Skip to content

LogLikelihoodRollingAction

dendron.actions.loglikelihood_rolling_action.LogLikelihoodRollingAction

Bases: ActionNode

An action node that uses a causal language model to calculate the log-likelihood of a given a prompt in the blackboard.

This node is based on the HFLM library, and will download the model that you specify by name. This can take a long time and/or use a lot of storage, depending on the model you name.

There are enough configuration options for this type of node that the options have all been placed in a dataclass config object. See the documentation for that object to learn about the many options available to you.

Parameters:

Name Type Description Default
name str

The given name of this node.

 required
cfg CausalLMActionConfig

The configuration object for this model.

 required
Source code in src/dendron/actions/loglikelihood_rolling_action.py 
 12
 13
 14
 15
 16
 17
 18
 19
 20
 21
 22
 23
 24
 25
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
class LogLikelihoodRollingAction(ActionNode):
    """
    An action node that uses a causal language model to calculate the log-likelihood
    of a given a prompt in the blackboard.

    This node is based on the HFLM library, and will
    download the model that you specify by name. This can take a long 
    time and/or use a lot of storage, depending on the model you name.

    There are enough configuration options for this type of node that
    the options have all been placed in a dataclass config object. See 
    the documentation for that object to learn about the many options
    available to you.

    Args:
        name (str):
            The given name of this node.
        cfg (CausalLMActionConfig):
            The configuration object for this model.
    """
    def __init__(self, model_cfg : HFLMConfig, node_cfg : LMActionConfig) -> None:
        super().__init__(node_cfg.node_name)

        self.input_key = node_cfg.input_key
        self.output_key = node_cfg.output_key
        self.max_new_tokens = node_cfg.max_new_tokens
        self.temperature = node_cfg.temperature

        if self.temperature == 0.0: 
            self.do_sample = False
        else:
            self.do_sample = True

        self.input_processor = None
        self.output_processor = None
        self.node_config = node_cfg
        self.model_config = model_cfg

    def set_model(self, new_model) -> None:
        """
        TODO: I'm not sure what type new_model should be
              if it is supposed to be an HFLM model, then we need to adjust this
        """
        self.model = new_model

    def set_input_processor(self, f : Callable) -> None:
        """
        Set the input processor to use during `tick()`s. 

        An input processor is applied to the prompt text stored in the 
        blackboard, and can be used to preprocess the prompt. The 
        processor function should be a map from `str` to `str`. During a 
        `tick()`, the output of this function will be what is tokenized 
        and sent to the model for generation.

        Args:
            f (Callable):
                The input processor function to use. Should be a callable
                object that maps (self, Any) to str.
        """
        self.input_processor = types.MethodType(f, self)

    def set_output_processor(self, f : Callable) -> None:
        """
        Set the output processor to use during `tick()`s.

        An output processor is applied to the text generated by the model,
        before that text is written to the output slot of the blackboard.
        The function should be a map from `str` to `str`.

        A typical example of an output processor would be a function that
        removes the prompt from the text returned by a model, so that only
        the newly generated text is written to the blackboard.

        Args:
            f (Callable):
                The output processor function. Should be a callable object
                that maps from (self, str) to Any.
        """
        self.output_processor = types.MethodType(f, self)

    def tick(self) -> NodeStatus:
        """
        Execute a tick, consisting of the following steps:

        - Retrieve a prompt from the node's blackboard, using the input_key.
        - Apply the input processor, if one exists.
        - Tokenize the prompt text.
        - Generate new tokens based on the prompt.
        - Decode the model output into a text string.
        - Apply the output processor, if one exists,
        - Write the result back to the blackboard, using the output_key.

        If any of the above fail, the exception text is printed and the node
        returns a status of `FAILURE`. Otherwise the node returns `SUCCESS`. If
        you want to use a language model to make decisions, consider looking at
        the `CompletionConditionNode`.
        """
        try:
            input_text = self.blackboard[self.input_key]

            if self.input_processor:
                input_text = self.input_processor(input_text)

            output_probs = self.tree.get_model(self.model_config.model_name).loglikelihood_rolling([input_text], disable_tqdm=True)[0]

            if self.output_processor:
                output_probs = self.output_processor(output_probs)

            self.blackboard[self.output_key] = output_probs

            return NodeStatus.SUCCESS
        except Exception as ex:
            print(f"Exception in node {self.name}:")
            print(traceback.format_exc())

            return NodeStatus.FAILURE

    def set_tree(self, tree : BehaviorTree) -> None:
        """
        Set the behavior tree for this node, which includes setting up the blackboard
        and registering the model configuration with the tree.

        Args:
            tree (BehaviorTree):
                The behavior tree this node belongs to.
        """
        self.tree = tree
        self.set_blackboard(tree.blackboard)
        tree.add_model(self.model_config)

set_model(new_model)

I'm not sure what type new_model should be

if it is supposed to be an HFLM model, then we need to adjust this

Source code in src/dendron/actions/loglikelihood_rolling_action.py 
50
51
52
53
54
55
def set_model(self, new_model) -> None:
    """
    TODO: I'm not sure what type new_model should be
          if it is supposed to be an HFLM model, then we need to adjust this
    """
    self.model = new_model

set_input_processor(f)

Set the input processor to use during tick()s.

An input processor is applied to the prompt text stored in the blackboard, and can be used to preprocess the prompt. The processor function should be a map from str to str. During a tick(), the output of this function will be what is tokenized and sent to the model for generation.

Parameters:

Name Type Description Default
f Callable

The input processor function to use. Should be a callable object that maps (self, Any) to str.

 required
Source code in src/dendron/actions/loglikelihood_rolling_action.py 
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
def set_input_processor(self, f : Callable) -> None:
    """
    Set the input processor to use during `tick()`s. 

    An input processor is applied to the prompt text stored in the 
    blackboard, and can be used to preprocess the prompt. The 
    processor function should be a map from `str` to `str`. During a 
    `tick()`, the output of this function will be what is tokenized 
    and sent to the model for generation.

    Args:
        f (Callable):
            The input processor function to use. Should be a callable
            object that maps (self, Any) to str.
    """
    self.input_processor = types.MethodType(f, self)

set_output_processor(f)

Set the output processor to use during tick()s.

An output processor is applied to the text generated by the model, before that text is written to the output slot of the blackboard. The function should be a map from str to str.

A typical example of an output processor would be a function that removes the prompt from the text returned by a model, so that only the newly generated text is written to the blackboard.

Parameters:

Name Type Description Default
f Callable

The output processor function. Should be a callable object that maps from (self, str) to Any.

 required
Source code in src/dendron/actions/loglikelihood_rolling_action.py 
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
def set_output_processor(self, f : Callable) -> None:
    """
    Set the output processor to use during `tick()`s.

    An output processor is applied to the text generated by the model,
    before that text is written to the output slot of the blackboard.
    The function should be a map from `str` to `str`.

    A typical example of an output processor would be a function that
    removes the prompt from the text returned by a model, so that only
    the newly generated text is written to the blackboard.

    Args:
        f (Callable):
            The output processor function. Should be a callable object
            that maps from (self, str) to Any.
    """
    self.output_processor = types.MethodType(f, self)

tick()

Execute a tick, consisting of the following steps:

  • Retrieve a prompt from the node's blackboard, using the input_key.
  • Apply the input processor, if one exists.
  • Tokenize the prompt text.
  • Generate new tokens based on the prompt.
  • Decode the model output into a text string.
  • Apply the output processor, if one exists,
  • Write the result back to the blackboard, using the output_key.

If any of the above fail, the exception text is printed and the node returns a status of FAILURE. Otherwise the node returns SUCCESS. If you want to use a language model to make decisions, consider looking at the CompletionConditionNode.

Source code in src/dendron/actions/loglikelihood_rolling_action.py 
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
def tick(self) -> NodeStatus:
    """
    Execute a tick, consisting of the following steps:

    - Retrieve a prompt from the node's blackboard, using the input_key.
    - Apply the input processor, if one exists.
    - Tokenize the prompt text.
    - Generate new tokens based on the prompt.
    - Decode the model output into a text string.
    - Apply the output processor, if one exists,
    - Write the result back to the blackboard, using the output_key.

    If any of the above fail, the exception text is printed and the node
    returns a status of `FAILURE`. Otherwise the node returns `SUCCESS`. If
    you want to use a language model to make decisions, consider looking at
    the `CompletionConditionNode`.
    """
    try:
        input_text = self.blackboard[self.input_key]

        if self.input_processor:
            input_text = self.input_processor(input_text)

        output_probs = self.tree.get_model(self.model_config.model_name).loglikelihood_rolling([input_text], disable_tqdm=True)[0]

        if self.output_processor:
            output_probs = self.output_processor(output_probs)

        self.blackboard[self.output_key] = output_probs

        return NodeStatus.SUCCESS
    except Exception as ex:
        print(f"Exception in node {self.name}:")
        print(traceback.format_exc())

        return NodeStatus.FAILURE

set_tree(tree)

Set the behavior tree for this node, which includes setting up the blackboard and registering the model configuration with the tree.

Parameters:

Name Type Description Default
tree BehaviorTree

The behavior tree this node belongs to.

 required
Source code in src/dendron/actions/loglikelihood_rolling_action.py 
130
131
132
133
134
135
136
137
138
139
140
141
def set_tree(self, tree : BehaviorTree) -> None:
    """
    Set the behavior tree for this node, which includes setting up the blackboard
    and registering the model configuration with the tree.

    Args:
        tree (BehaviorTree):
            The behavior tree this node belongs to.
    """
    self.tree = tree
    self.set_blackboard(tree.blackboard)
    tree.add_model(self.model_config)