fix issue with obtaining the decoder layer number when converting the…

… T5 model. (#17185)

### Description
fix issue with obtaining the decoder layer number when converting the T5
model.

### Motivation and Context
fix issue： #17072

Test with
[byt5-small](https://huggingface.co/google/byt5-small/tree/main) model,
which has 12 encoder layers and 4 decoder layers.
Here is the log.

![image](https://github.com/microsoft/onnxruntime/assets/3481539/ff1b69c5-f485-4301-a333-9ee2a984df07)

onnxruntime/python/tools/transformers/models/t5/t5_decoder.py

@@ -100,7 +100,8 @@ def __init__(self, decoder, lm_head, config):
         )
 
     def forward(self, decoder_input_ids, encoder_attention_mask, *past):
-        past_key_values = PastKeyValuesHelper.group_by_layer(past, self.config.num_layers)
+        num_decoder_layers = self.config.num_decoder_layers
+        past_key_values = PastKeyValuesHelper.group_by_layer(past, num_decoder_layers)
 
         # This is a hack since only the third dimension of encoder_hidden_states is used here
         dummy_encoder_hidden_states = encoder_attention_mask.unsqueeze(2)
@@ -162,7 +163,7 @@ def create_dummy(
             T5DecoderInputs: dummy inputs for decoder
         """
         num_attention_heads: int = config.num_heads
-        num_layers: int = config.num_layers
+        num_layers: int = config.num_decoder_layers
         vocab_size: int = config.vocab_size
 
         # Do not use head_size = hidden_size / num_attention_heads here.
@@ -263,9 +264,11 @@ def export_onnx(
         )
         input_list = inputs.to_list()
 
-        past_names = PastKeyValuesHelper.get_past_names(decoder.config.num_layers, present=False)
-        present_names = PastKeyValuesHelper.get_past_names(decoder.config.num_layers, present=True)
-        present_self_names = present_names[: 2 * decoder.config.num_layers]
+        num_decoder_layers = decoder.config.num_decoder_layers
+
+        past_names = PastKeyValuesHelper.get_past_names(num_decoder_layers, present=False)
+        present_names = PastKeyValuesHelper.get_past_names(num_decoder_layers, present=True)
+        present_self_names = present_names[: 2 * num_decoder_layers]
 
         input_past_names = past_names if isinstance(decoder, T5Decoder) else []
         output_present_names = present_self_names if isinstance(decoder, T5Decoder) else present_names
@@ -407,20 +410,21 @@ def verify_onnx(
                 torch_outputs = model(*input_list)
 
             ort_outputs = T5DecoderHelper.onnxruntime_inference(ort_session, inputs)
+            num_decoder_layers = model.config.num_decoder_layers
 
             max_diff = numpy.amax(numpy.abs(torch_outputs[0].cpu().numpy() - ort_outputs[0]))
             max_diff_all = max_diff
             logger.debug(f"logits max_diff={max_diff}")
 
-            for i in range(2 * model.config.num_layers):
+            for i in range(2 * num_decoder_layers):
                 max_diff = numpy.amax(numpy.abs(torch_outputs[1][i].cpu().numpy() - ort_outputs[1 + i]))
                 logger.debug(f"self attention past state {i} max_diff={max_diff}")
                 max_diff_all = max(max_diff_all, max_diff)
 
             if isinstance(model, T5DecoderInit):
-                for i in range(2 * model.config.num_layers):
+                for i in range(2 * num_decoder_layers):
                     max_diff = numpy.amax(
-                        numpy.abs(torch_outputs[2][i].cpu().numpy() - ort_outputs[1 + 2 * model.config.num_layers + i])
+                        numpy.abs(torch_outputs[2][i].cpu().numpy() - ort_outputs[1 + 2 * num_decoder_layers + i])
                     )
                     logger.debug(f"cross attention past state {i} max_diff={max_diff}")
                     max_diff_all = max(max_diff_all, max_diff)

onnxruntime/python/tools/transformers/models/t5/t5_encoder_decoder_init.py

@@ -125,7 +125,7 @@ def export_onnx(
         )
         input_list = inputs.to_list()
 
-        present_names = PastKeyValuesHelper.get_past_names(model.config.num_layers, present=True)
+        present_names = PastKeyValuesHelper.get_past_names(model.config.num_decoder_layers, present=True)
 
         output_names = ["logits", "encoder_hidden_states", *present_names]
 
@@ -271,6 +271,8 @@ def verify_onnx(
             input_list = inputs.to_list()
             torch_outputs = model(*input_list)
 
+            num_decoder_layers = model.config.num_decoder_layers
+
             assert torch_outputs[0].cpu().numpy().shape == ort_outputs[0].shape
             max_diff = numpy.amax(numpy.abs(torch_outputs[0].cpu().numpy() - ort_outputs[0]))
             logger.debug(f"logits max_diff={max_diff}")
@@ -281,13 +283,13 @@ def verify_onnx(
             logger.debug(f"encoder_hidden_states max_diff={max_diff}")
             max_diff_all = max(max_diff_all, max_diff)
 
-            for i in range(2 * model.config.num_layers):
+            for i in range(2 * num_decoder_layers):
                 max_diff = numpy.amax(numpy.abs(torch_outputs[2][i].cpu().numpy() - ort_outputs[2 + i]))
                 logger.debug(f"self attention past state {i} max_diff={max_diff}")
 
-            for i in range(2 * model.config.num_layers):
+            for i in range(2 * num_decoder_layers):
                 max_diff = numpy.amax(
-                    numpy.abs(torch_outputs[3][i].cpu().numpy() - ort_outputs[2 + 2 * model.config.num_layers + i])
+                    numpy.abs(torch_outputs[3][i].cpu().numpy() - ort_outputs[2 + 2 * num_decoder_layers + i])
                 )
                 logger.debug(f"cross attention past state {i} max_diff={max_diff}")
                 max_diff_all = max(max_diff_all, max_diff)