wehub-resource-sync
598 lines
21 KiB
Python
598 lines
21 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""Inference-only AfMoE model compatible with HuggingFace weights."""
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from collections.abc import Iterable
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from itertools import islice
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import torch
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from torch import nn
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from vllm.compilation.decorators import support_torch_compile
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from vllm.config import CacheConfig, VllmConfig, get_current_vllm_config
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from vllm.distributed import (
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get_ep_group,
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get_pp_group,
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get_tensor_model_parallel_world_size,
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)
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from vllm.logger import init_logger
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from vllm.model_executor.layers.attention import Attention
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from vllm.model_executor.layers.fused_moe import (
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FusedMoE,
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MoERunner,
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)
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from vllm.model_executor.layers.layernorm import RMSNorm
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from vllm.model_executor.layers.linear import (
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ColumnParallelLinear,
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QKVParallelLinear,
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RowParallelLinear,
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)
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from vllm.model_executor.layers.logits_processor import LogitsProcessor
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from vllm.model_executor.layers.quantization import QuantizationConfig
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from vllm.model_executor.layers.rotary_embedding import get_rope
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from vllm.model_executor.layers.vocab_parallel_embedding import (
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ParallelLMHead,
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VocabParallelEmbedding,
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)
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from vllm.model_executor.models.interfaces import (
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EagleModelMixin,
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MixtureOfExperts,
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SupportsEagle3,
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SupportsLoRA,
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SupportsPP,
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)
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from vllm.model_executor.models.llama import LlamaMLP as AfmoeMLP
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from vllm.model_executor.models.utils import (
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AutoWeightsLoader,
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PPMissingLayer,
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WeightsMapper,
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extract_layer_index,
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make_empty_intermediate_tensors_factory,
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make_layers,
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maybe_prefix,
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)
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from vllm.sequence import IntermediateTensors
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from vllm.v1.attention.backend import AttentionType
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logger = init_logger(__name__)
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class AfmoeMoE(nn.Module):
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def __init__(
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self,
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config, # AfmoeConfig
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quant_config: QuantizationConfig | None = None,
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prefix: str = "",
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enable_eplb: bool = False,
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):
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super().__init__()
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self.tp_size = get_tensor_model_parallel_world_size()
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self.route_scale = config.route_scale
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self.score_func = config.score_func
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self.route_norm = config.route_norm
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self.ep_group = get_ep_group().device_group
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self.ep_rank = self.ep_group.rank()
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self.ep_size = self.ep_group.size()
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self.n_routed_experts: int = config.num_experts
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self.n_shared_experts: int = config.num_shared_experts
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if config.hidden_act != "silu":
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raise ValueError(
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f"Unsupported activation: {config.hidden_act}. "
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"Only silu is supported for now."
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)
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# Router gate
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self.gate = nn.Linear(
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config.hidden_size,
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config.num_experts,
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bias=False,
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dtype=torch.float32,
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)
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self.expert_bias = nn.Parameter(
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torch.empty(config.num_experts, dtype=torch.float32)
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)
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# Load balancing settings
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vllm_config = get_current_vllm_config()
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eplb_config = vllm_config.parallel_config.eplb_config
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self.enable_eplb = enable_eplb
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self.n_redundant_experts = eplb_config.num_redundant_experts
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self.n_logical_experts = self.n_routed_experts
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self.n_physical_experts = self.n_logical_experts + self.n_redundant_experts
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self.n_local_physical_experts = self.n_physical_experts // self.ep_size
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self.physical_expert_start = self.ep_rank * self.n_local_physical_experts
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self.physical_expert_end = (
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self.physical_expert_start + self.n_local_physical_experts
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)
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self.shared_experts = None
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# Shared experts
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if config.num_shared_experts > 0:
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intermediate_size = config.moe_intermediate_size * config.num_shared_experts
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self.shared_experts = AfmoeMLP(
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hidden_size=config.hidden_size,
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intermediate_size=intermediate_size,
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hidden_act=config.hidden_act,
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quant_config=quant_config,
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reduce_results=False,
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prefix=f"{prefix}.shared_experts",
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)
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# Routed experts using FusedMoE
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self.experts = FusedMoE(
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shared_experts=self.shared_experts,
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num_experts=config.num_experts,
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top_k=config.num_experts_per_tok,
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hidden_size=config.hidden_size,
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intermediate_size=config.moe_intermediate_size,
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renormalize=self.route_norm if self.score_func == "sigmoid" else False,
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quant_config=quant_config,
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use_grouped_topk=True,
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num_expert_group=config.n_group,
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topk_group=config.topk_group,
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prefix=f"{prefix}.experts",
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scoring_func=self.score_func,
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routed_scaling_factor=self.route_scale,
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e_score_correction_bias=self.expert_bias,
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enable_eplb=self.enable_eplb,
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num_redundant_experts=self.n_redundant_experts,
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router_logits_dtype=torch.float32,
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)
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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num_tokens, hidden_dim = hidden_states.shape
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hidden_states = hidden_states.view(-1, hidden_dim)
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router_logits = self.gate(hidden_states.to(dtype=torch.float32))
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final_hidden_states = self.experts(
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hidden_states=hidden_states, router_logits=router_logits
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)
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return final_hidden_states.view(num_tokens, hidden_dim)
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class AfmoeAttention(nn.Module):
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def __init__(
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self,
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config, # AfmoeConfig
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layer_idx: int,
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hidden_size: int,
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num_heads: int,
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num_kv_heads: int,
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max_position_embeddings: int = 131072,
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head_dim: int | None = None,
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rms_norm_eps: float = 1e-05,
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cache_config: CacheConfig | None = None,
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quant_config: QuantizationConfig | None = None,
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prefix: str = "",
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attn_type: str = AttentionType.DECODER,
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) -> None:
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super().__init__()
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self.layer_idx = layer_idx
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self.hidden_size = hidden_size
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tp_size = get_tensor_model_parallel_world_size()
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self.total_num_heads = num_heads
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assert self.total_num_heads % tp_size == 0
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self.num_heads = self.total_num_heads // tp_size
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self.total_num_kv_heads = num_kv_heads
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if self.total_num_kv_heads >= tp_size:
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# Number of KV heads is greater than TP size, so we partition
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# the KV heads across multiple tensor parallel GPUs.
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assert self.total_num_kv_heads % tp_size == 0
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else:
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# Number of KV heads is less than TP size, so we replicate
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# the KV heads across multiple tensor parallel GPUs.
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assert tp_size % self.total_num_kv_heads == 0
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self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
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self.head_dim = head_dim or (hidden_size // self.total_num_heads)
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self.q_size = self.num_heads * self.head_dim
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self.kv_size = self.num_kv_heads * self.head_dim
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self.scaling = self.head_dim**-0.5
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self.max_position_embeddings = max_position_embeddings
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# Check if this is a local attention layer
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self.is_local_attention = config.layer_types[layer_idx] == "sliding_attention"
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self.sliding_window = config.sliding_window if self.is_local_attention else None
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self.qkv_proj = QKVParallelLinear(
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self.hidden_size,
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self.head_dim,
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self.total_num_heads,
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self.total_num_kv_heads,
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bias=False,
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quant_config=quant_config,
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prefix=f"{prefix}.qkv_proj",
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)
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self.o_proj = RowParallelLinear(
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self.total_num_heads * self.head_dim,
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self.hidden_size,
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bias=False,
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quant_config=quant_config,
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prefix=f"{prefix}.o_proj",
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)
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# Gating projection
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self.gate_proj = ColumnParallelLinear(
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hidden_size,
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self.total_num_heads * self.head_dim,
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bias=False,
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quant_config=quant_config,
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prefix=f"{prefix}.gate_proj",
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)
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# Q/K normalization
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self.q_norm = RMSNorm(self.head_dim, eps=config.rms_norm_eps)
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self.k_norm = RMSNorm(self.head_dim, eps=config.rms_norm_eps)
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# Only create rotary embeddings for local attention
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if self.is_local_attention:
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self.rotary_emb = get_rope(
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self.head_dim,
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max_position=max_position_embeddings,
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rope_parameters=config.rope_parameters,
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is_neox_style=True,
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)
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else:
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self.rotary_emb = None
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self.attn = Attention(
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self.num_heads,
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self.head_dim,
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self.scaling,
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num_kv_heads=self.num_kv_heads,
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cache_config=cache_config,
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quant_config=quant_config,
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per_layer_sliding_window=self.sliding_window,
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prefix=f"{prefix}.attn",
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attn_type=attn_type,
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)
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def forward(
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self,
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positions: torch.Tensor,
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hidden_states: torch.Tensor,
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) -> torch.Tensor:
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qkv, _ = self.qkv_proj(hidden_states)
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gate, _ = self.gate_proj(hidden_states)
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q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
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# Apply Q/K normalization
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q = self.q_norm(q.reshape(-1, self.num_heads, self.head_dim)).reshape(q.shape)
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k = self.k_norm(k.reshape(-1, self.num_kv_heads, self.head_dim)).reshape(
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k.shape
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)
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# Apply rotary embeddings only for local attention
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if self.is_local_attention and self.rotary_emb is not None:
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q, k = self.rotary_emb(positions, q, k)
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attn_output = self.attn(q, k, v)
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# Apply gating
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attn_output = attn_output * torch.sigmoid(gate)
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output, _ = self.o_proj(attn_output)
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return output
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class AfmoeDecoderLayer(nn.Module):
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def __init__(
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self,
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config, # AfmoeConfig
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cache_config: CacheConfig | None = None,
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quant_config: QuantizationConfig | None = None,
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prefix: str = "",
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enable_eplb: bool = False,
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) -> None:
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super().__init__()
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self.hidden_size = config.hidden_size
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max_position_embeddings = getattr(config, "max_position_embeddings", 131072)
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# DecoderLayers are created with `make_layers` which passes the prefix
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# with the layer's index.
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self.layer_idx = extract_layer_index(prefix)
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self.self_attn = AfmoeAttention(
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config=config,
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layer_idx=self.layer_idx,
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hidden_size=self.hidden_size,
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num_heads=config.num_attention_heads,
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num_kv_heads=config.num_key_value_heads,
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max_position_embeddings=max_position_embeddings,
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head_dim=config.head_dim,
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rms_norm_eps=config.rms_norm_eps,
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cache_config=cache_config,
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quant_config=quant_config,
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prefix=f"{prefix}.self_attn",
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)
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# MoE or dense FFN
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self.moe_enabled = self.layer_idx >= config.num_dense_layers
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if self.moe_enabled:
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self.mlp = AfmoeMoE(
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config=config,
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quant_config=quant_config,
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prefix=f"{prefix}.mlp",
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enable_eplb=enable_eplb,
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)
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else:
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self.mlp = AfmoeMLP(
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hidden_size=config.hidden_size,
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intermediate_size=config.intermediate_size,
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hidden_act=config.hidden_act,
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quant_config=quant_config,
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prefix=f"{prefix}.mlp",
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)
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self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.post_attention_layernorm = RMSNorm(
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config.hidden_size, eps=config.rms_norm_eps
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)
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self.pre_mlp_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.post_mlp_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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def forward(
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self,
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positions: torch.Tensor,
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hidden_states: torch.Tensor,
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residual: torch.Tensor | None,
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) -> tuple[torch.Tensor, torch.Tensor]:
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if residual is None:
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residual = hidden_states
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hidden_states = self.input_layernorm(hidden_states)
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else:
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hidden_states, residual = self.input_layernorm(hidden_states, residual)
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hidden_states = self.self_attn(
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positions=positions,
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hidden_states=hidden_states,
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)
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hidden_states = self.post_attention_layernorm(hidden_states) # attn norm b
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# Fully Connected
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hidden_states, residual = self.pre_mlp_layernorm( # ffn norm a
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hidden_states, residual
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)
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hidden_states = self.mlp(hidden_states)
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hidden_states = self.post_mlp_layernorm(hidden_states) # ffn norm b
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return hidden_states, residual
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@support_torch_compile(
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dynamic_arg_dims={
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"input_ids": 0,
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"positions": -1,
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"intermediate_tensors": 0,
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"inputs_embeds": 0,
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}
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)
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class AfmoeModel(nn.Module, EagleModelMixin):
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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super().__init__()
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config = vllm_config.model_config.hf_config
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cache_config = vllm_config.cache_config
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quant_config = vllm_config.quant_config
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enable_eplb = vllm_config.parallel_config.enable_eplb
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self.config = config
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self.vocab_size = config.vocab_size
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self.mup_enabled = config.mup_enabled
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if get_pp_group().is_first_rank:
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self.embed_tokens = VocabParallelEmbedding(
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config.vocab_size, config.hidden_size, prefix=f"{prefix}.embed_tokens"
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)
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else:
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self.embed_tokens = PPMissingLayer()
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self.start_layer, self.end_layer, self.layers = make_layers(
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config.num_hidden_layers,
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lambda prefix: AfmoeDecoderLayer(
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config=config,
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cache_config=cache_config,
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quant_config=quant_config,
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prefix=prefix,
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enable_eplb=enable_eplb,
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),
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prefix=f"{prefix}.layers",
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)
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if get_pp_group().is_last_rank:
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self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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else:
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self.norm = PPMissingLayer()
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self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
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["hidden_states", "residual"], config.hidden_size
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)
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def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.embed_tokens(input_ids)
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def forward(
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self,
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input_ids: torch.Tensor | None,
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positions: torch.Tensor,
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intermediate_tensors: IntermediateTensors | None = None,
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inputs_embeds: torch.Tensor | None = None,
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) -> torch.Tensor | IntermediateTensors | tuple[torch.Tensor, list[torch.Tensor]]:
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if get_pp_group().is_first_rank:
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if inputs_embeds is not None:
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hidden_states = inputs_embeds
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else:
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hidden_states = self.embed_input_ids(input_ids)
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# Apply muP input scaling if enabled
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if self.mup_enabled:
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hidden_states = hidden_states * (self.config.hidden_size**0.5)
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residual = None
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else:
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assert intermediate_tensors is not None
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hidden_states = intermediate_tensors["hidden_states"]
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residual = intermediate_tensors["residual"]
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aux_hidden_states = self._maybe_add_hidden_state([], 0, hidden_states, residual)
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for idx, layer in enumerate(
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islice(self.layers, self.start_layer, self.end_layer)
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):
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hidden_states, residual = layer(positions, hidden_states, residual)
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self._maybe_add_hidden_state(
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aux_hidden_states, idx + 1, hidden_states, residual
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)
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if not get_pp_group().is_last_rank:
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return IntermediateTensors(
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{"hidden_states": hidden_states, "residual": residual}
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)
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hidden_states, _ = self.norm(hidden_states, residual)
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if len(aux_hidden_states) > 0:
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return hidden_states, aux_hidden_states
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return hidden_states
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def make_empty_intermediate_tensors(
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self, batch_size: int, dtype: torch.dtype, device: torch.device
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) -> IntermediateTensors:
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return IntermediateTensors(
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{
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"hidden_states": torch.zeros(
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(batch_size, self.config.hidden_size), dtype=dtype, device=device
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),
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"residual": torch.zeros(
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(batch_size, self.config.hidden_size), dtype=dtype, device=device
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),
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}
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)
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class AfmoeForCausalLM(
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nn.Module, SupportsPP, SupportsEagle3, SupportsLoRA, MixtureOfExperts
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):
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packed_modules_mapping = {
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"qkv_proj": [
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"q_proj",
|
|
"k_proj",
|
|
"v_proj",
|
|
],
|
|
"gate_up_proj": [
|
|
"gate_proj",
|
|
"up_proj",
|
|
],
|
|
}
|
|
|
|
hf_to_vllm_mapper = WeightsMapper(
|
|
orig_to_new_suffix={
|
|
".router.gate.weight": ".gate.weight",
|
|
},
|
|
orig_to_new_stacked={
|
|
# weight_name: (param_name, shard_id)
|
|
".q_proj": (".qkv_proj", "q"),
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|
".k_proj": (".qkv_proj", "k"),
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|
".v_proj": (".qkv_proj", "v"),
|
|
# `.self_attn.gate_proj` is a gated-attention projection (not fused).
|
|
".mlp.gate_proj": (".mlp.gate_up_proj", 0),
|
|
".mlp.up_proj": (".mlp.gate_up_proj", 1),
|
|
".shared_experts.gate_proj": (".shared_experts.gate_up_proj", 0),
|
|
".shared_experts.up_proj": (".shared_experts.gate_up_proj", 1),
|
|
},
|
|
)
|
|
|
|
fall_back_to_pt_during_load = False
|
|
|
|
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
|
|
super().__init__()
|
|
config = vllm_config.model_config.hf_config
|
|
quant_config = vllm_config.quant_config
|
|
self.config = config
|
|
self.quant_config = quant_config
|
|
self.model = AfmoeModel(
|
|
vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
|
|
)
|
|
if get_pp_group().is_last_rank:
|
|
self.lm_head = ParallelLMHead(
|
|
config.vocab_size, config.hidden_size, quant_config=quant_config
|
|
)
|
|
else:
|
|
self.lm_head = PPMissingLayer()
|
|
self.logits_processor = LogitsProcessor(config.vocab_size)
|
|
self.make_empty_intermediate_tensors = (
|
|
self.model.make_empty_intermediate_tensors
|
|
)
|
|
# Set MoE hyperparameters
|
|
self.num_moe_layers = config.num_hidden_layers - config.num_dense_layers
|
|
self.num_expert_groups = config.n_group
|
|
|
|
self.moe_layers: list[MoERunner] = []
|
|
example_moe = None
|
|
for layer in self.model.layers:
|
|
if isinstance(layer, PPMissingLayer):
|
|
continue
|
|
|
|
assert isinstance(layer, AfmoeDecoderLayer)
|
|
if layer.moe_enabled:
|
|
example_moe = layer.mlp
|
|
self.moe_layers.append(layer.mlp.experts)
|
|
|
|
if example_moe is None and self.num_moe_layers > 0:
|
|
raise RuntimeError("No AfmoeMoE layer found in model.layers.")
|
|
|
|
if example_moe is not None:
|
|
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_shared_experts = example_moe.n_shared_experts
|
|
self.num_redundant_experts = example_moe.n_redundant_experts
|
|
|
|
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, PPMissingLayer):
|
|
continue
|
|
if layer.moe_enabled:
|
|
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 embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
|
|
return self.model.embed_input_ids(input_ids)
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor | None,
|
|
positions: torch.Tensor,
|
|
intermediate_tensors: IntermediateTensors | None = None,
|
|
inputs_embeds: torch.Tensor | None = None,
|
|
) -> torch.Tensor | IntermediateTensors | tuple[torch.Tensor, list[torch.Tensor]]:
|
|
hidden_states = self.model(
|
|
input_ids, positions, intermediate_tensors, inputs_embeds
|
|
)
|
|
return hidden_states
|
|
|
|
def compute_logits(self, hidden_states: torch.Tensor) -> torch.Tensor | None:
|
|
logits = self.logits_processor(self.lm_head, hidden_states)
|
|
return logits
|
|
|
|
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
|
|
loader = AutoWeightsLoader(self)
|
|
return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)
|