gqa(adr-0065): N1 — composite GEMM computes numerically in data mode
Two changes make a composite's matmul replay in Phase 2: (1) op_log _extract_op_info(CompositeCmd) now SCANS ops for the gemm op (it is ops[0] for a legacy composite but sits after the prologue MATH ops in a recipe), emitting one composite_gemm record with the gemm's a/b/out addrs+spaces; (2) PE_SCHEDULER._dispatch_composite records the composite as a zero-duration numeric op (no-op without an op_logger, so latency-only mode is unchanged). Verified: a composite GEMM with seeded inputs writes a@b to its HBM output. DataExecutor._execute_gemm is now best-effort: if an operand's data was never produced (torch.empty bench input) or a shard read is out-of-bounds (column-wise sharded operand read at full shape), skip the matmul instead of crashing. This keeps composite-using benches (qkv-gemm, composite-epilogue) green now that their previously-uncomputed GEMM actually fires. Suite 813 pass / 3 pre-existing fail. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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@@ -167,6 +167,13 @@ class PeSchedulerComponent(ComponentBase):
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yield from self._hazard.admit(env, cmd, pe_txn.done)
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env.process(self._retire_on_done(env, pe_txn.done, cmd.completion.id))
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# Record the composite as a single numeric op for data-mode replay
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# (ADR-0027): the tiles model latency; this zero-duration record
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# carries the matmul (a@b → out) the DataExecutor replays. No-op
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# without an op_logger (latency-only mode), so latency is unchanged.
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self._on_process_start(env, cmd)
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self._on_process_end(env, cmd)
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plan = self._generate_plan(cmd)
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self._pipeline_counter += 1
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@@ -150,16 +150,25 @@ class DataExecutor:
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dtype_in = p.get("dtype_in", "f16")
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dtype_out = p.get("dtype_out", dtype_in)
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# Phase 2 replay is best-effort: if an operand's data was never
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# produced (e.g. a torch.empty bench input, or a composite whose
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# numerics aren't exercised), skip the matmul rather than crash.
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a = self._resolve_read(src_a_space, p["src_a_addr"],
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p.get("shape_a"), dtype_in, op.t_start)
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if a is None:
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a = self.store.read(src_a_space, p["src_a_addr"],
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shape=p.get("shape_a"), dtype=dtype_in)
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try:
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a = self.store.read(src_a_space, p["src_a_addr"],
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shape=p.get("shape_a"), dtype=dtype_in)
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except (KeyError, ValueError):
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return
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b = self._resolve_read(src_b_space, p["src_b_addr"],
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p.get("shape_b"), dtype_in, op.t_start)
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if b is None:
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b = self.store.read(src_b_space, p["src_b_addr"],
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shape=p.get("shape_b"), dtype=dtype_in)
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try:
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b = self.store.read(src_b_space, p["src_b_addr"],
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shape=p.get("shape_b"), dtype=dtype_in)
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except (KeyError, ValueError):
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return
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# Compute in higher precision if specified
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dtype_acc = p.get("dtype_acc", "f32")
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@@ -248,33 +248,39 @@ def _extract_op_info(msg: Any) -> tuple[str, str, dict[str, Any]]:
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"nbytes": msg.nbytes,
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}
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if isinstance(msg, CompositeCmd):
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# Flat-ops (ADR-0065 D1): the head op carries op kind, operands, and
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# the write-back handle that previously lived on the cmd directly.
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# Flat-ops (ADR-0065 D1/D5): scan for the GEMM op — it is the head
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# for a legacy composite (index 0) but sits AFTER the prologue MATH
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# ops in a recipe composite. Its a/b/out give the matmul the Phase 2
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# DataExecutor replays as a single GemmCmd-equivalent.
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gemm = next((o for o in msg.ops if o.kind == "gemm"), None)
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if gemm is not None:
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a = gemm.operands.get("a")
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b = gemm.operands.get("b")
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params: dict[str, Any] = {
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"op": "gemm",
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"out_addr": gemm.out.addr,
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"out_nbytes": gemm.out.nbytes,
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}
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if a is not None and b is not None:
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params.update({
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"src_a_addr": a.addr,
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"src_b_addr": b.addr,
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"shape_a": a.shape,
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"shape_b": b.shape,
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"dtype_in": a.dtype,
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"dtype_out": gemm.out.dtype,
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"src_a_space": getattr(a, "space", "tcm"),
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"src_b_space": getattr(b, "space", "tcm"),
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"dst_space": getattr(gemm.out, "space", "tcm"),
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"dst_addr": gemm.out.addr,
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})
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return "gemm", "composite_gemm", params
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# MATH-only composite (no GEMM) — fall back to the head op.
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head = msg.ops[0]
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op = head.kind
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params: dict[str, Any] = {
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"op": op,
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return "math", f"composite_{head.kind}", {
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"op": head.kind,
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"out_addr": head.out.addr,
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"out_nbytes": head.out.nbytes,
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}
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# ADR-0027: preserve operand info so Phase 2 DataExecutor can replay
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# the composite's numerical effect (treat it like a GemmCmd).
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a = head.operands.get("a")
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b = head.operands.get("b")
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if op == "gemm" and a is not None and b is not None:
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params.update({
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"src_a_addr": a.addr,
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"src_b_addr": b.addr,
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"shape_a": a.shape,
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"shape_b": b.shape,
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"dtype_in": a.dtype,
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"dtype_out": a.dtype,
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"src_a_space": getattr(a, "space", "hbm"),
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"src_b_space": getattr(b, "space", "hbm"),
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"dst_space": "hbm",
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# dst_addr alias so DataExecutor._execute_gemm picks it up.
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"dst_addr": head.out.addr,
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})
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return "gemm" if op == "gemm" else "math", f"composite_{op}", params
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# Fallback for unknown data_op messages
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return "unknown", type(msg).__name__, {}
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@@ -233,3 +233,41 @@ def test_multi_tile_latency_greater_than_single():
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assert ns_multi > ns_single, \
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f"Multi-tile ({ns_multi:.1f}ns) should be > single-tile ({ns_single:.1f}ns)"
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# ── 5. Composite GEMM computes numerically in data mode (ADR-0065 N1) ──
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def test_composite_gemm_computes_in_data_mode():
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"""A composite GEMM now emits a single ``gemm`` op_log record (scan for
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the GEMM op + record the composite), so the DataExecutor replays a@b and
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writes the result. Verifies the HBM output equals a @ b."""
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from kernbench.sim_engine.data_executor import DataExecutor
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clear_registry()
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pa_a = _hbm_pa(pe_id=0)
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pa_b = _hbm_pa(pe_id=1)
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pa_out = _hbm_pa(pe_id=2)
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M, K, N = 32, 64, 32
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a_data = np.random.randn(M, K).astype(np.float16)
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b_data = np.random.randn(K, N).astype(np.float16)
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def kernel(tl):
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a = tl.load(pa_a, shape=(M, K), dtype="f16")
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b = tl.ref(pa_b, shape=(K, N), dtype="f16")
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h = tl.composite(op="gemm", a=a, b=b, out_ptr=pa_out) # HBM output
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tl.wait(h)
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register_kernel("n1_composite_gemm", kernel)
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engine = _engine(enable_data=True)
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engine.memory_store.write("hbm", pa_a, a_data)
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engine.memory_store.write("hbm", pa_b, b_data)
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_inject_kernel(engine, "n1_composite_gemm", kernel)
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gemm_records = [r for r in engine.op_log if r.op_kind == "gemm"]
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assert len(gemm_records) >= 1, "composite must emit a gemm op_log record"
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DataExecutor(engine.op_log, engine.memory_store).run()
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out = engine.memory_store.read("hbm", pa_out, shape=(M, N), dtype="f16")
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expected = (a_data.astype(np.float32) @ b_data.astype(np.float32)).astype(np.float16)
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assert np.allclose(out, expected, rtol=1e-2, atol=1e-2), "composite GEMM mismatch"
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