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Symbolic Tensor Graph (STG) Generator

Author: Changhai Man @ SynergyLab
Contact: [email protected]

Overview

The Symbolic Tensor Graph is a generator for Chakra Execution Trace (ET) files. This tool is designed to generate synthetic workload traces for use in parallel strategy exploration without gathering data from a real system or implementing actual workload codes. It supports various parallelization strategies like Data Parallelism (DP), Model Parallelism (MP), and Pipeline Parallelism (PP).

Key Features

• Generate synthetic transformer workloads in Chakra ET format.
• Supports multiple parallelism strategies (DP, MP, PP, SP).
• Support customized model dimensions (batch, seq, dmodel, dff, n_head)

Installation

To set up the environment and install the required dependencies, follow these steps:

# Clone the repository
git clone [email protected]:changhai0109/symbolic_tensor_network.git

# Navigate to the project directory
cd symbolic_tensor_network

# Install dependencies via conda
conda env create -n <env_name>
conda activate -n <env_name>
conda install numpy sympy python-graphviz protobuf pandas -c conda-forge

Usage

To generate symbolic workloads, use the following command:

python main.py –h

This will show all available options and their descriptions. Example of running the generator:

python main.py --output_dir generated/ \
               --output_name workload.%d.et \
               --comm_group_file comm_group.json \
               --dp 2 --mp 2 --pp 2 \
               --weight_sharded 0 

Example Output:

$ ls generated/
comm_group.json  workload.0.et  workload.1.et  workload.2.et  workload.3.et

Parameters

Parameter	Description	Example Values
--output_dir	Folder to place output files	./output
--output_name	Output file naming pattern	workload.%d.et
--comm_group_file	Communication group config file	comm_group.json
--chakra_schema_version	Chakra ET schema version	v0.0.4, v0.0.1, json
--dp	Data parallelism degree	32
--mp	Model parallelism degree	4
--pp	Pipeline parallelism degree	2
--sp	Token parallelism degree	4
--weight_sharded	Shard weights (FSDP enabled)	True/False
--din	Input embedding size	51200
--dout	Output embedding size	25600
--dmodel	Model feature size	25600
--dff	FFN feature size	25600*4
--seq	Sequence length	1024
--head	Number of attention heads	128
--num_stacks	Number of encoder stacks	32

Example Commands

• Generate with DP=8, MP=4, PP=4, no FSDP:

  python main.py --output_dir generated/ --output_name workload_1.%d.et --comm_group_file comm_group_1.json --dp 8 --mp 4 --pp 4 --sp 1 --weight_sharded 0 --chakra_schema_version v0.0.4

• Generate with DP=64, MP=1, PP=1, FSDP:

  python main.py --output_dir generated/ --output_name workload_2.%d.et --comm_group_file comm_group_2.json --dp 64 --mp 1 --pp 1 --sp 1 --weight_sharded 1 --chakra_schema_version v0.0.4

• Generate with DP=4, MP=4, PP=2, SP=2, FSDP, output in JSON format:

  python main.py --output_dir generated/ --output_name workload_3.%d.json --comm_group_file comm_group_3.json --dp 4 --mp 4 --pp 2 --sp 2 --weight_sharded 1 --chakra_schema_version json

Chakra Schema Version

The schema version used determines compatibility with different tools and repositories:

• v0.0.4: Current latest chakra version (by Oct.6 2024).
• v0.0.1: Supported for lagacy, not fully tested.

License

MIT

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