# Copyright 2025 Qwen Team # Copyright 2025 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. # ============================================================================== """Inference-only Qwen3.5 model and Qwen3.5 MoE model compatible with HuggingFace weights.""" import logging from functools import lru_cache from typing import Iterable, Optional, Set, Tuple, Union import torch import torch.nn as nn import triton from sglang.jit_kernel.triton.gdn_fused_proj import ( fused_qkvzba_split_reshape_cat_contiguous, ) # Configs from sglang.srt.configs.qwen3_5 import ( Qwen3_5Config, Qwen3_5MoeConfig, Qwen3_5TextConfig, ) # Distributed from sglang.srt.distributed import get_pp_group from sglang.srt.eplb.expert_distribution import get_global_expert_distribution_recorder from sglang.srt.eplb.expert_location import ModelConfigForExpertLocation # Layers - Attention from sglang.srt.layers.attention.fla.layernorm_gated import RMSNorm as RMSNormGated from sglang.srt.layers.attention.mamba.mamba import mamba_v2_sharded_weight_loader from sglang.srt.layers.communicator import LayerCommunicator, LayerScatterModes from sglang.srt.layers.dp_attention import ( is_dp_attention_enabled, ) from sglang.srt.layers.elementwise import fused_sigmoid_mul # Layers - Others from sglang.srt.layers.layernorm import GemmaRMSNorm # Layers - Linear from sglang.srt.layers.linear import ( ColumnParallelLinear, MergedColumnParallelLinear, QKVParallelLinear, RowParallelLinear, ) from sglang.srt.layers.moe.fused_moe_triton.layer import FusedMoE from sglang.srt.layers.parameter import ( BlockQuantScaleParameter, PerTensorScaleParameter, ) from sglang.srt.layers.quantization.base_config import QuantizationConfig from sglang.srt.layers.radix_attention import RadixAttention from sglang.srt.layers.radix_linear_attention import RadixLinearAttention from sglang.srt.layers.rotary_embedding import get_rope from sglang.srt.layers.utils import PPMissingLayer, get_layer_id from sglang.srt.layers.vocab_parallel_embedding import VocabParallelEmbedding from sglang.srt.model_executor.cuda_graph_config import ( Backend, Phase, check_cuda_graph_backend, ) from sglang.srt.model_executor.forward_batch_info import ForwardBatch, PPProxyTensors from sglang.srt.model_executor.runner import get_is_capture_mode from sglang.srt.model_loader.weight_utils import ( default_weight_loader, sharded_weight_loader, ) from sglang.srt.models.qwen2_moe import ( Qwen2MoeMLP, Qwen2MoeSparseMoeBlock, can_fuse_shared_expert, ) # Models from sglang.srt.models.qwen3_vl import Qwen3VLForConditionalGeneration from sglang.srt.models.utils import ( fused_qk_gemma_rmsnorm, fused_qk_gemma_rmsnorm_with_gate, ) from sglang.srt.runtime_context import ( get_forward, get_parallel, get_server_args, get_stream, ) # Utils from sglang.srt.utils import ( LazyValue, add_prefix, cpu_has_amx_support, get_bool_env_var, is_cpu, is_cuda, is_gfx95_supported, is_hip, is_npu, is_xpu, make_layers, set_weight_attrs, ) from sglang.srt.utils.hf_transformers_utils import get_processor, get_rope_config logger = logging.getLogger(__name__) _is_cuda = is_cuda() _is_npu = is_npu() _is_cpu = is_cpu() _is_gfx95 = is_gfx95_supported() _is_hip = is_hip() _is_xpu = is_xpu() _use_aiter = get_bool_env_var("SGLANG_USE_AITER") and _is_hip _hip_use_alt_stream = get_bool_env_var("SGLANG_ALT_STREAM") and _is_hip _gdn_use_alt_stream = _is_cuda or ( get_bool_env_var("SGLANG_GDN_QKVZ_BA_ALT_STREAM", "False") and _hip_use_alt_stream ) _qknorm_use_alt_stream = _is_cuda or ( get_bool_env_var("SGLANG_QK_NORM_ALT_STREAM", "False") and _hip_use_alt_stream ) _is_amx_available = cpu_has_amx_support() cached_get_processor = lru_cache(get_processor) def _disable_shared_experts_fusion() -> bool: # Resolved lazily: the global server args is not set at module import time # (e.g. when this module is imported by unit tests). return get_server_args().disable_shared_experts_fusion if _is_cuda: from sglang.srt.layers.fused_qk_rmsnorm_rope_gate import ( fused_qk_gemma_rmsnorm_rope_gate, ) if _is_cpu: fused_sigmoid_mul = torch.ops.sgl_kernel.fused_sigmoid_mul_cpu fused_qk_gemma_rmsnorm = torch.ops.sgl_kernel.fused_qk_gemma_rmsnorm_cpu fused_qk_gemma_rmsnorm_with_gate = ( torch.ops.sgl_kernel.fused_qk_gemma_rmsnorm_with_gate_cpu ) if _is_npu: from sgl_kernel_npu.norm.split_qkv_rmsnorm_rope import ( split_qkvgate_gemma_rmsnorm_rope, ) class Qwen3_5GatedDeltaNet(nn.Module): def __init__( self, config: Qwen3_5TextConfig, layer_id: int, quant_config: Optional[QuantizationConfig] = None, alt_stream: Optional[torch.cuda.Stream] = None, prefix: str = "", ) -> None: super().__init__() self.config = config self.attn_tp_rank = get_parallel().attn_tp_rank self.attn_tp_size = get_parallel().attn_tp_size self.hidden_size = config.hidden_size self.num_v_heads = ( config.linear_num_value_heads if not _is_cpu else config.linear_num_value_heads_cpu ) self.num_k_heads = ( config.linear_num_key_heads if not _is_cpu else config.linear_num_key_heads_cpu ) self.head_k_dim = config.linear_key_head_dim self.head_v_dim = config.linear_value_head_dim self.key_dim = self.head_k_dim * self.num_k_heads self.value_dim = self.head_v_dim * self.num_v_heads self.alt_stream = alt_stream self.conv_kernel_size = config.linear_conv_kernel_dim self.layer_id = layer_id self.activation = config.hidden_act self.output_gate_type = config.output_gate_type self.layer_norm_epsilon = config.rms_norm_eps # Conv1d layer self.conv_dim = self.key_dim * 2 + self.value_dim self.conv1d = ColumnParallelLinear( input_size=self.conv_kernel_size, output_size=self.conv_dim, bias=False, quant_config=None, tp_rank=self.attn_tp_rank, tp_size=self.attn_tp_size, prefix=add_prefix("conv1d", prefix), ) self.conv1d.weight.data = self.conv1d.weight.data.unsqueeze(1) # projection of the input hidden states self.in_proj_qkvz = self.create_qkvz_proj( hidden_size=self.hidden_size, key_dim=self.key_dim, value_dim=self.value_dim, quant_config=quant_config, prefix=add_prefix("in_proj_qkvz", prefix), tp_rank=self.attn_tp_rank, tp_size=self.attn_tp_size, ) self.in_proj_ba = self.create_ba_proj( hidden_size=self.hidden_size, num_v_heads=self.num_v_heads, quant_config=quant_config, prefix=add_prefix("in_proj_ba", prefix), tp_rank=self.attn_tp_rank, tp_size=self.attn_tp_size, ) # Override weight loaders for packed checkpoint format. # Important: for FP8, this must cover not only `.weight` but also # `weight_scale_inv` / `weight_scale` / `input_scale` if present. self._bind_packed_weight_loaders(self.in_proj_qkvz) self._bind_packed_weight_loaders(self.in_proj_ba) # Conv1d weight loader setup query_key_settings = (self.key_dim, 0, False) value_settings = (self.value_dim, 0, False) self._override_weight_loader( self.conv1d.weight, mamba_v2_sharded_weight_loader( [ query_key_settings, query_key_settings, value_settings, ], self.attn_tp_size, self.attn_tp_rank, ), ) # State parameters self.dt_bias = nn.Parameter( torch.ones(self.num_v_heads // self.attn_tp_size), ) self.A_log = nn.Parameter( torch.empty(self.num_v_heads // self.attn_tp_size, dtype=torch.float32), ) set_weight_attrs(self.A_log, {"weight_loader": sharded_weight_loader(0)}) set_weight_attrs(self.dt_bias, {"weight_loader": sharded_weight_loader(0)}) conv_weights = self.conv1d.weight.view( self.conv1d.weight.size(0), self.conv1d.weight.size(2) ) self.attn = RadixLinearAttention( layer_id=layer_id, num_q_heads=self.num_k_heads // self.attn_tp_size, num_k_heads=self.num_k_heads // self.attn_tp_size, num_v_heads=self.num_v_heads // self.attn_tp_size, head_q_dim=self.head_k_dim, head_k_dim=self.head_k_dim, head_v_dim=self.head_v_dim, conv_weights=conv_weights, bias=self.conv1d.bias, activation=self.activation, A_log=self.A_log, dt_bias=self.dt_bias, ) self.norm = RMSNormGated( self.head_v_dim, eps=self.layer_norm_epsilon, group_size=None, norm_before_gate=True, device=torch.get_device_module().current_device(), dtype=config.torch_dtype, **( {"activation": self.output_gate_type} if self.output_gate_type is not None else {} ), ) self.out_proj = RowParallelLinear( self.value_dim, self.hidden_size, bias=False, input_is_parallel=True, reduce_results=False, quant_config=quant_config, tp_rank=self.attn_tp_rank, tp_size=self.attn_tp_size, prefix=add_prefix("out_proj", prefix), ) @staticmethod def _override_weight_loader(param, loader): """Robustly override loader for: 1) BasevLLMParameter subclasses: real storage is `_weight_loader` 2) regular Parameters that already have mutable `weight_loader` 3) regular Parameters without `weight_loader` yet """ if hasattr(param, "_weight_loader"): # FP8 / quantized BasevLLMParameter path param._weight_loader = loader return if hasattr(param, "weight_loader"): # Regular parameter/tensor that already has a mutable attr. # Do NOT call set_weight_attrs here, because it asserts when # overwriting an existing attribute. param.weight_loader = loader return # Fresh attribute on a normal tensor/Parameter set_weight_attrs(param, {"weight_loader": loader}) def _bind_packed_weight_loaders(self, module): """Bind packed-checkpoint-aware loaders to all relevant params of a merged module.""" for attr_name in ("weight", "weight_scale_inv", "weight_scale", "input_scale"): param = getattr(module, attr_name, None) if param is None: continue original_loader = getattr(param, "weight_loader", None) if original_loader is None: continue wrapped_loader = self._make_packed_weight_loader(module, original_loader) self._override_weight_loader(param, wrapped_loader) @staticmethod def _get_split_sizes_for_param(module, param, loaded_shard_id): """Return checkpoint-side split sizes for this param type.""" if isinstance(param, BlockQuantScaleParameter): # Split by output blocks, not raw output sizes. block_n, _ = module.quant_method.quant_config.weight_block_size block_n = 1 if getattr(param, "format_ue8m0", False) else block_n return [ (module.output_sizes[idx] + block_n - 1) // block_n for idx in loaded_shard_id ] if isinstance(param, PerTensorScaleParameter): # One logical scale per logical shard. return [1 for _ in loaded_shard_id] # Normal weight / non-block quant tensor return [module.output_sizes[idx] for idx in loaded_shard_id] @classmethod def _make_packed_weight_loader(cls, module, original_weight_loader): """Wrap the param's original loader so split checkpoints: - in_proj_qkv + in_proj_z -> merged in_proj_qkvz - in_proj_b + in_proj_a -> merged in_proj_ba can load correctly for both normal and FP8 params. """ def weight_loader(param, loaded_weight, loaded_shard_id=None): # Only intercept split-checkpoint tuple shards. # int shard_id and None should preserve original behavior. if isinstance(loaded_shard_id, tuple): split_sizes = cls._get_split_sizes_for_param( module, param, loaded_shard_id ) if loaded_weight.numel() == 1: # Single-element tensor (scalar or [1]): # broadcast to each logical shard. chunks = [loaded_weight.view(-1)] * len(loaded_shard_id) else: split_dim = getattr(param, "output_dim", 0) if _is_cpu: cpu_split_sizes = [] split_size_sum = sum(split_sizes) target_size_sim = loaded_weight.size(split_dim) for i in range(len(split_sizes)): cpu_split_sizes.append( int(target_size_sim * split_sizes[i] / split_size_sum) ) assert ( sum(cpu_split_sizes) == target_size_sim ), f"Padding the loaded weight failed due to sizes are not divisible cleanly from {cpu_split_sizes} to {target_size_sim}" chunks = loaded_weight.split(cpu_split_sizes, dim=split_dim) else: chunks = loaded_weight.split(split_sizes, dim=split_dim) assert len(chunks) == len(loaded_shard_id), ( f"Chunk/shard mismatch: {len(chunks)=}, " f"{len(loaded_shard_id)=}, {split_sizes=}" ) for idx, chunk in zip(loaded_shard_id, chunks): # Delegate each chunk to the param's original int-shard loader. original_weight_loader(param, chunk, idx) return return original_weight_loader(param, loaded_weight, loaded_shard_id) return weight_loader def create_qkvz_proj( self, hidden_size: int, key_dim: int, value_dim: int, quant_config: QuantizationConfig | None, prefix: str, tp_rank: Optional[int] = None, tp_size: Optional[int] = None, ) -> MergedColumnParallelLinear: return MergedColumnParallelLinear( input_size=hidden_size, output_sizes=[key_dim, key_dim, value_dim, value_dim], bias=False, quant_config=quant_config, prefix=prefix, tp_rank=tp_rank, tp_size=tp_size, ) def create_ba_proj( self, hidden_size: int, num_v_heads: int, quant_config: QuantizationConfig | None, prefix: str, tp_rank: Optional[int] = None, tp_size: Optional[int] = None, ) -> MergedColumnParallelLinear: # Qwen3.5 has separate in_proj_b and in_proj_a weights in the # checkpoint, which are loaded into the fused in_proj_ba parameter # via stacked_params_mapping with shard_id 0 and 1 respectively. return MergedColumnParallelLinear( input_size=hidden_size, output_sizes=[num_v_heads, num_v_heads], bias=False, quant_config=quant_config, prefix=prefix, tp_rank=tp_rank, tp_size=tp_size, ) def fix_query_key_value_ordering( self, mixed_qkvz: torch.Tensor, mixed_ba: torch.Tensor, ): """ Derives `query`, `key` and `value` tensors from `mixed_qkvzba`. """ k_tp = self.key_dim // self.attn_tp_size v_tp = self.value_dim // self.attn_tp_size nv_tp = self.num_v_heads // self.attn_tp_size # Directly split, no head group reshape query, key, value, z = mixed_qkvz.split([k_tp, k_tp, v_tp, v_tp], dim=-1) b, a = mixed_ba.split([nv_tp, nv_tp], dim=-1) # value / z reshape to (seq, num_v_heads/tp, head_v_dim) value = value.reshape(value.size(0), -1, self.head_v_dim) z = z.reshape(z.size(0), -1, self.head_v_dim) return query, key, value, z, b, a def _forward_input_proj(self, hidden_states: torch.Tensor): if ( _is_cpu or _is_npu or check_cuda_graph_backend(Phase.PREFILL, Backend.TC_PIECEWISE) ): DUAL_STREAM_TOKEN_THRESHOLD = 0 else: DUAL_STREAM_TOKEN_THRESHOLD = 1024 seq_len, _ = hidden_states.shape if ( self.alt_stream is not None and get_is_capture_mode() and seq_len < DUAL_STREAM_TOKEN_THRESHOLD and _gdn_use_alt_stream ): current_stream = torch.cuda.current_stream() self.alt_stream.wait_stream(current_stream) projected_states_qkvz, _ = self.in_proj_qkvz(hidden_states) with torch.cuda.stream(self.alt_stream): projected_states_ba, _ = self.in_proj_ba(hidden_states) current_stream.wait_stream(self.alt_stream) else: projected_states_qkvz, _ = self.in_proj_qkvz(hidden_states) projected_states_ba, _ = self.in_proj_ba(hidden_states) return projected_states_qkvz, projected_states_ba def forward( self, hidden_states: torch.Tensor, forward_batch: ForwardBatch, ): """ Forward pass with three parts: 1. Input projection 2. Core attention (custom op) 3. Output projection """ projected_states_qkvz, projected_states_ba = self._forward_input_proj( hidden_states ) if ( self.num_v_heads // self.num_k_heads in [1, 2, 4] and not _is_cpu and not _is_npu ): mixed_qkv, z, b, a = fused_qkvzba_split_reshape_cat_contiguous( projected_states_qkvz, projected_states_ba, triton.cdiv(self.num_k_heads, self.attn_tp_size), triton.cdiv(self.num_v_heads, self.attn_tp_size), self.head_k_dim, self.head_v_dim, ) elif _is_cpu and _is_amx_available: mixed_qkv, z, b, a = ( torch.ops.sgl_kernel.fused_qkvzba_split_reshape_cat_contiguous_cpu( projected_states_qkvz, projected_states_ba, self.num_k_heads // self.attn_tp_size, self.num_v_heads // self.attn_tp_size, self.head_k_dim, self.head_v_dim, ) ) else: query, key, value, z, b, a = self.fix_query_key_value_ordering( projected_states_qkvz, projected_states_ba ) b = b.contiguous() a = a.contiguous() query, key, value = map( lambda x: x.reshape(x.shape[0], -1), (query, key, value) ) mixed_qkv = torch.cat((query, key, value), dim=-1) core_attn_out = self.attn( forward_batch, mixed_qkv=mixed_qkv, a=a, b=b, ) z_shape_og = z.shape # reshape input data into 2D tensor core_attn_out = core_attn_out.reshape(-1, core_attn_out.shape[-1]) z = z.reshape(-1, z.shape[-1]) # Add padding for DP-Attn if core_attn_out.shape != z.shape: core_attn_out_pad = torch.zeros_like(z) core_attn_out_pad[: core_attn_out.shape[0], :] = core_attn_out core_attn_out = core_attn_out_pad core_attn_out = self.norm(core_attn_out, z) core_attn_out = core_attn_out.reshape(z_shape_og) core_attn_out = core_attn_out.reshape(*core_attn_out.shape[:-2], -1) output, _ = self.out_proj(core_attn_out) return output class Qwen3_5LinearDecoderLayer(nn.Module): """Qwen3.5 Decoder Layer with Linear Attention (GatedDeltaNet).""" def __init__( self, config: Qwen3_5TextConfig, layer_id: int, quant_config: Optional[QuantizationConfig] = None, prefix: str = "", alt_stream: Optional[torch.cuda.Stream] = None, is_nextn: bool = False, ) -> None: super().__init__() self.config = config self.layer_id = layer_id linear_attn_quant_config = ( None if quant_config and quant_config.get_name() == "modelopt_fp4" else quant_config ) self.linear_attn = Qwen3_5GatedDeltaNet( config, layer_id, linear_attn_quant_config, alt_stream, prefix ) # NOTE: Determine the MLP type based on the model type # Qwen3.5 use all layers for MLP / Qwen3.5-MoE use sparse MoE blocks if config.model_type == "qwen3_5_moe_text": self.mlp = Qwen2MoeSparseMoeBlock( layer_id=layer_id, config=config, quant_config=quant_config, alt_stream=( alt_stream if (_is_cuda or _disable_shared_experts_fusion()) else None ), prefix=add_prefix("mlp", prefix.replace(".linear_attn", "")), is_nextn=is_nextn, support_shared_expert_fusion=not _disable_shared_experts_fusion(), ) is_layer_sparse = True is_previous_layer_sparse = True is_next_layer_sparse = True elif config.model_type == "qwen3_5_text": self.mlp = Qwen2MoeMLP( hidden_size=config.hidden_size, intermediate_size=config.intermediate_size, hidden_act=config.hidden_act, quant_config=quant_config, prefix=add_prefix("mlp", prefix.replace(".linear_attn", "")), ) is_layer_sparse = False is_previous_layer_sparse = False is_next_layer_sparse = False else: raise ValueError(f"Invalid model type: {config.model_type}") self.layer_scatter_modes = LayerScatterModes.init_new( layer_id=layer_id, num_layers=config.num_hidden_layers, is_layer_sparse=is_layer_sparse, is_previous_layer_sparse=is_previous_layer_sparse, is_next_layer_sparse=is_next_layer_sparse, ) self.input_layernorm = GemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.post_attention_layernorm = GemmaRMSNorm( config.hidden_size, eps=config.rms_norm_eps ) self.layer_communicator = LayerCommunicator( layer_scatter_modes=self.layer_scatter_modes, input_layernorm=self.input_layernorm, post_attention_layernorm=self.post_attention_layernorm, allow_reduce_scatter=True, is_last_layer=(layer_id == config.num_hidden_layers - 1), ) def forward( self, hidden_states: torch.Tensor, residual: Optional[torch.Tensor], **kwargs, ): forward_batch = kwargs.get("forward_batch", None) hidden_states, residual = ( self.layer_communicator.prepare_attn_and_capture_last_layer_outputs( hidden_states, residual, forward_batch, captured_last_layer_outputs=kwargs.get( "captured_last_layer_outputs", None ), ) ) if not forward_batch.forward_mode.is_idle(): hidden_states = self.linear_attn( hidden_states, forward_batch, ) # Fully Connected hidden_states, residual = self.layer_communicator.prepare_mlp( hidden_states, residual, forward_batch ) mlp_reduce_scatter = self.layer_communicator.should_use_reduce_scatter( forward_batch ) fuse_mlp_allreduce = ( self.layer_communicator.should_fuse_mlp_allreduce_with_next_layer( forward_batch ) ) with get_forward().scoped( fuse_mlp_allreduce=fuse_mlp_allreduce, mlp_reduce_scatter=mlp_reduce_scatter, ): if isinstance(self.mlp, Qwen2MoeSparseMoeBlock): hidden_states = self.mlp( hidden_states, forward_batch, ) else: hidden_states = self.mlp(hidden_states) if fuse_mlp_allreduce: hidden_states._sglang_needs_allreduce_fusion = True else: hidden_states, residual = self.layer_communicator.postprocess_layer( hidden_states, residual, forward_batch ) return hidden_states, residual class Qwen3_5AttentionDecoderLayer(nn.Module): """Qwen3.5 Decoder Layer with Full Attention.""" def __init__( self, config: Qwen3_5TextConfig, layer_id: int, quant_config: Optional[QuantizationConfig] = None, prefix: str = "", alt_stream: Optional[torch.cuda.Stream] = None, is_nextn: bool = False, ) -> None: super().__init__() self.config = config self.hidden_size = config.hidden_size self.attn_tp_rank = get_parallel().attn_tp_rank self.attn_tp_size = get_parallel().attn_tp_size self.total_num_heads = config.num_attention_heads assert self.total_num_heads % self.attn_tp_size == 0 self.num_heads = self.total_num_heads // self.attn_tp_size self.total_num_kv_heads = config.num_key_value_heads if self.total_num_kv_heads >= self.attn_tp_size: assert self.total_num_kv_heads % self.attn_tp_size == 0 else: assert self.attn_tp_size % self.total_num_kv_heads == 0 self.num_kv_heads = max(1, self.total_num_kv_heads // self.attn_tp_size) self.head_dim = config.head_dim or (self.hidden_size // self.num_heads) self.q_size = self.num_heads * self.head_dim self.kv_size = self.num_kv_heads * self.head_dim self.scaling = self.head_dim**-0.5 self.max_position_embeddings = getattr(config, "max_position_embeddings", 8192) self.rope_theta, rope_scaling = get_rope_config(config) self.partial_rotary_factor = getattr(config, "partial_rotary_factor", 1.0) self.layer_id = layer_id # If rope_scaling doesn't specify a scaling type, treat as no scaling if rope_scaling and not ("rope_type" in rope_scaling or "type" in rope_scaling): rope_scaling = None self.attn_output_gate = getattr(config, "attn_output_gate", True) if self.attn_output_gate: logger.warning_once("using attn output gate!") self.rotary_emb = get_rope( head_size=self.head_dim, rotary_dim=self.head_dim, max_position=self.max_position_embeddings, rope_scaling=rope_scaling, base=self.rope_theta, partial_rotary_factor=self.partial_rotary_factor, is_neox_style=True, dtype=torch.get_default_dtype(), ) attn_quant_config = ( None if quant_config and quant_config.get_name() == "modelopt_fp4" else quant_config ) self.qkv_proj = QKVParallelLinear( config.hidden_size, self.head_dim, self.total_num_heads * (1 + self.attn_output_gate), self.total_num_kv_heads, bias=False, quant_config=attn_quant_config, tp_rank=self.attn_tp_rank, tp_size=self.attn_tp_size, prefix=add_prefix("qkv_proj", prefix), ) self.o_proj = RowParallelLinear( self.total_num_heads * self.head_dim, config.hidden_size, bias=False, quant_config=attn_quant_config, reduce_results=False, tp_rank=self.attn_tp_rank, tp_size=self.attn_tp_size, prefix=add_prefix("o_proj", prefix), ) self.attn = RadixAttention( self.num_heads, self.head_dim, self.scaling, num_kv_heads=self.num_kv_heads, layer_id=layer_id, prefix=f"{prefix}.attn", ) # Dense MLP for non-MoE variant if config.model_type == "qwen3_5_text": self.mlp = Qwen2MoeMLP( hidden_size=config.hidden_size, intermediate_size=config.intermediate_size, hidden_act=config.hidden_act, quant_config=quant_config, prefix=add_prefix("mlp", prefix.replace(".self_attn", "")), ) is_layer_sparse = False is_previous_layer_sparse = False is_next_layer_sparse = False elif config.model_type == "qwen3_5_moe_text": self.mlp = Qwen2MoeSparseMoeBlock( layer_id=layer_id, config=config, quant_config=quant_config, alt_stream=( alt_stream if (_is_cuda or _disable_shared_experts_fusion()) else None ), prefix=add_prefix("mlp", prefix.replace(".self_attn", "")), is_nextn=is_nextn, support_shared_expert_fusion=not _disable_shared_experts_fusion(), ) is_layer_sparse = True is_previous_layer_sparse = True is_next_layer_sparse = True else: raise ValueError(f"Invalid model type: {config.model_type}") self.layer_scatter_modes = LayerScatterModes.init_new( layer_id=layer_id, num_layers=config.num_hidden_layers, is_layer_sparse=is_layer_sparse, is_previous_layer_sparse=is_previous_layer_sparse, is_next_layer_sparse=is_next_layer_sparse, ) self.input_layernorm = GemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.post_attention_layernorm = GemmaRMSNorm( config.hidden_size, eps=config.rms_norm_eps ) self.q_norm = GemmaRMSNorm(self.head_dim, eps=config.rms_norm_eps) self.k_norm = GemmaRMSNorm(self.head_dim, eps=config.rms_norm_eps) self.layer_communicator = LayerCommunicator( layer_scatter_modes=self.layer_scatter_modes, input_layernorm=self.input_layernorm, post_attention_layernorm=self.post_attention_layernorm, allow_reduce_scatter=True, is_last_layer=(layer_id == config.num_hidden_layers - 1), ) self.alt_stream = alt_stream def _apply_qk_norm( self, q: torch.Tensor, k: torch.Tensor ) -> Tuple[torch.Tensor, torch.Tensor]: """Apply Q/K normalization with optional alt_stream overlap.""" if ( self.alt_stream is not None and get_is_capture_mode() and _qknorm_use_alt_stream ): current_stream = torch.cuda.current_stream() self.alt_stream.wait_stream(current_stream) q_by_head = q.reshape(-1, self.head_dim) q_by_head = self.q_norm(q_by_head) with torch.cuda.stream(self.alt_stream): k_by_head = k.reshape(-1, self.head_dim) k_by_head = self.k_norm(k_by_head) current_stream.wait_stream(self.alt_stream) elif _is_hip or _is_xpu or _is_cpu: q_by_head, k_by_head = fused_qk_gemma_rmsnorm( q, k, self.q_norm.weight.data, self.k_norm.weight.data, self.q_norm.variance_epsilon, self.head_dim, ) else: q_by_head = q.reshape(-1, self.head_dim) q_by_head = self.q_norm(q_by_head) k_by_head = k.reshape(-1, self.head_dim) k_by_head = self.k_norm(k_by_head) q = q_by_head.view(q.shape) k = k_by_head.view(k.shape) return q, k def forward_prepare_cuda_fused(self, positions, hidden_states): """Fused QK GemmaRMSNorm + NeoX RoPE + gate deinterleave.""" qkv, _ = self.qkv_proj(hidden_states) if self.attn_output_gate: q_gate, k, v = qkv.split( [self.q_size * 2, self.kv_size, self.kv_size], dim=-1 ) else: q_gate, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1) q_out, k_out, gate_out = fused_qk_gemma_rmsnorm_rope_gate( q_gate, k, self.q_norm.weight.data, self.k_norm.weight.data, self.rotary_emb.cos_sin_cache, positions, self.q_norm.variance_epsilon, self.num_heads, self.num_kv_heads, self.head_dim, self.rotary_emb.rotary_dim, has_gate=self.attn_output_gate, ) seq_len = hidden_states.shape[0] q = q_out.view(seq_len, -1) k = k_out.view(seq_len, -1) gate = gate_out.view(seq_len, -1) if gate_out is not None else None return q, k, v, gate def forward_prepare_native(self, positions, hidden_states): qkv, _ = self.qkv_proj(hidden_states) if self.attn_output_gate: q_gate, k, v = qkv.split( [self.q_size * 2, self.kv_size, self.kv_size], dim=-1 ) orig_shape = q_gate.shape[:-1] q_gate = q_gate.view(*orig_shape, self.num_heads, -1) q, gate = torch.chunk(q_gate, 2, dim=-1) q = q.reshape(*orig_shape, -1) # gate stays as 3D strided view; fused_sigmoid_mul handles it directly else: q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1) gate = None q, k = self._apply_qk_norm(q, k) q, k = self.rotary_emb(positions, q, k) return q, k, v, gate def forward_prepare_fused_gate(self, positions, hidden_states): qkv, _ = self.qkv_proj(hidden_states) if self.attn_output_gate: q_gate, k, v = qkv.split( [self.q_size * 2, self.kv_size, self.kv_size], dim=-1 ) seq_len = q_gate.shape[0] q_flat, k_flat, gate_flat = fused_qk_gemma_rmsnorm_with_gate( q_gate, k, self.q_norm.weight.data, self.k_norm.weight.data, self.q_norm.variance_epsilon, self.head_dim, self.num_heads, ) q = q_flat.view(seq_len, -1) k = k_flat.view(seq_len, -1) gate = gate_flat.view(seq_len, -1) else: q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1) gate = None q, k = self._apply_qk_norm(q, k) q, k = self.rotary_emb(positions, q, k) return q, k, v, gate def forward_prepare_npu(self, positions, hidden_states, forward_batch): qkv, _ = self.qkv_proj(hidden_states) # Calculate first full attention layer ID based on config if self.attn.layer_id == (self.config.full_attention_interval - 1): self.rotary_emb.get_cos_sin_with_position(positions) q, k, v, gate = split_qkvgate_gemma_rmsnorm_rope( qkv, self.rotary_emb.position_sin, self.rotary_emb.position_cos, self.q_size, self.kv_size, self.head_dim, int(self.head_dim * self.partial_rotary_factor), eps=self.q_norm.variance_epsilon, q_weight=self.q_norm.weight, k_weight=self.k_norm.weight, ) return q, k, v, gate def self_attention( self, positions: torch.Tensor, hidden_states: torch.Tensor, forward_batch: ForwardBatch, ) -> torch.Tensor: """Full attention forward pass.""" if _is_cuda and self.attn_output_gate: q, k, v, gate = self.forward_prepare_cuda_fused( positions=positions, hidden_states=hidden_states, ) elif (_is_hip or _is_xpu or _is_cpu) and self.attn_output_gate: q, k, v, gate = self.forward_prepare_fused_gate( positions=positions, hidden_states=hidden_states, ) elif ( not _is_npu or forward_batch.forward_mode.is_extend_or_draft_extend_or_mixed() or not self.attn_output_gate ): q, k, v, gate = self.forward_prepare_native( positions=positions, hidden_states=hidden_states, ) else: q, k, v, gate = self.forward_prepare_npu( positions=positions, hidden_states=hidden_states, forward_batch=forward_batch, ) attn_output = self.attn(q, k, v, forward_batch) if self.attn_output_gate: if not _is_npu: attn_output = fused_sigmoid_mul(attn_output, gate, inplace=True) else: gate_val = gate.reshape(gate.shape[0], -1) if gate.ndim == 3 else gate attn_output.mul_(torch.sigmoid(gate_val)) output, _ = self.o_proj(attn_output) return output def forward( self, positions: torch.Tensor, hidden_states: torch.Tensor, residual: Optional[torch.Tensor], forward_batch: ForwardBatch, captured_last_layer_outputs: Optional[list[torch.Tensor]] = None, **kwargs, ): hidden_states, residual = ( self.layer_communicator.prepare_attn_and_capture_last_layer_outputs( hidden_states, residual, forward_batch, captured_last_layer_outputs=captured_last_layer_outputs, ) ) if not forward_batch.forward_mode.is_idle(): hidden_states = self.self_attention( positions=positions, hidden_states=hidden_states, forward_batch=forward_batch, ) # Fully Connected hidden_states, residual = self.layer_communicator.prepare_mlp( hidden_states, residual, forward_batch ) mlp_reduce_scatter = self.layer_communicator.should_use_reduce_scatter( forward_batch ) fuse_mlp_allreduce = ( self.layer_communicator.should_fuse_mlp_allreduce_with_next_layer( forward_batch ) ) with get_forward().scoped( fuse_mlp_allreduce=fuse_mlp_allreduce, mlp_reduce_scatter=mlp_reduce_scatter, ): if isinstance(self.mlp, Qwen2MoeSparseMoeBlock): hidden_states = self.mlp( hidden_states, forward_batch, ) else: hidden_states = self.mlp(hidden_states) if fuse_mlp_allreduce: hidden_states._sglang_needs_allreduce_fusion = True else: hidden_states, residual = self.layer_communicator.postprocess_layer( hidden_states, residual, forward_batch ) return hidden_states, residual ALL_DECODER_LAYER_TYPES = { "attention": Qwen3_5AttentionDecoderLayer, "linear_attention": Qwen3_5LinearDecoderLayer, } class Qwen3_5ForCausalLM(nn.Module): """Qwen3.5 Model with support for dense variant.""" packed_modules_mapping = { "qkv_proj": ["q_proj", "k_proj", "v_proj"], "gate_up_proj": ["gate_proj", "up_proj"], "in_proj_qkvz": ["in_proj_qkv", "in_proj_z"], "in_proj_ba": ["in_proj_b", "in_proj_a"], } supported_lora_modules = [ "qkv_proj", "o_proj", "out_proj", "in_proj_qkvz", "gate_up_proj", "down_proj", "lm_head", ] def get_hidden_dim(self, module_name: str, layer_idx: int): config = self.config head_dim = config.head_dim or (config.hidden_size // config.num_attention_heads) if module_name == "qkv_proj": attn_output_gate = getattr(config, "attn_output_gate", True) q_heads = config.num_attention_heads * (2 if attn_output_gate else 1) return ( config.hidden_size, head_dim * (q_heads + config.num_key_value_heads * 2), ) elif module_name == "o_proj": return config.num_attention_heads * head_dim, config.hidden_size elif module_name == "out_proj": value_dim = config.linear_value_head_dim * config.linear_num_value_heads return value_dim, config.hidden_size elif module_name == "in_proj_qkvz": key_dim = config.linear_key_head_dim * config.linear_num_key_heads value_dim = config.linear_value_head_dim * config.linear_num_value_heads return config.hidden_size, key_dim * 2 + value_dim * 2 elif module_name == "gate_up_proj": # MoE: shared expert uses shared_expert_intermediate_size # Dense: regular MLP uses intermediate_size is_moe = "moe" in getattr(config, "model_type", "") if is_moe: inter = config.shared_expert_intermediate_size else: inter = config.intermediate_size return config.hidden_size, inter * 2 elif module_name == "down_proj": is_moe = "moe" in getattr(config, "model_type", "") if is_moe: inter = config.shared_expert_intermediate_size else: inter = config.intermediate_size return inter, config.hidden_size elif module_name == "gate_up_proj_moe": return config.hidden_size, config.moe_intermediate_size * 2 elif module_name == "down_proj_moe": return config.moe_intermediate_size, config.hidden_size elif module_name == "embed_tokens": return config.vocab_size, config.hidden_size elif module_name == "lm_head": return config.hidden_size, config.vocab_size else: raise NotImplementedError( f"get_hidden_dim not implemented for {module_name}" ) def _maybe_autodisable_shared_experts_fusion(self, config, quant_config): # Auto-disable fusion when the checkpoint can't fuse (e.g. MXFP4 Qwen3.5) # so the model still gets the #25885 multi-streaming path. ROCm-only. if ( config.model_type == "qwen3_5_moe_text" and not get_server_args().disable_shared_experts_fusion and not can_fuse_shared_expert(config, quant_config) ): from sglang.srt.arg_groups.overrides import declare_load_time_override declare_load_time_override( "Qwen3_5ForCausalLM._maybe_autodisable_shared_experts_fusion", {"disable_shared_experts_fusion": True}, ) logger.info( "Qwen3.5: shared-expert fusion not supported for this checkpoint; " "auto-disabling (multi-streaming #25885 still applies)." ) def __init__( self, config: Qwen3_5TextConfig, quant_config: Optional[QuantizationConfig] = None, prefix: str = "", is_nextn: bool = False, ) -> None: super().__init__() self.config = config self.hidden_size = config.hidden_size self.pp_group = get_pp_group() if _is_hip: self._maybe_autodisable_shared_experts_fusion(config, quant_config) alt_stream = get_stream("alt") if _is_cuda or _hip_use_alt_stream else None # Embedding layer if self.pp_group.is_first_rank: self.embed_tokens = VocabParallelEmbedding( config.vocab_size, config.hidden_size, org_num_embeddings=config.vocab_size, enable_tp=not is_dp_attention_enabled(), ) else: self.embed_tokens = PPMissingLayer() # Decoder layers def get_layer(idx: int, prefix: str): layer_type = config.layers_block_type[idx] layer_class = ALL_DECODER_LAYER_TYPES[layer_type] if layer_type == "attention": prefix = add_prefix("self_attn", prefix) else: prefix = add_prefix("linear_attn", prefix) return layer_class( config=config, layer_id=idx, quant_config=quant_config, prefix=prefix, alt_stream=alt_stream, is_nextn=is_nextn, ) self.layers, self._start_layer, self._end_layer = make_layers( config.num_hidden_layers, get_layer, pp_rank=self.pp_group.rank_in_group, pp_size=self.pp_group.world_size, prefix=f"{prefix}.layers", ) # Final normalization if self.pp_group.is_last_rank: self.norm = GemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps) else: self.norm = PPMissingLayer() self.layers_to_capture = [] def get_input_embeddings(self): return self.embed_tokens def set_dflash_layers_to_capture(self, layers_to_capture: list[int]): self.layers_to_capture = layers_to_capture for layer_id in self.layers_to_capture: setattr(self.layers[layer_id], "_is_layer_to_capture", True) @property def start_layer(self) -> int: return self._start_layer @property def end_layer(self) -> int: return self._end_layer @torch.no_grad() def forward( self, input_ids: torch.Tensor, positions: torch.Tensor, forward_batch: ForwardBatch, input_embeds: Optional[torch.Tensor] = None, pp_proxy_tensors: Optional[PPProxyTensors] = None, input_deepstack_embeds: Optional[torch.Tensor] = None, ) -> Union[torch.Tensor, PPProxyTensors]: # Initialize hidden states if self.pp_group.is_first_rank: if input_embeds is None: hidden_states = self.embed_tokens(input_ids) else: hidden_states = input_embeds residual = None else: assert pp_proxy_tensors is not None hidden_states = pp_proxy_tensors["hidden_states"] residual = pp_proxy_tensors["residual"] aux_hidden_states = [] # Pass through decoder layers for layer_idx in range(self.start_layer, self.end_layer): layer = self.layers[layer_idx] with get_global_expert_distribution_recorder().with_current_layer( layer_idx ): hidden_states, residual = layer( positions=positions, hidden_states=hidden_states, residual=residual, forward_batch=forward_batch, captured_last_layer_outputs=( aux_hidden_states if getattr(layer, "_is_layer_to_capture", False) else None ), ) # Process deepstack embeddings if provided if ( input_deepstack_embeds is not None and input_deepstack_embeds.numel() > 0 and layer_idx < 3 ): sep = self.hidden_size * layer_idx hidden_states.add_( input_deepstack_embeds[:, sep : sep + self.hidden_size] ) # Return intermediate tensors for pipeline parallelism if not self.pp_group.is_last_rank: return PPProxyTensors( { "hidden_states": hidden_states, "residual": residual, } ) # Apply final normalization if hidden_states.shape[0] != 0: if residual is None: hidden_states = self.norm(hidden_states) else: hidden_states, _ = self.norm(hidden_states, residual) if len(aux_hidden_states) == 0: return hidden_states return hidden_states, aux_hidden_states def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]): stacked_params_mapping = [ # (param_name, shard_name, shard_id) ("qkv_proj", "q_proj", "q"), ("qkv_proj", "k_proj", "k"), ("qkv_proj", "v_proj", "v"), ("gate_up_proj", "gate_proj", 0), ("gate_up_proj", "up_proj", 1), # GDN ("in_proj_qkvz.", "in_proj_qkv.", (0, 1, 2)), ("in_proj_qkvz.", "in_proj_z.", 3), ("in_proj_ba.", "in_proj_b.", 0), ("in_proj_ba.", "in_proj_a.", 1), ] loaded_params: Set[str] = set() params_dict = dict(self.named_parameters(remove_duplicate=False)) for name, loaded_weight in weights: if "rotary_emb.inv_freq" in name: continue if "mtp" in name: continue if "visual" in name: continue if "language_model" in name: name = name.replace(r"model.language_model.", r"model.") if ".self_attn." in name: name = name.replace(".self_attn", "") layer_id = get_layer_id(name) if ( layer_id is not None and hasattr(self, "start_layer") and (layer_id < self.start_layer or layer_id >= self.end_layer) ): continue for param_name, weight_name, shard_id in stacked_params_mapping: if weight_name not in name: continue if "mlp.experts" in name: continue name = name.replace(weight_name, param_name) # Skip loading extra bias for GPTQ models. if name.endswith(".bias") and name not in params_dict: continue # Skip layers on other devices. # if is_pp_missing_parameter(name, self): # continue if name not in params_dict: continue param = params_dict[name] weight_loader = getattr(param, "weight_loader") weight_loader(param, loaded_weight, shard_id) break else: # Skip loading extra bias for GPTQ models. if name.endswith(".bias") and name not in params_dict: continue if name not in params_dict: logger.warning(f"Parameter {name} not found in params_dict") continue param = params_dict[name] weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, loaded_weight) loaded_params.add(name) return loaded_params @classmethod def get_model_config_for_expert_location(cls, config): return ModelConfigForExpertLocation( num_layers=config.num_hidden_layers, num_logical_experts=config.num_experts, num_groups=None, ) class Qwen3_5MoeForCausalLM(Qwen3_5ForCausalLM): def __init__( self, config: Qwen3_5TextConfig, quant_config: Optional[QuantizationConfig] = None, prefix: str = "", ) -> None: super().__init__(config=config, quant_config=quant_config, prefix=prefix) def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]): stacked_params_mapping = [ # (param_name, shard_name, shard_id) ("qkv_proj", "q_proj", "q"), ("qkv_proj", "k_proj", "k"), ("qkv_proj", "v_proj", "v"), ("gate_up_proj", "gate_proj", 0), ("gate_up_proj", "up_proj", 1), # GDN ("in_proj_qkvz.", "in_proj_qkv.", (0, 1, 2)), ("in_proj_qkvz.", "in_proj_z.", 3), ("in_proj_ba.", "in_proj_b.", 0), ("in_proj_ba.", "in_proj_a.", 1), ] # Params for weights, fp8 weight scales, fp8 activation scales # (param_name, weight_name, expert_id, shard_id) expert_params_mapping = FusedMoE.make_expert_params_mapping( ckpt_gate_proj_name="gate_proj", ckpt_down_proj_name="down_proj", ckpt_up_proj_name="up_proj", num_experts=self.config.num_experts, ) # Skip loading extra parameters for GPTQ/modelopt models. ignore_suffixes = ( ".bias", "_bias", ".k_scale", "_k_scale", ".v_scale", "_v_scale", ".weight_scale", "_weight_scale", ".input_scale", "_input_scale", ) is_fused_expert = False fused_expert_params_mapping = [ ("experts.w13_weight", "experts.gate_up_proj", 0, "w1"), ("experts.w2_weight", "experts.down_proj", 0, "w2"), ] num_experts = self.config.num_experts def load_fused_expert_weights( name: str, params_dict: dict, loaded_weight: torch.Tensor, shard_id: str, num_experts: int, ): if name not in params_dict: return False param = params_dict[name] weight_loader = param.weight_loader # let ep moe layer to gracefully handle expert_ids that do not belong to local moe rank for expert_id in range(num_experts): curr_expert_weight = loaded_weight[expert_id] weight_loader( param, curr_expert_weight, name, shard_id, expert_id, ) return True loaded_params: Set[str] = set() params_dict = dict(self.named_parameters(remove_duplicate=False)) for name, loaded_weight in weights: if "rotary_emb.inv_freq" in name: continue if "mtp" in name: continue if "visual" in name: continue if "language_model" in name: name = name.replace(r"model.language_model.", r"model.") if ".self_attn." in name: name = name.replace(".self_attn", "") layer_id = get_layer_id(name) if ( layer_id is not None and hasattr(self, "start_layer") and (layer_id < self.start_layer or layer_id >= self.end_layer) ): continue for param_name, weight_name, shard_id in stacked_params_mapping: if "experts.gate_up_proj" in name or "experts.down_proj" in name: is_fused_expert = True expert_params_mapping = fused_expert_params_mapping # Skip non-stacked layers and experts (experts handled below). if weight_name not in name: continue # We have mlp.experts[0].gate_proj in the checkpoint. # Since we handle the experts below in expert_params_mapping, # we need to skip here BEFORE we update the name, otherwise # name will be updated to mlp.experts[0].gate_up_proj, which # will then be updated below in expert_params_mapping # for mlp.experts[0].gate_gate_up_proj, which breaks load. if "mlp.experts" in name: continue name = name.replace(weight_name, param_name) # Skip loading extra parameters for GPTQ/modelopt models. if name.endswith(ignore_suffixes) and name not in params_dict: continue if name not in params_dict: continue param = params_dict[name] weight_loader = param.weight_loader weight_loader(param, loaded_weight, shard_id) break else: # Track if this is an expert weight to enable early skipping is_expert_weight = False for mapping in expert_params_mapping: param_name, weight_name, expert_id, shard_id = mapping if weight_name not in name: continue # Anyway, this is an expert weight and should not be # attempted to load as other weights later is_expert_weight = True name_mapped = name.replace(weight_name, param_name) if is_fused_expert: if "experts.gate_up_proj" in name: loaded_weight = loaded_weight.chunk(2, dim=-2) load_fused_expert_weights( name_mapped, params_dict, loaded_weight[0], "w1", num_experts, ) load_fused_expert_weights( name_mapped, params_dict, loaded_weight[1], "w3", num_experts, ) else: load_fused_expert_weights( name_mapped, params_dict, loaded_weight, shard_id, num_experts, ) else: # Skip loading extra parameters for GPTQ/modelopt models. if ( name_mapped.endswith(ignore_suffixes) and name_mapped not in params_dict ): continue param = params_dict[name_mapped] # We should ask the weight loader to return success or # not here since otherwise we may skip experts with # # other available replicas. weight_loader = param.weight_loader weight_loader( param, loaded_weight, name_mapped, shard_id=shard_id, expert_id=expert_id, ) name = name_mapped break else: if is_expert_weight: # This is an expert weight but not mapped to this rank, skip all remaining processing continue # Skip loading extra parameters for GPTQ/modelopt models. if name.endswith(ignore_suffixes) and name not in params_dict: continue if name in params_dict.keys(): param = params_dict[name] weight_loader = getattr( param, "weight_loader", default_weight_loader ) weight_loader(param, loaded_weight) else: logger.warning(f"Parameter {name} not found in params_dict") loaded_params.add(name) return loaded_params class Qwen3_5ForConditionalGeneration(Qwen3VLForConditionalGeneration): packed_modules_mapping = Qwen3_5ForCausalLM.packed_modules_mapping hf_to_sglang_mapper = None supported_lora_modules = Qwen3_5ForCausalLM.supported_lora_modules def __init__( self, config: Qwen3_5Config, quant_config: Optional[QuantizationConfig] = None, prefix: str = "", language_model_cls=Qwen3_5ForCausalLM, ): super().__init__(config, quant_config, prefix, language_model_cls) rope_config = getattr(self.config, "rope_parameters", None) or getattr( self.config, "rope_scaling", {} ) self.is_mrope_enabled = "mrope_section" in rope_config self.deepstack_visual_indexes = self.visual.deepstack_visual_indexes def get_hidden_dim(self, module_name: str, layer_idx: int): return self.model.get_hidden_dim(module_name, layer_idx) def should_apply_lora(self, module_name: str) -> bool: return module_name.startswith("model.layers.") @property def start_layer(self) -> int: return getattr(getattr(self, "model", None), "start_layer", 0) @property def end_layer(self) -> int: model = getattr(self, "model", None) end_layer = getattr(model, "end_layer", None) if end_layer is not None: return end_layer cfg = getattr(model, "config", None) return int(getattr(cfg, "num_hidden_layers", 0)) def get_embed_and_head(self): embed = self.model.embed_tokens.weight if self.pp_group.is_first_rank else None head = self.lm_head.weight if self.pp_group.is_last_rank else None return embed, head def set_embed_and_head(self, embed, head): if self.pp_group.is_first_rank and embed is not None: del self.model.embed_tokens.weight self.model.embed_tokens.weight = embed if self.pp_group.is_last_rank and head is not None: del self.lm_head.weight self.lm_head.weight = head if _is_xpu: torch.xpu.empty_cache() torch.xpu.synchronize() else: torch.cuda.empty_cache() torch.cuda.synchronize() def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]): stacked_params_mapping = [ # (param_name, shard_name, shard_id) ("qkv_proj", "q_proj", "q"), ("qkv_proj", "k_proj", "k"), ("qkv_proj", "v_proj", "v"), ("gate_up_proj", "gate_proj", 0), ("gate_up_proj", "up_proj", 1), # GDN fused projections ("in_proj_qkvz.", "in_proj_qkv.", (0, 1, 2)), ("in_proj_qkvz.", "in_proj_z.", 3), ("in_proj_ba.", "in_proj_b.", 0), ("in_proj_ba.", "in_proj_a.", 1), ] loaded_params: Set[str] = set() params_dict = dict(self.named_parameters(remove_duplicate=False)) for name, loaded_weight in weights: if "rotary_emb.inv_freq" in name: continue if "mtp" in name: continue if "language_model" in name: name = name.replace(r"model.language_model.", r"model.") if ".self_attn." in name: name = name.replace(".self_attn", "") if ( self.config.tie_word_embeddings and self.pp_group.is_last_rank and "model.embed_tokens.weight" in name ): if "lm_head.weight" in params_dict: lm_head_param = params_dict["lm_head.weight"] weight_loader = getattr( lm_head_param, "weight_loader", default_weight_loader ) weight_loader(lm_head_param, loaded_weight) layer_id = get_layer_id(name) if ( layer_id is not None and hasattr(self, "start_layer") and (layer_id < self.start_layer or layer_id >= self.end_layer) ): continue for param_name, weight_name, shard_id in stacked_params_mapping: if weight_name not in name: continue if "visual" in name or "mlp.experts" in name: continue name = name.replace(weight_name, param_name) # Skip loading extra bias for GPTQ models. if name.endswith(".bias") and name not in params_dict: continue # Skip layers on other devices. # if is_pp_missing_parameter(name, self): # continue if name not in params_dict: continue param = params_dict[name] weight_loader = getattr(param, "weight_loader") weight_loader(param, loaded_weight, shard_id) break else: if "visual" in name: # adapt to VisionAttention name = name.replace(r"attn.qkv.", r"attn.qkv_proj.") name = name.replace(r"model.visual.", r"visual.") # print(name, loaded_weight.shape) # Skip loading extra bias for GPTQ models. if name.endswith(".bias") and name not in params_dict: continue if name not in params_dict: logger.warning(f"Parameter {name} not found in params_dict") continue param = params_dict[name] weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, loaded_weight) if ( self.config.tie_word_embeddings and name == "model.embed_tokens.weight" and (_is_cpu and _is_amx_available) ): param_lm_head = params_dict["lm_head.weight"] weight_loader = getattr( param_lm_head, "weight_loader", default_weight_loader ) weight_loader(param_lm_head, loaded_weight) loaded_params.add(name) return loaded_params class Qwen3_5MoeForConditionalGeneration(Qwen3VLForConditionalGeneration): """Qwen3.5 MoE Vision-Language Model.""" packed_modules_mapping = Qwen3_5ForCausalLM.packed_modules_mapping hf_to_sglang_mapper = None supported_lora_modules = Qwen3_5ForCausalLM.supported_lora_modules def __init__( self, config: Qwen3_5MoeConfig, quant_config: Optional[QuantizationConfig] = None, prefix: str = "", language_model_cls=Qwen3_5MoeForCausalLM, ) -> None: super().__init__(config, quant_config, prefix, language_model_cls) rope_config = getattr(self.config, "rope_parameters", None) or getattr( self.config, "rope_scaling", {} ) self.is_mrope_enabled = "mrope_section" in rope_config self.deepstack_visual_indexes = self.visual.deepstack_visual_indexes self.num_fused_shared_experts = 0 if _use_aiter and not _disable_shared_experts_fusion(): self.num_fused_shared_experts = self._get_num_fused_shared_experts() self.enable_shared_expert_fusion = self.num_fused_shared_experts > 0 def get_hidden_dim(self, module_name: str, layer_idx: int): return self.model.get_hidden_dim(module_name, layer_idx) def should_apply_lora(self, module_name: str) -> bool: # Accept all language model layer modules (attention, linear_attn, mlp). return module_name.startswith("model.layers.") def _get_num_fused_shared_experts(self): if not ( hasattr(self.model, "layers") and len(self.model.layers) > 0 and hasattr(self.model.layers[0].mlp, "num_fused_shared_experts") ): return 0 return self.model.layers[0].mlp.num_fused_shared_experts def get_embed_and_head(self): embed = self.model.embed_tokens.weight if self.pp_group.is_first_rank else None head = self.lm_head.weight if self.pp_group.is_last_rank else None return embed, head def set_embed_and_head(self, embed, head): if self.pp_group.is_first_rank and embed is not None: del self.model.embed_tokens.weight self.model.embed_tokens.weight = embed if self.pp_group.is_last_rank and head is not None: del self.lm_head.weight self.lm_head.weight = head if _is_xpu: torch.xpu.empty_cache() torch.xpu.synchronize() else: torch.cuda.empty_cache() torch.cuda.synchronize() def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]): stacked_params_mapping = [ # (param_name, shard_name, shard_id) ("qkv_proj", "q_proj", "q"), ("qkv_proj", "k_proj", "k"), ("qkv_proj", "v_proj", "v"), ("gate_up_proj", "gate_proj", 0), ("gate_up_proj", "up_proj", 1), # GDN fused projections ("in_proj_qkvz.", "in_proj_qkv.", (0, 1, 2)), ("in_proj_qkvz.", "in_proj_z.", 3), ("in_proj_ba.", "in_proj_b.", 0), ("in_proj_ba.", "in_proj_a.", 1), ] num_experts = self.config.num_experts # Params for weights, fp8 weight scales, fp8 activation scales # (param_name, weight_name, expert_id, shard_id) expert_params_mapping = FusedMoE.make_expert_params_mapping( ckpt_gate_proj_name="gate_proj", ckpt_down_proj_name="down_proj", ckpt_up_proj_name="up_proj", num_experts=( num_experts if not self.enable_shared_expert_fusion else num_experts + self.num_fused_shared_experts ), ) # Skip loading extra parameters for GPTQ/modelopt models. ignore_suffixes = ( ".bias", "_bias", ".k_scale", "_k_scale", ".v_scale", "_v_scale", "_weight_scale", "_input_scale", ) is_fused_expert = False fused_expert_params_mapping = [ ("experts.w13_weight", "experts.gate_up_proj", 0, "w1"), ("experts.w2_weight", "experts.down_proj", 0, "w2"), ] if self.enable_shared_expert_fusion: """ When shared experts are fused, we need to map the shared experts to routed experts. mlp.share_expert.gate_up_proj.weight --> experts.512.gate_up_proj.weight -> experts.w13_weight, expert_id = 512 mlp.share_expert.down_proj.weight --> experts.512.down_proj.weight -> experts.w2_weight, expert_id = 512 """ fused_expert_params_mapping += [ ( "experts.w13_", f"experts.{num_experts}.gate_up_proj.", num_experts, "w1", ), ( "experts.w2_", f"experts.{num_experts}.down_proj.", num_experts, "w2", ), ## shared experts may contain gate_proj and up_proj instead of gate_up_proj ( "experts.w13_", f"experts.{num_experts}.gate_proj.", num_experts, "w1", ), ( "experts.w13_", f"experts.{num_experts}.up_proj.", num_experts, "w3", ), ] def load_fused_expert_weights( name: str, params_dict: dict, loaded_weight: torch.Tensor, shard_id: str, num_experts: int, ): if name not in params_dict: return False param = params_dict[name] weight_loader = param.weight_loader # let ep moe layer to gracefully handle expert_ids that do not belong to local moe rank for expert_id in range(num_experts): curr_expert_weight = loaded_weight[expert_id] weight_loader( param, curr_expert_weight, name, shard_id, expert_id, ) return True loaded_params: Set[str] = set() params_dict = dict(self.named_parameters(remove_duplicate=False)) for name, loaded_weight in weights: if "rotary_emb.inv_freq" in name: continue if "mtp" in name: continue if "language_model" in name: name = name.replace(r"model.language_model.", r"model.") if ".self_attn." in name: name = name.replace(".self_attn", "") if ( self.config.tie_word_embeddings and self.pp_group.is_last_rank and "model.embed_tokens.weight" in name ): if "lm_head.weight" in params_dict: lm_head_param = params_dict["lm_head.weight"] weight_loader = getattr( lm_head_param, "weight_loader", default_weight_loader ) weight_loader(lm_head_param, loaded_weight) layer_id = get_layer_id(name) if ( layer_id is not None and hasattr(self, "start_layer") and (layer_id < self.start_layer or layer_id >= self.end_layer) ): continue if self.enable_shared_expert_fusion: if "mlp.shared_expert." in name: # Firstly map mlp.shared_expert.xx_proj to mlp.experts.512.xx_proj name = name.replace( "mlp.shared_expert.", f"mlp.experts.{num_experts}.", ) for param_name, weight_name, shard_id in stacked_params_mapping: if name.endswith("experts.gate_up_proj") or name.endswith( "experts.down_proj" ): is_fused_expert = True expert_params_mapping = fused_expert_params_mapping # Skip non-stacked layers and experts (experts handled below). if weight_name not in name: continue if "visual" in name: continue # We have mlp.experts[0].gate_proj in the checkpoint. # Since we handle the experts below in expert_params_mapping, # we need to skip here BEFORE we update the name, otherwise # name will be updated to mlp.experts[0].gate_up_proj, which # will then be updated below in expert_params_mapping # for mlp.experts[0].gate_gate_up_proj, which breaks load. if "mlp.experts" in name: continue name = name.replace(weight_name, param_name) # Skip loading extra parameters for GPTQ/modelopt models. if name.endswith(ignore_suffixes) and name not in params_dict: continue if name not in params_dict: continue param = params_dict[name] weight_loader = param.weight_loader weight_loader(param, loaded_weight, shard_id) break else: # Track if this is an expert weight to enable early skipping is_expert_weight = False for mapping in expert_params_mapping: param_name, weight_name, expert_id, shard_id = mapping if weight_name not in name: continue if "visual" in name or self.config.encoder_only: continue # Anyway, this is an expert weight and should not be # attempted to load as other weights later is_expert_weight = True name_mapped = name.replace(weight_name, param_name) if is_fused_expert: # is_fused_expert is True, the checkpoint contains gate_up_proj and down_proj for each expert if "experts.gate_up_proj" in name: # experts.gate_up_proj contains all 512 routed experts, excluding shared experts # split into w1 and w3 loaded_weight = loaded_weight.chunk(2, dim=-2) load_fused_expert_weights( name_mapped, params_dict, loaded_weight[0], "w1", num_experts, ) load_fused_expert_weights( name_mapped, params_dict, loaded_weight[1], "w3", num_experts, ) elif "experts.down_proj" in name: # experts.down_proj contains all 512 routed experts, excluding shared experts load_fused_expert_weights( name_mapped, params_dict, loaded_weight, shard_id, num_experts, ) elif self.enable_shared_expert_fusion: # shared experts should be loaded to experts.w13_weight and experts.w2_weight param = params_dict[name_mapped] weight_loader = getattr( param, "weight_loader", default_weight_loader ) param = params_dict[name_mapped] if f"{num_experts}.gate_up_proj" in name: # split into w1 and w3 loaded_weight = loaded_weight.chunk(2, dim=-2) # load to experts.w13_weight, shard_id = w1, expert_id = 512 weight_loader( param, loaded_weight[0], name_mapped, "w1", expert_id, ) # load to experts.w13_weight, shard_id = w3, expert_id = 512 weight_loader( param, loaded_weight[1], name_mapped, "w3", expert_id, ) else: # load down_proj to experts.w2_weight, shard_id = w2, expert_id = 512 # Or load gate_proj and up_proj to experts.w13_weight, shard_id = w1/w3, expert_id = 512 weight_loader( param, loaded_weight, name_mapped, shard_id, expert_id, ) else: # Skip loading extra parameters for GPTQ models. if ( name_mapped.endswith(ignore_suffixes) and name_mapped not in params_dict ): continue param = params_dict[name_mapped] # We should ask the weight loader to return success or # not here since otherwise we may skip experts with # # other available replicas. weight_loader = param.weight_loader weight_loader( param, loaded_weight, name_mapped, shard_id=shard_id, expert_id=expert_id, ) name = name_mapped break else: if is_expert_weight: # This is an expert weight but not mapped to this rank, skip all remaining processing continue if "visual" in name: # adapt to VisionAttention name = name.replace(r"attn.qkv.", r"attn.qkv_proj.") name = name.replace(r"model.visual.", r"visual.") # Skip loading extra parameters for GPTQ/modelopt models. if name.endswith(ignore_suffixes) and name not in params_dict: continue if name in params_dict.keys(): param = params_dict[name] weight_loader = getattr( param, "weight_loader", default_weight_loader ) weight_loader(param, loaded_weight) else: logger.warning(f"Parameter {name} not found in params_dict") loaded_params.add(name) self._routed_experts_weights_of_layer = LazyValue( lambda: { layer_id: layer.mlp.get_moe_weights() for layer_id, layer in enumerate(self.model.layers) if isinstance(layer.mlp, Qwen2MoeSparseMoeBlock) } ) return loaded_params @property def routed_experts_weights_of_layer(self): return self._routed_experts_weights_of_layer.value @classmethod def get_model_config_for_expert_location(cls, config): text_config = getattr(config, "text_config", config) return ModelConfigForExpertLocation( num_layers=text_config.num_hidden_layers, num_logical_experts=text_config.num_experts, num_groups=None, ) EntryClass = [Qwen3_5MoeForConditionalGeneration, Qwen3_5ForConditionalGeneration]