test2除了qfeat全部改完

vllm/model_executor/layers/pooler.py

@@ -6,7 +6,7 @@ from dataclasses import dataclass
 from enum import IntEnum
 from itertools import groupby
 from typing import Callable, Optional, TypeVar, Union
-
+from torch.nn.utils.rnn import pad_sequence
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
@@ -641,7 +641,8 @@ class ClassifierPooler(Pooler):
     ) -> PoolerOutput:
         pooled_data = self.pooling(hidden_states, pooling_metadata)
         if isinstance(pooled_data, list):
-            pooled_data = torch.stack(pooled_data)
+            pooled_data = pad_sequence(pooled_data, batch_first=True, padding_value=0.0)
+            #pooled_data = torch.stack(pooled_data)
         # pooled_data shape: [batchsize, hidden_size]
 
         pooled_data = pooled_data.to(self.head_dtype)

vllm/model_executor/models/adapters_custom/adapters_classify.py

@@ -356,10 +356,12 @@ def new_hy_05b_dense_official_classification(cls: _T) -> _T:
             if isinstance(pooled_output, tuple):
                 pooled_output = pooled_output[0]
             pooled_output = torch.tanh(pooled_output)
-            pooled_output = self.pool_head2(pooled_output)
+            reward = self.pool_head2(pooled_output)
             if isinstance(pooled_output, tuple):
-                pooled_output = pooled_output[0]
-
+                reward = reward[0]
+            # last_token_idx = pooling_metadata.pooling_cursor.num_scheduled_tokens_cpu-1
+            # batch_indices = torch.arange(last_token_idx.size(0))
+            # reward=pooled_output[batch_indices, last_token_idx,:]
             # Select logits at the last non-pad token position per sequence
             # seq_length: [batch]
             # cursor = pooling_metadata.pooling_cursor
@@ -374,7 +376,7 @@ def new_hy_05b_dense_official_classification(cls: _T) -> _T:
             # reward = pooled_output[torch.arange(batch_size, device=pooled_output.device),
             #                        seq_length].squeeze(-1)
 
-            return pooled_output
+            return reward
         def forward(
             self,
             input_ids: torch.Tensor,
@@ -552,11 +554,16 @@ def hy_2b_dense_classification_official_hf_multihead_full_mask(cls: _T) -> _T:
             qhidden = self.encode_qfeat(qfeat)
             a_wei = self.qfeat_fc2(qhidden)
             a_bias = self.qfeat_fc3(qhidden)
-
-            sat_logits = pooled_output_sat[:,-1,:]
-            auth_logits = pooled_output_auth[:,-2,:]
-            time_logits = pooled_output_time[:,-3,:]
-            rel_logits = pooled_output_rel[:,-4,:]
+            if pooled_output.size()[1]<3:
+                batch_size = pooled_output.size(0)  # 或 pooled_output.shape[0]
+                reward = torch.full((batch_size, 1), float('inf'), device=pooled_output.device, dtype=pooled_output.dtype)
+                return reward
+            last_token_idx = pooling_metadata.pooling_cursor.num_scheduled_tokens_cpu-1
+            batch_indices = torch.arange(last_token_idx.size(0))
+            sat_logits = pooled_output_sat[batch_indices, last_token_idx,:]
+            auth_logits = pooled_output_auth[batch_indices, last_token_idx-1,:]
+            time_logits = pooled_output_time[batch_indices, last_token_idx-2,:]
+            rel_logits = pooled_output_rel[batch_indices, last_token_idx-3,:]
             multii_logits = torch.concat([rel_logits, time_logits, auth_logits], dim=1)
             task_logits = (a_wei * multii_logits + a_bias).sum(dim=1, keepdim=True)
             task_logits = torch.sigmoid(task_logits)