[TOPI] Add left_shift, right_shift, clip, cast (#504)

* [TOPI] Add left_shift, right_shift, clip, cast

* [TOPI] Add test

* [TOPI] Fix

topi/python/topi/math.py

@@ -122,3 +122,87 @@ def sigmoid(x):
         The result.
     """
     return tvm.compute(x.shape, lambda *i: tvm.sigmoid(x(*i)))
+
+
+@tvm.tag_scope(tag=tag.ELEMWISE)
+def left_shift(x, n):
+    """Take n bits left shift of input x.
+
+    Parameters
+    ----------
+    x : tvm.Tensor
+        Input argument.
+    n : int
+        Number of bits.
+
+    Returns
+    -------
+    y : tvm.Tensor
+        The result.
+    """
+    return tvm.compute(x.shape, lambda *i: x(*i) << n)
+
+
+@tvm.tag_scope(tag=tag.ELEMWISE)
+def right_shift(x, n):
+    """Take n bits right shift of input x.
+
+    Parameters
+    ----------
+    x : tvm.Tensor
+        Input argument.
+    n : int
+        Number of bits.
+
+    Returns
+    -------
+    y : tvm.Tensor
+        The result.
+    """
+    return tvm.compute(x.shape, lambda *i: x(*i) >> n)
+
+
+@tvm.tag_scope(tag=tag.ELEMWISE)
+def clip(x, a_min, a_max):
+    """Clip (limit) the values in an array. Given an interval, values
+    outside the interval are clipped to the interval edges.
+
+    Parameters
+    ----------
+    x : tvm.Tensor
+        Input argument.
+    a_min : int or float
+        Minimum value.
+    a_max : int or float
+        Maximum value.
+
+    Returns
+    -------
+    y : tvm.Tensor
+        The result.
+    """
+    def _compute(*indices):
+        value = x(*indices)
+        const_min = tvm.const(a_min, value.dtype)
+        const_max = tvm.const(a_max, value.dtype)
+        return tvm.max(tvm.min(value, const_max), const_min)
+    return tvm.compute(x.shape, _compute)
+
+
+@tvm.tag_scope(tag=tag.ELEMWISE)
+def cast(x, dtype):
+    """Cast input to specified data type.
+
+    Parameters
+    ----------
+    x : tvm.Tensor
+        Input argument.
+    dtype : str
+        Data type.
+
+    Returns
+    -------
+    y : tvm.Tensor
+        The result.
+    """
+    return tvm.compute(x.shape, lambda *i: x(*i).astype(dtype))

topi/tests/python/test_topi_clip.py

@@ -0,0 +1,43 @@
+"""Test code for clip operator"""
+import numpy as np
+import tvm
+import topi
+from topi.util import get_const_tuple
+from tvm.contrib.pickle_memoize import memoize
+
+
+def verify_clip(N, a_min, a_max, dtype):
+    A = tvm.placeholder((N, N), dtype=dtype, name='A')
+    B = topi.clip(A, a_min, a_max)
+    s = tvm.create_schedule([B.op])
+
+    # use memoize to pickle the test data for next time use
+    @memoize("topi.tests.test_topi_clip")
+    def get_ref_data():
+        a_np = np.random.uniform(a_min*2, a_max*2, size=(N, N)).astype(dtype)
+        b_np = np.clip(a_np, a_min, a_max)
+        return a_np, b_np
+    a_np, b_np = get_ref_data()
+
+    def check_device(device):
+        if not tvm.module.enabled(device):
+            print("Skip because %s is not enabled" % device)
+            return
+        ctx = tvm.cpu(0) if device == "llvm" else tvm.gpu(0)
+        a = tvm.nd.array(a_np, ctx)
+        b = tvm.nd.array(np.zeros(get_const_tuple(B.shape), dtype=dtype), ctx)
+        f = tvm.build(s, [A, B], device, name="clip")
+        f(a, b)
+        np.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5)
+
+    for device in ['llvm']:
+        check_device(device)
+
+def test_clip():
+    verify_clip(1024, -127, 127, 'int8')
+    verify_clip(1024, -127, 127, 'int16')
+    verify_clip(1024, -127, 127, 'float32')
+
+
+if __name__ == "__main__":
+    test_clip()