mirror of
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118 lines
4.2 KiB
Python
118 lines
4.2 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import Optional, Union
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import torch
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import transformers
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from ..._utils import torch_dtype_to_str
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from ...mapping import Mapping
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from ..modeling_utils import PretrainedConfig, QuantConfig
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class BERTConfig(PretrainedConfig):
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def __init__(self,
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*,
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is_roberta: bool = False,
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type_vocab_size,
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pad_token_id=None,
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num_labels=None,
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**kwargs):
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self.is_roberta = is_roberta
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self.type_vocab_size = type_vocab_size
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self.pad_token_id = pad_token_id
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self.num_labels = num_labels
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super().__init__(**kwargs)
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def to_dict(self):
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output = super().to_dict()
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output['is_roberta'] = self.is_roberta
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output['type_vocab_size'] = self.type_vocab_size
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output['pad_token_id'] = self.pad_token_id
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output['num_labels'] = self.num_labels
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return output
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@classmethod
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def from_hugging_face(
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cls,
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hf_config_or_dir: Union[str, 'transformers.PretrainedConfig'],
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dtype: str = 'auto',
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mapping: Optional[Mapping] = None,
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quant_config: Optional[QuantConfig] = None,
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**kwargs):
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import transformers
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if isinstance(hf_config_or_dir, transformers.PretrainedConfig):
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hf_config = hf_config_or_dir
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else:
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hf_config_dir = str(hf_config_or_dir)
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hf_config = transformers.AutoConfig.from_pretrained(hf_config_dir)
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num_key_value_heads = getattr(hf_config, "num_key_value_heads",
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hf_config.num_attention_heads)
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head_dim = getattr(
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hf_config, "head_dim",
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hf_config.hidden_size // hf_config.num_attention_heads)
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head_size = getattr(hf_config, "kv_channels", head_dim)
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num_labels = getattr(hf_config, "num_labels", None)
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if (hf_config.position_embedding_type == 'absolute'):
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position_embedding_type = 'learned_absolute'
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else:
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raise NotImplementedError(
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f"{hf_config.position_embedding_type} hasn't been supported")
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if hf_config.model_type == "bert":
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is_roberta = False
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else:
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is_roberta = True
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if dtype == 'auto':
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dtype = getattr(hf_config, 'torch_dtype', None)
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if dtype is None:
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dtype = 'float16'
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if isinstance(dtype, torch.dtype):
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dtype = torch_dtype_to_str(dtype)
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if dtype == 'float32':
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dtype = 'float16'
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return cls(
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architecture=hf_config.architectures[0],
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dtype=dtype,
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hidden_size=hf_config.hidden_size,
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num_hidden_layers=hf_config.num_hidden_layers,
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num_attention_heads=hf_config.num_attention_heads,
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vocab_size=hf_config.vocab_size,
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hidden_act=hf_config.hidden_act,
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logits_dtype='float32',
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norm_epsilon=hf_config.layer_norm_eps,
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position_embedding_type=position_embedding_type,
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max_position_embeddings=hf_config.max_position_embeddings,
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num_key_value_heads=num_key_value_heads,
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intermediate_size=hf_config.intermediate_size,
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head_size=head_size,
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quantization=quant_config,
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mapping=mapping,
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#BERT model args
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is_roberta=is_roberta,
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type_vocab_size=hf_config.type_vocab_size,
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pad_token_id=hf_config.pad_token_id,
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num_labels=num_labels,
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**kwargs)
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