add mla tuning script and configs

examples/mla/gen_mla_pa_tables.py

-'''
-QH=num_attention_heads KVH=1 QKD=kv_lora_rank + qk_rope_head_dim VD=kv_lora_rank
-deepseek v2: QH=16, QKD=512+64=576, VD=512
-deepseek v3: QH=128, QKD=512+64=576, VD=512
-
-python3 gen_mla_pa_tables.py --QH 16 --KVH 1 --QKD 576 --VD 512 --TP 1
-python3 gen_mla_pa_tables.py --QH 16 --KVH 1 --QKD 576 --VD 512 --TP 2
-python3 gen_mla_pa_tables.py --QH 16 --KVH 1 --QKD 576 --VD 512 --TP 4
-python3 gen_mla_pa_tables.py --QH 16 --KVH 1 --QKD 576 --VD 512 --TP 8
-python3 gen_mla_pa_tables.py --QH 128 --KVH 1 --QKD 576 --VD 512 --TP 32
-'''
-import argparse
-
-
-def parse_args():
-    parser = argparse.ArgumentParser(description="Generate mla pa tables (B, SEQ, QH, KVH, QKD, VD).")
-    parser.add_argument('--SEQSTART', type=int, required=False, default=100, help='Value for SEQSTART')
-    parser.add_argument('--SEQEND', type=int, required=False, default=8193, help='Value for SEQEND')
-    parser.add_argument('--QH', type=int, required=True, help='Value for QH')
-    parser.add_argument('--KVH', type=int, required=True, help='Value for KVH')
-    parser.add_argument('--QKD', type=int, required=True, help='Value for QKD')
-    parser.add_argument('--VD', type=int, required=True, help='Value for VD')
-    parser.add_argument('--TP', type=int, required=True, help='Value for TP')
-    return parser.parse_args()
-
-
-def generate_B():
-    # 1 4 8 16 ... 128，144 160 ... 256, 288 320 ... 512
-    B_values = [1, 4] + [i * 8 for i in range(1, 17)]
-    # B_values = [1, 4] + [i * 8 for i in range(1, 17)] + [(i + 1) * 16 for i in range(8, 16)] + [(i + 1) * 32 for i in range(8, 16)]
-    return B_values
-
-
-def generate_SEQ_bs1_32(seq_start, seq_end):
-    SEQ_values_part1 = list(range(seq_start, 5000, 300)) 
-    SEQ_values_part2 = list(range(5000, seq_end, 500))  
-    SEQ_values = [1] + SEQ_values_part1 + SEQ_values_part2
-    return SEQ_values
-
-def generate_SEQ_bs40_96(seq_start, seq_end):
-    SEQ_values_part1 = list(range(seq_start, 5000, 200)) 
-    SEQ_values_part2 = list(range(5000, seq_end, 300))  
-    SEQ_values = [1] + SEQ_values_part1 + SEQ_values_part2
-    return SEQ_values
-
-def generate_SEQ_other(seq_start, seq_end):
-    SEQ_values_part1 = list(range(seq_start, 5000, 300)) 
-    SEQ_values_part2 = list(range(5000, seq_end, 500))  
-    SEQ_values = [1] + SEQ_values_part1 + SEQ_values_part2
-    return SEQ_values
-
-
-def generate_tuples(seq_start, seq_end, QH, KVH, QKD, VD):
-    B_values = generate_B()
-    SEQ_values_b1_32 = generate_SEQ_bs1_32(seq_start, seq_end)
-    SEQ_values_b40_96 = generate_SEQ_bs40_96(seq_start, seq_end)
-    SEQ_values_other = generate_SEQ_other(seq_start, seq_end)
-
-    pa_tables = []
-    for B in B_values:
-        if B <= 32:
-            for SEQ in SEQ_values_b1_32:
-                pa_tables.append((B, SEQ, QH, KVH, QKD, VD))
-        elif B <= 96 and B > 32:
-            for SEQ in SEQ_values_b40_96:
-                pa_tables.append((B, SEQ, QH, KVH, QKD, VD))
-        else:
-            for SEQ in SEQ_values_other:
-                pa_tables.append((B, SEQ, QH, KVH, QKD, VD))
-    
-    return pa_tables
-
-
-if __name__ == "__main__":
-    args = parse_args()
-    assert args.QH % args.TP == 0, "error QH, QH % TP must be 0"
-    tuple_sizes = generate_tuples(args.SEQSTART, args.SEQEND, args.QH // args.TP, max(args.KVH // args.TP, 1), args.QKD, args.VD)
-
-    for t in tuple_sizes:
-        print(f"{t},")

examples/mla/test_triton_decode_attention.py

+# SPDX-License-Identifier: Apache-2.0
+
+import os
+import json
+import pytest
+import torch
+import triton
+from triton_decode_attention import decode_attentionv1_fwd, decode_attentionv2_fwd
+
+def cdiv(a, b):
+    return (a + b - 1) // b
+
+
+@pytest.mark.parametrize("B", [1])
+# @pytest.mark.parametrize("L", [100])
+# @pytest.mark.parametrize("L", [1,100,400,700,1000,1300,1600,1900,2200,2500,2800,3100,3400,3700,4000,4300,4600,4900,5000,5500,6000,6500,7000,7500,8000])
+@pytest.mark.parametrize("L", [1,100,400,700,1000,1300,1600,1900,2200,2500,2800,3100,3400,3700,4000,4300,4600,4900,5000,5500,6000,6500,7000,7500,8000,8500,9000,9500,10000,10500,11000,11500,12000,12500,13000,13500,14000,14500,15000,15500,16000,16500,17000,17500,18000,18500,19000,19500,20000,20500,21000,21500,22000,22500,23000,23500,24000,24500,25000,25500,26000,26500,27000,27500,28000,28500,29000,29500,30000,30500,31000,31500,32000,32500])
+@pytest.mark.parametrize("H_Q", [4, 8, 16])
+@pytest.mark.parametrize("H_KV", [1])
+@pytest.mark.parametrize("D_QK", [576])
+@pytest.mark.parametrize("D_V", [512])
+@pytest.mark.parametrize("CACHE_SIZE", [16384])
+@pytest.mark.parametrize("PAGE_SIZE", [16]) 
+
+def test_decode_attention(B, L, H_Q, H_KV, D_QK, D_V, CACHE_SIZE, PAGE_SIZE):
+    assert CACHE_SIZE % PAGE_SIZE == 0
+    dtype = torch.bfloat16
+    seq_len = L  # This represents the number of tokens already in the sequence
+    sm_scale = 1.0 / (D_QK**0.5)
+    num_kv_splits = 4
+
+    num_pages_per_batch = cdiv(seq_len, PAGE_SIZE) #这里为向上取整,65,(1027+16-1)//16
+    req_to_page = torch.randint(0,
+                                CACHE_SIZE // PAGE_SIZE,
+                                (B, num_pages_per_batch, 1), #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 + torch.arange(PAGE_SIZE, device="cuda").view(
+        1, 1, -1)
+    req_to_token = req_to_token.view(B, -1)
+    req_to_token = req_to_token[:, :seq_len].contiguous()
+
+
+    # q represents the new token being generated, one per batch
+    q = torch.randn(B, H_Q, D_QK, dtype=dtype, device="cuda")
+
+    # k_buffer and v_buffer represent all previous tokens
+    # Page size is 1.
+    k_buffer = torch.randn(CACHE_SIZE, H_KV, D_QK, dtype=dtype, device="cuda")
+    v_buffer = torch.randn(CACHE_SIZE, H_KV, D_V, dtype=dtype, device="cuda")
+
+    # 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")
+    attn_logits = torch.empty(
+        (B, H_Q, num_kv_splits, D_V + 1),
+        dtype=torch.float32,
+        device="cuda",
+    )
+    b_req_idx = torch.arange(B, device="cuda").to(torch.int32)
+    # Call the original implementation.
+    decode_attentionv2_fwd(
+        q,
+        k_buffer,
+        v_buffer,
+        o,
+        req_to_token,
+        b_seq_len,
+        attn_logits,
+        num_kv_splits,
+        sm_scale,
+    )
+
+
+    # Page size can be larger than 1.
+    k_buffer = k_buffer.view(CACHE_SIZE // PAGE_SIZE, PAGE_SIZE, H_KV, D_QK)
+    v_buffer = v_buffer.view(CACHE_SIZE // PAGE_SIZE, PAGE_SIZE, H_KV, D_V)
+
+    o1 = torch.zeros_like(o)
+    configs = {
+                  "v2_tc": {"stage1": {}, "stage2": {}},
+                  "v1_2stages_tc": {"stage1": {}, "stage2": {}},
+              }
+    ms = {
+         "v1_2stages_tc": 10000.0,
+         "v2_tc": 10000.0,
+     }
+    final_best_config = {
+                            "kernel_kind": "",
+                            "best_config": {},
+                            "best_us": 0.0,
+                        }
+    v2_tc_stage1_best_config, v2_tc_stage2_best_config = decode_attentionv2_fwd(
+        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)
+    quantiles = [0.5, 0.2, 0.8]
+    v2_tc_ms, v2_tc_min_ms, v2_tc_max_ms = triton.testing.do_bench(lambda:
+    decode_attentionv2_fwd(
+        q,
+        k_buffer,
+        v_buffer,
+        o1,
+        req_to_page,
+        b_seq_len,
+        attn_logits,
+        num_kv_splits,
+        sm_scale,
+        PAGE_SIZE,
+
+    ), quantiles=quantiles)
+    for key, value in v2_tc_stage1_best_config.kwargs.items():
+        configs["v2_tc"]["stage1"][key] = value
+    configs["v2_tc"]["stage1"]["num_stages"] = v2_tc_stage1_best_config.num_stages
+    configs["v2_tc"]["stage1"]["num_warps"] = v2_tc_stage1_best_config.num_warps
+    for key, value in v2_tc_stage2_best_config.kwargs.items():
+        configs["v2_tc"]["stage2"][key] = value
+    configs["v2_tc"]["stage2"]["num_stages"] = v2_tc_stage2_best_config.num_stages
+    configs["v2_tc"]["stage2"]["num_warps"]  = v2_tc_stage2_best_config.num_warps
+    ms["v2_tc"] = v2_tc_ms
+    print(f"v2_tc best configs is {configs['v2_tc']}")
+    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)
+    o2 = torch.zeros_like(o)
+
+    v1_tc_stage1_best_config, v1_tc_stage2_best_config = decode_attentionv1_fwd(
+        q,
+        k_buffer,
+        v_buffer,
+        o2,
+        req_to_page,
+        b_seq_len,
+        attn_logits,
+        num_kv_splits,
+        sm_scale,
+        PAGE_SIZE,
+    )
+    assert torch.allclose(o, o2, atol=1e-2, rtol=1e-2)
+    
+    v1_tc_ms, v1_tc_min_ms, v1_tc_max_ms = triton.testing.do_bench(lambda:
+    decode_attentionv1_fwd(
+        q,
+        k_buffer,
+        v_buffer,
+        o1,
+        req_to_page,
+        b_seq_len,
+        attn_logits,
+        num_kv_splits,
+        sm_scale,
+        PAGE_SIZE,
+
+    ), quantiles=quantiles)
+    for key, value in v1_tc_stage1_best_config.kwargs.items():
+        configs["v1_2stages_tc"]["stage1"][key] = value
+    configs["v1_2stages_tc"]["stage1"]["num_stages"] = v1_tc_stage1_best_config.num_stages
+    configs["v1_2stages_tc"]["stage1"]["num_warps"] = v1_tc_stage1_best_config.num_warps
+    configs["v1_2stages_tc"]["stage1"]["num_ldmatrixes"] = v1_tc_stage1_best_config.num_ldmatrixes
+    for key, value in v1_tc_stage2_best_config.kwargs.items():
+        configs["v1_2stages_tc"]["stage2"][key] = value
+    configs["v1_2stages_tc"]["stage2"]["num_stages"] = v1_tc_stage2_best_config.num_stages
+    configs["v1_2stages_tc"]["stage2"]["num_warps"]  = v1_tc_stage2_best_config.num_warps
+    configs["v1_2stages_tc"]["stage2"]["num_ldmatrixes"] = v1_tc_stage1_best_config.num_ldmatrixes
+    ms["v1_2stages_tc"] = v1_tc_ms 
+    min_key, min_ms = min(ms.items(), key=lambda x: x[1])
+    final_best_config["kernel_kind"] = min_key
+    final_best_config["best_config"] = configs[min_key]
+    final_best_config["best_us"] = min_ms * 1000
+    print(f"v1_2stages_tc best configs is {configs['v1_2stages_tc']}")
+
+    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)
+    print(f"Tuned_decode_attention choose {min_key} kernel, min cost {min_ms} ms, best config of {min_key} kernel is {configs[min_key]}")
+    assert torch.allclose(o, o2, atol=1e-2, rtol=1e-2)
+
+
+    #**************save config**************#
+    batch = b_req_idx.shape[0]
+    mean_seq_len = int((b_seq_len.sum() / max(1, batch)).item())
+
+    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"
+        file_name = f"QH={H_Q}_KVH={H_KV}_QKD={D_QK}_VD={D_V}_fp16_K100AI.json"
+    elif "BW" in device_name:
+        # return f"QH={QH}_KVH={KVH}_QKD={QKD}_VD={VD}_{cache_dtype}_BW.json"
+        file_name = f"QH={H_Q}_KVH={H_KV}_QKD={D_QK}_VD={D_V}_fp16_BW.json"
+    else:
+        raise ValueError(f"Unsurpport device name: {device_name}")
+    if os.path.exists(file_name):
+        with open(file_name, 'r') as file:
+            config_info = json.load(file)
+    else:
+        config_info = {}
+
+    # 如果 config_info 中没有当前的 batch，初始化它为一个空字典
+    # if f"{batch}" not in config_info:
+    #     config_info[f"{batch}"] = {}
+
+    # 把新的 mean_seq_len 配置加入到当前 batch 中
+    # config_info[f"{batch}"][f"{mean_seq_len}"] = final_best_config
+    config_info[f"{mean_seq_len}"] = final_best_config
+
+    # 保存最佳配置
+    with open(file_name, 'w') as file:
+        json.dump(config_info, file, indent=1)
+    #**************save config**************#
+

vllm/attention/backends/triton_config.py

@@ -9,14 +9,11 @@ from vllm.logger import init_logger
 logger = init_logger(__name__)
 
 class KERNLE_KINDS(Enum):
-    v1              = 0
-    v1_tc           = 1
-    v1_tc_2         = 2
-    v1_2stages      = 3
-    v1_2stages_tc   = 4
-    v2              = 5
-    v2_tc           = 6
-    TOTAL_KIND      = 7
+    v1_2stages      = 0
+    v1_2stages_tc   = 1
+    v2              = 2
+    v2_tc           = 3
+    TOTAL_KIND      = 4
 
 class BestConfig():
     def __init__(self):
@@ -24,8 +21,9 @@ class BestConfig():
         self.seq_len = 0
         self.kernel_kind = KERNLE_KINDS.TOTAL_KIND
         self.BLOCK_N = 0
-        self.BLOCK_SEQ = 0
-        self.SPLIT_K = 0
+        self.BLOCK_DIM = 0
+        # self.BLOCK_SEQ = 0
+        # self.SPLIT_K = 0
         self.num_stages = 0
         self.num_warps = 0
         self.NUM_KV_SPLITS = 0
@@ -49,19 +47,6 @@ def get_mla_config_file_name(QH: int, KVH: int, QKD: int, VD: int, cache_dtype:
         raise ValueError(f"Unsurpport device name: {device_name}")
 
 
-def get_config_file_name(QH: int, KVH: int, D: int, cache_dtype: Optional[str]) -> str:
-    if cache_dtype == "default":
-        return f"QH={QH}_KVH={KVH}_D={D}_default.json"
-    
-    device_name = torch.cuda.get_device_name().replace(" ", "_")
-    if "K100_AI" in device_name:
-        return f"QH={QH}_KVH={KVH}_D={D}_{cache_dtype}_K100AI.json"
-    elif "BW" in device_name:
-        return f"QH={QH}_KVH={KVH}_D={D}_{cache_dtype}_BW.json"
-    else:
-        raise ValueError(f"Unsurpport device name: {device_name}")
-
-
 def get_attention_mla_configs_json(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
@@ -73,7 +58,7 @@ def get_attention_mla_configs_json(QH: int, KVH: int, QKD: int, VD: int, cache_d
     )
     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)
+            # 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:
@@ -107,29 +92,11 @@ def get_config_map(attention_configs):
             int_seq_len = int(seq_len)
             kind_config = seq_configs[seq_len]
             configs = BestConfig()
-            configs.batch_size = int_bs
-            configs.seq_len = int_seq_len
+            # configs.batch_size = int_bs
+            # configs.seq_len = int_seq_len
             configs.best_us = kind_config['best_us']
             seq_map[int_seq_len] = configs
-            if kind_config['kernel_kind'] == 'v1':
-                best_config = kind_config['best_config']
-                configs.kernel_kind = KERNLE_KINDS.v1
-                configs.BLOCK_N = best_config['BLOCK_N']
-                configs.num_stages = best_config['num_stages']
-                configs.num_warps = best_config['num_warps']
-            elif kind_config['kernel_kind'] == 'v1_tc':
-                best_config = kind_config['best_config']
-                configs.kernel_kind = KERNLE_KINDS.v1_tc
-                configs.BLOCK_N = best_config['BLOCK_N']
-                configs.num_stages = best_config['num_stages']
-                configs.num_warps = best_config['num_warps']
-            elif kind_config['kernel_kind'] == 'v1_tc_2':
-                best_config = kind_config['best_config']
-                configs.kernel_kind = KERNLE_KINDS.v1_tc_2
-                configs.BLOCK_N = best_config['BLOCK_N']
-                configs.num_stages = best_config['num_stages']
-                configs.num_warps = best_config['num_warps']
-            elif kind_config['kernel_kind'] == 'v1_2stages':
+            if kind_config['kernel_kind'] == 'v1_2stages':
                 best_config = kind_config['best_config']
                 stage1 = best_config['stage1']
                 stage2 = best_config['stage2']
@@ -158,10 +125,10 @@ def get_config_map(attention_configs):
                 stage1 = best_config['stage1']
                 stage2 = best_config['stage2']
                 configs.kernel_kind = KERNLE_KINDS.v2
-                if 'BLOCK_SEQ' in stage1:
-                    configs.BLOCK_SEQ = stage1['BLOCK_SEQ']
-                else:
-                    configs.NUM_KV_SPLITS = stage1['NUM_KV_SPLITS']
+                # if 'BLOCK_SEQ' in stage1:
+                #     configs.BLOCK_SEQ = stage1['BLOCK_SEQ']
+                # else:
+                #     configs.NUM_KV_SPLITS = stage1['NUM_KV_SPLITS']
                 configs.BLOCK_N = stage1['BLOCK_N']
                 configs.num_stages = stage1['num_stages']
                 configs.num_warps = stage1['num_warps']
@@ -172,11 +139,12 @@ def get_config_map(attention_configs):
                 stage1 = best_config['stage1']
                 stage2 = best_config['stage2']
                 configs.kernel_kind = KERNLE_KINDS.v2_tc
-                if 'BLOCK_SEQ' in stage1:
-                    configs.BLOCK_SEQ = stage1['BLOCK_SEQ']
-                else:
-                    configs.NUM_KV_SPLITS = stage1['NUM_KV_SPLITS']
+                # if 'BLOCK_SEQ' in stage1:
+                #     configs.BLOCK_SEQ = stage1['BLOCK_SEQ']
+                # else:
+                #     configs.NUM_KV_SPLITS = stage1['NUM_KV_SPLITS']
                 configs.BLOCK_N = stage1['BLOCK_N']
+                configs.BLOCK_DIM = stage1['BLOCK_DIM']
                 configs.num_stages = stage1['num_stages']
                 configs.num_warps = stage1['num_warps']
                 configs.num_stages_2 = stage2['num_stages']

vllm/attention/backends/triton_mla.py

 # SPDX-License-Identifier: Apache-2.0
 
+import os
 from collections import defaultdict
 from contextlib import contextmanager
 from dataclasses import dataclass
@@ -742,9 +743,13 @@ class TritonMLAImpl(MLACommonImpl[TritonMLAMetadata]):
         PAGE_SIZE = kv_c_and_k_pe_cache.size(1)
         
         # TODO
-        for bs in self.attn_configs.keys():
-            for mean_seq_len in self.attn_configs[bs].keys():
-                best_config = get_config(bs, mean_seq_len, self.attn_configs)
+        max_seq_len = torch.max(decode_meta.seq_lens_tensor).item()
+        if os.environ.get('PA_MATCH_USE_MEAN_SEQ') == '1':
+            match_seq_len = int((decode_meta.seq_lens_tensor.sum()/ max(1, B)).item())
+        else:
+            match_seq_len = max_seq_len
+            
+        best_config = self.attn_configs[min(self.attn_configs.keys(), key=lambda x: abs(int(x) - match_seq_len))]
                 
         # Run MQA
         decode_attention_fwd(q, kv_c_and_k_pe_cache, kv_c_cache, o,

vllm/attention/ops/triton_decode_attention.py

@@ -36,7 +36,7 @@ import triton.language as tl
 
 from vllm.platforms import current_platform
 from vllm import envs
-from ..backends.triton_config import KERNLE_KINDS
+# from ..backends.triton_config import KERNLE_KINDS
 
 is_hip_ = current_platform.is_rocm()
 os.environ["TRITON_HIP_USE_NEW_STREAM_PIPELINE"] = f"0"
@@ -676,7 +676,7 @@ def _decode_v1_kernel_stage1_use_tc(
     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
+    # reduce_dtype = Att_Out.dtype.element_ty
 
     if BLOCK_H < kv_group_num:
         VALID_BLOCK_H: tl.constexpr = BLOCK_H
@@ -695,14 +695,14 @@ def _decode_v1_kernel_stage1_use_tc(
     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)
+    ) # .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)
+        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
@@ -726,7 +726,7 @@ def _decode_v1_kernel_stage1_use_tc(
             K_Buffer + offs_buf_k,
             mask=(offs_n[None, :] < split_k_end) & (offs_d[:, None] < Lk),
             other=0.0,
-        ).to(reduce_dtype)
+        ) # .to(reduce_dtype)
         qk = tl.dot(q, k)
         if BLOCK_DPE > 0:
             offs_buf_kpe = (
@@ -738,7 +738,7 @@ def _decode_v1_kernel_stage1_use_tc(
                 K_Buffer + offs_buf_kpe,
                 mask=offs_n[None, :] < split_k_end,
                 other=0.0,
-            ).to(reduce_dtype)
+            ) # .to(reduce_dtype)
             qk += tl.dot(qpe, kpe)
         qk *= sm_scale
 
@@ -917,18 +917,18 @@ def _decode_v1_stage1_use_tc(
     batch, head_num = q.shape[0], q.shape[1]
     kv_group_num = q.shape[1] // k_buffer.shape[-2]
     
-    BLOCK_N = best_config.BLOCK_N
+    BLOCK_N = best_config['stage1']['BLOCK_N']
     SPLIT_K = num_kv_splits  # best_config.SPLIT_K
-    num_stages = best_config.num_stages
-    num_warps = best_config.num_warps
+    num_stages = best_config['stage1']['num_stages']
+    num_warps = best_config['stage1']['num_warps']
+
     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,
     )
-    if best_config.decode_fwd_stage1 is None:
-        best_config.decode_fwd_stage1 = _decode_v1_kernel_stage1_use_tc[grid](
+    _decode_v1_kernel_stage1_use_tc[grid](
         q,
         k_buffer,
         sm_scale,
@@ -957,23 +957,6 @@ def _decode_v1_stage1_use_tc(
         Lk=Lk,
         kpack=2,
     )
-    else:
-        best_config.decode_fwd_stage1 = _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),
-        )
         
     # return _decode_v1_kernel_stage1_use_tc.best_config
 
@@ -992,16 +975,16 @@ def _decode_v1_stage2_use_tc(
     batch, head_num = b_seq_len.shape[0], logits.shape[0]
     kv_group_num = logits.shape[0] // v_buffer.shape[-2]
     
-    BLOCK_N = best_config.BLOCK_N_2
-    num_stages = best_config.num_stages_2
-    num_warps = best_config.num_warps_2
+    BLOCK_N = best_config['stage2']['BLOCK_N']
+    num_stages = best_config['stage2']['num_stages']
+    num_warps = best_config['stage2']['num_warps']
     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)
-    if best_config.decode_fwd_stage2 is None:
-        best_config.decode_fwd_stage2 = _decode_v1_kernel_stage2_use_tc[grid](
+   
+    _decode_v1_kernel_stage2_use_tc[grid](
         logits,
         v_buffer,
         o,
@@ -1025,22 +1008,6 @@ def _decode_v1_stage2_use_tc(
         num_warps=num_warps,
         num_stages=num_stages,
     )
-    else:
-        best_config.decode_fwd_stage2 = _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),
-        )
         
     # return _decode_v1_kernel_stage2_use_tc.best_config
 
@@ -1115,21 +1082,16 @@ def decode_attention_v1(
 
 # @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),
+#         triton.Config({"BLOCK_N": 16, "BLOCK_DIM":64}, num_warps=2, num_stages=1),
+#         triton.Config({"BLOCK_N": 16, "BLOCK_DIM":64}, num_warps=4, num_stages=1),
+#         triton.Config({"BLOCK_N": 32, "BLOCK_DIM":64}, num_warps=2, num_stages=1),
+#         triton.Config({"BLOCK_N": 32, "BLOCK_DIM":64}, num_warps=4, num_stages=1),
+#         triton.Config({"BLOCK_N": 64, "BLOCK_DIM":32}, num_warps=2, num_stages=1),
+#         triton.Config({"BLOCK_N": 64, "BLOCK_DIM":32}, num_warps=4, num_stages=1),
+#         triton.Config({"BLOCK_N": 128, "BLOCK_DIM":32}, num_warps=2, num_stages=1),
+#         triton.Config({"BLOCK_N": 128, "BLOCK_DIM":32}, num_warps=4, num_stages=1),
+#         triton.Config({"BLOCK_N": 256, "BLOCK_DIM":32}, num_warps=2, num_stages=1),
+#         triton.Config({"BLOCK_N": 256, "BLOCK_DIM":32}, num_warps=4, num_stages=1),
 #     ],
 #     key=["B_Seqlen","stride_qbs","stride_buf_kbs","stride_buf_kh", "stride_buf_vbs", "stride_buf_vh"]
 # )
@@ -1159,6 +1121,7 @@ def _decode_v2_kernel_stage1_use_tc(
     BLOCK_DPE: tl.constexpr,
     BLOCK_DV: tl.constexpr,
     BLOCK_N: tl.constexpr,
+    BLOCK_DIM: tl.constexpr,
     BLOCK_H: tl.constexpr,
     NUM_KV_SPLITS: tl.constexpr,
     PAGE_SIZE: tl.constexpr,
@@ -1179,16 +1142,16 @@ def _decode_v2_kernel_stage1_use_tc(
     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_d = tl.arange(0, BLOCK_DMODEL)
     offs_dv = tl.arange(0, BLOCK_DV)
-    mask_d = offs_d < Lk
+    # 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)
+    # 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)
@@ -1208,26 +1171,25 @@ def _decode_v2_kernel_stage1_use_tc(
     e_sum = tl.zeros([BLOCK_H], dtype=tl.float32)
     acc = tl.zeros([BLOCK_H, BLOCK_DV], dtype=tl.float32)
 
+    NUM_DIM_SPLIT = tl.cdiv(BLOCK_DMODEL, BLOCK_DIM)
     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))
+            
+            qk = tl.zeros([BLOCK_H, BLOCK_N], dtype=tl.float32)
+            for i in range(0, NUM_DIM_SPLIT):
+                offs_d = tl.arange(0, BLOCK_DIM) + i * BLOCK_DIM
+                mask_d = offs_d < Lk
+                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)
+                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
@@ -1311,9 +1273,10 @@ def _decode_v2_stage1_use_tc(
     logit_cap,
 ):
 
-    BLOCK = best_config.BLOCK_N
-    num_stages = best_config.num_stages
-    num_warps = best_config.num_warps
+    BLOCK = best_config['stage1']['BLOCK_N']
+    BLOCK_DIM = best_config['stage1']['BLOCK_DIM']
+    num_stages = best_config['stage1']['num_stages']
+    num_warps = best_config['stage1']['num_warps']
     
     Lk = k_buffer.shape[-1]
     Lv = v_buffer.shape[-1]
@@ -1341,8 +1304,7 @@ def _decode_v2_stage1_use_tc(
         NUM_KV_SPLITS,
     )
 
-    if best_config.decode_fwd_stage1 is None:
-        best_config.decode_fwd_stage1 =_decode_v2_kernel_stage1_use_tc[grid](
+    _decode_v2_kernel_stage1_use_tc[grid](
         q,
         k_buffer,
         v_buffer,
@@ -1367,6 +1329,7 @@ def _decode_v2_stage1_use_tc(
         BLOCK_DPE=BLOCK_DPE,
         BLOCK_DV=BLOCK_DV,
         BLOCK_N=BLOCK,
+        BLOCK_DIM=BLOCK_DIM,
         BLOCK_H=BLOCK_H,
         NUM_KV_SPLITS=NUM_KV_SPLITS,
         PAGE_SIZE=page_size,
@@ -1377,33 +1340,14 @@ def _decode_v2_stage1_use_tc(
         Lv=Lv,
         kpack=2,
     )
-    else:
-        best_config.decode_fwd_stage1[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),
-        )
+    
     # return _decode_v2_kernel_stage1_use_tc.best_config
 
 
 # @triton.autotune(
 #     configs=[
 #         triton.Config({}, num_warps=1, num_stages=1),
+#         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),
@@ -1476,8 +1420,8 @@ def _decode_v2_stage2_use_tc(
     num_kv_splits,
     best_config,
 ):
-    num_stages = best_config.num_stages_2
-    num_warps = best_config.num_warps_2
+    num_stages = best_config['stage2']['num_stages']
+    num_warps = best_config['stage2']['num_warps']
 
     batch, head_num = q.shape[0], q.shape[1]
     Lv = v_buffer.shape[-1]
@@ -1486,8 +1430,7 @@ def _decode_v2_stage2_use_tc(
     NUM_KV_SPLITS = num_kv_splits
 
     grid = (batch, head_num, 1)
-    if best_config.decode_fwd_stage2 is None:
-        best_config.decode_fwd_stage2 = _decode_v2_kernel_stage2[grid](
+    _decode_v2_kernel_stage2[grid](
         logits,
         o,
         b_seq_len,
@@ -1502,17 +1445,6 @@ def _decode_v2_stage2_use_tc(
         num_warps=num_warps,
         num_stages=num_stages,
     )
-    else:
-        best_config.decode_fwd_stage2[grid](
-            logits,
-            o,
-            b_seq_len,
-            logits.stride(0),
-            logits.stride(1),
-            logits.stride(2),
-            o.stride(0),
-            o.stride(1),
-        )
 
     # return _decode_v2_kernel_stage2.best_config
 
@@ -1580,7 +1512,7 @@ def decode_attention_fwd(
 ):
     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)
+    b_start_loc = torch.arange(0, req_to_token.shape[0]*req_to_token.shape[1], req_to_token.shape[0]*req_to_token.shape[1] // q.shape[0], device="cuda").to(torch.int32)
     if kv_group_num == 1:
         # MHA
         decode_attention_fwd_normal(
@@ -1614,8 +1546,8 @@ def decode_attention_fwd(
                 logit_cap,
             )
             attn_logits_v1 = torch.empty(
-               (q.shape[1],k_buffer.shape[0]*page_size),
-               dtype=torch.float16,
+                (q.shape[1],req_to_token.shape[0]*req_to_token.shape[1]*page_size),
+                dtype=torch.float32,
                 device="cuda")
             decode_attention_v1(
                q,
@@ -1632,10 +1564,10 @@ def decode_attention_fwd(
                logit_cap,
            )'''
         
-            if best_config.kernel_kind == KERNLE_KINDS.v1_2stages_tc:
+            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,
+                    (q.shape[1],req_to_token.shape[0]*req_to_token.shape[1]*page_size),
+                    dtype=torch.float32,
                     device="cuda")
                 decode_attention_v1(
                     q,
@@ -1648,11 +1580,11 @@ def decode_attention_fwd(
                     attn_logits_v1,
                     num_kv_splits,
                     sm_scale,
-                    best_config=best_config,
+                    best_config=best_config['best_config'],
                     page_size=page_size, 
                     logit_cap=logit_cap,
                 )
-            elif best_config.kernel_kind == KERNLE_KINDS.v2_tc:
+            elif best_config['kernel_kind'] == 'v2_tc':
                 decode_attention_v2(
                     q,
                     k_buffer,
@@ -1663,7 +1595,7 @@ def decode_attention_fwd(
                     attn_logits,
                     num_kv_splits,
                     sm_scale,
-                    best_config=best_config,
+                    best_config=best_config['best_config'],
                     page_size=page_size, 
                     logit_cap=logit_cap,
                 )