Add llama_eagle.py (#2640)

Co-authored-by: kavioyu <kavioyu@tencent.com>

python/sglang/srt/models/llama_eagle.py

+"""
+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 transformers import LlamaConfig
+
+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.llama import LlamaDecoderLayer, LlamaForCausalLM
+
+
+class LlamaDecoderLayer(LlamaDecoderLayer):
+    def __init__(
+        self,
+        config: LlamaConfig,
+        layer_id: int = 0,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__(config, layer_id, quant_config, prefix)
+
+        # 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 LlamaModel(nn.Module):
+    def __init__(
+        self,
+        config: LlamaConfig,
+        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(
+            [
+                LlamaDecoderLayer(
+                    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 LlamaForCausalLMEagle(LlamaForCausalLM):
+    def __init__(
+        self,
+        config: LlamaConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        cache_config=None,
+    ) -> None:
+        nn.Module.__init__(self)
+        self.config = config
+        self.quant_config = quant_config
+        self.model = LlamaModel(config, quant_config=quant_config)
+        # Llama 3.2 1B Instruct set tie_word_embeddings to True
+        # Llama 3.1 8B Instruct set tie_word_embeddings to False
+        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 = [LlamaForCausalLMEagle]