e0b2c0ea54
El SDK CI - dev / build-and-test (pull_request) Failing after 12m7s
Adds tests/native/test_lexer_scaling.el, the regression gate for el #132. Both directions are proven on LIVE workloads, not synthetic series: healthy per-character scan 1821 3251 6007 10422 us -> O(n) PASS rescan-from-zero (the #132 shape) 922 3667 13524 44792 -> O(n^2) FAIL A gate only proven to pass is decoration. The quadratic specimen exists so the gate is proven to FIRE. Also fixes elb_spread_ok to judge the ASYMPTOTIC TAIL (last three ratios) rather than the whole sweep. Measured on a genuinely linear scan the ratios ran 3.37 2.92 1.76 1.65 -- the head looks quadratic because it is cold cache, the tail is the truth. Whole-sweep spread rejected correct data. A complexity bound is an asymptotic claim and must be judged asymptotically. That fix came from the classifier refusing to rubber-stamp my own bad measurement: it reported INDETERMINATE on an unwarmed sweep rather than passing it. Warmup is now taken and discarded at every sweep point. Reverts the == workarounds in test_elbench.el now that el #137 has landed; the natural form generates no str_eq and all 13 fitter tests stay green. The workaround remains -- the Plus arm is still open.
112 lines
4.4 KiB
EmacsLisp
112 lines
4.4 KiB
EmacsLisp
import "../../runtime/eltest.el"
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import "../../runtime/elbench.el"
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// test_elbench.el — proves the growth-curve classifier against KNOWN curves.
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//
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// Every series below is real measured data from lang/tests/bench/fitprobe.el
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// on a geometric sweep n = 200/400/800/1600. The classifier must be provable
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// without depending on a live defect existing, which is the whole point of
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// keeping controlled specimens.
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fn _s4(a: Int, b: Int, c: Int, d: Int) -> [Int] {
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let l: [Int] = native_list_empty()
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let l = native_list_append(l, a)
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let l = native_list_append(l, b)
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let l = native_list_append(l, c)
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let l = native_list_append(l, d)
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return l
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}
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test "classifies a linear allocation series as O(n)" {
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// fitprobe `linear`, allocation count
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let v = _s4(208, 409, 810, 1611)
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assert elb_measured_curve(v, 10) == 2, "linear allocs should classify O(n)"
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}
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test "classifies a linear byte series as O(n)" {
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// fitprobe `linear`, allocation bytes
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let v = _s4(4786, 9682, 19474, 39658)
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assert elb_measured_curve(v, 10) == 2, "linear bytes should classify O(n)"
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}
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test "classifies a quadratic byte series as O(n^2)" {
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// fitprobe `accum`, allocation bytes -- the accumulator-rebuild shape
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let v = _s4(20300, 80600, 321200, 1282400)
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assert elb_measured_curve(v, 10) == 4, "accum bytes should classify O(n^2)"
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}
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test "accumulator count is linear -- proves count alone misses it" {
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// Same run as above. The COUNT is exactly linear while bytes are
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// quadratic. A count-only gate passes this defect clean.
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let v = _s4(200, 400, 800, 1600)
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assert elb_measured_curve(v, 10) == 2, "accum count classifies O(n)"
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assert elb_gate(v, 2, 10) == 0, "count-only gate PASSES the quadratic"
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}
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test "classifies a quadratic time series as O(n^2)" {
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// fitprobe `compute` -- el #132's shape: n scans over n characters
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let v = _s4(67, 205, 818, 3268)
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assert elb_measured_curve(v, 10) == 4, "compute time should classify O(n^2)"
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}
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test "REFUSES an all-zero series instead of calling it O(1)" {
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// fitprobe `compute` allocation count. Pure CPU, allocates nothing.
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// Reporting O(1) here would be a confident answer with nothing behind it.
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let v = _s4(0, 0, 0, 0)
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assert elb_gate(v, 2, 10) == 3, "all-zero series must be REFUSED"
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assert elb_measured_curve(v, 10) < 0, "unclassifiable returns -1"
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}
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test "REFUSES an implausibly flat series" {
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// The shape produced when clang closes a loop to a multiply: a real
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// answer, no work done, no movement across an 8x input range.
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let v = _s4(1000, 1001, 1002, 1003)
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assert elb_gate(v, 2, 10) == 3, "hard-flat series must be REFUSED"
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}
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test "gate FAILS a quadratic declared as linear" {
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let v = _s4(20300, 80600, 321200, 1282400)
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assert elb_gate(v, 2, 10) == 1, "O(n^2) measured vs O(n) declared must FAIL"
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}
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test "gate PASSES a linear series declared as linear" {
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let v = _s4(208, 409, 810, 1611)
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assert elb_gate(v, 2, 10) == 0, "O(n) measured vs O(n) declared must PASS"
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}
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test "gate reports BETTER when measured beats the declared bound" {
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let v = _s4(208, 409, 810, 1611)
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assert elb_gate(v, 4, 10) == 4, "O(n) measured vs O(n^2) declared is BETTER"
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}
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test "gate reports INDETERMINATE on disagreeing ratios" {
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// fitprobe `linear` WALL TIME at these sizes: 26/19/43/78 microseconds.
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// Ratios 0.73, 2.26, 1.81 disagree well past the noise threshold. The
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// honest answer is "cannot tell", not a classification -- this is exactly
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// why benchmarks need auto-scaled iteration counts rather than one shot.
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let v = _s4(26, 19, 43, 78)
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assert elb_gate(v, 2, 10) == 2, "disagreeing ratios must be INDETERMINATE"
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}
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test "black_box is a real barrier and returns its input" {
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assert el_black_box(42) == 42, "black_box is value-preserving"
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let s: Int = 0
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let i: Int = 0
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while i < 100 {
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// Bind the call before using it in arithmetic: `x + call(...)`
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// lowers to el_str_concat() on integers. Same inference defect
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// as `call(...) == y` lowering to str_eq().
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let bx: Int = el_black_box(1)
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let s = s + bx
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let i = i + 1
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}
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assert s == 100, "black_box does not disturb the computation"
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}
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test "curve names round-trip" {
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assert elb_curve_from_name("O(n)") == 2, "O(n) parses"
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assert elb_curve_from_name("O(n^2)") == 4, "O(n^2) parses"
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assert str_eq(elb_curve_name(4), "O(n^2)"), "O(n^2) renders"
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assert elb_curve_from_name("O(nonsense)") < 0, "unknown curve is -1"
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}
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