test_fpgadataflow_convinputgenerator.py

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import pytest

import numpy as np
from onnx import TensorProto, helper
from qonnx.core.datatype import DataType
from qonnx.core.modelwrapper import ModelWrapper
from qonnx.custom_op.registry import getCustomOp
from qonnx.transformation.general import GiveUniqueNodeNames
from qonnx.util.basic import gen_finn_dt_tensor

import finn.core.onnx_exec as oxe
from finn.analysis.fpgadataflow.exp_cycles_per_layer import exp_cycles_per_layer
from finn.transformation.fpgadataflow.compile_cppsim import CompileCppSim
from finn.transformation.fpgadataflow.hlssynth_ip import HLSSynthIP
from finn.transformation.fpgadataflow.prepare_cppsim import PrepareCppSim
from finn.transformation.fpgadataflow.prepare_ip import PrepareIP
from finn.transformation.fpgadataflow.prepare_rtlsim import PrepareRTLSim
from finn.transformation.fpgadataflow.set_exec_mode import SetExecMode


def make_single_im2col_modelwrapper(
    k, ifm_ch, ifm_dim, ofm_dim, simd, stride, dilation, idt
):
    odt = idt
    inp = helper.make_tensor_value_info(
        "inp", TensorProto.FLOAT, [1, ifm_dim, ifm_dim, ifm_ch]
    )
    outp = helper.make_tensor_value_info(
        "outp", TensorProto.FLOAT, [1, ofm_dim, ofm_dim, k * k * ifm_ch]
    )

    im2col_node = helper.make_node(
        "Im2Col",
        ["inp"],
        ["outp"],
        domain="qonnx.custom_op.general",
        stride=[stride, stride],
        kernel_size=[k, k],
        input_shape=str((1, ifm_dim, ifm_dim, ifm_ch)),
        pad_amount=[0, 0, 0, 0],
        pad_value=0,
        dilations=[dilation, dilation],
    )
    graph = helper.make_graph(
        nodes=[im2col_node], name="im2col_graph", inputs=[inp], outputs=[outp]
    )

    model = helper.make_model(graph, producer_name="im2col-model")
    model = ModelWrapper(model)

    model.set_tensor_datatype("inp", idt)
    model.set_tensor_datatype("outp", odt)

    return model


def make_single_slidingwindow_modelwrapper(
    k, ifm_ch, ifm_dim, ofm_dim, simd, stride, dilation, idt, dw=0
):
    odt = idt
    inp = helper.make_tensor_value_info(
        "inp", TensorProto.FLOAT, [1, ifm_dim, ifm_dim, ifm_ch]
    )
    outp = helper.make_tensor_value_info(
        "outp", TensorProto.FLOAT, [1, ofm_dim, ofm_dim, k * k * ifm_ch]
    )

    SlidingWindow_node = helper.make_node(
        "ConvolutionInputGenerator",
        ["inp"],
        ["outp"],
        domain="finn.custom_op.fpgadataflow",
        backend="fpgadataflow",
        ConvKernelDim=[k, k],
        IFMChannels=ifm_ch,
        IFMDim=[ifm_dim, ifm_dim],
        OFMDim=[ofm_dim, ofm_dim],
        SIMD=simd,
        Stride=[stride, stride],
        Dilation=[dilation, dilation],
        inputDataType=idt.name,
        outputDataType=odt.name,
        depthwise=dw,
    )
    graph = helper.make_graph(
        nodes=[SlidingWindow_node],
        name="slidingwindow_graph",
        inputs=[inp],
        outputs=[outp],
    )

    model = helper.make_model(graph, producer_name="slidingwindow-model")
    model = ModelWrapper(model)

    model.set_tensor_datatype("inp", idt)
    model.set_tensor_datatype("outp", odt)

    return model


def prepare_inputs(input_tensor):
    return {"inp": input_tensor}


# input datatype
@pytest.mark.parametrize("idt", [DataType["BIPOLAR"], DataType["INT2"]])
# kernel size
@pytest.mark.parametrize("k", [2, 3])
# input dimension
@pytest.mark.parametrize("ifm_dim", [6, 8])
# input channels
@pytest.mark.parametrize("ifm_ch", [2, 4])
# Stride
@pytest.mark.parametrize("stride", [1, 2])
# Dilation
# Currently only dilation value of 1 is supported
@pytest.mark.parametrize("dilation", [1])
# execution mode
@pytest.mark.parametrize("exec_mode", ["cppsim", "rtlsim"])
# input channel parallelism ("SIMD")
@pytest.mark.parametrize("simd", [1, 2])
# depthwise
@pytest.mark.parametrize("dw", [0, 1])
@pytest.mark.fpgadataflow
@pytest.mark.slow
@pytest.mark.vivado
def test_fpgadataflow_slidingwindow(
    idt, k, ifm_dim, ifm_ch, stride, dilation, exec_mode, simd, dw
):
    ofm_dim = int(((ifm_dim - k) / stride) + 1)

    x = gen_finn_dt_tensor(idt, (1, ifm_dim, ifm_dim, ifm_ch))
    model = make_single_slidingwindow_modelwrapper(
        k, ifm_ch, ifm_dim, ofm_dim, simd, stride, dilation, idt, dw
    )

    if exec_mode == "cppsim":
        model = model.transform(SetExecMode("cppsim"))
        model = model.transform(PrepareCppSim())
        model = model.transform(CompileCppSim())
    elif exec_mode == "rtlsim":
        model = model.transform(SetExecMode("rtlsim"))
        model = model.transform(GiveUniqueNodeNames())
        model = model.transform(PrepareIP("xc7z020clg400-1", 5))
        model = model.transform(HLSSynthIP())
        model = model.transform(PrepareRTLSim())
    else:
        raise Exception("Unknown exec_mode in test_fpgadataflow_slidingwindow")

    # prepare input data
    input_dict = prepare_inputs(x)
    # execute model
    y_produced = oxe.execute_onnx(model, input_dict)["outp"]
    golden = make_single_im2col_modelwrapper(
        k, ifm_ch, ifm_dim, ofm_dim, simd, stride, dilation, idt
    )
    y_expected = oxe.execute_onnx(golden, input_dict)["outp"]

    if dw == 0:
        assert (y_produced == y_expected).all()
    else:
        y_expected = y_expected.reshape(
            1, ofm_dim, ofm_dim, k * k, ifm_ch // simd, simd
        )
        y_expected = y_expected.transpose(0, 1, 2, 4, 3, 5)
        y_expected = y_expected.reshape(1, ofm_dim, ofm_dim, ifm_ch * k * k)
        assert (y_produced == y_expected).all()

    if exec_mode == "rtlsim":
        node = model.get_nodes_by_op_type("ConvolutionInputGenerator")[0]
        inst = getCustomOp(node)
        cycles_rtlsim = inst.get_nodeattr("cycles_rtlsim")
        exp_cycles_dict = model.analysis(exp_cycles_per_layer)
        exp_cycles = exp_cycles_dict[node.name]
        assert np.isclose(exp_cycles, cycles_rtlsim, atol=10)
        assert exp_cycles != 0