gqa(adr): rewrite GQA fused-attention ADR as ADR-0060, aligned to kernbench
Reframes the proposed GQA FlashAttention design onto kernbench's actual execution model (greenlet tl API + IPCQ), replacing the composite-centric mechanism that does not match the simulator: - Records relationship to existing baseline kernels (_attention_mesh_kv/mlo, milestone-gqa-llama70b) and their 3 deliberate limitations. - Mechanism is greenlet tl (per-op latency; no fusion benefit), not composites; running (m,l,O) is Python handles; reduction is tl.send/recv. - Tree reduction (log N) replaces baseline all-to-all fan-out (N-1). - Pseudocode rewritten in real tl.* signatures; depends on ADR-0061/62/63. - Rejects composite-IPCQ-push + composite-carried-state + flash-composite with documented efficient alternatives. - Adds verification plan. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
This commit is contained in:
@@ -0,0 +1,576 @@
|
||||
# ADR-0060: AHBM GQA Fused Attention Kernel (Llama3-70B)
|
||||
|
||||
## Status
|
||||
|
||||
Proposed
|
||||
|
||||
**Context model:** Llama3-70B.
|
||||
**Decision drivers:** agentic workload → low batch, long context;
|
||||
KV-load-bound decode; sequence-parallel (Ring KV) for long-context prefill.
|
||||
|
||||
**Supersedes / extends:** the existing mesh-native attention kernels
|
||||
`_attention_mesh_kv` (prefill) and `_attention_mesh_mlo` (decode) and the
|
||||
`milestone-gqa-llama70b` eval bench. See *§A. Relationship to existing
|
||||
kernbench work* — that code is the baseline this ADR upgrades to a real
|
||||
GQA, causal, long-context kernel.
|
||||
|
||||
**Supporting ADRs (prerequisites):**
|
||||
- **ADR-0061** `tl.broadcast` — data-faithful GQA head reuse.
|
||||
- **ADR-0062** `tl.load_async` — non-blocking HBM tile load (KV prefetch).
|
||||
- **ADR-0063** `tl.scratch_scope` — per-tile scratch recycling.
|
||||
|
||||
**Algorithm lineage.** This kernel is **FlashAttention** (tiling +
|
||||
online/streaming softmax with fused P·V — no full score matrix
|
||||
materialised). The KV-parallel split-and-combine in §4 is
|
||||
**FlashDecoding** (split-KV with log-sum-exp merge). The Ring path in
|
||||
§5.5 is **Ring Attention** (KV blocks rotated around the mesh, folded by
|
||||
the same online softmax). No new math is introduced; this ADR maps those
|
||||
known algorithms onto the kernbench **greenlet `tl` programming model**
|
||||
(ADR-0020, ADR-0046) and the **IPCQ** PE↔PE collective (ADR-0023/0025).
|
||||
|
||||
---
|
||||
|
||||
## A. Relationship to existing kernbench work (read first)
|
||||
|
||||
kernbench **already runs FlashAttention with an online-softmax `(m, ℓ, O)`
|
||||
merge over IPCQ today.** Two kernels exist:
|
||||
|
||||
| File | Role | Mechanism |
|
||||
|---|---|---|
|
||||
| `src/kernbench/benches/_attention_mesh_kv.py` | prefill (Ring K/V) | per-rank partial attention, bidirectional K/V fan-out, online-softmax fold |
|
||||
| `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 |
|
||||
|
||||
Both are driven by `milestone-gqa-llama70b` (4 panels:
|
||||
`{single,multi}_user × {prefill,decode}`,
|
||||
`src/kernbench/benches/milestone_gqa_llama70b.py`) and tested in
|
||||
`tests/attention/test_milestone_gqa_llama70b.py`.
|
||||
|
||||
**They are written in the greenlet `tl` API — not composites:** `tl.load`,
|
||||
`tl.dot`, `tl.softmax`/`tl.max`/`tl.sum`/`tl.exp`, `tl.send`/`tl.recv`,
|
||||
and Python `-`/`*`/`/` on `TensorHandle` (each emits a `MathCmd`). The
|
||||
running `(m, ℓ, O)` is just Python `TensorHandle`s threaded through the
|
||||
loop. This **matters for this ADR's design** (see §1).
|
||||
|
||||
**Three deliberate limitations of the baseline** — exactly what an
|
||||
*efficient GQA* kernel must lift:
|
||||
|
||||
1. **No GQA reuse.** `h_q == h_kv == 1`
|
||||
(`test_milestone_gqa_llama70b.py:137-142`). Real GQA (`h_q = G·h_kv`)
|
||||
is blocked by the MemoryStore byte-conservation check on the symbolic
|
||||
broadcast view → fixed by **ADR-0061**.
|
||||
2. **O(N) reduction.** The baseline does an all-to-all bidirectional
|
||||
fan-out so *every* rank ends with the full answer (`n_ranks − 1`
|
||||
steps). Attention only needs `O` at the query owner → a **tree
|
||||
reduction** to root is `⌈log₂ N⌉` steps (§4).
|
||||
3. **Validation scale only.** `S = 16` because the 1 MiB scratch bump
|
||||
allocator leaks per-tile temporaries
|
||||
(`test_milestone_gqa_llama70b.py:123-148`) and there is no causal
|
||||
masking / tiling → fixed by **ADR-0063** (recycling) + §5 (tiling,
|
||||
causal skip) + **ADR-0062** (prefetch).
|
||||
|
||||
**Documentation debt (out of scope but recorded):** the baseline cites
|
||||
ADR-0055/0056/0057/0058/0059, **none of which exist as files** — they are
|
||||
ghost references. This ADR does not retro-write them; see the Detailed
|
||||
Design Document's *Open Decisions* for the recommendation.
|
||||
|
||||
---
|
||||
|
||||
## 0. Reference dimensions (Llama3-70B)
|
||||
|
||||
| Symbol | Meaning | Value |
|
||||
|---|---|---|
|
||||
| `H_q` | query heads | 64 |
|
||||
| `H_kv` | KV heads | 8 |
|
||||
| `G` | GQA group size = `H_q / H_kv` | 8 |
|
||||
| `d` | head dim | 128 |
|
||||
| `L` | layers | 80 |
|
||||
| `D` | model dim | 8192 |
|
||||
|
||||
Hardware recap:
|
||||
- **AHBM (chip)** = set of **CUBE**s (memory cubes, each with a logic die
|
||||
containing **PE**s) + **IO die** (ADR-0003).
|
||||
- **IPCQ**: PE↔PE queues, 4 mesh-direction queue-pairs per PE
|
||||
(`N/S/E/W`, ADR-0023 D3; `global_*` for inter-SIP, ADR-0032). Kernel
|
||||
API: `tl.send(dir, src)` / `tl.recv(dir, shape, dtype)`
|
||||
(`tl_context.py:402-499`).
|
||||
- **Composite command** (`CompositeCmd`, `pe_commands.py:144-162`): a
|
||||
single GEMM (or MATH) *head* plus element-wise *epilogue* stages
|
||||
(`bias/relu/scale/add/...`). It is **not** a general multi-op DAG: it
|
||||
cannot chain two GEMMs, cannot carry register state across instances,
|
||||
and cannot pop/wait on IPCQ. This ADR therefore does **not** require
|
||||
composites for the attention inner loop (see §1, §8).
|
||||
- **Allocation policy (SP):** one query head per CUBE in the multi-user
|
||||
panels; the `G` query heads of a KV group map within a CUBE's PEs.
|
||||
|
||||
**Two orthogonal mapping layers** (round-robin placement):
|
||||
- **Layer 1 (KV-parallel, intra-request):** KV token `i` lands on PE
|
||||
`(start_pe + i) mod N` — round-robin so one long request is split
|
||||
across `N` PEs, balanced to ≤1 token.
|
||||
- **Layer 2 (inter-request):** `start_pe = request_id mod N` rotates the
|
||||
"PE-0 role" per request.
|
||||
- Combined: `pe = (request_id + global_token_idx) mod N`.
|
||||
|
||||
`N` = number of PEs in the KV-parallel reduction group for one
|
||||
(query head, request). Reduction stays **intra-CUBE** (a query head never
|
||||
spans CUBEs — explicit non-goal).
|
||||
|
||||
---
|
||||
|
||||
## 0.5 Kernel boundary, preconditions, I/O contract
|
||||
|
||||
### 0.5.1 Position in the decoder layer
|
||||
|
||||
```
|
||||
1. RMSNorm
|
||||
2. QKV projection (GEMM) ─┐ qkv_rope kernel (SEPARATE, upstream)
|
||||
3. RoPE on Q and K ─┤
|
||||
4. write new K,V → KV cache ─┘
|
||||
5. ===== THIS KERNEL: FlashAttention ===== (post-RoPE Q, K-cache, V-cache → O)
|
||||
6. Output projection (GEMM) out_proj kernel (SEPARATE, downstream)
|
||||
7. residual add → FFN ...
|
||||
```
|
||||
|
||||
**Preconditions (upstream `qkv_rope`, NOT this kernel):**
|
||||
- **P1.** Q is already RoPE-rotated. No rotation here.
|
||||
- **P2.** K-cache stores **post-RoPE** K (never re-rotated at attention
|
||||
time — the reason for post-RoPE caching).
|
||||
- **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.** ⇒ this kernel is **pure read** on the KV cache.
|
||||
- **P4.** V is not rotated; V-cache holds raw projected V.
|
||||
- **P5.** RoPE position upstream is the token's **absolute global
|
||||
position** `global_idx = local_slot·N + ((pe_id − start_pe) mod N)`
|
||||
(§2.1). Round-robin placement ≠ RoPE position.
|
||||
|
||||
### 0.5.2 Shape symbols
|
||||
`T_q` = query length this launch (decode: 1; prefill/chunk: chunk width).
|
||||
`S` = total context length. `S_pe` = keys owned by this PE ≈ `⌈S/N⌉`.
|
||||
Storage `bf16` (numpy proxy `f16`, `memory_store.py:16`); `m,ℓ,O`
|
||||
accumulators `f32` where precision matters.
|
||||
|
||||
### 0.5.3 INPUTS (per kernel launch)
|
||||
|
||||
| Input | Shape (per KV head) | Location | Notes |
|
||||
|---|---|---|---|
|
||||
| `Q` | `[G, T_q, d]` | per-PE HBM (loaded to TCM) | post-RoPE (P1). `G` query rows of the group batched. |
|
||||
| `K_cache` | `[S_pe, d]` | per-PE HBM, base `K_base[pe]`, contiguous | post-RoPE (P2). Read-only. Dense locally, strided in global index (§2.1). |
|
||||
| `V_cache` | `[S_pe, d]` | per-PE HBM, base `V_base[pe]` | raw V (P4). Read-only. |
|
||||
| `global_token_counter` | scalar | launch arg | kernel derives `S_pe`, slot↔global, causal bounds. |
|
||||
| `start_pe` (= `request_id mod N`) | scalar | launch arg | Layer-2 rotation. |
|
||||
| `pe_id`, `N` | scalars | launch / `tl.program_id` | reduction-group geometry. |
|
||||
| `q_block_meta` `{q_start, T_q}` | launch arg | prefill/SP causal masking & skip. |
|
||||
| `O_base` | address | launch arg | where final O is written (root PE only). |
|
||||
| `softmax_scale` | scalar | launch arg | `1/√d`. |
|
||||
|
||||
For **Ring Attention (§5.5)** add per ring step: incoming `K_block,
|
||||
V_block` via IPCQ into ping-pong buffers (post-RoPE), plus
|
||||
`step_kv_global_range` for the causal step-skip.
|
||||
|
||||
### 0.5.4 OUTPUTS
|
||||
|
||||
| Output | Shape | Location | Notes |
|
||||
|---|---|---|---|
|
||||
| `O` (final) | `[G, T_q, d]` | `O_base`, **root PE only** | normalised `O_acc / ℓ_acc`, cast to storage dtype. |
|
||||
|
||||
**Intermediate (non-root PE, on IPCQ — not a kernel-visible output):**
|
||||
`(m_i, ℓ_i, O_i)` (`m,ℓ`: `[G, T_q]`; `O_i`: `[G, T_q, d]` unnormalised)
|
||||
pushed to `tree_parent` via `tl.send` (§4). `O_i` is the heavy part.
|
||||
|
||||
**No-SP (`N=1`):** no IPCQ partials; the single PE's running `(m,ℓ,O)` is
|
||||
normalised in place and written to `O_base`.
|
||||
|
||||
**Explicitly NOT outputs:** KV-cache writes (upstream, P3), output
|
||||
projection (downstream), score `S` and probs `P` (never materialised).
|
||||
|
||||
---
|
||||
|
||||
## 1. Decision (mechanism)
|
||||
|
||||
**Implement the kernel in the greenlet `tl` programming model, not as
|
||||
composites.** Rationale grounded in kernbench's execution + latency model:
|
||||
|
||||
- The simulator charges latency **per op on its modelled component**
|
||||
(GEMM on PE_GEMM, vector ops on PE_MATH, DMA on PE_DMA — `pe_gemm.py`,
|
||||
`pe_math.py`, `pe_dma.py`). "Fusing" several ops into one
|
||||
`CompositeCmd` does **not** reduce modelled latency; the stages still
|
||||
run on the same engines. The win a real fused kernel gets — *overlap*
|
||||
(prefetch) and *small working set* (scratch recycling) — is obtained
|
||||
here by **ADR-0062** and **ADR-0063**, which are smaller and reusable.
|
||||
- The running `(m, ℓ, O)` flash state is naturally a set of Python
|
||||
`TensorHandle`s threaded through the loop (the baseline already does
|
||||
this). No composite-carried register state is needed.
|
||||
- Cross-PE combination uses kernel-level `tl.send`/`tl.recv` (the
|
||||
baseline already does this and it is tested). No composite-driven IPCQ
|
||||
push is needed.
|
||||
|
||||
So the per-tile inner pipeline is the op sequence
|
||||
|
||||
```
|
||||
K_j load → Q·Kⱼᵀ → (+ causal mask) → online-softmax update → V_j load → P·Vⱼ → running (m,ℓ,O) update
|
||||
```
|
||||
|
||||
issued as ordinary `tl.*` ops, with the **next** tile's K/V issued via
|
||||
`tl.load_async` (ADR-0062) so its DMA overlaps the current tile's
|
||||
compute, and each tile's temporaries wrapped in `tl.scratch_scope`
|
||||
(ADR-0063) so scratch stays O(1).
|
||||
|
||||
Cross-PE combination (KV-parallel / SP) is a **log-sum-exp tree
|
||||
reduction** over `(m, ℓ, O)` after P·V, flowed through IPCQ with
|
||||
kernel-level `tl.send`/`tl.recv` (§4).
|
||||
|
||||
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).
|
||||
|
||||
> **Why this is the efficient choice.** It reuses the proven baseline
|
||||
> kernels and the proven IPCQ collective; the only genuinely new
|
||||
> machinery is three small, general primitives (ADR-0061/0062/0063). The
|
||||
> originally-proposed "everything-in-one-composite + composite IPCQ push
|
||||
> + composite-carried state" would require a bespoke flash-composite
|
||||
> command type, a register-lifetime model across composites, and an
|
||||
> IPCQ-push epilogue — large, special-purpose, and with no latency
|
||||
> benefit over the greenlet path. Those are **rejected**; see §8.
|
||||
|
||||
---
|
||||
|
||||
## 2. Memory layout & driver responsibilities
|
||||
|
||||
### 2.1 KV cache allocation
|
||||
- Per-PE KV buffers sized to per-PE max context `⌈max_context / N⌉ × d ×
|
||||
dtype`, for K and V separately. In kernbench these are deployed with a
|
||||
`DPPolicy` whose `pe` (or `cube`) axis is `row_wise` over the sequence
|
||||
dimension (`policy/placement/dp.py`; the baseline uses
|
||||
`DPPolicy(pe="row_wise")` for K/V and `replicate` for Q).
|
||||
- Within a PE, assigned tokens are **appended contiguously** (slot
|
||||
0,1,2,…). Global indices are strided (`i, i+N, …`) but the per-PE
|
||||
buffer is dense ⇒ DMA stays contiguous.
|
||||
- Slot → global: `global_idx = local_slot·N + ((pe_id − start_pe) mod N)`.
|
||||
|
||||
### 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[pe]`, `V_base[pe]` | per-PE KV buffer bases (from tensor VA) |
|
||||
| `O_base` | attention output destination |
|
||||
| `global_token_counter` | current sequence position |
|
||||
| `start_pe = request_id mod N` | Layer-2 rotation |
|
||||
| `pe_id` (`tl.program_id`), `N` | geometry |
|
||||
| `q_block_meta` | prefill/SP query block start + length |
|
||||
|
||||
Kernel-derived (plain arithmetic):
|
||||
- **my turn to write this step:** `(start_pe + counter) mod N == pe_id`
|
||||
- **my valid length:** `base = counter // N; rem = counter % N;
|
||||
my_len = base + (1 if ((pe_id − start_pe) mod N) < 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 N` is the entire placement policy.
|
||||
|
||||
---
|
||||
|
||||
## 3. Per-tile op sequence (greenlet `tl`)
|
||||
|
||||
One iteration = one KV tile on one PE. Real `tl` names (`tl_context.py`),
|
||||
with `tl.load_async` (ADR-0062), `tl.broadcast` (ADR-0061),
|
||||
`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]
|
||||
|
||||
with tl.scratch_scope(): # per-tile temporaries recycled
|
||||
Kj = tl.wait(f_k[j]) # prefetched (ADR-0062)
|
||||
Sj = tl.dot(q_g, tl.trans(Kj)) * softmax_scale # [G, TILE]; q_g is GQA-batched
|
||||
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)
|
||||
Vj = tl.wait(f_v[j])
|
||||
O = O * corr + tl.dot(P, Vj) # P·V folded into running O
|
||||
m = m_new
|
||||
if j + PREFETCH < n_tiles: # keep the pipeline full
|
||||
f_k[j + PREFETCH] = tl.load_async(K_base + (j+PREFETCH)*TILE*d, (TILE, d))
|
||||
f_v[j + PREFETCH] = tl.load_async(V_base + (j+PREFETCH)*TILE*d, (TILE, d))
|
||||
```
|
||||
|
||||
Notes:
|
||||
- `q_g` is the GQA-batched query: `[G, d]` (decode) or `[G, T_q, d]`
|
||||
(prefill). One K/V tile load serves all `G` rows — the GQA reuse lever.
|
||||
Where the matmul needs the KV head expanded to `G`, use
|
||||
`tl.broadcast` (ADR-0061), never the symbolic `_view`.
|
||||
- The V load is issued (prefetched) *before* it is needed so its DMA
|
||||
overlaps the Q·Kᵀ + softmax of the same/earlier tile.
|
||||
- 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).
|
||||
- No `CompositeCmd` is used. (A future `tl.composite` flash kind is an
|
||||
*optional* optimisation — §8 item 4 — not a requirement.)
|
||||
|
||||
---
|
||||
|
||||
## 4. Reduction (KV-parallel / SP combine) — tree to root
|
||||
|
||||
After its tile sweep each PE holds `(m_i, ℓ_i, O_i)` (unnormalised).
|
||||
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 tree root
|
||||
```
|
||||
|
||||
- **Timing:** exchange is **after P·V** (each PE has its final `O_i`).
|
||||
- **Topology:** mesh tree, depth `⌈log₂ N⌉`, over physical neighbours.
|
||||
For `N=8`: level-0 `(0↔1,2↔3,4↔5,6↔7)`, level-1 `(1↔3,5↔7)`, level-2
|
||||
`(3↔7)`, root = `7`. Each tree pair must be a physical `N/S/E/W`
|
||||
neighbour so `tl.send(dir)`/`tl.recv(dir)` use a real link
|
||||
(tuning item §9; the SFR install that wires neighbours is
|
||||
`configure_sfr_intracube_pe_ring`, used by the baseline).
|
||||
- **Why a tree, not the baseline fan-out:** attention needs `O` at one
|
||||
place (the PE that writes `O_base`). A tree is `⌈log₂ N⌉` steps
|
||||
(3 for `N=8`) vs the baseline all-to-all's `N−1` (7). For decode, where
|
||||
the per-PE sweep is short, the reduction is the dominant cost, so this
|
||||
is a real win. (The baseline's fan-out replicates the answer to all
|
||||
ranks — unnecessary here.)
|
||||
- **Payload:** `O_i` (`d`-vector) is heavy; `m,ℓ` are scalars per `(g,
|
||||
T_q)`. Sent as separate `tl.send`s (the baseline sends the triplet as
|
||||
three sends — same pattern).
|
||||
|
||||
Kernel structure (greenlet):
|
||||
|
||||
```python
|
||||
# leaf / internal node: fold children that send to me, then forward up
|
||||
for child_dir in tree_children_dirs(pe_id, N):
|
||||
m_c = tl.recv(child_dir, m.shape); l_c = tl.recv(child_dir, l.shape)
|
||||
O_c = tl.recv(child_dir, O.shape)
|
||||
m, l, O = merge(m, l, O, m_c, l_c, O_c)
|
||||
if not is_root(pe_id):
|
||||
tl.send(parent_dir(pe_id), m); tl.send(parent_dir(pe_id), l); tl.send(parent_dir(pe_id), O)
|
||||
else:
|
||||
tl.store(O_base, O / l)
|
||||
```
|
||||
|
||||
`tl.recv` blocks until data arrives (`tl_context.py:446-499`); the merge
|
||||
order is fixed by the static tree, so no data-dependent `pop` is needed —
|
||||
which is why **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 `N`, 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, N,
|
||||
q_block, softmax_scale, *, tl):
|
||||
pe_id = tl.program_id(axis=rank_axis)
|
||||
my_len = valid_len(counter, start_pe, pe_id, N)
|
||||
n_tiles = ceil(my_len / TILE)
|
||||
q_g = load_Q_group(q_ptr) # [G,d] decode / [G,T_q,d] prefill; tl.broadcast for GQA
|
||||
m, l, O = init_running() # persistent arena: -inf, 0, zeros
|
||||
prime_prefetch(k_ptr, v_ptr, n_tiles) # tl.load_async first PREFETCH tiles
|
||||
for j in range(n_tiles):
|
||||
if tile_all_future(j, q_block): # causal skip (kernel if)
|
||||
continue
|
||||
run_tile(j, mask_or_null(j, q_block)) # §3 op sequence, in tl.scratch_scope
|
||||
if N == 1:
|
||||
tl.store(o_ptr, O / l) # no reduction
|
||||
else:
|
||||
tree_reduce_and_store(pe_id, N, o_ptr) # §4
|
||||
```
|
||||
|
||||
### 5.2 DECODE, no SP (`N=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 batched (`q_g = [G, d]`), so each K/V
|
||||
tile is loaded once and reused by 8 rows (Q·Kᵀ is an `8×TILE` batched
|
||||
GEMV, P·V an `8×d`). `tl.broadcast` expands the KV head where needed.
|
||||
- If `S_pe` fits in scratch (small/medium context) this degenerates to a
|
||||
**one-shot** partial attention (one `tl.dot` for Q·Kᵀ, one softmax, one
|
||||
`tl.dot` for P·V) — exactly the baseline `_attention_mesh_mlo`
|
||||
`_partial_attention`, just GQA-batched. Tiling (§3) only kicks in when
|
||||
`S_pe` exceeds the scratch scope's tile budget.
|
||||
|
||||
### 5.3 DECODE, with SP / KV-parallel (`N=8`)
|
||||
- One request's KV is round-robin across 8 PEs; each owns ≈`my_len`
|
||||
tokens. Each PE GQA-batches its `G=8` query rows over its local tiles.
|
||||
- `T_q=1` ⇒ short per-PE sweep ⇒ **reduction dominates** ⇒ the §4 tree
|
||||
(3 steps) is the structurally important part. Reduction latency is
|
||||
hidden by other concurrent decode tokens when batched, or by the long
|
||||
per-PE tile sweep for long single-stream context.
|
||||
|
||||
### 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 `N` PEs (the ring); 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).
|
||||
- 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 query-owning PE; if KV-parallel
|
||||
splits remain, §4 tree-merges them, then normalise + store.
|
||||
|
||||
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 PE
|
||||
has many KV tiles; the §4 tree's `tl.recv` blocks are covered by the
|
||||
sweep work of concurrent tokens / long context.
|
||||
- The §4 reduction uses a **static** tree, 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 tree) |
|
||||
| K/V load, Q·Kᵀ, mask add, softmax math, P·V | **`tl` ops** on PE engines |
|
||||
|
||||
The kernel decides; the `tl` ops execute already-decided work. This is
|
||||
exactly the greenlet model kernbench already supports — no new control
|
||||
abstraction.
|
||||
|
||||
---
|
||||
|
||||
## 8. Required kernbench changes
|
||||
|
||||
**Genuinely new (each has a supporting ADR):**
|
||||
|
||||
1. **GQA head broadcast in data mode** — **ADR-0061** (`tl.broadcast`).
|
||||
*The* blocker for `h_q > h_kv`. Without it the GQA reuse lever cannot
|
||||
run under `enable_data=True`.
|
||||
2. **Async HBM tile load / KV prefetch** — **ADR-0062** (`tl.load_async`
|
||||
+ `tl.wait`). The KV-load-bound overlap lever for decode/long-context.
|
||||
3. **Per-tile scratch recycling** — **ADR-0063** (`tl.scratch_scope`).
|
||||
Removes the `S=16` ceiling so realistic context lengths run.
|
||||
|
||||
**Algorithm work in the kernel (no new primitive; existing `tl` API):**
|
||||
|
||||
- GQA Q-axis batching (`G` rows share each K/V load) — `tl.broadcast` +
|
||||
2-D `tl.dot`.
|
||||
- Tree reduction to root (§4) replacing the baseline all-to-all fan-out —
|
||||
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 `+`.
|
||||
- Round-robin KV placement / valid-length arithmetic (§2) — launch-arg
|
||||
arithmetic + `DPPolicy(pe="row_wise")`.
|
||||
|
||||
**Explicitly REJECTED (efficient alternative chosen):**
|
||||
|
||||
4. ~~Composite that chains DMA→MM→VEC→DMA→MM→VEC with carried `(m,ℓ,O)`
|
||||
register state and a tail IPCQ push.~~ Replaced by the greenlet path:
|
||||
per-op latency is identical; overlap comes from ADR-0062; small
|
||||
working set from ADR-0063; running state is Python handles; the IPCQ
|
||||
push is `tl.send`. Building a bespoke flash-composite command type +
|
||||
cross-composite register lifetime + an IPCQ-push epilogue is large,
|
||||
special-purpose, and yields no latency benefit. **A tiled
|
||||
`tl.composite` "flash" kind remains a possible *future* optimisation**
|
||||
(it would fold prefetch+recycling into the scheduler), but it is not
|
||||
required for an efficient kernel and is out of scope here.
|
||||
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. **KV tile prefetch depth** (ADR-0062) — dominant for KV-load-bound;
|
||||
sweep 2→4.
|
||||
2. **PE↔mesh-neighbour mapping for the reduction tree** — ensure each
|
||||
depth-`⌈log₂ N⌉` pair is a physical `N/S/E/W` neighbour; bad mapping
|
||||
adds hops. Verify against the SFR install.
|
||||
3. **TILE size** — balance scratch residency (`K_tile + S/P + V_tile +
|
||||
O_acc + G-way GQA`) against DMA efficiency; interacts with ADR-0063.
|
||||
4. **Ring buffer ping-pong vs `recv_async` depth** in §5.5.
|
||||
5. **One-shot vs tiled crossover for decode** (§5.2) — the `S_pe`
|
||||
threshold where tiling beats a single `tl.dot`.
|
||||
|
||||
---
|
||||
|
||||
## 10. Coverage summary
|
||||
|
||||
| Case | KV placement | Inner loop | Reduction | Masking |
|
||||
|---|---|---|---|---|
|
||||
| Decode, no SP | 1 PE, all KV | one-shot or tiled, GQA-batched | none | last tile only |
|
||||
| Decode, SP | round-robin `N` PEs | tiled, GQA-batched | §4 tree (`⌈log₂ N⌉`) | last tile only |
|
||||
| Prefill, no SP | resident | tiled per q-block | none | skip future / triangular boundary |
|
||||
| Prefill, SP (Ring) | ring-sharded | per ring step (recv_async overlap) | running state + §4 tree | 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 PE | `O[G,1,d]` at `O_base` | none |
|
||||
| Decode, SP | `Q[G,1,d]`, per-PE `K/V[S_pe,d]` | `O[G,1,d]` (root) | `(m,ℓ,O_i)` per PE → tree |
|
||||
| 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]` (root) | running `(m,ℓ,O)` + tree |
|
||||
|
||||
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).
|
||||
|
||||
1. **Correctness vs reference (Phase 2, `enable_data=True`):** for a
|
||||
small `(G, T_q, S, N)`, kernel `O` matches a numpy FlashAttention
|
||||
reference within fp tolerance — for all four cases. This is the test
|
||||
that *requires* ADR-0061 (GQA) to even run with `h_q>h_kv`.
|
||||
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. **Tree reduction step count:** decode-SP issues `⌈log₂ N⌉` reduction
|
||||
rounds (not `N−1`); op_log `ipcq_send`/`recv` counts match the tree.
|
||||
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. **Prefetch overlap (ADR-0062):** end-to-end latency of the tiled sweep
|
||||
is below the serial `Σ(load+compute)` (overlap is real).
|
||||
7. **Determinism:** identical inputs → identical op_log + latency
|
||||
(SPEC §0.1).
|
||||
Reference in New Issue
Block a user