| { | |
| "SOAFusedAttention": { | |
| "id": "SOAFusedAttention", | |
| "name": "SOA Fused Attention: Low-Rank Latent Space Multi-Head Attention w", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/attentions/SOAFusedAttention.py" | |
| }, | |
| "adaptive_span_attention": { | |
| "id": "adaptive_span_attention", | |
| "name": "Adaptive Span Attention: Dynamic Head-Specific Context Window Gat", | |
| "category": "C", | |
| "subcategory": "sliding_window", | |
| "paradigm": "sparse", | |
| "file": "Codes/framework/attentions/adaptive_span_attention.py" | |
| }, | |
| "adaptive_span_inject": { | |
| "id": "adaptive_span_inject", | |
| "name": "Adaptive Span Attention: Dynamic Head-Specific Context Window Gat", | |
| "category": "C", | |
| "subcategory": "sliding_window", | |
| "paradigm": "sparse", | |
| "file": "Codes/framework/attentions/adaptive_span_inject.py" | |
| }, | |
| "adaptive_sparse_attention": { | |
| "id": "adaptive_sparse_attention", | |
| "name": "Adaptive Sparse Attention: Dynamic Top-K & Threshold-Based Sparsi", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "sparse", | |
| "file": "Codes/framework/attentions/adaptive_sparse_attention.py" | |
| }, | |
| "adaptive_sparse_inject": { | |
| "id": "adaptive_sparse_inject", | |
| "name": "Adaptive Sparse Attention: Dynamic Top-K & Threshold-Based Sparsi", | |
| "category": "B", | |
| "subcategory": "sparse_approximate", | |
| "paradigm": "sparse", | |
| "file": "Codes/framework/attentions/adaptive_sparse_inject.py" | |
| }, | |
| "adaptive_svd_attention": { | |
| "id": "adaptive_svd_attention", | |
| "name": "Adaptive SVD Attention: Low-Rank SVD Projections with Parallel Re", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/attentions/adaptive_svd_attention.py" | |
| }, | |
| "adasplash_attention": { | |
| "id": "adasplash_attention", | |
| "name": "AdaSplash Attention: Adaptive Splash Attention with alpha-Entmax ", | |
| "category": "C", | |
| "subcategory": "sliding_window", | |
| "paradigm": "moe", | |
| "file": "Codes/framework/attentions/adasplash_attention.py" | |
| }, | |
| "adasplash_inject": { | |
| "id": "adasplash_inject", | |
| "name": "AdaSplash Attention: Adaptive Splash Attention with alpha-Entmax ", | |
| "category": "B", | |
| "subcategory": "sparse_approximate", | |
| "paradigm": "sparse", | |
| "file": "Codes/framework/attentions/adasplash_inject.py" | |
| }, | |
| "aegis_hybrid": { | |
| "id": "aegis_hybrid", | |
| "name": "AEGIS-Hybrid Attention: Low-Rank Factorized Hybrid Attention with", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/attentions/aegis_hybrid.py" | |
| }, | |
| "aegis_hybrid_attention": { | |
| "id": "aegis_hybrid_attention", | |
| "name": "Compatibility shim for aegis_hybrid_attention.", | |
| "category": "B", | |
| "subcategory": "sparse_approximate", | |
| "paradigm": "sparse", | |
| "file": "Codes/framework/attentions/aegis_hybrid_attention.py" | |
| }, | |
| "aegis_spectron_fp8": { | |
| "id": "aegis_spectron_fp8", | |
| "name": "AEGIS-Spectron-FP8: Tri-Branch Hybrid Attention with Token-Level ", | |
| "category": "H", | |
| "subcategory": "quantization", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/attentions/aegis_spectron_fp8.py" | |
| }, | |
| "aegis_v1": { | |
| "id": "aegis_v1", | |
| "name": "AEGIS-Attention V1: Adaptive Enhanced Global-Local Integrated Spa", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/attentions/aegis_v1.py" | |
| }, | |
| "aegis_v2": { | |
| "id": "aegis_v2", | |
| "name": "AEGIS-Attention V2: Low-Rank Factorized Hybrid Attention with Ada", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/attentions/aegis_v2.py" | |
| }, | |
| "aegis_v3": { | |
| "id": "aegis_v3", | |
| "name": "AEGIS-Attention V3: Adaptive Enhanced Global-Local Integrated Spa", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/attentions/aegis_v3.py" | |
| }, | |
| "aegis_v4": { | |
| "id": "aegis_v4", | |
| "name": "AEGIS-Attention V4: Adaptive Enhanced Global-Local Integrated Spa", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/attentions/aegis_v4.py" | |
| }, | |
| "aegis_v5": { | |
| "id": "aegis_v5", | |
| "name": "AEGIS-Attention V5: Adaptive Enhanced Global-Local Integrated Spa", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/attentions/aegis_v5.py" | |
| }, | |
| "aegis_v6": { | |
| "id": "aegis_v6", | |
| "name": "AEGIS-Attention V6: Unified SVD-LoRA Hybrid Attention with Orthog", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/attentions/aegis_v6.py" | |
| }, | |
| "asvd_attention": { | |
| "id": "asvd_attention", | |
| "name": "ASVD Attention: Activation-Aware Singular Value Decomposition Arc", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/attentions/asvd_attention.py" | |
| }, | |
| "bam_umoe": { | |
| "id": "bam_umoe", | |
| "name": "BAM-uMoE: Bidirectional Attentive Mixture-of-Unified-Experts", | |
| "category": "F", | |
| "subcategory": "mixture_of_experts", | |
| "paradigm": "moe", | |
| "file": "Codes/framework/attentions/bam_umoe.py" | |
| }, | |
| "block_transformer_hiba": { | |
| "id": "block_transformer_hiba", | |
| "name": "Block Transformer with Hierarchical Intra-Block and Inter-Block A", | |
| "category": "A", | |
| "subcategory": "dense_attention", | |
| "paradigm": "mla", | |
| "file": "Codes/framework/attentions/block_transformer_hiba.py" | |
| }, | |
| "chela_slay": { | |
| "id": "chela_slay", | |
| "name": "CHELA-SLAY: Spherical Linearized Attention with Yat-Kernel.", | |
| "category": "B", | |
| "subcategory": "sparse_approximate", | |
| "paradigm": "mla", | |
| "file": "Codes/framework/attentions/chela_slay.py" | |
| }, | |
| "cluster_attention": { | |
| "id": "cluster_attention", | |
| "name": "ClusterFormer: Dynamic Token-Clustering Attention via Batch Itera", | |
| "category": "B", | |
| "subcategory": "sparse_approximate", | |
| "paradigm": "sparse", | |
| "file": "Codes/framework/attentions/cluster_attention.py" | |
| }, | |
| "cluster_inject": { | |
| "id": "cluster_inject", | |
| "name": "ClusterFormer: Dynamic Token-Clustering Attention via Batch Itera", | |
| "category": "B", | |
| "subcategory": "sparse_approximate", | |
| "paradigm": "sparse", | |
| "file": "Codes/framework/attentions/cluster_inject.py" | |
| }, | |
| "compressed_attention": { | |
| "id": "compressed_attention", | |
| "name": "Compressed Multi-Head Attention via Truncated Singular Value Deco", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/attentions/compressed_attention.py" | |
| }, | |
| "compressed_mha": { | |
| "id": "compressed_mha", | |
| "name": "Compressed Multi-Head Attention (Compressed-MHA)", | |
| "category": "B", | |
| "subcategory": "sparse_approximate", | |
| "paradigm": "sparse", | |
| "file": "Codes/framework/attentions/compressed_mha.py" | |
| }, | |
| "compressed_mha_attention": { | |
| "id": "compressed_mha_attention", | |
| "name": "Compressed Multi-Head Attention for DistilBERT and Multi-Architec", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/attentions/compressed_mha_attention.py" | |
| }, | |
| "cosformer": { | |
| "id": "cosformer", | |
| "name": "Cosformer: Rethinking Softmax in Attention with Linear Complexity", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/attentions/cosformer.py" | |
| }, | |
| "cosformer_inject": { | |
| "id": "cosformer_inject", | |
| "name": "Deprecated stub. Re-exports base module for registry compatibilit", | |
| "category": "A", | |
| "subcategory": "dense_attention", | |
| "paradigm": "ssm", | |
| "file": "Codes/framework/attentions/cosformer_inject.py" | |
| }, | |
| "decay_linear_attention": { | |
| "id": "decay_linear_attention", | |
| "name": "Decay Linear Attention (DecayLinearAttention).", | |
| "category": "D", | |
| "subcategory": "recurrent_linear", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/attentions/decay_linear_attention.py" | |
| }, | |
| "eat": { | |
| "id": "eat", | |
| "name": "EAT: Efficient Attention Transformer with Progressive Token Pruni", | |
| "category": "G", | |
| "subcategory": "pruning_sparsity", | |
| "paradigm": "pruning", | |
| "file": "Codes/framework/attentions/eat.py" | |
| }, | |
| "expert_choice_moe": { | |
| "id": "expert_choice_moe", | |
| "name": "Expert Choice Mixture of Experts (EC-MoE) Attention Block", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/attentions/expert_choice_moe.py" | |
| }, | |
| "factorised_matrix_state": { | |
| "id": "factorised_matrix_state", | |
| "name": "Factorised Matrix State Attention: Bidirectional Associative Matr", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/attentions/factorised_matrix_state.py" | |
| }, | |
| "flash_attention3": { | |
| "id": "flash_attention3", | |
| "name": "FlashAttention-3: Fast and Memory-Efficient Exact Attention with ", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/attentions/flash_attention3.py" | |
| }, | |
| "flash_attention3_fp8": { | |
| "id": "flash_attention3_fp8", | |
| "name": "FlashAttention-3 FP8: Low-Precision 8-Bit Float Transformer Atten", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/attentions/flash_attention3_fp8.py" | |
| }, | |
| "full_attention": { | |
| "id": "full_attention", | |
| "name": "Full Attention: Standard Multi-Head Scaled Dot-Product Attention ", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "sparse", | |
| "file": "Codes/framework/attentions/full_attention.py" | |
| }, | |
| "fused_svd_kernel": { | |
| "id": "fused_svd_kernel", | |
| "name": "Fused SVD-Linear Layer with SRAM-Resident RMSNorm & Dual-Projecti", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/attentions/fused_svd_kernel.py" | |
| }, | |
| "fusion_attn": { | |
| "id": "fusion_attn", | |
| "name": "FUSIONATTN: Multi-Paradigm Hybrid Sparse Attention Module.", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/attentions/fusion_attn.py" | |
| }, | |
| "galerkin": { | |
| "id": "galerkin", | |
| "name": "Petrov-Galerkin Projection Attention: Energy-Preserving Spectral ", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "sparse", | |
| "file": "Codes/framework/attentions/galerkin.py" | |
| }, | |
| "galerkin_attention": { | |
| "id": "galerkin_attention", | |
| "name": "Petrov-Galerkin Projection Attention: Energy-Preserving Spectral ", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/attentions/galerkin_attention.py" | |
| }, | |
| "galerkin_inject": { | |
| "id": "galerkin_inject", | |
| "name": "Petrov-Galerkin Projection Attention: Energy-Preserving Spectral ", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "sparse", | |
| "file": "Codes/framework/attentions/galerkin_inject.py" | |
| }, | |
| "gated_delta_net": { | |
| "id": "gated_delta_net", | |
| "name": "Gated DeltaNet: Multi-Head Bidirectional Delta Rule Linear Recurr", | |
| "category": "D", | |
| "subcategory": "recurrent_linear", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/attentions/gated_delta_net.py" | |
| }, | |
| "gated_deltanet_block": { | |
| "id": "gated_deltanet_block", | |
| "name": "Gated DeltaNet Block: Hybrid Multi-Head Attention and Recurrent D", | |
| "category": "D", | |
| "subcategory": "recurrent_linear", | |
| "paradigm": "moe", | |
| "file": "Codes/framework/attentions/gated_deltanet_block.py" | |
| }, | |
| "gated_linear_attention": { | |
| "id": "gated_linear_attention", | |
| "name": "Gated Linear Attention (GatedLinearAttention).", | |
| "category": "D", | |
| "subcategory": "recurrent_linear", | |
| "paradigm": "ssm", | |
| "file": "Codes/framework/attentions/gated_linear_attention.py" | |
| }, | |
| "goldfinch_ahn": { | |
| "id": "goldfinch_ahn", | |
| "name": "GoldFinch-AHN: Adaptive Hybrid Network Combining Sliding Window a", | |
| "category": "D", | |
| "subcategory": "recurrent_linear", | |
| "paradigm": "ssm", | |
| "file": "Codes/framework/attentions/goldfinch_ahn.py" | |
| }, | |
| "gqa": { | |
| "id": "gqa", | |
| "name": "Grouped-Query Attention (GQA): Memory-Efficient Shared Key-Value ", | |
| "category": "A", | |
| "subcategory": "grouped_query", | |
| "paradigm": "sparse", | |
| "file": "Codes/framework/attentions/gqa.py" | |
| }, | |
| "gqa_attention": { | |
| "id": "gqa_attention", | |
| "name": "Grouped-Query Attention (GQA): Memory-Efficient Shared Key-Value ", | |
| "category": "A", | |
| "subcategory": "dense_attention", | |
| "paradigm": "mla", | |
| "file": "Codes/framework/attentions/gqa_attention.py" | |
| }, | |
| "granite_hybrid": { | |
| "id": "granite_hybrid", | |
| "name": "Granite-Hybrid Attention: Interleaved Mamba-2 State Space Archite", | |
| "category": "I", | |
| "subcategory": "hybrid_composite", | |
| "paradigm": "moe", | |
| "file": "Codes/framework/attentions/granite_hybrid.py" | |
| }, | |
| "griffin_ssd": { | |
| "id": "griffin_ssd", | |
| "name": "Griffin SSD: Hybrid Local Sliding-Window Attention and Global Sta", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "ssm", | |
| "file": "Codes/framework/attentions/griffin_ssd.py" | |
| }, | |
| "halo_regla": { | |
| "id": "halo_regla", | |
| "name": "HALO-ReGLA: Hardware-Aware Linear Optics & Rectified Gated Linear", | |
| "category": "B", | |
| "subcategory": "sparse_approximate", | |
| "paradigm": "mla", | |
| "file": "Codes/framework/attentions/halo_regla.py" | |
| }, | |
| "hamming_attention": { | |
| "id": "hamming_attention", | |
| "name": "Hamming Attention Distillation (HAD): Sign-Binarized Attention wi", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/attentions/hamming_attention.py" | |
| }, | |
| "hgrn2_gated_deltanet": { | |
| "id": "hgrn2_gated_deltanet", | |
| "name": "HGRN-2 Gated DeltaNet Hybrid Attention Module.", | |
| "category": "D", | |
| "subcategory": "recurrent_linear", | |
| "paradigm": "ssm", | |
| "file": "Codes/framework/attentions/hgrn2_gated_deltanet.py" | |
| }, | |
| "hgrn2_layer": { | |
| "id": "hgrn2_layer", | |
| "name": "Hierarchically Gated Recurrent Network V2 (HGRN-2) Layer.", | |
| "category": "D", | |
| "subcategory": "recurrent_linear", | |
| "paradigm": "ssm", | |
| "file": "Codes/framework/attentions/hgrn2_layer.py" | |
| }, | |
| "hgrn2_outer": { | |
| "id": "hgrn2_outer", | |
| "name": "HGRN-2 Outer-Product Recurrent Linear Attention Module.", | |
| "category": "D", | |
| "subcategory": "recurrent_linear", | |
| "paradigm": "ssm", | |
| "file": "Codes/framework/attentions/hgrn2_outer.py" | |
| }, | |
| "hla_attention": { | |
| "id": "hla_attention", | |
| "name": "Hadamard Linear Attention (HLA).", | |
| "category": "D", | |
| "subcategory": "recurrent_linear", | |
| "paradigm": "ssm", | |
| "file": "Codes/framework/attentions/hla_attention.py" | |
| }, | |
| "hla_inject": { | |
| "id": "hla_inject", | |
| "name": "Deprecated stub. Re-exports base module for registry compatibilit", | |
| "category": "B", | |
| "subcategory": "sparse_approximate", | |
| "paradigm": "sparse", | |
| "file": "Codes/framework/attentions/hla_inject.py" | |
| }, | |
| "hymba_transx": { | |
| "id": "hymba_transx", | |
| "name": "HymbaTransX: Hybrid Differential Transformer & State Space Model ", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "mla", | |
| "file": "Codes/framework/attentions/hymba_transx.py" | |
| }, | |
| "infini_attention": { | |
| "id": "infini_attention", | |
| "name": "Infini-Attention with Compressive Memory and Sliding Window (Prod", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "mla", | |
| "file": "Codes/framework/attentions/infini_attention.py" | |
| }, | |
| "jamba": { | |
| "id": "jamba", | |
| "name": "Jamba-2 Hybrid Transformer: SVD-Mamba + MHA + MoE Attention Block", | |
| "category": "I", | |
| "subcategory": "hybrid_composite", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/attentions/jamba.py" | |
| }, | |
| "jamba_info": { | |
| "id": "jamba_info", | |
| "name": "Information-Bottlenecked Jamba Architecture with Dual Operational", | |
| "category": "I", | |
| "subcategory": "hybrid_composite", | |
| "paradigm": "ssm", | |
| "file": "Codes/framework/attentions/jamba_info.py" | |
| }, | |
| "kda_attention": { | |
| "id": "kda_attention", | |
| "name": "Kernelized Decay Attention (KDA) / Recurrent Linear Attention (Ha", | |
| "category": "D", | |
| "subcategory": "recurrent_linear", | |
| "paradigm": "ssm", | |
| "file": "Codes/framework/attentions/kda_attention.py" | |
| }, | |
| "kda_inject": { | |
| "id": "kda_inject", | |
| "name": "Deprecated stub. Re-exports base module for registry compatibilit", | |
| "category": "B", | |
| "subcategory": "sparse_approximate", | |
| "paradigm": "sparse", | |
| "file": "Codes/framework/attentions/kda_inject.py" | |
| }, | |
| "leka_v1": { | |
| "id": "leka_v1", | |
| "name": "LEKAv1: Linear-time Efficient Kernel Attention (Production-Harden", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "mla", | |
| "file": "Codes/framework/attentions/leka_v1.py" | |
| }, | |
| "leka_v1_inject": { | |
| "id": "leka_v1_inject", | |
| "name": "Deprecated stub. Re-exports base module for registry compatibilit", | |
| "category": "B", | |
| "subcategory": "sparse_approximate", | |
| "paradigm": "sparse", | |
| "file": "Codes/framework/attentions/leka_v1_inject.py" | |
| }, | |
| "leka_v2": { | |
| "id": "leka_v2", | |
| "name": "LEKAv2: Linear-time Efficient Kernel Attention with Landmark Dist", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "mla", | |
| "file": "Codes/framework/attentions/leka_v2.py" | |
| }, | |
| "leka_v2_inject": { | |
| "id": "leka_v2_inject", | |
| "name": "LEKA v2: Landmark-Enhanced Kernel Attention with Dual-Scale Disti", | |
| "category": "B", | |
| "subcategory": "sparse_approximate", | |
| "paradigm": "sparse", | |
| "file": "Codes/framework/attentions/leka_v2_inject.py" | |
| }, | |
| "leka_v3": { | |
| "id": "leka_v3", | |
| "name": "LEKAv3: Linear-time Efficient Kernel Attention with Value-Scored ", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "mla", | |
| "file": "Codes/framework/attentions/leka_v3.py" | |
| }, | |
| "leka_v3_inject": { | |
| "id": "leka_v3_inject", | |
| "name": "LEKA v3: Value-Aware Landmark Kernel Attention with Padding Invar", | |
| "category": "B", | |
| "subcategory": "sparse_approximate", | |
| "paradigm": "sparse", | |
| "file": "Codes/framework/attentions/leka_v3_inject.py" | |
| }, | |
| "linformer_attention": { | |
| "id": "linformer_attention", | |
| "name": "Linformer Attention: Linear-Complexity Sequence Projection via Lo", | |
| "category": "B", | |
| "subcategory": "linformer", | |
| "paradigm": "mla", | |
| "file": "Codes/framework/attentions/linformer_attention.py" | |
| }, | |
| "lola_attention": { | |
| "id": "lola_attention", | |
| "name": "Lola Attention: Low-Rank Localized Attention via Three-Memory Sys", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/attentions/lola_attention.py" | |
| }, | |
| "lora_attention": { | |
| "id": "lora_attention", | |
| "name": "LoRA Attention: Parameter-Efficient Low-Rank Adaptation", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/attentions/lora_attention.py" | |
| }, | |
| "lsh_attention": { | |
| "id": "lsh_attention", | |
| "name": "Locality-Sensitive Hashing Attention: Angular Random Hyperplane B", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "pruning", | |
| "file": "Codes/framework/attentions/lsh_attention.py" | |
| }, | |
| "mamba": { | |
| "id": "mamba", | |
| "name": "Mamba: Gated State Space Model (Mamba) linear attention replaceme", | |
| "category": "D", | |
| "subcategory": "recurrent_linear", | |
| "paradigm": "ssm", | |
| "file": "Codes/framework/attentions/mamba.py" | |
| }, | |
| "mamba_inject": { | |
| "id": "mamba_inject", | |
| "name": "Mamba Injection Module.", | |
| "category": "D", | |
| "subcategory": "recurrent_linear", | |
| "paradigm": "ssm", | |
| "file": "Codes/framework/attentions/mamba_inject.py" | |
| }, | |
| "mamba_svd_flash": { | |
| "id": "mamba_svd_flash", | |
| "name": "MambaSVD-Flash: Hybrid Bidirectional SSM-FlashAttention Module (P", | |
| "category": "D", | |
| "subcategory": "recurrent_linear", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/attentions/mamba_svd_flash.py" | |
| }, | |
| "minimal_aurora": { | |
| "id": "minimal_aurora", | |
| "name": "MinimalAURORA Attention: Energy-Preserving SVD-Factorized Attenti", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/attentions/minimal_aurora.py" | |
| }, | |
| "minimal_nova": { | |
| "id": "minimal_nova", | |
| "name": "MinimalNOVA Attention: SVD-Factorized Low-Rank Projection Attenti", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/attentions/minimal_nova.py" | |
| }, | |
| "minimal_prism": { | |
| "id": "minimal_prism", | |
| "name": "MinimalPRISM Attention: Singular Spectrum Energy-Factorized Low-R", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/attentions/minimal_prism.py" | |
| }, | |
| "minimal_svd": { | |
| "id": "minimal_svd", | |
| "name": "MinimalSVD: Cumulative Energy-Budgeted SVD Factorization Attentio", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/attentions/minimal_svd.py" | |
| }, | |
| "minimal_svd_v2": { | |
| "id": "minimal_svd_v2", | |
| "name": "MinimalSVD v2: Refined SVD Low-Rank Attention with Flexible Atten", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/attentions/minimal_svd_v2.py" | |
| }, | |
| "mixattention_moska": { | |
| "id": "mixattention_moska", | |
| "name": "MixAttention-MoSKA: Frequency/Variance-Aware Sparse Attention (FA", | |
| "category": "B", | |
| "subcategory": "sparse_approximate", | |
| "paradigm": "sparse", | |
| "file": "Codes/framework/attentions/mixattention_moska.py" | |
| }, | |
| "mla_attention": { | |
| "id": "mla_attention", | |
| "name": "Multi-Head Latent Attention (MLA): Low-Rank Latent Bottleneck Com", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "mla", | |
| "file": "Codes/framework/attentions/mla_attention.py" | |
| }, | |
| "moba": { | |
| "id": "moba", | |
| "name": "MoBA: Mixture of Block Attention for Long-Context Modeling.", | |
| "category": "B", | |
| "subcategory": "sparse_approximate", | |
| "paradigm": "sparse", | |
| "file": "Codes/framework/attentions/moba.py" | |
| }, | |
| "moba_attention": { | |
| "id": "moba_attention", | |
| "name": "MoBA Attention: Mixture-of-Block Attention via Centroid Cosine Ro", | |
| "category": "B", | |
| "subcategory": "sparse_approximate", | |
| "paradigm": "sparse", | |
| "file": "Codes/framework/attentions/moba_attention.py" | |
| }, | |
| "moe_deltakv_paged": { | |
| "id": "moe_deltakv_paged", | |
| "name": "MoE-DeltaKV Paged: Quantized & Paged KV-Cache Mixture-of-Experts ", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/attentions/moe_deltakv_paged.py" | |
| }, | |
| "nasc_attention": { | |
| "id": "nasc_attention", | |
| "name": "NASC: NEXUS-AEGIS Spectral Cascade Attention.", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/attentions/nasc_attention.py" | |
| }, | |
| "nexus": { | |
| "id": "nexus", | |
| "name": "NEXUS: Neural Efficient X-scoring Unified Sparse Attention.", | |
| "category": "B", | |
| "subcategory": "sparse_approximate", | |
| "paradigm": "moe", | |
| "file": "Codes/framework/attentions/nexus.py" | |
| }, | |
| "nha_attention": { | |
| "id": "nha_attention", | |
| "name": "Native Hybrid Attention (NHA) – Production-Hardened.", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "mla", | |
| "file": "Codes/framework/attentions/nha_attention.py" | |
| }, | |
| "nha_compressor": { | |
| "id": "nha_compressor", | |
| "name": "Native Hybrid Attention (NHA) Compressor.", | |
| "category": "B", | |
| "subcategory": "sparse_approximate", | |
| "paradigm": "sparse", | |
| "file": "Codes/framework/attentions/nha_compressor.py" | |
| }, | |
| "nsa_attention": { | |
| "id": "nsa_attention", | |
| "name": "Native Sparse Attention: Hybrid Local Sliding-Window and Dynamic ", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "moe", | |
| "file": "Codes/framework/attentions/nsa_attention.py" | |
| }, | |
| "nystrom_attention": { | |
| "id": "nystrom_attention", | |
| "name": "Nyström Attention: Low-Rank Landmark Approximation via Matrix Pse", | |
| "category": "B", | |
| "subcategory": "nystrom", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/attentions/nystrom_attention.py" | |
| }, | |
| "optimized_hybrid_attention": { | |
| "id": "optimized_hybrid_attention", | |
| "name": "Optimized Hybrid Attention (OHA): Fused Sliding-Window Local Self", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/attentions/optimized_hybrid_attention.py" | |
| }, | |
| "orion": { | |
| "id": "orion", | |
| "name": "ORION: Optimized Rectified Input-Aware Orthogonal Network with En", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/attentions/orion.py" | |
| }, | |
| "performer_attention": { | |
| "id": "performer_attention", | |
| "name": "Performer Attention – Fast Attention via Positive Orthogonal Rand", | |
| "category": "B", | |
| "subcategory": "performer", | |
| "paradigm": "mla", | |
| "file": "Codes/framework/attentions/performer_attention.py" | |
| }, | |
| "phantom": { | |
| "id": "phantom", | |
| "name": "PHANTOM: Parameter-Hashed AttentioN with Token-wise Orthogonal Ma", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/attentions/phantom.py" | |
| }, | |
| "primal_attention": { | |
| "id": "primal_attention", | |
| "name": "Primal Attention: Self-Attention through Asymmetric Kernel SVD in", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/attentions/primal_attention.py" | |
| }, | |
| "progressive_token_pruning": { | |
| "id": "progressive_token_pruning", | |
| "name": "Progressive Token Pruning (PTP) – Production-Hardened.", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "mla", | |
| "file": "Codes/framework/attentions/progressive_token_pruning.py" | |
| }, | |
| "projection_factorized_attn": { | |
| "id": "projection_factorized_attn", | |
| "name": "Projection Factorized SVD Linear Attention.", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/attentions/projection_factorized_attn.py" | |
| }, | |
| "qcqa_attention": { | |
| "id": "qcqa_attention", | |
| "name": "Quality and Capacity-Aware Grouped Query Attention (QCQA): Dynami", | |
| "category": "A", | |
| "subcategory": "dense_attention", | |
| "paradigm": "moe", | |
| "file": "Codes/framework/attentions/qcqa_attention.py" | |
| }, | |
| "rala_attention": { | |
| "id": "rala_attention", | |
| "name": "Rank-Augmented Linear Attention (RALA): Low-Rank Parametric Head ", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/attentions/rala_attention.py" | |
| }, | |
| "rala_compressor": { | |
| "id": "rala_compressor", | |
| "name": "RALA Compressor: Rank-Augmented Linear Attention Layer Architectu", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/attentions/rala_compressor.py" | |
| }, | |
| "reap_moe": { | |
| "id": "reap_moe", | |
| "name": "REAP-MoE: Robust Expert Allocation and Pruning Mixture of Experts", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/attentions/reap_moe.py" | |
| }, | |
| "regla_attention": { | |
| "id": "regla_attention", | |
| "name": "Refined Gated Linear Attention (ReGLA): Element-Wise Key Gating &", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "mla", | |
| "file": "Codes/framework/attentions/regla_attention.py" | |
| }, | |
| "replace": { | |
| "id": "replace", | |
| "name": "Model Attention Replacement and Dynamic Graph Surgery Utilities.", | |
| "category": "B", | |
| "subcategory": "sparse_approximate", | |
| "paradigm": "sparse", | |
| "file": "Codes/framework/attentions/replace.py" | |
| }, | |
| "res_svd": { | |
| "id": "res_svd", | |
| "name": "Spectral Residual Low-Rank SVD Attention (Res-SVD Attention) – Pr", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/attentions/res_svd.py" | |
| }, | |
| "routing_attention": { | |
| "id": "routing_attention", | |
| "name": "Routing Attention: Content-Adaptive Query-Centric Clustering via ", | |
| "category": "B", | |
| "subcategory": "sparse_approximate", | |
| "paradigm": "moe", | |
| "file": "Codes/framework/attentions/routing_attention.py" | |
| }, | |
| "rpc": { | |
| "id": "rpc", | |
| "name": "Random Projection Compressed (RPC) Attention.", | |
| "category": "B", | |
| "subcategory": "sparse_approximate", | |
| "paradigm": "sparse", | |
| "file": "Codes/framework/attentions/rpc.py" | |
| }, | |
| "rpc_attention": { | |
| "id": "rpc_attention", | |
| "name": "RPC Attention: Randomized Phase Cluster Attention via Fixed Rando", | |
| "category": "A", | |
| "subcategory": "dense_attention", | |
| "paradigm": "mla", | |
| "file": "Codes/framework/attentions/rpc_attention.py" | |
| }, | |
| "salt": { | |
| "id": "salt", | |
| "name": "SALT Attention: Singular Value Adaptation with Low-Rank Tuning.", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/attentions/salt.py" | |
| }, | |
| "salt_attention": { | |
| "id": "salt_attention", | |
| "name": "SALT Attention: Singular Value Adaptation with Low-Rank Tuning fo", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/attentions/salt_attention.py" | |
| }, | |
| "samba_m3": { | |
| "id": "samba_m3", | |
| "name": "SAMBA-M3: Hybrid Sliding-Window Attention and Spectral Mamba Bloc", | |
| "category": "D", | |
| "subcategory": "recurrent_linear", | |
| "paradigm": "ssm", | |
| "file": "Codes/framework/attentions/samba_m3.py" | |
| }, | |
| "samba_moe": { | |
| "id": "samba_moe", | |
| "name": "Samba-MoE: Selective State Space (SAMBA-M3) + REAP-MoE Hybrid Arc", | |
| "category": "D", | |
| "subcategory": "recurrent_linear", | |
| "paradigm": "moe", | |
| "file": "Codes/framework/attentions/samba_moe.py" | |
| }, | |
| "simple_ssm": { | |
| "id": "simple_ssm", | |
| "name": "Simple State Space Model (SimpleSSM).", | |
| "category": "A", | |
| "subcategory": "dense_attention", | |
| "paradigm": "ssm", | |
| "file": "Codes/framework/attentions/simple_ssm.py" | |
| }, | |
| "smart_weighted_ensemble": { | |
| "id": "smart_weighted_ensemble", | |
| "name": "Smart Weighted Ensemble: Task-Aware Attention & Dynamic Confidenc", | |
| "category": "B", | |
| "subcategory": "sparse_approximate", | |
| "paradigm": "sparse", | |
| "file": "Codes/framework/attentions/smart_weighted_ensemble.py" | |
| }, | |
| "soa_attn": { | |
| "id": "soa_attn", | |
| "name": "Shared Orthogonal Attention (SOA) with Per-Projection Latent Bott", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "mla", | |
| "file": "Codes/framework/attentions/soa_attn.py" | |
| }, | |
| "soa_fused_module": { | |
| "id": "soa_fused_module", | |
| "name": "SOA-Fused Attention: Low-Rank Latent Space Multi-Head Attention w", | |
| "category": "B", | |
| "subcategory": "sparse_approximate", | |
| "paradigm": "sparse", | |
| "file": "Codes/framework/attentions/soa_fused_module.py" | |
| }, | |
| "soa_module": { | |
| "id": "soa_module", | |
| "name": "SOA Attention: Shared Orthogonal Attention with Latent Bottleneck", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "sparse", | |
| "file": "Codes/framework/attentions/soa_module.py" | |
| }, | |
| "span_expanded_attention": { | |
| "id": "span_expanded_attention", | |
| "name": "Span-Expanded Attention: Receptive Field Extension with Parallel ", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/attentions/span_expanded_attention.py" | |
| }, | |
| "spectra_v2": { | |
| "id": "spectra_v2", | |
| "name": "SPECTRA-v2: Sparse Per-Head Efficient Context Transformer with Re", | |
| "category": "B", | |
| "subcategory": "sparse_approximate", | |
| "paradigm": "moe", | |
| "file": "Codes/framework/attentions/spectra_v2.py" | |
| }, | |
| "spectra_v2_attention": { | |
| "id": "spectra_v2_attention", | |
| "name": "Re-export module for SpectraV2Attention.", | |
| "category": "B", | |
| "subcategory": "sparse_approximate", | |
| "paradigm": "sparse", | |
| "file": "Codes/framework/attentions/spectra_v2_attention.py" | |
| }, | |
| "spectral_mamba": { | |
| "id": "spectral_mamba", | |
| "name": "Spectral-Mamba Attention Module: Hybrid Attention-State Space Mod", | |
| "category": "D", | |
| "subcategory": "recurrent_linear", | |
| "paradigm": "ssm", | |
| "file": "Codes/framework/attentions/spectral_mamba.py" | |
| }, | |
| "spectral_mamba_attention": { | |
| "id": "spectral_mamba_attention", | |
| "name": "Re-export module for SpectralMambaAttention.", | |
| "category": "D", | |
| "subcategory": "recurrent_linear", | |
| "paradigm": "ssm", | |
| "file": "Codes/framework/attentions/spectral_mamba_attention.py" | |
| }, | |
| "spectron": { | |
| "id": "spectron", | |
| "name": "SpectronAttention: Importance-Aware Low-Rank + Sparse Attention.", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/attentions/spectron.py" | |
| }, | |
| "spectron_attention": { | |
| "id": "spectron_attention", | |
| "name": "Spectron Attention Re-export Module.", | |
| "category": "H", | |
| "subcategory": "quantization", | |
| "paradigm": "sparse", | |
| "file": "Codes/framework/attentions/spectron_attention.py" | |
| }, | |
| "squeezing_heads": { | |
| "id": "squeezing_heads", | |
| "name": "Squeezing Heads Attention Re-export Module.", | |
| "category": "B", | |
| "subcategory": "sparse_approximate", | |
| "paradigm": "sparse", | |
| "file": "Codes/framework/attentions/squeezing_heads.py" | |
| }, | |
| "squeezing_heads_distill": { | |
| "id": "squeezing_heads_distill", | |
| "name": "Squeezing Heads Distillation: Multi-Head Receptive Field Distilla", | |
| "category": "B", | |
| "subcategory": "sparse_approximate", | |
| "paradigm": "sparse", | |
| "file": "Codes/framework/attentions/squeezing_heads_distill.py" | |
| }, | |
| "squeezing_heads_distiller": { | |
| "id": "squeezing_heads_distiller", | |
| "name": "Squeezing Heads Distiller: Progressive Multi-Head Knowledge Disti", | |
| "category": "B", | |
| "subcategory": "sparse_approximate", | |
| "paradigm": "sparse", | |
| "file": "Codes/framework/attentions/squeezing_heads_distiller.py" | |
| }, | |
| "svd_attention": { | |
| "id": "svd_attention", | |
| "name": "SVD Attention: Sequence-Dimension Singular Subspace Projection Ar", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/attentions/svd_attention.py" | |
| }, | |
| "svd_compressed": { | |
| "id": "svd_compressed", | |
| "name": "SVD-Compressed Attention: Importance-Aware Lagrangian Rank Alloca", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/attentions/svd_compressed.py" | |
| }, | |
| "svd_compressed_attention": { | |
| "id": "svd_compressed_attention", | |
| "name": "SVD-Compressed Attention Re-export Module.", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/attentions/svd_compressed_attention.py" | |
| }, | |
| "svd_linear_attention": { | |
| "id": "svd_linear_attention", | |
| "name": "SVD Factorized Linear Attention & Kernelized Linear Attention Mod", | |
| "category": "D", | |
| "subcategory": "recurrent_linear", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/attentions/svd_linear_attention.py" | |
| }, | |
| "svd_linear_decay": { | |
| "id": "svd_linear_decay", | |
| "name": "SVD Linear Decay Gated Attention Alias.", | |
| "category": "D", | |
| "subcategory": "recurrent_linear", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/attentions/svd_linear_decay.py" | |
| }, | |
| "svd_llm_v2": { | |
| "id": "svd_llm_v2", | |
| "name": "SVD-LLM V2 Attention Alias.", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/attentions/svd_llm_v2.py" | |
| }, | |
| "svd_llm_v2_attention": { | |
| "id": "svd_llm_v2_attention", | |
| "name": "SVD-LLM V2 Attention: Truncation-Aware Explicit Factor Buffer Arc", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/attentions/svd_llm_v2_attention.py" | |
| }, | |
| "svd_mamba": { | |
| "id": "svd_mamba", | |
| "name": "SVD-Mamba Attention: Hardware-Efficient State-Space Recurrence vi", | |
| "category": "D", | |
| "subcategory": "recurrent_linear", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/attentions/svd_mamba.py" | |
| }, | |
| "svd_proj_attn": { | |
| "id": "svd_proj_attn", | |
| "name": "SVD Projection Attention Alias.", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/attentions/svd_proj_attn.py" | |
| }, | |
| "svd_projection_attention": { | |
| "id": "svd_projection_attention", | |
| "name": "SVD Projection Attention: Explicit Low-Rank Matrix Reconstruction", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/attentions/svd_projection_attention.py" | |
| }, | |
| "svd_sequence_attention": { | |
| "id": "svd_sequence_attention", | |
| "name": "Low-Rank SVD Sequence Attention Re-export Module.", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/attentions/svd_sequence_attention.py" | |
| }, | |
| "template_attention": { | |
| "id": "template_attention", | |
| "name": "Template Attention: Reference Implementation & Standardization Ba", | |
| "category": "B", | |
| "subcategory": "sparse_approximate", | |
| "paradigm": "sparse", | |
| "file": "Codes/framework/attentions/template_attention.py" | |
| }, | |
| "token_compressed": { | |
| "id": "token_compressed", | |
| "name": "Token-Compressed Attention Wrapper: Structural Abstraction & Pass", | |
| "category": "B", | |
| "subcategory": "sparse_approximate", | |
| "paradigm": "sparse", | |
| "file": "Codes/framework/attentions/token_compressed.py" | |
| }, | |
| "token_compressed_attention": { | |
| "id": "token_compressed_attention", | |
| "name": "Token Compressed Attention: Saliency-Based Key-Value Pruning", | |
| "category": "G", | |
| "subcategory": "pruning_sparsity", | |
| "paradigm": "sparse", | |
| "file": "Codes/framework/attentions/token_compressed_attention.py" | |
| }, | |
| "tpa_attention": { | |
| "id": "tpa_attention", | |
| "name": "TPAttention: Tensor Product Attention with Factorised Query-Key P", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/attentions/tpa_attention.py" | |
| }, | |
| "tt": { | |
| "id": "tt", | |
| "name": "TokenCompressedAttention: A No‑Compression Baseline Wrapper for A", | |
| "category": "B", | |
| "subcategory": "sparse_approximate", | |
| "paradigm": "sparse", | |
| "file": "Codes/framework/attentions/tt.py" | |
| }, | |
| "unified_rope": { | |
| "id": "unified_rope", | |
| "name": "Universal Rotary Position Embedding (UnifiedRoPE): Positional Inv", | |
| "category": "B", | |
| "subcategory": "sparse_approximate", | |
| "paradigm": "sparse", | |
| "file": "Codes/framework/attentions/unified_rope.py" | |
| }, | |
| "upi_mamba": { | |
| "id": "upi_mamba", | |
| "name": "UPI-Stuffed Mamba: Uncertainty-Penalized Interpolated Mamba SSM", | |
| "category": "D", | |
| "subcategory": "recurrent_linear", | |
| "paradigm": "ssm", | |
| "file": "Codes/framework/attentions/upi_mamba.py" | |
| }, | |
| "upi_stuffed_mamba": { | |
| "id": "upi_stuffed_mamba", | |
| "name": "UPI-Stuffed Mamba: Uncertainty-Penalized Interpolated Mamba SSM B", | |
| "category": "D", | |
| "subcategory": "recurrent_linear", | |
| "paradigm": "ssm", | |
| "file": "Codes/framework/attentions/upi_stuffed_mamba.py" | |
| }, | |
| "vig_attention": { | |
| "id": "vig_attention", | |
| "name": "ViG Attention: Vision GNN-inspired 2D Grid Linear Attention with ", | |
| "category": "B", | |
| "subcategory": "sparse_approximate", | |
| "paradigm": "sparse", | |
| "file": "Codes/framework/attentions/vig_attention.py" | |
| }, | |
| "vspec": { | |
| "id": "vspec", | |
| "name": "VSpec++ Attention: Variational Spectral Token-Adaptive Attention ", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "sparse", | |
| "file": "Codes/framework/attentions/vspec.py" | |
| }, | |
| "weighted_ensemble": { | |
| "id": "weighted_ensemble", | |
| "name": "Smart Weighted Ensemble with Task-Aware Attention and Hybrid Comb", | |
| "category": "B", | |
| "subcategory": "sparse_approximate", | |
| "paradigm": "sparse", | |
| "file": "Codes/framework/attentions/weighted_ensemble.py" | |
| }, | |
| "wgqa_attention": { | |
| "id": "wgqa_attention", | |
| "name": "Weighted Grouped-Query Attention (WGQA).", | |
| "category": "A", | |
| "subcategory": "dense_attention", | |
| "paradigm": "mla", | |
| "file": "Codes/framework/attentions/wgqa_attention.py" | |
| }, | |
| "xattention": { | |
| "id": "xattention", | |
| "name": "XAttention: Block-Sparse Coarse-to-Fine Routing with Unified Soft", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "moe", | |
| "file": "Codes/framework/attentions/xattention.py" | |
| }, | |
| "zeros_attention": { | |
| "id": "zeros_attention", | |
| "name": "ZeroS Attention: Zero-Mean Normalized Linear Attention", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "sparse", | |
| "file": "Codes/framework/attentions/zeros_attention.py" | |
| }, | |
| "a3_compressor": { | |
| "id": "a3_compressor", | |
| "name": "A3 Compressor: Activation-Aware Factorized SVD Compression with F", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/compression_engine/a3_compressor.py" | |
| }, | |
| "aa_svd_compressor": { | |
| "id": "aa_svd_compressor", | |
| "name": "Activation-Aware Singular Value Decomposition (AASVD) Compressor.", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/compression_engine/aa_svd_compressor.py" | |
| }, | |
| "aasvd": { | |
| "id": "aasvd", | |
| "name": "Activation-Aware Singular Value Decomposition (AASVD): Streaming ", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/compression_engine/aasvd.py" | |
| }, | |
| "adaptive_compressor": { | |
| "id": "adaptive_compressor", | |
| "name": "AdaptiveTransformerCompressor: Dynamic Energy-Threshold Attention", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/compression_engine/adaptive_compressor.py" | |
| }, | |
| "adaptive_ranks": { | |
| "id": "adaptive_ranks", | |
| "name": "Adaptive Ranks SVD Compressor: dynamic rank selection per layer b", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/compression_engine/adaptive_ranks.py" | |
| }, | |
| "adasvd_compressor": { | |
| "id": "adasvd_compressor", | |
| "name": "AdaSVDCompressor: Adaptive SVD via Nuclear-Norm Layer Importance ", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/compression_engine/adasvd_compressor.py" | |
| }, | |
| "admm_compressor": { | |
| "id": "admm_compressor", | |
| "name": "ADMM Compressor: Alternating Direction Method of Multipliers for ", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "pruning", | |
| "file": "Codes/framework/compression_engine/admm_compressor.py" | |
| }, | |
| "admm_pruner": { | |
| "id": "admm_pruner", | |
| "name": "ADMM Pruner: Alternating Direction Method of Multipliers with Dyn", | |
| "category": "G", | |
| "subcategory": "pruning_sparsity", | |
| "paradigm": "pruning", | |
| "file": "Codes/framework/compression_engine/admm_pruner.py" | |
| }, | |
| "armor_pruner": { | |
| "id": "armor_pruner", | |
| "name": "ARMOR Pruner: Adaptive Resilient Matrix Orthogonal Rank Pruning (", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/compression_engine/armor_pruner.py" | |
| }, | |
| "ast_pruner": { | |
| "id": "ast_pruner", | |
| "name": "AST: Adaptive Semi-Structured 2:4 and N:M Pruning with Empirical ", | |
| "category": "G", | |
| "subcategory": "pruning_sparsity", | |
| "paradigm": "pruning", | |
| "file": "Codes/framework/compression_engine/ast_pruner.py" | |
| }, | |
| "astro_quantizer": { | |
| "id": "astro_quantizer", | |
| "name": "AstroQuantizer: Dynamic Uniform Range Quantization Engine with St", | |
| "category": "H", | |
| "subcategory": "quantization", | |
| "paradigm": "quant", | |
| "file": "Codes/framework/compression_engine/astro_quantizer.py" | |
| }, | |
| "bonsai_pruner": { | |
| "id": "bonsai_pruner", | |
| "name": "Bonsai: Iterative Magnitude and Taylor Pruning (IMP) with Gradien", | |
| "category": "G", | |
| "subcategory": "pruning_sparsity", | |
| "paradigm": "moe", | |
| "file": "Codes/framework/compression_engine/bonsai_pruner.py" | |
| }, | |
| "cachenotes_compressor": { | |
| "id": "cachenotes_compressor", | |
| "name": "CacheNotes Compressor: Task-Aware CPT Guidance for Static SVD & D", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/compression_engine/cachenotes_compressor.py" | |
| }, | |
| "come": { | |
| "id": "come", | |
| "name": "CoMe: Compress LLMs through Layer Concatenation and Shedding with", | |
| "category": "A", | |
| "subcategory": "dense_attention", | |
| "paradigm": "pruning", | |
| "file": "Codes/framework/compression_engine/come.py" | |
| }, | |
| "compot_compressor": { | |
| "id": "compot_compressor", | |
| "name": "COMPOT: Compressed Orthogonal Dictionary Learning Compressor.", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/compression_engine/compot_compressor.py" | |
| }, | |
| "coopq_quantizer": { | |
| "id": "coopq_quantizer", | |
| "name": "CoopQ: Cooperative Game-Theoretic Bit Allocation Quantizer with S", | |
| "category": "H", | |
| "subcategory": "quantization", | |
| "paradigm": "quant", | |
| "file": "Codes/framework/compression_engine/coopq_quantizer.py" | |
| }, | |
| "cr_compressor": { | |
| "id": "cr_compressor", | |
| "name": "CR Compressor: Interpolative Decomposition for Weight Compression", | |
| "category": "B", | |
| "subcategory": "sparse_approximate", | |
| "paradigm": "sparse", | |
| "file": "Codes/framework/compression_engine/cr_compressor.py" | |
| }, | |
| "cur_compressor": { | |
| "id": "cur_compressor", | |
| "name": "CUR Compressor: replaces Linear and Conv1D layers with CUR decomp", | |
| "category": "B", | |
| "subcategory": "sparse_approximate", | |
| "paradigm": "sparse", | |
| "file": "Codes/framework/compression_engine/cur_compressor.py" | |
| }, | |
| "cur_linear": { | |
| "id": "cur_linear", | |
| "name": "CUR Linear Layer: low-rank cross-approximation using column and r", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/compression_engine/cur_linear.py" | |
| }, | |
| "cur_moe": { | |
| "id": "cur_moe", | |
| "name": "CUR-MoE Compressor alias module.", | |
| "category": "F", | |
| "subcategory": "mixture_of_experts", | |
| "paradigm": "moe", | |
| "file": "Codes/framework/compression_engine/cur_moe.py" | |
| }, | |
| "cur_moe_compressor": { | |
| "id": "cur_moe_compressor", | |
| "name": "CUR-MoE Compressor: apply CUR cross-approximation to Mixture of E", | |
| "category": "F", | |
| "subcategory": "mixture_of_experts", | |
| "paradigm": "moe", | |
| "file": "Codes/framework/compression_engine/cur_moe_compressor.py" | |
| }, | |
| "cure": { | |
| "id": "cure", | |
| "name": "CURE: CUR Expert Compression Engine with Randomized Subspace Appr", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/compression_engine/cure.py" | |
| }, | |
| "delta_kv": { | |
| "id": "delta_kv", | |
| "name": "DeltaKV: Residual-Based Key-Value Cache Compression via Long-Rang", | |
| "category": "A", | |
| "subcategory": "dense_attention", | |
| "paradigm": "mla", | |
| "file": "Codes/framework/compression_engine/delta_kv.py" | |
| }, | |
| "direct_svd": { | |
| "id": "direct_svd", | |
| "name": "Direct SVD Compressor: Truncated SVD low-rank factorized layer re", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/compression_engine/direct_svd.py" | |
| }, | |
| "dobi": { | |
| "id": "dobi", | |
| "name": "DobiSVDLinearLayer & Compressor: Differentiable SVD with Learnabl", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/compression_engine/dobi.py" | |
| }, | |
| "dobi_svd": { | |
| "id": "dobi_svd", | |
| "name": "DobiSVDLinearLayer & Compressor: Differentiable SVD with Learnabl", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/compression_engine/dobi_svd.py" | |
| }, | |
| "dynamic_entropy": { | |
| "id": "dynamic_entropy", | |
| "name": "Dynamic Entropy (`dynamic_entropy`)", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/compression_engine/dynamic_entropy.py" | |
| }, | |
| "dynamic_rank": { | |
| "id": "dynamic_rank", | |
| "name": "Dynamic Entropy Singular Value Decomposition (Dynamic Entropy SVD", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/compression_engine/dynamic_rank.py" | |
| }, | |
| "enlighten_compressor": { | |
| "id": "enlighten_compressor", | |
| "name": "ENLighten: Low-Rank SVD + 2:4 Structured Sparsity (Photonic-Align", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/compression_engine/enlighten_compressor.py" | |
| }, | |
| "entropy_budget_allocation": { | |
| "id": "entropy_budget_allocation", | |
| "name": "EntropyBudgetAllocation: global rank assignment via whitened acti", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/compression_engine/entropy_budget_allocation.py" | |
| }, | |
| "erc_svd": { | |
| "id": "erc_svd", | |
| "name": "ERCCompressor: Error-Controlled Singular Value Energy Thresholdin", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/compression_engine/erc_svd.py" | |
| }, | |
| "faq_quantizer": { | |
| "id": "faq_quantizer", | |
| "name": "FAQ (Feature-Activation-Aware Quantization): adjusts quantization", | |
| "category": "H", | |
| "subcategory": "quantization", | |
| "paradigm": "quant", | |
| "file": "Codes/framework/compression_engine/faq_quantizer.py" | |
| }, | |
| "flap": { | |
| "id": "flap", | |
| "name": "FLAP: Fluctuation-Based Adaptive Structured Pruning.", | |
| "category": "G", | |
| "subcategory": "pruning_sparsity", | |
| "paradigm": "pruning", | |
| "file": "Codes/framework/compression_engine/flap.py" | |
| }, | |
| "flat_llm": { | |
| "id": "flat_llm", | |
| "name": "FLAT-LLM: Activation-Aware Structured Dimension & Neuron Pruning ", | |
| "category": "G", | |
| "subcategory": "pruning_sparsity", | |
| "paradigm": "pruning", | |
| "file": "Codes/framework/compression_engine/flat_llm.py" | |
| }, | |
| "frandi": { | |
| "id": "frandi", | |
| "name": "FRanDI Compressor alias module.", | |
| "category": "B", | |
| "subcategory": "sparse_approximate", | |
| "paradigm": "sparse", | |
| "file": "Codes/framework/compression_engine/frandi.py" | |
| }, | |
| "frandi_compressor": { | |
| "id": "frandi_compressor", | |
| "name": "FRanDI: Feature-Regression Aligned Singular Value Decomposition C", | |
| "category": "B", | |
| "subcategory": "sparse_approximate", | |
| "paradigm": "sparse", | |
| "file": "Codes/framework/compression_engine/frandi_compressor.py" | |
| }, | |
| "gfws_d_compressor": { | |
| "id": "gfws_d_compressor", | |
| "name": "GFWSVD: Fisher-weighted / Covariance-weighted SVD. Registered as ", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/compression_engine/gfws_d_compressor.py" | |
| }, | |
| "grecodec": { | |
| "id": "grecodec", | |
| "name": "GreCoDeC Compressor alias module.", | |
| "category": "B", | |
| "subcategory": "sparse_approximate", | |
| "paradigm": "sparse", | |
| "file": "Codes/framework/compression_engine/grecodec.py" | |
| }, | |
| "grecodec_compressor": { | |
| "id": "grecodec_compressor", | |
| "name": "GreCoDeC: Greedy Coordinate Descent Low-Rank Compressor.", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/compression_engine/grecodec_compressor.py" | |
| }, | |
| "hashed_linear": { | |
| "id": "hashed_linear", | |
| "name": "HashedNets: Vectorized Weight Sharing via Hash Tables with Sign H", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "sparse", | |
| "file": "Codes/framework/compression_engine/hashed_linear.py" | |
| }, | |
| "hashed_net_compressor": { | |
| "id": "hashed_net_compressor", | |
| "name": "HashedNet Compressor: replaces nn.Linear and Conv1D layers with H", | |
| "category": "B", | |
| "subcategory": "sparse_approximate", | |
| "paradigm": "sparse", | |
| "file": "Codes/framework/compression_engine/hashed_net_compressor.py" | |
| }, | |
| "huffman_coder": { | |
| "id": "huffman_coder", | |
| "name": "Huffman Coder & Entropy Compression Engine for Weight and KV Cach", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "mla", | |
| "file": "Codes/framework/compression_engine/huffman_coder.py" | |
| }, | |
| "hybrid_svd_layer": { | |
| "id": "hybrid_svd_layer", | |
| "name": "Hybrid SVD Layer: Single‑Head SVD Approximation with Positional E", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/compression_engine/hybrid_svd_layer.py" | |
| }, | |
| "hybridkv": { | |
| "id": "hybridkv", | |
| "name": "HybridKV: Hybrid KV Cache Compression for Multimodal & Long-Conte", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/compression_engine/hybridkv.py" | |
| }, | |
| "hybridkv_compressor": { | |
| "id": "hybridkv_compressor", | |
| "name": "HybridKV Compressor: Dual Static-Dynamic Head-Specific Hierarchic", | |
| "category": "I", | |
| "subcategory": "hybrid_composite", | |
| "paradigm": "mla", | |
| "file": "Codes/framework/compression_engine/hybridkv_compressor.py" | |
| }, | |
| "kmeans_quantize": { | |
| "id": "kmeans_quantize", | |
| "name": "KMeans-based weight clustering quantizer with STE. Registered as ", | |
| "category": "H", | |
| "subcategory": "quantization", | |
| "paradigm": "quant", | |
| "file": "Codes/framework/compression_engine/kmeans_quantize.py" | |
| }, | |
| "kronecker_compressor": { | |
| "id": "kronecker_compressor", | |
| "name": "Kronecker Product Compressor: replaces nn.Linear and Conv1D layer", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/compression_engine/kronecker_compressor.py" | |
| }, | |
| "kronecker_linear": { | |
| "id": "kronecker_linear", | |
| "name": "Kronecker Linear Layer: weight matrix represented as Kronecker pr", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/compression_engine/kronecker_linear.py" | |
| }, | |
| "kronecker_tt_lora": { | |
| "id": "kronecker_tt_lora", | |
| "name": "Kronecker-TT-LoRA: Hybrid Compression Engine for Parameter-Effici", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "sparse", | |
| "file": "Codes/framework/compression_engine/kronecker_tt_lora.py" | |
| }, | |
| "kvtc": { | |
| "id": "kvtc", | |
| "name": "KVTC: KV Cache Transform Coding - PCA decorrelation + adaptive qu", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/compression_engine/kvtc.py" | |
| }, | |
| "kvtc_compressor": { | |
| "id": "kvtc_compressor", | |
| "name": "KVTC Compressor: Key-Value Transform Coding via PCA Decorrelation", | |
| "category": "H", | |
| "subcategory": "quantization", | |
| "paradigm": "mla", | |
| "file": "Codes/framework/compression_engine/kvtc_compressor.py" | |
| }, | |
| "liftr": { | |
| "id": "liftr", | |
| "name": "LIFTR: Low-Rank In-Flight Truncated Reconstruction Compressor", | |
| "category": "A", | |
| "subcategory": "dense_attention", | |
| "paradigm": "pruning", | |
| "file": "Codes/framework/compression_engine/liftr.py" | |
| }, | |
| "lillama_compressor": { | |
| "id": "lillama_compressor", | |
| "name": "LiLLaMA: Local Distillation via Activation Cross-Covariance Singu", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/compression_engine/lillama_compressor.py" | |
| }, | |
| "lsc_compressor": { | |
| "id": "lsc_compressor", | |
| "name": "LSC Compressor: Low-Rank Spectral Fourier Compression Engine", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/compression_engine/lsc_compressor.py" | |
| }, | |
| "mamba_shedder": { | |
| "id": "mamba_shedder", | |
| "name": "Mamba-Shedder: Sensitivity-Based Structured Pruning and Layer She", | |
| "category": "D", | |
| "subcategory": "recurrent_linear", | |
| "paradigm": "ssm", | |
| "file": "Codes/framework/compression_engine/mamba_shedder.py" | |
| }, | |
| "mgie_svd": { | |
| "id": "mgie_svd", | |
| "name": "MGIE-SVD: Maximum Information Entropy SVD Rank Selection.", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/compression_engine/mgie_svd.py" | |
| }, | |
| "mgie_svd_compressor": { | |
| "id": "mgie_svd_compressor", | |
| "name": "MGIESVDBudgetCompressor: Information Entropy SVD Rank Allocation ", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/compression_engine/mgie_svd_compressor.py" | |
| }, | |
| "mlorq_compressor": { | |
| "id": "mlorq_compressor", | |
| "name": "MLoRQ Compressor: Multi-Layer Optimal Rank SVD and Joint Low-Bit ", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/compression_engine/mlorq_compressor.py" | |
| }, | |
| "modegpt": { | |
| "id": "modegpt", | |
| "name": "MoDeGPT: Modular Decomposition of Transformer Blocks.", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/compression_engine/modegpt.py" | |
| }, | |
| "modegpt_compressor": { | |
| "id": "modegpt_compressor", | |
| "name": "MoDeGPT: Modular Decomposition of Transformer Blocks.", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/compression_engine/modegpt_compressor.py" | |
| }, | |
| "nli_softmax": { | |
| "id": "nli_softmax", | |
| "name": "NLI Softmax approximation – a fast, quantisation‑friendly softmax", | |
| "category": "H", | |
| "subcategory": "quantization", | |
| "paradigm": "pruning", | |
| "file": "Codes/framework/compression_engine/nli_softmax.py" | |
| }, | |
| "nmf_compressor": { | |
| "id": "nmf_compressor", | |
| "name": "NMFCompressor: Non-Negative Matrix Factorization Compression Engi", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/compression_engine/nmf_compressor.py" | |
| }, | |
| "oats": { | |
| "id": "oats", | |
| "name": "OATS: Outlier-Aware Training Sparsification. Registered as 'oats'", | |
| "category": "B", | |
| "subcategory": "sparse_approximate", | |
| "paradigm": "sparse", | |
| "file": "Codes/framework/compression_engine/oats.py" | |
| }, | |
| "oats_compressor": { | |
| "id": "oats_compressor", | |
| "name": "OATS: Outlier-Aware Training & Saliency-Based Sparsification with", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/compression_engine/oats_compressor.py" | |
| }, | |
| "optimal_subspace_projection": { | |
| "id": "optimal_subspace_projection", | |
| "name": "Optimal Subspace Projection: Activation-Aware Subspace Projection", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/compression_engine/optimal_subspace_projection.py" | |
| }, | |
| "padre_attention": { | |
| "id": "padre_attention", | |
| "name": "PADRe: Polynomial Attention with Dropout Regularization.", | |
| "category": "B", | |
| "subcategory": "sparse_approximate", | |
| "paradigm": "mla", | |
| "file": "Codes/framework/compression_engine/padre_attention.py" | |
| }, | |
| "proxsparse": { | |
| "id": "proxsparse", | |
| "name": "ProxSparse: Proximal Gradient Regularized Structured & Semi-Struc", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/compression_engine/proxsparse.py" | |
| }, | |
| "proxsparse_pruner": { | |
| "id": "proxsparse_pruner", | |
| "name": "ProxSparse: regularized learning of semi-structured sparsity. Reg", | |
| "category": "G", | |
| "subcategory": "pruning_sparsity", | |
| "paradigm": "pruning", | |
| "file": "Codes/framework/compression_engine/proxsparse_pruner.py" | |
| }, | |
| "pruning_utils": { | |
| "id": "pruning_utils", | |
| "name": "Pruning Utilities and Saliency Estimation for Structural Pruning ", | |
| "category": "G", | |
| "subcategory": "pruning_sparsity", | |
| "paradigm": "pruning", | |
| "file": "Codes/framework/compression_engine/pruning_utils.py" | |
| }, | |
| "replaceme_compressor": { | |
| "id": "replaceme_compressor", | |
| "name": "ReplaceMe: Layer Removal and Replacement with Linear Residual Blo", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/compression_engine/replaceme_compressor.py" | |
| }, | |
| "roast_hashed_linear": { | |
| "id": "roast_hashed_linear", | |
| "name": "ROAST: Hardware-Aware Weight Sharing with Tiling.", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/compression_engine/roast_hashed_linear.py" | |
| }, | |
| "rotational_slicegpt_int4": { | |
| "id": "rotational_slicegpt_int4", | |
| "name": "Rotational SliceGPT INT4 Compressor: Rotational Invariance, Subsp", | |
| "category": "H", | |
| "subcategory": "quantization", | |
| "paradigm": "mla", | |
| "file": "Codes/framework/compression_engine/rotational_slicegpt_int4.py" | |
| }, | |
| "rpca": { | |
| "id": "rpca", | |
| "name": "RPCA Compressor alias module.", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/compression_engine/rpca.py" | |
| }, | |
| "rpca_compressor": { | |
| "id": "rpca_compressor", | |
| "name": "RPCACompressor: Robust Principal Component Analysis Low-Rank & Sp", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/compression_engine/rpca_compressor.py" | |
| }, | |
| "saes_svd": { | |
| "id": "saes_svd", | |
| "name": "SAES-SVD v2: error suppression with fixed rank ratio. Registered ", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/compression_engine/saes_svd.py" | |
| }, | |
| "saes_svd_compressor": { | |
| "id": "saes_svd_compressor", | |
| "name": "SAES-SVD Compressor: Joint intra- and inter-layer error compensat", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/compression_engine/saes_svd_compressor.py" | |
| }, | |
| "saes_svd_v2": { | |
| "id": "saes_svd_v2", | |
| "name": "SAES-SVD v2: Spectrum-Aware Error-Suppressed Singular Value Decom", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/compression_engine/saes_svd_v2.py" | |
| }, | |
| "salr": { | |
| "id": "salr", | |
| "name": "SALR: Structure-Aware / Sparse Low-Rank compression combining SVD", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/compression_engine/salr.py" | |
| }, | |
| "sdrm": { | |
| "id": "sdrm", | |
| "name": "SDRM: Scaled Dimension Rank Modulation Compressor. Registered as ", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/compression_engine/sdrm.py" | |
| }, | |
| "sdrm_compressor": { | |
| "id": "sdrm_compressor", | |
| "name": "SDRM: Rank Assignment Proportional to Parameter Budget for Edge I", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/compression_engine/sdrm_compressor.py" | |
| }, | |
| "slicegpt": { | |
| "id": "slicegpt", | |
| "name": "SliceGPT: Post-Training Slicing with Orthogonal Transformation & ", | |
| "category": "A", | |
| "subcategory": "dense_attention", | |
| "paradigm": "pruning", | |
| "file": "Codes/framework/compression_engine/slicegpt.py" | |
| }, | |
| "slicegpt_compressor": { | |
| "id": "slicegpt_compressor", | |
| "name": "SliceGPT PCA-based slicing compressor. Registered as 'slicegpt_pc", | |
| "category": "A", | |
| "subcategory": "dense_attention", | |
| "paradigm": "pruning", | |
| "file": "Codes/framework/compression_engine/slicegpt_compressor.py" | |
| }, | |
| "slicegpt_pca": { | |
| "id": "slicegpt_pca", | |
| "name": "SliceGPT PCA Compressor: Principal Component Analysis based Dimen", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/compression_engine/slicegpt_pca.py" | |
| }, | |
| "slim_compressor": { | |
| "id": "slim_compressor", | |
| "name": "SLiM: Structured Low-Rank Integrated Matrix Compression (Quantiza", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/compression_engine/slim_compressor.py" | |
| }, | |
| "soka": { | |
| "id": "soka", | |
| "name": "SoKA: Second-Order Knowledge-Aware Low-Rank Linear Compressor. Re", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/compression_engine/soka.py" | |
| }, | |
| "soka_compressor": { | |
| "id": "soka_compressor", | |
| "name": "SoKA: Second-Order Knowledge-Aware Low-Rank Linear Compressor.", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/compression_engine/soka_compressor.py" | |
| }, | |
| "sparsegpt": { | |
| "id": "sparsegpt", | |
| "name": "SparseGPT: One-Shot Second-Order Hessian-Guided Weight Pruning fo", | |
| "category": "G", | |
| "subcategory": "pruning_sparsity", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/compression_engine/sparsegpt.py" | |
| }, | |
| "spectraformer_attention": { | |
| "id": "spectraformer_attention", | |
| "name": "Spectraformer: Random Feature Linear Attention with Flexible Kern", | |
| "category": "B", | |
| "subcategory": "sparse_approximate", | |
| "paradigm": "mla", | |
| "file": "Codes/framework/compression_engine/spectraformer_attention.py" | |
| }, | |
| "subspace_projection": { | |
| "id": "subspace_projection", | |
| "name": "Subspace Projection Compressor: Low-Rank Subspace Projection and ", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/compression_engine/subspace_projection.py" | |
| }, | |
| "svd_linear": { | |
| "id": "svd_linear", | |
| "name": "SVD-based low-rank linear layer replacement with optional Fused S", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/compression_engine/svd_linear.py" | |
| }, | |
| "swift_svd": { | |
| "id": "swift_svd", | |
| "name": "Swift-SVD: fast closed-form truncated SVD with symmetric square-r", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/compression_engine/swift_svd.py" | |
| }, | |
| "swift_svd_act": { | |
| "id": "swift_svd_act", | |
| "name": "Swift-SVD Activation-Aware Compressor via cross-covariance / acti", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/compression_engine/swift_svd_act.py" | |
| }, | |
| "swift_svd_compressor": { | |
| "id": "swift_svd_compressor", | |
| "name": "Swift-SVD Activation-aware via cross-covariance. Registered as 's", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/compression_engine/swift_svd_compressor.py" | |
| }, | |
| "tlinformer_attention": { | |
| "id": "tlinformer_attention", | |
| "name": "TLinFormer: Exact linear‑time attention via low‑rank sequence pro", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/compression_engine/tlinformer_attention.py" | |
| }, | |
| "tomoe": { | |
| "id": "tomoe", | |
| "name": "ToMoE: Converting Dense MLP into Mixture of Experts via Dynamic S", | |
| "category": "F", | |
| "subcategory": "mixture_of_experts", | |
| "paradigm": "moe", | |
| "file": "Codes/framework/compression_engine/tomoe.py" | |
| }, | |
| "tomoe_compressor": { | |
| "id": "tomoe_compressor", | |
| "name": "ToMoE: converts dense linear and Conv1D layers (e.g. FFN and atte", | |
| "category": "F", | |
| "subcategory": "mixture_of_experts", | |
| "paradigm": "moe", | |
| "file": "Codes/framework/compression_engine/tomoe_compressor.py" | |
| }, | |
| "tt_compressor": { | |
| "id": "tt_compressor", | |
| "name": "Tensor Train Compressor: replaces eligible nn.Linear and Conv1D l", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/compression_engine/tt_compressor.py" | |
| }, | |
| "tt_linear": { | |
| "id": "tt_linear", | |
| "name": "Tensor-Train Linear Layer: parameterizes weight matrices as Tenso", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/compression_engine/tt_linear.py" | |
| }, | |
| "uniql_compressor": { | |
| "id": "uniql_compressor", | |
| "name": "UniQL: Joint low‑rank + quantisation using SVD and uniform quant.", | |
| "category": "E", | |
| "subcategory": "svd_low_rank", | |
| "paradigm": "svd", | |
| "file": "Codes/framework/compression_engine/uniql_compressor.py" | |
| }, | |
| "zerokv_neo": { | |
| "id": "zerokv_neo", | |
| "name": "ZeroKV-Neo: L2-norm based KV cache eviction with attention sink a", | |
| "category": "A", | |
| "subcategory": "dense_attention", | |
| "paradigm": "mla", | |
| "file": "Codes/framework/compression_engine/zerokv_neo.py" | |
| }, | |
| "zerokv_neo_compressor": { | |
| "id": "zerokv_neo_compressor", | |
| "name": "ZeroKV-Neo: L2-norm based KV cache eviction with attention sink a", | |
| "category": "A", | |
| "subcategory": "dense_attention", | |
| "paradigm": "mla", | |
| "file": "Codes/framework/compression_engine/zerokv_neo_compressor.py" | |
| } | |
| } |
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