add bigcode specific dummy generator

optimum/exporters/onnx/base.py

@@ -583,7 +583,8 @@ def generate_dummy_inputs(self, framework: str = "pt", **kwargs):
             and self.use_cache_branch is not False
             and "attention_mask" in dummy_inputs
         ):
-            past_length = dummy_inputs["past_key_values"][0][0].shape[2]
+            past_length = dummy_inputs["past_key_values"][0][0].shape[-2]
+
             dummy_inputs["attention_mask"] = DummyInputGenerator.pad_input_on_dim(
                 dummy_inputs["attention_mask"],
                 desired_length=past_length + 1,

optimum/exporters/onnx/model_configs.py

@@ -214,11 +214,6 @@ class LlamaOnnxConfig(TextDecoderOnnxConfig):
     NORMALIZED_CONFIG_CLASS = NormalizedTextConfig
 
 
-class GPTBigCodeOnnxConfig(TextDecoderOnnxConfig):
-    DEFAULT_ONNX_OPSET = 13
-    NORMALIZED_CONFIG_CLASS = NormalizedTextConfig
-
-
 class BloomDummyPastKeyValuesGenerator(DummyPastKeyValuesGenerator):
     def generate(self, input_name: str, framework: str = "pt"):
         past_key_shape = (
@@ -272,6 +267,45 @@ def add_past_key_values(self, inputs_or_outputs: Dict[str, Dict[int, str]], dire
             }
 
 
+class GPTBigCodeDummyPastKeyValuesGenerator(DummyPastKeyValuesGenerator):
+    def generate(self, input_name: str, framework: str = "pt"):
+        past_key_value_shape = (
+            self.batch_size,
+            self.sequence_length,
+            self.hidden_size // self.num_attention_heads * 2,
+        )
+        return [self.random_float_tensor(past_key_value_shape, framework=framework) for _ in range(self.num_layers)]
+
+
+class GPTBigCodeOnnxConfig(TextDecoderOnnxConfig):
+    DUMMY_INPUT_GENERATOR_CLASSES = (
+        GPTBigCodeDummyPastKeyValuesGenerator,
+    ) + TextDecoderOnnxConfig.DUMMY_INPUT_GENERATOR_CLASSES
+    DUMMY_PKV_GENERATOR_CLASS = GPTBigCodeDummyPastKeyValuesGenerator
+    NORMALIZED_CONFIG_CLASS = NormalizedTextConfig.with_args(num_layers="n_layer", num_attention_heads="n_head")
+
+    def add_past_key_values(self, inputs_or_outputs: Dict[str, Dict[int, str]], direction: str):
+        """
+        Refer to OnnxConfigWithPast in base.py
+        """
+        if direction not in ["inputs", "outputs"]:
+            raise ValueError(f'direction must either be "inputs" or "outputs", but {direction} was given')
+
+        if direction == "inputs":
+            decoder_sequence_name = "past_sequence_length"
+            name = "past_key_values"
+        else:
+            decoder_sequence_name = "past_sequence_length + 1"
+            name = "present"
+
+        for i in range(self._normalized_config.num_layers):
+            # No dim for `n_head` when using multi-query attention
+            inputs_or_outputs[f"{name}.{i}.key_value"] = {
+                0: "batch_size",
+                1: decoder_sequence_name,
+            }
+
+
 class T5DummySeq2SeqPastKeyValuesGenerator(DummySeq2SeqPastKeyValuesGenerator):
     def generate(self, input_name: str, framework: str = "pt"):
         encoder_shape = (