diff --git "a/attnserver.run_attnserver.slurm.sh.343240.err.log" "b/attnserver.run_attnserver.slurm.sh.343240.err.log" --- "a/attnserver.run_attnserver.slurm.sh.343240.err.log" +++ "b/attnserver.run_attnserver.slurm.sh.343240.err.log" @@ -9934,3 +9934,2090 @@ W0621 22:08:09.690000 1060210 site-packages/torch/distributed/run.py:766] ****** [rank11]:[W621 22:08:45.286598082 ProcessGroupNCCL.cpp:1476] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator()) [rank9]:[W621 22:08:45.337017583 ProcessGroupNCCL.cpp:1476] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator()) [rank3]:[W621 22:08:45.238234084 ProcessGroupNCCL.cpp:1476] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator()) +W0621 22:08:46.494000 1983977 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 1984046 closing signal SIGTERM +W0621 22:08:46.497000 1983977 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 1984047 closing signal SIGTERM +W0621 22:08:46.497000 1983977 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 1984048 closing signal SIGTERM +W0621 22:08:46.514000 1983977 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 1984049 closing signal SIGTERM +W0621 22:08:46.515000 1983977 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 1984050 closing signal SIGTERM +W0621 22:08:46.519000 1983977 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 1984051 closing signal SIGTERM +W0621 22:08:46.520000 1983977 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 1984052 closing signal SIGTERM +W0621 22:08:46.656000 1060210 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 1060282 closing signal SIGTERM +W0621 22:08:46.659000 1060210 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 1060283 closing signal SIGTERM +W0621 22:08:46.660000 1060210 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 1060284 closing signal SIGTERM +W0621 22:08:46.663000 1060210 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 1060285 closing signal SIGTERM +W0621 22:08:46.663000 1060210 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 1060286 closing signal SIGTERM +W0621 22:08:46.666000 1060210 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 1060287 closing signal SIGTERM +W0621 22:08:46.667000 1060210 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 1060288 closing signal SIGTERM +E0621 22:08:47.000000 1983977 site-packages/torch/distributed/elastic/multiprocessing/api.py:874] failed (exitcode: 1) local_rank: 7 (pid: 1984053) of binary: /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/python3 +Traceback (most recent call last): + File "", line 198, in _run_module_as_main + File "", line 88, in _run_code + File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launch.py", line 207, in + main() + File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/typing_extensions.py", line 3253, in wrapper + return arg(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^ + File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launch.py", line 203, in main + launch(args) + File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launch.py", line 188, in launch + run(args) + File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/run.py", line 883, in run + elastic_launch( + File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launcher/api.py", line 139, in __call__ + return launch_agent(self._config, self._entrypoint, list(args)) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launcher/api.py", line 270, in launch_agent + raise ChildFailedError( +torch.distributed.elastic.multiprocessing.errors.ChildFailedError: +============================================================ +./pretrain_gpt_profile.py FAILED +------------------------------------------------------------ +Failures: + +------------------------------------------------------------ +Root Cause (first observed failure): +[0]: + time : 2025-06-21_22:08:46 + host : fs-mbz-gpu-286 + rank : 15 (local_rank: 7) + exitcode : 1 (pid: 1984053) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +============================================================ +E0621 22:08:47.053000 1060210 site-packages/torch/distributed/elastic/multiprocessing/api.py:874] failed (exitcode: 1) local_rank: 7 (pid: 1060289) of binary: /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/python3 +Traceback (most recent call last): + File "", line 198, in _run_module_as_main + File "", line 88, in _run_code + File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launch.py", line 207, in + main() + File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/typing_extensions.py", line 3253, in wrapper + return arg(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^ + File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launch.py", line 203, in main + launch(args) + File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launch.py", line 188, in launch + run(args) + File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/run.py", line 883, in run + elastic_launch( + File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launcher/api.py", line 139, in __call__ + return launch_agent(self._config, self._entrypoint, list(args)) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launcher/api.py", line 270, in launch_agent + raise ChildFailedError( +torch.distributed.elastic.multiprocessing.errors.ChildFailedError: +============================================================ +./pretrain_gpt_profile.py FAILED +------------------------------------------------------------ +Failures: + +------------------------------------------------------------ +Root Cause (first observed failure): +[0]: + time : 2025-06-21_22:08:46 + host : fs-mbz-gpu-239 + rank : 7 (local_rank: 7) + exitcode : 1 (pid: 1060289) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +============================================================ ++ set +x ++ set +x ++ for ctx_length in 1024 2048 4096 8192 12288 16384 24576 32768 40960 49152 65536 81920 98304 131072 ++ export PROF_CTX_LENGTH=81920 ++ PROF_CTX_LENGTH=81920 ++ name='/mnt/sharefs/users/hao.zhang/junda/megatron-prof-data--unstable-v5/mytrace.L81920*tp2.cp8.bs8.json' ++ '[' -f '/mnt/sharefs/users/hao.zhang/junda/megatron-prof-data--unstable-v5/mytrace.L81920*tp2.cp8.bs8.json' ']' ++ echo 'Running ctx_length=81920, TP_SIZE=2, CP_SIZE=8, BATCH_SIZE=8' ++ srun bash ./attnserver.sh ++ which python3 ++ python3 -m torch.distributed.launch --nproc_per_node 8 --nnodes 2 --node_rank 0 --rdzv_id 343240 --rdzv_backend c10d --rdzv_endpoint fs-mbz-gpu-239:29500 ./pretrain_gpt_profile.py --tensor-model-parallel-size 2 --context-parallel-size 8 --num-layers 2 --hidden-size 4096 --num-attention-heads 64 --group-query-attention --num-query-groups 16 --seq-length 81920 --max-position-embeddings 81920 --micro-batch-size 1 --global-batch-size 1 --lr 0.0005 --train-iters 10 --lr-decay-iters 150000 --lr-decay-style cosine --lr-warmup-iters 2 --weight-decay .1 --adam-beta2 .999 --fp16 --log-interval 1 --save-interval 16 --eval-interval 16 --eval-iters 1 --vocab-file vocab.json --merge-file merges.txt --save gpt-checkpoint --load gpt-checkpoint --logging-level 0 --mock-data --tensorboard-dir tensorboard-logs/ ++ which python3 ++ python3 -m torch.distributed.launch --nproc_per_node 8 --nnodes 2 --node_rank 1 --rdzv_id 343240 --rdzv_backend c10d --rdzv_endpoint fs-mbz-gpu-239:29500 ./pretrain_gpt_profile.py --tensor-model-parallel-size 2 --context-parallel-size 8 --num-layers 2 --hidden-size 4096 --num-attention-heads 64 --group-query-attention --num-query-groups 16 --seq-length 81920 --max-position-embeddings 81920 --micro-batch-size 1 --global-batch-size 1 --lr 0.0005 --train-iters 10 --lr-decay-iters 150000 --lr-decay-style cosine --lr-warmup-iters 2 --weight-decay .1 --adam-beta2 .999 --fp16 --log-interval 1 --save-interval 16 --eval-interval 16 --eval-iters 1 --vocab-file vocab.json --merge-file merges.txt --save gpt-checkpoint --load gpt-checkpoint --logging-level 0 --mock-data --tensorboard-dir tensorboard-logs/ +/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launch.py:207: FutureWarning: The module torch.distributed.launch is deprecated +and will be removed in future. Use torchrun. +Note that --use-env is set by default in torchrun. +If your script expects `--local-rank` argument to be set, please +change it to read from `os.environ['LOCAL_RANK']` instead. See +https://pytorch.org/docs/stable/distributed.html#launch-utility for +further instructions + + main() +W0621 22:08:51.419000 1985777 site-packages/torch/distributed/run.py:766] +W0621 22:08:51.419000 1985777 site-packages/torch/distributed/run.py:766] ***************************************** +W0621 22:08:51.419000 1985777 site-packages/torch/distributed/run.py:766] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. +W0621 22:08:51.419000 1985777 site-packages/torch/distributed/run.py:766] ***************************************** +/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launch.py:207: FutureWarning: The module torch.distributed.launch is deprecated +and will be removed in future. Use torchrun. +Note that --use-env is set by default in torchrun. +If your script expects `--local-rank` argument to be set, please +change it to read from `os.environ['LOCAL_RANK']` instead. See +https://pytorch.org/docs/stable/distributed.html#launch-utility for +further instructions + + main() +W0621 22:08:51.436000 1062080 site-packages/torch/distributed/run.py:766] +W0621 22:08:51.436000 1062080 site-packages/torch/distributed/run.py:766] ***************************************** +W0621 22:08:51.436000 1062080 site-packages/torch/distributed/run.py:766] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. +W0621 22:08:51.436000 1062080 site-packages/torch/distributed/run.py:766] ***************************************** +[rank7]:[W621 22:09:14.145396059 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 7] using GPU 7 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device. +[rank5]:[W621 22:09:14.145394759 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 5] using GPU 5 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device. +[rank4]:[W621 22:09:14.145393717 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 4] using GPU 4 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device. +[rank1]:[W621 22:09:14.145596650 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 1] using GPU 1 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device. +[rank6]:[W621 22:09:14.145941938 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 6] using GPU 6 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device. +[rank13]:[W621 22:09:14.277615310 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 13] using GPU 5 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device. +[rank2]:[W621 22:09:14.150580832 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 2] using GPU 2 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device. +[rank3]:[W621 22:09:14.151479228 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 3] using GPU 3 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device. +[rank15]:[W621 22:09:14.288045524 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 15] using GPU 7 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device. +[rank14]:[W621 22:09:14.288092885 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 14] using GPU 6 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device. +[rank9]:[W621 22:09:14.288403518 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 9] using GPU 1 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device. +[rank10]:[W621 22:09:14.288476292 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 10] using GPU 2 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device. +[rank12]:[W621 22:09:14.288561958 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 12] using GPU 4 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device. +[rank11]:[W621 22:09:14.288578883 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 11] using GPU 3 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device. +[rank8]:[W621 22:09:14.370329084 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 8] using GPU 0 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device. +[rank0]:[W621 22:09:14.280953302 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 0] using GPU 0 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device. +/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly. + warnings.warn( +/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly. + warnings.warn( +/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly. + warnings.warn( +/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly. + warnings.warn( +/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly. + warnings.warn( +/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly. + warnings.warn( +/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly. + warnings.warn( +/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly. + warnings.warn( +/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly. + warnings.warn( +/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly. + warnings.warn( +/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly. + warnings.warn( +/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly. + warnings.warn( +/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly. + warnings.warn( +/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly. + warnings.warn( +/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly. + warnings.warn( +/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly. + warnings.warn( +/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect. + warnings.warn( +/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect. + warnings.warn( +/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect. + warnings.warn( +/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect. + warnings.warn( +/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect. + warnings.warn( +/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect. + warnings.warn( +/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect. + warnings.warn( +/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect. + warnings.warn( +/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect. + warnings.warn( +/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect. + warnings.warn( +/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect. + warnings.warn( +/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect. + warnings.warn( +/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect. + warnings.warn( +/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect. + warnings.warn( +/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect. + warnings.warn( +/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect. + warnings.warn( +[rank9]: Traceback (most recent call last): +[rank9]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in +[rank9]: pretrain( +[rank9]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain +[rank9]: iteration, num_floating_point_operations_so_far = train( +[rank9]: ^^^^^^ +[rank9]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train +[rank9]: ) = train_step( +[rank9]: ^^^^^^^^^^^ +[rank9]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step +[rank9]: losses_reduced = forward_backward_func( +[rank9]: ^^^^^^^^^^^^^^^^^^^^^^ +[rank9]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining +[rank9]: output_tensor, num_tokens = forward_step( +[rank9]: ^^^^^^^^^^^^^ +[rank9]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step +[rank9]: output_tensor, loss_func = forward_step_func(data_iterator, model) +[rank9]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank9]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step +[rank9]: (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator) +[rank9]: ^^^^^^^^^^^^^^^^^^^^^^^^ +[rank9]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch +[rank9]: batch = next(global_batches) +[rank9]: ^^^^^^^^^^^^^^^^^^^^ +[rank9]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches +[rank9]: attention_mask = torch.ones( +[rank9]: ^^^^^^^^^^^ +[rank9]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 3200.00 GiB. GPU 1 has a total capacity of 139.81 GiB of which 133.47 GiB is free. Including non-PyTorch memory, this process has 6.33 GiB memory in use. Of the allocated memory 4.68 GiB is allocated by PyTorch, and 174.39 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) +[rank15]: Traceback (most recent call last): +[rank15]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in +[rank15]: pretrain( +[rank15]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain +[rank15]: iteration, num_floating_point_operations_so_far = train( +[rank15]: ^^^^^^ +[rank15]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train +[rank15]: ) = train_step( +[rank15]: ^^^^^^^^^^^ +[rank15]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step +[rank15]: losses_reduced = forward_backward_func( +[rank15]: ^^^^^^^^^^^^^^^^^^^^^^ +[rank15]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining +[rank15]: output_tensor, num_tokens = forward_step( +[rank15]: ^^^^^^^^^^^^^ +[rank15]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step +[rank15]: output_tensor, loss_func = forward_step_func(data_iterator, model) +[rank15]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank15]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step +[rank15]: (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator) +[rank15]: ^^^^^^^^^^^^^^^^^^^^^^^^ +[rank15]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch +[rank15]: batch = next(global_batches) +[rank15]: ^^^^^^^^^^^^^^^^^^^^ +[rank15]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches +[rank15]: attention_mask = torch.ones( +[rank15]: ^^^^^^^^^^^ +[rank15]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 3200.00 GiB. GPU 7 has a total capacity of 139.81 GiB of which 133.47 GiB is free. Including non-PyTorch memory, this process has 6.33 GiB memory in use. Of the allocated memory 4.68 GiB is allocated by PyTorch, and 174.39 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) +[rank13]: Traceback (most recent call last): +[rank13]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in +[rank13]: pretrain( +[rank13]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain +[rank13]: iteration, num_floating_point_operations_so_far = train( +[rank13]: ^^^^^^ +[rank13]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train +[rank13]: ) = train_step( +[rank13]: ^^^^^^^^^^^ +[rank13]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step +[rank13]: losses_reduced = forward_backward_func( +[rank13]: ^^^^^^^^^^^^^^^^^^^^^^ +[rank13]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining +[rank13]: output_tensor, num_tokens = forward_step( +[rank13]: ^^^^^^^^^^^^^ +[rank13]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step +[rank3]: Traceback (most recent call last): +[rank3]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in +[rank3]: pretrain( +[rank3]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain +[rank3]: iteration, num_floating_point_operations_so_far = train( +[rank3]: ^^^^^^ +[rank3]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train +[rank3]: ) = train_step( +[rank3]: ^^^^^^^^^^^ +[rank3]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step +[rank3]: losses_reduced = forward_backward_func( +[rank3]: ^^^^^^^^^^^^^^^^^^^^^^ +[rank3]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining +[rank13]: output_tensor, loss_func = forward_step_func(data_iterator, model) +[rank13]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank13]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step +[rank13]: (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator) +[rank13]: ^^^^^^^^^^^^^^^^^^^^^^^^ +[rank13]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch +[rank13]: batch = next(global_batches) +[rank13]: ^^^^^^^^^^^^^^^^^^^^ +[rank13]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches +[rank13]: attention_mask = torch.ones( +[rank13]: ^^^^^^^^^^^ +[rank3]: output_tensor, num_tokens = forward_step( +[rank3]: ^^^^^^^^^^^^^ +[rank3]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step +[rank3]: output_tensor, loss_func = forward_step_func(data_iterator, model) +[rank3]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank3]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step +[rank3]: (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator) +[rank3]: ^^^^^^^^^^^^^^^^^^^^^^^^ +[rank3]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch +[rank3]: batch = next(global_batches) +[rank3]: ^^^^^^^^^^^^^^^^^^^^ +[rank13]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 3200.00 GiB. GPU 5 has a total capacity of 139.81 GiB of which 133.47 GiB is free. Including non-PyTorch memory, this process has 6.33 GiB memory in use. Of the allocated memory 4.68 GiB is allocated by PyTorch, and 174.39 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) +[rank3]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches +[rank3]: attention_mask = torch.ones( +[rank3]: ^^^^^^^^^^^ +[rank3]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 3200.00 GiB. GPU 3 has a total capacity of 139.81 GiB of which 133.46 GiB is free. Including non-PyTorch memory, this process has 6.35 GiB memory in use. Of the allocated memory 4.68 GiB is allocated by PyTorch, and 174.39 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) +[rank11]: Traceback (most recent call last): +[rank11]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in +[rank11]: pretrain( +[rank11]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain +[rank11]: iteration, num_floating_point_operations_so_far = train( +[rank11]: ^^^^^^ +[rank11]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train +[rank11]: ) = train_step( +[rank11]: ^^^^^^^^^^^ +[rank11]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step +[rank11]: losses_reduced = forward_backward_func( +[rank11]: ^^^^^^^^^^^^^^^^^^^^^^ +[rank1]: Traceback (most recent call last): +[rank1]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in +[rank1]: pretrain( +[rank1]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain +[rank1]: iteration, num_floating_point_operations_so_far = train( +[rank1]: ^^^^^^ +[rank1]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train +[rank1]: ) = train_step( +[rank1]: ^^^^^^^^^^^ +[rank1]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step +[rank1]: losses_reduced = forward_backward_func( +[rank1]: ^^^^^^^^^^^^^^^^^^^^^^ +[rank1]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining +[rank11]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining +[rank11]: output_tensor, num_tokens = forward_step( +[rank11]: ^^^^^^^^^^^^^ +[rank11]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step +[rank11]: output_tensor, loss_func = forward_step_func(data_iterator, model) +[rank11]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank11]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step +[rank11]: (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator) +[rank11]: ^^^^^^^^^^^^^^^^^^^^^^^^ +[rank1]: output_tensor, num_tokens = forward_step( +[rank1]: ^^^^^^^^^^^^^ +[rank1]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step +[rank1]: output_tensor, loss_func = forward_step_func(data_iterator, model) +[rank1]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank1]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step +[rank1]: (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator) +[rank1]: ^^^^^^^^^^^^^^^^^^^^^^^^ +[rank1]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch +[rank1]: batch = next(global_batches) +[rank1]: ^^^^^^^^^^^^^^^^^^^^ +[rank11]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch +[rank11]: batch = next(global_batches) +[rank11]: ^^^^^^^^^^^^^^^^^^^^ +[rank11]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches +[rank11]: attention_mask = torch.ones( +[rank11]: ^^^^^^^^^^^ +[rank11]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 3200.00 GiB. GPU 3 has a total capacity of 139.81 GiB of which 133.47 GiB is free. Including non-PyTorch memory, this process has 6.33 GiB memory in use. Of the allocated memory 4.68 GiB is allocated by PyTorch, and 174.39 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) +[rank1]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches +[rank1]: attention_mask = torch.ones( +[rank1]: ^^^^^^^^^^^ +[rank1]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 3200.00 GiB. GPU 1 has a total capacity of 139.81 GiB of which 133.46 GiB is free. Including non-PyTorch memory, this process has 6.35 GiB memory in use. Of the allocated memory 4.68 GiB is allocated by PyTorch, and 174.39 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) +[rank7]: Traceback (most recent call last): +[rank7]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in +[rank7]: pretrain( +[rank8]: Traceback (most recent call last): +[rank8]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in +[rank8]: pretrain( +[rank8]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain +[rank8]: iteration, num_floating_point_operations_so_far = train( +[rank8]: ^^^^^^ +[rank8]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train +[rank8]: ) = train_step( +[rank8]: ^^^^^^^^^^^ +[rank8]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step +[rank8]: losses_reduced = forward_backward_func( +[rank8]: ^^^^^^^^^^^^^^^^^^^^^^ +[rank8]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining +[rank7]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain +[rank7]: iteration, num_floating_point_operations_so_far = train( +[rank7]: ^^^^^^ +[rank7]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train +[rank7]: ) = train_step( +[rank7]: ^^^^^^^^^^^ +[rank7]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step +[rank7]: losses_reduced = forward_backward_func( +[rank7]: ^^^^^^^^^^^^^^^^^^^^^^ +[rank7]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining +[rank7]: output_tensor, num_tokens = forward_step( +[rank7]: ^^^^^^^^^^^^^ +[rank8]: output_tensor, num_tokens = forward_step( +[rank8]: ^^^^^^^^^^^^^ +[rank8]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step +[rank8]: output_tensor, loss_func = forward_step_func(data_iterator, model) +[rank8]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank8]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step +[rank8]: (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator) +[rank8]: ^^^^^^^^^^^^^^^^^^^^^^^^ +[rank8]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch +[rank8]: batch = next(global_batches) +[rank8]: ^^^^^^^^^^^^^^^^^^^^ +[rank7]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step +[rank8]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches +[rank8]: attention_mask = torch.ones( +[rank8]: ^^^^^^^^^^^ +[rank8]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 3200.00 GiB. GPU 0 has a total capacity of 139.81 GiB of which 133.46 GiB is free. Including non-PyTorch memory, this process has 6.35 GiB memory in use. Of the allocated memory 4.68 GiB is allocated by PyTorch, and 174.39 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) +[rank7]: output_tensor, loss_func = forward_step_func(data_iterator, model) +[rank7]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank7]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step +[rank7]: (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator) +[rank7]: ^^^^^^^^^^^^^^^^^^^^^^^^ +[rank7]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch +[rank7]: batch = next(global_batches) +[rank7]: ^^^^^^^^^^^^^^^^^^^^ +[rank7]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches +[rank7]: attention_mask = torch.ones( +[rank7]: ^^^^^^^^^^^ +[rank14]: Traceback (most recent call last): +[rank14]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in +[rank14]: pretrain( +[rank14]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain +[rank14]: iteration, num_floating_point_operations_so_far = train( +[rank14]: ^^^^^^ +[rank14]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train +[rank14]: ) = train_step( +[rank14]: ^^^^^^^^^^^ +[rank14]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step +[rank14]: losses_reduced = forward_backward_func( +[rank14]: ^^^^^^^^^^^^^^^^^^^^^^ +[rank7]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 3200.00 GiB. GPU 7 has a total capacity of 139.81 GiB of which 133.46 GiB is free. Including non-PyTorch memory, this process has 6.35 GiB memory in use. Of the allocated memory 4.68 GiB is allocated by PyTorch, and 174.39 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) +[rank14]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining +[rank14]: output_tensor, num_tokens = forward_step( +[rank14]: ^^^^^^^^^^^^^ +[rank14]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step +[rank14]: output_tensor, loss_func = forward_step_func(data_iterator, model) +[rank14]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank14]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step +[rank14]: (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator) +[rank14]: ^^^^^^^^^^^^^^^^^^^^^^^^ +[rank14]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch +[rank14]: batch = next(global_batches) +[rank14]: ^^^^^^^^^^^^^^^^^^^^ +[rank14]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches +[rank14]: attention_mask = torch.ones( +[rank14]: ^^^^^^^^^^^ +[rank14]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 3200.00 GiB. GPU 6 has a total capacity of 139.81 GiB of which 133.46 GiB is free. Including non-PyTorch memory, this process has 6.35 GiB memory in use. Of the allocated memory 4.68 GiB is allocated by PyTorch, and 174.39 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) +[rank10]: Traceback (most recent call last): +[rank10]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in +[rank10]: pretrain( +[rank10]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain +[rank10]: iteration, num_floating_point_operations_so_far = train( +[rank10]: ^^^^^^ +[rank10]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train +[rank10]: ) = train_step( +[rank10]: ^^^^^^^^^^^ +[rank10]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step +[rank10]: losses_reduced = forward_backward_func( +[rank10]: ^^^^^^^^^^^^^^^^^^^^^^ +[rank10]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining +[rank10]: output_tensor, num_tokens = forward_step( +[rank10]: ^^^^^^^^^^^^^ +[rank10]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step +[rank10]: output_tensor, loss_func = forward_step_func(data_iterator, model) +[rank10]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank10]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step +[rank10]: (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator) +[rank10]: ^^^^^^^^^^^^^^^^^^^^^^^^ +[rank4]: Traceback (most recent call last): +[rank4]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in +[rank4]: pretrain( +[rank4]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain +[rank4]: iteration, num_floating_point_operations_so_far = train( +[rank4]: ^^^^^^ +[rank4]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train +[rank4]: ) = train_step( +[rank4]: ^^^^^^^^^^^ +[rank4]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step +[rank4]: losses_reduced = forward_backward_func( +[rank4]: ^^^^^^^^^^^^^^^^^^^^^^ +[rank4]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining +[rank10]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch +[rank10]: batch = next(global_batches) +[rank10]: ^^^^^^^^^^^^^^^^^^^^ +[rank10]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches +[rank10]: attention_mask = torch.ones( +[rank10]: ^^^^^^^^^^^ +[rank10]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 3200.00 GiB. GPU 2 has a total capacity of 139.81 GiB of which 133.46 GiB is free. Including non-PyTorch memory, this process has 6.35 GiB memory in use. Of the allocated memory 4.68 GiB is allocated by PyTorch, and 174.39 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) +[rank4]: output_tensor, num_tokens = forward_step( +[rank4]: ^^^^^^^^^^^^^ +[rank4]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step +[rank4]: output_tensor, loss_func = forward_step_func(data_iterator, model) +[rank4]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank4]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step +[rank4]: (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator) +[rank4]: ^^^^^^^^^^^^^^^^^^^^^^^^ +[rank4]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch +[rank4]: batch = next(global_batches) +[rank4]: ^^^^^^^^^^^^^^^^^^^^ +[rank12]: Traceback (most recent call last): +[rank12]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in +[rank12]: pretrain( +[rank12]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain +[rank12]: iteration, num_floating_point_operations_so_far = train( +[rank12]: ^^^^^^ +[rank12]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train +[rank12]: ) = train_step( +[rank12]: ^^^^^^^^^^^ +[rank12]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step +[rank12]: losses_reduced = forward_backward_func( +[rank12]: ^^^^^^^^^^^^^^^^^^^^^^ +[rank4]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches +[rank4]: attention_mask = torch.ones( +[rank4]: ^^^^^^^^^^^ +[rank4]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 3200.00 GiB. GPU 4 has a total capacity of 139.81 GiB of which 133.47 GiB is free. Including non-PyTorch memory, this process has 6.33 GiB memory in use. Of the allocated memory 4.68 GiB is allocated by PyTorch, and 174.39 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) +[rank12]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining +[rank12]: output_tensor, num_tokens = forward_step( +[rank12]: ^^^^^^^^^^^^^ +[rank12]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step +[rank12]: output_tensor, loss_func = forward_step_func(data_iterator, model) +[rank12]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank12]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step +[rank12]: (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator) +[rank12]: ^^^^^^^^^^^^^^^^^^^^^^^^ +[rank12]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch +[rank12]: batch = next(global_batches) +[rank12]: ^^^^^^^^^^^^^^^^^^^^ +[rank12]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches +[rank12]: attention_mask = torch.ones( +[rank12]: ^^^^^^^^^^^ +[rank12]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 3200.00 GiB. GPU 4 has a total capacity of 139.81 GiB of which 133.46 GiB is free. Including non-PyTorch memory, this process has 6.35 GiB memory in use. Of the allocated memory 4.68 GiB is allocated by PyTorch, and 174.39 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) +[rank5]: Traceback (most recent call last): +[rank5]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in +[rank5]: pretrain( +[rank5]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain +[rank5]: iteration, num_floating_point_operations_so_far = train( +[rank5]: ^^^^^^ +[rank5]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train +[rank5]: ) = train_step( +[rank5]: ^^^^^^^^^^^ +[rank5]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step +[rank5]: losses_reduced = forward_backward_func( +[rank5]: ^^^^^^^^^^^^^^^^^^^^^^ +[rank5]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining +[rank5]: output_tensor, num_tokens = forward_step( +[rank5]: ^^^^^^^^^^^^^ +[rank5]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step +[rank5]: output_tensor, loss_func = forward_step_func(data_iterator, model) +[rank5]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank5]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step +[rank5]: (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator) +[rank5]: ^^^^^^^^^^^^^^^^^^^^^^^^ +[rank5]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch +[rank5]: batch = next(global_batches) +[rank5]: ^^^^^^^^^^^^^^^^^^^^ +[rank5]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches +[rank5]: attention_mask = torch.ones( +[rank5]: ^^^^^^^^^^^ +[rank5]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 3200.00 GiB. GPU 5 has a total capacity of 139.81 GiB of which 133.46 GiB is free. Including non-PyTorch memory, this process has 6.35 GiB memory in use. Of the allocated memory 4.68 GiB is allocated by PyTorch, and 174.39 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) +[rank0]: Traceback (most recent call last): +[rank0]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in +[rank0]: pretrain( +[rank0]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain +[rank0]: iteration, num_floating_point_operations_so_far = train( +[rank0]: ^^^^^^ +[rank0]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train +[rank0]: ) = train_step( +[rank0]: ^^^^^^^^^^^ +[rank0]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step +[rank0]: losses_reduced = forward_backward_func( +[rank0]: ^^^^^^^^^^^^^^^^^^^^^^ +[rank0]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining +[rank0]: output_tensor, num_tokens = forward_step( +[rank0]: ^^^^^^^^^^^^^ +[rank0]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step +[rank0]: output_tensor, loss_func = forward_step_func(data_iterator, model) +[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank0]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step +[rank0]: (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator) +[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^ +[rank0]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch +[rank0]: batch = next(global_batches) +[rank0]: ^^^^^^^^^^^^^^^^^^^^ +[rank0]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches +[rank0]: attention_mask = torch.ones( +[rank0]: ^^^^^^^^^^^ +[rank0]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 3200.00 GiB. GPU 0 has a total capacity of 139.81 GiB of which 133.47 GiB is free. Including non-PyTorch memory, this process has 6.33 GiB memory in use. Of the allocated memory 4.68 GiB is allocated by PyTorch, and 174.39 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) +[rank6]: Traceback (most recent call last): +[rank6]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in +[rank6]: pretrain( +[rank6]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain +[rank6]: iteration, num_floating_point_operations_so_far = train( +[rank6]: ^^^^^^ +[rank6]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train +[rank6]: ) = train_step( +[rank6]: ^^^^^^^^^^^ +[rank6]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step +[rank6]: losses_reduced = forward_backward_func( +[rank6]: ^^^^^^^^^^^^^^^^^^^^^^ +[rank6]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining +[rank6]: output_tensor, num_tokens = forward_step( +[rank6]: ^^^^^^^^^^^^^ +[rank6]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step +[rank6]: output_tensor, loss_func = forward_step_func(data_iterator, model) +[rank6]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank6]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step +[rank6]: (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator) +[rank6]: ^^^^^^^^^^^^^^^^^^^^^^^^ +[rank6]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch +[rank6]: batch = next(global_batches) +[rank6]: ^^^^^^^^^^^^^^^^^^^^ +[rank6]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches +[rank6]: attention_mask = torch.ones( +[rank6]: ^^^^^^^^^^^ +[rank6]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 3200.00 GiB. GPU 6 has a total capacity of 139.81 GiB of which 133.47 GiB is free. Including non-PyTorch memory, this process has 6.33 GiB memory in use. Of the allocated memory 4.68 GiB is allocated by PyTorch, and 174.39 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) +[rank2]: Traceback (most recent call last): +[rank2]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in +[rank2]: pretrain( +[rank2]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain +[rank2]: iteration, num_floating_point_operations_so_far = train( +[rank2]: ^^^^^^ +[rank2]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train +[rank2]: ) = train_step( +[rank2]: ^^^^^^^^^^^ +[rank2]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step +[rank2]: losses_reduced = forward_backward_func( +[rank2]: ^^^^^^^^^^^^^^^^^^^^^^ +[rank2]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining +[rank2]: output_tensor, num_tokens = forward_step( +[rank2]: ^^^^^^^^^^^^^ +[rank2]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step +[rank2]: output_tensor, loss_func = forward_step_func(data_iterator, model) +[rank2]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank2]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step +[rank2]: (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator) +[rank2]: ^^^^^^^^^^^^^^^^^^^^^^^^ +[rank2]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch +[rank2]: batch = next(global_batches) +[rank2]: ^^^^^^^^^^^^^^^^^^^^ +[rank2]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches +[rank2]: attention_mask = torch.ones( +[rank2]: ^^^^^^^^^^^ +[rank2]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 3200.00 GiB. GPU 2 has a total capacity of 139.81 GiB of which 133.47 GiB is free. Including non-PyTorch memory, this process has 6.33 GiB memory in use. Of the allocated memory 4.68 GiB is allocated by PyTorch, and 174.39 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) +[rank9]:[W621 22:09:27.234039112 ProcessGroupNCCL.cpp:1476] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator()) +[rank3]:[W621 22:09:27.135429891 ProcessGroupNCCL.cpp:1476] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator()) +[rank11]:[W621 22:09:27.395965961 ProcessGroupNCCL.cpp:1476] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator()) +[rank15]:[W621 22:09:27.446549216 ProcessGroupNCCL.cpp:1476] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator()) +[rank1]:[W621 22:09:27.386456984 ProcessGroupNCCL.cpp:1476] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator()) +[rank7]:[W621 22:09:27.468379165 ProcessGroupNCCL.cpp:1476] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator()) +[rank13]:[W621 22:09:27.628067694 ProcessGroupNCCL.cpp:1476] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator()) +[rank5]:[W621 22:09:27.559459752 ProcessGroupNCCL.cpp:1476] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator()) +W0621 22:09:28.664000 1062080 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 1062151 closing signal SIGTERM +W0621 22:09:28.668000 1062080 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 1062152 closing signal SIGTERM +W0621 22:09:28.669000 1062080 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 1062153 closing signal SIGTERM +W0621 22:09:28.696000 1062080 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 1062155 closing signal SIGTERM +W0621 22:09:28.700000 1062080 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 1062156 closing signal SIGTERM +W0621 22:09:28.700000 1062080 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 1062157 closing signal SIGTERM +W0621 22:09:28.704000 1985777 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 1985847 closing signal SIGTERM +W0621 22:09:28.708000 1062080 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 1062158 closing signal SIGTERM +W0621 22:09:28.708000 1985777 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 1985849 closing signal SIGTERM +W0621 22:09:28.711000 1985777 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 1985850 closing signal SIGTERM +W0621 22:09:28.712000 1985777 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 1985851 closing signal SIGTERM +W0621 22:09:28.714000 1985777 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 1985852 closing signal SIGTERM +W0621 22:09:28.715000 1985777 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 1985853 closing signal SIGTERM +W0621 22:09:28.717000 1985777 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 1985854 closing signal SIGTERM +E0621 22:09:29.208000 1062080 site-packages/torch/distributed/elastic/multiprocessing/api.py:874] failed (exitcode: 1) local_rank: 3 (pid: 1062154) of binary: /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/python3 +Traceback (most recent call last): + File "", line 198, in _run_module_as_main + File "", line 88, in _run_code + File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launch.py", line 207, in + main() + File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/typing_extensions.py", line 3253, in wrapper + return arg(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^ + File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launch.py", line 203, in main + launch(args) + File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launch.py", line 188, in launch + run(args) + File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/run.py", line 883, in run + elastic_launch( + File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launcher/api.py", line 139, in __call__ + return launch_agent(self._config, self._entrypoint, list(args)) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launcher/api.py", line 270, in launch_agent + raise ChildFailedError( +torch.distributed.elastic.multiprocessing.errors.ChildFailedError: +============================================================ +./pretrain_gpt_profile.py FAILED +------------------------------------------------------------ +Failures: + +------------------------------------------------------------ +Root Cause (first observed failure): +[0]: + time : 2025-06-21_22:09:28 + host : fs-mbz-gpu-239 + rank : 3 (local_rank: 3) + exitcode : 1 (pid: 1062154) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +============================================================ +E0621 22:09:29.370000 1985777 site-packages/torch/distributed/elastic/multiprocessing/api.py:874] failed (exitcode: 1) local_rank: 1 (pid: 1985848) of binary: /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/python3 +Traceback (most recent call last): + File "", line 198, in _run_module_as_main + File "", line 88, in _run_code + File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launch.py", line 207, in + main() + File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/typing_extensions.py", line 3253, in wrapper + return arg(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^ + File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launch.py", line 203, in main + launch(args) + File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launch.py", line 188, in launch + run(args) + File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/run.py", line 883, in run + elastic_launch( + File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launcher/api.py", line 139, in __call__ + return launch_agent(self._config, self._entrypoint, list(args)) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launcher/api.py", line 270, in launch_agent + raise ChildFailedError( +torch.distributed.elastic.multiprocessing.errors.ChildFailedError: +============================================================ +./pretrain_gpt_profile.py FAILED +------------------------------------------------------------ +Failures: + +------------------------------------------------------------ +Root Cause (first observed failure): +[0]: + time : 2025-06-21_22:09:28 + host : fs-mbz-gpu-286 + rank : 9 (local_rank: 1) + exitcode : 1 (pid: 1985848) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +============================================================ ++ set +x ++ set +x ++ for ctx_length in 1024 2048 4096 8192 12288 16384 24576 32768 40960 49152 65536 81920 98304 131072 ++ export PROF_CTX_LENGTH=98304 ++ PROF_CTX_LENGTH=98304 ++ name='/mnt/sharefs/users/hao.zhang/junda/megatron-prof-data--unstable-v5/mytrace.L98304*tp2.cp8.bs8.json' ++ '[' -f '/mnt/sharefs/users/hao.zhang/junda/megatron-prof-data--unstable-v5/mytrace.L98304*tp2.cp8.bs8.json' ']' ++ echo 'Running ctx_length=98304, TP_SIZE=2, CP_SIZE=8, BATCH_SIZE=8' ++ srun bash ./attnserver.sh ++ which python3 ++ python3 -m torch.distributed.launch --nproc_per_node 8 --nnodes 2 --node_rank 1 --rdzv_id 343240 --rdzv_backend c10d --rdzv_endpoint fs-mbz-gpu-239:29500 ./pretrain_gpt_profile.py --tensor-model-parallel-size 2 --context-parallel-size 8 --num-layers 2 --hidden-size 4096 --num-attention-heads 64 --group-query-attention --num-query-groups 16 --seq-length 98304 --max-position-embeddings 98304 --micro-batch-size 1 --global-batch-size 1 --lr 0.0005 --train-iters 10 --lr-decay-iters 150000 --lr-decay-style cosine --lr-warmup-iters 2 --weight-decay .1 --adam-beta2 .999 --fp16 --log-interval 1 --save-interval 16 --eval-interval 16 --eval-iters 1 --vocab-file vocab.json --merge-file merges.txt --save gpt-checkpoint --load gpt-checkpoint --logging-level 0 --mock-data --tensorboard-dir tensorboard-logs/ ++ which python3 ++ python3 -m torch.distributed.launch --nproc_per_node 8 --nnodes 2 --node_rank 0 --rdzv_id 343240 --rdzv_backend c10d --rdzv_endpoint fs-mbz-gpu-239:29500 ./pretrain_gpt_profile.py --tensor-model-parallel-size 2 --context-parallel-size 8 --num-layers 2 --hidden-size 4096 --num-attention-heads 64 --group-query-attention --num-query-groups 16 --seq-length 98304 --max-position-embeddings 98304 --micro-batch-size 1 --global-batch-size 1 --lr 0.0005 --train-iters 10 --lr-decay-iters 150000 --lr-decay-style cosine --lr-warmup-iters 2 --weight-decay .1 --adam-beta2 .999 --fp16 --log-interval 1 --save-interval 16 --eval-interval 16 --eval-iters 1 --vocab-file vocab.json --merge-file merges.txt --save gpt-checkpoint --load gpt-checkpoint --logging-level 0 --mock-data --tensorboard-dir tensorboard-logs/ +/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launch.py:207: FutureWarning: The module torch.distributed.launch is deprecated +and will be removed in future. Use torchrun. +Note that --use-env is set by default in torchrun. +If your script expects `--local-rank` argument to be set, please +change it to read from `os.environ['LOCAL_RANK']` instead. See +https://pytorch.org/docs/stable/distributed.html#launch-utility for +further instructions + + main() +/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launch.py:207: FutureWarning: The module torch.distributed.launch is deprecated +and will be removed in future. Use torchrun. +Note that --use-env is set by default in torchrun. +If your script expects `--local-rank` argument to be set, please +change it to read from `os.environ['LOCAL_RANK']` instead. See +https://pytorch.org/docs/stable/distributed.html#launch-utility for +further instructions + + main() +W0621 22:09:33.580000 1063932 site-packages/torch/distributed/run.py:766] +W0621 22:09:33.580000 1063932 site-packages/torch/distributed/run.py:766] ***************************************** +W0621 22:09:33.580000 1063932 site-packages/torch/distributed/run.py:766] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. +W0621 22:09:33.580000 1063932 site-packages/torch/distributed/run.py:766] ***************************************** +W0621 22:09:33.580000 1987594 site-packages/torch/distributed/run.py:766] +W0621 22:09:33.580000 1987594 site-packages/torch/distributed/run.py:766] ***************************************** +W0621 22:09:33.580000 1987594 site-packages/torch/distributed/run.py:766] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. +W0621 22:09:33.580000 1987594 site-packages/torch/distributed/run.py:766] ***************************************** +[rank11]:[W621 22:09:57.529258555 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 11] using GPU 3 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device. +[rank9]:[W621 22:09:57.529264436 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 9] using GPU 1 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device. +[rank5]:[W621 22:09:57.401541082 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 5] using GPU 5 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device. +[rank15]:[W621 22:09:57.529953470 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 15] using GPU 7 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device. +[rank13]:[W621 22:09:57.529972366 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 13] using GPU 5 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device. +[rank7]:[W621 22:09:57.401843726 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 7] using GPU 7 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device. +[rank14]:[W621 22:09:57.535980473 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 14] using GPU 6 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device. +[rank3]:[W621 22:09:57.402053297 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 3] using GPU 3 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device. +[rank1]:[W621 22:09:57.402080057 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 1] using GPU 1 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device. +[rank2]:[W621 22:09:57.403135412 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 2] using GPU 2 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device. +[rank6]:[W621 22:09:57.409828911 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 6] using GPU 6 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device. +[rank4]:[W621 22:09:57.409989089 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 4] using GPU 4 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device. +[rank12]:[W621 22:09:57.557682452 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 12] using GPU 4 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device. +[rank10]:[W621 22:09:57.557720993 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 10] using GPU 2 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device. +[rank8]:[W621 22:09:57.643415635 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 8] using GPU 0 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device. +[rank0]:[W621 22:09:57.552420622 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 0] using GPU 0 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device. +/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly. + warnings.warn( +/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly. + warnings.warn( +/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly. + warnings.warn( +/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly. + warnings.warn( +/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly. + warnings.warn( +/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly. + warnings.warn( +/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly. + warnings.warn( +/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly. + warnings.warn( +/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly. + warnings.warn( +/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly. + warnings.warn( +/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly. + warnings.warn( +/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly. + warnings.warn( +/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly. + warnings.warn( +/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly. + warnings.warn( +/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly. + warnings.warn( +/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly. + warnings.warn( +/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect. + warnings.warn( +/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect. + warnings.warn( +/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect. + warnings.warn( +/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect. + warnings.warn( +/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect. + warnings.warn( +/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect. + warnings.warn( +/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect. + warnings.warn( +/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect. + warnings.warn( +/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect. + warnings.warn( +/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect. + warnings.warn( +/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect. + warnings.warn( +/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect. + warnings.warn( +/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect. + warnings.warn( +/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect. + warnings.warn( +/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect. + warnings.warn( +/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect. + warnings.warn( +[rank13]: Traceback (most recent call last): +[rank13]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in +[rank13]: pretrain( +[rank13]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain +[rank13]: iteration, num_floating_point_operations_so_far = train( +[rank13]: ^^^^^^ +[rank13]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train +[rank13]: ) = train_step( +[rank13]: ^^^^^^^^^^^ +[rank13]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step +[rank13]: losses_reduced = forward_backward_func( +[rank13]: ^^^^^^^^^^^^^^^^^^^^^^ +[rank13]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining +[rank13]: output_tensor, num_tokens = forward_step( +[rank13]: ^^^^^^^^^^^^^ +[rank13]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step +[rank13]: output_tensor, loss_func = forward_step_func(data_iterator, model) +[rank13]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank13]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step +[rank13]: (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator) +[rank13]: ^^^^^^^^^^^^^^^^^^^^^^^^ +[rank13]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch +[rank13]: batch = next(global_batches) +[rank13]: ^^^^^^^^^^^^^^^^^^^^ +[rank13]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches +[rank13]: attention_mask = torch.ones( +[rank13]: ^^^^^^^^^^^ +[rank13]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 4608.00 GiB. GPU 5 has a total capacity of 139.81 GiB of which 132.92 GiB is free. Including non-PyTorch memory, this process has 6.89 GiB memory in use. Of the allocated memory 5.20 GiB is allocated by PyTorch, and 212.39 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) +[rank11]: Traceback (most recent call last): +[rank11]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in +[rank11]: pretrain( +[rank11]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain +[rank11]: iteration, num_floating_point_operations_so_far = train( +[rank11]: ^^^^^^ +[rank11]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train +[rank11]: ) = train_step( +[rank11]: ^^^^^^^^^^^ +[rank11]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step +[rank11]: losses_reduced = forward_backward_func( +[rank11]: ^^^^^^^^^^^^^^^^^^^^^^ +[rank11]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining +[rank11]: output_tensor, num_tokens = forward_step( +[rank11]: ^^^^^^^^^^^^^ +[rank11]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step +[rank11]: output_tensor, loss_func = forward_step_func(data_iterator, model) +[rank11]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank11]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step +[rank11]: (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator) +[rank11]: ^^^^^^^^^^^^^^^^^^^^^^^^ +[rank11]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch +[rank11]: batch = next(global_batches) +[rank11]: ^^^^^^^^^^^^^^^^^^^^ +[rank11]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches +[rank11]: attention_mask = torch.ones( +[rank11]: ^^^^^^^^^^^ +[rank11]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 4608.00 GiB. GPU 3 has a total capacity of 139.81 GiB of which 132.92 GiB is free. Including non-PyTorch memory, this process has 6.89 GiB memory in use. Of the allocated memory 5.20 GiB is allocated by PyTorch, and 212.39 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) +[rank12]: Traceback (most recent call last): +[rank12]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in +[rank12]: pretrain( +[rank12]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain +[rank12]: iteration, num_floating_point_operations_so_far = train( +[rank12]: ^^^^^^ +[rank12]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train +[rank12]: ) = train_step( +[rank12]: ^^^^^^^^^^^ +[rank12]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step +[rank12]: losses_reduced = forward_backward_func( +[rank12]: ^^^^^^^^^^^^^^^^^^^^^^ +[rank12]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining +[rank12]: output_tensor, num_tokens = forward_step( +[rank12]: ^^^^^^^^^^^^^ +[rank12]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step +[rank12]: output_tensor, loss_func = forward_step_func(data_iterator, model) +[rank12]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank12]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step +[rank12]: (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator) +[rank12]: ^^^^^^^^^^^^^^^^^^^^^^^^ +[rank12]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch +[rank12]: batch = next(global_batches) +[rank12]: ^^^^^^^^^^^^^^^^^^^^ +[rank12]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches +[rank12]: attention_mask = torch.ones( +[rank12]: ^^^^^^^^^^^ +[rank12]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 4608.00 GiB. GPU 4 has a total capacity of 139.81 GiB of which 132.90 GiB is free. Including non-PyTorch memory, this process has 6.90 GiB memory in use. Of the allocated memory 5.20 GiB is allocated by PyTorch, and 212.39 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) +[rank9]: Traceback (most recent call last): +[rank9]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in +[rank9]: pretrain( +[rank9]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain +[rank9]: iteration, num_floating_point_operations_so_far = train( +[rank9]: ^^^^^^ +[rank9]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train +[rank9]: ) = train_step( +[rank9]: ^^^^^^^^^^^ +[rank9]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step +[rank9]: losses_reduced = forward_backward_func( +[rank9]: ^^^^^^^^^^^^^^^^^^^^^^ +[rank9]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining +[rank9]: output_tensor, num_tokens = forward_step( +[rank9]: ^^^^^^^^^^^^^ +[rank9]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step +[rank9]: output_tensor, loss_func = forward_step_func(data_iterator, model) +[rank9]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank9]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step +[rank9]: (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator) +[rank9]: ^^^^^^^^^^^^^^^^^^^^^^^^ +[rank9]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch +[rank9]: batch = next(global_batches) +[rank9]: ^^^^^^^^^^^^^^^^^^^^ +[rank9]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches +[rank9]: attention_mask = torch.ones( +[rank9]: ^^^^^^^^^^^ +[rank9]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 4608.00 GiB. GPU 1 has a total capacity of 139.81 GiB of which 132.92 GiB is free. Including non-PyTorch memory, this process has 6.89 GiB memory in use. Of the allocated memory 5.20 GiB is allocated by PyTorch, and 212.39 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) +[rank15]: Traceback (most recent call last): +[rank15]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in +[rank15]: pretrain( +[rank15]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain +[rank15]: iteration, num_floating_point_operations_so_far = train( +[rank15]: ^^^^^^ +[rank15]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train +[rank15]: ) = train_step( +[rank15]: ^^^^^^^^^^^ +[rank15]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step +[rank15]: losses_reduced = forward_backward_func( +[rank15]: ^^^^^^^^^^^^^^^^^^^^^^ +[rank15]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining +[rank15]: output_tensor, num_tokens = forward_step( +[rank15]: ^^^^^^^^^^^^^ +[rank15]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step +[rank15]: output_tensor, loss_func = forward_step_func(data_iterator, model) +[rank15]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank15]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step +[rank15]: (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator) +[rank15]: ^^^^^^^^^^^^^^^^^^^^^^^^ +[rank15]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch +[rank15]: batch = next(global_batches) +[rank15]: ^^^^^^^^^^^^^^^^^^^^ +[rank15]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches +[rank15]: attention_mask = torch.ones( +[rank15]: ^^^^^^^^^^^ +[rank15]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 4608.00 GiB. GPU 7 has a total capacity of 139.81 GiB of which 132.92 GiB is free. Including non-PyTorch memory, this process has 6.89 GiB memory in use. Of the allocated memory 5.20 GiB is allocated by PyTorch, and 212.39 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) +[rank10]: Traceback (most recent call last): +[rank10]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in +[rank10]: pretrain( +[rank10]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain +[rank10]: iteration, num_floating_point_operations_so_far = train( +[rank10]: ^^^^^^ +[rank10]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train +[rank10]: ) = train_step( +[rank10]: ^^^^^^^^^^^ +[rank10]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step +[rank10]: losses_reduced = forward_backward_func( +[rank10]: ^^^^^^^^^^^^^^^^^^^^^^ +[rank10]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining +[rank10]: output_tensor, num_tokens = forward_step( +[rank10]: ^^^^^^^^^^^^^ +[rank10]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step +[rank10]: output_tensor, loss_func = forward_step_func(data_iterator, model) +[rank10]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank10]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step +[rank10]: (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator) +[rank10]: ^^^^^^^^^^^^^^^^^^^^^^^^ +[rank10]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch +[rank10]: batch = next(global_batches) +[rank10]: ^^^^^^^^^^^^^^^^^^^^ +[rank10]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches +[rank10]: attention_mask = torch.ones( +[rank10]: ^^^^^^^^^^^ +[rank10]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 4608.00 GiB. GPU 2 has a total capacity of 139.81 GiB of which 132.90 GiB is free. Including non-PyTorch memory, this process has 6.90 GiB memory in use. Of the allocated memory 5.20 GiB is allocated by PyTorch, and 212.39 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) +[rank8]: Traceback (most recent call last): +[rank8]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in +[rank8]: pretrain( +[rank8]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain +[rank8]: iteration, num_floating_point_operations_so_far = train( +[rank8]: ^^^^^^ +[rank8]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train +[rank8]: ) = train_step( +[rank8]: ^^^^^^^^^^^ +[rank8]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step +[rank8]: losses_reduced = forward_backward_func( +[rank8]: ^^^^^^^^^^^^^^^^^^^^^^ +[rank8]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining +[rank8]: output_tensor, num_tokens = forward_step( +[rank8]: ^^^^^^^^^^^^^ +[rank8]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step +[rank8]: output_tensor, loss_func = forward_step_func(data_iterator, model) +[rank8]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank8]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step +[rank8]: (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator) +[rank8]: ^^^^^^^^^^^^^^^^^^^^^^^^ +[rank8]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch +[rank8]: batch = next(global_batches) +[rank8]: ^^^^^^^^^^^^^^^^^^^^ +[rank8]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches +[rank8]: attention_mask = torch.ones( +[rank8]: ^^^^^^^^^^^ +[rank8]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 4608.00 GiB. GPU 0 has a total capacity of 139.81 GiB of which 132.90 GiB is free. Including non-PyTorch memory, this process has 6.90 GiB memory in use. Of the allocated memory 5.20 GiB is allocated by PyTorch, and 212.39 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) +[rank14]: Traceback (most recent call last): +[rank14]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in +[rank14]: pretrain( +[rank14]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain +[rank14]: iteration, num_floating_point_operations_so_far = train( +[rank14]: ^^^^^^ +[rank14]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train +[rank14]: ) = train_step( +[rank14]: ^^^^^^^^^^^ +[rank14]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step +[rank14]: losses_reduced = forward_backward_func( +[rank14]: ^^^^^^^^^^^^^^^^^^^^^^ +[rank14]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining +[rank14]: output_tensor, num_tokens = forward_step( +[rank14]: ^^^^^^^^^^^^^ +[rank14]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step +[rank14]: output_tensor, loss_func = forward_step_func(data_iterator, model) +[rank14]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank14]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step +[rank14]: (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator) +[rank14]: ^^^^^^^^^^^^^^^^^^^^^^^^ +[rank14]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch +[rank14]: batch = next(global_batches) +[rank14]: ^^^^^^^^^^^^^^^^^^^^ +[rank14]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches +[rank14]: attention_mask = torch.ones( +[rank14]: ^^^^^^^^^^^ +[rank14]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 4608.00 GiB. GPU 6 has a total capacity of 139.81 GiB of which 132.90 GiB is free. Including non-PyTorch memory, this process has 6.90 GiB memory in use. Of the allocated memory 5.20 GiB is allocated by PyTorch, and 212.39 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) +[rank7]: Traceback (most recent call last): +[rank7]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in +[rank7]: pretrain( +[rank7]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain +[rank7]: iteration, num_floating_point_operations_so_far = train( +[rank7]: ^^^^^^ +[rank7]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train +[rank7]: ) = train_step( +[rank7]: ^^^^^^^^^^^ +[rank7]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step +[rank7]: losses_reduced = forward_backward_func( +[rank7]: ^^^^^^^^^^^^^^^^^^^^^^ +[rank7]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining +[rank7]: output_tensor, num_tokens = forward_step( +[rank7]: ^^^^^^^^^^^^^ +[rank7]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step +[rank7]: output_tensor, loss_func = forward_step_func(data_iterator, model) +[rank7]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank7]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step +[rank7]: (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator) +[rank7]: ^^^^^^^^^^^^^^^^^^^^^^^^ +[rank7]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch +[rank7]: batch = next(global_batches) +[rank7]: ^^^^^^^^^^^^^^^^^^^^ +[rank7]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches +[rank7]: attention_mask = torch.ones( +[rank7]: ^^^^^^^^^^^ +[rank7]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 4608.00 GiB. GPU 7 has a total capacity of 139.81 GiB of which 132.90 GiB is free. Including non-PyTorch memory, this process has 6.90 GiB memory in use. Of the allocated memory 5.20 GiB is allocated by PyTorch, and 212.39 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) +[rank1]: Traceback (most recent call last): +[rank1]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in +[rank1]: pretrain( +[rank1]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain +[rank1]: iteration, num_floating_point_operations_so_far = train( +[rank1]: ^^^^^^ +[rank1]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train +[rank1]: ) = train_step( +[rank1]: ^^^^^^^^^^^ +[rank1]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step +[rank1]: losses_reduced = forward_backward_func( +[rank1]: ^^^^^^^^^^^^^^^^^^^^^^ +[rank1]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining +[rank1]: output_tensor, num_tokens = forward_step( +[rank1]: ^^^^^^^^^^^^^ +[rank1]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step +[rank1]: output_tensor, loss_func = forward_step_func(data_iterator, model) +[rank1]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank1]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step +[rank1]: (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator) +[rank1]: ^^^^^^^^^^^^^^^^^^^^^^^^ +[rank1]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch +[rank1]: batch = next(global_batches) +[rank1]: ^^^^^^^^^^^^^^^^^^^^ +[rank1]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches +[rank1]: attention_mask = torch.ones( +[rank1]: ^^^^^^^^^^^ +[rank1]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 4608.00 GiB. GPU 1 has a total capacity of 139.81 GiB of which 132.90 GiB is free. Including non-PyTorch memory, this process has 6.90 GiB memory in use. Of the allocated memory 5.20 GiB is allocated by PyTorch, and 212.39 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) +[rank5]: Traceback (most recent call last): +[rank5]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in +[rank5]: pretrain( +[rank5]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain +[rank5]: iteration, num_floating_point_operations_so_far = train( +[rank5]: ^^^^^^ +[rank5]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train +[rank5]: ) = train_step( +[rank5]: ^^^^^^^^^^^ +[rank5]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step +[rank5]: losses_reduced = forward_backward_func( +[rank5]: ^^^^^^^^^^^^^^^^^^^^^^ +[rank5]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining +[rank5]: output_tensor, num_tokens = forward_step( +[rank5]: ^^^^^^^^^^^^^ +[rank5]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step +[rank5]: output_tensor, loss_func = forward_step_func(data_iterator, model) +[rank5]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank5]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step +[rank5]: (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator) +[rank5]: ^^^^^^^^^^^^^^^^^^^^^^^^ +[rank5]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch +[rank5]: batch = next(global_batches) +[rank5]: ^^^^^^^^^^^^^^^^^^^^ +[rank5]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches +[rank5]: attention_mask = torch.ones( +[rank5]: ^^^^^^^^^^^ +[rank5]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 4608.00 GiB. GPU 5 has a total capacity of 139.81 GiB of which 132.90 GiB is free. Including non-PyTorch memory, this process has 6.90 GiB memory in use. Of the allocated memory 5.20 GiB is allocated by PyTorch, and 212.39 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) +[rank3]: Traceback (most recent call last): +[rank3]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in +[rank3]: pretrain( +[rank3]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain +[rank3]: iteration, num_floating_point_operations_so_far = train( +[rank3]: ^^^^^^ +[rank3]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train +[rank3]: ) = train_step( +[rank3]: ^^^^^^^^^^^ +[rank3]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step +[rank3]: losses_reduced = forward_backward_func( +[rank3]: ^^^^^^^^^^^^^^^^^^^^^^ +[rank3]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining +[rank3]: output_tensor, num_tokens = forward_step( +[rank3]: ^^^^^^^^^^^^^ +[rank3]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step +[rank3]: output_tensor, loss_func = forward_step_func(data_iterator, model) +[rank3]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank3]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step +[rank3]: (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator) +[rank3]: ^^^^^^^^^^^^^^^^^^^^^^^^ +[rank3]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch +[rank3]: batch = next(global_batches) +[rank3]: ^^^^^^^^^^^^^^^^^^^^ +[rank3]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches +[rank3]: attention_mask = torch.ones( +[rank3]: ^^^^^^^^^^^ +[rank3]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 4608.00 GiB. GPU 3 has a total capacity of 139.81 GiB of which 132.90 GiB is free. Including non-PyTorch memory, this process has 6.90 GiB memory in use. Of the allocated memory 5.20 GiB is allocated by PyTorch, and 212.39 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) +[rank6]: Traceback (most recent call last): +[rank6]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in +[rank6]: pretrain( +[rank6]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain +[rank6]: iteration, num_floating_point_operations_so_far = train( +[rank6]: ^^^^^^ +[rank6]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train +[rank6]: ) = train_step( +[rank6]: ^^^^^^^^^^^ +[rank6]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step +[rank6]: losses_reduced = forward_backward_func( +[rank6]: ^^^^^^^^^^^^^^^^^^^^^^ +[rank6]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining +[rank6]: output_tensor, num_tokens = forward_step( +[rank6]: ^^^^^^^^^^^^^ +[rank6]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step +[rank6]: output_tensor, loss_func = forward_step_func(data_iterator, model) +[rank6]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank6]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step +[rank6]: (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator) +[rank6]: ^^^^^^^^^^^^^^^^^^^^^^^^ +[rank6]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch +[rank6]: batch = next(global_batches) +[rank6]: ^^^^^^^^^^^^^^^^^^^^ +[rank6]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches +[rank6]: attention_mask = torch.ones( +[rank6]: ^^^^^^^^^^^ +[rank6]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 4608.00 GiB. GPU 6 has a total capacity of 139.81 GiB of which 132.92 GiB is free. Including non-PyTorch memory, this process has 6.89 GiB memory in use. Of the allocated memory 5.20 GiB is allocated by PyTorch, and 212.39 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) +[rank4]: Traceback (most recent call last): +[rank4]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in +[rank4]: pretrain( +[rank4]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain +[rank4]: iteration, num_floating_point_operations_so_far = train( +[rank4]: ^^^^^^ +[rank4]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train +[rank4]: ) = train_step( +[rank4]: ^^^^^^^^^^^ +[rank4]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step +[rank4]: losses_reduced = forward_backward_func( +[rank4]: ^^^^^^^^^^^^^^^^^^^^^^ +[rank4]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining +[rank4]: output_tensor, num_tokens = forward_step( +[rank4]: ^^^^^^^^^^^^^ +[rank4]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step +[rank4]: output_tensor, loss_func = forward_step_func(data_iterator, model) +[rank4]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank4]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step +[rank4]: (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator) +[rank4]: ^^^^^^^^^^^^^^^^^^^^^^^^ +[rank4]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch +[rank4]: batch = next(global_batches) +[rank4]: ^^^^^^^^^^^^^^^^^^^^ +[rank4]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches +[rank4]: attention_mask = torch.ones( +[rank4]: ^^^^^^^^^^^ +[rank4]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 4608.00 GiB. GPU 4 has a total capacity of 139.81 GiB of which 132.92 GiB is free. Including non-PyTorch memory, this process has 6.89 GiB memory in use. Of the allocated memory 5.20 GiB is allocated by PyTorch, and 212.39 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) +[rank0]: Traceback (most recent call last): +[rank0]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in +[rank0]: pretrain( +[rank0]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain +[rank0]: iteration, num_floating_point_operations_so_far = train( +[rank0]: ^^^^^^ +[rank0]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train +[rank0]: ) = train_step( +[rank0]: ^^^^^^^^^^^ +[rank0]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step +[rank0]: losses_reduced = forward_backward_func( +[rank0]: ^^^^^^^^^^^^^^^^^^^^^^ +[rank0]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining +[rank0]: output_tensor, num_tokens = forward_step( +[rank0]: ^^^^^^^^^^^^^ +[rank0]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step +[rank0]: output_tensor, loss_func = forward_step_func(data_iterator, model) +[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank0]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step +[rank0]: (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator) +[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^ +[rank0]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch +[rank0]: batch = next(global_batches) +[rank0]: ^^^^^^^^^^^^^^^^^^^^ +[rank0]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches +[rank0]: attention_mask = torch.ones( +[rank0]: ^^^^^^^^^^^ +[rank0]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 4608.00 GiB. GPU 0 has a total capacity of 139.81 GiB of which 132.92 GiB is free. Including non-PyTorch memory, this process has 6.89 GiB memory in use. Of the allocated memory 5.20 GiB is allocated by PyTorch, and 212.39 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) +[rank2]: Traceback (most recent call last): +[rank2]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in +[rank2]: pretrain( +[rank2]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain +[rank2]: iteration, num_floating_point_operations_so_far = train( +[rank2]: ^^^^^^ +[rank2]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train +[rank2]: ) = train_step( +[rank2]: ^^^^^^^^^^^ +[rank2]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step +[rank2]: losses_reduced = forward_backward_func( +[rank2]: ^^^^^^^^^^^^^^^^^^^^^^ +[rank2]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining +[rank2]: output_tensor, num_tokens = forward_step( +[rank2]: ^^^^^^^^^^^^^ +[rank2]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step +[rank2]: output_tensor, loss_func = forward_step_func(data_iterator, model) +[rank2]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank2]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step +[rank2]: (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator) +[rank2]: ^^^^^^^^^^^^^^^^^^^^^^^^ +[rank2]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch +[rank2]: batch = next(global_batches) +[rank2]: ^^^^^^^^^^^^^^^^^^^^ +[rank2]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches +[rank2]: attention_mask = torch.ones( +[rank2]: ^^^^^^^^^^^ +[rank2]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 4608.00 GiB. GPU 2 has a total capacity of 139.81 GiB of which 132.92 GiB is free. Including non-PyTorch memory, this process has 6.89 GiB memory in use. Of the allocated memory 5.20 GiB is allocated by PyTorch, and 212.39 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) +[rank5]:[W621 22:10:10.923786357 ProcessGroupNCCL.cpp:1476] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator()) +[rank11]:[W621 22:10:11.124210112 ProcessGroupNCCL.cpp:1476] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator()) +[rank15]:[W621 22:10:11.143827049 ProcessGroupNCCL.cpp:1476] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator()) +[rank9]:[W621 22:10:11.164012334 ProcessGroupNCCL.cpp:1476] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator()) +[rank13]:[W621 22:10:11.164079089 ProcessGroupNCCL.cpp:1476] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator()) +[rank7]:[W621 22:10:11.246089995 ProcessGroupNCCL.cpp:1476] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator()) +[rank1]:[W621 22:10:11.260413349 ProcessGroupNCCL.cpp:1476] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator()) +[rank3]:[W621 22:10:11.276549944 ProcessGroupNCCL.cpp:1476] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator()) +W0621 22:10:12.474000 1063932 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 1064003 closing signal SIGTERM +W0621 22:10:12.478000 1063932 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 1064004 closing signal SIGTERM +W0621 22:10:12.479000 1063932 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 1064005 closing signal SIGTERM +W0621 22:10:12.481000 1063932 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 1064006 closing signal SIGTERM +W0621 22:10:12.481000 1063932 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 1064007 closing signal SIGTERM +W0621 22:10:12.484000 1063932 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 1064009 closing signal SIGTERM +W0621 22:10:12.487000 1063932 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 1064010 closing signal SIGTERM +W0621 22:10:12.730000 1987594 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 1987665 closing signal SIGTERM +W0621 22:10:12.734000 1987594 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 1987667 closing signal SIGTERM +W0621 22:10:12.736000 1987594 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 1987669 closing signal SIGTERM +W0621 22:10:12.739000 1987594 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 1987671 closing signal SIGTERM +W0621 22:10:12.754000 1987594 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 1987672 closing signal SIGTERM +E0621 22:10:12.829000 1987594 site-packages/torch/distributed/elastic/multiprocessing/api.py:874] failed (exitcode: 1) local_rank: 1 (pid: 1987666) of binary: /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/python3 +Traceback (most recent call last): + File "", line 198, in _run_module_as_main + File "", line 88, in _run_code + File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launch.py", line 207, in + main() + File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/typing_extensions.py", line 3253, in wrapper + return arg(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^ + File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launch.py", line 203, in main + launch(args) + File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launch.py", line 188, in launch + run(args) + File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/run.py", line 883, in run + elastic_launch( + File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launcher/api.py", line 139, in __call__ + return launch_agent(self._config, self._entrypoint, list(args)) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launcher/api.py", line 270, in launch_agent + raise ChildFailedError( +torch.distributed.elastic.multiprocessing.errors.ChildFailedError: +============================================================ +./pretrain_gpt_profile.py FAILED +------------------------------------------------------------ +Failures: +[1]: + time : 2025-06-21_22:10:12 + host : fs-mbz-gpu-286 + rank : 11 (local_rank: 3) + exitcode : 1 (pid: 1987668) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +[2]: + time : 2025-06-21_22:10:12 + host : fs-mbz-gpu-286 + rank : 13 (local_rank: 5) + exitcode : 1 (pid: 1987670) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +------------------------------------------------------------ +Root Cause (first observed failure): +[0]: + time : 2025-06-21_22:10:12 + host : fs-mbz-gpu-286 + rank : 9 (local_rank: 1) + exitcode : 1 (pid: 1987666) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +============================================================ +E0621 22:10:13.048000 1063932 site-packages/torch/distributed/elastic/multiprocessing/api.py:874] failed (exitcode: 1) local_rank: 5 (pid: 1064008) of binary: /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/python3 +Traceback (most recent call last): + File "", line 198, in _run_module_as_main + File "", line 88, in _run_code + File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launch.py", line 207, in + main() + File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/typing_extensions.py", line 3253, in wrapper + return arg(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^ + File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launch.py", line 203, in main + launch(args) + File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launch.py", line 188, in launch + run(args) + File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/run.py", line 883, in run + elastic_launch( + File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launcher/api.py", line 139, in __call__ + return launch_agent(self._config, self._entrypoint, list(args)) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launcher/api.py", line 270, in launch_agent + raise ChildFailedError( +torch.distributed.elastic.multiprocessing.errors.ChildFailedError: +============================================================ +./pretrain_gpt_profile.py FAILED +------------------------------------------------------------ +Failures: + +------------------------------------------------------------ +Root Cause (first observed failure): +[0]: + time : 2025-06-21_22:10:12 + host : fs-mbz-gpu-239 + rank : 5 (local_rank: 5) + exitcode : 1 (pid: 1064008) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +============================================================ ++ set +x ++ set +x ++ for ctx_length in 1024 2048 4096 8192 12288 16384 24576 32768 40960 49152 65536 81920 98304 131072 ++ export PROF_CTX_LENGTH=131072 ++ PROF_CTX_LENGTH=131072 ++ name='/mnt/sharefs/users/hao.zhang/junda/megatron-prof-data--unstable-v5/mytrace.L131072*tp2.cp8.bs8.json' ++ '[' -f '/mnt/sharefs/users/hao.zhang/junda/megatron-prof-data--unstable-v5/mytrace.L131072*tp2.cp8.bs8.json' ']' ++ echo 'Running ctx_length=131072, TP_SIZE=2, CP_SIZE=8, BATCH_SIZE=8' ++ srun bash ./attnserver.sh ++ which python3 ++ python3 -m torch.distributed.launch --nproc_per_node 8 --nnodes 2 --node_rank 1 --rdzv_id 343240 --rdzv_backend c10d --rdzv_endpoint fs-mbz-gpu-239:29500 ./pretrain_gpt_profile.py --tensor-model-parallel-size 2 --context-parallel-size 8 --num-layers 2 --hidden-size 4096 --num-attention-heads 64 --group-query-attention --num-query-groups 16 --seq-length 131072 --max-position-embeddings 131072 --micro-batch-size 1 --global-batch-size 1 --lr 0.0005 --train-iters 10 --lr-decay-iters 150000 --lr-decay-style cosine --lr-warmup-iters 2 --weight-decay .1 --adam-beta2 .999 --fp16 --log-interval 1 --save-interval 16 --eval-interval 16 --eval-iters 1 --vocab-file vocab.json --merge-file merges.txt --save gpt-checkpoint --load gpt-checkpoint --logging-level 0 --mock-data --tensorboard-dir tensorboard-logs/ ++ which python3 ++ python3 -m torch.distributed.launch --nproc_per_node 8 --nnodes 2 --node_rank 0 --rdzv_id 343240 --rdzv_backend c10d --rdzv_endpoint fs-mbz-gpu-239:29500 ./pretrain_gpt_profile.py --tensor-model-parallel-size 2 --context-parallel-size 8 --num-layers 2 --hidden-size 4096 --num-attention-heads 64 --group-query-attention --num-query-groups 16 --seq-length 131072 --max-position-embeddings 131072 --micro-batch-size 1 --global-batch-size 1 --lr 0.0005 --train-iters 10 --lr-decay-iters 150000 --lr-decay-style cosine --lr-warmup-iters 2 --weight-decay .1 --adam-beta2 .999 --fp16 --log-interval 1 --save-interval 16 --eval-interval 16 --eval-iters 1 --vocab-file vocab.json --merge-file merges.txt --save gpt-checkpoint --load gpt-checkpoint --logging-level 0 --mock-data --tensorboard-dir tensorboard-logs/ +/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launch.py:207: FutureWarning: The module torch.distributed.launch is deprecated +and will be removed in future. Use torchrun. +Note that --use-env is set by default in torchrun. +If your script expects `--local-rank` argument to be set, please +change it to read from `os.environ['LOCAL_RANK']` instead. See +https://pytorch.org/docs/stable/distributed.html#launch-utility for +further instructions + + main() +W0621 22:10:17.509000 1989482 site-packages/torch/distributed/run.py:766] +W0621 22:10:17.509000 1989482 site-packages/torch/distributed/run.py:766] ***************************************** +W0621 22:10:17.509000 1989482 site-packages/torch/distributed/run.py:766] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. +W0621 22:10:17.509000 1989482 site-packages/torch/distributed/run.py:766] ***************************************** +/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launch.py:207: FutureWarning: The module torch.distributed.launch is deprecated +and will be removed in future. Use torchrun. +Note that --use-env is set by default in torchrun. +If your script expects `--local-rank` argument to be set, please +change it to read from `os.environ['LOCAL_RANK']` instead. See +https://pytorch.org/docs/stable/distributed.html#launch-utility for +further instructions + + main() +W0621 22:10:17.586000 1065872 site-packages/torch/distributed/run.py:766] +W0621 22:10:17.586000 1065872 site-packages/torch/distributed/run.py:766] ***************************************** +W0621 22:10:17.586000 1065872 site-packages/torch/distributed/run.py:766] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. +W0621 22:10:17.586000 1065872 site-packages/torch/distributed/run.py:766] ***************************************** +[rank3]:[W621 22:10:40.505699611 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 3] using GPU 3 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device. +[rank1]:[W621 22:10:40.506123506 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 1] using GPU 1 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device. +[rank6]:[W621 22:10:40.507070939 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 6] using GPU 6 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device. +[rank2]:[W621 22:10:40.507078680 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 2] using GPU 2 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device. +[rank5]:[W621 22:10:40.507556215 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 5] using GPU 5 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device. +[rank4]:[W621 22:10:40.507684175 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 4] using GPU 4 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device. +[rank7]:[W621 22:10:40.508353660 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 7] using GPU 7 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device. +[rank13]:[W621 22:10:40.657485476 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 13] using GPU 5 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device. +[rank10]:[W621 22:10:40.657490776 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 10] using GPU 2 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device. +[rank14]:[W621 22:10:40.657542408 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 14] using GPU 6 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device. +[rank15]:[W621 22:10:40.657551444 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 15] using GPU 7 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device. +[rank9]:[W621 22:10:40.657554082 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 9] using GPU 1 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device. +[rank12]:[W621 22:10:40.657627983 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 12] using GPU 4 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device. +[rank11]:[W621 22:10:40.657689673 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 11] using GPU 3 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device. +[rank8]:[W621 22:10:40.739290309 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 8] using GPU 0 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device. +[rank0]:[W621 22:10:40.644678473 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 0] using GPU 0 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device. +/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly. + warnings.warn( +/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly. + warnings.warn( +/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly. + warnings.warn( +/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly. + warnings.warn( +/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly. + warnings.warn( +/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly. + warnings.warn( +/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly. + warnings.warn( +/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly. + warnings.warn( +/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly. + warnings.warn( +/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly. + warnings.warn( +/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly. + warnings.warn( +/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly. + warnings.warn( +/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly. + warnings.warn( +/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly. + warnings.warn( +/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly. + warnings.warn( +/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly. + warnings.warn( +/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect. + warnings.warn( +/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect. + warnings.warn( +/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect. + warnings.warn( +/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect. + warnings.warn( +/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect. + warnings.warn( +/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect. + warnings.warn( +/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect. + warnings.warn( +/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect. + warnings.warn( +/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect. + warnings.warn( +/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect. + warnings.warn( +/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect. + warnings.warn( +/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect. + warnings.warn( +/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect. + warnings.warn( +/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect. + warnings.warn( +/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect. + warnings.warn( +/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect. + warnings.warn( +[rank9]: Traceback (most recent call last): +[rank9]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in +[rank9]: pretrain( +[rank9]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain +[rank9]: iteration, num_floating_point_operations_so_far = train( +[rank9]: ^^^^^^ +[rank9]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train +[rank9]: ) = train_step( +[rank9]: ^^^^^^^^^^^ +[rank9]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step +[rank9]: losses_reduced = forward_backward_func( +[rank9]: ^^^^^^^^^^^^^^^^^^^^^^ +[rank9]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining +[rank9]: output_tensor, num_tokens = forward_step( +[rank9]: ^^^^^^^^^^^^^ +[rank9]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step +[rank9]: output_tensor, loss_func = forward_step_func(data_iterator, model) +[rank9]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank9]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step +[rank9]: (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator) +[rank9]: ^^^^^^^^^^^^^^^^^^^^^^^^ +[rank9]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch +[rank9]: batch = next(global_batches) +[rank9]: ^^^^^^^^^^^^^^^^^^^^ +[rank9]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches +[rank9]: attention_mask = torch.ones( +[rank9]: ^^^^^^^^^^^ +[rank9]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 8192.00 GiB. GPU 1 has a total capacity of 139.81 GiB of which 131.42 GiB is free. Including non-PyTorch memory, this process has 8.39 GiB memory in use. Of the allocated memory 6.24 GiB is allocated by PyTorch, and 684.39 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) +[rank13]: Traceback (most recent call last): +[rank13]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in +[rank13]: pretrain( +[rank13]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain +[rank13]: iteration, num_floating_point_operations_so_far = train( +[rank13]: ^^^^^^ +[rank13]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train +[rank13]: ) = train_step( +[rank13]: ^^^^^^^^^^^ +[rank13]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step +[rank13]: losses_reduced = forward_backward_func( +[rank13]: ^^^^^^^^^^^^^^^^^^^^^^ +[rank13]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining +[rank13]: output_tensor, num_tokens = forward_step( +[rank13]: ^^^^^^^^^^^^^ +[rank13]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step +[rank13]: output_tensor, loss_func = forward_step_func(data_iterator, model) +[rank13]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank13]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step +[rank13]: (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator) +[rank13]: ^^^^^^^^^^^^^^^^^^^^^^^^ +[rank13]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch +[rank13]: batch = next(global_batches) +[rank13]: ^^^^^^^^^^^^^^^^^^^^ +[rank13]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches +[rank13]: attention_mask = torch.ones( +[rank13]: ^^^^^^^^^^^ +[rank13]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 8192.00 GiB. GPU 5 has a total capacity of 139.81 GiB of which 131.42 GiB is free. Including non-PyTorch memory, this process has 8.39 GiB memory in use. Of the allocated memory 6.24 GiB is allocated by PyTorch, and 684.39 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) +[rank15]: Traceback (most recent call last): +[rank15]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in +[rank15]: pretrain( +[rank15]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain +[rank15]: iteration, num_floating_point_operations_so_far = train( +[rank15]: ^^^^^^ +[rank15]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train +[rank15]: ) = train_step( +[rank15]: ^^^^^^^^^^^ +[rank15]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step +[rank15]: losses_reduced = forward_backward_func( +[rank15]: ^^^^^^^^^^^^^^^^^^^^^^ +[rank15]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining +[rank15]: output_tensor, num_tokens = forward_step( +[rank15]: ^^^^^^^^^^^^^ +[rank15]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step +[rank15]: output_tensor, loss_func = forward_step_func(data_iterator, model) +[rank15]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank15]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step +[rank15]: (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator) +[rank15]: ^^^^^^^^^^^^^^^^^^^^^^^^ +[rank15]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch +[rank15]: batch = next(global_batches) +[rank15]: ^^^^^^^^^^^^^^^^^^^^ +[rank15]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches +[rank15]: attention_mask = torch.ones( +[rank15]: ^^^^^^^^^^^ +[rank15]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 8192.00 GiB. GPU 7 has a total capacity of 139.81 GiB of which 131.42 GiB is free. Including non-PyTorch memory, this process has 8.39 GiB memory in use. Of the allocated memory 6.24 GiB is allocated by PyTorch, and 684.39 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) +[rank10]: Traceback (most recent call last): +[rank10]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in +[rank10]: pretrain( +[rank10]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain +[rank10]: iteration, num_floating_point_operations_so_far = train( +[rank10]: ^^^^^^ +[rank10]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train +[rank10]: ) = train_step( +[rank10]: ^^^^^^^^^^^ +[rank10]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step +[rank10]: losses_reduced = forward_backward_func( +[rank10]: ^^^^^^^^^^^^^^^^^^^^^^ +[rank10]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining +[rank10]: output_tensor, num_tokens = forward_step( +[rank10]: ^^^^^^^^^^^^^ +[rank10]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step +[rank10]: output_tensor, loss_func = forward_step_func(data_iterator, model) +[rank10]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank10]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step +[rank10]: (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator) +[rank10]: ^^^^^^^^^^^^^^^^^^^^^^^^ +[rank10]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch +[rank10]: batch = next(global_batches) +[rank10]: ^^^^^^^^^^^^^^^^^^^^ +[rank10]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches +[rank10]: attention_mask = torch.ones( +[rank10]: ^^^^^^^^^^^ +[rank10]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 8192.00 GiB. GPU 2 has a total capacity of 139.81 GiB of which 131.40 GiB is free. Including non-PyTorch memory, this process has 8.40 GiB memory in use. Of the allocated memory 6.24 GiB is allocated by PyTorch, and 684.39 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) +[rank11]: Traceback (most recent call last): +[rank11]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in +[rank11]: pretrain( +[rank11]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain +[rank11]: iteration, num_floating_point_operations_so_far = train( +[rank11]: ^^^^^^ +[rank11]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train +[rank11]: ) = train_step( +[rank11]: ^^^^^^^^^^^ +[rank11]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step +[rank11]: losses_reduced = forward_backward_func( +[rank11]: ^^^^^^^^^^^^^^^^^^^^^^ +[rank11]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining +[rank11]: output_tensor, num_tokens = forward_step( +[rank11]: ^^^^^^^^^^^^^ +[rank11]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step +[rank11]: output_tensor, loss_func = forward_step_func(data_iterator, model) +[rank11]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank11]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step +[rank11]: (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator) +[rank11]: ^^^^^^^^^^^^^^^^^^^^^^^^ +[rank11]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch +[rank11]: batch = next(global_batches) +[rank11]: ^^^^^^^^^^^^^^^^^^^^ +[rank11]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches +[rank11]: attention_mask = torch.ones( +[rank11]: ^^^^^^^^^^^ +[rank11]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 8192.00 GiB. GPU 3 has a total capacity of 139.81 GiB of which 131.42 GiB is free. Including non-PyTorch memory, this process has 8.39 GiB memory in use. Of the allocated memory 6.24 GiB is allocated by PyTorch, and 684.39 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) +[rank8]: Traceback (most recent call last): +[rank8]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in +[rank8]: pretrain( +[rank8]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain +[rank8]: iteration, num_floating_point_operations_so_far = train( +[rank8]: ^^^^^^ +[rank8]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train +[rank8]: ) = train_step( +[rank8]: ^^^^^^^^^^^ +[rank8]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step +[rank8]: losses_reduced = forward_backward_func( +[rank8]: ^^^^^^^^^^^^^^^^^^^^^^ +[rank8]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining +[rank8]: output_tensor, num_tokens = forward_step( +[rank8]: ^^^^^^^^^^^^^ +[rank8]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step +[rank8]: output_tensor, loss_func = forward_step_func(data_iterator, model) +[rank8]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank8]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step +[rank8]: (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator) +[rank8]: ^^^^^^^^^^^^^^^^^^^^^^^^ +[rank8]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch +[rank8]: batch = next(global_batches) +[rank8]: ^^^^^^^^^^^^^^^^^^^^ +[rank8]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches +[rank8]: attention_mask = torch.ones( +[rank8]: ^^^^^^^^^^^ +[rank8]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 8192.00 GiB. GPU 0 has a total capacity of 139.81 GiB of which 131.40 GiB is free. Including non-PyTorch memory, this process has 8.40 GiB memory in use. Of the allocated memory 6.24 GiB is allocated by PyTorch, and 684.39 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) +[rank12]: Traceback (most recent call last): +[rank12]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in +[rank12]: pretrain( +[rank12]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain +[rank12]: iteration, num_floating_point_operations_so_far = train( +[rank12]: ^^^^^^ +[rank12]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train +[rank12]: ) = train_step( +[rank12]: ^^^^^^^^^^^ +[rank12]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step +[rank12]: losses_reduced = forward_backward_func( +[rank12]: ^^^^^^^^^^^^^^^^^^^^^^ +[rank12]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining +[rank12]: output_tensor, num_tokens = forward_step( +[rank12]: ^^^^^^^^^^^^^ +[rank12]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step +[rank12]: output_tensor, loss_func = forward_step_func(data_iterator, model) +[rank12]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank12]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step +[rank12]: (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator) +[rank12]: ^^^^^^^^^^^^^^^^^^^^^^^^ +[rank12]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch +[rank12]: batch = next(global_batches) +[rank12]: ^^^^^^^^^^^^^^^^^^^^ +[rank12]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches +[rank12]: attention_mask = torch.ones( +[rank12]: ^^^^^^^^^^^ +[rank12]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 8192.00 GiB. GPU 4 has a total capacity of 139.81 GiB of which 131.40 GiB is free. Including non-PyTorch memory, this process has 8.40 GiB memory in use. Of the allocated memory 6.24 GiB is allocated by PyTorch, and 684.39 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) +[rank14]: Traceback (most recent call last): +[rank14]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in +[rank14]: pretrain( +[rank14]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain +[rank14]: iteration, num_floating_point_operations_so_far = train( +[rank14]: ^^^^^^ +[rank14]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train +[rank14]: ) = train_step( +[rank14]: ^^^^^^^^^^^ +[rank14]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step +[rank14]: losses_reduced = forward_backward_func( +[rank14]: ^^^^^^^^^^^^^^^^^^^^^^ +[rank14]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining +[rank14]: output_tensor, num_tokens = forward_step( +[rank14]: ^^^^^^^^^^^^^ +[rank14]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step +[rank14]: output_tensor, loss_func = forward_step_func(data_iterator, model) +[rank14]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank14]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step +[rank14]: (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator) +[rank14]: ^^^^^^^^^^^^^^^^^^^^^^^^ +[rank14]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch +[rank14]: batch = next(global_batches) +[rank14]: ^^^^^^^^^^^^^^^^^^^^ +[rank14]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches +[rank14]: attention_mask = torch.ones( +[rank14]: ^^^^^^^^^^^ +[rank14]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 8192.00 GiB. GPU 6 has a total capacity of 139.81 GiB of which 131.40 GiB is free. Including non-PyTorch memory, this process has 8.40 GiB memory in use. Of the allocated memory 6.24 GiB is allocated by PyTorch, and 684.39 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) +[rank5]: Traceback (most recent call last): +[rank5]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in +[rank5]: pretrain( +[rank5]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain +[rank5]: iteration, num_floating_point_operations_so_far = train( +[rank5]: ^^^^^^ +[rank5]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train +[rank5]: ) = train_step( +[rank5]: ^^^^^^^^^^^ +[rank5]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step +[rank5]: losses_reduced = forward_backward_func( +[rank5]: ^^^^^^^^^^^^^^^^^^^^^^ +[rank5]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining +[rank5]: output_tensor, num_tokens = forward_step( +[rank5]: ^^^^^^^^^^^^^ +[rank5]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step +[rank5]: output_tensor, loss_func = forward_step_func(data_iterator, model) +[rank5]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank5]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step +[rank5]: (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator) +[rank5]: ^^^^^^^^^^^^^^^^^^^^^^^^ +[rank5]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch +[rank5]: batch = next(global_batches) +[rank5]: ^^^^^^^^^^^^^^^^^^^^ +[rank5]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches +[rank5]: attention_mask = torch.ones( +[rank5]: ^^^^^^^^^^^ +[rank5]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 8192.00 GiB. GPU 5 has a total capacity of 139.81 GiB of which 131.40 GiB is free. Including non-PyTorch memory, this process has 8.40 GiB memory in use. Of the allocated memory 6.24 GiB is allocated by PyTorch, and 684.39 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) +[rank1]: Traceback (most recent call last): +[rank1]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in +[rank1]: pretrain( +[rank1]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain +[rank1]: iteration, num_floating_point_operations_so_far = train( +[rank1]: ^^^^^^ +[rank1]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train +[rank1]: ) = train_step( +[rank1]: ^^^^^^^^^^^ +[rank1]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step +[rank1]: losses_reduced = forward_backward_func( +[rank1]: ^^^^^^^^^^^^^^^^^^^^^^ +[rank1]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining +[rank1]: output_tensor, num_tokens = forward_step( +[rank1]: ^^^^^^^^^^^^^ +[rank1]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step +[rank1]: output_tensor, loss_func = forward_step_func(data_iterator, model) +[rank1]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank1]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step +[rank1]: (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator) +[rank1]: ^^^^^^^^^^^^^^^^^^^^^^^^ +[rank1]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch +[rank1]: batch = next(global_batches) +[rank1]: ^^^^^^^^^^^^^^^^^^^^ +[rank1]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches +[rank1]: attention_mask = torch.ones( +[rank1]: ^^^^^^^^^^^ +[rank1]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 8192.00 GiB. GPU 1 has a total capacity of 139.81 GiB of which 131.40 GiB is free. Including non-PyTorch memory, this process has 8.40 GiB memory in use. Of the allocated memory 6.24 GiB is allocated by PyTorch, and 684.39 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) +[rank6]: Traceback (most recent call last): +[rank6]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in +[rank6]: pretrain( +[rank6]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain +[rank6]: iteration, num_floating_point_operations_so_far = train( +[rank6]: ^^^^^^ +[rank6]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train +[rank6]: ) = train_step( +[rank6]: ^^^^^^^^^^^ +[rank6]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step +[rank6]: losses_reduced = forward_backward_func( +[rank6]: ^^^^^^^^^^^^^^^^^^^^^^ +[rank6]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining +[rank6]: output_tensor, num_tokens = forward_step( +[rank6]: ^^^^^^^^^^^^^ +[rank6]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step +[rank6]: output_tensor, loss_func = forward_step_func(data_iterator, model) +[rank6]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank6]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step +[rank6]: (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator) +[rank6]: ^^^^^^^^^^^^^^^^^^^^^^^^ +[rank6]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch +[rank6]: batch = next(global_batches) +[rank6]: ^^^^^^^^^^^^^^^^^^^^ +[rank6]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches +[rank6]: attention_mask = torch.ones( +[rank6]: ^^^^^^^^^^^ +[rank6]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 8192.00 GiB. GPU 6 has a total capacity of 139.81 GiB of which 131.42 GiB is free. Including non-PyTorch memory, this process has 8.39 GiB memory in use. Of the allocated memory 6.24 GiB is allocated by PyTorch, and 684.39 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) +[rank2]: Traceback (most recent call last): +[rank2]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in +[rank2]: pretrain( +[rank2]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain +[rank2]: iteration, num_floating_point_operations_so_far = train( +[rank2]: ^^^^^^ +[rank2]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train +[rank2]: ) = train_step( +[rank2]: ^^^^^^^^^^^ +[rank2]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step +[rank2]: losses_reduced = forward_backward_func( +[rank2]: ^^^^^^^^^^^^^^^^^^^^^^ +[rank2]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining +[rank2]: output_tensor, num_tokens = forward_step( +[rank2]: ^^^^^^^^^^^^^ +[rank2]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step +[rank2]: output_tensor, loss_func = forward_step_func(data_iterator, model) +[rank2]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank2]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step +[rank2]: (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator) +[rank2]: ^^^^^^^^^^^^^^^^^^^^^^^^ +[rank2]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch +[rank2]: batch = next(global_batches) +[rank2]: ^^^^^^^^^^^^^^^^^^^^ +[rank2]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches +[rank2]: attention_mask = torch.ones( +[rank2]: ^^^^^^^^^^^ +[rank2]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 8192.00 GiB. GPU 2 has a total capacity of 139.81 GiB of which 131.42 GiB is free. Including non-PyTorch memory, this process has 8.39 GiB memory in use. Of the allocated memory 6.24 GiB is allocated by PyTorch, and 684.39 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) +[rank0]: Traceback (most recent call last): +[rank0]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in +[rank0]: pretrain( +[rank0]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain +[rank0]: iteration, num_floating_point_operations_so_far = train( +[rank0]: ^^^^^^ +[rank0]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train +[rank0]: ) = train_step( +[rank0]: ^^^^^^^^^^^ +[rank0]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step +[rank0]: losses_reduced = forward_backward_func( +[rank0]: ^^^^^^^^^^^^^^^^^^^^^^ +[rank0]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining +[rank0]: output_tensor, num_tokens = forward_step( +[rank0]: ^^^^^^^^^^^^^ +[rank0]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step +[rank0]: output_tensor, loss_func = forward_step_func(data_iterator, model) +[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank0]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step +[rank0]: (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator) +[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^ +[rank0]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch +[rank0]: batch = next(global_batches) +[rank0]: ^^^^^^^^^^^^^^^^^^^^ +[rank0]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches +[rank0]: attention_mask = torch.ones( +[rank0]: ^^^^^^^^^^^ +[rank0]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 8192.00 GiB. GPU 0 has a total capacity of 139.81 GiB of which 131.42 GiB is free. Including non-PyTorch memory, this process has 8.39 GiB memory in use. Of the allocated memory 6.24 GiB is allocated by PyTorch, and 684.39 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) +[rank7]: Traceback (most recent call last): +[rank7]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in +[rank7]: pretrain( +[rank7]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain +[rank7]: iteration, num_floating_point_operations_so_far = train( +[rank7]: ^^^^^^ +[rank7]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train +[rank7]: ) = train_step( +[rank7]: ^^^^^^^^^^^ +[rank7]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step +[rank7]: losses_reduced = forward_backward_func( +[rank7]: ^^^^^^^^^^^^^^^^^^^^^^ +[rank7]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining +[rank7]: output_tensor, num_tokens = forward_step( +[rank7]: ^^^^^^^^^^^^^ +[rank7]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step +[rank7]: output_tensor, loss_func = forward_step_func(data_iterator, model) +[rank7]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank7]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step +[rank7]: (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator) +[rank7]: ^^^^^^^^^^^^^^^^^^^^^^^^ +[rank7]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch +[rank7]: batch = next(global_batches) +[rank7]: ^^^^^^^^^^^^^^^^^^^^ +[rank7]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches +[rank7]: attention_mask = torch.ones( +[rank7]: ^^^^^^^^^^^ +[rank7]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 8192.00 GiB. GPU 7 has a total capacity of 139.81 GiB of which 131.40 GiB is free. Including non-PyTorch memory, this process has 8.40 GiB memory in use. Of the allocated memory 6.24 GiB is allocated by PyTorch, and 684.39 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) +[rank3]: Traceback (most recent call last): +[rank3]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in +[rank3]: pretrain( +[rank3]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain +[rank3]: iteration, num_floating_point_operations_so_far = train( +[rank3]: ^^^^^^ +[rank3]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train +[rank3]: ) = train_step( +[rank3]: ^^^^^^^^^^^ +[rank3]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step +[rank3]: losses_reduced = forward_backward_func( +[rank3]: ^^^^^^^^^^^^^^^^^^^^^^ +[rank3]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining +[rank3]: output_tensor, num_tokens = forward_step( +[rank3]: ^^^^^^^^^^^^^ +[rank3]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step +[rank3]: output_tensor, loss_func = forward_step_func(data_iterator, model) +[rank3]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank3]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step +[rank3]: (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator) +[rank3]: ^^^^^^^^^^^^^^^^^^^^^^^^ +[rank3]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch +[rank3]: batch = next(global_batches) +[rank3]: ^^^^^^^^^^^^^^^^^^^^ +[rank3]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches +[rank3]: attention_mask = torch.ones( +[rank3]: ^^^^^^^^^^^ +[rank3]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 8192.00 GiB. GPU 3 has a total capacity of 139.81 GiB of which 131.40 GiB is free. Including non-PyTorch memory, this process has 8.40 GiB memory in use. Of the allocated memory 6.24 GiB is allocated by PyTorch, and 684.39 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) +[rank4]: Traceback (most recent call last): +[rank4]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in +[rank4]: pretrain( +[rank4]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain +[rank4]: iteration, num_floating_point_operations_so_far = train( +[rank4]: ^^^^^^ +[rank4]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train +[rank4]: ) = train_step( +[rank4]: ^^^^^^^^^^^ +[rank4]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step +[rank4]: losses_reduced = forward_backward_func( +[rank4]: ^^^^^^^^^^^^^^^^^^^^^^ +[rank4]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining +[rank4]: output_tensor, num_tokens = forward_step( +[rank4]: ^^^^^^^^^^^^^ +[rank4]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step +[rank4]: output_tensor, loss_func = forward_step_func(data_iterator, model) +[rank4]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank4]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step +[rank4]: (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator) +[rank4]: ^^^^^^^^^^^^^^^^^^^^^^^^ +[rank4]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch +[rank4]: batch = next(global_batches) +[rank4]: ^^^^^^^^^^^^^^^^^^^^ +[rank4]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches +[rank4]: attention_mask = torch.ones( +[rank4]: ^^^^^^^^^^^ +[rank4]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 8192.00 GiB. GPU 4 has a total capacity of 139.81 GiB of which 131.42 GiB is free. Including non-PyTorch memory, this process has 8.39 GiB memory in use. Of the allocated memory 6.24 GiB is allocated by PyTorch, and 684.39 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) +[rank9]:[W621 22:10:59.567885990 ProcessGroupNCCL.cpp:1476] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator()) +[rank13]:[W621 22:10:59.573695883 ProcessGroupNCCL.cpp:1476] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator()) +[rank11]:[W621 22:10:59.624036570 ProcessGroupNCCL.cpp:1476] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator()) +[rank15]:[W621 22:10:59.634106601 ProcessGroupNCCL.cpp:1476] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator()) +[rank5]:[W621 22:10:59.575454277 ProcessGroupNCCL.cpp:1476] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator()) +[rank1]:[W621 22:10:59.582626730 ProcessGroupNCCL.cpp:1476] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator()) +[rank7]:[W621 22:10:59.636085793 ProcessGroupNCCL.cpp:1476] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator()) +[rank3]:[W621 22:10:59.736824570 ProcessGroupNCCL.cpp:1476] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator()) +W0621 22:11:01.059000 1989482 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 1989552 closing signal SIGTERM +W0621 22:11:01.063000 1989482 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 1989554 closing signal SIGTERM +W0621 22:11:01.065000 1989482 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 1989555 closing signal SIGTERM +W0621 22:11:01.066000 1989482 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 1989556 closing signal SIGTERM +W0621 22:11:01.067000 1989482 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 1989558 closing signal SIGTERM +W0621 22:11:01.071000 1989482 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 1989559 closing signal SIGTERM +W0621 22:11:01.116000 1065872 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 1065943 closing signal SIGTERM +W0621 22:11:01.123000 1065872 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 1065944 closing signal SIGTERM +W0621 22:11:01.124000 1065872 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 1065945 closing signal SIGTERM +W0621 22:11:01.126000 1065872 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 1065946 closing signal SIGTERM +W0621 22:11:01.127000 1065872 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 1065947 closing signal SIGTERM +W0621 22:11:01.129000 1065872 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 1065949 closing signal SIGTERM +W0621 22:11:01.149000 1065872 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 1065950 closing signal SIGTERM +E0621 22:11:01.228000 1989482 site-packages/torch/distributed/elastic/multiprocessing/api.py:874] failed (exitcode: 1) local_rank: 1 (pid: 1989553) of binary: /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/python3 +Traceback (most recent call last): + File "", line 198, in _run_module_as_main + File "", line 88, in _run_code + File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launch.py", line 207, in + main() + File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/typing_extensions.py", line 3253, in wrapper + return arg(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^ + File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launch.py", line 203, in main + launch(args) + File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launch.py", line 188, in launch + run(args) + File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/run.py", line 883, in run + elastic_launch( + File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launcher/api.py", line 139, in __call__ + return launch_agent(self._config, self._entrypoint, list(args)) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launcher/api.py", line 270, in launch_agent + raise ChildFailedError( +torch.distributed.elastic.multiprocessing.errors.ChildFailedError: +============================================================ +./pretrain_gpt_profile.py FAILED +------------------------------------------------------------ +Failures: +[1]: + time : 2025-06-21_22:11:01 + host : fs-mbz-gpu-286 + rank : 13 (local_rank: 5) + exitcode : 1 (pid: 1989557) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +------------------------------------------------------------ +Root Cause (first observed failure): +[0]: + time : 2025-06-21_22:11:01 + host : fs-mbz-gpu-286 + rank : 9 (local_rank: 1) + exitcode : 1 (pid: 1989553) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +============================================================ +E0621 22:11:01.493000 1065872 site-packages/torch/distributed/elastic/multiprocessing/api.py:874] failed (exitcode: 1) local_rank: 5 (pid: 1065948) of binary: /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/python3 +Traceback (most recent call last): + File "", line 198, in _run_module_as_main + File "", line 88, in _run_code + File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launch.py", line 207, in + main() + File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/typing_extensions.py", line 3253, in wrapper + return arg(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^ + File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launch.py", line 203, in main + launch(args) + File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launch.py", line 188, in launch + run(args) + File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/run.py", line 883, in run + elastic_launch( + File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launcher/api.py", line 139, in __call__ + return launch_agent(self._config, self._entrypoint, list(args)) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launcher/api.py", line 270, in launch_agent + raise ChildFailedError( +torch.distributed.elastic.multiprocessing.errors.ChildFailedError: +============================================================ +./pretrain_gpt_profile.py FAILED +------------------------------------------------------------ +Failures: + +------------------------------------------------------------ +Root Cause (first observed failure): +[0]: + time : 2025-06-21_22:11:01 + host : fs-mbz-gpu-239 + rank : 5 (local_rank: 5) + exitcode : 1 (pid: 1065948) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +============================================================ ++ set +x ++ set +x