Model: Support Qwen 72B RM model. (#3772)

python/sglang/srt/configs/model_config.py

@@ -389,6 +389,7 @@ def is_generation_model(model_architectures: List[str], is_embedding: bool = Fal
         or "LlamaForSequenceClassification" in model_architectures
         or "LlamaForSequenceClassificationWithNormal_Weights" in model_architectures
         or "InternLM2ForRewardModel" in model_architectures
+        or "Qwen2ForRewardModel" in model_architectures
     ):
         return False
     else:

python/sglang/srt/models/qwen2.py

@@ -379,6 +379,8 @@ class Qwen2ForCausalLM(nn.Module):
                 continue
             if name.startswith("model.vision_tower") and name not in params_dict:
                 continue
+            if name.startswith("lm_head"):
+                continue
 
             for param_name, weight_name, shard_id in stacked_params_mapping:
                 if weight_name not in name:

python/sglang/srt/models/qwen2_rm.py

@@ -0,0 +1,70 @@
+# 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 Qwen2Config
+
+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.qwen2 import Qwen2ForCausalLM, Qwen2Model
+
+
+class Qwen2ForRewardModel(nn.Module):
+    def __init__(
+        self,
+        config: Qwen2Config,
+        quant_config: Optional[QuantizationConfig] = None,
+    ) -> None:
+        super().__init__()
+        self.config = config
+        self.quant_config = quant_config
+        self.num_labels = 1
+        self.model = Qwen2Model(config, quant_config=quant_config)
+        self.score = nn.Sequential(
+            nn.Linear(config.hidden_size, config.hidden_size),
+            nn.ReLU(),
+            nn.Linear(config.hidden_size, self.num_labels),
+        )
+        self.pooler = Pooler(pooling_type=PoolingType.LAST, normalize=False)
+
+        self.eos_token_id = config.eos_token_id
+
+    @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, "Qwen2ForRewardModel is only used for embedding"
+
+        hidden_states = self.model(input_ids, positions, forward_batch, input_embeds)
+        logits = self.score(hidden_states)
+        pooled_logits = self.pooler(logits, forward_batch).embeddings
+
+        return EmbeddingPoolerOutput(pooled_logits)
+
+    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
+        return Qwen2ForCausalLM.load_weights(self, weights)
+
+
+EntryClass = [
+    Qwen2ForRewardModel,
+]