Delete DecompX/src/globenc_utils.py

DecompX/src/globenc_utils.py

@@ -1,49 +0,0 @@
-import torch
-from dataclasses import dataclass
-from typing import List, Optional, Tuple, Union
-
-@dataclass
-class GlobencConfig():
-    include_biases: Optional[bool] = True
-    bias_decomp_type: Optional[str] = "absdot"  # "absdot": Based on the absolute value of dot products | "norm": Based on the norm of the attribution vectors | "equal": equal decomposition | "abssim": Based on the absolute value of cosine similarites | "cls": add to cls token
-    include_bias_token: Optional[bool] = False  # Adds an extra input token as a bias in the attribution vectors
-                                                # If the bias_decomp_type is None and include_bias_token is True then the final token in the input tokens of the attr. vectors will be the summation of the biases
-                                                # Otherwise the bias token will be decomposed with the specified decomp type
-
-    include_LN1: Optional[bool] = True
-
-    include_FFN: Optional[bool] = True
-    FFN_approx_type: Optional[str] = "GeLU_ZO"  # "GeLU_LA": GeLU-based linear approximation | "ReLU": Using ReLU as an approximation | "GeLU_ZO": Zero-origin slope approximation
-    FFN_fast_mode: Optional[bool] = False
-
-    include_LN2: Optional[bool] = True
-
-    aggregation: Optional[str] = None           # None: No aggregation | vector: Vector-based aggregation | rollout: Norm-based rollout aggregation 
-
-    include_classifier_w_pooler: Optional[bool] = True
-    tanh_approx_type: Optional[str] = "ZO"      # "ZO": Zero-origin slope approximation | "LA": Linear approximation
-
-    output_all_layers: Optional[bool] = False   # True: Output all layers | False: Output only last layer
-    output_attention: Optional[str] = None      # None | norm | vector | both
-    output_res1: Optional[str] = None           # None | norm | vector | both
-    output_LN1: Optional[str] = None            # None | norm | vector | both
-    output_FFN: Optional[str] = None            # None | norm | vector | both
-    output_res2: Optional[str] = None           # None | norm | vector | both
-    output_encoder: Optional[str] = None        # None | norm | vector | both
-    output_aggregated: Optional[str] = None     # None | norm | vector | both
-    output_pooler: Optional[str] = None         # None | norm | vector | both
-
-    output_classifier: Optional[bool] = True
-
-
-@dataclass
-class GlobencOutput():
-    attention: Optional[Union[Tuple[torch.Tensor, Tuple[torch.Tensor]], Tuple[torch.Tensor], torch.Tensor]] = None
-    res1: Optional[Union[Tuple[torch.Tensor, Tuple[torch.Tensor]], Tuple[torch.Tensor], torch.Tensor]] = None
-    LN1: Optional[Union[Tuple[torch.Tensor, Tuple[torch.Tensor]], Tuple[torch.Tensor], torch.Tensor]] = None
-    FFN: Optional[Union[Tuple[torch.Tensor, Tuple[torch.Tensor]], Tuple[torch.Tensor], torch.Tensor]] = None
-    res2: Optional[Union[Tuple[torch.Tensor, Tuple[torch.Tensor]], Tuple[torch.Tensor], torch.Tensor]] = None
-    encoder: Optional[Union[Tuple[torch.Tensor, Tuple[torch.Tensor]], Tuple[torch.Tensor], torch.Tensor]] = None
-    aggregated: Optional[Union[Tuple[torch.Tensor, Tuple[torch.Tensor]], Tuple[torch.Tensor], torch.Tensor]] = None
-    pooler: Optional[Union[Tuple[torch.Tensor], torch.Tensor]] = None
-    classifier: Optional[torch.Tensor] = None