diff --git "a/attnserver.run_attnserver.slurm.sh.343198.err.log" "b/attnserver.run_attnserver.slurm.sh.343198.err.log" --- "a/attnserver.run_attnserver.slurm.sh.343198.err.log" +++ "b/attnserver.run_attnserver.slurm.sh.343198.err.log" @@ -46036,3 +46036,1367 @@ W0621 21:13:23.166000 720480 site-packages/torch/distributed/run.py:766] W0621 21:13:23.166000 720480 site-packages/torch/distributed/run.py:766] ***************************************** W0621 21:13:23.166000 720480 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 21:13:23.166000 720480 site-packages/torch/distributed/run.py:766] ***************************************** +[rank8]:[W621 21:13:46.505198100 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. +[rank24]:[W621 21:13:46.163961186 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 24] 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. +[rank16]:[W621 21:13:46.330934538 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 16] 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. +[rank1]:[W621 21:13:46.469035347 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. +[rank17]:[W621 21:13:46.392995352 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 17] 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. +[rank0]:[W621 21:13:46.470625983 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. +[rank25]:[W621 21:13:46.524214314 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 25] 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. +[rank9]:[W621 21:13:46.905157855 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. +[rank11]:[W621 21:13:46.909014019 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. +[rank3]:[W621 21:13:46.488804945 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. +[rank19]:[W621 21:13:46.413217736 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 19] 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. +[rank27]:[W621 21:13:46.543368559 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 27] 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. +[rank21]:[W621 21:13:46.423876517 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 21] 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. +[rank6]:[W621 21:13:46.501400877 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. +[rank22]:[W621 21:13:46.425170291 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 22] 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. +[rank5]:[W621 21:13:46.502423395 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 21:13:46.502488113 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 21:13:46.503269430 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. +[rank30]:[W621 21:13:46.556585329 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 30] 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. +[rank29]:[W621 21:13:46.556669418 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 29] 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. +[rank28]:[W621 21:13:46.556802374 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 28] 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. +[rank20]:[W621 21:13:46.427256951 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 20] 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. +[rank31]:[W621 21:13:46.557820950 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 31] 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 21:13:46.926537870 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. +[rank14]:[W621 21:13:46.929227322 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. +[rank12]:[W621 21:13:46.931429860 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. +[rank15]:[W621 21:13:46.932518338 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. +[rank2]:[W621 21:13:46.511451034 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. +[rank23]:[W621 21:13:46.437354709 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 23] 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. +[rank10]:[W621 21:13:46.935298302 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. +[rank26]:[W621 21:13:46.567501541 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 26] 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. +[rank18]:[W621 21:13:46.444518401 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 18] 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. +/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/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( +/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( +[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 133.15 GiB is free. Including non-PyTorch memory, this process has 6.65 GiB memory in use. Of the allocated memory 4.68 GiB is allocated by PyTorch, and 463.51 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 133.14 GiB is free. Including non-PyTorch memory, this process has 6.67 GiB memory in use. Of the allocated memory 4.68 GiB is allocated by PyTorch, and 463.51 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 133.14 GiB is free. Including non-PyTorch memory, this process has 6.67 GiB memory in use. Of the allocated memory 4.68 GiB is allocated by PyTorch, and 463.51 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 8192.00 GiB. GPU 1 has a total capacity of 139.81 GiB of which 133.15 GiB is free. Including non-PyTorch memory, this process has 6.65 GiB memory in use. Of the allocated memory 4.68 GiB is allocated by PyTorch, and 463.51 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]: ^^^^^^^^^^^^^^^^^^^^^^ +[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 +[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]: ^^^^^^^^^^^^^^^^^^^^^^^^ +[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]: ^^^^^^^^^^^^^^^^^^^^ +[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 133.14 GiB is free. Including non-PyTorch memory, this process has 6.67 GiB memory in use. Of the allocated memory 4.68 GiB is allocated by PyTorch, and 463.51 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) +[rank31]: Traceback (most recent call last): +[rank31]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in +[rank31]: pretrain( +[rank31]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain +[rank31]: iteration, num_floating_point_operations_so_far = train( +[rank31]: ^^^^^^ +[rank31]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train +[rank31]: ) = train_step( +[rank31]: ^^^^^^^^^^^ +[rank31]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step +[rank31]: losses_reduced = forward_backward_func( +[rank31]: ^^^^^^^^^^^^^^^^^^^^^^ +[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 133.16 GiB is free. Including non-PyTorch memory, this process has 6.65 GiB memory in use. Of the allocated memory 4.68 GiB is allocated by PyTorch, and 463.51 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( +[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]: ^^^^^^^^^^^^^^^^^^^^^^ +[rank31]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining +[rank31]: output_tensor, num_tokens = forward_step( +[rank31]: ^^^^^^^^^^^^^ +[rank31]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step +[rank31]: output_tensor, loss_func = forward_step_func(data_iterator, model) +[rank31]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank31]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step +[rank31]: (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator) +[rank31]: ^^^^^^^^^^^^^^^^^^^^^^^^ +[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]: ^^^^^^^^^^^^^ +[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]: ^^^^^^^^^^^^^^^^^^^^^^^^ +[rank31]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch +[rank31]: batch = next(global_batches) +[rank31]: ^^^^^^^^^^^^^^^^^^^^ +[rank31]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches +[rank31]: attention_mask = torch.ones( +[rank31]: ^^^^^^^^^^^ +[rank31]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 8192.00 GiB. GPU 7 has a total capacity of 139.81 GiB of which 133.17 GiB is free. Including non-PyTorch memory, this process has 6.63 GiB memory in use. Of the allocated memory 4.68 GiB is allocated by PyTorch, and 463.51 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]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step +[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 133.14 GiB is free. Including non-PyTorch memory, this process has 6.67 GiB memory in use. Of the allocated memory 4.68 GiB is allocated by PyTorch, and 463.51 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) +[rank30]: Traceback (most recent call last): +[rank30]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in +[rank30]: pretrain( +[rank30]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain +[rank30]: iteration, num_floating_point_operations_so_far = train( +[rank30]: ^^^^^^ +[rank30]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train +[rank30]: ) = train_step( +[rank30]: ^^^^^^^^^^^ +[rank30]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step +[rank30]: losses_reduced = forward_backward_func( +[rank30]: ^^^^^^^^^^^^^^^^^^^^^^ +[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]: ^^^^^^^^^^^ +[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]: ^^^^^^^^^^^^^^^^^^^^^^ +[rank30]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining +[rank30]: output_tensor, num_tokens = forward_step( +[rank30]: ^^^^^^^^^^^^^ +[rank30]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step +[rank30]: output_tensor, loss_func = forward_step_func(data_iterator, model) +[rank30]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank30]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step +[rank30]: (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator) +[rank30]: ^^^^^^^^^^^^^^^^^^^^^^^^ +[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 133.16 GiB is free. Including non-PyTorch memory, this process has 6.65 GiB memory in use. Of the allocated memory 4.68 GiB is allocated by PyTorch, and 463.51 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]: 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 +[rank30]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch +[rank30]: batch = next(global_batches) +[rank30]: ^^^^^^^^^^^^^^^^^^^^ +[rank30]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches +[rank30]: attention_mask = torch.ones( +[rank30]: ^^^^^^^^^^^ +[rank30]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 8192.00 GiB. GPU 6 has a total capacity of 139.81 GiB of which 133.16 GiB is free. Including non-PyTorch memory, this process has 6.65 GiB memory in use. Of the allocated memory 4.68 GiB is allocated by PyTorch, and 463.51 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]: 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]: ^^^^^^^^^^^ +[rank27]: Traceback (most recent call last): +[rank27]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in +[rank27]: pretrain( +[rank27]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain +[rank27]: iteration, num_floating_point_operations_so_far = train( +[rank27]: ^^^^^^ +[rank27]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train +[rank27]: ) = train_step( +[rank27]: ^^^^^^^^^^^ +[rank27]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step +[rank27]: losses_reduced = forward_backward_func( +[rank27]: ^^^^^^^^^^^^^^^^^^^^^^ +[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 133.15 GiB is free. Including non-PyTorch memory, this process has 6.65 GiB memory in use. Of the allocated memory 4.68 GiB is allocated by PyTorch, and 463.51 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) +[rank27]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining +[rank27]: output_tensor, num_tokens = forward_step( +[rank27]: ^^^^^^^^^^^^^ +[rank27]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step +[rank27]: output_tensor, loss_func = forward_step_func(data_iterator, model) +[rank27]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank27]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step +[rank27]: (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator) +[rank27]: ^^^^^^^^^^^^^^^^^^^^^^^^ +[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]: ^^^^^^^^^^^^^^^^^^^^^^ +[rank27]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch +[rank27]: batch = next(global_batches) +[rank27]: ^^^^^^^^^^^^^^^^^^^^ +[rank27]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches +[rank27]: attention_mask = torch.ones( +[rank27]: ^^^^^^^^^^^ +[rank27]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 8192.00 GiB. GPU 3 has a total capacity of 139.81 GiB of which 133.17 GiB is free. Including non-PyTorch memory, this process has 6.63 GiB memory in use. Of the allocated memory 4.68 GiB is allocated by PyTorch, and 463.51 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 +[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]: ^^^^^^^^^^^^^^^^^^^^^^^^ +[rank26]: Traceback (most recent call last): +[rank26]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in +[rank26]: pretrain( +[rank26]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain +[rank26]: iteration, num_floating_point_operations_so_far = train( +[rank26]: ^^^^^^ +[rank26]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train +[rank26]: ) = train_step( +[rank26]: ^^^^^^^^^^^ +[rank26]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step +[rank26]: losses_reduced = forward_backward_func( +[rank26]: ^^^^^^^^^^^^^^^^^^^^^^ +[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]: ^^^^^^^^^^^^^^^^^^^^ +[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 133.15 GiB is free. Including non-PyTorch memory, this process has 6.65 GiB memory in use. Of the allocated memory 4.68 GiB is allocated by PyTorch, and 463.51 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) +[rank26]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining +[rank26]: output_tensor, num_tokens = forward_step( +[rank26]: ^^^^^^^^^^^^^ +[rank26]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step +[rank26]: output_tensor, loss_func = forward_step_func(data_iterator, model) +[rank26]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank26]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step +[rank26]: (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator) +[rank26]: ^^^^^^^^^^^^^^^^^^^^^^^^ +[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 133.17 GiB is free. Including non-PyTorch memory, this process has 6.63 GiB memory in use. Of the allocated memory 4.68 GiB is allocated by PyTorch, and 463.51 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) +[rank26]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch +[rank26]: batch = next(global_batches) +[rank26]: ^^^^^^^^^^^^^^^^^^^^ +[rank26]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches +[rank26]: attention_mask = torch.ones( +[rank26]: ^^^^^^^^^^^ +[rank26]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 8192.00 GiB. GPU 2 has a total capacity of 139.81 GiB of which 133.16 GiB is free. Including non-PyTorch memory, this process has 6.65 GiB memory in use. Of the allocated memory 4.68 GiB is allocated by PyTorch, and 463.51 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) +[rank22]: Traceback (most recent call last): +[rank22]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in +[rank22]: pretrain( +[rank22]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain +[rank22]: iteration, num_floating_point_operations_so_far = train( +[rank22]: ^^^^^^ +[rank22]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train +[rank22]: ) = train_step( +[rank22]: ^^^^^^^^^^^ +[rank22]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step +[rank22]: losses_reduced = forward_backward_func( +[rank22]: ^^^^^^^^^^^^^^^^^^^^^^ +[rank24]: Traceback (most recent call last): +[rank24]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in +[rank24]: pretrain( +[rank24]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain +[rank24]: iteration, num_floating_point_operations_so_far = train( +[rank24]: ^^^^^^ +[rank24]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train +[rank24]: ) = train_step( +[rank24]: ^^^^^^^^^^^ +[rank24]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step +[rank24]: losses_reduced = forward_backward_func( +[rank24]: ^^^^^^^^^^^^^^^^^^^^^^ +[rank22]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining +[rank22]: output_tensor, num_tokens = forward_step( +[rank22]: ^^^^^^^^^^^^^ +[rank22]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step +[rank22]: output_tensor, loss_func = forward_step_func(data_iterator, model) +[rank22]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank22]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step +[rank22]: (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator) +[rank22]: ^^^^^^^^^^^^^^^^^^^^^^^^ +[rank24]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining +[rank24]: output_tensor, num_tokens = forward_step( +[rank24]: ^^^^^^^^^^^^^ +[rank24]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step +[rank24]: output_tensor, loss_func = forward_step_func(data_iterator, model) +[rank24]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank24]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step +[rank24]: (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator) +[rank24]: ^^^^^^^^^^^^^^^^^^^^^^^^ +[rank22]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch +[rank22]: batch = next(global_batches) +[rank22]: ^^^^^^^^^^^^^^^^^^^^ +[rank22]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches +[rank22]: attention_mask = torch.ones( +[rank22]: ^^^^^^^^^^^ +[rank22]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 8192.00 GiB. GPU 6 has a total capacity of 139.81 GiB of which 133.15 GiB is free. Including non-PyTorch memory, this process has 6.65 GiB memory in use. Of the allocated memory 4.68 GiB is allocated by PyTorch, and 463.51 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) +[rank24]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch +[rank24]: batch = next(global_batches) +[rank24]: ^^^^^^^^^^^^^^^^^^^^ +[rank24]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches +[rank24]: attention_mask = torch.ones( +[rank24]: ^^^^^^^^^^^ +[rank24]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 8192.00 GiB. GPU 0 has a total capacity of 139.81 GiB of which 133.16 GiB is free. Including non-PyTorch memory, this process has 6.65 GiB memory in use. Of the allocated memory 4.68 GiB is allocated by PyTorch, and 463.51 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 +[rank20]: Traceback (most recent call last): +[rank20]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in +[rank20]: pretrain( +[rank20]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain +[rank20]: iteration, num_floating_point_operations_so_far = train( +[rank20]: ^^^^^^ +[rank20]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train +[rank20]: ) = train_step( +[rank20]: ^^^^^^^^^^^ +[rank20]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step +[rank20]: losses_reduced = forward_backward_func( +[rank20]: ^^^^^^^^^^^^^^^^^^^^^^ +[rank28]: Traceback (most recent call last): +[rank28]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in +[rank28]: pretrain( +[rank28]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain +[rank28]: iteration, num_floating_point_operations_so_far = train( +[rank28]: ^^^^^^ +[rank28]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train +[rank28]: ) = train_step( +[rank28]: ^^^^^^^^^^^ +[rank28]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step +[rank28]: losses_reduced = forward_backward_func( +[rank28]: ^^^^^^^^^^^^^^^^^^^^^^ +[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]: ^^^^^^^^^^^^^^^^^^^^ +[rank20]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining +[rank20]: output_tensor, num_tokens = forward_step( +[rank20]: ^^^^^^^^^^^^^ +[rank20]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step +[rank28]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining +[rank28]: output_tensor, num_tokens = forward_step( +[rank28]: ^^^^^^^^^^^^^ +[rank28]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step +[rank28]: output_tensor, loss_func = forward_step_func(data_iterator, model) +[rank28]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank28]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step +[rank28]: (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator) +[rank28]: ^^^^^^^^^^^^^^^^^^^^^^^^ +[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 133.16 GiB is free. Including non-PyTorch memory, this process has 6.65 GiB memory in use. Of the allocated memory 4.68 GiB is allocated by PyTorch, and 463.51 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) +[rank20]: output_tensor, loss_func = forward_step_func(data_iterator, model) +[rank20]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank20]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step +[rank20]: (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator) +[rank20]: ^^^^^^^^^^^^^^^^^^^^^^^^ +[rank20]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch +[rank20]: batch = next(global_batches) +[rank20]: ^^^^^^^^^^^^^^^^^^^^ +[rank20]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches +[rank20]: attention_mask = torch.ones( +[rank20]: ^^^^^^^^^^^ +[rank28]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch +[rank28]: batch = next(global_batches) +[rank28]: ^^^^^^^^^^^^^^^^^^^^ +[rank28]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches +[rank28]: attention_mask = torch.ones( +[rank28]: ^^^^^^^^^^^ +[rank28]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 8192.00 GiB. GPU 4 has a total capacity of 139.81 GiB of which 133.16 GiB is free. Including non-PyTorch memory, this process has 6.65 GiB memory in use. Of the allocated memory 4.68 GiB is allocated by PyTorch, and 463.51 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 +[rank20]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 8192.00 GiB. GPU 4 has a total capacity of 139.81 GiB of which 133.15 GiB is free. Including non-PyTorch memory, this process has 6.65 GiB memory in use. Of the allocated memory 4.68 GiB is allocated by PyTorch, and 463.51 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) +[rank25]: Traceback (most recent call last): +[rank25]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in +[rank25]: pretrain( +[rank25]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain +[rank25]: iteration, num_floating_point_operations_so_far = train( +[rank25]: ^^^^^^ +[rank25]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train +[rank25]: ) = train_step( +[rank25]: ^^^^^^^^^^^ +[rank25]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step +[rank25]: losses_reduced = forward_backward_func( +[rank25]: ^^^^^^^^^^^^^^^^^^^^^^ +[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]: ^^^^^^^^^^^^^^^^^^^^ +[rank25]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining +[rank25]: output_tensor, num_tokens = forward_step( +[rank25]: ^^^^^^^^^^^^^ +[rank25]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step +[rank25]: output_tensor, loss_func = forward_step_func(data_iterator, model) +[rank25]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank25]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step +[rank25]: (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator) +[rank25]: ^^^^^^^^^^^^^^^^^^^^^^^^ +[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 133.17 GiB is free. Including non-PyTorch memory, this process has 6.63 GiB memory in use. Of the allocated memory 4.68 GiB is allocated by PyTorch, and 463.51 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) +[rank25]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch +[rank25]: batch = next(global_batches) +[rank25]: ^^^^^^^^^^^^^^^^^^^^ +[rank25]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches +[rank25]: attention_mask = torch.ones( +[rank25]: ^^^^^^^^^^^ +[rank25]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 8192.00 GiB. GPU 1 has a total capacity of 139.81 GiB of which 133.17 GiB is free. Including non-PyTorch memory, this process has 6.63 GiB memory in use. Of the allocated memory 4.68 GiB is allocated by PyTorch, and 463.51 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 +[rank29]: Traceback (most recent call last): +[rank29]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in +[rank29]: pretrain( +[rank29]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain +[rank29]: iteration, num_floating_point_operations_so_far = train( +[rank29]: ^^^^^^ +[rank29]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train +[rank29]: ) = train_step( +[rank29]: ^^^^^^^^^^^ +[rank29]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step +[rank29]: losses_reduced = forward_backward_func( +[rank29]: ^^^^^^^^^^^^^^^^^^^^^^ +[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]: ^^^^^^^^^^^^^^^^^^^^ +[rank29]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining +[rank29]: output_tensor, num_tokens = forward_step( +[rank29]: ^^^^^^^^^^^^^ +[rank29]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step +[rank29]: output_tensor, loss_func = forward_step_func(data_iterator, model) +[rank29]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank29]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step +[rank29]: (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator) +[rank29]: ^^^^^^^^^^^^^^^^^^^^^^^^ +[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 133.17 GiB is free. Including non-PyTorch memory, this process has 6.63 GiB memory in use. Of the allocated memory 4.68 GiB is allocated by PyTorch, and 463.51 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) +[rank29]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch +[rank29]: batch = next(global_batches) +[rank29]: ^^^^^^^^^^^^^^^^^^^^ +[rank29]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches +[rank29]: attention_mask = torch.ones( +[rank29]: ^^^^^^^^^^^ +[rank29]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 8192.00 GiB. GPU 5 has a total capacity of 139.81 GiB of which 133.17 GiB is free. Including non-PyTorch memory, this process has 6.63 GiB memory in use. Of the allocated memory 4.68 GiB is allocated by PyTorch, and 463.51 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]: ^^^^^^^^^^^^^^^^^^^^ +[rank23]: Traceback (most recent call last): +[rank23]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in +[rank23]: pretrain( +[rank23]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain +[rank23]: iteration, num_floating_point_operations_so_far = train( +[rank23]: ^^^^^^ +[rank23]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train +[rank23]: ) = train_step( +[rank23]: ^^^^^^^^^^^ +[rank23]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step +[rank23]: losses_reduced = forward_backward_func( +[rank23]: ^^^^^^^^^^^^^^^^^^^^^^ +[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 133.16 GiB is free. Including non-PyTorch memory, this process has 6.65 GiB memory in use. Of the allocated memory 4.68 GiB is allocated by PyTorch, and 463.51 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) +[rank23]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining +[rank23]: output_tensor, num_tokens = forward_step( +[rank23]: ^^^^^^^^^^^^^ +[rank23]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step +[rank23]: output_tensor, loss_func = forward_step_func(data_iterator, model) +[rank23]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank23]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step +[rank23]: (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator) +[rank23]: ^^^^^^^^^^^^^^^^^^^^^^^^ +[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 +[rank23]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch +[rank23]: batch = next(global_batches) +[rank23]: ^^^^^^^^^^^^^^^^^^^^ +[rank23]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches +[rank23]: attention_mask = torch.ones( +[rank23]: ^^^^^^^^^^^ +[rank23]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 8192.00 GiB. GPU 7 has a total capacity of 139.81 GiB of which 133.14 GiB is free. Including non-PyTorch memory, this process has 6.67 GiB memory in use. Of the allocated memory 4.68 GiB is allocated by PyTorch, and 463.51 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]: 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 133.17 GiB is free. Including non-PyTorch memory, this process has 6.63 GiB memory in use. Of the allocated memory 4.68 GiB is allocated by PyTorch, and 463.51 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) +[rank19]: Traceback (most recent call last): +[rank19]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in +[rank19]: pretrain( +[rank19]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain +[rank19]: iteration, num_floating_point_operations_so_far = train( +[rank19]: ^^^^^^ +[rank19]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train +[rank19]: ) = train_step( +[rank19]: ^^^^^^^^^^^ +[rank19]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step +[rank19]: losses_reduced = forward_backward_func( +[rank19]: ^^^^^^^^^^^^^^^^^^^^^^ +[rank19]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining +[rank19]: output_tensor, num_tokens = forward_step( +[rank19]: ^^^^^^^^^^^^^ +[rank19]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step +[rank19]: output_tensor, loss_func = forward_step_func(data_iterator, model) +[rank19]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank19]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step +[rank19]: (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator) +[rank19]: ^^^^^^^^^^^^^^^^^^^^^^^^ +[rank19]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch +[rank19]: batch = next(global_batches) +[rank19]: ^^^^^^^^^^^^^^^^^^^^ +[rank19]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches +[rank19]: attention_mask = torch.ones( +[rank19]: ^^^^^^^^^^^ +[rank19]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 8192.00 GiB. GPU 3 has a total capacity of 139.81 GiB of which 133.14 GiB is free. Including non-PyTorch memory, this process has 6.67 GiB memory in use. Of the allocated memory 4.68 GiB is allocated by PyTorch, and 463.51 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) +[rank16]: Traceback (most recent call last): +[rank16]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in +[rank16]: pretrain( +[rank16]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain +[rank16]: iteration, num_floating_point_operations_so_far = train( +[rank16]: ^^^^^^ +[rank16]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train +[rank16]: ) = train_step( +[rank16]: ^^^^^^^^^^^ +[rank16]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step +[rank16]: losses_reduced = forward_backward_func( +[rank16]: ^^^^^^^^^^^^^^^^^^^^^^ +[rank16]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining +[rank16]: output_tensor, num_tokens = forward_step( +[rank16]: ^^^^^^^^^^^^^ +[rank16]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step +[rank16]: output_tensor, loss_func = forward_step_func(data_iterator, model) +[rank16]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank16]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step +[rank16]: (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator) +[rank16]: ^^^^^^^^^^^^^^^^^^^^^^^^ +[rank16]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch +[rank16]: batch = next(global_batches) +[rank16]: ^^^^^^^^^^^^^^^^^^^^ +[rank16]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches +[rank16]: attention_mask = torch.ones( +[rank16]: ^^^^^^^^^^^ +[rank16]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 8192.00 GiB. GPU 0 has a total capacity of 139.81 GiB of which 133.15 GiB is free. Including non-PyTorch memory, this process has 6.65 GiB memory in use. Of the allocated memory 4.68 GiB is allocated by PyTorch, and 463.51 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) +[rank21]: Traceback (most recent call last): +[rank21]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in +[rank21]: pretrain( +[rank21]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain +[rank21]: iteration, num_floating_point_operations_so_far = train( +[rank21]: ^^^^^^ +[rank21]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train +[rank21]: ) = train_step( +[rank21]: ^^^^^^^^^^^ +[rank21]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step +[rank21]: losses_reduced = forward_backward_func( +[rank21]: ^^^^^^^^^^^^^^^^^^^^^^ +[rank21]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining +[rank21]: output_tensor, num_tokens = forward_step( +[rank21]: ^^^^^^^^^^^^^ +[rank21]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step +[rank21]: output_tensor, loss_func = forward_step_func(data_iterator, model) +[rank21]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank21]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step +[rank21]: (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator) +[rank21]: ^^^^^^^^^^^^^^^^^^^^^^^^ +[rank21]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch +[rank21]: batch = next(global_batches) +[rank21]: ^^^^^^^^^^^^^^^^^^^^ +[rank21]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches +[rank21]: attention_mask = torch.ones( +[rank21]: ^^^^^^^^^^^ +[rank21]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 8192.00 GiB. GPU 5 has a total capacity of 139.81 GiB of which 133.14 GiB is free. Including non-PyTorch memory, this process has 6.67 GiB memory in use. Of the allocated memory 4.68 GiB is allocated by PyTorch, and 463.51 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) +[rank17]: Traceback (most recent call last): +[rank17]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in +[rank17]: pretrain( +[rank17]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain +[rank17]: iteration, num_floating_point_operations_so_far = train( +[rank17]: ^^^^^^ +[rank17]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train +[rank17]: ) = train_step( +[rank17]: ^^^^^^^^^^^ +[rank17]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step +[rank17]: losses_reduced = forward_backward_func( +[rank17]: ^^^^^^^^^^^^^^^^^^^^^^ +[rank17]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining +[rank17]: output_tensor, num_tokens = forward_step( +[rank17]: ^^^^^^^^^^^^^ +[rank17]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step +[rank17]: output_tensor, loss_func = forward_step_func(data_iterator, model) +[rank17]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank17]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step +[rank17]: (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator) +[rank17]: ^^^^^^^^^^^^^^^^^^^^^^^^ +[rank17]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch +[rank17]: batch = next(global_batches) +[rank17]: ^^^^^^^^^^^^^^^^^^^^ +[rank17]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches +[rank17]: attention_mask = torch.ones( +[rank17]: ^^^^^^^^^^^ +[rank17]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 8192.00 GiB. GPU 1 has a total capacity of 139.81 GiB of which 133.14 GiB is free. Including non-PyTorch memory, this process has 6.67 GiB memory in use. Of the allocated memory 4.68 GiB is allocated by PyTorch, and 463.51 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) +[rank18]: Traceback (most recent call last): +[rank18]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in +[rank18]: pretrain( +[rank18]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain +[rank18]: iteration, num_floating_point_operations_so_far = train( +[rank18]: ^^^^^^ +[rank18]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train +[rank18]: ) = train_step( +[rank18]: ^^^^^^^^^^^ +[rank18]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step +[rank18]: losses_reduced = forward_backward_func( +[rank18]: ^^^^^^^^^^^^^^^^^^^^^^ +[rank18]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining +[rank18]: output_tensor, num_tokens = forward_step( +[rank18]: ^^^^^^^^^^^^^ +[rank18]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step +[rank18]: output_tensor, loss_func = forward_step_func(data_iterator, model) +[rank18]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank18]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step +[rank18]: (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator) +[rank18]: ^^^^^^^^^^^^^^^^^^^^^^^^ +[rank18]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch +[rank18]: batch = next(global_batches) +[rank18]: ^^^^^^^^^^^^^^^^^^^^ +[rank18]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches +[rank18]: attention_mask = torch.ones( +[rank18]: ^^^^^^^^^^^ +[rank18]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 8192.00 GiB. GPU 2 has a total capacity of 139.81 GiB of which 133.15 GiB is free. Including non-PyTorch memory, this process has 6.65 GiB memory in use. Of the allocated memory 4.68 GiB is allocated by PyTorch, and 463.51 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]:[W621 21:14:03.385365097 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 21:14:03.988768163 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()) +[rank30]:[W621 21:14:03.057366568 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()) +[rank4]:[W621 21:14:04.038154115 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 21:14:04.040870193 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()) +[rank22]:[W621 21:14:04.968448457 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()) +[rank10]:[W621 21:14:04.474511066 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 21:14:04.074838142 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()) +[rank23]:[W621 21:14:04.009154646 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()) +[rank21]:[W621 21:14:04.011255416 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()) +[rank27]:[W621 21:14:04.146535983 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 21:14:04.524822188 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()) +[rank14]:[W621 21:14:04.526780416 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 21:14:04.528661980 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()) +[rank31]:[W621 21:14:04.179381385 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()) +[rank28]:[W621 21:14:04.187947312 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()) +[rank29]:[W621 21:14:04.191527447 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()) +[rank25]:[W621 21:14:04.211440293 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 21:14:04.172352878 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()) +[rank26]:[W621 21:14:04.247634249 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()) +[rank6]:[W621 21:14:04.207173868 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()) +[rank2]:[W621 21:14:04.221343550 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()) +[rank20]:[W621 21:14:04.169038618 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 21:14:04.680568703 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()) +[rank12]:[W621 21:14:04.727330428 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()) +[rank17]:[W621 21:14:04.242847991 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()) +[rank19]:[W621 21:14:04.369797528 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()) +[rank18]:[W621 21:14:04.379964925 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 21:14:04.835000 720480 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 720571 closing signal SIGTERM +W0621 21:14:04.837000 720480 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 720572 closing signal SIGTERM +W0621 21:14:04.838000 720480 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 720573 closing signal SIGTERM +W0621 21:14:04.839000 720480 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 720574 closing signal SIGTERM +W0621 21:14:04.839000 720480 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 720575 closing signal SIGTERM +W0621 21:14:04.839000 720480 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 720576 closing signal SIGTERM +W0621 21:14:04.840000 720480 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 720577 closing signal SIGTERM +E0621 21:14:05.331000 720480 site-packages/torch/distributed/elastic/multiprocessing/api.py:874] failed (exitcode: 1) local_rank: 7 (pid: 720578) of binary: /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/python3 +W0621 21:14:05.366000 2065164 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 2065237 closing signal SIGTERM +W0621 21:14:05.374000 2065164 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 2065239 closing signal SIGTERM +W0621 21:14:05.375000 2065164 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 2065240 closing signal SIGTERM +W0621 21:14:05.375000 2065164 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 2065241 closing signal SIGTERM +W0621 21:14:05.375000 2065164 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 2065242 closing signal SIGTERM +W0621 21:14:05.376000 2065164 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 2065243 closing signal SIGTERM +W0621 21:14:05.376000 2065164 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 2065244 closing signal SIGTERM +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_21:14:04 + host : fs-mbz-gpu-188 + rank : 15 (local_rank: 7) + exitcode : 1 (pid: 720578) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +============================================================ +W0621 21:14:05.414000 1890057 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 1890128 closing signal SIGTERM +W0621 21:14:05.417000 1890057 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 1890129 closing signal SIGTERM +W0621 21:14:05.418000 1890057 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 1890130 closing signal SIGTERM +W0621 21:14:05.418000 1890057 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 1890132 closing signal SIGTERM +W0621 21:14:05.418000 1890057 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 1890134 closing signal SIGTERM +W0621 21:14:05.419000 1890057 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 1890135 closing signal SIGTERM +W0621 21:14:05.427000 963888 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 963960 closing signal SIGTERM +W0621 21:14:05.435000 963888 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 963961 closing signal SIGTERM +W0621 21:14:05.436000 963888 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 963962 closing signal SIGTERM +W0621 21:14:05.436000 963888 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 963964 closing signal SIGTERM +W0621 21:14:05.436000 963888 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 963965 closing signal SIGTERM +W0621 21:14:05.436000 963888 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 963966 closing signal SIGTERM +W0621 21:14:05.437000 963888 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 963967 closing signal SIGTERM ++ set +x +E0621 21:14:05.709000 1890057 site-packages/torch/distributed/elastic/multiprocessing/api.py:874] failed (exitcode: 1) local_rank: 3 (pid: 1890131) of binary: /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/python3 +W0621 21:14:05.722000 1890057 site-packages/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1292] The node 'fs-mbz-gpu-286_1890057_0' has failed to shutdown the rendezvous '343198' due to an error of type RendezvousConnectionError. +[W621 21:14:05.789193594 TCPStore.cpp:106] [c10d] sendBytes failed on SocketImpl(fd=4, addr=[fs-mbz-gpu-286]:43940, remote=[fs-mbz-gpu-188]:29500): Broken pipe +Exception raised from sendBytes at /pytorch/torch/csrc/distributed/c10d/Utils.hpp:653 (most recent call first): +frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string, std::allocator >) + 0x98 (0x153f453785e8 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libc10.so) +frame #1: + 0x5ba8afe (0x153f2e25aafe in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so) +frame #2: + 0x5baa358 (0x153f2e25c358 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so) +frame #3: + 0x5babb3e (0x153f2e25db3e in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so) +frame #4: c10d::TCPStore::doWait(c10::ArrayRef, std::allocator > >, std::chrono::duration >) + 0x1a6 (0x153f2e257ac6 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so) +frame #5: c10d::TCPStore::doGet(std::__cxx11::basic_string, std::allocator > const&) + 0x33 (0x153f2e257ea3 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so) +frame #6: c10d::TCPStore::get(std::__cxx11::basic_string, std::allocator > const&) + 0xab (0x153f2e258f8b in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so) +frame #7: + 0xc0f526 (0x153f3d58b526 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_python.so) +frame #8: + 0x37f17d (0x153f3ccfb17d in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_python.so) + +frame #26: + 0x29d90 (0x153f463d8d90 in /lib/x86_64-linux-gnu/libc.so.6) +frame #27: __libc_start_main + 0x80 (0x153f463d8e40 in /lib/x86_64-linux-gnu/libc.so.6) + +W0621 21:14:05.733000 1890057 site-packages/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1292] The node 'fs-mbz-gpu-286_1890057_0' has failed to shutdown the rendezvous '343198' due to an error of type RendezvousConnectionError. +[W621 21:14:05.799893472 TCPStore.cpp:106] [c10d] sendBytes failed on SocketImpl(fd=4, addr=[fs-mbz-gpu-286]:43940, remote=[fs-mbz-gpu-188]:29500): Broken pipe +Exception raised from sendBytes at /pytorch/torch/csrc/distributed/c10d/Utils.hpp:653 (most recent call first): +frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string, std::allocator >) + 0x98 (0x153f453785e8 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libc10.so) +frame #1: + 0x5ba8afe (0x153f2e25aafe in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so) +frame #2: + 0x5baa358 (0x153f2e25c358 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so) +frame #3: + 0x5babb3e (0x153f2e25db3e in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so) +frame #4: c10d::TCPStore::doWait(c10::ArrayRef, std::allocator > >, std::chrono::duration >) + 0x1a6 (0x153f2e257ac6 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so) +frame #5: c10d::TCPStore::doGet(std::__cxx11::basic_string, std::allocator > const&) + 0x33 (0x153f2e257ea3 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so) +frame #6: c10d::TCPStore::get(std::__cxx11::basic_string, std::allocator > const&) + 0xab (0x153f2e258f8b in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so) +frame #7: + 0xc0f526 (0x153f3d58b526 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_python.so) +frame #8: + 0x37f17d (0x153f3ccfb17d in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_python.so) + +frame #26: + 0x29d90 (0x153f463d8d90 in /lib/x86_64-linux-gnu/libc.so.6) +frame #27: __libc_start_main + 0x80 (0x153f463d8e40 in /lib/x86_64-linux-gnu/libc.so.6) + +W0621 21:14:05.743000 1890057 site-packages/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1292] The node 'fs-mbz-gpu-286_1890057_0' has failed to shutdown the rendezvous '343198' due to an error of type RendezvousConnectionError. +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_21:14:05 + host : fs-mbz-gpu-286 + rank : 29 (local_rank: 5) + exitcode : 1 (pid: 1890133) + 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_21:14:05 + host : fs-mbz-gpu-286 + rank : 27 (local_rank: 3) + exitcode : 1 (pid: 1890131) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +============================================================ +E0621 21:14:05.754000 2065164 site-packages/torch/distributed/elastic/multiprocessing/api.py:874] failed (exitcode: 1) local_rank: 1 (pid: 2065238) of binary: /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/python3 +W0621 21:14:05.768000 2065164 site-packages/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1292] The node 'fs-mbz-gpu-141_2065164_0' has failed to shutdown the rendezvous '343198' due to an error of type RendezvousConnectionError. +[W621 21:14:05.781829107 TCPStore.cpp:106] [c10d] sendBytes failed on SocketImpl(fd=4, addr=[fs-mbz-gpu-141]:51012, remote=[fs-mbz-gpu-188]:29500): Broken pipe +Exception raised from sendBytes at /pytorch/torch/csrc/distributed/c10d/Utils.hpp:653 (most recent call first): +frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string, std::allocator >) + 0x98 (0x14ebf97785e8 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libc10.so) +frame #1: + 0x5ba8afe (0x14ebe2a5aafe in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so) +frame #2: + 0x5baa358 (0x14ebe2a5c358 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so) +frame #3: + 0x5babb3e (0x14ebe2a5db3e in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so) +frame #4: c10d::TCPStore::doWait(c10::ArrayRef, std::allocator > >, std::chrono::duration >) + 0x1a6 (0x14ebe2a57ac6 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so) +frame #5: c10d::TCPStore::doGet(std::__cxx11::basic_string, std::allocator > const&) + 0x33 (0x14ebe2a57ea3 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so) +frame #6: c10d::TCPStore::get(std::__cxx11::basic_string, std::allocator > const&) + 0xab (0x14ebe2a58f8b in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so) +frame #7: + 0xc0f526 (0x14ebf1d8b526 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_python.so) +frame #8: + 0x37f17d (0x14ebf14fb17d in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_python.so) + +frame #26: + 0x29d90 (0x14ebfab04d90 in /lib/x86_64-linux-gnu/libc.so.6) +frame #27: __libc_start_main + 0x80 (0x14ebfab04e40 in /lib/x86_64-linux-gnu/libc.so.6) + +W0621 21:14:05.780000 2065164 site-packages/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1292] The node 'fs-mbz-gpu-141_2065164_0' has failed to shutdown the rendezvous '343198' due to an error of type RendezvousConnectionError. +[W621 21:14:05.792934315 TCPStore.cpp:106] [c10d] sendBytes failed on SocketImpl(fd=4, addr=[fs-mbz-gpu-141]:51012, remote=[fs-mbz-gpu-188]:29500): Broken pipe +Exception raised from sendBytes at /pytorch/torch/csrc/distributed/c10d/Utils.hpp:653 (most recent call first): +frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string, std::allocator >) + 0x98 (0x14ebf97785e8 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libc10.so) +frame #1: + 0x5ba8afe (0x14ebe2a5aafe in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so) +frame #2: + 0x5baa358 (0x14ebe2a5c358 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so) +frame #3: + 0x5babb3e (0x14ebe2a5db3e in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so) +frame #4: c10d::TCPStore::doWait(c10::ArrayRef, std::allocator > >, std::chrono::duration >) + 0x1a6 (0x14ebe2a57ac6 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so) +frame #5: c10d::TCPStore::doGet(std::__cxx11::basic_string, std::allocator > const&) + 0x33 (0x14ebe2a57ea3 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so) +frame #6: c10d::TCPStore::get(std::__cxx11::basic_string, std::allocator > const&) + 0xab (0x14ebe2a58f8b in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so) +frame #7: + 0xc0f526 (0x14ebf1d8b526 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_python.so) +frame #8: + 0x37f17d (0x14ebf14fb17d in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_python.so) + +frame #26: + 0x29d90 (0x14ebfab04d90 in /lib/x86_64-linux-gnu/libc.so.6) +frame #27: __libc_start_main + 0x80 (0x14ebfab04e40 in /lib/x86_64-linux-gnu/libc.so.6) + +W0621 21:14:05.790000 2065164 site-packages/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1292] The node 'fs-mbz-gpu-141_2065164_0' has failed to shutdown the rendezvous '343198' due to an error of type RendezvousConnectionError. +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_21:14:05 + host : fs-mbz-gpu-141 + rank : 1 (local_rank: 1) + exitcode : 1 (pid: 2065238) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +============================================================ +E0621 21:14:05.828000 963888 site-packages/torch/distributed/elastic/multiprocessing/api.py:874] failed (exitcode: 1) local_rank: 3 (pid: 963963) of binary: /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/python3 +W0621 21:14:05.841000 963888 site-packages/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1292] The node 'fs-mbz-gpu-239_963888_0' has failed to shutdown the rendezvous '343198' due to an error of type RendezvousConnectionError. +[W621 21:14:05.779398700 TCPStore.cpp:106] [c10d] sendBytes failed on SocketImpl(fd=4, addr=[fs-mbz-gpu-239]:47680, remote=[fs-mbz-gpu-188]:29500): Broken pipe +Exception raised from sendBytes at /pytorch/torch/csrc/distributed/c10d/Utils.hpp:653 (most recent call first): +frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string, std::allocator >) + 0x98 (0x14cd345785e8 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libc10.so) +frame #1: + 0x5ba8afe (0x14cd1d85aafe in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so) +frame #2: + 0x5baa358 (0x14cd1d85c358 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so) +frame #3: + 0x5babb3e (0x14cd1d85db3e in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so) +frame #4: c10d::TCPStore::doWait(c10::ArrayRef, std::allocator > >, std::chrono::duration >) + 0x1a6 (0x14cd1d857ac6 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so) +frame #5: c10d::TCPStore::doGet(std::__cxx11::basic_string, std::allocator > const&) + 0x33 (0x14cd1d857ea3 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so) +frame #6: c10d::TCPStore::get(std::__cxx11::basic_string, std::allocator > const&) + 0xab (0x14cd1d858f8b in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so) +frame #7: + 0xc0f526 (0x14cd2cb8b526 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_python.so) +frame #8: + 0x37f17d (0x14cd2c2fb17d in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_python.so) + +frame #26: + 0x29d90 (0x14cd358a4d90 in /lib/x86_64-linux-gnu/libc.so.6) +frame #27: __libc_start_main + 0x80 (0x14cd358a4e40 in /lib/x86_64-linux-gnu/libc.so.6) + +W0621 21:14:05.853000 963888 site-packages/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1292] The node 'fs-mbz-gpu-239_963888_0' has failed to shutdown the rendezvous '343198' due to an error of type RendezvousConnectionError. +[W621 21:14:05.790369534 TCPStore.cpp:106] [c10d] sendBytes failed on SocketImpl(fd=4, addr=[fs-mbz-gpu-239]:47680, remote=[fs-mbz-gpu-188]:29500): Broken pipe +Exception raised from sendBytes at /pytorch/torch/csrc/distributed/c10d/Utils.hpp:653 (most recent call first): +frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string, std::allocator >) + 0x98 (0x14cd345785e8 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libc10.so) +frame #1: + 0x5ba8afe (0x14cd1d85aafe in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so) +frame #2: + 0x5baa358 (0x14cd1d85c358 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so) +frame #3: + 0x5babb3e (0x14cd1d85db3e in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so) +frame #4: c10d::TCPStore::doWait(c10::ArrayRef, std::allocator > >, std::chrono::duration >) + 0x1a6 (0x14cd1d857ac6 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so) +frame #5: c10d::TCPStore::doGet(std::__cxx11::basic_string, std::allocator > const&) + 0x33 (0x14cd1d857ea3 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so) +frame #6: c10d::TCPStore::get(std::__cxx11::basic_string, std::allocator > const&) + 0xab (0x14cd1d858f8b in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so) +frame #7: + 0xc0f526 (0x14cd2cb8b526 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_python.so) +frame #8: + 0x37f17d (0x14cd2c2fb17d in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_python.so) + +frame #26: + 0x29d90 (0x14cd358a4d90 in /lib/x86_64-linux-gnu/libc.so.6) +frame #27: __libc_start_main + 0x80 (0x14cd358a4e40 in /lib/x86_64-linux-gnu/libc.so.6) + +W0621 21:14:05.863000 963888 site-packages/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1292] The node 'fs-mbz-gpu-239_963888_0' has failed to shutdown the rendezvous '343198' due to an error of type RendezvousConnectionError. +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_21:14:05 + host : fs-mbz-gpu-239 + rank : 19 (local_rank: 3) + exitcode : 1 (pid: 963963) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +============================================================ ++ set +x ++ set +x ++ set +x