diff --git a/benchmarks/kernels/benchmark_moe.py b/benchmarks/kernels/benchmark_moe.py index 1531cc9692..9ea22dd130 100644 --- a/benchmarks/kernels/benchmark_moe.py +++ b/benchmarks/kernels/benchmark_moe.py @@ -777,6 +777,7 @@ def get_model_params(config): intermediate_size = config.moe_intermediate_size hidden_size = config.hidden_size elif architecture in ( + "BailingMoeV3ForCausalLM", "Qwen2MoeForCausalLM", "Qwen3MoeForCausalLM", "Qwen3NextForCausalLM", diff --git a/docs/models/supported_models.md b/docs/models/supported_models.md index de937ec520..1fcba8f021 100644 --- a/docs/models/supported_models.md +++ b/docs/models/supported_models.md @@ -348,6 +348,7 @@ th { | `BailingMoeForCausalLM` | Ling | `inclusionAI/Ling-lite-1.5`, `inclusionAI/Ling-plus`, etc. | ✅︎ | ✅︎ | | `BailingMoeV2ForCausalLM` | Ling | `inclusionAI/Ling-mini-2.0`, etc. | ✅︎ | ✅︎ | | `BailingMoeV2_5ForCausalLM` | Ling | `inclusionAI/Ling-2.5-1T`, `inclusionAI/Ring-2.5-1T` | | ✅︎ | +| `BailingMoeV3ForCausalLM` | Ling | `inclusionAI/Ling-3.0-flash` | | ✅︎ | | `BloomForCausalLM` | BLOOM, BLOOMZ, BLOOMChat | `bigscience/bloom`, `bigscience/bloomz`, etc. | | ✅︎ | | `ChatGLMModel`, `ChatGLMForConditionalGeneration` | ChatGLM | `zai-org/chatglm2-6b`, `zai-org/chatglm3-6b`, `thu-coai/ShieldLM-6B-chatglm3`, etc. | ✅︎ | ✅︎ | | `CohereForCausalLM`, `Cohere2ForCausalLM` | Command-R, Command-A | `CohereLabs/c4ai-command-r-v01`, `CohereLabs/c4ai-command-r7b-12-2024`, `CohereLabs/c4ai-command-a-03-2025`, `CohereLabs/command-a-reasoning-08-2025`, etc. | ✅︎ | ✅︎ | diff --git a/tests/models/registry.py b/tests/models/registry.py index dd36833258..a37d02299e 100644 --- a/tests/models/registry.py +++ b/tests/models/registry.py @@ -213,6 +213,9 @@ _TEXT_GENERATION_EXAMPLE_MODELS = { "BailingMoeV2_5ForCausalLM": _HfExamplesInfo( "inclusionAI/Ring-2.5-1T", trust_remote_code=True ), + "BailingMoeV3ForCausalLM": _HfExamplesInfo( + "inclusionAI/Ling-3.0-flash", trust_remote_code=True + ), "BloomForCausalLM": _HfExamplesInfo( "bigscience/bloom-560m", {"1b": "bigscience/bloomz-1b1"} ), @@ -1614,6 +1617,11 @@ _SPECULATIVE_DECODING_EXAMPLE_MODELS = { trust_remote_code=True, is_available_online=False, ), + "BailingMoeV3MTPModel": _HfExamplesInfo( + "inclusionAI/Ling-3.0-flash", + speculative_model="inclusionAI/Ling-3.0-flash", + trust_remote_code=True, + ), "DeepSeekMTPModel": _HfExamplesInfo( "luccafong/deepseek_mtp_main_random", speculative_model="luccafong/deepseek_mtp_draft_random", diff --git a/tests/parser/engine/test_ling3.py b/tests/parser/engine/test_ling3.py new file mode 100644 index 0000000000..7cde4dc45d --- /dev/null +++ b/tests/parser/engine/test_ling3.py @@ -0,0 +1,105 @@ +# SPDX-License-Identifier: Apache-2.0 +# SPDX-FileCopyrightText: Copyright contributors to the vLLM project + +import json + +from tests.parser.engine.replay_harness import MockTokenizer, _test_request +from vllm.reasoning import ReasoningParserManager +from vllm.tool_parsers import ToolParserManager + + +def _tokenizer() -> MockTokenizer: + vocab = { + "": 1, + "": 2, + "": 3, + "": 4, + "": 5, + "": 6, + "": 7, + "": 8, + } + return MockTokenizer(vocab=vocab, tokens=[]) + + +def test_ling3_registered(): + assert ReasoningParserManager.get_reasoning_parser("ling3") is not None + assert ToolParserManager.get_tool_parser("ling3") is not None + + +def test_ling3_defaults_thinking_on(): + parser_cls = ReasoningParserManager.get_reasoning_parser("ling3") + parser = parser_cls(_tokenizer()) + + assert parser.reasoning_start_str == "" + assert parser.reasoning_end_str == "" + + reasoning, content = parser.extract_reasoning( + "reasonanswer", _test_request() + ) + + assert reasoning == "reason" + assert content == "answer" + + +def test_ling3_disable_thinking_keeps_reasoning_as_content(): + parser_cls = ReasoningParserManager.get_reasoning_parser("ling3") + parser = parser_cls( + _tokenizer(), + chat_template_kwargs={"enable_thinking": False}, + ) + + reasoning, content = parser.extract_reasoning( + "reasonanswer", _test_request() + ) + + assert reasoning is None + assert content == "reasonanswer" + + +def test_ling3_enable_thinking_keeps_open_reasoning_as_content(): + 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() + ) + + assert reasoning is None + assert content == "only reasoning" + + +def test_ling3_tool_call_without_newline(): + parser_cls = ToolParserManager.get_tool_parser("ling3") + parser = parser_cls(_tokenizer()) + + result = parser.extract_tool_calls( + "get_weathercity" + "Beijing", + request=_test_request(), + ) + + assert result.tools_called + assert result.content is None + assert result.tool_calls[0].function.name == "get_weather" + assert json.loads(result.tool_calls[0].function.arguments) == { + "city": "Beijing", + } + + +def test_ling3_tool_call_without_args(): + parser_cls = ToolParserManager.get_tool_parser("ling3") + parser = parser_cls(_tokenizer()) + + result = parser.extract_tool_calls( + "ping", + request=_test_request(), + ) + + assert result.tools_called + assert result.content is None + assert result.tool_calls[0].function.name == "ping" + assert json.loads(result.tool_calls[0].function.arguments) == {} diff --git a/vllm/config/speculative.py b/vllm/config/speculative.py index e619097968..3a084acfc0 100644 --- a/vllm/config/speculative.py +++ b/vllm/config/speculative.py @@ -48,6 +48,7 @@ MTPModelTypes = Literal[ "qwen3_next_mtp", "qwen3_5_mtp", "longcat_flash_mtp", + "bailing_hybrid_v3_mtp", "minimax_m3_mtp", "bailing_hybrid_mtp", "mtp", @@ -489,7 +490,9 @@ class SpeculativeConfig: ) architectures = getattr(hf_config, "architectures", []) or [] - if ( + if initial_architecture == "BailingMoeV3ForCausalLM": + hf_config.model_type = "bailing_hybrid_v3_mtp" + elif ( hf_config.model_type == "bailing_hybrid" or "BailingMoeV2_5ForCausalLM" in architectures ): @@ -502,6 +505,14 @@ class SpeculativeConfig: "architectures": ["BailingMoeV25MTPModel"], } ) + if hf_config.model_type == "bailing_hybrid_v3_mtp": + n_predict = getattr(hf_config, "num_nextn_predict_layers", None) + hf_config.update( + { + "n_predict": n_predict, + "architectures": ["BailingMoeV3MTPModel"], + } + ) if hf_config.model_type == "exaone_moe": hf_config.model_type = "exaone_moe_mtp" diff --git a/vllm/model_executor/layers/fused_moe/configs/E=512,N=192,device_name=NVIDIA_H20-3e.json b/vllm/model_executor/layers/fused_moe/configs/E=512,N=192,device_name=NVIDIA_H20-3e.json new file mode 100644 index 0000000000..697944a71a --- /dev/null +++ b/vllm/model_executor/layers/fused_moe/configs/E=512,N=192,device_name=NVIDIA_H20-3e.json @@ -0,0 +1,147 @@ +{ + "triton_version": "3.6.0", + "1": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 32, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 4 + }, + "2": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 64, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 4 + }, + "4": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 64, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 32, + "num_warps": 4, + "num_stages": 4 + }, + "8": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 64, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 64, + "num_warps": 4, + "num_stages": 4 + }, + "16": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 3 + }, + "24": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 64, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 4 + }, + "32": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 64, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 3 + }, + "48": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 16, + "num_warps": 4, + "num_stages": 3 + }, + "64": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 16, + "num_warps": 4, + "num_stages": 3 + }, + "96": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 16, + "num_warps": 4, + "num_stages": 3 + }, + "128": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 16, + "num_warps": 4, + "num_stages": 3 + }, + "256": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 3 + }, + "512": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 16, + "num_warps": 4, + "num_stages": 3 + }, + "1024": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 16, + "num_warps": 4, + "num_stages": 3 + }, + "1536": { + "BLOCK_SIZE_M": 32, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 64, + "num_warps": 4, + "num_stages": 3 + }, + "2048": { + "BLOCK_SIZE_M": 64, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 2 + }, + "3072": { + "BLOCK_SIZE_M": 64, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 2 + }, + "4096": { + "BLOCK_SIZE_M": 64, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 2 + } +} diff --git a/vllm/model_executor/models/bailing_moe_v3.py b/vllm/model_executor/models/bailing_moe_v3.py new file mode 100644 index 0000000000..1bd3d2e14d --- /dev/null +++ b/vllm/model_executor/models/bailing_moe_v3.py @@ -0,0 +1,1289 @@ +# SPDX-License-Identifier: Apache-2.0 +# SPDX-FileCopyrightText: Copyright contributors to the vLLM project +"""vLLM implementation for BailingMoeV3ForCausalLM. + +The HuggingFace reference model mixes MLA full-attention layers with Kimi +Delta Attention linear layers and Bailing MoE blocks. This file keeps the V3 +module/weight names aligned with the reference implementation while reusing +vLLM's parallel linear layers, MLA kernel, KDA kernel and fused MoE loader. +""" + +import copy +from collections.abc import Iterable + +import torch +import torch.nn as nn +import torch.nn.functional as F +from einops import rearrange +from transformers.configuration_utils import PretrainedConfig + +from vllm.compilation.decorators import support_torch_compile +from vllm.config import CacheConfig, ModelConfig, VllmConfig, get_current_vllm_config +from vllm.distributed import ( + get_pp_group, + get_tensor_model_parallel_rank, + get_tensor_model_parallel_world_size, +) +from vllm.forward_context import ForwardContext, get_forward_context +from vllm.model_executor.custom_op import PluggableLayer +from vllm.model_executor.layers.activation import SiluAndMul, SwigluStepAndMul +from vllm.model_executor.layers.fused_moe import ( + FusedMoEFactory, + fused_moe_make_expert_params_mapping, +) +from vllm.model_executor.layers.layernorm import RMSNorm +from vllm.model_executor.layers.linear import ( + ColumnParallelLinear, + MergedColumnParallelLinear, + ReplicatedLinear, + RowParallelLinear, +) +from vllm.model_executor.layers.logits_processor import LogitsProcessor +from vllm.model_executor.layers.mamba.abstract import MambaBase +from vllm.model_executor.layers.mamba.mamba_utils import ( + MambaStateCopyFuncCalculator, + MambaStateDtypeCalculator, + MambaStateShapeCalculator, + is_conv_state_dim_first, +) +from vllm.model_executor.layers.mamba.ops.causal_conv1d import ( + causal_conv1d_fn, + causal_conv1d_update, +) +from vllm.model_executor.layers.mla import ( + MLAModules, + MultiHeadLatentAttentionWrapper, +) +from vllm.model_executor.layers.quantization.base_config import QuantizationConfig +from vllm.model_executor.layers.rotary_embedding import get_rope +from vllm.model_executor.layers.vocab_parallel_embedding import ( + ParallelLMHead, + VocabParallelEmbedding, +) +from vllm.model_executor.model_loader.weight_utils import ( + default_weight_loader, + maybe_remap_kv_scale_name, + sharded_weight_loader, +) +from vllm.model_executor.utils import set_weight_attrs +from vllm.sequence import IntermediateTensors +from vllm.third_party.flash_linear_attention.ops.kda import ( + FusedRMSNormGated, + chunk_kda, + fused_kda_gate, + fused_recurrent_kda, + fused_recurrent_kda_fwd, +) +from vllm.utils.torch_utils import direct_register_custom_op +from vllm.v1.attention.backend import AttentionMetadata +from vllm.v1.attention.backends.gdn_attn import GDNAttentionMetadata +from vllm.v1.attention.backends.registry import MambaAttentionBackendEnum + +from .interfaces import HasInnerState, IsHybrid, SupportsPP +from .utils import PPMissingLayer, is_pp_missing_parameter, make_layers, maybe_prefix + + +def bailing_v3_kda_attention( + q_proj_states: torch.Tensor, + k_proj_states: torch.Tensor, + v_proj_states: torch.Tensor, + g1: torch.Tensor, + beta: torch.Tensor, + core_attn_out: torch.Tensor, + layer_name: str, +) -> None: + """Run Bailing V3's KDA state update outside the compiled graph.""" + forward_context: ForwardContext = get_forward_context() + layer = forward_context.no_compile_layers[layer_name] + layer._forward( + q_proj_states=q_proj_states, + k_proj_states=k_proj_states, + v_proj_states=v_proj_states, + g1=g1, + beta=beta, + core_attn_out=core_attn_out, + ) + + +def bailing_v3_kda_attention_fake( + q_proj_states: torch.Tensor, + k_proj_states: torch.Tensor, + v_proj_states: torch.Tensor, + g1: torch.Tensor, + beta: torch.Tensor, + core_attn_out: torch.Tensor, + layer_name: str, +) -> None: + return + + +direct_register_custom_op( + op_name="bailing_v3_kda_attention", + op_func=bailing_v3_kda_attention, + mutates_args=["core_attn_out"], + fake_impl=bailing_v3_kda_attention_fake, +) + + +def _is_kda_layer( + layer_idx: int, layer_group_size: int, num_hidden_layers: int +) -> bool: + return not ( + (layer_idx + 1) % layer_group_size == 0 + or layer_idx >= num_hidden_layers // layer_group_size * layer_group_size + ) + + +def _get_kda_state_shape_for_config( + vllm_config: VllmConfig, +) -> tuple[tuple[int, ...], tuple[int, ...], tuple[int, ...], tuple[int, ...]]: + config = vllm_config.model_config.hf_config + num_spec = ( + vllm_config.speculative_config.num_speculative_tokens + if vllm_config.speculative_config + else 0 + ) + shapes = MambaStateShapeCalculator.kda_state_shape( + vllm_config.parallel_config.tensor_parallel_size, + config.num_attention_heads, + config.head_dim, + conv_kernel_size=config.short_conv_kernel_size, + num_spec=num_spec, + ) + + return shapes + + +def _build_rope_parameters(config: PretrainedConfig) -> dict | None: + rope_parameters = copy.deepcopy(getattr(config, "rope_parameters", None)) or {} + if "rope_theta" not in rope_parameters and hasattr(config, "rope_theta"): + rope_parameters["rope_theta"] = config.rope_theta + # BailingMoeV3 stores the MLA rotary width explicitly in qk_rope_head_dim. + # Some checkpoints also carry partial_rotary_factor=0.5, but applying it + # here would shrink the rotary cache to half of the qk_rope_head_dim. + rope_parameters.pop("partial_rotary_factor", None) + + rope_scaling = getattr(config, "rope_scaling", None) + if isinstance(rope_scaling, dict): + rope_scaling = copy.deepcopy(rope_scaling) + if "type" in rope_scaling and "rope_type" not in rope_scaling: + rope_scaling["rope_type"] = rope_scaling.pop("type") + rope_scaling.pop("partial_rotary_factor", None) + rope_parameters.update(rope_scaling) + + return rope_parameters or None + + +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 + limit = limit_list[layer_idx] + if limit in (None, 0): + return None + return float(limit) + + +def _load_a_log(param: torch.nn.Parameter, loaded_weight: torch.Tensor) -> None: + tp_rank = get_tensor_model_parallel_rank() + tp_size = get_tensor_model_parallel_world_size() + if loaded_weight.ndim == 1: + shard_size = loaded_weight.shape[0] // tp_size + shard = loaded_weight.narrow(0, tp_rank * shard_size, shard_size) + param.data.copy_(shard.view_as(param.data)) + else: + sharded_weight_loader(2)(param, loaded_weight) + + +class _IdentityProjection(nn.Module): + def forward(self, x: torch.Tensor) -> tuple[torch.Tensor, None]: + return x, None + + +class BailingMoeV3MLP(nn.Module): + def __init__( + self, + intermediate_size: int, + config: PretrainedConfig, + quant_config: QuantizationConfig | None = None, + reduce_results: bool | None = True, + prefix: str = "", + swiglu_limit: float | None = None, + ) -> None: + super().__init__() + self.gate_up_proj = MergedColumnParallelLinear( + config.hidden_size, + [intermediate_size] * 2, + bias=config.use_bias, + quant_config=quant_config, + prefix=f"{prefix}.gate_up_proj", + ) + self.down_proj = RowParallelLinear( + intermediate_size, + config.hidden_size, + bias=config.use_bias, + quant_config=quant_config, + reduce_results=reduce_results, + prefix=f"{prefix}.down_proj", + ) + self.act_fn = ( + SwigluStepAndMul(limit=swiglu_limit) + if swiglu_limit not in (None, 0) + else SiluAndMul() + ) + + def forward(self, x): + x, _ = self.gate_up_proj(x) + x = self.act_fn(x) + x, _ = self.down_proj(x) + return x + + +class BailingMoeV3MLAAttention(nn.Module): + def __init__( + self, + config: PretrainedConfig, + quant_config: QuantizationConfig | None = None, + layer_id: int = 0, + prefix: str = "attention", + cache_config: CacheConfig | None = None, + ) -> None: + super().__init__() + self.hidden_size = config.hidden_size + self.num_heads = config.num_attention_heads + self.layer_id = layer_id + + self.qk_nope_head_dim = getattr(config, "qk_nope_head_dim", 128) + self.qk_rope_head_dim = getattr(config, "qk_rope_head_dim", 64) + self.qk_head_dim = self.qk_nope_head_dim + self.qk_rope_head_dim + self.v_head_dim = getattr(config, "v_head_dim", 128) + self.q_lora_rank = getattr(config, "q_lora_rank", None) + self.kv_lora_rank = getattr(config, "kv_lora_rank", 512) + + tp_size = get_tensor_model_parallel_world_size() + assert self.num_heads % tp_size == 0 + self.num_local_heads = self.num_heads // tp_size + self.scaling = self.qk_head_dim**-0.5 + + if self.q_lora_rank is None: + self.q_proj = ColumnParallelLinear( + self.hidden_size, + self.num_heads * self.qk_head_dim, + bias=False, + quant_config=quant_config, + prefix=f"{prefix}.q_proj", + ) + self.fused_qkv_a_proj = None + self.q_a_layernorm = None + self.q_b_proj = None + self.kv_a_proj_with_mqa = ReplicatedLinear( + self.hidden_size, + self.kv_lora_rank + self.qk_rope_head_dim, + bias=False, + quant_config=quant_config, + prefix=f"{prefix}.kv_a_proj_with_mqa", + ) + else: + self.fused_qkv_a_proj = MergedColumnParallelLinear( + self.hidden_size, + [self.q_lora_rank, self.kv_lora_rank + self.qk_rope_head_dim], + bias=False, + quant_config=quant_config, + prefix=f"{prefix}.fused_qkv_a_proj", + disable_tp=True, + ) + self.q_a_layernorm = RMSNorm(self.q_lora_rank, eps=config.rms_norm_eps) + self.q_b_proj = ColumnParallelLinear( + self.q_lora_rank, + self.num_heads * self.qk_head_dim, + bias=False, + quant_config=quant_config, + prefix=f"{prefix}.q_b_proj", + ) + self.q_proj = None + self.kv_a_proj_with_mqa = None + + self.kv_a_layernorm = RMSNorm(self.kv_lora_rank, eps=config.rms_norm_eps) + self.kv_b_proj = ColumnParallelLinear( + self.kv_lora_rank, + self.num_heads * (self.qk_nope_head_dim + self.v_head_dim), + bias=False, + quant_config=quant_config, + prefix=f"{prefix}.kv_b_proj", + ) + self.gated_attention_proj_granularity_type = getattr( + config, "gated_attention_proj_granularity_type", None + ) + if self.gated_attention_proj_granularity_type == "head_wise": + g_out = self.num_heads + elif self.gated_attention_proj_granularity_type == "element_wise": + g_out = self.num_heads * self.v_head_dim + else: + g_out = 0 + self.g_proj = ( + ColumnParallelLinear( + self.hidden_size, + g_out, + bias=False, + quant_config=quant_config, + prefix=f"{prefix}.g_proj", + ) + if g_out > 0 + else None + ) + self.dense = RowParallelLinear( + self.num_heads * self.v_head_dim, + self.hidden_size, + bias=False, + quant_config=quant_config, + prefix=f"{prefix}.dense", + ) + + self.rotary_emb = get_rope( + 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, + kv_b_proj=self.kv_b_proj, + rotary_emb=self.rotary_emb, + o_proj=_IdentityProjection(), + fused_qkv_a_proj=self.fused_qkv_a_proj, + kv_a_proj_with_mqa=self.kv_a_proj_with_mqa, + q_a_layernorm=self.q_a_layernorm, + q_b_proj=self.q_b_proj, + q_proj=self.q_proj, + indexer=None, + is_sparse=False, + topk_indices_buffer=None, + ) + self.mla_attn = MultiHeadLatentAttentionWrapper( + self.hidden_size, + self.num_local_heads, + self.scaling, + self.qk_nope_head_dim, + self.qk_rope_head_dim, + self.v_head_dim, + self.q_lora_rank, + self.kv_lora_rank, + mla_modules, + cache_config, + quant_config, + prefix, + ) + + def forward(self, hidden_states: torch.Tensor, positions: torch.Tensor): + attn_out = self.mla_attn(positions, hidden_states) + if self.g_proj is not None: + gate = torch.sigmoid(self.g_proj(hidden_states)[0].float()).to( + hidden_states.dtype + ) + if self.gated_attention_proj_granularity_type == "head_wise": + attn_out = attn_out.view(-1, self.num_local_heads, self.v_head_dim) + attn_out = attn_out * gate.unsqueeze(-1) + attn_out = attn_out.reshape(hidden_states.shape[0], -1) + else: + attn_out = attn_out * gate + + return self.dense(attn_out)[0] + + +# --8<-- [start:bailing_moe_v3_kimi_delta_attention] +@PluggableLayer.register("bailing_moe_v3_kimi_delta_attention") +class BailingMoeV3KimiDeltaAttention(PluggableLayer, MambaBase): + # --8<-- [end:bailing_moe_v3_kimi_delta_attention] + + @property + def mamba_type(self) -> MambaAttentionBackendEnum: + return MambaAttentionBackendEnum.GDN_ATTN + + def get_state_dtype( + self, + ) -> tuple[torch.dtype, torch.dtype, torch.dtype, torch.dtype]: + if self.model_config is None or self.cache_config is None: + raise ValueError("model_config and cache_config must be set") + return MambaStateDtypeCalculator.kda_state_dtype( + self.model_config.dtype, self.cache_config.mamba_cache_dtype + ) + + def get_state_shape( + self, + ) -> tuple[tuple[int, ...], tuple[int, ...], tuple[int, ...], tuple[int, ...]]: + return MambaStateShapeCalculator.kda_state_shape( + self.tp_size, + self.num_heads, + self.head_dim, + conv_kernel_size=self.conv_size, + num_spec=self.num_speculative_tokens, + ) + + def __init__( + self, + config: PretrainedConfig, + quant_config: QuantizationConfig | None = None, + layer_id: int = 0, + prefix: str = "attention", + model_config: ModelConfig | None = None, + cache_config: CacheConfig | None = None, + num_speculative_tokens: int = 0, + ) -> None: + super().__init__() + self.tp_size = get_tensor_model_parallel_world_size() + self.hidden_size = config.hidden_size + self.head_dim = config.head_dim + self.num_heads = config.num_attention_heads + self.local_num_heads = self.num_heads // self.tp_size + self.conv_size = config.short_conv_kernel_size + self.layer_id = layer_id + self.prefix = prefix + self.model_config = model_config + self.cache_config = cache_config + self.num_speculative_tokens = num_speculative_tokens + self.safe_gate = getattr(config, "kda_safe_gate", True) + self.lower_bound = getattr(config, "kda_lower_bound", -5.0) + if not getattr(config, "no_kda_lora", False): + raise ValueError("BailingMoeV3 KDA currently expects no_kda_lora=True") + + projection_size = self.head_dim * self.num_heads + self.projection_size_per_partition = projection_size // self.tp_size + self.qkvb_proj = MergedColumnParallelLinear( + self.hidden_size, + [projection_size, projection_size, projection_size, self.num_heads], + bias=False, + quant_config=quant_config, + prefix=f"{prefix}.qkvb_proj", + ) + self.f_proj = ColumnParallelLinear( + self.hidden_size, + projection_size, + bias=False, + quant_config=quant_config, + prefix=f"{prefix}.f_proj", + ) + self.dt_bias = nn.Parameter( + torch.empty(projection_size // self.tp_size, dtype=torch.float32) + ) + set_weight_attrs(self.dt_bias, {"weight_loader": sharded_weight_loader(0)}) + + self.q_conv1d = ColumnParallelLinear( + input_size=self.conv_size, + output_size=projection_size, + bias=False, + params_dtype=torch.float32, + prefix=f"{prefix}.q_conv1d", + ) + self.k_conv1d = ColumnParallelLinear( + input_size=self.conv_size, + output_size=projection_size, + bias=False, + params_dtype=torch.float32, + prefix=f"{prefix}.k_conv1d", + ) + self.v_conv1d = ColumnParallelLinear( + input_size=self.conv_size, + output_size=projection_size, + bias=False, + params_dtype=torch.float32, + prefix=f"{prefix}.v_conv1d", + ) + self.q_conv1d.weight.data = self.q_conv1d.weight.data.unsqueeze(1) + self.k_conv1d.weight.data = self.k_conv1d.weight.data.unsqueeze(1) + self.v_conv1d.weight.data = self.v_conv1d.weight.data.unsqueeze(1) + + self.A_log = nn.Parameter( + torch.empty(1, 1, self.local_num_heads, 1, dtype=torch.float32) + ) + set_weight_attrs(self.A_log, {"weight_loader": _load_a_log}) + + self.g_proj = ColumnParallelLinear( + self.hidden_size, + projection_size, + bias=False, + quant_config=quant_config, + prefix=f"{prefix}.g_proj", + ) + self.o_norm = FusedRMSNormGated( + self.head_dim, eps=config.rms_norm_eps, activation="sigmoid" + ) + self.o_proj = RowParallelLinear( + projection_size, + self.hidden_size, + bias=False, + quant_config=quant_config, + prefix=f"{prefix}.o_proj", + ) + + compilation_config = get_current_vllm_config().compilation_config + if prefix in compilation_config.static_forward_context: + raise ValueError(f"Duplicate layer name: {prefix}") + compilation_config.static_forward_context[prefix] = self + splitting_op = "vllm::bailing_v3_kda_attention" + if ( + compilation_config.splitting_ops is not None + and splitting_op not in compilation_config.splitting_ops + ): + compilation_config.splitting_ops.append(splitting_op) + + def forward( + self, + hidden_states: torch.Tensor, + positions: torch.Tensor, + output: torch.Tensor, + ) -> None: + del positions + num_tokens = hidden_states.size(0) + qkvb = self.qkvb_proj(hidden_states)[0] + q, k, v, beta_logits = qkvb.split( + [ + self.projection_size_per_partition, + self.projection_size_per_partition, + self.projection_size_per_partition, + self.local_num_heads, + ], + dim=-1, + ) + beta = beta_logits.float().sigmoid().unsqueeze(0) + g1 = self.f_proj(hidden_states)[0] + g1 = fused_kda_gate( + g1, + self.A_log, + self.head_dim, + g_bias=self.dt_bias, + lower_bound=self.lower_bound if self.safe_gate else None, + ) + g1 = g1.unsqueeze(0) + g2 = rearrange( + self.g_proj(hidden_states)[0], "... (h d) -> ... h d", d=self.head_dim + ) + + core_attn_out = torch.zeros( + (1, num_tokens, self.local_num_heads, self.head_dim), + dtype=hidden_states.dtype, + device=hidden_states.device, + ) + torch.ops.vllm.bailing_v3_kda_attention( + q, k, v, g1, beta, core_attn_out, self.prefix + ) + core_attn_out = self.o_norm(core_attn_out, g2) + core_attn_out = rearrange(core_attn_out, "1 n h d -> n (h d)") + output[:] = self.o_proj(core_attn_out)[0] + + def _forward( + self, + q_proj_states: torch.Tensor, + k_proj_states: torch.Tensor, + v_proj_states: torch.Tensor, + g1: torch.Tensor, + beta: torch.Tensor, + core_attn_out: torch.Tensor, + ) -> None: + forward_context = get_forward_context() + attn_metadata: AttentionMetadata = forward_context.attn_metadata + if attn_metadata is None: + return + + assert isinstance(attn_metadata, dict) + attn_metadata = attn_metadata[self.prefix] + assert isinstance(attn_metadata, GDNAttentionMetadata) + has_initial_state = attn_metadata.has_initial_state + spec_query_start_loc = attn_metadata.spec_query_start_loc + query_start_loc = attn_metadata.non_spec_query_start_loc + spec_state_indices = attn_metadata.spec_state_indices_tensor + state_indices = attn_metadata.non_spec_state_indices_tensor + spec_sequence_masks = attn_metadata.spec_sequence_masks + spec_token_indx = attn_metadata.spec_token_indx + non_spec_token_indx = attn_metadata.non_spec_token_indx + num_accepted_tokens = attn_metadata.num_accepted_tokens + num_actual_tokens = attn_metadata.num_actual_tokens + conv_state, recurrent_state = self.kv_cache + recurrent_state_active = recurrent_state[..., : self.head_dim] + if not is_conv_state_dim_first(): + conv_state = conv_state.transpose(-1, -2) + conv_state_q, conv_state_k, conv_state_v = conv_state.chunk(3, dim=-2) + + q_proj_states = q_proj_states[:num_actual_tokens] + k_proj_states = k_proj_states[:num_actual_tokens] + v_proj_states = v_proj_states[:num_actual_tokens] + g1 = g1[:, :num_actual_tokens] + beta = beta[:, :num_actual_tokens] + + spec_only = ( + spec_sequence_masks is not None + and attn_metadata.num_prefills == 0 + and attn_metadata.num_decodes == 0 + ) + if spec_sequence_masks is not None: + assert spec_token_indx is not None + assert non_spec_token_indx is not None + if spec_only: + q_proj_states_spec = q_proj_states + k_proj_states_spec = k_proj_states + v_proj_states_spec = v_proj_states + g1_spec = g1 + beta_spec = beta + q_proj_states_non_spec = None + k_proj_states_non_spec = None + v_proj_states_non_spec = None + g1_non_spec = None + beta_non_spec = None + else: + q_proj_states_spec = q_proj_states.index_select(0, spec_token_indx) + k_proj_states_spec = k_proj_states.index_select(0, spec_token_indx) + v_proj_states_spec = v_proj_states.index_select(0, spec_token_indx) + g1_spec = g1.index_select(1, spec_token_indx) + beta_spec = beta.index_select(1, spec_token_indx) + q_proj_states_non_spec = q_proj_states.index_select( + 0, non_spec_token_indx + ) + k_proj_states_non_spec = k_proj_states.index_select( + 0, non_spec_token_indx + ) + v_proj_states_non_spec = v_proj_states.index_select( + 0, non_spec_token_indx + ) + g1_non_spec = g1.index_select(1, non_spec_token_indx) + beta_non_spec = beta.index_select(1, non_spec_token_indx) + else: + q_proj_states_spec = None + k_proj_states_spec = None + v_proj_states_spec = None + g1_spec = None + beta_spec = None + q_proj_states_non_spec = q_proj_states + k_proj_states_non_spec = k_proj_states + v_proj_states_non_spec = v_proj_states + g1_non_spec = g1 + beta_non_spec = beta + + q_conv_weights = self.q_conv1d.weight.view( + self.q_conv1d.weight.size(0), self.q_conv1d.weight.size(2) + ) + k_conv_weights = self.k_conv1d.weight.view( + self.k_conv1d.weight.size(0), self.k_conv1d.weight.size(2) + ) + v_conv_weights = self.v_conv1d.weight.view( + self.v_conv1d.weight.size(0), self.v_conv1d.weight.size(2) + ) + + if spec_sequence_masks is not None: + assert q_proj_states_spec is not None + assert k_proj_states_spec is not None + assert v_proj_states_spec is not None + assert spec_state_indices is not None + q_spec = causal_conv1d_update( + q_proj_states_spec, + conv_state_q, + q_conv_weights, + self.q_conv1d.bias, + activation="silu", + conv_state_indices=spec_state_indices[:, 0][ + : attn_metadata.num_spec_decodes + ], + num_accepted_tokens=num_accepted_tokens, + query_start_loc=spec_query_start_loc, + max_query_len=spec_state_indices.size(-1), + validate_data=False, + ) + k_spec = causal_conv1d_update( + k_proj_states_spec, + conv_state_k, + k_conv_weights, + self.k_conv1d.bias, + activation="silu", + conv_state_indices=spec_state_indices[:, 0][ + : attn_metadata.num_spec_decodes + ], + num_accepted_tokens=num_accepted_tokens, + query_start_loc=spec_query_start_loc, + max_query_len=spec_state_indices.size(-1), + validate_data=False, + ) + v_spec = causal_conv1d_update( + v_proj_states_spec, + conv_state_v, + v_conv_weights, + self.v_conv1d.bias, + activation="silu", + conv_state_indices=spec_state_indices[:, 0][ + : attn_metadata.num_spec_decodes + ], + num_accepted_tokens=num_accepted_tokens, + query_start_loc=spec_query_start_loc, + max_query_len=spec_state_indices.size(-1), + validate_data=False, + ) + else: + q_spec = k_spec = v_spec = None + + if attn_metadata.num_prefills > 0: + assert q_proj_states_non_spec is not None + assert k_proj_states_non_spec is not None + assert v_proj_states_non_spec is not None + q = causal_conv1d_fn( + q_proj_states_non_spec.transpose(0, 1), + q_conv_weights, + self.q_conv1d.bias, + activation="silu", + conv_states=conv_state_q, + has_initial_state=has_initial_state, + cache_indices=state_indices, + query_start_loc=query_start_loc, + metadata=attn_metadata, + ).transpose(0, 1) + k = causal_conv1d_fn( + k_proj_states_non_spec.transpose(0, 1), + k_conv_weights, + self.k_conv1d.bias, + activation="silu", + conv_states=conv_state_k, + has_initial_state=has_initial_state, + cache_indices=state_indices, + query_start_loc=query_start_loc, + metadata=attn_metadata, + ).transpose(0, 1) + v = causal_conv1d_fn( + v_proj_states_non_spec.transpose(0, 1), + v_conv_weights, + self.v_conv1d.bias, + activation="silu", + conv_states=conv_state_v, + has_initial_state=has_initial_state, + cache_indices=state_indices, + query_start_loc=query_start_loc, + metadata=attn_metadata, + ).transpose(0, 1) + elif attn_metadata.num_decodes > 0: + assert q_proj_states_non_spec is not None + assert k_proj_states_non_spec is not None + assert v_proj_states_non_spec is not None + assert state_indices is not None + decode_indices = state_indices[:num_actual_tokens] + q = causal_conv1d_update( + q_proj_states_non_spec, + conv_state_q, + q_conv_weights, + self.q_conv1d.bias, + activation="silu", + conv_state_indices=decode_indices, + validate_data=True, + ) + k = causal_conv1d_update( + k_proj_states_non_spec, + conv_state_k, + k_conv_weights, + self.k_conv1d.bias, + activation="silu", + conv_state_indices=decode_indices, + validate_data=True, + ) + v = causal_conv1d_update( + v_proj_states_non_spec, + conv_state_v, + v_conv_weights, + self.v_conv1d.bias, + activation="silu", + conv_state_indices=decode_indices, + validate_data=True, + ) + else: + q = k = v = None + + if q_spec is not None: + q_spec, k_spec, v_spec = map( + lambda x: rearrange(x, "n (h d) -> 1 n h d", d=self.head_dim), + (q_spec, k_spec, v_spec), + ) + if q is not None: + q, k, v = map( + lambda x: rearrange(x, "n (h d) -> 1 n h d", d=self.head_dim), + (q, k, v), + ) + if spec_sequence_masks is not None: + assert g1_spec is not None + assert beta_spec is not None + assert spec_query_start_loc is not None + assert spec_state_indices is not None + out_spec, _ = fused_recurrent_kda_fwd( + q=q_spec.contiguous(), + k=k_spec.contiguous(), + v=v_spec.contiguous(), + g=g1_spec.contiguous(), + beta=beta_spec.contiguous(), + scale=self.head_dim**-0.5, + initial_state=recurrent_state_active, + inplace_final_state=True, + cu_seqlens=spec_query_start_loc[: attn_metadata.num_spec_decodes + 1], + ssm_state_indices=spec_state_indices, + num_accepted_tokens=num_accepted_tokens, + use_qk_l2norm_in_kernel=True, + ) + else: + out_spec = None + + if attn_metadata.num_prefills > 0: + assert q is not None and k is not None and v is not None + assert g1_non_spec is not None and beta_non_spec is not None + assert state_indices is not None + zero_idx = state_indices[~has_initial_state] + recurrent_state[zero_idx] = 0 + initial_state = recurrent_state_active[state_indices].contiguous() + out, last_state = chunk_kda( + q=q, + k=k, + v=v, + g=g1_non_spec, + beta=beta_non_spec, + initial_state=initial_state, + output_final_state=True, + use_qk_l2norm_in_kernel=True, + safe_gate=self.safe_gate, + lower_bound=self.lower_bound, + cu_seqlens=query_start_loc, + ) + recurrent_state_active[state_indices] = last_state + elif attn_metadata.num_decodes > 0: + assert q is not None and k is not None and v is not None + assert g1_non_spec is not None and beta_non_spec is not None + assert state_indices is not None + out, _ = fused_recurrent_kda( + q=q, + k=k, + v=v, + g=g1_non_spec, + beta=beta_non_spec, + initial_state=recurrent_state_active, + use_qk_l2norm_in_kernel=True, + safe_gate=self.safe_gate, + lower_bound=self.lower_bound, + cu_seqlens=query_start_loc[: attn_metadata.num_decodes + 1], + ssm_state_indices=state_indices, + ) + else: + out = None + + if spec_sequence_masks is not None and out is not None: + assert spec_token_indx is not None and non_spec_token_indx is not None + merged_out = torch.empty( + (1, num_actual_tokens, *out_spec.shape[2:]), + dtype=out_spec.dtype, + device=out_spec.device, + ) + merged_out.index_copy_(1, spec_token_indx, out_spec) + merged_out.index_copy_(1, non_spec_token_indx, out) + core_attn_out[0, :num_actual_tokens] = merged_out[0, :num_actual_tokens] + elif spec_sequence_masks is not None: + core_attn_out[0, :num_actual_tokens] = out_spec[0, :num_actual_tokens] + else: + core_attn_out[0, :num_actual_tokens] = out[0, :num_actual_tokens] + + +class BailingMoeV3Gate(nn.Module): + def __init__( + self, + config: PretrainedConfig, + params_dtype: torch.dtype | None = None, + ) -> None: + super().__init__() + if params_dtype is None: + params_dtype = torch.float32 + self.weight = nn.Parameter( + torch.empty((config.num_experts, config.hidden_size), dtype=params_dtype) + ) + self.expert_bias = nn.Parameter( + torch.empty((config.num_experts,), dtype=torch.float32) + ) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + return F.linear(hidden_states.to(self.weight.dtype), self.weight).to( + hidden_states.dtype + ) + + +class BailingMoeV3MoE(nn.Module): + def __init__( + self, + config: PretrainedConfig, + quant_config: QuantizationConfig | None = None, + layer_id: int = 0, + prefix: str = "", + ) -> None: + super().__init__() + self.tp_size = get_tensor_model_parallel_world_size() + self.num_experts = config.num_experts + self.top_k = config.num_experts_per_tok + self.hidden_size = config.hidden_size + self.routed_scaling_factor = getattr(config, "routed_scaling_factor", 1.0) + self.gate = BailingMoeV3Gate(config) + self.expert_swiglu_limit = _get_layer_swiglu_limit( + getattr(config, "expert_swiglu_limit_list", None), layer_id + ) + self.share_expert_swiglu_limit = _get_layer_swiglu_limit( + getattr(config, "share_expert_swiglu_limit_list", None), layer_id + ) + shared_intermediate = ( + config.moe_shared_expert_intermediate_size * config.num_shared_experts + ) + self.shared_experts = BailingMoeV3MLP( + intermediate_size=shared_intermediate, + config=config, + quant_config=quant_config, + reduce_results=False, + prefix=f"{prefix}.shared_experts", + swiglu_limit=self.share_expert_swiglu_limit, + ) + self.experts = FusedMoEFactory( + shared_experts=self.shared_experts, + num_experts=self.num_experts, + top_k=self.top_k, + hidden_size=self.hidden_size, + intermediate_size=config.moe_intermediate_size, + renormalize=True, + quant_config=quant_config, + activation="silu", + swiglu_limit=self.expert_swiglu_limit, + prefix=f"{prefix}.experts", + scoring_func="sigmoid", + routed_scaling_factor=self.routed_scaling_factor, + e_score_correction_bias=self.gate.expert_bias, + num_expert_group=config.n_group, + topk_group=config.topk_group, + use_grouped_topk=True, + router_logits_dtype=torch.float32, + ) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + num_tokens, hidden_size = hidden_states.shape + hidden_states = hidden_states.contiguous().view(-1, hidden_size) + router_logits = self.gate(hidden_states.to(torch.float32)) + hidden_states = self.experts( + hidden_states=hidden_states, router_logits=router_logits + ) + return hidden_states.view(num_tokens, hidden_size) + + +class BailingMoeV3DecoderLayer(nn.Module): + def __init__( + self, + config: PretrainedConfig, + quant_config: QuantizationConfig | None = None, + layer_id: int = 0, + prefix: str = "layer", + model_config: ModelConfig | None = None, + cache_config: CacheConfig | None = None, + num_speculative_tokens: int = 0, + ) -> None: + super().__init__() + self.layer_id = layer_id + self.hidden_size = config.hidden_size + self.is_kda_layer = _is_kda_layer( + layer_id, config.layer_group_size, config.num_hidden_layers + ) + if self.is_kda_layer: + self.self_attn = BailingMoeV3KimiDeltaAttention( + config, + quant_config=quant_config, + layer_id=layer_id, + prefix=f"{prefix}.self_attn", + model_config=model_config, + cache_config=cache_config, + num_speculative_tokens=num_speculative_tokens, + ) + else: + self.self_attn = BailingMoeV3MLAAttention( + config, + quant_config=quant_config, + layer_id=layer_id, + prefix=f"{prefix}.self_attn", + cache_config=cache_config, + ) + + if config.num_experts is not None and layer_id >= config.first_k_dense_replace: + self.mlp = BailingMoeV3MoE( + config, + quant_config=quant_config, + layer_id=layer_id, + prefix=f"{prefix}.mlp", + ) + else: + self.mlp = BailingMoeV3MLP( + intermediate_size=config.intermediate_size, + config=config, + quant_config=quant_config, + reduce_results=True, + prefix=f"{prefix}.mlp", + ) + + self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.post_attention_layernorm = RMSNorm( + config.hidden_size, eps=config.rms_norm_eps + ) + + def forward( + self, + hidden_states: torch.Tensor, + positions: torch.Tensor, + residual: torch.Tensor | None = None, + ) -> tuple[torch.Tensor, torch.Tensor | None]: + if residual is None: + residual = hidden_states + hidden_states = self.input_layernorm(hidden_states) + else: + hidden_states, residual = self.input_layernorm(hidden_states, residual) + + if self.is_kda_layer: + attn_output = torch.zeros_like(hidden_states) + self.self_attn(hidden_states, positions, attn_output) + else: + attn_output = self.self_attn(hidden_states, positions) + + hidden_states, residual = self.post_attention_layernorm(attn_output, residual) + hidden_states = self.mlp(hidden_states) + return hidden_states, residual + + +@support_torch_compile( + dynamic_arg_dims={ + "input_ids": 0, + "positions": -1, + "intermediate_tensors": 0, + "inputs_embeds": 0, + } +) +class BailingMoeV3Model(nn.Module): + def __init__(self, *, vllm_config: VllmConfig, prefix: str = "") -> None: + super().__init__() + config = vllm_config.model_config.hf_config + model_config = vllm_config.model_config + quant_config = vllm_config.quant_config + cache_config = vllm_config.cache_config + num_speculative_tokens = ( + vllm_config.speculative_config.num_speculative_tokens + if vllm_config.speculative_config + else 0 + ) + + self.config = config + self.vocab_size = config.vocab_size + self.embed_dim = config.hidden_size + self.num_layers = config.num_hidden_layers + + if get_pp_group().is_first_rank: + self.word_embeddings = VocabParallelEmbedding( + self.vocab_size, + self.embed_dim, + org_num_embeddings=self.vocab_size, + ) + else: + self.word_embeddings = PPMissingLayer() + + def layer_fn(prefix: str): + layer_idx = int(prefix.split(".")[-1]) + return BailingMoeV3DecoderLayer( + config=config, + quant_config=quant_config, + layer_id=layer_idx, + prefix=prefix, + model_config=model_config, + cache_config=cache_config, + num_speculative_tokens=num_speculative_tokens, + ) + + self.start_layer, self.end_layer, self.layers = make_layers( + self.num_layers, layer_fn, prefix=f"{prefix}.layers" + ) + + if get_pp_group().is_last_rank: + self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) + else: + self.norm = PPMissingLayer() + + @property + def embed_tokens(self) -> nn.Module: + return self.word_embeddings + + def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor: + return self.word_embeddings(input_ids) + + def forward( + self, + input_ids: torch.Tensor | None, + positions: torch.Tensor, + intermediate_tensors: IntermediateTensors | None = None, + inputs_embeds: torch.Tensor | None = None, + ) -> torch.Tensor | IntermediateTensors: + if get_pp_group().is_first_rank: + hidden_states = ( + self.word_embeddings(input_ids) + if inputs_embeds is None + else inputs_embeds + ) + residual = None + else: + assert intermediate_tensors is not None + hidden_states = intermediate_tensors["hidden_states"] + residual = intermediate_tensors["residual"] + + for layer in self.layers[self.start_layer : self.end_layer]: + hidden_states, residual = layer(hidden_states, positions, residual) + + if not get_pp_group().is_last_rank: + return IntermediateTensors( + {"hidden_states": hidden_states, "residual": residual} + ) + if residual is not None: + hidden_states, _ = self.norm(hidden_states, residual) + else: + hidden_states = self.norm(hidden_states) + return hidden_states + + def get_expert_mapping(self) -> list[tuple[str, str, int, str]]: + return fused_moe_make_expert_params_mapping( + self, + ckpt_gate_proj_name="gate_proj", + ckpt_down_proj_name="down_proj", + ckpt_up_proj_name="up_proj", + num_experts=self.config.num_experts, + ) + + +class BailingMoeV3ForCausalLM(nn.Module, HasInnerState, IsHybrid, SupportsPP): + packed_modules_mapping = { + "gate_up_proj": ["gate_proj", "up_proj"], + "qkvb_proj": ["q_proj", "k_proj", "v_proj", "b_proj"], + } + + def __init__(self, *, vllm_config: VllmConfig, prefix: str = "") -> None: + super().__init__() + config = vllm_config.model_config.hf_config + quant_config = vllm_config.quant_config + self.config = config + self.quant_config = quant_config + self.model = BailingMoeV3Model( + vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model") + ) + if get_pp_group().is_last_rank: + self.lm_head = ParallelLMHead( + config.vocab_size, + config.hidden_size, + quant_config=quant_config, + prefix=maybe_prefix(prefix, "lm_head"), + ) + self.logits_processor = LogitsProcessor(config.vocab_size) + else: + self.lm_head = PPMissingLayer() + + def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor: + return self.model.embed_input_ids(input_ids) + + def forward( + self, + input_ids: torch.Tensor | None, + positions: torch.Tensor, + intermediate_tensors: IntermediateTensors | None = None, + inputs_embeds: torch.Tensor | None = None, + ) -> torch.Tensor | IntermediateTensors: + return self.model(input_ids, positions, intermediate_tensors, inputs_embeds) + + def compute_logits(self, hidden_states: torch.Tensor) -> torch.Tensor: + return self.logits_processor(self.lm_head, hidden_states) + + def make_empty_intermediate_tensors( + self, batch_size: int, dtype: torch.dtype, device: torch.device + ) -> IntermediateTensors: + return IntermediateTensors( + { + "hidden_states": torch.zeros( + (batch_size, self.config.hidden_size), dtype=dtype, device=device + ), + "residual": torch.zeros( + (batch_size, self.config.hidden_size), dtype=dtype, device=device + ), + } + ) + + @classmethod + def get_mamba_state_shape_from_config( + cls, vllm_config: VllmConfig + ) -> tuple[tuple[int, ...], tuple[int, ...], tuple[int, ...], tuple[int, ...]]: + return _get_kda_state_shape_for_config(vllm_config) + + @classmethod + def get_mamba_state_dtype_from_config( + cls, vllm_config: VllmConfig + ) -> tuple[torch.dtype, torch.dtype, torch.dtype, torch.dtype]: + return MambaStateDtypeCalculator.kda_state_dtype( + vllm_config.model_config.dtype, + vllm_config.cache_config.mamba_cache_dtype, + ) + + @classmethod + def get_mamba_state_copy_func(cls) -> tuple: + return MambaStateCopyFuncCalculator.kda_state_copy_func() + + def get_expert_mapping(self) -> list[tuple[str, str, int, str]]: + return self.model.get_expert_mapping() + + def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]: + params_dict = dict(self.named_parameters(remove_duplicate=False)) + loaded_params: set[str] = set() + stacked_mappings = [ + (".gate_up_proj", ".gate_proj", 0), + (".gate_up_proj", ".up_proj", 1), + (".qkvb_proj", ".q_proj", 0), + (".qkvb_proj", ".k_proj", 1), + (".qkvb_proj", ".v_proj", 2), + (".qkvb_proj", ".b_proj", 3), + (".fused_qkv_a_proj", ".q_a_proj", 0), + (".fused_qkv_a_proj", ".kv_a_proj_with_mqa", 1), + ] + expert_mappings = list(self.get_expert_mapping()) + + def load_param(name: str, tensor: torch.Tensor, shard_id=None) -> bool: + if name not in params_dict or is_pp_missing_parameter(name, self): + return False + param = params_dict[name] + weight_loader = getattr(param, "weight_loader", default_weight_loader) + if shard_id is None: + weight_loader(param, tensor) + elif isinstance(shard_id, int): + weight_loader(param, tensor, shard_id) + else: + weight_loader( + param, tensor, name, expert_id=shard_id[0], shard_id=shard_id[1] + ) + loaded_params.add(name) + return True + + def normalize_name(name: str) -> str | None: + if name.startswith("model.layers."): + layer_idx = int(name.split("model.layers.")[1].split(".")[0]) + if layer_idx >= self.config.num_hidden_layers: + return None + name = name.replace("attention.", "self_attn.") + return maybe_remap_kv_scale_name(name, params_dict) + + for orig_name, weight in weights: + name = normalize_name(orig_name) + if name is None: + continue + loaded = False + for param_suf, weight_suf, shard_id in stacked_mappings: + if weight_suf in name: + mapped = name.replace(weight_suf, param_suf) + if load_param(mapped, weight, shard_id): + loaded = True + break + if loaded: + continue + + if ".mlp.experts." in name: + for param_name, weight_name, expert_id, shard_id in expert_mappings: + if weight_name in name: + mapped = name.replace(weight_name, param_name) + load_param(mapped, weight, (expert_id, shard_id)) + break + continue + + load_param(name, weight) + return loaded_params diff --git a/vllm/model_executor/models/bailing_moe_v3_mtp.py b/vllm/model_executor/models/bailing_moe_v3_mtp.py new file mode 100644 index 0000000000..c89007b5b3 --- /dev/null +++ b/vllm/model_executor/models/bailing_moe_v3_mtp.py @@ -0,0 +1,389 @@ +# SPDX-License-Identifier: Apache-2.0 +# SPDX-FileCopyrightText: Copyright contributors to the vLLM project +"""Inference-only Bailing MoE v3 MTP model.""" + +from collections.abc import Iterable + +import torch +import torch.nn as nn +from transformers.configuration_utils import PretrainedConfig + +from vllm.compilation.decorators import support_torch_compile +from vllm.config import VllmConfig +from vllm.distributed import get_pp_group +from vllm.model_executor.layers.fused_moe import ( + fused_moe_make_expert_params_mapping, +) +from vllm.model_executor.layers.layernorm import RMSNorm +from vllm.model_executor.layers.logits_processor import LogitsProcessor +from vllm.model_executor.layers.vocab_parallel_embedding import ( + ParallelLMHead, + VocabParallelEmbedding, +) +from vllm.model_executor.model_loader.weight_utils import ( + default_weight_loader, + maybe_remap_kv_scale_name, +) +from vllm.model_executor.models.bailing_moe_v3 import ( + BailingMoeV3MLAAttention, + BailingMoeV3MoE, +) +from vllm.model_executor.models.interfaces import SupportsPP +from vllm.model_executor.models.utils import ( + PPMissingLayer, + is_pp_missing_parameter, + maybe_prefix, +) +from vllm.sequence import IntermediateTensors + + +def _get_draft_hf_config(vllm_config: VllmConfig) -> PretrainedConfig: + speculative_config = vllm_config.speculative_config + if speculative_config is not None: + draft_model_config = speculative_config.draft_model_config + if draft_model_config is not None: + return draft_model_config.hf_config + return vllm_config.model_config.hf_config + + +class BailingMoeV3MTPSharedHead(nn.Module): + def __init__( + self, + config: PretrainedConfig, + prefix: str, + vllm_config: VllmConfig, + ) -> None: + super().__init__() + self.head = ParallelLMHead( + config.vocab_size, + config.hidden_size, + quant_config=vllm_config.quant_config, + prefix=maybe_prefix(prefix, "head"), + ) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + return hidden_states + + +class BailingMoeV3MultiTokenPredictorLayer(nn.Module): + def __init__( + self, + *, + vllm_config: VllmConfig, + prefix: str, + layer_id: int, + ) -> None: + super().__init__() + config = _get_draft_hf_config(vllm_config) + self.config = config + self.layer_id = layer_id + self.enorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.hnorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.eh_proj = nn.Linear(config.hidden_size * 2, config.hidden_size, bias=False) + self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.self_attn = BailingMoeV3MLAAttention( + config, + quant_config=vllm_config.quant_config, + layer_id=layer_id, + prefix=maybe_prefix(prefix, "self_attn"), + cache_config=vllm_config.cache_config, + ) + self.post_attention_layernorm = RMSNorm( + config.hidden_size, eps=config.rms_norm_eps + ) + self.mlp = BailingMoeV3MoE( + config, + quant_config=vllm_config.quant_config, + layer_id=layer_id, + prefix=maybe_prefix(prefix, "mlp"), + ) + self.final_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.shared_head = BailingMoeV3MTPSharedHead( + config, + maybe_prefix(prefix, "shared_head"), + vllm_config, + ) + + def forward( + self, + input_ids: torch.Tensor, + positions: torch.Tensor, + previous_hidden_states: torch.Tensor, + inputs_embeds: torch.Tensor | None = None, + spec_step_index: int = 0, + ) -> torch.Tensor: + del input_ids, spec_step_index + assert inputs_embeds is not None + inputs_embeds = self.enorm(inputs_embeds) + previous_hidden_states = self.hnorm(previous_hidden_states) + + hidden_states = self.eh_proj( + torch.cat([inputs_embeds, previous_hidden_states], dim=-1) + ) + residual = hidden_states + hidden_states = self.input_layernorm(hidden_states) + + hidden_states = self.self_attn(hidden_states, positions) + hidden_states = residual + hidden_states + + residual = hidden_states + hidden_states = self.post_attention_layernorm(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = residual + hidden_states.to(residual.device) + return self.final_layernorm(hidden_states) + + +class BailingMoeV3MultiTokenPredictor(nn.Module): + def __init__( + self, + *, + vllm_config: VllmConfig, + prefix: str = "", + ) -> None: + super().__init__() + config = _get_draft_hf_config(vllm_config) + self.mtp_start_layer_idx = config.num_hidden_layers + self.num_mtp_layers = config.num_nextn_predict_layers + self.layers = nn.ModuleDict( + { + str(idx): BailingMoeV3MultiTokenPredictorLayer( + vllm_config=vllm_config, + prefix=f"{prefix}.layers.{idx}", + layer_id=idx, + ) + for idx in range( + self.mtp_start_layer_idx, + self.mtp_start_layer_idx + self.num_mtp_layers, + ) + } + ) + if get_pp_group().is_first_rank: + self.embed_tokens = VocabParallelEmbedding( + config.vocab_size, + config.hidden_size, + org_num_embeddings=config.vocab_size, + prefix=maybe_prefix(prefix, "embed_tokens"), + ) + else: + self.embed_tokens = PPMissingLayer() + self.logits_processor = LogitsProcessor(config.vocab_size) + + def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor: + return self.embed_tokens(input_ids) + + def forward( + self, + input_ids: torch.Tensor, + positions: torch.Tensor, + previous_hidden_states: torch.Tensor, + inputs_embeds: torch.Tensor | None = None, + spec_step_idx: int = 0, + ) -> torch.Tensor: + if inputs_embeds is None: + inputs_embeds = self.embed_tokens(input_ids) + current_step_idx = spec_step_idx % self.num_mtp_layers + return self.layers[str(self.mtp_start_layer_idx + current_step_idx)]( + input_ids, + positions, + previous_hidden_states, + inputs_embeds, + current_step_idx, + ) + + def compute_logits( + self, + hidden_states: torch.Tensor, + spec_step_idx: int = 0, + lm_head: nn.Module | None = None, + ) -> torch.Tensor: + current_step_idx = spec_step_idx % self.num_mtp_layers + mtp_layer = self.layers[str(self.mtp_start_layer_idx + current_step_idx)] + head = lm_head if lm_head is not None else mtp_layer.shared_head.head + return self.logits_processor(head, mtp_layer.shared_head(hidden_states)) + + +@support_torch_compile +class BailingMoeV3MTPModel(nn.Module, SupportsPP): + packed_modules_mapping = { + "gate_up_proj": ["gate_proj", "up_proj"], + "fused_qkv_a_proj": ["q_a_proj", "kv_a_proj_with_mqa"], + } + + def __init__( + self, + *, + vllm_config: VllmConfig, + prefix: str = "", + ) -> None: + super().__init__() + self.config = _get_draft_hf_config(vllm_config) + self.model = BailingMoeV3MultiTokenPredictor( + vllm_config=vllm_config, + prefix=maybe_prefix(prefix, "model"), + ) + self.lm_head: nn.Module | None = None + + 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 forward( + self, + input_ids: torch.Tensor | None, + positions: torch.Tensor, + hidden_states: torch.Tensor, + intermediate_tensors: IntermediateTensors | None = None, + inputs_embeds: torch.Tensor | None = None, + spec_step_idx: int = 0, + ) -> torch.Tensor: + del intermediate_tensors + return self.model( + input_ids, + positions, + hidden_states, + inputs_embeds, + spec_step_idx, + ) + + def compute_logits( + self, + hidden_states: torch.Tensor, + spec_step_idx: int = 0, + ) -> torch.Tensor: + return self.model.compute_logits(hidden_states, spec_step_idx, self.lm_head) + + def get_expert_mapping(self) -> list[tuple[str, str, int, str]]: + return fused_moe_make_expert_params_mapping( + self, + ckpt_gate_proj_name="gate_proj", + ckpt_down_proj_name="down_proj", + ckpt_up_proj_name="up_proj", + num_experts=self.config.num_experts, + ) + + def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]: + stacked_params_mapping = [ + (".fused_qkv_a_proj", ".q_a_proj", 0), + (".fused_qkv_a_proj", ".kv_a_proj_with_mqa", 1), + (".gate_up_proj", ".gate_proj", 0), + (".gate_up_proj", ".up_proj", 1), + ] + expert_params_mapping = list(self.get_expert_mapping()) + params_dict = dict(self.named_parameters(remove_duplicate=False)) + loaded_params: set[str] = set() + loaded_mtp_layers: set[int] = set() + + def load_param( + name: str, + loaded_weight: torch.Tensor, + shard_id=None, + ) -> bool: + name = maybe_remap_kv_scale_name(name, params_dict) + if name is None: + return False + if name not in params_dict or is_pp_missing_parameter(name, self): + return False + + param = params_dict[name] + weight_loader = getattr(param, "weight_loader", default_weight_loader) + if shard_id is None: + weight_loader(param, loaded_weight) + elif isinstance(shard_id, int): + weight_loader(param, loaded_weight, shard_id) + else: + weight_loader( + param, + loaded_weight, + name, + expert_id=shard_id[0], + shard_id=shard_id[1], + ) + loaded_params.add(name) + return True + + def get_spec_layer_idx(name: str) -> int | None: + if not name.startswith("model.layers."): + return None + try: + layer_idx = int(name.split("model.layers.", 1)[1].split(".", 1)[0]) + except (IndexError, ValueError): + return None + mtp_idx = layer_idx - self.config.num_hidden_layers + if 0 <= mtp_idx < self.config.num_nextn_predict_layers: + return layer_idx + return None + + def normalize_name(name: str) -> str: + name = name.replace(".attention.", ".self_attn.") + return name.replace( + "mlp.gate.e_score_correction_bias", + "mlp.gate.expert_bias", + ) + + def load_lm_head(loaded_weight: torch.Tensor) -> None: + for layer_idx in range( + self.model.mtp_start_layer_idx, + self.model.mtp_start_layer_idx + self.model.num_mtp_layers, + ): + name = f"model.layers.{layer_idx}.shared_head.head.weight" + load_param(name, loaded_weight) + + for name, loaded_weight in weights: + if "rotary_emb.inv_freq" in name: + continue + + if name == "model.word_embeddings.weight": + load_param("model.embed_tokens.weight", loaded_weight) + continue + if name == "lm_head.weight": + load_lm_head(loaded_weight) + continue + + spec_layer = get_spec_layer_idx(name) + if spec_layer is None: + continue + name = normalize_name(name) + + loaded = False + for param_name, weight_name, shard_id in stacked_params_mapping: + if weight_name not in name: + continue + if "mlp.experts." in name and name not in params_dict: + continue + mapped_name = name.replace(weight_name, param_name) + if load_param(mapped_name, loaded_weight, shard_id): + loaded = True + break + if loaded: + loaded_mtp_layers.add(spec_layer) + continue + + if "mlp.experts" in name: + for mapping in expert_params_mapping: + param_name, weight_name, expert_id, shard_id = mapping + if weight_name not in name: + continue + mapped_name = name.replace(weight_name, param_name) + if load_param(mapped_name, loaded_weight, (expert_id, shard_id)): + loaded = True + break + if loaded: + loaded_mtp_layers.add(spec_layer) + continue + + if load_param(name, loaded_weight): + loaded_mtp_layers.add(spec_layer) + + for layer_idx in range( + self.model.mtp_start_layer_idx, + self.model.mtp_start_layer_idx + self.model.num_mtp_layers, + ): + if layer_idx not in loaded_mtp_layers: + raise ValueError( + f"Bailing V3 MTP speculative decoding layer {layer_idx} " + "weights are missing from checkpoint. Use a checkpoint that " + "includes MTP layer weights, or disable speculative decoding." + ) + return loaded_params diff --git a/vllm/model_executor/models/registry.py b/vllm/model_executor/models/registry.py index 545b257794..6ac20cc6c9 100644 --- a/vllm/model_executor/models/registry.py +++ b/vllm/model_executor/models/registry.py @@ -79,6 +79,7 @@ _TEXT_GENERATION_MODELS = { "BailingMoeForCausalLM": ("bailing_moe", "BailingMoeForCausalLM"), "BailingMoeV2ForCausalLM": ("bailing_moe", "BailingMoeV2ForCausalLM"), "BailingMoeV2_5ForCausalLM": ("bailing_moe_linear", "BailingMoeV25ForCausalLM"), + "BailingMoeV3ForCausalLM": ("bailing_moe_v3", "BailingMoeV3ForCausalLM"), "BloomForCausalLM": ("bloom", "BloomForCausalLM"), "ChatGLMModel": ("chatglm", "ChatGLMForCausalLM"), "ChatGLMForConditionalGeneration": ("chatglm", "ChatGLMForCausalLM"), @@ -641,6 +642,7 @@ _SPECULATIVE_DECODING_MODELS = { "EagleDeepSeekMTPModel": ("deepseek_eagle", "EagleDeepseekV3ForCausalLM"), "DeepSeekMTPModel": ("deepseek_mtp", "DeepSeekMTP"), "DeepSeekV4MTPModel": ("vllm.models.deepseek_v4", "DeepSeekV4MTP"), + "BailingMoeV3MTPModel": ("bailing_moe_v3_mtp", "BailingMoeV3MTPModel"), "MiniMaxM3MTP": ("vllm.models.minimax_m3", "MiniMaxM3MTP"), "BailingMoeV25MTPModel": ("bailing_moe_mtp", "BailingMoeV25MTPModel"), "InklingMTPModel": ("vllm.models.inkling", "InklingMTP"), diff --git a/vllm/parser/ling3.py b/vllm/parser/ling3.py new file mode 100644 index 0000000000..b8f184cf29 --- /dev/null +++ b/vllm/parser/ling3.py @@ -0,0 +1,83 @@ +# SPDX-License-Identifier: Apache-2.0 +# SPDX-FileCopyrightText: Copyright contributors to the vLLM project +"""Ling3 parser for reasoning and tool calls. + +Ling3 uses the same XML tool-call format as GLM-4.7 and defaults thinking on. +It follows the GLM-style ```` / ```` reasoning format and +treats ```` as an implicit reasoning terminator. +""" + +from __future__ import annotations + +from dataclasses import replace +from typing import TYPE_CHECKING + +from vllm.entrypoints.openai.chat_completion.protocol import ChatCompletionRequest +from vllm.entrypoints.openai.responses.protocol import ResponsesRequest +from vllm.parser.engine.adapters import make_adapters +from vllm.parser.engine.parser_engine import ParserEngine +from vllm.parser.glm47_moe import ( + THINK_END, + THINK_START, + Glm47MoeParser, + glm47_moe_config, +) + +if TYPE_CHECKING: + from vllm.tokenizers import TokenizerLike + from vllm.tool_parsers.abstract_tool_parser import Tool + + +class Ling3Parser(Glm47MoeParser): + """Ling3 parser backed by the GLM XML parser engine.""" + + def __init__( + self, + tokenizer: TokenizerLike, + tools: list[Tool] | None = None, + **kwargs, + ) -> None: + chat_kwargs = kwargs.get("chat_template_kwargs", {}) or {} + thinking = chat_kwargs.get("thinking", None) + enable_thinking = chat_kwargs.get("enable_thinking", None) + self.thinking_enabled = ( + True + if thinking is None and enable_thinking is None + else bool(thinking) or bool(enable_thinking) + ) + parser_config = replace( + glm47_moe_config(thinking=self.thinking_enabled), + name="ling3", + ) + kwargs.setdefault( + "parser_engine_config", + parser_config, + ) + ParserEngine.__init__(self, tokenizer, tools, **kwargs) + + @property + def reasoning_start_str(self) -> str: + return THINK_START + + @property + def reasoning_end_str(self) -> str: + return THINK_END + + def extract_reasoning( + self, + model_output: str, + request: ChatCompletionRequest | ResponsesRequest, + ) -> tuple[str | None, str | None]: + 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 + + +( + Ling3ParserReasoningAdapter, + Ling3ParserToolAdapter, +) = make_adapters(Ling3Parser) diff --git a/vllm/reasoning/__init__.py b/vllm/reasoning/__init__.py index b5111a9ec6..6c761147c9 100644 --- a/vllm/reasoning/__init__.py +++ b/vllm/reasoning/__init__.py @@ -60,6 +60,10 @@ _REASONING_PARSERS_TO_REGISTER = { "glm47_moe_reasoning_parser", "Glm47MoeParserReasoningAdapter", ), + "ling3": ( + "ling3_reasoning_parser", + "Ling3ParserReasoningAdapter", + ), "openai_gptoss": ( "gptoss_reasoning_parser", "GptOssReasoningParser", diff --git a/vllm/reasoning/ling3_reasoning_parser.py b/vllm/reasoning/ling3_reasoning_parser.py new file mode 100644 index 0000000000..43105585c8 --- /dev/null +++ b/vllm/reasoning/ling3_reasoning_parser.py @@ -0,0 +1,6 @@ +# SPDX-License-Identifier: Apache-2.0 +# SPDX-FileCopyrightText: Copyright contributors to the vLLM project + +from vllm.parser.ling3 import Ling3ParserReasoningAdapter + +__all__ = ["Ling3ParserReasoningAdapter"] diff --git a/vllm/tool_parsers/__init__.py b/vllm/tool_parsers/__init__.py index 4db6f675fb..2292f25a85 100644 --- a/vllm/tool_parsers/__init__.py +++ b/vllm/tool_parsers/__init__.py @@ -58,6 +58,10 @@ _TOOL_PARSERS_TO_REGISTER = { "glm47_moe_tool_parser", "Glm47MoeModelToolParser", ), + "ling3": ( + "ling3_tool_parser", + "Ling3ToolParser", + ), "granite-20b-fc": ( "granite_20b_fc_tool_parser", "Granite20bFCToolParser", diff --git a/vllm/tool_parsers/ling3_tool_parser.py b/vllm/tool_parsers/ling3_tool_parser.py new file mode 100644 index 0000000000..9b309b1eac --- /dev/null +++ b/vllm/tool_parsers/ling3_tool_parser.py @@ -0,0 +1,11 @@ +# SPDX-License-Identifier: Apache-2.0 +# SPDX-FileCopyrightText: Copyright contributors to the vLLM project + +from __future__ import annotations + +from vllm.parser.ling3 import Ling3ParserToolAdapter + + +class Ling3ToolParser(Ling3ParserToolAdapter): # type: ignore[valid-type, misc] + supports_required_and_named = False + structural_tag_model = "glm_4_7" diff --git a/vllm/transformers_utils/model_arch_config_convertor.py b/vllm/transformers_utils/model_arch_config_convertor.py index 3425648df1..861633b06c 100644 --- a/vllm/transformers_utils/model_arch_config_convertor.py +++ b/vllm/transformers_utils/model_arch_config_convertor.py @@ -289,6 +289,7 @@ class ModelArchConfigConvertorBase: "pangu_ultra_moe_mtp", "bailing_hybrid", "bailing_hybrid_mtp", + "bailing_hybrid_v3_mtp", ): # check is deepseek_v4 model if hasattr(self.hf_text_config, "compress_ratios"): @@ -571,6 +572,12 @@ class BailingHybridMTPModelArchConfigConvertor(ModelArchConfigConvertorBase): return getattr(self.hf_text_config, "num_nextn_predict_layers", 0) +class BailingHybridV3MTPModelArchConfigConvertor( + BailingHybridMTPModelArchConfigConvertor +): + pass + + class Qwen3_5MTPModelArchConfigConvertor(ModelArchConfigConvertorBase): def get_num_hidden_layers(self) -> int: return getattr(self.hf_text_config, "mtp_num_hidden_layers", 0) @@ -693,6 +700,7 @@ MODEL_ARCH_CONFIG_CONVERTORS = { "moss_audio": MossAudioModelArchConfigConvertor, "mpt": MPTModelArchConfigConvertor, "nemotron-nas": NemotronNasModelArchConfigConvertor, + "bailing_hybrid_v3_mtp": BailingHybridV3MTPModelArchConfigConvertor, "pangu_ultra_moe_mtp": PanguUltraMoeMTPModelArchConfigConvertor, "qwen3_5_mtp": Qwen3_5MTPModelArchConfigConvertor, "qwen3_next_mtp": Qwen3NextMTPModelArchConfigConvertor,