actions 100% full lol, fixing

.github/workflows/container-image.yaml

@@ -58,6 +58,8 @@ jobs:
       # replaced on each successful push, just as main replaces `latest`.
       - name: Build and optionally publish
         uses: docker/build-push-action@v7
+        env:
+          DOCKER_BUILD_RECORD_RETENTION_DAYS: 3
         with:
           context: .
           platforms: linux/amd64

perf/act-benchmark.py

@@ -0,0 +1,209 @@
+#!/usr/bin/env python3
+"""Finite CPU benchmark of LeRobot ACT with ImageNet-pretrained ResNet-18.
+
+First run: python3 perf/act-benchmark.py --setup
+Repeat:    python3 perf/act-benchmark.py
+Quick:     python3 perf/act-benchmark.py --threads 4 --seconds 2 --iterations 2 --batch-size 2
+
+Python >=3.12, Linux. Setup uses a private cached environment. Only the visual
+backbone is pretrained; the action policy is randomly initialized. Synthetic
+in-memory inputs measure compute, NOT learning quality or driving competence.
+No rover connections, server changes, training checkpoints, or background jobs.
+"""
+import argparse
+import contextlib
+import importlib.metadata
+import json
+import math
+import os
+from pathlib import Path
+import platform
+import resource
+import statistics
+import subprocess
+import sys
+import tempfile
+import time
+import venv
+
+REVISION = '146d8474932c8b566717eb2738ba459337ab71c4'
+
+
+def positive(value):
+    number = int(value)
+    if number < 1:
+        raise argparse.ArgumentTypeError('Must be positive')
+    return number
+
+
+def arguments():
+    parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
+    parser.add_argument('--setup', action='store_true')
+    parser.add_argument('--cache', type=Path, default=Path.home() / '.cache/rover-act-benchmark')
+    parser.add_argument('--threads', nargs='+', type=positive, default=[4, 8, 16])
+    parser.add_argument('--seconds', type=positive, default=10)
+    parser.add_argument('--iterations', type=positive, default=10)
+    parser.add_argument('--batch-size', type=positive, default=8, help='Training only; inference batch is always one rover')
+    parser.add_argument('--chunk-size', type=positive, default=10, help='Future command steps per prediction, not observed history')
+    parser.add_argument('--width', type=positive, default=160)
+    parser.add_argument('--height', type=positive, default=120)
+    parser.add_argument('--sensors', type=positive, default=30)
+    parser.add_argument('--mode', choices=['both', 'inference', 'training'], default='both')
+    parser.add_argument('--timeout', type=positive, default=900, help='Hard per-case wall time limit in seconds')
+    parser.add_argument('--worker', action='store_true', help=argparse.SUPPRESS)
+    parser.add_argument('--result', type=Path, help=argparse.SUPPRESS)
+    return parser.parse_args()
+
+
+def setup(args):
+    python = args.cache / 'venv/bin/python'
+    if not python.exists():
+        venv.EnvBuilder(with_pip=True).create(args.cache / 'venv')
+    subprocess.run([str(python), '-m', 'pip', 'install', '--no-compile', 'torch', 'torchvision',
+                    '--index-url', 'https://download.pytorch.org/whl/cpu'], check=True)
+    # Install only ACT's import dependencies: the full robotics/dataset stack has
+    # unrelated version constraints and is not exercised by this benchmark.
+    subprocess.run([str(python), '-m', 'pip', 'install', '--no-compile', 'numpy', 'einops>=0.8,<0.9',
+                    'draccus>=0.11.6,<0.12', 'huggingface-hub>=1.6,<2', 'safetensors',
+                    'packaging', 'termcolor', 'tqdm', 'gymnasium'], check=True)
+    subprocess.run([str(python), '-m', 'pip', 'install', '--no-compile', '--no-deps',
+                    f'https://github.com/huggingface/lerobot/archive/{REVISION}.zip'], check=True)
+    (args.cache / 'source-revision').write_text(REVISION)
+
+
+def worker(args):
+    import torch
+    from lerobot.configs import FeatureType, PolicyFeature
+    from lerobot.policies.act.configuration_act import ACTConfig
+    from lerobot.policies.act.modeling_act import ACTPolicy
+
+    torch.set_num_threads(args.threads[0])
+    torch.set_num_interop_threads(1)
+    torch.manual_seed(7)
+    training = args.mode == 'training'
+    batch_size = args.batch_size if training else 1
+    config = ACTConfig(device='cpu', chunk_size=args.chunk_size, n_action_steps=1,
+        input_features={
+            'observation.images.front': PolicyFeature(type=FeatureType.VISUAL, shape=(3, args.height, args.width)),
+            'observation.state': PolicyFeature(type=FeatureType.STATE, shape=(args.sensors,)),
+        }, output_features={'action': PolicyFeature(type=FeatureType.ACTION, shape=(2,))})
+    policy = ACTPolicy(config).cpu().float()
+    policy.train(training)
+    optimizer_config = config.get_optimizer_preset()
+    optimizer = optimizer_config.build(policy.get_optim_params()) if training else None
+    # Already-normalized tensors stand in for a decoded observation and target
+    # sequence. Avoid video/dataset overhead in this compute-only measurement.
+    images = torch.rand(batch_size, 3, args.height, args.width)
+    mean = torch.tensor([.485, .456, .406]).view(1, 3, 1, 1)
+    std = torch.tensor([.229, .224, .225]).view(1, 3, 1, 1)
+    batch = {'observation.images.front': (images - mean) / std,
+             'observation.state': torch.randn(batch_size, args.sensors)}
+    if training:
+        batch['action'] = torch.rand(batch_size, args.chunk_size, 2) * 2 - 1
+        batch['action_is_pad'] = torch.zeros(batch_size, args.chunk_size, dtype=torch.bool)
+
+    def step():
+        if training:
+            optimizer.zero_grad(set_to_none=True)
+            loss, _ = policy(batch)
+            if not torch.isfinite(loss):
+                raise RuntimeError('Non-finite training loss')
+            loss.backward()
+            norm = torch.nn.utils.clip_grad_norm_(policy.parameters(), optimizer_config.grad_clip_norm,
+                                                   error_if_nonfinite=True)
+            optimizer.step()
+            return {'loss': loss.item(), 'gradientNorm': norm.item()}
+        # select_action can serve cached actions; directly time a full chunk.
+        prediction = policy.predict_action_chunk(batch)
+        if prediction.shape != (1, args.chunk_size, 2) or not torch.isfinite(prediction).all():
+            raise RuntimeError('Invalid action output')
+        return {'outputShape': list(prediction.shape)}
+
+    with (contextlib.nullcontext() if training else torch.inference_mode()):
+        for _ in range(3):
+            step()
+        timings = []
+        started, cpu_started = time.perf_counter(), time.process_time()
+        while len(timings) < args.iterations or time.perf_counter() - started < args.seconds:
+            tick = time.perf_counter()
+            last = step()
+            timings.append((time.perf_counter() - tick) * 1000)
+        elapsed, cpu = time.perf_counter() - started, time.process_time() - cpu_started
+    timings.sort()
+    result = dict(mode=args.mode, threads=args.threads[0], batchSize=batch_size,
+        iterations=len(timings), elapsedSeconds=elapsed, p50Ms=statistics.median(timings),
+        p95Ms=timings[math.ceil(.95*len(timings))-1], maxMs=max(timings),
+        stepsPerSecond=len(timings)/elapsed, examplesPerSecond=len(timings)*batch_size/elapsed,
+        cpuCores=cpu/elapsed, peakMiB=resource.getrusage(resource.RUSAGE_SELF).ru_maxrss/1024,
+        parameters=sum(p.numel() for p in policy.parameters()),
+        trainableParameters=sum(p.numel() for p in policy.parameters() if p.requires_grad),
+        lastStep=last, config={
+            'chunkSize': config.chunk_size, 'dimModel': config.dim_model, 'klWeight': config.kl_weight,
+            'pretrainedBackbone': config.pretrained_backbone_weights, 'useVae': config.use_vae,
+            'encoderLayers': config.n_encoder_layers, 'decoderLayers': config.n_decoder_layers,
+            'feedforwardDimension': config.dim_feedforward, 'observationSteps': config.n_obs_steps,
+            'learningRate': optimizer_config.lr, 'backboneLearningRate': config.optimizer_lr_backbone},
+        versions={key: importlib.metadata.version(key) for key in ['torch','torchvision','lerobot','numpy','einops','draccus']})
+    args.result.write_text(json.dumps(result))
+
+
+def main():
+    args = arguments()
+    if sys.version_info < (3, 12):
+        raise RuntimeError('Use Python 3.12 or newer')
+    args.cache = args.cache.expanduser().resolve()
+    os.environ['TORCH_HOME'] = str(args.cache / 'torch')
+    if args.worker:
+        worker(args)
+        return
+    if args.setup:
+        setup(args)
+    python = args.cache / 'venv/bin/python'
+    marker = args.cache / 'source-revision'
+    if not python.exists() or not marker.exists() or marker.read_text() != REVISION:
+        raise RuntimeError('Run python3 perf/act-benchmark.py --setup first')
+    print('CPU FP32 ACT: pretrained visual backbone, untrained action policy; synthetic data.', flush=True)
+    print(f'One {args.width}x{args.height} image + {args.sensors} sensors; {args.chunk_size} future left/right command pairs.', flush=True)
+    print('Inference times a fresh full prediction. Training includes backbone gradients and optimizer update.', flush=True)
+    print('mode       threads batch p50 ms p95 ms max ms steps/s examples/s CPU cores peak MiB', flush=True)
+    modes = ['inference', 'training'] if args.mode == 'both' else [args.mode]
+    results, failures = [], []
+    initial_load = os.getloadavg()
+    with tempfile.TemporaryDirectory(prefix='act-benchmark-') as directory:
+        for mode in modes:
+            for threads in args.threads:
+                result_path = Path(directory) / 'result.json'
+                command = [str(python), str(Path(__file__).resolve()), '--worker', '--cache', str(args.cache),
+                    '--mode', mode, '--threads', str(threads), '--result', str(result_path)]
+                for option in ['seconds','iterations','batch_size','chunk_size','width','height','sensors']:
+                    command += ['--'+option.replace('_','-'), str(getattr(args, option))]
+                try:
+                    subprocess.run(command, check=True, timeout=args.timeout,
+                                   env={**os.environ, 'PYTHONDONTWRITEBYTECODE':'1'})
+                    r = json.loads(result_path.read_text())
+                except (subprocess.CalledProcessError, subprocess.TimeoutExpired) as error:
+                    failures.append(dict(mode=mode, threads=threads, error=str(error)))
+                    print(f'Case failed: {mode}, {threads} threads: {error}', flush=True)
+                    break  # Avoid repeating the same setup/import/OOM error.
+                results.append(r)
+                print(f"{mode:10} {threads:7} {r['batchSize']:5} {r['p50Ms']:6.1f} {r['p95Ms']:6.1f} {r['maxMs']:6.1f} {r['stepsPerSecond']:7.2f} {r['examplesPerSecond']:10.2f} {r['cpuCores']:9.2f} {r['peakMiB']:8.0f}", flush=True)
+            if failures:
+                break
+    report = dict(benchmark='act-cpu-v1', sourceRevision=REVISION, platform=platform.platform(),
+        python=sys.version, logicalCpus=os.cpu_count(), initialLoadAverage=initial_load,
+        finalLoadAverage=os.getloadavg(), imageSize=[args.width,args.height], sensors=args.sensors,
+        chunkSize=args.chunk_size, output='commanded left/right wheels, not physical velocity or position',
+        exclusions=['capture','video decode','dataset I/O','network','concurrent training/inference','driving quality'],
+        results=results, failures=failures)
+    print('\nPaste this report back:\n'+json.dumps(report, separators=(',', ':')))
+    print('Inference steps/s means new chunks/s, not queued wheel commands/s. Training loss on random data is not a quality score.')
+    if failures:
+        sys.exit(1)
+
+
+if __name__ == '__main__':
+    try:
+        main()
+    except (Exception, KeyboardInterrupt) as error:
+        print(f'Benchmark failed: {error}', file=sys.stderr)
+        sys.exit(1)