1285 lines
47 KiB
Python
1285 lines
47 KiB
Python
# Copyright 2025-2026 SGLang Team
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Inference-only GLM-4.5, GLM-4.6 model compatible with HuggingFace weights"""
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import logging
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from typing import Any, Dict, Iterable, List, Optional, Tuple, Union
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import torch
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import torch.nn.functional as F
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from torch import nn
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from transformers import PretrainedConfig
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from sglang.srt.batch_overlap.two_batch_overlap import model_forward_maybe_tbo
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from sglang.srt.distributed import (
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get_moe_expert_parallel_world_size,
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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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parallel_state,
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tensor_model_parallel_all_reduce,
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)
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from sglang.srt.distributed.device_communicators.pynccl_allocator import (
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use_symmetric_memory,
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)
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from sglang.srt.eplb.expert_distribution import get_global_expert_distribution_recorder
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from sglang.srt.eplb.expert_location import ModelConfigForExpertLocation
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from sglang.srt.eplb.expert_location_dispatch import ExpertLocationDispatchInfo
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from sglang.srt.layers.activation import SiluAndMul
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from sglang.srt.layers.communicator import (
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LayerCommunicator,
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LayerScatterModes,
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enable_moe_dense_fully_dp,
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)
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from sglang.srt.layers.dp_attention import (
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get_attention_tp_rank,
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get_attention_tp_size,
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is_allocation_symmetric,
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is_dp_attention_enabled,
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)
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from sglang.srt.layers.layernorm import RMSNorm
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from sglang.srt.layers.linear import (
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MergedColumnParallelLinear,
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QKVParallelLinear,
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RowParallelLinear,
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)
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from sglang.srt.layers.logits_processor import LogitsProcessor
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from sglang.srt.layers.moe import (
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get_moe_a2a_backend,
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should_use_flashinfer_cutlass_moe_fp4_allgather,
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)
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from sglang.srt.layers.moe.ep_moe.layer import get_moe_impl_class
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from sglang.srt.layers.moe.fused_moe_triton.layer import FusedMoE
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from sglang.srt.layers.moe.topk import TopK
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from sglang.srt.layers.quantization.base_config import QuantizationConfig
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from sglang.srt.layers.quantization.fp8_kernel import is_fp8_fnuz
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from sglang.srt.layers.radix_attention import RadixAttention
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from sglang.srt.layers.rotary_embedding import get_rope
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from sglang.srt.layers.utils import PPMissingLayer
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from sglang.srt.layers.vocab_parallel_embedding import (
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ParallelLMHead,
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VocabParallelEmbedding,
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)
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from sglang.srt.model_executor.cuda_graph_runner import get_is_capture_mode
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from sglang.srt.model_executor.forward_batch_info import ForwardBatch, PPProxyTensors
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from sglang.srt.model_loader.weight_utils import default_weight_loader
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from sglang.srt.server_args import get_global_server_args
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from sglang.srt.utils import (
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add_prefix,
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cpu_has_amx_support,
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get_bool_env_var,
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get_device_sm,
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is_cpu,
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is_cuda,
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is_hip,
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is_non_idle_and_non_empty,
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log_info_on_rank0,
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make_layers,
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)
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_is_hip = is_hip()
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_is_cuda = is_cuda()
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_is_fp8_fnuz = is_fp8_fnuz()
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_use_aiter = get_bool_env_var("SGLANG_USE_AITER") and _is_hip
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_is_cpu_amx_available = cpu_has_amx_support()
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_is_cpu = is_cpu()
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_device_sm = get_device_sm()
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logger = logging.getLogger(__name__)
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class Glm4MoeMLP(nn.Module):
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def __init__(
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self,
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hidden_size: int,
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intermediate_size: int,
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hidden_act: str,
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quant_config: Optional[QuantizationConfig] = None,
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reduce_results: bool = True,
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prefix: str = "",
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tp_rank: Optional[int] = None,
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tp_size: Optional[int] = None,
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) -> None:
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super().__init__()
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self.tp_size = tp_size
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self.gate_up_proj = MergedColumnParallelLinear(
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hidden_size,
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[intermediate_size] * 2,
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bias=False,
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quant_config=quant_config,
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prefix=add_prefix("gate_up_proj", prefix),
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tp_rank=tp_rank,
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tp_size=tp_size,
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)
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self.down_proj = RowParallelLinear(
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intermediate_size,
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hidden_size,
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bias=False,
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quant_config=quant_config,
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reduce_results=reduce_results,
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prefix=add_prefix("down_proj", prefix),
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tp_rank=tp_rank,
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tp_size=tp_size,
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)
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if hidden_act != "silu":
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raise ValueError(
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f"Unsupported activation: {hidden_act}. Only silu is supported for now."
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)
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self.act_fn = SiluAndMul()
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def forward(
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self,
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x,
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forward_batch=None,
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should_allreduce_fusion: bool = False,
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use_reduce_scatter: bool = False,
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):
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if (self.tp_size == 1) and x.shape[0] == 0:
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return x
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gate_up, _ = self.gate_up_proj(x)
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x = self.act_fn(gate_up)
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x, _ = self.down_proj(
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x, skip_all_reduce=should_allreduce_fusion or use_reduce_scatter
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)
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return x
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class Glm4MoeAttention(nn.Module):
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def __init__(
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self,
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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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layer_id: int = 0,
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rope_theta: float = 10000,
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partial_rotary_factor: float = 0.5,
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rope_scaling: Optional[Dict[str, Any]] = None,
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max_position_embeddings: int = 8192,
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head_dim: Optional[int] = None,
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rms_norm_eps: float = 1e-05,
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attention_bias: bool = True,
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quant_config: Optional[QuantizationConfig] = None,
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use_qk_norm: bool = False,
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prefix: str = "",
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alt_stream: Optional[torch.cuda.Stream] = None,
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) -> None:
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super().__init__()
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self.hidden_size = hidden_size
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attn_tp_rank = get_attention_tp_rank()
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attn_tp_size = get_attention_tp_size()
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self.total_num_heads = num_heads
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assert self.total_num_heads % attn_tp_size == 0
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self.num_heads = self.total_num_heads // attn_tp_size
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self.total_num_kv_heads = num_kv_heads
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if self.total_num_kv_heads >= attn_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 % attn_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 attn_tp_size % self.total_num_kv_heads == 0
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self.num_kv_heads = max(1, self.total_num_kv_heads // attn_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.rope_theta = rope_theta
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self.use_qk_norm = use_qk_norm
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self.max_position_embeddings = max_position_embeddings
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self.tp_rank = get_tensor_model_parallel_rank()
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self.qkv_proj = QKVParallelLinear(
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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=attention_bias,
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quant_config=quant_config,
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tp_rank=attn_tp_rank,
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tp_size=attn_tp_size,
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prefix=add_prefix("qkv_proj", prefix),
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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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hidden_size,
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bias=False,
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quant_config=quant_config,
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tp_rank=attn_tp_rank,
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tp_size=attn_tp_size,
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reduce_results=False,
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prefix=add_prefix("o_proj", prefix),
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)
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self.rotary_emb = get_rope(
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self.head_dim,
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rotary_dim=self.head_dim,
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max_position=max_position_embeddings,
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partial_rotary_factor=partial_rotary_factor,
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base=rope_theta,
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rope_scaling=rope_scaling,
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)
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self.attn = RadixAttention(
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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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layer_id=layer_id,
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prefix=add_prefix("attn", prefix),
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)
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if self.use_qk_norm:
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self.q_norm = RMSNorm(self.head_dim, eps=rms_norm_eps)
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self.k_norm = RMSNorm(self.head_dim, eps=rms_norm_eps)
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self.alt_stream = alt_stream
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def _apply_qk_norm(
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self, q: torch.Tensor, k: torch.Tensor
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) -> Tuple[torch.Tensor, torch.Tensor]:
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# overlap qk norm
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if self.alt_stream is not None and get_is_capture_mode():
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current_stream = torch.cuda.current_stream()
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self.alt_stream.wait_stream(current_stream)
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q_by_head = q.reshape(-1, self.head_dim)
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q_by_head = self.q_norm(q_by_head)
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with torch.cuda.stream(self.alt_stream):
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k_by_head = k.reshape(-1, self.head_dim)
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k_by_head = self.k_norm(k_by_head)
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current_stream.wait_stream(self.alt_stream)
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else:
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q_by_head = q.reshape(-1, self.head_dim)
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q_by_head = self.q_norm(q_by_head)
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k_by_head = k.reshape(-1, self.head_dim)
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k_by_head = self.k_norm(k_by_head)
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q = q_by_head.view(q.shape)
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k = k_by_head.view(k.shape)
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return q, k
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def op_prepare(self, state):
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state.attn_intermediate_state = self.forward_prepare(
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positions=state.positions,
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hidden_states=state.pop("hidden_states_after_comm_pre_attn"),
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forward_batch=state.forward_batch,
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)
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def op_core(self, state):
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state.hidden_states_after_attn = self.forward_core(
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state.pop("attn_intermediate_state")
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)
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def forward_prepare(
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self,
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positions: torch.Tensor,
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hidden_states: torch.Tensor,
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forward_batch: ForwardBatch,
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):
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if hidden_states.shape[0] == 0:
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return hidden_states, forward_batch, None
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qkv, _ = self.qkv_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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if self.use_qk_norm:
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q, k = self._apply_qk_norm(q, k)
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q, k = self.rotary_emb(positions, q, k)
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inner_state = q, k, v, forward_batch
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return None, forward_batch, inner_state
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def forward_core(self, intermediate_state):
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hidden_states, forward_batch, inner_state = intermediate_state
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if inner_state is None:
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return hidden_states
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attn_output = self.attn(*inner_state)
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output, _ = self.o_proj(attn_output)
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return output
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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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forward_batch: ForwardBatch,
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) -> torch.Tensor:
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s = self.forward_prepare(
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positions=positions,
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hidden_states=hidden_states,
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forward_batch=forward_batch,
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)
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return self.forward_core(s)
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class Glm4MoeGate(nn.Module):
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def __init__(
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self,
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config,
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prefix: str = "",
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):
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super().__init__()
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self.weight = nn.Parameter(
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torch.empty((config.n_routed_experts, config.hidden_size))
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)
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self.e_score_correction_bias = nn.Parameter(
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torch.empty((config.n_routed_experts), dtype=torch.float32)
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)
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def forward(self, hidden_states):
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logits = F.linear(hidden_states, self.weight, None)
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return logits
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class Glm4MoeSparseMoeBlock(nn.Module):
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def __init__(
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self,
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config: PretrainedConfig,
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layer_id: int,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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alt_stream: Optional[torch.cuda.Stream] = None,
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):
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nn.Module.__init__(self)
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self.top_k = config.num_experts_per_tok
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self.tp_size = get_tensor_model_parallel_world_size()
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self.moe_ep_size = get_moe_expert_parallel_world_size()
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self.routed_scaling_factor = config.routed_scaling_factor
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self.n_shared_experts = config.n_shared_experts
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self.num_fused_shared_experts = (
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0
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if get_global_server_args().disable_shared_experts_fusion
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else config.n_shared_experts
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)
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self.config = config
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self.layer_id = layer_id
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self.alt_stream = alt_stream
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if self.tp_size > config.n_routed_experts:
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raise ValueError(
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f"Tensor parallel size {self.tp_size} is greater than "
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f"the number of experts {config.n_routed_experts}."
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)
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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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self.gate = Glm4MoeGate(config=config, prefix=add_prefix("gate", prefix))
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self.experts = get_moe_impl_class(quant_config)(
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num_experts=config.n_routed_experts + self.num_fused_shared_experts,
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num_fused_shared_experts=self.num_fused_shared_experts,
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top_k=self.top_k + self.num_fused_shared_experts,
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layer_id=self.layer_id,
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hidden_size=config.hidden_size,
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intermediate_size=config.moe_intermediate_size,
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quant_config=quant_config,
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routed_scaling_factor=self.routed_scaling_factor,
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prefix=add_prefix("experts", prefix),
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)
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self.topk = TopK(
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top_k=self.top_k + self.num_fused_shared_experts,
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renormalize=config.norm_topk_prob,
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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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correction_bias=self.gate.e_score_correction_bias,
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routed_scaling_factor=self.routed_scaling_factor,
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num_fused_shared_experts=self.num_fused_shared_experts,
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apply_routed_scaling_factor_on_output=getattr(
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self.experts, "should_fuse_routed_scaling_factor_in_topk", False
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),
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fused_shared_experts_scaling_factor=1,
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)
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# shared expert
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if config.n_shared_experts is not None and self.num_fused_shared_experts == 0:
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intermediate_size = config.moe_intermediate_size * config.n_shared_experts
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self.shared_experts = Glm4MoeMLP(
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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=add_prefix("shared_experts", prefix),
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**(
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dict(tp_rank=0, tp_size=1)
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if get_moe_a2a_backend().is_deepep()
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or get_moe_a2a_backend().is_mooncake()
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or should_use_flashinfer_cutlass_moe_fp4_allgather()
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else {}
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),
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)
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if get_moe_a2a_backend().is_deepep() or get_moe_a2a_backend().is_mooncake():
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# TODO: we will support tp < ep in the future
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self.ep_size = get_moe_expert_parallel_world_size()
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self.num_experts = (
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config.n_routed_experts
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+ get_global_server_args().ep_num_redundant_experts
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)
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self.renormalize = config.norm_topk_prob
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self.topk_group = config.topk_group
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self.num_expert_group = config.n_group
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self.correction_bias = (
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self.gate.e_score_correction_bias.data
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if self.gate.e_score_correction_bias is not None
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else None
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)
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self._enable_a2a_moe = (
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get_moe_a2a_backend().is_deepep() or get_moe_a2a_backend().is_mooncake()
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)
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def get_moe_weights(self):
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return [
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x.data
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for name, x in self.experts.named_parameters()
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if name not in ["correction_bias"]
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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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forward_batch: Optional[ForwardBatch] = None,
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should_allreduce_fusion: bool = False,
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use_reduce_scatter: bool = False,
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) -> torch.Tensor:
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|
if not get_moe_a2a_backend().is_deepep():
|
|
if (
|
|
self.alt_stream is not None
|
|
and self.num_fused_shared_experts == 0
|
|
and hidden_states.shape[0] > 0
|
|
and get_is_capture_mode()
|
|
):
|
|
return self.forward_normal_dual_stream(
|
|
hidden_states, should_allreduce_fusion, use_reduce_scatter
|
|
)
|
|
else:
|
|
return self.forward_normal(
|
|
hidden_states, should_allreduce_fusion, use_reduce_scatter
|
|
)
|
|
else:
|
|
return self.forward_deepep(hidden_states, forward_batch)
|
|
|
|
def forward_normal_dual_stream(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
should_allreduce_fusion: bool = False,
|
|
use_reduce_scatter: bool = False,
|
|
) -> torch.Tensor:
|
|
current_stream = torch.cuda.current_stream()
|
|
self.alt_stream.wait_stream(current_stream)
|
|
shared_output = self._forward_shared_experts(hidden_states)
|
|
|
|
with torch.cuda.stream(self.alt_stream):
|
|
# router_logits: (num_tokens, n_experts)
|
|
router_logits = self.gate(hidden_states)
|
|
topk_output = self.topk(hidden_states, router_logits)
|
|
|
|
final_hidden_states = self.experts(hidden_states, topk_output)
|
|
if not _is_cuda and not _use_aiter:
|
|
# fused in biased_grouped_topk so we can skip here
|
|
final_hidden_states *= self.routed_scaling_factor
|
|
|
|
current_stream.wait_stream(self.alt_stream)
|
|
|
|
with use_symmetric_memory(
|
|
parallel_state.get_tp_group(), disabled=not is_allocation_symmetric()
|
|
):
|
|
final_hidden_states_out = torch.empty_like(final_hidden_states)
|
|
torch.add(final_hidden_states, shared_output, out=final_hidden_states_out)
|
|
final_hidden_states = final_hidden_states_out
|
|
if (
|
|
self.tp_size > 1
|
|
and not should_allreduce_fusion
|
|
and not use_reduce_scatter
|
|
and not should_use_flashinfer_cutlass_moe_fp4_allgather()
|
|
):
|
|
final_hidden_states = tensor_model_parallel_all_reduce(final_hidden_states)
|
|
return final_hidden_states
|
|
|
|
def forward_normal(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
should_allreduce_fusion: bool = False,
|
|
use_reduce_scatter: bool = False,
|
|
) -> torch.Tensor:
|
|
if hidden_states.shape[0] > 0:
|
|
shared_output = self._forward_shared_experts(hidden_states)
|
|
# router_logits: (num_tokens, n_experts)
|
|
router_logits = self.gate(hidden_states)
|
|
topk_output = self.topk(hidden_states, router_logits)
|
|
else:
|
|
shared_output = None
|
|
topk_output = self.topk.empty_topk_output(hidden_states.device)
|
|
|
|
final_hidden_states = self.experts(hidden_states, topk_output)
|
|
if not _is_cuda and not _use_aiter:
|
|
final_hidden_states *= self.routed_scaling_factor
|
|
if shared_output is not None:
|
|
with use_symmetric_memory(
|
|
parallel_state.get_tp_group(), disabled=not is_allocation_symmetric()
|
|
):
|
|
final_hidden_states_out = torch.empty_like(final_hidden_states)
|
|
torch.add(final_hidden_states, shared_output, out=final_hidden_states_out)
|
|
final_hidden_states = final_hidden_states_out
|
|
if (
|
|
self.tp_size > 1
|
|
and not should_allreduce_fusion
|
|
and not use_reduce_scatter
|
|
and not should_use_flashinfer_cutlass_moe_fp4_allgather()
|
|
):
|
|
final_hidden_states = tensor_model_parallel_all_reduce(final_hidden_states)
|
|
return final_hidden_states
|
|
|
|
def forward_deepep(
|
|
self, hidden_states: torch.Tensor, forward_batch: ForwardBatch
|
|
) -> torch.Tensor:
|
|
shared_output = None
|
|
if hidden_states.shape[0] > 0:
|
|
# router_logits: (num_tokens, n_experts)
|
|
router_logits = self.gate(hidden_states)
|
|
shared_output = self._forward_shared_experts(hidden_states)
|
|
topk_output = self.topk(
|
|
hidden_states,
|
|
router_logits,
|
|
num_token_non_padded=forward_batch.num_token_non_padded,
|
|
expert_location_dispatch_info=ExpertLocationDispatchInfo.init_new(
|
|
layer_id=self.layer_id,
|
|
),
|
|
)
|
|
else:
|
|
topk_output = self.topk.empty_topk_output(hidden_states.device)
|
|
final_hidden_states = self.experts(
|
|
hidden_states=hidden_states,
|
|
topk_output=topk_output,
|
|
)
|
|
|
|
if shared_output is not None:
|
|
x = shared_output
|
|
if self.experts.should_fuse_routed_scaling_factor_in_topk:
|
|
x.add_(final_hidden_states)
|
|
else:
|
|
x.add_(final_hidden_states, alpha=self.routed_scaling_factor)
|
|
final_hidden_states = x
|
|
else:
|
|
if not self.experts.should_fuse_routed_scaling_factor_in_topk:
|
|
final_hidden_states *= self.routed_scaling_factor
|
|
|
|
return final_hidden_states
|
|
|
|
def _forward_shared_experts(self, hidden_states: torch.Tensor):
|
|
if (hidden_states.shape[0] > 0) and (self.num_fused_shared_experts == 0):
|
|
return self.shared_experts(hidden_states)
|
|
else:
|
|
return None
|
|
|
|
def op_gate(self, state):
|
|
if is_non_idle_and_non_empty(
|
|
state.forward_batch.forward_mode, state.hidden_states_mlp_input
|
|
):
|
|
# router_logits: (num_tokens, n_experts)
|
|
state.router_logits = self.gate(state.hidden_states_mlp_input)
|
|
else:
|
|
state.router_logits = None
|
|
|
|
def op_select_experts(self, state):
|
|
router_logits = state.pop("router_logits")
|
|
hidden_states = state.hidden_states_mlp_input
|
|
|
|
if router_logits is not None:
|
|
with get_global_expert_distribution_recorder().with_current_layer(
|
|
self.layer_id
|
|
):
|
|
state.topk_output = self.topk(
|
|
hidden_states=hidden_states,
|
|
router_logits=router_logits,
|
|
num_token_non_padded=state.forward_batch.num_token_non_padded,
|
|
expert_location_dispatch_info=ExpertLocationDispatchInfo.init_new(
|
|
layer_id=self.layer_id,
|
|
),
|
|
)
|
|
else:
|
|
state.topk_output = self.topk.empty_topk_output(hidden_states.device)
|
|
|
|
def op_dispatch_a(self, state):
|
|
if self.ep_size > 1:
|
|
self.experts.dispatcher.dispatch_a(
|
|
hidden_states=state.hidden_states_mlp_input,
|
|
topk_output=state.pop("topk_output"),
|
|
tbo_subbatch_index=state.get("tbo_subbatch_index"),
|
|
)
|
|
|
|
def op_dispatch_b(self, state):
|
|
if self.ep_size > 1:
|
|
with get_global_expert_distribution_recorder().with_current_layer(
|
|
self.layer_id
|
|
):
|
|
state.dispatch_output = self.experts.dispatcher.dispatch_b(
|
|
tbo_subbatch_index=state.get("tbo_subbatch_index"),
|
|
)
|
|
|
|
def op_experts(self, state):
|
|
state.combine_input = self.experts.run_moe_core(
|
|
dispatch_output=state.dispatch_output,
|
|
)
|
|
|
|
def op_combine_a(self, state):
|
|
if self.ep_size > 1:
|
|
self.experts.dispatcher.combine_a(
|
|
combine_input=state.pop("combine_input"),
|
|
tbo_subbatch_index=state.get("tbo_subbatch_index"),
|
|
)
|
|
state.pop("dispatch_output")
|
|
|
|
def op_combine_b(self, state):
|
|
if self.ep_size > 1:
|
|
state.hidden_states_after_combine = self.experts.dispatcher.combine_b(
|
|
tbo_subbatch_index=state.get("tbo_subbatch_index"),
|
|
)
|
|
|
|
def op_output(self, state):
|
|
final_hidden_states = state.pop("hidden_states_after_combine")
|
|
|
|
if (shared_output := state.pop("shared_output")) is not None:
|
|
x = shared_output
|
|
x.add_(final_hidden_states, alpha=self.routed_scaling_factor)
|
|
final_hidden_states = x
|
|
else:
|
|
final_hidden_states *= self.routed_scaling_factor
|
|
|
|
state.hidden_states_mlp_output = final_hidden_states
|
|
|
|
|
|
class Glm4MoeDecoderLayer(nn.Module):
|
|
def __init__(
|
|
self,
|
|
config: PretrainedConfig,
|
|
layer_id: int,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
is_nextn: bool = False,
|
|
prefix: str = "",
|
|
alt_stream: Optional[torch.cuda.Stream] = None,
|
|
) -> None:
|
|
nn.Module.__init__(self)
|
|
self.hidden_size = config.hidden_size
|
|
self.config = config
|
|
rope_theta = getattr(config, "rope_theta", 10000)
|
|
rope_scaling = getattr(config, "rope_scaling", None)
|
|
partial_rotary_factor = getattr(
|
|
getattr(config, "rope_parameters", None), "partial_rotary_factor", None
|
|
) or getattr(config, "partial_rotary_factor", 0.5)
|
|
max_position_embeddings = getattr(config, "max_position_embeddings", 8192)
|
|
head_dim = getattr(
|
|
config, "head_dim", config.hidden_size // config.num_attention_heads
|
|
)
|
|
rms_norm_eps = config.rms_norm_eps
|
|
attention_bias = config.attention_bias
|
|
self.layer_id = layer_id
|
|
|
|
self.self_attn = Glm4MoeAttention(
|
|
hidden_size=self.hidden_size,
|
|
num_heads=config.num_attention_heads,
|
|
num_kv_heads=config.num_key_value_heads,
|
|
layer_id=layer_id,
|
|
rope_theta=rope_theta,
|
|
rope_scaling=rope_scaling,
|
|
partial_rotary_factor=partial_rotary_factor,
|
|
max_position_embeddings=max_position_embeddings,
|
|
head_dim=head_dim,
|
|
rms_norm_eps=rms_norm_eps,
|
|
attention_bias=attention_bias,
|
|
quant_config=quant_config,
|
|
prefix=add_prefix("self_attn", prefix),
|
|
use_qk_norm=config.use_qk_norm,
|
|
alt_stream=alt_stream,
|
|
)
|
|
|
|
self.is_layer_sparse = self._is_layer_sparse(layer_id, is_nextn=is_nextn)
|
|
is_previous_layer_sparse = self._is_layer_sparse(layer_id - 1, is_nextn=False)
|
|
is_next_layer_sparse = self._is_layer_sparse(layer_id + 1, is_nextn=False)
|
|
|
|
self.layer_scatter_modes = LayerScatterModes.init_new(
|
|
layer_id=layer_id,
|
|
num_layers=1 if is_nextn else config.num_hidden_layers,
|
|
is_layer_sparse=self.is_layer_sparse,
|
|
is_previous_layer_sparse=is_previous_layer_sparse,
|
|
is_next_layer_sparse=is_next_layer_sparse,
|
|
)
|
|
|
|
if self.is_layer_sparse:
|
|
self.mlp = Glm4MoeSparseMoeBlock(
|
|
config=config,
|
|
quant_config=quant_config,
|
|
prefix=add_prefix("mlp", prefix),
|
|
layer_id=self.layer_id,
|
|
alt_stream=alt_stream,
|
|
)
|
|
else:
|
|
if enable_moe_dense_fully_dp():
|
|
mlp_tp_rank, mlp_tp_size = 0, 1
|
|
else:
|
|
mlp_tp_rank, mlp_tp_size = None, None
|
|
self.mlp = Glm4MoeMLP(
|
|
hidden_size=config.hidden_size,
|
|
intermediate_size=config.intermediate_size,
|
|
hidden_act=config.hidden_act,
|
|
quant_config=quant_config,
|
|
prefix=add_prefix("mlp", prefix),
|
|
tp_rank=mlp_tp_rank,
|
|
tp_size=mlp_tp_size,
|
|
)
|
|
|
|
self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
|
self.post_attention_layernorm = RMSNorm(
|
|
config.hidden_size, eps=config.rms_norm_eps
|
|
)
|
|
|
|
self.layer_communicator = LayerCommunicator(
|
|
layer_scatter_modes=self.layer_scatter_modes,
|
|
input_layernorm=self.input_layernorm,
|
|
post_attention_layernorm=self.post_attention_layernorm,
|
|
allow_reduce_scatter=True,
|
|
is_last_layer=(
|
|
is_nextn or (self.layer_id == self.config.num_hidden_layers - 1)
|
|
),
|
|
)
|
|
|
|
def _is_layer_sparse(self, layer_id: int, is_nextn: bool) -> bool:
|
|
return is_nextn or (
|
|
self.config.n_routed_experts is not None
|
|
and layer_id >= self.config.first_k_dense_replace
|
|
)
|
|
|
|
def forward(
|
|
self,
|
|
positions: torch.Tensor,
|
|
hidden_states: torch.Tensor,
|
|
forward_batch: ForwardBatch,
|
|
residual: Optional[torch.Tensor],
|
|
) -> torch.Tensor:
|
|
|
|
hidden_states, residual = self.layer_communicator.prepare_attn(
|
|
hidden_states, residual, forward_batch
|
|
)
|
|
|
|
hidden_states = self.self_attn(
|
|
positions=positions,
|
|
hidden_states=hidden_states,
|
|
forward_batch=forward_batch,
|
|
)
|
|
|
|
hidden_states, residual = self.layer_communicator.prepare_mlp(
|
|
hidden_states, residual, forward_batch
|
|
)
|
|
|
|
should_allreduce_fusion = (
|
|
self.layer_communicator.should_fuse_mlp_allreduce_with_next_layer(
|
|
forward_batch
|
|
)
|
|
)
|
|
|
|
# For DP with padding, reduce scatter can be used instead of all-reduce.
|
|
use_reduce_scatter = self.layer_communicator.should_use_reduce_scatter(
|
|
forward_batch
|
|
)
|
|
|
|
hidden_states = self.mlp(
|
|
hidden_states, forward_batch, should_allreduce_fusion, use_reduce_scatter
|
|
)
|
|
|
|
if should_allreduce_fusion:
|
|
hidden_states._sglang_needs_allreduce_fusion = True
|
|
else:
|
|
hidden_states, residual = self.layer_communicator.postprocess_layer(
|
|
hidden_states, residual, forward_batch
|
|
)
|
|
|
|
return hidden_states, residual
|
|
|
|
def op_comm_prepare_attn(
|
|
self,
|
|
state,
|
|
positions: torch.Tensor,
|
|
hidden_states: torch.Tensor,
|
|
forward_batch: ForwardBatch,
|
|
residual: Optional[torch.Tensor],
|
|
tbo_subbatch_index: Optional[int] = None,
|
|
):
|
|
state.hidden_states_after_comm_pre_attn, state.residual_after_input_ln = (
|
|
self.layer_communicator.prepare_attn(hidden_states, residual, forward_batch)
|
|
)
|
|
state.update(
|
|
dict(
|
|
forward_batch=forward_batch,
|
|
positions=positions,
|
|
tbo_subbatch_index=tbo_subbatch_index,
|
|
)
|
|
)
|
|
|
|
def op_comm_prepare_mlp(self, state):
|
|
state.hidden_states_mlp_input, state.residual_after_comm_pre_mlp = (
|
|
self.layer_communicator.prepare_mlp(
|
|
state.pop("hidden_states_after_attn"),
|
|
state.pop("residual_after_input_ln"),
|
|
state.forward_batch,
|
|
)
|
|
)
|
|
|
|
def op_mlp(self, state):
|
|
hidden_states = state.pop("hidden_states_mlp_input")
|
|
if not (
|
|
enable_moe_dense_fully_dp()
|
|
and (not self.is_layer_sparse)
|
|
and hidden_states.shape[0] == 0
|
|
):
|
|
state.hidden_states_mlp_output = self.mlp(
|
|
hidden_states, state.forward_batch
|
|
)
|
|
else:
|
|
state.hidden_states_mlp_output = hidden_states
|
|
|
|
def op_comm_postprocess_layer(self, state):
|
|
hidden_states, residual = self.layer_communicator.postprocess_layer(
|
|
state.pop("hidden_states_mlp_output"),
|
|
state.pop("residual_after_comm_pre_mlp"),
|
|
state.forward_batch,
|
|
)
|
|
|
|
output = dict(
|
|
positions=state.positions,
|
|
hidden_states=hidden_states,
|
|
residual=residual,
|
|
forward_batch=state.forward_batch,
|
|
tbo_subbatch_index=state.tbo_subbatch_index,
|
|
)
|
|
|
|
state.clear(
|
|
expect_keys={
|
|
"positions",
|
|
"forward_batch",
|
|
"tbo_subbatch_index",
|
|
}
|
|
)
|
|
return output
|
|
|
|
|
|
class Glm4MoeModel(nn.Module):
|
|
def __init__(
|
|
self,
|
|
config: PretrainedConfig,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
prefix: str = "",
|
|
):
|
|
super().__init__()
|
|
self.pp_group = get_pp_group()
|
|
self.config = config
|
|
self.vocab_size = config.vocab_size
|
|
self.first_k_dense_replace = config.first_k_dense_replace
|
|
self.embed_dim = config.hidden_size
|
|
if self.pp_group.is_first_rank:
|
|
self.embed_tokens = VocabParallelEmbedding(
|
|
config.vocab_size,
|
|
config.hidden_size,
|
|
enable_tp=not is_dp_attention_enabled(),
|
|
)
|
|
else:
|
|
self.embed_tokens = PPMissingLayer()
|
|
|
|
self.alt_stream = torch.cuda.Stream() if _is_cuda else None
|
|
self.layers, self.start_layer, self.end_layer = make_layers(
|
|
config.num_hidden_layers,
|
|
lambda idx, prefix: Glm4MoeDecoderLayer(
|
|
layer_id=idx,
|
|
config=config,
|
|
quant_config=quant_config,
|
|
prefix=prefix,
|
|
alt_stream=self.alt_stream,
|
|
),
|
|
pp_rank=self.pp_group.rank_in_group,
|
|
pp_size=self.pp_group.world_size,
|
|
prefix=add_prefix("layers", prefix),
|
|
)
|
|
if self.pp_group.is_last_rank:
|
|
self.norm = RMSNorm(self.embed_dim, eps=config.rms_norm_eps)
|
|
else:
|
|
self.norm = PPMissingLayer(return_tuple=True)
|
|
|
|
self.layers_to_capture = []
|
|
|
|
def get_input_embeddings(self) -> torch.Tensor:
|
|
return self.embed_tokens
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
forward_batch: ForwardBatch,
|
|
input_embeds: torch.Tensor = None,
|
|
pp_proxy_tensors: Optional[PPProxyTensors] = None,
|
|
) -> Union[torch.Tensor, PPProxyTensors]:
|
|
if self.pp_group.is_first_rank:
|
|
if input_embeds is None:
|
|
hidden_states = self.embed_tokens(input_ids)
|
|
else:
|
|
hidden_states = input_embeds
|
|
residual = None
|
|
else:
|
|
assert pp_proxy_tensors is not None
|
|
hidden_states = pp_proxy_tensors["hidden_states"]
|
|
residual = pp_proxy_tensors["residual"]
|
|
|
|
normal_start_layer = self.start_layer
|
|
normal_end_layer = self.end_layer
|
|
if forward_batch.can_run_tbo:
|
|
if (
|
|
self.first_k_dense_replace > normal_start_layer
|
|
and self.first_k_dense_replace < normal_end_layer
|
|
):
|
|
normal_end_layer = self.first_k_dense_replace
|
|
elif self.first_k_dense_replace < normal_start_layer:
|
|
normal_end_layer = normal_start_layer = 0
|
|
|
|
aux_hidden_states = []
|
|
for i in range(normal_start_layer, normal_end_layer):
|
|
with get_global_expert_distribution_recorder().with_current_layer(i):
|
|
if i in self.layers_to_capture:
|
|
aux_hidden_states.append(hidden_states + residual)
|
|
layer = self.layers[i]
|
|
hidden_states, residual = layer(
|
|
positions,
|
|
hidden_states,
|
|
forward_batch,
|
|
residual,
|
|
)
|
|
|
|
if normal_end_layer != self.end_layer:
|
|
hidden_states, residual = model_forward_maybe_tbo(
|
|
layers=self.layers[normal_end_layer : self.end_layer],
|
|
enable_tbo=True,
|
|
positions=positions,
|
|
forward_batch=forward_batch,
|
|
hidden_states=hidden_states,
|
|
residual=residual,
|
|
input_data_scatter_mode=self.layers[
|
|
normal_end_layer - 1
|
|
].layer_scatter_modes.layer_output_mode,
|
|
)
|
|
|
|
if not self.pp_group.is_last_rank:
|
|
return PPProxyTensors(
|
|
{
|
|
"hidden_states": hidden_states,
|
|
"residual": residual,
|
|
}
|
|
)
|
|
else:
|
|
if not forward_batch.forward_mode.is_idle():
|
|
if residual is None:
|
|
hidden_states = self.norm(hidden_states)
|
|
else:
|
|
hidden_states, _ = self.norm(hidden_states, residual)
|
|
if len(aux_hidden_states) == 0:
|
|
return hidden_states
|
|
return hidden_states, aux_hidden_states
|
|
|
|
|
|
class Glm4MoeForCausalLM(nn.Module):
|
|
def __init__(
|
|
self,
|
|
config: PretrainedConfig,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
prefix: str = "",
|
|
) -> None:
|
|
nn.Module.__init__(self)
|
|
self.pp_group = get_pp_group()
|
|
self.config = config
|
|
self.tp_size = get_tensor_model_parallel_world_size()
|
|
self.quant_config = quant_config
|
|
self.num_fused_shared_experts = 0
|
|
self.determine_num_fused_shared_experts()
|
|
self.model = Glm4MoeModel(
|
|
config, quant_config, prefix=add_prefix("model", prefix)
|
|
)
|
|
self.lm_head = ParallelLMHead(
|
|
config.vocab_size,
|
|
config.hidden_size,
|
|
quant_config=quant_config,
|
|
prefix=add_prefix("lm_head", prefix),
|
|
use_attn_tp_group=get_global_server_args().enable_dp_lm_head,
|
|
)
|
|
self.logits_processor = LogitsProcessor(config)
|
|
|
|
# For EAGLE3 support
|
|
self.capture_aux_hidden_states = False
|
|
|
|
def get_input_embeddings(self) -> nn.Embedding:
|
|
return self.model.embed_tokens
|
|
|
|
def determine_num_fused_shared_experts(self):
|
|
if get_global_server_args().disable_shared_experts_fusion:
|
|
return
|
|
|
|
disable_reason = None
|
|
if not getattr(self.config, "n_shared_experts", None):
|
|
disable_reason = "No shared experts are defined in the config."
|
|
elif not _is_cuda:
|
|
disable_reason = "Shared experts fusion currently requires CUDA devices."
|
|
elif _is_cuda and (_device_sm is not None) and (_device_sm < 80):
|
|
disable_reason = "Shared experts fusion requires SM80 or newer GPUs."
|
|
elif get_moe_expert_parallel_world_size() > 1:
|
|
disable_reason = "Shared experts fusion is not supported together with expert parallelism yet."
|
|
elif get_moe_a2a_backend().is_deepep():
|
|
disable_reason = "Shared experts fusion is not supported when Deepep MoE backend is enabled."
|
|
|
|
if disable_reason is not None:
|
|
get_global_server_args().disable_shared_experts_fusion = True
|
|
log_info_on_rank0(
|
|
logger,
|
|
f"{disable_reason} Shared experts fusion optimization is disabled.",
|
|
)
|
|
return
|
|
|
|
self.num_fused_shared_experts = self.config.n_shared_experts
|
|
assert (
|
|
self.num_fused_shared_experts == 1
|
|
), "Only 1 fused shared expert is supported for Glm4MoeForCausalLM"
|
|
log_info_on_rank0(logger, "Shared experts fusion optimization enabled.")
|
|
|
|
@torch.no_grad()
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
forward_batch: ForwardBatch,
|
|
input_embeds: torch.Tensor = None,
|
|
pp_proxy_tensors: Optional[PPProxyTensors] = None,
|
|
) -> torch.Tensor:
|
|
hidden_states = self.model(
|
|
input_ids, positions, forward_batch, input_embeds, pp_proxy_tensors
|
|
)
|
|
aux_hidden_states = None
|
|
if self.capture_aux_hidden_states:
|
|
hidden_states, aux_hidden_states = hidden_states
|
|
|
|
if self.pp_group.is_last_rank:
|
|
return self.logits_processor(
|
|
input_ids, hidden_states, self.lm_head, forward_batch, aux_hidden_states
|
|
)
|
|
else:
|
|
return hidden_states
|
|
|
|
@property
|
|
def start_layer(self):
|
|
return self.model.start_layer
|
|
|
|
@property
|
|
def end_layer(self):
|
|
return self.model.end_layer
|
|
|
|
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]], is_nextn=False):
|
|
if is_nextn:
|
|
if hasattr(self.config, "num_nextn_predict_layers"):
|
|
num_nextn_layers = self.config.num_nextn_predict_layers
|
|
assert num_nextn_layers == 1, "Only 1 nextn layer is supported"
|
|
# compatible with old design
|
|
nextn_layer_id = (
|
|
0
|
|
if self.config.num_hidden_layers == 1
|
|
else self.config.num_hidden_layers
|
|
)
|
|
else:
|
|
raise ValueError("num_nextn_predict_layers is not in the config")
|
|
|
|
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),
|
|
]
|
|
|
|
expert_params_mapping = FusedMoE.make_expert_params_mapping(
|
|
ckpt_gate_proj_name="gate_proj",
|
|
ckpt_down_proj_name="down_proj",
|
|
ckpt_up_proj_name="up_proj",
|
|
num_experts=self.config.n_routed_experts + self.num_fused_shared_experts,
|
|
)
|
|
|
|
if is_nextn:
|
|
nextn_layer_prefix = f"model.layers.{nextn_layer_id}"
|
|
nextn_spec_weight_names = [
|
|
"shared_head.norm",
|
|
"eh_proj",
|
|
"enorm",
|
|
"hnorm",
|
|
]
|
|
|
|
params_dict = dict(self.named_parameters())
|
|
weight_names = []
|
|
for name, loaded_weight in weights:
|
|
weight_names.append(name)
|
|
|
|
if self.num_fused_shared_experts > 0 and "mlp.shared_experts" in name:
|
|
# Map shared expert weights to the last expert slot
|
|
# Shared expert becomes expert ID = n_routed_experts
|
|
name = name.replace(
|
|
"mlp.shared_experts",
|
|
f"mlp.experts.{self.config.n_routed_experts}",
|
|
)
|
|
|
|
if not is_nextn:
|
|
if hasattr(self.config, "num_nextn_predict_layers"):
|
|
num_nextn_layers = self.config.num_nextn_predict_layers
|
|
if num_nextn_layers > 0 and name.startswith("model.layers"):
|
|
name_list = name.split(".")
|
|
if (
|
|
len(name_list) >= 3
|
|
and int(name_list[2]) >= self.config.num_hidden_layers
|
|
):
|
|
continue
|
|
else:
|
|
if not name.startswith(nextn_layer_prefix):
|
|
continue
|
|
|
|
# Use shared head and embed weights from target model
|
|
if "shared_head.head" in name or "embed_tokens" in name:
|
|
continue
|
|
|
|
is_decoder = True
|
|
# For nextn specific weights
|
|
for weight_name in nextn_spec_weight_names:
|
|
if weight_name in name:
|
|
name = name.replace(nextn_layer_prefix, "model")
|
|
is_decoder = False
|
|
break
|
|
# For decoder layer weights
|
|
if is_decoder:
|
|
name = name.replace(nextn_layer_prefix, "model.decoder")
|
|
|
|
if "rotary_emb.inv_freq" in name:
|
|
continue
|
|
for param_name, weight_name, shard_id in stacked_params_mapping:
|
|
# Skip non-stacked layers and experts (experts handled below).
|
|
if weight_name not in name:
|
|
continue
|
|
# We have mlp.experts[0].gate_proj in the checkpoint.
|
|
# Since we handle the experts below in expert_params_mapping,
|
|
# we need to skip here BEFORE we update the name, otherwise
|
|
# name will be updated to mlp.experts[0].gate_up_proj, which
|
|
# will then be updated below in expert_params_mapping
|
|
# for mlp.experts[0].gate_gate_up_proj, which breaks load.
|
|
if "mlp.experts" in name:
|
|
continue
|
|
name = name.replace(weight_name, param_name)
|
|
# Skip loading extra bias for GPTQ models.
|
|
if name.endswith(".bias") and name not in params_dict:
|
|
continue
|
|
if name not in params_dict:
|
|
continue
|
|
|
|
param = params_dict[name]
|
|
weight_loader = param.weight_loader
|
|
weight_loader(param, loaded_weight, shard_id)
|
|
break
|
|
else:
|
|
# Track if this is an expert weight to enable early skipping
|
|
is_expert_weight = False
|
|
|
|
for mapping in expert_params_mapping:
|
|
param_name, weight_name, expert_id, shard_id = mapping
|
|
if weight_name not in name:
|
|
continue
|
|
|
|
# Mark as expert weight regardless of whether we can process it
|
|
is_expert_weight = True
|
|
|
|
name = name.replace(weight_name, param_name)
|
|
if name not in params_dict:
|
|
# Expert weight not on this rank, will be skipped below
|
|
continue
|
|
|
|
param = params_dict[name]
|
|
weight_loader = param.weight_loader
|
|
weight_loader(
|
|
param,
|
|
loaded_weight,
|
|
name,
|
|
shard_id=shard_id,
|
|
expert_id=expert_id,
|
|
)
|
|
break
|
|
else:
|
|
if is_expert_weight:
|
|
# This is an expert weight but not mapped to this rank, skip all remaining processing
|
|
continue
|
|
|
|
# Skip loading extra bias for GPTQ models.
|
|
if name.endswith(".bias") and name not in params_dict:
|
|
continue
|
|
if name not in params_dict:
|
|
continue
|
|
|
|
if name in params_dict.keys():
|
|
param = params_dict[name]
|
|
weight_loader = getattr(
|
|
param, "weight_loader", default_weight_loader
|
|
)
|
|
weight_loader(param, loaded_weight)
|
|
else:
|
|
logger.warning(f"Parameter {name} not found in params_dict")
|
|
|
|
def get_embed_and_head(self):
|
|
return self.model.embed_tokens.weight, self.lm_head.weight
|
|
|
|
def set_embed_and_head(self, embed, head):
|
|
del self.model.embed_tokens.weight
|
|
del self.lm_head.weight
|
|
self.model.embed_tokens.weight = embed
|
|
self.lm_head.weight = head
|
|
torch.cuda.empty_cache()
|
|
torch.cuda.synchronize()
|
|
|
|
@classmethod
|
|
def get_model_config_for_expert_location(cls, config):
|
|
return ModelConfigForExpertLocation(
|
|
num_layers=config.num_hidden_layers,
|
|
num_logical_experts=config.n_routed_experts,
|
|
num_groups=config.n_group,
|
|
)
|
|
|
|
def set_eagle3_layers_to_capture(self, layer_ids: Optional[List[int]] = None):
|
|
if not self.pp_group.is_last_rank:
|
|
return
|
|
|
|
if layer_ids is None:
|
|
self.capture_aux_hidden_states = True
|
|
num_layers = self.config.num_hidden_layers
|
|
self.model.layers_to_capture = [2, num_layers // 2, num_layers - 3]
|
|
else:
|
|
self.capture_aux_hidden_states = True
|
|
# we plus 1 here because in sglang, for the ith layer, it takes the output
|
|
# of the (i-1)th layer as aux hidden state
|
|
self.model.layers_to_capture = [val + 1 for val in layer_ids]
|
|
|
|
|
|
EntryClass = [Glm4MoeForCausalLM]
|