FIX for ConvNd layers using the groups argument.

[gslama12]

As discussed in #2153.

Code example

[BenjaminBossan]

Thanks for the PR. The change seems to be much smaller than I initially thought, which is great. Before we proceed, could we do the following:

Let's add a test case for this. First, let's create an entry like this one:

peft/tests/test_custom_models.py

Line 114 in f51203f

("Conv2d 1 LoRA", "Conv2d", LoraConfig, {"target_modules": ["conv2d"]}),

Then we need to define a model with a conv layer that uses groups. Something similar to this with groups=5 should work:

peft/tests/test_custom_models.py

Lines 864 to 880 in f51203f

	class ModelConv2D(nn.Module):
	def __init__(self):
	super().__init__()
	self.conv2d = nn.Conv2d(5, 10, 3)
	self.relu = nn.ReLU()
	self.flat = nn.Flatten()
	self.lin0 = nn.Linear(10, 2)
	self.sm = nn.LogSoftmax(dim=-1)
	def forward(self, X):
	X = X.float().reshape(-1, 5, 3, 3)
	X = self.conv2d(X)
	X = self.relu(X)
	X = self.flat(X)
	X = self.lin0(X)
	X = self.sm(X)
	return X

Then make sure that the model is being used when it's model ID is passed by adding an entry similar to this one:

peft/tests/test_custom_models.py

Lines 967 to 968 in f51203f

	if model_id == "Conv2d":
	return ModelConv2D().to(torch_dtype)

LMK if anything is unclear.

Moreover, don't forget to run make style for the linter.

[gslama12]

thanks for the fast reply. I implemented the test as you described. I added one for lora and one for dora. LMK if there is something missing.

[BenjaminBossan]

Thanks for adding the tests. Unfortunately, a lot of them are failing for me locally. Do they pass for you? E.g. this one:

pytest tests/test_custom_models.py -k test_forward_output_finite_021_Conv2d_Groups_LoRA

[gslama12]

I had a bug in the test model, i fixed it now and the TC you stated should work. Let's see if anything else fails.