fix int16 overflow

Author: Valery Chernov

tests/python/relay/test_op_level2.py

@@ -2159,73 +2159,7 @@ def get_conv2d_nchw(
             out_dtype=out_dtype,
         )
 
-    # def verify_by_ort(x_data, w_data, b_data, data_dtype, out):
-    #     from onnx import helper, mapping, TensorProto
-    #     from onnxruntime import backend as ort_bk
-
-    #     def get_onnx_model(data_dtype, x_shape, w_shape, b_shape, out_shape):
-    #         x_dtype = data_dtype
-    #         w_dtype = "int8"
-    #         b_dtype = "int32"
-    #         x_proto_type = mapping.NP_TYPE_TO_TENSOR_TYPE[np.dtype(x_dtype)]
-    #         w_proto_type = mapping.NP_TYPE_TO_TENSOR_TYPE[np.dtype(w_dtype)]
-    #         b_proto_type = mapping.NP_TYPE_TO_TENSOR_TYPE[np.dtype(b_dtype)]
-
-    #         y_dtype = "int32"
-    #         y_proto_type = mapping.NP_TYPE_TO_TENSOR_TYPE[np.dtype(y_dtype)]
-
-    #         input_nodes = [
-    #             helper.make_tensor_value_info("x", x_proto_type, list(x_shape)),
-    #             helper.make_tensor_value_info("w", w_proto_type, list(w_shape)),
-    #             helper.make_tensor_value_info("B", b_proto_type, list(b_shape)),
-    #         ]
-    #         initializer = [
-    #             helper.make_tensor("x_scale", TensorProto.FLOAT, [], [1.]),
-    #             helper.make_tensor("x_zero_point", x_proto_type, [], [0]),
-    #             helper.make_tensor("w_scale", TensorProto.FLOAT, [], [1.]),
-    #             helper.make_tensor("w_zero_point", w_proto_type, [], [0]),
-    #             helper.make_tensor("y_scale", TensorProto.FLOAT, [], [1.]),
-    #             helper.make_tensor("y_zero_point", y_proto_type, [], [0]),
-    #         ]
-    #         input_names = [
-    #             "x",
-    #             "x_scale",
-    #             "x_zero_point",
-    #             "w",
-    #             "w_scale",
-    #             "w_zero_point",
-    #             "y_scale",
-    #             "y_zero_point",
-    #             "B"
-    #         ]
-
-    #         node_conv = helper.make_node(
-    #             "QLinearConv",
-    #             inputs=input_names,
-    #             outputs=["y"],
-    #         )
-
-    #         graph = helper.make_graph(
-    #             [node_conv],
-    #             "ort_conv2d_int8_test",
-    #             inputs=input_nodes,
-    #             initializer=initializer,
-    #             outputs=[helper.make_tensor_value_info("y", y_proto_type, list(out_shape))],
-    #         )
-    #         model = helper.make_model(graph, producer_name="ort_conv2d_int8_test")
-    #         return model
-
-    #     onnx_model = get_onnx_model(data_dtype, x_data.shape, w_data.shape, b_data.shape, out.shape)
-    #     ort_exec = ort_bk.prepare(onnx_model.SerializeToString(), "CPU")
-    #     ort_out = ort_exec.run([x_data, w_data, b_data])
-    #     # Unpack output if there's only a single value.
-    #     if len(ort_out) == 1:
-    #         ort_out = ort_out[0]
-    #     if len(out) == 1:
-    #         out = out[0]
-    #     np.testing.assert_equal(out, ort_out)
-
-    I, O, H, W = 64, 64, 56, 56
+    I, O, H, W = 1, 1, 56, 56
     kH = kW = 3
 
     data_shape = (1, I, H, W)
@@ -2271,11 +2205,6 @@ def get_conv2d_nchw(
 
     out = rt_mod.get_output(0).numpy()
 
-    # print("COMPARE ORT and OUT")
-    # verify_by_ort(data_np, weight_np, bias_np, data_dtype, out)
-    # print("COMPARE ORT and REF")
-    # verify_by_ort(data_np, weight_np, bias_np, data_dtype, ref)
-
     np.testing.assert_equal(out, ref)