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Author SHA1 Message Date
Aharrypotter 2786eb9886 bench: orchestrate a multi-card campaign without touching the measurement path 
campaign.sh fans a screened operator list across cards, one ab_repeat.sh per
card, serial within a card. It does no measuring: ab_repeat.sh is the path that
produced the four operator verdicts already on record, and it stays byte for
byte as it was.

Each card gets its own AB_ROOT. Eight ab_repeat.sh started in the same second
would otherwise compute the same timestamped directory name and two cards would
write into one.

ZL_ISOLATE_TUNER_CACHE=1 is forced, since eight processes on one sqlite is the
thing to avoid. The cost is that pinned configs differ from a shared-cache
batch, so absolute latencies cannot be quoted across the two; the caliber guard
refuses such comparisons on its own, which is why this is a note and not a
warning someone has to remember.

partition.py's shell output gained the per-card weight, because EXTRA_OPS has
to land on the lightest card and counting operators gets that wrong — one heavy
operator outweighs three light ones. It writes through stdout.buffer: Python
turns "\n" into CRLF on Windows, and the trailing \r then rides into the last
field and breaks the numeric comparison that picks the card.
 2026-09-10 19:43:37 +08:00
Aharrypotter 03fdcbab28 bench: partition operators across cards by measured cost, and rescore in place 
Two pieces the parallel campaign needs.

partition.py assigns screened operators to cards longest-first. run_batch.py's
existing hash partition ignores weight: on this batch it puts the critical path
at 120 minutes and leaves a card idle, where greedy gets 70. The floor is set
by the single heaviest operator — topk alone is 70.6 minutes against an ideal
share of 45.4 — so partitioning cannot beat it, only keep the other cards from
waiting. Weights come from screen.tsv's elapsed_before + elapsed_after, which
is one pair, i.e. 2 cells; N rounds is 2N cells, so the estimate is N times the
weight. Getting that wrong reported topk as 11.8 minutes.

RESCORE=<root> re-judges an existing screening root against the current
fingerprint and rewrites its screen.tsv, keeping the original alongside. The
criterion has already changed twice — index.tsv used to be hashed, and the
switch's own ttg.rlc-* annotations had to be stripped — and the second change
moved 24 operators from 23 IR_CHANGED to 16. Rescoring the artifacts already on
disk takes seconds; re-running that screen takes 79 minutes.
 2026-09-10 19:25:34 +08:00
Aharrypotter c6a7f834cb bench: ignore the switch's own annotations when fingerprinting the IR 
FlagTree's _apply_musa_rlc_policy(mod) stamps the whole RLC policy onto the
module as ttg.rlc-* attributes whenever the switch is on, regardless of whether
the pass then changes anything. Hashing the raw text therefore carried a
constant offset: every operator that compiles a kernel differed on that one
line and screened as IR_CHANGED. A filter that excludes nothing is not
filtering.

The null control could not catch it. Both of its sides run with the switch off,
so both carry the same attributes and the fingerprints match — it proved the
dump is deterministic, which was true, and said nothing about this.

Stripping ttg.rlc-* before hashing turns the screen back into a filter. Over
the same 24 operators, recomputed from the artifacts already on disk: 23
IR_CHANGED becomes 16, with 7 falling back to NO_IR_DIFF — tanh, gelu, mul,
softmax, mm, bmm, replication_pad3d, each byte-identical apart from that line.
replication_pad3d is the PR's largest NVIDIA win at +124.6%; on MUSA this pass
does not touch it.

The counts also show why the convert_layout count fails as a criterion in both
directions: addmm keeps cvt at 15 -> 15 while all 15 of its ttgir files differ
in substance, and tanh has no conversions and no change.
 2026-09-10 19:13:51 +08:00
Aharrypotter 4ac2a9acd8 bench: report when a foreign vendor's FlagGems implementation gets loaded 
FlagGems keeps per-vendor implementations under
flag_gems/runtime/backend/_<vendor>/ops/, and benchmarks commonly gate on
whether one imports:

    try:
        from flag_gems.runtime.backend._nvidia.hopper.ops.mm import router_gemm
        ROUTER_GEMM_AVAILABLE = True
    except Exception:
        ROUTER_GEMM_AVAILABLE = False

An import only proves the .py is on disk. It touches no device, so it succeeds
on every backend and the gate never fires. test_router_gemm.py therefore ran
the NVIDIA Hopper kernel on an S5000: its autotune and heuristics are keyed to
nvidia, supply no BLOCK_M/N/K/SPLIT_K on musa, and the run died deep inside the
JIT with "dynamic_func() missing 4 required positional arguments" — an error
that points nowhere near the cause. Meanwhile _mthreads/ops/mm.py holds a
working router_gemm the benchmark never imports. Gating on vendor_name first,
the way the Cambricon branch in the same file already does, makes it run: 10
shapes, 10 kernels, cvt 10 -> 2.

The plugin now scans sys.modules at session end and prints
FOREIGN_VENDOR_MODULE with the vendors it found. It reports rather than
refuses: loading another vendor's module is not proof of a problem, but when a
result looks wrong it is the first line to check. Vendor roots are compared
segment-wise, not by prefix, so a vendor whose name extends ours is not
mistaken for our own.

README gains the ordered checklist for an operator that will not run, with the
real instance behind each entry.
 2026-09-10 18:51:26 +08:00
Aharrypotter 2e456bdc74 bench: let the verdict govern the report, and put the caliber in the table 
A pair judged incomparable was still followed by a speedup range on the next
line. A NO_TRITON pair's range says nothing about the switch — nothing reached
the compiler — and a NO_IR_DIFF pair's range is the machine's noise floor for
that operator, which is useful but is not a result. Printing either unlabelled
produces exactly the thing this tooling exists to prevent: a table that looks
like a finding. NO_ROWS and NO_TRITON now print no range; NO_IR_DIFF prints one
and says what it is — how far it sits from zero is the confidence floor for a
single measurement of that operator.

screen.tsv gains the seven caliber columns, read back through
`batch_summary.py --caliber`, so CALIBER_FIELDS stays the single definition and
the screening table does not restate it. Two screen.tsv files taken at
different --level are otherwise indistinguishable side by side, and those
columns are the only thing that would stop the comparison.
 2026-09-10 17:10:59 +08:00
Aharrypotter a11863399a bench: bound each screening pair and record what it cost 
Screening is first contact with an operator, which makes it the place most
likely to meet one that hangs — and it had no upper bound at all, so a single
operator could hold the whole pass indefinitely. The entire argument for
having a screening step is that it is fast.

CELL_TIMEOUT (default 2400s, as in ab_repeat.sh) now caps each side, and a
truncated pair says so instead of being folded into the dtype explanation: rc
124/137 is a cost problem, not a verdict about the switch.

elapsed_before/elapsed_after join screen.tsv. The two-step design rests on the
ratio between a pair and six rounds — 0.8 minutes against 13-44 on S5000 — so
the pass should record its own half of that ratio rather than leave it to be
reconstructed from directory timestamps.
 2026-09-10 17:08:08 +08:00
Aharrypotter 1fd3abd56d bench: rename NO_KERNEL to NO_TRITON and say what it rules out 
"NO_KERNEL" reads as "this operator has no kernel", which is not what the
verdict means and not usually true. It means the run compiled no Triton
kernel: the operator may have run perfectly well through aten or a vendor
library, and a GPU kernel certainly executed. What is absent is anything for
the Triton compiler pass under test to act on.

The note now says that, and says the one alternative explanation is already
ruled out: run_pytest.sh gives every run its own TRITON_CACHE_DIR, so zero
.ttgir cannot be compilation skipped by a warm cache.
 2026-09-10 17:00:45 +08:00
Aharrypotter 4d69320139 bench: let ab_rlc_test.sh decide which operators are worth six rounds 
zl_bench already had the screening mechanics — ab_rlc_test.sh runs exactly one
config-pinned pair per operator (before records, after replays it) against the
vendor-prefixed RLC switch. I had started a second script for the same job
instead of extending this one; that is removed.

What the existing script could not tell you is whether the pair is worth
following with six rounds, which costs 13-44 minutes per operator against
about 0.8 for the pair. It now writes a verdict per operator to
<root>/screen.tsv, and three of the four are not "the switch does nothing":

  IR_CHANGED  the two sides' ttgir differ         -> measure it
  NO_IR_DIFF  byte-identical, configs pinned      -> skip it
  NO_KERNEL   rows but no .ttgir                  -> never reached Triton here
  NO_ROWS     no measurements                     -> usually the dtype

The criterion is the ttgir fingerprint, not the convert_layout count: equal
counts do not mean equal generated code, and what the A/B measures is whether
the generated code changed. Counts are still recorded — they say what kind of
change it was. The fingerprint comes from _ir_fingerprint.sh, shared with
ab_repeat.sh's round-1 check so the two cannot drift apart.

OPS entries take an optional per-operator dtype and default to the operator's
own set. Forcing one dtype across a list is how fft got written off as
unrunnable on MUSA: it only accepts complex64.

Output moves under runs/ab_rlc_<ts>/<op>_{before,after} with OUT_DIR set
explicitly, rather than recovering the directory with `ls -dt runs/<op>_*`.
That lookup is correct while one process runs operators in series, which is
the only way it has been used; it stops being correct the moment several cards
share runs/.
 2026-09-10 16:54:26 +08:00
Aharrypotter 2fbfe1dd16 bench: make screening a first-class step with the same caliber as the A/B 
Six rounds of A/B cost 13-44 minutes per operator on S5000 (rand, 15 shapes:
13.0 min; sort, 25 shapes: 43.4 min). Screening one costs about 0.8 minutes —
21 operators in 15.0 minutes, which knocked 13 of them out of the expensive
queue and cut it from ~7 hours to ~2.7. That ratio is the whole argument for
having a separate first step.

Screening was being done by ad-hoc scripts living in the results tree, and
those got two things wrong that this codebase already knows better than:

Both sides ran `record`. Each swept autotune independently, so the two sides
compiled different configs and produced different IR for reasons that have
nothing to do with the switch — every operator would screen as "changed".
screen.sh pins configs exactly as round 1 of ab_repeat does: A records, B
replays A.

The criterion was the convert_layout count. Equal counts do not mean equal
generated code, and what the A/B measures is whether the generated code
changed. The ttgir fingerprint decides; the cvt counts are still recorded,
because they explain what kind of change it was.

Four verdicts, and three of them are not "the switch does nothing":
IR_CHANGED (measure it), NO_IR_DIFF (skip it), NO_KERNEL (the operator never
reached Triton on this backend), NO_ROWS (it did not run — usually the dtype).
The last two are runnability problems being mistaken for switch results, which
is how `fft` got written off: the screen forced DTYPES=float16 on every
operator and fft only accepts complex64. OPS entries now carry an optional
per-operator dtype and default to the operator's own set.

ir_fingerprint moves to _ir_fingerprint.sh so screening and the A/B cannot
drift apart on the criterion.
 2026-09-10 16:49:32 +08:00
Aharrypotter 5236d0745f bench: fingerprint only the IR, and refuse to compare when there is none 
ir_fingerprint hashed every file under the dump directory, which includes
index.tsv and naming.md. An operator that compiles no Triton kernel at all
leaves only those two, identical on both sides, so the switch-effectiveness
check reported "byte-identical ttgir" and skipped the operator with
SWITCH_NO_IR_DIFF — "the switch changed no generated code" — when the truth
was that nothing was generated. router_gemm on MUSA is exactly this case: its
kernel cannot be instantiated there (missing BLOCK_M/N/K/SPLIT_K from the
autotune path), zero .ttgir files, and the check called it a no-op switch.

Now only *.ttgir files are hashed, and a run with none returns failure so the
caller treats it as "no comparable IR" rather than "identical". The message on
that path says so, and says explicitly that it does not mean the switch is
inert.
 2026-09-10 16:47:39 +08:00
Aharrypotter 551ffbbb77 bench: exit the sampler quietly when its reader goes away 
Piping `telemetry.py --run` into anything that stops reading first — head, or
the campaign shutting the sampler down — raised BrokenPipeError and printed a
traceback. Under ab_repeat that traceback lands in telemetry.err, where it
reads as "telemetry is broken" rather than "telemetry ended normally", and
telemetry.err is exactly the file someone checks when the clock record looks
short.

Swallowing the exception alone is not enough: the interpreter flushes stdout
again on exit and emits a second "Exception ignored" to stderr, so stdout is
pointed at devnull the way CPython documents.
 2026-09-10 16:10:15 +08:00
Aharrypotter 4329b0c835 docs: stop claiming non-CUDA backends never attempt graph capture 
Both the _timing_plugin docstring and the README env table still described the
behaviour from before MUSA declared supports_cuda_graph: "other backends do
not attempt CUDA Graph at all", "非 CUDA 即使设 1 也拒绝". Capture selection has
been driven by the backend capability, not by "is this CUDA", since the MUSA
fallback policy landed. Reading the old text is most likely why the S5000
campaigns were launched with ZL_ENABLE_CUDAGRAPH=0 without anyone checking
what auto would do.

Also states what timer=cuda_graph does and does not mean, since the two are
easy to conflate: it says capture was attempted. Measured on S5000 with auto
and configs pinned, dropout/rand/masked_select select the graph path and then
fall back on every single shape — BENCHMARK_DIRECT_NO_CUDAGRAPH on all 15
rows, zero poisoned, zero rows lost — so the whole table is direct-timed under
a cuda_graph label. ZL_ENABLE_CUDAGRAPH=0 selects the same path and records it
plainly, which is why the existing campaign data stands as measured.
 2026-09-10 16:00:44 +08:00
Aharrypotter ee5f70fa89 bench: record device clocks, temperature and power for the whole campaign 
A 6-round A/B here reports side noise at the 0.01% level. Clock throttling and
thermal drift move latency by far more than that and leave no trace in
run.log, so the report comes out looking exactly as clean as a run on a stable
machine. The NV run that reproduced PR763 sampled nvidia-smi every 5s
alongside the campaign; the MUSA campaigns had no equivalent, and the parallel
plan makes it worse by heating the whole box.

telemetry.py samples through the backend, so cuda and musa emit the same
columns (index, sm_clock_mhz, mem_clock_mhz, temp_c, power_w, util_pct) and
two machines' campaigns can be read side by side. mthreads-gmi offers no CSV
query mode, so the MUSA path parses --json and strips the units glued onto
each value ("36C", "1888MHz", "477.68W"); its "Power Draw " key carries a
trailing space, matched by stripping rather than by literal.

The device comes from the backend's own visibility variable when not pinned. A
list of devices resolves to no -i at all, sampling every one, rather than
silently picking the first. Dropping the placeholder drops its flag too: a
bare trailing -i fails the sampler instead of widening it.

Telemetry is evidence, not a gate. A missing or unpermitted sampler exits
quietly with its reason in telemetry.err and the campaign runs unchanged.
 2026-09-10 16:00:01 +08:00
Aharrypotter d45806eacd bench: log every A/B cell as it finishes, and cap it with a timeout 
The three markers the README requires be checked — AUTOTUNE_REPLAY_FALLBACK,
BENCHMARK_DIRECT_NO_CUDAGRAPH, BENCHMARK_CAPTURE_POISONED — were only
reachable by grepping run.log afterwards, which happens when someone
remembers to. The NV run that reproduced PR763 wrote a per-cell status row
instead; a 6-round campaign here had no equivalent, so a missing cell, a
stalled cell, or a marker count going from 0 to nonzero was invisible until
the batch ended.

run_cell now writes round/op/side/switch/rc/elapsed_s/success/failed plus the
three marker counts to <root>/status.tsv as each cell completes, and wraps the
cell in CELL_TIMEOUT (default 2400s, matching the NV run) so one hung cell
cannot hold the batch. rc 124/137 is called out by name.

grep -c prints 0 and exits 1 when it finds nothing, so each count swallows its
status; without that the bookkeeping itself would mark a clean cell failed.
 2026-09-10 15:55:36 +08:00
Aharrypotter 92f3d150c7 bench: preflight the interpreter, not just the paths 
triton normally comes from a PYTHONPATH pointing at a compiler build and
flag_gems from a source checkout, so a shell that reaches every correct path
can still be the wrong Python. run_pytest.sh checked that the FlagGems
directory and benchmark file exist and then handed the run to pytest, where
the real problem surfaced per cell as `pytest exit 4` from a conftest
ImportError.

A multi-round campaign burns every cell that way before anyone reads a log:
today's noise-calibration run lost 12 measurement cells and 160 load runs to
`ModuleNotFoundError: No module named 'triton'`, each failing in under a
second and each looking, from the outside, like a cell that ran.

The check names the interpreter it used, since which Python is on PATH is the
whole question. Its stderr redirect stays on the child — putting `2>/dev/null`
on the preflight call swallows preflight's own `>&2` message and turns a named
refusal into a bare exit 3.
 2026-09-10 15:18:00 +08:00
AharrypotterandClaude Opus 5 c95474a432 bench: refuse to report a comparison whose two sides have different calibers 
Two runs on different calibers produce a report that looks exactly like a
valid one — no step errors, the numbers just mean something else. This repo
has published three figures that were an artifact of such a difference:
router_gemm -8.2% (record vs record), mm "945 variants -> 33" (shared vs
inherited libtuner cache), replication_pad3d +43% vs +124.5% (timer).

batch_summary --ab / --ab-rounds now reads every participating run's
run_identity.json and compares backend/device/timer/bench_level/shape_mode/
tuner_cache/direct_repeat before parsing a single latency. Mismatch, or a
missing identity, prints !!! [caliber] and exits 4. autotune_mode is judged
separately: record pinning a replay is the normal shape, both sides record
is the exact trap above and is refused by name. --allow-caliber-mismatch
releases it, still printing what it found.

Recording on a *shared* tuner cache warns rather than refuses: the pinned
config depends on what else ran on the machine, which leaves relative
conclusions intact and absolute latencies unquotable. Refusing it would
reject the default every A/B here currently uses.

ab_repeat.sh closes the one axis that had no check: after round 1 it
fingerprints both sides' ttgir (relative path + content, so a changed
variant *set* counts too). Byte-identical means the switch changed no
generated code for this op, and the remaining N-1 rounds would measure
noise and report it as "no difference" — so the op is skipped with
SWITCH_NO_IR_DIFF unless SWITCH_ALLOW_NO_IR_DIFF=1.

README leads with a caliber table: each axis, its measured magnitude, what
pins it, and which code checks it. The fourth column is code because
documentation did not hold — the README already said "the B side must use
REPLAY_FROM" when the record-vs-record number was published.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
 2026-09-10 14:32:49 +08:00
AharrypotterandClaude Opus 5 aa147c938e feat: repeat an A/B over rounds and report whether the verdict holds 
A single pair cannot say whether its own verdict is stable, and on this device
that is not a theoretical gap. Measured on an S5000, topk eliminated 32
convert_layout ops yet 17 of its 42 shapes changed the SIGN of their delta
between two equally valid runs. The cause is not the timer: graph capture and
direct timing agree to 1.00x on that operator, and the verdict moved a median of
1.1pp between them, inside the 1.7pp noise floor a same-timer control
established. The deltas are simply near zero, and one measurement cannot orient
them.

ab_repeat.sh runs an A/B over N rounds in independent processes. Round 1's A
side records the autotune configs and every later run -- including A's own later
rounds -- replays that one set, so configs are pinned across rounds and sides and
only the switch under test and measurement noise remain. Round order alternates
to cancel one-directional drift. An operator with a failed round gets no report,
since a report missing rounds reads exactly like a complete one.

batch_summary --ab-rounds aggregates <root>/r<k>_<A|B>/run.log and separates the
two dispersions, which answer different questions and were previously conflated:
the spread within one side's samples says how noisy the machine was, while the
spread of the per-round deltas says whether the verdict holds. Shapes whose
delta changes sign across rounds are counted and called out, because their
direction is not a conclusion at any timer.

The path is already validated on NVIDIA: six rounds narrowed router_gemm to
-16.6% ~ -16.2% with 0.4% side noise, which is what turned a single pair into a
statement.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
 2026-09-10 13:11:37 +08:00
AharrypotterandClaude Opus 5 9665d36a7c feat: allow the direct timer to repeat and take the median 
do_bench already returns a median over the iterations inside one call, which
suppresses iteration-level jitter. It cannot see drift across a call: clocks
ramping, another process arriving. Direct timing is where that shows, because
it walks the full Python launch path once per iteration, so on small kernels the
number carries launch overhead and CPU jitter that graph capture removes.
ZL_DIRECT_REPEAT=N repeats the call and takes the median over calls, which is
the layer the inner median misses. It defaults to 1, so nothing changes unless
asked.

The knob covers both ways a row ends up on the direct timer: a run that selected
timer=direct outright, and the rows a captured run falls back on, whose marker
now carries repeat=N since those rows were already a different caliber from the
captured ones and can now also be a different sample count. Repetition is
skipped when the caller asks for quantiles or return_mode "all", because the
result is then a sequence and a median across calls is undefined for it.

On MThreads this is not a refinement. A convert-reduction screen over 21
operators puts the largest reductions at sort (65->15), dropout (48->0) and
rand (24->0), and all three are exactly the operators that cannot be
graph-captured there -- RNG seeding and a capture-illegal stream op. The
headline numbers on that device are produced by the noisiest timer, which until
now had no repetition at all.

_cudagraph_plugin imports triton at module scope, so the tests load it against a
stub: the repeat logic is arithmetic and should stay testable without a GPU.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
 2026-09-10 12:56:08 +08:00
AharrypotterandClaude Opus 5 d7cb7efc94 fix: have the campaign worker ask zl_bench for its plugin list 
The worker carried a hand-copied plugin array, taken from zl_bench before
backends/ existed, and it had drifted twice over. It still named
_device_guard_plugin, which is now a shim for _device_setup_plugin. More
consequentially it carried no timing plugin at all, so every measurement this
campaign ever sealed was timer=direct while zl_bench's own driver had been
capturing -- a difference the harness never reported, because the missing plugin
cannot announce itself.

It now calls plugin_plan.py with ZL_BACKEND=musa and uses whatever comes back,
so the caliber follows the backend's declared capabilities instead of a snapshot
of them. The plan is preflighted for non-emptiness, since an empty array would
silently run pytest with no plugins at all: no shape injection, no autotune
record, no IR dump, and a table that still looks well-formed.

Two effects on this campaign. _timing_plugin now enters the list, and since
MUSA declares graph capture the worker will capture where the operator allows
it; operators that cannot are marked per row, as on any other backend. And
_parallel_warmup_plugin joins, inert unless PARALLEL_WARMUP_GPUS is set.

The sealed 32-workload run therefore sits on the far side of a caliber change
and is not comparable with anything produced after this commit. Re-running it is
a campaign decision, not a harness one.

The worker test asserted the literal "-p _musa_benchmark_compat_plugin"; it now
asserts the delegation and that no -p is spelled out locally. What the plan
holds for musa is covered where it belongs, in zl_bench's test_backends.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
 2026-09-10 10:34:05 +08:00
AharrypotterandClaude Opus 5 75405cc555 Merge the S5000 MUSA RLC product campaign 
Brings in campaigns/musa_rlc_product/: the runner, its worker, manifest
validation, the sealed 32-workload manifest, a topology re-accounting tool and
their tests. Developed on a fork branch that never reached main, and it merges
without conflict because it only adds a new directory; the one file it shares
with main, _musa_benchmark_compat_plugin.py, is byte-identical to the copy
already committed here.

The campaign layer sits above zl_bench rather than inside it: freezing a
workload set, classifying outcomes, and producing receipts, where zl_bench turns
one operator into one reproducible measurement. Two of its ideas are worth
naming because they are absent below it. Narrowing what a workload measures --
which correctness variant, which benchmark case -- requires a stated reason that
manifest validation enforces, so an exclusion stays visible in review. And
reaccount_musa_rlc_topology.py re-derives verdicts from sealed artifacts under a
corrected accounting rule without re-running anything, while refusing to promote
a workload on the new count, only to demote it.

Its worker still hardcodes a plugin list copied from a pre-backends zl_bench,
which is why the campaign has been timing with timer=direct. Switching it to
plugin_plan.py is the next change and will alter its caliber, so the sealed run
is not comparable across that boundary.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
 2026-09-10 10:32:33 +08:00
AharrypotterandClaude Opus 5 29af59be9b feat: absorb the MThreads compat plugin and the A/B range report 
Three pieces from the fork's feature/musa-benchmark-support branch that never
reached main, rebased onto the backend abstraction rather than copied verbatim.

_musa_benchmark_compat_plugin drops FlagGems benchmark skip guards written as
`torch.cuda.is_available()` where the code under test dispatches through
flag_gems.device. On MThreads those turn a working operator into "not measured":
test_fft.py skipped entirely, and unskipped on an S5000 it produces 25 rows. The
plugin pins each override to one file and one exact skip reason and only acts
when flag_gems has dispatched to the mthreads vendor; two guards were probed on
device and deliberately left in place, which is why they are named in its
docstring rather than silently absent.

It is wired through a new DeviceBackend.compat_plugins rather than a name test
in the planner, so the policy stays where every other backend difference lives.
Compat plugins are planned first because they rewrite collection-time markers
and nothing may read the item list before them; a test pins that position and
another pins that they do not leak to other backends.

batch_summary gains --ab: the speedup column compares gems against torch, not
one build against another, so an A/B needs the two run.logs paired by operator,
dtype and size detail. Only rows that succeeded on both sides with a positive
latency count, so an incomplete pairing cannot look like a complete range, and
the range is annotated when either side carries a BENCHMARK_DIRECT_NO_CUDAGRAPH
marker, since a fallback row was timed under a different regime.

ab_rlc_test.sh is the same caliber as ab_fold_test.sh with the comparison axis
moved to FLAGTREE_<VENDOR>_RLC_ENHANCE. Both are caliber examples, not
regression scripts: if the switch under test no longer exists the script still
runs every round and prints a full table showing no difference, so confirm the
axis is live before reusing either.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
 2026-09-10 10:31:59 +08:00
AharrypotterandClaude Opus 5 c9f9b7885f feat: check the run's preconditions, and record which tuner cache it used 
Two failure modes that cost this harness real results, both of them silent.

A wrong path does not fail as a wrong path. pytest cannot collect a benchmark
directory that is not there, so benchmark/conftest.py never registers --level
and --mode, and the run dies with "unrecognized arguments: --level core --mode
kernel" -- an error about the flags, several layers away from the missing
directory. run_pytest.sh now checks the FlagGems directory, the benchmark file,
SHAPE_FILE and REPLAY_FROM before starting pytest, prints
"PREFLIGHT_FAILED check=<the named check>" and exits 3, distinct from pytest's
own 1 and 4 so the batch layer can tell a broken environment from a broken
operator. Only the file half of a node is checked; a missing ::test_<fn> already
produces a clear "not found: ..." from pytest and needs no help.

FlagGems keeps libtuner winners in a sqlite DB under $FLAGGEMS_CACHE_DIR
(default ~/.flaggems), shared by every run on the machine. Whichever side of an
A/B runs first therefore hands its winners to the second, which then compiles
only those: measured on router_gemm, the second of two shared-cache runs
compiled 10 variants instead of 234 and finished in 17s instead of 52s. That is
how an mm A/B in this repo produced "945 variants -> 33, 136 converts -> 10" and
read as a compiler result when it was cache inheritance; isolated, both runs
compile 234.

ZL_ISOLATE_TUNER_CACHE=1 gives a run its own cache directory, dropped afterwards
with the Triton one since the chosen configs are already in autotune_records/.
It defaults to 0, because cold tuning is expensive and the standard REPLAY_FROM
A/B pins configs anyway; only record-against-record is exposed. The default is
unchanged, but the caliber is no longer invisible: either way it goes into the
run header and run_identity.json as tuner_cache, which is what was missing when
the mm numbers could not be explained after the fact.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
 2026-09-10 10:31:36 +08:00
Aharrypotter e970ed94c2 docs: document the MUSA RLC campaign 
Covers the three screen cells and why replay locks them to one variable, how to
freeze and run a campaign, what the runner refuses to start on, the six
classifications, and the three scoping rules that decide whether a verdict
means anything.

Also states what a terminal campaign does not prove, since the manifest fixes
production_default_policy to unchanged-off and a passing run is not an argument
for flipping it.
 2026-09-02 11:36:33 +08:00
Aharrypotter 8a685638bb feat: re-account a sealed MUSA RLC run without re-running it 
A sealed run already holds every TTGIR dump and its launch index, so a change
in how convert topology is counted can be applied to it directly instead of
spending accelerator time.

The tool re-reads each cell's dump, recomputes the scoped count, re-runs the
campaign classifier against the sealed correctness verdicts, and writes a
derived receipt and matrix outside the run root. It refuses to write inside a
sealed run and rejects a directory with no campaign receipt.

A derived verdict can only demote a topology claim. Promoting one to G or C
needs reversed-order timing, which a sealed run holds only for tasks that
already showed a reduction, so a reduction appearing for the first time under
scoping is reported as X for missing round-b evidence rather than guessed.

Run against the sealed 32-workload S5000 campaign, a floor of 1 changes exactly
one task: gelu_backward moves from C to B, and the only excluded files in the
whole campaign are its five once-launched randn_kernel dumps. A floor of 0
reproduces the sealed counts with no task changed, which is the equivalence
that makes the scoped number auditable rather than merely different.
 2026-09-02 11:35:43 +08:00
Aharrypotter 8bbea472bb feat: add the S5000 MUSA RLC product campaign 
A frozen 32-workload campaign that measures how FlagTree's RemoveLayoutConversions
pass behaves on a Moore Threads S5000, through FlagGems public dispatch and this
repository's existing plugins.

The manifest is the contract: source commits, the single physical device, the
three screen cells off/mask0/candidate, the native-event timer, and per-workload
dtypes, timeouts and canary membership. prepare validates it against
ops/inventory_main.csv and freezes an immutable task matrix; the runner executes
the matrix serially, and the worker drives one operator cell through pytest with
_ir_meta_plugin, _autotune_record_plugin and the rest.

Three properties are worth calling out because they decide whether a verdict
means anything.

Convert topology is scoped to the measured operator. A benchmark call builds its
inputs once and then runs the operator many times under the timer, so counting
ttg.convert_layout over every executed kernel credits the candidate with
converts removed from input construction that no timer can observe. Each dumped
TTGIR file is joined to its launch count from index.tsv and only variants
launched more than measured_launch_floor contribute; the unscoped total and the
excluded files stay in the receipt, and a cell that cannot be joined to its
index is classified X rather than trusted.

Correctness is scoped to the variant a workload measures. Running a whole test
file lets an unrelated variant decide the verdict, so a workload may pin node
ids or a -k selector, never both, and any narrowing must state its reason.

Targets are resolved before the campaign starts. The inventory can lag the
pinned FlagGems commit, and such a task previously failed only inside the
worker's preflight, which exited silently and left an empty log. The runner now
resolves every benchmark and correctness target against the real tree, a
workload may name a target the inventory does not know about if it says why,
and each preflight check names itself on failure.
 2026-09-02 11:35:30 +08:00
Aharrypotter 22139e4b87 chore: pin shipped text formats to LF 
This repository's drivers are shell scripts executed on Linux accelerator
hosts, and its consumers record the SHA-256 of the files they ran. With Git's
default autocrlf on Windows a checkout rewrites those files with CRLF, which
breaks the shell drivers outright and changes every digest a receipt refers to.
 2026-09-02 11:34:48 +08:00
Aharrypotter 3b54f458e6 feat: add a MThreads benchmark compatibility plugin 
Several upstream FlagGems tests gate themselves on torch.cuda.is_available()
even though the code under test runs on whatever accelerator FlagGems
dispatches to. On MThreads those guards skip the operator instead of
exercising it, so a working path reports as "not measured".

The plugin drops such a guard only when it is pinned to one file and one exact
skip reason, only when FlagGems has dispatched to the MThreads vendor, and only
after the unskipped test was observed to pass on an S5000. It walks the node
chain rather than the item's own markers, because a module-level pytestmark
does not appear on the item.

Two guards were probed on device and deliberately left in place, which is why
they are named in the module docstring rather than silently absent.
test_grid_sample.py builds its inputs through a helper whose device argument
defaults to the literal "cuda": unskipped, all 90 collected cases fail in
aten::empty on a CUDA backend that is not present. test_cutlass_scaled_mm.py
gates on vLLM as well as CUDA, so removing its guard would raise on the missing
dependency instead of measuring anything.
 2026-09-02 11:34:04 +08:00
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README.md
+264 -5
View File
@@ -6,6 +6,30 @@
2. **可解释**：自动按 shape 收集每次运行实际使用的 ttgir（带可读的变体命名），供 IR 级 diff； 2. **可解释**：自动按 shape 收集每次运行实际使用的 ttgir（带可读的变体命名），供 IR 级 diff；
3. **口径统一**：cudagraph 计时消除 launch 开销，小 kernel 的对比不被 CPU 侧噪声淹没；capture 前按 warmup 预算显式预热吸收 autotune/JIT，首轮即稳态、多轮一致。 3. **口径统一**：cudagraph 计时消除 launch 开销，小 kernel 的对比不被 CPU 侧噪声淹没；capture 前按 warmup 预算显式预热吸收 autotune/JIT，首轮即稳态、多轮一致。
## 先读这个：口径决定数字的含义
同一个算子、同一台机器、同一份代码，换一条测试口径，测出来的数字可以完全不同。
下表每一行都是本仓踩过的，量级是实测的，不是估计：
| 口径轴 | 改一下的量级 | 由谁固定 | 由谁检查 |
|---|---|---|---|
| autotune config（record vs replay） | router_gemm 差 8pp | `REPLAY_FROM` | `caliber_complaints`：两侧都 record 直接拒绝出报告 |
| timer（cuda_graph vs direct） | replication_pad3d 从 +43% 变成 +124.5% | `ZL_TIMER` / backend 能力 | 同上，`timer` 字段不一致即拒绝 |
| libtuner 持久缓存（共享 vs 隔离） | mm 看起来从 945 个变体降到 33 个——实际是 B 侧继承了 A 侧的缓存 | `ZL_ISOLATE_TUNER_CACHE=1` | 共享缓存上 record 会打 `警告 [caliber]` |
| `--level core` vs `comprehensive` | mm 的 shape 数 5 → 29，能不能覆盖到大 GEMM 全看这个 | `BENCH_LEVEL` | `bench_level` 字段不一致即拒绝 |
| 重复轮数 | 收益落在零附近时，单次测量给不出方向：topk 42 个 shape 有 17 个在两次等效运行间翻了符号 | `ROUNDS` / `ZL_DIRECT_REPEAT` | `direct_repeat` 字段不一致即拒绝 |
| 被测开关本身是否真的接上 | 开关名拼错，两侧跑同一份代码，脚本照样报出"无差异" | 开关名正确 | `ab_repeat.sh`：第 1 轮后比两侧 ttgir 指纹，相同即跳过该算子 |
**结论：一次运行的数字没有意义，只有一对同口径运行的差值有意义。** 每次运行把自己的
口径写进 `runs/<run>/run_identity.json`；`batch_summary.py --ab` / `--ab-rounds` 在出
报告前核对这些字段，不一致就拒绝，因为**口径不同的两次运行会产出一份和有效报告长得
一模一样的表**——没有任何一步会报错，只是数字换了含义。确认两次运行确实可比时，用
`--allow-caliber-mismatch` 显式放行。
文档拦不住人：README 里早就写着"B 侧必须走 REPLAY_FROM"和"只有 record 数据支撑的
结论按未验证处理"，本仓仍然发布过一个 record 对 record 得到的 router_gemm 数字。
所以上表第四列是代码，不是提醒。
单算子精测流程之上另有批量筛查层（`run_batch.py`，见「批量多算子测试」），可对双仓库上千个算子做例行体检，产物结构与单算子完全同构。 单算子精测流程之上另有批量筛查层（`run_batch.py`，见「批量多算子测试」），可对双仓库上千个算子做例行体检，产物结构与单算子完全同构。
## 快速开始 ## 快速开始
@@ -66,9 +90,11 @@ softmax:
| `REPLAY_FROM` | 空（record 模式） | 指向某次历史 run 目录，replay 其 autotune 选择。做 A/B 时 B 侧必须设（见「A/B 对比测试标准流程」） | | `REPLAY_FROM` | 空（record 模式） | 指向某次历史 run 目录，replay 其 autotune 选择。做 A/B 时 B 侧必须设（见「A/B 对比测试标准流程」） |
| `USE_FLAGTUNE` | `0` | `0` 走普通 autotune（跳过搜索、快速验证）；`1` 用 FlagTune 扩展调优空间（首跑全量搜索、慢）。A/B 两侧须取同值 | | `USE_FLAGTUNE` | `0` | `0` 走普通 autotune（跳过搜索、快速验证）；`1` 用 FlagTune 扩展调优空间（首跑全量搜索、慢）。A/B 两侧须取同值 |
| `BENCH_LEVEL` | `core` | 透传上游 `--level`。`comprehensive` 相对 `core` 做两件事：① 在 `set_shapes()` 里并入子类 `set_more_shapes()`（`mm` 因此从 5 个 shape 变 29 个，含 N/K 到 152064 的大 K 与 `models_shapes/*.yaml` 的模型 shape）；② 在 `get_input_iter()` 里对每个 shape 再产出一次变体（BLAS 是转置侧），用例数翻倍。注意 ① 只在 `SHAPE_MODE=upstream` 下可见——传了 `SHAPE_FILE` 时 `_shape_inject_plugin` 会在上游合并之后再覆盖一次、把 shape 集拉回 yaml 内容（BLAS 那类 arity 不一致的情形除外，插件会退让）；② 不经插件，任何 shape 来源下都生效。sweep 成本同步放大，shape 多时配合 `PARALLEL_WARMUP_GPUS`。A/B 两侧须取同值 | | `BENCH_LEVEL` | `core` | 透传上游 `--level`。`comprehensive` 相对 `core` 做两件事：① 在 `set_shapes()` 里并入子类 `set_more_shapes()`（`mm` 因此从 5 个 shape 变 29 个，含 N/K 到 152064 的大 K 与 `models_shapes/*.yaml` 的模型 shape）；② 在 `get_input_iter()` 里对每个 shape 再产出一次变体（BLAS 是转置侧），用例数翻倍。注意 ① 只在 `SHAPE_MODE=upstream` 下可见——传了 `SHAPE_FILE` 时 `_shape_inject_plugin` 会在上游合并之后再覆盖一次、把 shape 集拉回 yaml 内容（BLAS 那类 arity 不一致的情形除外，插件会退让）；② 不经插件，任何 shape 来源下都生效。sweep 成本同步放大，shape 多时配合 `PARALLEL_WARMUP_GPUS`。A/B 两侧须取同值 |
| `ZL_ISOLATE_TUNER_CACHE` | `0` | FlagGems 的 libtuner winner 存在 `$FLAGGEMS_CACHE_DIR/config_cache` 的 sqlite 里（默认 `~/.flaggems`），跨 run 长存。`1` 给本次 run 一个私有缓存目录，两侧各自冷调优（README「各自最优」口径），跑完随 Triton 缓存一起删——选中的 config 已在 `autotune_records/` 里，DB 本身无留存价值。共享缓存的风险是单向的：先跑的一侧写入 winner，后跑的一侧直接命中、只编译那几个胜出变体。实测 `router_gemm` 共享模式下第二次运行编译变体从 234 掉到 10、耗时 52s→17s，隔离模式下两次都是 234 个变体。走 `REPLAY_FROM` 的标准 A/B 不受影响（config 本就锁死），record 对 record 才会中招。取值无论如何都写进 `run_identity.json` 的 `tuner_cache` |
| `ZL_DIRECT_REPEAT` | `1` | direct 计时的重复次数，取中位。只作用于 direct 路径：整体 `timer=direct` 的运行，以及图捕获运行里逐行回退的那些点（标记会带上 `repeat=N`）；捕获成功的行不受影响。`do_bench` 自己已在调用内取中位，压住的是迭代间抖动；跨调用重复补的是它看不见的那层——时钟爬坡、邻居进程进来这类漂移。调用方要 `quantiles` 或 `return_mode="all"` 时返回序列、跨调用中位无定义，自动不重复。在 MThreads 上这不是可选项：cvt 消除量最大的 `sort`/`dropout`/`rand` 恰好都无法捕获，那批结论正是由这条路径产出的。取值写进 `run_identity.json` 的 `direct_repeat` |
| `DTYPES` | 空（上游默认 dtype 扫描） | 空格分隔的 dtype 白名单（如 `"bfloat16 float16"`），逐个转为上游 `--dtypes`。算子不支持时上游报 `can't be supported by this op` | | `DTYPES` | 空（上游默认 dtype 扫描） | 空格分隔的 dtype 白名单（如 `"bfloat16 float16"`），逐个转为上游 `--dtypes`。算子不支持时上游报 `can't be supported by this op` |
| `PARALLEL_WARMUP_GPUS` | 空（串行） | 设为 `N`（≥2）时把 autotune sweep 分片到 N 卡并行，测量仍单卡串行；不设或 `<2` 则完全串行（见「加速：多卡并行 sweep」） | | `PARALLEL_WARMUP_GPUS` | 空（串行） | 设为 `N`（≥2）时把 autotune sweep 分片到 N 卡并行，测量仍单卡串行；不设或 `<2` 则完全串行（见「加速：多卡并行 sweep」） |
| `ZL_ENABLE_CUDAGRAPH` | `auto` | `auto`/`1` 仅在 CUDA 上走 Graph 计时；非 CUDA 即使设 `1` 也拒绝并保持 `timer=direct` | | `ZL_ENABLE_CUDAGRAPH` | `auto` | `auto`/`1` 在**声明了 `supports_cuda_graph` 的后端**上走 Graph 计时（目前 cuda 与 musa），其余后端即使设 `1` 也拒绝并保持 `timer=direct`。注意 `timer=cuda_graph` 只表示**尝试过**捕获，不表示有任何一行捕获成功——S5000 上 `dropout`/`rand`/`masked_select` 是逐 shape 全部回退（每行 `BENCHMARK_DIRECT_NO_CUDAGRAPH`，零毒化、不丢行），整张表实际是 direct 计时。这种情况下设 `0` 走的是同一条路径，只是记录更诚实 |
| `ZL_ENABLE_AUTOTUNE` | `1` | `0` 关闭 autotune record/replay 插件 | | `ZL_ENABLE_AUTOTUNE` | `1` | `0` 关闭 autotune record/replay 插件 |
| `ZL_ENABLE_IR` | `1` | `0` 关闭 TTGIR / 编译元数据插件 | | `ZL_ENABLE_IR` | `1` | `0` 关闭 TTGIR / 编译元数据插件 |
| `ZL_ENABLE_MM_CLUSTER` | `0` | `1` 且后端支持时加载 Hopper mm cluster 规避 | | `ZL_ENABLE_MM_CLUSTER` | `0` | `1` 且后端支持时加载 Hopper mm cluster 规避 |
@@ -87,7 +113,9 @@ softmax:
``` ```
runs/softmax_20260716-031752/ runs/softmax_20260716-031752/
├── run.log # 完整日志（含 SUCCESS 行的 latency/speedup 表；已去 ANSI 色的纯文本） ├── run.log # 完整日志（含 SUCCESS 行的 latency/speedup 表；已去 ANSI 色的纯文本）
├── run_identity.json # provider/backend/device/timer/vendor（插件合并写入） ├── run_identity.json # 本次运行的口径：provider/backend/device/timer/vendor/
│ # bench_level/shape_mode/tuner_cache/direct_repeat/autotune_mode
│ # （shell 写 stub，插件合并写入；对比工具据此判可比性）
├── shapes.yaml # 本次实际使用的 shape（存档） ├── shapes.yaml # 本次实际使用的 shape（存档）
├── autotune_records/ # Triton autotune 选中的 config（供 replay） ├── autotune_records/ # Triton autotune 选中的 config（供 replay）
│ ├── softmax.json │ ├── softmax.json
@@ -109,6 +137,137 @@ ttgir 关键设计：
- **命名 = 组内有区分度的参数**：同一 (shape, kernel) 组内取值相同的 constexpr 不进文件名；多词参数缩写为首字母（`BLOCK_SIZE_M` → `BSM`，图例见 naming.md）；同名冲突依次用 dtype（`__fp16`）、参数对齐特化（`__EMdiv16`）、hash 前缀消歧。 - **命名 = 组内有区分度的参数**：同一 (shape, kernel) 组内取值相同的 constexpr 不进文件名；多词参数缩写为首字母（`BLOCK_SIZE_M` → `BSM`，图例见 naming.md）；同名冲突依次用 dtype（`__fp16`）、参数对齐特化（`__EMdiv16`）、hash 前缀消歧。
- **崩溃也能拿到 IR**：dump 挂在进程 atexit 上，CUDA crash 后仍会落盘已编译部分。 - **崩溃也能拿到 IR**：dump 挂在进程 atexit 上，CUDA crash 后仍会落盘已编译部分。
## 多卡跑一批：campaign.sh
`ab_repeat.sh` 是单卡串行的。一批算子要并行时用 `campaign.sh` 编排——它只分活，
测量仍然全部由 `ab_repeat.sh` 做，那个脚本一行不动。
```bash
SWITCH=FLAGTREE_MUSA_RLC_ENHANCE DEVICES=0,1,2,3,4,5,6,7 SCREEN=runs/ab_rlc_XXX/screen.tsv ZL_BACKEND=musa FLAGGEMS_DIR=/path/to/FlagGems DRY_RUN=1 bash campaign.sh
```
`DRY_RUN=1` 先看方案，确认了再去掉它起跑。
- **只取筛选表里 `IR_CHANGED` 的算子**——第一步的判定在这里生效
- **按实测耗时贪心分片**（`partition.py`），不是 hash 取模。S5000 上 16 个算子实测：
hash 分片关键路径 120 分钟、有一张卡空转；贪心 70 分钟。**下界由最重的单个算子
决定**（`topk` 一个就 70.6 分钟），加卡不解决，分片只保证别的卡不空等
- **每卡独立 `AB_ROOT`**——八个 `ab_repeat.sh` 同一秒启动，默认的时间戳目录名会撞
- **强制 `ZL_ISOLATE_TUNER_CACHE=1`**。8 个进程写同一个 sqlite 是要避开的事。代价是
pin 的 config 与共享缓存批次不同，**两批的绝对延迟不可互引**——口径守卫会自己拒绝
- `EXTRA_OPS` 追加筛选表里没有的算子（如 benchmark 刚修好的），放到**负载最小**的卡上
- 跑完合并各卡 `status.tsv`（首列是卡号），并统计非零退出的格与三个标记的总数
设备可见性变量名从 backends 层取，不写死 `MUSA_VISIBLE_DEVICES`。
## 两步：先筛选，再实测
跑一个算子的六轮 A/B 要 13~44 分钟（S5000 实测：`rand` 15 shape 13.0 分钟，
`sort` 25 shape 43.4 分钟）。用 `ab_rlc_test.sh` 筛一对只要约 0.8 分钟。
**差 20~50 倍**，所以先筛后测：
```bash
# 第一步：一对定去留（A 侧 record，B 侧 REPLAY_FROM 它，config 锁死）
OPS="col2im:col2im topk:topk perf_fft:fft" VENDOR=MUSA bash ab_rlc_test.sh
# 第二步：只把 IR_CHANGED 的送进来
SWITCH=FLAGTREE_MUSA_RLC_ENHANCE ROUNDS=6 OPS="topk:topk" bash ab_repeat.sh
```
`ab_rlc_test.sh` 除了原有的 speedup 区间，还给每个算子写一行判定到
`<root>/screen.tsv`：
| 判定 | 含义 | 该怎么办 |
|---|---|---|
| `IR_CHANGED` | 两侧 ttgir 不同 | 排六轮 A/B |
| `NO_IR_DIFF` | 逐字节相同（config 已锁死） | 跳过。跑六轮只会测到噪声，并报成一个"无差异"结论 |
| `NO_TRITON` | 跑出了行，但没编出 `.ttgir` | 本次运行没有任何东西经过 Triton。算子本身可能完全正常——多半跑的是 aten 或厂商库的 kernel——只是被测的编译器 pass 没有作用对象。**既不是"开关无作用"，也不是"没有 kernel"**。`run_pytest.sh` 每个 run 用独立 `TRITON_CACHE_DIR`，所以"缓存命中没重新编译"这个解释已被排除 |
| `NO_ROWS` | 没有测量行 | 先修可运行性：多半是 dtype 不被该算子接受，或它在本后端拿不到 config |
两件容易做错的：
- **判据是 ttgir 指纹，不是 `convert_layout` 计数。** cvt 数相同不等于生成代码
相同，而要测的是"生成代码变了没有"。两个方向都会骗人：`addmm` 的 cvt 是 15→15
没变，15 个 ttgir 文件却实质全不同；`tanh`/`softmax` 的 cvt 是 0→0，生成代码也
真的没动。cvt 数照样记，它解释的是变化的性质。
- **指纹先抹掉开关自己盖的注解。** FlagTree 的 `_apply_musa_rlc_policy(mod)` 在开关
打开时把整套 RLC 策略旋钮盖成 module 属性（`ttg.rlc-*`），跟这个 pass 后面有没有
真的改动什么无关。不抹的话判据有个常量偏移——**凡是能编出 kernel 的算子都必然判成
"变了"，筛选器谁都不排除**。24 个算子实测：抹之前 23 个 `IR_CHANGED`，抹之后 7 个
落回 `NO_IR_DIFF`。零对照抓不到这个，因为两侧同值时属性也相同。
- **不要全局写死一个 `DTYPES`。** `OPS` 元素支持 `OP:OP_FILE:DTYPES`，留空表示用
该算子自己的全套。`fft` 只吃 complex64，被一刀切的 `DTYPES=float16` 判死过一次
——那是 harness 的问题，被记成了算子的问题。
同一份 `_ir_fingerprint.sh` 也被 `ab_repeat.sh` 用于第 1 轮后的对比轴有效性检查，
两处判据不会漂开。
判定还管住报告：`NO_ROWS` 与 `NO_TRITON` 不出 speedup 区间（前者没有可比的测量，
后者与被测开关无关）；`NO_IR_DIFF` 照出，但明说那是**本机对该算子的噪声底**，不是
开关的收益——它离零多远，就是该算子单次测量的可信下限。判定刚说完这一对不可比，
下一行就给个 `+0.3%`，正是这套工具一路在防的"看起来像结论的表"。
`screen.tsv` 末七列是口径（`backend device timer bench_level shape_mode
tuner_cache direct_repeat`），由 `python batch_summary.py --caliber <run_dir>` 取——
`CALIBER_FIELDS` 只有那一处定义，筛选表不重列一遍。两份不同 `--level` 的
`screen.tsv` 放一起比时，这几列是唯一能拦住的东西。
每对有 `CELL_TIMEOUT`（默认 2400 秒）兜底，超时打 `!!! CELL_TIMEOUT` 并按耗时问题
报告，不混进 dtype 那条解释里。`elapsed_before/after` 也进表：两步流程的全部依据
就是"一对 0.8 分钟对六轮 13~44 分钟"这个比例，那这一步该记下自己那一半。
## 算子跑不起来时的排查清单
`NO_ROWS` / `NO_TRITON` 说的是"这一格没测出东西"，不是"开关无作用"。按下面的顺序查，
每一条都踩过：
| # | 症状 | 查什么 | 实例 |
|---|---|---|---|
| 1 | `PREFLIGHT_FAILED check=interpreter imports triton + flag_gems` | 用的是哪个 python。triton 通常来自 PYTHONPATH 指向的编译器构建 | 一次噪声标定因此废掉 12 格 + 160 次负载 run |
| 2 | `PREFLIGHT_FAILED check=benchmark file exists` | `OPS` 元素是 `OP:OP_FILE`，写反就找不到文件 | `test_perf_sort` 在 `test_sort.py` 里，要写 `perf_sort:sort` |
| 3 | `ValueError: Given dtype(s) '...' can't be supported by this op` | 该算子只吃特定 dtype，别全局写死 `DTYPES` | `fft` 只吃 complex64，被一刀切的 fp16 判成"跑不出行"，而它是 cvt 消除量最大的算子 |
| 4 | `dynamic_func() missing ... BLOCK_M, BLOCK_N ...` 之类 JIT 深处的报错 | **benchmark 是不是导入了别家厂商的实现**（见下） | `router_gemm` 拿 NVIDIA Hopper 的 kernel 跑在 MUSA 上 |
| 5 | 跑出行但 `kernels=0` | 该算子在本后端没走 Triton，走的是 aten 或厂商库 | — |
### 第 4 条：import 成功不等于这个后端支持
FlagGems 的厂商实现放在 `flag_gems/runtime/backend/_<vendor>/ops/`。benchmark 里常见
这种可用性判据：
```python
try:
from flag_gems.runtime.backend._nvidia.hopper.ops.mm import router_gemm
ROUTER_GEMM_AVAILABLE = True # ← import 成功就置 True
except Exception:
ROUTER_GEMM_AVAILABLE = False
```
**`import` 只证明那个 `.py` 在磁盘上，它不碰设备，所以在任何后端上都成功。** gate 于是
形同虚设：MUSA 上照样置 True，benchmark 拿 NVIDIA Hopper 的 kernel 跑在摩尔线程卡上，
最后死在 JIT 深处的 `dynamic_func() missing BLOCK_M, BLOCK_N, BLOCK_K, SPLIT_K`——
**那个报错完全指不向真正的原因**。而 `_mthreads/ops/mm.py` 下明明有一份能用的实现，
benchmark 从头到尾没导入过。
正确写法是先判 vendor（同一个文件里 Cambricon 那支就是对的）：
```python
if base.vendor_name == "nvidia":
try:
from flag_gems.runtime.backend._nvidia.hopper.ops.mm import router_gemm
ROUTER_GEMM_AVAILABLE = True
except Exception:
pass
```
`_device_setup_plugin` 会在 session 结束时扫 `sys.modules`，载入过别家厂商实现时打：
```
FOREIGN_VENDOR_MODULE active=mthreads loaded=nvidia
```
**它不拦，只报**——载入别家模块本身不一定有问题（有些是共享工具），但一个算子结果可疑
时，这行是第一个该看的地方。
## A/B 对比测试标准流程 ## A/B 对比测试标准流程
```bash ```bash
@@ -148,7 +307,104 @@ libtuner 的持久缓存何时失效：FlagGems kernel 源码改动、tune_confi
同一层缓存也决定了 `_autotune_record_plugin` 的实现方式：record 不能靠"cache 新增了哪个 key"来判断本次选中的 config（缓存一热就走 cached 分支、不写新 key，键集差集恒为空），改为 `run()` 之后直接读回本次调用的 `self.cache[key]`；replay 同理不能加"key 不在 cache 里才注入"的前置条件，否则注入被跳过、该 run 表面在 replay 实际在自调优。想确认 record 真的覆盖到目标 kernel，查 `runs/<run>/autotune_records/<op>.json` 里有无对应 kernel 条目——漏记时该文件照样生成，只是少了 libtuner 那几个。 同一层缓存也决定了 `_autotune_record_plugin` 的实现方式：record 不能靠"cache 新增了哪个 key"来判断本次选中的 config（缓存一热就走 cached 分支、不写新 key，键集差集恒为空），改为 `run()` 之后直接读回本次调用的 `self.cache[key]`；replay 同理不能加"key 不在 cache 里才注入"的前置条件，否则注入被跳过、该 run 表面在 replay 实际在自调优。想确认 record 真的覆盖到目标 kernel，查 `runs/<run>/autotune_records/<op>.json` 里有无对应 kernel 条目——漏记时该文件照样生成，只是少了 libtuner 那几个。
### ab_fold_test.sh：A/B 测试参考示例 ### 前置检查
`run_pytest.sh` 在启动 pytest 之前校验 FlagGems 目录、benchmark 文件、`SHAPE_FILE`
与 `REPLAY_FROM`，失败时打 `!!! PREFLIGHT_FAILED check=<具名检查>` 并以 3 退出。
这些检查存在的理由是**静默失败会指向错误的方向**：路径不存在时 pytest 收集不到
benchmark 目录，注册 `--level`/`--mode` 的 `conftest.py` 就没被加载，报错变成
`unrecognized arguments: --level core --mode kernel`，看起来像参数或版本问题。
只校验节点的文件那一半；`::test_<fn>` 交给 pytest，函数不存在时它自己会清楚地报
`not found: ...`。
还校验**解释器**——`python -c "import triton, flag_gems"`。triton 通常来自 PYTHONPATH
指向的编译器构建产物、flag_gems 来自源码检出，所以一个能摸到全部正确路径的 shell
仍然可能是错的那个 Python。不查的话，失败以每格一次 `pytest exit 4`（conftest 的
ImportError）出现，多轮 campaign 会在有人读日志之前把每一格都烧掉——这不是假想，
2026-09-10 的噪声标定实验就这么废了 12 格加 160 次负载 run。报错里带上
`$(command -v python)`，因为要看的就是"用的到底是哪个解释器"。
### 口径核对：出报告前的拒绝
`batch_summary.py --ab` / `--ab-rounds` 在解析任何 latency 之前，先读参与对比的每个
run 目录的 `run_identity.json`，逐字段核对 `backend / device / timer / bench_level /
shape_mode / tuner_cache / direct_repeat`。任何一项不一致，或任何一侧缺 identity，
就打 `!!! [caliber] ...` 并以 **4** 退出，不出报告。
```
!!! [caliber] sort: timer 不一致：before=cuda_graph, after=direct
!!! [caliber] sort: 拒绝出报告。确认口径确实可比后加 --allow-caliber-mismatch
```
单独判 `autotune_mode`：一侧 record 定 config、另一侧 replay 它是正常形态；**两侧都
record 直接拒绝**——那正是本仓发布过错误 router_gemm 数字的那次形态。
不拒绝但会警告的一类：在**共享** libtuner 缓存上 record。被 pin 的 config 取决于此前
在这台机器上跑过什么，相对结论仍成立，绝对延迟与变体数不可跨机器引用。
`--allow-caliber-mismatch` 放行，仍会把问题打出来。用它之前先能说清为什么这两次运行
可比；跨口径引用别人的数字，比自己重跑一遍贵得多。
### 多轮重复：ab_repeat.sh 与 --ab-rounds
单对观测无法说明自己稳不稳。S5000 实测 `topk` 消除了 32 个 `convert_layout`，
42 个 shape 里却有 17 个在两次同等有效的运行之间**改变了 delta 的符号**——那不是
计时口径问题（图捕获与 direct 的延迟比是 1.00x），是收益本身落在零附近、单次
测量给不出方向。
```bash
SWITCH=FLAGTREE_MUSA_RLC_ENHANCE OPS="topk masked_select" ROUNDS=6 ZL_BACKEND=musa FLAGGEMS_DIR=/path/to/FlagGems bash ab_repeat.sh
```
口径：第 1 轮 A 侧 record config，此后每一次运行（含 A 侧的后续轮次）都
`REPLAY_FROM` 那一份，config 因此跨轮跨侧全部锁死；逐轮交替先后顺序抵消单向漂移。
有轮次失败的算子不出报告——缺轮的报告会让人误以为是完整的 N 轮。
每跑完一格往 `<root>/status.tsv` 追一行：`round op side switch rc elapsed_s success
failed replay_fallback no_cudagraph capture_poisoned`。README 要求必查的三个标记就在
这张表里，跑的时候就能看见它们从 0 变成非 0，不用等结束再 grep——事后 grep 只在有人
想起来的时候才发生。每格另有 `CELL_TIMEOUT`（默认 2400 秒）兜底，超时记 `rc=124` 并打
`!!! CELL_TIMEOUT`，不会让一格卡死拖住整批。
全程还采一份 `<root>/telemetry.tsv`：
```
timestamp index sm_clock_mhz mem_clock_mhz temp_c power_w util_pct
2026-09-10T07:41:12 7 1888 2500 36 477.68 87
```
列在 cuda 与 musa 上完全一致（`nvidia-smi --query-gpu` 与 `mthreads-gmi -q --json`
各自解析后归一），所以两台机器的 campaign 可以并排读。理由很直接：六轮 A/B 声称的是
0.01% 量级的单侧噪声，**降频和热漂移能移动比这大得多的延迟，而且在 run.log 里不留
任何痕迹**——报告照样干净。`TELEMETRY_INTERVAL`（默认 5 秒）设 0 关闭；采样器缺失或
没权限时 `telemetry.py` 安静退出并把原因写进 `telemetry.err`，不影响测试本身。
第 1 轮 A/B 跑完后，脚本比两侧 `ttgir/` 的指纹（相对路径 + 内容）。**逐字节相同就
跳过这个算子**，并打 `!!! SWITCH_NO_IR_DIFF op=... switch=...`：这个开关没改变该算子的
生成代码，剩下 N-1 轮测的全是噪声，最后还会报成一个"无差异"的结论。先查开关名拼写
与它在被测源码里的读取点；确属只影响运行时、不影响 IR 的开关，用
`SWITCH_ALLOW_NO_IR_DIFF=1` 放行。
报告分开给两种离散度，它们回答不同的问题：
| 指标 | 含义 |
|---|---|
| 逐轮区间 | N 轮各自 delta 的 min~max，说明**判定**稳不稳 |
| 单侧噪声 | 同一侧 N 次延迟的极差，说明**机器**吵不吵 |
| 符号不稳行数 | delta 在轮次间改变符号的 shape 数，这些行的方向不可结论 |
已有产物可以单独重出报告，目录形态是 `<root>/r<k>_<A|B>/run.log`：
```bash
python batch_summary.py --ab-rounds runs/ab_repeat_XXX/topk --label topk
```
NV 上验证过这条路径：`router_gemm` 六轮把逐轮区间收窄到 -16.6% ~ -16.2%、单侧
噪声 0.4%，把单对观测升级成了结论。
### ab_fold_test.sh / ab_rlc_test.sh：A/B 测试参考示例
`ab_fold_test.sh` 是上述 A/B 流程的现成参考：以 FlagGems `FLAGGEMS_MXFP4_FOLDSCALE`（fold_scale 优化）开关为对比对象，对 4 个 DeepSeek-V4-Flash 真实 trace shape 集（`shape/DeepSeek-V4-Flash-p{1024,4096,32768,65536}d1024.yaml`，随仓库提供）各跑一对 FOLD=0（基线，record）→ FOLD=1（replay 同 config），共 8 轮： `ab_fold_test.sh` 是上述 A/B 流程的现成参考：以 FlagGems `FLAGGEMS_MXFP4_FOLDSCALE`（fold_scale 优化）开关为对比对象，对 4 个 DeepSeek-V4-Flash 真实 trace shape 集（`shape/DeepSeek-V4-Flash-p{1024,4096,32768,65536}d1024.yaml`，随仓库提供）各跑一对 FOLD=0（基线，record）→ FOLD=1（replay 同 config），共 8 轮：
@@ -157,6 +413,8 @@ bash ab_fold_test.sh
# 汇总 log：runs/ab_fold_<时间戳>.log（已去色）；快速对账：grep -E '^#####|FAILED' <log> # 汇总 log：runs/ab_fold_<时间戳>.log（已去色）；快速对账：grep -E '^#####|FAILED' <log>
``` ```
`ab_rlc_test.sh` 是同一口径的另一个实例，对比轴换成 FlagTree 的 `FLAGTREE_<VENDOR>_RLC_ENHANCE`（`VENDOR` 默认 `MUSA`），算子表由 `OPS` 指定，跑完调 `batch_summary.py --ab <before> <after>` 出该算子的 speedup 区间。
可复用的是**口径**——同 config replay、cudagraph 计时、真实 trace shape 集、逐对成组——对比其它开关 / 编译器改动 / 算子，改循环变量与环境变量即可，口径部分不用动。 可复用的是**口径**——同 config replay、cudagraph 计时、真实 trace shape 集、逐对成组——对比其它开关 / 编译器改动 / 算子，改循环变量与环境变量即可，口径部分不用动。
但**它是口径示例，不是可长期直接执行的回归脚本**：对比轴是 FlagGems 侧的算子开关，会随上游演进被改名、改语义或移除，本仓库不跟随同步更新（该脚本引用的开关目前就已被上游移除）。失效后果是**静默的**：脚本照样跑完全部轮次、照样输出完整对比表，只是两侧执行同一份代码，得出"无差异"的假结论。所以套用前先确认对比轴仍有效——`grep -rn '<开关名>' $FLAGGEMS_DIR/src` 要有命中，且两侧 latency 表确有差异。 但**它是口径示例，不是可长期直接执行的回归脚本**：对比轴是 FlagGems 侧的算子开关，会随上游演进被改名、改语义或移除，本仓库不跟随同步更新（该脚本引用的开关目前就已被上游移除）。失效后果是**静默的**：脚本照样跑完全部轮次、照样输出完整对比表，只是两侧执行同一份代码，得出"无差异"的假结论。所以套用前先确认对比轴仍有效——`grep -rn '<开关名>' $FLAGGEMS_DIR/src` 要有命中，且两侧 latency 表确有差异。
@@ -253,8 +511,9 @@ python run_batch.py --ops-file my_ops.txt --replay-root runs/batch_xxx
| `_shape_iter_inject_plugin` | 覆盖在 `get_input_iter` 里硬编码 shape 的类（conv/pool 等） | 不关 | | `_shape_iter_inject_plugin` | 覆盖在 `get_input_iter` 里硬编码 shape 的类（conv/pool 等） | 不关 |
| `_bespoke_shape_plugin` | 覆盖特殊输入构造的算子（upsample/flash_mla/cutlass 等，按类名注册） | 测这些算子之外可关 | | `_bespoke_shape_plugin` | 覆盖特殊输入构造的算子（upsample/flash_mla/cutlass 等，按类名注册） | 测这些算子之外可关 |
| `_autotune_record_plugin` | record/replay Triton autotune 选择；`autotune_records/meta.json` 记录 backend，跨后端 replay 拒绝 | `ZL_ENABLE_AUTOTUNE=0` | | `_autotune_record_plugin` | record/replay Triton autotune 选择；`autotune_records/meta.json` 记录 backend，跨后端 replay 拒绝 | `ZL_ENABLE_AUTOTUNE=0` |
| `_timing_plugin` | 按后端选择计时路径：CUDA 可走 Graph（内部调用 `_cudagraph_plugin`），其余后端一开始就是上游 `do_bench`（`timer=direct`） | `ZL_ENABLE_CUDAGRAPH=0` 强制 direct | | `_timing_plugin` | 按后端的 `supports_cuda_graph` 选计时路径：声明了的走 Graph（内部调用 `_cudagraph_plugin`，逐 shape 回退），没声明的一开始就是上游 `do_bench`（`timer=direct`） | `ZL_ENABLE_CUDAGRAPH=0` 强制 direct |
| `_cudagraph_plugin` | 仅由 `_timing_plugin` 在 CUDA 上调用。`do_bench` → `do_bench_cudagraph`，capture 前按 warmup 预算显式预热；无法 capture 时回退并打 `BENCHMARK_DIRECT_NO_CUDAGRAPH` | 不要直接 `-p` 加到非 CUDA 运行 | | `_cudagraph_plugin` | 仅由 `_timing_plugin` 在声明了 `supports_cuda_graph` 的后端上调用。`do_bench` → `do_bench_cudagraph`，capture 前按 warmup 预算显式预热；无法 capture 时回退并打 `BENCHMARK_DIRECT_NO_CUDAGRAPH` | 不要直接 `-p` 加到非 CUDA 运行 |
| `_musa_benchmark_compat_plugin` | 剥掉 FlagGems 上游若干 benchmark 上过时的 `torch.cuda.is_available()` 跳过门——被测代码本就经 `flag_gems.device` 分发，在 MThreads 上那些门把可用算子变成「未测量」。每条覆盖都钉死到一个文件与一条确切的 skip 理由，且仅在 `flag_gems.vendor_name == "mthreads"` 时生效 | 由 `backends/musa.py` 的 `compat_plugins` 声明，其他后端不加载 |
| `_pretty_report_plugin` | 结果表整理 + 着色：全表相同的输入折叠成表头下一行图例，每行 Size Detail 只留随行变化的部分（shape 保持 `torch.Size([...])` 原样，MoE 类算子单行从 ~400 字符缩到一屏内）；SUCCESS 绿 / FAILED 红。列名与 `SUCCESS` 字样保持上游原文，`run.log` 的 grep/解析不受影响 | 需要与上游原始表格逐字对齐时去掉 | | `_pretty_report_plugin` | 结果表整理 + 着色：全表相同的输入折叠成表头下一行图例，每行 Size Detail 只留随行变化的部分（shape 保持 `torch.Size([...])` 原样，MoE 类算子单行从 ~400 字符缩到一屏内）；SUCCESS 绿 / FAILED 红。列名与 `SUCCESS` 字样保持上游原文，`run.log` 的 grep/解析不受影响 | 需要与上游原始表格逐字对齐时去掉 |
| `_ir_meta_plugin` | 编译期记录每个变体的 constexpr/签名/特化；launch 钩子统计每个 (kernel, shape) 的实际使用；退出时按上述结构 dump ttgir。Triton knobs 缺失时 fail-soft | `ZL_ENABLE_IR=0` | | `_ir_meta_plugin` | 编译期记录每个变体的 constexpr/签名/特化；launch 钩子统计每个 (kernel, shape) 的实际使用；退出时按上述结构 dump ttgir。Triton knobs 缺失时 fail-soft | `ZL_ENABLE_IR=0` |
| `_mm_cluster_fix_plugin` | Hopper fp16 mm cluster kernel 越界崩溃的运行时规避 | 默认关；`ZL_ENABLE_MM_CLUSTER=1` 或 `ZL_EXTRA_PLUGINS` | | `_mm_cluster_fix_plugin` | Hopper fp16 mm cluster kernel 越界崩溃的运行时规避 | 默认关；`ZL_ENABLE_MM_CLUSTER=1` 或 `ZL_EXTRA_PLUGINS` |
_cudagraph_plugin.py
+48 -9
View File
@@ -33,6 +33,47 @@ import triton.testing as _tt
_ORIGINAL_DO_BENCH = _tt.do_bench _ORIGINAL_DO_BENCH = _tt.do_bench
def direct_repeat() -> int:
"""ZL_DIRECT_REPEAT, clamped to >= 1. 1 keeps today's single call."""
try:
return max(1, int(os.environ.get("ZL_DIRECT_REPEAT", "1")))
except ValueError:
return 1
def run_direct(fn, warmup, rep, grad_to_none, quantiles, return_mode):
"""Upstream do_bench, optionally repeated with the median taken.
Direct timing walks the full Python launch path once per iteration, so on
small kernels it carries launch overhead and CPU jitter that graph capture
does not. do_bench already returns a median over the iterations inside one
call, which suppresses iteration-level jitter but not drift across a call:
clocks ramping, another process arriving. Repeating the call and taking the
median over calls is the part the inner median cannot see.
It matters most where it is least optional. On MThreads the operators with
the largest RLC convert reductions -- sort, dropout, rand -- are exactly the
ones that cannot be graph-captured, so the headline numbers there are
produced by this path.
Repetition is skipped when the caller asks for quantiles or return_mode
"all", because the result is then a sequence and a median across calls is
not defined for it; the single call is returned unchanged.
"""
n = direct_repeat()
if n == 1 or quantiles is not None or return_mode == "all":
return _ORIGINAL_DO_BENCH(
fn, warmup=warmup, rep=rep, grad_to_none=grad_to_none,
quantiles=quantiles, return_mode=return_mode)
import statistics
samples = [
_ORIGINAL_DO_BENCH(fn, warmup=warmup, rep=rep, grad_to_none=grad_to_none,
quantiles=None, return_mode=return_mode)
for _ in range(n)
]
return statistics.median(samples)
def _device_interface(): def _device_interface():
"""Triton's active device interface: torch.cuda on NVIDIA, torch_musa on MThreads.""" """Triton's active device interface: torch.cuda on NVIDIA, torch_musa on MThreads."""
from triton.runtime import driver from triton.runtime import driver
@@ -194,16 +235,14 @@ def _patched_do_bench(fn, warmup=25, rep=100, grad_to_none=None,
def _direct(note): def _direct(note):
from _term_style import YELLOW, paint from _term_style import YELLOW, paint
print(paint(f"BENCHMARK_DIRECT_NO_CUDAGRAPH op={op} {note}", YELLOW), n = direct_repeat()
# The repeat count belongs in the marker: these rows are already a
# different caliber from the captured ones, and now they can also be a
# different number of samples.
rpt = "" if n == 1 else f" repeat={n}"
print(paint(f"BENCHMARK_DIRECT_NO_CUDAGRAPH op={op} {note}{rpt}", YELLOW),
flush=True) flush=True)
return _ORIGINAL_DO_BENCH( return run_direct(fn, warmup, rep, grad_to_none, quantiles, return_mode)
fn,
warmup=warmup,
rep=rep,
grad_to_none=grad_to_none,
quantiles=quantiles,
return_mode=return_mode,
)
backend = None backend = None
try: try:
_device_setup_plugin.py
+43
View File
@@ -67,3 +67,46 @@ def pytest_configure(config):
_setup_at_import() _setup_at_import()
# FlagGems 的厂商实现按 flag_gems.runtime.backend._<vendor>.ops.* 组织。benchmark
# 常写成 `try: import <某厂商实现>; AVAILABLE = True`——但 import 只证明那个 .py 在
# 磁盘上，它不碰设备，所以在任何后端上都会成功。于是 gate 形同虚设，测出来的是别家
# 的 kernel 跑在本机上。router_gemm 就是这样：test_router_gemm.py 无条件导入
# _nvidia.hopper 版，在 MUSA 上 gate 照过，最后死在 JIT 深处的
# "dynamic_func() missing BLOCK_M, BLOCK_N, BLOCK_K, SPLIT_K"——那个报错完全指不
# 向真正的原因，而 _mthreads 下明明有一份能用的实现。
#
# 这里不拦，只报：跑完看一眼载入过谁家的实现。
_VENDOR_MOD_PREFIX = "flag_gems.runtime.backend._"
def foreign_vendor_modules(active_vendor: str, modules=None) -> list:
"""本次进程里载入过的、不属于当前厂商的 FlagGems 后端模块。"""
if modules is None:
modules = sys.modules
out = set()
for name in list(modules):
if not name.startswith(_VENDOR_MOD_PREFIX):
continue
# 只保留厂商根，_nvidia.hopper.ops.mm 与 _nvidia.ops.mm 算同一条。
# 按段比而不是按前缀：前缀会让 _<vendor>_something 被误认成自家。
root = name[len(_VENDOR_MOD_PREFIX):].split(".")[0]
if root != active_vendor:
out.add(root)
return sorted(out)
def pytest_sessionfinish(session, exitstatus):
from _term_style import tag
try:
vendor = get_backend().flaggems_vendor
except Exception:
return
foreign = foreign_vendor_modules(vendor)
if not foreign:
return
print(f"{tag('[device-setup-plugin]')} FOREIGN_VENDOR_MODULE active={vendor} "
f"loaded={','.join(foreign)}", file=sys.stderr, flush=True)
print(f"{tag('[device-setup-plugin]')} 本次载入了别家厂商的 FlagGems 实现。"
"如果这个算子的结果可疑，先查 benchmark 的可用性判据是不是只做了 import "
"而没判 vendor_name", file=sys.stderr, flush=True)
_ir_fingerprint.sh
+31
View File
@@ -0,0 +1,31 @@
#!/bin/bash
# 由 ab_repeat.sh 与 screen.sh 共用；两处必须用同一个判据，否则筛选放行的算子
# 到了 A/B 那步又被拦下，或者反过来。
# 一个 run 生成代码的指纹：只取 .ttgir 的相对路径 + 内容，并先抹掉开关自己盖上去的
# 注解。
#
# 只认 .ttgir：dump 目录里还有 index.tsv 和 naming.md，一个 kernel 都没编出来的 run
# 只剩这两个文件、两侧完全相同，会把"什么都没生成"报成"开关没改变生成代码"。
#
# 抹掉 ttg.rlc-*：FlagTree 的 _apply_musa_rlc_policy(mod) 在开关打开时把整套 RLC 策略
# 旋钮盖成 module 属性，跟这个 pass 后面有没有真的改动什么无关。不抹的话判据有个常量
# 偏移——凡是能编出 kernel 的算子都必然判成"变了"，筛选器谁都不排除。实测 24 个算子
# 里有 7 个（tanh gelu mul softmax mm bmm replication_pad3d）除这行属性外逐字节相同。
# 零对照抓不到它：两侧同值时属性也相同。
#
# 零 kernel 返回非零，交给调用方当作"没有可比的 IR"，而不是"一致"。
ir_fingerprint() {
local dir="$1/ttgir"
[[ -d "$dir" ]] || return 1
local n
n=$(find "$dir" -name '*.ttgir' -type f 2>/dev/null | wc -l)
(( n > 0 )) || return 1
local out
out=$( { find "$dir" -name '*.ttgir' -type f | LC_ALL=C sort | while IFS= read -r f; do
printf '%s
' "${f#"$dir"/}"
sed -E 's/"ttg[.]rlc-[^"]*" = [0-9-]+ : i32, ?//g' "$f"
done; } | sha256sum | cut -d' ' -f1 )
[[ -n "$out" ]] && printf '%s' "$out"
}
_musa_benchmark_compat_plugin.py
+89
View File
@@ -0,0 +1,89 @@
"""Narrow compatibility fixes for the frozen MUSA product corpus.
Several upstream FlagGems tests gate themselves on ``torch.cuda.is_available()``
even though the code under test runs on any accelerator FlagGems dispatches to.
On MThreads those guards skip the test instead of exercising the operator, which
turns a working path into a verdict of "not measured".
Every override here is pinned to one file and one exact skip reason, applies
only when FlagGems has dispatched to the MThreads vendor, and is only added
after the unskipped test was observed to pass on the device.
Two files were probed on S5000 and deliberately left out, because their guards
are not stale:
- ``test_cutlass_scaled_mm.py`` gates on vLLM as well as CUDA, so removing the
guard would raise on the missing dependency instead of measuring anything.
- ``test_grid_sample.py`` builds its inputs through a helper whose device
argument defaults to the literal ``"cuda"``. Its guard is consistent with its
own body: unskipped, all 90 collected cases fail in ``aten::empty`` on a CUDA
backend that does not exist here. Making that file portable means editing
upstream test source, which is outside what a compatibility plugin may do.
"""
from __future__ import annotations
import sys
from pathlib import Path
# file name -> exact skipif reasons that are stale on a dispatching backend.
_STALE_CUDA_GUARDS: dict[str, frozenset[str]] = {
"test_fft.py": frozenset({"CUDA is not available"}),
"test_top_k_per_row_decode.py": frozenset({"CUDA device required"}),
}
# Guards that are only stale for one test in the file stay pinned to it.
_PINNED_TESTS: dict[str, str] = {
"test_fft.py": "test_perf_fft",
}
def _is_stale_cuda_guard(item, mark) -> bool:
if mark.name != "skipif":
return False
name = Path(str(item.path)).name
reasons = _STALE_CUDA_GUARDS.get(name)
if reasons is None or mark.kwargs.get("reason") not in reasons:
return False
pinned = _PINNED_TESTS.get(name)
return pinned is None or item.name == pinned
# Retained under its historical name: the sealed campaign's tests reference it.
def _is_stale_fft_cuda_guard(item, mark) -> bool:
return Path(str(item.path)).name == "test_fft.py" and _is_stale_cuda_guard(
item, mark
)
def pytest_collection_modifyitems(items):
import flag_gems
if flag_gems.vendor_name != "mthreads":
return
removed: dict[str, int] = {}
# A guard can sit on the test, its class, or the module's pytestmark, and
# skipif is resolved by walking that whole chain. Strip every level.
seen: set[int] = set()
for item in items:
for node in item.listchain():
if id(node) in seen:
continue
seen.add(id(node))
markers = getattr(node, "own_markers", None)
if not markers:
continue
kept = [mark for mark in markers if not _is_stale_cuda_guard(item, mark)]
dropped = len(markers) - len(kept)
if dropped:
markers[:] = kept
name = Path(str(item.path)).name
removed[name] = removed.get(name, 0) + dropped
for name, count in sorted(removed.items()):
print(
f"[musa-benchmark-compat] removed {count} stale CUDA-only skip "
f"guard(s) from {name}",
file=sys.stderr,
flush=True,
)
_run_identity.py
+5
View File
@@ -71,6 +71,10 @@ def main(argv=None) -> int:
p.add_argument("--test-file", default=os.environ.get("TEST_FILE", "")) p.add_argument("--test-file", default=os.environ.get("TEST_FILE", ""))
p.add_argument("--shape-mode", default=os.environ.get("SHAPE_MODE", "")) p.add_argument("--shape-mode", default=os.environ.get("SHAPE_MODE", ""))
p.add_argument("--bench-level", default=os.environ.get("BENCH_LEVEL", "core")) p.add_argument("--bench-level", default=os.environ.get("BENCH_LEVEL", "core"))
# "isolated" or "shared(<path>)". Recorded because a shared libtuner cache
# lets whichever side runs first hand its winners to the other, and after
# the fact the two runs are otherwise indistinguishable.
p.add_argument("--tuner-cache", default="")
args = p.parse_args(argv) args = p.parse_args(argv)
init_identity( init_identity(
args.out_dir, args.out_dir,
@@ -83,6 +87,7 @@ def main(argv=None) -> int:
test_file=args.test_file, test_file=args.test_file,
shape_mode=args.shape_mode, shape_mode=args.shape_mode,
bench_level=args.bench_level, bench_level=args.bench_level,
tuner_cache=args.tuner_cache,
) )
return 0 return 0
_timing_plugin.py
+31 -8
View File
@@ -1,10 +1,15 @@
"""Select the timing path from backend capabilities, before any shape runs. """Select the timing path from backend capabilities, before any shape runs.
CUDA keeps the existing `_cudagraph_plugin` behaviour (graph capture, with Whether capture is attempted comes from the backend's `supports_cuda_graph`,
per-shape fallback to plain do_bench). Other backends do not attempt CUDA not from "is this CUDA". CUDA and MUSA both declare it, so both get graph
Graph at all: they leave `triton.testing.do_bench` in place and record capture with per-shape fallback to plain do_bench; a backend that declares
`timer=direct`. Trying capture-then-fallback on a non-CUDA device would nothing leaves `triton.testing.do_bench` in place and records `timer=direct`.
produce FATAL_DEVICE noise and mixed-caliber rows.
`timer=cuda_graph` therefore means capture was *attempted*, not that any row
was captured. On S5000 every shape of dropout/rand/masked_select falls back
(BENCHMARK_DIRECT_NO_CUDAGRAPH on every row, nothing poisoned, no rows lost),
so the whole table is direct-timed under a cuda_graph label. Setting
ZL_ENABLE_CUDAGRAPH=0 there selects the same timing path and says so plainly.
""" """
from __future__ import annotations from __future__ import annotations
@@ -37,11 +42,29 @@ def pytest_configure(config):
if _graph_requested(backend): if _graph_requested(backend):
import _cudagraph_plugin as _cg import _cudagraph_plugin as _cg
_cg.pytest_configure(config) _cg.pytest_configure(config)
update_identity(timer="cuda_graph", graph_capture="attempted") # Recorded on this branch too: the fallback rows a captured run produces
# are timed by the direct path and take the same repeat count.
update_identity(timer="cuda_graph", graph_capture="attempted",
direct_repeat=_cg.direct_repeat())
print(f"{tag('[timing-plugin]')} timer=cuda_graph backend={backend.name}", print(f"{tag('[timing-plugin]')} timer=cuda_graph backend={backend.name}",
file=sys.stderr, flush=True) file=sys.stderr, flush=True)
return return
update_identity(timer="direct", graph_capture="disabled") # A wholly-direct run gets the same repetition the fallback rows get, so the
# "put everything on one timer" advice does not silently cost the samples.
# Patching only when repeat > 1 keeps the default path untouched: upstream's
# do_bench stays in place exactly as before.
import _cudagraph_plugin as _cg
repeat = _cg.direct_repeat()
if repeat > 1:
def _repeated(fn, warmup=25, rep=100, grad_to_none=None,
quantiles=None, return_mode="mean"):
return _cg.run_direct(fn, warmup, rep, grad_to_none,
quantiles, return_mode)
import triton.testing as _tt
_tt.do_bench = _repeated
update_identity(timer="direct", graph_capture="disabled",
direct_repeat=repeat)
print(f"{tag('[timing-plugin]')} timer=direct backend={backend.name} " print(f"{tag('[timing-plugin]')} timer=direct backend={backend.name} "
"(upstream do_bench; graph capture not selected)", f"(upstream do_bench; graph capture not selected)"
+ (f"; median of {repeat} calls" if repeat > 1 else ""),
file=sys.stderr, flush=True) file=sys.stderr, flush=True)
ab_repeat.sh
+168
View File
@@ -0,0 +1,168 @@
#!/bin/bash
# 多轮 A/B：同一对比在 N 轮独立进程里各跑一次，逐 shape 取轮次中位。
#
# 为什么需要它：单对观测无法说明自己稳不稳。S5000 实测 topk 消除了 32 个
# convert_layout，42 个 shape 里却有 17 个在两次同等有效的运行之间改变了 delta
# 的符号——那不是计时口径的问题（图捕获与 direct 的延迟比是 1.00x），是收益本身
# 落在零附近、单次测量给不出方向。多轮之后才谈得上"这个判定成立"。
#
# 口径：第 1 轮 A 侧 record autotune config，此后每一次运行（含 A 侧自己的后续
# 轮次）都 REPLAY_FROM 那一份。config 因此跨轮跨侧全部锁死，剩下的变量只有被测
# 开关与测量噪声。逐轮交替先后顺序，抵消单向漂移。
#
# 用法：
# SWITCH=FLAGTREE_MUSA_RLC_ENHANCE OPS="topk masked_select" ROUNDS=6 \
# ZL_BACKEND=musa FLAGGEMS_DIR=/path/to/FlagGems bash ab_repeat.sh
#
# OPS 元素为 OP 或 OP:OP_FILE（省略时 OP_FILE=OP）。
# SWITCH 是被测环境变量，A 侧设 0、B 侧设 1。
#
# 对比轴有效性由脚本自己查：第 1 轮 A/B 跑完后比两侧的 ttgir 指纹，完全一致说明
# 这个开关在这个算子上没改变任何生成代码——继续跑满 N 轮只会得到一个"无差异"的假
# 结论（同 ab_fold_test.sh 的告诫）。这类算子默认跳过；开关确实只影响运行时而不影响
# IR 时，用 SWITCH_ALLOW_NO_IR_DIFF=1 放行。
#
# 产物：runs/ab_repeat_<开关>_<时间戳>/<op>/r<k>_<A|B>/，跑完对每个算子调用
# batch_summary.py --ab-rounds 出报告。
set -uo pipefail
cd "$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
SWITCH_ALLOW_NO_IR_DIFF="${SWITCH_ALLOW_NO_IR_DIFF:-0}"
source "$(dirname "${BASH_SOURCE[0]}")/_ir_fingerprint.sh"
: "${SWITCH:?SWITCH is required, e.g. FLAGTREE_MUSA_RLC_ENHANCE}"
OPS="${OPS:-}"
: "${OPS:?OPS is required, e.g. \"topk masked_select\"}"
ROUNDS="${ROUNDS:-6}"
CELL_TIMEOUT="${CELL_TIMEOUT:-2400}"
if [[ -t 1 && -z "${NO_COLOR:-}" ]]; then
export FLAGGEMS_PERF_COLOR=always
fi
# AB_ROOT 供 campaign.sh 指定每卡独立的产物根：八个 ab_repeat.sh 同一秒启动时，
# 默认的时间戳目录名会撞在一起，两张卡往同一个目录里写。
ROOT="${AB_ROOT:-runs/ab_repeat_${SWITCH}_$(date +%Y%m%d-%H%M%S)}"
mkdir -p "$ROOT"
LOG="$ROOT/summary.log"
STATUS="$ROOT/status.tsv"
# 全程采时钟/温度/功耗。六轮 A/B 声称的是 0.01% 量级的单侧噪声，降频和热漂移
# 能移动比这大得多的延迟，而且在 run.log 里不留痕迹——报告照样干净。
# TELEMETRY_INTERVAL=0 关闭。采不到时 telemetry.py 自己安静退出，不影响测试。
TELEMETRY_INTERVAL="${TELEMETRY_INTERVAL:-5}"
TELE_PID=""
if [[ "$TELEMETRY_INTERVAL" != "0" ]]; then
python telemetry.py --backend "${ZL_BACKEND:-cuda}" --device "${ZL_DEVICE:-}" \
--interval "$TELEMETRY_INTERVAL" --run \
> "$ROOT/telemetry.tsv" 2> "$ROOT/telemetry.err" &
TELE_PID=$!
trap '[[ -n "$TELE_PID" ]] && kill "$TELE_PID" 2>/dev/null' EXIT
fi
printf 'round\top\tside\tswitch\trc\telapsed_s\tsuccess\tfailed\treplay_fallback\tno_cudagraph\tcapture_poisoned\n' > "$STATUS"
# 跑一格并逐格登记。事后 grep 也能拿到这些数，但只在有人想起来时才发生；
# 缺格、超时、标记从 0 变成非 0，都要在跑的时候就看得见。
run_cell() { # round side op op_file out_dir [replay_from]
local round="$1" side="$2" op="$3" op_file="$4" dir="$5" replay="${6:-}"
local val=0; [[ "$side" == B ]] && val=1
local start rc elapsed log ok fail fb ng po
start=$(date +%s)
if [[ -n "$replay" ]]; then
OP="$op" OP_FILE="$op_file" OUT_DIR="$dir" REPLAY_FROM="$replay" \
timeout -k 30 "$CELL_TIMEOUT" env "$SWITCH=$val" bash run_pytest.sh
else
OP="$op" OP_FILE="$op_file" OUT_DIR="$dir" \
timeout -k 30 "$CELL_TIMEOUT" env "$SWITCH=$val" bash run_pytest.sh
fi
rc=$?
elapsed=$(( $(date +%s) - start ))
(( rc == 124 || rc == 137 )) && echo "!!! CELL_TIMEOUT ${CELL_TIMEOUT}s op=$op round=$round side=$side"
log="$dir/run.log"
# grep -c 没命中时打印 0 但以 1 退出；不吞掉的话会被当成本格失败
ok=$(grep -c '^SUCCESS' "$log" 2>/dev/null || true)
fail=$(grep -c '^FAILED' "$log" 2>/dev/null || true)
fb=$(grep -c 'AUTOTUNE_REPLAY_FALLBACK' "$log" 2>/dev/null || true)
ng=$(grep -c 'BENCHMARK_DIRECT_NO_CUDAGRAPH' "$log" 2>/dev/null || true)
po=$(grep -c 'BENCHMARK_CAPTURE_POISONED' "$log" 2>/dev/null || true)
printf '%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\n' \
"$round" "$op" "$side" "$val" "$rc" "$elapsed" \
"${ok:-0}" "${fail:-0}" "${fb:-0}" "${ng:-0}" "${po:-0}" >> "$STATUS"
return $rc
}
{
echo ">>> switch=$SWITCH rounds=$ROUNDS ops=$OPS"
for entry in $OPS; do
op="${entry%%:*}"
op_file="${entry#*:}"
[[ "$op_file" == "$entry" ]] && op_file="$op"
out="$ROOT/$op"
mkdir -p "$out"
# 第 1 轮 A 侧 record，其余全部回放它，config 跨轮跨侧一致
echo "########## $op | round 1 | A ($SWITCH=0, record) ##########"
if ! run_cell 1 A "$op" "$op_file" "$out/r1_A"; then
echo "!!! FAILED $op round 1 A; skipping this operator"
continue
fi
REC="$out/r1_A"
ok=1; reason="有失败轮次"
for ((k = 1; k <= ROUNDS; k++)); do
# 奇数轮先 A 后 B，偶数轮反过来
if (( k % 2 == 1 )); then order="A B"; else order="B A"; fi
for side in $order; do
[[ $k -eq 1 && $side == A ]] && continue # 已经跑过
val=$([[ $side == A ]] && echo 0 || echo 1)
echo "########## $op | round $k | $side ($SWITCH=$val, replay) ##########"
if ! run_cell "$k" "$side" "$op" "$op_file" "$out/r${k}_${side}" "$REC"; then
echo "!!! FAILED $op round $k $side"
ok=0
fi
done
if (( k == 1 && ok )); then
fa=$(ir_fingerprint "$out/r1_A") || fa=""
fb=$(ir_fingerprint "$out/r1_B") || fb=""
if [[ -z "$fa" || -z "$fb" ]]; then
echo "??? $op: 没有编出任何 .ttgir（可能该算子没走 Triton，或本轮失败），"
echo "??? 跳过对比轴有效性检查——这不等于开关无作用"
elif [[ "$fa" == "$fb" ]]; then
echo "!!! SWITCH_NO_IR_DIFF op=$op switch=$SWITCH"
echo "!!! 两侧 ttgir 逐字节相同：这个开关没有改变 $op 的生成代码。"
echo "!!! 继续跑 $ROUNDS 轮只会测出噪声，并报成一个'无差异'结论。"
echo "!!! 先确认开关名拼写与它在被测源码里的读取点；确属只影响运行时的"
echo "!!! 开关，用 SWITCH_ALLOW_NO_IR_DIFF=1 重跑。"
if [[ "$SWITCH_ALLOW_NO_IR_DIFF" != "1" ]]; then
ok=0; reason="开关未改变该算子的生成代码"
break
fi
else
echo ">>> 对比轴有效：$op 两侧 ttgir 不同（A=${fa:0:12} B=${fb:0:12}）"
fi
fi
done
echo "########## $op | report ##########"
if (( ok )); then
python batch_summary.py --ab-rounds "$out" --label "$op" \
|| echo "!!! report failed: $op"
else
# 缺轮的算子照样出报告会让人误以为是完整的 N 轮
echo "!!! $op $reason，不出报告；产物在 $out"
fi
done
echo "########## ALL DONE ##########"
} 2>&1 | tee "$LOG"
sed -i -E $'s/\x1b\\[[0-9;]*[A-Za-z]//g' "$LOG"
echo ">>> 总 log: $(pwd)/$LOG"
echo ">>> 逐格状态表: $(pwd)/$STATUS"
[[ -s "$ROOT/telemetry.tsv" ]] && echo ">>> 时钟/温度: $(pwd)/$ROOT/telemetry.tsv"
[[ -s "$ROOT/telemetry.err" ]] && echo ">>> 遥测提示: $(cat "$ROOT/telemetry.err")"
echo ">>> 逐算子重出报告: python batch_summary.py --ab-rounds $ROOT/<op> --label <op>"
echo ">>> 解读要点: 逐轮区间宽说明判定不稳；单侧噪声大说明机器不稳；两者含义不同"
ab_rlc_test.sh
+198
View File
@@ -0,0 +1,198 @@
#!/bin/bash
# A/B 对比：FlagTree RemoveLayoutConversions 增强关 vs 开，逐算子两轮。
#
# 口径：每个算子先跑关（record autotune config），再用 REPLAY_FROM 回放同一套
# config 跑开，两侧只差 FLAGTREE_<VENDOR>_RLC_ENHANCE 这一个开关。产物沿用
# run_pytest.sh 的目录结构，所以每个算子恰好留下两份 run.log：
#
# runs/<op>_<时间戳>/run.log 关（before）
# runs/<op>_<时间戳>/run.log 开（after）
#
# 跑完用 batch_summary.py --ab <before> <after> 得到该算子的 speedup 区间。
#
# 这一轮同时充当 ab_repeat.sh 的筛选前置：一对的代价约 0.8 分钟，六轮 A/B 是
# 13~44 分钟（S5000 实测），差 20~50 倍。所以先用它过一遍，只把 IR_CHANGED 的
# 算子送进 ab_repeat.sh。判定写在 <root>/screen.tsv，四种之一：
#
# IR_CHANGED 两侧 ttgir 不同 -> 排六轮
# NO_IR_DIFF 逐字节相同（config 已锁死）-> 跳过，跑六轮只会测到噪声
# NO_TRITON 跑出行但没编出 .ttgir -> 该算子在本后端没走 Triton，
# 编译器开关无从谈起。不是"开关无作用"
# NO_ROWS 没有测量行 -> 先修可运行性，多半是 dtype
#
# 判据是 ttgir 指纹而不是 convert_layout 计数：cvt 数相同不等于生成代码相同。
# cvt 数照样记，它解释的是变化的性质。
#
# 用法：
# OPS="softmax gelu_backward" bash ab_rlc_test.sh
# OPS="sort:perf_sort topk" bash ab_rlc_test.sh # OP:OP_FILE
# OPS="perf_fft:fft:complex64 topk:topk" bash ab_rlc_test.sh # 再加 DTYPES
#
# DTYPES 留空表示用该算子自己的全套。**不要全局写死一个 dtype**：fft 只吃
# complex64，被一刀切的 DTYPES=float16 判死过一次——那是 harness 的问题，
# 却被记成了算子的问题。
#
# 注意：RLC 开关名带厂商前缀，用 VENDOR 指定（默认 MUSA）。开关名拼错时两侧跑
# 同一份代码，此时判定会是 NO_IR_DIFF——先查拼写，再下"开关无作用"的结论。
set -uo pipefail
cd "$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
source "./_ir_fingerprint.sh"
# RESCORE=<已有的 root>：判据改了之后，用当前的 ir_fingerprint 对盘上已有的 ttgir
# 重新打分并改写该 root 的 screen.tsv，不重跑任何测量。
#
# 需要它是因为判据确实改过：_ir_fingerprint.sh 起初把 index.tsv 也算进哈希，后来又
# 发现要抹掉开关自己盖的 ttg.rlc-* 属性。同一批 24 个算子，结论从 23 个 IR_CHANGED
# 变成 16 个。重打分是秒级的，重跑那批筛选要 79 分钟。
#
# 原表存为 screen.tsv.before-rescore，判定有变化的算子逐个打出来。
RESCORE="${RESCORE:-}"
if [[ -n "$RESCORE" ]]; then
[[ -d "$RESCORE" ]] || { echo "!!! 目录不存在: $RESCORE" >&2; exit 2; }
TSV="$RESCORE/screen.tsv"
[[ -f "$TSV" ]] || { echo "!!! 没有 screen.tsv: $TSV" >&2; exit 2; }
cp "$TSV" "$TSV.before-rescore"
hdr=$(head -1 "$TSV.before-rescore")
# 列名 -> 序号，不写死列序
idx() { echo "$hdr" | tr '\t' '\n' | grep -nxF "$1" | cut -d: -f1; }
c_op=$(idx op); c_rb=$(idx rows_before); c_ra=$(idx rows_after); c_v=$(idx verdict)
[[ -n "$c_op" && -n "$c_v" ]] || { echo "!!! screen.tsv 缺 op/verdict 列" >&2; exit 2; }
printf '%s\n' "$hdr" > "$TSV"
changed=0
while IFS= read -r line; do
op=$(printf '%s' "$line" | cut -f"$c_op")
old=$(printf '%s' "$line" | cut -f"$c_v")
rb=$(printf '%s' "$line" | cut -f"${c_rb:-0}"); ra=$(printf '%s' "$line" | cut -f"${c_ra:-0}")
fa=$(ir_fingerprint "$RESCORE/${op}_before") || fa=""
fb=$(ir_fingerprint "$RESCORE/${op}_after") || fb=""
if [[ "${rb:-0}" == 0 || "${ra:-0}" == 0 ]]; then v=NO_ROWS
elif [[ -z "$fa" || -z "$fb" ]]; then v=NO_TRITON
elif [[ "$fa" == "$fb" ]]; then v=NO_IR_DIFF
else v=IR_CHANGED; fi
printf '%s\n' "$line" | awk -v FS='\t' -v OFS='\t' -v i="$c_v" -v v="$v" '{$i=v; print}' >> "$TSV"
if [[ "$old" != "$v" ]]; then
printf ' %-22s %-12s -> %s\n' "$op" "$old" "$v" >&2
changed=$((changed+1))
fi
done < <(tail -n +2 "$TSV.before-rescore")
echo ">>> 重打分完成，$changed 个算子的判定变了；原表存为 $TSV.before-rescore"
awk -v i="$c_v" -F'\t' 'NR>1{c[$i]++} END{for (k in c) printf(" %-12s %d\n", k, c[k])}' "$TSV"
exit 0
fi
VENDOR="${VENDOR:-MUSA}"
ENHANCE_VAR="FLAGTREE_${VENDOR}_RLC_ENHANCE"
# 空格分隔的算子表。元素为 OP 或 OP:OP_FILE（省略时 OP_FILE=OP）。
OPS="${OPS:-softmax}"
# 筛选是第一次接触一个算子的地方，最容易撞上挂死的那个。没有上限时一个算子
# 能把整批停在那里，而筛选的全部意义就是快。
CELL_TIMEOUT="${CELL_TIMEOUT:-2400}"
if [[ -t 1 && -z "${NO_COLOR:-}" ]]; then
export FLAGGEMS_PERF_COLOR=always
fi
# 显式 OUT_DIR，不再用 `ls -dt runs/<op>_*` 猜"最新那个就是我刚跑的"。串行下
# 那个假设成立，多卡并行共用 runs/ 就不成立了。
ROOT="runs/ab_rlc_$(date +%Y%m%d-%H%M%S)"
mkdir -p "$ROOT"
LOG="$ROOT/summary.log"
TSV="$ROOT/screen.tsv"
printf 'op op_file dtypes rows_before rows_after kernels_before kernels_after cvt_before cvt_after elapsed_before elapsed_after verdict backend device timer bench_level shape_mode tuner_cache direct_repeat\n' > "$TSV"
count_cvt() { find "$1/ttgir" -name '*.ttgir' -type f -exec cat {} + 2>/dev/null | grep -c convert_layout || true; }
count_kernels() { find "$1/ttgir" -name '*.ttgir' -type f 2>/dev/null | wc -l; }
count_rows() { grep -c '^SUCCESS' "$1/run.log" 2>/dev/null || true; }
{
echo ">>> vendor=$VENDOR switch=$ENHANCE_VAR ops=$OPS"
for entry in $OPS; do
op="${entry%%:*}"; rest="${entry#*:}"
if [[ "$rest" == "$entry" ]]; then op_file="$op"; dtypes=""
else
op_file="${rest%%:*}"; d="${rest#*:}"
[[ "$d" == "$rest" ]] && dtypes="" || dtypes="$d"
fi
BEFORE="$ROOT/${op}_before"; AFTER="$ROOT/${op}_after"
echo "########## $op | $ENHANCE_VAR=0 before (record) ##########"
t0=$(date +%s)
OP="$op" OP_FILE="$op_file" OUT_DIR="$BEFORE" DTYPES="$dtypes" \
timeout -k 30 "$CELL_TIMEOUT" env "$ENHANCE_VAR=0" bash run_pytest.sh
rc_before=$?; el_before=$(( $(date +%s) - t0 ))
echo "########## $op | $ENHANCE_VAR=1 after (replay $BEFORE) ##########"
t0=$(date +%s)
OP="$op" OP_FILE="$op_file" OUT_DIR="$AFTER" DTYPES="$dtypes" \
REPLAY_FROM="$BEFORE" timeout -k 30 "$CELL_TIMEOUT" env "$ENHANCE_VAR=1" bash run_pytest.sh
rc_after=$?; el_after=$(( $(date +%s) - t0 ))
for rc in "$rc_before" "$rc_after"; do
(( rc == 124 || rc == 137 )) && echo "!!! CELL_TIMEOUT ${CELL_TIMEOUT}s op=$op"
done
rb=$(count_rows "$BEFORE"); ra=$(count_rows "$AFTER")
kb=$(count_kernels "$BEFORE"); ka=$(count_kernels "$AFTER")
cb=$(count_cvt "$BEFORE"); ca=$(count_cvt "$AFTER")
fb=$(ir_fingerprint "$BEFORE") || fb=""
fa=$(ir_fingerprint "$AFTER") || fa=""
if (( rc_before != 0 || rc_after != 0 )) || [[ "${rb:-0}" == 0 || "${ra:-0}" == 0 ]]; then
verdict=NO_ROWS
if (( rc_before == 124 || rc_after == 124 || rc_before == 137 || rc_after == 137 )); then
note="超过 CELL_TIMEOUT=${CELL_TIMEOUT}s 被截断（rc=$rc_before/$rc_after）。这是耗时问题，不是对开关的判定——先看它为什么这么慢"
else
note="跑不出测量行（rc=$rc_before/$rc_after）。看 run.log——多半是 dtype 不被该算子接受，或它在本后端拿不到 config"
fi
elif [[ -z "$fb" || -z "$fa" ]]; then
verdict=NO_TRITON
note="跑出了行但没编出 .ttgir：本次运行没有任何东西经过 Triton。算子本身可能完全正常（多半跑的是 aten 或厂商库），只是被测的编译器 pass 没有作用对象。不是'开关无作用'，也不是'没有 kernel'"
elif [[ "$fb" == "$fa" ]]; then
verdict=NO_IR_DIFF
note="两侧 ttgir 逐字节相同（config 已锁死）：开关没改变生成代码。先查开关名拼写，再下结论"
else
verdict=IR_CHANGED
note="生成代码有变（cvt ${cb}→${ca}，指纹 ${fb:0:8}/${fa:0:8}）：值得排六轮 A/B"
fi
# 口径从 batch_summary.py 取，那里是 CALIBER_FIELDS 的唯一定义。两份不同
# --level 的 screen.tsv 放一起比时，这几列是唯一能拦住的东西。
cal=$(python batch_summary.py --caliber "$BEFORE" 2>/dev/null)
printf '%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\n' \
"$op" "$op_file" "${dtypes:-<all>}" "${rb:-0}" "${ra:-0}" \
"$kb" "$ka" "${cb:-0}" "${ca:-0}" "$el_before" "$el_after" "$verdict" \
"$cal" >> "$TSV"
echo ">>> $op: $verdict — $note"
# 判定说这一对不可比，就别在下一行给出一个看起来像结论的区间
case "$verdict" in
NO_ROWS)
echo "!!! $op 没有可对比的测量，不出 speedup 区间"; continue ;;
NO_TRITON)
echo "!!! $op 没有经过 Triton，speedup 区间与被测开关无关，不出"; continue ;;
NO_IR_DIFF)
echo ">>> $op 两侧生成代码相同：下面这个区间是本机对该算子的噪声底，"
echo ">>> 不是开关的收益。它离零多远，就是该算子单次测量的可信下限。" ;;
esac
echo "########## $op | summary ##########"
python batch_summary.py --ab "$BEFORE/run.log" "$AFTER/run.log" --label "$op" \
|| echo "!!! summary failed: $op"
done
echo "########## 筛选汇总 ##########"
awk -F'\t' 'NR>1{printf " %-12s %-22s cvt %s->%s kernels %s/%s rows %s/%s %ss\n", \
$12,$1,$8,$9,$6,$7,$4,$5,$10+$11}' "$TSV" | sort
echo ">>> 只把 IR_CHANGED 的算子送进 ab_repeat.sh。"
echo "########## ALL DONE ##########"
} 2>&1 | tee "$LOG"
# 去掉 ANSI 转义以便 grep/diff（终端输出保留颜色）
sed -i -E $'s/\x1b\\[[0-9;]*[A-Za-z]//g' "$LOG"
echo ">>> 总 log: $(pwd)/$LOG"
echo ">>> 筛选表: $(pwd)/$TSV"
echo ">>> 每算子两份 run.log 在 $ROOT/<op>_{before,after}/ 下"
backends/base.py
+35
View File
@@ -72,6 +72,34 @@ class DeviceBackend:
# backend. Skipped up front so one of them cannot cost a whole run. # backend. Skipped up front so one of them cannot cost a whole run.
capture_deny_ops: Tuple[str, ...] = () capture_deny_ops: Tuple[str, ...] = ()
# One-shot device telemetry sampler. Empty means this backend has none, and
# the campaign runs without a clock/thermal record rather than pretending.
# "{dev}" is dropped from the argv when no device is pinned, so the sampler
# covers every visible device instead of guessing an index.
telemetry_argv: Tuple[str, ...] = ()
def telemetry_command(self, device: str = "") -> List[str]:
if not self.telemetry_argv:
return []
out: List[str] = []
for a in self.telemetry_argv:
if "{dev}" in a:
if not device:
# Drop the flag the placeholder belonged to as well; a bare
# trailing "-i" makes the sampler fail instead of widening.
if out and out[-1].startswith("-"):
out.pop()
continue
a = a.replace("{dev}", device)
out.append(a)
return out
def parse_telemetry(self, raw: str) -> List[Dict[str, str]]:
"""One dict per device: index, sm_clock_mhz, mem_clock_mhz, temp_c,
power_w, util_pct. Normalised so a CUDA and a MUSA campaign produce the
same columns and can be read side by side."""
return []
def classify_capture_error(self, exc: BaseException) -> str: def classify_capture_error(self, exc: BaseException) -> str:
"""-> "fatal" | "poisoned" | "recoverable". """-> "fatal" | "poisoned" | "recoverable".
@@ -90,6 +118,13 @@ class DeviceBackend:
def denies_capture(self, op: str) -> bool: def denies_capture(self, op: str) -> bool:
return bool(op) and op in self.capture_deny_ops return bool(op) and op in self.capture_deny_ops
# Plugins that patch around upstream assumptions this backend violates, e.g.
# FlagGems benchmarks that gate themselves on torch.cuda.is_available() even
# though the operator under test runs on whatever device FlagGems dispatched
# to. Loaded first so collection-time overrides land before anything reads
# the item list. Each such plugin must scope itself to its own vendor.
compat_plugins: Tuple[str, ...] = ()
# Optional listing command; (argv, line_prefix). prefix="" means count # Optional listing command; (argv, line_prefix). prefix="" means count
# non-empty lines. Must be a real vendor CLI; do not invent flags. # non-empty lines. Must be a real vendor CLI; do not invent flags.
list_command: Optional[Tuple[Sequence[str], str]] = None list_command: Optional[Tuple[Sequence[str], str]] = None
backends/cuda.py
+21
View File
@@ -1,5 +1,7 @@
from __future__ import annotations from __future__ import annotations
from typing import Dict, List
from .base import DeviceBackend from .base import DeviceBackend
@@ -26,3 +28,22 @@ class CudaBackend(DeviceBackend):
# No CUDA wording is known to poison the context without also matching one # No CUDA wording is known to poison the context without also matching one
# of the fatal markers above, so nothing is listed here rather than guessed. # of the fatal markers above, so nothing is listed here rather than guessed.
poisoning_error_markers = () poisoning_error_markers = ()
telemetry_argv = (
"nvidia-smi",
"--query-gpu=index,clocks.sm,clocks.mem,temperature.gpu,power.draw,utilization.gpu",
"--format=csv,noheader,nounits",
"-i", "{dev}",
)
def parse_telemetry(self, raw: str) -> List[Dict[str, str]]:
out: List[Dict[str, str]] = []
for line in raw.splitlines():
parts = [p.strip() for p in line.split(",")]
if len(parts) != 6 or not parts[0].isdigit():
continue
out.append(dict(zip(
("index", "sm_clock_mhz", "mem_clock_mhz",
"temp_c", "power_w", "util_pct"), parts)))
return out
backends/musa.py
+54
View File
@@ -1,5 +1,7 @@
from __future__ import annotations from __future__ import annotations
from typing import Dict, List
from .base import DeviceBackend from .base import DeviceBackend
@@ -42,6 +44,58 @@ class MusaBackend(DeviceBackend):
# timing precision instead of its measurement. Revisit if a newer torch_musa # timing precision instead of its measurement. Revisit if a newer torch_musa
# changes the behaviour. # changes the behaviour.
capture_deny_ops = ("unique2",) capture_deny_ops = ("unique2",)
# Several FlagGems benchmarks skip on `not torch.cuda.is_available()` while
# the code under test dispatches through flag_gems.device. On MThreads that
# turns a working operator into "not measured"; the plugin drops those
# guards, pinned to one file and one exact skip reason each.
compat_plugins = ("_musa_benchmark_compat_plugin",)
# Device count comes from torch.musa or MUSA_VISIBLE_DEVICES. Do not guess # Device count comes from torch.musa or MUSA_VISIBLE_DEVICES. Do not guess
# a vendor CLI here; confirm the real listing tool before adding it. # a vendor CLI here; confirm the real listing tool before adding it.
list_command = None list_command = None
# mthreads-gmi has no CSV query mode; --json is the only machine-readable
# form it offers. Values arrive with their units glued on ("36C", "1888MHz",
# "477.68W", "87%"), so the parser strips trailing non-numerics rather than
# trusting a fixed suffix.
telemetry_argv = (
"mthreads-gmi", "-q",
"-d", "TEMPERATURE,CLOCK,POWER,UTILIZATION",
"--json",
"-i", "{dev}",
)
def parse_telemetry(self, raw: str) -> List[Dict[str, str]]:
import json
import re
def num(v):
m = re.match(r"^\s*(-?[\d.]+)", str(v or ""))
return m.group(1) if m else ""
start = raw.find("{")
if start < 0:
return []
try:
data = json.loads(raw[start:])
except Exception:
return []
gpus = data.get("GPU") or []
if isinstance(gpus, dict):
gpus = [gpus]
out: List[Dict[str, str]] = []
for g in gpus:
clocks = g.get("Clocks") or {}
power = g.get("Power Readings") or {}
# 键名带尾随空格（"Power Draw "），别按字面写死
draw = next((v for k, v in power.items()
if k.strip() == "Power Draw"), "")
out.append({
"index": str(g.get("Index", "")).strip(),
"sm_clock_mhz": num(clocks.get("Graphics")),
"mem_clock_mhz": num(clocks.get("Memory")),
"temp_c": num((g.get("Temperature") or {}).get("GPU Current Temp")),
"power_w": num(draw),
"util_pct": num((g.get("Utilization") or {}).get("Gpu")),
})
return out
batch_summary.py
+320 -1
View File
@@ -16,8 +16,10 @@ from __future__ import annotations
import argparse import argparse
import csv import csv
import itertools
import os import os
import re import re
import statistics
import sys import sys
from pathlib import Path from pathlib import Path
from typing import Dict, List, Optional, Tuple from typing import Dict, List, Optional, Tuple
@@ -186,12 +188,329 @@ def summarize(batch_dir: Path, out_path: Optional[Path] = None) -> Path:
return out_path return out_path
def ab_rows(before_text: str, after_text: str) -> List[Dict[str, str]]:
"""把两次 run.log 的测量行按 (op, dtype, size_detail) 配对。
speedup 列是 gems 对 torch，不是前后对比，所以这里用 gems_lat 自己算
before/after - 1。只有两侧都 SUCCESS 且延迟为正的行才计入；对不上的行
单独返回，避免用不完整的配对报一个看起来完整的区间。
"""
def index(text: str) -> Dict[Tuple[str, str, str], float]:
out: Dict[Tuple[str, str, str], float] = {}
for row in parse_run_log(text):
if row.get("row_status") != "SUCCESS":
continue
try:
lat = float(row.get("gems_lat", ""))
except ValueError:
continue
if lat > 0:
out[(row.get("fg_op_name", ""), row.get("dtype", ""),
row.get("size_detail", ""))] = lat
return out
before, after = index(before_text), index(after_text)
paired: List[Dict[str, str]] = []
for key in sorted(before.keys() & after.keys()):
op, dtype, size = key
b, a = before[key], after[key]
paired.append({
"fg_op_name": op, "dtype": dtype, "size_detail": size,
"before_lat": f"{b:.6f}", "after_lat": f"{a:.6f}",
"speedup_percent": f"{(b / a - 1.0) * 100.0:.2f}",
})
return paired
def report_ab(before: Path, after: Path, label: str = "",
strict: bool = True) -> List[Dict[str, str]]:
"""打印一个算子的前后对比：逐行 speedup 与整体区间。
先核对两侧口径；不一致时默认拒绝出报告，因为不可比的两次运行会产出
一份和有效报告长得一模一样的表。
"""
if not _report_caliber([("before", before.parent), ("after", after.parent)],
label or "ab", strict):
raise CaliberMismatch()
before_text = before.read_text(encoding="utf-8", errors="replace")
after_text = after.read_text(encoding="utf-8", errors="replace")
rows = ab_rows(before_text, after_text)
name = label or (rows[0]["fg_op_name"] if rows else "?")
if not rows:
print(f"[ab] {name}: 没有可配对的测量行"
f"（before={before} after={after}）")
return rows
values = [float(r["speedup_percent"]) for r in rows]
for r in rows:
print(f" {r['dtype']:<16} {float(r['speedup_percent']):+7.2f}% "
f"{r['before_lat']} -> {r['after_lat']} {r['size_detail']}")
fallback = ""
if DIRECT_NO_CUDAGRAPH_RE.search(before_text) or \
DIRECT_NO_CUDAGRAPH_RE.search(after_text):
# 回退行用的是 do_bench 口径，与 cudagraph 行不同表可比性
fallback = " [含 BENCHMARK_DIRECT_NO_CUDAGRAPH 回退行]"
print(f"[ab] {name}: {len(rows)} 行配对，speedup "
f"{min(values):+.2f}% ~ {max(values):+.2f}%{fallback}")
return rows
# Fields whose disagreement makes two runs incomparable. Measured magnitudes,
# so the list is not precautionary: an unpinned autotune config moved
# router_gemm by 8pp; the timer moved replication_pad3d from +43% to +124.5%;
# a shared tuner cache made mm look like 945 compiled variants had become 33;
# --level core sees 5 mm shapes where comprehensive sees 29.
CALIBER_FIELDS = (
"backend", "device", "timer", "bench_level", "shape_mode",
"tuner_cache", "direct_repeat",
)
def caliber_of(run_dir: Path) -> Optional[Dict[str, str]]:
"""Caliber fields from a run's run_identity.json, or None if unreadable."""
if not (run_dir / "run_identity.json").is_file():
return None
try:
from _run_identity import load_identity
ident = load_identity(str(run_dir))
except Exception:
return None
return {k: str(ident.get(k, "")) for k in CALIBER_FIELDS} | {
"autotune_mode": str(ident.get("autotune_mode", ""))
}
def caliber_complaints(runs: List[Tuple[str, Path]]) -> List[str]:
"""Everything about this set of runs that makes their comparison unsound.
Reported rather than silently tolerated because the failure is invisible in
the numbers: two runs on different calibers produce a report that looks
exactly like a valid one. This repository has already published three
figures that were an artifact of one such difference.
"""
out: List[str] = []
cals = [(name, caliber_of(d)) for name, d in runs]
missing = [n for n, c in cals if c is None]
if missing:
out.append(f"口径不可核实：{', '.join(missing)} 缺 run_identity.json"
"（早于该字段的产物）")
known = [(n, c) for n, c in cals if c is not None]
if len(known) < 2:
return out
for field in CALIBER_FIELDS:
seen = {c[field] for _, c in known}
if len(seen) > 1:
vals = ", ".join(f"{n}={c[field] or '<空>'}" for n, c in known)
out.append(f"{field} 不一致：{vals}")
# A/B needs one side to pin the configs the other replays. Two record runs
# each sweep independently, and the winner can differ; the README puts that
# at the 10% level and one-directional per shape, which is enough to swamp
# what is being measured.
modes = [(n, c["autotune_mode"]) for n, c in known]
if all(m.startswith("record") for _, m in modes):
out.append("两侧都是 record 模式：config 未锁定，各自 sweep 可能选出不同 "
"winner。按 README，只有 record 数据支撑的结论按未验证处理")
return out
def caliber_hazards(runs: List[Tuple[str, Path]]) -> List[str]:
"""Not mismatches — shared state that makes a matched caliber less solid.
Warned about rather than refused, because refusing would reject the
default configuration every A/B in this repo currently uses.
"""
cals = [(n, c) for n, c in ((n, caliber_of(d)) for n, d in runs) if c]
recording = [n for n, c in cals if c["autotune_mode"].startswith("record")]
shared = [n for n, c in cals if c["tuner_cache"].startswith("shared")]
if recording and shared:
# 一次 record 跑在共享 libtuner cache 上，sweep 的起点是别的运行留下的
# 结果。曾把 mm 的 945 个变体读成 33 个，误报为编译期收益。A/B 两侧都
# replay 同一份 config 时结论仍成立，绝对值不成立。
return [f"{', '.join(recording)} 在共享 tuner cache 上 record："
"被 pin 的 config 取决于此前跑过什么。相对结论不受影响，"
"绝对延迟/变体数不可跨机器引用（ZL_ISOLATE_TUNER_CACHE=1 可消除）"]
return []
class CaliberMismatch(SystemExit):
"""Strict-mode refusal. Exit 4 so a driver script can tell it from a crash."""
def __init__(self) -> None:
super().__init__(4)
def _report_caliber(runs: List[Tuple[str, Path]], label: str, strict: bool) -> bool:
"""Print caliber problems. Returns False when strict mode should stop."""
for h in caliber_hazards(runs):
print(f"警告 [caliber] {label}: {h}")
problems = caliber_complaints(runs)
if not problems:
return True
head = "!!!" if strict else "警告"
for p in problems:
print(f"{head} [caliber] {label}: {p}")
if strict:
print(f"!!! [caliber] {label}: 拒绝出报告。确认口径确实可比后加 "
"--allow-caliber-mismatch")
return False
return True
def _lat_index(text: str) -> Dict[Tuple[str, str, int], float]:
"""(dtype, size_detail, occurrence) -> gems latency, SUCCESS rows only.
Occurrence disambiguates a shape a benchmark measures twice in one table,
which BLAS does at --level comprehensive.
"""
out: Dict[Tuple[str, str, int], float] = {}
seen: Dict[Tuple[str, str], int] = {}
for row in parse_run_log(text):
if row.get("row_status") != "SUCCESS":
continue
try:
lat = float(row.get("gems_lat", ""))
except ValueError:
continue
if lat <= 0:
continue
base = (row.get("dtype", ""), row.get("size_detail", ""))
i = seen.get(base, 0)
seen[base] = i + 1
out[base + (i,)] = lat
return out
def ab_round_rows(root: Path) -> Tuple[List[Dict[str, str]], List[float], int]:
"""Aggregate an N-round A/B laid out as <root>/r<k>_<A|B>/run.log.
Returns the per-shape rows, the per-round deltas, and the round count.
A single pair cannot say whether its own verdict is stable, which is the
question that matters once a delta lands near zero: measured on an S5000,
topk eliminated 32 converts yet 17 of its 42 shapes changed the SIGN of
their delta between two equally valid runs. Two dispersions answer different
halves of that and both are reported:
side spread max/min-1 within one side's samples -> how noisy the machine
was while measuring
round spread min~max of the per-round deltas -> whether the VERDICT holds,
which is what a single pair cannot show
"""
rounds: List[Tuple[Dict, Dict]] = []
for k in itertools.count(1):
a, b = root / f"r{k}_A" / "run.log", root / f"r{k}_B" / "run.log"
if not (a.is_file() and b.is_file()):
break
rounds.append((
_lat_index(a.read_text(encoding="utf-8", errors="replace")),
_lat_index(b.read_text(encoding="utf-8", errors="replace")),
))
if not rounds:
return [], [], 0
keys = set(rounds[0][0]) & set(rounds[0][1])
for a, b in rounds[1:]:
keys &= set(a) & set(b)
per_round = [
statistics.median((a[k] / b[k] - 1.0) * 100.0 for k in keys)
for a, b in rounds
] if keys else []
rows: List[Dict[str, str]] = []
for key in sorted(keys):
av = [a[key] for a, _ in rounds]
bv = [b[key] for _, b in rounds]
am, bm = statistics.median(av), statistics.median(bv)
deltas = [(a / b - 1.0) * 100.0 for a, b in zip(av, bv)]
rows.append({
"dtype": key[0], "size_detail": key[1],
"before_lat": f"{am:.6f}", "after_lat": f"{bm:.6f}",
"speedup_percent": f"{(am / bm - 1.0) * 100.0:.2f}",
"delta_min": f"{min(deltas):.2f}", "delta_max": f"{max(deltas):.2f}",
"sign_stable": "yes" if all(d > 0 for d in deltas)
or all(d <= 0 for d in deltas) else "NO",
"before_spread": f"{(max(av) / min(av) - 1.0) * 100.0:.2f}",
"after_spread": f"{(max(bv) / min(bv) - 1.0) * 100.0:.2f}",
})
return rows, per_round, len(rounds)
def report_ab_rounds(root: Path, label: str = "",
strict: bool = True) -> List[Dict[str, str]]:
"""Print the multi-round A/B: per-shape medians plus both dispersions."""
name = label or root.name
cells = sorted(d for d in root.glob("r*_[AB]") if (d / "run.log").is_file())
if cells and not _report_caliber([(d.name, d) for d in cells], name, strict):
raise CaliberMismatch()
rows, per_round, n = ab_round_rows(root)
if n < 2:
print(f"[ab-rounds] {name}: 需要至少 2 轮 <root>/r<k>_<A|B>/run.log"
f"（找到 {n} 轮）")
return rows
if not rows:
print(f"[ab-rounds] {name}: {n} 轮，但没有全轮次都成功的配对行")
return rows
for r in rows:
flag = "" if r["sign_stable"] == "yes" else " 符号不稳"
print(f" {r['dtype']:<16} {float(r['speedup_percent']):+7.2f}% "
f"[{float(r['delta_min']):+.2f}%~{float(r['delta_max']):+.2f}%] "
f"{r['before_lat']} -> {r['after_lat']} "
f"{r['size_detail']}{flag}")
med = [float(r["speedup_percent"]) for r in rows]
spread = [float(r["before_spread"]) for r in rows] + \
[float(r["after_spread"]) for r in rows]
unstable = sum(1 for r in rows if r["sign_stable"] != "yes")
print(f"[ab-rounds] {name}: {n} 轮 × {len(rows)} shape，"
f"speedup 中位 {statistics.median(med):+.2f}%，"
f"逐轮 {min(per_round):+.2f}% ~ {max(per_round):+.2f}%，"
f"单侧噪声中位 {statistics.median(spread):.2f}%")
if unstable:
# The verdict, not the machine, is what these rows fail to pin down.
print(f"[ab-rounds] {name}: {unstable}/{len(rows)} 个 shape 的 delta "
"在轮次间改变符号——这些行的方向不可结论，与计时口径无关")
return rows
def main() -> None: def main() -> None:
p = argparse.ArgumentParser(description=__doc__.splitlines()[0]) p = argparse.ArgumentParser(description=__doc__.splitlines()[0])
p.add_argument("batch_dir", type=Path, help="run_batch.py 的产物目录") p.add_argument("batch_dir", type=Path, nargs="?",
help="run_batch.py 的产物目录")
p.add_argument("-o", "--output", type=Path, default=None, p.add_argument("-o", "--output", type=Path, default=None,
help="输出 CSV（默认 <batch_dir>/summary.csv）") help="输出 CSV（默认 <batch_dir>/summary.csv）")
p.add_argument("--ab", nargs=2, type=Path, metavar=("BEFORE", "AFTER"),
help="两份 run.log，按 shape 配对输出该算子的 speedup 区间")
p.add_argument("--ab-rounds", type=Path, default=None, metavar="ROOT",
help="多轮 A/B 目录，含 r<k>_A/run.log 与 r<k>_B/run.log；"
"逐 shape 取轮次中位，并报逐轮区间与符号稳定性")
p.add_argument("--label", default="", help="--ab / --ab-rounds 报告里的算子名")
p.add_argument("--caliber", type=Path, default=None, metavar="RUN_DIR",
help="打印该 run 的口径字段（TSV 一行），供筛选表复用")
p.add_argument("--allow-caliber-mismatch", action="store_true",
help="口径不一致时仍出报告（默认拒绝）。只在确认两次运行"
"确实可比时使用")
args = p.parse_args() args = p.parse_args()
if args.caliber is not None:
# 口径的定义只有 CALIBER_FIELDS 一处；筛选表等复用它，不要在别处重列
cal = caliber_of(args.caliber) or {}
print(" ".join(str(cal.get(k, "")) for k in CALIBER_FIELDS))
return
if args.ab_rounds:
if not args.ab_rounds.is_dir():
raise SystemExit(f"目录不存在: {args.ab_rounds}")
report_ab_rounds(args.ab_rounds, args.label,
strict=not args.allow_caliber_mismatch)
return
if args.ab:
for path in args.ab:
if not path.is_file():
raise SystemExit(f"run.log 不存在: {path}")
report_ab(args.ab[0], args.ab[1], args.label,
strict=not args.allow_caliber_mismatch)
return
if args.batch_dir is None:
raise SystemExit("需要 batch_dir，或用 --ab BEFORE AFTER")
if not args.batch_dir.is_dir(): if not args.batch_dir.is_dir():
raise SystemExit(f"目录不存在: {args.batch_dir}") raise SystemExit(f"目录不存在: {args.batch_dir}")
out = summarize(args.batch_dir, args.output) out = summarize(args.batch_dir, args.output)
campaign.sh
+128
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@@ -0,0 +1,128 @@
#!/bin/bash
# 多卡跑一批算子的六轮 A/B：每卡一个 ab_repeat.sh，卡内串行。
#
# 它只做编排，不做测量。ab_repeat.sh 一行不动——那是已经跑出结论的、验证过的串行
# 路径，不该为了并行去改它。分片交给 partition.py（按实测耗时贪心，比 hash 分片在
# 这批活上快 50 分钟）。
#
# 并行必须隔离 tuner cache：8 个进程写同一个 sqlite 是要避开的事。代价是 pin 下来
# 的 config 与共享缓存下跑的批次不同，两批的绝对延迟不可互引——batch_summary.py
# 的口径守卫会自己拒绝这种对比，不用记着。
#
# 用法：
# SWITCH=FLAGTREE_MUSA_RLC_ENHANCE DEVICES=0,1,2,3,4,5,6,7 \
# SCREEN=runs/ab_rlc_XXX/screen.tsv \
# ZL_BACKEND=musa FLAGGEMS_DIR=/path/to/FlagGems bash campaign.sh
#
# DRY_RUN=1 只打印分片方案，不起跑
# EXTRA_OPS=".." 追加筛选表里没有的算子（如 benchmark 刚修好的），塞给最空的卡
# ROUNDS=6 CELL_TIMEOUT=2400 VERDICT=IR_CHANGED
set -uo pipefail
cd "$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
: "${SWITCH:?SWITCH is required, e.g. FLAGTREE_MUSA_RLC_ENHANCE}"
: "${SCREEN:?SCREEN is required, 指向 ab_rlc_test.sh 的 screen.tsv}"
DEVICES="${DEVICES:-0}"
ROUNDS="${ROUNDS:-6}"
CELL_TIMEOUT="${CELL_TIMEOUT:-2400}"
VERDICT="${VERDICT:-IR_CHANGED}"
EXTRA_OPS="${EXTRA_OPS:-}"
DRY_RUN="${DRY_RUN:-0}"
TAB=$'\t'
[[ -f "$SCREEN" ]] || { echo "!!! 筛选表不存在: $SCREEN" >&2; exit 2; }
# 设备可见性变量名从 backends 层拿，不写死 MUSA_VISIBLE_DEVICES
VIS_KEY=$(python -c "import sys; sys.path.insert(0, '.')
from backends import get_backend
print(get_backend().visibility_env_keys[0])" 2>/dev/null) || VIS_KEY=""
[[ -n "$VIS_KEY" ]] || { echo "!!! 取不到后端的可见性变量名，检查 ZL_BACKEND" >&2; exit 2; }
echo ">>> switch=$SWITCH rounds=$ROUNDS devices=$DEVICES 可见性变量=$VIS_KEY"
python partition.py --screen "$SCREEN" --devices "$DEVICES" \
--verdict "$VERDICT" --rounds "$ROUNDS" || exit 2
# 每卡一行 "<device><TAB><OPS><TAB><权重秒>"
mapfile -t PLAN < <(python partition.py --screen "$SCREEN" --devices "$DEVICES" \
--verdict "$VERDICT" --format shell) || exit 2
if [[ -n "$EXTRA_OPS" ]]; then
# 追加到当前负载最小的卡。按算子个数选是错的——一个重算子能顶三个轻的，
# partition.py 的第三列给的就是权重秒数
lightest=0; least=""
for i in "${!PLAN[@]}"; do
IFS="$TAB" read -r _d _o _w <<< "${PLAN[$i]}"
if [[ -z "$least" ]] || (( ${_w:-0} < least )); then least=${_w:-0}; lightest=$i; fi
done
IFS="$TAB" read -r d o w <<< "${PLAN[$lightest]}"
PLAN[$lightest]="${d}${TAB}${o:+$o }${EXTRA_OPS}${TAB}${w}"
echo ">>> EXTRA_OPS 追加到卡${d}（当前最空，$(( ${w:-0} * ROUNDS / 60 )) 分钟）: $EXTRA_OPS"
fi
if [[ "$DRY_RUN" == "1" ]]; then
echo ">>> DRY_RUN=1，不起跑。逐卡将执行："
for row in "${PLAN[@]}"; do
IFS="$TAB" read -r d o w <<< "$row"
printf ' 卡%s %4s 分钟 OPS="%s"\n' "$d" "$(( ${w:-0} * ROUNDS / 60 ))" "$o"
done
exit 0
fi
ROOT="runs/campaign_${SWITCH}_$(date +%Y%m%d-%H%M%S)"
mkdir -p "$ROOT"
printf '%s\n' "${PLAN[@]}" > "$ROOT/plan.tsv"
echo ">>> 产物: $(pwd)/$ROOT"
declare -a PIDS=() CARDS=()
for row in "${PLAN[@]}"; do
IFS="$TAB" read -r dev ops _w <<< "$row"
[[ -n "$ops" ]] || { echo ">>> 卡$dev 没有分到活，跳过"; continue; }
out="$ROOT/card${dev}"
mkdir -p "$out"
# AB_ROOT 让每卡有独立产物根：八个 ab_repeat.sh 同一秒启动，默认的时间戳目录名会撞
env "$VIS_KEY=$dev" ZL_DEVICE="$dev" ZL_ISOLATE_TUNER_CACHE=1 \
AB_ROOT="$out" SWITCH="$SWITCH" ROUNDS="$ROUNDS" \
CELL_TIMEOUT="$CELL_TIMEOUT" OPS="$ops" NO_COLOR=1 \
bash ab_repeat.sh > "$ROOT/card${dev}.log" 2>&1 &
PIDS+=($!); CARDS+=("$dev")
echo ">>> 卡$dev pid=$! ops=$ops"
done
(( ${#PIDS[@]} )) || { echo "!!! 没有一张卡分到活" >&2; exit 2; }
echo ">>> ${#PIDS[@]} 张卡已启动，等待全部结束……"
fail=0
for i in "${!PIDS[@]}"; do
if wait "${PIDS[$i]}"; then
echo ">>> 卡${CARDS[$i]} 结束 rc=0"
else
rc=$?; fail=$((fail+1))
echo "!!! 卡${CARDS[$i]} 结束 rc=$rc —— 看 $ROOT/card${CARDS[$i]}.log"
fi
done
# 合并逐格记录：卡号加在最前面，便于事后按卡追
{
first=1
for d in "${CARDS[@]}"; do
f="$ROOT/card${d}/status.tsv"
[[ -f "$f" ]] || continue
if (( first )); then printf 'device%s%s\n' "$TAB" "$(head -1 "$f")"; first=0; fi
tail -n +2 "$f" | sed "s/^/${d}${TAB}/"
done
} > "$ROOT/status.tsv"
echo
echo "########## 汇总 ##########"
echo ">>> 失败的卡: $fail / ${#PIDS[@]}"
if [[ -s "$ROOT/status.tsv" ]]; then
echo ">>> 逐格记录: $(pwd)/$ROOT/status.tsv ($(( $(wc -l < "$ROOT/status.tsv") - 1 )) 格)"
awk -F"$TAB" 'NR>1 && ($6+0)!=0 {n++} END{printf ">>> 非零退出的格: %d\n", n+0}' "$ROOT/status.tsv"
awk -F"$TAB" 'NR>1 {fb+=$10; ng+=$11; po+=$12}
END{printf ">>> 三个标记合计: replay_fallback=%d no_cudagraph=%d capture_poisoned=%d\n",
fb+0, ng+0, po+0}' "$ROOT/status.tsv"
fi
echo ">>> 逐算子报告在各卡的 summary.log 里；单独重出："
echo " python batch_summary.py --ab-rounds $ROOT/card<N>/<op> --label <op>"
echo ">>> 注意：本批 ZL_ISOLATE_TUNER_CACHE=1，绝对延迟不可与共享缓存的批次互引"
campaigns/musa_rlc_product/README.md
+103
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@@ -0,0 +1,103 @@
# S5000 MUSA RLC 产品验证 campaign
在 Moore Threads S5000 上，经由 FlagGems public dispatch，测量 FlagTree
`RemoveLayoutConversions`（RLC）增强对 32 个冻结 workload 的影响。
本目录只提供 campaign 层：契约、冻结、执行、判定、重记账。单算子插件、shape、
inventory 仍然复用仓库根目录的既有能力。
## 组成
| 文件 | 作用 |
|---|---|
| `manifest/musa-rlc-32-product-v1.json` | 冻结契约：源码提交、设备、cell、计时口径、32 个 workload |
| `prepare_musa_rlc_product.py` | 校验 manifest 与 `ops/inventory_main.csv`，冻结不可变 task matrix |
| `run_musa_rlc_product.py` | 串行执行 task matrix，产出逐 cell、逐 task、campaign 三级收据 |
| `run_pytest_musa_rlc_product.sh` | 单个算子单个 cell 的 worker，挂载根目录插件 |
| `reaccount_musa_rlc_topology.py` | 对已封存 run 按新口径重记账，不占用设备 |
| `tests/` | 上述四者的单元测试 |
根目录的 `_musa_benchmark_compat_plugin.py` 属于插件族，不在本目录。
## 三个 cell
| cell | `FLAGTREE_MUSA_RLC_ENHANCE` | phase mask | 含义 |
|---|---|---|---|
| `off` | 0 | — | 增强关闭的基线 |
| `mask0` | 1 | 0 | 增强开启但所有 phase 关闭；它的 topology 必须等于 `off`，否则控制面本身有问题 |
| `candidate` | 1 | 13 | 1a、2、3 启用，1b 未启用 |
`off` 记录 autotune config，`mask0` 与 `candidate` 回放同一套 config。回放回退即判定失败，
这样 A/B 之间的差异才收敛到编译器改动这一个变量。
## 跑法
```bash
python campaigns/musa_rlc_product/prepare_musa_rlc_product.py \
campaigns/musa_rlc_product/manifest/musa-rlc-32-product-v1.json \
--inventory-dir ops --selection canary --dry-run
```
`--dry-run` 只做校验与预算估算。去掉它并给出 `--output-root` 才会冻结 task matrix。
`--selection all` 需要 `--canary-receipt` 指向一份通过的 canary 收据。
执行：
```bash
python campaigns/musa_rlc_product/run_musa_rlc_product.py \
--manifest <frozen>/manifest.json \
--task-matrix <frozen>/task-matrix.json \
--build-root <flagtree build root> \
--flaggems <flaggems> --zl-bench <this repo> \
--worker campaigns/musa_rlc_product/run_pytest_musa_rlc_product.sh \
--output-root <run root>
```
runner 在开跑前校验：manifest 与 matrix 的哈希绑定、FlagTree/FlagGems/zl_bench 三处源码身份、
Triton 与 FlagGems 确实从冻结路径导入、设备可见数为 1、所有 benchmark 与 correctness 目标
在真实源码树里存在、设备当前没有其它进程。任何一条不成立就整批拒绝。
单元测试直接跑文件：
```bash
python campaigns/musa_rlc_product/tests/test_musa_rlc_product.py
python campaigns/musa_rlc_product/tests/test_reaccount_musa_rlc_topology.py
```
## 判定
| 类 | 含义 |
|---|---|
| G | 正确，convert topology 下降，且双执行顺序下均稳定提速 ≥5% |
| C | 正确，topology 下降，但收益不足 5% |
| B | 正确，但 candidate 没有减少被测算子的 convert topology |
| R | 正确，但双顺序稳定退化 ≤ -3% |
| K | candidate 特有的正确性失败 |
| X | 不支持、超时、harness 失败，或同配置回放被破坏 |
只有 topology 相对 incumbent 下降时才追加反向顺序计时。convert 数下降不等于变快，
这一点在本 campaign 内已有反例。
## 三条口径边界
**convert 计数只算被测算子。** benchmark 调用会先构造一次输入再在计时区内反复执行算子。
把所有执行过的 kernel 都计入，会把输入构造 kernel 里消掉的 convert 算成收益，而计时器
根本观测不到它。因此每个 dump 的 TTGIR 都与 `index.tsv` 的 launch 数关联，只有 launch
数超过 `measured_launch_floor` 的变体才计入。未收敛总数与被排除文件保留在收据里；
无法与 index 关联的 cell 判为 X，而不是相信一个界定不了的 topology 声明。
**correctness 只跑该 workload 实际测量的变体。** 以整个测试文件为粒度，会让无关变体
决定被测算子的结论。workload 可以钉 node id 或 `-k` 选择器，两者互斥，且任何收窄都必须
写明理由。完整 argv 落在收据里。
**目标在开跑前解析。** inventory 可能落后于固定的 FlagGems 提交，例如上游把一个 benchmark
文件拆成多个。这类任务过去只在 worker 前置检查里静默失败，留下空日志。现在 runner 会
对真实源码树解析全部目标，workload 可以声明 inventory 里没有的目标但必须给出理由，
worker 的每个前置检查失败时自报名称。
## 不属于本 campaign 的结论
一次 terminal 的 campaign 只证明：该冻结语料在该设备该提交下的 dispatch 正确性、
layout topology 响应、以及同契约下的 native-event 延迟。它不证明跨厂商正确性、
性能对等、CI 状态，也不构成打开生产默认开关的依据。manifest 的
`production_default_policy` 固定为 `unchanged-off`。
campaigns/musa_rlc_product/manifest/musa-rlc-32-product-v1.json
+551
View File
@@ -0,0 +1,551 @@
{
"schema_version": 1,
"campaign_id": "s5000-musa-rlc-32-product-v1-20260902-v3",
"experiment_kind": "musa-rlc-product-validation",
"permitted_claim": "S5000 MUSA product-dispatch correctness, layout-topology response, and same-contract native-event latency for this frozen corpus only",
"production_default_policy": "unchanged-off",
"source": {
"flagtree_commit": "3059917e2df16f8ea35c5efe1ed1af2d87117396",
"flaggems_commit": "46ef8080329190d8da8738340240de2d5a475950",
"zl_bench_commit": "da2288564584039366e7be07fe4e94539a560c81"
},
"device": {
"ssh_alias": "S5000",
"container": "zixu_flagos_S5000",
"backend": "mthreads",
"model": "Moore Threads S5000",
"physical_device": "7",
"logical_device": "0",
"visible_device_count": 1
},
"protocol": {
"screen_cells": [
"off",
"mask0",
"candidate"
],
"timing_cells": [
"off",
"candidate"
],
"canary_order": [
"dropout",
"softmax",
"instance_norm",
"fft",
"sort",
"batch_norm",
"topk",
"conv1d"
],
"candidate_phase_mask": 13,
"candidate_profitability_policy": true,
"candidate_product_launch_count": 1,
"default_benchmark_dtypes": [
"float16"
],
"timer": "triton.testing.do_bench via MUSA device-interface Event",
"autotune_contract": "off-record then mask0/candidate replay from off",
"fresh_process_per_operator_cell": true,
"same_physical_device_for_all_cells": true,
"serial_measurement": true,
"full_campaign_requires_canary_terminal": true,
"correctness": "FlagGems public dispatch quick tests under candidate, narrowed to the variant each workload measures and run with the MThreads compatibility plugin, plus benchmark execution for every cell",
"classification": {
"G": "correct, convert count decreases, and stable speedup >= 5%",
"C": "correct and convert count decreases, but latency intervals overlap or speedup < 5%",
"B": "correct but candidate does not reduce the executed convert topology",
"R": "correct but stable regression <= -3%",
"K": "candidate correctness failure",
"X": "unsupported, timeout, harness failure, or same-contract replay violation"
},
"timeout_seconds_by_cost": {
"light": 300,
"medium": 900,
"heavy": 1800
},
"measured_launch_floor": 1,
"supported_benchmark_dtypes": [
"float16",
"float32",
"bfloat16",
"int16",
"int32",
"bool",
"complex64"
]
},
"workloads": [
{
"id": "batch_norm",
"repo": "main",
"op": "batch_norm",
"op_file": "batch_norm",
"shape_key": "batch_norm",
"correctness_file": "tests/test_batch_norm.py",
"family": "normalization",
"nv_evidence": "strong-positive",
"nv_gain_percent": 43.1,
"prior_s5000": "B 0->0",
"cost": "medium",
"canary": true
},
{
"id": "replication_pad3d",
"repo": "main",
"op": "replication_pad3d",
"op_file": "replication_pad3d",
"shape_key": "replication_pad3d",
"correctness_file": "tests/test_replication_pad3d.py",
"family": "padding",
"nv_evidence": "strong-positive",
"nv_gain_percent": 38.6,
"prior_s5000": "B 0->0 at author shape",
"cost": "medium",
"canary": false
},
{
"id": "fft",
"repo": "main",
"op": "perf_fft",
"op_file": "fft",
"shape_key": "fft",
"correctness_file": "tests/test_fft.py",
"family": "fft",
"nv_evidence": "strong-positive",
"nv_gain_percent": 36.5,
"prior_s5000": "G 38->2",
"cost": "heavy",
"canary": true,
"benchmark_dtypes": [
"complex64"
]
},
{
"id": "topk",
"repo": "main",
"op": "topk",
"op_file": "topk",
"shape_key": "topk",
"correctness_file": "tests/test_topk.py",
"family": "selection",
"nv_evidence": "strong-positive-shared-network-risk",
"nv_gain_percent": 20.8,
"prior_s5000": "B 0->0 at large author shape",
"cost": "medium",
"canary": true
},
{
"id": "instance_norm",
"repo": "main",
"op": "instance_norm",
"op_file": "instance_norm",
"shape_key": "instance_norm",
"correctness_file": "tests/test_instance_norm.py",
"family": "normalization",
"nv_evidence": "strong-positive",
"nv_gain_percent": 17.8,
"prior_s5000": "G 1->0",
"cost": "medium",
"canary": true
},
{
"id": "dropout",
"repo": "main",
"op": "dropout",
"op_file": "dropout",
"shape_key": "dropout",
"correctness_file": "tests/test_dropout.py",
"family": "rng",
"nv_evidence": "strong-positive-shared-network-risk",
"nv_gain_percent": 15.2,
"prior_s5000": "product G 8->0 +9.15%",
"cost": "light",
"canary": true
},
{
"id": "router_gemm",
"repo": "main",
"op": "perf_router_gemm",
"op_file": "router_gemm",
"shape_key": "router_gemm",
"correctness_file": "tests/test_router_gemm.py",
"family": "mma",
"nv_evidence": "strong-positive-mma",
"nv_gain_percent": 14.5,
"prior_s5000": "public screen available; product v1 pending",
"cost": "heavy",
"canary": false,
"benchmark_dtypes": [
"bfloat16"
]
},
{
"id": "fused_recurrent_gated_delta_rule",
"repo": "main",
"op": "perf_fused_recurrent_gated_delta_rule",
"op_file": "FLA/test_fused_recurrent_gated_delta_rule_perf",
"shape_key": null,
"correctness_file": "tests/test_FLA/test_fused_recurrent_gated_delta_rule.py",
"family": "recurrent",
"nv_evidence": "strong-positive-recurrent",
"nv_gain_percent": 14.2,
"prior_s5000": "not product-qualified",
"cost": "heavy",
"canary": false,
"benchmark_dtypes": [
"bfloat16"
]
},
{
"id": "top_k_per_row_decode",
"repo": "main",
"op": "top_k_per_row_decode",
"op_file": "top_k_per_row_decode",
"shape_key": "top_k_per_row_decode",
"correctness_file": "tests/test_top_k_per_row_decode.py",
"family": "selection",
"nv_evidence": "strong-positive-shared-network-risk",
"nv_gain_percent": 13.0,
"prior_s5000": "not product-qualified",
"cost": "medium",
"canary": false
},
{
"id": "sort",
"repo": "main",
"op": "perf_sort",
"op_file": "sort",
"shape_key": "sort",
"correctness_file": "tests/test_sort.py",
"family": "sort",
"nv_evidence": "strong-positive-shared-network-risk",
"nv_gain_percent": 12.3,
"prior_s5000": "G 2->1",
"cost": "medium",
"canary": true
},
{
"id": "sort_stable",
"repo": "main",
"op": "perf_sort_stable",
"op_file": "sort",
"shape_key": "sort_stable",
"correctness_file": "tests/test_sort.py",
"family": "sort",
"nv_evidence": "strong-positive-shared-network-risk",
"nv_gain_percent": 12.3,
"prior_s5000": "G 2->1",
"cost": "medium",
"canary": false
},
{
"id": "uniform",
"repo": "main",
"op": "uniform_",
"op_file": "uniform_",
"shape_key": "uniform_",
"correctness_file": "tests/test_uniform_.py",
"family": "rng",
"nv_evidence": "strong-positive-rng",
"nv_gain_percent": 10.3,
"prior_s5000": "public boundary risk; product v1 pending",
"cost": "light",
"canary": false
},
{
"id": "rand",
"repo": "main",
"op": "rand",
"op_file": "rand",
"shape_key": "rand",
"correctness_file": "tests/test_rand.py",
"family": "rng",
"nv_evidence": "strong-positive-rng",
"nv_gain_percent": 8.7,
"prior_s5000": "product v1 pending",
"cost": "light",
"canary": false
},
{
"id": "rand_like",
"repo": "main",
"op": "rand_like",
"op_file": "rand_like",
"shape_key": "rand_like",
"correctness_file": "tests/test_rand_like.py",
"family": "rng",
"nv_evidence": "strong-positive-rng",
"nv_gain_percent": 8.6,
"prior_s5000": "product v1 pending",
"cost": "light",
"canary": false
},
{
"id": "max_pool2d_with_indices",
"repo": "main",
"op": "max_pool2d_with_indices",
"op_file": "max_pool2d",
"shape_key": "max_pool2d_with_indices",
"correctness_file": "tests/test_max_pool2d.py",
"family": "reduction-index",
"nv_evidence": "positive-reduction-index",
"nv_gain_percent": 6.1,
"prior_s5000": "B 0->0",
"cost": "medium",
"canary": false
},
{
"id": "randperm",
"repo": "main",
"op": "randperm",
"op_file": "randperm",
"shape_key": "randperm",
"correctness_file": "tests/test_randperm.py",
"family": "rng",
"nv_evidence": "small-positive-rng",
"nv_gain_percent": 4.0,
"prior_s5000": "C 7->1 latency overlap",
"cost": "medium",
"canary": false,
"benchmark_dtypes": [
"int32"
]
},
{
"id": "randn",
"repo": "main",
"op": "randn",
"op_file": "randn",
"shape_key": "randn",
"correctness_file": "tests/test_randn.py",
"family": "rng",
"nv_evidence": "diluted-positive-rng",
"nv_gain_percent": 4.0,
"prior_s5000": "product v1 pending",
"cost": "light",
"canary": false
},
{
"id": "randn_like",
"repo": "main",
"op": "randn_like",
"op_file": "randn_like",
"shape_key": "randn_like",
"correctness_file": "tests/test_randn_like.py",
"family": "rng",
"nv_evidence": "diluted-positive-rng",
"nv_gain_percent": 4.0,
"prior_s5000": "product v1 pending",
"cost": "light",
"canary": false
},
{
"id": "upsample_bicubic2d_aa",
"repo": "main",
"op": "upsample_bicubic2d_aa",
"op_file": "upsample_bicubic2d_aa",
"shape_key": "upsample_bicubic2d_aa",
"correctness_file": "tests/test_upsample_bicubic2d_aa.py",
"family": "interpolation",
"nv_evidence": "diluted-positive-cold-path",
"nv_gain_percent": 1.7,
"prior_s5000": "C 10->0 latency flat",
"cost": "medium",
"canary": false,
"correctness_selector": "not backward",
"correctness_scope_reason": "the benchmarked op is the forward path (benchmark/test_upsample_bicubic2d_aa.py::test_upsample_bicubic2d_aa); the backward baseline defect fails identically under off/mask0/candidate and is tracked as a separate MUSA numerics issue"
},
{
"id": "grid_sample",
"repo": "main",
"op": "grid_sample",
"op_file": "grid_sample",
"shape_key": "grid_sample",
"correctness_file": "tests/test_grid_sample.py",
"family": "sampling",
"nv_evidence": "structure-change-no-win",
"nv_gain_percent": 0.0,
"prior_s5000": "B 0->0",
"cost": "medium",
"canary": false,
"benchmark_selector": "dtype0",
"benchmark_scope_reason": "benchmark/test_grid_sample.py parametrises over consts.FLOAT_DTYPES and declares dtypes=[dtype] per case, so a campaign-wide --dtypes float16 filter makes the float32 and bfloat16 cases raise; dtype0 is the float16 case this workload measures"
},
{
"id": "max_pool2d_backward",
"repo": "main",
"op": "max_pool2d_backward",
"op_file": "max_pool2d",
"shape_key": "max_pool2d_backward",
"correctness_file": "tests/test_max_pool2d.py",
"family": "reduction-index",
"nv_evidence": "structure-change-no-win",
"nv_gain_percent": 0.0,
"prior_s5000": "B 0->0",
"cost": "medium",
"canary": false
},
{
"id": "conv1d",
"repo": "main",
"op": "conv1d",
"op_file": "conv1d",
"shape_key": "conv1d",
"correctness_file": "tests/test_conv1d.py",
"family": "convolution",
"nv_evidence": "light-regression-review",
"nv_gain_percent": -4.2,
"prior_s5000": "C 1->0 via phase3, slight slowdown",
"cost": "heavy",
"canary": true
},
{
"id": "layer_norm_backward",
"repo": "main",
"op": "layer_norm_backward",
"op_file": "layer_norm",
"shape_key": "layer_norm_backward",
"correctness_file": "tests/test_layer_norm.py",
"family": "normalization",
"nv_evidence": "light-regression-review",
"nv_gain_percent": -1.9,
"prior_s5000": "C 4->2 latency flat",
"cost": "medium",
"canary": false
},
{
"id": "cutlass_scaled_mm",
"repo": "main",
"op": "cutlass_scaled_mm_benchmark",
"op_file": "cutlass_scaled_mm",
"shape_key": "cutlass_scaled_mm",
"correctness_file": "tests/test_cutlass_scaled_mm.py",
"family": "mma",
"nv_evidence": "light-regression-mma",
"nv_gain_percent": -1.2,
"prior_s5000": "likely unsupported CUDA-specific control",
"cost": "heavy",
"canary": false
},
{
"id": "gelu_backward",
"repo": "main",
"op": "gelu_backward",
"op_file": "gelu",
"shape_key": "gelu_backward",
"correctness_file": "tests/test_gelu.py",
"family": "elementwise-backward",
"nv_evidence": "direct-no-cudagraph-noise",
"nv_gain_percent": null,
"prior_s5000": "B 0->0 with LLVM optimizations",
"cost": "medium",
"canary": false
},
{
"id": "fused_marlin_moe_mxfp4",
"repo": "main",
"op": "fused_marlin_moe_w4a16_mxfp4",
"op_file": "fused_marlin_moe_w4a16_mxfp4",
"shape_key": null,
"correctness_file": "tests/test_fused_marlin_moe.py",
"family": "quantized-moe",
"nv_evidence": "autotune-config-change",
"nv_gain_percent": null,
"prior_s5000": "likely unsupported CUDA-specific control",
"cost": "heavy",
"canary": false,
"correctness_selector": "mxfp4",
"correctness_scope_reason": "tests/test_fused_marlin_moe.py also holds w4a16_int4 and w8a16_int8 variants; only the mxfp4 variant corresponds to this workload, and the int8 failures are a separate baseline defect",
"inventory_override_reason": "zl_bench da2288564584039366e7be07fe4e94539a560c81 lists benchmark/test_fused_marlin_moe.py, but FlagGems 46ef8080329190d8da8738340240de2d5a475950 split it into per-quantisation files; the mxfp4 target is benchmark/test_fused_marlin_moe_w4a16_mxfp4.py::test_fused_marlin_moe_w4a16_mxfp4"
},
{
"id": "bincount",
"repo": "main",
"op": "bincount",
"op_file": "bincount",
"shape_key": "bincount",
"correctness_file": "tests/test_bincount.py",
"family": "histogram",
"nv_evidence": "autotune-config-change",
"nv_gain_percent": null,
"prior_s5000": "product v1 pending",
"cost": "medium",
"canary": false,
"benchmark_dtypes": [
"float32"
]
},
{
"id": "masked_select",
"repo": "main",
"op": "masked_select",
"op_file": "masked_select",
"shape_key": "masked_select",
"correctness_file": "tests/test_masked_select.py",
"family": "selection",
"nv_evidence": "holdout-shared-network",
"nv_gain_percent": null,
"prior_s5000": "public qualify available",
"cost": "medium",
"canary": false
},
{
"id": "mm",
"repo": "main",
"op": "mm",
"op_file": "mm",
"shape_key": "mm",
"correctness_file": "tests/test_mm.py",
"family": "mma-control",
"nv_evidence": "holdout-mma-control",
"nv_gain_percent": null,
"prior_s5000": "control only",
"cost": "heavy",
"canary": false
},
{
"id": "scaled_dot_product_attention",
"repo": "main",
"op": "scaled_dot_product_attention",
"op_file": "scaled_dot_product_attention",
"shape_key": "scaled_dot_product_attention",
"correctness_file": "tests/test_scaled_dot_product_attention.py",
"family": "attention-control",
"nv_evidence": "holdout-attention-control",
"nv_gain_percent": null,
"prior_s5000": "control only",
"cost": "heavy",
"canary": false
},
{
"id": "softmax",
"repo": "main",
"op": "softmax",
"op_file": "softmax",
"shape_key": "softmax",
"correctness_file": "tests/test_softmax.py",
"family": "reduction-control",
"nv_evidence": "holdout-reduction-control",
"nv_gain_percent": null,
"prior_s5000": "product B 0->0",
"cost": "light",
"canary": true
},
{
"id": "layer_norm",
"repo": "main",
"op": "layer_norm",
"op_file": "layer_norm",
"shape_key": "layer_norm",
"correctness_file": "tests/test_layer_norm.py",
"family": "normalization-control",
"nv_evidence": "holdout-normalization-control",
"nv_gain_percent": null,
"prior_s5000": "control only",
"cost": "medium",
"canary": false
}
],
"predecessor_campaign_id": "s5000-musa-rlc-32-product-v1-20260901-v2"
}
campaigns/musa_rlc_product/prepare_musa_rlc_product.py
+478
View File
@@ -0,0 +1,478 @@
#!/usr/bin/env python3
"""Validate and freeze the single-device MUSA RLC product campaign."""
from __future__ import annotations
import argparse
import csv
import hashlib
import json
import os
import shutil
from pathlib import Path
from typing import Any
COSTS = {"light", "medium", "heavy"}
CELLS = ["off", "mask0", "candidate"]
TIMING_CELLS = ["off", "candidate"]
ESTIMATED_SECONDS = {"light": 180, "medium": 600, "heavy": 1200}
class CampaignError(RuntimeError):
pass
def _canonical(value: object) -> bytes:
return (json.dumps(value, indent=2, sort_keys=True) + "\n").encode("utf-8")
def _is_git_sha(value: object) -> bool:
return (
isinstance(value, str)
and len(value) == 40
and all(char in "0123456789abcdef" for char in value)
)
def _correctness_scope_errors(row: dict[str, Any], index: int) -> list[str]:
"""Check an optional narrowing of a workload's correctness gate.
Running a whole test file lets an unrelated variant decide the verdict for
the operator actually being measured. A workload may therefore pin node ids
or a ``-k`` selector, but never both, and narrowing always requires a stated
reason so the exclusion stays visible in review.
"""
name = row.get("id", index)
selector = row.get("correctness_selector")
node_ids = row.get("correctness_node_ids")
errors: list[str] = []
if selector is not None and node_ids is not None:
errors.append(
f"workload {name} must not set both correctness_selector and "
"correctness_node_ids"
)
if selector is not None and (not isinstance(selector, str) or not selector.strip()):
errors.append(f"workload {name} correctness_selector must be a non-empty string")
if node_ids is not None and (
not isinstance(node_ids, list)
or not node_ids
or not all(isinstance(value, str) and value.strip() for value in node_ids)
):
errors.append(
f"workload {name} correctness_node_ids must be non-empty strings"
)
if selector is None and node_ids is None:
return errors
reason = row.get("correctness_scope_reason")
if not isinstance(reason, str) or not reason.strip():
errors.append(
f"workload {name} narrows correctness and must state "
"correctness_scope_reason"
)
return errors
def _benchmark_scope_errors(row: dict[str, Any], index: int) -> list[str]:
"""Check an optional narrowing of a workload's benchmark run.
Some benchmarks parametrise one dtype per case, so the campaign-wide dtype
filter makes every non-matching case raise. A workload may pin the case it
measures, but narrowing what gets timed always requires a stated reason.
"""
name = row.get("id", index)
selector = row.get("benchmark_selector")
if selector is None:
return []
errors: list[str] = []
if not isinstance(selector, str) or not selector.strip():
errors.append(f"workload {name} benchmark_selector must be a non-empty string")
reason = row.get("benchmark_scope_reason")
if not isinstance(reason, str) or not reason.strip():
errors.append(
f"workload {name} narrows the benchmark and must state "
"benchmark_scope_reason"
)
return errors
def _inventory_override_errors(row: dict[str, Any], index: int) -> list[str]:
"""Check an explicit departure from the pinned zl_bench inventory.
The inventory can lag the pinned FlagGems commit, for example when upstream
splits one benchmark file into several. A workload may then name the target
that actually exists, but it must say why, so a stale inventory is never
silently routed around.
"""
name = row.get("id", index)
reason = row.get("inventory_override_reason")
if reason is None:
return []
if not isinstance(reason, str) or not reason.strip():
return [
f"workload {name} inventory_override_reason must be a non-empty string"
]
return []
def validate_manifest(manifest: dict[str, Any]) -> list[str]:
errors: list[str] = []
if manifest.get("schema_version") != 1:
errors.append("schema_version must be 1")
if manifest.get("experiment_kind") != "musa-rlc-product-validation":
errors.append("experiment_kind must be musa-rlc-product-validation")
if manifest.get("production_default_policy") != "unchanged-off":
errors.append("production_default_policy must be unchanged-off")
if "S5000 MUSA product-dispatch" not in manifest.get("permitted_claim", ""):
errors.append("permitted_claim must bind S5000 MUSA product dispatch")
source = manifest.get("source")
if not isinstance(source, dict):
errors.append("source must be an object")
else:
for key in ("flagtree_commit", "flaggems_commit", "zl_bench_commit"):
if not _is_git_sha(source.get(key)):
errors.append(f"source.{key} must be a lowercase 40-character Git SHA")
device = manifest.get("device")
expected_device = {
"ssh_alias": "S5000",
"container": "zixu_flagos_S5000",
"backend": "mthreads",
"model": "Moore Threads S5000",
"physical_device": "7",
"logical_device": "0",
"visible_device_count": 1,
}
if not isinstance(device, dict):
errors.append("device must be an object")
else:
for key, expected in expected_device.items():
if device.get(key) != expected:
errors.append(f"device.{key} must equal {expected!r}")
protocol = manifest.get("protocol")
if not isinstance(protocol, dict):
errors.append("protocol must be an object")
else:
if protocol.get("screen_cells") != CELLS:
errors.append("protocol.screen_cells must be off/mask0/candidate")
if protocol.get("timing_cells") != TIMING_CELLS:
errors.append("protocol.timing_cells must be off/candidate")
if protocol.get("candidate_phase_mask") != 13:
errors.append("candidate_phase_mask must be 13")
canary_order = protocol.get("canary_order")
if not isinstance(canary_order, list) or len(canary_order) != 8:
errors.append("protocol.canary_order must contain eight workload ids")
for flag in (
"candidate_profitability_policy",
"fresh_process_per_operator_cell",
"same_physical_device_for_all_cells",
"serial_measurement",
"full_campaign_requires_canary_terminal",
):
if protocol.get(flag) is not True:
errors.append(f"protocol.{flag} must be true")
if protocol.get("timer") != "triton.testing.do_bench via MUSA device-interface Event":
errors.append("protocol.timer must bind the MUSA native-event path")
default_dtypes = protocol.get("default_benchmark_dtypes")
if not isinstance(default_dtypes, list) or not default_dtypes or not all(
isinstance(value, str) and value for value in default_dtypes
):
errors.append("protocol.default_benchmark_dtypes must be non-empty strings")
supported = protocol.get("supported_benchmark_dtypes")
if not isinstance(supported, list) or not supported or not all(
isinstance(value, str) and value for value in supported
):
errors.append(
"protocol.supported_benchmark_dtypes must be non-empty strings"
)
timeouts = protocol.get("timeout_seconds_by_cost")
if not isinstance(timeouts, dict) or set(timeouts) != COSTS:
errors.append("timeout_seconds_by_cost must define light/medium/heavy")
elif not all(isinstance(value, int) and value > 0 for value in timeouts.values()):
errors.append("timeout_seconds_by_cost values must be positive integers")
floor = protocol.get("measured_launch_floor")
if not isinstance(floor, int) or isinstance(floor, bool) or floor < 0:
errors.append(
"protocol.measured_launch_floor must be a non-negative integer"
)
workloads = manifest.get("workloads")
if not isinstance(workloads, list) or len(workloads) != 32:
errors.append("workloads must contain exactly 32 entries")
return errors
ids: list[str] = []
nodes: list[tuple[str, str, str]] = []
canaries = 0
for index, row in enumerate(workloads):
if not isinstance(row, dict):
errors.append(f"workloads[{index}] must be an object")
continue
for key in (
"id", "repo", "op", "op_file", "correctness_file", "family",
"nv_evidence", "prior_s5000", "cost",
):
if not isinstance(row.get(key), str) or not row[key]:
errors.append(f"workloads[{index}] missing {key}")
if row.get("repo") != "main":
errors.append(f"workload {row.get('id', index)} must use the main FlagGems repo")
if row.get("cost") not in COSTS:
errors.append(f"workload {row.get('id', index)} has invalid cost")
if not isinstance(row.get("canary"), bool):
errors.append(f"workload {row.get('id', index)} canary must be boolean")
benchmark_dtypes = row.get(
"benchmark_dtypes", protocol.get("default_benchmark_dtypes", [])
)
if not isinstance(benchmark_dtypes, list) or not benchmark_dtypes or not all(
isinstance(value, str) and value for value in benchmark_dtypes
):
errors.append(
f"workload {row.get('id', index)} benchmark_dtypes must be non-empty strings"
)
else:
# The benchmark CLI rejects a dtype it does not define, and that
# only surfaced as an argparse error inside a device run.
vocabulary = protocol.get("supported_benchmark_dtypes")
if isinstance(vocabulary, list):
unknown = [
value for value in benchmark_dtypes if value not in vocabulary
]
if unknown:
errors.append(
f"workload {row.get('id', index)} declares benchmark dtype(s) "
f"the benchmark CLI does not accept: {', '.join(unknown)}"
)
errors.extend(_correctness_scope_errors(row, index))
errors.extend(_benchmark_scope_errors(row, index))
errors.extend(_inventory_override_errors(row, index))
canaries += row.get("canary") is True
ids.append(row.get("id", ""))
nodes.append((row.get("repo", ""), row.get("op", ""), row.get("op_file", "")))
if len(ids) != len(set(ids)):
errors.append("workload ids must be unique")
if len(nodes) != len(set(nodes)):
errors.append("workload repo/op/op_file triples must be unique")
if canaries != 8:
errors.append("exactly eight workloads must be canaries")
elif set(manifest["protocol"].get("canary_order", [])) != {
row["id"] for row in workloads if row["canary"]
}:
errors.append("protocol.canary_order must contain every canary exactly once")
return errors
def _load_inventory(inventory_dir: Path) -> dict[tuple[str, str, str], dict[str, str]]:
path = inventory_dir / "inventory_main.csv"
if not path.is_file():
raise CampaignError(f"missing inventory: {path}")
rows: dict[tuple[str, str, str], dict[str, str]] = {}
with path.open(newline="", encoding="utf-8") as handle:
for row in csv.DictReader(handle):
key = ("main", row.get("op", ""), row.get("op_file", ""))
if key in rows:
raise CampaignError(f"duplicate inventory row: {key}")
rows[key] = row
return rows
def validate_inventory(workloads: list[dict[str, Any]], inventory_dir: Path) -> list[str]:
inventory = _load_inventory(inventory_dir)
errors: list[str] = []
for row in workloads:
key = (row["repo"], row["op"], row["op_file"])
found = inventory.get(key)
if found is None:
# A stated override admits a target the pinned inventory does not
# know about. The runner still resolves it against the real tree.
if row.get("inventory_override_reason"):
continue
errors.append(f"inventory missing {key}")
continue
if found.get("collected") == "no":
errors.append(f"inventory marks {key} collected=no")
if found.get("marked_skip") == "yes":
errors.append(f"inventory marks {key} skipped")
return errors
def build_plan(manifest: dict[str, Any], selection: str) -> tuple[dict[str, Any], dict[str, Any]]:
workloads = manifest["workloads"]
if selection == "canary":
by_id = {row["id"]: row for row in workloads if row["canary"]}
workloads = [by_id[task_id] for task_id in manifest["protocol"]["canary_order"]]
elif selection != "all":
raise CampaignError(f"unsupported selection: {selection}")
timeouts = manifest["protocol"]["timeout_seconds_by_cost"]
tasks = []
ordinal = 0
for row in workloads:
ordinal += 1
tasks.append(
{
"ordinal": ordinal,
"task_id": row["id"],
"repo": row["repo"],
"op": row["op"],
"op_file": row["op_file"],
"shape_key": row.get("shape_key"),
"correctness_file": row["correctness_file"],
"correctness_selector": row.get("correctness_selector"),
"correctness_node_ids": row.get("correctness_node_ids"),
"correctness_scope_reason": row.get("correctness_scope_reason"),
"benchmark_selector": row.get("benchmark_selector"),
"benchmark_scope_reason": row.get("benchmark_scope_reason"),
"inventory_override_reason": row.get("inventory_override_reason"),
"measured_launch_floor": manifest["protocol"]["measured_launch_floor"],
"family": row["family"],
"nv_evidence": row["nv_evidence"],
"nv_gain_percent": row.get("nv_gain_percent"),
"prior_s5000": row["prior_s5000"],
"cost": row["cost"],
"benchmark_dtypes": row.get(
"benchmark_dtypes",
manifest["protocol"]["default_benchmark_dtypes"],
),
"timeout_seconds": timeouts[row["cost"]],
"estimated_seconds_per_cell": ESTIMATED_SECONDS[row["cost"]],
"cells": CELLS,
"status": "PENDING",
}
)
estimate = sum(task["estimated_seconds_per_cell"] * len(CELLS) for task in tasks)
matrix = {
"schema_version": 1,
"campaign_id": manifest["campaign_id"],
"selection": selection,
"device": manifest["device"],
"screen_cells": CELLS,
"timing_cells": TIMING_CELLS,
"tasks": tasks,
}
budget = {
"schema_version": 1,
"campaign_id": manifest["campaign_id"],
"selection": selection,
"task_count": len(tasks),
"gpu_count": 1,
"serial_measurement": True,
"estimated_wall_seconds": estimate,
"estimated_gpu_hours": round(estimate / 3600.0, 4),
"note": "Scheduling estimate only; it is not performance evidence.",
}
return matrix, budget
def validate_canary_receipt(
manifest: dict[str, Any], selection: str, canary_receipt: Path | None
) -> list[str]:
if selection != "all":
return []
if canary_receipt is None:
return ["full campaign requires --canary-receipt"]
try:
receipt = json.loads(canary_receipt.read_text(encoding="utf-8"))
except (OSError, json.JSONDecodeError) as exc:
return [f"cannot read canary receipt: {exc}"]
errors = []
if receipt.get("campaign_id") != manifest.get("campaign_id"):
errors.append("canary receipt campaign_id mismatch")
if receipt.get("selection") != "canary" or receipt.get("status") != "TERMINAL":
errors.append("canary receipt must be a terminal canary")
if receipt.get("canary_gate_pass") is not True:
errors.append("canary receipt does not authorize the full campaign")
return errors
def prepare(
manifest_path: Path,
inventory_dir: Path,
output_root: Path,
selection: str,
canary_receipt: Path | None = None,
) -> Path:
manifest = json.loads(manifest_path.read_text(encoding="utf-8"))
if not isinstance(manifest, dict):
raise CampaignError("manifest root must be an object")
errors = validate_manifest(manifest)
if not errors:
errors.extend(validate_inventory(manifest["workloads"], inventory_dir))
if not errors:
errors.extend(validate_canary_receipt(manifest, selection, canary_receipt))
if errors:
raise CampaignError("campaign validation failed:\n- " + "\n- ".join(errors))
matrix, budget = build_plan(manifest, selection)
output = output_root.resolve() / f"{manifest['campaign_id']}--{selection}"
if output.exists():
raise CampaignError(f"campaign output already exists and is immutable: {output}")
output.parent.mkdir(parents=True, exist_ok=True)
stage = output.parent / f".{output.name}.staging-{os.getpid()}"
if stage.exists():
raise CampaignError(f"staging directory already exists: {stage}")
try:
stage.mkdir()
shutil.copy2(manifest_path, stage / "manifest.json")
manifest_sha = hashlib.sha256((stage / "manifest.json").read_bytes()).hexdigest()
matrix["manifest_sha256"] = manifest_sha
budget["manifest_sha256"] = manifest_sha
(stage / "manifest.sha256").write_text(
f"{manifest_sha} manifest.json\n", encoding="utf-8", newline="\n"
)
(stage / "task-matrix.json").write_bytes(_canonical(matrix))
(stage / "budget.json").write_bytes(_canonical(budget))
(stage / "ops.txt").write_text(
"".join(f"{task['repo']}:{task['op']}@{task['op_file']}\n" for task in matrix["tasks"]),
encoding="utf-8",
newline="\n",
)
stage.replace(output)
except Exception:
if stage.exists():
shutil.rmtree(stage)
raise
return output
def main() -> int:
parser = argparse.ArgumentParser()
parser.add_argument("manifest", type=Path)
parser.add_argument("--inventory-dir", type=Path, required=True)
parser.add_argument("--selection", choices=("canary", "all"), default="canary")
parser.add_argument("--output-root", type=Path)
parser.add_argument("--canary-receipt", type=Path)
parser.add_argument("--dry-run", action="store_true")
args = parser.parse_args()
try:
manifest = json.loads(args.manifest.read_text(encoding="utf-8"))
errors = validate_manifest(manifest) if isinstance(manifest, dict) else ["manifest root must be an object"]
if not errors:
errors.extend(validate_inventory(manifest["workloads"], args.inventory_dir))
if not errors and not args.dry_run:
errors.extend(validate_canary_receipt(manifest, args.selection, args.canary_receipt))
if errors:
raise CampaignError("campaign validation failed:\n- " + "\n- ".join(errors))
matrix, budget = build_plan(manifest, args.selection)
if args.dry_run:
print(json.dumps({"status": "DRY_RUN", "budget": budget, "tasks": matrix["tasks"]}, indent=2, sort_keys=True))
return 0
if args.output_root is None:
raise CampaignError("--output-root is required unless --dry-run is used")
output = prepare(
args.manifest,
args.inventory_dir,
args.output_root,
args.selection,
args.canary_receipt,
)
except (OSError, json.JSONDecodeError, CampaignError) as exc:
print(str(exc))
return 2
print(json.dumps({"status": "PREPARED", "output": str(output)}, indent=2))
return 0
if __name__ == "__main__":
raise SystemExit(main())
campaigns/musa_rlc_product/reaccount_musa_rlc_topology.py
+223
View File
@@ -0,0 +1,223 @@
#!/usr/bin/env python3
"""Re-account a sealed MUSA RLC run under measured-operator convert scoping.
The sealed campaign counted every executed kernel's ``ttg.convert_layout`` ops,
including the kernels a benchmark launches once to build its inputs. Those sit
outside the timed region, so a candidate could be credited with a topology
reduction that no timer could ever observe.
This tool re-reads the sealed TTGIR dumps and their launch index, recomputes the
convert counts scoped to repeatedly launched kernels, and re-runs the campaign
classifier. It never writes inside the sealed run.
A re-accounted verdict can only demote a topology claim. Promoting a task to G
or C needs reversed-order timing, which a sealed run only holds for tasks that
already showed a reduction, so a newly appearing reduction is reported as X for
missing round-b evidence rather than guessed.
"""
from __future__ import annotations
import argparse
import csv
import importlib.util
import json
import sys
from pathlib import Path
from typing import Any
HERE = Path(__file__).resolve().parent
class ReaccountError(RuntimeError):
pass
def _load_runner():
spec = importlib.util.spec_from_file_location(
"run_musa_rlc_product", HERE / "run_musa_rlc_product.py"
)
if spec is None or spec.loader is None:
raise ReaccountError("cannot load run_musa_rlc_product.py")
module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(module)
return module
def _canonical(value: object) -> bytes:
return (json.dumps(value, indent=2, sort_keys=True) + "\n").encode("utf-8")
def reaccount_task(
runner,
task_root: Path,
receipt: dict[str, Any],
measured_launch_floor: int,
) -> dict[str, Any]:
"""Recompute one sealed task receipt under the scoped convert count."""
cells: dict[str, dict[str, Any]] = {}
for key, sealed in receipt.get("cells", {}).items():
cell = dict(sealed)
cell_dir = task_root / str(sealed.get("round")) / str(sealed.get("cell"))
if cell_dir.is_dir():
scope = runner._convert_count(
cell_dir, measured_launch_floor=measured_launch_floor
)
cell.update(
{
"convert_count": scope["measured_convert_count"],
"convert_count_all": scope["total_convert_count"],
"convert_scope_complete": scope["scope_complete"],
"measured_launch_floor": scope["measured_launch_floor"],
"convert_excluded_unmeasured": scope["excluded_unmeasured"],
"convert_unindexed_files": scope["unindexed_files"],
}
)
else:
cell["convert_scope_complete"] = False
cells[key] = cell
classification, detail = runner.classify_task(
receipt["correctness"], cells, receipt.get("correctness_isolation")
)
sealed_class = receipt.get("classification")
excluded = sorted(
{
row["file"]
for cell in cells.values()
for row in cell.get("convert_excluded_unmeasured", [])
}
)
return {
"task_id": receipt["task_id"],
"op": receipt.get("op"),
"family": receipt.get("family"),
"sealed_classification": sealed_class,
"reaccounted_classification": classification,
"changed": classification != sealed_class,
"sealed_reason": receipt.get("classification_detail", {}).get("reason"),
"reaccounted_reason": detail["reason"],
"sealed_convert": {
key: receipt["cells"][key].get("convert_count")
for key in sorted(receipt.get("cells", {}))
},
"reaccounted_convert": {
key: cells[key].get("convert_count") for key in sorted(cells)
},
"excluded_unmeasured_files": excluded,
"classification_detail": detail,
}
def reaccount(
run_root: Path, output_root: Path, measured_launch_floor: int
) -> dict[str, Any]:
run_root = run_root.resolve()
output_root = output_root.resolve()
if not (run_root / "campaign-receipt.json").is_file():
raise ReaccountError(f"not a sealed campaign run root: {run_root}")
if output_root == run_root or run_root in output_root.parents:
raise ReaccountError("refusing to write inside the sealed run root")
runner = _load_runner()
sealed = json.loads((run_root / "campaign-receipt.json").read_text(encoding="utf-8"))
rows: list[dict[str, Any]] = []
for path in sorted((run_root / "tasks").glob("*/task-receipt.json")):
receipt = json.loads(path.read_text(encoding="utf-8"))
rows.append(
reaccount_task(runner, path.parent, receipt, measured_launch_floor)
)
terminal = sorted(runner.TERMINAL)
result = {
"schema_version": 1,
"kind": "musa-rlc-topology-reaccount",
"sealed_campaign_id": sealed.get("campaign_id"),
"sealed_run_root": str(run_root),
"measured_launch_floor": measured_launch_floor,
"task_count": len(rows),
"sealed_counts": {
key: sum(row["sealed_classification"] == key for row in rows)
for key in terminal
},
"reaccounted_counts": {
key: sum(row["reaccounted_classification"] == key for row in rows)
for key in terminal
},
"changed_task_ids": [row["task_id"] for row in rows if row["changed"]],
"note": (
"Derived accounting over sealed artifacts. It supersedes no receipt "
"and produces no new device measurement."
),
"tasks": rows,
}
output_root.mkdir(parents=True, exist_ok=True)
(output_root / "reaccount-receipt.json").write_bytes(_canonical(result))
with (output_root / "reaccount-matrix.csv").open(
"w", newline="", encoding="utf-8"
) as handle:
writer = csv.writer(handle)
writer.writerow(
[
"task_id",
"sealed_class",
"reaccounted_class",
"changed",
"sealed_off",
"sealed_candidate",
"scoped_off",
"scoped_candidate",
"reaccounted_reason",
]
)
for row in rows:
writer.writerow(
[
row["task_id"],
row["sealed_classification"],
row["reaccounted_classification"],
"yes" if row["changed"] else "no",
row["sealed_convert"].get("a_off"),
row["sealed_convert"].get("a_candidate"),
row["reaccounted_convert"].get("a_off"),
row["reaccounted_convert"].get("a_candidate"),
row["reaccounted_reason"],
]
)
return result
def main() -> int:
parser = argparse.ArgumentParser()
parser.add_argument("--run-root", type=Path, required=True)
parser.add_argument("--output-root", type=Path, required=True)
parser.add_argument("--measured-launch-floor", type=int, default=1)
args = parser.parse_args()
try:
if args.measured_launch_floor < 0:
raise ReaccountError("--measured-launch-floor must be non-negative")
result = reaccount(
args.run_root, args.output_root, args.measured_launch_floor
)
except (OSError, json.JSONDecodeError, ReaccountError) as exc:
print(f"MUSA_RLC_REACCOUNT_ERROR: {exc}", file=sys.stderr)
return 2
summary = {
key: result[key]
for key in (
"sealed_campaign_id",
"measured_launch_floor",
"task_count",
"sealed_counts",
"reaccounted_counts",
"changed_task_ids",
)
}
print(json.dumps(summary, indent=2, sort_keys=True))
return 0
if __name__ == "__main__":
raise SystemExit(main())
campaigns/musa_rlc_product/run_musa_rlc_product.py
+834
View File
@@ -0,0 +1,834 @@
#!/usr/bin/env python3
"""Run the frozen S5000 MUSA RLC product campaign serially."""
from __future__ import annotations
import argparse
import csv
import hashlib
import importlib.util
import json
import math
import os
import signal
import statistics
import subprocess
import sys
import time
from pathlib import Path
from typing import Any
TERMINAL = {"G", "C", "B", "R", "K", "X"}
RLC_ENV_KEYS = {
"FLAGTREE_RLC_ENHANCE",
"FLAGTREE_RLC_PHASE_MASK",
"FLAGTREE_MUSA_RLC_ENHANCE",
"FLAGTREE_MUSA_RLC_PHASE_MASK",
"FLAGTREE_MUSA_RLC_PROFITABILITY_POLICY",
"FLAGTREE_MUSA_RLC_PRODUCT_LAUNCH_COUNT",
"FLAGTREE_MUSA_RLC_MIN_ADJUSTED_SAVED_COST_PER_TENSOR_OP",
"FLAGTREE_MUSA_RLC_PHASE3_SAVED_COST_MULTIPLIER",
"FLAGTREE_MUSA_RLC_MAX_EXTERNAL_USE_EDGES",
"FLAGTREE_MUSA_RLC_MIN_REMOVED_CONVERT_DENSITY_PER_1024_PROPOSAL_VALUES",
"FLAGTREE_MUSA_RLC_PRESERVE_INT_TO_FP_CONTIGUITY",
"FLAGTREE_MUSA_RLC_PRESERVE_FP_TO_FP_CONTIGUITY",
}
class CampaignError(RuntimeError):
pass
def _canonical(value: object) -> bytes:
return (json.dumps(value, indent=2, sort_keys=True) + "\n").encode("utf-8")
def _sha256(path: Path) -> str:
digest = hashlib.sha256()
with path.open("rb") as handle:
for chunk in iter(lambda: handle.read(1024 * 1024), b""):
digest.update(chunk)
return digest.hexdigest()
def _atomic_json(path: Path, value: object) -> None:
temporary = path.with_name(path.name + ".tmp")
temporary.write_bytes(_canonical(value))
os.replace(temporary, path)
def _load_module(path: Path, name: str):
spec = importlib.util.spec_from_file_location(name, path)
if spec is None or spec.loader is None:
raise CampaignError(f"cannot load module: {path}")
module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(module)
return module
def _identity(path: Path) -> dict[str, str]:
rows: dict[str, str] = {}
for raw in path.read_text(encoding="utf-8").splitlines():
key, separator, value = raw.partition("=")
if separator:
rows[key] = value
return rows
def _base_env(site: Path, flaggems: Path) -> dict[str, str]:
env = os.environ.copy()
for key in list(env):
if key.startswith("FLAGTREE_") and "RLC" in key:
env.pop(key)
env.pop("DISABLE_LLVM_OPT", None)
env.update(
{
"PYTHONNOUSERSITE": "1",
"PYTHONDONTWRITEBYTECODE": "1",
"PYTHONUNBUFFERED": "1",
"GEMS_VENDOR": "mthreads",
"FLAGGEMS_VENDOR": "mthreads",
"FLAGTREE_BACKEND": "mthreads",
"TRITON_BACKENDS_IN_TREE": "1",
"MUSA_VISIBLE_DEVICES": "7",
"MTHREADS_VISIBLE_DEVICES": "7",
"PYTHONPATH": f"{flaggems / 'src'}:{site}",
"FLAGTREE_RLC_TRACE_REJECTS": "1",
}
)
return env
def _cell_env(base: dict[str, str], cell: str) -> dict[str, str]:
env = base.copy()
configs = {
"off": {"enhance": "0", "mask": "13", "policy": "0", "launches": "0"},
"mask0": {"enhance": "1", "mask": "0", "policy": "1", "launches": "1"},
"candidate": {"enhance": "1", "mask": "13", "policy": "1", "launches": "1"},
}
try:
row = configs[cell]
except KeyError as exc:
raise CampaignError(f"unsupported cell: {cell}") from exc
env.update(
{
"FLAGTREE_MUSA_RLC_ENHANCE": row["enhance"],
"FLAGTREE_MUSA_RLC_PHASE_MASK": row["mask"],
"FLAGTREE_MUSA_RLC_PROFITABILITY_POLICY": row["policy"],
"FLAGTREE_MUSA_RLC_PRODUCT_LAUNCH_COUNT": row["launches"],
"FLAGTREE_MUSA_RLC_MIN_ADJUSTED_SAVED_COST_PER_TENSOR_OP": "1800",
"FLAGTREE_MUSA_RLC_PHASE3_SAVED_COST_MULTIPLIER": "2",
"FLAGTREE_MUSA_RLC_MAX_EXTERNAL_USE_EDGES": "8",
"FLAGTREE_MUSA_RLC_MIN_REMOVED_CONVERT_DENSITY_PER_1024_PROPOSAL_VALUES": "0",
"FLAGTREE_MUSA_RLC_PRESERVE_INT_TO_FP_CONTIGUITY": "1",
"FLAGTREE_MUSA_RLC_PRESERVE_FP_TO_FP_CONTIGUITY": "1",
}
)
return env
def _run_process(
command: list[str], *, cwd: Path, env: dict[str, str], log: Path, timeout: int
) -> tuple[int, bool, float]:
started = time.time()
with log.open("w", encoding="utf-8", newline="\n") as handle:
process = subprocess.Popen(
command,
cwd=str(cwd),
env=env,
stdout=handle,
stderr=subprocess.STDOUT,
text=True,
start_new_session=True,
)
timed_out = False
try:
return_code = process.wait(timeout=timeout)
except subprocess.TimeoutExpired:
timed_out = True
try:
os.killpg(process.pid, signal.SIGTERM)
process.wait(timeout=10)
except subprocess.TimeoutExpired:
os.killpg(process.pid, signal.SIGKILL)
process.wait()
return_code = 124
return return_code, timed_out, time.time() - started
def _indexed_launches(ttgir_root: Path) -> dict[Path, int] | None:
"""Map each dumped TTGIR file to its executed launch count.
Returns None when ``index.tsv`` is missing or unreadable, which means the
measured-operator scope cannot be established for this cell.
"""
index = ttgir_root / "index.tsv"
if not index.is_file():
return None
try:
lines = index.read_text(encoding="utf-8", errors="replace").splitlines()
except OSError:
return None
if not lines:
return None
header = lines[0].split("\t")
try:
shape_at = header.index("shape")
kernel_at = header.index("kernel")
file_at = header.index("file")
launch_at = header.index("launches")
except ValueError:
return None
width = max(shape_at, kernel_at, file_at, launch_at)
launches: dict[Path, int] = {}
for raw in lines[1:]:
fields = raw.split("\t")
if len(fields) <= width:
continue
name = fields[file_at].strip()
# Autotune losers are indexed for audit but never dumped; they carry a
# parenthesised placeholder instead of a file name.
if not name or name.startswith("("):
continue
try:
count = int(fields[launch_at])
except ValueError:
continue
path = ttgir_root / fields[shape_at] / fields[kernel_at] / name
launches[path] = max(launches.get(path, 0), count)
return launches
def _convert_count(path: Path, *, measured_launch_floor: int = 1) -> dict[str, Any]:
"""Count executed convert_layout ops, scoped to the measured operator.
A benchmark call constructs its inputs once and then runs the operator many
times under the timer. Counting every executed kernel therefore credits the
candidate with converts removed from input construction, outside the timed
region. Only variants launched more than ``measured_launch_floor`` times
contribute to the measured count; the unscoped total is preserved too.
"""
ttgir_root = path / "ttgir"
files = sorted(ttgir_root.glob("**/*.ttgir"))
launches = _indexed_launches(ttgir_root)
total = 0
measured = 0
excluded: list[dict[str, Any]] = []
unindexed: list[str] = []
for file in files:
converts = file.read_text(encoding="utf-8", errors="replace").count(
"ttg.convert_layout"
)
total += converts
relative = file.relative_to(ttgir_root).as_posix()
if launches is None:
measured += converts
continue
launch_count = launches.get(file)
if launch_count is None:
# Dumped but absent from the index: keep it and report the gap
# rather than silently dropping executed topology.
measured += converts
unindexed.append(relative)
continue
if launch_count > measured_launch_floor:
measured += converts
elif converts:
excluded.append(
{
"file": relative,
"launches": launch_count,
"convert_count": converts,
}
)
return {
"measured_convert_count": measured,
"total_convert_count": total,
"ttgir_files": len(files),
"measured_launch_floor": measured_launch_floor,
"scope_complete": launches is not None and not unindexed,
"excluded_unmeasured": excluded,
"unindexed_files": unindexed,
}
def _median_gems_latency(rows: list[dict[str, str]]) -> float | None:
values = []
for row in rows:
if row.get("row_status") != "SUCCESS":
continue
try:
value = float(row.get("gems_lat", ""))
except ValueError:
continue
if math.isfinite(value) and value > 0:
values.append(value)
return statistics.median(values) if values else None
def missing_targets(matrix: dict[str, Any], flaggems: Path) -> list[str]:
"""Resolve every task's benchmark and correctness target against the tree.
The inventory a matrix is built from can lag the pinned FlagGems commit, for
example when upstream splits one benchmark file into several. Before this
check, such a task only failed inside the worker's preflight, which exited
silently and left a zero-byte log and a cell receipt with nothing in it.
"""
missing: list[str] = []
for task in matrix["tasks"]:
benchmark = flaggems / "benchmark" / f"test_{task['op_file']}.py"
if not benchmark.is_file():
missing.append(f"{task['task_id']} benchmark -> {benchmark}")
correctness = flaggems / task["correctness_file"]
if not correctness.is_file():
missing.append(f"{task['task_id']} correctness -> {correctness}")
return missing
def validate_contract(
manifest_path: Path,
matrix_path: Path,
build_root: Path,
flaggems: Path,
zl_bench: Path,
python: Path,
) -> tuple[dict[str, Any], dict[str, Any], dict[str, Any]]:
manifest = json.loads(manifest_path.read_text(encoding="utf-8"))
matrix = json.loads(matrix_path.read_text(encoding="utf-8"))
if not isinstance(manifest, dict) or not isinstance(matrix, dict):
raise CampaignError("manifest and task matrix roots must be objects")
manifest_sha = _sha256(manifest_path)
if matrix.get("manifest_sha256") != manifest_sha:
raise CampaignError("task matrix manifest_sha256 does not match manifest")
if matrix.get("campaign_id") != manifest.get("campaign_id"):
raise CampaignError("campaign id mismatch")
if matrix.get("screen_cells") != ["off", "mask0", "candidate"]:
raise CampaignError("task matrix cell contract mismatch")
missing = missing_targets(matrix, flaggems)
if missing:
raise CampaignError(
"targets missing from the frozen FlagGems tree: " + "; ".join(missing)
)
site = build_root / "site"
identity = _identity(build_root / "identity.txt")
expected = manifest["source"]
if identity.get("git_commit") != expected["flagtree_commit"]:
raise CampaignError("FlagTree build identity does not match manifest")
if not (build_root / "build.done").is_file():
raise CampaignError("FlagTree product build is not terminal")
if (flaggems / ".source-commit").read_text(encoding="utf-8").strip() != expected["flaggems_commit"]:
raise CampaignError("FlagGems source identity does not match manifest")
if (zl_bench / ".source-commit").read_text(encoding="utf-8").strip() != expected["zl_bench_commit"]:
raise CampaignError("zl_bench source identity does not match manifest")
probe_env = _base_env(site, flaggems)
probe_code = (
"import json,torch,torch_musa,triton,flag_gems;"
"print(json.dumps({'count':torch.musa.device_count(),"
"'device':str(flag_gems.device),'vendor':str(flag_gems.vendor_name),"
"'triton':triton.__file__,'flaggems':flag_gems.__file__},sort_keys=True))"
)
probe = subprocess.run(
[str(python), "-B", "-c", probe_code],
cwd=str(flaggems),
env=probe_env,
text=True,
capture_output=True,
timeout=60,
)
if probe.returncode:
raise CampaignError(f"product import probe failed: {probe.stderr.strip()}")
observed = json.loads(probe.stdout.strip().splitlines()[-1])
if observed.get("count") != 1 or observed.get("vendor") != "mthreads":
raise CampaignError(f"MUSA device/vendor mismatch: {observed}")
if not Path(observed["triton"]).is_relative_to(site):
raise CampaignError("imported Triton does not come from the frozen product site")
if not Path(observed["flaggems"]).is_relative_to(flaggems / "src"):
raise CampaignError("imported FlagGems does not come from the frozen product source")
observed.update(
{
"flagtree_commit": identity["git_commit"],
"libtriton_sha256": _sha256(site / "triton/_C/libtriton.so"),
"flaggems_commit": expected["flaggems_commit"],
"zl_bench_commit": expected["zl_bench_commit"],
"python": str(python),
}
)
return manifest, matrix, observed
def run_cell(
task: dict[str, Any],
cell: str,
round_name: str,
replay_from: Path | None,
*,
output_root: Path,
worker: Path,
python: Path,
zl_bench: Path,
flaggems: Path,
site: Path,
shape_file: Path,
batch_summary: Any,
base_env: dict[str, str],
) -> dict[str, Any]:
out = output_root / "tasks" / task["task_id"] / round_name / cell
env = _cell_env(base_env, cell)
env.update(
{
"PYTHON_BIN": str(python),
"ZL_BENCH_DIR": str(zl_bench),
"FLAGGEMS_DIR": str(flaggems),
"FLAGTREE_SITE": str(site),
"OP": task["op"],
"OP_FILE": task["op_file"],
"SHAPE_FILE": str(shape_file),
"OUT_DIR": str(out),
"DTYPES": " ".join(task["benchmark_dtypes"]),
}
)
if task.get("benchmark_selector"):
env["BENCH_SELECTOR"] = str(task["benchmark_selector"])
if replay_from is not None:
env["REPLAY_FROM"] = str(replay_from)
command = ["bash", str(worker)]
# The shell worker owns run.log, so use a sibling controller log.
controller_log = out.parent / f"{cell}.controller.log"
controller_log.parent.mkdir(parents=True, exist_ok=True)
return_code, timed_out, elapsed = _run_process(
command,
cwd=worker.parent,
env=env,
log=controller_log,
timeout=int(task["timeout_seconds"]),
)
run_log = out / "run.log"
text = run_log.read_text(encoding="utf-8", errors="replace") if run_log.is_file() else ""
rows = batch_summary.parse_run_log(text)
if timed_out:
status = "TIMEOUT"
else:
status = batch_summary.classify(return_code, rows, text)
scope = _convert_count(
out, measured_launch_floor=int(task.get("measured_launch_floor", 1))
)
# A worker can fail its preflight before creating OUT_DIR (for example,
# when a frozen inventory entry has no backend benchmark file). Preserve
# that failure as a cell receipt instead of aborting the whole campaign.
out.mkdir(parents=True, exist_ok=True)
receipt = {
"task_id": task["task_id"],
"round": round_name,
"cell": cell,
"status": status,
"return_code": return_code,
"timed_out": timed_out,
"elapsed_seconds": round(elapsed, 3),
"rows": rows,
"rows_success": sum(row.get("row_status") == "SUCCESS" for row in rows),
"median_gems_latency_ms": _median_gems_latency(rows),
"convert_count": scope["measured_convert_count"],
"convert_count_all": scope["total_convert_count"],
"convert_scope_complete": scope["scope_complete"],
"measured_launch_floor": scope["measured_launch_floor"],
"convert_excluded_unmeasured": scope["excluded_unmeasured"],
"convert_unindexed_files": scope["unindexed_files"],
"ttgir_files": scope["ttgir_files"],
"replay_fallback_count": text.count("AUTOTUNE_REPLAY_FALLBACK"),
"benchmark_selector": task.get("benchmark_selector"),
"benchmark_dtypes": list(task["benchmark_dtypes"]),
"effective_rlc": {
key: env[key]
for key in sorted(RLC_ENV_KEYS)
if key in env
},
}
_atomic_json(out / "cell-receipt.json", receipt)
return receipt
def correctness_command(task: dict[str, Any], python: Path) -> list[str]:
"""Build the correctness argv, narrowed to the variant this task measures.
A task-level file runs every variant in that file, so an unrelated failing
variant can mask or fabricate a verdict for the operator under test. A
manifest may therefore pin explicit node ids or a ``-k`` selector; the
manifest schema requires a stated reason whenever it does.
"""
command = [str(python), "-B", "-m", "pytest", "-q"]
# The same vendor-scoped compatibility plugin the benchmark worker loads.
# Without it a stale CUDA-only guard skips the operator here too, and a
# SKIP would be read as "not measured" rather than "not gated".
command.extend(["-p", "_musa_benchmark_compat_plugin"])
node_ids = task.get("correctness_node_ids")
if node_ids:
command.extend(str(node) for node in node_ids)
else:
command.append(task["correctness_file"])
selector = task.get("correctness_selector")
if selector:
command.extend(["-k", str(selector)])
# -rs records why a variant was skipped, so a SKIP verdict stays auditable
# without a second run.
command.extend(["-rs", "--quick"])
return command
def run_correctness(
task: dict[str, Any],
cell: str,
*,
output_root: Path,
python: Path,
flaggems: Path,
site: Path,
base_env: dict[str, str],
plugin_dir: Path,
) -> dict[str, Any]:
out = output_root / "correctness" / task["task_id"] / cell
out.mkdir(parents=True, exist_ok=True)
env = _cell_env(base_env, cell)
env["PYTEST_DISABLE_PLUGIN_AUTOLOAD"] = "1"
env["PYTHONPATH"] = f"{plugin_dir}{os.pathsep}{env['PYTHONPATH']}"
env["TRITON_CACHE_DIR"] = str(out / "triton-cache")
env["FLAGGEMS_CACHE_DIR"] = str(out / "flaggems-cache")
(out / "triton-cache").mkdir()
(out / "flaggems-cache").mkdir()
command = correctness_command(task, python)
return_code, timed_out, elapsed = _run_process(
command,
cwd=flaggems,
env=env,
log=out / "correctness.log",
timeout=int(task["timeout_seconds"]),
)
log_text = (out / "correctness.log").read_text(encoding="utf-8", errors="replace")
if timed_out:
status = "TIMEOUT"
elif return_code != 0:
status = "FAIL"
elif " passed" in log_text:
status = "PASS"
elif " skipped" in log_text:
status = "SKIP"
else:
status = "FAIL"
receipt = {
"task_id": task["task_id"],
"correctness_file": task["correctness_file"],
"correctness_node_ids": task.get("correctness_node_ids"),
"correctness_selector": task.get("correctness_selector"),
"correctness_scope_reason": task.get("correctness_scope_reason"),
"command": command,
"cell": cell,
"status": status,
"return_code": return_code,
"timed_out": timed_out,
"elapsed_seconds": round(elapsed, 3),
}
_atomic_json(out / "correctness-receipt.json", receipt)
return receipt
def _speedup(off: dict[str, Any], candidate: dict[str, Any]) -> float | None:
left = off.get("median_gems_latency_ms")
right = candidate.get("median_gems_latency_ms")
if not isinstance(left, (int, float)) or not isinstance(right, (int, float)) or right <= 0:
return None
return (left / right - 1.0) * 100.0
def classify_task(
correctness: dict[str, Any],
cells: dict[str, dict[str, Any]],
correctness_isolation: dict[str, dict[str, Any]] | None = None,
) -> tuple[str, dict[str, Any]]:
detail: dict[str, Any] = {"reason": ""}
if correctness["status"] == "FAIL":
isolation = correctness_isolation or {}
off_status = isolation.get("off", {}).get("status")
mask0_status = isolation.get("mask0", {}).get("status")
detail.update(
{
"off_correctness_status": off_status,
"mask0_correctness_status": mask0_status,
"candidate_correctness_status": correctness["status"],
}
)
if off_status == "FAIL" and mask0_status == "FAIL":
detail["reason"] = "baseline correctness fails under off/mask0/candidate"
return "X", detail
detail["reason"] = "candidate correctness failed"
return "K", detail
if correctness["status"] != "PASS":
detail["reason"] = f"candidate correctness is {correctness['status']}"
return "X", detail
required = ("a_off", "a_mask0", "a_candidate")
if any(cells.get(key, {}).get("status") != "PASS" for key in required):
detail["reason"] = "one or more screen cells did not pass"
return "X", detail
if any(cells[key].get("replay_fallback_count") for key in ("a_mask0", "a_candidate")):
detail["reason"] = "same-config replay fell back"
return "X", detail
# A topology claim is only admissible when every screen cell could be scoped
# to the measured operator. Older receipts predate scoping and are trusted.
if any(cells[key].get("convert_scope_complete", True) is False for key in required):
detail["reason"] = "measured convert topology scope is incomplete"
return "X", detail
off_count = cells["a_off"]["convert_count"]
mask0_count = cells["a_mask0"]["convert_count"]
candidate_count = cells["a_candidate"]["convert_count"]
detail.update(
{
"off_convert_count": off_count,
"mask0_convert_count": mask0_count,
"candidate_convert_count": candidate_count,
"off_convert_count_all": cells["a_off"].get("convert_count_all"),
"candidate_convert_count_all": cells["a_candidate"].get("convert_count_all"),
"round_a_speedup_percent": _speedup(cells["a_off"], cells["a_candidate"]),
}
)
if off_count != mask0_count:
detail["reason"] = "mask0 topology differs from off"
return "X", detail
if candidate_count >= off_count:
speedup = detail["round_a_speedup_percent"]
if speedup is not None and speedup <= -3.0:
detail["reason"] = "no topology reduction and screening regression"
return "R", detail
detail["reason"] = "candidate did not reduce executed convert topology"
return "B", detail
if cells.get("b_off", {}).get("status") != "PASS" or cells.get("b_candidate", {}).get("status") != "PASS":
detail["reason"] = "topology changed but reversed-order timing did not pass"
return "X", detail
if cells["b_off"].get("replay_fallback_count") or cells["b_candidate"].get("replay_fallback_count"):
detail["reason"] = "reversed-order timing replay fell back"
return "X", detail
round_b = _speedup(cells["b_off"], cells["b_candidate"])
round_a = detail["round_a_speedup_percent"]
detail["round_b_speedup_percent"] = round_b
if round_a is None or round_b is None:
detail["reason"] = "missing comparable native-event latency"
return "X", detail
if round_a >= 5.0 and round_b >= 5.0:
detail["reason"] = "topology reduction and stable >=5% speedup"
return "G", detail
if round_a <= -3.0 and round_b <= -3.0:
detail["reason"] = "topology reduction but stable regression"
return "R", detail
detail["reason"] = "topology reduction without stable >=5% speedup"
return "C", detail
def run_campaign(
manifest: dict[str, Any],
matrix: dict[str, Any],
observed: dict[str, Any],
*,
output_root: Path,
worker: Path,
python: Path,
zl_bench: Path,
flaggems: Path,
build_root: Path,
resume: bool,
) -> dict[str, Any]:
if output_root.exists() and not resume:
raise CampaignError(f"output exists; use --resume: {output_root}")
observed = dict(observed)
observed.update(
{
"benchmark_runner_sha256": _sha256(Path(__file__)),
"benchmark_worker_sha256": _sha256(worker),
"musa_benchmark_compat_plugin_sha256": _sha256(
zl_bench / "_musa_benchmark_compat_plugin.py"
),
}
)
output_root.mkdir(parents=True, exist_ok=True)
site = build_root / "site"
shape_file = zl_bench / "ops/shapes_single.yaml"
batch_summary = _load_module(zl_bench / "batch_summary.py", "zl_batch_summary")
base_env = _base_env(site, flaggems)
status_csv = output_root / "status.csv"
rows: list[dict[str, Any]] = []
occupancy = subprocess.run(
["mthreads-gmi"], text=True, capture_output=True, timeout=30
)
if occupancy.returncode:
raise CampaignError(f"mthreads-gmi failed: {occupancy.stderr.strip()}")
(output_root / "occupancy-before.txt").write_text(occupancy.stdout, encoding="utf-8", newline="\n")
if "No running processes found" not in occupancy.stdout:
raise CampaignError("S5000 has an active process; campaign not admitted")
for task in matrix["tasks"]:
task_root = output_root / "tasks" / task["task_id"]
task_receipt_path = task_root / "task-receipt.json"
if resume and task_receipt_path.is_file():
receipt = json.loads(task_receipt_path.read_text(encoding="utf-8"))
if receipt.get("classification") in TERMINAL:
rows.append(receipt)
continue
cells: dict[str, dict[str, Any]] = {}
off_root = task_root / "round-a" / "off"
cells["a_off"] = run_cell(
task, "off", "round-a", None,
output_root=output_root, worker=worker, python=python,
zl_bench=zl_bench, flaggems=flaggems, site=site,
shape_file=shape_file, batch_summary=batch_summary, base_env=base_env,
)
cells["a_mask0"] = run_cell(
task, "mask0", "round-a", off_root,
output_root=output_root, worker=worker, python=python,
zl_bench=zl_bench, flaggems=flaggems, site=site,
shape_file=shape_file, batch_summary=batch_summary, base_env=base_env,
)
cells["a_candidate"] = run_cell(
task, "candidate", "round-a", off_root,
output_root=output_root, worker=worker, python=python,
zl_bench=zl_bench, flaggems=flaggems, site=site,
shape_file=shape_file, batch_summary=batch_summary, base_env=base_env,
)
correctness = run_correctness(
task, "candidate", output_root=output_root, python=python, flaggems=flaggems,
site=site, base_env=base_env, plugin_dir=zl_bench,
)
correctness_isolation: dict[str, dict[str, Any]] = {
"candidate": correctness
}
if correctness["status"] != "PASS":
correctness_isolation["off"] = run_correctness(
task, "off", output_root=output_root, python=python,
flaggems=flaggems, site=site, base_env=base_env,
plugin_dir=zl_bench,
)
correctness_isolation["mask0"] = run_correctness(
task, "mask0", output_root=output_root, python=python,
flaggems=flaggems, site=site, base_env=base_env,
plugin_dir=zl_bench,
)
if (
cells["a_off"]["status"] == "PASS"
and cells["a_candidate"]["status"] == "PASS"
and cells["a_candidate"]["convert_count"] < cells["a_off"]["convert_count"]
):
cells["b_candidate"] = run_cell(
task, "candidate", "round-b", off_root,
output_root=output_root, worker=worker, python=python,
zl_bench=zl_bench, flaggems=flaggems, site=site,
shape_file=shape_file, batch_summary=batch_summary, base_env=base_env,
)
cells["b_off"] = run_cell(
task, "off", "round-b", off_root,
output_root=output_root, worker=worker, python=python,
zl_bench=zl_bench, flaggems=flaggems, site=site,
shape_file=shape_file, batch_summary=batch_summary, base_env=base_env,
)
classification, detail = classify_task(
correctness, cells, correctness_isolation
)
receipt = {
"task_id": task["task_id"],
"op": task["op"],
"op_file": task["op_file"],
"family": task["family"],
"nv_evidence": task["nv_evidence"],
"nv_gain_percent": task.get("nv_gain_percent"),
"prior_s5000": task["prior_s5000"],
"classification": classification,
"classification_detail": detail,
"correctness": correctness,
"correctness_isolation": correctness_isolation,
"cells": cells,
}
task_root.mkdir(parents=True, exist_ok=True)
_atomic_json(task_receipt_path, receipt)
rows.append(receipt)
with status_csv.with_name(status_csv.name + ".tmp").open("w", newline="", encoding="utf-8") as handle:
writer = csv.DictWriter(
handle,
fieldnames=["task_id", "op", "family", "classification", "reason"],
)
writer.writeheader()
for row in rows:
writer.writerow(
{
"task_id": row["task_id"],
"op": row["op"],
"family": row["family"],
"classification": row["classification"],
"reason": row["classification_detail"]["reason"],
}
)
os.replace(status_csv.with_name(status_csv.name + ".tmp"), status_csv)
print(
f"MUSA_RLC_PRODUCT_TASK task={task['task_id']} class={classification} "
f"reason={detail['reason']}",
flush=True,
)
counts = {key: sum(row["classification"] == key for row in rows) for key in sorted(TERMINAL)}
key_rows = {row["task_id"]: row for row in rows}
canary_gate = None
if matrix.get("selection") == "canary":
canary_gate = (
key_rows.get("dropout", {}).get("classification") in {"G", "C"}
and key_rows.get("softmax", {}).get("classification") == "B"
and counts["K"] == 0
)
final = {
"schema_version": 1,
"campaign_id": manifest["campaign_id"],
"selection": matrix["selection"],
"status": "TERMINAL",
"task_count": len(rows),
"counts": counts,
"canary_gate_pass": canary_gate,
"source_observed": observed,
"production_default_policy": "unchanged-off",
"claim_scope": manifest["permitted_claim"],
}
_atomic_json(output_root / "campaign-receipt.json", final)
return final
def main() -> int:
parser = argparse.ArgumentParser()
parser.add_argument("--manifest", type=Path, required=True)
parser.add_argument("--task-matrix", type=Path, required=True)
parser.add_argument("--build-root", type=Path, required=True)
parser.add_argument("--flaggems", type=Path, required=True)
parser.add_argument("--zl-bench", type=Path, required=True)
parser.add_argument("--worker", type=Path, required=True)
parser.add_argument("--python", type=Path, default=Path("/usr/bin/python3"))
parser.add_argument("--output-root", type=Path, required=True)
parser.add_argument("--resume", action="store_true")
args = parser.parse_args()
try:
manifest, matrix, observed = validate_contract(
args.manifest.resolve(), args.task_matrix.resolve(), args.build_root.resolve(),
args.flaggems.resolve(), args.zl_bench.resolve(), args.python.resolve(),
)
final = run_campaign(
manifest, matrix, observed,
output_root=args.output_root.resolve(), worker=args.worker.resolve(),
python=args.python.resolve(), zl_bench=args.zl_bench.resolve(),
flaggems=args.flaggems.resolve(), build_root=args.build_root.resolve(),
resume=args.resume,
)
except (OSError, json.JSONDecodeError, CampaignError, subprocess.SubprocessError) as exc:
print(f"MUSA_RLC_PRODUCT_ERROR: {exc}", file=sys.stderr)
return 2
print(json.dumps(final, indent=2, sort_keys=True))
return 0 if final.get("canary_gate_pass") is not False else 1
if __name__ == "__main__":
raise SystemExit(main())
campaigns/musa_rlc_product/run_pytest_musa_rlc_product.sh
+110
View File
@@ -0,0 +1,110 @@
#!/usr/bin/env bash
set -Eeuo pipefail
: "${PYTHON_BIN:?PYTHON_BIN is required}"
: "${ZL_BENCH_DIR:?ZL_BENCH_DIR is required}"
: "${FLAGGEMS_DIR:?FLAGGEMS_DIR is required}"
: "${FLAGTREE_SITE:?FLAGTREE_SITE is required}"
: "${OP:?OP is required}"
: "${OP_FILE:?OP_FILE is required}"
: "${SHAPE_FILE:?SHAPE_FILE is required}"
: "${OUT_DIR:?OUT_DIR is required}"
worker_dir="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
# A silent preflight exit leaves a zero-byte controller log and no run.log, so
# the campaign records a failed cell with nothing to diagnose it. Name the check
# that failed instead.
preflight() {
local description="$1"
shift
if ! "$@"; then
printf 'MUSA_RLC_PRODUCT_PREFLIGHT_FAILED op=%s check=%s\n' \
"${OP:-?}" "$description" >&2
exit 3
fi
}
preflight "python interpreter is executable: $PYTHON_BIN" test -x "$PYTHON_BIN"
preflight "zl_bench directory exists: $ZL_BENCH_DIR" test -d "$ZL_BENCH_DIR"
preflight "ir meta plugin exists: $ZL_BENCH_DIR/_ir_meta_plugin.py" \
test -f "$ZL_BENCH_DIR/_ir_meta_plugin.py"
preflight "benchmark file exists: $FLAGGEMS_DIR/benchmark/test_${OP_FILE}.py" \
test -f "$FLAGGEMS_DIR/benchmark/test_${OP_FILE}.py"
preflight "FlagGems source exists: $FLAGGEMS_DIR/src/flag_gems" \
test -d "$FLAGGEMS_DIR/src/flag_gems"
preflight "libtriton is non-empty: $FLAGTREE_SITE/triton/_C/libtriton.so" \
test -s "$FLAGTREE_SITE/triton/_C/libtriton.so"
preflight "shape file exists: $SHAPE_FILE" test -f "$SHAPE_FILE"
preflight "output directory is fresh: $OUT_DIR" test ! -e "$OUT_DIR"
mkdir -p "$OUT_DIR/autotune_records" "$OUT_DIR/triton-cache" "$OUT_DIR/flaggems-cache"
export PYTHONNOUSERSITE=1
export PYTHONDONTWRITEBYTECODE=1
export PYTHONUNBUFFERED=1
export GEMS_VENDOR=mthreads
export FLAGGEMS_VENDOR=mthreads
export FLAGTREE_BACKEND=mthreads
export TRITON_BACKENDS_IN_TREE=1
export MUSA_VISIBLE_DEVICES=7
export MTHREADS_VISIBLE_DEVICES=7
export PYTHONPATH="$worker_dir:$ZL_BENCH_DIR:$FLAGGEMS_DIR/src:$FLAGTREE_SITE"
export FLAGGEMS_PERF_CURRENT_OP="$OP"
export FLAGGEMS_PERF_COLOR=never
export NO_COLOR=1
# The plugin list comes from zl_bench, not from here. Hand-copying it is how
# this campaign drifted: the copy was taken before backends/ existed, so it
# still named _device_guard_plugin (now _device_setup_plugin) and, more
# importantly, carried no timing plugin at all -- which is why every sealed
# measurement is timer=direct while zl_bench's own driver had been capturing.
# Asking the planner means the caliber tracks whatever the backend declares.
export ZL_BACKEND=musa
mapfile -t plugins < <("$PYTHON_BIN" "$ZL_BENCH_DIR/plugin_plan.py" \
--backend "$ZL_BACKEND" --pytest-args | tr ' ' '\n')
preflight "plugin plan is non-empty" test "${#plugins[@]}" -gt 0
dtype_args=()
for dtype in ${DTYPES:-float16}; do
dtype_args+=(--dtypes "$dtype")
done
# Some benchmarks parametrise one dtype per case, so a campaign-wide --dtypes
# filter makes every non-matching case raise. A workload may then pin the case
# it measures; the manifest requires a stated reason for that narrowing.
selector_args=()
if [[ -n "${BENCH_SELECTOR:-}" ]]; then
selector_args+=(-k "$BENCH_SELECTOR")
fi
if [[ -n "${REPLAY_FROM:-}" ]]; then
autotune_env="FLAGGEMS_PERF_AUTOTUNE_REPLAY_DIR=$REPLAY_FROM/autotune_records"
mode_desc="replay($REPLAY_FROM)"
else
autotune_env="FLAGGEMS_PERF_AUTOTUNE_RECORD_DIR=$OUT_DIR/autotune_records"
mode_desc=record
fi
cp "$SHAPE_FILE" "$OUT_DIR/shapes.yaml"
test_file="$FLAGGEMS_DIR/benchmark/test_${OP_FILE}.py::test_${OP}"
status=0
{
printf 'MUSA_RLC_PRODUCT_RUN op=%s mode=%s out=%s\n' "$OP" "$mode_desc" "$OUT_DIR"
env \
TRITON_CACHE_DIR="$OUT_DIR/triton-cache" \
FLAGGEMS_CACHE_DIR="$OUT_DIR/flaggems-cache" \
FLAGGEMS_PERF_TTGIR_DUMP_DIR="$OUT_DIR/ttgir" \
"$autotune_env" \
USE_FLAGTUNE=0 \
"$PYTHON_BIN" -u -m pytest -s "$test_file" \
"${plugins[@]}" \
--color=no \
--shape_file "$SHAPE_FILE" \
"${dtype_args[@]}" \
"${selector_args[@]}" \
--level core --mode kernel || status=$?
printf 'MUSA_RLC_PRODUCT_RUN_RC=%s\n' "$status"
exit "$status"
} 2>&1 | tee "$OUT_DIR/run.log" || status=$?
exit "$status"
campaigns/musa_rlc_product/tests/test_musa_rlc_product.py
+573
View File
@@ -0,0 +1,573 @@
from __future__ import annotations
import copy
import importlib.util
import json
import sys
import tempfile
import unittest
from types import SimpleNamespace
from pathlib import Path
HERE = Path(__file__).resolve().parent
CAMPAIGN = HERE.parent
REPO_ROOT = CAMPAIGN.parents[1]
REFERENCE = CAMPAIGN / "manifest" / "musa-rlc-32-product-v1.json"
SPEC = importlib.util.spec_from_file_location(
"prepare_musa_rlc_product", CAMPAIGN / "prepare_musa_rlc_product.py"
)
assert SPEC is not None and SPEC.loader is not None
MODULE = importlib.util.module_from_spec(SPEC)
SPEC.loader.exec_module(MODULE)
RUN_SPEC = importlib.util.spec_from_file_location(
"run_musa_rlc_product", CAMPAIGN / "run_musa_rlc_product.py"
)
assert RUN_SPEC is not None and RUN_SPEC.loader is not None
RUNNER = importlib.util.module_from_spec(RUN_SPEC)
RUN_SPEC.loader.exec_module(RUNNER)
COMPAT_SPEC = importlib.util.spec_from_file_location(
"_musa_benchmark_compat_plugin", REPO_ROOT / "_musa_benchmark_compat_plugin.py"
)
assert COMPAT_SPEC is not None and COMPAT_SPEC.loader is not None
COMPAT = importlib.util.module_from_spec(COMPAT_SPEC)
COMPAT_SPEC.loader.exec_module(COMPAT)
def _manifest():
return json.loads(REFERENCE.read_text(encoding="utf-8"))
class MusaRlcProductTests(unittest.TestCase):
def test_musa_fft_compatibility_override_is_narrow(self):
stale = SimpleNamespace(
name="skipif", args=(True,), kwargs={"reason": "CUDA is not available"}
)
unrelated = SimpleNamespace(
name="skipif", args=(True,), kwargs={"reason": "another reason"}
)
fft = SimpleNamespace(path=Path("benchmark/test_fft.py"), name="test_perf_fft")
other = SimpleNamespace(path=Path("benchmark/test_fft.py"), name="test_other")
self.assertTrue(COMPAT._is_stale_fft_cuda_guard(fft, stale))
self.assertFalse(COMPAT._is_stale_fft_cuda_guard(fft, unrelated))
self.assertFalse(COMPAT._is_stale_fft_cuda_guard(other, stale))
def test_runner_preserves_preflight_failure_receipts(self):
runner = (CAMPAIGN / "run_musa_rlc_product.py").read_text(encoding="utf-8")
ensure_parent = "out.mkdir(parents=True, exist_ok=True)"
write_receipt = '_atomic_json(out / "cell-receipt.json", receipt)'
self.assertIn(ensure_parent, runner)
self.assertLess(runner.index(ensure_parent), runner.index(write_receipt))
def test_worker_dumps_only_executed_ttgir_variants(self):
worker = (CAMPAIGN / "run_pytest_musa_rlc_product.sh").read_text(
encoding="utf-8"
)
self.assertIn(
'FLAGGEMS_PERF_TTGIR_DUMP_DIR="$OUT_DIR/ttgir"', worker
)
# The plugin list is asked for, not written here: a hand-copied list is
# what let this worker keep _device_guard_plugin and no timing plugin
# long after zl_bench had moved on. What the plan contains for musa --
# the compat plugin included -- is asserted in zl_bench's test_backends.
self.assertIn("plugin_plan.py", worker)
self.assertIn('--backend "$ZL_BACKEND"', worker)
self.assertNotIn("-p _", worker)
self.assertIn(
'PYTHONPATH="$worker_dir:$ZL_BENCH_DIR:$FLAGGEMS_DIR/src:$FLAGTREE_SITE"',
worker,
)
with tempfile.TemporaryDirectory() as temporary:
root = Path(temporary)
dumped = root / "ttgir" / "shape" / "kernel" / "used.ttgir"
cached = root / "triton-cache" / "hash" / "loser.ttgir"
dumped.parent.mkdir(parents=True)
cached.parent.mkdir(parents=True)
dumped.write_text("ttg.convert_layout\nttg.convert_layout\n", encoding="utf-8")
cached.write_text("ttg.convert_layout\n" * 99, encoding="utf-8")
scope = RUNNER._convert_count(root)
self.assertEqual(scope["total_convert_count"], 2)
self.assertEqual(scope["ttgir_files"], 1)
# Without an index the measured scope cannot be established, so the
# count stays unscoped and the cell is marked incomplete.
self.assertEqual(scope["measured_convert_count"], 2)
self.assertFalse(scope["scope_complete"])
@staticmethod
def _write_cell(root, rows):
"""Materialise a cell's ttgir dump plus the index the dumper writes."""
ttgir = root / "ttgir"
ttgir.mkdir(parents=True)
lines = [
"\t".join(
[
"shape", "kernel", "file", "launches", "sweep_launches",
"num_warps", "num_stages", "constexprs", "cache_hash",
]
)
]
for shape, kernel, name, launches, converts in rows:
if name.startswith("("):
lines.append(
"\t".join([shape, kernel, name, str(launches), "0", "4", "3", "{}", "H"])
)
continue
path = ttgir / shape / kernel / name
path.parent.mkdir(parents=True, exist_ok=True)
path.write_text("ttg.convert_layout\n" * converts, encoding="utf-8")
lines.append(
"\t".join([shape, kernel, name, str(launches), "0", "4", "3", "{}", "H"])
)
(ttgir / "index.tsv").write_text("\n".join(lines) + "\n", encoding="utf-8")
def test_convert_count_excludes_input_construction_kernels(self):
# Reproduces the sealed gelu_backward shape: every convert sits in the
# once-launched input builder, while the timed kernel has none.
with tempfile.TemporaryDirectory() as temporary:
root = Path(temporary)
self._write_cell(
root,
[
("1048576", "randn_kernel", "w16s3.ttgir", 1, 4),
("1048576", "gelu_none_kernel_rank_1", "w4s3.ttgir", 1, 0),
("1048576", "gelu_backward_none_kernel_rank_1", "w4s3.ttgir", 3795, 0),
],
)
scope = RUNNER._convert_count(root)
self.assertEqual(scope["total_convert_count"], 4)
self.assertEqual(scope["measured_convert_count"], 0)
self.assertTrue(scope["scope_complete"])
self.assertEqual(
[row["file"] for row in scope["excluded_unmeasured"]],
["1048576/randn_kernel/w16s3.ttgir"],
)
def test_convert_count_keeps_repeatedly_launched_kernels(self):
# Reproduces the sealed sort shape: converts in the dominant kernel and
# in an autotune loser that was indexed but never dumped.
with tempfile.TemporaryDirectory() as temporary:
root = Path(temporary)
self._write_cell(
root,
[
("1024x1024", "sweep", "w4s3.ttgir", 280, 3),
("1024x1024", "broadcast_to_kernel", "w4s3.ttgir", 140, 1),
("-", "arange_kernel", "(sweep loser, not dumped)", 0, 0),
],
)
scope = RUNNER._convert_count(root)
self.assertEqual(scope["measured_convert_count"], 4)
self.assertEqual(scope["total_convert_count"], 4)
self.assertTrue(scope["scope_complete"])
self.assertEqual(scope["excluded_unmeasured"], [])
def test_convert_count_reports_dumped_but_unindexed_files(self):
with tempfile.TemporaryDirectory() as temporary:
root = Path(temporary)
self._write_cell(root, [("s", "measured", "w4s3.ttgir", 140, 2)])
stray = root / "ttgir" / "s" / "stray" / "w4s3.ttgir"
stray.parent.mkdir(parents=True)
stray.write_text("ttg.convert_layout\n", encoding="utf-8")
scope = RUNNER._convert_count(root)
# An unindexed dump is counted rather than dropped, and flagged.
self.assertEqual(scope["measured_convert_count"], 3)
self.assertFalse(scope["scope_complete"])
self.assertEqual(scope["unindexed_files"], ["s/stray/w4s3.ttgir"])
def test_incomplete_convert_scope_fails_closed(self):
def cell(convert_count, latency, complete=True):
return {
"status": "PASS",
"convert_count": convert_count,
"convert_scope_complete": complete,
"median_gems_latency_ms": latency,
"replay_fallback_count": 0,
}
cells = {
"a_off": cell(8, 0.14),
"a_mask0": cell(8, 0.14),
"a_candidate": cell(0, 0.12, complete=False),
"b_candidate": cell(0, 0.121),
"b_off": cell(8, 0.141),
}
classification, detail = RUNNER.classify_task({"status": "PASS"}, cells)
self.assertEqual(classification, "X")
self.assertEqual(
detail["reason"], "measured convert topology scope is incomplete"
)
def test_correctness_command_narrows_to_the_measured_variant(self):
python = Path("/usr/bin/python3")
plain = RUNNER.correctness_command(
{"correctness_file": "tests/test_sort.py"}, python
)
self.assertEqual(plain[-3:], ["tests/test_sort.py", "-rs", "--quick"])
self.assertNotIn("-k", plain)
selected = RUNNER.correctness_command(
{
"correctness_file": "tests/test_upsample_bicubic2d_aa.py",
"correctness_selector": "not backward",
},
python,
)
self.assertIn("tests/test_upsample_bicubic2d_aa.py", selected)
self.assertEqual(selected[selected.index("-k") + 1], "not backward")
pinned = RUNNER.correctness_command(
{
"correctness_file": "tests/test_mm.py",
"correctness_node_ids": ["tests/test_mm.py::test_mm"],
},
python,
)
self.assertIn("tests/test_mm.py::test_mm", pinned)
self.assertNotIn("tests/test_mm.py", pinned)
def test_narrowed_correctness_requires_a_stated_reason(self):
manifest = copy.deepcopy(_manifest())
target = next(
row for row in manifest["workloads"] if row["id"] == "upsample_bicubic2d_aa"
)
self.assertEqual(target["correctness_selector"], "not backward")
target.pop("correctness_scope_reason")
errors = MODULE.validate_manifest(manifest)
self.assertIn(
"workload upsample_bicubic2d_aa narrows correctness and must state "
"correctness_scope_reason",
errors,
)
both = copy.deepcopy(_manifest())
row = next(item for item in both["workloads"] if item["id"] == "sort")
row["correctness_selector"] = "sort"
row["correctness_node_ids"] = ["tests/test_sort.py::test_sort"]
row["correctness_scope_reason"] = "reason"
self.assertIn(
"workload sort must not set both correctness_selector and "
"correctness_node_ids",
MODULE.validate_manifest(both),
)
def test_measured_launch_floor_is_a_frozen_protocol_field(self):
manifest = copy.deepcopy(_manifest())
self.assertEqual(manifest["protocol"]["measured_launch_floor"], 1)
plan, _ = MODULE.build_plan(manifest, "all")
self.assertTrue(
all(task["measured_launch_floor"] == 1 for task in plan["tasks"])
)
scoped = next(
task for task in plan["tasks"] if task["task_id"] == "fused_marlin_moe_mxfp4"
)
self.assertEqual(scoped["correctness_selector"], "mxfp4")
self.assertTrue(scoped["correctness_scope_reason"])
manifest["protocol"]["measured_launch_floor"] = -1
self.assertIn(
"protocol.measured_launch_floor must be a non-negative integer",
MODULE.validate_manifest(manifest),
)
def test_compat_plugin_only_overrides_verified_stale_guards(self):
def mark(reason, name="skipif"):
return SimpleNamespace(name=name, args=(True,), kwargs={"reason": reason})
def item(file_name, test_name):
return SimpleNamespace(path=Path("tests") / file_name, name=test_name)
# Verified on S5000: unskipped, these pass.
self.assertTrue(
COMPAT._is_stale_cuda_guard(
item("test_top_k_per_row_decode.py", "test_accuracy_top_k"),
mark("CUDA device required"),
)
)
self.assertTrue(
COMPAT._is_stale_fft_cuda_guard(
item("test_fft.py", "test_perf_fft"), mark("CUDA is not available")
)
)
# The fft guard stays pinned to the one test it was verified against.
self.assertFalse(
COMPAT._is_stale_cuda_guard(
item("test_fft.py", "test_other"), mark("CUDA is not available")
)
)
# Probed and rejected: grid_sample hardcodes device="cuda" in its own
# helper, and cutlass_scaled_mm gates on vLLM as well as CUDA.
self.assertFalse(
COMPAT._is_stale_cuda_guard(
item("test_grid_sample.py", "test_nearest_zeros_4d_small"),
mark("CUDA is required."),
)
)
self.assertFalse(
COMPAT._is_stale_cuda_guard(
item("test_cutlass_scaled_mm.py", "test_cutlass_scaled_mm"),
mark("Feature requires vLLM and NVIDIA Hopper architecture"),
)
)
# Only skipif marks, and only the exact reason string.
self.assertFalse(
COMPAT._is_stale_cuda_guard(
item("test_top_k_per_row_decode.py", "test_accuracy_top_k"),
mark("CUDA device required", name="xfail"),
)
)
self.assertFalse(
COMPAT._is_stale_cuda_guard(
item("test_top_k_per_row_decode.py", "test_accuracy_top_k"),
mark("some other reason"),
)
)
def test_compat_plugin_strips_module_level_guards(self):
# top_k_per_row_decode carries its guard as a module-level pytestmark,
# which resolves through the node chain rather than the item's own marks.
stale = SimpleNamespace(
name="skipif", args=(True,), kwargs={"reason": "CUDA device required"}
)
keep = SimpleNamespace(
name="skipif", args=(True,), kwargs={"reason": "vLLM not installed"}
)
module = SimpleNamespace(own_markers=[stale, keep])
function = SimpleNamespace(
path=Path("tests/test_top_k_per_row_decode.py"),
name="test_accuracy_top_k",
own_markers=[],
)
function.listchain = lambda: [module, function]
sys.modules.setdefault(
"flag_gems", SimpleNamespace(vendor_name="mthreads")
)
COMPAT.pytest_collection_modifyitems([function])
self.assertEqual(module.own_markers, [keep])
def test_benchmark_selector_requires_a_stated_reason(self):
manifest = copy.deepcopy(_manifest())
target = next(
row for row in manifest["workloads"] if row["id"] == "grid_sample"
)
self.assertEqual(target["benchmark_selector"], "dtype0")
target.pop("benchmark_scope_reason")
self.assertIn(
"workload grid_sample narrows the benchmark and must state "
"benchmark_scope_reason",
MODULE.validate_manifest(manifest),
)
def test_declared_benchmark_dtypes_must_exist_in_the_cli_vocabulary(self):
manifest = copy.deepcopy(_manifest())
self.assertEqual(
next(row for row in manifest["workloads"] if row["id"] == "randperm")[
"benchmark_dtypes"
],
["int32"],
)
# int64 is not a --dtypes choice; the sealed run only found that out on
# the device, as an argparse error inside a screen cell.
next(row for row in manifest["workloads"] if row["id"] == "randperm")[
"benchmark_dtypes"
] = ["int32", "int64"]
self.assertIn(
"workload randperm declares benchmark dtype(s) the benchmark CLI "
"does not accept: int64",
MODULE.validate_manifest(manifest),
)
def test_stale_inventory_needs_an_override_reason(self):
manifest = copy.deepcopy(_manifest())
marlin = next(
row
for row in manifest["workloads"]
if row["id"] == "fused_marlin_moe_mxfp4"
)
self.assertEqual(marlin["op_file"], "fused_marlin_moe_w4a16_mxfp4")
self.assertTrue(marlin["inventory_override_reason"])
with tempfile.TemporaryDirectory() as temporary:
inventory = Path(temporary)
(inventory / "inventory_main.csv").write_text(
"repo,op,op_file,file,marked_skip,collected,op_names\n"
+ "".join(
f"main,{row['op']},{row['op_file']},f.py,,yes,{row['id']}\n"
for row in manifest["workloads"]
if row["id"] != "fused_marlin_moe_mxfp4"
),
encoding="utf-8",
)
# The stated override lets the missing row through.
self.assertEqual(
MODULE.validate_inventory(manifest["workloads"], inventory), []
)
marlin.pop("inventory_override_reason")
self.assertEqual(
MODULE.validate_inventory(manifest["workloads"], inventory),
[
"inventory missing ('main', 'fused_marlin_moe_w4a16_mxfp4', "
"'fused_marlin_moe_w4a16_mxfp4')"
],
)
def test_missing_benchmark_target_fails_before_the_campaign_starts(self):
matrix = {
"tasks": [
{
"task_id": "present",
"op_file": "sort",
"correctness_file": "tests/test_sort.py",
},
{
# The shape of the sealed marlin defect: the inventory named
# a merged benchmark file that upstream had already split.
"task_id": "fused_marlin_moe_mxfp4",
"op_file": "fused_marlin_moe",
"correctness_file": "tests/test_fused_marlin_moe.py",
},
]
}
with tempfile.TemporaryDirectory() as temporary:
flaggems = Path(temporary)
(flaggems / "benchmark").mkdir()
(flaggems / "tests").mkdir()
(flaggems / "benchmark" / "test_sort.py").write_text("", encoding="utf-8")
(flaggems / "tests" / "test_sort.py").write_text("", encoding="utf-8")
(flaggems / "tests" / "test_fused_marlin_moe.py").write_text(
"", encoding="utf-8"
)
missing = RUNNER.missing_targets(matrix, flaggems)
self.assertEqual(len(missing), 1)
self.assertIn("fused_marlin_moe_mxfp4 benchmark", missing[0])
self.assertIn("test_fused_marlin_moe.py", missing[0])
def test_worker_names_the_failed_preflight_check(self):
worker = (CAMPAIGN / "run_pytest_musa_rlc_product.sh").read_text(encoding="utf-8")
self.assertIn("MUSA_RLC_PRODUCT_PREFLIGHT_FAILED", worker)
self.assertIn(
'preflight "benchmark file exists: '
'$FLAGGEMS_DIR/benchmark/test_${OP_FILE}.py"',
worker,
)
# The benchmark selector must reach pytest.
self.assertIn('selector_args+=(-k "$BENCH_SELECTOR")', worker)
self.assertIn('"${selector_args[@]}"', worker)
def test_correctness_runs_with_the_vendor_compat_plugin(self):
command = RUNNER.correctness_command(
{"correctness_file": "tests/test_top_k_per_row_decode.py"},
Path("/usr/bin/python3"),
)
self.assertEqual(
command[command.index("-p") + 1], "_musa_benchmark_compat_plugin"
)
def test_valid_manifest_and_plan_sizes(self):
manifest = _manifest()
self.assertEqual(MODULE.validate_manifest(manifest), [])
canary, canary_budget = MODULE.build_plan(manifest, "canary")
full, full_budget = MODULE.build_plan(manifest, "all")
self.assertEqual(len(canary["tasks"]), 8)
self.assertEqual(len(full["tasks"]), 32)
self.assertEqual(canary["tasks"][0]["task_id"], "dropout")
self.assertEqual(canary["tasks"][1]["task_id"], "softmax")
self.assertEqual(canary["tasks"][0]["benchmark_dtypes"], ["float16"])
self.assertEqual(
next(task for task in canary["tasks"] if task["task_id"] == "fft")[
"benchmark_dtypes"
],
["complex64"],
)
self.assertEqual(canary_budget["gpu_count"], 1)
self.assertEqual(full_budget["gpu_count"], 1)
self.assertTrue(canary_budget["serial_measurement"])
def test_invalid_device_and_canary_count_fail_closed(self):
manifest = copy.deepcopy(_manifest())
manifest["device"]["physical_device"] = "0"
manifest["workloads"][0]["canary"] = False
errors = MODULE.validate_manifest(manifest)
self.assertIn("device.physical_device must equal '7'", errors)
self.assertIn("exactly eight workloads must be canaries", errors)
def test_invalid_timer_and_default_policy_fail_closed(self):
manifest = copy.deepcopy(_manifest())
manifest["protocol"]["timer"] = "host wall clock"
manifest["production_default_policy"] = "enabled"
errors = MODULE.validate_manifest(manifest)
self.assertIn("protocol.timer must bind the MUSA native-event path", errors)
self.assertIn("production_default_policy must be unchanged-off", errors)
def test_runtime_classifier_positive_and_fail_closed_paths(self):
def cell(convert_count, latency, status="PASS", replay=0):
return {
"status": status,
"convert_count": convert_count,
"median_gems_latency_ms": latency,
"replay_fallback_count": replay,
}
correct = {"status": "PASS"}
cells = {
"a_off": cell(8, 0.14),
"a_mask0": cell(8, 0.14),
"a_candidate": cell(0, 0.12),
"b_candidate": cell(0, 0.121),
"b_off": cell(8, 0.141),
}
classification, _ = RUNNER.classify_task(correct, cells)
self.assertEqual(classification, "G")
invalid = copy.deepcopy(cells)
invalid["a_mask0"]["convert_count"] = 7
classification, detail = RUNNER.classify_task(correct, invalid)
self.assertEqual(classification, "X")
self.assertEqual(detail["reason"], "mask0 topology differs from off")
classification, _ = RUNNER.classify_task({"status": "FAIL"}, cells)
self.assertEqual(classification, "K")
classification, detail = RUNNER.classify_task(
{"status": "FAIL"},
cells,
{
"candidate": {"status": "FAIL"},
"mask0": {"status": "FAIL"},
"off": {"status": "FAIL"},
},
)
self.assertEqual(classification, "X")
self.assertEqual(
detail["reason"],
"baseline correctness fails under off/mask0/candidate",
)
def test_full_preparation_requires_passing_canary_receipt(self):
manifest = _manifest()
self.assertEqual(
MODULE.validate_canary_receipt(manifest, "all", None),
["full campaign requires --canary-receipt"],
)
with tempfile.TemporaryDirectory() as temporary:
receipt = Path(temporary) / "campaign-receipt.json"
receipt.write_text(
json.dumps(
{
"campaign_id": manifest["campaign_id"],
"selection": "canary",
"status": "TERMINAL",
"canary_gate_pass": True,
}
),
encoding="utf-8",
)
self.assertEqual(
MODULE.validate_canary_receipt(manifest, "all", receipt), []
)
if __name__ == "__main__":
unittest.main()
campaigns/musa_rlc_product/tests/test_reaccount_musa_rlc_topology.py
+157
View File
@@ -0,0 +1,157 @@
from __future__ import annotations
import importlib.util
import json
import tempfile
import unittest
from pathlib import Path
HERE = Path(__file__).resolve().parent
CAMPAIGN = HERE.parent
SPEC = importlib.util.spec_from_file_location(
"reaccount_musa_rlc_topology", CAMPAIGN / "reaccount_musa_rlc_topology.py"
)
assert SPEC is not None and SPEC.loader is not None
TOOL = importlib.util.module_from_spec(SPEC)
SPEC.loader.exec_module(TOOL)
def _write_cell(cell_dir: Path, rows):
ttgir = cell_dir / "ttgir"
ttgir.mkdir(parents=True)
lines = [
"\t".join(
[
"shape", "kernel", "file", "launches", "sweep_launches",
"num_warps", "num_stages", "constexprs", "cache_hash",
]
)
]
for shape, kernel, name, launches, converts in rows:
path = ttgir / shape / kernel / name
path.parent.mkdir(parents=True, exist_ok=True)
path.write_text("ttg.convert_layout\n" * converts, encoding="utf-8")
lines.append(
"\t".join([shape, kernel, name, str(launches), "0", "4", "3", "{}", "H"])
)
(ttgir / "index.tsv").write_text("\n".join(lines) + "\n", encoding="utf-8")
def _seal_run(root: Path):
"""A minimal sealed run shaped like the gelu_backward task."""
(root / "campaign-receipt.json").write_text(
json.dumps({"campaign_id": "sealed-v2"}), encoding="utf-8"
)
task_root = root / "tasks" / "gelu_backward"
cells = {}
for key, round_name, cell_name, setup, measured in (
("a_off", "round-a", "off", 4, 0),
("a_mask0", "round-a", "mask0", 4, 0),
("a_candidate", "round-a", "candidate", 0, 0),
("b_off", "round-b", "off", 4, 0),
("b_candidate", "round-b", "candidate", 0, 0),
):
cell_dir = task_root / round_name / cell_name
_write_cell(
cell_dir,
[
("s", "randn_kernel", "w16s3.ttgir", 1, setup),
("s", "gelu_backward_kernel", "w4s3.ttgir", 3795, measured),
],
)
cells[key] = {
"round": round_name,
"cell": cell_name,
"status": "PASS",
"convert_count": setup + measured,
"median_gems_latency_ms": 0.1168 if cell_name == "off" else 0.1166,
"replay_fallback_count": 0,
}
task_root.mkdir(parents=True, exist_ok=True)
(task_root / "task-receipt.json").write_text(
json.dumps(
{
"task_id": "gelu_backward",
"op": "gelu_backward",
"family": "elementwise-backward",
"classification": "C",
"classification_detail": {
"reason": "topology reduction without stable >=5% speedup"
},
"correctness": {"status": "PASS"},
"correctness_isolation": {"candidate": {"status": "PASS"}},
"cells": cells,
}
),
encoding="utf-8",
)
class ReaccountTests(unittest.TestCase):
def test_input_construction_reduction_is_demoted_to_b(self):
with tempfile.TemporaryDirectory() as temporary:
base = Path(temporary)
run_root = base / "sealed"
run_root.mkdir()
_seal_run(run_root)
result = TOOL.reaccount(run_root, base / "derived", 1)
self.assertEqual(result["changed_task_ids"], ["gelu_backward"])
task = result["tasks"][0]
self.assertEqual(task["sealed_classification"], "C")
self.assertEqual(task["reaccounted_classification"], "B")
self.assertEqual(task["sealed_convert"]["a_off"], 4)
self.assertEqual(task["reaccounted_convert"]["a_off"], 0)
self.assertEqual(
task["excluded_unmeasured_files"], ["s/randn_kernel/w16s3.ttgir"]
)
def test_a_zero_floor_reproduces_the_sealed_verdict(self):
with tempfile.TemporaryDirectory() as temporary:
base = Path(temporary)
run_root = base / "sealed"
run_root.mkdir()
_seal_run(run_root)
result = TOOL.reaccount(run_root, base / "derived", 0)
# Floor 0 admits the once-launched builder again, so the derived verdict
# must match the sealed one. That equivalence is what makes the scoped
# count auditable rather than an unexplained new number.
self.assertEqual(result["changed_task_ids"], [])
self.assertEqual(result["tasks"][0]["reaccounted_classification"], "C")
def test_derived_output_is_written_and_never_inside_the_sealed_run(self):
with tempfile.TemporaryDirectory() as temporary:
base = Path(temporary)
run_root = base / "sealed"
run_root.mkdir()
_seal_run(run_root)
with self.assertRaises(TOOL.ReaccountError):
TOOL.reaccount(run_root, run_root / "derived", 1)
with self.assertRaises(TOOL.ReaccountError):
TOOL.reaccount(run_root, run_root, 1)
output = base / "derived"
TOOL.reaccount(run_root, output, 1)
self.assertTrue((output / "reaccount-receipt.json").is_file())
self.assertTrue((output / "reaccount-matrix.csv").is_file())
# The sealed receipt must survive untouched.
sealed = json.loads(
(run_root / "tasks" / "gelu_backward" / "task-receipt.json").read_text(
encoding="utf-8"
)
)
self.assertEqual(sealed["classification"], "C")
self.assertEqual(sealed["cells"]["a_off"]["convert_count"], 4)
def test_a_directory_without_a_campaign_receipt_is_rejected(self):
with tempfile.TemporaryDirectory() as temporary:
base = Path(temporary)
with self.assertRaises(TOOL.ReaccountError):
TOOL.reaccount(base, base.parent / "derived", 1)
if __name__ == "__main__":
unittest.main()
partition.py
+145
View File
@@ -0,0 +1,145 @@
#!/usr/bin/env python3
"""把筛选通过的算子按实测耗时分到各张卡上。
为什么不用 `run_batch.py` 的 hash 分片：它按算子名的 hash 取模，不看重量。S5000 上
17 个算子的六轮 campaign，hash 分片关键路径 120 分钟、有一张卡空转；按耗时贪心是
70 分钟。同一批活，差 50 分钟。
贪心（最长优先塞进当前最空的卡）在这里够用，也不可能更好：`topk` 一个算子就要 70.6
分钟，而理想均分是 45.4——**关键路径的下界由最重的那个算子决定，加卡不解决**。分片
只能保证除它之外的卡不空等。
权重来自 `screen.tsv` 的 `elapsed_before + elapsed_after`（**一对 = 2 格**的秒数）。
N 轮是 2N 格，所以预估耗时是权重的 N 倍——不是 N/6 倍，这里错过一次，把 topk 的 70.6
分钟报成了 11.8。缺列时退化成等权重，并在 stderr 说明——等权重的分片仍然
比不分好，但别让人以为它按耗时排过。
用法：
python partition.py --screen runs/ab_rlc_XXX/screen.tsv --devices 0,1,2,3
python partition.py --screen ... --devices 0,1 --verdict IR_CHANGED --format shell
"""
from __future__ import annotations
import argparse
import csv
import sys
from pathlib import Path
from typing import Dict, List, Optional, Sequence, Tuple
Item = Tuple[str, float]
def plan(items: Sequence[Item], slots: Sequence[str]) -> Dict[str, List[Item]]:
"""最长优先贪心：每次把最重的活放进当前总量最小的卡。
卡少于 1 张是调用方的错，直接抛；活比卡少时后面的卡就是空的，这不是错误。
"""
if not slots:
raise ValueError("需要至少一张卡")
out: Dict[str, List[Item]] = {s: [] for s in slots}
load: Dict[str, float] = {s: 0.0 for s in slots}
# 同重量时按名字排，保证同一份输入每次得到同一个方案
for name, weight in sorted(items, key=lambda kv: (-kv[1], kv[0])):
target = min(slots, key=lambda s: (load[s], s))
out[target].append((name, weight))
load[target] += weight
return out
def imbalance(assignment: Dict[str, List[Item]]) -> float:
"""最慢的卡 / 理想均分。1.0 是完美；下界由最重的单个算子决定。"""
loads = [sum(w for _, w in v) for v in assignment.values()]
total = sum(loads)
if total <= 0:
return 1.0
return max(loads) / (total / len(loads))
def read_screen(path: Path, verdict: str = "IR_CHANGED"
) -> Tuple[List[Item], Dict[str, str], List[str]]:
"""(算子, 一对耗时秒), op -> op_file, 被判定挡掉的算子。"""
with path.open(newline="", encoding="utf-8") as f:
rows = list(csv.DictReader(f, delimiter="\t"))
if not rows:
raise SystemExit(f"筛选表没有数据行: {path}")
items: List[Item] = []
files: Dict[str, str] = {}
skipped: List[str] = []
warned = False
for r in rows:
op = r.get("op", "")
if not op:
continue
if verdict and r.get("verdict") != verdict:
skipped.append(f"{op}({r.get('verdict', '?')})")
continue
try:
w = float(r["elapsed_before"]) + float(r["elapsed_after"])
except (KeyError, TypeError, ValueError):
w = 1.0
if not warned:
print("!!! 筛选表没有 elapsed_* 列，退化成等权重分片——"
"分片仍然比不分好，但它没有按耗时排过", file=sys.stderr)
warned = True
items.append((op, w))
files[op] = r.get("op_file") or op
return items, files, skipped
def main(argv: Optional[List[str]] = None) -> int:
p = argparse.ArgumentParser(description=__doc__.splitlines()[0],
formatter_class=argparse.RawDescriptionHelpFormatter)
p.add_argument("--screen", type=Path, required=True, help="ab_rlc_test.sh 的 screen.tsv")
p.add_argument("--devices", required=True, help="逗号分隔的设备列表，如 0,1,2,3")
p.add_argument("--verdict", default="IR_CHANGED",
help="只取这个判定的算子；空字符串表示全要")
# 权重是一对（2 格）的秒数；N 轮是 2N 格，所以预估耗时是权重的 N 倍
p.add_argument("--rounds", type=int, default=6, help="仅用于打印预估耗时")
p.add_argument("--format", choices=("human", "shell"), default="human",
help="shell 输出每卡一行 `<device>\\t<OPS>`，供驱动脚本读")
args = p.parse_args(argv)
if not args.screen.is_file():
raise SystemExit(f"筛选表不存在: {args.screen}")
devices = [d.strip() for d in args.devices.split(",") if d.strip()]
if not devices:
raise SystemExit("--devices 为空")
items, files, skipped = read_screen(args.screen, args.verdict)
if not items:
raise SystemExit(f"筛选表里没有 verdict={args.verdict} 的算子——"
"先确认筛选跑过，且判据没有把所有算子都挡掉")
assignment = plan(items, devices)
if args.format == "shell":
# 第三列是该卡的权重秒数，供驱动脚本决定往哪张卡追加算子——按负载选，不是
# 按算子个数：一个重算子能顶三个轻的。
# 显式写 "\n" 而不用 print：Windows 上 print 会发 CRLF，读的一侧会把 \r
# 带进最后一个字段，数值比较就废了。
for d in devices:
ops = " ".join(f"{op}:{files[op]}" for op, _ in assignment[d])
load = sum(w for _, w in assignment[d])
sys.stdout.buffer.write(f"{d}\t{ops}\t{load:.0f}\n".encode())
sys.stdout.buffer.flush()
return 0
total = sum(w for _, w in items) * args.rounds
print(f">>> {len(items)} 个算子 / {len(devices)} 张卡 / {args.rounds} 轮")
if skipped:
print(f">>> 判定挡掉 {len(skipped)} 个: {', '.join(skipped)}")
for d in devices:
load = sum(w for _, w in assignment[d]) * args.rounds
names = " ".join(op for op, _ in assignment[d]) or "（空）"
print(f" 卡{d} {load/60:6.1f} 分钟 {names}")
crit = max(sum(w for _, w in v) for v in assignment.values()) * args.rounds
heaviest = max(items, key=lambda kv: kv[1])
print(f">>> 单卡串行 {total/3600:.2f} 小时；{len(devices)} 卡关键路径 "
f"{crit/60:.1f} 分钟，失衡 {imbalance(assignment):.2f}x")
print(f">>> 下界由最重的算子决定：{heaviest[0]} 单独就要 "
f"{heaviest[1]*args.rounds/60:.1f} 分钟，加卡不解决")
return 0
if __name__ == "__main__":
raise SystemExit(main())
plugin_plan.py
+3 -1
View File
@@ -41,7 +41,9 @@ def plan_plugins(
enable_mm_cluster: Optional[bool] = None, enable_mm_cluster: Optional[bool] = None,
) -> List[str]: ) -> List[str]:
backend = get_backend(backend_name) backend = get_backend(backend_name)
plugins: List[str] = list(CORE_PLUGINS) # Compat first: these override collection-time markers, so they have to be
# in place before anything else inspects the collected items.
plugins: List[str] = list(backend.compat_plugins) + list(CORE_PLUGINS)
autotune = ( autotune = (
backend.supports_triton_hooks backend.supports_triton_hooks
run_pytest.sh
+56 -3
View File
@@ -75,8 +75,30 @@ case "$BENCH_LEVEL" in
*) echo "!!! BENCH_LEVEL 只能是 core 或 comprehensive（当前：$BENCH_LEVEL）" >&2; exit 2 ;; *) echo "!!! BENCH_LEVEL 只能是 core 或 comprehensive（当前：$BENCH_LEVEL）" >&2; exit 2 ;;
esac esac
# FlagGems 的 libtuner winner 存在 $FLAGGEMS_CACHE_DIR/config_cache 的 sqlite 里，
# 默认 ~/.flaggems，跨 run 长存。1=本次 run 用独立缓存目录，两侧各自冷调优
# （README「各自最优」口径）；0=沿用共享缓存（默认，快）。
#
# 共享缓存的风险是单向的：先跑的一侧把 winner 写进去，后跑的一侧直接命中，
# 于是它只编译那几个胜出变体。实测 mm 上后跑侧的 TTGIR 变体从 945 塌到 33、
# cvt 从 136 变 10，看起来像被测改动的效果，其实只是继承了对侧的 config。
# 走 REPLAY_FROM 的标准 A/B 不受此影响（config 本来就锁死），record 对 record
# 才会中招。无论取哪个值都会写进 run_identity.json，口径不再隐形。
ZL_ISOLATE_TUNER_CACHE="${ZL_ISOLATE_TUNER_CACHE:-0}"
# ============================== 执行逻辑 ============================== # ============================== 执行逻辑 ==============================
# 前置检查。静默失败最贵：路径不存在时 pytest 收集不到 benchmark 目录，注册
# --level/--mode 的 conftest 就没加载，报错变成 "unrecognized arguments"，指向
# 完全错误的方向。每项检查自报名字，失败即停。
preflight() {
local what="$1"; shift
if ! "$@"; then
echo "!!! PREFLIGHT_FAILED check=$what" >&2
exit 3
fi
}
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
export PYTHONPATH="$SCRIPT_DIR${PYTHONPATH:+:$PYTHONPATH}" export PYTHONPATH="$SCRIPT_DIR${PYTHONPATH:+:$PYTHONPATH}"
eval "$(python "$SCRIPT_DIR/providers/flaggems.py" --emit-shell \ eval "$(python "$SCRIPT_DIR/providers/flaggems.py" --emit-shell \
@@ -88,6 +110,23 @@ eval "$(python "$SCRIPT_DIR/providers/flaggems.py" --emit-shell \
--shape-mode "$SHAPE_MODE")" --shape-mode "$SHAPE_MODE")"
: "${TEST_FILE:=$FLAGGEMS_DIR/benchmark/test_${OP_FILE}.py::test_${OP}}" : "${TEST_FILE:=$FLAGGEMS_DIR/benchmark/test_${OP_FILE}.py::test_${OP}}"
preflight "FlagGems directory exists: $FLAGGEMS_DIR" test -d "$FLAGGEMS_DIR"
# The node's file half; the ::test_<fn> half is left to pytest, which reports a
# missing function clearly ("not found: ...") while a missing FILE does not.
preflight "benchmark file exists: ${TEST_FILE%%::*}" test -f "${TEST_FILE%%::*}"
# The interpreter, not just the paths. triton and flag_gems normally come from a
# PYTHONPATH pointing at a compiler build and a source checkout, so a shell that
# merely reaches the right files can still be the wrong Python. Without this the
# failure surfaces per cell as `pytest exit 4` from a conftest ImportError, and a
# multi-round campaign burns every cell before anyone reads a log.
preflight "interpreter imports triton + flag_gems ($(command -v python))" sh -c 'python -c "import triton, flag_gems" 2>/dev/null'
if [[ -n "$SHAPE_FILE" ]]; then
preflight "shape file exists: $SHAPE_FILE" test -f "$SHAPE_FILE"
fi
if [[ -n "$REPLAY_FROM" ]]; then
preflight "replay run directory exists: $REPLAY_FROM" test -d "$REPLAY_FROM"
fi
# 产物目录，可用 OUT_DIR 指定。时间戳只到秒，并发启动会撞名，故用不带 -p 的 # 产物目录，可用 OUT_DIR 指定。时间戳只到秒，并发启动会撞名，故用不带 -p 的
# mkdir 抢占（已存在即失败），撞上就退让到 -2、-3…… # mkdir 抢占（已存在即失败），撞上就退让到 -2、-3……
if [[ -n "${OUT_DIR:-}" ]]; then if [[ -n "${OUT_DIR:-}" ]]; then
@@ -162,6 +201,18 @@ else
SHAPE_ARGS=(--shape_file "$SHAPE_FILE") SHAPE_ARGS=(--shape_file "$SHAPE_FILE")
fi fi
# libtuner 缓存：隔离时用本 run 的私有目录，跑完随 Triton 缓存一起删（选中的
# config 已经在 autotune_records/ 里，DB 本身没有留存价值）。
TUNER_CACHE_ARGS=()
if [[ "$ZL_ISOLATE_TUNER_CACHE" == "1" ]]; then
TUNER_CACHE_DIR="$OUT_DIR/.flaggems_cache"
rm -rf "$TUNER_CACHE_DIR"; mkdir -p "$TUNER_CACHE_DIR"
TUNER_CACHE_ARGS=(FLAGGEMS_CACHE_DIR="$TUNER_CACHE_DIR")
TUNER_DESC=isolated
else
TUNER_DESC="shared(${FLAGGEMS_CACHE_DIR:-~/.flaggems})"
fi
python "$SCRIPT_DIR/_run_identity.py" "$OUT_DIR" \ python "$SCRIPT_DIR/_run_identity.py" "$OUT_DIR" \
--backend "$ZL_BACKEND" \ --backend "$ZL_BACKEND" \
--op "$OP" \ --op "$OP" \
@@ -171,11 +222,12 @@ python "$SCRIPT_DIR/_run_identity.py" "$OUT_DIR" \
--device "${ZL_DEVICE:-}" \ --device "${ZL_DEVICE:-}" \
--test-file "$TEST_FILE" \ --test-file "$TEST_FILE" \
--shape-mode "$SHAPE_MODE" \ --shape-mode "$SHAPE_MODE" \
--bench-level "$BENCH_LEVEL" --bench-level "$BENCH_LEVEL" \
--tuner-cache "$TUNER_DESC"
status=0 status=0
{ {
echo "${C_BOLD}>>> op=$OP backend=$ZL_BACKEND mode=$MODE_DESC USE_FLAGTUNE=$USE_FLAGTUNE shape=$SHAPE_MODE level=$BENCH_LEVEL${C_RESET}" echo "${C_BOLD}>>> op=$OP backend=$ZL_BACKEND mode=$MODE_DESC USE_FLAGTUNE=$USE_FLAGTUNE shape=$SHAPE_MODE level=$BENCH_LEVEL tuner=$TUNER_DESC${C_RESET}"
echo "${C_DIM}>>> node=$TEST_FILE${C_RESET}" echo "${C_DIM}>>> node=$TEST_FILE${C_RESET}"
echo "${C_DIM}>>> out=$OUT_DIR${C_RESET}" echo "${C_DIM}>>> out=$OUT_DIR${C_RESET}"
@@ -186,13 +238,14 @@ status=0
status=0 status=0
TRITON_CACHE_DIR="$CACHE_DIR" \ TRITON_CACHE_DIR="$CACHE_DIR" \
FLAGGEMS_PERF_TTGIR_DUMP_DIR="$OUT_DIR/ttgir" \ FLAGGEMS_PERF_TTGIR_DUMP_DIR="$OUT_DIR/ttgir" \
env "$AUTOTUNE_ENV" \ env "$AUTOTUNE_ENV" ${TUNER_CACHE_ARGS[@]+"${TUNER_CACHE_ARGS[@]}"} \
USE_FLAGTUNE=$USE_FLAGTUNE python -u -m pytest -s "$TEST_FILE" \ USE_FLAGTUNE=$USE_FLAGTUNE python -u -m pytest -s "$TEST_FILE" \
"${PLUGINS[@]}" "${PYTEST_COLOR[@]}" \ "${PLUGINS[@]}" "${PYTEST_COLOR[@]}" \
${SHAPE_ARGS[@]+"${SHAPE_ARGS[@]}"} ${DTYPE_ARGS[@]+"${DTYPE_ARGS[@]}"} \ ${SHAPE_ARGS[@]+"${SHAPE_ARGS[@]}"} ${DTYPE_ARGS[@]+"${DTYPE_ARGS[@]}"} \
--level "$BENCH_LEVEL" --mode kernel || status=$? --level "$BENCH_LEVEL" --mode kernel || status=$?
rm -rf "$CACHE_DIR" rm -rf "$CACHE_DIR"
[[ -n "${TUNER_CACHE_DIR:-}" ]] && rm -rf "$TUNER_CACHE_DIR"
if (( status == 0 )); then if (( status == 0 )); then
echo "${C_GREEN}>>> done. outputs in $OUT_DIR${C_RESET}" echo "${C_GREEN}>>> done. outputs in $OUT_DIR${C_RESET}"
telemetry.py
+110
View File
@@ -0,0 +1,110 @@
#!/usr/bin/env python3
"""采样设备时钟/温度/功耗，跨后端归一化成同一组列。
为什么要有它：六轮 A/B 会声称单侧噪声在 0.01% 量级。降频和热漂移足以移动比这大
得多的延迟，而且在 run.log 里不留任何痕迹——报告照样干净。NV 上复现 PR763 的那次
全程 `nvidia-smi -l 5` 写 clocks.csv，S5000 这边一直没有对应的东西。
输出每设备每次采样一行 TSV：
timestamp index sm_clock_mhz mem_clock_mhz temp_c power_w util_pct
列在 cuda 与 musa 上完全一致，所以两边的 campaign 可以并排读。采样器不存在时安静
退出：拿不到遥测是缺一份证据，不是让整批测试失败的理由。
用法：
python telemetry.py --backend musa --device 7 --interval 5 --run > telemetry.tsv
python telemetry.py --backend cuda # 只打印将要执行的采样命令
"""
from __future__ import annotations
import argparse
import os
import subprocess
import sys
import time
from typing import List, Optional
FIELDS = ("index", "sm_clock_mhz", "mem_clock_mhz", "temp_c", "power_w", "util_pct")
def resolve_device(backend, explicit: Optional[str]) -> str:
"""显式设备优先，否则从后端自己的可见性变量取。
单值才用得上：`MUSA_VISIBLE_DEVICES=7` 指一张卡，`0,1,2` 指一组，后者不传
`-i`、采全部，比挑第一张更诚实。
"""
if explicit:
return explicit.strip()
for key in getattr(backend, "visibility_env_keys", ()):
raw = os.environ.get(key, "").strip()
if raw and "," not in raw:
return raw
return ""
def sample_once(backend, argv: List[str]) -> List[dict]:
try:
out = subprocess.run(argv, capture_output=True, text=True, timeout=30)
except (OSError, subprocess.SubprocessError):
return []
return backend.parse_telemetry(out.stdout or "")
def run(backend, device: str, interval: float, out=sys.stdout) -> int:
argv = backend.telemetry_command(device)
if not argv:
print(f"[telemetry] backend={backend.name} 没有采样器；本批不记时钟/温度",
file=sys.stderr, flush=True)
return 0
if not sample_once(backend, argv):
# 第一次就取不到，说明工具缺失或没权限。不要留一个空文件让人以为采过了
print(f"[telemetry] {argv[0]} 取不到读数；本批不记时钟/温度",
file=sys.stderr, flush=True)
return 0
print("timestamp\t" + "\t".join(FIELDS), file=out, flush=True)
while True:
ts = time.strftime("%Y-%m-%dT%H:%M:%S")
for row in sample_once(backend, argv):
print(ts + "\t" + "\t".join(row.get(f, "") for f in FIELDS),
file=out, flush=True)
time.sleep(interval)
def main(argv: Optional[List[str]] = None) -> int:
p = argparse.ArgumentParser(description=__doc__,
formatter_class=argparse.RawDescriptionHelpFormatter)
p.add_argument("--backend", default=os.environ.get("ZL_BACKEND", "cuda"))
p.add_argument("--device", default=os.environ.get("ZL_DEVICE", ""))
p.add_argument("--interval", type=float, default=5.0)
p.add_argument("--run", action="store_true",
help="持续采样直到被杀；不给则只打印采样命令")
args = p.parse_args(argv)
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
from backends import get_backend
backend = get_backend(args.backend)
device = resolve_device(backend, args.device)
if not args.run:
print(" ".join(backend.telemetry_command(device)))
return 0
try:
return run(backend, device, args.interval)
except KeyboardInterrupt:
return 0
except BrokenPipeError:
# 读端先走（管给 head，或 campaign 结束时收掉）。这是正常终止，不该在
# telemetry.err 里留一段 traceback 让人以为遥测本身坏了。按 CPython 的
# 惯用做法把 stdout 指到 devnull——只吞异常不够，解释器退出时还会 flush
# 一次，再抛一条 "Exception ignored" 到 stderr。
try:
devnull = os.open(os.devnull, os.O_WRONLY)
os.dup2(devnull, sys.stdout.fileno())
except Exception:
pass
return 0
if __name__ == "__main__":
raise SystemExit(main())
test_backends.py
+391
View File
@@ -10,6 +10,7 @@ import json
import os import os
import sys import sys
import tempfile import tempfile
import types
import unittest import unittest
from pathlib import Path from pathlib import Path
@@ -173,6 +174,17 @@ class PluginPlanTests(unittest.TestCase):
self.assertNotIn("_cudagraph_plugin", names) self.assertNotIn("_cudagraph_plugin", names)
self.assertNotIn("_mm_cluster_fix_plugin", names) self.assertNotIn("_mm_cluster_fix_plugin", names)
def test_musa_loads_its_compat_plugin_first(self):
names = self._plan("musa")
self.assertIn("_musa_benchmark_compat_plugin", names)
# It rewrites collection-time markers, so nothing may inspect the item
# list before it runs.
self.assertEqual(names[0], "_musa_benchmark_compat_plugin")
def test_compat_plugins_do_not_leak_to_other_backends(self):
for name in ("cuda", "rocm", "ascend"):
self.assertNotIn("_musa_benchmark_compat_plugin", self._plan(name), name)
def test_musa_same_core_without_cluster_fix(self): def test_musa_same_core_without_cluster_fix(self):
# _cudagraph_plugin is absent from the plan on every backend: the timing # _cudagraph_plugin is absent from the plan on every backend: the timing
# plugin loads it, so its absence here says nothing about capture support. # plugin loads it, so its absence here says nothing about capture support.
@@ -268,6 +280,82 @@ class CaptureClassificationTests(unittest.TestCase):
self.assertFalse(musa.denies_capture("unique")) self.assertFalse(musa.denies_capture("unique"))
class DirectRepeatTests(unittest.TestCase):
"""Repetition on the direct timer.
_cudagraph_plugin imports triton at module scope, so it is loaded here
against a stub: the repeat logic is plain arithmetic and should stay
testable on a machine with no GPU and no Triton.
"""
@classmethod
def setUpClass(cls):
cls._saved = {k: sys.modules.get(k) for k in ("triton", "triton.testing")}
triton = types.ModuleType("triton")
testing = types.ModuleType("triton.testing")
testing.do_bench = lambda *a, **k: 0.0
testing.do_bench_cudagraph = lambda *a, **k: 0.0
testing._summarize_statistics = lambda *a, **k: 0.0
triton.testing = testing
sys.modules["triton"] = triton
sys.modules["triton.testing"] = testing
import _cudagraph_plugin # noqa: E402
cls.cg = _cudagraph_plugin
@classmethod
def tearDownClass(cls):
for name, mod in cls._saved.items():
if mod is None:
sys.modules.pop(name, None)
else:
sys.modules[name] = mod
sys.modules.pop("_cudagraph_plugin", None)
def _run(self, value, **kwargs):
"""-> (result, how many times do_bench was called)"""
seq, calls = [3.0, 1.0, 2.0, 9.0, 5.0], []
self.cg._ORIGINAL_DO_BENCH = (
lambda fn, **kw: (calls.append(1), seq[(len(calls) - 1) % len(seq)])[1])
old = os.environ.get("ZL_DIRECT_REPEAT")
os.environ["ZL_DIRECT_REPEAT"] = value
try:
out = self.cg.run_direct(lambda: None, 25, 100, None,
kwargs.get("quantiles"),
kwargs.get("return_mode", "mean"))
finally:
if old is None:
os.environ.pop("ZL_DIRECT_REPEAT", None)
else:
os.environ["ZL_DIRECT_REPEAT"] = old
return out, len(calls)
def test_default_is_one_call(self):
self.assertEqual(self._run("1"), (3.0, 1))
def test_unset_is_one_call(self):
old = os.environ.pop("ZL_DIRECT_REPEAT", None)
try:
self.assertEqual(self.cg.direct_repeat(), 1)
finally:
if old is not None:
os.environ["ZL_DIRECT_REPEAT"] = old
def test_median_over_calls(self):
# 3, 1, 2, 9, 5 -> 3
self.assertEqual(self._run("5"), (3.0, 5))
def test_garbage_and_zero_fall_back_to_one(self):
self.assertEqual(self._run("abc"), (3.0, 1))
self.assertEqual(self._run("0"), (3.0, 1))
self.assertEqual(self._run("-4"), (3.0, 1))
def test_sequence_results_are_not_repeated(self):
# quantiles / return_mode="all" make do_bench return a sequence, and a
# median across calls is undefined for it; take the single call.
self.assertEqual(self._run("5", quantiles=[0.5])[1], 1)
self.assertEqual(self._run("5", return_mode="all")[1], 1)
class DeviceArgTests(unittest.TestCase): class DeviceArgTests(unittest.TestCase):
def test_devices_preferred(self): def test_devices_preferred(self):
self.assertEqual(resolve_device_arg("0,1", None), "0,1") self.assertEqual(resolve_device_arg("0,1", None), "0,1")
@@ -343,5 +431,308 @@ class AutotuneReplayBackendTests(unittest.TestCase):
self.assertFalse(_replay_backend_ok(tmp)) self.assertFalse(_replay_backend_ok(tmp))
class CaliberGuardTests(unittest.TestCase):
"""两次运行口径不同产出的报告，和有效报告长得一模一样。
这些用例钉住的是本仓已经踩过的三次：record 对 record、共享 tuner cache、
timer 不同。它们都不会让任何一步报错，只会让数字变成另一个含义。
"""
BASE = {
"backend": "musa", "device": "S5000", "timer": "cuda_graph",
"bench_level": "core", "shape_mode": "default",
"tuner_cache": "isolated", "direct_repeat": 5,
"autotune_mode": "replay",
}
def _run(self, root, name, **over):
d = Path(root, name)
d.mkdir(parents=True)
if over.get("_no_identity"):
return d
ident = dict(self.BASE)
ident.update({k: v for k, v in over.items() if not k.startswith("_")})
Path(d, "run_identity.json").write_text(
json.dumps(ident), encoding="utf-8")
return d
def test_same_caliber_has_no_complaints(self):
from batch_summary import caliber_complaints
with tempfile.TemporaryDirectory() as tmp:
runs = [("A", self._run(tmp, "a")), ("B", self._run(tmp, "b"))]
self.assertEqual(caliber_complaints(runs), [])
def test_record_side_pinning_the_other_is_normal(self):
"""A/B 的正常形态：一侧 record 定 config，另一侧 replay 它。"""
from batch_summary import caliber_complaints
with tempfile.TemporaryDirectory() as tmp:
runs = [("r1_A", self._run(tmp, "a", autotune_mode="record")),
("r1_B", self._run(tmp, "b"))]
self.assertEqual(caliber_complaints(runs), [])
def test_record_against_record_is_flagged(self):
from batch_summary import caliber_complaints
with tempfile.TemporaryDirectory() as tmp:
runs = [("A", self._run(tmp, "a", autotune_mode="record")),
("B", self._run(tmp, "b", autotune_mode="record"))]
self.assertTrue(any("record" in c for c in caliber_complaints(runs)))
def test_each_caliber_field_is_checked(self):
from batch_summary import CALIBER_FIELDS, caliber_complaints
others = {"backend": "cuda", "device": "H20", "timer": "direct",
"bench_level": "comprehensive", "shape_mode": "yaml",
"tuner_cache": "shared", "direct_repeat": 1}
self.assertEqual(set(others), set(CALIBER_FIELDS))
for field, other in others.items():
with tempfile.TemporaryDirectory() as tmp:
runs = [("A", self._run(tmp, "a")),
("B", self._run(tmp, "b", **{field: other}))]
got = caliber_complaints(runs)
self.assertTrue(any(c.startswith(field) for c in got),
f"{field} 不一致时没被拦下: {got}")
def test_missing_identity_is_flagged_not_assumed_equal(self):
from batch_summary import caliber_complaints
with tempfile.TemporaryDirectory() as tmp:
runs = [("A", self._run(tmp, "a")),
("B", self._run(tmp, "b", _no_identity=True))]
self.assertTrue(any("run_identity" in c
for c in caliber_complaints(runs)))
def test_shared_cache_record_warns_but_does_not_block(self):
from batch_summary import caliber_complaints, caliber_hazards
with tempfile.TemporaryDirectory() as tmp:
runs = [("r1_A", self._run(tmp, "a", autotune_mode="record",
tuner_cache="shared(~/.flaggems)")),
("r1_B", self._run(tmp, "b",
tuner_cache="shared(~/.flaggems)"))]
self.assertEqual(caliber_complaints(runs), [])
self.assertTrue(caliber_hazards(runs))
def test_isolated_cache_has_no_hazard(self):
from batch_summary import caliber_hazards
with tempfile.TemporaryDirectory() as tmp:
runs = [("A", self._run(tmp, "a", autotune_mode="record")),
("B", self._run(tmp, "b"))]
self.assertEqual(caliber_hazards(runs), [])
def test_strict_refuses_and_permissive_continues(self):
from batch_summary import _report_caliber
with tempfile.TemporaryDirectory() as tmp:
runs = [("A", self._run(tmp, "a")),
("B", self._run(tmp, "b", timer="direct"))]
self.assertFalse(_report_caliber(runs, "op", strict=True))
self.assertTrue(_report_caliber(runs, "op", strict=False))
LOG = ("Operator: op (dtype=torch.float16, mode=kernel, level=core)\n"
"SUCCESS 1.000 2.000 0.500 shape\n")
def test_report_ab_refuses_mismatched_pair(self):
from batch_summary import CaliberMismatch, report_ab
with tempfile.TemporaryDirectory() as tmp:
a = self._run(tmp, "a")
b = self._run(tmp, "b", timer="direct")
for d in (a, b):
Path(d, "run.log").write_text(self.LOG, encoding="utf-8")
with self.assertRaises(CaliberMismatch):
report_ab(a / "run.log", b / "run.log", "op")
# 放行开关在，但必须是显式的
self.assertEqual(
len(report_ab(a / "run.log", b / "run.log", "op", strict=False)), 1)
class TelemetryTests(unittest.TestCase):
"""采样器命令与解析。两边归一到同一组列，campaign 才能并排读。"""
# 取自 S5000 实机：值带单位，"Power Draw " 的键名有尾随空格
MUSA_JSON = """
{
"Attached GPUs": "8",
"GPU": [
{
"Index": "7",
"Temperature": {"GPU Current Temp": "36C"},
"Clocks": {"Graphics": "1888MHz", "Memory": "2500MHz", "Video": "0MHz"},
"Power Readings": {"Power Draw ": "477.68W", "Current Power Limit": "950.00W"},
"Utilization": {"Gpu": "87%", "Memory": "14%"}
}
]
}
"""
CUDA_CSV = "0, 1980, 2619, 41, 122.35, 63\n"
def test_musa_parses_units_off_values(self):
rows = get_backend("musa").parse_telemetry(self.MUSA_JSON)
self.assertEqual(rows, [{
"index": "7", "sm_clock_mhz": "1888", "mem_clock_mhz": "2500",
"temp_c": "36", "power_w": "477.68", "util_pct": "87"}])
def test_cuda_parses_csv(self):
rows = get_backend("cuda").parse_telemetry(self.CUDA_CSV)
self.assertEqual(rows, [{
"index": "0", "sm_clock_mhz": "1980", "mem_clock_mhz": "2619",
"temp_c": "41", "power_w": "122.35", "util_pct": "63"}])
def test_both_backends_yield_the_same_columns(self):
import telemetry
a = get_backend("musa").parse_telemetry(self.MUSA_JSON)[0]
b = get_backend("cuda").parse_telemetry(self.CUDA_CSV)[0]
self.assertEqual(set(a), set(b))
self.assertEqual(set(a), set(telemetry.FIELDS))
def test_garbage_parses_to_nothing_rather_than_raising(self):
for backend in ("musa", "cuda"):
for raw in ("", "no such tool", "{broken json"):
self.assertEqual(get_backend(backend).parse_telemetry(raw), [])
def test_no_device_drops_the_flag_not_just_the_value(self):
# 留下裸 "-i" 会让采样器直接失败，比采全部更糟
argv = get_backend("musa").telemetry_command("")
self.assertNotIn("-i", argv)
self.assertIn("--json", argv)
self.assertEqual(get_backend("musa").telemetry_command("7")[-2:], ["-i", "7"])
def test_backend_without_sampler_returns_empty(self):
from backends.base import DeviceBackend
self.assertEqual(DeviceBackend().telemetry_command("0"), [])
def test_device_comes_from_the_visibility_var_when_unpinned(self):
import telemetry
backend = get_backend("musa")
old = os.environ.get("MUSA_VISIBLE_DEVICES")
try:
os.environ["MUSA_VISIBLE_DEVICES"] = "7"
self.assertEqual(telemetry.resolve_device(backend, ""), "7")
# 一组卡不挑第一张，交给采样器采全部
os.environ["MUSA_VISIBLE_DEVICES"] = "0,1,2"
self.assertEqual(telemetry.resolve_device(backend, ""), "")
self.assertEqual(telemetry.resolve_device(backend, "3"), "3")
finally:
if old is None:
os.environ.pop("MUSA_VISIBLE_DEVICES", None)
else:
os.environ["MUSA_VISIBLE_DEVICES"] = old
class ForeignVendorModuleTests(unittest.TestCase):
"""benchmark 的可用性判据常写成 `try: import <厂商实现>; AVAILABLE = True`。
import 只证明那个 .py 在磁盘上——它不碰设备，所以在任何后端上都成功，gate 于是
形同虚设。router_gemm 就这样让 NVIDIA Hopper 的 kernel 跑在了 MUSA 卡上，最后
死在 JIT 深处的 "dynamic_func() missing BLOCK_M ..."，报错完全指不向真正的原因。
"""
P = "flag_gems.runtime.backend._"
def _mods(self, *names):
return {self.P + n: object() for n in names} | {"torch": object()}
def test_only_own_vendor_is_clean(self):
from _device_setup_plugin import foreign_vendor_modules
mods = self._mods("mthreads", "mthreads.ops.mm")
self.assertEqual(foreign_vendor_modules("mthreads", mods), [])
def test_foreign_vendor_is_reported_once_per_root(self):
from _device_setup_plugin import foreign_vendor_modules
# 同一厂商的多个子模块只报一条
mods = self._mods("mthreads.ops.mm",
"nvidia.hopper.ops.mm", "nvidia.ops.mm", "nvidia")
self.assertEqual(foreign_vendor_modules("mthreads", mods), ["nvidia"])
def test_several_foreign_vendors(self):
from _device_setup_plugin import foreign_vendor_modules
mods = self._mods("mthreads", "nvidia.ops.mm", "cambricon.ops.mm")
self.assertEqual(foreign_vendor_modules("mthreads", mods),
["cambricon", "nvidia"])
def test_a_vendor_whose_name_extends_ours_is_still_foreign(self):
# 按前缀比会把 _mthreads_extra 认成自家
from _device_setup_plugin import foreign_vendor_modules
mods = self._mods("mthreads", "mthreads_extra.ops")
self.assertEqual(foreign_vendor_modules("mthreads", mods),
["mthreads_extra"])
def test_unrelated_modules_are_ignored(self):
from _device_setup_plugin import foreign_vendor_modules
mods = {"torch": object(), "triton": object(),
"flag_gems.ops.mm": object()}
self.assertEqual(foreign_vendor_modules("mthreads", mods), [])
class PartitionTests(unittest.TestCase):
"""按耗时把算子分到各卡。hash 分片在这批活上比它慢 50 分钟。"""
HEADER = "op\top_file\telapsed_before\telapsed_after\tverdict"
def _load(self, assignment):
return sorted(sum(w for _, w in v) for v in assignment.values())
def _screen(self, tmp, rows, header=None):
p = Path(tmp, "screen.tsv")
p.write_text((header or self.HEADER) + "\n" + "\n".join(rows) + "\n",
encoding="utf-8")
return p
def test_heavy_first_keeps_cards_even(self):
from partition import plan
items = [("a", 10), ("b", 9), ("c", 8), ("d", 7), ("e", 6), ("f", 5)]
self.assertEqual(self._load(plan(items, ["0", "1", "2"])), [15, 15, 15])
def test_one_item_heavier_than_the_ideal_share_sets_the_floor(self):
# topk 单个 70 分钟、理想均分 45——加卡不解决，分片只保证别人不空等
from partition import imbalance, plan
items = [("topk", 70)] + [(f"op{i}", 5) for i in range(10)]
a = plan(items, [str(i) for i in range(8)])
self.assertEqual(max(sum(w for _, w in v) for v in a.values()), 70)
self.assertGreater(imbalance(a), 1.0)
def test_more_cards_than_operators_leaves_some_empty(self):
from partition import plan
a = plan([("a", 1), ("b", 1)], ["0", "1", "2", "3"])
self.assertEqual(sum(1 for v in a.values() if not v), 2)
def test_same_input_gives_the_same_plan(self):
from partition import plan
items = [("a", 5), ("b", 5), ("c", 5), ("d", 5)]
first = plan(items, ["0", "1"])
for _ in range(5):
self.assertEqual(plan(list(reversed(items)), ["0", "1"]), first)
def test_no_cards_is_the_callers_error(self):
from partition import plan
with self.assertRaises(ValueError):
plan([("a", 1)], [])
def test_perfect_balance_is_one(self):
from partition import imbalance, plan
self.assertEqual(imbalance(plan([("a", 5), ("b", 5)], ["0", "1"])), 1.0)
def test_verdict_filters_and_reports_what_it_dropped(self):
from partition import read_screen
with tempfile.TemporaryDirectory() as tmp:
f = self._screen(tmp, ["a\ta\t10\t10\tIR_CHANGED",
"b\tbb\t5\t5\tNO_IR_DIFF",
"c\tc\t1\t1\tNO_TRITON"])
items, files, skipped = read_screen(f)
self.assertEqual(items, [("a", 20.0)])
self.assertEqual(files["a"], "a")
self.assertEqual(len(skipped), 2)
def test_op_file_defaults_to_op_name(self):
from partition import read_screen
with tempfile.TemporaryDirectory() as tmp:
f = self._screen(tmp, ["a\t\t1\t1\tIR_CHANGED"])
_, files, _ = read_screen(f)
self.assertEqual(files["a"], "a")
def test_missing_elapsed_falls_back_to_equal_weights(self):
from partition import read_screen
with tempfile.TemporaryDirectory() as tmp:
f = self._screen(tmp, ["a\ta\tIR_CHANGED", "b\tb\tIR_CHANGED"],
header="op\top_file\tverdict")
items, _, _ = read_screen(f)
self.assertEqual([w for _, w in items], [1.0, 1.0])
if __name__ == "__main__": if __name__ == "__main__":
unittest.main() unittest.main()