diff --git a/optimum_transformers/generation_utils.py b/optimum_transformers/generation_utils.py
index 4b98c01..7ae0bfd 100644
--- a/optimum_transformers/generation_utils.py
+++ b/optimum_transformers/generation_utils.py
@@ -1531,13 +1531,22 @@ def greedy_search(
 
             # prepare model inputs
             model_inputs = self.model.prepare_inputs_for_generation(input_ids, **model_kwargs)
+            print(model_inputs.keys())
 
             # forward pass to get next token
             if self.use_onnx:
-                inputs_onnx = {
-                    "input_ids": model_inputs["input_ids"].cpu().detach().numpy(),
-                    "attention_mask": model_inputs["attention_mask"].cpu().detach().numpy()
-                }
+                if self.model.config.is_encoder_decoder:
+                    inputs_onnx = {
+                        "input_ids": input_ids.cpu().detach().numpy(),
+                        "attention_mask": model_inputs["attention_mask"].cpu().detach().numpy(),
+                        "decoder_input_ids": model_inputs["decoder_input_ids"].cpu().detach().numpy(),
+                        "decoder_attention_mask": model_inputs["attention_mask"].cpu().detach().numpy(),
+                    }
+                else:
+                    inputs_onnx = {
+                        "input_ids": model_inputs["input_ids"].cpu().detach().numpy(),
+                        "attention_mask": model_inputs["attention_mask"].cpu().detach().numpy()
+                    }
                 outputs = CausalLMOutputWithCrossAttentions(
                     logits=torch.tensor(self.onnx_model.run(None, inputs_onnx)[0]))
             else:
@@ -1783,10 +1792,19 @@ def sample(
 
             # forward pass to get next token
             if self.use_onnx:
-                inputs_onnx = {
-                    "input_ids": model_inputs["input_ids"].cpu().detach().numpy(),
-                    "attention_mask": model_inputs["attention_mask"].cpu().detach().numpy()
-                }
+                if self.model.config.is_encoder_decoder:
+                    inputs_onnx = {
+                        "input_ids": input_ids.cpu().detach().numpy(),
+                        "attention_mask": model_inputs["attention_mask"].cpu().detach().numpy(),
+                    }
+                    if self.model.use_past:
+                        inputs_onnx["decoder_inputs_ids"] = model_inputs["decoder_inputs_ids"].cpu().detach().numpy()
+                        inputs_onnx["decoder_attention_mask"] = model_inputs["decoder_attention_mask"].cpu().detach().numpy()
+                else:
+                    inputs_onnx = {
+                        "input_ids": model_inputs["input_ids"].cpu().detach().numpy(),
+                        "attention_mask": model_inputs["attention_mask"].cpu().detach().numpy()
+                    }
                 outputs = CausalLMOutputWithCrossAttentions(
                     logits=torch.tensor(self.onnx_model.run(None, inputs_onnx)[0]))
             else:
diff --git a/optimum_transformers/pipelines/__init__.py b/optimum_transformers/pipelines/__init__.py
index 5038c79..9b83be4 100644
--- a/optimum_transformers/pipelines/__init__.py
+++ b/optimum_transformers/pipelines/__init__.py
@@ -34,6 +34,7 @@
 from .question_answering import OptimumQuestionAnsweringPipeline
 from .text_classification import OptimumTextClassificationPipeline
 from .text_generation import OptimumTextGenerationPipeline
+from .text2text_generation import OptimumText2TextGenerationPipeline
 from .token_classification import (
     OptimumTokenClassificationPipeline,
 )
@@ -55,6 +56,7 @@
         TFAutoModelForCausalLM,
         TFAutoModelForMaskedLM,
         TFAutoModelForQuestionAnswering,
+        TFAutoModelForSeq2SeqLM,
         TFAutoModelForSequenceClassification,
         TFAutoModelForTokenClassification,
     )
@@ -67,6 +69,7 @@
         AutoModelForCausalLM,
         AutoModelForMaskedLM,
         AutoModelForQuestionAnswering,
+        AutoModelForSeq2SeqLM,
         AutoModelForSequenceClassification,
         AutoModelForTokenClassification,
     )
@@ -178,6 +181,17 @@
             "text_inputs": "HuggingFace is creating a tool that the community uses to solve NLP tasks."
         },
     },
+    "text2text-generation": {
+        "impl": OptimumText2TextGenerationPipeline,
+        "tf": (TFAutoModelForSeq2SeqLM,) if is_tf_available() else (),
+        "pt": (AutoModelForSeq2SeqLM,) if is_torch_available() else (),
+        "default": {"model": {"pt": "t5-base", "tf": "t5-base"}},
+        "type": "text",
+        "feature": "seq2seq-lm",
+        "example": {
+            "text_inputs": "HuggingFace is creating a tool that the community uses to solve NLP tasks."
+        },
+    },
 }
 
 
diff --git a/optimum_transformers/pipelines/text2text_generation.py b/optimum_transformers/pipelines/text2text_generation.py
new file mode 100644
index 0000000..4b44abc
--- /dev/null
+++ b/optimum_transformers/pipelines/text2text_generation.py
@@ -0,0 +1,36 @@
+from transformers import Text2TextGenerationPipeline
+from transformers.file_utils import is_tf_available
+
+from ..generation_utils import GenerationMixin
+from .base import _warmup_onnx_graph
+
+if is_tf_available():
+    import tensorflow as tf
+
+
+class OptimumText2TextGenerationPipeline(Text2TextGenerationPipeline):
+    def __init__(self, *args, onnx_model, example, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.onnx_model = onnx_model
+        self.example = example
+        _warmup_onnx_graph(self)
+    
+
+    def _forward(self, model_inputs, **generate_kwargs):
+        if self.framework == "pt":
+            in_b, input_length = model_inputs["input_ids"].shape
+        elif self.framework == "tf":
+            in_b, input_length = tf.shape(model_inputs["input_ids"]).numpy()
+
+        generate_kwargs["min_length"] = generate_kwargs.get("min_length", self.model.config.min_length)
+        generate_kwargs["max_length"] = generate_kwargs.get("max_length", self.model.config.max_length)
+        self.check_inputs(input_length, generate_kwargs["min_length"], generate_kwargs["max_length"])
+        generation_matrix = GenerationMixin(self.model, self.onnx_model)
+        output_ids = generation_matrix.generate(**model_inputs, **generate_kwargs)
+        out_b = output_ids.shape[0]
+
+        if self.framework == "pt":
+            output_ids = output_ids.reshape(in_b, out_b // in_b, *output_ids.shape[1:])
+        elif self.framework == "tf":
+            output_ids = tf.reshape(output_ids, (in_b, out_b // in_b, *output_ids.shape[1:]))
+        return {"output_ids": output_ids}