813 lines
29 KiB
Python
813 lines
29 KiB
Python
# SPDX-License-Identifier: Apache-2.0
|
|
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
|
"""Inference-only Qwen3Next model."""
|
|
|
|
from collections.abc import Iterable
|
|
from itertools import islice
|
|
|
|
import torch
|
|
from torch import nn
|
|
|
|
from vllm.compilation.decorators import support_torch_compile
|
|
from vllm.config import (
|
|
CacheConfig,
|
|
ModelConfig,
|
|
VllmConfig,
|
|
get_current_vllm_config,
|
|
)
|
|
from vllm.distributed import (
|
|
get_ep_group,
|
|
get_pp_group,
|
|
get_tensor_model_parallel_world_size,
|
|
tensor_model_parallel_all_gather,
|
|
)
|
|
from vllm.logger import init_logger
|
|
from vllm.model_executor.layers.attention import Attention
|
|
from vllm.model_executor.layers.fused_moe import SharedFusedMoE
|
|
from vllm.model_executor.layers.layernorm import (
|
|
GemmaRMSNorm as Qwen3NextRMSNorm,
|
|
)
|
|
from vllm.model_executor.layers.linear import (
|
|
QKVParallelLinear,
|
|
ReplicatedLinear,
|
|
RowParallelLinear,
|
|
)
|
|
from vllm.model_executor.layers.logits_processor import LogitsProcessor
|
|
from vllm.model_executor.layers.mamba.gdn_linear_attn import GatedDeltaNetAttention
|
|
from vllm.model_executor.layers.mamba.mamba_utils import (
|
|
MambaStateCopyFunc,
|
|
MambaStateCopyFuncCalculator,
|
|
MambaStateDtypeCalculator,
|
|
MambaStateShapeCalculator,
|
|
)
|
|
from vllm.model_executor.layers.quantization 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,
|
|
)
|
|
from vllm.model_executor.models.qwen2_moe import Qwen2MoeMLP as Qwen3NextMLP
|
|
from vllm.model_executor.models.utils import sequence_parallel_chunk
|
|
from vllm.sequence import IntermediateTensors
|
|
from vllm.transformers_utils.configs.qwen3_next import Qwen3NextConfig
|
|
|
|
from .interfaces import (
|
|
HasInnerState,
|
|
IsHybrid,
|
|
MixtureOfExperts,
|
|
SupportsLoRA,
|
|
SupportsPP,
|
|
)
|
|
from .utils import (
|
|
AutoWeightsLoader,
|
|
PPMissingLayer,
|
|
extract_layer_index,
|
|
is_pp_missing_parameter,
|
|
make_empty_intermediate_tensors_factory,
|
|
make_layers,
|
|
maybe_prefix,
|
|
)
|
|
|
|
logger = init_logger(__name__)
|
|
|
|
KVCache = tuple[torch.Tensor, torch.Tensor]
|
|
|
|
|
|
class Qwen3NextSparseMoeBlock(nn.Module):
|
|
def __init__(self, vllm_config: VllmConfig, prefix: str = ""):
|
|
super().__init__()
|
|
|
|
config = vllm_config.model_config.hf_text_config
|
|
parallel_config = vllm_config.parallel_config
|
|
quant_config = vllm_config.quant_config
|
|
|
|
self.tp_size = get_tensor_model_parallel_world_size()
|
|
|
|
self.ep_group = get_ep_group().device_group
|
|
self.ep_rank = get_ep_group().rank_in_group
|
|
self.ep_size = self.ep_group.size()
|
|
self.n_routed_experts = config.num_experts
|
|
|
|
self.is_sequence_parallel = parallel_config.use_sequence_parallel_moe
|
|
|
|
if self.tp_size > config.num_experts:
|
|
raise ValueError(
|
|
f"Tensor parallel size {self.tp_size} is greater than "
|
|
f"the number of experts {config.num_experts}."
|
|
)
|
|
|
|
# Load balancing settings.
|
|
vllm_config = get_current_vllm_config()
|
|
eplb_config = vllm_config.parallel_config.eplb_config
|
|
self.enable_eplb = parallel_config.enable_eplb
|
|
|
|
self.n_logical_experts = self.n_routed_experts
|
|
self.n_redundant_experts = eplb_config.num_redundant_experts
|
|
self.n_physical_experts = self.n_logical_experts + self.n_redundant_experts
|
|
self.n_local_physical_experts = self.n_physical_experts // self.ep_size
|
|
|
|
self.physical_expert_start = self.ep_rank * self.n_local_physical_experts
|
|
self.physical_expert_end = (
|
|
self.physical_expert_start + self.n_local_physical_experts
|
|
)
|
|
|
|
self.gate = ReplicatedLinear(
|
|
config.hidden_size,
|
|
config.num_experts,
|
|
bias=False,
|
|
quant_config=None,
|
|
prefix=f"{prefix}.gate",
|
|
)
|
|
|
|
self.shared_expert_gate = ReplicatedLinear(
|
|
config.hidden_size,
|
|
1,
|
|
bias=False,
|
|
quant_config=None,
|
|
prefix=f"{prefix}.shared_expert_gate",
|
|
)
|
|
|
|
if config.shared_expert_intermediate_size > 0:
|
|
self.shared_expert = Qwen3NextMLP(
|
|
hidden_size=config.hidden_size,
|
|
intermediate_size=config.shared_expert_intermediate_size,
|
|
hidden_act=config.hidden_act,
|
|
quant_config=quant_config,
|
|
reduce_results=False,
|
|
expert_gate=self.shared_expert_gate,
|
|
prefix=f"{prefix}.shared_expert",
|
|
)
|
|
else:
|
|
self.shared_expert = None
|
|
|
|
self.experts = SharedFusedMoE(
|
|
shared_experts=self.shared_expert,
|
|
gate=self.gate,
|
|
num_experts=self.n_routed_experts,
|
|
top_k=config.num_experts_per_tok,
|
|
hidden_size=config.hidden_size,
|
|
intermediate_size=config.moe_intermediate_size,
|
|
reduce_results=False,
|
|
renormalize=getattr(config, "norm_topk_prob", True),
|
|
quant_config=quant_config,
|
|
prefix=f"{prefix}.experts",
|
|
enable_eplb=self.enable_eplb,
|
|
num_redundant_experts=self.n_redundant_experts,
|
|
is_sequence_parallel=self.is_sequence_parallel,
|
|
)
|
|
|
|
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
|
# NOTE: hidden_states can have either 1D or 2D shape.
|
|
orig_shape = hidden_states.shape
|
|
num_tokens, hidden_dim = hidden_states.shape
|
|
hidden_states = hidden_states.view(-1, hidden_dim)
|
|
|
|
if self.is_sequence_parallel:
|
|
hidden_states = sequence_parallel_chunk(hidden_states)
|
|
|
|
if self.experts.is_internal_router:
|
|
# In this case, the gate/router runs inside the FusedMoE class
|
|
final_hidden_states = self.experts(
|
|
hidden_states=hidden_states, router_logits=hidden_states
|
|
)
|
|
else:
|
|
# 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.shared_expert is not None:
|
|
final_hidden_states = final_hidden_states[0] + final_hidden_states[1]
|
|
|
|
if self.is_sequence_parallel:
|
|
final_hidden_states = tensor_model_parallel_all_gather(
|
|
final_hidden_states, 0
|
|
)
|
|
final_hidden_states = final_hidden_states[:num_tokens]
|
|
elif self.tp_size > 1:
|
|
final_hidden_states = self.experts.maybe_all_reduce_tensor_model_parallel( # noqa E501
|
|
final_hidden_states
|
|
)
|
|
|
|
return final_hidden_states.view(orig_shape)
|
|
|
|
|
|
class Qwen3NextAttention(nn.Module):
|
|
def __init__(
|
|
self,
|
|
config: Qwen3NextConfig,
|
|
model_config: ModelConfig | None = None,
|
|
cache_config: CacheConfig | None = None,
|
|
quant_config: QuantizationConfig | None = None,
|
|
prefix: str = "",
|
|
) -> None:
|
|
super().__init__()
|
|
self.config = config
|
|
self.hidden_size = config.hidden_size
|
|
tp_size = get_tensor_model_parallel_world_size()
|
|
self.total_num_heads = config.num_attention_heads
|
|
assert self.total_num_heads % tp_size == 0
|
|
self.num_heads = self.total_num_heads // tp_size
|
|
self.total_num_kv_heads = config.num_key_value_heads
|
|
if self.total_num_kv_heads >= tp_size:
|
|
# Number of KV heads is greater than TP size, so we partition
|
|
# the KV heads across multiple tensor parallel GPUs.
|
|
assert self.total_num_kv_heads % tp_size == 0
|
|
else:
|
|
# Number of KV heads is less than TP size, so we replicate
|
|
# the KV heads across multiple tensor parallel GPUs.
|
|
assert tp_size % self.total_num_kv_heads == 0
|
|
self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
|
|
self.head_dim = config.head_dim or (self.hidden_size // self.num_heads)
|
|
self.q_size = self.num_heads * self.head_dim
|
|
self.kv_size = self.num_kv_heads * self.head_dim
|
|
self.scaling = self.head_dim**-0.5
|
|
self.dual_chunk_attention_config = getattr(
|
|
config, "dual_chunk_attention_config", None
|
|
)
|
|
self.attn_output_gate = getattr(config, "attn_output_gate", True)
|
|
|
|
self.qkv_proj = QKVParallelLinear(
|
|
config.hidden_size,
|
|
self.head_dim,
|
|
self.total_num_heads * (1 + self.attn_output_gate),
|
|
self.total_num_kv_heads,
|
|
bias=getattr(config, "qkv_bias", False),
|
|
quant_config=quant_config,
|
|
prefix=f"{prefix}.qkv_proj",
|
|
)
|
|
|
|
self.o_proj = RowParallelLinear(
|
|
self.total_num_heads * self.head_dim,
|
|
config.hidden_size,
|
|
bias=False,
|
|
quant_config=quant_config,
|
|
prefix=f"{prefix}.o_proj",
|
|
)
|
|
|
|
self.rotary_emb = get_rope(
|
|
head_size=self.head_dim,
|
|
max_position=config.max_position_embeddings,
|
|
rope_parameters=config.rope_parameters,
|
|
dual_chunk_attention_config=self.dual_chunk_attention_config,
|
|
)
|
|
|
|
self.attn = Attention(
|
|
self.num_heads,
|
|
self.head_dim,
|
|
self.scaling,
|
|
num_kv_heads=self.num_kv_heads,
|
|
cache_config=cache_config,
|
|
quant_config=quant_config,
|
|
prefix=f"{prefix}.attn",
|
|
**{
|
|
"layer_idx": extract_layer_index(prefix),
|
|
"dual_chunk_attention_config": self.dual_chunk_attention_config,
|
|
}
|
|
if self.dual_chunk_attention_config
|
|
else {},
|
|
)
|
|
|
|
self.q_norm = Qwen3NextRMSNorm(self.head_dim, eps=config.rms_norm_eps)
|
|
self.k_norm = Qwen3NextRMSNorm(self.head_dim, eps=config.rms_norm_eps)
|
|
|
|
def forward(
|
|
self,
|
|
positions: torch.Tensor,
|
|
output: torch.Tensor,
|
|
hidden_states: torch.Tensor,
|
|
):
|
|
qkv, _ = self.qkv_proj(hidden_states)
|
|
|
|
if self.attn_output_gate:
|
|
q_gate, k, v = qkv.split(
|
|
[self.q_size * 2, self.kv_size, self.kv_size], dim=-1
|
|
)
|
|
orig_shape = q_gate.shape[:-1]
|
|
q_gate = q_gate.view(*orig_shape, self.num_heads, -1)
|
|
q, gate = torch.chunk(q_gate, 2, dim=-1)
|
|
q = q.reshape(*orig_shape, -1)
|
|
gate = gate.reshape(*orig_shape, -1)
|
|
else:
|
|
q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
|
|
|
|
q = self.q_norm(q.view(-1, self.num_heads, self.head_dim)).view(
|
|
-1, self.num_heads * self.head_dim
|
|
)
|
|
k = self.k_norm(k.view(-1, self.num_kv_heads, self.head_dim)).view(
|
|
-1, self.num_kv_heads * self.head_dim
|
|
)
|
|
|
|
q, k = self.rotary_emb(positions, q, k)
|
|
|
|
attn_output = self.attn(q, k, v)
|
|
|
|
if self.attn_output_gate:
|
|
gate = torch.sigmoid(gate)
|
|
attn_output = attn_output * gate
|
|
|
|
output[:], _ = self.o_proj(attn_output)
|
|
|
|
|
|
class Qwen3NextDecoderLayer(nn.Module):
|
|
def __init__(
|
|
self,
|
|
vllm_config: VllmConfig,
|
|
layer_type: str,
|
|
prefix: str = "",
|
|
) -> None:
|
|
super().__init__()
|
|
|
|
config = vllm_config.model_config.hf_config
|
|
model_config = vllm_config.model_config
|
|
cache_config = vllm_config.cache_config
|
|
quant_config = vllm_config.quant_config
|
|
|
|
self.layer_type = layer_type
|
|
self.layer_idx = extract_layer_index(prefix)
|
|
|
|
if self.layer_type == "linear_attention":
|
|
self.linear_attn = GatedDeltaNetAttention(
|
|
config,
|
|
vllm_config=vllm_config,
|
|
prefix=f"{prefix}.linear_attn",
|
|
gqa_interleaved_layout=True,
|
|
)
|
|
elif self.layer_type == "full_attention":
|
|
self.self_attn = Qwen3NextAttention(
|
|
config,
|
|
model_config=model_config,
|
|
cache_config=cache_config,
|
|
quant_config=quant_config,
|
|
prefix=f"{prefix}.self_attn",
|
|
)
|
|
else:
|
|
raise ValueError(f"Invalid layer_type {self.layer_type}")
|
|
|
|
mlp_only_layers = (
|
|
[] if not hasattr(config, "mlp_only_layers") else config.mlp_only_layers
|
|
)
|
|
if (self.layer_idx not in mlp_only_layers) and (
|
|
config.num_experts > 0
|
|
and (self.layer_idx + 1) % config.decoder_sparse_step == 0
|
|
):
|
|
self.mlp = Qwen3NextSparseMoeBlock(
|
|
vllm_config=vllm_config,
|
|
prefix=f"{prefix}.mlp",
|
|
)
|
|
else:
|
|
self.mlp = Qwen3NextMLP(
|
|
hidden_size=config.hidden_size,
|
|
intermediate_size=config.intermediate_size,
|
|
hidden_act=config.hidden_act,
|
|
quant_config=quant_config,
|
|
prefix=f"{prefix}.mlp",
|
|
)
|
|
|
|
self.input_layernorm = Qwen3NextRMSNorm(
|
|
config.hidden_size, eps=config.rms_norm_eps
|
|
)
|
|
self.post_attention_layernorm = Qwen3NextRMSNorm(
|
|
config.hidden_size, eps=config.rms_norm_eps
|
|
)
|
|
|
|
self.layer_scale = getattr(config, "layer_scale", False)
|
|
if self.layer_scale:
|
|
self.attn_layer_scale = torch.nn.Parameter(
|
|
torch.zeros(
|
|
1,
|
|
1,
|
|
config.hidden_size,
|
|
),
|
|
)
|
|
self.ffn_layer_scale = torch.nn.Parameter(
|
|
torch.zeros(
|
|
1,
|
|
1,
|
|
config.hidden_size,
|
|
),
|
|
)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
residual: torch.Tensor | None,
|
|
positions: torch.Tensor = None,
|
|
**kwargs: object,
|
|
):
|
|
if residual is None:
|
|
residual = hidden_states
|
|
hidden_states = self.input_layernorm(hidden_states)
|
|
else:
|
|
hidden_states, residual = self.input_layernorm(hidden_states, residual)
|
|
|
|
self_attention_output = torch.empty_like(hidden_states)
|
|
if self.layer_type == "linear_attention":
|
|
self.linear_attn(
|
|
hidden_states=hidden_states,
|
|
output=self_attention_output,
|
|
)
|
|
elif self.layer_type == "full_attention":
|
|
self.self_attn(
|
|
hidden_states=hidden_states,
|
|
output=self_attention_output,
|
|
positions=positions,
|
|
)
|
|
else:
|
|
raise ValueError("Invalid layer_type")
|
|
hidden_states = self_attention_output
|
|
|
|
if self.layer_scale:
|
|
if len(hidden_states.shape) == 2:
|
|
hidden_states = hidden_states * (
|
|
self.attn_layer_scale.to(hidden_states.dtype)[0] + 1
|
|
)
|
|
else:
|
|
hidden_states = hidden_states * (
|
|
self.attn_layer_scale.to(hidden_states.dtype) + 1
|
|
)
|
|
|
|
# Fully Connected
|
|
hidden_states, residual = self.post_attention_layernorm(hidden_states, residual)
|
|
hidden_states = self.mlp(hidden_states)
|
|
|
|
if self.layer_scale:
|
|
if len(hidden_states.shape) == 2:
|
|
hidden_states = hidden_states * (
|
|
self.ffn_layer_scale.to(hidden_states.dtype)[0] + 1
|
|
)
|
|
else:
|
|
assert len(hidden_states.shape) == len(self.ffn_layer_scale.shape), (
|
|
f"shape must be the same {len(hidden_states.shape)}, "
|
|
f"{len(self.ffn_layer_scale.shape)}"
|
|
)
|
|
hidden_states = hidden_states * (
|
|
self.ffn_layer_scale.to(hidden_states.dtype) + 1
|
|
)
|
|
|
|
return hidden_states, residual
|
|
|
|
|
|
@support_torch_compile
|
|
class Qwen3NextModel(nn.Module):
|
|
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
|
|
super().__init__()
|
|
|
|
config: Qwen3NextConfig = vllm_config.model_config.hf_text_config
|
|
parallel_config = vllm_config.parallel_config
|
|
|
|
eplb_config = parallel_config.eplb_config
|
|
self.num_redundant_experts = eplb_config.num_redundant_experts
|
|
|
|
self.config = config
|
|
|
|
self.vocab_size = config.vocab_size
|
|
|
|
self.embed_tokens = VocabParallelEmbedding(
|
|
self.vocab_size,
|
|
config.hidden_size,
|
|
)
|
|
|
|
def get_layer(prefix: str):
|
|
return Qwen3NextDecoderLayer(
|
|
vllm_config,
|
|
layer_type=config.layer_types[extract_layer_index(prefix)],
|
|
prefix=prefix,
|
|
)
|
|
|
|
self.start_layer, self.end_layer, self.layers = make_layers(
|
|
config.num_hidden_layers, get_layer, 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 = Qwen3NextRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
|
else:
|
|
self.norm = PPMissingLayer()
|
|
|
|
self.aux_hidden_state_layers: tuple[int, ...] = ()
|
|
|
|
def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
|
|
return self.embed_tokens(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 | tuple[torch.Tensor, list[torch.Tensor]]:
|
|
if get_pp_group().is_first_rank:
|
|
if inputs_embeds is not None:
|
|
hidden_states = inputs_embeds
|
|
else:
|
|
hidden_states = self.embed_input_ids(input_ids)
|
|
residual = None
|
|
else:
|
|
assert intermediate_tensors is not None
|
|
hidden_states = intermediate_tensors["hidden_states"]
|
|
residual = intermediate_tensors["residual"]
|
|
|
|
aux_hidden_states = []
|
|
for layer_idx, layer in enumerate(
|
|
islice(self.layers, self.start_layer, self.end_layer),
|
|
start=self.start_layer,
|
|
):
|
|
if layer_idx in self.aux_hidden_state_layers:
|
|
aux_hidden_states.append(
|
|
hidden_states + residual if residual is not None else hidden_states
|
|
)
|
|
hidden_states, residual = layer(
|
|
positions=positions,
|
|
hidden_states=hidden_states,
|
|
residual=residual,
|
|
)
|
|
|
|
if not get_pp_group().is_last_rank:
|
|
return IntermediateTensors(
|
|
{"hidden_states": hidden_states, "residual": residual}
|
|
)
|
|
hidden_states, _ = self.norm(hidden_states, residual)
|
|
if aux_hidden_states:
|
|
return hidden_states, aux_hidden_states
|
|
return hidden_states
|
|
|
|
def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
|
|
# Params for weights, fp8 weight scales, fp8 activation scales
|
|
# (param_name, weight_name, expert_id, shard_id)
|
|
return SharedFusedMoE.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=getattr(self.config, "num_experts", 0),
|
|
num_redundant_experts=self.num_redundant_experts,
|
|
)
|
|
|
|
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
|
|
stacked_params_mapping = [
|
|
# (param_name, shard_name, shard_id)
|
|
("qkv_proj", "q_proj", "q"),
|
|
("qkv_proj", "k_proj", "k"),
|
|
("qkv_proj", "v_proj", "v"),
|
|
("gate_up_proj", "gate_proj", 0),
|
|
("gate_up_proj", "up_proj", 1),
|
|
]
|
|
|
|
params_dict = dict(self.named_parameters())
|
|
loaded_params: set[str] = set()
|
|
expert_params_mapping = self.get_expert_mapping()
|
|
for name, loaded_weight in weights:
|
|
if "rotary_emb.inv_freq" in name:
|
|
continue
|
|
|
|
if name.startswith("mtp."):
|
|
continue
|
|
|
|
# Remapping the name of FP8 kv-scale.
|
|
if name.endswith("scale"):
|
|
name = maybe_remap_kv_scale_name(name, params_dict)
|
|
if name is None:
|
|
continue
|
|
|
|
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
|
|
# Skip layers on other devices.
|
|
if is_pp_missing_parameter(name, self):
|
|
continue
|
|
# name = apply_attn_prefix(name, params_dict)
|
|
if name not in params_dict:
|
|
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)
|
|
# Skip layers on other devices.
|
|
if is_pp_missing_parameter(name, self):
|
|
continue
|
|
# Skip loading extra bias for GPTQ models.
|
|
if (
|
|
name.endswith(".bias") or name.endswith("_bias")
|
|
) and name not in params_dict:
|
|
continue
|
|
if name not in params_dict:
|
|
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:
|
|
# Skip loading extra bias for GPTQ models.
|
|
if name.endswith(".bias") and name not in params_dict:
|
|
continue
|
|
if is_pp_missing_parameter(name, self):
|
|
continue
|
|
if name not in params_dict:
|
|
logger.warning_once(
|
|
f"Parameter {name} not found in params_dict, skip loading"
|
|
)
|
|
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 QwenNextMixtureOfExperts(MixtureOfExperts):
|
|
def update_physical_experts_metadata(
|
|
self,
|
|
num_physical_experts: int,
|
|
num_local_physical_experts: int,
|
|
) -> None:
|
|
assert self.num_local_physical_experts == num_local_physical_experts
|
|
self.num_physical_experts = num_physical_experts
|
|
self.num_local_physical_experts = num_local_physical_experts
|
|
self.num_redundant_experts = num_physical_experts - self.num_logical_experts
|
|
for layer in self.model.layers:
|
|
if isinstance(layer.mlp, Qwen3NextSparseMoeBlock):
|
|
moe = layer.mlp
|
|
moe.n_local_physical_experts = num_local_physical_experts
|
|
moe.n_physical_experts = num_physical_experts
|
|
moe.n_redundant_experts = self.num_redundant_experts
|
|
moe.experts.update_expert_map()
|
|
|
|
def set_moe_parameters(self):
|
|
self.expert_weights = []
|
|
|
|
self.moe_layers = []
|
|
example_moe = None
|
|
for layer in self.model.layers:
|
|
if isinstance(layer, Qwen3NextDecoderLayer) and isinstance(
|
|
layer.mlp, Qwen3NextSparseMoeBlock
|
|
):
|
|
example_moe = layer.mlp
|
|
self.moe_layers.append(layer.mlp.experts)
|
|
|
|
if example_moe is None:
|
|
raise RuntimeError("No Qwen3Next layer found in the model.layers.")
|
|
|
|
# Set MoE hyperparameters
|
|
self.num_moe_layers = len(self.moe_layers)
|
|
self.num_expert_groups = 1
|
|
self.num_shared_experts = 0
|
|
self.num_logical_experts = example_moe.n_logical_experts
|
|
self.num_physical_experts = example_moe.n_physical_experts
|
|
self.num_local_physical_experts = example_moe.n_local_physical_experts
|
|
self.num_routed_experts = example_moe.n_routed_experts
|
|
self.num_redundant_experts = example_moe.n_redundant_experts
|
|
|
|
|
|
class Qwen3NextForCausalLM(
|
|
nn.Module,
|
|
HasInnerState,
|
|
SupportsLoRA,
|
|
SupportsPP,
|
|
QwenNextMixtureOfExperts,
|
|
IsHybrid,
|
|
):
|
|
packed_modules_mapping = {
|
|
"qkv_proj": [
|
|
"q_proj",
|
|
"k_proj",
|
|
"v_proj",
|
|
],
|
|
"gate_up_proj": ["gate_proj", "up_proj"],
|
|
"in_proj_qkvz": ["in_proj_qkvz"],
|
|
"in_proj_ba": ["in_proj_ba"],
|
|
}
|
|
|
|
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
|
|
config = vllm_config.model_config.hf_text_config
|
|
self.vllm_config = vllm_config
|
|
self.model_config = vllm_config.model_config
|
|
cache_config = vllm_config.cache_config
|
|
|
|
scheduler_config = vllm_config.scheduler_config
|
|
if cache_config.mamba_cache_mode == "all":
|
|
raise NotImplementedError(
|
|
"Qwen3Next currently does not support 'all' prefix caching, "
|
|
"please use '--mamba-cache-mode=align' instead"
|
|
)
|
|
self.quant_config = vllm_config.quant_config
|
|
|
|
super().__init__()
|
|
self.config = config
|
|
self.scheduler_config = scheduler_config
|
|
self.model = Qwen3NextModel(
|
|
vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
|
|
)
|
|
|
|
self.lm_head = ParallelLMHead(
|
|
config.vocab_size,
|
|
config.hidden_size,
|
|
prefix=maybe_prefix(prefix, "lm_head"),
|
|
)
|
|
self.logits_processor = LogitsProcessor(config.vocab_size)
|
|
self.make_empty_intermediate_tensors = (
|
|
self.model.make_empty_intermediate_tensors
|
|
)
|
|
|
|
# Set MoE hyperparameters
|
|
self.set_moe_parameters()
|
|
|
|
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,
|
|
**kwargs: object,
|
|
):
|
|
hidden_states = self.model(
|
|
input_ids, positions, intermediate_tensors, inputs_embeds
|
|
)
|
|
|
|
return hidden_states
|
|
|
|
@classmethod
|
|
def get_mamba_state_dtype_from_config(
|
|
cls,
|
|
vllm_config: "VllmConfig",
|
|
) -> tuple[torch.dtype, torch.dtype]:
|
|
return MambaStateDtypeCalculator.gated_delta_net_state_dtype(
|
|
vllm_config.model_config.dtype,
|
|
vllm_config.cache_config.mamba_cache_dtype,
|
|
vllm_config.cache_config.mamba_ssm_cache_dtype,
|
|
)
|
|
|
|
@classmethod
|
|
def get_mamba_state_shape_from_config(
|
|
cls, vllm_config: "VllmConfig"
|
|
) -> tuple[tuple[int, int], tuple[int, int]]:
|
|
parallel_config = vllm_config.parallel_config
|
|
hf_config = vllm_config.model_config.hf_text_config
|
|
tp_size = parallel_config.tensor_parallel_size
|
|
num_spec = (
|
|
vllm_config.speculative_config.num_speculative_tokens
|
|
if vllm_config.speculative_config
|
|
else 0
|
|
)
|
|
return MambaStateShapeCalculator.gated_delta_net_state_shape(
|
|
tp_size,
|
|
hf_config.linear_num_key_heads,
|
|
hf_config.linear_num_value_heads,
|
|
hf_config.linear_key_head_dim,
|
|
hf_config.linear_value_head_dim,
|
|
hf_config.linear_conv_kernel_dim,
|
|
num_spec,
|
|
)
|
|
|
|
@classmethod
|
|
def get_mamba_state_copy_func(cls) -> tuple[MambaStateCopyFunc, MambaStateCopyFunc]:
|
|
return MambaStateCopyFuncCalculator.gated_delta_net_state_copy_func()
|
|
|
|
def compute_logits(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
) -> torch.Tensor | None:
|
|
return self.logits_processor(self.lm_head, hidden_states)
|
|
|
|
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
|
|
loader = AutoWeightsLoader(
|
|
self,
|
|
skip_prefixes=["mtp."],
|
|
)
|
|
return loader.load_weights(weights)
|
|
|
|
def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
|
|
return self.model.get_expert_mapping()
|