# SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # 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. from typing import Optional, Union from ...layers import MoeConfig from ...mapping import Mapping from ..convert_utils import infer_dtype from ..modeling_utils import PretrainedConfig, QuantConfig class QWenConfig(PretrainedConfig): def __init__(self, *, mlp_bias: bool = False, attn_bias: bool = True, rotary_base: float = 10000.0, rotary_scaling: Optional[dict] = None, disable_weight_only_quant_plugin: bool = False, use_logn_attn: bool = False, moe: Optional[Union[MoeConfig, dict]] = None, num_labels: int = 1, mlp_only_layers: Optional[list] = None, decoder_sparse_step: int = 1, **kwargs): self.mlp_bias = mlp_bias self.attn_bias = attn_bias self.rotary_base = rotary_base self.rotary_scaling = rotary_scaling self.disable_weight_only_quant_plugin = disable_weight_only_quant_plugin self.num_labels = num_labels self.use_logn_attn = use_logn_attn self.mlp_only_layers = mlp_only_layers or [] self.decoder_sparse_step = decoder_sparse_step if moe is None: # Legacy MOE config fields moe = MoeConfig(num_experts=kwargs.pop('moe_num_experts', 0), top_k=kwargs.pop('moe_top_k', 0), normalization_mode=kwargs.pop( 'moe_normalization_mode', MoeConfig.ExpertScaleNormalizationMode.NONE)) elif isinstance(moe, dict): moe = MoeConfig.from_dict(moe) assert isinstance(moe, MoeConfig) self.moe = moe.validate() super().__init__(**kwargs) def to_dict(self): output = super().to_dict() # Serialize the fields added in QWenConfig output['mlp_bias'] = self.mlp_bias output['attn_bias'] = self.attn_bias output['rotary_base'] = self.rotary_base output['rotary_scaling'] = self.rotary_scaling output[ 'disable_weight_only_quant_plugin'] = self.disable_weight_only_quant_plugin output['use_logn_attn'] = self.use_logn_attn output['mlp_only_layers'] = self.mlp_only_layers output['decoder_sparse_step'] = self.decoder_sparse_step output['moe'] = self.moe.to_dict() return output @classmethod def from_hugging_face(cls, hf_config_or_dir: Union[ str, 'transformers.PretrainedConfig'], dtype: str = 'auto', mapping: Optional[Mapping] = None, quant_config: Optional[QuantConfig] = None, **kwargs) -> "QWenConfig": import transformers trust_remote_code = kwargs.pop('trust_remote_code', True) if isinstance(hf_config_or_dir, transformers.PretrainedConfig): hf_config = hf_config_or_dir else: hf_config_dir = str(hf_config_or_dir) hf_config = transformers.AutoConfig.from_pretrained( hf_config_dir, trust_remote_code=trust_remote_code) if hasattr(hf_config, 'llm_config'): hf_config = hf_config.llm_config qwen_type = hf_config.model_type # lmms llava onevision qwen if qwen_type == 'llava': qwen_type = 'qwen2' if hf_config.architectures and hf_config.architectures[ 0] == 'LlavaQwenForCausalLM': hf_config.architectures[0] = 'Qwen2ForCausalLM' # hf llava onevision qwen if qwen_type == 'llava_onevision': hf_config = hf_config.text_config qwen_type = f'{hf_config.model_type}_llava_onevision' # Qwen2-Audio if qwen_type == 'qwen2_audio': hf_config = hf_config.text_config hf_config.architectures = ['Qwen2ForCausalLM'] valid_types = ('qwen', 'qwen2', 'qwen2_moe', 'qwen2_llava_onevision', 'qwen2_vl', 'qwen2_audio', 'qwen3', 'qwen3_moe') assert qwen_type in valid_types, f"Unsupported Qwen type: {qwen_type}, only {valid_types} are acceptable." num_key_value_heads = getattr(hf_config, "num_key_value_heads", hf_config.num_attention_heads) head_dim = getattr( hf_config, "head_dim", hf_config.hidden_size // hf_config.num_attention_heads) head_size = getattr(hf_config, "kv_channels", head_dim) hidden_act = getattr(hf_config, "hidden_act", "silu") if qwen_type in ("qwen2_moe", "qwen3_moe"): hidden_act = "swiglu" # Qwen3 models have no attention bias, while legacy models have bias if qwen_type in ('qwen3', 'qwen3_moe'): attn_bias = False # Qwen3 models have no attn bias else: attn_bias = True # Legacy Qwen models have attn bias rotary_scaling = getattr(hf_config, "rope_scaling", None) seq_length = getattr(hf_config, "seq_length", 8192) use_logn_attn = getattr(hf_config, "use_logn_attn", False) disable_weight_only_quant_plugin = kwargs.pop( 'disable_weight_only_quant_plugin', False) if qwen_type == "qwen": rms_norm_eps = hf_config.layer_norm_epsilon rotary_base = getattr(hf_config, "rotary_emb_base", 10000.0) else: rms_norm_eps = hf_config.rms_norm_eps rotary_base = getattr(hf_config, "rope_theta", 100000.0) num_labels = 1 if hf_config.architectures[0] == "Qwen2ForSequenceClassification": num_labels = hf_config.num_labels moe_num_experts = getattr(hf_config, "num_experts", 0) moe_top_k = getattr(hf_config, "num_experts_per_tok", 0) moe_intermediate_size = getattr(hf_config, "moe_intermediate_size", 0) moe_shared_expert_intermediate_size = getattr( hf_config, "shared_expert_intermediate_size", 0) moe_normalization_mode = MoeConfig.ExpertScaleNormalizationMode.NONE # Add support for mlp_only_layers and decoder_sparse_step (Qwen3 MoE) mlp_only_layers = getattr(hf_config, "mlp_only_layers", []) decoder_sparse_step = getattr(hf_config, "decoder_sparse_step", 1) moe_config = MoeConfig(num_experts=moe_num_experts, top_k=moe_top_k, normalization_mode=moe_normalization_mode) moe_config.validate() dtype = infer_dtype(dtype, getattr(hf_config, 'torch_dtype', None)) tie_word_embeddings = getattr(hf_config, 'tie_word_embeddings', False) if qwen_type == 'qwen2_vl': pe_type = 'mrope' rotary_embedding_percentage = getattr(hf_config, 'rotary_pct', 1.0) rotary_embedding_dim = getattr( hf_config, 'rotary_dim', int(hf_config.hidden_size / hf_config.num_attention_heads * rotary_embedding_percentage)) rotary_scaling['type'] = 'mrope' else: pe_type = 'rope_gpt_neox' rotary_embedding_dim = None return cls( architecture=hf_config.architectures[0], dtype=dtype, num_hidden_layers=hf_config.num_hidden_layers, num_attention_heads=hf_config.num_attention_heads, hidden_size=hf_config.hidden_size, intermediate_size=hf_config.intermediate_size, num_key_value_heads=num_key_value_heads, head_size=head_size, vocab_size=hf_config.vocab_size, position_embedding_type=pe_type, max_position_embeddings=hf_config.max_position_embeddings, rotary_embedding_dim=rotary_embedding_dim, hidden_act=hidden_act, norm_epsilon=rms_norm_eps, attn_bias=attn_bias, rotary_base=rotary_base, rotary_scaling=rotary_scaling, disable_weight_only_quant_plugin=disable_weight_only_quant_plugin, seq_length=seq_length, use_logn_attn=use_logn_attn, qwen_type=qwen_type, moe_intermediate_size=moe_intermediate_size, moe_shared_expert_intermediate_size= moe_shared_expert_intermediate_size, mlp_only_layers=mlp_only_layers, decoder_sparse_step=decoder_sparse_step, moe=moe_config, mapping=mapping, quantization=quant_config, num_labels=num_labels, tie_word_embeddings=tie_word_embeddings, **kwargs)