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# to you under the Apache License, Version 2.0 (the
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#
#   http://www.apache.org/licenses/LICENSE-2.0
#
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"""Example code to do convolution."""
import os
import numpy as np
import scipy.signal
import tvm
from tvm import te
from tvm.contrib import nvcc
from tvm import topi
from tvm.topi.utils import get_const_tuple

TASK = "conv2d_hwcn_map"
USE_MANUAL_CODE = False


@tvm.register_func
def tvm_callback_cuda_compile(code):
    ptx = nvcc.compile_cuda(code, target="ptx")
    return ptx


def write_code(code, fname):
    with open(fname, "w") as f:
        f.write(code)


@tvm.register_func
def tvm_callback_cuda_postproc(code):
    if not os.path.exists("perf"):
        os.mkdir("perf")
    write_code(code, "perf/%s_generated.cu" % TASK)
    if USE_MANUAL_CODE:
        code = open("perf/%s_manual.cu" % TASK).read()
    return code


def test_conv2d_hwcn_map():
    batch = 64
    in_channel = 128
    in_height = 16
    in_width = 16
    num_filter = 128
    kernel = 3
    stride = 2
    padding = "SAME"

    A = te.placeholder((in_height, in_width, in_channel, batch), name="A")
    W = te.placeholder((kernel, kernel, in_channel, num_filter), name="W")
    B = topi.nn.conv2d_hwcn(A, W, stride, padding)
    C = topi.nn.relu(B)
    s1 = topi.cuda.schedule_conv2d_hwcn([B])
    s2 = topi.cuda.schedule_conv2d_hwcn([C])

    a_np = np.random.uniform(size=get_const_tuple(A.shape)).astype(A.dtype)
    w_np = np.random.uniform(size=get_const_tuple(W.shape)).astype(W.dtype)
    b_np = tvm.topi.testing.conv2d_hwcn_python(a_np, w_np, stride, padding)
    c_np = np.maximum(b_np, 0)

    def check_device(device):
        if not tvm.runtime.enabled(device):
            print("Skip because %s is not enabled" % device)
            return
        ctx = tvm.context(device, 0)
        a = tvm.nd.array(a_np, ctx)
        w = tvm.nd.array(w_np, ctx)
        b = tvm.nd.array(np.zeros(get_const_tuple(B.shape), dtype=B.dtype), ctx)
        c = tvm.nd.array(np.zeros(get_const_tuple(C.shape), dtype=C.dtype), ctx)

        with tvm.transform.PassContext(
            config={
                "tir.UrollLoop": {"auto_unroll_max_step": 128, "explicit_unroll": device == "rocm"}
            }
        ):
            func1 = tvm.build(s1, [A, W, B], device)
            func1(a, w, b)
            tvm.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5)
            func2 = tvm.build(s2, [A, W, C], device)
            func2(a, w, c)
            tvm.testing.assert_allclose(c.asnumpy(), c_np, rtol=1e-5)

    for device in ["cuda", "opencl", "rocm"]:
        check_device(device)


if __name__ == "__main__":
    test_conv2d_hwcn_map()
