File size: 18,822 Bytes
252711e |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# 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.
import torch
import torch.nn as nn
from transformers import AutoModelForCausalLM, AutoModelForSeq2SeqLM
from .modeling_base import PreTrainedModelWrapper
class ValueHead(nn.Module):
r"""
The ValueHead class implements a head for GPT2 that returns a scalar for each output token.
"""
def __init__(self, config, **kwargs):
super().__init__()
if not hasattr(config, "summary_dropout_prob"):
summary_dropout_prob = kwargs.pop("summary_dropout_prob", 0.1)
else:
summary_dropout_prob = config.summary_dropout_prob
self.dropout = nn.Dropout(summary_dropout_prob) if summary_dropout_prob else nn.Identity()
# some models such as OPT have a projection layer before the word embeddings - e.g. OPT-350m
if hasattr(config, "hidden_size"):
hidden_size = config.hidden_size
if hasattr(config, "word_embed_proj_dim"):
hidden_size = config.word_embed_proj_dim
elif hasattr(config, "is_encoder_decoder"):
if config.is_encoder_decoder and hasattr(config, "decoder"):
if hasattr(config.decoder, "hidden_size"):
hidden_size = config.decoder.hidden_size
self.summary = nn.Linear(hidden_size, 1)
self.flatten = nn.Flatten()
def forward(self, hidden_states):
output = self.dropout(hidden_states)
# For now force upcast in fp32 if needed. Let's keep the
# output in fp32 for numerical stability.
if output.dtype != self.summary.weight.dtype:
output = output.to(self.summary.weight.dtype)
output = self.summary(output)
return output
class AutoModelForCausalLMWithValueHead(PreTrainedModelWrapper):
r"""
An autoregressive model with a value head in addition to the language model head.
This class inherits from `~trl.PreTrainedModelWrapper` and wraps a
`transformers.PreTrainedModel` class. The wrapper class supports classic functions
such as `from_pretrained`, `push_to_hub` and `generate`. To call a method of the wrapped
model, simply manipulate the `pretrained_model` attribute of this class.
Class attributes:
- **transformers_parent_class** (`transformers.PreTrainedModel`) -- The parent class of the wrapped model. This
should be set to `transformers.AutoModelForCausalLM` for this class.
- **lm_head_namings** (`tuple`) -- A tuple of strings that are used to identify the language model head of the
wrapped model. This is set to `("lm_head", "embed_out")` for this class but can be changed for other models
in the future
- **supported_args** (`tuple`) -- A tuple of strings that are used to identify the arguments that are supported
by the `ValueHead` class. Currently, the supported args are:
- **summary_dropout_prob** (`float`, `optional`, defaults to `None`) -- The dropout probability for the
`ValueHead` class.
- **v_head_initializer_range** (`float`, `optional`, defaults to `0.2`) -- The initializer range for the
`ValueHead` if a specific initialization strategy is selected.
- **v_head_init_strategy** (`str`, `optional`, defaults to `None`) -- The initialization strategy for the
`ValueHead`. Currently, the supported strategies are:
- **`None`** -- Initializes the weights of the `ValueHead` with a random distribution. This is the default
strategy.
- **"normal"** -- Initializes the weights of the `ValueHead` with a normal distribution.
"""
transformers_parent_class = AutoModelForCausalLM
lm_head_namings = ["lm_head", "embed_out"]
supported_args = (
"summary_dropout_prob",
"v_head_initializer_range",
"v_head_init_strategy",
)
def __init__(self, pretrained_model, **kwargs):
r"""
Initializes the model.
Args:
pretrained_model (`transformers.PreTrainedModel`):
The model to wrap. It should be a causal language model such as GPT2.
or any model mapped inside the `AutoModelForCausalLM` class.
kwargs (`dict`, `optional`):
Additional keyword arguments, that are passed to the `ValueHead` class.
"""
super().__init__(pretrained_model, **kwargs)
v_head_kwargs, _, _ = self._split_kwargs(kwargs)
if not any(hasattr(self.pretrained_model, attribute) for attribute in self.lm_head_namings):
raise ValueError("The model does not have a language model head, please use a model that has one.")
self.v_head = ValueHead(self.pretrained_model.config, **v_head_kwargs)
self._init_weights(**v_head_kwargs)
def _init_weights(self, **kwargs):
r"""
Initializes the weights of the value head. The default initialization strategy is random.
Users can pass a different initialization strategy by passing the `v_head_init_strategy` argument
when calling `.from_pretrained`. Supported strategies are:
- `normal`: initializes the weights with a normal distribution.
Args:
**kwargs (`dict`, `optional`):
Additional keyword arguments, that are passed to the `ValueHead` class. These arguments
can contain the `v_head_init_strategy` argument as well as the `v_head_initializer_range`
argument.
"""
initializer_range = kwargs.pop("v_head_initializer_range", 0.2)
# random init by default
init_strategy = kwargs.pop("v_head_init_strategy", None)
if init_strategy is None:
# do nothing
pass
elif init_strategy == "normal":
self.v_head.summary.weight.data.normal_(mean=0.0, std=initializer_range)
self.v_head.summary.bias.data.zero_()
def forward(
self,
input_ids=None,
past_key_values=None,
attention_mask=None,
**kwargs,
):
r"""
Applies a forward pass to the wrapped model and returns the logits of the value head.
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary.
past_key_values (`tuple(tuple(torch.FloatTensor))`, `optional`):
Contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model
(see `past_key_values` input) to speed up sequential decoding.
attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, `optional`):
Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
kwargs (`dict`, `optional`):
Additional keyword arguments, that are passed to the wrapped model.
"""
kwargs["output_hidden_states"] = True # this had already been set in the LORA / PEFT examples
kwargs["past_key_values"] = past_key_values
if self.is_peft_model and self.pretrained_model.active_peft_config.peft_type == "PREFIX_TUNING":
kwargs.pop("past_key_values")
base_model_output = self.pretrained_model(
input_ids=input_ids,
attention_mask=attention_mask,
**kwargs,
)
last_hidden_state = base_model_output.hidden_states[-1]
lm_logits = base_model_output.logits
loss = base_model_output.loss
if last_hidden_state.device != self.v_head.summary.weight.device:
last_hidden_state = last_hidden_state.to(self.v_head.summary.weight.device)
value = self.v_head(last_hidden_state).squeeze(-1)
# force upcast in fp32 if logits are in half-precision
if lm_logits.dtype != torch.float32:
lm_logits = lm_logits.float()
return (lm_logits, loss, value)
def generate(self, *args, **kwargs):
r"""
A simple wrapper around the `generate` method of the wrapped model.
Please refer to the [`generate`](https://huggingface.co/docs/transformers/internal/generation_utils)
method of the wrapped model for more information about the supported arguments.
Args:
*args (`list`, *optional*):
Positional arguments passed to the `generate` method of the wrapped model.
**kwargs (`dict`, *optional*):
Keyword arguments passed to the `generate` method of the wrapped model.
"""
return self.pretrained_model.generate(*args, **kwargs)
def state_dict(self, *args, **kwargs):
r"""
Returns the state dictionary of the model. We add the state dictionary of the value head
to the state dictionary of the wrapped model by prepending the key with `v_head.`.
"""
if not self.is_peft_model:
pretrained_model_state_dict = self.pretrained_model.state_dict(*args, **kwargs)
else:
# if it is a peft model, only save the v_head
pretrained_model_state_dict = {}
v_head_state_dict = self.v_head.state_dict(*args, **kwargs)
for k, v in v_head_state_dict.items():
pretrained_model_state_dict[f"v_head.{k}"] = v
return pretrained_model_state_dict
def push_to_hub(self, *args, **kwargs):
setattr(self.pretrained_model, "v_head", self.v_head)
return self.pretrained_model.push_to_hub(*args, **kwargs)
def post_init(self, state_dict):
r"""
We add the state dictionary of the value head to the state dictionary of the wrapped model
by prepending the key with `v_head.`. This function removes the `v_head.` prefix from the
keys of the value head state dictionary.
"""
for k in list(state_dict.keys()):
if "v_head." in k:
state_dict[k.replace("v_head.", "")] = state_dict.pop(k)
self.v_head.load_state_dict(state_dict, strict=False)
del state_dict
if hasattr(self.pretrained_model, "hf_device_map"):
if "cpu" in self.pretrained_model.hf_device_map.values() or "disk" in self.pretrained_model.hf_device_map.values():
raise ValueError("The model is offloaded on CPU or disk - CPU & disk offloading is not supported for ValueHead models.")
first_device = list(set(self.pretrained_model.hf_device_map.values()))[0]
self.v_head = self.v_head.to(first_device)
def set_device_hook(module, input, outputs):
new_output = ()
for output in outputs:
if isinstance(output, torch.Tensor):
new_output += (output.to(first_device),)
else:
new_output += (output,)
return new_output
self.register_forward_hook(set_device_hook)
self.is_sequential_parallel = True
class AutoModelForSeq2SeqLMWithValueHead(PreTrainedModelWrapper):
r"""
A seq2seq model with a value head in addition to the language model head.
This class inherits from `~trl.PreTrainedModelWrapper` and wraps a
`transformers.PreTrainedModel` class. The wrapper class supports classic functions
such as `from_pretrained` and `push_to_hub` and also provides some additional
functionalities such as `generate`.
Args:
pretrained_model (`transformers.PreTrainedModel`):
The model to wrap. It should be a causal language model such as GPT2.
or any model mapped inside the `AutoModelForSeq2SeqLM` class.
kwargs:
Additional keyword arguments passed along to the `ValueHead` class.
"""
transformers_parent_class = AutoModelForSeq2SeqLM
lm_head_namings = ["lm_head", "embed_out", "output_projection"]
supported_args = (
"summary_dropout_prob",
"v_head_initializer_range",
"v_head_init_strategy",
)
def __init__(self, pretrained_model, **kwargs):
super().__init__(pretrained_model, **kwargs)
v_head_kwargs, _, _ = self._split_kwargs(kwargs)
self.is_encoder_decoder = True
if not self._has_lm_head():
raise ValueError("The model does not have a language model head, please use a model that has one.")
self.v_head = ValueHead(self.pretrained_model.config, **v_head_kwargs)
self._init_weights(**v_head_kwargs)
def _has_lm_head(self):
# check module names of all modules inside `pretrained_model` to find the language model head
for name, module in self.pretrained_model.named_modules():
if any(attribute in name for attribute in self.lm_head_namings):
return True
return False
def post_init(self, state_dict):
r"""
We add the state dictionary of the value head to the state dictionary of the wrapped model
by prepending the key with `v_head.`. This function removes the `v_head.` prefix from the
keys of the value head state dictionary.
"""
for k in list(state_dict.keys()):
if "v_head." in k:
state_dict[k.replace("v_head.", "")] = state_dict.pop(k)
self.v_head.load_state_dict(state_dict, strict=False)
del state_dict
if hasattr(self.pretrained_model, "hf_device_map"):
if "cpu" in self.pretrained_model.hf_device_map.values() or "disk" in self.pretrained_model.hf_device_map.values():
raise ValueError("The model is offloaded on CPU or disk - CPU & disk offloading is not supported for ValueHead models.")
# get the lm_head device
for name, module in self.pretrained_model.named_modules():
if any(attribute in name for attribute in self.lm_head_namings):
lm_head_device = module.weight.device
break
# put v_head on the same device as the lm_head to avoid issues
self.v_head = self.v_head.to(lm_head_device)
def set_device_hook(module, input, outputs):
r"""
A hook that sets the device of the output of the model to the device of the first
parameter of the model.
Args:
module (`nn.Module`):
The module to which the hook is attached.
input (`tuple`):
The input to the module.
outputs (`tuple`):
The output of the module.
"""
new_output = ()
for output in outputs:
if isinstance(output, torch.Tensor):
new_output += (output.to(lm_head_device),)
else:
new_output += (output,)
return new_output
self.register_forward_hook(set_device_hook)
self.is_sequential_parallel = True
def state_dict(self, *args, **kwargs):
r"""
Returns the state dictionary of the model. We add the state dictionary of the value head
to the state dictionary of the wrapped model by prepending the key with `v_head.`.
"""
if not self.is_peft_model:
pretrained_model_state_dict = self.pretrained_model.state_dict(*args, **kwargs)
else:
# if it is a peft model, only save the v_head
pretrained_model_state_dict = {}
v_head_state_dict = self.v_head.state_dict(*args, **kwargs)
for k, v in v_head_state_dict.items():
pretrained_model_state_dict[f"v_head.{k}"] = v
return pretrained_model_state_dict
def push_to_hub(self, *args, **kwargs):
setattr(self.pretrained_model, "v_head", self.v_head)
return self.pretrained_model.push_to_hub(*args, **kwargs)
def _init_weights(self, **kwargs):
r"""
We initialize the weights of the value head.
"""
initializer_range = kwargs.pop("v_head_initializer_range", 0.2)
# random init by default
init_strategy = kwargs.pop("v_head_init_strategy", None)
if init_strategy is None:
# do nothing
pass
elif init_strategy == "normal":
self.v_head.summary.weight.data.normal_(mean=0.0, std=initializer_range)
self.v_head.summary.bias.data.zero_()
def forward(
self,
input_ids=None,
past_key_values=None,
attention_mask=None,
**kwargs,
):
kwargs["past_key_values"] = past_key_values
if self.is_peft_model and self.pretrained_model.active_peft_config.peft_type == "PREFIX_TUNING":
kwargs.pop("past_key_values")
base_model_output = self.pretrained_model(
input_ids=input_ids,
attention_mask=attention_mask,
output_hidden_states=True, # We force the model to output hidden states
**kwargs,
)
last_hidden_state = base_model_output.decoder_hidden_states[-1]
lm_logits = base_model_output.logits
loss = base_model_output.loss
value = self.v_head(last_hidden_state).squeeze(-1)
# force upcast in fp32 if logits are in half-precision
if lm_logits.dtype != torch.float32:
lm_logits = lm_logits.float()
return (lm_logits, loss, value)
def generate(self, *args, **kwargs):
r"""
We call `generate` on the wrapped model.
"""
return self.pretrained_model.generate(*args, **kwargs)
|