chore: import upstream snapshot with attribution

python/sglang/srt/models/qwen3_classification.py

@@ -0,0 +1,151 @@
+# 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.
+# ==============================================================================
+
+import logging
+from typing import Iterable, Optional, Tuple
+
+import torch
+from torch import nn
+from transformers import Qwen2Config  # Qwen3 uses Qwen2Config
+
+from sglang.srt.layers.pooler import (
+    EmbeddingPoolerOutput,
+    Pooler,
+    PoolingType,
+    score_and_pool,
+)
+from sglang.srt.layers.quantization.base_config import QuantizationConfig
+from sglang.srt.model_executor.forward_batch_info import ForwardBatch
+from sglang.srt.model_loader.weight_utils import default_weight_loader
+from sglang.srt.models.qwen3 import Qwen3Model
+from sglang.srt.utils import add_prefix
+
+logger = logging.getLogger(__name__)
+
+
+class Qwen3ForPooledOutput(nn.Module):
+    """Base class for Qwen3 models that produce pooled output (classification, reward).
+
+    Subclasses should set self.score and self.pooler in their __init__.
+    """
+
+    def __init__(
+        self,
+        config: Qwen2Config,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.config = config
+        self.quant_config = quant_config
+        self.model = Qwen3Model(
+            config, quant_config=quant_config, prefix=add_prefix("model", prefix)
+        )
+        self.eos_token_id = config.eos_token_id
+        # Subclasses must set self.score and self.pooler
+
+    def get_input_embeddings(self) -> nn.Embedding:
+        return self.model.get_input_embeddings()
+
+    @torch.no_grad()
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        forward_batch: ForwardBatch,
+        input_embeds: Optional[torch.Tensor] = None,
+        get_embedding: bool = True,
+    ) -> EmbeddingPoolerOutput:
+        assert get_embedding, f"{self.__class__.__name__} is only used for embedding"
+
+        hidden_states = self.model(input_ids, positions, forward_batch, input_embeds)
+        return score_and_pool(
+            self.score, self.pooler, hidden_states, forward_batch, input_ids
+        )
+
+    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+            ("gate_up_proj", "gate_proj", 0),
+            ("gate_up_proj", "up_proj", 1),
+        ]
+
+        params_dict = dict(self.named_parameters())
+        for name, loaded_weight in weights:
+            # Skip lm_head weights (pooled output models don't have lm_head)
+            if name.startswith("lm_head"):
+                continue
+
+            # Skip rotary embeddings and other non-parameter tensors
+            if "rotary_emb.inv_freq" in name or "projector" in name:
+                continue
+            if "rotary_emb.cos_cached" in name or "rotary_emb.sin_cached" in name:
+                continue
+
+            # Handle stacked parameters (qkv_proj, gate_up_proj)
+            for param_name, weight_name, shard_id in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                if name not in params_dict:
+                    continue
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                # Skip loading extra bias for GPTQ models
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+
+                if name in params_dict:
+                    param = params_dict[name]
+                    weight_loader = getattr(
+                        param, "weight_loader", default_weight_loader
+                    )
+                    weight_loader(param, loaded_weight)
+                else:
+                    logger.warning(f"Parameter {name} not found in params_dict")
+
+
+class Qwen3ForSequenceClassification(Qwen3ForPooledOutput):
+    def __init__(
+        self,
+        config: Qwen2Config,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__(config, quant_config, prefix)
+        self.score = nn.Linear(config.hidden_size, config.num_labels)
+        # Use normalize=True for qwen3 embedding based on official implementation
+        # Reference: https://github.com/QwenLM/Qwen3-Embedding/blob/main/examples/qwen3_embedding_transformers.py#L55
+        # Official code: output = F.normalize(output, p=2, dim=1)
+        normalize = True
+
+        # We don't want to normalize the embedding if we have a classification head
+        if config.id2label is not None or config.label2id is not None:
+            normalize = False
+
+        self.pooler = Pooler(pooling_type=PoolingType.LAST, normalize=normalize)
+
+
+EntryClass = [
+    Qwen3ForSequenceClassification,
+]