6072170279

Committed 04 Sep 2023 10:21AM UTC coverage: 92.812% (-0.2%) from 93.023%

Build # 6072170279

Build Type

push

github

Committed by

web-flow

Commit Message

Make to/from_dict optional (#107)

* remove from/to dict from Protocol

* use a default marshaller

* example component with no serializers

* fix linting

* make it smarter

* fix linting

* thank you mypy protector of the dumb programmers

Run Details

151 of 155 branches covered (0.0%)

Branch coverage included in aggregate %.

585 of 638 relevant lines covered (91.69%)

0.92 hits per line

Source File
Press 'n' to go to next uncovered line, 'b' for previous

96.92

canals/component/component.py

# SPDX-FileCopyrightText: 2022-present deepset GmbH <info@deepset.ai>
#
# SPDX-License-Identifier: Apache-2.0
"""
    Attributes:

        component: Marks a class as a component. Any class decorated with `@component` can be used by a Pipeline.

    All components must follow the contract below. This docstring is the source of truth for components contract.

    <hr>

    `@component` decorator

    All component classes must be decorated with the `@component` decorator. This allows Canals to discover them.

    <hr>

    `__init__(self, **kwargs)`

    Optional method.

    Components may have an `__init__` method where they define:

    - `self.init_parameters = {same parameters that the __init__ method received}`:
        In this dictionary you can store any state the components wish to be persisted when they are saved.
        These values will be given to the `__init__` method of a new instance when the pipeline is loaded.
        Note that by default the `@component` decorator saves the arguments automatically.
        However, if a component sets their own `init_parameters` manually in `__init__()`, that will be used instead.
        Note: all of the values contained here **must be JSON serializable**. Serialize them manually if needed.

    Components should take only "basic" Python types as parameters of their `__init__` function, or iterables and
    dictionaries containing only such values. Anything else (objects, functions, etc) will raise an exception at init
    time. If there's the need for such values, consider serializing them to a string.

    _(TODO explain how to use classes and functions in init. In the meantime see `test/components/test_accumulate.py`)_

    The `__init__` must be extrememly lightweight, because it's a frequent operation during the construction and
    validation of the pipeline. If a component has some heavy state to initialize (models, backends, etc...) refer to
    the `warm_up()` method.

    <hr>

    `warm_up(self)`

    Optional method.

    This method is called by Pipeline before the graph execution. Make sure to avoid double-initializations,
    because Pipeline will not keep track of which components it called `warm_up()` on.

    <hr>

    `run(self, data)`

    Mandatory method.

    This is the method where the main functionality of the component should be carried out. It's called by
    `Pipeline.run()`.

    When the component should run, Pipeline will call this method with an instance of the dataclass returned by the
    method decorated with `@component.input`. This dataclass contains:

    - all the input values coming from other components connected to it,
    - if any is missing, the corresponding value defined in `self.defaults`, if it exists.

    `run()` must return a single instance of the dataclass declared through the method decorated with
    `@component.output`.

"""

import logging
import inspect
from typing import Protocol, Union, Dict, Any, get_origin, get_args
from functools import wraps

from canals.errors import ComponentError


logger = logging.getLogger(__name__)


class Component(Protocol):
    """
    Abstract interface of a Component.
    This is only used by type checking tools.
    If you want to create a new Component use the @component decorator.
    """

    def run(self, **kwargs) -> Dict[str, Any]:
        """
        Takes the Component input and returns its output.
        Inputs are defined explicitly by the run method's signature or with `component.set_input_types()` if dynamic.
        Outputs are defined by decorating the run method with `@component.output_types()`
        or with `component.set_output_types()` if dynamic.
        """


class _Component:
    """
    See module's docstring.

    Args:
        class_: the class that Canals should use as a component.
        serializable: whether to check, at init time, if the component can be saved with
        `save_pipelines()`.

    Returns:
        A class that can be recognized as a component.

    Raises:
        ComponentError: if the class provided has no `run()` method or otherwise doesn't respect the component contract.
    """

    def __init__(self):
        self.registry = {}

    def set_input_types(self, instance, **types):
        """
        Method that validates the input kwargs of the run method.

        Use as:

        ```python
        @component
        class MyComponent:

            def __init__(self, value: int):
                component.set_input_types(value_1=str, value_2=str)
                ...

            @component.output_types(output_1=int, output_2=str)
            def run(self, **kwargs):
                return {"output_1": kwargs["value_1"], "output_2": ""}
        ```
        """
        run_method = instance.run

        def wrapper(**kwargs):
            return run_method(**kwargs)

        # Store the input types in the run method
        wrapper.__canals_input__ = {
            name: {"name": name, "type": type_, "is_optional": _is_optional(type_)} for name, type_ in types.items()
        }
        wrapper.__canals_output__ = getattr(run_method, "__canals_output__", {})

        # Assigns the wrapped method to the instance's run()
        instance.run = wrapper

    def set_output_types(self, instance, **types):
        """
        Method that validates the output dictionary of the run method.

        Use as:

        ```python
        @component
        class MyComponent:

            def __init__(self, value: int):
                component.set_output_types(output_1=int, output_2=str)
                ...

            def run(self, value: int):
                return {"output_1": 1, "output_2": "2"}
        ```
        """
        if not types:
            return

        run_method = instance.run

        def wrapper(*args, **kwargs):
            return run_method(*args, **kwargs)

        # Store the output types in the run method
        wrapper.__canals_input__ = getattr(run_method, "__canals_input__", {})
        wrapper.__canals_output__ = {name: {"name": name, "type": type_} for name, type_ in types.items()}

        # Assigns the wrapped method to the instance's run()
        instance.run = wrapper

    def output_types(self, **types):
        """
        Decorator factory that validates the output dictionary of the run method.

        Use as:

        ```python
        @component
        class MyComponent:
            @component.output_types(output_1=int, output_2=str)
            def run(self, value: int):
                return {"output_1": 1, "output_2": "2"}
        ```
        """

        def output_types_decorator(run_method):
            """
            Decorator that validates the output dictionary of the run method.
            """
            # Store the output types in the run method - used by the pipeline to build the sockets.

            @wraps(run_method)
            def wrapper(self, *args, **kwargs):
                return run_method(self, *args, **kwargs)

            wrapper.__canals_input__ = getattr(run_method, "__canals_input__", {})
            wrapper.__canals_output__ = {name: {"name": name, "type": type_} for name, type_ in types.items()}

            return wrapper

        return output_types_decorator

    def _component(self, class_):
        """
        Decorator validating the structure of the component and registering it in the components registry.
        """
        logger.debug("Registering %s as a component", class_)

        # Check for required methods
        if not hasattr(class_, "run"):
            raise ComponentError(f"{class_.__name__} must have a 'run()' method. See the docs for more information.")
        run_signature = inspect.signature(class_.run)

        # Create the input sockets
        class_.run.__canals_input__ = {
            param: {
                "name": param,
                "type": run_signature.parameters[param].annotation,
                "is_optional": _is_optional(run_signature.parameters[param].annotation),
            }
            for param in list(run_signature.parameters)[1:]  # First is 'self' and it doesn't matter.
        }

        # Save the component in the class registry (for deserialization)
        if class_.__name__ in self.registry:
            # It may occur easily in notebooks by re-running cells.
            logger.debug(
                "Component %s is already registered. Previous imported from '%s', new imported from '%s'",
                class_.__name__,
                self.registry[class_.__name__],
                class_,
            )
        self.registry[class_.__name__] = class_
        logger.debug("Registered Component %s", class_)

        setattr(class_, "__canals_component__", True)

        return class_

    def __call__(self, class_=None):
        """Allows us to use this decorator with parenthesis and without."""
        if class_:
            return self._component(class_)

        return self._component


component = _Component()


def _is_optional(type_: type) -> bool:
    """
    Utility method that returns whether a type is Optional.
    """
    return get_origin(type_) is Union and type(None) in get_args(type_)

1	# SPDX-FileCopyrightText: 2022-present deepset GmbH <info@deepset.ai>
2	#
3	# SPDX-License-Identifier: Apache-2.0
4	"""	1✔
5	Attributes:
6
7	component: Marks a class as a component. Any class decorated with `@component` can be used by a Pipeline.
8
9	All components must follow the contract below. This docstring is the source of truth for components contract.
10
11	<hr>
12
13	`@component` decorator
14
15	All component classes must be decorated with the `@component` decorator. This allows Canals to discover them.
16
17	<hr>
18
19	`__init__(self, **kwargs)`
20
21	Optional method.
22
23	Components may have an `__init__` method where they define:
24
25	- `self.init_parameters = {same parameters that the __init__ method received}`:
26	In this dictionary you can store any state the components wish to be persisted when they are saved.
27	These values will be given to the `__init__` method of a new instance when the pipeline is loaded.
28	Note that by default the `@component` decorator saves the arguments automatically.
29	However, if a component sets their own `init_parameters` manually in `__init__()`, that will be used instead.
30	Note: all of the values contained here must be JSON serializable. Serialize them manually if needed.
31
32	Components should take only "basic" Python types as parameters of their `__init__` function, or iterables and
33	dictionaries containing only such values. Anything else (objects, functions, etc) will raise an exception at init
34	time. If there's the need for such values, consider serializing them to a string.
35
36	_(TODO explain how to use classes and functions in init. In the meantime see `test/components/test_accumulate.py`)_
37
38	The `__init__` must be extrememly lightweight, because it's a frequent operation during the construction and
39	validation of the pipeline. If a component has some heavy state to initialize (models, backends, etc...) refer to
40	the `warm_up()` method.
41
42	<hr>
43
44	`warm_up(self)`
45
46	Optional method.
47
48	This method is called by Pipeline before the graph execution. Make sure to avoid double-initializations,
49	because Pipeline will not keep track of which components it called `warm_up()` on.
50
51	<hr>
52
53	`run(self, data)`
54
55	Mandatory method.
56
57	This is the method where the main functionality of the component should be carried out. It's called by
58	`Pipeline.run()`.
59
60	When the component should run, Pipeline will call this method with an instance of the dataclass returned by the
61	method decorated with `@component.input`. This dataclass contains:
62
63	- all the input values coming from other components connected to it,
64	- if any is missing, the corresponding value defined in `self.defaults`, if it exists.
65
66	`run()` must return a single instance of the dataclass declared through the method decorated with
67	`@component.output`.
68
69	"""
70
71	import logging	1✔
72	import inspect	1✔
73	from typing import Protocol, Union, Dict, Any, get_origin, get_args	1✔
74	from functools import wraps	1✔
75
76	from canals.errors import ComponentError	1✔
77
78
79	logger = logging.getLogger(__name__)	1✔
80
81
82	class Component(Protocol):	1✔
83	"""
84	Abstract interface of a Component.
85	This is only used by type checking tools.
86	If you want to create a new Component use the @component decorator.
87	"""
88
89	def run(self, **kwargs) -> Dict[str, Any]:	1✔
90	"""
91	Takes the Component input and returns its output.
92	Inputs are defined explicitly by the run method's signature or with `component.set_input_types()` if dynamic.
93	Outputs are defined by decorating the run method with `@component.output_types()`
94	or with `component.set_output_types()` if dynamic.
95	"""
96
97
98	class _Component:	1✔
99	"""
100	See module's docstring.
101
102	Args:
103	class_: the class that Canals should use as a component.
104	serializable: whether to check, at init time, if the component can be saved with
105	`save_pipelines()`.
106
107	Returns:
108	A class that can be recognized as a component.
109
110	Raises:
111	ComponentError: if the class provided has no `run()` method or otherwise doesn't respect the component contract.
112	"""
113
114	def __init__(self):	1✔
115	self.registry = {}	1✔
116
117	def set_input_types(self, instance, **types):	1✔
118	"""
119	Method that validates the input kwargs of the run method.
120
121	Use as:
122
123	```python
124	@component
125	class MyComponent:
126
127	def __init__(self, value: int):
128	component.set_input_types(value_1=str, value_2=str)
129	...
130
131	@component.output_types(output_1=int, output_2=str)
132	def run(self, **kwargs):
133	return {"output_1": kwargs["value_1"], "output_2": ""}
134	```
135	"""
136	run_method = instance.run	1✔
137
138	def wrapper(**kwargs):	1✔
139	return run_method(**kwargs)	1✔
140
141	# Store the input types in the run method
142	wrapper.__canals_input__ = {	1✔
143	name: {"name": name, "type": type_, "is_optional": _is_optional(type_)} for name, type_ in types.items()
144	}
145	wrapper.__canals_output__ = getattr(run_method, "__canals_output__", {})	1✔
146
147	# Assigns the wrapped method to the instance's run()
148	instance.run = wrapper	1✔
149
150	def set_output_types(self, instance, **types):	1✔
151	"""
152	Method that validates the output dictionary of the run method.
153
154	Use as:
155
156	```python
157	@component
158	class MyComponent:
159
160	def __init__(self, value: int):
161	component.set_output_types(output_1=int, output_2=str)
162	...
163
164	def run(self, value: int):
165	return {"output_1": 1, "output_2": "2"}
166	```
167	"""
168	if not types:	1✔
169	return	×
170
171	run_method = instance.run	1✔
172
173	def wrapper(args, *kwargs):	1✔
174	return run_method(args, *kwargs)	1✔
175
176	# Store the output types in the run method
177	wrapper.__canals_input__ = getattr(run_method, "__canals_input__", {})	1✔
178	wrapper.__canals_output__ = {name: {"name": name, "type": type_} for name, type_ in types.items()}	1✔
179
180	# Assigns the wrapped method to the instance's run()
181	instance.run = wrapper	1✔
182
183	def output_types(self, **types):	1✔
184	"""
185	Decorator factory that validates the output dictionary of the run method.
186
187	Use as:
188
189	```python
190	@component
191	class MyComponent:
192	@component.output_types(output_1=int, output_2=str)
193	def run(self, value: int):
194	return {"output_1": 1, "output_2": "2"}
195	```
196	"""
197
198	def output_types_decorator(run_method):	1✔
199	"""
200	Decorator that validates the output dictionary of the run method.
201	"""
202	# Store the output types in the run method - used by the pipeline to build the sockets.
203
204	@wraps(run_method)	1✔
205	def wrapper(self, args, *kwargs):	1✔
206	return run_method(self, args, *kwargs)	1✔
207
208	wrapper.__canals_input__ = getattr(run_method, "__canals_input__", {})	1✔
209	wrapper.__canals_output__ = {name: {"name": name, "type": type_} for name, type_ in types.items()}	1✔
210
211	return wrapper	1✔
212
213	return output_types_decorator	1✔
214
215	def _component(self, class_):	1✔
216	"""
217	Decorator validating the structure of the component and registering it in the components registry.
218	"""
219	logger.debug("Registering %s as a component", class_)	1✔
220
221	# Check for required methods
222	if not hasattr(class_, "run"):	1✔
223	raise ComponentError(f"{class_.__name__} must have a 'run()' method. See the docs for more information.")	1✔
224	run_signature = inspect.signature(class_.run)	1✔
225
226	# Create the input sockets
227	class_.run.__canals_input__ = {	1✔
228	param: {
229	"name": param,
230	"type": run_signature.parameters[param].annotation,
231	"is_optional": _is_optional(run_signature.parameters[param].annotation),
232	}
233	for param in list(run_signature.parameters)[1:] # First is 'self' and it doesn't matter.
234	}
235
236	# Save the component in the class registry (for deserialization)
237	if class_.__name__ in self.registry:	1✔
238	# It may occur easily in notebooks by re-running cells.
239	logger.debug(	1✔
240	"Component %s is already registered. Previous imported from '%s', new imported from '%s'",
241	class_.__name__,
242	self.registry[class_.__name__],
243	class_,
244	)
245	self.registry[class_.__name__] = class_	1✔
246	logger.debug("Registered Component %s", class_)	1✔
247
248	setattr(class_, "__canals_component__", True)	1✔
249
250	return class_	1✔
251
252	def __call__(self, class_=None):	1✔
253	"""Allows us to use this decorator with parenthesis and without."""
254	if class_:	1✔
255	return self._component(class_)	1✔
256
257	return self._component	×
258
259
260	component = _Component()	1✔
261
262
263	def _is_optional(type_: type) -> bool:	1✔
264	"""
265	Utility method that returns whether a type is Optional.
266	"""
267	return get_origin(type_) is Union and type(None) in get_args(type_)	1✔

deepset-ai / canals / 6072170279

Source File Press 'n' to go to next uncovered line, 'b' for previous

Source File
Press 'n' to go to next uncovered line, 'b' for previous