GitLab

Root Cause:

1. '_get_all_test_cases()' was called in '_parser' while '_parser' was
defined in the base class.
2.  in '_get_all_test_cases()', cmd path was not included.

Fix:

1. Remove '_get_all_test_cases()' from '_parser'.
2. Construct path for cmd.

---------
Co-authored-by: hongtaozhang <hongtaozhang@microsoft.com>

**Description**

1. Fixed the bug that nvbandwidth benchmark need to handle 'N/A' values
in nvbandwidth cmd output.
2. Replaced the input format of test cases with a list.
3. Add nvbandwidth configuration example in default config files.

---------
Co-authored-by: hongtaozhang <hongtaozhang@microsoft.com>
Co-authored-by: Yifan Xiong <yifan.xiong@microsoft.com>

Added llama benchmark - training and inference in accordance with the
existing pytorch models implementation like gpt2, lstm etc.

- added llama fp8 unit test for better code coverage, to reduce memory
required
- updated transformers version >= 4.28.0 for LLamaConfig
- set tokenizers version <= 0.20.3 to avoid 0.20.4 version
[issues](https://github.com/huggingface/tokenizers/issues/1691

) with
py3.8
- added llama2 to tensorrt
- llama2 tests not added to test_tensorrt_inference_performance.py due
to large memory requirement for worker gpu. tests validated separately
on gh200

---------
Co-authored-by: dpatlolla <dpatlolla@microsoft.com>

Upgrade dependency versions in Azure pipeline:

* Remove Python 3.6 and add Python 3.10 for cpu-unit-test
* Upgrade CUDA from 11.1 to 12.4 for cuda-unit-test
* Update labels accordingly

---------
Co-authored-by: Dilip Patlolla <dilipreddi@gmail.com>

**Description**

Add nvbandwidth benchmark.

---------
Co-authored-by: hongtaozhang <hongtaozhang@microsoft.com>

**Description**
Add micro benchmark to measure general CPU bandwidth and latency without 'mlc'.

Test output:
```
{
"cpu-memory-bw-latency/return_code": 0,
"cpu-memory-bw-latency/mem_bandwidth_matrix_numa_0_1_bw": 5388.75021,
"cpu-memory-bw-latency/mem_bandwidth_matrix_numa_0_1_lat": 0.185571786,
"cpu-memory-bw-latency/mem_bandwidth_matrix_numa_1_0_bw": 4634.82028,
"cpu-memory-bw-latency/mem_bandwidth_matrix_numa_1_0_lat": 0.215758096,
}
```

---------
Co-authored-by: hongtaozhang <hongtaozhang@microsoft.com>

**Description**
Adds hipblasLt tuning to dist-inference cpp implementation.

**Description**

Cherry-pick bug fixes from v0.10.0 to main.

**Major Revisions**

* Benchmarks: Microbenchmark - Support different hipblasLt data types in dist_inference #590
* Benchmarks: Microbenchmark - Support in-place for NCCL/RCCL benchmark #591
* Bug Fix - Fix NUMA Domains Swap Issue in NDv4 Topology File #592
* Benchmarks: Microbenchmark - Add data type option for NCCL and RCCL tests #595
* Benchmarks: Bug Fix - Make metrics of dist-inference-cpp aligned with PyTorch version #596
* CI/CD - Add ndv5 topo file #597
* Benchmarks: Microbenchmark - Improve AMD GPU P2P performance with fine-grained GPU memory #593
* Benchmarks: Build Pipeline - fix nccl and nccl test version to 2.18.3 to resolve hang issue in cuda12.2 docker #599
* Dockerfile - Bug fix for rocm docker build and deploy #598
* Benchmarks: Microbenchmark - Adapt to hipblasLt data type changes #603
* Benchmarks: Micro benchmarks - Update hipblaslt metric unit to tflops #604
* Monitor - Upgrade pyrsmi to amdsmi python library. #601
* Benchmarks: Micro benchmarks - add fp8 and initialization for hipblaslt benchmark #605
* Dockerfile - Add rocm6.0 dockerfile #602
* Bug Fix - Bug fix for latest megatron-lm benchmark #600
* Docs - Upgrade version and release note #606
Co-authored-by: Ziyue Yang <ziyyang@microsoft.com>
Co-authored-by: Yang Wang <yangwang1@microsoft.com>
Co-authored-by: Yuting Jiang <yutingjiang@microsoft.com>
Co-authored-by: guoshzhao <guzhao@microsoft.com>

**Description**
Add distributed inference benchmark cpp implementation.

Benchmarks: Micro benchmark - Add one-to-all, all-to-one, all-to-all support to gpu_copy_bw_performance (#588)

**Description**
Add one-to-all, all-to-one, all-to-all support to
gpu_copy_bw_performance, and fix performance bug in gpu_copy

**Description**
Megatron-LM/Megatron-Deepspeed GPT pretrain benchmark

**Description**
Revise NCCL/RCCL benchmarks to graph mode add latency metrics.

**Description**
Benchmarks: micro benchmark - Support cpu-gpu and gpu-cpu in
ib-validation

**Major Revision**
- Support cpu-gpu and gpu-cpu in ib-validation


**Minor Revision**
- support multi msg size, multi direction, multi ib commands in
ib-validation

**Description**
add initialization options for rocm gemm flops.

**Description**
hipblaslt function benchmark and rebase cublaslt function benchmark.

**Description**
add int8 support for cublaslt function.

**Description**
remove cp ptx file in gpu burn test since the command is run inside
self.args.bin_dir dir.


https://github.com/microsoft/superbenchmark/blob/d246bab430adeb461072918a551b2e2b68c9bce5/superbench/benchmarks/micro_benchmarks/micro_base.py#L183

**Description**
add python code for DirectXGPUEncodingLatency.

**Description**
add python code for DirectXGPUCopy.

**Description**
add python code for DirecXGPUMemBw.

**Description**
add python code for DirectX core flops and init DirectX test pipeline.

**Major Revision**
- add python code for DirectX core flops 
- init DirectX test pipeline


**Minor Revision**
- add test for DirectX core flops

**Description**
add auto selecting algorithm support for cudnn functions.

**Major Revision**
- add auto selecting algorithm support for cudnn functions in source
code
- add 'auto_algo' option in benchmark
- add related test

* Update result parsing for newer tensorrt versions
* Update arguments when load torchvision models

**Description**
Model benchmarks can stop due to `num_steps` or `duration` config which
will take effect when the value is set greater than 0.
If both are set greater than 0, the earliest condition reached will
work.

**Description**

Cherry-pick bug fixes from v0.8.0 to main.

**Major Revisions**

* Monitor - Fix the cgroup version checking logic (#502)
* Benchmark - Fix matrix size overflow issue in cuBLASLt GEMM (#503)
* Fix wrong torch usage in communication wrapper for Distributed
Inference Benchmark (#505)
* Analyzer: Fix bug in python3.8 due to pandas api change (#504)
* Bug - Fix bug to get metric from cmd when error happens (#506)
* Monitor - Collect realtime GPU power when benchmarking (#507)
* Add num_workers argument in model benchmark (#511)
* Remove unreachable condition when write host list (#512)
* Update cuda11.8 image to cuda12.1 based on nvcr23.03 (#513)
* Doc - Fix wrong unit of cpu-memory-bw-latency in doc (#515)
* Docs - Upgrade version and release note (#508)
Co-authored-by: guoshzhao <guzhao@microsoft.com>
Co-authored-by: Ziyue Yang <ziyyang@microsoft.com>
Co-authored-by: Yuting Jiang <yutingjiang@microsoft.com>

**Description**
This PR adds a micro-benchmark of distributed model inference workloads.

**Major Revision**
- Add a new micro-benchmark dist-inference.
- Add corresponding example and unit tests.
- Update configuration files to include this new micro-benchmark.
- Update micro-benchmark README.

---------
Co-authored-by: Peng Cheng <chengpeng5555@outlook.com>

Support batch and shape range with multiplication factors in cublaslt
gemm benchmark.

**Description**

- Adding HPL benchmark

---------
Co-authored-by: Ubuntu <azureuser@sbtestvm.jzlku1oskncengjiado35wf1hd.ax.internal.cloudapp.net>
Co-authored-by: Peng Cheng <chengpeng5555@outlook.com>

**Description**

- Added stream benchmark
- Added stream unit test
- Added stream example
- Modified docker files to build stream

---------
Co-authored-by: Ubuntu <azureuser@sbtestvm.jzlku1oskncengjiado35wf1hd.ax.internal.cloudapp.net>
Co-authored-by: Peng Cheng <chengpeng5555@outlook.com>
Co-authored-by: Yifan Xiong <xiongyf@yandex.com>

Support traffic patterns under the different devices in NCCL/RCCL test
* change the metrics format if specified the pattern

Support FP8 in PyTorch BERT models:

* add fp8 hybrid/e4m3/e5m2 in precision arguments
* build BERT encoders with `te.TransformerLayer` to repalce
`transformers.BertModel`
* wrap forward steps with fp8 autocast

Integrate cublaslt-gemm micro-benchmark #451.

**Description**
 Add correctness check in cublas-function benchmark.

**Major Revision**
- add python code of correctness check in cublas-function benchmark and test

**Description**
Add stdout logging util module and enable real-time logging flushing in executor

**Major Revision**
- Add stdout logging util module to redirect stdout into file log
- enable stdout logging in executor to write benchmark output into both stdout and file `sb-bench.log`
- enable real-time log flushing in run_command of microbenchmarks through config `log_flushing`

**Minor Revision**
- add log_n_step args to enable regular step time log in model benchmarks 
- udpate related docs

**Description**
Add wait time option to resolve mem-bw unstable issue.

Benchmarks - Add support to allow list of custom config string in cudnn-functions and cublas-functions (#414)

**Description**
Add support to allow list of custom config string in cudnn-functions and cublas-functions.

**Description**

Cherry-pick bug fixes from v0.6.0 to main.

**Major Revisions**

* Enable latency test in ib traffic validation distributed benchmark (#396)
* Enhance parameter parsing to allow spaces in value (#397)
* Update apt packages in dockerfile (#398)
* Upgrade colorlog for NO_COLOR support (#404)
* Analyzer - Update error handling to support exit code of sb result diagnosis (#403)
* Analyzer - Make baseline file optional in data diagnosis and fix bugs (#399)
* Enhance timeout cleanup to avoid possible hanging (#405)
* Auto generate ibstat file by pssh (#402)
* Analyzer - Format int type and unify empty value to N/A in diagnosis output file (#406)
* Docs - Upgrade version and release note (#407)
* Docs - Fix issues in document (#408)
Co-authored-by: Yang Wang <yangwang1@microsoft.com>
Co-authored-by: Yuting Jiang <yutingjiang@microsoft.com>

* Gracefully exit when timeout, add corresponding log and return code.
* Set minimum timeout to 1 minute and enlarge Ansible timeout.

* Support topo-aware IB performance validation

Add a new pattern `topo-aware`, so the user can run IB performance
test based on VM's topology information. This way, the user can
validate the IB performance across VM pairs with different distance
as a quick test instead of pair-wise test.

To run with topo-aware pattern, user needs to specify three required
(and two optional) parameters in YAML config file:
--pattern	topo-aware
--ibstat	path to ibstat output
--ibnetdiscover	path to ibnetdiscover output
--min_dist	minimum distance of VM pairs (optional, default 2)
--max_dist	maximum distance of VM pairs (optional, default 6)

The newly added topo_aware module then parses the topology
information, builds a graph, and generates the VM pairs with
the specified distance (# hops).

The specified IB test will then be running across these
generated VM pairs.
Signed-off-by: Jie Zhang <jessezhang1010@gmail.com>

* Add description about topology aware ib traffic tests
Signed-off-by: Jie Zhang <jessezhang1010@gmail.com>

* Add unit test to verify generated topology aware config file

This commit adds unit test to verify the generated topology aware
config file is correct. To do so, four new data files are added in
order to invoke gen_topo_aware_config function to generate topology
aware config file, then compares it with the expected config file.
Signed-off-by: Jie Zhang <jessezhang1010@gmail.com>

* Fix lint issue on Azure pipeline
Signed-off-by: Jie Zhang <jessezhang1010@gmail.com>

Fix an unexpected result value (`-0.125`) issue in ib traffic benchmark when encountering `-1` in raw output
* Check if the value is valid before the base conversion
* Add a test case to cover this situation