add VLLM_USE_TRITON_OPT_MLA to use optimized MLA attention

update mla optest

setup.py

@@ -508,10 +508,10 @@ def get_version_add(sha: Optional[str] = None) -> str:
             if sha is None:
                 sha = get_sha(vllm_root)
             if (major, minor) == ('2', '4'):
-                version = 'das.opt1.cust1.' + sha[:7]
+                version = 'das.opt1.' + sha[:7]
     else:
         if (major, minor) == ('2', '4'):
-            version = 'das.opt1.cust1'
+            version = 'das.opt1'
 
 
     # dtk version
@@ -696,7 +696,7 @@ package_data = {
         "model_executor/layers/fused_moe/configs/*.json",
         "model_executor/layers/quantization/utils/configs/*.json",
         "benchmarks/*.py",
-        "model_executor/layers/quantization/configs/w8a8/*.json",
+        "attention/backends/configs/*.json",
         "model_executor/layers/quantization/configs/awq/*.json"
     ]
 }

tests/kernels/test_triton_decode_attention.py

@@ -2,9 +2,9 @@
 
 import pytest
 import torch
+import triton
 
-from vllm.attention.ops.triton_decode_attention import decode_attention_fwd
-
+from vllm.attention.ops.triton_decode_attention import decode_attention_fwd, decode_attention_v1, decode_attention_v2
 
 def cdiv(a, b):
     return (a + b - 1) // b
@@ -25,13 +25,13 @@ def test_decode_attention(B, L, H_Q, H_KV, D_QK, D_V, CACHE_SIZE, PAGE_SIZE):
     sm_scale = 1.0 / (D_QK**0.5)
     num_kv_splits = 8
 
-    num_pages_per_batch = cdiv(seq_len, PAGE_SIZE)
+    num_pages_per_batch = cdiv(seq_len, PAGE_SIZE) # 向上取整:65, (1027+16-1)//16
     req_to_page = torch.randint(0,
                                 CACHE_SIZE // PAGE_SIZE,
-                                (B, num_pages_per_batch, 1),
+                                (B, num_pages_per_batch, 1), #shape为（B, num_pages_per_batch, 1）的tensor,大小取值为0 至cache_size//page_size
                                 device="cuda")
     req_to_token = req_to_page * PAGE_SIZE
-    req_to_token = req_to_token.expand(B, num_pages_per_batch, PAGE_SIZE)
+    req_to_token = req_to_token.expand(B, num_pages_per_batch, PAGE_SIZE) # 维度扩展，从torch.Size([3, 65, 1])扩展至torch.Size([3, 65, 16])
     req_to_token = req_to_token + torch.arange(PAGE_SIZE, device="cuda").view(
         1, 1, -1)
     req_to_token = req_to_token.view(B, -1)
@@ -47,14 +47,22 @@ def test_decode_attention(B, L, H_Q, H_KV, D_QK, D_V, CACHE_SIZE, PAGE_SIZE):
 
     # o will have the same shape as q
     o = torch.zeros(B, H_Q, D_V, dtype=dtype, device="cuda")
-
+    
     b_seq_len = torch.full((B, ), seq_len, device="cuda")
 
+    b_start_loc = torch.arange(0, k_buffer.shape[0] * PAGE_SIZE, k_buffer.shape[0] * PAGE_SIZE // q.shape[0], device="cuda").to(torch.int32)
+    attn_logits_v1 = torch.empty(
+               (q.shape[1], k_buffer.shape[0]*PAGE_SIZE),
+               dtype=torch.float16,
+               device="cuda")
+
     attn_logits = torch.empty(
         (B, H_Q, num_kv_splits, D_V + 1),
         dtype=torch.float32,
         device="cuda",
     )
+    
+    quantiles = [0.5, 0.2, 0.8]
 
     # Call the original implementation.
     decode_attention_fwd(
@@ -87,5 +95,81 @@ def test_decode_attention(B, L, H_Q, H_KV, D_QK, D_V, CACHE_SIZE, PAGE_SIZE):
         sm_scale,
         PAGE_SIZE,
     )
-
     assert torch.allclose(o, o1)
+    
+    # v0_tc_ms, v0_tc_min_ms, v0_tc_max_ms = triton.testing.do_bench(lambda:
+    # decode_attention_fwd(
+    #     q,
+    #     k_buffer,
+    #     v_buffer,
+    #     o1,
+    #     req_to_page,
+    #     b_seq_len,
+    #     attn_logits,
+    #     num_kv_splits,
+    #     sm_scale,
+    #     PAGE_SIZE,
+
+    # ), quantiles=quantiles)
+    # print("print mla decode attention ori kernel [B, L, H_Q, H_KV, D_QK, D_V, CACHE_SIZE, PAGE_SIZE] min cost :",[B, L, H_Q, H_KV, D_QK, D_V, CACHE_SIZE, PAGE_SIZE], v0_tc_ms)
+    
+    decode_attention_v1(
+        q,
+        k_buffer,
+        v_buffer,
+        o1,
+        req_to_page,
+        b_start_loc,
+        b_seq_len,
+        attn_logits_v1,
+        num_kv_splits,
+        sm_scale,
+        PAGE_SIZE, 
+    )
+    assert torch.allclose(o, o1, atol=1e-2, rtol=1e-2)
+    
+    # v1_tc_ms, v1_tc_min_ms, v1_tc_max_ms = triton.testing.do_bench(lambda:
+    # decode_attention_v1(
+    #     q,
+    #     k_buffer,
+    #     v_buffer,
+    #     o1,
+    #     req_to_page,
+    #     b_start_loc,
+    #     b_seq_len,
+    #     attn_logits_v1,
+    #     num_kv_splits,
+    #     sm_scale,
+    #     PAGE_SIZE, 
+    # ), quantiles=quantiles)
+    # print("print mla decode attention v1 kernel [B, L, H_Q, H_KV, D_QK, D_V, CACHE_SIZE, PAGE_SIZE] min cost :",[B, L, H_Q, H_KV, D_QK, D_V, CACHE_SIZE, PAGE_SIZE], v1_tc_ms)
+    
+        
+    decode_attention_v2(
+        q,
+        k_buffer,
+        v_buffer,
+        o1,
+        req_to_page,
+        b_seq_len,
+        attn_logits,
+        num_kv_splits,
+        sm_scale,
+        PAGE_SIZE, 
+    )
+    assert torch.allclose(o, o1, atol=1e-2, rtol=1e-2)
+
+    # v2_tc_ms, v2_tc_min_ms, v2_tc_max_ms = triton.testing.do_bench(lambda:
+    # decode_attention_v2(
+    #     q,
+    #     k_buffer,
+    #     v_buffer,
+    #     o1,
+    #     req_to_page,
+    #     b_seq_len,
+    #     attn_logits,
+    #     num_kv_splits,
+    #     sm_scale,
+    #     PAGE_SIZE, 
+    # ), quantiles=quantiles)
+    # print("print mla decode attention v2 kernel [B, L, H_Q, H_KV, D_QK, D_V, CACHE_SIZE, PAGE_SIZE] min cost :",[B, L, H_Q, H_KV, D_QK, D_V, CACHE_SIZE, PAGE_SIZE], v2_tc_ms)
\ No newline at end of file

vllm/attention/backends/triton_mla.py

 # SPDX-License-Identifier: Apache-2.0
 
+import os
+import functools
+import json
 from collections import defaultdict
 from contextlib import contextmanager
 from dataclasses import dataclass
@@ -33,6 +36,57 @@ if TYPE_CHECKING:
     from vllm.worker.model_runner import (ModelInputForGPUBuilder,
                                           ModelInputForGPUWithSamplingMetadata)
 
+from vllm.logger import init_logger
+logger = init_logger(__name__)
+
+
+def get_mla_config_file_name(QH: int, KVH: int, QKD: int, VD: int, cache_dtype: Optional[str]) -> str:
+    if cache_dtype == "default":
+        return f"QH={QH}_KVH={KVH}_QKD={QKD}_VD={VD}_default.json"
+    
+    device_name = torch.cuda.get_device_name().replace(" ", "_")
+    if "K100_AI" in device_name:
+        return f"QH={QH}_KVH={KVH}_QKD={QKD}_VD={VD}_{cache_dtype}_K100AI.json"
+    elif "BW" in device_name:
+        return f"QH={QH}_KVH={KVH}_QKD={QKD}_VD={VD}_{cache_dtype}_BW.json"
+    else:
+        raise ValueError(f"Unsurpport device name: {device_name}")
+
+
+@functools.lru_cache
+def get_attention_mla_configs(QH: int, KVH: int, QKD: int, VD: int, cache_dtype: Optional[str]) -> Optional[Dict[Any, Any]]:
+
+    # First look up if an optimized configuration is available in the configs
+    # directory
+    json_file_name = get_mla_config_file_name(QH, KVH, QKD, VD, cache_dtype)
+
+    config_file_path = os.path.join(
+        os.path.dirname(os.path.realpath(__file__)), "configs", json_file_name
+    )
+    if os.path.exists(config_file_path):
+        with open(config_file_path) as f:
+            logger.info("Using decode attention configuration from %s for attention layer.", config_file_path)
+            # If a configuration has been found, return it
+            return json.load(f)
+    else:
+        logger.warning("Can not find best decode attention configuration %s for attention layer, it may not have the best performance to use default json. Please tune one. ", config_file_path)
+        
+        json_file_name = get_mla_config_file_name(16, 1, 576, 512, "default")
+        config_file_path = os.path.join(
+            os.path.dirname(os.path.realpath(__file__)), "configs", json_file_name
+        )
+        if os.path.exists(config_file_path):
+            with open(config_file_path) as f:
+                logger.warning("Using default decode attention configuration from %s for attention layer. It may not have the best performance to use default json. ", config_file_path)
+                # If a configuration has been found, return it
+                return json.load(f)
+        else:
+            raise ValueError("Please surpport default config can match 16 1 576 512")
+
+    # If no optimized configuration is available, we will use the default
+    # configuration
+    return None
+
 
 class TritonMLABackend(AttentionBackend):
 
@@ -735,12 +789,15 @@ class TritonMLAImpl(MLACommonImpl[TritonMLAMetadata]):
         kv_c_and_k_pe_cache = kv_c_and_k_pe_cache.unsqueeze(2)
         kv_c_cache = kv_c_and_k_pe_cache[..., :self.kv_lora_rank]
         PAGE_SIZE = kv_c_and_k_pe_cache.size(1)
+        
+        # TODO
+        # config = get_attention_mla_configs(self.num_heads, 1, self.kv_lora_rank + self.qk_rope_head_dim, self.kv_lora_rank, "fp16")
 
         # Run MQA
         decode_attention_fwd(q, kv_c_and_k_pe_cache, kv_c_cache, o,
                              decode_meta.block_tables,
                              decode_meta.seq_lens_tensor, attn_logits,
-                             attn_metadata.num_kv_splits, self.scale,
+                             attn_metadata.num_kv_splits, self.scale,  # config, 
                              PAGE_SIZE)
 
         return self._v_up_proj_and_o_proj(o)

vllm/attention/ops/triton_decode_attention.py

@@ -30,11 +30,12 @@ It supports page size >= 1.
 
 import os
 import logging
-
+import torch
 import triton
 import triton.language as tl
 
 from vllm.platforms import current_platform
+from vllm import envs
 
 is_hip_ = current_platform.is_rocm()
 os.environ["TRITON_HIP_USE_NEW_STREAM_PIPELINE"] = f"0"
@@ -221,7 +222,6 @@ def _decode_att_m_fwd(
         PAGE_SIZE=page_size,
         logit_cap=logit_cap,
         num_warps=num_warps,
-        num_stages=2,
         Lk=Lk,
         Lv=Lv,
     )
@@ -458,7 +458,6 @@ def _decode_grouped_att_m_fwd(
         PAGE_SIZE=page_size,
         logit_cap=logit_cap,
         num_warps=4,
-        num_stages=2,
         Lk=Lk,
         Lv=Lv,
         **extra_kargs,
@@ -541,7 +540,7 @@ def _decode_softmax_reducev_fwd(
         # https://rocm.docs.amd.com/en/docs-6.2.0/how-to/llm-fine-tuning-optimization/optimizing-triton-kernel.html
         # https://github.com/triton-lang/triton/blob/main/third_party/amd/backend/compiler.py
         extra_kargs = {
-            "waves_per_eu": 4,
+            "waves_per_eu": 0,
             "matrix_instr_nonkdim": 16,
             "kpack": 2
         }
@@ -560,7 +559,6 @@ def _decode_softmax_reducev_fwd(
         BLOCK_DV=BLOCK_DV,
         Lv=Lv,
         num_warps=4,
-        num_stages=2,
         **extra_kargs,
     )
 
@@ -623,6 +621,806 @@ def decode_attention_fwd_grouped(
                                 num_kv_splits)
 
 
+
+# opt
+@triton.autotune(
+    configs=[
+        triton.Config({"BLOCK_N": 16}, num_warps=2, num_ldmatrixes=0, num_stages=1),
+        triton.Config({"BLOCK_N": 16}, num_warps=4, num_ldmatrixes=0, num_stages=1),
+        triton.Config({"BLOCK_N": 16}, num_warps=8, num_ldmatrixes=0, num_stages=1),
+        triton.Config({"BLOCK_N": 32}, num_warps=2, num_ldmatrixes=0, num_stages=1),
+        triton.Config({"BLOCK_N": 32}, num_warps=4, num_ldmatrixes=0, num_stages=1),
+        triton.Config({"BLOCK_N": 32}, num_warps=8, num_ldmatrixes=0, num_stages=1),
+        triton.Config({"BLOCK_N": 64}, num_warps=2, num_ldmatrixes=0, num_stages=1),
+        triton.Config({"BLOCK_N": 64}, num_warps=4, num_ldmatrixes=0, num_stages=1),
+        triton.Config({"BLOCK_N": 64}, num_warps=8, num_ldmatrixes=0, num_stages=1),
+        triton.Config({"BLOCK_N": 128}, num_warps=2, num_ldmatrixes=0, num_stages=1),
+        triton.Config({"BLOCK_N": 128}, num_warps=4, num_ldmatrixes=0, num_stages=1),
+        triton.Config({"BLOCK_N": 128}, num_warps=8, num_ldmatrixes=0, num_stages=1),
+        triton.Config({"BLOCK_N": 256}, num_warps=2, num_ldmatrixes=0, num_stages=1),
+        triton.Config({"BLOCK_N": 256}, num_warps=4, num_ldmatrixes=0, num_stages=1),
+        triton.Config({"BLOCK_N": 256}, num_warps=8, num_ldmatrixes=0, num_stages=1),
+    ],
+    key=["B_Seqlen","stride_qbs","stride_buf_kbs","stride_buf_kh"]
+)
+@triton.jit
+def _decode_v1_kernel_stage1_use_tc(
+    Q,
+    K_Buffer,
+    sm_scale,
+    Req_to_tokens,
+    #B_req_idx,
+    B_Start_Loc,
+    B_Seqlen,
+    Att_Out,
+    stride_req_to_tokens_b,
+    stride_qbs,
+    stride_qh,
+    stride_buf_kbs,
+    stride_buf_kh,
+    att_stride_h,
+    kv_group_num: tl.constexpr,
+    q_head_num: tl.constexpr,
+    BLOCK_DMODEL: tl.constexpr,
+    BLOCK_DPE: tl.constexpr,
+    BLOCK_N: tl.constexpr,
+    BLOCK_H: tl.constexpr,
+    SPLIT_K: tl.constexpr,
+    PAGE_SIZE: tl.constexpr,
+    logit_cap: tl.constexpr,
+    Lk: tl.constexpr,
+):
+    cur_batch = tl.program_id(0)
+    cur_head_id = tl.program_id(1)
+    cur_kv_head = cur_head_id // tl.cdiv(kv_group_num, BLOCK_H)
+    split_k_id = tl.program_id(2)
+
+    reduce_dtype = Att_Out.dtype.element_ty
+
+    if BLOCK_H < kv_group_num:
+        VALID_BLOCK_H: tl.constexpr = BLOCK_H
+    else:
+        VALID_BLOCK_H: tl.constexpr = kv_group_num
+    cur_head = cur_head_id * VALID_BLOCK_H + tl.arange(0, BLOCK_H)
+    mask_h = cur_head < (cur_head_id + 1) * VALID_BLOCK_H
+    mask_h = mask_h & (cur_head < q_head_num)
+
+    offs_d = tl.arange(0, BLOCK_DMODEL)
+    cur_batch_seq_len = tl.load(B_Seqlen + cur_batch)
+    cur_batch_in_all_start_index = tl.load(B_Start_Loc + cur_batch)
+    # cur_batch_req_idx = tl.load(B_req_idx + cur_batch)
+    cur_batch_req_idx = cur_batch 
+
+    offs_q = cur_batch * stride_qbs + cur_head[:, None] * stride_qh + offs_d[None, :]
+    q = tl.load(
+        Q + offs_q, mask=(mask_h[:, None]) & (offs_d[None, :] < Lk), other=0.0
+    ).to(reduce_dtype)
+
+    if BLOCK_DPE > 0:
+        offs_dpe = BLOCK_DMODEL + tl.arange(0, BLOCK_DPE)
+        off_qpe = (
+            cur_batch * stride_qbs + cur_head[:, None] * stride_qh + offs_dpe[None, :]
+        )
+        qpe = tl.load(Q + off_qpe, mask=mask_h[:, None], other=0.0).to(reduce_dtype)
+
+    kv_len_per_split = tl.cdiv(cur_batch_seq_len, SPLIT_K)
+    split_k_start = kv_len_per_split * split_k_id
+    split_k_end = tl.minimum(split_k_start + kv_len_per_split, cur_batch_seq_len)
+
+    for start_n in range(split_k_start, split_k_end, BLOCK_N):
+        offs_n = start_n + tl.arange(0, BLOCK_N)
+        kv_page_number = tl.load(
+                Req_to_tokens + stride_req_to_tokens_b * cur_batch_req_idx +
+                offs_n // PAGE_SIZE,
+                mask=offs_n < split_k_end,
+                other=0,
+        )
+        k_loc = kv_page_number * PAGE_SIZE + offs_n % PAGE_SIZE 
+        offs_buf_k = (
+            k_loc[None, :] * stride_buf_kbs
+            + cur_kv_head * stride_buf_kh
+            + offs_d[:, None]
+        )
+        k = tl.load(
+            K_Buffer + offs_buf_k,
+            mask=(offs_n[None, :] < split_k_end) & (offs_d[:, None] < Lk),
+            other=0.0,
+        ).to(reduce_dtype)
+        qk = tl.dot(q, k)
+        if BLOCK_DPE > 0:
+            offs_buf_kpe = (
+                k_loc[None, :] * stride_buf_kbs
+                + cur_kv_head * stride_buf_kh
+                + offs_dpe[:, None]
+            )
+            kpe = tl.load(
+                K_Buffer + offs_buf_kpe,
+                mask=offs_n[None, :] < split_k_end,
+                other=0.0,
+            ).to(reduce_dtype)
+            qk += tl.dot(qpe, kpe)
+        qk *= sm_scale
+
+        if logit_cap > 0:
+            qk = logit_cap * tanh(qk / logit_cap)
+
+        offs_o = cur_head[:, None] * att_stride_h + (
+            cur_batch_in_all_start_index + offs_n[None, :]
+        )
+
+        tl.store(
+            Att_Out + offs_o,
+            qk,
+            mask=mask_h[:, None] & (offs_n[None, :] < split_k_end),
+        )
+
+@triton.autotune(
+    configs=[
+        triton.Config({"BLOCK_N": 32}, num_warps=1, num_ldmatrixes=1, num_stages=1),
+        triton.Config({"BLOCK_N": 32}, num_warps=2, num_ldmatrixes=1, num_stages=1),
+        triton.Config({"BLOCK_N": 32}, num_warps=4, num_ldmatrixes=1, num_stages=1),
+        triton.Config({"BLOCK_N": 32}, num_warps=8, num_ldmatrixes=1, num_stages=1),
+        triton.Config({"BLOCK_N": 64}, num_warps=1, num_ldmatrixes=1, num_stages=1),
+        triton.Config({"BLOCK_N": 64}, num_warps=2, num_ldmatrixes=1, num_stages=1),
+        triton.Config({"BLOCK_N": 64}, num_warps=4, num_ldmatrixes=1, num_stages=1),
+        triton.Config({"BLOCK_N": 64}, num_warps=8, num_ldmatrixes=1, num_stages=1),
+        triton.Config({"BLOCK_N": 8}, num_warps=1, num_ldmatrixes=0, num_stages=1),
+        triton.Config({"BLOCK_N": 8}, num_warps=2, num_ldmatrixes=0, num_stages=1),
+        triton.Config({"BLOCK_N": 8}, num_warps=4, num_ldmatrixes=0, num_stages=1),
+        triton.Config({"BLOCK_N": 8}, num_warps=8, num_ldmatrixes=0, num_stages=1),
+        triton.Config({"BLOCK_N": 16}, num_warps=1, num_ldmatrixes=0, num_stages=1),
+        triton.Config({"BLOCK_N": 16}, num_warps=2, num_ldmatrixes=0, num_stages=1),
+        triton.Config({"BLOCK_N": 16}, num_warps=4, num_ldmatrixes=0, num_stages=1),
+        triton.Config({"BLOCK_N": 16}, num_warps=8, num_ldmatrixes=0, num_stages=1),
+        triton.Config({"BLOCK_N": 32}, num_warps=1, num_ldmatrixes=0, num_stages=1),
+        triton.Config({"BLOCK_N": 32}, num_warps=2, num_ldmatrixes=0, num_stages=1),
+        triton.Config({"BLOCK_N": 32}, num_warps=4, num_ldmatrixes=0, num_stages=1),
+        triton.Config({"BLOCK_N": 32}, num_warps=8, num_ldmatrixes=0, num_stages=1),
+        triton.Config({"BLOCK_N": 64}, num_warps=1, num_ldmatrixes=0, num_stages=1),
+        triton.Config({"BLOCK_N": 64}, num_warps=2, num_ldmatrixes=0, num_stages=1),
+        triton.Config({"BLOCK_N": 64}, num_warps=4, num_ldmatrixes=0, num_stages=1),
+        triton.Config({"BLOCK_N": 64}, num_warps=8, num_ldmatrixes=0, num_stages=1),
+        triton.Config({"BLOCK_N": 128}, num_warps=1, num_ldmatrixes=1, num_stages=1),
+        triton.Config({"BLOCK_N": 128}, num_warps=2, num_ldmatrixes=1, num_stages=1),
+        triton.Config({"BLOCK_N": 128}, num_warps=4, num_ldmatrixes=1, num_stages=1),
+        triton.Config({"BLOCK_N": 128}, num_warps=8, num_ldmatrixes=1, num_stages=1),
+        triton.Config({"BLOCK_N": 256}, num_warps=1, num_ldmatrixes=1, num_stages=1),
+        triton.Config({"BLOCK_N": 256}, num_warps=2, num_ldmatrixes=1, num_stages=1),
+        triton.Config({"BLOCK_N": 256}, num_warps=4, num_ldmatrixes=1, num_stages=1),
+        triton.Config({"BLOCK_N": 256}, num_warps=8, num_ldmatrixes=1, num_stages=1),
+        triton.Config({"BLOCK_N": 512}, num_warps=1, num_ldmatrixes=1, num_stages=1),
+        triton.Config({"BLOCK_N": 512}, num_warps=2, num_ldmatrixes=1, num_stages=1),
+        triton.Config({"BLOCK_N": 512}, num_warps=4, num_ldmatrixes=1, num_stages=1),
+        triton.Config({"BLOCK_N": 512}, num_warps=8, num_ldmatrixes=1, num_stages=1),
+        triton.Config({"BLOCK_N": 128}, num_warps=1, num_ldmatrixes=0, num_stages=1),
+        triton.Config({"BLOCK_N": 128}, num_warps=2, num_ldmatrixes=0, num_stages=1),
+        triton.Config({"BLOCK_N": 128}, num_warps=4, num_ldmatrixes=0, num_stages=1),
+        triton.Config({"BLOCK_N": 128}, num_warps=8, num_ldmatrixes=0, num_stages=1),
+        triton.Config({"BLOCK_N": 256}, num_warps=1, num_ldmatrixes=0, num_stages=1),
+        triton.Config({"BLOCK_N": 256}, num_warps=2, num_ldmatrixes=0, num_stages=1),
+        triton.Config({"BLOCK_N": 256}, num_warps=4, num_ldmatrixes=0, num_stages=1),
+        triton.Config({"BLOCK_N": 256}, num_warps=8, num_ldmatrixes=0, num_stages=1),
+        triton.Config({"BLOCK_N": 512}, num_warps=1, num_ldmatrixes=0, num_stages=1),
+        triton.Config({"BLOCK_N": 512}, num_warps=2, num_ldmatrixes=0, num_stages=1),
+        triton.Config({"BLOCK_N": 512}, num_warps=4, num_ldmatrixes=0, num_stages=1),
+        triton.Config({"BLOCK_N": 512}, num_warps=8, num_ldmatrixes=0, num_stages=1),
+    ],
+    key=["B_Seqlen","stride_logic_h","stride_buf_vbs","stride_buf_vh"]
+)
+@triton.jit
+def _decode_v1_kernel_stage2_use_tc(
+    logits,
+    V_Buffer,
+    Out,
+    Req_to_tokens,
+    #B_req_idx,
+    B_Start_Loc,
+    B_Seqlen,
+    stride_logic_h,
+    stride_buf_vbs,
+    stride_buf_vh,
+    stride_obs,
+    stride_oh,
+    stride_req_to_token_b,
+    kv_group_num: tl.constexpr,
+    q_head_num: tl.constexpr,
+    BLOCK_DMODEL: tl.constexpr,
+    BLOCK_H: tl.constexpr,
+    PAGE_SIZE: tl.constexpr,
+    Lv: tl.constexpr,
+    BLOCK_N: tl.constexpr,
+):
+    cur_batch = tl.program_id(0)
+    cur_kv_head = tl.program_id(1)
+    cur_head = cur_kv_head * kv_group_num + tl.arange(0, BLOCK_H)
+    mask_h = cur_head < (cur_kv_head + 1) * kv_group_num
+    mask_h = mask_h & (cur_head < q_head_num)
+
+    cur_batch_seq_len = tl.load(B_Seqlen + cur_batch)
+    cur_batch_start_loc = tl.load(B_Start_Loc + cur_batch)
+    cur_batch_req_idx = cur_batch #tl.load(B_req_idx + cur_batch)
+
+    offs_n = tl.arange(0, BLOCK_N)
+    offs_d = tl.arange(0, BLOCK_DMODEL)
+
+    offs_buf_v = cur_kv_head * stride_buf_vh + offs_d[None, :]
+    v_ptrs = V_Buffer + offs_buf_v
+
+    e_max = tl.zeros([BLOCK_H], dtype=tl.float32) - float("inf")
+    e_sum = tl.zeros([BLOCK_H], dtype=tl.float32)
+    acc = tl.zeros([BLOCK_H, BLOCK_DMODEL], dtype=tl.float32)
+
+    for start_n in range(0, cur_batch_seq_len, BLOCK_N):
+        start_n = tl.multiple_of(start_n, BLOCK_N)
+        v_page_number = tl.load(
+            Req_to_tokens
+            + cur_batch_req_idx * stride_req_to_token_b
+            + (start_n + offs_n) // PAGE_SIZE,
+            mask=(start_n + offs_n) < cur_batch_seq_len,
+            other=0,
+        )
+        v_loc = v_page_number * PAGE_SIZE + (start_n + offs_n) % PAGE_SIZE
+        offs_qk = cur_head[:, None] * stride_logic_h + (
+            cur_batch_start_loc + start_n + offs_n[None, :]
+        )
+
+        qk = tl.load(
+            logits + offs_qk,
+            mask=mask_h[:, None] & (start_n + offs_n[None, :] < cur_batch_seq_len),
+            other=float("-inf"),
+        ) #[head, block_n] 
+
+        n_e_max = tl.maximum(tl.max(qk, 1), e_max)
+        old_scale = tl.exp(e_max - n_e_max)
+        p = tl.exp(qk - n_e_max[:, None])
+        e_sum = e_sum * old_scale + tl.sum(p, 1)
+        v = tl.load(
+            v_ptrs + v_loc[:, None] * stride_buf_vbs, mask=(offs_d[None, :] < Lv)
+        ) #[block_n,head_dim]
+        p = p.to(v.dtype)
+        acc = acc * old_scale[:, None] + tl.dot(p, v)
+        e_max = n_e_max
+
+    acc = acc / e_sum[:, None]
+    off_o = cur_batch * stride_obs + cur_head[:, None] * stride_oh + offs_d[None, :]
+    out_ptrs = Out + off_o
+    tl.store(out_ptrs, acc, mask=(mask_h[:, None]) & (offs_d[None, :] < Lv))
+
+def _decode_v1_stage1_use_tc(
+    q,
+    k_buffer,
+    att_out,
+    Req_to_tokens,
+    #B_req_idx,
+    B_Start_Loc,
+    B_Seqlen,
+    sm_scale,
+    page_size,
+    num_kv_splits,
+    logit_cap,
+):
+    Lk = k_buffer.shape[-1]
+
+    if Lk == 576:
+        BLOCK_DMODEL = 512
+        BLOCK_DPE = 64
+    elif Lk == 288:
+        BLOCK_DMODEL = 256
+        BLOCK_DPE = 32
+    else:
+        BLOCK_DMODEL = triton.next_power_of_2(Lk)
+        BLOCK_DPE = 0
+
+    # batch, head_num = B_req_idx.shape[0], q.shape[1]
+    batch, head_num = q.shape[0], q.shape[1]
+    kv_group_num = q.shape[1] // k_buffer.shape[-2]
+    SPLIT_K = num_kv_splits
+    BLOCK_H = max(16, min(64, triton.next_power_of_2(kv_group_num)))
+    grid = lambda META: (
+        batch,
+        triton.cdiv(head_num, min(BLOCK_H, kv_group_num)),
+        SPLIT_K,
+    )
+    _decode_v1_kernel_stage1_use_tc[grid](
+        q,
+        k_buffer,
+        sm_scale,
+        Req_to_tokens,
+        #B_req_idx,
+        B_Start_Loc,
+        B_Seqlen,
+        att_out,
+        Req_to_tokens.stride(0),
+        q.stride(0),
+        q.stride(1),
+        k_buffer.stride(-3),
+        k_buffer.stride(-2),
+        att_out.stride(0),
+        kv_group_num=kv_group_num,
+        q_head_num=head_num,
+        BLOCK_DMODEL=BLOCK_DMODEL,
+        BLOCK_DPE=BLOCK_DPE,
+        BLOCK_H=BLOCK_H,
+        SPLIT_K=SPLIT_K,
+        PAGE_SIZE=page_size,
+        logit_cap=logit_cap,
+        Lk=Lk,
+        kpack=2,
+    )
+    return _decode_v1_kernel_stage1_use_tc.best_config
+
+
+def _decode_v1_stage2_use_tc(
+    logits,
+    v_buffer,
+    o,
+    req_to_tokens,
+    #b_req_idx,
+    b_start_loc,
+    b_seq_len,
+    page_size,
+):
+    batch, head_num = b_seq_len.shape[0], logits.shape[0]
+    kv_group_num = logits.shape[0] // v_buffer.shape[-2]
+    BLOCK_H = max(16, triton.next_power_of_2(kv_group_num))
+    grid = (batch, triton.cdiv(head_num, min(BLOCK_H, kv_group_num)), 1)
+
+    Lv = v_buffer.shape[-1]
+    BLOCK_DMODEL = triton.next_power_of_2(Lv)
+    _decode_v1_kernel_stage2_use_tc[grid](
+        logits,
+        v_buffer,
+        o,
+        req_to_tokens,
+        #b_req_idx,
+        b_start_loc,
+        b_seq_len,
+        logits.stride(0),
+        v_buffer.stride(-3),
+        v_buffer.stride(-2),
+        o.stride(0),
+        o.stride(1),
+        req_to_tokens.stride(0),
+        kv_group_num=kv_group_num,
+        q_head_num=head_num,
+        BLOCK_DMODEL=BLOCK_DMODEL,
+        BLOCK_H=BLOCK_H,
+        PAGE_SIZE=page_size,
+        Lv=Lv,
+    )
+    return _decode_v1_kernel_stage2_use_tc.best_config
+
+
+def decode_attention_v1(
+    q,
+    k_buffer,
+    v_buffer,
+    o,
+    req_to_token,
+    #b_req_idx,
+    b_start_loc,
+    b_seq_len,
+    attn_logits,
+    num_kv_splits,
+    sm_scale,
+    page_size,
+    logit_cap=0.0,
+):
+    # GQA/MQA/MLA
+    _decode_v1_stage1_best_config = _decode_v1_stage1_use_tc(
+                                        q,
+                                        k_buffer,
+                                        attn_logits,
+                                        req_to_token,
+                                        #b_req_idx,
+                                        b_start_loc,
+                                        b_seq_len,
+                                        sm_scale,
+                                        page_size,
+                                        num_kv_splits,
+                                        logit_cap,
+                                    )
+    _decode_v1_stage2_best_config = _decode_v1_stage2_use_tc(
+                                        attn_logits,
+                                        v_buffer,
+                                        o,
+                                        req_to_token,
+                                        #b_req_idx,
+                                        b_start_loc,
+                                        b_seq_len,
+                                        page_size,
+                                    )
+    return _decode_v1_stage1_best_config, _decode_v1_stage2_best_config
+
+
+
+@triton.autotune(
+    configs=[ 
+        triton.Config({"BLOCK_N": 16}, num_warps=2, num_stages=1),
+        triton.Config({"BLOCK_N": 16}, num_warps=4, num_stages=1),
+        triton.Config({"BLOCK_N": 16}, num_warps=8, num_stages=1),
+        triton.Config({"BLOCK_N": 32}, num_warps=2, num_stages=1),
+        triton.Config({"BLOCK_N": 32}, num_warps=4, num_stages=1),
+        triton.Config({"BLOCK_N": 32}, num_warps=8, num_stages=1),
+        triton.Config({"BLOCK_N": 64}, num_warps=2, num_stages=1),
+        triton.Config({"BLOCK_N": 64}, num_warps=4, num_stages=1),
+        triton.Config({"BLOCK_N": 64}, num_warps=8, num_stages=1),
+        triton.Config({"BLOCK_N": 128}, num_warps=2, num_stages=1),
+        triton.Config({"BLOCK_N": 128}, num_warps=4, num_stages=1),
+        triton.Config({"BLOCK_N": 128}, num_warps=8, num_stages=1),
+        triton.Config({"BLOCK_N": 256}, num_warps=2, num_stages=1),
+        triton.Config({"BLOCK_N": 256}, num_warps=4, num_stages=1),
+        triton.Config({"BLOCK_N": 256}, num_warps=8, num_stages=1),
+    ],
+    key=["B_Seqlen","stride_qbs","stride_buf_kbs","stride_buf_kh", "stride_buf_vbs", "stride_buf_vh"]
+)
+@triton.jit
+def _decode_v2_kernel_stage1_use_tc(
+    Q,
+    K_Buffer,
+    V_Buffer,
+    sm_scale,
+    Req_to_tokens,
+    # B_req_idx,
+    B_Seqlen,
+    Att_Out,
+    stride_req_to_tokens_b,
+    stride_qbs,
+    stride_qh,
+    stride_buf_kbs,
+    stride_buf_kh,
+    stride_buf_vbs,
+    stride_buf_vh,
+    stride_mid_ob,
+    stride_mid_oh,
+    stride_mid_os,
+    kv_group_num: tl.constexpr,
+    q_head_num: tl.constexpr,
+    BLOCK_DMODEL: tl.constexpr,
+    BLOCK_DPE: tl.constexpr,
+    BLOCK_DV: tl.constexpr,
+    BLOCK_N: tl.constexpr,
+    BLOCK_H: tl.constexpr,
+    NUM_KV_SPLITS: tl.constexpr,
+    PAGE_SIZE: tl.constexpr,
+    logit_cap: tl.constexpr,
+    Lk: tl.constexpr,
+    Lv: tl.constexpr,
+):
+    cur_batch = tl.program_id(0)
+    cur_head_id = tl.program_id(1)
+    cur_kv_head = cur_head_id // tl.cdiv(kv_group_num, BLOCK_H)
+    split_kv_id = tl.program_id(2)
+
+    if BLOCK_H < kv_group_num:
+        VALID_BLOCK_H: tl.constexpr = BLOCK_H
+    else:
+        VALID_BLOCK_H: tl.constexpr = kv_group_num
+    cur_head = cur_head_id * VALID_BLOCK_H + tl.arange(0, BLOCK_H)
+    mask_h = cur_head < (cur_head_id + 1) * VALID_BLOCK_H
+    mask_h = mask_h & (cur_head < q_head_num)
+
+    offs_d = tl.arange(0, BLOCK_DMODEL)
+    offs_dv = tl.arange(0, BLOCK_DV)
+    mask_d = offs_d < Lk
+    mask_dv = offs_dv < Lv
+    cur_batch_seq_len = tl.load(B_Seqlen + cur_batch)
+    # cur_batch_req_idx = tl.load(B_req_idx + cur_batch)
+    cur_batch_req_idx = cur_batch
+
+    offs_q = cur_batch * stride_qbs + cur_head[:, None] * stride_qh + offs_d[None, :]
+    q = tl.load(Q + offs_q, mask=(mask_h[:, None]) & (mask_d[None, :]), other=0.0)
+
+    if BLOCK_DPE > 0:
+        offs_dpe = BLOCK_DMODEL + tl.arange(0, BLOCK_DPE)
+        mask_dpe = offs_dpe < Lk
+        off_qpe = (
+            cur_batch * stride_qbs + cur_head[:, None] * stride_qh + offs_dpe[None, :]
+        )
+        qpe = tl.load(
+            Q + off_qpe, mask=(mask_h[:, None]) & (mask_dpe[None, :]), other=0.0
+        )
+
+    kv_len_per_split = tl.cdiv(cur_batch_seq_len, NUM_KV_SPLITS)
+    split_kv_start = kv_len_per_split * split_kv_id
+    split_kv_end = tl.minimum(split_kv_start + kv_len_per_split, cur_batch_seq_len)
+
+    e_max = tl.zeros([BLOCK_H], dtype=tl.float32) - float("inf")
+    e_sum = tl.zeros([BLOCK_H], dtype=tl.float32)
+    acc = tl.zeros([BLOCK_H, BLOCK_DV], dtype=tl.float32)
+
+    if split_kv_end > split_kv_start:
+        for start_n in range(split_kv_start, split_kv_end, BLOCK_N):
+            offs_n = start_n + tl.arange(0, BLOCK_N)
+            kv_page_number = tl.load(
+                Req_to_tokens + stride_req_to_tokens_b * cur_batch_req_idx + offs_n // PAGE_SIZE,
+                mask=offs_n < split_kv_end,
+                other=0,
+            )
+            kv_loc = kv_page_number * PAGE_SIZE + offs_n % PAGE_SIZE
+            offs_buf_k = (
+                kv_loc[None, :] * stride_buf_kbs
+                + cur_kv_head * stride_buf_kh
+                + offs_d[:, None]
+            )
+            k = tl.load(
+                K_Buffer + offs_buf_k,
+                mask=(offs_n[None, :] < split_kv_end) & (mask_d[:, None]),
+                other=0.0,
+            )
+            qk = tl.dot(q, k.to(q.dtype))
+            if BLOCK_DPE > 0:
+                offs_buf_kpe = (
+                    kv_loc[None, :] * stride_buf_kbs
+                    + cur_kv_head * stride_buf_kh
+                    + offs_dpe[:, None]
+                )
+                kpe = tl.load(
+                    K_Buffer + offs_buf_kpe,
+                    mask=(offs_n[None, :] < split_kv_end) & (mask_dpe[:, None]),
+                    other=0.0,
+                )
+                qk += tl.dot(qpe, kpe.to(qpe.dtype))
+            qk *= sm_scale
+
+            if logit_cap > 0:
+                qk = logit_cap * tanh(qk / logit_cap)
+
+            qk = tl.where(
+                mask_h[:, None] & (offs_n[None, :] < split_kv_end), qk, float("-inf")
+            )
+
+            offs_buf_v = (
+                kv_loc[:, None] * stride_buf_vbs
+                + cur_kv_head * stride_buf_vh
+                + offs_dv[None, :]
+            )
+            v = tl.load(
+                V_Buffer + offs_buf_v,
+                mask=(offs_n[:, None] < split_kv_end) & (mask_dv[None, :]),
+                other=0.0,
+            )
+
+            n_e_max = tl.maximum(tl.max(qk, 1), e_max)
+            re_scale = tl.exp(e_max - n_e_max)
+            p = tl.exp(qk - n_e_max[:, None])
+            acc *= re_scale[:, None]
+            acc += tl.dot(p.to(v.dtype), v)
+
+            e_sum = e_sum * re_scale + tl.sum(p, 1)
+            e_max = n_e_max
+
+        offs_mid_o = (
+            cur_batch * stride_mid_ob
+            + cur_head[:, None] * stride_mid_oh
+            + split_kv_id * stride_mid_os
+            + offs_dv[None, :]
+        )
+
+        tl.store(
+            Att_Out + offs_mid_o,
+            acc / e_sum[:, None],
+            mask=(mask_h[:, None]) & (mask_dv[None, :]),
+        )
+
+        offs_mid_o_1 = (
+            cur_batch * stride_mid_ob
+            + cur_head * stride_mid_oh
+            + split_kv_id * stride_mid_os
+            + Lv
+        )
+
+        tl.store(
+            Att_Out + offs_mid_o_1,
+            e_max + tl.log(e_sum),
+            mask=mask_h,
+        )
+
+
+def _decode_v2_stage1_use_tc(
+    q,
+    k_buffer,
+    v_buffer,
+    att_out,
+    Req_to_tokens,
+    # B_req_idx,
+    B_Seqlen,
+    num_kv_splits,
+    sm_scale,
+    page_size,
+    logit_cap,
+):
+
+    Lk = k_buffer.shape[-1]
+    Lv = v_buffer.shape[-1]
+
+    if Lk == 576:
+        BLOCK_DMODEL = 512
+        BLOCK_DPE = 64
+    elif Lk == 288:
+        BLOCK_DMODEL = 256
+        BLOCK_DPE = 32
+    else:
+        BLOCK_DMODEL = triton.next_power_of_2(Lk)
+        BLOCK_DPE = 0
+    BLOCK_DV = triton.next_power_of_2(Lv)
+
+    # batch, head_num = B_req_idx.shape[0], q.shape[1]
+    batch, head_num = q.shape[0], q.shape[1]
+    kv_group_num = q.shape[1] // k_buffer.shape[-2]
+    BLOCK_H = 16
+    NUM_KV_SPLITS = num_kv_splits
+
+    grid = lambda META: (
+        batch,
+        triton.cdiv(head_num, min(BLOCK_H, kv_group_num)),
+        NUM_KV_SPLITS,
+    )
+
+    _decode_v2_kernel_stage1_use_tc[grid](
+        q,
+        k_buffer,
+        v_buffer,
+        sm_scale,
+        Req_to_tokens,
+        # B_req_idx,
+        B_Seqlen,
+        att_out,
+        Req_to_tokens.stride(0),
+        q.stride(0),
+        q.stride(1),
+        k_buffer.stride(-3),
+        k_buffer.stride(-2),
+        v_buffer.stride(-3),
+        v_buffer.stride(-2),
+        att_out.stride(0),
+        att_out.stride(1),
+        att_out.stride(2),
+        kv_group_num=kv_group_num,
+        q_head_num=head_num,
+        BLOCK_DMODEL=BLOCK_DMODEL,
+        BLOCK_DPE=BLOCK_DPE,
+        BLOCK_DV=BLOCK_DV,
+        BLOCK_H=BLOCK_H,
+        NUM_KV_SPLITS=NUM_KV_SPLITS,
+        PAGE_SIZE=page_size,
+        logit_cap=logit_cap,
+        Lk=Lk,
+        Lv=Lv,
+        kpack=2,
+    )
+    return _decode_v2_kernel_stage1_use_tc.best_config
+
+@triton.autotune(
+    configs=[
+        triton.Config({}, num_warps=1, num_stages=1),
+        triton.Config({}, num_warps=2, num_stages=1),
+        triton.Config({}, num_warps=4, num_stages=1),
+        triton.Config({}, num_warps=8, num_stages=1),
+
+    ],
+    key=["B_Seqlen", "stride_mid_ob", "stride_mid_oh", "stride_mid_os"]
+)
+@triton.jit
+def _decode_v2_kernel_stage2(
+    Mid_O,
+    O,
+    B_Seqlen,
+    stride_mid_ob,
+    stride_mid_oh,
+    stride_mid_os,
+    stride_obs,
+    stride_oh,
+    NUM_KV_SPLITS: tl.constexpr,
+    BLOCK_DV: tl.constexpr,
+    Lv: tl.constexpr,
+):
+    cur_batch = tl.program_id(0)
+    cur_head = tl.program_id(1)
+
+    cur_batch_seq_len = tl.load(B_Seqlen + cur_batch)
+
+    offs_d = tl.arange(0, BLOCK_DV)
+    mask_d = offs_d < Lv
+
+    e_sum = 0.0
+    e_max = -float("inf")
+    acc = tl.zeros([BLOCK_DV], dtype=tl.float32)
+
+    offs_v = cur_batch * stride_mid_ob + cur_head * stride_mid_oh + offs_d
+    offs_logic = cur_batch * stride_mid_ob + cur_head * stride_mid_oh + Lv
+
+    for split_kv_id in range(0, NUM_KV_SPLITS):
+        kv_len_per_split = tl.cdiv(cur_batch_seq_len, NUM_KV_SPLITS)
+        split_kv_start = kv_len_per_split * split_kv_id
+        split_kv_end = tl.minimum(split_kv_start + kv_len_per_split, cur_batch_seq_len)
+
+        if split_kv_end > split_kv_start:
+            tv = tl.load(
+                Mid_O + offs_v + split_kv_id * stride_mid_os, mask=mask_d, other=0.0
+            )
+            tlogic = tl.load(Mid_O + offs_logic + split_kv_id * stride_mid_os)
+            n_e_max = tl.maximum(tlogic, e_max)
+
+            old_scale = tl.exp(e_max - n_e_max)
+            acc *= old_scale
+            exp_logic = tl.exp(tlogic - n_e_max)
+            acc += exp_logic * tv
+
+            e_sum = e_sum * old_scale + exp_logic
+            e_max = n_e_max
+
+    tl.store(
+        O + cur_batch * stride_obs + cur_head * stride_oh + offs_d,
+        acc / e_sum,
+        mask=mask_d,
+    )
+
+
+def _decode_v2_stage2_use_tc(
+    logits,
+    q,
+    o,
+    v_buffer,
+    b_seq_len,
+    num_kv_splits,
+):
+
+    batch, head_num = q.shape[0], q.shape[1]
+    Lv = v_buffer.shape[-1]
+    BLOCK_DV = triton.next_power_of_2(Lv)
+
+    NUM_KV_SPLITS = num_kv_splits
+
+    grid = (batch, head_num)
+    _decode_v2_kernel_stage2[grid](
+        logits,
+        o,
+        b_seq_len,
+        logits.stride(0),
+        logits.stride(1),
+        logits.stride(2),
+        o.stride(0),
+        o.stride(1),
+        NUM_KV_SPLITS=NUM_KV_SPLITS,
+        BLOCK_DV=BLOCK_DV,
+        Lv=Lv,
+    )
+
+    return _decode_v2_kernel_stage2.best_config
+
+
+def decode_attention_v2(
+    q,
+    k_buffer,
+    v_buffer,
+    o,
+    req_to_token,
+    # b_req_idx,
+    b_seq_len,
+    attn_logits,
+    num_kv_splits,
+    sm_scale,
+    page_size,
+    logit_cap=0.0,
+):
+    _decode_v2_stage1_best_config = _decode_v2_stage1_use_tc(
+        q,
+        k_buffer,
+        v_buffer,
+        attn_logits,
+        req_to_token,
+        # b_req_idx,
+        b_seq_len,
+        num_kv_splits,
+        sm_scale,
+        page_size,
+        logit_cap,
+    )
+    _decode_v2_stage2_best_config = _decode_v2_stage2_use_tc(attn_logits, q, o, v_buffer, b_seq_len, num_kv_splits)
+    return _decode_v2_stage1_best_config, _decode_v2_stage2_best_config
+
+       
 def decode_attention_fwd(
     q,
     k_buffer,
@@ -633,12 +1431,13 @@ def decode_attention_fwd(
     attn_logits,
     num_kv_splits,
     sm_scale,
+    # config,
     page_size=1,
     logit_cap=0.0,
 ):
     assert num_kv_splits == attn_logits.shape[2]
     kv_group_num = q.shape[1] // v_buffer.shape[-2]
-
+    b_start_loc = torch.arange(0, k_buffer.shape[0] * page_size, k_buffer.shape[0] * page_size // q.shape[0], device="cuda").to(torch.int32)
     if kv_group_num == 1:
         # MHA
         decode_attention_fwd_normal(
@@ -656,16 +1455,88 @@ def decode_attention_fwd(
         )
     else:
         # GQA/MQA/MLA
-        decode_attention_fwd_grouped(
-            q,
-            k_buffer,
-            v_buffer,
-            o,
-            req_to_token,
-            b_seq_len,
-            attn_logits,
-            num_kv_splits,
-            sm_scale,
-            page_size,
-            logit_cap,
-        )
+        if envs.VLLM_USE_TRITON_OPT_MLA:
+            decode_attention_v2(
+                q,
+                k_buffer,
+                v_buffer,
+                o,
+                req_to_token,
+                b_seq_len,
+                attn_logits,
+                num_kv_splits,
+                sm_scale,
+                page_size, 
+                logit_cap,
+            )
+        #    attn_logits_v1 = torch.empty(
+        #        (q.shape[1],k_buffer.shape[0]*page_size),
+        #        dtype=torch.float16,
+        #        device="cuda")
+        #    decode_attention_v1(
+        #        q,
+        #        k_buffer,
+        #        v_buffer,
+        #        o,
+        #        req_to_token,
+        #        b_start_loc,
+        #        b_seq_len,
+        #        attn_logits_v1,
+        #        num_kv_splits, # sub
+        #        sm_scale,
+        #        page_size, 
+        #        logit_cap,
+        #    )
+        
+            # TODO
+            # if best_config['kernel_kind'] == 'v1_2stages_tc':
+            #     attn_logits_v1 = torch.empty(
+            #         (q.shape[1],k_buffer.shape[0]*page_size),
+            #         dtype=torch.float16,
+            #         device="cuda")
+            #     decode_attention_v1(
+            #         q,
+            #         k_buffer,
+            #         v_buffer,
+            #         o,
+            #         req_to_token,
+            #         b_start_loc,
+            #         b_seq_len,
+            #         attn_logits_v1,
+            #         num_kv_splits,
+            #         sm_scale,
+            #         config,
+            #         page_size, 
+            #         logit_cap,
+            #     )
+            # elif best_config['kernel_kind'] == 'v2_tc':
+            #     decode_attention_v2(
+            #         q,
+            #         k_buffer,
+            #         v_buffer,
+            #         o,
+            #         req_to_token,
+            #         b_seq_len,
+            #         attn_logits,
+            #         num_kv_splits,
+            #         sm_scale,
+            #         config,
+            #         page_size, 
+            #         logit_cap,
+            #     )
+            # else:
+            #     print("Unknown mla kernel kind: ", best_config['kernel_kind'])
+        else:
+            decode_attention_fwd_grouped(
+                q,
+                k_buffer,
+                v_buffer,
+                o,
+                req_to_token,
+                b_seq_len,
+                attn_logits,
+                num_kv_splits,
+                sm_scale,
+                page_size,
+                logit_cap,
+            )
\ No newline at end of file

vllm/envs.py

@@ -15,6 +15,7 @@ if TYPE_CHECKING:
     VLLM_NCCL_SO_PATH: Optional[str] = None
     LD_LIBRARY_PATH: Optional[str] = None
     VLLM_USE_TRITON_FLASH_ATTN: bool = False
+    VLLM_USE_TRITON_OPT_MLA: bool = False
     VLLM_USE_OPT_OP: bool = False
     VLLM_USE_TC_PAGED_ATTN: bool = False
     VLLM_USE_PA_PRINT_PARAM: bool = False 
@@ -573,6 +574,10 @@ environment_variables: Dict[str, Callable[[], Any]] = {
     # If set, vLLM will disable the MLA attention optimizations.
     "VLLM_MLA_DISABLE":
     lambda: bool(int(os.getenv("VLLM_MLA_DISABLE", "0"))),
+    
+    # If set, vLLM will use optimized MLA attention optimizations.
+    "VLLM_USE_TRITON_OPT_MLA":
+    lambda: bool(int(os.getenv("VLLM_USE_TRITON_OPT_MLA", "0"))),
 
     # Flag that can control whether or not we perform matrix-absorption for MLA
     # decode, i.e. absorb W_UK into W_Q/W_UK and W_UV into W_O, absorbing the