# 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 from einops import rearrange # Model Executor from sglang.srt.compilation.piecewise_context_manager import get_forward_context # 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 ( get_attention_tp_rank, get_attention_tp_size, is_dp_attention_enabled, ) # 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.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.vocab_parallel_embedding import VocabParallelEmbedding from sglang.srt.model_executor.cuda_graph_runner import get_is_capture_mode from sglang.srt.model_executor.forward_batch_info import ForwardBatch, PPProxyTensors from sglang.srt.model_loader.weight_utils import ( default_weight_loader, sharded_weight_loader, ) from sglang.srt.models.qwen2_moe import Qwen2MoeMLP, Qwen2MoeSparseMoeBlock # Models from sglang.srt.models.qwen3_next import gdn_with_output from sglang.srt.models.qwen3_vl import Qwen3VLForConditionalGeneration # Utils from sglang.srt.utils import add_prefix, is_cuda, is_npu, make_layers, set_weight_attrs from sglang.srt.utils.hf_transformers_utils import get_processor logger = logging.getLogger(__name__) _is_cuda = is_cuda() _is_npu = is_npu() cached_get_processor = lru_cache(get_processor) 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_attention_tp_rank() self.attn_tp_size = get_attention_tp_size() self.hidden_size = config.hidden_size self.num_v_heads = config.linear_num_value_heads self.num_k_heads = config.linear_num_key_heads 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.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) # Split projection layers (following vLLM's implementation) # Instead of fused in_proj_qkvz and in_proj_ba, use separate layers self.in_proj_qkv = MergedColumnParallelLinear( input_size=self.hidden_size, output_sizes=[self.key_dim, self.key_dim, self.value_dim], bias=False, quant_config=quant_config, tp_rank=self.attn_tp_rank, tp_size=self.attn_tp_size, prefix=add_prefix("in_proj_qkv", prefix), ) self.in_proj_z = ColumnParallelLinear( input_size=self.hidden_size, output_size=self.value_dim, bias=False, quant_config=quant_config, tp_rank=self.attn_tp_rank, tp_size=self.attn_tp_size, prefix=add_prefix("in_proj_z", prefix), ) self.in_proj_b = ColumnParallelLinear( input_size=self.hidden_size, output_size=self.num_v_heads, bias=False, quant_config=quant_config, tp_rank=self.attn_tp_rank, tp_size=self.attn_tp_size, prefix=add_prefix("in_proj_b", prefix), ) self.in_proj_a = ColumnParallelLinear( input_size=self.hidden_size, output_size=self.num_v_heads, bias=False, quant_config=quant_config, tp_rank=self.attn_tp_rank, tp_size=self.attn_tp_size, prefix=add_prefix("in_proj_a", prefix), ) # Conv1d weight loader setup query_key_settings = (self.key_dim, 0, False) value_settings = (self.value_dim, 0, False) delattr(self.conv1d.weight, "weight_loader") set_weight_attrs( self.conv1d.weight, { "weight_loader": 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), ) 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) ) # RadixLinearAttention layer 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, ) # Normalization layer 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, ) # Output projection 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), ) def fix_query_key_value_ordering( self, mixed_qkv, z, b, a, ): raise NotImplementedError( "Qwen3.5 Series dont need to fix query key value ordering" ) def forward( self, hidden_states: torch.Tensor, forward_batch: ForwardBatch, ): output = torch.empty_like(hidden_states) if forward_batch.forward_mode.is_extend() and get_forward_context() is not None: gdn_with_output( hidden_states, output, self.layer_id, ) return output else: return self._forward(hidden_states, forward_batch) 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 """ seq_len, _ = hidden_states.shape mixed_qkv, _ = self.in_proj_qkv(hidden_states) z, _ = self.in_proj_z(hidden_states) z = z.reshape(z.size(0), -1, self.head_v_dim) b, _ = self.in_proj_b(hidden_states) a, _ = self.in_proj_a(hidden_states) b = b.contiguous() a = a.contiguous() core_attn_out = self.attn.forward( forward_batch=forward_batch, mixed_qkv=mixed_qkv, a=a, b=b, ) z_shape_og = z.shape core_attn_out = core_attn_out.reshape(-1, core_attn_out.shape[-1]) z = z.reshape(-1, z.shape[-1]) core_attn_out = self.norm(core_attn_out, z) core_attn_out = core_attn_out.reshape(z_shape_og) core_attn_out = rearrange(core_attn_out, "... h d -> ... (h d)") 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, ) -> 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, prefix=add_prefix("mlp", prefix.replace(".linear_attn", "")), ) 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, ) 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( hidden_states, residual, forward_batch ) 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 ) use_reduce_scatter = self.layer_communicator.should_use_reduce_scatter( forward_batch ) hidden_states = self.mlp(hidden_states, forward_batch, use_reduce_scatter) 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, ) -> None: super().__init__() self.config = config self.hidden_size = config.hidden_size self.attn_tp_rank = get_attention_tp_rank() self.attn_tp_size = get_attention_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) if hasattr(config, "rope_parameters"): self.rope_scaling = getattr(config, "rope_parameters", None) else: self.rope_scaling = getattr(config, "rope_scaling", None) self.rope_theta = self.rope_scaling.get("rope_theta", 10000) self.partial_rotary_factor = self.rope_scaling.get("partial_rotary_factor", 1.0) self.layer_id = layer_id 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=self.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, prefix=add_prefix("mlp", prefix.replace(".self_attn", "")), ) 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, ) 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(): 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) 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 self_attention( self, positions: torch.Tensor, hidden_states: torch.Tensor, forward_batch: ForwardBatch, ) -> torch.Tensor: """Full attention forward pass.""" 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 = gate.reshape(*orig_shape, -1) else: q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1) q, k = self._apply_qk_norm(q, k) q, k = self.rotary_emb(positions, q, k) attn_output = self.attn(q, k, v, forward_batch) if self.attn_output_gate: gate = torch.sigmoid(gate) attn_output = attn_output * gate 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, **kwargs, ): hidden_states, residual = self.layer_communicator.prepare_attn( hidden_states, residual, forward_batch ) 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 ) use_reduce_scatter = self.layer_communicator.should_use_reduce_scatter( forward_batch ) hidden_states = self.mlp(hidden_states, forward_batch, use_reduce_scatter) 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.""" def __init__( self, config: Qwen3_5TextConfig, quant_config: Optional[QuantizationConfig] = None, prefix: str = "", ) -> None: super().__init__() self.config = config self.hidden_size = config.hidden_size self.pp_group = get_pp_group() alt_stream = torch.cuda.Stream() if _is_cuda 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(), ) # 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, ) self.layers = make_layers( config.num_hidden_layers, get_layer, prefix=f"{prefix}.layers", ) # Final normalization if self.pp_group.is_last_rank: self.norm = GemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps) def get_input_embeddings(self) -> nn.Embedding: return self.embed_tokens @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"] # Pass through decoder layers for layer_idx in range(len(self.layers)): 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, ) # 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) return 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), ] 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", "") 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 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), ] # 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, ): 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", "") 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): 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_embed_and_head(self): return self.model.embed_tokens.weight, self.lm_head.weight def set_embed_and_head(self, embed, head): del self.model.embed_tokens.weight del self.lm_head.weight self.model.embed_tokens.weight = embed self.lm_head.weight = head 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), ] 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", "") 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) loaded_params.add(name) return loaded_params class Qwen3_5MoeForConditionalGeneration(Qwen3VLForConditionalGeneration): """Qwen3.5 MoE Vision-Language Model.""" 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 def get_embed_and_head(self): return self.model.embed_tokens.weight, self.lm_head.weight def set_embed_and_head(self, embed, head): del self.model.embed_tokens.weight del self.lm_head.weight self.model.embed_tokens.weight = embed self.lm_head.weight = head 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), ] # 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", "_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, ): 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", "") 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: 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 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) return loaded_params @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]