Fix ONNX export for MobileBERT (#1539)

* Account for the possibility of the quantized embedddings to be in int8 format (conversion to uint8 occurs later)

* Set the padding value to match to the zero point accordingly.

* Style and quality fixes

Author: anmarques

src/sparseml/pytorch/sparsification/quantization/quantize_qat_export.py

@@ -1761,7 +1761,7 @@ def _propagate_mobilebert_embedding_quantization(model: ModelProto):
             continue
 
         embedding_array = numpy_helper.to_array(embedding_initializer)
-        if embedding_array.dtype != numpy.uint8:
+        if embedding_array.dtype not in [numpy.uint8, numpy.int8]:
             continue
 
         dequant_node = graph.get_node_single_child(gather_node)
@@ -1805,12 +1805,15 @@ def _propagate_mobilebert_embedding_quantization(model: ModelProto):
         # switch position of dequantize node
         for branch_node in graph.get_node_children(dequant_node):
             if branch_node.op_type == "Slice":
+                zero_point = graph.get_init_by_name(dequant_node.input[2])
+                zero_point_array = numpy_helper.to_array(zero_point)
                 branch_node.input[0] = gather_node.output[0]
                 pad_node = graph.get_node_single_child(branch_node)
                 pad_value = graph.get_init_by_name(pad_node.input[2])
                 pad_value_array = numpy_helper.to_array(pad_value)
-                pad_value_array = pad_value_array + 128
-                pad_value_array = pad_value_array.astype(numpy.uint8)
+                pad_value_array = (
+                    pad_value_array.astype(zero_point_array.dtype) + zero_point_array
+                )
                 model.graph.initializer.remove(pad_value)
                 pad_value = numpy_helper.from_array(
                     pad_value_array, name=pad_value.name