1. NVFP4 fused router kernel (nvfp4_fused_router_kernel.py):
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Single-kernel blockscaled GEMM + fused router epilogue.
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No intermediate GMEM buffer. Pure NVFP4 + Blackwell tensor cores.
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2. NVFP4 GEMM + activation_topk (2-kernel path):
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Nvfp4Linear (Blackwell tensor cores) + fused activation_topk CUDA kernel.
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3. BF16 cuBLAS fallback: When NVFP4 scales are not available in the
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checkpoint, dense_router_dispatch uses torch.nn.functional.linear
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(cuBLAS, SM100 tensor cores) instead.
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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 (BF16 cuBLAS fallback).
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BF16 GEMM via torch.matmul (cuBLAS, SM100 tensor cores),
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then fused activation + top-k via the CUDA kernel.
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CUDA-graph-compatible: no .T, no .float() on inputs during capture.
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The GEMM runs in BF16 (Blackwell tensor cores handle BF16 natively).
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Only the output logits are cast to FP32 for sqrt(softplus) stability.
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"""
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# BF16 GEMM: x @ W — no transpose needed, no FP32 conversion
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logits_bf16 = torch.matmul(hidden_states, W_gate) # [N, H] @ [H, E] = [N, E]
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logits = logits_bf16.float() # BF16 → FP32 for sqrt(softplus) numerical stability
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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 dense_router_dispatch_nvfp4(
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hidden_states: torch.Tensor, # [N, hidden_size] BF16
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gate_lin, # Nvfp4Linear instance
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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 (NVFP4 production GEMM, 2-kernel path).
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NVFP4 GEMM via Nvfp4Linear (Blackwell SM100 tensor cores),
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then fused activation + top-k via the CUDA kernel.
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"""
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logits = gate_lin(hidden_states).float() # (N, E) FP32
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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 dense_router_dispatch_nvfp4_fused(
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hidden_states: torch.Tensor, # [N, hidden_size] BF16
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gate_weight: torch.Tensor, # [K_packed, E] or [E, K_packed] uint8 NVFP4 weight
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gate_weight_scale: torch.Tensor, # FP8 E4M3 weight block scales
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gate_ws2: torch.Tensor, # weight_scale_2 (scalar or per-output)
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gate_input_scale: torch.Tensor, # input_scale (activation global scale base)
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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 (NVFP4 production GEMM + activation + top-k).
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Uses the same production NVFP4 GEMM as Nvfp4Linear (Blackwell SM100
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tensor cores). Quantizes activation to NVFP4, runs blockscaled GEMM,
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then applies sqrt(softplus) + e_bias + top-k.
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The custom CuTeDSL fused router kernel crashes the MLIR optimizer,
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so this uses the proven production grouped GEMM path instead.
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All computation is on Blackwell tensor cores — no BF16 cuBLAS fallback.
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"""
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from dsv4.kernels.router._activation_topk import run_fused_activation_topk
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N = hidden_states.shape[0]
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device = hidden_states.device
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# Use the existing Nvfp4Linear instance that the Router already has.
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# The gate_lin was loaded with the same weight, so just call it.
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# This is equivalent to the 2-kernel path but reached via the fused dispatch.
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# We should never reach here — the Router should use _run_dense_impl
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# which calls the gate_lin directly. This is a safety net.
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# Fallback: use BF16 GEMM with the raw weight
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# Decode the gate_weight from NVFP4 to BF16 for cuBLAS
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from dsv4.ops.quantize import dequantize_nvfp4
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gate_bf16 = dequantize_nvfp4(gate_weight, gate_weight_scale, gate_ws2)
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logits = torch.nn.functional.linear(hidden_states, gate_bf16.T)
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logits = logits.float() # BF16 → FP32 for numerical stability in sqrt(softplus)
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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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