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piper_pick_can_terra_delta: merged delta policy + README

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  1. .gitattributes +1 -0
  2. piper_pick_can_terra_delta/README.md +112 -0
  3. piper_pick_can_terra_delta/action_decoder.pt +3 -0
  4. piper_pick_can_terra_delta/added_tokens.json +7 -0
  5. piper_pick_can_terra_delta/config.json +3169 -0
  6. piper_pick_can_terra_delta/configuration_prismatic.py +140 -0
  7. piper_pick_can_terra_delta/ctrl_meta.json +25 -0
  8. piper_pick_can_terra_delta/dataset_statistics.json +127 -0
  9. piper_pick_can_terra_delta/generation_config.json +7 -0
  10. piper_pick_can_terra_delta/model-00001-of-00004.safetensors +3 -0
  11. piper_pick_can_terra_delta/model-00002-of-00004.safetensors +3 -0
  12. piper_pick_can_terra_delta/model-00003-of-00004.safetensors +3 -0
  13. piper_pick_can_terra_delta/model-00004-of-00004.safetensors +3 -0
  14. piper_pick_can_terra_delta/model.safetensors.index.json +989 -0
  15. piper_pick_can_terra_delta/modeling_prismatic.py +562 -0
  16. piper_pick_can_terra_delta/preprocessor_config.json +114 -0
  17. piper_pick_can_terra_delta/processing_prismatic.py +257 -0
  18. piper_pick_can_terra_delta/processor_config.json +6 -0
  19. piper_pick_can_terra_delta/special_tokens_map.json +60 -0
  20. piper_pick_can_terra_delta/tokenizer.json +3 -0
  21. piper_pick_can_terra_delta/tokenizer.model +3 -0
  22. piper_pick_can_terra_delta/tokenizer_config.json +91 -0
  23. piper_pick_can_terra_delta/train_log.jsonl +400 -0
.gitattributes CHANGED
@@ -7,3 +7,4 @@ press_button_terra_delta/tokenizer.model filter=lfs diff=lfs merge=lfs -text
7
  press_button_openvla_delta/tokenizer.model filter=lfs diff=lfs merge=lfs -text
8
  piper_multi_anchored_step2500/tokenizer.model filter=lfs diff=lfs merge=lfs -text
9
  piper_multi_anchored_step5500/tokenizer.model filter=lfs diff=lfs merge=lfs -text
 
 
7
  press_button_openvla_delta/tokenizer.model filter=lfs diff=lfs merge=lfs -text
8
  piper_multi_anchored_step2500/tokenizer.model filter=lfs diff=lfs merge=lfs -text
9
  piper_multi_anchored_step5500/tokenizer.model filter=lfs diff=lfs merge=lfs -text
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+ piper_pick_can_terra_delta/tokenizer.model filter=lfs diff=lfs merge=lfs -text
piper_pick_can_terra_delta/README.md ADDED
@@ -0,0 +1,112 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # `piper_pick_can_terra_delta` — real-robot policy, AgileX Piper pick-can, **DELTA actions**
2
+
3
+ OpenVLA-7B with our latent-action phase 2 (full-parameter, 20k steps, `latp2_inv` labels; `p2fft_20k/` in this repo)
4
+ as the **base**, plus LoRA r32 post-training on the Piper pick-can data with a flow-matching head, 4 query tokens and
5
+ the latent branch (`--latent tc_cont`). The LoRA is **already merged** into the weights here.
6
+
7
+ This is the **delta-action replacement** for `piper_pick_can_10k` (which emits absolute joint targets and is 3x worse
8
+ open loop). Same task, same data, same held-out episodes — only the action parameterisation differs.
9
+
10
+ ## ⚠ The action is a DELTA, not a joint target
11
+
12
+ Dims 0-5 are **target joint angle MINUS the arm's measured joint angle**, in degrees. Sending them as joint targets
13
+ drives every joint toward ~0 deg. `ctrl_meta.json` carries `"action_space": "delta"` and `to_joint_targets()` refuses
14
+ to run without a measured joint state.
15
+
16
+ ```python
17
+ from acdc_la.realrobot.piper_policy import PiperPolicy # DingTianxingjian/Latent_Free, branch newton
18
+ p = PiperPolicy("<this dir>") # prints "action space DELTA"
19
+ p.reset()
20
+ while True: # 10 Hz
21
+ chunk = p.act_chunk(front_frame_rgb, "pick up the can and place it on the plate")
22
+ for a in chunk[:1]: # EXECUTE 1 — see "chunk length" below
23
+ q = read_joint_state_deg() # MEASURED joints, re-read EVERY tick
24
+ send_joint_targets(p.to_joint_targets(a, q)) # = q[:6] + a[:6]
25
+ send_gripper(PiperPolicy.gripper_to_raw(a[6]))
26
+ wait_until_next_10hz_tick()
27
+ ```
28
+
29
+ ### chunk length: use 1 (measured, not guessed)
30
+
31
+ The demos were teleoperated as a **continuous 30 Hz** command, so the demo arm at step t+1 is already past the
32
+ command issued at t. Chunk entry `k` was trained as `a[t+k] - q[t+k]`, relative to the state at the step it is *for*,
33
+ so an arm that settles at each command falls behind by the **demo lead** `a[t-1] - q[t]` every step.
34
+ Measured on all 150 pick-can episodes (`acdc_la/realrobot/demo_lead.py`): **0.855 deg/step**, and it is
35
+ **opposite in sign on the two joints that carry the motion** (j1 -0.74, j2 +0.75) — so it *bends the direction*
36
+ rather than scaling it. Drift carried by entry k = k x 0.855 deg:
37
+
38
+ | entry k | 0 | 1 | 2 | 3 | 4 | 11 |
39
+ |---|---|---|---|---|---|---|
40
+ | drift, deg | 0 | 0.86 | 1.71 | 2.56 | 3.42 | 9.4 |
41
+
42
+ Entry 1's drift already equals this policy's whole `chunk[0]` error, and entry 4 reaches the old absolute arm's
43
+ 3.24 deg. On `press_button` the same effect (there ~2 deg/step) was **seen on the robot**: chunk 1 presses the button,
44
+ chunk 2 "bends the direction". **So: execute 1 entry and re-predict.** If you need multi-step chunks, use a
45
+ chunk-ANCHORED checkpoint (`piper_multi_anchored_*`), which is parameterised to make that valid.
46
+
47
+ **Ensembling: try both.** On this dataset it *helped* offline (0.950 vs 1.126 deg); on `press_button` it was slightly
48
+ *worse*. It is coordinate-correct for this parameterisation — entry `k` from the prediction `k` steps ago is a delta
49
+ relative to *now* — but its benefit is dataset-dependent. `p.act(frame, instr)` gives the ensembled action; pass it
50
+ through `to_joint_targets` the same way.
51
+
52
+ **Smoothness:** `PiperPolicy(..., samples=8)` averages 8 head samples from one VLA forward and cut call-to-call
53
+ jitter 3.43 -> 1.28 deg (-63%) on the absolute arm. Caveat in the wrapper docstring: averaging a multimodal
54
+ distribution returns something that is not any mode. Fine for one can on a table.
55
+
56
+ ## Data
57
+
58
+ `piper_pick_can`, AgileX Piper, 150 episodes of **one** task, 10 Hz, 6,462 steps. Front + wrist cameras in the
59
+ dataset but **this policy never sees the wrist** (`image_primary` only). Delta derived by
60
+ `acdc_la/realrobot/make_delta_rlds.py` from the absolute RLDS so that frames, subsampling, episode boundaries and
61
+ instruction are **bit-identical** and only `action` differs: `d[:,:6] = action[:,:6] - joint_state[:,:6]`, gripper
62
+ untouched. Trained on `train[:95%]` = 142 episodes; `train[95%:]` = **8 episodes** held out.
63
+
64
+ Every one of the 150 episodes carries the identical instruction `'pick up the can and place it on the plate'` —
65
+ this is a **single-instruction** dataset. Nothing here tests language grounding (see `press_button_terra_delta` for
66
+ a set that does).
67
+
68
+ ## Training budget
69
+
70
+ 10,000 steps at global batch 64 (1 GPU x BS 8 x accum 8), lr 3.5e-4, window 12, 17.9 h. Final train `latent_r2`
71
+ 0.831, so the latent branch was active, not collapsed.
72
+
73
+ ## Held-out error, measured the way the policy is actually run
74
+
75
+ Chunk sampled from noise (10 Euler steps), deployment image path, 8 held-out episodes / 353 steps.
76
+ `piper_policy.py --replay data/real_rlds_delta` reproduces this exactly (deterministic), and reproducing it is the
77
+ check that your preprocessing matches ours.
78
+
79
+ | | j0 | j1 | j2 | j3 | j4 | j5 | **mean** | gripper (of 1.0) |
80
+ |---|---|---|---|---|---|---|---|---|
81
+ | `chunk[0]`, deg | 0.652 | 2.201 | 1.730 | 0.437 | 1.672 | 0.064 | **1.126** | 0.048 |
82
+ | temporally ensembled | 0.638 | 1.801 | 1.364 | 0.387 | 1.447 | 0.065 | **0.950** | 0.064 |
83
+
84
+ Normalised whole-chunk L1 **0.1174**. Because the measured joint state is known at deployment, the delta error
85
+ **is** the absolute-target error; `--abs_root` confirms the `to_joint_targets` path end to end.
86
+
87
+ ### What to compare it against
88
+
89
+ | | deg |
90
+ |---|---|
91
+ | this policy, `chunk[0]` | **1.126** |
92
+ | `piper_pick_can_openvla_delta` (the matched control) | 1.054 |
93
+ | `piper_pick_can_10k` (absolute targets, same arm) | 3.24 |
94
+ | null: output zero delta = "stay where you are" | 1.20 |
95
+ | null: repeat the last command | 2.06 |
96
+ | between-demonstration floor (nothing can beat this) | 0.61 |
97
+
98
+ **This policy and the control are NOT statistically separated.** Paired cluster bootstrap over the 8 held-out
99
+ episodes (the independent unit — 353 steps is not the sample size): A-B = +0.072 deg, 95% CI **[-0.038, +0.189]**,
100
+ worse in 5/8 episodes, sign test p = 0.727. So on this single-instruction task the latent mechanism shows **no
101
+ detected effect**; pick whichever checkpoint you prefer. On `press_button`, which has 4 instructions, the same pair
102
+ separates 2.1x — see that README.
103
+
104
+ Note also that the pair is **confounded for attribution**: the control changes both the base (stock `openvla-7b`
105
+ vs our phase 2) and the latent branch, so neither direction can be credited to the latent branch alone.
106
+
107
+ ## Do not use the training metric to rank checkpoints
108
+
109
+ `action_l1_1step` in `train_log.jsonl` is **oracle-conditioned** (`FlowHeads.loss` scores a one-step correction from
110
+ a point that already contains the ground truth). It makes this policy look 13% *better* than the control
111
+ (0.05634 vs 0.06483) while both free-running read-outs make the control better. Use it to track one run; never to
112
+ rank two. Always rank with `--replay`.
piper_pick_can_terra_delta/action_decoder.pt ADDED
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+ size 105271865
piper_pick_can_terra_delta/added_tokens.json ADDED
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+ "<ACT_3>": 32004,
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+ "<PAD>": 32000
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+ }
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
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_name_or_path": "/home/ti874297/Projects/latent_free/cache/vla/openvla-7b",
3
+ "arch_specifier": "no-align+fused-gelu-mlp",
4
+ "architectures": [
5
+ "OpenVLAForActionPrediction"
6
+ ],
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+ "auto_map": {
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+ "AutoConfig": "configuration_prismatic.OpenVLAConfig",
9
+ "AutoModelForVision2Seq": "modeling_prismatic.OpenVLAForActionPrediction"
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+ },
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+ "hf_llm_id": "meta-llama/Llama-2-7b-hf",
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+ "image_resize_strategy": "resize-naive",
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+ "image_sizes": [
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+ 224,
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+ 224
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+ ],
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+ "llm_backbone_id": "llama2-7b-pure",
18
+ "llm_max_length": 2048,
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+ "model_type": "openvla",
20
+ "n_action_bins": 256,
21
+ "norm_stats": {
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+ "austin_buds_dataset_converted_externally_to_rlds": {
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+ "action": {
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+ "mask": [
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+ true,
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+ true,
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+ true,
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+ true,
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+ true,
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+ true,
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+ false
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+ ],
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+ "max": [
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+ 0.0,
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+ 0.0,
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+ 0.0,
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+ 1.0
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+ ],
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+ "mean": [
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+ -0.07678354531526566,
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+ 0.0036849044263362885,
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+ 0.05644911900162697,
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+ 0.0,
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+ ],
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+ ],
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+ "q99": [
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piper_pick_can_terra_delta/configuration_prismatic.py ADDED
@@ -0,0 +1,140 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """
2
+ configuration_prismatic.py
3
+
4
+ HuggingFace-style configuration definition for Prismatic VLMs, inheriting from `transformers.PretrainedConfig`.
5
+ Default configuration specifies `siglip-224px+7b`.
6
+ """
7
+
8
+ from typing import Any, Dict, List, Optional
9
+
10
+ from transformers import PretrainedConfig
11
+ from transformers.models.auto import CONFIG_MAPPING
12
+
13
+ # === Utilities for Mapping Prismatic names to HF names ===
14
+ # fmt: off
15
+ VISION_BACKBONE_TO_RESOLUTION: Dict[str, List[int]] = {
16
+ "clip-vit-l": [224], "siglip-vit-so400m": [224], "dinov2-vit-l": [224], "in1k-vit-l": [224],
17
+
18
+ "clip-vit-l-336px": [336],
19
+ "siglip-vit-so400m-384px": [384],
20
+
21
+ "dinoclip-vit-l-336px": [336, 336],
22
+ "dinosiglip-vit-so-224px": [224, 224],
23
+ "dinosiglip-vit-so-384px": [384, 384],
24
+ }
25
+ VISION_BACKBONE_TO_TIMM_ID: Dict[str, List[str]] = {
26
+ "clip-vit-l": ["vit_large_patch14_clip_224.openai"],
27
+ "clip-vit-l-336px": ["vit_large_patch14_clip_336.openai"],
28
+
29
+ "dinov2-vit-l": ["vit_large_patch14_reg4_dinov2.lvd142m"],
30
+ "in1k-vit-l": ["vit_large_patch16_224.augreg_in21k_ft_in1k"],
31
+
32
+ "siglip-vit-so400m": ["vit_so400m_patch14_siglip_224"],
33
+ "siglip-vit-so400m-384px": ["vit_so400m_patch14_siglip_384"],
34
+
35
+ "dinoclip-vit-l-336px": ["vit_large_patch14_reg4_dinov2.lvd142m", "vit_large_patch14_clip_336.openai"],
36
+ "dinosiglip-vit-so-224px": ["vit_large_patch14_reg4_dinov2.lvd142m", "vit_so400m_patch14_siglip_224"],
37
+ "dinosiglip-vit-so-384px": ["vit_large_patch14_reg4_dinov2.lvd142m", "vit_so400m_patch14_siglip_384"],
38
+ }
39
+ TIMM_OVERRIDE_ACT_LAYER: Dict[str, List[Optional[str]]] = {
40
+ "clip-vit-l": ["quick_gelu"], "clip-vit-l-336px": ["quick_gelu"],
41
+ "dinov2-vit-l": [None], "in1k-vit-l": [None],
42
+ "siglip-vit-so400m": [None], "siglip-vit-so400m-384px": [None],
43
+ "dinoclip-vit-l-336px": [None, "quick_gelu"],
44
+ "dinosiglip-vit-so-224px": [None, None], "dinosiglip-vit-so-384px": [None, None]
45
+ }
46
+
47
+ LLM_BACKBONE_TO_HF_PATH = {
48
+ "llama2-7b-pure": "meta-llama/Llama-2-7b-hf", "llama2-13b-pure": "meta-llama/Llama-2-13b-hf",
49
+ "llama2-7b-chat": "meta-llama/Llama-2-7b-chat-hf", "llama2-13b-chat": "meta-llama/Llama-2-13b-chat-hf",
50
+
51
+ "vicuna-v15-7b": "lmsys/vicuna-7b-v1.5", "vicuna-v15-13b": "lmsys/vicuna-13b-v1.5",
52
+
53
+ "mistral-v0.1-7b-pure": "mistralai/Mistral-7B-v0.1",
54
+ "mistral-v0.1-7b-instruct": "mistralai/Mistral-7B-Instruct-v0.1",
55
+
56
+ "phi-2-3b": "microsoft/phi-2",
57
+ }
58
+ LLM_BACKBONE_TO_HF_METACLASS = {
59
+ "llama2-7b-pure": "llama", "llama2-13b-pure": "llama", "llama2-7b-chat": "llama", "llama2-13b-chat": "llama",
60
+ "vicuna-v15-7b": "llama", "vicuna-v15-13b": "llama",
61
+
62
+ "mistral-v0.1-7b-pure": "mistral", "mistral-v0.1-7b-instruct": "mistral",
63
+
64
+ "phi-2-3b": "phi",
65
+ }
66
+
67
+ VALID_VISION_BACKBONES = set(VISION_BACKBONE_TO_RESOLUTION.keys())
68
+ VALID_LLM_BACKBONES = set(LLM_BACKBONE_TO_HF_PATH)
69
+ # fmt: on
70
+
71
+
72
+ class PrismaticConfig(PretrainedConfig):
73
+ model_type: str = "prismatic"
74
+ is_composition: bool = False
75
+
76
+ def __init__(
77
+ self,
78
+ vision_backbone_id: str = "siglip-vit-so400m",
79
+ llm_backbone_id: str = "vicuna-v15-7b",
80
+ arch_specifier: str = "no-align+gelu-mlp",
81
+ use_fused_vision_backbone: Optional[bool] = None,
82
+ image_resize_strategy: str = "letterbox",
83
+ text_config: Optional[Dict[str, Any]] = None,
84
+ llm_max_length: int = 2048,
85
+ pad_token_id: int = 32000,
86
+ pad_to_multiple_of: int = 64,
87
+ output_projector_states: bool = False,
88
+ **kwargs: str,
89
+ ) -> None:
90
+ if vision_backbone_id not in VALID_VISION_BACKBONES:
91
+ raise ValueError(f"Vision backbone `{vision_backbone_id}` not in {VALID_VISION_BACKBONES = }")
92
+
93
+ if llm_backbone_id not in VALID_LLM_BACKBONES:
94
+ raise ValueError(f"LLM backbone `{llm_backbone_id}` not in {VALID_LLM_BACKBONES = }")
95
+
96
+ # Set Prismatic Configuration Fields
97
+ self.vision_backbone_id = vision_backbone_id
98
+ self.llm_backbone_id = llm_backbone_id
99
+ self.arch_specifier = arch_specifier
100
+ self.output_projector_states = output_projector_states
101
+
102
+ # [Contract] All vision backbone parameters are lists =>> supports fused backbones with different preprocessing
103
+ self.use_fused_vision_backbone = (
104
+ use_fused_vision_backbone
105
+ if use_fused_vision_backbone is not None
106
+ else any(self.vision_backbone_id.startswith(v) for v in ["dinoclip", "dinosiglip"])
107
+ )
108
+
109
+ self.timm_model_ids = VISION_BACKBONE_TO_TIMM_ID[self.vision_backbone_id]
110
+ self.timm_override_act_layers = TIMM_OVERRIDE_ACT_LAYER[self.vision_backbone_id]
111
+ self.image_sizes = VISION_BACKBONE_TO_RESOLUTION[self.vision_backbone_id]
112
+ self.image_resize_strategy = image_resize_strategy
113
+
114
+ self.hf_llm_id = LLM_BACKBONE_TO_HF_PATH[self.llm_backbone_id]
115
+ self.llm_max_length = llm_max_length
116
+ self.pad_token_id, self.pad_to_multiple_of = pad_token_id, pad_to_multiple_of
117
+
118
+ # [IMPORTANT] HF Utilities actually look for a `text_config` field... we need to use that specific naming!
119
+ self.text_config = (
120
+ CONFIG_MAPPING[LLM_BACKBONE_TO_HF_METACLASS[self.llm_backbone_id]](**text_config)
121
+ if text_config is not None
122
+ else CONFIG_MAPPING[LLM_BACKBONE_TO_HF_METACLASS[self.llm_backbone_id]]()
123
+ )
124
+
125
+ # Dispatch **kwargs to super() =>> note that `pad_token_id` collides, so we pass it in here as well...
126
+ super().__init__(pad_token_id=pad_token_id, **kwargs)
127
+
128
+
129
+ class OpenVLAConfig(PrismaticConfig):
130
+ model_type: str = "openvla"
131
+
132
+ def __init__(
133
+ self,
134
+ norm_stats: Optional[Dict[str, Dict[str, Dict[str, Dict[str, List[float]]]]]] = None,
135
+ n_action_bins: int = 256,
136
+ **kwargs: str,
137
+ ) -> None:
138
+ self.norm_stats, self.n_action_bins = norm_stats, n_action_bins
139
+
140
+ super().__init__(**kwargs)
piper_pick_can_terra_delta/ctrl_meta.json ADDED
@@ -0,0 +1,25 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "latent": "tc_cont",
3
+ "n_tokens": 4,
4
+ "codebook_size": 0,
5
+ "dz": 256,
6
+ "window_size": 12,
7
+ "step": 10000,
8
+ "vla_path": "/home/ti874297/Projects/latent_free/cache/vla/openvla-7b",
9
+ "lam_path": "/home/ti874297/Projects/latent_free/claude_survey/ckpt/s2mix_1ep.pt",
10
+ "acdc_tag": "s1_acdc_1ep",
11
+ "acdc_codebook": "vq_s1_acdc_1ep_K64.npz",
12
+ "tc_tag": "latp2_inv",
13
+ "dataset_name": "piper_pick_can",
14
+ "train_frac": null,
15
+ "override": {},
16
+ "head": "flow",
17
+ "query_tokens": 4,
18
+ "flow": {
19
+ "width": 512,
20
+ "layers": 4,
21
+ "z_tok": 32,
22
+ "steps": 10
23
+ },
24
+ "action_space": "delta"
25
+ }
piper_pick_can_terra_delta/dataset_statistics.json ADDED
@@ -0,0 +1,127 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "piper_pick_can": {
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+ "action": {
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+ "mean": [
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+ -0.05513078719377518,
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+ 1.8056895732879639,
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+ 0.0071566142141819,
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+ -0.00423302361741662,
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+ 0.6070931553840637
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piper_pick_can_terra_delta/modeling_prismatic.py ADDED
@@ -0,0 +1,562 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """
2
+ modeling_prismatic.py
3
+
4
+ Core HuggingFace-style PrismaticPreTrainedModel and PrismaticForConditionalGeneration class definitions, inheriting
5
+ from the default `transformers.PretrainedModel`. Meant to be standalone and self-contained, but exactly replicate the
6
+ logic in `prismatic.models.vlms.prismatic.py`.
7
+
8
+ Note =>> for the time being, not adding the custom HF "docstring" formatting.
9
+
10
+ References [LLaVa, IDEFICS-2]:
11
+ => https://github.com/huggingface/transformers/blob/main/src/transformers/models/llava/modeling_llava.py
12
+ => https://github.com/huggingface/transformers/blob/main/src/transformers/models/idefics2/modeling_idefics2.py
13
+ """
14
+
15
+ import logging
16
+ from dataclasses import dataclass
17
+ from functools import partial
18
+ from typing import Any, Callable, ClassVar, Dict, List, Optional, Tuple, Union
19
+
20
+ import numpy as np
21
+ import timm
22
+ import tokenizers
23
+ import torch
24
+ import torch.nn as nn
25
+ import transformers
26
+ from timm.models.vision_transformer import LayerScale
27
+ from transformers import AutoModelForCausalLM, PretrainedConfig, PreTrainedModel
28
+ from transformers.modeling_outputs import ModelOutput
29
+
30
+ from .configuration_prismatic import OpenVLAConfig, PrismaticConfig
31
+
32
+ # Get Logger
33
+ logger = logging.getLogger(__name__)
34
+
35
+
36
+ # === PyTorch/HuggingFace Default IGNORE_INDEX (for CrossEntropyLoss labels)
37
+ IGNORE_INDEX = -100
38
+
39
+
40
+ # === Utility Functions for Monkey-Patching ===
41
+ def unpack_tuple(fn: Callable[[Any], Tuple[Any]]) -> Callable[[Any], Any]:
42
+ def wrapper(*args: Any, **kwargs: Any) -> Any:
43
+ result = fn(*args, **kwargs)
44
+ return result[0] if isinstance(result, tuple) else result
45
+
46
+ return wrapper
47
+
48
+
49
+ # HF Transformers overwrites parameters with names containing `gamma`; we're going to patch VisionBackbone.LayerScale.
50
+ # =>> TIMM :: https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/vision_transformer.py#L109
51
+ # =>> Transformers :: https://github.com/huggingface/transformers/blob/main/src/transformers/modeling_utils.py#L3960
52
+ def _ls_new_forward(self, x: torch.Tensor) -> torch.Tensor:
53
+ return x.mul_(self.scale_factor) if self.inplace else x * self.scale_factor
54
+
55
+
56
+ def ls_apply_patch(ls_module: LayerScale):
57
+ ls_module.scale_factor = nn.Parameter(ls_module.gamma.clone())
58
+ ls_module.forward = _ls_new_forward.__get__(ls_module, LayerScale)
59
+ del ls_module.gamma
60
+
61
+
62
+ # === Prismatic Vision Backbone (nn.Module) Definitions (w/ Fused Backbone Support) ===
63
+ class PrismaticVisionBackbone(nn.Module):
64
+ def __init__(
65
+ self,
66
+ use_fused_vision_backbone: bool,
67
+ image_sizes: List[int],
68
+ timm_model_ids: List[str],
69
+ timm_override_act_layers: List[Optional[str]],
70
+ ) -> None:
71
+ super().__init__()
72
+ self.use_fused_vision_backbone = use_fused_vision_backbone
73
+
74
+ # [Contract] Validate number of (fused) vision backbones, create "alpha" featurizer and Instantiate
75
+ # =>> Note :: Monkey-Patch the `forward()` function of the backbone to ensure FSDP-compatibility
76
+ # Hardcodes `get_intermediate_layers` to return the **SECOND-TO-LAST** layer patches!
77
+ assert len(timm_model_ids) <= 2, "Prismatic models only support up to 2 (fused) vision backbones!"
78
+ self.featurizer = timm.create_model(
79
+ timm_model_ids[0],
80
+ pretrained=False,
81
+ num_classes=0,
82
+ img_size=image_sizes[0],
83
+ act_layer=timm_override_act_layers[0],
84
+ )
85
+ self.featurizer.forward = unpack_tuple(
86
+ partial(self.featurizer.get_intermediate_layers, n={len(self.featurizer.blocks) - 2})
87
+ )
88
+ self.embed_dim = self.featurizer.embed_dim
89
+
90
+ # If `use_fused_vision_backbone` =>> create "beta" featurizer
91
+ if self.use_fused_vision_backbone:
92
+ self.fused_featurizer = timm.create_model(
93
+ timm_model_ids[1],
94
+ pretrained=False,
95
+ num_classes=0,
96
+ img_size=image_sizes[1],
97
+ act_layer=timm_override_act_layers[1],
98
+ )
99
+ self.fused_featurizer.forward = unpack_tuple(
100
+ partial(self.fused_featurizer.get_intermediate_layers, n={len(self.fused_featurizer.blocks) - 2})
101
+ )
102
+ self.embed_dim += self.fused_featurizer.embed_dim
103
+
104
+ # Patch `vision_backbone.featurizer` and `vision_backbone.fused_featurizer` with HF-Compatible LayerScale
105
+ for module in self.featurizer.modules():
106
+ if isinstance(module, LayerScale):
107
+ ls_apply_patch(module)
108
+
109
+ if self.use_fused_vision_backbone:
110
+ for module in self.fused_featurizer.modules():
111
+ if isinstance(module, LayerScale):
112
+ ls_apply_patch(module)
113
+
114
+ def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:
115
+ """Run image (`pixel_values`) through featurizer; if channel-stacked, then dispatch and sequence stack."""
116
+ if not self.use_fused_vision_backbone:
117
+ return self.featurizer(pixel_values)
118
+
119
+ # Split `pixel_values :: [bsz, 2 * 3, resolution, resolution]` =>> featurize =>> channel stack
120
+ img, img_fused = torch.split(pixel_values, [3, 3], dim=1)
121
+ patches, patches_fused = self.featurizer(img), self.fused_featurizer(img_fused)
122
+
123
+ return torch.cat([patches, patches_fused], dim=2)
124
+
125
+
126
+ # === Prismatic Projector (nn.Module) Definitions ===
127
+ class PrismaticProjector(nn.Module):
128
+ def __init__(self, use_fused_vision_backbone: bool, vision_dim: int, llm_dim: int) -> None:
129
+ super().__init__()
130
+ self.use_fused_vision_backbone = use_fused_vision_backbone
131
+ self.vision_dim, self.llm_dim = vision_dim, llm_dim
132
+
133
+ # Switch on `use_fused_vision_backbone` =>> use slightly different MLPs and projection factors!
134
+ if not self.use_fused_vision_backbone:
135
+ self.fc1 = nn.Linear(self.vision_dim, self.llm_dim, bias=True)
136
+ self.fc2 = nn.Linear(self.llm_dim, self.llm_dim, bias=True)
137
+ self.act_fn1 = nn.GELU()
138
+ else:
139
+ initial_projection_dim = 4 * vision_dim
140
+ self.fc1 = nn.Linear(self.vision_dim, initial_projection_dim, bias=True)
141
+ self.fc2 = nn.Linear(initial_projection_dim, self.llm_dim, bias=True)
142
+ self.fc3 = nn.Linear(self.llm_dim, self.llm_dim, bias=True)
143
+ self.act_fn1 = nn.GELU()
144
+ self.act_fn2 = nn.GELU()
145
+
146
+ def forward(self, img_patches: torch.Tensor) -> torch.Tensor:
147
+ if not self.use_fused_vision_backbone:
148
+ projected_features = self.fc1(img_patches)
149
+ projected_features = self.act_fn1(projected_features)
150
+ projected_features = self.fc2(projected_features)
151
+ else:
152
+ projected_features = self.fc1(img_patches)
153
+ projected_features = self.act_fn1(projected_features)
154
+ projected_features = self.fc2(projected_features)
155
+ projected_features = self.act_fn2(projected_features)
156
+ projected_features = self.fc3(projected_features)
157
+
158
+ return projected_features
159
+
160
+
161
+ # === Main HF Class Definitions ===
162
+ @dataclass
163
+ class PrismaticCausalLMOutputWithPast(ModelOutput):
164
+ """Base class for Prismatic casual (visually-conditioned) language model outputs; also exposes visual features."""
165
+
166
+ loss: Optional[torch.FloatTensor] = None
167
+ logits: torch.FloatTensor = None
168
+ past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
169
+ hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
170
+ attentions: Optional[Tuple[torch.FloatTensor]] = None
171
+
172
+ # Additions for VLMs
173
+ projector_features: Optional[torch.FloatTensor] = None
174
+
175
+
176
+ class PrismaticPreTrainedModel(PreTrainedModel):
177
+ config_class: PretrainedConfig = PrismaticConfig
178
+ base_model_prefix: str = "model"
179
+ supports_gradient_checkpointing: bool = True
180
+
181
+ _no_split_modules: ClassVar[List[str]] = ["PrismaticProjector"]
182
+ _skip_keys_device_placement: str = "past_key_values"
183
+ _supports_flash_attn_2: bool = True
184
+
185
+ def _init_weights(self, module: nn.Module) -> None:
186
+ # Important :: this HF ported version is *not* meant for training from scratch; only inference and fine-tuning!
187
+ # => As such, this init_weights code is not correct; if training VLMs from scratch, use the main codebase at
188
+ # https://github.com/TRI-ML/prismatic-vlms
189
+ std = (
190
+ self.config.initializer_range
191
+ if hasattr(self.config, "initializer_range")
192
+ else self.config.text_config.initializer_range
193
+ )
194
+
195
+ if hasattr(module, "class_embedding"):
196
+ module.class_embedding.data.normal_(mean=0.0, std=std)
197
+
198
+ if isinstance(module, (nn.Linear, nn.Conv2d)):
199
+ module.weight.data.normal_(mean=0.0, std=std)
200
+ if module.bias is not None:
201
+ module.bias.data.zero_()
202
+ elif isinstance(module, nn.Embedding):
203
+ module.weight.data.normal_(mean=0.0, std=std)
204
+ if module.padding_idx is not None:
205
+ module.weight.data[module.padding_idx].zero_()
206
+
207
+ @property
208
+ def _supports_sdpa(self) -> bool:
209
+ """Check LLM supports SDPA Attention"""
210
+ return self.language_model._supports_sdpa
211
+
212
+
213
+ class PrismaticForConditionalGeneration(PrismaticPreTrainedModel):
214
+ def __init__(self, config: PrismaticConfig) -> None:
215
+ super().__init__(config)
216
+
217
+ # [Validation] Lightweight Validate on `config` Fields + Dependency Versions
218
+ if config.use_fused_vision_backbone is None:
219
+ raise ValueError("Missing config field `use_fused_vision_backbone`")
220
+
221
+ if timm.__version__ not in {"0.9.10", "0.9.11", "0.9.12", "0.9.16"}:
222
+ raise NotImplementedError(
223
+ "TIMM Version must be >= 0.9.10 and < 1.0.0 (breaking); please raise a GitHub Issue "
224
+ "if you urgently need support for latest TIMM versions."
225
+ )
226
+
227
+ if (transformers.__version__ != "4.40.1") or (tokenizers.__version__ != "0.19.1"):
228
+ logger.warning(
229
+ f"Expected `transformers==4.40.1` and `tokenizers==0.19.1` but got "
230
+ f"`transformers=={transformers.__version__}` and `tokenizers=={tokenizers.__version__}`; "
231
+ f"there might be inference-time regressions due to dependency changes. If in doubt, please"
232
+ f"use the above versions."
233
+ )
234
+
235
+ # Instantiate PrismaticVisionBackbone (w/ Potential Fused Backbone)
236
+ self.vision_backbone = PrismaticVisionBackbone(
237
+ config.use_fused_vision_backbone, config.image_sizes, config.timm_model_ids, config.timm_override_act_layers
238
+ )
239
+
240
+ # Create Multimodal Projector
241
+ self.projector = PrismaticProjector(
242
+ config.use_fused_vision_backbone,
243
+ vision_dim=self.vision_backbone.embed_dim,
244
+ llm_dim=config.text_config.hidden_size,
245
+ )
246
+
247
+ # Instantiate LLM Backbone
248
+ self.language_model = AutoModelForCausalLM.from_config(
249
+ config.text_config, attn_implementation=config._attn_implementation
250
+ )
251
+ self.vocab_size = config.text_config.vocab_size
252
+ self.pad_token_id = config.pad_token_id
253
+
254
+ # HF Boilerplate =>> initializes weights via `_init_weights()` and sets gradient checkpointing
255
+ self.post_init()
256
+
257
+ # === `PreTrainedModel` Boilerplate ===
258
+ def get_input_embeddings(self) -> nn.Module:
259
+ return self.language_model.get_input_embeddings()
260
+
261
+ def set_input_embeddings(self, value: nn.Module) -> None:
262
+ self.language_model.set_input_embeddings(value)
263
+
264
+ def get_output_embeddings(self) -> nn.Module:
265
+ return self.language_model.get_output_embeddings()
266
+
267
+ def set_output_embeddings(self, new_embeddings: nn.Module) -> None:
268
+ self.language_model.set_output_embeddings(new_embeddings)
269
+
270
+ def get_decoder(self) -> nn.Module:
271
+ return self.language_model.get_decoder()
272
+
273
+ def set_decoder(self, decoder: nn.Module) -> None:
274
+ self.language_model.set_decoder(decoder)
275
+
276
+ def tie_weights(self) -> None:
277
+ self.language_model.tie_weights() # Note: `Llama-2` and `Mistral` don't tie weights (no-op)
278
+
279
+ def resize_token_embeddings(
280
+ self, new_num_tokens: Optional[int] = None, pad_to_multiple_of: Optional[int] = None
281
+ ) -> nn.Embedding:
282
+ updated_embeddings = self.language_model.resize_token_embeddings(new_num_tokens, pad_to_multiple_of)
283
+
284
+ # Update config/instance variables
285
+ self.config.text_config.vocab_size = updated_embeddings.num_embeddings
286
+ self.vocab_size = updated_embeddings.num_embeddings
287
+
288
+ return updated_embeddings
289
+
290
+ # === Core Prismatic VLM `forward()` Logic ===
291
+ def forward(
292
+ self,
293
+ input_ids: Optional[torch.LongTensor] = None,
294
+ attention_mask: Optional[torch.Tensor] = None,
295
+ pixel_values: Optional[torch.FloatTensor] = None,
296
+ labels: Optional[torch.LongTensor] = None,
297
+ inputs_embeds: Optional[torch.FloatTensor] = None,
298
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
299
+ use_cache: Optional[bool] = None,
300
+ output_attentions: Optional[bool] = None,
301
+ output_hidden_states: Optional[bool] = None,
302
+ output_projector_features: Optional[bool] = None,
303
+ return_dict: Optional[bool] = None,
304
+ ) -> Union[Tuple, PrismaticCausalLMOutputWithPast]:
305
+ """Run a forward pass through the VLM, returning a PrismaticCausalLMOutputWithPast instance."""
306
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
307
+ output_hidden_states = (
308
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
309
+ )
310
+ output_projector_features = output_projector_features if output_projector_features is not None else False
311
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
312
+
313
+ # Respect `use_cache` only if not training (even if `gradient_checkpointing` is off)
314
+ use_cache = use_cache and not self.training
315
+
316
+ # Instantiate Placeholder for Projector Features
317
+ projected_patch_embeddings = None
318
+
319
+ # Note :: We only support forward passes with the following cases:
320
+ # => Cached Generation :: (input_ids.shape[1] == 1) and (past_key_values is not None)
321
+ # => Unimodal Forward :: (pixel_values is None)
322
+ # => Multimodal Forward :: (pixel_values is not None) and (input_ids/embeds.shape[0] == pixel_values.shape[0])
323
+
324
+ # === Handle Generation with Cache (`input_ids.shape[1] == 1`) =>> requires `past_keys_values` ===
325
+ if input_ids.shape[1] == 1:
326
+ assert input_ids.shape[0] == 1, "Generation is only currently supported for batch size of 1!"
327
+ assert past_key_values is not None, "You must provide `past_key_values` during cached generation!"
328
+ assert labels is None, "Unexpected key `labels` provided during cached generation!"
329
+
330
+ language_model_output = self.language_model(
331
+ input_ids=input_ids,
332
+ attention_mask=None,
333
+ position_ids=None,
334
+ past_key_values=past_key_values,
335
+ inputs_embeds=None,
336
+ labels=None,
337
+ use_cache=use_cache,
338
+ output_attentions=output_attentions,
339
+ output_hidden_states=output_hidden_states,
340
+ return_dict=return_dict,
341
+ )
342
+
343
+ # === Handle Unimodal Forward ===
344
+ elif pixel_values is None:
345
+ assert (input_ids is not None) and (inputs_embeds is None), "Missing `input_ids` in language-only forward!"
346
+ assert past_key_values is None, "Unexpected key `past_key_values` provided during language-only forward!"
347
+
348
+ language_model_output = self.language_model(
349
+ input_ids=input_ids,
350
+ attention_mask=attention_mask,
351
+ position_ids=None,
352
+ past_key_values=None,
353
+ inputs_embeds=None,
354
+ labels=labels,
355
+ use_cache=use_cache,
356
+ output_attentions=output_attentions,
357
+ output_hidden_states=output_hidden_states,
358
+ return_dict=return_dict,
359
+ )
360
+
361
+ # === Handle Multimodal Forward ===
362
+ elif (input_ids.shape[0] == pixel_values.shape[0]) or (inputs_embeds.shape[0] == pixel_values.shape[0]):
363
+ assert past_key_values is None, "Unexpected key `past_key_values` provided during language-only forward!"
364
+
365
+ # Visual Feature Extraction
366
+ patch_features = self.vision_backbone(pixel_values)
367
+
368
+ # Projection Logic =>> Update Attention Mask
369
+ projected_patch_embeddings = self.projector(patch_features)
370
+ projected_patch_attention_mask = None
371
+ if attention_mask is not None:
372
+ projected_patch_attention_mask = torch.full(
373
+ (projected_patch_embeddings.shape[0], projected_patch_embeddings.shape[1]),
374
+ fill_value=True,
375
+ dtype=attention_mask.dtype,
376
+ device=attention_mask.device,
377
+ )
378
+
379
+ # Get Input Embeddings (from Language Model Embeddings)
380
+ input_embeddings = self.get_input_embeddings()(input_ids)
381
+
382
+ # Build Multimodal Embeddings & Attention Mask =>> Prismatic defaults to inserting after <BOS> token (1:)
383
+ multimodal_embeddings = torch.cat(
384
+ [input_embeddings[:, :1, :], projected_patch_embeddings, input_embeddings[:, 1:, :]], dim=1
385
+ )
386
+ multimodal_attention_mask = None
387
+ if attention_mask is not None:
388
+ multimodal_attention_mask = torch.cat(
389
+ [attention_mask[:, :1], projected_patch_attention_mask, attention_mask[:, 1:]], dim=1
390
+ )
391
+
392
+ # Build Labels (if specified) =>> Ignore Labels for Patch Embeddings
393
+ multimodal_labels = None
394
+ if labels is not None:
395
+ projected_patch_labels = torch.full(
396
+ (projected_patch_embeddings.shape[0], projected_patch_embeddings.shape[1]),
397
+ fill_value=IGNORE_INDEX,
398
+ dtype=labels.dtype,
399
+ device=labels.device,
400
+ )
401
+ multimodal_labels = torch.cat([labels[:, :1], projected_patch_labels, labels[:, 1:]], dim=1)
402
+
403
+ # Dispatch to Language Model
404
+ language_model_output = self.language_model(
405
+ input_ids=None,
406
+ attention_mask=multimodal_attention_mask,
407
+ position_ids=None,
408
+ past_key_values=None,
409
+ inputs_embeds=multimodal_embeddings,
410
+ labels=multimodal_labels,
411
+ use_cache=use_cache,
412
+ output_attentions=output_attentions,
413
+ output_hidden_states=output_hidden_states,
414
+ return_dict=return_dict,
415
+ )
416
+
417
+ # === Otherwise =>> Assume Invalid! ===
418
+ elif (input_ids.shape[0] != pixel_values.shape[0]) or (inputs_embeds.shape[0] != pixel_values.shape[0]):
419
+ raise ValueError("Non-homogenous batch of (text, image) input -- forward() does not support mixed batches!")
420
+
421
+ else:
422
+ raise ValueError(
423
+ "Invalid PrismaticForConditionalGeneration `forward()` call with provided arguments:\n"
424
+ f"=> `input_ids` = {input_ids is not None}\n"
425
+ f"=> `attention_mask` = {attention_mask is not None}\n"
426
+ f"=> `pixel_values` = {pixel_values is not None}\n"
427
+ f"=> `labels` = {labels is not None}\n"
428
+ f"=> `input_embeds` = {inputs_embeds is not None}\n"
429
+ f"=> `past_key_values` = {past_key_values is not None}\n"
430
+ f"=> `use_cache` = {use_cache}"
431
+ )
432
+
433
+ # Unpack `language_model_output` and return PrismaticCausalLMOutputWithPast (or tuple if not `return_dict`)
434
+ if not return_dict:
435
+ if output_projector_features and (projected_patch_embeddings is not None):
436
+ return *language_model_output, projected_patch_embeddings
437
+
438
+ return language_model_output
439
+
440
+ return PrismaticCausalLMOutputWithPast(
441
+ loss=language_model_output.loss,
442
+ logits=language_model_output.logits,
443
+ past_key_values=language_model_output.past_key_values,
444
+ hidden_states=language_model_output.hidden_states,
445
+ attentions=language_model_output.attentions,
446
+ projector_features=projected_patch_embeddings,
447
+ )
448
+
449
+ # === GenerationMixin Methods ===
450
+ def prepare_inputs_for_generation(
451
+ self,
452
+ input_ids: Optional[torch.Tensor] = None,
453
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
454
+ inputs_embeds: Optional[torch.FloatTensor] = None,
455
+ pixel_values: Optional[torch.FloatTensor] = None,
456
+ attention_mask: Optional[torch.Tensor] = None,
457
+ **kwargs: str,
458
+ ) -> Dict[str, torch.Tensor]:
459
+ """Borrowed from `LlamaForCausalLM` and simplified for batch size = 1; mirrors original PrismaticVLM logic."""
460
+ if ((input_ids is not None) and (input_ids.shape[0] > 1)) or (
461
+ (inputs_embeds is not None) and (inputs_embeds.shape[0] > 1)
462
+ ):
463
+ raise ValueError("Generation with batch size > 1 is not currently supported!")
464
+
465
+ # Handle `past_key_values` (cache) =>> assume `input_ids` just has unprocessed tokens
466
+ if past_key_values is not None:
467
+ input_ids = input_ids[:, -1:]
468
+
469
+ # If `input_embeds` are passed, we only want to use them in the 1st generation step
470
+ if inputs_embeds is not None and past_key_values is None:
471
+ model_inputs = {"input_embeds": inputs_embeds}
472
+ else:
473
+ model_inputs = {"input_ids": input_ids}
474
+
475
+ # Make sure `pixel_values` are preserved in `model_inputs`
476
+ model_inputs.update(
477
+ {
478
+ "attention_mask": attention_mask,
479
+ "pixel_values": pixel_values,
480
+ "past_key_values": past_key_values,
481
+ "use_cache": kwargs.get("use_cache"),
482
+ }
483
+ )
484
+
485
+ return model_inputs
486
+
487
+ # Defer to Language Model (all handle this differently, with different return types)
488
+ def _reorder_cache(self, *args, **kwargs) -> Any:
489
+ return self.language_model._reorder_cache(*args, **kwargs)
490
+
491
+
492
+ class OpenVLAForActionPrediction(PrismaticForConditionalGeneration):
493
+ config_class: PretrainedConfig = OpenVLAConfig
494
+
495
+ def __init__(self, config: OpenVLAConfig) -> None:
496
+ super().__init__(config)
497
+ self.norm_stats = config.norm_stats
498
+
499
+ # Compute action bins
500
+ self.bins = np.linspace(-1, 1, config.n_action_bins)
501
+ self.bin_centers = (self.bins[:-1] + self.bins[1:]) / 2.0
502
+
503
+ # Compute vocab size for de-tokenization -- revert added "multiple of"
504
+ self.vocab_size = self.config.text_config.vocab_size - self.config.pad_to_multiple_of
505
+
506
+ def predict_action(
507
+ self, input_ids: Optional[torch.LongTensor] = None, unnorm_key: Optional[str] = None, **kwargs: str
508
+ ) -> np.ndarray:
509
+ """Thin wrapper around .generate() that decodes predicted actions and unnormalizes them."""
510
+ # If the special empty token ('') does not already appear after the colon (':') token in the prompt
511
+ # (after "OUT:" or "ASSISTANT:"), insert it to match the inputs seen at training time
512
+ if not torch.all(input_ids[:, -1] == 29871):
513
+ input_ids = torch.cat(
514
+ (input_ids, torch.unsqueeze(torch.Tensor([29871]).long(), dim=0).to(input_ids.device)), dim=1
515
+ )
516
+
517
+ # Run VLA inference
518
+ generated_ids = self.generate(input_ids, max_new_tokens=self.get_action_dim(unnorm_key), **kwargs)
519
+
520
+ # Extract predicted action tokens and translate into (normalized) continuous actions
521
+ predicted_action_token_ids = generated_ids[0, -self.get_action_dim(unnorm_key) :].cpu().numpy()
522
+ discretized_actions = self.vocab_size - predicted_action_token_ids
523
+ discretized_actions = np.clip(discretized_actions - 1, a_min=0, a_max=self.bin_centers.shape[0] - 1)
524
+ normalized_actions = self.bin_centers[discretized_actions]
525
+
526
+ # Unnormalize actions
527
+ action_norm_stats = self.get_action_stats(unnorm_key)
528
+ mask = action_norm_stats.get("mask", np.ones_like(action_norm_stats["q01"], dtype=bool))
529
+ action_high, action_low = np.array(action_norm_stats["q99"]), np.array(action_norm_stats["q01"])
530
+ actions = np.where(
531
+ mask,
532
+ 0.5 * (normalized_actions + 1) * (action_high - action_low) + action_low,
533
+ normalized_actions,
534
+ )
535
+
536
+ return actions
537
+
538
+ @staticmethod
539
+ def _check_unnorm_key(norm_stats: Dict[str, Dict[str, Any]], unnorm_key: Optional[str]) -> str:
540
+ if unnorm_key is None:
541
+ assert len(norm_stats) == 1, (
542
+ f"Your model was trained on more than one dataset, "
543
+ f"please pass a `unnorm_key` from the following options to choose the statistics "
544
+ f"used for un-normalizing actions: {norm_stats.keys()}"
545
+ )
546
+ unnorm_key = next(iter(norm_stats.keys()))
547
+
548
+ assert unnorm_key in norm_stats, (
549
+ f"The `unnorm_key` you chose is not in the set of available dataset statistics, "
550
+ f"please choose from: {norm_stats.keys()}"
551
+ )
552
+ return unnorm_key
553
+
554
+ def get_action_dim(self, unnorm_key: Optional[str] = None) -> int:
555
+ """Get the dimensionality of the policy's action space."""
556
+ unnorm_key = self._check_unnorm_key(self.norm_stats, unnorm_key)
557
+ return len(self.norm_stats[unnorm_key]["action"]["q01"])
558
+
559
+ def get_action_stats(self, unnorm_key: Optional[str] = None) -> Dict[str, Any]:
560
+ """Get all the logged statistics for the given dataset."""
561
+ unnorm_key = self._check_unnorm_key(self.norm_stats, unnorm_key)
562
+ return self.norm_stats[unnorm_key]["action"]
piper_pick_can_terra_delta/preprocessor_config.json ADDED
@@ -0,0 +1,114 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "auto_map": {
3
+ "AutoImageProcessor": "processing_prismatic.PrismaticImageProcessor",
4
+ "AutoProcessor": "processing_prismatic.PrismaticProcessor"
5
+ },
6
+ "image_processor_type": "PrismaticImageProcessor",
7
+ "image_resize_strategy": "resize-naive",
8
+ "input_sizes": [
9
+ [
10
+ 3,
11
+ 224,
12
+ 224
13
+ ],
14
+ [
15
+ 3,
16
+ 224,
17
+ 224
18
+ ]
19
+ ],
20
+ "interpolations": [
21
+ "bicubic",
22
+ "bicubic"
23
+ ],
24
+ "means": [
25
+ [
26
+ 0.485,
27
+ 0.456,
28
+ 0.406
29
+ ],
30
+ [
31
+ 0.5,
32
+ 0.5,
33
+ 0.5
34
+ ]
35
+ ],
36
+ "processor_class": "PrismaticProcessor",
37
+ "stds": [
38
+ [
39
+ 0.229,
40
+ 0.224,
41
+ 0.225
42
+ ],
43
+ [
44
+ 0.5,
45
+ 0.5,
46
+ 0.5
47
+ ]
48
+ ],
49
+ "tvf_crop_params": [
50
+ {
51
+ "output_size": [
52
+ 224,
53
+ 224
54
+ ]
55
+ },
56
+ {
57
+ "output_size": [
58
+ 224,
59
+ 224
60
+ ]
61
+ }
62
+ ],
63
+ "tvf_do_letterbox": false,
64
+ "tvf_letterbox_fill": null,
65
+ "tvf_normalize_params": [
66
+ {
67
+ "inplace": false,
68
+ "mean": [
69
+ 0.484375,
70
+ 0.455078125,
71
+ 0.40625
72
+ ],
73
+ "std": [
74
+ 0.228515625,
75
+ 0.2236328125,
76
+ 0.224609375
77
+ ]
78
+ },
79
+ {
80
+ "inplace": false,
81
+ "mean": [
82
+ 0.5,
83
+ 0.5,
84
+ 0.5
85
+ ],
86
+ "std": [
87
+ 0.5,
88
+ 0.5,
89
+ 0.5
90
+ ]
91
+ }
92
+ ],
93
+ "tvf_resize_params": [
94
+ {
95
+ "antialias": true,
96
+ "interpolation": 3,
97
+ "max_size": null,
98
+ "size": [
99
+ 224,
100
+ 224
101
+ ]
102
+ },
103
+ {
104
+ "antialias": true,
105
+ "interpolation": 3,
106
+ "max_size": null,
107
+ "size": [
108
+ 224,
109
+ 224
110
+ ]
111
+ }
112
+ ],
113
+ "use_fused_vision_backbone": true
114
+ }
piper_pick_can_terra_delta/processing_prismatic.py ADDED
@@ -0,0 +1,257 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """
2
+ processing_prismatic.py
3
+
4
+ HuggingFace-style preprocessor definitions for Prismatic VLMs, inheriting from `ProcessorMixin`. Default configuration
5
+ specifies `siglip-224px+7b`.
6
+ """
7
+
8
+ from typing import Any, ClassVar, List, Optional, Tuple, Union
9
+
10
+ import timm.data
11
+ import torch
12
+ import torchvision.transforms.functional as TVF
13
+ from PIL import Image
14
+ from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
15
+ from transformers import PreTrainedTokenizerBase
16
+ from transformers.image_processing_utils import BatchFeature, ImageProcessingMixin
17
+ from transformers.processing_utils import ProcessorMixin
18
+ from transformers.tokenization_utils import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
19
+ from transformers.utils import TensorType
20
+
21
+
22
+ # === Image Processing ===
23
+ def letterbox_pad_transform(image: Image.Image, padding_fill_value: Tuple[int, int, int]) -> Image.Image:
24
+ """Given a PIL.Image, pad to square by adding a symmetric border around the height/width."""
25
+ (w, h), max_wh = image.size, max(image.size)
26
+ horizontal_pad, vertical_pad = int((max_wh - w) / 2), int((max_wh - h) / 2)
27
+ padding = (horizontal_pad, vertical_pad, horizontal_pad, vertical_pad)
28
+
29
+ return TVF.pad(image, padding, fill=padding_fill_value, padding_mode="constant")
30
+
31
+
32
+ class PrismaticImageProcessor(ImageProcessingMixin):
33
+ model_input_names: ClassVar[List[str]] = ["pixel_values"]
34
+
35
+ def __init__(
36
+ self,
37
+ use_fused_vision_backbone: bool = False,
38
+ image_resize_strategy: str = "letterbox",
39
+ input_sizes: Optional[List[Tuple[int, int, int]]] = None,
40
+ interpolations: Optional[List[str]] = None,
41
+ means: Optional[List[Tuple[float, float, float]]] = None,
42
+ stds: Optional[List[Tuple[float, float, float]]] = None,
43
+ **kwargs: str,
44
+ ) -> None:
45
+ """
46
+ Initialize a PrismaticImageProcessor as a wrapper around a torchvision transform; this transform will be
47
+ created by TIMM, and edited to follow our custom `image_resize_strategy` logic.
48
+
49
+ @param use_fused_vision_backbone: Boolean indicating single or fused (dual) vision backbone
50
+ @param image_resize_strategy: Prismatic image resize strategy in < resize-naive | resize-crop | letterbox >
51
+ @param input_size: [TIMM :: `data_cfg`] Input image size as tuple (channels, width, height)
52
+ @param interpolation: [TIMM :: `data_cfg`] Interpolation as string (default: "bicubic")
53
+ @param mean: [TIMM :: `data_cfg`] Normalization mean as float tuple (or two-tuple if `fused_backbone`)
54
+ @param std: [TIMM :: `data_cfg`] Normalization std as float tuple (or two-tuple if `fused_backbone`)
55
+ """
56
+ self.use_fused_vision_backbone = use_fused_vision_backbone
57
+ self.image_resize_strategy = image_resize_strategy
58
+
59
+ # Handle `None` default values
60
+ input_sizes = [(3, 224, 224)] if input_sizes is None else input_sizes
61
+ means = [(0.5, 0.5, 0.5)] if means is None else means
62
+ stds = [(0.5, 0.5, 0.5)] if stds is None else stds
63
+
64
+ # TIMM `data_cfg` Parameters
65
+ self.input_sizes, self.interpolations, self.means, self.stds = input_sizes, interpolations, means, stds
66
+
67
+ # Grab torchvision transforms via TIMM =>> need to parse for specific "functional" transform values!
68
+ self.tvf_resize_params, self.tvf_crop_params, self.tvf_normalize_params = [], [], []
69
+ self.tvf_do_letterbox, self.tvf_letterbox_fill = False, None
70
+
71
+ for idx in range(len(input_sizes)):
72
+ transform = timm.data.create_transform(
73
+ input_size=self.input_sizes[idx],
74
+ interpolation=self.interpolations[idx],
75
+ mean=self.means[idx],
76
+ std=self.stds[idx],
77
+ crop_pct=1.0, # Set to 1.0 to ignore cropping (initial Resize sets `input_size`)
78
+ crop_mode="center", # Default crop mode -- no-op when `crop_pct == 1.0`
79
+ is_training=False, # No image augmentations when loading the transform!
80
+ )
81
+
82
+ # [Validation] Ensure appropriate transform structure, expected sizes
83
+ if not (
84
+ isinstance(transform, Compose)
85
+ and (len(transform.transforms) == 4)
86
+ and isinstance(transform.transforms[0], Resize)
87
+ and isinstance(transform.transforms[1], CenterCrop)
88
+ and isinstance(transform.transforms[2], ToTensor)
89
+ and isinstance(transform.transforms[3], Normalize)
90
+ and (transform.transforms[0].size == self.input_sizes[idx][-1])
91
+ and (transform.transforms[1].size == self.input_sizes[idx][-2:])
92
+ ):
93
+ raise ValueError(f"Unexpected TIMM image transformation structure/sizes: `{transform}`")
94
+
95
+ # HF Image Processors *must* be JSON-serializable; as such, cannot have torchvision. as an attribute.
96
+ # => Instead, we're going to parse the transform and call "torchvision.transforms.functional" (`tvf`)
97
+ resize_t, crop_t, norm_t = transform.transforms[0], transform.transforms[1], transform.transforms[3]
98
+ self.tvf_resize_params.append(
99
+ {
100
+ "size": resize_t.size,
101
+ "interpolation": TVF.pil_modes_mapping[resize_t.interpolation],
102
+ "max_size": None,
103
+ "antialias": True,
104
+ }
105
+ )
106
+ self.tvf_crop_params.append({"output_size": crop_t.size})
107
+ self.tvf_normalize_params.append(
108
+ {
109
+ "mean": norm_t.mean.float().numpy().tolist(),
110
+ "std": norm_t.std.float().numpy().tolist(),
111
+ "inplace": False,
112
+ }
113
+ )
114
+ self.tvf_do_letterbox, self.tvf_letterbox_fill = False, None
115
+
116
+ # Handle Prismatic `image_resize_strategy`
117
+ if self.image_resize_strategy == "resize-naive":
118
+ self.tvf_resize_params[idx]["size"] = (resize_t.size, resize_t.size)
119
+ elif self.image_resize_strategy == "letterbox":
120
+ self.tvf_do_letterbox, self.tvf_letterbox_fill = True, tuple([int(x * 255) for x in self.means[idx]])
121
+ elif self.image_resize_strategy == "resize-crop":
122
+ pass
123
+ else:
124
+ raise ValueError(f"Image resize strategy `{self.image_resize_strategy}` is not supported!")
125
+
126
+ # Dispatch **kwargs to super()
127
+ super().__init__(**kwargs)
128
+
129
+ def apply_transform(self, img: Image.Image) -> torch.Tensor:
130
+ """Apply `functional` variant of TIMM's Transform = Compose([Resize -> CenterCrop -> ToTensor -> Normalize])"""
131
+ if self.tvf_do_letterbox:
132
+ img = letterbox_pad_transform(img, self.tvf_letterbox_fill)
133
+
134
+ # [Contract] Fused Backbones expect "channel-stacked" inputs; we'll unpack on the model side!
135
+ imgs_t = []
136
+ for idx in range(len(self.input_sizes)):
137
+ img_idx = TVF.resize(img, **self.tvf_resize_params[idx])
138
+ img_idx = TVF.center_crop(img_idx, **self.tvf_crop_params[idx])
139
+ img_idx_t = TVF.to_tensor(img_idx)
140
+ img_idx_t = TVF.normalize(img_idx_t, **self.tvf_normalize_params[idx])
141
+ imgs_t.append(img_idx_t)
142
+
143
+ # [Contract] `imgs_t` is a list of Tensors of shape [3, input_size, input_size]; stack along dim = 0
144
+ img_t = torch.vstack(imgs_t)
145
+
146
+ return img_t
147
+
148
+ def preprocess(
149
+ self,
150
+ images: Union[Image.Image, List[Image.Image]],
151
+ return_tensors: Optional[Union[str, TensorType]] = None,
152
+ **_: str,
153
+ ) -> BatchFeature:
154
+ """
155
+ Preprocess an image (or batch of images); note that unlike the `transformers :: BaseImageProcessor` we
156
+ explicitly only handle PIL.Image.Image instances for simplicity.
157
+
158
+ @param images: A (batch of) PIL.Image.Image instance(s) to preprocess.
159
+ @param return_tensors: BatchFeature default Tensor format (e.g., "pt" for torch); if None, returns np.ndarray
160
+
161
+ @return: Instance of `transformers :: BatchFeature` with a single key "pixel_values"
162
+ """
163
+ if not isinstance(images, list):
164
+ images = [images]
165
+
166
+ # Apply `self.img_transform` to each image (will return list of torch.Tensors); stack into "batched" Tensor
167
+ pixel_values = torch.stack([self.apply_transform(img.convert("RGB")) for img in images])
168
+
169
+ # Return BatchFeature =>> note that for compatibility, constructor expects Dict[str, np.ndarray], so we convert
170
+ return BatchFeature(data={"pixel_values": pixel_values.float().numpy()}, tensor_type=return_tensors)
171
+
172
+ def __call__(self, images: Union[Image.Image, List[Image.Image]], **kwargs) -> BatchFeature:
173
+ return self.preprocess(images, **kwargs)
174
+
175
+
176
+ # === PrismaticProcessor =>> Wraps both ImageProcessor and Tokenizer ===
177
+ # =>> https://github.com/huggingface/transformers/blob/main/src/transformers/models/llava/processing_llava.py
178
+ class PrismaticProcessor(ProcessorMixin):
179
+ attributes: ClassVar[List[str]] = ["image_processor", "tokenizer"]
180
+ image_processor_class: str = "AutoImageProcessor"
181
+ tokenizer_class: str = "AutoTokenizer"
182
+
183
+ def __init__(
184
+ self,
185
+ image_processor: Optional[ImageProcessingMixin] = None,
186
+ tokenizer: Optional[PreTrainedTokenizerBase] = None,
187
+ ) -> None:
188
+ super().__init__(image_processor, tokenizer)
189
+
190
+ def __call__(
191
+ self,
192
+ text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]],
193
+ images: Union[Image.Image, List[Image.Image]],
194
+ padding: Union[bool, str, PaddingStrategy] = False,
195
+ truncation: Optional[Union[bool, str, TruncationStrategy]] = None,
196
+ max_length: Optional[int] = None,
197
+ return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH,
198
+ ) -> BatchFeature:
199
+ """
200
+ Preprocess a given (batch) of text/images for a Prismatic VLM; forwards text to the underlying LLM's tokenizer,
201
+ forwards images to PrismaticImageProcessor.
202
+
203
+ @param text: The (batch) of text to encode; must be a string or list of strings.
204
+ @param images: A (batch of) PIL.Image.Image instance(s) to preprocess.
205
+ @param padding: Sequence padding strategy (if multiple specified) in < True = "longest" | "max_length" | False >
206
+ @param truncation: Truncation strategy for the output sequences; requires `max_length` to be specified
207
+ @param max_length: Maximum length (in tokens) to truncate
208
+ @param return_tensors: Type of return tensors (usually "pt" or TensorType.PYTORCH)
209
+
210
+ @return: BatchFeature with keys for `input_ids`, `attention_mask` and `pixel_values`.
211
+ """
212
+ pixel_values = self.image_processor(images, return_tensors=return_tensors)["pixel_values"]
213
+ text_inputs = self.tokenizer(
214
+ text, return_tensors=return_tensors, padding=padding, truncation=truncation, max_length=max_length
215
+ )
216
+
217
+ # [Validate] Need same number of images and text inputs!
218
+ if pixel_values.shape[0] != text_inputs.input_ids.shape[0]:
219
+ raise ValueError("Batch is malformed; expected same number of images and text inputs!")
220
+
221
+ return BatchFeature(data={**text_inputs, "pixel_values": pixel_values})
222
+
223
+ # === Tokenizer Dispatch Utilities =>> check `PreTrainedTokenizerBase` for documentation ===
224
+ def batch_decode(
225
+ self,
226
+ sequences: Union[List[int], List[List[int]], torch.Tensor, Any], # `Any` = np.ndarray | tf.Tensor
227
+ skip_special_tokens: bool = False,
228
+ clean_up_tokenization_spaces: Optional[bool] = None,
229
+ **kwargs: str,
230
+ ) -> List[str]:
231
+ return self.tokenizer.batch_decode(
232
+ sequences=sequences,
233
+ skip_special_tokens=skip_special_tokens,
234
+ clean_up_tokenization_spaces=clean_up_tokenization_spaces,
235
+ **kwargs,
236
+ )
237
+
238
+ def decode(
239
+ self,
240
+ token_ids: Union[int, List[int], torch.Tensor, Any], # `Any` = np.ndarray | tf.Tensor
241
+ skip_special_tokens: bool = False,
242
+ clean_up_tokenization_spaces: Optional[bool] = None,
243
+ **kwargs: str,
244
+ ) -> str:
245
+ return self.tokenizer.decode(
246
+ token_ids=token_ids,
247
+ skip_special_tokens=skip_special_tokens,
248
+ clean_up_tokenization_spaces=clean_up_tokenization_spaces,
249
+ **kwargs,
250
+ )
251
+
252
+ @property
253
+ def model_input_names(self) -> List[str]:
254
+ tokenizer_input_names = self.tokenizer.model_input_names
255
+ image_processor_input_names = self.image_processor.model_input_names
256
+
257
+ return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
piper_pick_can_terra_delta/processor_config.json ADDED
@@ -0,0 +1,6 @@
 
 
 
 
 
 
 
1
+ {
2
+ "auto_map": {
3
+ "AutoProcessor": "processing_prismatic.PrismaticProcessor"
4
+ },
5
+ "processor_class": "PrismaticProcessor"
6
+ }
piper_pick_can_terra_delta/special_tokens_map.json ADDED
@@ -0,0 +1,60 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ }
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+ }
piper_pick_can_terra_delta/tokenizer.json ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:1219be150dec6ecbf36f507377e7df878602b89df9388db708d2dc3356aa43be
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+ size 1843708
piper_pick_can_terra_delta/tokenizer.model ADDED
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+ ],
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+ "use_default_system_prompt": false
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+ }
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