Fix device handling and logits concatenation in OliveEvaluator #1615
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- Add exception handling for unsupported devices in `device_string_to_torch_device` method. - Correct logits concatenation in `OnnxEvaluator` by using `logits_dict` instead of `logits`. - Initialize `logits_dict` in `PyTorchEvaluator` to handle different result types. - Update `_inference` method in `PyTorchEvaluator` to handle different result types and concatenate logits correctly.
tezheng
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jambayk
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Feb 14, 2025
| @@ -196,7 +196,11 @@ def compute_throughput(metric: Metric, latencies: Any) -> MetricResult: | |||
| class _OliveEvaluator(OliveEvaluator): | |||
| @staticmethod | |||
| def device_string_to_torch_device(device: Device): | |||
| return torch.device("cuda") if device == Device.GPU else torch.device(device) | |||
| try: | |||
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maybe we can just do torch.device("cuda") if device == Device.GPU else torch.device("cpu"). Otherwise, this would raise the warning for npu targets everytime. since this method is only used when evaluating pytorch models, I think it's save to just return cuda or cpu only.
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So we will only consider cuda and cpu, for now at least? Are we planning to support device like Apple mps?
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yes, there are no plans for other torch devices like mps for now.
xiaoyu-work
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Feb 18, 2025
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| if isinstance(result, torch.Tensor): | ||
| logits.append(result.cpu()) | ||
| elif isinstance(result, (list, tuple)): | ||
| logits.append([r.cpu() for r in result]) | ||
| elif isinstance(result, dict): | ||
| for k in result: | ||
| logits_dict[k].append(result[k].cpu()) |
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How can we handle the case when result has logits attribute?
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You mean result like { "logits": tensor }?
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device_string_to_torch_devicemethod.OnnxEvaluatorby usinglogits_dictinstead oflogits.logits_dictinPyTorchEvaluatorto handle different result types._inferencemethod inPyTorchEvaluatorto handle different result types and concatenate logits correctly.Describe your changes
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