make easier for user to understand how tensor_intrin works

Signed-off-by: wangtongyu <wangtongyu6@huawei.com>

tests/python/unittest/test_te_schedule.py

@@ -252,20 +252,25 @@ def intrin_func(ins, outs):
         assert ins[0].shape[0].value == n
         return tvm.tir.call_packed("vadd", ins[0].data, outs[0].data, ins[0].shape[0])
 
-    intrin = te.decl_tensor_intrin(z.op, intrin_func)
+    intrin = te.decl_tensor_intrin(z.op, intrin_func, default_buffer_params={"offset_factor": n})
     assert intrin.op == z.op
     assert intrin.reduce_init is None
     assert tuple(intrin.inputs) == tuple(z.op.input_tensors)
     assert intrin.buffers[0].shape[0].value == n
     m = 32
-    x = te.placeholder((m,), name="x")
-    y = te.placeholder((m,), name="y")
-    z = te.compute(x.shape, lambda i: x[i] + y[i], name="z")
-    s = te.create_schedule(z.op)
-    xo, xi = s[z].split(z.op.axis[0], factor=n)
-    s[z].tensorize(xi, intrin)
-    assert s[z].iter_var_attrs[xi].tensor_intrin == intrin
-    assert s[z].iter_var_attrs[xi].iter_type == tvm.te.schedule.IterVar.Tensorized
+    X = te.placeholder((m,), name="X")
+    Y = te.placeholder((m,), name="Y")
+    Z = te.compute(X.shape, lambda i: X[i] + Y[i], name="Z")
+    s = te.create_schedule(Z.op)
+    xo, xi = s[Z].split(Z.op.axis[0], factor=n)
+    s[Z].tensorize(xi, intrin)
+    stmt = tvm.lower(s, [X, Y, Z])["main"].body
+    assert isinstance(stmt.body, tvm.tir.Evaluate)
+    assert str(stmt.body.value.args[0]) == '"vadd"'
+    assert str(stmt.body.value.args[1]) == "X"
+    assert str(stmt.body.value.args[2]) == "Z"
+    assert s[Z].iter_var_attrs[xi].tensor_intrin == intrin
+    assert s[Z].iter_var_attrs[xi].iter_type == tvm.te.schedule.IterVar.Tensorized
 
 
 def test_tensor_intrin_scalar_params():