diff --git a/changelog.d/calibrate-chi-square-l2-basis.added.md b/changelog.d/calibrate-chi-square-l2-basis.added.md index c3a0f15efb..3fc01b2fff 100644 --- a/changelog.d/calibrate-chi-square-l2-basis.added.md +++ b/changelog.d/calibrate-chi-square-l2-basis.added.md @@ -1 +1 @@ -Add two opt-in options to Adam calibration and the ACS local-area tool. `l2_basis="chi_square"` (`--l2-basis chi_square`) makes the `l2_lambda` penalty GREG's design-weighted chi-square distance `sum(d * (w / d - 1) ** 2) / sum(d)`, which pulls toward the design weights themselves; the historical record-weighted `mean((w / d) ** 2)`, whose mass-conserved optimum is `w ∝ d ** 2`, stays the default. `mass_parametrization="softmax"` (`--mass-parametrization softmax`) conserves mass through `w = total * softmax(log_w)`; the historical per-step uniform shift, still the default, can stall away from the constrained optimum when every record's gradient shares a sign (on the ACS local release it does not, and softmax's per-step cap rounds run out on most epochs there, which the solve counts). Defaults reproduce the previous solver's bytes. Results record both settings and `CalibrationResult.chi_square_distance`; the tool records them, with the softmax cap-exhaustion count, in its calibration summary and build manifest, names all three penalty flags in the manifest's refresh recipe, and refuses to resume a checkpoint solved under other settings. See docs/calibration-l2-basis.md. +Add two opt-in options to Adam calibration and the ACS local-area tool. `l2_basis="chi_square"` (`--l2-basis chi_square`) makes the `l2_lambda` penalty GREG's design-weighted chi-square distance `sum(d * (w / d - 1) ** 2) / sum(d)`, which pulls toward the design weights themselves; the historical record-weighted `mean((w / d) ** 2)`, whose mass-conserved optimum is `w ∝ d ** 2`, stays the default. `mass_parametrization="softmax"` (`--mass-parametrization softmax`) conserves mass through `w = total * softmax(log_w)`; the historical per-step uniform shift, still the default, can stall away from the constrained optimum when every record's gradient shares a sign (on the ACS local release it does not). Defaults reproduce the previous solver's bytes. Results record both settings and `CalibrationResult.chi_square_distance`; the tool records them in its calibration summary and build manifest, names all three penalty flags in the manifest's refresh recipe, and refuses to resume a checkpoint solved under other settings. See docs/calibration-l2-basis.md. diff --git a/changelog.d/calibrate-softmax-exact-cap.changed.md b/changelog.d/calibrate-softmax-exact-cap.changed.md new file mode 100644 index 0000000000..71a70ca363 --- /dev/null +++ b/changelog.d/calibrate-softmax-exact-cap.changed.md @@ -0,0 +1 @@ +`mass_parametrization="softmax"` now projects the log-weights exactly onto the capped simplex after every Adam step: one common shift, then a clamp to each cap. Active-set rounds find the shift in float64, and a sort finishes it in log space if they run long or the uncapped weights underflow float64. It replaces a renormalize/clamp loop of at most 32 rounds, which ran out on most epochs of the ACS local release because of float32 rounding (the overshoot it left was float32-sized, measured in `experiments/us-acs-local-l2-basis-20260928/results/exact_cap.md`). Softmax solves record `options["iterate_selection_receipt"]["softmax_in_loop_max_cap_ratio"]`, the largest in-loop weight over its cap, in place of the exhaustion count, and the ACS local-area tool carries it in its calibration summary and build manifest. The default `"projection"` parametrization is unchanged. diff --git a/docs/calibration-l2-basis.md b/docs/calibration-l2-basis.md index 9956fa7700..75ce41fcee 100644 --- a/docs/calibration-l2-basis.md +++ b/docs/calibration-l2-basis.md @@ -95,29 +95,84 @@ The total holds by construction, and autograd hands Adam the gradient with its component along the constraint already removed. For a smooth objective that reduced gradient vanishes at the constrained optimum; the capped-MAPE loss has kinks, so Adam still oscillates there on the scale of `lr`, as it does under -`mass="free"`. The ratio cap remains a per-step clamp, alternated with the -softmax-invariant renormalization for up to 32 rounds, plus the closing -float64 projection that both parametrizations share. It requires -`mass="conserve"` and no L0 gates. - -**Known limitation: the cap rounds run out at production scale.** Each -renormalization lifts the clamped records back over the cap by a shrinking -amount, so the rounds converge geometrically but need not reach float32 -exactness in 32. A step that runs out ends on a clamp, and its next forward -pass optimizes `total·softmax(log_w)` past the cap, by an amount that is not -recorded. -`options["iterate_selection_receipt"]["softmax_cap_rounds_exhausted_epochs"]` -counts those steps. On small problems it is rare. On the ACS local release it -is the normal state: 165-400 of the last 400 epochs in every run that records -it, including 400 at the share-0.5, `l2_lambda = 0.03` configuration on the -full surface and on one fold, and 400 in every share-0.9 run. Only the -closing projection makes the returned vector exact. The returned loss stays -within 0.3% of the last trajectory loss in every run that records the count, -but projection runs, which have no cap loop, show the same gap (0.01-0.33%), so -that says nothing about the overshoot; its size is not measured. An -exact capped-softmax step (water-filling the excess onto the uncapped records) -would remove the limitation. Until then, prefer `"projection"` at that scale, -where the stall does not bite (below). +`mass="free"`. After every step the log-weights are projected exactly onto +the capped simplex `{w : sum(w) = D, w <= cap * d}`, and the closing float64 +projection that both parametrizations share makes the returned vector exact. +It requires `mass="conserve"` and no L0 gates. + +**The per-step cap projection.** It is the Kullback-Leibler projection: one +common shift `s` for every log-weight, then a clamp to each log cap, +`log_w <- min(log_w + s, log(cap * d))`. Uncapped records keep their +relative weights, the direction the softmax cannot see, and only records +whose share would pass their caps are held at them. `s` solves +`sum(min(exp(log_w + s), cap * d)) = D`, whose left side is continuous and +nondecreasing in `s`. Active-set rounds find it: with `C` the records already +at their caps, the next shift solves +`sum(cap * d[C]) + exp(s) * sum(exp(log_w)[not C]) = D`. That shift is a lower +bound on the answer for every `C`, so `C` only grows, and a round that adds +no record is exact. If eight rounds pass without that, a sort over the +remaining records finishes it, which bounds the worst case at `O(n log n)`. +Both paths compute the shift in float64. The same bound keeps +`D - sum(cap * d[C])` at least `exp(s) * sum(exp(log_w)[not C])`, the +uncapped records' share, so when rounding takes it to zero or below, the +capped records' caps already hold `D`: the rounds stop at the current shift, +and the other records keep a share below float64 rounding of `D`. When the +uncapped weights sum below float64's normal range, the rounds hand over to +the sort, which sums them in log space, so a weight that underflows float64 +still counts. The solve records +`options["iterate_selection_receipt"]["softmax_in_loop_max_cap_ratio"]`, the +largest ratio of a realized in-loop weight to its float64 cap over every +forward pass and the closing iterate, before the closing projection. +`test_softmax_cap_projection.py` pins the projection for every drawn input, +including zero weights and zero caps drawn independently, a cap of exactly +1, and inputs where some records' caps meet the total while the rest sit +below float64 rounding of it or below float64's range. It conserves the +total, respects every cap, is idempotent and invariant to a constant added +to the log-weights, and moves an input already within its caps by the shift +alone. It also meets the Kullback-Leibler projection's KKT conditions, +equals a water-fill written from the definition in 60-digit decimal +arithmetic, and gives the same answer by its active-set and sorted paths, +including on an input built to need one round per record. Wherever the cap +step that the full-scale measurement below ran settles in its active-set +rounds, with every nonzero weight in float64's normal range, the test checks +that the current step returns the same shift and round count, bit for bit. + +**What the projection replaced.** Before, each step renormalized and clamped +in a loop of at most 32 rounds. On the ACS local release the rounds ran out on +most epochs, and how far that left the in-loop weights past their caps was +not recorded. It has since been measured +(`experiments/us-acs-local-l2-basis-20260928/results/exact_cap.md` and +`results/exact_cap_float32.json`): + +- **Why the loop ran out.** Each round's shift was `log(D)` in float64 minus a + float32 `logsumexp`. On a vector already within its caps that difference + does not reach zero. It bottoms out at the float32 rounding residual of + `log(D)` itself, 7.8e-8 here. That shift lifts the one to three capped + records with log caps below 2 in magnitude one float32 step over their caps, + the clamp puts them back, and the next round repeats it. On full-scale vectors + the projection had already made exact, the loop still ran out in 10 of 12 + trials. +- **How far past the cap it was.** Not materially. Two full-scale + configurations whose earlier runs ran out were rerun under both steps: share + 0.5 at `l2_lambda = 0.03`, and share 0.9 unpenalized. The largest in-loop + weight over its cap was 1 + 1.2e-5 and 1 + 8.7e-6 under the loop, and + 1 + 9.0e-6 under the projection at both. The loop ran out on 713 and 705 of + 800 steps. The projection settled in one to three rounds on every step and + took 12.7-22.5 ms a step, against the loop's 48.7-53.8 ms. +- **What sets that residual.** The float32 softmax. The projected + log-weights sit within their float32 log caps, but the solve realizes + `D * softmax(log_w)` with a float32 normalizer over 1.59M records, whose + total was off by up to 8.2e-6. The cap ratio exceeded that total error by + at most 6.1e-7. With a float64 normalizer on the same log-weights the largest + ratio was 1 + 4.9e-7, the float32 rounding of the log-weights themselves. + That decomposition covers only the projected vectors; the same softmax + plausibly sets the loop's residual, but no run measured it. +- **The solves.** National ESS agreed within 0.1% between the two steps + (21,834 against 21,835, and 93,609 against 93,598). At share 0.5 and + `l2_lambda = 0.03` the projection's final loss was 0.01899. Three loop runs + of that configuration gave 0.01910-0.01917, so the projection's loss is + 0.6-0.9% lower, from one run. At share 0.9 the loss was 0.2% lower (0.03129 + against 0.03137). Evidence: @@ -165,8 +220,10 @@ recalibrated from its own checkpoint (`experiments/us-acs-local-l2-basis-2026092 `l2_lambda` is not an equal-ESS comparison. At `0.03` the held-out comparison is a toss-up: softmax has slightly lower capped error, projection more held-out targets within 10% on both folds. Projection's training loss is - 6% lower there, against 0.3% run-to-run variation. With that and the - cap-loop limitation above, projection is the recommended parametrization. + 6% lower there, against 0.3% run-to-run variation, so projection is the + recommended parametrization. Rerun with the exact cap projection, softmax's + training loss at `0.03` is 0.01899 (one run), still 5% above projection's + 0.01806. - **Record basis.** The record basis at `0.1` lowers national ESS from 13,646 to 8,689, as its `w ∝ d ** 2` optimum predicts. - **Chi-square basis.** The chi-square basis raises ESS smoothly with @@ -191,7 +248,7 @@ result = calibrate( l2_lambda=..., l2_basis="chi_square", # mass_parametrization="softmax" for problems where every target presses - # the same way; see the cap-loop limitation above. + # the same way (above). ) result.options["l2_basis"], result.options["mass_parametrization"] result.chi_square_distance, result.effective_sample_size @@ -200,8 +257,9 @@ result.chi_square_distance, result.effective_sample_size The ACS local-area tool takes the same settings as `--l2-lambda`, `--l2-basis {record,chi_square}` and `--mass-parametrization {projection,softmax}`. It records them, with the -realized chi-square distance, in `calibration_summary.json` and in the build -manifest's `calibration` block. Both join the tool's solver-settings stamp, +realized chi-square distance and, under softmax, the last epoch batch's +largest in-loop weight over its cap, in `calibration_summary.json` and in the +build manifest's `calibration` block. Both join the tool's solver-settings stamp, so `--resume` and the already-complete shortcut refuse weights solved under any other solver setting (cap, loss cap, `l2_lambda`, `l2_basis`, `mass_parametrization`, seed, epoch batch); a stamp written before these two diff --git a/docs/evidence/spec-engine/us-f0-coverage.json b/docs/evidence/spec-engine/us-f0-coverage.json index d438bc7ef2..11a1c6d60f 100644 --- a/docs/evidence/spec-engine/us-f0-coverage.json +++ b/docs/evidence/spec-engine/us-f0-coverage.json @@ -1642,13 +1642,13 @@ "compiler_ir.node_slices" ], "expected": { - "map_sha256": "07fe9086b57a15ab1c2551fce5c30aebfa55175d76a308f7dbb268ac49da747a", - "protocol_sha256": "4ff992324004d4c09d4165bc8d24ccff15b9d84858836851b12d532c870920f4" + "map_sha256": "7ef47df73404e40d20e12292c35bc81ce18620e4608f8a52ce2b8f95ec32f4d1", + "protocol_sha256": "76c372eefaf7581557d530ec68123802b974860d9b2e581f45b30aa55cedfbf5" }, "failures": [], "observed": { - "map_sha256": "07fe9086b57a15ab1c2551fce5c30aebfa55175d76a308f7dbb268ac49da747a", - "protocol_sha256": "4ff992324004d4c09d4165bc8d24ccff15b9d84858836851b12d532c870920f4" + "map_sha256": "7ef47df73404e40d20e12292c35bc81ce18620e4608f8a52ce2b8f95ec32f4d1", + "protocol_sha256": "76c372eefaf7581557d530ec68123802b974860d9b2e581f45b30aa55cedfbf5" }, "status": "covered" }, @@ -1663,7 +1663,7 @@ "compiler_ir.seed_stream_map" ], "expected": { - "implementation_sha256": "4ff992324004d4c09d4165bc8d24ccff15b9d84858836851b12d532c870920f4", + "implementation_sha256": "76c372eefaf7581557d530ec68123802b974860d9b2e581f45b30aa55cedfbf5", "protocol": "legacy-v1", "streams": [ "build_model", @@ -1684,7 +1684,7 @@ }, "failures": [], "observed": { - "implementation_sha256": "4ff992324004d4c09d4165bc8d24ccff15b9d84858836851b12d532c870920f4", + "implementation_sha256": "76c372eefaf7581557d530ec68123802b974860d9b2e581f45b30aa55cedfbf5", "protocol": "legacy-v1", "streams": [ "build_model", @@ -2585,7 +2585,7 @@ "country": "us", "schema_id": "country_spec", "schema_version": 1, - "spec_sha256": "0c713aabfd0afc3703415fea86a8210a3f4346918f774f371fb16aa663348c1d" + "spec_sha256": "874294b24bf70671b3ec78bb1d1a2c3461fd585e2af1388265c27be26c18d0ba" } }, "report_schema_version": 3, @@ -2595,7 +2595,7 @@ "country": "us", "schema_id": "country_spec", "schema_version": 1, - "spec_sha256": "0c713aabfd0afc3703415fea86a8210a3f4346918f774f371fb16aa663348c1d" + "spec_sha256": "874294b24bf70671b3ec78bb1d1a2c3461fd585e2af1388265c27be26c18d0ba" }, "status": "pass" } diff --git a/experiments/us-acs-local-l2-basis-20260928/README.md b/experiments/us-acs-local-l2-basis-20260928/README.md index 0fa601ef11..8b854fc669 100644 --- a/experiments/us-acs-local-l2-basis-20260928/README.md +++ b/experiments/us-acs-local-l2-basis-20260928/README.md @@ -39,10 +39,44 @@ what that anchor buys, what it costs, and what limits it. - A larger ACS share raises that limit several-fold. At share 0.9 without a penalty: ESS 93,524, Massachusetts 2,704, no district below 50, and 95.8% of targets within 10%. - It also worsens held-out fit, mainly on SOI targets (held-out SOI within 10% falls from 64% to 54% at share 0.9), while held-out district populations improve. - That is consistent with how the staging builds the rows: the ACS rows' tax variables are transferred from the donor rows by QRF (the tool's `donor_sparse_selection_training_set` limitation), so the donor rows carry tax detail the ACS rows only approximate. -4. **The softmax parametrization is not needed at this scale for λ ≤ 0.03, and its cap loop runs out of rounds here.** +4. **The softmax parametrization is not needed at this scale for λ ≤ 0.03.** - The per-record gradients here are near Adam's `eps`, with mixed signs (`results/gradient_scale.json`). The same-sign stall that the small-problem evidence shows does not occur. - The two parametrizations trace the same training frontier for λ ≤ 0.03. At λ = 0 softmax reaches 18% more ESS (16,132 against 13,646) at a 3% higher loss (0.0160 against 0.0155), a loss gap about the size of the unpenalized solve's run-to-run variation (2.7%; see "How it was run"). At λ ≥ 0.1 projection falls inside softmax's training frontier. At λ = 0.1 projection reaches ESS 22,739 at loss 0.0228, where softmax's path (interpolated between its λ = 0.03 and 0.1 solves) gives about 0.021. At λ = 1 projection reaches 30,582 at 0.0785, against about 0.055 on softmax's path (between λ = 0.3 and 1). Equal λ is not an equal-ESS comparison. - - Softmax's per-step cap loop runs out of its 32 rounds on most epochs at this scale: in the runs that record it (heads 9ef71ed6, bc763cb4 and d82ff85b), 165-400 of the last batch's 400 epochs, including 400 at share 0.5, λ = 0.03 on the full surface (the `dup_` rerun) and on fold 0, and every share-0.9 run. Those epochs optimize total·softmax(log_w) past the 5x cap, by an amount that is not recorded; only the closing projection makes the returned weights exact. The returned loss is within 0.3% of the last trajectory loss in every softmax run that records the count, but projection runs, which have no cap loop, show the same gap (0.01-0.33%), so it says nothing about the overshoot. Projection is the recommended parametrization. + - Softmax's per-step cap loop runs out of its 32 rounds on most epochs at this scale: in the runs that record it (heads 9ef71ed6, bc763cb4 and d82ff85b), 165-400 of the last batch's 400 epochs, including 400 at share 0.5, λ = 0.03 on the full surface (the `dup_` rerun) and on fold 0, and every share-0.9 run. Those epochs optimize total·softmax(log_w) past the 5x cap, by an amount these runs did not record; only the closing projection makes the returned weights exact. An exact projection has since replaced the loop, and two reruns measured the overshoot: float32-sized, 1 + 1.2e-5 at most, against 1 + 9.0e-6 for the projection. Those two configurations' softmax results stand; the measurement did not rerun the grid's other softmax runs (see "The softmax cap step, re-measured" below). Projection is the recommended parametrization. + +## The softmax cap step, re-measured + +Softmax's per-step cap loop (finding 4) was replaced by an exact projection +onto the capped simplex (docs/calibration-l2-basis.md). `exact_cap_modal.py` +reran two configurations whose grid runs ran out of rounds, under the new +step and under the old loop, at kernel head 4bddde64. It recorded the largest +in-loop weight over its cap at every forward pass, the cap step's rounds and +its time (`results/exact_cap.md`, `results/exact_cap/`): + +| Configuration | Cap step | Largest in-loop weight over its cap | Steps out of rounds | Cap step | Final loss | National ESS | +|---|---|---:|---:|---:|---:|---:| +| Share 0.5, softmax, chi-square λ 0.03 | exact projection | 1 + 9.0e-6 | 0 of 800 | 12.7 ms | 0.018991 | 21,834 | +| Share 0.5, softmax, chi-square λ 0.03 | 32-round loop | 1 + 1.2e-5 | 713 of 800 | 53.8 ms | 0.019169 | 21,835 | +| Share 0.9, softmax, no penalty | exact projection | 1 + 9.0e-6 | 0 of 800 | 22.5 ms | 0.031293 | 93,609 | +| Share 0.9, softmax, no penalty | 32-round loop | 1 + 8.7e-6 | 705 of 800 | 48.7 ms | 0.031365 | 93,598 | + +- The loop's overshoot was float32-sized, about the same as the projection's. + The solve's float32 softmax sets the projection's: its normalizer missed + the total by up to 8.2e-6, and with a float64 normalizer the projected + vectors sit within 1 + 4.9e-7 of their caps (`exact_cap_float32.py`, + `results/exact_cap_float32.json`). That script decomposed only the + projected vectors; the same softmax plausibly sets the loop's overshoot, + but no run measured it. +- The loop ran out because of float32 too. Its shift was `log(total)` in + float64 minus a float32 `logsumexp`, which bottoms out at the float32 + rounding residual of `log(total)` (7.8e-8) and lifts a few capped records + over their caps every round. On vectors the projection had already made + exact it still ran out in 10 of 12 trials. +- The projection settled in one to three active-set rounds on every step. +- The loop run at share 0.5 reproduced the grid's `soft_chi_s050_0.03` + (0.019169, ESS 21,835); the `dup_` rerun gave 0.019104. The projection's + 0.018991 is 0.6-0.9% below those three, from one run. ESS agreed within + 0.1% at both configurations. ## Key configurations @@ -86,7 +120,7 @@ default `--mass-parametrization projection`. - the smallest district from 12 to 22. - Out of sample it is ahead on both measures and both folds: held-out error 0.1247 and 0.1191 against 0.1296 and 0.1236, and held-out within 10% 64.2% and 67.0% against 63.3% and 65.8%. Held-out run-to-run variation was not measured and λ was picked on these folds, so treat that as "no worse, probably slightly better". - In sample it costs a little: training loss rises from 0.0155 to 0.0181, and training targets within 10% fall from 97.6% to 97.3%. -- Softmax at λ = 0.03 gives a little more ESS (21,835). Against projection, its held-out fit is a toss-up (lower capped error, fewer targets within 10%, both by small margins). What separates them is training fit (loss 0.0192 against 0.0181, 6% higher, against 0.3% run-to-run variation at this λ) and softmax's cap loop (finding 4). Projection is the default and the better-supported choice. +- Softmax at λ = 0.03 gives a little more ESS (21,835). Against projection, its held-out fit is a toss-up (lower capped error, fewer targets within 10%, both by small margins). What separates them is training fit: loss 0.0192 against 0.0181, 6% higher, against 0.3% run-to-run variation at this λ (0.0190 and 5% higher with the exact cap projection, from one run). Projection is the default and the better-supported choice. - λ = 0.1 (softmax) buys more ESS (24,895; 132 districts below 50) and lower held-out error than the release, but lower held-out within 10% on both folds (61.5% and 63.8%), and 2 pp of training fit. - **λ is in units of the loss.** The ACS local build weights every target equally today (`calibrate` gets no `target_loss_weights`), and IRS SOI cells are 3,819 of the 4,459 targets. A change to weight them as the national release does is planned. Re-pick λ on that loss by rerunning this harness with its target weights before shipping a default. diff --git a/experiments/us-acs-local-l2-basis-20260928/exact_cap_float32.py b/experiments/us-acs-local-l2-basis-20260928/exact_cap_float32.py new file mode 100644 index 0000000000..6df9664294 --- /dev/null +++ b/experiments/us-acs-local-l2-basis-20260928/exact_cap_float32.py @@ -0,0 +1,179 @@ +"""What float32 does to the softmax cap step at full scale. + +Applies the kernel's exact projection to full-scale log-weights (a softmax +run's final weights, moved by a step-sized random perturbation), so every +log-weight is at or below its float32 log cap and the vector meets the total. +Two measurements on each projected vector: + +- **The realized weights.** The solve realizes ``total * softmax(log_w)`` in + float32, whose normalizer is a float32 sum over 1.59M records. Realize the + same float32 log-weights with a float64 normalizer too; the difference is + what the float32 softmax alone adds to the cap ratio. +- **microcosm#1078's 32-round loop on that vector.** It needs no cap work, so + an exact stopping test would pass in the first round. Record the shift the + loop computes each round, the records it lifts over their float32 log caps, + and whether the rounds run out. + +Run on Modal, off the shared host, from the sweep's volume (it writes +``results/exact_cap_float32.json``):: + + modal run experiments/us-acs-local-l2-basis-20260928/exact_cap_modal.py::float32 + +or locally against the build artifacts with +``uv run python experiments/us-acs-local-l2-basis-20260928/exact_cap_float32.py``. +""" + +from __future__ import annotations + +import json +import math +from pathlib import Path + +import numpy as np +import pandas as pd +import torch + +from microcosm.calibrate import solve + +HERE = Path(__file__).resolve().parent +ARTIFACTS = Path("/Users/maxghenis/PolicyEngine/_build_artifacts") +CHECKPOINT = ARTIFACTS / "acs-local-l2-basis-20260928/checkpoint" +RUN_WEIGHTS = ( + ARTIFACTS / "acs-local-l2-basis-20260928/runs/dup_soft_chi_s050_0.03/weights.npz" +) +MAX_WEIGHT_RATIO = 5.0 +#: Adam's learning rate in the release's solve: the size of one log-weight step. +STEP = 0.02 +TRIALS = 12 +SEED = 3 + + +def rounds_32_on(log_w: torch.Tensor, total: float, log_upper: torch.Tensor) -> dict: + """microcosm#1078's cap step (b7b4c2935) on a copy, with what each round did.""" + log_w = log_w.clone() + shifts, lifted, lifted_log_cap = [], [], 0.0 + for _ in range(32): + shift = math.log(total) - float(torch.logsumexp(log_w, dim=0).item()) + shifts.append(shift) + log_w.add_(shift) + over = log_w > log_upper + lifted.append(int(over.sum())) + if not lifted[-1]: + break + lifted_log_cap = max(lifted_log_cap, float(log_upper[over].abs().max())) + log_w.clamp_(max=log_upper) + return { + "rounds": len(shifts), + "ran_out": bool(lifted[-1]), + "shift_min": min(shifts), + "shift_max": max(shifts), + "records_lifted_over_their_caps_per_round_max": max(lifted), + "largest_abs_log_cap_among_lifted_records": lifted_log_cap, + } + + +def measure(checkpoint: Path = CHECKPOINT, run_weights: Path = RUN_WEIGHTS) -> dict: + design = pd.read_parquet(checkpoint / "households.parquet")[ + "design_weight" + ].to_numpy(np.float64) + weights = np.load(run_weights)["weights"].astype(np.float64) + total = float(design.sum()) + cap = torch.tensor(MAX_WEIGHT_RATIO * design, dtype=torch.float64) + log_upper = torch.log(torch.tensor(MAX_WEIGHT_RATIO * design, dtype=torch.float32)) + rng = np.random.default_rng(SEED) + trials = [] + for _ in range(TRIALS): + log_w = torch.tensor( + np.log(weights) + rng.normal(0.0, STEP, design.size), dtype=torch.float32 + ) + rounds = solve._project_softmax_log_weights_( + log_w, total, log_upper.to(torch.float64) + ) + if not bool((log_w <= log_upper).all()): + raise SystemExit("a projected log-weight passed its float32 log cap") + loop = rounds_32_on(log_w, total, log_upper) + float32 = total * torch.softmax(log_w, dim=0) + exact = log_w.to(torch.float64) + float64 = total * torch.exp(exact - torch.logsumexp(exact, dim=0)) + trials.append( + { + "active_set_rounds": int(rounds), + "records_at_cap": int((log_w >= log_upper).sum()), + "float64_shift_still_needed": math.log(total) + - float(torch.logsumexp(log_w.to(torch.float64), dim=0)), + "rounds_32_loop": loop, + "float32_softmax": { + "max_cap_ratio_minus_one": solve._max_cap_ratio(float32, cap) - 1.0, + "total_error": float(float32.to(torch.float64).sum()) / total - 1.0, + }, + "float64_normalizer": { + "max_cap_ratio_minus_one": solve._max_cap_ratio(float64, cap) - 1.0, + "total_error": float(float64.sum()) / total - 1.0, + }, + } + ) + + def spread(realization: str, key: str) -> dict: + values = [trial[realization][key] for trial in trials] + return {"min": min(values), "max": max(values)} + + out = { + "records": int(design.size), + "max_weight_ratio": MAX_WEIGHT_RATIO, + "log_weight_step_sd": STEP, + "trials": len(trials), + "seed": SEED, + "torch": str(torch.__version__), + "torch_num_threads": torch.get_num_threads(), + "float32_softmax": { + key: spread("float32_softmax", key) + for key in ("max_cap_ratio_minus_one", "total_error") + }, + "float64_normalizer": { + key: spread("float64_normalizer", key) + for key in ("max_cap_ratio_minus_one", "total_error") + }, + # How far the float32 softmax's cap ratio sits above its own total + # error: what is left once the normalizer's error is taken out. + "float32_cap_ratio_minus_total_error": { + "min": min( + t["float32_softmax"]["max_cap_ratio_minus_one"] + - t["float32_softmax"]["total_error"] + for t in trials + ), + "max": max( + t["float32_softmax"]["max_cap_ratio_minus_one"] + - t["float32_softmax"]["total_error"] + for t in trials + ), + }, + "rounds_32_loop_on_the_projected_vectors": { + "trials_ran_out": sum(t["rounds_32_loop"]["ran_out"] for t in trials), + "shift": { + "min": min(t["rounds_32_loop"]["shift_min"] for t in trials), + "max": max(t["rounds_32_loop"]["shift_max"] for t in trials), + }, + "float32_log_total_residual": math.log(total) + - float(torch.tensor(math.log(total), dtype=torch.float32)), + "records_lifted_over_their_caps_per_round_max": max( + t["rounds_32_loop"]["records_lifted_over_their_caps_per_round_max"] + for t in trials + ), + "largest_abs_log_cap_among_lifted_records": max( + t["rounds_32_loop"]["largest_abs_log_cap_among_lifted_records"] + for t in trials + ), + }, + "by_trial": trials, + } + return out + + +def write(out: dict) -> None: + path = HERE / "results" / "exact_cap_float32.json" + path.write_text(json.dumps(out, indent=1) + "\n") + print(json.dumps({k: v for k, v in out.items() if k != "by_trial"}, indent=1)) + + +if __name__ == "__main__": + write(measure()) diff --git a/experiments/us-acs-local-l2-basis-20260928/exact_cap_modal.py b/experiments/us-acs-local-l2-basis-20260928/exact_cap_modal.py new file mode 100644 index 0000000000..5b9cf3242b --- /dev/null +++ b/experiments/us-acs-local-l2-basis-20260928/exact_cap_modal.py @@ -0,0 +1,333 @@ +"""Measure the softmax cap step at full scale, before and after the exact projection. + +Each run is one ``sweep.py`` softmax calibration of the 2026-09-23 release's +surface (1,588,854 households, 4,459 targets, a 5x cap; 800 epochs as two +warm-started batches) on Modal, in one of two variants of the same committed +kernel: + +- ``water_fill``: the kernel as committed, whose per-step cap is the exact + log-space projection ``_project_softmax_log_weights_``. +- ``rounds_32``: the same kernel with that one function replaced by + microcosm#1078's renormalize/clamp loop (32 rounds at most), copied here. + +Both record what the kernel's receipt records, the largest in-loop weight +over its cap, plus this script's own instrumentation: the ratio at every +forward pass, the cap step's rounds and its wall time. + + modal run --detach experiments/us-acs-local-l2-basis-20260928/exact_cap_modal.py::launch + modal run experiments/us-acs-local-l2-basis-20260928/exact_cap_modal.py::collect + modal run experiments/us-acs-local-l2-basis-20260928/exact_cap_modal.py::float32 + +``launch`` reads the checkpoint the sweep already uploaded to the volume +(``sweep.py`` checks every file against ``MANIFEST.json``) and uploads +nothing. ``collect`` copies each finished run's metrics into +``results/exact_cap/`` and writes ``results/exact_cap.md``. Weights stay on +the volume under ``/exact_cap``. ``float32`` runs ``exact_cap_float32.py`` +against the volume's checkpoint and the sweep's ``dup_soft_chi_s050_0.03`` +weights and writes ``results/exact_cap_float32.json``. +""" + +from __future__ import annotations + +import json +import subprocess +from pathlib import Path + +import modal + +HERE = Path(__file__).resolve().parent +REPO = ( + HERE.parents[1] + if len(HERE.parents) > 1 and (HERE.parents[1] / ".git").exists() + else None +) +KERNEL = ( + "microcosm-frame", + "microcosm-graph", + "microcosm-diagnostics", + "microcosm-calibrate", +) +VOLUME_ROOT = "/sweep" +OUT_ROOT = f"{VOLUME_ROOT}/exact_cap" +CPUS = 8 +VARIANTS = ("water_fill", "rounds_32") +#: The two configurations whose earlier runs ran out of cap rounds on every +#: epoch of their last batch: the recommended penalty at the release's +#: seeding, and the unpenalized solve at an ACS share of 0.9. +CONFIGURATIONS = { + "soft_chi_s050_0.03": { + "l2_lambda": 0.03, + "l2_basis": "chi_square", + "mass_parametrization": "softmax", + }, + "soft_chi_s900_0": { + "l2_lambda": 0.0, + "l2_basis": "chi_square", + "mass_parametrization": "softmax", + "acs_share": 0.9, + }, +} + + +def _git(*args: str) -> str: + return subprocess.run( + ["git", "-C", str(REPO), *args], capture_output=True, text=True, check=True + ).stdout.strip() + + +GIT_SHA = _git("rev-parse", "HEAD") if REPO is not None else "container" + +image = ( + modal.Image.debian_slim(python_version="3.13") + .uv_pip_install("torch==2.12.0", index_url="https://download.pytorch.org/whl/cpu") + .uv_pip_install( + "numpy==2.4.6", + "scipy==1.17.1", + "pandas==3.0.3", + "pyarrow==25.0.0", + "pydantic>=2.10,<3", + ) + .env( + { + "OMP_NUM_THREADS": str(CPUS), + "MKL_NUM_THREADS": str(CPUS), + "OPENBLAS_NUM_THREADS": str(CPUS), + "MICROCOSM_GIT_SHA": GIT_SHA, + "MICROCOSM_REPO": "/opt/none", + } + ) +) +if REPO is not None: + for name in KERNEL: + image = image.add_local_dir( + str(REPO / "packages" / name), f"/opt/kernel/{name}", copy=True + ) + image = ( + image.run_commands( + "python -m pip install --no-deps " + + " ".join(f"/opt/kernel/{name}" for name in KERNEL) + ) + .add_local_file(str(HERE / "sweep.py"), "/opt/sweep/sweep.py") + .add_local_file( + str(HERE / "exact_cap_float32.py"), "/opt/sweep/exact_cap_float32.py" + ) + ) + +app = modal.App("microcosm-acs-exact-cap", image=image) +volume = modal.Volume.from_name("microcosm-acs-l2-basis-sweep") + + +def run_ids() -> list[tuple[str, str, str]]: + return [ + (f"{variant}_{name}", variant, name) + for name in CONFIGURATIONS + for variant in VARIANTS + ] + + +def _instrument(variant: str) -> dict: + """Swap in the variant's cap step and record what each step and pass did.""" + + import math + import time + + import torch + + from microcosm.calibrate import solve + + record = {"variant": variant, "seconds": 0.0, "rounds": [], "ratios": []} + water_fill = solve._project_softmax_log_weights_ + float32_caps: dict[int, torch.Tensor] = {} + + def rounds_32(log_w, total, log_upper): + """microcosm#1078's cap step (b7b4c2935), plus the round count. + + Shift so ``exp(log_w)`` sums to ``total``, stop if no record is over + its float32 log cap, else clamp and go again. Returns the rounds + taken, negated when all 32 ran out with a record still over. + """ + if log_upper is not None and id(log_upper) not in float32_caps: + float32_caps[id(log_upper)] = log_upper.to(torch.float32) + upper = None if log_upper is None else float32_caps[id(log_upper)] + for taken in range(1, 33): + log_w.add_(math.log(total) - float(torch.logsumexp(log_w, dim=0).item())) + if upper is None or not bool((log_w > upper).any().item()): + return taken + log_w.clamp_(max=upper) + return -32 + + step = water_fill if variant == "water_fill" else rounds_32 + + def timed(log_w, total, log_upper): + started = time.perf_counter() + taken = step(log_w, total, log_upper) + record["seconds"] += time.perf_counter() - started + record["rounds"].append(int(taken)) + return taken + + max_cap_ratio = solve._max_cap_ratio + + def recorded(weights, cap): + ratio = max_cap_ratio(weights, cap) + record["ratios"].append(ratio) + return ratio + + solve._project_softmax_log_weights_ = timed + solve._max_cap_ratio = recorded + return record + + +def _cap_step_summary(record: dict) -> dict: + import numpy as np + + rounds = np.asarray(record["rounds"]) + ratios = np.asarray(record["ratios"], dtype=np.float64) + values, counts = np.unique(rounds, return_counts=True) + return { + "variant": record["variant"], + "cap_steps": int(rounds.size), + "cap_step_seconds_total": record["seconds"], + "cap_step_seconds_mean": record["seconds"] / max(1, rounds.size), + "rounds_histogram": { + str(int(v)): int(c) for v, c in zip(values, counts, strict=True) + }, + "rounds_exhausted_steps": int((rounds < 0).sum()), + "forward_passes": int(ratios.size), + "in_loop_cap_ratio_minus_one": { + "max": float(ratios.max() - 1.0), + "p99": float(np.quantile(ratios, 0.99) - 1.0), + "median": float(np.median(ratios) - 1.0), + "min": float(ratios.min() - 1.0), + "passes_above_cap": int((ratios > 1.0).sum()), + }, + "in_loop_cap_ratio_by_pass": [float(r) for r in ratios], + } + + +@app.function( + cpu=CPUS, memory=24576, timeout=3 * 3600, volumes={VOLUME_ROOT: volume}, retries=0 +) +def run_variant(run_id: str, variant: str, name: str) -> dict: + import sys + + import torch + + torch.set_num_threads(CPUS) + sys.path.insert(0, "/opt/sweep") + import sweep + + volume.reload() + out_root = Path(OUT_ROOT) / "runs" + out_root.mkdir(parents=True, exist_ok=True) + record = _instrument(variant) + payload = sweep.calibrate_mode( + sweep.RunSpec.from_mapping({"run_id": run_id, **CONFIGURATIONS[name]}), + Path(VOLUME_ROOT) / "checkpoint", + out_root, + resume=False, + verify=True, + ) + cap_step = _cap_step_summary(record) + (out_root / run_id / "cap_step.json").write_text( + json.dumps(cap_step, indent=1) + "\n" + ) + volume.commit() + return { + "run_id": run_id, + "final_loss": payload["result"]["final_loss"], + "kish_ess": payload["metrics"]["concentration"]["national"]["kish_ess"], + "wall_seconds": payload["timing"]["total_wall_seconds"], + **{k: v for k, v in cap_step.items() if k != "in_loop_cap_ratio_by_pass"}, + } + + +@app.local_entrypoint() +def launch() -> None: + kernel = [f"packages/{name}/src" for name in KERNEL] + if _git("status", "--porcelain", "--", *kernel): + raise SystemExit("kernel sources differ from HEAD; commit before launching") + print(f"kernel HEAD {GIT_SHA}") + calls = [] + for run_id, variant, name in run_ids(): + calls.append(run_variant.spawn(run_id, variant, name)) + print(run_id, calls[-1].object_id, flush=True) + # The runs outlive this process under --detach; waiting only prints them. + for call in calls: + print(json.dumps(call.get()), flush=True) + + +@app.function(cpu=CPUS, memory=8192, timeout=1800, volumes={VOLUME_ROOT: volume}) +def measure_float32() -> str: + import sys + + import torch + + torch.set_num_threads(CPUS) + sys.path.insert(0, "/opt/sweep") + import exact_cap_float32 + + out = exact_cap_float32.measure( + Path(VOLUME_ROOT) / "checkpoint", + Path(VOLUME_ROOT) / "runs" / "dup_soft_chi_s050_0.03" / "weights.npz", + ) + # JSON text: the local client has no torch to unpickle torch-typed values. + return json.dumps(out) + + +@app.local_entrypoint() +def float32() -> None: + kernel = [f"packages/{name}/src" for name in KERNEL] + if _git("status", "--porcelain", "--", *kernel): + raise SystemExit("kernel sources differ from HEAD; commit before launching") + out = {"kernel_head": GIT_SHA, **json.loads(measure_float32.remote())} + path = HERE / "results" / "exact_cap_float32.json" + path.write_text(json.dumps(out, indent=1) + "\n") + print(json.dumps({k: v for k, v in out.items() if k != "by_trial"}, indent=1)) + + +def _read(path: str) -> bytes | None: + try: + return b"".join(volume.read_file(path)) + except FileNotFoundError: + return None + + +@app.local_entrypoint() +def collect() -> None: + results = HERE / "results" / "exact_cap" + results.mkdir(parents=True, exist_ok=True) + rows = [] + for run_id, variant, name in run_ids(): + metrics = _read(f"/exact_cap/runs/{run_id}/metrics.json") + cap_step = _read(f"/exact_cap/runs/{run_id}/cap_step.json") + if metrics is None or cap_step is None: + print(f"{run_id}: not finished") + continue + (results / f"{run_id}.json").write_bytes(metrics) + (results / f"{run_id}.cap_step.json").write_bytes(cap_step) + rows.append((name, variant, json.loads(metrics), json.loads(cap_step))) + lines = [ + "| Configuration | Cap step | Largest in-loop weight over its cap, minus 1 " + "(max / median over passes) | Steps out of rounds | Rounds per step " + "| Cap step ms | s/epoch | Receipt (batch 1 / 2) | Final loss | National ESS |", + "|---|---|---:|---:|---|---:|---:|---|---:|---:|", + ] + for name, variant, metrics, cap_step in rows: + ratio = cap_step["in_loop_cap_ratio_minus_one"] + batches = metrics["batches"] + receipts = " / ".join( + f"{(b.get('iterate_selection_receipt') or {}).get('softmax_in_loop_max_cap_ratio', float('nan')) - 1.0:.2e}" + for b in batches + ) + per_epoch = " / ".join(f"{b['seconds_per_epoch']:.2f}" for b in batches) + lines.append( + f"| `{name}` | `{variant}` | {ratio['max']:.2e} / {ratio['median']:.2e} " + f"| {cap_step['rounds_exhausted_steps']} of {cap_step['cap_steps']} " + f"| {json.dumps(cap_step['rounds_histogram'])} " + f"| {1000 * cap_step['cap_step_seconds_mean']:.1f} | {per_epoch} " + f"| {receipts} | {metrics['result']['final_loss']:.6f} " + f"| {metrics['metrics']['concentration']['national']['kish_ess']:,.0f} |" + ) + (HERE / "results" / "exact_cap.md").write_text("\n".join(lines) + "\n") + print("\n".join(lines)) + print(f"collected {len(rows)} of {len(run_ids())} runs") diff --git a/experiments/us-acs-local-l2-basis-20260928/results/exact_cap.md b/experiments/us-acs-local-l2-basis-20260928/results/exact_cap.md new file mode 100644 index 0000000000..792a131814 --- /dev/null +++ b/experiments/us-acs-local-l2-basis-20260928/results/exact_cap.md @@ -0,0 +1,6 @@ +| Configuration | Cap step | Largest in-loop weight over its cap, minus 1 (max / median over passes) | Steps out of rounds | Rounds per step | Cap step ms | s/epoch | Receipt (batch 1 / 2) | Final loss | National ESS | +|---|---|---:|---:|---|---:|---:|---|---:|---:| +| `soft_chi_s050_0.03` | `water_fill` | 8.97e-06 / 3.19e-06 | 0 of 800 | {"1": 598, "2": 202} | 12.7 | 1.00 / 0.95 | 8.97e-06 / 7.80e-06 | 0.018991 | 21,834 | +| `soft_chi_s050_0.03` | `rounds_32` | 1.22e-05 / 2.96e-06 | 713 of 800 | {"-32": 713, "1": 68, "2": 2, "3": 15, "22": 1, "32": 1} | 53.8 | 1.33 / 1.37 | 1.14e-05 / 1.22e-05 | 0.019169 | 21,835 | +| `soft_chi_s900_0` | `water_fill` | 8.99e-06 / 1.09e-06 | 0 of 800 | {"1": 70, "2": 103, "3": 627} | 22.5 | 1.34 / 1.38 | 8.99e-06 / 8.27e-06 | 0.031293 | 93,609 | +| `soft_chi_s900_0` | `rounds_32` | 8.66e-06 / 1.18e-06 | 705 of 800 | {"-32": 705, "1": 65, "2": 1, "3": 6, "4": 18, "5": 5} | 48.7 | 1.33 / 1.28 | 7.74e-06 / 8.66e-06 | 0.031365 | 93,598 | diff --git a/experiments/us-acs-local-l2-basis-20260928/results/exact_cap/rounds_32_soft_chi_s050_0.03.cap_step.json b/experiments/us-acs-local-l2-basis-20260928/results/exact_cap/rounds_32_soft_chi_s050_0.03.cap_step.json new file mode 100644 index 0000000000..8418f67b34 --- /dev/null +++ b/experiments/us-acs-local-l2-basis-20260928/results/exact_cap/rounds_32_soft_chi_s050_0.03.cap_step.json @@ -0,0 +1,827 @@ +{ + "variant": "rounds_32", + "cap_steps": 800, + "cap_step_seconds_total": 43.05271996800134, + "cap_step_seconds_mean": 0.053815899960001676, + "rounds_histogram": { + "-32": 713, + "1": 68, + "2": 2, + "3": 15, + "22": 1, + "32": 1 + }, + "rounds_exhausted_steps": 713, + "forward_passes": 802, + "in_loop_cap_ratio_minus_one": { + "max": 1.2231641854487663e-05, + "p99": 1.080651350005013e-05, + "median": 2.963704962555269e-06, + "min": -0.799999572981976, + "passes_above_cap": 606 + }, + "in_loop_cap_ratio_by_pass": [ + 0.20000042701802404, + 0.20542158747216505, + 0.21091188598917873, + 0.21642729027345184, + 0.2219198732032773, + 0.2273775342805713, + 0.23276169309679998, + 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"shift_min": 7.764958454004045e-08, + "shift_max": 7.764958454004045e-08, + "records_lifted_over_their_caps_per_round_max": 1, + "largest_abs_log_cap_among_lifted_records": 1.46820068359375 + }, + "float32_softmax": { + "max_cap_ratio_minus_one": -1.9945366699358758e-06, + "total_error": -2.1802007749149865e-06 + }, + "float64_normalizer": { + "max_cap_ratio_minus_one": 1.4614623444764163e-07, + "total_error": -5.551115123125783e-16 + } + }, + { + "active_set_rounds": 1, + "records_at_cap": 458, + "float64_shift_still_needed": -2.3115266500894904e-07, + "rounds_32_loop": { + "rounds": 32, + "ran_out": true, + "shift_min": 7.764958454004045e-08, + "shift_max": 7.764958454004045e-08, + "records_lifted_over_their_caps_per_round_max": 3, + "largest_abs_log_cap_among_lifted_records": 1.6906201839447021 + }, + "float32_softmax": { + "max_cap_ratio_minus_one": 8.506990349932408e-07, + "total_error": 7.134451469603675e-07 + }, + "float64_normalizer": { + "max_cap_ratio_minus_one": 1.1250317788480402e-08, + "total_error": 1.7763568394002505e-15 + } + }, + { + "active_set_rounds": 2, + "records_at_cap": 446, + "float64_shift_still_needed": 7.473493823795252e-08, + "rounds_32_loop": { + "rounds": 32, + "ran_out": true, + "shift_min": 7.764958454004045e-08, + "shift_max": 7.764958454004045e-08, + "records_lifted_over_their_caps_per_round_max": 2, + "largest_abs_log_cap_among_lifted_records": 1.46820068359375 + }, + "float32_softmax": { + "max_cap_ratio_minus_one": 8.549572118488058e-06, + "total_error": 8.156424846639254e-06 + }, + "float64_normalizer": { + "max_cap_ratio_minus_one": 3.171379714395073e-07, + "total_error": -1.9984014443252818e-15 + } + }, + { + "active_set_rounds": 1, + "records_at_cap": 455, + "float64_shift_still_needed": 1.6941939051662303e-07, + "rounds_32_loop": { + "rounds": 32, + "ran_out": true, + "shift_min": 7.764958454004045e-08, + "shift_max": 7.764958454004045e-08, + "records_lifted_over_their_caps_per_round_max": 1, + "largest_abs_log_cap_among_lifted_records": 1.6906201839447021 + }, + "float32_softmax": { + "max_cap_ratio_minus_one": -1.9108532669509515e-06, + "total_error": -2.4308093040659173e-06 + }, + "float64_normalizer": { + "max_cap_ratio_minus_one": 4.1182245813509155e-07, + "total_error": 8.881784197001252e-16 + } + }, + { + "active_set_rounds": 1, + "records_at_cap": 439, + "float64_shift_still_needed": 3.7992045065493585e-08, + "rounds_32_loop": { + "rounds": 32, + "ran_out": true, + "shift_min": 7.764958454004045e-08, + "shift_max": 7.764958454004045e-08, + "records_lifted_over_their_caps_per_round_max": 2, + "largest_abs_log_cap_among_lifted_records": 1.46820068359375 + }, + "float32_softmax": { + "max_cap_ratio_minus_one": 4.532768770548756e-06, + "total_error": 4.160417010901085e-06 + }, + "float64_normalizer": { + "max_cap_ratio_minus_one": 2.7849041250860296e-07, + "total_error": 1.1102230246251565e-15 + } + }, + { + "active_set_rounds": 2, + "records_at_cap": 468, + "float64_shift_still_needed": -9.350399565732914e-08, + "rounds_32_loop": { + "rounds": 32, + "ran_out": true, + "shift_min": 7.764958454004045e-08, + "shift_max": 7.764958454004045e-08, + "records_lifted_over_their_caps_per_round_max": 2, + "largest_abs_log_cap_among_lifted_records": 1.46820068359375 + }, + "float32_softmax": { + "max_cap_ratio_minus_one": 5.474207055211622e-06, + "total_error": 5.220840902708801e-06 + }, + "float64_normalizer": { + "max_cap_ratio_minus_one": 1.4889899824233055e-07, + "total_error": -1.4432899320127035e-15 + } + }, + { + "active_set_rounds": 1, + "records_at_cap": 468, + "float64_shift_still_needed": 2.439401605158764e-07, + "rounds_32_loop": { + "rounds": 1, + "ran_out": false, + "shift_min": 7.764958454004045e-08, + "shift_max": 7.764958454004045e-08, + "records_lifted_over_their_caps_per_round_max": 0, + "largest_abs_log_cap_among_lifted_records": 0.0 + }, + "float32_softmax": { + "max_cap_ratio_minus_one": -1.408752848375272e-06, + "total_error": -2.0149825884496764e-06 + }, + "float64_normalizer": { + "max_cap_ratio_minus_one": 4.863432616630803e-07, + "total_error": -7.771561172376096e-16 + } + }, + { + "active_set_rounds": 1, + "records_at_cap": 429, + "float64_shift_still_needed": -1.700978131680131e-07, + "rounds_32_loop": { + "rounds": 32, + "ran_out": true, + "shift_min": 7.764958454004045e-08, + "shift_max": 7.764958454004045e-08, + "records_lifted_over_their_caps_per_round_max": 1, + "largest_abs_log_cap_among_lifted_records": 1.46820068359375 + }, + "float32_softmax": { + "max_cap_ratio_minus_one": -1.074019236102508e-06, + "total_error": -1.2776027139560497e-06 + }, + "float64_normalizer": { + "max_cap_ratio_minus_one": 7.230517229395161e-08, + "total_error": 6.661338147750939e-16 + } + } + ] +} diff --git a/experiments/us-acs-local-l2-basis-20260928/sweep.py b/experiments/us-acs-local-l2-basis-20260928/sweep.py index c1a5251ce7..27c0155d3c 100644 --- a/experiments/us-acs-local-l2-basis-20260928/sweep.py +++ b/experiments/us-acs-local-l2-basis-20260928/sweep.py @@ -887,6 +887,9 @@ def on_progress(event: dict, _stamps=stamps, _done=done) -> None: "effective_sample_size": float(result.effective_sample_size), "chi_square_distance": float(result.chi_square_distance), "realized_max_weight_ratio": float(result.realized_max_weight_ratio), + "iterate_selection_receipt": jsonable( + dict(result.options.get("iterate_selection_receipt") or {}) + ), "peak_rss_gb": peak_rss_gb(), } batches.append(batch) diff --git a/packages/microcosm-build/src/microcosm/build/spec_engine/inventory_coverage.py b/packages/microcosm-build/src/microcosm/build/spec_engine/inventory_coverage.py index d8b26cb74b..77c4ef72cf 100644 --- a/packages/microcosm-build/src/microcosm/build/spec_engine/inventory_coverage.py +++ b/packages/microcosm-build/src/microcosm/build/spec_engine/inventory_coverage.py @@ -379,9 +379,11 @@ # gains the passive partnership/S-corp leaf, so the PUF QRF targets change. # microcosm#1259's recipient-share bound in puf_support moves both again. # microcosm#1261 moves both again: the PUF donor carries partnership and - # S-corp line-32 shares as separate leaves. - "seed_map": '07fe9086b57a15ab1c2551fce5c30aebfa55175d76a308f7dbb268ac49da747a', - "seed_protocol": '4ff992324004d4c09d4165bc8d24ccff15b9d84858836851b12d532c870920f4', + # S-corp line-32 shares as separate leaves. microcosm#1096 moves both + # again: solve's opt-in softmax mass parametrization projects exactly onto + # the capped simplex; the default projection parametrization is unchanged. + "seed_map": '7ef47df73404e40d20e12292c35bc81ce18620e4608f8a52ce2b8f95ec32f4d1', + "seed_protocol": '76c372eefaf7581557d530ec68123802b974860d9b2e581f45b30aa55cedfbf5', "source_manifest": "608c3621b1e4a108e57c8f83d16a184acdb3d05e16a81b895f7f72dbf5d0685c", "take_up": "9522ce40f7dea569312dd7a7beb474e5a5afadd2f1e10cb5876534c3ef623d35", "tail": "071d418bb49976f4682c320d2ad07097c177de3a645d2524f235e1ff33ac83f9", diff --git a/packages/microcosm-build/tests/engine_free/shared/test_spec_engine_loader.py b/packages/microcosm-build/tests/engine_free/shared/test_spec_engine_loader.py index 5837c69a8c..40a4e90a92 100644 --- a/packages/microcosm-build/tests/engine_free/shared/test_spec_engine_loader.py +++ b/packages/microcosm-build/tests/engine_free/shared/test_spec_engine_loader.py @@ -17,8 +17,9 @@ def test_semantic_hash_has_golden_vector_and_surface_separation(tmp_path) -> Non # chain step's caller-owned uniforms move it too. microcosm#1380's # self-employment feature in the imputation schema moves the schema-set # receipt, and its post-transfer step moves acs_transfer's attested bytes. + # microcosm#1096's exact softmax cap projection in solve moves it too. assert first.spec_sha256 == ( - 'e8ef234504dfaed9b5cc7d04355d1996ad0a2f965c06b34679ce216c4f899abe' + 'c8ed7c7226089829c36276ed7dec4704a4d4cde38b094189239cef34519c7302' ) second_root = _rich_minimal(tmp_path / "xy", note="second", store="local:b") diff --git a/packages/microcosm-build/tests/engine_free/us/test_us_acs_local_release_tool.py b/packages/microcosm-build/tests/engine_free/us/test_us_acs_local_release_tool.py index ace5cd97e9..656df0bc0b 100644 --- a/packages/microcosm-build/tests/engine_free/us/test_us_acs_local_release_tool.py +++ b/packages/microcosm-build/tests/engine_free/us/test_us_acs_local_release_tool.py @@ -3039,8 +3039,10 @@ def test_do_calibrate_stamps_and_records_the_penalty_settings( assert summary["l2_basis"] == "chi_square" assert summary["mass_parametrization"] == "softmax" assert summary["chi_square_distance"] >= 0.0 - exhausted = summary["softmax_cap_rounds_exhausted_epochs"] - assert isinstance(exhausted, int) and 0 <= exhausted <= 2 + in_loop = summary["softmax_in_loop_max_cap_ratio"] + # The float32 slack test_l2_basis.py allows on small solves. It holds for + # this tiny surface only: the release surface reached 1 + 9.0e-6. + assert 0.0 < in_loop <= 1.0 + 1e-5 assert summary["solver_settings"]["l2_basis"] == "chi_square" assert summary["solver_settings"]["mass_parametrization"] == "softmax" saved = np.load(args.checkpoint_dir / "weights_latest.npz") @@ -3226,7 +3228,7 @@ def test_package_manifest_reads_a_legacy_summary_as_the_historical_solve( assert manifest["calibration"]["l2_basis"] == "record" assert manifest["calibration"]["mass_parametrization"] == "projection" assert manifest["calibration"]["chi_square_distance"] is None - assert manifest["calibration"]["softmax_cap_rounds_exhausted_epochs"] is None + assert manifest["calibration"]["softmax_in_loop_max_cap_ratio"] is None def test_concentration_limitation_is_true_to_the_penalty_solved() -> None: diff --git a/packages/microcosm-build/tests/engine_workflow/us/test_us_multispine_pool_tool.py b/packages/microcosm-build/tests/engine_workflow/us/test_us_multispine_pool_tool.py index 80f7dfe560..1f6595ad9e 100644 --- a/packages/microcosm-build/tests/engine_workflow/us/test_us_multispine_pool_tool.py +++ b/packages/microcosm-build/tests/engine_workflow/us/test_us_multispine_pool_tool.py @@ -350,7 +350,8 @@ def capture_equality(expected: object, actual: object) -> None: # microcosm#1251's passive partnership/S-corp leaf in puf_support moves it. # microcosm#1259's recipient-share bound in puf_support moves it again. # microcosm#1261's entity split of line 32 moves it too. - "spec_sha256": '0c713aabfd0afc3703415fea86a8210a3f4346918f774f371fb16aa663348c1d', + # microcosm#1096's exact softmax cap projection in solve moves it. + "spec_sha256": '874294b24bf70671b3ec78bb1d1a2c3461fd585e2af1388265c27be26c18d0ba', }, } diff --git a/packages/microcosm-calibrate/src/microcosm/calibrate/solve.py b/packages/microcosm-calibrate/src/microcosm/calibrate/solve.py index 4fb658e2d9..677e4d91c9 100644 --- a/packages/microcosm-calibrate/src/microcosm/calibrate/solve.py +++ b/packages/microcosm-calibrate/src/microcosm/calibrate/solve.py @@ -171,24 +171,23 @@ #: For a smooth objective that reduced gradient vanishes at the constrained #: optimum, which the projection scheme's full gradient does not; with the #: capped-MAPE kink Adam still oscillates there on the scale of ``lr``, as it -#: does under ``mass="free"``. The ratio cap is a per-step clamp, alternated -#: with the softmax-invariant renormalization (``_SOFTMAX_CAP_ROUNDS``), plus -#: the closing float64 projection. +#: does under ``mass="free"``. After every step the log-weights are projected +#: exactly onto the capped simplex (:func:`_project_softmax_log_weights_`), +#: and the closing float64 projection is shared with ``"projection"``. MASS_PARAMETRIZATION_SOFTMAX = "softmax" MASS_PARAMETRIZATIONS = frozenset( {MASS_PARAMETRIZATION_PROJECTION, MASS_PARAMETRIZATION_SOFTMAX} ) -#: Upper bound on the per-step renormalize/clamp rounds under the softmax -#: parametrization. Each renormalization lifts the capped records back over -#: the cap by the capped records' share of the excess, so the excess shrinks -#: geometrically without reaching exactly zero; the rounds stop when no -#: record exceeds its bound in float32. A step that exhausts the rounds ends -#: on a clamp, so its next forward pass renormalizes slightly past the cap; -#: such epochs are counted in ``options["iterate_selection_receipt"]`` and the -#: closing float64 projection makes the returned vector exact regardless. -_SOFTMAX_CAP_ROUNDS = 32 +#: Active-set rounds the softmax cap projection takes before it finishes with +#: a sort. Each round is one pass over the records and only ever adds records +#: to the capped set, so the rounds alone are exact and finite, but an +#: adversarial input can need one per record. The sort bounds that worst case +#: at ``O(n log n)``. On the ACS local release (1.59M records, a 5x cap) every +#: step of two full-scale solves settled in one to three rounds +#: (experiments/us-acs-local-l2-basis-20260928/results/exact_cap.md). +_SOFTMAX_CAP_ACTIVE_SET_ROUNDS = 8 #: Threshold below which a weight counts as pruned (a "zero") when reporting the #: non-zero record count for the L0 path. Relative to the *initial* mean weight. @@ -1011,25 +1010,173 @@ def _l2_penalty( raise ValueError(f"l2_basis must be one of {sorted(L2_BASES)}, got {basis!r}.") -def _renormalize_softmax_log_weights_( +def _sorted_capped_log_shift( + cap_shift: torch.Tensor, + caps: torch.Tensor, + log_weights: torch.Tensor, + total: float, + capped: torch.Tensor, + lower: float, +) -> float: + """Finish :func:`_capped_log_shift` exactly with one sort, in log space. + + ``capped`` marks records known to be at their caps at the answer and + ``lower`` is a lower bound on it. Sort the other records that carry + weight by the shift at which each meets its cap. With the first ``k`` of + them capped too, the shift that meets ``total`` is + ``log(room_k) - log(free_k)``, ``room_k`` being the total less every + capped record's cap and ``free_k`` the weight of the records from the + ``k``-th on, summed in log space so that a weight below float64's range + still counts. The answer is that shift at the smallest ``k`` where it + does not pass the ``k``-th record's own cap shift: the ``k`` records + before it reach their caps at a smaller shift, so they are capped, and + every record from the ``k``-th on is not. A ``room_k`` that is not + positive means the caps of those ``k`` records already hold the total; + the lower-bound argument in :func:`_capped_log_shift` rules that out + beyond rounding, so the answer caps exactly those ``k`` and the rest keep + their rounding-sized share. If no ``k`` qualifies, the caps of every + record that carries weight hold less than the total, and the answer caps + them all. + """ + rest = ~capped & torch.isfinite(log_weights) + order = torch.argsort(cap_shift[rest]) + rest_shift = cap_shift[rest][order] + if not rest_shift.numel(): + return lower + rest_caps = caps[rest][order] + capped_before = torch.cat( + (rest_caps.new_zeros(1), torch.cumsum(rest_caps, dim=0)[:-1]) + ) + room = (total - float(caps[capped].sum())) - capped_before + log_free = torch.flip( + torch.logcumsumexp(torch.flip(log_weights[rest][order], (0,)), dim=0), (0,) + ) + candidate = torch.where( + room > 0.0, + torch.log(room.clamp_min(torch.finfo(torch.float64).tiny)) - log_free, + -math.inf, + ) + meets = candidate <= rest_shift + if not bool(meets.any()): + return max(lower, float(rest_shift[-1])) + k = int(meets.to(torch.uint8).argmax()) + # Exact arithmetic puts the answer past the (k-1)-th record's cap shift, + # since that record failed; the clamp keeps rounding from uncapping it. + previous = float(rest_shift[k - 1]) if k else lower + return max(lower, previous, float(candidate[k])) + + +def _capped_log_shift( + log_weights: torch.Tensor, + log_upper: torch.Tensor, + total: float, + *, + active_set_rounds: int = _SOFTMAX_CAP_ACTIVE_SET_ROUNDS, +) -> tuple[float, int]: + """The shift ``s`` with ``sum(min(exp(log_weights + s), exp(log_upper))) == total``. + + Float64 tensors; ``log_weights`` sums to ``total`` (``exp`` of it does), + so ``s >= 0``. The left side is continuous and nondecreasing in ``s``, so + the shift exists and the projection it defines is unique whenever the caps + of the records that carry weight sum to at least ``total``. + + Active-set rounds: with ``C`` the records whose cap shift + ``log_upper - log_weights`` is at most the current shift, the next shift + solves ``sum(caps[C]) + exp(s) * sum(mass[~C]) == total``. That left side + bounds the true one from above for every ``C`` (a record outside ``C`` + contributes ``exp(...)`` where the true sum has the smaller ``min``), so + every round's shift is a lower bound on the answer and ``C`` only grows. + When a round adds no record, the bound is attained and the shift is exact. + + The same bound gives ``total - sum(caps[C]) >= exp(s) * sum(mass[~C])`` + at every round, so a room that is not positive is float64 rounding: the + capped records' caps hold the total, and the records outside ``C`` hold a + share below that rounding. The current shift is then the answer, and + they keep that share. When the uncapped mass sums below float64's normal + range, every uncapped record's weight has underflowed or lost its + precision, and the rounds hand over to + :func:`_sorted_capped_log_shift`, which sums in log space. So do + ``active_set_rounds`` rounds that end without the bound attained; the sort + finishes from the current capped set and bound. Records with a zero cap + are capped at every shift; records with zero weight (a log-weight of + ``-inf``) never reach a positive cap and contribute nothing. A record + whose finite log-weight underflows float64 still carries weight. + + Returns the shift and the rounds taken (``active_set_rounds + 1`` when + the sort finished it). + """ + cap_shift = torch.where( + torch.isneginf(log_upper), + torch.full_like(log_upper, -math.inf), + log_upper - log_weights, + ) + caps = torch.exp(log_upper) + mass = torch.exp(log_weights) + zero = torch.zeros((), dtype=torch.float64) + shift = 0.0 + capped = cap_shift <= shift + for rounds in range(1, active_set_rounds + 1): + room = total - float(torch.where(capped, caps, zero).sum()) + if room <= 0.0: + return shift, rounds + free = float(torch.where(capped, zero, mass).sum()) + if free < torch.finfo(torch.float64).tiny: + break + shift = max(shift, math.log(room) - math.log(free)) + grown = capped | (cap_shift <= shift) + if int(grown.sum()) == int(capped.sum()): + return shift, rounds + capped = grown + return ( + _sorted_capped_log_shift(cap_shift, caps, log_weights, total, capped, shift), + active_set_rounds + 1, + ) + + +def _project_softmax_log_weights_( log_w: torch.Tensor, total: float, log_upper: torch.Tensor | None -) -> bool: - """Shift ``log_w`` so ``exp(log_w)`` sums to ``total``; clamp to the cap. - - In place, under ``no_grad``. The shift leaves ``total * softmax(log_w)`` - unchanged (softmax is shift-invariant); it only keeps ``exp(log_w)`` equal - to the realized weights, so the cap comparison is in weight units. Each - clamp lowers the normalizer, so the next shift lifts every record, - including the capped ones, by a shrinking amount. Returns ``True`` when - the rounds ran out with a record still over its bound after the last - shift (the vector then ends on a clamp; see ``_SOFTMAX_CAP_ROUNDS``). +) -> int: + """Project ``log_w`` exactly onto the capped simplex, in place. + + The softmax parametrization's per-step cap. The target is + ``{w : sum(w) == total, w <= exp(log_upper)}``, reached in log space: + every log-weight moves by one common shift ``s``, then each is clamped to + its log cap, ``log_w <- min(log_w + s, log_upper)``, with ``s`` from + :func:`_capped_log_shift` (computed in float64; exact in a finite number + of steps). This is the projection in Kullback-Leibler divergence: the + uncapped records keep their relative weights, which is the direction + ``total * softmax(log_w)`` cannot see, and only records whose share + would pass their caps are held at them. A vector already within its caps + after the shift moves by the shift alone. + + Afterwards ``exp(log_w)`` sums to ``total`` up to float32 rounding of + ``log_w`` and never passes ``exp(log_upper)``, so the realized weights + ``total * softmax(log_w)`` sit within the caps up to the float32 error of + the softmax's own normalizer. ``log_upper`` is the float64 copy of the + float32 log caps, so the clamp is exact in float32. Without a cap the + projection is the shift alone. Returns the active-set rounds taken (0 + without a cap). + """ + log_weights = log_w.detach().to(torch.float64) + log_weights = log_weights + ( + math.log(total) - float(torch.logsumexp(log_weights, dim=0)) + ) + if log_upper is None: + log_w.copy_(log_weights) + return 0 + shift, rounds = _capped_log_shift(log_weights, log_upper, total) + log_w.copy_(torch.minimum(log_weights + shift, log_upper)) + return rounds + + +def _max_cap_ratio(weights: torch.Tensor, cap: torch.Tensor) -> float: + """The largest ``weights / cap``, in float64 against the float64 cap. + + A record with a zero cap reads ``0`` while its weight is zero (``0 / 0``) + and ``inf`` once it is not. """ - for _ in range(_SOFTMAX_CAP_ROUNDS): - log_w.add_(math.log(total) - float(torch.logsumexp(log_w, dim=0).item())) - if log_upper is None or not bool((log_w > log_upper).any().item()): - return False - log_w.clamp_(max=log_upper) - return True + ratio = weights.detach().to(torch.float64) / cap + return float(torch.nan_to_num(ratio, nan=0.0, posinf=math.inf).max()) def _optimize( @@ -1094,7 +1241,6 @@ def _optimize( prune_atol = _PRUNE_REL_ATOL * float(np.mean(w0)) log_w = torch.tensor(np.log(start), dtype=torch.float32, requires_grad=True) softmax_mass = mass_parametrization == MASS_PARAMETRIZATION_SOFTMAX - softmax_cap_exhausted_epochs = 0 if softmax_mass and ( not conserve_mass or l0_lambda > 0.0 or target_records is not None ): @@ -1127,6 +1273,15 @@ def _optimize( if max_weight_ratio is not None else None ) + # Under softmax: the float32 log caps the per-step projection clamps to + # (float64 copies, so its arithmetic stays float64), and the float64 cap + # the in-loop receipt measures every realized weight against. + softmax_log_upper: torch.Tensor | None = None + softmax_cap: torch.Tensor | None = None + softmax_max_cap_ratio = 0.0 + if softmax_mass and upper is not None: + softmax_log_upper = torch.log(upper).to(torch.float64) + softmax_cap = torch.tensor(max_weight_ratio * w0, dtype=torch.float64) if l2_lambda > 0.0: # The L2 penalty's reference vector. "initial" divides by each record's # own starting weight; "uniform" divides by the shared mean weight; an @@ -1173,6 +1328,10 @@ def _optimize( optimizer.zero_grad() if softmax_mass: weights = total0 * torch.softmax(log_w, dim=0) + if softmax_cap is not None: + softmax_max_cap_ratio = max( + softmax_max_cap_ratio, _max_cap_ratio(weights, softmax_cap) + ) else: weights = torch.exp(log_w) if gates is not None: @@ -1226,11 +1385,9 @@ def _optimize( if softmax_mass: # The softmax weights sum to the input total whatever log_w is, so # there is no mass shift for Adam's normalized step to fight; only - # the ratio cap needs a per-step clamp. + # the ratio cap needs a per-step projection, exact in one call. with torch.no_grad(): - softmax_cap_exhausted_epochs += _renormalize_softmax_log_weights_( - log_w, total0, None if upper is None else torch.log(upper) - ) + _project_softmax_log_weights_(log_w, total0, softmax_log_upper) continue # Hard projections, applied to the realized weights every step so the @@ -1255,14 +1412,20 @@ def _optimize( # hard so it can never be violated mid-run. log_w.clamp_(max=torch.log(upper)) - if softmax_mass and selection_receipt is not None: - selection_receipt["softmax_cap_rounds_exhausted_epochs"] = ( - softmax_cap_exhausted_epochs - ) gate_open_probabilities: np.ndarray | None = None with torch.no_grad(): if softmax_mass: weights = total0 * torch.softmax(log_w, dim=0) + if softmax_cap is not None: + softmax_max_cap_ratio = max( + softmax_max_cap_ratio, _max_cap_ratio(weights, softmax_cap) + ) + if selection_receipt is not None: + # Every realized vector the solve evaluated, and the closing + # one, over its float64 cap, before the closing projection. + selection_receipt["softmax_in_loop_max_cap_ratio"] = ( + softmax_max_cap_ratio if softmax_cap is not None else None + ) else: weights = torch.exp(log_w) if best_log_w is not None: @@ -2198,12 +2361,16 @@ def calibrate( construction and Adam sees the gradient with its mass-constraint component removed. It requires ``mass="conserve"`` and no L0 gates. Recorded in ``options["mass_parametrization"]`` whatever - the mass mode, like ``l2_basis`` whatever ``l2_lambda``; a softmax - solve also counts, in ``options["iterate_selection_receipt"]``, - the epochs whose cap rounds ran out. On the ACS local release - (1.6M records, a 5x cap) that is most epochs, which then optimize - past the cap until the closing projection; prefer - ``"projection"`` at that scale (docs/calibration-l2-basis.md). + the mass mode, like ``l2_basis`` whatever ``l2_lambda``. Every + softmax step ends on an exact projection onto the capped simplex, + and a softmax solve records in + ``options["iterate_selection_receipt"] + ["softmax_in_loop_max_cap_ratio"]`` the largest ratio of a + realized weight it evaluated to its ``max_weight_ratio`` cap, + before the closing projection (``None`` without a cap). It is 1 + up to the float32 error of the softmax's normalizer. + docs/calibration-l2-basis.md compares the two parametrizations + at the ACS local release's scale. max_weight_ratio: If given, a hard per-record cap: no calibrated weight exceeds ``max_weight_ratio * initial_weight``. The landmine guard. target_records: If given, enable L0 pruning with **budget control**: the diff --git a/packages/microcosm-calibrate/tests/engine_free/shared/test_l2_basis.py b/packages/microcosm-calibrate/tests/engine_free/shared/test_l2_basis.py index 0a2af329fc..a56f368a92 100644 --- a/packages/microcosm-calibrate/tests/engine_free/shared/test_l2_basis.py +++ b/packages/microcosm-calibrate/tests/engine_free/shared/test_l2_basis.py @@ -14,7 +14,11 @@ 4. With no targets and ``mass="conserve"``, the chi-square solve returns the design weights from any warm start, under both mass parametrizations. 5. The softmax parametrization conserves the input total, respects the ratio - cap, and keeps every weight positive. + cap, and keeps every weight positive. Every vector it evaluates in the + loop, before the closing projection, also sits within the cap up to + float32 rounding (the receipt's ``softmax_in_loop_max_cap_ratio``), even + when the cap binds on nearly every record. The per-step projection's own + contracts are in ``test_softmax_cap_projection.py``. 6. Each solve under softmax-conserve and free mass lands within a bound of the exact optimum of the same convex program (differential against CLARABEL on these tests' own problems, fixture @@ -72,6 +76,13 @@ # few, derandomized examples; the pure penalty algebra runs many. _SOLVER_SETTINGS = settings(max_examples=8, deadline=None, derandomize=True) _ALGEBRA_SETTINGS = settings(max_examples=200, deadline=None) +#: Float32 slack for a softmax solve's in-loop weights over their caps: the +#: log-weights and the softmax's normalizer are float32, and on these sizes +#: their error stays below 1e-6 (measured 2e-7 to 6e-7). The bound holds for +#: these sizes only. On the ACS local release's 1,588,854 households the +#: largest in-loop ratio reached 1 + 9.0e-6, 90% of it +#: (experiments/us-acs-local-l2-basis-20260928/results/exact_cap.md). +_IN_LOOP_CAP_TOLERANCE = 1e-5 def _frame(columns: dict[str, np.ndarray], design: np.ndarray) -> Frame: @@ -320,10 +331,53 @@ def test_softmax_parametrization_conserves_mass_and_respects_the_cap( assert (result.weights <= cap * design * (1.0 + 1e-12)).all() assert result.options["mass_parametrization"] == MASS_PARAMETRIZATION_SOFTMAX assert result.options["iterate_selection"] == "closing_state" - exhausted = result.options["iterate_selection_receipt"][ - "softmax_cap_rounds_exhausted_epochs" + receipt = result.options["iterate_selection_receipt"] + assert set(receipt) == {"softmax_in_loop_max_cap_ratio"} + in_loop = receipt["softmax_in_loop_max_cap_ratio"] + if cap is None: + assert in_loop is None + else: + assert 0.0 < in_loop <= 1.0 + _IN_LOOP_CAP_TOLERANCE + + +@_SOLVER_SETTINGS +@given(st.integers(0, 2**32 - 1), st.sampled_from([1.0, 1.001, 1.05])) +def test_softmax_iterates_stay_at_a_cap_that_binds_almost_everywhere( + seed: int, cap: float +) -> None: + """Targets far above the design push every record into a tight cap. + + The per-step projection holds every in-loop vector at the cap, where it + binds on nearly every record (and on all of them at a cap of 1, whose + only feasible vector is the design itself). + """ + frame, targets, design = _problem(seed, n=60, k=4, factors=(1.5, 3.0)) + result = calibrate( + frame, + targets, + epochs=200, + learning_rate=0.05, + mass="conserve", + max_weight_ratio=cap, + mass_parametrization=MASS_PARAMETRIZATION_SOFTMAX, + seed=0, + ) + in_loop = result.options["iterate_selection_receipt"][ + "softmax_in_loop_max_cap_ratio" ] - assert isinstance(exhausted, int) and 0 <= exhausted <= 300 + assert 1.0 - _IN_LOOP_CAP_TOLERANCE <= in_loop <= 1.0 + _IN_LOOP_CAP_TOLERANCE + assert result.weights.sum() == pytest.approx(design.sum(), rel=1e-12) + assert (result.weights <= cap * design * (1.0 + 1e-12)).all() + if cap == 1.0: + np.testing.assert_allclose(result.weights, design, rtol=1e-12) + + +def test_projection_solves_record_no_softmax_receipt() -> None: + frame, targets, _ = _problem(0, n=40, k=3) + result = calibrate( + frame, targets, epochs=20, mass="conserve", max_weight_ratio=5.0, seed=0 + ) + assert result.options["iterate_selection_receipt"] == {} def test_retaining_the_best_objective_under_l2_never_returns_a_worse_epoch() -> None: diff --git a/packages/microcosm-calibrate/tests/engine_free/shared/test_softmax_cap_projection.py b/packages/microcosm-calibrate/tests/engine_free/shared/test_softmax_cap_projection.py new file mode 100644 index 0000000000..eb078c8687 --- /dev/null +++ b/packages/microcosm-calibrate/tests/engine_free/shared/test_softmax_cap_projection.py @@ -0,0 +1,558 @@ +"""Contracts for the softmax parametrization's per-step cap projection. + +``_project_softmax_log_weights_`` maps log-weights onto the capped simplex +``{w : sum(w) == total, w <= cap}`` in log space: one common shift ``s``, then +``min(log_w + s, log_cap)``. Invariants pinned here, each for every input the +strategies draw. One strategy draws zero weights and zero caps independently +and includes a cap of exactly 1, where the caps sum to the total. The other +lets the caps of a held set meet the total, or nearly, while the other records +sit 45 to 1,100 nats below their caps: below float64 rounding of the total, +and for the deeper ones below float64's range. + +1. It conserves the total and never passes a cap; a record with zero weight + or a zero cap stays at zero, and every other record keeps a finite + log-weight. +2. It is idempotent, and invariant to a constant added to the input (the + direction ``total * softmax(log_w)`` cannot see). +3. An input already within its caps after the shift moves by the shift alone. +4. It is the Kullback-Leibler projection: every record below its cap moved by + one common shift, and every record at its cap would pass it under that + shift (the KKT conditions of ``min KL(w || exp(log_w))`` on the set). +5. Differential: it equals a water-fill written from the definition in + 60-digit decimal arithmetic, and the active-set rounds and the sort that + bounds them agree (an input built to need one round per record exercises + the sort). Wherever the cap step the full-scale measurement ran settles in + its active-set rounds, with every nonzero weight in float64's normal range, + the installed step returns the same shift and round count, bit for bit. +6. In float32, as the solver runs it, the clamp is exact in log space and the + realized ``total * softmax(log_w)`` sits within the float64 caps up to the + float32 error of the softmax. +""" + +from __future__ import annotations + +import functools +import hashlib +import importlib.util +import math +from decimal import Decimal, localcontext + +import numpy as np +import pytest +import torch +from hypothesis import assume, given, settings +from hypothesis import strategies as st + +from microcosm.calibrate import solve as solve_module +from test_support.paths import paths_for + +_TEST_PATHS = paths_for("microcosm-calibrate") + +_SETTINGS = settings(max_examples=300, deadline=None) + +#: Float64 agreement in log space, for differences that involve no sum: the +#: shift added to every uncapped record, the clamp. +_EXACT = 1e-10 +#: Float64 agreement in weight units, as a share of the total. Each path +#: computes the shift from a sum of up to 40 weights, whose rounding is a few +#: ulps of the total. When the caps nearly meet the total, the uncapped +#: records hold only the small remainder, so that absolute error is large in +#: log space for them; in weight units it stays a few ulps of the total. +_EXACT_MASS = 1e-12 +#: Float32 tolerance for the realized weights over their float64 caps. Two +#: float32 errors reach them: rounding ``log_w`` (half an ulp of a log-weight +#: below 16 is 4.8e-7 relative) and the softmax's float32 normalizer. On these +#: sizes they stay below 1e-6; 1e-5 leaves an order of magnitude. +_FLOAT32 = 1e-5 +#: Significant digits of the reference water-fill. +_REFERENCE_DIGITS = 60 + + +@st.composite +def capped_problems(draw, *, max_size: int = 40): + """Log-weights, log caps and a total the caps can hold. + + The caps are ``ratio * design`` and the total a ``fill`` share of the caps + of the records that carry weight, so with every record carrying weight + ``fill = 1 / ratio`` is calibrate's own total ``sum(design)``, and + ``fill = 1`` leaves no slack. Zero caps (zero design weights) and zero + weights are drawn independently: a record can carry weight under a zero + cap, or have a cap and no weight. + """ + n = draw(st.integers(1, max_size)) + design = np.array( + draw( + st.lists( + st.one_of(st.just(0.0), st.floats(1e-3, 1e4)), + min_size=n, + max_size=n, + ) + ) + ) + weightless = np.array( + draw( + st.lists( + st.sampled_from((False, False, False, True)), min_size=n, max_size=n + ) + ) + ) + if not (design[~weightless] > 0.0).any(): + design[0], weightless[0] = 1.0, False + moves = np.array(draw(st.lists(st.floats(-8.0, 8.0), min_size=n, max_size=n))) + ratio = draw(st.one_of(st.just(1.0), st.floats(1.0, 10.0))) + fill = draw(st.one_of(st.just(1.0), st.just(1.0 / ratio), st.floats(0.05, 1.0))) + with np.errstate(divide="ignore"): + log_caps = np.log(ratio * design) + log_weights = np.where( + weightless, -np.inf, np.log(np.where(design > 0.0, design, 1.0)) + moves + ) + total = fill * float(np.exp(log_caps[~weightless]).sum()) + return log_weights, log_caps, total + + +@st.composite +def sunk_problems(draw, *, max_size: int = 40): + """The caps of a held set meet the total, or nearly; the rest sit far below. + + The held records start within 3 nats of their caps, above or below. The + sunk records start 45 to 1,100 nats below theirs: their weight is tiny + next to the total, for most draws below float64 rounding of it, and for + the deeper ones below float64's range. The total is the held caps plus a + ``room`` share of the sunk caps. At a share of 0 the held caps alone meet + the total: the sunk records keep their tiny share, and the cap of + whichever held record the rounds reach last fills what the others leave + to within rounding. Above 0 the sunk records must fill a real room, which + a weight below float64's range can only do in log space. + """ + held = draw(st.integers(1, max_size // 2)) + sunk = draw(st.integers(1, max_size // 2)) + design = np.array( + draw(st.lists(st.floats(1e-3, 1e4), min_size=held + sunk, max_size=held + sunk)) + ) + ratio = draw(st.one_of(st.just(1.0), st.floats(1.0, 10.0))) + log_caps = np.log(ratio * design) + above = draw(st.lists(st.floats(-3.0, 3.0), min_size=held, max_size=held)) + below = draw(st.lists(st.floats(45.0, 1100.0), min_size=sunk, max_size=sunk)) + log_weights = log_caps + np.concatenate((above, -np.asarray(below))) + room = draw(st.one_of(st.just(0.0), st.floats(0.01, 1.0))) + caps = np.exp(log_caps) + total = float(caps[:held].sum()) + room * float(caps[held:].sum()) + order = np.array(draw(st.permutations(range(held + sunk)))) + return log_weights[order], log_caps[order], total + + +#: Every drawn input; most properties hold on both families. +problems = st.one_of(capped_problems(), sunk_problems()) + + +def _project(log_weights: np.ndarray, log_caps: np.ndarray, total: float): + """The solver's projection on a float64 copy.""" + log_w = torch.tensor(log_weights, dtype=torch.float64) + solve_module._project_softmax_log_weights_( + log_w, total, torch.tensor(log_caps, dtype=torch.float64) + ) + return log_w.numpy() + + +def _normalized(log_weights: np.ndarray, log_caps: np.ndarray, total: float): + """The float64 inputs the solver hands :func:`_capped_log_shift`.""" + log_w = torch.tensor(log_weights, dtype=torch.float64) + log_w = log_w + (math.log(total) - float(torch.logsumexp(log_w, dim=0))) + return log_w, torch.tensor(log_caps, dtype=torch.float64) + + +def _reference_water_fill( + log_weights: np.ndarray, log_caps: np.ndarray, total: float +) -> np.ndarray: + """Water-fill from the definition, in 60-digit decimal arithmetic. + + Independent of the solver and of float64 rounding. Each weight + ``v = exp(log_weights)`` and cap ``u = exp(log_caps)`` is computed to 60 + digits, so a weight below float64's range still counts. Scale the weights + by ``c``, holding at its cap each record ``c * v`` would pass. Records + reach their caps in order of ``u / v``; try each count ``k`` of capped + records in that order and keep the one whose ``c`` caps exactly those + ``k``. + """ + with localcontext() as context: + context.prec = _REFERENCE_DIGITS + weights = [ + Decimal(float(x)).exp() if math.isfinite(x) else Decimal(0) + for x in log_weights + ] + caps = [ + Decimal(float(x)).exp() if math.isfinite(x) else Decimal(0) + for x in log_caps + ] + target = Decimal(float(total)) + breakpoints = [ + cap / weight if weight else Decimal("Infinity") + for weight, cap in zip(weights, caps, strict=True) + ] + order = sorted(range(len(weights)), key=breakpoints.__getitem__) + for k in range(len(order) + 1): + held, scaled = order[:k], order[k:] + room = target - sum((caps[i] for i in held), Decimal(0)) + free = sum((weights[i] for i in scaled), Decimal(0)) + if not free: + # Nothing left to scale: only a total the held caps already meet. + assert abs(room) <= Decimal("1e-9") * target + scale = Decimal(0) + break + if room <= 0: + continue + scale = room / free + below = k == 0 or scale >= breakpoints[order[k - 1]] + above = k == len(order) or scale <= breakpoints[order[k]] + if below and above: + break + else: # pragma: no cover - a feasible total always has a count + raise AssertionError("no consistent capped count") + capped = set(held) + result = [ + caps[i] if i in capped else scale * weights[i] for i in range(len(weights)) + ] + return np.array([float(w.ln()) if w > 0 else -np.inf for w in result]) + + +def _assert_same_weights( + actual: np.ndarray, expected: np.ndarray, total: float +) -> None: + """Log-weight vectors with the same support and weights equal to rounding.""" + assert np.array_equal(np.isneginf(actual), np.isneginf(expected)) + np.testing.assert_allclose( + np.exp(actual), np.exp(expected), rtol=1e-9, atol=_EXACT_MASS * total + ) + + +# --- 1: total, caps, support -------------------------------------------------- + + +@_SETTINGS +@given(problems) +def test_projection_conserves_the_total_and_respects_the_caps(problem) -> None: + log_weights, log_caps, total = problem + projected = _project(log_weights, log_caps, total) + assert (projected <= log_caps).all() + assert float(np.exp(projected).sum()) == pytest.approx(total, rel=1e-12) + zero = np.isneginf(log_weights) | np.isneginf(log_caps) + assert np.isneginf(projected[zero]).all() + assert np.isfinite(projected[~zero]).all() + + +# --- 2: idempotence and shift invariance ------------------------------------- + + +@_SETTINGS +@given(problems, st.floats(-30.0, 30.0)) +def test_projection_is_idempotent_and_shift_invariant(problem, offset) -> None: + log_weights, log_caps, total = problem + projected = _project(log_weights, log_caps, total) + _assert_same_weights(_project(projected, log_caps, total), projected, total) + _assert_same_weights( + _project(log_weights + offset, log_caps, total), projected, total + ) + + +# --- 3: a feasible input moves by the shift alone ---------------------------- + + +@_SETTINGS +@given( + st.lists(st.floats(1e-3, 1e4), min_size=1, max_size=40), + st.data(), + st.floats(0.05, 1.0), + st.floats(-30.0, 30.0), +) +def test_a_feasible_input_moves_by_the_shift_alone(caps, data, fill, offset) -> None: + """Below its caps once scaled to the total: the projection is the shift. + + Each record sits ``slack`` below its cap, and the total is at most the + records' own sum, so scaling to it only lowers them. + """ + log_caps = np.log(np.asarray(caps, dtype=np.float64)) + slack = np.array( + data.draw(st.lists(st.floats(0.0, 8.0), min_size=len(caps), max_size=len(caps))) + ) + log_weights = log_caps - slack + total = fill * float(np.exp(log_weights).sum()) + shifted = log_weights + offset + expected_shift = math.log(total) - float(np.logaddexp.reduce(shifted)) + _assert_same_weights( + _project(shifted, log_caps, total), shifted + expected_shift, total + ) + + +# --- 4: the KKT conditions of the KL projection ------------------------------ + + +@_SETTINGS +@given(problems) +def test_projection_is_the_kullback_leibler_projection(problem) -> None: + log_weights, log_caps, total = problem + projected = _project(log_weights, log_caps, total) + live = np.isfinite(projected) + at_cap = live & (projected >= log_caps - _EXACT) + below = live & ~at_cap + if below.any(): + moved = projected[below] - log_weights[below] + shift = float(np.median(moved)) + np.testing.assert_allclose(moved, shift, rtol=0.0, atol=_EXACT) + # Every capped record would pass its cap under the common shift. + assert (log_weights[at_cap] + shift >= log_caps[at_cap] - _EXACT).all() + else: + # Every live record is held: the caps alone meet the total. + assert float(np.exp(log_caps[live]).sum()) == pytest.approx(total, rel=1e-12) + + +def test_a_cap_of_one_holds_every_record_at_its_design_weight() -> None: + design = np.array([3.0, 0.0, 1.5, 40.0, 2.25]) + with np.errstate(divide="ignore"): + log_caps = np.log(design) + log_weights = log_caps + np.array([2.0, 0.0, -3.0, 0.5, -0.25]) + projected = _project(log_weights, log_caps, float(design.sum())) + assert np.array_equal(projected, log_caps) + + +# --- caps that hold the total, and weights below float64's range ------------- + + +@pytest.mark.parametrize("total", [1.0, 1000.0]) +@pytest.mark.parametrize("below", [46.0, 1000.0]) +def test_caps_that_hold_the_total_leave_the_other_records_their_share( + total: float, below: float +) -> None: + """A cap of 1.05 where one record's cap holds the total, to float64 rounding. + + The first record sits at its cap, which is the total. In float64 that cap + meets a total of 1 exactly and falls two ulps short of 1,000. The other + record sits ``below`` nats under the total: below float64 rounding of it + at 46 nats, below float64's range at 1,000. Its cap is 5% of the total. + The first cap leaves it at most that rounding, so its weight stays below + 1e-12 of the total. The cap step the full-scale measurement ran lifted it + to its cap instead, 5% over the total, in three of these four cases + (microcosm#1096 review, S2). + """ + log_caps = np.log([total, 0.05 * total]) + log_weights = np.array([math.log(total), math.log(total) - below]) + projected = _project(log_weights, log_caps, total) + assert float(np.exp(projected).sum()) == pytest.approx(total, rel=1e-12) + assert projected[0] == log_caps[0] + assert math.exp(projected[1]) <= _EXACT_MASS * total + + +@pytest.mark.parametrize("below", [742.0, 1000.0]) +def test_a_weight_below_float64s_range_still_fills_the_room(below: float) -> None: + """A finite log-weight carries weight even where float64 underflows it. + + The first record's cap holds 60% of the total. The second record sits + ``below`` nats lower, and its cap is 50% of the total; it alone can fill + the other 40%. At 742 nats its weight is subnormal in float64, with about + four significant digits; at 1,000 it is zero. The answer lifts it to + exactly 40%, which only log space can compute. A float64 sum of the + subnormal weight misses 40% by about 1e-5 of it; counting a zero weight + as none leaves the room empty, and lifting the record to its cap puts the + total 10% over (microcosm#1096 review, S2). + """ + total = 1000.0 + log_caps = np.log([0.6 * total, 0.5 * total]) + log_weights = np.array([math.log(total), math.log(total) - below]) + projected = _project(log_weights, log_caps, total) + np.testing.assert_allclose( + np.exp(projected), [0.6 * total, 0.4 * total], rtol=1e-12 + ) + + +# --- 5: differential against the reference water-fill ------------------------ + + +@_SETTINGS +@given(problems) +def test_projection_matches_the_reference_water_fill(problem) -> None: + log_weights, log_caps, total = problem + _assert_same_weights( + _project(log_weights, log_caps, total), + _reference_water_fill(log_weights, log_caps, total), + total, + ) + + +@_SETTINGS +@given(problems, st.sampled_from([0, 1, 2])) +def test_active_set_rounds_and_the_sort_agree(problem, rounds) -> None: + log_weights, log_caps, total = problem + log_w, log_upper = _normalized(log_weights, log_caps, total) + shift, taken = solve_module._capped_log_shift(log_w, log_upper, total) + assert 1 <= taken <= solve_module._SOFTMAX_CAP_ACTIVE_SET_ROUNDS + 1 + bounded, bounded_taken = solve_module._capped_log_shift( + log_w, log_upper, total, active_set_rounds=rounds + ) + assert 1 <= bounded_taken <= rounds + 1 + _assert_same_weights( + torch.minimum(log_w + bounded, log_upper).numpy(), + torch.minimum(log_w + shift, log_upper).numpy(), + total, + ) + + +def _active_set_chain(n: int, length: int): + """Unit masses with caps that the active-set rounds take one at a time. + + Round ``r`` caps the records whose cap shift is at most the previous + round's shift and solves for the next. Each record of the chain gets a + cap just inside the next round's root, so every round adds exactly one; + the margins shrink by more than the roots move, which keeps them in + order. The records after the chain never bind. + """ + caps = np.full(n, 10.0 * n) + caps[0] = 0.5 + previous, root = 1.0, (n - 0.5) / (n - 1) + margin = 0.5 * (root - previous) + for r in range(1, length): + caps[r] = root - margin + previous, root = root, root + margin / (n - r - 1) + margin = 0.5 * margin / (n - r - 1) + return np.zeros(n), np.log(caps), float(n) + + +def test_the_sort_caps_a_record_that_fails_with_no_room_left() -> None: + """The sort's rounding branch: no room is left after a record that failed. + + The first record's cap is the whole total and its weight sits below it; + the second record's weight is 800 nats lower. In exact arithmetic the + first record fails its own test only if room remains after its cap. For + some of these ``u``, float64 rounds ``log(exp(u))`` above ``u``, so the + first record fails with no room left, and the sort must cap it at its own + cap shift. Its lower bound, 0, would leave it at its weight, 22% to 39% + short of the total. + """ + nothing_capped = torch.zeros(2, dtype=torch.bool) + for u in np.linspace(0.25, 0.5, 64): + log_w = torch.tensor([0.0, -800.0], dtype=torch.float64) + log_upper = torch.tensor([u, math.log(10.0)], dtype=torch.float64) + caps = torch.exp(log_upper) + total = float(caps[0]) + shift = solve_module._sorted_capped_log_shift( + log_upper - log_w, caps, log_w, total, nothing_capped, 0.0 + ) + projected = torch.minimum(log_w + shift, log_upper) + assert float(projected.exp().sum()) == pytest.approx(total, rel=1e-15) + + +def test_a_long_active_set_chain_is_finished_by_the_sort() -> None: + log_weights, log_caps, total = _active_set_chain(14, 12) + log_w = torch.tensor(log_weights, dtype=torch.float64) + log_upper = torch.tensor(log_caps, dtype=torch.float64) + unbounded, unbounded_taken = solve_module._capped_log_shift( + log_w, log_upper, total, active_set_rounds=10_000 + ) + assert unbounded_taken == 12 + shift, taken = solve_module._capped_log_shift(log_w, log_upper, total) + assert taken == solve_module._SOFTMAX_CAP_ACTIVE_SET_ROUNDS + 1 + assert shift == pytest.approx(unbounded, abs=_EXACT) + _assert_same_weights( + _project(log_weights, log_caps, total), + _reference_water_fill(log_weights, log_caps, total), + total, + ) + assert int(np.sum(_project(log_weights, log_caps, total) >= log_caps)) == 12 + + +# The cap step the full-scale measurement ran, as a verbatim oracle. +_MEASURED_CAP_STEP = "cap_step_4bddde649.py" +_MEASURED_CAP_STEP_SHA256 = ( + "da94ee960c55e0c75bbac939ffd65bbcf8bdd57057cfb84e0c961279845f5c5f" +) +#: Calls the measured step made to its every-record cap: the exit microcosm#1096's +#: review (S2) replaced. Cleared before each comparison. +_MEASURED_CAPPED_EVERY_RECORD: list[float] = [] + + +@functools.cache +def _measured_cap_step(): + path = _TEST_PATHS.tests / "fixtures/pre_cap_step_fix" / _MEASURED_CAP_STEP + assert hashlib.sha256(path.read_bytes()).hexdigest() == _MEASURED_CAP_STEP_SHA256 + spec = importlib.util.spec_from_file_location("measured_cap_step", path) + oracle = importlib.util.module_from_spec(spec) + spec.loader.exec_module(oracle) + cap_every_record = oracle._cap_every_record_shift + + def recorded(cap_shift: torch.Tensor) -> float: + shift = cap_every_record(cap_shift) + _MEASURED_CAPPED_EVERY_RECORD.append(shift) + return shift + + oracle._cap_every_record_shift = recorded + return oracle + + +@_SETTINGS +@given(capped_problems()) +def test_the_measured_cap_step_is_unchanged_where_its_rounds_settle(problem) -> None: + """Pins the arithmetic the full-scale measurement ran (review S1). + + ``experiments/us-acs-local-l2-basis-20260928/results/exact_cap.md`` + measured the cap step at 4bddde649. Each of its 1,600 steps settled in + one to three active-set rounds, and none capped every record: after the + caps first bound, no forward pass's largest weight-to-cap ratio fell more + than 1% below the previous pass's, and that exit would have renormalized + a 5x-capped vector to about a fifth of its caps. Wherever the measured + step settles the same way, the installed one returns its shift and round + count bit for bit. These inputs keep every nonzero weight in float64's + normal range; below it the installed step hands its rounds over to the + log-space sort, which the measured step lacked + (``test_a_weight_below_float64s_range_still_fills_the_room``). + """ + measured = _measured_cap_step() + log_w, log_upper = _normalized(*problem) + total = problem[2] + _MEASURED_CAPPED_EVERY_RECORD.clear() + expected = measured._capped_log_shift(log_w, log_upper, total) + assume( + not _MEASURED_CAPPED_EVERY_RECORD + and expected[1] <= measured._SOFTMAX_CAP_ACTIVE_SET_ROUNDS + ) + assert solve_module._capped_log_shift(log_w, log_upper, total) == expected + + +# --- 6: float32, as the solver runs it ---------------------------------------- + + +@_SETTINGS +@given(problems) +def test_float32_projection_holds_the_realized_weights_at_their_caps(problem) -> None: + log_weights, log_caps, total = problem + caps = np.exp(log_caps) + upper = torch.tensor(caps, dtype=torch.float32) + log_upper32 = torch.log(upper) + log_w = torch.tensor(log_weights, dtype=torch.float32) + solve_module._project_softmax_log_weights_( + log_w, total, log_upper32.to(torch.float64) + ) + assert (log_w <= log_upper32).all() + realized = total * torch.softmax(log_w, dim=0) + ratio = solve_module._max_cap_ratio(realized, torch.tensor(caps)) + assert ratio <= 1.0 + _FLOAT32 + assert float(realized.double().sum()) == pytest.approx(total, rel=_FLOAT32) + + +def test_max_cap_ratio_reads_zero_caps() -> None: + caps = torch.tensor([2.0, 0.0, 4.0], dtype=torch.float64) + assert solve_module._max_cap_ratio( + torch.tensor([1.0, 0.0, 4.0]), caps + ) == pytest.approx(1.0) + assert solve_module._max_cap_ratio(torch.tensor([1.0, 1e-30, 1.0]), caps) == ( + math.inf + ) + + +def test_without_a_cap_the_projection_is_the_shift() -> None: + log_weights = np.array([0.0, -2.0, 3.5, 1.0]) + log_w = torch.tensor(log_weights, dtype=torch.float64) + assert solve_module._project_softmax_log_weights_(log_w, 50.0, None) == 0 + np.testing.assert_allclose( + log_w.numpy() - log_weights, + math.log(50.0) - float(np.logaddexp.reduce(log_weights)), + rtol=0.0, + atol=_EXACT, + ) diff --git a/packages/microcosm-calibrate/tests/fixtures/pre_cap_step_fix/cap_step_4bddde649.py b/packages/microcosm-calibrate/tests/fixtures/pre_cap_step_fix/cap_step_4bddde649.py new file mode 100644 index 0000000000..fdec05172b --- /dev/null +++ b/packages/microcosm-calibrate/tests/fixtures/pre_cap_step_fix/cap_step_4bddde649.py @@ -0,0 +1,125 @@ +"""Immutable softmax cap step the full-scale measurement ran, from commit 4bddde649. + +The three functions below are copied verbatim from +``packages/microcosm-calibrate/src/microcosm/calibrate/solve.py`` at +4bddde6499c5a9001700652999b00bd376aeace8, the head every receipt in +``experiments/us-acs-local-l2-basis-20260928/results/exact_cap/`` records +(solve.py's sha256 there is cf6d74a641a5...). Tests verify this module's SHA +before use and compare the installed cap step with it on the inputs where it +settles in its active-set rounds. This module is only an oracle; it never +replaces a solver function. +""" + +from __future__ import annotations + +import math + +import torch + +_SOFTMAX_CAP_ACTIVE_SET_ROUNDS = 8 + + +def _cap_every_record_shift(cap_shift: torch.Tensor) -> float: + """The smallest shift that puts every record able to reach its cap at it.""" + reachable = cap_shift[torch.isfinite(cap_shift)] + return float(reachable.max()) if reachable.numel() else 0.0 + + +def _sorted_capped_log_shift( + cap_shift: torch.Tensor, + caps: torch.Tensor, + mass: torch.Tensor, + total: float, + capped: torch.Tensor, + lower: float, +) -> float: + """Finish :func:`_capped_log_shift` exactly with one sort. + + ``capped`` marks records known to be at their caps at the answer and + ``lower`` is a lower bound on it. Sort the remaining records by the shift + at which each meets its cap. With the first ``k`` of them capped too, the + shift that meets ``total`` is ``log(room_k / free_k)``, ``room_k`` being + the total less every capped record's cap and ``free_k`` the mass of the + records after the ``k``-th. The answer is that shift at the smallest + ``k`` where it does not pass the ``k``-th record's own cap shift: the + ``k`` records before it reach their caps at a smaller shift, so they are + capped, and every record from the ``k``-th on is not. + """ + rest = ~capped & (mass > 0.0) + order = torch.argsort(cap_shift[rest]) + rest_shift = cap_shift[rest][order] + rest_caps = caps[rest][order] + rest_mass = mass[rest][order] + capped_before = torch.cat( + (rest_caps.new_zeros(1), torch.cumsum(rest_caps, dim=0)[:-1]) + ) + room = (total - float(caps[capped].sum())) - capped_before + free = torch.flip(torch.cumsum(torch.flip(rest_mass, (0,)), dim=0), (0,)) + # Once the capped records' caps reach the total, no shift meets it short + # of capping every record (a cap of exactly 1, up to rounding). + candidate = torch.where( + room > 0.0, + torch.log(room.clamp_min(torch.finfo(torch.float64).tiny)) - torch.log(free), + math.inf, + ) + meets = candidate <= rest_shift + if not bool(meets.any()): + return max(lower, _cap_every_record_shift(cap_shift)) + return max(lower, float(candidate[int(meets.to(torch.uint8).argmax())])) + + +def _capped_log_shift( + log_weights: torch.Tensor, + log_upper: torch.Tensor, + total: float, + *, + active_set_rounds: int = _SOFTMAX_CAP_ACTIVE_SET_ROUNDS, +) -> tuple[float, int]: + """The shift ``s`` with ``sum(min(exp(log_weights + s), exp(log_upper))) == total``. + + Float64 tensors; ``log_weights`` sums to ``total`` (``exp`` of it does), + so ``s >= 0``. The left side is continuous and nondecreasing in ``s``, so + the shift exists and the projection it defines is unique whenever the caps + sum to at least ``total``. + + Active-set rounds: with ``C`` the records whose cap shift + ``log_upper - log_weights`` is at most the current shift, the next shift + solves ``sum(caps[C]) + exp(s) * sum(mass[~C]) == total``. That left side + bounds the true one from above for every ``C`` (a record outside ``C`` + contributes ``exp(...)`` where the true sum has the smaller ``min``), so + every round's shift is a lower bound on the answer and ``C`` only grows. + When a round adds no record, the bound is attained and the shift is exact. + If ``active_set_rounds`` pass without that, :func:`_sorted_capped_log_shift` + finishes from the current capped set and bound. Records with a zero cap + are capped at every shift; records with zero mass never reach a positive + cap and contribute nothing. + + Returns the shift and the rounds taken (``active_set_rounds + 1`` when + the sort finished it). + """ + cap_shift = torch.where( + torch.isneginf(log_upper), + torch.full_like(log_upper, -math.inf), + log_upper - log_weights, + ) + caps = torch.exp(log_upper) + mass = torch.exp(log_weights) + zero = torch.zeros((), dtype=torch.float64) + shift = 0.0 + capped = cap_shift <= shift + for rounds in range(1, active_set_rounds + 1): + room = total - float(torch.where(capped, caps, zero).sum()) + free = float(torch.where(capped, zero, mass).sum()) + if room <= 0.0 or free <= 0.0: + # The capped records alone hold the total (a cap of exactly 1, up + # to rounding): every record that can reach its cap sits at it. + return max(shift, _cap_every_record_shift(cap_shift)), rounds + shift = max(shift, math.log(room) - math.log(free)) + grown = capped | (cap_shift <= shift) + if int(grown.sum()) == int(capped.sum()): + return shift, rounds + capped = grown + return ( + _sorted_capped_log_shift(cap_shift, caps, mass, total, capped, shift), + active_set_rounds + 1, + ) diff --git a/packages/microcosm-graph/tests/fixtures/parity/kernels/calibrate/pins.json b/packages/microcosm-graph/tests/fixtures/parity/kernels/calibrate/pins.json index 0e5581dd6e..17be198590 100644 --- a/packages/microcosm-graph/tests/fixtures/parity/kernels/calibrate/pins.json +++ b/packages/microcosm-graph/tests/fixtures/parity/kernels/calibrate/pins.json @@ -1 +1 @@ -{"dependencies":{"numpy":"2.4.6","pandas":"3.0.3","scipy":"1.17.1","torch":"2.12.0"},"implementation_hash":"3ee050b762e12fdb27229bd5c6aa5f5165f43a17b0b2f9b6b6de43a0bf7cfce3","kernel":"calibrate.adam@1","node":"calibrate","node_key":"ba5cd153ef0d459c5c8a83eae6bd420322467955e9af3213400f98c9e25118f2","numeric":"bitwise","platform":"arm64/darwin/py3.14","platforms":{"arm64/darwin/py3.14":{"direct":"direct.csv","node_key":"ba5cd153ef0d459c5c8a83eae6bd420322467955e9af3213400f98c9e25118f2"}},"seed":0} +{"dependencies":{"numpy":"2.4.6","pandas":"3.0.3","scipy":"1.17.1","torch":"2.12.0"},"implementation_hash":"1916c5286b83c4017cbba72c7c63746e0c22df37f964680e2677a8355d9fc63e","kernel":"calibrate.adam@1","node":"calibrate","node_key":"539d1bf5dc53e545af1bc536509e8d72ca7edb5c1417438c3d310736caa0c4fa","numeric":"bitwise","platform":"arm64/darwin/py3.14","platforms":{"arm64/darwin/py3.14":{"direct":"direct.csv","node_key":"539d1bf5dc53e545af1bc536509e8d72ca7edb5c1417438c3d310736caa0c4fa"}},"seed":0} diff --git a/tools/build_us_acs_local_release.py b/tools/build_us_acs_local_release.py index 3804c18c3b..f0ba9d2d34 100644 --- a/tools/build_us_acs_local_release.py +++ b/tools/build_us_acs_local_release.py @@ -2347,10 +2347,11 @@ def save(weights: np.ndarray, epochs_done: int) -> None: "effective_sample_size": round(result.effective_sample_size, 1), "ess_fraction": round(result.effective_sample_size / n_households, 4), "chi_square_distance": round(result.chi_square_distance, 6), - # Softmax only: epochs whose cap rounds ran out (the last epoch batch). - "softmax_cap_rounds_exhausted_epochs": ( + # Softmax only: the largest in-loop weight over its cap, before the + # closing projection (the last epoch batch). + "softmax_in_loop_max_cap_ratio": ( result.options.get("iterate_selection_receipt") or {} - ).get("softmax_cap_rounds_exhausted_epochs"), + ).get("softmax_in_loop_max_cap_ratio"), "realized_max_weight_ratio": round(result.realized_max_weight_ratio, 4), "mass_conserved_ratio": round( float(result.weights.sum()) / float(design_weights.sum()), 6 @@ -4161,7 +4162,7 @@ def _version(package: str) -> str: "effective_sample_size", "ess_fraction", "chi_square_distance", - "softmax_cap_rounds_exhausted_epochs", + "softmax_in_loop_max_cap_ratio", "realized_max_weight_ratio", "mass_conserved_ratio", ) @@ -4486,10 +4487,9 @@ def _parse_args(argv: list[str] | None = None) -> argparse.Namespace: "How the mass-conserving Adam solve holds the total " "(microcosm.calibrate mass_parametrization). 'projection' " "(default) is the historical per-step uniform shift; 'softmax' " - "optimizes total * softmax(log_w). At the ACS release's scale " - "softmax's per-step cap rounds run out on most epochs, so it " - "optimizes past the cap until the closing projection; the summary " - "records the count (docs/calibration-l2-basis.md)." + "optimizes total * softmax(log_w), projecting exactly onto the " + "capped simplex after every step; the summary records the largest " + "in-loop weight over its cap (docs/calibration-l2-basis.md)." ), ) parser.add_argument("--seed", type=int, default=0)