From 96838c4e9d522b952db9963c06e1fb3141ff37a4 Mon Sep 17 00:00:00 2001 From: benedettoleto Date: Fri, 7 Mar 2025 19:06:44 +0100 Subject: [PATCH 1/6] refactor metric dashboard --- neurobench/benchmarks/benchmark.py | 207 ++++++++++++++++++++++++----- pyproject.toml | 1 + 2 files changed, 177 insertions(+), 31 deletions(-) diff --git a/neurobench/benchmarks/benchmark.py b/neurobench/benchmarks/benchmark.py index ebe03b8c..e28c3620 100644 --- a/neurobench/benchmarks/benchmark.py +++ b/neurobench/benchmarks/benchmark.py @@ -1,7 +1,5 @@ import sys from contextlib import redirect_stdout -from os import mkdir -from tqdm import tqdm from snntorch import utils from neurobench.metrics.manager.static_manager import StaticMetricManager from neurobench.metrics.manager.workload_manager import WorkloadMetricManager @@ -20,9 +18,22 @@ import pathlib import snntorch from torch import Tensor - +from rich.console import Console +from rich.table import Table import torch import nir +from rich.live import Live +from rich.progress import ( + Progress, + SpinnerColumn, + BarColumn, + TextColumn, + TimeRemainingColumn, +) +from rich.panel import Panel +from rich.layout import Layout +from rich.columns import Columns +import rich class Benchmark: @@ -84,7 +95,6 @@ def run( """ with redirect_stdout(None if quiet else sys.stdout): - print("Running benchmark") self.results = None results = self.static_metric_manager.run_metrics(self.model) @@ -104,40 +114,140 @@ def run( self.model.__net__().to(device) batch_num = 0 - for data in tqdm(dataloader, total=len(dataloader), disable=quiet): - # convert data to tuple - data = tuple(data) if not isinstance(data, tuple) else data - - if device is not None: - data = (data[0].to(device), data[1].to(device)) - - batch_size = data[0].size(0) - - # Preprocessing data - data = self.processor_manager.preprocess(data) - - # Run model on test data - preds = self.model(data[0]) - - # Postprocessing data - preds = self.processor_manager.postprocess(preds) - - # Data metrics - batch_results = self.workload_metric_manager.run_metrics( - self.model, preds, data, batch_size, dataset_len - ) - self.workload_metric_manager.reset_hooks(self.model) + print("\n") + def make_layout(): + layout = Layout() if verbose: - results.update(batch_results) - print(f"\nBatch num {batch_num + 1}/{len(dataloader)}") - print(dict(results)) + layout.split( + Layout(name="progress", size=3), + Layout(name="content"), + ) + else: + layout.split( + Layout(name="progress", size=3), + ) + + return layout + + # Create a custom progress bar + progress = Progress( + SpinnerColumn(spinner_name="dots"), + TextColumn("[bold cyan]Processing[/bold cyan]"), + BarColumn(bar_width=40, style="magenta", complete_style="cyan"), + TextColumn("[progress.percentage]{task.percentage:>3.0f}%"), + TextColumn("•"), + TextColumn( + "[bold blue]{task.completed}/{task.total}[/bold blue] batches" + ), + TextColumn("•"), + TimeRemainingColumn(), + expand=True, + transient=False, + ) + + layout = make_layout() + layout["progress"].update(Panel(progress)) + if verbose: + layout["content"].update(Panel("")) + + console = Console() + console.clear() + + with Live( + layout, + refresh_per_second=10, + vertical_overflow="visible", + screen=True, # This will clear the screen and create a new canvas + console=console, + ) as live: + task = progress.add_task( + "[cyan]Running Benchmark...", total=len(dataloader) + ) - batch_num += 1 + for data in dataloader: + # convert data to tuple + data = tuple(data) if not isinstance(data, tuple) else data + + if device is not None: + data = (data[0].to(device), data[1].to(device)) + + batch_size = data[0].size(0) + + # Preprocessing data + data = self.processor_manager.preprocess(data) + + # Run model on test data + preds = self.model(data[0]) + + # Postprocessing data + preds = self.processor_manager.postprocess(preds) + + # Data metrics + batch_results = self.workload_metric_manager.run_metrics( + self.model, preds, data, batch_size, dataset_len + ) + self.workload_metric_manager.reset_hooks(self.model) + + progress.update(task, advance=1) + + if verbose: + # Create a list of panels for each metric + panels = [] + for key, value in batch_results.items(): + if isinstance(value, dict): + sub_panels = [ + Panel( + f"Value: {sub_value}", + title=sub_key, + border_style="green", + ) + for sub_key, sub_value in value.items() + ] + panels.append( + Panel( + Columns(sub_panels), + title=key, + border_style="blue", + ) + ) + else: + panels.append( + Panel( + f"Value: {value}", + title=key, + border_style="blue", + ) + ) + + # Create a structured panel for the content section + content_panel = Panel( + Columns(panels), + title=f"Processing batch {progress.tasks[0].completed}/{progress.tasks[0].total}", + title_align="left", + border_style="cyan", + ) + + # Update the content section with the history of panels + layout["content"].update( + Panel( + content_panel, + title="Batch History", + border_style="cyan", + ) + ) + + batch_num += 1 + + # Keep the progress bar base at the end without collapsing it + progress.update(task, completed=len(dataloader)) + live.refresh() + # live.stop() results.update(self.workload_metric_manager.results) self.workload_metric_manager.clean_results() self.results = dict(results) + self._show_results(console) return self.results def save_benchmark_results( @@ -262,3 +372,38 @@ def to_onnx(self, dummy_input: Tensor, filename: str, **kwargs) -> None: self.model.__net__().eval() torch.onnx.export(self.model.__net__(), dummy_input, filename, **kwargs) print(f"Model exported to {filename}") + + def _show_results(self, console): + """Print the results of the benchmark.""" + if self.results is None: + raise ValueError("No results to show. Run the benchmark first.") + + # Create console and table + + table = Table( + title="[bold magenta]\nBenchmark Results:[/bold magenta]", + show_lines=True, + expand=True, + header_style="bold cyan", + title_justify="center", + title_style="bold magenta", + border_style="cyan", + box=rich.box.ROUNDED, + ) + + # Define columns + table.add_column("Metric", style="bold magenta", justify="left") + table.add_column("Value", style="bold yellow", justify="right") + + # Add metrics to the table + for key, value in self.results.items(): + if isinstance(value, dict): + table.add_section() + table.add_row(f"[bold green]{key}[/]", "") + for sub_key, sub_value in value.items(): + table.add_row(f" {sub_key}", f"{sub_value}") + else: + table.add_row(key, f"{value}") + + # Print the table + console.print(table) diff --git a/pyproject.toml b/pyproject.toml index 07944294..9e1dd317 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -19,6 +19,7 @@ snntorch = {version = ">=0.7.0"} jitcdde = {version = "^1.8.1", optional = true} pytorch-lightning = {version = "^1.4.0", optional = true} gdown = {version = "^4.7.1", optional = true} +rich = ">=13.9.4" [tool.poetry.group.dev] optional = true From f4e986c5390d6c6c4e878419eeb9c3e8a3475744 Mon Sep 17 00:00:00 2001 From: benedettoleto Date: Fri, 7 Mar 2025 19:09:54 +0100 Subject: [PATCH 2/6] refactor progressbar --- neurobench/benchmarks/benchmark.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/neurobench/benchmarks/benchmark.py b/neurobench/benchmarks/benchmark.py index e28c3620..58b082a8 100644 --- a/neurobench/benchmarks/benchmark.py +++ b/neurobench/benchmarks/benchmark.py @@ -242,7 +242,7 @@ def make_layout(): # Keep the progress bar base at the end without collapsing it progress.update(task, completed=len(dataloader)) live.refresh() - # live.stop() + live.stop() results.update(self.workload_metric_manager.results) self.workload_metric_manager.clean_results() From 20ffe5a4c5bd37423c252e84288b6306f1d7afaf Mon Sep 17 00:00:00 2001 From: benedettoleto Date: Fri, 7 Mar 2025 19:24:29 +0100 Subject: [PATCH 3/6] poetry lock update --- examples/nehar/benchmark.py | 3 +- poetry.lock | 4548 +++++++++++++++-------------------- 2 files changed, 1956 insertions(+), 2595 deletions(-) diff --git a/examples/nehar/benchmark.py b/examples/nehar/benchmark.py index a6c272f3..ad2ad413 100644 --- a/examples/nehar/benchmark.py +++ b/examples/nehar/benchmark.py @@ -52,8 +52,7 @@ benchmark = Benchmark( model, test_set_loader, [], postprocessors, [static_metrics, workload_metrics] ) - results = benchmark.run(verbose=False) - print(results) + results = benchmark.run(verbose=True) results_path = os.path.join(file_path, "results") benchmark.save_benchmark_results(results_path) diff --git a/poetry.lock b/poetry.lock index 47f823a1..9a773d80 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,18 +1,105 @@ +# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand. + [[package]] name = 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+ +[extras] +mackey-glass = ["jitcdde"] +nehar = ["gdown", "pytorch-lightning"] + +[metadata] +lock-version = "2.0" +python-versions = "^3.10" +content-hash = "5c44d87b8282d216d917ff5807732ab54ec60337dc53abaaa6a1ff3fd1762fff" From c8697e8af2cb3ea57326731d9b2fd2f5f9934c9a Mon Sep 17 00:00:00 2001 From: benedettoleto Date: Wed, 12 Mar 2025 11:17:59 +0100 Subject: [PATCH 4/6] update benchmark --- neurobench/benchmarks/benchmark.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/neurobench/benchmarks/benchmark.py b/neurobench/benchmarks/benchmark.py index 58b082a8..d79e15b4 100644 --- a/neurobench/benchmarks/benchmark.py +++ b/neurobench/benchmarks/benchmark.py @@ -114,7 +114,7 @@ def run( self.model.__net__().to(device) batch_num = 0 - print("\n") + print("\n ") def make_layout(): layout = Layout() From e0abc920919d483f1433311ef27fb9a3eba8dd4c Mon Sep 17 00:00:00 2001 From: benedettoleto Date: Wed, 12 Mar 2025 11:21:05 +0100 Subject: [PATCH 5/6] revert changes --- neurobench/benchmarks/benchmark.py | 56 ++++++------------------------ 1 file changed, 10 insertions(+), 46 deletions(-) diff --git a/neurobench/benchmarks/benchmark.py b/neurobench/benchmarks/benchmark.py index e1218fdf..58b082a8 100644 --- a/neurobench/benchmarks/benchmark.py +++ b/neurobench/benchmarks/benchmark.py @@ -14,7 +14,7 @@ import json import csv import os -from typing import Literal, List, Type, Optional, Dict, Any, Callable, Tuple +from typing import Literal, List, Type, Optional, Dict, Any import pathlib import snntorch from torch import Tensor @@ -47,25 +47,18 @@ def __init__( self, model: NeuroBenchModel, dataloader: Optional[DataLoader], - preprocessors: Optional[ - List[ - NeuroBenchPreProcessor - | Callable[[Tuple[Tensor, Tensor]], Tuple[Tensor, Tensor]] - ] - ], - postprocessors: Optional[ - List[NeuroBenchPostProcessor | Callable[[Tensor], Tensor]] - ], + preprocessors: Optional[List[NeuroBenchPreProcessor]], + postprocessors: Optional[List[NeuroBenchPostProcessor]], metric_list: List[List[Type[StaticMetric | WorkloadMetric]]], ): """ Args: model: A NeuroBenchModel. dataloader: A PyTorch DataLoader. - preprocessors: A list of NeuroBenchPreProcessors or callable functions (e.g. lambda) with matching interfaces. - postprocessors: A list of NeuroBenchPostProcessors or callable functions (e.g. lambda) with matching interfaces. - metric_list: A list of lists of StaticMetric and WorkloadMetric classes of metrics to run. - First item is StaticMetrics, second item is WorkloadMetrics. + preprocessors: A list of NeuroBenchPreProcessors. + postprocessors: A list of NeuroBenchPostProcessors. + metric_list: A list of lists of strings of metrics to run. + First item is static metrics, second item is data metrics. """ self.model = model @@ -81,13 +74,8 @@ def run( quiet: bool = False, verbose: bool = False, dataloader: Optional[DataLoader] = None, - preprocessors: Optional[ - NeuroBenchPreProcessor - | Callable[[Tuple[Tensor, Tensor]], Tuple[Tensor, Tensor]] - ] = None, - postprocessors: Optional[ - NeuroBenchPostProcessor | Callable[[Tensor], Tensor] - ] = None, + preprocessors: Optional[NeuroBenchPreProcessor] = None, + postprocessors: Optional[NeuroBenchPostProcessor] = None, device: Optional[str] = None, ) -> Dict[str, Any]: """ @@ -126,31 +114,7 @@ def run( self.model.__net__().to(device) batch_num = 0 - - print("\n ") - for data in tqdm(dataloader, total=len(dataloader), disable=quiet): - # convert data to tuple - data = tuple(data) if not isinstance(data, tuple) else data - - if device is not None: - data = (data[0].to(device), data[1].to(device)) - - batch_size = data[0].size(0) - - # Preprocessing data - input, target = self.processor_manager.preprocess(data) - - # Run model on test data - preds = self.model(input) - - # Postprocessing data - preds = self.processor_manager.postprocess(preds) - - # Data metrics - batch_results = self.workload_metric_manager.run_metrics( - self.model, preds, data, batch_size, dataset_len - ) - self.workload_metric_manager.reset_hooks(self.model) + print("\n") def make_layout(): layout = Layout() From 3f9ef10846651299a2c68a64c464960ce94b00c0 Mon Sep 17 00:00:00 2001 From: benedettoleto Date: Thu, 27 Mar 2025 00:28:53 +0100 Subject: [PATCH 6/6] refactor: layout progressbar --- neurobench/benchmarks/benchmark.py | 82 +++------------------------- neurobench/benchmarks/utils.py | 85 ++++++++++++++++++++++++++++++ 2 files changed, 91 insertions(+), 76 deletions(-) create mode 100644 neurobench/benchmarks/utils.py diff --git a/neurobench/benchmarks/benchmark.py b/neurobench/benchmarks/benchmark.py index 58b082a8..af3d13e9 100644 --- a/neurobench/benchmarks/benchmark.py +++ b/neurobench/benchmarks/benchmark.py @@ -19,21 +19,13 @@ import snntorch from torch import Tensor from rich.console import Console +from rich.panel import Panel from rich.table import Table import torch import nir from rich.live import Live -from rich.progress import ( - Progress, - SpinnerColumn, - BarColumn, - TextColumn, - TimeRemainingColumn, -) -from rich.panel import Panel -from rich.layout import Layout -from rich.columns import Columns import rich +from .utils import make_layout, create_progress_bar, create_content_panel class Benchmark: @@ -116,37 +108,8 @@ def run( batch_num = 0 print("\n") - def make_layout(): - layout = Layout() - if verbose: - layout.split( - Layout(name="progress", size=3), - Layout(name="content"), - ) - else: - layout.split( - Layout(name="progress", size=3), - ) - - return layout - - # Create a custom progress bar - progress = Progress( - SpinnerColumn(spinner_name="dots"), - TextColumn("[bold cyan]Processing[/bold cyan]"), - BarColumn(bar_width=40, style="magenta", complete_style="cyan"), - TextColumn("[progress.percentage]{task.percentage:>3.0f}%"), - TextColumn("•"), - TextColumn( - "[bold blue]{task.completed}/{task.total}[/bold blue] batches" - ), - TextColumn("•"), - TimeRemainingColumn(), - expand=True, - transient=False, - ) - - layout = make_layout() + progress = create_progress_bar(total=len(dataloader)) + layout = make_layout(verbose) layout["progress"].update(Panel(progress)) if verbose: layout["content"].update(Panel("")) @@ -193,42 +156,9 @@ def make_layout(): if verbose: # Create a list of panels for each metric - panels = [] - for key, value in batch_results.items(): - if isinstance(value, dict): - sub_panels = [ - Panel( - f"Value: {sub_value}", - title=sub_key, - border_style="green", - ) - for sub_key, sub_value in value.items() - ] - panels.append( - Panel( - Columns(sub_panels), - title=key, - border_style="blue", - ) - ) - else: - panels.append( - Panel( - f"Value: {value}", - title=key, - border_style="blue", - ) - ) - - # Create a structured panel for the content section - content_panel = Panel( - Columns(panels), - title=f"Processing batch {progress.tasks[0].completed}/{progress.tasks[0].total}", - title_align="left", - border_style="cyan", + content_panel = create_content_panel( + progress, batch_results, verbose ) - - # Update the content section with the history of panels layout["content"].update( Panel( content_panel, diff --git a/neurobench/benchmarks/utils.py b/neurobench/benchmarks/utils.py new file mode 100644 index 00000000..a7228c33 --- /dev/null +++ b/neurobench/benchmarks/utils.py @@ -0,0 +1,85 @@ +from rich.layout import Layout +from rich.progress import ( + Progress, + SpinnerColumn, + BarColumn, + TextColumn, + TimeRemainingColumn, +) +from rich.panel import Panel +from rich.columns import Columns + + +def make_layout(verbose: bool = False) -> Layout: + """Create a layout for the benchmark runner.""" + layout = Layout() + if verbose: + layout.split( + Layout(name="progress", size=3), + Layout(name="content"), + ) + else: + layout.split( + Layout(name="progress", size=3), + ) + + return layout + + +def create_progress_bar(total: int) -> Progress: + """Create a progress bar.""" + + progress = Progress( + SpinnerColumn(spinner_name="dots"), + TextColumn("[bold cyan]Processing[/bold cyan]"), + BarColumn(bar_width=40, style="magenta", complete_style="cyan"), + TextColumn("[progress.percentage]{task.percentage:>3.0f}%"), + TextColumn("•"), + TextColumn("[bold blue]{task.completed}/{task.total}[/bold blue] batches"), + TextColumn("•"), + TimeRemainingColumn(), + expand=True, + transient=False, + ) + + return progress + + +def create_content_panel(progress, batch_results, verbose): + panels = [] + if verbose: + for key, value in batch_results.items(): + if isinstance(value, dict): + sub_panels = [ + Panel( + f"Value: {sub_value}", + title=sub_key, + border_style="green", + ) + for sub_key, sub_value in value.items() + ] + panels.append( + Panel( + Columns(sub_panels), + title=key, + border_style="blue", + ) + ) + else: + panels.append( + Panel( + f"Value: {value}", + title=key, + border_style="blue", + ) + ) + # Create a structured panel for the content section + content_panel = Panel( + Columns(panels), + title=f"Processing batch {progress.tasks[0].completed}/{progress.tasks[0].total}", + title_align="left", + border_style="cyan", + ) + + # Update the content section with the history of panels + return content_panel