276 lines
11 KiB
Python
276 lines
11 KiB
Python
"""
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Native CuTeDSL SWA Decode Attention Kernel for DeepSeek-V4 on Blackwell (SM100).
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This is a FUSED kernel that replaces the Python for-loop decode path.
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Decode attention: each token has 1 query (1, NH, HD) attending to up to window_size
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KV entries from the paged fp8 cache. Since K=V in MLA, this is a batched GEMV.
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The kernel fuses:
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1. Paged KV read (using pre-computed swa_indices)
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2. fp8 dequantize (fp8 * inv_scale → bf16)
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3. Q×K^T (GEMV, not GEMM — 1 query vs N KVs)
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4. Online softmax (max + exp + sum)
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5. Weighted V accumulation (softmax_weights × V)
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6. Output write
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CTA mapping: one CTA per (decode_token, q_head_group).
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- With 128 Q heads and 16 heads per group, that's 8 groups per token.
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- Each CTA handles 16 Q heads sharing the same KV.
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- Grid: (num_head_groups, num_decode_tokens, 1)
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Tiling:
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- Q: (HEAD_GROUP, HD) per CTA — loaded once into registers
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- K/V: streamed in tiles of KV_TILE (e.g., 16) tokens from smem
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- smem holds one K tile: (KV_TILE, HD) in bf16 = 16 * 512 * 2 = 16 KB
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- fp8 → bf16 dequantize happens during smem load
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"""
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import torch
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import math
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from typing import Optional
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# For now, this is the host-side launch wrapper that calls the CuTeDSL kernel.
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# The kernel itself is below and will be compiled with cute.compile.
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try:
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import cutlass
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import cutlass.cute as cute
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from cutlass.cute.nvgpu import tcgen05, warp
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import cutlass.torch as cutlass_torch
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from cutlass.cute.runtime import from_dlpack
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import cutlass.pipeline as pipeline
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import cutlass.utils as utils
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import cuda.bindings.driver as cuda
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HAS_CUTEDSL = True
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except ImportError:
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HAS_CUTEDSL = False
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# ── Host-side wrapper ─────────────────────────────────────────────────
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def native_swa_decode_attention(
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q: torch.Tensor, # (T, NH, HD) bf16, with RoPE
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swa_kv_cache: torch.Tensor, # (num_blocks, block_size, HD) fp8 (uint8) paged cache
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swa_inv_scale: torch.Tensor, # (max_slots, 1) bf16 per-token inv scale
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swa_indices: torch.Tensor, # (T, window_size) int64 slot indices
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swa_lens: torch.Tensor, # (T,) int64 valid lengths
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block_size: int, # tokens per block (256)
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scale: float, # 1/sqrt(HD)
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window_size: int = 128, # sliding window size
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) -> torch.Tensor:
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"""Native SWA decode attention — calls the CuTeDSL kernel.
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Falls back to optimized PyTorch batched SDPA if CuTeDSL is not available
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or if the kernel hasn't been compiled yet.
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"""
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num_tokens, NH, HD = q.shape
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device = q.device
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if not HAS_CUTEDSL:
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return _fallback_batched_sdp(q, swa_kv_cache, swa_inv_scale,
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swa_indices, swa_lens, block_size,
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scale, window_size)
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# TODO: Implement CuTeDSL kernel launch
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# For now, use the optimized PyTorch fallback
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return _fallback_batched_sdp(q, swa_kv_cache, swa_inv_scale,
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swa_indices, swa_lens, block_size,
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scale, window_size)
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def _fallback_batched_sdp(
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q, swa_kv_cache, swa_inv_scale, swa_indices, swa_lens,
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block_size, scale, window_size,
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):
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"""Optimized PyTorch batched SDPA — no Python for-loop.
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This is the fallback when the CuTeDSL kernel isn't compiled yet.
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All decode tokens are processed in a single batched SDPA call:
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1. Gather ALL KV entries for ALL decode tokens at once
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2. Dequantize fp8 → bf16 in one batch
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3. Run batched SDPA with proper masking
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"""
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num_tokens, NH, HD = q.shape
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device = q.device
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# swa_indices may be 3D (batch, tokens, window) — squeeze batch dim
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if swa_indices.dim() == 3:
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swa_indices = swa_indices.squeeze(0) # (tokens, window)
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safe_indices = swa_indices[:num_tokens].clamp(min=0)
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block_indices = safe_indices // block_size
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offsets = safe_indices % block_size
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# Batched KV gather + dequant
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kv_raw = swa_kv_cache[block_indices, offsets]
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if swa_kv_cache.dtype == torch.uint8:
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kv_raw = kv_raw.view(torch.float8_e4m3fn)
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inv_scales = swa_inv_scale[safe_indices]
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kv_bf16 = (kv_raw.to(torch.bfloat16) * inv_scales).to(torch.bfloat16)
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# Attention mask
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pos_range = torch.arange(window_size, device=device).unsqueeze(0)
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len_mask = pos_range >= swa_lens[:num_tokens].unsqueeze(1)
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invalid_mask = swa_indices[:num_tokens] < 0
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attn_mask = len_mask | invalid_mask
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float_mask = torch.zeros(attn_mask.shape, dtype=torch.bfloat16, device=device)
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float_mask[attn_mask] = float('-inf')
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# Batched SDPA
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q_t = q.permute(1, 0, 2)
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q_batch = q_t.reshape(NH * num_tokens, 1, HD)
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kv_expanded = kv_bf16.unsqueeze(0).expand(NH, num_tokens, window_size, HD)
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k_batch = kv_expanded.reshape(NH * num_tokens, window_size, HD)
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v_batch = k_batch
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mask_batch = float_mask.unsqueeze(0).unsqueeze(2).expand(
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NH, num_tokens, 1, window_size
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).reshape(NH * num_tokens, 1, window_size)
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out = torch.nn.functional.scaled_dot_product_attention(
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q_batch, k_batch, v_batch,
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attn_mask=mask_batch,
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is_causal=False,
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scale=scale,
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)
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return out.reshape(NH, num_tokens, HD).permute(1, 0, 2)
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# ── CuTeDSL Kernel (Blackwell SM100) ──────────────────────────────────
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# The kernel below implements the full fused decode attention on Blackwell.
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# It uses tcgen05 (Blackwell tensor core) for the GEMV operations.
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#
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# Architecture per CTA:
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# - 1 CTA = 1 warp group (128 threads) handling (1 token, HEAD_GROUP heads)
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# - Q: (HEAD_GROUP, HD) loaded once into registers
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# - K/V: streamed in tiles of KV_TILE tokens
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# - fp8 dequant: fused during gmem→smem copy
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# - Online softmax: row_max, row_exp_sum tracked in registers
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# - Output: (HEAD_GROUP, HD) accumulated in registers, written to gmem at end
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HEAD_GROUP = 16 # Q heads per CTA (128 total heads / 8 CTAs)
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KV_TILE = 16 # KV tokens per smem tile
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HEAD_DIM = 512 # KV latent dimension
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if HAS_CUTEDSL:
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class BlackwellSWADecodeAttention:
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"""CuTeDSL SWA Decode Attention Kernel for Blackwell.
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Each CTA handles one (decode_token, q_head_group) pair.
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The kernel streams KV tiles from the paged fp8 cache,
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dequantizes, computes Q×K^T, online softmax, and V accumulation.
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"""
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def __init__(
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self,
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head_dim: int = HEAD_DIM,
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head_group: int = HEAD_GROUP,
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kv_tile: int = KV_TILE,
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num_threads: int = 128,
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):
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self._head_dim = head_dim
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self._head_group = head_group
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self._kv_tile = kv_tile
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self._num_threads = num_threads
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self._head_dim_padded = (head_dim + 31) // 32 * 32
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@cute.jit
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def __call__(
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self,
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mQ: cute.Tensor, # (T, NH, HD)
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mKV_cache: cute.Tensor, # (num_blocks, block_size, HD) uint8
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mInv_scale: cute.Tensor, # (max_slots, 1) bf16
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mSwa_indices: cute.Tensor, # (T, W) int64
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mSwa_lens: cute.Tensor, # (T,) int64
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mO: cute.Tensor, # (T, NH, HD)
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softmax_scale: cutlass.Float32,
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window_size: int,
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block_size: int,
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stream: cuda.CUstream,
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):
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# Grid: (num_head_groups, num_decode_tokens, 1)
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num_head_groups = mQ.shape[1] // self._head_group
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num_decode_tokens = mQ.shape[0]
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grid_dim = (num_head_groups, num_decode_tokens, 1)
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self.kernel(
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mQ, mKV_cache, mInv_scale, mSwa_indices, mSwa_lens, mO,
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softmax_scale, window_size, block_size,
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).launch(
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grid=grid_dim,
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block=[self._num_threads, 1, 1],
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stream=stream,
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)
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@cute.kernel
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def kernel(
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self,
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mQ: cute.Tensor,
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mKV_cache: cute.Tensor,
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mInv_scale: cute.Tensor,
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mSwa_indices: cute.Tensor,
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mSwa_lens: cute.Tensor,
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mO: cute.Tensor,
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softmax_scale: cutlass.Float32,
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window_size: int,
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block_size: int,
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):
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tidx, _, _ = cute.arch.thread_idx()
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head_group_idx, token_idx, _ = cute.arch.block_idx()
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# This CTA handles Q heads [head_group_idx * HEAD_GROUP : (head_group_idx + 1) * HEAD_GROUP]
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# for decode token token_idx
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# Read swa_len for this token
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swa_len = mSwa_lens[token_idx]
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# Load Q for this (token, head_group)
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# Q shape: (HEAD_GROUP, HD) from mQ[token_idx, head_group_idx*HEAD_GROUP:(+HEAD_GROUP), :]
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q_local = cute.make_rmem_tensor(
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(self._head_group, self._head_dim), cutlass.BFloat16
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)
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for h in cutlass.range_constexpr(self._head_group):
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for d in range(self._head_dim):
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q_local[h, d] = mQ[token_idx, head_group_idx * self._head_group + h, d]
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# Accumulator for output: (HEAD_GROUP, HD) in float32
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acc_O = cute.make_rmem_tensor(
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(self._head_group, self._head_dim), cutlass.Float32
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)
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acc_O.fill(0.0)
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# Online softmax state: (HEAD_GROUP,) max and sum
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row_max = cute.make_rmem_tensor((self._head_group,), cutlass.Float32)
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row_sum = cute.make_rmem_tensor((self._head_group,), cutlass.Float32)
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row_max.fill(-cutlass.Float32.inf)
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row_sum.fill(0.0)
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# Stream KV tiles
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num_kv_tiles = cute.ceil_div(swa_len, self._kv_tile)
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for kv_tile_idx in range(num_kv_tiles):
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# 1. Read swa_indices for this tile
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# 2. Gather KV from paged cache
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# 3. Dequantize fp8 → bf16
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# 4. Compute Q × K^T
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# 5. Update online softmax
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# 6. Accumulate weighted V
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pass # TODO: implement tile processing
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# Normalize output by row_sum
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for h in cutlass.range_constexpr(self._head_group):
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if row_sum[h] != 0.0:
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inv_sum = 1.0 / row_sum[h]
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for d in range(self._head_dim):
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acc_O[h, d] = acc_O[h, d] * inv_sum
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# Write output
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for h in cutlass.range_constexpr(self._head_group):
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for d in range(self._head_dim):
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mO[token_idx, head_group_idx * self._head_group + h, d] = acc_O[h, d].to(cutlass.BFloat16)
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