sch and compute for reduce ops

tests/python/relay/test_op_level4.py

@@ -106,8 +106,11 @@ def test_where():
     assert zz.checked_type == relay.TensorType((3, 4), "float32")
 
 
-def verify_reduce(test_func, data, axis, keepdims, exclude, output):
-    x = relay.var("x", relay.TensorType(data, "float32"))
+def verify_reduce(funcs, data, axis, keepdims, exclude, output, dtype="float32"):
+    test_func = funcs[0]
+    ref_func = funcs[1]
+
+    x = relay.var("x", relay.TensorType(data, dtype))
     z = test_func(x, axis, keepdims, exclude)
     zz = relay.ir_pass.infer_type(z)
     if axis:
@@ -116,18 +119,54 @@ def verify_reduce(test_func, data, axis, keepdims, exclude, output):
         assert "keepdims=" in z.astext()
     if exclude:
         assert "exclude=" in z.astext()
-    out_type = "int32" if test_func in [relay.argmin, relay.argmax] else "float32"
+    out_type = "int32" if test_func in [relay.argmin, relay.argmax] else dtype
     assert zz.checked_type == relay.ty.TensorType(output, out_type)
 
+    if all(isinstance(v, tvm.expr.Var) == 1 for v in data) or len(output) == 0:
+        return
+
+    func = relay.Function([x], z)
+    x_data = np.random.uniform(size=data).astype(dtype)
+    if ref_func in [np.sum]:
+        ref_res = ref_func(x_data + 0, axis=axis, dtype=dtype, keepdims=keepdims)
+    elif ref_func in [np.max, np.min, np.mean, np.prod]:
+        ref_res = ref_func(x_data + 0, axis=axis, keepdims=keepdims)
+    else: #argmin/argmax
+        if axis and len(axis) > 1:
+            return
+        ref_res = ref_func(x_data + 0, axis=axis, keepdims=keepdims)
+
+    for target, ctx in ctx_list():
+        intrp1 = relay.create_executor("graph", ctx=ctx, target=target)
+        intrp2 = relay.create_executor("debug", ctx=ctx, target=target)
+        op_res1 = intrp1.evaluate(func)(x_data)
+        tvm.testing.assert_allclose(op_res1.asnumpy(), ref_res, rtol=1e-5)
+        op_res2 = intrp2.evaluate(func)(x_data)
+        tvm.testing.assert_allclose(op_res2.asnumpy(), ref_res, rtol=1e-5)
+
 def test_reduce_functions():
+    def _with_keepdims(func):
+        def _wrapper(data, axis=None, keepdims=False):
+            if not keepdims:
+                return func(data, axis=axis)
+            else:
+                if axis is not None:
+                    axis = axis[0]
+                    out_shape = list(data.shape)
+                    out_shape[axis] = 1
+                else:
+                    out_shape = [1 for _ in range(len(data.shape))]
+                return func(data, axis=axis).reshape(out_shape)
+        return _wrapper
+
     d1, d2, d3, d4 = tvm.var("d1"), tvm.var("d2"), tvm.var("d3"), tvm.var("d4")
-    for func in [relay.sum,
-                 relay.max,
-                 relay.min,
-                 relay.mean,
-                 relay.prod,
-                 relay.argmin,
-                 relay.argmax]:
+    for func in [[relay.sum, np.sum],
+                 [relay.max, np.max],
+                 [relay.min, np.min],
+                 [relay.mean, np.mean],
+                 [relay.prod, np.prod],
+                 [relay.argmin, _with_keepdims(np.argmin)],
+                 [relay.argmax, _with_keepdims(np.argmax)]]:
         verify_reduce(func, (d1, d2, d3, d4), (2,), True, False, (d1, d2, 1, d4))
         verify_reduce(func, (d1, d2, d3), (1,), True, False, (d1, 1, d3))
         verify_reduce(func, (d1, d2, d3), None, True, False, (1, 1, 1))