mukesh
a7fe785e5f
tl.composite: fused epilogue ops with per-op scope
...
Extend tl.composite() with an ordered epilogue list. Each op carries
a scope flag - output_tile (default, runs once per (m,n) before
STORE), k_tile (every K-tile right after GEMM), or kernel. Plan
generator slots MATH stages by scope; pe_math reuses pe_dma's
local-loop pattern so chained epilogues (bias->relu) skip the port
hop. op_log captures per-stage params for telemetry. Topology
gains a gemm->math edge (snapshot test updated).
API stays backward-compatible - `epilogue=` is opt-in.
Example:
h = tl.composite(
op="gemm", a=a, b=b, out_ptr=int(out),
epilogue=[
{"op": "dequant", "scale": s_per_k, "scope": "k_tile"},
{"op": "bias", "bias": bias_vec},
{"op": "relu"},
{"op": "scale", "factor": 0.5},
],
)
tl.wait(h)
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com >
2026-05-15 10:16:47 -07:00
ywkang
998cc85762
Add PE-level IPCQ collective infra + unified ccl_allreduce bench (ADR-0023)
...
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 >
2026-04-12 19:36:59 -07:00
ywkang
51004c311c
Implement ADR-0020: 2-pass data execution with greenlet kernel runner
...
Step 1 — Foundation:
- OpRecord/OpLogger: op log infrastructure with t_start stable ordering
- MemoryStore: numpy ndarray tensor-granular storage (reference semantics)
- data_op=True flag on DmaReadCmd, DmaWriteCmd, GemmCmd, MathCmd, CompositeCmd
- numpy/greenlet dependencies added to pyproject.toml
Step 2 — ComponentBase hooks:
- _on_process_start/end hooks in _forward_txn (fabric messages)
- _handle_with_hooks in PeEngineBase (PE-internal commands)
- op_logger optional — zero overhead when disabled
Step 3 — KernelRunner + greenlet:
- KernelRunner: greenlet ↔ SimPy bridge in triton_emu/kernel_runner.py
- TLContext: _emit() method routes to greenlet switch or command list
- tl.load() returns real numpy data in greenlet mode
- Dynamic control flow supported (memory-read based branching)
Step 4 — PE_CPU integration:
- Greenlet mode when ctx.memory_store is set, legacy fallback otherwise
- Refactored into _execute_greenlet/_execute_legacy/_send_response
- ComponentContext gains memory_store and op_logger fields
Step 5 — DataExecutor:
- Phase 2 numpy execution for GEMM/Math ops from op_log
- _compute_math: all unary/binary/reduction ops
- verify(): compare MemoryStore against expected with dtype tolerance
28 new tests, 366 total passing.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com >
2026-04-08 00:22:44 -07:00
ywkang
08812eda58
Add virtual memory support: PE_MMU, VA allocator, fabric MmuMapMsg
...
Implement VA/MMU layer (ADR-0011 Phase 1) enabling Triton kernels to use
contiguous virtual addresses on sharded tensors.
Key changes:
- PE_MMU component: hybrid inbox (MmuMapMsg) + sync translate() for PE_DMA
- VirtualAllocator + PEMemAllocator: free-list with coalescing
- MmuMapMsg/MmuUnmapMsg fabric path with SIP-level routing
- DPPolicy-based mapping: replicate=local, sharded=broadcast
- Tensor lifecycle: del + weakref cleanup, context manager
- Rename: TensorHandle.pa→addr, DmaReadCmd.src_pa→src_addr, ctx→torch
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com >
2026-03-26 00:01:47 -07:00
mukesh
ywkang