JUPITER Benchmark Suite: Megatron-LM

This benchmark is part of the JUPITER Benchmark Suite. See the repository of the suite for some general remarks.

This repository contains the Megatron-LM NLP/LLM benchmark. DESCRIPTION.md contains details for compilation, execution, and evaluation.

The required source code (Megatron-LM, Apex) is included in the ./src/ subdirectory as submodules from the upstream repositories; github.com/NVIDIA/Megatron-LM for Megatron-LM and github.com/NVIDIA/apex for Apex. Sample data files are also included.

Overview of Benchmark

Description Of Folder Structure

• benchmark
  • aux
    
    • tokenizers
    • script used for getting data and tokenizers; get_shrink_data_and_tokenizers.sh
    • script used for preprocessing data; job_preprocess_data.sbatch
    • sample 10MB oscar dataset got using get_shrink_data_and_tokenizers.sh
  • env
    • script for activating the python virtual env; activate.bash
    • script to set up python virtual env; setup_venv.sh
  • slurm
    • sbatch scripts for 13B and 175B model to be used when running without JUBE
  • jube
    • contains accompanying files for JUBE run and the JUBE yaml file
• src
  • data : contains the preprocessed data (*idx and *.bin files)
  • compile_build.sh : script to build the software dependencies
  • variables.bash : file that sets important paths
  • prebuild_kernels.py : script to prebuild fused kernels

Workflow Without JUBE:

Getting Data and Tokenizers

The following workflow can be done if data and tokenizers are not already present with this repository:

• Step 1: Set NLP_BENCH_ROOT variable as export NLP_BENCH_ROOT=<rootdir path of this benchmark> in your bash shell
• Step 2: cd benchmark/aux/
• Step 3: bash get_shrink_data_and_tokenizers.sh to get tokenizers and compress the raw data oscar-1GB.jsonl.xz to oscar-10MB.jsonl.xz

Prepocessing Data

If your src/data folder does not contain preprocessed data (*.idx and *.bin files), then execute sbatch job_preprocess_data.sbatch after Step 5 in "Workflow With Preprocessed Data And Tokenizers Available" from benchmark/aux directory.

The job_preprocess_data.sbatch script in benchmark/aux/ is used to preprocess the oscar-10MB.jsonl.xz and put it in src/data/. The file can be modified to preprocess any data of choice.

Workflow With Preprocessed Data And Tokenizers Available

• Step 1: cd into it the folder of this benchmark
• Step 2: Set NLP_BENCH_ROOT variable as export NLP_BENCH_ROOT=<rootdir path of this benchmark> in your bash shell
• Step 3: Set TORCH_CUDA_ARCH_LIST according to GPU's compute capability in benchmark/env/activate.bash
• Step 4: Run bash benchmark/env/setup_venv.sh
• Step 5: Run bash src/compile_build.sh
• Step 6: Run sbatch benchmark/slurm/jobscript_13B.sbatch or sbatch benchmark/slurm/jobscript_175B.sbatch

The output file *.out file would have result logs of the following form that are important : ``` [default3]: iteration 10/ 292968 | consumed samples: 10240 | elapsed time per iteration (s): 35.8651 | learning rate: 4.734E-06 | global batch size: 1024 | lm loss: 1.332803E+01 | loss scale: 4096.0 | grad norm: 42.627 | number of skipped iterations: 0 | number of nan iterations: 0 | samples per second: 28.551 | TFLOPs: 199.03 | [default3]: iteration 20/ 292968 | consumed samples: 20480 | elapsed time per iteration (s): 34.9991 | learning rate: 9.467E-06 | global batch size: 1024 | lm loss: 1.010884E+01 | loss scale: 4096.0 | grad norm: 13.038 | number of skipped iterations: 0 | number of nan iterations: 0 | samples per second: 29.258 | TFLOPs: 203.96 | [default3]: iteration 30/ 292968 | consumed samples: 30720 | elapsed time per iteration (s): 34.8709 | learning rate: 1.420E-05 | global batch size: 1024 | lm loss: 9.072961E+00 | loss scale: 4096.0 | grad norm: 26.640 | number of skipped iterations: 0 | number of nan iterations: 0 | samples per second: 29.365 | TFLOPs: 204.71 | [default3]: iteration 40/ 292968 | consumed samples: 40960 | elapsed time per iteration (s): 35.3346 | learning rate: 1.893E-05 | global batch size: 1024 | lm loss: 8.486469E+00 | loss scale: 4096.0 | grad norm: 3.441 | number of skipped iterations: 0 | number of nan iterations: 0 | samples per second: 28.980 | TFLOPs: 202.02 | [default3]: iteration 50/ 292968 | consumed samples: 51200 | elapsed time per iteration (s): 35.3357 | learning rate: 2.367E-05 | global batch size: 1024 | lm loss: 8.

```

The metric tokens_per_sec should be calculated as (1.0/$elapsed_time_per_iteration)*$global_batch_size*$sequence_length obtained from the *.out file.

For submission the throughput tokenspersec is converted into time, a hypothetical training would require. This conversion is done by assuming a training with 20 Million tokens, using the formula [ time_to_report_in_seconds ] =  [tokens] / [tokens/second]  Example: Using the 13B model result below (Tokens/sec: 59463.14), we obtain a duration of 20,000,000 / 59463.14 = 336.34 seconds.

Hint: sequence_length can be found in the jobscript.

Workflow With JUBE:

• Step 1: cd into it the folder of this benchmark
• Step 2: Set TORCH_CUDA_ARCH_LIST according to GPU's compute capability in benchmark/env/activate.bash
• Step 3: Execute either jube run benchmark/jube/nlp_benchmark.yaml --tag 175 for 175B model orjube run benchmark/jube/nlp_benchmark.yaml --tag 13 for 13B model
• Step 4: Wait for the benchmark to run and then do jube continue nlp_benchmark_run -i last until no Steps with the "wait" state remain
• Step 5: After the benchmark finishes, run jube result -a nlp_benchmark_run -i last to print the benchmark results

Example result from JUBE:

``` | system | version | queue | JobID | JobTime | ModelSize (Billion Param) | Nodes | BatchSize | PipelineParallel | TensorParallel | Iterations | AvgTFLOPs/GPU | Tokens/sec | timetoreportinseconds | |---------------|---------|---------|----------|------------|----------------------------|-------|------------|-------------------|-----------------|------------|----------------|------------|---------------------------| | juwelsbooster | 2024.01 | booster | 10011638 | "00:30:00" | 13 | 8 | 1024 | 4 | 2 | 20 | 206.885 | 60777.68 | 329.07 |

```