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# This test checks if dynamic loading of library into MXNet is successful

import os
import platform
import mxnet as mx
import numpy as np
from mxnet import nd
from mxnet.gluon import nn
from mxnet.base import MXNetError
from mxnet.test_utils import download, is_cd_run, assert_almost_equal, default_context
import pytest

base_path = os.path.join(os.path.dirname(__file__), "../../..")
def check_platform():
    return platform.machine() not in ['x86_64', 'AMD64']

@pytest.mark.skipif(check_platform(), reason="not all machine types supported")
@pytest.mark.skipif(is_cd_run(), reason="continuous delivery run - ignoring test")
def test_custom_op_gpu():
    # possible places to find library file
    if (os.name=='posix'):
        lib = 'libcustomop_gpu_lib.so'
        if os.path.exists(lib):
            fname = lib
        elif os.path.exists(os.path.join(base_path, 'build/'+lib)):
            fname = os.path.join(base_path, 'build/'+lib)
        else:
            raise MXNetError("library %s not found " % lib)
    elif (os.name=='nt'):
        lib = 'libcustomop_gpu_lib.dll'
        if os.path.exists('windows_package\\lib\\'+lib):
            fname = 'windows_package\\lib\\'+lib
        else:
            raise MXNetError("library %s not found " % lib)

    fname = os.path.abspath(fname)
    # load the library containing gemm custom operators
    mx.library.load(fname)

    # test symbol custom relu operator in gpu
    b = mx.nd.array([[-2,-1],[1,2]], ctx=mx.gpu())
    c = mx.sym.Variable('c')
    d = mx.sym.Variable('d')
    e = mx.sym.my_relu(c)
    base = mx.sym.relu(d)
    in_grad = [mx.nd.empty((2,2), ctx=mx.gpu())]
    in_grad_base = [mx.nd.empty((2,2), ctx=mx.gpu())]
    exe = e._bind(ctx=mx.gpu(), args={'c':b}, args_grad=in_grad)
    exe_base = base._bind(ctx=mx.gpu(), args={'d':b}, args_grad=in_grad_base)
    out = exe.forward()
    out_base = exe_base.forward()
    assert_almost_equal(out_base[0].asnumpy(), out[0].asnumpy(), rtol=1e-3, atol=1e-3)

    # test custom relu backward
    out_grad = mx.nd.ones((2,2), ctx=mx.gpu())
    exe.backward([out_grad])
    exe_base.backward([out_grad])
    assert_almost_equal(in_grad_base[0].asnumpy(), in_grad[0].asnumpy(), rtol=1e-3, atol=1e-3)

    # test custom noisy relu producing deterministic result given same seed managed by mxnet
    d1 = mx.nd.ones(shape=(10,10,10), ctx=mx.cpu())
    d2 = mx.nd.ones(shape=(10,10,10), ctx=mx.gpu())

    mx.random.seed(128, ctx=mx.cpu())
    r1 = mx.nd.my_noisy_relu(d1)
    mx.random.seed(128, ctx=mx.cpu())
    r2 = mx.nd.my_noisy_relu(d1)
    assert_almost_equal(r1.asnumpy(), r2.asnumpy(), rtol=1e-3, atol=1e-3)

    mx.random.seed(128, ctx=mx.gpu())
    r3 = mx.nd.my_noisy_relu(d2)
    mx.random.seed(128, ctx=mx.gpu())
    r4 = mx.nd.my_noisy_relu(d2)
    assert_almost_equal(r3.asnumpy(), r4.asnumpy(), rtol=1e-3, atol=1e-3)