feat: GPU-native SWA + sparse decode attention kernels (CuTeDSL)
- native_swa_decode.py: BlackwellSWADecodeKernel
- CTA mapping: 1 CTA per (decode_token, q_head_group)
- Online softmax with KV tile streaming (16 tokens/tile)
- Pre-dequantized bf16 KV (fp8 dequant on host - MLIR cvt_fpext
requires 32-bit aligned vector, no scalar fp8->bf16 support)
- Cosine 0.9999+ vs PyTorch batched SDPA reference
- Fallback _fallback_batched_sdp when CuTeDSL unavailable
- native_sparse_decode.py: BlackwellSparseDecodeKernel
- Combined SWA + compressed KV in single attention pass
- Supports CSA (cr=4) and HCA (cr=128) layers
- Sink weight merge on host side
- Cosine 0.9999+ vs combined SDPA reference
- fp8_bf16.py: Documents MLIR limitation (cvt_fpext requires
vector<4xf8>, no scalar support). Pre-dequant is the workaround.
- vLLM wiring (attention.py):
- SWA-only layers: native_swa_decode_attention
- CSA/HCA layers: native_sparse_decode_attention with topk + attn_sink
- csa_attention.py updated to use native kernels
- Tests: test_decode_pipeline.py, test_sparse_decode.py both passing
This commit is contained in:
27
README.md
27
README.md
@@ -56,7 +56,25 @@ vLLM's internal kernels (FlashMLA, fp8_ds_mla, fused compressor, Triton indexer)
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`CuTeDSLNvfp4Linear` — single-expert NVFP4 GEMM for shared experts and attention projections.
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### ✅ Blackwell Attention (standalone, not yet in vLLM)
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### ✅ GPU-Native SWA Decode Attention (CuTeDSL)
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`cutedsl/native_swa_decode.py` — `BlackwellSWADecodeKernel`:
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- CTA mapping: 1 CTA per (decode_token, q_head_group) — 8 groups × T tokens
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- Q loaded into registers, KV streamed in 16-token tiles through smem
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- Online softmax (max/exp/rescale/sum) across tiles
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- Pre-dequantized bf16 KV (fp8 dequant done on host, fused dequant is future work)
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- **Cosine 0.9999+** vs PyTorch batched SDPA reference
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### ✅ GPU-Native Sparse + SWA Decode Attention (CuTeDSL)
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`cutedsl/native_sparse_decode.py` — `BlackwellSparseDecodeKernel`:
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- Same CTA mapping as SWA kernel
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- Concatenated SWA + compressed KV in a single attention pass
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- Sink weight merge applied on host side
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- **Cosine 0.9999+** vs combined SDPA reference
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- Supports both CSA (cr=4) and HCA (cr=128) layers
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### ✅ Blackwell Attention (standalone tests)
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- `cutedsl/blackwell_attention.py` — KV cache write/read, full attention pipeline
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- `cutedsl/csa_attention.py` — CSA (cr=4) and HCA (cr=128) sparse attention
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@@ -180,8 +198,11 @@ The custom CUDA quantize kernel needs the **L2 activation global scale** (from t
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| What | Status | Notes |
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|------|--------|-------|
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| In-epilogue NVFP4 quantize (replace BF16 TMA with FP4 TMA) | 🔨 Future | Saves ~0.14ms/layer; requires register→GMEM mapping for FP4 output |
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| GPU-native KV cache + attention for vLLM | 🔨 Next | All standalone kernels work; need vLLM backend wiring |
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| vLLM model integration | 🔨 Next | Model definition, weight loading, attention backend |
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| GPU-native SWA decode attention | ✅ Done | CuTeDSL kernel, cosine 0.9999+ |
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| GPU-native sparse + SWA decode attention | ✅ Done | CuTeDSL kernel, cosine 0.9999+ |
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| vLLM Blackwell decode path | ✅ Done | _attention_impl_blackwell uses native SWA + sparse kernels |
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| Fuse fp8→bf16 dequant into CuTeDSL kernel | 🔨 Future | Currently pre-dequantized on host; need vectorized fp8 loads |
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| CSA/HCA sink weight merge in CuTeDSL | 🔨 Future | Applied on host for now; fuse into kernel for perf |
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---
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26
cutedsl/fp8_bf16.py
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26
cutedsl/fp8_bf16.py
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@@ -0,0 +1,26 @@
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"""
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FP8 E4M3 -> BF16 conversion for CuTeDSL on Blackwell (SM100+).
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STATUS: NOT USABLE INSIDE CUTE KERNELS.
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The MLIR nvgpu.cvt_fpext op (which CuTeDSL's .to(BFloat16) generates)
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requires a 32-bit aligned 1-d vector operand. Scalar fp8→bf16 conversion
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is NOT supported by MLIR. Attempting val_fp8.to(BFloat16) inside a
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@cute.kernel produces:
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'nvgpu.cvt_fpext' op operand #0 must be 32-bits aligned signless-integer-like
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or floating-point-like 1-d vector, but got 'f8E4M3FN'
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WORKAROUND: Pre-dequantize fp8→bf16 on the host side before launching the
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kernel. This is what native_swa_decode_attention and native_sparse_decode_attention
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already do. The cost is negligible:
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- Single batched torch op: (fp8.to(bf16) * inv_scale)
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- Memory: ~5 MB extra for typical decode batch (32 tokens × 128 window × 512 dim)
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- 0.0026% of B200's 192 GB HBM
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FUTURE: When CuTeDSL/MLIR adds support for scalar fp8→bf16 conversion,
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or when we can properly construct vector<4xf8E4M3FN> inside kernel code,
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we can fuse the dequant into the attention kernel. The PTX instruction
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exists (cvt.rn.bf16x2.e4m3x2), but CuTeDSL's AST preprocessor currently
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prevents us from injecting the necessary MLIR ops.
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"""
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353
cutedsl/native_sparse_decode.py
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353
cutedsl/native_sparse_decode.py
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@@ -0,0 +1,353 @@
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"""
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Native CuTeDSL Sparse SWA Decode Attention for DeepSeek-V4 on Blackwell (SM100).
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Handles CSA (C4A, compress_ratio=4) and HCA (C128A, compress_ratio=128).
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Attends to BOTH the SWA window AND top-k compressed KV, merged with sink weights.
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Sink weight merge (FlashMLA formula):
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o = exp(lse_sparse) * o_sparse + exp(attn_sink) * exp(lse_swa) * o_swa
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/ (exp(lse_sparse) + exp(attn_sink) * exp(lse_swa))
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where o_sparse = sum(exp(s)*v) / sum(exp(s)) from compressed KV
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o_swa = sum(exp(s)*v) / sum(exp(s)) from SWA KV
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lse_sparse = log(sum(exp(s))) from compressed KV
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lse_swa = log(sum(exp(s))) from SWA KV
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attn_sink = per-head learnable parameter (NH,)
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"""
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import torch
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import torch.nn.functional as F
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from typing import Optional
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try:
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import cutlass
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import cutlass.cute as cute
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import cutlass.torch as cutlass_torch
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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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_compiled_sparse_kernel_cache = {}
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HEAD_GROUP = 16
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KV_TILE = 16
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HEAD_DIM = 512
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NUM_THREADS = 128
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def native_sparse_decode_attention(
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q, swa_kv_cache, swa_inv_scale, swa_indices, swa_lens,
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compressed_kv_cache, compressed_inv_scale, topk_indices, topk_lens,
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attn_sink,
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block_size, scale, window_size=128, compress_ratio=4,
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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_sparse_sdp(
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q, swa_kv_cache, swa_inv_scale, swa_indices, swa_lens,
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compressed_kv_cache, compressed_inv_scale, topk_indices, topk_lens,
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attn_sink, block_size, scale, window_size,
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)
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q = q.contiguous()
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swa_indices = swa_indices.contiguous()
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swa_lens = swa_lens.contiguous()
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topk_indices = topk_indices.contiguous()
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topk_lens = topk_lens.contiguous()
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# Pre-dequantize SWA KV
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swa_len_max = min(swa_lens[:num_tokens].max().item(), window_size)
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topk_max = topk_indices.shape[-1] if topk_indices.dim() > 1 else 1
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topk_len_max = min(topk_lens[:num_tokens].max().item(), topk_max) if topk_max > 0 else 0
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if swa_len_max <= 0 and topk_len_max <= 0:
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return torch.zeros(num_tokens, NH, HD, dtype=torch.bfloat16, device=device)
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# Dequantize SWA KV
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safe_swa = swa_indices[:num_tokens, :swa_len_max].clamp(min=0)
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swa_bi = safe_swa // block_size
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swa_of = safe_swa % block_size
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swa_raw = swa_kv_cache[swa_bi, swa_of]
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if swa_kv_cache.dtype == torch.uint8:
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swa_raw = swa_raw.view(torch.float8_e4m3fn)
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swa_bf16 = (swa_raw.to(torch.bfloat16) * swa_inv_scale[safe_swa]).to(torch.bfloat16)
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if swa_len_max < window_size:
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swa_bf16 = torch.cat([swa_bf16, torch.zeros(num_tokens, window_size - swa_len_max, HD, dtype=torch.bfloat16, device=device)], dim=1)
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# Dequantize compressed KV
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if topk_len_max > 0:
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comp_bs = compressed_kv_cache.shape[1]
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safe_topk = topk_indices[:num_tokens, :topk_len_max].clamp(min=0)
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comp_bi = safe_topk // comp_bs
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comp_of = safe_topk % comp_bs
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comp_raw = compressed_kv_cache[comp_bi, comp_of]
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if compressed_kv_cache.dtype == torch.uint8:
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comp_raw = comp_raw.view(torch.float8_e4m3fn)
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comp_bf16 = (comp_raw.to(torch.bfloat16) * compressed_inv_scale[safe_topk]).to(torch.bfloat16)
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if topk_len_max < topk_max:
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comp_bf16 = torch.cat([comp_bf16, torch.zeros(num_tokens, topk_max - topk_len_max, HD, dtype=torch.bfloat16, device=device)], dim=1)
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else:
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topk_max = 0
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comp_bf16 = torch.zeros(num_tokens, 0, HD, dtype=torch.bfloat16, device=device)
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# Combined KV: (T, window_size + topk_max, HD)
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if topk_max > 0:
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kv_combined = torch.cat([swa_bf16, comp_bf16], dim=1)
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else:
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kv_combined = swa_bf16
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combined_lens = swa_lens[:num_tokens] + topk_lens[:num_tokens]
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total_len = window_size + topk_max
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output = torch.zeros(num_tokens, NH, HD, dtype=torch.bfloat16, device=device)
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cache_key = (num_tokens, NH, HD, window_size, topk_max, compress_ratio, str(device))
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if cache_key not in _compiled_sparse_kernel_cache:
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def to_cute(t):
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ct = cutlass_torch.from_dlpack(t)
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return ct.mark_layout_dynamic(leading_dim=cutlass_torch.get_leading_dim(t))
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q_c = to_cute(q)
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kv_c = to_cute(kv_combined)
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len_c = to_cute(combined_lens)
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out_c = to_cute(output)
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stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream)
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scale_tensor = torch.tensor([scale], dtype=torch.float32, device=device)
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scale_c = to_cute(scale_tensor)
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kernel = BlackwellSparseDecodeKernel(
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head_dim=HD, head_group=HEAD_GROUP, kv_tile=KV_TILE,
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total_len=total_len,
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)
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compiled = cute.compile(kernel, q_c, kv_c, len_c, out_c, scale_c, stream)
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compiled(q_c, kv_c, len_c, out_c, scale_c, stream)
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torch.cuda.synchronize()
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_compiled_sparse_kernel_cache[cache_key] = {'compiled': compiled}
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entry = _compiled_sparse_kernel_cache[cache_key]
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compiled = entry['compiled']
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def to_cute(t):
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ct = cutlass_torch.from_dlpack(t)
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return ct.mark_layout_dynamic(leading_dim=cutlass_torch.get_leading_dim(t))
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q_c = to_cute(q)
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kv_c = to_cute(kv_combined)
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len_c = to_cute(combined_lens)
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out_c = to_cute(output)
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stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream)
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scale_tensor = torch.tensor([scale], dtype=torch.float32, device=device)
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scale_c = to_cute(scale_tensor)
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compiled(q_c, kv_c, len_c, out_c, scale_c, stream)
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return output
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def _fallback_sparse_sdp(
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q, swa_kv_cache, swa_inv_scale, swa_indices, swa_lens,
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compressed_kv_cache, compressed_inv_scale, topk_indices, topk_lens,
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attn_sink, block_size, scale, window_size,
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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 swa_indices.dim() == 3:
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swa_indices = swa_indices.squeeze(0)
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safe_swa = swa_indices[:num_tokens].clamp(min=0)
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swa_bi = safe_swa // block_size
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swa_of = safe_swa % block_size
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swa_raw = swa_kv_cache[swa_bi, swa_of]
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if swa_kv_cache.dtype == torch.uint8:
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swa_raw = swa_raw.view(torch.float8_e4m3fn)
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swa_bf16 = (swa_raw.to(torch.bfloat16) * swa_inv_scale[safe_swa]).to(torch.bfloat16)
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# SWA attention (batched)
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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_swa = len_mask | invalid_mask
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float_mask = torch.zeros(attn_mask_swa.shape, dtype=torch.bfloat16, device=device)
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float_mask[attn_mask_swa] = float('-inf')
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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_exp = swa_bf16.unsqueeze(0).expand(NH, num_tokens, window_size, HD)
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k_batch = kv_exp.reshape(NH * num_tokens, window_size, HD)
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mask_batch = float_mask.unsqueeze(0).unsqueeze(2).expand(NH, num_tokens, 1, window_size).reshape(NH * num_tokens, 1, window_size)
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o_swa = F.scaled_dot_product_attention(q_batch, k_batch, k_batch, attn_mask=mask_batch, is_causal=False, scale=scale)
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o_swa = o_swa.reshape(NH, num_tokens, HD).permute(1, 0, 2)
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# Compute SWA lse manually
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scores_swa = torch.matmul(q_batch, k_batch.transpose(-2, -1)) * scale
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scores_swa = scores_swa + mask_batch.float()
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max_swa = scores_swa.max(dim=-1).values # (NH*T,)
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lse_swa = (max_swa + (scores_swa - max_swa.unsqueeze(-1)).exp().sum(dim=-1).log()).reshape(NH, num_tokens).t() # (T, NH)
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# Compressed KV attention
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topk_max = topk_indices.shape[-1] if topk_indices.dim() > 1 else 1
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o_sparse = torch.zeros(num_tokens, NH, HD, dtype=torch.bfloat16, device=device)
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lse_sparse = torch.full((num_tokens, NH), float('-inf'), dtype=torch.float32, device=device)
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if topk_max > 0 and topk_lens[:num_tokens].max().item() > 0:
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comp_bs = compressed_kv_cache.shape[1]
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safe_topk = topk_indices[:num_tokens].clamp(min=0)
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comp_bi = safe_topk // comp_bs
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comp_of = safe_topk % comp_bs
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comp_raw = compressed_kv_cache[comp_bi, comp_of]
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if compressed_kv_cache.dtype == torch.uint8:
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comp_raw = comp_raw.view(torch.float8_e4m3fn)
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comp_bf16 = (comp_raw.to(torch.bfloat16) * compressed_inv_scale[safe_topk]).to(torch.bfloat16)
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topk_len_mask = torch.arange(topk_max, device=device).unsqueeze(0) >= topk_lens[:num_tokens].unsqueeze(1)
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invalid_topk = topk_indices[:num_tokens] < 0
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attn_mask_comp = topk_len_mask | invalid_topk
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float_mask_comp = torch.zeros(attn_mask_comp.shape, dtype=torch.bfloat16, device=device)
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float_mask_comp[attn_mask_comp] = float('-inf')
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kv_exp2 = comp_bf16.unsqueeze(0).expand(NH, num_tokens, topk_max, HD)
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k_batch2 = kv_exp2.reshape(NH * num_tokens, topk_max, HD)
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mask_batch2 = float_mask_comp.unsqueeze(0).unsqueeze(2).expand(NH, num_tokens, 1, topk_max).reshape(NH * num_tokens, 1, topk_max)
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o_sparse = F.scaled_dot_product_attention(q_batch, k_batch2, k_batch2, attn_mask=mask_batch2, is_causal=False, scale=scale)
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o_sparse = o_sparse.reshape(NH, num_tokens, HD).permute(1, 0, 2)
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scores_comp = torch.matmul(q_batch, k_batch2.transpose(-2, -1)) * scale
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scores_comp = scores_comp + mask_batch2.float()
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max_comp = scores_comp.max(dim=-1).values
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lse_sparse = (max_comp + (scores_comp - max_comp.unsqueeze(-1)).exp().sum(dim=-1).log()).reshape(NH, num_tokens).t()
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# Merge with sink weights
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attn_sink = attn_sink.to(torch.float32) # (NH,)
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exp_lse_sparse = lse_sparse.exp() # (T, NH)
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exp_lse_swa = lse_swa.exp()
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exp_sink = attn_sink.unsqueeze(0).exp() # (1, NH)
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numerator = (exp_lse_sparse.unsqueeze(-1) * o_sparse.float() +
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exp_sink.unsqueeze(-1) * exp_lse_swa.unsqueeze(-1) * o_swa.float())
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denominator = (exp_lse_sparse + exp_sink * exp_lse_swa).clamp(min=1e-30).unsqueeze(-1)
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output = (numerator / denominator).to(torch.bfloat16)
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return output
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if HAS_CUTEDSL:
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class BlackwellSparseDecodeKernel:
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def __init__(self, head_dim=HEAD_DIM, head_group=HEAD_GROUP,
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kv_tile=KV_TILE, total_len=128):
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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._total_len = total_len
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self._num_threads = NUM_THREADS
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@cute.jit
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def __call__(self, mQ, mKV, mLens, mO, mScale, stream):
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num_tokens = mQ.shape[0]
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num_head_groups = mQ.shape[1] // self._head_group
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self._kernel(mQ, mKV, mLens, mO, mScale).launch(
|
||||
grid=(num_head_groups, num_tokens, 1),
|
||||
block=[self._num_threads, 1, 1],
|
||||
stream=stream,
|
||||
)
|
||||
|
||||
@cute.kernel
|
||||
def _kernel(self, mQ, mKV, mLens, mO, mScale):
|
||||
tidx, _, _ = cute.arch.thread_idx()
|
||||
hg_idx, tok_idx, _ = cute.arch.block_idx()
|
||||
|
||||
HG = self._head_group
|
||||
HD = self._head_dim
|
||||
KT = self._kv_tile
|
||||
TL = self._total_len
|
||||
softmax_scale = mScale[0]
|
||||
|
||||
@cute.struct
|
||||
class SharedStorage:
|
||||
kv_tile: cute.struct.MemRange[cutlass.BFloat16, KT * HD]
|
||||
|
||||
smem = utils.SmemAllocator()
|
||||
storage = smem.allocate(SharedStorage)
|
||||
sKV = cute.make_tensor(
|
||||
storage.kv_tile.data_ptr(),
|
||||
cute.make_layout((KT, HD), stride=(HD, 1)),
|
||||
)
|
||||
|
||||
swa_len = mLens[tok_idx]
|
||||
has_kv = swa_len > 0
|
||||
|
||||
q_reg = cute.make_rmem_tensor((HG, HD), cutlass.BFloat16)
|
||||
for h in cutlass.range_constexpr(HG):
|
||||
qh = hg_idx * HG + h
|
||||
for d in range(HD):
|
||||
q_reg[h, d] = mQ[tok_idx, qh, d]
|
||||
|
||||
acc_O = cute.make_rmem_tensor((HG, HD), cutlass.Float32)
|
||||
acc_O.fill(0.0)
|
||||
row_max = cute.make_rmem_tensor((HG,), cutlass.Float32)
|
||||
row_sum = cute.make_rmem_tensor((HG,), cutlass.Float32)
|
||||
row_max.fill(-1e30)
|
||||
row_sum.fill(0.0)
|
||||
|
||||
max_tiles = (TL + KT - 1) // KT
|
||||
for tile_idx in range(max_tiles):
|
||||
tile_start = tile_idx * KT
|
||||
for kv_pos in range(KT):
|
||||
global_kv = tile_start + kv_pos
|
||||
for d in range(HD):
|
||||
valid = global_kv < swa_len
|
||||
val = cutlass.BFloat16(0.0)
|
||||
if valid:
|
||||
val = mKV[tok_idx, global_kv, d]
|
||||
sKV[kv_pos, d] = val
|
||||
|
||||
cute.arch.sync_threads()
|
||||
|
||||
scores = cute.make_rmem_tensor((HG, KT), cutlass.Float32)
|
||||
scores.fill(0.0)
|
||||
for h in cutlass.range_constexpr(HG):
|
||||
for kv_pos in range(KT):
|
||||
dot = cutlass.Float32(0.0)
|
||||
for d in range(HD):
|
||||
q_val = q_reg[h, d].to(cutlass.Float32)
|
||||
k_val = sKV[kv_pos, d].to(cutlass.Float32)
|
||||
dot = dot + q_val * k_val
|
||||
scores[h, kv_pos] = dot * softmax_scale
|
||||
|
||||
for h in cutlass.range_constexpr(HG):
|
||||
tile_max = cutlass.Float32(-1e30)
|
||||
for kv_pos in range(KT):
|
||||
s = scores[h, kv_pos]
|
||||
if s > tile_max:
|
||||
tile_max = s
|
||||
new_max = row_max[h]
|
||||
if tile_max > new_max:
|
||||
new_max = tile_max
|
||||
rescale = cutlass.Float32(0.0)
|
||||
if row_max[h] > cutlass.Float32(-1e29):
|
||||
rescale = cute.exp(row_max[h] - new_max)
|
||||
for d in range(HD):
|
||||
acc_O[h, d] = acc_O[h, d] * rescale
|
||||
row_sum[h] = row_sum[h] * rescale
|
||||
for kv_pos in range(KT):
|
||||
exp_score = cute.exp(scores[h, kv_pos] - new_max)
|
||||
row_sum[h] = row_sum[h] + exp_score
|
||||
for d in range(HD):
|
||||
v_val = sKV[kv_pos, d].to(cutlass.Float32)
|
||||
acc_O[h, d] = acc_O[h, d] + exp_score * v_val
|
||||
row_max[h] = new_max
|
||||
|
||||
cute.arch.sync_threads()
|
||||
|
||||
for h in cutlass.range_constexpr(HG):
|
||||
qh = hg_idx * HG + h
|
||||
for d in range(HD):
|
||||
val_f32 = cutlass.Float32(0.0)
|
||||
if has_kv and row_sum[h] > cutlass.Float32(1e-30):
|
||||
val_f32 = acc_O[h, d] / row_sum[h]
|
||||
mO[tok_idx, qh, d] = val_f32.to(cutlass.BFloat16)
|
||||
@@ -1,68 +1,44 @@
|
||||
"""
|
||||
Native CuTeDSL SWA Decode Attention Kernel for DeepSeek-V4 on Blackwell (SM100).
|
||||
|
||||
This is a FUSED kernel that replaces the Python for-loop decode path.
|
||||
|
||||
Decode attention: each token has 1 query (1, NH, HD) attending to up to window_size
|
||||
KV entries from the paged fp8 cache. Since K=V in MLA, this is a batched GEMV.
|
||||
|
||||
The kernel fuses:
|
||||
1. Paged KV read (using pre-computed swa_indices)
|
||||
2. fp8 dequantize (fp8 * inv_scale → bf16)
|
||||
3. Q×K^T (GEMV, not GEMM — 1 query vs N KVs)
|
||||
4. Online softmax (max + exp + sum)
|
||||
5. Weighted V accumulation (softmax_weights × V)
|
||||
6. Output write
|
||||
FUSED kernel: paged KV read + Q*K^T + online softmax + V accumulation.
|
||||
fp8 dequant is done in a batched pre-step on the host side (fast with torch ops).
|
||||
Future optimization: fuse the fp8 dequant into the kernel using vectorized loads.
|
||||
|
||||
CTA mapping: one CTA per (decode_token, q_head_group).
|
||||
- With 128 Q heads and 16 heads per group, that's 8 groups per token.
|
||||
- Each CTA handles 16 Q heads sharing the same KV.
|
||||
- 128 Q heads / 16 per group = 8 groups per token
|
||||
- Grid: (num_head_groups, num_decode_tokens, 1)
|
||||
|
||||
Tiling:
|
||||
- Q: (HEAD_GROUP, HD) per CTA — loaded once into registers
|
||||
- K/V: streamed in tiles of KV_TILE (e.g., 16) tokens from smem
|
||||
- smem holds one K tile: (KV_TILE, HD) in bf16 = 16 * 512 * 2 = 16 KB
|
||||
- fp8 → bf16 dequantize happens during smem load
|
||||
"""
|
||||
|
||||
import torch
|
||||
import math
|
||||
from typing import Optional
|
||||
|
||||
# For now, this is the host-side launch wrapper that calls the CuTeDSL kernel.
|
||||
# The kernel itself is below and will be compiled with cute.compile.
|
||||
|
||||
try:
|
||||
import cutlass
|
||||
import cutlass.cute as cute
|
||||
from cutlass.cute.nvgpu import tcgen05, warp
|
||||
import cutlass.torch as cutlass_torch
|
||||
from cutlass.cute.runtime import from_dlpack
|
||||
import cutlass.pipeline as pipeline
|
||||
import cutlass.utils as utils
|
||||
import cuda.bindings.driver as cuda
|
||||
HAS_CUTEDSL = True
|
||||
except ImportError:
|
||||
HAS_CUTEDSL = False
|
||||
|
||||
_compiled_kernel_cache = {}
|
||||
|
||||
HEAD_GROUP = 16
|
||||
KV_TILE = 16
|
||||
HEAD_DIM = 512
|
||||
NUM_THREADS = 128
|
||||
|
||||
# ── Host-side wrapper ─────────────────────────────────────────────────
|
||||
|
||||
def native_swa_decode_attention(
|
||||
q: torch.Tensor, # (T, NH, HD) bf16, with RoPE
|
||||
swa_kv_cache: torch.Tensor, # (num_blocks, block_size, HD) fp8 (uint8) paged cache
|
||||
swa_inv_scale: torch.Tensor, # (max_slots, 1) bf16 per-token inv scale
|
||||
swa_indices: torch.Tensor, # (T, window_size) int64 slot indices
|
||||
swa_lens: torch.Tensor, # (T,) int64 valid lengths
|
||||
block_size: int, # tokens per block (256)
|
||||
scale: float, # 1/sqrt(HD)
|
||||
window_size: int = 128, # sliding window size
|
||||
) -> torch.Tensor:
|
||||
"""Native SWA decode attention — calls the CuTeDSL kernel.
|
||||
q, swa_kv_cache, swa_inv_scale, swa_indices, swa_lens,
|
||||
block_size, scale, window_size=128,
|
||||
):
|
||||
"""Native SWA decode attention.
|
||||
|
||||
Falls back to optimized PyTorch batched SDPA if CuTeDSL is not available
|
||||
or if the kernel hasn't been compiled yet.
|
||||
Pre-dequantizes fp8 KV cache to bf16 in a batched operation,
|
||||
then launches the CuTeDSL attention kernel on bf16 data.
|
||||
"""
|
||||
num_tokens, NH, HD = q.shape
|
||||
device = q.device
|
||||
@@ -72,44 +48,113 @@ def native_swa_decode_attention(
|
||||
swa_indices, swa_lens, block_size,
|
||||
scale, window_size)
|
||||
|
||||
# TODO: Implement CuTeDSL kernel launch
|
||||
# For now, use the optimized PyTorch fallback
|
||||
return _fallback_batched_sdp(q, swa_kv_cache, swa_inv_scale,
|
||||
swa_indices, swa_lens, block_size,
|
||||
scale, window_size)
|
||||
q = q.contiguous()
|
||||
swa_indices = swa_indices.contiguous()
|
||||
swa_lens = swa_lens.contiguous()
|
||||
|
||||
# Pre-dequantize fp8 KV cache to bf16
|
||||
# This is a batched gather + dequant: fast on GPU
|
||||
if swa_indices.dim() == 3:
|
||||
swa_indices_2d = swa_indices.squeeze(0)[:num_tokens]
|
||||
else:
|
||||
swa_indices_2d = swa_indices[:num_tokens]
|
||||
|
||||
max_len = swa_lens[:num_tokens].max().item()
|
||||
if max_len <= 0:
|
||||
return torch.zeros(num_tokens, NH, HD, dtype=torch.bfloat16, device=device)
|
||||
|
||||
# Clamp to window_size
|
||||
max_len = min(max_len, window_size)
|
||||
|
||||
# Gather all KV indices: (num_tokens, max_len)
|
||||
safe_indices = swa_indices_2d[:, :max_len].clamp(min=0)
|
||||
block_indices = safe_indices // block_size
|
||||
offsets = safe_indices % block_size
|
||||
|
||||
# Batched gather + dequant
|
||||
kv_raw = swa_kv_cache[block_indices, offsets] # (T, max_len, HD) fp8
|
||||
if swa_kv_cache.dtype == torch.uint8:
|
||||
kv_raw = kv_raw.view(torch.float8_e4m3fn)
|
||||
inv_scales = swa_inv_scale[safe_indices] # (T, max_len, 1)
|
||||
kv_bf16 = (kv_raw.to(torch.bfloat16) * inv_scales).to(torch.bfloat16)
|
||||
|
||||
# Pad to window_size if needed
|
||||
if max_len < window_size:
|
||||
pad = torch.zeros(num_tokens, window_size - max_len, HD,
|
||||
dtype=torch.bfloat16, device=device)
|
||||
kv_bf16 = torch.cat([kv_bf16, pad], dim=1)
|
||||
|
||||
# kv_bf16 is now (num_tokens, window_size, HD) bf16
|
||||
output = torch.zeros(num_tokens, NH, HD, dtype=torch.bfloat16, device=device)
|
||||
|
||||
cache_key = (num_tokens, NH, HD, window_size, str(device))
|
||||
|
||||
if cache_key not in _compiled_kernel_cache:
|
||||
def to_cute(t):
|
||||
ct = cutlass_torch.from_dlpack(t)
|
||||
return ct.mark_layout_dynamic(leading_dim=cutlass_torch.get_leading_dim(t))
|
||||
|
||||
q_c = to_cute(q)
|
||||
kv_c = to_cute(kv_bf16)
|
||||
len_c = to_cute(swa_lens[:num_tokens])
|
||||
out_c = to_cute(output)
|
||||
stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream)
|
||||
|
||||
scale_tensor = torch.tensor([scale], dtype=torch.float32, device=device)
|
||||
scale_c = to_cute(scale_tensor)
|
||||
|
||||
kernel = BlackwellSWADecodeKernel(
|
||||
head_dim=HD, head_group=HEAD_GROUP, kv_tile=KV_TILE,
|
||||
window_size=window_size,
|
||||
)
|
||||
|
||||
compiled = cute.compile(
|
||||
kernel, q_c, kv_c, len_c, out_c, scale_c, stream,
|
||||
)
|
||||
compiled(q_c, kv_c, len_c, out_c, scale_c, stream)
|
||||
torch.cuda.synchronize()
|
||||
|
||||
_compiled_kernel_cache[cache_key] = {'compiled': compiled}
|
||||
|
||||
entry = _compiled_kernel_cache[cache_key]
|
||||
compiled = entry['compiled']
|
||||
|
||||
def to_cute(t):
|
||||
ct = cutlass_torch.from_dlpack(t)
|
||||
return ct.mark_layout_dynamic(leading_dim=cutlass_torch.get_leading_dim(t))
|
||||
|
||||
q_c = to_cute(q)
|
||||
kv_c = to_cute(kv_bf16)
|
||||
len_c = to_cute(swa_lens[:num_tokens])
|
||||
out_c = to_cute(output)
|
||||
stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream)
|
||||
scale_tensor = torch.tensor([scale], dtype=torch.float32, device=device)
|
||||
scale_c = to_cute(scale_tensor)
|
||||
|
||||
compiled(q_c, kv_c, len_c, out_c, scale_c, stream)
|
||||
return output
|
||||
|
||||
|
||||
def _fallback_batched_sdp(
|
||||
q, swa_kv_cache, swa_inv_scale, swa_indices, swa_lens,
|
||||
block_size, scale, window_size,
|
||||
):
|
||||
"""Optimized PyTorch batched SDPA — no Python for-loop.
|
||||
|
||||
This is the fallback when the CuTeDSL kernel isn't compiled yet.
|
||||
All decode tokens are processed in a single batched SDPA call:
|
||||
1. Gather ALL KV entries for ALL decode tokens at once
|
||||
2. Dequantize fp8 → bf16 in one batch
|
||||
3. Run batched SDPA with proper masking
|
||||
"""
|
||||
num_tokens, NH, HD = q.shape
|
||||
device = q.device
|
||||
|
||||
# swa_indices may be 3D (batch, tokens, window) — squeeze batch dim
|
||||
if swa_indices.dim() == 3:
|
||||
swa_indices = swa_indices.squeeze(0) # (tokens, window)
|
||||
swa_indices = swa_indices.squeeze(0)
|
||||
|
||||
safe_indices = swa_indices[:num_tokens].clamp(min=0)
|
||||
block_indices = safe_indices // block_size
|
||||
offsets = safe_indices % block_size
|
||||
|
||||
# Batched KV gather + dequant
|
||||
kv_raw = swa_kv_cache[block_indices, offsets]
|
||||
if swa_kv_cache.dtype == torch.uint8:
|
||||
kv_raw = kv_raw.view(torch.float8_e4m3fn)
|
||||
inv_scales = swa_inv_scale[safe_indices]
|
||||
kv_bf16 = (kv_raw.to(torch.bfloat16) * inv_scales).to(torch.bfloat16)
|
||||
|
||||
# Attention mask
|
||||
pos_range = torch.arange(window_size, device=device).unsqueeze(0)
|
||||
len_mask = pos_range >= swa_lens[:num_tokens].unsqueeze(1)
|
||||
invalid_mask = swa_indices[:num_tokens] < 0
|
||||
@@ -117,7 +162,6 @@ def _fallback_batched_sdp(
|
||||
float_mask = torch.zeros(attn_mask.shape, dtype=torch.bfloat16, device=device)
|
||||
float_mask[attn_mask] = float('-inf')
|
||||
|
||||
# Batched SDPA
|
||||
q_t = q.permute(1, 0, 2)
|
||||
q_batch = q_t.reshape(NH * num_tokens, 1, HD)
|
||||
kv_expanded = kv_bf16.unsqueeze(0).expand(NH, num_tokens, window_size, HD)
|
||||
@@ -138,69 +182,25 @@ def _fallback_batched_sdp(
|
||||
return out.reshape(NH, num_tokens, HD).permute(1, 0, 2)
|
||||
|
||||
|
||||
# ── CuTeDSL Kernel (Blackwell SM100) ──────────────────────────────────
|
||||
|
||||
# The kernel below implements the full fused decode attention on Blackwell.
|
||||
# It uses tcgen05 (Blackwell tensor core) for the GEMV operations.
|
||||
#
|
||||
# Architecture per CTA:
|
||||
# - 1 CTA = 1 warp group (128 threads) handling (1 token, HEAD_GROUP heads)
|
||||
# - Q: (HEAD_GROUP, HD) loaded once into registers
|
||||
# - K/V: streamed in tiles of KV_TILE tokens
|
||||
# - fp8 dequant: fused during gmem→smem copy
|
||||
# - Online softmax: row_max, row_exp_sum tracked in registers
|
||||
# - Output: (HEAD_GROUP, HD) accumulated in registers, written to gmem at end
|
||||
|
||||
HEAD_GROUP = 16 # Q heads per CTA (128 total heads / 8 CTAs)
|
||||
KV_TILE = 16 # KV tokens per smem tile
|
||||
HEAD_DIM = 512 # KV latent dimension
|
||||
|
||||
|
||||
if HAS_CUTEDSL:
|
||||
|
||||
class BlackwellSWADecodeAttention:
|
||||
"""CuTeDSL SWA Decode Attention Kernel for Blackwell.
|
||||
|
||||
Each CTA handles one (decode_token, q_head_group) pair.
|
||||
The kernel streams KV tiles from the paged fp8 cache,
|
||||
dequantizes, computes Q×K^T, online softmax, and V accumulation.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
head_dim: int = HEAD_DIM,
|
||||
head_group: int = HEAD_GROUP,
|
||||
kv_tile: int = KV_TILE,
|
||||
num_threads: int = 128,
|
||||
):
|
||||
class BlackwellSWADecodeKernel:
|
||||
def __init__(self, head_dim=HEAD_DIM, head_group=HEAD_GROUP,
|
||||
kv_tile=KV_TILE, window_size=128):
|
||||
self._head_dim = head_dim
|
||||
self._head_group = head_group
|
||||
self._kv_tile = kv_tile
|
||||
self._num_threads = num_threads
|
||||
self._head_dim_padded = (head_dim + 31) // 32 * 32
|
||||
self._window_size = window_size
|
||||
self._num_threads = NUM_THREADS
|
||||
|
||||
@cute.jit
|
||||
def __call__(
|
||||
self,
|
||||
mQ: cute.Tensor, # (T, NH, HD)
|
||||
mKV_cache: cute.Tensor, # (num_blocks, block_size, HD) uint8
|
||||
mInv_scale: cute.Tensor, # (max_slots, 1) bf16
|
||||
mSwa_indices: cute.Tensor, # (T, W) int64
|
||||
mSwa_lens: cute.Tensor, # (T,) int64
|
||||
mO: cute.Tensor, # (T, NH, HD)
|
||||
softmax_scale: cutlass.Float32,
|
||||
window_size: int,
|
||||
block_size: int,
|
||||
stream: cuda.CUstream,
|
||||
):
|
||||
# Grid: (num_head_groups, num_decode_tokens, 1)
|
||||
def __call__(self, mQ, mKV, mLens, mO, mScale, stream):
|
||||
num_tokens = mQ.shape[0]
|
||||
num_head_groups = mQ.shape[1] // self._head_group
|
||||
num_decode_tokens = mQ.shape[0]
|
||||
grid_dim = (num_head_groups, num_decode_tokens, 1)
|
||||
grid_dim = (num_head_groups, num_tokens, 1)
|
||||
|
||||
self.kernel(
|
||||
mQ, mKV_cache, mInv_scale, mSwa_indices, mSwa_lens, mO,
|
||||
softmax_scale, window_size, block_size,
|
||||
self._kernel(
|
||||
mQ, mKV, mLens, mO, mScale,
|
||||
).launch(
|
||||
grid=grid_dim,
|
||||
block=[self._num_threads, 1, 1],
|
||||
@@ -208,68 +208,124 @@ if HAS_CUTEDSL:
|
||||
)
|
||||
|
||||
@cute.kernel
|
||||
def kernel(
|
||||
def _kernel(
|
||||
self,
|
||||
mQ: cute.Tensor,
|
||||
mKV_cache: cute.Tensor,
|
||||
mInv_scale: cute.Tensor,
|
||||
mSwa_indices: cute.Tensor,
|
||||
mSwa_lens: cute.Tensor,
|
||||
mO: cute.Tensor,
|
||||
softmax_scale: cutlass.Float32,
|
||||
window_size: int,
|
||||
block_size: int,
|
||||
mQ: cute.Tensor, # (T, NH, HD) bf16
|
||||
mKV: cute.Tensor, # (T, WS, HD) bf16 - pre-dequantized
|
||||
mLens: cute.Tensor, # (T,) int64
|
||||
mO: cute.Tensor, # (T, NH, HD) bf16
|
||||
mScale: cute.Tensor, # (1,) f32
|
||||
):
|
||||
tidx, _, _ = cute.arch.thread_idx()
|
||||
head_group_idx, token_idx, _ = cute.arch.block_idx()
|
||||
hg_idx, tok_idx, _ = cute.arch.block_idx()
|
||||
|
||||
# This CTA handles Q heads [head_group_idx * HEAD_GROUP : (head_group_idx + 1) * HEAD_GROUP]
|
||||
# for decode token token_idx
|
||||
HG = self._head_group
|
||||
HD = self._head_dim
|
||||
KT = self._kv_tile
|
||||
WS = self._window_size
|
||||
softmax_scale = mScale[0]
|
||||
|
||||
# Read swa_len for this token
|
||||
swa_len = mSwa_lens[token_idx]
|
||||
# ── Shared memory ──────────────────────────────────────
|
||||
@cute.struct
|
||||
class SharedStorage:
|
||||
kv_tile: cute.struct.MemRange[cutlass.BFloat16, KT * HD]
|
||||
|
||||
# Load Q for this (token, head_group)
|
||||
# Q shape: (HEAD_GROUP, HD) from mQ[token_idx, head_group_idx*HEAD_GROUP:(+HEAD_GROUP), :]
|
||||
q_local = cute.make_rmem_tensor(
|
||||
(self._head_group, self._head_dim), cutlass.BFloat16
|
||||
smem = utils.SmemAllocator()
|
||||
storage = smem.allocate(SharedStorage)
|
||||
|
||||
sKV = cute.make_tensor(
|
||||
storage.kv_tile.data_ptr(),
|
||||
cute.make_layout((KT, HD), stride=(HD, 1)),
|
||||
)
|
||||
for h in cutlass.range_constexpr(self._head_group):
|
||||
for d in range(self._head_dim):
|
||||
q_local[h, d] = mQ[token_idx, head_group_idx * self._head_group + h, d]
|
||||
|
||||
# Accumulator for output: (HEAD_GROUP, HD) in float32
|
||||
acc_O = cute.make_rmem_tensor(
|
||||
(self._head_group, self._head_dim), cutlass.Float32
|
||||
)
|
||||
# ── Read valid KV length ───────────────────────────────
|
||||
swa_len = mLens[tok_idx]
|
||||
has_kv = swa_len > 0
|
||||
|
||||
# ── Load Q into registers: (HG, HD) ───────────────────
|
||||
q_reg = cute.make_rmem_tensor((HG, HD), cutlass.BFloat16)
|
||||
for h in cutlass.range_constexpr(HG):
|
||||
qh = hg_idx * HG + h
|
||||
for d in range(HD):
|
||||
q_reg[h, d] = mQ[tok_idx, qh, d]
|
||||
|
||||
# ── Output accumulator: (HG, HD) f32 ──────────────────
|
||||
acc_O = cute.make_rmem_tensor((HG, HD), cutlass.Float32)
|
||||
acc_O.fill(0.0)
|
||||
|
||||
# Online softmax state: (HEAD_GROUP,) max and sum
|
||||
row_max = cute.make_rmem_tensor((self._head_group,), cutlass.Float32)
|
||||
row_sum = cute.make_rmem_tensor((self._head_group,), cutlass.Float32)
|
||||
row_max.fill(-cutlass.Float32.inf)
|
||||
# ── Online softmax state: (HG,) f32 ───────────────────
|
||||
row_max = cute.make_rmem_tensor((HG,), cutlass.Float32)
|
||||
row_sum = cute.make_rmem_tensor((HG,), cutlass.Float32)
|
||||
row_max.fill(-1e30)
|
||||
row_sum.fill(0.0)
|
||||
|
||||
# Stream KV tiles
|
||||
num_kv_tiles = cute.ceil_div(swa_len, self._kv_tile)
|
||||
# ── Stream KV tiles ────────────────────────────────────
|
||||
max_tiles = (WS + KT - 1) // KT
|
||||
|
||||
for kv_tile_idx in range(num_kv_tiles):
|
||||
# 1. Read swa_indices for this tile
|
||||
# 2. Gather KV from paged cache
|
||||
# 3. Dequantize fp8 → bf16
|
||||
# 4. Compute Q × K^T
|
||||
# 5. Update online softmax
|
||||
# 6. Accumulate weighted V
|
||||
pass # TODO: implement tile processing
|
||||
for tile_idx in range(max_tiles):
|
||||
tile_start = tile_idx * KT
|
||||
|
||||
# Normalize output by row_sum
|
||||
for h in cutlass.range_constexpr(self._head_group):
|
||||
if row_sum[h] != 0.0:
|
||||
inv_sum = 1.0 / row_sum[h]
|
||||
for d in range(self._head_dim):
|
||||
acc_O[h, d] = acc_O[h, d] * inv_sum
|
||||
# Load bf16 KV from contiguous tensor to smem
|
||||
for kv_pos in range(KT):
|
||||
global_kv = tile_start + kv_pos
|
||||
for d in range(HD):
|
||||
valid = global_kv < swa_len
|
||||
val = cutlass.BFloat16(0.0)
|
||||
if valid:
|
||||
val = mKV[tok_idx, global_kv, d]
|
||||
sKV[kv_pos, d] = val
|
||||
|
||||
# Write output
|
||||
for h in cutlass.range_constexpr(self._head_group):
|
||||
for d in range(self._head_dim):
|
||||
mO[token_idx, head_group_idx * self._head_group + h, d] = acc_O[h, d].to(cutlass.BFloat16)
|
||||
cute.arch.sync_threads()
|
||||
|
||||
# Q * K^T: (HG, KT) scores
|
||||
scores = cute.make_rmem_tensor((HG, KT), cutlass.Float32)
|
||||
scores.fill(0.0)
|
||||
|
||||
for h in cutlass.range_constexpr(HG):
|
||||
for kv_pos in range(KT):
|
||||
dot = cutlass.Float32(0.0)
|
||||
for d in range(HD):
|
||||
q_val = q_reg[h, d].to(cutlass.Float32)
|
||||
k_val = sKV[kv_pos, d].to(cutlass.Float32)
|
||||
dot = dot + q_val * k_val
|
||||
scores[h, kv_pos] = dot * softmax_scale
|
||||
|
||||
# Online softmax update
|
||||
for h in cutlass.range_constexpr(HG):
|
||||
tile_max = cutlass.Float32(-1e30)
|
||||
for kv_pos in range(KT):
|
||||
s = scores[h, kv_pos]
|
||||
if s > tile_max:
|
||||
tile_max = s
|
||||
|
||||
new_max = row_max[h]
|
||||
if tile_max > new_max:
|
||||
new_max = tile_max
|
||||
|
||||
rescale = cutlass.Float32(0.0)
|
||||
if row_max[h] > cutlass.Float32(-1e29):
|
||||
rescale = cute.exp(row_max[h] - new_max)
|
||||
|
||||
for d in range(HD):
|
||||
acc_O[h, d] = acc_O[h, d] * rescale
|
||||
row_sum[h] = row_sum[h] * rescale
|
||||
|
||||
for kv_pos in range(KT):
|
||||
exp_score = cute.exp(scores[h, kv_pos] - new_max)
|
||||
row_sum[h] = row_sum[h] + exp_score
|
||||
for d in range(HD):
|
||||
v_val = sKV[kv_pos, d].to(cutlass.Float32)
|
||||
acc_O[h, d] = acc_O[h, d] + exp_score * v_val
|
||||
|
||||
row_max[h] = new_max
|
||||
|
||||
cute.arch.sync_threads()
|
||||
|
||||
# ── Normalize and write output ─────────────────────────
|
||||
for h in cutlass.range_constexpr(HG):
|
||||
qh = hg_idx * HG + h
|
||||
for d in range(HD):
|
||||
val_f32 = cutlass.Float32(0.0)
|
||||
if has_kv and row_sum[h] > cutlass.Float32(1e-30):
|
||||
val_f32 = acc_O[h, d] / row_sum[h]
|
||||
mO[tok_idx, qh, d] = val_f32.to(cutlass.BFloat16)
|
||||
|
||||
140
tests/test_decode_pipeline.py
Normal file
140
tests/test_decode_pipeline.py
Normal file
@@ -0,0 +1,140 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Integration test: full decode attention pipeline on Blackwell.
|
||||
|
||||
Tests the end-to-end path that _attention_impl_blackwell uses:
|
||||
1. Project Q, KV (simulated)
|
||||
2. Apply RoPE to Q (in-place)
|
||||
3. Write KV to paged cache (RoPE + fp8 quantize + insert)
|
||||
4. Native SWA decode attention (CuTeDSL kernel)
|
||||
5. Inverse RoPE on output
|
||||
6. wo_a + wo_b projections
|
||||
|
||||
Compares against a pure-PyTorch reference path.
|
||||
"""
|
||||
import sys, torch, torch.nn.functional as F, math
|
||||
sys.path.insert(0, "/root/dsv4-nvfp4-workspace/vllm")
|
||||
sys.path.insert(0, "/root/dsv4-nvfp4-workspace/kernel")
|
||||
|
||||
from vllm.model_executor.layers.csa_attention import (
|
||||
fused_qnorm_rope_kv_insert_py,
|
||||
blackwell_attention_kv_write,
|
||||
causal_prefill_attention,
|
||||
kv_dequantize_fp8,
|
||||
apply_gptj_rope,
|
||||
apply_inv_gptj_rope,
|
||||
)
|
||||
from cutedsl.native_swa_decode import native_swa_decode_attention
|
||||
|
||||
torch.manual_seed(42)
|
||||
torch.cuda.set_device(0)
|
||||
|
||||
# ── Model params (DeepSeek-V4) ──────────────────────────────────────
|
||||
NH = 128
|
||||
HD = 512
|
||||
NOPE_DIM = 448
|
||||
ROPE_DIM = 64
|
||||
BLOCK_SIZE = 256
|
||||
WINDOW_SIZE = 128
|
||||
NUM_LAYERS = 61
|
||||
SCALE = HD ** -0.5
|
||||
EPS = 1e-6
|
||||
|
||||
# ── Cos/sin cache ────────────────────────────────────────────────────
|
||||
MAX_POS = 4096
|
||||
half_rope = ROPE_DIM // 2
|
||||
freqs = 1.0 / (10000 ** (torch.arange(0, ROPE_DIM, 2).float() / ROPE_DIM))
|
||||
t = torch.arange(MAX_POS).float()
|
||||
freqs = torch.outer(t, freqs)
|
||||
cos_sin_cache = torch.cat([freqs.cos(), freqs.sin()], dim=-1) # (MAX_POS, ROPE_DIM)
|
||||
|
||||
# ── Simulate decode tokens ──────────────────────────────────────────
|
||||
num_decode_tokens = 4
|
||||
positions = torch.tensor([100, 200, 300, 400], dtype=torch.int64, device="cuda:0")
|
||||
|
||||
# Create Q and KV (post-norm, pre-RoPE)
|
||||
q = torch.randn(num_decode_tokens, NH, HD, dtype=torch.bfloat16, device="cuda:0") * 0.1
|
||||
kv = torch.randn(num_decode_tokens, HD, dtype=torch.bfloat16, device="cuda:0") * 0.5
|
||||
|
||||
# ── Apply RoPE to Q ─────────────────────────────────────────────────
|
||||
fused_qnorm_rope_kv_insert_py(
|
||||
q, kv, None, None, positions, cos_sin_cache, EPS, 0,
|
||||
nope_dim=NOPE_DIM, rope_dim=ROPE_DIM,
|
||||
)
|
||||
# q is now RoPE'd in-place
|
||||
|
||||
# ── Create paged KV cache and write KV ──────────────────────────────
|
||||
num_blocks = 8
|
||||
swa_kv_cache = torch.zeros(
|
||||
num_blocks, BLOCK_SIZE, HD, dtype=torch.uint8, device="cuda:0",
|
||||
)
|
||||
max_slots = num_blocks * BLOCK_SIZE
|
||||
swa_inv_scale = torch.zeros(max_slots, 1, dtype=torch.bfloat16, device="cuda:0")
|
||||
|
||||
# Slot mapping: each decode token gets a unique slot
|
||||
slot_mapping = torch.zeros(num_decode_tokens, dtype=torch.int64, device="cuda:0")
|
||||
for i, pos in enumerate(positions):
|
||||
slot_mapping[i] = pos.item() # slot = position for simplicity
|
||||
|
||||
blackwell_attention_kv_write(
|
||||
kv, positions, swa_kv_cache, swa_inv_scale,
|
||||
slot_mapping, BLOCK_SIZE, cos_sin_cache,
|
||||
nope_dim=NOPE_DIM, rope_dim=ROPE_DIM,
|
||||
)
|
||||
|
||||
# ── Build SWA indices for decode ─────────────────────────────────────
|
||||
# Each decode token attends to the last window_size positions
|
||||
swa_indices = torch.zeros(num_decode_tokens, WINDOW_SIZE, dtype=torch.int64, device="cuda:0")
|
||||
swa_lens = torch.zeros(num_decode_tokens, dtype=torch.int64, device="cuda:0")
|
||||
|
||||
for i, pos in enumerate(positions):
|
||||
# This token can see positions 0..pos (inclusive)
|
||||
num_cached = min(pos.item() + 1, WINDOW_SIZE)
|
||||
swa_lens[i] = num_cached
|
||||
for j in range(WINDOW_SIZE):
|
||||
if j < num_cached:
|
||||
slot = pos.item() - (num_cached - 1 - j)
|
||||
swa_indices[i, j] = max(0, slot)
|
||||
else:
|
||||
swa_indices[i, j] = -1
|
||||
|
||||
# ── Native SWA decode attention ──────────────────────────────────────
|
||||
o_native = native_swa_decode_attention(
|
||||
q, swa_kv_cache, swa_inv_scale,
|
||||
swa_indices, swa_lens,
|
||||
BLOCK_SIZE, SCALE, WINDOW_SIZE,
|
||||
)
|
||||
|
||||
# ── Reference: full BF16 attention ──────────────────────────────────
|
||||
# Read all cached KV for each token, dequantize, attend
|
||||
o_ref = torch.zeros_like(o_native)
|
||||
for i, pos in enumerate(positions):
|
||||
num_cached = min(pos.item() + 1, WINDOW_SIZE)
|
||||
slots = torch.arange(pos.item() - num_cached + 1, pos.item() + 1, dtype=torch.int64, device="cuda:0")
|
||||
slots = slots.clamp(min=0)
|
||||
block_idx = slots // BLOCK_SIZE
|
||||
offsets = slots % BLOCK_SIZE
|
||||
kv_cached_raw = swa_kv_cache[block_idx, offsets].view(torch.float8_e4m3fn)
|
||||
inv_s = swa_inv_scale[slots]
|
||||
kv_cached = (kv_cached_raw.to(torch.bfloat16) * inv_s).to(torch.bfloat16)
|
||||
|
||||
qi = q[i:i+1] # (1, NH, HD)
|
||||
qi_t = qi.permute(1, 0, 2) # (NH, 1, HD)
|
||||
kv_exp = kv_cached.unsqueeze(0).expand(NH, -1, -1)
|
||||
out = F.scaled_dot_product_attention(qi_t, kv_exp, kv_exp, is_causal=False, scale=SCALE)
|
||||
o_ref[i] = out.permute(1, 0, 2).squeeze(0)
|
||||
|
||||
# ── Compare ──────────────────────────────────────────────────────────
|
||||
cos = F.cosine_similarity(o_ref.flatten().unsqueeze(0).float(),
|
||||
o_native.flatten().unsqueeze(0).float()).item()
|
||||
print(f"Full pipeline cosine (ref vs native): {cos:.6f} {'PASS' if cos >= 0.99 else 'FAIL'}")
|
||||
|
||||
# Per-token
|
||||
for i in range(num_decode_tokens):
|
||||
ct = F.cosine_similarity(o_ref[i].flatten().unsqueeze(0).float(),
|
||||
o_native[i].flatten().unsqueeze(0).float()).item()
|
||||
print(f" Token {i} (pos={positions[i].item()}) cosine: {ct:.6f}")
|
||||
|
||||
# Check for NaN
|
||||
print(f"NaN in native output: {torch.isnan(o_native).any()}")
|
||||
print(f"Native amax: {o_native.amax():.4f}")
|
||||
71
tests/test_sparse_decode.py
Normal file
71
tests/test_sparse_decode.py
Normal file
@@ -0,0 +1,71 @@
|
||||
import sys, torch, torch.nn.functional as F
|
||||
sys.path.insert(0, "/root/dsv4-nvfp4-workspace/kernel")
|
||||
from cutedsl.native_sparse_decode import native_sparse_decode_attention
|
||||
|
||||
torch.manual_seed(42)
|
||||
torch.cuda.set_device(0)
|
||||
NH, HD, BS, WIN, TOPK = 128, 512, 256, 128, 16
|
||||
|
||||
for nt, swa_l, topk_l in [(2,32,8), (2,64,16), (4,32,16), (4,64,8)]:
|
||||
q = torch.randn(nt, NH, HD, dtype=torch.bfloat16, device="cuda:0") * 0.1
|
||||
nb = 4
|
||||
# SWA cache
|
||||
kv_bf = torch.randn(nb*BS, HD, dtype=torch.bfloat16, device="cuda:0") * 0.5
|
||||
am = kv_bf.float().abs().amax(-1, keepdim=True).clamp(min=1e-12)
|
||||
f8m = torch.tensor(448.0, dtype=torch.float32, device="cuda:0")
|
||||
swa_cache = (kv_bf.float() * f8m / am).to(torch.float8_e4m3fn)[:nb*BS].reshape(nb,BS,HD).view(torch.uint8)
|
||||
inv_sc = (am / f8m).to(torch.bfloat16)
|
||||
# Compressed cache
|
||||
comp_bf = torch.randn(nb*BS, HD, dtype=torch.bfloat16, device="cuda:0") * 0.3
|
||||
am2 = comp_bf.float().abs().amax(-1, keepdim=True).clamp(min=1e-12)
|
||||
comp_cache = (comp_bf.float() * f8m / am2).to(torch.float8_e4m3fn)[:nb*BS].reshape(nb,BS,HD).view(torch.uint8)
|
||||
inv_sc2 = (am2 / f8m).to(torch.bfloat16)
|
||||
|
||||
si = torch.zeros(nt, WIN, dtype=torch.int64, device="cuda:0")
|
||||
sl = torch.zeros(nt, dtype=torch.int64, device="cuda:0")
|
||||
ti = torch.zeros(nt, TOPK, dtype=torch.int64, device="cuda:0")
|
||||
tl = torch.zeros(nt, dtype=torch.int64, device="cuda:0")
|
||||
for t in range(nt):
|
||||
sl[t] = swa_l
|
||||
for i in range(swa_l): si[t,i] = i
|
||||
for i in range(swa_l, WIN): si[t,i] = -1
|
||||
tl[t] = topk_l
|
||||
for i in range(topk_l): ti[t,i] = 1000+i
|
||||
for i in range(topk_l, TOPK): ti[t,i] = -1
|
||||
|
||||
sink = torch.full((NH,), float("-inf"), dtype=torch.float32, device="cuda:0")
|
||||
ascale = HD ** -0.5
|
||||
|
||||
# Reference: combined SDPA
|
||||
safe_swa = si.clamp(min=0)
|
||||
swa_raw = swa_cache[safe_swa//BS, safe_swa%BS].view(torch.float8_e4m3fn)
|
||||
swa_kv = (swa_raw.to(torch.bfloat16)*inv_sc[safe_swa]).to(torch.bfloat16)
|
||||
comp_bs = comp_cache.shape[1]
|
||||
safe_topk = ti.clamp(min=0)
|
||||
comp_raw = comp_cache[safe_topk//comp_bs, safe_topk%comp_bs].view(torch.float8_e4m3fn)
|
||||
comp_kv = (comp_raw.to(torch.bfloat16)*inv_sc2[safe_topk]).to(torch.bfloat16)
|
||||
kv_comb = torch.cat([swa_kv, comp_kv], dim=1)
|
||||
total = WIN + TOPK
|
||||
cl = sl + tl
|
||||
|
||||
# Build mask
|
||||
pos = torch.arange(total, device="cuda:0").unsqueeze(0)
|
||||
lm = pos >= cl.unsqueeze(1)
|
||||
inv_s = si < 0
|
||||
inv_t = ti < 0
|
||||
inv = torch.cat([inv_s, inv_t], dim=1)
|
||||
mask = lm | inv
|
||||
fm = torch.zeros(mask.shape, dtype=torch.bfloat16, device="cuda:0")
|
||||
fm[mask] = float("-inf")
|
||||
|
||||
qt = q.permute(1,0,2).reshape(NH*nt,1,HD)
|
||||
kve = kv_comb.unsqueeze(0).expand(NH,nt,total,HD).reshape(NH*nt,total,HD)
|
||||
mb = fm.unsqueeze(0).unsqueeze(2).expand(NH,nt,1,total).reshape(NH*nt,1,total)
|
||||
ref = F.scaled_dot_product_attention(qt, kve, kve, attn_mask=mb, is_causal=False, scale=ascale).reshape(NH,nt,HD).permute(1,0,2)
|
||||
|
||||
try:
|
||||
nat = native_sparse_decode_attention(q, swa_cache, inv_sc, si, sl, comp_cache, inv_sc2, ti, tl, sink, BS, ascale, WIN, compress_ratio=4)
|
||||
c = F.cosine_similarity(ref.flatten().unsqueeze(0).float(), nat.flatten().unsqueeze(0).float()).item()
|
||||
print(f"tokens={nt} swa={swa_l} topk={topk_l} cosine={c:.6f} {'OK' if c>=0.99 else 'LOW'}")
|
||||
except Exception as e:
|
||||
print(f"tokens={nt} swa={swa_l} topk={topk_l} FAILED: {e}")
|
||||
Reference in New Issue
Block a user