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FlashRT — Stable Public API

This document enumerates every symbol that is part of FlashRT's public stability contract. Symbols listed here will not be removed or have their signatures changed without a major version bump.

Symbols not listed here (internal modules, private functions, class internals) may change between minor releases.

Top-level (flash_rt)

import flash_rt

flash_rt.__version__   # str, e.g. "2.2.0"
flash_rt.load_model    # → VLAModel
flash_rt.VLAModel      # inference wrapper

flash_rt.load_model(...)

def load_model(
    checkpoint: str,
    framework: str = "torch",       # "torch" | "jax"
    num_views: int = 2,             # 1, 2, or 3
    autotune: int = 3,              # 0=off, 3=default, 5+=thorough
    recalibrate: bool = False,
    weight_cache: bool = True,      # JAX only
    config: str = "pi05",           # "pi05" | "pi0" | "groot" | "groot_n17" | "pi0fast" | "motus" | "wan22_ti2v_5b" | "cosmos3_video"
    device=None,                    # reserved
    # Pi0-FAST-specific:
    decode_cuda_graph: bool = False,
    decode_graph_steps: int = 80,
    max_decode_steps: int = 256,
    hardware: str = "auto",         # "auto" | "thor" | "rtx_sm120" | "rtx_sm89" | "rtx_sm87"
    # GROOT-specific:
    embodiment_tag: str | None = None,
    action_horizon: int | None = None,
    # Pi0.5 torch-specific:
    use_fp4: bool = False,
    fp4_layers: tuple[int, ...] | None = None,
    use_awq: bool | None = None,
    awq_alpha: float = 0.5,
    use_p1_split_gu: bool | None = None,
    num_steps: int | None = None,
    vision_pool_factor: int | None = None,
    vision_num_layers: int | None = None,
    cache_frames: int | None = None,
    # Frontends with an FP8/BF16 switch:
    use_fp8: bool = True,
    # Pi0.5 torch RTX SM120/SM89 opt-in:
    use_fp16: bool = False,
) -> VLAModel

Returns a VLAModel wrapping the appropriate frontend for the detected (or explicitly specified) GPU architecture.

  • decode_cuda_graph, decode_graph_steps, max_decode_steps apply to Pi0-FAST.
  • embodiment_tag and action_horizon apply to GROOT.
  • use_fp4, fp4_layers, use_awq, awq_alpha, and use_p1_split_gu apply to the Pi0.5 torch NVFP4 encoder path.
  • num_steps, vision_pool_factor, vision_num_layers, and cache_frames apply only to frontends that expose those constructor parameters today. The Pi0.5 torch RTX/Orin frontend validates vision_pool_factor in {1, 2, 4}, vision_num_layers in [1, 27], and cache_frames >= 1.
  • use_fp8=False disables FP8 where the selected frontend exposes a BF16 fallback; unsupported frontends ignore it.
  • use_fp16=True selects the opt-in Pi0.5 torch RTX SM120/SM89 full-FP16 CUDA Graph path. It requires use_fp8=False and is currently valid only for config="pi05", framework="torch", and hardware in {"rtx_sm120", "rtx_sm89"}.
  • config="motus" is a beta RTX SM120 frontend. It expects a Motus checkpoint plus Wan and VLM checkpoint paths supplied to the Motus quickstart/frontend; see docs/motus_usage_beta.md.
  • config="wan22_ti2v_5b" is an RTX SM120 official-pipeline Wan2.2 baseline. It exposes set_prompt(prompt, negative_prompt=...) and infer(mode="t2v"|"i2v", width=..., height=..., frames=..., steps=..., shift=..., guide_scale=..., seed=..., teacache=False, teacache_threshold=..., teacache_start_step=..., teacache_end_step=..., teacache_cache_device=...); predict() is not part of this video-generation API. See docs/wan22_usage.md.
  • config="cosmos3_video" is an RTX SM120 Cosmos3-Nano text2video FP8 denoise model (non-VLA). It exposes set_prompt(ref=<reference dump>) for conditioning and infer(teacache_skip=..., shift=..., compare_ref=..., return_metadata=...), returning the denoised vision latent; predict() is not part of this API. Precision is selected with load_model(..., use_fp8=True|False). See docs/cosmos3_video_usage.md.
  • config="groot_n17" is registered for framework="torch" and hardware="rtx_sm120". This route validates the N1.7 DiT self/cross-attention path on the vendored FA2 backend.

flash_rt.VLAModel

class VLAModel:
    def set_prompt(self, *args, **kwargs): ...
    def infer(self, *args, **kwargs): ...
    def predict(self, images, prompt=None, state=None) -> np.ndarray: ...
    def warm_state_prompt_buckets(self, images, prompt, states) -> list[int]: ...
    def recalibrate(self) -> None: ...

    @property
    def framework(self) -> str: ...
    @property
    def prompt(self) -> str | None: ...

  • set_prompt(*args, **kwargs) — delegate prompt setup to the selected frontend. GROOT N1.7 currently uses set_prompt(aux=..., prompt=...), where aux contains the captured Qwen3-VL setup tensors consumed by the N1.7 calibration path.

  • infer(*args, **kwargs) — delegate inference to the selected frontend. GROOT N1.7 currently uses infer(state_normalized, initial_noise=..., use_dit_graph=...) and returns normalized actions.

  • predict(images, prompt, state) — run one inference step. images: list of (224,224,3) uint8 numpy arrays, or a dict with "image" / "wrist_image" / "wrist_image_right" keys. prompt: required on first call, reused on subsequent calls if None. state: optional robot state array. predict() attaches it to the observation as "state" when the caller passes image lists, while preserving an explicit "state" already present in a dict observation. For frontends whose set_prompt() accepts state, predict() refreshes the prompt prefix when either the prompt text or that prompt-state value changes. Returns np.ndarray of shape (action_horizon, action_dim).

    state is part of the VLA observation schema; each model encodes it through its own reference contract:

    • Pi0 uses a continuous state token in the action-expert suffix.
    • Pi0.5 encodes state as openpi-compatible discretized prompt tokens: Task: <prompt>, State: <openpi state bins>;\nAction: . The bins use OpenPI's np.digitize(state, np.linspace(-1, 1, 257)[:-1]) - 1 convention: normalized in-range values usually become 0..255, while values below -1 become -1. RTX/Thor torch frontends and the JAX Thor Pi0.5 frontend accept state in set_prompt() and through predict(). Same-length state prompt updates reuse the captured graph; recurring RTX prompt lengths reuse the cached pipeline instead of rebuilding.
    • Pi0-FAST encodes state in the FAST token prefix.
    • GROOT N1.6 consumes proprioceptive state from obs["state"]; if omitted, the backend uses zeros.
    • GROOT N1.7 currently uses the lower-level normalize_state(...) + infer(state_normalized, initial_noise=...) contract rather than the image-list predict() contract.

  • warm_state_prompt_buckets(images, prompt, states) — Pi0.5 RTX torch helper for realtime loops that pass changing state through the prompt. It calls the selected frontend's bucket warmup hook, using images as the calibration/capture observation and each item in states as a representative robot state. The return value is the sorted list of warmed prompt lengths. The method preserves the OpenPI state prompt text format; it does not zero-pad or otherwise rewrite state tokens. A recurring prompt token length reuses its cached runtime pipeline, but a previously unseen state-token length still pays one first-time bucket build/capture cost. Serving code should keep state serialization stable (fixed state dimension and numeric rounding/precision policy) and prewarm representative states from the deployment range, for example reset, mid-rollout, near-goal, and a few recorded rollout observations whose discretized values may tokenize to different lengths.

  • recalibrate() — clear FP8 calibration cache and force re-calibration on the next predict() call.

Hardware dispatch (flash_rt.hardware)

from flash_rt.hardware import detect_arch, resolve_pipeline_class

detect_arch() -> str

Returns "thor", "rtx_sm120", "rtx_sm89", or "rtx_sm87" based on the current CUDA device's compute capability. SM87 is currently supported only for config="pi05", framework="torch".

resolve_pipeline_class(config, framework, arch)

Lazily imports and returns the concrete frontend class for the given (config, framework, arch) triple. Used internally by load_model. Motus beta is registered for (config="motus", framework="torch", arch="rtx_sm120"). GROOT N1.7 RTX is registered for (config="groot_n17", framework="torch", arch="rtx_sm120"). Wan2.2 TI2V-5B is registered for (config="wan22_ti2v_5b", framework="torch", arch="rtx_sm120").

_PIPELINE_MAP

flash_rt.hardware._PIPELINE_MAP: dict[tuple[str, str, str], tuple[str, str]]

The dispatch table mapping (config, framework, arch) to (module_path, class_name). External plugins may mutate this dict at import time to register new models — see Plugin Model Template.

AttentionBackend protocol (flash_rt.hardware.backend)

from flash_rt.hardware.backend import (
    AttentionBackend,    # Protocol class
    AttentionBackendBase,  # Optional base with accessor defaults
    AttentionSpec,       # Full model attention specification
    SiteSpec,            # One attention site descriptor
)

SiteSpec

@dataclass
class SiteSpec:
    num_layers: int
    num_q_heads: int
    num_kv_heads: int
    head_dim: int
    max_q_seq: int
    max_kv_seq: int | None = None     # None → self-attention
    batch_axis: int = 1
    sliding_window: int | None = None  # reserved for SWA models
    causal: bool = False
    extra: dict = field(default_factory=dict)

AttentionSpec

@dataclass
class AttentionSpec:
    sites: dict[str, SiteSpec]
    def add_site(self, name: str, **kwargs) -> AttentionSpec: ...
    def site(self, name: str) -> SiteSpec: ...

AttentionBackend (Protocol)

class AttentionBackend(Protocol):
    def sites(self) -> tuple[str, ...]: ...
    def get_slot_ptrs(self, site: str, layer_idx: int) -> dict[str, int]: ...
    def run(self, site: str, layer_idx: int, q_seq: int, *,
            kv_seq: int | None = None, stream: int = 0) -> int: ...
    def head_dim(self, site: str) -> int: ...
    def num_q_heads(self, site: str) -> int: ...
    def num_kv_heads(self, site: str) -> int: ...

Reference implementations (reusable, not stable)

These concrete backends are available for reuse by plugins. Their existence is stable, but their internal signatures may change between minor releases. Plugins that subclass or call into these should pin a minor version.

from flash_rt.hardware.rtx.attn_backend       import RtxFlashAttnBackend
from flash_rt.hardware.rtx.attn_backend_groot import RtxFlashAttnBackendGroot

Both classes are framework-neutral — used by torch and jax frontends alike. The old names TorchFlashAttnBackend / TorchFlashAttnBackendGroot are kept as deprecated module-level aliases and will be removed in the next major version.

Core utilities (flash_rt.core)

from flash_rt.core.cuda_buffer import CudaBuffer
from flash_rt.core.cuda_graph import CUDAGraph
from flash_rt.core.quant.calibrator import load_calibration, save_calibration

These are used by both pipelines and frontends. Their public API is stable; internal helper functions are not.

Native extension modules

FlashRT ships two pybind11 Python extension modules:

from flash_rt import flash_rt_kernels   # always present
from flash_rt import flash_rt_fa2       # RTX (SM80/86/89/120) only

flash_rt.flash_rt_kernels

The main kernel module — hand-written CUDA code plus cuBLASLt/ CUTLASS wrappers for memory-bound ops (norm, activation, fusion, FP8 quant, residual, gate-geglu, true-silu, etc.) and Thor-specific attention (fvk.attention_qkv_fp16). Binary name pattern: flash_rt_kernels.cpython-<abi>.so, ~3 MB.

All fvk.<symbol>(...) calls seen in pipeline code live here. Signatures are internal — plug-ins should go through the AttentionBackend protocol, not call fvk.* directly.

Motus beta SM120 kernels are built into flash_rt_kernels when CMake is configured with -DFLASHRT_ENABLE_MOTUS=ON (currently the default). They do not require a separate runtime kernel library directory.

Runtime utilities

from flash_rt.runtime.rtc import (
    ActionChunkAdapter,
    CallablePolicyAdapter,
    AsyncChunkRunner,
    RTCStats,
)

RTC-lite is a beta inference scheduling utility for action-chunk policies. It does not change model numerics or calibration; it only serves action chunks at a fixed controller rate while a background worker prepares the next chunk. See docs/rtc_lite_design.md.

flash_rt.flash_rt_fa2

Vendored Flash-Attention 2 v2.7.4.post1 (forward only, fp16 + bf16, SM80-family SASS). Binary name pattern: flash_rt_fa2.cpython-<abi>.so, ~135 MB. Only built when GPU_ARCH ∈ {80, 86, 87, 89, 120}. Exposes:

flash_rt_fa2.fwd_fp16(
    Q, K, V, O, softmax_lse,
    softmax_lse_accum=0, o_accum=0,   # splitkv scratch ptrs; 0 disables splitkv
    *,
    batch, seqlen_q, seqlen_k,
    num_heads_q, num_heads_kv, head_dim,
    q_strides, k_strides, v_strides, o_strides,   # 3-tuples (batch, row, head) in elements
    softmax_scale=1.0,
    num_sms=0,                         # required for splitkv heuristic
    stream=0,
)
flash_rt_fa2.fwd_bf16(...)            # same signature, bfloat16 dtype

All pointer args are int device pointers (tensor.data_ptr()). Pipeline code should go through RtxFlashAttnBackend (which calls this module internally) rather than invoking it directly — direct use is unstable and may change without notice. RtxFlashAttnBackend selects between this module and the pip flash-attn wheel via the FVK_RTX_FA2 env var (default "1" = use vendored FA2; "0" = fallback to flash_attn.flash_attn_func). The backend name reflects the hardware family (RTX), not the frontend framework — the same backend instance serves both torch and jax frontends.

Thor (SM110) builds do not produce this module — attention on Thor uses flash_rt_kernels.attention_qkv_fp16 (cuBLAS-decomposed) because FA2's Ampere tile shapes aren't tuned for Thor's unified LPDDR memory model. Code importing flash_rt_fa2 must therefore guard for ImportError on Thor deployments, or stay inside the AttentionBackend protocol which handles the dispatch transparently.

Directory structure (post-refactor)

flash_rt/
├── __init__.py              # load_model, VLAModel
├── api.py                   # load_model implementation
├── core/                    # shared utilities (CudaBuffer, CUDAGraph, calibrator)
├── hardware/
│   ├── __init__.py          # detect_arch, _PIPELINE_MAP, resolve_pipeline_class
│   ├── backend.py           # AttentionBackend protocol
│   ├── rtx/                 # rtx attention backends (hardware primitives only)
│   └── thor/
│       ├── attn_backend.py        # Thor FMHA wrapper
│       ├── attn_backend_groot.py  # GROOT-specific Thor attention
│       └── shared_primitives.py   # CLOSED SET: model-agnostic Thor helpers only
│                                  #   (_gpu_*, _measure_scale_gpu,
│                                  #    siglip_forward, encoder_forward,
│                                  #    encoder_forward_calibrate)
├── models/
│   ├── pi05/
│   │   ├── pipeline_thor.py       # Pi0.5 Thor compute (postln_project, decoder_*)
│   │   └── pipeline_rtx.py        # Pi0.5 RTX Pi05Pipeline class
│   ├── pi0/
│   │   ├── pipeline_thor.py       # Pi0 Thor decoder fns
│   │   └── pipeline_rtx.py        # Pi0 RTX Pi0Pipeline class
│   ├── pi0fast/
│   │   └── pipeline.py            # DEPRECATED PATTERN: Thor+SM120 runtime fork
│   │                              # do NOT copy this style for new models
│   └── groot/
│       ├── pipeline_thor.py       # GROOT Thor pipeline
│       ├── pipeline_rtx.py        # GROOT RTX pipeline
│       └── embodiments.py         # per-embodiment MLP slots
└── frontends/
    ├── torch/
    │   ├── pi05_thor.py    (Pi05TorchFrontendThor)
    │   ├── pi05_rtx.py     (Pi05TorchFrontendRtx)
    │   ├── pi0_thor.py     (Pi0TorchFrontendThor)
    │   ├── pi0_rtx.py      (Pi0TorchFrontendRtx)
    │   ├── pi0fast.py      (Pi0FastTorchFrontend)    DEPRECATED — Thor+RTX hybrid
    │   ├── groot_thor.py   (GrootTorchFrontendThor)
    │   └── groot_rtx.py    (GrootTorchFrontendRtx)
    └── jax/
        ├── pi05_thor.py    (Pi05JaxFrontendThor)
        ├── pi05_rtx.py     (Pi05JaxFrontendRtx)
        ├── pi0_thor.py     (Pi0JaxFrontendThor)
        ├── pi0_rtx.py      (Pi0JaxFrontendRtx)
        └── pi0fast.py      (Pi0FastJaxFrontend)      DEPRECATED — Thor+RTX hybrid

Naming convention (established 2026-04, stage 8 unified-pipeline-layout refactor):

  • Every (model, hardware) compute path is its own file: models/<m>/pipeline_<hw>.py where <hw> ∈ {thor, rtx}. No pipeline.py (no-suffix default entry) is allowed.
  • Every (model, framework, hardware) IO path is its own frontend file: frontends/<fw>/<m>_<hw>.py with class <Model><Fw>Frontend<Hw> (e.g. Pi05TorchFrontendThor, Pi05TorchFrontendRtx).
  • No runtime hardware forks (if self._has_sm100, hasattr(fvk, ...)): if a model needs different code on Thor vs RTX, those are separate files.
  • hardware/<hw>/shared_primitives.py is a closed set of model-agnostic helpers. Model-specific forwards/decoders go into models/<m>/pipeline_<hw>.py, never into shared_primitives.py.
  • _PIPELINE_MAP is one-to-one: each (model, framework, hw) tuple routes to exactly one frontend file/class.

Known historical exception (do NOT copy for new models):

  • pi0fast ships as a single pipeline.py with 14+ if self._has_sm100 branches and a single multi-hw frontend file. This in-file SM-fork pattern is retained for the existing Pi0-FAST implementation only; new models should follow the standard (model, framework, hw) split.

Declarative weight loading (stage 7)

Thor frontends' per-layer weight-loading loops are expressed as ModelWeightSpec objects in private spec modules next to the frontend. The public surface is three things:

from flash_rt.executors.weight_loader import (
    Item, LayerBlock, ModelWeightSpec, WeightLoader,
)
from flash_rt.executors.torch_weights import (  # torch side
    SafetensorsSource, DictSource,
    Cat, FusedQKV, FusedGateUp,
    ToFp16, ToFp32, T, tT, InterleaveQK, Quant, Mul,
    Attr, TensorList, FlatCat,
)
from flash_rt.executors.jax_weights import (  # jax side
    OrbaxDictSource,
    Transpose, Astype, Contiguous, JaxQuant,
    NumpyAttr, NumpyList, CudaBufferAttr, CudaBufferFlat,
)

Stability contract: the classes above and their constructor signatures are public. Adding new sink/transform/composite classes is backwards-compatible; existing ones will not be removed or renamed without a major version bump.

Spec file naming: flash_rt/frontends/{torch,jax}/_<model>_thor_spec.py, each exporting a build_spec() -> ModelWeightSpec. Shared block builders live in _thor_spec_common.py per framework.

Convention for scale lists: spec items that quant set scale_into="_<group>_scales"; frontends wrap these into device tensors after loader.run():

self._enc_w_dev = torch.tensor(self._enc_w_scales,
                               dtype=torch.float32, device='cuda')

See docs/adding_new_model.md for the end-to-end model adaptation walkthrough, and docs/plugin_model_template.md for registering an external-plugin model via _PIPELINE_MAP.

Adaptation / extension guides

When adding a new model or kernel, read these in order:

  1. docs/adding_new_model.md — end-to-end walkthrough for wiring a new VLA model into FlashRT on Thor (AttentionSpec → WEIGHT_SPEC → pipeline forward → frontend → calibration → graph capture → registration → tests).
  2. docs/calibration.md — FP8 weight/activation scale mechanics, alpha = act_scale × weight_scale invariants, calibration cache format, and the four historical bugs every new model's _calibrate should guard against.
  3. docs/kernel_fusion.md — the 93 public fvk kernels grouped by purpose, current production fusion patterns, what does and does not fuse, and the catalog of failed optimizations (OPT-3 / OPT-5 / v1.5-B2 / v1.5-B4) to avoid.