From c541ecc96a198cffde55e9f7b5262d9f183834ed Mon Sep 17 00:00:00 2001 From: Alexander Rubinstein Date: Wed, 28 Jan 2026 08:08:00 +0100 Subject: [PATCH 01/47] [Add MMLU task]: - Add scaffold for MMLU evaluation on anchor points --- examples/mmlu_benchmark/.gitignore | 7 + examples/mmlu_benchmark/README.md | 202 +++++++ examples/mmlu_benchmark/__init__.py | 5 + examples/mmlu_benchmark/mmlu_benchmark.py | 363 ++++++++++++ maseval/benchmark/mmlu/__init__.py | 45 ++ maseval/benchmark/mmlu/mmlu.py | 655 ++++++++++++++++++++++ 6 files changed, 1277 insertions(+) create mode 100644 examples/mmlu_benchmark/.gitignore create mode 100644 examples/mmlu_benchmark/README.md create mode 100644 examples/mmlu_benchmark/__init__.py create mode 100644 examples/mmlu_benchmark/mmlu_benchmark.py create mode 100644 maseval/benchmark/mmlu/__init__.py create mode 100644 maseval/benchmark/mmlu/mmlu.py diff --git a/examples/mmlu_benchmark/.gitignore b/examples/mmlu_benchmark/.gitignore new file mode 100644 index 00000000..69728145 --- /dev/null +++ b/examples/mmlu_benchmark/.gitignore @@ -0,0 +1,7 @@ +# Results +results/ +*.jsonl + +# Predictions +predictions/ +*.pkl diff --git a/examples/mmlu_benchmark/README.md b/examples/mmlu_benchmark/README.md new file mode 100644 index 00000000..03f60688 --- /dev/null +++ b/examples/mmlu_benchmark/README.md @@ -0,0 +1,202 @@ +# MMLU Benchmark Example + +This example demonstrates how to evaluate HuggingFace language models on MMLU (Massive Multitask Language Understanding) using MASEval, with optional anchor point-based task selection for DISCO prediction. + +## Overview + +The MMLU benchmark evaluates language models on multiple choice questions across 57 subjects including STEM, humanities, social sciences, and more. This implementation is compatible with the [disco-public](https://github.com/parameterlab/disco-public) evaluation methodology. + +### Key Features + +- **Anchor Point-Based Evaluation**: Evaluate only on selected anchor tasks for efficient DISCO-based performance prediction +- **Full Prompt Support**: Use few-shot examples from `full_prompt` field (like lm-evaluation-harness) +- **HuggingFace Integration**: Works with any HuggingFace transformers model +- **DISCO-Compatible Output**: Saves predictions in format compatible with DISCO predictor + +## Installation + +```bash +# Install MASEval with all extras (includes transformers) +pip install "maseval[all]" + +# Or install with specific extras +pip install "maseval[transformers]" +``` + +## Data Format + +The benchmark expects a JSON file in the `mmlu_prompts_examples.json` format: + +```json +[ + { + "query": "Question text with answer choices...\nA. ...\nB. ...\nC. ...\nD. ...\nAnswer:", + "full_prompt": "The following are multiple choice questions... [few-shot examples] ... [question]", + "choices": ["A", "B", "C", "D"], + "gold": 1 + }, + ... +] +``` + +## Usage + +### Basic Evaluation + +Evaluate a model on all MMLU tasks: + +```bash +python mmlu_benchmark.py \ + --model_id "meta-llama/Llama-2-7b-hf" \ + --data_path /path/to/mmlu_prompts_examples.json +``` + +### Anchor Points Evaluation (for DISCO) + +Evaluate only on anchor tasks for DISCO prediction: + +```bash +python mmlu_benchmark.py \ + --model_id "alignment-handbook/zephyr-7b-sft-full" \ + --data_path /path/to/mmlu_prompts_examples.json \ + --anchor_points_path /path/to/anchor_points_disagreement.pkl \ + --use_full_prompt \ + --predictions_path ./output/predictions.pkl +``` + +### Quick Test + +Run on a small subset for testing: + +```bash +python mmlu_benchmark.py \ + --model_id "meta-llama/Llama-2-7b-hf" \ + --data_path /path/to/mmlu_prompts_examples.json \ + --limit 10 +``` + +## Command Line Arguments + +| Argument | Description | Default | +|----------|-------------|---------| +| `--model_id` | HuggingFace model identifier (required) | - | +| `--data_path` | Path to MMLU prompts JSON file (required) | - | +| `--anchor_points_path` | Path to anchor points pickle file | None | +| `--output_dir` | Directory to save results | `./results` | +| `--predictions_path` | Path to save predictions pickle (for DISCO) | None | +| `--use_full_prompt` | Use full prompt with few-shot examples | False | +| `--limit` | Limit number of tasks to evaluate | None | +| `--device` | Device to run model on | `cuda:0` | +| `--trust_remote_code` | Trust remote code when loading model | False | +| `--num_workers` | Number of parallel workers | 1 | + +## Equivalent disco-public Command + +This MASEval benchmark provides equivalent functionality to: + +```bash +python scripts/run_lm_eval.py \ + --anchor_points_path=/path/to/anchor_points_disagreement.pkl \ + --batch_size=8 --device=cuda:0 \ + --gen_kwargs=max_gen_toks=128,output_scores=True,return_dict_in_generate=True \ + --metric=acc_norm --model=hf \ + --model_args=pretrained=alignment-handbook/zephyr-7b-sft-full,trust_remote_code=True \ + --num_fewshot=0 \ + --output_path=/path/to/output \ + --tasks=mmlu_prompts --log_samples --force_recompute \ + --use_full_prompt --skip_non_anchor_points +``` + +## Programmatic Usage + +```python +from maseval.benchmark.mmlu import ( + MMLUBenchmark, + load_tasks, + compute_benchmark_metrics, +) +from maseval.interface.inference import HuggingFaceModelAdapter + + +class MyMMLUBenchmark(MMLUBenchmark): + def __init__(self, model_id: str, **kwargs): + super().__init__(**kwargs) + self._model_id = model_id + self._pipeline = None + + def get_model_adapter(self, model_id: str, **kwargs): + from transformers import pipeline + + if self._pipeline is None: + self._pipeline = pipeline( + "text-generation", + model=self._model_id, + device="cuda:0", + ) + + return HuggingFaceModelAdapter( + model=self._pipeline, + model_id=self._model_id, + ) + + +# Load tasks with anchor points filtering +tasks = load_tasks( + data_path="/path/to/mmlu_prompts_examples.json", + anchor_points_path="/path/to/anchor_points.pkl", +) + +# Run benchmark +benchmark = MyMMLUBenchmark( + model_id="meta-llama/Llama-2-7b-hf", + use_full_prompt=True, +) +results = benchmark.run(tasks=tasks, agent_data={"model_id": "llama-7b"}) + +# Compute metrics +metrics = compute_benchmark_metrics(results) +print(f"Accuracy: {metrics['acc']:.4f}") +``` + +## Output Format + +### Results (JSONL) + +Each task result is saved in JSONL format: + +```json +{ + "task_id": "mmlu_42", + "status": "success", + "eval": [ + { + "acc": 1.0, + "acc_norm": 1.0, + "predicted": 1, + "gold": 1, + "correct": true, + "doc_id": 42 + } + ] +} +``` + +### Predictions (Pickle) + +When `--predictions_path` is specified, saves a numpy array of shape `(1, n_questions, n_choices)` for use with DISCO predictor. + +## Architecture + +The MMLU benchmark implementation follows MASEval patterns: + +- `MMLUBenchmark`: Main benchmark class (abstract, requires `get_model_adapter`) +- `MMLUEnvironment`: Simple environment holding task context (no tools needed) +- `MMLUEvaluator`: Evaluates model predictions against gold answers +- `AnchorPointsTaskQueue`: AdaptiveTaskQueue that iterates through anchor tasks +- `MMLUModelAgent`: Simple agent wrapper that forwards prompts to model + +## References + +- [MMLU Dataset](https://huggingface.co/datasets/cais/mmlu) +- [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) +- [disco-public](https://github.com/parameterlab/disco-public) diff --git a/examples/mmlu_benchmark/__init__.py b/examples/mmlu_benchmark/__init__.py new file mode 100644 index 00000000..87c0e5cf --- /dev/null +++ b/examples/mmlu_benchmark/__init__.py @@ -0,0 +1,5 @@ +"""MMLU Benchmark Example. + +This example demonstrates how to evaluate HuggingFace models on MMLU +using anchor point-based task selection for DISCO prediction. +""" diff --git a/examples/mmlu_benchmark/mmlu_benchmark.py b/examples/mmlu_benchmark/mmlu_benchmark.py new file mode 100644 index 00000000..4bb0d267 --- /dev/null +++ b/examples/mmlu_benchmark/mmlu_benchmark.py @@ -0,0 +1,363 @@ +""" +MMLU Benchmark Example - Evaluating Models on Anchor Points + +This example demonstrates how to evaluate a HuggingFace model on MMLU tasks +using anchor point-based task selection for DISCO prediction. + +Equivalent to the disco-public command: + python scripts/run_lm_eval.py \\ + --anchor_points_path=/path/to/anchor_points_disagreement.pkl \\ + --model=hf \\ + --model_args=pretrained=alignment-handbook/zephyr-7b-sft-full,trust_remote_code=True \\ + --tasks=mmlu_prompts \\ + --skip_non_anchor_points \\ + --use_full_prompt + +Usage: + # Run with default settings (evaluates on all tasks) + python mmlu_benchmark.py --model_id "meta-llama/Llama-2-7b-hf" --data_path /path/to/mmlu_prompts_examples.json + + # Run with anchor points filtering (for DISCO prediction) + python mmlu_benchmark.py \\ + --model_id "alignment-handbook/zephyr-7b-sft-full" \\ + --data_path /path/to/mmlu_prompts_examples.json \\ + --anchor_points_path /path/to/anchor_points_disagreement.pkl \\ + --use_full_prompt + + # Run on a subset of tasks for testing + python mmlu_benchmark.py \\ + --model_id "meta-llama/Llama-2-7b-hf" \\ + --data_path /path/to/mmlu_prompts_examples.json \\ + --limit 10 +""" + +import argparse +import json +import pickle +from pathlib import Path +from typing import Optional + +import numpy as np + +# MASEval imports +from maseval.core.callbacks.result_logger import FileResultLogger + +# MMLU benchmark imports +from maseval.benchmark.mmlu import ( + MMLUBenchmark, + load_tasks, + compute_benchmark_metrics, +) + + +def parse_args(): + """Parse command line arguments.""" + parser = argparse.ArgumentParser( + description="MMLU Benchmark - Evaluate models on MMLU multiple choice questions", + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + ) + + # Required arguments + parser.add_argument( + "--model_id", + type=str, + required=True, + help="HuggingFace model identifier (e.g., 'meta-llama/Llama-2-7b-hf')", + ) + parser.add_argument( + "--data_path", + type=str, + required=True, + help="Path to MMLU prompts JSON file (mmlu_prompts_examples.json format)", + ) + + # Optional arguments + parser.add_argument( + "--anchor_points_path", + type=str, + default=None, + help="Path to anchor points pickle file. If provided, evaluates only anchor tasks.", + ) + parser.add_argument( + "--output_dir", + type=str, + default="./results", + help="Directory to save results", + ) + parser.add_argument( + "--predictions_path", + type=str, + default=None, + help="Path to save predictions tensor as pickle (for DISCO predictor)", + ) + parser.add_argument( + "--use_full_prompt", + action="store_true", + help="Use full prompt with few-shot examples instead of just the query", + ) + parser.add_argument( + "--limit", + type=int, + default=None, + help="Limit number of tasks to evaluate (for testing)", + ) + parser.add_argument( + "--batch_size", + type=int, + default=1, + help="Batch size for evaluation (currently not implemented, reserved for future)", + ) + parser.add_argument( + "--device", + type=str, + default="cuda:0", + help="Device to run model on (e.g., 'cuda:0', 'cpu')", + ) + parser.add_argument( + "--trust_remote_code", + action="store_true", + help="Trust remote code when loading model", + ) + parser.add_argument( + "--num_workers", + type=int, + default=1, + help="Number of parallel workers for task execution", + ) + + return parser.parse_args() + + +class HuggingFaceMMLUBenchmark(MMLUBenchmark): + """MMLU Benchmark using HuggingFace transformers models. + + This concrete implementation loads a HuggingFace model via the + transformers pipeline and uses it for MCQ evaluation. + """ + + def __init__( + self, + model_id: str, + device: str = "cuda:0", + trust_remote_code: bool = False, + use_full_prompt: bool = False, + **kwargs, + ): + """Initialize HuggingFace MMLU benchmark. + + Args: + model_id: HuggingFace model identifier. + device: Device to run model on. + trust_remote_code: Trust remote code when loading model. + use_full_prompt: Use full prompt with few-shot examples. + **kwargs: Additional arguments passed to MMLUBenchmark. + """ + super().__init__(use_full_prompt=use_full_prompt, **kwargs) + self._model_id = model_id + self._device = device + self._trust_remote_code = trust_remote_code + self._pipeline = None + + def _get_pipeline(self): + """Lazy load the transformers pipeline.""" + if self._pipeline is None: + from transformers import pipeline + + self._pipeline = pipeline( + "text-generation", + model=self._model_id, + device=self._device if "cuda" in self._device else -1, + trust_remote_code=self._trust_remote_code, + ) + return self._pipeline + + def get_model_adapter(self, model_id: str, **kwargs): + """Provide a HuggingFace ModelAdapter. + + Args: + model_id: Model identifier (ignored, uses instance model_id). + **kwargs: Additional arguments (e.g., register_name). + + Returns: + HuggingFaceModelAdapter instance. + """ + from maseval.interface.inference import HuggingFaceModelAdapter + + pipe = self._get_pipeline() + adapter = HuggingFaceModelAdapter( + model=pipe, + model_id=self._model_id, + default_generation_params={ + "max_new_tokens": 32, + "do_sample": False, + "return_full_text": False, + }, + ) + + # Register for tracing if requested + register_name = kwargs.get("register_name") + if register_name: + self.register("models", register_name, adapter) + + return adapter + + +def save_predictions_for_disco( + results: list, + output_path: str, + anchor_points: Optional[list] = None, + n_choices: int = 4, +): + """Save predictions in format compatible with DISCO predictor. + + Creates a predictions tensor of shape (1, n_questions, n_choices) + where the values are logits (1.0 for predicted choice, 0.0 for others). + + Args: + results: Benchmark results list. + output_path: Path to save predictions pickle. + anchor_points: Optional anchor points for ordering. + n_choices: Number of answer choices (default 4 for A/B/C/D). + """ + # Build predictions array + # For now, we create simple binary predictions (1.0 for predicted, 0.0 for others) + # A more sophisticated implementation would capture actual logits + + predictions_list = [] + + if anchor_points is not None: + # Order results by anchor points + result_by_doc_id = {} + for res in results: + if res.get("status") == "success" and res.get("eval"): + for entry in res["eval"]: + doc_id = entry.get("doc_id") + if doc_id is not None: + result_by_doc_id[doc_id] = entry + + for doc_id in anchor_points: + entry = result_by_doc_id.get(doc_id, {}) + predicted = entry.get("predicted", -1) + pred_vec = [0.0] * n_choices + if 0 <= predicted < n_choices: + pred_vec[predicted] = 1.0 + predictions_list.append(pred_vec) + else: + # Use results in order + for res in results: + if res.get("status") == "success" and res.get("eval"): + for entry in res["eval"]: + predicted = entry.get("predicted", -1) + pred_vec = [0.0] * n_choices + if 0 <= predicted < n_choices: + pred_vec[predicted] = 1.0 + predictions_list.append(pred_vec) + + predictions = np.array(predictions_list) + predictions = predictions.reshape(1, -1, n_choices) # (1, n_questions, n_choices) + + with open(output_path, "wb") as f: + pickle.dump(predictions, f) + + print(f"Saved predictions tensor to {output_path}") + print(f" Shape: {predictions.shape}") + + +def main(): + """Main entry point.""" + args = parse_args() + + print("=" * 80) + print("MMLU Benchmark - MASEval") + print("=" * 80) + print(f"Model: {args.model_id}") + print(f"Data path: {args.data_path}") + print(f"Anchor points: {args.anchor_points_path or 'None (evaluate all)'}") + print(f"Use full prompt: {args.use_full_prompt}") + print(f"Device: {args.device}") + print(f"Output dir: {args.output_dir}") + print("=" * 80) + + # Load tasks + print("\nLoading tasks...") + tasks = load_tasks( + data_path=args.data_path, + anchor_points_path=args.anchor_points_path, + limit=args.limit, + ) + print(f"Loaded {len(tasks)} tasks") + + if args.anchor_points_path: + anchor_points = tasks._anchor_points + print(f"Filtering to {len(anchor_points)} anchor points") + else: + anchor_points = None + + # Create output directory + output_dir = Path(args.output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + + # Create result logger + logger = FileResultLogger( + output_dir=str(output_dir), + filename_pattern="mmlu_{timestamp}.jsonl", + validate_on_completion=False, + ) + + # Create benchmark + benchmark = HuggingFaceMMLUBenchmark( + model_id=args.model_id, + device=args.device, + trust_remote_code=args.trust_remote_code, + use_full_prompt=args.use_full_prompt, + callbacks=[logger], + num_workers=args.num_workers, + ) + + # Run evaluation + print("\nRunning evaluation...") + agent_data = { + "model_id": args.model_id, + "use_full_prompt": args.use_full_prompt, + } + results = benchmark.run(tasks=tasks, agent_data=agent_data) + + # Compute metrics + metrics = compute_benchmark_metrics(results) + + print("\n" + "=" * 80) + print("Results Summary") + print("=" * 80) + print(f"Total tasks: {metrics['total_tasks']}") + print(f"Correct: {metrics['correct_count']}") + print(f"Accuracy: {metrics['acc']:.4f}") + print(f"Accuracy (norm): {metrics['acc_norm']:.4f}") + + # Save predictions for DISCO if requested + if args.predictions_path: + save_predictions_for_disco( + results=results, + output_path=args.predictions_path, + anchor_points=anchor_points, + ) + + # Save summary + summary_path = output_dir / "summary.json" + with open(summary_path, "w") as f: + json.dump( + { + "model_id": args.model_id, + "data_path": str(args.data_path), + "anchor_points_path": str(args.anchor_points_path) if args.anchor_points_path else None, + "use_full_prompt": args.use_full_prompt, + "metrics": metrics, + }, + f, + indent=2, + ) + print(f"\nSummary saved to: {summary_path}") + print(f"Full results saved to: {logger.output_dir}") + + +if __name__ == "__main__": + main() diff --git a/maseval/benchmark/mmlu/__init__.py b/maseval/benchmark/mmlu/__init__.py new file mode 100644 index 00000000..d5fcb7bf --- /dev/null +++ b/maseval/benchmark/mmlu/__init__.py @@ -0,0 +1,45 @@ +"""MMLU Benchmark for MASEval. + +Implements MMLU evaluation with anchor point-based task selection for DISCO prediction. + +Usage: + from maseval.benchmark.mmlu import ( + MMLUBenchmark, + MMLUEnvironment, + MMLUEvaluator, + load_tasks, + AnchorPointsTaskQueue, + ) + + # Load tasks and anchor points + tasks = load_tasks( + data_path="path/to/mmlu_prompts_examples.json", + anchor_points_path="path/to/anchor_points.pkl", # Optional + ) + + # Create benchmark + benchmark = MMLUBenchmark() + results = benchmark.run(tasks=tasks, agent_data={"model_id": "gpt-4"}) +""" + +from .mmlu import ( + MMLUBenchmark, + MMLUEnvironment, + MMLUEvaluator, + MMLUModelAgent, + MMLUAgentAdapter, + AnchorPointsTaskQueue, + load_tasks, + compute_benchmark_metrics, +) + +__all__ = [ + "MMLUBenchmark", + "MMLUEnvironment", + "MMLUEvaluator", + "MMLUModelAgent", + "MMLUAgentAdapter", + "AnchorPointsTaskQueue", + "load_tasks", + "compute_benchmark_metrics", +] diff --git a/maseval/benchmark/mmlu/mmlu.py b/maseval/benchmark/mmlu/mmlu.py new file mode 100644 index 00000000..f6422a60 --- /dev/null +++ b/maseval/benchmark/mmlu/mmlu.py @@ -0,0 +1,655 @@ +"""MMLU Benchmark - Multiple Choice Question Answering Evaluation. + +Implements MMLU evaluation compatible with lm-evaluation-harness output format, +with anchor point-based task selection for DISCO prediction. + +Reference: Based on disco-public evaluation methodology. +Dataset: MMLU (Massive Multitask Language Understanding) + +Usage: + from maseval.benchmark.mmlu import ( + MMLUBenchmark, load_tasks, AnchorPointsTaskQueue + ) + + # Load tasks filtered to anchor points + tasks = load_tasks( + data_path="/path/to/mmlu_prompts_examples.json", + anchor_points_path="/path/to/anchor_points.pkl", + ) + + # Create benchmark with HuggingFace model + class MyMMLUBenchmark(MMLUBenchmark): + def get_model_adapter(self, model_id, **kwargs): + from transformers import pipeline + from maseval.interface.inference import HuggingFaceModelAdapter + pipe = pipeline("text-generation", model=model_id) + return HuggingFaceModelAdapter(model=pipe, model_id=model_id) + + benchmark = MyMMLUBenchmark() + results = benchmark.run(tasks=tasks, agent_data={"model_id": "meta-llama/Llama-2-7b"}) +""" + +import json +import pickle +from abc import abstractmethod +from pathlib import Path +from typing import Any, Dict, List, Optional, Sequence, Tuple, Union + +# numpy is optional - only needed for anchor points processing +try: + import numpy as np + + HAS_NUMPY = True +except ImportError: + np = None # type: ignore[assignment] + HAS_NUMPY = False + +from maseval import ( + AgentAdapter, + Benchmark, + Environment, + Evaluator, + ModelAdapter, + Task, +) +from maseval.core.task import AdaptiveTaskQueue, SequentialTaskQueue +from maseval.core.tracing import TraceableMixin +from maseval.core.config import ConfigurableMixin + + +# ============================================================================= +# Task Queue +# ============================================================================= + + +class AnchorPointsTaskQueue(AdaptiveTaskQueue): + """Task queue that iterates through tasks in anchor points order. + + This queue is used for DISCO-based evaluation where we only evaluate + on a subset of anchor tasks and predict performance on the full dataset. + + The queue iterates through tasks in the order specified by anchor_points, + and stops when all anchor tasks have been processed. + """ + + def __init__(self, tasks: List[Task], anchor_points: Optional[List[int]] = None): + """Initialize anchor points task queue. + + Args: + tasks: Full list of tasks (ordered by doc_id). + anchor_points: Optional list of task indices (doc_ids) to evaluate. + If None, evaluates all tasks in order. + """ + super().__init__(tasks) + self._anchor_points = anchor_points + self._anchor_idx = 0 + + # Build index mapping for quick lookup + self._task_by_doc_id: Dict[int, Task] = {} + for i, task in enumerate(tasks): + doc_id = task.metadata.get("doc_id", i) + self._task_by_doc_id[doc_id] = task + + def initial_state(self) -> Dict[str, Any]: + """Initialize state for anchor point iteration.""" + return { + "anchor_idx": 0, + "completed_anchors": [], + } + + def select_next_task(self, remaining: Sequence[Task], state: Dict[str, Any]) -> Optional[Task]: + """Select the next anchor task to execute. + + Args: + remaining: Tasks not yet executed. + state: Current state with anchor_idx. + + Returns: + Next anchor task, or None if all anchors processed. + """ + if self._anchor_points is None: + # No anchor filtering - return next remaining task + return remaining[0] if remaining else None + + anchor_idx = state["anchor_idx"] + + # Check if we've processed all anchor points + if anchor_idx >= len(self._anchor_points): + return None + + # Get the doc_id for the next anchor point + doc_id = self._anchor_points[anchor_idx] + + # Find the task with this doc_id + task = self._task_by_doc_id.get(doc_id) + + if task is None: + # Skip missing anchor points + state["anchor_idx"] = anchor_idx + 1 + return self.select_next_task(remaining, state) + + # Check if task is still in remaining + if task not in remaining: + # Task already processed, move to next anchor + state["anchor_idx"] = anchor_idx + 1 + return self.select_next_task(remaining, state) + + return task + + def update_state(self, task: Task, report: Dict[str, Any], state: Dict[str, Any]) -> Dict[str, Any]: + """Update state after task completion. + + Args: + task: Completed task. + report: Execution report. + state: Current state. + + Returns: + Updated state. + """ + doc_id = task.metadata.get("doc_id") + state["completed_anchors"].append(doc_id) + state["anchor_idx"] += 1 + + # Check if we've completed all anchors + if self._anchor_points is not None: + if state["anchor_idx"] >= len(self._anchor_points): + self.stop() + + return state + + +# ============================================================================= +# Environment +# ============================================================================= + + +class MMLUEnvironment(Environment): + """Simple environment for MMLU multiple choice evaluation. + + MMLU tasks don't require tools - the environment just holds + the task context (question, choices, etc.). + """ + + def setup_state(self, task_data: Dict[str, Any]) -> Dict[str, Any]: + """Initialize state from task data.""" + return { + "query": task_data.get("query", ""), + "choices": task_data.get("environment_data", {}).get("choices", []), + "full_prompt": task_data.get("environment_data", {}).get("full_prompt", ""), + "use_full_prompt": task_data.get("environment_data", {}).get("use_full_prompt", False), + } + + def create_tools(self) -> Dict[str, Any]: + """MMLU doesn't use tools.""" + return {} + + def get_prompt(self) -> str: + """Get the prompt to send to the model. + + Returns full_prompt if use_full_prompt is True, otherwise query. + """ + if self.state.get("use_full_prompt", False): + return self.state.get("full_prompt", self.state.get("query", "")) + return self.state.get("query", "") + + +# ============================================================================= +# Evaluator +# ============================================================================= + + +class MMLUEvaluator(Evaluator): + """Evaluator for MMLU multiple choice questions. + + Computes accuracy metrics (acc and acc_norm) by comparing model predictions + with gold answers. + """ + + def __init__( + self, + task: Task, + environment: Environment, + user: Optional[Any] = None, + ): + """Initialize MMLU evaluator. + + Args: + task: Task being evaluated (contains gold answer). + environment: Environment (provides choices). + user: Unused for MMLU. + """ + self.task = task + self.environment = environment + self.gold = task.evaluation_data.get("gold", 0) + self.choices = task.environment_data.get("choices", ["A", "B", "C", "D"]) + + def filter_traces(self, traces: Dict[str, Any]) -> Dict[str, Any]: + """Extract relevant traces for evaluation. + + For MMLU, we need the model's response from agent traces. + """ + # Get agent traces + agents = traces.get("agents", {}) + if agents: + # Get first agent's messages + first_agent = next(iter(agents.values()), {}) + messages = first_agent.get("messages", []) + return {"messages": messages} + return {"messages": []} + + def __call__(self, traces: Dict[str, Any], final_answer: Optional[str] = None) -> Dict[str, Any]: + """Evaluate the model's response. + + Args: + traces: Filtered traces with messages. + final_answer: The model's final answer. + + Returns: + Dict with acc, acc_norm, predicted, gold, and correct fields. + """ + # Parse the model's answer + predicted = self._parse_answer(final_answer or "") + + # Check if correct + correct = predicted == self.gold + + return { + "acc": 1.0 if correct else 0.0, + "acc_norm": 1.0 if correct else 0.0, + "predicted": predicted, + "gold": self.gold, + "correct": correct, + "doc_id": self.task.metadata.get("doc_id"), + } + + def _parse_answer(self, response: str) -> int: + """Parse model response to extract answer choice. + + Handles various formats: + - Direct letter: "A", "B", "C", "D" + - With period: "A." + - As part of sentence: "The answer is A" + + Args: + response: Model's response string. + + Returns: + Index of the predicted choice (0-3), or -1 if unparseable. + """ + if not response: + return -1 + + response = response.strip().upper() + + # Direct letter match + for i, choice in enumerate(["A", "B", "C", "D"]): + if response == choice or response.startswith(f"{choice}."): + return i + + # Look for "answer is X" pattern + for i, choice in enumerate(["A", "B", "C", "D"]): + if f"ANSWER IS {choice}" in response: + return i + if f"ANSWER: {choice}" in response: + return i + + # Last character check + last_char = response.rstrip(".")[-1] if response else "" + for i, choice in enumerate(["A", "B", "C", "D"]): + if last_char == choice: + return i + + return -1 + + +# ============================================================================= +# Model Adapter Wrapper for MCQ +# ============================================================================= + + +class MMLUModelAgent(TraceableMixin, ConfigurableMixin): + """Simple agent wrapper that passes prompts to a model for MCQ evaluation. + + This is a minimal agent that just forwards prompts to the model + and returns the response. It supports tracing for MASEval integration. + """ + + def __init__(self, model: ModelAdapter, name: str = "mmlu_agent"): + """Initialize MMLU model agent. + + Args: + model: ModelAdapter to use for generation. + name: Agent name for tracing. + """ + super().__init__() + self.model = model + self.name = name + self._messages: List[Dict[str, Any]] = [] + + def run(self, prompt: str) -> str: + """Run the model on a prompt. + + Args: + prompt: The prompt to send to the model. + + Returns: + Model's response string. + """ + # Record input message + self._messages.append({"role": "user", "content": prompt}) + + # Generate response + response = self.model.generate(prompt) + + # Record output message + self._messages.append({"role": "assistant", "content": response}) + + return response + + def gather_traces(self) -> Dict[str, Any]: + """Gather traces for this agent.""" + return { + **super().gather_traces(), + "name": self.name, + "messages": list(self._messages), + } + + def gather_config(self) -> Dict[str, Any]: + """Gather configuration.""" + return { + **super().gather_config(), + "name": self.name, + "model_id": self.model.model_id, + } + + +class MMLUAgentAdapter(AgentAdapter): + """AgentAdapter wrapper for MMLUModelAgent.""" + + def __init__(self, agent: MMLUModelAgent, name: str): + """Initialize adapter. + + Args: + agent: MMLUModelAgent instance. + name: Adapter name. + """ + super().__init__(agent, name) + + def _run_agent(self, query: str) -> Any: + """Execute the agent.""" + return self.agent.run(query) + + def get_messages(self) -> List[Dict[str, Any]]: + """Get agent messages.""" + return self.agent._messages + + +# ============================================================================= +# Benchmark +# ============================================================================= + + +class MMLUBenchmark(Benchmark): + """MMLU Benchmark - Framework-agnostic base class. + + Evaluates language models on MMLU multiple choice questions. + Supports anchor point-based evaluation for DISCO prediction. + + Users must subclass and implement: + - get_model_adapter() to provide model adapters + + Usage: + class MyMMLUBenchmark(MMLUBenchmark): + def get_model_adapter(self, model_id, **kwargs): + from transformers import pipeline + from maseval.interface.inference import HuggingFaceModelAdapter + pipe = pipeline("text-generation", model=model_id) + return HuggingFaceModelAdapter(model=pipe, model_id=model_id) + + benchmark = MyMMLUBenchmark() + results = benchmark.run(tasks=tasks, agent_data={"model_id": "llama-7b"}) + """ + + def __init__( + self, + use_full_prompt: bool = False, + callbacks: Optional[List[Any]] = None, + n_task_repeats: int = 1, + **kwargs: Any, + ): + """Initialize benchmark. + + Args: + use_full_prompt: If True, use full_prompt (with few-shot examples) + instead of just the query. + callbacks: Benchmark callbacks. + n_task_repeats: Repetitions per task. + """ + super().__init__(callbacks=callbacks, n_task_repeats=n_task_repeats, max_invocations=1, **kwargs) + self.use_full_prompt = use_full_prompt + + def setup_environment( + self, + agent_data: Dict[str, Any], + task: Task, + ) -> MMLUEnvironment: + """Create environment for a task.""" + task_data = { + "query": task.query, + "environment_data": { + **task.environment_data, + "use_full_prompt": self.use_full_prompt or agent_data.get("use_full_prompt", False), + }, + } + return MMLUEnvironment(task_data) + + def setup_user( + self, + agent_data: Dict[str, Any], + environment: MMLUEnvironment, + task: Task, + ) -> None: + """MMLU doesn't use a user simulator.""" + return None + + def setup_agents( + self, + agent_data: Dict[str, Any], + environment: MMLUEnvironment, + task: Task, + user: Optional[Any], + ) -> Tuple[Sequence[AgentAdapter], Dict[str, AgentAdapter]]: + """Create model agent for MCQ evaluation. + + Args: + agent_data: Agent config with model_id. + environment: MMLU environment. + task: Current task. + user: Unused. + + Returns: + Tuple of (agents_to_run, agents_dict). + """ + model_id = agent_data.get("model_id", "unknown") + model = self.get_model_adapter(model_id, register_name="mmlu_model") + + agent = MMLUModelAgent(model, name="mmlu_agent") + adapter = MMLUAgentAdapter(agent, "mmlu_agent") + + return [adapter], {"mmlu_agent": adapter} + + def setup_evaluators( + self, + environment: MMLUEnvironment, + task: Task, + agents: Sequence[AgentAdapter], + user: Optional[Any], + ) -> Sequence[Evaluator]: + """Create MMLU evaluator.""" + return [MMLUEvaluator(task, environment)] + + def run_agents( + self, + agents: Sequence[AgentAdapter], + task: Task, + environment: MMLUEnvironment, + query: str = "", + ) -> Any: + """Execute agent on the MMLU prompt.""" + # Get the prompt from environment + prompt = environment.get_prompt() + + # Run the agent + agent = agents[0] + return agent.run(prompt) + + @abstractmethod + def get_model_adapter(self, model_id: str, **kwargs) -> ModelAdapter: + """Provide a ModelAdapter for the model. + + Must be implemented by subclass. + + Args: + model_id: Model identifier. + **kwargs: Additional arguments (e.g., register_name for tracing). + + Returns: + ModelAdapter instance. + """ + pass + + def evaluate( + self, + evaluators: Sequence[Evaluator], + agents: Dict[str, AgentAdapter], + final_answer: Any, + traces: Dict[str, Any], + ) -> List[Dict[str, Any]]: + """Evaluate model response.""" + results = [] + for evaluator in evaluators: + filtered_traces = evaluator.filter_traces(traces) + result = evaluator(filtered_traces, final_answer) + results.append(result) + return results + + +# ============================================================================= +# Data Loading +# ============================================================================= + + +def load_pickle(path: Union[str, Path]) -> Any: + """Load a pickle file.""" + with open(path, "rb") as f: + return pickle.load(f) + + +def load_tasks( + data_path: Union[str, Path], + anchor_points_path: Optional[Union[str, Path]] = None, + limit: Optional[int] = None, +) -> Union[AnchorPointsTaskQueue, SequentialTaskQueue]: + """Load MMLU tasks from JSON file. + + Args: + data_path: Path to MMLU prompts JSON file (mmlu_prompts_examples.json format). + anchor_points_path: Optional path to anchor points pickle file. + If provided, returns an AnchorPointsTaskQueue that evaluates + only the anchor tasks in order. + limit: Optional limit on number of tasks to load. + + Returns: + TaskQueue containing MMLU tasks. + + Raises: + ImportError: If anchor_points_path is provided but numpy is not installed. + """ + data_path = Path(data_path) + + # Load JSON data + with open(data_path, "r") as f: + data = json.load(f) + + # Apply limit before filtering + if limit is not None: + data = data[:limit] + + # Convert to Tasks + tasks = [] + for i, item in enumerate(data): + task = Task( + query=item.get("query", item.get("example", "")), + id=f"mmlu_{i}", + environment_data={ + "choices": item.get("choices", ["A", "B", "C", "D"]), + "full_prompt": item.get("full_prompt", ""), + "example": item.get("example", ""), + }, + evaluation_data={ + "gold": item.get("gold", 0), + }, + metadata={ + "doc_id": i, + "task_type": "mmlu", + }, + ) + tasks.append(task) + + # Load anchor points if provided + anchor_points = None + if anchor_points_path is not None: + anchor_points = load_pickle(anchor_points_path) + # Convert numpy array to list if necessary + if HAS_NUMPY and isinstance(anchor_points, np.ndarray): + anchor_points = anchor_points.tolist() + elif not HAS_NUMPY and hasattr(anchor_points, "tolist"): + # Fallback: try tolist() method if it exists + anchor_points = anchor_points.tolist() + + # Create appropriate queue + if anchor_points is not None: + return AnchorPointsTaskQueue(tasks, anchor_points) + else: + return SequentialTaskQueue(tasks) + + +def compute_benchmark_metrics(results: List[Dict[str, Any]]) -> Dict[str, Any]: + """Compute summary metrics across all benchmark results. + + Args: + results: List of result dicts from benchmark.run(). + + Returns: + Dict with accuracy metrics and task counts. + """ + if not results: + return { + "total_tasks": 0, + "acc": 0.0, + "acc_norm": 0.0, + } + + total_tasks = len(results) + correct_count = 0 + acc_sum = 0.0 + acc_norm_sum = 0.0 + + for res in results: + if res.get("status") != "success": + continue + + evals = res.get("eval") or [] + for entry in evals: + acc_sum += entry.get("acc", 0.0) + acc_norm_sum += entry.get("acc_norm", 0.0) + if entry.get("correct", False): + correct_count += 1 + + return { + "total_tasks": total_tasks, + "correct_count": correct_count, + "acc": acc_sum / total_tasks if total_tasks > 0 else 0.0, + "acc_norm": acc_norm_sum / total_tasks if total_tasks > 0 else 0.0, + } From 49c83b36fbc1d64f6eff803dd7c01cb3a71d3c50 Mon Sep 17 00:00:00 2001 From: Alexander Rubinstein Date: Wed, 28 Jan 2026 08:16:06 +0100 Subject: [PATCH 02/47] [Add MMLU task]: - Add scaffold for DISCO prediction for MMLU --- examples/mmlu_benchmark/README.md | 24 ++ examples/mmlu_benchmark/mmlu_benchmark.py | 292 ++++++++++++++++++++-- 2 files changed, 293 insertions(+), 23 deletions(-) diff --git a/examples/mmlu_benchmark/README.md b/examples/mmlu_benchmark/README.md index 03f60688..3349f1dd 100644 --- a/examples/mmlu_benchmark/README.md +++ b/examples/mmlu_benchmark/README.md @@ -64,6 +64,26 @@ python mmlu_benchmark.py \ --predictions_path ./output/predictions.pkl ``` +### DISCO Prediction (Predict Full Benchmark from Anchor Points) + +Evaluate on anchor points and predict full benchmark performance using DISCO: + +```bash +python mmlu_benchmark.py \ + --model_id "alignment-handbook/zephyr-7b-sft-full" \ + --data_path /path/to/mmlu_prompts_examples.json \ + --anchor_points_path /path/to/anchor_points_disagreement.pkl \ + --use_full_prompt \ + --disco_prediction \ + --disco_model_path /path/to/fitted_weights.pkl \ + --disco_transform_path /path/to/transform.pkl \ + --pca 256 +``` + +This is equivalent to running: +1. The evaluation on anchor points (like `scripts/run_lm_eval.py --skip_non_anchor_points`) +2. The prediction step (like `scripts/predict_model_performance.py`) + ### Quick Test Run on a small subset for testing: @@ -89,6 +109,10 @@ python mmlu_benchmark.py \ | `--device` | Device to run model on | `cuda:0` | | `--trust_remote_code` | Trust remote code when loading model | False | | `--num_workers` | Number of parallel workers | 1 | +| `--disco_prediction` | Enable DISCO prediction from anchor points | False | +| `--disco_model_path` | Path to DISCO fitted weights pickle | None | +| `--disco_transform_path` | Path to DISCO PCA transform pickle | None | +| `--pca` | PCA dimension for DISCO embeddings | 256 | ## Equivalent disco-public Command diff --git a/examples/mmlu_benchmark/mmlu_benchmark.py b/examples/mmlu_benchmark/mmlu_benchmark.py index 4bb0d267..e477fed9 100644 --- a/examples/mmlu_benchmark/mmlu_benchmark.py +++ b/examples/mmlu_benchmark/mmlu_benchmark.py @@ -24,6 +24,17 @@ --anchor_points_path /path/to/anchor_points_disagreement.pkl \\ --use_full_prompt + # Run with DISCO prediction (predict full benchmark performance from anchor points) + python mmlu_benchmark.py \\ + --model_id "alignment-handbook/zephyr-7b-sft-full" \\ + --data_path /path/to/mmlu_prompts_examples.json \\ + --anchor_points_path /path/to/anchor_points_disagreement.pkl \\ + --use_full_prompt \\ + --disco_prediction \\ + --disco_model_path /path/to/fitted_weights.pkl \\ + --disco_transform_path /path/to/transform.pkl \\ + --pca 256 + # Run on a subset of tasks for testing python mmlu_benchmark.py \\ --model_id "meta-llama/Llama-2-7b-hf" \\ @@ -125,6 +136,31 @@ def parse_args(): help="Number of parallel workers for task execution", ) + # DISCO prediction arguments + parser.add_argument( + "--disco_prediction", + action="store_true", + help="Enable DISCO prediction of full benchmark performance from anchor points", + ) + parser.add_argument( + "--disco_model_path", + type=str, + default=None, + help="Path to DISCO fitted weights pickle file (required if --disco_prediction)", + ) + parser.add_argument( + "--disco_transform_path", + type=str, + default=None, + help="Path to DISCO PCA transform pickle file (required if --disco_prediction with --pca)", + ) + parser.add_argument( + "--pca", + type=int, + default=256, + help="PCA dimension for DISCO embeddings (default: 256)", + ) + return parser.parse_args() @@ -256,17 +292,182 @@ def save_predictions_for_disco( predictions = np.array(predictions_list) predictions = predictions.reshape(1, -1, n_choices) # (1, n_questions, n_choices) - with open(output_path, "wb") as f: - pickle.dump(predictions, f) + if output_path and output_path != "/dev/null": + with open(output_path, "wb") as f: + pickle.dump(predictions, f) + print(f"Saved predictions tensor to {output_path}") + print(f" Shape: {predictions.shape}") + else: + print(f"Built predictions tensor with shape: {predictions.shape}") + + return predictions + + +def compute_disco_embedding( + predictions: np.ndarray, + pca: int, + transform=None, + apply_softmax: bool = True, +) -> tuple: + """Compute DISCO embeddings from predictions. + + This implements the embedding computation from disco-public/experiments.py. + + Args: + predictions: Predictions tensor of shape (n_models, n_anchor_points, n_classes). + pca: PCA dimension for dimensionality reduction. + transform: Pre-fitted PCA transform. If None, a new one will be fitted. + apply_softmax: Whether to apply softmax to predictions. + + Returns: + Tuple of (embeddings, transform) where embeddings has shape (n_models, pca). + """ + try: + import torch + from sklearn.decomposition import PCA + except ImportError as e: + raise ImportError("DISCO prediction requires torch and sklearn. Install with: pip install torch scikit-learn") from e + + # Convert to torch tensor + preds_tensor = torch.Tensor(predictions) + + # Apply softmax if requested + if apply_softmax: + emb_unreduced = preds_tensor.softmax(dim=-1) + else: + emb_unreduced = preds_tensor + + # Flatten to (n_models, n_anchor_points * n_classes) + emb_unreduced = emb_unreduced.reshape(emb_unreduced.shape[0], -1) + + # Apply PCA + if pca is not None: + if transform is None: + # Fit new PCA transform + transform = PCA( + n_components=pca, + svd_solver="full", + random_state=42, + ).fit(emb_unreduced.numpy()) + + emb = transform.transform(emb_unreduced.numpy()) + emb = torch.Tensor(emb) + else: + emb = emb_unreduced + + return emb, transform - print(f"Saved predictions tensor to {output_path}") - print(f" Shape: {predictions.shape}") + +def predict_with_disco( + predictions: np.ndarray, + model_path: str, + transform_path: Optional[str] = None, + pca: int = 256, +) -> dict: + """Predict full benchmark performance using DISCO. + + This implements the prediction logic from disco-public/scripts/predict_model_performance.py. + + Args: + predictions: Predictions tensor of shape (n_models, n_anchor_points, n_classes). + model_path: Path to fitted weights pickle file. + transform_path: Path to PCA transform pickle file (optional, can be in model_path). + pca: PCA dimension (default: 256). + + Returns: + Dict with predicted accuracies and metadata. + """ + # Load model data + with open(model_path, "rb") as f: + model_data = pickle.load(f) + + if not isinstance(model_data, dict): + raise ValueError(f"model_path must contain a dict. Got {type(model_data)}") + + # Get transform + if transform_path is not None: + with open(transform_path, "rb") as f: + transform = pickle.load(f) + elif "transform" in model_data: + transform = model_data["transform"] + else: + raise ValueError("Transform not found. Provide --disco_transform_path or ensure the model file contains a 'transform' key.") + + # Get fitted weights + if "fitted_weights" in model_data: + fitted_weights = model_data["fitted_weights"] + else: + # Assume the dict itself is fitted_weights (excluding transform) + fitted_weights = {k: v for k, v in model_data.items() if k != "transform"} + if not fitted_weights: + raise ValueError("Could not find fitted_weights in model file.") + + # Get metadata or infer from structure + if "sampling_name" in model_data: + sampling_name = model_data["sampling_name"] + else: + sampling_name = list(fitted_weights.keys())[0] + + if "number_item" in model_data: + number_item = model_data["number_item"] + else: + number_item = list(fitted_weights[sampling_name].keys())[0] + + if "fitted_model_type" in model_data: + fitted_model_type = model_data["fitted_model_type"] + else: + fitted_model_type = list(fitted_weights[sampling_name][number_item].keys())[0] + + print(f" Using: sampling={sampling_name}, n_items={number_item}, model={fitted_model_type}") + + # Compute embeddings + embeddings, _ = compute_disco_embedding( + predictions, + pca=pca, + transform=transform, + apply_softmax=True, + ) + + # Get the fitted model + fitted_model = fitted_weights[sampling_name][number_item][fitted_model_type] + + # Predict accuracies for each model + predicted_accs = {} + for model_idx in range(embeddings.shape[0]): + model_embedding = embeddings[model_idx] + + # Convert to numpy if needed + if hasattr(model_embedding, "numpy"): + model_embedding_np = model_embedding.numpy() + else: + model_embedding_np = np.array(model_embedding) + + # Predict using fitted model + predicted_acc = fitted_model.predict(model_embedding_np.reshape(1, -1))[0] + predicted_accs[model_idx] = predicted_acc + + return { + "predicted_accuracies": predicted_accs, + "sampling_name": sampling_name, + "number_item": number_item, + "fitted_model_type": fitted_model_type, + "pca": pca, + } def main(): """Main entry point.""" args = parse_args() + # Validate DISCO prediction arguments + if args.disco_prediction: + if args.disco_model_path is None: + raise ValueError("--disco_model_path is required when --disco_prediction is enabled") + if args.anchor_points_path is None: + raise ValueError("--anchor_points_path is required when --disco_prediction is enabled") + if args.pca is not None and args.disco_transform_path is None: + print("Warning: --pca specified without --disco_transform_path. Transform will be loaded from model file if available.") + print("=" * 80) print("MMLU Benchmark - MASEval") print("=" * 80) @@ -276,6 +477,11 @@ def main(): print(f"Use full prompt: {args.use_full_prompt}") print(f"Device: {args.device}") print(f"Output dir: {args.output_dir}") + if args.disco_prediction: + print("DISCO prediction: ENABLED") + print(f" Model path: {args.disco_model_path}") + print(f" Transform path: {args.disco_transform_path or '(from model file)'}") + print(f" PCA dimension: {args.pca}") print("=" * 80) # Load tasks @@ -322,39 +528,79 @@ def main(): } results = benchmark.run(tasks=tasks, agent_data=agent_data) - # Compute metrics + # Compute metrics on evaluated tasks metrics = compute_benchmark_metrics(results) print("\n" + "=" * 80) - print("Results Summary") + print("Results Summary (Evaluated Tasks)") print("=" * 80) print(f"Total tasks: {metrics['total_tasks']}") print(f"Correct: {metrics['correct_count']}") - print(f"Accuracy: {metrics['acc']:.4f}") - print(f"Accuracy (norm): {metrics['acc_norm']:.4f}") + print(f"Accuracy (on anchor points): {metrics['acc']:.4f}") + print(f"Accuracy norm (on anchor points): {metrics['acc_norm']:.4f}") - # Save predictions for DISCO if requested - if args.predictions_path: - save_predictions_for_disco( + # Build predictions tensor for DISCO + predictions = None + if args.predictions_path or args.disco_prediction: + predictions = save_predictions_for_disco( results=results, - output_path=args.predictions_path, + output_path=args.predictions_path if args.predictions_path else "/dev/null", anchor_points=anchor_points, ) + # Run DISCO prediction if enabled + disco_results = None + if args.disco_prediction: + print("\n" + "=" * 80) + print("DISCO Prediction") + print("=" * 80) + print("Computing embeddings and predicting full benchmark accuracy...") + + disco_results = predict_with_disco( + predictions=predictions, + model_path=args.disco_model_path, + transform_path=args.disco_transform_path, + pca=args.pca, + ) + + print("\n" + "-" * 40) + print("DISCO Predicted Full Benchmark Accuracy:") + print("-" * 40) + for model_idx, acc in disco_results["predicted_accuracies"].items(): + print(f" Model {model_idx}: {acc:.6f}") + + # Compare with actual anchor accuracy + print("\n" + "-" * 40) + print("Comparison:") + print("-" * 40) + predicted_acc = disco_results["predicted_accuracies"][0] + actual_anchor_acc = metrics["acc"] + print(f" Actual accuracy (on anchor points): {actual_anchor_acc:.6f}") + print(f" DISCO predicted accuracy (full benchmark): {predicted_acc:.6f}") + print(f" Difference: {predicted_acc - actual_anchor_acc:+.6f}") + # Save summary + summary_data = { + "model_id": args.model_id, + "data_path": str(args.data_path), + "anchor_points_path": str(args.anchor_points_path) if args.anchor_points_path else None, + "use_full_prompt": args.use_full_prompt, + "metrics": metrics, + } + + if disco_results: + summary_data["disco_prediction"] = { + "predicted_accuracy": disco_results["predicted_accuracies"][0], + "sampling_name": disco_results["sampling_name"], + "number_item": disco_results["number_item"], + "fitted_model_type": disco_results["fitted_model_type"], + "pca": disco_results["pca"], + } + summary_path = output_dir / "summary.json" with open(summary_path, "w") as f: - json.dump( - { - "model_id": args.model_id, - "data_path": str(args.data_path), - "anchor_points_path": str(args.anchor_points_path) if args.anchor_points_path else None, - "use_full_prompt": args.use_full_prompt, - "metrics": metrics, - }, - f, - indent=2, - ) + json.dump(summary_data, f, indent=2) + print(f"\nSummary saved to: {summary_path}") print(f"Full results saved to: {logger.output_dir}") From 945c80aca7afee108cbd1c722bdf42a8f40b47bf Mon Sep 17 00:00:00 2001 From: Alexander Rubinstein Date: Fri, 30 Jan 2026 14:52:48 +0100 Subject: [PATCH 03/47] [Add MMLU task]: - Add lm_eval wrapper --- examples/mmlu_benchmark/lm_eval_wrapper.py | 232 +++++++++++++++++ tests/test_benchmark/__init__.py | 0 .../test_mmlu_disco_identity.py | 238 ++++++++++++++++++ 3 files changed, 470 insertions(+) create mode 100644 examples/mmlu_benchmark/lm_eval_wrapper.py create mode 100644 tests/test_benchmark/__init__.py create mode 100644 tests/test_benchmark/test_mmlu_disco_identity.py diff --git a/examples/mmlu_benchmark/lm_eval_wrapper.py b/examples/mmlu_benchmark/lm_eval_wrapper.py new file mode 100644 index 00000000..66a1490b --- /dev/null +++ b/examples/mmlu_benchmark/lm_eval_wrapper.py @@ -0,0 +1,232 @@ +"""LM-Eval Harness wrapper for MMLU evaluation. + +This module provides a thin wrapper around lm-evaluation-harness to produce +predictions in the exact same format as disco-public, enabling identical +output for unit testing. + +Usage: + from lm_eval_wrapper import evaluate_with_lm_eval + + predictions, correctness = evaluate_with_lm_eval( + model_id="alignment-handbook/zephyr-7b-sft-full", + anchor_points_path="/path/to/anchor_points.pkl", + data_path="/path/to/mmlu_prompts_examples.json", + pad_to_size=31, + ) +""" + +import pickle +import sys +from pathlib import Path +from typing import Optional + +import numpy as np + + +def pad_predictions(predictions: list, max_num_answers: int = 31) -> list: + """Pad predictions with -inf to max_num_answers. + + Args: + predictions: List of log probabilities. + max_num_answers: Target size for padding. + + Returns: + Padded list of predictions. + """ + if len(predictions) >= max_num_answers: + return predictions[:max_num_answers] + return predictions + [float("-inf")] * (max_num_answers - len(predictions)) + + +def evaluate_with_lm_eval( + model_id: str, + data_path: str, + anchor_points_path: Optional[str] = None, + output_path: Optional[str] = None, + pad_to_size: int = 31, + device: str = "cuda:0", + batch_size: int = 8, + trust_remote_code: bool = True, + use_full_prompt: bool = True, + disco_public_path: str = "/home/oh/arubinstein17/github/disco-public", +) -> tuple: + """Evaluate model using lm-evaluation-harness. + + This produces predictions in the exact same format as disco-public's + run_lm_eval.py script. + + Args: + model_id: HuggingFace model identifier. + data_path: Path to MMLU prompts JSON file. + anchor_points_path: Path to anchor points pickle file. + output_path: Path to save predictions pickle. + pad_to_size: Padding size for predictions (default 31). + device: Device to run model on. + batch_size: Batch size for evaluation. + trust_remote_code: Trust remote code when loading model. + use_full_prompt: Use full prompt with few-shot examples. + disco_public_path: Path to disco-public repository. + + Returns: + Tuple of (predictions, correctness) numpy arrays. + """ + # Add disco-public to path for imports + disco_scripts = Path(disco_public_path) / "scripts" + disco_root = Path(disco_public_path) + + if str(disco_scripts) not in sys.path: + sys.path.insert(0, str(disco_scripts)) + if str(disco_root) not in sys.path: + sys.path.insert(0, str(disco_root)) + + # Import lm-eval harness + lm_eval_path = Path(disco_public_path) / "external" / "lm-evaluation-harness" + if str(lm_eval_path) not in sys.path: + sys.path.insert(0, str(lm_eval_path)) + + try: + import lm_eval + from lm_eval_postprocess import ( + convert_jsonl_results_to_arrays, + load_jsonl, + find_jsonl_file_in_directory, + ) + from utils import load_pickle + from utils_for_notebooks import pad_predictions as disco_pad_predictions + except ImportError as e: + raise ImportError(f"Could not import lm-eval dependencies. Ensure disco-public is available at {disco_public_path}. Error: {e}") from e + + # Load anchor points if provided + anchor_points = None + if anchor_points_path: + anchor_points = load_pickle(anchor_points_path) + if isinstance(anchor_points, np.ndarray): + anchor_points = anchor_points.tolist() + + # Build model args + model_args = f"pretrained={model_id}" + if trust_remote_code: + model_args += ",trust_remote_code=True" + + # Build gen_kwargs + gen_kwargs = "max_gen_toks=128,output_scores=True,return_dict_in_generate=True" + + # Prepare lm_eval arguments + eval_args = [ + "--model", + "hf", + "--model_args", + model_args, + "--tasks", + "mmlu_prompts", + "--batch_size", + str(batch_size), + "--device", + device, + "--gen_kwargs", + gen_kwargs, + "--num_fewshot", + "0", + "--log_samples", + ] + + if use_full_prompt: + eval_args.append("--use_full_prompt") + + if anchor_points_path: + eval_args.extend(["--anchor_points_path", anchor_points_path]) + eval_args.append("--skip_non_anchor_points") + + # Create temporary output directory + import tempfile + + with tempfile.TemporaryDirectory() as tmpdir: + eval_args.extend(["--output_path", tmpdir]) + + # Run lm_eval + print(f"Running lm-eval with args: {' '.join(eval_args)}") + + # Use lm_eval CLI + from lm_eval.__main__ import cli_evaluate + + old_argv = sys.argv + sys.argv = ["lm_eval"] + eval_args + try: + cli_evaluate() + finally: + sys.argv = old_argv + + # Find and load results + jsonl_path = find_jsonl_file_in_directory(tmpdir) + if jsonl_path is None: + raise RuntimeError(f"Could not find JSONL results in {tmpdir}") + + jsonl_results = load_jsonl(jsonl_path) + + # Convert to arrays + predictions_2d, correctness_1d, n_questions = convert_jsonl_results_to_arrays(jsonl_results, "acc_norm", anchor_points=anchor_points) + + # Pad predictions + if pad_to_size is not None and predictions_2d.shape[1] < pad_to_size: + padded_predictions_list = [] + for row in predictions_2d: + padded_row = disco_pad_predictions(row.tolist(), max_num_answers=pad_to_size) + padded_predictions_list.append(padded_row) + predictions_2d = np.array(padded_predictions_list) + + # Reshape to (1, n_questions, n_choices) + predictions = predictions_2d.reshape(1, -1, predictions_2d.shape[-1]) + + # Save if output path provided + if output_path: + with open(output_path, "wb") as f: + pickle.dump(predictions, f) + print(f"Saved predictions to {output_path}") + print(f" Shape: {predictions.shape}") + + return predictions, correctness_1d + + +def main(): + """Command-line interface for lm-eval wrapper.""" + import argparse + + parser = argparse.ArgumentParser(description="Evaluate model using lm-evaluation-harness wrapper") + parser.add_argument("--model_id", type=str, required=True) + parser.add_argument("--data_path", type=str, required=True) + parser.add_argument("--anchor_points_path", type=str, default=None) + parser.add_argument("--output_path", type=str, default=None) + parser.add_argument("--pad_to_size", type=int, default=31) + parser.add_argument("--device", type=str, default="cuda:0") + parser.add_argument("--batch_size", type=int, default=8) + parser.add_argument("--trust_remote_code", action="store_true") + parser.add_argument("--use_full_prompt", action="store_true") + parser.add_argument( + "--disco_public_path", + type=str, + default="/home/oh/arubinstein17/github/disco-public", + ) + + args = parser.parse_args() + + predictions, correctness = evaluate_with_lm_eval( + model_id=args.model_id, + data_path=args.data_path, + anchor_points_path=args.anchor_points_path, + output_path=args.output_path, + pad_to_size=args.pad_to_size, + device=args.device, + batch_size=args.batch_size, + trust_remote_code=args.trust_remote_code, + use_full_prompt=args.use_full_prompt, + disco_public_path=args.disco_public_path, + ) + + print("\nResults:") + print(f" Predictions shape: {predictions.shape}") + print(f" Correctness shape: {correctness.shape}") + print(f" Accuracy: {correctness.mean():.4f}") + + +if __name__ == "__main__": + main() diff --git a/tests/test_benchmark/__init__.py b/tests/test_benchmark/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/test_benchmark/test_mmlu_disco_identity.py b/tests/test_benchmark/test_mmlu_disco_identity.py new file mode 100644 index 00000000..7d57b0f5 --- /dev/null +++ b/tests/test_benchmark/test_mmlu_disco_identity.py @@ -0,0 +1,238 @@ +"""Tests for MMLU benchmark DISCO prediction output identity. + +These tests verify that the MASEval MMLU benchmark produces output +that is compatible with (and optionally identical to) disco-public. + +To run the full identity test (requires lm-eval-harness and GPU): + pytest tests/test_benchmark/test_mmlu_disco_identity.py -v -m "slow" + +To run format-only tests: + pytest tests/test_benchmark/test_mmlu_disco_identity.py -v -m "not slow" +""" + +import pickle +from pathlib import Path + +import numpy as np +import pytest + +# Reference files from disco-public +DISCO_PUBLIC_PATH = Path("/home/oh/arubinstein17/github/disco-public") +REFERENCE_PREDICTIONS_PATH = DISCO_PUBLIC_PATH / "data/model_outputs/mmlu/local_predictions_r90.pkl" +ANCHOR_POINTS_PATH = DISCO_PUBLIC_PATH / "data/model_outputs/mmlu/anchor_points_disagreement.pkl" +MMLU_DATA_PATH = DISCO_PUBLIC_PATH / "notebooks/mmlu_prompts_examples.json" +FITTED_WEIGHTS_PATH = DISCO_PUBLIC_PATH / "data/model_outputs/mmlu/fitted_weights.pkl" +TRANSFORM_PATH = DISCO_PUBLIC_PATH / "data/model_outputs/mmlu/transform.pkl" + + +@pytest.fixture +def reference_predictions(): + """Load reference predictions from disco-public.""" + if not REFERENCE_PREDICTIONS_PATH.exists(): + pytest.skip(f"Reference file not found: {REFERENCE_PREDICTIONS_PATH}") + with open(REFERENCE_PREDICTIONS_PATH, "rb") as f: + return pickle.load(f) + + +@pytest.fixture +def anchor_points(): + """Load anchor points from disco-public.""" + if not ANCHOR_POINTS_PATH.exists(): + pytest.skip(f"Anchor points file not found: {ANCHOR_POINTS_PATH}") + with open(ANCHOR_POINTS_PATH, "rb") as f: + points = pickle.load(f) + if isinstance(points, np.ndarray): + return points.tolist() + return points + + +class TestPredictionsFormat: + """Test that predictions have the correct format for DISCO.""" + + def test_reference_predictions_shape(self, reference_predictions): + """Reference predictions should have shape (1, n_anchors, 31).""" + assert reference_predictions.ndim == 3 + assert reference_predictions.shape[0] == 1 + assert reference_predictions.shape[1] == 100 # n_anchor_points + assert reference_predictions.shape[2] == 31 # padded size + + def test_reference_predictions_dtype(self, reference_predictions): + """Reference predictions should be float64.""" + assert reference_predictions.dtype == np.float64 + + def test_reference_predictions_contains_logprobs(self, reference_predictions): + """Reference predictions should contain log probabilities (negative values).""" + # First 4 columns should be log probs (negative, but not -inf) + first_4 = reference_predictions[0, :, :4] + valid_mask = ~np.isinf(first_4) + assert valid_mask.sum() > 0 # At least some valid values + assert (first_4[valid_mask] < 0).all() # All negative (log probs) + + def test_reference_predictions_padding(self, reference_predictions): + """Reference predictions should be padded with -inf after first 4 columns.""" + padding = reference_predictions[0, :, 4:] + assert np.all(np.isinf(padding) & (padding < 0)) + + +class TestAnchorPoints: + """Test anchor points loading and format.""" + + def test_anchor_points_count(self, anchor_points): + """Should have 100 anchor points.""" + assert len(anchor_points) == 100 + + def test_anchor_points_are_integers(self, anchor_points): + """Anchor points should be integers (doc_ids).""" + assert all(isinstance(p, (int, np.integer)) for p in anchor_points) + + +class TestDISCOPredictionWorkflow: + """Test the DISCO prediction workflow with reference files.""" + + @pytest.mark.skipif(not all([FITTED_WEIGHTS_PATH.exists(), TRANSFORM_PATH.exists()]), reason="DISCO model files not available") + def test_disco_prediction_with_reference_predictions(self, reference_predictions): + """Test DISCO prediction produces reasonable accuracy estimate.""" + # Import DISCO prediction functions + try: + import torch + from sklearn.decomposition import PCA + except ImportError: + pytest.skip("torch or sklearn not available") + + # Load model and transform + with open(FITTED_WEIGHTS_PATH, "rb") as f: + model_data = pickle.load(f) + with open(TRANSFORM_PATH, "rb") as f: + transform = pickle.load(f) + + # Get fitted weights + if "fitted_weights" in model_data: + fitted_weights = model_data["fitted_weights"] + else: + fitted_weights = {k: v for k, v in model_data.items() if k != "transform"} + + sampling_name = list(fitted_weights.keys())[0] + number_item = list(fitted_weights[sampling_name].keys())[0] + fitted_model_type = list(fitted_weights[sampling_name][number_item].keys())[0] + fitted_model = fitted_weights[sampling_name][number_item][fitted_model_type] + + # Compute embeddings + preds_tensor = torch.Tensor(reference_predictions) + emb_unreduced = preds_tensor.softmax(dim=-1) + emb_unreduced = emb_unreduced.reshape(emb_unreduced.shape[0], -1) + emb = transform.transform(emb_unreduced.numpy()) + emb = torch.Tensor(emb) + + # Predict accuracy + predicted_acc = fitted_model.predict(emb.numpy())[0] + + # Check that predicted accuracy is reasonable (between 0 and 1) + assert 0.0 <= predicted_acc <= 1.0 + + # For zephyr-7b-sft-full on MMLU, we expect roughly 50-70% accuracy + assert 0.4 <= predicted_acc <= 0.8, f"Predicted accuracy {predicted_acc} seems unreasonable" + + print(f"DISCO predicted accuracy: {predicted_acc:.4f}") + + +@pytest.mark.slow +class TestOutputIdentity: + """Tests that verify exact output identity with disco-public. + + These tests are marked slow because they require running the full + lm-evaluation-harness evaluation, which takes significant time and GPU. + """ + + @pytest.mark.skipif(not all([MMLU_DATA_PATH.exists(), ANCHOR_POINTS_PATH.exists()]), reason="Required data files not available") + def test_lm_eval_wrapper_produces_identical_output(self, reference_predictions, anchor_points, tmp_path): + """Test that lm_eval_wrapper produces identical output to disco-public.""" + try: + from examples.mmlu_benchmark.lm_eval_wrapper import evaluate_with_lm_eval + except ImportError: + pytest.skip("lm_eval_wrapper not available") + + output_path = tmp_path / "test_predictions.pkl" + + # Run evaluation + predictions, correctness = evaluate_with_lm_eval( + model_id="alignment-handbook/zephyr-7b-sft-full", + data_path=str(MMLU_DATA_PATH), + anchor_points_path=str(ANCHOR_POINTS_PATH), + output_path=str(output_path), + pad_to_size=31, + device="cuda:0", + batch_size=8, + trust_remote_code=True, + use_full_prompt=True, + ) + + # Load produced predictions + with open(output_path, "rb") as f: + produced_predictions = pickle.load(f) + + # Assert identical shape + assert produced_predictions.shape == reference_predictions.shape, ( + f"Shape mismatch: {produced_predictions.shape} vs {reference_predictions.shape}" + ) + + # Assert identical dtype + assert produced_predictions.dtype == reference_predictions.dtype, ( + f"Dtype mismatch: {produced_predictions.dtype} vs {reference_predictions.dtype}" + ) + + # Assert values are very close (allowing for floating point differences) + # Use a relative tolerance since log probs can have large absolute values + np.testing.assert_allclose( + produced_predictions, reference_predictions, rtol=1e-5, atol=1e-10, err_msg="Predictions do not match reference" + ) + + print("Output identity test PASSED!") + + +def test_format_compatibility_with_maseval_output(): + """Test that MASEval's save_predictions_for_disco produces compatible format. + + Note: This tests format compatibility, not value identity. For identical + values, use the lm_eval_wrapper which uses lm-evaluation-harness directly. + """ + import sys + from pathlib import Path + + # Add examples to path for import + examples_path = Path(__file__).parent.parent.parent / "examples" / "mmlu_benchmark" + if str(examples_path.parent) not in sys.path: + sys.path.insert(0, str(examples_path.parent)) + + try: + from mmlu_benchmark.mmlu_benchmark import save_predictions_for_disco + except ImportError as e: + pytest.skip(f"mmlu_benchmark not available: {e}") + + # Create mock results + mock_results = [ + { + "status": "success", + "eval": [ + {"doc_id": 0, "predicted": 0, "acc": 1.0, "acc_norm": 1.0}, + ], + }, + { + "status": "success", + "eval": [ + {"doc_id": 1, "predicted": 2, "acc": 0.0, "acc_norm": 0.0}, + ], + }, + ] + + predictions = save_predictions_for_disco( + results=mock_results, + output_path=None, # Don't save, just return + anchor_points=[0, 1], + n_choices=4, + ) + + # Check format + assert predictions.ndim == 3 + assert predictions.shape[0] == 1 # n_models + assert predictions.shape[1] == 2 # n_questions + assert predictions.shape[2] == 4 # n_choices From 0890ce0f1602ad571db2ade32f418e49f0ffe125 Mon Sep 17 00:00:00 2001 From: Alexander Rubinstein Date: Fri, 30 Jan 2026 14:53:04 +0100 Subject: [PATCH 04/47] Update dependencies --- pyproject.toml | 9 +++ uv.lock | 188 ++++++++++++++++++++++++++++++++++++++++--------- 2 files changed, 162 insertions(+), 35 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 5bbf369d..3d2bdc01 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -85,6 +85,15 @@ tau2 = ["docstring-parser>=0.16"] # Dependencies for running examples (only what's actually used) examples = [ "maseval[smolagents,langgraph,llamaindex,camel,anthropic,openai,google-genai,litellm,langfuse,gaia2,macs,tau2]", +# DISCO prediction (for MMLU benchmark example) +disco = [ + "scikit-learn>=1.7.2", + "torch>=2.10.0", +] + +# Dependencies for running examples (only what's actually used) +examples = [ + "maseval[smolagents,langgraph,llamaindex,anthropic,openai,google-genai,litellm,langfuse,disco]", # Additional integrations used in examples "langchain>=0.3.27", "langchain-google-genai>=2.1.12", diff --git a/uv.lock b/uv.lock index 63de6d8d..eb930298 100644 --- a/uv.lock +++ b/uv.lock @@ -3,10 +3,10 @@ revision = 3 requires-python = ">=3.10" resolution-markers = [ "python_full_version >= '3.14' and sys_platform == 'linux'", + "python_full_version >= '3.14' and sys_platform != 'linux'", "python_full_version >= '3.12' and python_full_version < '3.14' and sys_platform == 'linux'", "python_full_version == '3.11.*' and sys_platform == 'linux'", "python_full_version < '3.11' and sys_platform == 'linux'", - "python_full_version >= '3.14' and sys_platform != 'linux'", "python_full_version >= '3.12' and python_full_version < '3.14' and sys_platform != 'linux'", "python_full_version == '3.11.*' and sys_platform != 'linux'", "python_full_version < '3.11' and sys_platform != 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++++++++++++++++------------------ 1 file changed, 36 insertions(+), 39 deletions(-) diff --git a/maseval/benchmark/mmlu/mmlu.py b/maseval/benchmark/mmlu/mmlu.py index f6422a60..b7715d83 100644 --- a/maseval/benchmark/mmlu/mmlu.py +++ b/maseval/benchmark/mmlu/mmlu.py @@ -33,7 +33,7 @@ def get_model_adapter(self, model_id, **kwargs): import pickle from abc import abstractmethod from pathlib import Path -from typing import Any, Dict, List, Optional, Sequence, Tuple, Union +from typing import Any, Dict, Iterator, List, Optional, Sequence, Tuple, Union # numpy is optional - only needed for anchor points processing try: @@ -80,15 +80,43 @@ def __init__(self, tasks: List[Task], anchor_points: Optional[List[int]] = None) anchor_points: Optional list of task indices (doc_ids) to evaluate. If None, evaluates all tasks in order. """ + # If anchor_points provided, filter tasks to only include anchor tasks + # This dramatically improves performance by avoiding O(n²) iteration + if anchor_points is not None: + # Build index mapping for quick lookup + task_by_doc_id: Dict[int, Task] = {} + for i, task in enumerate(tasks): + doc_id = task.metadata.get("doc_id", i) + task_by_doc_id[doc_id] = task + + # Filter to only anchor tasks, preserving anchor order + anchor_tasks = [] + for doc_id in anchor_points: + task = task_by_doc_id.get(doc_id) + if task is not None: + anchor_tasks.append(task) + + # Store original for reference + self._all_tasks = tasks + self._task_by_doc_id = task_by_doc_id + tasks = anchor_tasks + super().__init__(tasks) self._anchor_points = anchor_points self._anchor_idx = 0 - # Build index mapping for quick lookup - self._task_by_doc_id: Dict[int, Task] = {} - for i, task in enumerate(tasks): - doc_id = task.metadata.get("doc_id", i) - self._task_by_doc_id[doc_id] = task + # Initialize state immediately (since __iter__ is overridden and skips initial_state()) + self._state = self.initial_state() + + def __iter__(self) -> Iterator[Task]: + """Yield tasks in anchor point order. + + Since tasks are pre-filtered during __init__, we simply iterate + over the stored tasks in order. This avoids the infinite loop + issue in AdaptiveTaskQueue.__iter__ which relies on on_task_repeat_end + to remove tasks from _remaining. + """ + return iter(self._tasks) def initial_state(self) -> Dict[str, Any]: """Initialize state for anchor point iteration.""" @@ -107,34 +135,8 @@ def select_next_task(self, remaining: Sequence[Task], state: Dict[str, Any]) -> Returns: Next anchor task, or None if all anchors processed. """ - if self._anchor_points is None: - # No anchor filtering - return next remaining task - return remaining[0] if remaining else None - - anchor_idx = state["anchor_idx"] - - # Check if we've processed all anchor points - if anchor_idx >= len(self._anchor_points): - return None - - # Get the doc_id for the next anchor point - doc_id = self._anchor_points[anchor_idx] - - # Find the task with this doc_id - task = self._task_by_doc_id.get(doc_id) - - if task is None: - # Skip missing anchor points - state["anchor_idx"] = anchor_idx + 1 - return self.select_next_task(remaining, state) - - # Check if task is still in remaining - if task not in remaining: - # Task already processed, move to next anchor - state["anchor_idx"] = anchor_idx + 1 - return self.select_next_task(remaining, state) - - return task + # Simply return the first remaining task since we pre-filtered to anchor tasks only + return remaining[0] if remaining else None def update_state(self, task: Task, report: Dict[str, Any], state: Dict[str, Any]) -> Dict[str, Any]: """Update state after task completion. @@ -151,11 +153,6 @@ def update_state(self, task: Task, report: Dict[str, Any], state: Dict[str, Any] state["completed_anchors"].append(doc_id) state["anchor_idx"] += 1 - # Check if we've completed all anchors - if self._anchor_points is not None: - if state["anchor_idx"] >= len(self._anchor_points): - self.stop() - return state From 00bb500d704e7eb4fe006ba5ce949de63965af85 Mon Sep 17 00:00:00 2001 From: Alexander Rubinstein Date: Fri, 30 Jan 2026 15:03:09 +0100 Subject: [PATCH 06/47] [Add MMLU task]: - Save model predictions as intermediate outputs --- examples/mmlu_benchmark/mmlu_benchmark.py | 44 +++++-- .../test_mmlu_disco_identity.py | 117 ++++++++++++++++++ 2 files changed, 151 insertions(+), 10 deletions(-) diff --git a/examples/mmlu_benchmark/mmlu_benchmark.py b/examples/mmlu_benchmark/mmlu_benchmark.py index e477fed9..a2724452 100644 --- a/examples/mmlu_benchmark/mmlu_benchmark.py +++ b/examples/mmlu_benchmark/mmlu_benchmark.py @@ -160,6 +160,12 @@ def parse_args(): default=256, help="PCA dimension for DISCO embeddings (default: 256)", ) + parser.add_argument( + "--pad_to_size", + type=int, + default=None, + help="Pad predictions to this size with -inf (default: no padding, disco-public uses 31)", + ) return parser.parse_args() @@ -243,21 +249,26 @@ def save_predictions_for_disco( output_path: str, anchor_points: Optional[list] = None, n_choices: int = 4, + pad_to_size: Optional[int] = None, ): """Save predictions in format compatible with DISCO predictor. - Creates a predictions tensor of shape (1, n_questions, n_choices) - where the values are logits (1.0 for predicted choice, 0.0 for others). + Creates a predictions tensor of shape (1, n_questions, pad_to_size) + where the values are log-probabilities (0.0 for predicted choice, -inf for others). + + Note: This produces a simplified format using 0/-inf instead of actual log-likelihoods. + For identical output to lm-evaluation-harness, use lm_eval_wrapper.py instead. Args: results: Benchmark results list. output_path: Path to save predictions pickle. anchor_points: Optional anchor points for ordering. n_choices: Number of answer choices (default 4 for A/B/C/D). + pad_to_size: Pad predictions to this size with -inf (default: no padding). """ # Build predictions array - # For now, we create simple binary predictions (1.0 for predicted, 0.0 for others) - # A more sophisticated implementation would capture actual logits + # We use 0.0 for the predicted answer and -inf for others + # This mimics the log-probability format but with simplified values predictions_list = [] @@ -274,9 +285,10 @@ def save_predictions_for_disco( for doc_id in anchor_points: entry = result_by_doc_id.get(doc_id, {}) predicted = entry.get("predicted", -1) - pred_vec = [0.0] * n_choices + # Use -inf for non-predicted, 0.0 for predicted (log-prob style) + pred_vec = [float("-inf")] * n_choices if 0 <= predicted < n_choices: - pred_vec[predicted] = 1.0 + pred_vec[predicted] = 0.0 predictions_list.append(pred_vec) else: # Use results in order @@ -284,19 +296,30 @@ def save_predictions_for_disco( if res.get("status") == "success" and res.get("eval"): for entry in res["eval"]: predicted = entry.get("predicted", -1) - pred_vec = [0.0] * n_choices + pred_vec = [float("-inf")] * n_choices if 0 <= predicted < n_choices: - pred_vec[predicted] = 1.0 + pred_vec[predicted] = 0.0 predictions_list.append(pred_vec) predictions = np.array(predictions_list) - predictions = predictions.reshape(1, -1, n_choices) # (1, n_questions, n_choices) + + # Pad to specified size if requested + if pad_to_size is not None and predictions.shape[1] < pad_to_size: + padding = np.full( + (predictions.shape[0], pad_to_size - predictions.shape[1]), + float("-inf"), + dtype=predictions.dtype, + ) + predictions = np.concatenate([predictions, padding], axis=1) + + predictions = predictions.reshape(1, -1, predictions.shape[-1]) # (1, n_questions, n_choices) if output_path and output_path != "/dev/null": with open(output_path, "wb") as f: pickle.dump(predictions, f) print(f"Saved predictions tensor to {output_path}") print(f" Shape: {predictions.shape}") + print(f" Dtype: {predictions.dtype}") else: print(f"Built predictions tensor with shape: {predictions.shape}") @@ -544,8 +567,9 @@ def main(): if args.predictions_path or args.disco_prediction: predictions = save_predictions_for_disco( results=results, - output_path=args.predictions_path if args.predictions_path else "/dev/null", + output_path=args.predictions_path if args.predictions_path else None, anchor_points=anchor_points, + pad_to_size=args.pad_to_size, ) # Run DISCO prediction if enabled diff --git a/tests/test_benchmark/test_mmlu_disco_identity.py b/tests/test_benchmark/test_mmlu_disco_identity.py index 7d57b0f5..0a56bbc1 100644 --- a/tests/test_benchmark/test_mmlu_disco_identity.py +++ b/tests/test_benchmark/test_mmlu_disco_identity.py @@ -236,3 +236,120 @@ def test_format_compatibility_with_maseval_output(): assert predictions.shape[0] == 1 # n_models assert predictions.shape[1] == 2 # n_questions assert predictions.shape[2] == 4 # n_choices + + +# Path to MASEval-produced predictions (set by running mmlu_benchmark.py with --predictions_path) +MASEVAL_PREDICTIONS_PATH = Path("/weka/oh/arubinstein17/github/disco-public/output/mmlu_maseval/maseval_predictions.pkl") + + +class TestMASEvalPredictionsComparison: + """Tests that compare MASEval predictions with disco-public reference. + + These tests verify that predictions saved via mmlu_benchmark.py with + --predictions_path are format-compatible with disco-public output. + + To run these tests, first generate predictions by running: + python examples/mmlu_benchmark/mmlu_benchmark.py \\ + --model_id alignment-handbook/zephyr-7b-sft-full \\ + --data_path /path/to/mmlu_prompts_examples.json \\ + --anchor_points_path /path/to/anchor_points.pkl \\ + --predictions_path /path/to/maseval_predictions.pkl \\ + --pad_to_size 31 + """ + + @pytest.fixture + def maseval_predictions(self): + """Load MASEval-produced predictions.""" + if not MASEVAL_PREDICTIONS_PATH.exists(): + pytest.skip(f"MASEval predictions not found: {MASEVAL_PREDICTIONS_PATH}. Run mmlu_benchmark.py with --predictions_path first.") + with open(MASEVAL_PREDICTIONS_PATH, "rb") as f: + return pickle.load(f) + + def test_maseval_predictions_shape_matches_reference(self, maseval_predictions, reference_predictions): + """MASEval predictions should have same shape as reference.""" + assert maseval_predictions.shape == reference_predictions.shape, ( + f"Shape mismatch: MASEval {maseval_predictions.shape} vs reference {reference_predictions.shape}" + ) + + def test_maseval_predictions_has_correct_format(self, maseval_predictions): + """MASEval predictions should have correct format (padded with -inf).""" + # Check shape + assert maseval_predictions.ndim == 3 + assert maseval_predictions.shape[0] == 1 # n_models + assert maseval_predictions.shape[1] == 100 # n_anchor_points + assert maseval_predictions.shape[2] == 31 # padded size + + # Check padding (columns 4-31 should be -inf) + padding = maseval_predictions[0, :, 4:] + assert np.all(np.isinf(padding) & (padding < 0)), "Padding should be -inf" + + def test_maseval_predictions_argmax_matches_reference(self, maseval_predictions, reference_predictions): + """Argmax of predictions (predicted answers) should match. + + This test checks that both methods predict the same answer for each question, + even though the actual log-probability values differ. + """ + # Get argmax for first 4 columns (A, B, C, D choices) + maseval_choices = maseval_predictions[0, :, :4] + reference_choices = reference_predictions[0, :, :4] + + maseval_argmax = np.argmax(maseval_choices, axis=1) + reference_argmax = np.argmax(reference_choices, axis=1) + + # Count matches + matches = (maseval_argmax == reference_argmax).sum() + total = len(maseval_argmax) + match_rate = matches / total + + print(f"Answer match rate: {matches}/{total} ({match_rate:.2%})") + + # They should match exactly if using the same model + # Allow for some tolerance due to different evaluation methods + assert match_rate >= 0.9, f"Answer match rate {match_rate:.2%} is too low. Expected >= 90% if using same model." + + def test_disco_prediction_produces_similar_accuracy(self, maseval_predictions, reference_predictions): + """DISCO prediction from both prediction sets should give similar results.""" + try: + import torch + except ImportError: + pytest.skip("torch not available") + + if not all([FITTED_WEIGHTS_PATH.exists(), TRANSFORM_PATH.exists()]): + pytest.skip("DISCO model files not available") + + # Load model and transform + with open(FITTED_WEIGHTS_PATH, "rb") as f: + model_data = pickle.load(f) + with open(TRANSFORM_PATH, "rb") as f: + transform = pickle.load(f) + + # Get fitted model + if "fitted_weights" in model_data: + fitted_weights = model_data["fitted_weights"] + else: + fitted_weights = {k: v for k, v in model_data.items() if k != "transform"} + + sampling_name = list(fitted_weights.keys())[0] + number_item = list(fitted_weights[sampling_name].keys())[0] + fitted_model_type = list(fitted_weights[sampling_name][number_item].keys())[0] + fitted_model = fitted_weights[sampling_name][number_item][fitted_model_type] + + def compute_disco_accuracy(predictions): + """Compute DISCO-predicted accuracy from predictions tensor.""" + preds_tensor = torch.Tensor(predictions) + emb_unreduced = preds_tensor.softmax(dim=-1) + emb_unreduced = emb_unreduced.reshape(emb_unreduced.shape[0], -1) + emb = transform.transform(emb_unreduced.numpy()) + return fitted_model.predict(emb)[0] + + maseval_acc = compute_disco_accuracy(maseval_predictions) + reference_acc = compute_disco_accuracy(reference_predictions) + + print(f"DISCO predicted accuracy from MASEval predictions: {maseval_acc:.4f}") + print(f"DISCO predicted accuracy from reference predictions: {reference_acc:.4f}") + print(f"Difference: {abs(maseval_acc - reference_acc):.4f}") + + # The accuracies should be reasonably close + assert abs(maseval_acc - reference_acc) < 0.1, ( + f"DISCO predictions differ too much: MASEval={maseval_acc:.4f}, reference={reference_acc:.4f}" + ) From 92b2232baca5b21443426db3f2d07bf056a54821 Mon Sep 17 00:00:00 2001 From: Alexander Rubinstein Date: Fri, 30 Jan 2026 15:38:01 +0100 Subject: [PATCH 07/47] [Add MMLU task]: - Estimate likelihood for MCQ --- examples/mmlu_benchmark/mmlu_benchmark.py | 336 +++++++++++++++++++--- maseval/benchmark/mmlu/mmlu.py | 13 +- pyproject.toml | 1 + uv.lock | 22 ++ 4 files changed, 333 insertions(+), 39 deletions(-) diff --git a/examples/mmlu_benchmark/mmlu_benchmark.py b/examples/mmlu_benchmark/mmlu_benchmark.py index a2724452..79ed5739 100644 --- a/examples/mmlu_benchmark/mmlu_benchmark.py +++ b/examples/mmlu_benchmark/mmlu_benchmark.py @@ -173,8 +173,13 @@ def parse_args(): class HuggingFaceMMLUBenchmark(MMLUBenchmark): """MMLU Benchmark using HuggingFace transformers models. - This concrete implementation loads a HuggingFace model via the - transformers pipeline and uses it for MCQ evaluation. + This concrete implementation uses log-likelihood based MCQ evaluation + with the same optimizations as lm-evaluation-harness: + + 1. Single forward pass per question (one-token continuation optimization) + 2. Batching multiple questions together + 3. Efficient log-softmax computation + 4. Proper left-padding for batch processing """ def __init__( @@ -183,6 +188,7 @@ def __init__( device: str = "cuda:0", trust_remote_code: bool = False, use_full_prompt: bool = False, + batch_size: int = 8, **kwargs, ): """Initialize HuggingFace MMLU benchmark. @@ -192,30 +198,283 @@ def __init__( device: Device to run model on. trust_remote_code: Trust remote code when loading model. use_full_prompt: Use full prompt with few-shot examples. + batch_size: Batch size for evaluation (number of questions per batch). **kwargs: Additional arguments passed to MMLUBenchmark. """ super().__init__(use_full_prompt=use_full_prompt, **kwargs) self._model_id = model_id self._device = device self._trust_remote_code = trust_remote_code - self._pipeline = None - - def _get_pipeline(self): - """Lazy load the transformers pipeline.""" - if self._pipeline is None: - from transformers import pipeline - - self._pipeline = pipeline( - "text-generation", - model=self._model_id, - device=self._device if "cuda" in self._device else -1, + self._batch_size = batch_size + self._model = None + self._tokenizer = None + self._choice_token_ids_cache = None + + def _load_model(self): + """Lazy load the model and tokenizer for log-likelihood computation.""" + if self._model is None: + import torch + from transformers import AutoModelForCausalLM, AutoTokenizer + + print(f"Loading model: {self._model_id}") + self._tokenizer = AutoTokenizer.from_pretrained( + self._model_id, + trust_remote_code=self._trust_remote_code, + ) + self._tokenizer.padding_side = "left" + if self._tokenizer.pad_token is None: + self._tokenizer.pad_token = self._tokenizer.eos_token + + # Load model without device_map (doesn't require accelerate) + # Then move to device manually + self._model = AutoModelForCausalLM.from_pretrained( + self._model_id, trust_remote_code=self._trust_remote_code, + torch_dtype=torch.float16, ) - return self._pipeline + self._model = self._model.to(self._device) + self._model.eval() + + # Pre-compute token IDs for answer choices A, B, C, D + self._choice_token_ids_cache = {} + for choice in ["A", "B", "C", "D"]: + tokens = self._tokenizer.encode(choice, add_special_tokens=False) + self._choice_token_ids_cache[choice] = tokens[0] if tokens else None + + print(f"Answer token IDs: {self._choice_token_ids_cache}") + + return self._model, self._tokenizer + + @property + def _choice_token_ids(self): + """Get cached choice token IDs.""" + if self._choice_token_ids_cache is None: + self._load_model() + return self._choice_token_ids_cache + + def _compute_logprobs_single_token(self, prompt: str, choices: list) -> list: + """Compute log-likelihoods using single-token optimization. + + For MCQ with single-letter answers (A, B, C, D), we only need ONE + forward pass: compute logits for the prompt, then look up the + log-probability of each answer token at the last position. + + This is the key optimization from lm-evaluation-harness. + + Args: + prompt: The prompt/question text. + choices: List of answer choice strings (e.g., ["A", "B", "C", "D"]). + + Returns: + List of log-likelihoods, one per choice. + """ + import torch + + model, tokenizer = self._load_model() + + # Tokenize the prompt + input_ids = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt") + input_ids = input_ids.to(self._device) + + with torch.no_grad(): + outputs = model(input_ids) + # Get logits at the last position (where the answer token would be predicted) + # logits shape: (batch, seq_len, vocab_size) + last_logits = outputs.logits[0, -1, :] # (vocab_size,) + + # Compute log-softmax once + log_probs = torch.nn.functional.log_softmax(last_logits, dim=-1) + + # Extract log-probabilities for each answer choice + logprobs = [] + for choice in choices: + token_id = self._choice_token_ids.get(choice) + if token_id is not None: + logprobs.append(log_probs[token_id].item()) + else: + # Fallback: encode the choice and sum log-probs + # This handles multi-token answers if needed + logprobs.append(float("-inf")) + + return logprobs + + def _compute_logprobs_batched(self, prompts: list, choices_list: list) -> list: + """Compute log-likelihoods for a batch of prompts. + + Batches multiple questions together for efficient GPU utilization. + + Args: + prompts: List of prompt strings. + choices_list: List of choice lists (one per prompt). + + Returns: + List of log-likelihood lists, one per prompt. + """ + import torch + + model, tokenizer = self._load_model() + + # Batch encode with left padding + encoding = tokenizer( + prompts, + padding="longest", + return_tensors="pt", + add_special_tokens=False, + ) + input_ids = encoding["input_ids"].to(self._device) + attention_mask = encoding["attention_mask"].to(self._device) + + with torch.no_grad(): + outputs = model(input_ids, attention_mask=attention_mask) + # logits shape: (batch, seq_len, vocab_size) + logits = outputs.logits + + # For left-padded inputs, we need to find the actual last position for each sequence + # The last real token is where attention_mask is 1 + # For each sequence, get logits at position (seq_len - 1) accounting for padding + batch_size = input_ids.shape[0] + all_logprobs = [] + + for i in range(batch_size): + # Find the last non-padded position + seq_len = attention_mask[i].sum().item() + last_pos = seq_len - 1 + + # Get logits at the last real position + last_logits = logits[i, last_pos, :] + + # Compute log-softmax + log_probs = torch.nn.functional.log_softmax(last_logits, dim=-1) + + # Extract log-probabilities for each answer choice + choices = choices_list[i] + item_logprobs = [] + for choice in choices: + token_id = self._choice_token_ids.get(choice) + if token_id is not None: + item_logprobs.append(log_probs[token_id].item()) + else: + item_logprobs.append(float("-inf")) + + all_logprobs.append(item_logprobs) + + return all_logprobs + + def _compute_logprobs_multi_token(self, prompt: str, choices: list) -> list: + """Compute log-likelihoods for multi-token continuations. + + This is the fallback for when answer choices have multiple tokens. + Batches all choices together and computes log-probs in parallel. + + Args: + prompt: The prompt/question text. + choices: List of answer choice strings. + + Returns: + List of log-likelihoods, one per choice. + """ + import torch + + model, tokenizer = self._load_model() + + # Build batch: prompt + each choice + full_texts = [prompt + choice for choice in choices] + + # Encode all at once with padding + encoding = tokenizer( + full_texts, + padding="longest", + return_tensors="pt", + add_special_tokens=False, + ) + input_ids = encoding["input_ids"].to(self._device) + attention_mask = encoding["attention_mask"].to(self._device) + + # Also encode just the prompt to know where continuation starts + prompt_ids = tokenizer.encode(prompt, add_special_tokens=False) + prompt_len = len(prompt_ids) + + with torch.no_grad(): + outputs = model(input_ids, attention_mask=attention_mask) + logits = outputs.logits + + # Compute log-softmax over vocab dimension + log_probs = torch.nn.functional.log_softmax(logits, dim=-1) + + # For each choice, sum log-probs of continuation tokens + all_logprobs = [] + for i, choice in enumerate(choices): + choice_ids = tokenizer.encode(choice, add_special_tokens=False) + choice_len = len(choice_ids) + + # Sum log-probs for the continuation tokens + # Token at position j is predicted by logits at position j-1 + total_logprob = 0.0 + for j, token_id in enumerate(choice_ids): + pos = prompt_len + j - 1 # Position in logits that predicts this token + if pos >= 0: + total_logprob += log_probs[i, pos, token_id].item() + + all_logprobs.append(total_logprob) + + return all_logprobs + + def run_agents( + self, + agents, + task, + environment, + query: str = "", + ): + """Execute log-likelihood based MCQ evaluation. + + Uses single-forward-pass optimization for single-token answers, + or multi-token batched computation as fallback. + """ + # Get the prompt from environment + prompt = environment.get_prompt() + choices = environment.state.get("choices", ["A", "B", "C", "D"]) + + # Load model to check if single-token optimization applies + self._load_model() + + # Check if all choices are single tokens + all_single_token = all(choice in self._choice_token_ids and self._choice_token_ids[choice] is not None for choice in choices) + + if all_single_token: + # Use optimized single-token path (one forward pass) + logprobs = self._compute_logprobs_single_token(prompt, choices) + else: + # Fall back to multi-token batched computation + logprobs = self._compute_logprobs_multi_token(prompt, choices) + + # Select the choice with highest log-probability + best_idx = logprobs.index(max(logprobs)) + answer = choices[best_idx] + + # Store logprobs in environment for later retrieval if needed + environment.state["logprobs"] = logprobs + environment.state["predicted_idx"] = best_idx + + # Record in agent messages for tracing + agent = agents[0] + agent.agent._messages.append({"role": "user", "content": prompt}) + agent.agent._messages.append( + { + "role": "assistant", + "content": answer, + "logprobs": logprobs, + } + ) + + return answer def get_model_adapter(self, model_id: str, **kwargs): """Provide a HuggingFace ModelAdapter. + Note: For logprobs-based evaluation, we don't actually use the adapter + for generation. This is kept for API compatibility. + Args: model_id: Model identifier (ignored, uses instance model_id). **kwargs: Additional arguments (e.g., register_name). @@ -225,15 +484,15 @@ def get_model_adapter(self, model_id: str, **kwargs): """ from maseval.interface.inference import HuggingFaceModelAdapter - pipe = self._get_pipeline() + # Create a minimal adapter for compatibility + # The actual evaluation uses _compute_logprobs_* + class DummyCallable: + def __call__(self, prompt, **kwargs): + return "" + adapter = HuggingFaceModelAdapter( - model=pipe, + model=DummyCallable(), model_id=self._model_id, - default_generation_params={ - "max_new_tokens": 32, - "do_sample": False, - "return_full_text": False, - }, ) # Register for tracing if requested @@ -254,10 +513,10 @@ def save_predictions_for_disco( """Save predictions in format compatible with DISCO predictor. Creates a predictions tensor of shape (1, n_questions, pad_to_size) - where the values are log-probabilities (0.0 for predicted choice, -inf for others). + where the values are log-probabilities. - Note: This produces a simplified format using 0/-inf instead of actual log-likelihoods. - For identical output to lm-evaluation-harness, use lm_eval_wrapper.py instead. + If logprobs are available in the results (from logprobs-based evaluation), + uses actual log-likelihoods. Otherwise, falls back to 0/-inf format. Args: results: Benchmark results list. @@ -266,12 +525,22 @@ def save_predictions_for_disco( n_choices: Number of answer choices (default 4 for A/B/C/D). pad_to_size: Pad predictions to this size with -inf (default: no padding). """ - # Build predictions array - # We use 0.0 for the predicted answer and -inf for others - # This mimics the log-probability format but with simplified values - predictions_list = [] + def get_pred_vec(entry, n_choices): + """Extract prediction vector from entry, using logprobs if available.""" + # Check if actual logprobs are available + logprobs = entry.get("logprobs") + if logprobs is not None and len(logprobs) >= n_choices: + return logprobs[:n_choices] + + # Fall back to 0/-inf format based on predicted index + predicted = entry.get("predicted", -1) + pred_vec = [float("-inf")] * n_choices + if 0 <= predicted < n_choices: + pred_vec[predicted] = 0.0 + return pred_vec + if anchor_points is not None: # Order results by anchor points result_by_doc_id = {} @@ -284,21 +553,14 @@ def save_predictions_for_disco( for doc_id in anchor_points: entry = result_by_doc_id.get(doc_id, {}) - predicted = entry.get("predicted", -1) - # Use -inf for non-predicted, 0.0 for predicted (log-prob style) - pred_vec = [float("-inf")] * n_choices - if 0 <= predicted < n_choices: - pred_vec[predicted] = 0.0 + pred_vec = get_pred_vec(entry, n_choices) predictions_list.append(pred_vec) else: # Use results in order for res in results: if res.get("status") == "success" and res.get("eval"): for entry in res["eval"]: - predicted = entry.get("predicted", -1) - pred_vec = [float("-inf")] * n_choices - if 0 <= predicted < n_choices: - pred_vec[predicted] = 0.0 + pred_vec = get_pred_vec(entry, n_choices) predictions_list.append(pred_vec) predictions = np.array(predictions_list) diff --git a/maseval/benchmark/mmlu/mmlu.py b/maseval/benchmark/mmlu/mmlu.py index b7715d83..8f3c8915 100644 --- a/maseval/benchmark/mmlu/mmlu.py +++ b/maseval/benchmark/mmlu/mmlu.py @@ -243,7 +243,7 @@ def __call__(self, traces: Dict[str, Any], final_answer: Optional[str] = None) - final_answer: The model's final answer. Returns: - Dict with acc, acc_norm, predicted, gold, and correct fields. + Dict with acc, acc_norm, predicted, gold, correct, and optionally logprobs fields. """ # Parse the model's answer predicted = self._parse_answer(final_answer or "") @@ -251,7 +251,7 @@ def __call__(self, traces: Dict[str, Any], final_answer: Optional[str] = None) - # Check if correct correct = predicted == self.gold - return { + result = { "acc": 1.0 if correct else 0.0, "acc_norm": 1.0 if correct else 0.0, "predicted": predicted, @@ -260,6 +260,15 @@ def __call__(self, traces: Dict[str, Any], final_answer: Optional[str] = None) - "doc_id": self.task.metadata.get("doc_id"), } + # Extract logprobs from traces if available (for logprobs-based evaluation) + messages = traces.get("messages", []) + for msg in messages: + if isinstance(msg, dict) and "logprobs" in msg: + result["logprobs"] = msg["logprobs"] + break + + return result + def _parse_answer(self, response: str) -> int: """Parse model response to extract answer choice. diff --git a/pyproject.toml b/pyproject.toml index 3d2bdc01..7259bfb1 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -104,6 +104,7 @@ examples = [ "jupyter>=1.0.0", "ipykernel>=6.0.0", "ipywidgets>=8.0.0", + "accelerate>=1.12.0", ] # Complete installation with absolutely everything (uses self-reference for DRY) diff --git a/uv.lock b/uv.lock index eb930298..0b12716e 100644 --- a/uv.lock +++ b/uv.lock @@ -37,6 +37,25 @@ overrides = [ { name = "termcolor", specifier = ">=2.5.0" }, ] +[[package]] +name = "accelerate" +version = "1.12.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "huggingface-hub" }, + { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" }, + { name = "numpy", version = "2.4.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" }, + { name = "packaging" }, + { name = "psutil" }, + { name = "pyyaml" }, + { name = "safetensors" }, + { name = "torch" }, +] +sdist = { url = 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@@ docs = [ [package.metadata] requires-dist = [ + { name = "accelerate", marker = "extra == 'examples'", specifier = ">=1.12.0" }, { name = "anthropic", marker = "extra == 'anthropic'", specifier = ">=0.40.0" }, { name = "arxiv", marker = "extra == 'multiagentbench'", specifier = ">=2.1.0" }, { name = "beartype", marker = "extra == 'multiagentbench'" }, From 17b601e44fd7f046377b04645409ef966ed0b7d6 Mon Sep 17 00:00:00 2001 From: Alexander Rubinstein Date: Sun, 1 Feb 2026 14:34:41 +0100 Subject: [PATCH 08/47] [Add MMLU task]: - Add DISCO dependencies --- pyproject.toml | 12 + uv.lock | 770 +++++++++++++++++++++++++++++++++++++++++++------ 2 files changed, 699 insertions(+), 83 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 7259bfb1..fc6267e5 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -87,8 +87,20 @@ examples = [ "maseval[smolagents,langgraph,llamaindex,camel,anthropic,openai,google-genai,litellm,langfuse,gaia2,macs,tau2]", # DISCO prediction (for MMLU benchmark example) disco = [ + "click>=8.1.0", + "datasets>=2.14.0", + "gdown>=4.6.0", + "h5py>=3.0.0", + "ipython>=8.0.0", + "jsonlines>=4.0.0", + "matplotlib>=3.5.0", "scikit-learn>=1.7.2", + "scipy>=1.11.0", + "stnd @ git+https://github.com/arubique/stnd.git", "torch>=2.10.0", + "torchvision>=0.15.0", + "typer>=0.9.0", + "umap-learn>=0.5.0", ] # Dependencies for running examples (only what's actually used) diff --git a/uv.lock b/uv.lock index 0b12716e..770f57fc 100644 --- a/uv.lock +++ b/uv.lock @@ -4,10 +4,12 @@ requires-python = ">=3.10" resolution-markers = [ "python_full_version >= '3.14' and sys_platform == 'linux'", "python_full_version >= '3.14' and sys_platform != 'linux'", - "python_full_version >= '3.12' and python_full_version < '3.14' and sys_platform == 'linux'", + "python_full_version == '3.13.*' and sys_platform == 'linux'", + "python_full_version == '3.12.*' and sys_platform == 'linux'", "python_full_version == '3.11.*' and sys_platform == 'linux'", 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b/examples/mmlu_benchmark/lm_eval_wrapper.py @@ -16,6 +16,7 @@ """ import pickle +import subprocess import sys from pathlib import Path from typing import Optional @@ -70,19 +71,17 @@ def evaluate_with_lm_eval( Returns: Tuple of (predictions, correctness) numpy arrays. """ - # Add disco-public to path for imports - disco_scripts = Path(disco_public_path) / "scripts" + # Add disco-public to path for imports. Order matters: disco_root must be first + # so "from scripts import run_lm_eval" resolves to disco-public/scripts, not + # lm-evaluation-harness/scripts. disco_root = Path(disco_public_path) + disco_scripts = disco_root / "scripts" + lm_eval_path = disco_root / "external" / "lm-evaluation-harness" - if str(disco_scripts) not in sys.path: - sys.path.insert(0, str(disco_scripts)) - if str(disco_root) not in sys.path: - sys.path.insert(0, str(disco_root)) - - # Import lm-eval harness - lm_eval_path = Path(disco_public_path) / "external" / "lm-evaluation-harness" - if str(lm_eval_path) not in sys.path: - sys.path.insert(0, str(lm_eval_path)) + for path in (disco_scripts, lm_eval_path, disco_root): + path_str = str(path) + if path_str not in sys.path: + sys.path.insert(0, path_str) try: import lm_eval @@ -141,20 +140,19 @@ def evaluate_with_lm_eval( import tempfile with tempfile.TemporaryDirectory() as tmpdir: + eval_args.extend(["--target_outputs_path", str(output_path).replace(".pkl", "_target_outputs.pkl")]) eval_args.extend(["--output_path", tmpdir]) + eval_args.extend(["--predictions_path", str(output_path)]) + eval_args.append("--metric") + eval_args.append("acc_norm") + eval_args.append("--force_recompute") - # Run lm_eval - print(f"Running lm-eval with args: {' '.join(eval_args)}") - - # Use lm_eval CLI - from lm_eval.__main__ import cli_evaluate + # Run disco-public's run_lm_eval.py as a subprocess so scripts.* resolves to disco-public/scripts + # (in-process import would see lm-evaluation-harness/scripts first). + print(f"Running run_lm_eval with args: {' '.join(eval_args)}") - old_argv = sys.argv - sys.argv = ["lm_eval"] + eval_args - try: - cli_evaluate() - finally: - sys.argv = old_argv + cmd = [sys.executable, str(disco_root / "scripts" / "run_lm_eval.py")] + eval_args + subprocess.run(cmd, cwd=str(disco_root), check=True) # Find and load results jsonl_path = find_jsonl_file_in_directory(tmpdir) diff --git a/examples/mmlu_benchmark/mmlu_benchmark.py b/examples/mmlu_benchmark/mmlu_benchmark.py index 79ed5739..b79d2d7f 100644 --- a/examples/mmlu_benchmark/mmlu_benchmark.py +++ b/examples/mmlu_benchmark/mmlu_benchmark.py @@ -541,15 +541,25 @@ def get_pred_vec(entry, n_choices): pred_vec[predicted] = 0.0 return pred_vec + def extract_eval_entries(res): + """Extract evaluation entries from a result dict.""" + eval_data = res.get("eval") + if eval_data is None: + return [] + if isinstance(eval_data, list): + return eval_data + if isinstance(eval_data, dict): + return [eval_data] + return [] + if anchor_points is not None: # Order results by anchor points result_by_doc_id = {} for res in results: - if res.get("status") == "success" and res.get("eval"): - for entry in res["eval"]: - doc_id = entry.get("doc_id") - if doc_id is not None: - result_by_doc_id[doc_id] = entry + for entry in extract_eval_entries(res): + doc_id = entry.get("doc_id") + if doc_id is not None: + result_by_doc_id[doc_id] = entry for doc_id in anchor_points: entry = result_by_doc_id.get(doc_id, {}) @@ -558,10 +568,9 @@ def get_pred_vec(entry, n_choices): else: # Use results in order for res in results: - if res.get("status") == "success" and res.get("eval"): - for entry in res["eval"]: - pred_vec = get_pred_vec(entry, n_choices) - predictions_list.append(pred_vec) + for entry in extract_eval_entries(res): + pred_vec = get_pred_vec(entry, n_choices) + predictions_list.append(pred_vec) predictions = np.array(predictions_list) diff --git a/tests/test_benchmark/test_mmlu_disco_identity.py b/tests/test_benchmark/test_mmlu_disco_identity.py index 0a56bbc1..f0c9e478 100644 --- a/tests/test_benchmark/test_mmlu_disco_identity.py +++ b/tests/test_benchmark/test_mmlu_disco_identity.py @@ -95,9 +95,8 @@ def test_disco_prediction_with_reference_predictions(self, reference_predictions # Import DISCO prediction functions try: import torch - from sklearn.decomposition import PCA except ImportError: - pytest.skip("torch or sklearn not available") + pytest.skip("torch not available") # Load model and transform with open(FITTED_WEIGHTS_PATH, "rb") as f: @@ -153,18 +152,21 @@ def test_lm_eval_wrapper_produces_identical_output(self, reference_predictions, output_path = tmp_path / "test_predictions.pkl" - # Run evaluation - predictions, correctness = evaluate_with_lm_eval( - model_id="alignment-handbook/zephyr-7b-sft-full", - data_path=str(MMLU_DATA_PATH), - anchor_points_path=str(ANCHOR_POINTS_PATH), - output_path=str(output_path), - pad_to_size=31, - device="cuda:0", - batch_size=8, - trust_remote_code=True, - use_full_prompt=True, - ) + # Run evaluation (lm-eval deps are imported inside evaluate_with_lm_eval) + try: + predictions, correctness = evaluate_with_lm_eval( + model_id="alignment-handbook/zephyr-7b-sft-full", + data_path=str(MMLU_DATA_PATH), + anchor_points_path=str(ANCHOR_POINTS_PATH), + output_path=str(output_path), + pad_to_size=31, + device="cuda:0", + batch_size=8, + trust_remote_code=True, + use_full_prompt=True, + ) + except ImportError as e: + pytest.skip(f"lm-eval dependencies not available: {e}") # Load produced predictions with open(output_path, "rb") as f: @@ -284,10 +286,11 @@ def test_maseval_predictions_has_correct_format(self, maseval_predictions): assert np.all(np.isinf(padding) & (padding < 0)), "Padding should be -inf" def test_maseval_predictions_argmax_matches_reference(self, maseval_predictions, reference_predictions): - """Argmax of predictions (predicted answers) should match. + """Compare predicted answers between MASEval and disco-public. - This test checks that both methods predict the same answer for each question, - even though the actual log-probability values differ. + Note: MASEval uses text generation while disco-public uses log-likelihood + scoring. These methods can produce different results even with the same model, + so we only check for reasonable correlation, not exact match. """ # Get argmax for first 4 columns (A, B, C, D choices) maseval_choices = maseval_predictions[0, :, :4] @@ -302,13 +305,24 @@ def test_maseval_predictions_argmax_matches_reference(self, maseval_predictions, match_rate = matches / total print(f"Answer match rate: {matches}/{total} ({match_rate:.2%})") + print(f"MASEval argmax distribution: {np.bincount(maseval_argmax, minlength=4)}") + print(f"Reference argmax distribution: {np.bincount(reference_argmax, minlength=4)}") + + # Text generation and log-likelihood scoring can give different results + # We expect at least 50% agreement for the same model (random would be 25%) + assert match_rate >= 0.5, f"Answer match rate {match_rate:.2%} is too low. Expected >= 50% agreement between evaluation methods." + + def test_disco_prediction_produces_valid_accuracy(self, maseval_predictions, reference_predictions): + """DISCO prediction from MASEval predictions should produce valid accuracy. - # They should match exactly if using the same model - # Allow for some tolerance due to different evaluation methods - assert match_rate >= 0.9, f"Answer match rate {match_rate:.2%} is too low. Expected >= 90% if using same model." + Note: MASEval predictions use 0/-inf values (one-hot style) while disco-public + uses actual log-likelihoods. The DISCO predictor is trained on log-likelihood + distributions, so predictions from one-hot style inputs will be less accurate. - def test_disco_prediction_produces_similar_accuracy(self, maseval_predictions, reference_predictions): - """DISCO prediction from both prediction sets should give similar results.""" + This test checks that: + 1. MASEval predictions produce a valid (0-1) accuracy prediction + 2. Reference predictions produce a reasonable accuracy for the model + """ try: import torch except ImportError: @@ -342,14 +356,19 @@ def compute_disco_accuracy(predictions): emb = transform.transform(emb_unreduced.numpy()) return fitted_model.predict(emb)[0] - maseval_acc = compute_disco_accuracy(maseval_predictions) + # Reference should work normally reference_acc = compute_disco_accuracy(reference_predictions) - - print(f"DISCO predicted accuracy from MASEval predictions: {maseval_acc:.4f}") print(f"DISCO predicted accuracy from reference predictions: {reference_acc:.4f}") - print(f"Difference: {abs(maseval_acc - reference_acc):.4f}") + assert 0.4 <= reference_acc <= 0.8, f"Reference accuracy {reference_acc} seems unreasonable" - # The accuracies should be reasonably close - assert abs(maseval_acc - reference_acc) < 0.1, ( - f"DISCO predictions differ too much: MASEval={maseval_acc:.4f}, reference={reference_acc:.4f}" - ) + # MASEval predictions use 0/-inf which may not work well with DISCO + # since it's trained on actual log-likelihoods. We just check it doesn't crash. + try: + maseval_acc = compute_disco_accuracy(maseval_predictions) + print(f"DISCO predicted accuracy from MASEval predictions: {maseval_acc:.4f}") + print(f"Difference: {abs(maseval_acc - reference_acc):.4f}") + # Just check it's a valid number + assert not np.isnan(maseval_acc), "MASEval DISCO prediction is NaN" + except ValueError as e: + # If it fails due to NaN from softmax of -inf values, that's expected + pytest.skip(f"MASEval predictions not compatible with DISCO transform: {e}") From 62a6d45203326023097b6a5ba298b53aae8e42e3 Mon Sep 17 00:00:00 2001 From: Alexander Rubinstein Date: Sun, 1 Feb 2026 16:44:47 +0100 Subject: [PATCH 11/47] [Add MMLU task]: - Temporary update libs for reproducibility --- pyproject.toml | 2 +- uv.lock | 131 +++++++++++++++++++++---------------------------- 2 files changed, 56 insertions(+), 77 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 43f7278e..6fdf44e4 100644 --- a/pyproject.toml +++ 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Mon Sep 17 00:00:00 2001 From: Alexander Rubinstein Date: Sun, 1 Feb 2026 18:30:51 +0100 Subject: [PATCH 12/47] [Add MMLU task]: - Sync MMLU argmaxes between MASeval and DISCO --- examples/mmlu_benchmark/mmlu_benchmark.py | 81 ++++++++++++------- maseval/benchmark/mmlu/mmlu.py | 18 +++++ .../test_mmlu_disco_identity.py | 33 ++++---- 3 files changed, 84 insertions(+), 48 deletions(-) diff --git a/examples/mmlu_benchmark/mmlu_benchmark.py b/examples/mmlu_benchmark/mmlu_benchmark.py index b79d2d7f..3d12b69e 100644 --- a/examples/mmlu_benchmark/mmlu_benchmark.py +++ b/examples/mmlu_benchmark/mmlu_benchmark.py @@ -208,12 +208,10 @@ def __init__( self._batch_size = batch_size self._model = None self._tokenizer = None - self._choice_token_ids_cache = None def _load_model(self): """Lazy load the model and tokenizer for log-likelihood computation.""" if self._model is None: - import torch from transformers import AutoModelForCausalLM, AutoTokenizer print(f"Loading model: {self._model_id}") @@ -225,32 +223,48 @@ def _load_model(self): if self._tokenizer.pad_token is None: self._tokenizer.pad_token = self._tokenizer.eos_token - # Load model without device_map (doesn't require accelerate) + # Load model with torch_dtype="auto" to match lm-evaluation-harness exactly + # This uses the model's native dtype (bfloat16 for most modern models) # Then move to device manually self._model = AutoModelForCausalLM.from_pretrained( self._model_id, trust_remote_code=self._trust_remote_code, - torch_dtype=torch.float16, + torch_dtype="auto", ) self._model = self._model.to(self._device) self._model.eval() - # Pre-compute token IDs for answer choices A, B, C, D - self._choice_token_ids_cache = {} - for choice in ["A", "B", "C", "D"]: - tokens = self._tokenizer.encode(choice, add_special_tokens=False) - self._choice_token_ids_cache[choice] = tokens[0] if tokens else None - - print(f"Answer token IDs: {self._choice_token_ids_cache}") + # Note: We don't pre-cache choice token IDs here because they depend on context. + # Token IDs are computed dynamically in _get_choice_token_id_in_context() + # to match lm-evaluation-harness behavior exactly. return self._model, self._tokenizer - @property - def _choice_token_ids(self): - """Get cached choice token IDs.""" - if self._choice_token_ids_cache is None: - self._load_model() - return self._choice_token_ids_cache + def _get_choice_token_id_separate(self, choice: str) -> int: + """Get the token ID for a choice when tokenized SEPARATELY. + + CRITICAL: lm-evaluation-harness encodes context and continuation separately, + then concatenates. This means "A" is always tokenized standalone (token 330), + NOT in context after "Answer:" (which would be token 28741). + + We must match this behavior to get identical log-likelihood values. + + Args: + choice: The choice string (e.g., "A"). + + Returns: + Token ID for the choice (standalone tokenization). + """ + _, tokenizer = self._load_model() + + # Tokenize choice ALONE (not in context) - this is how lm-eval does it + choice_tokens = tokenizer.encode(choice, add_special_tokens=False) + + if len(choice_tokens) == 1: + return choice_tokens[0] + else: + # Multi-token choice - return None to trigger multi-token fallback + return None def _compute_logprobs_single_token(self, prompt: str, choices: list) -> list: """Compute log-likelihoods using single-token optimization. @@ -259,7 +273,9 @@ def _compute_logprobs_single_token(self, prompt: str, choices: list) -> list: forward pass: compute logits for the prompt, then look up the log-probability of each answer token at the last position. - This is the key optimization from lm-evaluation-harness. + IMPORTANT: To match lm-evaluation-harness exactly: + 1. Context is encoded WITH special tokens (BOS) - add_special_tokens=True + 2. Continuation tokens are encoded SEPARATELY (standalone) - add_special_tokens=False Args: prompt: The prompt/question text. @@ -272,8 +288,8 @@ def _compute_logprobs_single_token(self, prompt: str, choices: list) -> list: model, tokenizer = self._load_model() - # Tokenize the prompt - input_ids = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt") + # Tokenize the prompt WITH special tokens (BOS) to match lm-eval + input_ids = tokenizer.encode(prompt, add_special_tokens=True, return_tensors="pt") input_ids = input_ids.to(self._device) with torch.no_grad(): @@ -286,14 +302,15 @@ def _compute_logprobs_single_token(self, prompt: str, choices: list) -> list: log_probs = torch.nn.functional.log_softmax(last_logits, dim=-1) # Extract log-probabilities for each answer choice + # Use SEPARATE tokenization to match lm-evaluation-harness exactly + # (lm-eval encodes context and continuation separately, then concatenates) logprobs = [] for choice in choices: - token_id = self._choice_token_ids.get(choice) + token_id = self._get_choice_token_id_separate(choice) if token_id is not None: logprobs.append(log_probs[token_id].item()) else: - # Fallback: encode the choice and sum log-probs - # This handles multi-token answers if needed + # Fallback: multi-token choice, need different computation logprobs.append(float("-inf")) return logprobs @@ -315,11 +332,12 @@ def _compute_logprobs_batched(self, prompts: list, choices_list: list) -> list: model, tokenizer = self._load_model() # Batch encode with left padding + # Use add_special_tokens=True to match lm-evaluation-harness (adds BOS token) encoding = tokenizer( prompts, padding="longest", return_tensors="pt", - add_special_tokens=False, + add_special_tokens=True, ) input_ids = encoding["input_ids"].to(self._device) attention_mask = encoding["attention_mask"].to(self._device) @@ -347,10 +365,11 @@ def _compute_logprobs_batched(self, prompts: list, choices_list: list) -> list: log_probs = torch.nn.functional.log_softmax(last_logits, dim=-1) # Extract log-probabilities for each answer choice + # Use SEPARATE tokenization to match lm-evaluation-harness exactly choices = choices_list[i] item_logprobs = [] for choice in choices: - token_id = self._choice_token_ids.get(choice) + token_id = self._get_choice_token_id_separate(choice) if token_id is not None: item_logprobs.append(log_probs[token_id].item()) else: @@ -381,17 +400,18 @@ def _compute_logprobs_multi_token(self, prompt: str, choices: list) -> list: full_texts = [prompt + choice for choice in choices] # Encode all at once with padding + # Use add_special_tokens=True to match lm-evaluation-harness (adds BOS token) encoding = tokenizer( full_texts, padding="longest", return_tensors="pt", - add_special_tokens=False, + add_special_tokens=True, ) input_ids = encoding["input_ids"].to(self._device) attention_mask = encoding["attention_mask"].to(self._device) # Also encode just the prompt to know where continuation starts - prompt_ids = tokenizer.encode(prompt, add_special_tokens=False) + prompt_ids = tokenizer.encode(prompt, add_special_tokens=True) prompt_len = len(prompt_ids) with torch.no_grad(): @@ -405,7 +425,6 @@ def _compute_logprobs_multi_token(self, prompt: str, choices: list) -> list: all_logprobs = [] for i, choice in enumerate(choices): choice_ids = tokenizer.encode(choice, add_special_tokens=False) - choice_len = len(choice_ids) # Sum log-probs for the continuation tokens # Token at position j is predicted by logits at position j-1 @@ -435,11 +454,11 @@ def run_agents( prompt = environment.get_prompt() choices = environment.state.get("choices", ["A", "B", "C", "D"]) - # Load model to check if single-token optimization applies + # Load model self._load_model() - # Check if all choices are single tokens - all_single_token = all(choice in self._choice_token_ids and self._choice_token_ids[choice] is not None for choice in choices) + # Check if all choices are single tokens (using separate tokenization like lm-eval) + all_single_token = all(self._get_choice_token_id_separate(choice) is not None for choice in choices) if all_single_token: # Use optimized single-token path (one forward pass) diff --git a/maseval/benchmark/mmlu/mmlu.py b/maseval/benchmark/mmlu/mmlu.py index 8f3c8915..51eaabb7 100644 --- a/maseval/benchmark/mmlu/mmlu.py +++ b/maseval/benchmark/mmlu/mmlu.py @@ -390,6 +390,24 @@ def get_messages(self) -> List[Dict[str, Any]]: """Get agent messages.""" return self.agent._messages + def gather_traces(self) -> Dict[str, Any]: + """Gather execution traces from this agent. + + Override to handle plain list messages (not MessageHistory). + """ + from maseval.core.tracing import TraceableMixin + + messages = self.get_messages() + return { + **TraceableMixin.gather_traces(self), + "name": self.name, + "agent_type": type(self.agent).__name__, + "message_count": len(messages), + "messages": messages, # Already a list, no need for to_list() + "callbacks": [type(cb).__name__ for cb in self.callbacks], + "logs": self.logs, + } + # ============================================================================= # Benchmark diff --git a/tests/test_benchmark/test_mmlu_disco_identity.py b/tests/test_benchmark/test_mmlu_disco_identity.py index f0c9e478..b6ef13e4 100644 --- a/tests/test_benchmark/test_mmlu_disco_identity.py +++ b/tests/test_benchmark/test_mmlu_disco_identity.py @@ -308,20 +308,20 @@ def test_maseval_predictions_argmax_matches_reference(self, maseval_predictions, print(f"MASEval argmax distribution: {np.bincount(maseval_argmax, minlength=4)}") print(f"Reference argmax distribution: {np.bincount(reference_argmax, minlength=4)}") - # Text generation and log-likelihood scoring can give different results - # We expect at least 50% agreement for the same model (random would be 25%) - assert match_rate >= 0.5, f"Answer match rate {match_rate:.2%} is too low. Expected >= 50% agreement between evaluation methods." + # With contextual token ID computation, MASEval should produce identical + # predictions to lm-evaluation-harness (both use log-likelihood scoring) + assert match_rate == 1.0, f"Answer match rate {match_rate:.2%} is not 100%. Check that contextual token IDs are being used correctly." def test_disco_prediction_produces_valid_accuracy(self, maseval_predictions, reference_predictions): """DISCO prediction from MASEval predictions should produce valid accuracy. - Note: MASEval predictions use 0/-inf values (one-hot style) while disco-public - uses actual log-likelihoods. The DISCO predictor is trained on log-likelihood - distributions, so predictions from one-hot style inputs will be less accurate. + MASEval now uses the same log-likelihood scoring as lm-evaluation-harness, + so predictions should contain actual log-likelihoods (not 0/-inf values). This test checks that: 1. MASEval predictions produce a valid (0-1) accuracy prediction 2. Reference predictions produce a reasonable accuracy for the model + 3. Both predictions are very similar (since MASEval matches lm-eval) """ try: import torch @@ -361,14 +361,13 @@ def compute_disco_accuracy(predictions): print(f"DISCO predicted accuracy from reference predictions: {reference_acc:.4f}") assert 0.4 <= reference_acc <= 0.8, f"Reference accuracy {reference_acc} seems unreasonable" - # MASEval predictions use 0/-inf which may not work well with DISCO - # since it's trained on actual log-likelihoods. We just check it doesn't crash. - try: - maseval_acc = compute_disco_accuracy(maseval_predictions) - print(f"DISCO predicted accuracy from MASEval predictions: {maseval_acc:.4f}") - print(f"Difference: {abs(maseval_acc - reference_acc):.4f}") - # Just check it's a valid number - assert not np.isnan(maseval_acc), "MASEval DISCO prediction is NaN" - except ValueError as e: - # If it fails due to NaN from softmax of -inf values, that's expected - pytest.skip(f"MASEval predictions not compatible with DISCO transform: {e}") + # MASEval predictions should now have actual log-likelihoods (matching lm-eval) + maseval_acc = compute_disco_accuracy(maseval_predictions) + print(f"DISCO predicted accuracy from MASEval predictions: {maseval_acc:.4f}") + print(f"Difference: {abs(maseval_acc - reference_acc):.4f}") + + # Both should produce similar predictions since MASEval matches lm-eval + assert not np.isnan(maseval_acc), "MASEval DISCO prediction is NaN" + assert abs(maseval_acc - reference_acc) < 0.05, ( + f"DISCO predictions differ too much: MASEval={maseval_acc:.4f}, reference={reference_acc:.4f}. Expected difference < 0.05." + ) From 7e3b2b3dbf23171b24de96cdee4c44cfff39bea2 Mon Sep 17 00:00:00 2001 From: Alexander Rubinstein Date: Sun, 1 Feb 2026 19:55:58 +0100 Subject: [PATCH 13/47] [Add MMLU task]: - Achieve exact match with DISCO predictions for --lm_eval_batching --- examples/mmlu_benchmark/mmlu_benchmark.py | 349 ++++++++++++------ .../test_mmlu_disco_identity.py | 54 ++- 2 files changed, 279 insertions(+), 124 deletions(-) diff --git a/examples/mmlu_benchmark/mmlu_benchmark.py b/examples/mmlu_benchmark/mmlu_benchmark.py index 3d12b69e..0759c7c9 100644 --- a/examples/mmlu_benchmark/mmlu_benchmark.py +++ b/examples/mmlu_benchmark/mmlu_benchmark.py @@ -166,6 +166,11 @@ def parse_args(): default=None, help="Pad predictions to this size with -inf (default: no padding, disco-public uses 31)", ) + parser.add_argument( + "--use_lmeval_batching", + action="store_true", + help="Use lm-evaluation-harness batching for exact numerical match. This batches ALL requests together before computing logprobs.", + ) return parser.parse_args() @@ -266,16 +271,51 @@ def _get_choice_token_id_separate(self, choice: str) -> int: # Multi-token choice - return None to trigger multi-token fallback return None + def _encode_pair(self, context: str, continuation: str) -> tuple: + """Encode a context-continuation pair like lm-evaluation-harness. + + This matches lm-eval's _encode_pair method exactly: + 1. Encode whole = context + continuation + 2. Encode context alone + 3. continuation_enc = whole[len(context_enc):] + + This handles tokenization boundary effects correctly. + + Args: + context: The context/prompt string. + continuation: The continuation string (e.g., " A" with target_delimiter). + + Returns: + Tuple of (context_enc, continuation_enc) token lists. + """ + _, tokenizer = self._load_model() + + # Handle trailing spaces in context (move to continuation) + n_spaces = len(context) - len(context.rstrip()) + if n_spaces > 0: + continuation = context[-n_spaces:] + continuation + context = context[:-n_spaces] + + # Encode whole string together, then split + whole_enc = tokenizer.encode(context + continuation, add_special_tokens=True) + context_enc = tokenizer.encode(context, add_special_tokens=True) + + # Continuation tokens are what's left after context + continuation_enc = whole_enc[len(context_enc) :] + + return context_enc, continuation_enc + def _compute_logprobs_single_token(self, prompt: str, choices: list) -> list: """Compute log-likelihoods using single-token optimization. - For MCQ with single-letter answers (A, B, C, D), we only need ONE - forward pass: compute logits for the prompt, then look up the - log-probability of each answer token at the last position. + For MCQ with single-letter answers (A, B, C, D), we can compute all + choices in one forward pass since they share the same context. - IMPORTANT: To match lm-evaluation-harness exactly: - 1. Context is encoded WITH special tokens (BOS) - add_special_tokens=True - 2. Continuation tokens are encoded SEPARATELY (standalone) - add_special_tokens=False + IMPORTANT: To match lm-evaluation-harness EXACTLY: + 1. Use target_delimiter=" " before choices (e.g., " A" not "A") + 2. Use _encode_pair to handle tokenization boundaries correctly + 3. Input = (context + continuation)[:-1] + 4. Apply log_softmax to get log probabilities Args: prompt: The prompt/question text. @@ -286,39 +326,54 @@ def _compute_logprobs_single_token(self, prompt: str, choices: list) -> list: """ import torch - model, tokenizer = self._load_model() + model, _ = self._load_model() + + # lm-eval uses target_delimiter=" " for multiple choice tasks + target_delimiter = " " - # Tokenize the prompt WITH special tokens (BOS) to match lm-eval - input_ids = tokenizer.encode(prompt, add_special_tokens=True, return_tensors="pt") - input_ids = input_ids.to(self._device) + # Encode first choice to get the shared context + first_continuation = f"{target_delimiter}{choices[0]}" + context_enc, first_cont_enc = self._encode_pair(prompt, first_continuation) + + # Build input: (context + continuation)[:-1] + full_sequence = context_enc + first_cont_enc + input_tokens = full_sequence[:-1] # Remove last token + + input_ids = torch.tensor([input_tokens], dtype=torch.long, device=self._device) with torch.no_grad(): outputs = model(input_ids) - # Get logits at the last position (where the answer token would be predicted) - # logits shape: (batch, seq_len, vocab_size) - last_logits = outputs.logits[0, -1, :] # (vocab_size,) + logits = outputs.logits[0] # (seq_len, vocab_size) + + # Select logits at position where continuation is predicted + # For single-token continuation, this is the last position + inplen = len(input_tokens) + contlen = len(first_cont_enc) + selected_logits = logits[inplen - contlen : inplen] - # Compute log-softmax once - log_probs = torch.nn.functional.log_softmax(last_logits, dim=-1) + # Compute log-softmax + log_probs = torch.nn.functional.log_softmax(selected_logits, dim=-1) - # Extract log-probabilities for each answer choice - # Use SEPARATE tokenization to match lm-evaluation-harness exactly - # (lm-eval encodes context and continuation separately, then concatenates) + # Get log prob for each choice's continuation token logprobs = [] for choice in choices: - token_id = self._get_choice_token_id_separate(choice) - if token_id is not None: - logprobs.append(log_probs[token_id].item()) - else: - # Fallback: multi-token choice, need different computation - logprobs.append(float("-inf")) + continuation = f"{target_delimiter}{choice}" + _, cont_enc = self._encode_pair(prompt, continuation) + + # Sum log probs for multi-token continuations + total = 0.0 + for i, token_id in enumerate(cont_enc): + total += log_probs[i, token_id].item() + logprobs.append(total) return logprobs def _compute_logprobs_batched(self, prompts: list, choices_list: list) -> list: """Compute log-likelihoods for a batch of prompts. - Batches multiple questions together for efficient GPU utilization. + For exact match with lm-evaluation-harness, we process each prompt + individually using _compute_logprobs_single_token which uses the + correct _encode_pair tokenization logic. Args: prompts: List of prompt strings. @@ -327,63 +382,105 @@ def _compute_logprobs_batched(self, prompts: list, choices_list: list) -> list: Returns: List of log-likelihood lists, one per prompt. """ - import torch + # For exact match with lm-eval, process individually + # This ensures correct tokenization via _encode_pair + all_logprobs = [] + for prompt, choices in zip(prompts, choices_list): + logprobs = self._compute_logprobs_single_token(prompt, choices) + all_logprobs.append(logprobs) - model, tokenizer = self._load_model() + return all_logprobs - # Batch encode with left padding - # Use add_special_tokens=True to match lm-evaluation-harness (adds BOS token) - encoding = tokenizer( - prompts, - padding="longest", - return_tensors="pt", - add_special_tokens=True, - ) - input_ids = encoding["input_ids"].to(self._device) - attention_mask = encoding["attention_mask"].to(self._device) + def precompute_all_logprobs_lmeval(self, tasks) -> dict: + """Precompute log-likelihoods for ALL tasks using lm-eval's batching. - with torch.no_grad(): - outputs = model(input_ids, attention_mask=attention_mask) - # logits shape: (batch, seq_len, vocab_size) - logits = outputs.logits + CRITICAL: lm-evaluation-harness batches ALL requests together and uses + its Collator class to reorder/group them. This affects floating-point + precision for some edge cases. To get EXACT matches, we must process + ALL requests together in a single batch. - # For left-padded inputs, we need to find the actual last position for each sequence - # The last real token is where attention_mask is 1 - # For each sequence, get logits at position (seq_len - 1) accounting for padding - batch_size = input_ids.shape[0] - all_logprobs = [] + This method: + 1. Creates Instance objects for all task/choice combinations + 2. Calls lm-eval's HFLM.loglikelihood() with ALL instances + 3. Returns a mapping from doc_id to logprobs - for i in range(batch_size): - # Find the last non-padded position - seq_len = attention_mask[i].sum().item() - last_pos = seq_len - 1 + Args: + tasks: Iterable of Task objects with prompt and choices. - # Get logits at the last real position - last_logits = logits[i, last_pos, :] + Returns: + Dict mapping doc_id -> list of log-likelihoods for each choice. + """ + import sys - # Compute log-softmax - log_probs = torch.nn.functional.log_softmax(last_logits, dim=-1) + # Add lm-eval to path + sys.path.insert(0, "/home/oh/arubinstein17/github/disco-public") + sys.path.insert(0, "/home/oh/arubinstein17/github/disco-public/external/lm-evaluation-harness") - # Extract log-probabilities for each answer choice - # Use SEPARATE tokenization to match lm-evaluation-harness exactly - choices = choices_list[i] - item_logprobs = [] - for choice in choices: - token_id = self._get_choice_token_id_separate(choice) - if token_id is not None: - item_logprobs.append(log_probs[token_id].item()) - else: - item_logprobs.append(float("-inf")) + from lm_eval.models.huggingface import HFLM + from lm_eval.api.instance import Instance - all_logprobs.append(item_logprobs) + # Create HFLM model (this handles model loading internally) + lm = HFLM( + pretrained=self._model_id, + trust_remote_code=self._trust_remote_code, + batch_size=self._batch_size, + device=self._device, + ) - return all_logprobs + # lm-eval uses target_delimiter=" " for multiple choice tasks + target_delimiter = " " + choices = ["A", "B", "C", "D"] + continuations = [f"{target_delimiter}{c}" for c in choices] + + # Build ALL instances like lm-eval task system does + all_instances = [] + instance_map = {} # (doc_id, choice_idx) -> position in results + + for task in tasks: + doc_id = task.metadata.get("doc_id") + # Get prompt from task - use full_prompt from environment_data if available + if self.use_full_prompt and "full_prompt" in task.environment_data: + prompt = task.environment_data["full_prompt"] + else: + prompt = task.query + + for i, cont in enumerate(continuations): + inst = Instance( + request_type="loglikelihood", + doc={"doc_id": doc_id}, + arguments=(prompt, cont), + idx=i, + metadata=("mmlu_prompts", doc_id, 1), + ) + instance_map[(doc_id, i)] = len(all_instances) + all_instances.append(inst) + + print(f"Precomputing logprobs for {len(all_instances)} instances ({len(all_instances) // len(choices)} tasks)") + + # Call loglikelihood with ALL instances at once - this is the key! + results = lm.loglikelihood(all_instances) + + # Map results back to doc_ids + doc_logprobs = {} + for task in tasks: + doc_id = task.metadata.get("doc_id") + logprobs = [] + for i in range(len(choices)): + pos = instance_map[(doc_id, i)] + logprob, _ = results[pos] + logprobs.append(logprob) + doc_logprobs[doc_id] = logprobs + + # Store for later use + self._precomputed_logprobs = doc_logprobs + + return doc_logprobs def _compute_logprobs_multi_token(self, prompt: str, choices: list) -> list: """Compute log-likelihoods for multi-token continuations. This is the fallback for when answer choices have multiple tokens. - Batches all choices together and computes log-probs in parallel. + Uses _encode_pair to match lm-evaluation-harness exactly. Args: prompt: The prompt/question text. @@ -394,47 +491,42 @@ def _compute_logprobs_multi_token(self, prompt: str, choices: list) -> list: """ import torch - model, tokenizer = self._load_model() + model, _ = self._load_model() - # Build batch: prompt + each choice - full_texts = [prompt + choice for choice in choices] + # lm-eval uses target_delimiter=" " for multiple choice tasks + target_delimiter = " " - # Encode all at once with padding - # Use add_special_tokens=True to match lm-evaluation-harness (adds BOS token) - encoding = tokenizer( - full_texts, - padding="longest", - return_tensors="pt", - add_special_tokens=True, - ) - input_ids = encoding["input_ids"].to(self._device) - attention_mask = encoding["attention_mask"].to(self._device) + all_logprobs = [] + for choice in choices: + continuation = f"{target_delimiter}{choice}" - # Also encode just the prompt to know where continuation starts - prompt_ids = tokenizer.encode(prompt, add_special_tokens=True) - prompt_len = len(prompt_ids) + # Use _encode_pair for correct tokenization + context_enc, continuation_enc = self._encode_pair(prompt, continuation) - with torch.no_grad(): - outputs = model(input_ids, attention_mask=attention_mask) - logits = outputs.logits + # Build input: (context + continuation)[:-1] + full_sequence = context_enc + continuation_enc + input_tokens = full_sequence[:-1] + + input_ids = torch.tensor([input_tokens], dtype=torch.long, device=self._device) - # Compute log-softmax over vocab dimension - log_probs = torch.nn.functional.log_softmax(logits, dim=-1) + with torch.no_grad(): + outputs = model(input_ids) + logits = outputs.logits[0] # (seq_len, vocab_size) - # For each choice, sum log-probs of continuation tokens - all_logprobs = [] - for i, choice in enumerate(choices): - choice_ids = tokenizer.encode(choice, add_special_tokens=False) + # Select continuation logits + inplen = len(input_tokens) + contlen = len(continuation_enc) + selected = logits[inplen - contlen : inplen] - # Sum log-probs for the continuation tokens - # Token at position j is predicted by logits at position j-1 - total_logprob = 0.0 - for j, token_id in enumerate(choice_ids): - pos = prompt_len + j - 1 # Position in logits that predicts this token - if pos >= 0: - total_logprob += log_probs[i, pos, token_id].item() + # Compute log-softmax + log_probs = torch.nn.functional.log_softmax(selected, dim=-1) + + # Sum log probs for all continuation tokens + total = 0.0 + for i, token_id in enumerate(continuation_enc): + total += log_probs[i, token_id].item() - all_logprobs.append(total_logprob) + all_logprobs.append(total) return all_logprobs @@ -447,24 +539,62 @@ def run_agents( ): """Execute log-likelihood based MCQ evaluation. - Uses single-forward-pass optimization for single-token answers, - or multi-token batched computation as fallback. + Uses precomputed logprobs if available (for exact lm-eval match), + otherwise falls back to single-forward-pass optimization for + single-token answers, or multi-token batched computation. """ # Get the prompt from environment prompt = environment.get_prompt() choices = environment.state.get("choices", ["A", "B", "C", "D"]) - + doc_id = task.metadata.get("doc_id") if task else None + + # Check if we have precomputed logprobs (for exact lm-eval match) + if hasattr(self, "_precomputed_logprobs") and doc_id is not None: + logprobs = self._precomputed_logprobs.get(doc_id) + if logprobs is not None: + # Use precomputed values for exact match + best_idx = logprobs.index(max(logprobs)) + answer = choices[best_idx] + + # Store logprobs in environment for later retrieval + environment.state["logprobs"] = logprobs + environment.state["predicted_idx"] = best_idx + + # Record in agent messages for tracing + agent = agents[0] + agent.agent._messages.append({"role": "user", "content": prompt}) + agent.agent._messages.append( + { + "role": "assistant", + "content": answer, + "logprobs": logprobs, + } + ) + + return answer + + # Fall back to computing logprobs on-the-fly # Load model self._load_model() - # Check if all choices are single tokens (using separate tokenization like lm-eval) - all_single_token = all(self._get_choice_token_id_separate(choice) is not None for choice in choices) + # lm-eval uses target_delimiter=" " for multiple choice tasks + target_delimiter = " " + + # Check if all choices result in single-token continuations + # using _encode_pair to get the correct tokenization + all_single_token = True + for choice in choices: + continuation = f"{target_delimiter}{choice}" + _, cont_enc = self._encode_pair(prompt, continuation) + if len(cont_enc) != 1: + all_single_token = False + break if all_single_token: # Use optimized single-token path (one forward pass) logprobs = self._compute_logprobs_single_token(prompt, choices) else: - # Fall back to multi-token batched computation + # Fall back to multi-token computation logprobs = self._compute_logprobs_multi_token(prompt, choices) # Select the choice with highest log-probability @@ -833,6 +963,13 @@ def main(): num_workers=args.num_workers, ) + # Optionally precompute logprobs using lm-eval batching for exact match + if args.use_lmeval_batching: + print("\nPrecomputing logprobs using lm-eval batching (for exact numerical match)...") + # Get task list for precomputation + task_list = list(tasks._anchor_tasks if hasattr(tasks, "_anchor_tasks") else tasks._tasks) + benchmark.precompute_all_logprobs_lmeval(task_list) + # Run evaluation print("\nRunning evaluation...") agent_data = { diff --git a/tests/test_benchmark/test_mmlu_disco_identity.py b/tests/test_benchmark/test_mmlu_disco_identity.py index b6ef13e4..cd5fda01 100644 --- a/tests/test_benchmark/test_mmlu_disco_identity.py +++ b/tests/test_benchmark/test_mmlu_disco_identity.py @@ -285,32 +285,50 @@ def test_maseval_predictions_has_correct_format(self, maseval_predictions): padding = maseval_predictions[0, :, 4:] assert np.all(np.isinf(padding) & (padding < 0)), "Padding should be -inf" - def test_maseval_predictions_argmax_matches_reference(self, maseval_predictions, reference_predictions): - """Compare predicted answers between MASEval and disco-public. + def test_maseval_predictions_match_reference_exactly(self, maseval_predictions, reference_predictions): + """Compare log-likelihood values between MASEval and disco-public (lm-eval). - Note: MASEval uses text generation while disco-public uses log-likelihood - scoring. These methods can produce different results even with the same model, - so we only check for reasonable correlation, not exact match. + MASEval now uses identical computation to lm-evaluation-harness: + - Same tokenization (BOS token, separate context/continuation encoding) + - Same model dtype (torch_dtype="auto" -> bfloat16) + - Same log-softmax computation + + This test verifies the actual log-likelihood values are nearly identical, + not just that argmax matches. """ - # Get argmax for first 4 columns (A, B, C, D choices) + # Get choice columns (A, B, C, D) - exclude padding maseval_choices = maseval_predictions[0, :, :4] reference_choices = reference_predictions[0, :, :4] - maseval_argmax = np.argmax(maseval_choices, axis=1) - reference_argmax = np.argmax(reference_choices, axis=1) + # Check absolute closeness of log-likelihood values + # Use rtol=1e-3 and atol=1e-3 to allow for minor floating-point differences + # is_close = np.allclose(maseval_choices, reference_choices, rtol=1e-3, atol=1e-3) + is_close = np.allclose(maseval_choices, reference_choices) + + # Compute statistics for debugging + abs_diff = np.abs(maseval_choices - reference_choices) + max_diff = abs_diff.max() + mean_diff = abs_diff.mean() - # Count matches - matches = (maseval_argmax == reference_argmax).sum() - total = len(maseval_argmax) - match_rate = matches / total + # Find where max difference occurs + max_idx = np.unravel_index(abs_diff.argmax(), abs_diff.shape) - print(f"Answer match rate: {matches}/{total} ({match_rate:.2%})") - print(f"MASEval argmax distribution: {np.bincount(maseval_argmax, minlength=4)}") - print(f"Reference argmax distribution: {np.bincount(reference_argmax, minlength=4)}") + print(f"Max absolute difference: {max_diff:.6f} at index {max_idx}") + print(f"Mean absolute difference: {mean_diff:.6f}") + print("Sample comparison at max diff location:") + print(f" MASEval: {maseval_choices[max_idx[0], :]}") + print(f" Reference: {reference_choices[max_idx[0], :]}") - # With contextual token ID computation, MASEval should produce identical - # predictions to lm-evaluation-harness (both use log-likelihood scoring) - assert match_rate == 1.0, f"Answer match rate {match_rate:.2%} is not 100%. Check that contextual token IDs are being used correctly." + # Also check argmax agreement for reference + maseval_argmax = np.argmax(maseval_choices, axis=1) + reference_argmax = np.argmax(reference_choices, axis=1) + argmax_match_rate = (maseval_argmax == reference_argmax).mean() + print(f"Argmax match rate: {argmax_match_rate:.2%}") + + assert is_close, ( + f"Predictions not close enough. Max diff: {max_diff:.6f}, Mean diff: {mean_diff:.6f}. " + f"Check model dtype, tokenization, and log-softmax computation match lm-eval." + ) def test_disco_prediction_produces_valid_accuracy(self, maseval_predictions, reference_predictions): """DISCO prediction from MASEval predictions should produce valid accuracy. From 2f97f29f39fb5ea636a1ae612c8b5724ec87f4a9 Mon Sep 17 00:00:00 2001 From: Alexander Rubinstein Date: Thu, 5 Feb 2026 08:34:24 +0100 Subject: [PATCH 14/47] [Add DISCO to HF]: - Load DISCO predictor and transform from npz --- examples/mmlu_benchmark/README.md | 24 +++ .../mmlu_benchmark/extract_disco_weights.py | 133 ++++++++++++ examples/mmlu_benchmark/mmlu_benchmark.py | 203 +++++++++++++----- 3 files changed, 311 insertions(+), 49 deletions(-) create mode 100644 examples/mmlu_benchmark/extract_disco_weights.py diff --git a/examples/mmlu_benchmark/README.md b/examples/mmlu_benchmark/README.md index 3349f1dd..d0c112f4 100644 --- a/examples/mmlu_benchmark/README.md +++ b/examples/mmlu_benchmark/README.md @@ -84,6 +84,30 @@ This is equivalent to running: 1. The evaluation on anchor points (like `scripts/run_lm_eval.py --skip_non_anchor_points`) 2. The prediction step (like `scripts/predict_model_performance.py`) +#### Avoiding pickle / sklearn version warnings + +If you see `InconsistentVersionWarning` when loading the DISCO pickles (e.g. fitted_weights.pkl was saved with sklearn 1.7.2 but you use 1.8.0), you can export the model to NumPy-only `.npz` files and use those instead: + +```bash +# One-time extraction (run with the env that created the pickles, or ignore the warning) +python extract_disco_weights.py \ + --model_path /path/to/fitted_weights.pkl \ + --transform_path /path/to/transform.pkl \ + --output_dir /path/to/disco_npz +``` + +Then point the benchmark at the extracted files: + +```bash +python mmlu_benchmark.py ... \ + --disco_prediction \ + --disco_model_path /path/to/disco_npz/disco_model.npz \ + --disco_transform_path /path/to/disco_npz/disco_transform.npz \ + --pca 256 +``` + +No pickle or sklearn objects are loaded at runtime when using `.npz` paths. + ### Quick Test Run on a small subset for testing: diff --git a/examples/mmlu_benchmark/extract_disco_weights.py b/examples/mmlu_benchmark/extract_disco_weights.py new file mode 100644 index 00000000..26747fa2 --- /dev/null +++ b/examples/mmlu_benchmark/extract_disco_weights.py @@ -0,0 +1,133 @@ +""" +Extract numpy arrays from DISCO pickle files (fitted_weights.pkl, transform.pkl). + +Run this once with the same environment that created the pickles (or ignore +InconsistentVersionWarning). The output .npz files can be loaded without +pickle, avoiding sklearn version mismatch warnings. + +Usage: + python extract_disco_weights.py \\ + --model_path /path/to/fitted_weights.pkl \\ + --transform_path /path/to/transform.pkl \\ + --output_dir /path/to/output + +Output: + {output_dir}/disco_transform.npz - PCA: components_, mean_ + {output_dir}/disco_model.npz - RandomForest: tree arrays + {output_dir}/disco_meta.json - sampling_name, number_item, fitted_model_type +""" + +import argparse +import json +import pickle +import warnings +from pathlib import Path + +import numpy as np + + +def _extract_pca(transform) -> dict: + """Extract numpy arrays from a fitted sklearn PCA object.""" + return { + "components_": np.asarray(transform.components_, dtype=np.float64), + "mean_": np.asarray(transform.mean_, dtype=np.float64), + "n_components_": int(transform.n_components_), + "n_features_in_": int(transform.n_features_in_), + } + + +def _extract_tree(tree) -> dict: + """Extract numpy arrays from one DecisionTreeRegressor.tree_.""" + return { + "children_left": np.asarray(tree.children_left, dtype=np.int32), + "children_right": np.asarray(tree.children_right, dtype=np.int32), + "feature": np.asarray(tree.feature, dtype=np.int32), + "threshold": np.asarray(tree.threshold, dtype=np.float64), + "value": np.asarray(tree.value, dtype=np.float64).reshape(-1), + } + + +def _extract_rf(estimator) -> dict: + """Extract numpy arrays from a RandomForestRegressor (all trees).""" + trees = [] + for est in estimator.estimators_: + trees.append(_extract_tree(est.tree_)) + n_trees = len(trees) + # Concatenate per-tree arrays; store node count per tree for splitting + node_counts = np.array([len(t["children_left"]) for t in trees], dtype=np.int64) + return { + "n_trees": n_trees, + "tree_node_counts": node_counts, + "children_left": np.concatenate([t["children_left"] for t in trees]), + "children_right": np.concatenate([t["children_right"] for t in trees]), + "feature": np.concatenate([t["feature"] for t in trees]), + "threshold": np.concatenate([t["threshold"] for t in trees]), + "value": np.concatenate([t["value"] for t in trees]), + } + + +def _get_fitted_model(model_data: dict) -> tuple: + """Return (fitted_model, sampling_name, number_item, fitted_model_type).""" + if "fitted_weights" in model_data: + fitted_weights = model_data["fitted_weights"] + else: + fitted_weights = {k: v for k, v in model_data.items() if k != "transform"} + if not fitted_weights: + raise ValueError("Could not find fitted_weights in model file.") + sampling_name = list(fitted_weights.keys())[0] + number_item = list(fitted_weights[sampling_name].keys())[0] + fitted_model_type = list(fitted_weights[sampling_name][number_item].keys())[0] + fitted_model = fitted_weights[sampling_name][number_item][fitted_model_type] + return fitted_model, sampling_name, number_item, fitted_model_type + + +def main(): + parser = argparse.ArgumentParser( + description="Extract numpy arrays from DISCO pickle files for pickle-free loading.", + ) + parser.add_argument("--model_path", type=str, required=True, help="Path to fitted_weights.pkl") + parser.add_argument("--transform_path", type=str, required=True, help="Path to transform.pkl") + parser.add_argument( + "--output_dir", + type=str, + default=".", + help="Directory to write disco_transform.npz and disco_model.npz", + ) + args = parser.parse_args() + out_dir = Path(args.output_dir) + out_dir.mkdir(parents=True, exist_ok=True) + + # Load transform (PCA) + with warnings.catch_warnings(): + warnings.simplefilter("ignore", category=UserWarning) + with open(args.transform_path, "rb") as f: + transform = pickle.load(f) + pca_data = _extract_pca(transform) + transform_out = out_dir / "disco_transform.npz" + np.savez(transform_out, **pca_data) + print(f"Saved PCA arrays to {transform_out}") + + # Load model and extract RF + with warnings.catch_warnings(): + warnings.simplefilter("ignore", category=UserWarning) + with open(args.model_path, "rb") as f: + model_data = pickle.load(f) + fitted_model, sampling_name, number_item, fitted_model_type = _get_fitted_model(model_data) + rf_data = _extract_rf(fitted_model) + model_out = out_dir / "disco_model.npz" + np.savez(model_out, **rf_data) + print(f"Saved RF arrays to {model_out}") + meta = { + "sampling_name": str(sampling_name), + "number_item": str(number_item), + "fitted_model_type": str(fitted_model_type), + } + meta_out = out_dir / "disco_meta.json" + with open(meta_out, "w") as f: + json.dump(meta, f, indent=2) + print(f"Saved metadata to {meta_out}") + print(f" sampling={sampling_name}, n_items={number_item}, model={fitted_model_type}") + + +if __name__ == "__main__": + main() diff --git a/examples/mmlu_benchmark/mmlu_benchmark.py b/examples/mmlu_benchmark/mmlu_benchmark.py index 0759c7c9..cb5c62d2 100644 --- a/examples/mmlu_benchmark/mmlu_benchmark.py +++ b/examples/mmlu_benchmark/mmlu_benchmark.py @@ -420,6 +420,7 @@ def precompute_all_logprobs_lmeval(self, tasks) -> dict: from lm_eval.api.instance import Instance # Create HFLM model (this handles model loading internally) + print(f"Loading HFLM model for {self._model_id}") lm = HFLM( pretrained=self._model_id, trust_remote_code=self._trust_remote_code, @@ -746,10 +747,101 @@ def extract_eval_entries(res): return predictions +def _pca_transform_numpy(X: np.ndarray, components: np.ndarray, mean: np.ndarray) -> np.ndarray: + """Apply PCA transform using only numpy: (X - mean) @ components.T.""" + return (X - mean) @ components.T + + +def _predict_tree_numpy( + X: np.ndarray, + children_left: np.ndarray, + children_right: np.ndarray, + feature: np.ndarray, + threshold: np.ndarray, + value: np.ndarray, +) -> np.ndarray: + """Predict for one tree; X shape (n_samples, n_features). Returns (n_samples,).""" + out = np.empty(X.shape[0], dtype=np.float64) + for i in range(X.shape[0]): + node = 0 + while children_left[node] != -1: + if X[i, feature[node]] <= threshold[node]: + node = children_left[node] + else: + node = children_right[node] + out[i] = value[node] + return out + + +def _predict_rf_numpy( + X: np.ndarray, + tree_node_counts: np.ndarray, + children_left: np.ndarray, + children_right: np.ndarray, + feature: np.ndarray, + threshold: np.ndarray, + value: np.ndarray, +) -> np.ndarray: + """Predict using extracted RF tree arrays; X shape (n_samples, n_features). Returns (n_samples,).""" + offsets = np.concatenate([[0], np.cumsum(tree_node_counts)]) + n_trees = len(tree_node_counts) + preds = np.zeros((n_trees, X.shape[0]), dtype=np.float64) + for t in range(n_trees): + lo, hi = offsets[t], offsets[t + 1] + preds[t] = _predict_tree_numpy( + X, + children_left[lo:hi], + children_right[lo:hi], + feature[lo:hi], + threshold[lo:hi], + value[lo:hi], + ) + return np.mean(preds, axis=0) + + +def load_disco_from_npz( + model_npz_path: str, + transform_npz_path: str, + meta_path: Optional[str] = None, +) -> tuple: + """Load DISCO transform and model from .npz (no pickle). + + Returns: + (transform_fn, predict_fn, meta_dict). transform_fn(X) returns transformed + array; predict_fn(X) returns predicted accuracies; meta_dict has + sampling_name, number_item, fitted_model_type. + """ + transform_data = np.load(transform_npz_path) + components = np.asarray(transform_data["components_"]) + mean = np.asarray(transform_data["mean_"]) + + def transform_fn(X: np.ndarray) -> np.ndarray: + return _pca_transform_numpy(X, components, mean) + + model_data = np.load(model_npz_path) + tree_node_counts = np.asarray(model_data["tree_node_counts"], dtype=np.int64) + children_left = np.asarray(model_data["children_left"], dtype=np.int32) + children_right = np.asarray(model_data["children_right"], dtype=np.int32) + feature = np.asarray(model_data["feature"], dtype=np.int32) + threshold = np.asarray(model_data["threshold"], dtype=np.float64) + value = np.asarray(model_data["value"], dtype=np.float64) + + def predict_fn(X: np.ndarray) -> np.ndarray: + return _predict_rf_numpy(X, tree_node_counts, children_left, children_right, feature, threshold, value) + + if meta_path is None: + meta_path = str(Path(model_npz_path).parent / "disco_meta.json") + with open(meta_path) as f: + meta = json.load(f) + transform_npz = {"components_": components, "mean_": mean} + return transform_fn, predict_fn, meta, transform_npz + + def compute_disco_embedding( predictions: np.ndarray, pca: int, transform=None, + transform_npz: Optional[dict] = None, apply_softmax: bool = True, ) -> tuple: """Compute DISCO embeddings from predictions. @@ -759,7 +851,8 @@ def compute_disco_embedding( Args: predictions: Predictions tensor of shape (n_models, n_anchor_points, n_classes). pca: PCA dimension for dimensionality reduction. - transform: Pre-fitted PCA transform. If None, a new one will be fitted. + transform: Pre-fitted sklearn PCA transform. If None, a new one will be fitted (unless transform_npz). + transform_npz: If set, (components_, mean_) from npz; used instead of transform (no pickle). apply_softmax: Whether to apply softmax to predictions. Returns: @@ -782,19 +875,26 @@ def compute_disco_embedding( # Flatten to (n_models, n_anchor_points * n_classes) emb_unreduced = emb_unreduced.reshape(emb_unreduced.shape[0], -1) + X = emb_unreduced.numpy() # Apply PCA if pca is not None: - if transform is None: + if transform_npz is not None: + emb = _pca_transform_numpy(X, transform_npz["components_"], transform_npz["mean_"]) + emb = torch.Tensor(emb) + transform = None + elif transform is not None: + emb = transform.transform(X) + emb = torch.Tensor(emb) + else: # Fit new PCA transform transform = PCA( n_components=pca, svd_solver="full", random_state=42, - ).fit(emb_unreduced.numpy()) - - emb = transform.transform(emb_unreduced.numpy()) - emb = torch.Tensor(emb) + ).fit(X) + emb = transform.transform(X) + emb = torch.Tensor(emb) else: emb = emb_unreduced @@ -811,23 +911,53 @@ def predict_with_disco( This implements the prediction logic from disco-public/scripts/predict_model_performance.py. + Use .npz paths (from extract_disco_weights.py) to avoid pickle/sklearn version warnings. + Args: predictions: Predictions tensor of shape (n_models, n_anchor_points, n_classes). - model_path: Path to fitted weights pickle file. - transform_path: Path to PCA transform pickle file (optional, can be in model_path). + model_path: Path to fitted weights pickle file or disco_model.npz. + transform_path: Path to PCA transform pickle or disco_transform.npz (optional if in model_path). pca: PCA dimension (default: 256). Returns: Dict with predicted accuracies and metadata. """ - # Load model data + use_npz = model_path.endswith(".npz") and (transform_path or "").endswith(".npz") + if model_path.endswith(".npz") and not (transform_path or "").endswith(".npz"): + raise ValueError("When using .npz model path, provide --disco_transform_path to disco_transform.npz") + + if use_npz: + transform_fn, predict_fn, meta, transform_npz = load_disco_from_npz(model_path, transform_path, meta_path=None) + sampling_name = meta["sampling_name"] + number_item = meta["number_item"] + fitted_model_type = meta["fitted_model_type"] + print(f" Using: sampling={sampling_name}, n_items={number_item}, model={fitted_model_type} (from .npz)") + + embeddings, _ = compute_disco_embedding( + predictions, + pca=pca, + transform=None, + transform_npz=transform_npz, + apply_softmax=True, + ) + X_emb = embeddings.numpy() if hasattr(embeddings, "numpy") else np.asarray(embeddings) + pred_values = predict_fn(X_emb) + predicted_accs = {i: float(pred_values[i]) for i in range(len(pred_values))} + return { + "predicted_accuracies": predicted_accs, + "sampling_name": sampling_name, + "number_item": number_item, + "fitted_model_type": fitted_model_type, + "pca": pca, + } + + # Pickle path with open(model_path, "rb") as f: model_data = pickle.load(f) if not isinstance(model_data, dict): raise ValueError(f"model_path must contain a dict. Got {type(model_data)}") - # Get transform if transform_path is not None: with open(transform_path, "rb") as f: transform = pickle.load(f) @@ -836,34 +966,19 @@ def predict_with_disco( else: raise ValueError("Transform not found. Provide --disco_transform_path or ensure the model file contains a 'transform' key.") - # Get fitted weights if "fitted_weights" in model_data: fitted_weights = model_data["fitted_weights"] else: - # Assume the dict itself is fitted_weights (excluding transform) fitted_weights = {k: v for k, v in model_data.items() if k != "transform"} if not fitted_weights: raise ValueError("Could not find fitted_weights in model file.") - # Get metadata or infer from structure - if "sampling_name" in model_data: - sampling_name = model_data["sampling_name"] - else: - sampling_name = list(fitted_weights.keys())[0] - - if "number_item" in model_data: - number_item = model_data["number_item"] - else: - number_item = list(fitted_weights[sampling_name].keys())[0] - - if "fitted_model_type" in model_data: - fitted_model_type = model_data["fitted_model_type"] - else: - fitted_model_type = list(fitted_weights[sampling_name][number_item].keys())[0] + sampling_name = model_data.get("sampling_name") or list(fitted_weights.keys())[0] + number_item = model_data.get("number_item") or list(fitted_weights[sampling_name].keys())[0] + fitted_model_type = model_data.get("fitted_model_type") or list(fitted_weights[sampling_name][number_item].keys())[0] print(f" Using: sampling={sampling_name}, n_items={number_item}, model={fitted_model_type}") - # Compute embeddings embeddings, _ = compute_disco_embedding( predictions, pca=pca, @@ -871,21 +986,11 @@ def predict_with_disco( apply_softmax=True, ) - # Get the fitted model fitted_model = fitted_weights[sampling_name][number_item][fitted_model_type] - - # Predict accuracies for each model predicted_accs = {} for model_idx in range(embeddings.shape[0]): model_embedding = embeddings[model_idx] - - # Convert to numpy if needed - if hasattr(model_embedding, "numpy"): - model_embedding_np = model_embedding.numpy() - else: - model_embedding_np = np.array(model_embedding) - - # Predict using fitted model + model_embedding_np = model_embedding.numpy() if hasattr(model_embedding, "numpy") else np.array(model_embedding) predicted_acc = fitted_model.predict(model_embedding_np.reshape(1, -1))[0] predicted_accs[model_idx] = predicted_acc @@ -965,7 +1070,7 @@ def main(): # Optionally precompute logprobs using lm-eval batching for exact match if args.use_lmeval_batching: - print("\nPrecomputing logprobs using lm-eval batching (for exact numerical match)...") + print("\nPrecomputing logprobs using lm-eval batching ...") # Get task list for precomputation task_list = list(tasks._anchor_tasks if hasattr(tasks, "_anchor_tasks") else tasks._tasks) benchmark.precompute_all_logprobs_lmeval(task_list) @@ -1020,15 +1125,15 @@ def main(): for model_idx, acc in disco_results["predicted_accuracies"].items(): print(f" Model {model_idx}: {acc:.6f}") - # Compare with actual anchor accuracy - print("\n" + "-" * 40) - print("Comparison:") - print("-" * 40) - predicted_acc = disco_results["predicted_accuracies"][0] - actual_anchor_acc = metrics["acc"] - print(f" Actual accuracy (on anchor points): {actual_anchor_acc:.6f}") - print(f" DISCO predicted accuracy (full benchmark): {predicted_acc:.6f}") - print(f" Difference: {predicted_acc - actual_anchor_acc:+.6f}") + # # Compare with actual anchor accuracy + # print("\n" + "-" * 40) + # print("Comparison:") + # print("-" * 40) + # predicted_acc = disco_results["predicted_accuracies"][0] + # actual_anchor_acc = metrics["acc"] + # print(f" Actual accuracy (on anchor points): {actual_anchor_acc:.6f}") + # print(f" DISCO predicted accuracy (full benchmark): {predicted_acc:.6f}") + # print(f" Difference: {predicted_acc - actual_anchor_acc:+.6f}") # Save summary summary_data = { From 36e7482828fcb9e0fb775b8266bc97cbbcae8a16 Mon Sep 17 00:00:00 2001 From: Alexander Rubinstein Date: Thu, 5 Feb 2026 09:12:17 +0100 Subject: [PATCH 15/47] [Add to HF]: - DISCO model is loadable from hf --- .gitignore | 2 + examples/mmlu_benchmark/README.md | 18 ++ .../huggingface_disco/README.md | 69 ++++++ .../huggingface_disco/__init__.py | 6 + .../huggingface_disco/build_repo.py | 82 +++++++ .../huggingface_disco/configuration_disco.py | 24 ++ .../huggingface_disco/modeling_disco.py | 218 ++++++++++++++++++ examples/mmlu_benchmark/mmlu_benchmark.py | 28 ++- 8 files changed, 445 insertions(+), 2 deletions(-) create mode 100644 examples/mmlu_benchmark/huggingface_disco/README.md create mode 100644 examples/mmlu_benchmark/huggingface_disco/__init__.py create mode 100644 examples/mmlu_benchmark/huggingface_disco/build_repo.py create mode 100644 examples/mmlu_benchmark/huggingface_disco/configuration_disco.py create mode 100644 examples/mmlu_benchmark/huggingface_disco/modeling_disco.py diff --git a/.gitignore b/.gitignore index c2b9f6df..467eed97 100644 --- a/.gitignore +++ b/.gitignore @@ -5,6 +5,8 @@ .idea/ .DS_Store .devcontainer/ +results/ +DISCO-MMLU/ # Byte-compiled / optimized / DLL files __pycache__/ diff --git a/examples/mmlu_benchmark/README.md b/examples/mmlu_benchmark/README.md index d0c112f4..f5a7a428 100644 --- a/examples/mmlu_benchmark/README.md +++ b/examples/mmlu_benchmark/README.md @@ -108,6 +108,24 @@ python mmlu_benchmark.py ... \ No pickle or sklearn objects are loaded at runtime when using `.npz` paths. +#### Loading DISCO from Hugging Face + +You can upload the extracted weights as a custom Hugging Face model and load it with `AutoModel.from_pretrained(..., trust_remote_code=True)`. See [huggingface_disco/README.md](huggingface_disco/README.md) for: + +1. Extracting weights (`extract_disco_weights.py`) +2. Building the HF repo (`huggingface_disco/build_repo.py`) +3. Uploading to the Hub + +Then run the benchmark with a repo id instead of local paths: + +```bash +python mmlu_benchmark.py ... \ + --disco_prediction \ + --disco_model_path /my-disco-mmlu +``` + +(No `--disco_transform_path` needed; the Hub model contains everything.) + ### Quick Test Run on a small subset for testing: diff --git a/examples/mmlu_benchmark/huggingface_disco/README.md b/examples/mmlu_benchmark/huggingface_disco/README.md new file mode 100644 index 00000000..ab4ea55e --- /dev/null +++ b/examples/mmlu_benchmark/huggingface_disco/README.md @@ -0,0 +1,69 @@ +# DISCO predictor as a Hugging Face model + +This folder contains the code and layout to upload a DISCO predictor (PCA + Random Forest) to the Hugging Face Hub so it can be loaded with: + +```python +from transformers import AutoModel + +model = AutoModel.from_pretrained("/my-disco-mmlu", trust_remote_code=True) +# predictions: (n_models, n_anchor_points, n_classes) e.g. log-probabilities +acc = model.predict(predictions) +# acc: (n_models,) predicted full-benchmark accuracies +``` + +## 1. Extract weights to NumPy (no pickle) + +From the MASEval repo, run the extractor (once) to produce `disco_transform.npz`, `disco_model.npz`, and `disco_meta.json`: + +```bash +python examples/mmlu_benchmark/extract_disco_weights.py \ + --model_path /path/to/fitted_weights.pkl \ + --transform_path /path/to/transform.pkl \ + --output_dir /path/to/disco_weights +``` + +## 2. Build the Hugging Face repo + +Run the build script to create a directory you can upload: + +```bash +python examples/mmlu_benchmark/huggingface_disco/build_repo.py \ + --weights_dir /path/to/disco_weights \ + --output_dir ./my-disco-mmlu +``` + +This copies the NumPy weights and writes `config.json` with `auto_map` so `AutoModel.from_pretrained(..., trust_remote_code=True)` loads `DiscoPredictor`. + +## 3. Upload to the Hub + +```bash +cd my-disco-mmlu +huggingface-cli upload /my-disco-mmlu . . +``` + +Or from Python: + +```python +from huggingface_hub import HfApi +api = HfApi() +api.upload_folder(folder_path="./my-disco-mmlu", repo_id="/my-disco-mmlu", repo_type="model") +``` + +## Repo contents + +- `config.json` – `model_type: "disco"` and `auto_map` for AutoConfig / AutoModel +- `configuration_disco.py` – `DiscoConfig` +- `modeling_disco.py` – `DiscoPredictor` (loads npz, implements `predict`) +- `disco_transform.npz` – PCA components and mean +- `disco_model.npz` – Random Forest tree arrays +- `disco_meta.json` – sampling_name, number_item, fitted_model_type (optional, for display) + +## Dependencies + +Loading from the Hub only needs: + +- `transformers` +- `huggingface_hub` +- `numpy` + +No `scikit-learn` or pickle at runtime. diff --git a/examples/mmlu_benchmark/huggingface_disco/__init__.py b/examples/mmlu_benchmark/huggingface_disco/__init__.py new file mode 100644 index 00000000..d5964a6d --- /dev/null +++ b/examples/mmlu_benchmark/huggingface_disco/__init__.py @@ -0,0 +1,6 @@ +# DISCO predictor as a Hugging Face custom model. + +from .configuration_disco import DiscoConfig +from .modeling_disco import DiscoPredictor + +__all__ = ["DiscoConfig", "DiscoPredictor"] diff --git a/examples/mmlu_benchmark/huggingface_disco/build_repo.py b/examples/mmlu_benchmark/huggingface_disco/build_repo.py new file mode 100644 index 00000000..51b5379d --- /dev/null +++ b/examples/mmlu_benchmark/huggingface_disco/build_repo.py @@ -0,0 +1,82 @@ +""" +Build a directory ready to upload to Hugging Face as a DISCO predictor model. + +Usage: + python build_repo.py \\ + --weights_dir /path/to/disco_weights \\ + --output_dir ./my-disco-mmlu + +Then upload with: huggingface-cli upload /my-disco-mmlu ./my-disco-mmlu . +""" + +import argparse +import json +import shutil +from pathlib import Path + + +def main(): + parser = argparse.ArgumentParser( + description="Build HF repo directory for DISCO predictor (npz + config + code).", + ) + parser.add_argument( + "--weights_dir", + type=str, + required=True, + help="Directory containing disco_transform.npz, disco_model.npz, disco_meta.json", + ) + parser.add_argument( + "--output_dir", + type=str, + required=True, + help="Output directory to create (will contain config, code, and weights).", + ) + args = parser.parse_args() + + weights_dir = Path(args.weights_dir) + output_dir = Path(args.output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + + # Required weight files + for name in ("disco_transform.npz", "disco_model.npz", "disco_meta.json"): + src = weights_dir / name + if not src.exists(): + raise FileNotFoundError(f"Missing {name} in {weights_dir}. Run extract_disco_weights.py first.") + shutil.copy2(src, output_dir / name) + print(f"Copied weights from {weights_dir} -> {output_dir}") + + # Copy modeling and config code (so repo is self-contained) + this_dir = Path(__file__).resolve().parent + for name in ("configuration_disco.py", "modeling_disco.py"): + shutil.copy2(this_dir / name, output_dir / name) + print(f"Copied {name}") + + # config.json with auto_map for AutoModel.from_pretrained(..., trust_remote_code=True) + meta_path = output_dir / "disco_meta.json" + meta = {} + if meta_path.exists(): + with open(meta_path) as f: + meta = json.load(f) + + config = { + "model_type": "disco", + "auto_map": { + "AutoConfig": "configuration_disco.DiscoConfig", + "AutoModel": "modeling_disco.DiscoPredictor", + }, + "n_components": 256, + "sampling_name": meta.get("sampling_name", ""), + "number_item": meta.get("number_item", ""), + "fitted_model_type": meta.get("fitted_model_type", ""), + } + config_path = output_dir / "config.json" + with open(config_path, "w") as f: + json.dump(config, f, indent=2) + print(f"Wrote {config_path}") + + print(f"\nRepo ready at: {output_dir.resolve()}") + print("Upload with: huggingface-cli upload /my-disco-mmlu ./my-disco-mmlu .") + + +if __name__ == "__main__": + main() diff --git a/examples/mmlu_benchmark/huggingface_disco/configuration_disco.py b/examples/mmlu_benchmark/huggingface_disco/configuration_disco.py new file mode 100644 index 00000000..f6c15555 --- /dev/null +++ b/examples/mmlu_benchmark/huggingface_disco/configuration_disco.py @@ -0,0 +1,24 @@ +# Copyright 2025 MASEval contributors. DISCO predictor config for Hugging Face Hub. + +# Use configuration_utils so this works when transformers uses lazy top-level imports +from transformers.configuration_utils import PretrainedConfig + + +class DiscoConfig(PretrainedConfig): + """Configuration for DISCO predictor (PCA + Random Forest) on the Hub.""" + + model_type = "disco" + + def __init__( + self, + n_components: int = 256, + sampling_name: str = "", + number_item: str = "", + fitted_model_type: str = "", + **kwargs, + ): + super().__init__(**kwargs) + self.n_components = n_components + self.sampling_name = sampling_name + self.number_item = number_item + self.fitted_model_type = fitted_model_type diff --git a/examples/mmlu_benchmark/huggingface_disco/modeling_disco.py b/examples/mmlu_benchmark/huggingface_disco/modeling_disco.py new file mode 100644 index 00000000..789d7368 --- /dev/null +++ b/examples/mmlu_benchmark/huggingface_disco/modeling_disco.py @@ -0,0 +1,218 @@ +# Copyright 2025 MASEval contributors. DISCO predictor model for Hugging Face Hub. +# +# Self-contained: uses only numpy and huggingface_hub. Load with: +# from transformers import AutoModel +# model = AutoModel.from_pretrained("/my-disco-mmlu", trust_remote_code=True) +# acc = model.predict(predictions_tensor) # predictions: (n_models, n_anchor_points, n_classes) + +from pathlib import Path +from typing import Any, Optional, Union + +import numpy as np + + +def _pca_transform(X: np.ndarray, components: np.ndarray, mean: np.ndarray) -> np.ndarray: + """Apply PCA transform: (X - mean) @ components.T.""" + return (X - mean) @ components.T + + +def _predict_tree( + X: np.ndarray, + children_left: np.ndarray, + children_right: np.ndarray, + feature: np.ndarray, + threshold: np.ndarray, + value: np.ndarray, +) -> np.ndarray: + """Predict for one tree; X (n_samples, n_features) -> (n_samples,).""" + out = np.empty(X.shape[0], dtype=np.float64) + for i in range(X.shape[0]): + node = 0 + while children_left[node] != -1: + if X[i, feature[node]] <= threshold[node]: + node = children_left[node] + else: + node = children_right[node] + out[i] = value[node] + return out + + +def _predict_rf( + X: np.ndarray, + tree_node_counts: np.ndarray, + children_left: np.ndarray, + children_right: np.ndarray, + feature: np.ndarray, + threshold: np.ndarray, + value: np.ndarray, +) -> np.ndarray: + """Predict using RF tree arrays; X (n_samples, n_features) -> (n_samples,).""" + offsets = np.concatenate([[0], np.cumsum(tree_node_counts)]) + n_trees = len(tree_node_counts) + preds = np.zeros((n_trees, X.shape[0]), dtype=np.float64) + for t in range(n_trees): + lo, hi = offsets[t], offsets[t + 1] + preds[t] = _predict_tree( + X, + children_left[lo:hi], + children_right[lo:hi], + feature[lo:hi], + threshold[lo:hi], + value[lo:hi], + ) + return np.mean(preds, axis=0) + + +class DiscoPredictor: + """ + DISCO predictor: maps anchor-point prediction tensors to full-benchmark accuracy. + + Load from the Hub with: + from transformers import AutoModel + model = AutoModel.from_pretrained("/my-disco-mmlu", trust_remote_code=True) + + Then call model.predict(predictions) where predictions has shape + (n_models, n_anchor_points, n_classes) (e.g. log-probabilities per choice). + Returns a 1D array of predicted full-benchmark accuracies, one per model. + """ + + def __init__( + self, + components: np.ndarray, + mean: np.ndarray, + tree_node_counts: np.ndarray, + children_left: np.ndarray, + children_right: np.ndarray, + feature: np.ndarray, + threshold: np.ndarray, + value: np.ndarray, + config: Optional[Any] = None, + ): + self._components = np.asarray(components, dtype=np.float64) + self._mean = np.asarray(mean, dtype=np.float64) + self._tree_node_counts = np.asarray(tree_node_counts, dtype=np.int64) + self._children_left = np.asarray(children_left, dtype=np.int32) + self._children_right = np.asarray(children_right, dtype=np.int32) + self._feature = np.asarray(feature, dtype=np.int32) + self._threshold = np.asarray(threshold, dtype=np.float64) + self._value = np.asarray(value, dtype=np.float64) + self.config = config + + @classmethod + def register_for_auto_class(cls, auto_class=None): + """No-op: required by transformers when loading custom models with trust_remote_code.""" + pass + + @classmethod + def from_pretrained( + cls, + pretrained_model_name_or_path: Union[str, Path], + **kwargs, + ) -> "DiscoPredictor": + """Load DISCO weights from a Hugging Face repo or local directory.""" + try: + from huggingface_hub import snapshot_download + except ImportError as e: + raise ImportError("Loading from Hub requires huggingface_hub: pip install huggingface_hub") from e + + path = Path(pretrained_model_name_or_path) + if not path.exists() or not path.is_dir(): + path = Path(snapshot_download(pretrained_model_name_or_path)) + + # Load config if present + config = None + config_path = path / "config.json" + if config_path.exists(): + try: + from transformers import AutoConfig + + config = AutoConfig.from_pretrained(str(path), trust_remote_code=True) + except Exception: + pass + + # Load PCA (transform) + transform_data = np.load(path / "disco_transform.npz") + components = np.asarray(transform_data["components_"]) + mean = np.asarray(transform_data["mean_"]) + + # Load RF (model) + model_data = np.load(path / "disco_model.npz") + tree_node_counts = np.asarray(model_data["tree_node_counts"], dtype=np.int64) + children_left = np.asarray(model_data["children_left"], dtype=np.int32) + children_right = np.asarray(model_data["children_right"], dtype=np.int32) + feature = np.asarray(model_data["feature"], dtype=np.int32) + threshold = np.asarray(model_data["threshold"], dtype=np.float64) + value = np.asarray(model_data["value"], dtype=np.float64) + + return cls( + components=components, + mean=mean, + tree_node_counts=tree_node_counts, + children_left=children_left, + children_right=children_right, + feature=feature, + threshold=threshold, + value=value, + config=config, + ) + + def predict( + self, + predictions: np.ndarray, + apply_softmax: bool = True, + ) -> np.ndarray: + """ + Predict full-benchmark accuracy from anchor-point predictions. + + Args: + predictions: Shape (n_models, n_anchor_points, n_classes), e.g. log-probabilities. + apply_softmax: If True, apply softmax to predictions before PCA (default True). + + Returns: + Shape (n_models,) predicted full-benchmark accuracies. + """ + X = np.asarray(predictions, dtype=np.float64) + if X.ndim == 2: + X = X[np.newaxis, ...] + n_models = X.shape[0] + # Softmax over last dim + if apply_softmax: + X = np.exp(X - X.max(axis=-1, keepdims=True)) + X = X / X.sum(axis=-1, keepdims=True) + # Flatten to (n_models, n_anchor_points * n_classes) + X = X.reshape(n_models, -1) + # PCA + emb = _pca_transform(X, self._components, self._mean) + # RF + return _predict_rf( + emb, + self._tree_node_counts, + self._children_left, + self._children_right, + self._feature, + self._threshold, + self._value, + ) + + def save_pretrained(self, save_directory: Union[str, Path]) -> None: + """Save DISCO weights and config to a directory (e.g. for uploading to Hub).""" + from transformers import AutoConfig + + path = Path(save_directory) + path.mkdir(parents=True, exist_ok=True) + np.savez( + path / "disco_transform.npz", + components_=self._components, + mean_=self._mean, + ) + np.savez( + path / "disco_model.npz", + tree_node_counts=self._tree_node_counts, + children_left=self._children_left, + children_right=self._children_right, + feature=self._feature, + threshold=self._threshold, + value=self._value, + ) + if self.config is not None: + self.config.save_pretrained(save_directory) diff --git a/examples/mmlu_benchmark/mmlu_benchmark.py b/examples/mmlu_benchmark/mmlu_benchmark.py index cb5c62d2..161289dd 100644 --- a/examples/mmlu_benchmark/mmlu_benchmark.py +++ b/examples/mmlu_benchmark/mmlu_benchmark.py @@ -912,16 +912,40 @@ def predict_with_disco( This implements the prediction logic from disco-public/scripts/predict_model_performance.py. Use .npz paths (from extract_disco_weights.py) to avoid pickle/sklearn version warnings. + Use a Hugging Face repo id (e.g. "/my-disco-mmlu") to load via AutoModel.from_pretrained(..., trust_remote_code=True). Args: predictions: Predictions tensor of shape (n_models, n_anchor_points, n_classes). - model_path: Path to fitted weights pickle file or disco_model.npz. - transform_path: Path to PCA transform pickle or disco_transform.npz (optional if in model_path). + model_path: Path to fitted weights pickle, disco_model.npz, or HF repo id (USERNAME/repo). + transform_path: Path to PCA transform pickle or disco_transform.npz (optional if npz or HF). pca: PCA dimension (default: 256). Returns: Dict with predicted accuracies and metadata. """ + # Hugging Face repo: model_path like "username/repo-name", no local path + use_hf = "/" in model_path and not Path(model_path).exists() and (transform_path is None or transform_path == model_path) + if use_hf: + from transformers import AutoModel + + disco_model = AutoModel.from_pretrained(model_path, trust_remote_code=True) + pred_values = disco_model.predict(np.asarray(predictions), apply_softmax=True) + predicted_accs = {i: float(pred_values[i]) for i in range(len(pred_values))} + config = getattr(disco_model, "config", None) + meta = { + "sampling_name": getattr(config, "sampling_name", "") if config else "", + "number_item": getattr(config, "number_item", "") if config else "", + "fitted_model_type": getattr(config, "fitted_model_type", "") if config else "", + } + print(f" Using: DISCO predictor from Hugging Face ({model_path})") + return { + "predicted_accuracies": predicted_accs, + "sampling_name": meta["sampling_name"], + "number_item": meta["number_item"], + "fitted_model_type": meta["fitted_model_type"], + "pca": pca, + } + use_npz = model_path.endswith(".npz") and (transform_path or "").endswith(".npz") if model_path.endswith(".npz") and not (transform_path or "").endswith(".npz"): raise ValueError("When using .npz model path, provide --disco_transform_path to disco_transform.npz") From 6c48aa87735c0e663a73719b6a6b7376189a9216 Mon Sep 17 00:00:00 2001 From: Alexander Rubinstein Date: Thu, 5 Feb 2026 09:13:07 +0100 Subject: [PATCH 16/47] Remove unused import --- examples/mmlu_benchmark/huggingface_disco/modeling_disco.py | 1 - 1 file changed, 1 deletion(-) diff --git a/examples/mmlu_benchmark/huggingface_disco/modeling_disco.py b/examples/mmlu_benchmark/huggingface_disco/modeling_disco.py index 789d7368..8ed459b3 100644 --- a/examples/mmlu_benchmark/huggingface_disco/modeling_disco.py +++ b/examples/mmlu_benchmark/huggingface_disco/modeling_disco.py @@ -196,7 +196,6 @@ def predict( def save_pretrained(self, save_directory: Union[str, Path]) -> None: """Save DISCO weights and config to a directory (e.g. for uploading to Hub).""" - from transformers import AutoConfig path = Path(save_directory) path.mkdir(parents=True, exist_ok=True) From 8b034edde3b879f5e538e9c0d019fa75de261de4 Mon Sep 17 00:00:00 2001 From: Alexander Rubinstein Date: Fri, 6 Feb 2026 08:41:42 +0100 Subject: [PATCH 17/47] [Add to HF]: - Store anchor points in hf --- .../huggingface_disco/README.md | 10 +++++ .../huggingface_disco/build_repo.py | 38 +++++++++++++++++++ examples/mmlu_benchmark/mmlu_benchmark.py | 30 ++++++++++++++- maseval/benchmark/mmlu/mmlu.py | 25 ++++++++---- 4 files changed, 94 insertions(+), 9 deletions(-) diff --git a/examples/mmlu_benchmark/huggingface_disco/README.md b/examples/mmlu_benchmark/huggingface_disco/README.md index ab4ea55e..54cb5a1b 100644 --- a/examples/mmlu_benchmark/huggingface_disco/README.md +++ b/examples/mmlu_benchmark/huggingface_disco/README.md @@ -32,6 +32,15 @@ python examples/mmlu_benchmark/huggingface_disco/build_repo.py \ --output_dir ./my-disco-mmlu ``` +To include anchor points so users can run the benchmark without `--anchor_points_path`: + +```bash +python examples/mmlu_benchmark/huggingface_disco/build_repo.py \ + --weights_dir /path/to/disco_weights \ + --output_dir ./my-disco-mmlu \ + --anchor_points_path /path/to/anchor_points_disagreement.pkl +``` + This copies the NumPy weights and writes `config.json` with `auto_map` so `AutoModel.from_pretrained(..., trust_remote_code=True)` loads `DiscoPredictor`. ## 3. Upload to the Hub @@ -57,6 +66,7 @@ api.upload_folder(folder_path="./my-disco-mmlu", repo_id="/my-disco-mm - `disco_transform.npz` – PCA components and mean - `disco_model.npz` – Random Forest tree arrays - `disco_meta.json` – sampling_name, number_item, fitted_model_type (optional, for display) +- `anchor_points.json` – list of anchor doc_ids (optional; add with `--anchor_points_path` so users can omit `--anchor_points_path` when running the benchmark) ## Dependencies diff --git a/examples/mmlu_benchmark/huggingface_disco/build_repo.py b/examples/mmlu_benchmark/huggingface_disco/build_repo.py index 51b5379d..670adb31 100644 --- a/examples/mmlu_benchmark/huggingface_disco/build_repo.py +++ b/examples/mmlu_benchmark/huggingface_disco/build_repo.py @@ -6,15 +6,36 @@ --weights_dir /path/to/disco_weights \\ --output_dir ./my-disco-mmlu + # Include anchor points so users can omit --anchor_points_path when using the HF model + python build_repo.py \\ + --weights_dir /path/to/disco_weights \\ + --output_dir ./my-disco-mmlu \\ + --anchor_points_path /path/to/anchor_points_disagreement.pkl + Then upload with: huggingface-cli upload /my-disco-mmlu ./my-disco-mmlu . """ import argparse import json +import pickle import shutil from pathlib import Path +def _load_anchor_points(path: Path) -> list: + """Load anchor points from .pkl or .json; return a list.""" + path = Path(path) + if path.suffix.lower() == ".json": + with open(path) as f: + out = json.load(f) + else: + with open(path, "rb") as f: + out = pickle.load(f) + if hasattr(out, "tolist"): + out = out.tolist() + return list(out) + + def main(): parser = argparse.ArgumentParser( description="Build HF repo directory for DISCO predictor (npz + config + code).", @@ -31,6 +52,12 @@ def main(): required=True, help="Output directory to create (will contain config, code, and weights).", ) + parser.add_argument( + "--anchor_points_path", + type=str, + default=None, + help="Path to anchor points .pkl or .json; saved as anchor_points.json in the repo.", + ) args = parser.parse_args() weights_dir = Path(args.weights_dir) @@ -74,6 +101,17 @@ def main(): json.dump(config, f, indent=2) print(f"Wrote {config_path}") + # Optional: anchor points so users can omit --anchor_points_path when loading from HF + if args.anchor_points_path: + ap_path = Path(args.anchor_points_path) + if not ap_path.exists(): + raise FileNotFoundError(f"Anchor points file not found: {ap_path}") + anchor_points = _load_anchor_points(ap_path) + ap_out = output_dir / "anchor_points.json" + with open(ap_out, "w") as f: + json.dump(anchor_points, f, indent=2) + print(f"Wrote {ap_out} ({len(anchor_points)} anchor points)") + print(f"\nRepo ready at: {output_dir.resolve()}") print("Upload with: huggingface-cli upload /my-disco-mmlu ./my-disco-mmlu .") diff --git a/examples/mmlu_benchmark/mmlu_benchmark.py b/examples/mmlu_benchmark/mmlu_benchmark.py index 161289dd..8a4f5733 100644 --- a/examples/mmlu_benchmark/mmlu_benchmark.py +++ b/examples/mmlu_benchmark/mmlu_benchmark.py @@ -1027,6 +1027,26 @@ def predict_with_disco( } +def _resolve_anchor_points_path( + disco_model_path: str, + anchor_points_path: Optional[str], +) -> Optional[str]: + """If anchor_points_path is None and model_path is an HF repo with anchor_points.json, return its path.""" + if anchor_points_path is not None: + return anchor_points_path + if "/" not in disco_model_path or Path(disco_model_path).exists(): + return None + try: + from huggingface_hub import snapshot_download + except ImportError: + return None + repo_path = Path(snapshot_download(disco_model_path)) + ap_file = repo_path / "anchor_points.json" + if ap_file.exists(): + return str(ap_file) + return None + + def main(): """Main entry point.""" args = parse_args() @@ -1035,8 +1055,14 @@ def main(): if args.disco_prediction: if args.disco_model_path is None: raise ValueError("--disco_model_path is required when --disco_prediction is enabled") - if args.anchor_points_path is None: - raise ValueError("--anchor_points_path is required when --disco_prediction is enabled") + anchor_points_path_resolved = _resolve_anchor_points_path(args.disco_model_path, args.anchor_points_path) + if anchor_points_path_resolved is None and args.anchor_points_path is None: + raise ValueError( + "Anchor points required for DISCO prediction. Provide --anchor_points_path or use a " + "DISCO model repo that includes anchor_points.json (build with build_repo.py --anchor_points_path)." + ) + if args.anchor_points_path is None and anchor_points_path_resolved: + args.anchor_points_path = anchor_points_path_resolved if args.pca is not None and args.disco_transform_path is None: print("Warning: --pca specified without --disco_transform_path. Transform will be loaded from model file if available.") diff --git a/maseval/benchmark/mmlu/mmlu.py b/maseval/benchmark/mmlu/mmlu.py index 51eaabb7..e05b19ae 100644 --- a/maseval/benchmark/mmlu/mmlu.py +++ b/maseval/benchmark/mmlu/mmlu.py @@ -570,6 +570,23 @@ def load_pickle(path: Union[str, Path]) -> Any: return pickle.load(f) +def load_anchor_points(path: Union[str, Path]) -> List[int]: + """Load anchor points from a .json or .pkl file. Returns a list of doc_ids.""" + path = Path(path) + if not path.exists(): + raise FileNotFoundError(f"Anchor points file not found: {path}") + if path.suffix.lower() == ".json": + with open(path) as f: + anchor_points = json.load(f) + else: + anchor_points = load_pickle(path) + if HAS_NUMPY and isinstance(anchor_points, np.ndarray): + anchor_points = anchor_points.tolist() + elif not HAS_NUMPY and hasattr(anchor_points, "tolist"): + anchor_points = anchor_points.tolist() + return list(anchor_points) + + def load_tasks( data_path: Union[str, Path], anchor_points_path: Optional[Union[str, Path]] = None, @@ -624,13 +641,7 @@ def load_tasks( # Load anchor points if provided anchor_points = None if anchor_points_path is not None: - anchor_points = load_pickle(anchor_points_path) - # Convert numpy array to list if necessary - if HAS_NUMPY and isinstance(anchor_points, np.ndarray): - anchor_points = anchor_points.tolist() - elif not HAS_NUMPY and hasattr(anchor_points, "tolist"): - # Fallback: try tolist() method if it exists - anchor_points = anchor_points.tolist() + anchor_points = load_anchor_points(anchor_points_path) # Create appropriate queue if anchor_points is not None: From c75b9bea0201dcc6e1b1c699c824af97225a9066 Mon Sep 17 00:00:00 2001 From: Alexander Rubinstein Date: Fri, 6 Feb 2026 09:00:07 +0100 Subject: [PATCH 18/47] [Add to HF]: - Store predictor config in hf --- .../huggingface_disco/README.md | 12 +++- .../huggingface_disco/build_repo.py | 34 ++++++++++- examples/mmlu_benchmark/mmlu_benchmark.py | 60 ++++++++++++++----- 3 files changed, 87 insertions(+), 19 deletions(-) diff --git a/examples/mmlu_benchmark/huggingface_disco/README.md b/examples/mmlu_benchmark/huggingface_disco/README.md index 54cb5a1b..2fef3f8d 100644 --- a/examples/mmlu_benchmark/huggingface_disco/README.md +++ b/examples/mmlu_benchmark/huggingface_disco/README.md @@ -32,15 +32,20 @@ python examples/mmlu_benchmark/huggingface_disco/build_repo.py \ --output_dir ./my-disco-mmlu ``` -To include anchor points so users can run the benchmark without `--anchor_points_path`: +To include anchor points and recommended eval settings (so users can omit matching args when running the benchmark): ```bash python examples/mmlu_benchmark/huggingface_disco/build_repo.py \ --weights_dir /path/to/disco_weights \ --output_dir ./my-disco-mmlu \ - --anchor_points_path /path/to/anchor_points_disagreement.pkl + --anchor_points_path /path/to/anchor_points_disagreement.pkl \ + --pca 256 \ + --pad_to_size 31 \ + --use_lmeval_batching ``` +These options are stored in `config.json` as `eval_config`. When the benchmark loads the model from the Hub, it uses these values and raises an error if the user passes a different `--pca`, or passes `--pad_to_size` or `--use_lmeval_batching` (the user must omit these flags so the model's config is used). + This copies the NumPy weights and writes `config.json` with `auto_map` so `AutoModel.from_pretrained(..., trust_remote_code=True)` loads `DiscoPredictor`. ## 3. Upload to the Hub @@ -66,7 +71,8 @@ api.upload_folder(folder_path="./my-disco-mmlu", repo_id="/my-disco-mm - `disco_transform.npz` – PCA components and mean - `disco_model.npz` – Random Forest tree arrays - `disco_meta.json` – sampling_name, number_item, fitted_model_type (optional, for display) -- `anchor_points.json` – list of anchor doc_ids (optional; add with `--anchor_points_path` so users can omit `--anchor_points_path` when running the benchmark) +- `anchor_points.json` – list of anchor doc_ids (optional; add with `--anchor_points_path`) +- `config.json` may include `eval_config` – optional `pca`, `pad_to_size`, `use_lmeval_batching`; when loading from HF the benchmark uses these and errors if the user passes conflicting flags ## Dependencies diff --git a/examples/mmlu_benchmark/huggingface_disco/build_repo.py b/examples/mmlu_benchmark/huggingface_disco/build_repo.py index 670adb31..de9cdeeb 100644 --- a/examples/mmlu_benchmark/huggingface_disco/build_repo.py +++ b/examples/mmlu_benchmark/huggingface_disco/build_repo.py @@ -6,11 +6,14 @@ --weights_dir /path/to/disco_weights \\ --output_dir ./my-disco-mmlu - # Include anchor points so users can omit --anchor_points_path when using the HF model + # Include anchor points and eval config (pca, pad_to_size, use_lmeval_batching) python build_repo.py \\ --weights_dir /path/to/disco_weights \\ --output_dir ./my-disco-mmlu \\ - --anchor_points_path /path/to/anchor_points_disagreement.pkl + --anchor_points_path /path/to/anchor_points_disagreement.pkl \\ + --pca 256 --pad_to_size 31 --use_lmeval_batching + +Stored eval_config is applied by the benchmark when loading the model from HF if the user did not override those args. Then upload with: huggingface-cli upload /my-disco-mmlu ./my-disco-mmlu . """ @@ -58,6 +61,23 @@ def main(): default=None, help="Path to anchor points .pkl or .json; saved as anchor_points.json in the repo.", ) + parser.add_argument( + "--pca", + type=int, + default=None, + help="PCA dimension used for this model; stored in eval_config so the benchmark can use it when loading from HF.", + ) + parser.add_argument( + "--pad_to_size", + type=int, + default=None, + help="Pad predictions to this size; stored in eval_config for the benchmark when loading from HF.", + ) + parser.add_argument( + "--use_lmeval_batching", + action="store_true", + help="Whether lm-eval batching was used; stored in eval_config for the benchmark when loading from HF.", + ) args = parser.parse_args() weights_dir = Path(args.weights_dir) @@ -96,6 +116,16 @@ def main(): "number_item": meta.get("number_item", ""), "fitted_model_type": meta.get("fitted_model_type", ""), } + # Eval config: when the benchmark loads this model from HF, it can apply these if the user did not override + eval_config = {} + if args.pca is not None: + eval_config["pca"] = args.pca + if args.pad_to_size is not None: + eval_config["pad_to_size"] = args.pad_to_size + if args.use_lmeval_batching: + eval_config["use_lmeval_batching"] = True + if eval_config: + config["eval_config"] = eval_config config_path = output_dir / "config.json" with open(config_path, "w") as f: json.dump(config, f, indent=2) diff --git a/examples/mmlu_benchmark/mmlu_benchmark.py b/examples/mmlu_benchmark/mmlu_benchmark.py index 8a4f5733..4dd2d315 100644 --- a/examples/mmlu_benchmark/mmlu_benchmark.py +++ b/examples/mmlu_benchmark/mmlu_benchmark.py @@ -157,7 +157,7 @@ def parse_args(): parser.add_argument( "--pca", type=int, - default=256, + default=None, help="PCA dimension for DISCO embeddings (default: 256)", ) parser.add_argument( @@ -1027,35 +1027,64 @@ def predict_with_disco( } -def _resolve_anchor_points_path( +def _apply_eval_config_from_repo(repo_path: Path, args: "argparse.Namespace") -> None: + """Load eval_config from repo; forbid passing --pca/--pad_to_size/--use_lmeval_batching, then set args from eval_config.""" + config_path = repo_path / "config.json" + if not config_path.exists(): + return + with open(config_path) as f: + hf_config = json.load(f) + eval_config = hf_config.get("eval_config") or {} + if not eval_config: + return + # When using a Hub DISCO model with eval_config, user must not pass these; the model's values are used. + errors = [] + if "pca" in eval_config and args.pca is not None: + errors.append(f"do not pass --pca (model uses pca={eval_config['pca']})") + if "pad_to_size" in eval_config and args.pad_to_size is not None: + errors.append(f"do not pass --pad_to_size (model uses pad_to_size={eval_config['pad_to_size']})") + if "use_lmeval_batching" in eval_config and args.use_lmeval_batching: + errors.append("do not pass --use_lmeval_batching (model uses use_lmeval_batching=True)") + if errors: + raise ValueError("When using a DISCO model from the Hub, " + "; ".join(errors) + ". Omit these flags to use the model's eval_config.") + if "pca" in eval_config: + args.pca = eval_config["pca"] + if "pad_to_size" in eval_config: + args.pad_to_size = eval_config["pad_to_size"] + if "use_lmeval_batching" in eval_config: + args.use_lmeval_batching = eval_config["use_lmeval_batching"] + + +def _resolve_hf_disco_repo( disco_model_path: str, anchor_points_path: Optional[str], -) -> Optional[str]: - """If anchor_points_path is None and model_path is an HF repo with anchor_points.json, return its path.""" - if anchor_points_path is not None: - return anchor_points_path +) -> tuple: + """If model_path is an HF repo, download and return (anchor_points_path, repo_path). Else (anchor_points_path, None). + + anchor_points_path: path to anchor_points.json if repo has it and input was None; else input. + repo_path: path to the downloaded repo dir, or None if not an HF repo. + """ if "/" not in disco_model_path or Path(disco_model_path).exists(): - return None + return (anchor_points_path, None) try: from huggingface_hub import snapshot_download except ImportError: - return None + return (anchor_points_path, None) repo_path = Path(snapshot_download(disco_model_path)) - ap_file = repo_path / "anchor_points.json" - if ap_file.exists(): - return str(ap_file) - return None + if anchor_points_path is None and (repo_path / "anchor_points.json").exists(): + return (str(repo_path / "anchor_points.json"), repo_path) + return (anchor_points_path, repo_path) def main(): """Main entry point.""" args = parse_args() - # Validate DISCO prediction arguments + # Validate DISCO prediction arguments and resolve HF repo (anchor points + eval_config) if args.disco_prediction: if args.disco_model_path is None: raise ValueError("--disco_model_path is required when --disco_prediction is enabled") - anchor_points_path_resolved = _resolve_anchor_points_path(args.disco_model_path, args.anchor_points_path) + anchor_points_path_resolved, hf_repo_path = _resolve_hf_disco_repo(args.disco_model_path, args.anchor_points_path) if anchor_points_path_resolved is None and args.anchor_points_path is None: raise ValueError( "Anchor points required for DISCO prediction. Provide --anchor_points_path or use a " @@ -1063,6 +1092,9 @@ def main(): ) if args.anchor_points_path is None and anchor_points_path_resolved: args.anchor_points_path = anchor_points_path_resolved + # Apply eval_config from HF repo when user left script defaults (so model's training config is used) + if hf_repo_path is not None: + _apply_eval_config_from_repo(hf_repo_path, args) if args.pca is not None and args.disco_transform_path is None: print("Warning: --pca specified without --disco_transform_path. Transform will be loaded from model file if available.") From beb9cfcb55d1ac8ae49b6deb553c9c7e90816d0f Mon Sep 17 00:00:00 2001 From: Alexander Rubinstein Date: Fri, 6 Feb 2026 09:04:54 +0100 Subject: [PATCH 19/47] [Add to HF]: - Enforce empty args for transform_path and anchor_points_path when using hf model --- examples/mmlu_benchmark/mmlu_benchmark.py | 13 ++++++++++--- 1 file changed, 10 insertions(+), 3 deletions(-) diff --git a/examples/mmlu_benchmark/mmlu_benchmark.py b/examples/mmlu_benchmark/mmlu_benchmark.py index 4dd2d315..9734f788 100644 --- a/examples/mmlu_benchmark/mmlu_benchmark.py +++ b/examples/mmlu_benchmark/mmlu_benchmark.py @@ -1090,11 +1090,18 @@ def main(): "Anchor points required for DISCO prediction. Provide --anchor_points_path or use a " "DISCO model repo that includes anchor_points.json (build with build_repo.py --anchor_points_path)." ) - if args.anchor_points_path is None and anchor_points_path_resolved: - args.anchor_points_path = anchor_points_path_resolved - # Apply eval_config from HF repo when user left script defaults (so model's training config is used) if hf_repo_path is not None: + if args.anchor_points_path is not None: + raise ValueError( + "When using a DISCO model from the Hub, do not pass --anchor_points_path; anchor points are taken from the model repo." + ) + if args.disco_transform_path is not None: + raise ValueError( + "When using a DISCO model from the Hub, do not pass --disco_transform_path; the transform is included in the model repo." + ) _apply_eval_config_from_repo(hf_repo_path, args) + if args.anchor_points_path is None and anchor_points_path_resolved: + args.anchor_points_path = anchor_points_path_resolved if args.pca is not None and args.disco_transform_path is None: print("Warning: --pca specified without --disco_transform_path. Transform will be loaded from model file if available.") From 7037d4ea66e6aefc562f5f8494f0ba95b5dfba86 Mon Sep 17 00:00:00 2001 From: Alexander Rubinstein Date: Fri, 6 Feb 2026 09:11:19 +0100 Subject: [PATCH 20/47] Deprecate --disco_prediction arg --- examples/mmlu_benchmark/README.md | 6 +----- examples/mmlu_benchmark/mmlu_benchmark.py | 24 ++++++++--------------- 2 files changed, 9 insertions(+), 21 deletions(-) diff --git a/examples/mmlu_benchmark/README.md b/examples/mmlu_benchmark/README.md index f5a7a428..50714356 100644 --- a/examples/mmlu_benchmark/README.md +++ b/examples/mmlu_benchmark/README.md @@ -74,7 +74,6 @@ python mmlu_benchmark.py \ --data_path /path/to/mmlu_prompts_examples.json \ --anchor_points_path /path/to/anchor_points_disagreement.pkl \ --use_full_prompt \ - --disco_prediction \ --disco_model_path /path/to/fitted_weights.pkl \ --disco_transform_path /path/to/transform.pkl \ --pca 256 @@ -100,7 +99,6 @@ Then point the benchmark at the extracted files: ```bash python mmlu_benchmark.py ... \ - --disco_prediction \ --disco_model_path /path/to/disco_npz/disco_model.npz \ --disco_transform_path /path/to/disco_npz/disco_transform.npz \ --pca 256 @@ -120,7 +118,6 @@ Then run the benchmark with a repo id instead of local paths: ```bash python mmlu_benchmark.py ... \ - --disco_prediction \ --disco_model_path /my-disco-mmlu ``` @@ -151,8 +148,7 @@ python mmlu_benchmark.py \ | `--device` | Device to run model on | `cuda:0` | | `--trust_remote_code` | Trust remote code when loading model | False | | `--num_workers` | Number of parallel workers | 1 | -| `--disco_prediction` | Enable DISCO prediction from anchor points | False | -| `--disco_model_path` | Path to DISCO fitted weights pickle | None | +| `--disco_model_path` | If set, run DISCO prediction (.pkl, .npz, or HF repo id) | None | | `--disco_transform_path` | Path to DISCO PCA transform pickle | None | | `--pca` | PCA dimension for DISCO embeddings | 256 | diff --git a/examples/mmlu_benchmark/mmlu_benchmark.py b/examples/mmlu_benchmark/mmlu_benchmark.py index 9734f788..d7a4ea17 100644 --- a/examples/mmlu_benchmark/mmlu_benchmark.py +++ b/examples/mmlu_benchmark/mmlu_benchmark.py @@ -24,13 +24,12 @@ --anchor_points_path /path/to/anchor_points_disagreement.pkl \\ --use_full_prompt - # Run with DISCO prediction (predict full benchmark performance from anchor points) + # Run with DISCO prediction (passing --disco_model_path enables it) python mmlu_benchmark.py \\ --model_id "alignment-handbook/zephyr-7b-sft-full" \\ --data_path /path/to/mmlu_prompts_examples.json \\ --anchor_points_path /path/to/anchor_points_disagreement.pkl \\ --use_full_prompt \\ - --disco_prediction \\ --disco_model_path /path/to/fitted_weights.pkl \\ --disco_transform_path /path/to/transform.pkl \\ --pca 256 @@ -136,23 +135,18 @@ def parse_args(): help="Number of parallel workers for task execution", ) - # DISCO prediction arguments - parser.add_argument( - "--disco_prediction", - action="store_true", - help="Enable DISCO prediction of full benchmark performance from anchor points", - ) + # DISCO prediction: enabled when --disco_model_path is set parser.add_argument( "--disco_model_path", type=str, default=None, - help="Path to DISCO fitted weights pickle file (required if --disco_prediction)", + help="If set, run DISCO prediction (path to fitted weights .pkl, .npz, or Hugging Face repo id)", ) parser.add_argument( "--disco_transform_path", type=str, default=None, - help="Path to DISCO PCA transform pickle file (required if --disco_prediction with --pca)", + help="Path to DISCO PCA transform .pkl or .npz (required for local .pkl model when using --pca)", ) parser.add_argument( "--pca", @@ -1081,9 +1075,7 @@ def main(): args = parse_args() # Validate DISCO prediction arguments and resolve HF repo (anchor points + eval_config) - if args.disco_prediction: - if args.disco_model_path is None: - raise ValueError("--disco_model_path is required when --disco_prediction is enabled") + if args.disco_model_path is not None: anchor_points_path_resolved, hf_repo_path = _resolve_hf_disco_repo(args.disco_model_path, args.anchor_points_path) if anchor_points_path_resolved is None and args.anchor_points_path is None: raise ValueError( @@ -1114,7 +1106,7 @@ def main(): print(f"Use full prompt: {args.use_full_prompt}") print(f"Device: {args.device}") print(f"Output dir: {args.output_dir}") - if args.disco_prediction: + if args.disco_model_path is not None: print("DISCO prediction: ENABLED") print(f" Model path: {args.disco_model_path}") print(f" Transform path: {args.disco_transform_path or '(from model file)'}") @@ -1185,7 +1177,7 @@ def main(): # Build predictions tensor for DISCO predictions = None - if args.predictions_path or args.disco_prediction: + if args.predictions_path or args.disco_model_path is not None: predictions = save_predictions_for_disco( results=results, output_path=args.predictions_path if args.predictions_path else None, @@ -1195,7 +1187,7 @@ def main(): # Run DISCO prediction if enabled disco_results = None - if args.disco_prediction: + if args.disco_model_path is not None: print("\n" + "=" * 80) print("DISCO Prediction") print("=" * 80) From aa1c3e89d949de788a8ff09c989bfbf08f411f7b Mon Sep 17 00:00:00 2001 From: Alexander Rubinstein Date: Fri, 6 Feb 2026 09:29:33 +0100 Subject: [PATCH 21/47] [Add to HF]: - Add flattened MMLU to hf --- .gitignore | 1 + .../huggingface_disco/README.md | 26 +++++ .../huggingface_disco/build_data_repo.py | 99 +++++++++++++++++++ examples/mmlu_benchmark/mmlu_benchmark.py | 24 ++++- flattened-MMLU/README.md | 24 +++++ 5 files changed, 173 insertions(+), 1 deletion(-) create mode 100644 examples/mmlu_benchmark/huggingface_disco/build_data_repo.py create mode 100644 flattened-MMLU/README.md diff --git a/.gitignore b/.gitignore index 467eed97..a99befd4 100644 --- a/.gitignore +++ b/.gitignore @@ -7,6 +7,7 @@ .devcontainer/ results/ DISCO-MMLU/ +flattened-MMLU/ # Byte-compiled / optimized / DLL files __pycache__/ diff --git a/examples/mmlu_benchmark/huggingface_disco/README.md b/examples/mmlu_benchmark/huggingface_disco/README.md index 2fef3f8d..2b4ea0c8 100644 --- a/examples/mmlu_benchmark/huggingface_disco/README.md +++ b/examples/mmlu_benchmark/huggingface_disco/README.md @@ -74,6 +74,32 @@ api.upload_folder(folder_path="./my-disco-mmlu", repo_id="/my-disco-mm - `anchor_points.json` – list of anchor doc_ids (optional; add with `--anchor_points_path`) - `config.json` may include `eval_config` – optional `pca`, `pad_to_size`, `use_lmeval_batching`; when loading from HF the benchmark uses these and errors if the user passes conflicting flags +## MMLU prompts dataset (build_data_repo.py) + +To store the MMLU prompts JSON on the Hub so you can use it as `--data_path` from anywhere: + +```bash +python examples/mmlu_benchmark/huggingface_disco/build_data_repo.py \ + --data_path /path/to/mmlu_prompts_examples.json \ + --output_dir ./mmlu-prompts-examples +``` + +Upload as a dataset: + +```bash +cd mmlu-prompts-examples +huggingface-cli upload /mmlu-prompts-examples . . --repo-type dataset +``` + +Then run the benchmark with the repo id as `--data_path` (the script downloads the file automatically): + +```bash +python mmlu_benchmark.py \ + --model_id ... \ + --data_path /mmlu-prompts-examples \ + ... +``` + ## Dependencies Loading from the Hub only needs: diff --git a/examples/mmlu_benchmark/huggingface_disco/build_data_repo.py b/examples/mmlu_benchmark/huggingface_disco/build_data_repo.py new file mode 100644 index 00000000..16547339 --- /dev/null +++ b/examples/mmlu_benchmark/huggingface_disco/build_data_repo.py @@ -0,0 +1,99 @@ +""" +Build a directory ready to upload to Hugging Face as a dataset (e.g. MMLU prompts). + +Usage: + python build_data_repo.py \\ + --data_path /path/to/mmlu_prompts_examples.json \\ + --output_dir ./mmlu-prompts-examples + +Then upload as a dataset: + cd mmlu-prompts-examples + huggingface-cli upload /mmlu-prompts-examples . . --repo-type dataset + +Download in code: + from huggingface_hub import hf_hub_download + data_path = hf_hub_download( + repo_id="/mmlu-prompts-examples", + filename="mmlu_prompts_examples.json", + repo_type="dataset", + ) + # Then pass data_path to mmlu_benchmark.py --data_path +""" + +import argparse +import shutil +from pathlib import Path + + +def main(): + parser = argparse.ArgumentParser( + description="Build HF dataset repo directory (e.g. for MMLU prompts JSON).", + ) + parser.add_argument( + "--data_path", + type=str, + required=True, + help="Path to the prompts JSON file (e.g. mmlu_prompts_examples.json).", + ) + parser.add_argument( + "--output_dir", + type=str, + required=True, + help="Output directory to create; will contain the JSON and a README.", + ) + parser.add_argument( + "--filename", + type=str, + default="mmlu_prompts_examples.json", + help="Filename to use in the repo (default: mmlu_prompts_examples.json).", + ) + args = parser.parse_args() + + data_path = Path(args.data_path) + if not data_path.exists(): + raise FileNotFoundError(f"Data file not found: {data_path}") + + output_dir = Path(args.output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + + out_file = output_dir / args.filename + shutil.copy2(data_path, out_file) + print(f"Copied {data_path} -> {out_file}") + + readme = output_dir / "README.md" + readme.write_text( + f"""# MMLU prompts examples + +Dataset of MMLU (Massive Multitask Language Understanding) prompts in the format expected by MASEval's MMLU benchmark. + +## Data + +- **{args.filename}** – JSON list of items with `query`, `full_prompt`, `choices`, `gold`, and optional `example`. + +## Use with MASEval + +Download the file and pass it to the benchmark: + +```python +from huggingface_hub import hf_hub_download + +data_path = hf_hub_download( + repo_id="/mmlu-prompts-examples", + filename="{args.filename}", + repo_type="dataset", +) +# Then: python mmlu_benchmark.py --data_path ... +``` + +Or download once to a local path and use that as `--data_path`. +""", + encoding="utf-8", + ) + print(f"Wrote {readme}") + + print(f"\nRepo ready at: {output_dir.resolve()}") + print("Upload with: huggingface-cli upload /mmlu-prompts-examples . . --repo-type dataset") + + +if __name__ == "__main__": + main() diff --git a/examples/mmlu_benchmark/mmlu_benchmark.py b/examples/mmlu_benchmark/mmlu_benchmark.py index d7a4ea17..6a98b5ed 100644 --- a/examples/mmlu_benchmark/mmlu_benchmark.py +++ b/examples/mmlu_benchmark/mmlu_benchmark.py @@ -78,7 +78,7 @@ def parse_args(): "--data_path", type=str, required=True, - help="Path to MMLU prompts JSON file (mmlu_prompts_examples.json format)", + help="Path to MMLU prompts JSON file, or Hugging Face dataset repo id (e.g. username/mmlu-prompts-examples)", ) # Optional arguments @@ -1021,6 +1021,25 @@ def predict_with_disco( } +def _resolve_data_path(data_path: str) -> str: + """If data_path looks like an HF dataset repo id (user/repo), download and return local path.""" + if not data_path or "/" not in data_path or Path(data_path).exists(): + return data_path + try: + from huggingface_hub import hf_hub_download + except ImportError: + return data_path + try: + local = hf_hub_download( + repo_id=data_path, + filename="mmlu_prompts_examples.json", + repo_type="dataset", + ) + return local + except Exception: + return data_path + + def _apply_eval_config_from_repo(repo_path: Path, args: "argparse.Namespace") -> None: """Load eval_config from repo; forbid passing --pca/--pad_to_size/--use_lmeval_batching, then set args from eval_config.""" config_path = repo_path / "config.json" @@ -1074,6 +1093,9 @@ def main(): """Main entry point.""" args = parse_args() + # Resolve --data_path if it is an HF dataset repo id (e.g. username/mmlu-prompts-examples) + args.data_path = _resolve_data_path(args.data_path) + # Validate DISCO prediction arguments and resolve HF repo (anchor points + eval_config) if args.disco_model_path is not None: anchor_points_path_resolved, hf_repo_path = _resolve_hf_disco_repo(args.disco_model_path, args.anchor_points_path) diff --git a/flattened-MMLU/README.md b/flattened-MMLU/README.md new file mode 100644 index 00000000..88228c68 --- /dev/null +++ b/flattened-MMLU/README.md @@ -0,0 +1,24 @@ +# MMLU prompts examples + +Dataset of MMLU (Massive Multitask Language Understanding) prompts in the format expected by MASEval's MMLU benchmark. + +## Data + +- **mmlu_prompts_examples.json** – JSON list of items with `query`, `full_prompt`, `choices`, `gold`, and optional `example`. + +## Use with MASEval + +Download the file and pass it to the benchmark: + +```python +from huggingface_hub import hf_hub_download + +data_path = hf_hub_download( + repo_id="/mmlu-prompts-examples", + filename="mmlu_prompts_examples.json", + repo_type="dataset", +) +# Then: python mmlu_benchmark.py --data_path ... +``` + +Or download once to a local path and use that as `--data_path`. From 6022bd7745a0e5a4984475fa6438a09153f2c794 Mon Sep 17 00:00:00 2001 From: Alexander Rubinstein Date: Sat, 7 Feb 2026 09:58:01 +0100 Subject: [PATCH 22/47] [Add to HF]: - Add flattened-MMLU and --use-full-prompt to enforced config for DISCO-MMLU --- .../huggingface_disco/build_repo.py | 25 ++++++++++++++++++- .../huggingface_disco/configuration_disco.py | 4 +++ examples/mmlu_benchmark/mmlu_benchmark.py | 14 ++++++++--- 3 files changed, 39 insertions(+), 4 deletions(-) diff --git a/examples/mmlu_benchmark/huggingface_disco/build_repo.py b/examples/mmlu_benchmark/huggingface_disco/build_repo.py index de9cdeeb..6fb58448 100644 --- a/examples/mmlu_benchmark/huggingface_disco/build_repo.py +++ b/examples/mmlu_benchmark/huggingface_disco/build_repo.py @@ -78,6 +78,24 @@ def main(): action="store_true", help="Whether lm-eval batching was used; stored in eval_config for the benchmark when loading from HF.", ) + parser.add_argument( + "--use_full_prompt", + action="store_true", + default=True, + help="Whether full prompts were used; stored in eval_config (default: True).", + ) + parser.add_argument( + "--no_use_full_prompt", + action="store_false", + dest="use_full_prompt", + help="Disable use_full_prompt.", + ) + parser.add_argument( + "--data_path", + type=str, + default=None, + help="Data path or HF dataset repo id (e.g. arubique/flattened-MMLU); stored in eval_config.", + ) args = parser.parse_args() weights_dir = Path(args.weights_dir) @@ -115,8 +133,10 @@ def main(): "sampling_name": meta.get("sampling_name", ""), "number_item": meta.get("number_item", ""), "fitted_model_type": meta.get("fitted_model_type", ""), + "use_full_prompt": args.use_full_prompt, + "data_path": args.data_path or "", } - # Eval config: when the benchmark loads this model from HF, it can apply these if the user did not override + # Eval config: when the benchmark loads this model from HF, it validates these eval_config = {} if args.pca is not None: eval_config["pca"] = args.pca @@ -124,6 +144,9 @@ def main(): eval_config["pad_to_size"] = args.pad_to_size if args.use_lmeval_batching: eval_config["use_lmeval_batching"] = True + eval_config["use_full_prompt"] = args.use_full_prompt + if args.data_path is not None: + eval_config["data_path"] = args.data_path if eval_config: config["eval_config"] = eval_config config_path = output_dir / "config.json" diff --git a/examples/mmlu_benchmark/huggingface_disco/configuration_disco.py b/examples/mmlu_benchmark/huggingface_disco/configuration_disco.py index f6c15555..6eaacba9 100644 --- a/examples/mmlu_benchmark/huggingface_disco/configuration_disco.py +++ b/examples/mmlu_benchmark/huggingface_disco/configuration_disco.py @@ -15,6 +15,8 @@ def __init__( sampling_name: str = "", number_item: str = "", fitted_model_type: str = "", + use_full_prompt: bool = True, + data_path: str = "", **kwargs, ): super().__init__(**kwargs) @@ -22,3 +24,5 @@ def __init__( self.sampling_name = sampling_name self.number_item = number_item self.fitted_model_type = fitted_model_type + self.use_full_prompt = use_full_prompt + self.data_path = data_path diff --git a/examples/mmlu_benchmark/mmlu_benchmark.py b/examples/mmlu_benchmark/mmlu_benchmark.py index 6a98b5ed..b0004e9c 100644 --- a/examples/mmlu_benchmark/mmlu_benchmark.py +++ b/examples/mmlu_benchmark/mmlu_benchmark.py @@ -1060,6 +1060,13 @@ def _apply_eval_config_from_repo(repo_path: Path, args: "argparse.Namespace") -> errors.append("do not pass --use_lmeval_batching (model uses use_lmeval_batching=True)") if errors: raise ValueError("When using a DISCO model from the Hub, " + "; ".join(errors) + ". Omit these flags to use the model's eval_config.") + # Require use_full_prompt and data_path to match model config + if "use_full_prompt" in eval_config and args.use_full_prompt != eval_config["use_full_prompt"]: + raise ValueError(f"When using this DISCO model, --use_full_prompt must be {eval_config['use_full_prompt']!r} (model config).") + if "data_path" in eval_config: + # Compare before _resolve_data_path overwrites; user must pass the expected repo id or path + if args.data_path != eval_config["data_path"]: + raise ValueError(f"When using this DISCO model, --data_path must be {eval_config['data_path']!r} (model config).") if "pca" in eval_config: args.pca = eval_config["pca"] if "pad_to_size" in eval_config: @@ -1093,10 +1100,8 @@ def main(): """Main entry point.""" args = parse_args() - # Resolve --data_path if it is an HF dataset repo id (e.g. username/mmlu-prompts-examples) - args.data_path = _resolve_data_path(args.data_path) - # Validate DISCO prediction arguments and resolve HF repo (anchor points + eval_config) + # Must run before _resolve_data_path so we can validate args.data_path against model config if args.disco_model_path is not None: anchor_points_path_resolved, hf_repo_path = _resolve_hf_disco_repo(args.disco_model_path, args.anchor_points_path) if anchor_points_path_resolved is None and args.anchor_points_path is None: @@ -1119,6 +1124,9 @@ def main(): if args.pca is not None and args.disco_transform_path is None: print("Warning: --pca specified without --disco_transform_path. Transform will be loaded from model file if available.") + # Resolve --data_path if it is an HF dataset repo id (e.g. username/mmlu-prompts-examples) + args.data_path = _resolve_data_path(args.data_path) + print("=" * 80) print("MMLU Benchmark - MASEval") print("=" * 80) From 45737878b0eed8870db546c351f0f9b921c8ecc4 Mon Sep 17 00:00:00 2001 From: Alexander Rubinstein Date: Sat, 7 Feb 2026 10:38:27 +0100 Subject: [PATCH 23/47] Deprecate --use_full_prompt and --trust_remote_code args --- examples/mmlu_benchmark/README.md | 4 --- examples/mmlu_benchmark/mmlu_benchmark.py | 36 +++++++---------------- 2 files changed, 11 insertions(+), 29 deletions(-) diff --git a/examples/mmlu_benchmark/README.md b/examples/mmlu_benchmark/README.md index 50714356..9644d160 100644 --- a/examples/mmlu_benchmark/README.md +++ b/examples/mmlu_benchmark/README.md @@ -60,7 +60,6 @@ python mmlu_benchmark.py \ --model_id "alignment-handbook/zephyr-7b-sft-full" \ --data_path /path/to/mmlu_prompts_examples.json \ --anchor_points_path /path/to/anchor_points_disagreement.pkl \ - --use_full_prompt \ --predictions_path ./output/predictions.pkl ``` @@ -73,7 +72,6 @@ python mmlu_benchmark.py \ --model_id "alignment-handbook/zephyr-7b-sft-full" \ --data_path /path/to/mmlu_prompts_examples.json \ --anchor_points_path /path/to/anchor_points_disagreement.pkl \ - --use_full_prompt \ --disco_model_path /path/to/fitted_weights.pkl \ --disco_transform_path /path/to/transform.pkl \ --pca 256 @@ -143,10 +141,8 @@ python mmlu_benchmark.py \ | `--anchor_points_path` | Path to anchor points pickle file | None | | `--output_dir` | Directory to save results | `./results` | | `--predictions_path` | Path to save predictions pickle (for DISCO) | None | -| `--use_full_prompt` | Use full prompt with few-shot examples | False | | `--limit` | Limit number of tasks to evaluate | None | | `--device` | Device to run model on | `cuda:0` | -| `--trust_remote_code` | Trust remote code when loading model | False | | `--num_workers` | Number of parallel workers | 1 | | `--disco_model_path` | If set, run DISCO prediction (.pkl, .npz, or HF repo id) | None | | `--disco_transform_path` | Path to DISCO PCA transform pickle | None | diff --git a/examples/mmlu_benchmark/mmlu_benchmark.py b/examples/mmlu_benchmark/mmlu_benchmark.py index b0004e9c..57a710c3 100644 --- a/examples/mmlu_benchmark/mmlu_benchmark.py +++ b/examples/mmlu_benchmark/mmlu_benchmark.py @@ -21,15 +21,13 @@ python mmlu_benchmark.py \\ --model_id "alignment-handbook/zephyr-7b-sft-full" \\ --data_path /path/to/mmlu_prompts_examples.json \\ - --anchor_points_path /path/to/anchor_points_disagreement.pkl \\ - --use_full_prompt + --anchor_points_path /path/to/anchor_points_disagreement.pkl # Run with DISCO prediction (passing --disco_model_path enables it) python mmlu_benchmark.py \\ --model_id "alignment-handbook/zephyr-7b-sft-full" \\ --data_path /path/to/mmlu_prompts_examples.json \\ --anchor_points_path /path/to/anchor_points_disagreement.pkl \\ - --use_full_prompt \\ --disco_model_path /path/to/fitted_weights.pkl \\ --disco_transform_path /path/to/transform.pkl \\ --pca 256 @@ -100,11 +98,6 @@ def parse_args(): default=None, help="Path to save predictions tensor as pickle (for DISCO predictor)", ) - parser.add_argument( - "--use_full_prompt", - action="store_true", - help="Use full prompt with few-shot examples instead of just the query", - ) parser.add_argument( "--limit", type=int, @@ -123,11 +116,6 @@ def parse_args(): default="cuda:0", help="Device to run model on (e.g., 'cuda:0', 'cpu')", ) - parser.add_argument( - "--trust_remote_code", - action="store_true", - help="Trust remote code when loading model", - ) parser.add_argument( "--num_workers", type=int, @@ -185,8 +173,8 @@ def __init__( self, model_id: str, device: str = "cuda:0", - trust_remote_code: bool = False, - use_full_prompt: bool = False, + trust_remote_code: bool = True, + use_full_prompt: bool = True, batch_size: int = 8, **kwargs, ): @@ -195,8 +183,8 @@ def __init__( Args: model_id: HuggingFace model identifier. device: Device to run model on. - trust_remote_code: Trust remote code when loading model. - use_full_prompt: Use full prompt with few-shot examples. + trust_remote_code: Trust remote code when loading model (default True). + use_full_prompt: Use full prompt with few-shot examples (default True). batch_size: Batch size for evaluation (number of questions per batch). **kwargs: Additional arguments passed to MMLUBenchmark. """ @@ -1060,9 +1048,7 @@ def _apply_eval_config_from_repo(repo_path: Path, args: "argparse.Namespace") -> errors.append("do not pass --use_lmeval_batching (model uses use_lmeval_batching=True)") if errors: raise ValueError("When using a DISCO model from the Hub, " + "; ".join(errors) + ". Omit these flags to use the model's eval_config.") - # Require use_full_prompt and data_path to match model config - if "use_full_prompt" in eval_config and args.use_full_prompt != eval_config["use_full_prompt"]: - raise ValueError(f"When using this DISCO model, --use_full_prompt must be {eval_config['use_full_prompt']!r} (model config).") + # Require data_path to match model config (use_full_prompt is always True) if "data_path" in eval_config: # Compare before _resolve_data_path overwrites; user must pass the expected repo id or path if args.data_path != eval_config["data_path"]: @@ -1133,7 +1119,7 @@ def main(): print(f"Model: {args.model_id}") print(f"Data path: {args.data_path}") print(f"Anchor points: {args.anchor_points_path or 'None (evaluate all)'}") - print(f"Use full prompt: {args.use_full_prompt}") + print("Use full prompt: True") print(f"Device: {args.device}") print(f"Output dir: {args.output_dir}") if args.disco_model_path is not None: @@ -1173,8 +1159,8 @@ def main(): benchmark = HuggingFaceMMLUBenchmark( model_id=args.model_id, device=args.device, - trust_remote_code=args.trust_remote_code, - use_full_prompt=args.use_full_prompt, + trust_remote_code=True, + use_full_prompt=True, callbacks=[logger], num_workers=args.num_workers, ) @@ -1190,7 +1176,7 @@ def main(): print("\nRunning evaluation...") agent_data = { "model_id": args.model_id, - "use_full_prompt": args.use_full_prompt, + "use_full_prompt": True, } results = benchmark.run(tasks=tasks, agent_data=agent_data) @@ -1251,7 +1237,7 @@ def main(): "model_id": args.model_id, "data_path": str(args.data_path), "anchor_points_path": str(args.anchor_points_path) if args.anchor_points_path else None, - "use_full_prompt": args.use_full_prompt, + "use_full_prompt": True, "metrics": metrics, } From a5981c4828f949f362c35deab186a4ffeff31ce7 Mon Sep 17 00:00:00 2001 From: Alexander Rubinstein Date: Sat, 7 Feb 2026 11:35:09 +0100 Subject: [PATCH 24/47] [Add to HF]: - Update readme for flattened-MMLU --- .../huggingface_disco/build_data_repo.py | 64 +++++++++++++++++-- 1 file changed, 58 insertions(+), 6 deletions(-) diff --git a/examples/mmlu_benchmark/huggingface_disco/build_data_repo.py b/examples/mmlu_benchmark/huggingface_disco/build_data_repo.py index 16547339..842b46e8 100644 --- a/examples/mmlu_benchmark/huggingface_disco/build_data_repo.py +++ b/examples/mmlu_benchmark/huggingface_disco/build_data_repo.py @@ -13,7 +13,7 @@ Download in code: from huggingface_hub import hf_hub_download data_path = hf_hub_download( - repo_id="/mmlu-prompts-examples", + repo_id="arubique/flattened-MMLU", filename="mmlu_prompts_examples.json", repo_type="dataset", ) @@ -62,13 +62,31 @@ def main(): readme = output_dir / "README.md" readme.write_text( - f"""# MMLU prompts examples + f"""# MMLU prompts (flattened) -Dataset of MMLU (Massive Multitask Language Understanding) prompts in the format expected by MASEval's MMLU benchmark. +This dataset is a flattened reformatting of the original [MMLU](https://huggingface.co/datasets/cais/mmlu) benchmark, in the format expected by [MASEval](https://github.com/parameterlab/MASEval)'s MMLU benchmark with support for accelerated [DISCO](https://arubique.github.io/disco-site/) evaluation. -## Data +## What this dataset is - **{args.filename}** – JSON list of items with `query`, `full_prompt`, `choices`, `gold`, and optional `example`. +- A flattened structure suitable for anchor-point evaluation and DISCO prediction pipelines. + +### What "flattened" means + +The original MMLU has 57 subject categories (anatomy, abstract algebra, virology, etc.). This dataset drops the per-category structure and concatenates all questions into a single ordered list of ~14k questions, preserving a fixed ordering for reproducible evaluation. + +### The `full_prompt` field + +For each question, we randomly select 10 few-shot examples from the same category. The `full_prompt` field includes the question along with these in-context few-shot examples. This design ensures evaluation reproducibility by fixing the few-shot examples for every run. + +## Attribution / Acknowledgment + +This dataset is derived from the original **MMLU (Massive Multitask Language Understanding)** benchmark: + +- **Original authors:** Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt (ICLR 2021). +- **Original paper:** ["Measuring Massive Multitask Language Understanding"](https://arxiv.org/abs/2009.03300) (ICLR 2021). +- **Source dataset:** [cais/mmlu](https://huggingface.co/datasets/cais/mmlu) on Hugging Face. +- **License:** MIT License (inherited from cais/mmlu). ## Use with MASEval @@ -78,19 +96,53 @@ def main(): from huggingface_hub import hf_hub_download data_path = hf_hub_download( - repo_id="/mmlu-prompts-examples", - filename="{args.filename}", + repo_id="arubique/flattened-MMLU", + filename="mmlu_prompts_examples.json", repo_type="dataset", ) # Then: python mmlu_benchmark.py --data_path ... ``` Or download once to a local path and use that as `--data_path`. + +## License + +This dataset includes material derived from the **cais/mmlu** dataset (MIT License). +Original work by: Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt (ICLR 2021). + +MIT License terms are provided in the `LICENSE` file. """, encoding="utf-8", ) print(f"Wrote {readme}") + # Include MIT License from cais/mmlu for compliance + license_text = """MIT License + +Copyright (c) 2020 Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. +""" + license_path = output_dir / "LICENSE" + license_path.write_text(license_text, encoding="utf-8") + print(f"Wrote {license_path}") + print(f"\nRepo ready at: {output_dir.resolve()}") print("Upload with: huggingface-cli upload /mmlu-prompts-examples . . --repo-type dataset") From bdfd81b51785e06391b138122e5cb30b2333fd66 Mon Sep 17 00:00:00 2001 From: Alexander Rubinstein Date: Sat, 7 Feb 2026 11:40:42 +0100 Subject: [PATCH 25/47] [Add to HF]: - Update dataset metadata --- .../huggingface_disco/build_data_repo.py | 15 ++++++++++++++- 1 file changed, 14 insertions(+), 1 deletion(-) diff --git a/examples/mmlu_benchmark/huggingface_disco/build_data_repo.py b/examples/mmlu_benchmark/huggingface_disco/build_data_repo.py index 842b46e8..834d269f 100644 --- a/examples/mmlu_benchmark/huggingface_disco/build_data_repo.py +++ b/examples/mmlu_benchmark/huggingface_disco/build_data_repo.py @@ -62,7 +62,20 @@ def main(): readme = output_dir / "README.md" readme.write_text( - f"""# MMLU prompts (flattened) + f"""--- +language: +- en +pretty_name: MMLU prompts (flattened) +license: mit +task_categories: +- question-answering +tags: +- mmlu +- multiple-choice +- llm-evaluation +--- + +# MMLU prompts (flattened) This dataset is a flattened reformatting of the original [MMLU](https://huggingface.co/datasets/cais/mmlu) benchmark, in the format expected by [MASEval](https://github.com/parameterlab/MASEval)'s MMLU benchmark with support for accelerated [DISCO](https://arubique.github.io/disco-site/) evaluation. From b2fbf679581baa38160931d967cb8926b5f503b4 Mon Sep 17 00:00:00 2001 From: Alexander Rubinstein Date: Sat, 7 Feb 2026 12:17:35 +0100 Subject: [PATCH 26/47] [Add to HF]: - Minor readme update --- examples/mmlu_benchmark/huggingface_disco/build_data_repo.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/mmlu_benchmark/huggingface_disco/build_data_repo.py b/examples/mmlu_benchmark/huggingface_disco/build_data_repo.py index 834d269f..94fca3e9 100644 --- a/examples/mmlu_benchmark/huggingface_disco/build_data_repo.py +++ b/examples/mmlu_benchmark/huggingface_disco/build_data_repo.py @@ -90,7 +90,7 @@ def main(): ### The `full_prompt` field -For each question, we randomly select 10 few-shot examples from the same category. The `full_prompt` field includes the question along with these in-context few-shot examples. This design ensures evaluation reproducibility by fixing the few-shot examples for every run. +For each question, we randomly select 10 few-shot examples from the same category. The `full_prompt` field includes the question along with these in-context few-shot examples as well as category-specific prompt prefix. This design ensures evaluation reproducibility by fixing the few-shot examples for every run. ## Attribution / Acknowledgment From 829643f98bc8bc56003b1cb9580e06e8b4537832 Mon Sep 17 00:00:00 2001 From: Alexander Rubinstein Date: Sat, 7 Feb 2026 12:22:57 +0100 Subject: [PATCH 27/47] [Add to HF]: - Add fields description --- .../huggingface_disco/build_data_repo.py | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/examples/mmlu_benchmark/huggingface_disco/build_data_repo.py b/examples/mmlu_benchmark/huggingface_disco/build_data_repo.py index 94fca3e9..b5472286 100644 --- a/examples/mmlu_benchmark/huggingface_disco/build_data_repo.py +++ b/examples/mmlu_benchmark/huggingface_disco/build_data_repo.py @@ -88,10 +88,22 @@ def main(): The original MMLU has 57 subject categories (anatomy, abstract algebra, virology, etc.). This dataset drops the per-category structure and concatenates all questions into a single ordered list of ~14k questions, preserving a fixed ordering for reproducible evaluation. +### The `query` field + +The question text with answer choices, formatted for zero-shot evaluation (e.g. "Question...\\nA. ...\\nB. ...\\nC. ...\\nD. ...\\nAnswer:"). When the benchmark uses zero-shot mode instead of `full_prompt`, the model receives this input. + +### The `example` field + +Optional. The question text with the ground-truth answer inserted (query + correct answer). Used for few-shot examples mining and related applications. + ### The `full_prompt` field For each question, we randomly select 10 few-shot examples from the same category. The `full_prompt` field includes the question along with these in-context few-shot examples as well as category-specific prompt prefix. This design ensures evaluation reproducibility by fixing the few-shot examples for every run. +### The `gold` field + +0-based index into `choices` indicating the correct answer. For example, `gold: 0` means the first choice (A) is correct; `gold: 1` means the second choice (B) is correct. + ## Attribution / Acknowledgment This dataset is derived from the original **MMLU (Massive Multitask Language Understanding)** benchmark: From 93c9901d4647c459cadf90bb8d5ff03f0515a477 Mon Sep 17 00:00:00 2001 From: Alexander Rubinstein Date: Sat, 14 Feb 2026 09:44:08 +0100 Subject: [PATCH 28/47] [Create PR]: - Remove extra files --- .gitignore | 1 + .../mmlu_benchmark/extract_disco_weights.py | 133 ------ .../huggingface_disco/README.md | 111 ----- .../huggingface_disco/__init__.py | 6 - .../huggingface_disco/build_data_repo.py | 176 -------- .../huggingface_disco/build_repo.py | 173 -------- .../huggingface_disco/configuration_disco.py | 28 -- .../huggingface_disco/modeling_disco.py | 217 ---------- examples/mmlu_benchmark/lm_eval_wrapper.py | 230 ----------- flattened-MMLU/README.md | 24 -- tests/test_benchmark/__init__.py | 0 .../test_mmlu_disco_identity.py | 391 ------------------ 12 files changed, 1 insertion(+), 1489 deletions(-) delete mode 100644 examples/mmlu_benchmark/extract_disco_weights.py delete mode 100644 examples/mmlu_benchmark/huggingface_disco/README.md delete mode 100644 examples/mmlu_benchmark/huggingface_disco/__init__.py delete mode 100644 examples/mmlu_benchmark/huggingface_disco/build_data_repo.py delete mode 100644 examples/mmlu_benchmark/huggingface_disco/build_repo.py delete mode 100644 examples/mmlu_benchmark/huggingface_disco/configuration_disco.py delete mode 100644 examples/mmlu_benchmark/huggingface_disco/modeling_disco.py delete mode 100644 examples/mmlu_benchmark/lm_eval_wrapper.py delete mode 100644 flattened-MMLU/README.md delete mode 100644 tests/test_benchmark/__init__.py delete mode 100644 tests/test_benchmark/test_mmlu_disco_identity.py diff --git a/.gitignore b/.gitignore index a99befd4..26095155 100644 --- a/.gitignore +++ b/.gitignore @@ -8,6 +8,7 @@ results/ DISCO-MMLU/ flattened-MMLU/ +tmp/ # Byte-compiled / optimized / DLL files __pycache__/ diff --git a/examples/mmlu_benchmark/extract_disco_weights.py b/examples/mmlu_benchmark/extract_disco_weights.py deleted file mode 100644 index 26747fa2..00000000 --- a/examples/mmlu_benchmark/extract_disco_weights.py +++ /dev/null @@ -1,133 +0,0 @@ -""" -Extract numpy arrays from DISCO pickle files (fitted_weights.pkl, transform.pkl). - -Run this once with the same environment that created the pickles (or ignore -InconsistentVersionWarning). The output .npz files can be loaded without -pickle, avoiding sklearn version mismatch warnings. - -Usage: - python extract_disco_weights.py \\ - --model_path /path/to/fitted_weights.pkl \\ - --transform_path /path/to/transform.pkl \\ - --output_dir /path/to/output - -Output: - {output_dir}/disco_transform.npz - PCA: components_, mean_ - {output_dir}/disco_model.npz - RandomForest: tree arrays - {output_dir}/disco_meta.json - sampling_name, number_item, fitted_model_type -""" - -import argparse -import json -import pickle -import warnings -from pathlib import Path - -import numpy as np - - -def _extract_pca(transform) -> dict: - """Extract numpy arrays from a fitted sklearn PCA object.""" - return { - "components_": np.asarray(transform.components_, dtype=np.float64), - "mean_": np.asarray(transform.mean_, dtype=np.float64), - "n_components_": int(transform.n_components_), - "n_features_in_": int(transform.n_features_in_), - } - - -def _extract_tree(tree) -> dict: - """Extract numpy arrays from one DecisionTreeRegressor.tree_.""" - return { - "children_left": np.asarray(tree.children_left, dtype=np.int32), - "children_right": np.asarray(tree.children_right, dtype=np.int32), - "feature": np.asarray(tree.feature, dtype=np.int32), - "threshold": np.asarray(tree.threshold, dtype=np.float64), - "value": np.asarray(tree.value, dtype=np.float64).reshape(-1), - } - - -def _extract_rf(estimator) -> dict: - """Extract numpy arrays from a RandomForestRegressor (all trees).""" - trees = [] - for est in estimator.estimators_: - trees.append(_extract_tree(est.tree_)) - n_trees = len(trees) - # Concatenate per-tree arrays; store node count per tree for splitting - node_counts = np.array([len(t["children_left"]) for t in trees], dtype=np.int64) - return { - "n_trees": n_trees, - "tree_node_counts": node_counts, - "children_left": np.concatenate([t["children_left"] for t in trees]), - "children_right": np.concatenate([t["children_right"] for t in trees]), - "feature": np.concatenate([t["feature"] for t in trees]), - "threshold": np.concatenate([t["threshold"] for t in trees]), - "value": np.concatenate([t["value"] for t in trees]), - } - - -def _get_fitted_model(model_data: dict) -> tuple: - """Return (fitted_model, sampling_name, number_item, fitted_model_type).""" - if "fitted_weights" in model_data: - fitted_weights = model_data["fitted_weights"] - else: - fitted_weights = {k: v for k, v in model_data.items() if k != "transform"} - if not fitted_weights: - raise ValueError("Could not find fitted_weights in model file.") - sampling_name = list(fitted_weights.keys())[0] - number_item = list(fitted_weights[sampling_name].keys())[0] - fitted_model_type = list(fitted_weights[sampling_name][number_item].keys())[0] - fitted_model = fitted_weights[sampling_name][number_item][fitted_model_type] - return fitted_model, sampling_name, number_item, fitted_model_type - - -def main(): - parser = argparse.ArgumentParser( - description="Extract numpy arrays from DISCO pickle files for pickle-free loading.", - ) - parser.add_argument("--model_path", type=str, required=True, help="Path to fitted_weights.pkl") - parser.add_argument("--transform_path", type=str, required=True, help="Path to transform.pkl") - parser.add_argument( - "--output_dir", - type=str, - default=".", - help="Directory to write disco_transform.npz and disco_model.npz", - ) - args = parser.parse_args() - out_dir = Path(args.output_dir) - out_dir.mkdir(parents=True, exist_ok=True) - - # Load transform (PCA) - with warnings.catch_warnings(): - warnings.simplefilter("ignore", category=UserWarning) - with open(args.transform_path, "rb") as f: - transform = pickle.load(f) - pca_data = _extract_pca(transform) - transform_out = out_dir / "disco_transform.npz" - np.savez(transform_out, **pca_data) - print(f"Saved PCA arrays to {transform_out}") - - # Load model and extract RF - with warnings.catch_warnings(): - warnings.simplefilter("ignore", category=UserWarning) - with open(args.model_path, "rb") as f: - model_data = pickle.load(f) - fitted_model, sampling_name, number_item, fitted_model_type = _get_fitted_model(model_data) - rf_data = _extract_rf(fitted_model) - model_out = out_dir / "disco_model.npz" - np.savez(model_out, **rf_data) - print(f"Saved RF arrays to {model_out}") - meta = { - "sampling_name": str(sampling_name), - "number_item": str(number_item), - "fitted_model_type": str(fitted_model_type), - } - meta_out = out_dir / "disco_meta.json" - with open(meta_out, "w") as f: - json.dump(meta, f, indent=2) - print(f"Saved metadata to {meta_out}") - print(f" sampling={sampling_name}, n_items={number_item}, model={fitted_model_type}") - - -if __name__ == "__main__": - main() diff --git a/examples/mmlu_benchmark/huggingface_disco/README.md b/examples/mmlu_benchmark/huggingface_disco/README.md deleted file mode 100644 index 2b4ea0c8..00000000 --- a/examples/mmlu_benchmark/huggingface_disco/README.md +++ /dev/null @@ -1,111 +0,0 @@ -# DISCO predictor as a Hugging Face model - -This folder contains the code and layout to upload a DISCO predictor (PCA + Random Forest) to the Hugging Face Hub so it can be loaded with: - -```python -from transformers import AutoModel - -model = AutoModel.from_pretrained("/my-disco-mmlu", trust_remote_code=True) -# predictions: (n_models, n_anchor_points, n_classes) e.g. log-probabilities -acc = model.predict(predictions) -# acc: (n_models,) predicted full-benchmark accuracies -``` - -## 1. Extract weights to NumPy (no pickle) - -From the MASEval repo, run the extractor (once) to produce `disco_transform.npz`, `disco_model.npz`, and `disco_meta.json`: - -```bash -python examples/mmlu_benchmark/extract_disco_weights.py \ - --model_path /path/to/fitted_weights.pkl \ - --transform_path /path/to/transform.pkl \ - --output_dir /path/to/disco_weights -``` - -## 2. Build the Hugging Face repo - -Run the build script to create a directory you can upload: - -```bash -python examples/mmlu_benchmark/huggingface_disco/build_repo.py \ - --weights_dir /path/to/disco_weights \ - --output_dir ./my-disco-mmlu -``` - -To include anchor points and recommended eval settings (so users can omit matching args when running the benchmark): - -```bash -python examples/mmlu_benchmark/huggingface_disco/build_repo.py \ - --weights_dir /path/to/disco_weights \ - --output_dir ./my-disco-mmlu \ - --anchor_points_path /path/to/anchor_points_disagreement.pkl \ - --pca 256 \ - --pad_to_size 31 \ - --use_lmeval_batching -``` - -These options are stored in `config.json` as `eval_config`. When the benchmark loads the model from the Hub, it uses these values and raises an error if the user passes a different `--pca`, or passes `--pad_to_size` or `--use_lmeval_batching` (the user must omit these flags so the model's config is used). - -This copies the NumPy weights and writes `config.json` with `auto_map` so `AutoModel.from_pretrained(..., trust_remote_code=True)` loads `DiscoPredictor`. - -## 3. Upload to the Hub - -```bash -cd my-disco-mmlu -huggingface-cli upload /my-disco-mmlu . . -``` - -Or from Python: - -```python -from huggingface_hub import HfApi -api = HfApi() -api.upload_folder(folder_path="./my-disco-mmlu", repo_id="/my-disco-mmlu", repo_type="model") -``` - -## Repo contents - -- `config.json` – `model_type: "disco"` and `auto_map` for AutoConfig / AutoModel -- `configuration_disco.py` – `DiscoConfig` -- `modeling_disco.py` – `DiscoPredictor` (loads npz, implements `predict`) -- `disco_transform.npz` – PCA components and mean -- `disco_model.npz` – Random Forest tree arrays -- `disco_meta.json` – sampling_name, number_item, fitted_model_type (optional, for display) -- `anchor_points.json` – list of anchor doc_ids (optional; add with `--anchor_points_path`) -- `config.json` may include `eval_config` – optional `pca`, `pad_to_size`, `use_lmeval_batching`; when loading from HF the benchmark uses these and errors if the user passes conflicting flags - -## MMLU prompts dataset (build_data_repo.py) - -To store the MMLU prompts JSON on the Hub so you can use it as `--data_path` from anywhere: - -```bash -python examples/mmlu_benchmark/huggingface_disco/build_data_repo.py \ - --data_path /path/to/mmlu_prompts_examples.json \ - --output_dir ./mmlu-prompts-examples -``` - -Upload as a dataset: - -```bash -cd mmlu-prompts-examples -huggingface-cli upload /mmlu-prompts-examples . . --repo-type dataset -``` - -Then run the benchmark with the repo id as `--data_path` (the script downloads the file automatically): - -```bash -python mmlu_benchmark.py \ - --model_id ... \ - --data_path /mmlu-prompts-examples \ - ... -``` - -## Dependencies - -Loading from the Hub only needs: - -- `transformers` -- `huggingface_hub` -- `numpy` - -No `scikit-learn` or pickle at runtime. diff --git a/examples/mmlu_benchmark/huggingface_disco/__init__.py b/examples/mmlu_benchmark/huggingface_disco/__init__.py deleted file mode 100644 index d5964a6d..00000000 --- a/examples/mmlu_benchmark/huggingface_disco/__init__.py +++ /dev/null @@ -1,6 +0,0 @@ -# DISCO predictor as a Hugging Face custom model. - -from .configuration_disco import DiscoConfig -from .modeling_disco import DiscoPredictor - -__all__ = ["DiscoConfig", "DiscoPredictor"] diff --git a/examples/mmlu_benchmark/huggingface_disco/build_data_repo.py b/examples/mmlu_benchmark/huggingface_disco/build_data_repo.py deleted file mode 100644 index b5472286..00000000 --- a/examples/mmlu_benchmark/huggingface_disco/build_data_repo.py +++ /dev/null @@ -1,176 +0,0 @@ -""" -Build a directory ready to upload to Hugging Face as a dataset (e.g. MMLU prompts). - -Usage: - python build_data_repo.py \\ - --data_path /path/to/mmlu_prompts_examples.json \\ - --output_dir ./mmlu-prompts-examples - -Then upload as a dataset: - cd mmlu-prompts-examples - huggingface-cli upload /mmlu-prompts-examples . . --repo-type dataset - -Download in code: - from huggingface_hub import hf_hub_download - data_path = hf_hub_download( - repo_id="arubique/flattened-MMLU", - filename="mmlu_prompts_examples.json", - repo_type="dataset", - ) - # Then pass data_path to mmlu_benchmark.py --data_path -""" - -import argparse -import shutil -from pathlib import Path - - -def main(): - parser = argparse.ArgumentParser( - description="Build HF dataset repo directory (e.g. for MMLU prompts JSON).", - ) - parser.add_argument( - "--data_path", - type=str, - required=True, - help="Path to the prompts JSON file (e.g. mmlu_prompts_examples.json).", - ) - parser.add_argument( - "--output_dir", - type=str, - required=True, - help="Output directory to create; will contain the JSON and a README.", - ) - parser.add_argument( - "--filename", - type=str, - default="mmlu_prompts_examples.json", - help="Filename to use in the repo (default: mmlu_prompts_examples.json).", - ) - args = parser.parse_args() - - data_path = Path(args.data_path) - if not data_path.exists(): - raise FileNotFoundError(f"Data file not found: {data_path}") - - output_dir = Path(args.output_dir) - output_dir.mkdir(parents=True, exist_ok=True) - - out_file = output_dir / args.filename - shutil.copy2(data_path, out_file) - print(f"Copied {data_path} -> {out_file}") - - readme = output_dir / "README.md" - readme.write_text( - f"""--- -language: -- en -pretty_name: MMLU prompts (flattened) -license: mit -task_categories: -- question-answering -tags: -- mmlu -- multiple-choice -- llm-evaluation ---- - -# MMLU prompts (flattened) - -This dataset is a flattened reformatting of the original [MMLU](https://huggingface.co/datasets/cais/mmlu) benchmark, in the format expected by [MASEval](https://github.com/parameterlab/MASEval)'s MMLU benchmark with support for accelerated [DISCO](https://arubique.github.io/disco-site/) evaluation. - -## What this dataset is - -- **{args.filename}** – JSON list of items with `query`, `full_prompt`, `choices`, `gold`, and optional `example`. -- A flattened structure suitable for anchor-point evaluation and DISCO prediction pipelines. - -### What "flattened" means - -The original MMLU has 57 subject categories (anatomy, abstract algebra, virology, etc.). This dataset drops the per-category structure and concatenates all questions into a single ordered list of ~14k questions, preserving a fixed ordering for reproducible evaluation. - -### The `query` field - -The question text with answer choices, formatted for zero-shot evaluation (e.g. "Question...\\nA. ...\\nB. ...\\nC. ...\\nD. ...\\nAnswer:"). When the benchmark uses zero-shot mode instead of `full_prompt`, the model receives this input. - -### The `example` field - -Optional. The question text with the ground-truth answer inserted (query + correct answer). Used for few-shot examples mining and related applications. - -### The `full_prompt` field - -For each question, we randomly select 10 few-shot examples from the same category. The `full_prompt` field includes the question along with these in-context few-shot examples as well as category-specific prompt prefix. This design ensures evaluation reproducibility by fixing the few-shot examples for every run. - -### The `gold` field - -0-based index into `choices` indicating the correct answer. For example, `gold: 0` means the first choice (A) is correct; `gold: 1` means the second choice (B) is correct. - -## Attribution / Acknowledgment - -This dataset is derived from the original **MMLU (Massive Multitask Language Understanding)** benchmark: - -- **Original authors:** Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt (ICLR 2021). -- **Original paper:** ["Measuring Massive Multitask Language Understanding"](https://arxiv.org/abs/2009.03300) (ICLR 2021). -- **Source dataset:** [cais/mmlu](https://huggingface.co/datasets/cais/mmlu) on Hugging Face. -- **License:** MIT License (inherited from cais/mmlu). - -## Use with MASEval - -Download the file and pass it to the benchmark: - -```python -from huggingface_hub import hf_hub_download - -data_path = hf_hub_download( - repo_id="arubique/flattened-MMLU", - filename="mmlu_prompts_examples.json", - repo_type="dataset", -) -# Then: python mmlu_benchmark.py --data_path ... -``` - -Or download once to a local path and use that as `--data_path`. - -## License - -This dataset includes material derived from the **cais/mmlu** dataset (MIT License). -Original work by: Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt (ICLR 2021). - -MIT License terms are provided in the `LICENSE` file. -""", - encoding="utf-8", - ) - print(f"Wrote {readme}") - - # Include MIT License from cais/mmlu for compliance - license_text = """MIT License - -Copyright (c) 2020 Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. -""" - license_path = output_dir / "LICENSE" - license_path.write_text(license_text, encoding="utf-8") - print(f"Wrote {license_path}") - - print(f"\nRepo ready at: {output_dir.resolve()}") - print("Upload with: huggingface-cli upload /mmlu-prompts-examples . . --repo-type dataset") - - -if __name__ == "__main__": - main() diff --git a/examples/mmlu_benchmark/huggingface_disco/build_repo.py b/examples/mmlu_benchmark/huggingface_disco/build_repo.py deleted file mode 100644 index 6fb58448..00000000 --- a/examples/mmlu_benchmark/huggingface_disco/build_repo.py +++ /dev/null @@ -1,173 +0,0 @@ -""" -Build a directory ready to upload to Hugging Face as a DISCO predictor model. - -Usage: - python build_repo.py \\ - --weights_dir /path/to/disco_weights \\ - --output_dir ./my-disco-mmlu - - # Include anchor points and eval config (pca, pad_to_size, use_lmeval_batching) - python build_repo.py \\ - --weights_dir /path/to/disco_weights \\ - --output_dir ./my-disco-mmlu \\ - --anchor_points_path /path/to/anchor_points_disagreement.pkl \\ - --pca 256 --pad_to_size 31 --use_lmeval_batching - -Stored eval_config is applied by the benchmark when loading the model from HF if the user did not override those args. - -Then upload with: huggingface-cli upload /my-disco-mmlu ./my-disco-mmlu . -""" - -import argparse -import json -import pickle -import shutil -from pathlib import Path - - -def _load_anchor_points(path: Path) -> list: - """Load anchor points from .pkl or .json; return a list.""" - path = Path(path) - if path.suffix.lower() == ".json": - with open(path) as f: - out = json.load(f) - else: - with open(path, "rb") as f: - out = pickle.load(f) - if hasattr(out, "tolist"): - out = out.tolist() - return list(out) - - -def main(): - parser = argparse.ArgumentParser( - description="Build HF repo directory for DISCO predictor (npz + config + code).", - ) - parser.add_argument( - "--weights_dir", - type=str, - required=True, - help="Directory containing disco_transform.npz, disco_model.npz, disco_meta.json", - ) - parser.add_argument( - "--output_dir", - type=str, - required=True, - help="Output directory to create (will contain config, code, and weights).", - ) - parser.add_argument( - "--anchor_points_path", - type=str, - default=None, - help="Path to anchor points .pkl or .json; saved as anchor_points.json in the repo.", - ) - parser.add_argument( - "--pca", - type=int, - default=None, - help="PCA dimension used for this model; stored in eval_config so the benchmark can use it when loading from HF.", - ) - parser.add_argument( - "--pad_to_size", - type=int, - default=None, - help="Pad predictions to this size; stored in eval_config for the benchmark when loading from HF.", - ) - parser.add_argument( - "--use_lmeval_batching", - action="store_true", - help="Whether lm-eval batching was used; stored in eval_config for the benchmark when loading from HF.", - ) - parser.add_argument( - "--use_full_prompt", - action="store_true", - default=True, - help="Whether full prompts were used; stored in eval_config (default: True).", - ) - parser.add_argument( - "--no_use_full_prompt", - action="store_false", - dest="use_full_prompt", - help="Disable use_full_prompt.", - ) - parser.add_argument( - "--data_path", - type=str, - default=None, - help="Data path or HF dataset repo id (e.g. arubique/flattened-MMLU); stored in eval_config.", - ) - args = parser.parse_args() - - weights_dir = Path(args.weights_dir) - output_dir = Path(args.output_dir) - output_dir.mkdir(parents=True, exist_ok=True) - - # Required weight files - for name in ("disco_transform.npz", "disco_model.npz", "disco_meta.json"): - src = weights_dir / name - if not src.exists(): - raise FileNotFoundError(f"Missing {name} in {weights_dir}. Run extract_disco_weights.py first.") - shutil.copy2(src, output_dir / name) - print(f"Copied weights from {weights_dir} -> {output_dir}") - - # Copy modeling and config code (so repo is self-contained) - this_dir = Path(__file__).resolve().parent - for name in ("configuration_disco.py", "modeling_disco.py"): - shutil.copy2(this_dir / name, output_dir / name) - print(f"Copied {name}") - - # config.json with auto_map for AutoModel.from_pretrained(..., trust_remote_code=True) - meta_path = output_dir / "disco_meta.json" - meta = {} - if meta_path.exists(): - with open(meta_path) as f: - meta = json.load(f) - - config = { - "model_type": "disco", - "auto_map": { - "AutoConfig": "configuration_disco.DiscoConfig", - "AutoModel": "modeling_disco.DiscoPredictor", - }, - "n_components": 256, - "sampling_name": meta.get("sampling_name", ""), - "number_item": meta.get("number_item", ""), - "fitted_model_type": meta.get("fitted_model_type", ""), - "use_full_prompt": args.use_full_prompt, - "data_path": args.data_path or "", - } - # Eval config: when the benchmark loads this model from HF, it validates these - eval_config = {} - if args.pca is not None: - eval_config["pca"] = args.pca - if args.pad_to_size is not None: - eval_config["pad_to_size"] = args.pad_to_size - if args.use_lmeval_batching: - eval_config["use_lmeval_batching"] = True - eval_config["use_full_prompt"] = args.use_full_prompt - if args.data_path is not None: - eval_config["data_path"] = args.data_path - if eval_config: - config["eval_config"] = eval_config - config_path = output_dir / "config.json" - with open(config_path, "w") as f: - json.dump(config, f, indent=2) - print(f"Wrote {config_path}") - - # Optional: anchor points so users can omit --anchor_points_path when loading from HF - if args.anchor_points_path: - ap_path = Path(args.anchor_points_path) - if not ap_path.exists(): - raise FileNotFoundError(f"Anchor points file not found: {ap_path}") - anchor_points = _load_anchor_points(ap_path) - ap_out = output_dir / "anchor_points.json" - with open(ap_out, "w") as f: - json.dump(anchor_points, f, indent=2) - print(f"Wrote {ap_out} ({len(anchor_points)} anchor points)") - - print(f"\nRepo ready at: {output_dir.resolve()}") - print("Upload with: huggingface-cli upload /my-disco-mmlu ./my-disco-mmlu .") - - -if __name__ == "__main__": - main() diff --git a/examples/mmlu_benchmark/huggingface_disco/configuration_disco.py b/examples/mmlu_benchmark/huggingface_disco/configuration_disco.py deleted file mode 100644 index 6eaacba9..00000000 --- a/examples/mmlu_benchmark/huggingface_disco/configuration_disco.py +++ /dev/null @@ -1,28 +0,0 @@ -# Copyright 2025 MASEval contributors. DISCO predictor config for Hugging Face Hub. - -# Use configuration_utils so this works when transformers uses lazy top-level imports -from transformers.configuration_utils import PretrainedConfig - - -class DiscoConfig(PretrainedConfig): - """Configuration for DISCO predictor (PCA + Random Forest) on the Hub.""" - - model_type = "disco" - - def __init__( - self, - n_components: int = 256, - sampling_name: str = "", - number_item: str = "", - fitted_model_type: str = "", - use_full_prompt: bool = True, - data_path: str = "", - **kwargs, - ): - super().__init__(**kwargs) - self.n_components = n_components - self.sampling_name = sampling_name - self.number_item = number_item - self.fitted_model_type = fitted_model_type - self.use_full_prompt = use_full_prompt - self.data_path = data_path diff --git a/examples/mmlu_benchmark/huggingface_disco/modeling_disco.py b/examples/mmlu_benchmark/huggingface_disco/modeling_disco.py deleted file mode 100644 index 8ed459b3..00000000 --- a/examples/mmlu_benchmark/huggingface_disco/modeling_disco.py +++ /dev/null @@ -1,217 +0,0 @@ -# Copyright 2025 MASEval contributors. DISCO predictor model for Hugging Face Hub. -# -# Self-contained: uses only numpy and huggingface_hub. Load with: -# from transformers import AutoModel -# model = AutoModel.from_pretrained("/my-disco-mmlu", trust_remote_code=True) -# acc = model.predict(predictions_tensor) # predictions: (n_models, n_anchor_points, n_classes) - -from pathlib import Path -from typing import Any, Optional, Union - -import numpy as np - - -def _pca_transform(X: np.ndarray, components: np.ndarray, mean: np.ndarray) -> np.ndarray: - """Apply PCA transform: (X - mean) @ components.T.""" - return (X - mean) @ components.T - - -def _predict_tree( - X: np.ndarray, - children_left: np.ndarray, - children_right: np.ndarray, - feature: np.ndarray, - threshold: np.ndarray, - value: np.ndarray, -) -> np.ndarray: - """Predict for one tree; X (n_samples, n_features) -> (n_samples,).""" - out = np.empty(X.shape[0], dtype=np.float64) - for i in range(X.shape[0]): - node = 0 - while children_left[node] != -1: - if X[i, feature[node]] <= threshold[node]: - node = children_left[node] - else: - node = children_right[node] - out[i] = value[node] - return out - - -def _predict_rf( - X: np.ndarray, - tree_node_counts: np.ndarray, - children_left: np.ndarray, - children_right: np.ndarray, - feature: np.ndarray, - threshold: np.ndarray, - value: np.ndarray, -) -> np.ndarray: - """Predict using RF tree arrays; X (n_samples, n_features) -> (n_samples,).""" - offsets = np.concatenate([[0], np.cumsum(tree_node_counts)]) - n_trees = len(tree_node_counts) - preds = np.zeros((n_trees, X.shape[0]), dtype=np.float64) - for t in range(n_trees): - lo, hi = offsets[t], offsets[t + 1] - preds[t] = _predict_tree( - X, - children_left[lo:hi], - children_right[lo:hi], - feature[lo:hi], - threshold[lo:hi], - value[lo:hi], - ) - return np.mean(preds, axis=0) - - -class DiscoPredictor: - """ - DISCO predictor: maps anchor-point prediction tensors to full-benchmark accuracy. - - Load from the Hub with: - from transformers import AutoModel - model = AutoModel.from_pretrained("/my-disco-mmlu", trust_remote_code=True) - - Then call model.predict(predictions) where predictions has shape - (n_models, n_anchor_points, n_classes) (e.g. log-probabilities per choice). - Returns a 1D array of predicted full-benchmark accuracies, one per model. - """ - - def __init__( - self, - components: np.ndarray, - mean: np.ndarray, - tree_node_counts: np.ndarray, - children_left: np.ndarray, - children_right: np.ndarray, - feature: np.ndarray, - threshold: np.ndarray, - value: np.ndarray, - config: Optional[Any] = None, - ): - self._components = np.asarray(components, dtype=np.float64) - self._mean = np.asarray(mean, dtype=np.float64) - self._tree_node_counts = np.asarray(tree_node_counts, dtype=np.int64) - self._children_left = np.asarray(children_left, dtype=np.int32) - self._children_right = np.asarray(children_right, dtype=np.int32) - self._feature = np.asarray(feature, dtype=np.int32) - self._threshold = np.asarray(threshold, dtype=np.float64) - self._value = np.asarray(value, dtype=np.float64) - self.config = config - - @classmethod - def register_for_auto_class(cls, auto_class=None): - """No-op: required by transformers when loading custom models with trust_remote_code.""" - pass - - @classmethod - def from_pretrained( - cls, - pretrained_model_name_or_path: Union[str, Path], - **kwargs, - ) -> "DiscoPredictor": - """Load DISCO weights from a Hugging Face repo or local directory.""" - try: - from huggingface_hub import snapshot_download - except ImportError as e: - raise ImportError("Loading from Hub requires huggingface_hub: pip install huggingface_hub") from e - - path = Path(pretrained_model_name_or_path) - if not path.exists() or not path.is_dir(): - path = Path(snapshot_download(pretrained_model_name_or_path)) - - # Load config if present - config = None - config_path = path / "config.json" - if config_path.exists(): - try: - from transformers import AutoConfig - - config = AutoConfig.from_pretrained(str(path), trust_remote_code=True) - except Exception: - pass - - # Load PCA (transform) - transform_data = np.load(path / "disco_transform.npz") - components = np.asarray(transform_data["components_"]) - mean = np.asarray(transform_data["mean_"]) - - # Load RF (model) - model_data = np.load(path / "disco_model.npz") - tree_node_counts = np.asarray(model_data["tree_node_counts"], dtype=np.int64) - children_left = np.asarray(model_data["children_left"], dtype=np.int32) - children_right = np.asarray(model_data["children_right"], dtype=np.int32) - feature = np.asarray(model_data["feature"], dtype=np.int32) - threshold = np.asarray(model_data["threshold"], dtype=np.float64) - value = np.asarray(model_data["value"], dtype=np.float64) - - return cls( - components=components, - mean=mean, - tree_node_counts=tree_node_counts, - children_left=children_left, - children_right=children_right, - feature=feature, - threshold=threshold, - value=value, - config=config, - ) - - def predict( - self, - predictions: np.ndarray, - apply_softmax: bool = True, - ) -> np.ndarray: - """ - Predict full-benchmark accuracy from anchor-point predictions. - - Args: - predictions: Shape (n_models, n_anchor_points, n_classes), e.g. log-probabilities. - apply_softmax: If True, apply softmax to predictions before PCA (default True). - - Returns: - Shape (n_models,) predicted full-benchmark accuracies. - """ - X = np.asarray(predictions, dtype=np.float64) - if X.ndim == 2: - X = X[np.newaxis, ...] - n_models = X.shape[0] - # Softmax over last dim - if apply_softmax: - X = np.exp(X - X.max(axis=-1, keepdims=True)) - X = X / X.sum(axis=-1, keepdims=True) - # Flatten to (n_models, n_anchor_points * n_classes) - X = X.reshape(n_models, -1) - # PCA - emb = _pca_transform(X, self._components, self._mean) - # RF - return _predict_rf( - emb, - self._tree_node_counts, - self._children_left, - self._children_right, - self._feature, - self._threshold, - self._value, - ) - - def save_pretrained(self, save_directory: Union[str, Path]) -> None: - """Save DISCO weights and config to a directory (e.g. for uploading to Hub).""" - - path = Path(save_directory) - path.mkdir(parents=True, exist_ok=True) - np.savez( - path / "disco_transform.npz", - components_=self._components, - mean_=self._mean, - ) - np.savez( - path / "disco_model.npz", - tree_node_counts=self._tree_node_counts, - children_left=self._children_left, - children_right=self._children_right, - feature=self._feature, - threshold=self._threshold, - value=self._value, - ) - if self.config is not None: - self.config.save_pretrained(save_directory) diff --git a/examples/mmlu_benchmark/lm_eval_wrapper.py b/examples/mmlu_benchmark/lm_eval_wrapper.py deleted file mode 100644 index c31727f4..00000000 --- a/examples/mmlu_benchmark/lm_eval_wrapper.py +++ /dev/null @@ -1,230 +0,0 @@ -"""LM-Eval Harness wrapper for MMLU evaluation. - -This module provides a thin wrapper around lm-evaluation-harness to produce -predictions in the exact same format as disco-public, enabling identical -output for unit testing. - -Usage: - from lm_eval_wrapper import evaluate_with_lm_eval - - predictions, correctness = evaluate_with_lm_eval( - model_id="alignment-handbook/zephyr-7b-sft-full", - anchor_points_path="/path/to/anchor_points.pkl", - data_path="/path/to/mmlu_prompts_examples.json", - pad_to_size=31, - ) -""" - -import pickle -import subprocess -import sys -from pathlib import Path -from typing import Optional - -import numpy as np - - -def pad_predictions(predictions: list, max_num_answers: int = 31) -> list: - """Pad predictions with -inf to max_num_answers. - - Args: - predictions: List of log probabilities. - max_num_answers: Target size for padding. - - Returns: - Padded list of predictions. - """ - if len(predictions) >= max_num_answers: - return predictions[:max_num_answers] - return predictions + [float("-inf")] * (max_num_answers - len(predictions)) - - -def evaluate_with_lm_eval( - model_id: str, - data_path: str, - anchor_points_path: Optional[str] = None, - output_path: Optional[str] = None, - pad_to_size: int = 31, - device: str = "cuda:0", - batch_size: int = 8, - trust_remote_code: bool = True, - use_full_prompt: bool = True, - disco_public_path: str = "/home/oh/arubinstein17/github/disco-public", -) -> tuple: - """Evaluate model using lm-evaluation-harness. - - This produces predictions in the exact same format as disco-public's - run_lm_eval.py script. - - Args: - model_id: HuggingFace model identifier. - data_path: Path to MMLU prompts JSON file. - anchor_points_path: Path to anchor points pickle file. - output_path: Path to save predictions pickle. - pad_to_size: Padding size for predictions (default 31). - device: Device to run model on. - batch_size: Batch size for evaluation. - trust_remote_code: Trust remote code when loading model. - use_full_prompt: Use full prompt with few-shot examples. - disco_public_path: Path to disco-public repository. - - Returns: - Tuple of (predictions, correctness) numpy arrays. - """ - # Add disco-public to path for imports. Order matters: disco_root must be first - # so "from scripts import run_lm_eval" resolves to disco-public/scripts, not - # lm-evaluation-harness/scripts. - disco_root = Path(disco_public_path) - disco_scripts = disco_root / "scripts" - lm_eval_path = disco_root / "external" / "lm-evaluation-harness" - - for path in (disco_scripts, lm_eval_path, disco_root): - path_str = str(path) - if path_str not in sys.path: - sys.path.insert(0, path_str) - - try: - import lm_eval - from lm_eval_postprocess import ( - convert_jsonl_results_to_arrays, - load_jsonl, - find_jsonl_file_in_directory, - ) - from utils import load_pickle - from utils_for_notebooks import pad_predictions as disco_pad_predictions - except ImportError as e: - raise ImportError(f"Could not import lm-eval dependencies. Ensure disco-public is available at {disco_public_path}. Error: {e}") from e - - # Load anchor points if provided - anchor_points = None - if anchor_points_path: - anchor_points = load_pickle(anchor_points_path) - if isinstance(anchor_points, np.ndarray): - anchor_points = anchor_points.tolist() - - # Build model args - model_args = f"pretrained={model_id}" - if trust_remote_code: - model_args += ",trust_remote_code=True" - - # Build gen_kwargs - gen_kwargs = "max_gen_toks=128,output_scores=True,return_dict_in_generate=True" - - # Prepare lm_eval arguments - eval_args = [ - "--model", - "hf", - "--model_args", - model_args, - "--tasks", - "mmlu_prompts", - "--batch_size", - str(batch_size), - "--device", - device, - "--gen_kwargs", - gen_kwargs, - "--num_fewshot", - "0", - "--log_samples", - ] - - if use_full_prompt: - eval_args.append("--use_full_prompt") - - if anchor_points_path: - eval_args.extend(["--anchor_points_path", anchor_points_path]) - eval_args.append("--skip_non_anchor_points") - - # Create temporary output directory - import tempfile - - with tempfile.TemporaryDirectory() as tmpdir: - eval_args.extend(["--target_outputs_path", str(output_path).replace(".pkl", "_target_outputs.pkl")]) - eval_args.extend(["--output_path", tmpdir]) - eval_args.extend(["--predictions_path", str(output_path)]) - eval_args.append("--metric") - eval_args.append("acc_norm") - eval_args.append("--force_recompute") - - # Run disco-public's run_lm_eval.py as a subprocess so scripts.* resolves to disco-public/scripts - # (in-process import would see lm-evaluation-harness/scripts first). - print(f"Running run_lm_eval with args: {' '.join(eval_args)}") - - cmd = [sys.executable, str(disco_root / "scripts" / "run_lm_eval.py")] + eval_args - subprocess.run(cmd, cwd=str(disco_root), check=True) - - # Find and load results - jsonl_path = find_jsonl_file_in_directory(tmpdir) - if jsonl_path is None: - raise RuntimeError(f"Could not find JSONL results in {tmpdir}") - - jsonl_results = load_jsonl(jsonl_path) - - # Convert to arrays - predictions_2d, correctness_1d, n_questions = convert_jsonl_results_to_arrays(jsonl_results, "acc_norm", anchor_points=anchor_points) - - # Pad predictions - if pad_to_size is not None and predictions_2d.shape[1] < pad_to_size: - padded_predictions_list = [] - for row in predictions_2d: - padded_row = disco_pad_predictions(row.tolist(), max_num_answers=pad_to_size) - padded_predictions_list.append(padded_row) - predictions_2d = np.array(padded_predictions_list) - - # Reshape to (1, n_questions, n_choices) - predictions = predictions_2d.reshape(1, -1, predictions_2d.shape[-1]) - - # Save if output path provided - if output_path: - with open(output_path, "wb") as f: - pickle.dump(predictions, f) - print(f"Saved predictions to {output_path}") - print(f" Shape: {predictions.shape}") - - return predictions, correctness_1d - - -def main(): - """Command-line interface for lm-eval wrapper.""" - import argparse - - parser = argparse.ArgumentParser(description="Evaluate model using lm-evaluation-harness wrapper") - parser.add_argument("--model_id", type=str, required=True) - parser.add_argument("--data_path", type=str, required=True) - parser.add_argument("--anchor_points_path", type=str, default=None) - parser.add_argument("--output_path", type=str, default=None) - parser.add_argument("--pad_to_size", type=int, default=31) - parser.add_argument("--device", type=str, default="cuda:0") - parser.add_argument("--batch_size", type=int, default=8) - parser.add_argument("--trust_remote_code", action="store_true") - parser.add_argument("--use_full_prompt", action="store_true") - parser.add_argument( - "--disco_public_path", - type=str, - default="/home/oh/arubinstein17/github/disco-public", - ) - - args = parser.parse_args() - - predictions, correctness = evaluate_with_lm_eval( - model_id=args.model_id, - data_path=args.data_path, - anchor_points_path=args.anchor_points_path, - output_path=args.output_path, - pad_to_size=args.pad_to_size, - device=args.device, - batch_size=args.batch_size, - trust_remote_code=args.trust_remote_code, - use_full_prompt=args.use_full_prompt, - disco_public_path=args.disco_public_path, - ) - - print("\nResults:") - print(f" Predictions shape: {predictions.shape}") - print(f" Correctness shape: {correctness.shape}") - print(f" Accuracy: {correctness.mean():.4f}") - - -if __name__ == "__main__": - main() diff --git a/flattened-MMLU/README.md b/flattened-MMLU/README.md deleted file mode 100644 index 88228c68..00000000 --- a/flattened-MMLU/README.md +++ /dev/null @@ -1,24 +0,0 @@ -# MMLU prompts examples - -Dataset of MMLU (Massive Multitask Language Understanding) prompts in the format expected by MASEval's MMLU benchmark. - -## Data - -- **mmlu_prompts_examples.json** – JSON list of items with `query`, `full_prompt`, `choices`, `gold`, and optional `example`. - -## Use with MASEval - -Download the file and pass it to the benchmark: - -```python -from huggingface_hub import hf_hub_download - -data_path = hf_hub_download( - repo_id="/mmlu-prompts-examples", - filename="mmlu_prompts_examples.json", - repo_type="dataset", -) -# Then: python mmlu_benchmark.py --data_path ... -``` - -Or download once to a local path and use that as `--data_path`. diff --git a/tests/test_benchmark/__init__.py b/tests/test_benchmark/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/tests/test_benchmark/test_mmlu_disco_identity.py b/tests/test_benchmark/test_mmlu_disco_identity.py deleted file mode 100644 index cd5fda01..00000000 --- a/tests/test_benchmark/test_mmlu_disco_identity.py +++ /dev/null @@ -1,391 +0,0 @@ -"""Tests for MMLU benchmark DISCO prediction output identity. - -These tests verify that the MASEval MMLU benchmark produces output -that is compatible with (and optionally identical to) disco-public. - -To run the full identity test (requires lm-eval-harness and GPU): - pytest tests/test_benchmark/test_mmlu_disco_identity.py -v -m "slow" - -To run format-only tests: - pytest tests/test_benchmark/test_mmlu_disco_identity.py -v -m "not slow" -""" - -import pickle -from pathlib import Path - -import numpy as np -import pytest - -# Reference files from disco-public -DISCO_PUBLIC_PATH = Path("/home/oh/arubinstein17/github/disco-public") -REFERENCE_PREDICTIONS_PATH = DISCO_PUBLIC_PATH / "data/model_outputs/mmlu/local_predictions_r90.pkl" -ANCHOR_POINTS_PATH = DISCO_PUBLIC_PATH / "data/model_outputs/mmlu/anchor_points_disagreement.pkl" -MMLU_DATA_PATH = DISCO_PUBLIC_PATH / "notebooks/mmlu_prompts_examples.json" -FITTED_WEIGHTS_PATH = DISCO_PUBLIC_PATH / "data/model_outputs/mmlu/fitted_weights.pkl" -TRANSFORM_PATH = DISCO_PUBLIC_PATH / "data/model_outputs/mmlu/transform.pkl" - - -@pytest.fixture -def reference_predictions(): - """Load reference predictions from disco-public.""" - if not REFERENCE_PREDICTIONS_PATH.exists(): - pytest.skip(f"Reference file not found: {REFERENCE_PREDICTIONS_PATH}") - with open(REFERENCE_PREDICTIONS_PATH, "rb") as f: - return pickle.load(f) - - -@pytest.fixture -def anchor_points(): - """Load anchor points from disco-public.""" - if not ANCHOR_POINTS_PATH.exists(): - pytest.skip(f"Anchor points file not found: {ANCHOR_POINTS_PATH}") - with open(ANCHOR_POINTS_PATH, "rb") as f: - points = pickle.load(f) - if isinstance(points, np.ndarray): - return points.tolist() - return points - - -class TestPredictionsFormat: - """Test that predictions have the correct format for DISCO.""" - - def test_reference_predictions_shape(self, reference_predictions): - """Reference predictions should have shape (1, n_anchors, 31).""" - assert reference_predictions.ndim == 3 - assert reference_predictions.shape[0] == 1 - assert reference_predictions.shape[1] == 100 # n_anchor_points - assert reference_predictions.shape[2] == 31 # padded size - - def test_reference_predictions_dtype(self, reference_predictions): - """Reference predictions should be float64.""" - assert reference_predictions.dtype == np.float64 - - def test_reference_predictions_contains_logprobs(self, reference_predictions): - """Reference predictions should contain log probabilities (negative values).""" - # First 4 columns should be log probs (negative, but not -inf) - first_4 = reference_predictions[0, :, :4] - valid_mask = ~np.isinf(first_4) - assert valid_mask.sum() > 0 # At least some valid values - assert (first_4[valid_mask] < 0).all() # All negative (log probs) - - def test_reference_predictions_padding(self, reference_predictions): - """Reference predictions should be padded with -inf after first 4 columns.""" - padding = reference_predictions[0, :, 4:] - assert np.all(np.isinf(padding) & (padding < 0)) - - -class TestAnchorPoints: - """Test anchor points loading and format.""" - - def test_anchor_points_count(self, anchor_points): - """Should have 100 anchor points.""" - assert len(anchor_points) == 100 - - def test_anchor_points_are_integers(self, anchor_points): - """Anchor points should be integers (doc_ids).""" - assert all(isinstance(p, (int, np.integer)) for p in anchor_points) - - -class TestDISCOPredictionWorkflow: - """Test the DISCO prediction workflow with reference files.""" - - @pytest.mark.skipif(not all([FITTED_WEIGHTS_PATH.exists(), TRANSFORM_PATH.exists()]), reason="DISCO model files not available") - def test_disco_prediction_with_reference_predictions(self, reference_predictions): - """Test DISCO prediction produces reasonable accuracy estimate.""" - # Import DISCO prediction functions - try: - import torch - except ImportError: - pytest.skip("torch not available") - - # Load model and transform - with open(FITTED_WEIGHTS_PATH, "rb") as f: - model_data = pickle.load(f) - with open(TRANSFORM_PATH, "rb") as f: - transform = pickle.load(f) - - # Get fitted weights - if "fitted_weights" in model_data: - fitted_weights = model_data["fitted_weights"] - else: - fitted_weights = {k: v for k, v in model_data.items() if k != "transform"} - - sampling_name = list(fitted_weights.keys())[0] - number_item = list(fitted_weights[sampling_name].keys())[0] - fitted_model_type = list(fitted_weights[sampling_name][number_item].keys())[0] - fitted_model = fitted_weights[sampling_name][number_item][fitted_model_type] - - # Compute embeddings - preds_tensor = torch.Tensor(reference_predictions) - emb_unreduced = preds_tensor.softmax(dim=-1) - emb_unreduced = emb_unreduced.reshape(emb_unreduced.shape[0], -1) - emb = transform.transform(emb_unreduced.numpy()) - emb = torch.Tensor(emb) - - # Predict accuracy - predicted_acc = fitted_model.predict(emb.numpy())[0] - - # Check that predicted accuracy is reasonable (between 0 and 1) - assert 0.0 <= predicted_acc <= 1.0 - - # For zephyr-7b-sft-full on MMLU, we expect roughly 50-70% accuracy - assert 0.4 <= predicted_acc <= 0.8, f"Predicted accuracy {predicted_acc} seems unreasonable" - - print(f"DISCO predicted accuracy: {predicted_acc:.4f}") - - -@pytest.mark.slow -class TestOutputIdentity: - """Tests that verify exact output identity with disco-public. - - These tests are marked slow because they require running the full - lm-evaluation-harness evaluation, which takes significant time and GPU. - """ - - @pytest.mark.skipif(not all([MMLU_DATA_PATH.exists(), ANCHOR_POINTS_PATH.exists()]), reason="Required data files not available") - def test_lm_eval_wrapper_produces_identical_output(self, reference_predictions, anchor_points, tmp_path): - """Test that lm_eval_wrapper produces identical output to disco-public.""" - try: - from examples.mmlu_benchmark.lm_eval_wrapper import evaluate_with_lm_eval - except ImportError: - pytest.skip("lm_eval_wrapper not available") - - output_path = tmp_path / "test_predictions.pkl" - - # Run evaluation (lm-eval deps are imported inside evaluate_with_lm_eval) - try: - predictions, correctness = evaluate_with_lm_eval( - model_id="alignment-handbook/zephyr-7b-sft-full", - data_path=str(MMLU_DATA_PATH), - anchor_points_path=str(ANCHOR_POINTS_PATH), - output_path=str(output_path), - pad_to_size=31, - device="cuda:0", - batch_size=8, - trust_remote_code=True, - use_full_prompt=True, - ) - except ImportError as e: - pytest.skip(f"lm-eval dependencies not available: {e}") - - # Load produced predictions - with open(output_path, "rb") as f: - produced_predictions = pickle.load(f) - - # Assert identical shape - assert produced_predictions.shape == reference_predictions.shape, ( - f"Shape mismatch: {produced_predictions.shape} vs {reference_predictions.shape}" - ) - - # Assert identical dtype - assert produced_predictions.dtype == reference_predictions.dtype, ( - f"Dtype mismatch: {produced_predictions.dtype} vs {reference_predictions.dtype}" - ) - - # Assert values are very close (allowing for floating point differences) - # Use a relative tolerance since log probs can have large absolute values - np.testing.assert_allclose( - produced_predictions, reference_predictions, rtol=1e-5, atol=1e-10, err_msg="Predictions do not match reference" - ) - - print("Output identity test PASSED!") - - -def test_format_compatibility_with_maseval_output(): - """Test that MASEval's save_predictions_for_disco produces compatible format. - - Note: This tests format compatibility, not value identity. For identical - values, use the lm_eval_wrapper which uses lm-evaluation-harness directly. - """ - import sys - from pathlib import Path - - # Add examples to path for import - examples_path = Path(__file__).parent.parent.parent / "examples" / "mmlu_benchmark" - if str(examples_path.parent) not in sys.path: - sys.path.insert(0, str(examples_path.parent)) - - try: - from mmlu_benchmark.mmlu_benchmark import save_predictions_for_disco - except ImportError as e: - pytest.skip(f"mmlu_benchmark not available: {e}") - - # Create mock results - mock_results = [ - { - "status": "success", - "eval": [ - {"doc_id": 0, "predicted": 0, "acc": 1.0, "acc_norm": 1.0}, - ], - }, - { - "status": "success", - "eval": [ - {"doc_id": 1, "predicted": 2, "acc": 0.0, "acc_norm": 0.0}, - ], - }, - ] - - predictions = save_predictions_for_disco( - results=mock_results, - output_path=None, # Don't save, just return - anchor_points=[0, 1], - n_choices=4, - ) - - # Check format - assert predictions.ndim == 3 - assert predictions.shape[0] == 1 # n_models - assert predictions.shape[1] == 2 # n_questions - assert predictions.shape[2] == 4 # n_choices - - -# Path to MASEval-produced predictions (set by running mmlu_benchmark.py with --predictions_path) -MASEVAL_PREDICTIONS_PATH = Path("/weka/oh/arubinstein17/github/disco-public/output/mmlu_maseval/maseval_predictions.pkl") - - -class TestMASEvalPredictionsComparison: - """Tests that compare MASEval predictions with disco-public reference. - - These tests verify that predictions saved via mmlu_benchmark.py with - --predictions_path are format-compatible with disco-public output. - - To run these tests, first generate predictions by running: - python examples/mmlu_benchmark/mmlu_benchmark.py \\ - --model_id alignment-handbook/zephyr-7b-sft-full \\ - --data_path /path/to/mmlu_prompts_examples.json \\ - --anchor_points_path /path/to/anchor_points.pkl \\ - --predictions_path /path/to/maseval_predictions.pkl \\ - --pad_to_size 31 - """ - - @pytest.fixture - def maseval_predictions(self): - """Load MASEval-produced predictions.""" - if not MASEVAL_PREDICTIONS_PATH.exists(): - pytest.skip(f"MASEval predictions not found: {MASEVAL_PREDICTIONS_PATH}. Run mmlu_benchmark.py with --predictions_path first.") - with open(MASEVAL_PREDICTIONS_PATH, "rb") as f: - return pickle.load(f) - - def test_maseval_predictions_shape_matches_reference(self, maseval_predictions, reference_predictions): - """MASEval predictions should have same shape as reference.""" - assert maseval_predictions.shape == reference_predictions.shape, ( - f"Shape mismatch: MASEval {maseval_predictions.shape} vs reference {reference_predictions.shape}" - ) - - def test_maseval_predictions_has_correct_format(self, maseval_predictions): - """MASEval predictions should have correct format (padded with -inf).""" - # Check shape - assert maseval_predictions.ndim == 3 - assert maseval_predictions.shape[0] == 1 # n_models - assert maseval_predictions.shape[1] == 100 # n_anchor_points - assert maseval_predictions.shape[2] == 31 # padded size - - # Check padding (columns 4-31 should be -inf) - padding = maseval_predictions[0, :, 4:] - assert np.all(np.isinf(padding) & (padding < 0)), "Padding should be -inf" - - def test_maseval_predictions_match_reference_exactly(self, maseval_predictions, reference_predictions): - """Compare log-likelihood values between MASEval and disco-public (lm-eval). - - MASEval now uses identical computation to lm-evaluation-harness: - - Same tokenization (BOS token, separate context/continuation encoding) - - Same model dtype (torch_dtype="auto" -> bfloat16) - - Same log-softmax computation - - This test verifies the actual log-likelihood values are nearly identical, - not just that argmax matches. - """ - # Get choice columns (A, B, C, D) - exclude padding - maseval_choices = maseval_predictions[0, :, :4] - reference_choices = reference_predictions[0, :, :4] - - # Check absolute closeness of log-likelihood values - # Use rtol=1e-3 and atol=1e-3 to allow for minor floating-point differences - # is_close = np.allclose(maseval_choices, reference_choices, rtol=1e-3, atol=1e-3) - is_close = np.allclose(maseval_choices, reference_choices) - - # Compute statistics for debugging - abs_diff = np.abs(maseval_choices - reference_choices) - max_diff = abs_diff.max() - mean_diff = abs_diff.mean() - - # Find where max difference occurs - max_idx = np.unravel_index(abs_diff.argmax(), abs_diff.shape) - - print(f"Max absolute difference: {max_diff:.6f} at index {max_idx}") - print(f"Mean absolute difference: {mean_diff:.6f}") - print("Sample comparison at max diff location:") - print(f" MASEval: {maseval_choices[max_idx[0], :]}") - print(f" Reference: {reference_choices[max_idx[0], :]}") - - # Also check argmax agreement for reference - maseval_argmax = np.argmax(maseval_choices, axis=1) - reference_argmax = np.argmax(reference_choices, axis=1) - argmax_match_rate = (maseval_argmax == reference_argmax).mean() - print(f"Argmax match rate: {argmax_match_rate:.2%}") - - assert is_close, ( - f"Predictions not close enough. Max diff: {max_diff:.6f}, Mean diff: {mean_diff:.6f}. " - f"Check model dtype, tokenization, and log-softmax computation match lm-eval." - ) - - def test_disco_prediction_produces_valid_accuracy(self, maseval_predictions, reference_predictions): - """DISCO prediction from MASEval predictions should produce valid accuracy. - - MASEval now uses the same log-likelihood scoring as lm-evaluation-harness, - so predictions should contain actual log-likelihoods (not 0/-inf values). - - This test checks that: - 1. MASEval predictions produce a valid (0-1) accuracy prediction - 2. Reference predictions produce a reasonable accuracy for the model - 3. Both predictions are very similar (since MASEval matches lm-eval) - """ - try: - import torch - except ImportError: - pytest.skip("torch not available") - - if not all([FITTED_WEIGHTS_PATH.exists(), TRANSFORM_PATH.exists()]): - pytest.skip("DISCO model files not available") - - # Load model and transform - with open(FITTED_WEIGHTS_PATH, "rb") as f: - model_data = pickle.load(f) - with open(TRANSFORM_PATH, "rb") as f: - transform = pickle.load(f) - - # Get fitted model - if "fitted_weights" in model_data: - fitted_weights = model_data["fitted_weights"] - else: - fitted_weights = {k: v for k, v in model_data.items() if k != "transform"} - - sampling_name = list(fitted_weights.keys())[0] - number_item = list(fitted_weights[sampling_name].keys())[0] - fitted_model_type = list(fitted_weights[sampling_name][number_item].keys())[0] - fitted_model = fitted_weights[sampling_name][number_item][fitted_model_type] - - def compute_disco_accuracy(predictions): - """Compute DISCO-predicted accuracy from predictions tensor.""" - preds_tensor = torch.Tensor(predictions) - emb_unreduced = preds_tensor.softmax(dim=-1) - emb_unreduced = emb_unreduced.reshape(emb_unreduced.shape[0], -1) - emb = transform.transform(emb_unreduced.numpy()) - return fitted_model.predict(emb)[0] - - # Reference should work normally - reference_acc = compute_disco_accuracy(reference_predictions) - print(f"DISCO predicted accuracy from reference predictions: {reference_acc:.4f}") - assert 0.4 <= reference_acc <= 0.8, f"Reference accuracy {reference_acc} seems unreasonable" - - # MASEval predictions should now have actual log-likelihoods (matching lm-eval) - maseval_acc = compute_disco_accuracy(maseval_predictions) - print(f"DISCO predicted accuracy from MASEval predictions: {maseval_acc:.4f}") - print(f"Difference: {abs(maseval_acc - reference_acc):.4f}") - - # Both should produce similar predictions since MASEval matches lm-eval - assert not np.isnan(maseval_acc), "MASEval DISCO prediction is NaN" - assert abs(maseval_acc - reference_acc) < 0.05, ( - f"DISCO predictions differ too much: MASEval={maseval_acc:.4f}, reference={reference_acc:.4f}. Expected difference < 0.05." - ) From 92da22a7a9f3c8b06bd952b84b0c6b2060fef969 Mon Sep 17 00:00:00 2001 From: Alexander Rubinstein Date: Sun, 15 Feb 2026 10:52:25 +0100 Subject: [PATCH 29/47] [Create PR]: - Move HuggingFaceMMLUBenchmark to mmlu.py --- examples/mmlu_benchmark/mmlu_benchmark.py | 490 +--------------------- maseval/benchmark/mmlu/__init__.py | 2 + maseval/benchmark/mmlu/mmlu.py | 478 +++++++++++++++++++++ 3 files changed, 481 insertions(+), 489 deletions(-) diff --git a/examples/mmlu_benchmark/mmlu_benchmark.py b/examples/mmlu_benchmark/mmlu_benchmark.py index 57a710c3..72075c52 100644 --- a/examples/mmlu_benchmark/mmlu_benchmark.py +++ b/examples/mmlu_benchmark/mmlu_benchmark.py @@ -52,7 +52,7 @@ # MMLU benchmark imports from maseval.benchmark.mmlu import ( - MMLUBenchmark, + HuggingFaceMMLUBenchmark, load_tasks, compute_benchmark_metrics, ) @@ -157,484 +157,6 @@ def parse_args(): return parser.parse_args() -class HuggingFaceMMLUBenchmark(MMLUBenchmark): - """MMLU Benchmark using HuggingFace transformers models. - - This concrete implementation uses log-likelihood based MCQ evaluation - with the same optimizations as lm-evaluation-harness: - - 1. Single forward pass per question (one-token continuation optimization) - 2. Batching multiple questions together - 3. Efficient log-softmax computation - 4. Proper left-padding for batch processing - """ - - def __init__( - self, - model_id: str, - device: str = "cuda:0", - trust_remote_code: bool = True, - use_full_prompt: bool = True, - batch_size: int = 8, - **kwargs, - ): - """Initialize HuggingFace MMLU benchmark. - - Args: - model_id: HuggingFace model identifier. - device: Device to run model on. - trust_remote_code: Trust remote code when loading model (default True). - use_full_prompt: Use full prompt with few-shot examples (default True). - batch_size: Batch size for evaluation (number of questions per batch). - **kwargs: Additional arguments passed to MMLUBenchmark. - """ - super().__init__(use_full_prompt=use_full_prompt, **kwargs) - self._model_id = model_id - self._device = device - self._trust_remote_code = trust_remote_code - self._batch_size = batch_size - self._model = None - self._tokenizer = None - - def _load_model(self): - """Lazy load the model and tokenizer for log-likelihood computation.""" - if self._model is None: - from transformers import AutoModelForCausalLM, AutoTokenizer - - print(f"Loading model: {self._model_id}") - self._tokenizer = AutoTokenizer.from_pretrained( - self._model_id, - trust_remote_code=self._trust_remote_code, - ) - self._tokenizer.padding_side = "left" - if self._tokenizer.pad_token is None: - self._tokenizer.pad_token = self._tokenizer.eos_token - - # Load model with torch_dtype="auto" to match lm-evaluation-harness exactly - # This uses the model's native dtype (bfloat16 for most modern models) - # Then move to device manually - self._model = AutoModelForCausalLM.from_pretrained( - self._model_id, - trust_remote_code=self._trust_remote_code, - torch_dtype="auto", - ) - self._model = self._model.to(self._device) - self._model.eval() - - # Note: We don't pre-cache choice token IDs here because they depend on context. - # Token IDs are computed dynamically in _get_choice_token_id_in_context() - # to match lm-evaluation-harness behavior exactly. - - return self._model, self._tokenizer - - def _get_choice_token_id_separate(self, choice: str) -> int: - """Get the token ID for a choice when tokenized SEPARATELY. - - CRITICAL: lm-evaluation-harness encodes context and continuation separately, - then concatenates. This means "A" is always tokenized standalone (token 330), - NOT in context after "Answer:" (which would be token 28741). - - We must match this behavior to get identical log-likelihood values. - - Args: - choice: The choice string (e.g., "A"). - - Returns: - Token ID for the choice (standalone tokenization). - """ - _, tokenizer = self._load_model() - - # Tokenize choice ALONE (not in context) - this is how lm-eval does it - choice_tokens = tokenizer.encode(choice, add_special_tokens=False) - - if len(choice_tokens) == 1: - return choice_tokens[0] - else: - # Multi-token choice - return None to trigger multi-token fallback - return None - - def _encode_pair(self, context: str, continuation: str) -> tuple: - """Encode a context-continuation pair like lm-evaluation-harness. - - This matches lm-eval's _encode_pair method exactly: - 1. Encode whole = context + continuation - 2. Encode context alone - 3. continuation_enc = whole[len(context_enc):] - - This handles tokenization boundary effects correctly. - - Args: - context: The context/prompt string. - continuation: The continuation string (e.g., " A" with target_delimiter). - - Returns: - Tuple of (context_enc, continuation_enc) token lists. - """ - _, tokenizer = self._load_model() - - # Handle trailing spaces in context (move to continuation) - n_spaces = len(context) - len(context.rstrip()) - if n_spaces > 0: - continuation = context[-n_spaces:] + continuation - context = context[:-n_spaces] - - # Encode whole string together, then split - whole_enc = tokenizer.encode(context + continuation, add_special_tokens=True) - context_enc = tokenizer.encode(context, add_special_tokens=True) - - # Continuation tokens are what's left after context - continuation_enc = whole_enc[len(context_enc) :] - - return context_enc, continuation_enc - - def _compute_logprobs_single_token(self, prompt: str, choices: list) -> list: - """Compute log-likelihoods using single-token optimization. - - For MCQ with single-letter answers (A, B, C, D), we can compute all - choices in one forward pass since they share the same context. - - IMPORTANT: To match lm-evaluation-harness EXACTLY: - 1. Use target_delimiter=" " before choices (e.g., " A" not "A") - 2. Use _encode_pair to handle tokenization boundaries correctly - 3. Input = (context + continuation)[:-1] - 4. Apply log_softmax to get log probabilities - - Args: - prompt: The prompt/question text. - choices: List of answer choice strings (e.g., ["A", "B", "C", "D"]). - - Returns: - List of log-likelihoods, one per choice. - """ - import torch - - model, _ = self._load_model() - - # lm-eval uses target_delimiter=" " for multiple choice tasks - target_delimiter = " " - - # Encode first choice to get the shared context - first_continuation = f"{target_delimiter}{choices[0]}" - context_enc, first_cont_enc = self._encode_pair(prompt, first_continuation) - - # Build input: (context + continuation)[:-1] - full_sequence = context_enc + first_cont_enc - input_tokens = full_sequence[:-1] # Remove last token - - input_ids = torch.tensor([input_tokens], dtype=torch.long, device=self._device) - - with torch.no_grad(): - outputs = model(input_ids) - logits = outputs.logits[0] # (seq_len, vocab_size) - - # Select logits at position where continuation is predicted - # For single-token continuation, this is the last position - inplen = len(input_tokens) - contlen = len(first_cont_enc) - selected_logits = logits[inplen - contlen : inplen] - - # Compute log-softmax - log_probs = torch.nn.functional.log_softmax(selected_logits, dim=-1) - - # Get log prob for each choice's continuation token - logprobs = [] - for choice in choices: - continuation = f"{target_delimiter}{choice}" - _, cont_enc = self._encode_pair(prompt, continuation) - - # Sum log probs for multi-token continuations - total = 0.0 - for i, token_id in enumerate(cont_enc): - total += log_probs[i, token_id].item() - logprobs.append(total) - - return logprobs - - def _compute_logprobs_batched(self, prompts: list, choices_list: list) -> list: - """Compute log-likelihoods for a batch of prompts. - - For exact match with lm-evaluation-harness, we process each prompt - individually using _compute_logprobs_single_token which uses the - correct _encode_pair tokenization logic. - - Args: - prompts: List of prompt strings. - choices_list: List of choice lists (one per prompt). - - Returns: - List of log-likelihood lists, one per prompt. - """ - # For exact match with lm-eval, process individually - # This ensures correct tokenization via _encode_pair - all_logprobs = [] - for prompt, choices in zip(prompts, choices_list): - logprobs = self._compute_logprobs_single_token(prompt, choices) - all_logprobs.append(logprobs) - - return all_logprobs - - def precompute_all_logprobs_lmeval(self, tasks) -> dict: - """Precompute log-likelihoods for ALL tasks using lm-eval's batching. - - CRITICAL: lm-evaluation-harness batches ALL requests together and uses - its Collator class to reorder/group them. This affects floating-point - precision for some edge cases. To get EXACT matches, we must process - ALL requests together in a single batch. - - This method: - 1. Creates Instance objects for all task/choice combinations - 2. Calls lm-eval's HFLM.loglikelihood() with ALL instances - 3. Returns a mapping from doc_id to logprobs - - Args: - tasks: Iterable of Task objects with prompt and choices. - - Returns: - Dict mapping doc_id -> list of log-likelihoods for each choice. - """ - import sys - - # Add lm-eval to path - sys.path.insert(0, "/home/oh/arubinstein17/github/disco-public") - sys.path.insert(0, "/home/oh/arubinstein17/github/disco-public/external/lm-evaluation-harness") - - from lm_eval.models.huggingface import HFLM - from lm_eval.api.instance import Instance - - # Create HFLM model (this handles model loading internally) - print(f"Loading HFLM model for {self._model_id}") - lm = HFLM( - pretrained=self._model_id, - trust_remote_code=self._trust_remote_code, - batch_size=self._batch_size, - device=self._device, - ) - - # lm-eval uses target_delimiter=" " for multiple choice tasks - target_delimiter = " " - choices = ["A", "B", "C", "D"] - continuations = [f"{target_delimiter}{c}" for c in choices] - - # Build ALL instances like lm-eval task system does - all_instances = [] - instance_map = {} # (doc_id, choice_idx) -> position in results - - for task in tasks: - doc_id = task.metadata.get("doc_id") - # Get prompt from task - use full_prompt from environment_data if available - if self.use_full_prompt and "full_prompt" in task.environment_data: - prompt = task.environment_data["full_prompt"] - else: - prompt = task.query - - for i, cont in enumerate(continuations): - inst = Instance( - request_type="loglikelihood", - doc={"doc_id": doc_id}, - arguments=(prompt, cont), - idx=i, - metadata=("mmlu_prompts", doc_id, 1), - ) - instance_map[(doc_id, i)] = len(all_instances) - all_instances.append(inst) - - print(f"Precomputing logprobs for {len(all_instances)} instances ({len(all_instances) // len(choices)} tasks)") - - # Call loglikelihood with ALL instances at once - this is the key! - results = lm.loglikelihood(all_instances) - - # Map results back to doc_ids - doc_logprobs = {} - for task in tasks: - doc_id = task.metadata.get("doc_id") - logprobs = [] - for i in range(len(choices)): - pos = instance_map[(doc_id, i)] - logprob, _ = results[pos] - logprobs.append(logprob) - doc_logprobs[doc_id] = logprobs - - # Store for later use - self._precomputed_logprobs = doc_logprobs - - return doc_logprobs - - def _compute_logprobs_multi_token(self, prompt: str, choices: list) -> list: - """Compute log-likelihoods for multi-token continuations. - - This is the fallback for when answer choices have multiple tokens. - Uses _encode_pair to match lm-evaluation-harness exactly. - - Args: - prompt: The prompt/question text. - choices: List of answer choice strings. - - Returns: - List of log-likelihoods, one per choice. - """ - import torch - - model, _ = self._load_model() - - # lm-eval uses target_delimiter=" " for multiple choice tasks - target_delimiter = " " - - all_logprobs = [] - for choice in choices: - continuation = f"{target_delimiter}{choice}" - - # Use _encode_pair for correct tokenization - context_enc, continuation_enc = self._encode_pair(prompt, continuation) - - # Build input: (context + continuation)[:-1] - full_sequence = context_enc + continuation_enc - input_tokens = full_sequence[:-1] - - input_ids = torch.tensor([input_tokens], dtype=torch.long, device=self._device) - - with torch.no_grad(): - outputs = model(input_ids) - logits = outputs.logits[0] # (seq_len, vocab_size) - - # Select continuation logits - inplen = len(input_tokens) - contlen = len(continuation_enc) - selected = logits[inplen - contlen : inplen] - - # Compute log-softmax - log_probs = torch.nn.functional.log_softmax(selected, dim=-1) - - # Sum log probs for all continuation tokens - total = 0.0 - for i, token_id in enumerate(continuation_enc): - total += log_probs[i, token_id].item() - - all_logprobs.append(total) - - return all_logprobs - - def run_agents( - self, - agents, - task, - environment, - query: str = "", - ): - """Execute log-likelihood based MCQ evaluation. - - Uses precomputed logprobs if available (for exact lm-eval match), - otherwise falls back to single-forward-pass optimization for - single-token answers, or multi-token batched computation. - """ - # Get the prompt from environment - prompt = environment.get_prompt() - choices = environment.state.get("choices", ["A", "B", "C", "D"]) - doc_id = task.metadata.get("doc_id") if task else None - - # Check if we have precomputed logprobs (for exact lm-eval match) - if hasattr(self, "_precomputed_logprobs") and doc_id is not None: - logprobs = self._precomputed_logprobs.get(doc_id) - if logprobs is not None: - # Use precomputed values for exact match - best_idx = logprobs.index(max(logprobs)) - answer = choices[best_idx] - - # Store logprobs in environment for later retrieval - environment.state["logprobs"] = logprobs - environment.state["predicted_idx"] = best_idx - - # Record in agent messages for tracing - agent = agents[0] - agent.agent._messages.append({"role": "user", "content": prompt}) - agent.agent._messages.append( - { - "role": "assistant", - "content": answer, - "logprobs": logprobs, - } - ) - - return answer - - # Fall back to computing logprobs on-the-fly - # Load model - self._load_model() - - # lm-eval uses target_delimiter=" " for multiple choice tasks - target_delimiter = " " - - # Check if all choices result in single-token continuations - # using _encode_pair to get the correct tokenization - all_single_token = True - for choice in choices: - continuation = f"{target_delimiter}{choice}" - _, cont_enc = self._encode_pair(prompt, continuation) - if len(cont_enc) != 1: - all_single_token = False - break - - if all_single_token: - # Use optimized single-token path (one forward pass) - logprobs = self._compute_logprobs_single_token(prompt, choices) - else: - # Fall back to multi-token computation - logprobs = self._compute_logprobs_multi_token(prompt, choices) - - # Select the choice with highest log-probability - best_idx = logprobs.index(max(logprobs)) - answer = choices[best_idx] - - # Store logprobs in environment for later retrieval if needed - environment.state["logprobs"] = logprobs - environment.state["predicted_idx"] = best_idx - - # Record in agent messages for tracing - agent = agents[0] - agent.agent._messages.append({"role": "user", "content": prompt}) - agent.agent._messages.append( - { - "role": "assistant", - "content": answer, - "logprobs": logprobs, - } - ) - - return answer - - def get_model_adapter(self, model_id: str, **kwargs): - """Provide a HuggingFace ModelAdapter. - - Note: For logprobs-based evaluation, we don't actually use the adapter - for generation. This is kept for API compatibility. - - Args: - model_id: Model identifier (ignored, uses instance model_id). - **kwargs: Additional arguments (e.g., register_name). - - Returns: - HuggingFaceModelAdapter instance. - """ - from maseval.interface.inference import HuggingFaceModelAdapter - - # Create a minimal adapter for compatibility - # The actual evaluation uses _compute_logprobs_* - class DummyCallable: - def __call__(self, prompt, **kwargs): - return "" - - adapter = HuggingFaceModelAdapter( - model=DummyCallable(), - model_id=self._model_id, - ) - - # Register for tracing if requested - register_name = kwargs.get("register_name") - if register_name: - self.register("models", register_name, adapter) - - return adapter - - def save_predictions_for_disco( results: list, output_path: str, @@ -1222,16 +744,6 @@ def main(): for model_idx, acc in disco_results["predicted_accuracies"].items(): print(f" Model {model_idx}: {acc:.6f}") - # # Compare with actual anchor accuracy - # print("\n" + "-" * 40) - # print("Comparison:") - # print("-" * 40) - # predicted_acc = disco_results["predicted_accuracies"][0] - # actual_anchor_acc = metrics["acc"] - # print(f" Actual accuracy (on anchor points): {actual_anchor_acc:.6f}") - # print(f" DISCO predicted accuracy (full benchmark): {predicted_acc:.6f}") - # print(f" Difference: {predicted_acc - actual_anchor_acc:+.6f}") - # Save summary summary_data = { "model_id": args.model_id, diff --git a/maseval/benchmark/mmlu/__init__.py b/maseval/benchmark/mmlu/__init__.py index d5fcb7bf..ab08a4b4 100644 --- a/maseval/benchmark/mmlu/__init__.py +++ b/maseval/benchmark/mmlu/__init__.py @@ -24,6 +24,7 @@ from .mmlu import ( MMLUBenchmark, + HuggingFaceMMLUBenchmark, MMLUEnvironment, MMLUEvaluator, MMLUModelAgent, @@ -35,6 +36,7 @@ __all__ = [ "MMLUBenchmark", + "HuggingFaceMMLUBenchmark", "MMLUEnvironment", "MMLUEvaluator", "MMLUModelAgent", diff --git a/maseval/benchmark/mmlu/mmlu.py b/maseval/benchmark/mmlu/mmlu.py index e05b19ae..f13edca5 100644 --- a/maseval/benchmark/mmlu/mmlu.py +++ b/maseval/benchmark/mmlu/mmlu.py @@ -559,6 +559,484 @@ def evaluate( return results +class HuggingFaceMMLUBenchmark(MMLUBenchmark): + """MMLU Benchmark using HuggingFace transformers models. + + This concrete implementation uses log-likelihood based MCQ evaluation + with the same optimizations as lm-evaluation-harness: + + 1. Single forward pass per question (one-token continuation optimization) + 2. Batching multiple questions together + 3. Efficient log-softmax computation + 4. Proper left-padding for batch processing + """ + + def __init__( + self, + model_id: str, + device: str = "cuda:0", + trust_remote_code: bool = True, + use_full_prompt: bool = True, + batch_size: int = 8, + **kwargs, + ): + """Initialize HuggingFace MMLU benchmark. + + Args: + model_id: HuggingFace model identifier. + device: Device to run model on. + trust_remote_code: Trust remote code when loading model (default True). + use_full_prompt: Use full prompt with few-shot examples (default True). + batch_size: Batch size for evaluation (number of questions per batch). + **kwargs: Additional arguments passed to MMLUBenchmark. + """ + super().__init__(use_full_prompt=use_full_prompt, **kwargs) + self._model_id = model_id + self._device = device + self._trust_remote_code = trust_remote_code + self._batch_size = batch_size + self._model = None + self._tokenizer = None + + def _load_model(self): + """Lazy load the model and tokenizer for log-likelihood computation.""" + if self._model is None: + from transformers import AutoModelForCausalLM, AutoTokenizer + + print(f"Loading model: {self._model_id}") + self._tokenizer = AutoTokenizer.from_pretrained( + self._model_id, + trust_remote_code=self._trust_remote_code, + ) + self._tokenizer.padding_side = "left" + if self._tokenizer.pad_token is None: + self._tokenizer.pad_token = self._tokenizer.eos_token + + # Load model with torch_dtype="auto" to match lm-evaluation-harness exactly + # This uses the model's native dtype (bfloat16 for most modern models) + # Then move to device manually + self._model = AutoModelForCausalLM.from_pretrained( + self._model_id, + trust_remote_code=self._trust_remote_code, + torch_dtype="auto", + ) + self._model = self._model.to(self._device) + self._model.eval() + + # Note: We don't pre-cache choice token IDs here because they depend on context. + # Token IDs are computed dynamically in _get_choice_token_id_in_context() + # to match lm-evaluation-harness behavior exactly. + + return self._model, self._tokenizer + + def _get_choice_token_id_separate(self, choice: str) -> int: + """Get the token ID for a choice when tokenized SEPARATELY. + + CRITICAL: lm-evaluation-harness encodes context and continuation separately, + then concatenates. This means "A" is always tokenized standalone (token 330), + NOT in context after "Answer:" (which would be token 28741). + + We must match this behavior to get identical log-likelihood values. + + Args: + choice: The choice string (e.g., "A"). + + Returns: + Token ID for the choice (standalone tokenization). + """ + _, tokenizer = self._load_model() + + # Tokenize choice ALONE (not in context) - this is how lm-eval does it + choice_tokens = tokenizer.encode(choice, add_special_tokens=False) + + if len(choice_tokens) == 1: + return choice_tokens[0] + else: + # Multi-token choice - return None to trigger multi-token fallback + return None + + def _encode_pair(self, context: str, continuation: str) -> tuple: + """Encode a context-continuation pair like lm-evaluation-harness. + + This matches lm-eval's _encode_pair method exactly: + 1. Encode whole = context + continuation + 2. Encode context alone + 3. continuation_enc = whole[len(context_enc):] + + This handles tokenization boundary effects correctly. + + Args: + context: The context/prompt string. + continuation: The continuation string (e.g., " A" with target_delimiter). + + Returns: + Tuple of (context_enc, continuation_enc) token lists. + """ + _, tokenizer = self._load_model() + + # Handle trailing spaces in context (move to continuation) + n_spaces = len(context) - len(context.rstrip()) + if n_spaces > 0: + continuation = context[-n_spaces:] + continuation + context = context[:-n_spaces] + + # Encode whole string together, then split + whole_enc = tokenizer.encode(context + continuation, add_special_tokens=True) + context_enc = tokenizer.encode(context, add_special_tokens=True) + + # Continuation tokens are what's left after context + continuation_enc = whole_enc[len(context_enc) :] + + return context_enc, continuation_enc + + def _compute_logprobs_single_token(self, prompt: str, choices: list) -> list: + """Compute log-likelihoods using single-token optimization. + + For MCQ with single-letter answers (A, B, C, D), we can compute all + choices in one forward pass since they share the same context. + + IMPORTANT: To match lm-evaluation-harness EXACTLY: + 1. Use target_delimiter=" " before choices (e.g., " A" not "A") + 2. Use _encode_pair to handle tokenization boundaries correctly + 3. Input = (context + continuation)[:-1] + 4. Apply log_softmax to get log probabilities + + Args: + prompt: The prompt/question text. + choices: List of answer choice strings (e.g., ["A", "B", "C", "D"]). + + Returns: + List of log-likelihoods, one per choice. + """ + import torch + + model, _ = self._load_model() + + # lm-eval uses target_delimiter=" " for multiple choice tasks + target_delimiter = " " + + # Encode first choice to get the shared context + first_continuation = f"{target_delimiter}{choices[0]}" + context_enc, first_cont_enc = self._encode_pair(prompt, first_continuation) + + # Build input: (context + continuation)[:-1] + full_sequence = context_enc + first_cont_enc + input_tokens = full_sequence[:-1] # Remove last token + + input_ids = torch.tensor([input_tokens], dtype=torch.long, device=self._device) + + with torch.no_grad(): + outputs = model(input_ids) + logits = outputs.logits[0] # (seq_len, vocab_size) + + # Select logits at position where continuation is predicted + # For single-token continuation, this is the last position + inplen = len(input_tokens) + contlen = len(first_cont_enc) + selected_logits = logits[inplen - contlen : inplen] + + # Compute log-softmax + log_probs = torch.nn.functional.log_softmax(selected_logits, dim=-1) + + # Get log prob for each choice's continuation token + logprobs = [] + for choice in choices: + continuation = f"{target_delimiter}{choice}" + _, cont_enc = self._encode_pair(prompt, continuation) + + # Sum log probs for multi-token continuations + total = 0.0 + for i, token_id in enumerate(cont_enc): + total += log_probs[i, token_id].item() + logprobs.append(total) + + return logprobs + + def _compute_logprobs_batched(self, prompts: list, choices_list: list) -> list: + """Compute log-likelihoods for a batch of prompts. + + For exact match with lm-evaluation-harness, we process each prompt + individually using _compute_logprobs_single_token which uses the + correct _encode_pair tokenization logic. + + Args: + prompts: List of prompt strings. + choices_list: List of choice lists (one per prompt). + + Returns: + List of log-likelihood lists, one per prompt. + """ + # For exact match with lm-eval, process individually + # This ensures correct tokenization via _encode_pair + all_logprobs = [] + for prompt, choices in zip(prompts, choices_list): + logprobs = self._compute_logprobs_single_token(prompt, choices) + all_logprobs.append(logprobs) + + return all_logprobs + + def precompute_all_logprobs_lmeval(self, tasks) -> dict: + """Precompute log-likelihoods for ALL tasks using lm-eval's batching. + + CRITICAL: lm-evaluation-harness batches ALL requests together and uses + its Collator class to reorder/group them. This affects floating-point + precision for some edge cases. To get EXACT matches, we must process + ALL requests together in a single batch. + + This method: + 1. Creates Instance objects for all task/choice combinations + 2. Calls lm-eval's HFLM.loglikelihood() with ALL instances + 3. Returns a mapping from doc_id to logprobs + + Args: + tasks: Iterable of Task objects with prompt and choices. + + Returns: + Dict mapping doc_id -> list of log-likelihoods for each choice. + """ + import sys + + # Add lm-eval to path + sys.path.insert(0, "/home/oh/arubinstein17/github/disco-public") + sys.path.insert(0, "/home/oh/arubinstein17/github/disco-public/external/lm-evaluation-harness") + + from lm_eval.models.huggingface import HFLM + from lm_eval.api.instance import Instance + + # Create HFLM model (this handles model loading internally) + print(f"Loading HFLM model for {self._model_id}") + lm = HFLM( + pretrained=self._model_id, + trust_remote_code=self._trust_remote_code, + batch_size=self._batch_size, + device=self._device, + ) + + # lm-eval uses target_delimiter=" " for multiple choice tasks + target_delimiter = " " + choices = ["A", "B", "C", "D"] + continuations = [f"{target_delimiter}{c}" for c in choices] + + # Build ALL instances like lm-eval task system does + all_instances = [] + instance_map = {} # (doc_id, choice_idx) -> position in results + + for task in tasks: + doc_id = task.metadata.get("doc_id") + # Get prompt from task - use full_prompt from environment_data if available + if self.use_full_prompt and "full_prompt" in task.environment_data: + prompt = task.environment_data["full_prompt"] + else: + prompt = task.query + + for i, cont in enumerate(continuations): + inst = Instance( + request_type="loglikelihood", + doc={"doc_id": doc_id}, + arguments=(prompt, cont), + idx=i, + metadata=("mmlu_prompts", doc_id, 1), + ) + instance_map[(doc_id, i)] = len(all_instances) + all_instances.append(inst) + + print(f"Precomputing logprobs for {len(all_instances)} instances ({len(all_instances) // len(choices)} tasks)") + + # Call loglikelihood with ALL instances at once - this is the key! + results = lm.loglikelihood(all_instances) + + # Map results back to doc_ids + doc_logprobs = {} + for task in tasks: + doc_id = task.metadata.get("doc_id") + logprobs = [] + for i in range(len(choices)): + pos = instance_map[(doc_id, i)] + logprob, _ = results[pos] + logprobs.append(logprob) + doc_logprobs[doc_id] = logprobs + + # Store for later use + self._precomputed_logprobs = doc_logprobs + + return doc_logprobs + + def _compute_logprobs_multi_token(self, prompt: str, choices: list) -> list: + """Compute log-likelihoods for multi-token continuations. + + This is the fallback for when answer choices have multiple tokens. + Uses _encode_pair to match lm-evaluation-harness exactly. + + Args: + prompt: The prompt/question text. + choices: List of answer choice strings. + + Returns: + List of log-likelihoods, one per choice. + """ + import torch + + model, _ = self._load_model() + + # lm-eval uses target_delimiter=" " for multiple choice tasks + target_delimiter = " " + + all_logprobs = [] + for choice in choices: + continuation = f"{target_delimiter}{choice}" + + # Use _encode_pair for correct tokenization + context_enc, continuation_enc = self._encode_pair(prompt, continuation) + + # Build input: (context + continuation)[:-1] + full_sequence = context_enc + continuation_enc + input_tokens = full_sequence[:-1] + + input_ids = torch.tensor([input_tokens], dtype=torch.long, device=self._device) + + with torch.no_grad(): + outputs = model(input_ids) + logits = outputs.logits[0] # (seq_len, vocab_size) + + # Select continuation logits + inplen = len(input_tokens) + contlen = len(continuation_enc) + selected = logits[inplen - contlen : inplen] + + # Compute log-softmax + log_probs = torch.nn.functional.log_softmax(selected, dim=-1) + + # Sum log probs for all continuation tokens + total = 0.0 + for i, token_id in enumerate(continuation_enc): + total += log_probs[i, token_id].item() + + all_logprobs.append(total) + + return all_logprobs + + def run_agents( + self, + agents, + task, + environment, + query: str = "", + ): + """Execute log-likelihood based MCQ evaluation. + + Uses precomputed logprobs if available (for exact lm-eval match), + otherwise falls back to single-forward-pass optimization for + single-token answers, or multi-token batched computation. + """ + # Get the prompt from environment + prompt = environment.get_prompt() + choices = environment.state.get("choices", ["A", "B", "C", "D"]) + doc_id = task.metadata.get("doc_id") if task else None + + # Check if we have precomputed logprobs (for exact lm-eval match) + if hasattr(self, "_precomputed_logprobs") and doc_id is not None: + logprobs = self._precomputed_logprobs.get(doc_id) + if logprobs is not None: + # Use precomputed values for exact match + best_idx = logprobs.index(max(logprobs)) + answer = choices[best_idx] + + # Store logprobs in environment for later retrieval + environment.state["logprobs"] = logprobs + environment.state["predicted_idx"] = best_idx + + # Record in agent messages for tracing + agent = agents[0] + agent.agent._messages.append({"role": "user", "content": prompt}) + agent.agent._messages.append( + { + "role": "assistant", + "content": answer, + "logprobs": logprobs, + } + ) + + return answer + + # Fall back to computing logprobs on-the-fly + # Load model + self._load_model() + + # lm-eval uses target_delimiter=" " for multiple choice tasks + target_delimiter = " " + + # Check if all choices result in single-token continuations + # using _encode_pair to get the correct tokenization + all_single_token = True + for choice in choices: + continuation = f"{target_delimiter}{choice}" + _, cont_enc = self._encode_pair(prompt, continuation) + if len(cont_enc) != 1: + all_single_token = False + break + + if all_single_token: + # Use optimized single-token path (one forward pass) + logprobs = self._compute_logprobs_single_token(prompt, choices) + else: + # Fall back to multi-token computation + logprobs = self._compute_logprobs_multi_token(prompt, choices) + + # Select the choice with highest log-probability + best_idx = logprobs.index(max(logprobs)) + answer = choices[best_idx] + + # Store logprobs in environment for later retrieval if needed + environment.state["logprobs"] = logprobs + environment.state["predicted_idx"] = best_idx + + # Record in agent messages for tracing + agent = agents[0] + agent.agent._messages.append({"role": "user", "content": prompt}) + agent.agent._messages.append( + { + "role": "assistant", + "content": answer, + "logprobs": logprobs, + } + ) + + return answer + + def get_model_adapter(self, model_id: str, **kwargs): + """Provide a HuggingFace ModelAdapter. + + Note: For logprobs-based evaluation, we don't actually use the adapter + for generation. This is kept for API compatibility. + + Args: + model_id: Model identifier (ignored, uses instance model_id). + **kwargs: Additional arguments (e.g., register_name). + + Returns: + HuggingFaceModelAdapter instance. + """ + from maseval.interface.inference import HuggingFaceModelAdapter + + # Create a minimal adapter for compatibility + # The actual evaluation uses _compute_logprobs_* + class DummyCallable: + def __call__(self, prompt, **kwargs): + return "" + + adapter = HuggingFaceModelAdapter( + model=DummyCallable(), + model_id=self._model_id, + ) + + # Register for tracing if requested + register_name = kwargs.get("register_name") + if register_name: + self.register("models", register_name, adapter) + + return adapter + + # ============================================================================= # Data Loading # ============================================================================= From ceed87dfa327dac794dbab506d908ad01cb8da5e Mon Sep 17 00:00:00 2001 From: Alexander Rubinstein Date: Sun, 15 Feb 2026 11:01:45 +0100 Subject: [PATCH 30/47] [Create PR]: - Move hardcoded values to constants --- examples/mmlu_benchmark/mmlu_benchmark.py | 41 +++++++++++----- maseval/benchmark/mmlu/__init__.py | 20 ++++++++ maseval/benchmark/mmlu/mmlu.py | 60 ++++++++++++++--------- 3 files changed, 86 insertions(+), 35 deletions(-) diff --git a/examples/mmlu_benchmark/mmlu_benchmark.py b/examples/mmlu_benchmark/mmlu_benchmark.py index 72075c52..972c7925 100644 --- a/examples/mmlu_benchmark/mmlu_benchmark.py +++ b/examples/mmlu_benchmark/mmlu_benchmark.py @@ -41,6 +41,7 @@ import argparse import json +import os import pickle from pathlib import Path from typing import Optional @@ -52,12 +53,26 @@ # MMLU benchmark imports from maseval.benchmark.mmlu import ( + DEFAULT_DEVICE, HuggingFaceMMLUBenchmark, load_tasks, compute_benchmark_metrics, ) +# Example constants (configurable) +DEFAULT_OUTPUT_DIR = "./results" +DEFAULT_N_CHOICES = 4 +DEFAULT_PCA_DIM = 256 +PCA_RANDOM_STATE = 42 +DISCO_META_FILENAME = "disco_meta.json" +MMLU_PROMPTS_FILENAME = "mmlu_prompts_examples.json" +DISCO_CONFIG_FILENAME = "config.json" +ANCHOR_POINTS_FILENAME = "anchor_points.json" +RESULTS_FILENAME_PATTERN = "mmlu_{timestamp}.jsonl" +SUMMARY_FILENAME = "summary.json" + + def parse_args(): """Parse command line arguments.""" parser = argparse.ArgumentParser( @@ -89,7 +104,7 @@ def parse_args(): parser.add_argument( "--output_dir", type=str, - default="./results", + default=DEFAULT_OUTPUT_DIR, help="Directory to save results", ) parser.add_argument( @@ -113,7 +128,7 @@ def parse_args(): parser.add_argument( "--device", type=str, - default="cuda:0", + default=DEFAULT_DEVICE, help="Device to run model on (e.g., 'cuda:0', 'cpu')", ) parser.add_argument( @@ -161,7 +176,7 @@ def save_predictions_for_disco( results: list, output_path: str, anchor_points: Optional[list] = None, - n_choices: int = 4, + n_choices: int = DEFAULT_N_CHOICES, pad_to_size: Optional[int] = None, ): """Save predictions in format compatible with DISCO predictor. @@ -239,7 +254,7 @@ def extract_eval_entries(res): predictions = predictions.reshape(1, -1, predictions.shape[-1]) # (1, n_questions, n_choices) - if output_path and output_path != "/dev/null": + if output_path and output_path != os.devnull: with open(output_path, "wb") as f: pickle.dump(predictions, f) print(f"Saved predictions tensor to {output_path}") @@ -334,7 +349,7 @@ def predict_fn(X: np.ndarray) -> np.ndarray: return _predict_rf_numpy(X, tree_node_counts, children_left, children_right, feature, threshold, value) if meta_path is None: - meta_path = str(Path(model_npz_path).parent / "disco_meta.json") + meta_path = str(Path(model_npz_path).parent / DISCO_META_FILENAME) with open(meta_path) as f: meta = json.load(f) transform_npz = {"components_": components, "mean_": mean} @@ -395,7 +410,7 @@ def compute_disco_embedding( transform = PCA( n_components=pca, svd_solver="full", - random_state=42, + random_state=PCA_RANDOM_STATE, ).fit(X) emb = transform.transform(X) emb = torch.Tensor(emb) @@ -409,7 +424,7 @@ def predict_with_disco( predictions: np.ndarray, model_path: str, transform_path: Optional[str] = None, - pca: int = 256, + pca: int = DEFAULT_PCA_DIM, ) -> dict: """Predict full benchmark performance using DISCO. @@ -542,7 +557,7 @@ def _resolve_data_path(data_path: str) -> str: try: local = hf_hub_download( repo_id=data_path, - filename="mmlu_prompts_examples.json", + filename=MMLU_PROMPTS_FILENAME, repo_type="dataset", ) return local @@ -552,7 +567,7 @@ def _resolve_data_path(data_path: str) -> str: def _apply_eval_config_from_repo(repo_path: Path, args: "argparse.Namespace") -> None: """Load eval_config from repo; forbid passing --pca/--pad_to_size/--use_lmeval_batching, then set args from eval_config.""" - config_path = repo_path / "config.json" + config_path = repo_path / DISCO_CONFIG_FILENAME if not config_path.exists(): return with open(config_path) as f: @@ -599,8 +614,8 @@ def _resolve_hf_disco_repo( except ImportError: return (anchor_points_path, None) repo_path = Path(snapshot_download(disco_model_path)) - if anchor_points_path is None and (repo_path / "anchor_points.json").exists(): - return (str(repo_path / "anchor_points.json"), repo_path) + if anchor_points_path is None and (repo_path / ANCHOR_POINTS_FILENAME).exists(): + return (str(repo_path / ANCHOR_POINTS_FILENAME), repo_path) return (anchor_points_path, repo_path) @@ -673,7 +688,7 @@ def main(): # Create result logger logger = FileResultLogger( output_dir=str(output_dir), - filename_pattern="mmlu_{timestamp}.jsonl", + filename_pattern=RESULTS_FILENAME_PATTERN, validate_on_completion=False, ) @@ -762,7 +777,7 @@ def main(): "pca": disco_results["pca"], } - summary_path = output_dir / "summary.json" + summary_path = output_dir / SUMMARY_FILENAME with open(summary_path, "w") as f: json.dump(summary_data, f, indent=2) diff --git a/maseval/benchmark/mmlu/__init__.py b/maseval/benchmark/mmlu/__init__.py index ab08a4b4..19e8fd32 100644 --- a/maseval/benchmark/mmlu/__init__.py +++ b/maseval/benchmark/mmlu/__init__.py @@ -23,6 +23,16 @@ """ from .mmlu import ( + DEFAULT_AGENT_NAME, + DEFAULT_BATCH_SIZE, + DEFAULT_CHOICES, + DEFAULT_DEVICE, + DEFAULT_MODEL_REGISTER_NAME, + FALLBACK_MODEL_ID, + MMLU_TASK_NAME, + STATUS_SUCCESS, + TARGET_DELIMITER, + TASK_TYPE_MMLU, MMLUBenchmark, HuggingFaceMMLUBenchmark, MMLUEnvironment, @@ -35,6 +45,16 @@ ) __all__ = [ + "DEFAULT_AGENT_NAME", + "DEFAULT_BATCH_SIZE", + "DEFAULT_CHOICES", + "DEFAULT_DEVICE", + "DEFAULT_MODEL_REGISTER_NAME", + "FALLBACK_MODEL_ID", + "MMLU_TASK_NAME", + "STATUS_SUCCESS", + "TARGET_DELIMITER", + "TASK_TYPE_MMLU", "MMLUBenchmark", "HuggingFaceMMLUBenchmark", "MMLUEnvironment", diff --git a/maseval/benchmark/mmlu/mmlu.py b/maseval/benchmark/mmlu/mmlu.py index f13edca5..304c45c8 100644 --- a/maseval/benchmark/mmlu/mmlu.py +++ b/maseval/benchmark/mmlu/mmlu.py @@ -57,6 +57,22 @@ def get_model_adapter(self, model_id, **kwargs): from maseval.core.config import ConfigurableMixin +# ============================================================================= +# Constants (configurable defaults) +# ============================================================================= + +DEFAULT_CHOICES = ["A", "B", "C", "D"] +DEFAULT_DEVICE = "cuda:0" +DEFAULT_BATCH_SIZE = 8 +DEFAULT_AGENT_NAME = "mmlu_agent" +DEFAULT_MODEL_REGISTER_NAME = "mmlu_model" +TARGET_DELIMITER = " " # lm-eval convention for MCQ +MMLU_TASK_NAME = "mmlu_prompts" +TASK_TYPE_MMLU = "mmlu" +FALLBACK_MODEL_ID = "unknown" +STATUS_SUCCESS = "success" + + # ============================================================================= # Task Queue # ============================================================================= @@ -219,7 +235,7 @@ def __init__( self.task = task self.environment = environment self.gold = task.evaluation_data.get("gold", 0) - self.choices = task.environment_data.get("choices", ["A", "B", "C", "D"]) + self.choices = task.environment_data.get("choices", DEFAULT_CHOICES) def filter_traces(self, traces: Dict[str, Any]) -> Dict[str, Any]: """Extract relevant traces for evaluation. @@ -289,12 +305,12 @@ def _parse_answer(self, response: str) -> int: response = response.strip().upper() # Direct letter match - for i, choice in enumerate(["A", "B", "C", "D"]): + for i, choice in enumerate(DEFAULT_CHOICES): if response == choice or response.startswith(f"{choice}."): return i # Look for "answer is X" pattern - for i, choice in enumerate(["A", "B", "C", "D"]): + for i, choice in enumerate(DEFAULT_CHOICES): if f"ANSWER IS {choice}" in response: return i if f"ANSWER: {choice}" in response: @@ -302,7 +318,7 @@ def _parse_answer(self, response: str) -> int: # Last character check last_char = response.rstrip(".")[-1] if response else "" - for i, choice in enumerate(["A", "B", "C", "D"]): + for i, choice in enumerate(DEFAULT_CHOICES): if last_char == choice: return i @@ -321,7 +337,7 @@ class MMLUModelAgent(TraceableMixin, ConfigurableMixin): and returns the response. It supports tracing for MASEval integration. """ - def __init__(self, model: ModelAdapter, name: str = "mmlu_agent"): + def __init__(self, model: ModelAdapter, name: str = DEFAULT_AGENT_NAME): """Initialize MMLU model agent. Args: @@ -495,13 +511,13 @@ def setup_agents( Returns: Tuple of (agents_to_run, agents_dict). """ - model_id = agent_data.get("model_id", "unknown") - model = self.get_model_adapter(model_id, register_name="mmlu_model") + model_id = agent_data.get("model_id", FALLBACK_MODEL_ID) + model = self.get_model_adapter(model_id, register_name=DEFAULT_MODEL_REGISTER_NAME) - agent = MMLUModelAgent(model, name="mmlu_agent") - adapter = MMLUAgentAdapter(agent, "mmlu_agent") + agent = MMLUModelAgent(model, name=DEFAULT_AGENT_NAME) + adapter = MMLUAgentAdapter(agent, DEFAULT_AGENT_NAME) - return [adapter], {"mmlu_agent": adapter} + return [adapter], {DEFAULT_AGENT_NAME: adapter} def setup_evaluators( self, @@ -574,10 +590,10 @@ class HuggingFaceMMLUBenchmark(MMLUBenchmark): def __init__( self, model_id: str, - device: str = "cuda:0", + device: str = DEFAULT_DEVICE, trust_remote_code: bool = True, use_full_prompt: bool = True, - batch_size: int = 8, + batch_size: int = DEFAULT_BATCH_SIZE, **kwargs, ): """Initialize HuggingFace MMLU benchmark. @@ -713,7 +729,7 @@ def _compute_logprobs_single_token(self, prompt: str, choices: list) -> list: model, _ = self._load_model() # lm-eval uses target_delimiter=" " for multiple choice tasks - target_delimiter = " " + target_delimiter = TARGET_DELIMITER # Encode first choice to get the shared context first_continuation = f"{target_delimiter}{choices[0]}" @@ -813,8 +829,8 @@ def precompute_all_logprobs_lmeval(self, tasks) -> dict: ) # lm-eval uses target_delimiter=" " for multiple choice tasks - target_delimiter = " " - choices = ["A", "B", "C", "D"] + target_delimiter = TARGET_DELIMITER + choices = DEFAULT_CHOICES continuations = [f"{target_delimiter}{c}" for c in choices] # Build ALL instances like lm-eval task system does @@ -835,7 +851,7 @@ def precompute_all_logprobs_lmeval(self, tasks) -> dict: doc={"doc_id": doc_id}, arguments=(prompt, cont), idx=i, - metadata=("mmlu_prompts", doc_id, 1), + metadata=(MMLU_TASK_NAME, doc_id, 1), ) instance_map[(doc_id, i)] = len(all_instances) all_instances.append(inst) @@ -879,7 +895,7 @@ def _compute_logprobs_multi_token(self, prompt: str, choices: list) -> list: model, _ = self._load_model() # lm-eval uses target_delimiter=" " for multiple choice tasks - target_delimiter = " " + target_delimiter = TARGET_DELIMITER all_logprobs = [] for choice in choices: @@ -930,7 +946,7 @@ def run_agents( """ # Get the prompt from environment prompt = environment.get_prompt() - choices = environment.state.get("choices", ["A", "B", "C", "D"]) + choices = environment.state.get("choices", DEFAULT_CHOICES) doc_id = task.metadata.get("doc_id") if task else None # Check if we have precomputed logprobs (for exact lm-eval match) @@ -963,7 +979,7 @@ def run_agents( self._load_model() # lm-eval uses target_delimiter=" " for multiple choice tasks - target_delimiter = " " + target_delimiter = TARGET_DELIMITER # Check if all choices result in single-token continuations # using _encode_pair to get the correct tokenization @@ -1102,7 +1118,7 @@ def load_tasks( query=item.get("query", item.get("example", "")), id=f"mmlu_{i}", environment_data={ - "choices": item.get("choices", ["A", "B", "C", "D"]), + "choices": item.get("choices", DEFAULT_CHOICES), "full_prompt": item.get("full_prompt", ""), "example": item.get("example", ""), }, @@ -1111,7 +1127,7 @@ def load_tasks( }, metadata={ "doc_id": i, - "task_type": "mmlu", + "task_type": TASK_TYPE_MMLU, }, ) tasks.append(task) @@ -1150,7 +1166,7 @@ def compute_benchmark_metrics(results: List[Dict[str, Any]]) -> Dict[str, Any]: acc_norm_sum = 0.0 for res in results: - if res.get("status") != "success": + if res.get("status") != STATUS_SUCCESS: continue evals = res.get("eval") or [] From 03d220ce1fa76ac56a7fc89e8e1c8fede5493b32 Mon Sep 17 00:00:00 2001 From: Alexander Rubinstein Date: Sun, 15 Feb 2026 11:09:11 +0100 Subject: [PATCH 31/47] [Create PR]: - Add lm-eval to pyproject.toml --- maseval/benchmark/mmlu/mmlu.py | 6 ------ pyproject.toml | 5 ++++- uv.lock | 7 ++++--- 3 files changed, 8 insertions(+), 10 deletions(-) diff --git a/maseval/benchmark/mmlu/mmlu.py b/maseval/benchmark/mmlu/mmlu.py index 304c45c8..5c7e2747 100644 --- a/maseval/benchmark/mmlu/mmlu.py +++ b/maseval/benchmark/mmlu/mmlu.py @@ -810,12 +810,6 @@ def precompute_all_logprobs_lmeval(self, tasks) -> dict: Returns: Dict mapping doc_id -> list of log-likelihoods for each choice. """ - import sys - - # Add lm-eval to path - sys.path.insert(0, "/home/oh/arubinstein17/github/disco-public") - sys.path.insert(0, "/home/oh/arubinstein17/github/disco-public/external/lm-evaluation-harness") - from lm_eval.models.huggingface import HFLM from lm_eval.api.instance import Instance diff --git a/pyproject.toml b/pyproject.toml index 6fdf44e4..1b77f83a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -84,6 +84,9 @@ tau2 = ["docstring-parser>=0.16"] # Dependencies for running examples (only what's actually used) examples = [ "maseval[smolagents,langgraph,llamaindex,camel,anthropic,openai,google-genai,litellm,langfuse,gaia2,macs,tau2]", +# LM Evaluation Harness (for HuggingFaceMMLUBenchmark.precompute_all_logprobs_lmeval) +lm-eval = ["lm-eval @ git+https://github.com/arubique/lm-evaluation-harness.git@main"] + # DISCO prediction (for MMLU benchmark example) disco = [ "click>=8.1.0", @@ -92,7 +95,7 @@ disco = [ "h5py>=3.0.0", "ipython>=8.0.0", "jsonlines>=4.0.0", - "lm-eval @ git+https://github.com/arubique/lm-evaluation-harness.git", + "lm-eval @ git+https://github.com/arubique/lm-evaluation-harness.git@main", "matplotlib>=3.5.0", "scikit-learn>=1.7.2", "scipy>=1.11.0", diff --git a/uv.lock b/uv.lock index 4839cd90..b32c0bc3 100644 --- a/uv.lock +++ b/uv.lock @@ -2871,7 +2871,7 @@ wheels = [ [[package]] name = "lm-eval" version = "0.4.9.1" -source = { git = "https://github.com/arubique/lm-evaluation-harness.git#0ad42c62a675c3d9b5c8cc23d82729ede2181e26" } +source = { git = "https://github.com/arubique/lm-evaluation-harness.git?rev=main#0ad42c62a675c3d9b5c8cc23d82729ede2181e26" } dependencies = [ { name = "accelerate" }, { name = "datasets" }, @@ -3340,7 +3340,8 @@ requires-dist = [ { name = "litellm", marker = "extra == 'litellm'", specifier = ">=1.0.0" }, { name = "litellm", marker = "extra == 'multiagentbench'", specifier = ">=1.0.0" }, { name = "llama-index-core", marker = "extra == 'llamaindex'", specifier = ">=0.12.0" }, - { name = "lm-eval", marker = "extra == 'disco'", git = "https://github.com/arubique/lm-evaluation-harness.git" }, + { name = "lm-eval", marker = "extra == 'disco'", git = "https://github.com/arubique/lm-evaluation-harness.git?rev=main" }, + { name = "lm-eval", marker = "extra == 'lm-eval'", git = "https://github.com/arubique/lm-evaluation-harness.git?rev=main" }, { name = "maseval", extras = ["examples", "transformers", "wandb"], marker = "extra == 'all'" }, { name = "maseval", extras = ["smolagents", "langgraph", "llamaindex", "anthropic", "openai", "google-genai", "litellm", "langfuse", "disco"], marker = "extra == 'examples'" }, { name = "matplotlib", marker = "extra == 'disco'", specifier = ">=3.5.0" }, @@ -3370,7 +3371,7 @@ requires-dist = [ { name = "waitress", marker = "extra == 'multiagentbench'", specifier = ">=3.0.0" }, { name = "wandb", marker = "extra == 'wandb'", specifier = ">=0.15.0" }, ] -provides-extras = ["smolagents", "langgraph", "llamaindex", "anthropic", "openai", "google-genai", "transformers", "litellm", "wandb", "langfuse", "disco", "examples", "all"] +provides-extras = ["smolagents", "langgraph", "llamaindex", "anthropic", "openai", "google-genai", "transformers", "litellm", "wandb", "langfuse", "lm-eval", "disco", "examples", "all"] [package.metadata.requires-dev] dev = [ From 83c5dfd9e4d2b1383487b781ee5cec5c0e18ec09 Mon Sep 17 00:00:00 2001 From: Alexander Rubinstein Date: Sun, 15 Feb 2026 11:16:45 +0100 Subject: [PATCH 32/47] [Create PR]: - Align pyproject.toml with MasEval main --- pyproject.toml | 35 +++++------------------------------ 1 file changed, 5 insertions(+), 30 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 1b77f83a..2c6d4708 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -9,7 +9,7 @@ description = "A library for multi-agent systems evaluation and benchmarking." authors = [{ name = "Cornelius Emde", email = "cornelius@parameterlab.de" }] readme = "README.md" license = { file = "LICENSE" } -requires-python = ">=3.11" +requires-python = ">=3.10" classifiers = [ "Development Status :: 3 - Alpha", "Intended Audience :: Science/Research", @@ -21,7 +21,7 @@ classifiers = [ "Topic :: Scientific/Engineering :: Artificial Intelligence", ] dependencies = [ - "gitpython==3.1.46", + "gitpython>=3.1.0", "tqdm>=4.66.0", "rich>=14.1.0", "pydantic>=2.10.6", @@ -39,11 +39,11 @@ camel = ["camel-ai>=0.2.0"] anthropic = ["anthropic>=0.40.0"] openai = ["openai>=1.107.2"] google-genai = ["google-genai>=1.37.0"] -transformers = ["transformers==4.57.1"] +transformers = ["transformers>=4.37.0"] litellm = ["litellm>=1.0.0"] # Logging -wandb = ["wandb==0.24.0"] +wandb = ["wandb>=0.15.0"] langfuse = ["langfuse>=3.3.4"] # Benchmarks @@ -120,7 +120,7 @@ examples = [ "jupyter>=1.0.0", "ipykernel>=6.0.0", "ipywidgets>=8.0.0", - "accelerate==1.11.0", + "accelerate>=1.11.0", ] # Complete installation with absolutely everything (uses self-reference for DRY) @@ -132,31 +132,6 @@ all = ["maseval[examples,transformers,wandb,multiagentbench]"] "Documentation" = "https://maseval.readthedocs.io/en/stable/" "Changelog" = "https://github.com/parameterlab/MASEval/blob/main/CHANGELOG.md" -# Align transitive deps with disco-public env (disco_env_lm_eval) for MMLU identity test -[tool.uv] -override-dependencies = [ - "absl-py==2.3.1", - "aiohttp==3.13.3", - "anyio==4.12.1", - "certifi==2026.1.4", - "filelock==3.20.3", - "google-auth==2.41.1", - "google-auth-oauthlib==1.2.3", - "httplib2==0.31.1", - "numpy==2.3", - "nvidia-nvshmem-cu12==3.3.20", - "pillow==12.1.0", - "protobuf==6.33.4", - "psutil==7.2.1", - "pyarrow==22.0.0", - "pyparsing==3.3.1", - "regex==2026.1.15", - "sentry-sdk==2.49.0", - "tokenizers==0.22.2", - "triton==3.5.1", - "urllib3==2.6.3", -] - [dependency-groups] # Development tools (linting, testing) - for contributors only dev = [ From 7ce1daad266662e17e8b395eb8c62a78979f8ef2 Mon Sep 17 00:00:00 2001 From: Alexander Rubinstein Date: Sun, 15 Feb 2026 11:24:43 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a/pyproject.toml b/pyproject.toml index 2c6d4708..31e6b8d7 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -84,6 +84,8 @@ tau2 = ["docstring-parser>=0.16"] # Dependencies for running examples (only what's actually used) examples = [ "maseval[smolagents,langgraph,llamaindex,camel,anthropic,openai,google-genai,litellm,langfuse,gaia2,macs,tau2]", +] + # LM Evaluation Harness (for HuggingFaceMMLUBenchmark.precompute_all_logprobs_lmeval) lm-eval = ["lm-eval @ git+https://github.com/arubique/lm-evaluation-harness.git@main"] From 80b8a758d528934b5f8b6a34fa609efbdd711d36 Mon Sep 17 00:00:00 2001 From: Alexander Rubinstein Date: Sun, 15 Feb 2026 12:14:08 +0100 Subject: [PATCH 36/47] [Create PR]: - Remove duplicates from pyproject.toml --- pyproject.toml | 7 +------ 1 file changed, 1 insertion(+), 6 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 31e6b8d7..37e92259 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -81,11 +81,6 @@ multiagentbench = [ ] tau2 = ["docstring-parser>=0.16"] -# Dependencies for running examples (only what's actually used) -examples = [ - "maseval[smolagents,langgraph,llamaindex,camel,anthropic,openai,google-genai,litellm,langfuse,gaia2,macs,tau2]", -] - # LM Evaluation Harness (for HuggingFaceMMLUBenchmark.precompute_all_logprobs_lmeval) lm-eval = ["lm-eval @ git+https://github.com/arubique/lm-evaluation-harness.git@main"] @@ -111,7 +106,7 @@ disco = [ # Dependencies for running examples (only what's actually used) examples = [ - "maseval[smolagents,langgraph,llamaindex,anthropic,openai,google-genai,litellm,langfuse,disco]", + "maseval[smolagents,langgraph,llamaindex,camel,anthropic,openai,google-genai,litellm,langfuse,gaia2,macs,tau2,disco]", # Additional integrations used in examples "langchain>=0.3.27", "langchain-google-genai>=2.1.12", From f4a70d4db3cffaf493345b34c5ba2bd6b482e1b3 Mon Sep 17 00:00:00 2001 From: Alexander Rubinstein Date: Sun, 15 Feb 2026 12:14:16 +0100 Subject: [PATCH 37/47] [Create PR]: - Add **kwargs to MMLUBenchmark init --- maseval/benchmark/mmlu/mmlu.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/maseval/benchmark/mmlu/mmlu.py b/maseval/benchmark/mmlu/mmlu.py index 5c7e2747..8403b43e 100644 --- a/maseval/benchmark/mmlu/mmlu.py +++ b/maseval/benchmark/mmlu/mmlu.py @@ -473,6 +473,7 @@ def setup_environment( self, agent_data: Dict[str, Any], task: Task, + **kwargs: Any, ) -> MMLUEnvironment: """Create environment for a task.""" task_data = { @@ -489,6 +490,7 @@ def setup_user( agent_data: Dict[str, Any], environment: MMLUEnvironment, task: Task, + **kwargs: Any, ) -> None: """MMLU doesn't use a user simulator.""" return None @@ -499,6 +501,7 @@ def setup_agents( environment: MMLUEnvironment, task: Task, user: Optional[Any], + **kwargs: Any, ) -> Tuple[Sequence[AgentAdapter], Dict[str, AgentAdapter]]: """Create model agent for MCQ evaluation. @@ -525,6 +528,7 @@ def setup_evaluators( task: Task, agents: Sequence[AgentAdapter], user: Optional[Any], + **kwargs: Any, ) -> Sequence[Evaluator]: """Create MMLU evaluator.""" return [MMLUEvaluator(task, environment)] From 956508b908502eca6a234cd2d20ab83b72753b50 Mon Sep 17 00:00:00 2001 From: Alexander Rubinstein Date: Sun, 15 Feb 2026 12:20:32 +0100 Subject: [PATCH 38/47] [Create PR]: - Update DISCO deps --- pyproject.toml | 1 + 1 file changed, 1 insertion(+) diff --git a/pyproject.toml b/pyproject.toml index 37e92259..42c88ca5 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -86,6 +86,7 @@ lm-eval = ["lm-eval @ git+https://github.com/arubique/lm-evaluation-harness.git@ # DISCO prediction (for MMLU benchmark example) disco = [ + "aiohttp>=3.9.0", "click>=8.1.0", "datasets==4.4.1", "gdown>=4.6.0", From 57f763becc1c18373a6bc22bf974bc91725f409f Mon Sep 17 00:00:00 2001 From: Alexander Rubinstein Date: Sun, 15 Feb 2026 12:35:13 +0100 Subject: [PATCH 39/47] [Create PR]: - Add test for MMLU benchmark --- pyproject.toml | 1 + tests/README.md | 1 + tests/test_benchmarks/test_mmlu/__init__.py | 1 + .../test_mmlu/test_benchmark.py | 17 ++++ .../test_mmlu/test_integration.py | 85 +++++++++++++++++++ 5 files changed, 105 insertions(+) create mode 100644 tests/test_benchmarks/test_mmlu/__init__.py create mode 100644 tests/test_benchmarks/test_mmlu/test_benchmark.py create mode 100644 tests/test_benchmarks/test_mmlu/test_integration.py diff --git a/pyproject.toml b/pyproject.toml index 42c88ca5..8e02c329 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -204,6 +204,7 @@ markers = [ "llamaindex: Tests that specifically require llama-index-core", "gaia2: Tests that specifically require ARE (Agent Research Environments)", "camel: Tests that specifically require camel-ai", + "mmlu: Tests that specifically require MMLU benchmark (HuggingFace + DISCO)", "live: Tests requiring network access (downloads, external APIs)", "credentialed: Tests requiring API keys (implies live, costs money)", "slow: Tests taking >30 seconds (data downloads, large datasets)", diff --git a/tests/README.md b/tests/README.md index c73fca13..50416724 100644 --- a/tests/README.md +++ b/tests/README.md @@ -93,6 +93,7 @@ tests/ └── test_benchmarks/ # Benchmark tests (marked benchmark) ├── test_tau2/ # Tau2 benchmark + data integrity ├── test_macs/ # MACS benchmark + data integrity + ├── test_mmlu/ # MMLU benchmark + DISCO integration ├── test_gaia2/ # GAIA2 benchmark + data integrity └── test_multiagentbench/ # MultiAgentBench + data integrity ``` diff --git a/tests/test_benchmarks/test_mmlu/__init__.py b/tests/test_benchmarks/test_mmlu/__init__.py new file mode 100644 index 00000000..17b7f737 --- /dev/null +++ b/tests/test_benchmarks/test_mmlu/__init__.py @@ -0,0 +1 @@ +# MMLU benchmark tests diff --git a/tests/test_benchmarks/test_mmlu/test_benchmark.py b/tests/test_benchmarks/test_mmlu/test_benchmark.py new file mode 100644 index 00000000..16e2e369 --- /dev/null +++ b/tests/test_benchmarks/test_mmlu/test_benchmark.py @@ -0,0 +1,17 @@ +"""Structural tests for MMLU benchmark (Tier 1: offline, no real data). + +Validates that the MMLU benchmark module exposes the expected API. +""" + +import pytest + +pytestmark = pytest.mark.benchmark + + +def test_mmlu_module_exposes_load_tasks_and_compute_metrics(): + """MMLU benchmark module exposes load_tasks and compute_benchmark_metrics.""" + pytest.importorskip("torch") + from maseval.benchmark.mmlu import load_tasks, compute_benchmark_metrics + + assert callable(load_tasks) + assert callable(compute_benchmark_metrics) diff --git a/tests/test_benchmarks/test_mmlu/test_integration.py b/tests/test_benchmarks/test_mmlu/test_integration.py new file mode 100644 index 00000000..d7e3439b --- /dev/null +++ b/tests/test_benchmarks/test_mmlu/test_integration.py @@ -0,0 +1,85 @@ +"""Integration test for MMLU benchmark with DISCO prediction. + +Replicates the "Run MMLU Benchmark (Zephyr-7B SFT Disco)" launch configuration: +- model_id=alignment-handbook/zephyr-7b-sft-full +- data_path=arubique/flattened-MMLU +- device=cuda:0 +- disco_model_path=arubique/DISCO-MMLU + +Asserts that model 0 DISCO predicted accuracy equals the known-good value 0.606739. + +Requires: CUDA, network (HuggingFace), disco extra. Marked live + slow + benchmark + mmlu. + +Run with:: + + pytest -m "mmlu and live and slow" tests/test_benchmarks/test_mmlu/test_integration.py -v +""" + +import json +import sys +from pathlib import Path + +import pytest + +pytestmark = [ + pytest.mark.live, + pytest.mark.slow, + pytest.mark.benchmark, + pytest.mark.mmlu, +] + +# Known-good DISCO predicted accuracy for model 0 (Zephyr-7B SFT) from launch config +EXPECTED_MODEL_0_PREDICTED_ACCURACY = 0.606739 + + +def _ensure_examples_importable(): + """Ensure repo root is on path so examples.mmlu_benchmark is importable.""" + root = Path(__file__).resolve().parents[3] + if str(root) not in sys.path: + sys.path.insert(0, str(root)) + + +def test_mmlu_zephyr_disco_predicted_accuracy(tmp_path): + """Running MMLU benchmark with Zephyr-7B SFT and DISCO yields model 0 accuracy 0.606739.""" + pytest.importorskip("torch") + import torch + + if not torch.cuda.is_available(): + pytest.skip("CUDA required for MMLU DISCO integration test") + + _ensure_examples_importable() + from examples.mmlu_benchmark.mmlu_benchmark import main + + output_dir = tmp_path / "mmlu_output" + output_dir.mkdir(parents=True, exist_ok=True) + + argv = [ + "mmlu_benchmark.py", + "--model_id", + "alignment-handbook/zephyr-7b-sft-full", + "--data_path", + "arubique/flattened-MMLU", + "--device", + "cuda:0", + "--disco_model_path", + "arubique/DISCO-MMLU", + "--output_dir", + str(output_dir), + ] + old_argv = sys.argv + try: + sys.argv = argv + main() + finally: + sys.argv = old_argv + + summary_path = output_dir / "summary.json" + assert summary_path.exists(), f"Expected summary at {summary_path}" + with open(summary_path) as f: + summary = json.load(f) + + assert "disco_prediction" in summary, "DISCO prediction should be present in summary" + predicted = summary["disco_prediction"]["predicted_accuracy"] + assert predicted == pytest.approx(EXPECTED_MODEL_0_PREDICTED_ACCURACY, rel=1e-5, abs=1e-6), ( + f"Model 0 predicted accuracy: expected {EXPECTED_MODEL_0_PREDICTED_ACCURACY}, got {predicted}" + ) From 8f9119de37f8ced6447b013170eac0b1073e058a Mon Sep 17 00:00:00 2001 From: Alexander Rubinstein Date: Sun, 15 Feb 2026 12:40:02 +0100 Subject: [PATCH 40/47] [Create PR]: - Sync pyproject.toml with MasEval main --- pyproject.toml | 1 + 1 file changed, 1 insertion(+) diff --git a/pyproject.toml b/pyproject.toml index 8e02c329..d13c7506 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -15,6 +15,7 @@ classifiers = [ "Intended Audience :: Science/Research", "License :: OSI Approved :: MIT License", "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.10", "Programming Language :: Python :: 3.11", "Programming Language :: Python :: 3.12", "Programming Language :: Python :: 3.13", From 961fbf4f276d04357551cd5e855dc26649d1608e Mon Sep 17 00:00:00 2001 From: Alexander Rubinstein Date: Mon, 16 Feb 2026 21:10:08 +0100 Subject: [PATCH 41/47] [Create PR]: - Update types to pass autotests --- maseval/benchmark/mmlu/mmlu.py | 48 +++++++++++++++++----------------- 1 file changed, 24 insertions(+), 24 deletions(-) diff --git a/maseval/benchmark/mmlu/mmlu.py b/maseval/benchmark/mmlu/mmlu.py index 8403b43e..6506402c 100644 --- a/maseval/benchmark/mmlu/mmlu.py +++ b/maseval/benchmark/mmlu/mmlu.py @@ -33,7 +33,7 @@ def get_model_adapter(self, model_id, **kwargs): import pickle from abc import abstractmethod from pathlib import Path -from typing import Any, Dict, Iterator, List, Optional, Sequence, Tuple, Union +from typing import Any, Dict, Iterator, List, Optional, Sequence, Tuple, Union, cast # numpy is optional - only needed for anchor points processing try: @@ -49,8 +49,11 @@ def get_model_adapter(self, model_id, **kwargs): Benchmark, Environment, Evaluator, + MessageHistory, ModelAdapter, Task, + User, + SeedGenerator, ) from maseval.core.task import AdaptiveTaskQueue, SequentialTaskQueue from maseval.core.tracing import TraceableMixin @@ -402,15 +405,12 @@ def _run_agent(self, query: str) -> Any: """Execute the agent.""" return self.agent.run(query) - def get_messages(self) -> List[Dict[str, Any]]: + def get_messages(self) -> MessageHistory: """Get agent messages.""" - return self.agent._messages + return MessageHistory(self.agent._messages) def gather_traces(self) -> Dict[str, Any]: - """Gather execution traces from this agent. - - Override to handle plain list messages (not MessageHistory). - """ + """Gather execution traces from this agent.""" from maseval.core.tracing import TraceableMixin messages = self.get_messages() @@ -419,7 +419,7 @@ def gather_traces(self) -> Dict[str, Any]: "name": self.name, "agent_type": type(self.agent).__name__, "message_count": len(messages), - "messages": messages, # Already a list, no need for to_list() + "messages": messages.to_list(), "callbacks": [type(cb).__name__ for cb in self.callbacks], "logs": self.logs, } @@ -473,7 +473,7 @@ def setup_environment( self, agent_data: Dict[str, Any], task: Task, - **kwargs: Any, + seed_generator: SeedGenerator, ) -> MMLUEnvironment: """Create environment for a task.""" task_data = { @@ -488,20 +488,20 @@ def setup_environment( def setup_user( self, agent_data: Dict[str, Any], - environment: MMLUEnvironment, + environment: Environment, task: Task, - **kwargs: Any, - ) -> None: + seed_generator: SeedGenerator, + ) -> Optional[User]: """MMLU doesn't use a user simulator.""" return None def setup_agents( self, agent_data: Dict[str, Any], - environment: MMLUEnvironment, + environment: Environment, task: Task, - user: Optional[Any], - **kwargs: Any, + user: Optional[User], + seed_generator: SeedGenerator, ) -> Tuple[Sequence[AgentAdapter], Dict[str, AgentAdapter]]: """Create model agent for MCQ evaluation. @@ -524,11 +524,11 @@ def setup_agents( def setup_evaluators( self, - environment: MMLUEnvironment, + environment: Environment, task: Task, agents: Sequence[AgentAdapter], - user: Optional[Any], - **kwargs: Any, + user: Optional[User], + seed_generator: SeedGenerator, ) -> Sequence[Evaluator]: """Create MMLU evaluator.""" return [MMLUEvaluator(task, environment)] @@ -537,12 +537,12 @@ def run_agents( self, agents: Sequence[AgentAdapter], task: Task, - environment: MMLUEnvironment, - query: str = "", + environment: Environment, + query: str, ) -> Any: """Execute agent on the MMLU prompt.""" - # Get the prompt from environment - prompt = environment.get_prompt() + mmlu_env = cast(MMLUEnvironment, environment) + prompt = mmlu_env.get_prompt() # Run the agent agent = agents[0] @@ -649,7 +649,7 @@ def _load_model(self): return self._model, self._tokenizer - def _get_choice_token_id_separate(self, choice: str) -> int: + def _get_choice_token_id_separate(self, choice: str) -> Optional[int]: """Get the token ID for a choice when tokenized SEPARATELY. CRITICAL: lm-evaluation-harness encodes context and continuation separately, @@ -662,7 +662,7 @@ def _get_choice_token_id_separate(self, choice: str) -> int: choice: The choice string (e.g., "A"). Returns: - Token ID for the choice (standalone tokenization). + Token ID for the choice (standalone tokenization), or None if multi-token. """ _, tokenizer = self._load_model() From 3b122bb13b379525ef133177036187870a9fbeea Mon Sep 17 00:00:00 2001 From: Alexander Rubinstein Date: Mon, 16 Feb 2026 21:16:24 +0100 Subject: [PATCH 42/47] [Create PR]: - Update changelog --- CHANGELOG.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index f9011dfe..b89a2177 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -11,6 +11,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 **Benchmarks** +- MMLU Benchmark with DISCO support: Integration for evaluating language models on MMLU (Massive Multitask Language Understanding) multiple-choice questions, compatible with DISCO anchor-point methodology. Includes `MMLUBenchmark`, `HuggingFaceMMLUBenchmark`, `MMLUEnvironment`, `MMLUEvaluator`, `MMLUModelAgent`, `MMLUAgentAdapter`, `AnchorPointsTaskQueue`, `load_tasks()`, and `compute_benchmark_metrics()`. Optional extras: `lm-eval` (for `HuggingFaceMMLUBenchmark.precompute_all_logprobs_lmeval`), `disco` (for DISCO prediction in the example). (PR: #34) + - CONVERSE benchmark for contextual safety evaluation in adversarial agent-to-agent conversations, including `ConverseBenchmark`, `DefaultAgentConverseBenchmark`, `ConverseEnvironment`, `ConverseExternalAgent`, `PrivacyEvaluator`, `SecurityEvaluator`, and `load_tasks()` utilities for `travel`, `real_estate`, and `insurance` domains. Benchmark source files are now downloaded on first use via `ensure_data_exists()` instead of being bundled in the package. (PR: #28) - GAIA2 Benchmark: Integration with Meta's ARE (Agent Research Environments) platform for evaluating LLM-based agents on dynamic, multi-step scenarios (PR: #26) @@ -32,6 +34,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 **Examples** +- MMLU benchmark example at `examples/mmlu_benchmark/` for evaluating HuggingFace models on MMLU with optional DISCO prediction (`--disco_model_path`, `--disco_transform_path`). Supports local data, HuggingFace dataset repos, and DISCO weights from .pkl/.npz or HF repos. (PR: #34) - Added a dedicated runnable CONVERSE default benchmark example at `examples/converse_benchmark/default_converse_benchmark.py` for quick start with `DefaultAgentConverseBenchmark`. (PR: #28) - Gaia2 benchmark example with Google GenAI and OpenAI model support (PR: #26) @@ -62,6 +65,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 - Live API round-trip tests for all model adapters (`-m credentialed`) (PR: #29) - CI jobs for slow tests (with benchmark data caching) and credentialed tests (behind GitHub Environment approval) (PR: #29) - Added `respx` dev dependency for HTTP-level mocking (PR: #29) +- pytest marker `mmlu` for tests that require the MMLU benchmark (HuggingFace + DISCO). (PR: #34) ### Changed From 6c3cb22f32c9d83d7601b4de971cd37b2f00c2be Mon Sep 17 00:00:00 2001 From: Alexander Rubinstein Date: Mon, 16 Feb 2026 21:24:33 +0100 Subject: [PATCH 43/47] [Create PR]: - Add default value to --data_path --- examples/mmlu_benchmark/README.md | 10 ++++++++-- examples/mmlu_benchmark/mmlu_benchmark.py | 16 +++++++--------- 2 files changed, 15 insertions(+), 11 deletions(-) diff --git a/examples/mmlu_benchmark/README.md b/examples/mmlu_benchmark/README.md index 9644d160..56292ecb 100644 --- a/examples/mmlu_benchmark/README.md +++ b/examples/mmlu_benchmark/README.md @@ -43,7 +43,13 @@ The benchmark expects a JSON file in the `mmlu_prompts_examples.json` format: ### Basic Evaluation -Evaluate a model on all MMLU tasks: +Evaluate a model on all MMLU tasks (uses `arubique/flattened-MMLU` by default): + +```bash +python mmlu_benchmark.py --model_id "meta-llama/Llama-2-7b-hf" +``` + +To use a local JSON file or another Hugging Face dataset: ```bash python mmlu_benchmark.py \ @@ -137,7 +143,7 @@ python mmlu_benchmark.py \ | Argument | Description | Default | |----------|-------------|---------| | `--model_id` | HuggingFace model identifier (required) | - | -| `--data_path` | Path to MMLU prompts JSON file (required) | - | +| `--data_path` | Path to MMLU prompts JSON file or Hugging Face dataset repo id | `arubique/flattened-MMLU` | | `--anchor_points_path` | Path to anchor points pickle file | None | | `--output_dir` | Directory to save results | `./results` | | `--predictions_path` | Path to save predictions pickle (for DISCO) | None | diff --git a/examples/mmlu_benchmark/mmlu_benchmark.py b/examples/mmlu_benchmark/mmlu_benchmark.py index 972c7925..023915bd 100644 --- a/examples/mmlu_benchmark/mmlu_benchmark.py +++ b/examples/mmlu_benchmark/mmlu_benchmark.py @@ -14,29 +14,27 @@ --use_full_prompt Usage: - # Run with default settings (evaluates on all tasks) - python mmlu_benchmark.py --model_id "meta-llama/Llama-2-7b-hf" --data_path /path/to/mmlu_prompts_examples.json + # Run with default settings (evaluates on all tasks; uses arubique/flattened-MMLU by default) + python mmlu_benchmark.py --model_id "meta-llama/Llama-2-7b-hf" # Run with anchor points filtering (for DISCO prediction) python mmlu_benchmark.py \\ --model_id "alignment-handbook/zephyr-7b-sft-full" \\ - --data_path /path/to/mmlu_prompts_examples.json \\ --anchor_points_path /path/to/anchor_points_disagreement.pkl # Run with DISCO prediction (passing --disco_model_path enables it) python mmlu_benchmark.py \\ --model_id "alignment-handbook/zephyr-7b-sft-full" \\ - --data_path /path/to/mmlu_prompts_examples.json \\ --anchor_points_path /path/to/anchor_points_disagreement.pkl \\ --disco_model_path /path/to/fitted_weights.pkl \\ --disco_transform_path /path/to/transform.pkl \\ --pca 256 # Run on a subset of tasks for testing - python mmlu_benchmark.py \\ - --model_id "meta-llama/Llama-2-7b-hf" \\ - --data_path /path/to/mmlu_prompts_examples.json \\ - --limit 10 + python mmlu_benchmark.py --model_id "meta-llama/Llama-2-7b-hf" --limit 10 + + # Override data source (path to JSON or Hugging Face repo id) + python mmlu_benchmark.py --model_id "meta-llama/Llama-2-7b-hf" --data_path /path/to/mmlu_prompts_examples.json """ import argparse @@ -90,7 +88,7 @@ def parse_args(): parser.add_argument( "--data_path", type=str, - required=True, + default="arubique/flattened-MMLU", help="Path to MMLU prompts JSON file, or Hugging Face dataset repo id (e.g. username/mmlu-prompts-examples)", ) From 8b7343f9e9ce8e9f3806e36d7d88dc6cf6b96e24 Mon Sep 17 00:00:00 2001 From: Alexander Rubinstein Date: Mon, 16 Feb 2026 21:39:07 +0100 Subject: [PATCH 44/47] [Create PR]: - Shorten example's readme --- examples/mmlu_benchmark/README.md | 267 +++--------------------------- 1 file changed, 21 insertions(+), 246 deletions(-) diff --git a/examples/mmlu_benchmark/README.md b/examples/mmlu_benchmark/README.md index 56292ecb..48590770 100644 --- a/examples/mmlu_benchmark/README.md +++ b/examples/mmlu_benchmark/README.md @@ -1,266 +1,41 @@ # MMLU Benchmark Example -This example demonstrates how to evaluate HuggingFace language models on MMLU (Massive Multitask Language Understanding) using MASEval, with optional anchor point-based task selection for DISCO prediction. +Evaluate language models on MMLU (Massive Multitask Language Understanding) with optional efficient evaluation via DISCO. -## Overview +## Run without DISCO (full evaluation) -The MMLU benchmark evaluates language models on multiple choice questions across 57 subjects including STEM, humanities, social sciences, and more. This implementation is compatible with the [disco-public](https://github.com/parameterlab/disco-public) evaluation methodology. - -### Key Features - -- **Anchor Point-Based Evaluation**: Evaluate only on selected anchor tasks for efficient DISCO-based performance prediction -- **Full Prompt Support**: Use few-shot examples from `full_prompt` field (like lm-evaluation-harness) -- **HuggingFace Integration**: Works with any HuggingFace transformers model -- **DISCO-Compatible Output**: Saves predictions in format compatible with DISCO predictor - -## Installation - -```bash -# Install MASEval with all extras (includes transformers) -pip install "maseval[all]" - -# Or install with specific extras -pip install "maseval[transformers]" -``` - -## Data Format - -The benchmark expects a JSON file in the `mmlu_prompts_examples.json` format: - -```json -[ - { - "query": "Question text with answer choices...\nA. ...\nB. ...\nC. ...\nD. ...\nAnswer:", - "full_prompt": "The following are multiple choice questions... [few-shot examples] ... [question]", - "choices": ["A", "B", "C", "D"], - "gold": 1 - }, - ... -] -``` - -## Usage - -### Basic Evaluation - -Evaluate a model on all MMLU tasks (uses `arubique/flattened-MMLU` by default): - -```bash -python mmlu_benchmark.py --model_id "meta-llama/Llama-2-7b-hf" -``` - -To use a local JSON file or another Hugging Face dataset: - -```bash -python mmlu_benchmark.py \ - --model_id "meta-llama/Llama-2-7b-hf" \ - --data_path /path/to/mmlu_prompts_examples.json -``` - -### Anchor Points Evaluation (for DISCO) - -Evaluate only on anchor tasks for DISCO prediction: - -```bash -python mmlu_benchmark.py \ - --model_id "alignment-handbook/zephyr-7b-sft-full" \ - --data_path /path/to/mmlu_prompts_examples.json \ - --anchor_points_path /path/to/anchor_points_disagreement.pkl \ - --predictions_path ./output/predictions.pkl -``` - -### DISCO Prediction (Predict Full Benchmark from Anchor Points) - -Evaluate on anchor points and predict full benchmark performance using DISCO: - -```bash -python mmlu_benchmark.py \ - --model_id "alignment-handbook/zephyr-7b-sft-full" \ - --data_path /path/to/mmlu_prompts_examples.json \ - --anchor_points_path /path/to/anchor_points_disagreement.pkl \ - --disco_model_path /path/to/fitted_weights.pkl \ - --disco_transform_path /path/to/transform.pkl \ - --pca 256 -``` - -This is equivalent to running: -1. The evaluation on anchor points (like `scripts/run_lm_eval.py --skip_non_anchor_points`) -2. The prediction step (like `scripts/predict_model_performance.py`) - -#### Avoiding pickle / sklearn version warnings - -If you see `InconsistentVersionWarning` when loading the DISCO pickles (e.g. fitted_weights.pkl was saved with sklearn 1.7.2 but you use 1.8.0), you can export the model to NumPy-only `.npz` files and use those instead: - -```bash -# One-time extraction (run with the env that created the pickles, or ignore the warning) -python extract_disco_weights.py \ - --model_path /path/to/fitted_weights.pkl \ - --transform_path /path/to/transform.pkl \ - --output_dir /path/to/disco_npz -``` - -Then point the benchmark at the extracted files: +From the project root: ```bash -python mmlu_benchmark.py ... \ - --disco_model_path /path/to/disco_npz/disco_model.npz \ - --disco_transform_path /path/to/disco_npz/disco_transform.npz \ - --pca 256 +uv run python examples/mmlu_benchmark/mmlu_benchmark.py --model_id alignment-handbook/zephyr-7b-sft-full ``` -No pickle or sklearn objects are loaded at runtime when using `.npz` paths. - -#### Loading DISCO from Hugging Face - -You can upload the extracted weights as a custom Hugging Face model and load it with `AutoModel.from_pretrained(..., trust_remote_code=True)`. See [huggingface_disco/README.md](huggingface_disco/README.md) for: +Full evaluation results look like: -1. Extracting weights (`extract_disco_weights.py`) -2. Building the HF repo (`huggingface_disco/build_repo.py`) -3. Uploading to the Hub - -Then run the benchmark with a repo id instead of local paths: - -```bash -python mmlu_benchmark.py ... \ - --disco_model_path /my-disco-mmlu ``` - -(No `--disco_transform_path` needed; the Hub model contains everything.) - -### Quick Test - -Run on a small subset for testing: - -```bash -python mmlu_benchmark.py \ - --model_id "meta-llama/Llama-2-7b-hf" \ - --data_path /path/to/mmlu_prompts_examples.json \ - --limit 10 +================================================================================ +Results Summary (Evaluated Tasks) +================================================================================ +Total tasks: 100 +Correct: 35 +Accuracy (on anchor points): 0.3500 +Accuracy norm (on anchor points): 0.3500 +Built predictions tensor with shape: (1, 100, 31) ``` -## Command Line Arguments - -| Argument | Description | Default | -|----------|-------------|---------| -| `--model_id` | HuggingFace model identifier (required) | - | -| `--data_path` | Path to MMLU prompts JSON file or Hugging Face dataset repo id | `arubique/flattened-MMLU` | -| `--anchor_points_path` | Path to anchor points pickle file | None | -| `--output_dir` | Directory to save results | `./results` | -| `--predictions_path` | Path to save predictions pickle (for DISCO) | None | -| `--limit` | Limit number of tasks to evaluate | None | -| `--device` | Device to run model on | `cuda:0` | -| `--num_workers` | Number of parallel workers | 1 | -| `--disco_model_path` | If set, run DISCO prediction (.pkl, .npz, or HF repo id) | None | -| `--disco_transform_path` | Path to DISCO PCA transform pickle | None | -| `--pca` | PCA dimension for DISCO embeddings | 256 | +## Run with DISCO (predicted full-benchmark score) -## Equivalent disco-public Command - -This MASEval benchmark provides equivalent functionality to: +From the project root: ```bash -python scripts/run_lm_eval.py \ - --anchor_points_path=/path/to/anchor_points_disagreement.pkl \ - --batch_size=8 --device=cuda:0 \ - --gen_kwargs=max_gen_toks=128,output_scores=True,return_dict_in_generate=True \ - --metric=acc_norm --model=hf \ - --model_args=pretrained=alignment-handbook/zephyr-7b-sft-full,trust_remote_code=True \ - --num_fewshot=0 \ - --output_path=/path/to/output \ - --tasks=mmlu_prompts --log_samples --force_recompute \ - --use_full_prompt --skip_non_anchor_points +uv run python examples/mmlu_benchmark/mmlu_benchmark.py --model_id alignment-handbook/zephyr-7b-sft-full --disco_model_path arubique/DISCO-MMLU ``` -## Programmatic Usage - -```python -from maseval.benchmark.mmlu import ( - MMLUBenchmark, - load_tasks, - compute_benchmark_metrics, -) -from maseval.interface.inference import HuggingFaceModelAdapter - - -class MyMMLUBenchmark(MMLUBenchmark): - def __init__(self, model_id: str, **kwargs): - super().__init__(**kwargs) - self._model_id = model_id - self._pipeline = None +Predicted score output: - def get_model_adapter(self, model_id: str, **kwargs): - from transformers import pipeline - - if self._pipeline is None: - self._pipeline = pipeline( - "text-generation", - model=self._model_id, - device="cuda:0", - ) - - return HuggingFaceModelAdapter( - model=self._pipeline, - model_id=self._model_id, - ) - - -# Load tasks with anchor points filtering -tasks = load_tasks( - data_path="/path/to/mmlu_prompts_examples.json", - anchor_points_path="/path/to/anchor_points.pkl", -) - -# Run benchmark -benchmark = MyMMLUBenchmark( - model_id="meta-llama/Llama-2-7b-hf", - use_full_prompt=True, -) -results = benchmark.run(tasks=tasks, agent_data={"model_id": "llama-7b"}) - -# Compute metrics -metrics = compute_benchmark_metrics(results) -print(f"Accuracy: {metrics['acc']:.4f}") ``` - -## Output Format - -### Results (JSONL) - -Each task result is saved in JSONL format: - -```json -{ - "task_id": "mmlu_42", - "status": "success", - "eval": [ - { - "acc": 1.0, - "acc_norm": 1.0, - "predicted": 1, - "gold": 1, - "correct": true, - "doc_id": 42 - } - ] -} +---------------------------------------- +DISCO Predicted Full Benchmark Accuracy: +---------------------------------------- + Model 0: 0.606739 ``` - -### Predictions (Pickle) - -When `--predictions_path` is specified, saves a numpy array of shape `(1, n_questions, n_choices)` for use with DISCO predictor. - -## Architecture - -The MMLU benchmark implementation follows MASEval patterns: - -- `MMLUBenchmark`: Main benchmark class (abstract, requires `get_model_adapter`) -- `MMLUEnvironment`: Simple environment holding task context (no tools needed) -- `MMLUEvaluator`: Evaluates model predictions against gold answers -- `AnchorPointsTaskQueue`: AdaptiveTaskQueue that iterates through anchor tasks -- `MMLUModelAgent`: Simple agent wrapper that forwards prompts to model - -## References - -- [MMLU Dataset](https://huggingface.co/datasets/cais/mmlu) -- [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) -- [disco-public](https://github.com/parameterlab/disco-public) From d945cb2fd525a6b931a751ac782c7147b769b279 Mon Sep 17 00:00:00 2001 From: Alexander Rubinstein Date: Mon, 16 Feb 2026 21:47:46 +0100 Subject: [PATCH 45/47] [Create PR]: - Add args description to readme --- examples/mmlu_benchmark/README.md | 28 +++++++++++++++++++++++----- 1 file changed, 23 insertions(+), 5 deletions(-) diff --git a/examples/mmlu_benchmark/README.md b/examples/mmlu_benchmark/README.md index 48590770..9018d7fd 100644 --- a/examples/mmlu_benchmark/README.md +++ b/examples/mmlu_benchmark/README.md @@ -16,11 +16,10 @@ Full evaluation results look like: ================================================================================ Results Summary (Evaluated Tasks) ================================================================================ -Total tasks: 100 -Correct: 35 -Accuracy (on anchor points): 0.3500 -Accuracy norm (on anchor points): 0.3500 -Built predictions tensor with shape: (1, 100, 31) +Total tasks: 14042 +Correct: 8291 +Accuracy (on anchor points): 0.5904 +Accuracy norm (on anchor points): 0.5904 ``` ## Run with DISCO (predicted full-benchmark score) @@ -39,3 +38,22 @@ DISCO Predicted Full Benchmark Accuracy: ---------------------------------------- Model 0: 0.606739 ``` + +## Arguments + +| Argument | Description | Default | +|----------|-------------|---------| +| `--model_id` | HuggingFace model identifier (e.g. `meta-llama/Llama-2-7b-hf`) | *(required)* | +| `--data_path` | Path to MMLU prompts JSON file or Hugging Face dataset repo id | `arubique/flattened-MMLU` | +| `--anchor_points_path` | Path to anchor points pickle file; if set, only anchor tasks are evaluated | — | +| `--output_dir` | Directory to save results | `./results` | +| `--predictions_path` | Path to save predictions tensor as pickle (for DISCO) | — | +| `--limit` | Limit number of tasks to evaluate (for testing) | — | +| `--batch_size` | Batch size for evaluation (reserved for future use) | `1` | +| `--device` | Device to run model on (e.g. `cuda:0`, `cpu`) | `cuda:0` | +| `--num_workers` | Number of parallel workers for task execution | `1` | +| `--disco_model_path` | If set, run DISCO prediction; path to `.pkl`, `.npz`, or Hugging Face repo id | — | +| `--disco_transform_path` | Path to DISCO PCA transform `.pkl` or `.npz` (for local DISCO model when using `--pca`) | — | +| `--pca` | PCA dimension for DISCO embeddings | — | +| `--pad_to_size` | Pad predictions to this size with -inf | — | +| `--use_lmeval_batching` | Use lm-evaluation-harness-style batching for exact numerical match with DISCO repo | off | From 27e93a252999f64f57b78a9e4ca0e4bbf331eaec Mon Sep 17 00:00:00 2001 From: Alexander Rubinstein Date: Mon, 16 Feb 2026 21:58:08 +0100 Subject: [PATCH 46/47] [Create PR]: - Add links to papers in readme --- examples/mmlu_benchmark/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/mmlu_benchmark/README.md b/examples/mmlu_benchmark/README.md index 9018d7fd..58448111 100644 --- a/examples/mmlu_benchmark/README.md +++ b/examples/mmlu_benchmark/README.md @@ -1,6 +1,6 @@ # MMLU Benchmark Example -Evaluate language models on MMLU (Massive Multitask Language Understanding) with optional efficient evaluation via DISCO. +Evaluate language models on [MMLU (Massive Multitask Language Understanding)](https://arxiv.org/abs/2009.03300) with optional efficient evaluation via [DISCO](https://arxiv.org/abs/2510.07959). ## Run without DISCO (full evaluation) From 2c705a30246cd4d5b87a21b1bfa2689ee00f03bc Mon Sep 17 00:00:00 2001 From: Alexander Rubinstein Date: Wed, 18 Feb 2026 08:27:39 +0100 Subject: [PATCH 47/47] [Create PR]: - Add link to lm-evaluation-harness --- examples/mmlu_benchmark/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/mmlu_benchmark/README.md b/examples/mmlu_benchmark/README.md index 58448111..62c6bafc 100644 --- a/examples/mmlu_benchmark/README.md +++ b/examples/mmlu_benchmark/README.md @@ -56,4 +56,4 @@ DISCO Predicted Full Benchmark Accuracy: | `--disco_transform_path` | Path to DISCO PCA transform `.pkl` or `.npz` (for local DISCO model when using `--pca`) | — | | `--pca` | PCA dimension for DISCO embeddings | — | | `--pad_to_size` | Pad predictions to this size with -inf | — | -| `--use_lmeval_batching` | Use lm-evaluation-harness-style batching for exact numerical match with DISCO repo | off | +| `--use_lmeval_batching` | Use [lm-evaluation-harness-style](https://github.com/EleutherAI/lm-evaluation-harness) batching for exact numerical match with DISCO repo | off |