The speedup column in a result table compares gems against torch, not one build
against another, so an A/B needs the two runs paired by shape. batch_summary
gains an --ab mode that indexes both run.logs by operator, dtype and size
detail, computes before/after per pair, and prints the per-shape rows plus the
range. Only rows that succeeded on both sides with a positive latency count, so
an incomplete pairing cannot masquerade as a complete range. When either side
carries a BENCHMARK_DIRECT_NO_CUDAGRAPH marker the range is annotated, because
a fallback row was timed under a different regime than a captured one.
ab_rlc_test.sh drives the pairing for the FlagTree RemoveLayoutConversions
switch the way ab_fold_test.sh does for the FlagGems one: per operator, run with
the enhancement off while recording autotune configs, then replay those configs
with it on, so the two sides differ by that switch alone. Each operator
therefore leaves exactly two run.log files, and the summary of each pair is
printed as it completes.
Checked on an S5000 against gelu_backward over five shapes: both sides captured
with no fallback, five rows paired, range -0.43% to +0.09%.
run_batch.py drives run_pytest.sh per operator across GPUs (stable-hash
sharding, per-op subprocess isolation, process-group timeouts, retry with
deterministic-failure cutoff, two-level dtype fallback, .complete resume,
per-op REPLAY_FROM). batch_summary.py aggregates run.log tables into
summary.csv. ops/ holds the curated assets: dual-repo inventories rebuilt
via AST scan + pytest collect verification, shape sets migrated from the
old regression harness and merged with upstream core_shapes class-name
keys (upstream's set_shapes falls back op_name -> MRO class name ->
1-D DEFAULT_SHAPES, so replacing the shape file without class keys
crashes the BLAS family), and a dismiss list where all 76 entries carry
verified reasons. Validated end to end: 1036-op full screen with zero
failures.
Also fix two cudagraph plugin regressions: newer torch appends "enable
device-side assertions" to every CUDA error, so the loose fatal-error
marker disabled the documented do_bench fallback entirely; and an aborted
graph capture can leave the default CUDA RNG generator stuck in capturing
state, poisoning every later torch.randn - captures now run under a
throwaway RNG state. run_pytest.sh gains an optional DTYPES passthrough.
Aharrypotter
zhoulin