add option to load pretrained weights for T5 model (#15951)

### Description
<!-- Describe your changes. -->

Adds option to pass in pretrained weights file during T5 inference onnx
export. Mimics the changes made to whisper:
https://github.com/microsoft/onnxruntime/pull/15759

### Motivation and Context
<!-- - Why is this change required? What problem does it solve?
- If it fixes an open issue, please link to the issue here. -->

Required for ONNX Runtime demo being presented at BUILD.

onnxruntime/python/tools/transformers/models/t5/convert_to_onnx.py

@@ -127,6 +127,13 @@ def parse_arguments():
     )
     parser.set_defaults(use_int64_inputs=False)
 
+    parser.add_argument(
+        "--state_dict_path",
+        type=str,
+        default="",
+        help="filepath to load pre-trained model with custom state dictionary (e.g. pytorch_model.bin)",
+    )
+
     args = parser.parse_args()
 
     return args
@@ -147,10 +154,13 @@ def export_onnx_models(
     disable_auto_mixed_precision: bool = False,
     use_int32_inputs: bool = True,
     model_type: str = "t5",
+    state_dict_path: str = "",
 ):
     device = torch.device("cuda:0" if use_gpu else "cpu")
 
-    models = T5Helper.load_model(model_name_or_path, cache_dir, device, merge_encoder_and_decoder_init, model_type)
+    models = T5Helper.load_model(
+        model_name_or_path, cache_dir, device, merge_encoder_and_decoder_init, model_type, state_dict_path
+    )
     config = models["decoder"].config
 
     if (not use_external_data_format) and (config.num_layers > 24):

onnxruntime/python/tools/transformers/models/t5/t5_helper.py

@@ -66,6 +66,7 @@ class T5Helper:
         device: torch.device,
         merge_encoder_and_decoder_init: bool = True,
         model_type: str = "t5",
+        state_dict_path: str = "",
     ) -> Dict[str, torch.nn.Module]:
         """Load model given a pretrained name or path, then build models for ONNX conversion.
 
@@ -85,6 +86,9 @@ class T5Helper:
         else:
             raise ValueError("only support mode_type=t5 or mt5")
 
+        if state_dict_path:
+            model.load_state_dict(torch.load(state_dict_path))
+
         decoder = T5Decoder(model.decoder, model.lm_head, model.config)
         decoder.eval().to(device)