Method from_dict

benchmark_v2/framework/data_classes.py:139–158

(cls, data: dict[str, Any])

    @classmethod
    def from_dict(cls, data: dict[str, Any]) -> "BenchmarkResult":
        # Handle GPU metrics, which is saved as None if it contains only None values
        if data["gpu_metrics"] is None:
            gpu_metrics = [None for _ in range(len(data["e2e_latency"]))]
        else:
            gpu_metrics = [GPURawMetrics.from_dict(gm) for gm in data["gpu_metrics"]]
        # Handle timestamps, which can be saved as None to reduce file size
        if data["timestamps"] is None:
            timestamps = [None for _ in range(len(data["e2e_latency"]))]
        else:
            timestamps = data["timestamps"]
        # Create a new instance and accumulate the data
        new_instance = cls()
        new_instance.e2e_latency = data["e2e_latency"]
        new_instance._timestamps = timestamps
        new_instance.time_to_first_token = data["time_to_first_token"]
        new_instance.inter_token_latency = data["inter_token_latency"]
        new_instance.shape_and_decoded_outputs = data["shape_and_decoded_outputs"]
        new_instance.gpu_metrics = gpu_metrics
        return new_instance

    def get_throughput(self, total_generated_tokens: int) -> list[float]:
        return [total_generated_tokens / e2e_latency for e2e_latency in self.e2e_latency]