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# coding=utf-8
# Copyright 2023-present the HuggingFace Inc. team.
#
# 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 inspect
import os
import warnings
from contextlib import contextmanager
import torch
from accelerate import dispatch_model, infer_auto_device_map
from accelerate.hooks import AlignDevicesHook, add_hook_to_module, remove_hook_from_submodules
from accelerate.utils import get_balanced_memory
from huggingface_hub import hf_hub_download
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from transformers import PreTrainedModel
from transformers.modeling_outputs import SequenceClassifierOutput, TokenClassifierOutput
from transformers.utils import PushToHubMixin
from .tuners import AdaLoraModel, LoraModel, PrefixEncoder, PromptEmbedding, PromptEncoder
from .utils import (
TRANSFORMERS_MODELS_TO_PREFIX_TUNING_POSTPROCESS_MAPPING,
WEIGHTS_NAME,
PeftConfig,
PeftType,
PromptLearningConfig,
TaskType,
_set_adapter,
_set_trainable,
get_peft_model_state_dict,
set_peft_model_state_dict,
shift_tokens_right,
)
PEFT_TYPE_TO_MODEL_MAPPING = {
PeftType.LORA: LoraModel,
PeftType.PROMPT_TUNING: PromptEmbedding,
PeftType.P_TUNING: PromptEncoder,
PeftType.PREFIX_TUNING: PrefixEncoder,
PeftType.ADALORA: AdaLoraModel,
}
class PeftModel(PushToHubMixin, torch.nn.Module):
"""
Base model encompassing various Peft methods.
Args:
model ([`~transformers.PreTrainedModel`]): The base transformer model used for Peft.
peft_config ([`PeftConfig`]): The configuration of the Peft model.
**Attributes**:
- **base_model** ([`~transformers.PreTrainedModel`]) -- The base transformer model used for Peft.
- **peft_config** ([`PeftConfig`]) -- The configuration of the Peft model.
- **modules_to_save** (`list` of `str`) -- The list of sub-module names to save when
saving the model.
- **prompt_encoder** ([`PromptEncoder`]) -- The prompt encoder used for Peft if
using [`PromptLearningConfig`].
- **prompt_tokens** (`torch.Tensor`) -- The virtual prompt tokens used for Peft if
using [`PromptLearningConfig`].
- **transformer_backbone_name** (`str`) -- The name of the transformer
backbone in the base model if using [`PromptLearningConfig`].
- **word_embeddings** (`torch.nn.Embedding`) -- The word embeddings of the transformer backbone
in the base model if using [`PromptLearningConfig`].
"""
def __init__(self, model, peft_config: PeftConfig, adapter_name="default"):
super().__init__()
self.base_model = model
self.config = self.base_model.config
self.modules_to_save = None
self.peft_config = {}
self.active_adapter = adapter_name
self.peft_type = peft_config.peft_type
self.base_model_torch_dtype = getattr(model, "dtype", None)
if not isinstance(peft_config, PromptLearningConfig):
self.peft_config[adapter_name] = peft_config
print(PEFT_TYPE_TO_MODEL_MAPPING[peft_config.peft_type])
self.base_model = PEFT_TYPE_TO_MODEL_MAPPING[peft_config.peft_type](
self.base_model, self.peft_config, adapter_name
)
self.set_additional_trainable_modules(peft_config, adapter_name)
else:
self.add_adapter(adapter_name, peft_config)
def save_pretrained(self, save_directory, **kwargs):
r"""
This function saves the adapter model and the adapter configuration files to a directory, so that it can be
reloaded using the [`LoraModel.from_pretrained`] class method, and also used by the [`LoraModel.push_to_hub`]
method.
Args:
save_directory (`str`):
Directory where the adapter model and configuration files will be saved (will be created if it does not
exist).
kwargs (additional keyword arguments, *optional*):
Additional keyword arguments passed along to the `push_to_hub` method.
"""
if os.path.isfile(save_directory):
raise ValueError(f"Provided path ({save_directory}) should be a directory, not a file")
os.makedirs(save_directory, exist_ok=True)
for adapter_name, peft_config in self.peft_config.items():
# save only the trainable weights
output_state_dict = get_peft_model_state_dict(
self, state_dict=kwargs.get("state_dict", None), adapter_name=adapter_name
)
output_dir = os.path.join(save_directory, adapter_name) if adapter_name != "default" else save_directory
os.makedirs(output_dir, exist_ok=True)
torch.save(output_state_dict, os.path.join(output_dir, WEIGHTS_NAME))
# save the config and change the inference mode to `True`
if peft_config.base_model_name_or_path is None:
peft_config.base_model_name_or_path = (
self.base_model.__dict__.get("name_or_path", None)
if isinstance(peft_config, PromptLearningConfig)
else self.base_model.model.__dict__.get("name_or_path", None)
)
inference_mode = peft_config.inference_mode
peft_config.inference_mode = True
peft_config.save_pretrained(output_dir)
peft_config.inference_mode = inference_mode
@classmethod
def from_pretrained(cls, model, model_id, adapter_name="default", is_trainable=False, **kwargs):
r"""
Instantiate a [`LoraModel`] from a pretrained Lora configuration and weights.
Args:
model ([`~transformers.PreTrainedModel`]):
The model to be adapted. The model should be initialized with the
[`~transformers.PreTrainedModel.from_pretrained`] method from the 🤗 Transformers library.
model_id (`str` or `os.PathLike`):
The name of the Lora configuration to use. Can be either:
- A string, the `model id` of a Lora configuration hosted inside a model repo on the Hugging Face
Hub.
- A path to a directory containing a Lora configuration file saved using the `save_pretrained`
method (`./my_lora_config_directory/`).
"""
from .mapping import MODEL_TYPE_TO_PEFT_MODEL_MAPPING, PEFT_TYPE_TO_CONFIG_MAPPING
# load the config
config = PEFT_TYPE_TO_CONFIG_MAPPING[
PeftConfig.from_pretrained(model_id, subfolder=kwargs.get("subfolder", None)).peft_type
].from_pretrained(model_id, subfolder=kwargs.get("subfolder", None))
if (getattr(model, "hf_device_map", None) is not None) and len(
set(model.hf_device_map.values()).intersection({"cpu", "disk"})
) > 0:
remove_hook_from_submodules(model)
if isinstance(config, PromptLearningConfig) and is_trainable:
raise ValueError("Cannot set a prompt learning adapter to trainable when loading pretrained adapter.")
else:
config.inference_mode = not is_trainable
if config.task_type not in MODEL_TYPE_TO_PEFT_MODEL_MAPPING.keys():
model = cls(model, config, adapter_name)
else:
model = MODEL_TYPE_TO_PEFT_MODEL_MAPPING[config.task_type](model, config, adapter_name)
model.load_adapter(model_id, adapter_name, **kwargs)
return model
def _setup_prompt_encoder(self, adapter_name):
config = self.peft_config[adapter_name]
self.prompt_encoder = torch.nn.ModuleDict({})
self.prompt_tokens = {}
transformer_backbone = None
for name, module in self.base_model.named_children():
for param in module.parameters():
param.requires_grad = False
if isinstance(module, PreTrainedModel):
# Make sure to freeze Tranformers model
if transformer_backbone is None:
transformer_backbone = module
self.transformer_backbone_name = name
if config.num_transformer_submodules is None:
config.num_transformer_submodules = 2 if config.task_type == TaskType.SEQ_2_SEQ_LM else 1
for named_param, value in list(transformer_backbone.named_parameters()):
if value.shape[0] == self.base_model.config.vocab_size:
self.word_embeddings = transformer_backbone.get_submodule(named_param.replace(".weight", ""))
break
if config.peft_type == PeftType.PROMPT_TUNING:
prompt_encoder = PromptEmbedding(config, self.word_embeddings)
elif config.peft_type == PeftType.P_TUNING:
prompt_encoder = PromptEncoder(config)
elif config.peft_type == PeftType.PREFIX_TUNING:
prompt_encoder = PrefixEncoder(config)
else:
raise ValueError("Not supported")
self.prompt_encoder.update(torch.nn.ModuleDict({adapter_name: prompt_encoder}))
self.prompt_tokens[adapter_name] = torch.arange(
config.num_virtual_tokens * config.num_transformer_submodules
).long()
def get_prompt_embedding_to_save(self, adapter_name):
"""
Returns the prompt embedding to save when saving the model. Only applicable when `peft_config.peft_type !=
PeftType.LORA`.
"""
prompt_tokens = self.prompt_tokens[adapter_name].unsqueeze(0).expand(1, -1).to(self.device)
if self.peft_config[adapter_name].peft_type == PeftType.PREFIX_TUNING:
prompt_tokens = prompt_tokens[:, : self.peft_config[adapter_name].num_virtual_tokens]
prompt_embeddings = self.prompt_encoder[adapter_name](prompt_tokens)
return prompt_embeddings[0].detach().cpu()
def get_prompt(self, batch_size):
"""
Returns the virtual prompts to use for Peft. Only applicable when `peft_config.peft_type != PeftType.LORA`.
"""
peft_config = self.active_peft_config
prompt_encoder = self.prompt_encoder[self.active_adapter]
prompt_tokens = self.prompt_tokens[self.active_adapter].unsqueeze(0).expand(batch_size, -1).to(self.device)
if peft_config.peft_type == PeftType.PREFIX_TUNING:
prompt_tokens = prompt_tokens[:, : peft_config.num_virtual_tokens]
if peft_config.inference_mode:
past_key_values = prompt_encoder.embedding.weight.repeat(batch_size, 1, 1)
else:
past_key_values = prompt_encoder(prompt_tokens)
past_key_values = past_key_values.view(
batch_size,
peft_config.num_virtual_tokens,
peft_config.num_layers * 2,
peft_config.num_attention_heads,
peft_config.token_dim // peft_config.num_attention_heads,
)
if peft_config.num_transformer_submodules == 2:
past_key_values = torch.cat([past_key_values, past_key_values], dim=2)
past_key_values = past_key_values.permute([2, 0, 3, 1, 4]).split(
peft_config.num_transformer_submodules * 2
)
if TRANSFORMERS_MODELS_TO_PREFIX_TUNING_POSTPROCESS_MAPPING.get(self.config.model_type, None) is not None:
post_process_fn = TRANSFORMERS_MODELS_TO_PREFIX_TUNING_POSTPROCESS_MAPPING[self.config.model_type]
past_key_values = post_process_fn(past_key_values)
return past_key_values
else:
if peft_config.inference_mode:
prompts = prompt_encoder.embedding.weight.repeat(batch_size, 1, 1)
else:
prompts = prompt_encoder(prompt_tokens)
return prompts
def print_trainable_parameters(self):
"""
Prints the number of trainable parameters in the model.
"""
trainable_params = 0
all_param = 0
for _, param in self.named_parameters():
num_params = param.numel()
# if using DS Zero 3 and the weights are initialized empty
if num_params == 0 and hasattr(param, "ds_numel"):
num_params = param.ds_numel
all_param += num_params
if param.requires_grad:
trainable_params += num_params
print(
f"trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param}"
)
def __getattr__(self, name: str):
"""Forward missing attributes to the wrapped module."""
try:
return super().__getattr__(name) # defer to nn.Module's logic
except AttributeError:
return getattr(self.base_model, name)
def forward(self, *args, **kwargs):
"""
Forward pass of the model.
"""
return self.get_base_model()(*args, **kwargs)
@contextmanager
def disable_adapter(self):
"""
Disables the adapter module.
"""
if isinstance(self.active_peft_config, PromptLearningConfig):
old_forward = self.forward
self.forward = self.base_model.forward
else:
self.base_model.disable_adapter_layers()
yield
if isinstance(self.active_peft_config, PromptLearningConfig):
self.forward = old_forward
else:
self.base_model.enable_adapter_layers()
def get_base_model(self):
"""
Returns the base model.
"""
return self.base_model if isinstance(self.active_peft_config, PromptLearningConfig) else self.base_model.model
def add_adapter(self, adapter_name, peft_config):
if peft_config.peft_type != self.peft_type:
raise ValueError(
f"Cannot combine adapters with different peft types. "
f"Found {self.peft_type} and {peft_config.peft_type}."
)
self.peft_config[adapter_name] = peft_config
if isinstance(peft_config, PromptLearningConfig):
self._setup_prompt_encoder(adapter_name)
else:
self.base_model.add_adapter(adapter_name, peft_config)
self.set_additional_trainable_modules(peft_config, adapter_name)
def set_additional_trainable_modules(self, peft_config, adapter_name):
if getattr(peft_config, "modules_to_save", None) is not None:
if self.modules_to_save is None:
self.modules_to_save = set(peft_config.modules_to_save)
else:
self.modules_to_save.update(peft_config.modules_to_save)
_set_trainable(self, adapter_name)
def load_adapter(self, model_id, adapter_name, is_trainable=False, **kwargs):
from .mapping import PEFT_TYPE_TO_CONFIG_MAPPING
if adapter_name not in self.peft_config:
# load the config
peft_config = PEFT_TYPE_TO_CONFIG_MAPPING[
PeftConfig.from_pretrained(model_id, subfolder=kwargs.get("subfolder", None)).peft_type
].from_pretrained(model_id, subfolder=kwargs.get("subfolder", None))
if isinstance(peft_config, PromptLearningConfig) and is_trainable:
raise ValueError("Cannot set a prompt learning adapter to trainable when loading pretrained adapter.")
else:
peft_config.inference_mode = not is_trainable
self.add_adapter(adapter_name, peft_config)
# load weights if any
path = os.path.join(model_id, kwargs["subfolder"]) if kwargs.get("subfolder", None) is not None else model_id
if os.path.exists(os.path.join(path, WEIGHTS_NAME)):
filename = os.path.join(path, WEIGHTS_NAME)
else:
try:
filename = hf_hub_download(model_id, WEIGHTS_NAME, subfolder=kwargs.get("subfolder", None))
except: # noqa
raise ValueError(
f"Can't find weights for {model_id} in {model_id} or in the Hugging Face Hub. "
f"Please check that the file {WEIGHTS_NAME} is present at {model_id}."
)
adapters_weights = torch.load(
filename, map_location=torch.device("cuda" if torch.cuda.is_available() else "cpu")
)
# load the weights into the model
set_peft_model_state_dict(self, adapters_weights, adapter_name=adapter_name)
if (
(getattr(self, "hf_device_map", None) is not None)
and (len(set(self.hf_device_map.values()).intersection({"cpu", "disk"})) > 0)
and len(self.peft_config) == 1
):
device_map = kwargs.get("device_map", "auto")
max_memory = kwargs.get("max_memory", None)
offload_dir = kwargs.get("offload_folder", None)
offload_index = kwargs.get("offload_index", None)
dispatch_model_kwargs = {}
# Safety checker for previous `accelerate` versions
# `offload_index` was introduced in https://github.com/huggingface/accelerate/pull/873/
if "offload_index" in inspect.signature(dispatch_model).parameters:
dispatch_model_kwargs["offload_index"] = offload_index
no_split_module_classes = self._no_split_modules
if device_map != "sequential":
max_memory = get_balanced_memory(
self,
max_memory=max_memory,
no_split_module_classes=no_split_module_classes,
low_zero=(device_map == "balanced_low_0"),
)
if isinstance(device_map, str):
device_map = infer_auto_device_map(
self, max_memory=max_memory, no_split_module_classes=no_split_module_classes
)
dispatch_model(
self,
device_map=device_map,
offload_dir=offload_dir,
**dispatch_model_kwargs,
)
hook = AlignDevicesHook(io_same_device=True)
if isinstance(self.peft_config[adapter_name], PromptLearningConfig):
remove_hook_from_submodules(self.prompt_encoder)
add_hook_to_module(self.get_base_model(), hook)
# Set model in evaluation mode to deactivate Dropout modules by default
self.eval()
def set_adapter(self, adapter_name):
"""
Sets the active adapter.
"""
if adapter_name not in self.peft_config:
raise ValueError(f"Adapter {adapter_name} not found.")
self.active_adapter = adapter_name
if not isinstance(self.peft_config[adapter_name], PromptLearningConfig):
self.base_model.set_adapter(adapter_name)
_set_adapter(self, adapter_name)
@property
def active_peft_config(self):
return self.peft_config[self.active_adapter]
class PeftModelForCausalLM(PeftModel):
"""
Peft model for causal language modeling.
Args:
model ([`~transformers.PreTrainedModel`]): Base transformer model.
peft_config ([`PeftConfig`]): Peft config.
Example:
```py
>>> from transformers import AutoModelForCausalLM
>>> from peft import PeftModelForCausalLM, get_peft_config
>>> config = {
... "peft_type": "PREFIX_TUNING",
... "task_type": "CAUSAL_LM",
... "inference_mode": False,
... "num_virtual_tokens": 20,
... "token_dim": 1280,
... "num_transformer_submodules": 1,
... "num_attention_heads": 20,
... "num_layers": 36,
... "encoder_hidden_size": 1280,
... "prefix_projection": False,
... "postprocess_past_key_value_function": None,
... }
>>> peft_config = get_peft_config(config)
>>> model = AutoModelForCausalLM.from_pretrained("gpt2-large")
>>> peft_model = PeftModelForCausalLM(model, peft_config)
>>> peft_model.print_trainable_parameters()
trainable params: 1843200 || all params: 775873280 || trainable%: 0.23756456724479544
```
"""
def __init__(self, model, peft_config: PeftConfig, adapter_name="default"):
super().__init__(model, peft_config, adapter_name)
self.base_model_prepare_inputs_for_generation = self.base_model.prepare_inputs_for_generation
def forward(
self,
input_ids=None,
audio_input=None,
attention_mask=None,
inputs_embeds=None,
labels=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
**kwargs,
):
peft_config = self.active_peft_config
if not isinstance(peft_config, PromptLearningConfig):
return self.base_model(
input_ids=input_ids,
audio_input=audio_input,
attention_mask=attention_mask,
inputs_embeds=inputs_embeds,
labels=labels,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
**kwargs,
)
batch_size = input_ids.shape[0]
if attention_mask is not None:
# concat prompt attention mask
prefix_attention_mask = torch.ones(batch_size, peft_config.num_virtual_tokens).to(self.device)
attention_mask = torch.cat((prefix_attention_mask, attention_mask), dim=1)
if kwargs.get("position_ids", None) is not None:
warnings.warn("Position ids are not supported for parameter efficient tuning. Ignoring position ids.")
kwargs["position_ids"] = None
if kwargs.get("token_type_ids", None) is not None:
warnings.warn("Token type ids are not supported for parameter efficient tuning. Ignoring token type ids")
kwargs["token_type_ids"] = None
kwargs.update(
{
"attention_mask": attention_mask,
"labels": labels,
"output_attentions": output_attentions,
"output_hidden_states": output_hidden_states,
"return_dict": return_dict,
}
)
if peft_config.peft_type == PeftType.PREFIX_TUNING:
past_key_values = self.get_prompt(batch_size)
print('goes to loop forward 2')
return self.base_model(input_ids=input_ids, past_key_values=past_key_values, **kwargs)
else:
if inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_ids)
# concat prompt labels
if labels is not None:
prefix_labels = torch.full((batch_size, peft_config.num_virtual_tokens), -100).to(self.device)
kwargs["labels"] = torch.cat((prefix_labels, labels), dim=1)
prompts = self.get_prompt(batch_size=batch_size)
prompts = prompts.to(inputs_embeds.dtype)
inputs_embeds = torch.cat((prompts, inputs_embeds), dim=1)
print('goes to loop forward 3')
return self.base_model(inputs_embeds=inputs_embeds, **kwargs)
def generate(self, **kwargs):
peft_config = self.active_peft_config
self.base_model.prepare_inputs_for_generation = self.prepare_inputs_for_generation
try:
if not isinstance(peft_config, PromptLearningConfig):
outputs = self.base_model.generate(**kwargs)
else:
if "input_ids" not in kwargs:
raise ValueError("input_ids must be provided for Peft model generation")
# For gpt2 models, we construct postion_ids on the fly by using attention mask, and position ids need to match input_shape.
# for prefix tuning, input shape is determined using `input_ids`. Thus we should not expand 'attention_mask' here
# for prompt tuning input_ids is not passed but a concatenated input_embeds is passed. Thus attention_mask needs to be of same size of num_virtual_tokens + input_ids
if kwargs.get("attention_mask", None) is not None and peft_config.peft_type in [
PeftType.PROMPT_TUNING,
PeftType.P_TUNING,
]:
# concat prompt attention mask
prefix_attention_mask = torch.ones(
kwargs["input_ids"].shape[0], peft_config.num_virtual_tokens
).to(kwargs["input_ids"].device)
kwargs["attention_mask"] = torch.cat((prefix_attention_mask, kwargs["attention_mask"]), dim=1)
if kwargs.get("position_ids", None) is not None:
warnings.warn(
"Position ids are not supported for parameter efficient tuning. Ignoring position ids."
)
kwargs["position_ids"] = None
if kwargs.get("token_type_ids", None) is not None:
warnings.warn(
"Token type ids are not supported for parameter efficient tuning. Ignoring token type ids"
)
kwargs["token_type_ids"] = None
outputs = self.base_model.generate(**kwargs)
except:
self.base_model.prepare_inputs_for_generation = self.base_model_prepare_inputs_for_generation
raise
else:
self.base_model.prepare_inputs_for_generation = self.base_model_prepare_inputs_for_generation
return outputs
def prepare_inputs_for_generation(self, *args, **kwargs):
peft_config = self.active_peft_config
model_kwargs = self.base_model_prepare_inputs_for_generation(*args, **kwargs)
if isinstance(peft_config, PromptLearningConfig):
if peft_config.peft_type == PeftType.PREFIX_TUNING:
prefix_attention_mask = torch.ones(
model_kwargs["input_ids"].shape[0], peft_config.num_virtual_tokens
).to(model_kwargs["input_ids"].device)
model_kwargs["attention_mask"] = torch.cat(
(prefix_attention_mask, model_kwargs["attention_mask"]), dim=1
)
if model_kwargs["past_key_values"] is None and peft_config.peft_type == PeftType.PREFIX_TUNING:
past_key_values = self.get_prompt(batch_size=model_kwargs["input_ids"].shape[0])
if self.base_model_torch_dtype is not None:
# handle the case for Bloom where it outputs tuple of tuples
if isinstance(past_key_values[0], tuple):
past_key_values = tuple(
tuple(
past_key_value.to(self.base_model_torch_dtype)
for past_key_value in past_key_value_tuple
)
for past_key_value_tuple in past_key_values
)
else:
past_key_values = tuple(
past_key_value.to(self.base_model_torch_dtype) for past_key_value in past_key_values
)
model_kwargs["past_key_values"] = past_key_values
else:
if model_kwargs["past_key_values"] is None:
inputs_embeds = self.word_embeddings(model_kwargs["input_ids"])
prompts = self.get_prompt(batch_size=model_kwargs["input_ids"].shape[0])
prompts = prompts.to(inputs_embeds.dtype)
model_kwargs["inputs_embeds"] = torch.cat((prompts, inputs_embeds), dim=1)
model_kwargs["input_ids"] = None
return model_kwargs
class PeftModelForSeq2SeqLM(PeftModel):
"""
Peft model for sequence-to-sequence language modeling.
Args:
model ([`~transformers.PreTrainedModel`]): Base transformer model.
peft_config ([`PeftConfig`]): Peft config.
Example:
```py
>>> from transformers import AutoModelForSeq2SeqLM
>>> from peft import PeftModelForSeq2SeqLM, get_peft_config
>>> config = {
... "peft_type": "LORA",
... "task_type": "SEQ_2_SEQ_LM",
... "inference_mode": False,
... "r": 8,
... "target_modules": ["q", "v"],
... "lora_alpha": 32,
... "lora_dropout": 0.1,
... "merge_weights": False,
... "fan_in_fan_out": False,
... "enable_lora": None,
... "bias": "none",
... }
>>> peft_config = get_peft_config(config)
>>> model = AutoModelForSeq2SeqLM.from_pretrained("t5-base")
>>> peft_model = PeftModelForSeq2SeqLM(model, peft_config)
>>> peft_model.print_trainable_parameters()
trainable params: 884736 || all params: 223843584 || trainable%: 0.3952474242013566
```
"""
def __init__(self, model, peft_config: PeftConfig, adapter_name="default"):
super().__init__(model, peft_config, adapter_name)
self.base_model_prepare_inputs_for_generation = self.base_model.prepare_inputs_for_generation
self.base_model_prepare_encoder_decoder_kwargs_for_generation = (
self.base_model._prepare_encoder_decoder_kwargs_for_generation
)
def forward(
self,
input_ids=None,
attention_mask=None,
inputs_embeds=None,
decoder_input_ids=None,
decoder_attention_mask=None,
decoder_inputs_embeds=None,
labels=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
**kwargs,
):
peft_config = self.active_peft_config
if not isinstance(peft_config, PromptLearningConfig):
return self.base_model(
input_ids=input_ids,
attention_mask=attention_mask,
inputs_embeds=inputs_embeds,
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
decoder_inputs_embeds=decoder_inputs_embeds,
labels=labels,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
**kwargs,
)
batch_size = input_ids.shape[0]
if decoder_attention_mask is not None:
# concat prompt attention mask
prefix_attention_mask = torch.ones(batch_size, peft_config.num_virtual_tokens).to(self.device)
decoder_attention_mask = torch.cat((prefix_attention_mask, decoder_attention_mask), dim=1)
if kwargs.get("position_ids", None) is not None:
warnings.warn("Position ids are not supported for parameter efficient tuning. Ignoring position ids.")
kwargs["position_ids"] = None
if kwargs.get("token_type_ids", None) is not None:
warnings.warn("Token type ids are not supported for parameter efficient tuning. Ignoring token type ids")
kwargs["token_type_ids"] = None
kwargs.update(
{
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"labels": labels,
"output_attentions": output_attentions,
"output_hidden_states": output_hidden_states,
"return_dict": return_dict,
}
)
if peft_config.peft_type == PeftType.PREFIX_TUNING:
past_key_values = self.get_prompt(batch_size)
return self.base_model(
input_ids=input_ids, decoder_input_ids=decoder_input_ids, past_key_values=past_key_values, **kwargs
)
else:
if inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_ids)
if decoder_inputs_embeds is None and decoder_input_ids is None:
decoder_input_ids = shift_tokens_right(
labels, self.config.pad_token_id, self.config.decoder_start_token_id
)
decoder_inputs_embeds = self.word_embeddings(decoder_input_ids)
if attention_mask is not None:
# concat prompt attention mask
prefix_attention_mask = torch.ones(batch_size, peft_config.num_virtual_tokens).to(self.device)
kwargs["attention_mask"] = torch.cat((prefix_attention_mask, attention_mask), dim=1)
# concat prompt labels
if labels is not None:
if peft_config.num_transformer_submodules == 1:
kwargs["labels"] = labels
elif peft_config.num_transformer_submodules == 2:
prefix_labels = torch.full((batch_size, peft_config.num_virtual_tokens), -100).to(self.device)
kwargs["labels"] = torch.cat((prefix_labels, labels), dim=1)
prompts = self.get_prompt(batch_size=batch_size)
prompts = prompts.to(inputs_embeds.dtype)
inputs_embeds = torch.cat((prompts[:, : peft_config.num_virtual_tokens], inputs_embeds), dim=1)
if peft_config.num_transformer_submodules == 1:
return self.base_model(inputs_embeds=inputs_embeds, **kwargs)
elif peft_config.num_transformer_submodules == 2:
decoder_inputs_embeds = torch.cat(
(prompts[:, peft_config.num_virtual_tokens :], decoder_inputs_embeds), dim=1
)
return self.base_model(
inputs_embeds=inputs_embeds, decoder_inputs_embeds=decoder_inputs_embeds, **kwargs
)
def generate(self, **kwargs):
peft_config = self.active_peft_config
self.base_model.prepare_inputs_for_generation = self.prepare_inputs_for_generation
self.base_model._prepare_encoder_decoder_kwargs_for_generation = (
self._prepare_encoder_decoder_kwargs_for_generation
)
try:
if not isinstance(peft_config, PromptLearningConfig):
outputs = self.base_model.generate(**kwargs)
else:
if "input_ids" not in kwargs:
raise ValueError("input_ids must be provided for Peft model generation")
if kwargs.get("position_ids", None) is not None:
warnings.warn(
"Position ids are not supported for parameter efficient tuning. Ignoring position ids."
)
kwargs["position_ids"] = None
if kwargs.get("token_type_ids", None) is not None:
warnings.warn(
"Token type ids are not supported for parameter efficient tuning. Ignoring token type ids"
)
kwargs["token_type_ids"] = None
if peft_config.peft_type == PeftType.PREFIX_TUNING:
outputs = self.base_model.generate(**kwargs)
else:
raise NotImplementedError
except:
self.base_model.prepare_inputs_for_generation = self.base_model_prepare_inputs_for_generation
self.base_model._prepare_encoder_decoder_kwargs_for_generation = (
self.base_model_prepare_encoder_decoder_kwargs_for_generation
)
raise
else:
self.base_model.prepare_inputs_for_generation = self.base_model_prepare_inputs_for_generation
self.base_model._prepare_encoder_decoder_kwargs_for_generation = (
self.base_model_prepare_encoder_decoder_kwargs_for_generation
)
return outputs
def prepare_inputs_for_generation(self, *args, **kwargs):
peft_config = self.active_peft_config
model_kwargs = self.base_model_prepare_inputs_for_generation(*args, **kwargs)
if model_kwargs["past_key_values"] is None and peft_config.peft_type == PeftType.PREFIX_TUNING:
batch_size = model_kwargs["decoder_input_ids"].shape[0]
past_key_values = self.get_prompt(batch_size)
if self.base_model_torch_dtype is not None:
# handle the case for Bloom where it outputs tuple of tuples
if isinstance(past_key_values[0], tuple):
past_key_values = tuple(
tuple(
past_key_value.to(self.base_model_torch_dtype) for past_key_value in past_key_value_tuple
)
for past_key_value_tuple in past_key_values
)
else:
past_key_values = tuple(
past_key_value.to(self.base_model_torch_dtype) for past_key_value in past_key_values
)
model_kwargs["past_key_values"] = past_key_values
return model_kwargs
class PeftModelForTokenClassification(PeftModel):
"""
Peft model for token classification tasks.
Args:
model ([`~transformers.PreTrainedModel`]): Base transformer model.
peft_config ([`PeftConfig`]): Peft config.
**Attributes**:
- **config** ([`~transformers.PretrainedConfig`]) -- The configuration object of the base model.
- **cls_layer_name** (`str`) -- The name of the classification layer.
Example:
```py
>>> from transformers import AutoModelForSequenceClassification
>>> from peft import PeftModelForTokenClassification, get_peft_config
>>> config = {
... "peft_type": "PREFIX_TUNING",
... "task_type": "TOKEN_CLS",
... "inference_mode": False,
... "num_virtual_tokens": 20,
... "token_dim": 768,
... "num_transformer_submodules": 1,
... "num_attention_heads": 12,
... "num_layers": 12,
... "encoder_hidden_size": 768,
... "prefix_projection": False,
... "postprocess_past_key_value_function": None,
... }
>>> peft_config = get_peft_config(config)
>>> model = AutoModelForTokenClassification.from_pretrained("bert-base-cased")
>>> peft_model = PeftModelForTokenClassification(model, peft_config)
>>> peft_model.print_trainable_parameters()
trainable params: 370178 || all params: 108680450 || trainable%: 0.3406113979101117
```
"""
def __init__(self, model, peft_config: PeftConfig = None, adapter_name="default"):
super().__init__(model, peft_config, adapter_name)
if self.modules_to_save is None:
self.modules_to_save = {"classifier", "score"}
else:
self.modules_to_save.update({"classifier", "score"})
for name, _ in self.base_model.named_children():
if any(module_name in name for module_name in self.modules_to_save):
self.cls_layer_name = name
break
# to make sure classifier layer is trainable
_set_trainable(self, adapter_name)
def forward(
self,
input_ids=None,
attention_mask=None,
inputs_embeds=None,
labels=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
**kwargs,
):
peft_config = self.active_peft_config
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if not isinstance(peft_config, PromptLearningConfig):
return self.base_model(
input_ids=input_ids,
attention_mask=attention_mask,
inputs_embeds=inputs_embeds,
labels=labels,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
**kwargs,
)
batch_size = input_ids.shape[0]
if attention_mask is not None:
# concat prompt attention mask
prefix_attention_mask = torch.ones(batch_size, peft_config.num_virtual_tokens).to(self.device)
attention_mask = torch.cat((prefix_attention_mask, attention_mask), dim=1)
if kwargs.get("position_ids", None) is not None:
warnings.warn("Position ids are not supported for parameter efficient tuning. Ignoring position ids.")
kwargs["position_ids"] = None
kwargs.update(
{
"attention_mask": attention_mask,
"labels": labels,
"output_attentions": output_attentions,
"output_hidden_states": output_hidden_states,
"return_dict": return_dict,
}
)
if peft_config.peft_type == PeftType.PREFIX_TUNING:
return self._prefix_tuning_forward(input_ids=input_ids, **kwargs)
else:
if kwargs.get("token_type_ids", None) is not None:
kwargs["token_type_ids"] = torch.cat(
(
torch.zeros(batch_size, peft_config.num_virtual_tokens).to(self.device),
kwargs["token_type_ids"],
),
dim=1,
).long()
if inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_ids)
prompts = self.get_prompt(batch_size=batch_size)
prompts = prompts.to(inputs_embeds.dtype)
inputs_embeds = torch.cat((prompts, inputs_embeds), dim=1)
return self.base_model(inputs_embeds=inputs_embeds, **kwargs)
def _prefix_tuning_forward(
self,
input_ids=None,
attention_mask=None,
inputs_embeds=None,
labels=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
**kwargs,
):
batch_size = input_ids.shape[0]
past_key_values = self.get_prompt(batch_size)
fwd_params = list(inspect.signature(self.base_model.forward).parameters.keys())
kwargs.update(
{
"input_ids": input_ids,
"attention_mask": attention_mask,
"inputs_embeds": inputs_embeds,
"output_attentions": output_attentions,
"output_hidden_states": output_hidden_states,
"return_dict": return_dict,
"past_key_values": past_key_values,
}
)
if "past_key_values" in fwd_params:
return self.base_model(labels=labels, **kwargs)
else:
transformer_backbone_name = self.base_model.get_submodule(self.transformer_backbone_name)
fwd_params = list(inspect.signature(transformer_backbone_name.forward).parameters.keys())
if "past_key_values" not in fwd_params:
raise ValueError("Model does not support past key values which are required for prefix tuning.")
outputs = transformer_backbone_name(**kwargs)
sequence_output = outputs[0]
if "dropout" in [name for name, _ in list(self.base_model.named_children())]:
sequence_output = self.base_model.dropout(sequence_output)
logits = self.base_model.get_submodule(self.cls_layer_name)(sequence_output)
loss = None
loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TokenClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
class PeftModelForSequenceClassification(PeftModel):
"""
Peft model for sequence classification tasks.
Args:
model ([`~transformers.PreTrainedModel`]): Base transformer model.
peft_config ([`PeftConfig`]): Peft config.
**Attributes**:
- **config** ([`~transformers.PretrainedConfig`]) -- The configuration object of the base model.
- **cls_layer_name** (`str`) -- The name of the classification layer.
Example:
```py
>>> from transformers import AutoModelForSequenceClassification
>>> from peft import PeftModelForSequenceClassification, get_peft_config
>>> config = {
... "peft_type": "PREFIX_TUNING",
... "task_type": "SEQ_CLS",
... "inference_mode": False,
... "num_virtual_tokens": 20,
... "token_dim": 768,
... "num_transformer_submodules": 1,
... "num_attention_heads": 12,
... "num_layers": 12,
... "encoder_hidden_size": 768,
... "prefix_projection": False,
... "postprocess_past_key_value_function": None,
... }
>>> peft_config = get_peft_config(config)
>>> model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased")
>>> peft_model = PeftModelForSequenceClassification(model, peft_config)
>>> peft_model.print_trainable_parameters()
trainable params: 370178 || all params: 108680450 || trainable%: 0.3406113979101117
```
"""
def __init__(self, model, peft_config: PeftConfig, adapter_name="default"):
super().__init__(model, peft_config, adapter_name)
if self.modules_to_save is None:
self.modules_to_save = {"classifier", "score"}
else:
self.modules_to_save.update({"classifier", "score"})
for name, _ in self.base_model.named_children():
if any(module_name in name for module_name in self.modules_to_save):
self.cls_layer_name = name
break
# to make sure classifier layer is trainable
_set_trainable(self, adapter_name)
def forward(
self,
input_ids=None,
attention_mask=None,
inputs_embeds=None,
labels=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
**kwargs,
):
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
peft_config = self.active_peft_config
if not isinstance(peft_config, PromptLearningConfig):
return self.base_model(
input_ids=input_ids,
attention_mask=attention_mask,
inputs_embeds=inputs_embeds,
labels=labels,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
**kwargs,
)
batch_size = input_ids.shape[0]
if attention_mask is not None:
# concat prompt attention mask
prefix_attention_mask = torch.ones(batch_size, peft_config.num_virtual_tokens).to(self.device)
attention_mask = torch.cat((prefix_attention_mask, attention_mask), dim=1)
if kwargs.get("position_ids", None) is not None:
warnings.warn("Position ids are not supported for parameter efficient tuning. Ignoring position ids.")
kwargs["position_ids"] = None
kwargs.update(
{
"attention_mask": attention_mask,
"labels": labels,
"output_attentions": output_attentions,
"output_hidden_states": output_hidden_states,
"return_dict": return_dict,
}
)
if peft_config.peft_type == PeftType.PREFIX_TUNING:
return self._prefix_tuning_forward(input_ids=input_ids, **kwargs)
else:
if kwargs.get("token_type_ids", None) is not None:
kwargs["token_type_ids"] = torch.cat(
(
torch.zeros(batch_size, peft_config.num_virtual_tokens).to(self.device),
kwargs["token_type_ids"],
),
dim=1,
).long()
if inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_ids)
prompts = self.get_prompt(batch_size=batch_size)
prompts = prompts.to(inputs_embeds.dtype)
inputs_embeds = torch.cat((prompts, inputs_embeds), dim=1)
return self.base_model(inputs_embeds=inputs_embeds, **kwargs)
def _prefix_tuning_forward(
self,
input_ids=None,
attention_mask=None,
inputs_embeds=None,
labels=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
**kwargs,
):
batch_size = input_ids.shape[0]
past_key_values = self.get_prompt(batch_size)
fwd_params = list(inspect.signature(self.base_model.forward).parameters.keys())
kwargs.update(
{
"input_ids": input_ids,
"attention_mask": attention_mask,
"inputs_embeds": inputs_embeds,
"output_attentions": output_attentions,
"output_hidden_states": output_hidden_states,
"return_dict": return_dict,
"past_key_values": past_key_values,
}
)
if "past_key_values" in fwd_params:
return self.base_model(labels=labels, **kwargs)
else:
transformer_backbone_name = self.base_model.get_submodule(self.transformer_backbone_name)
fwd_params = list(inspect.signature(transformer_backbone_name.forward).parameters.keys())
if "past_key_values" not in fwd_params:
raise ValueError("Model does not support past key values which are required for prefix tuning.")
outputs = transformer_backbone_name(**kwargs)
pooled_output = outputs[1] if len(outputs) > 1 else outputs[0]
if "dropout" in [name for name, _ in list(self.base_model.named_children())]:
pooled_output = self.base_model.dropout(pooled_output)
logits = self.base_model.get_submodule(self.cls_layer_name)(pooled_output)
loss = None
if labels is not None:
if self.config.problem_type is None:
if self.base_model.num_labels == 1:
self.config.problem_type = "regression"
elif self.base_model.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification"
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.base_model.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type == "single_label_classification":
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.base_model.num_labels), labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(logits, labels)
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return SequenceClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
) |