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cuML Python Estimators Developer Guide

This guide is meant to help developers follow the correct patterns when creating/modifying any cuML Estimator object and ensure a uniform cuML API.

Note: This guide is long, because it includes internal details on how cuML manages input and output types for advanced use cases. But for the vast majority of estimators, the requirements are very simple and can follow the example patterns shown below in the Quick Start Guide.

Table of Contents

Recommended Scikit-Learn Documentation

To start, it's recommended to read the following Scikit-learn documentation:

  1. Scikit-learn's Estimator Docs
    1. cuML Estimator design follows Scikit-learn very closely. We will only cover portions where our design differs from this document
    2. If short on time, pay attention to these sections, which are the most important (and have caused pain points in the past):
      1. Instantiation
      2. Estimated Attributes
      3. get_params and set_params
      4. Cloning
      5. Estimator tags
  2. Scikit-learn's Docstring Guide
    1. We follow the same guidelines for specifying array-like objects, array shapes, dtypes, and default values

Quick Start Guide

At a high level, all cuML Estimators must:

  1. Inherit from cuml.common.base.Base

    from cuml.common.base import Base

class MyEstimator(Base):
   ...

  2. Follow the Scikit-learn estimator guidelines found here

  3. Include the Base.__init__() arguments available in the new Estimator's __init__()

    class MyEstimator(Base):

   def __init__(self, *, extra_arg=True, handle=None, verbose=False, output_type=None):
      super().__init__(handle=handle, verbose=verbose, output_type=output_type)
      ...

  4. Declare each array-like attribute the new Estimator will compute as a class variable for automatic array type conversion

    from cuml.common.array_descriptor import CumlArrayDescriptor

class MyEstimator(Base):

   labels_ = CumlArrayDescriptor()

   def __init__(self):
      ...

  5. Add input and return type annotations to public API functions OR wrap those functions explicitly with conversion decorators (see this example for a non-standard use case)

    class MyEstimator(Base):

   def fit(self, X) -> "MyEstimator":
      ...

   def predict(self, X) -> CumlArray:
      ...

  6. Implement get_param_names() including values returned by super().get_param_names()

       def get_param_names(self):
      return super().get_param_names() + [
         "eps",
         "min_samples",
      ]

  7. Implement the appropriate tags method if any of the default tags need to be overriden for the new estimator. There are some convenience Mixins, that the estimator can inherit, can be used for indicating the preferred order (column or row major) as well as for sparse input capability.

If other tags are needed, they are static (i.e. don't change depending on the instantiated estimator), and more than one estimator will use them, then implement a new Mixin, if the tag will be used by a single class then implement the _more_static_tags method:

 @staticmethod
 def _more_static_tags():
    return {
         "requires_y": True
    }

If the tags depend on an attribute that is defined at runtime or instantiation of the estimator, then implement the _more_tags method:

   def _more_tags(self):
        return {
            "allow_nan": is_scalar_nan(self.missing_values)
         }

For the majority of estimators, the above steps will be sufficient to correctly work with the cuML library and ensure a consistent API. However, situations may arise where an estimator differs from the standard pattern and some of the functionality needs to be customized. The remainder of this guide takes a deep dive into the estimator functionality to assist developers when building estimators.

Background

Some background is necessary to understand the design of estimators and how to work around any non-standard situations.

Input and Output Types in cuML

In cuML we support both ingesting and generating a variety of different object types. Estimators should be able to accept and return any array type. The types that are supported as of release 0.17:

  • cuDF DataFrame or Series
  • Pandas DataFrame or Series
  • NumPy Arrays
  • Numba Device Arrays
  • CuPy arrays
  • CumlArray type (Internal to the cuml API only.)

When converting between types, it's important to minimize the CPU<->GPU type conversions as much as possible. Conversions such as NumPy -> CuPy or Numba -> Pandas DataFrame will incur a performance penalty as memory is copied from device to host or vice-versa.

Converting between types of the same device, i.e. CPU<->CPU or GPU<->GPU, do not have as significant of a penalty, though they may still increase memory usage (this is particularly true for the array <-> dataframe conversion. i.e. when converting from CuPy to cuDF, memory usage may increase slightly).

Finally, conversions between Numba<->CuPy<->CumlArray incur the least amount of overhead since only the device pointer is moved from one class to another.

Internally, all arrays should be converted to CumlArray as much as possible since it is compatible with all output types and can be easily converted.

Specifying the Array Output Type

Users can choose which array type should be returned by cuml by either:

  1. Individually setting the output_type property on an estimator class (i.e Base(output_type="numpy"))
  2. Globally setting the cuml.global_output_type
  3. Temporarily setting the cuml.global_output_type via the cuml.using_output_type context manager

Note: Setting cuml.global_output_type (either directly or via cuml.set_output_type() or cuml.using_output_type()) will take precedence over any value in Base.output_type

Changing the array output type will alter the return value of estimator functions (i.e. predict(), transform()), and the return value for array-like estimator attributes (i.e. my_estimator.classes_ or my_estimator.coef_)

All output_types (including cuml.global_output_type) are specified using an all lowercase string. These strings can be passed in an estimators constructor or via cuml.set_global_output_type and cuml.using_output_type. Accepted values are:

  • None: (Default) No global value set. Will use individual values from estimators output_type
  • "input": Similar to None. Will mirror the same type as any array passed into the estimator
  • "numba": Returns Numba Device Arrays
  • "numpy": Returns Numpy Arrays
  • "cudf": Returns cuDF DataFrame if cols > 1, else cuDF Series
  • "cupy": Returns CuPy Device Arrays

Note: There is an additional option "mirror" which can only be set by internal API calls and is not user accessible. This value is only used internally by the CumlArrayDescriptor to mirror any input value set.

Ingesting Arrays

When the input array type isn't known, the correct and safest way to ingest arrays is using cuml.common.input_to_cuml_array. This method can handle all supported types, is capable of checking the array order, can enforce a specific dtype, and can raise errors on incorrect array sizes:

def fit(self, X):
    cuml_array, dtype, cols, rows = input_to_cuml_array(X, order="K")
    ...

Returning Arrays

The CumlArray class can convert to any supported array type using the to_output(output_type: str) method. However, doing this explicitly is almost never needed in practice and should be avoided. Directly converting arrays with to_output() will circumvent the automatic conversion system potentially causing extra or incorrect array conversions.

Estimator Design

All estimators (any class that is a child of cuml.common.base.Base) have a similar structure. In addition to the guidelines specified in the SkLearn Estimator Docs, cuML implements a few additional rules.

Initialization

All estimators should match the arguments (including the default value) in Base.__init__ and pass these values to super().__init__(). As of 0.17, all estimators should accept handle, verbose and output_type.

In addition, is recommended to force keyword arguments to prevent breaking changes if arguments are added or removed in future versions. For example, all arguments below after * must be passed by keyword:

def __init__(self, *, eps=0.5, min_samples=5, max_mbytes_per_batch=None,
             calc_core_sample_indices=True, handle=None, verbose=False, output_type=None):

Finally, do not alter any input arguments - if you do, it will prevent proper cloning of the estimator. See Scikit-learn's section on instantiation for more info.

For example, the following __init__ shows what NOT to do:

def __init__(self, my_option="option1"):
   if (my_option == "option1"):
      self.my_option = 1
   else:
      self.my_option = 2

This will break cloning since the value of self.my_option is not a valid input to __init__. Instead, my_option should be saved as an attribute as-is.

Implementing get_param_names()

To support cloning, estimators need to implement the function get_param_names(). The returned value should be a list of strings of all estimator attributes that are necessary to duplicate the estimator. This method is used in Base.get_params() which will collect the collect the estimator param values from this list and pass this dictionary to a new estimator constructor. Therefore, all strings returned by get_param_names() should be arguments in __init__() otherwise an invalid argument exception will be raised. Most estimators implement get_param_names() similar to:

def get_param_names(self):
   return super().get_param_names() + [
      "eps",
      "min_samples",
   ]

Note: Be sure to include super().get_param_names() in the returned list to properly set the super() attributes.

Estimator Tags and cuML-Specific Tags

Scikit-learn introduced estimator tags in version 0.21, which are used to programmatically inspect the capabilities of estimators. These capabilities include items like sparse matrix support and the need for positive inputs, among other things. cuML estimators support all of the tags defined by the Scikit-learn estimator developer guide, and will add support for any tag added there.

Additionaly, some tags specific to cuML have been added. These tags may or may not be specific to GPU data types and can even apply outside of automated testing, such as allowing for the optimization of data generation. This can be useful for pipelines and HPO, among other things. These are:

  • X_types_gpu (default=['2darray']) Analogous to X_types, indicates what types of GPU objects an estimator can take. 2darray includes GPU ndarray objects (like CuPy and Numba) and cuDF objects, since they are all processed the same by input_utils. sparse includes CuPy sparse arrays.
  • preferred_input_order (default=None) One of ['F', 'C', None]. Whether an estimator "prefers" data in column-major ('F') or row-major ('C') contiguous memory layout. If different methods prefer different layouts or neither format is benefitial, then it is defined to None unless there is a good reason to chose either F or C. For example, all of fit, predict, etc. in an estimator use F but only score usesC.
  • dynamic_tags (default=False) Most estimators only need to define the tags statically, which facilitates the usage of tags in general. But some estimators might need to modify the values of a tag based on runtime attributes, so this tag reflects whether an estimator needs to do that. This tag value is automatically set by the Base estimator class if an Estimator has defined the _more_tags instance method.

Note on MRO and tags: Tag resolution makes it so that multiple classes define the same tag in a composed class, classes closer to the final class overwrite the values of the farther ones. In Python, the MRO resolution makes it so that the uppermost classes are closer to the inheritting class, for example:

Class:

class DBSCAN(Base,
             ClusterMixin,
             CMajorInputTagMixin):

MRO:

>>> cuml.DBSCAN.__mro__
(<class 'cuml.cluster.dbscan.DBSCAN'>, <class 'cuml.common.base.Base'>, <class 'cuml.common.mixins.TagsMixin'>, <class 'cuml.common.mixins.ClusterMixin'>, <class 'cuml.common.mixins.CMajorInputTagMixin'>, <class 'object'>)

So this needs to be taken into account for tag resolution, for the case above, the tags in ClusterMixin would overwrite tags of CMajorInputTagMixin if they defined the same tags. So take this into consideration for the (uncommon) cases where there might be tags re-defined in your MRO. This is not common since most tag mixins define mutually exclusive tags (i.e. either prefer F or C major inputs).

Estimator Array-Like Attributes

Any array-like attribute stored in an estimator needs to be convertible to the user's desired output type. To make it easier to store array-like objects in a class that derives from Base, the cuml.common.array_descriptor.CumlArrayDescriptor was created. The CumlArrayDescriptor class is a Python descriptor object which allows cuML to implement customized attribute lookup, storage and deletion code that can be reused on all estimators.

The CumlArrayDescriptor behaves different when accessed internally (from within one of cuml's functions) vs. externally (for user code outside the cuml module). Internally, it behaves exactly like a normal attribute and will return the previous value set. Externally, the array will get converted to the user's desired output type lazily and repeated conversion will be cached.

Performing the arrray conversion lazily (i.e. converting the input array to the desired output type, only when the attribute it read from for the first time) can greatly help reduce memory consumption, but can have unintended impacts the developers should be aware of. For example, benchmarking should take into account the lazy evaluation and ensure the array conversion is included in any profiling.

Defining Array-Like Attributes

To use the CumlArrayDescriptor in an estimator, any array-like attributes need to be specified by creating a CumlArrayDescriptor as a class variable.

from cuml.common.array_descriptor import CumlArrayDescriptor

class TestEstimator(cuml.Base):

   # Class variables outside of any function
   my_cuml_array_ = CumlArrayDescriptor()

   def __init__(self, ...):
      ...

This gives the developer full control over which attributes are arrays and the name for the array-like attribute (something that was not true before 0.17).

Working with CumlArrayDescriptor

Once an CumlArrayDescriptor attribute has been defined, developers can use the attribute as they normally would. Consider the following example estimator:

import cupy as cp
import cuml
from cuml.common.array_descriptor import CumlArrayDescriptor

class SampleEstimator(cuml.Base):

   # Class variables outside of any function
   my_cuml_array_ = CumlArrayDescriptor()
   my_cupy_array_ = CumlArrayDescriptor()
   my_other_array_ = CumlArrayDescriptor()

   def __init__(self, ...):

      # Initialize to None (not mandatory)
      self.my_cuml_array_ = None

      # Init with a cupy array
      self.my_cupy_array_ = cp.zeros((10, 10))

   def fit(self, X):
      # Stores the type of `X` and sets the output type if self.output_type == "input"
      self._set_output_type(X)

      # Set my_cuml_array_ with a CumlArray
      self.my_cuml_array_, *_ = input_to_cuml_array(X, order="K")

      # Access `my_cupy_array_` normally and set to another attribute
      # The internal type of my_other_array_ will be a CuPy array
      self.my_other_array_ = cp.ones((10, 10)) + self.my_cupy_array_

      return self

Just like any normal attribute, CumlArrayDescriptor attributes will return the same value that was set into the attribute unless accessed externally (more on that below). However, developers can convert the type of an array-like attribute by using the cuml.global_output_type functionality and reading from the attribute. For example, we could add a score() function to TestEstimator:

def score(self):

   # Set the global output type to numpy
   with cuml.using_output_type("numpy"):
      # Accessing my_cuml_array_ will return a numpy array and
      # the result can be returned directly
      return np.sum(self.my_cuml_array_, axis=0)

This has the same benefits of lazy conversion and caching as when descriptors are used externally.

CumlArrayDescriptor External Functionality

Externally, when users read from a CumlArrayDescriptor attribute, the array data will be converted to the correct output type lazily when the attribute is read from. For example, building off the above TestEstimator:

my_est = SampleEstimator()

# Print the default output_type and value for `my_cuml_array_`
# By default, `output_type` is set to `cuml.global_output_type`
# If `cuml.global_output_type == None`, `output_type` is set to "input"
print(my_est.output_type) # Output: "input"
print(my_est.my_cuml_array_) # Output: None
print(my_est.my_other_array_) # Output: AttributeError! my_other_array_ was never set

# Call fit() with a numpy array as the input
np_arr = np.ones((10,))
my_est.fit(np_arr) # This will load data into attributes

# `my_cuml_array_` was set internally as a CumlArray. Externally, we can check the type
print(type(my_est.my_cuml_array_)) # Output: Numpy (saved from the input of `fit`)

# Calling fit again with cupy arrays, will have a similar effect
my_est.fit(cp.ones((10,)))
print(type(my_est.my_cuml_array_)) # Output: CuPy

# Setting the `output_type` will change all descriptor properties
# and ignore the input type
my_est.output_type = "cudf"

# Reading any of the attributes will convert the type lazily
print(type(my_est.my_cuml_array_)) # Output: cuDF object

# Setting the global_output_type, overrides the estimator output_type attribute
with cuml.using_output_type("cupy"):
  print(type(my_est.my_cuml_array_)) # Output: cupy

# Once the global_output_type is restored, we return to the estimator output_type
print(type(my_est.my_cuml_array_)) # Output: cuDF. Using a cached value!

For more information about CumlArrayDescriptor and it's implementation, see the CumlArrayDescriptor Details section of the Appendix.

Estimator Methods

To allow estimator methods to accept a wide variety of inputs and outputs, a set of decorators have been created to wrap estimator functions (and all cuml API functions as well) and perform the standard conversions automatically. cuML provides 2 options to for performing the standard array type conversions:

  1. For many common patterns used in functions like fit(), predict(), transform(), cuml.Base can automatically perform the data conversions as long as a method has the necessary type annotations.
  2. Decorators can be manually added to methods to handle more advanced use cases

Option 1: Automatic Array Conversion From Type Annotation

To automatically convert array-like objects being returned by an Estimator method, a new metaclass has been added to Base that can scan the return type information of an Estimator method and infer which, if any, array conversion should be done. For example, if a method returns a type of Base, cuML can assume this method is likely similar to fit() and should call Base._set_base_attributes() before calling the method. If a method returns a type of CumlArray, cuML can assume this method is similar to predict() or transform(), and the return value is an array that may need to be converted using the output type calculated in Base._get_output_type().

The full set of return types rules that will be applied by the Base metaclass are:

Return Type Converts Array Type? Common Methods Notes
Base No fit() Any type that inherits or isinstance of Base will work
CumlArray Yes predict(), transform() Functions can return any array-like object (np.ndarray, cp.ndarray, etc. all accepted)
SparseCumlArray Yes predict(), transform() Functions can return any sparse array-like object (scipy, cupyx.scipy sparse arrays accepted)
dict, tuple, list or typing.Union Yes Functions must return a generic object that contains an array-like object. No sparse arrays are supported

Simply setting the return type of a method is all that is necessary to automatically convert the return type (with the added benefit of adding more information to the code). Below are some examples to show simple methods using automatic array conversion.

fit()
def fit(self, X) -> "KMeans":

   # Convert the input to CumlArray
   self.coef_ = input_to_cuml_array(X, order="K").array

   return self

Notes:

  • Any type that derives from Base can be used as the return type for fit(). In python, to indicate returning self from a function, class type can be surrounded in quotes to prevent an import error.
predict()
def predict(self, X) -> CumlArray:
   # Convert to CumlArray
   X_m = input_to_cuml_array(X, order="K").array

   # Call a cuda function
   X_m = cp.asarray(X_m) + cp.ones(X_m.shape)

   # Directly return a cupy array
   return X_m

Notes:

  • It's not necessary to convert to CumlArray and cast with to_output before returning. This function directly returned a cp.ndarray object. Any array-like object can be returned.

Option 2: Manual Estimator Method Decoration

While the automatic converions from type annotations works for many estimator functions, sometimes its necessary to explicitly decorate an estimator method. This allows developers greater flexibility over the input argument, output type and output dtype.

Which decorator to use for an estimator function is determined by 2 factors:

  1. Function return type
  2. Whether the function is on a class deriving from Base

The full set of descriptors can be organized by these two factors:

Return Type-> Array-Like Sparse Array-Like Generic Any
Base @api_base_return_array @api_base_return_sparse_array @api_base_return_generic @api_base_return_any
Non-Base @api_return_array @api_return_sparse_array @api_return_generic @api_return_any

Simply choosing the decorator based off the return type and if the function is on Base will work most of the time. The decorator default options were designed to work on most estimator functions without much customization.

An in-depth discussion of how these decorators work, when each should be used, and their default options can be found in the Appendix. For now, we will show an example method that uses a non-standard input argument name, and also requires converting the array dtype:

Non-Standard predict()
@cuml.internals.api_base_return_array(input_arg="X_in", get_output_dtype=True)
def predict(self, X):
   # Convert to CumlArray
   X_m = input_to_cuml_array(X, order="K").array

   # Call a cuda function
   X_m = cp.asarray(X_m) + cp.ones(X_m.shape)

   # Return the cupy array directly
   return X_m

Notes:

  • The decorator argument input_arg can be used to specify which input should be considered the "input".
    • In reality, this isn't necessary for this example. The decorator will look for an argument named "X" or default to the first, non self, argument.
  • It's not necessary to convert to CumlArray and casting with to_output before returning. This function directly returned a cp.ndarray object. Any array-like object can be returned.
  • Specifying get_output_dtype=True in the decorator argument instructs the decorator to also calculate the dtype in addition to the output type.

Do's And Do Not's

Do: Add Return Typing Information to Estimator Functions

Adding the return type to estimator functions will allow the Base meta-class to automatically decorate functions based on their return type.

Do this:

def fit(self, X, y, convert_dtype=True) -> "KNeighborsRegressor":
def predict(self, X, convert_dtype=True) -> CumlArray:
def kneighbors_graph(self, X=None, n_neighbors=None, mode='connectivity') -> SparseCumlArray:
def predict(self, start=0, end=None, level=None) -> typing.Union[CumlArray, float]:

Not this:

def fit(self, X, y, convert_dtype=True):
def predict(self, X, convert_dtype=True):
def kneighbors_graph(self, X=None, n_neighbors=None, mode='connectivity'):
def predict(self, start=0, end=None, level=None):

Do: Return Array-Like Objects Directly

There is no need to convert the array type before returning it. Simply return any array-like object and it will be automatically converted

Do this:

def predict(self) -> CumlArray:
   cp_arr = cp.ones((10,))

   return cp_arr

Not this:

def predict(self, X, y) -> CumlArray:
   cp_arr = cp.ones((10,))

   # Don't be tempted to use `CumlArray(cp_arr)` here either
   cuml_arr = input_to_cuml_array(cp_arr, order="K").array

   return cuml_arr.to_output(self._get_output_type(X))

Don't: Use CumlArray.to_output() directly

Using CumlArray.to_output() is no longer necessary except in very rare circumstances. Converting array types is best handled with input_to_cuml_array or cuml.using_output_type() when retrieving CumlArrayDescriptor values.

Do this:

def _private_func(self) -> CumlArray:
   return cp.ones((10,))

def predict(self, X, y) -> CumlArray:

   self.my_cupy_attribute_ = cp.zeros((10,))

   with cuml.using_output_type("numpy"):
      np_arr = self._private_func()

      return self.my_cupy_attribute_ + np_arr

Not this:

def _private_func(self) -> CumlArray:
   return cp.ones((10,))

def predict(self, X, y) -> CumlArray:

   self.my_cupy_attribute_ = cp.zeros((10,))

   np_arr = CumlArray(self._private_func()).to_output("numpy")

   return CumlArray(self.my_cupy_attribute_).to_output("numpy") + np_arr

Don't: Perform parameter modification in __init__()

Input arguments to __init__() should be stored as they were passed in. Parameter modification, such as converting parameter strings to integers, should be done in fit() or a helper private function.

While it's more verbose, altering the parameters in __init__ will break the estimator's ability to be used in clone().

Do this:

class TestEstimator(cuml.Base):

   def __init__(self, method_name: str, ...):
      super().__init__(...)

      self.method_name = method_name

   def _method_int(self) -> int:
      return 1 if self.method_name == "type1" else 0

   def fit(self, X) -> "TestEstimator":

      # Call external code from Cython
      my_external_func(X.ptr, <int>self._method_int())

      return self

Not this:

class TestEstimator(cuml.Base):

   def __init__(self, method_name: str, ...):
      super().__init__(...)

      self.method_name = 1 if method_name == "type1" else 0

   def fit(self, X) -> "TestEstimator":

      # Call external code from Cython
      my_external_func(X.ptr, <int>self.method_name)

      return self

Appendix

This section contains more in-depth information about the decorators and descriptors to help developers understand whats going on behind the scenes

Estimator Array-Like Attributes

Automatic Decoration Rules

Adding decorators to every estimator function just to use the decorator default values would be very repetitive and unnecessary. Because most of estimator functions follow a similar pattern, a new meta-class has been created to automatically decorate estimator functions based off their return type. This meta class will decorate functions according to a few rules:

  1. If a functions has been manually decorated, it will not be automatically decorated
  2. If an estimator function returns an instance of Base, then @api_base_return_any() will be applied.
  3. If an estimator function returns a CumlArray, then @api_base_return_array() will be applied.
  4. If an estimator function returns a SparseCumlArray, then @api_base_return_sparse_array() will be applied.
  5. If an estimator function returns a dict, tuple, list or typing.Union, then @api_base_return_generic() will be applied.
Return Type Decorator Notes
Base @api_base_return_any(set_output_type=True, set_n_features_in=True) Any type that isinstance of Base will work
CumlArray @api_base_return_array(get_output_type=True) Functions can return any array-like object
SparseCumlArray @api_base_return_sparse_array(get_output_type=True) Functions can return any sparse array-like object
dict, tuple, list or typing.Union @api_base_return_generic(get_output_type=True) Functions must return a generic object that contains an array-like object. No sparse arrays are supported

CumlArrayDescriptor Internals

The internal representation of CumlArrayDescriptor is a CumlArrayDescriptorMeta object. To inspect the internal representation, the attribute value must be directly accessed from the estimator's __dict__ (getattr and __getattr__ will perform the conversion). For example:

my_est = TestEstimator()
my_est.fit(cp.ones((10,)))

# Access the CumlArrayDescriptorMeta value directly. No array conversion will occur
print(my_est.__dict__["my_cuml_array_"])
# Output: CumlArrayDescriptorMeta(input_type='cupy', values={'cuml': <cuml.common.array.CumlArray object at 0x7fd39174ae20>, 'numpy': array([ 0,  1,  1,  2,  2, -1, -1, ...

# Values from CumlArrayDescriptorMeta can be specifically read
print(my_est.__dict__["my_cuml_array_"].input_type)
# Output: "cupy"

# The input value can be accessed
print(my_est.__dict__["my_cuml_array_"].get_input_value())
# Output: CumlArray ...

Estimator Methods

Common Functionality

All of these decorators perform the same basic steps with a few small differences. The common steps performed by each decorator is:

  1. Set cuml.global_output_type = "mirror"
    1. When "mirror" is used as the global output type, that indicates we are in an internal cuML API call. The CumlArrayDescriptor keys off this value to change between internal and external functionality
  2. Set CuPy allocator to use RMM
    1. This replaces the existing decorator @with_cupy_rmm
    2. Unlike before, the CuPy allocator is only set once per API call
  3. Set the estimator input attributes. Can be broken down into 3 steps:
    1. Set _input_type attribute
    2. Set target_dtype attribute
    3. Set n_features attribute
  4. Call the desired function
  5. Get the estimator output type. Can be broken down into 2 steps:
    1. Get output_type
    2. Get output_dtype
  6. Convert the return value
    1. This will ultimately call `CumlArray.to_output(output_type=output_type, output_dtype=output_dtype)

While the above list of steps may seem excessive for every call, most functions follow this general form, but may skip a few steps depending on a couple of factors. For example, Step #3 is necessary on functions that modify the estimator's estimated attributes, such as fit(), but is not necessary for functions like predict() or transform(). And Step #5/6 are only necessary when returning array-like objects and are omitted when returning any other type.

Functionally, you can think of these decorators equivalent to the following pseudocode:

def my_func(self, X):
   with cuml.using_ouput_type("mirror"):
      with cupy.cuda.cupy_using_allocator(rmm.rmm_cupy_allocator):
         # Set the input properties
         self._set_base_attributes(output_type=X, n_features=X)

         # Do actual calculation returning an array-like object
         ret_val = self._my_func(X)

         # Get the output type
         output_type = self._get_output_type(X)

         # Convert array-like to CumlArray
         ret_val = input_to_cuml_array(ret_val, order="K").array

         # Convert CumlArray to desired output_type
         return ret_val.to_output(output_type)

Keep the above pseudocode in mind when working with these decorators since their goal is to replace many of these repetitive functions.

Decorator Defaults

Every function in cuml is slightly different and some fit() functions may need to set the target_dtype or some predict() functions may need to skip getting the output type. To handle these situations, all of the decorators take arguments to configure their functionality.

Since the decorator's functionality is very similar, so are their arguments. All of the decorators take similar arguments that will be outlined below.

Argument Type Default Meaning
input_arg str 'X' or 1st non-self argument Determines which input argument to use for _set_output_type() and _set_n_features_in()
target_arg str 'y' or 2nd non-self argument Determines which input argument to use for _set_target_dtype()
set_output_type bool Varies Whether to call _set_output_type(input_arg)
set_output_dtype bool False Whether to call _set_target_dtype(target_arg)
set_n_features_in bool Varies Whether to call _set_n_features_in(input_arg)
get_output_type bool Varies Whether to call _get_output_type(input_arg)
get_output_dtype bool False Whether to call _get_target_dtype()

An example of how these arguments can be used is below:

Before:

@with_cupy_rmm
def predict(self, X, y):
   # Determine the output type and dtype
   out_type = self._get_output_type(y)
   out_dtype = self._get_target_dtype()

   # Convert to CumlArray
   X_m = input_to_cuml_array(X, order="K").array

   # Call a cuda function
   someCudaFunction(X_m.ptr)

   # Convert the CudaArray to the desired output
   return X_m.to_output(output_type=out_type, output_dtype=out_dtype)

After:

@cuml.internals.api_base_return_array(input_arg="y", get_output_dtype=True)
def predict(self, X):
   # Convert to CumlArray
   X_m = input_to_cuml_array(X, order="K").array

   # Call a cuda function
   someCudaFunction(X_m.ptr)

   # Convert the CudaArray to the desired output
   return X_m

Before 0.17 and After Comparison

For developers used to the 0.16 architecture it can be helpful to see examples of estimator methods from 0.16 compared to 0.17 and after. This section shows a few examples side-by-side to illustrate the changes.

fit()
Before After 
@with_cupy_rmm
def fit(self, X):
   # Set the base input attributes
   self._set_base_attributes(output_type=X, n_features=X)

   self.coef_ = input_to_cuml_array(X, order="K").array

   return self
 
def fit(self, X) -> "KMeans":



   self.coef_ = input_to_cuml_array(X, order="K").array

   return self

Notes:

  • @with_cupy_rmm is no longer needed. This is automatically applied for every public method of estimators
  • self._set_base_attributes() no longer needs to be called.
predict()
Before After 
@with_cupy_rmm
def predict(self, X, y):
   # Determine the output type and dtype
   out_type = self._get_output_type(y)

   # Convert to CumlArray
   X_m = input_to_cuml_array(X, order="K").array

   # Do some calculation with cupy
   X_m = cp.asarray(X_m) + cp.ones(X_m.shape)

   # Convert back to CumlArray
   X_m = CumlArray(X_m)

   # Convert the CudaArray to the desired output
   return X_m.to_output(output_type=out_type)
 
def predict(self, X) -> CumlArray:



   # Convert to CumlArray
   X_m = input_to_cuml_array(X, order="K").array

   # Call a cuda function
   X_m = cp.asarray(X_m) + cp.ones(X_m.shape)




   # Directly return a cupy array
   return X_m

Notes:

  • @with_cupy_rmm is no longer needed. This is automatically applied for every public method of estimators
  • self._get_output_type() no longer needs to be called. The output type is determined automatically
  • Its not necessary to convert to CumlArray and casting with to_output before returning. This function directly returned a cp.ndarray object. Any array-like object can be returned.
predict() with dtype
Before After 
@with_cupy_rmm
def predict(self, X_in):
   # Determine the output_type
   out_type = self._get_output_type(X_in)
   out_dtype = self._get_target_dtype()

   # Convert to CumlArray
   X_m = input_to_cuml_array(X_in, order="K").array

   # Call a cuda function
   X_m = cp.asarray(X_m) + cp.ones(X_m.shape)

   # Convert back to CumlArray
   X_m = CumlArray(X_m)

   # Convert the CudaArray to the desired output and dtype
   return X_m.to_output(output_type=out_type, output_dtype=out_dtype)
 
@api_base_return_array(input_arg="X_in", get_output_dtype=True)
def predict(self, X):




   # Convert to CumlArray
   X_m = input_to_cuml_array(X, order="K").array

   # Call a cuda function
   X_m = cp.asarray(X_m) + cp.ones(X_m.shape)




   # Return the cupy array directly
   return X_m

Notes:

  • @with_cupy_rmm is no longer needed. This is automatically applied with every decorator
  • The decorator argument input_arg can be used to specify which input should be considered the "input".
    • In reality, this isn't necessary for this example. The decorator will look for an argument named "X" or default to the first, non self, argument.
  • self._get_output_type() and self._get_target_dtype() no longer needs to be called. Both the output type and dtype are determined automatically
  • It's not necessary to convert to CumlArray and casting with to_output before returning. This function directly returned a cp.ndarray object. Any array-like object can be returned.
  • Specifying get_output_dtype=True in the decorator argument instructs the decorator to also calculate the dtype in addition to the output type.