fix QAT->Quant conversion of repeated Gemm layers with no activation QDQ

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

@@ -1296,6 +1296,14 @@ def _convert_quantizable_ops(model: ModelProto, convert_qlinearconv: bool):
             orig_qconv_weight_name_to_node_ids[weight_name].append(qconv_node.output[0])
 
         if quantizable_node.op_type == "Gemm":
+            output_dequant = graph.get_node_single_child(output_quant)
+            if output_dequant and output_dequant.op_type in _QUANTIZE_OP_NAMES:
+                output_dequant_child = graph.get_node_single_child(output_dequant)
+                if output_dequant_child and output_dequant_child.op_type == "Gemm":
+                    # output quant is not a QDQ block for the current Gemm Node but,
+                    # the input QDQ block for a new Gemm block this Gemm should be
+                    # skipped and processed by _convert_quantizable_gemm_no_activations
+                    continue
             _convert_quantizable_gemm(
                 model,
                 quantizable_node,