test_op_grad_level3.py

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import numpy as np
import tvm
from tvm import relay
from tvm.relay.transform import gradient
from tvm.relay.testing import ctx_list, run_infer_type

def test_clip():
    ref = (lambda x: np.where(x > 10.0, np.zeros_like(x),
                     np.where(x < 1.0, np.zeros_like(x), np.ones_like(x))))
    x = relay.var("x", relay.TensorType((10, 4), "float32"))
    y = tvm.relay.clip(x, 1.0, 10.0)

    data = np.random.rand(10, 4).astype("float32") * 11.0
    ref_grad = ref(data)
    fwd_func = relay.Function([x], y)
    fwd_func = run_infer_type(fwd_func)
    bwd_func = run_infer_type(gradient(fwd_func))

    for target, ctx in ctx_list():
        intrp = relay.create_executor(ctx=ctx, target=target)
        op_res, (op_grad, ) = intrp.evaluate(bwd_func)(data)
        np.testing.assert_allclose(op_grad.asnumpy(), ref_grad, rtol=0.01)


if __name__ == "__main__":
    test_clip()