Upload Qwen2WithRegressionHead

config.json

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+  "transformers_version": "4.51.3",
+  "use_cache": false,
+  "use_sliding_window": false,
+  "vocab_size": 151665
+}

generation_config.json

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+  "transformers_version": "4.51.3"
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+}

qwen2_regression.py

@@ -0,0 +1,135 @@
+from dataclasses import dataclass
+from typing import Optional, Tuple, Union
+
+import torch
+from torch.nn import CrossEntropyLoss, MSELoss
+from transformers import Qwen2ForCausalLM, Cache
+from transformers.processing_utils import Unpack
+from transformers.utils import ModelOutput, LossKwargs
+from transformers.modeling_outputs import CausalLMOutputWithPast
+from transformers.modeling_flash_attention_utils import FlashAttentionKwargs
+import torch.nn as nn
+
+
+@dataclass
+class CausalLMOutputWithPastAndRegression(ModelOutput):
+    """
+    Class for causal language model (or autoregressive) outputs together with regression ouputs.
+
+    Args:
+        logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
+            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+        lm_loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
+            Language modeling loss (for next-token prediction).
+        regr_loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
+            Regression loss (for score prediction).
+        loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
+            Combined loss from language modelling loss and regression loss.
+        regr_output (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
+            Regression output.
+        past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+            Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
+            `(batch_size, num_heads, sequence_length, embed_size_per_head)`)
+
+            Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
+            `past_key_values` input) to speed up sequential decoding.
+        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+            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.
+        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
+            sequence_length)`.
+
+            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
+            heads.
+    """
+
+    logits: torch.FloatTensor = None
+    lm_loss: Optional[torch.FloatTensor] = None
+    regr_loss: Optional[torch.FloatTensor] = None
+    loss: Optional[torch.FloatTensor] = None
+    regr_output: Optional[torch.FloatTensor] = None
+    past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
+    hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
+    attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
+
+
+class KwargsForCausalLM(FlashAttentionKwargs, LossKwargs): ...
+
+
+class Qwen2WithRegressionHead(Qwen2ForCausalLM):
+    def __init__(self, config):
+        super().__init__(config)
+        self.regression_head = nn.Linear(config.hidden_size, 1)
+        self.post_init()
+
+    def r_loss_function(self, outputs, labels, regression_labels):
+        lm_loss = outputs.loss
+
+        regression_loss = None
+        if regression_labels is not None:
+            regression_output = outputs.regr_output
+            regression_loss_fct = MSELoss()
+            regression_loss = regression_loss_fct(regression_output, regression_labels)
+
+        total_loss = None
+        if lm_loss is not None and regression_loss is not None:
+            total_loss = lm_loss + regression_loss
+
+        return {
+            "loss": total_loss,
+            "lm_loss": lm_loss,
+            "regr_loss": regression_loss,
+        }
+
+    def forward(
+            self,
+            input_ids: Optional[torch.LongTensor] = None,
+            attention_mask: Optional[torch.Tensor] = None,
+            position_ids: Optional[torch.LongTensor] = None,
+            past_key_values: Optional[Cache] = None,
+            inputs_embeds: Optional[torch.FloatTensor] = None,
+            regression_labels: 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,
+            cache_position: Optional[torch.LongTensor] = None,
+            logits_to_keep: Union[int, torch.Tensor] = 0,
+            **kwargs: Unpack[KwargsForCausalLM],
+    ) -> CausalLMOutputWithPastAndRegression:
+        outputs = super().forward(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            labels=labels,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            cache_position=cache_position,
+            logits_to_keep=logits_to_keep,
+            output_hidden_states=True,
+            **kwargs,
+        )
+        hidden_states = outputs.hidden_states[-1]  # last layer hidden states (B x S x D)
+        pooled_output = hidden_states[:, -1, :]  # last token"s hidden state (B x D)
+        regression_output = self.regression_head(pooled_output)
+        regression_output = regression_output.squeeze(-1)
+        outputs.regr_output = regression_output
+
+        loss_dict = self.r_loss_function(outputs, labels, regression_labels)
+        loss_dict["logits"] = outputs.logits
+
+        return CausalLMOutputWithPastAndRegression(
+            loss=loss_dict["loss"],
+            lm_loss=loss_dict["lm_loss"],
+            regr_loss=loss_dict["regr_loss"],
+            logits=outputs.logits,
+            regr_output=outputs.regr_output,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )