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