Refactor: express the DSv4 MoE tile-loop control flow as dispatch predicates by ChaoZheng109 · Pull Request #1926 · hw-native-sys/simpler

ChaoZheng109 · 2026-08-20T09:13:13Z

What

Follows #1921 (merged) — that PR moved the DSv4 orchestrations' 30 data get_tensor_data reads (the hc_*_scale_* values) into the consuming kernels. This one finishes the job for the reads #1921 left behind: the ten recv_count_out[expert][0] reads driving the MoE per-expert tile loops, the last orchestration reads of a task-produced tensor.

on tensormap_and_ringbuffer they stall the AICPU orchestrator on a producer wait;
on host_build_graph they have no value to return at all — the host builds the whole graph before the device executes anything — so the ten sites stood HBG_RECV_ROWS_PER_EXPERT = 16 in as a temporary stand-in, and that copy encoded a routing the fixture does not have.

The count now steers the loops from the device side, split by role:

Trip count → dispatch predicate. Both runtimes build a static per-expert tile grid — the h_i8 [512, 2048] layout budgets 16 rows per expert and a tile is 16 rows, so one tile per expert — and each of a tile's six tasks (exp_gate_mm, exp_up_mm, exp_gate_up_act, exp_h_q, exp_w2_mm, exp_w2_act) carries a dispatch predicate on that element:

CoreTaskPredicate expert_rows_pred;                 // recv_count_out[expert][0] > t0
expert_rows_pred.operand.tensor = &recv_count_out_inline9338;
expert_rows_pred.operand.ndims = 2;
expert_rows_pred.operand.indices[0] = static_cast<uint32_t>(local_i_inline2779_inline9183);
expert_rows_pred.operand.indices[1] = 0;
expert_rows_pred.op = PredicateOp::GT;
expert_rows_pred.target = t0_inline2784_inline9444;

The scheduler evaluates it at the dispatch point, where the task is already ready and the count is therefore current. An expert whose count does not reach the tile's first row has the tile's tasks retired inline, never dispatched to a core. 10 sites × 2 files, 30 set_predicate calls each, symmetric.

valid_rows → kernel-side read. The one value the loops computed from the count, valid_rows = min(count - t0, 16), was a task scalar of exp_gate_up_act*. It moves into the kernels the same way #1921 moved the scales: the orchestrations pass recv_count_out as an extra tensor input at the former first-scalar slot, and each of the five kernels derives the row count from GM in kernel_entry, after a single-line dcci on the element (the count lives in the ring heap, so a line this core cached under an earlier tensor could otherwise serve it). The predicate guarantees count - t0 >= 1 whenever the kernel runs. The inner ptoas-generated functions are untouched; the five _KERNELS signatures grow a trailing input (oiiiii → oiiiiii, xiiiii → xiiiiii).

Why no dependency is declared

The predicate declares none on either path, by design — the caller does. None is needed here: every task in a tile consumes a dispatch_gather output, and dispatch_gather carries an explicit dependency on dispatch_meta, which writes recv_count_out. Inside the Graph body that edge is recorded as a fanin, so the ordering survives replay; the new tensor input additionally records a direct fanin on the dispatch_meta node.

Consequences

decode_fwd_graph.cpp carries no runtime-specific rewrite any more. It is the TMR orchestration recast as a Graph. HBG_RECV_ROWS_PER_EXPERT is gone; both files share one EXPERT_ROWS_BUDGET that only sizes the static grid.
One get_tensor_data read left in either file: ext_num_tokens_per_owner. It is an external tensor the runtime stages with a host view, and it feeds set_block_num — a launch parameter a predicate cannot express, because a predicate decides whether a task dispatches, not how wide it is.
The TMR ring heap grows 1 GiB → 2 GiB, matching what the HBG case already needed: tile scratch is now allocated for all 32 experts of every MoE layer whatever the routing turns out to be.
The TMR README's stall description drops the orchestrator TENSOR_WAIT_TIMEOUT on recv_count_out — that read no longer exists, and it was a consequence of the comm-window stall rather than a cause.

Verification

The five modified kernels compile clean under ccec (KernelCompiler.compile_incore, aiv, pto-isa pin); the two orchestrations compile clean under the host g++ and (TMR) the aarch64 AICPU cross-g++.
Every rewrite site was produced by scripted transforms with assertions on the site count (5 n_rows + 5 e_rows per file; 5 valid_rows sites per file) and on the number of submits found inside each rewritten loop body (4 and 2), so a site whose shape did not match would have failed the run rather than been silently skipped. Each consumer's add_input binds the nearest preceding predicate's count tensor, asserted per site — the five MoE blocks each have their own recv_count_out.
Record-time acceptance checked against graph_record_node's predicate refusals: the operand is [32,1] INT32 allocated inside the Graph body, so it classifies INTERNAL (not OWN_OUTPUT), ndims matches, and flat_offset - start_offset is the expert index, inside extent_elem_cache == 32.
Predicate scene tests pass on a2a3sim: host_build_graph/graph_predicated_dispatch and host_build_graph/predicated_dispatch.
Full pre-commit clean (clang-format/tidy, cpplint, markdownlint, ruff, pyright).

Not verified on hardware. Both DSv4 cases are manual, need two dies and a 368-kernel compile; this box is currently 16/16 occupied and npu-smi/dcmi fails to initialize for new processes (dcmi module initialize failed. ret is -8005), so the arch precheck cannot pass. An onboard SIMPLER_SKIP_DEVICE_RUN=1 HBG run (host orchestration + Graph recording, which is where a rejected predicate operand would surface) is the check still owed; happy to post it once a die frees up.

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📝 Walkthrough

Walkthrough

The DeepSeek decode examples now pass scale tensors to kernels, read scales from GM, and evaluate MoE receive-count predicates on the device. Both orchestration paths use static expert tile budgets. Kernel registrations, runtime ring sizing, and documentation reflect the new data flow.

Changes

DeepSeek decode orchestration

Layer / File(s)	Summary
Kernel scale tensor contracts `examples/a2a3/tensormap_and_ringbuffer/deepseek_v4_flash_decode/kernels/aiv/*` `examples/a2a3/tensormap_and_ringbuffer/deepseek_v4_flash_decode/test_deepseek_v4_flash_decode.py`	`split_pre_post` and `comb_sinkhorn` now load scale values from GM tensor arguments. Registered kernel signatures include the new tensor inputs.
Tensormap dispatch and scale wiring `examples/a2a3/tensormap_and_ringbuffer/deepseek_v4_flash_decode/kernels/orchestration/decode_fwd.cpp` `examples/a2a3/tensormap_and_ringbuffer/deepseek_v4_flash_decode/test_deepseek_v4_flash_decode.py`	Attention, FFN, and expert tasks use tensor scale inputs and fixed-budget tiles with device-side `recv_count_out` predicates. The ring heap increases from 1 GiB to 2 GiB.
Host-build-graph dispatch and scale wiring `examples/a2a3/host_build_graph/deepseek_v4_flash_decode/kernels/orchestration/decode_fwd_graph.cpp`	The graph builder removes host scale reads and scalar packing. It passes scale tensors to tasks and applies receive-count predicates across five MoE blocks.
Documentation and runtime validation `examples/a2a3/host_build_graph/deepseek_v4_flash_decode/README.md`, `examples/a2a3/host_build_graph/deepseek_v4_flash_decode/test_deepseek_v4_flash_decode.py`, `examples/a2a3/tensormap_and_ringbuffer/deepseek_v4_flash_decode/README.md`	Documentation describes static expert grids, GM scale loads, external token-count handling, graph behavior, task counts, ring sizing, and smoke-test scope.

Estimated code review effort: 4 (Complex) | ~60 minutes

Merge Risk: ⚪ Minimal · up to 21911

The PR changes DSv4 orchestration to predicate-based tile dispatch and has no demonstrated runtime or correctness blocker; only minor documentation follow-up remains for reconciling task counts and clarifying tile-grid wording. It is merge-ready after normal review.

Sequence Diagram(s)

sequenceDiagram
  participant Orchestration
  participant ScaleTensorGM
  participant decode_fwd
  participant recv_count_out
  participant ExpertTasks
  Orchestration->>ScaleTensorGM: Pass scale tensor inputs
  Orchestration->>decode_fwd: Build fixed-budget task grid
  ScaleTensorGM->>decode_fwd: Kernel loads scale values from GM
  recv_count_out->>ExpertTasks: Device receive counts
  ExpertTasks->>ExpertTasks: Evaluate dispatch predicates
  ExpertTasks->>Orchestration: Execute eligible expert tasks

Poem

I’m a rabbit with tensors tucked tight,
GM scales hop into kernels at night.
Expert tiles wait in a static row,
Predicates choose where the tasks may go.
The graph runs on, neat as can be—
With carrots reserved for all thirty-two, whee!

🚥 Pre-merge checks | ✅ 4 | ❌ 1

❌ Failed checks (1 warning)

Check name	Status	Explanation	Resolution
Docstring Coverage	⚠️ Warning	Docstring coverage is 0.00% which is insufficient. The required threshold is 80.00%.	Write docstrings for the functions missing them to satisfy the coverage threshold.

✅ Passed checks (4 passed)

Check name	Status	Explanation
Linked Issues check	✅ Passed	Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check	✅ Passed	Check skipped because no linked issues were found for this pull request.
Title check	✅ Passed	The title clearly summarizes the main change: replacing DSv4 MoE tile-loop control flow with dispatch predicates.
Description check	✅ Passed	The description directly explains the predicate refactor, related tensor changes, verification results, and remaining hardware-validation limitation.

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Resolve merge conflict in branch feat/dsv4-predicate-dispatch

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Inline comments:
In `@examples/a2a3/host_build_graph/deepseek_v4_flash_decode/README.md`:
- Around line 50-54: Reconcile the task-count figures in the README by comparing
the 15,971 device-side count with the later 15,991 individual-submission count.
If they represent different scopes, explicitly identify those scopes; otherwise
correct the inconsistent value so the documented Graph reduction is
reproducible.
- Around line 28-30: Replace “static one-tile-per-expert grid” with “static
per-expert tile grid” in both descriptions:
examples/a2a3/host_build_graph/deepseek_v4_flash_decode/README.md lines 28-30
and
examples/a2a3/host_build_graph/deepseek_v4_flash_decode/test_deepseek_v4_flash_decode.py
lines 75-77.

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Reviewing files that changed from the base of the PR and between aac09a6 and 2191106.

📒 Files selected for processing (26)

examples/a2a3/host_build_graph/deepseek_v4_flash_decode/README.md
examples/a2a3/host_build_graph/deepseek_v4_flash_decode/kernels/orchestration/decode_fwd_graph.cpp
examples/a2a3/host_build_graph/deepseek_v4_flash_decode/test_deepseek_v4_flash_decode.py
examples/a2a3/tensormap_and_ringbuffer/deepseek_v4_flash_decode/README.md
examples/a2a3/tensormap_and_ringbuffer/deepseek_v4_flash_decode/kernels/aiv/comb_sinkhorn.cpp
examples/a2a3/tensormap_and_ringbuffer/deepseek_v4_flash_decode/kernels/aiv/comb_sinkhorn_0.cpp
examples/a2a3/tensormap_and_ringbuffer/deepseek_v4_flash_decode/kernels/aiv/comb_sinkhorn_1.cpp
examples/a2a3/tensormap_and_ringbuffer/deepseek_v4_flash_decode/kernels/aiv/comb_sinkhorn_2.cpp
examples/a2a3/tensormap_and_ringbuffer/deepseek_v4_flash_decode/kernels/aiv/comb_sinkhorn_3.cpp
examples/a2a3/tensormap_and_ringbuffer/deepseek_v4_flash_decode/kernels/aiv/comb_sinkhorn_4.cpp
examples/a2a3/tensormap_and_ringbuffer/deepseek_v4_flash_decode/kernels/aiv/comb_sinkhorn_5.cpp
examples/a2a3/tensormap_and_ringbuffer/deepseek_v4_flash_decode/kernels/aiv/comb_sinkhorn_6.cpp
examples/a2a3/tensormap_and_ringbuffer/deepseek_v4_flash_decode/kernels/aiv/comb_sinkhorn_7.cpp
examples/a2a3/tensormap_and_ringbuffer/deepseek_v4_flash_decode/kernels/aiv/comb_sinkhorn_8.cpp
examples/a2a3/tensormap_and_ringbuffer/deepseek_v4_flash_decode/kernels/aiv/split_pre_post.cpp
examples/a2a3/tensormap_and_ringbuffer/deepseek_v4_flash_decode/kernels/aiv/split_pre_post_0.cpp
examples/a2a3/tensormap_and_ringbuffer/deepseek_v4_flash_decode/kernels/aiv/split_pre_post_1.cpp
examples/a2a3/tensormap_and_ringbuffer/deepseek_v4_flash_decode/kernels/aiv/split_pre_post_2.cpp
examples/a2a3/tensormap_and_ringbuffer/deepseek_v4_flash_decode/kernels/aiv/split_pre_post_3.cpp
examples/a2a3/tensormap_and_ringbuffer/deepseek_v4_flash_decode/kernels/aiv/split_pre_post_4.cpp
examples/a2a3/tensormap_and_ringbuffer/deepseek_v4_flash_decode/kernels/aiv/split_pre_post_5.cpp
examples/a2a3/tensormap_and_ringbuffer/deepseek_v4_flash_decode/kernels/aiv/split_pre_post_6.cpp
examples/a2a3/tensormap_and_ringbuffer/deepseek_v4_flash_decode/kernels/aiv/split_pre_post_7.cpp
examples/a2a3/tensormap_and_ringbuffer/deepseek_v4_flash_decode/kernels/aiv/split_pre_post_8.cpp
examples/a2a3/tensormap_and_ringbuffer/deepseek_v4_flash_decode/kernels/orchestration/decode_fwd.cpp
examples/a2a3/tensormap_and_ringbuffer/deepseek_v4_flash_decode/test_deepseek_v4_flash_decode.py

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…dicates The ten `recv_count_out[expert][0]` reads driving the MoE per-expert tile loops were the last orchestration reads of a task-produced tensor. On `tensormap_and_ringbuffer` they stall the AICPU orchestrator on a producer wait; on `host_build_graph` they have no value to return at all, because the host builds the whole graph before the device executes anything, so the ten sites stood a constant in and that copy encoded a routing the fixture does not have. The count now steers the loops from the device side, split by role: - Trip count -> dispatch predicate. Both runtimes build a static per-expert tile grid -- the `h_i8 [512, 2048]` layout budgets 16 rows per expert and a tile is 16 rows, so one tile per expert -- and each of a tile's six tasks carries a dispatch predicate on that element, `recv_count_out[expert][0] > t0`. The scheduler evaluates it at the dispatch point, where the task is already ready and the count is therefore current: an expert whose count does not reach the tile's first row has the tile's tasks retired inline, never dispatched to a core. - `valid_rows` -> kernel-side read. The one value the loops computed from the count, `valid_rows = min(count - t0, 16)`, was a task scalar of `exp_gate_up_act*`. The orchestrations pass `recv_count_out` as an extra tensor input instead, at the former first-scalar slot, and each of the five kernels derives the row count from GM in `kernel_entry` after a single-line dcci on the element; the predicate guarantees the difference is at least 1 whenever the kernel runs. The inner ptoas-generated functions are untouched; the five `_KERNELS` signatures grow a trailing input. The predicate declares no dependency, on either path, and needs none here: every task in a tile consumes a `dispatch_gather` output and `dispatch_gather` depends on `dispatch_meta`, which writes the count. Inside the Graph body the operand classifies as INTERNAL and rebinds through the same path a tensor arg does. `decode_fwd_graph.cpp` therefore carries no runtime-specific rewrite any more: it is the TMR orchestration recast as a Graph. `ext_num_tokens_per_owner` is the one `get_tensor_data` read left in either file -- an external tensor the runtime stages with a host view, feeding `set_block_num`, which a predicate cannot express because it decides whether a task dispatches, not how wide it is. Both cases are `skip_golden` completion/smoke cases with no full-network reference. The TMR ring heap grows to 2 GiB to match HBG's, since tile scratch is now allocated for all 32 experts of every MoE layer whatever the routing turns out to be.

`SIMPLER_HBG_BIND_BREAKDOWN_ENABLE=1` has itemized the bind path for a while, but nothing said which segments make up the number the < 1 ms target applies to, how to get the two decode cases to emit the lines at all, or which of the readings are wrong rather than merely noisy. Every one of those cost a wasted device job to learn. This is that page. The definition worth writing down: the control plane is `host_orch + graph_upload + relocate + sm_h2d + arena_h2d`, and it is a **sum of cost shares, not an interval** -- `arena_h2d` runs after `host_view_close`, some 250 ms later, so subtracting two timestamps overstates it by two orders of magnitude. `args` and `host_view_close` are excluded because they price the ~41-46 GB of weights each case stages, not the graph. Four traps have each produced a confidently wrong answer: - A `device_count > 1` case run through the module runner yields a **passing** log with zero `bind phase=` lines, because `run_jobs` prints a child's stdout only on failure. The L3 child command has to be issued directly. - `SIMPLER_SKIP_DEVICE_RUN` is presence-based, so `=0` still skips -- a run then reports PASSED having executed nothing. - Comparing two branches sequentially attributes a drift in host load to the branch. The conditions have to interleave, and the per-pass sign has to agree. - A sum of per-segment minima is a total no pass achieved, and it can invert the sign against the minimum of the per-pass sums -- observed at -0.30 ms against +0.16 ms on one dsv4 log. The reference numbers are `upstream/main` on both cases, as ranges rather than points, because the range is what tells a reader whether their effect is measurable at all: `host_orch` alone spans 2.6-4.9 ms on dsv4 across passes of an unmodified build. Where a segment's scatter swamps the change, the page says to instrument the mechanism instead of timing the segment. The table is pinned to a commit (`777d4171`) and says why: the counts move too. Re-measuring against current `main` before opening this found four of its numbers already stale -- dsv4's `graph_upload` bytes 613,424 -> 671,144 and one task moved between `submit_task` and `alloc_tensors` (839/272, not 838/273), both from hw-native-sys#1926's predicated MoE tile loops; the case's kernel sources are 369, not 367; and the checked-in dsv4 swimlane holds 24 host spans, not 20. Everything structural was unchanged -- task counts, Definition node counts, boundary widths, `heap_used`, `sm_h2d` bytes -- which is the distinction the page now draws for the reader. The page is listed in `mkdocs.yml`'s nav. The docs build runs `--strict`, so a page present under `docs/` but absent from the nav is a build failure, not a warning. Kept as one page at 277 lines and 8 H2 sections, above doc-consistency's soft trigger: the content is majority tables and command blocks rather than prose, and the reference numbers earn their place directly below Recipe A because that is where a reader compares a fresh measurement against them.

`SIMPLER_HBG_BIND_BREAKDOWN_ENABLE=1` has itemized the bind path for a while, but nothing said which segments make up the number the < 1 ms target applies to, how to get the two decode cases to emit the lines at all, or which of the readings are wrong rather than merely noisy. Every one of those cost a wasted device job to learn. This is that page. The definition worth writing down: the control plane is `host_orch + graph_upload + relocate + sm_h2d + arena_h2d`, and it is a **sum of cost shares, not an interval** -- `arena_h2d` runs after `host_view_close`, some 250 ms later, so subtracting two timestamps overstates it by two orders of magnitude. `args` and `host_view_close` are excluded because they price the ~41-46 GB of weights each case stages, not the graph. Four traps have each produced a confidently wrong answer: - A `device_count > 1` case run through the module runner yields a **passing** log with zero `bind phase=` lines, because `run_jobs` prints a child's stdout only on failure. The L3 child command has to be issued directly. - `SIMPLER_SKIP_DEVICE_RUN` is presence-based, so `=0` still skips -- a run then reports PASSED having executed nothing. - Comparing two branches sequentially attributes a drift in host load to the branch. The conditions have to interleave, and the per-pass sign has to agree. - A sum of per-segment minima is a total no pass achieved, and it can invert the sign against the minimum of the per-pass sums -- observed at -0.30 ms against +0.16 ms on one dsv4 log. The reference numbers are `upstream/main` on both cases, as ranges rather than points, because the range is what tells a reader whether their effect is measurable at all: `host_orch` alone spans 2.6-4.9 ms on dsv4 across passes of an unmodified build. Where a segment's scatter swamps the change, the page says to instrument the mechanism instead of timing the segment. The table is pinned to a commit (`777d4171`) and says why: the counts move too. Re-measuring against current `main` before opening this found four of its numbers already stale -- dsv4's `graph_upload` bytes 613,424 -> 671,144 and one task moved between `submit_task` and `alloc_tensors` (839/272, not 838/273), both from #1926's predicated MoE tile loops; the case's kernel sources are 369, not 367; and the checked-in dsv4 swimlane holds 24 host spans, not 20. Everything structural was unchanged -- task counts, Definition node counts, boundary widths, `heap_used`, `sm_h2d` bytes -- which is the distinction the page now draws for the reader. The page is listed in `mkdocs.yml`'s nav. The docs build runs `--strict`, so a page present under `docs/` but absent from the nav is a build failure, not a warning. Kept as one page at 277 lines and 8 H2 sections, above doc-consistency's soft trigger: the content is majority tables and command blocks rather than prose, and the reference numbers earn their place directly below Recipe A because that is where a reader compares a fresh measurement against them.

coderabbitai Bot reviewed Aug 20, 2026

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Comment thread examples/a2a3/host_build_graph/deepseek_v4_flash_decode/README.md Outdated

Comment thread examples/a2a3/host_build_graph/deepseek_v4_flash_decode/README.md Outdated

ChaoZheng109 force-pushed the feat/dsv4-predicate-dispatch branch 3 times, most recently from 2242ff1 to 0a13c96 Compare August 20, 2026 11:59

ChaoZheng109 merged commit 777d417 into hw-native-sys:main Aug 20, 2026
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ChaoWao mentioned this pull request Aug 20, 2026

docs: how to measure the host_build_graph bind path #1934

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ChaoWao mentioned this pull request Aug 20, 2026

host_build_graph: upload the bind image once, and only the bytes the device reads #1932

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Refactor: express the DSv4 MoE tile-loop control flow as dispatch predicates - #1926

Refactor: express the DSv4 MoE tile-loop control flow as dispatch predicates#1926
ChaoZheng109 merged 1 commit into
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ChaoZheng109:feat/dsv4-predicate-dispatch

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