ποΈ Spike Skipper
Implementation of a mechanism to skip bad-data training steps that cause loss spikes during LLM training.
Sam Foreman 2024-09-17
Details
We describe below our implementation for skipping individual steps during training.
π Example
Suppose we observe a large spike in our loss curve, as shown below:
Seemingly, this spike is being caused by a batch of βbad dataβ. In order to prevent this βbad dataβ sample from corrupting our training, we would like to βskipβ that particular training step.
This can be accomplished by passing the keyword argument
--train-range-to-skip and specifying the endpoints of the ranges to be
skipped.
e.g., if you would like to skip all steps from [10, 20] and from
[25, 30], we would specify:
PBS_O_WORKDIR=$(pwd) bash train_aGPT_7B.sh \
--train-range-to-skip 10 20 25 30π§ͺ Implementation
We discuss below the details of the implementation, and provide some simple results to confirm things are behaving how we expect.
-
Check if
args.train_range_to_skip is not None[here] -
If current iteration is in any of these pairs [here]
β Sanity Check
In order to confirm things are behaving as expected, we can explicitly look at the tokens drawn for each step, and ensure that they are the same regardless of whether or not that iteration was skipped.
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In particular, we see that:
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test 1:
# [2024-09-16 23:09:09.059118][INFO][training:1083] - iteration=2 [0/8]: (torch.Size([4, 4097])) _tokens[:10]=tensor( [[ 1858, 3851, 29889, ..., 500, 13, 13], [ 349, 6156, 1650, ..., 5806, 28557, 3519], [16554, 304, 1653, ..., 322, 6934, 14722], [ 4955, 310, 10465, ..., 1438, 3841, 29892]] ) # [2024-09-16 23:09:09.061999][INFO][training:1083] - iteration=2 [1/8]: (torch.Size([4, 4097])) _tokens[:10]=tensor( [[ 363, 1302, 16453, ..., 7967, 29891, 484], [ 367, 766, 4752, ..., 1, 29871, 30143], [29899, 855, 1503, ..., 3786, 29892, 5100], [ 465, 1974, 289, ..., 21588, 533, 304]] ) -
test 2:
# [2024-09-16 22:59:27.752277][INFO][pretrain_gpt_alcf:198] - args.iteration=2: data['text'][:10]=tensor( [[ 1858, 3851, 29889, ..., 500, 13, 13], [ 349, 6156, 1650, ..., 5806, 28557, 3519], [16554, 304, 1653, ..., 322, 6934, 14722], [ 4955, 310, 10465, ..., 1438, 3841, 29892]] ) # [2024-09-16 22:59:27,755] [INFO] [profiler.py:81:start_profile] Flops profiler started # [2024-09-16 22:59:28.568805][INFO][pretrain_gpt_alcf:198] - args.iteration=2: data['text'][:10]=tensor( [[363, 1302, 16453, ..., 7967, 29891, 484], [ 367, 766, 4752, ..., 1, 29871, 30143], [29899, 855, 1503, ..., 3786, 29892, 5100], [ 465, 1974, 289, ..., 21588, 533, 304]] )
as expected.
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π Details
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First 4 steps:
tokens:-
Iteration 0:
[2024-09-16 22:58:50.168667][INFO][pretrain_gpt_alcf:198] - args.iteration=0: data['text'][:10]=tensor([[ 304, 7344, 5146, ..., 9776, 29914, 26419], [29889, 13, 4706, ..., 9280, 30004, 13], [29943, 20774, 29908, ..., 304, 27391, 322], [ 2645, 445, 29871, ..., 16888, 4656, 10070]]) [2024-09-16 22:58:58.866409][INFO][pretrain_gpt_alcf:198] - args.iteration=0: data['text'][:10]=tensor([[ 2768, 596, 1788, ..., 27274, 393, 30010], [ 278, 5613, 4192, ..., 362, 310, 1950], [28038, 29892, 2022, ..., 3160, 278, 2087], [ 4149, 907, 29888, ..., 29896, 29892, 29896]]) [2024-09-16 22:59:02.043059][INFO][pretrain_gpt_alcf:198] - args.iteration=0: data['text'][:10]=tensor([[ 424, 322, 16232, ..., 366, 748, 467], [ 13, 462, 1678, ..., 2084, 29892, 3497], [ 7562, 310, 19320, ..., 8973, 22684, 358], [ 2089, 3633, 292, ..., 13774, 269, 2375]]) [2024-09-16 22:59:03.456919][INFO][pretrain_gpt_alcf:198] - args.iteration=0: data['text'][:10]=tensor([[21411, 322, 3896, ..., 2610, 29889, 319], [ 8003, 29898, 29900, ..., 12, 6658, 529], [ 278, 4148, 310, ..., 263, 12212, 282], [ 5977, 29871, 29906, ..., 15332, 310, 1749]]) [2024-09-16 22:59:04.596630][INFO][pretrain_gpt_alcf:198] - args.iteration=0: data['text'][:10]=tensor([[ 278, 1473, 24987, ..., 263, 2217, 3804], [ 2973, 263, 18778, ..., 263, 4642, 6673], [ 309, 323, 804, ..., 1063, 15296, 327], [ 278, 5864, 322, ..., 9409, 29889, 2178]]) [2024-09-16 22:59:05.486913][INFO][pretrain_gpt_alcf:198] - args.iteration=0: data['text'][:10]=tensor([[29892, 13731, 6617, ..., 29871, 29896, 29946], [ 2892, 1012, 1266, ..., 4036, 7512, 2068], [ 1473, 1556, 3619, ..., 3762, 338, 263], [23353, 29918, 2177, ..., 501, 567, 814]]) [2024-09-16 22:59:06.361333][INFO][pretrain_gpt_alcf:198] - args.iteration=0: data['text'][:10]=tensor([[ 5400, 14378, 4768, ..., 2107, 18677, 29889], [ 9200, 29887, 29914, ..., 293, 24235, 322], [30143, 4746, 2184, ..., 11891, 29974, 25760], [19263, 29914, 303, ..., 358, 29889, 13]]) [2024-09-16 22:59:07.230671][INFO][pretrain_gpt_alcf:198] - args.iteration=0: data['text'][:10]=tensor([[ 309, 1306, 681, ..., 310, 23186, 21809], [29896, 29929, 13, ..., 29871, 29900, 13], [ 9558, 964, 263, ..., 322, 282, 682], [ 278, 23904, 21767, ..., 313, 29929, 29889]]) -
Iteration 1:
[2024-09-16 22:59:19.287338][INFO][training_log:661] - iteration= 1/ 635782 | consumed_samples= 768 | consumed_tokens= 3145728 | elapsed_time_per_iteration_ms=29570.0 | learning_rate=9.4372e-09 | global_batch_size=768 | lm loss=11.167250 | loss_scale=1.0 | grad_norm=6.363 | actual_seqlen= 4096 | number_of_skipped_iterations= 0 | number_of_nan_iterations= 0 | samples_per_second=25.972 | tokens_per_gpu_per_second_tgs=4432.597 | [LM]TFLOPs=20.30 | [DS]TFLOPs=26.18 | [2024-09-16 22:59:19.289582][INFO][utils:207] - [Rank 0] (after 1 iterations) memory (MB) | allocated: 1894.57666015625 | max allocated: 9752.35498046875 | reserved: 11342.0 | max reserved: 11342.0 (min, max) time across ranks (ms): forward-backward ...............................: (26094.39, 26095.09) optimizer ......................................: (3407.56, 3409.92) [2024-09-16 22:59:19.297183][INFO][pretrain_gpt_alcf:198] - args.iteration=1: data['text'][:10]=tensor([[ 1472, 29892, 408, ..., 29892, 1584, 363], [ 967, 19475, 6593, ..., 8093, 29899, 11249], [ 1006, 2218, 13326, ..., 2355, 1304, 304], [29900, 29916, 29947, ..., 353, 1870, 29936]]) [2024-09-16 22:59:20.104352][INFO][pretrain_gpt_alcf:198] - args.iteration=1: data['text'][:10]=tensor([[ 2354, 274, 1041, ..., 29892, 13049, 9098], [ 8798, 9547, 10353, ..., 303, 3143, 29889], [ 1373, 4056, 7236, ..., 3186, 297, 5837], [ 1738, 29920, 7355, ..., 13, 29871, 3776]]) [2024-09-16 22:59:20.977036][INFO][utils:326] - >> building dataset for /flare/Aurora_deployment/AuroraGPT/datasets/dolma/data_v1.7_Llama2Tokenizer/c4-0000_text_document [2024-09-16 22:59:20.977877][INFO][utils:326] - > building dataset index ... [2024-09-16 22:59:20.977147][INFO][pretrain_gpt_alcf:198] - args.iteration=1: data['text'][:10]=tensor([[ 2020, 306, 1016, ..., 322, 920, 372], [ 5921, 1749, 7306, ..., 19252, 297, 5664], [ 970, 770, 28547, ..., 970, 894, 2577], [ 1907, 363, 14188, ..., 756, 3646, 287]]) [2024-09-16 22:59:21.851620][INFO][pretrain_gpt_alcf:198] - args.iteration=1: data['text'][:10]=tensor([[ 715, 25392, 3104, ..., 289, 5761, 616], [ 426, 13, 9651, ..., 9651, 1815, 22603], [ 7714, 1213, 13, ..., 13, 29876, 457], [29889, 28663, 1230, ..., 1546, 278, 6586]]) [2024-09-16 22:59:22.720945][INFO][pretrain_gpt_alcf:198] - args.iteration=1: data['text'][:10]=tensor([[29929, 13, 13, ..., 10739, 4770, 11277], [ 4528, 304, 2367, ..., 2501, 385, 4203], [ 869, 319, 794, ..., 3158, 29889, 3115], [ 592, 260, 4125, ..., 284, 1135, 18655]]) [2024-09-16 22:59:23.590149][INFO][pretrain_gpt_alcf:198] - args.iteration=1: data['text'][:10]=tensor([[14338, 25323, 3321, ..., 5607, 1806, 1164], [ 322, 278, 15352, ..., 6462, 313, 1552], [25738, 714, 29889, ..., 29915, 29879, 24842], [ 5122, 399, 29889, ..., 29947, 7284, 2305]]) [2024-09-16 22:59:24.457646][INFO][pretrain_gpt_alcf:198] - args.iteration=1: data['text'][:10]=tensor([[ 367, 19310, 1891, ..., 2408, 292, 263], [ 470, 3307, 5713, ..., 568, 2594, 19385], [29953, 29905, 1631, ..., 1118, 343, 29897], [10261, 373, 5490, ..., 511, 297, 1760]]) [2024-09-16 22:59:25.326699][INFO][pretrain_gpt_alcf:198] - args.iteration=1: data['text'][:10]=tensor([[ 1006, 326, 29901, ..., 14834, 6694, 9595], [12058, 5446, 29892, ..., 29889, 8246, 3310], [ 7483, 310, 278, ..., 402, 9851, 4423], [ 8041, 813, 322, ..., 3303, 3900, 393]]) -
Iteration 2:
[2024-09-16 22:59:27.744603][INFO][training_log:661] - iteration= 2/ 635782 | consumed_samples= 1536 | consumed_tokens= 6291456 | elapsed_time_per_iteration_ms=8457.2 | learning_rate=1.88744e-08 | global_batch_size=768 | lm loss=11.164009 | loss_scale=1.0 | grad_norm=6.271 | actual_seqlen= 4096 | number_of_skipped_iterations= 0 | number_of_nan_iterations= 0 | samples_per_second=90.810 | tokens_per_gpu_per_second_tgs=15498.234 | [LM]TFLOPs=70.98| [DS]TFLOPs=91.53 | (min, max) time across ranks (ms): forward-backward ...............................: (8384.83, 8385.57) optimizer ......................................: (55.03, 55.61) [2024-09-16 22:59:27.752277][INFO][pretrain_gpt_alcf:198] - args.iteration=2: data['text'][:10]=tensor([[ 1858, 3851, 29889, ..., 500, 13, 13], [ 349, 6156, 1650, ..., 5806, 28557, 3519], [16554, 304, 1653, ..., 322, 6934, 14722], [ 4955, 310, 10465, ..., 1438, 3841, 29892]]) [2024-09-16 22:59:27,755] [INFO] [profiler.py:81:start_profile] Flops profiler started [2024-09-16 22:59:28.568805][INFO][pretrain_gpt_alcf:198] - args.iteration=2: data['text'][:10]=tensor([[ 363, 1302, 16453, ..., 7967, 29891, 484], [ 367, 766, 4752, ..., 1, 29871, 30143], [29899, 855, 1503, ..., 3786, 29892, 5100], [ 465, 1974, 289, ..., 21588, 533, 304]]) [2024-09-16 22:59:28,571] [INFO] [profiler.py:81:start_profile] Flops profiler started [2024-09-16 22:59:29.440843][INFO][pretrain_gpt_alcf:198] - args.iteration=2: data['text'][:10]=tensor([[29889, 13, 4806, ..., 3086, 26040, 9220], [ 293, 7207, 355, ..., 18131, 520, 1247], [ 8619, 29889, 29871, ..., 304, 10029, 266], [ 363, 15202, 29892, ..., 482, 17162, 19104]]) [2024-09-16 22:59:29,443] [INFO] [profiler.py:81:start_profile] Flops profiler started [2024-09-16 22:59:30.313403][INFO][pretrain_gpt_alcf:198] - args.iteration=2: data['text'][:10]=tensor([[25561, 411, 278, ..., 297, 2898, 26163], [22574, 2607, 18134, ..., 13, 4706, 500], [20190, 24820, 1623, ..., 310, 901, 29892], [29892, 1951, 4486, ..., 869, 887, 30010]]) [2024-09-16 22:59:30,316] [INFO] [profiler.py:81:start_profile] Flops profiler started [2024-09-16 22:59:31.185339][INFO][pretrain_gpt_alcf:198] - args.iteration=2: data['text'][:10]=tensor([[ 5371, 22417, 29892, ..., 13, 6716, 901], [ 353, 1565, 29936, ..., 29878, 3567, 7196], [17296, 338, 1985, ..., 3741, 9089, 422], [ 694, 13331, 310, ..., 21180, 29892, 607]]) [2024-09-16 22:59:31,188] [INFO] [profiler.py:81:start_profile] Flops profiler started [2024-09-16 22:59:32.057207][INFO][pretrain_gpt_alcf:198] - args.iteration=2: data['text'][:10]=tensor([[ 292, 8818, 267, ..., 29892, 11275, 7407], [ 1870, 29897, 13, ..., 2697, 29901, 13], [29913, 338, 263, ..., 29892, 591, 3394], [ 2253, 472, 1554, ..., 982, 304, 376]]) [2024-09-16 22:59:32,060] [INFO] [profiler.py:81:start_profile] Flops profiler started [2024-09-16 22:59:32.930293][INFO][pretrain_gpt_alcf:198] - args.iteration=2: data['text'][:10]=tensor([[ 391, 2598, 29883, ..., 22629, 346, 440], [29871, 29896, 29906, ..., 407, 583, 2833], [ 4262, 1836, 13, ..., 310, 263, 10608], [ 1199, 411, 24770, ..., 272, 2153, 29889]]) [2024-09-16 22:59:32,932] [INFO] [profiler.py:81:start_profile] Flops profiler started [2024-09-16 22:59:33.803567][INFO][pretrain_gpt_alcf:198] - args.iteration=2: data['text'][:10]=tensor([[ 620, 20503, 428, ..., 297, 1009, 9443], [ 950, 25078, 892, ..., 408, 10636, 284], [ 1012, 2003, 364, ..., 7313, 29912, 19303], [29906, 29892, 29945, ..., 967, 26414, 472]]) -
Iteration 3:
[2024-09-16 22:59:34.881265][INFO][training_log:661] - iteration= 3/ 635782 | consumed_samples= 2304 | consumed_tokens= 9437184 | elapsed_time_per_iteration_ms=7136.5 | learning_rate=2.83116e-08 | global_batch_size=768 | lm loss=11.164038 | loss_scale=1.0 | grad_norm=6.279 | actual_seqlen= 4096 | number_of_skipped_iterations= 0 | number_of_nan_iterations= 0 | samples_per_second=107.615 | tokens_per_gpu_per_second_tgs=18366.372 | [LM]TFLOPs=84.12 | [DS]TFLOPs=108.46 | (min, max) time across ranks (ms): forward-backward ...............................: (7078.48, 7079.28) optimizer ......................................: (38.62, 43.08) [2024-09-16 22:59:34.888870][INFO][pretrain_gpt_alcf:198] - args.iteration=3: data['text'][:10]=tensor([[ 496, 313, 29941, ..., 1316, 408, 4857], [29899, 3204, 29889, ..., 1074, 330, 2547], [29916, 29900, 29946, ..., 18455, 29889, 4002], [26406, 338, 1641, ..., 670, 1914, 6900]]) [2024-09-16 22:59:35.719630][INFO][pretrain_gpt_alcf:198] - args.iteration=3: data['text'][:10]=tensor([[29945, 29900, 867, ..., 7601, 12091, 310], [ 975, 29871, 29896, ..., 3573, 825, 306], [29906, 29900, 4638, ..., 29227, 23145, 29892], [ 278, 14368, 322, ..., 14909, 29936, 25913]]) [2024-09-16 22:59:36.591343][INFO][pretrain_gpt_alcf:198] - args.iteration=3: data['text'][:10]=tensor([[ 988, 306, 1033, ..., 437, 408, 1532], [ 450, 10317, 310, ..., 322, 752, 13036], [11405, 8020, 29889, ..., 471, 18096, 287], [ 288, 3594, 19284, ..., 910, 338, 385]]) [2024-09-16 22:59:37.463941][INFO][pretrain_gpt_alcf:198] - args.iteration=3: data['text'][:10]=tensor([[ 322, 15151, 29946, ..., 11648, 1497, 29889], [24233, 362, 467, ..., 4513, 1353, 322], [ 3311, 13605, 29912, ..., 945, 29899, 4181], [ 1951, 366, 508, ..., 6589, 491, 777]]) [2024-09-16 22:59:38.343307][INFO][pretrain_gpt_alcf:198] - args.iteration=3: data['text'][:10]=tensor([[29889, 29900, 13, ..., 6017, 424, 1711], [ 297, 5500, 1489, ..., 310, 3802, 7875], [ 8078, 5314, 515, ..., 373, 278, 6991], [13763, 6204, 6359, ..., 4706, 2024, 1347]]) [2024-09-16 22:59:39.214871][INFO][pretrain_gpt_alcf:198] - args.iteration=3: data['text'][:10]=tensor([[ 13, 4806, 3512, ..., 278, 7824, 6438], [ 2294, 938, 903, ..., 4537, 3047, 449], [ 1230, 4123, 767, ..., 310, 963, 21003], [ 1152, 2319, 10365, ..., 367, 14040, 363]]) [2024-09-16 22:59:40.085368][INFO][utils:326] - >> building dataset for /flare/Aurora_deployment/AuroraGPT/datasets/dolma/data_v1.7_Llama2Tokenizer/tulu_flan-0000_text_document [2024-09-16 22:59:40.086224][INFO][utils:326] - > building dataset index ... [2024-09-16 22:59:40.085475][INFO][pretrain_gpt_alcf:198] - args.iteration=3: data['text'][:10]=tensor([[27297, 29924, 801, ..., 28947, 29892, 470], [12542, 5568, 703, ..., 426, 13, 4706], [ 6907, 800, 322, ..., 29892, 1661, 30304], [29900, 13630, 293, ..., 26552, 363, 975]]) [2024-09-16 22:59:40.958071][INFO][pretrain_gpt_alcf:198] - args.iteration=3: data['text'][:10]=tensor([[ 13, 29946, 29953, ..., 29953, 29945, 29871], [ 1283, 16578, 1156, ..., 408, 2215, 408], [29906, 4229, 7671, ..., 13, 1576, 1014], [ 526, 2898, 304, ..., 471, 4802, 29991]]) -
Iteration 4:
[2024-09-16 22:59:42.028460][INFO][training_log:661] - iteration= 4/ 635782 | consumed_samples= 3072 | consumed_tokens= 12582912 | elapsed_time_per_iteration_ms=7147.0 | learning_rate=3.77488e-08 | global_batch_size=768 | lm loss=11.171233 | loss_scale=1.0 | grad_norm=6.272 | actual_seqlen= 4096 | number_of_skipped_iterations= 0 | number_of_nan_iterations= 0 | samples_per_second=107.458 | tokens_per_gpu_per_second_tgs=18339.524 | [LM]TFLOPs=84.00 | [DS]TFLOPs=108.31 | (min, max) time across ranks (ms): forward-backward ...............................: (7091.77, 7092.56) optimizer ......................................: (39.21, 40.11) [2024-09-16 22:59:42.035716][INFO][pretrain_gpt_alcf:198] - args.iteration=4: data['text'][:10]=tensor([[ 443, 666, 10170, ..., 278, 619, 7323], [ 13, 11008, 338, ..., 2472, 363, 22049], [29871, 13, 29938, ..., 29962, 8521, 29896], [ 1165, 2280, 304, ..., 306, 471, 2086]]) [2024-09-16 22:59:42.860756][INFO][pretrain_gpt_alcf:198] - args.iteration=4: data['text'][:10]=tensor([[ 304, 679, 304, ..., 1475, 29889, 1570], [ 2184, 29936, 13, ..., 4706, 970, 1780], [29872, 352, 29901, ..., 29905, 4915, 29912], [16809, 304, 1438, ..., 13457, 29889, 13]]) [2024-09-16 22:59:43.731208][INFO][pretrain_gpt_alcf:198] - args.iteration=4: data['text'][:10]=tensor([[12015, 29901, 20549, ..., 322, 10752, 17906], [ 372, 30010, 29879, ..., 29892, 14595, 653], [18280, 29958, 13, ..., 18884, 736, 6251], [29889, 13, 13, ..., 599, 373, 17097]]) [2024-09-16 22:59:44.605047][INFO][pretrain_gpt_alcf:198] - args.iteration=4: data['text'][:10]=tensor([[ 3367, 567, 964, ..., 3353, 24870, 2181], [ 1262, 2609, 367, ..., 29974, 29896, 7570], [29871, 29941, 29900, ..., 341, 555, 265], [ 4225, 526, 6041, ..., 1925, 1623, 2748]]) [2024-09-16 22:59:45.479433][INFO][pretrain_gpt_alcf:198] - args.iteration=4: data['text'][:10]=tensor([[ 2283, 10162, 1496, ..., 30656, 30317, 30605], [29879, 9228, 292, ..., 7968, 29899, 7052], [ 884, 599, 367, ..., 29892, 278, 6054], [29879, 411, 278, ..., 367, 5019, 1183]]) [2024-09-16 22:59:46.351707][INFO][pretrain_gpt_alcf:198] - args.iteration=4: data['text'][:10]=tensor([[ 3867, 281, 761, ..., 11949, 338, 4922], [ 297, 1432, 2586, ..., 5414, 278, 29811], [29892, 278, 15562, ..., 10296, 310, 394], [ 1451, 2960, 3505, ..., 657, 14346, 8003]]) [2024-09-16 22:59:47.222016][INFO][pretrain_gpt_alcf:198] - args.iteration=4: data['text'][:10]=tensor([[ 6601, 2874, 414, ..., 302, 317, 7390], [16415, 297, 5146, ..., 763, 372, 471], [29941, 29906, 1118, ..., 29900, 29889, 29953], [ 4893, 304, 4808, ..., 2284, 2164, 18690]]) [2024-09-16 22:59:48.094752][INFO][pretrain_gpt_alcf:198] - args.iteration=4: data['text'][:10]=tensor([[ 901, 310, 2994, ..., 29873, 1641, 766], [ 304, 1716, 2562, ..., 3489, 304, 367], [ 1949, 6736, 29871, ..., 29965, 29909, 353], [ 13, 13, 29930, ..., 16497, 316, 474]])
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Skipping steps
[2, 3]:tokens:-
Iteration 0:
[2024-09-16 23:08:47.749839][INFO][pretrain_gpt_alcf:198] - args.iteration=0: data['text'][:10]=tensor([[ 304, 7344, 5146, ..., 9776, 29914, 26419], [29889, 13, 4706, ..., 9280, 30004, 13], [29943, 20774, 29908, ..., 304, 27391, 322], [ 2645, 445, 29871, ..., 16888, 4656, 10070]]) [2024-09-16 23:08:51.451183][INFO][pretrain_gpt_alcf:198] - args.iteration=0: data['text'][:10]=tensor([[ 2768, 596, 1788, ..., 27274, 393, 30010], [ 278, 5613, 4192, ..., 362, 310, 1950], [28038, 29892, 2022, ..., 3160, 278, 2087], [ 4149, 907, 29888, ..., 29896, 29892, 29896]]) [2024-09-16 23:08:54.073597][INFO][pretrain_gpt_alcf:198] - args.iteration=0: data['text'][:10]=tensor([[ 424, 322, 16232, ..., 366, 748, 467], [ 13, 462, 1678, ..., 2084, 29892, 3497], [ 7562, 310, 19320, ..., 8973, 22684, 358], [ 2089, 3633, 292, ..., 13774, 269, 2375]]) [2024-09-16 23:08:56.212476][INFO][pretrain_gpt_alcf:198] - args.iteration=0: data['text'][:10]=tensor([[21411, 322, 3896, ..., 2610, 29889, 319], [ 8003, 29898, 29900, ..., 12, 6658, 529], [ 278, 4148, 310, ..., 263, 12212, 282], [ 5977, 29871, 29906, ..., 15332, 310, 1749]]) [2024-09-16 23:08:57.207940][INFO][pretrain_gpt_alcf:198] - args.iteration=0: data['text'][:10]=tensor([[ 278, 1473, 24987, ..., 263, 2217, 3804], [ 2973, 263, 18778, ..., 263, 4642, 6673], [ 309, 323, 804, ..., 1063, 15296, 327], [ 278, 5864, 322, ..., 9409, 29889, 2178]]) [2024-09-16 23:08:58.083935][INFO][pretrain_gpt_alcf:198] - args.iteration=0: data['text'][:10]=tensor([[29892, 13731, 6617, ..., 29871, 29896, 29946], [ 2892, 1012, 1266, ..., 4036, 7512, 2068], [ 1473, 1556, 3619, ..., 3762, 338, 263], [23353, 29918, 2177, ..., 501, 567, 814]]) [2024-09-16 23:08:58.951793][INFO][pretrain_gpt_alcf:198] - args.iteration=0: data['text'][:10]=tensor([[ 5400, 14378, 4768, ..., 2107, 18677, 29889], [ 9200, 29887, 29914, ..., 293, 24235, 322], [30143, 4746, 2184, ..., 11891, 29974, 25760], [19263, 29914, 303, ..., 358, 29889, 13]]) [2024-09-16 23:08:59.820234][INFO][pretrain_gpt_alcf:198] - args.iteration=0: data['text'][:10]=tensor([[ 309, 1306, 681, ..., 310, 23186, 21809], [29896, 29929, 13, ..., 29871, 29900, 13], [ 9558, 964, 263, ..., 322, 282, 682], [ 278, 23904, 21767, ..., 313, 29929, 29889]]) -
Iteration 1:
[2024-09-16 23:09:08.943867][INFO][training_log:661] - iteration= 1/ 635782 | consumed_samples= 768 | consumed_tokens= 3145728 | elapsed_time_per_iteration_ms=21224.4 | learning_rate=9.4372e-09 | global_batch_size=768 | lm loss=11.167250 | loss_scale=1.0 | grad_norm=6.363 | actual_seqlen= 4096 | number_of_skipped_iterations= 0 | number_of_nan_iterations= 0 | samples_per_second=36.185 | tokens_per_gpu_per_second_tgs=6175.523 | [LM]TFLOPs=28.29 | [DS]TFLOPs=36.47 | [2024-09-16 23:09:08.953432][INFO][training:1083] - iteration=1 [0/8]: (torch.Size([4, 4097])) _tokens[:10]=tensor([[ 1472, 29892, 408, ..., 29892, 1584, 363], [ 967, 19475, 6593, ..., 8093, 29899, 11249], [ 1006, 2218, 13326, ..., 2355, 1304, 304], [29900, 29916, 29947, ..., 353, 1870, 29936]]) [2024-09-16 23:09:08.957524][INFO][training:1083] - iteration=1 [1/8]: (torch.Size([4, 4097])) _tokens[:10]=tensor([[ 2354, 274, 1041, ..., 29892, 13049, 9098], [ 8798, 9547, 10353, ..., 303, 3143, 29889], [ 1373, 4056, 7236, ..., 3186, 297, 5837], [ 1738, 29920, 7355, ..., 13, 29871, 3776]]) [2024-09-16 23:09:08.966648][INFO][training:1083] - iteration=1 [2/8]: (torch.Size([4, 4097])) _tokens[:10]=tensor([[ 2020, 306, 1016, ..., 322, 920, 372], [ 5921, 1749, 7306, ..., 19252, 297, 5664], [ 970, 770, 28547, ..., 970, 894, 2577], [ 1907, 363, 14188, ..., 756, 3646, 287]]) [2024-09-16 23:09:08.969989][INFO][training:1083] - iteration=1 [3/8]: (torch.Size([4, 4097])) _tokens[:10]=tensor([[ 715, 25392, 3104, ..., 289, 5761, 616], [ 426, 13, 9651, ..., 9651, 1815, 22603], [ 7714, 1213, 13, ..., 13, 29876, 457], [29889, 28663, 1230, ..., 1546, 278, 6586]]) [2024-09-16 23:09:08.990736][INFO][training:1083] - iteration=1 [4/8]: (torch.Size([4, 4097])) _tokens[:10]=tensor([[29929, 13, 13, ..., 10739, 4770, 11277], [ 4528, 304, 2367, ..., 2501, 385, 4203], [ 869, 319, 794, ..., 3158, 29889, 3115], [ 592, 260, 4125, ..., 284, 1135, 18655]]) [2024-09-16 23:09:08.993101][INFO][training:1083] - iteration=1 [5/8]: (torch.Size([4, 4097])) _tokens[:10]=tensor([[14338, 25323, 3321, ..., 5607, 1806, 1164], [ 322, 278, 15352, ..., 6462, 313, 1552], [25738, 714, 29889, ..., 29915, 29879, 24842], [ 5122, 399, 29889, ..., 29947, 7284, 2305]]) [2024-09-16 23:09:09.036896][INFO][training:1083] - iteration=1 [6/8]: (torch.Size([4, 4097])) _tokens[:10]=tensor([[ 367, 19310, 1891, ..., 2408, 292, 263], [ 470, 3307, 5713, ..., 568, 2594, 19385], [29953, 29905, 1631, ..., 1118, 343, 29897], [10261, 373, 5490, ..., 511, 297, 1760]]) [2024-09-16 23:09:09.039401][INFO][training:1083] - iteration=1 [7/8]: (torch.Size([4, 4097])) _tokens[:10]=tensor([[ 1006, 326, 29901, ..., 14834, 6694, 9595], [12058, 5446, 29892, ..., 29889, 8246, 3310], [ 7483, 310, 278, ..., 402, 9851, 4423], [ 8041, 813, 322, ..., 3303, 3900, 393]]) -
Iteration 2:
[2024-09-16 23:09:09.050766][INFO][training_log:661] - iteration= 2/ 635782 | consumed_samples= 1536 | consumed_tokens= 6291456 | elapsed_time_per_iteration_ms=106.8 | learning_rate=1.88744e-08 | global_batch_size= 768 | loss_scale=1.0 | grad_norm=6.363 | actual_seqlen= 4096 | number_of_skipped_iterations= 1 | number_of_nan_iterations= 0 | samples_per_second=7190.781 | tokens_per_gpu_per_second_tgs=1227226.651 | [LM]TFLOPs=5620.92 | [DS]TFLOPs=7247.49 | [2024-09-16 23:09:09.055864][INFO][training:1069] - Caught 3 in 'ranges_to_skip', skipping! [2024-09-16 23:09:09.057929][INFO][training:1082] - torch.Size([4, 4097]), len(train_data_iterator)=490723200 [2024-09-16 23:09:09.059118][INFO][training:1083] - iteration=2 [0/8]: (torch.Size([4, 4097])) _tokens[:10]=tensor([[ 1858, 3851, 29889, ..., 500, 13, 13], [ 349, 6156, 1650, ..., 5806, 28557, 3519], [16554, 304, 1653, ..., 322, 6934, 14722], [ 4955, 310, 10465, ..., 1438, 3841, 29892]]) [2024-09-16 23:09:09.061999][INFO][training:1083] - iteration=2 [1/8]: (torch.Size([4, 4097])) _tokens[:10]=tensor([[ 363, 1302, 16453, ..., 7967, 29891, 484], [ 367, 766, 4752, ..., 1, 29871, 30143], [29899, 855, 1503, ..., 3786, 29892, 5100], [ 465, 1974, 289, ..., 21588, 533, 304]]) [2024-09-16 23:09:09.065494][INFO][training:1083] - iteration=2 [2/8]: (torch.Size([4, 4097])) _tokens[:10]=tensor([[29889, 13, 4806, ..., 3086, 26040, 9220], [ 293, 7207, 355, ..., 18131, 520, 1247], [ 8619, 29889, 29871, ..., 304, 10029, 266], [ 363, 15202, 29892, ..., 482, 17162, 19104]]) [2024-09-16 23:09:09.069035][INFO][training:1083] - iteration=2 [3/8]: (torch.Size([4, 4097])) _tokens[:10]=tensor([[25561, 411, 278, ..., 297, 2898, 26163], [22574, 2607, 18134, ..., 13, 4706, 500], [20190, 24820, 1623, ..., 310, 901, 29892], [29892, 1951, 4486, ..., 869, 887, 30010]]) [2024-09-16 23:09:09.072577][INFO][training:1083] - iteration=2 [4/8]: (torch.Size([4, 4097])) _tokens[:10]=tensor([[ 5371, 22417, 29892, ..., 13, 6716, 901], [ 353, 1565, 29936, ..., 29878, 3567, 7196], [17296, 338, 1985, ..., 3741, 9089, 422], [ 694, 13331, 310, ..., 21180, 29892, 607]]) [2024-09-16 23:09:09.075789][INFO][training:1083] - iteration=2 [5/8]: (torch.Size([4, 4097])) _tokens[:10]=tensor([[ 292, 8818, 267, ..., 29892, 11275, 7407], [ 1870, 29897, 13, ..., 2697, 29901, 13], [29913, 338, 263, ..., 29892, 591, 3394], [ 2253, 472, 1554, ..., 982, 304, 376]]) [2024-09-16 23:09:09.079052][INFO][training:1083] - iteration=2 [6/8]: (torch.Size([4, 4097])) _tokens[:10]=tensor([[ 391, 2598, 29883, ..., 22629, 346, 440], [29871, 29896, 29906, ..., 407, 583, 2833], [ 4262, 1836, 13, ..., 310, 263, 10608], [ 1199, 411, 24770, ..., 272, 2153, 29889]]) [2024-09-16 23:09:09.082739][INFO][training:1083] - iteration=2 [7/8]: (torch.Size([4, 4097])) _tokens[:10]=tensor([[ 620, 20503, 428, ..., 297, 1009, 9443], [ 950, 25078, 892, ..., 408, 10636, 284], [ 1012, 2003, 364, ..., 7313, 29912, 19303], [29906, 29892, 29945, ..., 967, 26414, 472]]) -
Iteration 3:
[2024-09-16 23:09:09.135651][INFO][training_log:661] - iteration= 3/ 635782 | consumed_samples= 2304 | consumed_tokens= 9437184 | elapsed_time_per_iteration_ms=84.7 | learning_rate=2.83116e-08 | global_batch_size= 768 | loss_scale=1.0 | grad_norm=6.363 | actual_seqlen= 4096 | number_of_skipped_iterations= 1 | number_of_nan_iterations= 0 | samples_per_second=9070.783 | tokens_per_gpu_per_second_tgs=1548080.271 | [LM]TFLOPs=7090.49 | [DS]TFLOPs=9142.31 | [2024-09-16 23:09:09.143511][INFO][pretrain_gpt_alcf:198] - args.iteration=3: data['text'][:10]=tensor([[496, 313, 29941, ..., 1316, 408, 4857], [29899, 3204, 29889, ..., 1074, 330, 2547], [29916, 29900, 29946, ..., 18455, 29889, 4002], [26406, 338, 1641, ..., 670, 1914, 6900]]) [2024-09-16 23:09:09.971988][INFO][pretrain_gpt_alcf:198] - args.iteration=3: data['text'][:10]=tensor([[29945, 29900, 867, ..., 7601, 12091, 310], [ 975, 29871, 29896, ..., 3573, 825, 306], [29906, 29900, 4638, ..., 29227, 23145, 29892], [ 278, 14368, 322, ..., 14909, 29936, 25913]]) [2024-09-16 23:09:10.843966][INFO][pretrain_gpt_alcf:198] - args.iteration=3: data['text'][:10]=tensor([[988, 306, 1033, ..., 437, 408, 1532], [ 450, 10317, 310, ..., 322, 752, 13036], [11405, 8020, 29889, ..., 471, 18096, 287], [ 288, 3594, 19284, ..., 910, 338, 385]]) [2024-09-16 23:09:11.715513][INFO][pretrain_gpt_alcf:198] - args.iteration=3: data['text'][:10]=tensor([[322, 15151, 29946, ..., 11648, 1497, 29889], [24233, 362, 467, ..., 4513, 1353, 322], [ 3311, 13605, 29912, ..., 945, 29899, 4181], [ 1951, 366, 508, ..., 6589, 491, 777]]) [2024-09-16 23:09:12.584136][INFO][pretrain_gpt_alcf:198] - args.iteration=3: data['text'][:10]=tensor([[29889, 29900, 13, ..., 6017, 424, 1711], [ 297, 5500, 1489, ..., 310, 3802, 7875], [ 8078, 5314, 515, ..., 373, 278, 6991], [13763, 6204, 6359, ..., 4706, 2024, 1347]]) [2024-09-16 23:09:13.450767][INFO][pretrain_gpt_alcf:198] - args.iteration=3: data['text'][:10]=tensor([[13, 4806, 3512, ..., 278, 7824, 6438], [ 2294, 938, 903, ..., 4537, 3047, 449], [ 1230, 4123, 767, ..., 310, 963, 21003], [ 1152, 2319, 10365, ..., 367, 14040, 363]]) [2024-09-16 23:09:14.317517][INFO][pretrain_gpt_alcf:198] - args.iteration=3: data['text'][:10]=tensor([[27297, 29924, 801, ..., 28947, 29892, 470], [12542, 5568, 703, ..., 426, 13, 4706], [ 6907, 800, 322, ..., 29892, 1661, 30304], [29900, 13630, 293, ..., 26552, 363, 975]]) [2024-09-16 23:09:15.187191][INFO][pretrain_gpt_alcf:198] - args.iteration=3: data['text'][:10]=tensor([[13, 29946, 29953, ..., 29953, 29945, 29871], [ 1283, 16578, 1156, ..., 408, 2215, 408], [29906, 4229, 7671, ..., 13, 1576, 1014], [ 526, 2898, 304, ..., 471, 4802, 29991]])
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