Example Code Doesn't Work
#1
by
evanfrick
- opened
Hi,
I'm trying to run the model, however it seems that safe_rlhf.models can't be imported.
I've tried running pip install safe_rlhf as well as installing it directly from the Github link.
Note that safe_rlhf by itself can import, but doesn't have a models attribute.
Is there an updated example code block to run this model?
Thanks!
@evanfrick , I made a solution on my own that is a standalone file, if you want to use it. See the pipeline at the bottom too.
# Copyright 2023 AllenAI. 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.
# Mostly copied from:
# https://github.com/PKU-Alignment/safe-rlhf/blob/main/safe_rlhf/models/score_model/llama/modeling_llama.py
# https://github.com/PKU-Alignment/safe-rlhf/blob/main/safe_rlhf/models/score_model/__init__.py
# https://github.com/PKU-Alignment/safe-rlhf/blob/main/safe_rlhf/models/normalizer.py
from abc import abstractmethod
from dataclasses import dataclass
from typing import Any, ClassVar, Literal
import torch
import torch.nn as nn
from fastchat.conversation import Conversation, SeparatorStyle, register_conv_template
from torch import distributed as dist
from torch.types import Number
from transformers import (
LlamaModel,
LlamaPreTrainedModel,
PretrainedConfig,
PreTrainedModel,
)
from transformers.models.llama.modeling_llama import (
_CONFIG_FOR_DOC,
LLAMA_INPUTS_DOCSTRING,
)
from transformers.utils.doc import (
add_start_docstrings_to_model_forward,
replace_return_docstrings,
)
from transformers.utils.generic import ModelOutput
# UltraLM / UltraRM Chat Template
# Reference1: https://huggingface.co./openbmb/UltraLM-65b
# Reference2: https://huggingface.co./openbmb/UltraRM-13b
register_conv_template(
Conversation(
name="pku-align",
system_message="BEGINNING OF CONVERSATION:",
roles=("USER", "ASSISTANT"),
sep_style=SeparatorStyle.ADD_COLON_SPACE_SINGLE,
sep=" ",
)
)
NormalizeFunction = Literal["affine", "scale", "translate", "identity"]
NormalizerType = Literal["RunningMeanStd", "ExponentialMovingAverage"]
class Normalizer(nn.Module):
"""Normalize input to have zero mean and unit variance."""
mean: torch.Tensor
var: torch.Tensor
count: torch.LongTensor
normalize_function: NormalizeFunction
def __init__(
self,
normalize_function: NormalizeFunction,
shape: tuple[int, ...],
device: torch.device | str | None = None,
) -> None:
"""Initialize."""
super().__init__()
if normalize_function not in {"affine", "scale", "translate", "identity"}:
raise ValueError(
f"Invalid normalization function type: {normalize_function}. ",
'Expected one of "affine", "scale", "translate", "identity".',
)
self.normalize_function = normalize_function
self.register_buffer("mean", torch.zeros(shape, device=device))
self.register_buffer("var", torch.ones(shape, device=device))
self.register_buffer("count", torch.zeros(1, dtype=torch.long, device=device))
@abstractmethod
def update(self, data: torch.Tensor) -> None:
"""Update mean and variance."""
raise NotImplementedError
@property
def std(self) -> torch.Tensor:
"""Return standard deviation."""
return self.var.sqrt()
def set_mean_var(
self,
mean: torch.Tensor | list[float] | tuple[float, ...] | None,
var: torch.Tensor | list[float] | tuple[float, ...] | None,
) -> None:
"""Set mean and variance."""
mean = torch.as_tensor(mean, dtype=self.mean.dtype, device=self.mean.device) if mean is not None else self.mean
var = torch.as_tensor(var, dtype=self.var.dtype, device=self.var.device) if var is not None else self.var
assert mean.shape == self.mean.shape
assert var.shape == self.var.shape
self.mean = mean
self.var = var
def forward(
self,
data: torch.Tensor,
epsilon: Number = 1e-8,
) -> torch.Tensor:
"""Update and normalize input."""
if self.training:
self.update(data)
return self.normalize(data, epsilon=epsilon)
def normalize(
self,
data: torch.Tensor,
epsilon: Number = 1e-8,
) -> torch.Tensor:
"""Normalize input."""
if self.normalize_function == "affine":
return (data - self.mean.detach()) / (self.std.detach() + epsilon)
if self.normalize_function == "scale":
return data / (self.std.detach() + epsilon)
if self.normalize_function == "translate":
return data - self.mean.detach()
if self.normalize_function == "identity":
return data
raise ValueError(
f"Invalid normalization function type: {self.normalize_function}. ",
'Expected one of "affine", "scale", "translate", "identity".',
)
@classmethod
def instantiate(
cls,
normalizer_type: NormalizerType | None,
normalize_function: NormalizeFunction,
shape: tuple[int, ...],
device: torch.device | str | None = None,
**kwargs: Any,
):
"""Get a normalizer."""
if normalizer_type == "RunningMeanStd":
return RunningMeanStd(
normalize_function,
shape=shape,
device=device,
)
if normalizer_type == "ExponentialMovingAverage":
return ExponentialMovingAverage(
normalize_function,
shape=shape,
device=device,
**kwargs,
)
if normalizer_type is None:
return IdentityNormalizer(
normalize_function,
shape=shape,
device=device,
)
raise ValueError(
f"Invalid normalization function type: {normalizer_type}. "
'Expected one of "RunningMeanStd", "ExponentialMovingAverage".',
)
@dataclass
class ScoreModelOutput(ModelOutput):
"""
Output of the score model.
Args:
scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, score_dim)`):
Prediction scores of the score model.
end_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, score_dim)`):
Prediction scores of the end of the sequence.
last_hidden_state (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_dim)`):
Sequence of hidden-states at the output of the last layer of the model.
end_last_hidden_state (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, hidden_dim)`):
Last hidden state of the sequence at the output of the last layer of the model.
end_index (`torch.LongTensor` of shape `(batch_size,)`):
Indices of the end of the sequence.
"""
scores: torch.FloatTensor | None = None # size = (B, L, D)
end_scores: torch.FloatTensor | None = None # size = (B, D)
last_hidden_state: torch.FloatTensor | None = None # size = (B, L, E)
end_last_hidden_state: torch.FloatTensor | None = None # size = (B, E)
end_index: torch.LongTensor | None = None # size = (B,)
class RunningMeanStd(Normalizer):
"""Running mean and standard deviation."""
def update(self, data: torch.Tensor) -> None:
"""Update mean and variance."""
batch_mean = data.mean(dim=0)
batch_var = data.var(dim=0)
batch_count = data.size(0)
delta = batch_mean - self.mean
total_count = self.count + batch_count
new_mean = self.mean + delta * batch_count / total_count
m_a = self.var * self.count
m_b = batch_var * batch_count
m2 = m_a + m_b + torch.square(delta) * (self.count * batch_count / total_count) # pylint: disable=invalid-name
new_var = m2 / total_count
self.mean = new_mean
self.var = new_var
self.count = total_count
class ExponentialMovingAverage(Normalizer):
"""Exponential moving average."""
def __init__(
self,
normalize_function: NormalizeFunction,
shape: tuple[int, ...],
device: torch.device | str | None = None,
momentum: float = 0.9,
) -> None:
super().__init__(normalize_function, shape=shape, device=device)
self.momentum = momentum
def update(self, data: torch.Tensor) -> None:
"""Update mean and variance."""
batch_mean = data.mean(dim=0)
batch_var = data.var(dim=0)
batch_count = data.size(0)
self.mean = self.momentum * self.mean + (1.0 - self.momentum) * batch_mean
self.var = self.momentum * self.var + (1.0 - self.momentum) * batch_var
self.count += batch_count # pylint: disable=no-member
class IdentityNormalizer(Normalizer):
"""Identity normalizer."""
def update(self, data: torch.Tensor) -> None:
"""Update mean and variance."""
self.count += data.size(0) # pylint: disable=no-member
class ScoreModelMixin:
"""Base class for score models."""
score_head: nn.Linear
normalizer: Normalizer
do_normalize: bool = False
normalize_function: NormalizeFunction = "affine"
_is_score_head_initialized: bool = False
def init_score_head(self, config: PretrainedConfig, hidden_size: int, **kwargs: Any) -> None:
"""Initialize the score head."""
if self._is_score_head_initialized:
return
config.score_dim = kwargs.pop("score_dim", getattr(config, "score_dim", 1))
config.bias = kwargs.pop("bias", getattr(config, "bias", False))
config.score_type = kwargs.pop("score_type", getattr(config, "score_type", "reward"))
if config.score_type == "reward":
self.normalize_function = "affine"
elif config.score_type == "cost":
self.normalize_function = "scale"
elif config.score_type == "critic":
self.normalize_function = "identity"
else:
raise ValueError(
f"Invalid score type: {config.score_type}. Expected one of 'reward', 'cost', or 'critic'.",
)
config.do_normalize = kwargs.pop(
"do_normalize",
getattr(config, "do_normalize", False),
)
self.do_normalize = config.do_normalize
config.normalizer_type = kwargs.pop(
"normalizer_type",
getattr(config, "normalizer_type", None),
)
if config.normalizer_type not in {"RunningMeanStd", "ExponentialMovingAverage", None}:
raise ValueError(
f"Invalid norm type: {config.normalizer_type}."
"Expected one of 'RunningMeadStd', 'ExponentialMovingAverage', or None.",
)
if config.normalizer_type == "ExponentialMovingAverage":
config.momentum = kwargs.pop("momentum", getattr(config, "momentum", None))
momentum = getattr(config, "momentum", None)
self.score_head = nn.Linear(hidden_size, config.score_dim, bias=config.bias)
self.normalizer = Normalizer.instantiate(
normalizer_type=config.normalizer_type,
normalize_function=self.normalize_function,
shape=(config.score_dim,),
momentum=momentum,
)
mean = getattr(config, "mean", None)
var = getattr(config, "var", None)
self.normalizer.set_mean_var(mean, var)
self._is_score_head_initialized = True
def get_scores(
self,
last_hidden_state: torch.FloatTensor, # size = (B, L, E)
attention_mask: torch.BoolTensor | None = None, # size = (B, L)
return_dict: bool | None = None,
) -> tuple[torch.Tensor, torch.Tensor] | ScoreModelOutput:
"""Forward pass of the score model."""
B, L, E = last_hidden_state.size()
if attention_mask is None:
if B > 1:
raise ValueError("'attention_mask' is required when batch size > 1.")
attention_mask = last_hidden_state.new_ones(B, L, dtype=torch.bool) # size = (B, L)
scores = self.score_head(last_hidden_state).float() # size = (B, L, D)
end_index = torch.cat([m.nonzero()[-1] for m in attention_mask]) # size = (B,)
end_last_hidden_state = torch.gather( # size = (B, 1, E)
last_hidden_state,
dim=1,
index=(
end_index.to(last_hidden_state.device)
.unsqueeze(dim=1)
.unsqueeze(dim=2)
.expand(-1, -1, last_hidden_state.size(-1))
),
)
end_scores = torch.gather( # size = (B, 1, D)
scores,
dim=1,
index=(end_index.to(scores.device).unsqueeze(dim=1).unsqueeze(dim=2).expand(-1, -1, scores.size(-1))),
)
end_last_hidden_state = end_last_hidden_state.squeeze(dim=1) # size = (B, E)
end_scores = end_scores.squeeze(dim=1) # size = (B, D)
if self.training:
if dist.is_initialized():
gathered_end_scores_list = [torch.zeros_like(end_scores) for _ in range(dist.get_world_size())]
dist.all_gather(gathered_end_scores_list, end_scores)
gathered_end_scores = torch.cat(gathered_end_scores_list, dim=0)
self.normalizer.update(gathered_end_scores)
else:
self.normalizer.update(end_scores)
self.config.mean = self.normalizer.mean.tolist()
self.config.var = self.normalizer.var.tolist()
if self.do_normalize:
scores = self.normalizer.normalize(scores)
end_scores = self.normalizer.normalize(end_scores)
if not return_dict:
return scores, end_scores
return ScoreModelOutput(
scores=scores, # size = (B, L, D)
end_scores=end_scores, # size = (B, D)
last_hidden_state=last_hidden_state, # size = (B, L, E)
end_last_hidden_state=end_last_hidden_state, # size = (B, E)
end_index=end_index, # size = (B,)
)
def set_normalize(self, mode: bool = True) -> None:
if self.do_normalize == mode:
return
self.do_normalize = self.config.do_normalize = mode
class LlamaForScore(ScoreModelMixin, LlamaPreTrainedModel):
_keys_to_ignore_on_load_missing: ClassVar[list[str]] = ["lm_head.weight"]
def __init__(self, config: PretrainedConfig, **kwargs: Any) -> None:
super().__init__(config)
self.model = LlamaModel(config)
config.architectures = [self.__class__.__name__]
self.init_score_head(config, hidden_size=config.hidden_size, **kwargs)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self) -> nn.Embedding:
return self.model.embed_tokens
def set_input_embeddings(self, value: nn.Embedding) -> None:
self.model.embed_tokens = value
def get_output_embeddings(self) -> None:
return None
def set_decoder(self, decoder: PreTrainedModel) -> None:
self.model = decoder
def get_decoder(self) -> PreTrainedModel:
return self.model
@add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=ScoreModelOutput, config_class=_CONFIG_FOR_DOC)
def forward( # pylint: disable=too-many-arguments
self,
input_ids: torch.LongTensor | None = None,
attention_mask: torch.Tensor | None = None,
position_ids: torch.LongTensor | None = None,
past_key_values: tuple[torch.FloatTensor] | None = None,
inputs_embeds: torch.FloatTensor | None = None,
use_cache: bool | None = None,
return_dict: bool | None = None,
) -> tuple[torch.Tensor, torch.Tensor] | ScoreModelOutput:
"""
Args:
Returns:
Examples:
```python
>>> from safe_rlhf.models.score_model.llama.modeling_llama import LlamaForScore
>>> from transformers import LlamaTokenizer
>>> model = LlamaForScore.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
>>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
>>> prompt = "Hey, are you conscious? Can you talk to me?"
>>> inputs = tokenizer(prompt, return_tensors="pt")
# get score
>>> outputs = model(**inputs)
>>> end_scores = outputs.end_scores
>>> end_scores
tensor([[0.0000]])
```
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.model(
input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=False,
output_hidden_states=False,
return_dict=True,
)
last_hidden_state = outputs.last_hidden_state # size = (B, L, E)
return self.get_scores(
last_hidden_state,
attention_mask=attention_mask,
return_dict=return_dict,
)
# Pipeline addition
class BeaverPipeline:
# init loads task, tokenizer and model
def __init__(self, task, model, tokenizer):
self.task = task
self.model = model
self.tokenizer = tokenizer
def __call__(self, samples, **kwargs):
_ = kwargs.get("batch_size", 1)
truncation = kwargs.get("truncation", True)
padding = kwargs.get("padding", True)
max_length = kwargs.get("max_length", 2048)
inputs = self.tokenizer(
samples,
truncation=truncation,
max_length=max_length,
padding=padding,
return_tensors="pt",
).to("cuda")
with torch.no_grad():
outputs = self.model(**inputs)
return outputs.end_scores
Amazing, thank you!