aiexperiments-ai-duet/server/third_party/magenta/pipelines

Data processing in Magenta

Magenta has a lot of different models which require different types of inputs. Some models train on melodies, some on raw audio or midi data. Being able to convert easily between these different data types is essential. We define a Pipeline which is a data processing module that transforms input data types to output data types. By connecting pipelines together, new data pipelines can be quickly built for new models.

Files:

• pipeline.py defines the Pipeline abstract class and utility functions for running a Pipeline instance.
• pipelines_common.py contains some Pipeline implementations that convert to common data types, like QuantizedSequence and MonophonicMelody.
• dag_pipeline.py defines a Pipeline which connects arbitrary pipelines together inside it. These Pipelines can be connected into any directed acyclic graph (DAG).
• statistics.py defines the Statistic abstract class and implementations. Statistics are useful for reporting about data processing.

Pipeline

All pipelines implement the abstract class Pipeline. Each Pipeline defines what its input and output look like. A pipeline can take as input a single object or dictionary mapping names to inputs. The output is given as a list of objects or a dictionary mapping names to lists of outputs. This allows the pipeline to output multiple items from a single input.

Pipeline has two methods:

• transform(input_object) converts a single input to one or many outputs.
• get_stats() returns statistics (see Statistic objects below) about each call to transform.

And three important properties:

• input_type is the type signature that the pipeline expects for its inputs.
• output_type is the type signature of the pipeline's outputs.
• name is a unique string name of the pipeline. This is used as a namespace for Statistic objects the Pipeline produces.

For example,

class MyPipeline(Pipeline):
  ...

print MyPipeline.input_type
> MyType1

print MyPipeline.output_type
> MyType2

print MyPipeline.name
> "MyPipeline"

my_input = MyType1(1, 2, 3)
outputs = MyPipeline.transform(my_input)
print outputs
> [MyType2(1), MyType2(2), MyType2(3)]

for stat in MyPipeline.get_stats():
  print str(stat)
> MyPipeline_how_many_ints: 3
> MyPipeline_sum_of_ints: 6

An example where inputs and outputs are dictionaries,

class MyPipeline(Pipeline):
  ...

print MyPipeline.input_type
> {'property_1': MyType1, 'property_2': int}

print MyPipeline.output_type
> {'output_1': MyType2, 'output_2': str}

my_input = {'property_1': MyType1(1, 2, 3), 'property_2': 1007}
outputs = MyPipeline.transform(my_input)
print outputs
> {'output_1': [MyType2(1), MyType2(2), MyType2(3)], 'output_2': ['1007', '7001']}

for stat in MyPipeline.get_stats():
  print str(stat)
> MyPipeline_how_many_ints: 3
> MyPipeline_sum_of_ints: 6
> MyPipeline_property_2_digits: 4

If the output is a dictionary, the lengths of the output lists do not need to be the same. Also, this example should not imply that a Pipeline which takes a dictionary input must produce a dictionary output, or vice versa. Pipelines do need to produce the type signature given by output_type.

Declaring input_type and output_type allows pipelines to be strung together inside meta-pipelines. So if there a pipeline that converts TypeA to TypeB, and you need TypeA to TypeC, then you only need to create a TypeB to TypeC pipeline. The TypeA to TypeC pipeline just feeds TypeA-to-TypeB into TypeB-to-TypeC.

A pipeline can be run over a dataset using run_pipeline_serial, or load_pipeline. run_pipeline_serial saves the output to disk, while load_pipeline keeps the output in memory. Only pipelines that output protocol buffers can be used in run_pipeline_serial since the outputs are saved to TFRecord. If the pipeline's output_type is a dictionary, the keys are used as dataset names.

Functions are also provided for iteration over input data. file_iterator iterates over files in a directory, returning the raw bytes. tf_record_iterator iterates over TFRecords, returning protocol buffers.

Note that the pipeline name is prepended to the names of all the statistics in these examples. Pipeline.get_stats automatically prepends the pipeline name to the statistic name for each stat.

Implementing Pipeline

There are exactly two things a subclass of Pipeline is required to do:

1. Call Pipeline.__init__ from its constructor passing in input_type, output_type, and name.
2. Implement the abstract method transform.

DO NOT override get_stats. To emit Statistic objects, call the private method _set_stats from the transform method. _set_stats will prepend the Pipeline name to all the Statistic names to avoid namespace conflicts, and write them to the private attribute _stats.

A full example:

class FooExtractor(Pipeline):

  def __init__(self):
    super(FooExtractor, self).__init__(
        input_type=BarType,
        output_type=FooType,
        name='FooExtractor')

  def transform(self, bar_object):
    how_many_foo = Counter('how_many_foo')
    how_many_features = Counter('how_many_features')
    how_many_bar = Counter('how_many_bar', 1)

    features = extract_features(bar_object)
    foos = []
    for feature in features:
      if is_foo(feature):
        foos.append(make_foo(feature))
        how_many_foo.increment()
      how_many_features.increment()

    self._set_stats([how_many_foo, how_many_features, how_many_bar])
    return foos

foo_extractor = FooExtractor()
print FooExtractor.input_type
> BarType
print FooExtractor.output_type
> FooType
print FooExtractor.name
> "FooExtractor"

print foo_extractor.transform(BarType(5))
> [FooType(1), FooType(2)]

for stat in foo_extractor.get_stats():
  print str(stat)
> FooExtractor_how_many_foo: 2
> FooExtractor_how_many_features: 5
> FooExtractor_how_many_bar: 1

DAGPipeline

Connecting pipelines together

Pipeline transforms A to B - input data to output data. But almost always it is cleaner to decompose this mapping into smaller pipelines, each with their own output representations. The recommended way to do this is to make a third Pipeline that runs the first two inside it. Magenta provides DAGPipeline - a Pipeline which takes a directed asyclic graph, or DAG, of Pipeline objects and runs it.

Lets take a look at a real example. Magenta has Quantizer and MonophonicMelodyExtractor (defined here). Quantizer takes note data in seconds and snaps, or quantizes, everything to a discrete grid of timesteps. It maps NoteSequence protocol buffers to QuantizedSequence objects. MonophonicMelodyExtractor maps those QuantizedSequence objects to MonophonicMelody objects. Finally, we want to partition the output into a training and test set. MonophonicMelody objects are fed into RandomPartition, yet another Pipeline which outputs a dictionary of two lists: training output and test output.

All of this is strung together in a DAGPipeline (code is here). First each of the pipelines are intantiated with parameters:

quantizer = pipelines_common.Quantizer(steps_per_quarter=4)
melody_extractor = pipelines_common.MonophonicMelodyExtractor(
    min_bars=7, min_unique_pitches=5,
    gap_bars=1.0, ignore_polyphonic_notes=False)
encoder_pipeline = EncoderPipeline(melody_encoder_decoder)
partitioner = pipelines_common.RandomPartition(
    tf.train.SequenceExample,
    ['eval_melodies', 'training_melodies'],
    [FLAGS.eval_ratio])

Next, the DAG is defined. The DAG is encoded with a Python dictionary that maps each Pipeline instance to run to the pipelines it depends on. More on this below.

dag = {quantizer: Input(music_pb2.NoteSequence),
       melody_extractor: quantizer,
       encoder_pipeline: melody_extractor,
       partitioner: encoder_pipeline,
       Output(): partitioner}

Finally, the composite pipeline is created with a single line of code:

composite_pipeline = DAGPipeline(dag)

Statistics

Statistics are great for collecting information about a dataset, and inspecting why a dataset created by a Pipeline turned out the way it did. Stats collected by Pipelines need to be able to do three things: be copied, be merged together, and print out their information.

A Statistic abstract class is provided. Each Statistic needs to implement the _merge_from method which combines data from another statistic of the same type, the copy method, and the _pretty_print method.

Each Statistic also has a string name which identifies what is being measured. Statistics with the same names get merged downstream.

Two statistic types are defined here: Counter and Histogram.

Counter keeps a count as the name suggests, and has an increment function.

count = Counter('how_many_foo')
count.increment()
count.increment(5)
print str(count)
> how_many_foo: 6

count_2 = Counter('how_many_foo')
count_2.increment()
count.merge_from(count_2)
print str(count)
> how_many_foo: 7

Histogram keeps counts for ranges of values, and also has an increment function.

histogram = Histogram('bar_distribution', [0, 1, 2, 3])
histogram.increment(0.0)
histogram.increment(1.2)
histogram.increment(1.9)
histogram.increment(2.5)
print str(histogram)
> bar_distribution:
>   [0,1): 1
>   [1,2): 2
>   [2,3): 1

histogram_2 = Histogram('bar_distribution', [0, 1, 2, 3])
histogram_2.increment(0.1)
histogram_2.increment(1.0, 3)
histogram.merge_from(histogram_2)
print str(histogram)
> bar_distribution:
>   [0,1): 2
>   [1,2): 5
>   [2,3): 1

When running Pipeline.transform many times, you will likely want to merge the outputs of Pipeline.get_stats into previous statistics. Furthermore, its possible for a Pipeline to produce many unmerged statistics. The merge_statistics method is provided to easily merge any statistics with the same names in a list.

print my_pipeline.name
> "FooBarExtractor"

my_pipeline.transform(...)
stats = my_pipeline.get_stats()
for stat in stats:
  print str(stat)
> FooBarExtractor_how_many_foo: 1
> FooBarExtractor_how_many_foo: 1
> FooBarExtractor_how_many_bar: 1
> FooBarExtractor_how_many_foo: 0

merged = merge_statistics(stats)
for stat in merged:
  print str(stat)
> FooBarExtractor_how_many_foo: 2
> FooBarExtractor_how_many_bar: 1

my_pipeline.transform(...)
stats += my_pipeline.get_stats()
stats = merge_statistics(stats)
for stat in stats:
  print str(stat)
> FooBarExtractor_how_many_foo: 5
> FooBarExtractor_how_many_bar: 2

DAG Specification

DAGPipeline takes a single argument: the DAG encoded as a Python dictionary. The DAG specifies how data will flow via connections between pipelines.

Each (key, value) pair is in this form, destination: dependency.

Remember that the Pipeline object defines input_type and output_type which can be Python classes or dictionaries mapping string names to Python classes. Lets call these the type signature of the pipeline's input and output data. Both the destination and dependency have type signatures, and the rule for the DAG is that every (destination, dependency) has the same type signature.

Specifically, if the destination is a Pipeline, then destination.input_type must have the same type signature as the dependency.

Break down of dependency

The dependency tells DAGPipeline what Pipeline's need to be run before running the destination. Like type signatures, a dependency can be a single object or a dictionary mapping string names to objects. In this case the objects are Pipeline instances (there is also one other allowed type, but more on that below), and not classes.

Input and outputs

Finally, we need to tell DAGPipeline where its inputs go, and which pipelines produce its outputs. This is done with Input and Output objects. Input is given the input type that DAGPipeline will take, like Input(str). Output is given a string name, like Output('some_output'). DAGPipeline always outputs a dictionary, and each Output in the DAG produces another name, output pair in DAGPipeline's output. Currently, only 1 input is supported.

Usage examples

A basic DAG:

print (ToPipeline.output_type, ToPipeline.input_type)
> (OutputType, IntermediateType)

print (FromPipeline.output_type, FromPipeline.input_type)
> (IntermediateType, InputType)

to_pipe = ToPipeline()
from_pipe = FromPipeline()
dag = {from_pipe: Input(InputType),
       to_pipe: from_pipe,
       Output('my_output'): to_pipe}

dag_pipe = DAGPipeline(dag)
print dag_pipe.transform(InputType())
> {'my_output': [<__main__.OutputType object>]}

Using dictionaries:

print (ToPipeline.output_type, ToPipeline.input_type)
> (OutputType, {'intermediate_1': Type1, 'intermediate_2': Type2})

print (FromPipeline.output_type, FromPipeline.input_type)
> ({'intermediate_1': Type1, 'intermediate_2': Type2}, InputType)

to_pipe = ToPipeline()
from_pipe = FromPipeline()
dag = {from_pipe: Input(InputType),
       to_pipe: from_pipe,
       Output('my_output'): to_pipe}

dag_pipe = DAGPipeline(dag)
print dag_pipe.transform(InputType())
> {'my_output': [<__main__.OutputType object>]}

Multiple outputs can be created by connecting a Pipeline with dictionary output to Output() without a name.

print (MyPipeline.output_type, MyPipeline.input_type)
> ({'output_1': Type1, 'output_2': Type2}, InputType)

my_pipeline = MyPipeline()
dag = {my_pipeline: Input(InputType),
       Output(): my_pipeline}

dag_pipe = DAGPipeline(dag)
print dag_pipe.transform(InputType())
> {'output_1': [<__main__.Type1 object>], 'output_2': [<__main__.Type2 object>]}

What if you only want to use one output from a dictionary output, or connect multiple outputs to a dictionary input?

Heres how:

print (FirstPipeline.output_type, FirstPipeline.input_type)
> ({'type_1': Type1, 'type_2': Type2}, InputType)

print (SecondPipeline.output_type, SecondPipeline.input_type)
> (TypeX, Type1)

print (ThirdPipeline.output_type, ThirdPipeline.input_type)
> (TypeY, Type2)

print (LastPipeline.output_type, LastPipeline.input_type)
> (OutputType, {'type_x': TypeX, 'type_y': TypeY})

first_pipe = FirstPipeline()
second_pipe = SecondPipeline()
third_pipe = ThirdPipeline()
last_pipe = LastPipeline()
dag = {first_pipe: Input(InputType),
       second_pipe: first_pipe['type_1'],
       third_pipe: first_pipe['type_2'],
       last_pipe: {'type_x': second_pipe, 'type_y': third_pipe},
       Output('my_output'): last_pipe}

dag_pipe = DAGPipeline(dag)
print dag_pipe.transform(InputType())
> {'my_output': [<__main__.OutputType object>]}

List index syntax and dictionary dependencies can also be combined:

print (FirstPipeline.output_type, FirstPipeline.input_type)
> ({'type_1': TypeA, 'type_2': TypeB}, InputType)

print (LastPipeline.output_type, LastPipeline.input_type)
> (OutputType, {'type_x': TypeB, 'type_y': TypeA})

first_pipe = FirstPipeline()
last_pipe = LastPipeline()
dag = {first_pipe: Input(InputType),
       last_pipe: {'type_x': first_pipe['type_2'], 'type_y': first_pipe['type_1']},
       Output('my_output'): last_pipe}

dag_pipe = DAGPipeline(dag)
print dag_pipe.transform(InputType())
> {'my_output': [<__main__.OutputType object>]}

Note: For pipelines which output more than one thing, not every pipeline output needs to be connected to something as long as at least one output is used.

DAGPipelines will collect the Statistic objects from its Pipeline instances:

print pipeline_1.name
> 'Pipe1'
print pipeline_2.name
> 'Pipe2'

dag = {pipeline_1: Input(pipeline_1.input_type),
       pipeline_2: pipeline_1,
       Output('output'): pipeline_2}
dag_pipe = DAGPipeline(dag)
print dag_pipe.name
> 'DAGPipeline'

dag_pipe.transform(Type1(5))
for stat in dag_pipe.get_stats():
  print str(stat)
> DAGPipeline_Pipe1_how_many_foo: 5
> DAGPipeline_Pipe1_how_many_bar: 2
> DAGPipeline_Pipe2_how_many_bar: 6
> DAGPipeline_Pipe2_foobar: 7

DAGPipeline Exceptions

InvalidDAGException

Thrown when the DAG dictionary is not well formatted. This can be because a destination: dependency pair is not in the form Pipeline: Pipeline or Pipeline: {'name_1': Pipeline, ...} (Note that Pipeline or Key objects both are allowed in the dependency). It is also thrown when Input is given as a destination, or Output is given as a dependency.

DuplicateNameException

Thrown when two Pipeline instances in the DAG have the same name. Pipeline names will be used as name spaces for the statistics they produce and we don't want any conflicts.

No exception:

print pipeline_1.name
> 'my_name'

print pipeline_2.name
> 'hello'

dag = {pipeline_1: ...,
       pipeline_2: ...}
DAGPipeline(dag)

DuplicateNameException thrown:

print pipeline_1.name
> 'my_name'

print pipeline_2.name
> 'my_name'

dag = {pipeline_1: ...,
       pipeline_2: ...}
DAGPipeline(dag)

TypeMismatchException

Thrown when destination type signature doesn't match dependency type signature.

TypeMismatchException is thrown in these examples:

print DestPipeline.input_type
> Type1

print SrcPipeline.output_type
> Type2

dag = {DestPipeline(): SrcPipeline(), ...}
DAGPipeline(dag)

print DestPipeline.input_type
> {'name_1': Type1}

print SrcPipeline.output_type
> {'name_1': Type2}

dag = {DestPipeline(): SrcPipeline(), ...}
DAGPipeline(dag)

print DestPipeline.input_type
> {'name_1': MyType}

print SrcPipeline.output_type
> {'name_2': MyType}

dag = {DestPipeline(): SrcPipeline(), ...}
DAGPipeline(dag)

BadTopologyException

Thrown when there is a directed cycle.

BadTopologyException is thrown in these examples:

pipe1 = MyPipeline1()
pipe2 = MyPipeline2()
dag = {Output('output'): Input(MyType),
       pipe2: pipe1,
       pipe1: pipe2}
DAGPipeline(dag)

pipe1 = MyPipeline1()
pipe2 = MyPipeline2()
pipe3 = MyPipeline3()
dag = {pipe1: Input(pipe1.input_type),
       pipe2: {'a': pipe1, 'b': pipe3},
       pipe3: pipe2,
       Output(): {'pipe2': pipe2, 'pipe3': pipe3}}
DAGPipeline(dag)

NotConnectedException

Thrown when the DAG is disconnected somewhere. Either because a Pipeline used in a dependency has nothing feeding into it, or because a Pipeline given as a destination does not feed anywhere.

NotConnectedException is thrown in these examples:

pipe1 = MyPipeline1()
pipe2 = MyPipeline2()
dag = {pipe1: Input(pipe1.input_type),
       Output('output'): pipe2}
DAGPipeline(dag)

pipe1 = MyPipeline1()
pipe2 = MyPipeline2()
dag = {pipe1: Input(pipe1.input_type),
       Output(): {'pipe1': pipe1, 'pipe2': pipe2}}
DAGPipeline(dag)

pipe1 = MyPipeline1()
pipe2 = MyPipeline2()
dag = {pipe1: Input(pipe1.input_type),
       pipe2: pipe1,
       Output('pipe1'): pipe1}
DAGPipeline(dag)

BadInputOrOutputException

Thrown when Input or Output are not used in the graph correctly. Specifically when there are no Input objects, more than one Input with different types, or there is no Output object.

BadInputOrOutputException is thrown in these examples:

pipe1 = MyPipeline1()
dag = {pipe1: Input(pipe1.input_type)}
DAGPipeline(dag)

dag = {Output('output'): pipe1}
DAGPipeline(dag)

pipe2 = MyPipeline2()
dag = {pipe1: Input(Type1),
       pipe2: Input(Type2),
       Output(): {'pipe1': pipe1, 'pipe2': pipe2}}
DAGPipeline(dag)

Having multiple Input instances with the same type is allowed.

This example will not throw an exception:

pipeA = MyPipelineA()
pipeB = MyPipelineB()
dag = {pipeA: Input(MyType),
       pipeB: Input(MyType),
       Output(): {'pipeA': pipeA, 'pipeB': pipeB}}
DAGPipeline(dag)

InvalidStatisticsException

Thrown when a Pipeline in the DAG returns a value from its get_stats method which is not a list of Statistic instances.

Valid statistics:

print my_pipeline.get_stats()
> [<Counter object>, <Histogram object>]

Invalid statistics:

print my_pipeline_1.get_stats()
> ['hello', <Histogram object>]

print my_pipeline_2.get_stats()
> <Counter object>

InvalidDictionaryOutput

Thrown when Output and dictionaries are not used correctly. Specifically when Output() is used without a dictionary dependency, or Output(name) is used with a name and with a dictionary dependency.

InvalidDictionaryOutput is thrown in these examples:

pipe1 = MyPipeline1()
pipe2 = MyPipeline2()
dag = {Output('output'): {'pipe1': pipe1, 'pipe2': pipe2}, ...}
DAGPipeline(dag)

print MyPipeline.output_type
> MyType

pipe = MyPipeline()
dag = {Output(): pipe, ...}
DAGPipeline(dag)

InvalidTransformOutputException

Thrown when a Pipeline in the DAG does not output the type(s) it promised to output in output_type.

This Pipeline would cause InvalidTransformOutputException to be thrown if it was passed into a DAGPipeline. It would also cause problems in general, and should be fixed no matter where it is used.

print MyPipeline.output_type
> TypeA

print MyPipeline.transform(TypeA())
> [<__main__.TypeB object>]