* refactor tests
* refactor bloom model
* finish policy tests
* refactor tests
* fix test pure pipeline
* remove test pipeline and cutdown launch process
* refactor tests
* refactor bloom model
* finish policy tests
* refactor tests
* fix test pure pipeline
* remove test pipeline and cutdown launch process
* bloom policy
* llama pipeline forward and tests
* fix the output and attention_mask
* fix name
* bind argument to policy
* Revert "bloom policy"
This reverts commit 8dee68a0a2.
This policy should be revert and copied to feature/bloom
* revert the bloom changes
* cancel unneeded inputs
* gpt
* finish llama
* causal lm and sequence classification
* revision
* add pure pipeline test
* fixed version
* fixed version
* pure pipeline
* bloom policy
* llama pipeline forward and tests
* fix the output and attention_mask
* fix name
* bind argument to policy
* Revert "bloom policy"
This reverts commit 8dee68a0a2.
This policy should be revert and copied to feature/bloom
* revert the bloom changes
* cancel unneeded inputs
* gpt
* finish llama
* causal lm and sequence classification
* revision
* add pure pipeline test
* finish some bert models
* finish all bert models
* finish bert tests
* fix bugs
* fix bugs
* fix test pipeline
* fix data gen for qa
* update the set pipeline forward
* shared params
* fix bugs
* add pipeline policy and bert forward to be done
* add bertmodel pipeline forward and make tests
* add Bert_Policy and test for policy
* update formatting
* update formatting
* update the code
* fix bugs
* fix name confilt
* add bloom model and policy ,revise the base class of policy
* revise
* revision
* add bert_for_pretraining
* add bert_for_pretraining forward and policy
* fix typos
* cancel warning
* change the imediate output to default dict
* change the default output of get_shared_params
* add pipeline policy and bert forward to be done
* add bertmodel pipeline forward and make tests
* add Bert_Policy and test for policy
* update formatting
* update formatting
* update the code
* fix bugs
* fix name confilt
* add bloom model and policy ,revise the base class of policy
* revise
* revision
* add bert_for_pretraining
* add pipeline policy and bert forward to be done
* add bertmodel pipeline forward and make tests
* add Bert_Policy and test for policy
* update formatting
* update formatting
* update the code
* fix bugs
* fix name confilt
* add DAG test case
* fix datarace by adjusting theposition of lock
* polish code
* fix pytest for middleware
* remove test
Co-authored-by: Ziyue Jiang <ziyue.jiang@gmail.com>
* use Topo class to rewrite DAG
* polish code
* polish code
* polish code
* add comment
* add else to unended if
Co-authored-by: Ziyue Jiang <ziyue.jiang@gmail.com>
* add DAG to split_module
* add comment
* add test case for DAG
* remove print
* add DAG middleware in scheduler
* add test case for scheduler
* remove break
* recover old lifecycle
Co-authored-by: Ziyue Jiang <ziyue.jiang@gmail.com>
* [pipeline/tuning] improve dispatch performance both time and space cost
* [pipeline/converge] add interface for testing convergence
* [NFC] polish colossalai/utils/multi_tensor_apply/multi_tensor_apply.py code style
* Update PipelineBase.py
* [pipeline/chimera] reconstruct PipelineBase and Worker to support more feasible custom schedule | finish Chimera
* [pipeline/chimera] test chimera | fix bug of initializing
* [pipeline/pytree] add pytree to process args and kwargs | provide to process args and kwargs after forward
* [pipeline/fix-bug] num_microbatches support any integrate | stable chimera | launch tool for rpc pp framework
* [pipeline/tuning] improve dispatch performance both time and space cost
* [pipeline/converge] add interface for testing convergence
* [NFC] polish colossalai/utils/multi_tensor_apply/multi_tensor_apply.py code style
* Update PipelineBase.py
* [pipeline/chimera] reconstruct PipelineBase and Worker to support more feasible custom schedule | finish Chimera
* [pipeline/chimera] test chimera | fix bug of initializing
* [pipeline/pytree] add pytree to process args and kwargs | provide to process args and kwargs after forward
* [pipeline/tuning] improve dispatch performance both time and space cost
* [pipeline/converge] add interface for testing convergence
* [NFC] polish colossalai/utils/multi_tensor_apply/multi_tensor_apply.py code style
* Update PipelineBase.py
* [pipeline/chimera] reconstruct PipelineBase and Worker to support more feasible custom schedule | finish Chimera
* [pipeline/chimera] test chimera | fix bug of initializing
* [pipeline/pytree] add pytree to process args and kwargs | provide to process args and kwargs after forward
* [pipeline/tuning] improve dispatch performance both time and space cost
* [pipeline/converge] add interface for testing convergence
* [NFC] polish colossalai/utils/multi_tensor_apply/multi_tensor_apply.py code style
* Update PipelineBase.py
* [pipeline/chimera] reconstruct PipelineBase and Worker to support more feasible custom schedule | finish Chimera
* [pipeline/chimera] test chimera | fix bug of initializing
* [pipeline/tuning] improve dispatch performance both time and space cost
* [pipeline/converge] add interface for testing convergence
* [NFC] polish colossalai/utils/multi_tensor_apply/multi_tensor_apply.py code style
* Update PipelineBase.py
* [pipeline/chimera] reconstruct PipelineBase and Worker to support more feasible custom schedule | finish Chimera
* support p2p communication with any type of object | pass test
* reconstruct pipeline schedule with p2p_v2.py(support communication with List[Any]) | pass test
* [engin/schedule] use p2p_v2 to recontruct pipeline_schedule
* [pipeline/rpc] implement a demo for PP with cuda rpc framework
* [pipeline/rpc] support interleaving | fix checkpoint bug | change logic when dispatch data in work_list to ensure steady 1F1B
* [pipeline/rpc] implement distributed optimizer | test with assert_close
* [pipeline/rpc] implement distributed optimizer | test with assert_close
* [pipeline/rpc] update outstanding mechanism | optimize dispatching strategy
* [pipeline/rpc] update outstanding mechanism | optimize dispatching strategy
* [pipeline/rpc] update outstanding mechanism | optimize dispatching strategy
* [pipeline/pipleline_process_group] finish PipelineProcessGroup to manage local abd global rank in TP,DP and PP
* [pipeline/pipleline_process_group] remove comment
* [pipeline/pipleline_process_group] remove comment
* [pipeline/pipleline_process_group] skip process group test
* [pipeline/pipleline_process_group] remove test named function
* support p2p communication with any type of object | pass test
* reconstruct pipeline schedule with p2p_v2.py(support communication with List[Any]) | pass test
* [engin/schedule] use p2p_v2 to recontruct pipeline_schedule
* [pipeline/rpc] implement a demo for PP with cuda rpc framework
* [pipeline/rpc] support interleaving | fix checkpoint bug | change logic when dispatch data in work_list to ensure steady 1F1B
* [pipeline/rpc] implement distributed optimizer | test with assert_close
* [pipeline/rpc] implement distributed optimizer | test with assert_close
* [pipeline/rpc] update outstanding mechanism | optimize dispatching strategy
* [pipeline/rpc] update outstanding mechanism | optimize dispatching strategy
* [pipeline/rpc] update outstanding mechanism | optimize dispatching strategy
* support p2p communication with any type of object | pass test
* reconstruct pipeline schedule with p2p_v2.py(support communication with List[Any]) | pass test
* [engin/schedule] use p2p_v2 to recontruct pipeline_schedule
* [pipeline/rpc] implement a demo for PP with cuda rpc framework
* [pipeline/rpc] support interleaving | fix checkpoint bug | change logic when dispatch data in work_list to ensure steady 1F1B
* [pipeline/rpc] implement distributed optimizer | test with assert_close
* [pipeline/rpc] implement distributed optimizer | test with assert_close
* support p2p communication with any type of object | pass test
* reconstruct pipeline schedule with p2p_v2.py(support communication with List[Any]) | pass test
* [engin/schedule] use p2p_v2 to recontruct pipeline_schedule
* [pipeline/rpc] implement a demo for PP with cuda rpc framework
* [pipeline/rpc] support interleaving | fix checkpoint bug | change logic when dispatch data in work_list to ensure steady 1F1B
* support p2p communication with any type of object | pass test
* reconstruct pipeline schedule with p2p_v2.py(support communication with List[Any]) | pass test
* [engin/schedule] use p2p_v2 to recontruct pipeline_schedule
* [pipeline/rpc] implement a demo for PP with cuda rpc framework
* Delete p2p_v2.py
* Delete _pipeline_schedule_v2.py
* Delete test_object_list_p2p_v2.py
* Delete test_boardcast_send_recv_v2.py
* Delete test_cifar_with_data_pipeline_tensor_v2.py
flybird11111