Source code for transformers.models.t5.configuration_t5

# coding=utf-8
# 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
# Unless required by applicable law or agreed to in writing, software
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""" T5 model configuration """
from collections import OrderedDict
from typing import Any, Dict, Iterable, Mapping, Optional

from transformers import PreTrainedTokenizer, TensorType

from ... import is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging

logger = logging.get_logger(__name__)

    "t5-small": "",
    "t5-base": "",
    "t5-large": "",
    "t5-3b": "",
    "t5-11b": "",

[docs]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 <>`__ 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). """ 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, 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.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 super().__init__( pad_token_id=pad_token_id, eos_token_id=eos_token_id, is_encoder_decoder=is_encoder_decoder, **kwargs, )
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)