[ARCH] CCCL-derived paged attention kernel architecture + Triton rewrite
Architecture document: docs/paged_attention_kernel_architecture.md Defines every module from CCCL algorithm patterns before code. Three-level decomposition from CCCL: Level 1 (warp_reduce_shfl): shfl.down butterfly for per-thread QK scores Level 2 (block_reduce_warp_reductions): warp partials → SMEM → block aggregate Level 3 (agent_scan decoupled lookback): cross-partition combine Compound type (from summary_statistics.cu): attention_partial = (max_score, exp_sum, weighted_v[256]) combine(a, b) = online softmax rescaling (same math as Flash Attention) Key design change: Grid on num_kv_heads, not num_heads. Before: grid = (1, 24, 200) = 4800 blocks, KV loaded 6x redundantly After: grid = (1, 4, 200) = 800 blocks, KV loaded once per kv_head Each block computes GQA_RATIO=6 query heads with shared KV loads. Reduces KV cache bandwidth by 6x (the GQA ratio). SMEM budget verified: K tile [32, 256] fp16 = 16KB V tile [32, 256] fp16 = 16KB Total = 32KB ≤ 48KB ✓ Phase 1 kernel: _partition_attn_kernel Processes query heads sequentially within the GQA group to minimize register pressure (6 × 256 = 1536 registers too many if all loaded simultaneously). Phase 2 kernel: _reduce_partitions_kernel Also gridded on kv_heads, reduces all partitions for GQA_RATIO heads per block. This replaces the previous Triton V2 which was gridded on num_heads and had no GQA awareness at the kernel level.
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Claude
@@ -1,35 +1,32 @@
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"""
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paged_attention_v2_triton.py — Triton PagedAttention V2 for BI-V100
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paged_attention_v2_triton.py — CCCL-derived Triton PagedAttention V2
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=====================================================================
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Two-kernel V2 implementation using Triton:
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Phase 1: _paged_attn_v2_partition — per-partition attention (paged K/V gather)
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Phase 2: _paged_attn_v2_reduce — cross-partition log-sum-exp reduction
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Architecture: docs/paged_attention_kernel_architecture.md
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The K/V gather pattern is adapted from prefix_prefill.py (lines 100-170):
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bn = tl.load(block_tables + seq * stride + (token // block_size) * stride)
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off_k = bn * stride_kc_b + kv_head * stride_kc_h + (d // x) * stride_kc_dx + ...
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k = tl.load(key_cache + off_k, mask=...)
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Two-kernel design:
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Phase 1: _partition_attn — per-partition compound reduction (CCCL block_reduce pattern)
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Phase 2: _reduce_partitions — cross-partition combine (CCCL agent_reduce pattern)
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For decode (BLOCK_M=1), the Q tile is just one vector [HEAD_DIM].
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The inner loop iterates over BLOCK_N KV tokens per step.
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Online softmax accumulates (max, sum, weighted_V) across steps.
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Key CCCL derivations:
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1. Compound type: (max_score, exp_sum, weighted_v[D]) — from summary_statistics.cu
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2. Combine op: online softmax rescaling — from Flash Attention = CCCL's binary_op pattern
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3. Warp reduce: shfl.down butterfly — from warp_reduce_shfl.cuh (Triton does this via tl.sum/tl.max)
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4. Block reduce: warp partials → SMEM → serial combine — from block_reduce_warp_reductions.cuh
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5. Paged gather: indirect load via block_tables — from prefix_prefill.py (proven on BI-V100)
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6. GQA: grid on kv_heads, process gqa_ratio query heads per block — KV loaded once
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After all steps in a partition, we have:
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max_logits[seq, head, part]: running max
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exp_sums[seq, head, part]: running exp sum
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tmp_output[seq, head, part, :]: unnormalized weighted V
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Grid design:
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Phase 1: (num_seqs, num_kv_heads, num_partitions) — NOT (num_seqs, num_heads, num_partitions)
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Each block loads KV once for kv_head, computes gqa_ratio query heads.
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Reduces KV cache reads by gqa_ratio (6x for Qwen3.6).
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Phase 2: (num_seqs, num_kv_heads) — reduces partitions, writes all gqa_ratio outputs.
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Phase 2 combines partitions using the CCCL summary_statistics pattern:
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global_max = max(part_maxes)
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rescaled_sum = sum(exp(part_max - global_max) * part_sum)
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output = sum(weight[p] * part_output[p])
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SMEM analysis:
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Phase 1: K tile [BLOCK_N, HEAD_DIM] loaded via gather (no explicit SMEM tile)
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Triton manages register allocation for tl.load + tl.dot
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At BLOCK_N=32, HEAD_DIM=256: 32×256 fp16 values in registers = 16KB
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Phase 2: No SMEM needed (partitions ≈ 200, all in registers)
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SMEM budget (head_dim=256, BLOCK_N=32):
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K tile: 32×256×2 = 16KB
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V tile: 32×256×2 = 16KB
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Warp partials: negligible (in registers for Triton)
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Total: 32KB ≤ 48KB ✓
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"""
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import torch
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@@ -39,22 +36,22 @@ from typing import Optional
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@triton.jit
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def _paged_attn_v2_partition_kernel(
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# Outputs
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tmp_output_ptr, # [num_seqs, num_heads, max_num_parts, head_size]
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exp_sums_ptr, # [num_seqs, num_heads, max_num_parts]
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max_logits_ptr, # [num_seqs, num_heads, max_num_parts]
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def _partition_attn_kernel(
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# Outputs (per partition)
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tmp_output_ptr, # [num_seqs, num_heads, max_parts, head_size]
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exp_sums_ptr, # [num_seqs, num_heads, max_parts]
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max_logits_ptr, # [num_seqs, num_heads, max_parts]
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# Inputs
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query_ptr, # [num_seqs, num_heads, head_size]
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key_cache_ptr, # [num_blocks, num_kv_heads, head_size/x, block_size, x]
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value_cache_ptr, # [num_blocks, num_kv_heads, head_size, block_size]
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key_cache_ptr, # [num_blocks, kv_heads, head_size/x, block_size, x]
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value_cache_ptr, # [num_blocks, kv_heads, head_size, block_size]
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block_tables_ptr, # [num_seqs, max_blocks_per_seq]
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seq_lens_ptr, # [num_seqs]
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# Scalars
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scale: tl.float32,
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num_queries_per_kv: tl.int32,
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gqa_ratio: tl.int32, # num_heads // num_kv_heads
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block_size: tl.int32,
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x_pack: tl.int32, # key_cache packing factor: 16 // sizeof(dtype)
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x_pack: tl.int32, # key_cache packing factor
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# Strides: query [S, H, D]
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stride_qs: tl.int32, stride_qh: tl.int32, stride_qd: tl.int32,
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# Strides: key_cache [B, KH, D/X, BS, X]
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@@ -68,23 +65,36 @@ def _paged_attn_v2_partition_kernel(
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# Strides: tmp_output [S, H, P, D]
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stride_to_s: tl.int32, stride_to_h: tl.int32,
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stride_to_p: tl.int32, stride_to_d: tl.int32,
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# Strides: exp_sums / max_logits [S, H, P]
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# Strides: exp_sums/max_logits [S, H, P]
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stride_es_s: tl.int32, stride_es_h: tl.int32, stride_es_p: tl.int32,
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# Compile-time constants
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# Constants
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PARTITION_SIZE: tl.constexpr,
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HEAD_DIM: tl.constexpr,
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BLOCK_N: tl.constexpr,
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GQA_RATIO: tl.constexpr,
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):
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"""Phase 1: Per-partition paged attention for decode (BLOCK_M=1).
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"""Phase 1: Per-partition attention with GQA broadcast.
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Grid: (num_seqs, num_heads, max_num_partitions)
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Each program instance processes one (seq, head, partition) triple.
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Grid: (num_seqs, num_kv_heads, num_partitions)
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Each block processes one (seq, kv_head, partition), computing GQA_RATIO query heads.
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Adapted from prefix_prefill.py's paged K/V gather pattern.
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Key difference: BLOCK_M=1 (decode has 1 query token per head).
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Algorithm (CCCL compound reduction):
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For each BLOCK_N chunk of KV tokens in this partition:
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1. Paged K gather: block_tables → physical_block → K[BLOCK_N, HEAD_DIM]
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2. Scores: Q[g, HEAD_DIM] · K[HEAD_DIM, BLOCK_N] → [GQA_RATIO, BLOCK_N]
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3. Online softmax update (combine op from summary_statistics.cu):
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For each query head g:
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m_new = max(m_old, max(scores[g]))
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rescale_old = exp(m_old - m_new)
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p = exp(scores[g] - m_new)
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l_new = rescale_old * l_old + sum(p)
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acc[g] = rescale_old * acc[g] + p · V
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m_old, l_old = m_new, l_new
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4. Paged V gather → accumulate weighted V
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Write per-partition results for all GQA_RATIO heads.
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"""
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seq_idx = tl.program_id(0)
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head_idx = tl.program_id(1)
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kv_head_idx = tl.program_id(1)
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part_idx = tl.program_id(2)
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seq_len = tl.load(seq_lens_ptr + seq_idx)
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@@ -92,166 +102,168 @@ def _paged_attn_v2_partition_kernel(
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part_end = tl.minimum(part_start + PARTITION_SIZE, seq_len)
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if part_start >= seq_len:
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# Unused partition — write sentinel values
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tl.store(max_logits_ptr + seq_idx * stride_es_s + head_idx * stride_es_h + part_idx * stride_es_p,
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float('-inf'))
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tl.store(exp_sums_ptr + seq_idx * stride_es_s + head_idx * stride_es_h + part_idx * stride_es_p,
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0.0)
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# Unused partition — write sentinels for all GQA_RATIO heads
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for g in range(GQA_RATIO):
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head_idx = kv_head_idx * GQA_RATIO + g
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tl.store(max_logits_ptr + seq_idx * stride_es_s + head_idx * stride_es_h + part_idx * stride_es_p,
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float('-inf'))
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tl.store(exp_sums_ptr + seq_idx * stride_es_s + head_idx * stride_es_h + part_idx * stride_es_p,
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0.0)
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return
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# GQA: map query head → KV head
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kv_head_idx = head_idx // num_queries_per_kv
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# Load query vector: [HEAD_DIM]
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offs_d = tl.arange(0, HEAD_DIM)
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q = tl.load(query_ptr + seq_idx * stride_qs + head_idx * stride_qh + offs_d * stride_qd).to(tl.float32)
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# Online softmax state
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m_i = float('-inf') # running max
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l_i = 0.0 # running exp sum
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acc = tl.zeros([HEAD_DIM], dtype=tl.float32) # weighted V accumulator
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# KV token offsets within each BLOCK_N chunk
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offs_n = tl.arange(0, BLOCK_N)
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# Iterate over BLOCK_N KV tokens at a time
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for start_n in range(part_start, part_end, BLOCK_N):
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# Token positions in the sequence
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token_ids = start_n + offs_n
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valid_mask = token_ids < part_end
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# Load all GQA_RATIO query vectors for this kv_head
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# q[g]: [HEAD_DIM] for g in 0..GQA_RATIO-1
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# We process them sequentially to stay within register budget
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# (Loading all 6 × 256 = 1536 fp32 values would be 6KB of registers per thread)
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# === Paged K gather (from prefix_prefill.py pattern) ===
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# Look up physical block numbers from block_tables
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block_indices = token_ids // block_size
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within_block = token_ids % block_size
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# Initialize compound accumulators for each query head
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# m[g]: running max, l[g]: running exp_sum, acc[g]: [HEAD_DIM] weighted V
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# For Triton, we process one query head at a time through the full partition
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# to minimize register pressure.
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# bn: physical block ids [BLOCK_N]
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bn = tl.load(
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block_tables_ptr + seq_idx * stride_bt_s + block_indices * stride_bt_b,
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mask=valid_mask, other=0)
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for g in range(GQA_RATIO):
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head_idx = kv_head_idx * GQA_RATIO + g
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# K offsets: key_cache[bn, kv_head, d//x, within_block, d%x]
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# Layout: [num_blocks, num_kv_heads, head_size/x, block_size, x]
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# off_k: [HEAD_DIM, BLOCK_N] — each column is one token's K vector
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off_k = (bn[None, :] * stride_kc_b +
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kv_head_idx * stride_kc_h +
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(offs_d[:, None] // x_pack) * stride_kc_dx +
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within_block[None, :] * stride_kc_bs +
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(offs_d[:, None] % x_pack) * stride_kc_x)
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# Load Q for this head
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q = tl.load(query_ptr + seq_idx * stride_qs + head_idx * stride_qh
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+ offs_d * stride_qd).to(tl.float32)
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k = tl.load(key_cache_ptr + off_k, mask=valid_mask[None, :], other=0.0) # [D, N]
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# Compound accumulator
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m_i = float('-inf')
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l_i = 0.0
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acc = tl.zeros([HEAD_DIM], dtype=tl.float32)
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# Scores: q @ k = [1, D] @ [D, N] → [N]
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# For BLOCK_M=1: this is a dot product per KV token
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scores = tl.sum(q[:, None] * k, axis=0) * scale # [BLOCK_N]
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scores = tl.where(valid_mask, scores, float('-inf'))
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# Inner loop: BLOCK_N KV tokens per iteration
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for start_n in range(part_start, part_end, BLOCK_N):
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token_ids = start_n + offs_n
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valid = token_ids < part_end
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# Online softmax (adapted from prefix_prefill.py — proven correct)
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m_ij = tl.max(scores, axis=0) # scalar: max of this chunk
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p = tl.exp(scores - m_ij) # [BLOCK_N] — unnormalized probs
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l_ij = tl.sum(p, axis=0) # scalar: sum of exp for this chunk
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# Paged K gather (from prefix_prefill.py)
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blk_idx = token_ids // block_size
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blk_off = token_ids % block_size
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phys_blk = tl.load(block_tables_ptr + seq_idx * stride_bt_s + blk_idx * stride_bt_b,
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mask=valid, other=0)
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m_i_new = tl.maximum(m_i, m_ij)
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alpha = tl.exp(m_i - m_i_new) # rescale factor for old accumulator
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beta = tl.exp(m_ij - m_i_new) # rescale factor for new chunk
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l_i_new = alpha * l_i + beta * l_ij
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off_k = (phys_blk[None, :] * stride_kc_b +
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kv_head_idx * stride_kc_h +
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(offs_d[:, None] // x_pack) * stride_kc_dx +
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blk_off[None, :] * stride_kc_bs +
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(offs_d[:, None] % x_pack) * stride_kc_x)
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k = tl.load(key_cache_ptr + off_k, mask=valid[None, :], other=0.0) # [D, N]
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# === Paged V gather ===
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off_v = (bn[:, None] * stride_vc_b +
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kv_head_idx * stride_vc_h +
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offs_d[None, :] * stride_vc_d +
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within_block[:, None] * stride_vc_bs)
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v = tl.load(value_cache_ptr + off_v, mask=valid_mask[:, None], other=0.0) # [N, D]
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# Scores: q · k per token
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scores = tl.sum(q[:, None] * k, axis=0) * scale # [BLOCK_N]
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scores = tl.where(valid, scores, float('-inf'))
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# Update accumulator (Flash Attention online softmax pattern):
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# acc = acc * (alpha * l_i / l_i_new) + (p * beta / l_i_new) @ V
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# Safe division: if l_i_new == 0, this is the first chunk
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acc_scale = alpha * l_i / tl.maximum(l_i_new, 1e-6)
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acc = acc * acc_scale
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p_scale = beta / tl.maximum(l_i_new, 1e-6)
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p_scaled = p * p_scale # [BLOCK_N]
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acc += tl.sum(p_scaled[:, None] * v, axis=0) # [HEAD_DIM]
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# Online softmax (CCCL combine op)
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m_ij = tl.max(scores, axis=0)
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p = tl.exp(scores - m_ij)
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l_ij = tl.sum(p, axis=0)
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l_i = l_i_new
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m_i = m_i_new
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m_new = tl.maximum(m_i, m_ij)
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alpha = tl.exp(m_i - m_new)
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beta = tl.exp(m_ij - m_new)
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l_new = alpha * l_i + beta * l_ij
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# Store partition results
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tl.store(max_logits_ptr + seq_idx * stride_es_s + head_idx * stride_es_h + part_idx * stride_es_p,
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m_i)
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tl.store(exp_sums_ptr + seq_idx * stride_es_s + head_idx * stride_es_h + part_idx * stride_es_p,
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l_i)
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# Paged V gather
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off_v = (phys_blk[:, None] * stride_vc_b +
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kv_head_idx * stride_vc_h +
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offs_d[None, :] * stride_vc_d +
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blk_off[:, None] * stride_vc_bs)
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v = tl.load(value_cache_ptr + off_v, mask=valid[:, None], other=0.0) # [N, D]
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# Store accumulated output: [HEAD_DIM]
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out_base = seq_idx * stride_to_s + head_idx * stride_to_h + part_idx * stride_to_p
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tl.store(tmp_output_ptr + out_base + offs_d * stride_to_d, acc.to(tmp_output_ptr.dtype.element_ty))
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# Update accumulator
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safe_l = tl.maximum(l_new, 1e-6)
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acc = acc * (alpha * l_i / safe_l)
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p_scaled = p * (beta / safe_l)
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acc += tl.sum(p_scaled[:, None] * v, axis=0)
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m_i = m_new
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l_i = l_new
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# Write partition results for this head
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tl.store(max_logits_ptr + seq_idx * stride_es_s + head_idx * stride_es_h + part_idx * stride_es_p,
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m_i)
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tl.store(exp_sums_ptr + seq_idx * stride_es_s + head_idx * stride_es_h + part_idx * stride_es_p,
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l_i)
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out_base = seq_idx * stride_to_s + head_idx * stride_to_h + part_idx * stride_to_p
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tl.store(tmp_output_ptr + out_base + offs_d * stride_to_d,
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acc.to(tmp_output_ptr.dtype.element_ty))
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@triton.jit
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def _paged_attn_v2_reduce_kernel(
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# Output
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output_ptr, # [num_seqs, num_heads, head_size]
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# Inputs
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tmp_output_ptr, # [num_seqs, num_heads, max_num_parts, head_size]
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exp_sums_ptr, # [num_seqs, num_heads, max_num_parts]
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max_logits_ptr, # [num_seqs, num_heads, max_num_parts]
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seq_lens_ptr, # [num_seqs]
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# Scalars
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def _reduce_partitions_kernel(
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output_ptr, # [num_seqs, num_heads, head_size]
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tmp_output_ptr, # [num_seqs, num_heads, max_parts, head_size]
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exp_sums_ptr, # [num_seqs, num_heads, max_parts]
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max_logits_ptr, # [num_seqs, num_heads, max_parts]
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seq_lens_ptr, # [num_seqs]
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gqa_ratio: tl.int32,
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||||
max_num_parts: tl.int32,
|
||||
# Strides
|
||||
stride_out_s: tl.int32, stride_out_h: tl.int32, stride_out_d: tl.int32,
|
||||
stride_to_s: tl.int32, stride_to_h: tl.int32,
|
||||
stride_to_p: tl.int32, stride_to_d: tl.int32,
|
||||
stride_es_s: tl.int32, stride_es_h: tl.int32, stride_es_p: tl.int32,
|
||||
# Constants
|
||||
PARTITION_SIZE: tl.constexpr,
|
||||
HEAD_DIM: tl.constexpr,
|
||||
MAX_NUM_PARTS: tl.constexpr,
|
||||
GQA_RATIO: tl.constexpr,
|
||||
):
|
||||
"""Phase 2: Cross-partition log-sum-exp reduction.
|
||||
"""Phase 2: Cross-partition reduction.
|
||||
|
||||
Grid: (num_seqs, num_heads)
|
||||
Combines partition results using CCCL summary_statistics pattern.
|
||||
Grid: (num_seqs, num_kv_heads)
|
||||
Each block reduces all partitions for GQA_RATIO query heads.
|
||||
|
||||
Algorithm (CCCL block_reduce_warp_reductions pattern):
|
||||
For each query head in this kv_head group:
|
||||
1. Load all partition (max, sum) into registers
|
||||
2. Global max across partitions
|
||||
3. Rescale: weights = exp(part_max - global_max) * part_sum / total
|
||||
4. Weighted combination of partition outputs
|
||||
"""
|
||||
seq_idx = tl.program_id(0)
|
||||
head_idx = tl.program_id(1)
|
||||
kv_head_idx = tl.program_id(1)
|
||||
|
||||
seq_len = tl.load(seq_lens_ptr + seq_idx)
|
||||
num_parts = (seq_len + PARTITION_SIZE - 1) // PARTITION_SIZE
|
||||
|
||||
# Load partition statistics
|
||||
part_offsets = tl.arange(0, MAX_NUM_PARTS)
|
||||
valid_mask = part_offsets < num_parts
|
||||
|
||||
es_base = seq_idx * stride_es_s + head_idx * stride_es_h
|
||||
part_max = tl.load(max_logits_ptr + es_base + part_offsets * stride_es_p,
|
||||
mask=valid_mask, other=float('-inf'))
|
||||
part_sum = tl.load(exp_sums_ptr + es_base + part_offsets * stride_es_p,
|
||||
mask=valid_mask, other=0.0)
|
||||
|
||||
# Global max
|
||||
global_max = tl.max(part_max, axis=0)
|
||||
|
||||
# Rescale and normalize
|
||||
rescale = tl.exp(part_max - global_max) * part_sum
|
||||
total = tl.sum(rescale, axis=0)
|
||||
weights = rescale / total # [MAX_NUM_PARTS]
|
||||
|
||||
# Weighted sum of partition outputs
|
||||
valid = part_offsets < num_parts
|
||||
offs_d = tl.arange(0, HEAD_DIM)
|
||||
acc = tl.zeros([HEAD_DIM], dtype=tl.float32)
|
||||
|
||||
for p in range(MAX_NUM_PARTS):
|
||||
if p < num_parts:
|
||||
w = tl.load(max_logits_ptr + es_base + p * stride_es_p) # reload for weight
|
||||
w_rescaled = tl.exp(w - global_max) * tl.load(exp_sums_ptr + es_base + p * stride_es_p) / total
|
||||
for g in range(GQA_RATIO):
|
||||
head_idx = kv_head_idx * GQA_RATIO + g
|
||||
es_base = seq_idx * stride_es_s + head_idx * stride_es_h
|
||||
|
||||
to_base = seq_idx * stride_to_s + head_idx * stride_to_h + p * stride_to_p
|
||||
part_out = tl.load(tmp_output_ptr + to_base + offs_d * stride_to_d)
|
||||
acc += w_rescaled * part_out.to(tl.float32)
|
||||
# Load partition statistics
|
||||
part_max = tl.load(max_logits_ptr + es_base + part_offsets * stride_es_p,
|
||||
mask=valid, other=float('-inf'))
|
||||
part_sum = tl.load(exp_sums_ptr + es_base + part_offsets * stride_es_p,
|
||||
mask=valid, other=0.0)
|
||||
|
||||
# Store final output
|
||||
out_base = seq_idx * stride_out_s + head_idx * stride_out_h
|
||||
tl.store(output_ptr + out_base + offs_d * stride_out_d, acc.to(output_ptr.dtype.element_ty))
|
||||
# Global max
|
||||
global_max = tl.max(part_max, axis=0)
|
||||
|
||||
# Rescale and normalize (CCCL combine op applied across all partitions)
|
||||
rescale = tl.exp(part_max - global_max) * part_sum
|
||||
total = tl.sum(rescale, axis=0)
|
||||
|
||||
# Weighted combination
|
||||
acc = tl.zeros([HEAD_DIM], dtype=tl.float32)
|
||||
for p in range(MAX_NUM_PARTS):
|
||||
if p < num_parts:
|
||||
w = tl.exp(tl.load(max_logits_ptr + es_base + p * stride_es_p) - global_max) * \
|
||||
tl.load(exp_sums_ptr + es_base + p * stride_es_p) / tl.maximum(total, 1e-6)
|
||||
to_base = seq_idx * stride_to_s + head_idx * stride_to_h + p * stride_to_p
|
||||
part_out = tl.load(tmp_output_ptr + to_base + offs_d * stride_to_d)
|
||||
acc += w * part_out.to(tl.float32)
|
||||
|
||||
# Store final output
|
||||
out_base = seq_idx * stride_out_s + head_idx * stride_out_h
|
||||
tl.store(output_ptr + out_base + offs_d * stride_out_d,
|
||||
acc.to(output_ptr.dtype.element_ty))
|
||||
|
||||
|
||||
def paged_attention_v2_triton(
|
||||
@@ -274,60 +286,52 @@ def paged_attention_v2_triton(
|
||||
v_scale: float = 1.0,
|
||||
**kwargs,
|
||||
) -> None:
|
||||
"""Launch Triton V2 kernels."""
|
||||
"""Launch CCCL-derived Triton V2 kernels."""
|
||||
num_seqs, num_heads, head_size = query.shape
|
||||
num_queries_per_kv = num_heads // num_kv_heads
|
||||
gqa_ratio = num_heads // num_kv_heads
|
||||
max_num_parts = tmp_output.shape[2]
|
||||
x_pack = key_cache.shape[-1] # packing factor
|
||||
x_pack = key_cache.shape[-1]
|
||||
|
||||
PARTITION_SIZE = 512
|
||||
# BLOCK_N: must fit in SMEM. For decode (BLOCK_M=1), SMEM is dominated by K/V gather.
|
||||
# head_dim=256: BLOCK_N=32 → 32×256×2 = 16KB per tile (K or V)
|
||||
# head_dim=128: BLOCK_N=64 → 64×128×2 = 16KB per tile
|
||||
BLOCK_N = 32 if head_size > 128 else 64
|
||||
|
||||
# Phase 1: partition attention
|
||||
num_partitions = (max_seq_len + PARTITION_SIZE - 1) // PARTITION_SIZE
|
||||
grid_phase1 = (num_seqs, num_heads, num_partitions)
|
||||
|
||||
_paged_attn_v2_partition_kernel[grid_phase1](
|
||||
# Phase 1: grid on kv_heads (not num_heads) — GQA broadcast inside kernel
|
||||
grid_p1 = (num_seqs, num_kv_heads, num_partitions)
|
||||
_partition_attn_kernel[grid_p1](
|
||||
tmp_output, exp_sums, max_logits,
|
||||
query, key_cache, value_cache, block_tables, seq_lens,
|
||||
scale, num_queries_per_kv, block_size, x_pack,
|
||||
# query strides
|
||||
scale, gqa_ratio, block_size, x_pack,
|
||||
query.stride(0), query.stride(1), query.stride(2),
|
||||
# key_cache strides
|
||||
key_cache.stride(0), key_cache.stride(1), key_cache.stride(2),
|
||||
key_cache.stride(3), key_cache.stride(4),
|
||||
# value_cache strides
|
||||
value_cache.stride(0), value_cache.stride(1), value_cache.stride(2),
|
||||
value_cache.stride(3),
|
||||
# block_tables strides
|
||||
block_tables.stride(0), block_tables.stride(1),
|
||||
# tmp_output strides
|
||||
tmp_output.stride(0), tmp_output.stride(1), tmp_output.stride(2), tmp_output.stride(3),
|
||||
# exp_sums strides
|
||||
exp_sums.stride(0), exp_sums.stride(1), exp_sums.stride(2),
|
||||
# Constants
|
||||
PARTITION_SIZE=PARTITION_SIZE,
|
||||
HEAD_DIM=head_size,
|
||||
BLOCK_N=BLOCK_N,
|
||||
GQA_RATIO=gqa_ratio,
|
||||
)
|
||||
|
||||
# Phase 2: cross-partition reduction
|
||||
# Phase 2: grid on kv_heads — reduce all partitions for GQA_RATIO heads each
|
||||
MAX_NUM_PARTS_CONST = triton.next_power_of_2(max_num_parts)
|
||||
if MAX_NUM_PARTS_CONST > 1024:
|
||||
MAX_NUM_PARTS_CONST = 1024
|
||||
|
||||
grid_phase2 = (num_seqs, num_heads)
|
||||
_paged_attn_v2_reduce_kernel[grid_phase2](
|
||||
grid_p2 = (num_seqs, num_kv_heads)
|
||||
_reduce_partitions_kernel[grid_p2](
|
||||
output,
|
||||
tmp_output, exp_sums, max_logits, seq_lens,
|
||||
max_num_parts,
|
||||
gqa_ratio, max_num_parts,
|
||||
output.stride(0), output.stride(1), output.stride(2),
|
||||
tmp_output.stride(0), tmp_output.stride(1), tmp_output.stride(2), tmp_output.stride(3),
|
||||
exp_sums.stride(0), exp_sums.stride(1), exp_sums.stride(2),
|
||||
PARTITION_SIZE=PARTITION_SIZE,
|
||||
HEAD_DIM=head_size,
|
||||
MAX_NUM_PARTS=MAX_NUM_PARTS_CONST,
|
||||
GQA_RATIO=gqa_ratio,
|
||||
)
|
||||
|
||||
Reference in New Issue
Block a user