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# coding=utf-8
# Copyright 2021 The IDEA Authors. 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.
""" T5 model configuration """
from collections import OrderedDict
from typing import Any, Dict, Iterable, Mapping, Optional
from transformers import PreTrainedTokenizer, TensorType
from transformers import is_torch_available
from transformers.configuration_utils import PretrainedConfig
from transformers.onnx import OnnxConfigWithPast
from transformers.utils import logging
logger = logging.get_logger(__name__)
T5_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"T5-small": "https://huggingface.co/T5-small/resolve/main/config.json",
"T5-base": "https://huggingface.co/T5-base/resolve/main/config.json",
"T5-large": "https://huggingface.co/T5-large/resolve/main/config.json",
"T5-3b": "https://huggingface.co/T5-3b/resolve/main/config.json",
"T5-11b": "https://huggingface.co/T5-11b/resolve/main/config.json",
}
class T5Config(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a :class:`~transformers.T5Model` or a
:class:`~transformers.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 :class:`~transformers.PretrainedConfig` and can be used to control the model
outputs. Read the documentation from :class:`~transformers.PretrainedConfig` for more information.
Arguments:
vocab_size (:obj:`int`, `optional`, defaults to 32128):
Vocabulary size of the T5 model. Defines the number of different tokens that can be represented by the
:obj:`inputs_ids` passed when calling :class:`~transformers.T5Model` or :class:`~transformers.TFT5Model`.
d_model (:obj:`int`, `optional`, defaults to 512):
Size of the encoder layers and the pooler layer.
d_kv (:obj:`int`, `optional`, defaults to 64):
Size of the key, query, value projections per attention head. :obj:`d_kv` has to be equal to :obj:`d_model
// num_heads`.
d_ff (:obj:`int`, `optional`, defaults to 2048):
Size of the intermediate feed forward layer in each :obj:`T5Block`.
num_layers (:obj:`int`, `optional`, defaults to 6):
Number of hidden layers in the Transformer encoder.
num_decoder_layers (:obj:`int`, `optional`):
Number of hidden layers in the Transformer decoder. Will use the same value as :obj:`num_layers` if not
set.
num_heads (:obj:`int`, `optional`, defaults to 8):
Number of attention heads for each attention layer in the Transformer encoder.
relative_attention_num_buckets (:obj:`int`, `optional`, defaults to 32):
The number of buckets to use for each attention layer.
dropout_rate (:obj:`float`, `optional`, defaults to 0.1):
The ratio for all dropout layers.
layer_norm_eps (:obj:`float`, `optional`, defaults to 1e-6):
The epsilon used by the layer normalization layers.
initializer_factor (:obj:`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 (:obj:`string`, `optional`, defaults to :obj:`"relu"`):
Type of feed forward layer to be used. Should be one of :obj:`"relu"` or :obj:`"gated-gelu"`. T5v1.1 uses
the :obj:`"gated-gelu"` feed forward projection. Original T5 uses :obj:`"relu"`.
use_cache (:obj:`bool`, `optional`, defaults to :obj:`True`):
Whether or not the model should return the last key/values attentions (not used by all models).
gradient_checkpointing (:obj:`bool`, `optional`, defaults to :obj:`False`):
If True, use gradient checkpointing to save memory at the expense of slower backward pass.
"""
model_type = "T5"
keys_to_ignore_at_inference = ["past_key_values"]
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,
dropout_rate=0.1,
layer_norm_epsilon=1e-5,
initializer_factor=1.0,
feed_forward_proj="gelu",
is_encoder_decoder=True,
use_cache=True,
pad_token_id=0,
eos_token_id=1,
gradient_checkpointing=False,
**kwargs
):
super().__init__(
pad_token_id=pad_token_id,
eos_token_id=eos_token_id,
is_encoder_decoder=is_encoder_decoder,
**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.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
self.gradient_checkpointing = gradient_checkpointing
@property
def hidden_size(self):
return self.d_model
@property
def num_attention_heads(self):
return self.num_heads
@property
def num_hidden_layers(self):
return self.num_layers
class T5OnnxConfig(OnnxConfigWithPast):
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
common_inputs = OrderedDict(
[
("input_ids", {0: "batch", 1: "encoder_sequence"}),
("attention_mask", {0: "batch", 1: "encoder_sequence"}),
("decoder_input_ids", {0: "batch"}),
("decoder_attention_mask", {0: "batch"}),
]
)
if self.use_past:
for i in range(0, self._config.num_layers):
common_inputs[f"past_key_values.{i}.decoder.key"] = {
0: "batch", 2: "past_sequence"}
common_inputs[f"past_key_values.{i}.decoder.value"] = {
0: "batch", 2: "past_sequence"}
common_inputs[f"past_key_values.{i}.encoder.key"] = {
0: "batch", 2: "past_sequence"}
common_inputs[f"past_key_values.{i}.encoder.value"] = {
0: "batch", 2: "past_sequence"}
return common_inputs
@property
def outputs(self) -> Mapping[str, Mapping[int, str]]:
common_outputs = super().outputs
if "last_hidden_state" in common_outputs:
common_outputs["last_hidden_state"] = {
0: "batch", 1: "decoder_sequence"}
if self.use_past:
for i in range(self._config.num_layers):
common_outputs[f"present.{i}.decoder.key"] = {
0: "batch", 2: "decoder_sequence"}
common_outputs[f"present.{i}.decoder.value"] = {
0: "batch", 2: "decoder_sequence"}
common_outputs[f"present.{i}.encoder.key"] = {
0: "batch", 2: "encoder_sequence"}
common_outputs[f"present.{i}.encoder.value"] = {
0: "batch", 2: "encoder_sequence"}
if self.task == "default":
common_outputs["encoder_last_hidden_state"] = {
0: "batch", 2: "encoder_sequence"}
return common_outputs
def generate_dummy_inputs(
self,
tokenizer: PreTrainedTokenizer,
batch_size: int = -1,
seq_length: int = -1,
is_pair: bool = False,
framework: Optional[TensorType] = None,
) -> Mapping[str, Any]:
# Generate encoder inputs
encoder_inputs = super().generate_dummy_inputs(
tokenizer, batch_size, seq_length, is_pair, framework)
# Generate decoder inputs
decoder_inputs = super().generate_dummy_inputs(
tokenizer, batch_size, 1, is_pair, framework)
decoder_inputs = {f"decoder_{name}": tensor for name,
tensor in decoder_inputs.items()}
ordered_inputs = dict(**encoder_inputs, **decoder_inputs)
if self.use_past:
if not is_torch_available():
raise ValueError(
"Cannot generate dummy past_keys inputs without PyTorch installed.")
else:
import torch
batch = encoder_inputs["input_ids"].shape[0]
encoder_seq_length = encoder_inputs["input_ids"].shape[1]
encoder_shape = (
batch,
self._config.num_heads,
encoder_seq_length,
self._config.hidden_size // self._config.num_heads,
)
decoder_shape = (batch, self._config.num_heads, 1,
self._config.hidden_size // self._config.num_heads)
ordered_inputs["past_key_values"] = []
for _ in range(self._config.num_layers):
ordered_inputs["past_key_values"].append(
(
torch.zeros(decoder_shape),
torch.zeros(decoder_shape),
torch.zeros(encoder_shape),
torch.zeros(encoder_shape),
)
)
return ordered_inputs
@staticmethod
def flatten_output_collection_property(name: str, field: Iterable[Any]) -> Dict[str, Any]:
if name in ["present", "past_key_values"]:
flatten_output = {}
for idx, t in enumerate(field):
flatten_output[f"{name}.{idx}.decoder.key"] = t[0]
flatten_output[f"{name}.{idx}.decoder.value"] = t[1]
flatten_output[f"{name}.{idx}.encoder.key"] = t[2]
flatten_output[f"{name}.{idx}.encoder.value"] = t[3]
return flatten_output
return super().flatten_output_collection_property(name, field)
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