ywkang
a61fed3ce0
Placement pivot (user-approved): a CUBE Group = C CUBEs within one SIP that jointly own one KV head + its G=8 query heads. KV sequence sharded 2 levels (Level-1 inter-CUBE over C, Level-2 intra-CUBE PE over P=8 = C*P ranks); G folded into matmul M. C is a knob (8/4; C=1 = single-CUBE). Reverses the old 'a query head never spans CUBEs' non-goal (held only because the baseline was H_kv=1). device=SIP; the for-kv loop is gone (head picked by CUBE coord). Reduction is a 2-level reduce-to-root (not all-reduce): Level-2 PE tree -> Level-1 center-root CUBE-mesh reduce, adapting lrab_hierarchical_allreduce's inter-CUBE pattern as reduce-only + log-sum-exp. Data-driven (send on local P.V completion, no global barrier) + level-pipelined; per-level topology configurable (tree for decode, ring for long prefill). Rewrites SS0/2/4/5/0.5/8-11, pseudocode, and adds SSB items (4-SIP config, mesh partition, C knob, invariant-reversal check, index-math test). KO mirror updated. Topology grounded in topology.yaml (4x4 CUBE mesh/SIP, 8 PE/cube). Docs only; no production code changed. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
960 lines
50 KiB
Markdown
960 lines
50 KiB
Markdown
# ADR-0060: AHBM GQA Fused Attention Kernel (Llama3-70B)
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## Status
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Proposed
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**Context model:** Llama3-70B.
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**Decision drivers:** agentic workload → low batch, long context;
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KV-load-bound decode; sequence-parallel (Ring KV) for long-context prefill.
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**Supersedes / extends:** the existing mesh-native attention kernels
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`_attention_mesh_kv` (prefill) and `_attention_mesh_mlo` (decode) and the
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`milestone-gqa-llama70b` eval bench. See *§A. Relationship to existing
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kernbench work* — that code is the baseline this ADR upgrades to a real
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GQA, causal, long-context kernel.
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**Supporting ADRs** (efficiency / scale enablers — *not* GQA blockers;
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see §8 correction): **ADR-0063** `tl.scratch_scope` (per-tile scratch
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recycling — required for realistic context length), **ADR-0062** lazy
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`tl.load` (non-blocking load with auto-wait on first use → load/compute
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overlap), **ADR-0061** `tl.broadcast` (optional mask/general
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convenience). The two GEMMs (Q·Kᵀ, P·V) are issued as scheduler-managed
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`tl.composite` commands (the existing `CompositeCmd`; no new command
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kind); real GQA itself needs only kernel restructuring (§5.2).
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**Algorithm lineage.** This kernel is **FlashAttention** (tiling +
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online/streaming softmax with fused P·V — no full score matrix
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materialised). The KV-parallel split-and-combine in §4 is
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**FlashDecoding** (split-KV with log-sum-exp merge). The Ring path in
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§5.5 is **Ring Attention** (KV blocks rotated around the mesh, folded by
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the same online softmax). No new math is introduced; this ADR maps those
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known algorithms onto the kernbench **greenlet `tl` programming model**
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(ADR-0020, ADR-0046) and the **IPCQ** PE↔PE collective (ADR-0023/0025).
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---
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## TL;DR — final GQA kernel (composite hybrid) pseudocode
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The decision (§1) in one place: **GEMMs → `tl.composite`; softmax merge +
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2-level reduction → kernel; `tl.load` is lazy.** The KV head is selected by
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**CUBE coordinate** (one CUBE Group per head, §0) — **no `for kv` loop**.
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`G` is folded into the matmul M dim (real GQA, no broadcast). The KV
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sequence is sharded `C × P` ranks (Level-1 inter-CUBE × Level-2 intra-CUBE
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PE); the combine is a 2-level reduce-to-root (§4). Reference shape:
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```python
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def gqa_attention(q_ptr, k_ptr, v_ptr, o_ptr,
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counter, start_pe, q_block, scale, *, tl):
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# ---- geometry: this rank within its CUBE Group (§0, §2) ----
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cube_id = tl.program_id(axis=1) # which CUBE in the SIP mesh
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pe_id = tl.program_id(axis=0) # which PE in the CUBE
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kv = head_of_cube(cube_id) # CUBE coord → KV head (no for-kv loop)
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C, P = cube_group_size, pes_per_cube # Level-1 × Level-2 SP extents
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G = H_q // H_kv # query heads per KV head (=8)
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causal = q_block.is_prefill
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# Q group: G rows folded into M → [G·T_q, d]. Lazy load (ADR-0062).
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q_g = tl.load(q_base(kv), (G * q_block.T_q, d)) # [G·T_q, d]
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my_len = valid_len_2level(counter, start_pe, cube_id, pe_id, C, P) \
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if not causal else S_kv_local
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n_tiles = ceil(my_len / TILE)
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# persistent arena (outside scratch_scope, ADR-0063): -inf, 0, zeros
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m, l, O = init_running(G * q_block.T_q, d)
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for j in range(n_tiles):
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if causal and tile_all_future(j, q_block):
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continue # causal skip (kernel if)
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with tl.scratch_scope(): # per-tile temporaries (ADR-0063)
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# --- GEMM #1 on the scheduler: Q·Kⱼᵀ; K streamed by composite ---
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Sj = tl.composite("gemm", a=q_g,
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b=tl.ref(k_tile(kv, j), (d, TILE))) * scale # Kᵀ pre-stored [d,TILE]
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if causal and tile_partial(j, q_block):
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Sj = Sj + causal_mask(j, q_block) # additive boundary mask
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# --- online softmax merge in the kernel (MATH ops) ---
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m_new = tl.maximum(m, tl.max(Sj, axis=-1))
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P = tl.exp(Sj - m_new)
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corr = tl.exp(m - m_new)
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l = l * corr + tl.sum(P, axis=-1)
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# --- GEMM #2 on the scheduler: P·Vⱼ; V streamed by composite ---
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Oj = tl.composite("gemm", a=P,
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b=tl.ref(v_tile(kv, j), (TILE, d)))
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O = O * corr + Oj # running merge
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m = m_new
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# ---- 2-level combine to the CUBE Group root (§4); data-driven, no barrier ----
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hierarchical_reduce_and_store(m, l, O, cube_id, pe_id, C, P, o_base(kv))
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```
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> **Why this shape:** the two `tl.composite` calls offload tiling + K/V DMA
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> streaming + cross-tile pipelining to PE_SCHEDULER (CPU issues coarse
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> descriptors and runs ahead → engines stay saturated, §1); the softmax
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> merge and the IPCQ reduction stay in the kernel because the existing
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> `CompositeCmd` cannot carry cross-tile state. The reduction is **2-level**
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> (Level-2 PE tree within a CUBE → Level-1 CUBE-mesh reduce within the CUBE
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> Group), both reduce-to-root, log-sum-exp, sent as each rank finishes its
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> local sweep (§4). `K` is pre-stored transposed `[d, TILE]` to sidestep the
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> reshape-not-transpose caveat (§3, §B). A bespoke "flash-composite" command
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> kind is **not** introduced (§8 item 4).
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---
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## A. Relationship to existing kernbench work (read first)
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kernbench **already runs FlashAttention with an online-softmax `(m, ℓ, O)`
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merge over IPCQ today.** Two kernels exist:
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| File | Role | Mechanism |
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|---|---|---|
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| `src/kernbench/benches/_attention_mesh_kv.py` | prefill (Ring K/V) | per-rank partial attention, bidirectional K/V fan-out, online-softmax fold |
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| `src/kernbench/benches/_attention_mesh_mlo.py` | decode (split-KV) | per-rank one-shot partial attention, bidirectional `(m,ℓ,O)` fan-out, log-sum-exp merge |
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Both are driven by `milestone-gqa-llama70b` (4 panels:
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`{single,multi}_user × {prefill,decode}`,
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`src/kernbench/benches/milestone_gqa_llama70b.py`) and tested in
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`tests/attention/test_milestone_gqa_llama70b.py`.
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**They are written in the greenlet `tl` API:** `tl.load`, `tl.dot`,
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`tl.softmax`/`tl.max`/`tl.sum`/`tl.exp`, `tl.send`/`tl.recv`, and Python
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`-`/`*`/`/` on `TensorHandle` (each emits a `MathCmd`) — the GEMMs as
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**blocking `tl.dot`**, not composites. The running `(m, ℓ, O)` is just
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Python `TensorHandle`s threaded through the loop. This ADR keeps the
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running state and the softmax merge in the kernel but **moves the two
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GEMMs onto the scheduler-managed `tl.composite` path** (see §1) — this
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**matters for the design**.
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**Three deliberate limitations of the baseline** — exactly what an
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*efficient GQA* kernel must lift:
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1. **No GQA reuse.** `h_q == h_kv == 1`
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(`test_milestone_gqa_llama70b.py:137-142`). The test attributes this to
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a MemoryStore byte-conservation failure on a *broadcast view*, but that
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failure is a property of the baseline's **head-packing hack**
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(`_view(K, (h_q·d, S_kv))`, which conflates all heads into one matmul
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dim and only conserves bytes when `h_q == h_kv`). The correct fix is
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**kernel restructuring**, not a broadcast op: process **one KV head at
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a time** and fold the `G` group rows into the matmul **M** dimension
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(§5.2). That uses only byte-conserving reshapes, so real GQA
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(`h_q = G·h_kv`) runs with **no new primitive** — see §8.
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2. **O(N) reduction.** The baseline does an all-to-all bidirectional
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fan-out so *every* rank ends with the full answer (`n_ranks − 1`
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steps). Attention only needs `O` at the query owner → a **2-level
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reduce-to-root** (intra-CUBE tree + intra-CUBE-Group center-mesh, §4) is
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`⌈log₂ P⌉` + center-mesh-over-`C` steps.
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3. **Validation scale only.** `S = 16` because the 1 MiB scratch bump
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allocator leaks per-tile temporaries
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(`test_milestone_gqa_llama70b.py:123-148`) and there is no causal
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masking / tiling → fixed by **ADR-0063** (recycling) + §5 (tiling,
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causal skip) + composite K/V streaming (§3) + **ADR-0062** (lazy load
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overlap).
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**Documentation debt (out of scope but recorded):** the baseline cites
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ADR-0055/0056/0057/0058/0059, **none of which exist as files** — they are
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ghost references. This ADR does not retro-write them; see the Detailed
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Design Document's *Open Decisions* for the recommendation.
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---
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## 0. Reference dimensions (Llama3-70B)
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| Symbol | Meaning | Value |
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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
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containing **PE**s) + **IO die** (ADR-0003).
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- **IPCQ**: PE↔PE queues, 4 mesh-direction queue-pairs per PE
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(`N/S/E/W`, ADR-0023 D3; `global_*` for inter-SIP, ADR-0032). Kernel
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API: `tl.send(dir, src)` / `tl.recv(dir, shape, dtype)`
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(`tl_context.py:402-499`).
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- **Composite command** (`CompositeCmd`, `pe_commands.py:144-162`): a
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single GEMM (or MATH) *head* plus element-wise *epilogue* stages
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(`bias/relu/scale/add/...`), issued **non-blocking** to PE_SCHEDULER,
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which generates a tile plan and streams DMA→GEMM→write per tile
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(ADR-0014 D6; `pe_scheduler.py:104-143`). It is **not** a general
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multi-op DAG: it cannot chain two GEMMs, cannot carry register state
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across instances, and cannot pop/wait on IPCQ. This ADR therefore issues
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**each** of the two attention GEMMs (Q·Kᵀ, P·V) as its own composite and
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keeps the cross-GEMM softmax merge + the IPCQ reduction in the kernel —
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it does **not** need a new "flash-composite" command kind (see §1, §8).
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### CUBE Group — the placement unit (hierarchical SP)
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**A `CUBE Group` is `C` CUBEs (within one SIP) that jointly own one KV
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head and its `G` query heads.** One KV head's KV sequence is **sharded
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two levels** of sequence-parallelism (SP):
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- **Level-1 (inter-CUBE, within the CUBE Group):** the head's sequence is
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split across the `C` CUBEs of the group.
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- **Level-2 (intra-CUBE, across PEs):** each CUBE's slice is split again
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across its `P` PEs.
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So the KV-parallel reduction group for one (KV head, request) is **`C × P`
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ranks**, all within one SIP. The `G` query heads are folded into the
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matmul M dim (replicated across all ranks). `C` is a **tuning knob**
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(e.g. 8 or 4): large `C` for long-context prefill, small `C` (→ 1 = a
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single CUBE, intra-CUBE SP only) for short decode where reduction
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dominates.
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**Topology grounding** (`topology.yaml`): a SIP is a `4×4` CUBE mesh
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(16 CUBEs, ADR-0017 NOC); a CUBE has `P = 8` PEs
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(`hbm_pseudo_channels/hbm_channels_per_pe = 64/8`). With `C = 8` a SIP
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holds **2 CUBE Groups = 2 KV heads**; the full `H_kv = 8` model therefore
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spans **4 SIPs** (8/2). Each CUBE Group is **intra-SIP**, so one head's
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reduction uses the CUBE NOC (Level-1) + PE IPCQ (Level-2) — never UCIe.
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(The shipped `topology.yaml` has `sips: 2`; full scale needs a 4-SIP
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config — §B.)
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**`--device` enumerates SIPs** (CLI semantics): one SIP-device bench
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drives its 2 CUBE Groups (2 KV heads) — the head is selected spatially by
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CUBE coordinate, **not** an in-kernel `for kv` loop. The CLI runs the
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4 SIP-devices logically in parallel to cover all 8 heads.
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**Token → rank placement** within a CUBE Group (round-robin SP, balanced
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to ≤1 token): KV token `i` lands on Level-1 rank `(start + i) mod C`, then
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Level-2 rank `((i // C) + start_pe) mod P` — a hierarchical round-robin so
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each of the `C × P` ranks owns a dense, contiguous local slice.
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Reduction is **hierarchical and intra-SIP**: a KV head **does** span the
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`C` CUBEs of its group (Level-1), reduced over the CUBE NOC; this
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**reverses** the earlier "a query head never spans CUBEs" non-goal, which
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existed only because the `H_kv=1` baseline never needed it. Crossing
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**SIPs** for one head remains a non-goal (a head stays in one SIP).
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---
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## 0.5 Kernel boundary, preconditions, I/O contract
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### 0.5.1 Position 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, 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 (upstream `qkv_rope`, NOT this kernel):**
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- **P1.** Q is already RoPE-rotated. No rotation here.
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- **P2.** K-cache stores **post-RoPE** K (never re-rotated at attention
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time — the reason for post-RoPE caching).
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- **P3.** For decode, the new step's K row is RoPE-rotated and appended
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to its owning PE's K-cache slot **by `qkv_rope` before this kernel
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launches.** ⇒ this kernel is **pure read** on the KV cache.
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- **P4.** V is not rotated; V-cache holds raw projected V.
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- **P5.** RoPE position upstream is the token's **absolute global
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position** `global_idx = local_slot·(C·P) + rank` where `rank =
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cube_local·P + pe_local` (§2.1). Round-robin placement ≠ RoPE position.
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### 0.5.2 Shape symbols
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`T_q` = query length this launch (decode: 1; prefill/chunk: chunk width).
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`S` = total context length. `R = C·P` = ranks per CUBE Group;
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`S_rank` = keys owned by this rank ≈ `⌈S/R⌉`. Storage `bf16` (numpy proxy
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`f16`, `memory_store.py:16`); `m,ℓ,O` accumulators `f32` where precision
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matters.
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### 0.5.3 INPUTS (per kernel launch)
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| Input | Shape (per KV head) | Location | Notes |
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|---|---|---|---|
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| `Q` | `[G, T_q, d]` | per-rank HBM (loaded to TCM) | post-RoPE (P1). `G` query rows of the group batched. |
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| `K_cache` | `[S/(C·P), d]` | per-rank HBM, base `K_base[rank]`, contiguous | post-RoPE (P2). Read-only. Dense locally, strided by `C·P` in global index (§2.1). |
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| `V_cache` | `[S/(C·P), d]` | per-rank HBM, base `V_base[rank]` | raw V (P4). Read-only. |
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| `global_token_counter` | scalar | launch arg | kernel derives local len, slot↔global, causal bounds. |
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| `start_cube`, `start_pe` (= `f(request_id)`) | scalars | launch arg | Level-1/Level-2 rotation. |
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| `cube_id`, `pe_id`, `C`, `P` | scalars | launch / `tl.program_id` 1/0 | CUBE-Group reduction geometry (`R = C·P`). |
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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 (CUBE-Group root only). |
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| `softmax_scale` | scalar | launch arg | `1/√d`. |
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For **Ring Attention (§5.5)** add per ring step: incoming `K_block,
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V_block` via IPCQ into ping-pong buffers (post-RoPE), plus
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`step_kv_global_range` for the causal step-skip.
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### 0.5.4 OUTPUTS
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| Output | Shape | Location | Notes |
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|---|---|---|---|
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| `O` (final) | `[G, T_q, d]` | `O_base`, **CUBE-Group root only** | normalised `O_acc / ℓ_acc`, cast to storage dtype. |
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**Intermediate (non-root rank, on IPCQ — not a kernel-visible output):**
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`(m_i, ℓ_i, O_i)` (`m,ℓ`: `[G, T_q]`; `O_i`: `[G, T_q, d]` unnormalised)
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pushed up the 2-level tree to the parent (Level-2 PE parent, then Level-1
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CUBE parent) via `tl.send` (§4). `O_i` is the heavy part.
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**No-SP (`C=P=1`):** no IPCQ partials; the single rank's running `(m,ℓ,O)`
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is normalised in place and written to `O_base`.
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**Explicitly NOT outputs:** KV-cache writes (upstream, P3), output
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projection (downstream), score `S` and probs `P` (never materialised).
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---
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## 1. Decision (mechanism)
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**Implement the kernel as a *hybrid*: issue the two GEMMs (Q·Kᵀ and P·V)
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as scheduler-managed `tl.composite(op="gemm")` commands, and keep the
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online-softmax merge and the cross-PE reduction as kernel-level `tl`
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ops.** `tl.load` is **lazy** (non-blocking; the wait is auto-inserted at
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first use of the loaded data — ADR-0062), so explicit HBM loads overlap
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the compute that follows. Rationale grounded in kernbench's execution +
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latency model:
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- **GEMM tiling is offloaded to PE_SCHEDULER.** A `CompositeCmd` is
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non-blocking (`kernel_runner.py:182-191`, `pe_scheduler.py:104-121`):
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the kernel pushes **one coarse descriptor** (M = `G·T_q`, the whole
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per-rank tile sweep) and the scheduler generates the tile plan and streams
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DMA→GEMM→write per tile (ADR-0014 D6). K/V are `tl.ref` operands the
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scheduler streams from HBM, so per-tile **K/V prefetch is the
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scheduler's job** — no explicit prefetch op. The CPU (greenlet) is freed
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to issue the **next** composite while the current one runs, so the
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scheduler keeps the GEMM engine saturated across tiles.
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- **This reflects the hardware** and decouples CPU issue-rate from
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execution-rate. The blocking per-op `tl.dot` path, by contrast, stalls
|
||
the CPU on every GEMM and leaves GEMM-engine **bubbles** during the
|
||
interleaved softmax MATH ops; it is realistic only if the CPU can keep
|
||
up with fine-grained per-tile issue.
|
||
- **The running `(m, ℓ, O)` flash state stays Python `TensorHandle`s**
|
||
threaded through the loop (the baseline already does this); the softmax
|
||
merge (max/exp/sum/rescale) is kernel-level `tl` MATH **between** the two
|
||
GEMM composites. The existing `CompositeCmd` cannot chain two GEMMs or
|
||
carry cross-tile register state (§0), so the merge necessarily lives in
|
||
the kernel — this is the hybrid split, not a limitation worked around.
|
||
- **Cross-PE combination** is a log-sum-exp **tree** over `(m, ℓ, O)`
|
||
after P·V, via kernel-level `tl.send`/`tl.recv` (§4) — unchanged.
|
||
|
||
So the per-tile inner pipeline is:
|
||
|
||
```
|
||
q_g = tl.load(Q group) # lazy; auto-wait at first use
|
||
per tile j:
|
||
Sⱼ = tl.composite("gemm", a=q_g, b=tl.ref(Kⱼ)) → Sⱼ # scheduler streams Kⱼ DMA + GEMM
|
||
Sⱼ += maskⱼ # kernel MATH, boundary tile only
|
||
online-softmax: mⱼ, m_new, P, corr, ℓ # kernel MATH
|
||
Oⱼ = tl.composite("gemm", a=P, b=tl.ref(Vⱼ)) → Oⱼ # scheduler streams Vⱼ DMA + GEMM
|
||
O = O*corr + Oⱼ; m = m_new # kernel MATH (running merge)
|
||
```
|
||
|
||
with each tile's MATH temporaries wrapped in `tl.scratch_scope`
|
||
(ADR-0063) so scratch stays O(1), and the next tile's composites issued
|
||
before the current tile's results are waited on (non-blocking handles) so
|
||
the scheduler pipelines across tiles.
|
||
|
||
Control flow (tile skip, mask generation, reduction scheduling, address
|
||
arithmetic) lives in the **kernel** (plain Python `if`/arithmetic in the
|
||
greenlet body). This is exactly what kernbench's greenlet model already
|
||
permits (`kernel_runner.py`, ADR-0020 D3).
|
||
|
||
> **What this supersedes.** An earlier iteration proposed a pure greenlet
|
||
> primitive path (all `tl.dot`, no composite) on the argument that
|
||
> "composite yields no latency benefit." That holds **only because** the
|
||
> simulator currently charges **zero** per-op CPU issue cost
|
||
> (`dispatch_cycles=0`, `pe_cpu.py`) — it models away exactly the CPU
|
||
> issue-rate / DMA-program cost that descriptor offload exists to hide.
|
||
> The hybrid is the faithful representation of an efficient kernel. The
|
||
> **measurable** size of the win (can the CPU saturate the engines for
|
||
> many tiles?) is gated on modelling an op-type-differentiated issue cost,
|
||
> tracked as future work (cost model; §9). Even at `dispatch_cycles=0` the
|
||
> non-blocking composite path fills the GEMM-engine bubbles the blocking
|
||
> `tl.dot` path leaves.
|
||
>
|
||
> **Why this is the efficient choice.** GEMM tiling + DMA streaming +
|
||
> cross-tile pipelining are offloaded to the proven `CompositeCmd`
|
||
> scheduler path; the softmax merge and the proven IPCQ collective stay in
|
||
> the kernel. The only genuinely new machinery is two small, general
|
||
> primitives (ADR-0062 lazy `tl.load`, ADR-0063 `tl.scratch_scope`); the
|
||
> reduction reuses `tl.send`/`tl.recv`. A bespoke "flash-composite"
|
||
> command (one kind internalising the softmax merge + carried register
|
||
> state + an IPCQ-push epilogue) is **not** built — large, special-purpose,
|
||
> and its only delta over this hybrid (full softmax offload) is not
|
||
> justified at the current modelling fidelity; see §8.
|
||
|
||
---
|
||
|
||
## 2. Memory layout & driver responsibilities
|
||
|
||
### 2.1 KV cache allocation (2-level SP)
|
||
|
||
One KV head's sequence is sharded across `C × P` ranks (Level-1 inter-CUBE
|
||
× Level-2 intra-CUBE PE, §0). In kernbench this is a `DPPolicy` whose
|
||
**both** the `cube` axis (`row_wise` over `C`) **and** the `pe` axis
|
||
(`row_wise` over `P`) shard the sequence dimension, with Q `replicate`
|
||
(`policy/placement/dp.py`). The baseline's single-axis
|
||
`DPPolicy(pe="row_wise")` becomes a two-axis `row_wise` over (cube, pe).
|
||
|
||
- Per-rank KV buffers sized to `⌈max_context / (C·P)⌉ × d × dtype`, K and V
|
||
separately.
|
||
- Within a rank, assigned tokens are **appended contiguously** (slot
|
||
0,1,2,…); the per-rank buffer is dense ⇒ DMA stays contiguous, even
|
||
though global indices are strided by `C·P`.
|
||
- Slot → global (hierarchical round-robin): `rank = cube_local·P + pe_local`
|
||
where `cube_local = (cube_id − start_cube) mod C`,
|
||
`pe_local = (pe_id − start_pe) mod P`; then
|
||
`global_idx = local_slot·(C·P) + rank`.
|
||
|
||
### 2.2 Driver per-launch duties (minimal)
|
||
The driver supplies, per launch, the bases + counter + rotation; the
|
||
kernel derives the rest:
|
||
|
||
| Launch arg | Purpose |
|
||
|---|---|
|
||
| `K_base[rank]`, `V_base[rank]` | per-rank KV buffer bases (from tensor VA) |
|
||
| `O_base` | attention output destination |
|
||
| `global_token_counter` | current sequence position |
|
||
| `start_cube`, `start_pe = f(request_id)` | Level-1/Level-2 rotation |
|
||
| `cube_id`, `pe_id` (`tl.program_id` 1/0), `C`, `P` | CUBE-Group geometry |
|
||
| `q_block_meta` | prefill/SP query block start + length |
|
||
|
||
Let `R = C·P`, `cube_local = (cube_id − start_cube) mod C`,
|
||
`pe_local = (pe_id − start_pe) mod P`, and this rank's index
|
||
`rank = cube_local·P + pe_local`. Kernel-derived (plain arithmetic):
|
||
- **my turn to write this step:** `(start_rank + counter) mod R == rank`
|
||
- **my valid length:** `base = counter // R; rem = counter % R;
|
||
my_len = base + (1 if rank < rem else 0)`
|
||
- **read range:** tiles `0 .. ⌈my_len / TILE⌉`.
|
||
- **causal bounds / per-tile skip:** from global positions of the query
|
||
block vs each tile.
|
||
|
||
Driver = bases + counter + rotation. The single formula
|
||
`(request_id + token_idx) mod (C·P)` over the two SP axes is the entire
|
||
placement policy.
|
||
|
||
---
|
||
|
||
## 3. Per-tile op sequence (greenlet `tl`)
|
||
|
||
One iteration = one KV tile on one PE. The two GEMMs are
|
||
`tl.composite(op="gemm")` (scheduler-managed tiling + K/V DMA streaming);
|
||
the softmax merge is kernel `tl` MATH between them. Real `tl` names
|
||
(`tl_context.py`), with lazy `tl.load` (ADR-0062), `tl.scratch_scope`
|
||
(ADR-0063):
|
||
|
||
```python
|
||
# running state (persistent arena — allocated once, outside the scope)
|
||
# m: [G, T_q] l: [G, T_q] O: [G, T_q, d]
|
||
q_g = tl.load(Q_group_ptr, (G*T_q, d)) # lazy; auto-wait at first use (ADR-0062)
|
||
|
||
with tl.scratch_scope(): # per-tile MATH temporaries recycled
|
||
Sj = tl.composite("gemm", a=q_g, # [G·T_q, TILE]; scheduler streams Kⱼ DMA
|
||
b=tl.ref(K_base + j*TILE*d, (TILE, d))) * softmax_scale
|
||
if mask_j is not None:
|
||
Sj = Sj + mask_j # additive causal mask (boundary tile)
|
||
m_j = tl.max(Sj, axis=-1)
|
||
m_new = tl.maximum(m, m_j)
|
||
P = tl.exp(Sj - m_new) # no full-matrix softmax; streaming
|
||
corr = tl.exp(m - m_new) # rescale factor for old accumulators
|
||
l = l * corr + tl.sum(P, axis=-1)
|
||
Oj = tl.composite("gemm", a=P, # [G·T_q, d]; scheduler streams Vⱼ DMA
|
||
b=tl.ref(V_base + j*TILE*d, (TILE, d)))
|
||
O = O * corr + Oj # running merge (kernel MATH)
|
||
m = m_new
|
||
```
|
||
|
||
Notes:
|
||
- `q_g` is the GQA-batched query reshaped to `[G·T_q, d]` (the `G` group
|
||
rows folded into the matmul M dim; byte-conserving). One K/V tile serves
|
||
all `G·T_q` rows — the GQA reuse lever — with no broadcast.
|
||
- **K/V are `tl.ref` operands** the composite scheduler streams from HBM
|
||
per tile (`pe_scheduler.py:104-143`): that *is* the prefetch/pipeline,
|
||
so there is no explicit prefetch op. Issuing tile `j+1`'s composites
|
||
before waiting on tile `j` (non-blocking handles) keeps the scheduler
|
||
pipelined across tiles and the GEMM engine saturated.
|
||
- `tl.trans` is **metadata-only** in kernbench (`tl_context.py:390`) and
|
||
`MemoryStore.read` *reshapes* rather than transposes
|
||
(`memory_store.py:73`). For zero/structural runs this is harmless; for
|
||
non-trivial numeric data it yields a reshape-not-transpose, so Q·Kᵀ via
|
||
a transposed K needs care (§11) — store K pre-transposed `[d, TILE]`, or
|
||
add a real `tl.transpose` (a candidate further primitive, likely
|
||
unnecessary given the simulator's performance-modeling purpose).
|
||
- Masking: the kernel builds the boundary-tile mask from query/KV global
|
||
offsets and adds it (`Sj + mask_j`); full-past tiles pass `None`;
|
||
full-future tiles are **skipped** (the `if` never enqueues them).
|
||
- A **new** "flash-composite" command kind (one that internalises the
|
||
softmax merge + carried `(m,ℓ,O)`) is **not** used; the existing
|
||
`CompositeCmd` covers each GEMM and the merge stays in the kernel
|
||
(§1, §8 item 4).
|
||
|
||
---
|
||
|
||
## 4. Reduction (KV-parallel / SP combine) — 2-level reduce-to-root
|
||
|
||
After its tile sweep each rank holds `(m_i, ℓ_i, O_i)` (unnormalised) over
|
||
its `1/(C·P)` shard. Combine via the associative/commutative log-sum-exp
|
||
merge (identical math to the baseline's fold, `_attention_mesh_mlo.py:117-122`):
|
||
|
||
```python
|
||
def merge(m_a, l_a, O_a, m_b, l_b, O_b):
|
||
m = tl.maximum(m_a, m_b)
|
||
sa, sb = tl.exp(m_a - m), tl.exp(m_b - m)
|
||
return m, l_a*sa + l_b*sb, O_a*sa + O_b*sb
|
||
# final: O = O_root / l_root # normalise once at the CUBE-Group root
|
||
```
|
||
|
||
Because the merge is associative **and** commutative, the combine is
|
||
**reduce-to-root** (attention needs `O` at one place — the rank that writes
|
||
`O_base` — not an all-reduce) and proceeds in **two levels**:
|
||
|
||
### 4.1 Level-2 — intra-CUBE (P PEs → CUBE root PE)
|
||
|
||
- A **reduce-to-root tree** over the `P` PEs of a CUBE, depth `⌈log₂ P⌉`
|
||
(3 for `P=8`), over the PE IPCQ `N/S/E/W` mesh
|
||
(`configure_sfr_intracube_pe_ring`). Each tree pair is a 1-hop physical
|
||
neighbour. Result lands on the CUBE's root PE.
|
||
|
||
### 4.2 Level-1 — intra-CUBE-Group (C CUBE roots → Group root)
|
||
|
||
- A **center-root bidirectional CUBE-mesh reduce** over the `C` CUBEs of
|
||
the group (PE-root-only, `program_id(axis=1)` = `cube_id`), over the
|
||
CUBE NOC 2D mesh (ADR-0017). This **adapts the proven inter-CUBE pattern
|
||
of `lrab_hierarchical_allreduce.py` (Phases 1–2: row-then-col converge at
|
||
the center CUBE)** — but **reduce-only** (drop its broadcast-back Phases
|
||
4–5; attention needs root-only) and with the **log-sum-exp `merge`**
|
||
replacing its plain `+`. Center-root halves the critical path vs a corner
|
||
root (`lrab_hierarchical_allreduce.py:113-116`). No inter-SIP phase — the
|
||
CUBE Group is intra-SIP (§0).
|
||
|
||
### 4.3 Data-driven overlap, no global barrier
|
||
|
||
- A rank sends up the tree the **instant its own local P·V finishes** — it
|
||
does **not** wait for sibling ranks; internal nodes `merge` children as
|
||
they arrive. So the reduction **overlaps the compute of slower ranks**
|
||
(causal prefill imbalance, batched decode tokens).
|
||
- The two levels **pipeline**: a CUBE whose Level-2 reduction is done feeds
|
||
Level-1 immediately, while other CUBEs are still doing Level-2 — **no
|
||
barrier between levels**. (A rank cannot send before its *own* local
|
||
sweep completes; sending pre-final partials would multiply the heavy `O`
|
||
payload and is not worth it.)
|
||
- **Why reduce-to-root, not the baseline fan-out / an all-reduce:** the
|
||
baseline replicates the answer to every rank (`R−1` steps); attention
|
||
needs it once. Reduce-to-root is `⌈log₂ P⌉ + (center-mesh depth over C)`
|
||
— for decode (short sweep, reduction-dominated) this is the dominant win.
|
||
|
||
### 4.4 Configurable topology
|
||
|
||
Each level's collective is **selectable** (a tuning item, §9): the defaults
|
||
above (Level-2 tree, Level-1 center-root mesh) suit the latency-bound,
|
||
small-`O` decode case; a **ring** option (chunked `O`) suits bandwidth-bound
|
||
long-context prefill with large `T_q·d` payload. `C=1` degenerates to
|
||
Level-2 only (single-CUBE SP).
|
||
|
||
**Payload:** `O_i` (`[G·T_q, d]`) is heavy; `m,ℓ` are light. Sent as
|
||
separate `tl.send`s (baseline pattern).
|
||
|
||
Kernel structure (greenlet), both levels reuse `merge`:
|
||
|
||
```python
|
||
def hierarchical_reduce_and_store(m, l, O, cube_id, pe_id, C, P, o_base):
|
||
# ---- Level-2: PE tree within the CUBE → CUBE root PE ----
|
||
for child in tree_children_dirs(pe_id, P): # PE IPCQ N/S/E/W
|
||
m, l, O = merge(m, l, O, *recv_triplet(child))
|
||
if not is_cube_root(pe_id, P):
|
||
send_triplet(parent_dir(pe_id, P), m, l, O); return
|
||
# ---- Level-1: CUBE-mesh center-root reduce → Group root (cube roots only) ----
|
||
for child in mesh_children_dirs(cube_id, C): # CUBE NOC, center-root
|
||
m, l, O = merge(m, l, O, *recv_triplet(child))
|
||
if is_group_root(cube_id, C):
|
||
tl.store(o_base, O / l) # normalise once
|
||
else:
|
||
send_triplet(parent_dir_mesh(cube_id, C), m, l, O)
|
||
```
|
||
|
||
The reduction tree/mesh is **static** (fixed at compile time), so the recv
|
||
order is data-independent — no data-dependent `pop`, plain blocking
|
||
`tl.recv` suffices, and **no hardware/composite `pop`-as-dependency change
|
||
is required** (§6).
|
||
|
||
---
|
||
|
||
## 5. Per-case kernels
|
||
|
||
All four cases share §3 (tile sweep) + §4 (reduction); they differ only
|
||
in the SP extents `C·P`, query-block width, and which `tile_*` predicates
|
||
fire.
|
||
|
||
### 5.1 Common skeleton
|
||
|
||
```python
|
||
def attn_kernel(q_ptr, k_ptr, v_ptr, o_ptr, counter, start_pe, start_cube,
|
||
C, P, q_block, softmax_scale, *, tl):
|
||
cube_id = tl.program_id(axis=1); pe_id = tl.program_id(axis=0)
|
||
kv = head_of_cube(cube_id) # CUBE coord → KV head (no for-kv loop)
|
||
my_len = valid_len_2level(counter, start_cube, start_pe, cube_id, pe_id, C, P)
|
||
n_tiles = ceil(my_len / TILE)
|
||
q_g = load_Q_group(q_ptr, kv) # [G·T_q, d]; lazy tl.load, G folded into M (no broadcast)
|
||
m, l, O = init_running() # persistent arena: -inf, 0, zeros
|
||
for j in range(n_tiles): # K/V streamed per tile by the composite scheduler (§3)
|
||
if tile_all_future(j, q_block): # causal skip (kernel if)
|
||
continue
|
||
run_tile(j, mask_or_null(j, q_block)) # §3: 2 composites + softmax MATH, in tl.scratch_scope
|
||
if C == 1 and P == 1:
|
||
tl.store(o_base(kv), O / l) # no reduction (single rank)
|
||
else:
|
||
hierarchical_reduce_and_store(m, l, O, cube_id, pe_id, C, P, o_base(kv)) # §4
|
||
```
|
||
|
||
### 5.2 DECODE, no SP (`C=P=1`, one PE owns the head's KV)
|
||
|
||
- `T_q = 1`, attends to all past KV ⇒ no future tiles, mask only on the
|
||
final ragged tile.
|
||
- **GQA reuse is the whole game** (decode is KV-load-bound): the `G=8`
|
||
query rows of the KV head are folded into the matmul **M** dimension
|
||
(`q_g` reshaped `[G, T_q, d] → [G·T_q, d]`, a byte-conserving reshape).
|
||
Then `Q·Kᵀ` is `composite([G·T_q, d], Kᵀ[d, TILE]) → [G·T_q, TILE]` and
|
||
`P·V` is `composite([G·T_q, TILE], V[TILE, d]) → [G·T_q, d]`. The KV tile
|
||
(`[TILE, d]`) is the shared `K`/`V` operand — **streamed once, reused by
|
||
all `G·T_q` rows automatically** because they are the M rows of the
|
||
GEMM. No broadcast of K/V is needed; `m = G·T_q` in the composite's tile
|
||
plan also makes the timing count all `G` rows' work correctly (a leading
|
||
batch axis would *not* be counted — see §8).
|
||
- If `S_rank` fits in scratch (small/medium context) this degenerates to a
|
||
**one-shot** partial attention (one composite for Q·Kᵀ, one softmax, one
|
||
composite for P·V) — exactly the baseline `_attention_mesh_mlo`
|
||
`_partial_attention`, just GQA-batched and on the composite path. Tiling
|
||
(§3) only kicks in when `S_rank` exceeds the scratch scope's tile budget.
|
||
|
||
### 5.3 DECODE, with SP / KV-parallel (`C × P` ranks)
|
||
|
||
- One request's KV is hierarchical-round-robin across the `C·P` ranks of
|
||
the CUBE Group (Level-1 over `C` CUBEs, Level-2 over `P` PEs); each rank
|
||
owns ≈`my_len` tokens and GQA-batches its `G=8` query rows over its local
|
||
tiles.
|
||
- `T_q=1` ⇒ short per-rank sweep ⇒ **reduction dominates** ⇒ the §4 2-level
|
||
reduce (`⌈log₂ P⌉` + center-mesh over `C`) is the structurally important
|
||
part. Reduction latency is hidden by other concurrent decode tokens when
|
||
batched, by the long per-rank sweep for long single-stream context, and
|
||
by the §4.3 level-pipelining. For short decode prefer a **small `C`**
|
||
(less inter-CUBE reduction); push `C` up only when context length demands
|
||
the extra KV-parallelism.
|
||
|
||
### 5.4 PREFILL, no SP
|
||
- Whole prompt resident; query is a block of `T_q` tokens (chunked).
|
||
- Causality is real, per query block `[qs, qe)` vs KV tile `[ks, ke)`:
|
||
- `ke ≤ qs` → `tile_all_past` → `mask=None`, full compute.
|
||
- `ks ≥ qe` → `tile_all_future` → **skip** (kernel `if`).
|
||
- overlap → `tile_partial` → kernel builds a triangular additive mask,
|
||
the tile op sequence adds it (`Sj + mask_j`).
|
||
- GQA batches the group's query heads along the same axis as decode.
|
||
|
||
### 5.5 PREFILL, with SP (Ring KV)
|
||
|
||
- KV sharded across the `C·P` ranks in **ring order**; each step delivers a
|
||
peer's KV block over IPCQ. Running `(m,ℓ,O)` is carried **across ring
|
||
steps** (Python handles, exactly as `_attention_mesh_kv` does today). The
|
||
ring is the long-context, bandwidth-bound case where §4.4's **ring**
|
||
collective option (not the tree) is the natural choice.
|
||
- IPCQ overlaps next step's KV receive with current step's compute via
|
||
`tl.recv_async`/`tl.wait` (`tl_context.py:543-560` — already exists).
|
||
- **Causal ring skip:** if the incoming step's KV block is entirely
|
||
*after* the local query block, skip its compute (don't recv-consume /
|
||
don't fold) — eliminates ≈half the steps for causal attention.
|
||
- Receive buffers ping-pong (persistent arena, not recycled).
|
||
- After the ring, `(m,ℓ,O)` is final per rank; remaining KV-parallel splits
|
||
combine via the §4 2-level reduce, then normalise + store at the Group
|
||
root.
|
||
|
||
The baseline `_attention_mesh_kv` already implements the ring fold; this
|
||
ADR adds GQA reuse, causal step-skip, and `recv_async` overlap.
|
||
|
||
---
|
||
|
||
## 6. Why no hardware / composite change is needed
|
||
|
||
- The only place a HW/composite `pop`-as-dependency would help is a lone
|
||
reduction with no other work to hide a poll — i.e. batch=1 **and** short
|
||
context. The target is **agentic = low batch, long context** ⇒ each rank
|
||
has many KV tiles; the §4 reduce's `tl.recv` blocks are covered by the
|
||
sweep work of concurrent tokens / long context / the §4.3 level-pipeline.
|
||
- The §4 reduction uses a **static** 2-level tree/mesh, so the recv order is
|
||
fixed at compile time — no data-dependent pop. `tl.recv` (blocking)
|
||
suffices.
|
||
- **Decision: ship with the greenlet `tl.send`/`tl.recv` collective.**
|
||
Revisit a 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** (Python `if` + arithmetic in greenlet body) |
|
||
| address / offset / valid-length arithmetic | **kernel** (from counter) |
|
||
| reduction scheduling, IPCQ send/recv ordering | **kernel** (static 2-level tree/mesh) |
|
||
| Q·Kᵀ, P·V (incl. per-tile K/V DMA streaming + tiling) | **`tl.composite`** → PE_SCHEDULER |
|
||
| Q load, mask add, softmax math, running `(m,ℓ,O)` merge | **`tl` ops** on PE engines (kernel-issued) |
|
||
|
||
The kernel decides; the GEMMs are offloaded to the scheduler as
|
||
composites; the remaining `tl` ops execute already-decided work. This is
|
||
exactly the greenlet + composite model kernbench already supports — no new
|
||
control abstraction.
|
||
|
||
---
|
||
|
||
## 8. Required kernbench changes
|
||
|
||
**Correction from design iteration:** real GQA (`h_q > h_kv`) needs **no
|
||
new primitive** — only the kernel restructuring in §5.2 (per KV head,
|
||
`G` folded into M, byte-conserving reshapes). The supporting ADRs are
|
||
*efficiency / scale* enablers, not GQA blockers.
|
||
|
||
**Algorithm work in the kernel (no new primitive; existing `tl` API):**
|
||
|
||
- **GQA Q-axis batching** (the reuse lever) — fold `G·T_q` into the matmul
|
||
M dim per KV head (§5.2); `_view`-style byte-conserving reshape; the
|
||
GEMM is a `tl.composite(op="gemm")` with M = `G·T_q`. Runs today in both
|
||
timing and data mode.
|
||
- **GEMMs via composite** (§1/§3) — Q·Kᵀ and P·V each issued as a
|
||
non-blocking `tl.composite(op="gemm")`; PE_SCHEDULER tiles them and
|
||
streams the `tl.ref` K/V operands' DMA (existing `CompositeCmd`; no new
|
||
command kind).
|
||
- **2-level reduce-to-root** (§4) replacing the baseline all-to-all fan-out
|
||
— Level-2 PE tree (intra-CUBE) + Level-1 center-root CUBE-mesh reduce
|
||
(intra-CUBE-Group, adapting the `lrab_hierarchical_allreduce` inter-CUBE
|
||
pattern as reduce-only + log-sum-exp), data-driven and level-pipelined,
|
||
pure kernel control flow over `tl.send`/`tl.recv`.
|
||
- Causal tile skip + additive boundary mask (§3/§5.4) — kernel `if` +
|
||
a mask tensor added with `+`.
|
||
- 2-level round-robin KV placement / valid-length arithmetic (§0, §2) —
|
||
launch-arg arithmetic + `DPPolicy` `row_wise` over both `cube` and `pe`.
|
||
|
||
**New primitives for efficiency / scale (each has a supporting ADR):**
|
||
|
||
1. **Per-tile scratch recycling** — **ADR-0063** (`tl.scratch_scope`).
|
||
*Required for scale*: removes the `S=16` ceiling (1 MiB bump
|
||
allocator) so realistic context lengths run. Highest-value of the
|
||
three.
|
||
2. **Lazy `tl.load`** — **ADR-0062** (non-blocking load + auto-wait on
|
||
first use; API surface unchanged). *Efficiency*: overlaps explicit
|
||
loads (the Q group, non-composite kernels) with following compute. The
|
||
per-tile **K/V** prefetch is handled by the composite scheduler (§1),
|
||
so this covers the remaining explicit loads. Global semantics change →
|
||
existing goldens regenerate (ADR-0062 D3).
|
||
3. **GQA head / mask broadcast** — **ADR-0061** (`tl.broadcast`).
|
||
*Optional convenience*, not a GQA blocker (see correction above).
|
||
Useful for additive-mask construction across the `G·T_q` rows and for
|
||
general kernels; `np.matmul` already broadcasts in data mode, so it is
|
||
not needed for correctness. Lowest priority.
|
||
|
||
**Explicitly REJECTED (efficient alternative chosen):**
|
||
|
||
4. ~~A bespoke "flash-composite" command kind that internalises the whole
|
||
inner loop — DMA→MM→VEC→DMA→MM→VEC with carried `(m,ℓ,O)` register
|
||
state and a tail IPCQ push.~~ The two GEMMs **do** use the existing
|
||
`CompositeCmd` (§1/§3) — that gives scheduler-managed tiling, K/V DMA
|
||
streaming, and cross-tile pipelining. What is rejected is a **new**
|
||
command kind that also absorbs the softmax merge + cross-tile register
|
||
lifetime + an IPCQ-push epilogue: it is large and special-purpose, and
|
||
its only delta over the hybrid (full softmax offload) is not justified
|
||
at the current modelling fidelity (per-op CPU issue cost = 0; see §1).
|
||
Revisit if the cost model (§9) makes full offload measurably worthwhile.
|
||
5. ~~Hardware `pop`-as-dependency.~~ Out of scope (§6).
|
||
6. ~~RoPE / QKV projection / KV-cache write inside this kernel.~~ Upstream
|
||
`qkv_rope` (P1–P5). Folding RoPE in would force re-rotating past tiles
|
||
every decode step and break Ring Attention's post-RoPE pass-through.
|
||
|
||
---
|
||
|
||
## 9. Open tuning items (measured in kernbench, not blocking)
|
||
|
||
1. **Composite tile-pipeline depth** — dominant for KV-load-bound; how far
|
||
ahead the kernel issues non-blocking composites before waiting, and the
|
||
scheduler's per-tile streaming depth.
|
||
2. **Reduction topology per level (§4.4)** — Level-2 (intra-CUBE PE) and
|
||
Level-1 (intra-CUBE-Group CUBE-mesh) each selectable tree/ring/center-mesh;
|
||
sweep tree (latency, small-`O` decode) vs ring (bandwidth, large-`O`
|
||
prefill). Also the CUBE-Group size `C` knob (small for decode, large for
|
||
long context). Ensure each tree pair is a 1-hop physical neighbour; verify
|
||
against the SFR install.
|
||
3. **TILE size** — balance scratch residency (`S/P tiles + O_acc + G-way
|
||
GQA`) against DMA efficiency; interacts with ADR-0063 and the
|
||
scheduler's `TILE_M/K/N` (`pe_scheduler.py`).
|
||
4. **Ring buffer ping-pong vs `recv_async` depth** in §5.5.
|
||
5. **One-shot vs tiled crossover for decode** (§5.2) — the `S_rank`
|
||
threshold where tiling beats a single composite.
|
||
6. **Per-op CPU issue cost (cost model)** — currently `dispatch_cycles=0`
|
||
(`pe_cpu.py`), so composite-vs-primitive issue overhead is invisible.
|
||
An op-type-differentiated issue cost (a `tl.composite` descriptor push
|
||
≫ a primitive op) is what makes the hybrid's CPU-saturation win
|
||
**measurable** (§1). Specified in **ADR-0064**; tracked as separate
|
||
future work.
|
||
|
||
---
|
||
|
||
## 10. Coverage summary
|
||
|
||
| Case | KV placement | Inner loop | Reduction | Masking |
|
||
|---|---|---|---|---|
|
||
| Decode, no SP | 1 rank (`C=P=1`), all KV | one-shot or tiled, GQA-batched | none | last tile only |
|
||
| Decode, SP | 2-level RR over `C·P` ranks | tiled, GQA-batched | §4 2-level (`⌈log₂P⌉`+mesh over `C`) | last tile only |
|
||
| Prefill, no SP | resident | tiled per q-block | none | skip future / triangular boundary |
|
||
| Prefill, SP (Ring) | ring over `C·P` ranks | per ring step (recv_async overlap) | running state + §4 2-level | causal step-skip + boundary |
|
||
|
||
**I/O per case** (full contract §0.5):
|
||
|
||
| Case | Inputs | Output | IPCQ partials |
|
||
|---|---|---|---|
|
||
| Decode, no SP | `Q[G,1,d]`, full `K/V[S,d]` on 1 rank | `O[G,1,d]` at `O_base` | none |
|
||
| Decode, SP | `Q[G,1,d]`, per-rank `K/V[S/(C·P),d]` | `O[G,1,d]` (Group root) | `(m,ℓ,O_i)` per rank → 2-level |
|
||
| Prefill, no SP | `Q[G,T_q,d]`, `K/V[≤end,d]` | `O[G,T_q,d]` at `O_base` | none |
|
||
| Prefill, SP (Ring) | `Q[G,T_q,d]`, ring-delivered `K/V` blocks | `O[G,T_q,d]` (Group root) | running `(m,ℓ,O)` + 2-level |
|
||
|
||
In all cases: KV-cache writes and RoPE happen **upstream**; output
|
||
projection **downstream**; score `S` and probs `P` are never materialised.
|
||
|
||
---
|
||
|
||
## 11. Verification plan (Phase 1 test outline)
|
||
|
||
SPEC/ADR coverage: R5 (PE↔PE IPCQ, PE↔HBM), R2 (latency by traversal),
|
||
ADR-0023/0025 (IPCQ), ADR-0046 (`tl` contract), ADR-0054 (eval bench).
|
||
|
||
The simulator's contract is **latency by traversal + determinism +
|
||
structural correctness** (SPEC §0, §0.1), not bit-exact numerics — Phase 2
|
||
data exists mainly to exercise the data path, `tl.trans` is
|
||
reshape-not-transpose, and `bf16` is modelled as `f16`. Verification is
|
||
therefore **structural/timing-first**, with numeric parity as a bounded
|
||
secondary check.
|
||
|
||
1. **Runs in data mode (`enable_data=True`):** the GQA kernel
|
||
(`h_q = G·h_kv`) completes without the byte-conservation error that the
|
||
baseline head-packing hits — for all four cases. (This needs the §5.2
|
||
restructuring, *not* a new primitive.)
|
||
**Numeric parity (secondary):** for symmetric/identity inputs where
|
||
reshape-as-transpose is exact, kernel `O` matches a numpy
|
||
FlashAttention reference within fp tolerance. Full asymmetric parity is
|
||
gated on a real `tl.transpose` (out of scope; flagged).
|
||
2. **GQA reuse:** with `h_q = G·h_kv`, the K/V `dma_read_count` is
|
||
independent of `G` (one load per tile, reused across the group), while
|
||
GEMM work scales with `G`. Asserts the lever actually fires.
|
||
3. **2-level reduction step count:** decode-SP issues `⌈log₂ P⌉` (Level-2,
|
||
intra-CUBE) + center-mesh-depth-over-`C` (Level-1) reduction rounds — not
|
||
the baseline's `C·P − 1` all-to-all; op_log `ipcq_send`/`recv` counts
|
||
match the static 2-level tree/mesh, and the result lands at exactly one
|
||
rank (CUBE-Group root). A separate assertion checks Level-1 sends use the
|
||
CUBE NOC and Level-2 the PE IPCQ.
|
||
4. **Causal skip:** prefill skips all `tile_all_future` tiles — GEMM
|
||
count equals the lower-triangular tile count, not the full grid.
|
||
5. **Long context (ADR-0063):** a sweep at `S` that overflows 1 MiB
|
||
without scopes completes and matches the reference.
|
||
6. **Load/compute overlap:** end-to-end latency of the tiled sweep is
|
||
below the serial `Σ(load+compute)` — from the composite scheduler
|
||
streaming K/V per tile (§1/§3) and lazy `tl.load` (ADR-0062) overlapping
|
||
the Q load. (Overlap is real modelled concurrency, not a subtraction.)
|
||
7. **Composite GEMM offload (structural):** each tile's Q·Kᵀ and P·V emit a
|
||
`CompositeCmd` (non-blocking) to PE_SCHEDULER, not a blocking `tl.dot`;
|
||
op_log shows the composite tile plan and the kernel issues the next
|
||
tile's composites before waiting (cross-tile pipelining).
|
||
8. **Determinism:** identical inputs → identical op_log + latency
|
||
(SPEC §0.1).
|
||
|
||
---
|
||
|
||
## B. Open design items from the hybrid pivot (review later)
|
||
|
||
These arose when the decision moved from a pure greenlet primitive path to
|
||
the **composite hybrid + lazy `tl.load`** (this revision). None blocks the
|
||
design; each needs a verification pass during implementation. Recorded here
|
||
(rather than asked) per the working agreement — the recommendation is my
|
||
predicted default; revise on review.
|
||
|
||
1. **DDD-0060 is not yet synced.** The Detailed Design Document still
|
||
describes the old `tl.load_async` double-buffer path and primitive
|
||
`tl.dot` inner loop (its §4.3/§5/§10). It must be updated to the hybrid
|
||
(composite GEMMs, lazy load, K pre-transposed). *Left for review*
|
||
because the DDD is a derived how-to and a large rewrite; the ADR is now
|
||
the authoritative record. **Recommend:** sync DDD as a follow-up before
|
||
implementation starts.
|
||
|
||
2. **K operand orientation for the composite GEMM.** Q·Kᵀ needs `b =
|
||
[d, TILE]`, but the KV cache stores K as `[S_rank, d]`. `tl.trans` is
|
||
metadata-only and `MemoryStore.read` reshapes, not transposes
|
||
(`memory_store.py:73`) — so a runtime transpose is wrong for non-trivial
|
||
data. **Recommend:** store K **pre-transposed** `[d, S_rank]` in the cache
|
||
(the pseudocode and §3 assume this), making `tl.ref(k_tile, (d, TILE))`
|
||
a contiguous slice. Verify the upstream `qkv_rope` write layout supports
|
||
this, or add a real `tl.transpose` (heavier; deferred).
|
||
|
||
3. **Composite output buffer vs `tl.scratch_scope`.** Each Q·Kᵀ composite
|
||
writes `Sj` to an `out_addr`; the kernel then reads it for the softmax
|
||
MATH. That output buffer, and the in-flight composites' targets, must
|
||
live where the per-tile `scratch_scope` (ADR-0063) will **not** recycle
|
||
them before they are consumed — same discipline as in-flight lazy loads
|
||
(ADR-0062 D-Negative). **Recommend:** composite outputs for the *current*
|
||
tile live in the scoped arena (consumed same iteration); the persistent
|
||
`(m,ℓ,O)` stays outside. Verify no use-after-recycle when the next
|
||
tile's composites are issued early (cross-tile pipelining).
|
||
|
||
4. **GQA `dma_read_count` lever under composite streaming.** The lever
|
||
(§11.2: K/V `dma_read_count` independent of `G`) assumes the composite
|
||
emits **one** K/V tile DMA reused across all `G·T_q` M-rows. The
|
||
scheduler's `generate_gemm_plan` tiles by `TILE_M/K/N`
|
||
(`pe_scheduler.py:35-37`, 32/64/32) — confirm the M-tiling over `G·T_q`
|
||
does **not** re-issue the shared K/V tile DMA per M-tile (i.e. operand
|
||
DMA is shared across M-tiles, or the lever weakens). **Recommend:**
|
||
assert it in the levers test; if violated, the GQA win is in compute
|
||
only, not DMA — still correct, but the headline changes.
|
||
|
||
5. **Kernel TILE vs scheduler `TILE_M/K/N`.** The kernel reasons about a
|
||
logical KV `TILE`; the scheduler re-tiles internally at fixed
|
||
`TILE_M/K/N`. Two tiling layers interact (scratch residency, pipeline
|
||
depth). **Recommend:** treat the kernel TILE as the K/V streaming
|
||
granularity and let the scheduler sub-tile the GEMM; document the
|
||
relationship in the DDD and sweep both (§9 items 1, 3).
|
||
|
||
6. **Cost model is a separate ADR.** The hybrid's CPU-saturation benefit is
|
||
invisible while `dispatch_cycles=0`. The per-op-type issue-cost model is
|
||
specified in **ADR-0064**; this ADR's §1/§9 depend on it for the
|
||
*measurable* (not just structural) win. **Recommend:** land ADR-0064's
|
||
model before claiming hybrid latency wins in the eval.
|
||
|
||
7. **Ring path (§5.5) GEMMs.** §5.5 still describes the ring fold with
|
||
primitive ops + `recv_async`. For consistency the ring's per-step Q·Kᵀ /
|
||
P·V should also be composites; the IPCQ `recv_async` overlap is
|
||
orthogonal and stays. **Recommend:** apply the same hybrid shape to the
|
||
ring step during implementation; low risk, mirrors §3.
|
||
|
||
### Items from the hierarchical CUBE-Group SP pivot (this revision)
|
||
|
||
1. **A 4-SIP topology config is needed for full scale.** The shipped
|
||
`topology.yaml` has `sips: count: 2`; the full `H_kv=8` model at `C=8`
|
||
needs **4 SIPs** (2 KV heads each, §0). **Recommend:** add a 4-SIP
|
||
topology config (16-CUBE 4×4 mesh per SIP, 8 PE/CUBE — already the per-SIP
|
||
shape) for the headline eval; validation-scale runs can use fewer
|
||
CUBEs/SIPs. The bench must remain single-device per launch (one SIP).
|
||
|
||
2. **`head_of_cube` / CUBE-Group partition of the 4×4 mesh.** With `C=8`,
|
||
each SIP's 16-CUBE mesh splits into 2 CUBE Groups of 8. The exact sub-mesh
|
||
shape (4×2 vs 2×4) sets the Level-1 center-root mesh hop counts and which
|
||
CUBEs are 1-hop neighbours. **Recommend:** pick the partition that keeps
|
||
each CUBE Group a contiguous rectangular sub-mesh with a true center CUBE
|
||
(so `lrab`-style center-root applies); verify against the CUBE NOC routing
|
||
(ADR-0017).
|
||
|
||
3. **`C` as a per-operating-point knob.** `C` should differ by case (small
|
||
for decode where reduction dominates, large for long-context prefill).
|
||
**Recommend:** expose `C` (and per-level topology, §4.4) as launch/bench
|
||
config, sweep it in the milestone bench, and report the decode-vs-prefill
|
||
optimum. Default `C` TBD by measurement.
|
||
|
||
4. **Reverses the §0 "never spans CUBEs" non-goal — confirm no downstream
|
||
assumption depends on it.** Earlier text and possibly other ADRs treated
|
||
intra-CUBE-only reduction as invariant. **Recommend:** grep for that
|
||
assumption (SFR installs, address policy, diagrams) before implementation;
|
||
none found in this kernel's scope, but the `configure_sfr_*` neighbour
|
||
wiring must expose CUBE-NOC `N/S/E/W` for Level-1, not just PE IPCQ.
|
||
|
||
5. **`valid_len_2level` / hierarchical round-robin correctness.** The
|
||
two-axis `(cube_local·P + pe_local)` placement (§2.1) must tile the
|
||
sequence with no gaps/overlaps and keep each rank's local buffer dense.
|
||
**Recommend:** unit-test the index math (every global token maps to
|
||
exactly one rank; per-rank slices are contiguous) before the kernel.
|