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Add check for ctc_loss targets param (#150981)

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Fixes #150835

## Test Result

```python
# cuda
>>> import torch
>>> import torch.nn.functional as F
>>> device = "cuda" # "cpu" is fine
>>> num_classes = 4
>>> log_probs = torch.rand(0, 0, num_classes, device=device)
>>> targets = torch.tensor([], device=device, dtype=torch.long)
>>> input_lengths = torch.tensor([], device=device, dtype=torch.long)
>>> target_lengths = torch.tensor([], device=device, dtype=torch.long)
>>> result = F.ctc_loss(log_probs, targets, input_lengths, target_lengths, reduction='none')

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/zong/code/pytorch/torch/nn/functional.py", line 3079, in ctc_loss
    return torch.ctc_loss(
           ^^^^^^^^^^^^^^^
RuntimeError: log_probs tensor must not be empty

# cpu
>>> device = "cpu"
>>> num_classes = 4
>>> log_probs = torch.rand(0, 0, num_classes, device=device)
>>> targets = torch.tensor([], device=device, dtype=torch.long)
>>> input_lengths = torch.tensor([], device=device, dtype=torch.long)
>>> target_lengths = torch.tensor([], device=device, dtype=torch.long)
>>> result = F.ctc_loss(log_probs, targets, input_lengths, target_lengths, reduction='none')
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/zong/code/pytorch/torch/nn/functional.py", line 3079, in ctc_loss
    return torch.ctc_loss(
           ^^^^^^^^^^^^^^^
RuntimeError: log_probs tensor must not be empty

```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/150981
Approved by: https://github.com/eqy
This commit is contained in:
zeshengzong
2025-04-14 07:24:30 +00:00
committed by PyTorch MergeBot
parent bbc5fe8504
commit 01f226bfb8
3 changed files with 11 additions and 0 deletions
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9
test/test_nn.py
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@ -11532,6 +11532,15 @@ class TestNNDeviceType(NNTestCase):
grad_cudnn, = torch.autograd.grad(loss_cudnn, log_probs, grad_out)
self.assertEqual(grad_cudnn, grad_native, atol=1e-4, rtol=0)
@expectedFailureMPS
def test_ctc_loss_error(self, device):
log_probs = torch.rand(0, 0, 4, device=device)
targets = torch.tensor([], device=device, dtype=torch.long)
input_lengths = torch.tensor([], device=device, dtype=torch.long)
target_lengths = torch.tensor([], device=device, dtype=torch.long)
with self.assertRaisesRegex(RuntimeError, "log_probs tensor must not be empty"):
F.ctc_loss(log_probs, targets, input_lengths, target_lengths, reduction='none')
@expectedFailureMPS # RuntimeError: LSTM with projections is not currently supported with MPS.
@dtypesIfCUDA(torch.half, torch.float, torch.double)
@dtypes(torch.float)