2d885869c5
### What this PR does / why we need it?
Fixes #3096
1. Fix kv cache initialization error of attention layer. There are some
models with layer name like `attn.attn`, instead of `self_attn`, but the
initialization of kv cache tensors only check for `self_attn` and
`attn.attn`, which leding to the error `AssertionError: Some layers are
not correctly initialized`
2. Set the default value of input arg `sampling_metadata` in
`compute_logits` for the modeling files in vllm-ascend. Thus fixing the
error `Qwen3NextForCausalLM.compute_logits() missing 1 required
positional argument: 'sampling_metadata'`
### Does this PR introduce _any_ user-facing change?
N/A
### How was this patch tested?
test locally with internlm
- vLLM version: v0.10.2
- vLLM main:
5aeb925452
---------
Signed-off-by: MengqingCao <cmq0113@163.com>
1031 lines
41 KiB
Python
1031 lines
41 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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# mypy: ignore-errors
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"""Inference-only Qwen3Next model."""
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from collections.abc import Iterable
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from typing import Optional
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import torch
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from einops import rearrange
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from torch import nn
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from transformers.activations import ACT2FN
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from vllm import envs
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from vllm.attention import AttentionBackend, AttentionMetadata
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from vllm.compilation.decorators import support_torch_compile
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from vllm.config import (CacheConfig, ModelConfig, SpeculativeConfig,
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VllmConfig, get_current_vllm_config)
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from vllm.distributed import (divide, get_pp_group,
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get_tensor_model_parallel_rank,
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get_tensor_model_parallel_world_size)
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from vllm.forward_context import ForwardContext, get_forward_context
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from vllm.model_executor.layers.fla.ops import RMSNormGated
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from vllm.model_executor.layers.fla.ops.chunk import chunk_gated_delta_rule
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from vllm.model_executor.layers.fla.ops.fused_recurrent import \
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fused_recurrent_gated_delta_rule
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from vllm.model_executor.layers.fused_moe import FusedMoE
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# yapf conflicts with isort for this block
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# yapf: disable
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from vllm.model_executor.layers.layernorm import \
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GemmaRMSNorm as Qwen3NextRMSNorm
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# yapf: enable
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from vllm.model_executor.layers.linear import (ColumnParallelLinear,
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MergedColumnParallelLinear,
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RowParallelLinear)
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from vllm.model_executor.layers.logits_processor import LogitsProcessor
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from vllm.model_executor.layers.mamba.abstract import MambaBase
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from vllm.model_executor.layers.mamba.mamba_mixer2 import \
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mamba_v2_sharded_weight_loader
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from vllm.model_executor.layers.mamba.mamba_utils import (
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MambaStateDtypeCalculator, MambaStateShapeCalculator)
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from vllm.model_executor.layers.mamba.ops.causal_conv1d import (
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causal_conv1d_fn, causal_conv1d_update)
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from vllm.model_executor.layers.quantization import QuantizationConfig
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from vllm.model_executor.layers.vocab_parallel_embedding import (
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DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
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from vllm.model_executor.model_loader.weight_utils import (
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default_weight_loader, sharded_weight_loader)
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from vllm.model_executor.models.interfaces import (HasInnerState, IsHybrid,
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MixtureOfExperts,
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SupportsLoRA, SupportsPP)
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from vllm.model_executor.models.mamba_cache import MambaCacheParams
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from vllm.model_executor.models.qwen2_moe import Qwen2MoeMLP as Qwen3NextMLP
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from vllm.model_executor.models.qwen3_next import (Qwen3NextAttention,
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Qwen3NextSparseMoeBlock,
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fused_gdn_gating)
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from vllm.model_executor.models.utils import (
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AutoWeightsLoader, PPMissingLayer, extract_layer_index,
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is_pp_missing_parameter, make_empty_intermediate_tensors_factory,
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make_layers, maybe_prefix)
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from vllm.model_executor.utils import set_weight_attrs
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from vllm.platforms import current_platform
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from vllm.sequence import IntermediateTensors
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from vllm.transformers_utils.configs import Qwen3NextConfig
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from vllm.utils import direct_register_custom_op
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from vllm.v1.attention.backends.gdn_attn import GDNAttentionMetadata
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class Qwen3NextGatedDeltaNet(nn.Module, MambaBase):
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@property
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def mamba_type(self) -> str:
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return "linear_attention"
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def get_attn_backend(self) -> type["AttentionBackend"]:
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from vllm.v1.attention.backends.gdn_attn import GDNAttentionBackend
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return GDNAttentionBackend
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def get_state_dtype(self) -> tuple[torch.dtype, torch.dtype]:
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return MambaStateDtypeCalculator.gated_delta_net_state_dtype(
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self.model_config.dtype, self.cache_config.mamba_cache_dtype)
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def get_state_shape(self) -> tuple[tuple[int, ...], tuple[int, ...]]:
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return MambaStateShapeCalculator.gated_delta_net_state_shape(
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self.tp_size,
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self.num_k_heads,
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self.num_v_heads,
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self.head_k_dim,
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self.head_v_dim,
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self.conv_kernel_size,
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self.num_spec,
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use_v1=True)
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def __init__(
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self,
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config: Qwen3NextConfig,
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model_config: Optional[ModelConfig] = None,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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speculative_config: Optional[SpeculativeConfig] = None,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.tp_size = get_tensor_model_parallel_world_size()
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self.tp_rank = get_tensor_model_parallel_rank()
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self.hidden_size = config.hidden_size
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self.num_v_heads = config.linear_num_value_heads
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self.num_k_heads = config.linear_num_key_heads
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self.head_k_dim = config.linear_key_head_dim
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self.head_v_dim = config.linear_value_head_dim
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self.key_dim = self.head_k_dim * self.num_k_heads
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self.value_dim = self.head_v_dim * self.num_v_heads
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self.conv_kernel_size = config.linear_conv_kernel_dim
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self.layer_idx = extract_layer_index(prefix)
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self.activation = config.hidden_act
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self.act = ACT2FN[config.hidden_act]
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self.layer_norm_epsilon = config.rms_norm_eps
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self.prefix = prefix
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self.config = config
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self.model_config = model_config
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self.cache_config = cache_config
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self.quant_config = quant_config
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self.speculative_config = speculative_config
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self.num_spec = (self.speculative_config.num_speculative_tokens
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if self.speculative_config else 0)
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# QKV
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self.conv_dim = self.key_dim * 2 + self.value_dim
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self.conv1d = ColumnParallelLinear(
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input_size=self.conv_kernel_size,
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output_size=self.conv_dim,
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bias=False,
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prefix=f"{prefix}.conv1d",
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)
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self.conv1d.weight.data = self.conv1d.weight.data.unsqueeze(1)
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# projection of the input hidden states
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self.projection_size_qkvz = self.key_dim * 2 + self.value_dim * 2
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self.projection_size_ba = self.num_v_heads * 2
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self.in_proj = MergedColumnParallelLinear(
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input_size=self.hidden_size,
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output_sizes=[self.projection_size_qkvz, self.projection_size_ba],
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bias=False,
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quant_config=quant_config,
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prefix=f"{prefix}.in_proj",
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)
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query_key_settings = (self.key_dim, 0, False)
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value_settings = (self.value_dim, 0, False)
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delattr(self.conv1d.weight, "weight_loader")
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set_weight_attrs(
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self.conv1d.weight, {
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"weight_loader":
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mamba_v2_sharded_weight_loader([
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query_key_settings,
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query_key_settings,
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value_settings,
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], self.tp_size, self.tp_rank)
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})
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# selective projection used to make dt, B and C input dependent
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# time step projection (discretization)
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# instantiate once and copy inv_dt in init_weights of PretrainedModel
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self.dt_bias = nn.Parameter(
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torch.ones(self.num_v_heads // self.tp_size), )
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self.A_log = nn.Parameter(
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torch.empty(
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divide(self.num_v_heads, self.tp_size),
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dtype=torch.float32,
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))
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set_weight_attrs(self.A_log,
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{"weight_loader": sharded_weight_loader(0)})
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set_weight_attrs(self.dt_bias,
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{"weight_loader": sharded_weight_loader(0)})
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self.norm = RMSNormGated(
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self.head_v_dim,
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eps=self.layer_norm_epsilon,
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norm_before_gate=True,
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device="npu",
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)
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self.out_proj = RowParallelLinear(self.value_dim,
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self.hidden_size,
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bias=False,
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input_is_parallel=True,
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quant_config=quant_config,
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prefix=f"{prefix}.out_proj")
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compilation_config = get_current_vllm_config().compilation_config
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if prefix in compilation_config.static_forward_context:
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raise ValueError(f"Duplicate layer name: {prefix}")
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compilation_config.static_forward_context[prefix] = self
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def fix_query_key_value_ordering(
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self,
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mixed_qkvz,
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mixed_ba,
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):
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"""
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Derives `query`, `key` and `value` tensors from `mixed_qkvzba`.
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"""
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new_tensor_shape_qkvz = mixed_qkvz.size()[:-1] + (
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self.num_k_heads // self.tp_size,
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(self.head_k_dim + self.head_k_dim +
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(self.head_v_dim + self.head_v_dim) * self.num_v_heads //
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self.num_k_heads),
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)
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new_tensor_shape_ba = mixed_qkvz.size()[:-1] + (
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self.num_k_heads // self.tp_size,
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2 * self.num_v_heads // self.num_k_heads,
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)
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mixed_qkvz = mixed_qkvz.view(*new_tensor_shape_qkvz)
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mixed_ba = mixed_ba.view(*new_tensor_shape_ba)
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split_arg_list_qkvz = [
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self.head_k_dim,
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self.head_k_dim,
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(self.num_v_heads // self.num_k_heads * self.head_v_dim),
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(self.num_v_heads // self.num_k_heads * self.head_v_dim),
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]
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split_arg_list_ba = [
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self.num_v_heads // self.num_k_heads,
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self.num_v_heads // self.num_k_heads
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]
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# [b, sq, ng, (hn + hn + np/ng * hn + np/ng + np/ng)]
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# --> [b, sq, ng, hn], [b, sq, ng, hn], [b, sq, ng, np/ng * hn],
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# [b, sq, ng, np/ng * hn], [b, sq, ng, np/ng], [b, sq, ng, np/ng]
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(query, key, value, z) = torch.split(mixed_qkvz,
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split_arg_list_qkvz,
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dim=2)
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(b, a) = torch.split(mixed_ba, split_arg_list_ba, dim=2)
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# [b, sq, ng, np/ng * hn] -> [b, sq, np, hn]
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value = value.reshape(value.size(0), -1, self.head_v_dim)
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z = z.reshape(z.size(0), -1, self.head_v_dim)
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b = b.reshape(b.size(0), self.num_v_heads // self.tp_size)
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a = a.reshape(a.size(0), self.num_v_heads // self.tp_size)
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return query, key, value, z, b, a
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def rearrange_mixed_qkv(self, mixed_qkv):
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if mixed_qkv is None:
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return None, None, None
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query, key, value = torch.split(
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mixed_qkv,
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[
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self.key_dim // self.tp_size,
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self.key_dim // self.tp_size,
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self.value_dim // self.tp_size,
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],
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dim=-1,
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)
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query, key = map(
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lambda x: rearrange(x, 'l (h d) -> 1 l h d', d=self.head_k_dim),
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(query, key))
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value = rearrange(value, 'l (h d) -> 1 l h d', d=self.head_v_dim)
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return query, key, value
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def forward(
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self,
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hidden_states: torch.Tensor,
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output: torch.Tensor,
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cache_params: Optional[MambaCacheParams] = None,
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):
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return torch.ops.vllm.npu_gdn_attention(
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hidden_states,
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output,
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self.prefix,
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)
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def _forward(
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self,
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hidden_states: torch.Tensor,
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output: torch.Tensor,
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):
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forward_context = get_forward_context()
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attn_metadata: AttentionMetadata = forward_context.attn_metadata
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if attn_metadata is None:
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# V1 profile run
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return
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assert isinstance(attn_metadata, dict)
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attn_metadata = attn_metadata[self.prefix]
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assert isinstance(attn_metadata, GDNAttentionMetadata)
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has_initial_state = attn_metadata.has_initial_state
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spec_query_start_loc = attn_metadata.spec_query_start_loc
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non_spec_query_start_loc = attn_metadata.non_spec_query_start_loc
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spec_sequence_masks = attn_metadata.spec_sequence_masks
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spec_token_masks = attn_metadata.spec_token_masks
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spec_state_indices_tensor = attn_metadata.spec_state_indices_tensor # noqa: E501
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non_spec_state_indices_tensor = attn_metadata.non_spec_state_indices_tensor # noqa: E501
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self_kv_cache = self.kv_cache[forward_context.virtual_engine]
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conv_state = self_kv_cache[0].transpose(-1, -2)
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ssm_state = self_kv_cache[1]
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num_actual_tokens = (attn_metadata.num_prefill_tokens +
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attn_metadata.num_decode_tokens +
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attn_metadata.num_spec_decode_tokens)
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num_accepted_tokens = attn_metadata.num_accepted_tokens
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# 1. Set up dimensions for reshapes later
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projected_states, _ = self.in_proj(hidden_states[:num_actual_tokens])
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if spec_token_masks is not None:
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spec_token_masks = spec_token_masks[:num_actual_tokens]
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projected_states_qkvz, projected_states_ba = torch.split(
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projected_states,
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[
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self.projection_size_qkvz // self.tp_size,
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self.projection_size_ba // self.tp_size
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],
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dim=-1,
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)
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query, key, value, z, b, a = self.fix_query_key_value_ordering(
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projected_states_qkvz, projected_states_ba)
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query, key, value = map(lambda x: rearrange(x, 'l p d -> l (p d)'),
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(query, key, value))
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mixed_qkv = torch.cat((query, key, value), dim=-1)
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# 2. Convolution sequence transformation
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conv_weights = self.conv1d.weight.view(self.conv1d.weight.size(0),
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self.conv1d.weight.size(2))
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if spec_sequence_masks is not None:
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if (attn_metadata.num_prefills == 0
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and attn_metadata.num_decodes == 0):
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mixed_qkv_spec = mixed_qkv
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mixed_qkv_non_spec = None
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else:
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mixed_qkv_spec = mixed_qkv[spec_token_masks]
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mixed_qkv_non_spec = mixed_qkv[~spec_token_masks]
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else:
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mixed_qkv_spec = None
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mixed_qkv_non_spec = mixed_qkv
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# 2.1: process the mutli-query part
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# if spec_sequence_masks is not None:
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# mixed_qkv_spec = mixed_qkv_spec.view(
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# attn_metadata.num_spec_decodes, -1, mixed_qkv_spec.size(-1))
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# mixed_qkv_spec = rearrange(mixed_qkv_spec, 'b l d -> b d l')
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# mixed_qkv_spec = causal_conv1d_update(
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# mixed_qkv_spec,
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# conv_state,
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# conv_weights,
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# self.conv1d.bias,
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# self.activation,
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# conv_state_indices=spec_state_indices_tensor[:, 0]
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# [:attn_metadata.num_spec_decodes],
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# num_accepted_tokens=num_accepted_tokens,
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# validate_data=False,
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# )
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# mixed_qkv_spec = rearrange(mixed_qkv_spec, 'b d l -> (b l) d')
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# 2.2: process the remaining part
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if attn_metadata.num_prefills > 0:
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# - "cache_indices" updates the conv_state cache in positions
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# pointed to by "mamba_cache_params.state_indices_tensor"
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mixed_qkv_non_spec = causal_conv1d_fn(
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mixed_qkv_non_spec.transpose(0, 1),
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conv_weights,
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self.conv1d.bias,
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activation=self.activation,
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conv_states=conv_state,
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has_initial_state=has_initial_state,
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cache_indices=non_spec_state_indices_tensor,
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query_start_loc=non_spec_query_start_loc,
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).transpose(0, 1)
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elif attn_metadata.num_decodes > 0:
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mixed_qkv_non_spec = causal_conv1d_update(
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mixed_qkv_non_spec,
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conv_state,
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conv_weights,
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self.conv1d.bias,
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self.activation,
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conv_state_indices=non_spec_state_indices_tensor[:attn_metadata
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.num_decodes],
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# validate_data=True,
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)
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else:
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mixed_qkv_non_spec = None
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query_spec, key_spec, value_spec = self.rearrange_mixed_qkv(
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mixed_qkv_spec)
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query_non_spec, key_non_spec, value_non_spec = self.rearrange_mixed_qkv(
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mixed_qkv_non_spec)
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beta = b.sigmoid()
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g = fused_gdn_gating(self.A_log, a, self.dt_bias)
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g, beta = map(lambda x: rearrange(x, 'l d -> 1 l d'), (g, beta))
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if spec_sequence_masks is not None:
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if (attn_metadata.num_prefills == 0
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and attn_metadata.num_decodes == 0):
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g_spec = g
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beta_spec = beta
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g_non_spec = None
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beta_non_spec = None
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else:
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g_spec = g[:, spec_token_masks]
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beta_spec = beta[:, spec_token_masks]
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g_non_spec = g[:, ~spec_token_masks]
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beta_non_spec = beta[:, ~spec_token_masks]
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else:
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g_spec = None
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beta_spec = None
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g_non_spec = g
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beta_non_spec = beta
|
|
|
|
# 3. Recurrent attention
|
|
# 3.1: process the mutlti-query part
|
|
if spec_sequence_masks is not None:
|
|
core_attn_out_spec, last_recurrent_state = (
|
|
fused_recurrent_gated_delta_rule(
|
|
q=query_spec,
|
|
k=key_spec,
|
|
v=value_spec,
|
|
g=g_spec,
|
|
beta=beta_spec,
|
|
initial_state=ssm_state,
|
|
inplace_final_state=True,
|
|
cu_seqlens=spec_query_start_loc[:attn_metadata.
|
|
num_spec_decodes + 1],
|
|
ssm_state_indices=spec_state_indices_tensor,
|
|
num_accepted_tokens=num_accepted_tokens,
|
|
use_qk_l2norm_in_kernel=True,
|
|
))
|
|
else:
|
|
core_attn_out_spec, last_recurrent_state = None, None
|
|
|
|
# 3.2: process the remaining part
|
|
if attn_metadata.num_prefills > 0:
|
|
initial_state = ssm_state[
|
|
non_spec_state_indices_tensor].contiguous()
|
|
initial_state[~has_initial_state, ...] = 0
|
|
|
|
batch_size = initial_state.shape[0]
|
|
core_attn_out = []
|
|
last_recurrent_state = []
|
|
|
|
for b_idx in range(batch_size):
|
|
start, end = non_spec_query_start_loc[
|
|
b_idx], non_spec_query_start_loc[b_idx + 1]
|
|
cur_q = query_non_spec[:, start:end, ...]
|
|
cur_k = key_non_spec[:, start:end, ...]
|
|
cur_v = value_non_spec[:, start:end, ...]
|
|
cur_g = g_non_spec[:, start:end, ...]
|
|
cur_b = beta_non_spec[:, start:end, ...]
|
|
cur_state = initial_state[b_idx].unsqueeze(0)
|
|
|
|
(
|
|
cur_core_attn_out_non_spec,
|
|
cur_last_recurrent_state,
|
|
) = chunk_gated_delta_rule(
|
|
query=cur_q,
|
|
key=cur_k,
|
|
value=cur_v,
|
|
g=cur_g,
|
|
beta=cur_b,
|
|
initial_state=cur_state,
|
|
output_final_state=True,
|
|
use_qk_l2norm_in_kernel=True,
|
|
)
|
|
|
|
core_attn_out.append(cur_core_attn_out_non_spec)
|
|
last_recurrent_state.append(cur_last_recurrent_state)
|
|
|
|
tar_dtype = core_attn_out[0].dtype
|
|
tar_device = core_attn_out[0].device
|
|
tar_shape = list(core_attn_out[0].shape)
|
|
tar_shape[1] = non_spec_query_start_loc[-1]
|
|
core_attn_out_non_spec = torch.empty(tar_shape,
|
|
dtype=tar_dtype,
|
|
device=tar_device)
|
|
for b_idx in range(batch_size):
|
|
cur_core_attn_out = core_attn_out[b_idx]
|
|
start, end = non_spec_query_start_loc[
|
|
b_idx], non_spec_query_start_loc[b_idx + 1]
|
|
core_attn_out_non_spec[:, start:end, ...] = cur_core_attn_out
|
|
last_recurrent_state = torch.cat(last_recurrent_state, dim=0)
|
|
|
|
# Init cache
|
|
ssm_state[non_spec_state_indices_tensor] = last_recurrent_state.to(
|
|
ssm_state.dtype)
|
|
elif attn_metadata.num_decodes > 0:
|
|
core_attn_out_non_spec, last_recurrent_state = (
|
|
fused_recurrent_gated_delta_rule(
|
|
q=query_non_spec,
|
|
k=key_non_spec,
|
|
v=value_non_spec,
|
|
g=g_non_spec,
|
|
beta=beta_non_spec,
|
|
initial_state=ssm_state,
|
|
inplace_final_state=True,
|
|
cu_seqlens=non_spec_query_start_loc[:attn_metadata.
|
|
num_decodes + 1],
|
|
ssm_state_indices=non_spec_state_indices_tensor,
|
|
use_qk_l2norm_in_kernel=True,
|
|
))
|
|
else:
|
|
core_attn_out_non_spec, last_recurrent_state = None, None
|
|
|
|
# Merge core attention output
|
|
if (spec_sequence_masks is not None
|
|
and core_attn_out_non_spec is not None):
|
|
core_attn_out = torch.empty(
|
|
(1, num_actual_tokens, *core_attn_out_spec.shape[2:]),
|
|
dtype=core_attn_out_non_spec.dtype,
|
|
device=core_attn_out_non_spec.device,
|
|
)
|
|
core_attn_out[:, spec_token_masks] = core_attn_out_spec
|
|
core_attn_out[:, ~spec_token_masks] = core_attn_out_non_spec
|
|
elif spec_sequence_masks is not None:
|
|
core_attn_out = core_attn_out_spec
|
|
else:
|
|
core_attn_out = core_attn_out_non_spec
|
|
|
|
z_shape_og = z.shape
|
|
# reshape input data into 2D tensor
|
|
core_attn_out = core_attn_out.reshape(-1, core_attn_out.shape[-1])
|
|
z = z.reshape(-1, z.shape[-1])
|
|
core_attn_out = self.norm(core_attn_out, z)
|
|
core_attn_out = core_attn_out.reshape(z_shape_og)
|
|
core_attn_out = rearrange(core_attn_out, '... h d -> ... (h d)')
|
|
|
|
output[:num_actual_tokens], _ = self.out_proj(core_attn_out)
|
|
|
|
|
|
class Qwen3NextDecoderLayer(nn.Module):
|
|
|
|
def __init__(
|
|
self,
|
|
config: Qwen3NextConfig,
|
|
layer_type: str,
|
|
model_config: Optional[ModelConfig] = None,
|
|
cache_config: Optional[CacheConfig] = None,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
speculative_config: Optional[SpeculativeConfig] = None,
|
|
prefix: str = "",
|
|
enable_eplb: bool = False,
|
|
) -> None:
|
|
super().__init__()
|
|
self.config = config
|
|
|
|
self.layer_type = layer_type
|
|
self.layer_idx = extract_layer_index(prefix)
|
|
|
|
if self.layer_type == "linear_attention":
|
|
self.linear_attn = Qwen3NextGatedDeltaNet(
|
|
config,
|
|
model_config=model_config,
|
|
cache_config=cache_config,
|
|
quant_config=quant_config,
|
|
speculative_config=speculative_config,
|
|
prefix=f'{prefix}.linear_attn')
|
|
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(
|
|
config=config,
|
|
quant_config=quant_config,
|
|
prefix=f"{prefix}.mlp",
|
|
enable_eplb=enable_eplb,
|
|
)
|
|
else:
|
|
self.mlp = Qwen3NextMLP(
|
|
hidden_size=config.hidden_size,
|
|
intermediate_size=config.intermediate_size,
|
|
hidden_act=config.hidden_act,
|
|
quant_config=quant_config,
|
|
)
|
|
|
|
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,
|
|
self.config.hidden_size,
|
|
dtype=config.torch_dtype,
|
|
), )
|
|
self.ffn_layer_scale = torch.nn.Parameter(
|
|
torch.zeros(
|
|
1,
|
|
1,
|
|
self.config.hidden_size,
|
|
dtype=config.torch_dtype,
|
|
), )
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
residual: Optional[torch.Tensor],
|
|
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)}, {len(self.ffn_layer_scale.shape)}' # noqa: E501
|
|
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_config
|
|
model_config = vllm_config.model_config
|
|
cache_config = vllm_config.cache_config
|
|
quant_config = vllm_config.quant_config
|
|
parallel_config = vllm_config.parallel_config
|
|
lora_config = vllm_config.lora_config
|
|
speculative_config = vllm_config.speculative_config
|
|
enable_eplb = parallel_config.enable_eplb
|
|
eplb_config = parallel_config.eplb_config
|
|
self.num_redundant_experts = eplb_config.num_redundant_experts
|
|
|
|
self.config = config
|
|
lora_vocab = ((lora_config.lora_extra_vocab_size *
|
|
(lora_config.max_loras or 1)) if lora_config else 0)
|
|
self.vocab_size = config.vocab_size + lora_vocab
|
|
|
|
self.embed_tokens = VocabParallelEmbedding(
|
|
self.vocab_size,
|
|
config.hidden_size,
|
|
org_num_embeddings=config.vocab_size,
|
|
)
|
|
|
|
def get_layer(prefix: str):
|
|
return Qwen3NextDecoderLayer(
|
|
config,
|
|
layer_type=config.layer_types[extract_layer_index(prefix)],
|
|
model_config=model_config,
|
|
cache_config=cache_config,
|
|
quant_config=quant_config,
|
|
speculative_config=speculative_config,
|
|
prefix=prefix,
|
|
enable_eplb=enable_eplb,
|
|
)
|
|
|
|
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))
|
|
|
|
self.norm = Qwen3NextRMSNorm(config.hidden_size,
|
|
eps=config.rms_norm_eps)
|
|
|
|
def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
|
|
return self.embed_tokens(input_ids)
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
intermediate_tensors: Optional[IntermediateTensors] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
) -> torch.Tensor:
|
|
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 layer in self.layers:
|
|
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)
|
|
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 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,
|
|
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),
|
|
("in_proj", "in_proj_qkvz", 0),
|
|
("in_proj", "in_proj_ba", 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
|
|
|
|
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
|
|
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
|
|
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 Qwen3NextForCausalLM(nn.Module, HasInnerState, SupportsLoRA, SupportsPP,
|
|
MixtureOfExperts, IsHybrid):
|
|
|
|
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
|
|
config = vllm_config.model_config.hf_config
|
|
self.vllm_config = vllm_config
|
|
self.model_config = vllm_config.model_config
|
|
cache_config = vllm_config.cache_config
|
|
lora_config = vllm_config.lora_config
|
|
scheduler_config = vllm_config.scheduler_config
|
|
assert not cache_config.enable_prefix_caching, \
|
|
"Qwen3Next currently does not support prefix caching"
|
|
assert envs.VLLM_USE_V1, "Qwen3Next requires VLLM_USE_V1"
|
|
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.unpadded_vocab_size = config.vocab_size
|
|
if lora_config:
|
|
self.unpadded_vocab_size += lora_config.lora_extra_vocab_size
|
|
self.lm_head = ParallelLMHead(
|
|
self.unpadded_vocab_size,
|
|
config.hidden_size,
|
|
org_num_embeddings=config.vocab_size,
|
|
padding_size=DEFAULT_VOCAB_PADDING_SIZE
|
|
# We need bigger padding if using lora for kernel
|
|
# compatibility
|
|
if not lora_config else lora_config.lora_vocab_padding_size,
|
|
)
|
|
self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
|
|
config.vocab_size)
|
|
self.make_empty_intermediate_tensors = (
|
|
self.model.make_empty_intermediate_tensors)
|
|
|
|
# Set MoE hyperparameters
|
|
self.expert_weights = []
|
|
|
|
self.moe_layers: list[FusedMoE] = []
|
|
example_layer = None
|
|
for layer in self.model.layers:
|
|
if isinstance(layer, PPMissingLayer):
|
|
continue
|
|
|
|
assert isinstance(layer, Qwen3NextDecoderLayer)
|
|
if isinstance(layer.mlp, Qwen3NextSparseMoeBlock):
|
|
example_layer = layer.mlp
|
|
self.moe_layers.append(layer.mlp.experts)
|
|
|
|
if example_layer is None:
|
|
raise RuntimeError("No Qwen3Next layer found in the model.layers.")
|
|
|
|
self.num_moe_layers = len(self.moe_layers)
|
|
self.num_expert_groups = 1
|
|
self.num_shared_experts = 0
|
|
self.num_logical_experts = example_layer.n_logical_experts
|
|
self.num_physical_experts = example_layer.n_physical_experts
|
|
self.num_local_physical_experts = example_layer.n_local_physical_experts
|
|
self.num_routed_experts = example_layer.n_routed_experts
|
|
self.num_redundant_experts = example_layer.n_redundant_experts
|
|
|
|
def set_eplb_state(
|
|
self,
|
|
expert_load_view: torch.Tensor,
|
|
logical_to_physical_map: torch.Tensor,
|
|
logical_replica_count: torch.Tensor,
|
|
) -> None:
|
|
for layer_idx, layer in enumerate(self.moe_layers):
|
|
# Register the expert weights.
|
|
self.expert_weights.append(layer.get_expert_weights())
|
|
layer.set_eplb_state(
|
|
moe_layer_idx=layer_idx,
|
|
expert_load_view=expert_load_view,
|
|
logical_to_physical_map=logical_to_physical_map,
|
|
logical_replica_count=logical_replica_count,
|
|
)
|
|
|
|
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 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,
|
|
**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)
|
|
|
|
@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_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,
|
|
use_v1=True)
|
|
|
|
def compute_logits(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
sampling_metadata=None, # type: ignore
|
|
) -> Optional[torch.Tensor]:
|
|
return self.logits_processor(self.lm_head, hidden_states,
|
|
sampling_metadata)
|
|
|
|
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()
|
|
|
|
|
|
def npu_gdn_attention(
|
|
hidden_states: torch.Tensor,
|
|
output: torch.Tensor,
|
|
layer_name: str,
|
|
) -> None:
|
|
forward_context: ForwardContext = get_forward_context()
|
|
self = forward_context.no_compile_layers[layer_name]
|
|
self._forward(hidden_states=hidden_states, output=output)
|
|
|
|
|
|
def npu_gdn_attention_fake(
|
|
hidden_states: torch.Tensor,
|
|
output: torch.Tensor,
|
|
layer_name: str,
|
|
) -> None:
|
|
return
|
|
|
|
|
|
direct_register_custom_op(
|
|
op_name="npu_gdn_attention",
|
|
op_func=npu_gdn_attention,
|
|
mutates_args=["output"],
|
|
fake_impl=npu_gdn_attention_fake,
|
|
dispatch_key=current_platform.dispatch_key,
|
|
)
|