From f1e13f7df9ad360df756ffeced301df97b209414 Mon Sep 17 00:00:00 2001 From: Tiezhen WANG <38108242+xianbaoqian@users.noreply.github.com> Date: Fri, 12 Jun 2026 18:41:09 +0800 Subject: [PATCH] [Model] Remove Mono-InternVL (InternLM2VEForCausalLM) (#45129) Signed-off-by: Xianbao QIAN Signed-off-by: Isotr0py Co-authored-by: Claude Co-authored-by: Isotr0py --- docs/models/supported_models.md | 2 +- .../multimodal/generation/test_common.py | 2 - tests/models/registry.py | 11 -- vllm/model_executor/models/h2ovl.py | 29 ++-- vllm/model_executor/models/internlm2_ve.py | 139 ------------------ vllm/model_executor/models/internvl.py | 51 ++----- vllm/model_executor/models/nvlm_d.py | 35 ++--- vllm/model_executor/models/registry.py | 2 +- vllm/model_executor/models/skyworkr1v.py | 44 ++---- 9 files changed, 53 insertions(+), 262 deletions(-) delete mode 100644 vllm/model_executor/models/internlm2_ve.py diff --git a/docs/models/supported_models.md b/docs/models/supported_models.md index 1823ddcecc..31a550b95f 100644 --- a/docs/models/supported_models.md +++ b/docs/models/supported_models.md @@ -577,7 +577,7 @@ These models primarily accept the [`LLM.generate`](./generative_models.md#llmgen | `InternS1ForConditionalGeneration` | Intern-S1 | T + IE+ + VE+ | `internlm/Intern-S1`, `internlm/Intern-S1-mini`, etc. | ✅︎ | ✅︎ | | `InternS1ProForConditionalGeneration` | Intern-S1-Pro | T + IE+ + VE+ | `internlm/Intern-S1-Pro`, etc. | ✅︎ | ✅︎ | | `InternS2PreviewForConditionalGeneration` | Intern-S2-Preview | T + IE+ + VE+ | `internlm/Intern-S2-Preview`, etc. | ✅︎ | ✅︎ | -| `InternVLChatModel` | InternVL 3.5, InternVL 3.0, InternVideo 2.5, InternVL 2.5, Mono-InternVL, InternVL 2.0 | T + IE+ + (VE+) | `OpenGVLab/InternVL3_5-14B`, `OpenGVLab/InternVL3-9B`, `OpenGVLab/InternVideo2_5_Chat_8B`, `OpenGVLab/InternVL2_5-4B`, `OpenGVLab/Mono-InternVL-2B`, `OpenGVLab/InternVL2-4B`, etc. | ✅︎ | ✅︎ | +| `InternVLChatModel` | InternVL 3.5, InternVL 3.0, InternVideo 2.5, InternVL 2.5, InternVL 2.0 | T + IE+ + (VE+) | `OpenGVLab/InternVL3_5-14B`, `OpenGVLab/InternVL3-9B`, `OpenGVLab/InternVideo2_5_Chat_8B`, `OpenGVLab/InternVL2_5-4B`, `OpenGVLab/InternVL2-4B`, etc. | ✅︎ | ✅︎ | | `InternVLForConditionalGeneration` | InternVL 3.0 (HF format) | T + IE+ + VE+ | `OpenGVLab/InternVL3-1B-hf`, etc. | ✅︎ | ✅︎ | | `KananaVForConditionalGeneration` | Kanana-V | T + I+ | `kakaocorp/kanana-1.5-v-3b-instruct`, etc. | | ✅︎ | | `KeyeForConditionalGeneration` | Keye-VL-8B-Preview | T + IE+ + VE+ | `Kwai-Keye/Keye-VL-8B-Preview` | ✅︎ | ✅︎ | diff --git a/tests/models/multimodal/generation/test_common.py b/tests/models/multimodal/generation/test_common.py index e2dd0d9de7..a9afe73cad 100644 --- a/tests/models/multimodal/generation/test_common.py +++ b/tests/models/multimodal/generation/test_common.py @@ -604,8 +604,6 @@ VLM_TEST_SETTINGS = { models=[ "OpenGVLab/InternVL2-1B", "OpenGVLab/InternVL2-2B", - # FIXME: Config cannot be loaded in transformers 4.52 - # "OpenGVLab/Mono-InternVL-2B", ], test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE), prompt_formatter=lambda img_prompt: f"<|im_start|>User\n{img_prompt}<|im_end|>\n<|im_start|>Assistant\n", # noqa: E501 diff --git a/tests/models/registry.py b/tests/models/registry.py index ed15ac5f46..86641c9b15 100644 --- a/tests/models/registry.py +++ b/tests/models/registry.py @@ -341,17 +341,6 @@ _TEXT_GENERATION_EXAMPLE_MODELS = { "InternLM2ForCausalLM": _HfExamplesInfo( "internlm/internlm2-chat-7b", trust_remote_code=True ), - "InternLM2VEForCausalLM": _HfExamplesInfo( - "OpenGVLab/Mono-InternVL-2B", - trust_remote_code=True, - max_transformers_version="4.57", - transformers_version_reason={ - "vllm": ( - "Custom config cannot be loaded with Transformers " - "v5 because `vision_config` is not always set" - ) - }, - ), "InternLM3ForCausalLM": _HfExamplesInfo( "internlm/internlm3-8b-instruct", trust_remote_code=True ), diff --git a/vllm/model_executor/models/h2ovl.py b/vllm/model_executor/models/h2ovl.py index 1e3629eb42..40240d3e4e 100644 --- a/vllm/model_executor/models/h2ovl.py +++ b/vllm/model_executor/models/h2ovl.py @@ -157,27 +157,22 @@ class H2OVLChatModel(InternVLChatModel): config: PretrainedConfig, quant_config: QuantizationConfig | None, *, - is_mono: bool, prefix: str, ): - if not is_mono: - vision_feature_layer = config.select_layer - if vision_feature_layer < 0: - num_hidden_layers = ( - config.vision_config.num_hidden_layers + vision_feature_layer + 1 - ) - else: - num_hidden_layers = vision_feature_layer + 1 - - return InternVisionModel( - config.vision_config, - quant_config=quant_config, - num_hidden_layers_override=num_hidden_layers, - prefix=prefix, + vision_feature_layer = config.select_layer + if vision_feature_layer < 0: + num_hidden_layers = ( + config.vision_config.num_hidden_layers + vision_feature_layer + 1 ) else: - msg = "Monolith mode is not applicable to H2OVL" - raise NotImplementedError(msg) + num_hidden_layers = vision_feature_layer + 1 + + return InternVisionModel( + config.vision_config, + quant_config=quant_config, + num_hidden_layers_override=num_hidden_layers, + prefix=prefix, + ) def get_num_mm_encoder_tokens(self, num_image_tokens: int) -> int: if num_image_tokens <= 0 or self.num_image_token <= 0: diff --git a/vllm/model_executor/models/internlm2_ve.py b/vllm/model_executor/models/internlm2_ve.py deleted file mode 100644 index da0dfe73e6..0000000000 --- a/vllm/model_executor/models/internlm2_ve.py +++ /dev/null @@ -1,139 +0,0 @@ -# SPDX-License-Identifier: Apache-2.0 -# SPDX-FileCopyrightText: Copyright contributors to the vLLM project - -from itertools import islice - -import torch -from torch import nn -from transformers import PretrainedConfig - -from vllm.config import CacheConfig, VllmConfig -from vllm.distributed import get_pp_group -from vllm.model_executor.layers.layernorm import RMSNorm -from vllm.model_executor.layers.quantization import QuantizationConfig -from vllm.model_executor.models.internlm2 import ( - InternLM2Attention, - InternLM2ForCausalLM, - InternLM2MLP, - InternLM2Model, -) -from vllm.sequence import IntermediateTensors - - -class InternLM2VEDecoderLayer(nn.Module): - def __init__( - self, - config: PretrainedConfig, - cache_config: CacheConfig | None = None, - quant_config: QuantizationConfig | None = None, - prefix: str = "", - ) -> None: - super().__init__() - self.hidden_size = config.hidden_size - max_position_embeddings = getattr(config, "max_position_embeddings", 8192) - self.attention = InternLM2Attention( - hidden_size=self.hidden_size, - num_heads=config.num_attention_heads, - num_kv_heads=config.num_key_value_heads, - rope_parameters=config.rope_parameters, - max_position_embeddings=max_position_embeddings, - cache_config=cache_config, - quant_config=quant_config, - prefix=f"{prefix}.attention", - ) - self.feed_forward = InternLM2MLP( - hidden_size=self.hidden_size, - intermediate_size=config.intermediate_size, - hidden_act=config.hidden_act, - quant_config=quant_config, - prefix=f"{prefix}.feed_forward", - ) - self.feed_forward_ve = InternLM2MLP( - hidden_size=self.hidden_size, - intermediate_size=config.intermediate_size, - hidden_act=config.hidden_act, - quant_config=quant_config, - prefix=f"{prefix}.feed_forward_ve", - ) - self.attention_norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) - self.ffn_norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) - - def forward( - self, - positions: torch.Tensor, - hidden_states: torch.Tensor, - residual: torch.Tensor | None, - visual_token_mask: torch.Tensor | None = None, - ) -> tuple[torch.Tensor, torch.Tensor]: - # Self Attention - if residual is None: - residual = hidden_states - hidden_states = self.attention_norm(hidden_states) - else: - hidden_states, residual = self.attention_norm(hidden_states, residual) - hidden_states = self.attention( - positions=positions, - hidden_states=hidden_states, - ) - - # Fully Connected - hidden_states, residual = self.ffn_norm(hidden_states, residual) - if visual_token_mask is not None and visual_token_mask.any(): - visual_token_mask = visual_token_mask.repeat(1, self.hidden_size).bool() - text_token_mask = ~visual_token_mask - hidden_states[visual_token_mask] = self.feed_forward_ve( - hidden_states[visual_token_mask].reshape(-1, self.hidden_size) - ).flatten() - if text_token_mask.any(): - hidden_states[text_token_mask] = self.feed_forward( - hidden_states[text_token_mask].reshape(-1, self.hidden_size) - ).flatten() - else: - hidden_states = self.feed_forward(hidden_states) - return hidden_states, residual - - -class InternLM2VEModel(InternLM2Model): - def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): - super().__init__( - vllm_config=vllm_config, prefix=prefix, layer_type=InternLM2VEDecoderLayer - ) - - def forward( - self, - input_ids: torch.Tensor | None, - positions: torch.Tensor, - intermediate_tensors: IntermediateTensors | None = None, - inputs_embeds: torch.Tensor | None = None, - visual_token_mask: torch.Tensor | None = None, - ) -> torch.Tensor | IntermediateTensors: - if get_pp_group().is_first_rank: - if inputs_embeds is not None: - hidden_states = inputs_embeds - else: - hidden_states = self.tok_embeddings(input_ids) - residual = None - else: - assert intermediate_tensors is not None - hidden_states = intermediate_tensors["hidden_states"] - residual = intermediate_tensors["residual"] - for layer in islice(self.layers, self.start_layer, self.end_layer): - hidden_states, residual = layer( - positions, - hidden_states, - residual, - visual_token_mask=visual_token_mask, - ) - if not get_pp_group().is_last_rank: - return IntermediateTensors( - {"hidden_states": hidden_states, "residual": residual} - ) - hidden_states, _ = self.norm(hidden_states, residual) - return hidden_states - - -class InternLM2VEForCausalLM(InternLM2ForCausalLM): - def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): - super().__init__( - vllm_config=vllm_config, prefix=prefix, model_type=InternLM2VEModel - ) diff --git a/vllm/model_executor/models/internvl.py b/vllm/model_executor/models/internvl.py index d57614ea98..94f03a539c 100644 --- a/vllm/model_executor/models/internvl.py +++ b/vllm/model_executor/models/internvl.py @@ -23,7 +23,6 @@ from vllm.model_executor.layers.quantization import QuantizationConfig from vllm.model_executor.layers.quantization.awq import AWQConfig from vllm.model_executor.models.intern_vit import ( InternVisionModel, - InternVisionPatchModel, ) from vllm.model_executor.models.module_mapping import MultiModelKeys from vllm.multimodal import MULTIMODAL_REGISTRY @@ -582,14 +581,10 @@ class InternVLChatModel( self.downsample_ratio = config.downsample_ratio self.ps_version = config.ps_version - llm_arch_name = config.text_config.architectures[0] - self.is_mono = llm_arch_name == "InternLM2VEForCausalLM" - with self._mark_tower_model(vllm_config, {"image", "video"}): self.vision_model = self._init_vision_model( config, quant_config=quant_config, - is_mono=self.is_mono, prefix=maybe_prefix(prefix, "vision_model"), ) self.mlp1 = self._init_mlp1(config) @@ -604,7 +599,6 @@ class InternVLChatModel( self.img_context_token_id = None self.video_context_token_id = None - self.visual_token_mask = None self.make_empty_intermediate_tensors = ( self.language_model.make_empty_intermediate_tensors ) @@ -627,26 +621,22 @@ class InternVLChatModel( config: PretrainedConfig, quant_config: QuantizationConfig | None, *, - is_mono: bool, prefix: str, ): - if not is_mono: - vision_feature_layer = config.select_layer - if vision_feature_layer < 0: - num_hidden_layers = ( - config.vision_config.num_hidden_layers + vision_feature_layer + 1 - ) - else: - num_hidden_layers = vision_feature_layer + 1 - - return InternVisionModel( - config.vision_config, - quant_config=quant_config, - num_hidden_layers_override=num_hidden_layers, - prefix=prefix, + vision_feature_layer = config.select_layer + if vision_feature_layer < 0: + num_hidden_layers = ( + config.vision_config.num_hidden_layers + vision_feature_layer + 1 ) else: - return InternVisionPatchModel(config.vision_config) + num_hidden_layers = vision_feature_layer + 1 + + return InternVisionModel( + config.vision_config, + quant_config=quant_config, + num_hidden_layers_override=num_hidden_layers, + prefix=prefix, + ) def _init_mlp1(self, config: PretrainedConfig) -> nn.Module: vit_hidden_size = config.vision_config.hidden_size @@ -805,15 +795,6 @@ class InternVLChatModel( return modalities - def _set_visual_token_mask(self, input_ids: torch.Tensor) -> None: - if self.is_mono: - assert self.img_context_token_id is not None - self.visual_token_mask = (input_ids == self.img_context_token_id).reshape( - -1, 1 - ) - else: - self.visual_token_mask = None - def embed_multimodal(self, **kwargs: object) -> MultiModalEmbeddings: modalities = self._parse_and_validate_multimodal_inputs(**kwargs) if not modalities: @@ -844,9 +825,6 @@ class InternVLChatModel( *, is_multimodal: torch.Tensor | None = None, ) -> torch.Tensor: - if multimodal_embeddings is not None and len(multimodal_embeddings) > 0: - self._set_visual_token_mask(input_ids) - # This is to satisfy the type checker for each overload if multimodal_embeddings is None or is_multimodal is None: return super().embed_input_ids(input_ids) @@ -875,11 +853,6 @@ class InternVLChatModel( "inputs_embeds": inputs_embeds, } - # Only required if the model is mono-architecture - if self.visual_token_mask is not None: - forward_kwargs.update({"visual_token_mask": self.visual_token_mask}) - self.visual_token_mask = None - hidden_states = self.language_model.model(**forward_kwargs) return hidden_states diff --git a/vllm/model_executor/models/nvlm_d.py b/vllm/model_executor/models/nvlm_d.py index 9fd4cf0797..2222ab09e1 100644 --- a/vllm/model_executor/models/nvlm_d.py +++ b/vllm/model_executor/models/nvlm_d.py @@ -177,27 +177,22 @@ class NVLM_D_Model(InternVLChatModel): config: PretrainedConfig, quant_config: QuantizationConfig | None, *, - is_mono: bool, prefix: str, ): - if not is_mono: - vision_feature_layer = config.select_layer - if vision_feature_layer < 0: - num_hidden_layers = ( - config.vision_config.num_hidden_layers + vision_feature_layer + 1 - ) - else: - num_hidden_layers = vision_feature_layer + 1 - - # We added additional dummy heads to the original num of heads to - # make the number of heads divisible by 8. - return InternVisionModel( - config.vision_config, - quant_config=quant_config, - num_hidden_layers_override=num_hidden_layers, - num_dummy_heads=7, - prefix=prefix, + vision_feature_layer = config.select_layer + if vision_feature_layer < 0: + num_hidden_layers = ( + config.vision_config.num_hidden_layers + vision_feature_layer + 1 ) else: - msg = "Monolith mode is not applicable to NVLM_D" - raise NotImplementedError(msg) + num_hidden_layers = vision_feature_layer + 1 + + # We added additional dummy heads to the original num of heads to + # make the number of heads divisible by 8. + return InternVisionModel( + config.vision_config, + quant_config=quant_config, + num_hidden_layers_override=num_hidden_layers, + num_dummy_heads=7, + prefix=prefix, + ) diff --git a/vllm/model_executor/models/registry.py b/vllm/model_executor/models/registry.py index 722ba93d39..ecdbe3991c 100644 --- a/vllm/model_executor/models/registry.py +++ b/vllm/model_executor/models/registry.py @@ -141,7 +141,6 @@ _TEXT_GENERATION_MODELS = { "HCXVisionV2ForCausalLM": ("hyperclovax_vision_v2", "HCXVisionV2ForCausalLM"), "HyperCLOVAXForCausalLM": ("hyperclovax", "HyperCLOVAXForCausalLM"), "InternLM2ForCausalLM": ("internlm2", "InternLM2ForCausalLM"), - "InternLM2VEForCausalLM": ("internlm2_ve", "InternLM2VEForCausalLM"), "InternLM3ForCausalLM": ("llama", "LlamaForCausalLM"), "IQuestCoderForCausalLM": ("llama", "LlamaForCausalLM"), "IQuestLoopCoderForCausalLM": ("iquest_loopcoder", "IQuestLoopCoderForCausalLM"), @@ -716,6 +715,7 @@ _PREVIOUSLY_SUPPORTED_MODELS = { "ErnieModel": "0.23.0", "ErnieForSequenceClassification": "0.23.0", "ErnieForTokenClassification": "0.23.0", + "InternLM2VEForCausalLM": "0.23.0", "QWenLMHeadModel": "0.23.0", "QwenVLForConditionalGeneration": "0.23.0", "InternLMForCausalLM": "0.23.0", diff --git a/vllm/model_executor/models/skyworkr1v.py b/vllm/model_executor/models/skyworkr1v.py index a3415a20a9..685b980c3f 100644 --- a/vllm/model_executor/models/skyworkr1v.py +++ b/vllm/model_executor/models/skyworkr1v.py @@ -22,7 +22,6 @@ from vllm.model_executor.layers.quantization import QuantizationConfig from vllm.model_executor.layers.quantization.awq import AWQConfig from vllm.model_executor.models.intern_vit import ( InternVisionModel, - InternVisionPatchModel, ) from vllm.multimodal import MULTIMODAL_REGISTRY from vllm.multimodal.processing import BaseDummyInputsBuilder @@ -178,14 +177,10 @@ class SkyworkR1VChatModel(nn.Module, SupportsMultiModal, SupportsPP): self.downsample_ratio = config.downsample_ratio self.ps_version = config.ps_version - llm_arch_name = config.text_config.architectures[0] - self.is_mono = llm_arch_name == "SkyworkLM2VEForCausalLM" - with self._mark_tower_model(vllm_config, "image"): self.vision_model = self._init_vision_model( config, quant_config=quant_config, - is_mono=self.is_mono, prefix=maybe_prefix(prefix, "vision_model"), ) self.mlp1 = self._init_mlp1( @@ -223,26 +218,22 @@ class SkyworkR1VChatModel(nn.Module, SupportsMultiModal, SupportsPP): config: PretrainedConfig, quant_config: QuantizationConfig | None, *, - is_mono: bool, prefix: str, ): - if not is_mono: - vision_feature_layer = config.select_layer - if vision_feature_layer < 0: - num_hidden_layers = ( - config.vision_config.num_hidden_layers + vision_feature_layer + 1 - ) - else: - num_hidden_layers = vision_feature_layer + 1 - - return InternVisionModel( - config.vision_config, - quant_config=quant_config, - num_hidden_layers_override=num_hidden_layers, - prefix=prefix, + vision_feature_layer = config.select_layer + if vision_feature_layer < 0: + num_hidden_layers = ( + config.vision_config.num_hidden_layers + vision_feature_layer + 1 ) else: - return InternVisionPatchModel(config.vision_config) + num_hidden_layers = vision_feature_layer + 1 + + return InternVisionModel( + config.vision_config, + quant_config=quant_config, + num_hidden_layers_override=num_hidden_layers, + prefix=prefix, + ) def _init_mlp1( self, @@ -363,14 +354,6 @@ class SkyworkR1VChatModel(nn.Module, SupportsMultiModal, SupportsPP): ] return image_embeds.split(image_feature_sizes) - def _set_visual_token_mask(self, input_ids: torch.Tensor) -> None: - if self.is_mono: - self.visual_token_mask = (input_ids == self.img_context_token_id).reshape( - -1, 1 - ) - else: - self.visual_token_mask = None - def embed_multimodal(self, **kwargs: object) -> MultiModalEmbeddings: image_input = self._parse_and_validate_image_input(**kwargs) if image_input is None: @@ -385,9 +368,6 @@ class SkyworkR1VChatModel(nn.Module, SupportsMultiModal, SupportsPP): *, is_multimodal: torch.Tensor | None = None, ) -> torch.Tensor: - if multimodal_embeddings is not None and len(multimodal_embeddings) > 0: - self._set_visual_token_mask(input_ids) - # This is to satisfy the type checker for each overload if multimodal_embeddings is None or is_multimodal is None: return super().embed_input_ids(input_ids)