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# to you under the Apache License, Version 2.0 (the
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#
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"""Example code to do convolution."""

import numpy as np
import tvm
from tvm import te
from tvm import autotvm
from tvm import topi
import tvm.topi.testing
from tvm.contrib.pickle_memoize import memoize
from tvm.topi.nn.utils import get_pad_tuple
from tvm.topi.utils import get_const_tuple
from tvm.topi.nn.conv2d import _get_workload
from tvm.topi.x86.conv2d_avx_common import _fallback_schedule

import tvm.testing


def verify_conv2d_nchw(
    batch,
    in_channel,
    in_size,
    num_filter,
    kernel,
    stride,
    padding,
    dilation=1,
    add_bias=False,
    add_relu=False,
    use_cudnn=False,
):

    pad_top, pad_left, pad_bottom, pad_right = get_pad_tuple(padding, (kernel, kernel))
    padding_sum = pad_top + pad_left + pad_bottom + pad_right
    print(
        "Workload: (%d, %d, %d, %d, %d, %d, %d, %d)"
        % (batch, in_channel, in_size, num_filter, kernel, stride, padding_sum, dilation)
    )

    in_height = in_width = in_size

    A = te.placeholder((batch, in_channel, in_height, in_width), name="A")
    W = te.placeholder((num_filter, in_channel, kernel, kernel), name="W")
    bias = te.placeholder((num_filter, 1, 1), name="bias")

    a_shape = get_const_tuple(A.shape)
    w_shape = get_const_tuple(W.shape)
    bias_shape = get_const_tuple(bias.shape)
    dtype = A.dtype

    @memoize("topi.tests.test_topi_conv2d_nchw.verify_conv2d_nchw")
    def get_ref_data():
        a_np = np.random.uniform(size=a_shape).astype(dtype)
        w_np = np.random.uniform(size=w_shape).astype(dtype)
        b_np = np.random.uniform(size=bias_shape).astype(dtype)
        dw_np = tvm.topi.testing.dilate_python(w_np, (1, 1, dilation, dilation))
        c_np = tvm.topi.testing.conv2d_nchw_python(a_np, dw_np, stride, padding)
        if add_bias:
            c_np += b_np
        if add_relu:
            c_np = np.maximum(c_np, 0)
        return a_np, w_np, b_np, c_np

    a_np, w_np, b_np, c_np = get_ref_data()

    def verify_workload_padding():
        _, _, out_height, out_width = get_const_tuple(c_np.shape)
        wkl = _get_workload(A, W, (stride, stride), padding, dilation, dtype)

        # check if tile_ow candidates are the factors of the right output weight.
        cfg = autotvm.get_config()
        _fallback_schedule(cfg, wkl)
        ow_tile = np.prod(cfg["tile_ow"].size)

        tvm.testing.assert_allclose(ow_tile, out_width)

    def check_device(device):
        ctx = tvm.context(device, 0)
        if not tvm.testing.device_enabled(device):
            print("Skip because %s is not enabled" % device)
            return
        print("Running on target: %s" % device)

        if "cudnn" in device:
            fcompute, fschedule = topi.cuda.conv2d_cudnn, topi.cuda.schedule_conv2d_cudnn
        else:
            fcompute, fschedule = tvm.topi.testing.get_conv2d_nchw_implement(device)

        with tvm.target.Target(device):
            if "cudnn" in device:
                C = fcompute(
                    A, W, (stride, stride), padding, (dilation, dilation), 1, "NCHW", dtype
                )
            else:
                C = fcompute(A, W, (stride, stride), padding, (dilation, dilation), dtype)
            if add_bias:
                C = topi.add(C, bias)
            if add_relu:
                C = topi.nn.relu(C)
            s = fschedule([C])

            if "llvm" in device:
                verify_workload_padding()

        a = tvm.nd.array(a_np, ctx)
        w = tvm.nd.array(w_np, ctx)
        b = tvm.nd.array(b_np, ctx)

        c = tvm.nd.array(np.zeros(get_const_tuple(C.shape), dtype=C.dtype), ctx)
        if add_bias:
            func = tvm.build(
                s,
                [A, W, bias, C],
                device,
                name="relu_%d_%d_%d_%d_%d_%d_%d_%d"
                % (batch, in_channel, in_size, num_filter, kernel, stride, padding_sum, dilation),
            )
            func(a, w, b, c)
        else:
            func = tvm.build(
                s,
                [A, W, C],
                device,
                name="relu_%d_%d_%d_%d_%d_%d_%d_%d"
                % (batch, in_channel, in_size, num_filter, kernel, stride, padding_sum, dilation),
            )
            func(a, w, c)
        tvm.testing.assert_allclose(c.asnumpy(), c_np, rtol=1e-4)

    for device, ctx in tvm.testing.enabled_targets():
        with autotvm.tophub.context(device):  # load tophub pre-tuned parameters
            check_device(device)

    if use_cudnn:
        check_device("cuda -model=unknown -libs=cudnn")


@tvm.testing.uses_gpu
def test_conv2d_nchw():
    # ResNet18 workloads
    verify_conv2d_nchw(1, 3, 224, 64, 7, 2, 3)
    verify_conv2d_nchw(1, 64, 56, 64, 3, 1, 1)
    verify_conv2d_nchw(1, 64, 56, 64, 1, 1, 0)
    verify_conv2d_nchw(1, 64, 56, 128, 3, 2, 1)
    verify_conv2d_nchw(1, 64, 56, 128, 1, 2, 0)
    verify_conv2d_nchw(1, 128, 28, 128, 3, 1, 1)
    verify_conv2d_nchw(1, 128, 28, 256, 3, 2, 1)
    verify_conv2d_nchw(1, 128, 28, 256, 1, 2, 0)
    verify_conv2d_nchw(1, 256, 14, 256, 3, 1, 1)
    verify_conv2d_nchw(1, 256, 14, 512, 3, 2, 1)
    verify_conv2d_nchw(1, 256, 14, 512, 1, 2, 0)
    verify_conv2d_nchw(1, 512, 7, 512, 3, 1, 1)

    # bias, relu
    verify_conv2d_nchw(1, 64, 56, 64, 3, 1, 1, add_relu=True)
    verify_conv2d_nchw(1, 64, 56, 64, 3, 1, 1, add_bias=True)
    verify_conv2d_nchw(1, 64, 56, 64, 3, 1, 1, add_bias=True, add_relu=True)

    # dilation = 2
    verify_conv2d_nchw(1, 64, 56, 64, 3, 1, 1, dilation=2)

    # batch size
    verify_conv2d_nchw(4, 64, 56, 64, 3, 1, 1)
    verify_conv2d_nchw(9, 64, 56, 64, 3, 1, 1)

    # weird workloads
    verify_conv2d_nchw(2, 2, 2, 2, 2, 2, 2)
    verify_conv2d_nchw(3, 3, 3, 3, 3, 3, 3)
    verify_conv2d_nchw(4, 4, 4, 4, 4, 4, 4)
    verify_conv2d_nchw(5, 5, 5, 5, 5, 5, 5)
    verify_conv2d_nchw(6, 6, 6, 6, 6, 6, 6)

    # disable these tests due to some bugs of llvm with nvptx
    # verify_conv2d_nchw(1, 1, 1, 1, 1, 1, 1, dilation=1)
    # verify_conv2d_nchw(1, 1, 1, 1, 1, 1, 1, dilation=2)
    # verify_conv2d_nchw(2, 13, 71, 59, 3, 1, 1)

    # inception v3 workloads
    verify_conv2d_nchw(1, 3, 299, 32, 3, 2, 0)
    verify_conv2d_nchw(1, 32, 149, 32, 3, 1, 0)
    verify_conv2d_nchw(1, 32, 147, 64, 3, 1, 1)
    verify_conv2d_nchw(1, 64, 73, 80, 1, 1, 0)
    verify_conv2d_nchw(1, 80, 73, 192, 3, 1, 0)
    verify_conv2d_nchw(1, 192, 35, 64, 1, 1, 0)
    verify_conv2d_nchw(1, 192, 35, 48, 1, 1, 0)
    verify_conv2d_nchw(1, 48, 35, 64, 5, 1, 2)
    verify_conv2d_nchw(1, 64, 35, 96, 3, 1, 1)
    verify_conv2d_nchw(1, 96, 35, 96, 3, 1, 1)
    verify_conv2d_nchw(1, 192, 35, 32, 1, 1, 0)
    verify_conv2d_nchw(1, 256, 35, 64, 1, 1, 0)
    verify_conv2d_nchw(1, 256, 35, 48, 1, 1, 0)
    verify_conv2d_nchw(1, 288, 35, 64, 1, 1, 0)
    verify_conv2d_nchw(1, 288, 35, 48, 1, 1, 0)
    verify_conv2d_nchw(1, 288, 35, 384, 3, 2, 0)
    verify_conv2d_nchw(1, 96, 35, 96, 3, 2, 0)
    verify_conv2d_nchw(1, 768, 17, 192, 1, 1, 0)
    verify_conv2d_nchw(1, 768, 17, 128, 1, 1, 0)
    verify_conv2d_nchw(1, 128, 17, 128, 1, 1, 0)
    verify_conv2d_nchw(1, 128, 17, 192, 7, 1, 3)
    verify_conv2d_nchw(1, 128, 17, 128, 7, 1, 3)
    verify_conv2d_nchw(1, 128, 17, 192, 1, 1, 0)
    verify_conv2d_nchw(1, 768, 17, 160, 1, 1, 0)
    # disable these tests due to some bugs of llvm with nvptx
    # verify_conv2d_nchw(1,  160,  17, 160, 1, 1, 0)
    verify_conv2d_nchw(1, 160, 17, 192, 7, 1, 3)
    verify_conv2d_nchw(1, 160, 17, 160, 7, 1, 3)
    verify_conv2d_nchw(1, 160, 17, 192, 1, 1, 0)
    verify_conv2d_nchw(1, 192, 17, 192, 1, 1, 0)
    verify_conv2d_nchw(1, 192, 17, 192, 7, 1, 3)
    verify_conv2d_nchw(1, 192, 17, 320, 3, 2, 0)
    verify_conv2d_nchw(1, 192, 17, 192, 3, 2, 0)
    verify_conv2d_nchw(1, 1280, 8, 320, 1, 1, 0)
    verify_conv2d_nchw(1, 1280, 8, 384, 1, 1, 0)
    verify_conv2d_nchw(1, 384, 8, 384, 1, 1, 0)
    verify_conv2d_nchw(1, 384, 8, 384, 3, 1, 1)
    verify_conv2d_nchw(1, 1280, 8, 448, 1, 1, 0)
    verify_conv2d_nchw(1, 448, 8, 384, 3, 1, 1)
    verify_conv2d_nchw(1, 1280, 8, 192, 1, 1, 0)
    verify_conv2d_nchw(1, 2048, 8, 320, 1, 1, 0)
    verify_conv2d_nchw(1, 2048, 8, 384, 1, 1, 0)
    verify_conv2d_nchw(1, 2048, 8, 448, 1, 1, 0)
    verify_conv2d_nchw(1, 2048, 8, 192, 1, 1, 0)
    verify_conv2d_nchw(1, 1024, 19, 84, 3, 1, 1)
    verify_conv2d_nchw(1, 2048, 10, 126, 3, 1, 1)
    verify_conv2d_nchw(1, 512, 5, 126, 3, 1, 1)
    verify_conv2d_nchw(1, 256, 3, 126, 3, 1, 1)

    # Asymmetric padding
    verify_conv2d_nchw(1, 3, 35, 64, 7, 2, (0, 0, 1, 1))
    verify_conv2d_nchw(1, 64, 8, 128, 3, 1, (3, 3, 2, 2))
    verify_conv2d_nchw(1, 64, 8, 64, 1, 1, (1, 2, 2, 1))
    verify_conv2d_nchw(1, 64, 17, 192, 1, 1, (1, 2))
    verify_conv2d_nchw(1, 64, 8, 64, 3, 1, (3, 1))
    verify_conv2d_nchw(1, 128, 8, 384, 3, 1, (0, 2))
    verify_conv2d_nchw(1, 64, 35, 64, 3, 1, (1, 2), use_cudnn=True)
    verify_conv2d_nchw(1, 64, 8, 64, 1, 1, "VALID")
    verify_conv2d_nchw(1, 388, 8, 64, 3, 1, "VALID")
    verify_conv2d_nchw(1, 64, 10, 48, 3, 1, "VALID", use_cudnn=True)
    verify_conv2d_nchw(1, 512, 19, 64, 1, 1, "SAME")
    verify_conv2d_nchw(1, 64, 5, 32, 2, 1, "SAME")
    verify_conv2d_nchw(1, 64, 8, 64, 3, 1, "SAME", use_cudnn=True)
    verify_conv2d_nchw(1, 64, 8, 64, 3, 1, (1, 2, 2, 1), add_relu=True)
    verify_conv2d_nchw(1, 64, 8, 64, 5, 2, (1, 3), add_bias=True)
    verify_conv2d_nchw(1, 64, 8, 64, 3, 1, "VALID", add_bias=True, add_relu=True)
    verify_conv2d_nchw(1, 64, 8, 64, 24, 1, "SAME", add_bias=True, add_relu=True)
    verify_conv2d_nchw(1, 32, 35, 64, 7, 2, (0, 0, 2, 2))


if __name__ == "__main__":
    test_conv2d_nchw()
