# Copyright (c) ModelScope Contributors. All rights reserved. import sys from transformers import AutoModel, PretrainedConfig, PreTrainedModel from typing import Any, Dict from swift.template import TemplateType from swift.utils import Processor, git_clone_github from ..constant import LLMModelType, MLLMModelType from ..model_arch import ModelArch from ..model_meta import Model, ModelGroup, ModelMeta from ..patcher import patch_output_clone, patch_output_to_input_device from ..register import ModelLoader, register_model from ..utils import use_submodel_func class DeepseekLoader(ModelLoader): def get_model(self, model_dir: str, *args, **kwargs) -> PreTrainedModel: model = super().get_model(model_dir, *args, **kwargs) # fix dtype bug mlp_cls = model.model.layers[1].mlp.__class__ for module in model.modules(): if isinstance(module, mlp_cls): patch_output_to_input_device(module) return model register_model( ModelMeta( LLMModelType.deepseek, [ ModelGroup([ Model('deepseek-ai/deepseek-moe-16b-chat', 'deepseek-ai/deepseek-moe-16b-chat'), Model('deepseek-ai/deepseek-moe-16b-base', 'deepseek-ai/deepseek-moe-16b-base'), ], ), ], DeepseekLoader, template=TemplateType.deepseek, architectures=['DeepseekForCausalLM'], )) register_model( ModelMeta( LLMModelType.deepseek_v2, [ ModelGroup([ Model('deepseek-ai/DeepSeek-Coder-V2-Instruct', 'deepseek-ai/DeepSeek-Coder-V2-Instruct'), Model('deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct', 'deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct'), Model('deepseek-ai/DeepSeek-Coder-V2-Base', 'deepseek-ai/DeepSeek-Coder-V2-Base'), Model('deepseek-ai/DeepSeek-Coder-V2-Lite-Base', 'deepseek-ai/DeepSeek-Coder-V2-Lite-Base'), Model('deepseek-ai/DeepSeek-V2-Lite', 'deepseek-ai/DeepSeek-V2-Lite'), Model('deepseek-ai/DeepSeek-V2-Lite-Chat', 'deepseek-ai/DeepSeek-V2-Lite-Chat'), Model('deepseek-ai/DeepSeek-V2', 'deepseek-ai/DeepSeek-V2'), Model('deepseek-ai/DeepSeek-V2-Chat', 'deepseek-ai/DeepSeek-V2-Chat'), ], TemplateType.deepseek), ModelGroup([ Model('deepseek-ai/DeepSeek-V2.5', 'deepseek-ai/DeepSeek-V2.5'), Model('deepseek-ai/DeepSeek-V2.5-1210', 'deepseek-ai/DeepSeek-V2.5-1210') ], TemplateType.deepseek_v2_5) ], DeepseekLoader, model_arch=ModelArch.deepseek_v2, architectures=['DeepseekV2ForCausalLM'], requires=['transformers>=4.39.3'], )) register_model( ModelMeta( LLMModelType.deepseek_v3, [ ModelGroup([ Model('deepseek-ai/DeepSeek-V3-Base', 'deepseek-ai/DeepSeek-V3-Base'), Model('deepseek-ai/DeepSeek-V3', 'deepseek-ai/DeepSeek-V3'), Model('deepseek-ai/DeepSeek-V3-0324', 'deepseek-ai/DeepSeek-V3-0324'), ], TemplateType.deepseek_v2_5), ModelGroup([ Model('cognitivecomputations/DeepSeek-V3-awq', 'cognitivecomputations/DeepSeek-V3-AWQ'), Model('cognitivecomputations/DeepSeek-V3-0324-AWQ', 'cognitivecomputations/DeepSeek-V3-0324-AWQ') ], TemplateType.deepseek_v2_5), ModelGroup([ Model('deepseek-ai/DeepSeek-Prover-V2-7B', 'deepseek-ai/DeepSeek-Prover-V2-7B'), Model('deepseek-ai/DeepSeek-Prover-V2-671B', 'deepseek-ai/DeepSeek-Prover-V2-671B'), ], TemplateType.deepseek_v2_5), ModelGroup([ Model('unsloth/DeepSeek-V3-bf16', 'unsloth/DeepSeek-V3-bf16'), Model('unsloth/DeepSeek-V3-0324-BF16', 'unsloth/DeepSeek-V3-0324-BF16'), Model('unsloth/DeepSeek-Prover-V2-671B-BF16', 'unsloth/DeepSeek-Prover-V2-671B-BF16'), ], TemplateType.deepseek_v2_5), ModelGroup([ Model('deepseek-ai/DeepSeek-R1', 'deepseek-ai/DeepSeek-R1'), Model('deepseek-ai/DeepSeek-R1-Zero', 'deepseek-ai/DeepSeek-R1-Zero'), Model('deepseek-ai/DeepSeek-R1-0528', 'deepseek-ai/DeepSeek-R1-0528'), ], TemplateType.deepseek_r1), ModelGroup([ Model('cognitivecomputations/DeepSeek-R1-awq', 'cognitivecomputations/DeepSeek-R1-AWQ'), Model('cognitivecomputations/DeepSeek-R1-0528-AWQ', 'cognitivecomputations/DeepSeek-R1-0528-AWQ'), ], TemplateType.deepseek_r1), ModelGroup([ Model('unsloth/DeepSeek-R1-BF16', 'unsloth/DeepSeek-R1-BF16'), Model('unsloth/DeepSeek-R1-Zero-BF16', 'unsloth/DeepSeek-R1-Zero-BF16'), Model('unsloth/DeepSeek-R1-0528-BF16', 'unsloth/DeepSeek-R1-0528-BF16'), ], TemplateType.deepseek_r1), ModelGroup([ Model('moonshotai/Moonlight-16B-A3B', 'moonshotai/Moonlight-16B-A3B'), Model('moonshotai/Moonlight-16B-A3B-Instruct', 'moonshotai/Moonlight-16B-A3B-Instruct'), ], TemplateType.moonlight, requires=['transformers<4.49']), ModelGroup([ Model('moonshotai/Kimi-K2-Base', 'moonshotai/Kimi-K2-Base'), Model('moonshotai/Kimi-K2-Instruct', 'moonshotai/Kimi-K2-Instruct'), Model('moonshotai/Kimi-K2-Instruct-0905', 'moonshotai/Kimi-K2-Instruct-0905'), Model('moonshotai/Kimi-K2-Thinking', 'moonshotai/Kimi-K2-Thinking'), ], TemplateType.kimi_k2), ModelGroup([ Model('deepseek-ai/DeepSeek-V3.1-Base', 'deepseek-ai/DeepSeek-V3.1-Base'), Model('deepseek-ai/DeepSeek-V3.1', 'deepseek-ai/DeepSeek-V3.1'), Model('deepseek-ai/DeepSeek-V3.1-Terminus', 'deepseek-ai/DeepSeek-V3.1-Terminus'), ], TemplateType.deepseek_v3_1), ], DeepseekLoader, model_arch=ModelArch.deepseek_v2, architectures=['DeepseekV3ForCausalLM'], requires=['transformers>=4.39.3'], )) class DeepseekV32Loader(ModelLoader): def get_config(self, model_dir: str): try: from transformers.models.deepseek_v32 import DeepseekV32Config except ImportError: from transformers.models.deepseek_v3 import DeepseekV3Config as DeepseekV32Config return DeepseekV32Config.from_pretrained(model_dir) def get_model(self, model_dir: str, *args, **kwargs) -> PreTrainedModel: try: from transformers.models.deepseek_v32 import DeepseekV32ForCausalLM except ImportError: # It’s only for compatibility with Megatron training or vllm/sglang infer, # while we wait for Transformers to support deepseek_v32. from transformers.models.deepseek_v3 import DeepseekV3ForCausalLM as DeepseekV32ForCausalLM if not self.return_dummy_model: raise ValueError('DeepSeek-V3.2 is not supported in transformers.') self.auto_model_cls = DeepseekV32ForCausalLM return super().get_model(model_dir, *args, **kwargs) register_model( ModelMeta( LLMModelType.deepseek_v32, [ ModelGroup([ Model('deepseek-ai/DeepSeek-V3.2', 'deepseek-ai/DeepSeek-V3.2'), Model('deepseek-ai/DeepSeek-V3.2-Speciale', 'deepseek-ai/DeepSeek-V3.2-Speciale'), Model('deepseek-ai/DeepSeek-V3.2-Exp', 'deepseek-ai/DeepSeek-V3.2-Exp'), Model('deepseek-ai/DeepSeek-V3.2-Exp-Base', 'deepseek-ai/DeepSeek-V3.2-Exp-Base'), Model('deepseek-ai/DeepSeek-Math-V2', 'deepseek-ai/DeepSeek-Math-V2'), ]), ], DeepseekV32Loader, template=TemplateType.deepseek_v3_1, architectures=['DeepseekV32ForCausalLM'], )) class DeepseekVLLoader(ModelLoader): def get_config(self, model_dir: str): # compat with python==3.10 if sys.version_info.minor >= 10: import collections import collections.abc for type_name in collections.abc.__all__: setattr(collections, type_name, getattr(collections.abc, type_name)) local_repo_path = self.local_repo_path if not local_repo_path: local_repo_path = git_clone_github('https://github.com/deepseek-ai/DeepSeek-VL') sys.path.append(local_repo_path) from deepseek_vl.models import VLChatProcessor self.auto_tokenizer_cls = VLChatProcessor return super().get_config(model_dir) def _get_model(self, model_dir: str, llm_prefix, *args, **kwargs) -> PreTrainedModel: model = super().get_model(model_dir, *args, **kwargs) llm = getattr(model, llm_prefix) patch_output_clone(llm.model.embed_tokens) patch_output_to_input_device(llm.model.embed_tokens) use_submodel_func(model, llm_prefix) model.generation_config = llm.generation_config return model def get_model(self, model_dir: str, *args, **kwargs) -> PreTrainedModel: return self._get_model(model_dir, 'language_model', *args, **kwargs) register_model( ModelMeta( MLLMModelType.deepseek_vl, [ ModelGroup([ Model('deepseek-ai/deepseek-vl-1.3b-chat', 'deepseek-ai/deepseek-vl-1.3b-chat'), Model('deepseek-ai/deepseek-vl-7b-chat', 'deepseek-ai/deepseek-vl-7b-chat'), ], ), ], DeepseekVLLoader, template=TemplateType.deepseek_vl, architectures=['MultiModalityCausalLM'], model_arch=ModelArch.deepseek_vl, tags=['vision'], )) class DeepseekJanusLoader(DeepseekVLLoader): def get_model(self, model_dir: str, *args, **kwargs) -> PreTrainedModel: return self._get_model(model_dir, 'language_model', *args, **kwargs) def get_config(self, model_dir: str): local_repo_path = self.local_repo_path if not local_repo_path: local_repo_path = git_clone_github('https://github.com/deepseek-ai/Janus') sys.path.append(local_repo_path) from janus.models import VLChatProcessor self.auto_tokenizer_cls = VLChatProcessor return super(DeepseekVLLoader, self).get_config(model_dir) register_model( ModelMeta( MLLMModelType.deepseek_janus, [ ModelGroup([ Model('deepseek-ai/Janus-1.3B', 'deepseek-ai/Janus-1.3B'), ]), ], DeepseekJanusLoader, template=TemplateType.deepseek_janus, model_arch=ModelArch.deepseek_janus, tags=['vision'], )) register_model( ModelMeta( MLLMModelType.deepseek_janus_pro, [ ModelGroup([ Model('deepseek-ai/Janus-Pro-1B', 'deepseek-ai/Janus-Pro-1B'), Model('deepseek-ai/Janus-Pro-7B', 'deepseek-ai/Janus-Pro-7B'), ]), ], DeepseekJanusLoader, template=TemplateType.deepseek_janus_pro, model_arch=ModelArch.deepseek_janus, tags=['vision'], )) class DeepseekVL2Loader(DeepseekVLLoader): def get_config(self, model_dir: str): local_repo_path = self.local_repo_path if not local_repo_path: local_repo_path = git_clone_github('https://github.com/deepseek-ai/DeepSeek-VL2') sys.path.append(local_repo_path) try: from deepseek_vl2.models import DeepseekVLV2Processor except ImportError: # compat transformers>=4.42 import transformers transformers.models.llama.modeling_llama.LlamaFlashAttention2 = None from deepseek_vl2.models import DeepseekVLV2Processor self.auto_tokenizer_cls = DeepseekVLV2Processor return super(DeepseekVLLoader, self).get_config(model_dir) def get_model(self, model_dir: str, *args, **kwargs) -> PreTrainedModel: return super()._get_model(model_dir, 'language', *args, **kwargs) register_model( ModelMeta( MLLMModelType.deepseek_vl2, [ ModelGroup([ Model('deepseek-ai/deepseek-vl2-tiny', 'deepseek-ai/deepseek-vl2-tiny'), Model('deepseek-ai/deepseek-vl2-small', 'deepseek-ai/deepseek-vl2-small'), Model('deepseek-ai/deepseek-vl2', 'deepseek-ai/deepseek-vl2'), ]), ], DeepseekVL2Loader, template=TemplateType.deepseek_vl2, model_arch=ModelArch.deepseek_vl2, requires=['transformers<4.42'], tags=['vision'], )) class DeepseekOCRLoader(ModelLoader): visual_name = 'vision_model' def get_model(self, model_dir: str, *args, **kwargs) -> PreTrainedModel: self.auto_model_cls = self.auto_model_cls or AutoModel model = super().get_model(model_dir, *args, **kwargs) patch_output_clone(model.model.embed_tokens) patch_output_to_input_device(model.model.sam_model) patch_output_to_input_device(getattr(model.model, self.visual_name)) patch_output_to_input_device(model.model.projector) return model def get_processor(self, model_dir: str, config: PretrainedConfig) -> Processor: from transformers import AutoProcessor, AutoTokenizer # When not loading model (e.g., vllm backend), avoid triggering AutoConfig which would execute # trust_remote_code and cause transformers version compatibility issues # For vllm backend, we only need the processor/tokenizer try: processor = AutoProcessor.from_pretrained(model_dir, trust_remote_code=True) except Exception: # Fallback to AutoTokenizer if AutoProcessor is not available processor = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True) return processor class DeepseekOCR2Loader(DeepseekOCRLoader): visual_name = 'qwen2_model' register_model( ModelMeta( MLLMModelType.deepseek_ocr, [ ModelGroup([ Model('deepseek-ai/DeepSeek-OCR', 'deepseek-ai/DeepSeek-OCR'), ]), ], DeepseekOCRLoader, template=TemplateType.deepseek_ocr, model_arch=ModelArch.deepseek_ocr, architectures=['DeepseekOCRForCausalLM'], requires=['transformers==4.46.3', 'easydict'], tags=['vision'], )) register_model( ModelMeta( MLLMModelType.deepseek_ocr2, [ ModelGroup([ Model('deepseek-ai/DeepSeek-OCR-2', 'deepseek-ai/DeepSeek-OCR-2'), ]), ], DeepseekOCR2Loader, template=TemplateType.deepseek_ocr2, model_arch=ModelArch.deepseek_ocr2, architectures=['DeepseekOCR2ForCausalLM'], requires=['transformers==4.46.3', 'easydict'], tags=['vision'], ))