Fix Errors in trainer_sgdmf.py and movielens.py

I tried to run this example following the docs at https://federatedscope.io/docs/recommendation/ with the command:
`python federatedscope/main.py --cfg federatedscope/mf/baseline/hfl-sgdmf_fedavg_standalone_on_movielens1m.yaml
`
However, it did not run and reported some errors. The error occurred in the file ./federatedscope/mf/trainer/trainer_sgdmf.py. It might be caused by a torch type "Embedding". Specifically, ctx.model.embed_user.grad is incorrect, while ctx.model.embed_user.weight.grad is correct. Additionally, there are some other errors, such as "add(sparse, dense)".

I tried using ChatGPT to fix the code, and now the example can run. I checked my Git history. Here are my fixed records:

In `federatedscope/mf/dataset/movielens.py`, line 160-161
```
row = [mapping_user[mid] for _, mid in data["userId"].items()]
col = [mapping_item[mid] for _, mid in data["movieId"].items()]
```
In `federatedscope/mf/trainer/trainer_sgdmf.py` line 70, replace all funciton `def hook_on_batch_backward(ctx):`
```
def hook_on_batch_backward(ctx):
"""Private local updates in SGDMF

"""
ctx.optimizer.zero_grad()
ctx.loss_task.backward()

if ctx.model.embed_user.weight.grad.is_sparse:
dense_user_grad = ctx.model.embed_user.weight.grad.to_dense()
else:
dense_user_grad = ctx.model.embed_user.weight.grad

if ctx.model.embed_item.weight.grad.is_sparse:
dense_item_grad = ctx.model.embed_item.weight.grad.to_dense()
else:
dense_item_grad = ctx.model.embed_item.weight.grad

# Inject noise
dense_user_grad.data += get_random(
"Normal",
sample_shape=ctx.model.embed_user.weight.shape,
params={
"loc": 0,
"scale": ctx.scale
},
device=ctx.model.embed_user.weight.device)
dense_item_grad.data += get_random(
"Normal",
sample_shape=ctx.model.embed_item.weight.shape,
params={
"loc": 0,
"scale": ctx.scale
},
device=ctx.model.embed_item.weight.device)

ctx.model.embed_user.weight.grad = dense_user_grad.to_sparse()
ctx.model.embed_item.weight.grad = dense_item_grad.to_sparse()
ctx.optimizer.step()

# Embedding clipping
with torch.no_grad():
embedding_clip(ctx.model.embed_user.weight, ctx.sgdmf_R)
embedding_clip(ctx.model.embed_item.weight, ctx.sgdmf_R)

```
The code can now run, but I’m not sure if there are any other issues.
I rarely use GitHub. I might need to learn how to pull a request later.

Env.:
python 3.9
torch 1.10.1
cuda 11.3

Thank your work. Have a good day. ：）

Start by running the documented command for the MovieLens example and inspect federatedscope/mf/trainer/trainer_sgdmf.py and federatedscope/mf/dataset/movielens.py at the reported sections. Verify the embedding gradient handling and the reported add(sparse, dense) errors against the provided environment details. Done means the example runs successfully without the reported errors and the related behavior is covered by the project’s existing checks, if applicable.

Written by the indexing model from the issue text.