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from dataclasses import dataclass
from typing import Optional, List, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint
from transformers import LlamaModel, LlamaPreTrainedModel
from transformers.models.llama.modeling_llama import LLAMA_INPUTS_DOCSTRING
from transformers.utils import ModelOutput
from transformers.utils import add_start_docstrings_to_model_forward
class GatingNetwork(nn.Module):
def __init__(self, in_features: int, out_features: int, bias: bool = True, temperature: float = 10,
logit_scale: float = 1., hidden_dim: int = 1024, n_hidden: int = 3):
super().__init__()
self.temperature = temperature
self.logit_scale = nn.Parameter(torch.ones(1) * logit_scale)
layers = []
for _ in range(n_hidden):
layers.append(nn.Linear(in_features, hidden_dim))
in_features = hidden_dim
layers.append(nn.Linear(in_features, out_features, bias=bias))
self.layers = nn.ModuleList(layers)
def forward(self, x: torch.FloatTensor) -> torch.FloatTensor:
# Apply the linear layers with ReLU
for i, layer in enumerate(self.layers):
x = F.relu(layer(x)) if i < len(self.layers) - 1 else layer(x)
# Apply the conditional ReLU using the expanded mask
x = F.softmax(x / self.temperature, dim=1)
return x * self.logit_scale[0]
# token_pattern = tokenizer.encode("<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n", add_special_tokens=False, )
token_pattern = [128009, 128006, 78191, 128007, 271]
def find_token_for_gating(lst, ):
"""Find the last occurrence of a token_pattern in a list."""
token_pattern_len = len(token_pattern)
search_end = len(lst)
for j in range(search_end - token_pattern_len, -1, -1):
if lst[j:j + token_pattern_len] == token_pattern:
return j
raise ValueError("Token pattern not found in the list.")
@dataclass
class CustomOutput(ModelOutput):
"""
Base class for outputs of sentence classification models.
Args:
hidden_state (`Tuple[torch.FloatTensor]` of length `config.num_hidden_layers`):
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.
prompt_embedding (`torch.FloatTensor` of shape `(batch_size, hidden_size)`):
The embeddings of the prompt tokens.
gating_output (`torch.FloatTensor` of shape `(batch_size, config.num_objectives)`):
The logits for the gating network.
score (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
The final reward score.
logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
Same as score
"""
rewards: torch.FloatTensor = None
hidden_state: Optional[Tuple[torch.FloatTensor, ...]] = None
prompt_embedding: Optional[torch.FloatTensor] = None
gating_output: Optional[torch.FloatTensor] = None
score: Optional[torch.FloatTensor] = None
logits: Optional[torch.FloatTensor] = None
class LlamaForRewardModelWithGating(LlamaPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.model = LlamaModel(config)
config_dict = config.to_dict()
self.num_objectives = config_dict.get("num_objectives", 19)
self.regression_layer = nn.Linear(config.hidden_size, self.num_objectives, bias=False)
self.post_init()
# Not using torch.eye because it is not supported in BF16
I = torch.zeros(self.num_objectives, self.num_objectives)
I[range(self.num_objectives), range(self.num_objectives)] = 1.
self.reward_transform_matrix = nn.Parameter(I)
self.reward_transform_matrix.requires_grad = False
# Initialize weights and apply final processing
self.gating = GatingNetwork(config.hidden_size, config.num_objectives,
temperature=config_dict.get("gating_temperature", 10),
hidden_dim=config_dict.get("gating_hidden_dim", 1024),
n_hidden=config_dict.get("gating_n_hidden", 3))
@add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> CustomOutput:
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
transformer_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=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
tokens_hidden_states = transformer_outputs[0]
if input_ids is not None:
batch_size = input_ids.shape[0]
else:
batch_size = inputs_embeds.shape[0]
if self.config.pad_token_id is None and batch_size != 1:
raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
if self.config.pad_token_id is None:
sequence_lengths = -1
else:
if input_ids is not None:
# if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
sequence_lengths = sequence_lengths % input_ids.shape[-1]
sequence_lengths = sequence_lengths.to(tokens_hidden_states.device)
else:
sequence_lengths = -1
dummy_iterator = torch.arange(batch_size, device=tokens_hidden_states.device)
hidden_states = tokens_hidden_states[dummy_iterator, sequence_lengths]
assert hidden_states.shape == (batch_size, self.config.hidden_size)
rewards = self.regression_layer(hidden_states)
gating_token_positions = [find_token_for_gating(ids.tolist()) for ids in input_ids]
prompt_embedding = tokens_hidden_states[dummy_iterator, gating_token_positions, :]
gating_output = self.gating(prompt_embedding)
rewards_adjusted = rewards @ self.reward_transform_matrix
score = torch.sum(gating_output * rewards_adjusted, dim=1)
return CustomOutput(
rewards=rewards,
hidden_state=hidden_states,
prompt_embedding=prompt_embedding,
gating_output=gating_output,
score=score,
logits=score,
) |