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From "Joshua Z. Zhang" <notificati...@github.com>
Subject [apache/incubator-mxnet] [mxnet 2.0][item 4.8][RFC] Gluon Data API Extension and Fixes(Part 2) (#17269)
Date Fri, 10 Jan 2020 23:15:49 GMT
## Description
This is the part 2 of Gluon Data API extension and fixes, which mainly focus on speed up the
current data loading pipeline using gluon dataset and dataloader.

## Motivation

The current data loading pipeline is the major bottleneck for many training tasks. We can
summarize the entire flow as:

```bash
| Dataset.__getitem__ -> 
| Transform.__call__()/forward() ->
| Batchify ->
| (optional communicate through shared_mem) ->
| split_and_load(ctxs) ->
| <training on GPUs>
-> 
```
where there are performance concerns:
- performance of python dataset/transform functions aren't satisfying
- it's not easy to embrace multithreading to speed up dataloading due to global interpreter
lock
- python multiprocessing is unfortunately slow and error prune, not to mention the shared
memory implementations on different OS are quite difference and very annoying(e.g., it's very
likely to run out of shared memory if not properly taken care of)
- currently memory planing for batchify is non-exist, causing frequent alloc/dealloc for large
chunk of memory if the batch size is big
- batchify then split and load can be optimized to partial_batchify

## Proposal
To alleviate the existing troubles I propose to use a hybrid solution, that is to 
- provide C++ Datasets that can cover the most usecases
    ```python
    from gluon.data.dataset import TupleDataset, ImageFolderDataset, ArrayDataset
    # as long as TupleDataset, ImageSequenceDataset, ArrayDataset are supported by backend
    dataset = TupleDataset([ImageSequenceDataset(img_paths), ArrayDataset(image_labels)])
    # dataset is an image classification dataset while fully supported in C++
    # with TupleDataset we can combine as many data as possible

    # a C++ backed Dataset can have a magic __handle__ method to return the c++ handle for
reference
    class TupleDataset:
        def __init__(self, datasets):
            if all([callable(getattr(dataset, '__handle__')) for dataset in datasets]):
                # all supported by backend
                self._tuple_dataset = check_call(_LIB.MXTupleDatasetCreate([getattr(dataset,
'__handle__') for dataset in datasets]))
            else:
                self._tuple_dataset = None

            def __handle__(self):
                return self._tuple_dataset
                    
    ```
- provide common C++ batchify functions that are split and context aware. Batchify with memory
planner is TBD.
- provide a C++ `MultithreadingDataLoader` which inherit the same arguments as `gluon.data.DataLoader`
but use mxnet internal multithreading rather than python multiprocessing.
- fallback to python multiprocessing whenever 
    - the dataset is not fully supported by backend(e.g., there are custom python datasets)
    - Transform is not fully hybridizable
    - Batchify is not fully supported by backend

User will continue to use the existing `gluon.data.DataLoader`, and the conversion will be
applied automatically
```python

loader = gluon.data.DataLoader(hybrid_dataset.transform(hybrid_transform), batch_size=32,
batchify_fn=hybrid_batchify)

def DataLoader:
    def __init__(self, dataset, ...):
        if isinstance(dataset, _LazyTransformDataset) and is_hybrid(dataset._transform) and
is_hybrid(dataset) and is_hybrid(batchify_fn):
            self._mt_dataloader = check_call(_LIB.MXMultiThreadDataLoaderCreate(...))
    def __iter__(self):
        if self._mt_dataloader:
                return self._mt_dataloader
        else:
               # fallback to single thread normal dataloader or multiprocessing dataloader

```

With this change, mxnet 2.0 will get smooth transition to mixed data loaders. Please comment
with specific examples where this proposal fail to accommodate.

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