add qwen2 eagle model (#2863)

python/sglang/srt/models/qwen2.py

@@ -362,5 +362,16 @@ class Qwen2ForCausalLM(nn.Module):
                 weight_loader = getattr(param, "weight_loader", default_weight_loader)
                 weight_loader(param, loaded_weight)
 
+    def get_embed_and_head(self):
+        return self.model.embed_tokens.weight, self.lm_head.weight
+
+    def set_embed_and_head(self, embed, head):
+        del self.model.embed_tokens.weight
+        del self.lm_head.weight
+        self.model.embed_tokens.weight = embed
+        self.lm_head.weight = head
+        torch.cuda.empty_cache()
+        torch.cuda.synchronize()
+
 
 EntryClass = Qwen2ForCausalLM

python/sglang/srt/models/qwen2_eagle.py

@@ -0,0 +1,131 @@
+"""
+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.
+"""
+
+# Adapted from
+# https://github.com/SafeAILab/EAGLE/blob/main/eagle/model/cnets.py
+"""Inference-only LLaMA-EAGLE model compatible with HuggingFace weights."""
+
+from typing import Iterable, Optional, Tuple
+
+import torch
+from torch import nn
+
+from sglang.srt.layers.logits_processor import LogitsProcessor
+from sglang.srt.layers.quantization.base_config import QuantizationConfig
+from sglang.srt.layers.vocab_parallel_embedding import (
+    ParallelLMHead,
+    VocabParallelEmbedding,
+)
+from sglang.srt.model_executor.forward_batch_info import ForwardBatch
+from sglang.srt.models.qwen2 import Qwen2DecoderLayer, Qwen2ForCausalLM
+
+Qwen2Config = None
+
+
+class Qwen2DecoderLayer(Qwen2DecoderLayer):
+    def __init__(
+        self,
+        config: Qwen2Config,
+        layer_id: int = 0,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__(config, layer_id, quant_config)
+
+        # Skip the input_layernorm
+        # https://github.com/SafeAILab/EAGLE/blob/35c78f6cdc19a73e05cf5c330b4c358dad970c6a/eagle/model/cnets.py#L427
+        if layer_id == 0:
+            del self.input_layernorm
+            setattr(self, "input_layernorm", lambda x: x)
+
+
+class Qwen2Model(nn.Module):
+    def __init__(
+        self,
+        config: Qwen2Config,
+        quant_config: Optional[QuantizationConfig] = None,
+    ) -> None:
+        super().__init__()
+        self.config = config
+        self.vocab_size = config.vocab_size
+        self.embed_tokens = VocabParallelEmbedding(
+            config.vocab_size,
+            config.hidden_size,
+        )
+        self.layers = nn.ModuleList(
+            [
+                Qwen2DecoderLayer(
+                    config, i, quant_config=quant_config, prefix=f"model.layers.{i}"
+                )
+                for i in range(config.num_hidden_layers)
+            ]
+        )
+        self.fc = torch.nn.Linear(config.hidden_size * 2, config.hidden_size)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        forward_batch: ForwardBatch,
+        input_embeds: torch.Tensor = None,
+    ) -> torch.Tensor:
+        if input_embeds is None:
+            hidden_states = self.embed_tokens(input_ids)
+        else:
+            hidden_states = input_embeds
+
+        hidden_states = self.fc(
+            torch.cat((hidden_states, forward_batch.spec_info.hidden_states), dim=-1)
+        )
+
+        residual = None
+        for i in range(len(self.layers)):
+            layer = self.layers[i]
+            hidden_states, residual = layer(
+                positions,
+                hidden_states,
+                forward_batch,
+                residual,
+            )
+        return hidden_states + residual
+
+
+class Qwen2ForCausalLMEagle(Qwen2ForCausalLM):
+    def __init__(
+        self,
+        config: Qwen2Config,
+        quant_config: Optional[QuantizationConfig] = None,
+        cache_config=None,
+    ) -> None:
+        nn.Module.__init__(self)
+        self.config = config
+        self.quant_config = quant_config
+        self.model = Qwen2Model(config, quant_config=quant_config)
+        if self.config.tie_word_embeddings:
+            self.lm_head = self.model.embed_tokens
+        else:
+            self.lm_head = ParallelLMHead(
+                config.vocab_size, config.hidden_size, quant_config=quant_config
+            )
+        self.logits_processor = LogitsProcessor(config)
+
+    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
+        for name, loaded_weight in weights:
+            if "lm_head" not in name:
+                name = "model." + name
+                super().load_weights([(name, loaded_weight)])
+
+
+EntryClass = [Qwen2ForCausalLMEagle]