Witllm/prompt_clue/configuration.py

176 lines
7.4 KiB
Python

# Copyright (c) Alibaba, Inc. and its affiliates.
# Copyright 2020, The T5 Authors and HuggingFace Inc.
#
# 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.
""" T5 model configuration"""
from typing import Mapping
from transformers.configuration_utils import PretrainedConfig
from transformers.onnx import OnnxSeq2SeqConfigWithPast
from modelscope.utils.logger import get_logger
logger = get_logger()
class T5Config(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`T5Model`] or a [`TFT5Model`]. It is used to
instantiate a T5 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 T5
[t5-small](https://huggingface.co/t5-small) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Arguments:
vocab_size (`int`, *optional*, defaults to 32128):
Vocabulary size of the T5 model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`T5Model`] or [`TFT5Model`].
d_model (`int`, *optional*, defaults to 512):
Size of the encoder layers and the pooler layer.
d_kv (`int`, *optional*, defaults to 64):
Size of the key, query, value projections per attention head. `d_kv` has to be equal to `d_model //
num_heads`.
d_ff (`int`, *optional*, defaults to 2048):
Size of the intermediate feed forward layer in each `T5Block`.
num_layers (`int`, *optional*, defaults to 6):
Number of hidden layers in the Transformer encoder.
num_decoder_layers (`int`, *optional*):
Number of hidden layers in the Transformer decoder. Will use the same value as `num_layers` if not set.
num_heads (`int`, *optional*, defaults to 8):
Number of attention heads for each attention layer in the Transformer encoder.
relative_attention_num_buckets (`int`, *optional*, defaults to 32):
The number of buckets to use for each attention layer.
relative_attention_max_distance (`int`, *optional*, defaults to 128):
The maximum distance of the longer sequences for the bucket separation.
dropout_rate (`float`, *optional*, defaults to 0.1):
The ratio for all dropout layers.
layer_norm_eps (`float`, *optional*, defaults to 1e-6):
The epsilon used by the layer normalization layers.
initializer_factor (`float`, *optional*, defaults to 1):
A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
testing).
feed_forward_proj (`string`, *optional*, defaults to `"relu"`):
Type of feed forward layer to be used. Should be one of `"relu"` or `"gated-gelu"`. T5v1.1 uses the
`"gated-gelu"` feed forward projection. Original T5 uses `"relu"`.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models).
"""
model_type = 't5'
keys_to_ignore_at_inference = ['past_key_values']
attribute_map = {
'hidden_size': 'd_model',
'num_attention_heads': 'num_heads',
'num_hidden_layers': 'num_layers'
}
def __init__(self,
vocab_size=32128,
d_model=512,
d_kv=64,
d_ff=2048,
num_layers=6,
num_decoder_layers=None,
num_heads=8,
relative_attention_num_buckets=32,
relative_attention_max_distance=128,
dropout_rate=0.1,
layer_norm_epsilon=1e-6,
initializer_factor=1.0,
feed_forward_proj='relu',
is_encoder_decoder=True,
use_cache=True,
pad_token_id=0,
eos_token_id=1,
**kwargs):
self.vocab_size = vocab_size
self.d_model = d_model
self.d_kv = d_kv
self.d_ff = d_ff
self.num_layers = num_layers
self.num_decoder_layers = (num_decoder_layers if num_decoder_layers
is not None else self.num_layers
) # default = symmetry
self.num_heads = num_heads
self.relative_attention_num_buckets = relative_attention_num_buckets
self.relative_attention_max_distance = relative_attention_max_distance
self.dropout_rate = dropout_rate
self.layer_norm_epsilon = layer_norm_epsilon
self.initializer_factor = initializer_factor
self.feed_forward_proj = feed_forward_proj
self.use_cache = use_cache
act_info = self.feed_forward_proj.split('-')
self.dense_act_fn = act_info[-1]
self.is_gated_act = act_info[0] == 'gated'
if len(act_info) > 1 and act_info[0] != 'gated' or len(act_info) > 2:
raise ValueError(
f'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
"'gated-gelu' or 'relu'")
# for backwards compatibility
if feed_forward_proj == 'gated-gelu':
self.dense_act_fn = 'gelu_new'
super().__init__(
pad_token_id=pad_token_id,
eos_token_id=eos_token_id,
is_encoder_decoder=is_encoder_decoder,
**kwargs,
)
class T5OnnxConfig(OnnxSeq2SeqConfigWithPast):
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
common_inputs = {
'input_ids': {
0: 'batch',
1: 'encoder_sequence'
},
'attention_mask': {
0: 'batch',
1: 'encoder_sequence'
},
}
if self.use_past:
common_inputs['attention_mask'][
1] = 'past_encoder_sequence + sequence'
common_inputs['decoder_input_ids'] = {0: 'batch'}
common_inputs['decoder_attention_mask'] = {
0: 'batch',
1: 'past_decoder_sequence + sequence'
}
else:
common_inputs['decoder_input_ids'] = {
0: 'batch',
1: 'decoder_sequence'
}
common_inputs['decoder_attention_mask'] = {
0: 'batch',
1: 'decoder_sequence'
}
if self.use_past:
self.fill_with_past_key_values_(common_inputs, direction='inputs')
return common_inputs
@property
def default_onnx_opset(self) -> int:
return 13