Add K-EXAONE-236B-A23B (#31621)
Signed-off-by: lkm2835 <lkm2835@gmail.com> Signed-off-by: Cyrus Leung <cyrus.tl.leung@gmail.com> Co-authored-by: lgai-exaone <exaonemodels@lgresearch.ai> Co-authored-by: Cyrus Leung <cyrus.tl.leung@gmail.com>
This commit is contained in:
Kyungmin Lee
@@ -375,6 +375,7 @@ th {
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| `Ernie4_5ForCausalLM` | Ernie4.5 | `baidu/ERNIE-4.5-0.3B-PT`, etc. | ✅︎ | ✅︎ |
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| `Ernie4_5_MoeForCausalLM` | Ernie4.5MoE | `baidu/ERNIE-4.5-21B-A3B-PT`, `baidu/ERNIE-4.5-300B-A47B-PT`, etc. |✅︎| ✅︎ |
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| `ExaoneForCausalLM` | EXAONE-3 | `LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct`, etc. | ✅︎ | ✅︎ |
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| `ExaoneMoeCausalLM` | K-EXAONE | `LGAI-EXAONE/K-EXAONE-236B-A23B`, etc. | | |
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| `Exaone4ForCausalLM` | EXAONE-4 | `LGAI-EXAONE/EXAONE-4.0-32B`, etc. | ✅︎ | ✅︎ |
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| `Fairseq2LlamaForCausalLM` | Llama (fairseq2 format) | `mgleize/fairseq2-dummy-Llama-3.2-1B`, etc. | ✅︎ | ✅︎ |
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| `FalconForCausalLM` | Falcon | `tiiuae/falcon-7b`, `tiiuae/falcon-40b`, `tiiuae/falcon-rw-7b`, etc. | | ✅︎ |
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@@ -250,6 +250,9 @@ _TEXT_GENERATION_EXAMPLE_MODELS = {
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"LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct", trust_remote_code=True
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),
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"Exaone4ForCausalLM": _HfExamplesInfo("LGAI-EXAONE/EXAONE-4.0-32B"),
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"ExaoneMoEForCausalLM": _HfExamplesInfo(
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"LGAI-EXAONE/K-EXAONE-236B-A23B", min_transformers_version="5.0.0"
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),
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"Fairseq2LlamaForCausalLM": _HfExamplesInfo("mgleize/fairseq2-dummy-Llama-3.2-1B"),
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"FalconForCausalLM": _HfExamplesInfo("tiiuae/falcon-7b"),
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"FalconH1ForCausalLM": _HfExamplesInfo("tiiuae/Falcon-H1-0.5B-Base"),
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@@ -1005,6 +1008,11 @@ _SPECULATIVE_DECODING_EXAMPLE_MODELS = {
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trust_remote_code=True,
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speculative_model="baidu/ERNIE-4.5-21B-A3B-PT",
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),
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"ExaoneMoeMTP": _HfExamplesInfo(
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"LGAI-EXAONE/K-EXAONE-236B-A23B",
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speculative_model="LGAI-EXAONE/K-EXAONE-236B-A23B",
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min_transformers_version="5.0.0",
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),
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"Glm4MoeMTPModel": _HfExamplesInfo(
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"zai-org/GLM-4.5",
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speculative_model="zai-org/GLM-4.5",
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@@ -33,6 +33,7 @@ MTPModelTypes = Literal[
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"mimo_mtp",
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"glm4_moe_mtp",
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"ernie_mtp",
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"exaone_moe_mtp",
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"qwen3_next_mtp",
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"longcat_flash_mtp",
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"mtp",
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@@ -219,6 +220,15 @@ class SpeculativeConfig:
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hf_config.update(
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{"n_predict": n_predict, "architectures": ["Qwen3NextMTP"]}
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)
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if hf_config.model_type == "exaone_moe":
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hf_config.model_type = "exaone_moe_mtp"
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if hf_config.model_type == "exaone_moe_mtp":
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n_predict = getattr(hf_config, "num_nextn_predict_layers", None)
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hf_config.update(
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{"n_predict": n_predict, "architectures": ["ExaoneMoeMTP"]}
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)
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if hf_config.model_type == "longcat_flash":
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hf_config.model_type = "longcat_flash_mtp"
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n_predict = getattr(hf_config, "num_nextn_predict_layers", 1)
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@@ -72,6 +72,7 @@ class Exaone4GatedMLP(nn.Module):
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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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bias: bool = False,
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prefix: str = "",
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) -> None:
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@@ -88,6 +89,7 @@ class Exaone4GatedMLP(nn.Module):
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output_size=hidden_size,
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bias=bias,
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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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578
vllm/model_executor/models/exaone_moe.py
Normal file
578
vllm/model_executor/models/exaone_moe.py
Normal file
@@ -0,0 +1,578 @@
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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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# ruff: noqa: E501
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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 K-EXAONE-236B-A22B 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 itertools import islice
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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.compilation.decorators import support_torch_compile
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from vllm.config import CacheConfig, VllmConfig, get_current_vllm_config
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from vllm.distributed import (
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get_ep_group,
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get_pp_group,
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get_tensor_model_parallel_world_size,
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)
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from vllm.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 ReplicatedLinear
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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.vocab_parallel_embedding import (
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DEFAULT_VOCAB_PADDING_SIZE,
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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 (
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default_weight_loader,
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maybe_remap_kv_scale_name,
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)
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from vllm.sequence import IntermediateTensors
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from .exaone4 import Exaone4Attention as ExaoneMoeAttention
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from .exaone4 import Exaone4GatedMLP as ExaoneMoeGatedMLP
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from .interfaces import SupportsLoRA, SupportsPP
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from .utils import (
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AutoWeightsLoader,
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PPMissingLayer,
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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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class ExaoneMoe(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: QuantizationConfig | None = None,
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prefix: str = "",
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enable_eplb: bool = False,
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):
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super().__init__()
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self.tp_size = get_tensor_model_parallel_world_size()
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self.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 = config.num_experts
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if self.tp_size > config.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.num_experts}."
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)
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self.gate = ReplicatedLinear(
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config.hidden_size,
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config.num_experts,
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bias=False,
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quant_config=None,
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prefix=f"{prefix}.gate",
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)
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self.e_score_correction_bias = nn.Parameter(
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torch.empty(config.num_experts, dtype=torch.float32)
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)
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# Load balancing settings.
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vllm_config = get_current_vllm_config()
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eplb_config = vllm_config.parallel_config.eplb_config
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self.enable_eplb = enable_eplb
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self.n_logical_experts = self.n_routed_experts
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eplb_config.num_redundant_experts = (
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eplb_config.num_redundant_experts
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if eplb_config.num_redundant_experts is not None
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else 0
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)
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self.n_redundant_experts = 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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self.experts = FusedMoE(
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num_experts=self.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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routed_scaling_factor=self.routed_scaling_factor,
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e_score_correction_bias=self.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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)
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if getattr(config, "num_shared_experts", 0) > 0:
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intermediate_size = config.moe_intermediate_size * config.num_shared_experts
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self.shared_experts = ExaoneMoeGatedMLP(
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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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prefix=f"{prefix}.shared_experts",
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)
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else:
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self.shared_experts = None
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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# NOTE: hidden_states can have either 1D or 2D shape.
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orig_shape = hidden_states.shape
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hidden_dim = hidden_states.shape[-1]
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hidden_states = hidden_states.view(-1, hidden_dim)
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# router_logits: (num_tokens, n_experts)
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router_logits, _ = self.gate(hidden_states)
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final_hidden_states = self.experts(
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hidden_states=hidden_states, router_logits=router_logits
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)
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if self.shared_experts is not None:
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shared_output = self.shared_experts(hidden_states)
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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 = self.experts.maybe_all_reduce_tensor_model_parallel( # noqa E501
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final_hidden_states
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)
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return final_hidden_states.view(orig_shape)
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class ExaoneMoeDecoderLayer(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: CacheConfig | None = None,
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quant_config: QuantizationConfig | None = None,
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mtp_layer: bool = None,
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prefix: str = "",
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) -> None:
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super().__init__()
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layer_idx = extract_layer_index(prefix)
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self.hidden_size = config.hidden_size
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max_position_embeddings = getattr(config, "max_position_embeddings", 8192)
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# Support abacusai/Smaug-72B-v0.1 with attention_bias
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# Support internlm/internlm-7b with bias
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attention_bias = getattr(config, "attention_bias", False) or getattr(
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config, "bias", False
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)
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self.self_attn = ExaoneMoeAttention(
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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=getattr(
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config, "num_key_value_heads", config.num_attention_heads
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),
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max_position_embeddings=max_position_embeddings,
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quant_config=quant_config,
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bias=attention_bias,
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cache_config=cache_config,
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prefix=f"{prefix}.self_attn",
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)
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if config.is_moe_layer[layer_idx] and not mtp_layer:
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self.mlp = ExaoneMoe(
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config=config, quant_config=quant_config, prefix=f"{prefix}.mlp"
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)
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else:
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self.mlp = ExaoneMoeGatedMLP(
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hidden_size=self.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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bias=getattr(config, "mlp_bias", False),
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prefix=f"{prefix}.mlp",
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)
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self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.post_attention_layernorm = RMSNorm(
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config.hidden_size, eps=config.rms_norm_eps
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)
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def forward(
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self,
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positions: torch.Tensor,
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hidden_states: torch.Tensor,
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residual: torch.Tensor | None,
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) -> tuple[torch.Tensor, torch.Tensor]:
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if residual is None:
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residual = hidden_states
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hidden_states = self.input_layernorm(hidden_states)
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else:
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hidden_states, residual = self.input_layernorm(hidden_states, residual)
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# Self Attention
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hidden_states = self.self_attn(
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positions=positions,
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hidden_states=hidden_states,
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)
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# Fully Connected
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hidden_states, residual = self.post_attention_layernorm(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 ExaoneMoeModel(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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lora_config = vllm_config.lora_config
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self.num_redundant_experts = (
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vllm_config.parallel_config.eplb_config.num_redundant_experts
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)
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self.config = config
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self.quant_config = quant_config
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lora_vocab = (
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(lora_config.lora_extra_vocab_size * (lora_config.max_loras or 1))
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if lora_config
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else 0
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)
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self.vocab_size = config.vocab_size + lora_vocab
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if get_pp_group().is_first_rank or (
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config.tie_word_embeddings and get_pp_group().is_last_rank
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):
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self.embed_tokens = VocabParallelEmbedding(
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self.vocab_size,
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config.hidden_size,
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org_num_embeddings=config.vocab_size,
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quant_config=quant_config,
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)
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else:
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self.embed_tokens = PPMissingLayer()
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self.start_layer, self.end_layer, self.layers = make_layers(
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config.num_hidden_layers,
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lambda prefix: ExaoneMoeDecoderLayer(
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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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),
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prefix=f"{prefix}.layers",
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)
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if get_pp_group().is_last_rank:
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self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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else:
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self.norm = PPMissingLayer()
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self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
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["hidden_states", "residual"], config.hidden_size
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)
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def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.embed_tokens(input_ids)
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def forward(
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self,
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input_ids: torch.Tensor | None,
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positions: torch.Tensor,
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intermediate_tensors: IntermediateTensors | None,
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inputs_embeds: torch.Tensor | None = None,
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) -> 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.embed_input_ids(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 layer in islice(self.layers, self.start_layer, self.end_layer):
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hidden_states, residual = layer(
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positions,
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hidden_states,
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residual,
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)
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if not get_pp_group().is_last_rank:
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return IntermediateTensors(
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{"hidden_states": hidden_states, "residual": residual}
|
||||
)
|
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|
||||
hidden_states, _ = self.norm(hidden_states, residual)
|
||||
return hidden_states
|
||||
|
||||
def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
|
||||
# Params for weights, fp8 weight scales, fp8 activation scales
|
||||
# (param_name, weight_name, expert_id, shard_id)
|
||||
return FusedMoE.make_expert_params_mapping(
|
||||
ckpt_gate_proj_name="gate_proj",
|
||||
ckpt_down_proj_name="down_proj",
|
||||
ckpt_up_proj_name="up_proj",
|
||||
num_experts=self.config.num_experts,
|
||||
num_redundant_experts=self.num_redundant_experts,
|
||||
)
|
||||
|
||||
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
|
||||
stacked_params_mapping = [
|
||||
# (param_name, shard_name, shard_id)
|
||||
(".qkv_proj", ".q_proj", "q"),
|
||||
(".qkv_proj", ".k_proj", "k"),
|
||||
(".qkv_proj", ".v_proj", "v"),
|
||||
(".gate_up_proj", ".gate_proj", 0),
|
||||
(".gate_up_proj", ".up_proj", 1),
|
||||
]
|
||||
|
||||
# Skip loading extra parameters for GPTQ/modelopt models.
|
||||
ignore_suffixes = (
|
||||
".bias",
|
||||
"_bias",
|
||||
".k_scale",
|
||||
"_k_scale",
|
||||
".v_scale",
|
||||
"_v_scale",
|
||||
".weight_scale",
|
||||
"_weight_scale",
|
||||
".input_scale",
|
||||
"_input_scale",
|
||||
)
|
||||
|
||||
params_dict = dict(self.named_parameters())
|
||||
loaded_params: set[str] = set()
|
||||
expert_params_mapping = self.get_expert_mapping()
|
||||
for name, loaded_weight in weights:
|
||||
if name.startswith("mtp."):
|
||||
continue
|
||||
if "rotary_emb.inv_freq" in name:
|
||||
continue
|
||||
if "rotary_emb.cos_cached" in name or "rotary_emb.sin_cached" in name:
|
||||
# Models trained using ColossalAI may include these tensors in
|
||||
# the checkpoint. Skip them.
|
||||
continue
|
||||
if self.quant_config is not None and (
|
||||
scale_name := self.quant_config.get_cache_scale(name)
|
||||
):
|
||||
# Loading kv cache quantization scales
|
||||
param = params_dict[scale_name]
|
||||
weight_loader = getattr(param, "weight_loader", default_weight_loader)
|
||||
loaded_weight = (
|
||||
loaded_weight if loaded_weight.dim() == 0 else loaded_weight[0]
|
||||
)
|
||||
weight_loader(param, loaded_weight)
|
||||
loaded_params.add(scale_name)
|
||||
continue
|
||||
for param_name, weight_name, shard_id in stacked_params_mapping:
|
||||
if weight_name not in name:
|
||||
continue
|
||||
if "mlp.experts" in name:
|
||||
continue
|
||||
name = name.replace(weight_name, param_name)
|
||||
# Skip loading extra bias for GPTQ models.
|
||||
if name.endswith(".bias") and name not in params_dict:
|
||||
continue
|
||||
if is_pp_missing_parameter(name, self):
|
||||
continue
|
||||
if name not in params_dict:
|
||||
continue
|
||||
|
||||
param = params_dict[name]
|
||||
weight_loader = param.weight_loader
|
||||
weight_loader(param, loaded_weight, shard_id)
|
||||
break
|
||||
else:
|
||||
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
|
||||
|
||||
# Skip loading extra parameters for GPTQ/modelopt models.
|
||||
if (
|
||||
name_mapped.endswith(ignore_suffixes)
|
||||
and name_mapped not in params_dict
|
||||
):
|
||||
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:
|
||||
continue
|
||||
# Skip loading extra bias for GPTQ models.
|
||||
if name.endswith(".bias") and name not in params_dict:
|
||||
continue
|
||||
# Skip loading extra parameters for GPTQ/modelopt models.
|
||||
if name.endswith(ignore_suffixes) 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 ExaoneMoeForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
|
||||
packed_modules_mapping = {
|
||||
"qkv_proj": [
|
||||
"q_proj",
|
||||
"k_proj",
|
||||
"v_proj",
|
||||
],
|
||||
"gate_up_proj": [
|
||||
"gate_proj",
|
||||
"up_proj",
|
||||
],
|
||||
}
|
||||
|
||||
# LoRA specific attributes
|
||||
embedding_modules = {
|
||||
"embed_tokens": "input_embeddings",
|
||||
"lm_head": "output_embeddings",
|
||||
}
|
||||
embedding_padding_modules = ["lm_head"]
|
||||
|
||||
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
|
||||
super().__init__()
|
||||
config = vllm_config.model_config.hf_config.get_text_config()
|
||||
quant_config = vllm_config.quant_config
|
||||
lora_config = vllm_config.lora_config
|
||||
|
||||
self.config = config
|
||||
self.lora_config = lora_config
|
||||
self.quant_config = quant_config
|
||||
|
||||
self.model = ExaoneMoeModel(
|
||||
vllm_config=vllm_config,
|
||||
prefix=maybe_prefix(prefix, "model"),
|
||||
)
|
||||
if get_pp_group().is_last_rank:
|
||||
self.unpadded_vocab_size = config.vocab_size
|
||||
if lora_config:
|
||||
self.unpadded_vocab_size += lora_config.lora_extra_vocab_size
|
||||
self.lm_head = ParallelLMHead(
|
||||
self.unpadded_vocab_size,
|
||||
config.hidden_size,
|
||||
org_num_embeddings=config.vocab_size,
|
||||
padding_size=DEFAULT_VOCAB_PADDING_SIZE
|
||||
# We need bigger padding if using lora for kernel
|
||||
# compatibility
|
||||
if not lora_config
|
||||
else lora_config.lora_vocab_padding_size,
|
||||
quant_config=quant_config,
|
||||
)
|
||||
if config.tie_word_embeddings:
|
||||
self.lm_head.weight = self.model.embed_tokens.weight
|
||||
|
||||
logit_scale = getattr(config, "logit_scale", 1.0)
|
||||
self.logits_processor = LogitsProcessor(
|
||||
self.unpadded_vocab_size, config.vocab_size, logit_scale
|
||||
)
|
||||
else:
|
||||
self.lm_head = PPMissingLayer()
|
||||
|
||||
self.make_empty_intermediate_tensors = (
|
||||
self.model.make_empty_intermediate_tensors
|
||||
)
|
||||
|
||||
def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
|
||||
return self.model.embed_input_ids(input_ids)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.Tensor,
|
||||
positions: torch.Tensor,
|
||||
intermediate_tensors: IntermediateTensors | None = None,
|
||||
inputs_embeds: torch.Tensor | None = None,
|
||||
) -> torch.Tensor | IntermediateTensors:
|
||||
model_output = self.model(
|
||||
input_ids, positions, intermediate_tensors, inputs_embeds
|
||||
)
|
||||
return model_output
|
||||
|
||||
def compute_logits(
|
||||
self,
|
||||
hidden_states: torch.Tensor,
|
||||
) -> torch.Tensor | None:
|
||||
logits = self.logits_processor(self.lm_head, hidden_states)
|
||||
return logits
|
||||
|
||||
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
|
||||
loader = AutoWeightsLoader(
|
||||
self,
|
||||
# With tie_word_embeddings, we can skip lm_head.weight
|
||||
# The weight might appear unnecessarily in the files if the model is
|
||||
# processed with quantization, LoRA, fine-tuning, etc.
|
||||
skip_prefixes=(
|
||||
["lm_head.", "mtp."] if self.config.tie_word_embeddings else ["mtp."]
|
||||
),
|
||||
)
|
||||
return loader.load_weights(weights)
|
||||
255
vllm/model_executor/models/exaone_moe_mtp.py
Normal file
255
vllm/model_executor/models/exaone_moe_mtp.py
Normal file
@@ -0,0 +1,255 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
||||
"""Inference-only ExaoneMoe MTP model."""
|
||||
|
||||
from collections.abc import Iterable
|
||||
|
||||
import torch
|
||||
from torch import nn
|
||||
|
||||
from vllm.compilation.decorators import support_torch_compile
|
||||
from vllm.config import VllmConfig
|
||||
from vllm.distributed.parallel_state import get_pp_group
|
||||
from vllm.logger import init_logger
|
||||
from vllm.model_executor.layers.layernorm import RMSNorm
|
||||
from vllm.model_executor.layers.linear import ColumnParallelLinear
|
||||
from vllm.model_executor.layers.logits_processor import LogitsProcessor
|
||||
from vllm.model_executor.layers.vocab_parallel_embedding import (
|
||||
ParallelLMHead,
|
||||
VocabParallelEmbedding,
|
||||
)
|
||||
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
|
||||
from vllm.model_executor.models.exaone_moe import ExaoneMoeDecoderLayer
|
||||
from vllm.sequence import IntermediateTensors
|
||||
|
||||
from .utils import (
|
||||
AutoWeightsLoader,
|
||||
is_pp_missing_parameter,
|
||||
maybe_prefix,
|
||||
)
|
||||
|
||||
logger = init_logger(__name__)
|
||||
|
||||
KVCache = tuple[torch.Tensor, torch.Tensor]
|
||||
|
||||
|
||||
@support_torch_compile
|
||||
class ExaoneMoeMultiTokenPredictor(nn.Module):
|
||||
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
|
||||
super().__init__()
|
||||
|
||||
model_config = vllm_config.model_config
|
||||
quant_config = vllm_config.quant_config
|
||||
lora_config = vllm_config.lora_config
|
||||
config = model_config.hf_config
|
||||
|
||||
self.config = config
|
||||
lora_vocab = (
|
||||
(lora_config.lora_extra_vocab_size * (lora_config.max_loras or 1))
|
||||
if lora_config
|
||||
else 0
|
||||
)
|
||||
self.vocab_size = config.vocab_size + lora_vocab
|
||||
self.org_vocab_size = config.vocab_size
|
||||
|
||||
self.mtp_start_layer_idx = config.num_hidden_layers
|
||||
self.num_mtp_layers = getattr(config, "num_nextn_predict_layers", 1)
|
||||
|
||||
self.embed_tokens = VocabParallelEmbedding(
|
||||
self.vocab_size,
|
||||
config.hidden_size,
|
||||
org_num_embeddings=config.vocab_size,
|
||||
)
|
||||
|
||||
self.fc = ColumnParallelLinear(
|
||||
self.config.hidden_size * 2,
|
||||
self.config.hidden_size,
|
||||
gather_output=True,
|
||||
bias=False,
|
||||
return_bias=False,
|
||||
quant_config=quant_config,
|
||||
prefix=f"{prefix}.fc",
|
||||
)
|
||||
self.layers = nn.ModuleList(
|
||||
ExaoneMoeDecoderLayer(
|
||||
vllm_config.model_config.hf_config,
|
||||
quant_config=quant_config,
|
||||
prefix=f"{prefix}.layers.{idx}",
|
||||
mtp_layer=True,
|
||||
)
|
||||
for idx in range(self.num_mtp_layers)
|
||||
)
|
||||
|
||||
self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
||||
self.pre_fc_norm_hidden = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
||||
self.pre_fc_norm_embedding = RMSNorm(
|
||||
config.hidden_size, eps=config.rms_norm_eps
|
||||
)
|
||||
|
||||
def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
|
||||
return self.embed_tokens(input_ids)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.Tensor,
|
||||
positions: torch.Tensor,
|
||||
hidden_states: torch.Tensor,
|
||||
intermediate_tensors: IntermediateTensors | None = None,
|
||||
inputs_embeds: torch.Tensor | None = None,
|
||||
spec_step_idx: int = 0,
|
||||
) -> torch.Tensor:
|
||||
if get_pp_group().is_first_rank:
|
||||
if inputs_embeds is None:
|
||||
inputs_embeds = self.get_input_embeddings(input_ids)
|
||||
assert hidden_states.shape[-1] == inputs_embeds.shape[-1]
|
||||
inputs_embeds = self.pre_fc_norm_embedding(inputs_embeds)
|
||||
hidden_states = self.pre_fc_norm_hidden(hidden_states)
|
||||
hidden_states = torch.cat([inputs_embeds, hidden_states], dim=-1)
|
||||
hidden_states = self.fc(hidden_states)
|
||||
residual = None
|
||||
else:
|
||||
assert intermediate_tensors is not None
|
||||
hidden_states = intermediate_tensors["hidden_states"]
|
||||
residual = intermediate_tensors["residual"]
|
||||
|
||||
current_step_idx = spec_step_idx % self.num_mtp_layers
|
||||
hidden_states, residual = self.layers[current_step_idx](
|
||||
positions=positions,
|
||||
hidden_states=hidden_states,
|
||||
residual=residual,
|
||||
)
|
||||
|
||||
if not get_pp_group().is_last_rank:
|
||||
return IntermediateTensors(
|
||||
{"hidden_states": hidden_states, "residual": residual}
|
||||
)
|
||||
|
||||
hidden_states, _ = self.norm(hidden_states, residual)
|
||||
return hidden_states
|
||||
|
||||
def 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 for weights, fp8 weight scales, fp8 activation scales
|
||||
# (param_name, weight_name, expert_id, shard_id)
|
||||
params_dict = dict(self.named_parameters())
|
||||
loaded_params: set[str] = set()
|
||||
for name, loaded_weight in weights:
|
||||
if "rotary_emb.inv_freq" in name:
|
||||
continue
|
||||
|
||||
for param_name, weight_name, shard_id in stacked_params_mapping:
|
||||
if weight_name not in name:
|
||||
continue
|
||||
|
||||
if "mlp.experts" in name:
|
||||
continue
|
||||
|
||||
name = name.replace(weight_name, param_name)
|
||||
# Skip loading extra bias for GPTQ models.
|
||||
if name.endswith(".bias") and name not in params_dict:
|
||||
continue
|
||||
# Skip layers on other devices.
|
||||
if is_pp_missing_parameter(name, self):
|
||||
continue
|
||||
if name not in params_dict:
|
||||
continue
|
||||
param = params_dict[name]
|
||||
weight_loader = param.weight_loader
|
||||
weight_loader(param, loaded_weight, shard_id)
|
||||
break
|
||||
else:
|
||||
# Skip loading extra bias for GPTQ models.
|
||||
if name.endswith(".bias") and name not in params_dict:
|
||||
continue
|
||||
if is_pp_missing_parameter(name, self):
|
||||
continue
|
||||
|
||||
param = params_dict[name]
|
||||
weight_loader = getattr(param, "weight_loader", default_weight_loader)
|
||||
weight_loader(param, loaded_weight)
|
||||
loaded_params.add(name)
|
||||
return loaded_params
|
||||
|
||||
|
||||
@support_torch_compile
|
||||
class ExaoneMoeMTP(nn.Module):
|
||||
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
|
||||
config = vllm_config.model_config.hf_config
|
||||
self.vllm_config = vllm_config
|
||||
cache_config = vllm_config.cache_config
|
||||
assert not cache_config.enable_prefix_caching, (
|
||||
"ExaoneMoeMTP currently does not support prefix caching"
|
||||
)
|
||||
|
||||
self.quant_config = vllm_config.quant_config
|
||||
|
||||
super().__init__()
|
||||
self.config = config
|
||||
self.model = ExaoneMoeMultiTokenPredictor(
|
||||
vllm_config=vllm_config, prefix=maybe_prefix(prefix, "mtp")
|
||||
)
|
||||
self.unpadded_vocab_size = config.vocab_size
|
||||
self.lm_head = ParallelLMHead(
|
||||
self.unpadded_vocab_size,
|
||||
config.hidden_size,
|
||||
org_num_embeddings=config.vocab_size,
|
||||
# padding_size=DEFAULT_VOCAB_PADDING_SIZE,
|
||||
prefix=maybe_prefix(prefix, "lm_head"),
|
||||
)
|
||||
if config.tie_word_embeddings:
|
||||
self.lm_head.weight = self.model.embed_tokens.weight
|
||||
self.logits_processor = LogitsProcessor(
|
||||
self.unpadded_vocab_size, config.vocab_size
|
||||
)
|
||||
|
||||
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,
|
||||
hidden_states: torch.Tensor,
|
||||
intermediate_tensors: IntermediateTensors | None = None,
|
||||
inputs_embeds: torch.Tensor | None = None,
|
||||
spec_step_idx: int = 0,
|
||||
**kwargs: object,
|
||||
):
|
||||
hidden_states = self.model(
|
||||
input_ids,
|
||||
positions,
|
||||
hidden_states,
|
||||
intermediate_tensors,
|
||||
inputs_embeds,
|
||||
spec_step_idx,
|
||||
)
|
||||
return hidden_states
|
||||
|
||||
def compute_logits(
|
||||
self,
|
||||
hidden_states: torch.Tensor,
|
||||
spec_step_idx: int = 0,
|
||||
) -> torch.Tensor | None:
|
||||
return self.logits_processor(self.lm_head, hidden_states)
|
||||
|
||||
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
|
||||
shared_weight_names = ["embed_tokens", "lm_head"]
|
||||
|
||||
def remap_weight_names(weights):
|
||||
for name, weight in weights:
|
||||
if name.startswith("mtp."):
|
||||
name = name.replace("mtp.", "model.")
|
||||
elif not any(key in name for key in shared_weight_names):
|
||||
continue
|
||||
yield name, weight
|
||||
|
||||
loader = AutoWeightsLoader(self)
|
||||
return loader.load_weights(remap_weight_names(weights))
|
||||
@@ -98,6 +98,7 @@ _TEXT_GENERATION_MODELS = {
|
||||
"Ernie4_5_MoeForCausalLM": ("ernie45_moe", "Ernie4_5_MoeForCausalLM"),
|
||||
"ExaoneForCausalLM": ("exaone", "ExaoneForCausalLM"),
|
||||
"Exaone4ForCausalLM": ("exaone4", "Exaone4ForCausalLM"),
|
||||
"ExaoneMoEForCausalLM": ("exaone_moe", "ExaoneMoeForCausalLM"),
|
||||
"Fairseq2LlamaForCausalLM": ("fairseq2_llama", "Fairseq2LlamaForCausalLM"),
|
||||
"FalconForCausalLM": ("falcon", "FalconForCausalLM"),
|
||||
"FalconMambaForCausalLM": ("mamba", "MambaForCausalLM"),
|
||||
@@ -457,6 +458,7 @@ _SPECULATIVE_DECODING_MODELS = {
|
||||
"EagleDeepSeekMTPModel": ("deepseek_eagle", "EagleDeepseekV3ForCausalLM"),
|
||||
"DeepSeekMTPModel": ("deepseek_mtp", "DeepSeekMTP"),
|
||||
"ErnieMTPModel": ("ernie_mtp", "ErnieMTP"),
|
||||
"ExaoneMoeMTP": ("exaone_moe_mtp", "ExaoneMoeMTP"),
|
||||
"LongCatFlashMTPModel": ("longcat_flash_mtp", "LongCatFlashMTP"),
|
||||
"Glm4MoeMTPModel": ("glm4_moe_mtp", "Glm4MoeMTP"),
|
||||
"MedusaModel": ("medusa", "Medusa"),
|
||||
|
||||
Reference in New Issue
Block a user