CoSA: Scheduling by Constrained Optimization for Spatial Accelerators

Related tags

Job Scheduler cosa
Overview

CoSA: Scheduling by Constrained Optimization for Spatial Accelerators

CoSA is a scheduler for spatial DNN accelerators that generate high-performance schedules in one shot using mixed integer programming (MIP). For more details, please refer to:

CoSA leverages the regularities in DNN operators and hardware to formulate the DNN scheduling space into a MIP problem with algorithmic and architectural constraints, which can be solved to automatically generate a highly efficient schedule in one shot.

Installation

  1. CoSA Download the CoSA source code git clone [email protected]:ucb-bar/cosa.git
  2. Gurobi Please follow the instructions in Gurobi for Academics and Researchers to install Gurobi optimizer and obtain an academic license.
  3. Timeloop Please refer to the instructions in the Timeloop Tutorial to install Timeloop with Docker. To install from source code please, follow the instructions in Timeloop Github. The specific Timeloop version used for CoSA evaluation is commit 11920be.
  4. Python3 Install packages for Python3 with:
pip install numpy==1.19.0 PyYAML==5.3.1 yamlordereddictloader==0.4.0 seaborn==0.10.1 matplotlib==3.2.2 pandas==1.0.5
  1. Environment Set##up Update env.sh with the paths to COSA_DIR, TIMELOOP_DIR, GUROBI_HOME, and GRB_LICENSE_FILE, and source the environment source env.sh.

Run CoSA

To run one scheduling example:

python3 src/cosa.py

CoSA Inputs and Outputs

CoSA takes problem dimension, architecture constraints, relation encoding constants as inputs and returns a mapping with tiling, temporal/spatial, and permutation solved to optimize the user defined objective.

def cosa(prob, arch, A, B, part_ratios, global_buf_idx, Z=None): 
    """Run CoSA to generate a mapping with tiling, temporal/spatial, and permutation determined. 
        We currently assume there is a global buffer 
    Args:
        prob: An object defines the layer dimension.
        arch: An object defines the hardware architecture dimension. 
        A: A 2d binary constant matrix that encodes the layer dimension to data tensor relationship.
            1 means related, 0 means unrelated
            Note that the R,S to the input tensor relation is specially handled in the formulation,
            and are specified to 0. 
        B: A 2d binary constant matrix that encodes the data tensor to memory level mapping. 
            It can be derived from the mapspace bypass pattern in Timeloop. 
            Note it is intended to be used for even mapping among different data tensors to different memory levels.
        part_ratios: A 2d array to represent the partition ratios of different data tensors in different memory buffers. 
        global_buf_idx: An index point to the global buffer. 
        Z: Similar to B, but intended for uneven mapping among different data tensors to different memory levels.
            It is a 3d binary constant matrix that encodes the data tensor to memory level mapping.

    Returns: 
        factor_config: A 2d array specifying the allocation decision for each prime factor.
        spatial_config: A 2d array specifying the temporal/spatial decisions for each prime factor.
        perm_config: A 2d array specifying the ordering of R,S,P,Q,C,K,N factors at each 
    """

Even and Uneven Mapping

CoSA shall be able to support the even (using matrix B to encode bypassing scheme in Timeloop) and uneven mapping (using matrix Z to encode rank to memory mapping for different data tensors as in ZigZag)

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Comments
  • Timeloop command line and configuration used in the paper

    Timeloop command line and configuration used in the paper

    Hi I was wondering if you can post the timeloop-mapper yaml and command line you used for the paper. I installed the timeloop as you suggested in the readme and using simba arch in the repository along with resnet workloads in configs. I am adding mapper config file to explicitly set num-threads but letting everything else be default. But mapper is able to use only 1 processors in many cases. I was wondering if you can help with this.

    opened by vmiheer 3
  • Simba accelerator architecture.

    Simba accelerator architecture.

    Hi, the Simba accelerator architecture in the 'simba.yaml' of your source code is inconsistent with that in Table V. of your paper? Could you please explain this？Thanks!!!

    opened by FuyuWang 1
  • Make CoSA packagable

    Make CoSA packagable

    CoSA is now packagable. In order to build, install poetry, cd into the CoSA directory, and run poetry build; this will generate a whl file in dist/ that can be installed in one command:

    pip install dist/cosa_scheduler-0.1.0-py3-none-any.whl
    opened by gdinh 0
  • Sample usage on actual model and NoC simulator

    Sample usage on actual model and NoC simulator

    According to the paper, CoSA was tested on AlexNet, ResNet-50, ResNeXt-50 on the NoC simulator. Would be cool to see samples of it being used on these networks!

    opened by raikonenfnu 2
Owner
UC Berkeley Architecture Research
UC Berkeley Architecture Research
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