#!/usr/bin/env python3 """ Batch Agent Runner This module provides parallel batch processing capabilities for running the agent across multiple prompts from a dataset. It includes: - Dataset loading and batching - Parallel batch processing with multiprocessing - Checkpointing for fault tolerance and resumption - Trajectory saving in the proper format (from/value pairs) - Tool usage statistics aggregation across all batches Usage: python batch_runner.py --dataset_file=data.jsonl --batch_size=10 --run_name=my_run # Resume an interrupted run python batch_runner.py --dataset_file=data.jsonl --batch_size=10 --run_name=my_run --resume # Use a specific toolset distribution python batch_runner.py --dataset_file=data.jsonl --batch_size=10 --run_name=my_run --distribution=image_gen """ import json import logging import os import time from pathlib import Path from typing import List, Dict, Any, Optional, Tuple from datetime import datetime from multiprocessing import Pool, Lock import traceback from rich.progress import Progress, SpinnerColumn, BarColumn, TextColumn, TimeRemainingColumn, MofNCompleteColumn from rich.console import Console logger = logging.getLogger(__name__) import fire from run_agent import AIAgent from toolset_distributions import ( list_distributions, sample_toolsets_from_distribution, validate_distribution ) from model_tools import TOOL_TO_TOOLSET_MAP # Global configuration for worker processes _WORKER_CONFIG = {} # All possible tools - auto-derived from the master mapping in model_tools.py. # This stays in sync automatically when new tools are added to TOOL_TO_TOOLSET_MAP. # Used for consistent schema in Arrow/Parquet (HuggingFace datasets) and for # filtering corrupted entries during trajectory combination. ALL_POSSIBLE_TOOLS = set(TOOL_TO_TOOLSET_MAP.keys()) # Default stats for tools that weren't used DEFAULT_TOOL_STATS = {'count': 0, 'success': 0, 'failure': 0} def _normalize_tool_stats(tool_stats: Dict[str, Dict[str, int]]) -> Dict[str, Dict[str, int]]: """ Normalize tool_stats to include all possible tools with consistent schema. This ensures HuggingFace datasets can load the JSONL without schema mismatch errors. Tools that weren't used get zero counts. Args: tool_stats (Dict): Raw tool statistics from extraction Returns: Dict: Normalized tool statistics with all tools present """ normalized = {} # Add all possible tools with defaults for tool in ALL_POSSIBLE_TOOLS: if tool in tool_stats: normalized[tool] = tool_stats[tool].copy() else: normalized[tool] = DEFAULT_TOOL_STATS.copy() # Also include any unexpected tools (in case new tools are added) for tool, stats in tool_stats.items(): if tool not in normalized: normalized[tool] = stats.copy() return normalized def _normalize_tool_error_counts(tool_error_counts: Dict[str, int]) -> Dict[str, int]: """ Normalize tool_error_counts to include all possible tools. Args: tool_error_counts (Dict): Raw error counts mapping Returns: Dict: Normalized error counts with all tools present """ normalized = {} # Add all possible tools with zero defaults for tool in ALL_POSSIBLE_TOOLS: normalized[tool] = tool_error_counts.get(tool, 0) # Also include any unexpected tools for tool, count in tool_error_counts.items(): if tool not in normalized: normalized[tool] = count return normalized def _extract_tool_stats(messages: List[Dict[str, Any]]) -> Dict[str, Dict[str, int]]: """ Extract tool usage statistics from message history. Args: messages (List[Dict]): Message history Returns: Dict: Tool statistics with counts and success/failure rates """ tool_stats = {} # Track tool calls and their results tool_calls_map = {} # Map tool_call_id to tool name for msg in messages: # Track tool calls from assistant messages if msg["role"] == "assistant" and "tool_calls" in msg and msg["tool_calls"]: for tool_call in msg["tool_calls"]: if not tool_call or not isinstance(tool_call, dict): continue tool_name = tool_call["function"]["name"] tool_call_id = tool_call["id"] # Initialize stats for this tool if not exists if tool_name not in tool_stats: tool_stats[tool_name] = { "count": 0, "success": 0, "failure": 0 } tool_stats[tool_name]["count"] += 1 tool_calls_map[tool_call_id] = tool_name # Track tool responses elif msg["role"] == "tool": tool_call_id = msg.get("tool_call_id", "") content = msg.get("content", "") # Determine if tool call was successful is_success = True try: # Try to parse as JSON and check for actual error values content_json = json.loads(content) if isinstance(content, str) else content if isinstance(content_json, dict): # Check if error field exists AND has a non-null value if "error" in content_json and content_json["error"] is not None: is_success = False # Special handling for terminal tool responses # Terminal wraps its response in a "content" field if "content" in content_json and isinstance(content_json["content"], dict): inner_content = content_json["content"] # Check for actual error (non-null error field) # Note: non-zero exit codes are not failures - the model can self-correct if inner_content.get("error") is not None: is_success = False # Check for "success": false pattern used by some tools if content_json.get("success") is False: is_success = False except (json.JSONDecodeError, ValueError, TypeError): # If not JSON, check if content is empty or explicitly states an error # Note: We avoid simple substring matching to prevent false positives if not content: is_success = False # Only mark as failure if it explicitly starts with "Error:" or "ERROR:" elif content.strip().lower().startswith("error:"): is_success = False # Update success/failure count if tool_call_id in tool_calls_map: tool_name = tool_calls_map[tool_call_id] if is_success: tool_stats[tool_name]["success"] += 1 else: tool_stats[tool_name]["failure"] += 1 return tool_stats def _extract_reasoning_stats(messages: List[Dict[str, Any]]) -> Dict[str, int]: """ Count how many assistant turns have reasoning vs no reasoning. Checks for in content or a non-empty 'reasoning' field (native thinking tokens). Returns counts for tracking reasoning coverage. Args: messages: Message history Returns: Dict with 'total_assistant_turns', 'turns_with_reasoning', 'turns_without_reasoning' """ total = 0 with_reasoning = 0 for msg in messages: if msg.get("role") != "assistant": continue total += 1 content = msg.get("content", "") or "" has_scratchpad = "" in content has_native_reasoning = bool(msg.get("reasoning", "").strip()) if msg.get("reasoning") else False if has_scratchpad or has_native_reasoning: with_reasoning += 1 return { "total_assistant_turns": total, "turns_with_reasoning": with_reasoning, "turns_without_reasoning": total - with_reasoning, "has_any_reasoning": with_reasoning > 0, } def _process_single_prompt( prompt_index: int, prompt_data: Dict[str, Any], batch_num: int, config: Dict[str, Any] ) -> Dict[str, Any]: """ Process a single prompt with the agent. Args: prompt_index (int): Index of prompt in dataset prompt_data (Dict): Prompt data containing 'prompt' field and optional 'image' field batch_num (int): Batch number config (Dict): Configuration dict with agent parameters Returns: Dict: Result containing trajectory, stats, and metadata """ prompt = prompt_data["prompt"] task_id = f"task_{prompt_index}" # Per-prompt container image override: if the dataset row has an 'image' field, # register it for this task's sandbox. Works with Docker, Modal, Singularity, and Daytona. container_image = prompt_data.get("image") or prompt_data.get("docker_image") if container_image: # Verify the image is accessible before spending tokens on the agent loop. # For Docker: check local cache, then try pulling. # For Modal: skip local check (Modal pulls server-side). env_type = os.getenv("TERMINAL_ENV", "local") if env_type == "docker": import subprocess as _sp try: probe = _sp.run( ["docker", "image", "inspect", container_image], capture_output=True, timeout=10, ) if probe.returncode != 0: if config.get("verbose"): print(f" Prompt {prompt_index}: Pulling docker image {container_image}...", flush=True) pull = _sp.run( ["docker", "pull", container_image], capture_output=True, text=True, timeout=600, ) if pull.returncode != 0: return { "success": False, "prompt_index": prompt_index, "error": f"Docker image not available: {container_image}\n{pull.stderr[:500]}", "trajectory": None, "tool_stats": {}, "toolsets_used": [], "metadata": {"batch_num": batch_num, "timestamp": datetime.now().isoformat()}, } except FileNotFoundError: pass # Docker CLI not installed β€” skip check (e.g., Modal backend) except Exception as img_err: if config.get("verbose"): print(f" Prompt {prompt_index}: Docker image check failed: {img_err}", flush=True) from tools.terminal_tool import register_task_env_overrides overrides = { "docker_image": container_image, "modal_image": container_image, "singularity_image": f"docker://{container_image}", "daytona_image": container_image, } if prompt_data.get("cwd"): overrides["cwd"] = prompt_data["cwd"] register_task_env_overrides(task_id, overrides) if config.get("verbose"): print(f" Prompt {prompt_index}: Using container image {container_image}") try: # Sample toolsets from distribution for this prompt selected_toolsets = sample_toolsets_from_distribution(config["distribution"]) if config.get("verbose"): print(f" Prompt {prompt_index}: Using toolsets {selected_toolsets}") # Initialize agent with sampled toolsets and log prefix for identification log_prefix = f"[B{batch_num}:P{prompt_index}]" agent = AIAgent( base_url=config.get("base_url"), api_key=config.get("api_key"), model=config["model"], max_iterations=config["max_iterations"], enabled_toolsets=selected_toolsets, save_trajectories=False, # We handle saving ourselves verbose_logging=config.get("verbose", False), ephemeral_system_prompt=config.get("ephemeral_system_prompt"), log_prefix_chars=config.get("log_prefix_chars", 100), log_prefix=log_prefix, providers_allowed=config.get("providers_allowed"), providers_ignored=config.get("providers_ignored"), providers_order=config.get("providers_order"), provider_sort=config.get("provider_sort"), max_tokens=config.get("max_tokens"), reasoning_config=config.get("reasoning_config"), prefill_messages=config.get("prefill_messages"), skip_context_files=True, # Don't pollute trajectories with SOUL.md/AGENTS.md skip_memory=True, # Don't use persistent memory in batch runs ) # Run the agent with task_id to ensure each task gets its own isolated VM result = agent.run_conversation(prompt, task_id=task_id) # Extract tool usage statistics tool_stats = _extract_tool_stats(result["messages"]) # Extract reasoning coverage stats reasoning_stats = _extract_reasoning_stats(result["messages"]) # Convert to trajectory format (using existing method) trajectory = agent._convert_to_trajectory_format( result["messages"], prompt, result["completed"] ) return { "success": True, "prompt_index": prompt_index, "trajectory": trajectory, "tool_stats": tool_stats, "reasoning_stats": reasoning_stats, "completed": result["completed"], "partial": result.get("partial", False), "api_calls": result["api_calls"], "toolsets_used": selected_toolsets, "metadata": { "batch_num": batch_num, "timestamp": datetime.now().isoformat(), "model": config["model"] } } except Exception as e: print(f"❌ Error processing prompt {prompt_index}: {e}") if config.get("verbose"): traceback.print_exc() return { "success": False, "prompt_index": prompt_index, "error": str(e), "trajectory": None, "tool_stats": {}, "toolsets_used": [], "metadata": { "batch_num": batch_num, "timestamp": datetime.now().isoformat() } } def _process_batch_worker(args: Tuple) -> Dict[str, Any]: """ Worker function to process a single batch of prompts. Args: args (Tuple): (batch_num, batch_data, output_dir, completed_prompts, config) Returns: Dict: Batch results with statistics """ batch_num, batch_data, output_dir, completed_prompts_set, config = args output_dir = Path(output_dir) print(f"\nπŸ”„ Batch {batch_num}: Starting ({len(batch_data)} prompts)") # Output file for this batch batch_output_file = output_dir / f"batch_{batch_num}.jsonl" # Filter out already completed prompts prompts_to_process = [ (idx, data) for idx, data in batch_data if idx not in completed_prompts_set ] if not prompts_to_process: print(f"βœ… Batch {batch_num}: Already completed (skipping)") return { "batch_num": batch_num, "processed": 0, "skipped": len(batch_data), "tool_stats": {}, "completed_prompts": [] } print(f" Processing {len(prompts_to_process)} prompts (skipping {len(batch_data) - len(prompts_to_process)} already completed)") # Initialize aggregated stats for this batch batch_tool_stats = {} batch_reasoning_stats = {"total_assistant_turns": 0, "turns_with_reasoning": 0, "turns_without_reasoning": 0} completed_in_batch = [] discarded_no_reasoning = 0 # Process each prompt sequentially in this batch for prompt_index, prompt_data in prompts_to_process: # Process the prompt result = _process_single_prompt( prompt_index, prompt_data, batch_num, config ) # Save trajectory if successful if result["success"] and result["trajectory"]: # Discard samples with zero reasoning across all turns reasoning = result.get("reasoning_stats", {}) if not reasoning.get("has_any_reasoning", True): print(f" 🚫 Prompt {prompt_index} discarded (no reasoning in any turn)") discarded_no_reasoning += 1 completed_in_batch.append(prompt_index) continue # Get and normalize tool stats for consistent schema across all entries raw_tool_stats = result.get("tool_stats", {}) tool_stats = _normalize_tool_stats(raw_tool_stats) # Create normalized tool_error_counts mapping tool names to their failure counts raw_error_counts = { tool_name: stats.get("failure", 0) for tool_name, stats in raw_tool_stats.items() } tool_error_counts = _normalize_tool_error_counts(raw_error_counts) trajectory_entry = { "prompt_index": prompt_index, "conversations": result["trajectory"], "metadata": result["metadata"], "completed": result["completed"], "partial": result.get("partial", False), # True if stopped due to invalid tool calls "api_calls": result["api_calls"], "toolsets_used": result["toolsets_used"], "tool_stats": tool_stats, # Full stats: {tool: {count, success, failure}} - normalized "tool_error_counts": tool_error_counts # Simple: {tool: failure_count} - normalized } # Append to batch output file with open(batch_output_file, 'a', encoding='utf-8') as f: f.write(json.dumps(trajectory_entry, ensure_ascii=False) + "\n") # Aggregate tool statistics for tool_name, stats in result.get("tool_stats", {}).items(): if tool_name not in batch_tool_stats: batch_tool_stats[tool_name] = { "count": 0, "success": 0, "failure": 0 } batch_tool_stats[tool_name]["count"] += stats["count"] batch_tool_stats[tool_name]["success"] += stats["success"] batch_tool_stats[tool_name]["failure"] += stats["failure"] # Aggregate reasoning stats for key in batch_reasoning_stats: batch_reasoning_stats[key] += result.get("reasoning_stats", {}).get(key, 0) # Only mark as completed if successfully saved (failed prompts can be retried on resume) if result["success"] and result["trajectory"]: completed_in_batch.append(prompt_index) status = "⚠️ partial" if result.get("partial") else "βœ…" print(f" {status} Prompt {prompt_index} completed") else: print(f" ❌ Prompt {prompt_index} failed (will retry on resume)") print(f"βœ… Batch {batch_num}: Completed ({len(prompts_to_process)} prompts processed)") return { "batch_num": batch_num, "processed": len(prompts_to_process), "skipped": len(batch_data) - len(prompts_to_process), "tool_stats": batch_tool_stats, "reasoning_stats": batch_reasoning_stats, "discarded_no_reasoning": discarded_no_reasoning, "completed_prompts": completed_in_batch } class BatchRunner: """ Manages batch processing of agent prompts with checkpointing and statistics. """ def __init__( self, dataset_file: str, batch_size: int, run_name: str, distribution: str = "default", max_iterations: int = 10, base_url: str = None, api_key: str = None, model: str = "claude-opus-4-20250514", num_workers: int = 4, verbose: bool = False, ephemeral_system_prompt: str = None, log_prefix_chars: int = 100, providers_allowed: List[str] = None, providers_ignored: List[str] = None, providers_order: List[str] = None, provider_sort: str = None, max_tokens: int = None, reasoning_config: Dict[str, Any] = None, prefill_messages: List[Dict[str, Any]] = None, max_samples: int = None, ): """ Initialize the batch runner. Args: dataset_file (str): Path to the dataset JSONL file with 'prompt' field batch_size (int): Number of prompts per batch run_name (str): Name for this run (used for checkpointing and output) distribution (str): Toolset distribution to use (default: "default") max_iterations (int): Max iterations per agent run base_url (str): Base URL for model API api_key (str): API key for model model (str): Model name to use num_workers (int): Number of parallel workers verbose (bool): Enable verbose logging ephemeral_system_prompt (str): System prompt used during agent execution but NOT saved to trajectories (optional) log_prefix_chars (int): Number of characters to show in log previews for tool calls/responses (default: 20) providers_allowed (List[str]): OpenRouter providers to allow (optional) providers_ignored (List[str]): OpenRouter providers to ignore (optional) providers_order (List[str]): OpenRouter providers to try in order (optional) provider_sort (str): Sort providers by price/throughput/latency (optional) max_tokens (int): Maximum tokens for model responses (optional, uses model default if not set) reasoning_config (Dict): OpenRouter reasoning config override (e.g. {"effort": "none"} to disable thinking) prefill_messages (List[Dict]): Messages to prepend as prefilled conversation context (few-shot priming). NOTE: Anthropic Sonnet 4.6+ and Opus 4.6+ reject a trailing assistant-role prefill (400 error). For those models use output_config.format or structured-output schemas instead. Safe here for user-role priming and for older Claude / non-Claude models. max_samples (int): Only process the first N samples from the dataset (optional, processes all if not set) """ self.dataset_file = Path(dataset_file) self.batch_size = batch_size self.run_name = run_name self.distribution = distribution self.max_iterations = max_iterations self.base_url = base_url self.api_key = api_key self.model = model self.num_workers = num_workers self.verbose = verbose self.ephemeral_system_prompt = ephemeral_system_prompt self.log_prefix_chars = log_prefix_chars self.providers_allowed = providers_allowed self.providers_ignored = providers_ignored self.providers_order = providers_order self.provider_sort = provider_sort self.max_tokens = max_tokens self.reasoning_config = reasoning_config self.prefill_messages = prefill_messages self.max_samples = max_samples # Validate distribution if not validate_distribution(distribution): raise ValueError(f"Unknown distribution: {distribution}. Available: {list(list_distributions().keys())}") # Setup output directory self.output_dir = Path("data") / run_name self.output_dir.mkdir(parents=True, exist_ok=True) # Checkpoint file self.checkpoint_file = self.output_dir / "checkpoint.json" # Statistics file self.stats_file = self.output_dir / "statistics.json" # Load dataset (and optionally truncate to max_samples) self.dataset = self._load_dataset() if self.max_samples and self.max_samples < len(self.dataset): full_count = len(self.dataset) self.dataset = self.dataset[:self.max_samples] print(f"βœ‚οΈ Truncated dataset from {full_count} to {self.max_samples} samples (--max_samples)") # Create batches self.batches = self._create_batches() print("πŸ“Š Batch Runner Initialized") print(f" Dataset: {self.dataset_file} ({len(self.dataset)} prompts)") print(f" Batch size: {self.batch_size}") print(f" Total batches: {len(self.batches)}") print(f" Run name: {self.run_name}") print(f" Distribution: {self.distribution}") print(f" Output directory: {self.output_dir}") print(f" Workers: {self.num_workers}") if self.ephemeral_system_prompt: prompt_preview = self.ephemeral_system_prompt[:60] + "..." if len(self.ephemeral_system_prompt) > 60 else self.ephemeral_system_prompt print(f" πŸ”’ Ephemeral system prompt: '{prompt_preview}'") def _load_dataset(self) -> List[Dict[str, Any]]: """ Load dataset from JSONL file. Returns: List[Dict]: List of dataset entries """ if not self.dataset_file.exists(): raise FileNotFoundError(f"Dataset file not found: {self.dataset_file}") dataset = [] with open(self.dataset_file, 'r', encoding='utf-8') as f: for line_num, line in enumerate(f, 1): line = line.strip() if not line: continue try: entry = json.loads(line) if 'prompt' not in entry: print(f"⚠️ Warning: Line {line_num} missing 'prompt' field, skipping") continue dataset.append(entry) except json.JSONDecodeError as e: print(f"⚠️ Warning: Invalid JSON on line {line_num}: {e}") continue if not dataset: raise ValueError(f"No valid entries found in dataset file: {self.dataset_file}") return dataset def _create_batches(self) -> List[List[Tuple[int, Dict[str, Any]]]]: """ Split dataset into batches with indices. Returns: List of batches, where each batch is a list of (index, entry) tuples """ batches = [] for i in range(0, len(self.dataset), self.batch_size): batch = [(idx, entry) for idx, entry in enumerate(self.dataset[i:i + self.batch_size], start=i)] batches.append(batch) return batches def _load_checkpoint(self) -> Dict[str, Any]: """ Load checkpoint data if it exists. Returns: Dict: Checkpoint data with completed prompt indices """ if not self.checkpoint_file.exists(): return { "run_name": self.run_name, "completed_prompts": [], "batch_stats": {}, "last_updated": None } try: with open(self.checkpoint_file, 'r', encoding='utf-8') as f: return json.load(f) except Exception as e: print(f"⚠️ Warning: Failed to load checkpoint: {e}") return { "run_name": self.run_name, "completed_prompts": [], "batch_stats": {}, "last_updated": None } def _save_checkpoint(self, checkpoint_data: Dict[str, Any], lock: Optional[Lock] = None): """ Save checkpoint data. Args: checkpoint_data (Dict): Checkpoint data to save lock (Lock): Optional lock for thread-safe access """ checkpoint_data["last_updated"] = datetime.now().isoformat() from utils import atomic_json_write if lock: with lock: atomic_json_write(self.checkpoint_file, checkpoint_data) else: atomic_json_write(self.checkpoint_file, checkpoint_data) def _scan_completed_prompts_by_content(self) -> set: """ Scan all batch files and extract completed prompts by their actual content. This provides a more robust resume mechanism that matches on prompt text rather than indices, allowing recovery even if indices don't match. Returns: set: Set of prompt texts that have been successfully processed """ completed_prompts = set() batch_files = sorted(self.output_dir.glob("batch_*.jsonl")) if not batch_files: return completed_prompts print(f"πŸ“‚ Scanning {len(batch_files)} batch files for completed prompts...") for batch_file in batch_files: try: with open(batch_file, 'r', encoding='utf-8') as f: for line in f: try: entry = json.loads(line.strip()) # Skip failed entries - we want to retry these if entry.get("failed", False): continue # Extract the human/user prompt from conversations conversations = entry.get("conversations", []) for msg in conversations: if msg.get("from") == "human": prompt_text = msg.get("value", "").strip() if prompt_text: completed_prompts.add(prompt_text) break # Only need the first human message except json.JSONDecodeError: continue except Exception as e: print(f" ⚠️ Warning: Error reading {batch_file.name}: {e}") return completed_prompts def _filter_dataset_by_completed(self, completed_prompts: set) -> Tuple[List[Dict], List[int]]: """ Filter the dataset to exclude prompts that have already been completed. Args: completed_prompts: Set of prompt texts that have been completed Returns: Tuple of (filtered_dataset, skipped_indices) """ filtered_dataset = [] skipped_indices = [] for idx, entry in enumerate(self.dataset): # Extract prompt from the dataset entry prompt_text = entry.get("prompt", "").strip() # Also check conversations format if not prompt_text: conversations = entry.get("conversations", []) for msg in conversations: role = msg.get("role") or msg.get("from") if role in ("user", "human"): prompt_text = (msg.get("content") or msg.get("value", "")).strip() break if prompt_text in completed_prompts: skipped_indices.append(idx) else: # Keep original index for tracking filtered_dataset.append((idx, entry)) return filtered_dataset, skipped_indices def run(self, resume: bool = False): """ Run the batch processing pipeline. Args: resume (bool): Whether to resume from checkpoint """ print("\n" + "=" * 70) print("πŸš€ Starting Batch Processing") print("=" * 70) # Smart resume: scan batch files by content to find completed prompts completed_prompt_texts = set() if resume: completed_prompt_texts = self._scan_completed_prompts_by_content() if completed_prompt_texts: print(f" Found {len(completed_prompt_texts)} already-completed prompts by content matching") # Filter dataset to only include unprocessed prompts if resume and completed_prompt_texts: filtered_entries, skipped_indices = self._filter_dataset_by_completed(completed_prompt_texts) if not filtered_entries: print("\nβœ… All prompts have already been processed!") return # Recreate batches from filtered entries (keeping original indices for tracking) batches_to_process = [] for i in range(0, len(filtered_entries), self.batch_size): batch = filtered_entries[i:i + self.batch_size] batches_to_process.append(batch) self.batches = batches_to_process # Print prominent resume summary print("\n" + "=" * 70) print("πŸ“Š RESUME SUMMARY") print("=" * 70) print(f" Original dataset size: {len(self.dataset):,} prompts") print(f" Already completed: {len(skipped_indices):,} prompts") print(" ─────────────────────────────────────────") print(f" 🎯 RESUMING WITH: {len(filtered_entries):,} prompts") print(f" New batches created: {len(batches_to_process)}") print("=" * 70 + "\n") # Load existing checkpoint (so resume doesn't clobber prior progress) checkpoint_data = self._load_checkpoint() if checkpoint_data.get("run_name") != self.run_name: checkpoint_data = { "run_name": self.run_name, "completed_prompts": [], "batch_stats": {}, "last_updated": None } # Prepare configuration for workers config = { "distribution": self.distribution, "model": self.model, "max_iterations": self.max_iterations, "base_url": self.base_url, "api_key": self.api_key, "verbose": self.verbose, "ephemeral_system_prompt": self.ephemeral_system_prompt, "log_prefix_chars": self.log_prefix_chars, "providers_allowed": self.providers_allowed, "providers_ignored": self.providers_ignored, "providers_order": self.providers_order, "provider_sort": self.provider_sort, "max_tokens": self.max_tokens, "reasoning_config": self.reasoning_config, "prefill_messages": self.prefill_messages, } # For backward compatibility, still track by index (but this is secondary to content matching) completed_prompts_set = set(checkpoint_data.get("completed_prompts", [])) # Aggregate statistics across all batches total_tool_stats = {} start_time = time.time() print(f"\nπŸ”§ Initializing {self.num_workers} worker processes...") # Checkpoint writes happen in the parent process; keep a lock for safety. checkpoint_lock = Lock() # Process batches in parallel with Pool(processes=self.num_workers) as pool: # Create tasks for each batch tasks = [ ( batch_num, batch_data, str(self.output_dir), # Convert Path to string for pickling completed_prompts_set, config ) for batch_num, batch_data in enumerate(self.batches) ] print(f"βœ… Created {len(tasks)} batch tasks") print("πŸš€ Starting parallel batch processing...\n") # Use rich Progress for better visual tracking with persistent bottom bar # redirect_stdout/stderr lets rich manage all output so progress bar stays clean results = [] console = Console(force_terminal=True) with Progress( SpinnerColumn(), TextColumn("[bold blue]πŸ“¦ Batches"), BarColumn(bar_width=40), MofNCompleteColumn(), TextColumn("β€’"), TimeRemainingColumn(), console=console, refresh_per_second=2, transient=False, redirect_stdout=False, redirect_stderr=False, ) as progress: task = progress.add_task("Processing", total=len(tasks)) # Temporarily suppress DEBUG logging to avoid bar interference root_logger = logging.getLogger() original_level = root_logger.level root_logger.setLevel(logging.WARNING) try: for result in pool.imap_unordered(_process_batch_worker, tasks): results.append(result) progress.update(task, advance=1) # Incremental checkpoint update (so resume works after crash) try: batch_num = result.get('batch_num') completed = result.get('completed_prompts', []) or [] completed_prompts_set.update(completed) if isinstance(batch_num, int): checkpoint_data.setdefault('batch_stats', {})[str(batch_num)] = { 'processed': result.get('processed', 0), 'skipped': result.get('skipped', 0), 'discarded_no_reasoning': result.get('discarded_no_reasoning', 0), } checkpoint_data['completed_prompts'] = sorted(completed_prompts_set) self._save_checkpoint(checkpoint_data, lock=checkpoint_lock) except Exception as ckpt_err: # Don't fail the run if checkpoint write fails print(f"⚠️ Warning: Failed to save incremental checkpoint: {ckpt_err}") except Exception as e: logger.error("Batch worker failed: %s", e, exc_info=True) raise finally: root_logger.setLevel(original_level) # Aggregate all batch statistics and update checkpoint total_reasoning_stats = {"total_assistant_turns": 0, "turns_with_reasoning": 0, "turns_without_reasoning": 0} for batch_result in results: # Aggregate tool stats for tool_name, stats in batch_result.get("tool_stats", {}).items(): if tool_name not in total_tool_stats: total_tool_stats[tool_name] = { "count": 0, "success": 0, "failure": 0 } total_tool_stats[tool_name]["count"] += stats["count"] total_tool_stats[tool_name]["success"] += stats["success"] total_tool_stats[tool_name]["failure"] += stats["failure"] # Aggregate reasoning stats for key in total_reasoning_stats: total_reasoning_stats[key] += batch_result.get("reasoning_stats", {}).get(key, 0) # Save final checkpoint (best-effort; incremental writes already happened) try: checkpoint_data["completed_prompts"] = sorted(completed_prompts_set) self._save_checkpoint(checkpoint_data, lock=checkpoint_lock) except Exception as ckpt_err: print(f"Òő ï¸ Warning: Failed to save final checkpoint: {ckpt_err}") # Calculate success rates for tool_name in total_tool_stats: stats = total_tool_stats[tool_name] total_calls = stats["success"] + stats["failure"] if total_calls > 0: stats["success_rate"] = round(stats["success"] / total_calls * 100, 2) stats["failure_rate"] = round(stats["failure"] / total_calls * 100, 2) else: stats["success_rate"] = 0.0 stats["failure_rate"] = 0.0 # Combine ALL batch files in directory into a single trajectories.jsonl file # This includes both old batches (from previous runs) and new batches (from resume) # Also filter out corrupted entries (where model generated invalid tool names) combined_file = self.output_dir / "trajectories.jsonl" print(f"\nπŸ“¦ Combining ALL batch files into {combined_file.name}...") # Valid tools auto-derived from model_tools.py β€” no manual updates needed VALID_TOOLS = ALL_POSSIBLE_TOOLS total_entries = 0 filtered_entries = 0 batch_files_found = 0 # Find ALL batch files in the output directory (handles resume merging old + new) all_batch_files = sorted(self.output_dir.glob("batch_*.jsonl")) with open(combined_file, 'w', encoding='utf-8') as outfile: for batch_file in all_batch_files: batch_files_found += 1 batch_num = batch_file.stem.split("_")[1] # Extract batch number for logging with open(batch_file, 'r', encoding='utf-8') as infile: for line in infile: total_entries += 1 try: data = json.loads(line) tool_stats = data.get('tool_stats', {}) # Check for invalid tool names (model hallucinations) invalid_tools = [k for k in tool_stats if k not in VALID_TOOLS] if invalid_tools: filtered_entries += 1 invalid_preview = invalid_tools[0][:50] + "..." if len(invalid_tools[0]) > 50 else invalid_tools[0] print(f" ⚠️ Filtering corrupted entry (batch {batch_num}): invalid tool '{invalid_preview}'") continue outfile.write(line) except json.JSONDecodeError: filtered_entries += 1 print(f" ⚠️ Filtering invalid JSON entry (batch {batch_num})") if filtered_entries > 0: print(f"⚠️ Filtered {filtered_entries} corrupted entries out of {total_entries} total") print(f"βœ… Combined {batch_files_found} batch files into trajectories.jsonl ({total_entries - filtered_entries} entries)") # Save final statistics final_stats = { "run_name": self.run_name, "distribution": self.distribution, "total_prompts": len(self.dataset), "total_batches": len(self.batches), "batch_size": self.batch_size, "model": self.model, "completed_at": datetime.now().isoformat(), "duration_seconds": round(time.time() - start_time, 2), "tool_statistics": total_tool_stats, "reasoning_statistics": total_reasoning_stats, } with open(self.stats_file, 'w', encoding='utf-8') as f: json.dump(final_stats, f, indent=2, ensure_ascii=False) # Print summary print("\n" + "=" * 70) print("πŸ“Š BATCH PROCESSING COMPLETE") print("=" * 70) print(f"βœ… Prompts processed this run: {sum(r.get('processed', 0) for r in results)}") print(f"βœ… Total trajectories in merged file: {total_entries - filtered_entries}") print(f"βœ… Total batch files merged: {batch_files_found}") print(f"⏱️ Total duration: {round(time.time() - start_time, 2)}s") print("\nπŸ“ˆ Tool Usage Statistics:") print("-" * 70) if total_tool_stats: # Sort by count descending sorted_tools = sorted( total_tool_stats.items(), key=lambda x: x[1]["count"], reverse=True ) print(f"{'Tool Name':<25} {'Count':<10} {'Success':<10} {'Failure':<10} {'Success Rate':<12}") print("-" * 70) for tool_name, stats in sorted_tools: print( f"{tool_name:<25} " f"{stats['count']:<10} " f"{stats['success']:<10} " f"{stats['failure']:<10} " f"{stats['success_rate']:.1f}%" ) else: print("No tool calls were made during this run.") # Print reasoning coverage stats total_discarded = sum(r.get("discarded_no_reasoning", 0) for r in results) print("\n🧠 Reasoning Coverage:") print("-" * 70) total_turns = total_reasoning_stats["total_assistant_turns"] with_reasoning = total_reasoning_stats["turns_with_reasoning"] without_reasoning = total_reasoning_stats["turns_without_reasoning"] if total_turns > 0: pct_with = round(with_reasoning / total_turns * 100, 1) pct_without = round(without_reasoning / total_turns * 100, 1) print(f" Total assistant turns: {total_turns:,}") print(f" With reasoning: {with_reasoning:,} ({pct_with}%)") print(f" Without reasoning: {without_reasoning:,} ({pct_without}%)") else: print(" No assistant turns recorded.") if total_discarded > 0: print(f" 🚫 Samples discarded (zero reasoning): {total_discarded:,}") print(f"\nπŸ’Ύ Results saved to: {self.output_dir}") print(" - Trajectories: trajectories.jsonl (combined)") print(" - Individual batches: batch_*.jsonl (for debugging)") print(f" - Statistics: {self.stats_file.name}") print(f" - Checkpoint: {self.checkpoint_file.name}") def main( dataset_file: str = None, batch_size: int = None, run_name: str = None, distribution: str = "default", model: str = "anthropic/claude-sonnet-4.6", api_key: str = None, base_url: str = "https://openrouter.ai/api/v1", max_turns: int = 10, num_workers: int = 4, resume: bool = False, verbose: bool = False, list_distributions: bool = False, ephemeral_system_prompt: str = None, log_prefix_chars: int = 100, providers_allowed: str = None, providers_ignored: str = None, providers_order: str = None, provider_sort: str = None, max_tokens: int = None, reasoning_effort: str = None, reasoning_disabled: bool = False, prefill_messages_file: str = None, max_samples: int = None, ): """ Run batch processing of agent prompts from a dataset. Args: dataset_file (str): Path to JSONL file with 'prompt' field in each entry batch_size (int): Number of prompts per batch run_name (str): Name for this run (used for output and checkpointing) distribution (str): Toolset distribution to use (default: "default") model (str): Model name to use (default: "claude-opus-4-20250514") api_key (str): API key for model authentication base_url (str): Base URL for model API max_turns (int): Maximum number of tool calling iterations per prompt (default: 10) num_workers (int): Number of parallel worker processes (default: 4) resume (bool): Resume from checkpoint if run was interrupted (default: False) verbose (bool): Enable verbose logging (default: False) list_distributions (bool): List available toolset distributions and exit ephemeral_system_prompt (str): System prompt used during agent execution but NOT saved to trajectories (optional) log_prefix_chars (int): Number of characters to show in log previews for tool calls/responses (default: 20) providers_allowed (str): Comma-separated list of OpenRouter providers to allow (e.g. "anthropic,openai") providers_ignored (str): Comma-separated list of OpenRouter providers to ignore (e.g. "together,deepinfra") providers_order (str): Comma-separated list of OpenRouter providers to try in order (e.g. "anthropic,openai,google") provider_sort (str): Sort providers by "price", "throughput", or "latency" (OpenRouter only) max_tokens (int): Maximum tokens for model responses (optional, uses model default if not set) reasoning_effort (str): OpenRouter reasoning effort level: "none", "minimal", "low", "medium", "high", "xhigh" (default: "medium") reasoning_disabled (bool): Completely disable reasoning/thinking tokens (default: False) prefill_messages_file (str): Path to JSON file containing prefill messages (list of {role, content} dicts) max_samples (int): Only process the first N samples from the dataset (optional, processes all if not set) Examples: # Basic usage python batch_runner.py --dataset_file=data.jsonl --batch_size=10 --run_name=my_run # Resume interrupted run python batch_runner.py --dataset_file=data.jsonl --batch_size=10 --run_name=my_run --resume # Use specific distribution python batch_runner.py --dataset_file=data.jsonl --batch_size=10 --run_name=image_test --distribution=image_gen # With disabled reasoning and max tokens python batch_runner.py --dataset_file=data.jsonl --batch_size=10 --run_name=my_run \\ --reasoning_disabled --max_tokens=128000 # With prefill messages from file python batch_runner.py --dataset_file=data.jsonl --batch_size=10 --run_name=my_run \\ --prefill_messages_file=configs/prefill_opus.json # List available distributions python batch_runner.py --list_distributions """ # Handle list distributions if list_distributions: from toolset_distributions import print_distribution_info print("πŸ“Š Available Toolset Distributions") print("=" * 70) all_dists = list_distributions() for dist_name in sorted(all_dists.keys()): print_distribution_info(dist_name) print("\nπŸ’‘ Usage:") print(" python batch_runner.py --dataset_file=data.jsonl --batch_size=10 \\") print(" --run_name=my_run --distribution=") return # Validate required arguments if not dataset_file: print("❌ Error: --dataset_file is required") return if not batch_size or batch_size < 1: print("❌ Error: --batch_size must be a positive integer") return if not run_name: print("❌ Error: --run_name is required") return # Parse provider preferences (comma-separated strings to lists) providers_allowed_list = [p.strip() for p in providers_allowed.split(",")] if providers_allowed else None providers_ignored_list = [p.strip() for p in providers_ignored.split(",")] if providers_ignored else None providers_order_list = [p.strip() for p in providers_order.split(",")] if providers_order else None # Build reasoning_config from CLI flags # --reasoning_disabled takes priority, then --reasoning_effort, then default (medium) reasoning_config = None if reasoning_disabled: # Completely disable reasoning/thinking tokens reasoning_config = {"effort": "none"} print("🧠 Reasoning: DISABLED (effort=none)") elif reasoning_effort: # Use specified effort level valid_efforts = ["none", "minimal", "low", "medium", "high", "xhigh"] if reasoning_effort not in valid_efforts: print(f"❌ Error: --reasoning_effort must be one of: {', '.join(valid_efforts)}") return reasoning_config = {"enabled": True, "effort": reasoning_effort} print(f"🧠 Reasoning effort: {reasoning_effort}") # Load prefill messages from JSON file if provided prefill_messages = None if prefill_messages_file: try: with open(prefill_messages_file, 'r', encoding='utf-8') as f: prefill_messages = json.load(f) if not isinstance(prefill_messages, list): print("❌ Error: prefill_messages_file must contain a JSON array of messages") return print(f"πŸ’¬ Loaded {len(prefill_messages)} prefill messages from {prefill_messages_file}") except Exception as e: print(f"❌ Error loading prefill messages: {e}") return # Initialize and run batch runner try: runner = BatchRunner( dataset_file=dataset_file, batch_size=batch_size, run_name=run_name, distribution=distribution, max_iterations=max_turns, base_url=base_url, api_key=api_key, model=model, num_workers=num_workers, verbose=verbose, ephemeral_system_prompt=ephemeral_system_prompt, log_prefix_chars=log_prefix_chars, providers_allowed=providers_allowed_list, providers_ignored=providers_ignored_list, providers_order=providers_order_list, provider_sort=provider_sort, max_tokens=max_tokens, reasoning_config=reasoning_config, prefill_messages=prefill_messages, max_samples=max_samples, ) runner.run(resume=resume) except Exception as e: print(f"\n❌ Fatal error: {e}") if verbose: traceback.print_exc() return 1 if __name__ == "__main__": fire.Fire(main)