gqa(adr): add supporting feature ADRs 0061-0063 for GQA fused attention
Proposed prerequisites surfaced while evaluating the GQA fused-attention ADR against the actual kernbench tl/sim_engine implementation: - ADR-0061 tl.broadcast: data-faithful GQA head reuse (fixes the MemoryStore nbytes check that forces h_q==h_kv==1 today). - ADR-0062 tl.load_async: non-blocking HBM tile load for KV prefetch (KV-load-bound decode/long-context overlap). - ADR-0063 tl.scratch_scope: per-tile scratch recycling (removes the 1 MiB bump-allocator ceiling that caps context at S=16). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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# ADR-0061: `tl.broadcast` / `tl.repeat` — data-faithful GQA head reuse
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## Status
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Proposed
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> Supporting ADR for **ADR-0060** (AHBM GQA Fused Attention). This ADR
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> removes the single hard blocker that keeps the existing attention
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> kernels at `h_q == h_kv == 1`: there is no way to expand a KV head to
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> the `G` query heads that share it without violating the MemoryStore
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> byte-conservation check.
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## Context
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### The GQA reuse lever
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Grouped-Query Attention amortises K/V loads: `G = H_q / H_kv` query heads
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share one KV head. For Llama3-70B `G = 8`. In a kernel this means a KV
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tile of shape `[S_kv, d]` (for one KV head) must be matmul'd against the
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`G` query rows of its group. Loading K/V **once** and reusing it across
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the group is the decode-time efficiency win (ADR-0060 §5.2).
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### Why it does not work today
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The existing mesh kernels reshape tensors with a metadata-only helper
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`_view` (`src/kernbench/benches/_attention_mesh_kv.py:25-36`,
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`_attention_mesh_mlo.py:25-36`):
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```python
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def _view(handle, new_shape):
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return TensorHandle(..., shape=new_shape, nbytes=handle.nbytes, ...)
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# ^^^ new shape ^^^ OLD nbytes (unchanged)
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```
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This rewrites `shape` but keeps the original `nbytes`. It is purely
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symbolic. It survives **Phase 1 (timing)** because Phase 1 never reads
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tensor bytes. It fails **Phase 2 (`enable_data=True`)** because the
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data executor materialises every op through `MemoryStore.read`, which
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enforces byte conservation
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(`src/kernbench/sim_engine/memory_store.py:67-73`):
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```python
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if shape is not None and arr.shape != shape:
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if arr.nbytes != np.prod(shape) * arr.dtype.itemsize:
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raise ValueError(f"Shape mismatch: stored {arr.shape} ({arr.nbytes}B) "
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f"vs requested {shape} ({...}B)")
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arr = arr.reshape(shape)
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```
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A reshape conserves bytes; a **broadcast does not** (`[S_kv, 1, d]` →
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`[S_kv, G, d]` multiplies the element count by `G`). So the moment a
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kernel tries to view a `h_kv`-headed K as if it had `h_q` heads, the
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stored array has `1/G` of the requested bytes and `read` raises.
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The current test suite documents this as a deliberate limitation
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(`tests/attention/test_milestone_gqa_llama70b.py:137-142`):
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> "v1 uses `h_q == h_kv == 1` to avoid … GQA broadcast view (which is
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> symbolic and does not survive MemoryStore's nbytes check under
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> simulator data execution). Real GQA (`h_q > h_kv`) is deferred."
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There is **no** broadcast / repeat / expand primitive in the `tl` API
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today (`src/kernbench/triton_emu/tl_context.py` exposes `trans`,
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`zeros`, `full`, `arange` as metadata-only ops; none replicate data).
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## Decision
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Add a **data-faithful broadcast op** to the `tl` API and the data
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executor. Unlike `_view`, it updates `nbytes` and, in Phase 2, actually
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replicates the underlying ndarray. The latency it charges models the
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on-chip register/SRAM fan-out, not an HBM re-load (the KV tile is read
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from HBM once; broadcast is an on-chip replication).
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### D1. `tl` surface
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```python
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def broadcast(self, x: TensorHandle, new_shape: tuple[int, ...]) -> TensorHandle:
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"""Replicate x along size-1 axes to new_shape (numpy broadcast rules).
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new_shape must be broadcast-compatible with x.shape: every axis is
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either equal to x's, or x's is 1 (the axis being expanded). nbytes of
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the result reflect new_shape (unlike the metadata-only _view).
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"""
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```
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`tl.repeat(x, axis, n)` is offered as a thin convenience wrapper over
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`broadcast` for the common "insert a group axis and expand it to G"
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pattern, but `broadcast` is the primitive.
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### D2. Command + op_log
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Emit a new `BroadcastCmd` (sibling of `MathCmd`):
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```python
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@dataclass(frozen=True)
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class BroadcastCmd:
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src: TensorHandle
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out: TensorHandle # out.nbytes reflects new_shape
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new_shape: tuple[int, ...]
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data_op: bool = True
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```
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op_log entry: `op_kind="math"`, `op_name="broadcast"` (it runs on the
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MATH/vector engine, like the other element-wise ops). The output handle
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is allocated from PE scratch via the existing `_make_compute_out`
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(`tl_context.py:148-156`), so `out.nbytes` is computed from `new_shape`
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and the byte-conservation check passes in Phase 2.
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### D3. Phase 2 data execution
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Add `_execute_broadcast` to the data executor
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(`src/kernbench/sim_engine/data_executor.py`):
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```python
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def _execute_broadcast(self, op):
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p = op.params
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src = self._resolve_read(p["src_space"], p["src_addr"],
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p["src_shape"], p["dtype"], op.t_start)
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result = np.broadcast_to(src, p["new_shape"]).copy() # .copy() → writable, owns bytes
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self.store.write(p["dst_space"], p["dst_addr"], result)
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```
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`.copy()` is required: `np.broadcast_to` returns a zero-stride view, and
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MemoryStore stores references — a later consumer's nbytes check must see
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a real `prod(new_shape)`-element array.
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### D4. Latency model
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Broadcast latency = vector-engine cost of writing `prod(new_shape)`
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elements (reuse `pe_math._compute_ns(num_elements)`,
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`src/kernbench/components/builtin/pe_math.py:46-51`). This models the
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on-chip replication into the register file / SRAM that real GQA hardware
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performs. It is **not** an HBM transaction — no `dma_read` is logged,
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consistent with "K/V tile loaded once" (ADR-0060 §5.2).
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## Alternatives
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### A1. Indexed per-head dot (no broadcast op)
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Loop the `G` query heads, each doing its own `tl.dot(Q_g, Kᵀ)` against
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the *same* (re-`ref`'d, not re-loaded) KV tile:
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```python
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for g in range(G):
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s_g = tl.dot(Q[g], K_T) # G separate GEMMs, KV reused via tl.ref
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```
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- Pro: no new primitive; works in Phase 2 today.
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- Con: `G×` more GEMM commands (op_log inflation), no batched-GEMM
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modelling, and the per-call overhead is charged `G` times. It also
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obscures the "single batched matmul over the group" the hardware does.
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Rejected as the primary path; acceptable as a fallback if the batched
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2D dot's shape handling proves awkward.
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### A2. Keep `_view`, relax the nbytes check
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Make `MemoryStore.read` tolerate broadcast (stride-0) shapes. Rejected:
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it silently breaks byte conservation for *all* readers and would mask
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genuine shape bugs elsewhere. The check is a correctness invariant
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(ADR-0052), not an obstacle to route around.
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### A3. Materialise the broadcast at deploy time
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Store K/V already expanded to `h_q` heads in HBM. Rejected: defeats the
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entire point of GQA (it `G×`'s KV-cache HBM footprint and bandwidth).
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## Consequences
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### Positive
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- Unblocks real GQA (`h_q > h_kv`) in Phase 2 — the headline blocker for
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ADR-0060 and the deferred milestone work.
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- A general, reusable primitive (any kernel needing numpy-style
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broadcast benefits, not just attention).
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- Byte conservation stays intact; no weakening of MemoryStore.
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### Negative
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- One new command type + one data-executor handler + one `tl` method.
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- Phase 2 `.copy()` allocates the expanded array in the store; for very
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large groups this is real memory. Bounded by tile size in the tiled
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design (ADR-0060), so acceptable.
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## Test Requirements
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1. **Phase 1 timing**: `tl.broadcast([S,1,d] → [S,G,d])` logs one
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`math/broadcast` op with `num_elements == S*G*d`; no `dma_read`.
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2. **Phase 2 data**: broadcasting a known array yields the numpy
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`broadcast_to(...).copy()` result; a downstream `tl.dot` consuming it
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passes the MemoryStore nbytes check (the exact case that fails today).
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3. **GQA end-to-end**: re-run a reduced `milestone-gqa-llama70b` panel
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with `h_q=8, h_kv=1` under `enable_data=True`; it must complete (today
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it raises `ValueError: Shape mismatch`).
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4. **Broadcast-incompatible shape** (e.g. axis size 3 → 8) raises a
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clear error at emit time, not deep in Phase 2.
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# ADR-0062: `tl.load_async` — non-blocking HBM tile load for KV prefetch
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## Status
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Proposed
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> Supporting ADR for **ADR-0060** (AHBM GQA Fused Attention). Decode and
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> long-context attention are **KV-load-bound**; the dominant lever is
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> overlapping the next K/V tile's DMA with the current tile's compute.
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> Today the only async primitive is for IPCQ comms, not for HBM loads.
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## Context
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### What overlap requires
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FlashAttention streams K/V tiles: while the MATH/GEMM engine works on
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tile `j` (Q·Kⱼᵀ → softmax → P·V), the DMA engine should already be
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pulling tile `j+1` (and `j+2` at prefetch depth 2). With that overlap a
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KV-load-bound kernel runs at roughly `max(compute, dma)` per tile instead
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of `compute + dma`.
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### What exists
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- `tl.load(ptr, shape, dtype)` is **blocking**: it emits `DmaReadCmd`
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and the greenlet kernel suspends until PE_DMA signals completion
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(`src/kernbench/triton_emu/tl_context.py:177-203`; greenlet drive in
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`kernel_runner.py`). No two `tl.load`s can be in flight from one
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kernel.
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- An async pattern **does** exist, but only for IPCQ:
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`tl.recv_async(dir, ...) -> RecvFuture` + `tl.wait(future)`
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(`tl_context.py:543-560`, `tl_context.py:660-693`). It proves the
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machinery — a non-blocking command that returns a future, resolved
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later by `tl.wait` — works in the greenlet model.
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- The DMA engine already models a read channel as a SimPy resource
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separate from the write channel
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(`src/kernbench/components/builtin/pe_dma.py`), so concurrent in-flight
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reads are representable at the component layer; only the *kernel-facing
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API* serialises them.
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There is **no** `tl.load_async`. Prefetch/double-buffering cannot be
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expressed.
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## Decision
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Add a non-blocking HBM load that mirrors the existing `recv_async`/`wait`
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contract.
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### D1. `tl` surface
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```python
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def load_async(self, ptr: int, shape: tuple[int, ...],
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dtype: str = "f16") -> LoadFuture:
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"""Issue a DMA read and return immediately. Resolve with tl.wait(fut)
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-> TensorHandle. Multiple loads may be in flight; ordering of
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resolution is by tl.wait calls, not issue order."""
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```
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`tl.wait` is extended to accept a `LoadFuture` (it already dispatches on
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`CompletionHandle` vs `RecvFuture` — add a third arm), returning the
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loaded `TensorHandle`.
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### D2. Command
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Reuse the existing `DmaReadCmd` with a `blocking=False` flag (mirroring
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`IpcqRecvCmd.blocking`), or a thin `DmaReadAsyncCmd` sibling — whichever
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keeps PE_DMA's handler simplest. The op_log entry is unchanged
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(`memory/dma_read`); asynchrony is a scheduling property, not a new op
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kind, so existing op_log consumers and the `dma_read_count` metric
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(milestone bench) keep working.
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### D3. Latency / overlap semantics
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- `load_async` charges the **issue** (descriptor push) only; the kernel
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proceeds.
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- The DMA transfer occupies the read channel for its modelled duration
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in parallel with whatever compute the kernel issues next.
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- `tl.wait(fut)` blocks only if the transfer has not finished; if it has,
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it returns immediately (same fast-path as `recv_async`/`wait`).
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- Determinism is preserved: completion is a scheduled event on the
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modelled DMA channel/link (SPEC §0.1, R8). No magic — the overlap is
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real modelled concurrency, not a hand-waved latency subtraction.
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### D4. Double-buffer usage (the intended pattern)
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```python
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# Prime: issue first two tile loads.
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f0 = tl.load_async(K_base + 0*tile_bytes, (TILE, d))
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f1 = tl.load_async(K_base + 1*tile_bytes, (TILE, d))
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for j in range(n_tiles):
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Kj = tl.wait(f_cur) # resolves the j-th load
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if j + 2 < n_tiles: # keep depth-2 pipeline full
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f_next2 = tl.load_async(K_base + (j+2)*tile_bytes, (TILE, d))
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s = tl.dot(q, tl.trans(Kj)) # compute overlaps f_{j+1}'s DMA
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...
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```
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(V tiles double-buffer the same way; ADR-0060 §3 schedules the V load so
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it overlaps the Q·Kᵀ + softmax of the same tile.)
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## Alternatives
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### A1. Use a tiled `tl.composite` instead
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`tl.composite` already pipelines tiles inside PE_SCHEDULER (DMA of tile
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`j+1` overlaps compute of tile `j`) and recycles per-tile scratch — so
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the composite path gets prefetch "for free." Rejected as the *sole*
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mechanism because today's composite is a single GEMM head + math
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epilogues (`pe_commands.py:144-162`); it cannot express the
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two-GEMM-with-running-`(m,l,O)` flash inner loop. Building a bespoke
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"flash composite" kind is a much larger change than `load_async`, which
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is a small, general primitive that also helps non-composite kernels.
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`load_async` and a future flash-composite are not mutually exclusive
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(ADR-0060 §8).
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### A2. Rely on `recv_async` only (no HBM async)
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Only works for the Ring-Attention path where KV arrives over IPCQ
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(comms, already overlappable). The decode and no-SP paths load KV from
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**local HBM**, which has no async path today. Insufficient.
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## Consequences
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### Positive
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- Enables the headline KV-load-bound optimisation for decode and
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long-context, in the proven greenlet model.
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- Symmetric with `recv_async`/`wait` — low conceptual surface area.
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- General: any bandwidth-bound kernel can prefetch.
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### Negative
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- Kernels must manage futures and buffer lifetimes explicitly (the
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double-buffer dance). Mitigated by keeping the pattern in a shared
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helper used by all four ADR-0060 cases.
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- Interacts with scratch lifetime (ADR-0063): in-flight buffers must not
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be recycled before their `tl.wait`. The recycling scope must exclude
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live prefetch buffers.
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## Test Requirements
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1. **Overlap is real**: a kernel that issues `load_async` then a GEMM of
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comparable duration completes in ≈`max(load, gemm)`, not `load+gemm`
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(assert end-to-end latency strictly below the serial sum).
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2. **Correctness**: `tl.wait(load_async(...))` returns the same bytes as
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blocking `tl.load(...)` for the same address (Phase 2).
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3. **Two in flight**: two outstanding `load_async`s to distinct
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addresses both resolve to correct, independent tensors.
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4. **op_log compatibility**: each `load_async` still logs exactly one
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`memory/dma_read`, so `dma_read_count` metrics are unchanged vs the
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blocking version.
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@@ -0,0 +1,162 @@
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# ADR-0063: `tl.scratch_scope` — per-tile scratch recycling for long context
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## Status
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Proposed
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> Supporting ADR for **ADR-0060** (AHBM GQA Fused Attention). Long-context
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> attention sweeps many K/V tiles; each tile's intermediates
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> (`scores`, `P`, `exp`, partial `O`, …) currently allocate fresh
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> scratch that is never freed within a kernel invocation. The 1 MiB
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> per-PE scratch budget is exhausted long before a realistic context
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> length.
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## Context
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### The bump allocator
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`TLContext` allocates every math/compute output handle from a per-PE
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scratch pool with a **linear bump cursor**
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(`src/kernbench/triton_emu/tl_context.py`; `_scratch_alloc`):
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```python
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def _scratch_alloc(self, nbytes):
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aligned = (nbytes + 15) & ~15
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addr = self._scratch_base + self._scratch_cursor
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self._scratch_cursor += aligned
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if self._scratch_cursor > self._scratch_size: # default 1 << 20 = 1 MiB
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raise RuntimeError("TLContext scratch overflow: ...")
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return addr
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```
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The docstring states the cursor **"resets on every kernel invocation"** —
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i.e. only at kernel entry, never *within* the kernel body. Every
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`tl.dot`, `tl.softmax`, `tl.exp`, `tl.sum`, `a - b`, `a * b`, … grabs a
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fresh slice and nothing is reclaimed until the kernel returns.
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### Why this bites the GQA kernel
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The current milestone bench keeps `S_q = S_kv_per_rank = 16` **explicitly
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because** of this limit
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(`tests/attention/test_milestone_gqa_llama70b.py:123-148`):
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> "S_q_prefill and S_kv_per_rank are deliberately small (16 each) so the
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> simulator's 1 MB per-PE TCM kernel scratch is not exhausted by the
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> bump-allocated handle outputs of softmax/exp/dot/sum chains over
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> n_ranks ring steps."
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A FlashAttention sweep over `n_tiles` tiles allocates O(`n_tiles` ×
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per-tile-intermediates). For any realistic context this overflows. The
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*math* of flash attention needs only **O(1)** live scratch — the running
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`(m, l, O)` plus the current tile's working set — because each tile's
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temporaries are dead once that tile is folded into the running state. The
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allocator just doesn't know they're dead.
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## Decision
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Add a **scratch scope** that lets a kernel mark a region of allocations
|
||||
as reclaimable, so per-tile temporaries are recycled while live running
|
||||
state (and in-flight prefetch buffers) are preserved.
|
||||
|
||||
### D1. `tl` surface — context manager
|
||||
|
||||
```python
|
||||
with tl.scratch_scope():
|
||||
s = tl.dot(q, k_t) # all handles allocated inside the `with`
|
||||
p = tl.softmax(s) # share a region that is rewound on exit
|
||||
o_j = tl.dot(p, v)
|
||||
# ... fold o_j into running (m,l,O) which live OUTSIDE the scope ...
|
||||
# on __exit__: cursor rewinds to its value at __enter__
|
||||
```
|
||||
|
||||
Semantics:
|
||||
- `__enter__` records the current `_scratch_cursor` as a save-point.
|
||||
- `__exit__` restores the cursor to the save-point, freeing everything
|
||||
allocated inside.
|
||||
- Handles allocated **outside** the scope (running `m,l,O`, prefetch
|
||||
buffers held by `LoadFuture` from ADR-0062) keep their addresses —
|
||||
they were allocated before the save-point or in an enclosing scope.
|
||||
|
||||
### D2. Safety contract
|
||||
|
||||
A handle allocated inside a scope **must not** be read after the scope
|
||||
exits — its bytes may be overwritten by the next scope's allocations.
|
||||
The flash loop respects this naturally: the only values that survive a
|
||||
tile iteration are the running accumulators, which are allocated outside
|
||||
the per-tile scope and updated by ops *inside* it writing to outside
|
||||
addresses (the merge writes new running state — see D3).
|
||||
|
||||
### D3. Interaction with running accumulators
|
||||
|
||||
The online-softmax merge reads the old running `(m, l, O)` and the
|
||||
current tile's `(m_j, l_j, O_j)`, producing new running values. To keep
|
||||
the new running values outside the recycled region, the merge writes them
|
||||
to **stable scratch** allocated once before the loop (a small fixed
|
||||
"running-state" arena, distinct from the per-tile scope). Concretely the
|
||||
kernel keeps two arenas:
|
||||
|
||||
- **persistent arena** (allocated once): `m, l, O` (and their
|
||||
double-buffer if needed for the merge).
|
||||
- **scoped arena** (rewound each tile): `scores, P, exp, O_j, scale_*`.
|
||||
|
||||
This mirrors how real flash-attention SRAM budgeting works: a small
|
||||
persistent accumulator region + a recycled tile working set.
|
||||
|
||||
### D4. Nesting
|
||||
|
||||
Scopes nest (stack of save-points). Inner scope exit rewinds to the inner
|
||||
save-point; outer exit rewinds further. This supports an outer
|
||||
"per-query-block" scope around an inner "per-KV-tile" scope for prefill.
|
||||
|
||||
## Alternatives
|
||||
|
||||
### A1. Round-trip temporaries through HBM
|
||||
|
||||
Store intermediates to HBM and reload to "free" TCM scratch. Rejected:
|
||||
turns a TCM-resident streaming kernel into an HBM-bandwidth-bound one —
|
||||
the opposite of the goal, and it pollutes op_log with spurious DMA.
|
||||
|
||||
### A2. Tiled `tl.composite` (scheduler-managed scratch)
|
||||
|
||||
`tl.composite` recycles per-tile scratch inside PE_SCHEDULER
|
||||
automatically. As in ADR-0062 A1, this is attractive but blocked on a
|
||||
flash-capable composite kind (two GEMMs + carried `(m,l,O)`), a much
|
||||
larger change. `scratch_scope` gives the same memory behaviour for the
|
||||
greenlet kernel with a tiny, general primitive. The two can coexist.
|
||||
|
||||
### A3. Grow the scratch budget
|
||||
|
||||
Bump `pe_tcm.kernel_scratch_mb`. Rejected as a fix: it only pushes the
|
||||
context-length ceiling out linearly while still leaking O(`n_tiles`)
|
||||
scratch, and it misrepresents the hardware (real TCM is small; the point
|
||||
of flash attention is O(1) working set). Useful only as a coarse knob,
|
||||
not a substitute for recycling.
|
||||
|
||||
## Consequences
|
||||
|
||||
### Positive
|
||||
- Removes the artificial `S = 16` validation-scale ceiling; enables
|
||||
realistic context lengths in both timing and data modes.
|
||||
- Faithfully models flash attention's O(1) working-set property.
|
||||
- Small, general primitive (any tiled kernel benefits).
|
||||
|
||||
### Negative
|
||||
- A use-after-scope bug silently reads stale bytes. Mitigated by the D2
|
||||
contract, by keeping the scope discipline inside a shared attention
|
||||
helper, and (optionally) by a debug build that poisons rewound regions.
|
||||
- Must coordinate with ADR-0062: prefetch buffers are live across tile
|
||||
iterations, so they belong to the persistent arena, not the scoped one.
|
||||
|
||||
## Test Requirements
|
||||
|
||||
1. **Recycling**: a loop of `N` tiles inside `tl.scratch_scope()` keeps
|
||||
peak `_scratch_cursor` bounded by one tile's footprint, independent of
|
||||
`N` (today it grows linearly and overflows).
|
||||
2. **Correctness**: a flash sweep with scopes produces the same `O` as
|
||||
the same sweep without scopes at a small `N` that fits without
|
||||
recycling (Phase 2).
|
||||
3. **Long context**: a sweep at an `N` that would overflow 1 MiB without
|
||||
scopes completes (the exact failure the `S=16` cap avoids today).
|
||||
4. **Persistent-vs-scoped isolation**: running `(m,l,O)` allocated
|
||||
outside the scope retains correct values across `__exit__`.
|
||||
5. **Nesting**: nested scopes rewind to the correct save-points.
|
||||
Reference in New Issue
Block a user