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lora_layers.py
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lora_layers.py
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# Copyright 2024 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from jax import numpy as jnp
from praxis import base_layer
from praxis.layers import attentions, linears
WeightInit = base_layer.WeightInit
WeightHParams = base_layer.WeightHParams
class LoraTheta(base_layer.Theta):
def __init__(self, module):
self.module = module
def _lora_initialized(self):
if (
self.module.has_variable("params", "lora_a")
and self.module.has_variable("params", "lora_b")
and "lora_a" in self.module._weight_hparams
and "lora_b" in self.module._weight_hparams
):
return True
else:
return False
def _lorafy_var(self, w):
lora_a = super().__getattr__("lora_a")
lora_b = super().__getattr__("lora_b")
lora_delta = self.module.einsum("...dr,...nr->...dn", lora_a, lora_b)
lora_delta = jnp.reshape(lora_delta, w.shape)
w_prime = w + lora_delta
return w_prime
def __getattr__(self, k):
var = super().__getattr__(k)
if not self._lora_initialized():
return var
if k == "w":
return self._lorafy_var(var)
return var
def __getitem__(self, k):
var = super().__getattr__(k)
if not self._lora_initialized():
return var
if k == "w":
return self._lorafy_var(var)
return var
class LoraThetaDescriptor:
"""Dot syntax accession descriptor."""
def __get__(self, obj, objtype=None):
return LoraTheta(obj)
class LoraLinear(linears.Linear):
rank: int = 0
lora_init: WeightInit | None = None
theta = LoraThetaDescriptor()
def setup(self) -> None:
lora_init = self.lora_init if self.lora_init else self.weight_init
super().setup()
self.create_variable(
"lora_a",
WeightHParams(
shape=[self.input_dims, self.rank],
init=lora_init,
mesh_shape=self.mesh_shape,
tensor_split_dims_mapping=[None, None],
),
)
self.create_variable(
"lora_b",
WeightHParams(
shape=[self.output_dims, self.rank],
init=WeightInit.Constant(scale=0.0),
mesh_shape=self.mesh_shape,
tensor_split_dims_mapping=[None, None],
),
)
class LoraAttentionProjection(attentions.AttentionProjection):
rank: int = 0
lora_init: WeightInit | None = None
theta = LoraThetaDescriptor()
def setup(self) -> None:
super().setup()
w_weight_params = self._weight_hparams["w"]
lora_init = self.lora_init if self.lora_init else w_weight_params.init
self.create_variable(
"lora_a",
WeightHParams(
shape=[self.input_dim, self.rank],
init=lora_init,
mesh_shape=self.mesh_shape,
tensor_split_dims_mapping=[
None,
None,
],
),
)
self.create_variable(
"lora_b",
WeightHParams(
shape=[self.dim_per_head * self.num_heads, self.rank],
init=WeightInit.Constant(scale=0.0),
mesh_shape=self.mesh_shape,
tensor_split_dims_mapping=[
None,
None,
],
),
)
class LoraCombinedQKVProjection(attentions.CombinedQKVProjectionLayer):
rank: int = 0
lora_init: WeightInit | None = None
theta = LoraThetaDescriptor()
def setup(self) -> None:
super().setup()
w_weight_params = self._weight_hparams["w"]
lora_init = self.lora_init if self.lora_init else w_weight_params.init
self.create_variable(
"lora_a",
WeightHParams(
shape=[3, self.input_dim, self.rank],
init=lora_init,
mesh_shape=self.mesh_shape,
tensor_split_dims_mapping=[None, None, None],
),
)
self.create_variable(
"lora_b",
WeightHParams(
shape=[3, self.dim_per_head * self.num_heads, self.rank],
init=WeightInit.Constant(scale=0.0),
mesh_shape=self.mesh_shape,
tensor_split_dims_mapping=[None, None, None],
),
)