
param
PArametrized Recommendation and Ai Model benchmark is a repository for development of numerous uBenchmarks as well as end to end nets for evaluation of training and inference platforms.
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PARAM Benchmarks is a repository of communication and compute micro-benchmarks as well as full workloads for evaluating training and inference platforms. It complements commonly used benchmarks by focusing on AI training with PyTorch based collective benchmarks, GEMM, embedding lookup, linear layer, and DLRM communication patterns. The tool bridges the gap between stand-alone C++ benchmarks and PyTorch/Tensorflow based application benchmarks, providing deep insights into system architecture and framework-level overheads.
README:
PARAM Benchmarks is a repository of communication and compute micro-benchmarks as well as full workloads for evaluating training and inference platforms.
PARAM complements two broad categories of commonly used benchmarks:
- C++ based stand-alone compute and communication benchmarks using cuDNN, MKL, NCCL, MPI libraries - e.g., NCCL tests (https://github.com/NVIDIA/nccl-tests), OSU MPI benchmarks (https://mvapich.cse.ohio-state.edu/benchmarks/), and DeepBench (https://github.com/baidu-research/DeepBench).
- Application benchmarks such as Deep Learning Recommendation Model (DLRM) and the broader MLPerf benchmarks. Its worth noting that while MLPerf is the de-facto industry standard for benchmarking ML applications we hope to compliment this effort with broader workloads that are of more interest to Facebook with more in-depth analysis of each within this branch of Application benchmarks.
Our initial release of PARAM benchmarks focuses on AI training and comprises of:
- Communication: PyTorch based collective benchmarks across arbitrary message sizes, effectiveness of compute-communication overlap, and DLRM communication patterns in fwd/bwd pass
- Compute: PyTorch based GEMM, embedding lookup, and linear layer
- DLRM: tracks the
ext_dist
branch of DRLM benchmark use Facebook's DLRM benchmark (https://github.com/facebookresearch/dlrm). In short, PARAM fully relies on DLRM benchmark for end-to-end workload evaluation; with additional extensions as required for scale-out AI training platforms. - PyTorch Execution Trace (ET) replay based tests: The PyTorch ET capturing capabilities, which have recently been introduced, allow for the recording of runtime information of a model at the operator level. This capability enables the creation of replay-based benchmarks (https://dl.acm.org/doi/abs/10.1145/3579371.3589072) to accurately reproduce the original performance.
In essence, PARAM bridges the gap between stand-alone C++ benchmarks and PyTorch/Tensorflow based application benchmarks. This enables us to gain deep insights into the inner workings of the system architecture as well as identify framework-level overheads by stressing all subcomponents of a system.
0.1 : Initial release
- pytorch
- future
- numpy
- apex
PARAM benchmarks is released under the MIT license. Please see the LICENSE
file for more information.
We actively welcome your pull requests! Please see CONTRIBUTING.md
and CODE_OF_CONDUCT.md
for more info.
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