176 lines
7.4 KiB
Python
176 lines
7.4 KiB
Python
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# Copyright (c) Alibaba, Inc. and its affiliates.
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# Copyright 2020, The T5 Authors and HuggingFace Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" T5 model configuration"""
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from typing import Mapping
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from transformers.configuration_utils import PretrainedConfig
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from transformers.onnx import OnnxSeq2SeqConfigWithPast
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from modelscope.utils.logger import get_logger
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logger = get_logger()
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class T5Config(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`T5Model`] or a [`TFT5Model`]. It is used to
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instantiate a T5 model according to the specified arguments, defining the model architecture. Instantiating a
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configuration with the defaults will yield a similar configuration to that of the T5
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[t5-small](https://huggingface.co/t5-small) architecture.
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Arguments:
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vocab_size (`int`, *optional*, defaults to 32128):
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Vocabulary size of the T5 model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`T5Model`] or [`TFT5Model`].
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d_model (`int`, *optional*, defaults to 512):
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Size of the encoder layers and the pooler layer.
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d_kv (`int`, *optional*, defaults to 64):
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Size of the key, query, value projections per attention head. `d_kv` has to be equal to `d_model //
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num_heads`.
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d_ff (`int`, *optional*, defaults to 2048):
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Size of the intermediate feed forward layer in each `T5Block`.
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num_layers (`int`, *optional*, defaults to 6):
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Number of hidden layers in the Transformer encoder.
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num_decoder_layers (`int`, *optional*):
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Number of hidden layers in the Transformer decoder. Will use the same value as `num_layers` if not set.
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num_heads (`int`, *optional*, defaults to 8):
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Number of attention heads for each attention layer in the Transformer encoder.
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relative_attention_num_buckets (`int`, *optional*, defaults to 32):
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The number of buckets to use for each attention layer.
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relative_attention_max_distance (`int`, *optional*, defaults to 128):
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The maximum distance of the longer sequences for the bucket separation.
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dropout_rate (`float`, *optional*, defaults to 0.1):
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The ratio for all dropout layers.
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layer_norm_eps (`float`, *optional*, defaults to 1e-6):
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The epsilon used by the layer normalization layers.
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initializer_factor (`float`, *optional*, defaults to 1):
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A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
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testing).
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feed_forward_proj (`string`, *optional*, defaults to `"relu"`):
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Type of feed forward layer to be used. Should be one of `"relu"` or `"gated-gelu"`. T5v1.1 uses the
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`"gated-gelu"` feed forward projection. Original T5 uses `"relu"`.
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use_cache (`bool`, *optional*, defaults to `True`):
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Whether or not the model should return the last key/values attentions (not used by all models).
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"""
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model_type = 't5'
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keys_to_ignore_at_inference = ['past_key_values']
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attribute_map = {
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'hidden_size': 'd_model',
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'num_attention_heads': 'num_heads',
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'num_hidden_layers': 'num_layers'
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}
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def __init__(self,
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vocab_size=32128,
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d_model=512,
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d_kv=64,
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d_ff=2048,
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num_layers=6,
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num_decoder_layers=None,
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num_heads=8,
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relative_attention_num_buckets=32,
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relative_attention_max_distance=128,
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dropout_rate=0.1,
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layer_norm_epsilon=1e-6,
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initializer_factor=1.0,
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feed_forward_proj='relu',
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is_encoder_decoder=True,
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use_cache=True,
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pad_token_id=0,
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eos_token_id=1,
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**kwargs):
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self.vocab_size = vocab_size
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self.d_model = d_model
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self.d_kv = d_kv
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self.d_ff = d_ff
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self.num_layers = num_layers
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self.num_decoder_layers = (num_decoder_layers if num_decoder_layers
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is not None else self.num_layers
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) # default = symmetry
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self.num_heads = num_heads
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self.relative_attention_num_buckets = relative_attention_num_buckets
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self.relative_attention_max_distance = relative_attention_max_distance
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self.dropout_rate = dropout_rate
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self.layer_norm_epsilon = layer_norm_epsilon
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self.initializer_factor = initializer_factor
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self.feed_forward_proj = feed_forward_proj
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self.use_cache = use_cache
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act_info = self.feed_forward_proj.split('-')
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self.dense_act_fn = act_info[-1]
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self.is_gated_act = act_info[0] == 'gated'
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if len(act_info) > 1 and act_info[0] != 'gated' or len(act_info) > 2:
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raise ValueError(
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f'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
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'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
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"'gated-gelu' or 'relu'")
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# for backwards compatibility
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if feed_forward_proj == 'gated-gelu':
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self.dense_act_fn = 'gelu_new'
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super().__init__(
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pad_token_id=pad_token_id,
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eos_token_id=eos_token_id,
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is_encoder_decoder=is_encoder_decoder,
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**kwargs,
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)
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class T5OnnxConfig(OnnxSeq2SeqConfigWithPast):
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@property
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def inputs(self) -> Mapping[str, Mapping[int, str]]:
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common_inputs = {
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'input_ids': {
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0: 'batch',
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1: 'encoder_sequence'
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},
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'attention_mask': {
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0: 'batch',
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1: 'encoder_sequence'
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},
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}
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if self.use_past:
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common_inputs['attention_mask'][
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1] = 'past_encoder_sequence + sequence'
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common_inputs['decoder_input_ids'] = {0: 'batch'}
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common_inputs['decoder_attention_mask'] = {
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0: 'batch',
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1: 'past_decoder_sequence + sequence'
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}
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else:
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common_inputs['decoder_input_ids'] = {
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0: 'batch',
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1: 'decoder_sequence'
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}
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common_inputs['decoder_attention_mask'] = {
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0: 'batch',
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1: 'decoder_sequence'
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}
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if self.use_past:
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self.fill_with_past_key_values_(common_inputs, direction='inputs')
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return common_inputs
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@property
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def default_onnx_opset(self) -> int:
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return 13
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