[Torch] Various updates for PyTorch frontend #7348
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t-vi
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Jan 27, 2021
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Looks good!
Since torch.sort returns both sorted values and indices while the Relay one doesn't, torch.sort conversion is not efficient, especially for multidimensional input (currently does both sort and argsort!). Suggestions for a better implementation are welcome.
I think gather is the direct equivalent of how you use take on 1d. Maybe it's worth fixing this in this PR.
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@t-vi Thanks, I think I tried |
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Thanks @siju-samuel @t-vi |
alexwong
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Feb 11, 2021
* add conversion for detr * remove explicit broadcast_to before batched matmul * use take with wrap mode * add test for transformer and negative indices * add sort and argsort * add logical_and * support masked_select * add gpu targets to masked_select test * improve sort conversion
electriclilies
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Feb 18, 2021
* add conversion for detr * remove explicit broadcast_to before batched matmul * use take with wrap mode * add test for transformer and negative indices * add sort and argsort * add logical_and * support masked_select * add gpu targets to masked_select test * improve sort conversion
Lokiiiiii
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Mar 2, 2021
* add conversion for detr * remove explicit broadcast_to before batched matmul * use take with wrap mode * add test for transformer and negative indices * add sort and argsort * add logical_and * support masked_select * add gpu targets to masked_select test * improve sort conversion
trevor-m
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Mar 2, 2021
* add conversion for detr * remove explicit broadcast_to before batched matmul * use take with wrap mode * add test for transformer and negative indices * add sort and argsort * add logical_and * support masked_select * add gpu targets to masked_select test * improve sort conversion
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This PR adds various updates to the PyTorch frontend, to fully support the recent transformer based object detection model from facebook, DETR https://github.com/facebookresearch/detr. After this PR, DETR runs on TVM and gets the correct results. TVM with auto scheduled GPU conv2d and batched matmul is 1.4x faster than PyTorch in my environment.
Also added various missing ops reported by hummingbird projects. @interesaaat
logical_and,masked_select,sortandargsort.masked_selectrequires VM to run.cumsumandmasked_fillop, to enable importing DETR https://github.com/facebookresearch/detr.cumsumis also requested by hummingbird.mode="wrap"in Relaytakeop. Without this, the result doesn't match with DETR.broadcast_tobeforebatch_matmul. Without it, memory usage blows up during constant evaluation of DETR. This is the same issue as [Topi] Allow batch_matmul to broadcast along batch dimension. #6616, please see the explanation there.torch.nn.Transformerto the tests.Since
torch.sortreturns both sorted values and indices while the Relay one doesn't,torch.sortconversion is not efficient,especially for multidimensional input (currently does both sort and argsort!). Suggestions for a better implementation are welcome.UPDATE: fixedplease review @siju-samuel @jwfromm @kevinthesun @t-vi