Training parameters in loss function as well as model

username: jungminc88
cooked: <p>I train a BERT model along with a loss function.<br>
The system makes sentence representations from BERT; computes cosine similarity between sentences; the loss function applies linear interpolation between the cosine similarity values and some constant values and the interpolation weight is supposed to be trainable.</p>
<p>When I use nn.CosineEmbeddingLoss instead of my custom loss function, it runs without a problem. However, the above gives error message halfway through training (around 400-th iteration), as memory usage increases and eventually it goes out of memory.</p>
<p>I suspect that memory is not cleared properly somewhere but I cannot figure out where.<br>
Could you point out my mistake or suggest what might be wrong?<br>
I would greatly appreciate your comment. Thank you!</p>
<p>Here is the train loop:</p>
<pre><code class="lang-auto">def train(dataloader, model):
    Loss_main = list()
    Loss = list()
    logger.info("starting train")
    model.train() 
    cos_sim = nn.CosineSimilarity(dim=1, eps=1e-6)
    
    for i, batch in enumerate(dataloader):
        que_iis = batch[0].to(device)
        que_ams = batch[1].to(device)
        art_iis = batch[2].to(device)
        art_ams = batch[3].to(device)
        labels = torch.where(batch[4]==0, -1, 1).to(device)
        article_ids = batch[5]
        section_ids = [art_to_hier[a.item()][-1] for a in article_ids]
        tfidf_sim = batch[6].to(device)
        query_out = model(input_ids=que_iis, attention_mask=que_ams).last_hidden_state
        article_out = model(input_ids=art_iis, attention_mask=art_ams).last_hidden_state
        query_emb = _mean_pooling(query_out, que_ams)
        article_emb = _mean_pooling(article_out, art_ams)
        sim = cos_sim(article_emb, query_emb)
        loss = loss_fn(sim, tfidf_sim, labels)
        loss = torch.mean(loss)
        Loss_main.append(loss.item())
        loss = loss / args.accumulation_size
        loss.backward()
        if (i + 1) % args.accumulation_size == 0:
            if args.grad_clip &gt; 0.:
                torch.nn.utils.clip_grad_norm_(
                    itertools.chain(model.parameters(), loss_fn.parameters()), max_norm=args.grad_clip, norm_type=2
                )
            optimizer.step()
            optimizer.zero_grad()

        Loss.append(loss.item())
    Loss = sum(Loss) / len(Loss)
    Loss_main = sum(Loss_main) / len(Loss_main)
    return Loss, Loss_main, loss_fn.lerp_weight
</code></pre>
<p>The loss function is defined as follows:</p>
<pre><code class="lang-auto">class CosSimLossWithLerp(nn.Module):
    def __init__(self):
        super().__init__()
        self.lerp_weight = nn.Parameter(torch.tensor([0.5]))

    def forward(self, sim, tfidf_sim, target):
        sim = torch.lerp(sim, tfidf_sim, self.lerp_weight)
        loss = [1 - sim[i] if target[i] == 1 else max(torch.tensor(0., requires_grad=True).to(device), sim[i]) for i in range(len(sim))]
        loss = torch.stack(loss)
        loss = torch.mean(loss)
        return loss
</code></pre>
<p>and called outside the train loop:</p>
<pre><code class="lang-auto">loss_fn = CosSimLossWithLerp()
loss_fn.to(device)
</code></pre>
<p>I included the loss function parameters in the optimizer:</p>
<pre><code class="lang-auto">optimizer = torch.optim.Adam(itertools.chain(model.parameters(), loss_fn.parameters()), lr=args.lr1)
</code></pre>