mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
synced 2026-01-14 06:27:45 +08:00
16d2467ea8
* Update TensorRT-LLM --------- Co-authored-by: Denis Kayshev <topenkoff@gmail.com> Co-authored-by: akhoroshev <arthoroshev@gmail.com> Co-authored-by: Patrick Reiter Horn <patrick.horn@gmail.com> Update
105 lines
4.2 KiB
Python
Executable File
105 lines
4.2 KiB
Python
Executable File
# 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 DeepSeekV1Config(PretrainedConfig):
|
|
|
|
def __init__(self,
|
|
*,
|
|
rotary_base: float = 10000.0,
|
|
rotary_scaling: Optional[dict] = None,
|
|
moe: Optional[Union[MoeConfig, dict]] = None,
|
|
**kwargs):
|
|
|
|
self.rotary_base = rotary_base
|
|
self.rotary_scaling = rotary_scaling
|
|
if 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 DeepSeekV1Config
|
|
|
|
output['rotary_base'] = self.rotary_base
|
|
output['rotary_scaling'] = self.rotary_scaling
|
|
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):
|
|
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)
|
|
|
|
num_key_value_heads = getattr(hf_config, "num_key_value_heads",
|
|
hf_config.num_attention_heads)
|
|
rotary_scaling = getattr(hf_config, "rope_scaling", None)
|
|
rotary_base = getattr(hf_config, "rope_theta", 10000.0)
|
|
dtype = infer_dtype(dtype, getattr(hf_config, 'torch_dtype', None))
|
|
moe_config = MoeConfig(
|
|
num_experts=getattr(hf_config, 'n_routed_experts', 0),
|
|
shared_expert_intermediate_size=getattr(hf_config,
|
|
'n_shared_experts', 0) *
|
|
getattr(hf_config, "moe_intermediate_size", 0),
|
|
top_k=getattr(hf_config, 'num_experts_per_tok', 0),
|
|
normalization_mode=getattr(
|
|
hf_config, 'moe_normalization_mode',
|
|
MoeConfig.ExpertScaleNormalizationMode.NONE),
|
|
)
|
|
moe_config.validate()
|
|
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,
|
|
vocab_size=hf_config.vocab_size,
|
|
position_embedding_type='rope_gpt_neox',
|
|
max_position_embeddings=hf_config.max_position_embeddings,
|
|
hidden_act='swiglu',
|
|
rotary_base=rotary_base,
|
|
rotary_scaling=rotary_scaling,
|
|
norm_epsilon=hf_config.rms_norm_eps,
|
|
mapping=mapping,
|
|
quantization=quant_config,
|
|
moe=moe_config,
|
|
moe_intermediate_size=hf_config.moe_intermediate_size,
|
|
**kwargs)
|