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https://git.datalinker.icu/vllm-project/vllm.git
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694 lines
28 KiB
Python
694 lines
28 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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# Copyright 2025 The ZhipuAI Team.
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# Copyright 2023 The vLLM team.
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# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
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#
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# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
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# and OPT implementations in this library. It has been modified from its
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# original forms to accommodate minor architectural differences compared
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# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
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#
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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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"""Inference-only GLM-4.5 model compatible with HuggingFace weights."""
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import typing
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from collections.abc import Callable, Iterable
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from typing import Any, Optional, Union
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import torch
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from torch import nn
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from transformers import PretrainedConfig
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from vllm.attention import Attention
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from vllm.compilation.decorators import support_torch_compile
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from vllm.config import CacheConfig, VllmConfig, get_current_vllm_config
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from vllm.distributed import (get_ep_group, get_pp_group,
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get_tensor_model_parallel_world_size)
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from vllm.logger import init_logger
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from vllm.model_executor.layers.activation import SiluAndMul
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from vllm.model_executor.layers.fused_moe import FusedMoE
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from vllm.model_executor.layers.layernorm import RMSNorm
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from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
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QKVParallelLinear,
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ReplicatedLinear,
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RowParallelLinear)
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from vllm.model_executor.layers.logits_processor import LogitsProcessor
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from vllm.model_executor.layers.quantization import QuantizationConfig
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from vllm.model_executor.layers.rotary_embedding import get_rope
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from vllm.model_executor.layers.vocab_parallel_embedding import (
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ParallelLMHead, VocabParallelEmbedding)
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from vllm.model_executor.model_loader.weight_utils import (
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default_weight_loader, maybe_remap_kv_scale_name)
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from vllm.model_executor.sampling_metadata import SamplingMetadata
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from vllm.sequence import IntermediateTensors
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from .interfaces import SupportsLoRA, SupportsPP
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from .utils import (AutoWeightsLoader, PPMissingLayer, is_pp_missing_parameter,
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make_empty_intermediate_tensors_factory, make_layers,
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maybe_prefix)
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logger = init_logger(__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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) -> None:
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super().__init__()
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self.gate_up_proj = MergedColumnParallelLinear(
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hidden_size, [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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self.down_proj = RowParallelLinear(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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if hidden_act != "silu":
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raise ValueError(f"Unsupported activation: {hidden_act}. "
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"Only silu is supported for now.")
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self.act_fn = SiluAndMul()
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def forward(self, 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(x)
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return x
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class Glm4MoE(nn.Module):
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def __init__(
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self,
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config: PretrainedConfig,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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enable_eplb: bool = False,
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):
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super().__init__()
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self.tp_size = get_tensor_model_parallel_world_size()
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self.routed_scaling_factor = config.routed_scaling_factor
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self.ep_group = get_ep_group().device_group
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self.ep_rank = self.ep_group.rank()
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self.ep_size = self.ep_group.size()
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self.n_routed_experts: int = config.n_routed_experts
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self.n_shared_experts: int = config.n_shared_experts
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if config.hidden_act != "silu":
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raise ValueError(f"Unsupported activation: {config.hidden_act}. "
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"Only silu is supported for now.")
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self.gate = ReplicatedLinear(config.hidden_size,
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config.n_routed_experts,
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bias=False,
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quant_config=None,
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params_dtype=torch.float32,
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prefix=f"{prefix}.gate")
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self.gate.e_score_correction_bias = nn.Parameter(
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torch.empty(config.n_routed_experts, dtype=torch.float32))
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# Load balancing settings.
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vllm_config = get_current_vllm_config()
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parallel_config = vllm_config.parallel_config
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self.enable_eplb = enable_eplb
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self.n_redundant_experts = parallel_config.num_redundant_experts
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self.n_logical_experts = self.n_routed_experts
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self.n_physical_experts = (self.n_logical_experts +
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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 *
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self.n_local_physical_experts)
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self.physical_expert_end = (self.physical_expert_start +
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self.n_local_physical_experts)
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self.experts = FusedMoE(
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num_experts=config.n_routed_experts,
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top_k=config.num_experts_per_tok,
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hidden_size=config.hidden_size,
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intermediate_size=config.moe_intermediate_size,
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reduce_results=False,
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renormalize=config.norm_topk_prob,
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quant_config=quant_config,
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use_grouped_topk=True,
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num_expert_group=config.n_group,
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topk_group=config.topk_group,
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prefix=f"{prefix}.experts",
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scoring_func="sigmoid",
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e_score_correction_bias=self.gate.e_score_correction_bias,
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enable_eplb=self.enable_eplb,
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num_redundant_experts=self.n_redundant_experts)
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if config.n_shared_experts is not None:
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intermediate_size = (config.moe_intermediate_size *
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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=self.experts.must_reduce_shared_expert_outputs(
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),
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prefix=f"{prefix}.shared_experts",
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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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if self.n_shared_experts is not None:
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shared_output = self.shared_experts(hidden_states)
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router_logits, _ = self.gate(hidden_states.to(dtype=torch.float32))
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final_hidden_states = self.experts(
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hidden_states=hidden_states,
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router_logits=router_logits) * self.routed_scaling_factor
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if shared_output is not None:
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final_hidden_states = final_hidden_states + shared_output
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if self.tp_size > 1:
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final_hidden_states = (
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self.experts.maybe_all_reduce_tensor_model_parallel(
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final_hidden_states))
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return final_hidden_states.view(num_tokens, hidden_dim)
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class Glm4MoeAttention(nn.Module):
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def __init__(
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self,
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config: PretrainedConfig,
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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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rope_theta: float = 10000,
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rope_scaling: Optional[dict[str, Any]] = None,
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max_position_embeddings: int = 131072,
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head_dim: Optional[int] = None,
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rms_norm_eps: float = 1e-05,
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qkv_bias: bool = False,
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use_qk_norm: bool = False,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.hidden_size = hidden_size
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tp_size = get_tensor_model_parallel_world_size()
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self.total_num_heads = num_heads
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assert self.total_num_heads % tp_size == 0
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self.num_heads = self.total_num_heads // tp_size
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self.total_num_kv_heads = num_kv_heads
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if self.total_num_kv_heads >= tp_size:
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# Number of KV heads is greater than TP size, so we partition
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# the KV heads across multiple tensor parallel GPUs.
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assert self.total_num_kv_heads % tp_size == 0
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else:
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# Number of KV heads is less than TP size, so we replicate
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# the KV heads across multiple tensor parallel GPUs.
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assert tp_size % self.total_num_kv_heads == 0
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self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
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self.head_dim = head_dim or (hidden_size // self.total_num_heads)
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self.q_size = self.num_heads * self.head_dim
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self.kv_size = self.num_kv_heads * self.head_dim
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self.scaling = self.head_dim**-0.5
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self.rope_theta = rope_theta
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self.max_position_embeddings = max_position_embeddings
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self.use_qk_norm = use_qk_norm
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self.qkv_proj = QKVParallelLinear(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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self.o_proj = RowParallelLinear(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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partial_rotary_factor = getattr(config, "partial_rotary_factor", 0.5)
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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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base=rope_theta,
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rope_scaling=rope_scaling,
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partial_rotary_factor=partial_rotary_factor,
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)
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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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)
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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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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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if self.use_qk_norm:
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q = self.q_norm(q.reshape(-1, self.num_heads,
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self.head_dim)).reshape(q.shape)
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k = self.k_norm(k.reshape(-1, self.num_kv_heads,
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self.head_dim)).reshape(k.shape)
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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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output, _ = self.o_proj(attn_output)
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return output
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class Glm4MoeDecoderLayer(nn.Module):
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def __init__(
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self,
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config: PretrainedConfig,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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enable_eplb: bool = False,
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) -> None:
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super().__init__()
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self.hidden_size = config.hidden_size
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rope_theta = getattr(config, "rope_theta", 10000)
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rope_scaling = getattr(config, "rope_scaling", None)
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max_position_embeddings = getattr(config, "max_position_embeddings",
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131072)
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# DecoderLayers are created with `make_layers` which passes the prefix
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# with the layer's index.
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layer_idx = int(prefix.split(sep='.')[-1])
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self.layer_idx = layer_idx
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self.self_attn = Glm4MoeAttention(
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config=config,
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hidden_size=self.hidden_size,
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num_heads=config.num_attention_heads,
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num_kv_heads=config.num_key_value_heads,
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rope_theta=rope_theta,
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rope_scaling=rope_scaling,
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max_position_embeddings=max_position_embeddings,
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head_dim=config.head_dim,
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rms_norm_eps=config.rms_norm_eps,
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qkv_bias=config.attention_bias,
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cache_config=cache_config,
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quant_config=quant_config,
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prefix=f"{prefix}.self_attn",
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use_qk_norm=config.use_qk_norm,
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)
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if (config.n_routed_experts is not None
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and layer_idx >= config.first_k_dense_replace):
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self.mlp = Glm4MoE(
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config=config,
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quant_config=quant_config,
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prefix=f"{prefix}.mlp",
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enable_eplb=enable_eplb,
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)
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else:
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self.mlp = Glm4MoeMLP(hidden_size=config.hidden_size,
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intermediate_size=config.intermediate_size,
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hidden_act=config.hidden_act,
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quant_config=quant_config,
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prefix=f"{prefix}.mlp")
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self.input_layernorm = RMSNorm(config.hidden_size,
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eps=config.rms_norm_eps)
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self.post_attention_layernorm = RMSNorm(config.hidden_size,
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eps=config.rms_norm_eps)
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self.routed_scaling_factor = config.routed_scaling_factor
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def forward(
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self,
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positions: torch.Tensor,
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hidden_states: torch.Tensor,
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residual: Optional[torch.Tensor],
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) -> tuple[torch.Tensor, torch.Tensor]:
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if residual is None:
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residual = hidden_states
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hidden_states = self.input_layernorm(hidden_states)
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else:
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hidden_states, residual = self.input_layernorm(
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hidden_states, residual)
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hidden_states = self.self_attn(positions=positions,
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hidden_states=hidden_states)
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hidden_states, residual = self.post_attention_layernorm(
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hidden_states, residual)
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hidden_states = self.mlp(hidden_states)
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return hidden_states, residual
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@support_torch_compile
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class Glm4MoeModel(nn.Module):
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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super().__init__()
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config = vllm_config.model_config.hf_config
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cache_config = vllm_config.cache_config
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quant_config = vllm_config.quant_config
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enable_eplb = vllm_config.parallel_config.enable_eplb
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self.config = config
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self.vocab_size = config.vocab_size
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if get_pp_group().is_first_rank:
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self.embed_tokens = VocabParallelEmbedding(
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config.vocab_size,
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config.hidden_size,
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prefix=f"{prefix}.embed_tokens")
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else:
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self.embed_tokens = PPMissingLayer()
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self.start_layer, self.end_layer, self.layers = make_layers(
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config.num_hidden_layers,
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lambda prefix: Glm4MoeDecoderLayer(
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config=config,
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cache_config=cache_config,
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quant_config=quant_config,
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prefix=prefix,
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enable_eplb=enable_eplb,
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),
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prefix=f"{prefix}.layers")
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if get_pp_group().is_last_rank:
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self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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else:
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self.norm = PPMissingLayer()
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self.make_empty_intermediate_tensors = (
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make_empty_intermediate_tensors_factory(
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["hidden_states", "residual"], config.hidden_size))
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def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.embed_tokens(input_ids)
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def forward(
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self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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intermediate_tensors: Optional[IntermediateTensors] = None,
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inputs_embeds: Optional[torch.Tensor] = None,
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) -> Union[torch.Tensor, IntermediateTensors]:
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if get_pp_group().is_first_rank:
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if inputs_embeds is not None:
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hidden_states = inputs_embeds
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else:
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hidden_states = self.get_input_embeddings(input_ids)
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residual = None
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else:
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assert intermediate_tensors is not None
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hidden_states = intermediate_tensors["hidden_states"]
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residual = intermediate_tensors["residual"]
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for i in range(self.start_layer, self.end_layer):
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layer = self.layers[i]
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hidden_states, residual = layer(positions, hidden_states, residual)
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if not get_pp_group().is_last_rank:
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return IntermediateTensors({
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"hidden_states": hidden_states,
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"residual": residual
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})
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hidden_states, _ = self.norm(hidden_states, residual)
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return hidden_states
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def make_empty_intermediate_tensors(
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self, batch_size: int, dtype: torch.dtype,
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device: torch.device) -> IntermediateTensors:
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return IntermediateTensors({
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"hidden_states":
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torch.zeros((batch_size, self.config.hidden_size),
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dtype=dtype,
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device=device),
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"residual":
|
|
torch.zeros((batch_size, self.config.hidden_size),
|
|
dtype=dtype,
|
|
device=device),
|
|
})
|
|
|
|
def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
|
|
# Params for weights, fp8 weight scales, fp8 activation scales
|
|
# (param_name, weight_name, expert_id, shard_id)
|
|
return FusedMoE.make_expert_params_mapping(
|
|
ckpt_gate_proj_name="gate_proj",
|
|
ckpt_down_proj_name="down_proj",
|
|
ckpt_up_proj_name="up_proj",
|
|
num_experts=self.config.n_routed_experts)
|
|
|
|
def load_weights(self, weights: Iterable[tuple[str,
|
|
torch.Tensor]]) -> set[str]:
|
|
stacked_params_mapping = [
|
|
# (param_name, shard_name, shard_id)
|
|
("qkv_proj", "q_proj", "q"),
|
|
("qkv_proj", "k_proj", "k"),
|
|
("qkv_proj", "v_proj", "v"),
|
|
("gate_up_proj", "gate_proj", 0),
|
|
("gate_up_proj", "up_proj", 1),
|
|
]
|
|
|
|
params_dict = dict(self.named_parameters())
|
|
loaded_params: set[str] = set()
|
|
expert_params_mapping = self.get_expert_mapping()
|
|
for name, loaded_weight in weights:
|
|
spec_layer = get_spec_layer_idx_from_weight_name(self.config, name)
|
|
if spec_layer is not None:
|
|
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) and name not in params_dict):
|
|
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 is_pp_missing_parameter(name, self):
|
|
continue
|
|
|
|
param = params_dict[name]
|
|
weight_loader = param.weight_loader
|
|
weight_loader(param, loaded_weight, shard_id)
|
|
break
|
|
else:
|
|
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
|
|
|
|
# Anyway, this is an expert weight and should not be
|
|
# attempted to load as other weights later
|
|
is_expert_weight = True
|
|
|
|
# Do not modify `name` since the loop may continue here
|
|
# Instead, create a new variable
|
|
name_mapped = name.replace(weight_name, param_name)
|
|
|
|
if is_pp_missing_parameter(name_mapped, self):
|
|
continue
|
|
|
|
param = params_dict[name_mapped]
|
|
# We should ask the weight loader to return success or not
|
|
# here since otherwise we may skip experts with other
|
|
# available replicas.
|
|
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:
|
|
name = name_mapped
|
|
break
|
|
else:
|
|
if is_expert_weight:
|
|
# We've checked that this is an expert weight
|
|
# However it's not mapped locally to this rank
|
|
# So we simply skip it
|
|
continue
|
|
|
|
# Skip loading extra bias for GPTQ models.
|
|
if name.endswith(".bias") and name not in params_dict:
|
|
continue
|
|
|
|
# Remapping the name of FP8 kv-scale.
|
|
name = maybe_remap_kv_scale_name(name, params_dict)
|
|
if name is None:
|
|
continue
|
|
|
|
if is_pp_missing_parameter(name, self):
|
|
continue
|
|
|
|
param = params_dict[name]
|
|
weight_loader = getattr(param, "weight_loader",
|
|
default_weight_loader)
|
|
weight_loader(param, loaded_weight)
|
|
loaded_params.add(name)
|
|
|
|
return loaded_params
|
|
|
|
|
|
class Glm4MoeForCausalLM(nn.Module, SupportsPP, SupportsLoRA):
|
|
packed_modules_mapping = {
|
|
"qkv_proj": [
|
|
"q_proj",
|
|
"k_proj",
|
|
"v_proj",
|
|
],
|
|
"gate_up_proj": [
|
|
"gate_proj",
|
|
"up_proj",
|
|
],
|
|
}
|
|
|
|
fall_back_to_pt_during_load = False
|
|
|
|
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
|
|
super().__init__()
|
|
config = vllm_config.model_config.hf_config
|
|
quant_config = vllm_config.quant_config
|
|
self.config = config
|
|
self.quant_config = quant_config
|
|
self.model = Glm4MoeModel(vllm_config=vllm_config,
|
|
prefix=maybe_prefix(prefix, "model"))
|
|
if get_pp_group().is_last_rank:
|
|
self.lm_head = ParallelLMHead(config.vocab_size,
|
|
config.hidden_size,
|
|
quant_config=quant_config)
|
|
else:
|
|
self.lm_head = PPMissingLayer()
|
|
self.logits_processor = LogitsProcessor(config.vocab_size)
|
|
self.make_empty_intermediate_tensors = (
|
|
self.model.make_empty_intermediate_tensors)
|
|
self.expert_weights = []
|
|
|
|
# Set MoE hyperparameters
|
|
self.num_moe_layers = (config.num_hidden_layers -
|
|
config.first_k_dense_replace)
|
|
self.num_expert_groups = config.n_group
|
|
|
|
self.moe_layers: list[FusedMoE] = []
|
|
example_moe = None
|
|
for layer in self.model.layers:
|
|
if isinstance(layer, PPMissingLayer):
|
|
continue
|
|
|
|
assert isinstance(layer, Glm4MoeDecoderLayer)
|
|
if isinstance(layer.mlp, Glm4MoE):
|
|
# Pick last one layer since the first ones may be dense layers.
|
|
example_moe = layer.mlp
|
|
self.moe_layers.append(layer.mlp.experts)
|
|
|
|
if example_moe is None:
|
|
raise RuntimeError("No Glm4MoE layer found in model.layers.")
|
|
|
|
self.num_logical_experts = example_moe.n_logical_experts
|
|
self.num_physical_experts = example_moe.n_physical_experts
|
|
self.num_local_physical_experts = example_moe.n_local_physical_experts
|
|
self.num_routed_experts = example_moe.n_routed_experts
|
|
self.num_shared_experts = example_moe.n_shared_experts
|
|
self.num_redundant_experts = example_moe.n_redundant_experts
|
|
|
|
def set_eplb_state(
|
|
self,
|
|
expert_load_view: torch.Tensor,
|
|
logical_to_physical_map: torch.Tensor,
|
|
logical_replica_count: torch.Tensor,
|
|
) -> None:
|
|
for layer_idx, layer in enumerate(self.moe_layers):
|
|
# Register the expert weights.
|
|
self.expert_weights.append(layer.get_expert_weights())
|
|
layer.set_eplb_state(
|
|
moe_layer_idx=layer_idx,
|
|
expert_load_view=expert_load_view,
|
|
logical_to_physical_map=logical_to_physical_map,
|
|
logical_replica_count=logical_replica_count,
|
|
)
|
|
|
|
def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
|
|
return self.model.get_input_embeddings(input_ids)
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
intermediate_tensors: Optional[IntermediateTensors] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
) -> Union[torch.Tensor, IntermediateTensors]:
|
|
hidden_states = self.model(input_ids, positions, intermediate_tensors,
|
|
inputs_embeds)
|
|
return hidden_states
|
|
|
|
def compute_logits(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
sampling_metadata: SamplingMetadata,
|
|
) -> Optional[torch.Tensor]:
|
|
logits = self.logits_processor(self.lm_head, hidden_states,
|
|
sampling_metadata)
|
|
return logits
|
|
|
|
def load_weights(self, weights: Iterable[tuple[str,
|
|
torch.Tensor]]) -> set[str]:
|
|
loader = AutoWeightsLoader(self)
|
|
return loader.load_weights(weights)
|
|
|
|
def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
|
|
return self.model.get_expert_mapping()
|
|
|
|
|
|
def get_spec_layer_idx_from_weight_name(config: PretrainedConfig,
|
|
weight_name: str) -> Optional[int]:
|
|
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 f"layers.{layer_idx+i}." in weight_name:
|
|
return layer_idx + i
|
|
return None
|