feat(distributed): widen InCore reduce_scatter to all four reduce ops - #2556
feat(distributed): widen InCore reduce_scatter to all four reduce ops#2556georgebisbas wants to merge 3 commits into
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📝 WalkthroughWalkthrough
ChangesReduce-scatter operators
Estimated code review effort: 3 (Moderate) | ~20 minutes Merge Risk: 🔵 Low · up to InCore reduce_scatter now supports Sum, Max, Min, and Prod, while HOST remains Sum-only; non-Sum HOST requests may fail later in compilation, and stale API/developer text can mislead users about support. The PR is mergeable with explicit owner awareness or follow-up on the rail-specific validation and documentation updates. Sequence Diagram(s)sequenceDiagram
participant Caller
participant ReduceScatter
participant LowerCompositeOpsPass
participant TileReduction
Caller->>ReduceScatter: submit tensor and ReduceOp
ReduceScatter->>LowerCompositeOpsPass: validate and lower operation
LowerCompositeOpsPass->>TileReduction: reduce peer chunks with selected operator
TileReduction-->>Caller: produce reduced scatter output
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🚥 Pre-merge checks | ✅ 4 | ❌ 1❌ Failed checks (1 warning)
✅ Passed checks (4 passed)
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| CHECK(op_value == static_cast<int>(ReduceOp::kSum)) | ||
| << "pld.tensor.reduce_scatter op must be ReduceOp.Sum (got int " << op_value | ||
| << "); Max / Min / Prod lowerings are not yet implemented"; | ||
| CHECK(op_value >= static_cast<int>(ReduceOp::kSum) && op_value <= static_cast<int>(ReduceOp::kProd)) |
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Once this check accepts Max, Min, and Prod for InCore calls, both public Python entry points still tell callers those variants are unavailable: python/pypto/ir/op/distributed/tensor_ops.py:428 says only Sum is supported, while python/pypto/language/distributed/op/tensor_ops.py:926-927 marks the other operations as reserved. The generic pass documentation at docs/en/dev/passes/12-lower_composite_ops.md:239 and the lowering comment at src/ir/transforms/lower_composite_ops_pass.cpp:1835 retain the same stale assumption, so users inspecting these interfaces can incorrectly conclude that the new behavior is unsupported. Update these descriptions to explain the InCore-versus-HOST distinction. .claude/rules/documentation.mdL34-L49
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Addressed in 71ba89a — the Python API docstring (python/pypto/ir/op/distributed/tensor_ops.py), the DSL docstring (python/pypto/language/distributed/op/tensor_ops.py), the pass doc (docs/en/dev/passes/12-lower_composite_ops.md), and the lowering comment (src/ir/transforms/lower_composite_ops_pass.cpp) now all state the InCore-all-four / HOST-Sum-only split.
| | AllReduce | `pld.tensor.allreduce` | `mesh` (InCore + HOST), `ring` (InCore + HOST) | `Sum`, `Max`, `Min`, `Prod` (mesh); `Sum` only (HOST ring) | — | FP16, FP32 (mesh; hard compile-time check); HOST ring: FP32 only (4-byte) | Mesh: O(N) remote traffic per step. Ring: O(N/P) remote traffic per step, 2(P-1) steps. | | ||
| | AllGather | `pld.tensor.allgather` | — | — | — | FP32 only (HOST builtin); any GM dtype (InCore) | Push-based. Input and target must be different buffers. | | ||
| | ReduceScatter | `pld.tensor.reduce_scatter` | — | `Sum` only | — | FP32 only (HOST builtin); any GM dtype (InCore) | Every rank stages all NR chunks before the call. | | ||
| | ReduceScatter | `pld.tensor.reduce_scatter` | — | `Sum`, `Max`, `Min`, `Prod` (InCore); `Sum` only (HOST builtin) | — | FP32 only (HOST builtin); any GM dtype (InCore) | Every rank stages all NR chunks before the call. | |
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When readers use the Chinese operation catalog, docs/zh/user/ops/01-catalog.md:193 still states that ReduceScatter supports only Sum, contradicting this updated English row and the newly accepted InCore behavior. Update the corresponding Chinese row with the same four-op InCore and Sum-only HOST distinction. .claude/rules/documentation.mdL89-L98
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Addressed in 71ba89a — docs/zh/user/ops/01-catalog.md ReduceScatter row now matches the English catalog: Sum/Max/Min/Prod (InCore), Sum only (HOST builtin).
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Inline comments:
In `@docs/en/user/distributed/02-primitives.md`:
- Line 17: Update the ReduceOp documentation in ir.pyi and both reduce_scatter
docstrings to state that InCore supports Sum, Max, Min, and Prod, while the HOST
builtin rail supports only Sum; remove the stale claims that reduce_scatter
supports Sum exclusively.
Apply the same fix in `@src/ir/transforms/lower_composite_ops_pass.cpp` around
lines 1854 - 1857: Chinese developer documentation still describes summation
rather than the selected reduction.
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📒 Files selected for processing (9)
docs/en/dev/passes/12-lower_composite_ops.mddocs/en/user/distributed/02-primitives.mddocs/en/user/ops/01-catalog.mddocs/zh/dev/passes/12-lower_composite_ops.mddocs/zh/user/distributed/02-primitives.mdsrc/ir/op/distributed/collective.cppsrc/ir/transforms/lower_composite_ops_pass.cpptests/st/distributed/collectives/test_l3_tensor_reduce_scatter_intrinsic.pytests/ut/ir/transforms/test_lower_composite_ops.py
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pld.tensor.reduce_scatter previously accepted ReduceOp.Sum only: the composite deducer rejected Max/Min/Prod and the LowerCompositeOps rule hard-coded a tile.add accumulate. Route the accumulate step through the shared Reduce() helper (tile.add / tile.maximum / tile.minimum / tile.mul - the same dispatch the allreduce rules use) and widen the deducer to the kSum..kProd bound. Add a composite-UT that asserts each op lowers to its matching tile op, and parametrize the InCore reduce_scatter ST over Sum/Max/Min/Prod at P=2 and P=4. Sum and Prod pass on sim; Max/Min hit a pre-existing tile.maximum/tile.minimum sim SIGSEGV (reproduced on pristine main via the allreduce Max/Min intrinsic STs), so those two ops are the NPU developer gate.
… InCore rail The InCore composite deducer and LowerCompositeOps rule now accept ReduceOp.Sum/Max/Min/Prod (routed through the shared Reduce() helper); the HOST builtin rail still lowers only Sum. Update the ReduceOp primitive table and the LowerCompositeOps reduce_scatter section (en + zh).
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… and docs Address review feedback (Codex P2 + CodeRabbit): the InCore reduce_scatter widening updated the C++ and EN docs but several surfaces still claimed Sum-only support. Synchronize all of them to the InCore-all-four / HOST-Sum-only split: - python API docstrings (ir/op + language/distributed tensor_ops.py) and ir.pyi ReduceOp note — drop 'Sum only in first version' / 'reserved' - ZH op catalog ReduceScatter row — Sum/Max/Min/Prod (InCore), Sum only (HOST) - EN+ZH lower-composite-ops dev docs — 'element-wise sum' -> 'element-wise reduction (Sum/Max/Min/Prod by ReduceOp)' - lowering rule comment — 'kSum only' -> all four ReduceOps dispatch
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Summary
pld.tensor.reduce_scatteron the InCore rail now supports all four reduce ops —ReduceOp.Sum,Max,Min,Prod— instead ofSumonly.What changes
DeduceTensorReduceScatterType): accepts the fullkSum..kProdbound instead of rejecting Max/Min/Prod.LowerTensorReduceScatterRule): the accumulate step routes through the sharedReduce()helper, mapping each op to the matching tile op (tile.add/tile.maximum/tile.minimum/tile.mul) — the same dispatch the allreduce rules use.reduce_scattercall is fully removed.test_l3_tensor_reduce_scatter_intrinsic.py): parametrized over all four ops at P=2 and P=4 with per-op golden (prod uses dyadic-rational inputs so products stay exact in FP32).ReduceOpcapability in02-primitives.md, theLowerCompositeOpsreduce_scatter section, and the op catalog — InCore accepts all four; the HOST builtin rail remainsSum-only.Verification
tests/ut/ir/transforms/test_lower_composite_ops.py: 126 passed--all-files: 21/21 green (ruff, pyright, clang-format, cpplint, markdownlint, docs parity)