fix dp-sgd example (#873)

see [this issue](#467)

examples/dp_cifar10/cifar10_transforms.py

@@ -51,14 +51,14 @@ def compute_norms(sample_grads):
     batch_size = sample_grads[0].shape[0]
     norms = [sample_grad.view(batch_size, -1).norm(2, dim=-1) for sample_grad in sample_grads]
     norms = torch.stack(norms, dim=0).norm(2, dim=0)
-    return norms
+    return norms, batch_size
 
 
 def clip_and_accumulate_and_add_noise(model, max_per_sample_grad_norm=1.0, noise_multiplier=1.0):
     sample_grads = tuple(param.grad_sample for param in model.parameters())
 
     # step 0: compute the norms
-    sample_norms = compute_norms(sample_grads)
+    sample_norms, batch_size = compute_norms(sample_grads)
 
     # step 1: compute clipping factors
     clip_factor = max_per_sample_grad_norm / (sample_norms + 1e-6)
@@ -76,7 +76,7 @@ def clip_and_accumulate_and_add_noise(model, max_per_sample_grad_norm=1.0, noise
 
     # step 4: assign the new grads, delete the sample grads
     for param, param_grad in zip(model.parameters(), grads):
-        param.grad = param_grad
+        param.grad = param_grad/batch_size
         del param.grad_sample
 
 
@@ -492,4 +492,4 @@ def parse_args():
 
 
 if __name__ == "__main__":
-    main()
+    main()