多卡流水导出

.gitignore

@@ -129,4 +129,14 @@ Data/utils.py
 Experiment/checkpoint
 Experiment/log
 
-*.ckpt
+*.ckpt
+ckpts/unifolm_wma_dual.ckpt.prepared.pt
+trt_engines/video_backbone.onnx
+trt_engines/video_backbone.engine
+*.weight
+*.bias
+onnx__Mul*
+onnx__MatMul*
+onnx__Add_*
+Constant_*_attr__value
+*.0

configs/inference/world_model_interaction.yaml

@@ -222,7 +222,7 @@ data:
     test:
       target: unifolm_wma.data.wma_data.WMAData
       params:
-        data_dir: '/mnt/ASC1637/unifolm-world-model-action/examples/world_model_interaction_prompts'
+        data_dir: 'examples/world_model_interaction_prompts'
         video_length: ${model.params.wma_config.params.temporal_length}
         frame_stride: 2
         load_raw_resolution: True

scripts/evaluation/analyze_backbone_blocks.py

@@ -0,0 +1,148 @@
+import argparse
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+
+MID_STEP_MIN = 15
+MID_STEP_MAX = 35
+TAIL_STEP_MIN = 40
+TAIL_STEP_MAX = 49
+
+
+def describe(series: pd.Series) -> dict[str, float]:
+    numeric = pd.to_numeric(series, errors='coerce').dropna()
+    if numeric.empty:
+        return {'count': 0.0, 'mean': np.nan, 'median': np.nan, 'p90': np.nan}
+    return {
+        'count': float(numeric.count()),
+        'mean': float(numeric.mean()),
+        'median': float(numeric.median()),
+        'p90': float(numeric.quantile(0.90)),
+    }
+
+
+def make_parser() -> argparse.ArgumentParser:
+    parser = argparse.ArgumentParser(
+        description="Aggregate backbone block profiling results.")
+    parser.add_argument("--input_csv",
+                        type=str,
+                        required=True,
+                        help="Path to backbone_block_log.csv")
+    parser.add_argument("--output_dir",
+                        type=str,
+                        default=None,
+                        help="Directory to store summaries; defaults to input parent.")
+    return parser
+
+
+def main() -> None:
+    args = make_parser().parse_args()
+    input_path = Path(args.input_csv)
+    if not input_path.exists():
+        raise FileNotFoundError(f"Missing file: {input_path}")
+
+    output_dir = Path(args.output_dir) if args.output_dir else input_path.parent / "analysis"
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    df = pd.read_csv(input_path)
+    numeric_cols = [
+        'forward_time_ms',
+        'l2_delta_vs_prev',
+        'rel_l2_delta_vs_prev',
+        'cosine_vs_prev',
+        'l2_delta_vs_full50',
+        'cosine_vs_full50',
+    ]
+    for col in numeric_cols:
+        df[col] = pd.to_numeric(df[col], errors='coerce')
+
+    step_aggregate = df.groupby(
+        ['block_stage', 'block_name', 'block_index', 'step'])[numeric_cols].mean().reset_index()
+    step_aggregate.to_csv(output_dir / 'block_step_aggregate.csv', index=False)
+
+    block_summary = df.groupby(['block_stage', 'block_name', 'block_index']).agg(
+        mean_forward_time_ms=('forward_time_ms', 'mean'),
+        mean_rel_l2_delta_vs_prev=('rel_l2_delta_vs_prev', 'mean'),
+        mean_cosine_vs_prev=('cosine_vs_prev', 'mean'),
+        mean_l2_delta_vs_full50=('l2_delta_vs_full50', 'mean'),
+        mean_cosine_vs_full50=('cosine_vs_full50', 'mean'),
+    ).reset_index()
+
+    mid_df = df[(df['step'] >= MID_STEP_MIN) & (df['step'] <= MID_STEP_MAX)]
+    tail_df = df[(df['step'] >= TAIL_STEP_MIN) & (df['step'] <= TAIL_STEP_MAX)]
+    block_summary = block_summary.merge(
+        mid_df.groupby(['block_stage', 'block_name', 'block_index']).agg(
+            mid_mean_rel_l2_delta_vs_prev=('rel_l2_delta_vs_prev', 'mean'),
+            mid_mean_cosine_vs_prev=('cosine_vs_prev', 'mean'),
+            mid_mean_forward_time_ms=('forward_time_ms', 'mean'),
+        ).reset_index(),
+        on=['block_stage', 'block_name', 'block_index'],
+        how='left',
+    )
+    block_summary = block_summary.merge(
+        tail_df.groupby(['block_stage', 'block_name', 'block_index']).agg(
+            tail_mean_rel_l2_delta_vs_prev=('rel_l2_delta_vs_prev', 'mean'),
+            tail_mean_cosine_vs_prev=('cosine_vs_prev', 'mean'),
+            tail_mean_forward_time_ms=('forward_time_ms', 'mean'),
+        ).reset_index(),
+        on=['block_stage', 'block_name', 'block_index'],
+        how='left',
+    )
+    block_summary = block_summary.sort_values(
+        ['tail_mean_rel_l2_delta_vs_prev', 'mean_forward_time_ms'],
+        ascending=[True, False])
+    block_summary.to_csv(output_dir / 'block_summary.csv', index=False)
+
+    stage_summary = []
+    for stage_name, stage_df in [('all', df), ('mid', mid_df), ('tail', tail_df)]:
+        grouped = stage_df.groupby('block_stage').agg(
+            mean_forward_time_ms=('forward_time_ms', 'mean'),
+            mean_rel_l2_delta_vs_prev=('rel_l2_delta_vs_prev', 'mean'),
+            mean_cosine_vs_prev=('cosine_vs_prev', 'mean'),
+            mean_l2_delta_vs_full50=('l2_delta_vs_full50', 'mean'),
+            mean_cosine_vs_full50=('cosine_vs_full50', 'mean'),
+        ).reset_index()
+        grouped.insert(0, 'window', stage_name)
+        stage_summary.append(grouped)
+    stage_summary_df = pd.concat(stage_summary, ignore_index=True)
+    stage_summary_df.to_csv(output_dir / 'stage_summary.csv', index=False)
+
+    best_cache_candidates = block_summary.sort_values(
+        ['tail_mean_rel_l2_delta_vs_prev', 'mean_forward_time_ms'],
+        ascending=[True, False]).head(10)
+
+    lines = [
+        "# Backbone Block Profiling Report",
+        "",
+        "## Dataset Overview",
+        "",
+        f"- Rows: {len(df)}",
+        f"- Blocks: {df['block_name'].nunique()}",
+        f"- Steps: {int(df['step'].min())}-{int(df['step'].max())}",
+        f"- Pass types: {', '.join(sorted(df['pass_type'].dropna().unique()))}",
+        "",
+        "## Block Timing",
+        "",
+        f"- `forward_time_ms`: mean={describe(df['forward_time_ms'])['mean']:.4f}, median={describe(df['forward_time_ms'])['median']:.4f}, p90={describe(df['forward_time_ms'])['p90']:.4f}",
+        "",
+        "## Stability",
+        "",
+        f"- `rel_l2_delta_vs_prev`: mean={describe(df['rel_l2_delta_vs_prev'])['mean']:.6f}, median={describe(df['rel_l2_delta_vs_prev'])['median']:.6f}, p90={describe(df['rel_l2_delta_vs_prev'])['p90']:.6f}",
+        f"- `cosine_vs_prev`: mean={describe(df['cosine_vs_prev'])['mean']:.6f}, median={describe(df['cosine_vs_prev'])['median']:.6f}, p90={describe(df['cosine_vs_prev'])['p90']:.6f}",
+        "",
+        "## Top Cache Candidates",
+        "",
+    ]
+    for _, row in best_cache_candidates.iterrows():
+        lines.append(
+            f"- `{row['block_name']}` ({row['block_stage']}): tail_rel_l2={row['tail_mean_rel_l2_delta_vs_prev']:.6f}, mean_forward_time_ms={row['mean_forward_time_ms']:.4f}"
+        )
+
+    (output_dir / 'backbone_profile_report.md').write_text("\n".join(lines),
+                                                           encoding='utf-8')
+
+
+if __name__ == "__main__":
+    main()

scripts/evaluation/analyze_metrics.py

@@ -0,0 +1,611 @@
+import argparse
+import json
+import os
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+try:
+    import matplotlib.pyplot as plt
+except ModuleNotFoundError:
+    plt = None
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(
+        description="Analyze DDIM convergence logs for the five research directions."
+    )
+    parser.add_argument(
+        "--input_dir",
+        type=str,
+        required=True,
+        help="Directory containing stepwise_log.csv, sample_summary.csv, and round_summary.csv.",
+    )
+    parser.add_argument(
+        "--output_dir",
+        type=str,
+        default=None,
+        help="Directory to write analysis outputs. Defaults to <input_dir>/analysis.",
+    )
+    parser.add_argument(
+        "--mid_step_start",
+        type=int,
+        default=15,
+        help="Start of the step window used for cross-step latent-delta analysis.",
+    )
+    parser.add_argument(
+        "--mid_step_end",
+        type=int,
+        default=35,
+        help="End of the step window used for cross-step latent-delta analysis.",
+    )
+    parser.add_argument(
+        "--full50_step_cap",
+        type=int,
+        default=50,
+        help="Upper bound used when summarizing step-based metrics.",
+    )
+    parser.add_argument(
+        "--stability_threshold",
+        type=float,
+        default=None,
+        help="Optional threshold for offline first_stable_step analysis. If unset, stable-step metrics are not computed.",
+    )
+    parser.add_argument(
+        "--stability_window",
+        type=int,
+        default=3,
+        help="Consecutive-step window for offline first_stable_step analysis.",
+    )
+    return parser.parse_args()
+
+
+def safe_float(value) -> float:
+    if pd.isna(value):
+        return float("nan")
+    return float(value)
+
+
+def describe_series(series: pd.Series) -> dict[str, float]:
+    series = pd.to_numeric(series, errors="coerce").dropna()
+    if series.empty:
+        return {
+            "count": 0.0,
+            "mean": float("nan"),
+            "std": float("nan"),
+            "min": float("nan"),
+            "p25": float("nan"),
+            "median": float("nan"),
+            "p75": float("nan"),
+            "p90": float("nan"),
+            "p95": float("nan"),
+            "max": float("nan"),
+        }
+    return {
+        "count": float(series.count()),
+        "mean": float(series.mean()),
+        "std": float(series.std(ddof=0)),
+        "min": float(series.min()),
+        "p25": float(series.quantile(0.25)),
+        "median": float(series.median()),
+        "p75": float(series.quantile(0.75)),
+        "p90": float(series.quantile(0.90)),
+        "p95": float(series.quantile(0.95)),
+        "max": float(series.max()),
+    }
+
+
+def ensure_columns(frame: pd.DataFrame, required: list[str], frame_name: str) -> None:
+    missing = [column for column in required if column not in frame.columns]
+    if missing:
+        raise ValueError(f"{frame_name} is missing required columns: {missing}")
+
+
+def first_consecutive_below(values: pd.Series, threshold: float,
+                            window: int) -> float:
+    cleaned = pd.to_numeric(values, errors="coerce").tolist()
+    if window <= 0 or len(cleaned) < window:
+        return float("nan")
+    for start in range(len(cleaned) - window + 1):
+        chunk = cleaned[start:start + window]
+        if all(pd.notna(value) and value < threshold for value in chunk):
+            return float(start + 1)
+    return float("nan")
+
+
+def load_tables(input_dir: Path) -> tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]:
+    stepwise_path = input_dir / "stepwise_log.csv"
+    sample_summary_path = input_dir / "sample_summary.csv"
+    round_summary_path = input_dir / "round_summary.csv"
+
+    if not stepwise_path.exists():
+        raise FileNotFoundError(f"Missing file: {stepwise_path}")
+    if not sample_summary_path.exists():
+        raise FileNotFoundError(f"Missing file: {sample_summary_path}")
+    if not round_summary_path.exists():
+        raise FileNotFoundError(f"Missing file: {round_summary_path}")
+
+    stepwise_df = pd.read_csv(stepwise_path)
+    sample_summary_df = pd.read_csv(sample_summary_path)
+    round_summary_df = pd.read_csv(round_summary_path)
+
+    ensure_columns(
+        stepwise_df,
+        [
+            "sample_id",
+            "scene",
+            "pass_type",
+            "round_id",
+            "step",
+            "latent_delta",
+            "action_delta",
+            "state_delta",
+            "action_cosine_vs_full50",
+            "state_cosine_vs_full50",
+            "latent_l2_vs_full50",
+        ],
+        "stepwise_log.csv",
+    )
+    ensure_columns(
+        sample_summary_df,
+        [
+            "sample_id",
+            "scene",
+            "pass_type",
+            "pass_total_time_s",
+            "action_first_stable_step",
+            "state_first_stable_step",
+            "latent_first_stable_step",
+            "action_vs_full50_90pct_step",
+            "action_vs_full50_95pct_step",
+            "oracle_budget_action",
+            "oracle_budget_state",
+        ],
+        "sample_summary.csv",
+    )
+    ensure_columns(
+        round_summary_df,
+        [
+            "sample_id",
+            "scene",
+            "round_id",
+            "policy_pass_total_time_s",
+            "world_model_pass_total_time_s",
+            "round_total_time_s",
+            "latent_init_dist_to_prev_round",
+            "action_drift_vs_prev_round",
+        ],
+        "round_summary.csv",
+    )
+    return stepwise_df, sample_summary_df, round_summary_df
+
+
+def aggregate_stepwise(stepwise_df: pd.DataFrame) -> pd.DataFrame:
+    metric_columns = [
+        "step_time_s",
+        "latent_delta",
+        "action_delta",
+        "state_delta",
+        "action_cosine_vs_full50",
+        "state_cosine_vs_full50",
+        "latent_l2_vs_full50",
+    ]
+    stepwise_df = stepwise_df.copy()
+    for column in metric_columns:
+        stepwise_df[column] = pd.to_numeric(stepwise_df[column], errors="coerce")
+    grouped = stepwise_df.groupby(["pass_type", "step"])[metric_columns]
+    return grouped.agg(["mean", "median", "std"]).reset_index()
+
+
+def aggregate_scene_summary(sample_summary_df: pd.DataFrame) -> pd.DataFrame:
+    metrics = [
+        "pass_total_time_s",
+        "action_first_stable_step",
+        "state_first_stable_step",
+        "latent_first_stable_step",
+        "action_vs_full50_90pct_step",
+        "action_vs_full50_95pct_step",
+        "oracle_budget_action",
+        "oracle_budget_state",
+        "round_total_time_s",
+        "policy_pass_total_time_s",
+        "world_model_pass_total_time_s",
+        "latent_init_dist_to_prev_round",
+        "action_drift_vs_prev_round",
+    ]
+    sample_summary_df = sample_summary_df.copy()
+    for column in metrics:
+        sample_summary_df[column] = pd.to_numeric(sample_summary_df[column],
+                                                  errors="coerce")
+    grouped = sample_summary_df.groupby(["scene", "pass_type"])[metrics]
+    return grouped.agg(["mean", "median", "std"]).reset_index()
+
+
+def compute_stability_summary(stepwise_df: pd.DataFrame, threshold: float,
+                              window: int) -> pd.DataFrame:
+    rows = []
+    grouped = stepwise_df.sort_values("step").groupby(
+        ["sample_id", "scene", "pass_type"])
+    for (sample_id, scene, pass_type), group in grouped:
+        rows.append({
+            "sample_id": sample_id,
+            "scene": scene,
+            "pass_type": pass_type,
+            "action_first_stable_step": first_consecutive_below(
+                group["action_delta"], threshold, window),
+            "state_first_stable_step": first_consecutive_below(
+                group["state_delta"], threshold, window),
+            "latent_first_stable_step": first_consecutive_below(
+                group["latent_delta"], threshold, window),
+        })
+    return pd.DataFrame(rows)
+
+
+def compute_direction_summary(stepwise_df: pd.DataFrame,
+                              sample_summary_df: pd.DataFrame,
+                              round_summary_df: pd.DataFrame,
+                              mid_step_start: int,
+                              mid_step_end: int,
+                              stability_threshold: float | None,
+                              stability_window: int) -> dict:
+    policy_summary = sample_summary_df[
+        sample_summary_df["pass_type"] == "policy"].copy()
+    world_summary = sample_summary_df[
+        sample_summary_df["pass_type"] == "world_model"].copy()
+
+    latent_mid = stepwise_df[
+        (stepwise_df["step"] >= mid_step_start)
+        & (stepwise_df["step"] <= mid_step_end)].copy()
+
+    action_before_latent = policy_summary[[
+        "action_first_stable_step",
+        "latent_first_stable_step",
+        "action_vs_full50_95pct_step",
+    ]].dropna()
+    if action_before_latent.empty:
+        action_first_beats_latent = float("nan")
+        action_95_beats_latent = float("nan")
+    else:
+        action_first_beats_latent = float(
+            (action_before_latent["action_first_stable_step"] <
+             action_before_latent["latent_first_stable_step"]).mean())
+        action_95_beats_latent = float(
+            (action_before_latent["action_vs_full50_95pct_step"] <
+             action_before_latent["latent_first_stable_step"]).mean())
+
+    direction_summary = {
+        "analysis_config": {
+            "stability_threshold": stability_threshold,
+            "stability_window": int(stability_window),
+            "mid_step_window": [int(mid_step_start), int(mid_step_end)],
+        },
+        "dataset_overview": {
+            "num_step_rows": int(len(stepwise_df)),
+            "num_sample_rows": int(len(sample_summary_df)),
+            "num_round_rows": int(len(round_summary_df)),
+            "num_unique_samples": int(sample_summary_df["sample_id"].nunique()),
+            "scenes": sorted(sample_summary_df["scene"].dropna().unique().tolist()),
+            "pass_types": sorted(sample_summary_df["pass_type"].dropna().unique().tolist()),
+        },
+        "direction_original_early_stop": {
+            "latent_first_stable_step_policy":
+            describe_series(policy_summary["latent_first_stable_step"]),
+            "latent_first_stable_step_world_model":
+            describe_series(world_summary["latent_first_stable_step"]),
+            "latent_l2_vs_full50":
+            describe_series(stepwise_df["latent_l2_vs_full50"]),
+        },
+        "direction_c_action_converges_first": {
+            "action_first_stable_step":
+            describe_series(policy_summary["action_first_stable_step"]),
+            "latent_first_stable_step":
+            describe_series(policy_summary["latent_first_stable_step"]),
+            "action_vs_full50_95pct_step":
+            describe_series(policy_summary["action_vs_full50_95pct_step"]),
+            "share_action_first_stable_before_latent":
+            action_first_beats_latent,
+            "share_action_95pct_before_latent":
+            action_95_beats_latent,
+        },
+        "direction_d_cross_step_similarity": {
+            "latent_delta_mid_steps":
+            describe_series(latent_mid["latent_delta"]),
+            "action_delta_mid_steps":
+            describe_series(latent_mid["action_delta"]),
+            "state_delta_mid_steps":
+            describe_series(latent_mid["state_delta"]),
+        },
+        "direction_a_budget_heterogeneity": {
+            "oracle_budget_action":
+            describe_series(policy_summary["oracle_budget_action"]),
+            "oracle_budget_state":
+            describe_series(world_summary["oracle_budget_state"]),
+            "oracle_budget_action_by_scene": {
+                scene: describe_series(group["oracle_budget_action"])
+                for scene, group in policy_summary.groupby("scene")
+            },
+        },
+        "direction_b_round_reuse": {
+            "latent_init_dist_to_prev_round":
+            describe_series(round_summary_df["latent_init_dist_to_prev_round"]),
+            "action_drift_vs_prev_round":
+            describe_series(round_summary_df["action_drift_vs_prev_round"]),
+            "round_total_time_s":
+            describe_series(round_summary_df["round_total_time_s"]),
+            "policy_pass_total_time_s":
+            describe_series(round_summary_df["policy_pass_total_time_s"]),
+            "world_model_pass_total_time_s":
+            describe_series(round_summary_df["world_model_pass_total_time_s"]),
+        },
+    }
+    return direction_summary
+
+
+def save_json(path: Path, payload: dict) -> None:
+    with open(path, "w", encoding="utf-8") as file:
+        json.dump(payload, file, indent=2, ensure_ascii=False)
+
+
+def write_markdown_report(path: Path, direction_summary: dict) -> None:
+    lines = [
+        "# DDIM Analysis Report",
+        "",
+        "## Analysis Config",
+        "",
+    ]
+    config = direction_summary["analysis_config"]
+    lines.extend([
+        f"- Stability threshold: {config['stability_threshold']}",
+        f"- Stability window: {config['stability_window']}",
+        f"- Mid-step window: {config['mid_step_window'][0]}-{config['mid_step_window'][1]}",
+        "",
+        "## Dataset Overview",
+        "",
+    ])
+    overview = direction_summary["dataset_overview"]
+    lines.extend([
+        f"- Unique samples: {overview['num_unique_samples']}",
+        f"- Step rows: {overview['num_step_rows']}",
+        f"- Sample rows: {overview['num_sample_rows']}",
+        f"- Round rows: {overview['num_round_rows']}",
+        f"- Scenes: {', '.join(overview['scenes'])}",
+        f"- Pass types: {', '.join(overview['pass_types'])}",
+        "",
+        "## Five Directions",
+        "",
+    ])
+
+    def append_stats(title: str, payload: dict) -> None:
+        lines.append(f"### {title}")
+        lines.append("")
+        for key, value in payload.items():
+            if isinstance(value, dict):
+                if {"median", "mean", "p90"} <= set(value.keys()):
+                    lines.append(
+                        f"- `{key}`: mean={value['mean']:.4f}, median={value['median']:.4f}, p90={value['p90']:.4f}, p95={value['p95']:.4f}"
+                    )
+                else:
+                    lines.append(f"- `{key}`:")
+                    for sub_key, sub_value in value.items():
+                        if isinstance(sub_value, dict) and {"median", "mean"} <= set(
+                                sub_value.keys()):
+                            lines.append(
+                                f"  - `{sub_key}`: mean={sub_value['mean']:.4f}, median={sub_value['median']:.4f}, std={sub_value['std']:.4f}"
+                            )
+                        else:
+                            lines.append(f"  - `{sub_key}`: {sub_value}")
+            else:
+                if isinstance(value, float):
+                    lines.append(f"- `{key}`: {value:.4f}")
+                else:
+                    lines.append(f"- `{key}`: {value}")
+        lines.append("")
+
+    append_stats("Original Early Stop",
+                 direction_summary["direction_original_early_stop"])
+    append_stats("Direction C: Action Converges First",
+                 direction_summary["direction_c_action_converges_first"])
+    append_stats("Direction D: Cross-Step Similarity",
+                 direction_summary["direction_d_cross_step_similarity"])
+    append_stats("Direction A: Budget Heterogeneity",
+                 direction_summary["direction_a_budget_heterogeneity"])
+    append_stats("Direction B: Round Reuse",
+                 direction_summary["direction_b_round_reuse"])
+
+    with open(path, "w", encoding="utf-8") as file:
+        file.write("\n".join(lines) + "\n")
+
+
+def save_convergence_plot(stepwise_df: pd.DataFrame, output_path: Path) -> None:
+    if plt is None:
+        return
+    fig, axes = plt.subplots(2, 2, figsize=(14, 10))
+    pass_types = sorted(stepwise_df["pass_type"].dropna().unique().tolist())
+    for pass_type in pass_types:
+        subset = stepwise_df[stepwise_df["pass_type"] == pass_type]
+        grouped = subset.groupby("step").mean(numeric_only=True).reset_index()
+        axes[0, 0].plot(grouped["step"],
+                        grouped["latent_delta"],
+                        label=pass_type)
+        axes[0, 1].plot(grouped["step"],
+                        grouped["action_cosine_vs_full50"],
+                        label=pass_type)
+        axes[1, 0].plot(grouped["step"],
+                        grouped["state_cosine_vs_full50"],
+                        label=pass_type)
+        axes[1, 1].plot(grouped["step"],
+                        grouped["latent_l2_vs_full50"],
+                        label=pass_type)
+
+    axes[0, 0].set_title("Mean Latent Delta")
+    axes[0, 1].set_title("Mean Action Cosine vs Full50")
+    axes[1, 0].set_title("Mean State Cosine vs Full50")
+    axes[1, 1].set_title("Mean Latent L2 vs Full50")
+    for axis in axes.flatten():
+        axis.set_xlabel("Step")
+        axis.grid(alpha=0.3)
+        axis.legend()
+    fig.tight_layout()
+    fig.savefig(output_path, dpi=200, bbox_inches="tight")
+    plt.close(fig)
+
+
+def save_budget_distribution_plot(sample_summary_df: pd.DataFrame,
+                                  output_path: Path) -> None:
+    if plt is None:
+        return
+    policy_summary = sample_summary_df[
+        sample_summary_df["pass_type"] == "policy"].copy()
+    scenes = sorted(policy_summary["scene"].dropna().unique().tolist())
+    fig, axes = plt.subplots(1, 2, figsize=(14, 5))
+
+    for scene in scenes:
+        subset = policy_summary[policy_summary["scene"] == scene]
+        axes[0].hist(subset["oracle_budget_action"].dropna(),
+                     bins=15,
+                     alpha=0.5,
+                     label=scene)
+    axes[0].set_title("Oracle Budget Action Distribution")
+    axes[0].set_xlabel("Step")
+    axes[0].set_ylabel("Count")
+    axes[0].legend()
+    axes[0].grid(alpha=0.3)
+
+    boxplot_data = [
+        policy_summary[policy_summary["scene"] == scene]["oracle_budget_action"].dropna()
+        for scene in scenes
+    ]
+    if scenes and any(len(values) > 0 for values in boxplot_data):
+        axes[1].boxplot(boxplot_data, labels=scenes, showfliers=False)
+    axes[1].set_title("Oracle Budget Action by Scene")
+    axes[1].set_ylabel("Step")
+    axes[1].tick_params(axis="x", rotation=20)
+    axes[1].grid(alpha=0.3)
+
+    fig.tight_layout()
+    fig.savefig(output_path, dpi=200, bbox_inches="tight")
+    plt.close(fig)
+
+
+def save_midstep_delta_plot(stepwise_df: pd.DataFrame, output_path: Path,
+                            mid_step_start: int, mid_step_end: int) -> None:
+    if plt is None:
+        return
+    subset = stepwise_df[(stepwise_df["step"] >= mid_step_start)
+                         & (stepwise_df["step"] <= mid_step_end)].copy()
+    steps = sorted(subset["step"].dropna().unique().tolist())
+    data = [subset[subset["step"] == step]["latent_delta"].dropna() for step in steps]
+
+    fig, ax = plt.subplots(figsize=(14, 5))
+    if steps and any(len(values) > 0 for values in data):
+        ax.boxplot(data, labels=steps, showfliers=False)
+    ax.set_title(
+        f"Latent Delta Distribution for Steps {mid_step_start}-{mid_step_end}")
+    ax.set_xlabel("Step")
+    ax.set_ylabel("latent_delta")
+    ax.grid(alpha=0.3)
+    fig.tight_layout()
+    fig.savefig(output_path, dpi=200, bbox_inches="tight")
+    plt.close(fig)
+
+
+def save_round_reuse_plot(round_summary_df: pd.DataFrame, output_path: Path) -> None:
+    if plt is None:
+        return
+    fig, axes = plt.subplots(1, 2, figsize=(14, 5))
+
+    subset = round_summary_df.dropna(
+        subset=["latent_init_dist_to_prev_round", "action_drift_vs_prev_round"])
+    if not subset.empty:
+        for scene, group in subset.groupby("scene"):
+            axes[0].scatter(group["latent_init_dist_to_prev_round"],
+                            group["action_drift_vs_prev_round"],
+                            alpha=0.6,
+                            label=scene)
+        axes[0].legend()
+    axes[0].set_title("Round Reuse: Latent Init Dist vs Action Drift")
+    axes[0].set_xlabel("latent_init_dist_to_prev_round")
+    axes[0].set_ylabel("action_drift_vs_prev_round")
+    axes[0].grid(alpha=0.3)
+
+    scene_groups = []
+    scene_labels = []
+    for scene, group in round_summary_df.groupby("scene"):
+        values = group["round_total_time_s"].dropna()
+        if len(values) > 0:
+            scene_groups.append(values)
+            scene_labels.append(scene)
+    if scene_groups:
+        axes[1].boxplot(scene_groups, labels=scene_labels, showfliers=False)
+    axes[1].set_title("Round Total Time by Scene")
+    axes[1].set_ylabel("Seconds")
+    axes[1].tick_params(axis="x", rotation=20)
+    axes[1].grid(alpha=0.3)
+
+    fig.tight_layout()
+    fig.savefig(output_path, dpi=200, bbox_inches="tight")
+    plt.close(fig)
+
+
+def main() -> None:
+    args = parse_args()
+    input_dir = Path(args.input_dir)
+    output_dir = Path(args.output_dir) if args.output_dir else input_dir / "analysis"
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    stepwise_df, sample_summary_df, round_summary_df = load_tables(input_dir)
+
+    stability_summary_df = None
+    sample_summary_for_analysis = sample_summary_df.copy()
+    if args.stability_threshold is not None:
+        stability_summary_df = compute_stability_summary(stepwise_df,
+                                                         args.stability_threshold,
+                                                         args.stability_window)
+        sample_summary_for_analysis = sample_summary_for_analysis.drop(
+            columns=[
+                "action_first_stable_step",
+                "state_first_stable_step",
+                "latent_first_stable_step",
+            ],
+            errors="ignore",
+        ).merge(stability_summary_df,
+                on=["sample_id", "scene", "pass_type"],
+                how="left")
+
+    step_aggregate_df = aggregate_stepwise(stepwise_df)
+    scene_summary_df = aggregate_scene_summary(sample_summary_for_analysis)
+    direction_summary = compute_direction_summary(
+        stepwise_df=stepwise_df,
+        sample_summary_df=sample_summary_for_analysis,
+        round_summary_df=round_summary_df,
+        mid_step_start=args.mid_step_start,
+        mid_step_end=args.mid_step_end,
+        stability_threshold=args.stability_threshold,
+        stability_window=args.stability_window,
+    )
+
+    step_aggregate_df.to_csv(output_dir / "step_aggregate.csv", index=False)
+    scene_summary_df.to_csv(output_dir / "scene_summary.csv", index=False)
+    if stability_summary_df is not None:
+        stability_summary_df.to_csv(output_dir / "stability_summary.csv",
+                                    index=False)
+    save_json(output_dir / "direction_summary.json", direction_summary)
+    write_markdown_report(output_dir / "analysis_report.md", direction_summary)
+
+    if plt is not None:
+        save_convergence_plot(stepwise_df, output_dir / "convergence_curves.png")
+        save_budget_distribution_plot(sample_summary_df,
+                                      output_dir / "budget_distribution.png")
+        save_midstep_delta_plot(stepwise_df,
+                                output_dir / "midstep_latent_delta.png",
+                                args.mid_step_start, args.mid_step_end)
+        save_round_reuse_plot(round_summary_df, output_dir / "round_reuse.png")
+    else:
+        print("matplotlib not installed; skipped PNG plots.")
+
+    print(f"Analysis written to: {output_dir}")
+
+
+if __name__ == "__main__":
+    main()

scripts/evaluation/simulate_sparse_head_execution.py

@@ -0,0 +1,380 @@
+import argparse
+import json
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+
+DEFAULT_SCHEMES = {
+    "dense": list(range(50)),
+    "sparse_10": list(range(0, 50, 5)),
+    "sparse_5": list(range(0, 50, 10)),
+    "tail_heavy_10": [0, 5, 10, 20, 30, 38, 43, 46, 48, 49],
+    "tail_only_6": [40, 43, 46, 47, 48, 49],
+    "sparse_8": [0, 7, 14, 21, 28, 35, 42, 49],
+    "sparse_4": [0, 16, 32, 49],
+    "tail_only_4": [40, 43, 46, 49],
+    "tail_heavy_6": [0, 32, 40, 44, 47, 49],
+    "tail_heavy_8": [0, 10, 20, 30, 38, 43, 46, 49],
+}
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(
+        description="Offline sparse head execution simulation from existing DDIM logs."
+    )
+    parser.add_argument(
+        "--root_dir",
+        type=str,
+        default=".",
+        help="Repository root or directory under which case outputs are stored.",
+    )
+    parser.add_argument(
+        "--output_dir",
+        type=str,
+        default=None,
+        help="Directory to write sparse-head simulation outputs. Defaults to <root_dir>/sparse_head_simulation.",
+    )
+    parser.add_argument(
+        "--target_threshold",
+        type=float,
+        default=0.95,
+        help="Target cosine threshold used by experiment 1.2.",
+    )
+    return parser.parse_args()
+
+
+def discover_stepwise_logs(root_dir: Path) -> list[Path]:
+    return sorted(root_dir.glob("unitree_*/case*/output/inference/stepwise_log.csv"))
+
+
+def load_stepwise_table(stepwise_paths: list[Path]) -> pd.DataFrame:
+    frames = []
+    for path in stepwise_paths:
+        frame = pd.read_csv(path)
+        frame["dataset"] = path.parts[-5]
+        frame["case"] = path.parts[-4]
+        frames.append(frame)
+    if not frames:
+        raise FileNotFoundError("No stepwise_log.csv files found.")
+    stepwise_df = pd.concat(frames, ignore_index=True)
+    for column in [
+            "step",
+            "step_time_s",
+            "latent_delta",
+            "action_delta",
+            "state_delta",
+            "action_cosine_vs_full50",
+            "state_cosine_vs_full50",
+            "latent_l2_vs_full50",
+    ]:
+        stepwise_df[column] = pd.to_numeric(stepwise_df[column], errors="coerce")
+    return stepwise_df
+
+
+def simulate_schemes(stepwise_df: pd.DataFrame,
+                     schemes: dict[str, list[int]]) -> pd.DataFrame:
+    rows = []
+    group_columns = [
+        "dataset",
+        "case",
+        "sample_id",
+        "scene",
+        "pass_type",
+        "round_id",
+    ]
+    grouped = stepwise_df.groupby(group_columns)
+    for keys, group in grouped:
+        group = group.sort_values("step").reset_index(drop=True)
+        action_curve = dict(zip(group["step"] - 1, group["action_cosine_vs_full50"]))
+        state_curve = dict(zip(group["step"] - 1, group["state_cosine_vs_full50"]))
+
+        for scheme_name, checkpoints in schemes.items():
+            normalized_checkpoints = sorted(
+                checkpoint for checkpoint in checkpoints if 0 <= checkpoint <= 49)
+            if not normalized_checkpoints:
+                continue
+            last_checkpoint = normalized_checkpoints[-1]
+            rows.append({
+                "dataset": keys[0],
+                "case": keys[1],
+                "sample_id": keys[2],
+                "scene": keys[3],
+                "pass_type": keys[4],
+                "round_id": keys[5],
+                "scheme": scheme_name,
+                "head_exec_steps_zero_based": json.dumps(normalized_checkpoints),
+                "head_exec_count": len(normalized_checkpoints),
+                "head_compute_saving_ratio": 1.0 - len(normalized_checkpoints) / 50.0,
+                "final_checkpoint_zero_based": last_checkpoint,
+                "final_checkpoint_one_based": last_checkpoint + 1,
+                "final_action_cosine_vs_dense": action_curve[last_checkpoint],
+                "final_state_cosine_vs_dense": state_curve[last_checkpoint],
+            })
+    return pd.DataFrame(rows)
+
+
+def summarize_scheme_results(simulation_df: pd.DataFrame) -> tuple[pd.DataFrame, pd.DataFrame]:
+    metric_columns = [
+        "final_action_cosine_vs_dense",
+        "final_state_cosine_vs_dense",
+        "head_exec_count",
+        "head_compute_saving_ratio",
+    ]
+    overall = simulation_df.groupby(["scheme", "pass_type"])[metric_columns].agg(
+        ["mean", "median", "std"]).reset_index()
+    per_case = simulation_df.groupby(
+        ["dataset", "case", "scheme", "pass_type"])[metric_columns].mean().reset_index()
+    return overall, per_case
+
+
+def compute_min_steps_needed(stepwise_df: pd.DataFrame,
+                             threshold: float) -> pd.DataFrame:
+    rows = []
+    group_columns = [
+        "dataset",
+        "case",
+        "sample_id",
+        "scene",
+        "pass_type",
+        "round_id",
+    ]
+    grouped = stepwise_df.groupby(group_columns)
+    for keys, group in grouped:
+        group = group.sort_values("step").reset_index(drop=True)
+        action_hits = group[group["action_cosine_vs_full50"] >= threshold]
+        state_hits = group[group["state_cosine_vs_full50"] >= threshold]
+
+        action_step = np.nan if action_hits.empty else float(action_hits.iloc[0]["step"] - 1)
+        state_step = np.nan if state_hits.empty else float(state_hits.iloc[0]["step"] - 1)
+        rows.append({
+            "dataset": keys[0],
+            "case": keys[1],
+            "sample_id": keys[2],
+            "scene": keys[3],
+            "pass_type": keys[4],
+            "round_id": keys[5],
+            "target_threshold": threshold,
+            "action_min_head_steps_needed": 1.0 if pd.notna(action_step) else np.nan,
+            "state_min_head_steps_needed": 1.0 if pd.notna(state_step) else np.nan,
+            "action_earliest_checkpoint_zero_based": action_step,
+            "state_earliest_checkpoint_zero_based": state_step,
+            "action_max_head_compute_saving_ratio": 0.98 if pd.notna(action_step) else np.nan,
+            "state_max_head_compute_saving_ratio": 0.98 if pd.notna(state_step) else np.nan,
+        })
+    return pd.DataFrame(rows)
+
+
+def compare_tail_vs_uniform(simulation_df: pd.DataFrame) -> pd.DataFrame:
+    comparison_specs = [
+        ("sparse_8", "tail_heavy_8", "budget_8_uniform_vs_tail"),
+        ("sparse_4", "tail_only_4", "budget_4_uniform_vs_tail"),
+        ("sparse_10", "tail_heavy_10", "budget_10_uniform_vs_tail"),
+    ]
+    base_columns = [
+        "dataset",
+        "case",
+        "sample_id",
+        "scene",
+        "pass_type",
+        "round_id",
+    ]
+    rows = []
+    for uniform_scheme, tail_scheme, comparison_name in comparison_specs:
+        uniform_df = simulation_df[simulation_df["scheme"] == uniform_scheme].copy()
+        tail_df = simulation_df[simulation_df["scheme"] == tail_scheme].copy()
+        merged = uniform_df.merge(
+            tail_df,
+            on=base_columns,
+            suffixes=("_uniform", "_tail"),
+            how="inner",
+        )
+        for _, row in merged.iterrows():
+            rows.append({
+                "comparison": comparison_name,
+                "dataset": row["dataset"],
+                "case": row["case"],
+                "sample_id": row["sample_id"],
+                "scene": row["scene"],
+                "pass_type": row["pass_type"],
+                "round_id": row["round_id"],
+                "uniform_scheme": uniform_scheme,
+                "tail_scheme": tail_scheme,
+                "uniform_head_exec_count": row["head_exec_count_uniform"],
+                "tail_head_exec_count": row["head_exec_count_tail"],
+                "uniform_action_cosine_vs_dense": row["final_action_cosine_vs_dense_uniform"],
+                "tail_action_cosine_vs_dense": row["final_action_cosine_vs_dense_tail"],
+                "uniform_state_cosine_vs_dense": row["final_state_cosine_vs_dense_uniform"],
+                "tail_state_cosine_vs_dense": row["final_state_cosine_vs_dense_tail"],
+                "tail_minus_uniform_action_cosine":
+                row["final_action_cosine_vs_dense_tail"] -
+                row["final_action_cosine_vs_dense_uniform"],
+                "tail_minus_uniform_state_cosine":
+                row["final_state_cosine_vs_dense_tail"] -
+                row["final_state_cosine_vs_dense_uniform"],
+                "tail_better_action": row["final_action_cosine_vs_dense_tail"] >
+                row["final_action_cosine_vs_dense_uniform"],
+                "tail_better_state": row["final_state_cosine_vs_dense_tail"] >
+                row["final_state_cosine_vs_dense_uniform"],
+            })
+    return pd.DataFrame(rows)
+
+
+def build_summary_payload(simulation_df: pd.DataFrame,
+                          min_steps_df: pd.DataFrame,
+                          tail_compare_df: pd.DataFrame,
+                          target_threshold: float) -> dict:
+    payload: dict[str, dict] = {
+        "config": {
+            "target_threshold": target_threshold,
+            "schemes": DEFAULT_SCHEMES,
+        },
+        "experiment_1_1": {},
+        "experiment_1_2": {},
+        "experiment_1_3": {},
+    }
+
+    for scheme, group in simulation_df.groupby("scheme"):
+        payload["experiment_1_1"][scheme] = {
+            "num_rows": int(len(group)),
+            "head_exec_count": float(group["head_exec_count"].iloc[0]),
+            "head_compute_saving_ratio": float(
+                group["head_compute_saving_ratio"].iloc[0]),
+            "final_action_cosine_vs_dense_mean": float(
+                group["final_action_cosine_vs_dense"].mean()),
+            "final_action_cosine_vs_dense_median": float(
+                group["final_action_cosine_vs_dense"].median()),
+            "final_state_cosine_vs_dense_mean": float(
+                group["final_state_cosine_vs_dense"].mean()),
+            "final_state_cosine_vs_dense_median": float(
+                group["final_state_cosine_vs_dense"].median()),
+        }
+
+    payload["experiment_1_2"] = {
+        "action_earliest_checkpoint_zero_based": {
+            "mean": float(
+                pd.to_numeric(
+                    min_steps_df["action_earliest_checkpoint_zero_based"],
+                    errors="coerce").dropna().mean()),
+            "median": float(
+                pd.to_numeric(
+                    min_steps_df["action_earliest_checkpoint_zero_based"],
+                    errors="coerce").dropna().median()),
+        },
+        "state_earliest_checkpoint_zero_based": {
+            "mean": float(
+                pd.to_numeric(
+                    min_steps_df["state_earliest_checkpoint_zero_based"],
+                    errors="coerce").dropna().mean()),
+            "median": float(
+                pd.to_numeric(
+                    min_steps_df["state_earliest_checkpoint_zero_based"],
+                    errors="coerce").dropna().median()),
+        },
+        "min_head_steps_needed_action_unique": sorted(
+            pd.to_numeric(min_steps_df["action_min_head_steps_needed"],
+                          errors="coerce").dropna().unique().tolist()),
+        "min_head_steps_needed_state_unique": sorted(
+            pd.to_numeric(min_steps_df["state_min_head_steps_needed"],
+                          errors="coerce").dropna().unique().tolist()),
+    }
+
+    for comparison, group in tail_compare_df.groupby("comparison"):
+        payload["experiment_1_3"][comparison] = {
+            "num_rows": int(len(group)),
+            "tail_better_action_share": float(group["tail_better_action"].mean()),
+            "tail_better_state_share": float(group["tail_better_state"].mean()),
+            "tail_minus_uniform_action_cosine_mean": float(
+                group["tail_minus_uniform_action_cosine"].mean()),
+            "tail_minus_uniform_state_cosine_mean": float(
+                group["tail_minus_uniform_state_cosine"].mean()),
+        }
+    return payload
+
+
+def write_markdown_report(path: Path, payload: dict) -> None:
+    lines = [
+        "# Sparse Head Execution Simulation",
+        "",
+        "This report uses zero-order hold over logged stepwise action/state outputs.",
+        "For a sparse scheme, the final output at step 49 is approximated by the most recent checkpoint output.",
+        "",
+        "## Experiment 1.1",
+        "",
+    ]
+
+    for scheme, stats in payload["experiment_1_1"].items():
+        lines.extend([
+            f"### {scheme}",
+            "",
+            f"- Head exec count: {stats['head_exec_count']:.0f}",
+            f"- Head compute saving ratio: {stats['head_compute_saving_ratio']:.4f}",
+            f"- Final action cosine vs dense: mean={stats['final_action_cosine_vs_dense_mean']:.4f}, median={stats['final_action_cosine_vs_dense_median']:.4f}",
+            f"- Final state cosine vs dense: mean={stats['final_state_cosine_vs_dense_mean']:.4f}, median={stats['final_state_cosine_vs_dense_median']:.4f}",
+            "",
+        ])
+
+    lines.extend([
+        "## Experiment 1.2",
+        "",
+        f"- Target threshold: {payload['config']['target_threshold']}",
+        f"- Action earliest checkpoint mean: {payload['experiment_1_2']['action_earliest_checkpoint_zero_based']['mean']:.4f}",
+        f"- State earliest checkpoint mean: {payload['experiment_1_2']['state_earliest_checkpoint_zero_based']['mean']:.4f}",
+        f"- Unique min head steps needed for action: {payload['experiment_1_2']['min_head_steps_needed_action_unique']}",
+        f"- Unique min head steps needed for state: {payload['experiment_1_2']['min_head_steps_needed_state_unique']}",
+        "",
+        "## Experiment 1.3",
+        "",
+    ])
+
+    for comparison, stats in payload["experiment_1_3"].items():
+        lines.extend([
+            f"### {comparison}",
+            "",
+            f"- Tail better action share: {stats['tail_better_action_share']:.4f}",
+            f"- Tail better state share: {stats['tail_better_state_share']:.4f}",
+            f"- Mean tail-minus-uniform action cosine: {stats['tail_minus_uniform_action_cosine_mean']:.4f}",
+            f"- Mean tail-minus-uniform state cosine: {stats['tail_minus_uniform_state_cosine_mean']:.4f}",
+            "",
+        ])
+
+    with open(path, "w", encoding="utf-8") as file:
+        file.write("\n".join(lines) + "\n")
+
+
+def main() -> None:
+    args = parse_args()
+    root_dir = Path(args.root_dir).resolve()
+    output_dir = Path(args.output_dir).resolve(
+    ) if args.output_dir else root_dir / "sparse_head_simulation"
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    stepwise_paths = discover_stepwise_logs(root_dir)
+    stepwise_df = load_stepwise_table(stepwise_paths)
+
+    simulation_df = simulate_schemes(stepwise_df, DEFAULT_SCHEMES)
+    scheme_overall_df, scheme_per_case_df = summarize_scheme_results(simulation_df)
+    min_steps_df = compute_min_steps_needed(stepwise_df, args.target_threshold)
+    tail_compare_df = compare_tail_vs_uniform(simulation_df)
+    summary_payload = build_summary_payload(simulation_df, min_steps_df,
+                                            tail_compare_df,
+                                            args.target_threshold)
+
+    simulation_df.to_csv(output_dir / "scheme_simulation_per_round.csv",
+                         index=False)
+    scheme_overall_df.to_csv(output_dir / "scheme_simulation_overall.csv",
+                             index=False)
+    scheme_per_case_df.to_csv(output_dir / "scheme_simulation_per_case.csv",
+                              index=False)
+    min_steps_df.to_csv(output_dir / "min_head_steps_needed.csv", index=False)
+    tail_compare_df.to_csv(output_dir / "tail_vs_uniform.csv", index=False)
+    with open(output_dir / "summary.json", "w", encoding="utf-8") as file:
+        json.dump(summary_payload, file, indent=2, ensure_ascii=False)
+    write_markdown_report(output_dir / "report.md", summary_payload)
+
+    print(f"Sparse head simulation written to: {output_dir}")
+
+
+if __name__ == "__main__":
+    main()

scripts/export_trt.py

@@ -0,0 +1,209 @@
+"""Export video UNet backbone to ONNX, then convert to TensorRT engine.
+
+Usage:
+    python scripts/export_trt.py \
+        --ckpt ckpts/unifolm_wma_dual.ckpt.prepared.pt \
+        --config configs/inference/world_model_interaction.yaml \
+        --out_dir trt_engines
+
+    python scripts/export_trt.py \
+        --ckpt ckpts/unifolm_wma_dual.ckpt.prepared.pt \
+        --config configs/inference/world_model_interaction.yaml \
+        --engine_path trt_engines/video_backbone_multigpu.engine \
+        --onnx_path trt_engines/video_backbone_multigpu.onnx
+"""
+
+import os
+import sys
+import argparse
+import json
+
+import torch
+import tensorrt as trt
+from omegaconf import OmegaConf
+
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..', 'src'))
+from unifolm_wma.utils.utils import instantiate_from_config
+from unifolm_wma.trt_utils import export_backbone_onnx
+
+
+class TacticRecorder(trt.IAlgorithmSelector):
+    """Pass 1: record all candidate tactics and the auto-selected winner."""
+
+    def __init__(self):
+        super().__init__()
+        self.records = {}  # layer_name -> {candidates: [...], selected: ...}
+
+    def select_algorithms(self, ctx, choices):
+        name = ctx.name
+        # Collect input/output shapes
+        inputs = []
+        for j in range(ctx.num_inputs):
+            try:
+                inputs.append([int(d) for d in ctx.get_shape(j)])
+            except Exception:
+                inputs.append(None)
+        outputs = []
+        for j in range(ctx.num_outputs):
+            try:
+                outputs.append([int(d) for d in ctx.get_shape(ctx.num_inputs + j)])
+            except Exception:
+                outputs.append(None)
+        self.records[name] = {
+            "input_shapes": inputs,
+            "output_shapes": outputs,
+            "candidates": [],
+            "selected": None,
+        }
+        for i, c in enumerate(choices):
+            v = c.algorithm_variant
+            self.records[name]["candidates"].append({
+                "index": i,
+                "implementation": v.implementation,
+                "tactic": v.tactic,
+                "timing_msec": c.timing_msec,
+                "workspace_size": c.workspace_size,
+            })
+        # return all indices -> let TRT auto-pick the fastest
+        return list(range(len(choices)))
+
+    def report_algorithms(self, ctx, choices):
+        # Both ctx and choices are lists in report_algorithms
+        for c, alg in zip(ctx, choices):
+            name = c.name
+            if name in self.records:
+                v = alg.algorithm_variant
+                self.records[name]["selected"] = {
+                    "implementation": v.implementation,
+                    "tactic": v.tactic,
+                    "timing_msec": alg.timing_msec,
+                    "workspace_size": alg.workspace_size,
+                }
+
+    def save(self, path):
+        with open(path, "w") as f:
+            json.dump(self.records, f, indent=2)
+        print(f">>> Tactic info saved to {path} ({len(self.records)} layers)")
+
+
+class TacticForcer(trt.IAlgorithmSelector):
+    """Pass 2: force user-specified tactics from a JSON file."""
+
+    def __init__(self, path):
+        super().__init__()
+        with open(path) as f:
+            self.overrides = json.load(f)
+        n = sum(1 for v in self.overrides.values() if v.get("force"))
+        print(f">>> Loaded tactic overrides: {n} layers with 'force' set")
+
+    def select_algorithms(self, ctx, choices):
+        name = ctx.name
+        override = self.overrides.get(name)
+        if override and override.get("force"):
+            target_impl = override["force"]["implementation"]
+            target_tactic = override["force"]["tactic"]
+            for i, c in enumerate(choices):
+                v = c.algorithm_variant
+                if v.implementation == target_impl and v.tactic == target_tactic:
+                    return [i]
+            print(f"    WARN: forced tactic not found for {name}, using auto")
+        return list(range(len(choices)))
+
+    def report_algorithms(self, ctx, choices):
+        pass
+
+
+def load_model(config_path, ckpt_path, device):
+    if ckpt_path.endswith('.prepared.pt'):
+        model = torch.load(ckpt_path, map_location='cpu')
+    else:
+        config = OmegaConf.load(config_path)
+        model = instantiate_from_config(config.model)
+        state_dict = torch.load(ckpt_path, map_location='cpu')
+        if 'state_dict' in state_dict:
+            state_dict = state_dict['state_dict']
+        model.load_state_dict(state_dict, strict=False)
+    model.eval().to(device)
+    return model
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--ckpt', required=True)
+    parser.add_argument('--config', default='configs/inference/world_model_interaction.yaml')
+    parser.add_argument('--out_dir', default='trt_engines')
+    parser.add_argument('--gpu_id',
+                        type=int,
+                        default=0,
+                        help='CUDA device id used for ONNX export and TRT build.')
+    parser.add_argument('--onnx_path',
+                        default=None,
+                        help='Optional explicit ONNX output path. Overrides --out_dir default name.')
+    parser.add_argument('--engine_path',
+                        default=None,
+                        help='Optional explicit TensorRT engine output path. Overrides --out_dir default name.')
+    parser.add_argument('--context_len', type=int, default=95)
+    parser.add_argument('--fp16', action='store_true', default=True)
+    parser.add_argument('--dump-tactics', default=None, help='Pass 1: dump tactic info to JSON')
+    parser.add_argument('--load-tactics', default=None, help='Pass 2: force tactics from JSON')
+    args = parser.parse_args()
+    device = torch.device('cuda', args.gpu_id)
+    torch.cuda.set_device(device)
+
+    onnx_path = args.onnx_path or os.path.join(args.out_dir,
+                                               'video_backbone.onnx')
+    engine_path = args.engine_path or os.path.join(args.out_dir,
+                                                   'video_backbone.engine')
+    os.makedirs(os.path.dirname(os.path.abspath(onnx_path)), exist_ok=True)
+    os.makedirs(os.path.dirname(os.path.abspath(engine_path)), exist_ok=True)
+
+    if os.path.exists(onnx_path):
+        print(f">>> ONNX already exists at {onnx_path}, skipping export.")
+        n_outputs = 10
+    else:
+        print(">>> Loading model ...")
+        model = load_model(args.config, args.ckpt, device)
+        print(">>> Exporting ONNX ...")
+        with torch.no_grad():
+            n_outputs = export_backbone_onnx(model, onnx_path, context_len=args.context_len)
+        del model
+        torch.cuda.empty_cache()
+
+    print(">>> Converting ONNX -> TensorRT engine ...")
+    logger = trt.Logger(trt.Logger.WARNING)
+    builder = trt.Builder(logger)
+    network = builder.create_network(1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH))
+    parser = trt.OnnxParser(network, logger)
+
+    if not parser.parse_from_file(os.path.abspath(onnx_path)):
+        for i in range(parser.num_errors):
+            print(f"    ONNX parse error: {parser.get_error(i)}")
+        raise RuntimeError("ONNX parsing failed")
+
+    config = builder.create_builder_config()
+    config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, 16 << 30)
+    if args.fp16:
+        config.set_flag(trt.BuilderFlag.FP16)
+
+    # Tactic selection
+    recorder = None
+    if args.dump_tactics:
+        recorder = TacticRecorder()
+        config.algorithm_selector = recorder
+    elif args.load_tactics:
+        config.algorithm_selector = TacticForcer(args.load_tactics)
+
+    engine_bytes = builder.build_serialized_network(network, config)
+
+    if recorder and args.dump_tactics:
+        recorder.save(args.dump_tactics)
+
+    with open(engine_path, 'wb') as f:
+        f.write(engine_bytes)
+
+    print(f"\n>>> Done! Engine saved to {engine_path}")
+    print(f"    Outputs: 1 y + {n_outputs - 1} hs_a tensors")
+
+
+if __name__ == '__main__':
+    main()

scripts/run_all_world_model_cases.sh

@@ -0,0 +1,60 @@
+#!/usr/bin/env bash
+
+set -euo pipefail
+
+repo_root="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)"
+cd "${repo_root}"
+
+run_analysis="${RUN_ANALYSIS:-1}"
+case_filter="${CASE_FILTER:-}"
+python_bin="${PYTHON_BIN:-}"
+
+if [[ -z "${python_bin}" ]]; then
+    if [[ -n "${CONDA_PREFIX:-}" ]] && [[ -x "${CONDA_PREFIX}/bin/python" ]]; then
+        python_bin="${CONDA_PREFIX}/bin/python"
+    elif [[ -n "${VIRTUAL_ENV:-}" ]] && [[ -x "${VIRTUAL_ENV}/bin/python" ]]; then
+        python_bin="${VIRTUAL_ENV}/bin/python"
+    else
+        python_bin="$(command -v python || true)"
+    fi
+fi
+
+if [[ -z "${python_bin}" ]] || [[ ! -x "${python_bin}" ]]; then
+    echo "Unable to resolve a usable Python interpreter. Set PYTHON_BIN explicitly." >&2
+    exit 1
+fi
+
+case_scripts=(
+    "unitree_z1_stackbox/case1/run_world_model_interaction.sh"
+    "unitree_z1_stackbox/case2/run_world_model_interaction.sh"
+    "unitree_z1_stackbox/case3/run_world_model_interaction.sh"
+    "unitree_z1_stackbox/case4/run_world_model_interaction.sh"
+    "unitree_z1_dual_arm_stackbox/case1/run_world_model_interaction.sh"
+    "unitree_z1_dual_arm_stackbox/case2/run_world_model_interaction.sh"
+    "unitree_z1_dual_arm_stackbox/case3/run_world_model_interaction.sh"
+    "unitree_z1_dual_arm_stackbox/case4/run_world_model_interaction.sh"
+    "unitree_z1_dual_arm_stackbox_v2/case1/run_world_model_interaction.sh"
+    "unitree_z1_dual_arm_stackbox_v2/case2/run_world_model_interaction.sh"
+    "unitree_z1_dual_arm_stackbox_v2/case3/run_world_model_interaction.sh"
+    "unitree_z1_dual_arm_stackbox_v2/case4/run_world_model_interaction.sh"
+    "unitree_z1_dual_arm_cleanup_pencils/case1/run_world_model_interaction.sh"
+    "unitree_z1_dual_arm_cleanup_pencils/case2/run_world_model_interaction.sh"
+    "unitree_z1_dual_arm_cleanup_pencils/case3/run_world_model_interaction.sh"
+    "unitree_z1_dual_arm_cleanup_pencils/case4/run_world_model_interaction.sh"
+    "unitree_g1_pack_camera/case1/run_world_model_interaction.sh"
+    "unitree_g1_pack_camera/case2/run_world_model_interaction.sh"
+    "unitree_g1_pack_camera/case3/run_world_model_interaction.sh"
+    "unitree_g1_pack_camera/case4/run_world_model_interaction.sh"
+)
+
+for case_script in "${case_scripts[@]}"; do
+    if [[ -n "${case_filter}" ]] && [[ "${case_script}" != *"${case_filter}"* ]]; then
+        continue
+    fi
+
+    echo "============================================================"
+    echo "Running ${case_script}"
+    echo "============================================================"
+    echo "Using Python: ${python_bin}"
+    RUN_ANALYSIS="${run_analysis}" PYTHON_BIN="${python_bin}" bash "${repo_root}/${case_script}"
+done

scripts/run_backbone_reuse_worst_cases.sh

@@ -0,0 +1,20 @@
+#!/usr/bin/env bash
+
+set -euo pipefail
+
+repo_root="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)"
+cd "${repo_root}"
+
+case_scripts=(
+    "unitree_z1_dual_arm_stackbox/case1/run_world_model_backbone_reuse_output546.sh"
+    "unitree_z1_dual_arm_stackbox/case3/run_world_model_backbone_reuse_output546.sh"
+    "unitree_z1_dual_arm_stackbox_v2/case2/run_world_model_backbone_reuse_output546.sh"
+    "unitree_z1_dual_arm_stackbox_v2/case3/run_world_model_backbone_reuse_output546.sh"
+)
+
+for case_script in "${case_scripts[@]}"; do
+    echo "============================================================"
+    echo "Running ${case_script}"
+    echo "============================================================"
+    bash "${repo_root}/${case_script}"
+done

scripts/run_case3_backbone_profiles.sh

@@ -0,0 +1,53 @@
+#!/usr/bin/env bash
+
+set -uo pipefail
+
+repo_root="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)"
+cd "${repo_root}"
+
+gpu_devices="${CUDA_VISIBLE_DEVICES:-1}"
+python_bin="${PYTHON_BIN:-}"
+
+if [[ -z "${python_bin}" ]]; then
+    if [[ -n "${CONDA_PREFIX:-}" ]] && [[ -x "${CONDA_PREFIX}/bin/python" ]]; then
+        python_bin="${CONDA_PREFIX}/bin/python"
+    elif [[ -n "${VIRTUAL_ENV:-}" ]] && [[ -x "${VIRTUAL_ENV}/bin/python" ]]; then
+        python_bin="${VIRTUAL_ENV}/bin/python"
+    else
+        python_bin="$(command -v python || true)"
+    fi
+fi
+
+if [[ -z "${python_bin}" ]] || [[ ! -x "${python_bin}" ]]; then
+    echo "Unable to resolve a usable Python interpreter. Set PYTHON_BIN explicitly." >&2
+    exit 1
+fi
+
+case_scripts=(
+    "unitree_z1_dual_arm_stackbox/case3/run_world_model_backbone_profile.sh"
+    "unitree_z1_dual_arm_stackbox_v2/case3/run_world_model_backbone_profile.sh"
+    "unitree_z1_dual_arm_cleanup_pencils/case3/run_world_model_backbone_profile.sh"
+    "unitree_g1_pack_camera/case3/run_world_model_backbone_profile.sh"
+)
+
+failed_cases=()
+
+for case_script in "${case_scripts[@]}"; do
+    echo "============================================================"
+    echo "Running ${case_script} on GPU ${gpu_devices}"
+    echo "============================================================"
+    echo "Using Python: ${python_bin}"
+    if ! CUDA_VISIBLE_DEVICES="${gpu_devices}" PYTHON_BIN="${python_bin}" bash "${repo_root}/${case_script}"; then
+        echo "FAILED: ${case_script}" >&2
+        failed_cases+=("${case_script}")
+    fi
+done
+
+if [[ ${#failed_cases[@]} -gt 0 ]]; then
+    echo "============================================================" >&2
+    echo "Backbone profiling finished with failures:" >&2
+    for case_script in "${failed_cases[@]}"; do
+        echo "  - ${case_script}" >&2
+    done
+    exit 1
+fi

scripts/run_world_model_backbone_profile_case.sh

@@ -0,0 +1,86 @@
+#!/usr/bin/env bash
+
+set -euo pipefail
+
+if [[ $# -ne 4 ]]; then
+    echo "Usage: $0 <res_dir> <dataset> <frame_stride> <n_iter>" >&2
+    exit 1
+fi
+
+res_dir="$1"
+dataset="$2"
+frame_stride="$3"
+n_iter="$4"
+
+repo_root="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)"
+cd "${repo_root}"
+
+gpu_devices="${CUDA_VISIBLE_DEVICES:-1}"
+python_bin="${PYTHON_BIN:-}"
+if [[ -z "${python_bin}" ]]; then
+    if [[ -n "${CONDA_PREFIX:-}" ]] && [[ -x "${CONDA_PREFIX}/bin/python" ]]; then
+        python_bin="${CONDA_PREFIX}/bin/python"
+    elif [[ -n "${VIRTUAL_ENV:-}" ]] && [[ -x "${VIRTUAL_ENV}/bin/python" ]]; then
+        python_bin="${VIRTUAL_ENV}/bin/python"
+    else
+        python_bin="$(command -v python || true)"
+    fi
+fi
+if [[ -z "${python_bin}" ]] || [[ ! -x "${python_bin}" ]]; then
+    echo "Unable to resolve a usable Python interpreter. Set PYTHON_BIN explicitly." >&2
+    exit 1
+fi
+save_dir="${res_dir}/backbone_profile_dense"
+log_path="${save_dir}/run.log"
+
+mkdir -p "${save_dir}"
+
+interaction_cmd=(
+    "${python_bin}" scripts/evaluation/world_model_interaction.py
+    --seed 123
+    --ckpt_path ckpts/unifolm_wma_dual.ckpt
+    --config configs/inference/world_model_interaction.yaml
+    --savedir "${save_dir}"
+    --bs 1
+    --height 320
+    --width 512
+    --unconditional_guidance_scale 1.0
+    --ddim_steps 50
+    --ddim_eta 1.0
+    --prompt_dir "${res_dir}/world_model_interaction_prompts"
+    --dataset "${dataset}"
+    --video_length 16
+    --frame_stride "${frame_stride}"
+    --n_action_steps 16
+    --exe_steps 16
+    --n_iter "${n_iter}"
+    --timestep_spacing uniform_trailing
+    --guidance_rescale 0.7
+    --perframe_ae
+    --analysis_log_metrics
+    --analysis_reference_steps 50
+    --analysis_profile_backbone_blocks
+)
+
+metrics_analysis_cmd=(
+    "${python_bin}" scripts/evaluation/analyze_metrics.py
+    --input_dir "${save_dir}/inference"
+    --output_dir "${save_dir}/inference/analysis"
+)
+
+backbone_analysis_cmd=(
+    "${python_bin}" scripts/evaluation/analyze_backbone_blocks.py
+    --input_csv "${save_dir}/inference/backbone_block_log.csv"
+    --output_dir "${save_dir}/inference/backbone_analysis"
+)
+
+{
+    echo "============================================================"
+    echo "Running dense backbone profiling for ${res_dir} on GPU ${gpu_devices}"
+    echo "============================================================"
+    echo "Using Python: ${python_bin}"
+    time env CUDA_VISIBLE_DEVICES="${gpu_devices}" "${interaction_cmd[@]}"
+
+    "${metrics_analysis_cmd[@]}"
+    "${backbone_analysis_cmd[@]}"
+} 2>&1 | tee "${log_path}"

scripts/run_world_model_case_schemes.sh

@@ -0,0 +1,106 @@
+#!/usr/bin/env bash
+
+set -euo pipefail
+
+if [[ $# -ne 4 ]]; then
+    echo "Usage: $0 <res_dir> <dataset> <frame_stride> <n_iter>" >&2
+    exit 1
+fi
+
+res_dir="$1"
+dataset="$2"
+frame_stride="$3"
+n_iter="$4"
+
+repo_root="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)"
+cd "${repo_root}"
+
+run_analysis="${RUN_ANALYSIS:-1}"
+scheme_filter="${SCHEME_FILTER:-}"
+gpu_devices="${CUDA_VISIBLE_DEVICES:-0}"
+python_bin="${PYTHON_BIN:-}"
+if [[ -z "${python_bin}" ]]; then
+    if [[ -n "${CONDA_PREFIX:-}" ]] && [[ -x "${CONDA_PREFIX}/bin/python" ]]; then
+        python_bin="${CONDA_PREFIX}/bin/python"
+    elif [[ -n "${VIRTUAL_ENV:-}" ]] && [[ -x "${VIRTUAL_ENV}/bin/python" ]]; then
+        python_bin="${VIRTUAL_ENV}/bin/python"
+    else
+        python_bin="$(command -v python || true)"
+    fi
+fi
+if [[ -z "${python_bin}" ]] || [[ ! -x "${python_bin}" ]]; then
+    echo "Unable to resolve a usable Python interpreter. Set PYTHON_BIN explicitly." >&2
+    exit 1
+fi
+head_log_steps=(40 43 46 47 48 49)
+scheme_names=(sparse_8)
+
+get_head_schedule() {
+    case "$1" in
+        sparse_8)
+            echo "0 7 14 21 28 35 42 49"
+            ;;
+        sparse_4)
+            echo "0 16 32 49"
+            ;;
+        tail_only)
+            echo "40 43 46 49"
+            ;;
+        tail_heavy)
+            echo "0 32 40 44 47 49"
+            ;;
+        *)
+            echo "Unknown scheme: $1" >&2
+            exit 1
+            ;;
+    esac
+}
+
+mkdir -p "${res_dir}/output_logs"
+
+for scheme_name in "${scheme_names[@]}"; do
+    if [[ -n "${scheme_filter}" ]] && [[ "${scheme_name}" != *"${scheme_filter}"* ]]; then
+        continue
+    fi
+
+    read -r -a head_schedule <<< "$(get_head_schedule "${scheme_name}")"
+    scheme_savedir="${res_dir}/output/${scheme_name}"
+    scheme_log_path="${res_dir}/output_logs/${scheme_name}.log"
+
+    {
+        echo "============================================================"
+        echo "Running ${res_dir} with ${scheme_name}"
+        echo "============================================================"
+        echo "Using Python: ${python_bin}"
+        time CUDA_VISIBLE_DEVICES="${gpu_devices}" "${python_bin}" scripts/evaluation/world_model_interaction.py \
+            --seed 123 \
+            --ckpt_path ckpts/unifolm_wma_dual.ckpt \
+            --config configs/inference/world_model_interaction.yaml \
+            --savedir "${scheme_savedir}" \
+            --bs 1 --height 320 --width 512 \
+            --unconditional_guidance_scale 1.0 \
+            --ddim_steps 50 \
+            --ddim_eta 1.0 \
+            --prompt_dir "${res_dir}/world_model_interaction_prompts" \
+            --dataset "${dataset}" \
+            --video_length 16 \
+            --frame_stride "${frame_stride}" \
+            --n_action_steps 16 \
+            --exe_steps 16 \
+            --n_iter "${n_iter}" \
+            --timestep_spacing uniform_trailing \
+            --guidance_rescale 0.7 \
+            --perframe_ae \
+            --analysis_log_metrics \
+            --analysis_reference_steps 50 \
+            --head_schedule_steps "${head_schedule[@]}" \
+            --head_skip_mode reuse_prediction \
+            --head_log_steps "${head_log_steps[@]}"
+
+        if [[ "${run_analysis}" == "1" ]]; then
+            "${python_bin}" scripts/evaluation/analyze_metrics.py \
+                --input_dir "${scheme_savedir}/inference" \
+                --output_dir "${scheme_savedir}/inference/analysis"
+        fi
+    } 2>&1 | tee "${scheme_log_path}"
+done

scripts/run_world_model_interaction.sh

@@ -3,6 +3,8 @@ ckpt=/path/to/model/checkpoint
 config=configs/inference/world_model_interaction.yaml
 seed=123
 res_dir="/path/to/result/directory"
+analysis_reference_steps=50
+# analysis_psnr_path="/path/to/psnr_full50.csv"
 
 datasets=(
     "unitree_z1_stackbox"
@@ -19,6 +21,13 @@ for i in "${!datasets[@]}"; do
     dataset=${datasets[$i]}
     n_iter=${n_iters[$i]}
     fs=${fses[$i]}
+    analysis_args=(
+        --analysis_log_metrics
+        --analysis_reference_steps ${analysis_reference_steps}
+    )
+    if [ -n "${analysis_psnr_path:-}" ]; then
+        analysis_args+=(--analysis_psnr_path "${analysis_psnr_path}")
+    fi
 
     CUDA_VISIBLE_DEVICES=0 python3 scripts/evaluation/world_model_interaction.py \
     --seed ${seed} \
@@ -38,5 +47,6 @@ for i in "${!datasets[@]}"; do
     --n_iter ${n_iter} \
     --timestep_spacing 'uniform_trailing' \
     --guidance_rescale 0.7 \
-    --perframe_ae
+    --perframe_ae \
+    "${analysis_args[@]}"
 done

sparse_head_simulation/min_head_steps_needed.csv

@@ -0,0 +1,393 @@
+dataset,case,sample_id,scene,pass_type,round_id,target_threshold,action_min_head_steps_needed,state_min_head_steps_needed,action_earliest_checkpoint_zero_based,state_earliest_checkpoint_zero_based,action_max_head_compute_saving_ratio,state_max_head_compute_saving_ratio
+unitree_g1_pack_camera,case1,unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,policy,0,0.95,1.0,1.0,45.0,45.0,0.98,0.98
+unitree_g1_pack_camera,case1,unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,policy,1,0.95,1.0,1.0,45.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case1,unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,policy,2,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case1,unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,policy,3,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case1,unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,policy,4,0.95,1.0,1.0,45.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case1,unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,policy,5,0.95,1.0,1.0,45.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case1,unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,policy,6,0.95,1.0,1.0,45.0,45.0,0.98,0.98
+unitree_g1_pack_camera,case1,unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,policy,7,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case1,unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,policy,8,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case1,unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,policy,9,0.95,1.0,1.0,47.0,47.0,0.98,0.98
+unitree_g1_pack_camera,case1,unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,policy,10,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case1,unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,world_model,0,0.95,1.0,1.0,45.0,45.0,0.98,0.98
+unitree_g1_pack_camera,case1,unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,world_model,1,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case1,unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,world_model,2,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case1,unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,world_model,3,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case1,unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,world_model,4,0.95,1.0,1.0,46.0,45.0,0.98,0.98
+unitree_g1_pack_camera,case1,unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,world_model,5,0.95,1.0,1.0,45.0,45.0,0.98,0.98
+unitree_g1_pack_camera,case1,unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,world_model,6,0.95,1.0,1.0,45.0,45.0,0.98,0.98
+unitree_g1_pack_camera,case1,unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,world_model,7,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case1,unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,world_model,8,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case1,unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,world_model,9,0.95,1.0,1.0,46.0,47.0,0.98,0.98
+unitree_g1_pack_camera,case1,unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,world_model,10,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case2,unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,policy,0,0.95,1.0,1.0,45.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case2,unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,policy,1,0.95,1.0,1.0,45.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case2,unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,policy,2,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case2,unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,policy,3,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case2,unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,policy,4,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case2,unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,policy,5,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case2,unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,policy,6,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case2,unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,policy,7,0.95,1.0,1.0,46.0,47.0,0.98,0.98
+unitree_g1_pack_camera,case2,unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,policy,8,0.95,1.0,1.0,46.0,47.0,0.98,0.98
+unitree_g1_pack_camera,case2,unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,policy,9,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case2,unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,policy,10,0.95,1.0,1.0,45.0,45.0,0.98,0.98
+unitree_g1_pack_camera,case2,unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,world_model,0,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case2,unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,world_model,1,0.95,1.0,1.0,45.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case2,unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,world_model,2,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case2,unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,world_model,3,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case2,unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,world_model,4,0.95,1.0,1.0,46.0,45.0,0.98,0.98
+unitree_g1_pack_camera,case2,unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,world_model,5,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case2,unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,world_model,6,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case2,unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,world_model,7,0.95,1.0,1.0,46.0,47.0,0.98,0.98
+unitree_g1_pack_camera,case2,unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,world_model,8,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case2,unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,world_model,9,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case2,unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,world_model,10,0.95,1.0,1.0,45.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case3,unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,policy,0,0.95,1.0,1.0,45.0,45.0,0.98,0.98
+unitree_g1_pack_camera,case3,unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,policy,1,0.95,1.0,1.0,45.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case3,unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,policy,2,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case3,unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,policy,3,0.95,1.0,1.0,45.0,45.0,0.98,0.98
+unitree_g1_pack_camera,case3,unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,policy,4,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case3,unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,policy,5,0.95,1.0,1.0,46.0,47.0,0.98,0.98
+unitree_g1_pack_camera,case3,unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,policy,6,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case3,unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,policy,7,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case3,unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,policy,8,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case3,unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,policy,9,0.95,1.0,1.0,46.0,45.0,0.98,0.98
+unitree_g1_pack_camera,case3,unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,policy,10,0.95,1.0,1.0,45.0,45.0,0.98,0.98
+unitree_g1_pack_camera,case3,unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,world_model,0,0.95,1.0,1.0,45.0,45.0,0.98,0.98
+unitree_g1_pack_camera,case3,unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,world_model,1,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case3,unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,world_model,2,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case3,unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,world_model,3,0.95,1.0,1.0,46.0,45.0,0.98,0.98
+unitree_g1_pack_camera,case3,unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,world_model,4,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case3,unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,world_model,5,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case3,unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,world_model,6,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case3,unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,world_model,7,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case3,unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,world_model,8,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case3,unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,world_model,9,0.95,1.0,1.0,45.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case3,unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,world_model,10,0.95,1.0,1.0,45.0,45.0,0.98,0.98
+unitree_g1_pack_camera,case4,unitree_g1_pack_camera-vid200-fs6,unitree_g1_pack_camera,policy,0,0.95,1.0,1.0,45.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case4,unitree_g1_pack_camera-vid200-fs6,unitree_g1_pack_camera,policy,1,0.95,1.0,1.0,45.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case4,unitree_g1_pack_camera-vid200-fs6,unitree_g1_pack_camera,policy,2,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case4,unitree_g1_pack_camera-vid200-fs6,unitree_g1_pack_camera,policy,3,0.95,1.0,1.0,46.0,45.0,0.98,0.98
+unitree_g1_pack_camera,case4,unitree_g1_pack_camera-vid200-fs6,unitree_g1_pack_camera,policy,4,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case4,unitree_g1_pack_camera-vid200-fs6,unitree_g1_pack_camera,policy,5,0.95,1.0,1.0,46.0,47.0,0.98,0.98
+unitree_g1_pack_camera,case4,unitree_g1_pack_camera-vid200-fs6,unitree_g1_pack_camera,policy,6,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case4,unitree_g1_pack_camera-vid200-fs6,unitree_g1_pack_camera,policy,7,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case4,unitree_g1_pack_camera-vid200-fs6,unitree_g1_pack_camera,policy,8,0.95,1.0,1.0,45.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case4,unitree_g1_pack_camera-vid200-fs6,unitree_g1_pack_camera,policy,9,0.95,1.0,1.0,46.0,45.0,0.98,0.98
+unitree_g1_pack_camera,case4,unitree_g1_pack_camera-vid200-fs6,unitree_g1_pack_camera,policy,10,0.95,1.0,1.0,45.0,45.0,0.98,0.98
+unitree_g1_pack_camera,case4,unitree_g1_pack_camera-vid200-fs6,unitree_g1_pack_camera,world_model,0,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case4,unitree_g1_pack_camera-vid200-fs6,unitree_g1_pack_camera,world_model,1,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case4,unitree_g1_pack_camera-vid200-fs6,unitree_g1_pack_camera,world_model,2,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case4,unitree_g1_pack_camera-vid200-fs6,unitree_g1_pack_camera,world_model,3,0.95,1.0,1.0,46.0,45.0,0.98,0.98
+unitree_g1_pack_camera,case4,unitree_g1_pack_camera-vid200-fs6,unitree_g1_pack_camera,world_model,4,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case4,unitree_g1_pack_camera-vid200-fs6,unitree_g1_pack_camera,world_model,5,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case4,unitree_g1_pack_camera-vid200-fs6,unitree_g1_pack_camera,world_model,6,0.95,1.0,1.0,45.0,45.0,0.98,0.98
+unitree_g1_pack_camera,case4,unitree_g1_pack_camera-vid200-fs6,unitree_g1_pack_camera,world_model,7,0.95,1.0,1.0,45.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case4,unitree_g1_pack_camera-vid200-fs6,unitree_g1_pack_camera,world_model,8,0.95,1.0,1.0,45.0,45.0,0.98,0.98
+unitree_g1_pack_camera,case4,unitree_g1_pack_camera-vid200-fs6,unitree_g1_pack_camera,world_model,9,0.95,1.0,1.0,45.0,45.0,0.98,0.98
+unitree_g1_pack_camera,case4,unitree_g1_pack_camera-vid200-fs6,unitree_g1_pack_camera,world_model,10,0.95,1.0,1.0,45.0,45.0,0.98,0.98
+unitree_z1_dual_arm_cleanup_pencils,case1,unitree_z1_dual_arm_cleanup_pencils-vid0-fs4,unitree_z1_dual_arm_cleanup_pencils,policy,0,0.95,1.0,1.0,45.0,45.0,0.98,0.98
+unitree_z1_dual_arm_cleanup_pencils,case1,unitree_z1_dual_arm_cleanup_pencils-vid0-fs4,unitree_z1_dual_arm_cleanup_pencils,policy,1,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_z1_dual_arm_cleanup_pencils,case1,unitree_z1_dual_arm_cleanup_pencils-vid0-fs4,unitree_z1_dual_arm_cleanup_pencils,policy,2,0.95,1.0,1.0,46.0,47.0,0.98,0.98
+unitree_z1_dual_arm_cleanup_pencils,case1,unitree_z1_dual_arm_cleanup_pencils-vid0-fs4,unitree_z1_dual_arm_cleanup_pencils,policy,3,0.95,1.0,1.0,46.0,47.0,0.98,0.98
+unitree_z1_dual_arm_cleanup_pencils,case1,unitree_z1_dual_arm_cleanup_pencils-vid0-fs4,unitree_z1_dual_arm_cleanup_pencils,policy,4,0.95,1.0,1.0,44.0,46.0,0.98,0.98
+unitree_z1_dual_arm_cleanup_pencils,case1,unitree_z1_dual_arm_cleanup_pencils-vid0-fs4,unitree_z1_dual_arm_cleanup_pencils,policy,5,0.95,1.0,1.0,45.0,46.0,0.98,0.98
+unitree_z1_dual_arm_cleanup_pencils,case1,unitree_z1_dual_arm_cleanup_pencils-vid0-fs4,unitree_z1_dual_arm_cleanup_pencils,policy,6,0.95,1.0,1.0,45.0,45.0,0.98,0.98
+unitree_z1_dual_arm_cleanup_pencils,case1,unitree_z1_dual_arm_cleanup_pencils-vid0-fs4,unitree_z1_dual_arm_cleanup_pencils,policy,7,0.95,1.0,1.0,47.0,47.0,0.98,0.98
+unitree_z1_dual_arm_cleanup_pencils,case1,unitree_z1_dual_arm_cleanup_pencils-vid0-fs4,unitree_z1_dual_arm_cleanup_pencils,world_model,0,0.95,1.0,1.0,45.0,45.0,0.98,0.98
+unitree_z1_dual_arm_cleanup_pencils,case1,unitree_z1_dual_arm_cleanup_pencils-vid0-fs4,unitree_z1_dual_arm_cleanup_pencils,world_model,1,0.95,1.0,1.0,46.0,46.0,0.98,0.98
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+unitree_z1_dual_arm_cleanup_pencils,case1,unitree_z1_dual_arm_cleanup_pencils-vid0-fs4,unitree_z1_dual_arm_cleanup_pencils,world_model,3,0.95,1.0,1.0,46.0,47.0,0.98,0.98
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+unitree_z1_stackbox,case4,unitree_z1_stackbox-vid35-fs4,unitree_z1_stackbox,world_model,5,0.95,1.0,1.0,47.0,47.0,0.98,0.98
+unitree_z1_stackbox,case4,unitree_z1_stackbox-vid35-fs4,unitree_z1_stackbox,world_model,6,0.95,1.0,1.0,47.0,48.0,0.98,0.98
+unitree_z1_stackbox,case4,unitree_z1_stackbox-vid35-fs4,unitree_z1_stackbox,world_model,7,0.95,1.0,1.0,47.0,48.0,0.98,0.98
+unitree_z1_stackbox,case4,unitree_z1_stackbox-vid35-fs4,unitree_z1_stackbox,world_model,8,0.95,1.0,1.0,47.0,48.0,0.98,0.98
+unitree_z1_stackbox,case4,unitree_z1_stackbox-vid35-fs4,unitree_z1_stackbox,world_model,9,0.95,1.0,1.0,46.0,47.0,0.98,0.98
+unitree_z1_stackbox,case4,unitree_z1_stackbox-vid35-fs4,unitree_z1_stackbox,world_model,10,0.95,1.0,1.0,47.0,48.0,0.98,0.98
+unitree_z1_stackbox,case4,unitree_z1_stackbox-vid35-fs4,unitree_z1_stackbox,world_model,11,0.95,1.0,1.0,47.0,48.0,0.98,0.98

sparse_head_simulation/report.md

@@ -0,0 +1,108 @@
+# Sparse Head Execution Simulation
+
+This report uses zero-order hold over logged stepwise action/state outputs.
+For a sparse scheme, the final output at step 49 is approximated by the most recent checkpoint output.
+
+## Experiment 1.1
+
+### dense
+
+- Head exec count: 50
+- Head compute saving ratio: 0.0000
+- Final action cosine vs dense: mean=1.0000, median=1.0000
+- Final state cosine vs dense: mean=1.0000, median=1.0000
+
+### sparse_10
+
+- Head exec count: 10
+- Head compute saving ratio: 0.8000
+- Final action cosine vs dense: mean=0.9341, median=0.9357
+- Final state cosine vs dense: mean=0.9130, median=0.9207
+
+### sparse_4
+
+- Head exec count: 4
+- Head compute saving ratio: 0.9200
+- Final action cosine vs dense: mean=1.0000, median=1.0000
+- Final state cosine vs dense: mean=1.0000, median=1.0000
+
+### sparse_5
+
+- Head exec count: 5
+- Head compute saving ratio: 0.9000
+- Final action cosine vs dense: mean=0.7747, median=0.7703
+- Final state cosine vs dense: mean=0.7463, median=0.7481
+
+### sparse_8
+
+- Head exec count: 8
+- Head compute saving ratio: 0.8400
+- Final action cosine vs dense: mean=1.0000, median=1.0000
+- Final state cosine vs dense: mean=1.0000, median=1.0000
+
+### tail_heavy_10
+
+- Head exec count: 10
+- Head compute saving ratio: 0.8000
+- Final action cosine vs dense: mean=1.0000, median=1.0000
+- Final state cosine vs dense: mean=1.0000, median=1.0000
+
+### tail_heavy_6
+
+- Head exec count: 6
+- Head compute saving ratio: 0.8800
+- Final action cosine vs dense: mean=1.0000, median=1.0000
+- Final state cosine vs dense: mean=1.0000, median=1.0000
+
+### tail_heavy_8
+
+- Head exec count: 8
+- Head compute saving ratio: 0.8400
+- Final action cosine vs dense: mean=1.0000, median=1.0000
+- Final state cosine vs dense: mean=1.0000, median=1.0000
+
+### tail_only_4
+
+- Head exec count: 4
+- Head compute saving ratio: 0.9200
+- Final action cosine vs dense: mean=1.0000, median=1.0000
+- Final state cosine vs dense: mean=1.0000, median=1.0000
+
+### tail_only_6
+
+- Head exec count: 6
+- Head compute saving ratio: 0.8800
+- Final action cosine vs dense: mean=1.0000, median=1.0000
+- Final state cosine vs dense: mean=1.0000, median=1.0000
+
+## Experiment 1.2
+
+- Target threshold: 0.95
+- Action earliest checkpoint mean: 46.0179
+- State earliest checkpoint mean: 46.5357
+- Unique min head steps needed for action: [1.0]
+- Unique min head steps needed for state: [1.0]
+
+## Experiment 1.3
+
+### budget_10_uniform_vs_tail
+
+- Tail better action share: 1.0000
+- Tail better state share: 1.0000
+- Mean tail-minus-uniform action cosine: 0.0659
+- Mean tail-minus-uniform state cosine: 0.0870
+
+### budget_4_uniform_vs_tail
+
+- Tail better action share: 0.0000
+- Tail better state share: 0.0000
+- Mean tail-minus-uniform action cosine: 0.0000
+- Mean tail-minus-uniform state cosine: 0.0000
+
+### budget_8_uniform_vs_tail
+
+- Tail better action share: 0.0000
+- Tail better state share: 0.0000
+- Mean tail-minus-uniform action cosine: 0.0000
+- Mean tail-minus-uniform state cosine: 0.0000
+

sparse_head_simulation/scheme_simulation_overall.csv

@@ -0,0 +1,22 @@
+scheme,pass_type,final_action_cosine_vs_dense,final_action_cosine_vs_dense,final_action_cosine_vs_dense,final_state_cosine_vs_dense,final_state_cosine_vs_dense,final_state_cosine_vs_dense,head_exec_count,head_exec_count,head_exec_count,head_compute_saving_ratio,head_compute_saving_ratio,head_compute_saving_ratio
+,,mean,median,std,mean,median,std,mean,median,std,mean,median,std
+dense,policy,1.000000006386212,1.0,8.6717418229413e-08,0.9999999939178934,1.0,9.826164033417286e-08,50.0,50.0,0.0,0.0,0.0,0.0
+dense,world_model,1.0000000069944226,1.0,9.373933947591167e-08,0.9999999936137881,1.0,9.102258232536112e-08,50.0,50.0,0.0,0.0,0.0,0.0
+sparse_10,policy,0.9357762607384701,0.9370943009853362,0.018183880133080282,0.9124557038350981,0.9198094010353088,0.03185505383051429,10.0,10.0,0.0,0.8,0.8,0.0
+sparse_10,world_model,0.9325069240769561,0.9341191649436951,0.0185309603092533,0.9134697318077087,0.9217162132263184,0.032954335242965455,10.0,10.0,0.0,0.8,0.8,0.0
+sparse_4,policy,1.000000006386212,1.0,8.6717418229413e-08,0.9999999939178934,1.0,9.826164033417286e-08,4.0,4.0,0.0,0.9200000000000002,0.92,0.0
+sparse_4,world_model,1.0000000069944226,1.0,9.373933947591167e-08,0.9999999936137881,1.0,9.102258232536112e-08,4.0,4.0,0.0,0.9200000000000002,0.92,0.0
+sparse_5,policy,0.7812822941614657,0.7787671983242035,0.03869909771001223,0.7465329766273499,0.7454181015491486,0.05109581488207182,5.0,5.0,0.0,0.9,0.9,0.0
+sparse_5,world_model,0.7681533368874569,0.7654141485691071,0.03844469190746414,0.7460347663383095,0.7492875158786774,0.05257641591339498,5.0,5.0,0.0,0.9,0.9,0.0
+sparse_8,policy,1.000000006386212,1.0,8.6717418229413e-08,0.9999999939178934,1.0,9.826164033417286e-08,8.0,8.0,0.0,0.84,0.84,0.0
+sparse_8,world_model,1.0000000069944226,1.0,9.373933947591167e-08,0.9999999936137881,1.0,9.102258232536112e-08,8.0,8.0,0.0,0.84,0.84,0.0
+tail_heavy_10,policy,1.000000006386212,1.0,8.6717418229413e-08,0.9999999939178934,1.0,9.826164033417286e-08,10.0,10.0,0.0,0.8,0.8,0.0
+tail_heavy_10,world_model,1.0000000069944226,1.0,9.373933947591167e-08,0.9999999936137881,1.0,9.102258232536112e-08,10.0,10.0,0.0,0.8,0.8,0.0
+tail_heavy_6,policy,1.000000006386212,1.0,8.6717418229413e-08,0.9999999939178934,1.0,9.826164033417286e-08,6.0,6.0,0.0,0.8799999999999999,0.88,0.0
+tail_heavy_6,world_model,1.0000000069944226,1.0,9.373933947591167e-08,0.9999999936137881,1.0,9.102258232536112e-08,6.0,6.0,0.0,0.8799999999999999,0.88,0.0
+tail_heavy_8,policy,1.000000006386212,1.0,8.6717418229413e-08,0.9999999939178934,1.0,9.826164033417286e-08,8.0,8.0,0.0,0.84,0.84,0.0
+tail_heavy_8,world_model,1.0000000069944226,1.0,9.373933947591167e-08,0.9999999936137881,1.0,9.102258232536112e-08,8.0,8.0,0.0,0.84,0.84,0.0
+tail_only_4,policy,1.000000006386212,1.0,8.6717418229413e-08,0.9999999939178934,1.0,9.826164033417286e-08,4.0,4.0,0.0,0.9200000000000002,0.92,0.0
+tail_only_4,world_model,1.0000000069944226,1.0,9.373933947591167e-08,0.9999999936137881,1.0,9.102258232536112e-08,4.0,4.0,0.0,0.9200000000000002,0.92,0.0
+tail_only_6,policy,1.000000006386212,1.0,8.6717418229413e-08,0.9999999939178934,1.0,9.826164033417286e-08,6.0,6.0,0.0,0.8799999999999999,0.88,0.0
+tail_only_6,world_model,1.0000000069944226,1.0,9.373933947591167e-08,0.9999999936137881,1.0,9.102258232536112e-08,6.0,6.0,0.0,0.8799999999999999,0.88,0.0

sparse_head_simulation/scheme_simulation_per_case.csv

@@ -0,0 +1,401 @@
+dataset,case,scheme,pass_type,final_action_cosine_vs_dense,final_state_cosine_vs_dense,head_exec_count,head_compute_saving_ratio
+unitree_g1_pack_camera,case1,dense,policy,1.000000005418604,1.0000000216744163,50.0,0.0
+unitree_g1_pack_camera,case1,dense,world_model,1.0000000162558123,0.9999999891627919,50.0,0.0
+unitree_g1_pack_camera,case1,sparse_10,policy,0.9458914236588911,0.9406864263794639,10.0,0.8
+unitree_g1_pack_camera,case1,sparse_10,world_model,0.943276660008864,0.94043137810447,10.0,0.8
+unitree_g1_pack_camera,case1,sparse_4,policy,1.000000005418604,1.0000000216744163,4.0,0.92
+unitree_g1_pack_camera,case1,sparse_4,world_model,1.0000000162558123,0.9999999891627919,4.0,0.92
+unitree_g1_pack_camera,case1,sparse_5,policy,0.7835968299345537,0.7882764935493469,5.0,0.9
+unitree_g1_pack_camera,case1,sparse_5,world_model,0.768740404735912,0.7820447520776228,5.0,0.9
+unitree_g1_pack_camera,case1,sparse_8,policy,1.000000005418604,1.0000000216744163,8.0,0.84
+unitree_g1_pack_camera,case1,sparse_8,world_model,1.0000000162558123,0.9999999891627919,8.0,0.84
+unitree_g1_pack_camera,case1,tail_heavy_10,policy,1.000000005418604,1.0000000216744163,10.0,0.8
+unitree_g1_pack_camera,case1,tail_heavy_10,world_model,1.0000000162558123,0.9999999891627919,10.0,0.8
+unitree_g1_pack_camera,case1,tail_heavy_6,policy,1.000000005418604,1.0000000216744163,6.0,0.88
+unitree_g1_pack_camera,case1,tail_heavy_6,world_model,1.0000000162558123,0.9999999891627919,6.0,0.88
+unitree_g1_pack_camera,case1,tail_heavy_8,policy,1.000000005418604,1.0000000216744163,8.0,0.84
+unitree_g1_pack_camera,case1,tail_heavy_8,world_model,1.0000000162558123,0.9999999891627919,8.0,0.84
+unitree_g1_pack_camera,case1,tail_only_4,policy,1.000000005418604,1.0000000216744163,4.0,0.92
+unitree_g1_pack_camera,case1,tail_only_4,world_model,1.0000000162558123,0.9999999891627919,4.0,0.92
+unitree_g1_pack_camera,case1,tail_only_6,policy,1.000000005418604,1.0000000216744163,6.0,0.88
+unitree_g1_pack_camera,case1,tail_only_6,world_model,1.0000000162558123,0.9999999891627919,6.0,0.88
+unitree_g1_pack_camera,case2,dense,policy,1.0,1.0000000108372082,50.0,0.0
+unitree_g1_pack_camera,case2,dense,world_model,1.0000000433488325,1.0,50.0,0.0
+unitree_g1_pack_camera,case2,sparse_10,policy,0.9434121738780629,0.9354661919853904,10.0,0.8
+unitree_g1_pack_camera,case2,sparse_10,world_model,0.9378488930788907,0.9352135224775835,10.0,0.8
+unitree_g1_pack_camera,case2,sparse_4,policy,1.0,1.0000000108372082,4.0,0.92
+unitree_g1_pack_camera,case2,sparse_4,world_model,1.0000000433488325,1.0,4.0,0.92
+unitree_g1_pack_camera,case2,sparse_5,policy,0.7797025496309454,0.7743967554785989,5.0,0.9
+unitree_g1_pack_camera,case2,sparse_5,world_model,0.7510665926066312,0.7674615762450478,5.0,0.9
+unitree_g1_pack_camera,case2,sparse_8,policy,1.0,1.0000000108372082,8.0,0.84
+unitree_g1_pack_camera,case2,sparse_8,world_model,1.0000000433488325,1.0,8.0,0.84
+unitree_g1_pack_camera,case2,tail_heavy_10,policy,1.0,1.0000000108372082,10.0,0.8
+unitree_g1_pack_camera,case2,tail_heavy_10,world_model,1.0000000433488325,1.0,10.0,0.8
+unitree_g1_pack_camera,case2,tail_heavy_6,policy,1.0,1.0000000108372082,6.0,0.88
+unitree_g1_pack_camera,case2,tail_heavy_6,world_model,1.0000000433488325,1.0,6.0,0.88
+unitree_g1_pack_camera,case2,tail_heavy_8,policy,1.0,1.0000000108372082,8.0,0.84
+unitree_g1_pack_camera,case2,tail_heavy_8,world_model,1.0000000433488325,1.0,8.0,0.84
+unitree_g1_pack_camera,case2,tail_only_4,policy,1.0,1.0000000108372082,4.0,0.92
+unitree_g1_pack_camera,case2,tail_only_4,world_model,1.0000000433488325,1.0,4.0,0.92
+unitree_g1_pack_camera,case2,tail_only_6,policy,1.0,1.0000000108372082,6.0,0.88
+unitree_g1_pack_camera,case2,tail_only_6,world_model,1.0000000433488325,1.0,6.0,0.88
+unitree_g1_pack_camera,case3,dense,policy,0.9999999729069796,0.9999999945813959,50.0,0.0
+unitree_g1_pack_camera,case3,dense,world_model,0.9999999891627919,0.9999999729069796,50.0,0.0
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+unitree_g1_pack_camera,case4,tail_only_6,world_model,1.000000005418604,1.0000000162558123,6.0,0.88
+unitree_z1_dual_arm_cleanup_pencils,case1,dense,policy,1.0000000074505806,0.9999999850988388,50.0,0.0
+unitree_z1_dual_arm_cleanup_pencils,case1,dense,world_model,0.9999999925494194,1.000000037252903,50.0,0.0
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+unitree_z1_dual_arm_cleanup_pencils,case1,sparse_10,world_model,0.9465975016355515,0.9307577759027481,10.0,0.8
+unitree_z1_dual_arm_cleanup_pencils,case1,sparse_4,policy,1.0000000074505806,0.9999999850988388,4.0,0.92
+unitree_z1_dual_arm_cleanup_pencils,case1,sparse_4,world_model,0.9999999925494194,1.000000037252903,4.0,0.92
+unitree_z1_dual_arm_cleanup_pencils,case1,sparse_5,policy,0.8056009188294411,0.7804089784622192,5.0,0.9
+unitree_z1_dual_arm_cleanup_pencils,case1,sparse_5,world_model,0.800347164273262,0.7744432389736176,5.0,0.9
+unitree_z1_dual_arm_cleanup_pencils,case1,sparse_8,policy,1.0000000074505806,0.9999999850988388,8.0,0.84
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src/unifolm_wma/models/samplers/ddim.py

@@ -6,6 +6,8 @@ from unifolm_wma.utils.diffusion import make_ddim_sampling_parameters, make_ddim
 from unifolm_wma.utils.common import noise_like
 from unifolm_wma.utils.common import extract_into_tensor
 from tqdm import tqdm
+from unifolm_wma.modules.attention import enable_cross_attn_kv_cache, disable_cross_attn_kv_cache
+from unifolm_wma.modules.networks.wma_model import enable_ctx_cache, disable_ctx_cache
 
 
 class DDIMSampler(object):
@@ -19,8 +21,9 @@ class DDIMSampler(object):
 
     def register_buffer(self, name, attr):
         if type(attr) == torch.Tensor:
-            if attr.device != torch.device("cuda"):
-                attr = attr.to(torch.device("cuda"))
+            target_device = self.model.device
+            if attr.device != target_device:
+                attr = attr.to(target_device)
         setattr(self, name, attr)
 
     def make_schedule(self,
@@ -67,11 +70,12 @@ class DDIMSampler(object):
             ddim_timesteps=self.ddim_timesteps,
             eta=ddim_eta,
             verbose=verbose)
-        self.register_buffer('ddim_sigmas', ddim_sigmas)
-        self.register_buffer('ddim_alphas', ddim_alphas)
-        self.register_buffer('ddim_alphas_prev', ddim_alphas_prev)
+        # Ensure tensors are on correct device for efficient indexing
+        self.register_buffer('ddim_sigmas', to_torch(torch.as_tensor(ddim_sigmas)))
+        self.register_buffer('ddim_alphas', to_torch(torch.as_tensor(ddim_alphas)))
+        self.register_buffer('ddim_alphas_prev', to_torch(torch.as_tensor(ddim_alphas_prev)))
         self.register_buffer('ddim_sqrt_one_minus_alphas',
-                             np.sqrt(1. - ddim_alphas))
+                             to_torch(torch.as_tensor(np.sqrt(1. - ddim_alphas))))
         sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(
             (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) *
             (1 - self.alphas_cumprod / self.alphas_cumprod_prev))
@@ -106,6 +110,9 @@ class DDIMSampler(object):
             fs=None,
             timestep_spacing='uniform',  #uniform_trailing for starting from last timestep
             guidance_rescale=0.0,
+            handoff_step: int = 0,
+            handoff_callback=None,
+            stop_at_handoff: bool = False,
             **kwargs):
 
         # Check condition bs
@@ -161,6 +168,9 @@ class DDIMSampler(object):
             precision=precision,
             fs=fs,
             guidance_rescale=guidance_rescale,
+            handoff_step=handoff_step,
+            handoff_callback=handoff_callback,
+            stop_at_handoff=stop_at_handoff,
             **kwargs)
         return samples, actions, states, intermediates
 
@@ -187,6 +197,9 @@ class DDIMSampler(object):
                       precision=None,
                       fs=None,
                       guidance_rescale=0.0,
+                      handoff_step: int = 0,
+                      handoff_callback=None,
+                      stop_at_handoff: bool = False,
                       **kwargs):
         device = self.model.betas.device
         dp_ddim_scheduler_action = self.model.dp_noise_scheduler_action
@@ -195,22 +208,12 @@ class DDIMSampler(object):
         b = shape[0]
         if x_T is None:
             img = torch.randn(shape, device=device)
-            action = torch.randn((b, 16, self.model.agent_action_dim),
-                                 device=device)
-            state = torch.randn((b, 16, self.model.agent_state_dim),
-                                device=device)
+            action = torch.randn((b, 16, self.model.agent_action_dim), device=device)
+            state = torch.randn((b, 16, self.model.agent_state_dim), device=device)
         else:
             img = x_T
-            action = torch.randn((b, 16, self.model.agent_action_dim),
-                                 device=device)
-            state = torch.randn((b, 16, self.model.agent_state_dim),
-                                device=device)
-
-        if precision is not None:
-            if precision == 16:
-                img = img.to(dtype=torch.float16)
-                action = action.to(dtype=torch.float16)
-                state = state.to(dtype=torch.float16)
+            action = torch.randn((b, 16, self.model.agent_action_dim), device=device)
+            state = torch.randn((b, 16, self.model.agent_state_dim), device=device)
 
         if timesteps is None:
             timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps
@@ -238,67 +241,91 @@ class DDIMSampler(object):
             iterator = time_range
 
         clean_cond = kwargs.pop("clean_cond", False)
-
         dp_ddim_scheduler_action.set_timesteps(len(timesteps))
         dp_ddim_scheduler_state.set_timesteps(len(timesteps))
-        for i, step in enumerate(iterator):
-            index = total_steps - i - 1
-            ts = torch.full((b, ), step, device=device, dtype=torch.long)
+        ts = torch.empty((b, ), device=device, dtype=torch.long)
+        enable_cross_attn_kv_cache(self.model)
+        enable_ctx_cache(self.model)
+        handoff = {}
+        try:
+            for i, step in enumerate(iterator):
+                index = total_steps - i - 1
+                ts.fill_(step)
 
-            # Use mask to blend noised original latent (img_orig) & new sampled latent (img)
-            if mask is not None:
-                assert x0 is not None
-                if clean_cond:
-                    img_orig = x0
-                else:
-                    img_orig = self.model.q_sample(x0, ts)
-                img = img_orig * mask + (1. - mask) * img
+                # Use mask to blend noised original latent (img_orig) & new sampled latent (img)
+                if mask is not None:
+                    assert x0 is not None
+                    if clean_cond:
+                        img_orig = x0
+                    else:
+                        img_orig = self.model.q_sample(x0, ts)
+                    img = img_orig * mask + (1. - mask) * img
 
-            outs = self.p_sample_ddim(
-                img,
-                action,
-                state,
-                cond,
-                ts,
-                index=index,
-                use_original_steps=ddim_use_original_steps,
-                quantize_denoised=quantize_denoised,
-                temperature=temperature,
-                noise_dropout=noise_dropout,
-                score_corrector=score_corrector,
-                corrector_kwargs=corrector_kwargs,
-                unconditional_guidance_scale=unconditional_guidance_scale,
-                unconditional_conditioning=unconditional_conditioning,
-                mask=mask,
-                x0=x0,
-                fs=fs,
-                guidance_rescale=guidance_rescale,
-                **kwargs)
+                outs = self.p_sample_ddim(
+                    img,
+                    action,
+                    state,
+                    cond,
+                    ts,
+                    index=index,
+                    precision=precision,
+                    use_original_steps=ddim_use_original_steps,
+                    quantize_denoised=quantize_denoised,
+                    temperature=temperature,
+                    noise_dropout=noise_dropout,
+                    score_corrector=score_corrector,
+                    corrector_kwargs=corrector_kwargs,
+                    unconditional_guidance_scale=unconditional_guidance_scale,
+                    unconditional_conditioning=unconditional_conditioning,
+                    mask=mask,
+                    x0=x0,
+                    fs=fs,
+                    guidance_rescale=guidance_rescale,
+                    **kwargs)
 
-            img, pred_x0, model_output_action, model_output_state = outs
+                img, pred_x0, model_output_action, model_output_state = outs
 
-            action = dp_ddim_scheduler_action.step(
-                model_output_action,
-                step,
-                action,
-                generator=None,
-            ).prev_sample
-            state = dp_ddim_scheduler_state.step(
-                model_output_state,
-                step,
-                state,
-                generator=None,
-            ).prev_sample
+                action = dp_ddim_scheduler_action.step(
+                    model_output_action,
+                    step,
+                    action,
+                    generator=None,
+                ).prev_sample
+                state = dp_ddim_scheduler_state.step(
+                    model_output_state,
+                    step,
+                    state,
+                    generator=None,
+                ).prev_sample
 
-            if callback: callback(i)
-            if img_callback: img_callback(pred_x0, i)
+                if callback: callback(i)
+                if img_callback: img_callback(pred_x0, i)
 
-            if index % log_every_t == 0 or index == total_steps - 1:
-                intermediates['x_inter'].append(img)
-                intermediates['pred_x0'].append(pred_x0)
-                intermediates['x_inter_action'].append(action)
-                intermediates['x_inter_state'].append(state)
+                if handoff_step > 0 and (i + 1) == handoff_step:
+                    handoff = {
+                        'samples': img.clone(),
+                        'actions': action.clone(),
+                        'states': state.clone(),
+                        'pred_x0': pred_x0.clone(),
+                        'step': i + 1,
+                    }
+                    if handoff_callback is not None:
+                        handoff_callback(handoff)
+                    if stop_at_handoff:
+                        intermediates['handoff'] = handoff
+                        break
 
+                if index % log_every_t == 0 or index == total_steps - 1:
+                    intermediates['x_inter'].append(img)
+                    intermediates['pred_x0'].append(pred_x0)
+                    intermediates['x_inter_action'].append(action)
+                    intermediates['x_inter_state'].append(state)
+        finally:
+            disable_cross_attn_kv_cache(self.model)
+            disable_ctx_cache(self.model)
+
+        if handoff_step > 0:
+            intermediates['handoff'] = handoff
         return img, action, state, intermediates
 
     @torch.no_grad()
@@ -309,6 +336,7 @@ class DDIMSampler(object):
                       c,
                       t,
                       index,
+                      precision=None,
                       repeat_noise=False,
                       use_original_steps=False,
                       quantize_denoised=False,
@@ -325,42 +353,41 @@ class DDIMSampler(object):
                       guidance_rescale=0.0,
                       **kwargs):
         b, *_, device = *x.shape, x.device
-        if x.dim() == 5:
-            is_video = True
-        else:
-            is_video = False
 
-        if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
-            model_output, model_output_action, model_output_state = self.model.apply_model(
-                x, x_action, x_state, t, c, **kwargs)  # unet denoiser
-        else:
-            # do_classifier_free_guidance
-            if isinstance(c, torch.Tensor) or isinstance(c, dict):
-                e_t_cond, e_t_cond_action, e_t_cond_state = self.model.apply_model(
-                    x, x_action, x_state, t, c, **kwargs)
-                e_t_uncond, e_t_uncond_action, e_t_uncond_state = self.model.apply_model(
-                    x, x_action, x_state, t, unconditional_conditioning,
-                    **kwargs)
+        use_autocast = precision == 16 and device.type == 'cuda'
+        with torch.cuda.amp.autocast(enabled=use_autocast,
+                                     dtype=torch.float16):
+            if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
+                model_output, model_output_action, model_output_state = self.model.apply_model(
+                    x, x_action, x_state, t, c, **kwargs)  # unet denoiser
             else:
-                raise NotImplementedError
-            model_output = e_t_uncond + unconditional_guidance_scale * (
-                e_t_cond - e_t_uncond)
-            model_output_action = e_t_uncond_action + unconditional_guidance_scale * (
-                e_t_cond_action - e_t_uncond_action)
-            model_output_state = e_t_uncond_state + unconditional_guidance_scale * (
-                e_t_cond_state - e_t_uncond_state)
+                # do_classifier_free_guidance
+                if isinstance(c, torch.Tensor) or isinstance(c, dict):
+                    e_t_cond, e_t_cond_action, e_t_cond_state = self.model.apply_model(
+                        x, x_action, x_state, t, c, **kwargs)
+                    e_t_uncond, e_t_uncond_action, e_t_uncond_state = self.model.apply_model(
+                        x, x_action, x_state, t, unconditional_conditioning,
+                        **kwargs)
+                else:
+                    raise NotImplementedError
+                model_output = e_t_uncond + unconditional_guidance_scale * (
+                    e_t_cond - e_t_uncond)
+                model_output_action = e_t_uncond_action + unconditional_guidance_scale * (
+                    e_t_cond_action - e_t_uncond_action)
+                model_output_state = e_t_uncond_state + unconditional_guidance_scale * (
+                    e_t_cond_state - e_t_uncond_state)
 
-            if guidance_rescale > 0.0:
-                model_output = rescale_noise_cfg(
-                    model_output, e_t_cond, guidance_rescale=guidance_rescale)
-                model_output_action = rescale_noise_cfg(
-                    model_output_action,
-                    e_t_cond_action,
-                    guidance_rescale=guidance_rescale)
-                model_output_state = rescale_noise_cfg(
-                    model_output_state,
-                    e_t_cond_state,
-                    guidance_rescale=guidance_rescale)
+                if guidance_rescale > 0.0:
+                    model_output = rescale_noise_cfg(
+                        model_output, e_t_cond, guidance_rescale=guidance_rescale)
+                    model_output_action = rescale_noise_cfg(
+                        model_output_action,
+                        e_t_cond_action,
+                        guidance_rescale=guidance_rescale)
+                    model_output_state = rescale_noise_cfg(
+                        model_output_state,
+                        e_t_cond_state,
+                        guidance_rescale=guidance_rescale)
 
         if self.model.parameterization == "v":
             e_t = self.model.predict_eps_from_z_and_v(x, t, model_output)
@@ -377,17 +404,11 @@ class DDIMSampler(object):
         sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
         sigmas = self.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
 
-        if is_video:
-            size = (b, 1, 1, 1, 1)
-        else:
-            size = (b, 1, 1, 1)
-
-        a_t = torch.full(size, alphas[index], device=device)
-        a_prev = torch.full(size, alphas_prev[index], device=device)
-        sigma_t = torch.full(size, sigmas[index], device=device)
-        sqrt_one_minus_at = torch.full(size,
-                                       sqrt_one_minus_alphas[index],
-                                       device=device)
+        # Use 0-d tensors directly (already on device); broadcasting handles shape
+        a_t = alphas[index]
+        a_prev = alphas_prev[index]
+        sigma_t = sigmas[index]
+        sqrt_one_minus_at = sqrt_one_minus_alphas[index]
 
         if self.model.parameterization != "v":
             pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
@@ -395,12 +416,8 @@ class DDIMSampler(object):
             pred_x0 = self.model.predict_start_from_z_and_v(x, t, model_output)
 
         if self.model.use_dynamic_rescale:
-            scale_t = torch.full(size,
-                                 self.ddim_scale_arr[index],
-                                 device=device)
-            prev_scale_t = torch.full(size,
-                                      self.ddim_scale_arr_prev[index],
-                                      device=device)
+            scale_t = self.ddim_scale_arr[index]
+            prev_scale_t = self.ddim_scale_arr_prev[index]
             rescale = (prev_scale_t / scale_t)
             pred_x0 *= rescale
 

src/unifolm_wma/modules/networks/wma_model.py

@@ -685,6 +685,37 @@ class WMAModel(nn.Module):
         self.action_token_projector = instantiate_from_config(
             stem_process_config)
 
+        # Context precomputation cache
+        self._ctx_cache_enabled = False
+        self._ctx_cache = {}
+        self._trt_backbone = None  # TRT engine for video UNet backbone
+        # Reusable CUDA stream for parallel state_unet / action_unet
+        self._state_stream = torch.cuda.Stream()
+
+    def __getstate__(self):
+        state = self.__dict__.copy()
+        state.pop('_state_stream', None)
+        return state
+
+    def __setstate__(self, state):
+        self.__dict__.update(state)
+        if not hasattr(self, '_ctx_cache_enabled'):
+            self._ctx_cache_enabled = False
+        if not hasattr(self, '_ctx_cache'):
+            self._ctx_cache = {}
+        if not hasattr(self, '_trt_backbone'):
+            self._trt_backbone = None
+        self._state_stream = torch.cuda.Stream()
+
+    def load_trt_backbone(self, engine_path, n_hs_a=9):
+        """Load a TensorRT engine for the video UNet backbone."""
+        from unifolm_wma.trt_utils import TRTBackbone
+        device = next(self.parameters()).device
+        self._trt_backbone = TRTBackbone(engine_path,
+                                         n_hs_a=n_hs_a,
+                                         device=device)
+        print(f">>> TRT backbone loaded from {engine_path} on {device}")
+
     def forward(self,
                 x: Tensor,
                 x_action: Tensor,
@@ -713,65 +744,70 @@ class WMAModel(nn.Module):
             Tuple of Tensors for predictions:
 
         """
-
         b, _, t, _, _ = x.shape
         t_emb = timestep_embedding(timesteps,
                                    self.model_channels,
                                    repeat_only=False).type(x.dtype)
         emb = self.time_embed(t_emb)
 
-        bt, l_context, _ = context.shape
-        if self.base_model_gen_only:
-            assert l_context == 77 + self.n_obs_steps * 16, ">>> ERROR Context dim 1 ..."  ## NOTE HANDCODE
+        _ctx_key = context.data_ptr()
+        if self._ctx_cache_enabled and _ctx_key in self._ctx_cache:
+            context = self._ctx_cache[_ctx_key]
         else:
-            if l_context == self.n_obs_steps + 77 + t * 16:
-                context_agent_state = context[:, :self.n_obs_steps]
-                context_text = context[:, self.n_obs_steps:self.n_obs_steps +
-                                       77, :]
-                context_img = context[:, self.n_obs_steps + 77:, :]
-                context_agent_state = context_agent_state.repeat_interleave(
-                    repeats=t, dim=0)
-                context_text = context_text.repeat_interleave(repeats=t, dim=0)
-                context_img = rearrange(context_img,
-                                        'b (t l) c -> (b t) l c',
-                                        t=t)
-                context = torch.cat(
-                    [context_agent_state, context_text, context_img], dim=1)
-            elif l_context == self.n_obs_steps + 16 + 77 + t * 16:
-                context_agent_state = context[:, :self.n_obs_steps]
-                context_agent_action = context[:, self.
-                                               n_obs_steps:self.n_obs_steps +
-                                               16, :]
-                context_agent_action = rearrange(
-                    context_agent_action.unsqueeze(2), 'b t l d -> (b t) l d')
-                context_agent_action = self.action_token_projector(
-                    context_agent_action)
-                context_agent_action = rearrange(context_agent_action,
-                                                 '(b o) l d -> b o l d',
-                                                 o=t)
-                context_agent_action = rearrange(context_agent_action,
-                                                 'b o (t l) d -> b o t l d',
-                                                 t=t)
-                context_agent_action = context_agent_action.permute(
-                    0, 2, 1, 3, 4)
-                context_agent_action = rearrange(context_agent_action,
-                                                 'b t o l d -> (b t) (o l) d')
+            bt, l_context, _ = context.shape
+            if self.base_model_gen_only:
+                assert l_context == 77 + self.n_obs_steps * 16, ">>> ERROR Context dim 1 ..."  ## NOTE HANDCODE
+            else:
+                if l_context == self.n_obs_steps + 77 + t * 16:
+                    context_agent_state = context[:, :self.n_obs_steps]
+                    context_text = context[:, self.n_obs_steps:self.n_obs_steps +
+                                           77, :]
+                    context_img = context[:, self.n_obs_steps + 77:, :]
+                    context_agent_state = context_agent_state.repeat_interleave(
+                        repeats=t, dim=0)
+                    context_text = context_text.repeat_interleave(repeats=t, dim=0)
+                    context_img = rearrange(context_img,
+                                            'b (t l) c -> (b t) l c',
+                                            t=t)
+                    context = torch.cat(
+                        [context_agent_state, context_text, context_img], dim=1)
+                elif l_context == self.n_obs_steps + 16 + 77 + t * 16:
+                    context_agent_state = context[:, :self.n_obs_steps]
+                    context_agent_action = context[:, self.
+                                                   n_obs_steps:self.n_obs_steps +
+                                                   16, :]
+                    context_agent_action = rearrange(
+                        context_agent_action.unsqueeze(2), 'b t l d -> (b t) l d')
+                    context_agent_action = self.action_token_projector(
+                        context_agent_action)
+                    context_agent_action = rearrange(context_agent_action,
+                                                     '(b o) l d -> b o l d',
+                                                     o=t)
+                    context_agent_action = rearrange(context_agent_action,
+                                                     'b o (t l) d -> b o t l d',
+                                                     t=t)
+                    context_agent_action = context_agent_action.permute(
+                        0, 2, 1, 3, 4)
+                    context_agent_action = rearrange(context_agent_action,
+                                                     'b t o l d -> (b t) (o l) d')
 
-                context_text = context[:, self.n_obs_steps +
-                                       16:self.n_obs_steps + 16 + 77, :]
-                context_text = context_text.repeat_interleave(repeats=t, dim=0)
+                    context_text = context[:, self.n_obs_steps +
+                                           16:self.n_obs_steps + 16 + 77, :]
+                    context_text = context_text.repeat_interleave(repeats=t, dim=0)
 
-                context_img = context[:, self.n_obs_steps + 16 + 77:, :]
-                context_img = rearrange(context_img,
-                                        'b (t l) c -> (b t) l c',
-                                        t=t)
-                context_agent_state = context_agent_state.repeat_interleave(
-                    repeats=t, dim=0)
-                context = torch.cat([
-                    context_agent_state, context_agent_action, context_text,
-                    context_img
-                ],
-                                    dim=1)
+                    context_img = context[:, self.n_obs_steps + 16 + 77:, :]
+                    context_img = rearrange(context_img,
+                                            'b (t l) c -> (b t) l c',
+                                            t=t)
+                    context_agent_state = context_agent_state.repeat_interleave(
+                        repeats=t, dim=0)
+                    context = torch.cat([
+                        context_agent_state, context_agent_action, context_text,
+                        context_img
+                    ],
+                                        dim=1)
+            if self._ctx_cache_enabled:
+                self._ctx_cache[_ctx_key] = context
 
         emb = emb.repeat_interleave(repeats=t, dim=0)
 
@@ -791,58 +827,95 @@ class WMAModel(nn.Module):
             fs_embed = fs_embed.repeat_interleave(repeats=t, dim=0)
             emb = emb + fs_embed
 
-        h = x.type(self.dtype)
-        adapter_idx = 0
-        hs = []
-        hs_a = []
-        for id, module in enumerate(self.input_blocks):
-            h = module(h, emb, context=context, batch_size=b)
-            if id == 0 and self.addition_attention:
-                h = self.init_attn(h, emb, context=context, batch_size=b)
-            # plug-in adapter features
-            if ((id + 1) % 3 == 0) and features_adapter is not None:
-                h = h + features_adapter[adapter_idx]
-                adapter_idx += 1
-            if id != 0:
-                if isinstance(module[0], Downsample):
+        if self._trt_backbone is not None:
+            # TRT path: run backbone via TensorRT engine
+            h_in = x.type(self.dtype).contiguous()
+            y, hs_a = self._trt_backbone(h_in, emb.contiguous(), context.contiguous())
+        else:
+            # PyTorch path: original backbone
+            h = x.type(self.dtype)
+            adapter_idx = 0
+            hs = []
+            hs_a = []
+            for id, module in enumerate(self.input_blocks):
+                h = module(h, emb, context=context, batch_size=b)
+                if id == 0 and self.addition_attention:
+                    h = self.init_attn(h, emb, context=context, batch_size=b)
+                # plug-in adapter features
+                if ((id + 1) % 3 == 0) and features_adapter is not None:
+                    h = h + features_adapter[adapter_idx]
+                    adapter_idx += 1
+                if id != 0:
+                    if isinstance(module[0], Downsample):
+                        hs_a.append(
+                            rearrange(hs[-1], '(b t) c h w -> b t c h w', b=b))
+                hs.append(h)
+            hs_a.append(rearrange(h, '(b t) c h w -> b t c h w', b=b))
+
+            if features_adapter is not None:
+                assert len(
+                    features_adapter) == adapter_idx, 'Wrong features_adapter'
+            h = self.middle_block(h, emb, context=context, batch_size=b)
+            hs_a.append(rearrange(h, '(b t) c h w -> b t c h w', b=b))
+
+            hs_out = []
+            for module in self.output_blocks:
+                h = torch.cat([h, hs.pop()], dim=1)
+                h = module(h, emb, context=context, batch_size=b)
+                if isinstance(module[-1], Upsample):
                     hs_a.append(
-                        rearrange(hs[-1], '(b t) c h w -> b t c h w', t=t))
-            hs.append(h)
-        hs_a.append(rearrange(h, '(b t) c h w -> b t c h w', t=t))
+                        rearrange(hs_out[-1], '(b t) c h w -> b t c h w', b=b))
+                hs_out.append(h)
+            h = h.type(x.dtype)
+            hs_a.append(rearrange(hs_out[-1], '(b t) c h w -> b t c h w', b=b))
 
-        if features_adapter is not None:
-            assert len(
-                features_adapter) == adapter_idx, 'Wrong features_adapter'
-        h = self.middle_block(h, emb, context=context, batch_size=b)
-        hs_a.append(rearrange(h, '(b t) c h w -> b t c h w', t=t))
-
-        hs_out = []
-        for module in self.output_blocks:
-            h = torch.cat([h, hs.pop()], dim=1)
-            h = module(h, emb, context=context, batch_size=b)
-            if isinstance(module[-1], Upsample):
-                hs_a.append(
-                    rearrange(hs_out[-1], '(b t) c h w -> b t c h w', t=t))
-            hs_out.append(h)
-        h = h.type(x.dtype)
-        hs_a.append(rearrange(hs_out[-1], '(b t) c h w -> b t c h w', t=t))
-
-        y = self.out(h)
-        y = rearrange(y, '(b t) c h w -> b c t h w', b=b)
+            y = self.out(h)
+            y = rearrange(y, '(b t) c h w -> b c t h w', b=b)
 
         if not self.base_model_gen_only:
             ba, _, _ = x_action.shape
-            a_y = self.action_unet(x_action, timesteps[:ba], hs_a,
-                                   context_action[:2], **kwargs)
-            # Predict state
-            if b > 1:
-                s_y = self.state_unet(x_state, timesteps[:ba], hs_a,
+            ts_state = timesteps[:ba] if b > 1 else timesteps
+            is_sim_mode = context_action[2] if len(context_action) > 2 else False
+
+            if is_sim_mode:
+                # WM mode: only need state_unet, skip action_unet
+                s_y = self.state_unet(x_state, ts_state, hs_a,
                                       context_action[:2], **kwargs)
+                a_y = torch.zeros_like(x_action)
             else:
-                s_y = self.state_unet(x_state, timesteps, hs_a,
-                                      context_action[:2], **kwargs)
+                # DM mode: only need action_unet, skip state_unet
+                a_y = self.action_unet(x_action, timesteps[:ba], hs_a,
+                                       context_action[:2], **kwargs)
+                s_y = torch.zeros_like(x_state)
         else:
             a_y = torch.zeros_like(x_action)
             s_y = torch.zeros_like(x_state)
 
         return y, a_y, s_y
+
+
+def enable_ctx_cache(model):
+    """Enable context precomputation cache on WMAModel and its action/state UNets."""
+    for m in model.modules():
+        if isinstance(m, WMAModel):
+            m._ctx_cache_enabled = True
+            m._ctx_cache = {}
+    # conditional_unet1d cache
+    from unifolm_wma.models.diffusion_head.conditional_unet1d import ConditionalUnet1D
+    for m in model.modules():
+        if isinstance(m, ConditionalUnet1D):
+            m._global_cond_cache_enabled = True
+            m._global_cond_cache = {}
+
+
+def disable_ctx_cache(model):
+    """Disable and clear context precomputation cache."""
+    for m in model.modules():
+        if isinstance(m, WMAModel):
+            m._ctx_cache_enabled = False
+            m._ctx_cache = {}
+    from unifolm_wma.models.diffusion_head.conditional_unet1d import ConditionalUnet1D
+    for m in model.modules():
+        if isinstance(m, ConditionalUnet1D):
+            m._global_cond_cache_enabled = False
+            m._global_cond_cache = {}

src/unifolm_wma/trt_utils.py

@@ -0,0 +1,196 @@
+"""TensorRT acceleration utilities for the video UNet backbone."""
+
+import torch
+import torch.nn as nn
+from einops import rearrange
+from unifolm_wma.modules.networks.wma_model import Downsample, Upsample
+
+
+def _normalize_cuda_device(device) -> torch.device:
+    if device is None:
+        return torch.device('cuda', torch.cuda.current_device())
+    if isinstance(device, torch.device):
+        if device.type != 'cuda':
+            raise ValueError(f"TensorRT requires a CUDA device, got {device}.")
+        return device
+    if isinstance(device, int):
+        return torch.device('cuda', device)
+    normalized = torch.device(device)
+    if normalized.type != 'cuda':
+        raise ValueError(f"TensorRT requires a CUDA device, got {normalized}.")
+    return normalized
+
+
+class VideoBackboneForExport(nn.Module):
+    """Wrapper that isolates the video UNet backbone for ONNX export.
+
+    Takes already-preprocessed inputs (after context/time embedding prep)
+    and returns y + hs_a as a flat tuple.
+    """
+
+    def __init__(self, wma_model):
+        super().__init__()
+        self.input_blocks = wma_model.input_blocks
+        self.middle_block = wma_model.middle_block
+        self.output_blocks = wma_model.output_blocks
+        self.out = wma_model.out
+        self.addition_attention = wma_model.addition_attention
+        if self.addition_attention:
+            self.init_attn = wma_model.init_attn
+        self.dtype = wma_model.dtype
+
+    def forward(self, h, emb, context):
+        t = 16
+        b = 1
+
+        hs = []
+        hs_a = []
+        h = h.type(self.dtype)
+        for id, module in enumerate(self.input_blocks):
+            h = module(h, emb, context=context, batch_size=b)
+            if id == 0 and self.addition_attention:
+                h = self.init_attn(h, emb, context=context, batch_size=b)
+            if id != 0:
+                if isinstance(module[0], Downsample):
+                    hs_a.append(rearrange(hs[-1], '(b t) c h w -> b t c h w', t=t))
+            hs.append(h)
+        hs_a.append(rearrange(h, '(b t) c h w -> b t c h w', t=t))
+
+        h = self.middle_block(h, emb, context=context, batch_size=b)
+        hs_a.append(rearrange(h, '(b t) c h w -> b t c h w', t=t))
+
+        hs_out = []
+        for module in self.output_blocks:
+            h = torch.cat([h, hs.pop()], dim=1)
+            h = module(h, emb, context=context, batch_size=b)
+            if isinstance(module[-1], Upsample):
+                hs_a.append(rearrange(hs_out[-1], '(b t) c h w -> b t c h w', t=t))
+            hs_out.append(h)
+        hs_a.append(rearrange(hs_out[-1], '(b t) c h w -> b t c h w', t=t))
+
+        y = self.out(h.type(h.dtype))
+        y = rearrange(y, '(b t) c h w -> b c t h w', b=b)
+        return (y, *hs_a)
+
+
+def export_backbone_onnx(model, save_path, context_len=95):
+    wma = model.model.diffusion_model
+    wrapper = VideoBackboneForExport(wma)
+    device = next(wma.parameters()).device
+    wrapper.eval().to(device)
+
+    for m in wrapper.modules():
+        if hasattr(m, 'checkpoint'):
+            m.checkpoint = False
+        if hasattr(m, 'use_checkpoint'):
+            m.use_checkpoint = False
+
+    import xformers.ops
+    _orig_mea = xformers.ops.memory_efficient_attention
+    def _sdpa_replacement(q, k, v, attn_bias=None, op=None, **kw):
+        return torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=attn_bias)
+    xformers.ops.memory_efficient_attention = _sdpa_replacement
+
+    BT = 16
+    emb_dim = wma.model_channels * 4
+    ctx_dim = 1024
+    in_ch = wma.in_channels
+
+    dummy_h = torch.randn(BT,
+                          in_ch,
+                          40,
+                          64,
+                          device=device,
+                          dtype=torch.float32)
+    dummy_emb = torch.randn(BT,
+                            emb_dim,
+                            device=device,
+                            dtype=torch.float32)
+    dummy_ctx = torch.randn(BT,
+                            context_len,
+                            ctx_dim,
+                            device=device,
+                            dtype=torch.float32)
+
+    with torch.no_grad():
+        outputs = wrapper(dummy_h, dummy_emb, dummy_ctx)
+        n_outputs = len(outputs)
+        print(f">>> Backbone has {n_outputs} outputs (1 y + {n_outputs-1} hs_a)")
+        for i, o in enumerate(outputs):
+            print(f"    output[{i}]: {o.shape} {o.dtype}")
+
+    output_names = ['y'] + [f'hs_a_{i}' for i in range(n_outputs - 1)]
+
+    torch.onnx.export(
+        wrapper,
+        (dummy_h, dummy_emb, dummy_ctx),
+        save_path,
+        input_names=['h', 'emb', 'context'],
+        output_names=output_names,
+        opset_version=17,
+        do_constant_folding=True,
+    )
+    print(f">>> ONNX exported to {save_path}")
+    xformers.ops.memory_efficient_attention = _orig_mea
+    return n_outputs
+
+
+class TRTBackbone:
+    """TensorRT runtime wrapper for the video UNet backbone."""
+
+    def __init__(self, engine_path, n_hs_a=9, device=None):
+        import tensorrt as trt
+
+        self.device = _normalize_cuda_device(device)
+        self.logger = trt.Logger(trt.Logger.WARNING)
+        with torch.cuda.device(self.device):
+            with open(engine_path, 'rb') as f:
+                runtime = trt.Runtime(self.logger)
+                self.engine = runtime.deserialize_cuda_engine(f.read())
+            self.context = self.engine.create_execution_context()
+        self.n_hs_a = n_hs_a
+
+        import numpy as np
+        self.output_buffers = {}
+        with torch.cuda.device(self.device):
+            for i in range(self.engine.num_io_tensors):
+                name = self.engine.get_tensor_name(i)
+                if self.engine.get_tensor_mode(name) == trt.TensorIOMode.OUTPUT:
+                    shape = self.engine.get_tensor_shape(name)
+                    np_dtype = trt.nptype(self.engine.get_tensor_dtype(name))
+                    buf = torch.empty(
+                        list(shape),
+                        dtype=torch.from_numpy(np.empty(0, dtype=np_dtype)).dtype,
+                        device=self.device)
+                    self.output_buffers[name] = buf
+                    print(
+                        f"    TRT output '{name}': {list(shape)} {buf.dtype} on {self.device}"
+                    )
+
+    def __call__(self, h, emb, context):
+        import numpy as np
+        import tensorrt as trt
+
+        bound_inputs = {}
+        with torch.cuda.device(self.device):
+            for name, tensor in [('h', h), ('emb', emb), ('context', context)]:
+                expected_dtype = trt.nptype(self.engine.get_tensor_dtype(name))
+                torch_expected = torch.from_numpy(
+                    np.empty(0, dtype=expected_dtype)).dtype
+                if tensor.device != self.device or tensor.dtype != torch_expected:
+                    tensor = tensor.to(device=self.device,
+                                       dtype=torch_expected,
+                                       non_blocking=True)
+                tensor = tensor.contiguous()
+                bound_inputs[name] = tensor
+                self.context.set_tensor_address(name, tensor.data_ptr())
+
+            for name, buf in self.output_buffers.items():
+                self.context.set_tensor_address(name, buf.data_ptr())
+
+            stream = torch.cuda.current_stream(device=self.device)
+            self.context.execute_async_v3(stream.cuda_stream)
+
+        y = self.output_buffers['y']
+        hs_a = [self.output_buffers[f'hs_a_{i}'] for i in range(self.n_hs_a)]
+        return y, hs_a

unitree_g1_pack_camera/case1/output/inference/analysis/analysis_report.md

@@ -0,0 +1,54 @@
+# DDIM Analysis Report
+
+## Analysis Config
+
+- Stability threshold: None
+- Stability window: 3
+- Mid-step window: 15-35
+
+## Dataset Overview
+
+- Unique samples: 1
+- Step rows: 1100
+- Sample rows: 2
+- Round rows: 11
+- Scenes: unitree_g1_pack_camera
+- Pass types: policy, world_model
+
+## Five Directions
+
+### Original Early Stop
+
+- `latent_first_stable_step_policy`: mean=nan, median=nan, p90=nan, p95=nan
+- `latent_first_stable_step_world_model`: mean=nan, median=nan, p90=nan, p95=nan
+- `latent_l2_vs_full50`: mean=172.5522, median=175.0850, p90=194.0716, p95=200.4957
+
+### Direction C: Action Converges First
+
+- `action_first_stable_step`: mean=nan, median=nan, p90=nan, p95=nan
+- `latent_first_stable_step`: mean=nan, median=nan, p90=nan, p95=nan
+- `action_vs_full50_95pct_step`: mean=47.2727, median=47.2727, p90=47.2727, p95=47.2727
+- `share_action_first_stable_before_latent`: nan
+- `share_action_95pct_before_latent`: nan
+
+### Direction D: Cross-Step Similarity
+
+- `latent_delta_mid_steps`: mean=0.3526, median=0.3486, p90=0.4168, p95=0.4280
+- `action_delta_mid_steps`: mean=0.0301, median=0.0287, p90=0.0431, p95=0.0455
+- `state_delta_mid_steps`: mean=0.0312, median=0.0299, p90=0.0444, p95=0.0465
+
+### Direction A: Budget Heterogeneity
+
+- `oracle_budget_action`: mean=46.8182, median=46.8182, p90=46.8182, p95=46.8182
+- `oracle_budget_state`: mean=46.5455, median=46.5455, p90=46.5455, p95=46.5455
+- `oracle_budget_action_by_scene`:
+  - `unitree_g1_pack_camera`: mean=46.8182, median=46.8182, std=0.0000
+
+### Direction B: Round Reuse
+
+- `latent_init_dist_to_prev_round`: mean=231.3224, median=234.0656, p90=246.2483, p95=251.1132
+- `action_drift_vs_prev_round`: mean=0.1699, median=0.1659, p90=0.2361, p95=0.3048
+- `round_total_time_s`: mean=136.4094, median=136.1244, p90=137.2721, p95=137.4062
+- `policy_pass_total_time_s`: mean=33.5494, median=33.5109, p90=33.7524, p95=33.8025
+- `world_model_pass_total_time_s`: mean=33.6186, median=33.5301, p90=33.9147, p95=34.0011
+

unitree_g1_pack_camera/case1/output/inference/analysis/scene_summary.csv

@@ -0,0 +1,4 @@
+scene,pass_type,pass_total_time_s,pass_total_time_s,pass_total_time_s,action_first_stable_step,action_first_stable_step,action_first_stable_step,state_first_stable_step,state_first_stable_step,state_first_stable_step,latent_first_stable_step,latent_first_stable_step,latent_first_stable_step,action_vs_full50_90pct_step,action_vs_full50_90pct_step,action_vs_full50_90pct_step,action_vs_full50_95pct_step,action_vs_full50_95pct_step,action_vs_full50_95pct_step,oracle_budget_action,oracle_budget_action,oracle_budget_action,oracle_budget_state,oracle_budget_state,oracle_budget_state,round_total_time_s,round_total_time_s,round_total_time_s,policy_pass_total_time_s,policy_pass_total_time_s,policy_pass_total_time_s,world_model_pass_total_time_s,world_model_pass_total_time_s,world_model_pass_total_time_s,latent_init_dist_to_prev_round,latent_init_dist_to_prev_round,latent_init_dist_to_prev_round,action_drift_vs_prev_round,action_drift_vs_prev_round,action_drift_vs_prev_round
+,,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std
+unitree_g1_pack_camera,policy,33.54937371340665,33.54937371340665,,,,,,,,,,,45.18181818181818,45.18181818181818,,47.27272727272727,47.27272727272727,,46.81818181818182,46.81818181818182,,46.63636363636363,46.63636363636363,,136.40941752086985,136.40941752086985,,33.54937371340665,33.54937371340665,,33.61860912496393,33.61860912496393,,231.32244110107425,231.32244110107425,,0.1698735475540161,0.1698735475540161,
+unitree_g1_pack_camera,world_model,33.61860912496393,33.61860912496393,,,,,,,,,,,45.6,45.6,,47.9,47.9,,46.81818181818182,46.81818181818182,,46.54545454545455,46.54545454545455,,136.40941752086985,136.40941752086985,,33.54937371340665,33.54937371340665,,33.61860912496393,33.61860912496393,,231.32244110107425,231.32244110107425,,0.1698735475540161,0.1698735475540161,

unitree_g1_pack_camera/case1/output/inference/analysis/step_aggregate.csv

@@ -0,0 +1,102 @@
+pass_type,step,step_time_s,step_time_s,step_time_s,latent_delta,latent_delta,latent_delta,action_delta,action_delta,action_delta,state_delta,state_delta,state_delta,action_cosine_vs_full50,action_cosine_vs_full50,action_cosine_vs_full50,state_cosine_vs_full50,state_cosine_vs_full50,state_cosine_vs_full50,latent_l2_vs_full50,latent_l2_vs_full50,latent_l2_vs_full50
+,,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std
+policy,1,0.6878367554057728,0.6880619525909424,0.0033110620523770797,1.414315397089178,1.4148871898651123,0.0020325428650545185,0.04114515435966572,0.0385311245918273,0.006729751254617576,0.03353392942385237,0.0324590355157852,0.005213655934941631,0.05104054273529482,0.0594044253230094,0.08061243367448104,0.04970239153639833,0.0510599464178085,0.0433968937741515,171.53487188165838,173.13885498046875,9.664412595735614
+policy,2,0.6476822332902388,0.6467409133911133,0.0033061949903357337,1.0219056389548562,1.0213992595672607,0.0021199227925337992,0.03117037869312541,0.0313579067587852,0.00128079844960254,0.03157321905547918,0.0311720389872789,0.001233561510136463,0.05620139464735983,0.0597818084061145,0.08012955603299711,0.04808159629729659,0.051007229834795,0.042785685991889186,170.7512831254439,172.5921630859375,8.884331795400193
+policy,3,0.6487778316844593,0.6475324630737305,0.003142656369118312,0.8511151563037526,0.8503890037536621,0.0022678079357062162,0.030918842012231953,0.0312048215419054,0.0013150623303982193,0.0314352546225894,0.0309654027223587,0.0012646352731930529,0.061267804015766474,0.0607415698468685,0.07969713662891846,0.046423285352912784,0.0509076341986656,0.042235624551005185,170.08949279785156,171.49826049804688,8.215684603325563
+policy,4,0.6487634832208807,0.6478748321533203,0.004029844748812948,0.7509499354795977,0.7515787482261658,0.002085653877243151,0.030670592392032747,0.0310523509979248,0.0013543947943310654,0.03129686889323317,0.0308115370571613,0.0012925313653133462,0.06623721884732894,0.0637962371110916,0.07931629750658403,0.04472967067902734,0.0506284832954406,0.0417506693755794,171.22090981223366,173.73974609375,9.969544995601543
+policy,5,0.6487428275021639,0.6482586860656738,0.003670227295705804,0.6813994104211981,0.6816902160644531,0.0012989316136776511,0.03042646480554879,0.0309010632336139,0.0013973579091322895,0.03115857053886756,0.030674846842885,0.0013169888755812213,0.07110742039301177,0.0669608265161514,0.07898798133724029,0.0430029126900163,0.0474095977842807,0.04133433106447461,171.7985049161044,173.38519287109375,10.522559323839568
+policy,6,0.6903667883439497,0.6897494792938232,0.004114674566720337,0.631840321150693,0.6317303776741028,0.0010384486047498446,0.017322231338105374,0.017490204423666,0.002910828001447578,0.015140305696563227,0.0140756284818053,0.0030282631265205373,0.07776769216765055,0.0715667605400085,0.07839767970195065,0.051973039961673954,0.0585936158895492,0.04022245959692242,172.16226612437856,174.35243225097656,10.048036575294242
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unitree_g1_pack_camera/case1/output/inference/round_summary.csv

@@ -0,0 +1,12 @@
+sample_id,scene,round_id,policy_pass_total_time_s,world_model_pass_total_time_s,round_total_time_s,latent_init_dist_to_prev_round,action_drift_vs_prev_round,psnr_full50
+unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,0,33.611624240875244,34.08755350112915,137.2721483707428,,,
+unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,1,33.85250473022461,33.91472029685974,137.54015183448792,214.96084594726562,0.18166154623031616,
+unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,2,33.75244617462158,33.62014126777649,137.18953275680542,240.2042236328125,0.19001662731170654,
+unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,3,33.52527594566345,33.530078172683716,136.1212797164917,206.36373901367188,0.02651798725128174,
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+unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,5,33.47249174118042,33.59989905357361,136.12437462806702,228.9165802001953,0.373574435710907,
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+unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,7,33.510878801345825,33.56129765510559,136.194073677063,245.16720581054688,0.13505703210830688,
+unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,8,33.4688138961792,33.51669669151306,136.05504417419434,255.97816467285156,0.1348034143447876,
+unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,9,33.46222972869873,33.503395318984985,136.02690505981445,243.15744018554688,0.10453599691390991,
+unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,10,33.459078788757324,33.49174499511719,135.9250843524933,222.74342346191406,0.1651395559310913,

unitree_g1_pack_camera/case1/output/inference/sample_summary.csv

@@ -0,0 +1,3 @@
+sample_id,scene,pass_type,pass_total_time_s,action_first_stable_step,state_first_stable_step,latent_first_stable_step,action_vs_full50_90pct_step,action_vs_full50_95pct_step,oracle_budget_action,oracle_budget_state,oracle_budget_latent,latent_init_dist_to_prev_round,action_drift_vs_prev_round,round_total_time_s,policy_pass_total_time_s,world_model_pass_total_time_s,psnr_full50
+unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,policy,33.54937371340665,,,,45.18181818181818,47.27272727272727,46.81818181818182,46.63636363636363,,231.32244110107422,0.16987354755401612,136.40941752086985,33.54937371340665,33.61860912496393,
+unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,world_model,33.61860912496393,,,,45.6,47.9,46.81818181818182,46.54545454545455,,231.32244110107422,0.16987354755401612,136.40941752086985,33.54937371340665,33.61860912496393,

unitree_g1_pack_camera/case1/output/sparse_8/inference/round_summary.csv

@@ -0,0 +1,12 @@
+sample_id,scene,round_id,policy_pass_total_time_s,world_model_pass_total_time_s,round_total_time_s,latent_init_dist_to_prev_round,action_drift_vs_prev_round,psnr_full50
+unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,0,35.013712882995605,34.73551678657532,143.6849582195282,,,
+unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,1,35.05562353134155,34.18100881576538,143.12280178070068,214.95654296875,0.1811339259147644,
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+unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,4,34.88056492805481,34.83683705329895,145.21864223480225,216.09486389160156,0.16485852003097534,
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+unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,10,35.301815032958984,34.37025237083435,144.55097150802612,241.2891845703125,0.16251075267791748,

unitree_g1_pack_camera/case1/output/sparse_8/inference/sample_summary.csv

@@ -0,0 +1,3 @@
+sample_id,scene,pass_type,pass_total_time_s,action_first_stable_step,state_first_stable_step,latent_first_stable_step,action_vs_full50_90pct_step,action_vs_full50_95pct_step,oracle_budget_action,oracle_budget_state,oracle_budget_latent,latent_init_dist_to_prev_round,action_drift_vs_prev_round,round_total_time_s,policy_pass_total_time_s,world_model_pass_total_time_s,psnr_full50
+unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,policy,34.943129019303754,,,,45.36363636363637,47.36363636363637,46.81818181818182,46.72727272727273,,230.12705993652344,0.17005489468574525,143.59034397385338,34.943129019303754,34.55225768956271,
+unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,world_model,34.55225768956271,,,,45.9,48.0,46.81818181818182,46.54545454545455,,230.12705993652344,0.17005489468574525,143.59034397385338,34.943129019303754,34.55225768956271,

unitree_g1_pack_camera/case1/run_world_model_interaction.sh

@@ -2,11 +2,11 @@ res_dir="unitree_g1_pack_camera/case1"
 dataset="unitree_g1_pack_camera"
 
 {
-    time CUDA_VISIBLE_DEVICES=0 python3 scripts/evaluation/world_model_interaction.py \
+    time CUDA_VISIBLE_DEVICES=0 "${PYTHON_BIN:-python}" scripts/evaluation/world_model_interaction.py \
         --seed 123 \
         --ckpt_path ckpts/unifolm_wma_dual.ckpt \
         --config configs/inference/world_model_interaction.yaml \
-        --savedir "${res_dir}/output" \
+        --savedir "${res_dir}/output/sparse_8" \
         --bs 1 --height 320 --width 512 \
         --unconditional_guidance_scale 1.0 \
         --ddim_steps 50 \
@@ -20,5 +20,10 @@ dataset="unitree_g1_pack_camera"
         --n_iter 11 \
         --timestep_spacing 'uniform_trailing' \
         --guidance_rescale 0.7 \
-        --perframe_ae
+        --perframe_ae \
+        --analysis_log_metrics \
+        --analysis_reference_steps 50 \
+        --head_schedule_steps 0 7 14 21 28 35 42 49 \
+        --head_skip_mode reuse_prediction \
+        --head_log_steps 40 43 46 47 48 49
 } 2>&1 | tee "${res_dir}/output.log"

unitree_g1_pack_camera/case2/output/inference/analysis/analysis_report.md

@@ -0,0 +1,54 @@
+# DDIM Analysis Report
+
+## Analysis Config
+
+- Stability threshold: None
+- Stability window: 3
+- Mid-step window: 15-35
+
+## Dataset Overview
+
+- Unique samples: 1
+- Step rows: 1100
+- Sample rows: 2
+- Round rows: 11
+- Scenes: unitree_g1_pack_camera
+- Pass types: policy, world_model
+
+## Five Directions
+
+### Original Early Stop
+
+- `latent_first_stable_step_policy`: mean=nan, median=nan, p90=nan, p95=nan
+- `latent_first_stable_step_world_model`: mean=nan, median=nan, p90=nan, p95=nan
+- `latent_l2_vs_full50`: mean=118.1448, median=137.2515, p90=165.2197, p95=172.7743
+
+### Direction C: Action Converges First
+
+- `action_first_stable_step`: mean=nan, median=nan, p90=nan, p95=nan
+- `latent_first_stable_step`: mean=nan, median=nan, p90=nan, p95=nan
+- `action_vs_full50_95pct_step`: mean=46.7273, median=46.7273, p90=46.7273, p95=46.7273
+- `share_action_first_stable_before_latent`: nan
+- `share_action_95pct_before_latent`: nan
+
+### Direction D: Cross-Step Similarity
+
+- `latent_delta_mid_steps`: mean=0.3529, median=0.3489, p90=0.4169, p95=0.4276
+- `action_delta_mid_steps`: mean=0.0313, median=0.0298, p90=0.0451, p95=0.0483
+- `state_delta_mid_steps`: mean=0.0310, median=0.0300, p90=0.0436, p95=0.0460
+
+### Direction A: Budget Heterogeneity
+
+- `oracle_budget_action`: mean=46.7273, median=46.7273, p90=46.7273, p95=46.7273
+- `oracle_budget_state`: mean=47.0000, median=47.0000, p90=47.0000, p95=47.0000
+- `oracle_budget_action_by_scene`:
+  - `unitree_g1_pack_camera`: mean=46.7273, median=46.7273, std=0.0000
+
+### Direction B: Round Reuse
+
+- `latent_init_dist_to_prev_round`: mean=236.4804, median=243.4704, p90=257.5054, p95=261.9766
+- `action_drift_vs_prev_round`: mean=0.2621, median=0.2435, p90=0.4256, p95=0.5909
+- `round_total_time_s`: mean=151.4911, median=151.4212, p90=152.2260, p95=152.4231
+- `policy_pass_total_time_s`: mean=74.2250, median=74.3476, p90=75.0393, p95=75.0564
+- `world_model_pass_total_time_s`: mean=74.2693, median=74.3542, p90=74.6399, p95=74.6751
+

unitree_g1_pack_camera/case2/output/inference/analysis/scene_summary.csv

@@ -0,0 +1,4 @@
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+,,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std
+unitree_g1_pack_camera,policy,74.22499192844738,74.22499192844738,,,,,,,,,,,44.90909090909091,44.90909090909091,,46.72727272727273,46.72727272727273,,46.72727272727273,46.72727272727273,,47.09090909090909,47.09090909090909,,151.4910772713748,151.4910772713748,,74.22499192844738,74.22499192844738,,74.26925277709961,74.26925277709961,,236.48041229248048,236.48041229248048,,0.2620766624808311,0.2620766624808311,
+unitree_g1_pack_camera,world_model,74.26925277709961,74.26925277709961,,,,,,,,,,,45.09090909090909,45.09090909090909,,46.81818181818182,46.81818181818182,,46.81818181818182,46.81818181818182,,47.0,47.0,,151.4910772713748,151.4910772713748,,74.22499192844738,74.22499192844738,,74.26925277709961,74.26925277709961,,236.48041229248048,236.48041229248048,,0.2620766624808311,0.2620766624808311,

unitree_g1_pack_camera/case2/output/inference/analysis/step_aggregate.csv

@@ -0,0 +1,102 @@
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+unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,1,74.23106241226196,73.55208587646484,150.9755437374115,214.81951904296875,0.24937200546264648,
+unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,2,75.07349109649658,74.42506957054138,152.62019896507263,202.9042205810547,0.24527257680892944,
+unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,3,73.82551193237305,74.7103636264801,150.684387922287,223.25729370117188,0.0750700831413269,
+unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,4,74.71211385726929,74.36838006973267,152.22601079940796,210.7346954345703,0.241740882396698,
+unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,5,73.80026388168335,74.63985013961792,151.58087468147278,266.4477844238281,0.7560845464468002,
+unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,6,74.41286873817444,74.4742693901062,151.42124199867249,239.9493865966797,0.1602541208267212,
+unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,7,74.38386011123657,74.27649450302124,151.78351974487305,247.0240936279297,0.13166594505310059,
+unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,8,73.25273704528809,74.35415315628052,150.77343583106995,256.5118408203125,0.06467467546463013,
+unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,9,74.34760189056396,73.83727645874023,151.31240582466125,256.1639709472656,0.30772316455841064,
+unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,10,73.39611744880676,74.33566379547119,150.86029887199402,246.99131774902344,0.38890862464904785,

unitree_g1_pack_camera/case2/output/inference/sample_summary.csv

@@ -0,0 +1,3 @@
+sample_id,scene,pass_type,pass_total_time_s,action_first_stable_step,state_first_stable_step,latent_first_stable_step,action_vs_full50_90pct_step,action_vs_full50_95pct_step,oracle_budget_action,oracle_budget_state,oracle_budget_latent,latent_init_dist_to_prev_round,action_drift_vs_prev_round,round_total_time_s,policy_pass_total_time_s,world_model_pass_total_time_s,psnr_full50
+unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,policy,74.22499192844738,,,,44.90909090909091,46.72727272727273,46.72727272727273,47.09090909090909,,236.48041229248048,0.26207666248083117,151.4910772713748,74.22499192844738,74.26925277709961,
+unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,world_model,74.26925277709961,,,,45.09090909090909,46.81818181818182,46.81818181818182,47.0,,236.48041229248048,0.26207666248083117,151.4910772713748,74.22499192844738,74.26925277709961,

unitree_g1_pack_camera/case2/output/sparse_8/inference/round_summary.csv

@@ -0,0 +1,12 @@
+sample_id,scene,round_id,policy_pass_total_time_s,world_model_pass_total_time_s,round_total_time_s,latent_init_dist_to_prev_round,action_drift_vs_prev_round,psnr_full50
+unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,0,34.508949756622314,34.254820823669434,140.46258544921875,,,
+unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,1,34.90244817733765,34.26566195487976,141.24445700645447,216.52996826171875,0.24411267042160034,
+unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,2,34.450573444366455,33.74520945549011,140.44232392311096,210.6576385498047,0.211828351020813,
+unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,3,34.227173805236816,34.19744801521301,139.57855439186096,231.77476501464844,0.10076236724853516,
+unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,4,34.42516374588013,33.859012603759766,140.9917664527893,196.30520629882812,0.24305355548858643,
+unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,5,34.275354862213135,34.270713329315186,143.11559915542603,270.99072265625,0.9365180283784866,
+unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,6,34.44929575920105,33.572381019592285,141.9271743297577,227.52503967285156,0.14944738149642944,
+unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,7,34.52991771697998,34.2509183883667,141.21305179595947,256.9947509765625,0.1445719599723816,
+unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,8,34.30908131599426,33.91361689567566,140.56905364990234,263.05572509765625,0.0568811297416687,
+unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,9,34.12396168708801,34.13224148750305,139.65425729751587,249.03759765625,0.11548089981079102,
+unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,10,34.32340431213379,34.3162624835968,142.47088241577148,246.99588012695312,0.5883344411849976,

unitree_g1_pack_camera/case2/output/sparse_8/inference/sample_summary.csv

@@ -0,0 +1,3 @@
+sample_id,scene,pass_type,pass_total_time_s,action_first_stable_step,state_first_stable_step,latent_first_stable_step,action_vs_full50_90pct_step,action_vs_full50_95pct_step,oracle_budget_action,oracle_budget_state,oracle_budget_latent,latent_init_dist_to_prev_round,action_drift_vs_prev_round,round_total_time_s,policy_pass_total_time_s,world_model_pass_total_time_s,psnr_full50
+unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,policy,34.41139314391396,,,,45.90909090909091,47.81818181818182,47.0,47.09090909090909,,236.98672943115236,0.279099078476429,141.06088235161522,34.41139314391396,34.07075331427834,
+unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,world_model,34.07075331427834,,,,46.27272727272727,47.77777777777778,47.0,47.09090909090909,,236.98672943115236,0.279099078476429,141.06088235161522,34.41139314391396,34.07075331427834,

unitree_g1_pack_camera/case2/run_world_model_interaction.sh

@@ -2,11 +2,11 @@ res_dir="unitree_g1_pack_camera/case2"
 dataset="unitree_g1_pack_camera"
 
 {
-    time CUDA_VISIBLE_DEVICES=0 python3 scripts/evaluation/world_model_interaction.py \
+    time CUDA_VISIBLE_DEVICES=0 "${PYTHON_BIN:-python}" scripts/evaluation/world_model_interaction.py \
         --seed 123 \
         --ckpt_path ckpts/unifolm_wma_dual.ckpt \
         --config configs/inference/world_model_interaction.yaml \
-        --savedir "${res_dir}/output" \
+        --savedir "${res_dir}/output/sparse_8" \
         --bs 1 --height 320 --width 512 \
         --unconditional_guidance_scale 1.0 \
         --ddim_steps 50 \
@@ -20,5 +20,10 @@ dataset="unitree_g1_pack_camera"
         --n_iter 11 \
         --timestep_spacing 'uniform_trailing' \
         --guidance_rescale 0.7 \
-        --perframe_ae
+        --perframe_ae \
+        --analysis_log_metrics \
+        --analysis_reference_steps 50 \
+        --head_schedule_steps 0 7 14 21 28 35 42 49 \
+        --head_skip_mode reuse_prediction \
+        --head_log_steps 40 43 46 47 48 49
 } 2>&1 | tee "${res_dir}/output.log"

unitree_g1_pack_camera/case3/backbone_profile_dense/inference/analysis/analysis_report.md

@@ -0,0 +1,54 @@
+# DDIM Analysis Report
+
+## Analysis Config
+
+- Stability threshold: None
+- Stability window: 3
+- Mid-step window: 15-35
+
+## Dataset Overview
+
+- Unique samples: 1
+- Step rows: 1100
+- Sample rows: 2
+- Round rows: 11
+- Scenes: unitree_g1_pack_camera
+- Pass types: policy, world_model
+
+## Five Directions
+
+### Original Early Stop
+
+- `latent_first_stable_step_policy`: mean=nan, median=nan, p90=nan, p95=nan
+- `latent_first_stable_step_world_model`: mean=nan, median=nan, p90=nan, p95=nan
+- `latent_l2_vs_full50`: mean=118.1510, median=137.4783, p90=165.7335, p95=172.5465
+
+### Direction C: Action Converges First
+
+- `action_first_stable_step`: mean=nan, median=nan, p90=nan, p95=nan
+- `latent_first_stable_step`: mean=nan, median=nan, p90=nan, p95=nan
+- `action_vs_full50_95pct_step`: mean=46.7273, median=46.7273, p90=46.7273, p95=46.7273
+- `share_action_first_stable_before_latent`: nan
+- `share_action_95pct_before_latent`: nan
+
+### Direction D: Cross-Step Similarity
+
+- `latent_delta_mid_steps`: mean=0.3528, median=0.3487, p90=0.4164, p95=0.4276
+- `action_delta_mid_steps`: mean=0.0317, median=0.0304, p90=0.0455, p95=0.0480
+- `state_delta_mid_steps`: mean=0.0317, median=0.0308, p90=0.0444, p95=0.0465
+
+### Direction A: Budget Heterogeneity
+
+- `oracle_budget_action`: mean=46.7273, median=46.7273, p90=46.7273, p95=46.7273
+- `oracle_budget_state`: mean=46.8182, median=46.8182, p90=46.8182, p95=46.8182
+- `oracle_budget_action_by_scene`:
+  - `unitree_g1_pack_camera`: mean=46.7273, median=46.7273, std=0.0000
+
+### Direction B: Round Reuse
+
+- `latent_init_dist_to_prev_round`: mean=226.9825, median=228.4588, p90=251.2165, p95=255.6648
+- `action_drift_vs_prev_round`: mean=0.2812, median=0.1662, p90=0.6238, p95=0.6602
+- `round_total_time_s`: mean=559.6007, median=588.8439, p90=625.1090, p95=625.4309
+- `policy_pass_total_time_s`: mean=231.2165, median=252.6857, p90=269.4816, p95=270.8949
+- `world_model_pass_total_time_s`: mean=239.7890, median=256.9119, p90=265.6770, p95=268.7066
+

unitree_g1_pack_camera/case3/backbone_profile_dense/inference/analysis/scene_summary.csv

@@ -0,0 +1,4 @@
+scene,pass_type,pass_total_time_s,pass_total_time_s,pass_total_time_s,action_first_stable_step,action_first_stable_step,action_first_stable_step,state_first_stable_step,state_first_stable_step,state_first_stable_step,latent_first_stable_step,latent_first_stable_step,latent_first_stable_step,action_vs_full50_90pct_step,action_vs_full50_90pct_step,action_vs_full50_90pct_step,action_vs_full50_95pct_step,action_vs_full50_95pct_step,action_vs_full50_95pct_step,oracle_budget_action,oracle_budget_action,oracle_budget_action,oracle_budget_state,oracle_budget_state,oracle_budget_state,round_total_time_s,round_total_time_s,round_total_time_s,policy_pass_total_time_s,policy_pass_total_time_s,policy_pass_total_time_s,world_model_pass_total_time_s,world_model_pass_total_time_s,world_model_pass_total_time_s,latent_init_dist_to_prev_round,latent_init_dist_to_prev_round,latent_init_dist_to_prev_round,action_drift_vs_prev_round,action_drift_vs_prev_round,action_drift_vs_prev_round
+,,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std
+unitree_g1_pack_camera,policy,231.21650622107765,231.21650622107765,,,,,,,,,,,44.90909090909091,44.90909090909091,,46.72727272727273,46.72727272727273,,46.72727272727273,46.72727272727273,,46.54545454545455,46.54545454545455,,559.6006835373965,559.6006835373965,,231.21650622107765,231.21650622107765,,239.78899411721665,239.78899411721665,,226.98248596191405,226.98248596191405,,0.2811613976955414,0.2811613976955414,
+unitree_g1_pack_camera,world_model,239.78899411721665,239.78899411721665,,,,,,,,,,,45.18181818181818,45.18181818181818,,46.90909090909091,46.90909090909091,,46.90909090909091,46.90909090909091,,46.81818181818182,46.81818181818182,,559.6006835373965,559.6006835373965,,231.21650622107765,231.21650622107765,,239.78899411721665,239.78899411721665,,226.98248596191405,226.98248596191405,,0.2811613976955414,0.2811613976955414,

unitree_g1_pack_camera/case3/backbone_profile_dense/inference/analysis/step_aggregate.csv

@@ -0,0 +1,102 @@
+pass_type,step,step_time_s,step_time_s,step_time_s,latent_delta,latent_delta,latent_delta,action_delta,action_delta,action_delta,state_delta,state_delta,state_delta,action_cosine_vs_full50,action_cosine_vs_full50,action_cosine_vs_full50,state_cosine_vs_full50,state_cosine_vs_full50,state_cosine_vs_full50,latent_l2_vs_full50,latent_l2_vs_full50,latent_l2_vs_full50
+,,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std
+policy,1,2.380662376230413,2.44140100479126,0.541582686726123,1.4142944487658413,1.4138203859329224,0.0021484383984422544,0.03989342092113057,0.0394162014126777,0.005337447312609944,0.03817017190158364,0.0320768542587757,0.019583009653493806,0.05996392396363342,0.052498735487461,0.060609924998752535,0.011052970520474692,0.0090840049088001,0.06042950452013075,170.6597373268821,169.39004516601562,11.4294487197575
+policy,2,4.017352884465998,4.391762018203735,1.1861475768626526,1.0213339003649624,1.0217310190200806,0.0024567828937138454,0.030183969742872463,0.0292389243841171,0.003662848656161182,0.024038863114335264,0.0234161801636219,0.0032601327954641397,0.05937811004167251,0.0544382371008396,0.060633432820552245,0.012036324648017217,0.0120233483612537,0.06094917187960682,170.7233359596946,170.29335021972656,11.105094617742372
+policy,3,4.122266856106845,4.671510457992554,1.2492590505795216,0.8495142514055426,0.8491835594177246,0.0019042913048274144,0.024150980805808797,0.0233028475195169,0.003964547134585321,0.018858069130642793,0.017807736992836,0.003598174444944763,0.062450069764798305,0.0594579800963401,0.06071553439145037,0.016193428500132116,0.0185147151350975,0.06139902092154736,169.90216341885653,168.07110595703125,11.037017541704445
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+policy,5,4.495447418906472,5.103692531585693,1.3613295706748316,0.6822635748169639,0.6824440956115723,0.001260418909258719,0.016741364432329427,0.0162334702908992,0.0016007561725265203,0.014321604574268464,0.0139080937951803,0.002077126750241558,0.07432248680429021,0.0766021013259887,0.06090986389856854,0.030568414621732418,0.0359245762228965,0.06248923823081076,168.0842548717152,167.58656311035156,9.961248376480912
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unitree_g1_pack_camera/case3/backbone_profile_dense/inference/backbone_analysis/backbone_profile_report.md

@@ -0,0 +1,30 @@
+# Backbone Block Profiling Report
+
+## Dataset Overview
+
+- Rows: 27500
+- Blocks: 25
+- Steps: 1-50
+- Pass types: policy, world_model
+
+## Block Timing
+
+- `forward_time_ms`: mean=29.5324, median=33.0049, p90=53.1777
+
+## Stability
+
+- `rel_l2_delta_vs_prev`: mean=0.144351, median=0.122058, p90=0.238142
+- `cosine_vs_prev`: mean=0.986104, median=0.993058, p90=0.998209
+
+## Top Cache Candidates
+
+- `output_5` (output_blocks): tail_rel_l2=0.047653, mean_forward_time_ms=40.3060
+- `output_4` (output_blocks): tail_rel_l2=0.055735, mean_forward_time_ms=38.0808
+- `output_3` (output_blocks): tail_rel_l2=0.059179, mean_forward_time_ms=38.4340
+- `output_2` (output_blocks): tail_rel_l2=0.059520, mean_forward_time_ms=4.2037
+- `output_1` (output_blocks): tail_rel_l2=0.069876, mean_forward_time_ms=3.3372
+- `output_0` (output_blocks): tail_rel_l2=0.071423, mean_forward_time_ms=3.4297
+- `output_6` (output_blocks): tail_rel_l2=0.074268, mean_forward_time_ms=40.6642
+- `middle` (middle_block): tail_rel_l2=0.075425, mean_forward_time_ms=24.7022
+- `input_7` (input_blocks): tail_rel_l2=0.075788, mean_forward_time_ms=38.4152
+- `input_11` (input_blocks): tail_rel_l2=0.077308, mean_forward_time_ms=2.5828

unitree_g1_pack_camera/case3/backbone_profile_dense/inference/backbone_analysis/block_summary.csv

@@ -0,0 +1,26 @@
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unitree_g1_pack_camera/case3/backbone_profile_dense/inference/backbone_analysis/stage_summary.csv

@@ -0,0 +1,10 @@
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unitree_g1_pack_camera/case3/backbone_profile_dense/inference/round_summary.csv

@@ -0,0 +1,12 @@
+sample_id,scene,round_id,policy_pass_total_time_s,world_model_pass_total_time_s,round_total_time_s,latent_init_dist_to_prev_round,action_drift_vs_prev_round,psnr_full50
+unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,0,272.30813097953796,258.77083349227905,625.7526919841766,,,
+unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,1,186.9196274280548,263.2243719100952,540.1022210121155,221.82896423339844,0.33534055948257446,
+unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,2,244.72048449516296,254.23945784568787,588.8439288139343,235.9840545654297,0.1256718635559082,
+unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,3,263.5875928401947,254.7561550140381,607.7824947834015,199.4680633544922,0.18907040357589722,
+unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,4,217.18552041053772,256.91192841529846,562.6434659957886,260.11322021484375,0.6156945526599884,
+unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,5,252.68572807312012,261.74088191986084,602.5384485721588,190.06353759765625,0.43064773082733154,
+unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,6,255.90784645080566,249.05019426345825,593.9222755432129,228.24305725097656,0.09470903873443604,
+unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,7,226.4355342388153,271.73611545562744,585.1254336833954,228.67453002929688,0.14335262775421143,
+unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,8,269.48164653778076,265.67703223228455,625.1090137958527,250.22793579101562,0.6966221630573273,
+unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,9,259.6198160648346,210.60337662696838,558.2484686374664,248.954345703125,0.14137792587280273,
+unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,10,94.52964091300964,90.96858811378479,265.539076089859,206.26715087890625,0.03912711143493652,

unitree_g1_pack_camera/case3/backbone_profile_dense/inference/sample_summary.csv

@@ -0,0 +1,3 @@
+sample_id,scene,pass_type,pass_total_time_s,action_first_stable_step,state_first_stable_step,latent_first_stable_step,action_vs_full50_90pct_step,action_vs_full50_95pct_step,oracle_budget_action,oracle_budget_state,oracle_budget_latent,latent_init_dist_to_prev_round,action_drift_vs_prev_round,round_total_time_s,policy_pass_total_time_s,world_model_pass_total_time_s,psnr_full50
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unitree_g1_pack_camera/case3/output/inference/analysis/analysis_report.md

@@ -0,0 +1,54 @@
+# DDIM Analysis Report
+
+## Analysis Config
+
+- Stability threshold: None
+- Stability window: 3
+- Mid-step window: 15-35
+
+## Dataset Overview
+
+- Unique samples: 1
+- Step rows: 1100
+- Sample rows: 2
+- Round rows: 11
+- Scenes: unitree_g1_pack_camera
+- Pass types: policy, world_model
+
+## Five Directions
+
+### Original Early Stop
+
+- `latent_first_stable_step_policy`: mean=nan, median=nan, p90=nan, p95=nan
+- `latent_first_stable_step_world_model`: mean=nan, median=nan, p90=nan, p95=nan
+- `latent_l2_vs_full50`: mean=116.0635, median=135.1986, p90=161.0852, p95=169.8322
+
+### Direction C: Action Converges First
+
+- `action_first_stable_step`: mean=nan, median=nan, p90=nan, p95=nan
+- `latent_first_stable_step`: mean=nan, median=nan, p90=nan, p95=nan
+- `action_vs_full50_95pct_step`: mean=46.6364, median=46.6364, p90=46.6364, p95=46.6364
+- `share_action_first_stable_before_latent`: nan
+- `share_action_95pct_before_latent`: nan
+
+### Direction D: Cross-Step Similarity
+
+- `latent_delta_mid_steps`: mean=0.3529, median=0.3489, p90=0.4169, p95=0.4276
+- `action_delta_mid_steps`: mean=0.0320, median=0.0307, p90=0.0455, p95=0.0488
+- `state_delta_mid_steps`: mean=0.0312, median=0.0301, p90=0.0439, p95=0.0465
+
+### Direction A: Budget Heterogeneity
+
+- `oracle_budget_action`: mean=46.6364, median=46.6364, p90=46.6364, p95=46.6364
+- `oracle_budget_state`: mean=46.7273, median=46.7273, p90=46.7273, p95=46.7273
+- `oracle_budget_action_by_scene`:
+  - `unitree_g1_pack_camera`: mean=46.6364, median=46.6364, std=0.0000
+
+### Direction B: Round Reuse
+
+- `latent_init_dist_to_prev_round`: mean=215.2431, median=230.0003, p90=237.2570, p95=239.0189
+- `action_drift_vs_prev_round`: mean=0.2697, median=0.2050, p90=0.4657, p95=0.6923
+- `round_total_time_s`: mean=147.1026, median=152.2600, p90=152.9385, p95=152.9569
+- `policy_pass_total_time_s`: mean=71.2595, median=74.7608, p90=74.9093, p95=75.1472
+- `world_model_pass_total_time_s`: mean=73.1823, median=74.7396, p90=74.9509, p95=74.9599
+

unitree_g1_pack_camera/case3/output/inference/analysis/scene_summary.csv

@@ -0,0 +1,4 @@
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+,,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std
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unitree_g1_pack_camera/case3/output/inference/analysis/step_aggregate.csv

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+world_model,44,1.3944376165216619,1.4681918621063232,0.23179871564208238,0.1918082237243652,0.1917132884263992,0.0011266145758682539,0.07411486790938808,0.0744659677147865,0.0053340624463093615,0.06928501210429448,0.0674033015966415,0.005014592152386143,0.8842862573536959,0.8876720070838928,0.014797954371457913,0.8867509202523665,0.8930128812789917,0.01875954773917384,47.76955067027699,47.98651123046875,2.1773311004029763
+world_model,45,1.3962215943769976,1.4676592350006104,0.2336440161230607,0.18133696642788968,0.1811342984437942,0.0011126272096607466,0.07610171288251871,0.0750593394041061,0.0052231679024188175,0.07038058814677318,0.0686565786600112,0.0052372791660646265,0.9166603738611395,0.9177016615867616,0.010872336039513642,0.9161591854962435,0.921278953552246,0.014550500550090738,41.6348710493608,41.83378601074219,2.0061604610575925
+world_model,46,1.3959437933835117,1.4677042961120603,0.2334022765221959,0.1709649183533408,0.170794740319252,0.0009963828332423168,0.07662165910005567,0.0747245773673057,0.005026059803525407,0.0733391405506567,0.0711352825164794,0.0056793837645177404,0.9443203156644647,0.9427329897880554,0.007506113742280097,0.9426048723134127,0.946301519870758,0.010402146317958022,35.394454609264024,35.55489730834961,1.8793098762078355
+world_model,47,1.3919809081337668,1.4672982692718506,0.22987843257648233,0.16032402759248557,0.1601537615060806,0.001161449976876946,0.07885337891903786,0.0767847225069999,0.00498972098124497,0.07695143127983263,0.0743001103401184,0.006415993376916115,0.9672025279565291,0.9668025970458984,0.0046594976464106605,0.9653134400194342,0.9674853682518004,0.006501365069714958,29.084233544089578,29.25230026245117,1.7000212458647328
+world_model,48,1.3910502953962847,1.4627628326416016,0.2319554021951319,0.14953668144616208,0.1492391377687454,0.0010335816728866416,0.08014944602142676,0.0796511098742485,0.005092453726361779,0.07987065748734902,0.0776736214756965,0.0069555070170309605,0.9843516566536643,0.9842669367790222,0.002386530317992733,0.9830300320278514,0.9840595722198486,0.0032879741632763707,22.524729295210406,22.64668846130371,1.3865496963055841
+world_model,49,1.394185109571977,1.4651310443878174,0.23014187168674302,0.13735197755423456,0.1375972926616668,0.001190005738145348,0.0817304903810674,0.0838558226823806,0.005614900667317128,0.0799507004293528,0.0789465606212616,0.00683753993571891,0.9953602010553534,0.9953088164329528,0.0007703843441456976,0.99452528628436,0.9947948455810548,0.0011420244573810449,15.268142700195312,15.346698760986328,0.95061216057606
+world_model,50,1.3988349654457786,1.4692156314849854,0.2322583024738879,0.11424645578319371,0.1143975555896759,0.0020879029661737196,0.0990189205516468,0.1001605242490768,0.007705735583356467,0.10550027137452901,0.1046335324645042,0.010376522856520558,0.9999999891627919,1.0,6.429670929923101e-08,0.9999999729069796,1.0,7.233379796249756e-08,0.0,0.0,0.0

unitree_g1_pack_camera/case3/output/inference/round_summary.csv

@@ -0,0 +1,12 @@
+sample_id,scene,round_id,policy_pass_total_time_s,world_model_pass_total_time_s,round_total_time_s,latent_init_dist_to_prev_round,action_drift_vs_prev_round,psnr_full50
+unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,0,75.38506054878235,74.50816583633423,151.84783244132996,,,
+unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,1,74.49218130111694,74.62240505218506,152.25997352600098,224.97146606445312,0.2796536087989807,
+unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,2,74.69758439064026,74.56192207336426,151.35133409500122,228.21055603027344,0.15541577339172363,
+unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,3,74.7608425617218,74.91416335105896,152.6145670413971,236.63555908203125,0.25457870960235596,
+unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,4,74.69965195655823,74.96893620491028,151.8847451210022,231.79010009765625,0.918834924697876,
+unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,5,74.81953477859497,74.73957371711731,152.68081712722778,198.5364227294922,0.08309799432754517,
+unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,6,74.90933179855347,74.88512587547302,152.9385278224945,231.7943572998047,0.11995601654052734,
+unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,7,74.84911894798279,74.95093822479248,152.97526359558105,236.86546325683594,0.39963626861572266,
+unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,8,74.88821697235107,67.77415251731873,145.36549997329712,240.78086853027344,0.4154035449028015,
+unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,9,35.5922589302063,64.26668500900269,101.50574612617493,198.4535675048828,0.06449002027511597,
+unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,10,74.76077079772949,74.81333827972412,152.70444345474243,124.39263153076172,0.005869686603546143,

unitree_g1_pack_camera/case3/output/inference/sample_summary.csv

@@ -0,0 +1,3 @@
+sample_id,scene,pass_type,pass_total_time_s,action_first_stable_step,state_first_stable_step,latent_first_stable_step,action_vs_full50_90pct_step,action_vs_full50_95pct_step,oracle_budget_action,oracle_budget_state,oracle_budget_latent,latent_init_dist_to_prev_round,action_drift_vs_prev_round,round_total_time_s,policy_pass_total_time_s,world_model_pass_total_time_s,psnr_full50
+unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,policy,71.25950481674887,,,,44.72727272727273,46.63636363636363,46.63636363636363,46.72727272727273,,215.24309921264648,0.2696936547756195,147.10261366584083,71.25950481674887,73.18230964920737,
+unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,world_model,73.18230964920737,,,,45.0,46.72727272727273,46.72727272727273,46.72727272727273,,215.24309921264648,0.2696936547756195,147.10261366584083,71.25950481674887,73.18230964920737,

unitree_g1_pack_camera/case3/output/sparse_8/inference/round_summary.csv

@@ -0,0 +1,12 @@
+sample_id,scene,round_id,policy_pass_total_time_s,world_model_pass_total_time_s,round_total_time_s,latent_init_dist_to_prev_round,action_drift_vs_prev_round,psnr_full50
+unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,0,34.73047733306885,34.12665319442749,143.00190567970276,,,
+unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,1,34.77882504463196,35.024171590805054,142.87922620773315,238.14430236816406,0.34216785430908203,
+unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,2,34.65710210800171,34.31918025016785,141.75025510787964,244.6288299560547,0.10849124193191528,
+unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,3,34.64808392524719,34.763160705566406,142.0598931312561,250.288330078125,0.4585834741592407,
+unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,4,34.43521761894226,34.34122323989868,141.00554966926575,224.81471252441406,0.6429577469825745,
+unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,5,34.357038497924805,34.85403919219971,141.1495542526245,195.2209014892578,0.1021263599395752,
+unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,6,35.0233428478241,34.67922353744507,144.0218186378479,226.24966430664062,0.08848029375076294,
+unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,7,34.45149564743042,34.6253707408905,143.35860419273376,245.75660705566406,0.17197155952453613,
+unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,8,34.7300283908844,34.399852991104126,143.3491985797882,238.90292358398438,0.6555709540843964,
+unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,9,34.11410880088806,34.445759534835815,142.07709193229675,226.96424865722656,0.0632086992263794,
+unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,10,34.794456481933594,34.530436992645264,141.20812225341797,223.38699340820312,0.012062489986419678,

unitree_g1_pack_camera/case3/output/sparse_8/inference/sample_summary.csv

@@ -0,0 +1,3 @@
+sample_id,scene,pass_type,pass_total_time_s,action_first_stable_step,state_first_stable_step,latent_first_stable_step,action_vs_full50_90pct_step,action_vs_full50_95pct_step,oracle_budget_action,oracle_budget_state,oracle_budget_latent,latent_init_dist_to_prev_round,action_drift_vs_prev_round,round_total_time_s,policy_pass_total_time_s,world_model_pass_total_time_s,psnr_full50
+unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,policy,34.610925154252485,,,,45.54545454545455,47.45454545454545,46.72727272727273,46.72727272727273,,231.43575134277344,0.26456206738948823,142.35101996768606,34.610925154252485,34.555370179089635,
+unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,world_model,34.555370179089635,,,,45.90909090909091,48.0,46.90909090909091,46.72727272727273,,231.43575134277344,0.26456206738948823,142.35101996768606,34.610925154252485,34.555370179089635,

unitree_g1_pack_camera/case3/run_world_model_backbone_profile.sh

@@ -0,0 +1,11 @@
+#!/usr/bin/env bash
+
+set -euo pipefail
+
+repo_root="$(cd "$(dirname "${BASH_SOURCE[0]}")/../.." && pwd)"
+CUDA_VISIBLE_DEVICES="${CUDA_VISIBLE_DEVICES:-1}" \
+bash "${repo_root}/scripts/run_world_model_backbone_profile_case.sh" \
+    "unitree_g1_pack_camera/case3" \
+    "unitree_g1_pack_camera" \
+    "6" \
+    "11"

unitree_g1_pack_camera/case3/run_world_model_interaction.sh

@@ -2,11 +2,11 @@ res_dir="unitree_g1_pack_camera/case3"
 dataset="unitree_g1_pack_camera"
 
 {
-    time CUDA_VISIBLE_DEVICES=0 python3 scripts/evaluation/world_model_interaction.py \
+    time CUDA_VISIBLE_DEVICES=0 "${PYTHON_BIN:-python}" scripts/evaluation/world_model_interaction.py \
         --seed 123 \
         --ckpt_path ckpts/unifolm_wma_dual.ckpt \
         --config configs/inference/world_model_interaction.yaml \
-        --savedir "${res_dir}/output" \
+        --savedir "${res_dir}/output/sparse_8" \
         --bs 1 --height 320 --width 512 \
         --unconditional_guidance_scale 1.0 \
         --ddim_steps 50 \
@@ -20,5 +20,10 @@ dataset="unitree_g1_pack_camera"
         --n_iter 11 \
         --timestep_spacing 'uniform_trailing' \
         --guidance_rescale 0.7 \
-        --perframe_ae
+        --perframe_ae \
+        --analysis_log_metrics \
+        --analysis_reference_steps 50 \
+        --head_schedule_steps 0 7 14 21 28 35 42 49 \
+        --head_skip_mode reuse_prediction \
+        --head_log_steps 40 43 46 47 48 49
 } 2>&1 | tee "${res_dir}/output.log"

unitree_g1_pack_camera/case4/output/inference/analysis/analysis_report.md

@@ -0,0 +1,54 @@
+# DDIM Analysis Report
+
+## Analysis Config
+
+- Stability threshold: None
+- Stability window: 3
+- Mid-step window: 15-35
+
+## Dataset Overview
+
+- Unique samples: 1
+- Step rows: 1100
+- Sample rows: 2
+- Round rows: 11
+- Scenes: unitree_g1_pack_camera
+- Pass types: policy, world_model
+
+## Five Directions
+
+### Original Early Stop
+
+- `latent_first_stable_step_policy`: mean=nan, median=nan, p90=nan, p95=nan
+- `latent_first_stable_step_world_model`: mean=nan, median=nan, p90=nan, p95=nan
+- `latent_l2_vs_full50`: mean=123.5010, median=143.1178, p90=173.0218, p95=183.1080
+
+### Direction C: Action Converges First
+
+- `action_first_stable_step`: mean=nan, median=nan, p90=nan, p95=nan
+- `latent_first_stable_step`: mean=nan, median=nan, p90=nan, p95=nan
+- `action_vs_full50_95pct_step`: mean=46.6364, median=46.6364, p90=46.6364, p95=46.6364
+- `share_action_first_stable_before_latent`: nan
+- `share_action_95pct_before_latent`: nan
+
+### Direction D: Cross-Step Similarity
+
+- `latent_delta_mid_steps`: mean=0.3512, median=0.3474, p90=0.4163, p95=0.4272
+- `action_delta_mid_steps`: mean=0.0318, median=0.0302, p90=0.0453, p95=0.0481
+- `state_delta_mid_steps`: mean=0.0313, median=0.0303, p90=0.0440, p95=0.0461
+
+### Direction A: Budget Heterogeneity
+
+- `oracle_budget_action`: mean=46.6364, median=46.6364, p90=46.6364, p95=46.6364
+- `oracle_budget_state`: mean=46.5455, median=46.5455, p90=46.5455, p95=46.5455
+- `oracle_budget_action_by_scene`:
+  - `unitree_g1_pack_camera`: mean=46.6364, median=46.6364, std=0.0000
+
+### Direction B: Round Reuse
+
+- `latent_init_dist_to_prev_round`: mean=231.5603, median=231.6096, p90=244.0158, p95=252.6133
+- `action_drift_vs_prev_round`: mean=0.2541, median=0.1514, p90=0.5899, p95=0.6868
+- `round_total_time_s`: mean=149.5150, median=149.4089, p90=150.2778, p95=150.4611
+- `policy_pass_total_time_s`: mean=73.3453, median=73.5693, p90=73.9486, p95=74.0089
+- `world_model_pass_total_time_s`: mean=73.2888, median=73.4965, p90=73.8629, p95=74.1038
+

unitree_g1_pack_camera/case4/output/inference/analysis/scene_summary.csv

@@ -0,0 +1,4 @@
+scene,pass_type,pass_total_time_s,pass_total_time_s,pass_total_time_s,action_first_stable_step,action_first_stable_step,action_first_stable_step,state_first_stable_step,state_first_stable_step,state_first_stable_step,latent_first_stable_step,latent_first_stable_step,latent_first_stable_step,action_vs_full50_90pct_step,action_vs_full50_90pct_step,action_vs_full50_90pct_step,action_vs_full50_95pct_step,action_vs_full50_95pct_step,action_vs_full50_95pct_step,oracle_budget_action,oracle_budget_action,oracle_budget_action,oracle_budget_state,oracle_budget_state,oracle_budget_state,round_total_time_s,round_total_time_s,round_total_time_s,policy_pass_total_time_s,policy_pass_total_time_s,policy_pass_total_time_s,world_model_pass_total_time_s,world_model_pass_total_time_s,world_model_pass_total_time_s,latent_init_dist_to_prev_round,latent_init_dist_to_prev_round,latent_init_dist_to_prev_round,action_drift_vs_prev_round,action_drift_vs_prev_round,action_drift_vs_prev_round
+,,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std
+unitree_g1_pack_camera,policy,73.34526510672136,73.34526510672136,,,,,,,,,,,44.54545454545455,44.54545454545455,,46.63636363636363,46.63636363636363,,46.63636363636363,46.63636363636363,,46.81818181818182,46.81818181818182,,149.51496249979192,149.51496249979192,,73.34526510672136,73.34526510672136,,73.28878194635564,73.28878194635564,,231.5602996826172,231.5602996826172,,0.2541187256574631,0.2541187256574631,
+unitree_g1_pack_camera,world_model,73.28878194635564,73.28878194635564,,,,,,,,,,,44.72727272727273,44.72727272727273,,46.54545454545455,46.54545454545455,,46.54545454545455,46.54545454545455,,46.54545454545455,46.54545454545455,,149.51496249979192,149.51496249979192,,73.34526510672136,73.34526510672136,,73.28878194635564,73.28878194635564,,231.5602996826172,231.5602996826172,,0.2541187256574631,0.2541187256574631,

unitree_g1_pack_camera/case4/output/inference/analysis/step_aggregate.csv

@@ -0,0 +1,102 @@
+pass_type,step,step_time_s,step_time_s,step_time_s,latent_delta,latent_delta,latent_delta,action_delta,action_delta,action_delta,state_delta,state_delta,state_delta,action_cosine_vs_full50,action_cosine_vs_full50,action_cosine_vs_full50,state_cosine_vs_full50,state_cosine_vs_full50,state_cosine_vs_full50,latent_l2_vs_full50,latent_l2_vs_full50,latent_l2_vs_full50
+,,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std
+policy,1,1.4640577706423672,1.4665131568908691,0.08630888295557096,1.4148840795863757,1.414720058441162,0.0017993882006632994,0.04190375960685985,0.0391346029937267,0.008968017415382092,0.034809943796558766,0.0358276963233947,0.0055891172503613204,0.07068525026129048,0.1094346269965171,0.09493404684793554,0.05545064823871308,0.0559474006295204,0.07515747173465141,179.16388633034447,178.22361755371094,12.54116874782092
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unitree_g1_pack_camera/case4/output/sparse_8/inference/sample_summary.csv

@@ -0,0 +1,3 @@
+sample_id,scene,pass_type,pass_total_time_s,action_first_stable_step,state_first_stable_step,latent_first_stable_step,action_vs_full50_90pct_step,action_vs_full50_95pct_step,oracle_budget_action,oracle_budget_state,oracle_budget_latent,latent_init_dist_to_prev_round,action_drift_vs_prev_round,round_total_time_s,policy_pass_total_time_s,world_model_pass_total_time_s,psnr_full50
+unitree_g1_pack_camera-vid200-fs6,unitree_g1_pack_camera,policy,34.51030624996532,,,,45.81818181818182,47.6,46.63636363636363,46.72727272727273,,243.6261001586914,0.25435117483139036,141.94035014239225,34.51030624996532,34.607211329720236,
+unitree_g1_pack_camera-vid200-fs6,unitree_g1_pack_camera,world_model,34.607211329720236,,,,46.36363636363637,47.77777777777778,46.81818181818182,46.81818181818182,,243.6261001586914,0.25435117483139036,141.94035014239225,34.51030624996532,34.607211329720236,

unitree_g1_pack_camera/case4/run_world_model_interaction.sh

@@ -2,11 +2,11 @@ res_dir="unitree_g1_pack_camera/case4"
 dataset="unitree_g1_pack_camera"
 
 {
-    time CUDA_VISIBLE_DEVICES=0 python3 scripts/evaluation/world_model_interaction.py \
+    time CUDA_VISIBLE_DEVICES=0 "${PYTHON_BIN:-python}" scripts/evaluation/world_model_interaction.py \
         --seed 123 \
         --ckpt_path ckpts/unifolm_wma_dual.ckpt \
         --config configs/inference/world_model_interaction.yaml \
-        --savedir "${res_dir}/output" \
+        --savedir "${res_dir}/output/sparse_8" \
         --bs 1 --height 320 --width 512 \
         --unconditional_guidance_scale 1.0 \
         --ddim_steps 50 \
@@ -20,5 +20,10 @@ dataset="unitree_g1_pack_camera"
         --n_iter 11 \
         --timestep_spacing 'uniform_trailing' \
         --guidance_rescale 0.7 \
-        --perframe_ae
+        --perframe_ae \
+        --analysis_log_metrics \
+        --analysis_reference_steps 50 \
+        --head_schedule_steps 0 7 14 21 28 35 42 49 \
+        --head_skip_mode reuse_prediction \
+        --head_log_steps 40 43 46 47 48 49
 } 2>&1 | tee "${res_dir}/output.log"

unitree_z1_dual_arm_cleanup_pencils/case1/output/inference/analysis/analysis_report.md

@@ -0,0 +1,54 @@
+# DDIM Analysis Report
+
+## Analysis Config
+
+- Stability threshold: None
+- Stability window: 3
+- Mid-step window: 15-35
+
+## Dataset Overview
+
+- Unique samples: 1
+- Step rows: 800
+- Sample rows: 2
+- Round rows: 8
+- Scenes: unitree_z1_dual_arm_cleanup_pencils
+- Pass types: policy, world_model
+
+## Five Directions
+
+### Original Early Stop
+
+- `latent_first_stable_step_policy`: mean=nan, median=nan, p90=nan, p95=nan
+- `latent_first_stable_step_world_model`: mean=nan, median=nan, p90=nan, p95=nan
+- `latent_l2_vs_full50`: mean=116.1877, median=137.0989, p90=163.0532, p95=167.3646
+
+### Direction C: Action Converges First
+
+- `action_first_stable_step`: mean=nan, median=nan, p90=nan, p95=nan
+- `latent_first_stable_step`: mean=nan, median=nan, p90=nan, p95=nan
+- `action_vs_full50_95pct_step`: mean=46.5000, median=46.5000, p90=46.5000, p95=46.5000
+- `share_action_first_stable_before_latent`: nan
+- `share_action_95pct_before_latent`: nan
+
+### Direction D: Cross-Step Similarity
+
+- `latent_delta_mid_steps`: mean=0.3542, median=0.3501, p90=0.4173, p95=0.4280
+- `action_delta_mid_steps`: mean=0.0295, median=0.0283, p90=0.0414, p95=0.0439
+- `state_delta_mid_steps`: mean=0.0287, median=0.0276, p90=0.0404, p95=0.0427
+
+### Direction A: Budget Heterogeneity
+
+- `oracle_budget_action`: mean=46.5000, median=46.5000, p90=46.5000, p95=46.5000
+- `oracle_budget_state`: mean=47.1250, median=47.1250, p90=47.1250, p95=47.1250
+- `oracle_budget_action_by_scene`:
+  - `unitree_z1_dual_arm_cleanup_pencils`: mean=46.5000, median=46.5000, std=0.0000
+
+### Direction B: Round Reuse
+
+- `latent_init_dist_to_prev_round`: mean=242.4520, median=230.2455, p90=269.5033, p95=273.0074
+- `action_drift_vs_prev_round`: mean=0.6782, median=0.6411, p90=0.9664, p95=0.9760
+- `round_total_time_s`: mean=146.9209, median=150.5785, p90=151.7969, p95=152.0286
+- `policy_pass_total_time_s`: mean=70.4979, median=73.9005, p90=74.3969, p95=74.5318
+- `world_model_pass_total_time_s`: mean=73.7692, median=74.0447, p90=74.5227, p95=74.5574
+

unitree_z1_dual_arm_cleanup_pencils/case1/output/inference/analysis/scene_summary.csv

@@ -0,0 +1,4 @@
+scene,pass_type,pass_total_time_s,pass_total_time_s,pass_total_time_s,action_first_stable_step,action_first_stable_step,action_first_stable_step,state_first_stable_step,state_first_stable_step,state_first_stable_step,latent_first_stable_step,latent_first_stable_step,latent_first_stable_step,action_vs_full50_90pct_step,action_vs_full50_90pct_step,action_vs_full50_90pct_step,action_vs_full50_95pct_step,action_vs_full50_95pct_step,action_vs_full50_95pct_step,oracle_budget_action,oracle_budget_action,oracle_budget_action,oracle_budget_state,oracle_budget_state,oracle_budget_state,round_total_time_s,round_total_time_s,round_total_time_s,policy_pass_total_time_s,policy_pass_total_time_s,policy_pass_total_time_s,world_model_pass_total_time_s,world_model_pass_total_time_s,world_model_pass_total_time_s,latent_init_dist_to_prev_round,latent_init_dist_to_prev_round,latent_init_dist_to_prev_round,action_drift_vs_prev_round,action_drift_vs_prev_round,action_drift_vs_prev_round
+,,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std
+unitree_z1_dual_arm_cleanup_pencils,policy,70.49793663620949,70.49793663620949,,,,,,,,,,,44.25,44.25,,46.5,46.5,,46.5,46.5,,47.125,47.125,,146.92093321681023,146.92093321681023,,70.49793663620949,70.49793663620949,,73.76916161179543,73.76916161179543,,242.45204162597656,242.45204162597656,,0.6781738067844084,0.6781738067844084,
+unitree_z1_dual_arm_cleanup_pencils,world_model,73.76916161179543,73.76916161179543,,,,,,,,,,,44.625,44.625,,46.5,46.5,,46.5,46.5,,47.125,47.125,,146.92093321681023,146.92093321681023,,70.49793663620949,70.49793663620949,,73.76916161179543,73.76916161179543,,242.45204162597656,242.45204162597656,,0.6781738067844084,0.6781738067844084,