mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
synced 2026-01-14 06:27:45 +08:00
115 lines
4.5 KiB
Python
115 lines
4.5 KiB
Python
# 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.
|
|
|
|
import json
|
|
from typing import Optional, Union
|
|
|
|
from ...mapping import Mapping
|
|
from ..convert_utils import infer_dtype
|
|
from ..llama.config import LLaMAConfig
|
|
from ..modeling_utils import PretrainedConfig, QuantConfig
|
|
from ..qwen.config import QWenConfig
|
|
|
|
|
|
# Medusa-specific config is stored and retrieved from GenericMedusaConfig.
|
|
class MedusaConfig(PretrainedConfig):
|
|
|
|
def __init__(self,
|
|
*,
|
|
num_medusa_heads: int = 4,
|
|
num_medusa_layers: int = 1,
|
|
max_draft_len: int = 63,
|
|
**kwargs):
|
|
|
|
model_type = str(kwargs.get('model_type', '')).lower()
|
|
generic_medusa_config = QWenConfig if 'qwen' in model_type else LLaMAConfig
|
|
self.config = generic_medusa_config(**kwargs)
|
|
|
|
# Add objects
|
|
self.config.num_medusa_heads = num_medusa_heads
|
|
self.config.num_medusa_layers = num_medusa_layers
|
|
self.config.max_draft_len = max_draft_len
|
|
|
|
def to_dict(self):
|
|
output = self.config.to_dict()
|
|
output['num_medusa_heads'] = self.config.num_medusa_heads
|
|
output['num_medusa_layers'] = self.config.num_medusa_layers
|
|
output['max_draft_len'] = self.config.max_draft_len
|
|
return output
|
|
|
|
# Specialization to redirect accesses to self.config
|
|
def __getattr__(self, name):
|
|
return getattr(self.config, name)
|
|
|
|
def __getstate__(self):
|
|
return self.__dict__
|
|
|
|
def __setstate__(self, state):
|
|
self.__dict__.update(state)
|
|
|
|
@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)
|
|
speculative_config_or_dir = kwargs.pop('speculative_model_dir', None)
|
|
speculative_config = kwargs.pop("speculative_config", None)
|
|
|
|
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)
|
|
dtype = infer_dtype(dtype, getattr(hf_config, 'torch_dtype', None))
|
|
|
|
if hasattr(hf_config, "medusa"):
|
|
# is modelOpt ckpt
|
|
num_medusa_heads = hf_config.medusa["num_medusa_heads"]
|
|
num_medusa_layers = hf_config.medusa["num_medusa_layers"]
|
|
else:
|
|
config_file = speculative_config_or_dir / "config.json"
|
|
with open(config_file) as fp:
|
|
config = json.load(fp)
|
|
|
|
num_medusa_heads = speculative_config.num_medusa_heads if speculative_config is not None else config.get(
|
|
'num_medusa_heads', None)
|
|
num_medusa_layers = config.get('medusa_num_layers', None)
|
|
|
|
return cls(architecture="MedusaForCausalLM",
|
|
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=hf_config.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=hf_config.hidden_act,
|
|
norm_epsilon=hf_config.rms_norm_eps,
|
|
mapping=mapping,
|
|
quantization=quant_config,
|
|
num_medusa_heads=num_medusa_heads,
|
|
num_medusa_layers=num_medusa_layers,
|
|
**kwargs)
|