contrib: add SmolVLA-Libero (HuggingFaceVLA/smolvla_libero)

contrib/models/SmolVLA-Libero/README.md

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+# Contrib Model: SmolVLA-Libero
+
+NeuronX Distributed Inference port of `HuggingFaceVLA/smolvla_libero` — a
+SmolVLM2-backed flow-matching vision-language-action (VLA) policy fine-tuned
+on the LIBERO benchmark. Three compiled subgraphs, maximally on-Neuron,
+written in the per-model NxDI structure.
+
+## Model Information
+
+- **HuggingFace ID:** `HuggingFaceVLA/smolvla_libero`
+- **Backbone:** `HuggingFaceTB/SmolVLM2-500M-Instruct` (full 32-layer text decoder)
+- **Model Type:** Flow-matching VLA (SigLIP vision + SmolLM-style VLM + action expert)
+- **Action head:** 32-layer expert with self/cross-attn alternation, 50-step action chunk, 10-step Euler denoising
+- **License:** Check HuggingFace model card
+
+## Architecture Details
+
+| Component                                              | Where      | Subgraph |
+|--------------------------------------------------------|------------|----------|
+| SigLIP vision encoder (12 layers, hidden=768)          | **Neuron** | #1       |
+| Pixel-shuffle 4× + connector + scale by sqrt(960)      | **Neuron** | #1       |
+| Lang token embed + scale by sqrt(960)                  | **Neuron** | #2       |
+| State projection (32 → 960)                            | **Neuron** | #2       |
+| VLM 32-layer text decoder (eager GQA, RoPE, RMSNorm)   | **Neuron** | #2       |
+| Pad-aware position_ids + 2D attention mask             | **Neuron** | #2 / #3  |
+| Action expert: 16× self-attn (concat past KV) layers   | **Neuron** | #3       |
+| Action expert: 16× cross-attn (Q from suffix) layers   | **Neuron** | #3       |
+| Sinusoidal timestep embedding                          | **Neuron** | #3       |
+| Action in/out projections + time MLP                   | **Neuron** | #3       |
+| Image preprocessing (resize-with-pad, normalize)       | CPU        | —        |
+| Tokenization                                           | CPU        | —        |
+| 10-step Euler denoising loop                           | CPU        | —        |
+
+**Deviations from "everything on Neuron":**
+
+1. The 10-step Euler loop runs on CPU. Static-shape compilation cannot host
+   a Python `for step in range(N)` as a single graph; the loop body is the
+   compiled subgraph. Each step calls NEFF #3 with the updated `noisy_actions`.
+2. Tokenization, image flip / resize-with-pad, and state-vector composition
+   run on CPU because they are data-loading, not model compute.
+
+**Hardware constraint flagged:** `tp_degree = 1` because
+`num_attention_heads = 15` and `num_kv_heads = 5` — neither divides cleanly
+into the 4 Neuron cores on `trn3pd98.3xlarge`. The NxDI parallel primitives
+(`ColumnParallelLinear`, `RowParallelLinear`, `ParallelEmbedding`) are still
+used throughout, so this code is portable to instances with divisor-friendly
+head counts. On this instance, 3 of 4 cores idle; the model fits comfortably
+in one core's HBM with vast headroom.
+
+## Validation Results
+
+**Validated:** 2026-05-06
+**Configuration:** TP=1, batch_size=1, bfloat16
+**Instance:** trn3pd98.3xlarge
+**NxDI:** 2.29 (`/opt/aws_neuronx_venv_pytorch_2_9_nxd_inference`)
+
+### Test Results
+
+| Check                                                        | Result                                       |
+|--------------------------------------------------------------|----------------------------------------------|
+| Vision NEFF vs HF SmolVLM2 vision (single image)             | cos_sim = 0.99990                            |
+| Prefix KV layer 0..31 vs lerobot CPU                         | max abs diff ≤ 0.4 (BF16)                    |
+| Full action chunk vs lerobot CPU (matched noise)             | cos_sim = 0.9999, mean abs diff = 0.007      |
+| Full action chunk: Neuron vs lerobot CPU (this `test_model.py`) | cos_sim = 0.999921, mean abs diff = 0.0015 |
+| Closed-loop LIBERO `libero_object` task 0                    | success                                      |
+| End-to-end inference latency (one chunk)                     | warm p50 = 62.5 ms (10 iters)                |
+
+The numerical match against the lerobot CPU reference replicates four
+lerobot-specific quirks that aren't in the SmolVLM2 HF config:
+
+1. **`resize_with_pad` pads top+left only** (image lands in the bottom-right
+   corner of the 512×512 frame), not centered.
+2. **Pad-aware attention**: dynamic 2D mask + cumsum-based position_ids that
+   skip padding tokens. A static prefix-LM mask leaks attention into pad-token
+   positions.
+3. **RoPE max_wavelength = 10000** (lerobot hardcodes this in `apply_rope`);
+   the SmolVLM2 HF config says 100000, but lerobot trained the model with 10000.
+4. **Image flip** in the LIBERO env (180° rotate, both H and W) per the
+   `libero_processor` step in `lerobot.processor.env_processor`.
+
+## Inference Flow
+
+```
+images [2 cams × [B, 3, 512, 512]]   lang_token_ids [B, 48]   lang_mask [B, 48]   state [B, 32]
+                |                              |                    |                 |
+        [Neuron NEFF #1]                       |                    |                 |
+        Vision (per camera)                    |                    |                 |
+                |                              |                    |                 |
+        [B, 128, 960] vision_features          |                    |                 |
+                |______________________________|____________________|_________________|
+                                                |
+                                       [Neuron NEFF #2]
+                                       VLM Prefix (32 layers, pad-aware)
+                                                |
+                                       prefix_keys, prefix_values
+                                       [32, B, 177, 5, 64] each
+                                                |
+                          ┌─────────────────────┴─────────────────────┐
+                          │  CPU Euler loop (10 steps)                │
+                          │     for t in [1.0, 0.9, ..., 0.1]:        │
+                          │         v_t = NEFF#3(x_t, t, K, V, pad)   │
+                          │         x_t += dt * v_t                   │
+                          └─────────────────────┬─────────────────────┘
+                                                |
+                                       action_chunk [B, 50, 32]
+                                       (first 7 dims used by env)
+```
+
+## Source Layout
+
+```
+SmolVLA-Libero/
+├── README.md
+├── src/
+│   ├── __init__.py
+│   ├── config_constants.py          # All architecture constants from the checkpoint
+│   ├── modeling_smolvla.py          # SmolVLAPolicy: orchestrator (compile / load / generate)
+│   ├── modeling_smolvla_vision.py   # SigLIP-12L + connector  (NEFF #1)
+│   ├── modeling_smolvla_text.py     # VLM 32L prefix + Action expert 32L denoiser  (NEFF #2 + #3)
+│   ├── neuron_action_head_base.py   # NeuronDenoisingConfig — ModelWrapper-compatible config shim
+│   ├── weight_mapping.py            # HF safetensors -> 3 per-subgraph state dicts
+│   └── run_inference.py             # CLI: compile / run / benchmark (synthetic inputs)
+└── test/
+    ├── __init__.py
+    ├── integration/
+    │   ├── __init__.py
+    │   └── test_model.py            # Smoke + numerical tests against lerobot CPU
+    └── unit/
+        └── __init__.py
+```
+
+We add `_text` because SmolVLA has separate text-prefix and action-expert
+subgraphs that the existing per-model layout (e.g. `pixtral/` with
+`modeling_pixtral.py` + `modeling_pixtral_vision.py`) does not need.
+
+## Usage
+
+### Setup
+
+```bash
+source /opt/aws_neuronx_venv_pytorch_2_9_nxd_inference/bin/activate
+
+# Download checkpoint (one-time)
+python -c "from huggingface_hub import snapshot_download; \
+    print(snapshot_download(repo_id='HuggingFaceVLA/smolvla_libero'))"
+```
+
+### Compile + run via CLI
+
+```bash
+cd contrib/models/SmolVLA-Libero/src
+
+# Compile (one-time, ~90s wall clock for 3 NEFFs)
+python run_inference.py --action compile \
+    --hf-checkpoint /home/ubuntu/.cache/huggingface/hub/models--HuggingFaceVLA--smolvla_libero/snapshots/<hash>/ \
+    --neff-dir      /home/ubuntu/smol_vla_neff_libero
+
+# Run inference (synthetic inputs, p50 / p99 latency, NaN check)
+python run_inference.py --action run \
+    --hf-checkpoint /home/ubuntu/.cache/huggingface/hub/models--HuggingFaceVLA--smolvla_libero/snapshots/<hash>/ \
+    --neff-dir      /home/ubuntu/smol_vla_neff_libero
+```
+
+### Programmatic
+
+```python
+import sys
+from pathlib import Path
+sys.path.insert(0, str(Path("contrib/models/SmolVLA-Libero/src")))
+
+from modeling_smolvla import SmolVLAPolicy
+
+policy = SmolVLAPolicy(hf_checkpoint_dir="<path>", tp_degree=1)
+policy.load("/home/ubuntu/smol_vla_neff_libero")
+
+# images: list of NUM_CAMERAS tensors, each [B, 3, 512, 512] BF16
+# lang_token_ids: [B, 48] INT32
+# lang_mask: [B, 48] BOOL  (True = real token, False = pad)
+# state: [B, 32] FP32  (already normalized, zero-padded)
+action_chunk = policy.generate(images, lang_token_ids, state, lang_mask=lang_mask)
+# action_chunk: [B, 50, 32] FP32  (first 7 dims used by LIBERO)
+```
+
+## Compatibility Matrix
+
+| Instance / NxDI | 2.29 |
+|-----------------|------|
+| Trn3            | ✅ Working |
+| Trn2            | Not tested |
+| Trn1 / Inf2     | Not tested |
+
+## Testing
+
+The integration test compiles the three NEFFs (or reuses an already-compiled
+directory), loads them, and runs three checks:
+
+1. **Smoke** — full pipeline returns a finite `[B, 50, 32]` action chunk with
+   non-zero variance.
+2. **Warm latency** — p50 latency over 5 iterations is under a generous bound
+   (1 s; expected ~65 ms on `trn3pd98.3xlarge`).
+3. **Neuron vs lerobot CPU parity** (NxDI accuracy check) — loads the
+   upstream `lerobot.SmolVLAPolicy` from the same HF checkpoint, runs a
+   CPU forward with identical inputs and identical seeded initial noise,
+   and asserts cosine similarity ≥ 0.99 and mean abs diff < 0.05 against
+   the Neuron action chunk. This is the SmolVLA equivalent of the logit
+   validation NxDI uses for CausalLM contrib models — it validates that
+   the Neuron port reproduces the reference implementation, not just
+   self-consistency. Skipped automatically if `lerobot` is not installed.
+
+```bash
+# One-time, point the test at a checkpoint and a NEFF output directory:
+export SMOLVLA_CKPT=/home/ubuntu/.cache/huggingface/hub/models--HuggingFaceVLA--smolvla_libero/snapshots/<hash>/
+export SMOLVLA_NEFF=/home/ubuntu/smol_vla_neff_libero
+
+# Run
+pytest contrib/models/SmolVLA-Libero/test/integration/test_model.py --capture=tee-sys
+
+# Or directly
+cd contrib/models/SmolVLA-Libero
+python test/integration/test_model.py
+```
+
+The first invocation compiles the three NEFFs into `$SMOLVLA_NEFF` (~90 s).
+Subsequent runs reuse the compiled artifacts and only re-load + execute.
+
+## Example Checkpoints
+
+- [`HuggingFaceVLA/smolvla_libero`](https://huggingface.co/HuggingFaceVLA/smolvla_libero)
+  — used for the validation results above
+
+## Maintainer
+
+Community contribution.
+
+**Last Updated:** 2026-05-06

contrib/models/SmolVLA-Libero/src/__init__.py

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+"""SmolVLA-Libero NxDI port for AWS Trainium.
+
+This directory is intended to be imported flat — add it to ``sys.path`` and
+import the modules directly (e.g. ``from modeling_smolvla import SmolVLAPolicy``).
+This matches the convention used by other models under ``contrib/models/``.
+"""

contrib/models/SmolVLA-Libero/src/config_constants.py

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+"""
+SmolVLA architecture constants.
+
+All numbers extracted directly from the HuggingFaceVLA/smolvla_libero
+checkpoint and config.json. Every other file in this port imports from here
+— no hardcoded shapes anywhere else.
+
+Source:
+    HF model id : HuggingFaceVLA/smolvla_libero
+    Backbone    : HuggingFaceTB/SmolVLM2-500M-Instruct (full 32-layer text model)
+    expert_width_multiplier : 0.5  (expert hidden = 480)
+    chunk size  : 50  (action prediction horizon)
+    num steps   : 10  (Euler denoising steps, on CPU)
+"""
+
+# ---------------------------------------------------------------------------
+# Vision encoder (SigLIP)
+# ---------------------------------------------------------------------------
+
+VISION_NUM_LAYERS         = 12
+VISION_HIDDEN             = 768
+VISION_INTERMEDIATE       = 3072
+VISION_NUM_HEADS          = 12          # 768 / 64
+VISION_HEAD_DIM           = 64
+VISION_PATCH_SIZE         = 16
+VISION_IMAGE_SIZE         = 512
+VISION_NUM_PATCHES        = (VISION_IMAGE_SIZE // VISION_PATCH_SIZE) ** 2  # 1024
+VISION_LAYER_NORM_EPS     = 1e-6
+
+# Connector pixel-shuffle: scale_factor=4 → 4x4 spatial merge → 16x token reduction
+PIXEL_SHUFFLE_SCALE       = 4
+VISION_TOKENS_PER_IMAGE   = VISION_NUM_PATCHES // (PIXEL_SHUFFLE_SCALE ** 2)  # 64
+CONNECTOR_INPUT_DIM       = VISION_HIDDEN * (PIXEL_SHUFFLE_SCALE ** 2)        # 12288
+
+# ---------------------------------------------------------------------------
+# VLM text backbone (SmolLM2-style — LlamaDecoderLayer with GQA)
+# ---------------------------------------------------------------------------
+
+VLM_NUM_LAYERS            = 32
+VLM_HIDDEN                = 960
+VLM_INTERMEDIATE          = 2560
+VLM_NUM_HEADS             = 15
+VLM_NUM_KV_HEADS          = 5
+VLM_HEAD_DIM              = 64
+VLM_KV_DIM                = VLM_NUM_KV_HEADS * VLM_HEAD_DIM   # 320
+VLM_RMS_NORM_EPS          = 1e-5
+VLM_ROPE_THETA            = 10000.0     # lerobot.smolvlm_with_expert.apply_rope hardcodes max_wavelength=10000
+VLM_VOCAB_SIZE            = 49280
+
+# ---------------------------------------------------------------------------
+# Action expert (Llama-style with cross-attn override every other layer)
+# ---------------------------------------------------------------------------
+
+EXPERT_NUM_LAYERS         = 32
+EXPERT_HIDDEN             = 480         # round(960 * 0.5)
+EXPERT_INTERMEDIATE       = 1280        # confirmed from checkpoint
+EXPERT_NUM_HEADS          = 15          # same as VLM
+EXPERT_NUM_KV_HEADS       = 5           # same as VLM
+EXPERT_HEAD_DIM           = 64
+EXPERT_KV_DIM             = EXPERT_NUM_KV_HEADS * EXPERT_HEAD_DIM   # 320
+EXPERT_Q_DIM              = EXPERT_NUM_HEADS * EXPERT_HEAD_DIM      # 960
+SELF_ATTN_EVERY_N_LAYERS  = 2
+# Even-indexed expert layers (0,2,...,14) are "self-attn" — concat past VLM K/V
+# with new expert K/V along seq dim. k/v_proj input dim = expert hidden (720).
+# Odd-indexed expert layers (1,3,...,15) are "cross-attn" — Q from expert,
+# K/V from past VLM K/V projected through k/v_proj. k/v_proj input dim = 320.
+
+# ---------------------------------------------------------------------------
+# Action / state projections (flow-matching action head)
+# ---------------------------------------------------------------------------
+
+MAX_STATE_DIM             = 32
+MAX_ACTION_DIM            = 32
+ACTION_CHUNK_SIZE         = 50          # config.chunk_size / n_action_steps
+NUM_DENOISE_STEPS         = 10
+
+# Sinusoidal timestep embedding parameters
+TIMESTEP_EMBED_DIM        = EXPERT_HIDDEN     # 720 — output dim of sin/cos block
+TIMESTEP_MIN_PERIOD       = 0.004
+TIMESTEP_MAX_PERIOD       = 4.0
+
+# action_time_mlp_in: Linear(1440, 720)  — (action_emb 720 ⊕ time_emb 720) → 720
+# action_time_mlp_out: Linear(720, 720)
+ACTION_TIME_MLP_IN_DIM    = EXPERT_HIDDEN * 2  # 1440
+
+# ---------------------------------------------------------------------------
+# Sequence layout (static at compile time)
+# ---------------------------------------------------------------------------
+
+NUM_CAMERAS               = 2                   # image (agentview), image2 (wrist)
+NUM_TEXT_TOKENS           = 48                  # tokenizer_max_length
+NUM_VISION_TOKENS_TOTAL   = NUM_CAMERAS * VISION_TOKENS_PER_IMAGE  # 192
+NUM_STATE_TOKENS          = 1
+PREFIX_LEN                = NUM_VISION_TOKENS_TOTAL + NUM_TEXT_TOKENS + NUM_STATE_TOKENS  # 241
+SUFFIX_LEN                = ACTION_CHUNK_SIZE                                              # 50
+FULL_LEN                  = PREFIX_LEN + SUFFIX_LEN                                        # 291
+
+# ---------------------------------------------------------------------------
+# Neuron runtime
+# ---------------------------------------------------------------------------
+
+# DEVIATION FLAG: tp_degree=1 because num_attention_heads=15 and
+# num_kv_heads=5 — neither divides cleanly into the 4 cores available on
+# trn3pd98.3xlarge. Production NxDI parallel primitives are still used so the
+# code stays portable to instances where head counts allow real TP, but on
+# this hardware sharding effectively no-ops.
+DEFAULT_TP_DEGREE         = 1
+BATCH_SIZE                = 1
+TORCH_DTYPE_STR           = "bfloat16"