Fix llama ONNX export

optimum/exporters/onnx/model_configs.py

@@ -216,7 +216,48 @@ class OPTOnnxConfig(TextDecoderOnnxConfig):
     NORMALIZED_CONFIG_CLASS = NormalizedTextConfig
 
 
+class LlamaDummyPastKeyValuesGenerator(DummyPastKeyValuesGenerator):
+    def __init__(
+        self,
+        task: str,
+        normalized_config: NormalizedTextConfig,
+        batch_size: int = DEFAULT_DUMMY_SHAPES["batch_size"],
+        sequence_length: int = DEFAULT_DUMMY_SHAPES["sequence_length"],
+        random_batch_size_range: Optional[Tuple[int, int]] = None,
+        random_sequence_length_range: Optional[Tuple[int, int]] = None,
+        **kwargs,
+    ):
+        super().__init__(
+            task=task,
+            normalized_config=normalized_config,
+            batch_size=batch_size,
+            sequence_length=sequence_length,
+            random_batch_size_range=random_batch_size_range,
+            random_sequence_length_range=random_sequence_length_range,
+            **kwargs,
+        )
+        self.num_key_value_heads = normalized_config.num_key_value_heads
+
+    def generate(self, input_name: str, framework: str = "pt", int_dtype: str = "int64", float_dtype: str = "fp32"):
+        shape = (
+            self.batch_size,
+            self.num_key_value_heads,
+            self.sequence_length,
+            self.hidden_size // self.num_attention_heads,
+        )
+        return [
+            (
+                self.random_float_tensor(shape, framework=framework, dtype=float_dtype),
+                self.random_float_tensor(shape, framework=framework, dtype=float_dtype),
+            )
+            for _ in range(self.num_layers)
+        ]
+
+
 class LlamaOnnxConfig(TextDecoderWithPositionIdsOnnxConfig):
+    DUMMY_INPUT_GENERATOR_CLASSES = (DummyTextInputGenerator, LlamaDummyPastKeyValuesGenerator)
+    DUMMY_PKV_GENERATOR_CLASS = LlamaDummyPastKeyValuesGenerator
+
     DEFAULT_ONNX_OPSET = 13
     NORMALIZED_CONFIG_CLASS = NormalizedTextConfig