Spaces:
Sleeping
Sleeping
File size: 11,955 Bytes
563c5bc |
1 2 3 4 5 6 7 8 9 10 11 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 |
'''
Copyright 2024 Infosys Ltd.
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"),
to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense,
and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies
or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED,
INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE
AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
'''
import os
import json
# import logging
from typing import List
from ..language_models import AbstractLanguageModel
from ..operations import GraphOfOperations, Thought
from ..prompter import Prompter
from ..parser import Parser
from llm_explain.config.logger import CustomLogger
logging = CustomLogger()
class Controller:
"""
Controller class to manage the execution flow of the Graph of Operations,
generating the Graph Reasoning State.
This involves language models, graph operations, prompting, and parsing.
"""
def __init__(
self,
lm: AbstractLanguageModel,
graph: GraphOfOperations,
prompter: Prompter,
parser: Parser,
problem_parameters: dict,
) -> None:
"""
Initialize the Controller instance with the language model,
operations graph, prompter, parser, and problem parameters.
:param lm: An instance of the AbstractLanguageModel.
:type lm: AbstractLanguageModel
:param graph: The Graph of Operations to be executed.
:type graph: OperationsGraph
:param prompter: An instance of the Prompter class, used to generate prompts.
:type prompter: Prompter
:param parser: An instance of the Parser class, used to parse responses.
:type parser: Parser
:param problem_parameters: Initial parameters/state of the problem.
:type problem_parameters: dict
"""
self.logger = CustomLogger()
self.lm = lm
self.graph = graph
self.prompter = prompter
self.parser = parser
self.problem_parameters = problem_parameters
self.run_executed = False
def run(self) -> None:
"""
Run the controller and execute the operations from the Graph of
Operations based on their readiness.
Ensures the program is in a valid state before execution.
:raises AssertionError: If the Graph of Operation has no roots.
:raises AssertionError: If the successor of an operation is not in the Graph of Operations.
"""
# self.logger.debug("Checking that the program is in a valid state")
assert self.graph.roots is not None, "The operations graph has no root"
# self.logger.debug("The program is in a valid state")
execution_queue = [
operation
for operation in self.graph.operations
if operation.can_be_executed()
]
# self.logger.info(execution_queue)
while len(execution_queue) > 0:
current_operation = execution_queue.pop(0)
# self.logger.info("Executing operation %s", current_operation.operation_type)
current_operation.execute(
self.lm, self.prompter, self.parser, **self.problem_parameters
)
# self.logger.debug("Operation %s executed", current_operation.operation_type)
for operation in current_operation.successors:
assert (
operation in self.graph.operations
), "The successor of an operation is not in the operations graph"
if operation.can_be_executed():
execution_queue.append(operation)
# self.logger.info("All operations executed")
self.run_executed = True
def get_final_thoughts(self) -> List[List[Thought]]:
"""
Retrieve the final thoughts after all operations have been executed.
:return: List of thoughts for each operation in the graph's leaves.
:rtype: List[List[Thought]]
:raises AssertionError: If the `run` method hasn't been executed yet.
"""
assert self.run_executed, "The run method has not been executed"
return [operation.get_thoughts() for operation in self.graph.leaves]
def output_graph(self, path: str) -> None:
"""
Serialize the state and results of the operations graph to a JSON file.
:param path: The path to the output file.
:type path: str
"""
output = []
for operation in self.graph.operations:
operation_serialized = {
"operation": operation.operation_type.name,
"thoughts": [thought.state for thought in operation.get_thoughts()],
}
if any([thought.scored for thought in operation.get_thoughts()]):
operation_serialized["scored"] = [
thought.scored for thought in operation.get_thoughts()
]
operation_serialized["scores"] = [
thought.score for thought in operation.get_thoughts()
]
if any([thought.validated for thought in operation.get_thoughts()]):
operation_serialized["validated"] = [
thought.validated for thought in operation.get_thoughts()
]
operation_serialized["validity"] = [
thought.valid for thought in operation.get_thoughts()
]
if any(
[
thought.compared_to_ground_truth
for thought in operation.get_thoughts()
]
):
operation_serialized["compared_to_ground_truth"] = [
thought.compared_to_ground_truth
for thought in operation.get_thoughts()
]
operation_serialized["problem_solved"] = [
thought.solved for thought in operation.get_thoughts()
]
output.append(operation_serialized)
output.append(
{
"prompt_tokens": self.lm.prompt_tokens,
"completion_tokens": self.lm.completion_tokens,
"cost": self.lm.cost,
}
)
with open(path, "w") as file:
file.write(json.dumps(output, indent=2))
def format_graph(self, source: str):
def count_unique_matches(l1, l2):
l1_set = set(l1) # Convert l1 to a set for unique elements
l2_set = set(l2) # Convert l2 to a set for unique elements
matches = l1_set & l2_set # Find the intersection
return len(matches)
import copy
with open(source, "r") as file:
data = json.load(file)
data_new = copy.deepcopy(data)
global_thoughts = []
global_thoughts_num = []
data_thoughts = {}
# generate
l = []
for i in range(len(data[0]['thoughts'])):
l.append(data[0]['thoughts'][i]['current'])
if data[0]['thoughts'][i]['current'] not in global_thoughts:
global_thoughts.append(data[0]['thoughts'][i]['current'])
global_thoughts_num.append(f"thought_{i+1}")
data_new[0]['thoughts'][i]['current'] = f"thought_{i+1}"
data_new[0]['thoughts'][i]['score'] = data_new[1]['scores'][i]
data_thoughts[f"thought_{i+1}"] = data[0]['thoughts'][i]['current']
# score
for i in range(len(data[1]['thoughts'])):
data_new[1]['thoughts'][i]['current'] = f"thought_{i+1}"
# keep_best_n
prev_thoughts = {}
for i in range(len(data[2]['thoughts'])):
if data[2]['thoughts'][i]['current'] in l:
data_new[2]['thoughts'][i]['current'] = f"thought_{l.index(data[2]['thoughts'][i]['current'])+1}"
data_new[2]['thoughts'][i]['score'] = data_new[2]['scores'][i]
# data_thoughts[f"thought_{l.index(data[2]['thoughts'][i]['current'])+1}"] = data[2]['thoughts'][i]['current']
elif data[2]['thoughts'][i]['current'] in global_thoughts:
data_new[2]['thoughts'][i]['current'] = f"thought_{global_thoughts_num[global_thoughts.index(data[2]['thoughts'][i]['current'])]}"
data_new[2]['thoughts'][i]['score'] = data_new[2]['scores'][i]
# data_thoughts[f"thought_{global_thoughts_num[global_thoughts.index(data[2]['thoughts'][i]['current'])]}"] = data[2]['thoughts'][i]['current']
prev_thoughts[str(i)] = data[2]['thoughts'][i]['current']
# aggregate
len1 = len(data[0]['thoughts'])
l, l3 = [], []
for i in range(len(data[3]['thoughts'])):
l.append(data[3]['thoughts'][i]['current'])
temp = []
for j in range(len(data[2]['thoughts'])):
temp.append(count_unique_matches(data[2]['thoughts'][j]['current'].split(), data[3]['thoughts'][i]['current'].split()))
val = data_new[2]['thoughts'][temp.index(max(temp))]['current']
if data[3]['thoughts'][i]['current'] not in global_thoughts:
global_thoughts.append(data[3]['thoughts'][i]['current'])
global_thoughts_num.append(f"aggregate_{val}")
data_new[3]['thoughts'][i]['current'] = f"aggregate_{val}"
data_new[3]['thoughts'][i]['score'] = data_new[4]['scores'][i]
# data_thoughts[f"{val}_thought_{i+1+len1}"] = data[3]['thoughts'][i]['current']
l3.append(f"aggregate_{val}")
# score
data_new[4]['thoughts'] = data_new[3]['thoughts']
# keep_best_n
for i in range(len(data[5]['thoughts'])):
if data[5]['thoughts'][i]['current'] in l:
data_new[5]['thoughts'][i]['current'] = l3[l.index(data[5]['thoughts'][0]['current'])]
data_new[5]['thoughts'][i]['score'] = data_new[5]['scores'][i]
data_thoughts[l3[l.index(data[5]['thoughts'][0]['current'])]] = data[5]['thoughts'][i]['current']
# data_thoughts['final_thought'] = data[5]['thoughts'][i]['current']
elif data[5]['thoughts'][i]['current'] in global_thoughts:
data_new[5]['thoughts'][i]['current'] = global_thoughts_num[global_thoughts.index(data[5]['thoughts'][i]['current'])]
data_new[5]['thoughts'][i]['score'] = data_new[5]['scores'][i]
data_thoughts[global_thoughts_num[global_thoughts.index(data[5]['thoughts'][i]['current'])]] = data[5]['thoughts'][i]['current']
# data_thoughts['final_thought'] = data[5]['thoughts'][i]['current']
# data_new[5]['thoughts'][i]['current'] = 'final_thought'
for i in range(len(data_new)):
if i >= len(data_new):
break
if 'operation' in data_new[i] and data_new[i]['operation'] == 'score':
del data_new[i]
if 'operation' in data_new[i] and data_new[i]['operation'] == 'keep_best_n':
del data_new[i]['scored']
del data_new[i]['scores']
os.remove(source)
return data_new, data_thoughts
|