diff --git a/benchmark/config.yml b/benchmark/config.yml index 86e626e..c47ac56 100644 --- a/benchmark/config.yml +++ b/benchmark/config.yml @@ -1,8 +1,8 @@ environment: - n_of_agents: 2 - n_of_backend_slots: 3 - n_of_tool_calls_per_agent: 2 - tool_execution_duration_time: 2 -run_description: 'Debugging new event model. Removed plots errors' -run_name: debug_new_event_model_2 -workload_id: langgraph_asyncflow + n_of_agents: 6 + n_of_backend_slots: 4 + n_of_tool_calls_per_agent: 10 + tool_execution_duration_time: 3 +run_description: 'Debugging AsyncFlow vs Parsl in the new flowgentic benchmarking repo!' +run_name: testing_backend_comparison +workload_id: backend_comparison diff --git a/benchmark/data_generation/experiments/backend_comparison/main.py b/benchmark/data_generation/experiments/backend_comparison/main.py new file mode 100644 index 0000000..89fdb2c --- /dev/null +++ b/benchmark/data_generation/experiments/backend_comparison/main.py @@ -0,0 +1,128 @@ +from enum import Enum +from typing import Any, Callable, Dict, Literal + +from data_generation.experiments.base.base_experiment import ( + BaseExperiment, +) +from data_generation.experiments.base.base_plots import BasePlotter +from data_generation.experiments.backend_comparison.utils.plots import ( + BackendComparisonPlotter, +) +from data_generation.utils.schemas import ( + BenchmarkConfig, + BenchmarkedRecord, + EngineIDs, + WorkloadConfig, + WorkloadResult, +) +from data_generation.workload.langgraph import LangraphWorkload +from data_generation.workload.autogen import AutogenWorkload +from data_generation.workload.academy import AcademyWorkload + +import logging +import os +import requests +from pathlib import Path + +from dotenv import load_dotenv + +load_dotenv() + +logger = logging.getLogger(__name__) + + +WORKLOADS = { + "langraph": (LangraphWorkload, EngineIDs.ASYNCFLOW.value), + "parsl": (LangraphWorkload, EngineIDs.PARSL.value), + "autogen": (AutogenWorkload, EngineIDs.ASYNCFLOW.value), + "academy": (AcademyWorkload, EngineIDs.ASYNCFLOW.value), +} + + +def send_discord_notifaction(msg: str): + webhook_url = os.getenv("DISCORD_WEBHOOK") + data = {"content": msg} + requests.post(webhook_url, json=data) + + +class BackendComparison(BaseExperiment): + def __init__( + self, benchmark_config: BenchmarkConfig, data_dir: str, plots_dir: str + ) -> None: + super().__init__(data_dir, Path(plots_dir)) + self.benchmark_config = benchmark_config + self.plotter = BackendComparisonPlotter(plots_dir=Path(plots_dir)) + self.results: Dict[str, Any] = {} + + async def _run_for_engine( + self, + config: BenchmarkConfig, + engine_id: str, + ) -> Dict: + workload_cls, hpc_backend_id = WORKLOADS[engine_id] + + logger.info(f"=== BACKEND COMPARISON: {engine_id} ===") + logger.info(f"Config is: {config.model_dump_json(indent=4)}") + + workload_config = WorkloadConfig( + n_of_agents=config.n_of_agents, + n_of_tool_calls_per_agent=config.n_of_tool_calls_per_agent, + n_of_backend_slots=config.n_of_backend_slots, + tool_execution_duration_time=config.tool_execution_duration_time, + engine_id=hpc_backend_id, + ) + + workload_result: WorkloadResult = await self.run_workload( + workload_orchestrator=workload_cls, + workload_config=workload_config, + ) + logger.debug(f"Workload result is: {workload_result}") + + record = BenchmarkedRecord( + run_name=config.run_name, + run_description=config.run_description, + workload_id=config.workload_id, + n_of_agents=config.n_of_agents, + n_of_tool_calls_per_agent=config.n_of_tool_calls_per_agent, + n_of_backend_slots=config.n_of_backend_slots, + workload_type=config.workload_type, + tool_execution_duration_time=config.tool_execution_duration_time, + total_makespan=workload_result.total_makespan, + events=workload_result.events, + ).model_dump(mode="json") + + msg = ( + f"🚀 **Iteration Complete: {config.run_name}**\n" + f"**Engine:** `{engine_id}`\n" + f"**Agents:** {config.n_of_agents} | **Calls/Agent:** {config.n_of_tool_calls_per_agent}\n" + f"⏱️ **Makespan:** `{workload_result.total_makespan:.2f}s`" + ) + send_discord_notifaction(msg) + + self.results[engine_id] = record + self.store_data_to_disk(self.results) + return record + + async def run_experiment(self) -> None: + """Run the workload for each engine sequentially.""" + for engine_id in WORKLOADS: + self.results[engine_id] = await self._run_for_engine( + self.benchmark_config, engine_id + ) + + def generate_plots(self, data: Dict[Any, Any]): + plots_dir = self.plotter.plots_dir + + # Backend comparison: LangGraph on AsyncFlow vs Parsl + self.plotter.set_plots_dir(plots_dir / "backend_comparison") + self.plotter.plot_results( + data={k: data[k] for k in ("langraph", "parsl") if k in data}, + engine_labels={"langraph": "AsyncFlow", "parsl": "Parsl"}, + ) + + # Orchestrator comparison: LangGraph vs AutoGen vs Academy, all on AsyncFlow + self.plotter.set_plots_dir(plots_dir / "orchestrator_comparison") + self.plotter.plot_results( + data={k: data[k] for k in ("langraph", "autogen", "academy") if k in data}, + engine_labels={"langraph": "LangGraph", "autogen": "AutoGen", "academy": "Academy"}, + ) diff --git a/benchmark/data_generation/experiments/backend_comparison/utils/plots.py b/benchmark/data_generation/experiments/backend_comparison/utils/plots.py new file mode 100644 index 0000000..b957642 --- /dev/null +++ b/benchmark/data_generation/experiments/backend_comparison/utils/plots.py @@ -0,0 +1,473 @@ +import logging +from pathlib import Path +from typing import Any, Dict, List, Optional + +import matplotlib.pyplot as plt +import numpy as np +from matplotlib.patches import Patch + +from data_generation.experiments.base.base_plots import BasePlotter +from data_generation.utils.io_utils import DiscordNotifier + +# Silence matplotlib's verbose font manager DEBUG logs +logging.getLogger("matplotlib.font_manager").setLevel(logging.WARNING) + +logger = logging.getLogger(__name__) + + +ENGINE_COLORS = { + "asyncflow": "#2196F3", + "parsl": "#FF9800", + "langraph": "#2196F3", + "autogen": "#4CAF50", + "academy": "#9C27B0", +} + +ENGINE_LABELS = { + "asyncflow": "AsyncFlow", + "parsl": "Parsl", + "langraph": "LangGraph and AsyncFlow", + "autogen": "AutoGen and AsyncFlow", + "academy": "Academy and AsyncFlow", +} + +METRIC_COLORS = { + "d_resolve": ENGINE_COLORS["asyncflow"], + "d_collect": ENGINE_COLORS["parsl"], +} + +METRIC_LABELS = { + "d_resolve": "D_resolve", + "d_collect": "D_collect", +} + + +def _parse_events(events: List[Dict]) -> tuple: + """ + Raw events into wrap timestamps and invocation timestamps. + + Returns: + - wrap_starts: {(tool_name, wrap_id): ts} + - wrap_ends: {(tool_name, wrap_id): ts} + - invocations: {invocation_id: {event_type: ts, ...}} + """ + wrap_starts: Dict = {} + wrap_ends: Dict = {} + invocations: Dict = {} + + for event in events: + event_type = event["event"] + if event_type == "tool_wrap_start": + wrap_starts[(event["tool_name"], event["wrap_id"])] = event["ts"] + elif event_type == "tool_wrap_end": + wrap_ends[(event["tool_name"], event["wrap_id"])] = event["ts"] + elif event_type in ("tool_invoke_start", "tool_resolve_end", + "tool_collect_start", "tool_invoke_end"): + invocation_id = event["invocation_id"] + if invocation_id not in invocations: + invocations[invocation_id] = {} + invocations[invocation_id][event_type] = event["ts"] + if "tool_name" in event: + invocations[invocation_id]["tool_name"] = event["tool_name"] + + return wrap_starts, wrap_ends, invocations + + +def _extract_event_durations(events: List[Dict]) -> Dict: + """ + Compute per-invocation durations from raw events. + + Returns: + - d_wrap_by_tool: {tool_name: [D_wrap durations]} + - d_wrap: all D_wrap durations combined + - d_resolve: list of D_resolve durations (tool_resolve_end - tool_invoke_start) + - d_backend: list of D_backend durations (tool_collect_start - tool_resolve_end) + - d_collect: list of D_collect durations (tool_invoke_end - tool_collect_start) + - d_total: list of D_total durations (tool_invoke_end - tool_invoke_start) + - inv_by_tool: {tool_name: count of invocations} + """ + wrap_starts, wrap_ends, invocations = _parse_events(events) + + # D_wrap: duration to wrap/register each tool, grouped by tool name + d_wrap_by_tool: Dict[str, List[float]] = {} + for (tool_name, wrap_id), ts_start in wrap_starts.items(): + if (tool_name, wrap_id) in wrap_ends: + duration = wrap_ends[(tool_name, wrap_id)] - ts_start + if tool_name not in d_wrap_by_tool: + d_wrap_by_tool[tool_name] = [] + d_wrap_by_tool[tool_name].append(duration) + + # Per-invocation stage durations + d_resolve, d_backend, d_collect, d_total = [], [], [], [] + d_resolve_ss, d_overhead_ss = [], [] # steady-state: first invocation per tool dropped + inv_by_tool: Dict[str, int] = {} + required_keys = ("tool_invoke_start", "tool_resolve_end", + "tool_collect_start", "tool_invoke_end") + + # Sort by invoke start time to correctly identify first invocation per tool + sorted_invocations = sorted( + (inv for inv in invocations.values() if all(k in inv for k in required_keys)), + key=lambda inv: inv["tool_invoke_start"], + ) + + seen_tools: set = set() + for invocation in sorted_invocations: + t_invoke_start = invocation["tool_invoke_start"] + t_resolve_end = invocation["tool_resolve_end"] + t_collect_start = invocation["tool_collect_start"] + t_invoke_end = invocation["tool_invoke_end"] + + dr = t_resolve_end - t_invoke_start + db = t_collect_start - t_resolve_end + dc = t_invoke_end - t_collect_start + dt = t_invoke_end - t_invoke_start + + d_resolve.append(dr) + d_backend.append(db) + d_collect.append(dc) + d_total.append(dt) + + tool_name = invocation.get("tool_name") + if tool_name is not None: + inv_by_tool[tool_name] = inv_by_tool.get(tool_name, 0) + 1 + if tool_name not in seen_tools: + seen_tools.add(tool_name) + else: + d_resolve_ss.append(dr) + d_overhead_ss.append(dr + dc) + else: + d_resolve_ss.append(dr) + d_overhead_ss.append(dr + dc) + + return { + "d_wrap_by_tool": d_wrap_by_tool, + "d_wrap": [v for vals in d_wrap_by_tool.values() for v in vals], + "d_resolve": d_resolve, + "d_resolve_ss": d_resolve_ss, + "d_backend": d_backend, + "d_collect": d_collect, + "d_total": d_total, + "d_overhead_ss": d_overhead_ss, + "inv_by_tool": inv_by_tool, + } + + +def _compute_invocation_metrics(data: Dict[str, Dict]) -> Dict[str, Dict]: + """ + Run _extract_event_durations for each engine and compute derived metrics. + + Returns one entry per engine_id: + - All raw durations from _extract_event_durations + - makespan: total experiment wall time + - n_inv: number of complete invocations + - d_overhead: D_resolve + D_collect per invocation (flowgentic overhead) + - d_wrap_amortized_ms: total D_wrap divided by n_inv, in ms + """ + metrics = {} + for engine_id, record in data.items(): + d = _extract_event_durations(record["events"]) + + n_inv = len(d["d_resolve"]) or 1 + d_overhead = [r + c for r, c in zip(d["d_resolve"], d["d_collect"])] + d_wrap_amortized_ms = 0.0 + for tool, inv_count in d["inv_by_tool"].items(): + if inv_count == 0: + continue + tool_wrap_time = sum(d["d_wrap_by_tool"].get(tool, [])) + d_wrap_amortized_ms += tool_wrap_time / inv_count * 1000 + + metrics[engine_id] = { + **d, + "makespan": record["total_makespan"], + "n_inv": n_inv, + "d_overhead": d_overhead, + "d_wrap_amortized_ms": d_wrap_amortized_ms, + } + return metrics + + +class BackendComparisonPlotter(BasePlotter): + """Generates comparison plots for backend-adaptive execution experiment.""" + + def __init__(self, plots_dir: Optional[Path] = None) -> None: + super().__init__() + self.plots_dir = plots_dir + self.discord_notifier = DiscordNotifier() + + def set_plots_dir(self, plots_dir: Path) -> None: + """Set the plots directory after initialization.""" + self.plots_dir = plots_dir + + def _plot_resolve_collect_distributions(self, metrics: Dict, engines: List[str], subdir: str, labels: Dict[str, str] = ENGINE_LABELS) -> None: + """4 box plots: D_resolve and D_collect per engine side by side.""" + fig, ax = plt.subplots(figsize=(10, 6)) + + box_data = [] + tick_labels = [] + colors = [] + positions = [] + pos = 1 + group_centers = [] + + for e in engines: + group_start = pos + for data_key, label_key in (("d_resolve_ss", "d_resolve"), ("d_collect", "d_collect")): + box_data.append([v * 1000 for v in metrics[e][data_key]]) + tick_labels.append(METRIC_LABELS[label_key]) + colors.append(METRIC_COLORS[label_key]) + positions.append(pos) + pos += 1 + group_centers.append((group_start + pos - 1) / 2.0) + pos += 1 + + bp = ax.boxplot( + box_data, + positions=positions, + patch_artist=True, + medianprops={"color": "black", "linewidth": 2}, + widths=0.6, + ) + for patch, color in zip(bp["boxes"], colors): + patch.set_facecolor(color) + patch.set_alpha(0.7) + + ax.set_xticks(group_centers) + ax.set_xticklabels([labels.get(e, e) for e in engines], fontsize=12, fontweight="bold") + + legend_handles = [Patch(facecolor=METRIC_COLORS[k], alpha=0.7, label=METRIC_LABELS[k]) + for k in ("d_resolve", "d_collect")] + ax.legend(handles=legend_handles, loc="upper right") + + ax.set_ylabel("Duration (ms)", fontsize=11) + ax.set_title(f"D_resolve & D_collect Distributions per Engine\n{self._subtitle}", fontsize=13) + ax.grid(True, alpha=0.3, axis="y") + + plt.tight_layout() + self._save_plot(fig, "resolve_collect_distributions.png", subdir) + plt.close(fig) + + def _plot_wrap_distribution(self, metrics: Dict, engines: List[str], subdir: str, labels: Dict[str, str] = ENGINE_LABELS) -> None: + """Box plot: D_wrap per engine (one box per engine, all tools combined).""" + fig, ax = plt.subplots(figsize=(7, 6)) + + box_data = [[v * 1000 for v in metrics[e]["d_wrap"]] for e in engines] + tick_labels = [labels.get(e, e) for e in engines] + colors = [ENGINE_COLORS.get(e, "#999") for e in engines] + + bp = ax.boxplot( + box_data, + labels=tick_labels, + patch_artist=True, + medianprops={"color": "black", "linewidth": 2}, + widths=0.5, + ) + for patch, color in zip(bp["boxes"], colors): + patch.set_facecolor(color) + patch.set_alpha(0.7) + + ax.set_ylabel("Duration (ms)", fontsize=11) + ax.set_title(f"D_wrap Distribution per Engine\n{self._subtitle}", fontsize=13) + ax.grid(True, alpha=0.3, axis="y") + + plt.tight_layout() + self._save_plot(fig, "wrap_distribution.png", subdir) + plt.close(fig) + + def _plot_overhead_distribution(self, metrics: Dict, engines: List[str], subdir: str, labels: Dict[str, str] = ENGINE_LABELS) -> None: + """Box plot: D_overhead per engine (2 boxes).""" + fig, ax = plt.subplots(figsize=(7, 6)) + + box_data = [[v * 1000 for v in metrics[e]["d_overhead_ss"]] for e in engines] + labels = [labels.get(e, e) for e in engines] + colors = [ENGINE_COLORS.get(e, "#999") for e in engines] + + bp = ax.boxplot( + box_data, + labels=labels, + patch_artist=True, + medianprops={"color": "black", "linewidth": 2}, + widths=0.5, + ) + for patch, color in zip(bp["boxes"], colors): + patch.set_facecolor(color) + patch.set_alpha(0.7) + + ax.set_ylabel("Duration (ms)", fontsize=11) + ax.set_title(f"D_overhead Distribution per Engine\n(D_resolve + D_collect)\n{self._subtitle}", fontsize=13) + ax.grid(True, alpha=0.3, axis="y") + + plt.tight_layout() + self._save_plot(fig, "overhead_distribution.png", subdir) + plt.close(fig) + + + def _plot_makespan(self, metrics: Dict, engines: List[str], subdir: str, labels: Dict[str, str] = ENGINE_LABELS) -> None: + """Total execution time (makespan) per engine.""" + fig, ax = plt.subplots(figsize=(8, 6)) + + makespans = [metrics[e]["makespan"] for e in engines] + colors = [ENGINE_COLORS.get(e, "#999") for e in engines] + labels = [labels.get(e, e) for e in engines] + + bars = ax.bar(labels, makespans, color=colors, width=0.5) + for bar in bars: + ax.text( + bar.get_x() + bar.get_width() / 2, bar.get_height(), + f"{bar.get_height():.3f}s", + ha="center", va="bottom", fontsize=10, + ) + + ax.set_xlabel("Backend Engine", fontsize=12) + ax.set_ylabel("Makespan (seconds)", fontsize=12) + ax.set_title(f"Makespan Comparison\n{self._subtitle}", fontsize=14) + ax.grid(True, alpha=0.3, axis="y") + ax.set_ylim(bottom=0) + + plt.tight_layout() + self._save_plot(fig, "makespan.png", subdir) + plt.close(fig) + + def _plot_results_table(self, metrics: Dict, engines: List[str], labels: Dict[str, str] = ENGINE_LABELS) -> None: + """Summary table with totals and per-invocation means, grouped by section.""" + fig, ax = plt.subplots(figsize=(12, 9)) + ax.axis("off") + + def _mean_ms(values: list) -> float: + return float(np.mean(values)) * 1000 if values else 0.0 + + def _overhead_fraction(m: Dict) -> float: + d_total = np.asarray(m["d_total"], dtype=float) + mask = d_total > 0 + if not mask.any(): + return 0.0 + overhead = np.asarray(m["d_resolve"])[mask] + np.asarray(m["d_collect"])[mask] + return float(np.mean(overhead / d_total[mask])) + + row_defs = [ + ("Overview", None), + ("Makespan (s)", [f"{metrics[e]['makespan']:.3f}" for e in engines]), + ("N invocations", [str(metrics[e]["n_inv"]) for e in engines]), + ("Totals (sum across all invocations)", None), + ("D_total total (ms)", [f"{sum(metrics[e]['d_total']) * 1000:.3f}" for e in engines]), + ("D_backend total (ms)", [f"{sum(metrics[e]['d_backend']) * 1000:.3f}" for e in engines]), + ("D_overhead total (ms)", [f"{sum(metrics[e]['d_overhead_ss']) * 1000:.3f}" for e in engines]), + ("D_resolve total (ms)", [f"{sum(metrics[e]['d_resolve_ss']) * 1000:.3f}" for e in engines]), + ("D_collect total (ms)", [f"{sum(metrics[e]['d_collect']) * 1000:.3f}" for e in engines]), + ("D_wrap total (ms)", [f"{sum(metrics[e]['d_wrap']) * 1000:.3f}" for e in engines]), + ("Per Invocation (mean)", None), + ("D_total mean (ms)", [f"{_mean_ms(metrics[e]['d_total']):.3f}" for e in engines]), + ("D_backend mean (ms)", [f"{_mean_ms(metrics[e]['d_backend']):.3f}" for e in engines]), + ("D_overhead mean (ms)", [f"{_mean_ms(metrics[e]['d_overhead_ss']):.3f}" for e in engines]), + ("D_resolve mean (ms)", [f"{_mean_ms(metrics[e]['d_resolve_ss']):.3f}" for e in engines]), + ("D_collect mean (ms)", [f"{_mean_ms(metrics[e]['d_collect']):.3f}" for e in engines]), + ("D_wrap amortized (ms)", [f"{metrics[e]['d_wrap_amortized_ms']:.3f}" for e in engines]), + ("Overhead fraction", [f"{_overhead_fraction(metrics[e]):.4f}" for e in engines]), + ] + + col_labels = [labels.get(e, e) for e in engines] + rows = [] + for label, values in row_defs: + row = [label] + (values if values is not None else [""] * len(engines)) + rows.append(row) + + table = ax.table( + cellText=rows, + colLabels=["Metric"] + col_labels, + loc="center", + cellLoc="center", + ) + table.auto_set_font_size(False) + table.set_fontsize(10) + table.scale(1.5, 1.9) + + # Header row + for j in range(len(engines) + 1): + cell = table[0, j] + cell.set_facecolor("#2C3E50") + cell.set_text_props(color="white", fontweight="bold") + + # Data rows + data_row_idx = 0 + for i, (_, values) in enumerate(row_defs): + row_i = i + 1 + if values is None: + for j in range(len(engines) + 1): + cell = table[row_i, j] + cell.set_facecolor("#4A6FA5") + cell.set_text_props(color="white", fontweight="bold") + cell.set_edgecolor("#2C3E50") + else: + color = "#F2F2F2" if data_row_idx % 2 == 0 else "#FFFFFF" + for j in range(len(engines) + 1): + table[row_i, j].set_facecolor(color) + table[row_i, j].set_edgecolor("#CCCCCC") + data_row_idx += 1 + + ax.set_title(f"Results Summary\n{self._subtitle}", fontsize=14, pad=40) + plt.tight_layout() + self._save_plot(fig, "results_table.png", None) + plt.close(fig) + + + def plot_results(self, data: Dict[Any, Any], engine_labels: Optional[Dict[str, str]] = None) -> None: + """ + Generate all plots from experiment data. + + Data structure expected: + { + 'asyncflow': BenchmarkedRecord dict, # AsyncFlow engine results + 'parsl': BenchmarkedRecord dict, # Parsl engine results + } + """ + engines = list(data.keys()) + if not engines: + logger.warning("No data to plot.") + return + + labels = {**ENGINE_LABELS, **(engine_labels or {})} + + sample = data[engines[0]] + self._subtitle = ( + f"({sample['n_of_agents']} agents, " + f"{sample['n_of_tool_calls_per_agent']} tool calls/agent, " + f"{sample['n_of_backend_slots']} slots)" + ) + + metrics = _compute_invocation_metrics(data) + + self._plot_makespan(metrics, engines, "makespan", labels) + self._plot_resolve_collect_distributions(metrics, engines, "overhead", labels) + self._plot_overhead_distribution(metrics, engines, "overhead", labels) + self._plot_wrap_distribution(metrics, engines, "overhead", labels) + self._plot_results_table(metrics, engines, labels) + + + def _save_plot(self, fig: plt.Figure, filename: str, subdirectory: Optional[str] = None) -> None: + """Save a plot to the configured directory.""" + if self.plots_dir: + if subdirectory: + subdir_path = self.plots_dir / subdirectory + subdir_path.mkdir(parents=True, exist_ok=True) + plot_path = subdir_path / filename + else: + plot_path = self.plots_dir / filename + plot_path.parent.mkdir(parents=True, exist_ok=True) + + fig.savefig(plot_path, dpi=150, bbox_inches="tight") + logger.info(f"Saved plot: {plot_path}") + + # Send plot to Discord + plot_description = ( + f"📊 **{subdirectory}/{filename}**" + if subdirectory + else f"📊 **{filename}**" + ) + try: + self.discord_notifier.send_discord_notification( + msg=plot_description, image_path=str(plot_path) + ) + logger.info(f"Sent plot to Discord: {plot_path}") + except Exception as e: + logger.warning(f"Failed to send plot to Discord: {e}") + else: + logger.warning(f"No plots_dir set, cannot save {filename}") diff --git a/benchmark/data_generation/experiments/backend_comparison/utils/schemas.py b/benchmark/data_generation/experiments/backend_comparison/utils/schemas.py new file mode 100644 index 0000000..f80e7b5 --- /dev/null +++ b/benchmark/data_generation/experiments/backend_comparison/utils/schemas.py @@ -0,0 +1 @@ +from pydantic import BaseModel \ No newline at end of file diff --git a/benchmark/data_generation/run_experiments.py b/benchmark/data_generation/run_experiments.py index 6e3d3ee..b434e3d 100644 --- a/benchmark/data_generation/run_experiments.py +++ b/benchmark/data_generation/run_experiments.py @@ -13,6 +13,9 @@ from data_generation.experiments.synthethic_adaptive.main import ( SynthethicAdaptive, ) +from data_generation.experiments.backend_comparison.main import ( + BackendComparison, +) from data_generation.utils.io_utils import IOUtils from data_generation.utils.schemas import ( BenchmarkConfig, @@ -83,8 +86,9 @@ async def main(): benchmark = FlowGenticBenchmarkManager() # Experiment 2 - benchmark.register_experiment("syntethic_adaptive", SynthethicAdaptive) - + # benchmark.register_experiment("syntethic_adaptive", SynthethicAdaptive) + # Experiment 3 + benchmark.register_experiment("backend_comparison", BackendComparison) # Execution of experiments await benchmark.run_registerd_experiments() diff --git a/benchmark/data_generation/utils/io_utils.py b/benchmark/data_generation/utils/io_utils.py index c4bb30e..b9598f1 100644 --- a/benchmark/data_generation/utils/io_utils.py +++ b/benchmark/data_generation/utils/io_utils.py @@ -90,6 +90,7 @@ def __init__(self): self.webhook_url = os.getenv("DISCORD_WEBHOOK") def send_discord_notification(self, msg: str, image_path: str = None): + return None if not self.webhook_url: return None diff --git a/benchmark/data_generation/workload/academy.py b/benchmark/data_generation/workload/academy.py new file mode 100644 index 0000000..df94183 --- /dev/null +++ b/benchmark/data_generation/workload/academy.py @@ -0,0 +1,67 @@ +import asyncio +import logging +import time +from typing import Callable + +from academy.agent import Agent, action +from academy.exchange.local import LocalExchangeFactory +from academy.manager import Manager + +from flowgentic.agent_orchestration_frameworks.academy import AcademyOrchestrator +from flowgentic.backend_engines.base import BaseEngine + +from data_generation.utils.schemas import WorkloadConfig +from data_generation.workload.base_workload import BaseWorkload + + +logger = logging.getLogger(__name__) + + +class HpcAgent(Agent): + """Academy agent that invokes an HPC-backed tool N times.""" + + def __init__(self, fetch_fn: Callable, n_calls: int) -> None: + self._fetch_fn = fetch_fn + self._n_calls = n_calls + + @action + async def run_tasks(self) -> None: + for _ in range(self._n_calls): + await self._fetch_fn(location="SFO") + + +class AcademyWorkload(BaseWorkload): + def __init__(self, workload_config: WorkloadConfig) -> None: + super().__init__(workload_config=workload_config) + + async def run(self, engine: BaseEngine) -> float: + t_execution_start = time.perf_counter() + + orchestrator = AcademyOrchestrator(engine) + + @orchestrator.hpc_task + async def fetch_temperature(location: str = "SFO") -> dict: + logger.debug("Executing fetch_temperature tool") + await asyncio.sleep(self.tool_execution_duration_time) + return {"temperature": 70, "location": location} + + manager = await Manager.from_exchange_factory( + factory=LocalExchangeFactory(), + executors=None, + ) + + async with manager: + handles = await asyncio.gather( + *[ + manager.launch( + HpcAgent, + args=(fetch_temperature, self.n_of_tool_calls_per_agent), + ) + for _ in range(self.n_of_agents) + ] + ) + await asyncio.gather(*[handle.run_tasks() for handle in handles]) + + t_execution_end = time.perf_counter() + + return t_execution_end - t_execution_start diff --git a/benchmark/data_generation/workload/autogen.py b/benchmark/data_generation/workload/autogen.py new file mode 100644 index 0000000..9571b6f --- /dev/null +++ b/benchmark/data_generation/workload/autogen.py @@ -0,0 +1,77 @@ +import asyncio +import logging +import time + +import autogen +from autogen import UserProxyAgent + +from flowgentic.agent_orchestration_frameworks.autogen import AutoGenOrchestrator +from flowgentic.backend_engines.base import BaseEngine +from flowgentic.core.models.implementations.dummy.autogen import ( + DummyAutoGenClient, + create_assistant_with_dummy_model, +) + +from data_generation.utils.schemas import WorkloadConfig +from data_generation.workload.base_workload import BaseWorkload + +logger = logging.getLogger(__name__) + + +class AutogenWorkload(BaseWorkload): + """ + AutoGen workload with configurable number of agents and tool calls per agent. + Each agent is an independent AssistantAgent + UserProxyAgent conversation, + all sharing the same HPC-wrapped tools and running concurrently. + """ + + def __init__(self, workload_config: WorkloadConfig) -> None: + super().__init__(workload_config=workload_config) + + async def run(self, engine: BaseEngine) -> float: + t_execution_start = time.perf_counter() + + orchestrator = AutoGenOrchestrator(engine) + + @orchestrator.hpc_task + async def fetch_temperature(location: str = "SFO") -> dict: + """Fetches temperature of a given city.""" + logger.debug("Executing temperature tool") + await asyncio.sleep(self.tool_execution_duration_time) + return {"temperature": 70, "location": location} + + tools = [fetch_temperature] + + async def run_agent(): + assistant = create_assistant_with_dummy_model( + name="hpc_assistant", + system_message="You are a helpful assistant. Use available tools to fetch weather data.", + calls_per_tool=self.n_of_tool_calls_per_agent, + ) + user_proxy = UserProxyAgent( + name="hpc_executor", + human_input_mode="NEVER", + max_consecutive_auto_reply=10, + is_termination_msg=lambda x: "TERMINATE" in x.get("content", ""), + code_execution_config=False, + ) + for tool in tools: + autogen.register_function( + tool, + caller=assistant, + executor=user_proxy, + description=tool.__doc__ or tool.__name__, + ) + if hasattr(assistant, "client") and assistant.client: + assistant.client.register_model_client(model_client_cls=DummyAutoGenClient) + + await user_proxy.a_initiate_chat( + assistant, + message="Fetch temperature in SFO. When done, reply TERMINATE.", + ) + + await asyncio.gather(*[run_agent() for _ in range(self.n_of_agents)]) + + t_execution_end = time.perf_counter() + + return t_execution_end - t_execution_start diff --git a/benchmark/data_generation/workload/utils/engine.py b/benchmark/data_generation/workload/utils/engine.py index 1baf217..71f6bce 100644 --- a/benchmark/data_generation/workload/utils/engine.py +++ b/benchmark/data_generation/workload/utils/engine.py @@ -1,9 +1,15 @@ +import tempfile from concurrent.futures import ProcessPoolExecutor from contextlib import asynccontextmanager from typing import Any, Callable, Dict, Optional from radical.asyncflow import LocalExecutionBackend, WorkflowEngine +import parsl +from flowgentic.backend_engines.parsl import ParslEngine +from parsl.config import Config +from parsl.executors import HighThroughputExecutor + from flowgentic.backend_engines.radical_asyncflow import AsyncFlowEngine import multiprocessing @@ -28,5 +34,13 @@ async def resolve_engine( # 3. Shutdown the flow, then manually shut down the executor await flow.shutdown() executor.shutdown(wait=True) + elif engine_id == "parsl": + parsl_config = Config( + executors=[HighThroughputExecutor(max_workers_per_node=n_of_backend_slots, label="local", encrypted=False)] + ) + try: + yield ParslEngine(config=parsl_config, observer=observer) + finally: + parsl.dfk().cleanup() else: raise Exception(f"Didnt match any engine for engine_id: {engine_id}") diff --git a/benchmark/pyproject.toml b/benchmark/pyproject.toml index c7e8745..143718a 100644 --- a/benchmark/pyproject.toml +++ b/benchmark/pyproject.toml @@ -5,7 +5,7 @@ description = "Add your description here" readme = "README.md" requires-python = ">=3.10" dependencies = [ - "flowgentic[langgraph,asyncflow,academy] @ git+https://github.com/stride-research/flowgentic.git@hotfix/benchmarking_redesign", + "flowgentic[langgraph,asyncflow,autogen,parsl,academy] @ git+https://github.com/stride-research/flowgentic.git@experiment/exp3-benchmarking-hotfix", "radical-asyncflow", "academy-py", "pyyaml", @@ -24,6 +24,9 @@ dev = [ requires = ["setuptools>=61.0", "wheel"] build-backend = "setuptools.build_meta" +[tool.setuptools.packages.find] +include = ["data_generation*"] + [tool.uv.sources] academy-py = { git = "https://github.com/proxystore/academy" } radical-asyncflow = { git = "https://github.com/radical-cybertools/radical.asyncflow.git", branch = "main" } diff --git a/benchmark/results/testing_backend_comparison/config/config.yml b/benchmark/results/testing_backend_comparison/config/config.yml new file mode 100644 index 0000000..c47ac56 --- /dev/null +++ b/benchmark/results/testing_backend_comparison/config/config.yml @@ -0,0 +1,8 @@ +environment: + n_of_agents: 6 + n_of_backend_slots: 4 + n_of_tool_calls_per_agent: 10 + tool_execution_duration_time: 3 +run_description: 'Debugging AsyncFlow vs Parsl in the new flowgentic benchmarking repo!' +run_name: testing_backend_comparison +workload_id: backend_comparison diff --git a/benchmark/results/testing_backend_comparison/experiments/backend_comparison/data/data.json b/benchmark/results/testing_backend_comparison/experiments/backend_comparison/data/data.json new file mode 100644 index 0000000..8ab0081 --- /dev/null +++ b/benchmark/results/testing_backend_comparison/experiments/backend_comparison/data/data.json @@ -0,0 +1,6150 @@ +{ + "langraph": { + "run_name": "testing_backend_comparison", + "run_description": "Debugging AsyncFlow vs Parsl in the new flowgentic benchmarking repo!", + "workload_id": "backend_comparison", + "n_of_agents": 6, + "n_of_tool_calls_per_agent": 10, + "n_of_backend_slots": 4, + "workload_type": "fixed_agents_vary_tools", + "tool_execution_duration_time": 3, + "total_makespan": 79.0169142599998, + "events": [ + { + "event": "tool_wrap_start", + "ts": 5647.470985085, + "tool_name": "fetch_temperature", + "wrap_id": "d796edc4-1581-42d7-a637-5d3c9d641034" 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