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>
81 lines
2.7 KiB
YAML
81 lines
2.7 KiB
YAML
# ccl.yaml — CCL backend (ahbm) configuration (ADR-0023 D11)
|
||
#
|
||
# Loaded by AhbmCCLBackend at init_process_group time.
|
||
# defaults.algorithm chooses which kernel + topology is installed
|
||
# into PE_IPCQ neighbor tables. Host code is unaware of these settings.
|
||
|
||
defaults:
|
||
# Algorithm to run for this benchmark execution.
|
||
algorithm: ring_allreduce_tcm
|
||
|
||
# NOTE: world_size is not set here by default. AhbmCCLBackend derives it
|
||
# from the chosen algorithm's entry (if it sets ``world_size``) or from
|
||
# topology.yaml (``sips × cubes_per_sip × pes_per_cube``). This mirrors
|
||
# real PyTorch DDP where ranks/world_size come from env vars, not code.
|
||
|
||
# IPCQ ring buffer location.
|
||
# tcm — PE-local TCM (fast, small, conflicts with compute TCM access)
|
||
# hbm — PE-local HBM (large, slower DMA latency)
|
||
# sram — Cube-shared SRAM (medium, cube-internal contention)
|
||
buffer_kind: tcm
|
||
|
||
# Backpressure mode.
|
||
# poll — spin-loop polling of cached peer pointers
|
||
# sleep — yield SimPy event, wake on credit return
|
||
backpressure: sleep
|
||
|
||
# Ring depth: number of slots per (direction, tx|rx) buffer.
|
||
n_slots: 4
|
||
|
||
# Slot size in bytes (must hold one tile worth of data).
|
||
slot_size: 4096
|
||
|
||
# PE_DMA virtual channel chunk size (D8). First implementation does not
|
||
# use chunk-level interleave; this is reserved for future precision.
|
||
vc_chunk_size: 256
|
||
|
||
# Credit return fast path message size (D9). Used by bottleneck-BW
|
||
# latency calculation. 16-64 bytes typical.
|
||
ipcq_credit_size_bytes: 16
|
||
|
||
algorithms:
|
||
# ── ring all-reduce, buffer in PE_TCM ──
|
||
# Defaults to topology-derived world_size (full system, 256 ranks).
|
||
# Use a smaller tile size at high rank counts so f16 sums stay within
|
||
# the verification tolerance and op_log replay scales.
|
||
ring_allreduce_tcm:
|
||
module: kernbench.ccl.algorithms.ring_allreduce
|
||
topology: ring_1d
|
||
buffer_kind: tcm
|
||
n_elem: 8
|
||
|
||
# ── ring all-reduce, buffer in PE-local HBM ──
|
||
ring_allreduce_hbm:
|
||
module: kernbench.ccl.algorithms.ring_allreduce
|
||
topology: ring_1d
|
||
buffer_kind: hbm
|
||
n_elem: 8
|
||
|
||
# ── ring all-reduce, buffer in cube SRAM ──
|
||
ring_allreduce_sram:
|
||
module: kernbench.ccl.algorithms.ring_allreduce
|
||
topology: ring_1d
|
||
buffer_kind: sram
|
||
n_elem: 8
|
||
|
||
# ── 2D mesh all-reduce: perfect square only (2×2 = 4 PEs) ──
|
||
mesh_allreduce_4:
|
||
module: kernbench.ccl.algorithms.mesh_allreduce
|
||
topology: mesh_2d
|
||
buffer_kind: tcm
|
||
world_size: 4
|
||
n_elem: 16
|
||
|
||
# ── tree all-reduce (binary, 7 PEs) ──
|
||
tree_allreduce_7:
|
||
module: kernbench.ccl.algorithms.tree_allreduce
|
||
topology: tree_binary
|
||
buffer_kind: tcm
|
||
world_size: 7
|
||
n_elem: 16
|