[Runtime][KVCache] Initial interface setup for MLA

src/runtime/relax_vm/kv_state.h

@@ -181,6 +181,69 @@ class AttentionKVCacheObj : public KVStateObj {
   virtual void AttentionWithFusedQKV(int64_t layer_id, NDArray qkv_data, Optional<NDArray> mask,
                                      NDArray o_data, double attn_score_scaling_factor) = 0;
 
+  /*!
+   * \brief Compute attention with Q/K/V data.
+   * \param layer_id The model layer where the attention compute happens.
+   * \param q_data The input Q data, in layout `(total_length, num_qo_heads, head_dim)`
+   * \param k_data The input K data, in layout `(total_length, num_kv_heads, head_dim)`
+   * \param v_data The input V data, in layout `(total_length, num_kv_heads, head_dim)`
+   * \param mask The input mask data, in layout `(total_sqr_length)`.
+   * \param o_data The output O data, in layout `(total_length, num_qo_heads, head_dim)`.
+   * \param attn_score_scaling_factor The additional attention scaling factor.
+   */
+  virtual void AttentionWithSeparateQKV(int64_t layer_id, NDArray q_data, NDArray k_data,
+                                        NDArray v_data, Optional<NDArray> mask, NDArray o_data,
+                                        double attn_score_scaling_factor) = 0;
+
+  /*!
+   * \brief Compute multi-head latent attention after applying weight absorption.
+   * \param layer_id The model layer where the attention compute happens.
+   * \param q_data The input Q data, in layout `(total_length, num_qo_heads, qk_head_dim)`
+   * \param compressed_kv_data The compressed latent KV data, in layout
+   * `(total_length, num_kv_heads, kv_lora_rank)`
+   * \param k_pe_data The positional embedding part of K data, in layout
+   * `(total_length, num_kv_heads, qk_rope_head_dim)`, where `kv_lora_rank + qk_rope_head_dim`
+   * equals qk_head_dim
+   * \param o_data The output O data, in layout `(total_length, num_qo_heads, v_head_dim)`.
+   * \param attn_score_scaling_factor The additional attention scaling factor.
+   */
+  virtual void MLAAbsorbed(int64_t layer_id, NDArray q_data, NDArray compressed_kv_data,
+                           NDArray k_pe_data, NDArray o_data, double attn_score_scaling_factor) = 0;
+
+  /*!
+   * \brief Compute multi-head latent attention in normal style.
+   * \param layer_id The model layer where the attention compute happens.
+   * \param q_data The input Q data, in layout
+   * `(total_length, num_qo_heads, qk_nope_head_dim + qk_rope_head_dim)`
+   * \param k_data The input K data, in layout
+   * `(total_length, num_qo_heads, qk_nope_head_dim + qk_rope_head_dim)`
+   * \param v_data The input V data, in layout
+   * `(total_length, num_qo_heads, v_head_dim)`
+   * \param compressed_kv_data The compressed latent KV data, in layout
+   * `(total_length, num_kv_heads, kv_lora_rank)`
+   * \param k_pe_data The positional embedding part of K data, in layout
+   * `(total_length, num_kv_heads, qk_rope_head_dim)`, where `kv_lora_rank + qk_rope_head_dim`
+   * equals qk_head_dim
+   * \param o_data The output O data, in layout `(total_length, num_qo_heads, v_head_dim)`.
+   * \param attn_score_scaling_factor The additional attention scaling factor.
+   */
+  virtual void MLANormal(int64_t layer_id, NDArray q_data, NDArray k_data, NDArray v_data,
+                         NDArray compressed_kv_data, NDArray k_pe_data, NDArray o_data,
+                         double attn_score_scaling_factor) = 0;
+
+  /*!
+   * \brief Compute linear attention with Q/K/V data.
+   * \param layer_id The model layer where the attention compute happens.
+   * \param q_data The input Q data, in layout `(total_length, num_qo_heads, head_dim)`.
+   * \param k_data The input K data, in layout `(total_length, num_kv_heads, head_dim)`.
+   * \param v_data The input V data, in layout `(total_length, num_kv_heads, head_dim)`.
+   * \param o_data The output O data, in layout `(total_length, num_qo_heads, head_dim)`.
+   * \param attn_score_scaling_factor The additional attention scaling factor.
+   * \sa AttentionKVCache::Attention
+   */
+  virtual void LinearAttention(int64_t layer_id, NDArray q_data, NDArray k_data, NDArray v_data,
+                               double attn_score_scaling_factor) = 0;
+
   /************** Positions **************/
 
   /*!