Python APIs¶
This section covers the direct MATE Python APIs. Use these interfaces only when your framework or model architecture requires direct symbol-level integration.
Note
Check wrapper availability first. The wrapper-first workflow preserves more of the upstream package behavior and usually requires less code change.
Use direct MATE Python APIs when:
no wrapper matches your framework’s package surface
a wrapper exists but does not cover the feature you need
you need symbol-level control over a specific operator path
Supported API Entrypoints¶
FlashInfer wrapper¶
The FlashInfer wrapper API reference documents the
supported flashinfer.rope and flashinfer.decode functions, including
their signatures, parameters, and return values.
Attention¶
Optimized entrypoints for FlashAttention, varlen, KV-cache, scheduler metadata, and MLA-related attention paths.
mate.flash_attn_combinemate.flash_attn_varlen_funcmate.flash_attn_with_kvcachemate.get_scheduler_metadatamate.get_mla_metadatamate.flash_mla_with_kvcachemate.flashmla.flash_mla_sparse_fwdmate.flashmla.flash_mla_sparse_fwd_pack8
Sparse MLA¶
Native sparse MLA entrypoints for fused RoPE FP8 quantization, reusable decode scheduler metadata, and FP8 sparse decode.
mate.sparse_mla_interface.mla_rope_quantize_fp8mate.sparse_mla_interface.sparse_mla_fp8_decode
SageAttention¶
Low-level pre-quantized SageAttention entrypoints for direct integration when
the sageattention wrapper is not the right surface.
mate.sage_attn_quantizedmate.sage_attn_quantized_with_kvcache
GEMM¶
Low-precision GEMM entrypoints, including 8-bit and mixed-dtype MoE GEMM, batched GEMM, and DeepGEMM-specific metadata / logits helpers.
mate.gemm.ragged_m_moe_gemm_8bitmate.gemm.ragged_m_moe_gemm_16bitmate.gemm.ragged_k_moe_gemm_8bitmate.gemm.ragged_k_moe_gemm_16bitmate.gemm.masked_moe_gemm_8bitmate.gemm.masked_moe_gemm_16bitmate.gemm.ragged_moe_gemm_mixed_dtypemate.gemm.masked_moe_gemm_mixed_dtypemate.gemm.bmmmate.deep_gemm.fp8_einsummate.deep_gemm.fp8_mqa_logitsmate.deep_gemm.tf32_hc_prenorm_gemmmate.deep_gemm.fp8_gemm_nt_skip_head_midmate.deep_gemm.get_paged_mqa_logits_metadatamate.deep_gemm.fp8_paged_mqa_logits
Mega MoE¶
Distributed Mega MoE entrypoints for DeepGEMM-style
expert execution across a process group. Use this path when you need the
fused distributed FP8 expert runtime rather than a single MoE GEMM operator.
The same APIs are available through mate.deep_gemm and mate.mega_moe;
the mate.deep_gemm aliases are the usual integration surface.
mate.deep_gemm.get_symm_buffer_for_mega_moemate.deep_gemm.transform_weights_for_mega_moemate.deep_gemm.fp8_fp8_mega_moe
Hyperconnection¶
Direct Hyperconnection APIs exposed at the
top-level mate package and through mate.hyperconnection.
mate.mhc_premate.mhc_prenorm_gemm_sqrsummate.mhc_pre_big_fuse
GDN¶
Direct Gated Delta Network APIs for decode and prefill. See the GDN support page for support details and workflow guidance.
mate.gated_delta_rule_decodemate.gdn_prefill.chunk_gated_delta_rule
KDA¶
Direct KDA entrypoints for fused chunked KDA and decode when
the flash_kda wrapper is not the right integration surface.
mate.chunk_kdamate.kda.chunk_kdamate.kda.gated_delta_rule_decode
MSA¶
MSA (MiniMax Sparse Attention) covers MATE’s direct dense, paged, and sparse
attention APIs on MUSA. Use the fmha_sm100 wrapper first when your project
already targets that package surface. Use the direct MATE APIs below when you
need the native planning and runtime contract.
mate.msa_interface.msa_planmate.msa_interface.msamate.msa_interface.sparse_msa_planmate.msa_interface.sparse_msamate.msa_interface.sparse_topk_selectmate.msa_interface.sparse_decode_atten_func
The detail page documents the plan types, runtime metadata, and page table helper used by the direct API path.
MoE Routing & Gating¶
Direct MoE routing and gating entrypoints for workflows that need MATE’s native router path instead of a wrapper-level integration.
mate.hash_topkmate.moe_fused_gate