Fix dense Embedding to work with double backward (#9078)

Fix dense Embedding to work with double backward (#9078)

Summary:
Fixes : #6469

1. `ATen/native/native_functions.yml` had [dispatch](https://github.com/pytorch/pytorch/blob/03e7953a98875c0164cb8e2c19b45800e85f4347/aten/src/ATen/native/native_functions.yaml#L451-L455) variants for for `embedding_dense_backward` , however `embedding_backward` explicitly made [call](https://github.com/pytorch/pytorch/blob/03e7953a98875c0164cb8e2c19b45800e85f4347/aten/src/ATen/native/Embedding.cpp#L35-L45) to it, thus leading to error.

2. In case of CUDA type tensor, the function crashed used to crash on dereferencing of indices's data [pointer](https://github.com/pytorch/pytorch/blob/03e7953a98875c0164cb8e2c19b45800e85f4347/aten/src/ATen/native/Embedding.cpp#L93).

Both have been solved and checked against (on CUDA and CPU)

1.  As mentioned in the issue
```
import torch

class Test(torch.nn.Module):

    def __init__(self):
        super(Test,self).__init__()
        self.embd = torch.nn.Embedding(1000, 100)
        self.dense = torch.nn.Linear(100, 1)

    def forward(self, inp):
        inp = self.embd(inp)
        return self.dense(inp)

test = Test()
inp = torch.tensor([0,1,2,1,1])
out = test(inp)
raw_loss = out.mean(dim=0)

loss_grad = torch.autograd.grad(outputs=raw_loss,
                         inputs=list(test.parameters()),
                         retain_graph=True, create_graph=True, only_inputs=True)
norm = sum([param.norm()**2 for param in loss_grad])
loss = raw_loss + norm

loss.backward(retain_graph=True)

print(test.embd.weight.grad)

```

2. Test Script
```
import torch
import time
start = time.time()
l = [1,1]*100
input = torch.tensor([[1,0],[1,0]],device='cpu')
embedding_matrix = torch.tensor([[1.0,3.0],[2.0,4]],requires_grad=True,device='cpu')

sq = embedding_matrix * embedding_matrix
emb = torch.nn.functional.embedding(input, sq,scale_grad_by_freq=False)

print('Embedding Matrix')
print(embedding_matrix)
print('-----------------')

sum_ = emb.sum()#prod.sum()

loss_grad, = torch.autograd.grad(outputs=sum_,inputs=embedding_matrix,create_graph=True)

print('Gradient')
print(loss_grad)
print('-----------------')

sum2_ = sum_ + loss_grad.sum()
print(sum2_)
sum2_.backward()

print(embedding_matrix.grad)
print(time.time() - start)
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9078

Reviewed By: ezyang

Differential Revision: D14691901

Pulled By: soumith

fbshipit-source-id: 78e2612ba39080be564c876311671eb5a0119a0f