[0-size Tensor Job2 No.53] Add 0-size Tensor support for paddle.nn.functional.adaptive_log_softmax_with_loss [fluid_ops]

python/paddle/nn/functional/loss.py

@@ -4636,7 +4636,7 @@ def adaptive_log_softmax_with_loss(
  target_dim = label.dim()
 
  if target_dim == 1:
- if input.shape[0] != label.shape[0]:
+ if input.shape[0] != label.shape[0] and label.shape[0] != 0:
  raise ValueError(
  'Input and label should have the same size '
  'in the batch dimension.'
@@ -4733,9 +4733,10 @@ def adaptive_log_softmax_with_loss(
  x=input, weight=head_weight, bias=head_bias
  )
  head_logprob = paddle.nn.functional.log_softmax(head_output, axis=1)
- output += paddle.take_along_axis(
- head_logprob, gather_inds.unsqueeze(1), axis=1
- ).squeeze()
+ if gather_inds.size != 0:
+ output += paddle.take_along_axis(
+ head_logprob, gather_inds.unsqueeze(1), axis=1
+ ).squeeze()
  loss = (-output).mean()
 
  if not is_batched:

test/legacy_test/test_adaptive_log_softmax_with_loss.py

@@ -393,6 +393,7 @@ def test_forward(self):
  assert not np.any(before != after)
 
  def test_cluster(self):
+ paddle.disable_static()
  model = SimpleModel(16, 20, [5, 10, 15], div_value=2.0)
  x = paddle.randn((128, 16))
  y = paddle.randint(low=0, high=20, shape=[128])
@@ -574,5 +575,28 @@ def test_grad(self):
  np.allclose(analytic_grads, grad_numerical, rtol=1e-5, atol=1e-5)
 
 
+class TestAdaptiveLogSoftmaxWithLossAPI_ZeroSize(unittest.TestCase):
+ def setUp(self):
+ self.places = get_places()
+
+ def test_dygraph_api(self):
+ for place in self.places:
+ paddle.disable_static(place)
+ input = paddle.to_tensor(np.random.random([4, 8]))
+ label = paddle.to_tensor(np.random.random([0]))
+ head_weight = paddle.to_tensor(np.random.random([8, 3]))
+ tail_weights = [
+ [
+ paddle.to_tensor(np.random.random([8, 4])),
+ paddle.to_tensor(np.random.random([4, 2])),
+ ]
+ ]
+ cutoffs = [2, 4]
+ out = paddle.nn.functional.adaptive_log_softmax_with_loss(
+ input, label, head_weight, tail_weights, cutoffs
+ )
+ np.testing.assert_allclose(np.array([]), out[0].numpy())
+
+
 if __name__ == "__main__":
  unittest.main()