ywkang
998cc85762
Major changes:
PE-level IPCQ infrastructure:
- New PE_IPCQ component: ring-buffer control plane with 4-direction
neighbor mapping, head/tail pointers, backpressure (poll/sleep).
- PE_DMA extended with vc_comm channel for IPCQ outbound/inbound DMA,
including in-flight data snapshot (D9) and op_log recording at
outbound time for Phase 2 replay correctness.
- IpcqDmaToken piggyback model: data + metadata travel together,
atomic visibility at receiver (invariant I6).
- Credit return fast path: bottleneck-BW latency, no fabric vc_comm.
Phase 2 data execution (ADR-0020 integration):
- op_log extended: DmaWriteCmd now captures src_space/src_addr for
Phase 2 dma_write copy; ipcq_copy ops recorded at outbound time.
- DataExecutor replays dma_write + ipcq_copy in t_start order.
- Engine._flush_data_phase: incremental cursor-based replay after
each engine.wait() so host reads see post-Phase-2 data.
- KernelRunner Phase 1 writes disabled when op_log is active to
prevent stale data from corrupting the MemoryStore snapshot.
TLContext / kernel API:
- tl.send(dir, src=TensorHandle), tl.recv(dir, shape, dtype),
tl.recv_async, tl.wait(RecvFuture), copy_to_dst mode.
- TensorHandle operator overloading (add/sub/mul/div) via thread-local
active TLContext → MathCmd dispatch through PE_MATH.
- PE-local scratch allocator for math output handles.
- tl.load returns space="hbm" handles for correct Phase 2 addressing.
- Additional math functions: maximum, minimum, fma, clamp, softmax, cdiv.
Unified ccl_allreduce bench (PyTorch-compat host code):
- Single benches/ccl_allreduce.py with run() + worker(rank, ws, torch)
split matching real PyTorch DDP worker pattern.
- torch.distributed facade: init_process_group, get_world_size,
get_rank, get_backend, all_reduce, barrier — only real PyTorch names.
- AhbmCCLBackend: eager install_ipcq at init, all_reduce dispatches
kernel via tensor shard metadata (n_elem from shards[0].nbytes).
- world_size derived from topology spec (sips × cubes × pes_per_cube)
with optional algorithm-level override in ccl.yaml.
Tensor API (PyTorch-compat surface):
- Tensor.numpy(): gather-aware (all shards via VA-based addressing).
- Tensor.copy_(source): scatter from host tensor into sharded target.
- RuntimeContext.from_numpy(arr): host-side staging tensor.
- Tensor.data property fixed to use numpy() (was shards[0]-only).
Algorithm modules moved to src/kernbench/ccl/algorithms/:
- ring_allreduce, mesh_allreduce, tree_allreduce, hello_send.
- Each module exports kernel_args(world_size, n_elem) helper.
- ccl.yaml module paths updated to kernbench.ccl.algorithms.*.
Dead code removed:
- 7 per-variant bench files (ccl_allreduce_{tcm,hbm,sram}, etc.).
- _run_ccl_bench greenlet-per-SIP scheduler.
- benches.loader.is_ccl_bench + run_rank detection.
- benches/ccl/ directory.
Tests:
- New test_ccl_allreduce_matrix.py: 7 parametrized cases
(ring×3 buffers, ring 8/16, mesh 4, tree 7).
- New test_runtime_api_tensor.py: copy_/numpy/from_numpy unit tests.
- Existing tests updated for new import paths + world_size_override.
Docs:
- Korean ccl-author-guide.md and ADR-0023 paths updated.
- New English versions: ccl-author-guide.en.md, ADR-0023.en.md.
502 tests pass.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
96 lines
3.2 KiB
Python
96 lines
3.2 KiB
Python
from __future__ import annotations
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from collections.abc import Callable
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from enum import Enum
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from typing import Any
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from kernbench.common.types import Completion, SimEngine, Trace
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from .context import RuntimeContext
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from .types import BenchResult, DeviceSelector
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class CompletionPolicy(str, Enum):
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LAST_SUBMITTED = "last_submitted"
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LAST_COMPLETED = "last_completed" # requires trace/timestamps or engine support; stub for now
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ALL_OK_FAIL_FAST = "all_ok_fail_fast"
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BenchFn = Callable[[RuntimeContext], Any]
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EngineFactory = Callable[[object, DeviceSelector], SimEngine]
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def run_bench(
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*,
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topology: object,
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bench_fn: BenchFn,
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device: DeviceSelector,
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engine_factory: EngineFactory,
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correlation_id: str = "bench0",
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completion_policy: CompletionPolicy = CompletionPolicy.LAST_SUBMITTED,
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) -> BenchResult:
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"""Minimal bench runner.
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- topology: compiled topology object (opaque to runtime here)
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- bench_fn: callable ``run(torch)`` receiving a RuntimeContext
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- device: DeviceSelector ("all" or "sip:<N>")
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- engine_factory: builds sim_engine for given topology & device
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- completion_policy: how to determine overall completion/result
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"""
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engine = engine_factory(topology, device)
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# Extract spec from TopologyHandle or TopologyGraph
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topo_obj = getattr(topology, "topology_obj", topology)
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spec = getattr(topo_obj, "spec", None)
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ctx = RuntimeContext(
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engine=engine, target_device=device,
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correlation_id=correlation_id, spec=spec,
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)
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bench_fn(ctx)
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ctx.wait_all()
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collected_traces = ctx._traces or None
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handles = ctx.handles()
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if not handles:
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return BenchResult(
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completion=Completion(
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ok=False, error_code="NO_REQUESTS", error_message="Bench submitted no requests"
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),
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correlation_id=correlation_id,
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trace=None,
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traces=collected_traces,
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engine=engine,
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)
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if completion_policy == CompletionPolicy.LAST_SUBMITTED:
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last = handles[-1]
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completion, trace = engine.get_completion(last)
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return BenchResult(
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completion=completion, correlation_id=correlation_id,
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trace=trace, traces=collected_traces, engine=engine,
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)
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if completion_policy == CompletionPolicy.ALL_OK_FAIL_FAST:
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last_trace: Trace | None = None
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for h in handles:
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c, t = engine.get_completion(h)
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last_trace = t if t is not None else last_trace
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if not c.ok:
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return BenchResult(
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completion=c, correlation_id=correlation_id,
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trace=last_trace, traces=collected_traces, engine=engine,
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)
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return BenchResult(
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completion=Completion(ok=True), correlation_id=correlation_id,
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trace=last_trace, traces=collected_traces, engine=engine,
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)
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# LAST_COMPLETED placeholder (needs engine support for timing). Fall back.
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last = handles[-1]
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completion, trace = engine.get_completion(last)
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return BenchResult(
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completion=completion, correlation_id=correlation_id,
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trace=trace, traces=collected_traces, engine=engine,
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)
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