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from typing import Optional, Dict, cast
import paddle
from . import RearrangeMixin, ReduceMixin
from ._einmix import _EinmixMixin
__author__ = "PaddlePaddle"
class Rearrange(RearrangeMixin, paddle.nn.Layer):
def forward(self, input):
return self._apply_recipe(input)
class Reduce(ReduceMixin, paddle.nn.Layer):
def forward(self, input):
return self._apply_recipe(input)
class EinMix(_EinmixMixin, paddle.nn.Layer):
def _create_parameters(self, weight_shape, weight_bound, bias_shape, bias_bound):
self.weight = self.create_parameter(
weight_shape, default_initializer=paddle.nn.initializer.Uniform(-weight_bound, weight_bound)
)
if bias_shape is not None:
self.bias = self.create_parameter(
bias_shape, default_initializer=paddle.nn.initializer.Uniform(-bias_bound, bias_bound)
)
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 = paddle.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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