45 lines
1.4 KiB
Python
45 lines
1.4 KiB
Python
from abc import ABC, abstractmethod
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import torch
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from torch import nn
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def init_states(
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model: nn.Module, batch_size: int, sequence_length: int
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) -> dict[str, dict[str, torch.Tensor]]:
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result = {}
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for module_name, module in model.named_modules():
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if not isinstance(module, StatefulModule):
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continue
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module_state = module.init_state(batch_size, sequence_length=sequence_length)
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result[module_name] = module_state
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return result
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def increment_steps(
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module: nn.Module, model_state: dict[str, dict[str, torch.Tensor]], increment: int = 1
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):
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# print("incrementing steps by", increment)
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for module_name, module in module.named_modules():
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if not isinstance(module, StatefulModule):
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continue
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module.increment_step(model_state[module_name], increment)
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class StatefulModule(ABC, nn.Module):
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def __init__(self, *args, **kwds):
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self._module_absolute_name = None
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return super().__init__(*args, **kwds)
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@abstractmethod
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def init_state(self, batch_size: int, sequence_length: int):
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"""Initialize the state."""
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raise NotImplementedError
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def increment_step(self, state: dict, increment: int = 1):
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pass
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def get_state(self, model_state: dict[str, dict[str, torch.Tensor]]) -> dict[str, torch.Tensor]:
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"""Get the state for this module from the model state."""
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return model_state[self._module_absolute_name]
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