def execute()

in optimum/runs_base.py [0:0]

• 34 lines of code
• 10 McCabe index (conditional complexity)

    def execute(self):
        inputs = {}

        checked_inputs = {"input_ids", "attention_mask", "token_type_ids", "pixel_values"}
        if "input_ids" in self.model_input_names:
            inputs["input_ids"] = torch.randint(high=1000, size=(self.batch_size, self.input_length))
        if "attention_mask" in self.model_input_names:
            inputs["attention_mask"] = torch.ones(self.batch_size, self.input_length, dtype=torch.int64)
        if "token_type_ids" in self.model_input_names:
            inputs["token_type_ids"] = torch.ones(self.batch_size, self.input_length, dtype=torch.int64)
        if "pixel_values" in self.model_input_names:
            # TODO support grayscale?
            inputs["pixel_values"] = torch.rand(
                self.batch_size, 3, self.model.config.image_size, self.model.config.image_size, dtype=torch.float32
            )

        if np.any([k not in checked_inputs for k in self.model_input_names]):
            raise NotImplementedError(
                f"At least an input in {self.model_input_names} has no dummy generation for time benchmark."
            )

        # Warmup
        for _ in trange(self.warmup_runs, desc="Warming up"):
            self.model.forward(**inputs)

        if self.benchmark_duration != 0:
            benchmark_duration_ns = self.benchmark_duration * SEC_TO_NS_SCALE
            print(f"Running time tracking in {self.benchmark_duration:.1f}s.")
            while sum(self.latencies) < benchmark_duration_ns:
                # TODO not trak GPU/CPU <--> numpy/torch, need to change the implementation of forward
                with self.track():
                    self.model.forward(**inputs)

            self.finalize(benchmark_duration_ns)

            return self.to_dict()
        else:
            benchmarks_stats = {
                "nb_forwards": 0,
                "throughput": -1,
                "latency_mean": -1,
            }
            return benchmarks_stats