fused attention kernel design - in-progress
This commit is contained in:
Yangwook Kang
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# ADR-001: AHBM GQA Fused Attention Kernel (Llama3-70B)
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**Status:** Proposed
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**Context model:** Llama3-70B
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**Decision drivers:** agentic workload → low batch, long context; KV-load-bound decode; SP (Ring KV) for long-context prefill.
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**Algorithm lineage.** This kernel is **FlashAttention** (tiling + online/streaming softmax with fused P·V — no full score matrix materialized). The KV-parallel split-and-combine in §4 is **FlashDecoding** (split-KV with log-sum-exp merge). The SP path in §5.5 is **Ring Attention** (block-wise KV rotated around the ring, accumulated by the same online softmax). No new math is introduced; this ADR maps those known algorithms onto AHBM composite commands + IPCQ.
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---
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## 0. Reference dimensions (Llama3-70B)
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| Symbol | Meaning | Value |
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|---|---|---|
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| `H_q` | query heads | 64 |
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| `H_kv` | KV heads | 8 |
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| `G` | GQA group size = `H_q / H_kv` | 8 |
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| `d` | head dim | 128 |
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| `L` | layers | 80 |
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| `D` | model dim | 8192 |
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Hardware recap:
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- **AHBM (chip)** = set of **CUBE**s (memory cubes, each with a logic die containing **PE**s) + **IO die**.
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- **IPCQ**: PE↔PE queues. Each PE holds 4 neighbor queue-pairs (one per mesh direction).
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- **Composite command**: a fused multi-stage command (load → matmul → vector op → ...) executed as one unit. *Cannot* express a data-dependent `pop` as an internal dependency (see §7). It *can* take a precomputed mask tile as an input and apply it as a stage.
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- **Allocation policy (SP):** one query head per CUBE. For GQA-8, one KV head's 8 query heads map to 8 CUBEs.
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**Two orthogonal mapping layers** (decided in design discussion):
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- **Layer 1 (KV-parallel, intra-request):** within a request, KV token `i` lands on PE `(start_pe + i) mod N` — round-robin so a single long request is split across `N` PEs and all PEs stay balanced (chunk length differs by ≤1 token).
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- **Layer 2 (inter-request):** `start_pe = request_id mod N` rotates the "PE-0 role" per request to spread write/start load.
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- Combined destination PE for a token: `pe = (request_id + global_token_idx) mod N`.
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Here `N` = number of PEs participating in the KV-parallel reduction group for one (query head, request). Within a CUBE the reduction group is the set of PEs splitting that head's sequence; reduction stays **intra-CUBE** (query head never spans CUBEs — explicit non-goal).
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---
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## 0.5 Kernel boundary, preconditions, and I/O contract
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### 0.5.1 Where this kernel sits in the decoder layer
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```
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1. RMSNorm
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2. QKV projection (GEMM) ─┐ qkv_rope kernel (SEPARATE, upstream)
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3. RoPE on Q and K ─┤
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4. write new K,V → KV cache ─┘
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5. ===== THIS KERNEL: FlashAttention ===== (post-RoPE Q, post-RoPE K-cache, V-cache → O)
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6. Output projection (GEMM) out_proj kernel (SEPARATE, downstream)
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7. residual add → FFN ...
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```
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**Preconditions (responsibilities of the upstream `qkv_rope` kernel, NOT this kernel):**
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- **P1.** Q is already RoPE-rotated. This kernel does **no** rotation on Q.
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- **P2.** K-cache stores **post-RoPE** K. Past tiles are never re-rotated at attention time (this is the reason for post-RoPE caching).
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- **P3.** For decode, the new step's K row is RoPE-rotated **and appended to its owning PE's K-cache slot by `qkv_rope` before this kernel launches.** For prefill, the whole query block's K is post-RoPE in cache. ⇒ this kernel is **pure read** on the KV cache; it never writes KV.
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- **P4.** V is **not** rotated (RoPE applies to Q,K only). V-cache holds raw projected V.
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- **P5.** RoPE position used upstream is the token's **absolute global position**, i.e. `global_idx = local_slot*N + ((pe_id - start_pe) mod N)` (§2.1). Round-robin placement must not be confused with RoPE position — the angle depends on global index, never on the local slot.
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(If a fused megakernel is ever desired, stages 2–4 would prepend to §5.1; the chosen design keeps them separate so KV cache is the clean interface and Ring Attention only ever passes post-RoPE tensors.)
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### 0.5.2 Symbols for shapes
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`T_q` = query length this launch (decode: 1; prefill/chunk: chunk width). `S` = total context length (keys seen). `S_pe` = keys owned by this PE = `valid_len` (§2.2), `≈ ceil(S/N)`. `bf16` storage, `fp32` for `m,l,O` accumulators.
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### 0.5.3 INPUTS (per kernel launch)
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| Input | Shape (per head) | Layout / location | Notes |
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|---|---|---|---|
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| `Q` | `[G, T_q, d]` | on-chip regs/SRAM of the PE | post-RoPE (P1). `G=8` query rows of the GQA group batched together. |
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| `K_cache` | `[S_pe, d]` | per-PE HBM buffer, **contiguous**, base = `K_base[pe]` | post-RoPE (P2). Read-only. Strided in global index, dense locally (§2.1). |
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| `V_cache` | `[S_pe, d]` | per-PE HBM buffer, base = `V_base[pe]` | raw V (P4). Read-only. |
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| `global_token_counter` | scalar | launch arg | kernel derives `S_pe`, slot↔global, causal bounds. |
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| `start_pe` | scalar = `request_id mod N` | launch arg | Layer-2 rotation. |
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| `pe_id`, `N` | scalars | known to PE / launch | reduction-group geometry. |
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| `q_block_meta` | `{q_start, T_q}` | launch arg | prefill/SP causal masking & skip. |
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| `O_base` | address | launch arg | where final O is written (root PE only). |
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| `softmax_scale` | scalar | launch arg | `1/sqrt(d)` (=1/√128). |
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For **Ring Attention (§5.5)** add: incoming `K_block, V_block` per ring step arrive via IPCQ/comms into ping-pong receive buffers (post-RoPE), plus `step_kv_global_range` so the kernel can apply the causal step-skip.
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### 0.5.4 OUTPUTS
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| Output | Shape (per head) | Location | Notes |
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|---|---|---|---|
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| `O` (final) | `[G, T_q, d]` | `O_base`, **written by tree-root PE only** | normalized: `O_acc / l_acc`. fp32→bf16 cast on store. |
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**Intermediate (per non-root PE, on IPCQ, not a kernel-visible output):**
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| Partial | Shape | Channel | Notes |
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|---|---|---|---|
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| `(m_i, l_i, O_i)` | `m,l`: `[G, T_q]` scalars; `O_i`: `[G, T_q, d]` unnormalized | pushed to `tree_parent` via neighbor IPCQ | log-sum-exp merge payload (§4). `O_i` is the heavy part. |
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**No-SP cases (`N=1`):** there are no IPCQ partials; the single PE's running `(m,l,O)` is normalized in place and written to `O_base`. The kernel's only output is `O`.
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**Explicitly NOT outputs of this kernel:** KV cache writes (done upstream, P3), output projection (downstream stage 6), the score matrix `S` and probabilities `P` (never materialized — FlashAttention).
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---
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## 1. Decision
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Fuse the attention inner pipeline into a single composite per KV tile:
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```
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K_j load → Q·Kjᵀ → (+ mask stage) → online-softmax update → V_j load → P·V → running (m,l,O) update → downstream IPCQ push
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```
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Cross-PE combination (KV-parallel / SP) is a **log-sum-exp tree reduction** over `(m, l, O)` performed **after P·V**, flowed through IPCQ with a **kernel-driven async-submit + bounded-polling loop** (no hardware `pop`-as-dependency change required for the target workload).
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Control flow (tile skip, mask generation, reduction scheduling, address arithmetic) lives in the **kernel**; the **composite** only executes already-decided work.
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---
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## 2. Memory layout & driver responsibilities
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### 2.1 KV cache allocation
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- Pre-allocate per-PE KV buffers sized to **per-PE max context** = `ceil(max_context / N)` tokens × `d` × dtype, for K and V separately.
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- Within a PE, tokens assigned to it are **appended contiguously** (slot 0,1,2,...). Even though global indices are strided (`i, i+N, i+2N, ...`), the per-PE buffer is dense ⇒ DMA stays contiguous, bandwidth not fragmented.
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- Slot → global index is implicit: `global_idx = local_slot * N + ((pe_id - start_pe) mod N)`.
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### 2.2 Driver per-step duties (kept minimal)
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The driver does **not** compute write slots or read ranges. It supplies, per kernel launch:
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| Launch arg | Purpose |
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|---|---|
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| `K_base[pe]`, `V_base[pe]` | base address of each PE's KV buffer |
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| `O_base` | attention output destination (separate from KV write) |
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| `global_token_counter` | current sequence position; kernel derives everything from it |
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| `start_pe` (= `request_id mod N`) | Layer-2 rotation |
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| `pe_id` | known to PE; used with counter for `mod`/`div` |
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| `q_block_meta` | for prefill/SP: query block start + length |
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From `global_token_counter`, `start_pe`, `pe_id` the kernel derives:
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- **"is it my turn to write this step":** `(start_pe + counter) mod N == pe_id`
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- **my valid length:** `base = counter / N; rem = counter % N; my_len = base + ( ((pe_id - start_pe) mod N) < rem ? 1 : 0 )`
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- **write offset:** `my_len` (next free slot) — kernel computes `K_base[pe] + my_len*d`.
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- **read range:** tiles `0 .. ceil(my_len / TILE)`.
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- **causal bounds & per-tile skip:** from global positions of query block vs each tile.
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Driver = address bases + counter + rotation. One formula (`(request_id + token_idx) mod N`) is the entire placement policy.
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---
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## 3. Composite structure
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One composite instance = one KV tile's worth of work on one PE.
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```
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COMPOSITE attn_tile(q_reg, K_base, V_base, tile_idx, mask_tile_or_null,
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m_reg, l_reg, O_reg, push_target):
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S1: DMA K_tile ← K_base + tile_idx*TILE*d # contiguous load
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S2: MM S = q_reg · K_tileᵀ (scale 1/sqrt(d))
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S3: VEC if mask_tile != null: S += mask_tile # masked boundary tile
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S4: VEC online softmax:
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m_new = max(m_reg, rowmax(S))
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P = exp(S - m_new)
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l_reg = l_reg*exp(m_reg - m_new) + rowsum(P)
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O_reg = O_reg*exp(m_reg - m_new) # rescale accumulator
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m_reg = m_new
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S5: DMA V_tile ← V_base + tile_idx*TILE*d # issued early, hidden behind S2-S4
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S6: MM O_reg += P · V_tile
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S7: (tail) PUSH (m_reg,l_reg,O_reg) → push_target # only on reduction-producing composite
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```
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Notes:
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- `m_reg, l_reg, O_reg` are **carried across composites** (running state), so consecutive tile composites form an in-order chain on one PE.
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- S5 (V load) is scheduled so its latency overlaps S2–S4; with prefetch depth ≥ 2 the next tile's K/V are already in flight.
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- S7 push is only on the **last** composite of a PE's tile sweep (when its `O_local` is final). Push is a tail *action*, not a dependency — composites can express push, not pop.
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- Mask handling: composite only **applies** a mask tile it is *given*. The kernel generates the boundary-tile mask from query/KV offsets and passes it in. Full-past tiles get `null`; full-future tiles are never enqueued (skipped in kernel).
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---
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## 4. Reduction (KV-parallel / SP combine)
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After every PE finishes its tile sweep it holds `(m_i, l_i, O_i)` (unnormalized). Combine via associative/commutative log-sum-exp merge:
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```
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merge((m_a,l_a,O_a),(m_b,l_b,O_b)):
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m = max(m_a,m_b)
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l = l_a*exp(m_a-m) + l_b*exp(m_b-m)
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O = O_a*exp(m_a-m) + O_b*exp(m_b-m)
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return (m,l,O)
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final O = O_root / l_root # normalize once at tree root
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```
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- **Timing: exchange is AFTER P·V** (each PE must finish its `O_local`).
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- **Topology:** mesh tree, depth `ceil(log2(N))`. For `N=8`: level-0 pairs (0↔1,2↔3,4↔5,6↔7), level-1 (1↔3,5↔7), level-2 (3↔7), root = PE matching last-in-group. PE↔mesh-neighbor numbering must be chosen so each tree pair is a physical 4-direction neighbor (tuning item, §9).
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- **Payload:** `O` is the heavy part (`d`-vector = 128 elems); `m,l` are scalars appended.
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---
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## 5. Kernel pseudocode
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### 5.1 Common loop skeleton (all four cases share this)
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```python
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def attn_kernel(pe_id, start_pe, counter, K_base, V_base, O_base, q_block):
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# ---- derive geometry ----
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my_len = valid_len(counter, start_pe, pe_id, N)
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n_tiles = ceil(my_len / TILE)
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q_reg = load_Q(q_block) # decode: 1 row; prefill: q_block rows
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m,l,O = -inf, 0, zeros(d)
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PREFETCH = 2
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issued = 0
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# prime prefetch
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for j in range(min(PREFETCH, n_tiles)):
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if not tile_all_future(j, q_block): # kernel-level if (§7)
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enqueue(build_composite(j, q_block)); issued += 1
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# main sweep
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for j in range(n_tiles):
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wait_complete(j) # in-order; running m,l,O updated
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nxt = j + PREFETCH
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if nxt < n_tiles and not tile_all_future(nxt, q_block):
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enqueue(build_composite(nxt, q_block))
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# opportunistic reduction progress (see 5.5) — only when this PE
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# already produced O_local AND has spare queue depth
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try_drain_reduction()
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# ---- reduction phase ----
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push_local((m,l,O), to=tree_parent(pe_id))
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if is_tree_internal_or_root(pe_id):
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reduce_tree_node(pe_id) # async-submit + bounded poll
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if is_root(pe_id):
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O_final = O_acc / l_acc
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store(O_base, O_final)
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def build_composite(j, q_block):
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if tile_partial(j, q_block): # boundary / diagonal tile
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mask = make_mask_tile(j, q_block) # kernel computes from offsets
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else: # tile_all_past
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mask = null
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return composite.attn_tile(q_reg, K_base, V_base, j, mask, m,l,O, push_target)
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```
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`tile_all_future / tile_partial / tile_all_past` are pure position comparisons (query global pos vs tile's KV global-pos range). Decode degenerates them (see below).
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### 5.2 DECODE, **no SP** (single PE owns the whole head's KV)
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- `N = 1` for the reduction group → no cross-PE merge, no tree.
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- Query length = 1; it attends to **all** past KV ⇒ `tile_all_future` never true, `tile_partial` only on the final ragged tile, no real masking.
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- GQA reuse: the `G=8` query heads sharing one KV head are processed in the **same** PE/composite stream so each K/V tile is loaded once and reused by 8 query rows (Q·Kᵀ becomes an 8×TILE GEMV-batch, P·V an 8×d). This is the main lever; decode is KV-load-bound, so amortizing K/V load over the group is where the win is.
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- Bottleneck = K/V tile DMA. Tune `PREFETCH` (≥2) so the queue never starves.
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```
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load q_reg = [8 query rows for this KV head] # GQA group batched
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for j in 0..n_tiles:
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composite.attn_tile(...) # 8×TILE Q·Kᵀ, mask=null (except last)
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store O[8 rows] # no reduction
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```
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### 5.3 DECODE, **with SP / KV-parallel** (N=8, head split across 8 PEs)
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- One request's KV is round-robin across 8 PEs; each PE owns ~`my_len` tokens (≤1 apart).
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- Each PE still batches its `G=8` query rows (GQA) over its local tiles.
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- Query length 1 ⇒ no future tiles, minimal mask ⇒ tile sweep is short per PE; **reduction is the structurally interesting part**, done once per token at sweep end.
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- Reduction latency for a single token is hidden by the remaining tiles of *other* concurrent decode tokens if batched; for strict batch=1 long-context, hidden by the fact that each PE still has many tiles (long context) — sweep work >> reduction handshake.
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```
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per PE:
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my tiles sweep (short, GQA-batched) → (m_i,l_i,O_i)
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push to tree_parent
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tree merge (depth 3) → root normalizes → O[8 rows] for this KV head
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```
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### 5.4 PREFILL, **no SP**
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- Whole prompt resident on the PE(s) for the head; query is a block of `T` tokens (chunked).
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- Now causality is real: for query block `[qs, qe)` and KV tile covering `[ks, ke)`:
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- `ke <= qs` → `tile_all_past` → mask=null, full compute.
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- `ks >= qe` → `tile_all_future`→ **skip enqueue** (kernel `if`).
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- overlap → `tile_partial` → kernel builds triangular mask tile, composite applies it.
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- Chunked prefill walks query blocks; for each query block runs the tile sweep with the skip/mask decisions above.
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- GQA: 8 query heads of a group share K/V tiles within the block ⇒ batched along the same axis as decode.
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```
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for q_block in chunks(prompt):
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m,l,O = reset
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for j in tiles_up_to(q_block.end):
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if tile_all_future(j,q_block): continue # skip
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composite.attn_tile(..., mask=mask_or_null(j,q_block))
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store O[q_block]
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```
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### 5.5 PREFILL, **with SP (Ring KV)**
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- KV is sharded across `N` PEs (the SP ring); each ring step delivers a different KV block from a peer.
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- **Composite is instantiated per ring step's KV block:** `K load → Q·Kᵀ → mask → online-softmax update → V → P·V → running (m,l,O)`. Running `(m,l,O)` is carried **across ring steps**.
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- IPCQ overlaps **next step's KV receive (comms)** with **current step's compute**. KV and V of a step arrive together (it's comms, not local DMA) ⇒ V is already present when P·V runs.
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- **Causal ring optimization via kernel `if`:** if the incoming step's KV block is entirely *after* the local query block, the whole step's compute is **skipped** (don't enqueue the composite) — can eliminate ~half the ring steps for causal attention.
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- Receive buffers ping-pong; buffer swap aligned to composite boundary.
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- After the ring completes, `(m,l,O)` is final per PE → same log-sum-exp tree reduction to produce O. (If the ring already linearly accumulated the full sequence on each query-owning PE, the "reduction" is just the running state; if KV-parallel splits remain, tree-merge them.)
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```
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init m,l,O
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recv KV_block[0]
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for step in 0..N-1:
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issue_recv(KV_block[step+1]) # overlap comms
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if step_all_future(step, q_block): # causal ring skip
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continue
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mask = step_mask(step, q_block) # null if fully past
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composite.attn_tile(q_reg, Kbuf[step%2], Vbuf[step%2], 0, mask, m,l,O, _)
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swap buffers each step
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# m,l,O now final for this PE's query block
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reduce_tree → normalize → store O
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```
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### 5.6 Reduction drain loop (the no-hardware-change mechanism)
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Because composites cannot `pop` as a dependency, the kernel does it explicitly. The poll never sits on the critical path because there is always other tile work to enqueue (long context):
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```python
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def reduce_tree_node(pe_id):
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children = tree_children(pe_id)
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acc = local_state
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pending = set(children)
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while pending:
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for c in list(pending):
|
||||
if ipcq_nonempty(dir_to(c)): # poll ONLY the child direction
|
||||
part = ipcq_pop(dir_to(c))
|
||||
acc = merge(acc, part)
|
||||
pending.remove(c)
|
||||
if pending:
|
||||
# miss → do useful work instead of busy-wait
|
||||
if has_unissued_tiles(): enqueue(next_tile_composite())
|
||||
else: spin_short() # rare: only batch=1 + short ctx
|
||||
if not is_root(pe_id):
|
||||
ipcq_push(dir_to(parent), acc)
|
||||
else:
|
||||
global_acc = acc
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 6. Why no hardware change for the target workload
|
||||
|
||||
- The only place a HW `pop`-as-dependency helps is a **lone reduction with no other work to hide the poll** — i.e. batch=1 *and* short context.
|
||||
- Target is **agentic = low batch but long context** ⇒ each PE has many KV tiles; a reduction poll-miss is always covered by issuing the next tile composite. The handshake cost is hidden, not on the critical path.
|
||||
- HW `pop` would also **remove the kernel's ability to choose "do another tile instead of waiting"** and complicate buffer-lifetime/deadlock reasoning (a blocked composite holds PE resources). For long-context that flexibility is worth more than the saved handshake.
|
||||
- **Decision: ship with software async-submit + bounded poll. Revisit HW `pop` only if a short-context, single-stream, latency-critical target emerges.**
|
||||
|
||||
---
|
||||
|
||||
## 7. Control vs execution split (the load-bearing principle)
|
||||
|
||||
| Concern | Owner |
|
||||
|---|---|
|
||||
| tile skip (future), mask generation, causal bounds | **kernel** (`if`, arithmetic) |
|
||||
| address/offset/valid-length arithmetic | **kernel** (from counter) |
|
||||
| reduction scheduling, IPCQ poll/push timing | **kernel** |
|
||||
| K/V load, Q·Kᵀ, mask-apply, softmax, P·V, push | **composite** (no internal branch) |
|
||||
|
||||
Composites never branch on data and never `pop`. They receive fully-decided inputs (which tile, which mask, where to push). This is what lets the design avoid HW changes.
|
||||
|
||||
---
|
||||
|
||||
## 8. Required changes to the composite command implementation
|
||||
|
||||
1. **Multi-stage fusion across DMA + 2×MM + VEC in one command.** Current composites must support the chain `DMA(K) → MM → VEC(mask+softmax) → DMA(V) → MM(P·V) → VEC(accumulate)` as a single fused unit, with the two DMAs schedulable at *different* dependency points (K early, V mid) rather than both up front.
|
||||
2. **Carried register state across composite instances.** `(m, l, O)` accumulators must persist between consecutive composites on a PE (running flash state) instead of being command-local. Needs a stable register/SRAM binding the kernel passes in and out.
|
||||
3. **Mask tile as an input operand.** Composite must accept an optional mask tile and apply it as a VEC stage (`S += mask`). No mask generation inside the composite.
|
||||
4. **Tail push action to a named IPCQ direction.** Composite must be able to, as a terminal action, write `(m,l,O)` to a neighbor queue-pair identified by direction. Push only — **pop remains a kernel operation** (no internal pop-dependency).
|
||||
5. **Early/decoupled V DMA scheduling.** The V load stage must be issuable so its latency overlaps the preceding MM/VEC stages (prefetch depth ≥ 2 across instances), not serialized after softmax.
|
||||
6. **GQA batching on the Q axis.** Q·Kᵀ and P·V stages must accept a batched Q of `G=8` rows so one K/V tile load serves the whole query group (single KV-head reuse).
|
||||
7. **(NOT required) internal data-dependent branch / pop-as-dependency.** Explicitly out of scope; kernel handles all branching. Listed so reviewers don't add it speculatively.
|
||||
8. **(NOT required here) RoPE / QKV projection.** Handled by the upstream `qkv_rope` kernel (§0.5). This kernel assumes post-RoPE Q and post-RoPE K-cache and performs **no rotation and no KV-cache write**. Listed so reviewers don't fold RoPE into the attention composite (doing so would force re-rotating past tiles every decode step and break Ring Attention's post-RoPE pass-through).
|
||||
|
||||
---
|
||||
|
||||
## 9. Open tuning items (not blocking; measured in Kernbench)
|
||||
|
||||
1. **KV tile prefetch depth** — dominant for KV-load-bound decode/prefill; sweep 2→4.
|
||||
2. **PE↔mesh-neighbor mapping for the reduction tree** — ensure each depth-3 tree pair is a physical 4-direction neighbor; bad mapping adds hops.
|
||||
3. **TILE size** — balance SRAM residency (K_tile + S/P + V_tile + O_acc + 8-way GQA) against DMA efficiency.
|
||||
4. **Ring buffer ping-pong vs prefetch depth** interaction in §5.5.
|
||||
|
||||
---
|
||||
|
||||
## 10. Coverage summary
|
||||
|
||||
| Case | KV placement | Composite unit | Reduction | Masking |
|
||||
|---|---|---|---|---|
|
||||
| Decode, no SP | 1 PE, all KV | per tile, GQA-batched | none | last tile only |
|
||||
| Decode, SP | round-robin N PEs | per tile, GQA-batched | tree (depth 3) | last tile only |
|
||||
| Prefill, no SP | resident | per tile per q-block | none | skip future / triangular boundary |
|
||||
| Prefill, SP (Ring) | ring-sharded | per ring step | running state + tree | causal step-skip + boundary |
|
||||
|
||||
All four share the §5.1 skeleton and the §3 composite; they differ only in `N`, query-block width, and which `tile_*` predicates fire.
|
||||
|
||||
**I/O per case** (see §0.5 for full contract):
|
||||
|
||||
| Case | Inputs | Output | Partials on IPCQ |
|
||||
|---|---|---|---|
|
||||
| Decode, no SP | `Q[G,1,d]` post-RoPE, full `K_cache[S,d]`, `V_cache[S,d]` on 1 PE | `O[G,1,d]` at `O_base` | none |
|
||||
| Decode, SP | `Q[G,1,d]`, per-PE `K_cache[S_pe,d]`, `V_cache[S_pe,d]` | `O[G,1,d]` (root PE) | `(m,l,O_i)` per PE → tree |
|
||||
| Prefill, no SP | `Q[G,T_q,d]` post-RoPE, `K_cache[≤end,d]`, `V_cache` | `O[G,T_q,d]` at `O_base` | none |
|
||||
| Prefill, SP (Ring) | `Q[G,T_q,d]`, ring-delivered `K_block,V_block` (post-RoPE) per step | `O[G,T_q,d]` (root PE) | running `(m,l,O)` + tree partials |
|
||||
|
||||
In all cases: KV-cache writes and RoPE happen **upstream**; output projection happens **downstream**; score `S` and probs `P` are never materialized.
|
||||
Reference in New Issue
Block a user