[Feature] Support reward model LxzGordon/URM-LLaMa-3.1-8B (#1525)

python/sglang/srt/models/llama_reward.py

@@ -0,0 +1,142 @@
+"""
+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 LlamaConfig
+from vllm.config import CacheConfig
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+
+from sglang.srt.layers.logits_processor import LogitsProcessorOutput
+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 InputMetadata
+from sglang.srt.models.llama import LlamaForCausalLM, LlamaModel
+
+
+class LlamaForSequenceClassification(nn.Module):
+    def __init__(
+        self,
+        config: LlamaConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        cache_config: Optional[CacheConfig] = None,
+    ) -> None:
+        super().__init__()
+        self.config = config
+        self.torchao_config = None
+        self.quant_config = quant_config
+        self.num_labels = config.num_labels
+        self.model = LlamaModel(config, quant_config=quant_config)
+        self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
+        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,
+        input_metadata: InputMetadata,
+        input_embeds: torch.Tensor = None,
+    ) -> EmbeddingPoolerOutput:
+        hidden_states = self.model(input_ids, positions, input_metadata, input_embeds)
+        scores = self.score(hidden_states)
+
+        return self.pooler(scores, input_metadata)
+
+    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
+        params_dict = dict(self.named_parameters())
+
+        for name, loaded_weight in weights:
+            if "classification_head" in name:
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader", default_weight_loader)
+                weight_loader(param, loaded_weight)
+            elif "lm_head" in name:
+                continue
+            else:
+                LlamaForCausalLM.load_weights(self, [(name, loaded_weight)])
+
+
+class LlamaForSequenceClassificationWithNormal_Weights(LlamaForSequenceClassification):
+    class Weights(torch.nn.Module):
+        def __init__(self, hidden_size, num_label):
+            super().__init__()
+            self.fc = torch.nn.Sequential(
+                torch.nn.Linear(hidden_size, hidden_size, dtype=torch.float16),
+                torch.nn.SELU(),
+                torch.nn.Linear(hidden_size, hidden_size, dtype=torch.float16),
+                torch.nn.SELU(),
+                torch.nn.Linear(hidden_size, num_label // 2, dtype=torch.float16),
+            )
+
+        def forward(self, x):
+            return self.fc(x.to(torch.float16))
+
+    def __init__(
+        self,
+        config: LlamaConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        cache_config: Optional[CacheConfig] = None,
+    ) -> None:
+        super().__init__(config, quant_config, cache_config)
+        self.weights = self.Weights(config.hidden_size, self.num_labels)
+
+    @torch.no_grad()
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        input_metadata: InputMetadata,
+        input_embeds: torch.Tensor = None,
+        get_embedding: bool = True,
+    ) -> EmbeddingPoolerOutput:
+        assert (
+            get_embedding
+        ), "LlamaForSequenceClassification is only used for embedding"
+        hidden_states = self.model(input_ids, positions, input_metadata, input_embeds)
+        logits = self.score(hidden_states)
+        weights = self.weights(hidden_states)
+
+        pooled_logits = self.pooler(logits, input_metadata).embeddings
+        pooled_weights = self.pooler(weights, input_metadata).embeddings
+
+        rews = pooled_logits.view(-1, self.num_labels // 2, 2)[:, :, 0].view(
+            -1, self.num_labels // 2
+        )
+        scores = (rews * pooled_weights).sum(dim=-1).view(-1, 1)
+        return EmbeddingPoolerOutput(scores)
+
+    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
+        params_dict = dict(self.named_parameters())
+
+        for name, loaded_weight in weights:
+            if "classification_head" in name:
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader", default_weight_loader)
+                weight_loader(param, loaded_weight)
+            elif "lm_head" in name:
+                continue
+            else:
+                LlamaForCausalLM.load_weights(self, [(name, loaded_weight)])
+
+
+EntryClass = [
+    LlamaForSequenceClassification,
+    LlamaForSequenceClassificationWithNormal_Weights,
+]