File size: 2,686 Bytes
884b8f8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 | # marble/core/base_transform.py
import torch
import torch.nn as nn
from abc import ABC, abstractmethod
from typing import Sequence, Union, Dict
class BaseEmbTransform(nn.Module, ABC):
"""
Abstract base class for post‐processing transformer outputs.
Safely intercepts ModelOutput objects to extract `.hidden_states`
while preserving all of PyTorch's hook, no_grad, and JIT behavior.
"""
def __init__(self):
super().__init__()
def __call__(
self,
outputs: Union[Sequence[torch.Tensor], object],
*args,
**kwargs
) -> torch.Tensor:
"""
Safely override nn.Module.__call__ to:
1. Extract `hidden_states` if `outputs` has that attribute.
2. Delegate into the original nn.Module.__call__, which
will handle hooks, no_grad, tracing, etc., then call forward().
Args:
outputs: Either
- A tuple/list of Tensors, each of shape (B, T, H), or
- A model‐output object (e.g. BaseModelOutput) with `.hidden_states`.
*args, **kwargs: Passed through to forward().
Returns:
Tensor: Whatever your forward() returns.
"""
# 1. Normalize to a Sequence[Tensor]
hidden_states = (
outputs.hidden_states
if hasattr(outputs, "hidden_states")
else outputs
)
# 2. Call nn.Module.__call__, which invokes hooks and then forward()
return super().__call__(hidden_states, *args, **kwargs)
@abstractmethod
def forward(
self,
hidden_states: Sequence[torch.Tensor],
*args,
**kwargs
) -> torch.Tensor:
"""
Core transform logic. You must implement this in subclasses.
Args:
hidden_states (Sequence[Tensor]): List/tuple of N tensors,
each of shape (batch_size, seq_len, hidden_size).
Returns:
Tensor: Transformed output; shape is up to the subclass.
"""
raise NotImplementedError("Subclasses must implement forward()")
class BaseAudioTransform(nn.Module, ABC):
"""
Base class for dict‐based audio transforms.
Inherit from nn.Module so that __call__() → forward() is wired up automatically.
"""
@abstractmethod
def forward(self, sample: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
"""
Args:
sample (dict): must contain at least "waveform": Tensor[C, T].
Returns:
sample (dict): with same keys (possibly modified in place).
"""
raise NotImplementedError("Subclasses must implement forward()")
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