configuration_t5mimo.py

from typing import Mapping
from transformers.configuration_utils import PretrainedConfig
from transformers.onnx import OnnxSeq2SeqConfigWithPast
from transformers.utils import logging


logger = logging.get_logger(__name__)


class T5MIMOConfig(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
 [google-t5/t5-small](https://huggingface.co/google-t5/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. The `inner_dim` of the projection layer will
 be defined as `num_heads * d_kv`.
 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.
 classifier_dropout (`float`, *optional*, defaults to 0.0):
 The dropout ratio for classifier.
 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 = "t5mimo"
 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,
 decoder_start_token_id = 0,
 classifier_dropout=0.0,
 num_seqs=3,
 num_filters=64,
 is_mimo=True,
 **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.classifier_dropout = classifier_dropout
 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.num_seqs = num_seqs
 self.num_filters = num_filters
 self.is_mimo = is_mimo

 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,
 decoder_start_token_id=decoder_start_token_id,
 is_encoder_decoder=is_encoder_decoder,
 **kwargs,
 )


class T5MIMOOnnxConfig(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