libmir-cuda 0.2.0

CUDA inference backend for libmir
mod backend;
mod checkpoint;
mod config;
mod engine;
mod error;
pub mod kernels;
mod tensor;

pub use backend::{
    AffineGatedDeltaLayerConfig, AffineGatedDeltaLayerWeights, AffineGatedDeltaMoeLayerConfig,
    AffineGatedFullAttentionConfig, AffineGatedFullAttentionMoeLayerConfig,
    AffineGatedFullAttentionWeights, AffineQuantizedBf16Linear, AffineQuantizedBf16Qmm,
    AffineQuantizedConfig, AffineQuantizedEmbedding, AffineQuantizedPairTensors,
    AffineQuantizedTensors, AffineQuantizedWeight, AffineRouterBf16, AffineSharedExpertMoeConfig,
    AffineSharedExpertMoeWeights, AttentionExecution, AttentionPlan, AttentionPlanRequest,
    BatchedDecodeAttentionBf16, BatchedDecodeMoeBlockBf16, BatchedDecodeMoeLayer,
    BatchedPagedAttentionBf16, Bf16Embedding, Bf16Linear, Bf16LinearPack, Bf16LinearPackWeights,
    Bf16LinearPair, Bf16LinearPairWeights, Bf16Projection, Bf16VectorLinear, BlockFp8LinearWeight,
    BucketedNvFp4MoeBf16, CapturedDecodeAttentionBf16, CapturedDecodeMoeBlockBf16,
    CudaAffineGatedDeltaExecution, CudaAffineGatedDeltaLayer, CudaAffineGatedDeltaMoeExecution,
    CudaAffineGatedDeltaMoeLayer, CudaAffineGatedFullAttention,
    CudaAffineGatedFullAttentionExecution, CudaAffineGatedFullAttentionMoeExecution,
    CudaAffineGatedFullAttentionMoeLayer, CudaAffineGatedFullAttentionState, CudaAffineOutputHead,
    CudaAffineSharedExpertMoe, CudaAffineSharedExpertMoeExecution, CudaAttentionPolicy,
    CudaBackend, CudaClampedRoutedModelSession, CudaClampedRoutedModelTemplate, CudaDecodeBatch,
    CudaDenseVectorPolicy, CudaDenseWeightPolicy, CudaExecutionPlanner, CudaGatedDeltaState,
    CudaHardwareProfile, CudaKernelAdmission, CudaMemoryArchitecture, CudaModelSessionConfig,
    CudaMoeBatchPolicy, CudaMoeFusionPolicy, CudaMoeModelSession, CudaMoeModelTemplate,
    CudaNumericalPolicy, CudaOutputHead, CudaOutputHeadPolicy, CudaPlanningPolicy,
    CudaSharedRoutedLayerState, CudaSharedRoutedModelSession, CudaSharedRoutedModelTemplate,
    DecodeAttentionBf16, DecodeAttentionConfig, DecodeAttentionOutputWeight,
    DecodeAttentionWeights, DecodeDenseSwiGlu, DecodeGraphAction, DecodeMoeBlockBf16,
    DecodeMoeBlockConfig, DecodeMoeBlockExecutor, DecodeMoeBlockWeights, DecodeMoeLayerTemplate,
    DecodeQkvWeights, DenseDownSource, DenseDownWeight, DenseExecution, DenseGateUpSource,
    DenseGateUpWeights, DenseOutputSource, DensePlan, DensePlanRequest, DenseQkvSource, DenseRole,
    DenseSwiGluConfig, DenseSwiGluLayerTemplate, DenseSwiGluWeights, DenseWeightSource,
    DeviceBatchSamplerBf16, DeviceSamplerBf16, DirectNvFp4MoeBf16, ExecutionPhase,
    Fp8ResidualLinearWeight, GatedActivation, GatedDeltaInputs, GatedDeltaStateConfig,
    GroupedNvFp4MoeBf16, HybridNvFp4MoeBf16, MoeExecution, MoePlan, MoePlanRequest,
    MoeQuantization, NvFp4Bf16Linear, NvFp4Bf16Pack, NvFp4Config, NvFp4ExpertBank,
    NvFp4ExpertBankConfig, NvFp4ExpertSource, NvFp4LinearWeight, NvFp4Tensors, OutputHeadExecution,
    OutputHeadPlan, OutputHeadPlanRequest, PagedAttentionBf16, PagedDecodeBatch, PagedKvCache,
    PlanSource, PrefillAttentionBf16, PrefillDenseSwiGlu, PrefillMoeBlockBf16, ProjectionFormat,
    RmsNormBf16, RopeBf16, RouterBf16, RouterSelection, RouterTensors,
    SelectedAffineGatedBf16Linear, SelectedAffinePairBf16Linear, SelectedAffineReduceBf16Linear,
    SelectedNvFp4LinearBf16, SelectedNvFp4MoeBf16, SelectedNvFp4TensorCoreMoeBf16,
};
pub use checkpoint::{
    DenseSwiGluLayerLoadConfig, NvFp4MoeLayerLoadConfig, SharedRoutedModelLoadConfig,
};
pub use config::CudaConfig;
pub use engine::{CudaEngine, CudaMemoryStats};
pub use error::{Error, Result};
pub use kernels::{RopeSpec, RouterSpec};
pub use tensor::{CudaTensor, CudaTensorDType, CudaTensorSet, TensorUploadBatch};