cleanup

i6_models/assemblies/lstm/lstm_v1.py

@@ -1,18 +1,15 @@
 __all__ = [
     "LstmEncoderV1Config",
     "LstmEncoderV1",
-    "LstmEncoderV2Config",
-    "LstmEncoderV2",
 ]
 
 from dataclasses import dataclass
-import math
 import torch
 from torch import nn
 from typing import Any, Dict, Optional, Tuple, Union
 
 from i6_models.config import ModelConfiguration
-from i6_models.parts.lstm import LstmBlockV1Config, LstmBlockV1, LstmBlockV2Config, LstmBlockV2
+from i6_models.parts.lstm import LstmBlockV1Config, LstmBlockV1
 
 
 @dataclass
@@ -46,62 +43,6 @@ def __init__(self, model_cfg: Union[LstmEncoderV1Config, Dict], **kwargs):
         if self.cfg.init_args is not None:
             self._param_init(**self.cfg.init_args)
 
-    def _param_init(self, init_args_w=None, init_args_b=None):
-        for m in self.modules():
-            for name, param in m.named_parameters():
-                if "bias" in name:
-                    if init_args_b["func"] == "normal":
-                        init_func = nn.init.normal_
-                    else:
-                        raise NotImplementedError
-                    hyp = init_args_b["arg"]
-                else:
-                    if init_args_w["func"] == "normal":
-                        init_func = nn.init.normal_
-                    else:
-                        raise NotImplementedError
-                    hyp = init_args_w["arg"]
-                init_func(param, **hyp)
-
-            if m is self.output:
-                m.bias.data.fill_(-math.log(self.cfg.input_dim))
-
-    def forward(self, data_tensor: torch.Tensor, seq_len: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
-        embed = self.embedding(data_tensor)
-        embed = self.embed_dropout(embed)
-
-        out, _ = self.lstm_block(embed, seq_len)
-        out = self.lstm_dropout(out)
-
-        return out, seq_len
-
-
-@dataclass
-class LstmEncoderV2Config(LstmEncoderV1Config):
-    lstm_layers_cfg: LstmBlockV2Config
-
-    @classmethod
-    def from_dict(cls, model_cfg_dict: Dict):
-        model_cfg_dict = model_cfg_dict.copy()
-        model_cfg_dict["lstm_layers_cfg"] = LstmBlockV2Config.from_dict(model_cfg_dict["lstm_layers_cfg"])
-        return cls(**model_cfg_dict)
-
-
-class LstmEncoderV2(nn.Module):
-    def __init__(self, model_cfg: Union[LstmEncoderV2Config, Dict], **kwargs):
-        super().__init__()
-
-        self.cfg = LstmEncoderV2Config.from_dict(model_cfg) if isinstance(model_cfg, Dict) else model_cfg
-
-        self.embedding = nn.Embedding(self.cfg.input_dim, self.cfg.embed_dim)
-        self.embed_dropout = nn.Dropout(self.cfg.embed_dropout)
-
-        self.lstm_block = LstmBlockV2(self.cfg.lstm_layers_cfg)
-        self.lstm_dropout = nn.Dropout(self.cfg.lstm_dropout)
-
-        if self.cfg.init_args is not None:
-            self._param_init(**self.cfg.init_args)
-
     def _param_init(self, init_args_w=None, init_args_b=None):
         for m in self.modules():
             for name, param in m.named_parameters():