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https://github.com/hanzoai/open-instruct.git
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20d70d9958
* Add dataset cache / mixing support * prototyping * makesure to shuffle with seed * quick fix * add finetune1 * support preference tuning as well * refactord * allow passing in an SFT message key * allow customizing chosen / rejected key * fix tests * add huggingface card * refactor * quick fix * add an option to cache dataset only * Add some logic for caching. * make mason work with the latest change * Use the latest dataset caching logic * restore change * push docs * update docs * quick update * quick push * push * Just replace the existing dpo / finetune * remove unused files * Apply suggestions from code review Co-authored-by: Nathan Lambert <[email protected]> * quick change * update docs and fix mason * quick update on docs * use the default entity * format --------- Co-authored-by: Nathan Lambert <[email protected]>
235 lines
10 KiB
Python
235 lines
10 KiB
Python
from typing import List, Optional, Tuple, Union
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import torch
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from torch import nn
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from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
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from transformers.modeling_outputs import SequenceClassifierOutputWithPast
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from transformers.models.olmo2.modeling_olmo2 import (
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Olmo2Config,
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Olmo2Model,
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Olmo2PreTrainedModel,
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)
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from transformers.models.olmoe.modeling_olmoe import (
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OlmoeConfig,
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OlmoeModel,
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OlmoePreTrainedModel,
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)
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class Olmo2ForSequenceClassification(Olmo2PreTrainedModel):
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def __init__(self, config: Olmo2Config):
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super().__init__(config)
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self.num_labels = config.num_labels
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self.model = Olmo2Model(config)
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self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
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# Initialize weights and apply final processing
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self.post_init()
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def get_input_embeddings(self):
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return self.model.embed_tokens
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def set_input_embeddings(self, value):
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self.model.embed_tokens = value
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def forward(
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self,
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input_ids: Optional[torch.LongTensor] = None,
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attention_mask: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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past_key_values: Optional[Union[List[torch.FloatTensor]]] = None,
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inputs_embeds: Optional[torch.FloatTensor] = None,
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labels: Optional[torch.LongTensor] = None,
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use_cache: Optional[bool] = None,
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output_attentions: Optional[bool] = None,
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output_hidden_states: Optional[bool] = None,
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return_dict: Optional[bool] = None,
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) -> Union[Tuple, "SequenceClassifierOutputWithPast"]:
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r"""
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labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
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Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
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config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
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`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
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"""
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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transformer_outputs = self.model(
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input_ids,
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attention_mask=attention_mask,
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position_ids=position_ids,
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past_key_values=past_key_values,
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inputs_embeds=inputs_embeds,
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use_cache=use_cache,
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output_attentions=output_attentions,
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output_hidden_states=output_hidden_states,
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return_dict=return_dict,
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)
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hidden_states = transformer_outputs[0]
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logits = self.score(hidden_states)
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if input_ids is not None:
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batch_size = input_ids.shape[0]
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else:
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batch_size = inputs_embeds.shape[0]
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if self.config.pad_token_id is None and batch_size != 1:
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raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
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if self.config.pad_token_id is None:
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sequence_lengths = -1
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else:
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if input_ids is not None:
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# if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
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sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
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sequence_lengths = sequence_lengths % input_ids.shape[-1]
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sequence_lengths = sequence_lengths.to(logits.device)
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else:
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sequence_lengths = -1
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pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
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loss = None
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if labels is not None:
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labels = labels.to(logits.device)
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if self.config.problem_type is None:
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if self.num_labels == 1:
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self.config.problem_type = "regression"
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elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
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self.config.problem_type = "single_label_classification"
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else:
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self.config.problem_type = "multi_label_classification"
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if self.config.problem_type == "regression":
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loss_fct = MSELoss()
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if self.num_labels == 1:
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loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
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else:
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loss = loss_fct(pooled_logits, labels)
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elif self.config.problem_type == "single_label_classification":
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loss_fct = CrossEntropyLoss()
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loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
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elif self.config.problem_type == "multi_label_classification":
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loss_fct = BCEWithLogitsLoss()
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loss = loss_fct(pooled_logits, labels)
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if not return_dict:
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output = (pooled_logits,) + transformer_outputs[1:]
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return ((loss,) + output) if loss is not None else output
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return SequenceClassifierOutputWithPast(
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loss=loss,
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logits=pooled_logits,
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past_key_values=transformer_outputs.past_key_values,
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hidden_states=transformer_outputs.hidden_states,
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attentions=transformer_outputs.attentions,
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)
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class OlmoeForSequenceClassification(OlmoePreTrainedModel):
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def __init__(self, config: OlmoeConfig):
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super().__init__(config)
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self.num_labels = config.num_labels
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self.model = OlmoeModel(config)
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self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
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# Initialize weights and apply final processing
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self.post_init()
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def get_input_embeddings(self):
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return self.model.embed_tokens
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def set_input_embeddings(self, value):
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self.model.embed_tokens = value
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def forward(
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self,
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input_ids: Optional[torch.LongTensor] = None,
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attention_mask: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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past_key_values: Optional[Union[List[torch.FloatTensor]]] = None,
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inputs_embeds: Optional[torch.FloatTensor] = None,
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labels: Optional[torch.LongTensor] = None,
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use_cache: Optional[bool] = None,
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output_attentions: Optional[bool] = None,
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output_hidden_states: Optional[bool] = None,
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return_dict: Optional[bool] = None,
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output_router_logits: Optional[bool] = None,
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cache_position: Optional[torch.LongTensor] = None,
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) -> Union[Tuple, SequenceClassifierOutputWithPast]:
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r"""
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labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
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Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
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config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
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`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
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"""
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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transformer_outputs = self.model(
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input_ids=input_ids,
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attention_mask=attention_mask,
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position_ids=position_ids,
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past_key_values=past_key_values,
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inputs_embeds=inputs_embeds,
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use_cache=use_cache,
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output_attentions=output_attentions,
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output_hidden_states=output_hidden_states,
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output_router_logits=output_router_logits,
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return_dict=return_dict,
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cache_position=cache_position,
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)
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hidden_states = transformer_outputs[0]
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logits = self.score(hidden_states)
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if input_ids is not None:
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batch_size = input_ids.shape[0]
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else:
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batch_size = inputs_embeds.shape[0]
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if self.config.pad_token_id is None and batch_size != 1:
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raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
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if self.config.pad_token_id is None:
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sequence_lengths = -1
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else:
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if input_ids is not None:
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# if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
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sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
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sequence_lengths = sequence_lengths % input_ids.shape[-1]
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sequence_lengths = sequence_lengths.to(logits.device)
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else:
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sequence_lengths = -1
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pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
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loss = None
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if labels is not None:
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labels = labels.to(logits.device)
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if self.config.problem_type is None:
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if self.num_labels == 1:
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self.config.problem_type = "regression"
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elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
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self.config.problem_type = "single_label_classification"
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else:
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self.config.problem_type = "multi_label_classification"
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if self.config.problem_type == "regression":
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loss_fct = MSELoss()
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if self.num_labels == 1:
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loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
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else:
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loss = loss_fct(pooled_logits, labels)
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elif self.config.problem_type == "single_label_classification":
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loss_fct = CrossEntropyLoss()
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loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
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elif self.config.problem_type == "multi_label_classification":
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loss_fct = BCEWithLogitsLoss()
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loss = loss_fct(pooled_logits, labels)
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if not return_dict:
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output = (pooled_logits,) + transformer_outputs[1:]
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return ((loss,) + output) if loss is not None else output
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return SequenceClassifierOutputWithPast(
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loss=loss,
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logits=pooled_logits,
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past_key_values=transformer_outputs.past_key_values,
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hidden_states=transformer_outputs.hidden_states,
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attentions=transformer_outputs.attentions,
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)
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