[Frontend][MXNet] add _npi_stack, issue apache#7186 (apache#7209)

- apache#7186
- add MxNet stack, `_npi_stack`
- https://mxnet.apache.org/versions/master/api/python/docs/api/np/generated/mxnet.np.stack.html?highlight=stack

python/tvm/relay/frontend/mxnet.py

@@ -2335,6 +2335,14 @@ def _mx_npi_concatenate(inputs, attrs):
         return _op.concatenate(tuple(inputs), axis=int(axis))
 
 
+def _mx_npi_stack(inputs, attrs):
+    axis = attrs.get_str("axis", "0")
+    if axis == "None":
+        return _op.reshape(_op.stack(tuple(inputs), axis=0), (-1,))
+    else:
+        return _op.stack(tuple(inputs), axis=int(axis))
+
+
 def _mx_npx_reshape(inputs, attrs):
     shape = attrs.get_int_tuple("newshape")
     reverse = attrs.get_bool("reverse", False)
@@ -2700,6 +2708,7 @@ def _mx_npi_where_rscalar(inputs, attrs):
     "_npi_less_equal": _mx_compare(_op.less_equal, _rename),
     "_npi_tanh": _rename(_op.tanh),
     "_npi_true_divide_scalar": _binop_scalar(_op.divide),
+    "_npi_stack": _mx_npi_stack,
 }
 
 # set identity list

tests/python/frontend/mxnet/test_forward.py

@@ -2012,6 +2012,34 @@ def test_forward_npi_concatenate(data_shape1, data_shape2, axis, dtype, target,
     tvm.testing.assert_allclose(op_res.asnumpy(), ref_res.asnumpy(), rtol=1e-5)
 
 
+@pytest.mark.parametrize(
+    "data_shape1, data_shape2, axis",
+    [
+        ((3,), (3,), 0),
+        ((3,), (3,), -1),
+        ((1, 3, 2), (1, 3, 2), 2),
+        ((1, 3, 3), (1, 3, 3), 1),
+        ((1, 3), (1, 3), 0),
+    ],
+)
+@pytest.mark.parametrize("dtype", ["float64", "float32", "int64", "int32"])
+@tvm.testing.parametrize_targets
+@pytest.mark.parametrize("kind", ["graph", "vm", "debug"])
+def test_forward_npi_stack(data_shape1, data_shape2, axis, dtype, target, ctx, kind):
+    data_np1 = np.random.uniform(size=data_shape1).astype(dtype)
+    data_np2 = np.random.uniform(size=data_shape2).astype(dtype)
+    data1 = mx.sym.var("data1")
+    data2 = mx.sym.var("data2")
+    ref_res = mx.np.stack([mx.np.array(data_np1), mx.np.array(data_np2)], axis=axis)
+    mx_sym = mx.sym.np.stack([data1.as_np_ndarray(), data2.as_np_ndarray()], axis=axis)
+    mod, _ = relay.frontend.from_mxnet(
+        mx_sym, shape={"data1": data_shape1, "data2": data_shape2}, dtype=dtype
+    )
+    intrp = relay.create_executor(kind, mod=mod, ctx=ctx, target=target)
+    op_res = intrp.evaluate()(data_np1, data_np2)
+    tvm.testing.assert_allclose(op_res.asnumpy(), ref_res.asnumpy(), rtol=1e-5)
+
+
 @pytest.mark.parametrize("data_shape", [(2, 2, 2), (2, 7, 2), (2, 2, 2, 1, 2, 3, 1), (1, 8)])
 @pytest.mark.parametrize("dtype", ["float64", "float32", "int64", "int32", "bool"])
 @tvm.testing.parametrize_targets