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# Usage
# Model loading
```python
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss, BCEWithLogitsLoss
from transformers import LlamaPreTrainedModel,LlamaModel,Gemma2PreTrainedModel,Gemma2Model,Cache
from transformers.modeling_outputs import SequenceClassifierOutputWithPast
from typing import Optional, List, Union, Tuple
@dataclass
class Config:
gemma_dir = '/kaggle/input/v7-dpo-16bit-01234-8bit-all/v7_dpo_16bit_01234_8bit_all'
max_length = 2000
batch_size = 8
device = torch.device("cuda") if torch.cuda_is_available() else torch.device("cpu")
cfg = Config()
class Gemma2ForSequenceClassificationV1(Gemma2PreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.model = Gemma2Model(config)
self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.model.embed_tokens
def set_input_embeddings(self, value):
self.model.embed_tokens = value
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = 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,
) -> Union[Tuple, SequenceClassifierOutputWithPast]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
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,
)
hidden_states = transformer_outputs[0]
# logits = self.score(hidden_states)
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(hidden_states.device)
else:
sequence_lengths = -1
hidden_states = hidden_states[
torch.arange(batch_size, device=hidden_states.device), sequence_lengths] # eos
pooled_logits = self.score(hidden_states)
return pooled_logits
tokenizer = GemmaTokenizerFast.from_pretrained(cfg.gemma_dir)
model = Gemma2ForSequenceClassificationV1.from_pretrained(
cfg.gemma_dir,
num_labels=3,
device_map=cfg.device,
use_cache=False,
)
model.config.pad_token_id = tokenizer.pad_token_id
```
# Inference
```python
def create_rounds(query: str,
answer_a: str,
answer_b: str) -> str:
prompt =f"""User question:
\"""{query}\"""
Answer A:
\"""{answer_a}\"""
Answer B:
\"""{answer_b}\"""
"""
return prompt
@torch.no_grad()
@torch.cuda.amp.autocast()
def single_prompt_inference(prompt, model, device, max_length=cfg.max_length):
"""
Perform inference on a single prompt.
Args:
prompt (str): The input prompt for inference.
model (torch.nn.Module): The model used for inference.
device (torch.device): The device to run inference on.
tokenizer (Tokenizer): Tokenizer for preprocessing input text.
max_length (int): Maximum sequence length for tokenization.
Returns:
dict: Probabilities for "a_win", "b_win", and "tie".
"""
# Tokenize the input prompt
input_ids = tokenizer(prompt, truncation=True, max_length=max_length)['input_ids']
input_ids.append(tokenizer.eos_token_id)
# Prepare inputs
inputs = pad_without_fast_tokenizer_warning(
tokenizer,
{"input_ids": [input_ids]}, # Wrap in a list for compatibility
padding="max_length",
pad_to_multiple_of=None,
max_length=max_length,
return_tensors="pt",
)
# Move inputs to the appropriate device
inputs = inputs.to(cfg.device)
# Run the model
outputs = model(**inputs)
# Get probabilities using softmax
proba = outputs.softmax(-1).cpu().squeeze()
return {
"winner_model_a": proba[0].item(),
"winner_model_b": proba[1].item(),
"tie": proba[2].item(),
}
query = "What is the height of the reassembled blind product?"
answer_a = "You can find all the technical information directly on the product sheet on our site."
answer_b = "The height of the aluminum Venetian blind is 130 cm."
prompt_direct = create_rounds(query, answer_a, answer_b)
single_prompt_inference(prompt_direct, model, device)
```
Credits to @sayoulala on kaggle for winnig the competition https://www.kaggle.com/competitions/lmsys-chatbot-arena and submitting this model. |