Lyken17 commented on pull request #9465:
URL: https://github.com/apache/tvm/pull/9465#issuecomment-963906327
Hi @vin13,
This is bit complex. Let me explain the situation. The bug was initially
found when I tried to calculate the gradients of `nn.Conv2d` with `groups`
```python
program = """
def @main(%input0: Tensor[(1, 32, 224, 224), float32],
%v0_0_weight: Tensor[(32, 1, 3, 3), float32],
%v1_conv_0_0_weight: Tensor[(32, 1, 3, 3), float32],
%v1_conv_1_weight: Tensor[(16, 32, 1, 1), float32]) -> Tensor[(1,
32, 224, 224), float32] {
%0 = nn.conv2d(%input0, %v0_0_weight, strides=[1, 1], padding=[1, 1, 1,
1], groups=32, channels=32, kernel_size=[3, 3]);
%0
}
"""
mod = parse_module(program)
mod = relay.transform.InferType()(mod)
bwd_ir = relay.transform.gradient(mod['main'], mode="first_order")
bwd_mod = tvm.IRModule.from_expr(bwd_ir)
"""
fn (%input0: Tensor[(1, 32, 224, 224), float32], %v0_0_weight: Tensor[(32,
1, 3, 3), float32], %v1_conv_0_0_weight: Tensor[(32, 1, 3, 3), float32],
%v1_conv_1_weight: Tensor[(16, 32, 1, 1), float32]) -> (Tensor[(1, 32, 224,
224), float32], (Tensor[(1, 32, 224, 224), float32], Tensor[(32, 1, 3, 3),
float32], Tensor[(32, 1, 3, 3), float32], Tensor[(16, 32, 1, 1), float32])) {
let %x = %input0;
let %x1 = zeros_like(%x);
let %x2 = %v0_0_weight;
let %x3 = zeros_like(%x2);
let %x4 = %v1_conv_0_0_weight;
let %x5 = zeros_like(%x4);
let %x6 = %v1_conv_1_weight;
let %x7 = zeros_like(%x6);
let %x8 = nn.conv2d(%x, %x2, padding=[1, 1, 1, 1], groups=32, channels=32,
kernel_size=[3, 3]) /* ty=Tensor[(1, 32, 224, 224), float32] */;
let %x9 = zeros_like(%x8);
%0 = ones_like(%x8);
%1 = nn.conv2d_transpose(%0, %x2, padding=[1, 1, 1, 1], groups=32);
%9 = (
let %x10 = add(%x1, %1);
%2 = tile(%0, reps=[1, 1, 1, 1]);
%3 = reshape(%x, newshape=[1, -1, 0, 0]);
%4 = reshape(%2, newshape=[-1, 1, 0, 0]);
%5 = nn.conv2d(%3, %4, padding=[1, 1, 1, 1], groups=32);
%6 = reshape(%5, newshape=[1, 1, 32, 3, 3]);
%7 = sum(%6, axis=[0]);
%8 = transpose(%7, axes=[1, 0, 2, 3]);
let %x11 = add(%x3, %8);
(%x10, %x11, %x5, %x7)
);
(%x8, %9)
}
"""
```
It is shown that `%1 = nn.conv2d_transpose(%0, %x2, padding=[1, 1, 1, 1],
groups=32);` is transformed to `%1 = nn.conv2d_transpose(%0, %x2, padding=[1,
1, 1, 1], groups=32);`. The shape of `%2` is (32, 1, 3, 3), which is `OIHW` for
Conv2d and `IOHW` for Conv2dTransposed. This is consistent with PyTorch
[[1](https://pytorch.org/docs/stable/generated/torch.nn.functional.conv2d.html),[2](https://pytorch.org/docs/stable/generated/torch.nn.functional.conv_transpose2d.html#torch.nn.functional.conv_transpose2d)]
and
PaddlePaddle[[1](https://www.paddlepaddle.org.cn/documentation/docs/en/1.8/api/dygraph/Conv2DTranspose.html)].
If the original purpose of `topi.nn.conv2d_transpose` is to use `OIHW` rather
than `IOHW`, then the primal gradients registration is wrong.
From my personal perspective, I prefer `IOHW` more since `conv2dtranspose`
should have an `transposed` weight and this make tvm consistent pytorch. Is
there any specific reason (e.g., performance tuning) that makes `OIHW` better
than `IOHW`?
In either case, the `groups` support for conv2dtranspose is missing and
should added.
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