fengshen/models/megatron_t5/configuration_megatron_t5.py

# coding=utf-8
# Copyright 2021 The IDEA Authors. All rights reserved.

# 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,
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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 transformers import is_torch_available
from transformers.configuration_utils import PretrainedConfig
from transformers.onnx import OnnxConfigWithPast
from transformers.utils import logging


logger = logging.get_logger(__name__)

T5_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "T5-small": "https://huggingface.co./T5-small/resolve/main/config.json",
    "T5-base": "https://huggingface.co./T5-base/resolve/main/config.json",
    "T5-large": "https://huggingface.co./T5-large/resolve/main/config.json",
    "T5-3b": "https://huggingface.co./T5-3b/resolve/main/config.json",
    "T5-11b": "https://huggingface.co./T5-11b/resolve/main/config.json",
}


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 <https://huggingface.co./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).
        gradient_checkpointing (:obj:`bool`, `optional`, defaults to :obj:`False`):
            If True, use gradient checkpointing to save memory at the expense of slower backward pass.
    """
    model_type = "T5"
    keys_to_ignore_at_inference = ["past_key_values"]

    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-5,
        initializer_factor=1.0,
        feed_forward_proj="gelu",
        is_encoder_decoder=True,
        use_cache=True,
        pad_token_id=0,
        eos_token_id=1,
        gradient_checkpointing=False,
        **kwargs
    ):
        super().__init__(
            pad_token_id=pad_token_id,
            eos_token_id=eos_token_id,
            is_encoder_decoder=is_encoder_decoder,
            **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
        self.gradient_checkpointing = gradient_checkpointing

    @property
    def hidden_size(self):
        return self.d_model

    @property
    def num_attention_heads(self):
        return self.num_heads

    @property
    def num_hidden_layers(self):
        return self.num_layers


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)