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952
vllm/model_executor/models/step3p5.py
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952
vllm/model_executor/models/step3p5.py
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""Inference-only Jurassic model."""
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import typing
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from collections.abc import Callable, Iterable
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from typing import Any
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import torch
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from torch import nn
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from torch.nn.parameter import Parameter
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from vllm.compilation.decorators import support_torch_compile
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from vllm.config import CacheConfig, ModelConfig, VllmConfig
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from vllm.distributed import (
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get_dp_group,
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get_ep_group,
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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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get_tp_group,
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)
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from vllm.logger import init_logger
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from vllm.model_executor.layers.activation import SiluAndMul, SwigluStepAndMul
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from vllm.model_executor.layers.attention import Attention
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from vllm.model_executor.layers.fused_moe import FusedMoE
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from vllm.model_executor.layers.fused_moe.shared_fused_moe import SharedFusedMoE
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from vllm.model_executor.layers.layernorm import GemmaRMSNorm
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from vllm.model_executor.layers.linear import (
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ColumnParallelLinear,
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MergedColumnParallelLinear,
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QKVParallelLinear,
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ReplicatedLinear,
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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.base_config 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.model_loader.weight_utils import default_weight_loader
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from vllm.sequence import IntermediateTensors
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from vllm.v1.attention.backend import AttentionType
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from .interfaces import MixtureOfExperts, SupportsPP
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from .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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is_pp_missing_parameter,
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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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logger = init_logger(__name__)
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class FP32ReplicatedLinear(ReplicatedLinear):
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"""
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Use FP32 for higher precision.
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"""
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def forward(
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self,
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x: torch.Tensor,
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) -> torch.Tensor | tuple[torch.Tensor, Parameter | None]:
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assert self.params_dtype == torch.float32
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return super().forward(x.to(torch.float32))
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class Step3p5MLP(nn.Module):
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def __init__(
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self,
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config: ModelConfig,
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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: QuantizationConfig | None = None,
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reduce_results: bool = True,
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prefix: str = "",
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) -> None:
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super().__init__()
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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=f"{prefix}.gate_up_proj",
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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=f"{prefix}.down_proj",
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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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self.prefix = prefix
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self.hidden_size = hidden_size
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self.limit = None
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layer_idx = extract_layer_index(prefix)
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if (
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config.swiglu_limits_shared
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and config.swiglu_limits_shared[layer_idx] is not None
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and config.swiglu_limits_shared[layer_idx] != 0
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):
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self.limit = config.swiglu_limits_shared[layer_idx]
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self.act_fn = SwigluStepAndMul(limit=self.limit)
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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gate_up, _ = self.gate_up_proj(hidden_states)
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intermediate_act = self.act_fn(gate_up)
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output, _ = self.down_proj(intermediate_act)
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return output
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class Step3p5Attention(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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max_position: int = 4096 * 32,
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head_dim: int | None = None,
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rms_norm_eps: float = 1e-06,
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qkv_bias: bool = False,
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rope_theta: float | list[float] | None = 10000,
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cache_config: CacheConfig | None = None,
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quant_config: QuantizationConfig | None = None,
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rope_scaling: dict[str, Any] | None = None,
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prefix: str = "",
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attn_type: str = AttentionType.DECODER,
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# Step3p5 specific args
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sliding_window: int | None = None,
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use_head_wise_attn_gate: bool = False,
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layer_types: list = None,
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use_rope_layers: list = None,
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yarn_only_types: list = None,
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swa_num_attention_heads: int | None = None,
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partial_rotary_factor: float = 1.0,
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):
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super().__init__()
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self.hidden_size = hidden_size
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self.total_num_heads = num_heads
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tp_size = get_tensor_model_parallel_world_size()
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self.layer_idx = extract_layer_index(prefix)
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if layer_types:
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enable_sliding_window = layer_types[self.layer_idx] == "sliding_attention"
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else:
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enable_sliding_window = self.layer_idx % 2 == 0
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if yarn_only_types and layer_types[self.layer_idx] not in yarn_only_types:
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rope_scaling = None
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if sliding_window is not None and enable_sliding_window:
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sliding_window = sliding_window
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if swa_num_attention_heads is not None:
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num_heads = swa_num_attention_heads
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self.total_num_heads = swa_num_attention_heads
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else:
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sliding_window = None
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if isinstance(rope_theta, list):
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rope_theta = rope_theta[self.layer_idx]
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self.rank = get_tensor_model_parallel_rank()
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self.partial_rotary_factor = partial_rotary_factor
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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.rope_theta = rope_theta
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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=qkv_bias,
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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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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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if rope_scaling is not None and not isinstance(rope_scaling, dict):
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raise ValueError("rope_scaling must be a dict for Step3p5Attention.")
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rope_parameters: dict[str, Any] = (
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dict(rope_scaling) if rope_scaling is not None else {}
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)
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rope_parameters.setdefault("rope_type", "default")
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rope_parameters["rope_theta"] = self.rope_theta
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rope_parameters["partial_rotary_factor"] = partial_rotary_factor
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self.rotary_emb = get_rope(
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head_size=self.head_dim,
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max_position=max_position,
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rope_parameters=rope_parameters,
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)
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self.q_norm = GemmaRMSNorm(self.head_dim, rms_norm_eps)
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self.k_norm = GemmaRMSNorm(self.head_dim, rms_norm_eps)
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self.use_head_wise_attn_gate = use_head_wise_attn_gate
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if use_head_wise_attn_gate:
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self.g_proj = ColumnParallelLinear(
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hidden_size,
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self.total_num_heads,
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bias=False,
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quant_config=quant_config,
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prefix=f"{prefix}.g_proj",
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)
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self.use_rope = True
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if use_rope_layers:
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self.use_rope = use_rope_layers[self.layer_idx]
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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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prefix=f"{prefix}.attn",
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per_layer_sliding_window=sliding_window,
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attn_type=attn_type,
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)
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self.max_position_embeddings = max_position
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assert self.partial_rotary_factor == 1 or self.partial_rotary_factor == 0.5
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self.rotary_dim = (
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self.head_dim if self.partial_rotary_factor == 1 else self.head_dim // 2
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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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q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
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# Add qk-norm inline similar to Qwen3 MOE attention
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q_by_head = q.view(*q.shape[:-1], q.shape[-1] // self.head_dim, self.head_dim)
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q_by_head = self.q_norm(q_by_head.contiguous())
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q = q_by_head.view(q.shape)
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k_by_head = k.view(*k.shape[:-1], k.shape[-1] // self.head_dim, self.head_dim)
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k_by_head = self.k_norm(k_by_head.contiguous())
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k = k_by_head.view(k.shape)
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if self.use_rope:
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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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if self.use_head_wise_attn_gate:
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extra_dims, _ = self.g_proj(hidden_states)
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output = (
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attn_output.view(*attn_output.shape[:-1], self.num_heads, self.head_dim)
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* extra_dims.unsqueeze(-1).sigmoid()
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)
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attn_output = output.view(*attn_output.shape)
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output, _ = self.o_proj(attn_output)
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return output
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class FusedMoEBlock(nn.Module):
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def __init__(
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self,
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vllm_config: VllmConfig,
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prefix: str = "",
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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.layer_idx = extract_layer_index(prefix)
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self.ep_size = get_ep_group().device_group.size()
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self.ep_rank = get_ep_group().device_group.rank()
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config = vllm_config.model_config.hf_config
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quant_config = vllm_config.quant_config
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parallel_config = vllm_config.parallel_config
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self.hidden_size = config.hidden_size
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self.enable_eplb = parallel_config.enable_eplb
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self.n_routed_experts = config.moe_num_experts
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self.n_logical_experts = self.n_routed_experts
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self.n_redundant_experts = parallel_config.eplb_config.num_redundant_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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if self.tp_size > config.moe_num_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.moe_num_experts}."
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)
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self.gate = FP32ReplicatedLinear(
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config.hidden_size,
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config.moe_num_experts,
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bias=False,
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quant_config=None,
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params_dtype=torch.float32, # Use FP32 for higher precision.
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prefix=f"{prefix}.gate",
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)
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self.use_moe_router_bias = config.use_moe_router_bias
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assert self.use_moe_router_bias, "Only support use_moe_router_bias is true."
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self.routed_scaling_factor = config.moe_router_scaling_factor
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self.router_bias = nn.Parameter(
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torch.zeros(config.moe_num_experts, dtype=torch.float32),
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requires_grad=False,
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)
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self.need_fp32_gate = config.need_fp32_gate
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assert self.need_fp32_gate, (
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"Router logits must use FP32 precision for numerical stability."
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)
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activation = "silu"
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swiglu_limits = config.swiglu_limits or []
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swiglu_limit = (
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swiglu_limits[self.layer_idx]
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if self.layer_idx < len(swiglu_limits)
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else None
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)
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if swiglu_limit not in (None, 0):
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swiglu_limit = float(swiglu_limit)
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assert swiglu_limit == 7.0, (
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"Swiglu limit in fused moe block only suport 7.0 now."
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)
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activation = "swiglustep"
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logger.debug(
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"step3p5 layer_idx: %s, activation: %s, limit: %s",
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self.layer_idx,
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activation,
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swiglu_limit,
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)
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self.share_expert = Step3p5MLP(
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config=config,
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hidden_size=self.hidden_size,
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intermediate_size=config.share_expert_dim,
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hidden_act="silu",
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reduce_results=False,
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quant_config=quant_config,
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prefix=f"{prefix}.share_expert",
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)
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self.experts = SharedFusedMoE(
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shared_experts=self.share_expert,
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gate=self.gate,
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num_experts=config.moe_num_experts,
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top_k=config.moe_top_k,
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hidden_size=config.hidden_size,
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intermediate_size=config.moe_intermediate_size,
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reduce_results=False,
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renormalize=config.norm_expert_weight,
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quant_config=quant_config,
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activation=activation,
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prefix=f"{prefix}.experts",
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scoring_func=getattr(config, "moe_router_activation", "sigmoid"),
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e_score_correction_bias=self.router_bias,
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routed_scaling_factor=config.moe_router_scaling_factor,
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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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||||
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if self.experts.is_internal_router:
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# In this case, the gate/router runs inside the FusedMoE class
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fused_moe_out = self.experts(
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hidden_states=hidden_states, router_logits=hidden_states
|
||||
)
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else:
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# router_logits: (num_tokens, n_experts)
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router_logits, _ = self.gate(hidden_states)
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fused_moe_out = self.experts(
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hidden_states=hidden_states, router_logits=router_logits
|
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)
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shared_output, final_hidden_states = fused_moe_out
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if self.share_expert is None:
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assert shared_output is None
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||||
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||||
if self.share_expert is not None:
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assert shared_output is not None
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||||
final_hidden_states += shared_output
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||||
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if self.tp_size > 1:
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||||
final_hidden_states = self.experts.maybe_all_reduce_tensor_model_parallel(
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||||
final_hidden_states
|
||||
)
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||||
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return final_hidden_states.view(num_tokens, hidden_dim)
|
||||
|
||||
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||||
class Step3p5DecoderLayer(nn.Module):
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||||
def __init__(
|
||||
self,
|
||||
vllm_config: VllmConfig,
|
||||
prefix: str = "",
|
||||
) -> None:
|
||||
super().__init__()
|
||||
config = vllm_config.model_config.hf_config
|
||||
self.hidden_size = config.hidden_size
|
||||
layer_idx = extract_layer_index(prefix)
|
||||
self.layer_idx = layer_idx
|
||||
cache_config = vllm_config.cache_config
|
||||
quant_config = vllm_config.quant_config
|
||||
if cache_config is not None:
|
||||
cache_config.sliding_window = None
|
||||
if config.att_impl_type == "GQA":
|
||||
num_attention_heads = None
|
||||
num_attention_groups = None
|
||||
head_dim = None
|
||||
if (
|
||||
getattr(config, "attention_other_setting", None)
|
||||
and getattr(config, "layer_types", [])
|
||||
and config.layer_types[layer_idx]
|
||||
== config.attention_other_setting["attention_type"]
|
||||
):
|
||||
num_attention_heads = config.attention_other_setting[
|
||||
"num_attention_heads"
|
||||
]
|
||||
num_attention_groups = config.attention_other_setting[
|
||||
"num_attention_groups"
|
||||
]
|
||||
head_dim = config.attention_other_setting["head_dim"]
|
||||
partial_rotary_factors = getattr(config, "partial_rotary_factors", [])
|
||||
self.self_attn = Step3p5Attention(
|
||||
hidden_size=self.hidden_size,
|
||||
num_heads=num_attention_heads
|
||||
if num_attention_heads
|
||||
else config.num_attention_heads,
|
||||
max_position=config.max_position_embeddings,
|
||||
num_kv_heads=num_attention_groups
|
||||
if num_attention_groups
|
||||
else config.num_attention_groups,
|
||||
rope_theta=config.rope_theta,
|
||||
rms_norm_eps=config.rms_norm_eps,
|
||||
qkv_bias=getattr(config, "attention_bias", False),
|
||||
head_dim=head_dim if head_dim else getattr(config, "head_dim", None),
|
||||
cache_config=cache_config,
|
||||
quant_config=quant_config,
|
||||
rope_scaling=getattr(config, "rope_scaling", None),
|
||||
sliding_window=getattr(config, "sliding_window", None),
|
||||
use_head_wise_attn_gate=getattr(
|
||||
config, "use_head_wise_attn_gate", False
|
||||
),
|
||||
layer_types=getattr(config, "layer_types", []),
|
||||
use_rope_layers=getattr(config, "use_rope_layers", []),
|
||||
yarn_only_types=getattr(config, "yarn_only_types", []),
|
||||
partial_rotary_factor=partial_rotary_factors[layer_idx]
|
||||
if partial_rotary_factors
|
||||
else 1.0,
|
||||
prefix=f"{prefix}.self_attn",
|
||||
)
|
||||
else:
|
||||
raise ValueError(
|
||||
f"Unsupported attention implementation: {config.att_impl_type}"
|
||||
)
|
||||
self.use_moe = False
|
||||
self.tp_group = get_tp_group()
|
||||
self.use_fused_all_reduce = (
|
||||
get_tensor_model_parallel_world_size() > 1
|
||||
and get_dp_group().world_size == 1
|
||||
)
|
||||
if self.use_fused_all_reduce:
|
||||
logger.warning_once("Enable custom fused all reduce...")
|
||||
else:
|
||||
logger.warning_once("Disable custom fused all reduce...")
|
||||
|
||||
moe_layers_enum = getattr(config, "moe_layers_enum", None)
|
||||
if moe_layers_enum is not None:
|
||||
moe_layers_idx = [int(i) for i in moe_layers_enum.strip().split(",")]
|
||||
else:
|
||||
moe_layers_idx = [i for i in range(1, config.num_hidden_layers)]
|
||||
if layer_idx in moe_layers_idx:
|
||||
self.moe = FusedMoEBlock(
|
||||
vllm_config,
|
||||
prefix=f"{prefix}.moe",
|
||||
)
|
||||
self.use_moe = True
|
||||
else:
|
||||
self.mlp = Step3p5MLP(
|
||||
config=config,
|
||||
hidden_size=config.hidden_size,
|
||||
intermediate_size=config.intermediate_size,
|
||||
hidden_act="silu",
|
||||
quant_config=quant_config,
|
||||
reduce_results=True,
|
||||
prefix=f"{prefix}.mlp",
|
||||
)
|
||||
self.input_layernorm = GemmaRMSNorm(config.hidden_size, config.rms_norm_eps)
|
||||
self.post_attention_layernorm = GemmaRMSNorm(
|
||||
config.hidden_size, config.rms_norm_eps
|
||||
)
|
||||
self.prefix = prefix
|
||||
|
||||
def add_and_maybe_inplace_all_reduce(
|
||||
self, in1: torch.Tensor, in2: torch.Tensor
|
||||
) -> torch.Tensor:
|
||||
if not self.use_fused_all_reduce:
|
||||
return in1 + in2
|
||||
return self.tp_group.all_reduce(in1 + in2)
|
||||
|
||||
def forward(
|
||||
self, positions: torch.Tensor, hidden_states: torch.Tensor
|
||||
) -> torch.Tensor:
|
||||
residual = hidden_states
|
||||
hidden_states = self.input_layernorm(hidden_states)
|
||||
|
||||
hidden_states = self.self_attn(
|
||||
positions=positions,
|
||||
hidden_states=hidden_states,
|
||||
)
|
||||
hidden_states += residual
|
||||
residual = hidden_states
|
||||
hidden_states = self.post_attention_layernorm(hidden_states)
|
||||
|
||||
if self.use_moe:
|
||||
ffn_output = self.moe(hidden_states)
|
||||
else:
|
||||
ffn_output = self.mlp(hidden_states)
|
||||
hidden_states = ffn_output + residual
|
||||
return hidden_states
|
||||
|
||||
|
||||
@support_torch_compile
|
||||
class Step3p5Model(nn.Module):
|
||||
def __init__(self, vllm_config: VllmConfig, prefix: str = "") -> None:
|
||||
super().__init__()
|
||||
|
||||
self.vllm_config = vllm_config
|
||||
config = vllm_config.model_config.hf_config
|
||||
self.vocab_size = config.vocab_size
|
||||
self.config = config
|
||||
|
||||
self.moe_num_experts = config.moe_num_experts
|
||||
|
||||
if get_pp_group().is_first_rank or (
|
||||
config.tie_word_embeddings and get_pp_group().is_last_rank
|
||||
):
|
||||
self.embed_tokens = VocabParallelEmbedding(
|
||||
self.vocab_size,
|
||||
config.hidden_size,
|
||||
)
|
||||
else:
|
||||
self.embed_tokens = PPMissingLayer()
|
||||
|
||||
self.start_layer, self.end_layer, self.layers = make_layers(
|
||||
config.num_hidden_layers,
|
||||
lambda prefix: Step3p5DecoderLayer(
|
||||
vllm_config,
|
||||
prefix=prefix,
|
||||
),
|
||||
prefix=f"{prefix}.layers",
|
||||
)
|
||||
if get_pp_group().is_last_rank:
|
||||
self.norm = GemmaRMSNorm(config.hidden_size, config.rms_norm_eps)
|
||||
else:
|
||||
self.norm = PPMissingLayer()
|
||||
|
||||
self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
|
||||
["hidden_states"], config.hidden_size
|
||||
)
|
||||
|
||||
def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
|
||||
return self.embed_tokens(input_ids)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.Tensor,
|
||||
positions: torch.Tensor,
|
||||
intermediate_tensors: IntermediateTensors | None = None,
|
||||
inputs_embeds: torch.Tensor | None = None,
|
||||
) -> torch.Tensor:
|
||||
if get_pp_group().is_first_rank:
|
||||
if inputs_embeds is not None:
|
||||
hidden_states = inputs_embeds
|
||||
else:
|
||||
hidden_states = self.embed_input_ids(input_ids)
|
||||
else:
|
||||
assert intermediate_tensors is not None
|
||||
hidden_states = intermediate_tensors["hidden_states"]
|
||||
for i in range(self.start_layer, self.end_layer):
|
||||
layer = self.layers[i]
|
||||
hidden_states = layer(positions, hidden_states)
|
||||
|
||||
if not get_pp_group().is_last_rank:
|
||||
return IntermediateTensors(
|
||||
{
|
||||
"hidden_states": hidden_states,
|
||||
}
|
||||
)
|
||||
|
||||
return hidden_states
|
||||
|
||||
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
|
||||
config = self.config
|
||||
assert config.num_attention_groups > 1, "Only support GQA"
|
||||
qkv_params_mapping = []
|
||||
stacked_params_mapping = [
|
||||
# (param_name, shard_name, shard_id)
|
||||
("qkv_proj", "q_proj", "q"),
|
||||
("qkv_proj", "k_proj", "k"),
|
||||
("qkv_proj", "v_proj", "v"),
|
||||
("gate_up_proj", "gate_proj", 0),
|
||||
("gate_up_proj", "up_proj", 1),
|
||||
]
|
||||
|
||||
params_dict = dict(self.named_parameters())
|
||||
loaded_params: set[str] = set()
|
||||
|
||||
# Old packed 3D format: .moe.gate_proj.weight [num_experts, out, in]
|
||||
expert_params_mapping = [
|
||||
(".moe.experts.w13_weight", ".moe.gate_proj.weight", "w1"),
|
||||
(".moe.experts.w13_weight", ".moe.up_proj.weight", "w3"),
|
||||
(".moe.experts.w2_weight", ".moe.down_proj.weight", "w2"),
|
||||
]
|
||||
|
||||
# New per-expert format: .moe.experts.E.gate_proj.weight_packed [out, in]
|
||||
per_expert_mapping = FusedMoE.make_expert_params_mapping(
|
||||
self,
|
||||
ckpt_gate_proj_name="gate_proj",
|
||||
ckpt_down_proj_name="down_proj",
|
||||
ckpt_up_proj_name="up_proj",
|
||||
num_experts=self.moe_num_experts,
|
||||
)
|
||||
|
||||
disable_moe_stacked_params = [data[1] for data in expert_params_mapping]
|
||||
|
||||
for name, loaded_weight in weights:
|
||||
if name.startswith("model."):
|
||||
local_name = name[len("model.") :]
|
||||
full_name = name
|
||||
else:
|
||||
local_name = name
|
||||
full_name = f"model.{name}" if name else "model"
|
||||
|
||||
spec_layer = get_spec_layer_idx_from_weight_name(config, full_name)
|
||||
if spec_layer is not None:
|
||||
continue # skip spec decode layers for main model
|
||||
|
||||
# Skip any layers beyond the main model's depth (e.g., MTP layers)
|
||||
if full_name.startswith("model.layers."):
|
||||
parts = full_name.split(".")
|
||||
if len(parts) > 2 and parts[2].isdigit():
|
||||
layer_idx = int(parts[2])
|
||||
if layer_idx >= config.num_hidden_layers:
|
||||
continue
|
||||
|
||||
# Per-expert MoE weights (new format from LLM Compressor):
|
||||
# .moe.experts.{E}.{gate,up,down}_proj.{weight_packed,scale,...}
|
||||
# Each weight is individual per-expert, not stacked 3D.
|
||||
if ".moe.experts." in local_name:
|
||||
is_expert_weight = False
|
||||
for mapping in per_expert_mapping:
|
||||
param_name, weight_name, expert_id, shard_id = mapping
|
||||
if weight_name not in local_name:
|
||||
continue
|
||||
is_expert_weight = True
|
||||
name_mapped = local_name.replace(weight_name, param_name)
|
||||
if is_pp_missing_parameter(name_mapped, self):
|
||||
continue
|
||||
if name_mapped not in params_dict:
|
||||
continue
|
||||
param = params_dict[name_mapped]
|
||||
weight_loader = typing.cast(
|
||||
Callable[..., bool], param.weight_loader
|
||||
)
|
||||
success = weight_loader(
|
||||
param,
|
||||
loaded_weight,
|
||||
name_mapped,
|
||||
shard_id=shard_id,
|
||||
expert_id=expert_id,
|
||||
return_success=True,
|
||||
)
|
||||
if success:
|
||||
loaded_params.add(name_mapped)
|
||||
break
|
||||
else:
|
||||
if (
|
||||
not is_expert_weight
|
||||
and not is_pp_missing_parameter(local_name, self)
|
||||
and local_name in params_dict
|
||||
):
|
||||
# Not an expert proj — use default loader
|
||||
# (e.g. share_expert weights if they matched)
|
||||
param = params_dict[local_name]
|
||||
weight_loader = getattr(
|
||||
param,
|
||||
"weight_loader",
|
||||
default_weight_loader,
|
||||
)
|
||||
weight_loader(param, loaded_weight)
|
||||
loaded_params.add(local_name)
|
||||
continue
|
||||
|
||||
for param_name, weight_name, shard_id in stacked_params_mapping:
|
||||
if weight_name not in local_name:
|
||||
continue
|
||||
if any(
|
||||
disable_moe_stacked_param in local_name
|
||||
for disable_moe_stacked_param in disable_moe_stacked_params
|
||||
):
|
||||
continue
|
||||
replaced_name = local_name.replace(weight_name, param_name)
|
||||
if is_pp_missing_parameter(replaced_name, self):
|
||||
continue
|
||||
if replaced_name not in params_dict:
|
||||
continue
|
||||
param = params_dict[replaced_name]
|
||||
weight_loader = param.weight_loader
|
||||
weight_loader(param, loaded_weight, shard_id)
|
||||
loaded_params.add(replaced_name)
|
||||
break
|
||||
else:
|
||||
for param_name, weight_name, shard_id in expert_params_mapping:
|
||||
if weight_name not in local_name:
|
||||
continue
|
||||
replaced_name = local_name.replace(weight_name, param_name)
|
||||
if is_pp_missing_parameter(replaced_name, self):
|
||||
continue
|
||||
if (
|
||||
replaced_name.endswith(".bias")
|
||||
or replaced_name.endswith("_bias")
|
||||
) and replaced_name not in params_dict:
|
||||
continue
|
||||
if replaced_name not in params_dict:
|
||||
continue
|
||||
param = params_dict[replaced_name]
|
||||
weight_loader = param.weight_loader
|
||||
moe_expert_num = self.moe_num_experts
|
||||
# Per-tensor global scales (e.g. weight_global_scale)
|
||||
# have shape [1] in compressed-tensors NVFP4 checkpoints.
|
||||
# Expand to per-expert before the iteration loop.
|
||||
if (
|
||||
loaded_weight.shape[0] == 1
|
||||
and loaded_weight.shape[0] != moe_expert_num
|
||||
):
|
||||
loaded_weight = loaded_weight.expand(
|
||||
moe_expert_num, *loaded_weight.shape[1:]
|
||||
)
|
||||
assert loaded_weight.shape[0] == moe_expert_num
|
||||
for expert_id in range(moe_expert_num):
|
||||
loaded_weight_expert = loaded_weight[expert_id]
|
||||
weight_loader(
|
||||
param,
|
||||
loaded_weight_expert,
|
||||
replaced_name,
|
||||
shard_id=shard_id,
|
||||
expert_id=expert_id,
|
||||
)
|
||||
loaded_params.add(replaced_name)
|
||||
break
|
||||
else:
|
||||
for (
|
||||
param_name,
|
||||
weight_name,
|
||||
start_idx,
|
||||
end_idx,
|
||||
) in qkv_params_mapping:
|
||||
if weight_name not in local_name:
|
||||
continue
|
||||
replaced_name = local_name.replace(weight_name, param_name)
|
||||
if is_pp_missing_parameter(replaced_name, self):
|
||||
continue
|
||||
if replaced_name not in params_dict:
|
||||
continue
|
||||
param = params_dict[replaced_name]
|
||||
dim = param.shape[param.output_dim]
|
||||
begin_idx = int(start_idx * dim)
|
||||
end_idx = int(end_idx * dim)
|
||||
param_slice = param.narrow(
|
||||
param.output_dim, begin_idx, end_idx - begin_idx
|
||||
)
|
||||
param_slice.copy_(loaded_weight)
|
||||
loaded_params.add(replaced_name)
|
||||
break
|
||||
else:
|
||||
if is_pp_missing_parameter(local_name, self):
|
||||
continue
|
||||
if "expert_bias" in local_name:
|
||||
logger.warning_once("ignore expert_bias")
|
||||
continue
|
||||
if local_name not in params_dict:
|
||||
continue
|
||||
param = params_dict[local_name]
|
||||
weight_loader = getattr(
|
||||
param, "weight_loader", default_weight_loader
|
||||
)
|
||||
weight_loader(param, loaded_weight)
|
||||
loaded_params.add(local_name)
|
||||
return loaded_params
|
||||
|
||||
|
||||
class Step3p5ForCausalLM(nn.Module, SupportsPP, MixtureOfExperts):
|
||||
hf_to_vllm_mapper = WeightsMapper(
|
||||
orig_to_new_substr={".share_expert.": ".moe.share_expert."}
|
||||
)
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
*,
|
||||
vllm_config: VllmConfig,
|
||||
prefix: str = "",
|
||||
):
|
||||
super().__init__()
|
||||
config = vllm_config.model_config.hf_config
|
||||
self.model = Step3p5Model(
|
||||
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=vllm_config.quant_config,
|
||||
prefix=maybe_prefix(prefix, "lm_head"),
|
||||
)
|
||||
self.logits_processor = LogitsProcessor(config.vocab_size)
|
||||
else:
|
||||
self.lm_head = PPMissingLayer()
|
||||
|
||||
self.make_empty_intermediate_tensors = (
|
||||
self.model.make_empty_intermediate_tensors
|
||||
)
|
||||
|
||||
# Set MoE hyperparameters
|
||||
self.moe_layers: list[FusedMoEBlock] = []
|
||||
for layer in self.model.layers:
|
||||
if isinstance(layer, PPMissingLayer):
|
||||
continue
|
||||
assert isinstance(layer, Step3p5DecoderLayer)
|
||||
if hasattr(layer, "moe") and isinstance(layer.moe, FusedMoEBlock):
|
||||
self.moe_layers.append(layer.moe)
|
||||
|
||||
self.expert_weights = []
|
||||
assert len(self.moe_layers) > 0, "No MoE layers found in the model."
|
||||
example_layer = self.moe_layers[0]
|
||||
self.num_moe_layers = len(self.moe_layers)
|
||||
self.num_expert_groups = 1
|
||||
self.num_shared_experts = 0
|
||||
self.num_logical_experts = example_layer.n_logical_experts
|
||||
self.num_physical_experts = example_layer.n_physical_experts
|
||||
self.num_local_physical_experts = example_layer.n_local_physical_experts
|
||||
self.num_routed_experts = example_layer.n_routed_experts
|
||||
self.num_redundant_experts = example_layer.n_redundant_experts
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.Tensor,
|
||||
positions: torch.Tensor,
|
||||
intermediate_tensors: IntermediateTensors | None = None,
|
||||
inputs_embeds: torch.Tensor | None = None,
|
||||
):
|
||||
hidden_states = self.model(
|
||||
input_ids, positions, intermediate_tensors, inputs_embeds
|
||||
)
|
||||
return hidden_states
|
||||
|
||||
def compute_logits(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
||||
hidden_states = self.model.norm(hidden_states)
|
||||
logits = self.logits_processor(self.lm_head, hidden_states)
|
||||
return logits
|
||||
|
||||
def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
|
||||
return self.model.embed_tokens(input_ids)
|
||||
|
||||
def set_eplb_state(
|
||||
self,
|
||||
expert_load_view: torch.Tensor,
|
||||
logical_to_physical_map: torch.Tensor,
|
||||
logical_replica_count: torch.Tensor,
|
||||
) -> None:
|
||||
for layer_idx, layer in enumerate(self.moe_layers):
|
||||
experts = layer.experts
|
||||
assert isinstance(experts, FusedMoE)
|
||||
# Register the expert weights.
|
||||
self.expert_weights.append(experts.get_expert_weights())
|
||||
experts.set_eplb_state(
|
||||
moe_layer_idx=layer_idx,
|
||||
expert_load_view=expert_load_view,
|
||||
logical_to_physical_map=logical_to_physical_map,
|
||||
logical_replica_count=logical_replica_count,
|
||||
)
|
||||
|
||||
def update_physical_experts_metadata(
|
||||
self,
|
||||
num_physical_experts: int,
|
||||
num_local_physical_experts: int,
|
||||
) -> None:
|
||||
assert self.num_local_physical_experts == num_local_physical_experts
|
||||
self.num_physical_experts = num_physical_experts
|
||||
self.num_local_physical_experts = num_local_physical_experts
|
||||
self.num_redundant_experts = num_physical_experts - self.num_logical_experts
|
||||
for layer in self.moe_layers:
|
||||
assert isinstance(layer, FusedMoEBlock)
|
||||
layer.n_local_physical_experts = num_local_physical_experts
|
||||
layer.n_physical_experts = num_physical_experts
|
||||
layer.n_redundant_experts = self.num_redundant_experts
|
||||
layer.experts.update_expert_map()
|
||||
|
||||
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)
|
||||
|
||||
|
||||
def get_spec_layer_idx_from_weight_name(
|
||||
config: ModelConfig, weight_name: str
|
||||
) -> int | None:
|
||||
if hasattr(config, "num_nextn_predict_layers") and (
|
||||
config.num_nextn_predict_layers > 0
|
||||
):
|
||||
layer_idx = config.num_hidden_layers
|
||||
for i in range(config.num_nextn_predict_layers):
|
||||
if weight_name.startswith(
|
||||
f"layers.{layer_idx + i}." # Step3p5Model
|
||||
) or weight_name.startswith(f"model.layers.{layer_idx + i}."): # Step3p5MTP
|
||||
return layer_idx + i
|
||||
return None
|
||||
Reference in New Issue
Block a user