the-algorithm-ml/projects/home/recap/model/feature_transform.py

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from typing import Mapping, Sequence, Union
from tml.projects.home.recap.model.config import (
BatchNormConfig,
DoubleNormLogConfig,
FeaturizationConfig,
LayerNormConfig,
)
import torch
def log_transform(x: torch.Tensor) -> torch.Tensor:
"""Safe log transform that works across both negative, zero, and positive floats."""
return torch.sign(x) * torch.log1p(torch.abs(x))
class BatchNorm(torch.nn.Module):
def __init__(self, num_features: int, config: BatchNormConfig):
super().__init__()
self.layer = torch.nn.BatchNorm1d(num_features, affine=config.affine, momentum=config.momentum)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.layer(x)
class LayerNorm(torch.nn.Module):
def __init__(self, normalized_shape: Union[int, Sequence[int]], config: LayerNormConfig):
super().__init__()
if config.axis != -1:
raise NotImplementedError
if config.center != config.scale:
raise ValueError(
f"Center and scale must match in torch, received {config.center}, {config.scale}"
)
self.layer = torch.nn.LayerNorm(
normalized_shape, eps=config.epsilon, elementwise_affine=config.center
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.layer(x)
class Log1pAbs(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
return log_transform(x)
class InputNonFinite(torch.nn.Module):
def __init__(self, fill_value: float = 0):
super().__init__()
self.register_buffer(
"fill_value", torch.as_tensor(fill_value, dtype=torch.float32), persistent=False
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return torch.where(torch.isfinite(x), x, self.fill_value)
class Clamp(torch.nn.Module):
def __init__(self, min_value: float, max_value: float):
super().__init__()
# Using buffer to make sure they are on correct device (and not moved every time).
# Will also be part of state_dict.
self.register_buffer(
"min_value", torch.as_tensor(min_value, dtype=torch.float32), persistent=True
)
self.register_buffer(
"max_value", torch.as_tensor(max_value, dtype=torch.float32), persistent=True
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return torch.clamp(x, min=self.min_value, max=self.max_value)
class DoubleNormLog(torch.nn.Module):
"""Performs a batch norm and clamp on continuous features followed by a layer norm on binary and continuous features."""
def __init__(
self,
input_shapes: Mapping[str, Sequence[int]],
config: DoubleNormLogConfig,
):
super().__init__()
_before_concat_layers = [
InputNonFinite(),
Log1pAbs(),
]
if config.batch_norm_config:
_before_concat_layers.append(
BatchNorm(input_shapes["continuous"][-1], config.batch_norm_config)
)
_before_concat_layers.append(
Clamp(min_value=-config.clip_magnitude, max_value=config.clip_magnitude)
)
self._before_concat_layers = torch.nn.Sequential(*_before_concat_layers)
self.layer_norm = None
if config.layer_norm_config:
last_dim = input_shapes["continuous"][-1] + input_shapes["binary"][-1]
self.layer_norm = LayerNorm(last_dim, config.layer_norm_config)
def forward(
self, continuous_features: torch.Tensor, binary_features: torch.Tensor
) -> torch.Tensor:
x = self._before_concat_layers(continuous_features)
x = torch.cat([x, binary_features], dim=1)
if self.layer_norm:
return self.layer_norm(x)
return x
def build_features_preprocessor(
config: FeaturizationConfig, input_shapes: Mapping[str, Sequence[int]]
):
"""Trivial right now, but we will change in the future."""
return DoubleNormLog(input_shapes, config.double_norm_log_config)