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Use the PyTorch BatchNorm folding instead of a handwritten converter when possible #31

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narendasan opened this issue Apr 1, 2020 · 1 comment
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Use the PyTorch BatchNorm folding instead of a handwritten converter when possible #31

narendasan opened this issue Apr 1, 2020 · 1 comment
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component: lowering Issues re: The lowering / preprocessing passes feature request New feature or request good first issue Good for newcomers help wanted Extra attention is needed priority: high
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v0.1.0

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@narendasan
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We currently use a handwritten converter for batch norm which fuses the operations into a single convolution. It would be easier to use the _jit_pass_fold_convbn pass in lowering instead where we can.
@narendasan narendasan added feature request New feature or request good first issue Good for newcomers component: lowering Issues re: The lowering / preprocessing passes priority: high help wanted Extra attention is needed labels Apr 1, 2020
@narendasan narendasan added this to the v0.0.2 milestone Apr 1, 2020
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pytorch/pytorch#34835

@narendasan narendasan mentioned this issue Apr 29, 2020
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frank-wei pushed a commit that referenced this issue Jun 4, 2022
@alexbeloi
[fx] add acc_ops.expand(_as) and shape inference (#31)
c7fc9f4 
Summary:
Pull Request resolved: https://github.com/pytorch/fx2trt/pull/31

1. acc_ops
    a. 'call_method' `torch.Tensor.expand` will map to `acc_op.expand`
    b. 'call_method' `torch.Tensor.expand_as` will map to `acc_op.expand`
2. shape inference will run for `acc_op.expand`
3. acc_normalizer will map `acc_op.expand` to `acc_op.tile` using the required shape information

Reviewed By: 842974287

Differential Revision: D34565863

fbshipit-source-id: ad94694e548f5b03af9cf3ef8e7c35e236bb0745
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component: lowering Issues re: The lowering / preprocessing passes feature request New feature or request good first issue Good for newcomers help wanted Extra attention is needed priority: high
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