test_fifosizing.py

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import pytest

import json
import shutil
from brevitas.export.onnx.generic.manager import BrevitasONNXManager

import finn.builder.build_dataflow as build
import finn.builder.build_dataflow_config as build_cfg
from finn.util.basic import make_build_dir
from finn.util.test import get_trained_network_and_ishape


def fetch_test_model(topology, wbits=2, abits=2):
    tmp_output_dir = make_build_dir("build_fifosizing_%s_" % topology)
    (model, ishape) = get_trained_network_and_ishape(topology, wbits, abits)
    chkpt_name = tmp_output_dir + "/model.onnx"
    BrevitasONNXManager.export(model, ishape, chkpt_name)
    return tmp_output_dir


@pytest.mark.slow
@pytest.mark.vivado
@pytest.mark.fpgadataflow
def test_fifosizing_linear():
    tmp_output_dir = fetch_test_model("tfc")
    cfg = build_cfg.DataflowBuildConfig(
        output_dir=tmp_output_dir,
        auto_fifo_depths=True,
        auto_fifo_strategy="characterize",
        target_fps=10000,
        synth_clk_period_ns=10.0,
        board="Pynq-Z1",
        rtlsim_batch_size=100,
        shell_flow_type=build_cfg.ShellFlowType.VIVADO_ZYNQ,
        generate_outputs=[
            build_cfg.DataflowOutputType.ESTIMATE_REPORTS,
            build_cfg.DataflowOutputType.STITCHED_IP,
            build_cfg.DataflowOutputType.RTLSIM_PERFORMANCE,
        ],
        default_mem_mode=build_cfg.ComputeEngineMemMode.DECOUPLED,
    )
    build.build_dataflow_cfg(tmp_output_dir + "/model.onnx", cfg)
    with open(tmp_output_dir + "/report/estimate_network_performance.json") as f:
        est_data = json.load(f)
    with open(tmp_output_dir + "/report/rtlsim_performance.json") as f:
        sim_data = json.load(f)
    assert (
        float(sim_data["throughput[images/s]"])
        / float(est_data["estimated_throughput_fps"])
        > 0.9
    )
    shutil.rmtree(tmp_output_dir)