# 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