243 lines
12 KiB
Python
243 lines
12 KiB
Python
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# coding=utf-8
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# Copyright 2023 The Mega Authors and The HuggingFace Inc. team.
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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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""" MEGA configuration"""
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from collections import OrderedDict
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from typing import Mapping
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from ...configuration_utils import PretrainedConfig
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from ...onnx import OnnxConfig
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from ...utils import logging
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logger = logging.get_logger(__name__)
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from ..deprecated._archive_maps import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402
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class MegaConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`MegaModel`]. It is used to instantiate a Mega
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model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
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defaults will yield a similar configuration to that of the Mega
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[mnaylor/mega-base-wikitext](https://huggingface.co/mnaylor/mega-base-wikitext) architecture.
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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vocab_size (`int`, *optional*, defaults to 30522):
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Vocabulary size of the Mega model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`MegaModel`].
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hidden_size (`int`, *optional*, defaults to 128):
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Dimensionality of the encoder layers and the pooler layer.
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num_hidden_layers (`int`, *optional*, defaults to 4):
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Number of hidden layers in the Mega encoder.
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intermediate_size (`int`, *optional*, defaults to 256):
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Dimensionality of the hidden size (self-attention value projection) within the Mega encoder
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ema_projection_size (`int`, *optional*, defaults to 16):
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Dimensionality of the MegaMultiDimensionDampedEma
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bidirectional (`bool`, *optional*, defaults to `True`):
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Whether the MegaMultiDimensionDampedEma used in Mega's self-attention should work bidirectionally (`True`)
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or unidirectionally (`False`). Bidirectional EMA is incompatible with causal decoding, so this should be
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False if you intend to use the model as a decoder.
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shared_representation_size (`int`, *optional*, defaults to 64):
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Dimensionality of the linear projection for shared representation of self-attention queries and keys
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use_chunking (`bool`, *optional*, defaults to `False`):
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Whether to chunk inputs for linear self-attention complexity (described as Mega-chunk in the paper)
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chunk_size (`int`, *optional*, defaults to -1):
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If `use_chunking` is set to `True`, determines the size of the chunks to apply to the input sequence. If
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chunking is used, input sequences must be padded to a multiple of `chunk_size`
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truncation (`int`, *optional*):
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If specified, the sequence length for which to truncate MegaMultiDimensionDampedEma
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normalize_before_mega (`bool`, *optional*, defaults to `True`):
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Whether to normalize before (`True`) or after (`False`) passing through Mega encoder blocks
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normalization_type (`str`, *optional*, defaults to `"scalenorm"`):
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Type of normalization to use in Mega encoder blocks. Choose one of `"scalenorm"`, `"layernorm"`,
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`"rmsnorm"`, `"batchnorm"`, or `"syncbatchnorm"` (GPU required for syncbatchnorm)
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norm_affine (`bool`, *optional*, defaults to `True`):
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If `True`, applies a parameterized affine transformation to inputs during normalization
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activation (`str`, *optional*, defaults to `"silu"`):
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Activation function to apply within Mega encoder blocks. Choose one of `"silu"`, `"relu"`, `"linear"`,
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`"gelu"`, or `"gelu_accurate"`
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attention_activation (`str`, *optional*, defaults to `"softmax"`):
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Activation function to apply for single-headed self-attention (a la Transformer). Choose one of
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`"softmax"`, `"laplace"`, or `"relu2"`
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dropout_prob (`float`, *optional*, defaults to 0.1):
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The dropout probability for EMA self-attention
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hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
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The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
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attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
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The dropout ratio for the attention probabilities.
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use_feature_dropout (`bool`, *optional*, defaults to `False`):
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Whether to use feature-based (`True`) or standard dropout (`False`)
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use_normalized_ffn (`bool`, *optional*, defaults to `True`):
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Whether to use the normalized feed-forward sub-layer in Mega blocks (`True`) or pass Mega encoder output
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as-is (`False`)
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nffn_hidden_size (`int`, *optional*, defaults to 256):
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If using the normalized feed-forward network (NFFN) layer within Mega (`use_normalized_ffn = True`), this
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is the hidden size of the NFFN
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normalize_before_ffn (`bool`, *optional*, defaults to `True`):
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Whether to normalize before (`True`) or after (`False`) the feed-forward portion of NFFN
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nffn_activation_dropout_prob (`float`, *optional*, defaults to 0.1):
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The dropout ratio for the NFFN component.
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max_positions (`int`, *optional*, defaults to 2048):
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The maximum sequence length to use for positional representations. For `"simple"` relative positional bias,
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this is a hard limit on input length; `"rotary"` relative positional bias will extrapolate to longer
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sequences
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add_token_type_embeddings (`bool`, *optional*, defaults to `True`):
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Whether to account for token types in embeddings. Left as optional to maintain compatibility with original
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implementation while adding support for token types.
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type_vocab_size (`int`, *optional*, defaults to 2):
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The vocabulary size of the `token_type_ids` passed when calling [`MegaModel`]. Only used if
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`add_token_type_embeddings = True`
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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ema_delta_alpha_range (`float`, *optional*, defaults to 0.2):
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The standard deviation for initializing the delta (damping factor) and alpha (decay factor) parameters in
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MegaMultiDimensionDampedEma.
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ema_beta_range (`float`, *optional*, defaults to 0.02):
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The standard deviation for initializing the beta parameter (expansion matrix) in
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MegaMultiDimensionDampedEma.
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ema_gamma_omega_range (`float`, *optional*, defaults to 1.0):
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The standard deviation for initializing the gamma (projection matrix) and omega (residual weight)
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parameters in MultiDimensionEMA.
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relative_positional_bias (`str`, *optional*, defaults to `"rotary"`):
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Type of relative positional encoding. Choose one of `"rotary"` or `"simple"`. If `"simple"` is selected,
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`max_positions` is used as a limit on input size, while `"rotary"` extrapolates beyond `max_positions`.
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is_decoder (`bool`, *optional*, defaults to `False`):
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Whether the model is used as a decoder or not. If `False`, the model is used as an encoder.
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use_cache (`bool`, *optional*, defaults to `True`):
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Whether or not the model should return the last key/values attentions (not used by all models). Only
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relevant if `config.is_decoder=True`.
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classifier_dropout (`float`, *optional*):
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The dropout ratio for the classification head.
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add_lm_hidden_dense_layer (`bool`, *optional*, defaults to `True`):
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Whether to include a hidden layer for projection between encoder outputs and LM heads (`True`) or pass
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hidden states directly to LM head (`False`). Remains optional for compatibility with original
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implementation
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Examples:
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```python
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>>> from transformers import MegaConfig, MegaModel
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>>> # Initializing a Mega configuration
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>>> configuration = MegaConfig()
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>>> # Initializing a model (with random weights) from the configuration
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>>> model = MegaModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "mega"
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def __init__(
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self,
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vocab_size=30522,
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hidden_size=128,
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num_hidden_layers=4,
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intermediate_size=256,
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ema_projection_size=16,
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bidirectional=True,
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shared_representation_size=64,
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use_chunking=False,
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chunk_size=-1,
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truncation=None,
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normalize_before_mega=True,
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normalization_type="scalenorm",
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norm_affine=True,
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activation="silu",
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attention_activation="softmax",
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dropout_prob=0.1,
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hidden_dropout_prob=0.1,
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attention_probs_dropout_prob=0.1,
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use_feature_dropout=False,
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use_normalized_ffn=True,
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nffn_hidden_size=256,
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normalize_before_ffn=True,
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nffn_activation_dropout_prob=0.1,
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max_positions=2048,
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add_token_type_embeddings=False,
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type_vocab_size=2,
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initializer_range=0.02,
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ema_delta_alpha_range=0.2,
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ema_beta_range=0.02,
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ema_gamma_omega_range=1.0,
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pad_token_id=1,
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bos_token_id=0,
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eos_token_id=2,
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relative_positional_bias="rotary",
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classifier_dropout=None,
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use_cache=True,
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add_lm_hidden_dense_layer=True,
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**kwargs,
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):
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super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.activation = activation
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self.attention_activation = attention_activation
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self.intermediate_size = intermediate_size
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self.ema_projection_size = ema_projection_size
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self.bidirectional = bidirectional
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self.shared_representation_size = shared_representation_size
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self.use_chunking = use_chunking
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self.chunk_size = chunk_size
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self.truncation = truncation
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self.normalize_before_mega = normalize_before_mega
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self.normalization_type = normalization_type
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self.norm_affine = norm_affine
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self.dropout_prob = dropout_prob
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self.hidden_dropout_prob = hidden_dropout_prob
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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self.use_feature_dropout = use_feature_dropout
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self.use_normalized_ffn = use_normalized_ffn
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self.nffn_hidden_size = nffn_hidden_size
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self.normalize_before_ffn = normalize_before_ffn
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self.nffn_activation_dropout_prob = nffn_activation_dropout_prob
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self.max_positions = max_positions
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self.add_token_type_embeddings = add_token_type_embeddings
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self.type_vocab_size = type_vocab_size
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self.initializer_range = initializer_range
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self.ema_delta_alpha_range = ema_delta_alpha_range
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self.ema_beta_range = ema_beta_range
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self.ema_gamma_omega_range = ema_gamma_omega_range
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self.relative_positional_bias = relative_positional_bias
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self.use_cache = use_cache
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self.classifier_dropout = classifier_dropout
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self.add_lm_hidden_dense_layer = add_lm_hidden_dense_layer
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self.num_attention_heads = 1 # not used but required by Hugging Face
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class MegaOnnxConfig(OnnxConfig):
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@property
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def inputs(self) -> Mapping[str, Mapping[int, str]]:
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if self.task == "multiple-choice":
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dynamic_axis = {0: "batch", 1: "choice", 2: "sequence"}
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else:
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dynamic_axis = {0: "batch", 1: "sequence"}
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return OrderedDict(
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[
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("input_ids", dynamic_axis),
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("attention_mask", dynamic_axis),
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]
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)
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