File size: 9,097 Bytes
204da06 |
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 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 |
# Copyright 2024 **AUTHORS_TODO**
# License: Apache-2.0
# Copyright 2022 MosaicML Examples authors
# SPDX-License-Identifier: Apache-2.0
# Copyright 2023 MosaicML Examples authors
# SPDX-License-Identifier: Apache-2.0
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018-2021, NVIDIA CORPORATION. All rights reserved.
# Copyright (c) 2023, Tri Dao.
from typing import Optional
import torch
import torch.nn as nn
from .configuration_bert import FlexBertConfig
from .activation import get_act_fn
from .normalization import get_norm_layer
from .initialization import ModuleType, init_weights
class BertResidualGLU(nn.Module):
"""Applies the FFN at the end of each Mosaic BERT layer.
Compared to the default BERT architecture, this block replaces :class:`~transformers.model.bert.modeling_bert.BertIntermediate`
and :class:`~transformers.model.bert.modeling_bert.SelfOutput` with a single module that has similar functionality, but
introduces Gated Linear Units.
Note: Mosaic BERT adds parameters in order to implement Gated Linear Units. To keep parameter count consistent with that of a
standard Hugging Face BERT, scale down `config.intermediate_size` by 2/3. For example, a Mosaic BERT constructed with
`config.intermediate_size=2048` will have the same parameter footprint as its Hugging Face BERT counterpart constructed
with the `config.intermediate_size=3072`.
However, in most cases it will not be necessary to adjust `config.intermediate_size` since, despite the increased
parameter size, Mosaic BERT typically offers a net higher throughput than a Hugging Face BERT built from the same `config`.
"""
def __init__(
self,
config,
):
super().__init__()
self.config = config
self.gated_layers = nn.Linear(config.hidden_size, config.intermediate_size * 2, bias=False)
self.act = get_act_fn(config.hidden_act)
self.wo = nn.Linear(config.intermediate_size, config.hidden_size)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.layernorm = get_norm_layer(config)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
"""Compute new hidden states from current hidden states.
Args:
hidden_states (torch.Tensor): The (unpadded) hidden states from
the attention layer [nnz, dim].
"""
residual_connection = hidden_states
# compute the activation
hidden_states = self.gated_layers(hidden_states)
gated = hidden_states[:, : self.config.intermediate_size]
non_gated = hidden_states[:, self.config.intermediate_size :]
hidden_states = self.act(gated) * non_gated
hidden_states = self.dropout(hidden_states)
# multiply by the second matrix
hidden_states = self.wo(hidden_states)
# add the residual connection and post-LN
hidden_states = self.layernorm(hidden_states + residual_connection)
return hidden_states
class FlexBertMLPBase(nn.Module):
"""A FlexBERT MLP base class for type hints."""
def __init__(self, config: FlexBertConfig, layer_id: Optional[int] = None):
super().__init__()
self.config = config
self.layer_id = layer_id
def _init_weights(self, reset_params: bool = False):
raise NotImplementedError("This is a base class and should not be used directly.")
def reset_parameters(self):
self._init_weights(reset_params=True)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
raise NotImplementedError("This is a base class and should not be used directly.")
class FlexBertMLP(FlexBertMLPBase):
"""Applies the MLP at the end of each FlexBERT layer.
Compared to the default BERT architecture, this block replaces :class:`~transformers.model.bert.modeling_bert.BertIntermediate`
and :class:`~transformers.model.bert.modeling_bert.SelfOutput` with a single module that has similar functionality.
"""
def __init__(self, config: FlexBertConfig, layer_id: Optional[int] = None):
super().__init__(config=config, layer_id=layer_id)
self.Wi = nn.Linear(config.hidden_size, config.intermediate_size, bias=config.mlp_in_bias)
self.act = get_act_fn(config.hidden_act)
self.drop = nn.Dropout(config.mlp_dropout_prob) if config.mlp_dropout_prob > 0.0 else nn.Identity()
self.Wo = nn.Linear(config.intermediate_size, config.hidden_size, bias=config.mlp_out_bias)
def _init_weights(self, reset_params: bool = False):
init_weights(
self.config,
self.Wi,
layer_dim=self.config.hidden_size,
layer_id=None,
type_of_module=ModuleType.in_module,
)
init_weights(
self.config,
self.Wo,
layer_dim=self.config.intermediate_size,
layer_id=self.layer_id,
type_of_module=ModuleType.out_module,
)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
"""Compute new hidden states from current hidden states.
Args:
hidden_states (torch.Tensor): The (unpadded) hidden states from
the attention layer [nnz, dim].
"""
return self.Wo(self.drop(self.act(self.Wi(hidden_states))))
class FlexBertGLU(FlexBertMLPBase):
"""Applies the GLU at the end of each FlexBERT layer.
Compared to the default BERT architecture, this block replaces :class:`~transformers.model.bert.modeling_bert.BertIntermediate`
and :class:`~transformers.model.bert.modeling_bert.SelfOutput` with a single module that has similar functionality.
"""
def __init__(self, config: FlexBertConfig, layer_id: Optional[int] = None):
super().__init__(config=config, layer_id=layer_id)
self.Wi = nn.Linear(config.hidden_size, int(config.intermediate_size) * 2, bias=config.mlp_in_bias)
self.act = get_act_fn(config.hidden_act)
self.drop = nn.Dropout(config.mlp_dropout_prob) if config.mlp_dropout_prob > 0.0 else nn.Identity()
self.Wo = nn.Linear(config.intermediate_size, config.hidden_size, bias=config.mlp_out_bias)
def _init_weights(self, reset_params: bool = False):
init_weights(
self.config,
self.Wi,
layer_dim=self.config.hidden_size,
layer_id=None,
type_of_module=ModuleType.in_module,
)
init_weights(
self.config,
self.Wo,
layer_dim=self.config.intermediate_size,
layer_id=self.layer_id,
type_of_module=ModuleType.out_module,
)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
input, gate = self.Wi(hidden_states).chunk(2, dim=-1)
return self.Wo(self.drop(self.act(input) * gate))
class FlexBertParallelGLU(FlexBertMLPBase):
"""Applies the GLU at the end of each FlexBERT layer using intermediate_ff computed in parallel of the attention.
Compared to the default BERT architecture, this block replaces :class:`~transformers.model.bert.modeling_bert.BertIntermediate`
and :class:`~transformers.model.bert.modeling_bert.SelfOutput` with a single module that has similar functionality.
"""
def __init__(self, config: FlexBertConfig, layer_id: Optional[int] = None):
super().__init__(config=config, layer_id=layer_id)
self.act = get_act_fn(config.hidden_act)
self.drop = nn.Dropout(config.mlp_dropout_prob) if config.mlp_dropout_prob > 0.0 else nn.Identity()
self.Wo = nn.Linear(config.intermediate_size, config.hidden_size, bias=config.mlp_out_bias)
def _init_weights(self, reset_params: bool = False):
init_weights(
self.config,
self.Wo,
layer_dim=self.config.intermediate_size,
layer_id=self.layer_id,
type_of_module=ModuleType.out_module,
)
def forward(self, intermediate_ff: torch.Tensor) -> torch.Tensor:
input, gate = intermediate_ff.chunk(2, dim=-1)
return self.Wo(self.drop(self.act(input) * gate))
MLP2CLS = {
"mlp": FlexBertMLP,
"glu": FlexBertGLU,
"parallel_glu": FlexBertParallelGLU,
}
def get_mlp_layer(config: FlexBertConfig, layer_id: Optional[int] = None) -> FlexBertMLPBase:
try:
mlp_layer = (
config.initial_mlp_layer
if layer_id < config.num_initial_layers and getattr(config, "initial_mlp_layer", None) is not None
else config.mlp_layer
)
return MLP2CLS[mlp_layer](config, layer_id=layer_id)
except KeyError as e:
if layer_id < config.num_initial_layers and getattr(config, "initial_mlp_layer", None) is not None:
raise ValueError(
f"Invalid MLP layer type: {config.initial_mlp_layer=}, must be one of {MLP2CLS.keys()}. {e}"
)
else:
raise ValueError(f"Invalid MLP layer type: {config.mlp_layer=}, must be one of {MLP2CLS.keys()}. {e}")
|