361 lines
13 KiB
Python
361 lines
13 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 Qwen3_5 MTP model."""
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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.eplb.expert_distribution import get_global_expert_distribution_recorder
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from sglang.srt.eplb.expert_location import ModelConfigForExpertLocation
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from sglang.srt.layers.layernorm import GemmaRMSNorm
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from sglang.srt.layers.logits_processor import LogitsProcessor
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from sglang.srt.layers.moe.fused_moe_triton.layer import FusedMoE
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from sglang.srt.layers.vocab_parallel_embedding import ParallelLMHead
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from sglang.srt.model_executor.forward_batch_info import ForwardBatch
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from sglang.srt.model_loader.weight_utils import default_weight_loader
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from sglang.srt.models.qwen3_5 import Qwen3_5ForCausalLM
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from sglang.srt.utils import add_prefix
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logger = logging.getLogger(__name__)
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class Qwen3_5ForCausalLMMTP(nn.Module):
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def __init__(
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self,
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config: PretrainedConfig,
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quant_config=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.is_multimodal = hasattr(config, "text_config")
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if self.is_multimodal:
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config = config.text_config
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# The MTP model is unquantized in the nvfp4 checkpoint.
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if quant_config and quant_config.get_name() == "modelopt_fp4":
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quant_config = None
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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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self.pp_group = get_pp_group()
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self.fc = nn.Linear(2 * config.hidden_size, config.hidden_size, bias=False)
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RMSNorm_cls = GemmaRMSNorm
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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 = Qwen3_5ForCausalLM(
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config,
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quant_config,
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prefix=add_prefix("mtp", prefix),
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is_nextn=True,
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)
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if get_pp_group().is_last_rank:
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if 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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@classmethod
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def get_model_config_for_expert_location(cls, config):
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text_config = getattr(config, "text_config", config)
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return ModelConfigForExpertLocation(
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num_layers=text_config.num_hidden_layers,
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num_logical_experts=text_config.num_experts,
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num_groups=None,
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)
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def get_embed_and_head(self):
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return self.model.embed_tokens.weight, self.lm_head.weight
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def set_embed_and_head(self, embed, head):
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del self.model.embed_tokens.weight
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if not self.config.tie_word_embeddings:
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del self.lm_head.weight
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self.model.embed_tokens.weight = embed
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self.lm_head.weight = head
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torch.cuda.empty_cache()
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torch.cuda.synchronize()
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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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assert input_embeds is None
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input_embeds = forward_batch.mm_input_embeds
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if (
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forward_batch.forward_mode.is_extend()
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and forward_batch.contains_mm_inputs()
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and not forward_batch.forward_mode.is_draft_extend(include_v2=True)
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):
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assert input_embeds is not None
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input_embeds = torch.cat(
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[input_embeds[:-1], self.model.embed_tokens(input_ids[-1].unsqueeze(0))]
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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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hidden_states = forward_batch.spec_info.hidden_states
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if not forward_batch.forward_mode.is_idle():
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input_embeds = self.pre_fc_norm_embedding(input_embeds)
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hidden_states = self.pre_fc_norm_hidden(hidden_states)
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hidden_states = torch.cat([input_embeds, hidden_states], dim=-1)
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hidden_states = self.fc(hidden_states)
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with get_global_expert_distribution_recorder().disable_this_region():
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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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stacked_params_mapping = [
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# (param_name, shard_name, shard_id)
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("qkv_proj", "q_proj", "q"),
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("qkv_proj", "k_proj", "k"),
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("qkv_proj", "v_proj", "v"),
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("gate_up_proj", "gate_proj", 0),
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("gate_up_proj", "up_proj", 1),
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]
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# Params for MoE experts (non-fused/fused)
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num_experts = getattr(self.config, "num_experts", None)
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if num_experts is not None:
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expert_params_mapping = FusedMoE.make_expert_params_mapping(
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ckpt_gate_proj_name="gate_proj",
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ckpt_down_proj_name="down_proj",
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ckpt_up_proj_name="up_proj",
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num_experts=num_experts,
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)
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else:
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expert_params_mapping = []
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# Skip loading extra parameters for GPTQ/modelopt models.
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ignore_suffixes = (
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".bias",
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"_bias",
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".k_scale",
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"_k_scale",
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".v_scale",
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"_v_scale",
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".weight_scale",
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"_weight_scale",
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".input_scale",
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"_input_scale",
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)
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# fused experts: experts.w13_weight / experts.w2_weight
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is_fused_expert = False
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fused_expert_params_mapping = [
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("experts.w13_weight", "experts.gate_up_proj", 0, "w1"),
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("experts.w2_weight", "experts.down_proj", 0, "w2"),
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]
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def load_fused_expert_weights(
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name: str,
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params_dict: dict,
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loaded_weight: torch.Tensor,
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shard_id: str,
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num_experts: int,
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):
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param = params_dict[name]
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weight_loader = param.weight_loader
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# Let EP MoE layer handle expert_ids that do not belong to local moe rank
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for expert_id in range(num_experts):
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curr_expert_weight = loaded_weight[expert_id]
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weight_loader(
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param,
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curr_expert_weight,
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name,
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shard_id,
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expert_id,
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)
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return True
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params_dict = dict(self.named_parameters())
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loaded_params: set[str] = set()
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for name, loaded_weight in weights:
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if "rotary_emb.inv_freq" in name:
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continue
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# Only process MTP branch weights
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if "mtp" not in name:
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continue
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if name.startswith("mtp."):
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# Remove the mtp. prefix for processing
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name = name.replace("mtp.", "model.")
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name = name.replace("model.fc", "fc")
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name = name.replace("model.pre_fc", "pre_fc")
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if ".self_attn." in name:
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name = name.replace(".self_attn", "")
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# 1) Process stacked parameters (q_proj/k_proj/v_proj & gate_proj/up_proj)
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for param_name, weight_name, shard_id in stacked_params_mapping:
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# Check if this is a fused expert weight
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if "experts.gate_up_proj" in name or "experts.down_proj" in name:
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is_fused_expert = True
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expert_params_mapping = fused_expert_params_mapping
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# Skip non-matching weights
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if weight_name not in name:
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continue
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# Skip MoE experts.* here, handled separately below
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if "mlp.experts" in name:
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continue
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name_mapped = name.replace(weight_name, param_name)
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# Skip loading extra parameters for GPTQ/modelopt models.
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if (
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name_mapped.endswith(ignore_suffixes)
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and name_mapped not in params_dict
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):
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continue
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if name_mapped not in params_dict:
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continue
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param = params_dict[name_mapped]
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weight_loader = getattr(param, "weight_loader", default_weight_loader)
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weight_loader(param, loaded_weight, shard_id)
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name = name_mapped
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break
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else:
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# 2) Process MoE expert weights (including fused experts)
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is_expert_weight = False
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for mapping in expert_params_mapping:
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param_name, weight_name, expert_id, shard_id = mapping
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if weight_name not in name:
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continue
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is_expert_weight = True
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name_mapped = name.replace(weight_name, param_name)
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# Fused experts: single checkpoint weight contains multiple experts
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if is_fused_expert and num_experts is not None:
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if "experts.gate_up_proj" in name:
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# gate_up_proj fused: split into w1 / w3
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loaded_w1, loaded_w3 = loaded_weight.chunk(2, dim=-2)
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load_fused_expert_weights(
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name_mapped,
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params_dict,
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loaded_w1,
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"w1",
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num_experts,
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)
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load_fused_expert_weights(
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name_mapped,
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params_dict,
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loaded_w3,
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"w3",
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num_experts,
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)
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else:
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# down_proj fused: distribute entire weight
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load_fused_expert_weights(
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name_mapped,
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params_dict,
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loaded_weight,
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shard_id,
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num_experts,
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)
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else:
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# Non-fused expert, load by expert_id/shard
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if (
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name_mapped.endswith(ignore_suffixes)
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and name_mapped not in params_dict
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):
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continue
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if name_mapped not in params_dict:
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break
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param = params_dict[name_mapped]
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weight_loader = param.weight_loader
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weight_loader(
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param,
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loaded_weight,
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name_mapped,
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shard_id=shard_id,
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expert_id=expert_id,
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)
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name = name_mapped
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break
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else:
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# Skip expert weight if not handled by current rank
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if is_expert_weight:
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continue
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# 3) Regular non-stacked / non-expert parameters, use default loader
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if name.endswith(ignore_suffixes) and name not in params_dict:
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continue
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if name in params_dict:
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param = params_dict[name]
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weight_loader = getattr(
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param, "weight_loader", default_weight_loader
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)
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weight_loader(param, loaded_weight)
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else:
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logger.warning_once(
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f"Parameter {name} not found in params_dict, skip loading"
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)
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loaded_params.add(name)
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return loaded_params
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EntryClass = [Qwen3_5ForCausalLMMTP]
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