tests/python/contrib/test_arm_compute_lib/test_runtime.py

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"""Arm Compute Library runtime tests."""

import numpy as np

import tvm
from tvm import relay

from .infrastructure import skip_runtime_test, build_and_run, verify
from .infrastructure import Device


def test_multiple_ops():
    """
    Test multiple operators destined for ACL.
    The ACL runtime will expect these ops as 2 separate functions for
    the time being.
    """
    Device.load("test_config.json")

    if skip_runtime_test():
        return

    device = Device()
    np.random.seed(0)

    def get_model(input_shape, var_names):
        """Return a model and any parameters it may have."""
        a = relay.var(next(var_names), shape=input_shape, dtype="float32")
        out = relay.reshape(a, (1, 1, 1000))
        out = relay.reshape(out, (1, 1000))
        return out

    inputs = {"a": tvm.nd.array(np.random.uniform(0, 1, (1, 1, 1, 1000)).astype("float32"))}

    outputs = []
    for acl in [False, True]:
        func = get_model(inputs["a"].shape, iter(inputs))
        outputs.append(
            build_and_run(func, inputs, 1, None, device, enable_acl=acl, acl_partitions=2)[0]
        )
    verify(outputs, atol=0.002, rtol=0.01)


def test_heterogeneous():
    """
    Test to check if offloading only supported operators works,
    while leaving unsupported operators computed via tvm.
    """
    Device.load("test_config.json")

    if skip_runtime_test():
        return

    device = Device()
    np.random.seed(0)

    def get_model(input_shape, var_names):
        """Return a model and any parameters it may have."""
        a = relay.var(next(var_names), shape=input_shape, dtype="float32")
        out = relay.reshape(a, (1, 1, 1000))
        out = relay.sigmoid(out)
        out = relay.reshape(out, (1, 1000))
        return out

    inputs = {"a": tvm.nd.array(np.random.uniform(-127, 128, (1, 1, 1, 1000)).astype("float32"))}

    outputs = []
    for acl in [False, True]:
        func = get_model(inputs["a"].shape, iter(inputs))
        outputs.append(
            build_and_run(
                func, inputs, 1, None, device, enable_acl=acl, tvm_ops=1, acl_partitions=2
            )[0]
        )
    verify(outputs, atol=0.002, rtol=0.01)


def test_multiple_runs():
    """
    Test that multiple runs of an operator work.
    """
    Device.load("test_config.json")

    if skip_runtime_test():
        return

    device = Device()

    def get_model():
        a = relay.var("a", shape=(1, 28, 28, 512), dtype="float32")
        w = tvm.nd.array(np.ones((256, 1, 1, 512), dtype="float32"))
        weights = relay.const(w, "float32")
        conv = relay.nn.conv2d(
            a,
            weights,
            kernel_size=(1, 1),
            data_layout="NHWC",
            kernel_layout="OHWI",
            strides=(1, 1),
            padding=(0, 0),
            dilation=(1, 1),
        )
        params = {"w": w}
        return conv, params

    inputs = {
        "a": tvm.nd.array(np.random.uniform(-127, 128, (1, 28, 28, 512)).astype("float32")),
    }

    func, params = get_model()
    outputs = build_and_run(func, inputs, 1, params, device, enable_acl=True, no_runs=3)
    verify(outputs, atol=0.002, rtol=0.01)


if __name__ == "__main__":
    test_multiple_ops()
    test_heterogeneous()
    test_multiple_runs()