opinfo : nn.functional.embedding (#66622)
Summary:
Adds opinfo for `nn.functional.embedding`
Few cases where `numerical` gradient doesn't match (gradcheck fails)
```python
import torch
try:
t = torch.randn(2, 1, dtype=torch.float64, requires_grad=True)
idx = torch.tensor([0, 1])
torch.autograd.gradcheck(lambda idx, t : torch.nn.functional.embedding(idx, t, padding_idx=1), (idx, t, ))
except Exception as e:
print("PADDING IDX:", e)
try:
t = torch.ones(2, 1, dtype=torch.float64, requires_grad=True)
idx = torch.tensor([0, 1])
torch.autograd.gradcheck(lambda idx, t : torch.nn.functional.embedding(idx, t, max_norm=1.), (idx, t, ))
except Exception as e:
print("MAX NORM:", e)
try:
t = torch.randn(2, 1, dtype=torch.float64, requires_grad=True)
idx = torch.tensor([0, 1, 1])
torch.autograd.gradcheck(lambda idx, t : torch.nn.functional.embedding(idx, t, scale_grad_by_freq=True), (idx, t, ))
except Exception as e:
print("SCALE GRAD BY FREQUENCY:", e)
try:
t = torch.randn(2, 1, dtype=torch.float64, requires_grad=True)
idx = torch.tensor([0, 1])
torch.autograd.gradcheck(lambda idx, t : torch.nn.functional.embedding(idx, t, sparse=True), (idx, t, ))
except Exception as e:
print("SPARSE", e)
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/66622
Reviewed By: gchanan
Differential Revision: D31761594
Pulled By: zou3519
fbshipit-source-id: d24f44728d049e6276d6c3165aa1fba458214959
