File size: 29,999 Bytes

fbc96c0

============================= test session starts ==============================
platform linux -- Python 3.12.3, pytest-8.1.1, pluggy-1.6.0 -- /usr/bin/python
cachedir: .pytest_cache
hypothesis profile 'default' -> database=DirectoryBasedExampleDatabase(PosixPath('/home/yiliu7/workspace/vllm/tests/kernels/attention/.hypothesis/examples'))
rootdir: /home/yiliu7/workspace/vllm
configfile: pyproject.toml
plugins: anyio-4.9.0, rerunfailures-15.1, shard-0.1.2, xdoctest-1.0.2, xdist-3.6.1, flakefinder-1.1.0, hypothesis-6.130.8, typeguard-4.3.0
collecting ... WARNING 11-29 09:56:20 [interface.py:508] Current platform cuda does not have '_pytestfixturefunction' attribute.
WARNING 11-29 09:56:20 [interface.py:508] Current platform cuda does not have '__test__' attribute.
WARNING 11-29 09:56:20 [interface.py:508] Current platform cuda does not have '__bases__' attribute.
WARNING 11-29 09:56:20 [interface.py:508] Current platform cuda does not have '__test__' attribute.
collected 1 item
Running 1 items in this shard: tests/kernels/attention/test_triton_unified_attention.py::test_triton_unified_attn[q_dtype0-2048-None-dtype0-None-16-8-num_heads0-seq_lens0]

test_triton_unified_attention.py::test_triton_unified_attn[q_dtype0-2048-None-dtype0-None-16-8-num_heads0-seq_lens0] query shape: torch.Size([5, 8, 8])
query_idx: 
tensor([[[  0,   1,   2,   3,   4,   5,   6,   7],
         [  8,   9,  10,  11,  12,  13,  14,  15],
         [ 16,  17,  18,  19,  20,  21,  22,  23],
         [ 24,  25,  26,  27,  28,  29,  30,  31],
         [ 32,  33,  34,  35,  36,  37,  38,  39],
         [ 40,  41,  42,  43,  44,  45,  46,  47],
         [ 48,  49,  50,  51,  52,  53,  54,  55],
         [ 56,  57,  58,  59,  60,  61,  62,  63]],

        [[ 64,  65,  66,  67,  68,  69,  70,  71],
         [ 72,  73,  74,  75,  76,  77,  78,  79],
         [ 80,  81,  82,  83,  84,  85,  86,  87],
         [ 88,  89,  90,  91,  92,  93,  94,  95],
         [ 96,  97,  98,  99, 100, 101, 102, 103],
         [104, 105, 106, 107, 108, 109, 110, 111],
         [112, 113, 114, 115, 116, 117, 118, 119],
         [120, 121, 122, 123, 124, 125, 126, 127]],

        [[128, 129, 130, 131, 132, 133, 134, 135],
         [136, 137, 138, 139, 140, 141, 142, 143],
         [144, 145, 146, 147, 148, 149, 150, 151],
         [152, 153, 154, 155, 156, 157, 158, 159],
         [160, 161, 162, 163, 164, 165, 166, 167],
         [168, 169, 170, 171, 172, 173, 174, 175],
         [176, 177, 178, 179, 180, 181, 182, 183],
         [184, 185, 186, 187, 188, 189, 190, 191]],

        [[192, 193, 194, 195, 196, 197, 198, 199],
         [200, 201, 202, 203, 204, 205, 206, 207],
         [208, 209, 210, 211, 212, 213, 214, 215],
         [216, 217, 218, 219, 220, 221, 222, 223],
         [224, 225, 226, 227, 228, 229, 230, 231],
         [232, 233, 234, 235, 236, 237, 238, 239],
         [240, 241, 242, 243, 244, 245, 246, 247],
         [248, 249, 250, 251, 252, 253, 254, 255]],

        [[256, 257, 258, 259, 260, 261, 262, 263],
         [264, 265, 266, 267, 268, 269, 270, 271],
         [272, 273, 274, 275, 276, 277, 278, 279],
         [280, 281, 282, 283, 284, 285, 286, 287],
         [288, 289, 290, 291, 292, 293, 294, 295],
         [296, 297, 298, 299, 300, 301, 302, 303],
         [304, 305, 306, 307, 308, 309, 310, 311],
         [312, 313, 314, 315, 316, 317, 318, 319]]], device='cuda:0')
query_uint8: 
tensor([[[183, 174, 195,  33, 160, 177, 178, 170],
         [185, 172, 189,  65,  42, 164,  58,  57],
         [160,  58,  60,  56, 176, 187, 180, 186],
         [ 25, 132,  63,  51, 154, 181,  59, 169],
         [178, 170, 183, 181, 180, 149,  49,  60],
         [  7,  46,  61,  51, 164,  56, 188, 180],
         [164,  44,  45, 155,  54, 185,  30,  48],
         [177, 170, 177,  54,  41,  32,  47, 185]],

        [[145, 184,  55, 185, 184,  31,  51, 173],
         [179, 177,  51, 188,  44, 147,  44, 176],
         [170,  52, 196,  43, 142,  60, 129,  36],
         [ 38, 167,  56,  61,  33,  48,  57, 183],
         [155, 188, 182, 174,  24, 146,  49,  54],
         [189,  46,  52, 182,  51, 171, 191, 163],
         [ 63,  48,  57, 187, 168, 188,  60,   6],
         [ 65, 183,  62, 168, 182, 166, 171, 165]],

        [[185, 177, 178,  64, 187,  58, 192, 182],
         [ 41, 180, 164, 171,  43, 161,  43, 184],
         [173,  49,  21,  61, 173, 166, 163,  34],
         [ 24, 187, 145, 167,  56,  58,  52, 181],
         [ 45, 175, 184,  55,  44, 175, 189,  56],
         [177,  41, 169,  34, 193,  49, 186,  60],
         [ 29, 175,  47,  34, 187,  63, 185,  41],
         [179,  51, 181, 156,  36,  38, 180, 174]],

        [[161, 187, 182, 173,  44,  39, 187, 176],
         [173, 188, 185, 168, 179, 193, 174,  23],
         [ 41,  58, 136,  45, 186,  61,  48, 187],
         [190,  55, 166, 177,  42, 175,  65,  49],
         [ 55,  41, 193,  46,  57,  50,  42,  58],
         [ 29, 192,  49,  34,  53,  40, 179, 134],
         [194,  43,  43, 154,  38, 194,  25, 192],
         [ 57, 177,  59, 190, 185,  59,  69, 182]],

        [[ 39,  57, 168, 176, 185,  59,  55, 151],
         [ 45, 175,  28,  57,  51, 164, 175, 190],
         [ 25, 184, 162,  50,  45, 175,  55, 181],
         [145, 186, 178,  57, 179,  57, 179, 161],
         [ 51, 179,  36, 190, 177, 179, 160, 178],
         [165,  62,  49, 152,  64, 168, 190, 174],
         [185,  58,  43, 179, 174, 184, 183,  49],
         [ 58, 191,  55, 180,  19,  63, 181, 179]]], device='cuda:0',
       dtype=torch.uint8)

>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> PDB set_trace >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
> /home/yiliu7/workspace/vllm/tests/kernels/attention/test_triton_unified_attention.py(219)test_triton_unified_attn()
-> ref_output = ref_paged_attn(
(Pdb) 
>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> PDB continue >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>

>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> PDB set_trace >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
> /home/yiliu7/workspace/vllm/tests/kernels/attention/test_triton_unified_attention.py(56)ref_paged_attn()
-> for i in range(num_seqs):
(Pdb) 
>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> PDB continue >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
k shape: torch.Size([7, 2, 8])
 k data: 
tensor([[[186,  58, 189,  59, 179, 172,  49,  53],
         [ 57,  54,  58,  51,  49, 134, 181,  63]],

        [[168,  56,  35, 177,  66,  40,  43,  50],
         [ 42, 157, 176, 184,  40,  57, 176, 181]],

        [[186, 191,  54,  48, 180,  43,  38, 157],
         [ 50, 164,  52, 189,  51,  64,  46, 191]],

        [[ 51, 178, 180,  61, 179,  45,  60,  41],
         [ 60,  52, 152, 182,  41, 173, 191, 178]],

        [[132, 174,  50, 181, 180,  59, 180,  46],
         [ 67,  42,  41,  43, 192,  64, 152, 186]],

        [[170, 171, 184,  48,  43, 162, 169, 190],
         [ 52, 179,  57,  50, 169, 168,  59,  40]],

        [[ 47,  49,  57,  49,  57, 177, 178, 169],
         [183, 189, 186, 164,  51,  33, 184,  48]]], device='cuda:0',
       dtype=torch.uint8)
v shape: torch.Size([7, 2, 8])
 v data: 
tensor([[[181,  50, 178,  57, 177, 187,  59,  21],
         [ 68,  57,  42, 179,  61,  50, 169, 147]],

        [[173,  57,  39,  41,  42, 194, 183,  54],
         [ 50, 188, 160,  42, 170, 173, 172, 170]],

        [[ 17, 179, 181,  53, 151, 162,  60, 186],
         [187, 182, 182,  41,  51,  37, 186, 183]],

        [[177, 170,  39, 179, 188,  43,  59,  55],
         [184,  64, 183,  53, 174, 160,  58, 194]],

        [[ 34,  36, 191,  44,  32,  34,  50,  39],
         [181,  59,  47, 184, 188,  50,  57,  61]],

        [[ 46,  52, 147,  59,  43, 180,  21,  54],
         [186,  36, 171,  49,  63,  61,  61, 129]],

        [[  1, 193, 157, 154, 183, 175,  43,  61],
         [ 55,  54, 173,  56,  63,  60,  45,  56]]], device='cuda:0',
       dtype=torch.uint8)
q shape : torch.Size([5, 8, 8]), k/v shape torch.Size([7, 8, 8])
attn shape: torch.Size([8, 5, 7]), v shape: torch.Size([7, 8, 8]), out shape: torch.Size([5, 8, 8])
num_seqs=1, num_query_heads=8, num_kv_heads=2
num_queries_per_kv=4, head_size=8
Using BLOCK_M=16, BLOCK_Q=4

>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> PDB set_trace >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
> /home/yiliu7/workspace/vllm/vllm/attention/ops/triton_unified_attention.py(762)unified_attention()
-> print(f"Launch parameters: total_num_q_blocks={total_num_q_blocks}, num_kv_heads={num_kv_heads}")
(Pdb) 
>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> PDB continue >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
Launch parameters: total_num_q_blocks=2, num_kv_heads=2
q_block_global_idx: [0], kv_head_idx: [0]
q_block_global_idx: [0], kv_head_idx: [0]
query_offset_0: 
[0 0 0 0 1 1 1 1 2 2 2 2 3 3 3 3]
query_offset_1: 
[0 1 2 3 0 1 2 3 0 1 2 3 0 1 2 3]
query_offset: 
[[  0   1   2   3   4   5   6   7]
 [  8   9  10  11  12  13  14  15]
 [ 16  17  18  19  20  21  22  23]
 [ 24  25  26  27  28  29  30  31]
 [ 64  65  66  67  68  69  70  71]
 [ 72  73  74  75  76  77  78  79]
 [ 80  81  82  83  84  85  86  87]
 [ 88  89  90  91  92  93  94  95]
 [128 129 130 131 132 133 134 135]
 [136 137 138 139 140 141 142 143]
 [144 145 146 147 148 149 150 151]
 [152 153 154 155 156 157 158 159]
 [192 193 194 195 196 197 198 199]
 [200 201 202 203 204 205 206 207]
 [208 209 210 211 212 213 214 215]
 [216 217 218 219 220 221 222 223]]
Q: 
[[183 174 195  33 160 177 178 170]
 [185 172 189  65  42 164  58  57]
 [160  58  60  56 176 187 180 186]
 [ 25 132  63  51 154 181  59 169]
 [145 184  55 185 184  31  51 173]
 [179 177  51 188  44 147  44 176]
 [170  52 196  43 142  60 129  36]
 [ 38 167  56  61  33  48  57 183]
 [185 177 178  64 187  58 192 182]
 [ 41 180 164 171  43 161  43 184]
 [173  49  21  61 173 166 163  34]
 [ 24 187 145 167  56  58  52 181]
 [161 187 182 173  44  39 187 176]
 [173 188 185 168 179 193 174  23]
 [ 41  58 136  45 186  61  48 187]
 [190  55 166 177  42 175  65  49]]
q_block_global_idx: [0], kv_head_idx: [0]
k_offset: 
[[469248 469264 469280 469296 469312 469328 469344 469360 469376 469392
  469408 469424 469440 469456 469472 469488]
 [469249 469265 469281 469297 469313 469329 469345 469361 469377 469393
  469409 469425 469441 469457 469473 469489]
 [469250 469266 469282 469298 469314 469330 469346 469362 469378 469394
  469410 469426 469442 469458 469474 469490]
 [469251 469267 469283 469299 469315 469331 469347 469363 469379 469395
  469411 469427 469443 469459 469475 469491]
 [469252 469268 469284 469300 469316 469332 469348 469364 469380 469396
  469412 469428 469444 469460 469476 469492]
 [469253 469269 469285 469301 469317 469333 469349 469365 469381 469397
  469413 469429 469445 469461 469477 469493]
 [469254 469270 469286 469302 469318 469334 469350 469366 469382 469398
  469414 469430 469446 469462 469478 469494]
 [469255 469271 469287 469303 469319 469335 469351 469367 469383 469399
  469415 469431 469447 469463 469479 469495]]
K_load: 
[[186 168 186  51 132 170   0   0   0   0   0   0   0   0   0   0]
 [ 58  56 191 178 174 171   0   0   0   0   0   0   0   0   0   0]
 [189  35  54 180  50 184   0   0   0   0   0   0   0   0   0   0]
 [ 59 177  48  61 181  48   0   0   0   0   0   0   0   0   0   0]
 [179  66 180 179 180  43   0   0   0   0   0   0   0   0   0   0]
 [172  40  43  45  59 162   0   0   0   0   0   0   0   0   0   0]
 [ 49  43  38  60 180 169   0   0   0   0   0   0   0   0   0   0]
 [ 53  50 157  41  46 190   0   0   0   0   0   0   0   0   0   0]]
v_offset: 
[[469248 469249 469250 469251 469252 469253 469254 469255]
 [469264 469265 469266 469267 469268 469269 469270 469271]
 [469280 469281 469282 469283 469284 469285 469286 469287]
 [469296 469297 469298 469299 469300 469301 469302 469303]
 [469312 469313 469314 469315 469316 469317 469318 469319]
 [469328 469329 469330 469331 469332 469333 469334 469335]
 [469344 469345 469346 469347 469348 469349 469350 469351]
 [469360 469361 469362 469363 469364 469365 469366 469367]
 [469376 469377 469378 469379 469380 469381 469382 469383]
 [469392 469393 469394 469395 469396 469397 469398 469399]
 [469408 469409 469410 469411 469412 469413 469414 469415]
 [469424 469425 469426 469427 469428 469429 469430 469431]
 [469440 469441 469442 469443 469444 469445 469446 469447]
 [469456 469457 469458 469459 469460 469461 469462 469463]
 [469472 469473 469474 469475 469476 469477 469478 469479]
 [469488 469489 469490 469491 469492 469493 469494 469495]]
V_load: 
[[181  50 178  57 177 187  59  21]
 [173  57  39  41  42 194 183  54]
 [ 17 179 181  53 151 162  60 186]
 [177 170  39 179 188  43  59  55]
 [ 34  36 191  44  32  34  50  39]
 [ 46  52 147  59  43 180  21  54]
 [  0   0   0   0   0   0   0   0]
 [  0   0   0   0   0   0   0   0]
 [  0   0   0   0   0   0   0   0]
 [  0   0   0   0   0   0   0   0]
 [  0   0   0   0   0   0   0   0]
 [  0   0   0   0   0   0   0   0]
 [  0   0   0   0   0   0   0   0]
 [  0   0   0   0   0   0   0   0]
 [  0   0   0   0   0   0   0   0]
 [  0   0   0   0   0   0   0   0]]

>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> PDB set_trace >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
> /home/yiliu7/workspace/vllm/vllm/attention/ops/triton_unified_attention.py(264)kernel_unified_attention_2d()
-> S = tl.where(
(Pdb) 
>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> PDB continue >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
q_block_global_idx: [0], kv_head_idx: [1]
q_block_global_idx: [0], kv_head_idx: [1]
query_offset_0: 
[0 0 0 0 1 1 1 1 2 2 2 2 3 3 3 3]
query_offset_1: 
[4 5 6 7 4 5 6 7 4 5 6 7 4 5 6 7]
query_offset: 
[[ 32  33  34  35  36  37  38  39]
 [ 40  41  42  43  44  45  46  47]
 [ 48  49  50  51  52  53  54  55]
 [ 56  57  58  59  60  61  62  63]
 [ 96  97  98  99 100 101 102 103]
 [104 105 106 107 108 109 110 111]
 [112 113 114 115 116 117 118 119]
 [120 121 122 123 124 125 126 127]
 [160 161 162 163 164 165 166 167]
 [168 169 170 171 172 173 174 175]
 [176 177 178 179 180 181 182 183]
 [184 185 186 187 188 189 190 191]
 [224 225 226 227 228 229 230 231]
 [232 233 234 235 236 237 238 239]
 [240 241 242 243 244 245 246 247]
 [248 249 250 251 252 253 254 255]]
Q: 
[[178 170 183 181 180 149  49  60]
 [  7  46  61  51 164  56 188 180]
 [164  44  45 155  54 185  30  48]
 [177 170 177  54  41  32  47 185]
 [155 188 182 174  24 146  49  54]
 [189  46  52 182  51 171 191 163]
 [ 63  48  57 187 168 188  60   6]
 [ 65 183  62 168 182 166 171 165]
 [ 45 175 184  55  44 175 189  56]
 [177  41 169  34 193  49 186  60]
 [ 29 175  47  34 187  63 185  41]
 [179  51 181 156  36  38 180 174]
 [ 55  41 193  46  57  50  42  58]
 [ 29 192  49  34  53  40 179 134]
 [194  43  43 154  38 194  25 192]
 [ 57 177  59 190 185  59  69 182]]
q_block_global_idx: [0], kv_head_idx: [1]
k_offset: 
[[469256 469272 469288 469304 469320 469336 469352 469368 469384 469400
  469416 469432 469448 469464 469480 469496]
 [469257 469273 469289 469305 469321 469337 469353 469369 469385 469401
  469417 469433 469449 469465 469481 469497]
 [469258 469274 469290 469306 469322 469338 469354 469370 469386 469402
  469418 469434 469450 469466 469482 469498]
 [469259 469275 469291 469307 469323 469339 469355 469371 469387 469403
  469419 469435 469451 469467 469483 469499]
 [469260 469276 469292 469308 469324 469340 469356 469372 469388 469404
  469420 469436 469452 469468 469484 469500]
 [469261 469277 469293 469309 469325 469341 469357 469373 469389 469405
  469421 469437 469453 469469 469485 469501]
 [469262 469278 469294 469310 469326 469342 469358 469374 469390 469406
  469422 469438 469454 469470 469486 469502]
 [469263 469279 469295 469311 469327 469343 469359 469375 469391 469407
  469423 469439 469455 469471 469487 469503]]
K_load: 
[[ 57  42  50  60  67  52   0   0   0   0   0   0   0   0   0   0]
 [ 54 157 164  52  42 179   0   0   0   0   0   0   0   0   0   0]
 [ 58 176  52 152  41  57   0   0   0   0   0   0   0   0   0   0]
 [ 51 184 189 182  43  50   0   0   0   0   0   0   0   0   0   0]
 [ 49  40  51  41 192 169   0   0   0   0   0   0   0   0   0   0]
 [134  57  64 173  64 168   0   0   0   0   0   0   0   0   0   0]
 [181 176  46 191 152  59   0   0   0   0   0   0   0   0   0   0]
 [ 63 181 191 178 186  40   0   0   0   0   0   0   0   0   0   0]]
v_offset: 
[[469256 469257 469258 469259 469260 469261 469262 469263]
 [469272 469273 469274 469275 469276 469277 469278 469279]
 [469288 469289 469290 469291 469292 469293 469294 469295]
 [469304 469305 469306 469307 469308 469309 469310 469311]
 [469320 469321 469322 469323 469324 469325 469326 469327]
 [469336 469337 469338 469339 469340 469341 469342 469343]
 [469352 469353 469354 469355 469356 469357 469358 469359]
 [469368 469369 469370 469371 469372 469373 469374 469375]
 [469384 469385 469386 469387 469388 469389 469390 469391]
 [469400 469401 469402 469403 469404 469405 469406 469407]
 [469416 469417 469418 469419 469420 469421 469422 469423]
 [469432 469433 469434 469435 469436 469437 469438 469439]
 [469448 469449 469450 469451 469452 469453 469454 469455]
 [469464 469465 469466 469467 469468 469469 469470 469471]
 [469480 469481 469482 469483 469484 469485 469486 469487]
 [469496 469497 469498 469499 469500 469501 469502 469503]]
V_load: 
[[ 68  57  42 179  61  50 169 147]
 [ 50 188 160  42 170 173 172 170]
 [187 182 182  41  51  37 186 183]
 [184  64 183  53 174 160  58 194]
 [181  59  47 184 188  50  57  61]
 [186  36 171  49  63  61  61 129]
 [  0   0   0   0   0   0   0   0]
 [  0   0   0   0   0   0   0   0]
 [  0   0   0   0   0   0   0   0]
 [  0   0   0   0   0   0   0   0]
 [  0   0   0   0   0   0   0   0]
 [  0   0   0   0   0   0   0   0]
 [  0   0   0   0   0   0   0   0]
 [  0   0   0   0   0   0   0   0]
 [  0   0   0   0   0   0   0   0]
 [  0   0   0   0   0   0   0   0]]

>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> PDB set_trace >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
> /home/yiliu7/workspace/vllm/vllm/attention/ops/triton_unified_attention.py(264)kernel_unified_attention_2d()
-> S = tl.where(
(Pdb) 
>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> PDB continue >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
q_block_global_idx: [1], kv_head_idx: [0]
q_block_global_idx: [1], kv_head_idx: [0]
query_offset_0: 
[4 4 4 4 5 5 5 5 6 6 6 6 7 7 7 7]
query_offset_1: 
[0 1 2 3 0 1 2 3 0 1 2 3 0 1 2 3]
query_offset: 
[[256 257 258 259 260 261 262 263]
 [264 265 266 267 268 269 270 271]
 [272 273 274 275 276 277 278 279]
 [280 281 282 283 284 285 286 287]
 [320 321 322 323 324 325 326 327]
 [328 329 330 331 332 333 334 335]
 [336 337 338 339 340 341 342 343]
 [344 345 346 347 348 349 350 351]
 [384 385 386 387 388 389 390 391]
 [392 393 394 395 396 397 398 399]
 [400 401 402 403 404 405 406 407]
 [408 409 410 411 412 413 414 415]
 [448 449 450 451 452 453 454 455]
 [456 457 458 459 460 461 462 463]
 [464 465 466 467 468 469 470 471]
 [472 473 474 475 476 477 478 479]]
Q: 
[[ 39  57 168 176 185  59  55 151]
 [ 45 175  28  57  51 164 175 190]
 [ 25 184 162  50  45 175  55 181]
 [145 186 178  57 179  57 179 161]
 [  0   0   0   0   0   0   0   0]
 [  0   0   0   0   0   0   0   0]
 [  0   0   0   0   0   0   0   0]
 [  0   0   0   0   0   0   0   0]
 [  0   0   0   0   0   0   0   0]
 [  0   0   0   0   0   0   0   0]
 [  0   0   0   0   0   0   0   0]
 [  0   0   0   0   0   0   0   0]
 [  0   0   0   0   0   0   0   0]
 [  0   0   0   0   0   0   0   0]
 [  0   0   0   0   0   0   0   0]
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q_block_global_idx: [1], kv_head_idx: [0]
k_offset: 
[[469248 469264 469280 469296 469312 469328 469344 469360 469376 469392
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K_load: 
[[186 168 186  51 132 170  47   0   0   0   0   0   0   0   0   0]
 [ 58  56 191 178 174 171  49   0   0   0   0   0   0   0   0   0]
 [189  35  54 180  50 184  57   0   0   0   0   0   0   0   0   0]
 [ 59 177  48  61 181  48  49   0   0   0   0   0   0   0   0   0]
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v_offset: 
[[469248 469249 469250 469251 469252 469253 469254 469255]
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 [469408 469409 469410 469411 469412 469413 469414 469415]
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V_load: 
[[181  50 178  57 177 187  59  21]
 [173  57  39  41  42 194 183  54]
 [ 17 179 181  53 151 162  60 186]
 [177 170  39 179 188  43  59  55]
 [ 34  36 191  44  32  34  50  39]
 [ 46  52 147  59  43 180  21  54]
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>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> PDB set_trace >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
> /home/yiliu7/workspace/vllm/vllm/attention/ops/triton_unified_attention.py(264)kernel_unified_attention_2d()
-> S = tl.where(
(Pdb) 
>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> PDB continue >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
q_block_global_idx: [1], kv_head_idx: [1]
q_block_global_idx: [1], kv_head_idx: [1]
query_offset_0: 
[4 4 4 4 5 5 5 5 6 6 6 6 7 7 7 7]
query_offset_1: 
[4 5 6 7 4 5 6 7 4 5 6 7 4 5 6 7]
query_offset: 
[[288 289 290 291 292 293 294 295]
 [296 297 298 299 300 301 302 303]
 [304 305 306 307 308 309 310 311]
 [312 313 314 315 316 317 318 319]
 [352 353 354 355 356 357 358 359]
 [360 361 362 363 364 365 366 367]
 [368 369 370 371 372 373 374 375]
 [376 377 378 379 380 381 382 383]
 [416 417 418 419 420 421 422 423]
 [424 425 426 427 428 429 430 431]
 [432 433 434 435 436 437 438 439]
 [440 441 442 443 444 445 446 447]
 [480 481 482 483 484 485 486 487]
 [488 489 490 491 492 493 494 495]
 [496 497 498 499 500 501 502 503]
 [504 505 506 507 508 509 510 511]]
Q: 
[[ 51 179  36 190 177 179 160 178]
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q_block_global_idx: [1], kv_head_idx: [1]
k_offset: 
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K_load: 
[[ 57  42  50  60  67  52 183   0   0   0   0   0   0   0   0   0]
 [ 54 157 164  52  42 179 189   0   0   0   0   0   0   0   0   0]
 [ 58 176  52 152  41  57 186   0   0   0   0   0   0   0   0   0]
 [ 51 184 189 182  43  50 164   0   0   0   0   0   0   0   0   0]
 [ 49  40  51  41 192 169  51   0   0   0   0   0   0   0   0   0]
 [134  57  64 173  64 168  33   0   0   0   0   0   0   0   0   0]
 [181 176  46 191 152  59 184   0   0   0   0   0   0   0   0   0]
 [ 63 181 191 178 186  40  48   0   0   0   0   0   0   0   0   0]]
v_offset: 
[[469256 469257 469258 469259 469260 469261 469262 469263]
 [469272 469273 469274 469275 469276 469277 469278 469279]
 [469288 469289 469290 469291 469292 469293 469294 469295]
 [469304 469305 469306 469307 469308 469309 469310 469311]
 [469320 469321 469322 469323 469324 469325 469326 469327]
 [469336 469337 469338 469339 469340 469341 469342 469343]
 [469352 469353 469354 469355 469356 469357 469358 469359]
 [469368 469369 469370 469371 469372 469373 469374 469375]
 [469384 469385 469386 469387 469388 469389 469390 469391]
 [469400 469401 469402 469403 469404 469405 469406 469407]
 [469416 469417 469418 469419 469420 469421 469422 469423]
 [469432 469433 469434 469435 469436 469437 469438 469439]
 [469448 469449 469450 469451 469452 469453 469454 469455]
 [469464 469465 469466 469467 469468 469469 469470 469471]
 [469480 469481 469482 469483 469484 469485 469486 469487]
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V_load: 
[[ 68  57  42 179  61  50 169 147]
 [ 50 188 160  42 170 173 172 170]
 [187 182 182  41  51  37 186 183]
 [184  64 183  53 174 160  58 194]
 [181  59  47 184 188  50  57  61]
 [186  36 171  49  63  61  61 129]
 [ 55  54 173  56  63  60  45  56]
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>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> PDB set_trace >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
> /home/yiliu7/workspace/vllm/vllm/attention/ops/triton_unified_attention.py(264)kernel_unified_attention_2d()
-> S = tl.where(
(Pdb) 
>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> PDB continue >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>

>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> PDB set_trace >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
> /home/yiliu7/workspace/vllm/tests/kernels/attention/test_triton_unified_attention.py(256)test_triton_unified_attn()
-> torch.testing.assert_close(output, ref_output, atol=atol, rtol=rtol),
(Pdb) 
>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> PDB continue >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
PASSED

=============================== warnings summary ===============================
<frozen importlib._bootstrap>:488
  <frozen importlib._bootstrap>:488: DeprecationWarning: builtin type SwigPyPacked has no __module__ attribute

<frozen importlib._bootstrap>:488
  <frozen importlib._bootstrap>:488: DeprecationWarning: builtin type SwigPyObject has no __module__ attribute

tests/kernels/attention/test_triton_unified_attention.py::test_triton_unified_attn[q_dtype0-2048-None-dtype0-None-16-8-num_heads0-seq_lens0]
tests/kernels/attention/test_triton_unified_attention.py::test_triton_unified_attn[q_dtype0-2048-None-dtype0-None-16-8-num_heads0-seq_lens0]
tests/kernels/attention/test_triton_unified_attention.py::test_triton_unified_attn[q_dtype0-2048-None-dtype0-None-16-8-num_heads0-seq_lens0]
tests/kernels/attention/test_triton_unified_attention.py::test_triton_unified_attn[q_dtype0-2048-None-dtype0-None-16-8-num_heads0-seq_lens0]
tests/kernels/attention/test_triton_unified_attention.py::test_triton_unified_attn[q_dtype0-2048-None-dtype0-None-16-8-num_heads0-seq_lens0]
tests/kernels/attention/test_triton_unified_attention.py::test_triton_unified_attn[q_dtype0-2048-None-dtype0-None-16-8-num_heads0-seq_lens0]
tests/kernels/attention/test_triton_unified_attention.py::test_triton_unified_attn[q_dtype0-2048-None-dtype0-None-16-8-num_heads0-seq_lens0]
tests/kernels/attention/test_triton_unified_attention.py::test_triton_unified_attn[q_dtype0-2048-None-dtype0-None-16-8-num_heads0-seq_lens0]
  /usr/local/lib/python3.12/dist-packages/triton/runtime/interpreter.py:818: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)
    tensor.__index__ = lambda self: int(self.handle.data)

tests/kernels/attention/test_triton_unified_attention.py::test_triton_unified_attn[q_dtype0-2048-None-dtype0-None-16-8-num_heads0-seq_lens0]
  /usr/local/lib/python3.12/dist-packages/triton/runtime/interpreter.py:463: RuntimeWarning: invalid value encountered in divide
    return TensorHandle(op(lhs.data, rhs.data), lhs.dtype.scalar)

-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
======================= 1 passed, 11 warnings in 10.85s ========================
sys:1: DeprecationWarning: builtin type swigvarlink has no __module__ attribute