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# Copyright 2024 Nvidia Corporation. All rights reserved.
#
# 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 dataclasses
import warnings
from typing import Dict, Any
from transformers.utils import is_flash_attn_2_available
from .block_config import BlockConfig
from .transformers_4_44_2__configuration_llama import LlamaConfig
from .transformers_4_44_2__modeling_rope_utils import \
rope_config_validation # fake import to make AutoConfig infer the dependency
rope_config_validation # this line is here to make sure that auto-formatting doesn't remove the import
class DeciLMConfig(LlamaConfig):
model_type = "nemotron-nas"
def __init__(
self,
block_configs: list[dict] | list[BlockConfig] = None,
**kwargs,
):
attn_implementation = kwargs.pop("attn_implementation", None)
if attn_implementation is None and is_flash_attn_2_available():
attn_implementation = "flash_attention_2"
if block_configs is not None:
if isinstance(block_configs[0], dict):
block_configs = [BlockConfig(**conf) for conf in block_configs]
using_unshifted_sink = any([block_config.attention.unshifted_sink for block_config in block_configs])
if using_unshifted_sink and attn_implementation != "eager":
warnings.warn("Forcing attn_implementation='eager' since some attention layers use unshifted sink")
attn_implementation = "eager"
super().__init__(attn_implementation=attn_implementation, **kwargs)
self.intermediate_size = None
self.num_key_value_heads = None
if block_configs is not None:
assert len(block_configs) == self.num_hidden_layers
self.block_configs: list[BlockConfig] = block_configs
def to_dict(self) -> Dict[str, Any]:
self_dict = super().to_dict()
if self.block_configs is not None:
self_dict["block_configs"] = [dataclasses.asdict(conf) for conf in self.block_configs]
return self_dict
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