144 lines
4.5 KiB
Python
144 lines
4.5 KiB
Python
"""
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Copyright 2023-2024 SGLang Team
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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"""
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from sglang.srt.utils import add_prefix
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# Adapted from
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# https://github.com/SafeAILab/EAGLE/blob/main/eagle/model/cnets.py
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"""Inference-only LLaMA-EAGLE model compatible with HuggingFace weights."""
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from typing import Iterable, Optional, Tuple
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import torch
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from torch import nn
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from sglang.srt.layers.logits_processor import LogitsProcessor
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from sglang.srt.layers.quantization.base_config import QuantizationConfig
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from sglang.srt.layers.vocab_parallel_embedding import (
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ParallelLMHead,
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VocabParallelEmbedding,
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)
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from sglang.srt.model_executor.forward_batch_info import ForwardBatch
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from sglang.srt.models.qwen2 import Qwen2DecoderLayer, Qwen2ForCausalLM
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Qwen2Config = None
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class Qwen2DecoderLayer(Qwen2DecoderLayer):
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def __init__(
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self,
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config: Qwen2Config,
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layer_id: int = 0,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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) -> None:
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super().__init__(config, layer_id, quant_config, prefix=prefix)
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# Skip the input_layernorm
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# https://github.com/SafeAILab/EAGLE/blob/35c78f6cdc19a73e05cf5c330b4c358dad970c6a/eagle/model/cnets.py#L427
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if layer_id == 0:
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del self.input_layernorm
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setattr(self, "input_layernorm", lambda x: x)
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class Qwen2Model(nn.Module):
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def __init__(
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self,
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config: Qwen2Config,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.config = config
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self.vocab_size = config.vocab_size
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self.embed_tokens = VocabParallelEmbedding(
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config.vocab_size,
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config.hidden_size,
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prefix=add_prefix("embed_tokens", prefix),
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)
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self.layers = nn.ModuleList(
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[
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Qwen2DecoderLayer(
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config,
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i,
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quant_config=quant_config,
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prefix=add_prefix(f"layers.{i}", prefix),
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)
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for i in range(config.num_hidden_layers)
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]
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)
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self.fc = torch.nn.Linear(config.hidden_size * 2, config.hidden_size)
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def forward(
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self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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forward_batch: ForwardBatch,
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input_embeds: torch.Tensor = None,
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) -> torch.Tensor:
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if input_embeds is None:
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hidden_states = self.embed_tokens(input_ids)
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else:
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hidden_states = input_embeds
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hidden_states = self.fc(
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torch.cat((hidden_states, forward_batch.spec_info.hidden_states), dim=-1)
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)
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residual = None
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for i in range(len(self.layers)):
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layer = self.layers[i]
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hidden_states, residual = layer(
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positions,
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hidden_states,
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forward_batch,
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residual,
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)
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return hidden_states + residual
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class Qwen2ForCausalLMEagle(Qwen2ForCausalLM):
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def __init__(
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self,
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config: Qwen2Config,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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) -> None:
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nn.Module.__init__(self)
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self.config = config
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self.quant_config = quant_config
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self.model = Qwen2Model(
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config, quant_config=quant_config, prefix=add_prefix("model", prefix)
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)
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if self.config.tie_word_embeddings:
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self.lm_head = self.model.embed_tokens
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else:
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self.lm_head = ParallelLMHead(
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config.vocab_size,
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config.hidden_size,
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quant_config=quant_config,
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prefix=add_prefix("lm_head", prefix),
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)
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self.logits_processor = LogitsProcessor(config)
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def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
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for name, loaded_weight in weights:
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if "lm_head" not in name:
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name = "model." + name
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super().load_weights([(name, loaded_weight)])
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EntryClass = [Qwen2ForCausalLMEagle]
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