mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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80f918cc22
Signed-off-by: gkswns0531 <gkswns0531@gmail.com> Signed-off-by: hanjuncho <gkswns0531@gmail.com> Co-authored-by: bhsueh_NV <11360707+byshiue@users.noreply.github.com>
211 lines
9.2 KiB
Python
211 lines
9.2 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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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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from typing import Optional, Union
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from ...layers import MoeConfig
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from ...mapping import Mapping
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from ..convert_utils import infer_dtype
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from ..modeling_utils import PretrainedConfig, QuantConfig
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class QWenConfig(PretrainedConfig):
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def __init__(self,
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*,
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mlp_bias: bool = False,
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attn_bias: bool = True,
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rotary_base: float = 10000.0,
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rotary_scaling: Optional[dict] = None,
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disable_weight_only_quant_plugin: bool = False,
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use_logn_attn: bool = False,
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moe: Optional[Union[MoeConfig, dict]] = None,
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num_labels: int = 1,
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mlp_only_layers: Optional[list] = None,
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decoder_sparse_step: int = 1,
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**kwargs):
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self.mlp_bias = mlp_bias
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self.attn_bias = attn_bias
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self.rotary_base = rotary_base
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self.rotary_scaling = rotary_scaling
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self.disable_weight_only_quant_plugin = disable_weight_only_quant_plugin
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self.num_labels = num_labels
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self.use_logn_attn = use_logn_attn
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self.mlp_only_layers = mlp_only_layers or []
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self.decoder_sparse_step = decoder_sparse_step
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if moe is None:
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# Legacy MOE config fields
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moe = MoeConfig(num_experts=kwargs.pop('moe_num_experts', 0),
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top_k=kwargs.pop('moe_top_k', 0),
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normalization_mode=kwargs.pop(
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'moe_normalization_mode',
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MoeConfig.ExpertScaleNormalizationMode.NONE))
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elif isinstance(moe, dict):
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moe = MoeConfig.from_dict(moe)
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assert isinstance(moe, MoeConfig)
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self.moe = moe.validate()
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super().__init__(**kwargs)
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def to_dict(self):
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output = super().to_dict()
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# Serialize the fields added in QWenConfig
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output['mlp_bias'] = self.mlp_bias
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output['attn_bias'] = self.attn_bias
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output['rotary_base'] = self.rotary_base
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output['rotary_scaling'] = self.rotary_scaling
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output[
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'disable_weight_only_quant_plugin'] = self.disable_weight_only_quant_plugin
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output['use_logn_attn'] = self.use_logn_attn
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output['mlp_only_layers'] = self.mlp_only_layers
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output['decoder_sparse_step'] = self.decoder_sparse_step
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output['moe'] = self.moe.to_dict()
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return output
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@classmethod
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def from_hugging_face(cls,
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hf_config_or_dir: Union[
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str, 'transformers.PretrainedConfig'],
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dtype: str = 'auto',
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mapping: Optional[Mapping] = None,
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quant_config: Optional[QuantConfig] = None,
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**kwargs) -> "QWenConfig":
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import transformers
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trust_remote_code = kwargs.pop('trust_remote_code', True)
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if isinstance(hf_config_or_dir, transformers.PretrainedConfig):
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hf_config = hf_config_or_dir
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else:
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hf_config_dir = str(hf_config_or_dir)
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hf_config = transformers.AutoConfig.from_pretrained(
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hf_config_dir, trust_remote_code=trust_remote_code)
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if hasattr(hf_config, 'llm_config'):
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hf_config = hf_config.llm_config
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qwen_type = hf_config.model_type
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# lmms llava onevision qwen
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if qwen_type == 'llava':
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qwen_type = 'qwen2'
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if hf_config.architectures and hf_config.architectures[
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0] == 'LlavaQwenForCausalLM':
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hf_config.architectures[0] = 'Qwen2ForCausalLM'
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# hf llava onevision qwen
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if qwen_type == 'llava_onevision':
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hf_config = hf_config.text_config
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qwen_type = f'{hf_config.model_type}_llava_onevision'
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# Qwen2-Audio
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if qwen_type == 'qwen2_audio':
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hf_config = hf_config.text_config
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hf_config.architectures = ['Qwen2ForCausalLM']
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valid_types = ('qwen', 'qwen2', 'qwen2_moe', 'qwen2_llava_onevision',
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'qwen2_vl', 'qwen2_audio', 'qwen3', 'qwen3_moe')
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assert qwen_type in valid_types, f"Unsupported Qwen type: {qwen_type}, only {valid_types} are acceptable."
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num_key_value_heads = getattr(hf_config, "num_key_value_heads",
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hf_config.num_attention_heads)
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head_dim = getattr(
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hf_config, "head_dim",
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hf_config.hidden_size // hf_config.num_attention_heads)
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head_size = getattr(hf_config, "kv_channels", head_dim)
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hidden_act = getattr(hf_config, "hidden_act", "silu")
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if qwen_type in ("qwen2_moe", "qwen3_moe"):
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hidden_act = "swiglu"
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# Qwen3 models have no attention bias, while legacy models have bias
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if qwen_type in ('qwen3', 'qwen3_moe'):
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attn_bias = False # Qwen3 models have no attn bias
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else:
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attn_bias = True # Legacy Qwen models have attn bias
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rotary_scaling = getattr(hf_config, "rope_scaling", None)
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seq_length = getattr(hf_config, "seq_length", 8192)
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use_logn_attn = getattr(hf_config, "use_logn_attn", False)
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disable_weight_only_quant_plugin = kwargs.pop(
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'disable_weight_only_quant_plugin', False)
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if qwen_type == "qwen":
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rms_norm_eps = hf_config.layer_norm_epsilon
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rotary_base = getattr(hf_config, "rotary_emb_base", 10000.0)
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else:
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rms_norm_eps = hf_config.rms_norm_eps
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rotary_base = getattr(hf_config, "rope_theta", 100000.0)
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num_labels = 1
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if hf_config.architectures[0] == "Qwen2ForSequenceClassification":
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num_labels = hf_config.num_labels
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moe_num_experts = getattr(hf_config, "num_experts", 0)
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moe_top_k = getattr(hf_config, "num_experts_per_tok", 0)
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moe_intermediate_size = getattr(hf_config, "moe_intermediate_size", 0)
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moe_shared_expert_intermediate_size = getattr(
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hf_config, "shared_expert_intermediate_size", 0)
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moe_normalization_mode = MoeConfig.ExpertScaleNormalizationMode.NONE
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# Add support for mlp_only_layers and decoder_sparse_step (Qwen3 MoE)
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mlp_only_layers = getattr(hf_config, "mlp_only_layers", [])
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decoder_sparse_step = getattr(hf_config, "decoder_sparse_step", 1)
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moe_config = MoeConfig(num_experts=moe_num_experts,
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top_k=moe_top_k,
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normalization_mode=moe_normalization_mode)
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moe_config.validate()
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dtype = infer_dtype(dtype, getattr(hf_config, 'torch_dtype', None))
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tie_word_embeddings = getattr(hf_config, 'tie_word_embeddings', False)
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if qwen_type == 'qwen2_vl':
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pe_type = 'mrope'
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rotary_embedding_percentage = getattr(hf_config, 'rotary_pct', 1.0)
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rotary_embedding_dim = getattr(
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hf_config, 'rotary_dim',
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int(hf_config.hidden_size / hf_config.num_attention_heads *
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rotary_embedding_percentage))
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rotary_scaling['type'] = 'mrope'
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else:
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pe_type = 'rope_gpt_neox'
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rotary_embedding_dim = None
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return cls(
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architecture=hf_config.architectures[0],
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dtype=dtype,
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num_hidden_layers=hf_config.num_hidden_layers,
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num_attention_heads=hf_config.num_attention_heads,
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hidden_size=hf_config.hidden_size,
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intermediate_size=hf_config.intermediate_size,
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num_key_value_heads=num_key_value_heads,
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head_size=head_size,
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vocab_size=hf_config.vocab_size,
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position_embedding_type=pe_type,
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max_position_embeddings=hf_config.max_position_embeddings,
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rotary_embedding_dim=rotary_embedding_dim,
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hidden_act=hidden_act,
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norm_epsilon=rms_norm_eps,
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attn_bias=attn_bias,
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rotary_base=rotary_base,
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rotary_scaling=rotary_scaling,
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disable_weight_only_quant_plugin=disable_weight_only_quant_plugin,
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seq_length=seq_length,
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use_logn_attn=use_logn_attn,
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qwen_type=qwen_type,
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moe_intermediate_size=moe_intermediate_size,
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moe_shared_expert_intermediate_size=
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moe_shared_expert_intermediate_size,
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mlp_only_layers=mlp_only_layers,
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decoder_sparse_step=decoder_sparse_step,
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moe=moe_config,
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mapping=mapping,
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quantization=quant_config,
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num_labels=num_labels,
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tie_word_embeddings=tie_word_embeddings,
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**kwargs)
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