mirror of
https://github.com/wassname/autoresearch_template.git
synced 2026-06-27 16:29:19 +08:00
wip
This commit is contained in:
wassname
@@ -25,6 +25,13 @@ We optimize: **{metric name}** ({lower/higher} is better)
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Baseline (untrained or current best): **{value}**
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Baseline std across 3 seeds: **{value}** (must be < effect size of a meaningful improvement)
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Train + eval wall time: **{X} minutes** (target: 5-40 min)
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If variance is too high relative to effect size, use more eval data, a smaller proxy task,
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or a lower-variance metric -- before running any experiments.
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## Data
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{FILL_IN: dataset, size, splits}
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@@ -1,30 +0,0 @@
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<!-- APPEND-ONLY: new entries at top. Never edit old entries. -->
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<!-- Written by agents (not humans -- see human_journal.md). -->
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<!-- Format: ## YYYY-MM-DD HH:MM | commit: SHORT_SHA | branch: NAME | agent: NAME -->
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# Agent Journal
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Short-term notes from agents: what was attempted in this session,
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blockers hit, context for the next agent picking this up.
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For lasting research learnings, write to RESEARCH_JOURNAL.md instead.
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---
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## Template entry
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```
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## YYYY-MM-DD HH:MM | commit: SHORT_SHA | branch: NAME | agent: NAME
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### Session goal
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[what this agent was asked to do]
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### What was done
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[brief -- the commit history has the details]
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### Blockers / open questions
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[anything the next agent needs to know]
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### Pueue queue state
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[any jobs queued: `pueue status` output or summary]
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```
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@@ -21,7 +21,7 @@ import tyro
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from loguru import logger
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# {FILL_IN}: import your model
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# from model import Config, build_model
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# from train import Config, build_model
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EVAL_SEED = 42 # FROZEN: never change
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@@ -85,6 +85,7 @@ def main(cfg: EvalConfig):
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data = load_eval_data()
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# {FILL_IN}: build and load your model
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# from train import Config, build_model
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# model_cfg = Config()
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# model = build_model(model_cfg)
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# if cfg.checkpoint:
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@@ -13,7 +13,7 @@ default:
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# Fast smoke test: shape checks on CPU, short run
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smoke:
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#!/usr/bin/env bash
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BEARTYPE=1 JAX_PLATFORMS=cpu uv run python train.py --max_steps=1
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BEARTYPE=1 uv run python train.py --max_steps=2
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echo smoke passed
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# --- Evaluation (FROZEN logic -- see eval.py) ---
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@@ -1,74 +0,0 @@
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"""
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Model definition and config.
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This file is NOT frozen -- agents modify it freely in worktrees.
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Keep eval.py's evaluate() interface stable: it calls build_model(cfg) and model.forward(x).
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"""
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from dataclasses import dataclass
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import torch
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import torch.nn as nn
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import tyro
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from jaxtyping import Float
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from loguru import logger
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from torch import Tensor
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# beartype checking enabled only when BEARTYPE=1 (smoke tests)
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import os
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if os.environ.get("BEARTYPE"):
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from beartype import beartype as typechecker
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from jaxtyping import jaxtyped
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else:
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def typechecker(f): return f
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def jaxtyped(**_): return lambda f: f
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@dataclass
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class Config:
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"""Model and training hyperparameters. Edit freely."""
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# model
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d_model: int = 256
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n_layers: int = 4
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# {FILL_IN}: add your architecture params
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# training
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lr: float = 3e-4
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batch_size: int = 32
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max_steps: int = 1000
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seed: int = 42
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# data
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# {FILL_IN}: add data params
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class Model(nn.Module):
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"""
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{FILL_IN}: replace with your actual model.
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"""
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def __init__(self, cfg: Config):
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super().__init__()
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self.cfg = cfg
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# {FILL_IN}: define layers
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# e.g. self.embed = nn.Embedding(vocab_size, cfg.d_model)
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@jaxtyped(typechecker=typechecker)
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def forward(self, x: Float[Tensor, "b s"]) -> Float[Tensor, "b s d"]:
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# {FILL_IN}: implement forward pass
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raise NotImplementedError("{FILL_IN}: implement forward()")
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def build_model(cfg: Config) -> Model:
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torch.manual_seed(cfg.seed)
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model = Model(cfg)
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n_params = sum(p.numel() for p in model.parameters())
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logger.info(f"Model: {n_params:,} parameters")
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return model
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if __name__ == "__main__":
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cfg = tyro.cli(Config)
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model = build_model(cfg)
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logger.info(f"Config: {cfg}")
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+44
-163
@@ -1,21 +1,15 @@
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<!-- FROZEN: only edit in meta-mode (META_MODE=1) -->
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<!-- Symlinked to AGENTS.md and CLAUDE.md -- always loaded by Claude/Cursor/etc. -->
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<!-- Symlinked to AGENTS.md and CLAUDE.md -- always loaded. -->
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# Research Program
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**Project**: {FILL_IN: one sentence describing the research problem}
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**Project**: {FILL_IN: one sentence}
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**Metric**: {FILL_IN: what we optimize, e.g. val_bpb, accuracy, F1} (lower/higher is better)
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**Metric**: {FILL_IN: metric name} ({lower/higher} is better). Target: 5-40 min per run.
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**Metric design requirements** (enforce before first real experiment):
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- Train + eval runs in 5-40 minutes on your GPU
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- Variance across seeds < effect size of a meaningful improvement (run baseline x3, check std)
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- Deterministic given same seed (fixed data order, fixed eval split)
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- If variance is too high: use more eval data, smaller model, or a proxy metric with less noise
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**Hypothesis space**: {FILL_IN: what class of approaches}
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**Hypothesis space**: {FILL_IN: what class of approaches are in scope}
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Read `0_docs/problem.md` for full context.
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See `0_docs/problem.md` for full problem context and baseline numbers.
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---
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@@ -24,182 +18,69 @@ Read `0_docs/problem.md` for full context.
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| Type | Files | Rule |
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|------|-------|------|
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| FROZEN | `program.md`, `eval.py`, `meta_journal.md` | Never edit without `META_MODE=1` |
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| GLOBAL | `RESEARCH_JOURNAL.md`, `results.tsv` | Only commit from main; worktrees append to root copy |
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| APPEND-ONLY | `*_journal.md` | New entries at top, never edit old ones |
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| GLOBAL | `RESEARCH_JOURNAL.md`, `results.tsv` | Commit from main only; worktrees append to root copy |
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| APPEND-ONLY | `RESEARCH_JOURNAL.md`, `human_journal.md`, `meta_journal.md` | New entries at top, never edit old entries |
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| REGULAR | everything else | Modify freely in your worktree |
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---
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## Agent Algorithm
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```
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YOU ARE AN AGENT. Follow this loop:
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```python
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# MUST read before starting:
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read RESEARCH_JOURNAL.md # what has been tried, what worked/failed
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read "Lessons Learned" below # gotchas -- don't repeat past failures
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read RESEARCH_JOURNAL.md # what has been tried
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read 0_docs/problem.md # what we're solving
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n_ideas = count files in 1_ideas/ (not _TEMPLATE.md)
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n_ideas = count(1_ideas/*.md) - 1 # minus _TEMPLATE.md
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if n_ideas < 30:
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## IDEATE
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- Read at least one file from 0_docs/papers/ (or fetch a new paper)
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- Do at least one web search for recent approaches
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- Fetch papers: use /semantic-search or /exa-search skills
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-> save FULL paper text to 0_docs/papers/{slug}.md (not summaries -- full text)
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-> optionally add a vargdown-style argument map to 0_docs/papers/{slug}_analysis.argdown
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-> add key insight (1-3 observations with sources) to RESEARCH_JOURNAL.md
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- Brainstorm ideas. Quality bar:
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* Novel (not in RESEARCH_JOURNAL.md already)
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* Mechanistically grounded (not just hyperparameter tuning)
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* Not sklearn slop -- must be a real ML research contribution
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* Bold enough that it could be a paper contribution
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- For each idea:
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write 1_ideas/{YYYY-MM-DD}_{slug}.md (use _TEMPLATE.md format)
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spawn subagent to critique the idea (prompt: "Is this idea sound?
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What are the failure modes? Is the hypothesis testable?")
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append subagent feedback to the idea file
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- Append summary of new ideas + paper insights to RESEARCH_JOURNAL.md
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# IDEATE
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# MUST read: 0_docs/ideation_guide.md (epistemics, paper fetching, brainstorm discipline)
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read 0_docs/problem.md
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search 1+ papers (semantic-search, exa-search, bibtex MCP)
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save full paper text to 0_docs/papers/{slug}.md # full text, not summaries
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for each idea:
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write 1_ideas/{YYYY-MM-DD}_{slug}.md # use _TEMPLATE.md
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subagent critique: "Is this sound? Failure modes? Testable?"
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append subagent feedback to idea file
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append paper insights + ideas to RESEARCH_JOURNAL.md
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else:
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## IMPLEMENT
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pick the best idea from 1_ideas/ based on:
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- subagent rating (see feedback section in idea file)
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- novelty relative to RESEARCH_JOURNAL.md
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- expected impact on metric
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- implementation feasibility
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# IMPLEMENT
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# MUST read: 0_docs/conventions.md (coding style)
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pick best idea (subagent rating + novelty + expected impact)
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just worktree {slug} # creates 5_worktrees/{slug} on branch exp/{slug}
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implement in worktree (edit train.py; do NOT touch eval.py, program.md)
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slug = idea filename slug
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run: git worktree add 5_worktrees/{slug} -b exp/{slug}
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cd 5_worktrees/{slug}
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# TEST
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subagent code review vs idea doc
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just smoke
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just eval # appends row to results.tsv
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implement the idea (modify train.py, model.py, etc.)
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do NOT modify: eval.py, program.md, meta_journal.md
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# REPORT
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write 9_reports/{YYYY-MM-DD}_{slug}.md # use _TEMPLATE.md
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append to RESEARCH_JOURNAL.md: what tried, delta metric, observation vs inference
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## TEST
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spawn subagent: "Code review this against the idea doc 1_ideas/{slug}.md.
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Does the implementation match the hypothesis? Any bugs?"
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run: just smoke # fast sanity check
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run: just eval # appends to results.tsv
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## REPORT
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write 9_reports/{YYYY-MM-DD}_{slug}.md (use _TEMPLATE.md format)
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append short summary to RESEARCH_JOURNAL.md:
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- what was tried, what metric changed, what you learned
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- key observation vs inference distinction
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## SUBMIT
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git commit -m "exp({slug}): {one-line description}"
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# SUBMIT
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git commit -m "exp({slug}): {one line}"
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git push origin exp/{slug}
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if result beats best in results.tsv:
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create PR for human to merge
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if beats best in results.tsv: open PR for human
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## QUEUING EXPERIMENTS (pueue)
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Use pueue to queue experiments for the single GPU -- one at a time, no collision:
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# Queue with a label showing the question and expected resolution
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pueue add --label "Q: does X help? H: expect +0.05 metric" -- just eval --config=path
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# Check queue / status / logs
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pueue status
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pueue log {task_id} # full stdout
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pueue follow {task_id} # live tail
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Labels encode the hypothesis being tested. After the run, append observed vs expected
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to RESEARCH_JOURNAL.md. The label shows up in `pueue status` so you can track what
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question each running/queued job is answering.
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# Example: multiple experiments queued with different hypotheses
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pueue add --label "Q: rotary vs sinusoidal? H: rotary saves 0.1 bpb" -- just eval rotary
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pueue add --label "Q: flash-attn memory? H: 2x batch size same speed" -- just eval flash
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pueue add --label "Q: does layer norm placement matter? H: pre-norm better" -- just eval prenorm
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# GPU QUEUE (pueue -- one GPU, no collision)
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just queue "Q: does X help? H: expect +delta" eval {args}
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pueue status # shows hypothesis label for each queued/running job
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```
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---
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## Coding Conventions
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## Lessons Learned and Gotchas
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Fail fast. No defensive programming. No silent fallbacks.
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Format: `YYYY-MM-DD | title | lesson (one line)`
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```python
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# shape ops: einops for clarity
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from einops import rearrange, reduce
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x = rearrange(x, 'b s h d -> b h s d')
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# einsum for explicit contraction
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out = torch.einsum('b h s d, b h d v -> b h s v', q, k)
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# jaxtyping on function boundaries (docs + smoke-test checking)
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from jaxtyping import Float
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from torch import Tensor
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def encode(x: Float[Tensor, 'b s d']) -> Float[Tensor, 'b s h']:
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...
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# logging: loguru not print
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from loguru import logger
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||||
logger.info(f"loss={loss:.4f}")
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# dataframes: polars v1
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import polars as pl
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df.group_by("exp").agg(pl.col("metric").mean())
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# config: tyro dataclass
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||||
import tyro
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from dataclasses import dataclass
|
||||
|
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@dataclass
|
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class Config:
|
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lr: float = 3e-4
|
||||
# {FILL_IN}
|
||||
|
||||
cfg = tyro.cli(Config)
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||||
```
|
||||
|
||||
---
|
||||
|
||||
## Research Epistemics
|
||||
|
||||
Separate observations from inferences:
|
||||
- **Observation**: "val_bpb dropped from 3.2 to 2.9 on run X" (measured fact)
|
||||
- **Inference**: "this suggests the attention head is learning positional structure" (interpretation)
|
||||
- **Claim from paper**: "authors claim X" -- not "X is true" unless you verified it
|
||||
|
||||
For complex arguments, use `/vargdown` skill: verified argument maps with credences.
|
||||
|
||||
Trust signals: community adoption > papers citing it > open source code > author reputation.
|
||||
|
||||
---
|
||||
|
||||
## Available Skills
|
||||
|
||||
Assume installed at `~/.claude/skills/` (from https://github.com/wassname/skills):
|
||||
|
||||
| Skill | Use for |
|
||||
|-------|---------|
|
||||
| `/semantic-search` | Search arXiv, Semantic Scholar, DBLP, OpenAlex |
|
||||
| `/arxiv-fetch` | Download full paper text given arXiv ID/URL |
|
||||
| `/exa-search` | Neural web search for recent approaches |
|
||||
| `/vargdown` | Verified argument maps with credences for complex reasoning |
|
||||
| `/gsd` | Get Shit Done: spec -> implement -> test -> review -> wrap |
|
||||
| `/jaxtyping` | Runtime tensor shape/dtype checking |
|
||||
| `/justfile` | Project recipes (`just smoke`, `just eval`, `just queue`) |
|
||||
| `/ml_debug` | ML convergence, gradient analysis, sweep methodology |
|
||||
| `/brainstorm` | Wide + deep ideation without tunnel vision |
|
||||
| `/external-review` | Code/plan review via a different model |
|
||||
| `pueue` | Queue GPU jobs sequentially; label each with Q/hypothesis |
|
||||
|
||||
Also available: bibtex MCP (search_reference, fetch), wandb MCP (query runs).
|
||||
<!-- {FILL_IN: add entries as experiments run} -->
|
||||
|
||||
---
|
||||
|
||||
## Meta-Mode
|
||||
|
||||
Human sets `META_MODE=1` to enable editing of FROZEN files and committing to main.
|
||||
|
||||
Use meta-mode to:
|
||||
- Revise this program.md (agent instructions)
|
||||
- Update eval.py (e.g., add new metric columns)
|
||||
- Reflect on the overall research process in meta_journal.md
|
||||
- Exit-interview style: what worked, what didn't, what would you change?
|
||||
|
||||
To enter: human writes `META_MODE=1` in human_journal.md entry before asking agent.
|
||||
Human writes `META_MODE=1` in `human_journal.md` to unlock editing FROZEN files and committing to main. Use for: revising this program.md, updating eval.py, exit-interview style process reflection in meta_journal.md.
|
||||
|
||||
@@ -1,25 +1,90 @@
|
||||
"""
|
||||
Training loop. NOT frozen -- agents modify freely in worktrees.
|
||||
Model + training. NOT frozen -- agents modify freely in worktrees.
|
||||
|
||||
Convention: eval.py runs the frozen evaluation. This file handles training.
|
||||
Keep them separate so eval is never accidentally changed during experimentation.
|
||||
eval.py is frozen (anti-p-hacking). This file is not.
|
||||
Everything agents change lives here: architecture, optimizer, loss, data pipeline.
|
||||
"""
|
||||
|
||||
import os
|
||||
from dataclasses import dataclass
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import tyro
|
||||
import wandb
|
||||
from einops import rearrange # noqa: F401 -- available for use
|
||||
from jaxtyping import Float
|
||||
from loguru import logger
|
||||
from torch import Tensor
|
||||
|
||||
from model import Config, build_model
|
||||
if os.environ.get("BEARTYPE"):
|
||||
from beartype import beartype as typechecker
|
||||
from jaxtyping import jaxtyped
|
||||
else:
|
||||
def typechecker(f): return f
|
||||
def jaxtyped(**_): return lambda f: f
|
||||
|
||||
|
||||
# --- Config -------------------------------------------------------------------
|
||||
|
||||
@dataclass
|
||||
class Config:
|
||||
"""All hyperparameters. Edit freely. eval.py imports this."""
|
||||
|
||||
# model
|
||||
d_model: int = 256
|
||||
n_layers: int = 4
|
||||
# {FILL_IN}: add architecture params
|
||||
|
||||
# training
|
||||
lr: float = 3e-4
|
||||
batch_size: int = 32
|
||||
max_steps: int = 1000
|
||||
seed: int = 42
|
||||
|
||||
# data
|
||||
# {FILL_IN}: add data params
|
||||
|
||||
# logging
|
||||
wandb_project: str = "{FILL_IN}"
|
||||
|
||||
|
||||
# --- Model --------------------------------------------------------------------
|
||||
|
||||
class Model(nn.Module):
|
||||
"""
|
||||
{FILL_IN}: replace with your architecture.
|
||||
Agents: this is the main thing you modify between experiments.
|
||||
"""
|
||||
|
||||
def __init__(self, cfg: Config):
|
||||
super().__init__()
|
||||
self.cfg = cfg
|
||||
# {FILL_IN}: define layers
|
||||
|
||||
@jaxtyped(typechecker=typechecker)
|
||||
def forward(self, x: Float[Tensor, "b s"]) -> Float[Tensor, "b s d"]:
|
||||
# {FILL_IN}: implement forward pass
|
||||
raise NotImplementedError("{FILL_IN}: implement forward()")
|
||||
|
||||
|
||||
def build_model(cfg: Config) -> Model:
|
||||
torch.manual_seed(cfg.seed)
|
||||
model = Model(cfg)
|
||||
n_params = sum(p.numel() for p in model.parameters())
|
||||
logger.info(f"Model: {n_params:,} parameters")
|
||||
return model
|
||||
|
||||
|
||||
# --- Training -----------------------------------------------------------------
|
||||
|
||||
def train(cfg: Config):
|
||||
torch.manual_seed(cfg.seed)
|
||||
|
||||
wandb.init(
|
||||
project="{FILL_IN}", # replace with your W&B project name
|
||||
project=cfg.wandb_project,
|
||||
config=vars(cfg),
|
||||
# group is set by justfile sweep recipes via WANDB_RUN_GROUP env var
|
||||
# WANDB_RUN_GROUP env var set by justfile sweep recipes
|
||||
)
|
||||
|
||||
model = build_model(cfg)
|
||||
|
||||
Reference in New Issue
Block a user