* add infer_struct and infer_config
* update codes
* change InferConfig
* Add hf_model_config to the engine
* rm _get_hf_model_config
* update codes
* made adjustments according to the feedback from the reviewer.
* update codes
* add ci test for config and struct
* Add the logic of the inference engine
* update engine and test
* Recover cache_manager.py
* add logger
* fix conflict
* update codes
* update codes
* update model and tokenizer
* fix add the logic about shardformer
* change kvcache_manager docstring
* add policy
* fix ci bug in test_kvcache_manager.py
* remove codes related o tokenizer and move model_policy
* fix code style
* add ordered_set to requirements-infer.txt
* Delete extra empty lines
* add ordered_set to requirements-test.txt
* [Inference] Add KVCache Manager
* function refactored
* add test for KVCache Manager
* add attr beam width
* Revise alloc func in CacheManager
* Fix docs and pytests
* add tp slicing for head number
* optimize shapes of tensors used as physical cache
* Apply using InferenceConfig on KVCacheManager
* rm duplicate config file
* Optimize cache allocation: use contiguous cache
* Fix config in pytest (and config)
* add infer_struct and infer_config
* update codes
* change InferConfig
* Add hf_model_config to the engine
* rm _get_hf_model_config
* update codes
* made adjustments according to the feedback from the reviewer.
* update codes
* add ci test for config and struct
* A more general _communicate
* feat: finish tree_flatten version p2p
* fix: update p2p api calls
---------
Co-authored-by: Wenhao Chen <cwher@outlook.com>
* test: add more p2p tests
* fix: remove send_forward_recv_forward as p2p op list need to use the same group
* fix: make send and receive atomic
* feat: update P2PComm fn
* feat: add metadata cache in 1f1b
* feat: add metadata cache in interleaved pp
* feat: modify is_xx_stage fn
* revert: add _broadcast_object_list
* feat: add interleaved pp in llama policy
* feat: set NCCL_BUFFSIZE in HybridParallelPlugin
* fix 3d checkpoint load when booster boost without optimizer
fix 3d checkpoint load when booster boost without optimizer
* test ci
* revert ci
* fix
fix
* [shardformer] implement policy for all GPT-J models and test
* [shardformer] support interleaved pipeline parallel for bert finetune
* [shardformer] shardformer support falcon (#4883)
* [shardformer]: fix interleaved pipeline for bert model (#5048)
* [hotfix]: disable seq parallel for gptj and falcon, and polish code (#5093)
* Add Mistral support for Shardformer (#5103)
* [shardformer] add tests to mistral (#5105)
---------
Co-authored-by: Pengtai Xu <henryxu880@gmail.com>
Co-authored-by: ppt0011 <143150326+ppt0011@users.noreply.github.com>
Co-authored-by: flybird11111 <1829166702@qq.com>
Co-authored-by: eric8607242 <e0928021388@gmail.com>
* [npu] setup device utils (#5047)
* [npu] add npu device support
* [npu] support low level zero
* [test] update npu zero plugin test
* [hotfix] fix import
* [test] recover tests
* [npu] gemini support npu (#5052)
* [npu] refactor device utils
* [gemini] support npu
* [example] llama2+gemini support npu
* [kernel] add arm cpu adam kernel (#5065)
* [kernel] add arm cpu adam
* [optim] update adam optimizer
* [kernel] arm cpu adam remove bf16 support
* Use p2p
* Cannot bidirectonal send p2p
* Refactor tensor creation and serialization in P2P
communication
* Fix llama forward args in flash attention
* Add flop estimate from megatron
* Support loading weight not in weight_map when strict=False in hybrid_parallel
* Use send_forward_recv_backward, etc in 1f1b
* Use dataclass for metdata
Remove torch.cuda.synchronize() as suggested
* Add comment about the torch.cuda.synchronize for potential error
* Typo
* Update hybrid_parallel_checkpoint_io.py
* Update p2p.py
* Update one_f_one_b.py
* Update p2p.py
---------
Co-authored-by: flybird11111 <1829166702@qq.com>
Yuanheng Zhao