MIDAS leverages the power of the DLPack protocol to export highly efficient tf.data.Dataset to other Deep Learning frameworks, like JAX or PyTorch. Hence, MIDAS is a multi-framework DataLoader that can be leveraged to produce highly efficient data pipelines.
One of the selling points of MIDAS is its simplicity and ease of use, consider the below example, where we build a python generator as our source data that is automatically converted to an efficient DataLoader that distributes the data between two GPUs in jax.
from midas import DataLoader
def dummy_data_generator():
for i in range(1000):
yield {"x": i, "y": i*3}
dl = DataLoader(dummy_data_generator)
# at this point dl wraps the tf.data.Dataset, so every tf.data.Dataset can be used over the
# dl object as shown bellow
dl = dl.map(lambda x: {**x, "is_y_even": x["y"]%2==0}) # tf.data.Dataset.map
dl = dl.cache() # tf.data.Dataset.cache
dl = dl.shuffle(1000) # tf.data.Dataset.shuffle
dl = dl.batch(10) # tf.data.Dataset.batch
# the .to_jax is a conversion method that will convert the Tensors from tf.data.Dataset to JAX tensors
jdl = dl.to_jax()
# Now jdl is a JaxDataLoader that supports jax-specific types of transformations.
jdl = jdl.shard() # shard the data between two GPUs (Number of GPUs available on this machine)
jdl = jdl.prefetch_to_devices() # send the data to the GPUs
for data in jdl:
# iterating over the dataloader
pass
print(data)Output:
{'is_y_even': ShardedDeviceArray([[0, 0, 0, 0, 0],
[1, 0, 1, 0, 1]], dtype=uint8),
'x': ShardedDeviceArray([[689, 97, 9, 129, 945],
[274, 761, 42, 937, 470]], dtype=int32),
'y': ShardedDeviceArray([[2067, 291, 27, 387, 2835],
[ 822, 2283, 126, 2811, 1410]], dtype=int32)}Since MIDAS work directly with python iterable, you can use any function or class iterable to instantiate a midas.DataLoader. For instance, we can load the mnist dataset from tensorflow_datasets to build a dataset that converts and shard the samples to jax:
import tensorflow as tf
import tensorflow_datasets as tfds
from midas import DataLoader
def preprocess(batch):
batch['image'] = tf.image.convert_image_dtype(batch['image'], tf.float32)
batch['image'] = (batch['image'] - 0.5) / 0.5 # tanh range is -1, 1
batch['label'] = tf.one_hot(batch['label'], 10)
return batch
mnist_data = tfds.load('mnist')
# Here since mnist_data["train"] is already a tf.data.Dataset, the DataLoader will use it
# instead of rebuilding a new one
train_dl = DataLoader(mnist_data["train"]) \
.map(preprocess, num_parallel_calls=tf.data.AUTOTUNE) \
.cache() \
.shuffle(5000) \
.batch(128, drop_remainder=True) \
.to_jax() \
.shard() \
.prefetch_to_devices()
for step, batch_train in enumerate(train_dl):
# call the train_step here with the already prepared data
...From pypi
TODO
From this repository (last commit in the master branch)
pip install git+https://github.com/bioinformatics-ua/midas.git
The only hard requirement of MIDAS is the installation of tensorflow if not yet installed. Although it is not required, if you are aiming to convert the tensors to another framework it is expectable that framwork would be already installed.
TL;DR: Python iterable -> tf.data.Dataset -> tf.data.Dataset data transformations -> DLPack -> Another DL framework (like JAX or PyTorch)
There are three basic steps to build DataLoaders with MIDAS:
- Definition of python iterable: This corresponds to the source of your data. Here, the midas.DataLoader will fetch your data in order to build the associated tf.data.Dataset. The only restriction here is that the iterable (python generator or class iterable) must yield a python dictionary (or similar structure) with your data. We decided that the data flow when using MIDAS should always be represented as a python dictionary given its high interpretability. Therefore, midas.DataLoader will automatically raise an exception if any sample is not formatted as a dictionary.
- Using fast tf.data.Dataset transformations: After creating the midas.DataLoader the further preprocessing should be done by leveraging the tf.data.Dataset API. For instance, to transform the data call
.map, to shuffle use.shuffle, to batch use.batchas you would normally do as a normal tf.data.Dataset. (To check the complete list of transformations click here) - Tensor conversion for the desired DL framework: At the last step, after we have defined all of the preprocessing steps, we can now convert the resulting tf.Tensor to our framework tensors. For this, we have exposed the following methods (
.to_jax,.to_torch,.to_numpyand.to_lambdaDataLoader, where this last one is for functional DataLoader conversion.)
First reason, It is really simple to use, you just need to specify your data as a python iterable (generation function or a class) and that's it the midas.DataLoader will take care of the conversions to tf.data.Dataset and will expose transformation methods to change the data between frameworks.
Second reason, it is FAST!, MIDAS is a direct wrapper of the tf.data.Dataset class and its transformation, which converts all of the defined processing steps into a computation graph on CPU, then thanks to the DLPack the conversion to other DL frameworks is achieved with almost no overhead.
Third reason, it is highly extensible, thanks to a modular coding approach new Deep Learning frameworks can be added following a functional programming style. For that, the user just needs to call .to_lambdaDataLoader(NEW_CLASS_CONVERTER_DATALOADER), and implement a new class that extends midas.LambdaDataLoader (Note: the DLPack should be used for fast conversion between different frameworks Tensors, however, this is not required).
Additional functionality for other DL frameworks, besides the framework conversion, MIDAS also exposes framework-specific transformations. For instance, when converting to a JaxDataLoader it is possible to follow the dataloader with the shard and prefetch_to_devices transformations, which automatically shards and distributes the data to the best accelerators devices found by jax.
But I am already a TF user, why should I care?. Well, MIDAS also offers the additional functionality of automatically converting python iterable (even for data with variable lengths, like text!) to tf.data.Dataset. For that it automatically infers the output_signature produced by your iterable, making it easier to build DataLoaders from generators.
Currently, MIDAS supports the following tensor conversions:
- tensorflow -> jax
- tensorflow -> torch
- tensorflow -> numpy
Additionally, dynamic new conversion can be added by extending LambdaDataLoader and passing the resulting class to the .to_lambdaDataLoadermethod.
When no output_signature is specified, the midas.DataLoader will automatically infer the correct output_signature from the samples of the python iterable. For that, a specific number of samples are gathered from the python iterable, which are specified by the infer_k=3 argument. So, by default, the DataLoader consumes 3 samples from the python iterable and then the values of each sample are automatically converted to a tf.Tensor, which we use to extract the associated data type and shape. Next, a consistency check is performed to ensure that the values from different samples follow the same data type and shape. If the data type differs, then it is an Error. On the other hand, if the shape differs, the data has a variable length, which is automatically handled by assigning the None shape instead of a fixed number.
Consider this example with fixed size data first:
from midas import DataLoader
def dummy_data_generator():
for i in range(1000):
yield {"x": i, "y": i*3}
dl = DataLoader(dummy_data_generator)
dl.output_signature
>>> {'x': TensorSpec(shape=(), dtype=tf.int32, name='x_input'), 'y': TensorSpec(shape=(), dtype=tf.int32, name='y_input')}Then consider this example, where the values of x randomly change:
from midas import DataLoader
import random
def dummy_data_generator():
for i in range(1000):
yield {"x": [i for _ in range(random.randint(1,5))], "y": i*3}
dl = DataLoader(dummy_data_generator)
dl.output_signature
>>> {'x': TensorSpec(shape=(None,), dtype=tf.int32, name='x_input'), 'y': TensorSpec(shape=(), dtype=tf.int32, name='y_input')}Here, since x is represented as a list that ranges from 1 element to 5 elements, the midas.DataLoader detected an inconsistency in the shape size, making the assumption that must be a Tensor with variable length. Therefore, it has represented with an unknown shape (None,).
Furthermore, infer_k controls how many samples are consumed to infer the correct shape. For performance reasons, the default value is low (3), which may produce some errors on datasets where data with variable length is rare since the DataLoader will only detect this if one of the first three samples has a different shape. So for cases like that, consider increasing the value of infer_k. As an extreme resource setting, infer_k=-1 will force the DataLoader to check the shapes of every sample on the python iterable.
Consider the following example that addresses this issue:
from midas import DataLoader
import random
def dummy_data_generator():
for i in range(3): # three samples where x is a list with two elements
yield {"x": [i for _ in range(2)], "y": i*3}
for i in range(5): # more five samples where x is a list with two elements
yield {"x": [i for _ in range(3)], "y": i*3}
dl = DataLoader(dummy_data_generator)
dl.output_signature
>>> {'x': TensorSpec(shape=(2,), dtype=tf.int32, name='x_input'), 'y': TensorSpec(shape=(), dtype=tf.int32, name='y_input')}
for data in dl:
pass
>>> TypeError: `generator` yielded an element of shape (3,) where an element of shape (2,) was expected.In the above example, since the three first samples have the same length, the midas.DataLoader will infer that x has shape (2,). However, the following samples will have a different shape, resulting in an Error coutch by the tf.data.Dataset. The main problem is that the default value of infer_k is set to 3, which in this example was not enough to detect that x has a variable lenght. To fix this, we can simply increase the infer_k value as shown:
from midas import DataLoader
import random
def dummy_data_generator():
for i in range(3): # three samples where x is a list with two elements
yield {"x": [i for _ in range(2)], "y": i*3}
for i in range(5): # more five samples where x is a list with two elements
yield {"x": [i for _ in range(3)], "y": i*3}
dl = DataLoader(dummy_data_generator, infer_k=5)
dl.output_signature
>>> {'x': TensorSpec(shape=(None,), dtype=tf.int32, name='x_input'), 'y': TensorSpec(shape=(), dtype=tf.int32, name='y_input')}
for data in dl:
pass
data
>>> {'x': <tf.Tensor: shape=(3,), dtype=int32, numpy=array([4, 4, 4], dtype=int32)>, 'y': <tf.Tensor: shape=(), dtype=int32, numpy=12>}Besides handling the DataLoaders conversions, MIDAS also adds some utility functions like:
get_python_iterator_n_samples will return the number of samples output by the python iterable, if this value is not specified during the initialization of a midas.DataLoader would be inferred by automatically transversing the python iterable. Therefore, do not call get_python_iterator_n_samples if your python iterable does not stop.
# continuation with the jdl created in the previous example
>>> jdl.get_python_iterator_n_samples()
1000get_n_samples will return the number of samples output by the DataLoader. Note that this number will differ from the get_python_iterator_n_samples if any data aggregation was performed. For instance, in this case, we are using the .batch(10) transformation that aggregates 10 sequential samples into one. Therefore, the current value for get_n_samples would be jdl.get_python_iterator_n_samples()/10.
# continuation with the jdl created in the previous example
>>> jdl.get_n_samples()
100get_transformation_list returns a list with all the transformations applied to the current DataLoader. For now, midas.DataLoder does not take advantage of this information. However, in a future version, this can be the starting point to implement a DataLoader chain optimizer that rearranges the specified transformations into a more proper order of execution that maximizes performance.
# continuation with the jdl created in the previous example
>>> jdl.get_transformation_list()
['tf.data.Dataset.from_generator',
'tf.data.Dataset.map',
'tf.data.Dataset.cache',
'tf.data.Dataset.shuffle',
'tf.data.Dataset.batch',
'midas.DataLoader.to_jax',
'midas.JaxDataLoader.shard',
'midas.JaxDataLoader.prefetch_to_devices']