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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.autotvm.task.space import FallbackConfigEntity
import topi
import topi.testing
from tvm.contrib.pickle_memoize import memoize
from topi.nn.util import get_pad_tuple
from topi.util import get_const_tuple


_conv2d_nchw_winograd_implement = {
    "arm_cpu": (topi.arm_cpu.conv2d_nchw_winograd, topi.arm_cpu.schedule_conv2d_nchw_winograd),
    "cuda": (topi.cuda.conv2d_nchw_winograd, topi.cuda.schedule_conv2d_nchw_winograd),
    "mali": (topi.mali.conv2d_nchw_winograd, topi.mali.schedule_conv2d_nchw_winograd),
}


def verify_conv2d_nchw(batch, in_channel, in_size, num_filter, kernel, stride, padding, dilation=1, add_bias=False, add_relu=False,
        devices=['cuda', 'llvm -device=arm_cpu', 'opencl -device=mali']):
    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 = topi.testing.dilate_python(w_np, (1, 1, dilation, dilation))
        c_np = topi.testing.conv2d_nchw_python(a_np, dw_np, stride, padding)
        if add_bias:
            b_np = np.random.uniform(size=bias_shape).astype(dtype)
            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 check_device(device):
        ctx = tvm.context(device, 0)
        if not ctx.exist:
            print("Skip because %s is not enabled" % device)
            return
        print("Running on target: %s" % device)
        with tvm.target.create(device):
            fcompute, fschedule = topi.testing.dispatch(device, _conv2d_nchw_winograd_implement)
            C = fcompute(A, W, stride, padding, dilation, dtype)
            if add_bias:
                C = topi.add(C, bias)
            if add_relu:
                C = topi.nn.relu(C)
            s = fschedule([C])

        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)

        rtol = 1e-3
        tvm.testing.assert_allclose(c.asnumpy(), c_np, rtol=rtol)


    for device in devices:
        check_device(device)


def test_conv2d_nchw():
    # inception v3 workloads
    verify_conv2d_nchw(1, 128, 17, 192, 7, 1, 3, devices=['cuda'])
    verify_conv2d_nchw(1, 128, 17, 128, 7, 1, 3, devices=['cuda'])
    verify_conv2d_nchw(1, 160, 17, 160, 7, 1, 3, devices=['cuda'])

    # resnet 18 workloads
    verify_conv2d_nchw(1, 64, 56, 64, 3, 1, 1)
    verify_conv2d_nchw(1, 128, 28, 128, 3, 1, 1)
    verify_conv2d_nchw(1, 256, 14, 256, 3, 1, 1)
    verify_conv2d_nchw(1, 512, 7, 512, 3, 1, 1)
    verify_conv2d_nchw(1, 48,  35, 64, 5, 1, 2, devices=['cuda'])

    # batch size = 2
    verify_conv2d_nchw(2, 64, 56, 64, 3, 1, 1)

    # relu, bias
    verify_conv2d_nchw(2, 64, 56, 64, 3, 1, 1, add_bias=True)
    verify_conv2d_nchw(2, 64, 56, 64, 3, 1, 1, add_relu=True)
    verify_conv2d_nchw(2, 64, 56, 64, 3, 1, 1, add_relu=True, add_bias=True)

    # weird workloads
    verify_conv2d_nchw(1, 1, 1, 1, 3, 1, 1)
    verify_conv2d_nchw(3, 3, 3, 3, 3, 1, 1)
    verify_conv2d_nchw(2, 13, 71, 59, 3, 1, 1)

    # Asymmetric padding
    verify_conv2d_nchw(1,  48, 56,  48, 3, 1, (1, 1, 1, 1))
    verify_conv2d_nchw(1,  64, 28,  64, 3, 1, (1, 1, 1, 1))
    verify_conv2d_nchw(1, 128, 14, 128, 3, 1, (1, 1))
    verify_conv2d_nchw(1, 512,  7, 512, 3, 1, "SAME")
    verify_conv2d_nchw(2, 13,  71,  59, 3, 1, (1, 1, 1, 1))
    verify_conv2d_nchw(2,  48, 56,  48, 3, 1, (1, 1, 1, 1), add_bias=True)
    verify_conv2d_nchw(2,  48, 56,  48, 3, 1, (1, 1), add_relu=True)
    verify_conv2d_nchw(2,  48, 56,  48, 3, 1, "SAME", add_relu=True, add_bias=True)
    verify_conv2d_nchw(1,  64, 17, 192, 7, 1, (3, 1), devices=['cuda'])
    verify_conv2d_nchw(1,  64, 17,  64, 7, 1, (3, 3, 2, 2), devices=['cuda'])
    verify_conv2d_nchw(1, 160, 17, 160, 7, 1, "SAME", devices=['cuda'])
    verify_conv2d_nchw(1,  48, 35,  48, 5, 1, "VALID", devices=['cuda'])


if __name__ == "__main__":
    test_conv2d_nchw()
