2.0.0 initial
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siriuz42
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# Copyright 2024 The Google Research Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from jax import numpy as jnp
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from praxis import base_layer
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from praxis.layers import attentions, linears
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WeightInit = base_layer.WeightInit
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WeightHParams = base_layer.WeightHParams
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class DoraTheta(base_layer.Theta):
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def __init__(self, module):
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self.module = module
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def _dora_initialized(self):
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if (
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self.module.has_variable("params", "lora_a")
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and self.module.has_variable("params", "lora_b")
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and self.module.has_variable("params", "dora_m")
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and "lora_a" in self.module._weight_hparams
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and "lora_b" in self.module._weight_hparams
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and "dora_m" in self.module._weight_hparams
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):
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return True
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else:
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return False
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def _dorafy_var(self, w):
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lora_a = super().__getattr__("lora_a")
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lora_b = super().__getattr__("lora_b")
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dora_m = super().__getattr__("dora_m")
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lora_delta = self.module.einsum("...dr,...nr->...dn", lora_a, lora_b)
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lora_delta = jnp.reshape(lora_delta, w.shape)
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w_prime = w + lora_delta
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column_norm = jnp.linalg.norm(w_prime, ord=2, axis=0, keepdims=True)
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norm_adapted = w_prime / column_norm
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w_prime = dora_m * norm_adapted
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return w_prime
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def __getattr__(self, k):
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var = super().__getattr__(k)
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if not self._dora_initialized():
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return var
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if k == "w":
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return self._dorafy_var(var)
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return var
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def __getitem__(self, k):
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var = super().__getattr__(k)
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if not self._dora_initialized():
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return var
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if k == "w":
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return self._dorafy_var(var)
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return var
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class DoraThetaDescriptor:
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"""Dot syntax accession descriptor."""
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def __get__(self, obj, objtype=None):
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return DoraTheta(obj)
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class DoraLinear(linears.Linear):
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rank: int = 0
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lora_init: WeightInit | None = None
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theta = DoraThetaDescriptor()
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def setup(self) -> None:
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lora_init = self.lora_init if self.lora_init else self.weight_init
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super().setup()
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self.create_variable(
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"lora_a",
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WeightHParams(
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shape=[self.input_dims, self.rank],
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init=lora_init,
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mesh_shape=self.mesh_shape,
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tensor_split_dims_mapping=[None, None],
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),
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)
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self.create_variable(
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"lora_b",
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WeightHParams(
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shape=[self.output_dims, self.rank],
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init=WeightInit.Constant(scale=0.0),
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mesh_shape=self.mesh_shape,
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tensor_split_dims_mapping=[None, None],
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),
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)
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self.create_variable(
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"dora_m",
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WeightHParams(
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shape=[1, self.output_dims],
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init=lora_init,
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mesh_shape=self.mesh_shape,
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tensor_split_dims_mapping=[None, None],
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),
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)
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class DoraAttentionProjection(attentions.AttentionProjection):
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rank: int = 0
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lora_init: WeightInit | None = None
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theta = DoraThetaDescriptor()
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def setup(self) -> None:
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super().setup()
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w_weight_params = self._weight_hparams["w"]
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lora_init = self.lora_init if self.lora_init else w_weight_params.init
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self.create_variable(
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"lora_a",
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WeightHParams(
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shape=[self.input_dim, self.rank],
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init=lora_init,
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mesh_shape=self.mesh_shape,
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tensor_split_dims_mapping=[
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None,
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None,
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],
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),
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)
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self.create_variable(
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"lora_b",
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WeightHParams(
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shape=[self.dim_per_head * self.num_heads, self.rank],
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init=WeightInit.Constant(scale=0.0),
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mesh_shape=self.mesh_shape,
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tensor_split_dims_mapping=[
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None,
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None,
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],
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),
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)
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self.create_variable(
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"dora_m",
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WeightHParams(
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shape=[1, self.num_heads, self.dim_per_head],
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init=lora_init,
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mesh_shape=self.mesh_shape,
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tensor_split_dims_mapping=[None, None, None],
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),
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)
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class DoraCombinedQKVProjection(attentions.CombinedQKVProjectionLayer):
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rank: int = 0
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lora_init: WeightInit | None = None
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theta = DoraThetaDescriptor()
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def setup(self) -> None:
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super().setup()
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w_weight_params = self._weight_hparams["w"]
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lora_init = self.lora_init if self.lora_init else w_weight_params.init
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self.create_variable(
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"lora_a",
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WeightHParams(
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shape=[3, self.input_dim, self.rank],
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init=lora_init,
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mesh_shape=self.mesh_shape,
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tensor_split_dims_mapping=[None, None, None],
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),
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)
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self.create_variable(
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"lora_b",
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WeightHParams(
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shape=[3, self.dim_per_head * self.num_heads, self.rank],
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init=WeightInit.Constant(scale=0.0),
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mesh_shape=self.mesh_shape,
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tensor_split_dims_mapping=[None, None, None],
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),
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)
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self.create_variable(
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"dora_m",
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WeightHParams(
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shape=[3, 1, self.num_heads, self.dim_per_head],
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init=lora_init,
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mesh_shape=self.mesh_shape,
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tensor_split_dims_mapping=[None, None, None, None],
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),
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)
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