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from typing import Optional, Dict, cast
import torch
from . import RearrangeMixin, ReduceMixin
from ._einmix import _EinmixMixin
from .._torch_specific import apply_for_scriptable_torch
__author__ = "Alex Rogozhnikov"
class Rearrange(RearrangeMixin, torch.nn.Module):
def forward(self, input):
recipe = self._multirecipe[input.ndim]
return apply_for_scriptable_torch(recipe, input, reduction_type="rearrange", axes_dims=self._axes_lengths)
def _apply_recipe(self, x):
# overriding parent method to prevent it's scripting
pass
class Reduce(ReduceMixin, torch.nn.Module):
def forward(self, input):
recipe = self._multirecipe[input.ndim]
return apply_for_scriptable_torch(recipe, input, reduction_type=self.reduction, axes_dims=self._axes_lengths)
def _apply_recipe(self, x):
# overriding parent method to prevent it's scripting
pass
class EinMix(_EinmixMixin, torch.nn.Module):
def _create_parameters(self, weight_shape, weight_bound, bias_shape, bias_bound):
self.weight = torch.nn.Parameter(
torch.zeros(weight_shape).uniform_(-weight_bound, weight_bound), requires_grad=True
)
if bias_shape is not None:
self.bias = torch.nn.Parameter(
torch.zeros(bias_shape).uniform_(-bias_bound, bias_bound), requires_grad=True
)
else:
self.bias = None
def _create_rearrange_layers(
self,
pre_reshape_pattern: Optional[str],
pre_reshape_lengths: Optional[Dict],
post_reshape_pattern: Optional[str],
post_reshape_lengths: Optional[Dict],
):
self.pre_rearrange = None
if pre_reshape_pattern is not None:
self.pre_rearrange = Rearrange(pre_reshape_pattern, **cast(dict, pre_reshape_lengths))
self.post_rearrange = None
if post_reshape_pattern is not None:
self.post_rearrange = Rearrange(post_reshape_pattern, **cast(dict, post_reshape_lengths))
def forward(self, input):
if self.pre_rearrange is not None:
input = self.pre_rearrange(input)
result = torch.einsum(self.einsum_pattern, input, self.weight)
if self.bias is not None:
result += self.bias
if self.post_rearrange is not None:
result = self.post_rearrange(result)
return result
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