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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

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

To improve benchmark debugging, the following debug methods were added:

pytorch profiler in model benchmark

- SB_ENABLE_PYTORCH_PROFILER: switch to enable/disable
- SB_TORCH_PROFILER_TRACE_DIR: log path
These 2 runtime variables need to be configured in SB config file.

nsys in SB runner

- SB_ENABLE_NSYS: switch to enable/disable 
- SB_NSYS_TRACE_DIR: log path
These 2 runtime variables need to be configured in runner's ENV

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

**Description**
Add cuda 12.9 dockerfile and build in pipeline.

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Co-authored-by: Guoshuai Zhao <microsoft@microsoft.com>
Co-authored-by: Hongtao Zhang <hongtaozhang@microsoft.com>
Co-authored-by: Hongtao Zhang <garyworkzht@gmail.com>

**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__

Update TE FP8 model conversion.

__Major Revisions__
* Add 16-byte alignment comment.
* Fix TE layer parameters type.

Support Transformer Engine FP8 in existing PyTorch BERT/GPT2 models by
converting linear/layernorm to TE layers.

Fix potential barrier timeout in init_process_group due to race
condition of using the same port. Change to different ports when running
multiple models sequentially in one process.
For example, when running vgg11/13/16/19, will use port 29501~29504
respectively.

**Description**

Cherry-pick  bug fixes from v0.5.0 to main.

**Major Revisions**

* Bug - Force to fix ort version as '1.10.0' (#343)
* Bug - Support no matching rules and unify the output name in result_summary (#345)
* Analyzer - Support regex in annotations of benchmark naming for metrics in rules (#344)
* Bug - Fix bugs in sync results on root rank for e2e model benchmarks (#342)
* Bug - Fix bug of duration feature for model benchmarks in distributed mode (#347)
* Docs - Upgrade version and release note (#348)
Co-authored-by: Yuting Jiang <v-yutjiang@microsoft.com>

**Description**
Add support for pytorch>=1.9.0 of init_process_group.

**Major Revision**
- Use PrefixStore(TCPStore) to init_process_group manully for each model run

**Description**
Please write a brief description and link the related issue if have.

**Major Revision**
- Sync (do allreduce max) the E2E training results among all workers.
- Avoid using ':0' in metric name if there has only one rank having output.

**Description**
Fix typo when set force_fp32 option.

**Description**
Add option `force_fp32` to use fp32 instead of tf32, only takes effect on Ampere or newer GPUs.

**Description**

Cherry-pick  bug fixes from v0.3.0 to main.

**Major Revisions**
* Docs - Upgrade version and release note (#209)
* Benchmarks: Build Pipeline - Update rccl-test git submodule to dc1ad48 (#210)
* Benchmarks: Update - Update benchmarks in configuration file (#208)
* CI/CD - Update GitHub Action VM (#211)
* Benchmarks: Fix Bug - Fix wrong parameters for gpu-sm-copy-bw in configuration examples (#203)
* CI/CD - Fix bug in build image for push event (#205)
* Benchmark: Fix Bug - fix error message of communication-computation-overlap (#204)
* Tool: Fix bug - Fix function naming issue in system info  (#200)
* CI/CD - Push images in GitHub Action (#202)
* Bug - Fix torch.distributed command for single node (#201)
* CLI - Integrate system info for node (#199)
* Benchmarks: Code Revision - Revise CMake files for microbenchmarks. (#196)
* CI/CD - Add ROCm image build in GitHub Actions (#194)
* Bug: Fix bug - fix bug of hipBusBandwidth build (#193)
* Benchmarks: Build Pipeline - Restore rocblas build logic (#197)
* Bug: Fix Bug - Add barrier before 'destroy_process_group' in model benchmarks (#198)
* Bug - Revise 'docker run' in sb deploy (#195)
* Bug - Fix Bug : fix bug of error param operations to operation in rccl-bw of hpe config (#190)
Co-authored-by: Yuting Jiang <v-yujiang@microsoft.com>
Co-authored-by: Guoshuai Zhao <guzhao@microsoft.com>
Co-authored-by: Ziyue Yang <ziyyang@microsoft.com>

__Description__
Cherry-pick bug fixes from v0.2.1 to main.

__Major Revisions__
* Fix bug of VGG models failed on A100 GPU with batch_size=128.
* Fix Ansible connection issue when running in localhost.
* Update version in packages and docs.

* replace torch.optim.AdamW with transformers.AdamW.

* Clean up the cache.

Co-authored-by: Guoshuai Zhao <guzhao@microsoft.com>

Co-authored-by: Guoshuai Zhao <guzhao@microsoft.com>

* add flag to disable GPU.

* fix spelling

* fix unittest.

* address comments.
Co-authored-by: Guoshuai Zhao <guzhao@microsoft.com>

Co-authored-by: Guoshuai Zhao <guzhao@microsoft.com>

* add pytorch base class

* address comments
Co-authored-by: Guoshuai Zhao <guzhao@microsoft.com>