AutoAndroidController/py/venv/Lib/site-packages/modelscope/models/nlp/T5/configuration.py

# 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.
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#
#     http://www.apache.org/licenses/LICENSE-2.0
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# distributed under the License is distributed on an "AS IS" BASIS,
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""" 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