diff --git a/docs/models/supported_models.md b/docs/models/supported_models.md index d2c22aef16f7..3a85d33df044 100644 --- a/docs/models/supported_models.md +++ b/docs/models/supported_models.md @@ -330,6 +330,8 @@ Specified using `--task generate`. | `DeepseekV2ForCausalLM` | DeepSeek-V2 | `deepseek-ai/DeepSeek-V2`, `deepseek-ai/DeepSeek-V2-Chat` etc. | | ✅︎ | ✅︎ | | `DeepseekV3ForCausalLM` | DeepSeek-V3 | `deepseek-ai/DeepSeek-V3-Base`, `deepseek-ai/DeepSeek-V3` etc. | | ✅︎ | ✅︎ | | `Dots1ForCausalLM` | dots.llm1 | `rednote-hilab/dots.llm1.base`, `rednote-hilab/dots.llm1.inst` etc. | | ✅︎ | ✅︎ | +| `Ernie4_5_ForCausalLM` | Ernie4.5 | `baidu/ERNIE-4.5-0.3B-PT`,etc. | | ✅︎ | ✅︎ | +| `Ernie4_5_MoeForCausalLM` | Ernie4.5MoE | `baidu/ERNIE-4.5-21B-A3B-PT`, `baidu/ERNIE-4.5-300B-A47B-PT`, etc. | | ✅︎ | ✅︎ | | `ExaoneForCausalLM` | EXAONE-3 | `LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct`, etc. | ✅︎ | ✅︎ | ✅︎ | | `FalconForCausalLM` | Falcon | `tiiuae/falcon-7b`, `tiiuae/falcon-40b`, `tiiuae/falcon-rw-7b`, etc. | | ✅︎ | ✅︎ | | `FalconMambaForCausalLM` | FalconMamba | `tiiuae/falcon-mamba-7b`, `tiiuae/falcon-mamba-7b-instruct`, etc. | | ✅︎ | ✅︎ | diff --git a/tests/models/registry.py b/tests/models/registry.py index faae46d286b3..11dfbc4ab651 100644 --- a/tests/models/registry.py +++ b/tests/models/registry.py @@ -162,6 +162,10 @@ def check_available_online( trust_remote_code=True), "DeepseekV3ForCausalLM": _HfExamplesInfo("deepseek-ai/DeepSeek-V3", # noqa: E501 trust_remote_code=True), + "Ernie4_5_ForCausalLM": _HfExamplesInfo("baidu/ERNIE-4.5-0.3B-PT", + trust_remote_code=True), + "Ernie4_5_MoeForCausalLM": _HfExamplesInfo("baidu/ERNIE-4.5-21B-A3B-PT", + trust_remote_code=True), "ExaoneForCausalLM": _HfExamplesInfo("LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct"), # noqa: E501 "Fairseq2LlamaForCausalLM": _HfExamplesInfo("mgleize/fairseq2-dummy-Llama-3.2-1B"), # noqa: E501 "FalconForCausalLM": _HfExamplesInfo("tiiuae/falcon-7b"), diff --git a/vllm/model_executor/models/ernie45.py b/vllm/model_executor/models/ernie45.py new file mode 100644 index 000000000000..2a89fffe80e3 --- /dev/null +++ b/vllm/model_executor/models/ernie45.py @@ -0,0 +1,43 @@ +# SPDX-License-Identifier: Apache-2.0 +# SPDX-FileCopyrightText: Copyright contributors to the vLLM project + +# Copyright 2025 The Baidu team. +# Copyright 2023 The vLLM team. +# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved. +# +# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX +# and OPT implementations in this library. It has been modified from its +# original forms to accommodate minor architectural differences compared +# to GPT-NeoX and OPT used by the Meta AI team that trained the model. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Inference-only Erine model compatible with HuggingFace weights.""" +from vllm.config import VllmConfig +from vllm.model_executor.models.llama import LlamaForCausalLM + +from .utils import PPMissingLayer + + +class Ernie4_5_ForCausalLM(LlamaForCausalLM): + + def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): + super().__init__(vllm_config=vllm_config, prefix=prefix) + # Hack Llama model to fit HF format Ernie4.5 dense implementation + # Attention difference between Ernie and Llama: + # 1. rotary_dim and no Neox style. + # 2. There is no bias for o_proj in attention + for layer in self.model.layers: + if not isinstance(layer, PPMissingLayer): + layer.self_attn.rotary_emb.is_neox_style = False + layer.self_attn.o_proj.bias = None + layer.self_attn.o_proj.skip_bias_add = True diff --git a/vllm/model_executor/models/ernie45_moe.py b/vllm/model_executor/models/ernie45_moe.py new file mode 100644 index 000000000000..e7a50ff7a1c9 --- /dev/null +++ b/vllm/model_executor/models/ernie45_moe.py @@ -0,0 +1,583 @@ +# SPDX-License-Identifier: Apache-2.0 +# SPDX-FileCopyrightText: Copyright contributors to the vLLM project + +# Copyright 2025 The Baidu team. +# Copyright 2023 The vLLM team. +# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved. +# +# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX +# and OPT implementations in this library. It has been modified from its +# original forms to accommodate minor architectural differences compared +# to GPT-NeoX and OPT used by the Meta AI team that trained the model. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Inference-only ErineMoE model compatible with HuggingFace weights.""" +from collections.abc import Iterable +from typing import Any, Optional, Union + +import torch +from torch import nn +from transformers import PretrainedConfig + +from vllm.attention import Attention +from vllm.compilation.decorators import support_torch_compile +from vllm.config import CacheConfig, VllmConfig +from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size +from vllm.logger import init_logger +from vllm.model_executor.layers.activation import SiluAndMul +from vllm.model_executor.layers.fused_moe import FusedMoE +from vllm.model_executor.layers.layernorm import RMSNorm +from vllm.model_executor.layers.linear import (MergedColumnParallelLinear, + QKVParallelLinear, + ReplicatedLinear, + RowParallelLinear) +from vllm.model_executor.layers.logits_processor import LogitsProcessor +from vllm.model_executor.layers.quantization import QuantizationConfig +from vllm.model_executor.layers.rotary_embedding import get_rope +from vllm.model_executor.layers.vocab_parallel_embedding import ( + ParallelLMHead, VocabParallelEmbedding) +from vllm.model_executor.model_loader.weight_utils import ( + default_weight_loader, maybe_remap_kv_scale_name) +from vllm.model_executor.sampling_metadata import SamplingMetadata +from vllm.sequence import IntermediateTensors + +from .interfaces import SupportsPP +from .utils import (PPMissingLayer, extract_layer_index, + is_pp_missing_parameter, + make_empty_intermediate_tensors_factory, make_layers, + maybe_prefix) + +logger = init_logger(__name__) + + +class Ernie4_5_MoeMLP(nn.Module): + + def __init__( + self, + hidden_size: int, + intermediate_size: int, + hidden_act: str, + use_bias: bool = False, + quant_config: Optional[QuantizationConfig] = None, + reduce_results: bool = True, + prefix: str = "", + ) -> None: + super().__init__() + self.gate_up_proj = MergedColumnParallelLinear( + hidden_size, [intermediate_size] * 2, + bias=use_bias, + quant_config=quant_config, + prefix=f"{prefix}.gate_up_proj") + self.down_proj = RowParallelLinear(intermediate_size, + hidden_size, + bias=use_bias, + quant_config=quant_config, + reduce_results=reduce_results, + prefix=f"{prefix}.down_proj") + if hidden_act != "silu": + raise ValueError(f"Unsupported activation: {hidden_act}. " + "Only silu is supported for now.") + self.act_fn = SiluAndMul() + + def forward(self, x): + gate_up, _ = self.gate_up_proj(x) + x = self.act_fn(gate_up) + x, _ = self.down_proj(x) + return x + + +class Ernie4_5_MoeMoE(nn.Module): + + def __init__( + self, + config: PretrainedConfig, + quant_config: Optional[QuantizationConfig] = None, + prefix: str = "", + ): + super().__init__() + + layer_idx = extract_layer_index(prefix) + self.layer_idx = layer_idx + self.tp_size = get_tensor_model_parallel_world_size() + self.moe_num_shared_experts = getattr(config, "moe_num_shared_experts", + None) + + if self.tp_size > config.moe_num_experts: + raise ValueError( + f"Tensor parallel size {self.tp_size} is greater than " + f"the number of experts {config.moe_num_experts}.") + + self.gate = ReplicatedLinear(config.hidden_size, + config.moe_num_experts, + bias=False, + quant_config=None, + prefix=f"{prefix}.gate") + + self.experts = FusedMoE(num_experts=config.moe_num_experts, + top_k=config.moe_k, + hidden_size=config.hidden_size, + intermediate_size=config.moe_intermediate_size, + reduce_results=False, + renormalize=True, + quant_config=quant_config, + prefix=f"{prefix}.experts") + + if self.moe_num_shared_experts is not None: + intermediate_size = (config.moe_intermediate_size * + config.moe_num_shared_experts) + self.shared_experts = Ernie4_5_MoeMLP( + hidden_size=config.hidden_size, + intermediate_size=intermediate_size, + hidden_act=config.hidden_act, + quant_config=quant_config, + prefix=f"{prefix}.shared_experts", + reduce_results=self.experts.must_reduce_shared_expert_outputs( + )) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + orig_shape = hidden_states.shape + hidden_dim = hidden_states.shape[-1] + hidden_states = hidden_states.view(-1, hidden_dim) + if self.moe_num_shared_experts is not None: + shared_output = self.shared_experts(hidden_states) + + router_logits, _ = self.gate(hidden_states) + + final_hidden_states = self.experts(hidden_states=hidden_states, + router_logits=router_logits) + + if self.moe_num_shared_experts is not None and \ + shared_output is not None: + final_hidden_states = final_hidden_states + shared_output + + if self.tp_size > 1: + final_hidden_states = ( + self.experts.maybe_all_reduce_tensor_model_parallel( + final_hidden_states)) + + return final_hidden_states.view(orig_shape) + + +class Ernie4_5_MoeAttention(nn.Module): + + def __init__( + self, + hidden_size: int, + num_heads: int, + num_kv_heads: int, + head_dim: Optional[int] = None, + rope_theta: float = 500000, + rope_scaling: Optional[dict[str, Any]] = None, + max_position_embeddings: int = 131072, + rms_norm_eps: float = 1e-05, + qkv_bias: bool = False, + cache_config: Optional[CacheConfig] = None, + quant_config: Optional[QuantizationConfig] = None, + prefix: str = "", + ) -> None: + super().__init__() + layer_idx = extract_layer_index(prefix) if len(prefix) > 0 else 0 + self.layer_idx = layer_idx + self.hidden_size = hidden_size + tp_size = get_tensor_model_parallel_world_size() + self.total_num_heads = num_heads + assert self.total_num_heads % tp_size == 0 + self.num_heads = self.total_num_heads // tp_size + + self.total_num_kv_heads = num_kv_heads + if self.total_num_kv_heads >= tp_size: + # Number of KV heads is greater than TP size, so we partition + # the KV heads across multiple tensor parallel GPUs. + assert self.total_num_kv_heads % tp_size == 0 + else: + # Number of KV heads is less than TP size, so we replicate + # the KV heads across multiple tensor parallel GPUs. + assert tp_size % self.total_num_kv_heads == 0 + self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size) + self.head_dim = head_dim or (hidden_size // self.total_num_heads) + + self.q_size = self.num_heads * self.head_dim + self.kv_size = self.num_kv_heads * self.head_dim + self.scaling = self.head_dim**-0.5 + self.rope_theta = rope_theta + self.max_position_embeddings = max_position_embeddings + + self.qkv_proj = QKVParallelLinear(hidden_size, + self.head_dim, + self.total_num_heads, + self.total_num_kv_heads, + bias=qkv_bias, + quant_config=quant_config, + prefix=f"{prefix}.qkv_proj") + + self.o_proj = RowParallelLinear(self.total_num_heads * self.head_dim, + hidden_size, + bias=False, + quant_config=quant_config, + prefix=f"{prefix}.o_proj") + + self.rotary_emb = get_rope( + self.head_dim, + rotary_dim=self.head_dim, + max_position=max_position_embeddings, + base=rope_theta, + is_neox_style=False, + rope_scaling=rope_scaling, + ) + self.attn = Attention(self.num_heads, + self.head_dim, + self.scaling, + num_kv_heads=self.num_kv_heads, + cache_config=cache_config, + quant_config=quant_config, + prefix=f"{prefix}.attn") + + def forward( + self, + positions: torch.Tensor, + hidden_states: torch.Tensor, + ) -> torch.Tensor: + + qkv, _ = self.qkv_proj(hidden_states) + + q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1) + q, k = self.rotary_emb(positions, q, k) + + # Attention + attn_output = self.attn(q, k, v) + # Output projection + output, _ = self.o_proj(attn_output) + return output + + +class Ernie4_5_MoeDecoderLayer(nn.Module): + + def __init__( + self, + config: PretrainedConfig, + cache_config: Optional[CacheConfig] = None, + quant_config: Optional[QuantizationConfig] = None, + prefix: str = "", + ) -> None: + super().__init__() + self.hidden_size = config.hidden_size + rope_theta = getattr(config, "rope_theta", 500000) + rope_scaling = getattr(config, "rope_scaling", None) + max_position_embeddings = getattr(config, "max_position_embeddings", + 131072) + self.self_attn = Ernie4_5_MoeAttention( + hidden_size=self.hidden_size, + num_heads=config.num_attention_heads, + num_kv_heads=config.num_key_value_heads, + head_dim=getattr(config, 'head_dim', None), + rope_theta=rope_theta, + rope_scaling=rope_scaling, + max_position_embeddings=max_position_embeddings, + rms_norm_eps=config.rms_norm_eps, + qkv_bias=getattr(config, 'use_bias', False), + cache_config=cache_config, + quant_config=quant_config, + prefix=f"{prefix}.self_attn", + ) + + layer_idx = extract_layer_index(prefix) + self.layer_idx = layer_idx + + # MoE + moe_num_experts = getattr(config, "moe_num_experts", 0) + moe_layer_start_index = getattr(config, "moe_layer_start_index", 0) + moe_layer_end_index = getattr(config, "moe_layer_end_index", + config.num_hidden_layers - 1) + moe_layer_interval = getattr(config, "moe_layer_interval", 1) + use_moe = getattr(config, "use_moe", moe_num_experts > 0) + + if (use_moe and ((layer_idx + 1) % moe_layer_interval == 0) + and layer_idx >= moe_layer_start_index + and layer_idx <= moe_layer_end_index): + self.mlp = Ernie4_5_MoeMoE(config=config, + quant_config=quant_config, + prefix=f"{prefix}.mlp") + else: + self.mlp = Ernie4_5_MoeMLP( + hidden_size=config.hidden_size, + intermediate_size=config.intermediate_size, + hidden_act=config.hidden_act, + use_bias=getattr(config, 'use_bias', False), + quant_config=quant_config, + prefix=f"{prefix}.mlp") + + self.input_layernorm = RMSNorm(config.hidden_size, + eps=config.rms_norm_eps) + self.post_attention_layernorm = RMSNorm(config.hidden_size, + eps=config.rms_norm_eps) + + def forward( + self, + positions: torch.Tensor, + hidden_states: torch.Tensor, + residual: Optional[torch.Tensor], + ) -> torch.Tensor: + + # Self Attention + if residual is None: + residual = hidden_states + hidden_states = self.input_layernorm(hidden_states) + else: + hidden_states, residual = self.input_layernorm( + hidden_states, residual) + + hidden_states = self.self_attn( + positions=positions, + hidden_states=hidden_states, + ) + + # Fully Connected + hidden_states, residual = self.post_attention_layernorm( + hidden_states, residual) + + hidden_states = self.mlp(hidden_states) + + return hidden_states, residual + + +@support_torch_compile +class Ernie4_5_MoeModel(nn.Module): + + def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): + super().__init__() + + config = vllm_config.model_config.hf_config + cache_config = vllm_config.cache_config + quant_config = vllm_config.quant_config + + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + self.config = config + + if get_pp_group().is_first_rank: + self.embed_tokens = VocabParallelEmbedding( + config.vocab_size, + config.hidden_size, + quant_config=quant_config, + prefix=f"{prefix}.embed_tokens") + else: + self.embed_tokens = PPMissingLayer() + + self.start_layer, self.end_layer, self.layers = make_layers( + config.num_hidden_layers, + lambda prefix: Ernie4_5_MoeDecoderLayer(config=config, + cache_config=cache_config, + quant_config=quant_config, + prefix=prefix), + prefix=f"{prefix}.layers", + ) + + if get_pp_group().is_last_rank: + self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) + else: + self.norm = PPMissingLayer() + + self.make_empty_intermediate_tensors = ( + make_empty_intermediate_tensors_factory( + ["hidden_states", "residual"], config.hidden_size)) + + 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, + intermediate_tensors: Optional[IntermediateTensors] = None, + inputs_embeds: Optional[torch.Tensor] = None, + ) -> Union[torch.Tensor, IntermediateTensors]: + + if get_pp_group().is_first_rank: + if inputs_embeds is not None: + hidden_states = inputs_embeds + else: + hidden_states = self.get_input_embeddings(input_ids) + residual = None + else: + assert intermediate_tensors is not None + hidden_states = intermediate_tensors["hidden_states"] + residual = intermediate_tensors["residual"] + + for i in range(self.start_layer, self.end_layer): + layer = self.layers[i] + hidden_states, residual = layer(positions, hidden_states, 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 + + +class Ernie4_5_MoeForCausalLM(nn.Module, SupportsPP): + 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 = Ernie4_5_MoeModel(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() + + if self.config.tie_word_embeddings: + self.lm_head.weight = self.model.embed_tokens.weight + self.logits_processor = LogitsProcessor(config.vocab_size) + self.make_empty_intermediate_tensors = ( + self.model.make_empty_intermediate_tensors) + + 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]: + 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) + expert_params_mapping = FusedMoE.make_expert_params_mapping( + ckpt_gate_proj_name="gate_proj", + ckpt_down_proj_name="down_proj", + ckpt_up_proj_name="up_proj", + num_experts=self.config.moe_num_experts) + + params_dict = dict(self.named_parameters()) + loaded_params: set[str] = set() + for name, loaded_weight in weights: + if self.config.tie_word_embeddings and name.endswith( + "lm_head.weight"): + continue + # MTP will be supported soon. + if "mtp" in name: + continue + + for (param_name, weight_name, shard_id) in stacked_params_mapping: + # Skip non-stacked layers and experts (experts handled below). + if weight_name not in name: + continue + + 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") or name.endswith("_bias")) + and name not in params_dict): + continue + # Skip layers on other devices. + 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: + for mapping in expert_params_mapping: + param_name, weight_name, expert_id, shard_id = mapping + + if weight_name not in name: + continue + + name = name.replace(weight_name, param_name) + # Skip layers on other devices. + if is_pp_missing_parameter(name, self): + continue + + # Skip loading extra bias for GPTQ models. + if ((name.endswith(".bias") or name.endswith("_bias")) + and name not in params_dict): + continue + param = params_dict[name] + + weight_loader = param.weight_loader + weight_loader(param, + loaded_weight, + name, + shard_id=shard_id, + expert_id=expert_id) + break + else: + # Skip loading extra bias for GPTQ models. + if ((name.endswith(".bias") or name.endswith("_bias")) + and name not in params_dict): + continue + # Skip layers on other devices. + if is_pp_missing_parameter(name, self): + continue + # Remapping the name of FP8 kv-scale. + name = maybe_remap_kv_scale_name(name, params_dict) + if name is None: + 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 diff --git a/vllm/model_executor/models/registry.py b/vllm/model_executor/models/registry.py index 5d0a1d77339a..de09db8add1c 100644 --- a/vllm/model_executor/models/registry.py +++ b/vllm/model_executor/models/registry.py @@ -53,6 +53,8 @@ "DeepseekV2ForCausalLM": ("deepseek_v2", "DeepseekV2ForCausalLM"), "DeepseekV3ForCausalLM": ("deepseek_v2", "DeepseekV3ForCausalLM"), "Dots1ForCausalLM": ("dots1", "Dots1ForCausalLM"), + "Ernie4_5_ForCausalLM": ("ernie45", "Ernie4_5_ForCausalLM"), + "Ernie4_5_MoeForCausalLM": ("ernie45_moe", "Ernie4_5_MoeForCausalLM"), "ExaoneForCausalLM": ("exaone", "ExaoneForCausalLM"), "FalconForCausalLM": ("falcon", "FalconForCausalLM"), "Fairseq2LlamaForCausalLM": ("fairseq2_llama", "Fairseq2LlamaForCausalLM"),