[1] Test case modified for int type (apache#5012)

tests/python/relay/test_op_level4.py

@@ -88,33 +88,54 @@ def test_cmp_type():
                 tvm.testing.assert_allclose(op_res.asnumpy(), ref_res)
 
 
-def test_binary_int_broadcast():
+def test_binary_int_broadcast_1():
     for op, ref in [(relay.right_shift, np.right_shift),
-               (relay.left_shift, np.left_shift),
-                (relay.mod, np.mod),
-               (relay.maximum, np.maximum),
-               (relay.minimum, np.minimum)]:
+                    (relay.left_shift, np.left_shift)]:
         x = relay.var("x", relay.TensorType((10, 4), "int32"))
         y = relay.var("y", relay.TensorType((5, 10, 1), "int32"))
         z = op(x, y)
         zz = run_infer_type(z)
         assert zz.checked_type == relay.TensorType((5, 10, 4), "int32")
 
-    if ref is not None:
-        x_shape = (10, 4)
-        y_shape = (5, 10, 1)
-        t1 = relay.TensorType(x_shape, 'int32')
-        t2 = relay.TensorType(y_shape, 'int32')
-        x_data = np.random.rand(*x_shape).astype(t1.dtype)
-        y_data = np.random.rand(*y_shape).astype(t2.dtype)
-        func = relay.Function([x, y], z)
-        ref_res = ref(x_data, y_data)
+        if ref is not None:
+            x_shape = (10, 4)
+            y_shape = (5, 10, 1)
+            t1 = relay.TensorType(x_shape, 'int32')
+            t2 = relay.TensorType(y_shape, 'int32')
+            x_data = np.random.randint(1, 10000, size=(x_shape)).astype(t1.dtype)
+            y_data = np.random.randint(1, 31, size=(y_shape)).astype(t2.dtype)
+            func = relay.Function([x, y], z)
+            ref_res = ref(x_data, y_data)
 
-        for target, ctx in ctx_list():
-            intrp = relay.create_executor("graph", ctx=ctx, target=target)
-            op_res = intrp.evaluate(func)(x_data, y_data)
-            tvm.testing.assert_allclose(op_res.asnumpy(), ref_res)
+            for target, ctx in ctx_list():
+                intrp = relay.create_executor("graph", ctx=ctx, target=target)
+                op_res = intrp.evaluate(func)(x_data, y_data)
+                tvm.testing.assert_allclose(op_res.asnumpy(), ref_res)
+
+def test_binary_int_broadcast_2():
+    for op, ref in [(relay.maximum, np.maximum),
+                    (relay.minimum, np.minimum),
+                    (relay.mod, np.mod)]:
+        x = relay.var("x", relay.TensorType((10, 4), "int32"))
+        y = relay.var("y", relay.TensorType((5, 10, 1), "int32"))
+        z = op(x, y)
+        zz = run_infer_type(z)
+        assert zz.checked_type == relay.TensorType((5, 10, 4), "int32")
+
+        if ref is not None:
+            x_shape = (10, 4)
+            y_shape = (5, 10, 1)
+            t1 = relay.TensorType(x_shape, 'int32')
+            t2 = relay.TensorType(y_shape, 'int32')
+            x_data = np.random.randint(1, 10000, size=(x_shape)).astype(t1.dtype)
+            y_data = np.random.randint(1, 10000, size=(y_shape)).astype(t2.dtype)
+            func = relay.Function([x, y], z)
+            ref_res = ref(x_data, y_data)
 
+            for target, ctx in ctx_list():
+                intrp = relay.create_executor("graph", ctx=ctx, target=target)
+                op_res = intrp.evaluate(func)(x_data, y_data)
+                tvm.testing.assert_allclose(op_res.asnumpy(), ref_res)
 
 def test_where():
     shape = (3, 4)
@@ -341,7 +362,8 @@ def verify(dshape, begin, end, strides, vshape, test_ref=True):
     test_strided_set()
     test_binary_op()
     test_cmp_type()
-    test_binary_int_broadcast()
+    test_binary_int_broadcast_1()
+    test_binary_int_broadcast_2()
     test_where()
     test_reduce_functions()
     test_mean_var_std()