From a92bf2bd0406e8a33a7b2f1ac5bac77bf3e72f5d Mon Sep 17 00:00:00 2001 From: Nikita Bondarev Date: Thu, 24 Sep 2026 15:33:44 +0400 Subject: [PATCH 1/2] Feat/llm benchmark (#5) * Add end-to-end LLM benchmark workflow --- .env.example | 19 + AGENTS.md | 148 +++++ README.md | 44 ++ doc/llm_benchmark/README.md | 107 ++++ doc/llm_benchmark/benchmark.ipynb | 139 +++++ pyproject.toml | 6 +- scripts/__init__.py | 1 + scripts/benchmark_llm.py | 7 + scripts/llm_benchmark/__init__.py | 3 + scripts/llm_benchmark/__main__.py | 5 + scripts/llm_benchmark/pipeline.py | 316 ++++++++++ scripts/llm_benchmark/sampling.py | 149 +++++ scripts/llm_benchmark/scoring.py | 116 ++++ scripts/llm_benchmark/templates.py | 92 +++ tests/unit/test_llm_benchmark.py | 146 +++++ uv.lock | 920 ++++++++++++++++++++++++++++- 16 files changed, 2212 insertions(+), 6 deletions(-) create mode 100644 .env.example create mode 100644 AGENTS.md create mode 100644 doc/llm_benchmark/README.md create mode 100644 doc/llm_benchmark/benchmark.ipynb create mode 100644 scripts/__init__.py create mode 100644 scripts/benchmark_llm.py create mode 100644 scripts/llm_benchmark/__init__.py create mode 100644 scripts/llm_benchmark/__main__.py create mode 100644 scripts/llm_benchmark/pipeline.py create mode 100644 scripts/llm_benchmark/sampling.py create mode 100644 scripts/llm_benchmark/scoring.py create mode 100644 scripts/llm_benchmark/templates.py create mode 100644 tests/unit/test_llm_benchmark.py diff --git a/.env.example b/.env.example new file mode 100644 index 0000000..708bc5f --- /dev/null +++ b/.env.example @@ -0,0 +1,19 @@ +# Copy this file to .env. The real .env is ignored by Git. +# +LLM_API_KEY= + +# Set only for a custom, proxy, or local endpoint. +# LLM_BASE_URL=http://localhost:4000 + +# LiteLLM also recognizes provider-specific keys. You can use one of these +# instead of LLM_API_KEY: +# OPENAI_API_KEY= +# GEMINI_API_KEY= +# ANTHROPIC_API_KEY= +# XAI_API_KEY= + +# Azure OpenAI uses these names directly. Pass the deployment at runtime as +# `--model azure/`. +# AZURE_OPENAI_ENDPOINT=https://.openai.azure.com/ +# AZURE_OPENAI_API_KEY= +# AZURE_OPENAI_API_VERSION=2024-10-21 diff --git a/AGENTS.md b/AGENTS.md new file mode 100644 index 0000000..7e0e56e --- /dev/null +++ b/AGENTS.md @@ -0,0 +1,148 @@ +# Repository agent guide + +This file is tool-agnostic and applies to any coding agent working in URSA. + +## Repository orientation (required) + +Before running commands or editing files: + +1. Read the root `README.md` for project scope, installation, data assets, + supported inputs, Solv-N semantics, and license restrictions. +2. Check `git status` and preserve unrelated or user-authored changes. +3. Read `pyproject.toml` before changing dependencies or tooling. +4. For changes to the scoring pipeline or domain model, inspect + `doc/uml/architecture.uml` and the relevant tests first. +5. Read the task-specific documentation listed below before launching an + expensive or externally billed operation. + +Use `uv` for environment and command execution. Do not commit generated data, +credentials, benchmark results, or unrelated untracked files unless the user +explicitly requests it. Do not commit, push, or publish without authorization. + +Before handing off code changes, run: + +```bash +uv sync --extra dev +uv run pre-commit run --all-files +uv run pytest +``` + +## LLM benchmark workflow + +Use the following sections when a user asks to configure, run, resume, inspect, +or compare an LLM benchmark. + +## Read first + +- Project overview and Solv-N semantics: `README.md` +- User guide and notebook: `doc/llm_benchmark/` +- Exact prompt templates: `scripts/llm_benchmark/templates.py` +- Scoring implementation: `scripts/llm_benchmark/scoring.py` +- Entry point: `scripts/benchmark_llm.py` + +## Gather the run configuration + +Before making API calls, determine the following. Ask only for missing choices: + +1. Model identifier and provider. Examples: `openai/gpt-5`, + `gemini/gemini-2.5-pro`, `anthropic/claude-sonnet-4-5`, `xai/grok-4`, or + `azure/`. +2. Benchmark: `EXPERT_2026`, `DRUGS_CLINICALS_2026`, or a custom CSV path. +3. Protocol size: target limit and samples per target. The published protocol + is all targets with 10 samples; a smoke test is 10 targets with 1 sample. +4. Concurrency: inference request workers and ChemCensor scoring workers. +5. Output directory and whether to resume or start fresh. + +State the total planned API calls (`targets × samples`) before a large run. +If the user already supplied all choices and explicitly requested the run, do +not ask them again. + +## Credentials + +Credentials are loaded automatically from the repository `.env`. + +- Generic: `LLM_API_KEY`, optionally `LLM_BASE_URL`. +- Azure: `AZURE_OPENAI_ENDPOINT`, `AZURE_OPENAI_API_KEY`, and + `AZURE_OPENAI_API_VERSION`. +- LiteLLM also understands provider-specific variables such as + `OPENAI_API_KEY`, `GEMINI_API_KEY`, `ANTHROPIC_API_KEY`, and `XAI_API_KEY`. + +Never ask the user to paste a secret into chat, source code, a notebook, or a +command. Ask whether the required variable is configured. If diagnosis is +needed, report only whether variables are set and sanitize endpoint output. +Never commit `.env`. + +For Azure, `--model` must be `azure/`. The deployment name is +the configured Azure deployment, which may differ from the base model name. + +## Setup and navigation + +```bash +uv sync --extra llm-benchmark +uv run python scripts/benchmark_llm.py --help +``` + +Use a dry run to verify target loading, canonicalization, and prompt format +without calling a model: + +```bash +uv run python scripts/benchmark_llm.py \ + --model MODEL \ + --benchmark EXPERT_2026 \ + --output data/results/RUN_NAME \ + --dry-run +``` + +Recommended smoke test: + +```bash +uv run python scripts/benchmark_llm.py \ + --model MODEL \ + --benchmark EXPERT_2026 \ + --limit 3 \ + --samples 1 \ + --request-workers 8 \ + --score-workers 0 \ + --output data/results/RUN_NAME +``` + +Full best-of-10: + +```bash +uv run python scripts/benchmark_llm.py \ + --model MODEL \ + --benchmark EXPERT_2026 \ + --samples 10 \ + --request-workers 100 \ + --score-workers 0 \ + --output data/results/RUN_NAME +``` + +## Run behavior + +- Target SMILES are canonicalized with RDKit before prompting. +- Completions are appended immediately to `OUTPUT/completions.jsonl`. +- Repeating a command resumes missing samples; `--fresh` discards existing + completions. +- Use a separate output directory for every model and protocol. Resume counting + is target-based and does not distinguish models. +- `--sample-only` performs inference only; `--score-only` reuses completions. +- `--request-workers` controls concurrent API calls. +- `--score-workers 0` lets ChemCensor choose the process count automatically. + +## Verify and report + +After completion, verify the process exit code and report: + +- adapted/total completions and `adapt_rate`; +- Solv-0, Solv-1, and Solv-2; +- `molecules_with_route / total_molecules`; +- output paths and elapsed time. + +Expected artifacts are `completions.jsonl`, `llm_metrics.json`, and +`llm_best_paths.json`. Do not commit generated results unless the user +explicitly requests it. + +CUDA-driver and `pkg_resources` warnings are non-fatal when scoring completes +successfully on CPU. A `KeyboardInterrupt` means a user stopped the run; resume +without `--fresh` to continue from saved completions. diff --git a/README.md b/README.md index c2507d4..3e645a7 100644 --- a/README.md +++ b/README.md @@ -204,6 +204,50 @@ dataset = BenchmarkDataset.from_csv( result = ursa.score_dataset(paths, target_smiles=dataset.target_smiles) ``` +## Benchmarking an LLM + +The script in `scripts/llm_benchmark/` runs the complete best-of-N protocol: +canonicalize targets, build randomized prompts, sample a model through +[LiteLLM](https://docs.litellm.ai/), adapt the XML-like answers with RetroCast, +and calculate Solv-N metrics. Usage documentation and an interactive notebook +are available in [`doc/llm_benchmark/`](doc/llm_benchmark/). + +Install the optional inference dependencies: + +```bash +uv sync --extra llm-benchmark +cp .env.example .env +``` + +Put API credentials in `.env`; select the model for each run with `--model`. +LiteLLM model identifiers support OpenAI, Gemini, Anthropic, xAI, Azure OpenAI, +and other providers: + +```bash +uv run python scripts/benchmark_llm.py \ + --model anthropic/claude-sonnet-4-5 \ + --benchmark EXPERT_2026 \ + --samples 10 \ + --request-workers 8 \ + --score-workers 0 \ + --output data/results/claude +``` + +For Azure OpenAI, set `AZURE_OPENAI_ENDPOINT`, `AZURE_OPENAI_API_KEY`, and +`AZURE_OPENAI_API_VERSION` in `.env`, then pass the deployment name: + +```bash +uv run python scripts/benchmark_llm.py \ + --model azure/my-deployment \ + --benchmark EXPERT_2026 \ + --output data/results/azure +``` + +Completions are appended to `OUTPUT/completions.jsonl` as they arrive. Repeating +the command resumes missing samples; `--fresh` starts over. `--sample-only` and +`--score-only` run one phase, `--limit N` selects the first N targets for a smoke +test, and `--dry-run` prints one prompt without calling the model. + --- ## License diff --git a/doc/llm_benchmark/README.md b/doc/llm_benchmark/README.md new file mode 100644 index 0000000..c1bdf3b --- /dev/null +++ b/doc/llm_benchmark/README.md @@ -0,0 +1,107 @@ +# LLM retrosynthesis benchmark + +This workflow evaluates an LLM on multi-step retrosynthesis with URSA. For each +target it builds a randomized prompt, samples one or more completions, adapts +the generated `` blocks into route trees, and reports Solv-0/1/2. + +Use [`benchmark.ipynb`](benchmark.ipynb) for an interactive run or +`scripts/benchmark_llm.py` from the command line. + +## Setup + +From the repository root: + +```bash +uv sync --extra llm-benchmark +cp .env.example .env +``` + +Put credentials in `.env`; never put secrets in the notebook. Generic +LiteLLM-compatible providers can use: + +```dotenv +LLM_API_KEY=... +# LLM_BASE_URL=https://custom-provider.example/v1 +``` + +Azure OpenAI uses: + +```dotenv +AZURE_OPENAI_ENDPOINT=https://.openai.azure.com/ +AZURE_OPENAI_API_KEY=... +AZURE_OPENAI_API_VERSION=2024-10-21 +``` + +The model is selected per run, not in `.env`. Examples include +`openai/gpt-5`, `gemini/gemini-2.5-pro`, +`anthropic/claude-sonnet-4-5`, `xai/grok-4`, and +`azure/`. + +## Benchmark sets + +Two built-in sets are available: + +- `EXPERT_2026` — 100 expert targets. +- `DRUGS_CLINICALS_2026` — drugs and clinical candidates. + +A custom CSV can be passed instead. Its default SMILES column is +`product_smiles`; use `--smiles-col` to select another column. Target SMILES +are canonicalized with RDKit before prompting and scoring. + +## Run from the CLI + +Small smoke test: + +```bash +uv run python scripts/benchmark_llm.py \ + --model azure/my-deployment \ + --benchmark EXPERT_2026 \ + --limit 10 \ + --samples 1 \ + --request-workers 8 \ + --score-workers 0 \ + --output data/results/azure-smoke +``` + +Full best-of-10 protocol: + +```bash +uv run python scripts/benchmark_llm.py \ + --model azure/my-deployment \ + --benchmark EXPERT_2026 \ + --samples 10 \ + --request-workers 100 \ + --score-workers 0 \ + --output data/results/azure-best-of-10 +``` + +`--request-workers` controls concurrent inference requests. +`--score-workers` controls ChemCensor worker processes; `0` auto-selects the +process count. + +## Resume and rerun + +Completions are appended to `OUTPUT/completions.jsonl` immediately. Repeating +the same command requests only missing samples. Use: + +- `--sample-only` to stop after inference. +- `--score-only` to rescore an existing completion file. +- `--fresh` to delete existing completions before inference. +- `--dry-run` to print one prompt without calling the model. + +Use a separate output directory for each model and protocol. Resume counting +is based on target SMILES, so reusing a directory for another model would mix +their completions. + +## Outputs + +The output directory contains: + +- `completions.jsonl` — raw model responses and target metadata. +- `llm_metrics.json` — Solv-N metrics, completion/adaptation counts, + `adapt_rate`, and retained route count. +- `llm_best_paths.json` — best route and per-step scores for each target. + +Every Solv-N rate uses the full target list as its denominator. Targets with no +parsable route therefore lower the score. `adapt_rate` is the fraction of +completions successfully converted by the RetroCast `ursa` adapter. diff --git a/doc/llm_benchmark/benchmark.ipynb b/doc/llm_benchmark/benchmark.ipynb new file mode 100644 index 0000000..8e0dcca --- /dev/null +++ b/doc/llm_benchmark/benchmark.ipynb @@ -0,0 +1,139 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "9a3fe31f", + "metadata": {}, + "source": [ + "# LLM retrosynthesis benchmark\n", + "\n", + "Run the end-to-end URSA best-of-N benchmark: sample an LLM, adapt generated routes, score them, and inspect Solv-N metrics.\n", + "\n", + "Before running this notebook:\n", + "\n", + "1. Install dependencies from the repository root: `uv sync --extra llm-benchmark`.\n", + "2. Copy `.env.example` to `.env` and add provider credentials.\n", + "3. Select the model or Azure deployment in the configuration cell below.\n", + "\n", + "See [`README.md`](README.md) for provider setup, CLI examples, resume behavior, and output descriptions. Secrets stay in `.env`; do not paste API keys into this notebook." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "27fd8e0f", + "metadata": {}, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "\n", + "\n", + "def find_repository_root(start: Path) -> Path:\n", + " for candidate in (start, *start.parents):\n", + " if (candidate / \"pyproject.toml\").exists():\n", + " return candidate\n", + " raise RuntimeError(\"Run this notebook from inside the URSA repository\")\n", + "\n", + "\n", + "ROOT = find_repository_root(Path.cwd().resolve())\n", + "\n", + "# LiteLLM model identifier. For Azure, use azure/.\n", + "MODEL = \"openai/gpt-6-astra\"\n", + "\n", + "BENCHMARK = \"EXPERT_2026\" # or \"DRUGS_CLINICALS_2026\"\n", + "OUTPUT = ROOT / \"data/results/notebook\"\n", + "\n", + "LIMIT = 0 # number of target molecules to sample, use 0 for all\n", + "SAMPLES = 1 # number of completions per target\n", + "REQUEST_WORKERS = 8 # number of parallel requests\n", + "SCORE_WORKERS = 8 # number of ChemCensor worker processes, use 0 for auto-tuning\n", + "FRESH = False # whether to discard existing completions" + ] + }, + { + "cell_type": "markdown", + "id": "0bcfd800", + "metadata": {}, + "source": [ + "## Run the benchmark\n", + "\n", + "The command resumes an existing `completions.jsonl`. Use a separate `OUTPUT` directory for each model. Set `FRESH = True` only when you intentionally want to discard previous completions and start again." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "04ac8e01", + "metadata": {}, + "outputs": [], + "source": [ + "import subprocess\n", + "import sys\n", + "\n", + "command = [\n", + " sys.executable,\n", + " str(ROOT / \"scripts/benchmark_llm.py\"),\n", + " \"--model\",\n", + " MODEL,\n", + " \"--benchmark\",\n", + " BENCHMARK,\n", + " \"--samples\",\n", + " str(SAMPLES),\n", + " \"--request-workers\",\n", + " str(REQUEST_WORKERS),\n", + " \"--score-workers\",\n", + " str(SCORE_WORKERS),\n", + " \"--output\",\n", + " str(OUTPUT),\n", + "]\n", + "if LIMIT:\n", + " command.extend([\"--limit\", str(LIMIT)])\n", + "if FRESH:\n", + " command.append(\"--fresh\")\n", + "\n", + "print(\" \".join(command))\n", + "subprocess.run(command, cwd=ROOT, check=True)" + ] + }, + { + "cell_type": "markdown", + "id": "85409f47", + "metadata": {}, + "source": [ + "## Inspect results\n", + "\n", + "The metrics include Solv-0/1/2, parser adaptation rate, completion counts, and the number of routes retained for scoring." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "03f2dec9", + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "\n", + "from IPython.display import JSON, display\n", + "\n", + "metrics_path = OUTPUT / \"llm_metrics.json\"\n", + "metrics = json.loads(metrics_path.read_text())\n", + "display(JSON(metrics, expanded=True))\n", + "metrics" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/pyproject.toml b/pyproject.toml index 696b441..7ebf7fc 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -9,13 +9,17 @@ dependencies = [ "huggingface-hub>=0.26.0", "numpy==1.26.4", "rdkit>=2025.3.5", - "retrocast>=0.7.0", + "retrocast>=0.7.0,<0.8", ] [project.scripts] ursa-bench = "ursa.cli:main" [project.optional-dependencies] +llm-benchmark = [ + "litellm>=1.0,<2.0", + "python-dotenv>=1.0,<2.0", +] dev = [ "flake8==7.0.0", "pre-commit==4.2.0", diff --git a/scripts/__init__.py b/scripts/__init__.py new file mode 100644 index 0000000..7ca1b78 --- /dev/null +++ b/scripts/__init__.py @@ -0,0 +1 @@ +"""Repository maintenance and benchmark scripts.""" diff --git a/scripts/benchmark_llm.py b/scripts/benchmark_llm.py new file mode 100644 index 0000000..c0061da --- /dev/null +++ b/scripts/benchmark_llm.py @@ -0,0 +1,7 @@ +#!/usr/bin/env python3 +# Compatibility entry point; implementation lives in scripts/llm_benchmark/. +from llm_benchmark.pipeline import main + + +if __name__ == "__main__": + main() diff --git a/scripts/llm_benchmark/__init__.py b/scripts/llm_benchmark/__init__.py new file mode 100644 index 0000000..1d4de9f --- /dev/null +++ b/scripts/llm_benchmark/__init__.py @@ -0,0 +1,3 @@ +from .pipeline import main + +__all__ = ["main"] diff --git a/scripts/llm_benchmark/__main__.py b/scripts/llm_benchmark/__main__.py new file mode 100644 index 0000000..85798aa --- /dev/null +++ b/scripts/llm_benchmark/__main__.py @@ -0,0 +1,5 @@ +from .pipeline import main + + +if __name__ == "__main__": + main() diff --git a/scripts/llm_benchmark/pipeline.py b/scripts/llm_benchmark/pipeline.py new file mode 100644 index 0000000..90ee832 --- /dev/null +++ b/scripts/llm_benchmark/pipeline.py @@ -0,0 +1,316 @@ +from __future__ import annotations + +import argparse +import csv +import json +import os +import random +import sys +from pathlib import Path + +from .sampling import sample_completions +from .scoring import score_records +from .templates import build_prompt + +_PRESETS = ("EXPERT_2026", "DRUGS_CLINICALS_2026") +_DEFAULT_ENV_FILE = Path(__file__).resolve().parents[2] / ".env" + + +def canonicalize_targets(targets: list[str]) -> list[str]: + """Canonicalize target SMILES with RDKit while preserving stereochemistry.""" + from rdkit import Chem + + canonical = [] + for index, smiles in enumerate(targets, 1): + molecule = Chem.MolFromSmiles(smiles) + if molecule is None: + raise SystemExit( + f"error: invalid target SMILES at benchmark row {index}: {smiles}" + ) + canonical.append( + Chem.MolToSmiles(molecule, canonical=True, isomericSmiles=True) + ) + return canonical + + +def load_targets(benchmark: str, smiles_col: str) -> list[str]: + """Load and canonicalize target SMILES from a preset or custom CSV.""" + if benchmark in _PRESETS: + from ursa import BenchmarkDataset + + dataset = getattr(BenchmarkDataset, benchmark) + return canonicalize_targets([entry.smiles for entry in dataset.load()]) + + path = Path(benchmark) + if not path.exists(): + raise SystemExit( + f"error: benchmark not recognised and file not found: {benchmark}" + ) + with path.open(newline="") as handle: + reader = csv.DictReader(handle) + if reader.fieldnames is None or smiles_col not in reader.fieldnames: + raise SystemExit( + f"error: column {smiles_col!r} not in {path}. " + "Pass --smiles-col (URSA preset CSVs use 'SMILES')." + ) + targets = [ + value for row in reader if (value := (row.get(smiles_col) or "").strip()) + ] + if not targets: + raise SystemExit(f"error: no target SMILES in {path}") + return canonicalize_targets(targets) + + +def load_records(path: Path) -> list[dict]: + """Load and validate completion records from JSONL.""" + if not path.exists(): + return [] + records = [] + with path.open() as handle: + for line_no, line in enumerate(handle, 1): + text = line.strip() + if not text: + continue + record = json.loads(text) + completion = record.get("completion") + meta = record.get("meta") or {} + product = meta.get("product_smiles") + if not isinstance(completion, str) or not isinstance(product, str): + raise SystemExit( + f"error: {path}:{line_no} needs string 'completion' and " + "'meta.product_smiles'" + ) + records.append(record) + return records + + +def _env_int(name: str) -> int | None: + raw = os.environ.get(name) + if raw is None or raw == "": + return None + return int(raw) + + +def load_environment(argv: list[str]) -> None: + """Load an env file before environment-backed CLI defaults are evaluated.""" + env_parser = argparse.ArgumentParser(add_help=False) + env_parser.add_argument("--env-file", type=Path, default=_DEFAULT_ENV_FILE) + env_args, _ = env_parser.parse_known_args(argv) + + try: + from dotenv import load_dotenv + except ModuleNotFoundError as exc: + raise ModuleNotFoundError( + "Environment-file support requires the llm-benchmark extra. " + "Install it with `uv sync --extra llm-benchmark`." + ) from exc + load_dotenv(env_args.env_file, override=False) + + +def build_parser() -> argparse.ArgumentParser: + """Build the end-to-end benchmark CLI.""" + parser = argparse.ArgumentParser( + prog="scripts/benchmark_llm.py", + description="Sample an LLM on multi-step retrosynthesis and score with URSA.", + epilog=( + "LiteLLM model examples: openai/gpt-5, gemini/gemini-2.5-pro, " + "anthropic/claude-sonnet-4-5, xai/grok-4. " + "Use --base-url for a custom or local endpoint." + ), + ) + parser.add_argument( + "--env-file", + type=Path, + default=_DEFAULT_ENV_FILE, + help="Environment file to load (default: repository .env).", + ) + parser.add_argument( + "--benchmark", + required=True, + help=( + "Built-in preset (EXPERT_2026, DRUGS_CLINICALS_2026) or a CSV path. " + "This list is the denominator for every Solv-N rate." + ), + ) + parser.add_argument( + "--smiles-col", + default="product_smiles", + help="SMILES column for a custom CSV (default: product_smiles).", + ) + parser.add_argument( + "--limit", + type=int, + default=0, + help="Use only the first N benchmark targets. 0 keeps all.", + ) + parser.add_argument( + "--output", + type=Path, + required=True, + help="Directory for metrics JSON and best-path JSON.", + ) + parser.add_argument( + "--completions", + type=Path, + default=None, + help="JSONL of completions. Default: OUTPUT/completions.jsonl.", + ) + parser.add_argument( + "--stem", default="llm", help="Filename prefix for score files." + ) + parser.add_argument( + "--model", + default="", + help="LiteLLM model identifier for this benchmark run.", + ) + parser.add_argument( + "--base-url", + default=( + os.environ.get("LLM_BASE_URL") + or os.environ.get("AZURE_OPENAI_ENDPOINT") + or None + ), + help=( + "Optional provider endpoint. Reads LLM_BASE_URL or " + "AZURE_OPENAI_ENDPOINT." + ), + ) + parser.add_argument( + "--api-key", + default=( + os.environ.get("LLM_API_KEY") + or os.environ.get("AZURE_OPENAI_API_KEY") + or None + ), + help=( + "Provider key. Reads LLM_API_KEY or AZURE_OPENAI_API_KEY; optional " + "when LiteLLM can read a provider-specific variable." + ), + ) + parser.add_argument( + "--api-version", + default=( + os.environ.get("LLM_API_VERSION") + or os.environ.get("AZURE_OPENAI_API_VERSION") + or os.environ.get("AZURE_API_VERSION") + or None + ), + help="Optional API version, required by some Azure OpenAI deployments.", + ) + parser.add_argument("--samples", type=int, default=10, help="Samples per target.") + parser.add_argument( + "--request-workers", + "--api-workers", + dest="request_workers", + type=int, + default=8, + help="Concurrent inference requests (default: 8).", + ) + parser.add_argument("--timeout", type=float, default=180.0) + parser.add_argument( + "--temperature", + type=float, + default=None, + help="Sampling temperature. Omit to use the provider default.", + ) + top_k_default = _env_int("URSA_TOP_K") + parser.add_argument( + "--top-k", + type=int, + default=top_k_default if top_k_default is not None else 10, + help="Routes kept per target after parsing. 0 keeps all. Default: 10.", + ) + parser.add_argument( + "--score-workers", + type=int, + default=_env_int("URSA_WORKERS"), + help="ChemCensor worker processes. Omit for sequential; 0 auto-scales.", + ) + parser.add_argument( + "--bb-catalog", + type=Path, + default=os.environ.get("URSA_BB_CATALOG") or None, + ) + parser.add_argument( + "--chemcensor-db", + type=Path, + default=os.environ.get("URSA_CHEMCENSOR_DB") or None, + ) + parser.add_argument( + "--fresh", + action="store_true", + help="Delete an existing completions file before sampling.", + ) + parser.add_argument("--sample-only", action="store_true") + parser.add_argument("--score-only", action="store_true") + parser.add_argument( + "--dry-run", + action="store_true", + help="Print one prompt for the first target and exit.", + ) + parser.add_argument("--seed", type=int, default=None) + return parser + + +def main(argv: list[str] | None = None) -> None: + """Run sampling and scoring, or either phase independently.""" + argv = sys.argv[1:] if argv is None else argv + load_environment(argv) + parser = build_parser() + args = parser.parse_args(argv) + if args.sample_only and args.score_only: + parser.error("choose one of --sample-only and --score-only") + if args.samples < 1: + parser.error("--samples must be >= 1") + if args.limit < 0: + parser.error("--limit must be >= 0") + if args.request_workers < 1: + parser.error("--request-workers must be >= 1") + if args.seed is not None: + random.seed(args.seed) + + targets = load_targets(args.benchmark, args.smiles_col) + if args.limit: + targets = targets[: args.limit] + print(f"Benchmark: {args.benchmark} ({len(targets)} targets)") + if args.dry_run: + print(build_prompt(targets[0])) + return + + completions_path = args.completions or (args.output / "completions.jsonl") + if not args.score_only: + if not args.model: + parser.error("--model is required when sampling") + sample_completions( + targets, + load_records(completions_path), + n_samples=args.samples, + completions_path=completions_path, + base_url=args.base_url, + api_key=args.api_key, + api_version=args.api_version, + model=args.model, + timeout=args.timeout, + temperature=args.temperature, + request_workers=args.request_workers, + fresh=args.fresh, + ) + print(f"Completions: {completions_path}") + + if args.sample_only: + return + if not completions_path.exists(): + raise SystemExit(f"error: completions file not found: {completions_path}") + + records = load_records(completions_path) + print(f"Scoring {len(records)} completions") + score_records( + records, + targets, + top_k=args.top_k, + score_workers=args.score_workers, + bb_catalog=args.bb_catalog, + chemcensor_db=args.chemcensor_db, + output=args.output, + stem=args.stem, + ) diff --git a/scripts/llm_benchmark/sampling.py b/scripts/llm_benchmark/sampling.py new file mode 100644 index 0000000..da8409f --- /dev/null +++ b/scripts/llm_benchmark/sampling.py @@ -0,0 +1,149 @@ +from __future__ import annotations + +import json +import sys +import time +from collections import defaultdict +from concurrent.futures import as_completed +from concurrent.futures import ThreadPoolExecutor +from pathlib import Path + +from .templates import build_prompt + + +def chat_completion( + *, + base_url: str | None, + api_key: str | None, + api_version: str | None, + model: str, + prompt: str, + timeout: float, + temperature: float | None, + attempts: int = 4, +) -> str: + """Request one completion from any provider supported by LiteLLM.""" + try: + import litellm + except ModuleNotFoundError as exc: + raise ModuleNotFoundError( + "LLM sampling requires LiteLLM. Install it with " + "`uv sync --extra llm-benchmark`." + ) from exc + # Custom Azure deployment names are absent from LiteLLM's public model + # catalog. Routing still works, but LiteLLM otherwise prints a misleading + # provider-list hint for every request. + litellm.suppress_debug_info = True + + request: dict = { + "model": model, + "messages": [{"role": "user", "content": prompt}], + "timeout": timeout, + } + if api_key: + request["api_key"] = api_key + if base_url: + request["api_base"] = base_url + if api_version: + request["api_version"] = api_version + if temperature is not None: + request["temperature"] = temperature + + last_error: Exception | None = None + for attempt in range(attempts): + try: + response = litellm.completion(**request) + content = response.choices[0].message.content + if not isinstance(content, str) or not content.strip(): + raise RuntimeError("model returned an empty completion") + return content + except Exception as exc: + last_error = exc + if attempt == attempts - 1: + raise + time.sleep(2**attempt) + raise RuntimeError(f"chat completion failed: {last_error}") + + +def sample_completions( + targets: list[str], + existing_records: list[dict], + *, + n_samples: int, + completions_path: Path, + base_url: str | None, + api_key: str | None, + api_version: str | None, + model: str, + timeout: float, + temperature: float | None, + request_workers: int, + fresh: bool, +) -> None: + """Append missing best-of-N samples to the completion JSONL.""" + completions_path.parent.mkdir(parents=True, exist_ok=True) + if fresh and completions_path.exists(): + completions_path.unlink() + existing_records = [] + + have: dict[str, int] = defaultdict(int) + for record in existing_records: + have[record["meta"]["product_smiles"]] += 1 + + unique_targets = list(dict.fromkeys(targets)) + jobs: list[tuple[str, str]] = [] + for target in unique_targets: + for _ in range(max(n_samples - have[target], 0)): + jobs.append((target, build_prompt(target))) + + done = sum(min(have[target], n_samples) for target in unique_targets) + total = len(unique_targets) * n_samples + print(f"Sampling {model}: {len(jobs)} requests ({done}/{total} already on disk)") + if not jobs: + return + + with completions_path.open("a") as handle: + # Validate provider configuration before starting the worker pool. + target, prompt = jobs.pop(0) + completion = chat_completion( + base_url=base_url, + api_key=api_key, + api_version=api_version, + model=model, + prompt=prompt, + timeout=timeout, + temperature=temperature, + ) + record = { + "completion": completion, + "meta": {"product_smiles": target}, + } + handle.write(json.dumps(record, ensure_ascii=False) + "\n") + handle.flush() + finished = done + 1 + print(f" {finished}/{total} {target}", file=sys.stderr) + + with ThreadPoolExecutor(max_workers=request_workers) as pool: + futures = { + pool.submit( + chat_completion, + base_url=base_url, + api_key=api_key, + api_version=api_version, + model=model, + prompt=prompt, + timeout=timeout, + temperature=temperature, + ): target + for target, prompt in jobs + } + for future in as_completed(futures): + target = futures[future] + record = { + "completion": future.result(), + "meta": {"product_smiles": target}, + } + handle.write(json.dumps(record, ensure_ascii=False) + "\n") + handle.flush() + finished += 1 + print(f" {finished}/{total} {target}", file=sys.stderr) diff --git a/scripts/llm_benchmark/scoring.py b/scripts/llm_benchmark/scoring.py new file mode 100644 index 0000000..c17ecfe --- /dev/null +++ b/scripts/llm_benchmark/scoring.py @@ -0,0 +1,116 @@ +from __future__ import annotations + +import json +from collections import defaultdict +from pathlib import Path + + +def _to_node(molecule): + """Convert a RetroCast molecule tree into an URSA node tree.""" + from ursa import RetrosyntheticNode + + if molecule.product_of is None: + return RetrosyntheticNode(smiles=molecule.smiles) + children = tuple(_to_node(reactant) for reactant in molecule.product_of.reactants) + return RetrosyntheticNode(smiles=molecule.smiles, children=children) + + +def completions_to_paths( + records: list[dict], targets: list[str], top_k: int +) -> tuple[list, int, int]: + """Parse completions and return paths, adapted count, and attempted count.""" + from retrocast import Target + from retrocast import adapt_routes + from retrocast import chem + from retrocast import get_adapter + from ursa import RetrosyntheticPath + + wanted = set(targets) + grouped: dict[str, list[dict]] = defaultdict(list) + for record in records: + smiles = record["meta"]["product_smiles"] + if smiles in wanted: + grouped[smiles].append(record) + + adapter = get_adapter("ursa") + paths = [] + n_adapted = 0 + n_completions = 0 + for target, group in grouped.items(): + n_completions += len(group) + target_obj = Target( + id=target, + smiles=chem.canonicalize_smiles(target), + inchikey=chem.get_inchi_key(target), + ) + routes = adapt_routes(group, adapter, target=target_obj) + n_adapted += len(routes) + if top_k > 0: + routes = routes[:top_k] + for rank, route in enumerate(routes, 1): + paths.append( + RetrosyntheticPath( + path_id=f"{target}__r{rank}", + root=_to_node(route.target), + ) + ) + return paths, n_adapted, n_completions + + +def score_records( + records: list[dict], + targets: list[str], + *, + top_k: int, + score_workers: int | None, + bb_catalog: Path | None, + chemcensor_db: Path | None, + output: Path, + stem: str, +) -> None: + """Score records while retaining the full target list as denominator.""" + from ursa import Ursa + + paths, n_adapted, n_completions = completions_to_paths(records, targets, top_k) + rate = (n_adapted / n_completions) if n_completions else 0.0 + print( + f"Adapted {n_adapted}/{n_completions} completions ({rate:.4f}); " + f"{len(paths)} routes kept" + ) + + parallel = score_workers is not None + n_workers = score_workers if score_workers else None + ursa = Ursa( + parallel=parallel, + n_workers=n_workers, + bb_catalog_path=bb_catalog, + chemcensor_db_path=chemcensor_db, + ) + result = ursa.score_dataset(paths, target_smiles=targets) + metrics = result.metrics + print( + f"\nResults:\n" + f" total_molecules: {metrics.total_molecules}\n" + f" molecules_with_route: {metrics.molecules_with_route}\n" + f" Solv-0 (STR): {metrics.solv_0:.4f} " + f"({metrics.routes_solv_0} routes)\n" + f" Solv-1: {metrics.solv_1:.4f} " + f"({metrics.routes_solv_1} routes)\n" + f" Solv-2: {metrics.solv_2:.4f} " + f"({metrics.routes_solv_2} routes)\n" + f" mean_score_without_fg: {metrics.mean_score_without_fg:.4f}\n" + f" mean_score_with_fg: {metrics.mean_score_with_fg:.4f}\n" + f" adapt_rate: {rate:.4f}" + ) + metrics_path, paths_path = result.save(output, stem=stem) + metrics_payload = json.loads(metrics_path.read_text()) + metrics_payload.update( + { + "completions_total": n_completions, + "completions_adapted": n_adapted, + "adapt_rate": rate, + "routes_kept": len(paths), + } + ) + metrics_path.write_text(json.dumps(metrics_payload, indent=2) + "\n") + print(f"\nSaved:\n {metrics_path}\n {paths_path}") diff --git a/scripts/llm_benchmark/templates.py b/scripts/llm_benchmark/templates.py new file mode 100644 index 0000000..81e5bd4 --- /dev/null +++ b/scripts/llm_benchmark/templates.py @@ -0,0 +1,92 @@ +from __future__ import annotations + +import random + +QUERY_TEMPLATES = ( + ( + "Please propose a plausible synthetic route for the target molecule " + "{query}. The route must converge to commercially " + "available starting materials (purchasable building blocks)." + ), + ( + "Please propose a stepwise retrosynthetic plan for the target molecule " + "{query} in which the final precursors are limited to " + "commercially supplied reagents and building blocks." + ), + ( + "Please propose a convergent retrosynthetic pathway for the target " + "molecule {query}. It is mandatory that the route " + "ends on commercially available starting materials, not bespoke " + "intermediates." + ), + ( + "Design the entire synthetic route to reach the target molecule " + "{query} from catalog chemicals. Explicitly ensure " + "the retrosynthesis bottoms out in commercially accessible building " + "blocks." + ), + ( + "For the target molecule {query}, outline a " + "retrosynthetic analysis and propose a synthetic route. The route " + "must terminate in reagents and building blocks that are commercially " + "available from standard suppliers. Please propose the full route." + ), + ( + "Please propose a sequence of retrosynthetic disconnections that " + "constitutes a valid synthetic route for the target molecule " + "{query}. The route must lead to commercially " + "available starting materials." + ), + ( + "Please perform retrosynthetic analysis and propose a synthetic route " + "for the molecule {query}. The route is acceptable " + "only if every terminal precursor can be sourced as a commercially " + "available building block." + ), + ( + "Please propose a practical multi-step retrosynthetic plan for the " + "target molecule {query}, in which all reactions " + "ultimately rest on commercially catalogued building blocks as the " + "inputs." + ), + ( + "Please propose the retrosynthetic pathway from the target molecule " + "{query} down to commercially available building " + "blocks." + ), + ( + "Please propose a retrosynthetic scheme for the target molecule " + "{query}. The route must lead to commercially " + "purchasable reagents and building blocks." + ), +) + +FORMAT_INSTRUCTION = ( + "Return only blocks, one per reaction. Use the following " + "route only as an output-format example for the target " + "CC1CCN(Cc2ccccc2COC2(C(F)F)CCCC2)CC1:\n" + "query" + "CC1CCN(Cc2ccccc2COC2(C(F)F)CCCC2)CC1" + "FC(F)C1(O[Na])CCCC1" + "intermediate 1" + "CC1CCN(Cc2ccccc2CCl)CC1" + "intermediate 2" + "intermediate 1" + "FC(F)C1(O[Na])CCCC1" + "OC1(C(F)F)CCCC1" + "building block 1" + "intermediate 2" + "CC1CCN(Cc2ccccc2CCl)CC1" + "CC1CCN(Cc2ccccc2CO)CC1" + "building block 2\n" + "Use query for the target, intermediate N for a reactant that must still " + "be prepared, building block N for a purchasable one. Each intermediate " + "N is the product of some later block; every branch bottoms out in a " + "building block. Add no other text." +) + + +def build_prompt(target_smiles: str) -> str: + """Build one randomized benchmark prompt for a target.""" + query = random.choice(QUERY_TEMPLATES).replace("{query}", target_smiles) + return f"{query}\n\n{FORMAT_INSTRUCTION}" diff --git a/tests/unit/test_llm_benchmark.py b/tests/unit/test_llm_benchmark.py new file mode 100644 index 0000000..c09e181 --- /dev/null +++ b/tests/unit/test_llm_benchmark.py @@ -0,0 +1,146 @@ +import json +import sys +from pathlib import Path +from types import SimpleNamespace + +import pytest + +from scripts.llm_benchmark import sampling +from scripts.llm_benchmark import templates +from scripts.llm_benchmark.pipeline import canonicalize_targets +from scripts.llm_benchmark.pipeline import load_records +from scripts.llm_benchmark.scoring import completions_to_paths +from scripts.llm_benchmark.scoring import score_records + + +def test_build_prompt_uses_target_and_format_example(monkeypatch): + monkeypatch.setattr( + templates.random, + "choice", + lambda choices: choices[0], + ) + + prompt = templates.build_prompt("CCO") + + assert "CCO" in prompt + assert "only as an output-format example" in prompt + assert prompt.endswith("Add no other text.") + + +def test_canonicalize_targets_preserves_stereochemistry(): + assert canonicalize_targets(["OC(C)", "F/C=C/F"]) == ["CCO", "F/C=C/F"] + + +def test_canonicalize_targets_rejects_invalid_smiles(): + with pytest.raises(SystemExit, match="invalid target SMILES"): + canonicalize_targets(["not-a-smiles"]) + + +def test_sampling_resumes_existing_jsonl(tmp_path: Path, monkeypatch): + output = tmp_path / "completions.jsonl" + existing = { + "completion": "existing", + "meta": {"product_smiles": "CCO"}, + } + output.write_text(json.dumps(existing) + "\n") + + calls = [] + + def fake_completion(**kwargs): + calls.append(kwargs) + return "new route" + + monkeypatch.setattr(sampling, "chat_completion", fake_completion) + sampling.sample_completions( + ["CCO", "CCN"], + [existing], + n_samples=2, + completions_path=output, + base_url=None, + api_key=None, + api_version=None, + model="test/model", + timeout=1, + temperature=None, + request_workers=2, + fresh=False, + ) + + records = load_records(output) + assert len(calls) == 3 + assert len(records) == 4 + assert [r["meta"]["product_smiles"] for r in records].count("CCO") == 2 + assert [r["meta"]["product_smiles"] for r in records].count("CCN") == 2 + + +def test_format_example_adapts_to_a_route(): + target = "CC1CCN(Cc2ccccc2COC2(C(F)F)CCCC2)CC1" + records = [ + { + "completion": templates.FORMAT_INSTRUCTION, + "meta": {"product_smiles": target}, + } + ] + + paths, adapted, attempted = completions_to_paths(records, [target], top_k=10) + + assert (adapted, attempted, len(paths)) == (1, 1, 1) + assert paths[0].root.canonical_smiles + assert len(paths[0].root.children) == 2 + + +def test_score_records_persists_adaptation_metrics(tmp_path: Path, monkeypatch): + metrics = SimpleNamespace( + total_molecules=1, + molecules_with_route=1, + routes_solv_0=1, + routes_solv_1=1, + routes_solv_2=1, + solv_0=1.0, + solv_1=1.0, + solv_2=1.0, + mean_score_without_fg=2.0, + mean_score_with_fg=1.0, + ) + + class FakeResult: + def __init__(self): + self.metrics = metrics + + def save(self, output, stem=""): + output.mkdir(parents=True, exist_ok=True) + metrics_path = output / f"{stem}_metrics.json" + paths_path = output / f"{stem}_best_paths.json" + metrics_path.write_text("{}") + paths_path.write_text("[]") + return metrics_path, paths_path + + class FakeUrsa: + def __init__(self, **kwargs): + pass + + def score_dataset(self, paths, target_smiles): + return FakeResult() + + monkeypatch.setitem(sys.modules, "ursa", SimpleNamespace(Ursa=FakeUrsa)) + monkeypatch.setattr( + "scripts.llm_benchmark.scoring.completions_to_paths", + lambda records, targets, top_k: ([object()], 8, 10), + ) + + score_records( + [], + ["CCO"], + top_k=10, + score_workers=None, + bb_catalog=None, + chemcensor_db=None, + output=tmp_path, + stem="llm", + ) + + saved = json.loads((tmp_path / "llm_metrics.json").read_text()) + assert saved["completions_total"] == 10 + assert saved["completions_adapted"] == 8 + assert saved["adapt_rate"] == 0.8 + assert saved["routes_kept"] == 1 diff --git a/uv.lock b/uv.lock index 1d23126..c268cf4 100644 --- a/uv.lock +++ b/uv.lock @@ -8,6 +8,128 @@ resolution-markers = [ "python_full_version < '3.14'", ] +[[package]] +name = "aiohappyeyeballs" +version = "2.7.1" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/ce/f4/eec0465c2f67b2664688d0240b3212d5196fd89e741df67ddb81f8d35658/aiohappyeyeballs-2.7.1.tar.gz", hash = "sha256:065665c041c42a5938ed220bdcd7230f22527fbec085e1853d2402c8a3615d9d", size = 24757, upload-time = "2026-07-01T17:11:55.501Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/71/43/1947f06babed6b3f1d7f38b0c767f52df66bfb2bc10b468c4a7de9eceff2/aiohappyeyeballs-2.7.1-py3-none-any.whl", 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