Fix missing argument when calling _get_quantize_input_nodes (#20245)

### Description
The current code is calling one method with a missing argument.



### Motivation and Context
It breaks Olive's unittests.

---------

Co-authored-by: Xavier Dupré <[email protected]>

Author: xadupre

onnxruntime/python/tools/quantization/onnx_quantizer.py

@@ -306,20 +306,19 @@ def is_float_tensor(self, tensor_name):
         )
         return False
 
-    def _get_dynamic_input_quantization_params(self, input_name, nodes_list, qType):
+    def _get_dynamic_input_quantization_params(self, input_name, nodes_list, qType, initial_type):
         """
         Create nodes for dynamic quantization of input and add them to nodes_list.
             parameter input_name: Name of the input.
             parameter nodes_list: new nodes are appended to this list.
             parameter qType: type to quantize to.
+            parameter initial_type: type to quantize from
             return: scale_name, zero_point_name, scale_shape, zero_point_shape.
         """
         if qType == onnx_proto.TensorProto.INT8:
-            return self._get_dynamic_input_quantization_params_int8(input_name, nodes_list)
+            return self._get_dynamic_input_quantization_params_int8(input_name, nodes_list, initial_type)
         if qType == onnx_proto.TensorProto.UINT8:
-            return self._get_dynamic_input_quantization_params_uint8(input_name, nodes_list)
-        if qType == onnx_proto.TensorProto.FLOAT8E4M3FN:
-            return self._get_dynamic_input_quantization_params_float8e4m3fn(input_name, nodes_list)
+            return self._get_dynamic_input_quantization_params_uint8(input_name, nodes_list, initial_type)
         raise ValueError(f"Unexpected value for qType={qType}.")
 
     def _get_dynamic_input_quantization_params_int8(self, input_name, nodes_list, initial_type):
@@ -559,7 +558,9 @@ def _get_quantization_params(self, param_name, use_scale=None, use_zeropoint=Non
 
         return True, scale_name, zero_point_name, scale_shape, zero_point_shape
 
-    def _get_quantize_input_nodes(self, node, input_index, qType, given_scale_name=None, given_zp_name=None):
+    def _get_quantize_input_nodes(
+        self, node, input_index, qType, given_scale_name=None, given_zp_name=None, initial_type=None
+    ):
         """
         Given an input for a node (which is not a initializer), this function
 
@@ -571,6 +572,7 @@ def _get_quantize_input_nodes(self, node, input_index, qType, given_scale_name=N
         :param qType: type to quantize to.
         :param given_scale_name: if those inputs need to be quanitzed using this scale tensor.
         :param given_zp_name: if those inputs to be quantized using this zeropoint tensor.
+        :param initial_type: type of the weight to quantize
         :return: List of newly created nodes in NodeProto format.
         """
         input_name = node.input[input_index]
@@ -606,12 +608,16 @@ def _get_quantize_input_nodes(self, node, input_index, qType, given_scale_name=N
                     ql_node_name,
                 )
             else:
+                assert initial_type is not None, (
+                    f"Cannot quantize input without knowing the initial type, "
+                    f"input_name={input_name!r}, input_index={input_index}, qType={qType}, node={node}"
+                )
                 (
                     scale_name,
                     zp_name,
                     scale_shape,
                     zp_shape,
-                ) = self._get_dynamic_input_quantization_params(input_name, nodes, qType)
+                ) = self._get_dynamic_input_quantization_params(input_name, nodes, qType, initial_type=initial_type)
                 qlinear_node = onnx.helper.make_node(
                     "QuantizeLinear",
                     [input_name, scale_name, zp_name],
@@ -794,7 +800,23 @@ def __quantize_inputs(
                     node_input + "_QuantizeLinear", self.new_nodes, self.model.graph()
                 )
                 if qlinear_node is None:
-                    quantize_input_nodes = self._get_quantize_input_nodes(node, input_index, self.activation_qType)
+                    input_name = node.input[input_index]
+                    if input_name in self.value_infos:
+                        value_info = self.value_infos[input_name]
+                        assert value_info.HasField("type"), f"value_info={value_info} has no type."
+                        assert value_info.type.HasField("tensor_type"), f"value_info={value_info} is not a tensor."
+                        initial_type = value_info.type.tensor_type.elem_type
+                    else:
+                        # Shape inference failed. Fallback to self.tensor_names.
+                        assert input_name in self.tensor_names, (
+                            f"shape inference failed for {input_name!r} and "
+                            f"attribute 'tensor_names' does not have any value for "
+                            f"this tensor."
+                        )
+                        initial_type = self.tensor_names[input_name]
+                    quantize_input_nodes = self._get_quantize_input_nodes(
+                        node, input_index, self.activation_qType, initial_type=initial_type
+                    )
                     if quantize_input_nodes is None:
                         return (None, None, None, None)
                     if from_subgraph:

onnxruntime/python/tools/quantization/operators/pad.py

@@ -68,6 +68,7 @@ def quantize(self):
                         self.quantizer.activation_qType,
                         quantized_input_value.scale_name,
                         quantized_input_value.zp_name,
+                        initial_type=scale_tensor.data_type,
                     )
                     self.quantizer.new_nodes.extend(pad_value_qnodes)
                     node.input[2] = pad_value_qnodes[0].output[0]

onnxruntime/test/python/quantization/test_op_gemm.py

@@ -784,7 +784,52 @@ def test_qgemm_ref_uint8_specific_example(self):
         got = ref.run(None, feeds)[0]
         assert_allclose(expected, got)
 
+    def test_dynamic_quantization(self):
+        # dummy_model.onnx from Olive
+        model = helper.make_model(
+            helper.make_graph(
+                [
+                    helper.make_node(
+                        "Gemm", ["input", "fc1.weight", "fc1.bias"], ["gemm0"], alpha=1.0, beta=1.0, transB=1
+                    ),
+                    helper.make_node("Relu", ["gemm0"], ["output"]),
+                ],
+                "g",
+                [helper.make_tensor_value_info("input", onnx.TensorProto.FLOAT, [1, 1])],
+                [helper.make_tensor_value_info("output", onnx.TensorProto.FLOAT, [1, 10])],
+                [
+                    onnx.numpy_helper.from_array(np.random.randn(10, 1).astype(np.float32), name="fc1.weight"),
+                    onnx.numpy_helper.from_array(np.random.randn(10).astype(np.float32), name="fc1.bias"),
+                ],
+            ),
+            opset_imports=[helper.make_opsetid("", 18)],
+            ir_version=9,
+        )
+        onnx.checker.check_model(model)
+        run_config = {
+            "weight_type": QuantType.QInt8,
+            "op_types_to_quantize": None,
+            "nodes_to_quantize": None,
+            "nodes_to_exclude": None,
+            "per_channel": False,
+            "reduce_range": False,
+            "extra_options": {
+                "extra.Sigmoid.nnapi": False,
+                "ActivationSymmetric": False,
+                "WeightSymmetric": True,
+                "EnableSubgraph": False,
+                "ForceQuantizeNoInputCheck": False,
+                "MatMulConstBOnly": True,
+            },
+        }
+        model_path = "test_dynamic_quantization.onnx"
+        with open(model_path, "wb") as f:
+            f.write(model.SerializeToString())
+        qpath = "test_dynamic_quantization.quantized.onnx"
+        quantize_dynamic(model_input=model_path, model_output=qpath, use_external_data_format=True, **run_config)
+        onx = onnx.load(qpath)
+        self.assertIn("DynamicQuantizeLinear", set(n.op_type for n in onx.graph.node))
+
 
 if __name__ == "__main__":
-    TestOpGemm().test_quantize_gemm_e4m3fn_p3()
     unittest.main(verbosity=2)