diff --git a/examples/README.md b/examples/README.md index 90c707c..297f3db 100644 --- a/examples/README.md +++ b/examples/README.md @@ -103,6 +103,29 @@ This example is self-contained and doesn't require external data. Use it to unde --- +### [target_coverage.ipynb](target_coverage.ipynb) +**Duration**: <1 min | **GPU**: Not required + +**What it demonstrates**: +- Why a fixed `base_window` in `logarithmic_windows()` makes analysis coverage balloon across a + batch-size sweep at a fixed token budget, and how `target_coverage` holds it roughly constant +- How to reproduce the exact before/after coverage numbers from a real packed-corpus sweep +- How many training tokens a single measurement window actually costs, and how that scales + (or doesn't) with batch size under `target_coverage` on vs. off +- How `tokens_per_step`/`tokens_per_window` holds tokens-per-window *exactly* constant instead, by + deriving `base_window` straight from a token budget rather than via `max_steps` + +**Key Insight**: `target_coverage` derives `base_window` from `max_steps` instead of fixing it, which +keeps both the realized coverage *and* the token cost of one measurement roughly constant across a +batch-size sweep -- trading window width for batch size as the budget shifts between them. +`tokens_per_step`/`tokens_per_window` goes further: it derives `base_window` directly from a token +budget, holding tokens-per-window constant exactly (up to step rounding) rather than roughly. + +This example only imports `perspic.logger`, `pandas`, and `matplotlib` -- no model, training loop, or +GPU needed. + +--- + ### [core/hutchinson_convergence.py](core/hutchinson_convergence.py) **Duration**: ~2 min | **GPU**: Not required @@ -155,6 +178,7 @@ examples/ │ ├── mlps.py │ └── utils.py ├── batch_size_scaling_analysis.ipynb # Theory + implementation +├── target_coverage.ipynb # target_coverage vs. fixed base_window ├── cifar10.ipynb # Core workflow ├── logging_scheduler.ipynb # Efficient logging ├── cross10_cross_response.ipynb # Response analysis diff --git a/examples/target_coverage.ipynb b/examples/target_coverage.ipynb new file mode 100644 index 0000000..8fb250a --- /dev/null +++ b/examples/target_coverage.ipynb @@ -0,0 +1,368 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": "# Target Coverage for Logarithmic Windows\n\nThis notebook is a companion to [`logging_scheduler.ipynb`](logging_scheduler.ipynb). It zooms in on\none parameter of `logarithmic_windows()`: **`target_coverage`**.\n\n**Background.** In a batch-size sweep at a *fixed token budget*, `max_steps` shrinks linearly as\nbatch size grows (`max_steps = total_blocks // batch_size`). A fixed `base_window` can't compensate\nfor that: window *count* only grows with `log10(max_steps)`, so realized analysis coverage\n(`len(schedule.steps) / max_steps`) balloons as batch size increases. `target_coverage` fixes this by\nderiving `base_window` from `max_steps` instead, so coverage stays roughly constant across the sweep.\n\n**What this notebook does:**\n1. Reproduces the exact before/after coverage table from the 2026-08-21 thesis-log entry, against the\n real packed-corpus token count, as a sanity check that we're using `logarithmic_windows()` the same way.\n2. Extends that anchor sweep to a denser range of batch sizes.\n3. Answers the question behind the fix: **how many training tokens does one measurement window cost,\n and how does that change with batch size** -- with `target_coverage` on vs. off.\n4. Shows a more direct alternative, `tokens_per_step`/`tokens_per_window`, that holds tokens-per-window\n *exactly* constant (up to step rounding) instead of only roughly constant." + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "from IPython.display import display\n", + "\n", + "from perspic.logger import logarithmic_windows" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fixed token budget\n", + "\n", + "We hold the *training data* fixed -- same packed corpus, same sequence length -- and only vary batch\n", + "size. `max_steps` is then a consequence of the budget, not something chosen independently:\n", + "\n", + "```\n", + "max_steps(batch_size) = total_blocks // batch_size\n", + "```\n", + "\n", + "The numbers below are the real packed-corpus size from the thesis-log entry (`meta.json`:\n", + "608,617,592 train tokens at `max_seq_len=512`)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "TOTAL_TRAIN_TOKENS = 608_617_592 # packed-corpus size, from meta.json\n", + "MAX_SEQ_LEN = 512\n", + "TOTAL_BLOCKS = TOTAL_TRAIN_TOKENS // MAX_SEQ_LEN # one step of batch_size=1 consumes one block\n", + "\n", + "REFERENCE_BATCH_SIZE = 128 # the sweep's existing wall-time reference batch size\n", + "BASE_WINDOW = 50 # fixed base_window used before the fix (target_coverage \"off\")\n", + "TARGET_COVERAGE = 0.2154 # calibrated so target_coverage reproduces base_window=50 at bs=128 exactly\n", + "\n", + "print(f\"total_blocks = {TOTAL_BLOCKS:,}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Reproducing the thesis-log anchor table\n", + "\n", + "Before extending anything, check that `logarithmic_windows()` reproduces the exact coverage numbers\n", + "from the log entry for its five batch sizes. \"`target_coverage` off\" means passing a fixed\n", + "`base_window`; \"`target_coverage` on\" means passing `target_coverage` instead and letting it derive\n", + "`base_window` from `max_steps`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def sweep_coverage(batch_sizes, base_window=BASE_WINDOW, target_coverage=TARGET_COVERAGE,\n", + " points_per_decade=10):\n", + " \"\"\"Build fixed-vs-target-coverage schedules across a batch-size sweep at a fixed token budget.\"\"\"\n", + " rows = []\n", + " for bs in batch_sizes:\n", + " max_steps = TOTAL_BLOCKS // bs\n", + " fixed = logarithmic_windows(\n", + " max_steps=max_steps, points_per_decade=points_per_decade, base_window=base_window\n", + " )\n", + " target = logarithmic_windows(\n", + " max_steps=max_steps, points_per_decade=points_per_decade, target_coverage=target_coverage\n", + " )\n", + " # adaptive_scale=0 (the default) means every window in a schedule has the same width\n", + " fixed_width = len(next(iter(fixed.windows.values())))\n", + " target_width = len(next(iter(target.windows.values())))\n", + " tokens_per_step = bs * MAX_SEQ_LEN\n", + "\n", + " rows.append({\n", + " \"batch_size\": bs,\n", + " \"max_steps\": max_steps,\n", + " \"window_width_fixed\": fixed_width,\n", + " \"window_width_target\": target_width,\n", + " \"coverage_fixed_%\": 100 * len(fixed.steps) / max_steps,\n", + " \"coverage_target_%\": 100 * len(target.steps) / max_steps,\n", + " \"tokens_per_step\": tokens_per_step,\n", + " \"tokens_per_window_fixed\": fixed_width * tokens_per_step,\n", + " \"tokens_per_window_target\": target_width * tokens_per_step,\n", + " \"analysis_tokens_total_fixed\": len(fixed.steps) * tokens_per_step,\n", + " \"analysis_tokens_total_target\": len(target.steps) * tokens_per_step,\n", + " })\n", + " return pd.DataFrame(rows)\n", + "\n", + "\n", + "anchor_batch_sizes = [4, 32, 128, 256, 1024]\n", + "anchor_df = sweep_coverage(anchor_batch_sizes)\n", + "display(anchor_df[[\"batch_size\", \"max_steps\", \"coverage_fixed_%\", \"coverage_target_%\"]].round(2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These match the 2026-08-21 log entry exactly: coverage collapses from **0.59% -> 49.40%** across\n", + "the sweep with a fixed `base_window=50` down to **8.55% -> 13.28%** with `target_coverage=0.2154` --\n", + "and bs=128 is bit-for-bit identical between the two, since `target_coverage` was calibrated to\n", + "reproduce that exact anchor schedule." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## A denser batch-size sweep\n", + "\n", + "The log entry's table only has five points. Filling in the powers of two between them (which include\n", + "all five anchor points above) makes the trend, and the crossover at bs=128, easier to see." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "batch_sizes = [4, 8, 16, 32, 64, 128, 256, 512, 1024]\n", + "df = sweep_coverage(batch_sizes)\n", + "display(df.round(2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Coverage: the spread that motivated the fix" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(7, 4.5))\n", + "ax.plot(df[\"batch_size\"], df[\"coverage_fixed_%\"], \"o-\", label=\"target_coverage off (base_window=50)\")\n", + "ax.plot(df[\"batch_size\"], df[\"coverage_target_%\"], \"o-\", label=f\"target_coverage on ({TARGET_COVERAGE})\")\n", + "ax.axvline(REFERENCE_BATCH_SIZE, color=\"gray\", linestyle=\":\", linewidth=1,\n", + " label=f\"anchor bs={REFERENCE_BATCH_SIZE}\")\n", + "ax.set_xscale(\"log\", base=2)\n", + "ax.set_xticks(batch_sizes)\n", + "ax.set_xticklabels(batch_sizes)\n", + "ax.set_xlabel(\"batch size\")\n", + "ax.set_ylabel(\"realized coverage (%)\")\n", + "ax.set_title(\"Analysis-window coverage vs. batch size, fixed token budget\")\n", + "ax.grid(True, alpha=0.3)\n", + "ax.legend()\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The actual question: tokens per measurement window\n", + "\n", + "Coverage is a useful summary, but it hides the mechanism. Each scheduled step processes\n", + "`batch_size * max_seq_len` tokens, and -- since `adaptive_scale=0` here, so every window in a\n", + "schedule has the same width -- a whole measurement window costs:\n", + "\n", + "```\n", + "tokens_per_window = window_width * batch_size * max_seq_len\n", + "```\n", + "\n", + "With a **fixed `base_window`**, `window_width` doesn't know about `batch_size` at all, so\n", + "`tokens_per_window` scales linearly with batch size purely because each step got heavier. With\n", + "**`target_coverage`**, `window_width` is derived from `max_steps` (which shrinks as batch size\n", + "grows), and that shrinkage largely cancels the growth in tokens-per-step." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, axes = plt.subplots(1, 2, figsize=(12, 4.5))\n", + "\n", + "ax = axes[0]\n", + "ax.plot(df[\"batch_size\"], df[\"window_width_fixed\"], \"o-\", label=\"target_coverage off\")\n", + "ax.plot(df[\"batch_size\"], df[\"window_width_target\"], \"o-\", label=\"target_coverage on\")\n", + "ax.set_xscale(\"log\", base=2)\n", + "ax.set_yscale(\"log\")\n", + "ax.set_xticks(batch_sizes)\n", + "ax.set_xticklabels(batch_sizes)\n", + "ax.set_xlabel(\"batch size\")\n", + "ax.set_ylabel(\"window width (steps)\")\n", + "ax.set_title(\"Steps per measurement window\")\n", + "ax.grid(True, alpha=0.3, which=\"both\")\n", + "ax.legend()\n", + "\n", + "ax = axes[1]\n", + "ax.plot(df[\"batch_size\"], df[\"tokens_per_window_fixed\"], \"o-\", label=\"target_coverage off\")\n", + "ax.plot(df[\"batch_size\"], df[\"tokens_per_window_target\"], \"o-\", label=\"target_coverage on\")\n", + "ax.set_xscale(\"log\", base=2)\n", + "ax.set_yscale(\"log\")\n", + "ax.set_xticks(batch_sizes)\n", + "ax.set_xticklabels(batch_sizes)\n", + "ax.set_xlabel(\"batch size\")\n", + "ax.set_ylabel(\"tokens per measurement window\")\n", + "ax.set_title(\"Training tokens consumed per window\")\n", + "ax.grid(True, alpha=0.3, which=\"both\")\n", + "ax.legend()\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Across the bs=4..1024 sweep (a 256x range in batch size):\n", + "\n", + "- **`target_coverage` off**: window width is pinned at 50 steps, so tokens-per-window scales with\n", + " batch size directly -- **~102K -> ~26.2M tokens**, a 256x spread.\n", + "- **`target_coverage` on**: window width falls from 1164 steps (bs=4) to 8 steps (bs=1024), almost\n", + " exactly canceling the 256x growth in tokens-per-step -- so tokens-per-window only moves\n", + " **~2.4M -> ~4.2M tokens**, under 2x.\n", + "\n", + "So `target_coverage` isn't just holding *coverage* constant -- it's holding the *token cost of a\n", + "single measurement* roughly constant, by trading window width for batch size as the budget shifts\n", + "between them." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Total analysis token spend across a full run\n", + "\n", + "Zooming back out: multiplying coverage by the total token budget gives the total number of training\n", + "tokens analyzed over the *whole* run, in tokens rather than as a percentage -- a more concrete number\n", + "if you're budgeting analysis compute." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(7, 4.5))\n", + "ax.plot(df[\"batch_size\"], df[\"analysis_tokens_total_fixed\"], \"o-\", label=\"target_coverage off\")\n", + "ax.plot(df[\"batch_size\"], df[\"analysis_tokens_total_target\"], \"o-\", label=\"target_coverage on\")\n", + "ax.axhline(TOTAL_TRAIN_TOKENS, color=\"gray\", linestyle=\":\", linewidth=1,\n", + " label=\"full corpus (608.6M tokens)\")\n", + "ax.set_xscale(\"log\", base=2)\n", + "ax.set_yscale(\"log\")\n", + "ax.set_xticks(batch_sizes)\n", + "ax.set_xticklabels(batch_sizes)\n", + "ax.set_xlabel(\"batch size\")\n", + "ax.set_ylabel(\"total tokens spent on analysis\")\n", + "ax.set_title(\"Total analysis-token spend across the whole run\")\n", + "ax.grid(True, alpha=0.3, which=\"both\")\n", + "ax.legend()\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Bonus: combining with `adaptive_scale`\n", + "\n", + "`target_coverage` only sets the *base* window width for a schedule; `adaptive_scale` still grows\n", + "individual windows later in training on top of that base\n", + "(`window_size = base_window + adaptive_scale * log10(center)`). The two combine independently:\n", + "`target_coverage` picks the starting width for a given batch size, `adaptive_scale` then grows\n", + "windows as `center` increases within that schedule." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "demo_bs = 128\n", + "demo_max_steps = TOTAL_BLOCKS // demo_bs\n", + "\n", + "for scale in (0.0, 2.0):\n", + " schedule = logarithmic_windows(\n", + " max_steps=demo_max_steps, target_coverage=TARGET_COVERAGE, adaptive_scale=scale\n", + " )\n", + " widths = [len(steps) for steps in schedule.windows.values()]\n", + " print(f\"adaptive_scale={scale}: window width ranges {min(widths)}-{max(widths)} steps \"\n", + " f\"over {len(schedule.windows)} windows\")" + ] + }, + { + "cell_type": "markdown", + "id": "eda28be4", + "source": "## Takeaways\n\n- At a fixed token budget, `max_steps` shrinks linearly with batch size, but a fixed `base_window`\n doesn't shrink with it -- so both coverage *and* the token cost of one measurement window balloon\n as batch size grows.\n- `target_coverage` derives `base_window` from `max_steps`, which keeps both roughly constant across\n a batch-size sweep instead.\n- `target_coverage` is calibrated once, at whatever batch size you already trust (here, bs=128, the\n sweep's wall-time reference point) -- pick it so the anchor schedule matches what you had before,\n then let it drive the rest of the sweep.\n- `tokens_per_step`/`tokens_per_window` is a more direct alternative when you need tokens-per-window\n held *exactly* constant rather than roughly: it derives `base_window` straight from a token budget,\n with no dependence on `max_steps`/`num_points`.\n\nSee [`perspic/logger.py`](../perspic/logger.py)'s `logarithmic_windows()` docstring and\n[`tests/unit/test_logger.py`](../tests/unit/test_logger.py)'s `TestTargetCoverage` and\n`TestTokensPerWindow` classes for both parameters' exact semantics and edge cases (e.g. the\nminimum-window-width clamp at very low `target_coverage`/`tokens_per_window`, or `max_steps <= 0`).", + "metadata": {} + }, + { + "cell_type": "code", + "id": "c6386217", + "source": "TOKENS_PER_WINDOW = BASE_WINDOW * REFERENCE_BATCH_SIZE * MAX_SEQ_LEN # 50 * 128 * 512 == 3,276,800\n\n\ndef sweep_tokens_per_window(batch_sizes, tokens_per_window=TOKENS_PER_WINDOW):\n \"\"\"Build tokens_per_window-derived schedules across the same batch-size sweep.\"\"\"\n rows = []\n for bs in batch_sizes:\n max_steps = TOTAL_BLOCKS // bs\n tokens_per_step = bs * MAX_SEQ_LEN\n exact = logarithmic_windows(\n max_steps=max_steps, tokens_per_step=tokens_per_step, tokens_per_window=tokens_per_window\n )\n exact_width = len(next(iter(exact.windows.values())))\n rows.append({\n \"batch_size\": bs,\n \"max_steps\": max_steps,\n \"window_width_exact\": exact_width,\n \"tokens_per_window_exact\": exact_width * tokens_per_step,\n })\n return pd.DataFrame(rows)\n\n\nexact_df = sweep_tokens_per_window(batch_sizes)\ndf = df.merge(exact_df, on=[\"batch_size\", \"max_steps\"])\ndisplay(df[[\"batch_size\", \"window_width_fixed\", \"window_width_target\", \"window_width_exact\",\n \"tokens_per_window_fixed\", \"tokens_per_window_target\", \"tokens_per_window_exact\"]].round(2))", + "metadata": {}, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "id": "fe96d019", + "source": "exact_ratio = df[\"tokens_per_window_exact\"].max() / df[\"tokens_per_window_exact\"].min()\ntarget_ratio = df[\"tokens_per_window_target\"].max() / df[\"tokens_per_window_target\"].min()\nprint(f\"tokens_per_window (exact): {df['tokens_per_window_exact'].min():>12,.0f} -> \"\n f\"{df['tokens_per_window_exact'].max():>12,.0f} tokens (ratio {exact_ratio:.4f})\")\nprint(f\"target_coverage (roughly): {df['tokens_per_window_target'].min():>12,.0f} -> \"\n f\"{df['tokens_per_window_target'].max():>12,.0f} tokens (ratio {target_ratio:.4f})\")", + "metadata": {}, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "id": "476692f9", + "source": "fig, axes = plt.subplots(1, 2, figsize=(12, 4.5))\n\nax = axes[0]\nax.plot(df[\"batch_size\"], df[\"window_width_fixed\"], \"o-\", label=\"fixed base_window\")\nax.plot(df[\"batch_size\"], df[\"window_width_target\"], \"o-\", label=\"target_coverage\")\nax.plot(df[\"batch_size\"], df[\"window_width_exact\"], \"o-\", label=\"tokens_per_window (exact)\")\nax.axvline(REFERENCE_BATCH_SIZE, color=\"gray\", linestyle=\":\", linewidth=1,\n label=f\"anchor bs={REFERENCE_BATCH_SIZE}\")\nax.set_xscale(\"log\", base=2)\nax.set_yscale(\"log\")\nax.set_xticks(batch_sizes)\nax.set_xticklabels(batch_sizes)\nax.set_xlabel(\"batch size\")\nax.set_ylabel(\"window width (steps)\")\nax.set_title(\"Steps per measurement window\")\nax.grid(True, alpha=0.3, which=\"both\")\nax.legend()\n\nax = axes[1]\nax.plot(df[\"batch_size\"], df[\"tokens_per_window_fixed\"], \"o-\", label=\"fixed base_window\")\nax.plot(df[\"batch_size\"], df[\"tokens_per_window_target\"], \"o-\", label=\"target_coverage\")\nax.plot(df[\"batch_size\"], df[\"tokens_per_window_exact\"], \"o-\", label=\"tokens_per_window (exact)\")\nax.axvline(REFERENCE_BATCH_SIZE, color=\"gray\", linestyle=\":\", linewidth=1,\n label=f\"anchor bs={REFERENCE_BATCH_SIZE}\")\nax.set_xscale(\"log\", base=2)\nax.set_yscale(\"log\")\nax.set_xticks(batch_sizes)\nax.set_xticklabels(batch_sizes)\nax.set_xlabel(\"batch size\")\nax.set_ylabel(\"tokens per measurement window\")\nax.set_title(\"Exact vs. roughly-constant tokens-per-window\")\nax.grid(True, alpha=0.3, which=\"both\")\nax.legend()\n\nplt.tight_layout()\nplt.show()", + "metadata": {}, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "id": "f091af53", + "source": "`tokens_per_window` stays within a couple of percent of its target across the whole 256x batch-size\nrange (up to step rounding and the 1-step floor), versus `target_coverage`'s ~2x drift.\n\n**Caveat:** this exactness applies to the `base_window` component only. Combining `tokens_per_window`\nwith `adaptive_scale > 0` reintroduces drift, since the adaptive growth term depends on\n`log10(center)` rather than `tokens_per_step` -- `center`'s range shifts with `max_steps` across a\nsweep just like it does for `target_coverage`.", + "metadata": {} + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": "## Takeaways\n\n- At a fixed token budget, `max_steps` shrinks linearly with batch size, but a fixed `base_window`\n doesn't shrink with it -- so both coverage *and* the token cost of one measurement window balloon\n as batch size grows.\n- `target_coverage` derives `base_window` from `max_steps`, which keeps both roughly constant across\n a batch-size sweep instead.\n- `target_coverage` is calibrated once, at whatever batch size you already trust (here, bs=128, the\n sweep's wall-time reference point) -- pick it so the anchor schedule matches what you had before,\n then let it drive the rest of the sweep.\n\nSee [`perspic/logger.py`](../perspic/logger.py)'s `logarithmic_windows()` docstring and\n[`tests/unit/test_logger.py`](../tests/unit/test_logger.py)'s `TestTargetCoverage` for the\nparameter's exact semantics and edge cases (e.g. the minimum-window-width clamp at very low\n`target_coverage`, or `max_steps <= 0`)." + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file diff --git a/perspic/logger.py b/perspic/logger.py index 0f15c2c..2f0f6fc 100644 --- a/perspic/logger.py +++ b/perspic/logger.py @@ -57,6 +57,9 @@ def logarithmic_windows( points_per_decade: int = 10, base_window: int = 5, adaptive_scale: float = 0.0, + target_coverage: Optional[float] = None, + tokens_per_step: Optional[float] = None, + tokens_per_window: Optional[float] = None, ) -> LogarithmicWindowSchedule: """Generate logarithmically spaced measurement windows. @@ -69,13 +72,52 @@ def logarithmic_windows( points_per_decade: Number of log points per 10x increase in steps. Higher values give denser coverage. Default is 10. base_window: Minimum window size (number of consecutive measurements - per log point). Default is 5. + per log point). Default is 5. Ignored if target_coverage or + tokens_per_window is set. adaptive_scale: Controls how window size grows with step number. - 0.0: Fixed window size (always base_window) - 1.0: Window grows by ~1 step per decade - 2.0: Window grows by ~2 steps per decade The formula is: window_size = base_window + adaptive_scale * log10(step) Default is 0.0 (fixed windows). + target_coverage: If set, overrides base_window with a value derived + from max_steps so that num_points * base_window / ( max_steps + 1 ) + approximates this fraction (clamped to at least 1; the *actual* + coverage -- len(schedule.steps) / (max_steps + 1) -- will still differ + somewhat, since overlapping/truncated windows collapse below this + nominal estimate). Use this instead of a hand-picked base_window + when comparing schedules across runs whose max_steps varies for + reasons unrelated to how much you want measured -- e.g. a + batch-size sweep at a fixed token budget, where max_steps shrinks + linearly with batch size. With a fixed base_window, window + *count* (num_points above) only grows with log10(max_steps), so + realized coverage balloons as max_steps shrinks; deriving + base_window from target_coverage instead keeps it roughly + constant across such a sweep. None (default) preserves the + original fixed-base_window behavior exactly. + tokens_per_step: Tokens consumed by one training step (e.g. + batch_size * seq_len, computed by the caller -- this function + stays agnostic of both). Must be set together with + tokens_per_window (both or neither); mutually exclusive with + target_coverage. + tokens_per_window: If set (together with tokens_per_step), overrides + base_window with round(tokens_per_window / tokens_per_step) + (clamped to at least 1), making the token cost of one + measurement window equal to tokens_per_window exactly, up to + that rounding. Use this instead of target_coverage when you need + tokens-per-window held constant *exactly* rather than roughly + across a batch-size sweep at a fixed token budget: holding + tokens_per_window fixed while tokens_per_step grows with batch + size shrinks window width in steps by the same factor, + canceling the growth exactly (subject to rounding and the + 1-step floor) -- unlike target_coverage, which derives + base_window via max_steps/num_points as a proxy and only + approximates this. None (default) leaves base_window and + target_coverage's behavior unaffected. Note: combining + tokens_per_window with adaptive_scale > 0 reintroduces drift, + since the adaptive growth added on top of base_window depends + on log10(center) rather than tokens_per_step -- the exact + guarantee applies to the base_window component only. Returns: LogarithmicWindowSchedule containing: @@ -96,6 +138,22 @@ def logarithmic_windows( adaptive_scale=2.0 ) + # Batch-size sweep: hold ~11% coverage constant across runs whose + # max_steps varies with batch size, instead of a fixed base_window + # that would make coverage balloon as batch size grows and max_steps + # shrinks. + schedule = logarithmic_windows(max_steps=max_steps, target_coverage=0.11) + + # Batch-size sweep: hold tokens-per-window exactly constant (up to + # rounding) as batch size varies, instead of target_coverage's + # roughly-constant behavior. + tokens_per_step = batch_size * seq_len # computed by the caller + schedule = logarithmic_windows( + max_steps=max_steps, + tokens_per_step=tokens_per_step, + tokens_per_window=2_000_000, + ) + # Use with analyzer model = analyzer(MyModule, analysis_schedule=schedule, ...) """ @@ -109,6 +167,24 @@ def logarithmic_windows( # Generate log-spaced center points in the range [10^0, 10^log10(max_steps)] # Step 0 will be added separately below num_points = int(points_per_decade * math.log10(max_steps)) + 1 + if (tokens_per_step is None) != (tokens_per_window is None): + raise ValueError( + "tokens_per_step and tokens_per_window must both be set, or both left as None" + ) + if tokens_per_window is not None: + if target_coverage is not None: + raise ValueError( + "tokens_per_window is mutually exclusive with target_coverage; set only one" + ) + if not math.isfinite(tokens_per_step) or tokens_per_step <= 0: + raise ValueError("tokens_per_step must be a finite positive float") + if not math.isfinite(tokens_per_window) or tokens_per_window <= 0: + raise ValueError("tokens_per_window must be a finite positive float") + base_window = max(1, round(tokens_per_window / tokens_per_step)) + if target_coverage is not None: + if not math.isfinite(target_coverage) or target_coverage <= 0: + raise ValueError("target_coverage must be a finite positive float") + base_window = max(1, round(target_coverage * (max_steps + 1) / num_points)) # Generate logspace without numpy: 10^(start + i * step) for i in range(num_points) log_start, log_end = 0, math.log10(max_steps) log_step = (log_end - log_start) / (num_points - 1) if num_points > 1 else 0 diff --git a/pyproject.toml b/pyproject.toml index f9e6886..fbfd6bf 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,7 +1,35 @@ +[build-system] +requires = ["setuptools>=80"] +build-backend = "setuptools.build_meta" + [tool.black] line-length = 88 preview = true [tool.isort] profile = 'black' -multi_line_output = 3 \ No newline at end of file +multi_line_output = 3 + +[tool.uv.sources] +perspic = { workspace = true } + +[project] +name = "perspic" +version = "0.0.1" +requires-python = ">=3.11" +dependencies = [ + "ipykernel>=7.3.0", + "matplotlib>=3.11.1", + "nngeometry==0.3.2", + "opacus==1.5.4", + "pandas>=3.0.5", + "psutil==7.1.3", + "pytorch-lightning==2.5.5", + "setuptools==80.9.0", + "torch==2.8.0", +] + +[dependency-groups] +dev = [ + "perspic", +] diff --git a/setup.py b/setup.py deleted file mode 100644 index 443961e..0000000 --- a/setup.py +++ /dev/null @@ -1,24 +0,0 @@ -""" -Configuration file for the package. -""" - -from setuptools import find_packages, setup - -with open("README.md", "r") as fh: - long_description = fh.read() - -with open("requirements.txt") as f: - required = f.read().splitlines() - -setup( - name="perspic", - version="0.0.1", - author="Konstantin Nikolaou and Jonas Scheunemann", - author_email="jscheunemann@icp.uni-stuttgart.de", - description="A tool to study neural network training dynamics.", - long_description=long_description, - long_description_content_type="text/markdown", - packages=find_packages(), - python_requires=">=3.11", - install_requires=required, -) diff --git a/tests/unit/test_logger.py b/tests/unit/test_logger.py index b5e661d..232623a 100644 --- a/tests/unit/test_logger.py +++ b/tests/unit/test_logger.py @@ -129,3 +129,207 @@ def test_windows_roughly_log_spaced(self): early_gap = centers[2] - centers[1] late_gap = centers[-1] - centers[-2] assert late_gap > early_gap + + +class TestTargetCoverage: + """Test the target_coverage parameter of logarithmic_windows.""" + + def test_target_coverage_overrides_explicit_base_window(self): + """Test target_coverage ignores an explicitly passed base_window.""" + schedule = logarithmic_windows( + max_steps=1000, + points_per_decade=10, + target_coverage=0.2, + base_window=999, + ) + sizes = {len(steps) for steps in schedule.windows.values()} + assert sizes == {6} + + def test_target_coverage_derives_expected_base_window(self): + """Test target_coverage derives base_window from max_steps/num_points. + + num_points = int(10 * log10(1000)) + 1 == 31, so + base_window = max(1, round(0.2 * 1000 / 31)) == 6. + """ + schedule = logarithmic_windows( + max_steps=1000, points_per_decade=10, target_coverage=0.2 + ) + sizes = {len(steps) for steps in schedule.windows.values()} + assert sizes == {6} + assert len(schedule.steps) == 121 + + def test_target_coverage_clamps_to_minimum_one(self): + """Test target_coverage clamps a sub-1 derived base_window to 1. + + base_window = max(1, round(0.001 * 1000 / 31)) == max(1, 0) == 1. + """ + schedule = logarithmic_windows( + max_steps=1000, points_per_decade=10, target_coverage=0.001 + ) + sizes = {len(steps) for steps in schedule.windows.values()} + assert sizes == {1} + assert len(schedule.steps) == 28 + + def test_target_coverage_combines_with_adaptive_scale(self): + """Test target_coverage's derived base grows via adaptive_scale. + + Base window is 6 (as in test_target_coverage_derives_expected_base_window); + with adaptive_scale=2.0, window size at center=794 (the largest + non-truncated center) should be 6 + int(2.0 * log10(794)) == 11. + """ + schedule = logarithmic_windows( + max_steps=1000, + points_per_decade=10, + target_coverage=0.2, + adaptive_scale=2.0, + ) + window_id_at_794 = next( + wid for wid, c in schedule.window_centers.items() if c == 794 + ) + window_id_at_0 = next( + wid for wid, c in schedule.window_centers.items() if c == 0 + ) + assert len(schedule.windows[window_id_at_0]) == 6 + assert len(schedule.windows[window_id_at_794]) == 11 + + def test_target_coverage_ignored_for_non_positive_max_steps(self): + """Test target_coverage has no effect when max_steps <= 0.""" + for val in [0, -10]: + schedule = logarithmic_windows(max_steps=val, target_coverage=0.2) + assert schedule.steps == {0} + assert len(schedule.windows) == 1 + + def test_target_coverage_holds_roughly_constant_across_batch_size_sweep(self): + """Test target_coverage keeps realized coverage roughly constant + across a batch-size sweep at a fixed sample budget, unlike a fixed + base_window (even one picked to give a comparable measurement-count + regime, so the comparison isolates constancy rather than raw count). + + This is the scenario target_coverage's docstring is written for: a + batch-size sweep at a fixed token budget, where max_steps shrinks + linearly with batch size. + """ + budget = 1_000_000 + batch_sizes = [8, 16, 24, 32, 40, 48, 56, 64] + max_steps_values = [budget // bs for bs in batch_sizes] + + target_coverages = [ + len(logarithmic_windows(max_steps=m, target_coverage=0.11).steps) / m + for m in max_steps_values + ] + fixed_coverages = [ + len(logarithmic_windows(max_steps=m, base_window=128).steps) / m + for m in max_steps_values + ] + + target_ratio = max(target_coverages) / min(target_coverages) + fixed_ratio = max(fixed_coverages) / min(fixed_coverages) + + assert target_ratio < 1.5 + assert fixed_ratio > 3 + assert fixed_ratio > target_ratio + + +class TestTokensPerWindow: + """Test the tokens_per_step/tokens_per_window parameters of logarithmic_windows.""" + + def test_tokens_per_window_overrides_explicit_base_window(self): + """Test tokens_per_window ignores an explicitly passed base_window. + + 8192 / 2048 == 4.0 exactly, so base_window == 4 with no rounding. + """ + schedule = logarithmic_windows( + max_steps=1000, + tokens_per_step=2048, + tokens_per_window=8192, + base_window=999, + ) + sizes = {len(steps) for steps in schedule.windows.values()} + assert sizes == {4} + + def test_tokens_per_window_derives_expected_base_window(self): + """Test tokens_per_window derives base_window via rounding division. + + base_window = round(10000 / 3000) = round(3.333...) == 3. + """ + schedule = logarithmic_windows( + max_steps=1000, tokens_per_step=3000, tokens_per_window=10000 + ) + sizes = {len(steps) for steps in schedule.windows.values()} + assert sizes == {3} + + def test_tokens_per_window_clamps_to_minimum_one(self): + """Test tokens_per_window clamps a sub-1 derived base_window to 1. + + base_window = max(1, round(100 / 2048)) == max(1, 0) == 1. + """ + schedule = logarithmic_windows( + max_steps=1000, tokens_per_step=2048, tokens_per_window=100 + ) + sizes = {len(steps) for steps in schedule.windows.values()} + assert sizes == {1} + assert len(schedule.steps) == 28 + + def test_tokens_per_window_and_target_coverage_are_mutually_exclusive(self): + """Test setting both tokens_per_window and target_coverage raises.""" + with pytest.raises(ValueError, match="mutually exclusive"): + logarithmic_windows( + max_steps=1000, + target_coverage=0.2, + tokens_per_step=2048, + tokens_per_window=8192, + ) + + def test_tokens_per_step_and_tokens_per_window_must_be_provided_together(self): + """Test providing only one of the pair raises.""" + with pytest.raises(ValueError, match="must both be set"): + logarithmic_windows(max_steps=1000, tokens_per_step=2048) + with pytest.raises(ValueError, match="must both be set"): + logarithmic_windows(max_steps=1000, tokens_per_window=8192) + + def test_tokens_per_window_ignored_for_non_positive_max_steps(self): + """Test tokens_per_step/tokens_per_window have no effect when + max_steps <= 0 -- using an invalid pairing (tokens_per_step alone) + to prove validation doesn't run at all, not just that valid inputs + pass through unchanged. + """ + for val in [0, -10]: + schedule = logarithmic_windows(max_steps=val, tokens_per_step=2048) + assert schedule.steps == {0} + assert len(schedule.windows) == 1 + + def test_tokens_per_window_holds_exactly_constant_across_batch_size_sweep(self): + """Test tokens_per_window keeps realized tokens-per-window essentially + exact across a batch-size sweep at a fixed token budget, in contrast + to target_coverage's only-roughly-constant behavior (already bounded + at <1.5 in test_target_coverage_holds_roughly_constant_across_batch_size_sweep). + """ + budget = 1_000_000 + batch_sizes = [8, 16, 24, 32, 40, 48, 56, 64] + seq_len = 512 + + exact_tokens = [] + coverage_tokens = [] + for bs in batch_sizes: + max_steps = budget // bs + tokens_per_step = bs * seq_len + + exact_schedule = logarithmic_windows( + max_steps=max_steps, + tokens_per_step=tokens_per_step, + tokens_per_window=2_000_000, + ) + exact_width = len(next(iter(exact_schedule.windows.values()))) + exact_tokens.append(exact_width * tokens_per_step) + + coverage_schedule = logarithmic_windows( + max_steps=max_steps, target_coverage=0.11 + ) + coverage_width = len(next(iter(coverage_schedule.windows.values()))) + coverage_tokens.append(coverage_width * tokens_per_step) + + exact_ratio = max(exact_tokens) / min(exact_tokens) + coverage_ratio = max(coverage_tokens) / min(coverage_tokens) + + assert exact_ratio < 1.05 + assert coverage_ratio > exact_ratio diff --git a/uv.lock b/uv.lock new file mode 100644 index 0000000..12664bd --- /dev/null +++ b/uv.lock @@ -0,0 +1,2494 @@ +version = 1 +revision = 3 +requires-python = ">=3.11" +resolution-markers = [ + "python_full_version >= '3.14' and sys_platform == 'win32'", + "python_full_version >= '3.14' and sys_platform == 'emscripten'", + "python_full_version >= '3.14' and sys_platform != 'emscripten' and 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