[KVCache] TIR attention kernel support for MLA

This PR introduces the MLA attention kernels written in TIR.
It also implements the KV cache MLA computation logic.

A new unit test file is added to ensure the correctness of the
TIR kernels.

This PR also fixes a few TIR prefill kernel tile size initialization.

Author: MasterJH5574

python/tvm/relax/frontend/nn/llm/kv_cache.py

@@ -320,7 +320,17 @@ def tree_attn(
 
     bdx = 32
     num_warps = 4
-    tile_x, tile_y, tile_z = 64 // ((DataType(dtype).bits + 7) // 8) // max(d // 128, 1), d, 16
+    tile_x, tile_y, tile_z = (
+        64 // ((DataType(dtype).bits + 7) // 8) // max(d // 128, 1),
+        d,
+        64 // ((DataType(dtype).bits + 7) // 8) // max(d // 128, 1),
+    )
+    original_tile_y = tile_y
+    original_tile_z = tile_z
+    while (tile_x * tile_z) % (bdx * num_warps) != 0:
+        tile_z += original_tile_z
+    while (tile_x * tile_y) % (bdx * num_warps) != 0:
+        tile_y += original_tile_y
 
     # Otherwise we would exceed maxComputeWorkgroupStorageSize
     if (
@@ -881,7 +891,17 @@ def tree_attn_with_paged_kv_cache(
 
     bdx = 32
     num_warps = 4
-    tile_x, tile_y, tile_z = 64 // ((DataType(dtype).bits + 7) // 8) // max(d // 128, 1), d, 16
+    tile_x, tile_y, tile_z = (
+        64 // ((DataType(dtype).bits + 7) // 8) // max(d // 128, 1),
+        d,
+        64 // ((DataType(dtype).bits + 7) // 8) // max(d // 128, 1),
+    )
+    original_tile_y = tile_y
+    original_tile_z = tile_z
+    while (tile_x * tile_z) % (bdx * num_warps) != 0:
+        tile_z += original_tile_z
+    while (tile_x * tile_y) % (bdx * num_warps) != 0:
+        tile_y += original_tile_y
 
     # Otherwise we would exceed maxComputeWorkgroupStorageSize
     if (

python/tvm/relax/frontend/nn/llm/tree_attn.py

@@ -74,12 +74,21 @@ TVM_REGISTER_GLOBAL("vm.builtin.attention_kv_cache_get_query_positions")
     .set_body_method<AttentionKVCache>(&AttentionKVCacheObj::GetQueryPositions);
 TVM_REGISTER_GLOBAL("vm.builtin.attention_kv_cache_debug_get_kv")
     .set_body_method<AttentionKVCache>(&AttentionKVCacheObj::DebugGetKV);
+TVM_REGISTER_GLOBAL("vm.builtin.attention_kv_cache_debug_get_kv_mla")
+    .set_body_method<AttentionKVCache>(&AttentionKVCacheObj::DebugGetKVMLA);
 TVM_REGISTER_GLOBAL("vm.builtin.attention_kv_cache_attention_with_fused_qkv")
     .set_body_typed([](AttentionKVCache kv_cache, int64_t layer_id,
                        double attn_score_scaling_factor, NDArray qkv_data, NDArray o_data) {
       kv_cache->AttentionWithFusedQKV(layer_id, std::move(qkv_data), NullOpt, std::move(o_data),
                                       attn_score_scaling_factor);
     });
+TVM_REGISTER_GLOBAL("vm.builtin.attention_kv_cache_mla_absorbed")
+    .set_body_typed([](AttentionKVCache kv_cache, int64_t layer_id,
+                       double attn_score_scaling_factor, NDArray q_data, NDArray compressed_kv_data,
+                       NDArray k_pe_data, NDArray o_data) {
+      kv_cache->MLAAbsorbed(layer_id, std::move(q_data), std::move(compressed_kv_data),
+                            std::move(k_pe_data), std::move(o_data), attn_score_scaling_factor);
+    });
 
 // RNN State methods
 TVM_REGISTER_GLOBAL("vm.builtin.rnn_state_get").set_body_method<RNNState>(&RNNStateObj::Get);

src/runtime/relax_vm/kv_state.cc

@@ -181,20 +181,6 @@ 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.
@@ -275,6 +261,16 @@ class AttentionKVCacheObj : public KVStateObj {
   virtual void DebugGetKV(int64_t seq_id,  //
                           int64_t start_pos, int64_t end_pos, NDArray k_data, NDArray v_data) = 0;
 
+  /*!
+   * \brief Fetch the compact K/V data of the given sequence for MLA cache.
+   * \param seq_id The sequence whose K/V data is to be fetched.
+   * \param start_pos The start position (inclusive) of the K/V data to fetch.
+   * \param end_pos The end position (exclusive) of the K/V data to fetch.
+   * \param kv_data The output KV data of the given sequence in layout elaborated above.
+   */
+  virtual void DebugGetKVMLA(int64_t seq_id, int64_t start_pos, int64_t end_pos,
+                             NDArray kv_data) = 0;
+
   /*!
    * \brief Set the K/V data of the given sequence from input K/V data.
    * `start_pos` (inclusive) controls starting position of K/V data