cudf.pandas#

The use of the cuDF pandas accelerator mode (cudf.pandas) is explained in the user guide. The purpose of this document is to explain how the fast-slow proxy mechanism works and document internal environment variables that can be used to debug cudf.pandas itself.

fast-slow proxy mechanism#

The core of cudf.pandas is implemented through proxy types defined in fast_slow_proxy.py, which link a pair of “fast” and “slow” libraries. cudf.pandas works by wrapping each “slow” type and its corresponding “fast” type in a new proxy type, also known as a fast-slow proxy type. The purpose of these proxy types is so we can first attempt computations on the fast object, and then fall back to the slow object if the fast version fails. While the core wrapping functionality is generic, the current usage mainly involves providing a proxy pair using cuDF and Pandas. In the rest of this document, to maintain a concrete pair of libraries in mind, we use cuDF and Pandas interchangeably as names for the “fast” and “slow” libraries, respectively, with the understanding that any pair of API-matching libraries could be used. For example, future support could include pairs such as CuPy (as the “fast” library) and NumPy (as the “slow” library).

Note

We currently do not wrap the entire NumPy library because it exposes a C API. But we do wrap NumPy’s numpy.ndarray and CuPy’s cupy.ndarray in a proxy type.

Types:#

Wrapped Types and Proxy Types#

The “wrapped” types/classes are the Pandas and cuDF specific types that have been wrapped into proxy types. Wrapped objects and proxy objects are instances of wrapped types and proxy types, respectively. In the snippet below s1 and s2 are wrapped objects and s3 is a fast-slow proxy object. Also note that the module xpd is a wrapped module and contains cuDF and Pandas modules as attributes. To check if an object is a proxy type, we can use cudf.pandas.is_proxy_object.

import cudf.pandas
cudf.pandas.install()
import pandas as xpd

cudf = xpd._fsproxy_fast
pd = xpd._fsproxy_slow

s1 = cudf.Series([1,2])
s2 = pd.Series([1,2])
s3 = xpd.Series([1,2])

from cudf.pandas import is_proxy_object

is_proxy_object(s1) # returns False

is_proxy_object(s2) # returns False

is_proxy_object(s3) # returns True

Note

Note that users should never have to interact with the wrapped objects directly in this way. This code is purely for demonstrative purposes.

The Different Kinds of Proxy Types#

In cudf.pandas, there are two main kinds of proxy types: final types and intermediate types.

Final and Intermediate Proxy Types#

Final types are types for which known operations exist for converting an object of a “fast” type to a “slow” type and vice versa. For example, cudf.DataFrame can be converted to Pandas using the method to_pandas, and pd.DataFrame can be converted to cuDF using the function cudf.from_pandas. Intermediate types are the types of the results of operations invoked on final types. For example, xpd.DataFrameGroupBy is an intermediate type that will be created during a groupby operation on the final type xpd.DataFrame.

Attributes and Callable Proxy Types#

Final proxy types are typically classes or modules, both of which have attributes. Classes also have methods. These attributes and methods must be wrapped as well to support the fast-slow proxy scheme.

Creating New Proxy Types#

_FinalProxy and _IntermediateProxy types are created using the functions make_final_proxy_type and make_intermediate_proxy type, respectively. Creating a new final type looks like this.

DataFrame = make_final_proxy_type(
    "DataFrame",
    cudf.DataFrame,
    pd.DataFrame,
    fast_to_slow=lambda fast: fast.to_pandas(),
    slow_to_fast=cudf.from_pandas,
)

The Fallback Mechanism#

Proxied calls are implemented with fallback via _fast_slow_function_call. This implements the mechanism by which we attempt operations the fast way (using cuDF) and then fall back to the slow way (using Pandas) on failure. The function looks like this:

def _fast_slow_function_call(func: Callable, *args, **kwargs):
    try:
        ...
        fast_args, fast_kwargs = _fast_arg(args), _fast_arg(kwargs)
        result = func(*fast_args, **fast_kwargs)
        ...
    except Exception:
        ...
        slow_args, slow_kwargs = _slow_arg(args), _slow_arg(kwargs)
        result = func(*slow_args, **slow_kwargs)
        ...
    return _maybe_wrap_result(result, func, *args, **kwargs), fast

As we can see the function attempts to call func the fast way using cuDF and if any Exception occurs, it calls the function using Pandas. In essence, this try-except is what allows cudf.pandas to support the bulk of the Pandas API.

At the end, the function wraps the result from either path in a fast-slow proxy object, if necessary.

Converting Proxy Objects#

Note that before the func is called, the proxy object and its attributes need to be converted to either their cuDF or Pandas implementations. This conversion is handled in the function _transform_arg which both _fast_arg and _slow_arg call.

_transform_arg is a recursive function that will call itself depending on the type or argument passed to it (eg. _transform_arg is called for each element in a list of arguments).

Using Metaclasses#

cudf.pandas uses a metaclass called (_FastSlowProxyMeta) to find class attributes and classmethods of fast-slow proxy types. For example, in the snippet below, the xpd.Series type is an instance of _FastSlowProxyMeta. Therefore we can access the property _fsproxy_fast defined in the metaclass.

import cudf.pandas
cudf.pandas.install()
import pandas as xpd

print(xpd.Series._fsproxy_fast) # output is cudf.core.series.Series

debugging cudf.pandas#

Several environment variables are available for debugging purposes.

Setting the environment variable CUDF_PANDAS_DEBUGGING produces a warning when the results from cuDF and Pandas differ from one another. For example, the snippet below produces the warning below.

import cudf.pandas
cudf.pandas.install()
import pandas as pd
import numpy as np

setattr(pd.Series.mean, "_fsproxy_slow", lambda self, *args, **kwargs: np.float64(1))
s = pd.Series([1,2,3])
s.mean()

UserWarning: The results from cudf and pandas were different. The exception was
Arrays are not almost equal to 7 decimals
 ACTUAL: 1.0
 DESIRED: 2.0.
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