GPU out of memory with torch.nn.Bilinear

Memory Format
1

Hello,

I am currently trying to train a model which includes among other modules the torch.nn.Bilinear one :

self.batch_size = 1
input_len = 100
output_len = 2
self.bilinear = nn.Bilinear(
            input_len,
            input_len,
            output_len,
            bias=False,
        )
 self.left_indices = [
            index
            for index in range(config["max_seq_len"])
            for _ in range(config["max_seq_len"] - index)
        ]
self.right_indices = [
            higher_index
            for index in range(config["max_seq_len"])
            for higher_index in range(index, config["max_seq_len"])
        ]

Which I use this way during training :

...
output_tensor = torch.zeros(
            self.batch_size,
            self.max_seq_len,
            self.max_seq_len,
            self.output_len,
            device=used_device,
        )
output_tensor[
                :, self.left_indices, self.right_indices
            ] = self.bilinear(
            input_left[:, self.left_indices],
            input_right[:, self.right_indices],
        )
....

However I hit the traditional “CUDA out of memory.” (which seems to happen when processing a second batch)

I tried varying the size of the two input tensors (from 1 to 100) and the resource allocated to the GPU takes up to 8GB with a Batch size of 1.

Is this normal or am I doing something wrong ? (assigning the module output to a tensor like that ?)

Thank you for your help ! =)