[Apache TVM Discuss] [Questions] How dose TVM elimitate calls of conv weights layout_transform?

Thanks for the reply Kevin! Those two layout trans make sense, but for filter parameters, they're loaded from .pth with OIHW by default(relay/frontend/pytorch.py) and I set desired_layout for HWIO. Will these filter parameters be transformed in advanced or by a cuda kernel in each inference?

[Apache TVM Discuss] [Questions] How dose TVM elimitate calls of conv weights layout_transform?

If original model layout is NCHW and you convert to NHWC in TVM, at least two layout transformation are required: one at the beginning and one at the end. --- [Visit Topic](https://discuss.tvm.apache.org/t/how-dose-tvm-elimitate-calls-of-conv-weights-layout-transform/8208/3) to respond.

[Apache TVM Discuss] [Questions] Auto-scheduling for lstm operator

@Jiali this could be due to the fact that the ratio of valid schedules over an entire search space is relatively low, so population sampling cannot find any valid schedule. If the evolutionary search is based on a set of invalid schedules, it is highly possible that it will be trapped in inval

[Apache TVM Discuss] [Questions] How dose TVM elimitate calls of conv weights layout_transform?

After reading these two links: [https://discuss.tvm.apache.org/t/layout-conversion-pass/4009/15](https://tvm.apache.org/docs/dev/convert_layout.html) [https://tvm.apache.org/docs/dev/convert_layout.html](https://tvm.apache.org/docs/dev/convert_layout.html) I'm still confused that

[Apache TVM Discuss] [Questions] How dose TVM elimitate calls of conv weights layout_transform?

Hi everyone! I modified this sample(https://tvm.apache.org/docs/tutorials/frontend/from_pytorch.html) to add desired_layout NHWC to the network saved from pytorch(which uses NCHW): ```python desired_layouts = {'qnn.conv2d': ['NHWC', 'HWIO'], 'nn.conv2d': ['NHWC', 'HWIO