[bug fix][dist] Handle the case that a dict entry is a list of tensors.

[xwjiang2010]

Fixes #5905 (comment)

Handles the case like tensor_dict["multi_modal_kwargs"]["pixel_values"] = a_list_of_tensors

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[DarkLight1337]

Can this support arbitrary layers of nested lists? This will be required for supporting multi-image inputs in the future.

[xwjiang2010]

hmm, @DarkLight1337
actually let me try making it more generic than this. So that we can handle nested dict and list.

[xwjiang2010]

yeah, @DarkLight1337 was just thinking of this and saw your comment :)
will try that so that we don't run into this issue again and again.

[xwjiang2010]

This will be required for supporting multi-image inputs in the future.

Give me a concrete example?

[njhill]

Could we stack the tensors in this case? To also reduce the number of broadcast ops…

[DarkLight1337]

This will be required for supporting multi-image inputs in the future.

Give me a concrete example?

Please first read my comment in the RFC.

Consider LLaVA-NeXT, which currently accepts shape [B, M, C, H, W] where M is the number of patches/crops. If M is different for each B, the output becomes a list of B elements each with shape [M, C, H, W].

When we support multi-image inputs, LLaVA-NeXT would then accept [B, N, M, C, H, W] where N is the number of input images. In this case, since M can also be different for each N, we need a 2-D nested list (over the B and N dimensions) since the value of M can be different for each combination of B and N.

[DarkLight1337]

Could we stack the tensors in this case? To also reduce the number of broadcast ops…

I guess this is technically possible but also quite complicated. We would have to first identify the tensors with a common shape and concatenate them for broadcasting, then afterwards split them back into the original shapes.

[youkaichao]

Please don't make the logic too complicated. If this is just CPU tensor, you can directly treat it as opaque python object and broadcast it.

If it is GPU tensor, try to avoid broadcasting GPU tensors.

[DarkLight1337]

If this is just CPU tensor, you can directly treat it as opaque python object and broadcast it.
If it is GPU tensor, try to avoid broadcasting GPU tensors.

Didn't know this. Let me try deferring the device move until after the broadcast then. Hope that this would simplify the logic.

[DarkLight1337]

I have updated #6836 to fix the issue while also simplifying the code.