diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_full_original_job85088_task1/case0_full_original.json b/results/worldmem-baseline-evals/matrix-game2/diagnostic_full_original_job85088_task1/case0_full_original.json new file mode 100644 index 0000000000000000000000000000000000000000..7b8fe9a8a5a38e554a597798e8d9e78afd3e9f65 --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_full_original_job85088_task1/case0_full_original.json @@ -0,0 +1,81 @@ +{ + "case": "case0_full_original", + "raw_rgb_psnr": [ + 25.77519416809082, + 4.8833327293396, + 4.636667251586914, + 5.688905715942383, + 7.4164934158325195, + 7.8217339515686035, + 8.03274154663086, + 8.007763862609863, + 8.02673625946045, + 8.09578800201416, + 8.218385696411133, + 8.326114654541016, + 7.63754940032959, + 7.489852428436279, + 7.3153157234191895, + 7.299378395080566, + 7.418322563171387, + 7.375185012817383, + 7.256275177001953, + 7.181506156921387, + 7.253871917724609, + 7.2649149894714355, + 7.2623491287231445, + 7.290315628051758 + ], + "raw_rgb_mse": [ + 0.002645334927365184, + 0.32483792304992676, + 0.3438216745853424, + 0.2698419392108917, + 0.18128031492233276, + 0.16513024270534515, + 0.1572989672422409, + 0.1582062542438507, + 0.15751661360263824, + 0.1550319343805313, + 0.15071670711040497, + 0.14702408015727997, + 0.17228403687477112, + 0.178243950009346, + 0.18555320799350739, + 0.18623535335063934, + 0.18120399117469788, + 0.18301282823085785, + 0.1880929172039032, + 0.19135920703411102, + 0.1881970316171646, + 0.18771910667419434, + 0.18783006072044373, + 0.18662439286708832 + ], + "mean_rgb": [ + 0.6862332224845886, + 0.1697441190481186, + 0.156618133187294, + 0.2281637042760849, + 0.31492793560028076, + 0.33408209681510925, + 0.34403756260871887, + 0.3432576060295105, + 0.3432428538799286, + 0.34543678164482117, + 0.35083115100860596, + 0.357102632522583, + 0.3239423930644989, + 0.3186531066894531, + 0.3103964030742645, + 0.3092810809612274, + 0.3162357807159424, + 0.31385642290115356, + 0.30743733048439026, + 0.3035033941268921, + 0.3069319725036621, + 0.3079564869403839, + 0.30708178877830505, + 0.30761298537254333 + ] +} \ No newline at end of file diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_full_original_job85088_task1/case1_full_original.json b/results/worldmem-baseline-evals/matrix-game2/diagnostic_full_original_job85088_task1/case1_full_original.json new file mode 100644 index 0000000000000000000000000000000000000000..78f08b20a3bf3be4575b8603721c8bb1de24201e --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_full_original_job85088_task1/case1_full_original.json @@ -0,0 +1,81 @@ +{ + "case": "case1_full_original", + "raw_rgb_psnr": [ + 20.75506591796875, + 5.314362525939941, + 4.540973663330078, + 5.3272600173950195, + 5.330643653869629, + 5.8266520500183105, + 5.940614223480225, + 6.0602803230285645, + 6.2254180908203125, + 6.322708606719971, + 6.403367042541504, + 6.356753826141357, + 6.32526159286499, + 6.268328666687012, + 6.2384490966796875, + 6.203701496124268, + 6.242549896240234, + 6.298856735229492, + 6.294210433959961, + 6.316972732543945, + 6.402440547943115, + 6.451478481292725, + 6.471812725067139, + 6.447417736053467 + ], + "raw_rgb_mse": [ + 0.00840414222329855, + 0.2941465377807617, + 0.3514816462993622, + 0.29327428340911865, + 0.29304590821266174, + 0.26141756772994995, + 0.25464698672294617, + 0.2477262318134308, + 0.23848342895507812, + 0.2332003265619278, + 0.22890923917293549, + 0.23137935996055603, + 0.23306328058242798, + 0.23613868653774261, + 0.23776891827583313, + 0.23967893421649933, + 0.23754450678825378, + 0.23448461294174194, + 0.23473559319972992, + 0.23350851237773895, + 0.22895805537700653, + 0.22638735175132751, + 0.22532984614372253, + 0.22659912705421448 + ], + "mean_rgb": [ + 0.6869287490844727, + 0.22597603499889374, + 0.16900615394115448, + 0.21825699508190155, + 0.21941502392292023, + 0.248577281832695, + 0.2554280757904053, + 0.26183179020881653, + 0.27177396416664124, + 0.27722224593162537, + 0.281747430562973, + 0.27876004576683044, + 0.27639126777648926, + 0.2728942036628723, + 0.2711937129497528, + 0.2694670855998993, + 0.2724168300628662, + 0.27583178877830505, + 0.2756144106388092, + 0.27678048610687256, + 0.2816024720668793, + 0.284187912940979, + 0.2853650152683258, + 0.283825546503067 + ] +} \ No newline at end of file diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_full_original_job85088_task1/complete.json b/results/worldmem-baseline-evals/matrix-game2/diagnostic_full_original_job85088_task1/complete.json new file mode 100644 index 0000000000000000000000000000000000000000..f8897e0d913b412511feb3e07ae959cd293fe1c5 --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_full_original_job85088_task1/complete.json @@ -0,0 +1 @@ +{"mode": "full_original", "cases": 2, "frames": 24} \ No newline at end of file diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_full_original_job85088_task1/launcher.sh b/results/worldmem-baseline-evals/matrix-game2/diagnostic_full_original_job85088_task1/launcher.sh new file mode 100644 index 0000000000000000000000000000000000000000..339a8b9d8bd588ab4ac7cd6b47a03c94b93c1f4e --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_full_original_job85088_task1/launcher.sh @@ -0,0 +1,35 @@ +#!/usr/bin/env bash +#SBATCH --job-name=matrix-game2-diagnostic +#SBATCH --array=0-4 +#SBATCH --nodes=1 +#SBATCH --ntasks=1 +#SBATCH --cpus-per-task=14 +#SBATCH --gres=gpu:1 +#SBATCH --time=01:00:00 +#SBATCH --account=berzelius-2026-196 +#SBATCH --chdir=/proj/cvl/users/x_fahkh2/worldmem-baseline-evals +#SBATCH --output=slurm_logs/%x-%A_%a.out +#SBATCH --error=slurm_logs/%x-%A_%a.out +set -euo pipefail +module load buildenv-gcccuda/12.9.1-gcc11 +ROOT=/proj/cvl/users/x_fahkh2/worldmem-baseline-evals +PYTHON=/proj/cvl/users/x_fahkh2/envs/matrix-game2-eval/bin/python +DATA_ROOT=/proj/cvl/users/x_fahkh2/WorldMem_Repro/datasets/minecraft +MODES=(wan_oracle full_original full_weighted i2v_original i2v_weighted) +MODE=${MODES[$SLURM_ARRAY_TASK_ID]} +OUTPUT=$ROOT/Matrix-Game/Matrix-Game-2/outputs/diagnostic_${MODE}_job${SLURM_ARRAY_JOB_ID}_task${SLURM_ARRAY_TASK_ID} +export TMPDIR=$ROOT/cache +export TRITON_CACHE_DIR=$ROOT/cache +export HF_HOME=$ROOT/cache/huggingface +export TORCH_HOME=$ROOT/cache/torch +export XDG_CACHE_HOME=$ROOT/cache +export MPLCONFIGDIR=$ROOT/cache/matplotlib +export PYTHONNOUSERSITE=1 PYTHONUNBUFFERED=1 OMP_NUM_THREADS=4 MKL_NUM_THREADS=4 +export WANDB_MODE=disabled HF_HUB_OFFLINE=1 TRANSFORMERS_OFFLINE=1 +mkdir -p "$TMPDIR" "$OUTPUT" +"$PYTHON" "$ROOT/deployment/verify_node.py" --expected-gpus 1 --output "$OUTPUT" +cp "$0" "$OUTPUT/launcher.sh" +cp "$ROOT/deployment/diagnose_matrix_game2.py" "$OUTPUT/probe.py" +srun --ntasks=1 --gpus-per-task=1 --cpus-per-task=14 --cpu-bind=cores \ + "$PYTHON" "$ROOT/deployment/diagnose_matrix_game2.py" \ + --mode "$MODE" --data-root "$DATA_ROOT" --output "$OUTPUT" diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_full_original_job85088_task1/manifest.json b/results/worldmem-baseline-evals/matrix-game2/diagnostic_full_original_job85088_task1/manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..4bb432a293d70f000f4bb8a6b01bfb5e98d1f04c --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_full_original_job85088_task1/manifest.json @@ -0,0 +1,59 @@ +{ + "diagnostic_only": true, + "mode": "full_original", + "checkpoint": "/proj/cvl/users/x_fahkh2/worldmem-baseline-evals/checkpoints/matrix-game2", + "weight_source": "released raw distilled", + "optimizer": null, + "lr": null, + "lora": null, + "activation_checkpointing": false, + "batch_size": 1, + "trainable_parameters": 0, + "denoising_steps": 3, + "shift": 5, + "local_attention_latents": 6, + "selector": "native sliding cache", + "reference": "raw RGB resized352x640, not benchmark Oasis references", + "targets": [ + 700, + 724 + ], + "full_history": [ + 100, + 700 + ], + "full_prefix_latents": 153, + "oracle": "9copies+RGB100:724 encodedonce;159latents; discard153latent prefix", + "i2v": { + "anchor": 699, + "decoded_slice": [ + 1, + 25 + ], + "controls": [ + 700, + 724 + ], + "right_noops": 9 + }, + "normalization_change": "weighted modes apply scales and accumulate RMS in fp32", + "cases": [ + { + "index": "0", + "path": "test/val_plains/2/000034.mp4", + "seed": 42000000 + }, + { + "index": "1", + "path": "test/val_desert/1/000029.mp4", + "seed": 42000001 + } + ], + "parameter_counts": { + "generator_total": 1619242304, + "generator_trainable": 0, + "vae_total": 126892531, + "clip_total": 1193014785 + }, + "action_norm_instances": 60 +} \ No newline at end of file diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_full_original_job85088_task1/node.json b/results/worldmem-baseline-evals/matrix-game2/diagnostic_full_original_job85088_task1/node.json new file mode 100644 index 0000000000000000000000000000000000000000..0898d32f4ec93b04cf7a29f6da916d1dac21b487 --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_full_original_job85088_task1/node.json @@ -0,0 +1,7 @@ +{ + "devices": [ + "NVIDIA H200" + ], + "torch": "2.7.0+cu126", + "slurm_job_id": "85090" +} diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_full_original_job85088_task1/probe.py b/results/worldmem-baseline-evals/matrix-game2/diagnostic_full_original_job85088_task1/probe.py new file mode 100644 index 0000000000000000000000000000000000000000..f5b01f8975ad3f66be61f6e960f05c64d14fd6e0 --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_full_original_job85088_task1/probe.py @@ -0,0 +1,113 @@ +"""Frozen Matrix diagnostics; raw-RGB PSNR is separate from benchmark scoring.""" +import argparse +import json +import sys +from pathlib import Path +import torch +import torch.nn.functional as F +from PIL import Image + +ROOT = Path(__file__).resolve().parents[1] +sys.path[:0] = [str(ROOT / 'shared'), str(ROOT / 'Matrix-Game/Matrix-Game-2')] +from protocol import load_minecraft_case, select_cases +from worldmem_adapter import MatrixGameAdapter, map_actions +from demo_utils.constant import ZERO_VAE_CACHE + +MODES = ['wan_oracle', 'full_original', 'full_weighted', 'i2v_original', 'i2v_weighted'] +parser = argparse.ArgumentParser(description=__doc__) +parser.add_argument('--mode', choices=MODES, required=True) +parser.add_argument('--data-root', type=Path, required=True) +parser.add_argument('--output', type=Path, required=True) +parser.add_argument('--dry-run', action='store_true') + +def weighted_norm(self, x): + y = x.float() + return (y * torch.rsqrt(y.square().mean(-1, keepdim=True) + self.eps)).to(x.dtype) * self.weight + +def save_result(output, name, prediction, reference): + prediction = prediction.float().cpu() + if prediction.shape != reference.shape or not torch.isfinite(prediction).all(): + raise ValueError(f'Invalid Matrix prediction: {name}, shape={tuple(prediction.shape)}') + prediction = prediction.clamp(0, 1) + mse = (prediction - reference).square().mean((1, 2, 3)) + report = {'case': name, 'raw_rgb_psnr': (-10 * mse.clamp_min(1e-12).log10()).tolist(), + 'raw_rgb_mse': mse.tolist(), 'mean_rgb': prediction.mean((1, 2, 3)).tolist()} + (output / f'{name}.json').write_text(json.dumps(report, indent=2)) + grid = torch.cat([torch.cat([v[t] for t in [0, 1, 2, 5, 11, 23]], dim=2) + for v in (reference, prediction)], dim=1) + Image.fromarray(grid.mul(255).byte().permute(1, 2, 0).numpy()).save(output / f'{name}.jpg') + print(json.dumps({'case': name, 'mean_psnr': sum(report['raw_rgb_psnr']) / 24, + 'first_psnr': report['raw_rgb_psnr'][:3]}), flush=True) + +@torch.inference_mode() +def main(): + args = parser.parse_args() + rows = select_cases(ROOT / 'deployment/manifests/minecraft_test_300.csv', limit=2) + checkpoint = ROOT / 'checkpoints/matrix-game2' + manifest = {'diagnostic_only': True, 'mode': args.mode, 'checkpoint': str(checkpoint), + 'weight_source': 'released raw distilled', 'optimizer': None, 'lr': None, 'lora': None, + 'activation_checkpointing': False, 'batch_size': 1, 'trainable_parameters': 0, + 'denoising_steps': 3, 'shift': 5, 'local_attention_latents': 6, + 'selector': 'native sliding cache', 'reference': 'raw RGB resized352x640, not benchmark Oasis references', + 'targets': [700, 724], 'full_history': [100, 700], 'full_prefix_latents': 153, + 'oracle': '9copies+RGB100:724 encodedonce;159latents; discard153latent prefix', + 'i2v': {'anchor': 699, 'decoded_slice': [1, 25], 'controls': [700, 724], 'right_noops': 9}, + 'normalization_change': 'weighted modes apply scales and accumulate RMS in fp32', + 'cases': [{'index': r['global_test_index'], 'path': r['relative_path'], + 'seed': 42000000 + int(r['global_test_index'])} for r in rows]} + if args.dry_run: + print(json.dumps(manifest, indent=2)) + return + args.output.mkdir(parents=True, exist_ok=True) + (args.output / 'manifest.json').write_text(json.dumps(manifest, indent=2)) + from wan.modules.action_module import WanRMSNorm + adapter = MatrixGameAdapter(checkpoint) + manifest['parameter_counts'] = adapter.parameter_counts + manifest['action_norm_instances'] = sum(isinstance(m, WanRMSNorm) for m in adapter.pipeline.modules()) + (args.output / 'manifest.json').write_text(json.dumps(manifest, indent=2)) + if args.mode.endswith('weighted'): + WanRMSNorm.forward = weighted_norm + for row in rows: + row.update(target_end='724', target_frames='24') + case = load_minecraft_case(row, args.data_root) + reference = F.interpolate(case['rgb'][600:624], (352, 640), mode='bilinear', align_corners=False, antialias=True) + seed = 42000000 + int(row['global_test_index']) + if args.mode == 'wan_oracle': + rgb = F.interpolate(case['rgb'].cuda(), (352, 640), mode='bilinear', align_corners=False, antialias=True) + rgb = torch.cat([rgb[:1].expand(9, -1, -1, -1), rgb]) + latent = adapter.vae.encode(rgb.transpose(0, 1)[None].mul(2).sub(1).bfloat16(), device=adapter.device, tiled=False) + cache, chunks = [None] * len(ZERO_VAE_CACHE), [] + for start in range(0, latent.shape[2], 3): + decoded, cache = adapter.pipeline.vae_decoder(latent[:, :, start:start + 3].transpose(1, 2).half(), *cache) + if start >= 153: + chunks.append(decoded.cpu()) + prediction = torch.cat(chunks, dim=1)[0].float().add(1).div(2) + del latent, rgb, cache, decoded, chunks + elif args.mode.startswith('full'): + prediction, _ = adapter.generate(case, target_frames=24, seed=seed) + else: + torch.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + anchor = F.interpolate(case['rgb'][599:600].cuda(), (352, 640), mode='bilinear', align_corners=False, antialias=True) + anchor = anchor.transpose(0, 1)[None].mul(2).sub(1).bfloat16() + image = anchor.new_zeros((1, 3, 33, 352, 640)) + image[:, :, :1] = anchor + image_latents = adapter.vae.encode(image, device=adapter.device, tiled=False) + mask = torch.zeros_like(image_latents[:, :4]) + mask[:, :, :1] = 1 + actions = torch.cat([case['actions'][600:624], torch.zeros(9, 25)]).cuda().bfloat16() + keyboard, mouse = map_actions(actions, 1.0) + condition = {'cond_concat': torch.cat([mask, image_latents], dim=1), + 'visual_context': adapter.vae.clip.encode_video(anchor).bfloat16(), + 'keyboard_cond': keyboard[None], 'mouse_cond': mouse[None]} + noise = torch.randn((1, 16, 9, 44, 80), device='cuda', dtype=torch.bfloat16) + videos = adapter.pipeline.inference(noise, condition, initial_latent=None, mode='universal') + prediction = torch.cat(videos, dim=1)[0, 1:25].float().add(1).div(2) + del image, anchor, image_latents, condition, videos, noise + save_result(args.output, f"case{row['global_test_index']}_{args.mode}", prediction, reference) + del prediction, reference, case + (args.output / 'complete.json').write_text(json.dumps({'mode': args.mode, 'cases': 2, 'frames': 24})) + print('DIAGNOSTICS_COMPLETE', flush=True) + +if __name__ == '__main__': + main() diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_full_weighted_job85088_task2/case0_full_weighted.json b/results/worldmem-baseline-evals/matrix-game2/diagnostic_full_weighted_job85088_task2/case0_full_weighted.json new file mode 100644 index 0000000000000000000000000000000000000000..22608060f1939ff02f9fa6dd9baef6b7a78d6ae9 --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_full_weighted_job85088_task2/case0_full_weighted.json @@ -0,0 +1,81 @@ +{ + "case": "case0_full_weighted", + "raw_rgb_psnr": [ + 25.945024490356445, + 4.916481018066406, + 4.617745876312256, + 5.818724632263184, + 7.579941272735596, + 7.90878963470459, + 8.117096900939941, + 8.039791107177734, + 8.028121948242188, + 8.168718338012695, + 8.279888153076172, + 8.406937599182129, + 7.733737945556641, + 7.428074359893799, + 7.25970983505249, + 7.244600296020508, + 7.345211505889893, + 7.292293548583984, + 7.148654937744141, + 7.099469184875488, + 7.157524108886719, + 7.194796085357666, + 7.167351245880127, + 7.1892571449279785 + ], + "raw_rgb_mse": [ + 0.0025438847951591015, + 0.32236796617507935, + 0.34532293677330017, + 0.26189520955085754, + 0.17458458244800568, + 0.1618531197309494, + 0.1542731374502182, + 0.15704382956027985, + 0.1574663519859314, + 0.15245023369789124, + 0.14859738945960999, + 0.1443132609128952, + 0.16851019859313965, + 0.1807975471019745, + 0.187944233417511, + 0.18859927356243134, + 0.18428027629852295, + 0.1865394413471222, + 0.19281220436096191, + 0.1950082778930664, + 0.19241884350776672, + 0.19077451527118683, + 0.19198393821716309, + 0.1910180002450943 + ], + "mean_rgb": [ + 0.6870425343513489, + 0.1719239205121994, + 0.1551111340522766, + 0.23643054068088531, + 0.32379114627838135, + 0.3390358090400696, + 0.3493805527687073, + 0.3448195159435272, + 0.3432111144065857, + 0.349094033241272, + 0.3539511561393738, + 0.360940158367157, + 0.32845252752304077, + 0.3160686194896698, + 0.3070259988307953, + 0.30618816614151, + 0.3123764097690582, + 0.3096041679382324, + 0.30161795020103455, + 0.29898521304130554, + 0.30154550075531006, + 0.3034396171569824, + 0.3025643229484558, + 0.302489310503006 + ] +} \ No newline at end of file diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_full_weighted_job85088_task2/case1_full_weighted.json b/results/worldmem-baseline-evals/matrix-game2/diagnostic_full_weighted_job85088_task2/case1_full_weighted.json new file mode 100644 index 0000000000000000000000000000000000000000..b717cf0c843ddaa8d5f8f0bf9e5baf859005fe37 --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_full_weighted_job85088_task2/case1_full_weighted.json @@ -0,0 +1,81 @@ +{ + "case": "case1_full_weighted", + "raw_rgb_psnr": [ + 20.358261108398438, + 5.021709442138672, + 4.7442827224731445, + 5.203425407409668, + 5.046121597290039, + 5.245299339294434, + 5.384633541107178, + 5.533613681793213, + 5.719480514526367, + 5.826648712158203, + 5.863295078277588, + 5.830295085906982, + 5.900106906890869, + 5.944562911987305, + 6.028284072875977, + 5.974111080169678, + 6.014407157897949, + 6.091710090637207, + 6.0647077560424805, + 6.101002216339111, + 6.22338342666626, + 6.275542259216309, + 6.29188346862793, + 6.269043922424316 + ], + "raw_rgb_mse": [ + 0.009208180010318756, + 0.3146509528160095, + 0.33540669083595276, + 0.3017570674419403, + 0.3128872513771057, + 0.298861563205719, + 0.28942540287971497, + 0.27966535091400146, + 0.2679488956928253, + 0.26141780614852905, + 0.2592211663722992, + 0.26119837164878845, + 0.25703325867652893, + 0.2544155716896057, + 0.24955807626247406, + 0.25269049406051636, + 0.2503567337989807, + 0.24593989551067352, + 0.2474737912416458, + 0.24541424214839935, + 0.2385951578617096, + 0.23574677109718323, + 0.23486140370368958, + 0.23609979450702667 + ], + "mean_rgb": [ + 0.6835651397705078, + 0.20514607429504395, + 0.18633019924163818, + 0.21573315560817719, + 0.20562198758125305, + 0.21696747839450836, + 0.2257290482521057, + 0.23421454429626465, + 0.24657680094242096, + 0.2528727352619171, + 0.25491902232170105, + 0.25266510248184204, + 0.25558704137802124, + 0.25767818093299866, + 0.26240184903144836, + 0.25888851284980774, + 0.2621479630470276, + 0.26704472303390503, + 0.2655029892921448, + 0.267596572637558, + 0.2748774290084839, + 0.27775564789772034, + 0.2788831293582916, + 0.2775684595108032 + ] +} \ No newline at end of file diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_full_weighted_job85088_task2/complete.json b/results/worldmem-baseline-evals/matrix-game2/diagnostic_full_weighted_job85088_task2/complete.json new file mode 100644 index 0000000000000000000000000000000000000000..03674bf425468d5fc6f957e1087856bdab8a2467 --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_full_weighted_job85088_task2/complete.json @@ -0,0 +1 @@ +{"mode": "full_weighted", "cases": 2, "frames": 24} \ No newline at end of file diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_full_weighted_job85088_task2/launcher.sh b/results/worldmem-baseline-evals/matrix-game2/diagnostic_full_weighted_job85088_task2/launcher.sh new file mode 100644 index 0000000000000000000000000000000000000000..339a8b9d8bd588ab4ac7cd6b47a03c94b93c1f4e --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_full_weighted_job85088_task2/launcher.sh @@ -0,0 +1,35 @@ +#!/usr/bin/env bash +#SBATCH --job-name=matrix-game2-diagnostic +#SBATCH --array=0-4 +#SBATCH --nodes=1 +#SBATCH --ntasks=1 +#SBATCH --cpus-per-task=14 +#SBATCH --gres=gpu:1 +#SBATCH --time=01:00:00 +#SBATCH --account=berzelius-2026-196 +#SBATCH --chdir=/proj/cvl/users/x_fahkh2/worldmem-baseline-evals +#SBATCH --output=slurm_logs/%x-%A_%a.out +#SBATCH --error=slurm_logs/%x-%A_%a.out +set -euo pipefail +module load buildenv-gcccuda/12.9.1-gcc11 +ROOT=/proj/cvl/users/x_fahkh2/worldmem-baseline-evals +PYTHON=/proj/cvl/users/x_fahkh2/envs/matrix-game2-eval/bin/python +DATA_ROOT=/proj/cvl/users/x_fahkh2/WorldMem_Repro/datasets/minecraft +MODES=(wan_oracle full_original full_weighted i2v_original i2v_weighted) +MODE=${MODES[$SLURM_ARRAY_TASK_ID]} +OUTPUT=$ROOT/Matrix-Game/Matrix-Game-2/outputs/diagnostic_${MODE}_job${SLURM_ARRAY_JOB_ID}_task${SLURM_ARRAY_TASK_ID} +export TMPDIR=$ROOT/cache +export TRITON_CACHE_DIR=$ROOT/cache +export HF_HOME=$ROOT/cache/huggingface +export TORCH_HOME=$ROOT/cache/torch +export XDG_CACHE_HOME=$ROOT/cache +export MPLCONFIGDIR=$ROOT/cache/matplotlib +export PYTHONNOUSERSITE=1 PYTHONUNBUFFERED=1 OMP_NUM_THREADS=4 MKL_NUM_THREADS=4 +export WANDB_MODE=disabled HF_HUB_OFFLINE=1 TRANSFORMERS_OFFLINE=1 +mkdir -p "$TMPDIR" "$OUTPUT" +"$PYTHON" "$ROOT/deployment/verify_node.py" --expected-gpus 1 --output "$OUTPUT" +cp "$0" "$OUTPUT/launcher.sh" +cp "$ROOT/deployment/diagnose_matrix_game2.py" "$OUTPUT/probe.py" +srun --ntasks=1 --gpus-per-task=1 --cpus-per-task=14 --cpu-bind=cores \ + "$PYTHON" "$ROOT/deployment/diagnose_matrix_game2.py" \ + --mode "$MODE" --data-root "$DATA_ROOT" --output "$OUTPUT" diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_full_weighted_job85088_task2/manifest.json b/results/worldmem-baseline-evals/matrix-game2/diagnostic_full_weighted_job85088_task2/manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..5d571eb0dd0a85d0089a44336229f4f0b43712a5 --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_full_weighted_job85088_task2/manifest.json @@ -0,0 +1,59 @@ +{ + "diagnostic_only": true, + "mode": "full_weighted", + "checkpoint": "/proj/cvl/users/x_fahkh2/worldmem-baseline-evals/checkpoints/matrix-game2", + "weight_source": "released raw distilled", + "optimizer": null, + "lr": null, + "lora": null, + "activation_checkpointing": false, + "batch_size": 1, + "trainable_parameters": 0, + "denoising_steps": 3, + "shift": 5, + "local_attention_latents": 6, + "selector": "native sliding cache", + "reference": "raw RGB resized352x640, not benchmark Oasis references", + "targets": [ + 700, + 724 + ], + "full_history": [ + 100, + 700 + ], + "full_prefix_latents": 153, + "oracle": "9copies+RGB100:724 encodedonce;159latents; discard153latent prefix", + "i2v": { + "anchor": 699, + "decoded_slice": [ + 1, + 25 + ], + "controls": [ + 700, + 724 + ], + "right_noops": 9 + }, + "normalization_change": "weighted modes apply scales and accumulate RMS in fp32", + "cases": [ + { + "index": "0", + "path": "test/val_plains/2/000034.mp4", + "seed": 42000000 + }, + { + "index": "1", + "path": "test/val_desert/1/000029.mp4", + "seed": 42000001 + } + ], + "parameter_counts": { + "generator_total": 1619242304, + "generator_trainable": 0, + "vae_total": 126892531, + "clip_total": 1193014785 + }, + "action_norm_instances": 60 +} \ No newline at end of file diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_full_weighted_job85088_task2/node.json b/results/worldmem-baseline-evals/matrix-game2/diagnostic_full_weighted_job85088_task2/node.json new file mode 100644 index 0000000000000000000000000000000000000000..512d05230f9f013a94a3517c71c947270e315112 --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_full_weighted_job85088_task2/node.json @@ -0,0 +1,7 @@ +{ + "devices": [ + "NVIDIA H200" + ], + "torch": "2.7.0+cu126", + "slurm_job_id": "85091" +} diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_full_weighted_job85088_task2/probe.py b/results/worldmem-baseline-evals/matrix-game2/diagnostic_full_weighted_job85088_task2/probe.py new file mode 100644 index 0000000000000000000000000000000000000000..f5b01f8975ad3f66be61f6e960f05c64d14fd6e0 --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_full_weighted_job85088_task2/probe.py @@ -0,0 +1,113 @@ +"""Frozen Matrix diagnostics; raw-RGB PSNR is separate from benchmark scoring.""" +import argparse +import json +import sys +from pathlib import Path +import torch +import torch.nn.functional as F +from PIL import Image + +ROOT = Path(__file__).resolve().parents[1] +sys.path[:0] = [str(ROOT / 'shared'), str(ROOT / 'Matrix-Game/Matrix-Game-2')] +from protocol import load_minecraft_case, select_cases +from worldmem_adapter import MatrixGameAdapter, map_actions +from demo_utils.constant import ZERO_VAE_CACHE + +MODES = ['wan_oracle', 'full_original', 'full_weighted', 'i2v_original', 'i2v_weighted'] +parser = argparse.ArgumentParser(description=__doc__) +parser.add_argument('--mode', choices=MODES, required=True) +parser.add_argument('--data-root', type=Path, required=True) +parser.add_argument('--output', type=Path, required=True) +parser.add_argument('--dry-run', action='store_true') + +def weighted_norm(self, x): + y = x.float() + return (y * torch.rsqrt(y.square().mean(-1, keepdim=True) + self.eps)).to(x.dtype) * self.weight + +def save_result(output, name, prediction, reference): + prediction = prediction.float().cpu() + if prediction.shape != reference.shape or not torch.isfinite(prediction).all(): + raise ValueError(f'Invalid Matrix prediction: {name}, shape={tuple(prediction.shape)}') + prediction = prediction.clamp(0, 1) + mse = (prediction - reference).square().mean((1, 2, 3)) + report = {'case': name, 'raw_rgb_psnr': (-10 * mse.clamp_min(1e-12).log10()).tolist(), + 'raw_rgb_mse': mse.tolist(), 'mean_rgb': prediction.mean((1, 2, 3)).tolist()} + (output / f'{name}.json').write_text(json.dumps(report, indent=2)) + grid = torch.cat([torch.cat([v[t] for t in [0, 1, 2, 5, 11, 23]], dim=2) + for v in (reference, prediction)], dim=1) + Image.fromarray(grid.mul(255).byte().permute(1, 2, 0).numpy()).save(output / f'{name}.jpg') + print(json.dumps({'case': name, 'mean_psnr': sum(report['raw_rgb_psnr']) / 24, + 'first_psnr': report['raw_rgb_psnr'][:3]}), flush=True) + +@torch.inference_mode() +def main(): + args = parser.parse_args() + rows = select_cases(ROOT / 'deployment/manifests/minecraft_test_300.csv', limit=2) + checkpoint = ROOT / 'checkpoints/matrix-game2' + manifest = {'diagnostic_only': True, 'mode': args.mode, 'checkpoint': str(checkpoint), + 'weight_source': 'released raw distilled', 'optimizer': None, 'lr': None, 'lora': None, + 'activation_checkpointing': False, 'batch_size': 1, 'trainable_parameters': 0, + 'denoising_steps': 3, 'shift': 5, 'local_attention_latents': 6, + 'selector': 'native sliding cache', 'reference': 'raw RGB resized352x640, not benchmark Oasis references', + 'targets': [700, 724], 'full_history': [100, 700], 'full_prefix_latents': 153, + 'oracle': '9copies+RGB100:724 encodedonce;159latents; discard153latent prefix', + 'i2v': {'anchor': 699, 'decoded_slice': [1, 25], 'controls': [700, 724], 'right_noops': 9}, + 'normalization_change': 'weighted modes apply scales and accumulate RMS in fp32', + 'cases': [{'index': r['global_test_index'], 'path': r['relative_path'], + 'seed': 42000000 + int(r['global_test_index'])} for r in rows]} + if args.dry_run: + print(json.dumps(manifest, indent=2)) + return + args.output.mkdir(parents=True, exist_ok=True) + (args.output / 'manifest.json').write_text(json.dumps(manifest, indent=2)) + from wan.modules.action_module import WanRMSNorm + adapter = MatrixGameAdapter(checkpoint) + manifest['parameter_counts'] = adapter.parameter_counts + manifest['action_norm_instances'] = sum(isinstance(m, WanRMSNorm) for m in adapter.pipeline.modules()) + (args.output / 'manifest.json').write_text(json.dumps(manifest, indent=2)) + if args.mode.endswith('weighted'): + WanRMSNorm.forward = weighted_norm + for row in rows: + row.update(target_end='724', target_frames='24') + case = load_minecraft_case(row, args.data_root) + reference = F.interpolate(case['rgb'][600:624], (352, 640), mode='bilinear', align_corners=False, antialias=True) + seed = 42000000 + int(row['global_test_index']) + if args.mode == 'wan_oracle': + rgb = F.interpolate(case['rgb'].cuda(), (352, 640), mode='bilinear', align_corners=False, antialias=True) + rgb = torch.cat([rgb[:1].expand(9, -1, -1, -1), rgb]) + latent = adapter.vae.encode(rgb.transpose(0, 1)[None].mul(2).sub(1).bfloat16(), device=adapter.device, tiled=False) + cache, chunks = [None] * len(ZERO_VAE_CACHE), [] + for start in range(0, latent.shape[2], 3): + decoded, cache = adapter.pipeline.vae_decoder(latent[:, :, start:start + 3].transpose(1, 2).half(), *cache) + if start >= 153: + chunks.append(decoded.cpu()) + prediction = torch.cat(chunks, dim=1)[0].float().add(1).div(2) + del latent, rgb, cache, decoded, chunks + elif args.mode.startswith('full'): + prediction, _ = adapter.generate(case, target_frames=24, seed=seed) + else: + torch.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + anchor = F.interpolate(case['rgb'][599:600].cuda(), (352, 640), mode='bilinear', align_corners=False, antialias=True) + anchor = anchor.transpose(0, 1)[None].mul(2).sub(1).bfloat16() + image = anchor.new_zeros((1, 3, 33, 352, 640)) + image[:, :, :1] = anchor + image_latents = adapter.vae.encode(image, device=adapter.device, tiled=False) + mask = torch.zeros_like(image_latents[:, :4]) + mask[:, :, :1] = 1 + actions = torch.cat([case['actions'][600:624], torch.zeros(9, 25)]).cuda().bfloat16() + keyboard, mouse = map_actions(actions, 1.0) + condition = {'cond_concat': torch.cat([mask, image_latents], dim=1), + 'visual_context': adapter.vae.clip.encode_video(anchor).bfloat16(), + 'keyboard_cond': keyboard[None], 'mouse_cond': mouse[None]} + noise = torch.randn((1, 16, 9, 44, 80), device='cuda', dtype=torch.bfloat16) + videos = adapter.pipeline.inference(noise, condition, initial_latent=None, mode='universal') + prediction = torch.cat(videos, dim=1)[0, 1:25].float().add(1).div(2) + del image, anchor, image_latents, condition, videos, noise + save_result(args.output, f"case{row['global_test_index']}_{args.mode}", prediction, reference) + del prediction, reference, case + (args.output / 'complete.json').write_text(json.dumps({'mode': args.mode, 'cases': 2, 'frames': 24})) + print('DIAGNOSTICS_COMPLETE', flush=True) + +if __name__ == '__main__': + main() diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_i2v_original_job85088_task3/case0_i2v_original.json b/results/worldmem-baseline-evals/matrix-game2/diagnostic_i2v_original_job85088_task3/case0_i2v_original.json new file mode 100644 index 0000000000000000000000000000000000000000..5d21b106d545969c8f9b71357b39dcdada6bc66b --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_i2v_original_job85088_task3/case0_i2v_original.json @@ -0,0 +1,81 @@ +{ + "case": "case0_i2v_original", + "raw_rgb_psnr": [ + 28.51933479309082, + 5.62062931060791, + 6.261198997497559, + 7.325035095214844, + 7.525331497192383, + 7.780514240264893, + 7.777151107788086, + 7.821002006530762, + 7.603299617767334, + 7.544827461242676, + 7.494100570678711, + 7.490266799926758, + 7.607268810272217, + 7.717894554138184, + 7.877525329589844, + 7.909063816070557, + 7.8667497634887695, + 7.888334274291992, + 7.912131309509277, + 7.934691905975342, + 7.701882839202881, + 6.664777755737305, + 7.160373687744141, + 7.919079780578613 + ], + "raw_rgb_mse": [ + 0.001406263094395399, + 0.27411770820617676, + 0.23652665317058563, + 0.18513838946819305, + 0.17679372429847717, + 0.16670498251914978, + 0.16683414578437805, + 0.16515807807445526, + 0.17364810407161713, + 0.17600186169147491, + 0.17806968092918396, + 0.17822694778442383, + 0.17348946630954742, + 0.16912606358528137, + 0.1630224734544754, + 0.16184288263320923, + 0.1634274572134018, + 0.16261722147464752, + 0.1617286056280136, + 0.16089065372943878, + 0.16975075006484985, + 0.2155371904373169, + 0.19229261577129364, + 0.16147007048130035 + ], + "mean_rgb": [ + 0.7052807807922363, + 0.22267355024814606, + 0.26383358240127563, + 0.31955206394195557, + 0.3294263184070587, + 0.3413337469100952, + 0.34034264087677, + 0.3422088921070099, + 0.3322543799877167, + 0.3306882083415985, + 0.32795271277427673, + 0.32645344734191895, + 0.32989341020584106, + 0.33399924635887146, + 0.34038686752319336, + 0.34092986583709717, + 0.33980700373649597, + 0.3406497836112976, + 0.3414357602596283, + 0.3423357307910919, + 0.3307926654815674, + 0.2806178331375122, + 0.30535194277763367, + 0.34129393100738525 + ] +} \ No newline at end of file diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_i2v_original_job85088_task3/case1_i2v_original.json b/results/worldmem-baseline-evals/matrix-game2/diagnostic_i2v_original_job85088_task3/case1_i2v_original.json new file mode 100644 index 0000000000000000000000000000000000000000..513c77766babe9a78cebb64b01cbd6d5276d8f03 --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_i2v_original_job85088_task3/case1_i2v_original.json @@ -0,0 +1,81 @@ +{ + "case": "case1_i2v_original", + "raw_rgb_psnr": [ + 15.054824829101562, + 11.44872760772705, + 9.014760971069336, + 7.8256916999816895, + 6.933335781097412, + 6.524610996246338, + 6.566136360168457, + 6.591427803039551, + 6.48160982131958, + 6.368434906005859, + 4.373075008392334, + 5.369548320770264, + 5.37614631652832, + 5.41501522064209, + 5.501518726348877, + 5.463625907897949, + 5.47122859954834, + 5.503015041351318, + 5.575506210327148, + 5.547548294067383, + 5.560653209686279, + 5.55626106262207, + 5.5676751136779785, + 5.580542087554932 + ], + "raw_rgb_mse": [ + 0.031226083636283875, + 0.07163531333208084, + 0.12546537816524506, + 0.16497983038425446, + 0.20261257886886597, + 0.22260703146457672, + 0.22048871219158173, + 0.21920840442180634, + 0.22482208907604218, + 0.23075786232948303, + 0.36533603072166443, + 0.29043248295783997, + 0.289991557598114, + 0.2874077558517456, + 0.281739741563797, + 0.284208744764328, + 0.28371164202690125, + 0.28164270520210266, + 0.27698060870170593, + 0.27876943349838257, + 0.27792951464653015, + 0.27821075916290283, + 0.27748051285743713, + 0.27665963768959045 + ], + "mean_rgb": [ + 0.6844472289085388, + 0.6284617781639099, + 0.5540845394134521, + 0.49505874514579773, + 0.4436206817626953, + 0.41198185086250305, + 0.4130478501319885, + 0.41542676091194153, + 0.406199187040329, + 0.39254048466682434, + 0.16926062107086182, + 0.22599884867668152, + 0.22546076774597168, + 0.22791211307048798, + 0.23338943719863892, + 0.23143425583839417, + 0.23280683159828186, + 0.23524777591228485, + 0.23973938822746277, + 0.23811960220336914, + 0.23764897882938385, + 0.2369965761899948, + 0.23754198849201202, + 0.23855453729629517 + ] +} \ No newline at end of file diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_i2v_original_job85088_task3/complete.json b/results/worldmem-baseline-evals/matrix-game2/diagnostic_i2v_original_job85088_task3/complete.json new file mode 100644 index 0000000000000000000000000000000000000000..6e89b0ff2b87619db998672bd36a218bdc30e49e --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_i2v_original_job85088_task3/complete.json @@ -0,0 +1 @@ +{"mode": "i2v_original", "cases": 2, "frames": 24} \ No newline at end of file diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_i2v_original_job85088_task3/launcher.sh b/results/worldmem-baseline-evals/matrix-game2/diagnostic_i2v_original_job85088_task3/launcher.sh new file mode 100644 index 0000000000000000000000000000000000000000..339a8b9d8bd588ab4ac7cd6b47a03c94b93c1f4e --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_i2v_original_job85088_task3/launcher.sh @@ -0,0 +1,35 @@ +#!/usr/bin/env bash +#SBATCH --job-name=matrix-game2-diagnostic +#SBATCH --array=0-4 +#SBATCH --nodes=1 +#SBATCH --ntasks=1 +#SBATCH --cpus-per-task=14 +#SBATCH --gres=gpu:1 +#SBATCH --time=01:00:00 +#SBATCH --account=berzelius-2026-196 +#SBATCH --chdir=/proj/cvl/users/x_fahkh2/worldmem-baseline-evals +#SBATCH --output=slurm_logs/%x-%A_%a.out +#SBATCH --error=slurm_logs/%x-%A_%a.out +set -euo pipefail +module load buildenv-gcccuda/12.9.1-gcc11 +ROOT=/proj/cvl/users/x_fahkh2/worldmem-baseline-evals +PYTHON=/proj/cvl/users/x_fahkh2/envs/matrix-game2-eval/bin/python +DATA_ROOT=/proj/cvl/users/x_fahkh2/WorldMem_Repro/datasets/minecraft +MODES=(wan_oracle full_original full_weighted i2v_original i2v_weighted) +MODE=${MODES[$SLURM_ARRAY_TASK_ID]} +OUTPUT=$ROOT/Matrix-Game/Matrix-Game-2/outputs/diagnostic_${MODE}_job${SLURM_ARRAY_JOB_ID}_task${SLURM_ARRAY_TASK_ID} +export TMPDIR=$ROOT/cache +export TRITON_CACHE_DIR=$ROOT/cache +export HF_HOME=$ROOT/cache/huggingface +export TORCH_HOME=$ROOT/cache/torch +export XDG_CACHE_HOME=$ROOT/cache +export MPLCONFIGDIR=$ROOT/cache/matplotlib +export PYTHONNOUSERSITE=1 PYTHONUNBUFFERED=1 OMP_NUM_THREADS=4 MKL_NUM_THREADS=4 +export WANDB_MODE=disabled HF_HUB_OFFLINE=1 TRANSFORMERS_OFFLINE=1 +mkdir -p "$TMPDIR" "$OUTPUT" +"$PYTHON" "$ROOT/deployment/verify_node.py" --expected-gpus 1 --output "$OUTPUT" +cp "$0" "$OUTPUT/launcher.sh" +cp "$ROOT/deployment/diagnose_matrix_game2.py" "$OUTPUT/probe.py" +srun --ntasks=1 --gpus-per-task=1 --cpus-per-task=14 --cpu-bind=cores \ + "$PYTHON" "$ROOT/deployment/diagnose_matrix_game2.py" \ + --mode "$MODE" --data-root "$DATA_ROOT" --output "$OUTPUT" diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_i2v_original_job85088_task3/manifest.json b/results/worldmem-baseline-evals/matrix-game2/diagnostic_i2v_original_job85088_task3/manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..26d835740d2d28c9a1855210665503c51eca313f --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_i2v_original_job85088_task3/manifest.json @@ -0,0 +1,59 @@ +{ + "diagnostic_only": true, + "mode": "i2v_original", + "checkpoint": "/proj/cvl/users/x_fahkh2/worldmem-baseline-evals/checkpoints/matrix-game2", + "weight_source": "released raw distilled", + "optimizer": null, + "lr": null, + "lora": null, + "activation_checkpointing": false, + "batch_size": 1, + "trainable_parameters": 0, + "denoising_steps": 3, + "shift": 5, + "local_attention_latents": 6, + "selector": "native sliding cache", + "reference": "raw RGB resized352x640, not benchmark Oasis references", + "targets": [ + 700, + 724 + ], + "full_history": [ + 100, + 700 + ], + "full_prefix_latents": 153, + "oracle": "9copies+RGB100:724 encodedonce;159latents; discard153latent prefix", + "i2v": { + "anchor": 699, + "decoded_slice": [ + 1, + 25 + ], + "controls": [ + 700, + 724 + ], + "right_noops": 9 + }, + "normalization_change": "weighted modes apply scales and accumulate RMS in fp32", + "cases": [ + { + "index": "0", + "path": "test/val_plains/2/000034.mp4", + "seed": 42000000 + }, + { + "index": "1", + "path": "test/val_desert/1/000029.mp4", + "seed": 42000001 + } + ], + "parameter_counts": { + "generator_total": 1619242304, + "generator_trainable": 0, + "vae_total": 126892531, + "clip_total": 1193014785 + }, + "action_norm_instances": 60 +} \ No newline at end of file diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_i2v_original_job85088_task3/node.json b/results/worldmem-baseline-evals/matrix-game2/diagnostic_i2v_original_job85088_task3/node.json new file mode 100644 index 0000000000000000000000000000000000000000..8d35ac451d1dcf5414d2ebc6a5be73f728cd25f8 --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_i2v_original_job85088_task3/node.json @@ -0,0 +1,7 @@ +{ + "devices": [ + "NVIDIA H200" + ], + "torch": "2.7.0+cu126", + "slurm_job_id": "85092" +} diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_i2v_original_job85088_task3/probe.py b/results/worldmem-baseline-evals/matrix-game2/diagnostic_i2v_original_job85088_task3/probe.py new file mode 100644 index 0000000000000000000000000000000000000000..f5b01f8975ad3f66be61f6e960f05c64d14fd6e0 --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_i2v_original_job85088_task3/probe.py @@ -0,0 +1,113 @@ +"""Frozen Matrix diagnostics; raw-RGB PSNR is separate from benchmark scoring.""" +import argparse +import json +import sys +from pathlib import Path +import torch +import torch.nn.functional as F +from PIL import Image + +ROOT = Path(__file__).resolve().parents[1] +sys.path[:0] = [str(ROOT / 'shared'), str(ROOT / 'Matrix-Game/Matrix-Game-2')] +from protocol import load_minecraft_case, select_cases +from worldmem_adapter import MatrixGameAdapter, map_actions +from demo_utils.constant import ZERO_VAE_CACHE + +MODES = ['wan_oracle', 'full_original', 'full_weighted', 'i2v_original', 'i2v_weighted'] +parser = argparse.ArgumentParser(description=__doc__) +parser.add_argument('--mode', choices=MODES, required=True) +parser.add_argument('--data-root', type=Path, required=True) +parser.add_argument('--output', type=Path, required=True) +parser.add_argument('--dry-run', action='store_true') + +def weighted_norm(self, x): + y = x.float() + return (y * torch.rsqrt(y.square().mean(-1, keepdim=True) + self.eps)).to(x.dtype) * self.weight + +def save_result(output, name, prediction, reference): + prediction = prediction.float().cpu() + if prediction.shape != reference.shape or not torch.isfinite(prediction).all(): + raise ValueError(f'Invalid Matrix prediction: {name}, shape={tuple(prediction.shape)}') + prediction = prediction.clamp(0, 1) + mse = (prediction - reference).square().mean((1, 2, 3)) + report = {'case': name, 'raw_rgb_psnr': (-10 * mse.clamp_min(1e-12).log10()).tolist(), + 'raw_rgb_mse': mse.tolist(), 'mean_rgb': prediction.mean((1, 2, 3)).tolist()} + (output / f'{name}.json').write_text(json.dumps(report, indent=2)) + grid = torch.cat([torch.cat([v[t] for t in [0, 1, 2, 5, 11, 23]], dim=2) + for v in (reference, prediction)], dim=1) + Image.fromarray(grid.mul(255).byte().permute(1, 2, 0).numpy()).save(output / f'{name}.jpg') + print(json.dumps({'case': name, 'mean_psnr': sum(report['raw_rgb_psnr']) / 24, + 'first_psnr': report['raw_rgb_psnr'][:3]}), flush=True) + +@torch.inference_mode() +def main(): + args = parser.parse_args() + rows = select_cases(ROOT / 'deployment/manifests/minecraft_test_300.csv', limit=2) + checkpoint = ROOT / 'checkpoints/matrix-game2' + manifest = {'diagnostic_only': True, 'mode': args.mode, 'checkpoint': str(checkpoint), + 'weight_source': 'released raw distilled', 'optimizer': None, 'lr': None, 'lora': None, + 'activation_checkpointing': False, 'batch_size': 1, 'trainable_parameters': 0, + 'denoising_steps': 3, 'shift': 5, 'local_attention_latents': 6, + 'selector': 'native sliding cache', 'reference': 'raw RGB resized352x640, not benchmark Oasis references', + 'targets': [700, 724], 'full_history': [100, 700], 'full_prefix_latents': 153, + 'oracle': '9copies+RGB100:724 encodedonce;159latents; discard153latent prefix', + 'i2v': {'anchor': 699, 'decoded_slice': [1, 25], 'controls': [700, 724], 'right_noops': 9}, + 'normalization_change': 'weighted modes apply scales and accumulate RMS in fp32', + 'cases': [{'index': r['global_test_index'], 'path': r['relative_path'], + 'seed': 42000000 + int(r['global_test_index'])} for r in rows]} + if args.dry_run: + print(json.dumps(manifest, indent=2)) + return + args.output.mkdir(parents=True, exist_ok=True) + (args.output / 'manifest.json').write_text(json.dumps(manifest, indent=2)) + from wan.modules.action_module import WanRMSNorm + adapter = MatrixGameAdapter(checkpoint) + manifest['parameter_counts'] = adapter.parameter_counts + manifest['action_norm_instances'] = sum(isinstance(m, WanRMSNorm) for m in adapter.pipeline.modules()) + (args.output / 'manifest.json').write_text(json.dumps(manifest, indent=2)) + if args.mode.endswith('weighted'): + WanRMSNorm.forward = weighted_norm + for row in rows: + row.update(target_end='724', target_frames='24') + case = load_minecraft_case(row, args.data_root) + reference = F.interpolate(case['rgb'][600:624], (352, 640), mode='bilinear', align_corners=False, antialias=True) + seed = 42000000 + int(row['global_test_index']) + if args.mode == 'wan_oracle': + rgb = F.interpolate(case['rgb'].cuda(), (352, 640), mode='bilinear', align_corners=False, antialias=True) + rgb = torch.cat([rgb[:1].expand(9, -1, -1, -1), rgb]) + latent = adapter.vae.encode(rgb.transpose(0, 1)[None].mul(2).sub(1).bfloat16(), device=adapter.device, tiled=False) + cache, chunks = [None] * len(ZERO_VAE_CACHE), [] + for start in range(0, latent.shape[2], 3): + decoded, cache = adapter.pipeline.vae_decoder(latent[:, :, start:start + 3].transpose(1, 2).half(), *cache) + if start >= 153: + chunks.append(decoded.cpu()) + prediction = torch.cat(chunks, dim=1)[0].float().add(1).div(2) + del latent, rgb, cache, decoded, chunks + elif args.mode.startswith('full'): + prediction, _ = adapter.generate(case, target_frames=24, seed=seed) + else: + torch.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + anchor = F.interpolate(case['rgb'][599:600].cuda(), (352, 640), mode='bilinear', align_corners=False, antialias=True) + anchor = anchor.transpose(0, 1)[None].mul(2).sub(1).bfloat16() + image = anchor.new_zeros((1, 3, 33, 352, 640)) + image[:, :, :1] = anchor + image_latents = adapter.vae.encode(image, device=adapter.device, tiled=False) + mask = torch.zeros_like(image_latents[:, :4]) + mask[:, :, :1] = 1 + actions = torch.cat([case['actions'][600:624], torch.zeros(9, 25)]).cuda().bfloat16() + keyboard, mouse = map_actions(actions, 1.0) + condition = {'cond_concat': torch.cat([mask, image_latents], dim=1), + 'visual_context': adapter.vae.clip.encode_video(anchor).bfloat16(), + 'keyboard_cond': keyboard[None], 'mouse_cond': mouse[None]} + noise = torch.randn((1, 16, 9, 44, 80), device='cuda', dtype=torch.bfloat16) + videos = adapter.pipeline.inference(noise, condition, initial_latent=None, mode='universal') + prediction = torch.cat(videos, dim=1)[0, 1:25].float().add(1).div(2) + del image, anchor, image_latents, condition, videos, noise + save_result(args.output, f"case{row['global_test_index']}_{args.mode}", prediction, reference) + del prediction, reference, case + (args.output / 'complete.json').write_text(json.dumps({'mode': args.mode, 'cases': 2, 'frames': 24})) + print('DIAGNOSTICS_COMPLETE', flush=True) + +if __name__ == '__main__': + main() diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_i2v_weighted_job85088_task4/case0_i2v_weighted.json b/results/worldmem-baseline-evals/matrix-game2/diagnostic_i2v_weighted_job85088_task4/case0_i2v_weighted.json new file mode 100644 index 0000000000000000000000000000000000000000..bebf4db7a768b5c77a6cf17432c0301e6508f470 --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_i2v_weighted_job85088_task4/case0_i2v_weighted.json @@ -0,0 +1,81 @@ +{ + "case": "case0_i2v_weighted", + "raw_rgb_psnr": [ + 28.400230407714844, + 5.406888008117676, + 6.059108257293701, + 7.271352767944336, + 7.498164653778076, + 7.74724006652832, + 7.7596211433410645, + 7.7729315757751465, + 7.52515983581543, + 7.486701488494873, + 7.524697303771973, + 7.638741493225098, + 7.781850814819336, + 7.818033695220947, + 7.900123119354248, + 7.916782379150391, + 7.866796493530273, + 7.875070095062256, + 7.894111633300781, + 7.932208061218262, + 7.702528953552246, + 7.582130432128906, + 7.766695976257324, + 7.993913650512695 + ], + "raw_rgb_mse": [ + 0.0014453631592914462, + 0.28794607520103455, + 0.2477930635213852, + 0.18744103610515594, + 0.17790310084819794, + 0.16798712313175201, + 0.16750890016555786, + 0.1669963002204895, + 0.17680072784423828, + 0.1783733069896698, + 0.1768195480108261, + 0.17223675549030304, + 0.16665369272232056, + 0.16527098417282104, + 0.16217641532421112, + 0.16155549883842468, + 0.16342569887638092, + 0.16311465203762054, + 0.16240103542804718, + 0.16098269820213318, + 0.16972550749778748, + 0.1744966059923172, + 0.16723622381687164, + 0.15871158242225647 + ], + "mean_rgb": [ + 0.7053292989730835, + 0.20904700458049774, + 0.2530539929866791, + 0.3162554204463959, + 0.3277013599872589, + 0.33897286653518677, + 0.3385704457759857, + 0.3386596143245697, + 0.32705172896385193, + 0.3246579170227051, + 0.32665011286735535, + 0.33084917068481445, + 0.33509644865989685, + 0.335825651884079, + 0.34004831314086914, + 0.34061479568481445, + 0.338895708322525, + 0.339072585105896, + 0.33963221311569214, + 0.3410741984844208, + 0.3300783038139343, + 0.32414621114730835, + 0.33280378580093384, + 0.3427526652812958 + ] +} \ No newline at end of file diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_i2v_weighted_job85088_task4/case1_i2v_weighted.json b/results/worldmem-baseline-evals/matrix-game2/diagnostic_i2v_weighted_job85088_task4/case1_i2v_weighted.json new file mode 100644 index 0000000000000000000000000000000000000000..7c4d312f0d40b1d2584c959cd3bf42e73f8781d7 --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_i2v_weighted_job85088_task4/case1_i2v_weighted.json @@ -0,0 +1,81 @@ +{ + "case": "case1_i2v_weighted", + "raw_rgb_psnr": [ + 15.572078704833984, + 11.67737865447998, + 9.182900428771973, + 7.9456071853637695, + 6.916970729827881, + 6.555791854858398, + 6.522993564605713, + 6.507280349731445, + 6.314092636108398, + 6.2913408279418945, + 6.392267227172852, + 6.423827171325684, + 6.575774669647217, + 4.436397075653076, + 4.66413688659668, + 4.843669414520264, + 4.8888068199157715, + 4.991722106933594, + 5.058438301086426, + 5.032224178314209, + 5.076114654541016, + 5.092604637145996, + 5.115626335144043, + 5.137517929077148 + ], + "raw_rgb_mse": [ + 0.027719931676983833, + 0.06796137243509293, + 0.12070075422525406, + 0.16048678755760193, + 0.20337752997875214, + 0.22101451456546783, + 0.22268995642662048, + 0.22349712252616882, + 0.2336634248495102, + 0.23489077389240265, + 0.22949503362178802, + 0.2278333306312561, + 0.21999992430210114, + 0.3600479066371918, + 0.34165385365486145, + 0.32781821489334106, + 0.3244287371635437, + 0.3168310821056366, + 0.31200113892555237, + 0.31389009952545166, + 0.3107338547706604, + 0.3095562160015106, + 0.30791962146759033, + 0.3063713610172272 + ], + "mean_rgb": [ + 0.6906836032867432, + 0.6348850727081299, + 0.5620357394218445, + 0.5025600790977478, + 0.4471380114555359, + 0.41820263862609863, + 0.4126875400543213, + 0.41116610169410706, + 0.3962208926677704, + 0.3935016095638275, + 0.3975541591644287, + 0.39638906717300415, + 0.40340858697891235, + 0.16516827046871185, + 0.18019184470176697, + 0.19274631142616272, + 0.1964321732521057, + 0.20392490923404694, + 0.20804454386234283, + 0.2059834748506546, + 0.20835448801517487, + 0.20949353277683258, + 0.21102812886238098, + 0.2126505970954895 + ] +} \ No newline at end of file diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_i2v_weighted_job85088_task4/complete.json b/results/worldmem-baseline-evals/matrix-game2/diagnostic_i2v_weighted_job85088_task4/complete.json new file mode 100644 index 0000000000000000000000000000000000000000..bbb479b1d32904d630d48268d290a07cad100fdc --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_i2v_weighted_job85088_task4/complete.json @@ -0,0 +1 @@ +{"mode": "i2v_weighted", "cases": 2, "frames": 24} \ No newline at end of file diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_i2v_weighted_job85088_task4/launcher.sh b/results/worldmem-baseline-evals/matrix-game2/diagnostic_i2v_weighted_job85088_task4/launcher.sh new file mode 100644 index 0000000000000000000000000000000000000000..339a8b9d8bd588ab4ac7cd6b47a03c94b93c1f4e --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_i2v_weighted_job85088_task4/launcher.sh @@ -0,0 +1,35 @@ +#!/usr/bin/env bash +#SBATCH --job-name=matrix-game2-diagnostic +#SBATCH --array=0-4 +#SBATCH --nodes=1 +#SBATCH --ntasks=1 +#SBATCH --cpus-per-task=14 +#SBATCH --gres=gpu:1 +#SBATCH --time=01:00:00 +#SBATCH --account=berzelius-2026-196 +#SBATCH --chdir=/proj/cvl/users/x_fahkh2/worldmem-baseline-evals +#SBATCH --output=slurm_logs/%x-%A_%a.out +#SBATCH --error=slurm_logs/%x-%A_%a.out +set -euo pipefail +module load buildenv-gcccuda/12.9.1-gcc11 +ROOT=/proj/cvl/users/x_fahkh2/worldmem-baseline-evals +PYTHON=/proj/cvl/users/x_fahkh2/envs/matrix-game2-eval/bin/python +DATA_ROOT=/proj/cvl/users/x_fahkh2/WorldMem_Repro/datasets/minecraft +MODES=(wan_oracle full_original full_weighted i2v_original i2v_weighted) +MODE=${MODES[$SLURM_ARRAY_TASK_ID]} +OUTPUT=$ROOT/Matrix-Game/Matrix-Game-2/outputs/diagnostic_${MODE}_job${SLURM_ARRAY_JOB_ID}_task${SLURM_ARRAY_TASK_ID} +export TMPDIR=$ROOT/cache +export TRITON_CACHE_DIR=$ROOT/cache +export HF_HOME=$ROOT/cache/huggingface +export TORCH_HOME=$ROOT/cache/torch +export XDG_CACHE_HOME=$ROOT/cache +export MPLCONFIGDIR=$ROOT/cache/matplotlib +export PYTHONNOUSERSITE=1 PYTHONUNBUFFERED=1 OMP_NUM_THREADS=4 MKL_NUM_THREADS=4 +export WANDB_MODE=disabled HF_HUB_OFFLINE=1 TRANSFORMERS_OFFLINE=1 +mkdir -p "$TMPDIR" "$OUTPUT" +"$PYTHON" "$ROOT/deployment/verify_node.py" --expected-gpus 1 --output "$OUTPUT" +cp "$0" "$OUTPUT/launcher.sh" +cp "$ROOT/deployment/diagnose_matrix_game2.py" "$OUTPUT/probe.py" +srun --ntasks=1 --gpus-per-task=1 --cpus-per-task=14 --cpu-bind=cores \ + "$PYTHON" "$ROOT/deployment/diagnose_matrix_game2.py" \ + --mode "$MODE" --data-root "$DATA_ROOT" --output "$OUTPUT" diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_i2v_weighted_job85088_task4/manifest.json b/results/worldmem-baseline-evals/matrix-game2/diagnostic_i2v_weighted_job85088_task4/manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..fc7a4966cd18484da8df5e0e69e7385846596768 --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_i2v_weighted_job85088_task4/manifest.json @@ -0,0 +1,59 @@ +{ + "diagnostic_only": true, + "mode": "i2v_weighted", + "checkpoint": "/proj/cvl/users/x_fahkh2/worldmem-baseline-evals/checkpoints/matrix-game2", + "weight_source": "released raw distilled", + "optimizer": null, + "lr": null, + "lora": null, + "activation_checkpointing": false, + "batch_size": 1, + "trainable_parameters": 0, + "denoising_steps": 3, + "shift": 5, + "local_attention_latents": 6, + "selector": "native sliding cache", + "reference": "raw RGB resized352x640, not benchmark Oasis references", + "targets": [ + 700, + 724 + ], + "full_history": [ + 100, + 700 + ], + "full_prefix_latents": 153, + "oracle": "9copies+RGB100:724 encodedonce;159latents; discard153latent prefix", + "i2v": { + "anchor": 699, + "decoded_slice": [ + 1, + 25 + ], + "controls": [ + 700, + 724 + ], + "right_noops": 9 + }, + "normalization_change": "weighted modes apply scales and accumulate RMS in fp32", + "cases": [ + { + "index": "0", + "path": "test/val_plains/2/000034.mp4", + "seed": 42000000 + }, + { + "index": "1", + "path": "test/val_desert/1/000029.mp4", + "seed": 42000001 + } + ], + "parameter_counts": { + "generator_total": 1619242304, + "generator_trainable": 0, + "vae_total": 126892531, + "clip_total": 1193014785 + }, + "action_norm_instances": 60 +} \ No newline at end of file diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_i2v_weighted_job85088_task4/node.json b/results/worldmem-baseline-evals/matrix-game2/diagnostic_i2v_weighted_job85088_task4/node.json new file mode 100644 index 0000000000000000000000000000000000000000..50e45a1cc83c562895d18b9251e356c2df478418 --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_i2v_weighted_job85088_task4/node.json @@ -0,0 +1,7 @@ +{ + "devices": [ + "NVIDIA H200" + ], + "torch": "2.7.0+cu126", + "slurm_job_id": "85088" +} diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_i2v_weighted_job85088_task4/probe.py b/results/worldmem-baseline-evals/matrix-game2/diagnostic_i2v_weighted_job85088_task4/probe.py new file mode 100644 index 0000000000000000000000000000000000000000..f5b01f8975ad3f66be61f6e960f05c64d14fd6e0 --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_i2v_weighted_job85088_task4/probe.py @@ -0,0 +1,113 @@ +"""Frozen Matrix diagnostics; raw-RGB PSNR is separate from benchmark scoring.""" +import argparse +import json +import sys +from pathlib import Path +import torch +import torch.nn.functional as F +from PIL import Image + +ROOT = Path(__file__).resolve().parents[1] +sys.path[:0] = [str(ROOT / 'shared'), str(ROOT / 'Matrix-Game/Matrix-Game-2')] +from protocol import load_minecraft_case, select_cases +from worldmem_adapter import MatrixGameAdapter, map_actions +from demo_utils.constant import ZERO_VAE_CACHE + +MODES = ['wan_oracle', 'full_original', 'full_weighted', 'i2v_original', 'i2v_weighted'] +parser = argparse.ArgumentParser(description=__doc__) +parser.add_argument('--mode', choices=MODES, required=True) +parser.add_argument('--data-root', type=Path, required=True) +parser.add_argument('--output', type=Path, required=True) +parser.add_argument('--dry-run', action='store_true') + +def weighted_norm(self, x): + y = x.float() + return (y * torch.rsqrt(y.square().mean(-1, keepdim=True) + self.eps)).to(x.dtype) * self.weight + +def save_result(output, name, prediction, reference): + prediction = prediction.float().cpu() + if prediction.shape != reference.shape or not torch.isfinite(prediction).all(): + raise ValueError(f'Invalid Matrix prediction: {name}, shape={tuple(prediction.shape)}') + prediction = prediction.clamp(0, 1) + mse = (prediction - reference).square().mean((1, 2, 3)) + report = {'case': name, 'raw_rgb_psnr': (-10 * mse.clamp_min(1e-12).log10()).tolist(), + 'raw_rgb_mse': mse.tolist(), 'mean_rgb': prediction.mean((1, 2, 3)).tolist()} + (output / f'{name}.json').write_text(json.dumps(report, indent=2)) + grid = torch.cat([torch.cat([v[t] for t in [0, 1, 2, 5, 11, 23]], dim=2) + for v in (reference, prediction)], dim=1) + Image.fromarray(grid.mul(255).byte().permute(1, 2, 0).numpy()).save(output / f'{name}.jpg') + print(json.dumps({'case': name, 'mean_psnr': sum(report['raw_rgb_psnr']) / 24, + 'first_psnr': report['raw_rgb_psnr'][:3]}), flush=True) + +@torch.inference_mode() +def main(): + args = parser.parse_args() + rows = select_cases(ROOT / 'deployment/manifests/minecraft_test_300.csv', limit=2) + checkpoint = ROOT / 'checkpoints/matrix-game2' + manifest = {'diagnostic_only': True, 'mode': args.mode, 'checkpoint': str(checkpoint), + 'weight_source': 'released raw distilled', 'optimizer': None, 'lr': None, 'lora': None, + 'activation_checkpointing': False, 'batch_size': 1, 'trainable_parameters': 0, + 'denoising_steps': 3, 'shift': 5, 'local_attention_latents': 6, + 'selector': 'native sliding cache', 'reference': 'raw RGB resized352x640, not benchmark Oasis references', + 'targets': [700, 724], 'full_history': [100, 700], 'full_prefix_latents': 153, + 'oracle': '9copies+RGB100:724 encodedonce;159latents; discard153latent prefix', + 'i2v': {'anchor': 699, 'decoded_slice': [1, 25], 'controls': [700, 724], 'right_noops': 9}, + 'normalization_change': 'weighted modes apply scales and accumulate RMS in fp32', + 'cases': [{'index': r['global_test_index'], 'path': r['relative_path'], + 'seed': 42000000 + int(r['global_test_index'])} for r in rows]} + if args.dry_run: + print(json.dumps(manifest, indent=2)) + return + args.output.mkdir(parents=True, exist_ok=True) + (args.output / 'manifest.json').write_text(json.dumps(manifest, indent=2)) + from wan.modules.action_module import WanRMSNorm + adapter = MatrixGameAdapter(checkpoint) + manifest['parameter_counts'] = adapter.parameter_counts + manifest['action_norm_instances'] = sum(isinstance(m, WanRMSNorm) for m in adapter.pipeline.modules()) + (args.output / 'manifest.json').write_text(json.dumps(manifest, indent=2)) + if args.mode.endswith('weighted'): + WanRMSNorm.forward = weighted_norm + for row in rows: + row.update(target_end='724', target_frames='24') + case = load_minecraft_case(row, args.data_root) + reference = F.interpolate(case['rgb'][600:624], (352, 640), mode='bilinear', align_corners=False, antialias=True) + seed = 42000000 + int(row['global_test_index']) + if args.mode == 'wan_oracle': + rgb = F.interpolate(case['rgb'].cuda(), (352, 640), mode='bilinear', align_corners=False, antialias=True) + rgb = torch.cat([rgb[:1].expand(9, -1, -1, -1), rgb]) + latent = adapter.vae.encode(rgb.transpose(0, 1)[None].mul(2).sub(1).bfloat16(), device=adapter.device, tiled=False) + cache, chunks = [None] * len(ZERO_VAE_CACHE), [] + for start in range(0, latent.shape[2], 3): + decoded, cache = adapter.pipeline.vae_decoder(latent[:, :, start:start + 3].transpose(1, 2).half(), *cache) + if start >= 153: + chunks.append(decoded.cpu()) + prediction = torch.cat(chunks, dim=1)[0].float().add(1).div(2) + del latent, rgb, cache, decoded, chunks + elif args.mode.startswith('full'): + prediction, _ = adapter.generate(case, target_frames=24, seed=seed) + else: + torch.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + anchor = F.interpolate(case['rgb'][599:600].cuda(), (352, 640), mode='bilinear', align_corners=False, antialias=True) + anchor = anchor.transpose(0, 1)[None].mul(2).sub(1).bfloat16() + image = anchor.new_zeros((1, 3, 33, 352, 640)) + image[:, :, :1] = anchor + image_latents = adapter.vae.encode(image, device=adapter.device, tiled=False) + mask = torch.zeros_like(image_latents[:, :4]) + mask[:, :, :1] = 1 + actions = torch.cat([case['actions'][600:624], torch.zeros(9, 25)]).cuda().bfloat16() + keyboard, mouse = map_actions(actions, 1.0) + condition = {'cond_concat': torch.cat([mask, image_latents], dim=1), + 'visual_context': adapter.vae.clip.encode_video(anchor).bfloat16(), + 'keyboard_cond': keyboard[None], 'mouse_cond': mouse[None]} + noise = torch.randn((1, 16, 9, 44, 80), device='cuda', dtype=torch.bfloat16) + videos = adapter.pipeline.inference(noise, condition, initial_latent=None, mode='universal') + prediction = torch.cat(videos, dim=1)[0, 1:25].float().add(1).div(2) + del image, anchor, image_latents, condition, videos, noise + save_result(args.output, f"case{row['global_test_index']}_{args.mode}", prediction, reference) + del prediction, reference, case + (args.output / 'complete.json').write_text(json.dumps({'mode': args.mode, 'cases': 2, 'frames': 24})) + print('DIAGNOSTICS_COMPLETE', flush=True) + +if __name__ == '__main__': + main() diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_demo_actions_job85112_task1/case0/comparison.json b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_demo_actions_job85112_task1/case0/comparison.json new file mode 100644 index 0000000000000000000000000000000000000000..aee79444d615c6f7d66476a611cf4e545ad10ee5 --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_demo_actions_job85112_task1/case0/comparison.json @@ -0,0 +1,81 @@ +{ + "case": "comparison", + "raw_rgb_psnr": [ + 28.761943817138672, + 24.894990921020508, + 5.066094875335693, + 6.884883880615234, + 7.142206192016602, + 7.228761196136475, + 7.280294895172119, + 7.196128845214844, + 7.207036972045898, + 7.207610130310059, + 7.2647624015808105, + 7.177729606628418, + 7.162692546844482, + 7.2452826499938965, + 7.280104637145996, + 7.2675628662109375, + 7.268505096435547, + 7.322355270385742, + 7.28434944152832, + 7.270142555236816, + 7.088680267333984, + 6.997251510620117, + 6.956180572509766, + 7.0359296798706055 + ], + "raw_rgb_mse": [ + 0.0013298585545271635, + 0.0032396710012108088, + 0.3114515542984009, + 0.20488570630550385, + 0.19309870898723602, + 0.18928833305835724, + 0.18705551326274872, + 0.1907159984111786, + 0.19023756682872772, + 0.19021247327327728, + 0.1877257078886032, + 0.1915256828069687, + 0.1921899914741516, + 0.18856962025165558, + 0.18706370890140533, + 0.18760469555854797, + 0.18756400048732758, + 0.18525265157222748, + 0.186880961060524, + 0.18749330937862396, + 0.19549334049224854, + 0.1996525377035141, + 0.20154961943626404, + 0.19788235425949097 + ], + "mean_rgb": [ + 0.7190113663673401, + 0.7229689359664917, + 0.19768567383289337, + 0.296479195356369, + 0.30714768171310425, + 0.31132906675338745, + 0.31336742639541626, + 0.3084028661251068, + 0.3105626106262207, + 0.3106946647167206, + 0.3127269446849823, + 0.3085455596446991, + 0.30667993426322937, + 0.3099704384803772, + 0.31143423914909363, + 0.31037086248397827, + 0.31108224391937256, + 0.3149312436580658, + 0.3144523501396179, + 0.31461331248283386, + 0.30496296286582947, + 0.3001496493816376, + 0.2973284423351288, + 0.30110853910446167 + ] +} \ No newline at end of file diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_demo_actions_job85112_task1/case0/inputs.npz b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_demo_actions_job85112_task1/case0/inputs.npz new file mode 100644 index 0000000000000000000000000000000000000000..9c075a1c3e7d55459d3371b66ffd0048f6f93d23 --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_demo_actions_job85112_task1/case0/inputs.npz @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:493fc4dd35246cc265ebffe2892af27a49cb3021a298008755a8cf455b536125 +size 952269 diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_demo_actions_job85112_task1/case1/comparison.json b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_demo_actions_job85112_task1/case1/comparison.json new file mode 100644 index 0000000000000000000000000000000000000000..251027d1d4d87980c1bf72bb8ab7f3ec6fdca5aa --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_demo_actions_job85112_task1/case1/comparison.json @@ -0,0 +1,81 @@ +{ + "case": "comparison", + "raw_rgb_psnr": [ + 19.81435775756836, + 18.266035079956055, + 17.720809936523438, + 17.051300048828125, + 17.8521785736084, + 17.96320152282715, + 17.770442962646484, + 17.66672706604004, + 17.461271286010742, + 17.219820022583008, + 17.17270851135254, + 17.01397132873535, + 16.4091739654541, + 16.703092575073242, + 17.036758422851562, + 16.95575523376465, + 16.949073791503906, + 16.948787689208984, + 16.964191436767578, + 17.009931564331055, + 16.958953857421875, + 16.964096069335938, + 16.84088134765625, + 16.837522506713867 + ], + "raw_rgb_mse": [ + 0.010436725802719593, + 0.014907212927937508, + 0.01690126210451126, + 0.019718321040272713, + 0.016397668048739433, + 0.01598379760980606, + 0.016709202900528908, + 0.01711304485797882, + 0.01794208399951458, + 0.018967844545841217, + 0.01917472667992115, + 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a/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_demo_actions_job85112_task1/case1/inputs.npz b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_demo_actions_job85112_task1/case1/inputs.npz new file mode 100644 index 0000000000000000000000000000000000000000..32b512a4e66b60c335e58aaed28925b84544f4cb --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_demo_actions_job85112_task1/case1/inputs.npz @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:000846f182a7383bde76fede303b37ca054efd047e17d6f941aeb95bd24cb7e5 +size 951982 diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_demo_actions_job85112_task1/complete.json b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_demo_actions_job85112_task1/complete.json new file mode 100644 index 0000000000000000000000000000000000000000..776d63197fb8e94e961a6ed416536c218e84b85f --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_demo_actions_job85112_task1/complete.json @@ -0,0 +1 @@ +{"mode": "demo_actions", "complete": true} \ No newline at end of file diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_demo_actions_job85112_task1/inference.py b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_demo_actions_job85112_task1/inference.py new file mode 100644 index 0000000000000000000000000000000000000000..f4df03bda84a07a365507a5146938effb5352ffe --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_demo_actions_job85112_task1/inference.py @@ -0,0 +1,169 @@ +import os +import argparse +import torch +import numpy as np + +from omegaconf import OmegaConf +from torchvision.transforms import v2 +from diffusers.utils import load_image +from einops import rearrange +from pipeline import CausalInferencePipeline +from wan.vae.wanx_vae import get_wanx_vae_wrapper +from demo_utils.vae_block3 import VAEDecoderWrapper +from utils.visualize import process_video +from utils.misc import set_seed +from utils.conditions import * +from utils.wan_wrapper import WanDiffusionWrapper +from safetensors.torch import load_file + +def parse_args(): + parser = argparse.ArgumentParser() + parser.add_argument("--config_path", type=str, default="configs/inference_yaml/inference_universal.yaml", help="Path to the config file") + parser.add_argument("--checkpoint_path", type=str, default="", help="Path to the checkpoint") + parser.add_argument("--img_path", type=str, default="demo_images/universal/0000.png", help="Path to the image") + parser.add_argument("--output_folder", type=str, default="outputs/", help="Output folder") + parser.add_argument("--num_output_frames", type=int, default=150, + help="Number of output latent frames") + parser.add_argument("--seed", type=int, default=0, help="Random seed") + parser.add_argument("--pretrained_model_path", type=str, default="Matrix-Game-2.0", help="Path to the VAE model folder") + args = parser.parse_args() + return args + +class InteractiveGameInference: + def __init__(self, args): + self.args = args + self.device = torch.device("cuda") + self.weight_dtype = torch.bfloat16 + + self._init_config() + self._init_models() + + self.frame_process = v2.Compose([ + v2.Resize(size=(352, 640), antialias=True), + v2.ToTensor(), + v2.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]), + ]) + + def _init_config(self): + self.config = OmegaConf.load(self.args.config_path) + + def _init_models(self): + # Initialize pipeline + generator = WanDiffusionWrapper( + **getattr(self.config, "model_kwargs", {}), is_causal=True) + current_vae_decoder = VAEDecoderWrapper() + vae_state_dict = torch.load(os.path.join(self.args.pretrained_model_path, "Wan2.1_VAE.pth"), map_location="cpu") + decoder_state_dict = {} + for key, value in vae_state_dict.items(): + if 'decoder.' in key or 'conv2' in key: + decoder_state_dict[key] = value + current_vae_decoder.load_state_dict(decoder_state_dict) + current_vae_decoder.to(self.device, torch.float16) + current_vae_decoder.requires_grad_(False) + current_vae_decoder.eval() + current_vae_decoder.compile(mode="max-autotune-no-cudagraphs") + pipeline = CausalInferencePipeline(self.config, generator=generator, vae_decoder=current_vae_decoder) + if self.args.checkpoint_path: + print("Loading Pretrained Model...") + state_dict = load_file(self.args.checkpoint_path) + pipeline.generator.load_state_dict(state_dict) + + self.pipeline = pipeline.to(device=self.device, dtype=self.weight_dtype) + self.pipeline.vae_decoder.to(torch.float16) + + vae = get_wanx_vae_wrapper(self.args.pretrained_model_path, torch.float16) + vae.requires_grad_(False) + vae.eval() + self.vae = vae.to(self.device, self.weight_dtype) + + def _resizecrop(self, image, th, tw): + w, h = image.size + if h / w > th / tw: + new_w = int(w) + new_h = int(new_w * th / tw) + else: + new_h = int(h) + new_w = int(new_h * tw / th) + left = (w - new_w) / 2 + top = (h - new_h) / 2 + right = (w + new_w) / 2 + bottom = (h + new_h) / 2 + image = image.crop((left, top, right, bottom)) + return image + + def generate_videos(self): + mode = self.config.pop('mode') + assert mode in ['universal', 'gta_drive', 'templerun'] + + image = load_image(self.args.img_path) + image = self._resizecrop(image, 352, 640) + image = self.frame_process(image)[None, :, None, :, :].to(dtype=self.weight_dtype, device=self.device) + # Encode the input image as the first latent + padding_video = torch.zeros_like(image).repeat(1, 1, 4 * (self.args.num_output_frames - 1), 1, 1) + img_cond = torch.concat([image, padding_video], dim=2) + tiler_kwargs={"tiled": True, "tile_size": [44, 80], "tile_stride": [23, 38]} + img_cond = self.vae.encode(img_cond, device=self.device, **tiler_kwargs).to(self.device) + mask_cond = torch.ones_like(img_cond) + mask_cond[:, :, 1:] = 0 + cond_concat = torch.cat([mask_cond[:, :4], img_cond], dim=1) + visual_context = self.vae.clip.encode_video(image) + sampled_noise = torch.randn( + [1, 16,self.args.num_output_frames, 44, 80], device=self.device, dtype=self.weight_dtype + ) + num_frames = (self.args.num_output_frames - 1) * 4 + 1 + + conditional_dict = { + "cond_concat": cond_concat.to(device=self.device, dtype=self.weight_dtype), + "visual_context": visual_context.to(device=self.device, dtype=self.weight_dtype) + } + + if mode == 'universal': + cond_data = Bench_actions_universal(num_frames) + mouse_condition = cond_data['mouse_condition'].unsqueeze(0).to(device=self.device, dtype=self.weight_dtype) + conditional_dict['mouse_cond'] = mouse_condition + elif mode == 'gta_drive': + cond_data = Bench_actions_gta_drive(num_frames) + mouse_condition = cond_data['mouse_condition'].unsqueeze(0).to(device=self.device, dtype=self.weight_dtype) + conditional_dict['mouse_cond'] = mouse_condition + else: + cond_data = Bench_actions_templerun(num_frames) + keyboard_condition = cond_data['keyboard_condition'].unsqueeze(0).to(device=self.device, dtype=self.weight_dtype) + conditional_dict['keyboard_cond'] = keyboard_condition + + with torch.no_grad(): + videos = self.pipeline.inference( + noise=sampled_noise, + conditional_dict=conditional_dict, + return_latents=False, + mode=mode, + profile=False + ) + + videos_tensor = torch.cat(videos, dim=1) + videos = rearrange(videos_tensor, "B T C H W -> B T H W C") + videos = ((videos.float() + 1) * 127.5).clip(0, 255).cpu().numpy().astype(np.uint8)[0] + video = np.ascontiguousarray(videos) + mouse_icon = 'assets/images/mouse.png' + if mode != 'templerun': + config = ( + keyboard_condition[0].float().cpu().numpy(), + mouse_condition[0].float().cpu().numpy() + ) + else: + config = ( + keyboard_condition[0].float().cpu().numpy() + ) + process_video(video.astype(np.uint8), self.args.output_folder+f'/demo.mp4', config, mouse_icon, mouse_scale=0.1, process_icon=False, mode=mode) + process_video(video.astype(np.uint8), self.args.output_folder+f'/demo_icon.mp4', config, mouse_icon, mouse_scale=0.1, process_icon=True, mode=mode) + print("Done") + +def main(): + """Main entry point for video generation.""" + args = parse_args() + set_seed(args.seed) + os.makedirs(args.output_folder, exist_ok=True) + pipeline = InteractiveGameInference(args) + pipeline.generate_videos() + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_demo_actions_job85112_task1/inference_universal.yaml b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_demo_actions_job85112_task1/inference_universal.yaml new file mode 100644 index 0000000000000000000000000000000000000000..adec83e8175fbe1c0159e0e39e34e7635c319de4 --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_demo_actions_job85112_task1/inference_universal.yaml @@ -0,0 +1,21 @@ +denoising_step_list: +- 1000 +- 666 +- 333 +warp_denoising_step: true +ts_schedule: false +mixed_precision: true +seed: 42 +image_or_video_shape: +- 1 +- 16 +- 15 +- 44 +- 80 +num_frame_per_block: 3 +context_noise: 0 +mode: universal +causal: true +model_kwargs: + timestep_shift: 5.0 + model_config: configs/distilled_model/universal \ No newline at end of file diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_demo_actions_job85112_task1/launcher.sh b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_demo_actions_job85112_task1/launcher.sh new file mode 100644 index 0000000000000000000000000000000000000000..b8daac36b612494e3b61040c1e52ac4e5de3b54b --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_demo_actions_job85112_task1/launcher.sh @@ -0,0 +1,38 @@ +#!/usr/bin/env bash +#SBATCH --job-name=matrix-game2-native-control +#SBATCH --array=0-4 +#SBATCH --nodes=1 +#SBATCH --ntasks=1 +#SBATCH --cpus-per-task=14 +#SBATCH --gres=gpu:1 +#SBATCH --time=02:00:00 +#SBATCH --account=berzelius-2026-196 +#SBATCH --chdir=/proj/cvl/users/x_fahkh2/worldmem-baseline-evals +#SBATCH --output=slurm_logs/%x-%A_%a.out +#SBATCH --error=slurm_logs/%x-%A_%a.out +set -euo pipefail +module load buildenv-gcccuda/12.9.1-gcc11 +ROOT=/proj/cvl/users/x_fahkh2/worldmem-baseline-evals +PYTHON=/proj/cvl/users/x_fahkh2/envs/matrix-game2-eval/bin/python +DATA_ROOT=/proj/cvl/users/x_fahkh2/WorldMem_Repro/datasets/minecraft +MODES=(official_demo demo_actions neutral recorded small_mouse) +MODE=${MODES[$SLURM_ARRAY_TASK_ID]} +OUTPUT=$ROOT/Matrix-Game/Matrix-Game-2/outputs/diagnostic_native_${MODE}_job${SLURM_ARRAY_JOB_ID}_task${SLURM_ARRAY_TASK_ID} +export TMPDIR=$ROOT/cache +export TRITON_CACHE_DIR=$ROOT/cache +export TORCHINDUCTOR_CACHE_DIR=$ROOT/cache/torchinductor +export HF_HOME=$ROOT/cache/huggingface +export TORCH_HOME=$ROOT/cache/torch +export XDG_CACHE_HOME=$ROOT/cache +export MPLCONFIGDIR=$ROOT/cache/matplotlib +export PYTHONPATH=$ROOT/Matrix-Game/Matrix-Game-2 +export PYTHONNOUSERSITE=1 PYTHONUNBUFFERED=1 OMP_NUM_THREADS=4 MKL_NUM_THREADS=4 +export WANDB_MODE=disabled HF_HUB_OFFLINE=1 TRANSFORMERS_OFFLINE=1 +mkdir -p "$TMPDIR" "$TORCHINDUCTOR_CACHE_DIR" "$OUTPUT" +"$PYTHON" "$ROOT/deployment/verify_node.py" --expected-gpus 1 --output "$OUTPUT" +cp "$0" "$OUTPUT/launcher.sh" +cp "$ROOT/deployment/matrix_native_controls.py" "$OUTPUT/probe.py" +cp "$ROOT/Matrix-Game/Matrix-Game-2/inference.py" "$ROOT/Matrix-Game/Matrix-Game-2/configs/inference_yaml/inference_universal.yaml" "$OUTPUT/" +srun --ntasks=1 --gpus-per-task=1 --cpus-per-task=14 --cpu-bind=cores \ + "$PYTHON" "$ROOT/deployment/matrix_native_controls.py" \ + --mode "$MODE" --data-root "$DATA_ROOT" --output "$OUTPUT" diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_demo_actions_job85112_task1/manifest.json b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_demo_actions_job85112_task1/manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..0a4a31d1d4056e4f00fde62ac39976990ac73193 --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_demo_actions_job85112_task1/manifest.json @@ -0,0 +1,50 @@ +{ + "mode": "demo_actions", + "diagnostic_only": true, + "checkpoint": "/proj/cvl/users/x_fahkh2/worldmem-baseline-evals/checkpoints/matrix-game2", + "upstream_revision": "71c3cd7f741311f8100f6cf9cde942b6c1378d11", + "weight_policy": "released raw distilled; original normalization; no optimizer or updates", + "expected_generator_parameters": 1619242304, + "expected_vae_parameters": 126892531, + "expected_clip_parameters": 1193014785, + "batch_size": 1, + "lora": null, + "lr": null, + "activation_checkpointing": false, + "steps": 3, + "shift": 5, + "cache_latents": 6, + "decoder": "official compiled max-autotune-no-cudagraphs", + "official_demo": "unchanged inference.py; supplied painting0000.png; seed0;150latents=597RGB", + "minecraft": { + "anchor": 699, + "native_latents": 9, + "native_rgb": 33, + "scored_slice": [ + 1, + 25 + ], + "targets": [ + 700, + 724 + ], + "recorded_control_alignment": "incoming700:724 plus9noops; small_mouse scales camera only", + "reference": "rawRGB with official center crop and frame_process" + }, + "score_caveat": "Only recorded controls replay GT actions; other modes test stability, not benchmark accuracy", + "cases": [ + { + "index": "0", + "path": "test/val_plains/2/000034.mp4", + "seed": 42000000 + }, + { + "index": "1", + "path": "test/val_desert/1/000029.mp4", + "seed": 42000001 + } + ], + "output_frames_per_case": 33, + "generator_requires_grad_parameters": 1619242304, + "inference_grad_policy": "unchanged native no_grad; parameter flags unmodified" +} \ No newline at end of file diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_demo_actions_job85112_task1/node.json b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_demo_actions_job85112_task1/node.json new file mode 100644 index 0000000000000000000000000000000000000000..974ca5ab6fc954d4606da20cd158791744fe9bad --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_demo_actions_job85112_task1/node.json @@ -0,0 +1,7 @@ +{ + "devices": [ + "NVIDIA H200" + ], + "torch": "2.7.0+cu126", + "slurm_job_id": "85114" +} diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_demo_actions_job85112_task1/probe.py b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_demo_actions_job85112_task1/probe.py new file mode 100644 index 0000000000000000000000000000000000000000..8c5e386fea438328f89a79c076453c94e2f09ecb --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_demo_actions_job85112_task1/probe.py @@ -0,0 +1,106 @@ +"""Official Matrix demo and native-image controls; all scores are diagnostic.""" +import argparse +import json +import os +import subprocess +import sys +from pathlib import Path +import numpy as np +import torch +from PIL import Image +from diagnose_matrix_game2 import save_result +from protocol import minecraft_actions, read_frames, select_cases +from worldmem_adapter import map_actions + +ROOT = Path(__file__).resolve().parents[1] +REPO = ROOT / 'Matrix-Game/Matrix-Game-2' +MODES = ['official_demo', 'demo_actions', 'neutral', 'recorded', 'small_mouse'] + +def replay_controls(raw, mode, length): + keyboard, mouse = map_actions(minecraft_actions(raw), 1.0) + keys, turns = torch.zeros(length, 4), torch.zeros(length, 2) + if mode != 'neutral': + keys[:24], turns[:24] = keyboard, mouse * (0.1 if mode == 'small_mouse' else 1.0) + return {'keyboard_condition': keys, 'mouse_condition': turns} + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument('--mode', choices=MODES, required=True) + parser.add_argument('--data-root', type=Path, required=True) + parser.add_argument('--output', type=Path, required=True) + parser.add_argument('--dry-run', action='store_true') + args = parser.parse_args() + checkpoint = ROOT / 'checkpoints/matrix-game2' + rows = select_cases(ROOT / 'deployment/manifests/minecraft_test_300.csv', limit=2) + manifest = {'mode': args.mode, 'diagnostic_only': True, 'checkpoint': str(checkpoint), + 'upstream_revision': '71c3cd7f741311f8100f6cf9cde942b6c1378d11', + 'weight_policy': 'released raw distilled; original normalization; no optimizer or updates', + 'expected_generator_parameters': 1619242304, 'expected_vae_parameters': 126892531, + 'expected_clip_parameters': 1193014785, 'batch_size': 1, 'lora': None, 'lr': None, + 'activation_checkpointing': False, 'steps': 3, 'shift': 5, 'cache_latents': 6, + 'decoder': 'official compiled max-autotune-no-cudagraphs', + 'official_demo': 'unchanged inference.py; supplied painting0000.png; seed0;150latents=597RGB', + 'minecraft': {'anchor': 699, 'native_latents': 9, 'native_rgb': 33, 'scored_slice': [1, 25], + 'targets': [700, 724], 'recorded_control_alignment': 'incoming700:724 plus9noops; small_mouse scales camera only', + 'reference': 'rawRGB with official center crop and frame_process'}, + 'score_caveat': 'Only recorded controls replay GT actions; other modes test stability, not benchmark accuracy', + 'cases': ([{'image': 'demo_images/universal/0000.png', 'seed': 0}] if args.mode == 'official_demo' + else [{'index': r['global_test_index'], 'path': r['relative_path'], + 'seed': 42000000 + int(r['global_test_index'])} for r in rows]), + 'output_frames_per_case': 597 if args.mode == 'official_demo' else 33} + if args.dry_run: + print(json.dumps(manifest, indent=2)) + return + args.output.mkdir(parents=True, exist_ok=True) + (args.output / 'manifest.json').write_text(json.dumps(manifest, indent=2)) + os.chdir(REPO) + if args.mode == 'official_demo': + subprocess.run([sys.executable, 'inference.py', '--checkpoint_path', str(checkpoint / 'base_distilled_model/base_distill.safetensors'), + '--pretrained_model_path', str(checkpoint), '--img_path', 'demo_images/universal/0000.png', + '--config_path', 'configs/inference_yaml/inference_universal.yaml', '--seed', '0', + '--num_output_frames', '150', '--output_folder', str(args.output)], check=True) + from torchvision.io import read_video + video, _, _ = read_video(str(args.output / 'demo.mp4'), pts_unit='sec') + assert len(video) == 597 + Image.fromarray(torch.cat([video[t] for t in [0, 1, 2, 24, 100, 596]], dim=1).numpy()).save(args.output / 'samples.jpg') + else: + import inference as native + native.set_seed(0) + model_args = argparse.Namespace(config_path='configs/inference_yaml/inference_universal.yaml', + checkpoint_path=str(checkpoint / 'base_distilled_model/base_distill.safetensors'), + pretrained_model_path=str(checkpoint), num_output_frames=9, seed=0, img_path='', output_folder='') + engine = native.InteractiveGameInference(model_args) + manifest['generator_requires_grad_parameters'] = sum(p.numel() for p in engine.pipeline.generator.parameters() if p.requires_grad) + manifest['inference_grad_policy'] = 'unchanged native no_grad; parameter flags unmodified' + (args.output / 'manifest.json').write_text(json.dumps(manifest, indent=2)) + original_inference, native_controls = engine.pipeline.inference, native.Bench_actions_universal + captured = {} + def capture(**kwargs): + condition = kwargs['conditional_dict'] + np.savez_compressed(case_dir / 'inputs.npz', initial_noise=kwargs['noise'].float().cpu().numpy(), + keyboard=condition['keyboard_cond'][0].float().cpu().numpy(), mouse=condition['mouse_cond'][0].float().cpu().numpy()) + videos = original_inference(**kwargs) + captured['prediction'] = torch.cat(videos, dim=1)[0, 1:25].detach().float().cpu().add(1).div(2) + return videos + engine.pipeline.inference = capture + for row in rows: + case_dir = args.output / f"case{row['global_test_index']}" + case_dir.mkdir(exist_ok=True) + path = args.data_root / row['relative_path'] + rgb = read_frames(path, torch.arange(699, 724)) + images = [Image.fromarray(frame.mul(255).round().byte().permute(1, 2, 0).numpy()) for frame in rgb] + images[0].save(case_dir / 'input.png') + reference = torch.stack([engine.frame_process(engine._resizecrop(im, 352, 640)).add(1).div(2) for im in images[1:]]) + with np.load(path.with_suffix('.npz')) as annotation: + controls = replay_controls(annotation['actions'][700:724], args.mode, 33) + native.Bench_actions_universal = native_controls if args.mode == 'demo_actions' else lambda length: controls + model_args.img_path, model_args.output_folder = str(case_dir / 'input.png'), str(case_dir) + engine.config['mode'] = 'universal' + native.set_seed(42000000 + int(row['global_test_index'])) + engine.generate_videos() + save_result(case_dir, 'comparison', captured.pop('prediction'), reference) + (args.output / 'complete.json').write_text(json.dumps({'mode': args.mode, 'complete': True})) + print('NATIVE_CONTROL_COMPLETE', flush=True) + +if __name__ == '__main__': + main() diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_neutral_job85112_task2/case0/comparison.json b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_neutral_job85112_task2/case0/comparison.json new file mode 100644 index 0000000000000000000000000000000000000000..ec55e812e9e1974d5a68d694697fffd7dcd9d52a --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_neutral_job85112_task2/case0/comparison.json @@ -0,0 +1,81 @@ +{ + "case": "comparison", + "raw_rgb_psnr": [ + 29.00463104248047, + 25.083839416503906, + 5.1293206214904785, + 7.163086891174316, + 7.246057510375977, + 7.313009262084961, + 7.356295585632324, + 7.3129777908325195, + 7.402219772338867, + 7.4208784103393555, + 7.457307815551758, + 7.469139099121094, + 7.432450771331787, + 7.4313764572143555, + 7.441709518432617, + 7.446588516235352, + 7.415165901184082, + 7.416438579559326, + 7.437826156616211, + 7.455380439758301, + 7.600482940673828, + 7.668927192687988, + 7.7082390785217285, + 7.726196765899658 + ], + "raw_rgb_mse": [ + 0.0012575836153700948, + 0.0031018152367323637, + 0.3069502115249634, + 0.19217252731323242, + 0.18853598833084106, + 0.1856517493724823, + 0.1838105469942093, + 0.18565309047698975, + 0.18187710642814636, + 0.1810973733663559, + 0.17958466708660126, + 0.1790960729122162, + 0.18061546981334686, + 0.18066012859344482, + 0.18023081123828888, + 0.18002846837043762, + 0.18133574724197388, + 0.1812826246023178, + 0.1803920418024063, + 0.17966437339782715, + 0.1737607717514038, + 0.1710437834262848, + 0.16950249671936035, + 0.16880306601524353 + ], + "mean_rgb": [ + 0.71062171459198, + 0.712151288986206, + 0.20236869156360626, + 0.3093110918998718, + 0.31275486946105957, + 0.316022664308548, + 0.31764835119247437, + 0.31517931818962097, + 0.31939250230789185, + 0.32033178210258484, + 0.32227084040641785, + 0.3230479955673218, + 0.32040339708328247, + 0.31977322697639465, + 0.3199549615383148, + 0.32007846236228943, + 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b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_neutral_job85112_task2/case1/comparison.json new file mode 100644 index 0000000000000000000000000000000000000000..0e38318d69f0dd8c7215d689b4f84ee159af2895 --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_neutral_job85112_task2/case1/comparison.json @@ -0,0 +1,81 @@ +{ + "case": "comparison", + "raw_rgb_psnr": [ + 20.059900283813477, + 18.597415924072266, + 18.113113403320312, + 17.338909149169922, + 18.3916072845459, + 18.688901901245117, + 18.53306007385254, + 18.357519149780273, + 18.232343673706055, + 18.186416625976562, + 18.135757446289062, + 18.113117218017578, + 18.16193389892578, + 18.166234970092773, + 18.167783737182617, + 18.165790557861328, + 18.197973251342773, + 18.205169677734375, + 18.255691528320312, + 18.27703285217285, + 18.182619094848633, + 18.162242889404297, + 18.10346794128418, + 18.056255340576172 + ], + "raw_rgb_mse": [ + 0.009863022714853287, + 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0.7046799063682556, + 0.7081876993179321, + 0.7094244956970215, + 0.7100866436958313, + 0.7104617953300476 + ] +} \ No newline at end of file diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_neutral_job85112_task2/case1/inputs.npz b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_neutral_job85112_task2/case1/inputs.npz new file mode 100644 index 0000000000000000000000000000000000000000..7479ec314114016c657db7e7bd7a045acc542c3f --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_neutral_job85112_task2/case1/inputs.npz @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:95cf1a93c298840e6226640ffa742607ab41f4aee3b1b5ae60a50691a67e83bd +size 951965 diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_neutral_job85112_task2/complete.json b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_neutral_job85112_task2/complete.json new file mode 100644 index 0000000000000000000000000000000000000000..88c3c8bfa67f8146186ce05642413c70c483f5ba --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_neutral_job85112_task2/complete.json @@ -0,0 +1 @@ +{"mode": "neutral", "complete": true} \ No newline at end of file diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_neutral_job85112_task2/inference.py b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_neutral_job85112_task2/inference.py new file mode 100644 index 0000000000000000000000000000000000000000..f4df03bda84a07a365507a5146938effb5352ffe --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_neutral_job85112_task2/inference.py @@ -0,0 +1,169 @@ +import os +import argparse +import torch +import numpy as np + +from omegaconf import OmegaConf +from torchvision.transforms import v2 +from diffusers.utils import load_image +from einops import rearrange +from pipeline import CausalInferencePipeline +from wan.vae.wanx_vae import get_wanx_vae_wrapper +from demo_utils.vae_block3 import VAEDecoderWrapper +from utils.visualize import process_video +from utils.misc import set_seed +from utils.conditions import * +from utils.wan_wrapper import WanDiffusionWrapper +from safetensors.torch import load_file + +def parse_args(): + parser = argparse.ArgumentParser() + parser.add_argument("--config_path", type=str, default="configs/inference_yaml/inference_universal.yaml", help="Path to the config file") + parser.add_argument("--checkpoint_path", type=str, default="", help="Path to the checkpoint") + parser.add_argument("--img_path", type=str, default="demo_images/universal/0000.png", help="Path to the image") + parser.add_argument("--output_folder", type=str, default="outputs/", help="Output folder") + parser.add_argument("--num_output_frames", type=int, default=150, + help="Number of output latent frames") + parser.add_argument("--seed", type=int, default=0, help="Random seed") + parser.add_argument("--pretrained_model_path", type=str, default="Matrix-Game-2.0", help="Path to the VAE model folder") + args = parser.parse_args() + return args + +class InteractiveGameInference: + def __init__(self, args): + self.args = args + self.device = torch.device("cuda") + self.weight_dtype = torch.bfloat16 + + self._init_config() + self._init_models() + + self.frame_process = v2.Compose([ + v2.Resize(size=(352, 640), antialias=True), + v2.ToTensor(), + v2.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]), + ]) + + def _init_config(self): + self.config = OmegaConf.load(self.args.config_path) + + def _init_models(self): + # Initialize pipeline + generator = WanDiffusionWrapper( + **getattr(self.config, "model_kwargs", {}), is_causal=True) + current_vae_decoder = VAEDecoderWrapper() + vae_state_dict = torch.load(os.path.join(self.args.pretrained_model_path, "Wan2.1_VAE.pth"), map_location="cpu") + decoder_state_dict = {} + for key, value in vae_state_dict.items(): + if 'decoder.' in key or 'conv2' in key: + decoder_state_dict[key] = value + current_vae_decoder.load_state_dict(decoder_state_dict) + current_vae_decoder.to(self.device, torch.float16) + current_vae_decoder.requires_grad_(False) + current_vae_decoder.eval() + current_vae_decoder.compile(mode="max-autotune-no-cudagraphs") + pipeline = CausalInferencePipeline(self.config, generator=generator, vae_decoder=current_vae_decoder) + if self.args.checkpoint_path: + print("Loading Pretrained Model...") + state_dict = load_file(self.args.checkpoint_path) + pipeline.generator.load_state_dict(state_dict) + + self.pipeline = pipeline.to(device=self.device, dtype=self.weight_dtype) + self.pipeline.vae_decoder.to(torch.float16) + + vae = get_wanx_vae_wrapper(self.args.pretrained_model_path, torch.float16) + vae.requires_grad_(False) + vae.eval() + self.vae = vae.to(self.device, self.weight_dtype) + + def _resizecrop(self, image, th, tw): + w, h = image.size + if h / w > th / tw: + new_w = int(w) + new_h = int(new_w * th / tw) + else: + new_h = int(h) + new_w = int(new_h * tw / th) + left = (w - new_w) / 2 + top = (h - new_h) / 2 + right = (w + new_w) / 2 + bottom = (h + new_h) / 2 + image = image.crop((left, top, right, bottom)) + return image + + def generate_videos(self): + mode = self.config.pop('mode') + assert mode in ['universal', 'gta_drive', 'templerun'] + + image = load_image(self.args.img_path) + image = self._resizecrop(image, 352, 640) + image = self.frame_process(image)[None, :, None, :, :].to(dtype=self.weight_dtype, device=self.device) + # Encode the input image as the first latent + padding_video = torch.zeros_like(image).repeat(1, 1, 4 * (self.args.num_output_frames - 1), 1, 1) + img_cond = torch.concat([image, padding_video], dim=2) + tiler_kwargs={"tiled": True, "tile_size": [44, 80], "tile_stride": [23, 38]} + img_cond = self.vae.encode(img_cond, device=self.device, **tiler_kwargs).to(self.device) + mask_cond = torch.ones_like(img_cond) + mask_cond[:, :, 1:] = 0 + cond_concat = torch.cat([mask_cond[:, :4], img_cond], dim=1) + visual_context = self.vae.clip.encode_video(image) + sampled_noise = torch.randn( + [1, 16,self.args.num_output_frames, 44, 80], device=self.device, dtype=self.weight_dtype + ) + num_frames = (self.args.num_output_frames - 1) * 4 + 1 + + conditional_dict = { + "cond_concat": cond_concat.to(device=self.device, dtype=self.weight_dtype), + "visual_context": visual_context.to(device=self.device, dtype=self.weight_dtype) + } + + if mode == 'universal': + cond_data = Bench_actions_universal(num_frames) + mouse_condition = cond_data['mouse_condition'].unsqueeze(0).to(device=self.device, dtype=self.weight_dtype) + conditional_dict['mouse_cond'] = mouse_condition + elif mode == 'gta_drive': + cond_data = Bench_actions_gta_drive(num_frames) + mouse_condition = cond_data['mouse_condition'].unsqueeze(0).to(device=self.device, dtype=self.weight_dtype) + conditional_dict['mouse_cond'] = mouse_condition + else: + cond_data = Bench_actions_templerun(num_frames) + keyboard_condition = cond_data['keyboard_condition'].unsqueeze(0).to(device=self.device, dtype=self.weight_dtype) + conditional_dict['keyboard_cond'] = keyboard_condition + + with torch.no_grad(): + videos = self.pipeline.inference( + noise=sampled_noise, + conditional_dict=conditional_dict, + return_latents=False, + mode=mode, + profile=False + ) + + videos_tensor = torch.cat(videos, dim=1) + videos = rearrange(videos_tensor, "B T C H W -> B T H W C") + videos = ((videos.float() + 1) * 127.5).clip(0, 255).cpu().numpy().astype(np.uint8)[0] + video = np.ascontiguousarray(videos) + mouse_icon = 'assets/images/mouse.png' + if mode != 'templerun': + config = ( + keyboard_condition[0].float().cpu().numpy(), + mouse_condition[0].float().cpu().numpy() + ) + else: + config = ( + keyboard_condition[0].float().cpu().numpy() + ) + process_video(video.astype(np.uint8), self.args.output_folder+f'/demo.mp4', config, mouse_icon, mouse_scale=0.1, process_icon=False, mode=mode) + process_video(video.astype(np.uint8), self.args.output_folder+f'/demo_icon.mp4', config, mouse_icon, mouse_scale=0.1, process_icon=True, mode=mode) + print("Done") + +def main(): + """Main entry point for video generation.""" + args = parse_args() + set_seed(args.seed) + os.makedirs(args.output_folder, exist_ok=True) + pipeline = InteractiveGameInference(args) + pipeline.generate_videos() + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_neutral_job85112_task2/inference_universal.yaml b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_neutral_job85112_task2/inference_universal.yaml new file mode 100644 index 0000000000000000000000000000000000000000..adec83e8175fbe1c0159e0e39e34e7635c319de4 --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_neutral_job85112_task2/inference_universal.yaml @@ -0,0 +1,21 @@ +denoising_step_list: +- 1000 +- 666 +- 333 +warp_denoising_step: true +ts_schedule: false +mixed_precision: true +seed: 42 +image_or_video_shape: +- 1 +- 16 +- 15 +- 44 +- 80 +num_frame_per_block: 3 +context_noise: 0 +mode: universal +causal: true +model_kwargs: + timestep_shift: 5.0 + model_config: configs/distilled_model/universal \ No newline at end of file diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_neutral_job85112_task2/launcher.sh b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_neutral_job85112_task2/launcher.sh new file mode 100644 index 0000000000000000000000000000000000000000..b8daac36b612494e3b61040c1e52ac4e5de3b54b --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_neutral_job85112_task2/launcher.sh @@ -0,0 +1,38 @@ +#!/usr/bin/env bash +#SBATCH --job-name=matrix-game2-native-control +#SBATCH --array=0-4 +#SBATCH --nodes=1 +#SBATCH --ntasks=1 +#SBATCH --cpus-per-task=14 +#SBATCH --gres=gpu:1 +#SBATCH --time=02:00:00 +#SBATCH --account=berzelius-2026-196 +#SBATCH --chdir=/proj/cvl/users/x_fahkh2/worldmem-baseline-evals +#SBATCH --output=slurm_logs/%x-%A_%a.out +#SBATCH --error=slurm_logs/%x-%A_%a.out +set -euo pipefail +module load buildenv-gcccuda/12.9.1-gcc11 +ROOT=/proj/cvl/users/x_fahkh2/worldmem-baseline-evals +PYTHON=/proj/cvl/users/x_fahkh2/envs/matrix-game2-eval/bin/python +DATA_ROOT=/proj/cvl/users/x_fahkh2/WorldMem_Repro/datasets/minecraft +MODES=(official_demo demo_actions neutral recorded small_mouse) +MODE=${MODES[$SLURM_ARRAY_TASK_ID]} +OUTPUT=$ROOT/Matrix-Game/Matrix-Game-2/outputs/diagnostic_native_${MODE}_job${SLURM_ARRAY_JOB_ID}_task${SLURM_ARRAY_TASK_ID} +export TMPDIR=$ROOT/cache +export TRITON_CACHE_DIR=$ROOT/cache +export TORCHINDUCTOR_CACHE_DIR=$ROOT/cache/torchinductor +export HF_HOME=$ROOT/cache/huggingface +export TORCH_HOME=$ROOT/cache/torch +export XDG_CACHE_HOME=$ROOT/cache +export MPLCONFIGDIR=$ROOT/cache/matplotlib +export PYTHONPATH=$ROOT/Matrix-Game/Matrix-Game-2 +export PYTHONNOUSERSITE=1 PYTHONUNBUFFERED=1 OMP_NUM_THREADS=4 MKL_NUM_THREADS=4 +export WANDB_MODE=disabled HF_HUB_OFFLINE=1 TRANSFORMERS_OFFLINE=1 +mkdir -p "$TMPDIR" "$TORCHINDUCTOR_CACHE_DIR" "$OUTPUT" +"$PYTHON" "$ROOT/deployment/verify_node.py" --expected-gpus 1 --output "$OUTPUT" +cp "$0" "$OUTPUT/launcher.sh" +cp "$ROOT/deployment/matrix_native_controls.py" "$OUTPUT/probe.py" +cp "$ROOT/Matrix-Game/Matrix-Game-2/inference.py" "$ROOT/Matrix-Game/Matrix-Game-2/configs/inference_yaml/inference_universal.yaml" "$OUTPUT/" +srun --ntasks=1 --gpus-per-task=1 --cpus-per-task=14 --cpu-bind=cores \ + "$PYTHON" "$ROOT/deployment/matrix_native_controls.py" \ + --mode "$MODE" --data-root "$DATA_ROOT" --output "$OUTPUT" diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_neutral_job85112_task2/manifest.json b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_neutral_job85112_task2/manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..32b94bb62892768b4bfbff8926a8b61abb2960f5 --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_neutral_job85112_task2/manifest.json @@ -0,0 +1,50 @@ +{ + "mode": "neutral", + "diagnostic_only": true, + "checkpoint": "/proj/cvl/users/x_fahkh2/worldmem-baseline-evals/checkpoints/matrix-game2", + "upstream_revision": "71c3cd7f741311f8100f6cf9cde942b6c1378d11", + "weight_policy": "released raw distilled; original normalization; no optimizer or updates", + "expected_generator_parameters": 1619242304, + "expected_vae_parameters": 126892531, + "expected_clip_parameters": 1193014785, + "batch_size": 1, + "lora": null, + "lr": null, + "activation_checkpointing": false, + "steps": 3, + "shift": 5, + "cache_latents": 6, + "decoder": "official compiled max-autotune-no-cudagraphs", + "official_demo": "unchanged inference.py; supplied painting0000.png; seed0;150latents=597RGB", + "minecraft": { + "anchor": 699, + "native_latents": 9, + "native_rgb": 33, + "scored_slice": [ + 1, + 25 + ], + "targets": [ + 700, + 724 + ], + "recorded_control_alignment": "incoming700:724 plus9noops; small_mouse scales camera only", + "reference": "rawRGB with official center crop and frame_process" + }, + "score_caveat": "Only recorded controls replay GT actions; other modes test stability, not benchmark accuracy", + "cases": [ + { + "index": "0", + "path": "test/val_plains/2/000034.mp4", + "seed": 42000000 + }, + { + "index": "1", + "path": "test/val_desert/1/000029.mp4", + "seed": 42000001 + } + ], + "output_frames_per_case": 33, + "generator_requires_grad_parameters": 1619242304, + "inference_grad_policy": "unchanged native no_grad; parameter flags unmodified" +} \ No newline at end of file diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_neutral_job85112_task2/node.json b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_neutral_job85112_task2/node.json new file mode 100644 index 0000000000000000000000000000000000000000..cd248956781ecb5915405f39a53efe22b8069c8f --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_neutral_job85112_task2/node.json @@ -0,0 +1,7 @@ +{ + "devices": [ + "NVIDIA H200" + ], + "torch": "2.7.0+cu126", + "slurm_job_id": "85115" +} diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_neutral_job85112_task2/probe.py b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_neutral_job85112_task2/probe.py new file mode 100644 index 0000000000000000000000000000000000000000..8c5e386fea438328f89a79c076453c94e2f09ecb --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_neutral_job85112_task2/probe.py @@ -0,0 +1,106 @@ +"""Official Matrix demo and native-image controls; all scores are diagnostic.""" +import argparse +import json +import os +import subprocess +import sys +from pathlib import Path +import numpy as np +import torch +from PIL import Image +from diagnose_matrix_game2 import save_result +from protocol import minecraft_actions, read_frames, select_cases +from worldmem_adapter import map_actions + +ROOT = Path(__file__).resolve().parents[1] +REPO = ROOT / 'Matrix-Game/Matrix-Game-2' +MODES = ['official_demo', 'demo_actions', 'neutral', 'recorded', 'small_mouse'] + +def replay_controls(raw, mode, length): + keyboard, mouse = map_actions(minecraft_actions(raw), 1.0) + keys, turns = torch.zeros(length, 4), torch.zeros(length, 2) + if mode != 'neutral': + keys[:24], turns[:24] = keyboard, mouse * (0.1 if mode == 'small_mouse' else 1.0) + return {'keyboard_condition': keys, 'mouse_condition': turns} + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument('--mode', choices=MODES, required=True) + parser.add_argument('--data-root', type=Path, required=True) + parser.add_argument('--output', type=Path, required=True) + parser.add_argument('--dry-run', action='store_true') + args = parser.parse_args() + checkpoint = ROOT / 'checkpoints/matrix-game2' + rows = select_cases(ROOT / 'deployment/manifests/minecraft_test_300.csv', limit=2) + manifest = {'mode': args.mode, 'diagnostic_only': True, 'checkpoint': str(checkpoint), + 'upstream_revision': '71c3cd7f741311f8100f6cf9cde942b6c1378d11', + 'weight_policy': 'released raw distilled; original normalization; no optimizer or updates', + 'expected_generator_parameters': 1619242304, 'expected_vae_parameters': 126892531, + 'expected_clip_parameters': 1193014785, 'batch_size': 1, 'lora': None, 'lr': None, + 'activation_checkpointing': False, 'steps': 3, 'shift': 5, 'cache_latents': 6, + 'decoder': 'official compiled max-autotune-no-cudagraphs', + 'official_demo': 'unchanged inference.py; supplied painting0000.png; seed0;150latents=597RGB', + 'minecraft': {'anchor': 699, 'native_latents': 9, 'native_rgb': 33, 'scored_slice': [1, 25], + 'targets': [700, 724], 'recorded_control_alignment': 'incoming700:724 plus9noops; small_mouse scales camera only', + 'reference': 'rawRGB with official center crop and frame_process'}, + 'score_caveat': 'Only recorded controls replay GT actions; other modes test stability, not benchmark accuracy', + 'cases': ([{'image': 'demo_images/universal/0000.png', 'seed': 0}] if args.mode == 'official_demo' + else [{'index': r['global_test_index'], 'path': r['relative_path'], + 'seed': 42000000 + int(r['global_test_index'])} for r in rows]), + 'output_frames_per_case': 597 if args.mode == 'official_demo' else 33} + if args.dry_run: + print(json.dumps(manifest, indent=2)) + return + args.output.mkdir(parents=True, exist_ok=True) + (args.output / 'manifest.json').write_text(json.dumps(manifest, indent=2)) + os.chdir(REPO) + if args.mode == 'official_demo': + subprocess.run([sys.executable, 'inference.py', '--checkpoint_path', str(checkpoint / 'base_distilled_model/base_distill.safetensors'), + '--pretrained_model_path', str(checkpoint), '--img_path', 'demo_images/universal/0000.png', + '--config_path', 'configs/inference_yaml/inference_universal.yaml', '--seed', '0', + '--num_output_frames', '150', '--output_folder', str(args.output)], check=True) + from torchvision.io import read_video + video, _, _ = read_video(str(args.output / 'demo.mp4'), pts_unit='sec') + assert len(video) == 597 + Image.fromarray(torch.cat([video[t] for t in [0, 1, 2, 24, 100, 596]], dim=1).numpy()).save(args.output / 'samples.jpg') + else: + import inference as native + native.set_seed(0) + model_args = argparse.Namespace(config_path='configs/inference_yaml/inference_universal.yaml', + checkpoint_path=str(checkpoint / 'base_distilled_model/base_distill.safetensors'), + pretrained_model_path=str(checkpoint), num_output_frames=9, seed=0, img_path='', output_folder='') + engine = native.InteractiveGameInference(model_args) + manifest['generator_requires_grad_parameters'] = sum(p.numel() for p in engine.pipeline.generator.parameters() if p.requires_grad) + manifest['inference_grad_policy'] = 'unchanged native no_grad; parameter flags unmodified' + (args.output / 'manifest.json').write_text(json.dumps(manifest, indent=2)) + original_inference, native_controls = engine.pipeline.inference, native.Bench_actions_universal + captured = {} + def capture(**kwargs): + condition = kwargs['conditional_dict'] + np.savez_compressed(case_dir / 'inputs.npz', initial_noise=kwargs['noise'].float().cpu().numpy(), + keyboard=condition['keyboard_cond'][0].float().cpu().numpy(), mouse=condition['mouse_cond'][0].float().cpu().numpy()) + videos = original_inference(**kwargs) + captured['prediction'] = torch.cat(videos, dim=1)[0, 1:25].detach().float().cpu().add(1).div(2) + return videos + engine.pipeline.inference = capture + for row in rows: + case_dir = args.output / f"case{row['global_test_index']}" + case_dir.mkdir(exist_ok=True) + path = args.data_root / row['relative_path'] + rgb = read_frames(path, torch.arange(699, 724)) + images = [Image.fromarray(frame.mul(255).round().byte().permute(1, 2, 0).numpy()) for frame in rgb] + images[0].save(case_dir / 'input.png') + reference = torch.stack([engine.frame_process(engine._resizecrop(im, 352, 640)).add(1).div(2) for im in images[1:]]) + with np.load(path.with_suffix('.npz')) as annotation: + controls = replay_controls(annotation['actions'][700:724], args.mode, 33) + native.Bench_actions_universal = native_controls if args.mode == 'demo_actions' else lambda length: controls + model_args.img_path, model_args.output_folder = str(case_dir / 'input.png'), str(case_dir) + engine.config['mode'] = 'universal' + native.set_seed(42000000 + int(row['global_test_index'])) + engine.generate_videos() + save_result(case_dir, 'comparison', captured.pop('prediction'), reference) + (args.output / 'complete.json').write_text(json.dumps({'mode': args.mode, 'complete': True})) + print('NATIVE_CONTROL_COMPLETE', flush=True) + +if __name__ == '__main__': + main() diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_official_demo_job85112_task0/complete.json b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_official_demo_job85112_task0/complete.json new file mode 100644 index 0000000000000000000000000000000000000000..12b6cd28d85775b5be35f2407dc7351442c6ba25 --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_official_demo_job85112_task0/complete.json @@ -0,0 +1 @@ +{"mode": "official_demo", "complete": true} \ No newline at end of file diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_official_demo_job85112_task0/inference.py b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_official_demo_job85112_task0/inference.py new file mode 100644 index 0000000000000000000000000000000000000000..f4df03bda84a07a365507a5146938effb5352ffe --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_official_demo_job85112_task0/inference.py @@ -0,0 +1,169 @@ +import os +import argparse +import torch +import numpy as np + +from omegaconf import OmegaConf +from torchvision.transforms import v2 +from diffusers.utils import load_image +from einops import rearrange +from pipeline import CausalInferencePipeline +from wan.vae.wanx_vae import get_wanx_vae_wrapper +from demo_utils.vae_block3 import VAEDecoderWrapper +from utils.visualize import process_video +from utils.misc import set_seed +from utils.conditions import * +from utils.wan_wrapper import WanDiffusionWrapper +from safetensors.torch import load_file + +def parse_args(): + parser = argparse.ArgumentParser() + parser.add_argument("--config_path", type=str, default="configs/inference_yaml/inference_universal.yaml", help="Path to the config file") + parser.add_argument("--checkpoint_path", type=str, default="", help="Path to the checkpoint") + parser.add_argument("--img_path", type=str, default="demo_images/universal/0000.png", help="Path to the image") + parser.add_argument("--output_folder", type=str, default="outputs/", help="Output folder") + parser.add_argument("--num_output_frames", type=int, default=150, + help="Number of output latent frames") + parser.add_argument("--seed", type=int, default=0, help="Random seed") + parser.add_argument("--pretrained_model_path", type=str, default="Matrix-Game-2.0", help="Path to the VAE model folder") + args = parser.parse_args() + return args + +class InteractiveGameInference: + def __init__(self, args): + self.args = args + self.device = torch.device("cuda") + self.weight_dtype = torch.bfloat16 + + self._init_config() + self._init_models() + + self.frame_process = v2.Compose([ + v2.Resize(size=(352, 640), antialias=True), + v2.ToTensor(), + v2.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]), + ]) + + def _init_config(self): + self.config = OmegaConf.load(self.args.config_path) + + def _init_models(self): + # Initialize pipeline + generator = WanDiffusionWrapper( + **getattr(self.config, "model_kwargs", {}), is_causal=True) + current_vae_decoder = VAEDecoderWrapper() + vae_state_dict = torch.load(os.path.join(self.args.pretrained_model_path, "Wan2.1_VAE.pth"), map_location="cpu") + decoder_state_dict = {} + for key, value in vae_state_dict.items(): + if 'decoder.' in key or 'conv2' in key: + decoder_state_dict[key] = value + current_vae_decoder.load_state_dict(decoder_state_dict) + current_vae_decoder.to(self.device, torch.float16) + current_vae_decoder.requires_grad_(False) + current_vae_decoder.eval() + current_vae_decoder.compile(mode="max-autotune-no-cudagraphs") + pipeline = CausalInferencePipeline(self.config, generator=generator, vae_decoder=current_vae_decoder) + if self.args.checkpoint_path: + print("Loading Pretrained Model...") + state_dict = load_file(self.args.checkpoint_path) + pipeline.generator.load_state_dict(state_dict) + + self.pipeline = pipeline.to(device=self.device, dtype=self.weight_dtype) + self.pipeline.vae_decoder.to(torch.float16) + + vae = get_wanx_vae_wrapper(self.args.pretrained_model_path, torch.float16) + vae.requires_grad_(False) + vae.eval() + self.vae = vae.to(self.device, self.weight_dtype) + + def _resizecrop(self, image, th, tw): + w, h = image.size + if h / w > th / tw: + new_w = int(w) + new_h = int(new_w * th / tw) + else: + new_h = int(h) + new_w = int(new_h * tw / th) + left = (w - new_w) / 2 + top = (h - new_h) / 2 + right = (w + new_w) / 2 + bottom = (h + new_h) / 2 + image = image.crop((left, top, right, bottom)) + return image + + def generate_videos(self): + mode = self.config.pop('mode') + assert mode in ['universal', 'gta_drive', 'templerun'] + + image = load_image(self.args.img_path) + image = self._resizecrop(image, 352, 640) + image = self.frame_process(image)[None, :, None, :, :].to(dtype=self.weight_dtype, device=self.device) + # Encode the input image as the first latent + padding_video = torch.zeros_like(image).repeat(1, 1, 4 * (self.args.num_output_frames - 1), 1, 1) + img_cond = torch.concat([image, padding_video], dim=2) + tiler_kwargs={"tiled": True, "tile_size": [44, 80], "tile_stride": [23, 38]} + img_cond = self.vae.encode(img_cond, device=self.device, **tiler_kwargs).to(self.device) + mask_cond = torch.ones_like(img_cond) + mask_cond[:, :, 1:] = 0 + cond_concat = torch.cat([mask_cond[:, :4], img_cond], dim=1) + visual_context = self.vae.clip.encode_video(image) + sampled_noise = torch.randn( + [1, 16,self.args.num_output_frames, 44, 80], device=self.device, dtype=self.weight_dtype + ) + num_frames = (self.args.num_output_frames - 1) * 4 + 1 + + conditional_dict = { + "cond_concat": cond_concat.to(device=self.device, dtype=self.weight_dtype), + "visual_context": visual_context.to(device=self.device, dtype=self.weight_dtype) + } + + if mode == 'universal': + cond_data = Bench_actions_universal(num_frames) + mouse_condition = cond_data['mouse_condition'].unsqueeze(0).to(device=self.device, dtype=self.weight_dtype) + conditional_dict['mouse_cond'] = mouse_condition + elif mode == 'gta_drive': + cond_data = Bench_actions_gta_drive(num_frames) + mouse_condition = cond_data['mouse_condition'].unsqueeze(0).to(device=self.device, dtype=self.weight_dtype) + conditional_dict['mouse_cond'] = mouse_condition + else: + cond_data = Bench_actions_templerun(num_frames) + keyboard_condition = cond_data['keyboard_condition'].unsqueeze(0).to(device=self.device, dtype=self.weight_dtype) + conditional_dict['keyboard_cond'] = keyboard_condition + + with torch.no_grad(): + videos = self.pipeline.inference( + noise=sampled_noise, + conditional_dict=conditional_dict, + return_latents=False, + mode=mode, + profile=False + ) + + videos_tensor = torch.cat(videos, dim=1) + videos = rearrange(videos_tensor, "B T C H W -> B T H W C") + videos = ((videos.float() + 1) * 127.5).clip(0, 255).cpu().numpy().astype(np.uint8)[0] + video = np.ascontiguousarray(videos) + mouse_icon = 'assets/images/mouse.png' + if mode != 'templerun': + config = ( + keyboard_condition[0].float().cpu().numpy(), + mouse_condition[0].float().cpu().numpy() + ) + else: + config = ( + keyboard_condition[0].float().cpu().numpy() + ) + process_video(video.astype(np.uint8), self.args.output_folder+f'/demo.mp4', config, mouse_icon, mouse_scale=0.1, process_icon=False, mode=mode) + process_video(video.astype(np.uint8), self.args.output_folder+f'/demo_icon.mp4', config, mouse_icon, mouse_scale=0.1, process_icon=True, mode=mode) + print("Done") + +def main(): + """Main entry point for video generation.""" + args = parse_args() + set_seed(args.seed) + os.makedirs(args.output_folder, exist_ok=True) + pipeline = InteractiveGameInference(args) + pipeline.generate_videos() + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_official_demo_job85112_task0/inference_universal.yaml b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_official_demo_job85112_task0/inference_universal.yaml new file mode 100644 index 0000000000000000000000000000000000000000..adec83e8175fbe1c0159e0e39e34e7635c319de4 --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_official_demo_job85112_task0/inference_universal.yaml @@ -0,0 +1,21 @@ +denoising_step_list: +- 1000 +- 666 +- 333 +warp_denoising_step: true +ts_schedule: false +mixed_precision: true +seed: 42 +image_or_video_shape: +- 1 +- 16 +- 15 +- 44 +- 80 +num_frame_per_block: 3 +context_noise: 0 +mode: universal +causal: true +model_kwargs: + timestep_shift: 5.0 + model_config: configs/distilled_model/universal \ No newline at end of file diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_official_demo_job85112_task0/launcher.sh b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_official_demo_job85112_task0/launcher.sh new file mode 100644 index 0000000000000000000000000000000000000000..b8daac36b612494e3b61040c1e52ac4e5de3b54b --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_official_demo_job85112_task0/launcher.sh @@ -0,0 +1,38 @@ +#!/usr/bin/env bash +#SBATCH --job-name=matrix-game2-native-control +#SBATCH --array=0-4 +#SBATCH --nodes=1 +#SBATCH --ntasks=1 +#SBATCH --cpus-per-task=14 +#SBATCH --gres=gpu:1 +#SBATCH --time=02:00:00 +#SBATCH --account=berzelius-2026-196 +#SBATCH --chdir=/proj/cvl/users/x_fahkh2/worldmem-baseline-evals +#SBATCH --output=slurm_logs/%x-%A_%a.out +#SBATCH --error=slurm_logs/%x-%A_%a.out +set -euo pipefail +module load buildenv-gcccuda/12.9.1-gcc11 +ROOT=/proj/cvl/users/x_fahkh2/worldmem-baseline-evals +PYTHON=/proj/cvl/users/x_fahkh2/envs/matrix-game2-eval/bin/python +DATA_ROOT=/proj/cvl/users/x_fahkh2/WorldMem_Repro/datasets/minecraft +MODES=(official_demo demo_actions neutral recorded small_mouse) +MODE=${MODES[$SLURM_ARRAY_TASK_ID]} +OUTPUT=$ROOT/Matrix-Game/Matrix-Game-2/outputs/diagnostic_native_${MODE}_job${SLURM_ARRAY_JOB_ID}_task${SLURM_ARRAY_TASK_ID} +export TMPDIR=$ROOT/cache +export TRITON_CACHE_DIR=$ROOT/cache +export TORCHINDUCTOR_CACHE_DIR=$ROOT/cache/torchinductor +export HF_HOME=$ROOT/cache/huggingface +export TORCH_HOME=$ROOT/cache/torch +export XDG_CACHE_HOME=$ROOT/cache +export MPLCONFIGDIR=$ROOT/cache/matplotlib +export PYTHONPATH=$ROOT/Matrix-Game/Matrix-Game-2 +export PYTHONNOUSERSITE=1 PYTHONUNBUFFERED=1 OMP_NUM_THREADS=4 MKL_NUM_THREADS=4 +export WANDB_MODE=disabled HF_HUB_OFFLINE=1 TRANSFORMERS_OFFLINE=1 +mkdir -p "$TMPDIR" "$TORCHINDUCTOR_CACHE_DIR" "$OUTPUT" +"$PYTHON" "$ROOT/deployment/verify_node.py" --expected-gpus 1 --output "$OUTPUT" +cp "$0" "$OUTPUT/launcher.sh" +cp "$ROOT/deployment/matrix_native_controls.py" "$OUTPUT/probe.py" +cp "$ROOT/Matrix-Game/Matrix-Game-2/inference.py" "$ROOT/Matrix-Game/Matrix-Game-2/configs/inference_yaml/inference_universal.yaml" "$OUTPUT/" +srun --ntasks=1 --gpus-per-task=1 --cpus-per-task=14 --cpu-bind=cores \ + "$PYTHON" "$ROOT/deployment/matrix_native_controls.py" \ + --mode "$MODE" --data-root "$DATA_ROOT" --output "$OUTPUT" diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_official_demo_job85112_task0/manifest.json b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_official_demo_job85112_task0/manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..2baa50ccab29231aff2a5f4a1664adb0bf31303a --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_official_demo_job85112_task0/manifest.json @@ -0,0 +1,42 @@ +{ + "mode": "official_demo", + "diagnostic_only": true, + "checkpoint": "/proj/cvl/users/x_fahkh2/worldmem-baseline-evals/checkpoints/matrix-game2", + "upstream_revision": "71c3cd7f741311f8100f6cf9cde942b6c1378d11", + "weight_policy": "released raw distilled; original normalization; no optimizer or updates", + "expected_generator_parameters": 1619242304, + "expected_vae_parameters": 126892531, + "expected_clip_parameters": 1193014785, + "batch_size": 1, + "lora": null, + "lr": null, + "activation_checkpointing": false, + "steps": 3, + "shift": 5, + "cache_latents": 6, + "decoder": "official compiled max-autotune-no-cudagraphs", + "official_demo": "unchanged inference.py; supplied painting0000.png; seed0;150latents=597RGB", + "minecraft": { + "anchor": 699, + "native_latents": 9, + "native_rgb": 33, + "scored_slice": [ + 1, + 25 + ], + "targets": [ + 700, + 724 + ], + "recorded_control_alignment": "incoming700:724 plus9noops; small_mouse scales camera only", + "reference": "rawRGB with official center crop and frame_process" + }, + "score_caveat": "Only recorded controls replay GT actions; other modes test stability, not benchmark accuracy", + "cases": [ + { + "image": "demo_images/universal/0000.png", + "seed": 0 + } + ], + "output_frames_per_case": 597 +} \ No newline at end of file diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_official_demo_job85112_task0/node.json b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_official_demo_job85112_task0/node.json new file mode 100644 index 0000000000000000000000000000000000000000..01a74644010f15c061d958eb712b8d295c18e56d --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_official_demo_job85112_task0/node.json @@ -0,0 +1,7 @@ +{ + "devices": [ + "NVIDIA H200" + ], + "torch": "2.7.0+cu126", + "slurm_job_id": "85113" +} diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_official_demo_job85112_task0/probe.py b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_official_demo_job85112_task0/probe.py new file mode 100644 index 0000000000000000000000000000000000000000..8c5e386fea438328f89a79c076453c94e2f09ecb --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_official_demo_job85112_task0/probe.py @@ -0,0 +1,106 @@ +"""Official Matrix demo and native-image controls; all scores are diagnostic.""" +import argparse +import json +import os +import subprocess +import sys +from pathlib import Path +import numpy as np +import torch +from PIL import Image +from diagnose_matrix_game2 import save_result +from protocol import minecraft_actions, read_frames, select_cases +from worldmem_adapter import map_actions + +ROOT = Path(__file__).resolve().parents[1] +REPO = ROOT / 'Matrix-Game/Matrix-Game-2' +MODES = ['official_demo', 'demo_actions', 'neutral', 'recorded', 'small_mouse'] + +def replay_controls(raw, mode, length): + keyboard, mouse = map_actions(minecraft_actions(raw), 1.0) + keys, turns = torch.zeros(length, 4), torch.zeros(length, 2) + if mode != 'neutral': + keys[:24], turns[:24] = keyboard, mouse * (0.1 if mode == 'small_mouse' else 1.0) + return {'keyboard_condition': keys, 'mouse_condition': turns} + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument('--mode', choices=MODES, required=True) + parser.add_argument('--data-root', type=Path, required=True) + parser.add_argument('--output', type=Path, required=True) + parser.add_argument('--dry-run', action='store_true') + args = parser.parse_args() + checkpoint = ROOT / 'checkpoints/matrix-game2' + rows = select_cases(ROOT / 'deployment/manifests/minecraft_test_300.csv', limit=2) + manifest = {'mode': args.mode, 'diagnostic_only': True, 'checkpoint': str(checkpoint), + 'upstream_revision': '71c3cd7f741311f8100f6cf9cde942b6c1378d11', + 'weight_policy': 'released raw distilled; original normalization; no optimizer or updates', + 'expected_generator_parameters': 1619242304, 'expected_vae_parameters': 126892531, + 'expected_clip_parameters': 1193014785, 'batch_size': 1, 'lora': None, 'lr': None, + 'activation_checkpointing': False, 'steps': 3, 'shift': 5, 'cache_latents': 6, + 'decoder': 'official compiled max-autotune-no-cudagraphs', + 'official_demo': 'unchanged inference.py; supplied painting0000.png; seed0;150latents=597RGB', + 'minecraft': {'anchor': 699, 'native_latents': 9, 'native_rgb': 33, 'scored_slice': [1, 25], + 'targets': [700, 724], 'recorded_control_alignment': 'incoming700:724 plus9noops; small_mouse scales camera only', + 'reference': 'rawRGB with official center crop and frame_process'}, + 'score_caveat': 'Only recorded controls replay GT actions; other modes test stability, not benchmark accuracy', + 'cases': ([{'image': 'demo_images/universal/0000.png', 'seed': 0}] if args.mode == 'official_demo' + else [{'index': r['global_test_index'], 'path': r['relative_path'], + 'seed': 42000000 + int(r['global_test_index'])} for r in rows]), + 'output_frames_per_case': 597 if args.mode == 'official_demo' else 33} + if args.dry_run: + print(json.dumps(manifest, indent=2)) + return + args.output.mkdir(parents=True, exist_ok=True) + (args.output / 'manifest.json').write_text(json.dumps(manifest, indent=2)) + os.chdir(REPO) + if args.mode == 'official_demo': + subprocess.run([sys.executable, 'inference.py', '--checkpoint_path', str(checkpoint / 'base_distilled_model/base_distill.safetensors'), + '--pretrained_model_path', str(checkpoint), '--img_path', 'demo_images/universal/0000.png', + '--config_path', 'configs/inference_yaml/inference_universal.yaml', '--seed', '0', + '--num_output_frames', '150', '--output_folder', str(args.output)], check=True) + from torchvision.io import read_video + video, _, _ = read_video(str(args.output / 'demo.mp4'), pts_unit='sec') + assert len(video) == 597 + Image.fromarray(torch.cat([video[t] for t in [0, 1, 2, 24, 100, 596]], dim=1).numpy()).save(args.output / 'samples.jpg') + else: + import inference as native + native.set_seed(0) + model_args = argparse.Namespace(config_path='configs/inference_yaml/inference_universal.yaml', + checkpoint_path=str(checkpoint / 'base_distilled_model/base_distill.safetensors'), + pretrained_model_path=str(checkpoint), num_output_frames=9, seed=0, img_path='', output_folder='') + engine = native.InteractiveGameInference(model_args) + manifest['generator_requires_grad_parameters'] = sum(p.numel() for p in engine.pipeline.generator.parameters() if p.requires_grad) + manifest['inference_grad_policy'] = 'unchanged native no_grad; parameter flags unmodified' + (args.output / 'manifest.json').write_text(json.dumps(manifest, indent=2)) + original_inference, native_controls = engine.pipeline.inference, native.Bench_actions_universal + captured = {} + def capture(**kwargs): + condition = kwargs['conditional_dict'] + np.savez_compressed(case_dir / 'inputs.npz', initial_noise=kwargs['noise'].float().cpu().numpy(), + keyboard=condition['keyboard_cond'][0].float().cpu().numpy(), mouse=condition['mouse_cond'][0].float().cpu().numpy()) + videos = original_inference(**kwargs) + captured['prediction'] = torch.cat(videos, dim=1)[0, 1:25].detach().float().cpu().add(1).div(2) + return videos + engine.pipeline.inference = capture + for row in rows: + case_dir = args.output / f"case{row['global_test_index']}" + case_dir.mkdir(exist_ok=True) + path = args.data_root / row['relative_path'] + rgb = read_frames(path, torch.arange(699, 724)) + images = [Image.fromarray(frame.mul(255).round().byte().permute(1, 2, 0).numpy()) for frame in rgb] + images[0].save(case_dir / 'input.png') + reference = torch.stack([engine.frame_process(engine._resizecrop(im, 352, 640)).add(1).div(2) for im in images[1:]]) + with np.load(path.with_suffix('.npz')) as annotation: + controls = replay_controls(annotation['actions'][700:724], args.mode, 33) + native.Bench_actions_universal = native_controls if args.mode == 'demo_actions' else lambda length: controls + model_args.img_path, model_args.output_folder = str(case_dir / 'input.png'), str(case_dir) + engine.config['mode'] = 'universal' + native.set_seed(42000000 + int(row['global_test_index'])) + engine.generate_videos() + save_result(case_dir, 'comparison', captured.pop('prediction'), reference) + (args.output / 'complete.json').write_text(json.dumps({'mode': args.mode, 'complete': True})) + print('NATIVE_CONTROL_COMPLETE', flush=True) + +if __name__ == '__main__': + main() diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_recorded_job85112_task3/case0/comparison.json b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_recorded_job85112_task3/case0/comparison.json new file mode 100644 index 0000000000000000000000000000000000000000..d519fbd6282e1bd673ec985512b58babe400c9fc --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_recorded_job85112_task3/case0/comparison.json @@ -0,0 +1,81 @@ +{ + "case": "comparison", + "raw_rgb_psnr": [ + 25.711793899536133, + 5.244641304016113, + 5.923252582550049, + 7.243335247039795, + 7.474766731262207, + 7.688778877258301, + 7.724562644958496, + 7.724119186401367, + 7.586732864379883, + 7.602225303649902, + 7.612397193908691, + 7.641911506652832, + 7.730371952056885, + 7.78223991394043, + 7.874336242675781, + 7.907952785491943, + 7.875397682189941, + 7.89320707321167, + 7.920524597167969, + 7.950427532196045, + 7.6845479011535645, + 7.524905204772949, + 7.746468544006348, + 8.102394104003906 + ], + "raw_rgb_mse": [ + 0.002684235805645585, + 0.2989068329334259, + 0.2556670308113098, + 0.18865419924259186, + 0.17886415123939514, + 0.17026373744010925, + 0.1688665747642517, + 0.168883815407753, + 0.1743117719888687, + 0.1736910492181778, + 0.17328473925590515, + 0.17211107909679413, + 0.1686408668756485, + 0.16663876175880432, + 0.16314223408699036, + 0.16188429296016693, + 0.16310235857963562, + 0.1624348908662796, + 0.16141636669635773, + 0.16030874848365784, + 0.17042966187000275, + 0.176811084151268, + 0.1680169552564621, + 0.154796302318573 + ], + "mean_rgb": [ + 0.7045056819915771, + 0.19928812980651855, + 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+version https://git-lfs.github.com/spec/v1 +oid sha256:2d97d56ea4798f49e8c43134111292f0d84a9a4bdd5656a98c8378c1569d5ff9 +size 952295 diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_recorded_job85112_task3/case1/comparison.json b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_recorded_job85112_task3/case1/comparison.json new file mode 100644 index 0000000000000000000000000000000000000000..a2aca32182a5056a9f2236c6def88e646bb355b6 --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_recorded_job85112_task3/case1/comparison.json @@ -0,0 +1,81 @@ +{ + "case": "comparison", + "raw_rgb_psnr": [ + 15.494953155517578, + 11.288851737976074, + 8.964729309082031, + 7.841015815734863, + 6.886514663696289, + 6.564419269561768, + 6.5279998779296875, + 6.482372760772705, + 6.38002872467041, + 6.4821319580078125, + 3.9035301208496094, + 5.6491379737854, + 5.5482330322265625, + 5.530768394470215, + 5.589770317077637, + 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a/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_recorded_job85112_task3/complete.json b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_recorded_job85112_task3/complete.json new file mode 100644 index 0000000000000000000000000000000000000000..1958c57556c216b3d1f4a30daedc1d62e728b91d --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_recorded_job85112_task3/complete.json @@ -0,0 +1 @@ +{"mode": "recorded", "complete": true} \ No newline at end of file diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_recorded_job85112_task3/inference.py b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_recorded_job85112_task3/inference.py new file mode 100644 index 0000000000000000000000000000000000000000..f4df03bda84a07a365507a5146938effb5352ffe --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_recorded_job85112_task3/inference.py @@ -0,0 +1,169 @@ +import os +import argparse +import torch +import numpy as np + +from omegaconf import OmegaConf +from torchvision.transforms import v2 +from diffusers.utils import load_image +from einops import rearrange +from pipeline import CausalInferencePipeline +from wan.vae.wanx_vae import get_wanx_vae_wrapper +from demo_utils.vae_block3 import VAEDecoderWrapper +from utils.visualize import process_video +from utils.misc import set_seed +from utils.conditions import * +from utils.wan_wrapper import WanDiffusionWrapper +from safetensors.torch import load_file + +def parse_args(): + parser = argparse.ArgumentParser() + parser.add_argument("--config_path", type=str, default="configs/inference_yaml/inference_universal.yaml", help="Path to the config file") + parser.add_argument("--checkpoint_path", type=str, default="", help="Path to the checkpoint") + parser.add_argument("--img_path", type=str, default="demo_images/universal/0000.png", help="Path to the image") + parser.add_argument("--output_folder", type=str, default="outputs/", help="Output folder") + parser.add_argument("--num_output_frames", type=int, default=150, + help="Number of output latent frames") + parser.add_argument("--seed", type=int, default=0, help="Random seed") + parser.add_argument("--pretrained_model_path", type=str, default="Matrix-Game-2.0", help="Path to the VAE model folder") + args = parser.parse_args() + return args + +class InteractiveGameInference: + def __init__(self, args): + self.args = args + self.device = torch.device("cuda") + self.weight_dtype = torch.bfloat16 + + self._init_config() + self._init_models() + + self.frame_process = v2.Compose([ + v2.Resize(size=(352, 640), antialias=True), + v2.ToTensor(), + v2.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]), + ]) + + def _init_config(self): + self.config = OmegaConf.load(self.args.config_path) + + def _init_models(self): + # Initialize pipeline + generator = WanDiffusionWrapper( + **getattr(self.config, "model_kwargs", {}), is_causal=True) + current_vae_decoder = VAEDecoderWrapper() + vae_state_dict = torch.load(os.path.join(self.args.pretrained_model_path, "Wan2.1_VAE.pth"), map_location="cpu") + decoder_state_dict = {} + for key, value in vae_state_dict.items(): + if 'decoder.' in key or 'conv2' in key: + decoder_state_dict[key] = value + current_vae_decoder.load_state_dict(decoder_state_dict) + current_vae_decoder.to(self.device, torch.float16) + current_vae_decoder.requires_grad_(False) + current_vae_decoder.eval() + current_vae_decoder.compile(mode="max-autotune-no-cudagraphs") + pipeline = CausalInferencePipeline(self.config, generator=generator, vae_decoder=current_vae_decoder) + if self.args.checkpoint_path: + print("Loading Pretrained Model...") + state_dict = load_file(self.args.checkpoint_path) + pipeline.generator.load_state_dict(state_dict) + + self.pipeline = pipeline.to(device=self.device, dtype=self.weight_dtype) + self.pipeline.vae_decoder.to(torch.float16) + + vae = get_wanx_vae_wrapper(self.args.pretrained_model_path, torch.float16) + vae.requires_grad_(False) + vae.eval() + self.vae = vae.to(self.device, self.weight_dtype) + + def _resizecrop(self, image, th, tw): + w, h = image.size + if h / w > th / tw: + new_w = int(w) + new_h = int(new_w * th / tw) + else: + new_h = int(h) + new_w = int(new_h * tw / th) + left = (w - new_w) / 2 + top = (h - new_h) / 2 + right = (w + new_w) / 2 + bottom = (h + new_h) / 2 + image = image.crop((left, top, right, bottom)) + return image + + def generate_videos(self): + mode = self.config.pop('mode') + assert mode in ['universal', 'gta_drive', 'templerun'] + + image = load_image(self.args.img_path) + image = self._resizecrop(image, 352, 640) + image = self.frame_process(image)[None, :, None, :, :].to(dtype=self.weight_dtype, device=self.device) + # Encode the input image as the first latent + padding_video = torch.zeros_like(image).repeat(1, 1, 4 * (self.args.num_output_frames - 1), 1, 1) + img_cond = torch.concat([image, padding_video], dim=2) + tiler_kwargs={"tiled": True, "tile_size": [44, 80], "tile_stride": [23, 38]} + img_cond = self.vae.encode(img_cond, device=self.device, **tiler_kwargs).to(self.device) + mask_cond = torch.ones_like(img_cond) + mask_cond[:, :, 1:] = 0 + cond_concat = torch.cat([mask_cond[:, :4], img_cond], dim=1) + visual_context = self.vae.clip.encode_video(image) + sampled_noise = torch.randn( + [1, 16,self.args.num_output_frames, 44, 80], device=self.device, dtype=self.weight_dtype + ) + num_frames = (self.args.num_output_frames - 1) * 4 + 1 + + conditional_dict = { + "cond_concat": cond_concat.to(device=self.device, dtype=self.weight_dtype), + "visual_context": visual_context.to(device=self.device, dtype=self.weight_dtype) + } + + if mode == 'universal': + cond_data = Bench_actions_universal(num_frames) + mouse_condition = cond_data['mouse_condition'].unsqueeze(0).to(device=self.device, dtype=self.weight_dtype) + conditional_dict['mouse_cond'] = mouse_condition + elif mode == 'gta_drive': + cond_data = Bench_actions_gta_drive(num_frames) + mouse_condition = cond_data['mouse_condition'].unsqueeze(0).to(device=self.device, dtype=self.weight_dtype) + conditional_dict['mouse_cond'] = mouse_condition + else: + cond_data = Bench_actions_templerun(num_frames) + keyboard_condition = cond_data['keyboard_condition'].unsqueeze(0).to(device=self.device, dtype=self.weight_dtype) + conditional_dict['keyboard_cond'] = keyboard_condition + + with torch.no_grad(): + videos = self.pipeline.inference( + noise=sampled_noise, + conditional_dict=conditional_dict, + return_latents=False, + mode=mode, + profile=False + ) + + videos_tensor = torch.cat(videos, dim=1) + videos = rearrange(videos_tensor, "B T C H W -> B T H W C") + videos = ((videos.float() + 1) * 127.5).clip(0, 255).cpu().numpy().astype(np.uint8)[0] + video = np.ascontiguousarray(videos) + mouse_icon = 'assets/images/mouse.png' + if mode != 'templerun': + config = ( + keyboard_condition[0].float().cpu().numpy(), + mouse_condition[0].float().cpu().numpy() + ) + else: + config = ( + keyboard_condition[0].float().cpu().numpy() + ) + process_video(video.astype(np.uint8), self.args.output_folder+f'/demo.mp4', config, mouse_icon, mouse_scale=0.1, process_icon=False, mode=mode) + process_video(video.astype(np.uint8), self.args.output_folder+f'/demo_icon.mp4', config, mouse_icon, mouse_scale=0.1, process_icon=True, mode=mode) + print("Done") + +def main(): + """Main entry point for video generation.""" + args = parse_args() + set_seed(args.seed) + os.makedirs(args.output_folder, exist_ok=True) + pipeline = InteractiveGameInference(args) + pipeline.generate_videos() + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_recorded_job85112_task3/inference_universal.yaml b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_recorded_job85112_task3/inference_universal.yaml new file mode 100644 index 0000000000000000000000000000000000000000..adec83e8175fbe1c0159e0e39e34e7635c319de4 --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_recorded_job85112_task3/inference_universal.yaml @@ -0,0 +1,21 @@ +denoising_step_list: +- 1000 +- 666 +- 333 +warp_denoising_step: true +ts_schedule: false +mixed_precision: true +seed: 42 +image_or_video_shape: +- 1 +- 16 +- 15 +- 44 +- 80 +num_frame_per_block: 3 +context_noise: 0 +mode: universal +causal: true +model_kwargs: + timestep_shift: 5.0 + model_config: configs/distilled_model/universal \ No newline at end of file diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_recorded_job85112_task3/launcher.sh b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_recorded_job85112_task3/launcher.sh new file mode 100644 index 0000000000000000000000000000000000000000..b8daac36b612494e3b61040c1e52ac4e5de3b54b --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_recorded_job85112_task3/launcher.sh @@ -0,0 +1,38 @@ +#!/usr/bin/env bash +#SBATCH --job-name=matrix-game2-native-control +#SBATCH --array=0-4 +#SBATCH --nodes=1 +#SBATCH --ntasks=1 +#SBATCH --cpus-per-task=14 +#SBATCH --gres=gpu:1 +#SBATCH --time=02:00:00 +#SBATCH --account=berzelius-2026-196 +#SBATCH --chdir=/proj/cvl/users/x_fahkh2/worldmem-baseline-evals +#SBATCH --output=slurm_logs/%x-%A_%a.out +#SBATCH --error=slurm_logs/%x-%A_%a.out +set -euo pipefail +module load buildenv-gcccuda/12.9.1-gcc11 +ROOT=/proj/cvl/users/x_fahkh2/worldmem-baseline-evals +PYTHON=/proj/cvl/users/x_fahkh2/envs/matrix-game2-eval/bin/python +DATA_ROOT=/proj/cvl/users/x_fahkh2/WorldMem_Repro/datasets/minecraft +MODES=(official_demo demo_actions neutral recorded small_mouse) +MODE=${MODES[$SLURM_ARRAY_TASK_ID]} +OUTPUT=$ROOT/Matrix-Game/Matrix-Game-2/outputs/diagnostic_native_${MODE}_job${SLURM_ARRAY_JOB_ID}_task${SLURM_ARRAY_TASK_ID} +export TMPDIR=$ROOT/cache +export TRITON_CACHE_DIR=$ROOT/cache +export TORCHINDUCTOR_CACHE_DIR=$ROOT/cache/torchinductor +export HF_HOME=$ROOT/cache/huggingface +export TORCH_HOME=$ROOT/cache/torch +export XDG_CACHE_HOME=$ROOT/cache +export MPLCONFIGDIR=$ROOT/cache/matplotlib +export PYTHONPATH=$ROOT/Matrix-Game/Matrix-Game-2 +export PYTHONNOUSERSITE=1 PYTHONUNBUFFERED=1 OMP_NUM_THREADS=4 MKL_NUM_THREADS=4 +export WANDB_MODE=disabled HF_HUB_OFFLINE=1 TRANSFORMERS_OFFLINE=1 +mkdir -p "$TMPDIR" "$TORCHINDUCTOR_CACHE_DIR" "$OUTPUT" +"$PYTHON" "$ROOT/deployment/verify_node.py" --expected-gpus 1 --output "$OUTPUT" +cp "$0" "$OUTPUT/launcher.sh" +cp "$ROOT/deployment/matrix_native_controls.py" "$OUTPUT/probe.py" +cp "$ROOT/Matrix-Game/Matrix-Game-2/inference.py" "$ROOT/Matrix-Game/Matrix-Game-2/configs/inference_yaml/inference_universal.yaml" "$OUTPUT/" +srun --ntasks=1 --gpus-per-task=1 --cpus-per-task=14 --cpu-bind=cores \ + "$PYTHON" "$ROOT/deployment/matrix_native_controls.py" \ + --mode "$MODE" --data-root "$DATA_ROOT" --output "$OUTPUT" diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_recorded_job85112_task3/manifest.json b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_recorded_job85112_task3/manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..65c1615bf5440f9967e2ab180a9de2c98fa82d50 --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_recorded_job85112_task3/manifest.json @@ -0,0 +1,50 @@ +{ + "mode": "recorded", + "diagnostic_only": true, + "checkpoint": "/proj/cvl/users/x_fahkh2/worldmem-baseline-evals/checkpoints/matrix-game2", + "upstream_revision": "71c3cd7f741311f8100f6cf9cde942b6c1378d11", + "weight_policy": "released raw distilled; original normalization; no optimizer or updates", + "expected_generator_parameters": 1619242304, + "expected_vae_parameters": 126892531, + "expected_clip_parameters": 1193014785, + "batch_size": 1, + "lora": null, + "lr": null, + "activation_checkpointing": false, + "steps": 3, + "shift": 5, + "cache_latents": 6, + "decoder": "official compiled max-autotune-no-cudagraphs", + "official_demo": "unchanged inference.py; supplied painting0000.png; seed0;150latents=597RGB", + "minecraft": { + "anchor": 699, + "native_latents": 9, + "native_rgb": 33, + "scored_slice": [ + 1, + 25 + ], + "targets": [ + 700, + 724 + ], + "recorded_control_alignment": "incoming700:724 plus9noops; small_mouse scales camera only", + "reference": "rawRGB with official center crop and frame_process" + }, + "score_caveat": "Only recorded controls replay GT actions; other modes test stability, not benchmark accuracy", + "cases": [ + { + "index": "0", + "path": "test/val_plains/2/000034.mp4", + "seed": 42000000 + }, + { + "index": "1", + "path": "test/val_desert/1/000029.mp4", + "seed": 42000001 + } + ], + "output_frames_per_case": 33, + "generator_requires_grad_parameters": 1619242304, + "inference_grad_policy": "unchanged native no_grad; parameter flags unmodified" +} \ No newline at end of file diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_recorded_job85112_task3/node.json b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_recorded_job85112_task3/node.json new file mode 100644 index 0000000000000000000000000000000000000000..847792e59a31650c8f9ea26e5452d285c5914cf2 --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_recorded_job85112_task3/node.json @@ -0,0 +1,7 @@ +{ + "devices": [ + "NVIDIA H200" + ], + "torch": "2.7.0+cu126", + "slurm_job_id": "85116" +} diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_recorded_job85112_task3/probe.py b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_recorded_job85112_task3/probe.py new file mode 100644 index 0000000000000000000000000000000000000000..8c5e386fea438328f89a79c076453c94e2f09ecb --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_recorded_job85112_task3/probe.py @@ -0,0 +1,106 @@ +"""Official Matrix demo and native-image controls; all scores are diagnostic.""" +import argparse +import json +import os +import subprocess +import sys +from pathlib import Path +import numpy as np +import torch +from PIL import Image +from diagnose_matrix_game2 import save_result +from protocol import minecraft_actions, read_frames, select_cases +from worldmem_adapter import map_actions + +ROOT = Path(__file__).resolve().parents[1] +REPO = ROOT / 'Matrix-Game/Matrix-Game-2' +MODES = ['official_demo', 'demo_actions', 'neutral', 'recorded', 'small_mouse'] + +def replay_controls(raw, mode, length): + keyboard, mouse = map_actions(minecraft_actions(raw), 1.0) + keys, turns = torch.zeros(length, 4), torch.zeros(length, 2) + if mode != 'neutral': + keys[:24], turns[:24] = keyboard, mouse * (0.1 if mode == 'small_mouse' else 1.0) + return {'keyboard_condition': keys, 'mouse_condition': turns} + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument('--mode', choices=MODES, required=True) + parser.add_argument('--data-root', type=Path, required=True) + parser.add_argument('--output', type=Path, required=True) + parser.add_argument('--dry-run', action='store_true') + args = parser.parse_args() + checkpoint = ROOT / 'checkpoints/matrix-game2' + rows = select_cases(ROOT / 'deployment/manifests/minecraft_test_300.csv', limit=2) + manifest = {'mode': args.mode, 'diagnostic_only': True, 'checkpoint': str(checkpoint), + 'upstream_revision': '71c3cd7f741311f8100f6cf9cde942b6c1378d11', + 'weight_policy': 'released raw distilled; original normalization; no optimizer or updates', + 'expected_generator_parameters': 1619242304, 'expected_vae_parameters': 126892531, + 'expected_clip_parameters': 1193014785, 'batch_size': 1, 'lora': None, 'lr': None, + 'activation_checkpointing': False, 'steps': 3, 'shift': 5, 'cache_latents': 6, + 'decoder': 'official compiled max-autotune-no-cudagraphs', + 'official_demo': 'unchanged inference.py; supplied painting0000.png; seed0;150latents=597RGB', + 'minecraft': {'anchor': 699, 'native_latents': 9, 'native_rgb': 33, 'scored_slice': [1, 25], + 'targets': [700, 724], 'recorded_control_alignment': 'incoming700:724 plus9noops; small_mouse scales camera only', + 'reference': 'rawRGB with official center crop and frame_process'}, + 'score_caveat': 'Only recorded controls replay GT actions; other modes test stability, not benchmark accuracy', + 'cases': ([{'image': 'demo_images/universal/0000.png', 'seed': 0}] if args.mode == 'official_demo' + else [{'index': r['global_test_index'], 'path': r['relative_path'], + 'seed': 42000000 + int(r['global_test_index'])} for r in rows]), + 'output_frames_per_case': 597 if args.mode == 'official_demo' else 33} + if args.dry_run: + print(json.dumps(manifest, indent=2)) + return + args.output.mkdir(parents=True, exist_ok=True) + (args.output / 'manifest.json').write_text(json.dumps(manifest, indent=2)) + os.chdir(REPO) + if args.mode == 'official_demo': + subprocess.run([sys.executable, 'inference.py', '--checkpoint_path', str(checkpoint / 'base_distilled_model/base_distill.safetensors'), + '--pretrained_model_path', str(checkpoint), '--img_path', 'demo_images/universal/0000.png', + '--config_path', 'configs/inference_yaml/inference_universal.yaml', '--seed', '0', + '--num_output_frames', '150', '--output_folder', str(args.output)], check=True) + from torchvision.io import read_video + video, _, _ = read_video(str(args.output / 'demo.mp4'), pts_unit='sec') + assert len(video) == 597 + Image.fromarray(torch.cat([video[t] for t in [0, 1, 2, 24, 100, 596]], dim=1).numpy()).save(args.output / 'samples.jpg') + else: + import inference as native + native.set_seed(0) + model_args = argparse.Namespace(config_path='configs/inference_yaml/inference_universal.yaml', + checkpoint_path=str(checkpoint / 'base_distilled_model/base_distill.safetensors'), + pretrained_model_path=str(checkpoint), num_output_frames=9, seed=0, img_path='', output_folder='') + engine = native.InteractiveGameInference(model_args) + manifest['generator_requires_grad_parameters'] = sum(p.numel() for p in engine.pipeline.generator.parameters() if p.requires_grad) + manifest['inference_grad_policy'] = 'unchanged native no_grad; parameter flags unmodified' + (args.output / 'manifest.json').write_text(json.dumps(manifest, indent=2)) + original_inference, native_controls = engine.pipeline.inference, native.Bench_actions_universal + captured = {} + def capture(**kwargs): + condition = kwargs['conditional_dict'] + np.savez_compressed(case_dir / 'inputs.npz', initial_noise=kwargs['noise'].float().cpu().numpy(), + keyboard=condition['keyboard_cond'][0].float().cpu().numpy(), mouse=condition['mouse_cond'][0].float().cpu().numpy()) + videos = original_inference(**kwargs) + captured['prediction'] = torch.cat(videos, dim=1)[0, 1:25].detach().float().cpu().add(1).div(2) + return videos + engine.pipeline.inference = capture + for row in rows: + case_dir = args.output / f"case{row['global_test_index']}" + case_dir.mkdir(exist_ok=True) + path = args.data_root / row['relative_path'] + rgb = read_frames(path, torch.arange(699, 724)) + images = [Image.fromarray(frame.mul(255).round().byte().permute(1, 2, 0).numpy()) for frame in rgb] + images[0].save(case_dir / 'input.png') + reference = torch.stack([engine.frame_process(engine._resizecrop(im, 352, 640)).add(1).div(2) for im in images[1:]]) + with np.load(path.with_suffix('.npz')) as annotation: + controls = replay_controls(annotation['actions'][700:724], args.mode, 33) + native.Bench_actions_universal = native_controls if args.mode == 'demo_actions' else lambda length: controls + model_args.img_path, model_args.output_folder = str(case_dir / 'input.png'), str(case_dir) + engine.config['mode'] = 'universal' + native.set_seed(42000000 + int(row['global_test_index'])) + engine.generate_videos() + save_result(case_dir, 'comparison', captured.pop('prediction'), reference) + (args.output / 'complete.json').write_text(json.dumps({'mode': args.mode, 'complete': True})) + print('NATIVE_CONTROL_COMPLETE', flush=True) + +if __name__ == '__main__': + main() diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_small_mouse_job85112_task4/case0/comparison.json b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_small_mouse_job85112_task4/case0/comparison.json new file mode 100644 index 0000000000000000000000000000000000000000..0d7d5b8786599f6d9fc4da276abb183a7d5fc123 --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_small_mouse_job85112_task4/case0/comparison.json @@ -0,0 +1,81 @@ +{ + "case": "comparison", + "raw_rgb_psnr": [ + 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0000000000000000000000000000000000000000..93daefe44801c87c363e8f0cdb7835db2a7335e2 --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_small_mouse_job85112_task4/case0/inputs.npz @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:72838d12ebb3dc19ef298830f68d94bf9f85a5ff50ba8851bbee595c913fc309 +size 952295 diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_small_mouse_job85112_task4/case1/comparison.json b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_small_mouse_job85112_task4/case1/comparison.json new file mode 100644 index 0000000000000000000000000000000000000000..cf94359e75669f7dcf859810b43a0c60f575879e --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_small_mouse_job85112_task4/case1/comparison.json @@ -0,0 +1,81 @@ +{ + "case": "comparison", + "raw_rgb_psnr": [ + 19.905391693115234, + 18.204139709472656, + 17.303102493286133, + 16.15292739868164, + 15.123891830444336, + 14.838737487792969, + 15.071209907531738, + 15.314916610717773, + 15.406350135803223, + 15.718154907226562, + 15.977568626403809, + 15.988106727600098, + 15.825580596923828, + 15.36284351348877, + 14.823203086853027, + 14.14748764038086, + 14.317941665649414, + 15.000207901000977, + 15.61587142944336, + 16.046274185180664, + 16.163511276245117, + 16.180030822753906, + 15.998974800109863, + 15.597412109375 + ], + "raw_rgb_mse": [ + 0.010220236144959927, + 0.015121194534003735, + 0.018607575446367264, + 0.024249756708741188, + 0.030733417719602585, + 0.032819073647260666, + 0.031108494848012924, + 0.02941090054810047, + 0.0287981815636158, + 0.026803065091371536, + 0.0252489373087883, + 0.025187749415636063, + 0.02614821307361126, + 0.02908812090754509, + 0.032936666160821915, + 0.03848142921924591, + 0.037000350654125214, + 0.03162126988172531, + 0.02744181640446186, + 0.02485264651477337, + 0.02419072389602661, + 0.02409888431429863, + 0.025124792009592056, + 0.02755870297551155 + ], + "mean_rgb": [ + 0.7165936827659607, + 0.7167513370513916, + 0.7154880166053772, + 0.709321916103363, + 0.6940903067588806, + 0.6899892091751099, + 0.6924728155136108, + 0.6968027353286743, + 0.6972663402557373, + 0.7005922198295593, + 0.7014625668525696, + 0.700640857219696, + 0.6989141702651978, + 0.6952425241470337, + 0.690796434879303, + 0.6834492087364197, + 0.6836293339729309, + 0.6919822692871094, + 0.6983417868614197, + 0.7024022936820984, + 0.7042455673217773, + 0.7036704421043396, + 0.7022319436073303, + 0.6999043822288513 + ] +} \ No newline at end of file diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_small_mouse_job85112_task4/case1/inputs.npz b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_small_mouse_job85112_task4/case1/inputs.npz new file mode 100644 index 0000000000000000000000000000000000000000..2c0556d95d70117252d9d743a93a4a206c2b8606 --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_small_mouse_job85112_task4/case1/inputs.npz @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fd706d5d2daa33ae15c69bdb605527b2a0bfda59c64eb9c71a593ba7a26843f2 +size 951986 diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_small_mouse_job85112_task4/complete.json b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_small_mouse_job85112_task4/complete.json new file mode 100644 index 0000000000000000000000000000000000000000..efcf1d7d0c4ba1ac5c58aca0c1bac96dadaff6c6 --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_small_mouse_job85112_task4/complete.json @@ -0,0 +1 @@ +{"mode": "small_mouse", "complete": true} \ No newline at end of file diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_small_mouse_job85112_task4/inference.py b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_small_mouse_job85112_task4/inference.py new file mode 100644 index 0000000000000000000000000000000000000000..f4df03bda84a07a365507a5146938effb5352ffe --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_small_mouse_job85112_task4/inference.py @@ -0,0 +1,169 @@ +import os +import argparse +import torch +import numpy as np + +from omegaconf import OmegaConf +from torchvision.transforms import v2 +from diffusers.utils import load_image +from einops import rearrange +from pipeline import CausalInferencePipeline +from wan.vae.wanx_vae import get_wanx_vae_wrapper +from demo_utils.vae_block3 import VAEDecoderWrapper +from utils.visualize import process_video +from utils.misc import set_seed +from utils.conditions import * +from utils.wan_wrapper import WanDiffusionWrapper +from safetensors.torch import load_file + +def parse_args(): + parser = argparse.ArgumentParser() + parser.add_argument("--config_path", type=str, default="configs/inference_yaml/inference_universal.yaml", help="Path to the config file") + parser.add_argument("--checkpoint_path", type=str, default="", help="Path to the checkpoint") + parser.add_argument("--img_path", type=str, default="demo_images/universal/0000.png", help="Path to the image") + parser.add_argument("--output_folder", type=str, default="outputs/", help="Output folder") + parser.add_argument("--num_output_frames", type=int, default=150, + help="Number of output latent frames") + parser.add_argument("--seed", type=int, default=0, help="Random seed") + parser.add_argument("--pretrained_model_path", type=str, default="Matrix-Game-2.0", help="Path to the VAE model folder") + args = parser.parse_args() + return args + +class InteractiveGameInference: + def __init__(self, args): + self.args = args + self.device = torch.device("cuda") + self.weight_dtype = torch.bfloat16 + + self._init_config() + self._init_models() + + self.frame_process = v2.Compose([ + v2.Resize(size=(352, 640), antialias=True), + v2.ToTensor(), + v2.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]), + ]) + + def _init_config(self): + self.config = OmegaConf.load(self.args.config_path) + + def _init_models(self): + # Initialize pipeline + generator = WanDiffusionWrapper( + **getattr(self.config, "model_kwargs", {}), is_causal=True) + current_vae_decoder = VAEDecoderWrapper() + vae_state_dict = torch.load(os.path.join(self.args.pretrained_model_path, "Wan2.1_VAE.pth"), map_location="cpu") + decoder_state_dict = {} + for key, value in vae_state_dict.items(): + if 'decoder.' in key or 'conv2' in key: + decoder_state_dict[key] = value + current_vae_decoder.load_state_dict(decoder_state_dict) + current_vae_decoder.to(self.device, torch.float16) + current_vae_decoder.requires_grad_(False) + current_vae_decoder.eval() + current_vae_decoder.compile(mode="max-autotune-no-cudagraphs") + pipeline = CausalInferencePipeline(self.config, generator=generator, vae_decoder=current_vae_decoder) + if self.args.checkpoint_path: + print("Loading Pretrained Model...") + state_dict = load_file(self.args.checkpoint_path) + pipeline.generator.load_state_dict(state_dict) + + self.pipeline = pipeline.to(device=self.device, dtype=self.weight_dtype) + self.pipeline.vae_decoder.to(torch.float16) + + vae = get_wanx_vae_wrapper(self.args.pretrained_model_path, torch.float16) + vae.requires_grad_(False) + vae.eval() + self.vae = vae.to(self.device, self.weight_dtype) + + def _resizecrop(self, image, th, tw): + w, h = image.size + if h / w > th / tw: + new_w = int(w) + new_h = int(new_w * th / tw) + else: + new_h = int(h) + new_w = int(new_h * tw / th) + left = (w - new_w) / 2 + top = (h - new_h) / 2 + right = (w + new_w) / 2 + bottom = (h + new_h) / 2 + image = image.crop((left, top, right, bottom)) + return image + + def generate_videos(self): + mode = self.config.pop('mode') + assert mode in ['universal', 'gta_drive', 'templerun'] + + image = load_image(self.args.img_path) + image = self._resizecrop(image, 352, 640) + image = self.frame_process(image)[None, :, None, :, :].to(dtype=self.weight_dtype, device=self.device) + # Encode the input image as the first latent + padding_video = torch.zeros_like(image).repeat(1, 1, 4 * (self.args.num_output_frames - 1), 1, 1) + img_cond = torch.concat([image, padding_video], dim=2) + tiler_kwargs={"tiled": True, "tile_size": [44, 80], "tile_stride": [23, 38]} + img_cond = self.vae.encode(img_cond, device=self.device, **tiler_kwargs).to(self.device) + mask_cond = torch.ones_like(img_cond) + mask_cond[:, :, 1:] = 0 + cond_concat = torch.cat([mask_cond[:, :4], img_cond], dim=1) + visual_context = self.vae.clip.encode_video(image) + sampled_noise = torch.randn( + [1, 16,self.args.num_output_frames, 44, 80], device=self.device, dtype=self.weight_dtype + ) + num_frames = (self.args.num_output_frames - 1) * 4 + 1 + + conditional_dict = { + "cond_concat": cond_concat.to(device=self.device, dtype=self.weight_dtype), + "visual_context": visual_context.to(device=self.device, dtype=self.weight_dtype) + } + + if mode == 'universal': + cond_data = Bench_actions_universal(num_frames) + mouse_condition = cond_data['mouse_condition'].unsqueeze(0).to(device=self.device, dtype=self.weight_dtype) + conditional_dict['mouse_cond'] = mouse_condition + elif mode == 'gta_drive': + cond_data = Bench_actions_gta_drive(num_frames) + mouse_condition = cond_data['mouse_condition'].unsqueeze(0).to(device=self.device, dtype=self.weight_dtype) + conditional_dict['mouse_cond'] = mouse_condition + else: + cond_data = Bench_actions_templerun(num_frames) + keyboard_condition = cond_data['keyboard_condition'].unsqueeze(0).to(device=self.device, dtype=self.weight_dtype) + conditional_dict['keyboard_cond'] = keyboard_condition + + with torch.no_grad(): + videos = self.pipeline.inference( + noise=sampled_noise, + conditional_dict=conditional_dict, + return_latents=False, + mode=mode, + profile=False + ) + + videos_tensor = torch.cat(videos, dim=1) + videos = rearrange(videos_tensor, "B T C H W -> B T H W C") + videos = ((videos.float() + 1) * 127.5).clip(0, 255).cpu().numpy().astype(np.uint8)[0] + video = np.ascontiguousarray(videos) + mouse_icon = 'assets/images/mouse.png' + if mode != 'templerun': + config = ( + keyboard_condition[0].float().cpu().numpy(), + mouse_condition[0].float().cpu().numpy() + ) + else: + config = ( + keyboard_condition[0].float().cpu().numpy() + ) + process_video(video.astype(np.uint8), self.args.output_folder+f'/demo.mp4', config, mouse_icon, mouse_scale=0.1, process_icon=False, mode=mode) + process_video(video.astype(np.uint8), self.args.output_folder+f'/demo_icon.mp4', config, mouse_icon, mouse_scale=0.1, process_icon=True, mode=mode) + print("Done") + +def main(): + """Main entry point for video generation.""" + args = parse_args() + set_seed(args.seed) + os.makedirs(args.output_folder, exist_ok=True) + pipeline = InteractiveGameInference(args) + pipeline.generate_videos() + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_small_mouse_job85112_task4/inference_universal.yaml b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_small_mouse_job85112_task4/inference_universal.yaml new file mode 100644 index 0000000000000000000000000000000000000000..adec83e8175fbe1c0159e0e39e34e7635c319de4 --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_small_mouse_job85112_task4/inference_universal.yaml @@ -0,0 +1,21 @@ +denoising_step_list: +- 1000 +- 666 +- 333 +warp_denoising_step: true +ts_schedule: false +mixed_precision: true +seed: 42 +image_or_video_shape: +- 1 +- 16 +- 15 +- 44 +- 80 +num_frame_per_block: 3 +context_noise: 0 +mode: universal +causal: true +model_kwargs: + timestep_shift: 5.0 + model_config: configs/distilled_model/universal \ No newline at end of file diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_small_mouse_job85112_task4/launcher.sh b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_small_mouse_job85112_task4/launcher.sh new file mode 100644 index 0000000000000000000000000000000000000000..b8daac36b612494e3b61040c1e52ac4e5de3b54b --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_small_mouse_job85112_task4/launcher.sh @@ -0,0 +1,38 @@ +#!/usr/bin/env bash +#SBATCH --job-name=matrix-game2-native-control +#SBATCH --array=0-4 +#SBATCH --nodes=1 +#SBATCH --ntasks=1 +#SBATCH --cpus-per-task=14 +#SBATCH --gres=gpu:1 +#SBATCH --time=02:00:00 +#SBATCH --account=berzelius-2026-196 +#SBATCH --chdir=/proj/cvl/users/x_fahkh2/worldmem-baseline-evals +#SBATCH --output=slurm_logs/%x-%A_%a.out +#SBATCH --error=slurm_logs/%x-%A_%a.out +set -euo pipefail +module load buildenv-gcccuda/12.9.1-gcc11 +ROOT=/proj/cvl/users/x_fahkh2/worldmem-baseline-evals +PYTHON=/proj/cvl/users/x_fahkh2/envs/matrix-game2-eval/bin/python +DATA_ROOT=/proj/cvl/users/x_fahkh2/WorldMem_Repro/datasets/minecraft +MODES=(official_demo demo_actions neutral recorded small_mouse) +MODE=${MODES[$SLURM_ARRAY_TASK_ID]} +OUTPUT=$ROOT/Matrix-Game/Matrix-Game-2/outputs/diagnostic_native_${MODE}_job${SLURM_ARRAY_JOB_ID}_task${SLURM_ARRAY_TASK_ID} +export TMPDIR=$ROOT/cache +export TRITON_CACHE_DIR=$ROOT/cache +export TORCHINDUCTOR_CACHE_DIR=$ROOT/cache/torchinductor +export HF_HOME=$ROOT/cache/huggingface +export TORCH_HOME=$ROOT/cache/torch +export XDG_CACHE_HOME=$ROOT/cache +export MPLCONFIGDIR=$ROOT/cache/matplotlib +export PYTHONPATH=$ROOT/Matrix-Game/Matrix-Game-2 +export PYTHONNOUSERSITE=1 PYTHONUNBUFFERED=1 OMP_NUM_THREADS=4 MKL_NUM_THREADS=4 +export WANDB_MODE=disabled HF_HUB_OFFLINE=1 TRANSFORMERS_OFFLINE=1 +mkdir -p "$TMPDIR" "$TORCHINDUCTOR_CACHE_DIR" "$OUTPUT" +"$PYTHON" "$ROOT/deployment/verify_node.py" --expected-gpus 1 --output "$OUTPUT" +cp "$0" "$OUTPUT/launcher.sh" +cp "$ROOT/deployment/matrix_native_controls.py" "$OUTPUT/probe.py" +cp "$ROOT/Matrix-Game/Matrix-Game-2/inference.py" "$ROOT/Matrix-Game/Matrix-Game-2/configs/inference_yaml/inference_universal.yaml" "$OUTPUT/" +srun --ntasks=1 --gpus-per-task=1 --cpus-per-task=14 --cpu-bind=cores \ + "$PYTHON" "$ROOT/deployment/matrix_native_controls.py" \ + --mode "$MODE" --data-root "$DATA_ROOT" --output "$OUTPUT" diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_small_mouse_job85112_task4/manifest.json b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_small_mouse_job85112_task4/manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..eb66b217d45d969e9ba985073f6d07ea542c8603 --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_small_mouse_job85112_task4/manifest.json @@ -0,0 +1,50 @@ +{ + "mode": "small_mouse", + "diagnostic_only": true, + "checkpoint": "/proj/cvl/users/x_fahkh2/worldmem-baseline-evals/checkpoints/matrix-game2", + "upstream_revision": "71c3cd7f741311f8100f6cf9cde942b6c1378d11", + "weight_policy": "released raw distilled; original normalization; no optimizer or updates", + "expected_generator_parameters": 1619242304, + "expected_vae_parameters": 126892531, + "expected_clip_parameters": 1193014785, + "batch_size": 1, + "lora": null, + "lr": null, + "activation_checkpointing": false, + "steps": 3, + "shift": 5, + "cache_latents": 6, + "decoder": "official compiled max-autotune-no-cudagraphs", + "official_demo": "unchanged inference.py; supplied painting0000.png; seed0;150latents=597RGB", + "minecraft": { + "anchor": 699, + "native_latents": 9, + "native_rgb": 33, + "scored_slice": [ + 1, + 25 + ], + "targets": [ + 700, + 724 + ], + "recorded_control_alignment": "incoming700:724 plus9noops; small_mouse scales camera only", + "reference": "rawRGB with official center crop and frame_process" + }, + "score_caveat": "Only recorded controls replay GT actions; other modes test stability, not benchmark accuracy", + "cases": [ + { + "index": "0", + "path": "test/val_plains/2/000034.mp4", + "seed": 42000000 + }, + { + "index": "1", + "path": "test/val_desert/1/000029.mp4", + "seed": 42000001 + } + ], + "output_frames_per_case": 33, + "generator_requires_grad_parameters": 1619242304, + "inference_grad_policy": "unchanged native no_grad; parameter flags unmodified" +} \ No newline at end of file diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_small_mouse_job85112_task4/node.json b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_small_mouse_job85112_task4/node.json new file mode 100644 index 0000000000000000000000000000000000000000..c7b31051c846c653dcca93c4fe5bee55bce45b69 --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_small_mouse_job85112_task4/node.json @@ -0,0 +1,7 @@ +{ + "devices": [ + "NVIDIA H200" + ], + "torch": "2.7.0+cu126", + "slurm_job_id": "85112" +} diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_small_mouse_job85112_task4/probe.py b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_small_mouse_job85112_task4/probe.py new file mode 100644 index 0000000000000000000000000000000000000000..8c5e386fea438328f89a79c076453c94e2f09ecb --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_native_small_mouse_job85112_task4/probe.py @@ -0,0 +1,106 @@ +"""Official Matrix demo and native-image controls; all scores are diagnostic.""" +import argparse +import json +import os +import subprocess +import sys +from pathlib import Path +import numpy as np +import torch +from PIL import Image +from diagnose_matrix_game2 import save_result +from protocol import minecraft_actions, read_frames, select_cases +from worldmem_adapter import map_actions + +ROOT = Path(__file__).resolve().parents[1] +REPO = ROOT / 'Matrix-Game/Matrix-Game-2' +MODES = ['official_demo', 'demo_actions', 'neutral', 'recorded', 'small_mouse'] + +def replay_controls(raw, mode, length): + keyboard, mouse = map_actions(minecraft_actions(raw), 1.0) + keys, turns = torch.zeros(length, 4), torch.zeros(length, 2) + if mode != 'neutral': + keys[:24], turns[:24] = keyboard, mouse * (0.1 if mode == 'small_mouse' else 1.0) + return {'keyboard_condition': keys, 'mouse_condition': turns} + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument('--mode', choices=MODES, required=True) + parser.add_argument('--data-root', type=Path, required=True) + parser.add_argument('--output', type=Path, required=True) + parser.add_argument('--dry-run', action='store_true') + args = parser.parse_args() + checkpoint = ROOT / 'checkpoints/matrix-game2' + rows = select_cases(ROOT / 'deployment/manifests/minecraft_test_300.csv', limit=2) + manifest = {'mode': args.mode, 'diagnostic_only': True, 'checkpoint': str(checkpoint), + 'upstream_revision': '71c3cd7f741311f8100f6cf9cde942b6c1378d11', + 'weight_policy': 'released raw distilled; original normalization; no optimizer or updates', + 'expected_generator_parameters': 1619242304, 'expected_vae_parameters': 126892531, + 'expected_clip_parameters': 1193014785, 'batch_size': 1, 'lora': None, 'lr': None, + 'activation_checkpointing': False, 'steps': 3, 'shift': 5, 'cache_latents': 6, + 'decoder': 'official compiled max-autotune-no-cudagraphs', + 'official_demo': 'unchanged inference.py; supplied painting0000.png; seed0;150latents=597RGB', + 'minecraft': {'anchor': 699, 'native_latents': 9, 'native_rgb': 33, 'scored_slice': [1, 25], + 'targets': [700, 724], 'recorded_control_alignment': 'incoming700:724 plus9noops; small_mouse scales camera only', + 'reference': 'rawRGB with official center crop and frame_process'}, + 'score_caveat': 'Only recorded controls replay GT actions; other modes test stability, not benchmark accuracy', + 'cases': ([{'image': 'demo_images/universal/0000.png', 'seed': 0}] if args.mode == 'official_demo' + else [{'index': r['global_test_index'], 'path': r['relative_path'], + 'seed': 42000000 + int(r['global_test_index'])} for r in rows]), + 'output_frames_per_case': 597 if args.mode == 'official_demo' else 33} + if args.dry_run: + print(json.dumps(manifest, indent=2)) + return + args.output.mkdir(parents=True, exist_ok=True) + (args.output / 'manifest.json').write_text(json.dumps(manifest, indent=2)) + os.chdir(REPO) + if args.mode == 'official_demo': + subprocess.run([sys.executable, 'inference.py', '--checkpoint_path', str(checkpoint / 'base_distilled_model/base_distill.safetensors'), + '--pretrained_model_path', str(checkpoint), '--img_path', 'demo_images/universal/0000.png', + '--config_path', 'configs/inference_yaml/inference_universal.yaml', '--seed', '0', + '--num_output_frames', '150', '--output_folder', str(args.output)], check=True) + from torchvision.io import read_video + video, _, _ = read_video(str(args.output / 'demo.mp4'), pts_unit='sec') + assert len(video) == 597 + Image.fromarray(torch.cat([video[t] for t in [0, 1, 2, 24, 100, 596]], dim=1).numpy()).save(args.output / 'samples.jpg') + else: + import inference as native + native.set_seed(0) + model_args = argparse.Namespace(config_path='configs/inference_yaml/inference_universal.yaml', + checkpoint_path=str(checkpoint / 'base_distilled_model/base_distill.safetensors'), + pretrained_model_path=str(checkpoint), num_output_frames=9, seed=0, img_path='', output_folder='') + engine = native.InteractiveGameInference(model_args) + manifest['generator_requires_grad_parameters'] = sum(p.numel() for p in engine.pipeline.generator.parameters() if p.requires_grad) + manifest['inference_grad_policy'] = 'unchanged native no_grad; parameter flags unmodified' + (args.output / 'manifest.json').write_text(json.dumps(manifest, indent=2)) + original_inference, native_controls = engine.pipeline.inference, native.Bench_actions_universal + captured = {} + def capture(**kwargs): + condition = kwargs['conditional_dict'] + np.savez_compressed(case_dir / 'inputs.npz', initial_noise=kwargs['noise'].float().cpu().numpy(), + keyboard=condition['keyboard_cond'][0].float().cpu().numpy(), mouse=condition['mouse_cond'][0].float().cpu().numpy()) + videos = original_inference(**kwargs) + captured['prediction'] = torch.cat(videos, dim=1)[0, 1:25].detach().float().cpu().add(1).div(2) + return videos + engine.pipeline.inference = capture + for row in rows: + case_dir = args.output / f"case{row['global_test_index']}" + case_dir.mkdir(exist_ok=True) + path = args.data_root / row['relative_path'] + rgb = read_frames(path, torch.arange(699, 724)) + images = [Image.fromarray(frame.mul(255).round().byte().permute(1, 2, 0).numpy()) for frame in rgb] + images[0].save(case_dir / 'input.png') + reference = torch.stack([engine.frame_process(engine._resizecrop(im, 352, 640)).add(1).div(2) for im in images[1:]]) + with np.load(path.with_suffix('.npz')) as annotation: + controls = replay_controls(annotation['actions'][700:724], args.mode, 33) + native.Bench_actions_universal = native_controls if args.mode == 'demo_actions' else lambda length: controls + model_args.img_path, model_args.output_folder = str(case_dir / 'input.png'), str(case_dir) + engine.config['mode'] = 'universal' + native.set_seed(42000000 + int(row['global_test_index'])) + engine.generate_videos() + save_result(case_dir, 'comparison', captured.pop('prediction'), reference) + (args.output / 'complete.json').write_text(json.dumps({'mode': args.mode, 'complete': True})) + print('NATIVE_CONTROL_COMPLETE', flush=True) + +if __name__ == '__main__': + main() diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_wan_oracle_job85088_task0/case0_wan_oracle.json b/results/worldmem-baseline-evals/matrix-game2/diagnostic_wan_oracle_job85088_task0/case0_wan_oracle.json new file mode 100644 index 0000000000000000000000000000000000000000..2defd9abf0e2255aaa894ae3b7912ff61fcc30ba --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_wan_oracle_job85088_task0/case0_wan_oracle.json @@ -0,0 +1,81 @@ +{ + "case": "case0_wan_oracle", + "raw_rgb_psnr": [ + 34.50311279296875, + 34.654510498046875, + 34.57817459106445, + 34.601837158203125, + 34.46246337890625, + 34.741241455078125, + 34.625450134277344, + 34.658409118652344, + 34.566619873046875, + 34.880271911621094, + 34.68211364746094, + 34.755611419677734, + 34.450016021728516, + 34.66584777832031, + 34.53730773925781, + 34.55168533325195, + 34.38592529296875, + 34.73810577392578, + 34.56845474243164, + 34.62205123901367, + 34.4306755065918, + 34.77874755859375, + 34.602718353271484, + 34.67219924926758 + ], + "raw_rgb_mse": [ + 0.00035455895704217255, + 0.00034241206594742835, + 0.00034848388168029487, + 0.0003465902991592884, + 0.00035789355752058327, + 0.00033564178738743067, + 0.00034471091930754483, + 0.000342104904120788, + 0.00034941223566420376, + 0.0003250669687986374, + 0.00034024243359453976, + 0.00033453290234319866, + 0.00035892060259357095, + 0.00034151942236348987, + 0.0003517784643918276, + 0.00035061591188423336, + 0.0003642567025963217, + 0.0003358841931913048, + 0.00034926453372463584, + 0.0003449807409197092, + 0.0003605225356295705, + 0.0003327555605210364, + 0.00034651983878575265, + 0.00034102011704817414 + ], + "mean_rgb": [ + 0.7217753529548645, + 0.7219029068946838, + 0.7213197946548462, + 0.7208141684532166, + 0.7212034463882446, + 0.7211069464683533, + 0.7204988598823547, + 0.7204425930976868, + 0.7205440998077393, + 0.7204944491386414, + 0.7203000783920288, + 0.7199990153312683, + 0.7209401726722717, + 0.7213990092277527, + 0.7206678986549377, + 0.7205467820167542, + 0.7213102579116821, + 0.7215085625648499, + 0.7212632894515991, + 0.7209525108337402, + 0.7212324142456055, + 0.7212611436843872, + 0.7209208607673645, + 0.7207081317901611 + ] +} \ No newline at end of file diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_wan_oracle_job85088_task0/case1_wan_oracle.json b/results/worldmem-baseline-evals/matrix-game2/diagnostic_wan_oracle_job85088_task0/case1_wan_oracle.json new file mode 100644 index 0000000000000000000000000000000000000000..41d7ea33785ac198d7e4b27091bdd50f96b94771 --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_wan_oracle_job85088_task0/case1_wan_oracle.json @@ -0,0 +1,81 @@ +{ + "case": "case1_wan_oracle", + "raw_rgb_psnr": [ + 31.08868408203125, + 30.543432235717773, + 31.288101196289062, + 30.8697509765625, + 30.309547424316406, + 31.53420639038086, + 32.109039306640625, + 31.969955444335938, + 32.03971862792969, + 32.365684509277344, + 33.04118347167969, + 32.62156677246094, + 32.28874588012695, + 32.467716217041016, + 33.07372283935547, + 32.700599670410156, + 32.59062194824219, + 32.697750091552734, + 33.1352653503418, + 32.91297912597656, + 32.108211517333984, + 32.93695068359375, + 32.958858489990234, + 32.73663330078125 + ], + "raw_rgb_mse": [ + 0.0007782725733704865, + 0.0008823824464343488, + 0.0007433437858708203, + 0.0008185115875676274, + 0.0009312047041021287, + 0.0007023914949968457, + 0.0006153129506856203, + 0.0006353375501930714, + 0.0006252131424844265, + 0.000580004823859781, + 0.0004964572144672275, + 0.0005468189483508468, + 0.000590371317230165, + 0.0005665370845235884, + 0.0004927514819428325, + 0.0005369579303078353, + 0.0005507288151420653, + 0.0005373102030716836, + 0.0004858178144786507, + 0.0005113309598527849, + 0.0006154304137453437, + 0.0005085160955786705, + 0.0005059578106738627, + 0.000532520585693419 + ], + "mean_rgb": [ + 0.7130449414253235, + 0.7113716006278992, + 0.7166961431503296, + 0.7147504687309265, + 0.722019374370575, + 0.7232353687286377, + 0.7230417132377625, + 0.722819983959198, + 0.7228452563285828, + 0.7228851914405823, + 0.7225772738456726, + 0.7221559286117554, + 0.7226071953773499, + 0.7228122353553772, + 0.7235471606254578, + 0.723677396774292, + 0.7240967750549316, + 0.7239828109741211, + 0.7240568399429321, + 0.7238571047782898, + 0.7236796021461487, + 0.7233341336250305, + 0.7234703302383423, + 0.7233763933181763 + ] +} \ No newline at end of file diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_wan_oracle_job85088_task0/complete.json b/results/worldmem-baseline-evals/matrix-game2/diagnostic_wan_oracle_job85088_task0/complete.json new file mode 100644 index 0000000000000000000000000000000000000000..eff65ba1d001083cdc408dead1a5989d50fd48c8 --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_wan_oracle_job85088_task0/complete.json @@ -0,0 +1 @@ +{"mode": "wan_oracle", "cases": 2, "frames": 24} \ No newline at end of file diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_wan_oracle_job85088_task0/launcher.sh b/results/worldmem-baseline-evals/matrix-game2/diagnostic_wan_oracle_job85088_task0/launcher.sh new file mode 100644 index 0000000000000000000000000000000000000000..339a8b9d8bd588ab4ac7cd6b47a03c94b93c1f4e --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_wan_oracle_job85088_task0/launcher.sh @@ -0,0 +1,35 @@ +#!/usr/bin/env bash +#SBATCH --job-name=matrix-game2-diagnostic +#SBATCH --array=0-4 +#SBATCH --nodes=1 +#SBATCH --ntasks=1 +#SBATCH --cpus-per-task=14 +#SBATCH --gres=gpu:1 +#SBATCH --time=01:00:00 +#SBATCH --account=berzelius-2026-196 +#SBATCH --chdir=/proj/cvl/users/x_fahkh2/worldmem-baseline-evals +#SBATCH --output=slurm_logs/%x-%A_%a.out +#SBATCH --error=slurm_logs/%x-%A_%a.out +set -euo pipefail +module load buildenv-gcccuda/12.9.1-gcc11 +ROOT=/proj/cvl/users/x_fahkh2/worldmem-baseline-evals +PYTHON=/proj/cvl/users/x_fahkh2/envs/matrix-game2-eval/bin/python +DATA_ROOT=/proj/cvl/users/x_fahkh2/WorldMem_Repro/datasets/minecraft +MODES=(wan_oracle full_original full_weighted i2v_original i2v_weighted) +MODE=${MODES[$SLURM_ARRAY_TASK_ID]} +OUTPUT=$ROOT/Matrix-Game/Matrix-Game-2/outputs/diagnostic_${MODE}_job${SLURM_ARRAY_JOB_ID}_task${SLURM_ARRAY_TASK_ID} +export TMPDIR=$ROOT/cache +export TRITON_CACHE_DIR=$ROOT/cache +export HF_HOME=$ROOT/cache/huggingface +export TORCH_HOME=$ROOT/cache/torch +export XDG_CACHE_HOME=$ROOT/cache +export MPLCONFIGDIR=$ROOT/cache/matplotlib +export PYTHONNOUSERSITE=1 PYTHONUNBUFFERED=1 OMP_NUM_THREADS=4 MKL_NUM_THREADS=4 +export WANDB_MODE=disabled HF_HUB_OFFLINE=1 TRANSFORMERS_OFFLINE=1 +mkdir -p "$TMPDIR" "$OUTPUT" +"$PYTHON" "$ROOT/deployment/verify_node.py" --expected-gpus 1 --output "$OUTPUT" +cp "$0" "$OUTPUT/launcher.sh" +cp "$ROOT/deployment/diagnose_matrix_game2.py" "$OUTPUT/probe.py" +srun --ntasks=1 --gpus-per-task=1 --cpus-per-task=14 --cpu-bind=cores \ + "$PYTHON" "$ROOT/deployment/diagnose_matrix_game2.py" \ + --mode "$MODE" --data-root "$DATA_ROOT" --output "$OUTPUT" diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_wan_oracle_job85088_task0/manifest.json b/results/worldmem-baseline-evals/matrix-game2/diagnostic_wan_oracle_job85088_task0/manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..5ee365c0f509a6e9776f02eefd475cbf4b4893fb --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_wan_oracle_job85088_task0/manifest.json @@ -0,0 +1,59 @@ +{ + "diagnostic_only": true, + "mode": "wan_oracle", + "checkpoint": "/proj/cvl/users/x_fahkh2/worldmem-baseline-evals/checkpoints/matrix-game2", + "weight_source": "released raw distilled", + "optimizer": null, + "lr": null, + "lora": null, + "activation_checkpointing": false, + "batch_size": 1, + "trainable_parameters": 0, + "denoising_steps": 3, + "shift": 5, + "local_attention_latents": 6, + "selector": "native sliding cache", + "reference": "raw RGB resized352x640, not benchmark Oasis references", + "targets": [ + 700, + 724 + ], + "full_history": [ + 100, + 700 + ], + "full_prefix_latents": 153, + "oracle": "9copies+RGB100:724 encodedonce;159latents; discard153latent prefix", + "i2v": { + "anchor": 699, + "decoded_slice": [ + 1, + 25 + ], + "controls": [ + 700, + 724 + ], + "right_noops": 9 + }, + "normalization_change": "weighted modes apply scales and accumulate RMS in fp32", + "cases": [ + { + "index": "0", + "path": "test/val_plains/2/000034.mp4", + "seed": 42000000 + }, + { + "index": "1", + "path": "test/val_desert/1/000029.mp4", + "seed": 42000001 + } + ], + "parameter_counts": { + "generator_total": 1619242304, + "generator_trainable": 0, + "vae_total": 126892531, + "clip_total": 1193014785 + }, + "action_norm_instances": 60 +} \ No newline at end of file diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_wan_oracle_job85088_task0/node.json b/results/worldmem-baseline-evals/matrix-game2/diagnostic_wan_oracle_job85088_task0/node.json new file mode 100644 index 0000000000000000000000000000000000000000..01d5cbe166895b65c92d903d934786242361090c --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_wan_oracle_job85088_task0/node.json @@ -0,0 +1,7 @@ +{ + "devices": [ + "NVIDIA H200" + ], + "torch": "2.7.0+cu126", + "slurm_job_id": "85089" +} diff --git a/results/worldmem-baseline-evals/matrix-game2/diagnostic_wan_oracle_job85088_task0/probe.py b/results/worldmem-baseline-evals/matrix-game2/diagnostic_wan_oracle_job85088_task0/probe.py new file mode 100644 index 0000000000000000000000000000000000000000..f5b01f8975ad3f66be61f6e960f05c64d14fd6e0 --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/diagnostic_wan_oracle_job85088_task0/probe.py @@ -0,0 +1,113 @@ +"""Frozen Matrix diagnostics; raw-RGB PSNR is separate from benchmark scoring.""" +import argparse +import json +import sys +from pathlib import Path +import torch +import torch.nn.functional as F +from PIL import Image + +ROOT = Path(__file__).resolve().parents[1] +sys.path[:0] = [str(ROOT / 'shared'), str(ROOT / 'Matrix-Game/Matrix-Game-2')] +from protocol import load_minecraft_case, select_cases +from worldmem_adapter import MatrixGameAdapter, map_actions +from demo_utils.constant import ZERO_VAE_CACHE + +MODES = ['wan_oracle', 'full_original', 'full_weighted', 'i2v_original', 'i2v_weighted'] +parser = argparse.ArgumentParser(description=__doc__) +parser.add_argument('--mode', choices=MODES, required=True) +parser.add_argument('--data-root', type=Path, required=True) +parser.add_argument('--output', type=Path, required=True) +parser.add_argument('--dry-run', action='store_true') + +def weighted_norm(self, x): + y = x.float() + return (y * torch.rsqrt(y.square().mean(-1, keepdim=True) + self.eps)).to(x.dtype) * self.weight + +def save_result(output, name, prediction, reference): + prediction = prediction.float().cpu() + if prediction.shape != reference.shape or not torch.isfinite(prediction).all(): + raise ValueError(f'Invalid Matrix prediction: {name}, shape={tuple(prediction.shape)}') + prediction = prediction.clamp(0, 1) + mse = (prediction - reference).square().mean((1, 2, 3)) + report = {'case': name, 'raw_rgb_psnr': (-10 * mse.clamp_min(1e-12).log10()).tolist(), + 'raw_rgb_mse': mse.tolist(), 'mean_rgb': prediction.mean((1, 2, 3)).tolist()} + (output / f'{name}.json').write_text(json.dumps(report, indent=2)) + grid = torch.cat([torch.cat([v[t] for t in [0, 1, 2, 5, 11, 23]], dim=2) + for v in (reference, prediction)], dim=1) + Image.fromarray(grid.mul(255).byte().permute(1, 2, 0).numpy()).save(output / f'{name}.jpg') + print(json.dumps({'case': name, 'mean_psnr': sum(report['raw_rgb_psnr']) / 24, + 'first_psnr': report['raw_rgb_psnr'][:3]}), flush=True) + +@torch.inference_mode() +def main(): + args = parser.parse_args() + rows = select_cases(ROOT / 'deployment/manifests/minecraft_test_300.csv', limit=2) + checkpoint = ROOT / 'checkpoints/matrix-game2' + manifest = {'diagnostic_only': True, 'mode': args.mode, 'checkpoint': str(checkpoint), + 'weight_source': 'released raw distilled', 'optimizer': None, 'lr': None, 'lora': None, + 'activation_checkpointing': False, 'batch_size': 1, 'trainable_parameters': 0, + 'denoising_steps': 3, 'shift': 5, 'local_attention_latents': 6, + 'selector': 'native sliding cache', 'reference': 'raw RGB resized352x640, not benchmark Oasis references', + 'targets': [700, 724], 'full_history': [100, 700], 'full_prefix_latents': 153, + 'oracle': '9copies+RGB100:724 encodedonce;159latents; discard153latent prefix', + 'i2v': {'anchor': 699, 'decoded_slice': [1, 25], 'controls': [700, 724], 'right_noops': 9}, + 'normalization_change': 'weighted modes apply scales and accumulate RMS in fp32', + 'cases': [{'index': r['global_test_index'], 'path': r['relative_path'], + 'seed': 42000000 + int(r['global_test_index'])} for r in rows]} + if args.dry_run: + print(json.dumps(manifest, indent=2)) + return + args.output.mkdir(parents=True, exist_ok=True) + (args.output / 'manifest.json').write_text(json.dumps(manifest, indent=2)) + from wan.modules.action_module import WanRMSNorm + adapter = MatrixGameAdapter(checkpoint) + manifest['parameter_counts'] = adapter.parameter_counts + manifest['action_norm_instances'] = sum(isinstance(m, WanRMSNorm) for m in adapter.pipeline.modules()) + (args.output / 'manifest.json').write_text(json.dumps(manifest, indent=2)) + if args.mode.endswith('weighted'): + WanRMSNorm.forward = weighted_norm + for row in rows: + row.update(target_end='724', target_frames='24') + case = load_minecraft_case(row, args.data_root) + reference = F.interpolate(case['rgb'][600:624], (352, 640), mode='bilinear', align_corners=False, antialias=True) + seed = 42000000 + int(row['global_test_index']) + if args.mode == 'wan_oracle': + rgb = F.interpolate(case['rgb'].cuda(), (352, 640), mode='bilinear', align_corners=False, antialias=True) + rgb = torch.cat([rgb[:1].expand(9, -1, -1, -1), rgb]) + latent = adapter.vae.encode(rgb.transpose(0, 1)[None].mul(2).sub(1).bfloat16(), device=adapter.device, tiled=False) + cache, chunks = [None] * len(ZERO_VAE_CACHE), [] + for start in range(0, latent.shape[2], 3): + decoded, cache = adapter.pipeline.vae_decoder(latent[:, :, start:start + 3].transpose(1, 2).half(), *cache) + if start >= 153: + chunks.append(decoded.cpu()) + prediction = torch.cat(chunks, dim=1)[0].float().add(1).div(2) + del latent, rgb, cache, decoded, chunks + elif args.mode.startswith('full'): + prediction, _ = adapter.generate(case, target_frames=24, seed=seed) + else: + torch.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + anchor = F.interpolate(case['rgb'][599:600].cuda(), (352, 640), mode='bilinear', align_corners=False, antialias=True) + anchor = anchor.transpose(0, 1)[None].mul(2).sub(1).bfloat16() + image = anchor.new_zeros((1, 3, 33, 352, 640)) + image[:, :, :1] = anchor + image_latents = adapter.vae.encode(image, device=adapter.device, tiled=False) + mask = torch.zeros_like(image_latents[:, :4]) + mask[:, :, :1] = 1 + actions = torch.cat([case['actions'][600:624], torch.zeros(9, 25)]).cuda().bfloat16() + keyboard, mouse = map_actions(actions, 1.0) + condition = {'cond_concat': torch.cat([mask, image_latents], dim=1), + 'visual_context': adapter.vae.clip.encode_video(anchor).bfloat16(), + 'keyboard_cond': keyboard[None], 'mouse_cond': mouse[None]} + noise = torch.randn((1, 16, 9, 44, 80), device='cuda', dtype=torch.bfloat16) + videos = adapter.pipeline.inference(noise, condition, initial_latent=None, mode='universal') + prediction = torch.cat(videos, dim=1)[0, 1:25].float().add(1).div(2) + del image, anchor, image_latents, condition, videos, noise + save_result(args.output, f"case{row['global_test_index']}_{args.mode}", prediction, reference) + del prediction, reference, case + (args.output / 'complete.json').write_text(json.dumps({'mode': args.mode, 'cases': 2, 'frames': 24})) + print('DIAGNOSTICS_COMPLETE', flush=True) + +if __name__ == '__main__': + main() diff --git a/results/worldmem-baseline-evals/matrix-game2/matrix-game2_minecraft_300cases_seed42_job84977/cases/g000000_test__val_plains__2__000034/case.json b/results/worldmem-baseline-evals/matrix-game2/matrix-game2_minecraft_300cases_seed42_job84977/cases/g000000_test__val_plains__2__000034/case.json new file mode 100644 index 0000000000000000000000000000000000000000..1765b665450ec058717e3ada5c4f6e3db30aa90f --- /dev/null +++ b/results/worldmem-baseline-evals/matrix-game2/matrix-game2_minecraft_300cases_seed42_job84977/cases/g000000_test__val_plains__2__000034/case.json @@ -0,0 +1,1755 @@ +{ + "case": { + "manifest_index": "0", + "global_test_index": "0", + "dataset_index": "450", + "relative_path": "test/val_plains/2/000034.mp4", + "clip_id": "test/val_plains/2/000034", + "source_offset": "100", + "context_start": "100", + "context_end": "700", + "target_start": "700", + "target_end": "1200", + "n_frames_valid": "1100", + "context_frames": "600", + "target_frames": "500", + "frame_skip": "1", 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