[model] support qwen3_5 / qwen3_5_moe

swift/megatron/model/constant.py

@@ -15,6 +15,7 @@ class MLLMMegatronModelType:
     qwen3_vl = 'qwen3_vl'
     qwen2_5_omni = 'qwen2_5_omni'
     qwen3_omni = 'qwen3_omni'
+    qwen3_5 = 'qwen3_5'
     ovis2_5 = 'ovis2_5'
 
     internvl3 = 'internvl3'

swift/megatron/model/gpt_bridge.py

@@ -713,7 +713,7 @@ def _get_hf_grouped(self):
         if self.args.hf_model_type in {
                 'qwen2_moe', 'qwen3_moe', 'deepseek_v2', 'deepseek_v3', 'dots1', 'ernie4_5_moe', 'glm4_moe',
                 'glm4_moe_lite', 'glm4v_moe', 'minimax_m2', 'olmoe', 'qwen3_next', 'kimi_vl', 'qwen3_omni_moe',
-                'qwen3_vl_moe'
+                'qwen3_vl_moe', 'qwen3_5_moe'
         }:
             return False, False
         return None, None

swift/megatron/model/gpts/qwen3_next.py

@@ -13,6 +13,7 @@
 from megatron.core.tensor_parallel import (gather_from_sequence_parallel_region,
                                            reduce_scatter_to_sequence_parallel_region)
 from megatron.core.transformer.attention import SelfAttention, SelfAttentionSubmodules
+from megatron.core.transformer.identity_op import IdentityOp
 from megatron.core.transformer.spec_utils import build_module
 from megatron.core.transformer.transformer_block import TransformerBlockSubmodules
 from megatron.core.transformer.transformer_config import TransformerConfig
@@ -475,7 +476,7 @@ def forward(self, hidden_states: torch.Tensor, **kwargs):
         return res, None
 
 
-def get_qwen3_next_transformer_layer_spec(config, vp_stage=None):
+def get_qwen3_next_transformer_layer_spec(config, vp_stage=None, gated_delta_net=None):
     config.hetereogenous_dist_checkpoint = True
     # compat Qwen3NextGatedDeltaNet
     args = get_args()
@@ -500,16 +501,18 @@ def get_qwen3_next_transformer_layer_spec(config, vp_stage=None):
         **kwargs,
     )
     layer_specs = []
+    gated_delta_net = gated_delta_net or Qwen3NextGatedDeltaNet
     for layer_type in args.layer_types:
         layer_spec = deepcopy(moe_layer_spec)
         if layer_type == 'linear_attention':
-            layer_spec.submodules.self_attention.module = Qwen3NextGatedDeltaNet
+            layer_spec.submodules.self_attention.module = gated_delta_net
         elif layer_type == 'full_attention':
             layer_spec.submodules.self_attention.submodules.linear_qkv = TEColumnParallelLinear
             layer_spec.submodules.self_attention.module = Qwen3NextSelfAttention
         # Replace ALL layernorms with Qwen3NextRMSNorm (Zero-Centered)
         layer_spec.submodules.input_layernorm = layer_norm_impl
-        if hasattr(layer_spec.submodules, 'pre_mlp_layernorm'):
+        if hasattr(layer_spec.submodules,
+                   'pre_mlp_layernorm') and layer_spec.submodules.pre_mlp_layernorm is not IdentityOp:
             layer_spec.submodules.pre_mlp_layernorm = layer_norm_impl
         # Replace qk_layernorm if present
         if hasattr(layer_spec.submodules.self_attention.submodules, 'q_layernorm'):

swift/megatron/model/mm_gpts/__init__.py

@@ -1,2 +1,2 @@
 # Copyright (c) ModelScope Contributors. All rights reserved.
-from . import glm, internvl, kimi_vl, llama4, qwen, qwen3_vl
+from . import glm, internvl, kimi_vl, llama4, qwen, qwen3_5, qwen3_vl

swift/megatron/model/mm_gpts/qwen3_5.py

@@ -0,0 +1,105 @@
+# Copyright (c) ModelScope Contributors. All rights reserved.
+from functools import partial
+
+import torch
+from megatron.core.extensions.transformer_engine import _get_extra_te_kwargs
+from megatron.core.models.huggingface import HuggingFaceModule as _HuggingFaceModule
+from megatron.core.tensor_parallel import (gather_from_sequence_parallel_region,
+                                           reduce_scatter_to_sequence_parallel_region)
+from megatron.core.transformer.attention import SelfAttentionSubmodules
+from megatron.core.transformer.transformer_config import TransformerConfig
+from megatron.training import get_args
+
+from swift.model import ModelType
+from swift.template import Template
+from ..constant import MegatronModelType
+from ..gpts.qwen3_next import Qwen3NextBridge, get_qwen3_next_mtp_block_spec, get_qwen3_next_transformer_layer_spec
+from ..register import MegatronModelMeta, register_megatron_model
+from .utils import HuggingFaceModule
+
+try:
+    from transformers.models.qwen3_5_moe.modeling_qwen3_5_moe import Qwen3_5MoeGatedDeltaNet as _Qwen3_5MoeGatedDeltaNet
+except ImportError:
+    _Qwen3_5MoeGatedDeltaNet = object
+
+
+class Qwen3_5MoeGatedDeltaNet(_HuggingFaceModule, _Qwen3_5MoeGatedDeltaNet):
+
+    def __init__(self, config: TransformerConfig, submodules: SelfAttentionSubmodules, layer_number: int, **kwargs):
+        assert config.context_parallel_size == 1, 'Qwen3_5 currently does not support context parallel.'
+        assert _Qwen3_5MoeGatedDeltaNet is not object, 'please update the `transformers` version.'
+        _Qwen3_5MoeGatedDeltaNet.__init__(self, config, layer_number)
+        self.config = config
+        extra_kwargs = _get_extra_te_kwargs(config)
+        self.to(dtype=extra_kwargs['params_dtype'], device=extra_kwargs['device'])
+
+    def forward(self, hidden_states: torch.Tensor, **kwargs):
+        args = get_args()
+        if args.sequence_parallel and args.tensor_model_parallel_size > 1:
+            hidden_states = gather_from_sequence_parallel_region(hidden_states)
+        seq_len = hidden_states.shape[0]
+        packed_seq_params = kwargs.get('packed_seq_params')
+        thd_format = packed_seq_params is not None and packed_seq_params.qkv_format == 'thd'
+        # Note: for packed inputs, we do not perform padding_free unpadding.
+        # Doing so would allow different sequences to see each other; for efficiency we keep this implementation.
+        if thd_format and not args.packing:
+            new_hidden_states = hidden_states.new_zeros(
+                (packed_seq_params.num_samples, packed_seq_params.max_seqlen_q.item(), hidden_states.shape[-1]))
+            attention_mask = hidden_states.new_zeros(
+                (packed_seq_params.num_samples, packed_seq_params.max_seqlen_q.item()), dtype=torch.bool)
+            cu_seqlens_q = packed_seq_params.cu_seqlens_q
+            for i in range(packed_seq_params.num_samples):
+                start, end = cu_seqlens_q[i], cu_seqlens_q[i + 1]
+                attention_mask[i, :end - start] = True
+                new_hidden_states[i, :end - start] = hidden_states[start:end, 0]
+            hidden_states = new_hidden_states
+        else:
+            hidden_states = hidden_states.transpose(0, 1)
+            attention_mask = kwargs.get('attention_mask')
+            if attention_mask is not None:
+                attention_mask = (~attention_mask).sum(dim=(1, 2)) > 0
+        res = super().forward(hidden_states=hidden_states, attention_mask=attention_mask)
+        if thd_format and not args.packing:
+            res = res[attention_mask][:, None]
+            res = torch.concat([res, res.new_zeros(seq_len - res.shape[0], 1, res.shape[2])])
+        else:
+            res = res.transpose(0, 1)
+        if args.sequence_parallel and args.tensor_model_parallel_size > 1:
+            # Quick fix for dropout issue, awaiting ms-swift 4.0 refactor
+            res = reduce_scatter_to_sequence_parallel_region(res) / args.tensor_model_parallel_size
+        return res, None
+
+
+class Qwen3_5Vit(HuggingFaceModule):
+    module_mapping = {'model.visual': 'visual'}
+    _vision_tower = ['visual']
+    _aligner = ['visual.merger']
+
+    def __init__(self, config):
+        from transformers.models.qwen3_5.modeling_qwen3_5 import Qwen3_5TextModel
+        from transformers.models.qwen3_5_moe.modeling_qwen3_5_moe import Qwen3_5MoeTextModel
+        super().__init__(config, [Qwen3_5TextModel, Qwen3_5MoeTextModel])
+
+    def get_inputs_embeds(self, inputs_embeds, **kwargs):
+        return Template._get_inputs_embeds_hf(inputs_embeds, kwargs, self.visual, self.processor, self.model_config)
+
+
+class Qwen3_5Bridge(Qwen3NextBridge):
+    hf_layers_prefix = 'model.language_model.layers'
+    hf_embed_key = 'model.language_model.embed_tokens.weight'
+    hf_final_layernorm_key = 'model.language_model.norm.weight'
+
+
+register_megatron_model(
+    MegatronModelMeta(
+        MegatronModelType.qwen3_5,
+        [
+            ModelType.qwen3_5,
+            ModelType.qwen3_5_moe,
+        ],
+        get_transformer_layer_spec=partial(
+            get_qwen3_next_transformer_layer_spec, gated_delta_net=Qwen3_5MoeGatedDeltaNet),
+        get_mtp_block_spec=get_qwen3_next_mtp_block_spec,
+        bridge_cls=Qwen3_5Bridge,
+        visual_cls=Qwen3_5Vit,
+    ))

swift/megatron/utils/config.py

@@ -106,13 +106,14 @@ def convert_hf_config(config) -> Dict[str, Any]:
     interleave_moe_layer_step = res.pop('interleave_moe_layer_step', None)
     window_size = res.pop('window_size', None)
     rope_scaling = res.get('rope_scaling') or {}
-    if llm_model_type in {'qwen3', 'qwen3_moe', 'qwen3_next'
-                          } or hf_model_type in {'qwen3_omni_moe', 'qwen3_omni', 'qwen3_vl', 'qwen3_vl_moe'}:
+    if llm_model_type in {'qwen3', 'qwen3_moe', 'qwen3_next'} or hf_model_type in {
+            'qwen3_omni_moe', 'qwen3_omni', 'qwen3_vl', 'qwen3_vl_moe', 'qwen3_5', 'qwen3_5_moe'
+    }:
         res['qk_layernorm'] = True
     if llm_model_type in {'qwen2_moe', 'qwen3_moe', 'qwen3_next'
-                          } or hf_model_type in {'qwen3_omni_moe', 'qwen3_vl_moe'}:
+                          } or hf_model_type in {'qwen3_omni_moe', 'qwen3_vl_moe', 'qwen3_5_moe'}:
         res.pop('ffn_hidden_size', None)
-        if llm_model_type in {'qwen2_moe', 'qwen3_next'}:
+        if llm_model_type in {'qwen2_moe', 'qwen3_next'} or hf_model_type == 'qwen3_5_moe':
             res['use_shared_expert_gate'] = True
     if llm_model_type in {
             'deepseek',
@@ -156,8 +157,8 @@ def convert_hf_config(config) -> Dict[str, Any]:
         if llm_model_type == 'glm4_moe_lite':
             res['qk_layernorm'] = True
             res.pop('num_query_groups', None)
-    elif llm_model_type == 'qwen3_next':
-        full_attention_interval = res.pop('full_attention_interval')
+    elif llm_model_type == 'qwen3_next' or hf_model_type in {'qwen3_5', 'qwen3_5_moe'}:
+        full_attention_interval = res.pop('full_attention_interval', 4)
         num_layers = res['num_layers']
         res['layer_types'] = [
             'full_attention' if (i + 1) % full_attention_interval == 0 else 'linear_attention'

swift/model/constant.py

@@ -144,6 +144,9 @@ class MLLMModelType:
     qwen3_vl_moe = 'qwen3_vl_moe'
     qwen3_vl_emb = 'qwen3_vl_emb'
     qwen3_vl_reranker = 'qwen3_vl_reranker'
+    qwen3_5 = 'qwen3_5'
+    qwen3_5_moe = 'qwen3_5_moe'
+
     qwen2_gme = 'qwen2_gme'
     ovis1_6 = 'ovis1_6'
     ovis2 = 'ovis2'

swift/model/models/qwen.py

@@ -1111,6 +1111,46 @@ def get_model(self, model_dir: str, config, processor, model_kwargs) -> PreTrain
         tags=['vision', 'video']))
 
 
+class Qwen3_5MoeLoader(Qwen3VLLoader):
+
+    def get_model(self, model_dir: str, config, processor, model_kwargs) -> PreTrainedModel:
+        from transformers import Qwen3_5MoeForConditionalGeneration
+        self.auto_model_cls = self.auto_model_cls or Qwen3_5MoeForConditionalGeneration
+        return Qwen2VLLoader.get_model(self, model_dir, config, processor, model_kwargs)
+
+
+register_model(
+    ModelMeta(
+        MLLMModelType.qwen3_5_moe, [
+            ModelGroup([], TemplateType.qwen3_5),
+        ],
+        Qwen3_5MoeLoader,
+        model_arch=ModelArch.qwen2_vl,
+        architectures=['Qwen3_5MoeForConditionalGeneration'],
+        requires=['transformers>=5.0.0.dev', 'qwen_vl_utils>=0.0.14', 'decord'],
+        tags=['vision', 'video']))
+
+
+class Qwen3_5Loader(Qwen3VLLoader):
+
+    def get_model(self, model_dir: str, config, processor, model_kwargs) -> PreTrainedModel:
+        from transformers import Qwen3_5ForConditionalGeneration
+        self.auto_model_cls = self.auto_model_cls or Qwen3_5ForConditionalGeneration
+        return Qwen2VLLoader.get_model(self, model_dir, config, processor, model_kwargs)
+
+
+register_model(
+    ModelMeta(
+        MLLMModelType.qwen3_5, [
+            ModelGroup([], TemplateType.qwen3_5),
+        ],
+        Qwen3_5Loader,
+        model_arch=ModelArch.qwen2_vl,
+        architectures=['Qwen3_5ForConditionalGeneration'],
+        requires=['transformers>=5.0.0.dev', 'qwen_vl_utils>=0.0.14', 'decord'],
+        tags=['vision', 'video']))
+
+
 class Qwen2_5OmniLoader(ModelLoader):
 
     def get_config(self, model_dir):

swift/template/constant.py

@@ -134,6 +134,8 @@ class MLLMTemplateType:
     qwen3_vl = 'qwen3_vl'
     qwen3_vl_emb = 'qwen3_vl_emb'
     qwen3_vl_reranker = 'qwen3_vl_reranker'
+    qwen3_5 = 'qwen3_5'
+
     qwen2_gme = 'qwen2_gme'
     qvq = 'qvq'
     ovis1_6 = 'ovis1_6'

swift/template/templates/qwen.py

@@ -552,6 +552,19 @@ def _post_encode(self, model, inputs: Dict[str, Any]) -> Dict[str, Any]:
         MLLMTemplateType.qwen3_vl, template_cls=Qwen3VLTemplate, default_system=None, thinking_prefix='<think>\n'))
 
 
+class Qwen3_5Template(Qwen3VLTemplate):
+    image_token_id = 248056
+    video_token_id = 248057
+
+    def _post_encode(self, model, inputs: Dict[str, Any]) -> Dict[str, Any]:
+        return Qwen2VLTemplate._post_encode(self, model, inputs)
+
+
+register_template(
+    QwenTemplateMeta(
+        MLLMTemplateType.qwen3_5, template_cls=Qwen3_5Template, default_system=None, thinking_prefix='<think>\n'))
+
+
 class Qwen3VLEmbTemplate(Qwen3VLTemplate):
 
     def _preprocess_inputs(self, inputs: StdTemplateInputs) -> None:

swift/utils/utils.py

@@ -87,7 +87,7 @@ def format_time(seconds):
     days = int(seconds // (24 * 3600))
     hours = int((seconds % (24 * 3600)) // 3600)
     minutes = int((seconds % 3600) // 60)
-    seconds = round(seconds % 60, 2)
+    seconds = int(seconds % 60)
 
     if days > 0:
         time_str = f'{days}d {hours}h {minutes}m {seconds}s'