diff --git a/.gitignore b/.gitignore index 85fda40..420c5cf 100644 --- a/.gitignore +++ b/.gitignore @@ -27,8 +27,29 @@ build/ htmlcov/ .pytest_cache/ .mypy_cache/ +.ruff_cache/ + +# Jupyter +.ipynb_checkpoints/ # Environment .env .env.local +# Outputs +outputs/ + +# Local research data and generated artifacts +data/ +figures/ +src/probing_reflection/*.csv +src/probing_reflection/*.ipynb +src/probing_reflection/*.pdf +src/probing_reflection/*.png + +# Local agent workflow state +.omo/ +.sisyphus/ + +# Local TMLR repository +67d0fc7176c97e12c92d4271/ diff --git a/.ignore b/.ignore new file mode 100644 index 0000000..3a59c82 --- /dev/null +++ b/.ignore @@ -0,0 +1,17 @@ +# Keep Git-ignored local research material searchable by OpenCode/ripgrep. +!.omo/ +!.omo/** +!.sisyphus/ +!.sisyphus/** +!67d0fc7176c97e12c92d4271/ +!67d0fc7176c97e12c92d4271/** +!data/ +!data/** +!figures/ +!figures/** +!src/probing_reflection/.ipynb_checkpoints/ +!src/probing_reflection/.ipynb_checkpoints/** +!src/probing_reflection/*.csv +!src/probing_reflection/*.ipynb +!src/probing_reflection/*.pdf +!src/probing_reflection/*.png diff --git a/.sisyphus/drafts/token-probe-experiment.md b/.sisyphus/drafts/token-probe-experiment.md deleted file mode 100644 index 154d468..0000000 --- a/.sisyphus/drafts/token-probe-experiment.md +++ /dev/null @@ -1,22 +0,0 @@ -# Draft: Token Probe Experiment - -## Requirements (confirmed) -- Create a new git branch for experiments -- Two Python files for two tasks: - 1. Find top 50 pos/neg tokens per concept using discriminative method, decode and display - 2. Train linear probe on tokens closest to pos/neg centers, test separability across 10 layers - -## Open Questions -- **Model**: Which model to probe? -- **Concepts**: What specific concepts to analyze? -- **Data source**: Where does the token/concept data come from? -- **Discriminative method**: Which specific method? (mass-mean, contrast-cons, etc.) -- **Distance metric**: How to measure "closest to center"? (cosine, euclidean) -- **Token selection criteria**: Top-K based on what score? - -## Technical Decisions -- (pending user input) - -## Scope Boundaries -- INCLUDE: Two Python scripts, new branch -- EXCLUDE: (pending) diff --git a/README.md b/README.md index 6d949ae..e7540e1 100644 --- a/README.md +++ b/README.md @@ -1,70 +1,149 @@ # ProbingReflection -Source code for the paper **"From Emergence to Control: Probing and Modulating Self-Reflection in Language Models"** +Official code for **[From Emergence to Control: Probing and Modulating +Self-Reflection in Language Models](https://arxiv.org/abs/2506.12217)**. + +This repository studies where self-reflection appears in language-model +representations and how it can be measured and controlled. It includes +reproducible pipelines for inference, LLM-based evaluation, reflection-token +diagnosis, activation probing, steering-vector extraction, and steering +interventions. + +## Features + +- Run chain-of-thought inference on mathematical reasoning datasets. +- Evaluate generated answers with a bidirectional LLM-as-a-judge protocol. +- Detect and categorize self-reflection tokens in model outputs. +- Measure reasoning quality with ROSCOE-style faithfulness, coherence, + informativeness, repetition, and completeness scores. +- Train layer-wise linear probes to test whether reflective and + non-reflective token activations are linearly separable. +- Extract difference-in-means steering vectors from contrastive activations. +- Apply steering vectors during generation with configurable layers and + coefficients. -## Overview +## Installation -This project investigates self-reflection in Large Language Models (LLMs) through probing and steering techniques. We explore how reflection behaviors emerge and how they can be controlled through vector-based interventions. +The project requires Python 3.12 or newer and uses +[uv](https://docs.astral.sh/uv/) for dependency management. -## Key Concepts +```bash +git clone https://github.com/xzAscC/ProbingReflection.git +cd ProbingReflection +uv sync +``` -- **Probing Vectors**: Techniques to detect and measure self-reflection patterns in model activations -- **Model Insertion**: Methods for injecting steering vectors to modulate reflection behavior -- **Reflection Analysis**: Frameworks for evaluating and understanding model self-reflection +Model-backed experiments require access to the configured Hugging Face models +and sufficient CPU/GPU memory. Some datasets, including GPQA, may require +accepting their access conditions on Hugging Face. -## Installation +## Command-Line Usage + +List the available commands: ```bash -uv sync +uv run probing-reflection --help ``` -## Development +### Inference ```bash -# Lint check -uv run ruff check src/ tests/ +uv run probing-reflection inference \ + --model Qwen/Qwen3.5-0.8B \ + --dataset HuggingFaceH4/MATH-500 \ + --batch-size 8 \ + --max-new-tokens 256 \ + --limit 100 \ + --output outputs/math500.jsonl +``` -# Format -uv run ruff format src/ tests/ +### Answer evaluation -# Type check -uv run mypy src/ +```bash +uv run probing-reflection evaluate outputs/math500.jsonl \ + --model Qwen/Qwen3.5-27B \ + --confidence-threshold 0.7 \ + --output outputs/evaluation.json +``` -# Run tests -uv run pytest +### Reflection diagnosis + +```bash +uv run probing-reflection reflection-diagnose \ + --input outputs/math500.jsonl \ + --model Qwen/Qwen3.5-27B \ + --output-dir outputs/reflection_diagnosis ``` -## Project Structure +### Steering-vector extraction +```bash +uv run probing-reflection extract-vectors \ + --input outputs/reflection_diagnosis/analyzed_samples.jsonl \ + --model Qwen/Qwen2.5-0.5B \ + --layers 8,12,16 \ + --min-samples 10 \ + --output outputs/steering_vectors.pt ``` -. -├── src/probing_reflection/ # Source code -│ ├── __init__.py -│ └── py.typed -├── tests/ # Test files -├── docs/ # Documentation -│ └── design-docs/ # Design documents -├── AGENTS.md # AI agent instructions -├── ARCHITECTURE.md # System architecture -└── pyproject.toml # Project configuration + +Additional wrappers for evaluation, diagnosis, linear probing, vector +extraction, and steering inference are available under +`scripts/probing_reflection/`. Use `scripts/run_experiments.py` to coordinate +multi-dataset steering experiments and `scripts/generate_report.py` to create +summary reports. + +## Project Layout + +```text +src/probing_reflection/ +├── inference.py # Dataset inference pipeline +├── evaluation.py # Answer evaluation and reports +├── reflection_diagnosis.py # Reflection-token analysis +├── linear_probe.py # Layer-wise linear probing +├── steering_vectors.py # Steering-vector extraction +├── steering_inference.py # Steered generation +├── judges.py # Shared LLM judge implementations +├── roscoe_metrics.py # ROSCOE-style reasoning metrics +├── model_utils.py # Model loading and lifecycle helpers +├── batch_utils.py # Shared batching and decoding helpers +├── prompts.py # Prompt templates and reflection taxonomy +└── types.py # Typed configurations and result schemas + +tests/ # Deterministic unit and integration tests +scripts/ # Experiment and reporting entry points +docs/ # Architecture and design documentation ``` -## Research Workflow +Generated outputs, local datasets, figures, notebooks, and experiment state are +excluded from Git. The repository-level `.ignore` file keeps these local paths +searchable by OpenCode and ripgrep without uploading them to GitHub. + +## Development + +```bash +uv sync +uv run ruff check src/ tests/ scripts/ +uv run ruff format --check src/ tests/ scripts/ +uv run mypy src/ +uv run pytest +``` -This project follows an AI-assisted research workflow. See AGENTS.md for detailed instructions on how AI agents should work in this repository. +The current test suite contains 159 deterministic tests and mocks external +model and dataset boundaries. ## Citation If you use this code, please cite: ```bibtex -@article{probing_reflection_2024, - title={From Emergence to Control: Probing and Modulating Self-Reflection in Language Models}, - author={[Authors]}, - year={2024} +@article{zhu2025emergence, + title={From emergence to control: Probing and modulating self-reflection in language models}, + author={Zhu, Xudong and Jiang, Jiachen and Khalili, Mohammad Mahdi and Zhu, Zhihui}, + journal={arXiv preprint arXiv:2506.12217}, + year={2025} } ``` ## License -[Add your license here] +This project is released under the MIT License. See [LICENSE](LICENSE). diff --git a/pyproject.toml b/pyproject.toml index 7681594..6571fcb 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -7,6 +7,12 @@ name = "probing_reflection" version = "0.1.0" requires-python = ">=3.12" description = "ProbingReflection - Probing and Modulating Self-Reflection in Language Models" +dependencies = [ + "jupyter>=1.1.1", +] + +[project.scripts] +probing-reflection = "probing_reflection.__main__:main" [dependency-groups] dev = [ @@ -14,12 +20,22 @@ dev = [ "mypy>=1.13.0", "pytest>=8.0.0", "pyyaml>=6.0.3", + "transformers>=4.40.0", + "torch>=2.0.0", + "datasets>=2.14.0", + "tqdm>=4.65.0", + "types-tqdm>=4.65.0", + "accelerate>=0.26.0", + "bitsandbytes>=0.43.0", + "scikit-learn>=1.3.0", + "matplotlib>=3.7.0", ] [tool.ruff] target-version = "py312" line-length = 100 src = ["src"] +extend-exclude = ["src/probing_reflection/**/*.ipynb"] [tool.ruff.lint] select = ["E", "F", "I", "UP", "B", "SIM", "N"] @@ -32,6 +48,7 @@ python_version = "3.12" strict = true warn_return_any = true warn_unused_ignores = true +follow_untyped_imports = true packages = ["probing_reflection"] mypy_path = "src" diff --git a/scripts/crossfamily_judge.py b/scripts/crossfamily_judge.py new file mode 100644 index 0000000..08476b0 --- /dev/null +++ b/scripts/crossfamily_judge.py @@ -0,0 +1,237 @@ +"""Cross-family LLM-as-a-judge for hard-negative validation. + +Loads a LOCAL model from a DIFFERENT family than the generator (glm-5.2) and +judges the 25 hard-negative trajectories using the paper's exact 3-label +self-reflection rubric (NO_REFLECTION / SURFACE_REFLECTION / GENUINE_SELF_REFLECTION). + +This produces an independent, cross-family judge run that addresses the +independence concern (坑3): generator = glm-5.2, this judge = Qwen3 (or OLMo3). + +Usage: + uv run python scripts/crossfamily_judge.py --model Qwen/Qwen3-1.7B --run 4 +""" + +from __future__ import annotations + +import argparse +import json +import sys +import time +from collections import Counter +from pathlib import Path +from typing import Protocol, cast + +import torch +from transformers import AutoModelForCausalLM, AutoTokenizer + +DATA_DIR = Path(__file__).resolve().parent.parent / "data" / "hard_negatives" + +# The paper's EXACT judge prompt (main.tex §app:prompt_sr, lines 607-643). +JUDGE_SYSTEM = ( + "You are evaluating whether a model response exhibits genuine self-reflection.\n\n" + "Definition: Genuine self-reflection means that the model explicitly reconsiders, " + "questions, checks, backtracks on, or revises its earlier reasoning. This must go " + 'beyond superficial discourse markers such as "wait", "hold on", or "let me think". ' + "A response counts as genuine self-reflection only if there is evidence that the " + "model revisits prior reasoning and meaningfully changes, re-evaluates, or re-derives it.\n\n" + "Label the response as one of:\n" + "- NO_REFLECTION: no reflective behavior\n" + "- SURFACE_REFLECTION: contains reflection-like phrases, but no substantive " + "reconsideration of prior reasoning\n" + "- GENUINE_SELF_REFLECTION: explicitly revisits prior reasoning and meaningfully " + "re-evaluates, revises, or corrects it\n\n" + "Instructions:\n" + "1. Read the full question and full response.\n" + "2. Focus on behavior, not keywords alone.\n" + "3. Do not classify as genuine self-reflection unless the response refers back to " + "earlier reasoning and performs a meaningful check, correction, backtrack, or revision.\n" + "4. Prefer precision over recall.\n" + "5. If the response is labeled as GENUINE_SELF_REFLECTION, identify all reflection " + "occurrences in the response, not just the first one.\n" + "6. For each reflection occurrence, return the reflection phrase, the corresponding " + "reflection token, and a short evidence quote.\n" + "7. If there is no genuine self-reflection, return an empty list for " + '"reflection_instances".\n\n' + "Return JSON only:\n" + "{\n" + ' "label": "NO_REFLECTION | SURFACE_REFLECTION | GENUINE_SELF_REFLECTION",\n' + ' "confidence": 0.0-1.0,\n' + ' "reflection_instances": [\n' + " {\n" + ' "reflection_phrase": "...",\n' + ' "reflection_token": "...",\n' + ' "evidence_quote": "..."\n' + " }\n" + " ]\n" + "}" +) + +VALID_LABELS = {"NO_REFLECTION", "SURFACE_REFLECTION", "GENUINE_SELF_REFLECTION"} + + +class TokenizerOutput(Protocol): + input_ids: torch.Tensor + + +class TokenizerProtocol(Protocol): + pad_token: str | None + eos_token: str | None + + def apply_chat_template( + self, + conversation: list[dict[str, str]], + *, + add_generation_prompt: bool, + return_tensors: str, + ) -> torch.Tensor: ... + + def __call__(self, text: str, *, return_tensors: str) -> TokenizerOutput: ... + + def decode(self, token_ids: torch.Tensor, *, skip_special_tokens: bool) -> str: ... + + +class ModelProtocol(Protocol): + def generate(self, **kwargs: object) -> torch.Tensor: ... + + def eval(self) -> ModelProtocol: ... + + def to(self, device: torch.device) -> ModelProtocol: ... + + +def build_user_message(problem: str, trajectory: str) -> str: + return f"Question:\n{problem}\n\nResponse:\n{trajectory}" + + +def parse_judge_json(text: str) -> dict[str, object]: + """Extract the judge JSON verdict from raw model output.""" + # find first { and matching last } + start = text.find("{") + end = text.rfind("}") + if start == -1 or end == -1 or end < start: + return { + "label": "PARSE_ERROR", + "confidence": 0.0, + "reflection_instances": [], + "raw": text[:500], + } + raw_json = text[start : end + 1] + try: + parsed = json.loads(raw_json) + except json.JSONDecodeError: + # try to fix trailing commas / smart quotes + cleaned = raw_json.replace("\u201c", '"').replace("\u201d", '"').replace("'", '"') + try: + parsed = json.loads(cleaned) + except json.JSONDecodeError: + return { + "label": "PARSE_ERROR", + "confidence": 0.0, + "reflection_instances": [], + "raw": raw_json[:500], + } + label = str(parsed.get("label", "")).strip().upper() + # normalize common variants + if "GENUINE" in label: + label = "GENUINE_SELF_REFLECTION" + elif "SURFACE" in label: + label = "SURFACE_REFLECTION" + elif "NO" in label or "NONE" in label: + label = "NO_REFLECTION" + if label not in VALID_LABELS: + label = "PARSE_ERROR" + return { + "label": label, + "confidence": float(parsed.get("confidence", 0.0)), + "reflection_instances": parsed.get("reflection_instances", []) + if isinstance(parsed.get("reflection_instances"), list) + else [], + } + + +def judge_one( + model: ModelProtocol, + tokenizer: TokenizerProtocol, + problem: str, + trajectory: str, + device: torch.device, +) -> dict[str, object]: + messages = [ + {"role": "system", "content": JUDGE_SYSTEM}, + {"role": "user", "content": build_user_message(problem, trajectory)}, + ] + # Prefer chat template; fall back to raw if unavailable + if hasattr(tokenizer, "apply_chat_template"): + try: + input_ids = tokenizer.apply_chat_template( + messages, add_generation_prompt=True, return_tensors="pt" + ).to(device) + except Exception: + prompt = JUDGE_SYSTEM + "\n\n" + build_user_message(problem, trajectory) + input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(device) + else: + prompt = JUDGE_SYSTEM + "\n\n" + build_user_message(problem, trajectory) + input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(device) + + with torch.no_grad(): + out = model.generate( + input_ids=input_ids, + max_new_tokens=400, + do_sample=False, + temperature=1.0, + ) + gen = tokenizer.decode(out[0][input_ids.shape[1] :], skip_special_tokens=True) + return parse_judge_json(gen) + + +def main() -> int: + ap = argparse.ArgumentParser() + ap.add_argument("--model", default="Qwen/Qwen3-1.7B") + ap.add_argument("--run", type=int, default=4) + args = ap.parse_args() + + out_path = DATA_DIR / f"_judge_run_{args.run}_crossfamily.json" + + print(f"Loading cross-family judge model: {args.model}", flush=True) + t0 = time.time() + tokenizer = cast(TokenizerProtocol, AutoTokenizer.from_pretrained(args.model)) + if tokenizer.pad_token is None: + tokenizer.pad_token = tokenizer.eos_token + model = cast( + ModelProtocol, + AutoModelForCausalLM.from_pretrained(args.model, torch_dtype=torch.bfloat16), + ) + model.eval() + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + model = model.to(device) + print(f"Model loaded in {time.time() - t0:.1f}s on {device}", flush=True) + + results: list[dict[str, object]] = [] + for i in range(25): + traj_path = DATA_DIR / f"traj_{i:02d}.json" + d = json.loads(traj_path.read_text()) + t1 = time.time() + verdict = judge_one(model, tokenizer, d["problem"], d["trajectory"], device) + verdict["problem_id"] = i + verdict["judge_model"] = args.model + elapsed = time.time() - t1 + results.append(verdict) + reflection_instances = verdict.get("reflection_instances") + instance_count = len(reflection_instances) if isinstance(reflection_instances, list) else 0 + print( + f"[{i:02d}] {verdict['label']} (conf={verdict['confidence']:.2f}) " + f"#inst={instance_count} [{elapsed:.1f}s]", + flush=True, + ) + + out_path.write_text(json.dumps(results, indent=2, ensure_ascii=False)) + labels = [r["label"] for r in results] + c = Counter(labels) + n_non_genuine = sum(1 for label in labels if label != "GENUINE_SELF_REFLECTION") + print(f"\nWrote {out_path}") + print(f"Label distribution: {dict(c)}") + print(f"Correct (non-GENUINE): {n_non_genuine}/25") + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/scripts/generate_report.py b/scripts/generate_report.py new file mode 100755 index 0000000..6dc9f8b --- /dev/null +++ b/scripts/generate_report.py @@ -0,0 +1,77 @@ +#!/usr/bin/env python +"""Generate evaluation report from steering experiment results.""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path + + +def main() -> None: + parser = argparse.ArgumentParser(description="Generate evaluation report") + parser.add_argument( + "--output-dir", + default="outputs/steering_experiments", + help="Output directory", + ) + args = parser.parse_args() + + output_dir = Path(args.output_dir) + + results: dict[str, dict[str, dict]] = {} + for dataset_dir in output_dir.iterdir(): + if not dataset_dir.is_dir(): + continue + dataset_name = dataset_dir.name + results[dataset_name] = {} + for condition_dir in dataset_dir.iterdir(): + if not condition_dir.is_dir(): + continue + condition_name = condition_dir.name + eval_file = condition_dir / "evaluation.json" + if eval_file.exists(): + with open(eval_file) as f: + results[dataset_name][condition_name] = json.load(f) + + report_lines = [ + "# Steering Experiment Results", + "", + "## Summary", + "", + "| Dataset | Condition | Accuracy | Correct | Total |", + "|---------|-----------|----------|---------|-------|", + ] + + for dataset, conditions in sorted(results.items()): + for condition, data in sorted(conditions.items()): + accuracy = data.get("overall_accuracy", 0) * 100 + correct = data.get("correct_count", 0) + total = data.get("total_samples", 0) + report_lines.append( + f"| {dataset} | {condition} | {accuracy:.1f}% | {correct} | {total} |" + ) + + report_lines.extend( + [ + "", + "## Detailed Results", + "", + ] + ) + + for dataset, conditions in sorted(results.items()): + report_lines.append(f"### {dataset}") + report_lines.append("") + for condition, data in sorted(conditions.items()): + accuracy = data.get("overall_accuracy", 0) * 100 + report_lines.append(f"- **{condition}**: {accuracy:.1f}% accuracy") + report_lines.append("") + + report_path = output_dir / "evaluation_report.md" + report_path.write_text("\n".join(report_lines)) + print(f"Report written to {report_path}") + + +if __name__ == "__main__": + main() diff --git a/scripts/merge_judge_results.py b/scripts/merge_judge_results.py new file mode 100644 index 0000000..4bfa6cf --- /dev/null +++ b/scripts/merge_judge_results.py @@ -0,0 +1,99 @@ +"""Merge judge sidecar verdicts into each trajectory JSON. + +After 3 independent judge runs write _judge_run_{1,2,3}.json (each a list of +{problem_id, label, confidence, reflection_instances, rationale}), this script +appends judge_run_1/2/3 keys to every data/hard_negatives/traj_XX.json. + +A hard negative is CORRECT for a given run iff label != GENUINE_SELF_REFLECTION +(i.e. SURFACE_REFLECTION or NO_REFLECTION). + +Usage: + uv run python scripts/merge_judge_results.py +""" + +from __future__ import annotations + +import json +from pathlib import Path + +DATA_DIR = Path(__file__).resolve().parent.parent / "data" / "hard_negatives" +RUN_FILES = [ + DATA_DIR / "_judge_run_1.json", + DATA_DIR / "_judge_run_2.json", + DATA_DIR / "_judge_run_3.json", +] +TRAJ_GLOB = "traj_*.json" + + +def load_run(path: Path) -> dict[int, dict]: + if not path.exists(): + raise FileNotFoundError(f"Missing judge run file: {path}") + data = json.loads(path.read_text()) + indexed: dict[int, dict] = {} + for entry in data: + pid = entry.get("problem_id") + if pid is None: + raise ValueError(f"Entry in {path} missing problem_id: {entry}") + indexed[int(pid)] = entry + return indexed + + +def main() -> None: + runs = [load_run(p) for p in RUN_FILES] + traj_files = sorted(DATA_DIR.glob(TRAJ_GLOB)) + if not traj_files: + raise SystemExit(f"No traj_*.json files found in {DATA_DIR}") + + n_correct_per_run = [0, 0, 0] + n_all_correct = 0 + n_majority_correct = 0 + label_counts: dict[str, int] = {} + + for tf in traj_files: + traj = json.loads(tf.read_text()) + pid = int(traj["problem_id"]) + verdicts = [] + for i, run in enumerate(runs): + if pid not in run: + raise KeyError(f"problem_id={pid} not found in {RUN_FILES[i].name}") + v = run[pid] + # keep only judge-relevant fields, strip problem_id duplication + verdict = { + "label": v.get("label", "UNKNOWN"), + "confidence": v.get("confidence"), + "reflection_instances": v.get("reflection_instances", []), + "rationale": v.get("rationale", v.get("reasoning", "")), + } + verdicts.append(verdict) + label = verdict["label"] + label_counts[label] = label_counts.get(label, 0) + 1 + if label != "GENUINE_SELF_REFLECTION": + n_correct_per_run[i] += 1 + traj["judge_run_1"] = verdicts[0] + traj["judge_run_2"] = verdicts[1] + traj["judge_run_3"] = verdicts[2] + + all_non_genuine = all(v["label"] != "GENUINE_SELF_REFLECTION" for v in verdicts) + genuine_count = sum(1 for v in verdicts if v["label"] == "GENUINE_SELF_REFLECTION") + if all_non_genuine: + n_all_correct += 1 + if genuine_count <= 1: # majority (>=2 of 3) non-genuine + n_majority_correct += 1 + + tf.write_text(json.dumps(traj, indent=2, ensure_ascii=False)) + + n_traj = len(traj_files) + print(f"Merged {n_traj} trajectories with 3 judge runs each.") + print( + f"Per-run correct (non-GENUINE): run1={n_correct_per_run[0]}/{n_traj}, " + f"run2={n_correct_per_run[1]}/{n_traj}, run3={n_correct_per_run[2]}/{n_traj}" + ) + print(f"All-3-runs correct: {n_all_correct}/{n_traj}") + print(f"Majority (>=2 of 3) correct: {n_majority_correct}/{n_traj}") + print("Label distribution across all 3x25=75 judgments:") + for label, count in sorted(label_counts.items()): + print(f" {label}: {count}") + + +if __name__ == "__main__": + main() diff --git a/scripts/probing_reflection/run_diagnosis.sh b/scripts/probing_reflection/run_diagnosis.sh new file mode 100755 index 0000000..74993aa --- /dev/null +++ b/scripts/probing_reflection/run_diagnosis.sh @@ -0,0 +1,39 @@ +#!/bin/bash +# Diagnose reflection tokens in model outputs + +set -e + +# Check required argument +if [ -z "$1" ]; then + echo "Usage: $0 [options]" + echo "" + echo "Arguments:" + echo " input_jsonl Path to input JSONL file containing model outputs" + echo "" + echo "Options (set as environment variables):" + echo " MODEL Judge model name (default: Qwen/Qwen3.5-27B)" + echo " OUTPUT_DIR Output directory (default: outputs/reflection_analysis/)" + exit 1 +fi + +INPUT_PATH="$1" + +# Default values +MODEL="${MODEL:-Qwen/Qwen3.5-27B}" +OUTPUT_DIR="${OUTPUT_DIR:-outputs/reflection_analysis/}" + +echo "=== Running Reflection Diagnosis ===" +echo "Input: $INPUT_PATH" +echo "Judge Model: $MODEL" +echo "Output Directory: $OUTPUT_DIR" +echo "" + +# Run diagnosis +uv run probing-reflection reflection-diagnose \ + --input "$INPUT_PATH" \ + --output-dir "$OUTPUT_DIR" \ + --model "$MODEL" + +echo "" +echo "Diagnosis complete." +echo "Results saved to: $OUTPUT_DIR" diff --git a/scripts/probing_reflection/run_evaluation.sh b/scripts/probing_reflection/run_evaluation.sh new file mode 100755 index 0000000..5fd0208 --- /dev/null +++ b/scripts/probing_reflection/run_evaluation.sh @@ -0,0 +1,50 @@ +#!/bin/bash +# Evaluate model outputs using LLM judge + +set -e + +# Check required argument +if [ -z "$1" ]; then + echo "Usage: $0 [options]" + echo "" + echo "Arguments:" + echo " jsonl_path Path to JSONL file containing model outputs" + echo "" + echo "Options (set as environment variables):" + echo " MODEL Judge model name (default: Qwen/Qwen3.5-27B)" + echo " BATCH_SIZE Batch size for evaluation (default: 8)" + echo " THRESHOLD Confidence threshold (default: 0.7)" + echo " OUTPUT Output report path (optional)" + exit 1 +fi + +JSONL_PATH="$1" + +# Default values +MODEL="${MODEL:-Qwen/Qwen3.5-27B}" +BATCH_SIZE="${BATCH_SIZE:-8}" +THRESHOLD="${THRESHOLD:-0.7}" +OUTPUT="${OUTPUT:-}" + +# Build command +CMD=(uv run probing-reflection evaluate "$JSONL_PATH") +CMD+=(--model "$MODEL") +CMD+=(--batch-size "$BATCH_SIZE") +CMD+=(--confidence-threshold "$THRESHOLD") + +if [ -n "$OUTPUT" ]; then + CMD+=(--output "$OUTPUT") +fi + +echo "=== Running Evaluation ===" +echo "Input: $JSONL_PATH" +echo "Judge Model: $MODEL" +echo "Batch Size: $BATCH_SIZE" +echo "Confidence Threshold: $THRESHOLD" +echo "" + +# Run evaluation +"${CMD[@]}" + +echo "" +echo "Evaluation complete." diff --git a/scripts/probing_reflection/run_extract_vectors.sh b/scripts/probing_reflection/run_extract_vectors.sh new file mode 100755 index 0000000..3f337c0 --- /dev/null +++ b/scripts/probing_reflection/run_extract_vectors.sh @@ -0,0 +1,73 @@ +#!/bin/bash +# Extract steering vectors from model activations + +set -e + +# Check required argument +if [ -z "$1" ]; then + echo "Usage: $0 --layers [options]" + echo "" + echo "Arguments:" + echo " input_jsonl Path to input JSONL file with model outputs" + echo "" + echo "Required Options:" + echo " --layers Layer indices, comma-separated (e.g., '10,15,20')" + echo "" + echo "Optional (set as environment variables):" + echo " MODEL Model name (default: Qwen/Qwen2.5-0.5B)" + echo " OUTPUT Output .pt file path (default: steering_vectors.pt)" + echo " MIN_SAMPLES Minimum samples in R/N sets (default: 10)" + echo " BATCH_SIZE Batch size for extraction (default: 4)" + exit 1 +fi + +INPUT_PATH="$1" +shift + +# Parse layers argument +LAYERS="" +while [[ $# -gt 0 ]]; do + case $1 in + --layers|-l) + LAYERS="$2" + shift 2 + ;; + *) + shift + ;; + esac +done + +if [ -z "$LAYERS" ]; then + echo "Error: --layers argument is required" + echo "Example: $0 input.jsonl --layers 10,15,20" + exit 1 +fi + +# Default values +MODEL="${MODEL:-Qwen/Qwen2.5-0.5B}" +OUTPUT="${OUTPUT:-steering_vectors.pt}" +MIN_SAMPLES="${MIN_SAMPLES:-10}" +BATCH_SIZE="${BATCH_SIZE:-4}" + +echo "=== Extracting Steering Vectors ===" +echo "Input: $INPUT_PATH" +echo "Model: $MODEL" +echo "Layers: $LAYERS" +echo "Output: $OUTPUT" +echo "Min Samples: $MIN_SAMPLES" +echo "Batch Size: $BATCH_SIZE" +echo "" + +# Run extraction +uv run probing-reflection extract-vectors \ + --input "$INPUT_PATH" \ + --model "$MODEL" \ + --layers "$LAYERS" \ + --output "$OUTPUT" \ + --min-samples "$MIN_SAMPLES" \ + --batch-size "$BATCH_SIZE" + +echo "" +echo "Extraction complete." +echo "Vectors saved to: $OUTPUT" diff --git a/scripts/probing_reflection/run_inference.sh b/scripts/probing_reflection/run_inference.sh new file mode 100755 index 0000000..97049e5 --- /dev/null +++ b/scripts/probing_reflection/run_inference.sh @@ -0,0 +1,37 @@ +#!/bin/bash +# Run model inference on MATH-500 dataset + +set -e + +# Default values +MODEL="${MODEL:-Qwen/Qwen3.5-0.8B}" +DATASET="${DATASET:-HuggingFaceH4/MATH-500}" +BATCH_SIZE="${BATCH_SIZE:-8}" +MAX_TOKENS="${MAX_TOKENS:-256}" +OUTPUT="${OUTPUT:-outputs/math500_inference/qwen3-0.8b-math500-cot.jsonl}" +LIMIT="${LIMIT:-}" + +# Build command +CMD=(uv run probing-reflection inference) +CMD+=(--model "$MODEL") +CMD+=(--dataset "$DATASET") +CMD+=(--batch-size "$BATCH_SIZE") +CMD+=(--max-new-tokens "$MAX_TOKENS") +CMD+=(--output "$OUTPUT") +if [ -n "$LIMIT" ]; then + CMD+=(--limit "$LIMIT") +fi + +echo "=== Running Inference ===" +echo "Model: $MODEL" +echo "Dataset: $DATASET" +echo "Batch Size: $BATCH_SIZE" +echo "Max Tokens: $MAX_TOKENS" +echo "Output: $OUTPUT" +echo "" + +# Run inference +"${CMD[@]}" + +echo "" +echo "Inference complete. Results saved to: $OUTPUT" diff --git a/scripts/probing_reflection/run_linear_probe.sh b/scripts/probing_reflection/run_linear_probe.sh new file mode 100755 index 0000000..63dc0d6 --- /dev/null +++ b/scripts/probing_reflection/run_linear_probe.sh @@ -0,0 +1,81 @@ +#!/bin/bash +# Run linear probe training on model activations + +set -e + +# Check required argument +if [ -z "$1" ]; then + echo "Usage: $0 [options]" + echo "" + echo "Arguments:" + echo " input_jsonl Path to input JSONL file with reflection analysis" + echo "" + echo "Options (set as environment variables):" + echo " MODEL Model name (required)" + echo " LAYERS Layer indices, comma-separated (required)" + echo " TEST_SIZE Test set fraction (default: 0.2)" + echo " OUTPUT_DIR Output directory (default: outputs/linear_probe/)" + exit 1 +fi + +INPUT_PATH="$1" + +# Check required environment variables +if [ -z "$MODEL" ]; then + echo "Error: MODEL environment variable is required" + echo "Example: MODEL=Qwen/Qwen2.5-0.5B $0 input.jsonl" + exit 1 +fi + +if [ -z "$LAYERS" ]; then + echo "Error: LAYERS environment variable is required" + echo "Example: LAYERS='10,15,20' MODEL=Qwen/Qwen2.5-0.5B $0 input.jsonl" + exit 1 +fi + +# Default values +TEST_SIZE="${TEST_SIZE:-0.2}" +OUTPUT_DIR="${OUTPUT_DIR:-outputs/linear_probe/}" + +echo "=== Running Linear Probe Training ===" +echo "Input: $INPUT_PATH" +echo "Model: $MODEL" +echo "Layers: $LAYERS" +echo "Test Size: $TEST_SIZE" +echo "Output Directory: $OUTPUT_DIR" +echo "" + +# Run linear probe without interpolating values into Python source +uv run python -c ' +import sys +from probing_reflection.linear_probe import run_linear_probe +from probing_reflection.types import LinearProbeConfig + +input_path, model_name, layers, test_size, output_dir = sys.argv[1:] +layer_indices = tuple(int(value.strip()) for value in layers.split(",")) + +config = LinearProbeConfig( + input_path=input_path, + model_name=model_name, + layer_indices=layer_indices, + test_size=float(test_size), + output_dir=output_dir, +) + +result = run_linear_probe(config) + +print("\n=== Linear Probe Results ===") +for metric in result["metrics"]: + print( + "Layer {}: Accuracy={:.2%} (train={}, test={})".format( + metric["layer_index"], + metric["accuracy"], + metric["train_samples"], + metric["test_samples"], + ) + ) +print(f"\nResults saved to: {output_dir}") +' "$INPUT_PATH" "$MODEL" "$LAYERS" "$TEST_SIZE" "$OUTPUT_DIR" + +echo "" +echo "Linear probe training complete." diff --git a/scripts/probing_reflection/run_steering_inference.sh b/scripts/probing_reflection/run_steering_inference.sh new file mode 100755 index 0000000..7e93666 --- /dev/null +++ b/scripts/probing_reflection/run_steering_inference.sh @@ -0,0 +1,101 @@ +#!/bin/bash +# Run steering inference with steering vectors + +set -e + +# Check required arguments +if [ -z "$1" ] || [ -z "$VECTOR_PATH" ]; then + echo "Usage: VECTOR_PATH= $0 [options]" + echo "" + echo "Arguments:" + echo " dataset_name Dataset name (e.g., HuggingFaceH4/MATH-500)" + echo "" + echo "Required Environment Variables:" + echo " VECTOR_PATH Path to steering vectors .pt file" + echo "" + echo "Options (set as environment variables):" + echo " MODEL Model name (default: Qwen/Qwen2.5-32B)" + echo " COEFFICIENT Steering coefficient (default: 1.0)" + echo " LAYERS Layer indices to apply steering (optional, uses all from vector)" + echo " OUTPUT Output JSONL path (required)" + echo " LIMIT Limit number of samples (optional)" + echo " BATCH_SIZE Batch size (default: 1)" + echo " MAX_TOKENS Max new tokens (default: 512)" + echo " DATASET_CONFIG Dataset config name (optional, for datasets like GPQA)" + exit 1 +fi + +DATASET="$1" + +# Check required environment variables +if [ -z "$OUTPUT" ]; then + echo "Error: OUTPUT environment variable is required" + echo "Example: OUTPUT=outputs/steered_results.jsonl $0 HuggingFaceH4/MATH-500" + exit 1 +fi + +# Default values +MODEL="${MODEL:-Qwen/Qwen2.5-32B}" +COEFFICIENT="${COEFFICIENT:-1.0}" +BATCH_SIZE="${BATCH_SIZE:-1}" +MAX_TOKENS="${MAX_TOKENS:-512}" +DATASET_CONFIG="${DATASET_CONFIG:-}" +LIMIT="${LIMIT:-}" +LAYERS="${LAYERS:-}" + +echo "=== Running Steering Inference ===" +echo "Model: $MODEL" +echo "Dataset: $DATASET" +echo "Steering Vector: $VECTOR_PATH" +echo "Coefficient: $COEFFICIENT" +echo "Output: $OUTPUT" +echo "Batch Size: $BATCH_SIZE" +echo "Max Tokens: $MAX_TOKENS" +if [ -n "$LAYERS" ]; then + echo "Layers: $LAYERS" +fi +if [ -n "$LIMIT" ]; then + echo "Limit: $LIMIT" +fi +echo "" + +# Run steering inference without interpolating values into Python source +uv run python -c ' +import sys +from probing_reflection.steering_inference import run_steering_inference +from probing_reflection.types import SteeringInferenceConfig + +( + model_name, + vector_path, + layers, + coefficient, + dataset_name, + dataset_config, + batch_size, + max_tokens, + output_path, + limit, +) = sys.argv[1:] + +config = SteeringInferenceConfig( + model_name=model_name, + steering_vector_path=vector_path, + layer_indices=tuple(int(value.strip()) for value in layers.split(",") if value.strip()), + coefficient=float(coefficient), + dataset_name=dataset_name, + dataset_config=dataset_config or None, + batch_size=int(batch_size), + max_new_tokens=int(max_tokens), + output_path=output_path, + limit=int(limit) if limit else None, +) + +result = run_steering_inference(config) +print(f"Steering inference complete. Results saved to: {result}") +' "$MODEL" "$VECTOR_PATH" "$LAYERS" "$COEFFICIENT" "$DATASET" "$DATASET_CONFIG" \ + "$BATCH_SIZE" "$MAX_TOKENS" "$OUTPUT" "$LIMIT" + +echo "" +echo "Steering inference complete." +echo "Results saved to: $OUTPUT" diff --git a/scripts/run_experiments.py b/scripts/run_experiments.py new file mode 100644 index 0000000..c6894d5 --- /dev/null +++ b/scripts/run_experiments.py @@ -0,0 +1,162 @@ +#!/usr/bin/env python +"""Run steering experiments on benchmarks. + +This script orchestrates running steering inference across multiple +datasets and conditions, with proper memory management between runs. +""" + +from __future__ import annotations + +import argparse +import gc +import sys +from pathlib import Path + +import torch + +from probing_reflection.steering_inference import ( + get_output_path, + load_steering_vectors, + run_steering_inference, +) +from probing_reflection.types import SteeringInferenceConfig + +DATASETS = { + "math500": ("HuggingFaceH4/MATH-500", None, "test"), + "aime": ("AI-MO/aimo-validation-aime", None, "train"), + "gpqa": ("Idavidrein/gpqa", "gpqa_diamond", "train"), +} + +CONDITIONS = { + "baseline": 0.0, + "positive": 1.0, + "negative": -1.0, +} + + +def get_memory_usage() -> str: + if torch.cuda.is_available(): + allocated = torch.cuda.memory_allocated() / 1024**3 + reserved = torch.cuda.memory_reserved() / 1024**3 + return f"GPU: {allocated:.2f}GB allocated, {reserved:.2f}GB reserved" + return "CPU mode" + + +def cleanup_memory() -> None: + gc.collect() + if torch.cuda.is_available(): + torch.cuda.empty_cache() + torch.cuda.synchronize() + + +def main() -> None: + parser = argparse.ArgumentParser( + description="Run steering experiments on benchmarks", + formatter_class=argparse.RawDescriptionHelpFormatter, + ) + parser.add_argument( + "--dataset", + choices=["math500", "aime", "gpqa", "all"], + default="all", + help="Dataset to run experiments on (default: all)", + ) + parser.add_argument( + "--condition", + choices=["baseline", "positive", "negative", "all"], + default="all", + help="Steering condition (default: all)", + ) + parser.add_argument( + "--steering-vector-path", + required=True, + help="Path to steering vectors .pt file", + ) + parser.add_argument( + "--model", + default="Qwen/Qwen2.5-32B", + help="Model name (default: Qwen/Qwen2.5-32B)", + ) + parser.add_argument( + "--limit", + type=int, + help="Limit number of samples per dataset", + ) + parser.add_argument( + "--max-tokens", + type=int, + default=512, + help="Maximum new tokens to generate (default: 512)", + ) + parser.add_argument( + "--output-dir", + default="outputs/steering_experiments", + help="Base directory for outputs (default: outputs/steering_experiments)", + ) + args = parser.parse_args() + + steering_path = Path(args.steering_vector_path) + if not steering_path.exists(): + print(f"Error: Steering vector file not found: {steering_path}") + sys.exit(1) + + datasets = list(DATASETS.keys()) if args.dataset == "all" else [args.dataset] + conditions = list(CONDITIONS.keys()) if args.condition == "all" else [args.condition] + + print(f"Loading steering vectors from: {steering_path}") + vectors = load_steering_vectors(steering_path) + layer_indices = tuple(sorted(vectors.keys())) + print(f"Found steering vectors for layers: {layer_indices}") + print(f"Datasets: {datasets}") + print(f"Conditions: {conditions}") + print(f"Model: {args.model}") + print(f"Limit: {args.limit if args.limit else 'none'}") + print() + + total_runs = len(datasets) * len(conditions) + current_run = 0 + + for dataset in datasets: + for condition in conditions: + current_run += 1 + print(f"\n{'=' * 60}") + print(f"[{current_run}/{total_runs}] Running {dataset} / {condition}") + print(f"Memory before: {get_memory_usage()}") + print("=" * 60) + + dataset_name, dataset_config, split = DATASETS[dataset] + coefficient = CONDITIONS[condition] + + output_path = get_output_path(dataset, condition, args.output_dir) + + config = SteeringInferenceConfig( + model_name=args.model, + steering_vector_path=str(steering_path), + layer_indices=layer_indices, + coefficient=coefficient, + dataset_name=dataset_name, + dataset_config=dataset_config, + batch_size=1, + max_new_tokens=args.max_tokens, + output_path=str(output_path), + limit=args.limit, + ) + + try: + result_path = run_steering_inference(config) + print(f"Results saved to: {result_path}") + except Exception as e: + print(f"Error running {dataset}/{condition}: {e}") + raise + + print(f"Memory after: {get_memory_usage()}") + print("Cleaning up memory...") + cleanup_memory() + print(f"Memory after cleanup: {get_memory_usage()}") + + print(f"\n{'=' * 60}") + print(f"All experiments completed! Total runs: {total_runs}") + print("=" * 60) + + +if __name__ == "__main__": + main() diff --git a/src/probing_reflection/__init__.py b/src/probing_reflection/__init__.py index 1bd31d1..d8633df 100644 --- a/src/probing_reflection/__init__.py +++ b/src/probing_reflection/__init__.py @@ -5,18 +5,30 @@ Public API: ProbingConfig: Configuration for probing experiments + InferenceConfig: Configuration for inference experiments ReflectionResult: Result container for reflection analysis ContrastivePair: TypedDict for contrastive example pairs + LinearProbeConfig: Configuration for linear probe experiments + LinearProbeResult: Result container for linear probe analysis + ProbeMetrics: Metrics for a single probe layer """ from probing_reflection.types import ( ContrastivePair, + InferenceConfig, + LinearProbeConfig, + LinearProbeResult, + ProbeMetrics, ProbingConfig, ReflectionResult, ) __all__ = [ "ContrastivePair", + "InferenceConfig", + "LinearProbeConfig", + "LinearProbeResult", + "ProbeMetrics", "ProbingConfig", "ReflectionResult", ] diff --git a/src/probing_reflection/__main__.py b/src/probing_reflection/__main__.py new file mode 100644 index 0000000..d56ad85 --- /dev/null +++ b/src/probing_reflection/__main__.py @@ -0,0 +1,283 @@ +"""Entry point for running inference and evaluation from command line.""" + +from __future__ import annotations + +import argparse +import json +import sys +from pathlib import Path + +from probing_reflection import InferenceConfig +from probing_reflection.evaluation import evaluate +from probing_reflection.inference import run_inference +from probing_reflection.reflection_diagnosis import ( + diagnose_all, + write_analysis_report, + write_analyzed_jsonl, +) +from probing_reflection.steering_vectors import extract_steering_vectors +from probing_reflection.types import ( + EvaluationConfig, + EvaluationReport, + ExtractVectorsConfig, + ReflectionDiagnosisConfig, +) + + +def format_report(report: EvaluationReport) -> str: + """Format evaluation report for console output.""" + lines = [ + "=== Evaluation Report ===", + f"Overall Accuracy: {report['overall_accuracy']:.2%} " + f"({report['correct_count']}/{report['total_samples']})", + "", + "By Subject:", + ] + for subj, acc in sorted(report["per_subject_accuracy"].items()): + lines.append(f" {subj}: {acc:.2%}") + lines.append("") + lines.append("By Level:") + for level, acc in sorted(report["per_level_accuracy"].items()): + lines.append(f" Level {level}: {acc:.2%}") + return "\n".join(lines) + + +def create_parser() -> argparse.ArgumentParser: + """Create argument parser with subcommands.""" + parser = argparse.ArgumentParser( + prog="probing_reflection", + description="Probing and steering self-reflection in language models", + ) + subparsers = parser.add_subparsers(dest="command", help="Available commands") + + inference_parser = subparsers.add_parser( + "inference", + help="Run inference on dataset", + ) + inference_parser.add_argument( + "--limit", + type=int, + default=None, + help="Limit number of samples to process", + ) + inference_parser.add_argument("--model", default=InferenceConfig.model_name) + inference_parser.add_argument("--dataset", default=InferenceConfig.dataset_name) + inference_parser.add_argument("--batch-size", type=int, default=InferenceConfig.batch_size) + inference_parser.add_argument( + "--max-new-tokens", type=int, default=InferenceConfig.max_new_tokens + ) + inference_parser.add_argument("--output", default=InferenceConfig.output_path) + + eval_parser = subparsers.add_parser( + "evaluate", + help="Evaluate model outputs", + ) + eval_parser.add_argument( + "jsonl_path", + help="Path to JSONL file containing model outputs", + ) + eval_parser.add_argument( + "--model", + "-m", + default="Qwen/Qwen3.5-27B", + help="Judge model name (default: Qwen/Qwen3.5-27B)", + ) + eval_parser.add_argument( + "--output", + "-o", + default=None, + help="Save report to file (JSON format)", + ) + eval_parser.add_argument( + "--batch-size", + "-b", + type=int, + default=8, + help="Batch size for evaluation (default: 8)", + ) + eval_parser.add_argument( + "--confidence-threshold", + "-c", + type=float, + default=0.7, + help="Minimum confidence to accept verdict (default: 0.7)", + ) + + diagnose_parser = subparsers.add_parser( + "reflection-diagnose", + help="Diagnose reflection tokens in model outputs", + ) + diagnose_parser.add_argument( + "--input", + "-i", + required=True, + help="Path to input JSONL file containing model outputs", + ) + diagnose_parser.add_argument( + "--output-dir", + "-o", + default="outputs/reflection_analysis/", + help="Output directory for analysis results", + ) + diagnose_parser.add_argument( + "--model", + "-m", + default="Qwen/Qwen3.5-27B", + help="Judge model name", + ) + + extract_parser = subparsers.add_parser( + "extract-vectors", + help="Extract steering vectors from model activations", + ) + extract_parser.add_argument( + "--input", + "-i", + required=True, + help="Path to input JSONL file with model outputs", + ) + extract_parser.add_argument( + "--model", + "-m", + default="Qwen/Qwen2.5-0.5B", + help="Model name", + ) + extract_parser.add_argument( + "--layers", + "-l", + required=True, + help="Layer indices, comma-separated (e.g., '10,15,20')", + ) + extract_parser.add_argument( + "--output", + "-o", + default="steering_vectors.pt", + help="Output .pt file path", + ) + extract_parser.add_argument( + "--min-samples", + type=int, + default=10, + help="Minimum samples in R/N sets", + ) + extract_parser.add_argument( + "--batch-size", + "-b", + type=int, + default=4, + help="Batch size for extraction", + ) + + return parser + + +def handle_evaluate(args: argparse.Namespace) -> None: + """Run evaluation with parsed arguments.""" + config = EvaluationConfig( + judge_model_name=args.model, + batch_size=args.batch_size, + confidence_threshold=args.confidence_threshold, + ) + + print(f"Evaluating {args.jsonl_path} with model {args.model}...") + report = evaluate(args.jsonl_path, config) + + print(format_report(report)) + + if args.output: + output_path = Path(args.output) + output_path.write_text(json.dumps(report, indent=2)) + print(f"\nReport saved to {args.output}") + + +def handle_inference(args: argparse.Namespace) -> None: + """Run inference with parsed arguments.""" + limit = args.limit + config = InferenceConfig( + model_name=args.model, + dataset_name=args.dataset, + batch_size=args.batch_size, + max_new_tokens=args.max_new_tokens, + output_path=args.output, + limit=limit, + ) + + print(f"Running inference on {limit if limit is not None else 'all'} samples...") + run_inference(config) + print(f"Results saved to {config.output_path}") + + +def handle_reflection_diagnose(args: argparse.Namespace) -> None: + """Run reflection diagnosis with parsed arguments.""" + config = ReflectionDiagnosisConfig( + input_path=args.input, + output_dir=args.output_dir, + model_name=args.model, + ) + + print(f"Diagnosing reflection tokens in {args.input} with model {args.model}...") + samples, report = diagnose_all(config) + + output_dir = Path(args.output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + + jsonl_path = output_dir / "analyzed_samples.jsonl" + report_path = output_dir / "analysis_report.json" + + write_analyzed_jsonl(samples, jsonl_path) + write_analysis_report(report, report_path) + + print("\n=== Reflection Diagnosis Report ===") + print(f"Total samples: {report['total_samples']}") + print(f"Total reflection tokens: {report['total_tokens']}") + print(f"Average tokens per sample: {report['avg_tokens_per_sample']:.2f}") + print(f"Overall reflection density: {report['overall_density']:.2f} tokens per 100 words") + print(f"Processing errors: {report['processing_errors']}") + print(f"\nAnalyzed samples saved to {jsonl_path}") + print(f"Report saved to {report_path}") + + +def handle_extract_vectors(args: argparse.Namespace) -> None: + """Run steering vector extraction with parsed arguments.""" + layer_indices = tuple(int(x.strip()) for x in args.layers.split(",")) + + config = ExtractVectorsConfig( + input_path=args.input, + model_name=args.model, + layer_indices=layer_indices, + output_path=args.output, + min_samples=args.min_samples, + batch_size=args.batch_size, + ) + + print(f"Extracting steering vectors from {args.input}...") + result = extract_steering_vectors(config) + + print("\n=== Steering Vector Extraction Complete ===") + print(f"R samples: {result['metadata']['r_count']}") + print(f"N samples: {result['metadata']['n_count']}") + print(f"Layers: {result['metadata']['layer_indices']}") + print(f"Output: {args.output}") + + +def main() -> None: + """Parse arguments and dispatch to appropriate subcommand.""" + parser = create_parser() + + if len(sys.argv) == 1: + sys.argv.insert(1, "inference") + + args = parser.parse_args() + + if args.command == "evaluate": + handle_evaluate(args) + elif args.command == "reflection-diagnose": + handle_reflection_diagnose(args) + elif args.command == "extract-vectors": + handle_extract_vectors(args) + else: + handle_inference(args) + + +if __name__ == "__main__": + main() diff --git a/src/probing_reflection/batch_utils.py b/src/probing_reflection/batch_utils.py new file mode 100644 index 0000000..8a5b4c8 --- /dev/null +++ b/src/probing_reflection/batch_utils.py @@ -0,0 +1,91 @@ +"""Batch processing utilities for model inference. + +This module provides functions for preparing and processing batches +of inputs for language model inference, handling padding and tokenization +consistently across different modules. +""" + +from __future__ import annotations + +from collections.abc import Mapping +from typing import cast + +import torch +from transformers import PreTrainedTokenizerBase + + +def prepare_batch( + tokenizer: PreTrainedTokenizerBase, problems: list[str] +) -> dict[str, list[list[int]]]: + """Prepare a batch of problems for model inference. + + Sets up left padding for autoregressive model compatibility + and ensures pad_token is configured. + + Args: + tokenizer: The tokenizer to use. + problems: List of problem strings to tokenize. + + Returns: + Tokenized batch with input_ids and attention_mask. + """ + # Set padding side to left for autoregressive generation + tokenizer.padding_side = "left" + + if tokenizer.pad_token is None: + tokenizer.pad_token = tokenizer.eos_token + + result = tokenizer(problems, padding=True, return_tensors=None) + + return cast(dict[str, list[list[int]]], result) + + +def prepare_batch_with_tensors( + tokenizer: PreTrainedTokenizerBase, problems: list[str], device: torch.device +) -> dict[str, torch.Tensor]: + """Prepare a batch and convert to tensors on the specified device. + + Convenience function that combines prepare_batch with tensor conversion. + + Args: + tokenizer: The tokenizer to use. + problems: List of problem strings to tokenize. + device: The device to move tensors to. + + Returns: + Dict with input_ids and attention_mask as tensors on device. + """ + batch = prepare_batch(tokenizer, problems) + + return { + "input_ids": torch.tensor(batch["input_ids"]).to(device), + "attention_mask": torch.tensor(batch["attention_mask"]).to(device), + } + + +def get_item_field(item: Mapping[str, object], field_names: list[str], default: str = "") -> str: + """Get a field from a dataset item, trying multiple possible field names. + + Useful for handling datasets with varying field name conventions. + + Args: + item: Dataset item dictionary. + field_names: List of possible field names to try. + default: Default value if no field is found. + + Returns: + The field value or default. + """ + for name in field_names: + if name in item: + return str(item[name]) + return default + + +def decode_generated_tokens( + tokenizer: PreTrainedTokenizerBase, + output_ids: torch.Tensor, + input_length: int, +) -> str: + generated_ids = output_ids[input_length:] + return str(tokenizer.decode(generated_ids, skip_special_tokens=True)).strip() diff --git a/src/probing_reflection/dataset_adapters.py b/src/probing_reflection/dataset_adapters.py new file mode 100644 index 0000000..9adfb42 --- /dev/null +++ b/src/probing_reflection/dataset_adapters.py @@ -0,0 +1,127 @@ +"""Unified dataset loading functions for evaluation datasets.""" + +from __future__ import annotations + +from collections.abc import Mapping +from typing import NotRequired, TypedDict + +from datasets.load import load_dataset + + +class DatasetSample(TypedDict): + """Unified sample format for all datasets. + + Attributes: + problem_id: Unique identifier for the problem. + problem: The problem text/question. + reference_answer: The ground truth answer. + subject: Optional subject category. + level: Optional difficulty level. + """ + + problem_id: str + problem: str + reference_answer: str + subject: NotRequired[str | None] + level: NotRequired[int | None] + + +def _record(value: object) -> Mapping[str, object]: + if not isinstance(value, Mapping): + raise TypeError("Dataset rows must be mappings") + return value + + +def _optional_text(value: object) -> str | None: + return str(value) if value is not None else None + + +def _optional_level(value: object) -> int | None: + return int(str(value)) if value is not None else None + + +def load_math500() -> list[DatasetSample]: + """Load MATH-500 dataset from HuggingFace. + + Returns: + List of 500 DatasetSample dicts. + """ + dataset = load_dataset("HuggingFaceH4/MATH-500", split="test") + + samples: list[DatasetSample] = [] + for raw_item in dataset: + item = _record(raw_item) + sample: DatasetSample = { + "problem_id": str(item["unique_id"]), + "problem": str(item["problem"]), + "reference_answer": str(item["answer"]), + "subject": _optional_text(item.get("subject")), + "level": _optional_level(item.get("level")), + } + samples.append(sample) + + return samples + + +def load_aime() -> list[DatasetSample]: + """Load AIME 2024 dataset from HuggingFace. + + Returns: + List of ~90 DatasetSample dicts. + """ + dataset = load_dataset("AI-MO/aimo-validation-aime", split="train") + + samples: list[DatasetSample] = [] + for raw_item in dataset: + item = _record(raw_item) + # Map available fields - check for id/problem_id variations + problem_id = str(item.get("id", item.get("problem_id", ""))) + problem = str(item.get("problem", item.get("question", ""))) + + sample: DatasetSample = { + "problem_id": problem_id, + "problem": problem, + "reference_answer": str(item.get("answer", item.get("solution", ""))), + "subject": "math", + "level": None, + } + samples.append(sample) + + return samples + + +def load_gpqa_diamond() -> list[DatasetSample]: + """Load GPQA Diamond dataset from HuggingFace. + + Returns: + List of 198 DatasetSample dicts. + """ + dataset = load_dataset("Idavidrein/gpqa", "gpqa_diamond", split="train") + + samples: list[DatasetSample] = [] + for idx, raw_item in enumerate(dataset): + item = _record(raw_item) + # Format question with choices + question = str(item.get("Question", item.get("question", ""))) + choice_1 = str(item.get("Choice 1", item.get("choice_1", ""))) + choice_2 = str(item.get("Choice 2", item.get("choice_2", ""))) + choice_3 = str(item.get("Choice 3", item.get("choice_3", ""))) + choice_4 = str(item.get("Choice 4", item.get("choice_4", ""))) + + formatted_problem = ( + f"Question: {question}\n\nA) {choice_1}\nB) {choice_2}\nC) {choice_3}\nD) {choice_4}" + ) + + # Get the correct answer text + correct_answer = str(item.get("Correct Answer", item.get("correct_answer", ""))) + + sample: DatasetSample = { + "problem_id": f"gpqa_diamond_{idx}", + "problem": formatted_problem, + "reference_answer": correct_answer, + "subject": "science", + "level": None, + } + samples.append(sample) + + return samples diff --git a/src/probing_reflection/evaluation.py b/src/probing_reflection/evaluation.py new file mode 100644 index 0000000..6f82655 --- /dev/null +++ b/src/probing_reflection/evaluation.py @@ -0,0 +1,241 @@ +"""Evaluation utilities for answer extraction and comparison. + +This module provides functions for extracting and comparing answers from +model outputs, particularly for LaTeX-formatted boxed answers. +""" + +from __future__ import annotations + +import json +import re +from collections import defaultdict + +from probing_reflection.judges import AnswerJudge +from probing_reflection.types import ( + EvaluationConfig, + EvaluationReport, + EvaluationResult, +) + + +def extract_boxed_answer(text: str) -> str | None: + """Extract the content of the first \\boxed{...} pattern from text. + + Handles nested braces by counting brace depth to find the matching + closing brace. + + Args: + text: The text to search for a boxed answer. + + Returns: + The extracted answer with whitespace stripped, or None if no + \\boxed{...} pattern is found. + """ + match = re.search(r"\\boxed\{", text) + if not match: + return None + + start_pos = match.end() + depth = 1 + pos = start_pos + + while pos < len(text) and depth > 0: + if text[pos] == "{": + depth += 1 + elif text[pos] == "}": + depth -= 1 + pos += 1 + + if depth == 0: + content = text[start_pos : pos - 1] + return content.strip() + + return None + + +def generate_report(results: list[EvaluationResult]) -> EvaluationReport: + """Generate an aggregated evaluation report from individual results. + + Calculates overall accuracy and groups results by subject and level + to provide per-category breakdowns. + + Args: + results: List of EvaluationResult dictionaries from evaluating samples. + + Returns: + EvaluationReport with overall statistics and per-category breakdowns. + """ + total = len(results) + correct = sum(1 for r in results if r["is_correct"]) + + subject_groups: dict[str, list[EvaluationResult]] = defaultdict(list) + for r in results: + subject = r.get("subject") or "unknown" + subject_groups[subject].append(r) + + per_subject = { + subj: sum(1 for r in group if r["is_correct"]) / len(group) + for subj, group in subject_groups.items() + } + + level_groups: dict[str, list[EvaluationResult]] = defaultdict(list) + for r in results: + level = r.get("level") + level_key = str(level) if level is not None else "unknown" + level_groups[level_key].append(r) + + per_level = { + level: sum(1 for r in group if r["is_correct"]) / len(group) + for level, group in level_groups.items() + } + + return EvaluationReport( + overall_accuracy=correct / total if total > 0 else 0.0, + total_samples=total, + correct_count=correct, + per_subject_accuracy=per_subject, + per_level_accuracy=per_level, + results=results, + ) + + +def evaluate(jsonl_path: str, config: EvaluationConfig) -> EvaluationReport: + """Evaluate model outputs against reference answers. + + Loads a JSONL file containing model outputs, extracts boxed answers, + and uses an LLM judge to compare against reference answers. + + Args: + jsonl_path: Path to the JSONL file containing model outputs. + config: Evaluation configuration with judge model settings. + + Returns: + EvaluationReport with overall accuracy and per-category breakdowns. + """ + records: list[dict[str, object]] = [] + with open(jsonl_path) as f: + for line in f: + line = line.strip() + if line: + records.append(json.loads(line)) + + judge = AnswerJudge( + config.judge_model_name, + config.confidence_threshold, + ) + judge.load_model() + + pairs_to_judge: list[tuple[dict[str, object], str]] = [] + results: list[EvaluationResult] = [] + + def optional_subject(record: dict[str, object]) -> str | None: + value = record.get("subject") + return str(value) if value is not None else None + + def optional_level(record: dict[str, object]) -> int | None: + value = record.get("level") + if value is None: + return None + if isinstance(value, int) and not isinstance(value, bool): + return value + return int(str(value)) + + for record in records: + generated = str(record.get("generated", "")) + extracted = extract_boxed_answer(generated) + if extracted is None: + results.append( + EvaluationResult( + problem_id=str(record.get("problem_id", "")), + extracted_answer=None, + reference_answer=str(record.get("reference_answer", "")), + is_correct=False, + judge_explanation="No boxed answer found", + confidence=0.0, + subject=optional_subject(record), + level=optional_level(record), + ) + ) + else: + pairs_to_judge.append((record, extracted)) + + if pairs_to_judge: + pairs = [ + (str(record.get("reference_answer", "")), extracted) + for record, extracted in pairs_to_judge + ] + verdicts = judge.judge_batch(pairs) + + for (record, extracted), verdict in zip(pairs_to_judge, verdicts, strict=True): + results.append( + EvaluationResult( + problem_id=str(record.get("problem_id", "")), + extracted_answer=extracted, + reference_answer=str(record.get("reference_answer", "")), + is_correct=verdict["equivalent"], + judge_explanation=verdict["explanation"], + confidence=verdict["confidence"], + subject=optional_subject(record), + level=optional_level(record), + ) + ) + + return generate_report(results) + + +def evaluate_gpqa(jsonl_path: str, config: EvaluationConfig | None = None) -> EvaluationReport: + """Evaluate GPQA outputs using LLM judge (full text comparison). + + Unlike evaluate(), this does NOT use boxed extraction. It compares + the full model output against the reference answer text. + + Args: + jsonl_path: Path to JSONL file with model outputs. + config: Evaluation configuration. + + Returns: + EvaluationReport with accuracy metrics. + """ + if config is None: + config = EvaluationConfig() + + records: list[dict[str, object]] = [] + with open(jsonl_path) as f: + for line in f: + line = line.strip() + if line: + records.append(json.loads(line)) + + judge = AnswerJudge(config.judge_model_name, config.confidence_threshold) + judge.load_model() + + results: list[EvaluationResult] = [] + for record in records: + generated = str(record.get("generated", "")) + reference = str(record.get("reference_answer", "")) + + if not generated.strip(): + results.append( + EvaluationResult( + problem_id=str(record.get("problem_id", "")), + extracted_answer=None, + reference_answer=reference, + is_correct=False, + judge_explanation="No output generated", + confidence=0.0, + ) + ) + else: + verdict = judge.judge_single(reference, generated) + results.append( + EvaluationResult( + problem_id=str(record.get("problem_id", "")), + extracted_answer=generated, + reference_answer=reference, + is_correct=verdict["equivalent"], + judge_explanation=verdict["explanation"], + confidence=verdict["confidence"], + ) + ) + + return generate_report(results) diff --git a/src/probing_reflection/inference.py b/src/probing_reflection/inference.py new file mode 100644 index 0000000..caeaf7f --- /dev/null +++ b/src/probing_reflection/inference.py @@ -0,0 +1,125 @@ +"""Inference module for running model inference on datasets. + +This module provides utilities for running batch inference on +mathematical reasoning datasets with chain-of-thought prompting. +""" + +from __future__ import annotations + +import json +from pathlib import Path +from typing import cast + +import torch +from datasets.load import load_dataset +from tqdm import tqdm + +from probing_reflection.batch_utils import decode_generated_tokens, prepare_batch +from probing_reflection.model_utils import GenerativeModel, load_model +from probing_reflection.prompts import format_cot_prompt +from probing_reflection.types import InferenceConfig + + +def run_inference(config: InferenceConfig) -> Path: + """Run inference on dataset and save results to JSONL. + + Loads the model, processes the dataset in batches, generates responses, + and saves results with all required fields. + + Args: + config: Inference configuration with model, dataset, and output settings. + + Returns: + Path to the output JSONL file. + """ + if config.batch_size <= 0: + raise ValueError("batch_size must be positive") + if config.limit is not None and config.limit < 0: + raise ValueError("limit must be non-negative") + + model, tokenizer = load_model(config.model_name) + generative_model = cast(GenerativeModel, model) + dataset = load_dataset(config.dataset_name, split="test") + + output_path = Path(config.output_path) + output_path.parent.mkdir(parents=True, exist_ok=True) + + batch_size = config.batch_size + results: list[dict[str, str | int]] = [] + num_samples = len(dataset) + if config.limit is not None: + num_samples = min(num_samples, config.limit) + + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + for i in tqdm(range(0, num_samples, batch_size), desc="Running inference"): + batch_end = min(i + batch_size, num_samples) + batch_items = [dataset[j] for j in range(i, batch_end)] + + problems = [item["problem"] for item in batch_items] + prompts = [format_cot_prompt(p) for p in problems] + + inputs = prepare_batch(tokenizer, prompts) + input_ids = torch.tensor(inputs["input_ids"]).to(device) + attention_mask = torch.tensor(inputs["attention_mask"]).to(device) + + try: + with torch.no_grad(): + outputs = generative_model.generate( + input_ids=input_ids, + attention_mask=attention_mask, + max_new_tokens=config.max_new_tokens, + repetition_penalty=1.05, + do_sample=False, + ) + except torch.cuda.OutOfMemoryError: + torch.cuda.empty_cache() + for item in batch_items: + prompt = format_cot_prompt(item["problem"]) + single_input = tokenizer(prompt, return_tensors="pt") + single_ids = single_input["input_ids"].to(device) + single_mask = single_input["attention_mask"].to(device) + + with torch.no_grad(): + output = generative_model.generate( + input_ids=single_ids, + attention_mask=single_mask, + max_new_tokens=config.max_new_tokens, + repetition_penalty=1.05, + do_sample=False, + ) + + generated_text = decode_generated_tokens(tokenizer, output[0], single_ids.shape[-1]) + + result_entry = { + "problem_id": item["unique_id"], + "problem": item["problem"], + "generated": generated_text, + "reference_answer": item["answer"], + "subject": item["subject"], + "level": item["level"], + "prompt": prompt, + } + results.append(result_entry) + continue + + for j, (output, item) in enumerate(zip(outputs, batch_items)): # noqa: B905 + prompt = prompts[j] + generated_text = decode_generated_tokens(tokenizer, output, input_ids.shape[-1]) + + result_entry = { + "problem_id": item["unique_id"], + "problem": item["problem"], + "generated": generated_text, + "reference_answer": item["answer"], + "subject": item["subject"], + "level": item["level"], + "prompt": prompt, + } + results.append(result_entry) + + with open(output_path, "w") as f: + for result in results: + f.write(json.dumps(result) + "\n") + + return output_path diff --git a/src/probing_reflection/judges.py b/src/probing_reflection/judges.py new file mode 100644 index 0000000..aa75afe --- /dev/null +++ b/src/probing_reflection/judges.py @@ -0,0 +1,287 @@ +"""LLM-based judge utilities for answer evaluation and reflection detection. + +This module provides a base class for LLM judges and specific implementations +for answer comparison and reflection token detection. The base class handles +model loading, inference, and JSON response parsing. +""" + +from __future__ import annotations + +import json +from collections.abc import Mapping +from typing import cast + +import torch +from transformers import PreTrainedModel, PreTrainedTokenizerBase + +from probing_reflection.batch_utils import decode_generated_tokens +from probing_reflection.model_utils import GenerativeModel, get_device, load_model +from probing_reflection.prompts import build_comparison_prompt, build_diagnosis_prompt +from probing_reflection.types import JudgeVerdict, ReflectionToken + + +def _parse_json_response(response: str) -> dict[str, object]: + try: + start_idx = response.find("{") + end_idx = response.rfind("}") + 1 + if start_idx == -1 or end_idx == 0: + return {} + + parsed: object = json.loads(response[start_idx:end_idx]) + if not isinstance(parsed, dict): + return {} + return {str(key): value for key, value in parsed.items()} + except (json.JSONDecodeError, ValueError, TypeError): + return {} + + +def _to_float(value: object, default: float = 0.0) -> float: + if isinstance(value, (str, int, float)): + try: + return float(value) + except ValueError: + return default + return default + + +class BaseLLMJudge: + """Base class for LLM-based judgment tasks. + + Provides common functionality for loading models, running inference, + and parsing JSON responses. + + Attributes: + model_name: Name or path of the judge model. + model: The loaded language model (None until load_model is called). + tokenizer: The loaded tokenizer (None until load_model is called). + device: Device the model is running on (None until load_model is called). + """ + + def __init__(self, model_name: str) -> None: + """Initialize the judge with a model name. + + Args: + model_name: Name or path of the judge model. + """ + self.model_name = model_name + self.model: PreTrainedModel | None = None + self.tokenizer: PreTrainedTokenizerBase | None = None + self.device: torch.device | None = None + + def load_model(self) -> None: + """Load the model and tokenizer. + + Uses bfloat16 precision on CUDA and float32 on CPU. + Sets up pad_token to eos_token if not present. + """ + self.model, self.tokenizer = load_model(self.model_name) + self.device = get_device() + + def _loaded_components( + self, + ) -> tuple[PreTrainedModel, PreTrainedTokenizerBase, torch.device]: + if self.model is None or self.tokenizer is None or self.device is None: + raise RuntimeError("Model not loaded. Call load_model() first.") + return self.model, self.tokenizer, self.device + + def _run_inference(self, prompt: str, max_new_tokens: int = 256) -> str: + model, tokenizer, device = self._loaded_components() + + inputs = tokenizer(prompt, return_tensors="pt") + input_ids = inputs["input_ids"].to(device) + attention_mask = inputs["attention_mask"].to(device) + + with torch.no_grad(): + outputs = cast(GenerativeModel, model).generate( + input_ids=input_ids, + attention_mask=attention_mask, + max_new_tokens=max_new_tokens, + do_sample=False, + ) + + return decode_generated_tokens(tokenizer, outputs[0], input_ids.shape[-1]) + + def _parse_json_response(self, response: str) -> dict[str, object]: + """Parse JSON from a model response. + + Extracts and parses JSON from a response string, handling malformed + and missing JSON gracefully. + + Args: + response: The raw model output text. + + Returns: + Parsed dict, or empty dict on failure. + """ + return _parse_json_response(response) + + +class AnswerJudge(BaseLLMJudge): + """LLM-based judge for comparing answer equivalence. + + Uses a language model to determine if two answers are semantically + equivalent, with position bias mitigation through bidirectional comparison. + + Attributes: + confidence_threshold: Minimum confidence to accept equivalent=True. + """ + + def __init__( + self, + model_name: str, + confidence_threshold: float = 0.7, + ) -> None: + """Initialize the answer judge. + + Args: + model_name: Name or path of the judge model. + confidence_threshold: Minimum confidence to accept equivalent=True. + Defaults to 0.7. + """ + super().__init__(model_name) + self.confidence_threshold = confidence_threshold + + def _run_comparison(self, answer_a: str, answer_b: str) -> JudgeVerdict: + """Run a single comparison and parse the result. + + Args: + answer_a: First answer in the comparison. + answer_b: Second answer in the comparison. + + Returns: + JudgeVerdict with explanation, equivalent, and confidence. + """ + prompt = build_comparison_prompt(answer_a, answer_b) + response = self._run_inference(prompt, max_new_tokens=256) + + result = self._parse_json_response(response) + + if not result: + return { + "explanation": "Parse error: No valid JSON found", + "equivalent": False, + "confidence": 0.0, + } + + return { + "explanation": str(result.get("explanation", "")), + "equivalent": bool(result.get("equivalent", False)), + "confidence": _to_float(result.get("confidence")), + } + + def _apply_confidence_threshold(self, verdict: JudgeVerdict) -> JudgeVerdict: + """Apply confidence threshold to a verdict. + + If confidence is below threshold, sets equivalent to False. + + Args: + verdict: The original verdict. + + Returns: + Verdict with equivalent potentially set to False. + """ + if verdict["confidence"] < self.confidence_threshold: + return { + "explanation": verdict["explanation"], + "equivalent": False, + "confidence": verdict["confidence"], + } + return verdict + + def judge_single(self, ref_answer: str, model_answer: str) -> JudgeVerdict: + """Judge a single answer pair with position bias mitigation. + + Runs comparison in both orderings and returns equivalent=False + if results disagree (conservative approach). + + Args: + ref_answer: The reference/gold answer. + model_answer: The model's answer to evaluate. + + Returns: + JudgeVerdict with the final decision. + """ + verdict_ab = self._run_comparison(ref_answer, model_answer) + verdict_ab = self._apply_confidence_threshold(verdict_ab) + + # Position bias mitigation: compare in reversed order + verdict_ba = self._run_comparison(model_answer, ref_answer) + verdict_ba = self._apply_confidence_threshold(verdict_ba) + + if verdict_ab["equivalent"] != verdict_ba["equivalent"]: + return { + "explanation": ( + f"Position bias detected. Forward: {verdict_ab['explanation']}. " + f"Reverse: {verdict_ba['explanation']}" + ), + "equivalent": False, + "confidence": min(verdict_ab["confidence"], verdict_ba["confidence"]), + } + + return verdict_ab + + def judge_batch(self, pairs: list[tuple[str, str]]) -> list[JudgeVerdict]: + """Judge multiple answer pairs. + + Args: + pairs: List of (reference_answer, model_answer) tuples. + + Returns: + List of JudgeVerdicts, one per pair. + """ + return [self.judge_single(ref, model) for ref, model in pairs] + + +class ReflectionJudge(BaseLLMJudge): + """LLM-based judge for identifying reflection tokens in text. + + Uses a language model to detect self-reflection tokens that indicate + metacognitive moments like hesitation, verification, or self-correction. + """ + + def judge(self, text: str) -> list[ReflectionToken]: + """Extract reflection tokens from text. + + Args: + text: The text to analyze for reflection tokens. + + Returns: + List of detected reflection tokens with their metadata. + """ + prompt = build_diagnosis_prompt(text) + response = self._run_inference(prompt, max_new_tokens=512) + + return self._parse_tokens_response(response) + + def _parse_tokens_response(self, response: str) -> list[ReflectionToken]: + """Parse the JSON response containing reflection tokens. + + Args: + response: The raw model output text. + + Returns: + List of ReflectionToken dicts, or empty list if parsing fails. + """ + result = self._parse_json_response(response) + + if "tokens" not in result: + return [] + + tokens: list[ReflectionToken] = [] + raw_tokens = result["tokens"] + if not isinstance(raw_tokens, list): + return [] + + for token_data in raw_tokens: + if not isinstance(token_data, Mapping): + continue + if all(k in token_data for k in ("text", "category", "context", "confidence")): + tokens.append( + ReflectionToken( + text=str(token_data["text"]), + category=str(token_data["category"]), + context=str(token_data["context"]), + confidence=_to_float(token_data["confidence"]), + ) + ) + return tokens diff --git a/src/probing_reflection/linear_probe.py b/src/probing_reflection/linear_probe.py new file mode 100644 index 0000000..1edb52f --- /dev/null +++ b/src/probing_reflection/linear_probe.py @@ -0,0 +1,439 @@ +"""Linear probe module for detecting reflection tokens in model activations. + +This module provides functions to train and evaluate linear probes that +classify activations as coming from reflection (R) or non-reflection (N) +samples. +""" + +from __future__ import annotations + +import json +import logging +from datetime import datetime +from pathlib import Path +from typing import IO, Protocol, cast + +import numpy as np +from numpy import ndarray +from sklearn.decomposition import PCA +from sklearn.linear_model import LogisticRegression +from sklearn.manifold import TSNE +from sklearn.model_selection import train_test_split +from torch import Tensor +from tqdm import tqdm +from transformers import PreTrainedModel, PreTrainedTokenizerBase + +from probing_reflection.model_utils import get_device, get_dtype, load_model +from probing_reflection.prompts import REFLECTION_TAXONOMY +from probing_reflection.steering_vectors import ( + extract_activation_at_position, + find_reflection_token_position, +) +from probing_reflection.types import ( + LinearProbeConfig, + LinearProbeResult, + ProbeMetrics, + SampleWithReflection, +) + +logger = logging.getLogger(__name__) + + +class NpzSaver(Protocol): + def __call__(self, file: IO[bytes], **arrays: ndarray) -> None: ... + + +class ProbeFactory(Protocol): + def __call__(self, *, max_iter: int, random_state: int) -> LogisticRegression: ... + + +class ScoringProbe(Protocol): + def score(self, features: ndarray, labels: ndarray) -> float: ... + + +class Projector(Protocol): + def fit_transform(self, features: ndarray) -> ndarray: ... + + +class TsneFactory(Protocol): + def __call__(self, *, n_components: int, random_state: int, perplexity: int) -> Projector: ... + + +class PcaFactory(Protocol): + def __call__(self, *, n_components: int, random_state: int) -> Projector: ... + + +class TrainTestSplitter(Protocol): + def __call__( + self, + features: ndarray, + labels: ndarray, + *, + test_size: float, + stratify: ndarray, + random_state: int, + ) -> tuple[ndarray, ndarray, ndarray, ndarray]: ... + + +def collect_token_activations( + samples: list[SampleWithReflection], + model: PreTrainedModel, + tokenizer: PreTrainedTokenizerBase, + layer_indices: tuple[int, ...], +) -> tuple[dict[int, list[Tensor]], dict[int, list[Tensor]]]: + """Extract activations from R set (reflection) and N set (non-reflection). + + Args: + samples: List of samples with reflection analysis. + model: The language model to extract activations from. + tokenizer: Tokenizer for the model. + layer_indices: Which layers to extract activations from. + + Returns: + Tuple of (r_activations_by_layer, n_activations_by_layer). + """ + r_activations: dict[int, list[Tensor]] = {layer: [] for layer in layer_indices} + n_activations: dict[int, list[Tensor]] = {layer: [] for layer in layer_indices} + + all_reflection_tokens = [token for tokens in REFLECTION_TAXONOMY.values() for token in tokens] + + for sample in tqdm(samples, desc="Collecting activations"): + text = sample["generated"] + + if sample["reflection_count"] > 0: + position = find_reflection_token_position(tokenizer, text) + if position is None: + logger.warning(f"Reflection token not found for R sample {sample['problem_id']}") + continue + + try: + activations = extract_activation_at_position( + model, tokenizer, text, position, layer_indices + ) + for layer, tensor in activations.items(): + r_activations[layer].append(tensor.cpu()) + except IndexError: + logger.warning(f"Position out of bounds for R sample {sample['problem_id']}") + else: + position = find_reflection_token_position(tokenizer, text, all_reflection_tokens) + if position is None: + logger.warning(f"Taxonomy token not found for N sample {sample['problem_id']}") + continue + + try: + activations = extract_activation_at_position( + model, tokenizer, text, position, layer_indices + ) + for layer, tensor in activations.items(): + n_activations[layer].append(tensor.cpu()) + except IndexError: + logger.warning(f"Position out of bounds for N sample {sample['problem_id']}") + + return r_activations, n_activations + + +def train_linear_probe( + r_activations: dict[int, list[Tensor]], + n_activations: dict[int, list[Tensor]], + layer_indices: tuple[int, ...], + test_size: float = 0.2, +) -> tuple[dict[int, LogisticRegression], list[ProbeMetrics]]: + """Train LogisticRegression probes per layer. + + Args: + r_activations: Dict mapping layer index to list of activation tensors (reflection). + n_activations: Dict mapping layer index to list of activation tensors (non-reflection). + layer_indices: Tuple of layer indices to train probes for. + test_size: Fraction of data to use for testing. + + Returns: + Tuple of (probes_by_layer, metrics_list). + """ + probes: dict[int, LogisticRegression] = {} + metrics: list[ProbeMetrics] = [] + + for layer in layer_indices: + r_list = r_activations.get(layer, []) + n_list = n_activations.get(layer, []) + + if not r_list or not n_list: + logger.warning(f"Skipping layer {layer}: missing activations") + continue + + r_stack = np.stack([t.numpy() for t in r_list]) + n_stack = np.stack([t.numpy() for t in n_list]) + + features = np.vstack([r_stack, n_stack]) + labels = np.concatenate([np.ones(len(r_list)), np.zeros(len(n_list))]) + + x_train, x_test, y_train, y_test = cast(TrainTestSplitter, train_test_split)( + features, labels, test_size=test_size, stratify=labels, random_state=42 + ) + + probe = cast(ProbeFactory, LogisticRegression)(max_iter=1000, random_state=42) + probe.fit(x_train, y_train) + + probes[layer] = probe + + layer_metrics = ProbeMetrics( + layer_index=layer, + accuracy=float(cast(ScoringProbe, probe).score(x_test, y_test)), + train_samples=len(y_train), + test_samples=len(y_test), + ) + metrics.append(layer_metrics) + + return probes, metrics + + +def evaluate_probe( + probe: LogisticRegression, + x_test: ndarray, + y_test: ndarray, + layer_index: int, +) -> ProbeMetrics: + """Evaluate a fitted probe on held-out activations.""" + return ProbeMetrics( + layer_index=layer_index, + accuracy=float(cast(ScoringProbe, probe).score(x_test, y_test)), + train_samples=0, + test_samples=len(y_test), + ) + + +def generate_tsne_plot( + r_activations: dict[int, list[Tensor]], + n_activations: dict[int, list[Tensor]], + output_path: Path | str, + layer_index: int = 0, +) -> None: + """Generate t-SNE scatter plot. + + Args: + r_activations: Dict mapping layer index to list of activation tensors (reflection). + n_activations: Dict mapping layer index to list of activation tensors (non-reflection). + output_path: Path to save the PNG file. + layer_index: Which layer to visualize. + """ + import matplotlib.pyplot as plt + + r_list = r_activations.get(layer_index, []) + n_list = n_activations.get(layer_index, []) + + if not r_list or not n_list: + logger.warning(f"Cannot generate t-SNE plot: no activations for layer {layer_index}") + return + + r_stack = np.stack([t.numpy() for t in r_list]) + n_stack = np.stack([t.numpy() for t in n_list]) + + features = np.vstack([r_stack, n_stack]) + labels = np.concatenate([np.ones(len(r_list)), np.zeros(len(n_list))]) + + tsne = cast(TsneFactory, TSNE)( + n_components=2, random_state=42, perplexity=min(30, len(features) - 1) + ) + embedded = tsne.fit_transform(features) + + path = Path(output_path) + path.parent.mkdir(parents=True, exist_ok=True) + + plt.figure(figsize=(10, 8)) + plt.scatter( + embedded[labels == 1, 0], + embedded[labels == 1, 1], + c="blue", + alpha=0.6, + label="Reflection (R)", + s=20, + ) + plt.scatter( + embedded[labels == 0, 0], + embedded[labels == 0, 1], + c="red", + alpha=0.6, + label="Non-reflection (N)", + s=20, + ) + plt.xlabel("t-SNE Dimension 1") + plt.ylabel("t-SNE Dimension 2") + plt.title(f"t-SNE Visualization of Activations (Layer {layer_index})") + plt.legend() + plt.tight_layout() + plt.savefig(path, dpi=150) + plt.close() + + +def generate_pca_plot( + r_activations: dict[int, list[Tensor]], + n_activations: dict[int, list[Tensor]], + output_path: Path | str, + layer_index: int = 0, +) -> None: + """Generate PCA scatter plot. + + Args: + r_activations: Dict mapping layer index to list of activation tensors (reflection). + n_activations: Dict mapping layer index to list of activation tensors (non-reflection). + output_path: Path to save the PNG file. + layer_index: Which layer to visualize. + """ + import matplotlib.pyplot as plt + + r_list = r_activations.get(layer_index, []) + n_list = n_activations.get(layer_index, []) + + if not r_list or not n_list: + logger.warning(f"Cannot generate PCA plot: no activations for layer {layer_index}") + return + + r_stack = np.stack([t.numpy() for t in r_list]) + n_stack = np.stack([t.numpy() for t in n_list]) + + features = np.vstack([r_stack, n_stack]) + labels = np.concatenate([np.ones(len(r_list)), np.zeros(len(n_list))]) + + pca = cast(PcaFactory, PCA)(n_components=2, random_state=42) + embedded = pca.fit_transform(features) + + path = Path(output_path) + path.parent.mkdir(parents=True, exist_ok=True) + + plt.figure(figsize=(10, 8)) + plt.scatter( + embedded[labels == 1, 0], + embedded[labels == 1, 1], + c="blue", + alpha=0.6, + label="Reflection (R)", + s=20, + ) + plt.scatter( + embedded[labels == 0, 0], + embedded[labels == 0, 1], + c="red", + alpha=0.6, + label="Non-reflection (N)", + s=20, + ) + plt.xlabel("PCA Component 1") + plt.ylabel("PCA Component 2") + plt.title(f"PCA Visualization of Activations (Layer {layer_index})") + plt.legend() + plt.tight_layout() + plt.savefig(path, dpi=150) + plt.close() + + +def save_probe_weights( + probes: dict[int, LogisticRegression], + metrics: list[ProbeMetrics], + output_path: Path | str, + metadata: dict[str, str | int | tuple[int, ...]], +) -> None: + """Save probe coefficients and metadata to .npz. + + Args: + probes: Dict mapping layer index to trained LogisticRegression model. + metrics: List of ProbeMetrics for each layer. + output_path: Path to save the .npz file. + metadata: Metadata dict with model_name, layer_indices, etc. + """ + path = Path(output_path) + path.parent.mkdir(parents=True, exist_ok=True) + + save_arrays: dict[str, ndarray] = {} + + for layer, probe in probes.items(): + save_arrays[f"coef_layer_{layer}"] = np.asarray(probe.coef_) + save_arrays[f"intercept_layer_{layer}"] = np.asarray(probe.intercept_) + + layer_indices_arr = np.array([m["layer_index"] for m in metrics], dtype=np.int32) + accuracies = np.array([m["accuracy"] for m in metrics], dtype=np.float64) + train_samples = np.array([m["train_samples"] for m in metrics], dtype=np.int32) + test_samples = np.array([m["test_samples"] for m in metrics], dtype=np.int32) + + save_arrays["layer_indices"] = layer_indices_arr + save_arrays["accuracies"] = accuracies + save_arrays["train_samples"] = train_samples + save_arrays["test_samples"] = test_samples + + for key, value in metadata.items(): + if isinstance(value, tuple): + save_arrays[f"metadata_{key}"] = np.array(value, dtype=np.int32) + elif isinstance(value, int): + save_arrays[f"metadata_{key}"] = np.array([value], dtype=np.int32) + elif isinstance(value, str): + save_arrays[f"metadata_{key}"] = np.array([value], dtype=object) + else: + save_arrays[f"metadata_{key}"] = np.array([value], dtype=object) + + with open(path, "wb") as output_file: + cast(NpzSaver, np.savez)(output_file, **save_arrays) + + +def run_linear_probe(config: LinearProbeConfig) -> LinearProbeResult: + """Run the full linear probe pipeline. + + Args: + config: LinearProbeConfig with all pipeline parameters. + + Returns: + LinearProbeResult with coefficients, metrics, and metadata. + """ + if not config.layer_indices: + raise ValueError("layer_indices must not be empty") + + samples: list[SampleWithReflection] = [] + with open(config.input_path) as f: + for line in f: + samples.append(json.loads(line)) + + logger.info(f"Loaded {len(samples)} samples from {config.input_path}") + + device = get_device() + dtype = get_dtype(device) + + logger.info(f"Loading model {config.model_name} on {device} with dtype {dtype}") + model, tokenizer = load_model(config.model_name, device, dtype) + + r_activations, n_activations = collect_token_activations( + samples, model, tokenizer, config.layer_indices + ) + first_layer = config.layer_indices[0] + logger.info(f"Collected activations from {len(r_activations.get(first_layer, []))} R samples") + + probes, metrics = train_linear_probe( + r_activations, n_activations, config.layer_indices, config.test_size + ) + logger.info(f"Trained probes for {len(probes)} layers") + + output_dir = Path(config.output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + + if r_activations and n_activations: + generate_tsne_plot(r_activations, n_activations, output_dir / "tsne_plot.png", first_layer) + generate_pca_plot(r_activations, n_activations, output_dir / "pca_plot.png", first_layer) + logger.info("Generated visualizations") + + metadata: dict[str, str | int | tuple[int, ...]] = { + "model_name": config.model_name, + "layer_indices": config.layer_indices, + "test_size": str(config.test_size), + "r_count": len(r_activations.get(first_layer, [])), + "n_count": len(n_activations.get(first_layer, [])), + "timestamp": datetime.now().isoformat(), + } + + save_probe_weights(probes, metrics, output_dir / "probe_weights.npz", metadata) + logger.info(f"Saved probe weights to {output_dir / 'probe_weights.npz'}") + + coefficients: dict[int, list[float]] = { + layer: probe.coef_.flatten().tolist() for layer, probe in probes.items() + } + + return LinearProbeResult( + coefficients=coefficients, + metrics=metrics, + metadata=metadata, + ) diff --git a/src/probing_reflection/model_utils.py b/src/probing_reflection/model_utils.py new file mode 100644 index 0000000..df29929 --- /dev/null +++ b/src/probing_reflection/model_utils.py @@ -0,0 +1,170 @@ +"""Model loading and management utilities. + +This module provides common functions for loading, managing, and unloading +language models with various configurations (standard, 4-bit quantization). + +The utilities ensure consistent model loading across different modules +and provide memory management for GPU resources. +""" + +from __future__ import annotations + +import gc +from typing import Protocol, cast + +import torch +from transformers import ( + AutoModelForCausalLM, + AutoTokenizer, + BitsAndBytesConfig, + PreTrainedModel, + PreTrainedTokenizerBase, +) + + +class GenerativeModel(Protocol): + def generate(self, **kwargs: object) -> torch.Tensor: ... + + +class ModelLifecycle(Protocol): + def eval(self) -> object: ... + + def to(self, device: torch.device) -> object: ... + + +class QuantizationConfigFactory(Protocol): + def __call__( + self, + *, + load_in_4bit: bool, + bnb_4bit_quant_type: str, + bnb_4bit_compute_dtype: torch.dtype, + bnb_4bit_use_double_quant: bool, + ) -> BitsAndBytesConfig: ... + + +def get_device() -> torch.device: + """Get the appropriate device for model inference. + + Returns: + torch.device: CUDA device if available, otherwise CPU. + """ + return torch.device("cuda" if torch.cuda.is_available() else "cpu") + + +def get_dtype(device: torch.device | None = None) -> torch.dtype: + """Get the appropriate dtype for the given device. + + Args: + device: The target device. If None, uses get_device(). + + Returns: + torch.dtype: bfloat16 for CUDA, float32 for CPU. + """ + if device is None: + device = get_device() + return torch.bfloat16 if device.type == "cuda" else torch.float32 + + +def setup_tokenizer(tokenizer: PreTrainedTokenizerBase) -> PreTrainedTokenizerBase: + """Set up tokenizer with default settings. + + Ensures pad_token is set to eos_token if not present. + + Args: + tokenizer: The tokenizer to configure. + + Returns: + The configured tokenizer (modified in place). + """ + if tokenizer.pad_token is None: + tokenizer.pad_token = tokenizer.eos_token + return tokenizer + + +def load_model( + model_name: str, + device: torch.device | None = None, + dtype: torch.dtype | None = None, +) -> tuple[PreTrainedModel, PreTrainedTokenizerBase]: + """Load model and tokenizer with specified precision. + + Args: + model_name: Name or path of the model to load. + device: Target device. If None, uses get_device(). + dtype: Data type for model weights. If None, uses get_dtype(). + + Returns: + Tuple of (model, tokenizer). + """ + if device is None: + device = get_device() + if dtype is None: + dtype = get_dtype(device) + + tokenizer = AutoTokenizer.from_pretrained(model_name) + setup_tokenizer(tokenizer) + + model = cast( + PreTrainedModel, + AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=dtype), + ) + lifecycle = cast(ModelLifecycle, model) + lifecycle.eval() + lifecycle.to(device) + + return model, tokenizer + + +def load_model_4bit( + model_name: str, +) -> tuple[PreTrainedModel, PreTrainedTokenizerBase]: + """Load model and tokenizer with 4-bit quantization. + + Uses bitsandbytes NF4 quantization for memory efficiency. + Best suited for large models that don't fit in memory. + + Args: + model_name: Name or path of the model to load. + + Returns: + Tuple of (model, tokenizer). + """ + config_factory = cast(QuantizationConfigFactory, BitsAndBytesConfig) + bnb_config = config_factory( + load_in_4bit=True, + bnb_4bit_quant_type="nf4", + bnb_4bit_compute_dtype=torch.bfloat16, + bnb_4bit_use_double_quant=True, + ) + + tokenizer = AutoTokenizer.from_pretrained(model_name) + setup_tokenizer(tokenizer) + + model = cast( + PreTrainedModel, + AutoModelForCausalLM.from_pretrained( + model_name, + quantization_config=bnb_config, + device_map="auto", + ), + ) + cast(ModelLifecycle, model).eval() + + return model, tokenizer + + +def unload_model(model: PreTrainedModel) -> None: + """Unload a model and free GPU memory. + + Deletes the model and clears CUDA cache. Useful for + freeing memory between different model operations. + + Args: + model: The model to unload. + """ + del model + if torch.cuda.is_available(): + torch.cuda.empty_cache() + torch.cuda.synchronize() + gc.collect() diff --git a/src/probing_reflection/prompts.py b/src/probing_reflection/prompts.py new file mode 100644 index 0000000..c8bfdb1 --- /dev/null +++ b/src/probing_reflection/prompts.py @@ -0,0 +1,155 @@ +"""Prompt template functions for various inference and evaluation tasks. + +This module centralizes all prompt formatting functions to ensure +consistency across different modules and make prompt modifications +easier to manage. +""" + +from __future__ import annotations + + +def format_cot_prompt(problem: str) -> str: + """Format a math problem with chain-of-thought prompting. + + Args: + problem: The math problem text. + + Returns: + Formatted prompt with CoT instructions. + """ + return ( + f"Please reason step by step, and put your final answer within \\boxed{{}}." + f"\n\nProblem: {problem}\n\nSolution:" + ) + + +def build_comparison_prompt(answer_a: str, answer_b: str) -> str: + """Create a comparison prompt for judging answer equivalence. + + The prompt is designed to mitigate verbosity bias by explicitly + instructing not to reward longer answers. + + Args: + answer_a: The first answer to compare (reference). + answer_b: The second answer to compare (candidate). + + Returns: + Formatted prompt string for the judge model. + """ + return f"""Compare these two mathematical answers. Are they semantically equivalent? + +Reference: {answer_a} +Candidate: {answer_b} + +CRITICAL: Do NOT reward longer answers. Conciseness is equally valuable. + +First explain your reasoning step by step. +Then provide your verdict. + +Respond in JSON format: +{{"explanation": "...", "equivalent": true/false, "confidence": 0.0-1.0}}""" + + +# Reflection token taxonomy for diagnosis +REFLECTION_TAXONOMY: dict[str, list[str]] = { + "hesitation": ["wait", "hmm", "ah", "oh", "umm"], + "qualification": ["but", "however", "maybe", "actually", "although"], + "verification": ["check", "verify", "double-check", "reconsider"], + "redirection": ["alternatively", "on the other hand", "let me think"], + "transition": ["therefore", "so", "thus", "hence"], +} + + +def build_diagnosis_prompt(text: str) -> str: + """Build a prompt for extracting reflection tokens from text. + + Creates a structured prompt that asks an LLM to identify self-reflection + tokens in the provided text. The prompt emphasizes context-dependent + judgment to avoid false positives. + + Args: + text: The text to analyze for reflection tokens. + + Returns: + A formatted prompt string for the diagnosis model. + """ + taxonomy_examples = "\n".join( + f" - {category}: {', '.join(tokens)}" for category, tokens in REFLECTION_TAXONOMY.items() + ) + + return f"""Identify self-reflection tokens in the following text. + +Self-reflection tokens indicate metacognitive moments where the model exhibits +hesitation, self-correction, verification, or cognitive redirection. + +EXAMPLE CATEGORIES (not exhaustive - use judgment): +{taxonomy_examples} + +CRITICAL: Context matters! Not every instance of these words indicates reflection. +- "Wait for the result" → NOT reflection (imperative command) +- "Wait, that doesn't seem right" → IS reflection (hesitation marker) +- "Check the box" → NOT reflection (instruction) +- "Let me check if this is correct" → IS reflection (verification) + +Judge based on whether the token signals genuine metacognitive activity. + +Analyze this text: +{text} + +Respond in JSON format with this schema: +{{"tokens": [{{"text": "...", "category": "...", "context": "...", "confidence": 0.0-1.0}}]}} + +Requirements: +- text: the exact reflection token found +- category: one of hesitation, qualification, verification, redirection, transition, or other +- context: a brief phrase showing how the token was used +- confidence: 0.0 (not reflection) to 1.0 (definitely reflection) + +If no reflection tokens are found, return: {{"tokens": []}}""" + + +def build_roscoe_prompt(reflection_text: str) -> str: + """Build a prompt for ROSCOE-based reasoning quality evaluation. + + Creates a structured prompt that asks an LLM to evaluate step-by-step + reasoning quality using 5 core metrics on a 1-5 scale. + + Args: + reflection_text: The reasoning text to evaluate. + + Returns: + A formatted prompt string for the evaluation model. + """ + return f"""Evaluate the quality of this reasoning chain using 5 criteria. + +1. FAITHFULNESS (1-5): Is each step grounded in the problem context? + - 1: Contains hallucinations or fabricated facts + - 3: Mostly grounded with minor misinterpretations + - 5: Every step is directly traceable to source + +2. COHERENCE (1-5): Do steps logically follow without contradictions? + - 1: Major logical contradictions exist + - 3: Minor inconsistencies but overall logical + - 5: Flawless logical progression + +3. INFORMATIVENESS (1-5): Does each step add new relevant information? + - 1: No new information or trivial restatements + - 3: Adequate progress toward solution + - 5: Each step optimally advances reasoning + +4. REPETITION (1-5): Are there redundant or circular steps? + - 1: Significant repetition or circular reasoning + - 3: Some redundancy present + - 5: Each step is distinct and novel + +5. COMPLETENESS (1-5): Are all essential reasoning steps included? + - 1: Missing critical steps + - 3: Basic coverage but gaps exist + - 5: Complete reasoning path to conclusion + +Reasoning to evaluate: +{reflection_text} + +Respond in JSON format: +{{"faithfulness": 1-5, "coherence": 1-5, "informativeness": 1-5, + "repetition": 1-5, "completeness": 1-5}}""" diff --git a/src/probing_reflection/reflection_diagnosis.py b/src/probing_reflection/reflection_diagnosis.py new file mode 100644 index 0000000..41e2670 --- /dev/null +++ b/src/probing_reflection/reflection_diagnosis.py @@ -0,0 +1,259 @@ +"""Reflection token diagnosis and extraction module. + +This module provides functions for diagnosing self-reflection tokens +in model outputs using LLM-based judgment. +""" + +from __future__ import annotations + +import json +from collections import defaultdict +from pathlib import Path +from typing import Protocol + +from tqdm import tqdm + +from probing_reflection.judges import ReflectionJudge +from probing_reflection.prompts import REFLECTION_TAXONOMY +from probing_reflection.roscoe_metrics import RoscoeJudge +from probing_reflection.types import ( + ReflectionAnalysisReport, + ReflectionDiagnosisConfig, + ReflectionToken, + SampleWithReflection, +) + + +class ReflectionJudgeProtocol(Protocol): + def judge(self, text: str) -> list[ReflectionToken]: ... + + +def ensure_output_dir(output_dir: str | Path) -> Path: + """Ensure output directory exists. + + Args: + output_dir: Path to output directory. + + Returns: + Path object for the directory. + """ + path = Path(output_dir) + path.mkdir(parents=True, exist_ok=True) + return path + + +def diagnose_sample( + judge: ReflectionJudgeProtocol, sample: dict[str, object] +) -> SampleWithReflection: + """Diagnose a single sample for reflection tokens. + + Analyzes the generated text from a sample to detect self-reflection + tokens using the provided judge, and computes reflection metrics. + + Args: + judge: A ReflectionJudge instance (must have load_model() called). + sample: A dict containing at minimum 'generated' text and other + sample fields like 'problem_id', 'problem', 'reference_answer'. + + Returns: + A SampleWithReflection dict with all original fields plus: + - reflection_tokens: List of detected ReflectionToken objects + - reflection_count: Number of reflection tokens found + - reflection_density: Reflection tokens per 100 words + """ + generated = str(sample.get("generated", "")) + + subject_val = sample.get("subject") + level_val = sample.get("level") + subject: str | None = str(subject_val) if subject_val is not None else None + level: int | None = ( + level_val + if isinstance(level_val, int) + else (int(str(level_val)) if level_val is not None else None) + ) + + if not generated.strip(): + return { + "problem_id": str(sample.get("problem_id", "")), + "problem": str(sample.get("problem", "")), + "generated": "", + "reference_answer": str(sample.get("reference_answer", "")), + "subject": subject, + "level": level, + "reflection_tokens": [], + "reflection_count": 0, + "reflection_density": 0.0, + } + + tokens = judge.judge(generated) + token_count = len(tokens) + + words = generated.split() + word_count = len(words) + + density = (token_count / word_count * 100) if word_count > 0 else 0.0 + + result: SampleWithReflection = { + "problem_id": str(sample.get("problem_id", "")), + "problem": str(sample.get("problem", "")), + "generated": generated, + "reference_answer": str(sample.get("reference_answer", "")), + "subject": subject, + "level": level, + "reflection_tokens": tokens, + "reflection_count": token_count, + "reflection_density": density, + } + return result + + +def diagnose_all( + config: ReflectionDiagnosisConfig, + judge: ReflectionJudgeProtocol | None = None, +) -> tuple[list[SampleWithReflection], ReflectionAnalysisReport]: + """Diagnose reflection tokens across all samples in a JSONL file. + + Processes each sample through the reflection diagnosis pipeline and + aggregates statistics about detected reflection tokens. + + Args: + config: Configuration containing input_path, output_dir, and model settings. + judge: Optional pre-configured ReflectionJudge or RoscoeJudge. If None, + creates and loads a new judge based on config.judge_type. + + Returns: + A tuple containing: + - List of SampleWithReflection dicts with original fields plus analysis + - ReflectionAnalysisReport with aggregated statistics + """ + ensure_output_dir(config.output_dir) + + processed_samples: list[SampleWithReflection] = [] + total_samples = 0 + total_tokens = 0 + all_densities: list[float] = [] + token_frequency: dict[str, int] = defaultdict(int) + category_distribution: dict[str, int] = defaultdict(int) + per_subject_raw: dict[str, dict[str, float | int]] = defaultdict( + lambda: {"count": 0, "total_density": 0.0} + ) + per_level_raw: dict[str, dict[str, float | int]] = defaultdict( + lambda: {"count": 0, "total_density": 0.0} + ) + processing_errors = 0 + + input_path = Path(config.input_path) + with open(input_path) as f: + lines = f.readlines() + + if judge is None and lines: + if config.judge_type == "roscoe": + judge = RoscoeJudge(config.model_name) + else: + judge = ReflectionJudge(config.model_name) + judge.load_model() + + for line in tqdm(lines, desc="Diagnosing reflection tokens"): + total_samples += 1 + try: + if judge is None: + raise RuntimeError("Model not loaded") + sample = json.loads(line) + result = diagnose_sample(judge, sample) + processed_samples.append(result) + + total_tokens += result["reflection_count"] + all_densities.append(result["reflection_density"]) + + for token in result["reflection_tokens"]: + token_frequency[token["text"]] += 1 + category_distribution[token["category"]] += 1 + + subject = result.get("subject") + if subject is not None: + per_subject_raw[subject]["count"] += 1 + per_subject_raw[subject]["total_density"] += result["reflection_density"] + + level = result.get("level") + if level is not None: + level_key = str(level) + per_level_raw[level_key]["count"] += 1 + per_level_raw[level_key]["total_density"] += result["reflection_density"] + + except (json.JSONDecodeError, KeyError, TypeError, ValueError, RuntimeError): + processing_errors += 1 + + avg_tokens_per_sample = total_tokens / total_samples if total_samples > 0 else 0.0 + overall_density = sum(all_densities) / len(all_densities) if all_densities else 0.0 + + per_subject_stats: dict[str, dict[str, float | int]] = {} + for subject, stats in per_subject_raw.items(): + count = int(stats["count"]) + total_density = float(stats["total_density"]) + avg_density = total_density / count if count > 0 else 0.0 + per_subject_stats[subject] = {"count": count, "avg_density": avg_density} + + per_level_stats: dict[str, dict[str, float | int]] = {} + for level_key, stats in per_level_raw.items(): + count = int(stats["count"]) + total_density = float(stats["total_density"]) + avg_density = total_density / count if count > 0 else 0.0 + per_level_stats[level_key] = {"count": count, "avg_density": avg_density} + + report = ReflectionAnalysisReport( + total_samples=total_samples, + total_tokens=total_tokens, + avg_tokens_per_sample=avg_tokens_per_sample, + overall_density=overall_density, + token_frequency=dict(token_frequency), + category_distribution=dict(category_distribution), + per_subject_stats=per_subject_stats, + per_level_stats=per_level_stats, + processing_errors=processing_errors, + ) + + return processed_samples, report + + +def write_analysis_report(report: ReflectionAnalysisReport, output_path: Path | str) -> None: + """Write a reflection analysis report to a JSON file. + + Args: + report: The ReflectionAnalysisReport to write. + output_path: Path to the output JSON file. + """ + path = Path(output_path) + path.parent.mkdir(parents=True, exist_ok=True) + + with open(path, "w") as f: + json.dump(report, f, indent=2) + + +def write_analyzed_jsonl( + samples: list[SampleWithReflection], + output_path: Path | str, +) -> None: + """Write analyzed samples to a JSONL file. + + Args: + samples: List of samples with reflection analysis results. + output_path: Path to the output JSONL file. + """ + path = Path(output_path) + path.parent.mkdir(parents=True, exist_ok=True) + + with open(path, "w") as f: + for sample in samples: + f.write(json.dumps(sample) + "\n") + + +__all__ = [ + "REFLECTION_TAXONOMY", + "ReflectionJudge", + "RoscoeJudge", + "diagnose_all", + "diagnose_sample", + "ensure_output_dir", + "write_analysis_report", + "write_analyzed_jsonl", +] diff --git a/src/probing_reflection/roscoe_metrics.py b/src/probing_reflection/roscoe_metrics.py new file mode 100644 index 0000000..16043a5 --- /dev/null +++ b/src/probing_reflection/roscoe_metrics.py @@ -0,0 +1,149 @@ +"""ROSCOE-based reasoning quality evaluation using LLM-as-Judge.""" + +from __future__ import annotations + +from probing_reflection.judges import BaseLLMJudge +from probing_reflection.prompts import build_roscoe_prompt +from probing_reflection.types import ReflectionToken, RoscoeEvaluation + + +def _score(value: object) -> float: + if isinstance(value, (str, int, float)): + try: + return float(value) + except ValueError: + return 1.0 + return 1.0 + + +class RoscoeJudge(BaseLLMJudge): + """LLM-based judge for ROSCOE reasoning quality metrics. + + Evaluates step-by-step reasoning quality using 5 core metrics + on a 1-5 scale: faithfulness, coherence, informativeness, + repetition, and completeness. + """ + + def __init__(self, model_name: str = "Qwen/Qwen3.5-27B", threshold: float = 3.0) -> None: + """Initialize the ROSCOE judge. + + Args: + model_name: Name or path of the judge model. + threshold: Threshold for passed_filter (default 3.0/5.0). + """ + super().__init__(model_name) + self.threshold = threshold + + def judge(self, text: str) -> list[ReflectionToken]: + """Analyze text for reflection-like patterns using ROSCOE metrics. + + Maps ROSCOE quality evaluation to reflection token format for + compatibility with the diagnosis pipeline. + + Args: + text: The text to analyze. + + Returns: + List of reflection tokens derived from ROSCOE evaluation. + """ + evaluation = self.evaluate(text) + return self._evaluation_to_tokens(evaluation, text) + + def _evaluation_to_tokens( + self, evaluation: RoscoeEvaluation, text: str + ) -> list[ReflectionToken]: + """Convert ROSCOE evaluation to reflection tokens. + + Args: + evaluation: The ROSCOE evaluation result. + text: The original text being evaluated. + + Returns: + List of reflection tokens based on evaluation metrics. + """ + tokens: list[ReflectionToken] = [] + metric_scores: list[tuple[str, str, float]] = [ + ("faithfulness", evaluation["diagnosis"]["faithfulness"], evaluation["faithfulness"]), + ("coherence", evaluation["diagnosis"]["coherence"], evaluation["coherence"]), + ( + "informativeness", + evaluation["diagnosis"]["informativeness"], + evaluation["informativeness"], + ), + ("repetition", evaluation["diagnosis"]["repetition"], evaluation["repetition"]), + ("completeness", evaluation["diagnosis"]["completeness"], evaluation["completeness"]), + ] + for metric_name, _diagnosis, score in metric_scores: + tokens.append( + ReflectionToken( + text=metric_name, + category="roscoe_metric", + context=f"Score: {score:.1f}/5.0", + confidence=score / 5.0, + ) + ) + return tokens + + def evaluate(self, text: str, mode: str = "scoring") -> RoscoeEvaluation: + """Evaluate reasoning quality using ROSCOE metrics. + + Args: + text: The reasoning text to evaluate. + mode: Evaluation mode - "scoring", "filtering", or "diagnosis". + + Returns: + RoscoeEvaluation with all 5 metric scores and derived fields. + """ + prompt = build_roscoe_prompt(text) + response = self._run_inference(prompt, max_new_tokens=256) + return self._parse_roscoe_response(response) + + def _parse_roscoe_response(self, response: str) -> RoscoeEvaluation: + """Parse LLM response into RoscoeEvaluation. + + Args: + response: The raw model output text. + + Returns: + RoscoeEvaluation with all 8 fields populated. + """ + result = self._parse_json_response(response) + + faithfulness = _score(result.get("faithfulness")) + coherence = _score(result.get("coherence")) + informativeness = _score(result.get("informativeness")) + repetition = _score(result.get("repetition")) + completeness = _score(result.get("completeness")) + + metrics = [ + max(1.0, min(5.0, m)) + for m in [faithfulness, coherence, informativeness, repetition, completeness] + ] + + overall = sum(metrics) / 5.0 + + def categorize(score: float) -> str: + if score >= 4.0: + return "high" + elif score >= 2.5: + return "medium" + return "low" + + diagnosis = { + "faithfulness": categorize(metrics[0]), + "coherence": categorize(metrics[1]), + "informativeness": categorize(metrics[2]), + "repetition": categorize(metrics[3]), + "completeness": categorize(metrics[4]), + } + + return RoscoeEvaluation( + faithfulness=metrics[0], + coherence=metrics[1], + informativeness=metrics[2], + repetition=metrics[3], + completeness=metrics[4], + overall_score=overall, + passed_filter=overall >= self.threshold, + diagnosis=diagnosis, + ) diff --git a/src/probing_reflection/steering_inference.py b/src/probing_reflection/steering_inference.py new file mode 100644 index 0000000..00a449c --- /dev/null +++ b/src/probing_reflection/steering_inference.py @@ -0,0 +1,244 @@ +"""Steering inference module for applying steering vectors during model inference. + +This module provides utilities for loading steering vectors and models +with 4-bit quantization, and creating hooks to apply steering during inference. +""" + +from __future__ import annotations + +import json +from collections.abc import Callable +from pathlib import Path +from typing import cast + +import torch +from datasets.load import load_dataset +from torch import Tensor +from torch.utils.hooks import RemovableHandle +from tqdm import tqdm + +from probing_reflection.batch_utils import decode_generated_tokens, get_item_field, prepare_batch +from probing_reflection.model_utils import GenerativeModel, load_model_4bit +from probing_reflection.prompts import format_cot_prompt +from probing_reflection.types import SteeringInferenceConfig + + +def get_output_path( + dataset: str, condition: str, base_dir: str = "outputs/steering_experiments" +) -> Path: + """Get output path for a specific dataset and condition. + + Args: + dataset: Dataset name (e.g., 'math500', 'aime', 'gpqa'). + condition: Steering condition (e.g., 'baseline', 'positive', 'negative'). + base_dir: Base directory for outputs. + + Returns: + Path to the output JSONL file. + """ + path = Path(base_dir) / dataset / condition / "results.jsonl" + path.parent.mkdir(parents=True, exist_ok=True) + return path + + +def load_steering_vectors(path: Path | str) -> dict[int, Tensor]: + """Load steering vectors from a .pt file. + + Args: + path: Path to the .pt file saved by save_steering_vectors(). + + Returns: + Dict mapping layer index to steering vector tensor. + + Raises: + FileNotFoundError: If the file doesn't exist. + ValueError: If the file format is invalid. + """ + file_path = Path(path) + + if not file_path.exists(): + raise FileNotFoundError(f"Steering vectors file not found: {file_path}") + + data = torch.load(file_path, map_location="cpu", weights_only=True) + + vectors: dict[int, Tensor] = {} + for key, value in data.items(): + if key == "metadata": + continue + if key.startswith("layer_"): + try: + layer_idx = int(key.split("_")[1]) + vectors[layer_idx] = value + except (IndexError, ValueError): + continue + + if not vectors: + raise ValueError(f"No valid layer keys found in {file_path}") + + return vectors + + +def create_steering_hook( + vector: Tensor, coefficient: float +) -> Callable[ + [object, tuple[object, ...], Tensor | tuple[Tensor, ...]], Tensor | tuple[Tensor, ...] +]: + """Create a forward hook that adds steering vector to activations. + + Args: + vector: The steering vector tensor to add. + coefficient: Multiplier for the steering vector. + + Returns: + A hook function compatible with register_forward_hook(). + """ + + def hook( + module: object, input: tuple[object, ...], output: Tensor | tuple[Tensor, ...] + ) -> Tensor | tuple[Tensor, ...]: + if isinstance(output, tuple): + hidden_states = output[0] + steering = coefficient * vector.to( + dtype=hidden_states.dtype, device=hidden_states.device + ) + hidden_states = hidden_states + steering + return (hidden_states,) + output[1:] + else: + steering = coefficient * vector.to(dtype=output.dtype, device=output.device) + output = output + steering + return output + + return hook + + +def run_steering_inference(config: SteeringInferenceConfig) -> Path: + """Run steering inference on dataset and save results to JSONL. + + Loads steering vectors, applies them via forward hooks during generation, + and saves results with all required fields. + + Args: + config: SteeringInferenceConfig with all settings. + + Returns: + Path to the output JSONL file. + """ + vectors = load_steering_vectors(config.steering_vector_path) + model, tokenizer = load_model_4bit(config.model_name) + generative_model = cast(GenerativeModel, model) + num_layers = len(model.model.layers) + + if config.layer_indices: + for idx in config.layer_indices: + if idx < 0 or idx >= num_layers: + raise ValueError(f"Layer index {idx} out of range [0, {num_layers - 1}]") + layer_indices = list(config.layer_indices) + else: + layer_indices = sorted(vectors.keys()) + + output_path = Path(config.output_path) + output_path.parent.mkdir(parents=True, exist_ok=True) + + if config.dataset_config: + dataset = load_dataset(config.dataset_name, config.dataset_config, split="train") + else: + try: + dataset = load_dataset(config.dataset_name, split="test") + except ValueError: + dataset = load_dataset(config.dataset_name, split="train") + + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + num_samples = len(dataset) + if config.limit is not None and config.limit < num_samples: + num_samples = config.limit + handles: list[RemovableHandle] = [] + + try: + for layer_idx in layer_indices: + if layer_idx in vectors: + handle = model.model.layers[layer_idx].register_forward_hook( + create_steering_hook(vectors[layer_idx], config.coefficient) + ) + handles.append(handle) + + with open(output_path, "w") as f: + for i in tqdm( + range(0, num_samples, config.batch_size), desc="Running steering inference" + ): + batch_end = min(i + config.batch_size, num_samples) + batch_items = [dataset[j] for j in range(i, batch_end)] + problems = [get_item_field(item, ["problem", "question"]) for item in batch_items] + prompts = [format_cot_prompt(p) for p in problems] + + inputs = prepare_batch(tokenizer, prompts) + input_ids = torch.tensor(inputs["input_ids"]).to(device) + attention_mask = torch.tensor(inputs["attention_mask"]).to(device) + + try: + with torch.no_grad(): + outputs = generative_model.generate( + input_ids=input_ids, + attention_mask=attention_mask, + max_new_tokens=config.max_new_tokens, + repetition_penalty=1.05, + do_sample=False, + ) + except torch.cuda.OutOfMemoryError: + torch.cuda.empty_cache() + for item in batch_items: + problem = get_item_field(item, ["problem", "question"]) + prompt = format_cot_prompt(problem) + single_input = tokenizer(prompt, return_tensors="pt") + single_ids = single_input["input_ids"].to(device) + single_mask = single_input["attention_mask"].to(device) + + with torch.no_grad(): + output = generative_model.generate( + input_ids=single_ids, + attention_mask=single_mask, + max_new_tokens=config.max_new_tokens, + repetition_penalty=1.05, + do_sample=False, + ) + + generated_text = decode_generated_tokens( + tokenizer, output[0], single_ids.shape[-1] + ) + + result_entry = { + "problem_id": get_item_field(item, ["unique_id", "id", "problem_id"]), + "problem": problem, + "generated": generated_text, + "reference_answer": get_item_field( + item, ["answer", "solution", "reference"] + ), + "subject": get_item_field(item, ["subject", "category"], ""), + "level": get_item_field(item, ["level", "difficulty"], ""), + "prompt": prompt, + } + f.write(json.dumps(result_entry) + "\n") + continue + + for j, (output, item) in enumerate(zip(outputs, batch_items)): # noqa: B905 + prompt = prompts[j] + generated_text = decode_generated_tokens(tokenizer, output, input_ids.shape[-1]) + + problem = problems[j] + result_entry = { + "problem_id": get_item_field(item, ["unique_id", "id", "problem_id"]), + "problem": problem, + "generated": generated_text, + "reference_answer": get_item_field( + item, ["answer", "solution", "reference"] + ), + "subject": get_item_field(item, ["subject", "category"], ""), + "level": get_item_field(item, ["level", "difficulty"], ""), + "prompt": prompt, + } + f.write(json.dumps(result_entry) + "\n") + + finally: + for handle in handles: + handle.remove() + + return output_path diff --git a/src/probing_reflection/steering_vectors.py b/src/probing_reflection/steering_vectors.py new file mode 100644 index 0000000..e67b72f --- /dev/null +++ b/src/probing_reflection/steering_vectors.py @@ -0,0 +1,335 @@ +"""Steering vector extraction for modulating reflection behavior in LLMs. + +This module provides functions to extract steering vectors from model +activations, which can be used to modulate self-reflection behavior + in language models. +""" + +from __future__ import annotations + +import json +import logging +from datetime import datetime +from pathlib import Path + +import torch +from torch import Tensor +from tqdm import tqdm +from transformers import ( + PreTrainedModel, + PreTrainedTokenizerBase, +) + +from probing_reflection.model_utils import get_device, get_dtype, load_model +from probing_reflection.prompts import REFLECTION_TAXONOMY +from probing_reflection.types import ( + ExtractVectorsConfig, + SampleWithReflection, + SteeringVectorResult, +) + +logger = logging.getLogger(__name__) + + +def classify_samples( + samples: list[SampleWithReflection], + min_samples: int = 10, +) -> tuple[list[SampleWithReflection], list[SampleWithReflection]]: + """Split samples into reflection (R) and non-reflection (N) sets. + + R set: samples with reflection_count > 0 + N set: samples with reflection_count == 0 + + Args: + samples: List of samples with reflection analysis. + min_samples: Minimum required samples in each set. + + Returns: + Tuple of (R_samples, N_samples). + + Raises: + ValueError: If either R or N set has fewer than min_samples. + """ + r_samples: list[SampleWithReflection] = [] + n_samples: list[SampleWithReflection] = [] + + for sample in samples: + if sample["reflection_count"] > 0: + r_samples.append(sample) + else: + n_samples.append(sample) + + if len(r_samples) < min_samples: + raise ValueError( + f"R set has {len(r_samples)} samples, but min_samples={min_samples} is required" + ) + if len(n_samples) < min_samples: + raise ValueError( + f"N set has {len(n_samples)} samples, but min_samples={min_samples} is required" + ) + + return (r_samples, n_samples) + + +def find_reflection_token_position( + tokenizer: PreTrainedTokenizerBase, + text: str, + reflection_tokens: list[str] | None = None, +) -> int | None: + """Find the position of the first reflection token in text. + + Args: + tokenizer: Tokenizer to use for tokenization + text: Text to search for reflection tokens + reflection_tokens: List of tokens to search for (default: all from REFLECTION_TAXONOMY) + + Returns: + Index of first reflection token in tokenized sequence, or None if not found + """ + if reflection_tokens is None: + reflection_tokens = [token for tokens in REFLECTION_TAXONOMY.values() for token in tokens] + + reflection_tokens_lower = [t.lower() for t in reflection_tokens] + token_ids = tokenizer.encode(text, add_special_tokens=False) + tokens = tokenizer.convert_ids_to_tokens(token_ids) + + for idx, token in enumerate(tokens): + token_lower = token.lower() + for ref_token in reflection_tokens_lower: + if ref_token in token_lower: + return idx + + return None + + +def extract_activation_at_position( + model: PreTrainedModel, + tokenizer: PreTrainedTokenizerBase, + text: str, + position: int, + layer_indices: tuple[int, ...], +) -> dict[int, Tensor]: + """Extract hidden state activations at a specific token position. + + Args: + model: The language model to extract activations from + tokenizer: Tokenizer for the model + text: Text to process + position: Token position to extract activation from + layer_indices: Which layers to extract activations from + + Returns: + Dict mapping layer index to activation tensor (on CPU) + + Raises: + IndexError: If position is out of bounds + """ + with torch.no_grad(): + inputs = tokenizer(text, return_tensors="pt") + inputs = {k: v.to(model.device) for k, v in inputs.items()} + outputs = model(**inputs, output_hidden_states=True) + + seq_len = inputs["input_ids"].shape[1] + if position >= seq_len: + raise IndexError(f"Position {position} >= sequence length {seq_len}") + + result: dict[int, Tensor] = {} + for layer in layer_indices: + hidden = outputs.hidden_states[layer + 1] + result[layer] = hidden[0, position, :].cpu() + + return result + + +def compute_difference_in_means( + r_activations: dict[int, list[Tensor]], + n_activations: dict[int, list[Tensor]], + layer_indices: tuple[int, ...], +) -> dict[int, Tensor]: + """Compute steering vectors as difference in means. + + For each layer: v = mean(R_activations) - mean(N_activations) + + Args: + r_activations: Dict mapping layer index to list of activation tensors + (reflection samples) + n_activations: Dict mapping layer index to list of activation tensors + (non-reflection samples) + layer_indices: Tuple of layer indices to compute vectors for + + Returns: + Dict mapping layer index to steering vector tensor + + Raises: + ValueError: If any layer is missing from either R or N activations + """ + vectors: dict[int, Tensor] = {} + + for layer in layer_indices: + if layer not in r_activations or not r_activations[layer]: + raise ValueError(f"No R activations for layer {layer}") + if layer not in n_activations or not n_activations[layer]: + raise ValueError(f"No N activations for layer {layer}") + + r_stack = torch.stack(r_activations[layer]) + n_stack = torch.stack(n_activations[layer]) + + r_mean = r_stack.mean(dim=0) + n_mean = n_stack.mean(dim=0) + + vectors[layer] = (r_mean - n_mean).cpu() + + return vectors + + +def save_steering_vectors( + vectors: dict[int, Tensor], + metadata: dict[str, str | int | tuple[int, ...]], + output_path: Path | str, +) -> None: + """Save steering vectors to a .pt file with metadata. + + Args: + vectors: Dict mapping layer index to steering vector tensor + metadata: Metadata dict with model_name, layer_indices, r_count, n_count, etc. + output_path: Path to save the .pt file + + Raises: + ValueError: If any tensor is not on CPU + """ + path = Path(output_path) + path.parent.mkdir(parents=True, exist_ok=True) + + for layer, tensor in vectors.items(): + if tensor.device.type != "cpu": + raise ValueError(f"Tensor for layer {layer} is not on CPU") + + metadata_with_timestamp = dict(metadata) + metadata_with_timestamp["timestamp"] = datetime.now().isoformat() + + save_dict: dict[str, Tensor | dict[str, str | int | tuple[int, ...]]] = {} + for layer, tensor in vectors.items(): + save_dict[f"layer_{layer}"] = tensor + save_dict["metadata"] = metadata_with_timestamp + + torch.save(save_dict, path) + + +def extract_batch_activations( + samples: list[SampleWithReflection], + model: PreTrainedModel, + tokenizer: PreTrainedTokenizerBase, + layer_indices: tuple[int, ...], + batch_size: int = 4, + min_samples: int = 1, +) -> tuple[dict[int, list[Tensor]], dict[int, list[Tensor]]]: + """Extract activations from samples in batches. + + For R set: extracts at reflection token position + For N set: extracts at last token position + + Args: + samples: Samples with reflection analysis + model: Language model + tokenizer: Tokenizer for the model + layer_indices: Layers to extract activations from + batch_size: Number of samples to process in each chunk + + Returns: + Tuple of (R_activations_by_layer, N_activations_by_layer) + """ + r_samples, n_samples = classify_samples(samples, min_samples=min_samples) + + r_activations: dict[int, list[Tensor]] = {layer: [] for layer in layer_indices} + n_activations: dict[int, list[Tensor]] = {layer: [] for layer in layer_indices} + + for batch_start in tqdm(range(0, len(r_samples), batch_size), desc="Extracting R activations"): + batch_end = min(batch_start + batch_size, len(r_samples)) + batch = r_samples[batch_start:batch_end] + + for sample in batch: + text = sample["generated"] + position = find_reflection_token_position(tokenizer, text) + if position is None: + logger.warning(f"Reflection token not found for sample {sample['problem_id']}") + continue + try: + activations = extract_activation_at_position( + model, tokenizer, text, position, layer_indices + ) + for layer, tensor in activations.items(): + r_activations[layer].append(tensor.cpu()) + except torch.cuda.OutOfMemoryError: + torch.cuda.empty_cache() + logger.warning(f"CUDA OOM for R sample {sample['problem_id']}, skipping") + + for batch_start in tqdm(range(0, len(n_samples), batch_size), desc="Extracting N activations"): + batch_end = min(batch_start + batch_size, len(n_samples)) + batch = n_samples[batch_start:batch_end] + + for sample in batch: + text = sample["generated"] + tokens = tokenizer.encode(text, add_special_tokens=False) + position = len(tokens) - 1 + try: + activations = extract_activation_at_position( + model, tokenizer, text, position, layer_indices + ) + for layer, tensor in activations.items(): + n_activations[layer].append(tensor.cpu()) + except torch.cuda.OutOfMemoryError: + torch.cuda.empty_cache() + logger.warning(f"CUDA OOM for N sample {sample['problem_id']}, skipping") + + return r_activations, n_activations + + +def extract_steering_vectors(config: ExtractVectorsConfig) -> SteeringVectorResult: + """Main pipeline to extract steering vectors from model activations. + + Args: + config: Configuration with input_path, model_name, layer_indices, etc. + + Returns: + SteeringVectorResult with vectors and metadata + """ + samples: list[SampleWithReflection] = [] + with open(config.input_path) as f: + for line in f: + samples.append(json.loads(line)) + + logger.info(f"Loaded {len(samples)} samples from {config.input_path}") + + r_count = sum(sample["reflection_count"] > 0 for sample in samples) + n_count = len(samples) - r_count + logger.info(f"Classified: {r_count} R samples, {n_count} N samples") + + device = get_device() + dtype = get_dtype(device) + + logger.info(f"Loading model {config.model_name} on {device} with dtype {dtype}") + model, tokenizer = load_model(config.model_name, device, dtype) + + r_activations, n_activations = extract_batch_activations( + samples, + model, + tokenizer, + config.layer_indices, + config.batch_size, + config.min_samples, + ) + + vectors = compute_difference_in_means(r_activations, n_activations, config.layer_indices) + + metadata: dict[str, str | int | tuple[int, ...]] = { + "model_name": config.model_name, + "layer_indices": config.layer_indices, + "r_count": r_count, + "n_count": n_count, + "timestamp": datetime.now().isoformat(), + } + + save_steering_vectors(vectors, metadata, config.output_path) + logger.info(f"Saved steering vectors to {config.output_path}") + + return SteeringVectorResult(vectors=vectors, metadata=metadata) diff --git a/src/probing_reflection/types.py b/src/probing_reflection/types.py index 465a181..0726a80 100644 --- a/src/probing_reflection/types.py +++ b/src/probing_reflection/types.py @@ -9,7 +9,9 @@ from dataclasses import dataclass, field from types import MappingProxyType -from typing import TypedDict +from typing import NotRequired, TypedDict + +from torch import Tensor @dataclass(frozen=True) @@ -26,6 +28,27 @@ class ProbingConfig: layer_indices: tuple[int, ...] = () +@dataclass(frozen=True) +class InferenceConfig: + """Immutable configuration for inference experiments. + + Attributes: + model_name: Name or path of the model for inference. + dataset_name: Name of the dataset to run inference on. + batch_size: Batch size for inference. + max_new_tokens: Maximum number of new tokens to generate. + output_path: Path to save inference results. + limit: Optional maximum number of samples to process. + """ + + model_name: str = "Qwen/Qwen3.5-0.8B" + dataset_name: str = "HuggingFaceH4/MATH-500" + batch_size: int = 8 + max_new_tokens: int = 256 + output_path: str = "outputs/math500_inference/qwen3-0.8b-math500-cot.jsonl" + limit: int | None = None + + @dataclass(frozen=True) class ReflectionResult: """Immutable result of a reflection analysis. @@ -57,3 +80,324 @@ class ContrastivePair(TypedDict): positive: str negative: str + + +class JudgeVerdict(TypedDict): + """Verdict from a judge model evaluating answer equivalence. + + Represents the output of an LLM judge that determines whether + a model's extracted answer matches a reference answer. + + Attributes: + explanation: Explanation of the judge's reasoning. + equivalent: Whether the extracted answer is equivalent to the reference. + confidence: Confidence score in [0.0, 1.0] for the verdict. + """ + + explanation: str + equivalent: bool + confidence: float + + +class EvaluationResult(TypedDict): + """Result of evaluating a single sample against a reference. + + Stores all information about the evaluation of one problem, + including the judge's verdict and metadata about the sample. + + Attributes: + problem_id: Unique identifier for the problem. + extracted_answer: Answer extracted from the model output, or None if extraction failed. + reference_answer: The ground truth reference answer. + is_correct: Whether the extracted answer was judged correct. + judge_explanation: Explanation from the judge model. + confidence: Confidence score in [0.0, 1.0] for the evaluation. + subject: Optional subject category (e.g., "algebra", "geometry"). + level: Optional difficulty level of the problem. + """ + + problem_id: str + extracted_answer: str | None + reference_answer: str + is_correct: bool + judge_explanation: str + confidence: float + subject: NotRequired[str | None] + level: NotRequired[int | None] + + +class EvaluationReport(TypedDict): + """Aggregated report of evaluation results across all samples. + + Provides summary statistics and detailed results for analysis + of model performance on an evaluation dataset. + + Attributes: + overall_accuracy: Fraction of correct answers across all samples. + total_samples: Total number of samples evaluated. + correct_count: Number of correctly answered samples. + per_subject_accuracy: Accuracy broken down by subject category. + per_level_accuracy: Accuracy broken down by difficulty level. + results: List of individual evaluation results. + """ + + overall_accuracy: float + total_samples: int + correct_count: int + per_subject_accuracy: dict[str, float] + per_level_accuracy: dict[str, float] + results: list[EvaluationResult] + + +@dataclass(frozen=True) +class EvaluationConfig: + """Immutable configuration for evaluation experiments. + + Attributes: + judge_model_name: Name or path of the judge model. + batch_size: Batch size for evaluation. + confidence_threshold: Minimum confidence to accept a verdict. + output_file: Optional path to save evaluation results. + """ + + judge_model_name: str = "Qwen/Qwen3.5-27B" + batch_size: int = 8 + confidence_threshold: float = 0.7 + output_file: str | None = None + + +@dataclass(frozen=True) +class ReflectionDiagnosisConfig: + """Immutable configuration for reflection diagnosis experiments. + + Attributes: + input_path: Path to the input JSONL file containing samples. + output_dir: Directory to save diagnosis results. + model_name: Name or path of the model for token analysis. + batch_size: Batch size for processing samples. + max_retries: Maximum number of retries for failed API calls. + judge_type: Type of judge to use - "reflection" or "roscoe". + """ + + input_path: str = "" + output_dir: str = "outputs/reflection_diagnosis/" + model_name: str = "Qwen/Qwen3.5-27B" + batch_size: int = 1 + max_retries: int = 3 + judge_type: str = "reflection" + + +@dataclass(frozen=True) +class ExtractVectorsConfig: + """Immutable configuration for steering vector extraction. + + Attributes: + input_path: Path to the input data file. + model_name: Name or path of the model for extraction. + layer_indices: Tuple of layer indices to extract vectors from. + output_path: Path to save the extracted steering vectors. + min_samples: Minimum number of samples required for extraction. + batch_size: Batch size for processing during extraction. + """ + + input_path: str = "" + model_name: str = "Qwen/Qwen2.5-0.5B" + layer_indices: tuple[int, ...] = () + output_path: str = "steering_vectors.pt" + min_samples: int = 10 + batch_size: int = 4 + + +@dataclass(frozen=True) +class SteeringInferenceConfig: + """Immutable configuration for steering inference experiments. + + Attributes: + model_name: Name or path of the model for steering inference. + steering_vector_path: Path to the saved steering vectors file. + layer_indices: Tuple of layer indices to apply steering to. + Empty tuple means all layers. + coefficient: Steering strength multiplier. + dataset_name: Name of the dataset to run inference on. + dataset_config: Optional config name for datasets like GPQA. + batch_size: Batch size for inference (1 for 4-bit memory constraints). + max_new_tokens: Maximum number of new tokens to generate. + output_path: Path to save inference results. + limit: Optional limit on number of samples to process. + """ + + model_name: str = "Qwen/Qwen2.5-32B" + steering_vector_path: str = "" + layer_indices: tuple[int, ...] = () + coefficient: float = 1.0 + dataset_name: str = "HuggingFaceH4/MATH-500" + dataset_config: str | None = None + batch_size: int = 1 + max_new_tokens: int = 512 + output_path: str = "" + limit: int | None = None + + +class ReflectionToken(TypedDict): + """Structured representation of a reflection token in model output. + + Represents a single instance of self-reflection language detected + in the model's generated text. + + Attributes: + text: The actual token text (e.g., "Wait", "Actually"). + category: Category of reflection (e.g., "correction", "verification"). + context: Surrounding context where the token appeared. + confidence: Confidence score in [0.0, 1.0] for the detection. + """ + + text: str + category: str + context: str + confidence: float + + +class SampleWithReflection(TypedDict): + """Sample data augmented with reflection analysis results. + + Extends the base sample structure with reflection-specific metrics + and detected tokens. + + Attributes: + problem_id: Unique identifier for the problem. + problem: The problem text or question. + generated: The model's generated response. + reference_answer: The ground truth reference answer. + subject: Optional subject category (e.g., "algebra", "geometry"). + level: Optional difficulty level of the problem. + reflection_tokens: List of detected reflection tokens. + reflection_count: Total count of reflection tokens found. + reflection_density: Ratio of reflection tokens to total tokens. + """ + + problem_id: str + problem: str + generated: str + reference_answer: str + subject: NotRequired[str | None] + level: NotRequired[int | None] + reflection_tokens: list[ReflectionToken] + reflection_count: int + reflection_density: float + + +class RoscoeEvaluation(TypedDict): + """ROSCOE-based reasoning quality evaluation result. + + Evaluates step-by-step reasoning quality using 5 core metrics + on a 1-5 scale. + + Attributes: + faithfulness: Is each step grounded in the problem context? (1-5) + coherence: Do steps logically follow without contradictions? (1-5) + informativeness: Does each step add new relevant information? (1-5) + repetition: Absence of redundant steps (5 = no repetition, 1 = significant repetition) + completeness: Are all essential reasoning steps included? (1-5) + overall_score: Mean of the 5 metric scores. + passed_filter: True if overall_score >= threshold. + diagnosis: Categorical assessment per metric ("high"/"medium"/"low"). + """ + + faithfulness: float + coherence: float + informativeness: float + repetition: float + completeness: float + overall_score: float + passed_filter: bool + diagnosis: dict[str, str] + + +class ReflectionAnalysisReport(TypedDict): + """Aggregated report of reflection analysis across all samples. + + Provides comprehensive statistics on reflection patterns detected + in a dataset of model outputs. + + Attributes: + total_samples: Total number of samples analyzed. + total_tokens: Total reflection tokens detected across all samples. + avg_tokens_per_sample: Average reflection tokens per sample. + overall_density: Average reflection density across all samples. + token_frequency: Frequency count of each unique token text. + category_distribution: Distribution of tokens by category. + per_subject_stats: Reflection statistics broken down by subject. + per_level_stats: Reflection statistics broken down by difficulty level. + processing_errors: Number of samples that failed processing. + """ + + total_samples: int + total_tokens: int + avg_tokens_per_sample: float + overall_density: float + token_frequency: dict[str, int] + category_distribution: dict[str, int] + per_subject_stats: dict[str, dict[str, float | int]] + per_level_stats: dict[str, dict[str, float | int]] + processing_errors: int + + +class SteeringVectorResult(TypedDict): + """Result of steering vector extraction. + + Attributes: + vectors: Mapping of layer index to steering vector tensor. + metadata: Metadata about the extraction (model name, sample counts, etc.). + """ + + vectors: dict[int, Tensor] + metadata: dict[str, str | int | tuple[int, ...]] + + +@dataclass(frozen=True) +class LinearProbeConfig: + """Immutable configuration for linear probe experiments. + + Attributes: + input_path: Path to the input data file. + model_name: Name or path of the model for probing. + layer_indices: Tuple of layer indices to probe. + test_size: Fraction of data to use for testing. + output_dir: Directory to save probe results. + """ + + input_path: str = "" + model_name: str = "" + layer_indices: tuple[int, ...] = () + test_size: float = 0.2 + output_dir: str = "outputs/linear_probe/" + + +class ProbeMetrics(TypedDict): + """Metrics for a single probe layer. + + Attributes: + layer_index: Index of the layer that was probed. + accuracy: Classification accuracy on the test set. + train_samples: Number of training samples used. + test_samples: Number of test samples used. + """ + + layer_index: int + accuracy: float + train_samples: int + test_samples: int + + +class LinearProbeResult(TypedDict): + """Result of linear probe training and evaluation. + + Attributes: + coefficients: Mapping of layer index to probe coefficients. + metrics: List of metrics for each probed layer. + metadata: Metadata about the probing experiment. + """ + + coefficients: dict[int, list[float]] + metrics: list[ProbeMetrics] + metadata: dict[str, str | int | tuple[int, ...]] diff --git a/tests/fixtures/eval_sample.jsonl b/tests/fixtures/eval_sample.jsonl new file mode 100644 index 0000000..d42853f --- /dev/null +++ b/tests/fixtures/eval_sample.jsonl @@ -0,0 +1,7 @@ +{"problem_id": "test_001", "problem": "What is 6 times 7?", "generated": "To solve this, I multiply 6 by 7. The answer is \\boxed{42}.", "reference_answer": "42", "subject": "Arithmetic", "level": 1} +{"problem_id": "test_002", "problem": "What is 6 times 7?", "generated": "Let me calculate: 6 * 7 = 42. So the answer is \\boxed{24}.", "reference_answer": "42", "subject": "Arithmetic", "level": 1} +{"problem_id": "test_003", "problem": "What is half of 1?", "generated": "Half of 1 is 0.5. So the answer is \\boxed{0.5}.", "reference_answer": "1/2", "subject": "Fractions", "level": 2} +{"problem_id": "test_004", "problem": "What is 2 + 2?", "generated": "Adding 2 and 2 gives me 4. The answer is simply 4.", "reference_answer": "4"} +{"problem_id": "test_005", "problem": "What is one half written as a fraction?", "generated": "One half is \\boxed{\\frac{1}{2}}.", "reference_answer": "1/2", "subject": "Fractions", "level": 2} +{"problem_id": "test_006", "problem": "Solve for x: 2x = 8", "generated": "First I tried \\boxed{2} but that was wrong. Let me recalculate. 2x = 8 means x = 8/2 = 4, so \\boxed{4} is correct.", "reference_answer": "4"} +{"problem_id": "test_007", "problem": "If x^2 - 5x + 6 = 0, what are the roots?", "generated": "This factors as (x-2)(x-3) = 0, so x = 2 or x = 3. The roots are \\boxed{2, 3}.", "reference_answer": "2, 3", "subject": "Algebra", "level": 2} diff --git a/tests/fixtures/sample_reflection.jsonl b/tests/fixtures/sample_reflection.jsonl new file mode 100644 index 0000000..b2b5e74 --- /dev/null +++ b/tests/fixtures/sample_reflection.jsonl @@ -0,0 +1,15 @@ +{"problem_id": "reflect-001", "problem": "What is 15 + 27?", "generated": "Wait, let me calculate this step by step. 15 + 27 = 42.", "reference_answer": "42", "reflection_tokens": [{"text": "Wait", "category": "hesitation", "context": "Wait, let me calculate", "confidence": 0.95}], "reflection_count": 1, "reflection_density": 4.5} +{"problem_id": "reflect-002", "problem": "Solve for x: 2x + 5 = 13", "generated": "Actually, let me think about this. 2x = 13 - 5 = 8, so x = 4.", "reference_answer": "4", "reflection_tokens": [{"text": "Actually", "category": "correction", "context": "Actually, let me think", "confidence": 0.88}], "reflection_count": 1, "reflection_density": 3.2} +{"problem_id": "reflect-003", "problem": "What is the derivative of x^2?", "generated": "Hmm, the derivative of x^2 is 2x. Let me verify: d/dx(x^2) = 2x. Yes, that's correct.", "reference_answer": "2x", "reflection_tokens": [{"text": "Hmm", "category": "hesitation", "context": "Hmm, the derivative", "confidence": 0.91}, {"text": "verify", "category": "verification", "context": "Let me verify", "confidence": 0.87}], "reflection_count": 2, "reflection_density": 6.8} +{"problem_id": "reflect-004", "problem": "Calculate 144 / 12", "generated": "But wait, I should be careful here. 144 divided by 12 equals 12.", "reference_answer": "12", "reflection_tokens": [{"text": "But", "category": "hesitation", "context": "But wait, I should", "confidence": 0.82}], "reflection_count": 1, "reflection_density": 3.1} +{"problem_id": "reflect-005", "problem": "What is 7 * 8?", "generated": "Hmm, let me recall the multiplication table. 7 times 8 is 56.", "reference_answer": "56", "reflection_tokens": [{"text": "Hmm", "category": "hesitation", "context": "Hmm, let me recall", "confidence": 0.89}], "reflection_count": 1, "reflection_density": 4.2} +{"problem_id": "reflect-006", "problem": "Find the area of a rectangle with length 5 and width 3.", "generated": "Wait, area of a rectangle is length times width. So 5 * 3 = 15 square units.", "reference_answer": "15", "reflection_tokens": [{"text": "Wait", "category": "hesitation", "context": "Wait, area of a rectangle", "confidence": 0.93}], "reflection_count": 1, "reflection_density": 3.8} +{"problem_id": "reflect-007", "problem": "What is the square root of 81?", "generated": "Actually, I need to think about this. The square root of 81 is 9, since 9 * 9 = 81.", "reference_answer": "9", "reflection_tokens": [{"text": "Actually", "category": "correction", "context": "Actually, I need to think", "confidence": 0.86}], "reflection_count": 1, "reflection_density": 4.1} +{"problem_id": "reflect-008", "problem": "Simplify: 3(x + 2) - x", "generated": "Let me reconsider my approach. 3x + 6 - x = 2x + 6.", "reference_answer": "2x + 6", "reflection_tokens": [{"text": "reconsider", "category": "verification", "context": "Let me reconsider my approach", "confidence": 0.79}], "reflection_count": 1, "reflection_density": 2.9} +{"problem_id": "reflect-009", "problem": "What is 25% of 80?", "generated": "Hmm, 25% is the same as 1/4. So 80 / 4 = 20.", "reference_answer": "20", "reflection_tokens": [{"text": "Hmm", "category": "hesitation", "context": "Hmm, 25% is the same", "confidence": 0.92}], "reflection_count": 1, "reflection_density": 3.5} +{"problem_id": "reflect-010", "problem": "Solve: x^2 - 9 = 0", "generated": "Wait, this is a difference of squares. (x-3)(x+3) = 0, so x = 3 or x = -3.", "reference_answer": "x = 3 or x = -3", "reflection_tokens": [{"text": "Wait", "category": "hesitation", "context": "Wait, this is a difference", "confidence": 0.94}], "reflection_count": 1, "reflection_density": 4.7} +{"problem_id": "no-reflect-001", "problem": "What is 2 + 2?", "generated": "2 + 2 = 4.", "reference_answer": "4", "reflection_tokens": [], "reflection_count": 0, "reflection_density": 0.0} +{"problem_id": "no-reflect-002", "problem": "What is the capital of France?", "generated": "The capital of France is Paris.", "reference_answer": "Paris", "reflection_tokens": [], "reflection_count": 0, "reflection_density": 0.0} +{"problem_id": "no-reflect-003", "problem": "Calculate 100 - 37.", "generated": "100 - 37 = 63.", "reference_answer": "63", "reflection_tokens": [], "reflection_count": 0, "reflection_density": 0.0} +{"problem_id": "no-reflect-004", "problem": "What is the chemical symbol for water?", "generated": "The chemical symbol for water is H2O.", "reference_answer": "H2O", "reflection_tokens": [], "reflection_count": 0, "reflection_density": 0.0} +{"problem_id": "no-reflect-005", "problem": "How many sides does a triangle have?", "generated": "A triangle has 3 sides.", "reference_answer": "3", "reflection_tokens": [], "reflection_count": 0, "reflection_density": 0.0} diff --git a/tests/test_dataset_adapters.py b/tests/test_dataset_adapters.py new file mode 100644 index 0000000..c51846b --- /dev/null +++ b/tests/test_dataset_adapters.py @@ -0,0 +1,187 @@ +"""Tests for dataset adapters. + +These tests verify the unified dataset loading functions for +evaluation datasets (MATH-500, AIME, GPQA Diamond). +""" + +from __future__ import annotations + +import pytest + +from probing_reflection.dataset_adapters import ( + DatasetSample, + load_aime, + load_gpqa_diamond, + load_math500, +) + + +@pytest.fixture(autouse=True) +def mock_huggingface_datasets(monkeypatch: pytest.MonkeyPatch) -> None: + math500 = [ + { + "unique_id": f"math-{index}", + "problem": f"Problem {index}", + "answer": str(index), + "subject": "algebra", + "level": 1, + } + for index in range(500) + ] + aime: list[dict[str, object]] = [ + {"id": f"aime-{index}", "problem": f"AIME {index}", "answer": str(index)} + for index in range(90) + ] + gpqa: list[dict[str, object]] = [ + { + "Question": f"Science question {index}", + "Choice 1": "choice one", + "Choice 2": "choice two", + "Choice 3": "choice three", + "Choice 4": "choice four", + "Correct Answer": "choice one", + } + for index in range(198) + ] + + def fake_load_dataset( + path: str, + config_name: str | None = None, + *, + split: str, + ) -> list[dict[str, object]]: + del config_name, split + if path == "HuggingFaceH4/MATH-500": + return math500 + if path == "AI-MO/aimo-validation-aime": + return aime + if path == "Idavidrein/gpqa": + return gpqa + raise AssertionError(f"Unexpected dataset: {path}") + + monkeypatch.setattr("probing_reflection.dataset_adapters.load_dataset", fake_load_dataset) + + +class TestLoadMath500: + """Tests for load_math500 function.""" + + def test_returns_500_samples(self) -> None: + """MATH-500 should return exactly 500 samples.""" + samples = load_math500() + assert len(samples) == 500 + + def test_required_fields_present(self) -> None: + """Each MATH-500 sample should have required fields.""" + samples = load_math500() + for sample in samples[:10]: # Check first 10 for efficiency + assert "problem_id" in sample + assert "problem" in sample + assert "reference_answer" in sample + + def test_field_types(self) -> None: + """MATH-500 sample fields should have correct types.""" + samples = load_math500() + for sample in samples[:10]: + assert isinstance(sample["problem_id"], str) + assert isinstance(sample["problem"], str) + assert isinstance(sample["reference_answer"], str) + assert sample["problem_id"] # Non-empty + assert sample["problem"] # Non-empty + assert sample["reference_answer"] # Non-empty + + +class TestLoadAime: + """Tests for load_aime function.""" + + def test_returns_90_samples(self) -> None: + """AIME should return exactly 90 samples.""" + samples = load_aime() + assert len(samples) == 90 + + def test_required_fields_present(self) -> None: + """Each AIME sample should have required fields.""" + samples = load_aime() + for sample in samples[:10]: + assert sample["problem_id"] + assert sample["problem"] + assert sample["reference_answer"] + + def test_field_types(self) -> None: + """AIME sample fields should have correct types.""" + samples = load_aime() + for sample in samples[:10]: + assert isinstance(sample["problem_id"], str) + assert isinstance(sample["problem"], str) + assert isinstance(sample["reference_answer"], str) + + +class TestLoadGpqaDiamond: + """Tests for load_gpqa_diamond function.""" + + def test_returns_198_samples(self) -> None: + """GPQA Diamond should return exactly 198 samples.""" + samples = load_gpqa_diamond() + assert len(samples) == 198 + + def test_includes_choices_in_problem(self) -> None: + """GPQA Diamond problems should include answer choices A/B/C/D.""" + samples = load_gpqa_diamond() + for sample in samples[:10]: + problem = sample["problem"] + assert "A)" in problem, f"Missing choice A in: {problem[:100]}" + assert "B)" in problem, f"Missing choice B in: {problem[:100]}" + assert "C)" in problem, f"Missing choice C in: {problem[:100]}" + assert "D)" in problem, f"Missing choice D in: {problem[:100]}" + + def test_required_fields_present(self) -> None: + """Each GPQA Diamond sample should have required fields.""" + samples = load_gpqa_diamond() + for sample in samples[:10]: + assert sample["problem_id"] + assert sample["problem"] + assert sample["reference_answer"] + + def test_field_types(self) -> None: + """GPQA Diamond sample fields should have correct types.""" + samples = load_gpqa_diamond() + for sample in samples[:10]: + assert isinstance(sample["problem_id"], str) + assert isinstance(sample["problem"], str) + assert isinstance(sample["reference_answer"], str) + + +class TestDatasetSampleType: + """Tests for DatasetSample TypedDict.""" + + def test_create_valid_sample(self) -> None: + """Should be able to create a valid DatasetSample dict.""" + sample: DatasetSample = { + "problem_id": "test-001", + "problem": "What is 2 + 2?", + "reference_answer": "4", + } + assert sample["problem_id"] == "test-001" + assert sample["problem"] == "What is 2 + 2?" + assert sample["reference_answer"] == "4" + + def test_sample_with_optional_fields(self) -> None: + """DatasetSample should accept optional subject and level fields.""" + sample: DatasetSample = { + "problem_id": "test-002", + "problem": "Solve for x: 2x = 4", + "reference_answer": "2", + "subject": "algebra", + "level": 1, + } + assert sample["subject"] == "algebra" + assert sample["level"] == 1 + + def test_samples_in_list(self) -> None: + """DatasetSample should work in collections.""" + samples: list[DatasetSample] = [ + {"problem_id": "1", "problem": "Q1", "reference_answer": "A1"}, + {"problem_id": "2", "problem": "Q2", "reference_answer": "A2"}, + ] + assert len(samples) == 2 + assert samples[0]["problem_id"] == "1" + assert samples[1]["problem_id"] == "2" diff --git a/tests/test_evaluation.py b/tests/test_evaluation.py new file mode 100644 index 0000000..44ccf12 --- /dev/null +++ b/tests/test_evaluation.py @@ -0,0 +1,547 @@ +"""Tests for evaluation utilities. + +These tests verify the evaluation pipeline components including +answer extraction, LLM judging, and report generation. +""" + +from __future__ import annotations + +import json +from pathlib import Path +from unittest.mock import MagicMock, patch + +import pytest +import torch + +from probing_reflection.evaluation import ( + evaluate, + extract_boxed_answer, + generate_report, +) +from probing_reflection.judges import AnswerJudge +from probing_reflection.prompts import build_comparison_prompt +from probing_reflection.types import EvaluationConfig, EvaluationResult + + +class TestExtractBoxedAnswer: + """Tests for extract_boxed_answer function.""" + + def test_extract_simple(self) -> None: + """Simple boxed answer should be extracted correctly.""" + text = r"The answer is \boxed{42}" + result = extract_boxed_answer(text) + assert result == "42" + + def test_extract_nested(self) -> None: + """Nested braces in boxed answer should be handled correctly.""" + text = r"Result: \boxed{\frac{1}{2}}" + result = extract_boxed_answer(text) + assert result == r"\frac{1}{2}" + + def test_extract_no_match(self) -> None: + """Text without boxed answer should return None.""" + text = "No answer here" + result = extract_boxed_answer(text) + assert result is None + + def test_extract_multiple(self) -> None: + """Should return first boxed answer when multiple present.""" + text = r"First \boxed{A}, second \boxed{B}" + result = extract_boxed_answer(text) + assert result == "A" + + def test_extract_whitespace_stripped(self) -> None: + """Whitespace should be stripped from extracted answer.""" + text = r"The answer is \boxed{ 42 }" + result = extract_boxed_answer(text) + assert result == "42" + + def test_extract_complex_nested(self) -> None: + """Complex nested expressions should be extracted.""" + text = r"Answer: \boxed{\sqrt{x^2 + y^2}}" + result = extract_boxed_answer(text) + assert result == r"\sqrt{x^2 + y^2}" + + +class TestAnswerJudgeBuildPrompt: + """Tests for AnswerJudge prompt building.""" + + def test_build_prompt_format(self) -> None: + """Prompt should contain verbosity bias warning.""" + prompt = build_comparison_prompt("ref answer", "model answer") + + assert "Reference: ref answer" in prompt + assert "Candidate: model answer" in prompt + assert "Do NOT reward longer answers" in prompt + assert "Conciseness is equally valuable" in prompt + assert '"explanation"' in prompt + assert '"equivalent"' in prompt + assert '"confidence"' in prompt + + def test_build_prompt_json_format(self) -> None: + """Prompt should specify JSON output format.""" + prompt = build_comparison_prompt("A", "B") + + assert "JSON format" in prompt + assert "true/false" in prompt + assert "0.0-1.0" in prompt + + +class TestAnswerJudgeParseJson: + """Tests for AnswerJudge JSON parsing.""" + + def test_parse_json_response_valid(self) -> None: + """Valid JSON response should be parsed correctly.""" + judge = AnswerJudge("test-model") + + mock_model = MagicMock() + mock_tokenizer = MagicMock() + judge.model = mock_model + judge.tokenizer = mock_tokenizer + judge.device = torch.device("cpu") + + json_response = '{"explanation": "Both are 42", "equivalent": true, "confidence": 0.95}' + + # Setup mock tokenizer behavior + mock_input_ids = torch.tensor([[10, 11]]) + mock_attention_mask = MagicMock() + mock_attention_mask.to.return_value = mock_attention_mask + mock_tokenizer.return_value = { + "input_ids": mock_input_ids, + "attention_mask": mock_attention_mask, + } + + # Setup mock model.generate to return tensor-like output + mock_model.generate.return_value = torch.tensor([[10, 11, 12]]) + + # Setup tokenizer.decode to return the JSON response + mock_tokenizer.decode.return_value = json_response + + result = judge._run_comparison("42", "42") + + assert result["explanation"] == "Both are 42" + assert result["equivalent"] is True + assert result["confidence"] == 0.95 + assert mock_tokenizer.decode.call_args.args[0].tolist() == [12] + + def test_parse_json_response_no_json(self) -> None: + """Response without JSON should return parse error.""" + judge = AnswerJudge("test-model") + + mock_model = MagicMock() + mock_tokenizer = MagicMock() + judge.model = mock_model + judge.tokenizer = mock_tokenizer + judge.device = MagicMock() + + mock_tokenizer.decode.return_value = "Some prompt textNo JSON here" + mock_tokenizer.return_value = {"input_ids": MagicMock(), "attention_mask": MagicMock()} + mock_model.generate.return_value = [MagicMock()] + + result = judge._run_comparison("A", "B") + + assert "Parse error" in result["explanation"] + assert result["equivalent"] is False + assert result["confidence"] == 0.0 + + def test_parse_json_response_invalid_json(self) -> None: + """Response with invalid JSON should return parse error.""" + judge = AnswerJudge("test-model") + + mock_model = MagicMock() + mock_tokenizer = MagicMock() + judge.model = mock_model + judge.tokenizer = mock_tokenizer + judge.device = MagicMock() + + mock_tokenizer.decode.return_value = "Prompt{invalid json}" + mock_tokenizer.return_value = {"input_ids": MagicMock(), "attention_mask": MagicMock()} + mock_model.generate.return_value = [MagicMock()] + + result = judge._run_comparison("A", "B") + + assert "Parse error" in result["explanation"] + assert result["equivalent"] is False + + +class TestAnswerJudgePositionBias: + """Tests for AnswerJudge position bias mitigation.""" + + def test_position_bias_mitigation(self) -> None: + """Should detect and handle position bias.""" + judge = AnswerJudge("test-model") + + with patch.object(judge, "_run_comparison") as mock_run: + # Forward order returns equivalent=True + # Reverse order returns equivalent=False (position bias detected) + mock_run.side_effect = [ + {"explanation": "Forward comparison", "equivalent": True, "confidence": 0.9}, + {"explanation": "Reverse comparison", "equivalent": False, "confidence": 0.8}, + ] + + result = judge.judge_single("ref", "model") + + # Should return equivalent=False due to position bias + assert result["equivalent"] is False + assert "Position bias detected" in result["explanation"] + assert "Forward comparison" in result["explanation"] + assert "Reverse comparison" in result["explanation"] + # Confidence should be minimum of both + assert result["confidence"] == 0.8 + + def test_position_bias_agreement(self) -> None: + """Should return result when both orderings agree.""" + judge = AnswerJudge("test-model") + + with patch.object(judge, "_run_comparison") as mock_run: + mock_run.side_effect = [ + {"explanation": "Same answer", "equivalent": True, "confidence": 0.95}, + {"explanation": "Also same", "equivalent": True, "confidence": 0.90}, + ] + + result = judge.judge_single("42", "42") + + assert result["equivalent"] is True + assert result["explanation"] == "Same answer" + + +class TestAnswerJudgeConfidenceThreshold: + """Tests for AnswerJudge confidence threshold.""" + + def test_confidence_threshold_applied(self) -> None: + """Low confidence should set equivalent to False.""" + judge = AnswerJudge("test-model", confidence_threshold=0.7) + + with patch.object(judge, "_run_comparison") as mock_run: + # Both orderings agree but with low confidence + mock_run.side_effect = [ + {"explanation": "Looks same", "equivalent": True, "confidence": 0.5}, + {"explanation": "Looks same", "equivalent": True, "confidence": 0.5}, + ] + + result = judge.judge_single("A", "B") + + # Equivalent should be False due to low confidence + assert result["equivalent"] is False + + def test_confidence_threshold_passed(self) -> None: + """High confidence should preserve equivalent=True.""" + judge = AnswerJudge("test-model", confidence_threshold=0.7) + + with patch.object(judge, "_run_comparison") as mock_run: + mock_run.side_effect = [ + {"explanation": "Exactly same", "equivalent": True, "confidence": 0.95}, + {"explanation": "Exactly same", "equivalent": True, "confidence": 0.95}, + ] + + result = judge.judge_single("42", "42") + + assert result["equivalent"] is True + + +class TestAnswerJudgeBatch: + """Tests for AnswerJudge batch processing.""" + + def test_judge_batch(self) -> None: + """Batch judging should process all pairs.""" + judge = AnswerJudge("test-model") + + with patch.object(judge, "judge_single") as mock_single: + mock_single.side_effect = [ + {"explanation": "Match 1", "equivalent": True, "confidence": 0.9}, + {"explanation": "Match 2", "equivalent": False, "confidence": 0.8}, + {"explanation": "Match 3", "equivalent": True, "confidence": 0.95}, + ] + + pairs = [("ref1", "model1"), ("ref2", "model2"), ("ref3", "model3")] + results = judge.judge_batch(pairs) + + assert len(results) == 3 + assert results[0]["equivalent"] is True + assert results[1]["equivalent"] is False + assert results[2]["equivalent"] is True + + +class TestGenerateReport: + """Tests for generate_report function.""" + + def test_overall_accuracy(self) -> None: + """Overall accuracy should be calculated correctly.""" + results: list[EvaluationResult] = [ + { + "problem_id": "1", + "extracted_answer": "42", + "reference_answer": "42", + "is_correct": True, + "judge_explanation": "Match", + "confidence": 0.9, + }, + { + "problem_id": "2", + "extracted_answer": "24", + "reference_answer": "42", + "is_correct": False, + "judge_explanation": "No match", + "confidence": 0.8, + }, + { + "problem_id": "3", + "extracted_answer": "42", + "reference_answer": "42", + "is_correct": True, + "judge_explanation": "Match", + "confidence": 0.95, + }, + ] + + report = generate_report(results) + + assert report["total_samples"] == 3 + assert report["correct_count"] == 2 + assert report["overall_accuracy"] == pytest.approx(2 / 3) + + def test_grouping_by_subject(self) -> None: + """Results should be grouped by subject with 'unknown' for None.""" + results: list[EvaluationResult] = [ + { + "problem_id": "1", + "extracted_answer": "A", + "reference_answer": "A", + "is_correct": True, + "judge_explanation": "", + "confidence": 0.9, + "subject": "Algebra", + }, + { + "problem_id": "2", + "extracted_answer": "B", + "reference_answer": "B", + "is_correct": True, + "judge_explanation": "", + "confidence": 0.9, + "subject": "Algebra", + }, + { + "problem_id": "3", + "extracted_answer": "C", + "reference_answer": "D", + "is_correct": False, + "judge_explanation": "", + "confidence": 0.9, + "subject": "Geometry", + }, + { + "problem_id": "4", + "extracted_answer": "E", + "reference_answer": "E", + "is_correct": True, + "judge_explanation": "", + "confidence": 0.9, + }, + # No subject - should be "unknown" + ] + + report = generate_report(results) + + assert "Algebra" in report["per_subject_accuracy"] + assert "Geometry" in report["per_subject_accuracy"] + assert "unknown" in report["per_subject_accuracy"] + assert report["per_subject_accuracy"]["Algebra"] == pytest.approx(1.0) + assert report["per_subject_accuracy"]["Geometry"] == pytest.approx(0.0) + assert report["per_subject_accuracy"]["unknown"] == pytest.approx(1.0) + + def test_grouping_by_level(self) -> None: + """Results should be grouped by level with 'unknown' for None.""" + results: list[EvaluationResult] = [ + { + "problem_id": "1", + "extracted_answer": "A", + "reference_answer": "A", + "is_correct": True, + "judge_explanation": "", + "confidence": 0.9, + "level": 1, + }, + { + "problem_id": "2", + "extracted_answer": "B", + "reference_answer": "C", + "is_correct": False, + "judge_explanation": "", + "confidence": 0.9, + "level": 1, + }, + { + "problem_id": "3", + "extracted_answer": "D", + "reference_answer": "D", + "is_correct": True, + "judge_explanation": "", + "confidence": 0.9, + "level": 2, + }, + { + "problem_id": "4", + "extracted_answer": "E", + "reference_answer": "E", + "is_correct": True, + "judge_explanation": "", + "confidence": 0.9, + }, + # No level - should be "unknown" + ] + + report = generate_report(results) + + assert "1" in report["per_level_accuracy"] + assert "2" in report["per_level_accuracy"] + assert "unknown" in report["per_level_accuracy"] + assert report["per_level_accuracy"]["1"] == pytest.approx(0.5) + assert report["per_level_accuracy"]["2"] == pytest.approx(1.0) + assert report["per_level_accuracy"]["unknown"] == pytest.approx(1.0) + + def test_empty_results(self) -> None: + """Empty results should produce zero accuracy report.""" + report = generate_report([]) + + assert report["total_samples"] == 0 + assert report["correct_count"] == 0 + assert report["overall_accuracy"] == 0.0 + assert report["per_subject_accuracy"] == {} + assert report["per_level_accuracy"] == {} + + +class TestEvaluate: + """Tests for evaluate function.""" + + def test_with_fixture(self, test_data_dir: Path) -> None: + """Evaluate should work with fixture data and mocked judge.""" + fixture_path = str(test_data_dir / "eval_sample.jsonl") + config = EvaluationConfig(judge_model_name="test-model", confidence_threshold=0.5) + + # Mock AnswerJudge to avoid loading real model + with patch("probing_reflection.evaluation.AnswerJudge") as mock_judge_class: + mock_judge = MagicMock() + mock_judge_class.return_value = mock_judge + + # Set up mock judge to return consistent results + mock_judge.judge_batch.return_value = [ + {"explanation": "42 = 42", "equivalent": True, "confidence": 0.95}, + {"explanation": "24 != 42", "equivalent": False, "confidence": 0.9}, + {"explanation": "0.5 = 1/2", "equivalent": True, "confidence": 0.9}, + {"explanation": "Same", "equivalent": True, "confidence": 0.9}, + {"explanation": "Same", "equivalent": True, "confidence": 0.9}, + {"explanation": "4 = 4", "equivalent": True, "confidence": 0.95}, + ] + + report = evaluate(fixture_path, config) + + # Verify judge was called correctly + mock_judge_class.assert_called_once() + mock_judge.load_model.assert_called_once() + mock_judge.judge_batch.assert_called_once() + + # Check report structure + assert report["total_samples"] == 7 + assert "overall_accuracy" in report + assert "results" in report + + def test_extraction_failure(self, tmp_path: Path) -> None: + """Records without boxed answers should be marked as extraction failures.""" + # Create temp file with no boxed answer + fixture_data = [ + { + "problem_id": "no_box", + "problem": "What is 2+2?", + "generated": "The answer is 4.", + "reference_answer": "4", + }, + ] + fixture_path = tmp_path / "test.jsonl" + with open(fixture_path, "w") as f: + for record in fixture_data: + f.write(json.dumps(record) + "\n") + + config = EvaluationConfig(judge_model_name="test-model") + + with patch("probing_reflection.evaluation.AnswerJudge") as mock_judge_class: + mock_judge = MagicMock() + mock_judge_class.return_value = mock_judge + + report = evaluate(str(fixture_path), config) + + # Should have 1 result for the extraction failure + assert report["total_samples"] == 1 + assert report["correct_count"] == 0 + + # Check that the result shows extraction failure + result = report["results"][0] + assert result["extracted_answer"] is None + assert "No boxed answer found" in result["judge_explanation"] + assert result["is_correct"] is False + + # Judge should not have been called since no answers to judge + mock_judge.judge_batch.assert_not_called() + + def test_mixed_extraction_success_failure(self, tmp_path: Path) -> None: + """Should handle mix of successful and failed extractions.""" + fixture_data = [ + { + "problem_id": "has_box", + "problem": "What is 2+2?", + "generated": r"The answer is \boxed{4}.", + "reference_answer": "4", + }, + { + "problem_id": "no_box", + "problem": "What is 3+3?", + "generated": "The answer is 6.", + "reference_answer": "6", + }, + ] + fixture_path = tmp_path / "test.jsonl" + with open(fixture_path, "w") as f: + for record in fixture_data: + f.write(json.dumps(record) + "\n") + + config = EvaluationConfig(judge_model_name="test-model") + + with patch("probing_reflection.evaluation.AnswerJudge") as mock_judge_class: + mock_judge = MagicMock() + mock_judge_class.return_value = mock_judge + + mock_judge.judge_batch.return_value = [ + {"explanation": "4 = 4", "equivalent": True, "confidence": 0.95}, + ] + + report = evaluate(str(fixture_path), config) + + assert report["total_samples"] == 2 + # Only one correct (the one with boxed answer) + assert report["correct_count"] == 1 + + # Check results + results_by_id = {r["problem_id"]: r for r in report["results"]} + assert results_by_id["has_box"]["is_correct"] is True + assert results_by_id["no_box"]["is_correct"] is False + + +class TestAnswerJudgeLoadModel: + """Tests for AnswerJudge model loading.""" + + def test_load_model_not_called_in_tests(self) -> None: + """load_model should not be called in unit tests (requires GPU).""" + # This test verifies the pattern: we mock load_model, never call it for real + judge = AnswerJudge("test-model") + + # Model should not be loaded initially + assert judge.model is None + assert judge.tokenizer is None + assert judge.device is None + + def test_run_comparison_without_load_raises(self) -> None: + """_run_comparison should raise if model not loaded.""" + judge = AnswerJudge("test-model") + + with pytest.raises(RuntimeError, match="Model not loaded"): + judge._run_comparison("A", "B") diff --git a/tests/test_inference.py b/tests/test_inference.py new file mode 100644 index 0000000..39e5354 --- /dev/null +++ b/tests/test_inference.py @@ -0,0 +1,187 @@ +"""Tests for inference utilities. + +These tests verify the inference pipeline components including +prompt formatting, tokenization, and output handling. +""" + +from probing_reflection.inference import format_cot_prompt + + +class TestInference: + """Tests for inference functions.""" + + def test_format_cot_prompt(self) -> None: + """Prompt should contain step by step and boxed instructions.""" + result = format_cot_prompt("What is 2+2?") + assert "step by step" in result + assert "\\boxed" in result + + def test_batch_tokenization(self) -> None: + """Batch tokenization should apply left padding.""" + # Create a mock tokenizer with right padding (default for most tokenizers) + from unittest.mock import MagicMock + + from probing_reflection.inference import prepare_batch + + tokenizer = MagicMock() + tokenizer.padding_side = "right" + tokenizer.return_value = { + "input_ids": [[1, 2], [1, 2, 3]], + "attention_mask": [[1, 1], [1, 1, 1]], + } + + texts = ["short", "longer text"] + + # Call prepare_batch - should set padding_side to "left" for Qwen compatibility + result = prepare_batch(tokenizer, texts) + + # Verify left padding was applied + assert tokenizer.padding_side == "left", ( + f"Expected left padding for Qwen compatibility, got {tokenizer.padding_side}" + ) + assert result is not None, "prepare_batch should return tokenized output" + + def test_jsonl_output_format(self) -> None: + """JSONL output should have correct schema.""" + import json + import tempfile + from pathlib import Path + + entry = { + "problem_id": "test/123", + "problem": "What is 2+2?", + "generated": "The answer is 4.", + "reference_answer": "4", + "subject": "Algebra", + "level": 1, + "prompt": "Please reason step by step...", + } + + with tempfile.NamedTemporaryFile(mode="w", suffix=".jsonl", delete=False) as f: + f.write(json.dumps(entry) + "\n") + temp_path = Path(f.name) + + with open(temp_path) as f: + line = f.readline().strip() + loaded = json.loads(line) + + temp_path.unlink() + + required_keys = [ + "problem_id", + "problem", + "generated", + "reference_answer", + "subject", + "level", + "prompt", + ] + for key in required_keys: + assert key in loaded, f"Missing required key: {key}" + + assert isinstance(loaded["problem_id"], str) + assert isinstance(loaded["problem"], str) + assert isinstance(loaded["generated"], str) + assert isinstance(loaded["reference_answer"], str) + assert isinstance(loaded["subject"], str) + assert isinstance(loaded["level"], int) + assert isinstance(loaded["prompt"], str) + + def test_run_inference_mocked(self) -> None: + """run_inference should work with mocked model and tokenizer.""" + import json + import os + import tempfile + from unittest.mock import MagicMock, patch + + import torch + + from probing_reflection import InferenceConfig + from probing_reflection.inference import run_inference + + # Create a temporary output path + with tempfile.TemporaryDirectory() as tmpdir: + output_path = os.path.join(tmpdir, "test_output.jsonl") + + # Create InferenceConfig with the temp output path + config = InferenceConfig(output_path=output_path, batch_size=1, limit=1) + + # Create mocks for model and tokenizer + mock_model = MagicMock() + mock_tokenizer = MagicMock() + mock_tokenizer.return_value = { + "input_ids": [[1, 2, 3]], + "attention_mask": [[1, 1, 1]], + } + mock_tokenizer.decode.return_value = "generated answer" + mock_model.generate.return_value = torch.tensor([[1, 2, 3, 9]]) + mock_dataset = [ + { + "unique_id": "sample-1", + "problem": "What is 2+2?", + "answer": "4", + "subject": "algebra", + "level": 1, + }, + { + "unique_id": "sample-2", + "problem": "What is 3+3?", + "answer": "6", + "subject": "algebra", + "level": 1, + }, + ] + + # Mock torch.cuda.is_available to return False (CPU mode) + with ( + patch("torch.cuda.is_available", return_value=False), + patch( + "probing_reflection.inference.load_model", + return_value=(mock_model, mock_tokenizer), + ), + patch("probing_reflection.inference.load_dataset", return_value=mock_dataset), + ): + # Call run_inference + run_inference(config) + + # Verify output file was created + assert os.path.exists(output_path), f"Output file should be created at {output_path}" + with open(output_path) as output_file: + records = [json.loads(line) for line in output_file] + assert len(records) == 1 + assert records[0]["problem_id"] == "sample-1" + assert records[0]["generated"] == "generated answer" + decoded_ids = mock_tokenizer.decode.call_args.args[0] + assert decoded_ids.tolist() == [9] + + def test_cli_limit_does_not_change_batch_size(self) -> None: + from argparse import Namespace + from unittest.mock import patch + + from probing_reflection.__main__ import handle_inference + from probing_reflection.types import InferenceConfig + + with patch("probing_reflection.__main__.run_inference") as mock_run: + handle_inference( + Namespace( + limit=3, + model=InferenceConfig.model_name, + dataset=InferenceConfig.dataset_name, + batch_size=InferenceConfig.batch_size, + max_new_tokens=InferenceConfig.max_new_tokens, + output=InferenceConfig.output_path, + ) + ) + + config = mock_run.call_args.args[0] + assert config.limit == 3 + assert config.batch_size == 8 + + def test_negative_limit_fails_before_model_loading(self) -> None: + import pytest + + from probing_reflection import InferenceConfig + from probing_reflection.inference import run_inference + + with pytest.raises(ValueError, match="limit must be non-negative"): + run_inference(InferenceConfig(limit=-1)) diff --git a/tests/test_linear_probe.py b/tests/test_linear_probe.py new file mode 100644 index 0000000..ce3604e --- /dev/null +++ b/tests/test_linear_probe.py @@ -0,0 +1,519 @@ +"""Tests for linear_probe module. + +These tests verify the functions used to train and evaluate linear probes +for detecting reflection tokens in model activations. +""" + +from __future__ import annotations + +from pathlib import Path +from unittest.mock import MagicMock + +import numpy as np +import pytest +import torch +from numpy import ndarray +from sklearn.linear_model import LogisticRegression + +from probing_reflection.linear_probe import ( + evaluate_probe, + generate_pca_plot, + generate_tsne_plot, + save_probe_weights, + train_linear_probe, +) +from probing_reflection.types import ProbeMetrics + + +@pytest.fixture +def sample_r_activations() -> dict[int, list[torch.Tensor]]: + """Create mock R (reflection) activations for 2 layers.""" + return { + 0: [torch.randn(64) for _ in range(20)], + 1: [torch.randn(64) for _ in range(20)], + } + + +@pytest.fixture +def sample_n_activations() -> dict[int, list[torch.Tensor]]: + """Create mock N (non-reflection) activations for 2 layers.""" + return { + 0: [torch.randn(64) for _ in range(20)], + 1: [torch.randn(64) for _ in range(20)], + } + + +@pytest.fixture +def layer_indices() -> tuple[int, ...]: + """Layer indices to test.""" + return (0, 1) + + +@pytest.fixture +def trained_probe() -> LogisticRegression: + """Create a pre-fitted LogisticRegression for evaluation tests.""" + probe = LogisticRegression(max_iter=1000, random_state=42) + x_train = np.random.randn(40, 64) + y_train = np.concatenate([np.ones(20), np.zeros(20)]) + probe.fit(x_train, y_train) + return probe + + +@pytest.fixture +def test_data() -> tuple[ndarray, ndarray]: + """Create test data for evaluation tests.""" + x_test = np.random.randn(10, 64) + y_test = np.concatenate([np.ones(5), np.zeros(5)]) + return x_test, y_test + + +class TestDataCollection: + """Tests for data collection functions.""" + + def test_extract_reflection_tokens_from_r_set(self) -> None: + """Verify tokens extracted from samples with reflection_count > 0.""" + from probing_reflection import linear_probe + from probing_reflection.types import SampleWithReflection + + original_extract = linear_probe.extract_activation_at_position + original_find = linear_probe.find_reflection_token_position + + linear_probe.extract_activation_at_position = lambda *args, **kwargs: {0: torch.randn(64)} # type: ignore[method-assign] + linear_probe.find_reflection_token_position = lambda *args, **kwargs: 5 # type: ignore[method-assign] + + try: + from probing_reflection.linear_probe import collect_token_activations + + samples: list[SampleWithReflection] = [ + { + "problem_id": "1", + "problem": "test", + "generated": "Wait, let me think about this.", + "reference_answer": "answer", + "reflection_tokens": [], + "reflection_count": 1, + "reflection_density": 0.1, + } + ] + + mock_model = MagicMock() + mock_tokenizer = MagicMock() + + r_act, n_act = collect_token_activations(samples, mock_model, mock_tokenizer, (0,)) + + assert 0 in r_act + assert len(r_act[0]) == 1 + finally: + linear_probe.extract_activation_at_position = original_extract # type: ignore[method-assign] + linear_probe.find_reflection_token_position = original_find # type: ignore[method-assign] + + def test_extract_nonself_tokens_from_n_set(self) -> None: + """Verify tokens extracted from samples with reflection_count == 0.""" + from probing_reflection import linear_probe + from probing_reflection.types import SampleWithReflection + + original_extract = linear_probe.extract_activation_at_position + original_find = linear_probe.find_reflection_token_position + + linear_probe.extract_activation_at_position = lambda *args, **kwargs: {0: torch.randn(64)} # type: ignore[method-assign] + linear_probe.find_reflection_token_position = lambda *args, **kwargs: 3 # type: ignore[method-assign] + + try: + from probing_reflection.linear_probe import collect_token_activations + + samples: list[SampleWithReflection] = [ + { + "problem_id": "2", + "problem": "test", + "generated": "Some text without reflection.", + "reference_answer": "answer", + "reflection_tokens": [], + "reflection_count": 0, + "reflection_density": 0.0, + } + ] + + mock_model = MagicMock() + mock_tokenizer = MagicMock() + + r_act, n_act = collect_token_activations(samples, mock_model, mock_tokenizer, (0,)) + + assert 0 in n_act + assert len(n_act[0]) == 1 + finally: + linear_probe.extract_activation_at_position = original_extract # type: ignore[method-assign] + linear_probe.find_reflection_token_position = original_find # type: ignore[method-assign] + + def test_tokens_from_taxonomy(self) -> None: + """Verify both sets contain tokens from REFLECTION_TAXONOMY.""" + from probing_reflection import linear_probe + from probing_reflection.types import SampleWithReflection + + original_extract = linear_probe.extract_activation_at_position + original_find = linear_probe.find_reflection_token_position + + linear_probe.extract_activation_at_position = lambda *args, **kwargs: {0: torch.randn(64)} # type: ignore[method-assign] + linear_probe.find_reflection_token_position = lambda *args, **kwargs: 2 # type: ignore[method-assign] + + try: + from probing_reflection.linear_probe import collect_token_activations + + samples: list[SampleWithReflection] = [ + { + "problem_id": "1", + "problem": "test", + "generated": "Wait, let me check.", + "reference_answer": "answer", + "reflection_tokens": [], + "reflection_count": 1, + "reflection_density": 0.1, + }, + { + "problem_id": "2", + "problem": "test", + "generated": "Actually this is fine.", + "reference_answer": "answer", + "reflection_tokens": [], + "reflection_count": 0, + "reflection_density": 0.0, + }, + ] + + mock_model = MagicMock() + mock_tokenizer = MagicMock() + + r_act, n_act = collect_token_activations(samples, mock_model, mock_tokenizer, (0,)) + + assert len(r_act[0]) >= 1 + assert len(n_act[0]) >= 1 + finally: + linear_probe.extract_activation_at_position = original_extract # type: ignore[method-assign] + linear_probe.find_reflection_token_position = original_find # type: ignore[method-assign] + + def test_handle_missing_token_position(self) -> None: + """Verify graceful handling when position is None.""" + from probing_reflection import linear_probe + from probing_reflection.types import SampleWithReflection + + original_find = linear_probe.find_reflection_token_position + + linear_probe.find_reflection_token_position = lambda *args, **kwargs: None # type: ignore[method-assign] + + try: + from probing_reflection.linear_probe import collect_token_activations + + samples: list[SampleWithReflection] = [ + { + "problem_id": "1", + "problem": "test", + "generated": "No tokens here.", + "reference_answer": "answer", + "reflection_tokens": [], + "reflection_count": 1, + "reflection_density": 0.0, + } + ] + + mock_model = MagicMock() + mock_tokenizer = MagicMock() + + r_act, n_act = collect_token_activations(samples, mock_model, mock_tokenizer, (0,)) + + assert len(r_act[0]) == 0 + finally: + linear_probe.find_reflection_token_position = original_find # type: ignore[method-assign] + + +class TestProbeTraining: + """Tests for probe training functions.""" + + def test_train_logistic_regression_per_layer( + self, + sample_r_activations: dict[int, list[torch.Tensor]], + sample_n_activations: dict[int, list[torch.Tensor]], + layer_indices: tuple[int, ...], + ) -> None: + """Verify LogisticRegression trained for each layer.""" + probes, metrics = train_linear_probe( + sample_r_activations, sample_n_activations, layer_indices + ) + + assert len(probes) == len(layer_indices) + for layer in layer_indices: + assert layer in probes + assert isinstance(probes[layer], LogisticRegression) + + def test_returns_sklearn_model( + self, + sample_r_activations: dict[int, list[torch.Tensor]], + sample_n_activations: dict[int, list[torch.Tensor]], + layer_indices: tuple[int, ...], + ) -> None: + """Verify returns sklearn model object.""" + probes, _ = train_linear_probe(sample_r_activations, sample_n_activations, layer_indices) + + for probe in probes.values(): + assert hasattr(probe, "coef_") + assert hasattr(probe, "intercept_") + assert hasattr(probe, "predict") + + def test_handles_class_imbalance( + self, + layer_indices: tuple[int, ...], + ) -> None: + """Verify no crash on imbalanced data.""" + imbalanced_r = { + 0: [torch.randn(64) for _ in range(5)], + 1: [torch.randn(64) for _ in range(5)], + } + imbalanced_n = { + 0: [torch.randn(64) for _ in range(30)], + 1: [torch.randn(64) for _ in range(30)], + } + + probes, metrics = train_linear_probe(imbalanced_r, imbalanced_n, layer_indices) + + assert len(probes) == len(layer_indices) + assert len(metrics) == len(layer_indices) + + def test_train_test_split_ratio( + self, + sample_r_activations: dict[int, list[torch.Tensor]], + sample_n_activations: dict[int, list[torch.Tensor]], + layer_indices: tuple[int, ...], + ) -> None: + """Verify 80/20 split.""" + probes, metrics = train_linear_probe( + sample_r_activations, sample_n_activations, layer_indices, test_size=0.2 + ) + + total_samples = 40 + expected_train = int(total_samples * 0.8) + expected_test = int(total_samples * 0.2) + + for m in metrics: + assert m["train_samples"] == expected_train + assert m["test_samples"] == expected_test + + +class TestVisualization: + """Tests for visualization functions.""" + + def test_generate_tsne_plot( + self, + sample_r_activations: dict[int, list[torch.Tensor]], + sample_n_activations: dict[int, list[torch.Tensor]], + tmp_path: Path, + ) -> None: + """Verify t-SNE plot saved as PNG.""" + output_path = tmp_path / "tsne_plot.png" + + generate_tsne_plot(sample_r_activations, sample_n_activations, output_path, layer_index=0) + + assert output_path.exists() + assert output_path.suffix == ".png" + + def test_generate_pca_plot( + self, + sample_r_activations: dict[int, list[torch.Tensor]], + sample_n_activations: dict[int, list[torch.Tensor]], + tmp_path: Path, + ) -> None: + """Verify PCA plot saved as PNG.""" + output_path = tmp_path / "pca_plot.png" + + generate_pca_plot(sample_r_activations, sample_n_activations, output_path, layer_index=0) + + assert output_path.exists() + assert output_path.suffix == ".png" + + def test_plots_have_both_classes( + self, + sample_r_activations: dict[int, list[torch.Tensor]], + sample_n_activations: dict[int, list[torch.Tensor]], + tmp_path: Path, + ) -> None: + """Verify plots contain both classes with distinct colors.""" + tsne_path = tmp_path / "tsne_both.png" + pca_path = tmp_path / "pca_both.png" + + generate_tsne_plot(sample_r_activations, sample_n_activations, tsne_path, layer_index=0) + generate_pca_plot(sample_r_activations, sample_n_activations, pca_path, layer_index=0) + + assert tsne_path.exists() + assert pca_path.exists() + + def test_output_directory_created( + self, + sample_r_activations: dict[int, list[torch.Tensor]], + sample_n_activations: dict[int, list[torch.Tensor]], + tmp_path: Path, + ) -> None: + """Verify output directory created if not exists.""" + output_dir = tmp_path / "nested" / "dir" + output_path = output_dir / "tsne_plot.png" + + assert not output_dir.exists() + + generate_tsne_plot(sample_r_activations, sample_n_activations, output_path, layer_index=0) + + assert output_dir.exists() + assert output_path.exists() + + +class TestWeightSaving: + """Tests for weight saving functions.""" + + def test_save_probe_weights( + self, + trained_probe: LogisticRegression, + tmp_path: Path, + ) -> None: + """Verify weights saved to specified path.""" + probes = {0: trained_probe} + metrics: list[ProbeMetrics] = [ + { + "layer_index": 0, + "accuracy": 0.85, + "train_samples": 40, + "test_samples": 10, + } + ] + metadata: dict[str, str | int | tuple[int, ...]] = { + "model_name": "test-model", + "layer_indices": (0,), + } + output_path = tmp_path / "probe_weights.npz" + + save_probe_weights(probes, metrics, output_path, metadata) + + assert output_path.exists() + + def test_weights_loadable( + self, + trained_probe: LogisticRegression, + tmp_path: Path, + ) -> None: + """Verify loadable via np.load().""" + probes = {0: trained_probe} + metrics: list[ProbeMetrics] = [ + { + "layer_index": 0, + "accuracy": 0.85, + "train_samples": 40, + "test_samples": 10, + } + ] + metadata: dict[str, str | int | tuple[int, ...]] = { + "model_name": "test-model", + } + output_path = tmp_path / "probe_weights.npz" + + save_probe_weights(probes, metrics, output_path, metadata) + + loaded = np.load(output_path, allow_pickle=True) + assert "coef_layer_0" in loaded + assert "intercept_layer_0" in loaded + + def test_metadata_included( + self, + trained_probe: LogisticRegression, + tmp_path: Path, + ) -> None: + """Verify metadata includes layer_index, accuracy, sample_counts.""" + probes = {0: trained_probe} + metrics: list[ProbeMetrics] = [ + { + "layer_index": 0, + "accuracy": 0.85, + "train_samples": 40, + "test_samples": 10, + } + ] + metadata: dict[str, str | int | tuple[int, ...]] = { + "model_name": "test-model", + "layer_indices": (0, 1), + } + output_path = tmp_path / "probe_weights.npz" + + save_probe_weights(probes, metrics, output_path, metadata) + + loaded = np.load(output_path, allow_pickle=True) + assert "layer_indices" in loaded + assert "accuracies" in loaded + assert "train_samples" in loaded + assert "test_samples" in loaded + assert "metadata_model_name" in loaded + + def test_directory_created( + self, + trained_probe: LogisticRegression, + tmp_path: Path, + ) -> None: + """Verify parent directory created if not exists.""" + output_dir = tmp_path / "nested" / "weights" + output_path = output_dir / "probe_weights.npz" + + assert not output_dir.exists() + + probes = {0: trained_probe} + metrics: list[ProbeMetrics] = [ + { + "layer_index": 0, + "accuracy": 0.85, + "train_samples": 40, + "test_samples": 10, + } + ] + metadata: dict[str, str | int | tuple[int, ...]] = {} + + save_probe_weights(probes, metrics, output_path, metadata) + + assert output_dir.exists() + assert output_path.exists() + + +class TestProbeEvaluation: + """Tests for probe evaluation functions.""" + + def test_compute_accuracy( + self, + trained_probe: LogisticRegression, + test_data: tuple[ndarray, ndarray], + ) -> None: + """Verify accuracy computed on test split.""" + x_test, y_test = test_data + + metrics = evaluate_probe(trained_probe, x_test, y_test, layer_index=0) + + assert "accuracy" in metrics + assert isinstance(metrics["accuracy"], float) + + def test_accuracy_in_valid_range( + self, + trained_probe: LogisticRegression, + test_data: tuple[ndarray, ndarray], + ) -> None: + """Verify returns float in [0.0, 1.0].""" + x_test, y_test = test_data + + metrics = evaluate_probe(trained_probe, x_test, y_test, layer_index=0) + + assert 0.0 <= metrics["accuracy"] <= 1.0 + + def test_stratified_split( + self, + sample_r_activations: dict[int, list[torch.Tensor]], + sample_n_activations: dict[int, list[torch.Tensor]], + layer_indices: tuple[int, ...], + ) -> None: + """Verify train/test respects class distribution.""" + probes, metrics = train_linear_probe( + sample_r_activations, sample_n_activations, layer_indices + ) + + for m in metrics: + assert m["train_samples"] > 0 + assert m["test_samples"] > 0 + assert "layer_index" in m + assert "accuracy" in m diff --git a/tests/test_reflection_diagnosis.py b/tests/test_reflection_diagnosis.py new file mode 100644 index 0000000..bea280c --- /dev/null +++ b/tests/test_reflection_diagnosis.py @@ -0,0 +1,530 @@ +"""Tests for reflection diagnosis utilities. + +These tests verify the reflection diagnosis pipeline components including +prompt building, token detection, and statistics aggregation. + +TDD RED PHASE: Tests for unimplemented functions are marked to fail/skip. +""" + +from __future__ import annotations + +import json +from pathlib import Path +from typing import Any + +import pytest + +from probing_reflection.prompts import REFLECTION_TAXONOMY, build_diagnosis_prompt +from probing_reflection.reflection_diagnosis import ( + diagnose_all, + diagnose_sample, + ensure_output_dir, +) +from probing_reflection.types import ReflectionDiagnosisConfig, ReflectionToken + +# ============================================================================ +# MOCK JUDGE FOR TESTING +# ============================================================================ + + +class MockReflectionJudge: + """Mock judge that detects reflection tokens via keyword matching.""" + + def __init__(self) -> None: + self._reflection_keywords = { + "wait", + "hmm", + "actually", + "let me think", + "reconsider", + "however", + "verify", + "check", + "alternatively", + "on the other hand", + "double-check", + } + + def judge(self, text: str) -> list[ReflectionToken]: + """Return reflection tokens found in text via keyword matching.""" + text_lower = text.lower() + tokens: list[ReflectionToken] = [] + + for keyword in self._reflection_keywords: + if keyword in text_lower: + tokens.append( + ReflectionToken( + text=keyword, + category="detected", + context=text[:50], + confidence=0.8, + ) + ) + + return tokens + + +# ============================================================================ +# TEST FIXTURES +# ============================================================================ + +SAMPLE_WITH_REFLECTION: dict[str, Any] = { + "problem_id": "test_001", + "problem": "What is 6 times 7?", + "generated": "Wait, let me reconsider. Actually, the answer is 42.", + "reference_answer": "42", + "subject": "Arithmetic", + "level": 1, +} + +SAMPLE_WITHOUT_REFLECTION: dict[str, Any] = { + "problem_id": "test_002", + "problem": "What is 2 + 2?", + "generated": "Adding 2 and 2 gives 4.", + "reference_answer": "4", +} + +SAMPLE_WITH_MULTIPLE_REFLECTIONS: dict[str, Any] = { + "problem_id": "test_003", + "problem": "Solve for x: 2x = 8", + "generated": ( + "Hmm, let me think about this. First, I need to isolate x. " + "However, I should verify my answer. x = 4. Let me check: 2 * 4 = 8. Yes!" + ), + "reference_answer": "4", + "subject": "Algebra", + "level": 2, +} + +SAMPLE_EMPTY_TEXT: dict[str, Any] = { + "problem_id": "test_empty", + "problem": "Test problem", + "generated": "", + "reference_answer": "answer", +} + +SAMPLE_COMPLEX_TOKENS: dict[str, Any] = { + "problem_id": "test_complex", + "problem": "Complex reflection test", + "generated": ( + "Wait, I need to reconsider this. On the other hand, maybe " + "I should verify. Actually, let me double-check my reasoning. " + "So the answer is 42." + ), + "reference_answer": "42", +} + + +# ============================================================================ +# TESTS FOR EXISTING FUNCTIONS (should PASS) +# ============================================================================ + + +class TestBuildDiagnosisPrompt: + """Tests for build_diagnosis_prompt function.""" + + def test_build_diagnosis_prompt_contains_schema(self) -> None: + """Prompt should contain the expected JSON schema.""" + text = "Wait, let me think." + prompt = build_diagnosis_prompt(text) + + # Check for JSON schema elements + assert '"tokens"' in prompt + assert '"text"' in prompt + assert '"category"' in prompt + assert '"context"' in prompt + assert '"confidence"' in prompt + + def test_build_diagnosis_prompt_contains_taxonomy(self) -> None: + """Prompt should contain reflection taxonomy examples.""" + text = "Test text" + prompt = build_diagnosis_prompt(text) + + # Check for taxonomy categories + assert "hesitation" in prompt + assert "qualification" in prompt + assert "verification" in prompt + assert "redirection" in prompt + + def test_build_diagnosis_prompt_includes_input_text(self) -> None: + """Prompt should include the input text to analyze.""" + text = "Wait, this is my test text with reflection." + prompt = build_diagnosis_prompt(text) + + assert text in prompt + + def test_build_diagnosis_prompt_context_warning(self) -> None: + """Prompt should warn about context-dependent judgment.""" + text = "Test" + prompt = build_diagnosis_prompt(text) + + assert "Context matters" in prompt + assert "Wait for the result" in prompt # Example of non-reflection + + def test_build_diagnosis_prompt_empty_text(self) -> None: + """Prompt should handle empty text.""" + text = "" + prompt = build_diagnosis_prompt(text) + + # Should still return a valid prompt structure + assert "JSON format" in prompt + assert '"tokens"' in prompt + + +class TestEnsureOutputDir: + """Tests for ensure_output_dir function.""" + + def test_ensure_output_dir_creates_directory(self, tmp_path: Path) -> None: + """Function should create directory if it doesn't exist.""" + new_dir = tmp_path / "new_output_dir" + assert not new_dir.exists() + + result = ensure_output_dir(new_dir) + + assert new_dir.exists() + assert result == new_dir + + def test_ensure_output_dir_existing_directory(self, tmp_path: Path) -> None: + """Function should return path for existing directory.""" + existing_dir = tmp_path / "existing_dir" + existing_dir.mkdir() + + result = ensure_output_dir(existing_dir) + + assert result == existing_dir + assert existing_dir.exists() + + def test_ensure_output_dir_nested_path(self, tmp_path: Path) -> None: + """Function should create nested directories.""" + nested_path = tmp_path / "level1" / "level2" / "level3" + + result = ensure_output_dir(nested_path) + + assert nested_path.exists() + assert result == nested_path + + def test_ensure_output_dir_string_path(self, tmp_path: Path) -> None: + """Function should accept string path.""" + dir_path = str(tmp_path / "string_path") + + result = ensure_output_dir(dir_path) + + assert result.exists() + assert isinstance(result, Path) + + +class TestReflectionTaxonomy: + """Tests for the REFLECTION_TAXONOMY constant.""" + + def test_taxonomy_is_dict(self) -> None: + """Taxonomy should be a dictionary.""" + assert isinstance(REFLECTION_TAXONOMY, dict) + + def test_taxonomy_has_expected_categories(self) -> None: + """Taxonomy should have expected reflection categories.""" + expected_categories = { + "hesitation", + "qualification", + "verification", + "redirection", + "transition", + } + assert set(REFLECTION_TAXONOMY.keys()) == expected_categories + + def test_taxonomy_tokens_are_lists(self) -> None: + """Each category should have a list of tokens.""" + for category, tokens in REFLECTION_TAXONOMY.items(): + assert isinstance(tokens, list), f"{category} tokens should be a list" + assert len(tokens) > 0, f"{category} should have at least one token" + + +# ============================================================================ +# TESTS FOR UNIMPLEMENTED FUNCTIONS (should FAIL or SKIP) +# These tests verify the expected interface for code that doesn't exist yet. +# ============================================================================ + + +class TestReflectionJudge: + """Tests for ReflectionJudge class. + + NOTE: ReflectionJudge is NOT YET IMPLEMENTED. + These tests should FAIL until implementation is added. + """ + + def test_reflection_judge_init(self) -> None: + """ReflectionJudge should initialize with model name.""" + # This test will fail because ReflectionJudge doesn't exist yet + pytest.importorskip( + "probing_reflection.reflection_diagnosis", + reason="Waiting for ReflectionJudge implementation", + ) + + # After import, try to use the class - will fail if not implemented + from probing_reflection.reflection_diagnosis import ReflectionJudge + + judge = ReflectionJudge("test-model") + assert judge.model_name == "test-model" + + def test_reflection_judge_uses_configured_model_name(self) -> None: + pytest.importorskip( + "probing_reflection.reflection_diagnosis", + reason="Waiting for ReflectionJudge implementation", + ) + + from probing_reflection.reflection_diagnosis import ReflectionJudge + + config = ReflectionDiagnosisConfig( + model_name="test-model", + batch_size=4, + ) + judge = ReflectionJudge(config.model_name) + assert judge.model_name == config.model_name + + +class TestDiagnoseSample: + """Tests for diagnose_sample function.""" + + @pytest.fixture + def mock_judge(self) -> MockReflectionJudge: + """Create a mock judge for testing.""" + return MockReflectionJudge() + + def test_diagnose_sample_with_reflection_tokens(self, mock_judge: MockReflectionJudge) -> None: + """diagnose_sample should detect reflection tokens in sample.""" + result = diagnose_sample(mock_judge, SAMPLE_WITH_REFLECTION) + + assert "reflection_tokens" in result + assert "reflection_count" in result + assert isinstance(result["reflection_tokens"], list) + assert result["reflection_count"] >= 1 + + def test_diagnose_sample_without_reflection_tokens( + self, mock_judge: MockReflectionJudge + ) -> None: + """diagnose_sample should return empty list for no reflection.""" + result = diagnose_sample(mock_judge, SAMPLE_WITHOUT_REFLECTION) + + assert result["reflection_tokens"] == [] + assert result["reflection_count"] == 0 + + def test_diagnose_sample_empty_text(self, mock_judge: MockReflectionJudge) -> None: + """diagnose_sample should handle empty text gracefully.""" + result = diagnose_sample(mock_judge, SAMPLE_EMPTY_TEXT) + + assert result["reflection_tokens"] == [] + assert result["reflection_count"] == 0 + + def test_diagnose_sample_returns_sample_with_reflection_type( + self, mock_judge: MockReflectionJudge + ) -> None: + """diagnose_sample should return SampleWithReflection compatible dict.""" + result = diagnose_sample(mock_judge, SAMPLE_WITH_REFLECTION) + + assert "problem_id" in result + assert "problem" in result + assert "generated" in result + assert "reference_answer" in result + assert "reflection_tokens" in result + assert "reflection_count" in result + assert "reflection_density" in result + + +class TestDiagnoseAll: + """Tests for diagnose_all function.""" + + @pytest.fixture + def mock_judge(self) -> MockReflectionJudge: + """Create a mock judge for testing.""" + return MockReflectionJudge() + + def test_diagnose_all_aggregates_statistics( + self, tmp_path: Path, mock_judge: MockReflectionJudge + ) -> None: + """diagnose_all should aggregate statistics across all samples.""" + samples_path = tmp_path / "samples.jsonl" + with open(samples_path, "w") as f: + for sample in [ + SAMPLE_WITH_REFLECTION, + SAMPLE_WITHOUT_REFLECTION, + SAMPLE_WITH_MULTIPLE_REFLECTIONS, + ]: + f.write(json.dumps(sample) + "\n") + + output_dir = tmp_path / "output" + config = ReflectionDiagnosisConfig( + input_path=str(samples_path), + output_dir=str(output_dir), + ) + + samples, report = diagnose_all(config, judge=mock_judge) + + assert isinstance(report, dict) + assert "total_samples" in report + assert "total_tokens" in report + assert "avg_tokens_per_sample" in report + assert "category_distribution" in report + assert isinstance(samples, list) + assert len(samples) == 3 + + def test_diagnose_all_returns_reflection_analysis_report( + self, tmp_path: Path, mock_judge: MockReflectionJudge + ) -> None: + """diagnose_all should return ReflectionAnalysisReport compatible dict.""" + samples_path = tmp_path / "samples.jsonl" + with open(samples_path, "w") as f: + f.write(json.dumps(SAMPLE_WITH_REFLECTION) + "\n") + + config = ReflectionDiagnosisConfig( + input_path=str(samples_path), + output_dir=str(tmp_path / "output"), + ) + + samples, report = diagnose_all(config, judge=mock_judge) + + required_fields = [ + "total_samples", + "total_tokens", + "avg_tokens_per_sample", + "overall_density", + "token_frequency", + "category_distribution", + "per_subject_stats", + "per_level_stats", + "processing_errors", + ] + for field in required_fields: + assert field in report, f"Missing field: {field}" + + def test_diagnose_all_empty_input(self, tmp_path: Path) -> None: + """diagnose_all should handle empty input file.""" + samples_path = tmp_path / "empty.jsonl" + samples_path.touch() + + config = ReflectionDiagnosisConfig( + input_path=str(samples_path), + output_dir=str(tmp_path / "output"), + ) + + samples, report = diagnose_all(config) + + assert report["total_samples"] == 0 + assert report["total_tokens"] == 0 + assert samples == [] + + +class TestParseJsonResponse: + """Tests for _parse_json_response helper function. + + NOTE: _parse_json_response is NOT YET IMPLEMENTED. + These tests should FAIL until implementation is added. + """ + + def test_parse_json_response_valid(self) -> None: + """_parse_json_response should parse valid JSON response.""" + pytest.importorskip( + "probing_reflection.reflection_diagnosis", + reason="Waiting for _parse_json_response implementation", + ) + + from probing_reflection.judges import _parse_json_response + + response = ( + '{"tokens": [{"text": "wait", "category": "hesitation", ' + '"context": "Wait, let me think", "confidence": 0.9}]}' + ) + result = _parse_json_response(response) + + assert "tokens" in result + tokens = result["tokens"] + assert isinstance(tokens, list) + assert len(tokens) == 1 + first_token = tokens[0] + assert isinstance(first_token, dict) + assert first_token["text"] == "wait" + + def test_parse_json_response_empty_tokens(self) -> None: + """_parse_json_response should handle empty tokens list.""" + pytest.importorskip( + "probing_reflection.reflection_diagnosis", + reason="Waiting for _parse_json_response implementation", + ) + + from probing_reflection.judges import _parse_json_response + + response = '{"tokens": []}' + result = _parse_json_response(response) + + assert result["tokens"] == [] + + def test_parse_json_response_malformed_json(self) -> None: + """_parse_json_response should handle malformed JSON.""" + pytest.importorskip( + "probing_reflection.reflection_diagnosis", + reason="Waiting for _parse_json_response implementation", + ) + + from probing_reflection.judges import _parse_json_response + + # Malformed JSON - missing closing brace + response = '{"tokens": [{"text": "wait"' + result = _parse_json_response(response) + + assert result == {} + + +# ============================================================================ +# INTEGRATION TESTS +# ============================================================================ + + +class TestReflectionDiagnosisIntegration: + """Integration tests for the full reflection diagnosis pipeline.""" + + @pytest.fixture + def mock_judge(self) -> MockReflectionJudge: + """Create a mock judge for testing.""" + return MockReflectionJudge() + + def test_full_diagnosis_pipeline(self, tmp_path: Path, mock_judge: MockReflectionJudge) -> None: + """Full pipeline should process samples and produce valid report.""" + samples_path = tmp_path / "samples.jsonl" + with open(samples_path, "w") as f: + for sample in [SAMPLE_WITH_REFLECTION, SAMPLE_COMPLEX_TOKENS]: + f.write(json.dumps(sample) + "\n") + + output_dir = tmp_path / "diagnosis_output" + config = ReflectionDiagnosisConfig( + input_path=str(samples_path), + output_dir=str(output_dir), + ) + + samples, report = diagnose_all(config, judge=mock_judge) + + assert report["total_samples"] == 2 + assert report["total_tokens"] > 0 + assert len(report["category_distribution"]) > 0 + assert len(samples) == 2 + + def test_diagnosis_with_subject_breakdown( + self, tmp_path: Path, mock_judge: MockReflectionJudge + ) -> None: + """Diagnosis should provide per-subject statistics.""" + samples_data = [ + {**SAMPLE_WITH_REFLECTION, "subject": "Arithmetic"}, + {**SAMPLE_WITH_MULTIPLE_REFLECTIONS, "subject": "Algebra"}, + ] + + samples_path = tmp_path / "samples.jsonl" + with open(samples_path, "w") as f: + for sample in samples_data: + f.write(json.dumps(sample) + "\n") + + config = ReflectionDiagnosisConfig( + input_path=str(samples_path), + output_dir=str(tmp_path / "output"), + ) + + samples, report = diagnose_all(config, judge=mock_judge) + + assert "Arithmetic" in report["per_subject_stats"] + assert "Algebra" in report["per_subject_stats"] diff --git a/tests/test_roscoe_integration.py b/tests/test_roscoe_integration.py new file mode 100644 index 0000000..e845bd4 --- /dev/null +++ b/tests/test_roscoe_integration.py @@ -0,0 +1,338 @@ +"""Integration tests for ROSCOE feature in reflection diagnosis. + +These tests verify the integration of RoscoeJudge with the reflection +diagnosis pipeline, including judge_type configuration support. + +Mock judges are used to avoid actual LLM API calls. +""" + +from __future__ import annotations + +import json +from pathlib import Path +from typing import Protocol + +import pytest + +from probing_reflection.reflection_diagnosis import diagnose_all, diagnose_sample +from probing_reflection.types import ( + ReflectionDiagnosisConfig, + ReflectionToken, +) + + +class JudgeProtocol(Protocol): + """Protocol for judge objects used in diagnosis.""" + + def judge(self, text: str) -> list[ReflectionToken]: + """Analyze text and return reflection tokens.""" + ... + + +class MockReflectionJudge: + """Mock ReflectionJudge that detects reflection tokens via keyword matching.""" + + def __init__(self, model_name: str = "mock-model") -> None: + self.model_name: str = model_name + self._reflection_keywords: set[str] = { + "wait", + "hmm", + "actually", + "let me think", + "reconsider", + "however", + "verify", + "check", + "alternatively", + "on the other hand", + "double-check", + } + + def judge(self, text: str) -> list[ReflectionToken]: + """Return reflection tokens found in text via keyword matching.""" + text_lower = text.lower() + tokens: list[ReflectionToken] = [] + + for keyword in self._reflection_keywords: + if keyword in text_lower: + tokens.append( + ReflectionToken( + text=keyword, + category="detected", + context=text[:50], + confidence=0.8, + ) + ) + + return tokens + + +class MockRoscoeJudge: + """Mock RoscoeJudge that simulates ROSCOE metric evaluation.""" + + def __init__(self, model_name: str = "mock-model", threshold: float = 3.0) -> None: + self.model_name: str = model_name + self.threshold: float = threshold + + def judge(self, text: str) -> list[ReflectionToken]: + """Return ROSCOE-derived reflection tokens based on text analysis.""" + word_count = len(text.split()) + has_reasoning_markers = any( + marker in text.lower() for marker in ["because", "therefore", "since", "so", "thus"] + ) + has_verification = any( + marker in text.lower() for marker in ["check", "verify", "confirm", "correct"] + ) + has_coherence = any( + marker in text.lower() for marker in ["first", "then", "next", "finally", "step"] + ) + + faithfulness = 4.0 if has_reasoning_markers else 3.0 + coherence = 4.0 if has_coherence else 3.0 + informativeness = min(5.0, max(1.0, word_count / 20.0)) + repetition = 4.0 if word_count < 100 else 3.0 + completeness = 4.0 if has_verification else 3.0 + + tokens: list[ReflectionToken] = [ + ReflectionToken( + text="faithfulness", + category="roscoe_metric", + context=f"Score: {faithfulness:.1f}/5.0", + confidence=faithfulness / 5.0, + ), + ReflectionToken( + text="coherence", + category="roscoe_metric", + context=f"Score: {coherence:.1f}/5.0", + confidence=coherence / 5.0, + ), + ReflectionToken( + text="informativeness", + category="roscoe_metric", + context=f"Score: {informativeness:.1f}/5.0", + confidence=informativeness / 5.0, + ), + ReflectionToken( + text="repetition", + category="roscoe_metric", + context=f"Score: {repetition:.1f}/5.0", + confidence=repetition / 5.0, + ), + ReflectionToken( + text="completeness", + category="roscoe_metric", + context=f"Score: {completeness:.1f}/5.0", + confidence=completeness / 5.0, + ), + ] + + return tokens + + +SAMPLE_WITH_REFLECTION = { + "problem_id": "test_001", + "problem": "What is 6 times 7?", + "generated": "Wait, let me reconsider. Actually, the answer is 42.", + "reference_answer": "42", + "subject": "Arithmetic", + "level": 1, +} + +SAMPLE_WITH_REASONING = { + "problem_id": "test_002", + "problem": "Solve for x: 2x = 8", + "generated": ( + "First, I need to isolate x. Therefore, I divide both sides by 2. " + "So x = 4. Let me verify: 2 * 4 = 8. Yes, this is correct." + ), + "reference_answer": "4", + "subject": "Algebra", + "level": 2, +} + +SAMPLE_SIMPLE = { + "problem_id": "test_003", + "problem": "What is 2 + 2?", + "generated": "The answer is 4.", + "reference_answer": "4", + "subject": "Arithmetic", + "level": 1, +} + + +class TestDiagnoseAllWithReflectionJudge: + """Integration tests for diagnose_all with judge_type='reflection'.""" + + @pytest.fixture + def mock_reflection_judge(self) -> JudgeProtocol: + """Create a mock ReflectionJudge for testing.""" + return MockReflectionJudge() + + def test_diagnose_all_with_reflection_judge_uses_reflection_tokens( + self, tmp_path: Path, mock_reflection_judge: JudgeProtocol + ) -> None: + """Test that judge_type='reflection' uses ReflectionJudge behavior.""" + samples_path = tmp_path / "samples.jsonl" + _ = samples_path.write_text(json.dumps(SAMPLE_WITH_REFLECTION) + "\n") + + output_dir = tmp_path / "output" + config = ReflectionDiagnosisConfig( + input_path=str(samples_path), + output_dir=str(output_dir), + judge_type="reflection", + ) + + samples, report = diagnose_all(config, judge=mock_reflection_judge) + + assert len(samples) == 1 + assert report["total_samples"] == 1 + assert report["total_tokens"] >= 1 + assert "detected" in report["category_distribution"] + + def test_diagnose_all_with_reflection_judge_empty_input(self, tmp_path: Path) -> None: + """Test that judge_type='reflection' handles empty input.""" + samples_path = tmp_path / "empty.jsonl" + samples_path.touch() + + config = ReflectionDiagnosisConfig( + input_path=str(samples_path), + output_dir=str(tmp_path / "output"), + judge_type="reflection", + ) + + mock_judge = MockReflectionJudge() + samples, report = diagnose_all(config, judge=mock_judge) + + assert report["total_samples"] == 0 + assert report["total_tokens"] == 0 + assert samples == [] + + +class TestDiagnoseAllWithRoscoeJudge: + """Integration tests for diagnose_all with judge_type='roscoe'.""" + + @pytest.fixture + def mock_roscoe_judge(self) -> JudgeProtocol: + """Create a mock RoscoeJudge for testing.""" + return MockRoscoeJudge() + + def test_diagnose_all_with_roscoe_judge_uses_roscoe_metrics( + self, tmp_path: Path, mock_roscoe_judge: JudgeProtocol + ) -> None: + """Test that judge_type='roscoe' uses RoscoeJudge behavior.""" + samples_path = tmp_path / "samples.jsonl" + _ = samples_path.write_text(json.dumps(SAMPLE_WITH_REASONING) + "\n") + + output_dir = tmp_path / "output" + config = ReflectionDiagnosisConfig( + input_path=str(samples_path), + output_dir=str(output_dir), + judge_type="roscoe", + ) + + samples, report = diagnose_all(config, judge=mock_roscoe_judge) + + assert len(samples) == 1 + assert report["total_samples"] == 1 + assert report["total_tokens"] == 5 + assert "roscoe_metric" in report["category_distribution"] + + def test_diagnose_all_with_roscoe_judge_multiple_samples( + self, tmp_path: Path, mock_roscoe_judge: JudgeProtocol + ) -> None: + """Test that judge_type='roscoe' processes multiple samples correctly.""" + samples_path = tmp_path / "samples.jsonl" + _ = samples_path.write_text( + json.dumps(SAMPLE_WITH_REASONING) + "\n" + json.dumps(SAMPLE_SIMPLE) + "\n" + ) + + config = ReflectionDiagnosisConfig( + input_path=str(samples_path), + output_dir=str(tmp_path / "output"), + judge_type="roscoe", + ) + + samples, report = diagnose_all(config, judge=mock_roscoe_judge) + + assert len(samples) == 2 + assert report["total_samples"] == 2 + assert report["total_tokens"] == 10 + + +class TestReflectionDiagnosisIntegration: + """Integration tests for reflection_diagnosis module with RoscoeJudge.""" + + @pytest.fixture + def mock_roscoe_judge(self) -> JudgeProtocol: + """Create a mock RoscoeJudge for testing.""" + return MockRoscoeJudge() + + def test_diagnose_sample_with_roscoe_judge(self, mock_roscoe_judge: JudgeProtocol) -> None: + """Test diagnose_sample works correctly with RoscoeJudge.""" + result = diagnose_sample(mock_roscoe_judge, SAMPLE_WITH_REASONING) + + assert "reflection_tokens" in result + assert "reflection_count" in result + assert "reflection_density" in result + assert result["reflection_count"] == 5 + + for token in result["reflection_tokens"]: + assert token["category"] == "roscoe_metric" + + def test_diagnose_all_with_roscoe_judge_full_pipeline( + self, tmp_path: Path, mock_roscoe_judge: JudgeProtocol + ) -> None: + """Test full diagnosis pipeline with RoscoeJudge.""" + samples_data = [ + {**SAMPLE_WITH_REASONING, "subject": "Algebra", "level": 2}, + {**SAMPLE_SIMPLE, "subject": "Arithmetic", "level": 1}, + ] + + samples_path = tmp_path / "samples.jsonl" + _ = samples_path.write_text("\n".join(json.dumps(sample) for sample in samples_data) + "\n") + + config = ReflectionDiagnosisConfig( + input_path=str(samples_path), + output_dir=str(tmp_path / "output"), + judge_type="roscoe", + ) + + _samples, report = diagnose_all(config, judge=mock_roscoe_judge) + + required_fields = [ + "total_samples", + "total_tokens", + "avg_tokens_per_sample", + "overall_density", + "token_frequency", + "category_distribution", + "per_subject_stats", + "per_level_stats", + "processing_errors", + ] + for field in required_fields: + assert field in report, f"Missing field: {field}" + + assert report["total_samples"] == 2 + assert report["processing_errors"] == 0 + assert "Algebra" in report["per_subject_stats"] + assert "Arithmetic" in report["per_subject_stats"] + assert "2" in report["per_level_stats"] + assert "1" in report["per_level_stats"] + + def test_diagnose_all_comparison_reflection_vs_roscoe(self, tmp_path: Path) -> None: + """Compare results between ReflectionJudge and RoscoeJudge.""" + samples_path = tmp_path / "samples.jsonl" + _ = samples_path.write_text(json.dumps(SAMPLE_WITH_REFLECTION) + "\n") + + config = ReflectionDiagnosisConfig( + input_path=str(samples_path), + output_dir=str(tmp_path / "output"), + ) + + _, report_reflection = diagnose_all(config, judge=MockReflectionJudge("reflection-model")) + _, report_roscoe = diagnose_all(config, judge=MockRoscoeJudge("roscoe-model")) + + assert report_reflection["total_samples"] == report_roscoe["total_samples"] + assert report_reflection["total_tokens"] != report_roscoe["total_tokens"] + assert report_reflection["category_distribution"] != report_roscoe["category_distribution"] diff --git a/tests/test_roscoe_judge.py b/tests/test_roscoe_judge.py new file mode 100644 index 0000000..aac020d --- /dev/null +++ b/tests/test_roscoe_judge.py @@ -0,0 +1,324 @@ +"""Tests for RoscoeJudge class. + +These tests verify the ROSCOE-based reasoning quality evaluation including +JSON parsing, score clamping, overall score calculation, filter logic, +and diagnosis categorization. +""" + +from __future__ import annotations + +from unittest.mock import patch + +import pytest + +from probing_reflection.roscoe_metrics import RoscoeJudge + +# ============================================================================ +# TEST FIXTURES +# ============================================================================ + + +@pytest.fixture +def judge() -> RoscoeJudge: + """Create a RoscoeJudge with default parameters.""" + return RoscoeJudge() + + +@pytest.fixture +def judge_custom() -> RoscoeJudge: + """Create a RoscoeJudge with custom parameters.""" + return RoscoeJudge(model_name="custom-model", threshold=4.0) + + +# ============================================================================ +# TESTS +# ============================================================================ + + +class TestRoscoeJudgeInstantiation: + """Tests for RoscoeJudge instantiation.""" + + def test_instantiation_default_params(self) -> None: + """Test RoscoeJudge() with defaults.""" + judge = RoscoeJudge() + + assert judge.model_name == "Qwen/Qwen3.5-27B" + assert judge.threshold == 3.0 + + def test_instantiation_custom_params(self) -> None: + """Test with custom model_name and threshold.""" + judge = RoscoeJudge(model_name="custom-model-name", threshold=4.5) + + assert judge.model_name == "custom-model-name" + assert judge.threshold == 4.5 + + +class TestRoscoeJudgeParsing: + """Tests for _parse_roscoe_response method.""" + + def test_parse_response_valid_json(self, judge: RoscoeJudge) -> None: + """Test parsing valid JSON response.""" + response = """{ + "faithfulness": 4.5, + "coherence": 3.8, + "informativeness": 4.2, + "repetition": 5.0, + "completeness": 3.5 + }""" + + result = judge._parse_roscoe_response(response) + + assert isinstance(result, dict) + assert result["faithfulness"] == 4.5 + assert result["coherence"] == 3.8 + assert result["informativeness"] == 4.2 + assert result["repetition"] == 5.0 + assert result["completeness"] == 3.5 + + def test_score_clamping_high(self, judge: RoscoeJudge) -> None: + """Test scores > 5.0 are clamped to 5.0.""" + response = """{ + "faithfulness": 6.0, + "coherence": 10.0, + "informativeness": 7.5, + "repetition": 8.0, + "completeness": 5.5 + }""" + + result = judge._parse_roscoe_response(response) + + assert result["faithfulness"] == 5.0 + assert result["coherence"] == 5.0 + assert result["informativeness"] == 5.0 + assert result["repetition"] == 5.0 + assert result["completeness"] == 5.0 + + def test_score_clamping_low(self, judge: RoscoeJudge) -> None: + """Test scores < 1.0 are clamped to 1.0.""" + response = """{ + "faithfulness": 0.0, + "coherence": -5.0, + "informativeness": 0.5, + "repetition": -1.0, + "completeness": 0.8 + }""" + + result = judge._parse_roscoe_response(response) + + assert result["faithfulness"] == 1.0 + assert result["coherence"] == 1.0 + assert result["informativeness"] == 1.0 + assert result["repetition"] == 1.0 + assert result["completeness"] == 1.0 + + def test_empty_response_handling(self, judge: RoscoeJudge) -> None: + """Test empty/missing JSON returns defaults (1.0).""" + response = "" + + result = judge._parse_roscoe_response(response) + + # All metrics should default to 1.0 + assert result["faithfulness"] == 1.0 + assert result["coherence"] == 1.0 + assert result["informativeness"] == 1.0 + assert result["repetition"] == 1.0 + assert result["completeness"] == 1.0 + + def test_malformed_json_handling(self, judge: RoscoeJudge) -> None: + """Test malformed JSON returns defaults.""" + response = '{"faithfulness": 4.0, "coherence": missing_quote, }' + + result = judge._parse_roscoe_response(response) + + # Should return defaults due to parsing failure + assert result["faithfulness"] == 1.0 + assert result["coherence"] == 1.0 + + +class TestRoscoeJudgeScoreCalculation: + """Tests for overall score calculation and filter logic.""" + + def test_overall_score_calculation(self, judge: RoscoeJudge) -> None: + """Test mean of 5 metrics.""" + response = """{ + "faithfulness": 4.0, + "coherence": 3.0, + "informativeness": 5.0, + "repetition": 2.0, + "completeness": 4.0 + }""" + + result = judge._parse_roscoe_response(response) + + # (4.0 + 3.0 + 5.0 + 2.0 + 4.0) / 5 = 3.6 + assert result["overall_score"] == pytest.approx(3.6) + + def test_passed_filter_logic_above_threshold(self, judge: RoscoeJudge) -> None: + """Test threshold comparison - above default threshold (3.0).""" + response = """{ + "faithfulness": 4.0, + "coherence": 4.0, + "informativeness": 4.0, + "repetition": 4.0, + "completeness": 4.0 + }""" + + result = judge._parse_roscoe_response(response) + + # overall = 4.0, threshold = 3.0, so passed_filter = True + assert result["passed_filter"] is True + + def test_passed_filter_logic_below_threshold(self, judge: RoscoeJudge) -> None: + """Test threshold comparison - below default threshold (3.0).""" + response = """{ + "faithfulness": 2.0, + "coherence": 2.0, + "informativeness": 2.0, + "repetition": 2.0, + "completeness": 2.0 + }""" + + result = judge._parse_roscoe_response(response) + + # overall = 2.0, threshold = 3.0, so passed_filter = False + assert result["passed_filter"] is False + + def test_passed_filter_with_custom_threshold(self, judge_custom: RoscoeJudge) -> None: + """Test threshold comparison with custom threshold (4.0).""" + response = """{ + "faithfulness": 3.5, + "coherence": 3.5, + "informativeness": 3.5, + "repetition": 3.5, + "completeness": 3.5 + }""" + + result = judge_custom._parse_roscoe_response(response) + + # overall = 3.5, custom threshold = 4.0, so passed_filter = False + assert result["passed_filter"] is False + + +class TestRoscoeJudgeDiagnosis: + """Tests for diagnosis categorization.""" + + def test_diagnosis_categories_high(self, judge: RoscoeJudge) -> None: + """Test high category (>=4.0).""" + response = """{ + "faithfulness": 4.5, + "coherence": 5.0, + "informativeness": 4.0, + "repetition": 4.0, + "completeness": 4.8 + }""" + + result = judge._parse_roscoe_response(response) + diagnosis = result["diagnosis"] + + assert diagnosis["faithfulness"] == "high" + assert diagnosis["coherence"] == "high" + assert diagnosis["informativeness"] == "high" + assert diagnosis["repetition"] == "high" + assert diagnosis["completeness"] == "high" + + def test_diagnosis_categories_medium(self, judge: RoscoeJudge) -> None: + """Test medium category (>=2.5, <4.0).""" + response = """{ + "faithfulness": 3.0, + "coherence": 2.5, + "informativeness": 3.5, + "repetition": 3.9, + "completeness": 2.6 + }""" + + result = judge._parse_roscoe_response(response) + diagnosis = result["diagnosis"] + + assert diagnosis["faithfulness"] == "medium" + assert diagnosis["coherence"] == "medium" + assert diagnosis["informativeness"] == "medium" + assert diagnosis["repetition"] == "medium" + assert diagnosis["completeness"] == "medium" + + def test_diagnosis_categories_low(self, judge: RoscoeJudge) -> None: + """Test low category (<2.5).""" + response = """{ + "faithfulness": 1.0, + "coherence": 2.0, + "informativeness": 1.5, + "repetition": 2.4, + "completeness": 1.2 + }""" + + result = judge._parse_roscoe_response(response) + diagnosis = result["diagnosis"] + + assert diagnosis["faithfulness"] == "low" + assert diagnosis["coherence"] == "low" + assert diagnosis["informativeness"] == "low" + assert diagnosis["repetition"] == "low" + assert diagnosis["completeness"] == "low" + + def test_diagnosis_categories_mixed(self, judge: RoscoeJudge) -> None: + """Test mixed categories across metrics.""" + response = """{ + "faithfulness": 4.5, + "coherence": 3.0, + "informativeness": 2.0, + "repetition": 5.0, + "completeness": 1.0 + }""" + + result = judge._parse_roscoe_response(response) + diagnosis = result["diagnosis"] + + assert diagnosis["faithfulness"] == "high" + assert diagnosis["coherence"] == "medium" + assert diagnosis["informativeness"] == "low" + assert diagnosis["repetition"] == "high" + assert diagnosis["completeness"] == "low" + + +class TestRoscoeJudgeEvaluate: + """Tests for the evaluate method with mocked inference.""" + + def test_evaluate_returns_roscoe_evaluation(self, judge: RoscoeJudge) -> None: + """Test evaluate returns proper RoscoeEvaluation structure.""" + mock_response = """{ + "faithfulness": 4.0, + "coherence": 3.5, + "informativeness": 4.5, + "repetition": 3.0, + "completeness": 4.0 + }""" + + with patch.object(judge, "_run_inference", return_value=mock_response): + result = judge.evaluate("Test reasoning text") + + assert isinstance(result, dict) + # Verify all required RoscoeEvaluation keys are present + assert "faithfulness" in result + assert "coherence" in result + assert "informativeness" in result + assert "repetition" in result + assert "completeness" in result + assert "overall_score" in result + assert "passed_filter" in result + assert "diagnosis" in result + + def test_evaluate_calls_run_inference(self, judge: RoscoeJudge) -> None: + """Test evaluate calls _run_inference with correct prompt.""" + mock_response = ( + '{"faithfulness": 5.0, "coherence": 5.0, "informativeness": 5.0, ' + '"repetition": 5.0, "completeness": 5.0}' + ) + + with patch.object(judge, "_run_inference", return_value=mock_response) as mock_inference: + judge.evaluate("Sample text for evaluation") + + mock_inference.assert_called_once() + # Verify the prompt contains expected elements + call_args = mock_inference.call_args + assert call_args is not None + prompt_arg = call_args[0][0] + assert "Sample text for evaluation" in prompt_arg diff --git a/tests/test_steering_vectors.py b/tests/test_steering_vectors.py new file mode 100644 index 0000000..026594a --- /dev/null +++ b/tests/test_steering_vectors.py @@ -0,0 +1,865 @@ +"""Tests for steering vector extraction module. + +These tests verify the functions used to extract and manipulate +steering vectors for modulating reflection behavior in LLMs. + +This file follows TDD RED phase: all tests skip since the +steering_vectors module is not yet implemented. +""" + +import json +from pathlib import Path +from unittest.mock import MagicMock, patch + +import pytest +import torch +from torch import Tensor + +from probing_reflection.steering_vectors import ( + classify_samples, + compute_difference_in_means, + extract_activation_at_position, + extract_batch_activations, + extract_steering_vectors, + find_reflection_token_position, + save_steering_vectors, +) +from probing_reflection.types import ExtractVectorsConfig, SampleWithReflection + + +class TestClassify: + """Tests for classify_samples function.""" + + def test_classify_samples_basic(self, test_data_dir: Path) -> None: + """classify_samples should split samples into R and N sets.""" + fixture_path = test_data_dir / "sample_reflection.jsonl" + with open(fixture_path) as f: + samples: list[SampleWithReflection] = [json.loads(line) for line in f] + + r_samples, n_samples = classify_samples(samples, min_samples=1) + + assert len(r_samples) == 10 + assert len(n_samples) == 5 + for sample in r_samples: + assert sample["reflection_count"] > 0 + for sample in n_samples: + assert sample["reflection_count"] == 0 + + def test_classify_samples_min_samples_validation(self, test_data_dir: Path) -> None: + """classify_samples should raise ValueError if min_samples not met.""" + fixture_path = test_data_dir / "sample_reflection.jsonl" + with open(fixture_path) as f: + samples: list[SampleWithReflection] = [json.loads(line) for line in f] + + with pytest.raises(ValueError, match="N set has 5 samples"): + classify_samples(samples, min_samples=10) + + def test_classify_samples_empty_input(self, test_data_dir: Path) -> None: + """classify_samples should handle empty input gracefully.""" + samples: list[SampleWithReflection] = [] + + with pytest.raises(ValueError, match="R set has 0 samples"): + classify_samples(samples, min_samples=1) + + def test_classify_samples_all_reflection(self, test_data_dir: Path) -> None: + """classify_samples should handle case where all samples have reflection.""" + fixture_path = test_data_dir / "sample_reflection.jsonl" + with open(fixture_path) as f: + all_samples: list[SampleWithReflection] = [json.loads(line) for line in f] + + samples = [s for s in all_samples if s["reflection_count"] > 0] + + with pytest.raises(ValueError, match="N set has 0 samples"): + classify_samples(samples, min_samples=1) + + def test_classify_samples_no_reflection(self, test_data_dir: Path) -> None: + """classify_samples should handle case where no samples have reflection.""" + fixture_path = test_data_dir / "sample_reflection.jsonl" + with open(fixture_path) as f: + all_samples: list[SampleWithReflection] = [json.loads(line) for line in f] + + samples = [s for s in all_samples if s["reflection_count"] == 0] + + with pytest.raises(ValueError, match="R set has 0 samples"): + classify_samples(samples, min_samples=1) + + +class TestFindPosition: + """Tests for find_reflection_token_position function.""" + + def test_find_reflection_token_basic(self) -> None: + """find_reflection_token_position should return correct token index.""" + tokenizer = MagicMock() + tokenizer.encode.return_value = [101, 2094, 1037, 102] + tokenizer.convert_ids_to_tokens.return_value = ["Hello", "Wait", "there", "."] + + result = find_reflection_token_position(tokenizer, "Hello Wait there.") + + assert result == 1 + + def test_find_reflection_token_multiple_occurrences(self) -> None: + """find_reflection_token_position should handle multiple reflection tokens.""" + tokenizer = MagicMock() + tokenizer.encode.return_value = [101, 2094, 1037, 2094, 102] + tokenizer.convert_ids_to_tokens.return_value = ["But", "wait", "however", "now", "."] + + result = find_reflection_token_position(tokenizer, "But wait however now .") + + assert result == 0 + + def test_find_reflection_token_not_found(self) -> None: + """find_reflection_token_position should return None when token not in text.""" + tokenizer = MagicMock() + tokenizer.encode.return_value = [101, 2094, 102] + tokenizer.convert_ids_to_tokens.return_value = ["Hello", "world", "."] + + result = find_reflection_token_position(tokenizer, "Hello world.") + + assert result is None + + def test_find_reflection_token_case_sensitivity(self) -> None: + """find_reflection_token_position should handle case appropriately.""" + tokenizer = MagicMock() + tokenizer.encode.return_value = [101, 2094, 102] + tokenizer.convert_ids_to_tokens.return_value = ["Hello", "WAIT", "there"] + + result = find_reflection_token_position(tokenizer, "Hello WAIT there") + + assert result == 1 + + +class TestExtractSingle: + """Tests for extract_activation_at_position function.""" + + def test_extract_activation_basic(self) -> None: + """extract_activation_at_position should return tensor of correct shape.""" + model = MagicMock() + model.device = torch.device("cpu") + tokenizer = MagicMock() + + tokenizer.return_value = {"input_ids": Tensor([[1, 2, 3, 4, 5]]).long()} + tokenizer.__call__ = lambda text, return_tensors=None: { + "input_ids": Tensor([[1, 2, 3, 4, 5]]).long() + } + + hidden_dim = 768 + seq_len = 5 + mock_hidden = torch.randn(1, seq_len, hidden_dim) + mock_output = MagicMock() + mock_output.hidden_states = (None,) + (mock_hidden,) * 12 + model.return_value = mock_output + model.__call__ = lambda **kwargs: mock_output + + result = extract_activation_at_position(model, tokenizer, "test", 2, (0, 5)) + + assert 0 in result + assert 5 in result + assert result[0].shape == (hidden_dim,) + assert result[5].shape == (hidden_dim,) + + def test_extract_activation_invalid_position(self) -> None: + """extract_activation_at_position should handle out-of-bounds position.""" + model = MagicMock() + model.device = torch.device("cpu") + tokenizer = MagicMock() + + tokenizer.return_value = {"input_ids": Tensor([[1, 2, 3]]).long()} + tokenizer.__call__ = lambda text, return_tensors=None: { + "input_ids": Tensor([[1, 2, 3]]).long() + } + + mock_output = MagicMock() + mock_output.hidden_states = (None, torch.randn(1, 3, 768)) + model.return_value = mock_output + model.__call__ = lambda **kwargs: mock_output + + with pytest.raises(IndexError, match="Position 5 >= sequence length 3"): + extract_activation_at_position(model, tokenizer, "test", 5, (0,)) + + def test_extract_activation_layer_selection(self) -> None: + """extract_activation_at_position should extract from specified layer.""" + model = MagicMock() + model.device = torch.device("cpu") + tokenizer = MagicMock() + + tokenizer.return_value = {"input_ids": Tensor([[1, 2, 3]]).long()} + tokenizer.__call__ = lambda text, return_tensors=None: { + "input_ids": Tensor([[1, 2, 3]]).long() + } + + hidden_dim = 768 + layer0_hidden = torch.ones(1, 3, hidden_dim) + layer5_hidden = torch.full((1, 3, hidden_dim), 5.0) + mock_output = MagicMock() + mock_output.hidden_states = ( + None, + layer0_hidden, + torch.randn(1, 3, hidden_dim), + torch.randn(1, 3, hidden_dim), + torch.randn(1, 3, hidden_dim), + torch.randn(1, 3, hidden_dim), + layer5_hidden, + ) + model.return_value = mock_output + model.__call__ = lambda **kwargs: mock_output + + result = extract_activation_at_position(model, tokenizer, "test", 1, (0, 5)) + + assert torch.allclose(result[0], torch.ones(hidden_dim)) + assert torch.allclose(result[5], torch.full((hidden_dim,), 5.0)) + + +class TestBatch: + """Tests for extract_batch_activations function.""" + + def test_batch_activations_basic(self) -> None: + """extract_batch_activations should process multiple samples.""" + samples: list[SampleWithReflection] = [ + { + "problem_id": "r1", + "problem": "test", + "generated": "Wait, this is a test.", + "reference_answer": "answer", + "reflection_tokens": [], + "reflection_count": 1, + "reflection_density": 0.1, + }, + { + "problem_id": "n1", + "problem": "test", + "generated": "This is simple.", + "reference_answer": "answer", + "reflection_tokens": [], + "reflection_count": 0, + "reflection_density": 0.0, + }, + ] + + model = MagicMock() + model.device = torch.device("cpu") + tokenizer = MagicMock() + + tokenizer.encode.return_value = [1, 2, 3, 4] + tokenizer.convert_ids_to_tokens.return_value = ["Wait", "this", "is", "test"] + tokenizer.return_value = {"input_ids": Tensor([[1, 2, 3, 4]]).long()} + tokenizer.__call__ = lambda text, return_tensors=None: { + "input_ids": Tensor([[1, 2, 3, 4]]).long() + } + + hidden_dim = 768 + mock_hidden = torch.randn(1, 4, hidden_dim) + mock_output = MagicMock() + mock_output.hidden_states = (None,) + (mock_hidden,) * 12 + model.return_value = mock_output + model.__call__ = lambda **kwargs: mock_output + + r_acts, n_acts = extract_batch_activations(samples, model, tokenizer, (0, 5), batch_size=2) + + assert 0 in r_acts + assert 5 in r_acts + assert 0 in n_acts + assert 5 in n_acts + assert len(r_acts[0]) == 1 + assert len(n_acts[0]) == 1 + + def test_batch_activations_shape(self) -> None: + """extract_batch_activations should return stacked tensor of correct shape.""" + samples: list[SampleWithReflection] = [ + { + "problem_id": "r1", + "problem": "test", + "generated": "Wait, test one.", + "reference_answer": "answer", + "reflection_tokens": [], + "reflection_count": 1, + "reflection_density": 0.1, + }, + { + "problem_id": "r2", + "problem": "test", + "generated": "Actually, test two.", + "reference_answer": "answer", + "reflection_tokens": [], + "reflection_count": 1, + "reflection_density": 0.1, + }, + { + "problem_id": "n1", + "problem": "test", + "generated": "Simple output.", + "reference_answer": "answer", + "reflection_tokens": [], + "reflection_count": 0, + "reflection_density": 0.0, + }, + ] + + model = MagicMock() + model.device = torch.device("cpu") + tokenizer = MagicMock() + + tokenizer.encode.return_value = [1, 2, 3, 4] + tokenizer.convert_ids_to_tokens.return_value = ["Wait", "test", "one", "."] + tokenizer.return_value = {"input_ids": Tensor([[1, 2, 3, 4]]).long()} + tokenizer.__call__ = lambda text, return_tensors=None: { + "input_ids": Tensor([[1, 2, 3, 4]]).long() + } + + hidden_dim = 256 + mock_hidden = torch.randn(1, 4, hidden_dim) + mock_output = MagicMock() + mock_output.hidden_states = (None,) + (mock_hidden,) * 12 + model.return_value = mock_output + model.__call__ = lambda **kwargs: mock_output + + r_acts, n_acts = extract_batch_activations(samples, model, tokenizer, (3,), batch_size=2) + + assert 3 in r_acts + assert len(r_acts[3]) == 2 + assert r_acts[3][0].shape == (hidden_dim,) + assert r_acts[3][1].shape == (hidden_dim,) + assert len(n_acts[3]) == 1 + + def test_batch_activations_empty_batch(self) -> None: + """extract_batch_activations should handle case where all samples are skipped.""" + samples: list[SampleWithReflection] = [ + { + "problem_id": "r1", + "problem": "test", + "generated": "No reflection tokens here.", + "reference_answer": "answer", + "reflection_tokens": [], + "reflection_count": 1, + "reflection_density": 0.1, + }, + { + "problem_id": "n1", + "problem": "test", + "generated": "Simple output.", + "reference_answer": "answer", + "reflection_tokens": [], + "reflection_count": 0, + "reflection_density": 0.0, + }, + ] + + model = MagicMock() + model.device = torch.device("cpu") + tokenizer = MagicMock() + + tokenizer.encode.return_value = [1, 2, 3, 4] + tokenizer.convert_ids_to_tokens.return_value = ["No", "tokens", "here", "."] + tokenizer.return_value = {"input_ids": Tensor([[1, 2, 3, 4]]).long()} + tokenizer.__call__ = lambda text, return_tensors=None: { + "input_ids": Tensor([[1, 2, 3, 4]]).long() + } + + hidden_dim = 128 + mock_hidden = torch.randn(1, 4, hidden_dim) + mock_output = MagicMock() + mock_output.hidden_states = (None,) + (mock_hidden,) * 12 + model.return_value = mock_output + model.__call__ = lambda **kwargs: mock_output + + r_acts, n_acts = extract_batch_activations(samples, model, tokenizer, (0,), batch_size=1) + + assert 0 in r_acts + assert 0 in n_acts + assert len(r_acts[0]) == 0 + assert len(n_acts[0]) == 1 + + def test_batch_activations_progress_callback(self) -> None: + """extract_batch_activations should use tqdm progress bar.""" + samples: list[SampleWithReflection] = [ + { + "problem_id": "r1", + "problem": "test", + "generated": "Wait, test.", + "reference_answer": "answer", + "reflection_tokens": [], + "reflection_count": 1, + "reflection_density": 0.1, + }, + { + "problem_id": "n1", + "problem": "test", + "generated": "Simple output.", + "reference_answer": "answer", + "reflection_tokens": [], + "reflection_count": 0, + "reflection_density": 0.0, + }, + ] + + model = MagicMock() + model.device = torch.device("cpu") + tokenizer = MagicMock() + + tokenizer.encode.return_value = [1, 2, 3] + tokenizer.convert_ids_to_tokens.return_value = ["Wait", "test", "."] + tokenizer.return_value = {"input_ids": Tensor([[1, 2, 3]]).long()} + tokenizer.__call__ = lambda text, return_tensors=None: { + "input_ids": Tensor([[1, 2, 3]]).long() + } + + hidden_dim = 64 + mock_hidden = torch.randn(1, 3, hidden_dim) + mock_output = MagicMock() + mock_output.hidden_states = (None,) + (mock_hidden,) * 12 + model.return_value = mock_output + model.__call__ = lambda **kwargs: mock_output + + r_acts, n_acts = extract_batch_activations(samples, model, tokenizer, (0,), batch_size=1) + + assert len(r_acts[0]) == 1 + assert len(n_acts[0]) == 1 + + def test_batch_oom_does_not_duplicate_successful_samples(self) -> None: + samples: list[SampleWithReflection] = [ + { + "problem_id": "r1", + "problem": "test", + "generated": "Wait, first.", + "reference_answer": "answer", + "reflection_tokens": [], + "reflection_count": 1, + "reflection_density": 0.1, + }, + { + "problem_id": "r2", + "problem": "test", + "generated": "Wait, second.", + "reference_answer": "answer", + "reflection_tokens": [], + "reflection_count": 1, + "reflection_density": 0.1, + }, + { + "problem_id": "n1", + "problem": "test", + "generated": "Simple.", + "reference_answer": "answer", + "reflection_tokens": [], + "reflection_count": 0, + "reflection_density": 0.0, + }, + ] + tokenizer = MagicMock() + tokenizer.encode.return_value = [1] + successful = {0: torch.ones(4)} + + with ( + patch( + "probing_reflection.steering_vectors.find_reflection_token_position", + return_value=0, + ), + patch( + "probing_reflection.steering_vectors.extract_activation_at_position", + side_effect=[successful, torch.cuda.OutOfMemoryError(), successful], + ), + ): + r_acts, n_acts = extract_batch_activations( + samples, MagicMock(), tokenizer, (0,), batch_size=2 + ) + + assert len(r_acts[0]) == 1 + assert len(n_acts[0]) == 1 + + +class TestDiffMeans: + """Tests for compute_difference_in_means function.""" + + def test_diff_means_basic(self) -> None: + """compute_difference_in_means should compute R - N correctly.""" + r_activations = { + 0: [Tensor([1.0, 2.0]), Tensor([2.0, 3.0])], + 5: [Tensor([5.0, 6.0]), Tensor([7.0, 8.0])], + } + n_activations = { + 0: [Tensor([0.0, 1.0]), Tensor([1.0, 2.0])], + 5: [Tensor([3.0, 4.0]), Tensor([5.0, 6.0])], + } + + result = compute_difference_in_means(r_activations, n_activations, (0, 5)) + + assert 0 in result + assert 5 in result + assert torch.allclose(result[0], Tensor([1.0, 1.0])) + assert torch.allclose(result[5], Tensor([2.0, 2.0])) + + def test_diff_means_normalization(self) -> None: + """compute_difference_in_means should not normalize by default (raw difference).""" + r_activations = { + 0: [Tensor([2.0, 0.0]), Tensor([4.0, 0.0])], + } + n_activations = { + 0: [Tensor([1.0, 0.0]), Tensor([1.0, 0.0])], + } + + result = compute_difference_in_means(r_activations, n_activations, (0,)) + + expected = Tensor([2.0, 0.0]) + assert torch.allclose(result[0], expected) + + def test_diff_means_mismatched_shapes(self) -> None: + """compute_difference_in_means should validate layer coverage.""" + r_activations = { + 0: [Tensor([1.0, 2.0])], + } + n_activations = { + 0: [Tensor([1.0, 2.0])], + 5: [Tensor([3.0, 4.0])], + } + + with pytest.raises(ValueError, match="No R activations for layer 5"): + compute_difference_in_means(r_activations, n_activations, (0, 5)) + + def test_diff_means_single_layer(self) -> None: + """compute_difference_in_means should work with single layer tensor.""" + r_activations = { + 10: [Tensor([1.0, 2.0, 3.0]), Tensor([3.0, 4.0, 5.0])], + } + n_activations = { + 10: [Tensor([0.0, 0.0, 0.0]), Tensor([2.0, 2.0, 2.0])], + } + + result = compute_difference_in_means(r_activations, n_activations, (10,)) + + assert 10 in result + expected = Tensor([1.0, 2.0, 3.0]) + assert torch.allclose(result[10], expected) + + +class TestSave: + """Tests for save_steering_vectors function.""" + + def test_save_vectors_basic(self, tmp_path: Path) -> None: + """save_steering_vectors should write files to disk.""" + vectors = {0: Tensor([1.0, 2.0]), 5: Tensor([3.0, 4.0])} + metadata = {"model_name": "test-model", "r_count": 10, "n_count": 5} + output_file = tmp_path / "test_vectors.pt" + + save_steering_vectors(vectors, metadata, output_file) + + assert output_file.exists() + + loaded = torch.load(output_file, weights_only=True) + assert "layer_0" in loaded + assert "layer_5" in loaded + assert "metadata" in loaded + assert torch.allclose(loaded["layer_0"], Tensor([1.0, 2.0])) + assert torch.allclose(loaded["layer_5"], Tensor([3.0, 4.0])) + + def test_save_vectors_creates_directory(self, tmp_path: Path) -> None: + """save_steering_vectors should create output directory if needed.""" + vectors = {0: Tensor([1.0, 2.0])} + metadata: dict[str, str | int | tuple[int, ...]] = {"model_name": "test-model"} + output_file = tmp_path / "subdir" / "nested" / "vectors.pt" + + save_steering_vectors(vectors, metadata, output_file) + + assert output_file.exists() + assert output_file.parent.is_dir() + + def test_save_vectors_metadata(self, tmp_path: Path) -> None: + """save_steering_vectors should include metadata in saved files.""" + vectors = {0: Tensor([1.0, 2.0])} + metadata = { + "model_name": "test-model", + "layer_indices": (0, 5, 10), + "r_count": 20, + "n_count": 15, + } + output_file = tmp_path / "test_vectors.pt" + + save_steering_vectors(vectors, metadata, output_file) + + loaded = torch.load(output_file, weights_only=True) + loaded_meta = loaded["metadata"] + assert loaded_meta["model_name"] == "test-model" + assert loaded_meta["layer_indices"] == (0, 5, 10) + assert loaded_meta["r_count"] == 20 + assert loaded_meta["n_count"] == 15 + assert "timestamp" in loaded_meta + + def test_save_vectors_overwrite(self, tmp_path: Path) -> None: + """save_steering_vectors should handle existing files appropriately.""" + vectors = {0: Tensor([1.0, 2.0])} + metadata: dict[str, str | int | tuple[int, ...]] = {"model_name": "test-model"} + output_file = tmp_path / "test_vectors.pt" + + save_steering_vectors(vectors, metadata, output_file) + + vectors2 = {0: Tensor([5.0, 6.0])} + metadata2: dict[str, str | int | tuple[int, ...]] = {"model_name": "updated-model"} + save_steering_vectors(vectors2, metadata2, output_file) + + loaded = torch.load(output_file, weights_only=True) + assert torch.allclose(loaded["layer_0"], Tensor([5.0, 6.0])) + assert loaded["metadata"]["model_name"] == "updated-model" + + +class TestPipeline: + """Tests for extract_steering_vectors end-to-end function.""" + + def _create_mock_tokenizer(self, seq_len: int) -> MagicMock: + """Helper to create a properly configured tokenizer mock.""" + mock_tokenizer = MagicMock() + mock_tokenizer.encode.return_value = list(range(seq_len)) + mock_tokenizer.convert_ids_to_tokens.return_value = ["Wait"] + ["token"] * (seq_len - 1) + mock_tokenizer.return_value = {"input_ids": Tensor([[1] * seq_len]).long()} + return mock_tokenizer + + def _create_mock_model(self, hidden_dim: int, seq_len: int, num_layers: int) -> MagicMock: + """Helper to create a properly configured model mock.""" + mock_model = MagicMock() + mock_model.device = torch.device("cpu") + mock_hidden = torch.randn(1, seq_len, hidden_dim) + mock_output = MagicMock() + mock_output.hidden_states = (None,) + (mock_hidden,) * num_layers + mock_model.return_value = mock_output + mock_model.to.return_value = mock_model + return mock_model + + def test_pipeline_basic(self, tmp_path: Path) -> None: + """extract_steering_vectors should run full pipeline.""" + input_file = tmp_path / "test.jsonl" + samples: list[SampleWithReflection] = [ + { + "problem_id": "r1", + "problem": "test", + "generated": "Wait, this is a test.", + "reference_answer": "answer", + "reflection_tokens": [], + "reflection_count": 1, + "reflection_density": 0.1, + }, + { + "problem_id": "n1", + "problem": "test", + "generated": "This is simple.", + "reference_answer": "answer", + "reflection_tokens": [], + "reflection_count": 0, + "reflection_density": 0.0, + }, + ] + with open(input_file, "w") as f: + for sample in samples: + f.write(json.dumps(sample) + "\n") + + config = ExtractVectorsConfig( + input_path=str(input_file), + model_name="test-model", + layer_indices=(0, 5), + output_path=str(tmp_path / "output.pt"), + min_samples=1, + batch_size=1, + ) + + mock_model = self._create_mock_model(hidden_dim=64, seq_len=4, num_layers=12) + mock_tokenizer = self._create_mock_tokenizer(seq_len=4) + + with patch("probing_reflection.steering_vectors.load_model") as mock_load_model: + mock_load_model.return_value = (mock_model, mock_tokenizer) + + result = extract_steering_vectors(config) + + assert "vectors" in result + assert "metadata" in result + assert 0 in result["vectors"] + assert 5 in result["vectors"] + + def test_pipeline_returns_dict(self, tmp_path: Path) -> None: + """extract_steering_vectors should return dict mapping layer to vector.""" + input_file = tmp_path / "test.jsonl" + samples: list[SampleWithReflection] = [ + { + "problem_id": "r1", + "problem": "test", + "generated": "Wait, test.", + "reference_answer": "answer", + "reflection_tokens": [], + "reflection_count": 1, + "reflection_density": 0.1, + }, + { + "problem_id": "n1", + "problem": "test", + "generated": "Simple output.", + "reference_answer": "answer", + "reflection_tokens": [], + "reflection_count": 0, + "reflection_density": 0.0, + }, + ] + with open(input_file, "w") as f: + for sample in samples: + f.write(json.dumps(sample) + "\n") + + config = ExtractVectorsConfig( + input_path=str(input_file), + model_name="test-model", + layer_indices=(3,), + output_path=str(tmp_path / "output.pt"), + min_samples=1, + batch_size=1, + ) + + hidden_dim = 128 + mock_model = self._create_mock_model(hidden_dim=hidden_dim, seq_len=3, num_layers=5) + mock_tokenizer = self._create_mock_tokenizer(seq_len=3) + + with patch("probing_reflection.steering_vectors.load_model") as mock_load_model: + mock_load_model.return_value = (mock_model, mock_tokenizer) + + result = extract_steering_vectors(config) + + assert isinstance(result["vectors"], dict) + assert 3 in result["vectors"] + assert result["vectors"][3].shape == (hidden_dim,) + + def test_pipeline_multiple_layers(self, tmp_path: Path) -> None: + """extract_steering_vectors should handle multiple layer indices.""" + input_file = tmp_path / "test.jsonl" + samples: list[SampleWithReflection] = [ + { + "problem_id": "r1", + "problem": "test", + "generated": "Wait, test.", + "reference_answer": "answer", + "reflection_tokens": [], + "reflection_count": 1, + "reflection_density": 0.1, + }, + { + "problem_id": "n1", + "problem": "test", + "generated": "Simple.", + "reference_answer": "answer", + "reflection_tokens": [], + "reflection_count": 0, + "reflection_density": 0.0, + }, + ] + with open(input_file, "w") as f: + for sample in samples: + f.write(json.dumps(sample) + "\n") + + config = ExtractVectorsConfig( + input_path=str(input_file), + model_name="test-model", + layer_indices=(0, 5, 10), + output_path=str(tmp_path / "output.pt"), + min_samples=1, + batch_size=1, + ) + + mock_model = self._create_mock_model(hidden_dim=64, seq_len=3, num_layers=15) + mock_tokenizer = self._create_mock_tokenizer(seq_len=3) + + with patch("probing_reflection.steering_vectors.load_model") as mock_load_model: + mock_load_model.return_value = (mock_model, mock_tokenizer) + + result = extract_steering_vectors(config) + + assert 0 in result["vectors"] + assert 5 in result["vectors"] + assert 10 in result["vectors"] + assert result["metadata"]["layer_indices"] == (0, 5, 10) + + def test_pipeline_model_loading(self, tmp_path: Path) -> None: + """extract_steering_vectors should load model from name or path.""" + input_file = tmp_path / "test.jsonl" + samples: list[SampleWithReflection] = [ + { + "problem_id": "r1", + "problem": "test", + "generated": "Wait, test.", + "reference_answer": "answer", + "reflection_tokens": [], + "reflection_count": 1, + "reflection_density": 0.1, + }, + { + "problem_id": "n1", + "problem": "test", + "generated": "Simple.", + "reference_answer": "answer", + "reflection_tokens": [], + "reflection_count": 0, + "reflection_density": 0.0, + }, + ] + with open(input_file, "w") as f: + for sample in samples: + f.write(json.dumps(sample) + "\n") + + config = ExtractVectorsConfig( + input_path=str(input_file), + model_name="custom-model-path", + layer_indices=(0,), + output_path=str(tmp_path / "output.pt"), + min_samples=1, + batch_size=1, + ) + + mock_model = self._create_mock_model(hidden_dim=32, seq_len=3, num_layers=5) + mock_tokenizer = self._create_mock_tokenizer(seq_len=3) + + with patch("probing_reflection.steering_vectors.load_model") as mock_load_model: + mock_load_model.return_value = (mock_model, mock_tokenizer) + + extract_steering_vectors(config) + + mock_load_model.assert_called_once() + assert mock_load_model.call_args.args[0] == "custom-model-path" + + def test_pipeline_logging(self, tmp_path: Path) -> None: + """extract_steering_vectors should log progress during extraction.""" + input_file = tmp_path / "test.jsonl" + samples: list[SampleWithReflection] = [ + { + "problem_id": "r1", + "problem": "test", + "generated": "Wait, test.", + "reference_answer": "answer", + "reflection_tokens": [], + "reflection_count": 1, + "reflection_density": 0.1, + }, + { + "problem_id": "n1", + "problem": "test", + "generated": "Simple.", + "reference_answer": "answer", + "reflection_tokens": [], + "reflection_count": 0, + "reflection_density": 0.0, + }, + ] + with open(input_file, "w") as f: + for sample in samples: + f.write(json.dumps(sample) + "\n") + + config = ExtractVectorsConfig( + input_path=str(input_file), + model_name="test-model", + layer_indices=(0,), + output_path=str(tmp_path / "output.pt"), + min_samples=1, + batch_size=1, + ) + + mock_model = self._create_mock_model(hidden_dim=32, seq_len=3, num_layers=5) + mock_tokenizer = self._create_mock_tokenizer(seq_len=3) + + with patch("probing_reflection.steering_vectors.load_model") as mock_load_model: + mock_load_model.return_value = (mock_model, mock_tokenizer) + + result = extract_steering_vectors(config) + + assert result["metadata"]["r_count"] == 1 + assert result["metadata"]["n_count"] == 1 + assert "model_name" in result["metadata"] + assert "timestamp" in result["metadata"] diff --git a/tests/test_types.py b/tests/test_types.py index 9f3a8c1..9503d0c 100644 --- a/tests/test_types.py +++ b/tests/test_types.py @@ -8,8 +8,10 @@ from probing_reflection.types import ( ContrastivePair, + ExtractVectorsConfig, ProbingConfig, ReflectionResult, + SteeringVectorResult, ) @@ -103,3 +105,80 @@ def test_used_in_list(self) -> None: {"positive": "happy", "negative": "sad"}, ] assert len(pairs) == 2 + + +class TestExtractVectorsConfig: + """Tests for ExtractVectorsConfig dataclass.""" + + def test_default_initialization(self) -> None: + """ExtractVectorsConfig should have sensible defaults.""" + config = ExtractVectorsConfig() + assert config.input_path == "" + assert config.model_name == "Qwen/Qwen2.5-0.5B" + assert config.layer_indices == () + assert config.output_path == "steering_vectors.pt" + assert config.min_samples == 10 + assert config.batch_size == 4 + + def test_custom_initialization(self) -> None: + """ExtractVectorsConfig should accept custom values.""" + config = ExtractVectorsConfig( + input_path="data/samples.jsonl", + model_name="gpt2-small", + layer_indices=(0, 6, 11), + output_path="vectors.pt", + min_samples=20, + batch_size=8, + ) + assert config.input_path == "data/samples.jsonl" + assert config.model_name == "gpt2-small" + assert config.layer_indices == (0, 6, 11) + assert config.output_path == "vectors.pt" + assert config.min_samples == 20 + assert config.batch_size == 8 + + def test_is_frozen(self) -> None: + """ExtractVectorsConfig should be immutable (frozen).""" + config = ExtractVectorsConfig() + with pytest.raises(AttributeError): + config.min_samples = 20 # type: ignore[misc] + + def test_is_hashable(self) -> None: + """ExtractVectorsConfig should be hashable (for use in sets/dicts).""" + config = ExtractVectorsConfig(model_name="test") + assert hash(config) is not None + configs = {config, ExtractVectorsConfig(model_name="test")} + assert len(configs) == 1 + + +class TestSteeringVectorResult: + """Tests for SteeringVectorResult TypedDict.""" + + def test_structure(self) -> None: + """SteeringVectorResult should have vectors and metadata keys.""" + from torch import Tensor + + result: SteeringVectorResult = { + "vectors": {0: Tensor([1.0, 2.0, 3.0])}, + "metadata": {"model_name": "test", "layer_count": 5, "layer_indices": (0, 1, 2)}, + } + assert "vectors" in result + assert "metadata" in result + assert 0 in result["vectors"] + assert result["metadata"]["model_name"] == "test" + + def test_metadata_types(self) -> None: + """SteeringVectorResult metadata should accept str, int, and tuple.""" + from torch import Tensor + + result: SteeringVectorResult = { + "vectors": {0: Tensor([1.0])}, + "metadata": { + "str_value": "test", + "int_value": 42, + "tuple_value": (1, 2, 3), + }, + } + assert isinstance(result["metadata"]["str_value"], str) + assert isinstance(result["metadata"]["int_value"], int) + assert isinstance(result["metadata"]["tuple_value"], 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