spiraltorch 0.4.10

Rust-first learning primitives for SpiralTorch

SpiralTorch Python bindings

This package exposes a thin, dependency-light bridge to SpiralTorch's Rust-first training stack. The wheel ships the same Z-space tensors, hypergrad tapes, and unified rank planners that power the Rust API—no NumPy, no PyTorch, and no shim layers.

Install

pip install -U spiraltorch

The published wheel is WGPU-first with CPU fallback. In plain Python terms, start with three handles:

• spiraltorch.Tensor for dependency-light native tensors.
• spiraltorch.nn for modules, losses, trainers, LoRA adapters, and checked checkpoint handoff.
• spiraltorch.ecosystem when a PyTorch/JAX/CuPy/TensorFlow tensor needs to cross the Z-space membrane.

python - <<'PY'
import spiraltorch as st
from spiraltorch.nn import Linear, Sequential

print("runtime:", st.describe_device("cpu")["backend"])

model = Sequential()
model.add(Linear(2, 2, name="head"))
print("forward:", model(st.Tensor(1, 2, [0.25, 0.75])).tolist())

head = Linear(2, 2, name="head")
native = dict(head.state_dict())
external = {
    "lm_head.weight": native["head::weight"],
    "lm_head.bias": native["head::bias"],
}
report = head.state_dict_compatibility_with_key_map(
    external,
    {"lm_head.weight": "head::weight", "lm_head.bias": "head::bias"},
)
print("checkpoint compatible:", report["compatible"])
PY

What's included

• Tensor, ComplexTensor, and OpenTopos for dependency-free geometry experiments.
• Native neural layers via spiraltorch.nn—Linear, Embedding, Sequential, losses, ModuleTrainer, LoraLinear, and ZSpaceProjector.
• Checked checkpoint handoff helpers for exact or key-mapped state_dict reports, subset loads, overlap resize/projection preflight, and HF-style checkpoint presets.
• LanguageWaveEncoder + Hypergrad so Python callers can stream Z-space text, accumulate gradients, and project back into the Poincaré ball.
• TensorBiome to cultivate open-topos rewrites, weight shoots, stack the harvest, and guard tensors that can be re-imported into Z-space.
• Unified planning helpers (plan, plan_topk, describe_device, probe_gpu_path) that reuse the same heuristics as the Rust executors.
• ROCm probing (hip_probe) so Python callers can reflect the stubbed device hints shared with the Rust runtime.
• Z-space barycentre solver (z_space_barycenter) to mix colour-field priors and chart couplings directly from Python.
• Loss-monotone barycenter intermediates (BarycenterIntermediate) that plug into Hypergrad.accumulate_barycenter_path so tapes converge along the same Z-space corridor as the solver.
• Lightweight runtime orchestration via SpiralSession so callers can record backend intent, inspect device preflight evidence, and reuse the same RankPlan helpers as the Rust executors.
• Language desire controls via st.nn.DesirePipeline, DesireTrainerBridge, DesireRoundtableBridge, and downstream hook adapters so notebooks can inspect phase/temperature/entropy offsets without making the symbolic kernel internals part of the stable public surface.
• Native trainer harnesses via spiraltorch.nn.ModuleTrainer and RoundtableConfig for quick notebook experiments without leaving the Rust training loop.
• Event observability via spiraltorch.plugin—subscribe, listen queues, or record JSONL streams with plugin.record(...).
• Python plugin registry via spiraltorch.plugin.register_python_plugin(...) (and plugin.load_entrypoints(...) / plugin.load_path(...) / plugin.reload_path(...) / plugin.watch_path(...) for discovery + hot reload). The spiral-plugin CLI can introspect plugin graphs (list, graph, dot, explain, validate).
• Custom operator registration via spiraltorch.ops with flexible register calls, ops.signature(...), and a human-friendly ops.describe(...).
• Built-in module + state-dict serialization helpers (spiraltorch.nn.save_json / spiraltorch.nn.load_json, plus bincode equivalents) for Linear, Sequential, and core layer modules; pass None to load_json to get a state dict back. The higher-level spiraltorch.nn.save / load helpers auto-detect JSON vs bincode and emit a compact manifest alongside weights.
• Expanded loss surface: MeanSquaredError, HyperbolicCrossEntropy (CrossEntropy alias), FocalLoss, ContrastiveLoss, and TripletLoss.
• Direct access to the core A/B/C roundtable trainer via spiraltorch.nn.ModuleTrainer (RoundtableConfig, RoundtableSchedule, EpochStats) including prepare/step/zero, optional realgrad toggles, and curvature-scheduler controls plus spectral/coherence bridge toggles (with tunable SpectralLearningRatePolicy) for long-running adaptive training loops.
• Attentionless sequence layers via spiraltorch.nn—WaveRnn, WaveGate, ZSpaceMixer, and FeatureReorder2d for Conv/RNN-style language baselines.
• Coherence VAE primitives via spiraltorch.nn—MellinBasis, ZSpaceVae, and ZSpaceTextVae for encoder+decoder reconstruction training loops, batch metrics, SGD/Adam/RMSProp optimizer state, and Atlas-ready telemetry.
• Streaming dataset helpers via spiraltorch.dataset—build a shuffle/batch/prefetch pipeline entirely in Rust using the native DataLoader.
• Trace and artifact utilities via spiraltorch.zspace_trace, spiraltorch.trainer_trace, and Atlas adapters so JSONL telemetry can be loaded, summarized, compared, and rendered from Python.
• SoT-3Dφ spiral planners (spiraltorch.sot) that collapse to Z-space tensors, grow full TensorBiomes via SoT3DPlan.grow_biome(...), and feed geometry-aware experiments or trace artifacts without requiring a Python session-side trace builder.
• Z-space projector bindings (spiraltorch.nn.ZSpaceProjector) so spiral trajectories can be rendered onto the canvas or reused inside sequential transformer stacks.
• Atlas adapters (spiraltorch.zspace_atlas) to convert JSONL traces + trainer events into telemetry.AtlasRoute summaries.
• Deployment and optimisation bridges via spiraltorch.integrations: archive TorchServe models, persist BentoML runners, explore hyperparameters with Optuna or Ray Tune, and emit deployment JSON artefacts (ONNX/TFLite planned) - all behind ergonomic Python call sites. Use the spiral-export CLI to generate export artefacts.
• Ecosystem helpers via spiraltorch.ecosystem to shuttle tensors between PyTorch, JAX, CuPy, and TensorFlow through zero-copy DLPack bridges.
• Reinforcement learning harness via spiraltorch.spiral_rl—SpiralTorchRL keeps policy gradients inside Z-space tensors, exposes hypergrad-enabled updates, and streams geometric rewards without leaving Rust.
• Recommendation toolkit via spiraltorch.rec—SpiralTorchRec factors user/item lattices under open-cartesian topos guards so embeddings stay psychoid-safe while training entirely in Rust.
• Model-zoo orchestration via spiraltorch.model_zoo—discover recipes, filter by task/family, resolve script paths, rank recommendations with suggest_models(...)/recommend_model(...), and run models with a stable Python API or the spiral-model-zoo CLI.
• Stream telemetry interop via vision.ChronoSnapshot, vision.ZSpaceStreamFrame, vision.StreamedVolume, and vision.ZSpaceStreamFrameAggregator so Python can attach chrono summaries, aggregate live frame streams, and ingest temporal updates without dropping to Rust glue code.
• Online stream-loop helpers vision.vision_online_step(...) and vision.stream_vision_training(...) to wire frame streams into SpiralTorchVision + ZSpaceTrainer loops directly from Python.

Tokenizerless FT diagnostics

The examples in examples/byte_lm_*.py provide a bounded byte-LM fine-tune diagnostic surface for local HF/PyTorch-style checkpoints without making Torch, safetensors, or Transformers hard dependencies of the binding. Start with examples/byte_lm_profile_smoke.py --hf-state-dict <path> --key-preset auto to run checkpoint preflight, native LoRA/source/profile comparisons, promotion manifests, and dry-run continuation plans before scaling into heavier training runs. spiraltorch.nn.Linear, Embedding, and LoraLinear expose checked exact and key-mapped load reports, while ZSpaceProjector can be inserted when you want a bounded residual projection instead of silently trusting an imported state dict. For a practical Transformers fine-tune readiness smoke, add --ft-readiness-preset hf-wgpu-balanced; this turns on checkpoint audit, Transformers trace, produced-manifest validation, same-process transformers/torch/tokenizers co-import evidence, the Transformers/trainer runtime bridge gate, describe_device("wgpu") runtime readiness evidence, and WGPU run-summary/promotion gates. The recipe expands to --runtime-contract-preset hf-runtime --wgpu-readiness-preset balanced; use hf-wgpu-observed to only require WGPU metrics/report presence or hf-wgpu-strict for a high-readiness gate that also requires WGPU runtime-ready evidence. Lower-level runtime/WGPU presets, explicit --runtime-device-report-backend, and explicit run, promotion, or manifest WGPU thresholds override the recipe defaults. Add --transformers-audit when a local Transformers config/tokenizer should be co-imported into the same JSONL evidence without making Transformers mandatory. For pre-FT inference evidence, examples/byte_lm_transformers_trace.py loads a local Transformers model, records prompt-level next-token top-k logits and hidden-state summaries, co-imports config/tokenizer/model runtime metadata, and can attach --zspace-project projection metrics. Add --runtime-contract-preset hf-runtime to require same-process transformers/torch/tokenizers co-import evidence without going through the profile ladder, or use checkpoint_preflight.py --transformers-runtime-contract-preset hf-runtime for the matching checkpoint audit shortcut. Add --require-runtime-metadata-match when comparing traces to fail fast on config/tokenizer/model swaps before reading prompt-level drift.

Building wheels

The binding mirrors the Rust feature flags. Pick the backend(s) you need and maturin will bake the appropriate artefact:

pip install maturin==1.*

# macOS wheel targets (Apple Silicon requires >= 11.0):
# - macOS 11+ (broad compatibility): export MACOSX_DEPLOYMENT_TARGET=11.0
# - macOS 14+ (separate wheel build): export MACOSX_DEPLOYMENT_TARGET=14.0
export MACOSX_DEPLOYMENT_TARGET=11.0

# Default binding build (WGPU-first; CPU fallback remains available)
maturin build -m bindings/st-py/Cargo.toml --release --locked

# Release-equivalent (matches PyPI wheels: default WGPU route + logic/kdsl)
maturin build -m bindings/st-py/Cargo.toml --release --locked --features logic,kdsl

# CPU-only (drop the default WGPU route but keep the standard Python surface)
maturin build -m bindings/st-py/Cargo.toml --release --locked --no-default-features --features python-default

# Add CUDA or HIP alongside the default WGPU-first wheel
maturin build -m bindings/st-py/Cargo.toml --release --locked --features cuda,logic,kdsl
maturin build -m bindings/st-py/Cargo.toml --release --locked --features hip,logic,kdsl

# Backend-specific builds without the default WGPU route
maturin build -m bindings/st-py/Cargo.toml --release --locked --no-default-features --features python-default,cuda,logic,kdsl
maturin build -m bindings/st-py/Cargo.toml --release --locked --no-default-features --features python-default,hip,logic,kdsl

# Install the wheel you just built
pip install --force-reinstall --no-cache-dir target/wheels/spiraltorch-*.whl

Smoke tests (no pytest required)

PYTHONNOUSERSITE=1 python3 -s -m unittest bindings/st-py/tests/test_unittest_smoke.py

Minimal usage

Model Zoo discovery + launch

import spiraltorch as st

entries = st.model_zoo.list_models(task="classification")
print("classification recipes:", [entry.key for entry in entries[:5]])

suggested = st.model_zoo.suggest_models(
    "llm_char",
    task="language-modeling",
    prefer_tags=["coherence"],
)
print("suggested:", [entry.key for entry in suggested[:3]])

zspace_stream = st.model_zoo.suggest_models(
    focus="zspace_stream",
    available_only=True,
    limit=3,
)
print("zspace stream track:", [entry.key for entry in zspace_stream])

cmd = st.model_zoo.build_model_command("mlp_regression", "--help")
print("command:", " ".join(cmd))

spiral-model-zoo focuses
spiral-model-zoo list --task language-modeling
spiral-model-zoo suggest llm_char --task language-modeling --prefer-tag coherence
spiral-model-zoo suggest --focus zspace_stream --available-only
spiral-model-zoo run zspace_stream_online_vision -- --steps 16 --flush-every 2
spiral-model-zoo run zspace_stream_frame_aggregator -- --steps 12 --native-frames
spiral-model-zoo run mlp_regression -- --help

Rank-K execution

import spiraltorch as st

plan = st.plan_topk(rows=2, cols=4, k=2, backend="auto")
print(plan.kind, plan.effective_backend)
print("tile/workgroup:", plan.tile, plan.workgroup)
print(plan.to_unison_script().splitlines()[0])
print(plan.fft_spiralk_hint().splitlines()[0])

Hello SpiralSession

python bindings/st-py/examples/hello_session.py

Aligns a barycenter with a hypergrad tape, prepares a Sequential module, and finishes a roundtable epoch entirely from Python.

from spiraltorch import Tensor, Hypergrad, LanguageWaveEncoder

encoder = LanguageWaveEncoder(-1.0, 0.5)
wave = encoder.encode_z_space("SpiralTorch in Rust")
rows, cols = wave.shape()
z = Tensor(rows, cols, [0.0] * (rows * cols))

tape = Hypergrad(-1.0, 0.05, *z.shape())
tape.accumulate_pair(z, wave)
tape.apply(z)
print(z.tolist())

Canvas projector quickstart

python bindings/st-py/examples/canvas_projector_quickstart.py

Renders a radial energy field into an RGBA surface (written as spiraltorch_canvas.ppm) and prints the row-wise FFT power spectrum tensor shape.

Atlas telemetry quickstart

python bindings/st-py/examples/atlas_quickstart.py

Builds an AtlasFrame from a Python dict and prints the district aggregation.

import spiraltorch as st

route = st.telemetry.AtlasRoute()
route.push_bounded(
    st.telemetry.AtlasFrame.from_metrics({"psi.total": 1.0}, timestamp=0.0),
    bound=32,
)
route.push_bounded(
    st.telemetry.AtlasFrame.from_metrics({"psi.total": 1.5}, timestamp=1.0),
    bound=32,
)
print("summary:", route.summary()["frames"], "frames")
print("psi perspective:", route.perspective_for("Concourse", focus_prefixes=["psi."])["guidance"])

Z-space + optim quickstart

python bindings/st-py/examples/zspace_optim_quickstart.py

import spiraltorch as st

opt = st.optim.Amegagrad((1, 3), curvature=-0.9, hyper_learning_rate=0.03, real_learning_rate=0.02)
weights = st.Tensor(1, 3, [0.2, -0.1, 0.05])

opt.accumulate_wave(st.Tensor(1, 3, [0.4, -0.6, 0.2]))
opt.step(weights)  # tunes rates via DesireGradientControl + applies both tapes
print("weights:", weights.tolist())

trainer = st.ZSpaceTrainer(z_dim=4)
loss = trainer.step({"speed": 0.2, "memory": 0.1, "stability": 0.9, "gradient": opt.real.gradient()})
print("z:", trainer.state, "loss:", loss)

AmegagradSession quickstart

python bindings/st-py/examples/amegagrad_session_quickstart.py

Canvas → Atlas → Session quickstart

python bindings/st-py/examples/canvas_atlas_session_quickstart.py

Runs a small closed-loop demo:

• AmegagradSession updates weights
• CanvasProjector renders + emits a loopback patch
• CanvasProjector.emit_atlas_frame(...) streams metrics into AtlasRoute

Text → optim → zspace quickstart

python bindings/st-py/examples/text_optim_zspace_quickstart.py

import spiraltorch as st

encoder = st.LanguageWaveEncoder(-1.0, 0.5)
rows, cols = encoder.encode_z_space("SpiralTorch").shape()
opt = st.optim.Amegagrad((rows, cols), curvature=encoder.curvature())
weights = st.Tensor(rows, cols, [0.0] * (rows * cols))

opt.absorb_text(encoder, "Z-space wants to be steerable.")  # pads/truncates to (rows, cols)
opt.step(weights)
print("weights (head):", [v for row in weights.tolist() for v in row][:5])

trainer = st.ZSpaceTrainer(z_dim=4)
trainer.step({"speed": 0.2, "memory": 0.1, "stability": 0.9, "gradient": opt.real.gradient()})

Z-space inference quickstart

python bindings/st-py/examples/zspace_inference_quickstart.py

import spiraltorch as st

trainer = st.ZSpaceTrainer(z_dim=4)
loss = trainer.step_partial({"speed": 0.2, "memory": 0.1, "stability": 0.9, "gradient": [0.1, 0.0, 0.0, 0.0]})
print("z:", trainer.state, "loss:", loss)
print("last inference:", trainer.last_inference.residual, trainer.last_inference.confidence)

SoT-3Dφ → TensorBiome quickstart

python bindings/st-py/examples/sot_biome_quickstart.py

SpiralK KDSl plan rewrite quickstart

python bindings/st-py/examples/spiralk_plan_rewrite_quickstart.py

Maxwell-coded envelopes → SpiralK hints quickstart

python bindings/st-py/examples/maxwell_spiralk_bridge_quickstart.py

Streaming Z-space trainer quickstart

python bindings/st-py/examples/zspace_stream_training_quickstart.py

Ecosystem bridges

SpiralTorch tensors can flow into PyTorch or JAX without copies thanks to the spiraltorch.ecosystem helpers. CuPy round-trips also accept optional CUDA streams so you can coordinate asynchronous pipelines, and the helpers can resolve friendly stream aliases on demand:

import spiraltorch as st
from spiraltorch.ecosystem import (
    tensor_to_cupy,
    tensor_to_jax,
    tensor_to_tensorflow,
    tensor_to_torch,
    cupy_to_tensor,
    jax_to_tensor,
    tensorflow_to_tensor,
    torch_to_tensor,
)

spiral = st.Tensor(2, 2, [1.0, 2.0, 3.0, 4.0])

try:
    import torch

    torch_tensor = tensor_to_torch(spiral, dtype=torch.float32)
    roundtrip = torch_to_tensor(torch_tensor)
    print("torch:", roundtrip.shape())
except Exception as exc:
    print("torch bridge skipped:", type(exc).__name__)

try:
    jax_array = tensor_to_jax(spiral)
    spiral_again = jax_to_tensor(jax_array)
    print("jax:", spiral_again.shape())
except Exception as exc:
    print("jax bridge skipped:", type(exc).__name__)

try:
    # stream can be an explicit cupy.cuda.Stream, or a lazy alias such as
    # "current" (resolve the active stream) or "null" (select the default stream).
    cupy_array = tensor_to_cupy(spiral, stream="current")
    spiral_from_cupy = cupy_to_tensor(cupy_array, stream="current")
    print("cupy:", spiral_from_cupy.shape())
except Exception as exc:
    print("cupy bridge skipped:", type(exc).__name__)

try:
    tf_tensor = tensor_to_tensorflow(spiral)
    spiral_from_tf = tensorflow_to_tensor(tf_tensor)
    print("tensorflow:", spiral_from_tf.shape())
except Exception as exc:
    print("tensorflow bridge skipped:", type(exc).__name__)

import spiraltorch as st
from spiraltorch.nn import Linear, MeanSquaredError, Sequential

trainer = st.nn.ModuleTrainer(
    backend="cpu",
    curvature=-1.0,
    hyper_learning_rate=1e-2,
    fallback_learning_rate=1e-2,
)
schedule = trainer.roundtable(
    2,
    1,
    st.nn.RoundtableConfig(top_k=1, mid_k=1, bottom_k=1, here_tolerance=1e-5),
)
model = Sequential()
model.add(Linear(2, 1, name="layer"))
model.attach_hypergrad(curvature=-1.0, learning_rate=1e-2)

loss = MeanSquaredError()
x = st.Tensor.rand(2, 2, seed=3)
y = st.Tensor.rand(2, 1, seed=4)
stats = trainer.train_epoch(model, loss, [(x, y)], schedule)

print(f"roundtable avg loss {stats.average_loss:.6f} over {stats.batches} batch")

Desire pipeline orchestration

import spiraltorch as st

pipeline = st.nn.DesirePipeline(vocab_size=2, concepts=2)
step = pipeline.step([1.2, -0.4], previous_token=0, concept=[0.6, 0.4])
print("phase", step["phase"], "entropy", step["entropy"])

adapter = st.build_desire_adapter_from_downstream_hook(
    {
        "geometry_bias_coherence": {"score": 0.7},
        "top_probability": 0.8,
    }
)
pipeline.ingest_geometry_bias(adapter["geometry_bias_signal"], source="zspace")
print("geometry bias:", pipeline.geometry_bias_metrics())

Native trainer harness

Python callers can keep the training loop small while still using the Rust roundtable trainer. For heavier HPO/serving flows, use this loop as the inner objective and wrap it with your Optuna/Ray/BentoML/TorchServe tool of choice.

import spiraltorch as st
from spiraltorch.nn import Linear, MeanSquaredError, ModuleTrainer, RoundtableConfig, Sequential

dataset = [
    (st.Tensor(1, 2, [0.0, 1.0]), st.Tensor(1, 1, [1.0])),
    (st.Tensor(1, 2, [1.0, 0.0]), st.Tensor(1, 1, [0.0])),
]

for label, lr in [("warmup", 1e-2), ("refine", 5e-3)]:
    trainer = ModuleTrainer(
        backend="cpu",
        curvature=-1.0,
        hyper_learning_rate=lr,
        fallback_learning_rate=lr,
    )
    schedule = trainer.roundtable(
        1,
        1,
        RoundtableConfig(top_k=1, mid_k=1, bottom_k=1, here_tolerance=1e-5),
    )
    model = Sequential()
    model.add(Linear(2, 1, name="layer"))
    model.attach_hypergrad(curvature=-1.0, learning_rate=lr)
    stats = trainer.train_epoch(model, MeanSquaredError(), dataset, schedule)
    print(label, f"avg_loss={stats.average_loss:.6f}")

SpiralTorchRL quickstart

spiraltorch.spiral_rl packages the Rust-side reinforcement-learning harness so Python notebooks can select actions, update native agents, and inspect compact state dictionaries without reimplementing the loop in Python.

Legacy rl imports

Older notebooks sometimes import rl directly. The Python binding now discovers whether the native wheel exposes spiraltorch.rl before wiring a lazy import hook. If another library has already populated sys.modules["rl"] we leave it untouched; otherwise importing rl defers to the SpiralTorch module on demand. Wheels built without SpiralTorchRL skip the hook entirely so third-party modules remain unaffected.

from spiraltorch.spiral_rl import stAgent

agent = stAgent(state_dim=4, action_dim=2, discount=0.97, learning_rate=0.02)

state = 0
next_state = 1
action = agent.select_action(state)
agent.update(state, int(action), reward=1.0, next_state=next_state)

print("action:", action)
print("epsilon:", agent.state_dict()["epsilon"])

The generic spiraltorch.rl.Agent wrapper exposes the same loop with an explicit config object and exploration schedule:

from spiraltorch.rl import Agent, AgentConfig, EpsilonGreedy

config = AgentConfig(
    "dqn",
    state_dim=4,
    action_dim=2,
    gamma=0.97,
    lr=0.02,
    exploration=EpsilonGreedy(0.2, 0.05, 100),
    seed=7,
)
agent = Agent(config)
action = agent.select_action(0)
agent.update(0, int(action), 1.0, 1)
print(agent.algo, agent.state_dict()["epsilon"])

The RL surface is intentionally compact today: keep state/action loops native, then export state_dict() for audit or handoff. Geometry-aware policy traces remain Rust-first until their Python facade is stable enough to document as a copy-paste path.

SpiralTorchRec quickstart

spiraltorch.rec brings the SpiralTorchRec factorisation stack to notebooks and production jobs alike. Embeddings stay guarded by the open-cartesian topos so psychoid limits never drift while running alternating updates in pure Rust.

from spiraltorch.rec import Recommender

rec = Recommender(users=8, items=12, factors=4, learning_rate=0.05, regularization=0.002)

ratings = [
    (0, 0, 5.0),
    (0, 1, 3.0),
    (1, 0, 4.0),
    (1, 2, 4.5),
]

report = rec.train_epoch(ratings)
print(report.rmse, report.samples)
print("score:", rec.predict(0, 0))
print("top-k:", rec.recommend_top_k(0, 3))

SpiralSession backend planning

SpiralSession is intentionally small: it records the requested backend, captures device preflight evidence, and exposes planner helpers that match the Rust runtime. Use it as the first runtime object before deciding whether a run should stay on CPU, ask for WGPU, or escalate into a heavier training recipe.

from spiraltorch import SpiralSession

session = SpiralSession(backend="wgpu")
print(session.requested_backend, "->", session.effective_backend)
print("runtime:", session.device_preflight["runtime_status"])

plan = session.plan_topk(rows=8, cols=64, k=4)
print(plan.kind, plan.effective_backend, plan.tile)