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import copy
import ujson
class BaseModule:
def __init__(self):
pass
def named_parameters(self):
"""
Unlike PyTorch, handles (non-recursive) lists of parameters too.
"""
from dspy.predict.parameter import Parameter
visited = set()
named_parameters = []
def add_parameter(param_name, param_value):
if isinstance(param_value, Parameter) and id(param_value) not in visited:
visited.add(id(param_value))
named_parameters.append((param_name, param_value))
for name, value in self.__dict__.items():
if isinstance(value, Parameter):
add_parameter(name, value)
elif isinstance(value, BaseModule):
# When a sub-module is pre-compiled, keep it frozen.
if not getattr(value, '_compiled', False):
for sub_name, param in value.named_parameters():
add_parameter(f"{name}.{sub_name}", param)
elif isinstance(value, (list, tuple)):
for idx, item in enumerate(value):
add_parameter(f"{name}[{idx}]", item)
elif isinstance(value, dict):
for key, item in value.items():
add_parameter(f"{name}['{key}']", item)
return named_parameters
def parameters(self):
return [param for _, param in self.named_parameters()]
def deepcopy(self):
return copy.deepcopy(self)
def reset_copy(self):
obj = copy.deepcopy(self)
for param in obj.parameters():
param.reset()
return obj
def dump_state(self):
return {name: param.dump_state() for name, param in self.named_parameters()}
def load_state(self, state):
for name, param in self.named_parameters():
param.load_state(state[name])
def save(self, path):
with open(path, "w") as f:
f.write(ujson.dumps(self.dump_state(), indent=2))
def load(self, path):
with open(path, "r") as f:
self.load_state(ujson.loads(f.read()))
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