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// RLX — versatile ML compiler + runtime.
// Copyright (C) 2026 Eugene Hauptmann, Nataliya Kosmyna.
//
// This program is free software: you can redistribute it and/or modify
// it under the terms of the GNU General Public License as published by
// the Free Software Foundation, version 3.
//
// This program is distributed in the hope that it will be useful,
// but WITHOUT ANY WARRANTY; without even the implied warranty of
// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU General Public License
// along with this program. If not, see <https://www.gnu.org/licenses/>.
//! OpKinds this backend claims for legalization (`Backend::supported_ops`).
//!
//! Source of truth for the coverage matrix in `docs/op-coverage.md`.
//! Kept in the backend crate so adding an op is a local edit, not a change
//! to `rlx-runtime`'s mega-`backend.rs`.
pub const SUPPORTED_OPS: &[rlx_ir::OpKind] = {
use rlx_ir::OpKind::*;
&[
Input,
Param,
Constant,
Activation,
Cast,
StopGradient,
Binary,
Compare,
Where,
Fma,
ElementwiseRegion,
MatMul,
DotGeneral,
DenseSolve,
BatchedDenseSolve,
Scan,
ScanBackward,
ScanBackwardXs,
LayerNorm,
LayerNorm2d,
GroupNorm,
BatchNormInference,
RmsNorm,
ResizeNearest2x,
AxialRope2d,
Attention,
Rope,
Reshape,
Transpose,
Narrow,
Concat,
Expand,
Gather,
Reverse,
Reduce,
Softmax,
Cumsum,
ArgMax,
ArgMin,
TopK,
Sample,
RngNormal,
RngUniform,
Conv,
Im2Col,
ConvTranspose2d,
Conv3d,
ConvTranspose3d,
Pool,
GroupedMatMul,
DequantGroupedMatMul,
DequantMoEWeights,
ScatterAdd,
ScatterNd,
ScatterElements,
GatherNd,
GatherElements,
LoraMatMul,
DequantMatMul,
ScaledMatMul,
ScaledQuantize,
ScaledQuantScale,
ScaledDequantize,
SelectiveScan,
GatedDeltaNet,
Lstm,
Gru,
Rnn,
Mamba2,
FusedSwiGLU,
FusedMatMulBiasAct,
FusedResidualLN,
FusedResidualRmsNorm,
FusedAttentionBlock,
AdaLayerNorm,
GatedResidual,
AdaLayerNormBackward,
GatedResidualBackward,
// Backward ops emitted by `rlx_opt::autodiff::grad_with_loss`.
// Their thunks live in rlx-cpu/src/thunk.rs alongside the
// forward kernels; without these entries the legalize step
// below would reject any compiled gradient graph.
ReluBackward,
ActivationBackward,
FakeQuantize,
FakeQuantizeBackward,
// LSQ (learned step size) QAT — native CPU thunks in thunk.rs.
FakeQuantizeLSQ,
FakeQuantizeLSQBackwardX,
FakeQuantizeLSQBackwardScale,
MaxPool2dBackward,
Conv2dBackwardInput,
Conv2dBackwardWeight,
SoftmaxCrossEntropy,
SoftmaxCrossEntropyWithLogits,
SoftmaxCrossEntropyBackward,
AttentionBackward,
LayerNormBackwardInput,
LayerNormBackwardGamma,
BatchNormInferenceBackwardInput,
BatchNormInferenceBackwardGamma,
BatchNormInferenceBackwardBeta,
// GroupNorm backward (native thunks in rlx-cpu/training_bwd):
GroupNormBackwardInput,
GroupNormBackwardGamma,
GroupNormBackwardBeta,
RmsNormBackwardInput,
RmsNormBackwardGamma,
RmsNormBackwardBeta,
RopeBackward,
CumsumBackward,
GatherBackward,
// 3D Gaussian splat CPU reference render/backward (requires `rlx-cpu/splat`).
GaussianSplatRender,
GaussianSplatRenderBackward,
GaussianSplatPrepare,
GaussianSplatRasterize,
// User-registered custom ops dispatched through
// `rlx_cpu::op_registry`. Lowering panics with a clear
// message if the named CPU kernel isn't registered.
Custom,
// User-defined sub-graph with optional override AD rules
// (JAX-shaped custom_vjp / custom_jvp). Body is a regular
// Graph compiled recursively in compile_thunks.
CustomFn,
// FFT primitive (1D last-axis, 2N real-block layout, f64
// power-of-2 sizes). Other backends panic at lowering;
// pin FFT-containing graphs to Device::Cpu for now.
Fft,
FftButterflyStage,
LogMel,
LogMelBackward,
WelchPeaks,
// C64 Wirtinger AD surface. ComplexNormSq is the canonical
// real-valued loss for complex inputs; Conjugate is emitted
// by the new Wirtinger VJP rules for BinaryOp::Mul/Div on
// C64. Both have CPU thunks in rlx-cpu.
ComplexNormSq,
ComplexNormSqBackward,
Conjugate,
// Riemannian / SPD-manifold layers (SPDNet + SPD batch-norm).
// CPU-first (F64); other backends fall through their catch-alls.
BiMap,
ReEig,
LogEig,
SpdBatchNorm,
SpdKarcherMean,
SpdKarcherMeanWeighted,
SpdLogMap,
SpdExpMap,
SpdParallelTransport,
SpdMatrixFnBatch,
ReEigBackward,
LogEigBackward,
SpdBatchNormBackwardX,
SpdBatchNormBackwardG,
SpdLogMapBackward,
SpdExpMapBackward,
SpdParallelTransportBackward,
SpdMatrixFnBatchBackward,
Eigh,
EighBackward,
EighBatch,
EighBatchBackward,
]
};