[GitHub] [tvm] mbs-octoml removed a comment on issue #9013: [Bug] tutorials do not build from a clean source tree

[GitBox <gi...@apache.org>]

mbs-octoml removed a comment on issue #9013:
URL: https://github.com/apache/tvm/issues/9013#issuecomment-924418401


   The AlterOpLayout pass is behaving differently.
   
   On the GOOD runs:
   ```
   [14:53:31] /home/mbs/github/mbs-tvm/src/relay/ir/transform.cc:135: AlterOpLayout: Input module:
   def @main(%data: Tensor[(1, 3, 10, 10), float32]) -> Tensor[(1, 6, 10, 10), float32] {
     nn.conv2d(%data, meta[relay.Constant][0] /* ty=Tensor[(6, 3, 5, 5), float32] */, padding=[2, 2, 2, 2], kernel_size=[5, 5], out_dtype="float32") /* ty=Tensor[(1, 6, 10, 10), float32] */
   }
   
   
   [14:53:31] /home/mbs/github/mbs-tvm/src/relay/ir/transform.cc:159: AlterOpLayout: Output module:
   def @main(%data: Tensor[(1, 3, 10, 10), float32]) -> Tensor[(1, 6, 10, 10), float32] {
     %0 = layout_transform(%data, src_layout="NCHW", dst_layout="NCHW3c");
     %1 = layout_transform(meta[relay.Constant][0] /* ty=Tensor[(6, 3, 5, 5), float32] */, src_layout="OIHW", dst_layout="OIHW3i3o");
     %2 = nn.contrib_conv2d_NCHWc(%0, %1, padding=[2, 2, 2, 2], channels=6, kernel_size=[5, 5], data_layout="NCHW3c", kernel_layout="OIHW3i3o", out_layout="NCHW3c", out_dtype="float32");
     layout_transform(%2, src_layout="NCHW3c", dst_layout="NCHW")
   }
   ```
   
   On the BAD runs:
   ```
   [14:56:21] /home/mbs/github/mbs-tvm/src/relay/ir/transform.cc:134: AlterOpLayout: Executing function pass with opt level: 3
   [14:56:21] /home/mbs/github/mbs-tvm/src/relay/ir/transform.cc:135: AlterOpLayout: Input module:
   def @main(%data: Tensor[(1, 3, 10, 10), float32]) -> Tensor[(1, 6, 10, 10), float32] {
     nn.conv2d(%data, meta[relay.Constant][0] /* ty=Tensor[(6, 3, 5, 5), float32] */, padding=[2, 2, 2, 2], kernel_size=[5, 5], out_dtype="float32") /* ty=Tensor[(1, 6, 10, 10), float32] */
   }
   
   
   [14:56:21] /home/mbs/github/mbs-tvm/src/relay/ir/transform.cc:159: AlterOpLayout: Output module:
   def @main(%data: Tensor[(1, 3, 10, 10), float32]) -> Tensor[(1, 6, 10, 10), float32] {
     %0 = layout_transform(%data, src_layout="NCHW", dst_layout="NCHW16c");
     %1 = nn.conv2d(%0, meta[relay.Constant][0] /* ty=Tensor[(6, 3, 5, 5), float32] */, padding=[2, 2, 2, 2], kernel_size=[5, 5], data_layout="NCHW16c", out_dtype="float32");
     layout_transform(%1, src_layout="NCHW16c", dst_layout="NCHW")
   }
   ```


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