* add coverage report

* define env vars in shared action

* reduce time for longest running tests

* fix broken shared action

* reduce test time

* reducing Pipeline test times

* further reducing test times

* rework Z3 test

* testing new mp.pool and persistent dist envs

* fix import

* reuse distributed environment for tests with lots of param combos

* fix for dist teardown

* fix pickling issue with pool cache

* actually fix pickling problem

* avoid running pool cache stuff on non-distributed tests

* fix issues with nested mp.pool

* fix for nested pools in Pipeline Engine

* re-add params

* update workflows with pytest opts

* implement feedback

* resolve race condition with port selection

* Update tests/unit/common.py

---------
Co-authored-by: NOlatunji Ruwase <olruwase@microsoft.com>

* update workflow triggers for merge queue

* add branch specifier to trigger

* utilize shorter tests for MII

* use cached torch download

* rework zero++ unit tests

* formatting

---------
Co-authored-by: NHeyangQin <heyangqin@microsoft.com>

* Disable AMD workflows in the YML

* Switch from PR to nightly so we can enable the flows here

* move torch19 tests to nightly

* make megatron apex install persistent on blob storage

This reverts commit 2b2be85f.

Co-authored-by: NStephen Youn <styoun@microsoft.com>
Co-authored-by: NArash Bakhtiari <arash@bakhtiari.org>
Co-authored-by: NCheng Li <pistasable@gmail.com>
Co-authored-by: NEthan Doe <yidoe@microsoft.com>
Co-authored-by: Nyidoe <68296935+yidoe@users.noreply.github.com>
Co-authored-by: NJeff Rasley <jerasley@microsoft.com>

* Add H100 workflow

* Switch to nightly from PR - will let us check stability first

* Fix apex installation

* Switch install flag from build-opt to global-opt to fix missing cpp_ext

* Try installing with support for newer pip

* Add build packaging

* Update to latest

* Pin to specific commit while pyproject.toml is fixed

* skip test for docs-only changes

* add missing skip to blog changes

* add fallback path for kernels used in megatron

* temporary numactl WA for SPR 56core

* adapt core allocation according to number of ranks

* add switch to turn on numactl

* detect number of cores on the system

* allow select a subset of the cores on the system to bind

* remove unneeded changes

* add ccl backend

* change nccl to ccl

* remove unused code

* add comm/ccl to ops

* initial ccl comm support

* first broadcast case passed

* add CCL_Backend to DeepSpeed

* support comm timer for CPU

* support barrier for comm backend

* support specify master address from deepspeed command line

* support pytorch 2.0

* remove 'block' from api

* Tweak for debug
Signed-off-by: NCao, Zhong Z <zhong.z.cao@intel.com>

* Remove unecessary directory
Signed-off-by: NCao, Zhong Z <zhong.z.cao@intel.com>

* Add bf16 kernel support for inference

* Add temporary torch implement for cpu inference

* Add softmax ops cpu fallback for inference

* bind cores to numa domain as well

* merge latest change in gma/numactl

* initial bf16 kernel support with fallback path

* initial fallback path for bloom kernel injection

* fix softmax attn mask

* check KMP_AFFINITY to avoid conflict with numactl

* New CCLBackend which utilize TorchBackend for initialization

* rollback last change because there is result error

* fix bloom injection policy TP could not work issue.

injection_policy={BloomBlock: ("self_attention.dense", "mlp.dense_4h_to_h")}

* Use TorchBackend to initialize CCLBackend, make behavior consistent

* remove comm under deepspeed/ops

* add license header

* code clean up

* fix format issue

* remove magic number in main address

* add caching support but not turn on by default

* change name of inference_cuda_module to inference_module

* Check for is_synchronized_device in accelerator before get Event

* fix typo

* Fix fallback path of softmax kernel on CUDA device for BF16 data type, because CUDA tril does not support BF16 datatype, enforce fp32 data type

* add cpu backend files

* change CPU_Accelerator op_builder_dir

* remove cpu_kernel_path

* using CPU_Accelerator on non-cuda device

* fix deepspeed.op_builder => deepspeed.ops.op_builder

* add alias for num_gpus: num_accelerators

* allow loading cpu_builder in build stage

* Assume cuda available if torch not installed

* add oneccl_binding_pt to requirements

* move oneccl-binding-pt to seperate requiremetns-cpu.txt

* add missing file

* use dependency_links in setuptools.setup() call for additional dependency links

* install oneccl_bind_pt in workflows

* change oneccl_bind_pt's version from 1.13 to 2.0

* use intel_exention_for_pytorch as indicator that CPU_Accelerator should be used

* Add indicator for Accelerator used

* change foo.c to foo.cpp

* exclude 'cpu' directory in CUDA op builder reflection

* add a cpu-inference workflow

* run cpu-inference workflow on self-hosted instance

* change cpu runs-on node to v100 node

* print out python version in workflow

* add verbose in pip command to understand oneccl_bind_pt install issue

* update cpu-inference workflow

* add a stage to detect instance instruction sets

* add back bf16 support for CPU inference

* enable autoTP for bloom
Signed-off-by: NWang, Yi A <yi.a.wang@intel.com>

* update workflow to detect cpu instruction sets

* temporary WA for Intel Extension for PyTorch AVX2 instructioon set detection

* change cpu-inference workflow machine to ubuntu-20.04

* add sharded checkpoint loading for AutoTP path to reduce the peak memory in initialization stage
Signed-off-by: NWang, Yi A <yi.a.wang@intel.com>

* enable policy for llama

* use a special build ipex to test avx2 detection fix

* fix format

* fix test fail issue
Signed-off-by: NWang, Yi A <yi.a.wang@intel.com>

* fix gptj sharded checkpoint loading problem
Signed-off-by: NWang, Yi A <yi.a.wang@intel.com>

* return a not implemented build in get_op_builder in cpu_backend

* support cpu device in tests

* use cpuinfo to extract number of CPUs

* use ~/tmp as transfomer cache rather than /blob/

* Add support for mpich launcher with prefer_deepspeed_comm

* add missing modification in accelerator

* enable IMPI launcher

* remove unused file and fix formatting

* clean up ccl.cpp

* Less confusing error message when certin op builder are not implemented

* Fix license header

* Add license header

* add license headers

* add license header

* fix cuda specific code in test

* update CPU workflow

* use numactl to bind to core

* allow bind_cores_to_rank in multi-node impi runner

* fix format error

* Remove InferenceBuilder

* fix format error in numa.py

* check whether op is in installed ops in ds_report.py

* allow override accelerator with DS_ACCELERATOR='cuda','cpu' or 'xpu'

* lazy init class_dict in CUDA_Accelerator to avoid cyclic initialization of CUDA_Accelerator

* put short path in the beginning in real_accelerator.py

* device_count return number of NUMA nodes

* fix typo

* install numactl in cpu workflow

* Follow comments

* Better implementation of device_count() and current_device()

* remove dependency_link for Intel Extension for DeepSpeed

* use check is_synchronized_device in timer only once

* remove env mapping WA in cpu_accelerator

* fix duplicate definition

* fix format error

* refine ccl backend selection

* move comments to the right place

* remove prefer_deepspeed_comm, use CCLBackend by default

* refractor fallback path

* Fix execution failure in kernel injection path

* do not refractory kernel injection fallback path in  residual_add because it contains function call with side-effect

* guard residual_add fallback path with environ DS_KI_FALLBACK=True

* fix format error

* add test for allreduce on CPU workflow

* fix format error

* Fallback to TorchBackend if CCLBackend kernel are not implemented

* Update Intel Extension for Pytorch installation link

* Don't specify version number of Intel Extension for PyTorch

* install oneCCL for CCLBackend

* fix link path for CPU comm kernels

* fix source oneCCL environment

* source oneCCL env before run UT

* Give more specific instruction when CCL_ROOT not defined

---------
Signed-off-by: NCao, Zhong Z <zhong.z.cao@intel.com>
Signed-off-by: NWang, Yi A <yi.a.wang@intel.com>
Co-authored-by: Nsdp <sdp@aia-sdp-spr-108864.jf.intel.com>
Co-authored-by: NCao, Zhong Z <zhong.z.cao@intel.com>
Co-authored-by: NZhenhuan Chen <zhenhuan.chen@intel.com>
Co-authored-by: Nbaodii <di.bao@intel.com>
Co-authored-by: NWang, Yi A <yi.a.wang@intel.com>
Co-authored-by: Njianan-gu <jianan.gu@intel.com>
Co-authored-by: NOlatunji Ruwase <olruwase@microsoft.com>
Co-authored-by: NLogan Adams <114770087+loadams@users.noreply.github.com>

Co-authored-by: NJeff Rasley <jerasley@microsoft.com>

* request log output

* add more details

Co-authored-by: NJeff Rasley <jerasley@microsoft.com>

* updated cupy install

* do non-isolated pip install

* Update action.yml

* update docs to reflect changes in deepspeed-chat training script

* add blogs to ignored changes in unit tests

* Update AMD workflows

* Update MI200 test flow to use torch latest

* Update tolerances to values that pass (will fix before completing PR)

* Revert chyanges to atol

* Rename workflows

* Fix CI badges

Co-authored-by: NLogan Adams <114770087+loadams@users.noreply.github.com>
Co-authored-by: NJeff Rasley <jerasley@microsoft.com>

* bump torch18 -> torch19
* fix gptj

---------
Co-authored-by: NJeff Rasley <jerasley@microsoft.com>

Co-authored-by: NJeff Rasley <jerasley@microsoft.com>