# 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 ...mapping import Mapping
from ..convert_utils import infer_dtype
from ..modeling_utils import PretrainedConfig, QuantConfig
[docs]
class FalconConfig(PretrainedConfig):
def __init__(self,
*,
bias: bool = False,
parallel_attention: bool = False,
num_ln_in_parallel_attn: int | None = None,
new_decoder_architecture: bool = False,
rotary_base: float = 10000.0,
**kwargs):
self.bias = bias
self.parallel_attention = parallel_attention
self.num_ln_in_parallel_attn = num_ln_in_parallel_attn
self.new_decoder_architecture = new_decoder_architecture
self.rotary_base = rotary_base
super().__init__(**kwargs)
[docs]
def to_dict(self):
output = super().to_dict()
# Serialize the fields added in LLaMAConfig
output['bias'] = self.bias
output['parallel_attention'] = self.parallel_attention
output['new_decoder_architecture'] = self.new_decoder_architecture
return output
[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)
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)
# Falcon-7B config may not have num_kv_heads or n_head_kv.
# Although Falcon-180B uses GQA (num_kv_heads=8), its config
# has multi_query=True.
if getattr(hf_config, 'multi_query', False) and not getattr(
hf_config, 'new_decoder_architecture', False):
hf_config.num_kv_heads = 1
if hf_config.model_type == 'RefinedWeb':
# Case 1. Falcon-40B / Falcon-40B-instruct
# https://huggingface.co/tiiuae/falcon-40b/blob/main/config.json
hf_config.num_hidden_layers = hf_config.n_layer
hf_config.num_attention_heads = hf_config.n_head
hf_config.num_kv_heads = hf_config.n_head_kv
hf_config.new_decoder_architecture = True
elif hf_config.model_type == 'RefinedWebModel':
# Case 2. Falcon-7B / Falcon-7B-instruct
# https://huggingface.co/tiiuae/falcon-7b/blob/main/config.json
hf_config.num_hidden_layers = hf_config.n_layer
hf_config.num_attention_heads = hf_config.n_head
hf_config.num_kv_heads = 1 if hf_config.multi_query else hf_config.n_head
hf_config.new_decoder_architecture = False
elif hf_config.model_type != 'falcon':
raise ValueError("Shouldn't reach here.")
hf_config.model_type = 'falcon'
dtype = infer_dtype(dtype, getattr(hf_config, 'torch_dtype', None))
return cls(architecture='FalconForCausalLM',
dtype=dtype,
num_hidden_layers=hf_config.num_hidden_layers,
num_attention_heads=hf_config.num_attention_heads,
num_key_value_heads=hf_config.num_kv_heads,
hidden_size=hf_config.hidden_size,
norm_epsilon=hf_config.layer_norm_epsilon,
vocab_size=hf_config.vocab_size,
position_embedding_type='alibi_with_scale'
if hf_config.alibi else 'rope_gpt_neox',
hidden_act='gelu',
bias=hf_config.bias,
parallel_attention=hf_config.parallel_attn,
num_ln_in_parallel_attn=getattr(hf_config,
'num_ln_in_parallel_attn',
None),
new_decoder_architecture=hf_config.new_decoder_architecture,
max_position_embeddings=getattr(hf_config,
'max_position_embeddings',
2048),
rotary_base=getattr(hf_config, 'rope_theta', 10000.0),
intermediate_size=getattr(hf_config, 'ffn_hidden_size',
None),
mapping=mapping,
quantization=quant_config,
**kwargs)