test_select_implementation.py

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""" Tests strategy selection for Relay ops """

import pytest
import numpy as np
from unittest.mock import MagicMock

import tvm
from tvm import relay
from tvm import te
from tvm.relay.testing import run_infer_type
import tvm.testing
from tvm import topi


@pytest.mark.parametrize(
    "target, expected_implementation",
    [("llvm", "concatenate.cpu"), ("llvm -device=arm_cpu", "concatenate.arm_cpu")],
)
def test_concatenate(target, expected_implementation):
    target = tvm.target.Target(target)

    shape = (1, 1, 1, 3)
    dtype = "float32"
    axis = 1
    inputs = []
    inputs.append(relay.var("var0", shape=shape, dtype=dtype))
    inputs.append(relay.var("var1", shape=shape, dtype=dtype))
    input_tuple = relay.Tuple(inputs)
    out = relay.op.concatenate(input_tuple, axis)
    out = run_infer_type(out)

    impl, xx = relay.backend.te_compiler.select_implementation(
        relay.op.get("concatenate"),
        out.attrs,
        [te.placeholder(shape)],
        out.checked_type,
        target,
        use_autotvm=False,
    )
    assert impl.name == expected_implementation


@pytest.mark.parametrize(
    "target,expected_impl",
    [
        ("llvm -device=arm_cpu", "conv2d_nhwc_spatial_pack.arm_cpu"),
        (
            "llvm -device=arm_cpu -mtriple=aarch64-linux-gnu -mattr=+neon",
            "conv2d_NHWC_quantized_interleaved.arm_cpu",
        ),
        (
            "llvm -device=arm_cpu -mtriple=armv8l-linux-gnu -mattr=+neon",
            "conv2d_nhwc_spatial_pack.arm_cpu",
        ),
    ],
)
def test_int8_conv2d(target, expected_impl):
    target = tvm.target.Target(target)

    dtype = "int8"
    data_shape = (1, 1, 1, 4)
    weight_shape = (1, 1, 4, 4)
    data_layout = "NHWC"
    kernel_layout = "HWIO"
    channels = 4
    kernel_size = (1, 1)

    out = relay.nn.conv2d(
        relay.var("data", shape=data_shape, dtype=dtype),
        relay.var("weight", shape=weight_shape, dtype=dtype),
        kernel_size=kernel_size,
        channels=channels,
        data_layout=data_layout,
        kernel_layout=kernel_layout,
    )
    out = run_infer_type(out)

    with target:
        impl, _ = relay.backend.te_compiler.select_implementation(
            out.op,
            out.attrs,
            [te.placeholder(data_shape, dtype), te.placeholder(weight_shape, dtype)],
            out.checked_type,
            target,
        )

    assert impl.name == expected_impl


@pytest.mark.parametrize(
    "target,expected_impl",
    [
        ("llvm -device=arm_cpu", "depthwise_conv2d_nhwc.generic"),
        (
            "llvm -device=arm_cpu -mtriple=aarch64-linux-gnu -mattr=+neon",
            "depthwise_conv2d_nhwc.arm_cpu",
        ),
        (
            "llvm -device=arm_cpu -mtriple=armv8l-linux-gnu -mattr=+neon",
            "depthwise_conv2d_nhwc.generic",
        ),
        ("c -device=arm_cpu -mcpu=cortex-m55", "depthwise_conv2d_nhwc_dsp.arm_cpu"),
    ],
)
def test_int8_depthwise_conv2d(target, expected_impl):
    target = tvm.target.Target(target)

    dtype = "int8"
    out_dtype = "int32"
    data_shape = (2, 2, 4, 8)
    weight_shape = (2, 2, 8, 1)
    data_layout = "NHWC"
    kernel_layout = "HWOI"
    groups = 8
    kernel_size = (2, 2)

    out = relay.nn.conv2d(
        relay.var("data", shape=data_shape, dtype=dtype),
        relay.var("weight", shape=weight_shape, dtype=dtype),
        kernel_size=kernel_size,
        data_layout=data_layout,
        kernel_layout=kernel_layout,
        groups=groups,
        out_dtype=out_dtype,
    )
    out = run_infer_type(out)

    with target:
        impl, _ = relay.backend.te_compiler.select_implementation(
            out.op,
            out.attrs,
            [te.placeholder(data_shape, dtype), te.placeholder(weight_shape, dtype)],
            out.checked_type,
            target,
        )

    assert impl.name == expected_impl


@pytest.mark.parametrize(
    "target,expected_valid_impl,expected_impl",
    [("llvm -device=arm_cpu", ["dense_pack.x86", "dense_nopack.x86"], "dense_pack.x86")],
)
def test_dense(target, expected_valid_impl, expected_impl):
    target = tvm.target.Target(target)

    data_shape = (30, 40)
    weight_shape = (30, 40)
    dtype = "float32"

    out = relay.nn.dense(
        relay.var("data", shape=data_shape, dtype=dtype),
        relay.var("weight", shape=weight_shape, dtype=dtype),
        out_dtype=dtype,
    )
    out = run_infer_type(out)

    with target:
        args = [
            out.op,
            out.attrs,
            [te.placeholder(data_shape, dtype), te.placeholder(weight_shape, dtype)],
            out.checked_type,
            target,
        ]
        valid_impl = relay.backend.te_compiler.get_valid_implementations(*args)
        selected_impl, _ = relay.backend.te_compiler.select_implementation(*args, use_autotvm=False)

    assert len(valid_impl) == len(expected_valid_impl)
    for impl in valid_impl:
        assert impl.name in expected_valid_impl
    assert selected_impl.name == expected_impl


@pytest.mark.parametrize(
    "target,schedule_func",
    [
        ("llvm -device=arm_cpu", topi.x86),
        ("c -device=arm_cpu -mcpu=cortex-m55", topi.arm_cpu),
    ],
)
def test_pool2d(target, schedule_func, monkeypatch):
    target = tvm.target.Target(target)

    data_shape = (1, 2, 2, 4)
    dtype = "float32"

    out = relay.nn.avg_pool2d(relay.var("data", shape=data_shape, dtype=dtype))
    placeholders = [te.placeholder(data_shape, dtype)]

    mock_schedule = MagicMock()
    monkeypatch.setattr(schedule_func, "schedule_pool", mock_schedule)

    # Since pool does not use OpStrategy to determine the relevant schedule,
    # we cannot simply check the schedule name that was selected with
    # `select_implementation`. With this implementation of schedule selection,
    # "pool.arm_cpu" will always be the schedule name, regardless of what schedule
    # was selected. Instead, this test checks that the relevant schedule function
    # is called when selecting the pooling from schedule from arm_cpu.
    relay.op.strategy.arm_cpu.schedule_pool_arm_cpu(out.attrs, placeholders, target)
    mock_schedule.assert_called()


if __name__ == "__main__":
    tvm.testing.main()