Update adaround_optimizer.py #3709
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Make sure that the input and output end up as tensors end up on the right device Signed-off-by: Nikil Ragav <[email protected]>
quic-mtuttle
approved these changes
Jan 7, 2025
| @@ -176,6 +176,8 @@ def _optimize_rounding(cls, module: ModuleInfo, quantized_input_name, | |||
| else: | |||
| model_inputs = cached_dataset[np.random.randint(len(cached_dataset))] | |||
| inp_data, orig_out_data = act_sampler.sample_acts(create_input_dict(orig_model.model, model_inputs)) | |||
| inp_data, orig_out_data = torch.from_numpy(inp_data[0]).to(torch_device), torch.from_numpy(out_data[0]).to(torch_device) | |||
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I did not understand one aspect of the change. There are two parts to the change - 1. we are converting numpy tensors to torch, 2. we are placing the torch tensors on a device.
How does the rest of the code work if we are now passing torch tensors instead of numpy?
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The rest of the code expects a torch tensor on the correct device, since it is using torch to backprop through the layer
quic-hitameht
approved these changes
Jan 13, 2025
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Make sure that the input and output end up as tensors end up on the right device
Referencing this issue: #3708