#!/usr/bin/env python

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#   http://www.apache.org/licenses/LICENSE-2.0
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"""Test script for tf op module"""
import tempfile
import os
import logging
import tensorflow as tf
import numpy as np
import tvm
from tvm import te
from tvm.contrib import tf_op


def test_use_tvmdso_op():
    """main test function"""

    def export_cpu_add_lib():
        """create cpu add op lib"""
        n = te.var("n")
        ph_a = te.placeholder((n,), name='ph_a')
        ph_b = te.placeholder((n,), name='ph_b')
        ph_c = te.compute(ph_a.shape, lambda i: ph_a[i] + ph_b[i], name='ph_c')
        sched = te.create_schedule(ph_c.op)
        fadd_dylib = tvm.build(sched, [ph_a, ph_b, ph_c], "c", name="vector_add")
        lib_path = tempfile.mktemp("tvm_add_dll.so")
        fadd_dylib.export_library(lib_path)
        return lib_path


    def export_gpu_add_lib():
        """create gpu add op lib"""
        n = te.var("n")
        ph_a = te.placeholder((n,), name='ph_a')
        ph_b = te.placeholder((n,), name='ph_b')
        ph_c = te.compute(ph_a.shape, lambda i: ph_a[i] + ph_b[i], name='ph_c')
        sched = te.create_schedule(ph_c.op)
        b_axis, t_axis = sched[ph_c].split(ph_c.op.axis[0], factor=64)
        sched[ph_c].bind(b_axis, te.thread_axis("blockIdx.x"))
        sched[ph_c].bind(t_axis, te.thread_axis("threadIdx.x"))
        fadd_dylib = tvm.build(sched, [ph_a, ph_b, ph_c], "cuda", name="vector_add")
        lib_path = tempfile.mktemp("tvm_add_cuda_dll.so")
        fadd_dylib.export_library(lib_path)
        return lib_path


    def test_add(session, lib_path, tf_device):
        """test add lib with TensorFlow wrapper"""
        module = tf_op.OpModule(lib_path)

        left = tf.placeholder("float32", shape=[4])
        right = tf.placeholder("float32", shape=[4])

        feed_dict = {left: [1.0, 2.0, 3.0, 4.0], right: [5.0, 6.0, 7.0, 8.0]}
        expect = np.asarray([6.0, 8.0, 10.0, 12.0])

        add1 = module.func("vector_add", output_shape=[4], output_dtype="float")
        add2 = module.func("vector_add", output_shape=tf.shape(left), output_dtype="float")
        add3 = module.func("vector_add", output_shape=[tf.shape(left)[0]], output_dtype="float")

        with tf.device(tf_device):
            output1 = session.run(add1(left, right), feed_dict)
            np.testing.assert_equal(output1, expect)

            output2 = session.run(add2(left, right), feed_dict)
            np.testing.assert_equal(output2, expect)

            output3 = session.run(add3(left, right), feed_dict)
            np.testing.assert_equal(output3, expect)


    def cpu_test(session):
        """test function for cpu"""
        cpu_lib = None
        try:
            cpu_lib = export_cpu_add_lib()
            test_add(session, cpu_lib, "/cpu:0")
        finally:
            if cpu_lib is not None:
                os.remove(cpu_lib)


    def gpu_test(session):
        """test function for gpu"""
        gpu_lib = None
        try:
            gpu_lib = export_gpu_add_lib()
            test_add(session, gpu_lib, "/gpu:0")
        finally:
            if gpu_lib is not None:
                os.remove(gpu_lib)

    with tf.Session() as session:
        if tvm.runtime.enabled("cpu"):
            logging.info("Test TensorFlow op on cpu kernel")
            cpu_test(session)
        if tvm.runtime.enabled("gpu"):
            logging.info("Test TensorFlow op on gpu kernel")
            gpu_test(session)


if __name__ == "__main__":
    test_use_tvmdso_op()
