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+}

modeling_custom.py

@@ -0,0 +1,166 @@
+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,
+        )