## 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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