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Initial text cleanup

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This commit is contained in:
Jeffrey Ward
2026-08-19 12:55:19 -04:00
parent 819a4cce7a
commit cc84d70d52
26 changed files with 454 additions and 457 deletions
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20
bin/bench_model.jl
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@@ -1,6 +1,6 @@
#!/usr/bin/env julia
#
# bench_model.jl — microbenchmark the classifier in isolation, with no server,
# bench_model.jl: microbenchmark the classifier in isolation, with no server,
# no queue, and no disk in the way.
#
# bin/bench.jl measures the *pipeline*: it reports stage 1 as one number, the
@@ -12,7 +12,7 @@
#
# read_features open, read 16 bytes, seek, read 16 bytes, scale
# Lux.apply the network itself, on a feature vector already in memory
# classify both together — what stage 1 actually calls per file
# classify both together: what stage 1 actually calls per file
#
# Three properties are worth checking beyond the raw per-file cost:
#
@@ -21,7 +21,7 @@
# (This is the same claim bin/bench.jl makes about memory, on the CPU axis.)
# * Batching should be much cheaper per file. A 32x1 matmul wastes most of a
# BLAS call; if batch-64 inference is many times cheaper per file, that is
# the headroom a batching stage-1 would buy — worth knowing before building
# the headroom a batching stage-1 would buy, worth knowing before building
# one, since today the pipeline classifies strictly one file at a time.
# * Inference should scale across threads. `Classifier` is shared read-only by
# the whole stage-1 pool on the claim that Lux inference is pure. If per-file
@@ -48,7 +48,7 @@ using Statistics
using Printf
# The script is run directly, not as part of the package, so pull in exactly the
# pieces the classifier needs. `Lux`/`JLD2` first — model.jl and classify.jl both
# pieces the classifier needs. `Lux`/`JLD2` first, because model.jl and classify.jl both
# assume the including scope already has them (see the note at the top of model.jl).
using Lux
using JLD2
@@ -156,7 +156,7 @@ fmt2(x::Real) = @sprintf("%.2f", x)
"""
control_kernel(x) -> Float64
Pure arithmetic, no allocation, no library call — deliberately dependent
Pure arithmetic, no allocation, no library call, and deliberately dependent
(each step needs the last) so the compiler can't vectorize it away, and sized to
land in the same microsecond neighbourhood as one `Lux.apply`.
"""
@@ -209,7 +209,7 @@ end
"""
Write a file of exactly `size` random bytes, in bounded chunks.
Content is random rather than zeros so the classifier sees a realistic input —
Content is random rather than zeros so the classifier sees a realistic input,
and so the filesystem can't cheat with a sparse file, which would make the tail
`seek` unrepresentatively fast.
"""
@@ -252,7 +252,7 @@ function main(argv)
Lux.apply(clf.model, x1, clf.ps, clf.st)
end
println()
println("INFERENCE (Lux.apply, batch 1 — features already in memory)")
println("INFERENCE (Lux.apply, batch 1; features already in memory)")
println(" per call $(human_time(infer_ns)) $(human_rate(rate(infer_ns)))")
println(" allocations $(human_bytes(infer_bytes)) per call")
results["inference_batch1"] = (; ns = infer_ns, bytes = infer_bytes, per_sec = rate(infer_ns))
@@ -302,14 +302,14 @@ function main(argv)
results["read_features"] = read_rows
flat = length(read_rows) > 1 ?
maximum(r.ns for r in read_rows) / minimum(r.ns for r in read_rows) : 1.0
@printf(" spread across a %.0fx size range: %.1fx — %s\n",
@printf(" spread across a %.0fx size range: %.1fx, %s\n",
maximum(sizes) / minimum(sizes), flat,
flat < 3 ? "flat, as designed (it seeks to the tail)" :
"NOT flat: something is reading more than 32 bytes")
# --- 4. classify(): what stage 1 calls, I/O and inference together.
println()
println("CLASSIFY (read_features + Lux.apply — one whole stage-1 file)")
println("CLASSIFY (read_features + Lux.apply; one whole stage-1 file)")
dir2 = mktempdir(; prefix = "fsmodel-")
classify_ns = 0.0
try
@@ -378,7 +378,7 @@ function main(argv)
end
results["thread_scaling"] = thread_rows
# A poor scaling curve has two possible authors — the model or the box —
# A poor scaling curve has two possible authors, the model or the box,
# and the table alone can't tell them apart. So run the same sweep on a
# kernel that is pure arithmetic with no allocation and no library
# underneath: whatever *it* achieves is this machine's ceiling for