# 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
# Medusa-specific config is stored and retrieved from GenericMedusaConfig.
[docs]
class MedusaConfig(PretrainedConfig):
def __init__(self,
*,
num_medusa_heads: int = 4,
num_medusa_layers: int = 1,
max_draft_len: int = 63,
**kwargs):
GenericMedusaConfig = QWenConfig if hasattr(
kwargs,
'model_type') and "qwen" in kwargs['model_type'] else LLaMAConfig
self.config = GenericMedusaConfig(**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
[docs]
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)
[docs]
@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', 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))
config_file = speculative_config_or_dir / "config.json"
with open(config_file) as fp:
config = json.load(fp)
num_medusa_heads = kwargs.pop(
"medusa_num_heads",
None) if "medusa_num_heads" in kwargs 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)