197 lines
6.5 KiB
Python
197 lines
6.5 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 transformers import LlamaConfig
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from sglang.srt.layers.layernorm import RMSNorm
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from sglang.srt.layers.linear import QKVParallelLinear, RowParallelLinear
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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.llama import LlamaAttention, LlamaDecoderLayer, LlamaForCausalLM
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class LlamaDecoderLayer(LlamaDecoderLayer):
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def __init__(
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self,
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config: LlamaConfig,
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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)
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# override qkv
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self.self_attn.qkv_proj = QKVParallelLinear(
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2 * self.hidden_size,
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self.self_attn.head_dim,
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self.self_attn.total_num_heads,
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self.self_attn.total_num_kv_heads,
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bias=False,
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quant_config=quant_config,
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prefix=add_prefix("qkv_proj", prefix),
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)
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self.hidden_norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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def forward(
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self,
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positions: torch.Tensor,
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embeds: torch.Tensor,
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hidden_states: torch.Tensor,
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forward_batch: ForwardBatch,
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residual: Optional[torch.Tensor],
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) -> Tuple[torch.Tensor, torch.Tensor]:
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residual = hidden_states
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embeds = self.input_layernorm(embeds)
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hidden_states = self.hidden_norm(hidden_states)
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hidden_states = torch.cat([embeds, hidden_states], dim=-1)
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# Self Attention
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hidden_states = self.self_attn(
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positions=positions,
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hidden_states=hidden_states,
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forward_batch=forward_batch,
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)
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hidden_states, residual = self.post_attention_layernorm(hidden_states, residual)
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# Fully Connected
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hidden_states = self.mlp(hidden_states)
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return hidden_states, residual
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class LlamaModel(nn.Module):
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def __init__(
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self,
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config: LlamaConfig,
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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.midlayer = LlamaDecoderLayer(config, 0, quant_config, prefix)
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if hasattr(config, "target_hidden_size"):
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self.fc = torch.nn.Linear(config.target_hidden_size * 3, config.hidden_size)
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else:
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self.fc = torch.nn.Linear(config.hidden_size * 3, config.hidden_size)
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self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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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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embeds = self.embed_tokens(input_ids)
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else:
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embeds = input_embeds
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hidden_states = forward_batch.spec_info.hidden_states
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if hidden_states.shape[-1] != embeds.shape[-1]:
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hidden_states = self.fc(hidden_states)
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residual = None
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hidden_states, residual = self.midlayer(
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positions,
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embeds,
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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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hidden_states_to_logits, hidden_states_to_aux = self.norm(
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hidden_states, residual
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)
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# For draft decode, we capture the hidden state before norm
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return hidden_states_to_logits, [hidden_states_to_aux]
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class LlamaForCausalLMEagle3(LlamaForCausalLM):
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def __init__(
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self,
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config: LlamaConfig,
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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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if self.config.num_hidden_layers != 1:
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raise ValueError("EAGLE3 currently only supports 1 layer")
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self.model = LlamaModel(
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config, quant_config=quant_config, prefix=add_prefix("model", prefix)
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)
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# Llama 3.2 1B Instruct set tie_word_embeddings to True
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# Llama 3.1 8B Instruct set tie_word_embeddings to False
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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.draft_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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self.capture_aux_hidden_states = True
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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 "d2t" in name:
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# d2t stores diffs between draft id and target id
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self.hot_token_id = loaded_weight + torch.arange(loaded_weight.shape[0])
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if "d2t" not in name and "t2d" not in name and "lm_head" not in name:
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new_name = f"model.{name}"
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super().load_weights([(new_name, loaded_weight)])
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elif "lm_head" in name:
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super().load_weights([(name, loaded_weight)])
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def get_hot_token_id(self):
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return self.hot_token_id
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EntryClass = [LlamaForCausalLMEagle3]
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