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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 further extended
in the future to support non-FP16 output as well for FP8 inputs.
2. Currently, the input matrices are initialized with values of 1.0 and
2.0 which makes them less demanding in terms of power. Another future
extension could be to enable another fill mode for, say, uniform random
numbers between -1 and 1.
3. cuBLAS workspace recommendations are listed under
https://docs.nvidia.com/cuda/cublas/#cublassetworkspace



Update (June 10, 2025): verified using higher level test driver with
these commands:

1. inline:
```
python3 -c "                                                                            
from superbench.benchmarks import BenchmarkRegistry, Platform
from superbench.common.utils import logger

parameters = (
    '--num_warmup 10 --num_steps 50 '
    '--shapes 512,512,512 1024,1024,1024 --in_types fp16 fp32 '
    '--enable_autotune --num_warmup_autotune 20 --num_steps_autotune 50'
)
context = BenchmarkRegistry.create_benchmark_context(
    'cublaslt-gemm', platform=Platform.CUDA, parameters=parameters
)
benchmark = BenchmarkRegistry.launch_benchmark(context)
logger.info('Result: {}'.format(benchmark.result))
"
```

2. newly added script: 
`python3 examples/benchmarks/cublaslt_function.py`

---------
Co-authored-by: Babak Hejazi <babakh@nvidia.com>

**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.