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Adds opt-in deterministic training mode to SuperBench's PyTorch model
benchmarks. When enabled --enable-determinism. PyTorch deterministic
algorithms are enforced, and per-step numerical fingerprints (loss,
activation means) are recorded as metrics. These can be compared across
runs using the existing sb result diagnosis pipeline to verify bit-exact
reproducibility — useful for hardware validation and platform
comparison.
 
Flags added - 

--enable-determinism
--check-frequency: Number of steps after which you want the metrics to
be recorded
--deterministic-seed

Changes - 

Updated pytorch_base.py to handle deterministic settings, logging.
Added a new example script: pytorch_deterministic_example.py
Added a test file: test_pytorch_determinism_all.py to verify everything
works as expected.

Usage - 

Step 1: Run 1 - Run with --enable-determinism and the necessary metrics
will be recorded in the results-summary.jsonl file
Step 2: Generate the baseline file from the Run 1 results using - sb
result generate-baseline
Step 3: Run 2 - Run with --enable-determinism and the necessary metrics
will be recorded in the results-summary.jsonl file on a different
machine (or the same machine)
Step 4: Run diagnosis on the results generated from the 2 runs using the
- sb result diagnosis command

Note - 
1. Make sure all the parameters are constant between the 2 runs 
2. Running the diagnosis command requires the rules.yaml file

---------
Co-authored-by: Aishwarya Tonpe <aishwarya.tonpe25@gmail.com>
Co-authored-by: Ubuntu <rdadmin@HPCPLTNODE0.n3kgq4m0lhoednrx3hxtad2nha.cdmx.internal.cloudapp.net>

**Description**
Enable autotuning as an opt-in mode when benchmarking cublasLt via
`cublaslt_gemm`

The implementation is based on
https://github.com/NVIDIA/CUDALibrarySamples/blob/master/cuBLASLt/LtSgemmSimpleAutoTuning/sample_cublasLt_LtSgemmSimpleAutoTuning.cu

The behavior of original benchmark command remains unchanged, e.g.:
- `cublaslt_gemm -m 2048 -n 12288 -k 1536 -w10000 -i 1000 -t fp8e4m3`

The new opt-in options are `-a` (for autotune) and `-I` (for autotune
iterations, default is 50, same as the default for `-i`) and `-W` (for
autotune warmups, default=20, same as the default for `-w`), e.g.:
- `cublaslt_gemm -m 2048 -n 12288 -k 1536 -w 10000 -i 1000 -t fp8e4m3
-a`
- `cublaslt_gemm -m 2048 -n 12288 -k 1536 -w 10000 -i 1000 -t fp8e4m3 -a
-I 10 -W 10`

**Note:** This PR also changes the default `gemm_compute_type` for BF16
and FP16 to `CUBLAS_COMPUTE_32F`.

**Further observations:** 
1. The support matrix of the `cublaslt_gemm` could be furt...

**Description**
Added support for Grace CPU neo2 architecture in CPU Stream. Now CPU
Stream supports dual socket benchmarking.

Example config for this arch support:
```yaml
    cpu-stream:numa0:
      timeout: *default_timeout
      modes:
      - name: local
        parallel: no
      parameters:
        cpu_arch: neo2
        numa_mem_nodes: 0
        cores: 0 1 2 3 4 5 6 7 8
    cpu-stream:numa1:
      timeout: *default_timeout
      modes:
      - name: local
        parallel: no
      parameters:
        cpu_arch: neo2
        numa_mem_nodes: 1
        cores: 64 65 66 67 68 69 70 71 72
    cpu-stream:numa-spread:
      timeout: *default_timeout
      modes:
      - name: local
        parallel: no
      parameters:
        cpu_arch: neo2
        numa_mem_nodes: 0 1
        cores: 0 1 2 3 4 5 6 7 8 64 65 66 67 68 69 70 71 72
```

---------
Co-authored-by: dpower4 <dilipreddi@gmail.com>

Added GPU Stream benchmark - measures the GPU memory bandwidth and
efficiency for double datatype through various memory operations
including copy, scale, add, and triad.
- added documentation for `gpu-stream` detailing its introduction,
metrics, and descriptions.
- added unit tests for `gpu-stream`. Example output is in
`superbenchmark/tests/data/gpu_stream.log`.

**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>

**Description**

Add nvbandwidth benchmark.

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

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**
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>

**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>

**Description**
Integrate FAMBench into superbench based on docker implementation:
https://github.com/facebookresearch/FAMBench

The script to run all benchmarks is:
https://github.com/facebookresearch/FAMBench/blob/main/benchmarks/run_all.sh

**Description**
Modifications adding GPU-Burn to SuperBench.
- added third party submodule
- modified Makefile to make gpu-burn binary
- added/modified microbenchmarks to add gpu-burn python scripts
- modified default and azure_ndv4 configs to add gpu-burn

This commit makes data checking in gpu_copy optional, because it will take too long time if message size is large.

**Description**
This commit adds bidirectional tests in gpu_copy benchmark for both device-host transfer and device-device transfer, and revises related tests.

**Description**
Add mlc memory bandwidth and latency micro benchmark to Superbench.

**Major Revision**
- Add mlc benchmark with test and example files

**Description**
Add ONNXRuntime inference benchmark based on ORT python API.

**Major Revision**
- Add `ORTInferenceBenchmark` class to export pytorch model to onnx model and do inference
- Add tests and example for `ort-inference` benchmark
- Update the introduction docs.

**Description**
Fix typo in description of kernel_launch_overhead.py

__Description__

Add TensorRT inference benchmark for torchvision models.

__Major Revision__
- Measure TensorRT inference performance.

**Description**
Add ib traffic validation distributed benchmark.

**Major Revision**
- Add ib traffic validation distributed benchmark, example and test

**Description**
This commit does the following:
1) Adds CPU-initiated copy benchmark;
2) Adds dtod benchmark;
3) Support scanning NUMA nodes and GPUs inside the benchmark program;
4) Change the name of gpu-sm-copy to gpu-copy.

Add RocmOnnxModelBenchmark class to run benchmarks packaged in superbench/benchmark:rocm4.3.1-onnxruntime1.9.0

**Description**
Add gpcnet microbenchmark

**Major Revision**
- add 2 microbenmark for gpcnet, gpc-network-test, gpc-network-load-test
- add related test and example file

**Description**
Add tcp connectivity validation microbenchmark which is to validate TCP connectivity between current node and several nodes in the hostfile.

**Major Revision**
- Add tcp connectivity validation microbenchmark and related test, example

**Description**
This commit adds gpu_sm_copy benchmark and related tests.

**Description**
Add gemm flops microbenchmark for amd.

**Major Revision**
- Add gemm flops microbenchmark for amd.
- Add related example and test file.

**Description**
Add memory bus bandwidth performance microbenchmark for amd.

**Major Revision**
- Add memory bus bandwidth performance microbenchmark for amd.
- Add related example and test file.

**Description**
Add rccl bandwidth microbenchmark for rocm.

**Major Revision**
- Register rccl-bw benchmark.

**Description**
Add NCCL performance microbenchmark.

**Major Revision**
- Add microbenchmark, example, test, config for NCCL

**Description**
Add RDMA Loopback performance microbenchmark.

**Major Revision**
- Add microbenchmark, example, test, config for RDMA Loopback

**Description**
Add disk performance microbenchmark.

**Major Revision**
- Add microbenchmark, example, test, config for disk performance.

**Minor Revision**
- Fix bugs in executor unit test related to default enabled tests.

Add microbenchmark, example, test, config for cuda memory performance and Add cuda-samples(tag with cuda version) as git submodule and update related makefile

* add cuda flops performance benchmark.

* add python related cudnn microbenchmark

* add benchmark for cublas test

* format

* revise error handling and test

* add interface to read json file, revise json file path and include .json in packaging

* add random_seed in arguments

* revise preprocess of cublas benchmark

* fix lint error and note error in source code

* update according comments

* revise input arguments from json file to custom str and convert json file to built-in dict list

* restore package config

* fit lint issue

* update platform and comments

* rename files to match source code dir and fix comments error
Co-authored-by: root <root@sb-validation-000001.51z1chmys5fuzfqyo4niepozre.bx.internal.cloudapp.net>

* add kernel launch overhead benchmark.

* revise example settings of cnn models.

* Benchmarks: Add Benchmark - Add LSTM model benchmarks.