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[Model] Add Ling implementation (#20680)

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Signed-off-by: vito.yy <vito.yy@antgroup.com>
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ant-yy
2025-07-14 22:10:32 +08:00
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1
docs/models/supported_models.md
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@ -316,6 +316,7 @@ Specified using `--task generate`.
| `AquilaForCausalLM` | Aquila, Aquila2 | `BAAI/Aquila-7B`, `BAAI/AquilaChat-7B`, etc. | ✅︎ | ✅︎ | ✅︎ |
| `ArcticForCausalLM` | Arctic | `Snowflake/snowflake-arctic-base`, `Snowflake/snowflake-arctic-instruct`, etc. | | ✅︎ | ✅︎ |
| `BaiChuanForCausalLM` | Baichuan2, Baichuan | `baichuan-inc/Baichuan2-13B-Chat`, `baichuan-inc/Baichuan-7B`, etc. | ✅︎ | ✅︎ | ✅︎ |
| `BailingMoeForCausalLM` | Ling | `inclusionAI/Ling-lite-1.5`, `inclusionAI/Ling-plus`, etc. | | ✅︎ | ✅︎ |
| `BambaForCausalLM` | Bamba | `ibm-ai-platform/Bamba-9B-fp8`, `ibm-ai-platform/Bamba-9B` | ✅︎ | ✅︎ | ✅︎ |
| `BloomForCausalLM` | BLOOM, BLOOMZ, BLOOMChat | `bigscience/bloom`, `bigscience/bloomz`, etc. | | ✅︎ | |
| `BartForConditionalGeneration` | BART | `facebook/bart-base`, `facebook/bart-large-cnn`, etc. | | | |

2
tests/models/registry.py
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@ -141,6 +141,8 @@ _TEXT_GENERATION_EXAMPLE_MODELS = {
trust_remote_code=True),
"BaichuanForCausalLM": _HfExamplesInfo("baichuan-inc/Baichuan2-7B-chat",
trust_remote_code=True),
"BailingMoeForCausalLM": _HfExamplesInfo("inclusionAI/Ling-lite-1.5",
trust_remote_code=True),
"BambaForCausalLM": _HfExamplesInfo("ibm-ai-platform/Bamba-9B",
extras={"tiny": "hmellor/tiny-random-BambaForCausalLM"}), # noqa: E501
"BloomForCausalLM": _HfExamplesInfo("bigscience/bloom-560m",

530
vllm/model_executor/models/bailing_moe.py Normal file
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@ -0,0 +1,530 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# Adapted from
# https://github.com/inclusionAI/Ling/blob/master/models/modeling_bailing_moe.py
# Copyright 2023 The vLLM team.
# Copyright 2023 Antgroup and The HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Inference-only BailingMoE model compatible with HuggingFace weights."""
from collections.abc import Iterable
from typing import Optional, Union
import torch
import torch.nn.functional as F
from torch import nn
from transformers.configuration_utils import PretrainedConfig
from vllm.attention import Attention
from vllm.config import CacheConfig, VllmConfig
from vllm.distributed import (get_pp_group, get_tensor_model_parallel_rank,
get_tensor_model_parallel_world_size,
tensor_model_parallel_all_reduce)
from vllm.model_executor.layers.activation import SiluAndMul
from vllm.model_executor.layers.fused_moe import FusedMoE
from vllm.model_executor.layers.layernorm import RMSNorm
from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
QKVParallelLinear,
ReplicatedLinear,
RowParallelLinear)
from vllm.model_executor.layers.logits_processor import LogitsProcessor
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.sampler import SamplerOutput, get_sampler
from vllm.model_executor.layers.vocab_parallel_embedding import (
ParallelLMHead, VocabParallelEmbedding)
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
from vllm.model_executor.sampling_metadata import SamplingMetadata
from vllm.sequence import IntermediateTensors
from .interfaces import SupportsPP
from .utils import (AutoWeightsLoader, PPMissingLayer, is_pp_missing_parameter,
make_empty_intermediate_tensors_factory, make_layers,
maybe_prefix)
class BailingAttention(nn.Module):
def __init__(
self,
config: PretrainedConfig,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",
):
super().__init__()
self.hidden_size = config.hidden_size
self.total_num_heads = config.num_attention_heads
self.total_kv_heads = config.num_key_value_heads
tp_size = get_tensor_model_parallel_world_size()
assert self.total_num_heads % tp_size == 0
assert self.total_kv_heads % tp_size == 0
assert self.total_num_heads >= self.total_kv_heads
self.num_heads = self.total_num_heads // tp_size
self.head_dim = config.head_dim or (self.hidden_size //
self.total_num_heads)
self.q_size_per_rank = self.head_dim * self.num_heads
self.num_kv_heads = self.total_kv_heads // tp_size
self.kv_size_per_rank = self.num_kv_heads * self.head_dim
self.scale = self.head_dim**-0.5
self.query_key_value = QKVParallelLinear(
self.hidden_size,
self.head_dim,
self.total_num_heads,
self.total_kv_heads,
bias=(config.use_bias or config.use_qkv_bias),
quant_config=quant_config,
prefix=f"{prefix}.query_key_value",
)
self.dense = RowParallelLinear(
self.total_num_heads * self.head_dim,
self.hidden_size,
bias=config.use_bias,
quant_config=quant_config,
prefix=f"{prefix}.dense",
)
self.attn = Attention(self.num_heads,
self.head_dim,
self.scale,
num_kv_heads=self.num_kv_heads,
cache_config=cache_config,
prefix=f"{prefix}.attn")
self.rotary_emb = get_rope(
self.head_dim,
rotary_dim=self.head_dim,
max_position=config.max_position_embeddings,
base=config.rope_theta,
is_neox_style=True,
rope_scaling=config.rope_scaling,
)
def forward(
self,
hidden_states: torch.Tensor,
position_ids: torch.Tensor,
) -> torch.Tensor:
qkv, _ = self.query_key_value(hidden_states)
q, k, v = qkv.split([
self.q_size_per_rank, self.kv_size_per_rank, self.kv_size_per_rank
],
dim=-1)
q, k = self.rotary_emb(position_ids, q, k)
context_layer = self.attn(q, k, v)
attn_output, _ = self.dense(context_layer)
return attn_output
class BailingMLP(nn.Module):
def __init__(
self,
intermediate_size: int,
config: PretrainedConfig,
quant_config: Optional[QuantizationConfig] = None,
reduce_results: Optional[bool] = True,
prefix: str = "",
) -> 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 = SiluAndMul()
def forward(self, x):
x, _ = self.gate_up_proj(x)
x = self.act_fn(x)
x, _ = self.down_proj(x)
return x
class BailingMoE(nn.Module):
def __init__(
self,
intermediate_size: int,
config: PretrainedConfig,
quant_config: Optional[QuantizationConfig] = None,
reduce_results: Optional[bool] = True,
prefix: str = "",
):
super().__init__()
self.tp_size = get_tensor_model_parallel_world_size()
self.tp_rank = get_tensor_model_parallel_rank()
self.num_experts = config.num_experts
self.top_k = config.num_experts_per_tok
self.norm_expert_prob = config.norm_topk_prob
self.hidden_size = config.hidden_size
self.quant_config = quant_config
self.num_shared_experts = config.num_shared_experts
# Gate always runs at half / full precision for now.
self.gate = ReplicatedLinear(self.hidden_size,
self.num_experts,
bias=False,
quant_config=None)
self.experts = FusedMoE(num_experts=self.num_experts,
top_k=self.top_k,
hidden_size=self.hidden_size,
intermediate_size=config.moe_intermediate_size,
reduce_results=False,
renormalize=self.norm_expert_prob,
quant_config=quant_config,
prefix=f"{prefix}.experts")
if self.num_shared_experts > 0:
intermediate_size = (config.moe_intermediate_size *
self.num_shared_experts)
self.shared_experts = BailingMLP(
intermediate_size=intermediate_size,
config=config,
quant_config=quant_config,
reduce_results=False,
prefix=f"{prefix}.shared_experts")
else:
self.shared_experts = None
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
num_tokens, hidden_size = hidden_states.shape
hidden_states = hidden_states.view(-1, hidden_size)
if self.num_shared_experts > 0:
shared_output = self.shared_experts(hidden_states)
# router_logits: (num_tokens, n_experts)
router_logits, _ = self.gate(hidden_states)
final_hidden_states = self.experts(hidden_states=hidden_states,
router_logits=router_logits)
if self.num_shared_experts > 0:
final_hidden_states = final_hidden_states + shared_output
if self.tp_size > 1:
final_hidden_states = tensor_model_parallel_all_reduce(
final_hidden_states)
return final_hidden_states.view(num_tokens, hidden_size)
class BailingMoeBlock(nn.Module):
def __init__(
self,
config: PretrainedConfig,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",
):
super().__init__()
hidden_size = config.hidden_size
intermediate_size = config.intermediate_size
self.input_layernorm = RMSNorm(hidden_size, eps=config.rms_norm_eps)
self.attention = BailingAttention(config,
cache_config,
quant_config,
prefix=f"{prefix}.attention")
self.post_attention_layernorm = RMSNorm(hidden_size,
eps=config.rms_norm_eps)
self.mlp = BailingMoE(intermediate_size,
config,
quant_config,
True,
prefix=f"{prefix}.mlp")
def forward(
self,
hidden_states: torch.Tensor,
position_ids: torch.Tensor,
residual: Optional[torch.Tensor],
) -> torch.Tensor:
if residual is None:
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
else:
hidden_states, residual = self.input_layernorm(
hidden_states, residual)
hidden_states = self.attention(
hidden_states=hidden_states,
position_ids=position_ids,
)
hidden_states, residual = self.post_attention_layernorm(
hidden_states, residual)
hidden_states = self.mlp(hidden_states)
return hidden_states, residual
class BailingMoeModel(nn.Module):
def __init__(
self,
*,
vllm_config: VllmConfig,
prefix: str = "",
):
super().__init__()
config = vllm_config.model_config.hf_config
cache_config = vllm_config.cache_config
quant_config = vllm_config.quant_config
self.config = config
self.vocab_size = config.vocab_size
self.embed_dim = config.hidden_size
if get_pp_group().is_first_rank or (config.tie_word_embeddings
and get_pp_group().is_last_rank):
self.word_embeddings = VocabParallelEmbedding(
self.vocab_size, self.embed_dim)
else:
self.word_embeddings = PPMissingLayer()
self.embedding_dropout = torch.nn.Dropout(config.embedding_dropout)
self.start_layer, self.end_layer, self.layers = make_layers(
config.num_hidden_layers,
lambda prefix: BailingMoeBlock(
config=config,
cache_config=cache_config,
quant_config=quant_config,
prefix=prefix,
),
prefix=f"{prefix}.layers")
self.make_empty_intermediate_tensors = (
make_empty_intermediate_tensors_factory(
["hidden_states", "residual"], config.hidden_size))
if get_pp_group().is_last_rank:
self.norm = RMSNorm(self.embed_dim, eps=config.rms_norm_eps)
else:
self.norm = PPMissingLayer()
def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
return self.word_embeddings(input_ids)
def forward(
self,
input_ids: torch.Tensor,
position_ids: torch.Tensor,
intermediate_tensors: Optional[IntermediateTensors],
inputs_embeds: Optional[torch.Tensor] = None,
) -> Union[torch.Tensor, IntermediateTensors]:
if get_pp_group().is_first_rank:
if inputs_embeds is not None:
hidden_states = inputs_embeds
else:
hidden_states = self.get_input_embeddings(input_ids)
residual = None
else:
assert intermediate_tensors is not None
hidden_states = intermediate_tensors["hidden_states"]
residual = intermediate_tensors["residual"]
for i in range(self.start_layer, self.end_layer):
layer = self.layers[i]
hidden_states, residual = layer(
hidden_states,
position_ids,
residual,
)
if not get_pp_group().is_last_rank:
return IntermediateTensors({
"hidden_states": hidden_states,
"residual": residual
})
hidden_states, _ = self.norm(hidden_states, residual)
return hidden_states
def load_weights(self, weights: Iterable[tuple[str,
torch.Tensor]]) -> set[str]:
stacked_params_mapping = [
# (param_name, shard_name, shard_id)
("gate_up_proj", "gate_proj", 0),
("gate_up_proj", "up_proj", 1),
]
expert_params_mapping = FusedMoE.make_expert_params_mapping(
ckpt_gate_proj_name="gate_proj",
ckpt_down_proj_name="down_proj",
ckpt_up_proj_name="up_proj",
num_experts=self.config.num_experts)
params_dict = dict(self.named_parameters(remove_duplicate=False))
loaded_params: set[str] = set()
for name, loaded_weight in weights:
if self.config.norm_head and "lm_head.weight" in name:
loaded_weight = F.normalize(loaded_weight,
dim=0,
p=2,
eps=1e-7)
for (param_name, weight_name, shard_id) in stacked_params_mapping:
if weight_name not in name:
continue
if "mlp.experts" in name:
continue
name = name.replace(weight_name, param_name)
# Skip loading extra bias for GPTQ models.
if name.endswith(".bias") and name not in params_dict:
continue
if name not in params_dict:
continue
if is_pp_missing_parameter(name, self):
continue
param = params_dict[name]
weight_loader = param.weight_loader
weight_loader(param, loaded_weight, shard_id)
break
else:
for mapping in expert_params_mapping:
param_name, weight_name, expert_id, shard_id = mapping
if weight_name not in name:
continue
name = name.replace(weight_name, param_name)
if is_pp_missing_parameter(name, self):
continue
param = params_dict[name]
weight_loader = param.weight_loader
weight_loader(param,
loaded_weight,
name,
shard_id=shard_id,
expert_id=expert_id)
break
else:
if name.endswith(".bias") and name not in params_dict:
continue
if name not in params_dict:
continue
if is_pp_missing_parameter(name, self):
continue
param = params_dict[name]
weight_loader = getattr(param, "weight_loader",
default_weight_loader)
weight_loader(param, loaded_weight)
loaded_params.add(name)
return loaded_params
class BailingMoeForCausalLM(nn.Module, SupportsPP):
packed_modules_mapping = {
"query_key_value": ["query_key_value"],
"gate_up_proj": [
"gate_proj",
"up_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.max_position_embeddings = config.max_position_embeddings
self.model = BailingMoeModel(vllm_config=vllm_config,
prefix=maybe_prefix(prefix, "model"))
if get_pp_group().is_last_rank:
self.lm_head = (self.word_embeddings if config.tie_word_embeddings
else ParallelLMHead(config.vocab_size,
config.hidden_size,
quant_config=quant_config))
self.logits_processor = LogitsProcessor(config.vocab_size)
else:
self.lm_head = PPMissingLayer()
self.sampler = get_sampler()
self.make_empty_intermediate_tensors = (
self.model.make_empty_intermediate_tensors)
def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
return self.model.get_input_embeddings(input_ids)
def forward(
self,
input_ids: torch.Tensor,
positions: torch.Tensor,
intermediate_tensors: Optional[IntermediateTensors] = None,
inputs_embeds: Optional[torch.Tensor] = None,
) -> Union[torch.Tensor, IntermediateTensors]:
model_output = self.model(input_ids, positions, intermediate_tensors,
inputs_embeds)
return model_output
def compute_logits(
self,
hidden_states: torch.Tensor,
sampling_metadata: SamplingMetadata,
) -> Optional[torch.Tensor]:
logits = self.logits_processor(self.lm_head, hidden_states,
sampling_metadata)
return logits
def sample(
self,
logits: torch.Tensor,
sampling_metadata: SamplingMetadata,
) -> Optional[SamplerOutput]:
next_tokens = self.sampler(logits, sampling_metadata)
return next_tokens
def load_weights(self, weights: Iterable[tuple[str,
torch.Tensor]]) -> set[str]:
loader = AutoWeightsLoader(
self,
skip_prefixes=(["lm_head."]
if self.config.tie_word_embeddings else None),
)
return loader.load_weights(weights)

1
vllm/model_executor/models/registry.py
View File

@ -41,6 +41,7 @@ _TEXT_GENERATION_MODELS = {
"BaiChuanForCausalLM": ("baichuan", "BaiChuanForCausalLM"),
# baichuan-13b, lower case 'c' in the class name
"BaichuanForCausalLM": ("baichuan", "BaichuanForCausalLM"),
"BailingMoeForCausalLM": ("bailing_moe", "BailingMoeForCausalLM"),
"BambaForCausalLM": ("bamba", "BambaForCausalLM"),
"BloomForCausalLM": ("bloom", "BloomForCausalLM"),
"ChatGLMModel": ("chatglm", "ChatGLMForCausalLM"),