diff --git a/docs/models/supported_models.md b/docs/models/supported_models.md index 71deac53bc..d06154fb5b 100644 --- a/docs/models/supported_models.md +++ b/docs/models/supported_models.md @@ -538,6 +538,7 @@ These models primarily accept the [`LLM.generate`](./generative_models.md#llmgen | `AriaForConditionalGeneration` | Aria | T + I+ | `rhymes-ai/Aria` | | | | `AudioFlamingo3ForConditionalGeneration` | AudioFlamingo3 | T + A | `nvidia/audio-flamingo-3-hf`, `nvidia/music-flamingo-hf` | ✅︎ | ✅︎ | | `BagelForConditionalGeneration` | BAGEL | T + I+ | `ByteDance-Seed/BAGEL-7B-MoT` | ✅︎ | ✅︎ | +| `BailingMoeV3VLForConditionalGeneration` | Ling 3.0 Flash VL | T + IE+ | `inclusionAI/Ling-3.0-flash-VL` | | ✅︎ | | `BeeForConditionalGeneration` | Bee-8B | T + IE+ | `Open-Bee/Bee-8B-RL`, `Open-Bee/Bee-8B-SFT` | | ✅︎ | | `Blip2ForConditionalGeneration` | BLIP-2 | T + IE | `Salesforce/blip2-opt-2.7b`, `Salesforce/blip2-opt-6.7b`, etc. | ✅︎ | ✅︎ | | `Cohere2VisionForConditionalGeneration` | Command A Vision, Command-A+ | T + I+ | `CohereLabs/command-a-vision-07-2025`, `CohereLabs/command-a-plus-05-2026`, etc. | | ✅︎ | diff --git a/tests/model_executor/test_bailing_mrope.py b/tests/model_executor/test_bailing_mrope.py new file mode 100644 index 0000000000..e44a683495 --- /dev/null +++ b/tests/model_executor/test_bailing_mrope.py @@ -0,0 +1,176 @@ +# SPDX-License-Identifier: Apache-2.0 +# SPDX-FileCopyrightText: Copyright contributors to the vLLM project + +import copy +from types import SimpleNamespace + +import pytest +import torch + +from vllm.config import DeviceConfig, VllmConfig, set_current_vllm_config +from vllm.model_executor.layers.rotary_embedding import get_rope +from vllm.model_executor.layers.rotary_embedding.bailing_mrope import ( + BailingMRotaryEmbedding, +) +from vllm.model_executor.models.bailing_moe_v3 import _build_mla_rotary_embedding +from vllm.platforms import current_platform + + +def test_bailing_mrope_selects_alternating_spatial_then_temporal(): + with set_current_vllm_config(VllmConfig(device_config=DeviceConfig("cpu"))): + rotary_emb = BailingMRotaryEmbedding( + head_size=12, + rotary_dim=12, + max_position_embeddings=8, + base=10_000, + is_neox_style=False, + dtype=torch.float32, + mrope_section=[2, 2, 2], + ) + + positions = torch.tensor([[1, 2], [3, 4], [5, 6]]) + frequencies = torch.arange(6).expand(8, -1) + position_values = torch.arange(8).unsqueeze(1) * 10 + cos = position_values + frequencies + sin = cos + 100 + cos_sin_cache = torch.cat((cos, sin), dim=-1) + + actual_cos, actual_sin = rotary_emb.select_cos_sin( + positions, + cos_sin_cache, + ) + + expected_cos = torch.tensor( + [ + [30, 51, 32, 53, 14, 15], + [40, 61, 42, 63, 24, 25], + ] + ) + torch.testing.assert_close(actual_cos, expected_cos) + torch.testing.assert_close(actual_sin, expected_cos + 100) + + +def test_bailing_factory_preserves_mla_width_and_config(): + config = SimpleNamespace( + max_position_embeddings=128, + rope_parameters={ + "rope_type": "default", + "rope_theta": 6_000_000, + "partial_rotary_factor": 0.5, + "mrope_section": [8, 12, 12], + }, + rope_scaling={"partial_rotary_factor": 0.5}, + ) + original = copy.deepcopy(vars(config)) + with set_current_vllm_config(VllmConfig(device_config=DeviceConfig("cpu"))): + rope = _build_mla_rotary_embedding(config, head_size=64) + assert rope is _build_mla_rotary_embedding(config, head_size=64) + generic = get_rope( + head_size=64, + max_position=128, + is_neox_style=False, + rope_parameters={ + "rope_type": "default", + "rope_theta": 6_000_000, + "mrope_section": [8, 12, 12], + }, + ) + assert isinstance(rope, BailingMRotaryEmbedding) + assert rope is not generic + assert rope.rotary_dim == 64 + assert vars(config) == original + + +def test_bailing_factory_rejects_unsupported_scaling(): + config = SimpleNamespace( + rope_parameters={ + "rope_type": "yarn", + "factor": 2.0, + "mrope_section": [8, 12, 12], + } + ) + with pytest.raises(ValueError, match="only supports rope_type='default'"): + _build_mla_rotary_embedding(config, head_size=64) + + +@pytest.mark.skipif( + not current_platform.is_cuda_alike(), + reason="Skipping CUDA/ROCm-only test.", +) +@pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16]) +@pytest.mark.parametrize("position_kind", ["text", "multimodal"]) +@torch.inference_mode() +def test_bailing_mrope_cuda_matches_native_for_strided_mla_views( + dtype: torch.dtype, + position_kind: str, +): + device = torch.device("cuda") + num_tokens = 17 + num_query_heads = 8 + with set_current_vllm_config(VllmConfig()): + rotary_emb = BailingMRotaryEmbedding( + head_size=64, + rotary_dim=64, + max_position_embeddings=64, + base=6_000_000, + is_neox_style=False, + dtype=dtype, + mrope_section=[8, 12, 12], + ).to(device) + + torch.manual_seed(0) + multimodal_positions = torch.randint( + 0, + 64, + (3, 2 * num_tokens), + dtype=torch.int32, + device=device, + )[:, ::2] + if position_kind == "multimodal": + positions = multimodal_positions + reference_positions = positions + else: + positions = torch.randint( + 0, + 64, + (2 * num_tokens,), + dtype=torch.int64, + device=device, + )[::2] + reference_positions = positions.expand(3, -1) + query_buffer = torch.randn( + num_tokens, + num_query_heads, + 192, + dtype=dtype, + device=device, + ) + key_buffer = torch.randn( + num_tokens, + 576, + dtype=dtype, + device=device, + ) + query = query_buffer[..., 128:] + key = key_buffer[:, 512:].unsqueeze(1) + assert not query.is_contiguous() + assert not key.is_contiguous() + + query_prefix = query_buffer[..., :128].clone() + key_prefix = key_buffer[:, :512].clone() + expected_query, expected_key = rotary_emb.forward_native( + reference_positions, + query.clone(), + key.clone(), + ) + actual_query, actual_key = rotary_emb.forward_cuda(positions, query, key) + assert expected_key is not None + assert actual_key is not None + + torch.testing.assert_close(actual_query, expected_query, atol=1e-2, rtol=1.6e-2) + torch.testing.assert_close(actual_key, expected_key, atol=1e-2, rtol=1.6e-2) + assert torch.equal(query_buffer[..., :128], query_prefix) + assert torch.equal(key_buffer[:, :512], key_prefix) + if position_kind == "multimodal": + assert actual_query.data_ptr() == query.data_ptr() + assert actual_key.data_ptr() == key.data_ptr() diff --git a/tests/models/multimodal/processing/test_bailing_moe_v3_vl.py b/tests/models/multimodal/processing/test_bailing_moe_v3_vl.py new file mode 100644 index 0000000000..0ebc61909f --- /dev/null +++ b/tests/models/multimodal/processing/test_bailing_moe_v3_vl.py @@ -0,0 +1,126 @@ +# SPDX-License-Identifier: Apache-2.0 +# SPDX-FileCopyrightText: Copyright contributors to the vLLM project +"""Bailing prompt expansion tests without downloading a checkpoint.""" + +from types import SimpleNamespace + +import pytest +import torch +from PIL import Image +from tokenizers import Tokenizer, decoders, models, pre_tokenizers +from transformers import PreTrainedTokenizerFast + +from vllm.model_executor.models.bailing_moe_v3_vl import ( + IMAGE_PLACEHOLDER, + IMAGE_TOKEN, + BailingMoeV3VLMultiModalProcessor, +) +from vllm.multimodal.inputs import MultiModalFieldConfig, MultiModalKwargsItems +from vllm.multimodal.parse import ImageProcessorItems, MultiModalDataItems + +pytestmark = pytest.mark.cpu_test + + +@pytest.fixture +def processor(): + vocab = { + token: index + for index, token in enumerate(sorted(pre_tokenizers.ByteLevel.alphabet())) + } + # A newline added by the processor must merge with an existing newline. + vocab["ĊĊ"] = len(vocab) + backend = Tokenizer(models.BPE(vocab=vocab, merges=[("Ċ", "Ċ")])) + backend.pre_tokenizer = pre_tokenizers.ByteLevel( + add_prefix_space=False, use_regex=False + ) + backend.decoder = decoders.ByteLevel() + tokenizer = PreTrainedTokenizerFast( + tokenizer_object=backend, + additional_special_tokens=[ + "<|vision_start|>", + IMAGE_TOKEN, + "<|vision_end|>", + ], + clean_up_tokenization_spaces=False, + ) + processor = object.__new__(BailingMoeV3VLMultiModalProcessor) + processor.info = SimpleNamespace( + get_tokenizer=lambda: tokenizer, + get_hf_config=lambda: SimpleNamespace( + vision_config=SimpleNamespace(spatial_merge_size=2) + ), + ) + return processor + + +@pytest.mark.parametrize( + ("prompt", "expected_text", "token_counts"), + [ + ( + f"before{IMAGE_PLACEHOLDER}after", + f"before<|vision_start|>{IMAGE_TOKEN * 2}<|vision_end|>\nafter", + [2], + ), + ( + f"before{IMAGE_TOKEN}after", + f"before<|vision_start|>{IMAGE_TOKEN * 2}<|vision_end|>\nafter", + [2], + ), + ( + f"before{IMAGE_PLACEHOLDER}\nafter", + f"before<|vision_start|>{IMAGE_TOKEN * 2}<|vision_end|>\n\nafter", + [2], + ), + ( + f"before{IMAGE_PLACEHOLDER}{IMAGE_PLACEHOLDER}after", + f"before<|vision_start|>{IMAGE_TOKEN * 2}<|vision_end|>\n" + f"<|vision_start|>{IMAGE_TOKEN * 3}<|vision_end|>\nafter", + [2, 3], + ), + ], + ids=["wrapped", "bare", "existing_newline", "adjacent_images"], +) +def test_image_expansion_matches_wrappers_and_newline_tokenization( + processor, prompt, expected_text, token_counts +): + tokenizer = processor.info.get_tokenizer() + image_id = tokenizer.get_vocab()[IMAGE_TOKEN] + grids = torch.tensor([[1, 2, 2 * count] for count in token_counts]) + mm_kwargs = MultiModalKwargsItems.from_hf_inputs( + {"image_grid_thw": grids}, + {"image_grid_thw": MultiModalFieldConfig.batched("image")}, + ) + mm_items = MultiModalDataItems( + {"image": ImageProcessorItems([Image.new("RGB", (1, 1))] * len(token_counts))} + ) + updates = processor._get_mm_prompt_updates(mm_items, {}, mm_kwargs) + prompt_ids = tokenizer.encode(prompt, add_special_tokens=False) + expected_ids = tokenizer.encode(expected_text, add_special_tokens=False) + + actual_ids, placeholders = processor._apply_prompt_updates(prompt_ids, updates) + + assert actual_ids == expected_ids + assert len(placeholders["image"]) == len(token_counts) + found = processor._find_mm_placeholders(actual_ids, updates) + search_offset = 0 + for index, count in enumerate(token_counts): + placeholder = placeholders["image"][index] + expected_offset = expected_ids.index(image_id, search_offset) + assert placeholder.start_idx == expected_offset + assert placeholder.tokens == [image_id] * count + assert placeholder.to_range().get_num_embeds() == count + assert placeholder.to_range() == found["image"][index].to_range() + search_offset = expected_offset + count + + +def test_text_only_prompt_preserves_original_token_ids(processor): + tokenizer = processor.info.get_tokenizer() + prompt_ids = tokenizer.encode("before\n", add_special_tokens=False) + prompt_ids += tokenizer.encode("\nafter", add_special_tokens=False) + # Deliberately use two newline tokens instead of their merged BPE token. + assert prompt_ids != tokenizer.encode("before\n\nafter", add_special_tokens=False) + + actual_ids, placeholders = processor._apply_prompt_updates(prompt_ids, {}) + + assert actual_ids == prompt_ids + assert placeholders == {} diff --git a/tests/models/multimodal/test_mapping.py b/tests/models/multimodal/test_mapping.py index 21ca71c6da..b762ab2a13 100644 --- a/tests/models/multimodal/test_mapping.py +++ b/tests/models/multimodal/test_mapping.py @@ -184,6 +184,36 @@ def test_cosmos3_edge_checkpoint_weights_mapper(): ) +@pytest.mark.cpu_test +def test_bailing_vl_mapper_handles_module_and_parameter_names(): + from vllm.model_executor.models.bailing_moe_v3_vl import ( + BailingMoeV3VLForConditionalGeneration, + ) + + mapper = BailingMoeV3VLForConditionalGeneration.hf_to_vllm_mapper + names = { + "model.visual": "visual", + "model.visual.blocks.0.attn.qkv.weight": "visual.blocks.0.attn.qkv.weight", + "model.linear_proj": "linear_proj", + "linear_proj.0": "linear_proj.linear_fc1", + "model.linear_proj.0.weight": "linear_proj.linear_fc1.weight", + "linear_proj.2": "linear_proj.linear_fc2", + "linear_proj.2.weight_scale": "linear_proj.linear_fc2.weight_scale", + "model.linear_proj.2.bias": "linear_proj.linear_fc2.bias", + "linear_proj.linear_fc1.weight": "linear_proj.linear_fc1.weight", + "model.linear_proj.linear_fc2.weight": "linear_proj.linear_fc2.weight", + "model.layers.0.attention.q_proj.weight": ( + "language_model.model.layers.0.self_attn.q_proj.weight" + ), + "model.layers.0.mlp.experts.3.up_proj.weight": ( + "language_model.model.layers.0.mlp.experts.3.up_proj.weight" + ), + "lm_head": "language_model.lm_head", + "lm_head.weight": "language_model.lm_head.weight", + } + assert mapper.apply_list(list(names)) == list(names.values()) + + def create_repo_dummy_weights(repo: str) -> Iterable[tuple[str, torch.Tensor]]: """Create weights from safetensors checkpoint metadata""" metadata = try_get_safetensors_metadata(repo) diff --git a/tests/models/registry.py b/tests/models/registry.py index 1651eb68ce..c3973c4d0b 100644 --- a/tests/models/registry.py +++ b/tests/models/registry.py @@ -796,6 +796,18 @@ _MULTIMODAL_EXAMPLE_MODELS = { }, ), "BagelForConditionalGeneration": _HfExamplesInfo("ByteDance-Seed/BAGEL-7B-MoT"), + "BailingMoeV3VLForConditionalGeneration": _HfExamplesInfo( + "inclusionAI/Ling-3.0-flash-VL", + trust_remote_code=True, + is_available_online=True, + use_original_num_layers=True, + hf_overrides={ + "text_config": { + "num_hidden_layers": 6, + "layer_types": ["linear_attention"] * 5 + ["full_attention"], + } + }, + ), "BeeForConditionalGeneration": _HfExamplesInfo( "Open-Bee/Bee-8B-RL", trust_remote_code=True, diff --git a/tests/parser/engine/test_ling3.py b/tests/parser/engine/test_ling3.py index 7cde4dc45d..d3c5318bcc 100644 --- a/tests/parser/engine/test_ling3.py +++ b/tests/parser/engine/test_ling3.py @@ -3,6 +3,8 @@ import json +import pytest + from tests.parser.engine.replay_harness import MockTokenizer, _test_request from vllm.reasoning import ReasoningParserManager from vllm.tool_parsers import ToolParserManager @@ -57,19 +59,18 @@ def test_ling3_disable_thinking_keeps_reasoning_as_content(): assert content == "reasonanswer" -def test_ling3_enable_thinking_keeps_open_reasoning_as_content(): +@pytest.mark.parametrize("model_output", ["only reasoning", "only reasoning"]) +def test_ling3_truncated_reasoning_stays_reasoning(model_output): parser_cls = ReasoningParserManager.get_reasoning_parser("ling3") parser = parser_cls( _tokenizer(), chat_template_kwargs={"enable_thinking": True}, ) - reasoning, content = parser.extract_reasoning( - "only reasoning", _test_request() - ) + reasoning, content = parser.extract_reasoning(model_output, _test_request()) - assert reasoning is None - assert content == "only reasoning" + assert reasoning == "only reasoning" + assert content is None def test_ling3_tool_call_without_newline(): diff --git a/tests/transformers_utils/test_bailing_moe_v3_vl_config.py b/tests/transformers_utils/test_bailing_moe_v3_vl_config.py new file mode 100644 index 0000000000..f10fd408ed --- /dev/null +++ b/tests/transformers_utils/test_bailing_moe_v3_vl_config.py @@ -0,0 +1,159 @@ +# SPDX-License-Identifier: Apache-2.0 +# SPDX-FileCopyrightText: Copyright contributors to the vLLM project + +import copy +import json +from pathlib import Path +from typing import Any + +import pytest +from transformers import AutoConfig + +from vllm.transformers_utils.config import get_config, uses_mrope +from vllm.transformers_utils.configs.bailing_moe_v3_vl import ( + BailingMoeV3TextConfig, + BailingMoeV3VisionConfig, + BailingMoeV3VLConfig, +) + + +def _bailing_v3_vl_config() -> dict[str, Any]: + return { + "architectures": ["BailingMoeV3VLForConditionalGeneration"], + "auto_map": { + "AutoConfig": ("configuration_bailing_moe_v3_vl.BailingMoeV3VLConfig") + }, + "model_type": "bailing_moe_v3_vl", + "mrope_section": [8, 12, 12], + "text_config": { + "num_hidden_layers": 42, + "layer_group_size": 6, + "rope_theta": 6_000_000, + }, + "vision_config": {"disable_merger_proj": True}, + } + + +def _write_config(path: Path, config: dict[str, Any]) -> None: + path.mkdir() + (path / "config.json").write_text(json.dumps(config), encoding="utf-8") + + +def test_bailing_v3_vl_config_loads_without_remote_code(tmp_path: Path): + model_path = tmp_path / "model" + _write_config(model_path, _bailing_v3_vl_config()) + + config = get_config(model_path, trust_remote_code=False) + + assert isinstance(config, BailingMoeV3VLConfig) + assert isinstance(config.text_config, BailingMoeV3TextConfig) + assert isinstance(config.vision_config, BailingMoeV3VisionConfig) + assert isinstance( + AutoConfig.from_pretrained(model_path, trust_remote_code=False), + BailingMoeV3VLConfig, + ) + assert config.mrope_section == [8, 12, 12] + assert uses_mrope(config) + + text_config = config.text_config + assert text_config.model_type == "bailing_hybrid" + assert text_config.architectures == ["BailingMoeV3ForCausalLM"] + assert text_config.rope_parameters["mrope_section"] == [8, 12, 12] + assert text_config.rope_parameters["rope_theta"] == 6_000_000 + assert text_config.layer_types.count("linear_attention") == 35 + assert [ + layer_idx + for layer_idx, layer_type in enumerate(text_config.layer_types) + if layer_type == "full_attention" + ] == [5, 11, 17, 23, 29, 35, 41] + assert config.vision_config.disable_merger_proj is True + + normalized = config.to_dict() + restored = BailingMoeV3VLConfig.from_dict(normalized) + assert isinstance(restored.text_config, BailingMoeV3TextConfig) + assert isinstance(restored.vision_config, BailingMoeV3VisionConfig) + assert restored.to_dict() == normalized + + +def test_bailing_v3_vl_config_preserves_explicit_nested_values(): + config_dict = _bailing_v3_vl_config() + config_dict["architectures"] = ["CustomBailingVLForConditionalGeneration"] + text_config = config_dict["text_config"] + text_config["architectures"] = ["CustomBailingForCausalLM"] + text_config["layer_types"] = (["linear_attention"] * 5 + ["full_attention"]) * 7 + text_config["rope_theta"] = 1_000_000 + text_config["rope_parameters"] = { + "rope_type": "default", + "rope_theta": 1_000_000, + "mrope_section": [8, 12, 12], + } + original = copy.deepcopy(config_dict) + + config = BailingMoeV3VLConfig(**config_dict) + + assert config_dict == original + assert config.architectures == ["CustomBailingVLForConditionalGeneration"] + assert config.text_config.architectures == ["CustomBailingForCausalLM"] + assert config.text_config.layer_types == text_config["layer_types"] + assert config.text_config.rope_parameters["rope_theta"] == 1_000_000 + assert config.text_config.rope_parameters["mrope_section"] == [8, 12, 12] + + +def test_bailing_v3_vl_config_normalizes_config_instance(): + text_config = BailingMoeV3TextConfig() + + config = BailingMoeV3VLConfig( + text_config=text_config, + mrope_section=[8, 12, 12], + ) + + assert config.text_config is text_config + assert text_config.rope_parameters["mrope_section"] == [8, 12, 12] + + +def test_bailing_v3_text_config_trailing_and_legacy_layers(): + expected = ["linear_attention", "linear_attention", "full_attention"] * 2 + [ + "full_attention" + ] * 2 + text_config = BailingMoeV3TextConfig( + num_hidden_layers=8, + layer_group_size=3, + ) + assert text_config.layer_types == expected + + text_config = BailingMoeV3TextConfig( + num_hidden_layers=8, + layer_group_size=3, + layers_block_type=["mamba", "mamba", "attention"] * 2 + ["attention"] * 2, + ) + assert text_config.layer_types == expected + + +def test_bailing_v3_text_config_accepts_legacy_rope_type(): + text_config = BailingMoeV3TextConfig(rope_scaling={"type": "linear", "factor": 2.0}) + + assert text_config.rope_parameters["type"] == "linear" + assert text_config.rope_parameters["rope_type"] == "linear" + + +def test_bailing_v3_vl_config_rejects_invalid_values(): + with pytest.raises(ValueError, match="layer_group_size must be positive"): + BailingMoeV3TextConfig(layer_group_size=0) + + with pytest.raises(ValueError, match="must match the layer_group_size schedule"): + BailingMoeV3TextConfig( + num_hidden_layers=8, + layer_group_size=3, + layer_types=["full_attention"] * 8, + ) + + with pytest.raises(ValueError, match="conflicting M-RoPE sections"): + BailingMoeV3VLConfig( + text_config={ + "rope_parameters": { + "rope_type": "default", + "mrope_section": [4, 14, 14], + } + }, + mrope_section=[8, 12, 12], + ) diff --git a/vllm/config/speculative.py b/vllm/config/speculative.py index 06ed2313d9..39d1f0ab4b 100644 --- a/vllm/config/speculative.py +++ b/vllm/config/speculative.py @@ -847,7 +847,18 @@ class SpeculativeConfig: ) architectures = getattr(hf_config, "architectures", []) or [] - if initial_architecture == "BailingMoeV3ForCausalLM": + if initial_architecture == "BailingMoeV3VLForConditionalGeneration": + quantization_config = getattr(hf_config, "quantization_config", None) + hf_config = copy.deepcopy(hf_config.text_config) + if ( + quantization_config is not None + and getattr(hf_config, "quantization_config", None) is None + ): + hf_config.quantization_config = copy.deepcopy(quantization_config) + if initial_architecture in ( + "BailingMoeV3ForCausalLM", + "BailingMoeV3VLForConditionalGeneration", + ): hf_config.model_type = "bailing_hybrid_v3_mtp" elif ( hf_config.model_type == "bailing_hybrid" diff --git a/vllm/model_executor/layers/rotary_embedding/__init__.py b/vllm/model_executor/layers/rotary_embedding/__init__.py index 110c9790f0..ab06448957 100644 --- a/vllm/model_executor/layers/rotary_embedding/__init__.py +++ b/vllm/model_executor/layers/rotary_embedding/__init__.py @@ -6,6 +6,7 @@ from typing import Any import torch +from .bailing_mrope import BailingMRotaryEmbedding from .base import RotaryEmbedding from .deepseek_scaling_rope import ( DeepseekScalingRotaryEmbedding, @@ -97,6 +98,16 @@ def get_rope( dtype, **extra_kwargs, ) + elif scaling_type == "bailing_mrope": + rotary_emb = BailingMRotaryEmbedding( + head_size, + rotary_dim, + max_position, + base, + is_neox_style, + dtype, + mrope_section=rope_parameters["mrope_section"], + ) elif scaling_type == "default": if "mrope_section" in rope_parameters: rotary_emb = MRotaryEmbedding( diff --git a/vllm/model_executor/layers/rotary_embedding/bailing_mrope.py b/vllm/model_executor/layers/rotary_embedding/bailing_mrope.py new file mode 100644 index 0000000000..fc58cfa960 --- /dev/null +++ b/vllm/model_executor/layers/rotary_embedding/bailing_mrope.py @@ -0,0 +1,351 @@ +# SPDX-License-Identifier: Apache-2.0 +# SPDX-FileCopyrightText: Copyright contributors to the vLLM project + +import torch + +from vllm.triton_utils import tl, triton + +from .mrope import MRotaryEmbedding + + +def _validate_bailing_mrope_config( + head_size: int, + rotary_dim: int, + mrope_section: list[int], +) -> int: + if rotary_dim <= 0 or rotary_dim % 2 != 0 or rotary_dim > head_size: + raise ValueError( + "Bailing M-RoPE requires an even rotary_dim in (0, head_size], " + f"got rotary_dim={rotary_dim} and head_size={head_size}" + ) + if len(mrope_section) != 3: + raise ValueError( + "Bailing M-RoPE expects [T, H, W] sections, " + f"got mrope_section={mrope_section}" + ) + + temporal_size, height_size, width_size = mrope_section + if any(section_size < 0 for section_size in mrope_section): + raise ValueError( + "Bailing M-RoPE sections must be non-negative, " + f"got mrope_section={mrope_section}" + ) + if height_size != width_size: + raise ValueError( + "Bailing M-RoPE requires equal H and W sections, " + f"got height_size={height_size} and width_size={width_size}" + ) + spatial_size = height_size + width_size + if spatial_size + temporal_size != rotary_dim // 2: + raise ValueError( + "Bailing M-RoPE sections must sum to rotary_dim // 2, got " + f"mrope_section={mrope_section} and rotary_dim={rotary_dim}" + ) + return spatial_size + + +def _validate_bailing_positions( + positions: torch.Tensor, + *, + require_2d: bool = False, +) -> None: + if positions.dtype not in (torch.int32, torch.int64): + raise ValueError( + "Bailing M-RoPE positions must have int32 or int64 dtype, got " + f"dtype={positions.dtype}" + ) + if positions.ndim == 1 and not require_2d: + return + if positions.ndim == 2: + if positions.shape[0] == 3: + return + raise ValueError( + "Bailing M-RoPE expects T/H/W positions with shape [3, N], " + f"got shape={tuple(positions.shape)}" + ) + if require_2d: + raise ValueError( + "Bailing M-RoPE expects T/H/W positions with shape [3, N], " + f"got shape={tuple(positions.shape)}" + ) + raise ValueError( + f"Bailing M-RoPE positions must be 1D or 2D, got shape={tuple(positions.shape)}" + ) + + +@triton.jit +def _triton_bailing_mrope_forward( + q_ptr, + k_ptr, + positions_ptr, + cos_sin_cache_ptr, + q_stride_token, + q_stride_head, + q_stride_dim, + k_stride_token, + k_stride_head, + k_stride_dim, + positions_stride_axis, + positions_stride_token, + cache_stride_position, + cache_stride_dim, + n_qh: tl.constexpr, + n_kh: tl.constexpr, + rotary_dim: tl.constexpr, + pad_n_qh: tl.constexpr, + pad_n_kh: tl.constexpr, + pad_half_rotary_dim: tl.constexpr, + spatial_size: tl.constexpr, +): + """Apply GPT-J-style H/W-interleaved, T-tail M-RoPE in place.""" + token_idx = tl.program_id(0) + position_offset = token_idx * positions_stride_token + temporal_position = tl.load(positions_ptr + position_offset).to(tl.int64) + height_position = tl.load( + positions_ptr + positions_stride_axis + position_offset + ).to(tl.int64) + width_position = tl.load( + positions_ptr + 2 * positions_stride_axis + position_offset + ).to(tl.int64) + + frequency_offsets = tl.arange(0, pad_half_rotary_dim) + frequency_mask = frequency_offsets < rotary_dim // 2 + spatial_position = tl.where( + frequency_offsets % 2 == 0, + height_position, + width_position, + ) + selected_position = tl.where( + frequency_offsets < spatial_size, + spatial_position, + temporal_position, + ) + cache_offsets = ( + selected_position * cache_stride_position + frequency_offsets * cache_stride_dim + ) + cos_row = tl.load( + cos_sin_cache_ptr + cache_offsets, + mask=frequency_mask, + other=0.0, + ).to(tl.float32) + sin_row = tl.load( + cos_sin_cache_ptr + cache_offsets + (rotary_dim // 2) * cache_stride_dim, + mask=frequency_mask, + other=0.0, + ).to(tl.float32) + + q_head_offsets = tl.arange(0, pad_n_qh)[:, None] + k_head_offsets = tl.arange(0, pad_n_kh)[:, None] + pair_offsets = frequency_offsets[None, :] + q_mask = (q_head_offsets < n_qh) & frequency_mask[None, :] + k_mask = (k_head_offsets < n_kh) & frequency_mask[None, :] + + q_base = q_ptr + token_idx * q_stride_token + q_even_offsets = q_head_offsets * q_stride_head + 2 * pair_offsets * q_stride_dim + q_odd_offsets = q_even_offsets + q_stride_dim + q_even = tl.load(q_base + q_even_offsets, mask=q_mask, other=0.0).to(tl.float32) + q_odd = tl.load(q_base + q_odd_offsets, mask=q_mask, other=0.0).to(tl.float32) + q_even_cos = (q_even * cos_row[None, :]).to(q_ptr.type.element_ty).to(tl.float32) + q_odd_sin = (q_odd * sin_row[None, :]).to(q_ptr.type.element_ty).to(tl.float32) + q_odd_cos = (q_odd * cos_row[None, :]).to(q_ptr.type.element_ty).to(tl.float32) + q_even_sin = (q_even * sin_row[None, :]).to(q_ptr.type.element_ty).to(tl.float32) + tl.store( + q_base + q_even_offsets, + q_even_cos - q_odd_sin, + mask=q_mask, + ) + tl.store( + q_base + q_odd_offsets, + q_odd_cos + q_even_sin, + mask=q_mask, + ) + + k_base = k_ptr + token_idx * k_stride_token + k_even_offsets = k_head_offsets * k_stride_head + 2 * pair_offsets * k_stride_dim + k_odd_offsets = k_even_offsets + k_stride_dim + k_even = tl.load(k_base + k_even_offsets, mask=k_mask, other=0.0).to(tl.float32) + k_odd = tl.load(k_base + k_odd_offsets, mask=k_mask, other=0.0).to(tl.float32) + k_even_cos = (k_even * cos_row[None, :]).to(k_ptr.type.element_ty).to(tl.float32) + k_odd_sin = (k_odd * sin_row[None, :]).to(k_ptr.type.element_ty).to(tl.float32) + k_odd_cos = (k_odd * cos_row[None, :]).to(k_ptr.type.element_ty).to(tl.float32) + k_even_sin = (k_even * sin_row[None, :]).to(k_ptr.type.element_ty).to(tl.float32) + tl.store( + k_base + k_even_offsets, + k_even_cos - k_odd_sin, + mask=k_mask, + ) + tl.store( + k_base + k_odd_offsets, + k_odd_cos + k_even_sin, + mask=k_mask, + ) + + +def triton_bailing_mrope( + query: torch.Tensor, + key: torch.Tensor, + positions: torch.Tensor, + cos_sin_cache: torch.Tensor, + spatial_size: int, + head_size: int, + rotary_dim: int, +) -> tuple[torch.Tensor, torch.Tensor]: + """Fused Bailing M-RoPE for packed or strided Q/K.""" + _validate_bailing_positions(positions, require_2d=True) + num_tokens = positions.shape[-1] + if num_tokens == 0: + return query, key + + query_3d = query.view(num_tokens, -1, head_size) + key_3d = key.view(num_tokens, -1, head_size) + n_q_heads = query_3d.shape[1] + n_k_heads = key_3d.shape[1] + pad_n_q_heads = triton.next_power_of_2(n_q_heads) + pad_n_k_heads = triton.next_power_of_2(n_k_heads) + pad_half_rotary_dim = triton.next_power_of_2(rotary_dim // 2) + _triton_bailing_mrope_forward[(num_tokens,)]( + query_3d, + key_3d, + positions, + cos_sin_cache, + query_3d.stride(0), + query_3d.stride(1), + query_3d.stride(2), + key_3d.stride(0), + key_3d.stride(1), + key_3d.stride(2), + positions.stride(0), + positions.stride(1), + cos_sin_cache.stride(0), + cos_sin_cache.stride(1), + n_q_heads, + n_k_heads, + rotary_dim, + pad_n_q_heads, + pad_n_k_heads, + pad_half_rotary_dim, + spatial_size, + num_warps=4, + ) + return query, key + + +class BailingMRotaryEmbedding(MRotaryEmbedding): + """Bailing M-RoPE with alternating H/W frequencies and a temporal tail.""" + + def __init__( + self, + head_size: int, + rotary_dim: int, + max_position_embeddings: int, + base: float, + is_neox_style: bool, + dtype: torch.dtype, + mrope_section: list[int], + ) -> None: + self.spatial_size = _validate_bailing_mrope_config( + head_size, rotary_dim, mrope_section + ) + super().__init__( + head_size, + rotary_dim, + max_position_embeddings, + base, + is_neox_style, + dtype, + mrope_section=mrope_section, + ) + + def select_cos_sin( + self, + positions: torch.Tensor, + cos_sin_cache: torch.Tensor | None = None, + ) -> tuple[torch.Tensor, torch.Tensor]: + _validate_bailing_positions(positions) + if cos_sin_cache is None: + cos_sin_cache = self.cos_sin_cache + cos_sin = cos_sin_cache[positions.to(torch.long)] + cos, sin = cos_sin.chunk(2, dim=-1) + if positions.ndim == 1: + return cos, sin + + spatial_mask = torch.arange(self.spatial_size, device=cos.device) % 2 == 0 + spatial_mask = spatial_mask.unsqueeze(0) + spatial_cos = torch.where( + spatial_mask, + cos[1, :, : self.spatial_size], + cos[2, :, : self.spatial_size], + ) + spatial_sin = torch.where( + spatial_mask, + sin[1, :, : self.spatial_size], + sin[2, :, : self.spatial_size], + ) + temporal_slice = slice(self.spatial_size, self.rotary_dim // 2) + return ( + torch.cat((spatial_cos, cos[0, :, temporal_slice]), dim=-1), + torch.cat((spatial_sin, sin[0, :, temporal_slice]), dim=-1), + ) + + def forward_native( + self, + positions: torch.Tensor, + query: torch.Tensor, + key: torch.Tensor | None = None, + offsets: torch.Tensor | None = None, + ) -> tuple[torch.Tensor, torch.Tensor | None]: + if key is None: + raise ValueError("Bailing M-RoPE requires key states") + + cos_sin_cache = self._match_cos_sin_cache_dtype(query) + cos, sin = self.select_cos_sin(positions, cos_sin_cache) + num_tokens = positions.shape[-1] + query_shape = query.shape + query = query.view(num_tokens, -1, self.head_size) + query_rot = self.apply_rotary_emb.forward_native( + query[..., : self.rotary_dim], + cos, + sin, + ) + query = torch.cat((query_rot, query[..., self.rotary_dim :]), dim=-1) + + key_shape = key.shape + key = key.view(num_tokens, -1, self.head_size) + key_rot = self.apply_rotary_emb.forward_native( + key[..., : self.rotary_dim], + cos, + sin, + ) + key = torch.cat((key_rot, key[..., self.rotary_dim :]), dim=-1) + return query.reshape(query_shape), key.reshape(key_shape) + + def forward_cuda( + self, + positions: torch.Tensor, + query: torch.Tensor, + key: torch.Tensor | None = None, + offsets: torch.Tensor | None = None, + ) -> tuple[torch.Tensor, torch.Tensor | None]: + if positions.ndim == 2 and not self.is_neox_style: + if key is None: + raise ValueError("Bailing M-RoPE requires key states") + cos_sin_cache = self._match_cos_sin_cache_dtype(query) + return triton_bailing_mrope( + query, + key, + positions, + cos_sin_cache, + self.spatial_size, + self.head_size, + self.rotary_dim, + ) + return self.forward_native(positions, query, key, offsets) + + def forward_cpu( + self, + positions: torch.Tensor, + query: torch.Tensor, + key: torch.Tensor | None = None, + offsets: torch.Tensor | None = None, + ) -> tuple[torch.Tensor, torch.Tensor | None]: + return self.forward_native(positions, query, key, offsets) diff --git a/vllm/model_executor/models/bailing_moe_v3.py b/vllm/model_executor/models/bailing_moe_v3.py index e788771f74..710a37268e 100644 --- a/vllm/model_executor/models/bailing_moe_v3.py +++ b/vllm/model_executor/models/bailing_moe_v3.py @@ -171,6 +171,27 @@ def _build_rope_parameters(config: PretrainedConfig) -> dict | None: return rope_parameters or None +def _build_mla_rotary_embedding( + config: PretrainedConfig, + head_size: int, +) -> nn.Module: + rope_parameters = _build_rope_parameters(config) + if rope_parameters is not None and "mrope_section" in rope_parameters: + rope_type = rope_parameters.get("rope_type", "default") + if rope_type != "default": + raise ValueError( + f"Bailing M-RoPE only supports rope_type='default', got {rope_type!r}" + ) + rope_parameters["rope_type"] = "bailing_mrope" + + return get_rope( + head_size=head_size, + max_position=getattr(config, "max_position_embeddings", 8192), + is_neox_style=False, + rope_parameters=rope_parameters, + ) + + def _get_layer_swiglu_limit(limit_list: list | None, layer_idx: int) -> float | None: if limit_list is None or layer_idx >= len(limit_list): return None @@ -524,11 +545,9 @@ class BailingMoeV3MLAAttention(nn.Module): prefix=f"{prefix}.dense", ) - self.rotary_emb = get_rope( + self.rotary_emb = _build_mla_rotary_embedding( + config, head_size=self.qk_rope_head_dim, - max_position=getattr(config, "max_position_embeddings", 8192), - is_neox_style=False, - rope_parameters=_build_rope_parameters(config), ) mla_modules = MLAModules( kv_a_layernorm=self.kv_a_layernorm, diff --git a/vllm/model_executor/models/bailing_moe_v3_mtp.py b/vllm/model_executor/models/bailing_moe_v3_mtp.py index 037239b21c..2b4c3c8d78 100644 --- a/vllm/model_executor/models/bailing_moe_v3_mtp.py +++ b/vllm/model_executor/models/bailing_moe_v3_mtp.py @@ -33,9 +33,14 @@ from vllm.model_executor.models.bailing_moe_v3 import ( _maybe_pad_block_fp8_shared_expert_checkpoint_tensor, _maybe_remap_ling_mxfp4_weight_names, ) -from vllm.model_executor.models.interfaces import SupportsPP +from vllm.model_executor.models.interfaces import ( + MultiModalEmbeddings, + SupportsMultiModalEmbeddings, + SupportsPP, +) from vllm.model_executor.models.utils import ( PPMissingLayer, + _merge_multimodal_embeddings, is_pp_missing_parameter, maybe_prefix, ) @@ -214,8 +219,16 @@ class BailingMoeV3MultiTokenPredictor(nn.Module): return self.logits_processor(head, mtp_layer.shared_head(hidden_states)) -@support_torch_compile -class BailingMoeV3MTPModel(nn.Module, SupportsPP): +@support_torch_compile( + dynamic_arg_dims={ + "input_ids": 0, + "positions": -1, + "hidden_states": 0, + "intermediate_tensors": 0, + "inputs_embeds": 0, + } +) +class BailingMoeV3MTPModel(nn.Module, SupportsPP, SupportsMultiModalEmbeddings): hf_to_vllm_mapper = BailingMoeV3ForCausalLM.hf_to_vllm_mapper packed_modules_mapping = { @@ -242,8 +255,22 @@ class BailingMoeV3MTPModel(nn.Module, SupportsPP): def share_lm_head(self, lm_head: nn.Module) -> None: self.lm_head = lm_head - def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor: - return self.model.embed_input_ids(input_ids) + def embed_input_ids( + self, + input_ids: torch.Tensor, + multimodal_embeddings: MultiModalEmbeddings | None = None, + *, + is_multimodal: torch.Tensor | None = None, + ) -> torch.Tensor: + inputs_embeds = self.model.embed_input_ids(input_ids) + if multimodal_embeddings is None or len(multimodal_embeddings) == 0: + return inputs_embeds + assert is_multimodal is not None + return _merge_multimodal_embeddings( + inputs_embeds=inputs_embeds, + multimodal_embeddings=multimodal_embeddings, + is_multimodal=is_multimodal, + ) def forward( self, diff --git a/vllm/model_executor/models/bailing_moe_v3_vl.py b/vllm/model_executor/models/bailing_moe_v3_vl.py new file mode 100644 index 0000000000..c926c49ea8 --- /dev/null +++ b/vllm/model_executor/models/bailing_moe_v3_vl.py @@ -0,0 +1,527 @@ +# SPDX-License-Identifier: Apache-2.0 +# SPDX-FileCopyrightText: Copyright contributors to the vLLM project +"""Inference-only Bailing MoE V3 vision-language model.""" + +import copy +from collections.abc import Iterable, Mapping, Sequence +from typing import Any + +import numpy as np +import regex as re +import torch +import torch.nn as nn +from transformers import AutoProcessor, BatchFeature + +from vllm.config import VllmConfig +from vllm.config.multimodal import BaseDummyOptions +from vllm.inputs import MultiModalDataDict +from vllm.model_executor.layers.linear import ( + ColumnParallelLinear, + RowParallelLinear, +) +from vllm.model_executor.layers.quantization import QuantizationConfig +from vllm.model_executor.layers.quantization.fp8 import Fp8Config +from vllm.model_executor.models.module_mapping import MultiModelKeys +from vllm.multimodal import MULTIMODAL_REGISTRY +from vllm.multimodal.inputs import ( + MultiModalFeatureSpec, + MultiModalFieldConfig, + MultiModalKwargsItems, +) +from vllm.multimodal.parse import MultiModalDataItems +from vllm.multimodal.processing import ( + BaseDummyInputsBuilder, + BaseMultiModalProcessor, + PromptReplacement, + PromptUpdate, +) +from vllm.multimodal.processing.processor import ( + MultiModalPromptUpdates, + PlaceholderFeaturesInfo, +) +from vllm.sequence import IntermediateTensors + +from .bailing_moe_v3 import BailingMoeV3ForCausalLM +from .interfaces import ( + HasInnerState, + IsHybrid, + MultiModalEmbeddings, + SupportsMRoPE, + SupportsMultiModal, + SupportsPP, + _require_is_multimodal, +) +from .qwen2_5_vl import ( + Qwen2_5_VLImageEmbeddingInputs, + Qwen2_5_VLImageInputs, + Qwen2_5_VLImagePixelInputs, +) +from .qwen2_vl import ( + Qwen2VLProcessingInfo, + _create_qwen2vl_field_factory, +) +from .qwen3_vl import Qwen3_VisionTransformer +from .utils import ( + AutoWeightsLoader, + WeightsMapper, + _merge_multimodal_embeddings, + maybe_prefix, +) + +IMAGE_TOKEN = "<|image_pad|>" +IMAGE_PLACEHOLDER = f"<|vision_start|>{IMAGE_TOKEN}<|vision_end|>" + + +class BailingMoeV3VLProcessingInfo(Qwen2VLProcessingInfo): + """Processing metadata for the remote Bailing multimodal processor.""" + + def get_hf_config(self): + return self.ctx.get_hf_config() + + def get_hf_processor(self, **kwargs: object) -> AutoProcessor: + # The checkpoint supplies its processor through trust_remote_code. + return self.ctx.get_hf_processor(**kwargs) + + def get_supported_mm_limits(self) -> Mapping[str, int | None]: + return {"image": None} + + def get_mm_max_tokens_per_item( + self, + seq_len: int, + mm_counts: Mapping[str, int], + ) -> Mapping[str, int]: + return {"image": self.get_max_image_tokens()} + + +class BailingMoeV3VLDummyInputsBuilder( + BaseDummyInputsBuilder[BailingMoeV3VLProcessingInfo] +): + def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str: + return IMAGE_PLACEHOLDER * mm_counts.get("image", 0) + + def get_dummy_mm_data( + self, + seq_len: int, + mm_counts: Mapping[str, int], + mm_options: Mapping[str, BaseDummyOptions], + ) -> MultiModalDataDict: + target_width, target_height = self.info.get_image_size_with_most_features() + return { + "image": self._get_dummy_images( + width=target_width, + height=target_height, + num_images=mm_counts.get("image", 0), + overrides=mm_options.get("image"), + ) + } + + +class BailingMoeV3VLMultiModalProcessor( + BaseMultiModalProcessor[BailingMoeV3VLProcessingInfo] +): + def _get_hf_processor_text(self, mm_counts: Mapping[str, int]) -> str: + return self.dummy_inputs.get_dummy_text(mm_counts) + + def _apply_prompt_updates( + self, + token_ids: list[int], + mm_prompt_updates: MultiModalPromptUpdates, + ) -> tuple[list[int], Mapping[str, list[PlaceholderFeaturesInfo]]]: + if image_updates := mm_prompt_updates.get("image"): + tokenizer = self.info.get_tokenizer() + # HF adds a newline after each image before tokenization. Re-encode + # the text so it can merge with existing whitespace at the boundary. + text = re.sub( + f"{re.escape(IMAGE_PLACEHOLDER)}|{re.escape(IMAGE_TOKEN)}", + lambda _: IMAGE_PLACEHOLDER + "\n", + tokenizer.decode(token_ids), + count=len(image_updates), + ) + token_ids = tokenizer.encode(text, add_special_tokens=False) + + return super()._apply_prompt_updates(token_ids, mm_prompt_updates) + + def _get_mm_fields_config( + self, + hf_inputs: BatchFeature, + hf_processor_mm_kwargs: Mapping[str, object], + ) -> Mapping[str, MultiModalFieldConfig]: + merge_size = self.info.get_hf_config().vision_config.spatial_merge_size + fields = _create_qwen2vl_field_factory(merge_size)(hf_inputs) + return { + key: fields[key] + for key in ("pixel_values", "image_embeds", "image_grid_thw") + } + + def _get_prompt_updates( + self, + mm_items: MultiModalDataItems, + hf_processor_mm_kwargs: Mapping[str, Any], + out_mm_kwargs: MultiModalKwargsItems, + ) -> Sequence[PromptUpdate]: + tokenizer = self.info.get_tokenizer() + image_token_id = tokenizer.get_vocab()[IMAGE_TOKEN] + merge_size = self.info.get_hf_config().vision_config.spatial_merge_size + merge_unit = merge_size**2 + + def get_image_replacement(item_idx: int) -> list[int]: + out_item = out_mm_kwargs["image"][item_idx] + image_grid_thw = out_item["image_grid_thw"].data + assert isinstance(image_grid_thw, torch.Tensor) + num_image_tokens = int(image_grid_thw.prod()) // merge_unit + return [image_token_id] * num_image_tokens + + return [ + PromptReplacement( + modality="image", + target=[image_token_id], + replacement=get_image_replacement, + ) + ] + + +class BailingMoeV3VisionPatchMerger(nn.Module): + """Normalize patches and merge each 2x2 group without an MLP.""" + + def __init__( + self, + context_dim: int, + spatial_merge_size: int, + norm_eps: float = 1e-6, + ) -> None: + super().__init__() + self.hidden_size = context_dim * spatial_merge_size**2 + self.norm = nn.LayerNorm(context_dim, eps=norm_eps) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.norm(hidden_states) + return hidden_states.reshape(-1, self.hidden_size) + + +class BailingMoeV3VisionTransformer(Qwen3_VisionTransformer): + """Qwen3 ViT with Bailing's parameter-free patch merger.""" + + def __init__( + self, + vision_config: Any, + norm_eps: float = 1e-6, + quant_config: QuantizationConfig | None = None, + prefix: str = "", + ) -> None: + # Honor whole-tower exclusions before the parent creates linear layers. + if ( + isinstance(quant_config, Fp8Config) + and prefix in quant_config.ignored_layers + ): + quant_config = None + + vision_config = copy.deepcopy(vision_config) + vision_config.deepstack_visual_indexes = [] + merged_hidden_size = ( + vision_config.hidden_size * vision_config.spatial_merge_size**2 + ) + vision_config.out_hidden_size = merged_hidden_size + + super().__init__( + vision_config, + norm_eps=norm_eps, + quant_config=quant_config, + prefix=prefix, + ) + self.merger = BailingMoeV3VisionPatchMerger( + context_dim=vision_config.hidden_size, + spatial_merge_size=vision_config.spatial_merge_size, + norm_eps=norm_eps, + ) + self.deepstack_visual_indexes = [] + self.deepstack_merger_list = nn.ModuleList() + self.out_hidden_size = merged_hidden_size + + +class BailingMoeV3VLProjector(nn.Module): + """Tensor-parallel MLP mapping merged vision features to text width.""" + + def __init__( + self, + vision_hidden_size: int, + text_hidden_size: int, + quant_config: QuantizationConfig | None = None, + prefix: str = "", + ) -> None: + super().__init__() + if ( + isinstance(quant_config, Fp8Config) + and prefix in quant_config.ignored_layers + ): + quant_config = None + + self.linear_fc1 = ColumnParallelLinear( + vision_hidden_size, + text_hidden_size, + bias=True, + quant_config=quant_config, + prefix=maybe_prefix(prefix, "linear_fc1"), + ) + self.act_fn = nn.GELU() + self.linear_fc2 = RowParallelLinear( + text_hidden_size, + text_hidden_size, + bias=True, + quant_config=quant_config, + prefix=maybe_prefix(prefix, "linear_fc2"), + ) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states, _ = self.linear_fc1(hidden_states) + hidden_states = self.act_fn(hidden_states) + hidden_states, _ = self.linear_fc2(hidden_states) + return hidden_states + + +@MULTIMODAL_REGISTRY.register_processor( + BailingMoeV3VLMultiModalProcessor, + info=BailingMoeV3VLProcessingInfo, + dummy_inputs=BailingMoeV3VLDummyInputsBuilder, +) +class BailingMoeV3VLForConditionalGeneration( + nn.Module, + HasInnerState, + IsHybrid, + SupportsMultiModal, + SupportsPP, + SupportsMRoPE, +): + """Native vLLM wrapper for ``BailingMoeV3VLConfig`` checkpoints.""" + + packed_modules_mapping = { + **BailingMoeV3ForCausalLM.packed_modules_mapping, + # The vision checkpoint already stores Q/K/V in one tensor. + "qkv": ["qkv"], + } + + hf_to_vllm_mapper = BailingMoeV3ForCausalLM.hf_to_vllm_mapper | WeightsMapper( + orig_to_new_prefix={ + # Match module names as well as parameter names for quantization. + "model.visual": "visual", + "model.linear_proj": "linear_proj", + "linear_proj.0": "linear_proj.linear_fc1", + "linear_proj.2": "linear_proj.linear_fc2", + "lm_head": "language_model.lm_head", + "model.": "language_model.model.", + } + ) + + @classmethod + def get_placeholder_str(cls, modality: str, i: int) -> str | None: + if modality.startswith("image"): + return IMAGE_PLACEHOLDER + raise ValueError("Only image modality is supported") + + @classmethod + def get_mamba_state_shape_from_config( + cls, vllm_config: VllmConfig + ) -> tuple[tuple[int, int], tuple[int, int, int]]: + text_config = vllm_config.model_config.hf_config.text_config + return BailingMoeV3ForCausalLM.get_mamba_state_shape_from_config( + vllm_config.with_hf_config(text_config) + ) + + @classmethod + def get_mamba_state_dtype_from_config( + cls, vllm_config: VllmConfig + ) -> tuple[torch.dtype, torch.dtype]: + text_config = vllm_config.model_config.hf_config.text_config + return BailingMoeV3ForCausalLM.get_mamba_state_dtype_from_config( + vllm_config.with_hf_config(text_config) + ) + + @classmethod + def get_mamba_state_copy_func(cls) -> tuple: + return BailingMoeV3ForCausalLM.get_mamba_state_copy_func() + + def __init__(self, *, vllm_config: VllmConfig, prefix: str = "model") -> None: + super().__init__() + config = vllm_config.model_config.hf_config + vision_config = config.vision_config + text_config = config.text_config + quant_config = vllm_config.quant_config + + self.config = config + self.model_config = vllm_config.model_config + + merged_vision_size = vision_config.hidden_size * ( + vision_config.spatial_merge_size**2 + ) + with self._mark_tower_model(vllm_config, "image"): + self.visual = BailingMoeV3VisionTransformer( + vision_config, + norm_eps=getattr(config, "rms_norm_eps", 1e-6), + quant_config=quant_config, + prefix=maybe_prefix(prefix, "visual"), + ) + self.linear_proj = BailingMoeV3VLProjector( + vision_hidden_size=merged_vision_size, + text_hidden_size=text_config.hidden_size, + quant_config=quant_config, + prefix=maybe_prefix(prefix, "linear_proj"), + ) + + with self._mark_language_model(vllm_config): + self.language_model = BailingMoeV3ForCausalLM( + vllm_config=vllm_config.with_hf_config(text_config), + prefix=maybe_prefix(prefix, "language_model"), + ) + + self.make_empty_intermediate_tensors = ( + self.language_model.make_empty_intermediate_tensors + ) + + def _parse_and_validate_image_input( + self, **kwargs: object + ) -> Qwen2_5_VLImageInputs | None: + pixel_values = kwargs.pop("pixel_values", None) + image_embeds = kwargs.pop("image_embeds", None) + image_grid_thw = kwargs.pop("image_grid_thw", None) + + if pixel_values is None and image_embeds is None: + return None + if pixel_values is not None: + return Qwen2_5_VLImagePixelInputs( + type="pixel_values", + pixel_values=pixel_values, + image_grid_thw=image_grid_thw, + ) + return Qwen2_5_VLImageEmbeddingInputs( + type="image_embeds", + image_embeds=image_embeds, + image_grid_thw=image_grid_thw, + ) + + def _process_image_input( + self, image_input: Qwen2_5_VLImageInputs + ) -> tuple[torch.Tensor, ...]: + image_grid_thw = image_input["image_grid_thw"] + assert image_grid_thw.ndim == 2 + + if image_input["type"] == "image_embeds": + image_embeds = image_input["image_embeds"] + else: + pixel_values = image_input["pixel_values"].type(self.visual.dtype) + vision_features = self.visual( + pixel_values, + grid_thw=image_grid_thw, + ) + image_embeds = self.linear_proj(vision_features) + + image_embeds = image_embeds.to(dtype=self.visual.dtype) + merge_unit = self.visual.spatial_merge_size**2 + sizes = (image_grid_thw.prod(-1) // merge_unit).tolist() + return image_embeds.split(sizes) + + def embed_multimodal(self, **kwargs: object) -> MultiModalEmbeddings: + image_input = self._parse_and_validate_image_input(**kwargs) + if image_input is None: + return [] + return self._process_image_input(image_input) + + def embed_input_ids( + self, + input_ids: torch.Tensor, + multimodal_embeddings: MultiModalEmbeddings | None = None, + *, + is_multimodal: torch.Tensor | None = None, + ) -> torch.Tensor: + inputs_embeds = self._embed_text_input_ids( + input_ids, + self.language_model.embed_input_ids, + is_multimodal=is_multimodal, + ) + if multimodal_embeddings is None or len(multimodal_embeddings) == 0: + return inputs_embeds + + return _merge_multimodal_embeddings( + inputs_embeds=inputs_embeds, + multimodal_embeddings=multimodal_embeddings, + is_multimodal=_require_is_multimodal(is_multimodal), + ) + + def get_mrope_input_positions( + self, + input_tokens: list[int], + mm_features: list[MultiModalFeatureSpec], + ) -> tuple[torch.Tensor, int]: + position_blocks: list[np.ndarray] = [] + consumed = 0 + merge_size = self.config.vision_config.spatial_merge_size + + for mm_feature in sorted(mm_features, key=lambda f: f.mm_position.offset): + if mm_feature.modality != "image": + raise ValueError(f"Unsupported modality: {mm_feature.modality}") + + offset = mm_feature.mm_position.offset + text_len = offset - consumed + start = position_blocks[-1].max() + 1 if position_blocks else 0 + position_blocks.append( + np.broadcast_to(np.arange(text_len), (3, text_len)) + start + ) + + assert mm_feature.data is not None + image_grid_thw = mm_feature.data["image_grid_thw"].data + assert isinstance(image_grid_thw, torch.Tensor) + t, h, w = image_grid_thw.tolist() + assert t == 1, f"Image must have one temporal grid, got {t}" + grid = np.indices((t, h // merge_size, w // merge_size)) + position_blocks.append(grid.reshape(3, -1) + text_len + start) + consumed = offset + t * (h // merge_size) * (w // merge_size) + + if consumed < len(input_tokens): + start = position_blocks[-1].max() + 1 if position_blocks else 0 + text_len = len(input_tokens) - consumed + position_blocks.append( + np.broadcast_to(np.arange(text_len), (3, text_len)) + start + ) + + if not position_blocks: + positions = np.empty((3, 0), dtype=np.int64) + return torch.from_numpy(positions), 0 + + positions = np.concatenate(position_blocks, axis=1).reshape(3, -1) + position_delta = int(positions.max() + 1 - len(input_tokens)) + return torch.from_numpy(positions), position_delta + + def forward( + self, + input_ids: torch.Tensor | None, + positions: torch.Tensor, + intermediate_tensors: IntermediateTensors | None = None, + inputs_embeds: torch.Tensor | None = None, + **kwargs: object, + ) -> torch.Tensor | IntermediateTensors: + if intermediate_tensors is not None: + inputs_embeds = None + return self.language_model( + input_ids=input_ids, + positions=positions, + intermediate_tensors=intermediate_tensors, + inputs_embeds=inputs_embeds, + ) + + def compute_logits(self, hidden_states: torch.Tensor) -> torch.Tensor: + return self.language_model.compute_logits(hidden_states) + + def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]: + loader = AutoWeightsLoader(self) + return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper) + + def get_mm_mapping(self) -> MultiModelKeys: + return MultiModelKeys.from_string_field( + language_model="language_model", + connector="linear_proj", + tower_model="visual.", + ) + + def get_num_mm_encoder_tokens(self, num_image_tokens: int) -> int: + return num_image_tokens * self.visual.spatial_merge_size**2 + + def get_num_mm_connector_tokens(self, num_vision_tokens: int) -> int: + return num_vision_tokens // self.visual.spatial_merge_size**2 diff --git a/vllm/model_executor/models/registry.py b/vllm/model_executor/models/registry.py index aa1e613765..3488d00272 100644 --- a/vllm/model_executor/models/registry.py +++ b/vllm/model_executor/models/registry.py @@ -350,6 +350,10 @@ _MULTIMODAL_MODELS = { "AudioFlamingo3ForConditionalGeneration", ), "BagelForConditionalGeneration": ("bagel", "BagelForConditionalGeneration"), + "BailingMoeV3VLForConditionalGeneration": ( + "bailing_moe_v3_vl", + "BailingMoeV3VLForConditionalGeneration", + ), "BeeForConditionalGeneration": ("bee", "BeeForConditionalGeneration"), "Blip2ForConditionalGeneration": ("blip2", "Blip2ForConditionalGeneration"), "Cohere2VisionForConditionalGeneration": ( diff --git a/vllm/parser/ling3.py b/vllm/parser/ling3.py index b8f184cf29..187407b409 100644 --- a/vllm/parser/ling3.py +++ b/vllm/parser/ling3.py @@ -71,10 +71,7 @@ class Ling3Parser(Glm47MoeParser): if not self.thinking_enabled: return None, model_output - reasoning, content = super().extract_reasoning(model_output, request) - if reasoning and not content and "" not in model_output: - return None, reasoning - return reasoning, content + return super().extract_reasoning(model_output, request) ( diff --git a/vllm/transformers_utils/config.py b/vllm/transformers_utils/config.py index b64dc5e090..93e3ad4de8 100644 --- a/vllm/transformers_utils/config.py +++ b/vllm/transformers_utils/config.py @@ -88,6 +88,7 @@ _CONFIG_REGISTRY: dict[str, type[PretrainedConfig]] = LazyConfigDict( afmoe="AfmoeConfig", axk1="AXK1Config", bagel="BagelConfig", + bailing_moe_v3_vl="BailingMoeV3VLConfig", chatglm="ChatGLMConfig", modernvbert="ColModernVBertConfig", colpali="ColPaliConfig", diff --git a/vllm/transformers_utils/configs/__init__.py b/vllm/transformers_utils/configs/__init__.py index fda48fd20d..971b6ffcb2 100644 --- a/vllm/transformers_utils/configs/__init__.py +++ b/vllm/transformers_utils/configs/__init__.py @@ -18,6 +18,9 @@ _CLASS_TO_MODULE: dict[str, str] = { "AfmoeConfig": "vllm.transformers_utils.configs.afmoe", "AXK1Config": "vllm.transformers_utils.configs.AXK1", "BagelConfig": "vllm.transformers_utils.configs.bagel", + "BailingMoeV3TextConfig": "vllm.transformers_utils.configs.bailing_moe_v3_vl", + "BailingMoeV3VisionConfig": "vllm.transformers_utils.configs.bailing_moe_v3_vl", + "BailingMoeV3VLConfig": "vllm.transformers_utils.configs.bailing_moe_v3_vl", "ChatGLMConfig": "vllm.transformers_utils.configs.chatglm", "ColModernVBertConfig": "vllm.transformers_utils.configs.colmodernvbert", "ColPaliConfig": "vllm.transformers_utils.configs.colpali", @@ -114,6 +117,9 @@ __all__ = [ "AfmoeConfig", "AXK1Config", "BagelConfig", + "BailingMoeV3TextConfig", + "BailingMoeV3VisionConfig", + "BailingMoeV3VLConfig", "ChatGLMConfig", "ColModernVBertConfig", "ColPaliConfig", diff --git a/vllm/transformers_utils/configs/bailing_moe_v3_vl.py b/vllm/transformers_utils/configs/bailing_moe_v3_vl.py new file mode 100644 index 0000000000..484e27991e --- /dev/null +++ b/vllm/transformers_utils/configs/bailing_moe_v3_vl.py @@ -0,0 +1,345 @@ +# SPDX-License-Identifier: Apache-2.0 +# SPDX-FileCopyrightText: Copyright contributors to the vLLM project +# Copyright 2025 Antgroup and The HuggingFace Inc. team. All rights reserved. +# Adapted from +# https://huggingface.co/inclusionAI/Ling-3.0-flash/blob/51fd444268df5267074ef0b289de0e1c4cb9b381/configuration_bailing_moe_v3.py +"""Bailing V3 vision-language model configuration.""" + +from __future__ import annotations + +from typing import Any + +from transformers.configuration_utils import PretrainedConfig + + +def _normalize_mrope_parameters( + config: PretrainedConfig, + mrope_section: list[int], +) -> None: + """Add the top-level Bailing M-RoPE split to a text config.""" + rope_parameters = dict(getattr(config, "rope_parameters", None) or {}) + text_mrope_section = rope_parameters.get("mrope_section") + if text_mrope_section is not None and list(text_mrope_section) != mrope_section: + raise ValueError( + "Bailing V3 VL has conflicting M-RoPE sections: " + f"text={text_mrope_section}, top-level={mrope_section}" + ) + + if "rope_type" not in rope_parameters and "type" not in rope_parameters: + rope_parameters["rope_type"] = "default" + rope_parameters.setdefault("rope_theta", getattr(config, "rope_theta", 10_000)) + rope_parameters["mrope_section"] = list(mrope_section) + config.rope_parameters = rope_parameters + + +def _build_layer_types( + num_hidden_layers: int, + layer_group_size: int, +) -> list[str]: + if layer_group_size <= 0: + raise ValueError( + f"Bailing V3 VL layer_group_size must be positive, got {layer_group_size}" + ) + + grouped_layers = num_hidden_layers // layer_group_size * layer_group_size + return [ + "linear_attention" + if ((layer_idx + 1) % layer_group_size != 0 and layer_idx < grouped_layers) + else "full_attention" + for layer_idx in range(num_hidden_layers) + ] + + +class BailingMoeV3TextConfig(PretrainedConfig): + model_type = "bailing_hybrid" + base_config_key = "text_config" + keys_to_ignore_at_inference = ["past_key_values"] + ignore_keys_at_rope_validation = {"mrope_section"} + + def __init__( + self, + *, + vocab_size: int = 157184, + hidden_size: int = 2048, + intermediate_size: int = 5120, + num_hidden_layers: int = 20, + num_attention_heads: int = 16, + num_key_value_heads: int = 4, + hidden_act: str = "silu", + use_qkv_bias: bool = False, + use_bias: bool = False, + rms_norm_eps: float = 1e-6, + tie_word_embeddings: bool = False, + embedding_dropout: float = 0.0, + attention_dropout: float = 0.0, + output_dropout: float = 0.0, + initializer_range: float = 0.02, + max_position_embeddings: int = 32768, + rope_theta: float = 600000.0, + rope_parameters: dict[str, Any] | None = None, + rope_scaling: dict[str, Any] | None = None, + partial_rotary_factor: float | None = None, + mrope_section: list[int] | tuple[int, ...] | None = None, + use_cache: bool = True, + max_window_layers: int = 20, + pad_token_id: int = 156892, + eos_token_id: int = 156892, + num_experts: int = 256, + num_shared_experts: int = 1, + num_experts_per_tok: int = 8, + n_group: int = 8, + topk_group: int = 4, + moe_intermediate_size: int = 512, + moe_shared_expert_intermediate_size: int = 512, + first_k_dense_replace: int = 1, + head_dim: int | None = 128, + output_router_logits: bool = False, + use_qk_norm: bool = True, + num_nextn_predict_layers: int = 0, + mtp_loss_scaling_factor: float = 0, + moe_router_enable_expert_bias: bool = True, + routed_scaling_factor: float = 1.0, + layer_group_size: int = 5, + layer_types: list[str] | None = None, + layers_block_type: list[str] | None = None, + kv_lora_rank: int = 512, + q_lora_rank: int | None = None, + qk_rope_head_dim: int = 64, + v_head_dim: int = 128, + qk_nope_head_dim: int = 128, + rope_interleave: bool = True, + score_function: str = "sigmoid", + scoring_func: str = "sigmoid", + seq_aux: bool = True, + topk_method: str = "noaux_tc", + router_dtype: str = "fp32", + gated_attention_proj_granularity_type: str | None = None, + no_kda_lora: bool = False, + kda_safe_gate: bool = False, + kda_lower_bound: float | None = None, + short_conv_kernel_size: int = 4, + architectures: list[str] | None = None, + **kwargs: Any, + ) -> None: + kwargs.pop("model_type", None) + if architectures is None: + architectures = ["BailingMoeV3ForCausalLM"] + + if rope_parameters is not None: + self.rope_parameters = dict(rope_parameters) + elif rope_scaling is not None: + self.rope_parameters = dict(rope_scaling) + else: + self.rope_parameters = {} + if ( + "rope_type" not in self.rope_parameters + and "type" not in self.rope_parameters + ): + self.rope_parameters["rope_type"] = "default" + self.rope_parameters.setdefault("rope_theta", rope_theta) + if mrope_section is not None: + _normalize_mrope_parameters(self, list(mrope_section)) + + self.vocab_size = vocab_size + self.hidden_size = hidden_size + self.intermediate_size = intermediate_size + self.num_hidden_layers = num_hidden_layers + self.num_attention_heads = num_attention_heads + self.num_key_value_heads = num_key_value_heads + self.hidden_act = hidden_act + self.use_qkv_bias = use_qkv_bias + self.use_bias = use_bias + self.rms_norm_eps = rms_norm_eps + self.embedding_dropout = embedding_dropout + self.attention_dropout = attention_dropout + self.output_dropout = output_dropout + self.initializer_range = initializer_range + self.max_position_embeddings = max_position_embeddings + self.rope_theta = rope_theta + self.partial_rotary_factor = partial_rotary_factor + self.use_cache = use_cache + self.max_window_layers = max_window_layers + self.num_experts = num_experts + self.num_shared_experts = num_shared_experts + self.num_experts_per_tok = num_experts_per_tok + self.n_group = n_group + self.topk_group = topk_group + self.moe_intermediate_size = moe_intermediate_size + self.moe_shared_expert_intermediate_size = moe_shared_expert_intermediate_size + self.first_k_dense_replace = first_k_dense_replace + self.head_dim = head_dim or hidden_size // num_attention_heads + self.output_router_logits = output_router_logits + self.use_qk_norm = use_qk_norm + self.num_nextn_predict_layers = num_nextn_predict_layers + self.mtp_loss_scaling_factor = mtp_loss_scaling_factor + self.moe_router_enable_expert_bias = moe_router_enable_expert_bias + self.routed_scaling_factor = routed_scaling_factor + self.layer_group_size = layer_group_size + self.kv_lora_rank = kv_lora_rank + self.q_lora_rank = q_lora_rank + self.qk_rope_head_dim = qk_rope_head_dim + self.v_head_dim = v_head_dim + self.qk_nope_head_dim = qk_nope_head_dim + self.qk_head_dim = qk_nope_head_dim + qk_rope_head_dim + self.rope_interleave = rope_interleave + self.score_function = score_function + self.scoring_func = scoring_func + self.seq_aux = seq_aux + self.topk_method = topk_method + self.router_dtype = router_dtype + self.gated_attention_proj_granularity_type = ( + gated_attention_proj_granularity_type + ) + self.no_kda_lora = no_kda_lora + self.kda_safe_gate = kda_safe_gate + self.kda_lower_bound = kda_lower_bound + self.short_conv_kernel_size = short_conv_kernel_size + + expected_layer_types = _build_layer_types( + num_hidden_layers, + layer_group_size, + ) + if layers_block_type is not None: + legacy_type_mapping = { + "attention": "full_attention", + "mamba": "linear_attention", + "conv": "linear_attention", + } + normalized_block_types = [ + legacy_type_mapping.get(layer_type, layer_type) + for layer_type in layers_block_type + ] + if layer_types is not None and layer_types != normalized_block_types: + raise ValueError( + "Bailing V3 VL has conflicting layer_types and layers_block_type" + ) + layer_types = normalized_block_types + if layer_types is not None and layer_types != expected_layer_types: + raise ValueError( + "Bailing V3 VL layer_types must match the layer_group_size schedule" + ) + self.layer_types = expected_layer_types + + super().__init__( + architectures=architectures, + pad_token_id=pad_token_id, + eos_token_id=eos_token_id, + tie_word_embeddings=tie_word_embeddings, + **kwargs, + ) + self.validate_layer_type() + + +class BailingMoeV3VisionConfig(PretrainedConfig): + model_type = "qwen3_moe_vit" + base_config_key = "vision_config" + + def __init__( + self, + *, + depth: int = 27, + hidden_size: int = 1152, + hidden_act: str = "gelu_pytorch_tanh", + intermediate_size: int = 4304, + num_heads: int = 16, + in_channels: int = 3, + patch_size: int = 16, + spatial_merge_size: int = 2, + temporal_patch_size: int = 2, + out_hidden_size: int = 3584, + num_position_embeddings: int = 2304, + deepstack_visual_indexes: list[int] | None = None, + initializer_range: float = 0.02, + **kwargs: Any, + ) -> None: + kwargs.pop("model_type", None) + super().__init__(**kwargs) + self.depth = depth + self.hidden_size = hidden_size + self.hidden_act = hidden_act + self.intermediate_size = intermediate_size + self.num_heads = num_heads + self.in_channels = in_channels + self.patch_size = patch_size + self.spatial_merge_size = spatial_merge_size + self.temporal_patch_size = temporal_patch_size + self.out_hidden_size = out_hidden_size + self.num_position_embeddings = num_position_embeddings + self.deepstack_visual_indexes = ( + [8, 16, 24] + if deepstack_visual_indexes is None + else deepstack_visual_indexes + ) + self.initializer_range = initializer_range + + +class BailingMoeV3VLConfig(PretrainedConfig): + model_type = "bailing_moe_v3_vl" + keys_to_ignore_at_inference = ["past_key_values"] + sub_configs = { + "text_config": BailingMoeV3TextConfig, + "vision_config": BailingMoeV3VisionConfig, + } + + def __init__( + self, + *, + text_config: BailingMoeV3TextConfig | dict[str, Any] | None = None, + vision_config: BailingMoeV3VisionConfig | dict[str, Any] | None = None, + image_token_id: int = 151655, + video_token_id: int = 151656, + vision_start_token_id: int = 151652, + vision_end_token_id: int = 151653, + tie_word_embeddings: bool = False, + mrope_section: list[int] | tuple[int, ...] | None = None, + architectures: list[str] | None = None, + **kwargs: Any, + ) -> None: + kwargs.pop("model_type", None) + if mrope_section is None: + mrope_section = [12, 10, 10] + self.mrope_section = list(mrope_section) + + if text_config is None: + text_config = BailingMoeV3TextConfig() + elif isinstance(text_config, dict): + text_config = dict(text_config) + nested_mrope_section = text_config.pop("mrope_section", None) + if ( + nested_mrope_section is not None + and list(nested_mrope_section) != self.mrope_section + ): + raise ValueError( + "Bailing V3 VL has conflicting M-RoPE sections: " + f"text={nested_mrope_section}, " + f"top-level={self.mrope_section}" + ) + text_config = BailingMoeV3TextConfig(**text_config) + self.text_config = text_config + _normalize_mrope_parameters(self.text_config, self.mrope_section) + + if vision_config is None: + vision_config = BailingMoeV3VisionConfig() + elif isinstance(vision_config, dict): + vision_config = BailingMoeV3VisionConfig(**vision_config) + self.vision_config = vision_config + + self.image_token_id = image_token_id + self.video_token_id = video_token_id + self.vision_start_token_id = vision_start_token_id + self.vision_end_token_id = vision_end_token_id + if architectures is None: + architectures = ["BailingMoeV3VLForConditionalGeneration"] + + super().__init__( + architectures=architectures, + tie_word_embeddings=tie_word_embeddings, + **kwargs, + ) + + +__all__ = [ + "BailingMoeV3TextConfig", + "BailingMoeV3VisionConfig", + "BailingMoeV3VLConfig", +] diff --git a/vllm/v1/spec_decode/llm_base_proposer.py b/vllm/v1/spec_decode/llm_base_proposer.py index 1b30118069..4b078c2aea 100644 --- a/vllm/v1/spec_decode/llm_base_proposer.py +++ b/vllm/v1/spec_decode/llm_base_proposer.py @@ -1356,6 +1356,7 @@ class SpecDecodeBaseProposer: # handle multimodality assert hasattr(target_model, "config") if self.get_model_name(target_model) in [ + "BailingMoeV3VLForConditionalGeneration", "Cohere2VisionForConditionalGeneration", "Exaone4_5_ForConditionalGeneration", "GlmOcrForConditionalGeneration",