No inf checks were recorded for this optimizer error for custom loss

Vishal_Jain1: <p>Ive written a custom loss function to measure the average circularity of channel segmentations in a batch.</p>
<pre><code class="lang-auto">def M(i,j,I):
    '''calculates the i,jth moment of image I'''
    x_grid,y_grid = torch.meshgrid(torch.arange(0,I.shape[0],dtype=torch.float,device = I.device,requires_grad=True),torch.arange(0,I.shape[1],dtype=torch.float,device = I.device,requires_grad=True))
    x_grid = x_grid**i 
    y_grid = y_grid**j
    moment = torch.sum(x_grid*y_grid*I)

    return moment

class MomentCircScore(nn.Module):
    def __init__(self, weight: torch.Tensor = None):
        super(MomentCircScore, self).__init__()

        # Sanity checks
        assert weight is None or isinstance(weight, torch.Tensor), "Class Loss weight must be a tensor"
        assert weight is None or weight.ndim == 1, "Class Loss weight must be a 1D list"
        assert weight is None or weight.sum() == 1, "Class Loss weight must sum to 1"

        device = torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu')
        assert weight is None or device == weight.device, "Loss weight on inconsistent device"

        # Declare loss variables
        self.weights = weight # get rd of background weight 
        self.pi = torch.tensor(math.pi)
        self.device = device

    def forward(self,y_pred):
        # softmax pred
        y_pred = F.softmax(y_pred, dim=1)
        shape = y_pred.shape
        # init scores tensor B x C
        circ_scores = torch.zeros((shape[0],shape[1]),device =self.device,requires_grad=True,dtype=torch.float)
        # label each pixel based on max channel value
        with torch.no_grad():
            labelled_y_pred = torch.argmax(y_pred,dim=1)
        
        # calc circularity score for each channel
        for batch in range(shape[0]):
            for class_ in range(0,shape[1]): # not calculating background for computation speed up
                with torch.no_grad():
                    # generate binary class seg 
                    class_map = torch.where(labelled_y_pred[batch]==class_,1,0)
                     
                # calculate moments
                M_0_0 = M(0,0,class_map)
                M_1_0 = M(1,0,class_map)
                M_0_1 = M(0,1,class_map)

                x_bar = M_1_0/M_0_0
                y_bar = M_0_1/M_0_0
                
                #calculate circularity score for class
                c_s = (1/(2*self.pi)) * (M_0_0**2/(M(2,0,class_map) + M(0,2,class_map) - y_bar*M_0_1 - x_bar*M_1_0))
                if torch.isnan(c_s):
                    # if no pixels belong to class, set score to 0
                    c_s = torch.tensor(0,device=self.device,requires_grad=True,dtype=torch.float)
                
                # add score to scores tensor
                with torch.no_grad():
                    circ_scores[batch,class_]= c_s
        
        # weight / average scores along channel
        if self.weights is not None:
            weighted_circ_scores = circ_scores * self.weights.repeat((shape[0], 1))
            average_class_circ_scores = weighted_circ_scores.sum(1)
        
        else:
            average_class_circ_scores = circ_scores.mean(1)
        
        return 1-average_class_circ_scores.mean()  
</code></pre>
<p>Since the loss requires me to convert the model’s output to a binary format I need to use torch.argmax and torch.where, which both break gradients. As such, I put the calls to these functions within torch.no_grad() blocks.</p>
<p>I was hoping that the pytorch autograd engine would work its magic but during run time I’m getting the assertion error:</p>
<pre><code class="lang-auto">Traceback (most recent call last):
  File "/home/imagingscience/Desktop/ia-vj/new_python_scripts/pytorch_scripts/UNet/train.py", line 457, in &lt;module&gt;
    train(args)
  File "/home/imagingscience/Desktop/ia-vj/new_python_scripts/pytorch_scripts/UNet/train.py", line 358, in train
    scaler.step(optimiser)
  File "/home/imagingscience/miniconda3/envs/aorta-seg/lib/python3.9/site-packages/torch/cuda/amp/grad_scaler.py", line 336, in step
    assert len(optimizer_state["found_inf_per_device"]) &gt; 0, "No inf checks were recorded for this optimizer."
AssertionError: No inf checks were recorded for this optimizer.
</code></pre>
<p>Although, this is after the <code>.backward()</code> call in the code and so I am not sure if the issue is in the binarisation process or if it’s something else.</p>
<p>EDIT-------------------------------------</p>
<p>Error disappears when turning off mixed precision training, so calling <code>scaler.step(optimiser)</code> ouside the <code>with torch.cuda.amp.autocast</code> block.</p>
<p>Now I have 2 questions, why is this the fix, and why does my loss work when Ive got parts of it working in <code>torch.no_grad()</code> blocks?</p>
<p>The relevant block from <code>train.py</code></p>
<pre><code class="lang-auto">                # zero parameter gradients
                optimiser.zero_grad()
                # forward + backward + optmise
                with torch.cuda.amp.autocast(params['mp_training']):
                    outputs = net(inputs) # convert datatype to float32 for training
                    # calculate loss
                    loss = criterion(outputs) if params['loss_function']['loss'] == 'moment_circ_loss' else criterion(outputs,labels) 
                    loss_value = loss.item()

                    if params['mp_training']:
                        scaler.scale(loss).backward()

                # backpropagation
                if params['mp_training']:
                    scaler.step(optimiser)
                    scaler.update()
                else:
                    loss.backward()
                    optimiser.step()
</code></pre>

ptrblck: <p>I’m not sure, if you’ve fixed the issue or if the model is just not updated, since (as you’ve already explained) <code>argmax</code> would break the computation graph.<br>
You could check the <code>.grad</code> attribute of all parameters of the model after the <code>backward</code> call and see if they are containing valid gradients.</p>