File size: 11,532 Bytes
4caa505 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 |
# Copyright 2024 MosaicML ComposeRL authors
# SPDX-License-Identifier: Apache-2.0
import os
from copy import deepcopy
from dataclasses import dataclass
from typing import (
Any,
Optional,
Union,
)
import numpy as np
import torch
import torch.nn as nn
from transformers import (
AutoConfig,
AutoModelForCausalLM,
PretrainedConfig,
PreTrainedModel,
)
from transformers.modeling_outputs import ModelOutput
@dataclass
class SequenceClassifierOutput(ModelOutput):
"""Sequence Classification Output.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Classification (or regression if config.num_labels==1) loss.
scores (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
Classification (or regression if config.num_labels==1) scores (before SoftMax).
logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
`(batch_size, num_heads, sequence_length, embed_size_per_head)`)
Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
`past_key_values` input) to speed up sequential decoding.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
loss: Optional[torch.FloatTensor] = None
scores: Optional[torch.FloatTensor] = None
logits: Optional[torch.FloatTensor] = None
past_key_values: Optional[tuple[tuple[torch.FloatTensor]]] = None
hidden_states: Optional[tuple[torch.FloatTensor, ...]] = None
attentions: Optional[tuple[torch.FloatTensor, ...]] = None
class ValueHead(nn.Module):
"""Value head for the transformer which outputs n_labels values."""
def __init__(self, n_labels: int, hidden_size: int, p_dropout: float = 0.0):
super().__init__()
self.dense = nn.Linear(hidden_size, hidden_size)
self.dropout = nn.Dropout(p_dropout)
self.score = nn.Linear(hidden_size, n_labels)
torch.nn.init.normal_(
self.score.weight,
std=1 / np.sqrt(hidden_size + 1),
)
torch.nn.init.constant_(self.score.bias, val=0.0)
def forward(
self,
hidden_states: torch.Tensor,
**kwargs: Any,
) -> torch.Tensor:
hidden_states = self.dropout(hidden_states)
hidden_states = self.dense(hidden_states)
hidden_states = torch.tanh(hidden_states)
hidden_states = self.dropout(hidden_states)
output = self.score(hidden_states)
return output
class RewardModelConfig(PretrainedConfig):
model_type = 'pairwise_rm'
def __init__(
self,
base_model: Optional[Union[str, os.PathLike]
] = 'meta-llama/Meta-Llama-3-70B-Instruct',
base_config: Optional[PretrainedConfig] = None,
p_dropout: float = 0.0,
n_labels: int = 1,
bias: float = 0.0,
return_logits: bool = False,
pretrain_cfg: Optional[dict[str, Any]] = None,
pretrained: bool = False,
**kwargs: Any,
):
super().__init__(**kwargs)
self.base_model = base_model
self.base_config = base_config if base_config is not None else AutoConfig.from_pretrained(
base_model,
)
temp_config = deepcopy(self.base_config)
if not isinstance(temp_config, dict):
temp_config = temp_config.__dict__
for key, value in temp_config.items():
if key not in ['_name_or_path', 'architectures']:
setattr(self, key, value)
self.p_dropout = p_dropout
self.n_labels = n_labels
self.bias = bias
self.return_logits = return_logits
self.pretrain_cfg = pretrain_cfg if pretrain_cfg is not None else {}
self.pretrained = pretrained
class AutoModelForCausalLMWithRM(PreTrainedModel):
config_class = RewardModelConfig
_supports_flash_attn_2 = True
def __init__(self, config: PretrainedConfig, *args: Any, **kwargs: Any):
super().__init__(config)
self.config = config
pretrain_cfg = config.pretrain_cfg
pretrained = config.pretrained
if pretrained:
self.lm_backbone = AutoModelForCausalLM.from_pretrained(
config.base_model,
config=config.base_config,
**pretrain_cfg,
)
else:
# When downloading from hub, base config gets converted to dict
# Redownload to make type PretrainedConfig
if isinstance(config.base_config, dict):
config.base_config = AutoConfig.from_pretrained(
config.base_model,
**config.base_config,
)
self.lm_backbone = AutoModelForCausalLM.from_config(
config.base_config,
**kwargs,
)
self.value_head = ValueHead(
n_labels=self.config.n_labels,
hidden_size=self.config.hidden_size,
p_dropout=self.config.p_dropout,
)
def generate(self, *args: Any, **kwargs: Any):
return self.lm_backbone.generate(**kwargs)
def resize_token_embeddings(
self,
new_num_tokens: Optional[int] = None,
pad_to_multiple_of: Optional[int] = None,
) -> nn.Embedding:
# Note need to update vocab size in base config as well so lm_head modification happens
self.config.base_config.vocab_size = new_num_tokens
model_embeds = super().resize_token_embeddings(
new_num_tokens=new_num_tokens,
pad_to_multiple_of=pad_to_multiple_of,
)
return model_embeds
def set_input_embeddings(self, new_embeddings: Any):
return self.lm_backbone.set_input_embeddings(new_embeddings)
def get_input_embeddings(self):
return self.lm_backbone.get_input_embeddings()
def set_output_embeddings(self, new_embeddings: Any):
return self.lm_backbone.set_output_embeddings(new_embeddings)
def get_output_embeddings(self):
return self.lm_backbone.get_output_embeddings()
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Any] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
**kwargs: Any,
):
output = self.lm_backbone(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
labels=labels,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=True,
return_dict=True,
cache_position=cache_position,
)
scores = self.value_head(
output.hidden_states[-1],
).squeeze(-1) - self.config.bias
logits = None
if self.config.return_logits:
logits = output.logits
return SequenceClassifierOutput(
loss=output.loss,
scores=scores,
logits=logits,
past_key_values=output.past_key_values,
hidden_states=output.hidden_states,
attentions=output.attentions,
)
@classmethod
def from_config(
cls,
config: PretrainedConfig,
**kwargs: Any,
) -> PreTrainedModel:
config.pretrained = False
model = cls(config, **kwargs)
return model
@classmethod
def from_pretrained(
cls,
pretrained_model_name_or_path: Optional[Union[str, os.PathLike]],
*model_args: Any,
config: Optional[Union[PretrainedConfig, str, os.PathLike]] = None,
cache_dir: Optional[Union[str, os.PathLike]] = None,
ignore_mismatched_sizes: bool = False,
force_download: bool = False,
local_files_only: bool = False,
token: Optional[Union[str, bool]] = True,
revision: str = 'main',
use_safetensors: Optional[bool] = None,
**kwargs: Any,
) -> PreTrainedModel:
trust_remote_code = kwargs.pop('trust_remote_code', None)
attn_implementation = kwargs.pop(
'attn_implementation',
'eager',
)
return_lm_logits = kwargs.pop('return_lm_logits', False)
load_in_8bit = kwargs.pop('load_in_8bit', False)
pretrained_model_config = AutoConfig.from_pretrained(
pretrained_model_name_or_path,
trust_remote_code=trust_remote_code,
token=token,
attn_implementation=attn_implementation,
use_cache=False,
)
if isinstance(pretrained_model_config, cls.config_class):
return super().from_pretrained(
pretrained_model_name_or_path,
*model_args,
config,
cache_dir,
ignore_mismatched_sizes,
force_download,
local_files_only,
token,
revision,
use_safetensors,
**kwargs,
)
pretrain_cfg = {
'trust_remote_code': trust_remote_code,
'token': token,
'load_in_8bit': load_in_8bit,
'attn_implementation': attn_implementation,
}
reward_model_config = RewardModelConfig(
base_model=pretrained_model_name_or_path,
base_config=pretrained_model_config,
hidden_size=pretrained_model_config.hidden_size,
return_logits=return_lm_logits,
vocab_size=pretrained_model_config.vocab_size,
pretrained=True,
pretrain_cfg=pretrain_cfg,
)
model = cls(reward_model_config)
return model
|