support internlm2-reward (#1994)

python/sglang/srt/configs/model_config.py

@@ -210,6 +210,7 @@ def is_generation_model(model_architectures: List[str], is_embedding: bool = Fal
         or "MistralModel" in model_architectures
         or "LlamaForSequenceClassification" in model_architectures
         or "LlamaForSequenceClassificationWithNormal_Weights" in model_architectures
+        or "InternLM2ForRewardModel" in model_architectures
     ):
         return False
     else:

python/sglang/srt/models/internlm2_reward.py

@@ -0,0 +1,62 @@
+"""
+Copyright 2023-2024 SGLang Team
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+"""
+
+from typing import Iterable, Optional, Tuple
+
+import torch
+from torch import nn
+from transformers import PretrainedConfig
+
+from sglang.srt.layers.pooler import EmbeddingPoolerOutput, Pooler, PoolingType
+from sglang.srt.layers.quantization.base_config import QuantizationConfig
+from sglang.srt.model_executor.forward_batch_info import ForwardBatch
+from sglang.srt.models.internlm2 import InternLM2ForCausalLM, InternLM2Model
+
+
+class InternLM2ForRewardModel(nn.Module):
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        cache_config=None,
+    ) -> None:
+        super().__init__()
+        self.config = config
+        self.quant_config = quant_config
+        self.vocab_size = config.vocab_size
+        self.model = InternLM2Model(config, quant_config)
+        self.v_head = nn.Linear(config.hidden_size, 1, bias=False)
+        self.pooler = Pooler(pooling_type=PoolingType.LAST, normalize=False)
+
+    @torch.no_grad()
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        forward_batch: ForwardBatch,
+        input_embeds: torch.Tensor = None,
+        get_embedding: bool = True,
+    ) -> EmbeddingPoolerOutput:
+        assert get_embedding, "InternLM2ForRewardModel is only used for embedding"
+        hidden_states = self.model(input_ids, positions, forward_batch, input_embeds)
+        last_token_hidden = self.pooler(hidden_states, forward_batch).embeddings
+        scores = self.v_head(last_token_hidden)
+        return EmbeddingPoolerOutput(scores)
+
+    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
+        return InternLM2ForCausalLM.load_weights(self, weights)
+
+
+EntryClass = InternLM2ForRewardModel