30c6e1f569
Co-authored-by: cao1zhg <114661107+cao1zhg@users.noreply.github.com> Co-authored-by: ispobock <ispobaoke@gmail.com> Co-authored-by: Binyao Jiang <byjiang1996@gmail.com> Co-authored-by: hebiao064 <hebiaobuaa@gmail.com> Co-authored-by: Lifu Huang <lifu.hlf@gmail.com> Co-authored-by: qingquansong <ustcsqq@gmail.com> Co-authored-by: Yaoyao Ding <dingyaoyao.cs@gmail.com> Co-authored-by: Ke Bao <ISPObaoke@163.com> Co-authored-by: Minglei Zhu <mingleizhu1122@gmail.com>
118 lines
4.5 KiB
Python
118 lines
4.5 KiB
Python
# 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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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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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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"""Inference-only Qwen3Next MTP Speculative Decoding."""
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import logging
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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 PretrainedConfig
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from sglang.srt.distributed import get_pp_group, get_tensor_model_parallel_world_size
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from sglang.srt.layers.layernorm import GemmaRMSNorm, RMSNorm
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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 ParallelLMHead
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from sglang.srt.managers.schedule_batch import global_server_args_dict
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from sglang.srt.model_executor.forward_batch_info import ForwardBatch
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from sglang.srt.models.qwen3_moe import Qwen3MoeModel
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from sglang.srt.models.qwen3_next import Qwen3NextForCausalLM, Qwen3NextModel
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from sglang.srt.utils import add_prefix
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logger = logging.getLogger(__name__)
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class Qwen3NextForCausalLMMTP(Qwen3NextForCausalLM):
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def __init__(
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self,
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config: PretrainedConfig,
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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.tp_size = get_tensor_model_parallel_world_size()
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self.quant_config = quant_config
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# if not set, model load will be broken in Qwen3NextForCausalLM load_weights()
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self.pp_group = get_pp_group()
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# self.determine_num_fused_shared_experts("Qwen3NextForCausalLMMTP")
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# currently based on the provided ckpt, we:
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# (1) do not use_dedicated_mtp_embeddings provided in ckpt since not provided and directly use the target model embeddings
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# (2) hardcode bias=False since not provided
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self.fc = nn.Linear(2 * config.hidden_size, config.hidden_size, bias=False)
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if getattr(
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config, "use_gemma_rms_norm", getattr(config, "apply_layernorm_1p", False)
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):
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logger.warning_once(
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"Using Gemma RMSNorm for input normalization and post attn normalization."
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)
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RMSNorm_cls = GemmaRMSNorm
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else:
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RMSNorm_cls = RMSNorm
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self.pre_fc_norm_embedding = RMSNorm_cls(
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config.hidden_size, config.rms_norm_eps
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)
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self.pre_fc_norm_hidden = RMSNorm_cls(config.hidden_size, config.rms_norm_eps)
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config.num_hidden_layers = 1
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config.full_attention_interval = 1
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self.model = Qwen3NextModel(
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config, quant_config, prefix=add_prefix("model", prefix)
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)
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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("model.shared_head.head", prefix),
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use_attn_tp_group=global_server_args_dict["enable_dp_lm_head"],
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)
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self.logits_processor = LogitsProcessor(config)
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@torch.no_grad()
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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: Optional[torch.Tensor] = None,
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**kwargs,
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):
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if input_embeds is None:
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input_embeds = self.model.embed_tokens(input_ids)
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input_embeds = self.pre_fc_norm_embedding(input_embeds)
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hidden_states = self.pre_fc_norm_hidden(forward_batch.spec_info.hidden_states)
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hidden_states = self.fc(torch.cat((input_embeds, hidden_states), dim=-1))
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hidden_states = self.model(
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input_ids,
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positions,
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forward_batch,
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hidden_states,
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)
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return self.logits_processor(
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input_ids, hidden_states, self.lm_head, forward_batch
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)
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def load_weights(
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self, weights: Iterable[Tuple[str, torch.Tensor]], is_mtp: bool = False
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):
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super().load_weights(weights, is_mtp=True)
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EntryClass = [Qwen3NextForCausalLMMTP]
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