Update GPTBigCodeOnnxConfig

optimum/exporters/onnx/model_configs.py

@@ -195,6 +195,10 @@ class ImageGPTOnnxConfig(GPT2OnnxConfig):
     pass
 
 
+class GPTBigCodeOnnxConfig(GPT2OnnxConfig):
+    pass
+
+
 class GPTNeoOnnxConfig(TextDecoderOnnxConfig):
     DEFAULT_ONNX_OPSET = 13
     NORMALIZED_CONFIG_CLASS = NormalizedTextConfig.with_args(num_attention_heads="num_heads")
@@ -268,45 +272,6 @@ 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 = (