def generate_dummy_inputs()

in optimum/exporters/openvino/model_configs.py [0:0]

• 39 lines of code
• 14 McCabe index (conditional complexity)

    def generate_dummy_inputs(self, framework: str = "pt", **kwargs):
        dummy_inputs_generators = self._create_dummy_input_generator_classes(**kwargs)

        dummy_inputs = {}
        input_names = [key for key in self.inputs.keys() if not key.startswith("past_key_values")]
        if self.use_past_in_inputs and self.use_cache_branch is not False:
            input_names.append("past_key_values")

        for input_name in input_names:
            input_was_inserted = False
            for dummy_input_gen in dummy_inputs_generators:
                if dummy_input_gen.supports_input(input_name):
                    dummy_inputs[input_name] = self.overwrite_shape_and_generate_input(
                        dummy_input_gen,
                        input_name,
                        framework,
                        input_shapes=kwargs,
                    )
                    input_was_inserted = True
                    break
            if not input_was_inserted:
                raise RuntimeError(
                    f'Could not generate dummy input for "{input_name}". Try adding a proper dummy input generator to the model ONNX config.'
                )

        # refer to https://github.com/huggingface/optimum/pull/764
        if (
            self.use_past_in_inputs
            and self.PAD_ATTENTION_MASK_TO_PAST
            and self.use_cache_branch is not False
            and "attention_mask" in dummy_inputs
        ):
            # Obtain the past sequence length from the value instead of the key (Bloom). ChatGLM has seq_len in 0 dim instead of -2
            seq_len_dim = 0 if not hasattr(self._normalized_config, "rope_ratio") else -2
            past_present_length = (
                dummy_inputs["input_ids"].shape[1] + dummy_inputs["past_key_values"][0][1].shape[seq_len_dim]
            )

            dummy_inputs["attention_mask"] = DummyInputGenerator.pad_input_on_dim(
                dummy_inputs["attention_mask"],
                desired_length=past_present_length,
                dim=1,
                dtype=dummy_inputs["attention_mask"].dtype,
            )

        return dummy_inputs