Add PyTorch golden reference dumps

ref/vjepa2-ac-vitg/cem.cem_action.npy

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ref/vjepa2-ac-vitg/cem.cem_noise.npy

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ref/vjepa2-ac-vitg/cem.context.npy

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ref/vjepa2-ac-vitg/cem.goal.npy

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ref/vjepa2-ac-vitg/cem.state0.npy

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ref/vjepa2-ac-vitg/frame1.context.npy

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ref/vjepa2-ac-vitg/frame1.frames_u8.npy

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ref/vjepa2-ac-vitg/frame1.input.npy

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ref/vjepa2-ac-vitg/frame1.last_hidden_state.npy

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ref/vjepa2-ac-vitg/frame1.pooled_mean.npy

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ref/vjepa2-ac-vitg/manifest.json

@@ -7,10 +7,10 @@
   "torch": "2.13.0+cpu",
   "transformers": "5.16.1",
   "python": "3.12.13",
-  "threads": 32,
+  "threads": 16,
   "cpu": "x86_64"
  },
- "created": "2026-09-01T19:58:11+0300",
+ "created": "2026-09-01T21:43:50+0300",
  "source_url": "https://dl.fbaipublicfiles.com/vjepa2/vjepa2-ac-vitg.pt",
  "code": "https://github.com/facebookresearch/vjepa2 (hubconf vjepa2_ac_vit_giant, src/models/ac_predictor.py, notebooks/utils/mpc_utils.py + world_model_wrapper.py), local clone at /home/overseer2/workdir/jepa.cpp/tmp/vjepa2-src",
  "checkpoint": {
@@ -147,10 +147,10 @@
   "next_state_ref": "[K, 7] compute_new_pose(state0, actions[:, 0]) -- the jepa_ac_next_state check"
  },
  "timing_s": {
-  "model_load": 8.727,
-  "forward_total": 3.22,
-  "forward_mean": 0.644,
-  "wall_total": 3.257
+  "model_load": 8.863,
+  "forward_total": 21.136,
+  "forward_mean": 2.642,
+  "wall_total": 21.193
  },
  "samples": [
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    "media": "franka_example_traj.npz[0]",
    "timing_s": {
     "preprocess_s": 0.0,
-    "forward_s": 0.5169
+    "forward_s": 0.7271
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    "tensors": {
     "frames_u8": {
@@ -214,12 +214,73 @@
    "frame_index": 0,
    "frames": 2
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+  {
+   "name": "frame1",
+   "media": "franka_example_traj.npz[1]",
+   "timing_s": {
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+    "forward_s": 0.6842
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+      256,
+      3
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+     "dtype": "uint8",
+     "layout": "THWC uint8 (the observation frame, repeated to the 2-frame clip)"
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+     "file": "frame1.input.npy",
+     "shape": [
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+      3,
+      2,
+      256,
+      256
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+     "dtype": "float32",
+     "layout": "NCTHW (T=2, the frame repeated)"
+    },
+    "last_hidden_state": {
+     "file": "frame1.last_hidden_state.npy",
+     "shape": [
+      256,
+      1408
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+     "dtype": "float32",
+     "layout": "[256, D] h-major then w"
+    },
+    "pooled_mean": {
+     "file": "frame1.pooled_mean.npy",
+     "shape": [
+      1408
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+     "dtype": "float32",
+     "layout": "[D]"
+    },
+    "context": {
+     "file": "frame1.context.npy",
+     "shape": [
+      256,
+      1408
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+     "dtype": "float32",
+     "layout": "[256, D] after the non-affine LayerNorm"
+    }
+   },
+   "frame_index": 1,
+   "frames": 2
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   {
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    "media": "franka_example_traj.npz[1]",
    "timing_s": {
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    "tensors": {
     "context": {
@@ -452,6 +513,161 @@
    },
    "candidates": 4,
    "horizon": 2
+  },
+  {
+   "name": "rollout_seed2",
+   "media": "franka_example_traj.npz",
+   "timing_s": {
+    "preprocess_s": 0.0,
+    "forward_s": 3.5616
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+   "tensors": {
+    "context": {
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+     "shape": [
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+      1408
+     ],
+     "dtype": "float32",
+     "layout": "[2*256, D] two OBSERVED frames"
+    },
+    "goal": {
+     "file": "rollout_seed2.goal.npy",
+     "shape": [
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+      1408
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+     "dtype": "float32",
+     "layout": "[256, D]"
+    },
+    "seed_actions": {
+     "file": "rollout_seed2.seed_actions.npy",
+     "shape": [
+      1,
+      7
+     ],
+     "dtype": "float32",
+     "layout": "[n_seed-1, 7] the action between the observed frames"
+    },
+    "seed_states": {
+     "file": "rollout_seed2.seed_states.npy",
+     "shape": [
+      2,
+      7
+     ],
+     "dtype": "float32",
+     "layout": "[2, 7]"
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+    "actions": {
+     "file": "rollout_seed2.actions.npy",
+     "shape": [
+      4,
+      2,
+      7
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+     "dtype": "float32",
+     "layout": "[4, 2, 7]"
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+    "states_seq": {
+     "file": "rollout_seed2.states_seq.npy",
+     "shape": [
+      4,
+      2,
+      7
+     ],
+     "dtype": "float32",
+     "layout": "[4, 2, 7]"
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+    "rollout": {
+     "file": "rollout_seed2.rollout.npy",
+     "shape": [
+      4,
+      2,
+      256,
+      1408
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+     "dtype": "float32",
+     "layout": "[4, 2, 256, D]"
+    },
+    "rollout_energy": {
+     "file": "rollout_seed2.rollout_energy.npy",
+     "shape": [
+      4
+     ],
+     "dtype": "float32",
+     "layout": "[4]"
+    }
+   },
+   "candidates": 4,
+   "horizon": 2,
+   "n_seed": 2
+  },
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+   "name": "cem",
+   "media": "franka_example_traj.npz",
+   "timing_s": {
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+    "forward_s": 12.5391
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+     "dtype": "float32",
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+    "goal": {
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+     "shape": [
+      256,
+      1408
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+     "dtype": "float32",
+     "layout": "[256, D]"
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+    "state0": {
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+     "shape": [
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+     "dtype": "float32",
+     "layout": "[7]"
+    },
+    "cem_noise": {
+     "file": "cem.cem_noise.npy",
+     "shape": [
+      3,
+      2,
+      8,
+      4
+     ],
+     "dtype": "float32",
+     "layout": "[3, 2, 8, 4] every torch.randn draw, in Meta's order"
+    },
+    "cem_action": {
+     "file": "cem.cem_action.npy",
+     "shape": [
+      2,
+      7
+     ],
+     "dtype": "float32",
+     "layout": "[2, 7] the plan cem() returned"
+    }
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+   "cem": {
+    "samples": 8,
+    "topk": 4,
+    "cem_steps": 3,
+    "rollout": 2,
+    "maxnorm": 0.05,
+    "momentum_mean": 0.15,
+    "momentum_std": 0.75,
+    "momentum_mean_gripper": 0.15,
+    "momentum_std_gripper": 0.15
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   }
  ]
 }

ref/vjepa2-ac-vitg/rollout_seed2.actions.npy

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ref/vjepa2-ac-vitg/rollout_seed2.goal.npy

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ref/vjepa2-ac-vitg/rollout_seed2.rollout.npy

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ref/vjepa2-ac-vitg/rollout_seed2.rollout_energy.npy

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ref/vjepa2-ac-vitg/rollout_seed2.seed_actions.npy

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ref/vjepa2-ac-vitg/rollout_seed2.seed_states.npy

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