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bigbird_pegasus

mindnlp.transformers.models.bigbird_pegasus.configuration_bigbird_pegasus.BigBirdPegasusConfig

Bases: PretrainedConfig

This is the configuration class to store the configuration of a [BigBirdPegasusModel]. It is used to instantiate an BigBirdPegasus 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 BigBirdPegasus google/bigbird-pegasus-large-arxiv architecture.

Configuration objects inherit from [PretrainedConfig] and can be used to control the model outputs. Read the documentation from [PretrainedConfig] for more information.

PARAMETER DESCRIPTION
vocab_size

Vocabulary size of the BigBirdPegasus model. Defines the number of different tokens that can be represented by the inputs_ids passed when calling [BigBirdPegasusModel].

TYPE: `int`, *optional*, defaults to 96103 DEFAULT: 96103

d_model

Dimension of the layers and the pooler layer.

TYPE: `int`, *optional*, defaults to 1024 DEFAULT: 1024

encoder_layers

Number of encoder layers.

TYPE: `int`, *optional*, defaults to 16 DEFAULT: 16

decoder_layers

Number of decoder layers.

TYPE: `int`, *optional*, defaults to 16 DEFAULT: 16

encoder_attention_heads

Number of attention heads for each attention layer in the Transformer encoder.

TYPE: `int`, *optional*, defaults to 16 DEFAULT: 16

decoder_attention_heads

Number of attention heads for each attention layer in the Transformer decoder.

TYPE: `int`, *optional*, defaults to 16 DEFAULT: 16

decoder_ffn_dim

Dimension of the "intermediate" (often named feed-forward) layer in decoder.

TYPE: `int`, *optional*, defaults to 4096 DEFAULT: 4096

encoder_ffn_dim

Dimension of the "intermediate" (often named feed-forward) layer in decoder.

TYPE: `int`, *optional*, defaults to 4096 DEFAULT: 4096

activation_function

The non-linear activation function (function or string) in the encoder and pooler. If string, "gelu", "relu", "silu" and "gelu_new" are supported.

TYPE: `str` or `function`, *optional*, defaults to `"gelu_new"` DEFAULT: 'gelu_new'

dropout

The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.

TYPE: `float`, *optional*, defaults to 0.1 DEFAULT: 0.1

attention_dropout

The dropout ratio for the attention probabilities.

TYPE: `float`, *optional*, defaults to 0.0 DEFAULT: 0.0

activation_dropout

The dropout ratio for activations inside the fully connected layer.

TYPE: `float`, *optional*, defaults to 0.0 DEFAULT: 0.0

classifier_dropout

The dropout ratio for classifier.

TYPE: `float`, *optional*, defaults to 0.0 DEFAULT: 0.0

max_position_embeddings

The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 1024 or 2048 or 4096).

TYPE: `int`, *optional*, defaults to 4096 DEFAULT: 4096

init_std

The standard deviation of the truncated_normal_initializer for initializing all weight matrices.

TYPE: `float`, *optional*, defaults to 0.02 DEFAULT: 0.02

encoder_layerdrop

The LayerDrop probability for the encoder. See the LayerDrop paper for more details.

TYPE: `float`, *optional*, defaults to 0.0 DEFAULT: 0.0

decoder_layerdrop

The LayerDrop probability for the decoder. See the LayerDrop paper for more details.

TYPE: `float`, *optional*, defaults to 0.0 DEFAULT: 0.0

use_cache

Whether or not the model should return the last key/values attentions (not used by all models).

TYPE: `bool`, *optional*, defaults to `True` DEFAULT: True

Example 
>>> from transformers import BigBirdPegasusConfig, BigBirdPegasusModel
...
>>> # Initializing a BigBirdPegasus bigbird-pegasus-base style configuration
>>> configuration = BigBirdPegasusConfig()
...
>>> # Initializing a model (with random weights) from the bigbird-pegasus-base style configuration
>>> model = BigBirdPegasusModel(configuration)
...
>>> # Accessing the model configuration
>>> configuration = model.config
Source code in mindnlp\transformers\models\bigbird_pegasus\configuration_bigbird_pegasus.py 
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class BigBirdPegasusConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`BigBirdPegasusModel`]. It is used to instantiate
    an BigBirdPegasus 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 BigBirdPegasus
    [google/bigbird-pegasus-large-arxiv](https://hf-mirror.com/google/bigbird-pegasus-large-arxiv) architecture.

    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PretrainedConfig`] for more information.


    Args:
        vocab_size (`int`, *optional*, defaults to 96103):
            Vocabulary size of the BigBirdPegasus model. Defines the number of different tokens that can be represented
            by the `inputs_ids` passed when calling [`BigBirdPegasusModel`].
        d_model (`int`, *optional*, defaults to 1024):
            Dimension of the layers and the pooler layer.
        encoder_layers (`int`, *optional*, defaults to 16):
            Number of encoder layers.
        decoder_layers (`int`, *optional*, defaults to 16):
            Number of decoder layers.
        encoder_attention_heads (`int`, *optional*, defaults to 16):
            Number of attention heads for each attention layer in the Transformer encoder.
        decoder_attention_heads (`int`, *optional*, defaults to 16):
            Number of attention heads for each attention layer in the Transformer decoder.
        decoder_ffn_dim (`int`, *optional*, defaults to 4096):
            Dimension of the "intermediate" (often named feed-forward) layer in decoder.
        encoder_ffn_dim (`int`, *optional*, defaults to 4096):
            Dimension of the "intermediate" (often named feed-forward) layer in decoder.
        activation_function (`str` or `function`, *optional*, defaults to `"gelu_new"`):
            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
            `"relu"`, `"silu"` and `"gelu_new"` are supported.
        dropout (`float`, *optional*, defaults to 0.1):
            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
        attention_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.
        activation_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for activations inside the fully connected layer.
        classifier_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for classifier.
        max_position_embeddings (`int`, *optional*, defaults to 4096):
            The maximum sequence length that this model might ever be used with. Typically set this to something large
            just in case (e.g., 1024 or 2048 or 4096).
        init_std (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        encoder_layerdrop (`float`, *optional*, defaults to 0.0):
            The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
            for more details.
        decoder_layerdrop (`float`, *optional*, defaults to 0.0):
            The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
            for more details.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models).
        attention_type (`str`, *optional*, defaults to `"block_sparse"`)
            Whether to use block sparse attention (with n complexity) as introduced in paper or original attention
            layer (with n^2 complexity) in encoder. Possible values are `"original_full"` and `"block_sparse"`.
        use_bias (`bool`, *optional*, defaults to `False`)
            Whether to use bias in query, key, value.
        block_size (`int`, *optional*, defaults to 64)
            Size of each block. Useful only when `attention_type == "block_sparse"`.
        num_random_blocks (`int`, *optional*, defaults to 3)
            Each query is going to attend these many number of random blocks. Useful only when `attention_type ==
            "block_sparse"`.
        scale_embeddings (`bool`, *optional*, defaults to `True`)
            Whether to rescale embeddings with (hidden_size ** 0.5).

    Example:
        ```python
        >>> from transformers import BigBirdPegasusConfig, BigBirdPegasusModel
        ...
        >>> # Initializing a BigBirdPegasus bigbird-pegasus-base style configuration
        >>> configuration = BigBirdPegasusConfig()
        ...
        >>> # Initializing a model (with random weights) from the bigbird-pegasus-base style configuration
        >>> model = BigBirdPegasusModel(configuration)
        ...
        >>> # Accessing the model configuration
        >>> configuration = model.config
        ```
    """
    model_type = "bigbird_pegasus"
    keys_to_ignore_at_inference = ["past_key_values"]
    attribute_map = {
        "num_attention_heads": "encoder_attention_heads",
        "hidden_size": "d_model",
        "attention_probs_dropout_prob": "attention_dropout",
    }

    def __init__(
        self,
        vocab_size=96103,
        max_position_embeddings=4096,
        encoder_layers=16,
        encoder_ffn_dim=4096,
        encoder_attention_heads=16,
        decoder_layers=16,
        decoder_ffn_dim=4096,
        decoder_attention_heads=16,
        encoder_layerdrop=0.0,
        decoder_layerdrop=0.0,
        use_cache=True,
        is_encoder_decoder=True,
        activation_function="gelu_new",
        d_model=1024,
        dropout=0.1,
        attention_dropout=0.0,
        activation_dropout=0.0,
        init_std=0.02,
        decoder_start_token_id=2,
        classifier_dropout=0.0,
        scale_embedding=True,
        pad_token_id=0,
        bos_token_id=2,
        eos_token_id=1,
        attention_type="block_sparse",  # only for encoder
        block_size=64,
        num_random_blocks=3,
        use_bias=False,
        **kwargs,
    ):
        """
        Initializes a new instance of the BigBirdPegasusConfig class.

        Args:
            self: The instance of the class.
            vocab_size (int, optional): The size of the vocabulary. Defaults to 96103.
            max_position_embeddings (int, optional): The maximum number of positional embeddings. Defaults to 4096.
            encoder_layers (int, optional): The number of encoder layers. Defaults to 16.
            encoder_ffn_dim (int, optional): The dimension of the encoder feed-forward network. Defaults to 4096.
            encoder_attention_heads (int, optional): The number of attention heads in the encoder. Defaults to 16.
            decoder_layers (int, optional): The number of decoder layers. Defaults to 16.
            decoder_ffn_dim (int, optional): The dimension of the decoder feed-forward network. Defaults to 4096.
            decoder_attention_heads (int, optional): The number of attention heads in the decoder. Defaults to 16.
            encoder_layerdrop (float, optional): The probability of dropping an encoder layer. Defaults to 0.0.
            decoder_layerdrop (float, optional): The probability of dropping a decoder layer. Defaults to 0.0.
            use_cache (bool, optional): Whether to use cache. Defaults to True.
            is_encoder_decoder (bool, optional): Whether the model is an encoder-decoder. Defaults to True.
            activation_function (str, optional): The activation function to be used. Defaults to 'gelu_new'.
            d_model (int, optional): The model dimension. Defaults to 1024.
            dropout (float, optional): The dropout probability. Defaults to 0.1.
            attention_dropout (float, optional): The dropout probability for attention layers. Defaults to 0.0.
            activation_dropout (float, optional): The dropout probability for activation layers. Defaults to 0.0.
            init_std (float, optional): The standard deviation for weight initialization. Defaults to 0.02.
            decoder_start_token_id (int, optional): The start token id for the decoder. Defaults to 2.
            classifier_dropout (float, optional): The dropout probability for the classifier. Defaults to 0.0.
            scale_embedding (bool, optional): Whether to scale the embeddings. Defaults to True.
            pad_token_id (int, optional): The id for padding tokens. Defaults to 0.
            bos_token_id (int, optional): The id for the beginning of sequence token. Defaults to 2.
            eos_token_id (int, optional): The id for the end of sequence token. Defaults to 1.
            attention_type (str, optional): The type of attention mechanism. Defaults to 'block_sparse'.
            block_size (int, optional): The size of blocks for block_sparse attention. Defaults to 64.
            num_random_blocks (int, optional): The number of random blocks for block_sparse attention. Defaults to 3.
            use_bias (bool, optional): Whether to use bias. Defaults to False.

        Returns:
            None.

        Raises:
            None.
        """
        self.vocab_size = vocab_size
        self.max_position_embeddings = max_position_embeddings
        self.d_model = d_model
        self.encoder_ffn_dim = encoder_ffn_dim
        self.encoder_layers = encoder_layers
        self.encoder_attention_heads = encoder_attention_heads
        self.decoder_ffn_dim = decoder_ffn_dim
        self.decoder_layers = decoder_layers
        self.decoder_attention_heads = decoder_attention_heads
        self.dropout = dropout
        self.attention_dropout = attention_dropout
        self.activation_dropout = activation_dropout
        self.activation_function = activation_function
        self.init_std = init_std
        self.encoder_layerdrop = encoder_layerdrop
        self.decoder_layerdrop = decoder_layerdrop
        self.classifier_dropout = classifier_dropout
        self.use_cache = use_cache
        self.num_hidden_layers = encoder_layers
        self.scale_embedding = scale_embedding  # scale factor will be sqrt(d_model) if True

        # extra config
        self.attention_type = attention_type
        self.block_size = block_size
        self.num_random_blocks = num_random_blocks
        self.use_bias = use_bias

        super().__init__(
            pad_token_id=pad_token_id,
            bos_token_id=bos_token_id,
            eos_token_id=eos_token_id,
            is_encoder_decoder=is_encoder_decoder,
            decoder_start_token_id=decoder_start_token_id,
            **kwargs,
        )

mindnlp.transformers.models.bigbird_pegasus.configuration_bigbird_pegasus.BigBirdPegasusConfig.__init__(vocab_size=96103, max_position_embeddings=4096, encoder_layers=16, encoder_ffn_dim=4096, encoder_attention_heads=16, decoder_layers=16, decoder_ffn_dim=4096, decoder_attention_heads=16, encoder_layerdrop=0.0, decoder_layerdrop=0.0, use_cache=True, is_encoder_decoder=True, activation_function='gelu_new', d_model=1024, dropout=0.1, attention_dropout=0.0, activation_dropout=0.0, init_std=0.02, decoder_start_token_id=2, classifier_dropout=0.0, scale_embedding=True, pad_token_id=0, bos_token_id=2, eos_token_id=1, attention_type='block_sparse', block_size=64, num_random_blocks=3, use_bias=False, **kwargs)

Initializes a new instance of the BigBirdPegasusConfig class.

PARAMETER DESCRIPTION
self

The instance of the class.
vocab_size

The size of the vocabulary. Defaults to 96103.

TYPE: int DEFAULT: 96103

max_position_embeddings

The maximum number of positional embeddings. Defaults to 4096.

TYPE: int DEFAULT: 4096

encoder_layers

The number of encoder layers. Defaults to 16.

TYPE: int DEFAULT: 16

encoder_ffn_dim

The dimension of the encoder feed-forward network. Defaults to 4096.

TYPE: int DEFAULT: 4096

encoder_attention_heads

The number of attention heads in the encoder. Defaults to 16.

TYPE: int DEFAULT: 16

decoder_layers

The number of decoder layers. Defaults to 16.

TYPE: int DEFAULT: 16

decoder_ffn_dim

The dimension of the decoder feed-forward network. Defaults to 4096.

TYPE: int DEFAULT: 4096

decoder_attention_heads

The number of attention heads in the decoder. Defaults to 16.

TYPE: int DEFAULT: 16

encoder_layerdrop

The probability of dropping an encoder layer. Defaults to 0.0.

TYPE: float DEFAULT: 0.0

decoder_layerdrop

The probability of dropping a decoder layer. Defaults to 0.0.

TYPE: float DEFAULT: 0.0

use_cache

Whether to use cache. Defaults to True.

TYPE: bool DEFAULT: True

is_encoder_decoder

Whether the model is an encoder-decoder. Defaults to True.

TYPE: bool DEFAULT: True

activation_function

The activation function to be used. Defaults to 'gelu_new'.

TYPE: str DEFAULT: 'gelu_new'

d_model

The model dimension. Defaults to 1024.

TYPE: int DEFAULT: 1024

dropout

The dropout probability. Defaults to 0.1.

TYPE: float DEFAULT: 0.1

attention_dropout

The dropout probability for attention layers. Defaults to 0.0.

TYPE: float DEFAULT: 0.0

activation_dropout

The dropout probability for activation layers. Defaults to 0.0.

TYPE: float DEFAULT: 0.0

init_std

The standard deviation for weight initialization. Defaults to 0.02.

TYPE: float DEFAULT: 0.02

decoder_start_token_id

The start token id for the decoder. Defaults to 2.

TYPE: int DEFAULT: 2

classifier_dropout

The dropout probability for the classifier. Defaults to 0.0.

TYPE: float DEFAULT: 0.0

scale_embedding

Whether to scale the embeddings. Defaults to True.

TYPE: bool DEFAULT: True

pad_token_id

The id for padding tokens. Defaults to 0.

TYPE: int DEFAULT: 0

bos_token_id

The id for the beginning of sequence token. Defaults to 2.

TYPE: int DEFAULT: 2

eos_token_id

The id for the end of sequence token. Defaults to 1.

TYPE: int DEFAULT: 1

attention_type

The type of attention mechanism. Defaults to 'block_sparse'.

TYPE: str DEFAULT: 'block_sparse'

block_size

The size of blocks for block_sparse attention. Defaults to 64.

TYPE: int DEFAULT: 64

num_random_blocks

The number of random blocks for block_sparse attention. Defaults to 3.

TYPE: int DEFAULT: 3

use_bias

Whether to use bias. Defaults to False.

TYPE: bool DEFAULT: False

RETURNS DESCRIPTION

None.
Source code in mindnlp\transformers\models\bigbird_pegasus\configuration_bigbird_pegasus.py 
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def __init__(
    self,
    vocab_size=96103,
    max_position_embeddings=4096,
    encoder_layers=16,
    encoder_ffn_dim=4096,
    encoder_attention_heads=16,
    decoder_layers=16,
    decoder_ffn_dim=4096,
    decoder_attention_heads=16,
    encoder_layerdrop=0.0,
    decoder_layerdrop=0.0,
    use_cache=True,
    is_encoder_decoder=True,
    activation_function="gelu_new",
    d_model=1024,
    dropout=0.1,
    attention_dropout=0.0,
    activation_dropout=0.0,
    init_std=0.02,
    decoder_start_token_id=2,
    classifier_dropout=0.0,
    scale_embedding=True,
    pad_token_id=0,
    bos_token_id=2,
    eos_token_id=1,
    attention_type="block_sparse",  # only for encoder
    block_size=64,
    num_random_blocks=3,
    use_bias=False,
    **kwargs,
):
    """
    Initializes a new instance of the BigBirdPegasusConfig class.

    Args:
        self: The instance of the class.
        vocab_size (int, optional): The size of the vocabulary. Defaults to 96103.
        max_position_embeddings (int, optional): The maximum number of positional embeddings. Defaults to 4096.
        encoder_layers (int, optional): The number of encoder layers. Defaults to 16.
        encoder_ffn_dim (int, optional): The dimension of the encoder feed-forward network. Defaults to 4096.
        encoder_attention_heads (int, optional): The number of attention heads in the encoder. Defaults to 16.
        decoder_layers (int, optional): The number of decoder layers. Defaults to 16.
        decoder_ffn_dim (int, optional): The dimension of the decoder feed-forward network. Defaults to 4096.
        decoder_attention_heads (int, optional): The number of attention heads in the decoder. Defaults to 16.
        encoder_layerdrop (float, optional): The probability of dropping an encoder layer. Defaults to 0.0.
        decoder_layerdrop (float, optional): The probability of dropping a decoder layer. Defaults to 0.0.
        use_cache (bool, optional): Whether to use cache. Defaults to True.
        is_encoder_decoder (bool, optional): Whether the model is an encoder-decoder. Defaults to True.
        activation_function (str, optional): The activation function to be used. Defaults to 'gelu_new'.
        d_model (int, optional): The model dimension. Defaults to 1024.
        dropout (float, optional): The dropout probability. Defaults to 0.1.
        attention_dropout (float, optional): The dropout probability for attention layers. Defaults to 0.0.
        activation_dropout (float, optional): The dropout probability for activation layers. Defaults to 0.0.
        init_std (float, optional): The standard deviation for weight initialization. Defaults to 0.02.
        decoder_start_token_id (int, optional): The start token id for the decoder. Defaults to 2.
        classifier_dropout (float, optional): The dropout probability for the classifier. Defaults to 0.0.
        scale_embedding (bool, optional): Whether to scale the embeddings. Defaults to True.
        pad_token_id (int, optional): The id for padding tokens. Defaults to 0.
        bos_token_id (int, optional): The id for the beginning of sequence token. Defaults to 2.
        eos_token_id (int, optional): The id for the end of sequence token. Defaults to 1.
        attention_type (str, optional): The type of attention mechanism. Defaults to 'block_sparse'.
        block_size (int, optional): The size of blocks for block_sparse attention. Defaults to 64.
        num_random_blocks (int, optional): The number of random blocks for block_sparse attention. Defaults to 3.
        use_bias (bool, optional): Whether to use bias. Defaults to False.

    Returns:
        None.

    Raises:
        None.
    """
    self.vocab_size = vocab_size
    self.max_position_embeddings = max_position_embeddings
    self.d_model = d_model
    self.encoder_ffn_dim = encoder_ffn_dim
    self.encoder_layers = encoder_layers
    self.encoder_attention_heads = encoder_attention_heads
    self.decoder_ffn_dim = decoder_ffn_dim
    self.decoder_layers = decoder_layers
    self.decoder_attention_heads = decoder_attention_heads
    self.dropout = dropout
    self.attention_dropout = attention_dropout
    self.activation_dropout = activation_dropout
    self.activation_function = activation_function
    self.init_std = init_std
    self.encoder_layerdrop = encoder_layerdrop
    self.decoder_layerdrop = decoder_layerdrop
    self.classifier_dropout = classifier_dropout
    self.use_cache = use_cache
    self.num_hidden_layers = encoder_layers
    self.scale_embedding = scale_embedding  # scale factor will be sqrt(d_model) if True

    # extra config
    self.attention_type = attention_type
    self.block_size = block_size
    self.num_random_blocks = num_random_blocks
    self.use_bias = use_bias

    super().__init__(
        pad_token_id=pad_token_id,
        bos_token_id=bos_token_id,
        eos_token_id=eos_token_id,
        is_encoder_decoder=is_encoder_decoder,
        decoder_start_token_id=decoder_start_token_id,
        **kwargs,
    )

mindnlp.transformers.models.bigbird_pegasus.modeling_bigbird_pegasus.BigBirdPegasusForCausalLM

Bases: BigBirdPegasusPreTrainedModel

Source code in mindnlp\transformers\models\bigbird_pegasus\modeling_bigbird_pegasus.py 
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class BigBirdPegasusForCausalLM(BigBirdPegasusPreTrainedModel):
    _tied_weights_keys = ["lm_head.weight"]

    def __init__(self, config):
        config = copy.deepcopy(config)
        config.is_decoder = True
        config.is_encoder_decoder = False
        super().__init__(config)
        self.model = BigBirdPegasusDecoderWrapper(config)

        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

        # Initialize weights and apply final processing
        self.post_init()

    def get_input_embeddings(self):
        return self.model.decoder.embed_tokens

    def set_input_embeddings(self, value):
        self.model.decoder.embed_tokens = value

    def get_output_embeddings(self):
        return self.lm_head

    def set_output_embeddings(self, new_embeddings):
        self.lm_head = new_embeddings

    def set_decoder(self, decoder):
        self.model.decoder = decoder

    def get_decoder(self):
        return self.model.decoder

    def forward(
        self,
        input_ids: mindspore.Tensor = None,
        attention_mask: Optional[mindspore.Tensor] = None,
        encoder_hidden_states: Optional[mindspore.Tensor] = None,
        encoder_attention_mask: Optional[mindspore.Tensor] = None,
        head_mask: Optional[mindspore.Tensor] = None,
        cross_attn_head_mask: Optional[mindspore.Tensor] = None,
        past_key_values: Optional[Tuple[Tuple[mindspore.Tensor]]] = None,
        inputs_embeds: Optional[mindspore.Tensor] = None,
        labels: Optional[mindspore.Tensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, CausalLMOutputWithCrossAttentions]:
        r"""
        Args:
            input_ids (`mindspore.Tensor` of shape `(batch_size, sequence_length)`):
                Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
                provide it.

                Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
                [`PreTrainedTokenizer.__call__`] for details.

                [What are input IDs?](../glossary#input-ids)
            attention_mask (`mindspore.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
                Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

                - 1 for tokens that are **not masked**,
                - 0 for tokens that are **masked**.

                [What are attention masks?](../glossary#attention-mask)
            encoder_hidden_states  (`mindspore.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
                Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
                if the model is configured as a decoder.
            encoder_attention_mask (`mindspore.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
                Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used
                in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:
            head_mask (`mindspore.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
                Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:

                - 1 indicates the head is **not masked**,
                - 0 indicates the head is **masked**.

            cross_attn_head_mask (`mindspore.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
                Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`:

                - 1 indicates the head is **not masked**,
                - 0 indicates the head is **masked**.

            past_key_values (`tuple(tuple(mindspore.Tensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
                Tuple of `tuple(mindspore.Tensor)` of length `config.n_layers`, with each tuple having 2 tensors of
                shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of
                shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. The two additional
                tensors are only required when the model is used as a decoder in a Sequence to Sequence model.

                Contains pre-computed hidden-states (key and values in the self-attention blocks and in the
                cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.

                If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
                that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
                all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
            labels (`mindspore.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
            use_cache (`bool`, *optional*):
                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
                (see `past_key_values`).

                - 1 for tokens that are **not masked**,
                - 0 for tokens that are **masked**.
            output_attentions (`bool`, *optional*):
                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                returned tensors for more detail.
            output_hidden_states (`bool`, *optional*):
                Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
                for more detail.
            return_dict (`bool`, *optional*):
                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.

        Returns:

        Example:

        ```python
        >>> from transformers import AutoTokenizer, BigBirdPegasusForCausalLM

        >>> tokenizer = AutoTokenizer.from_pretrained("google/bigbird-pegasus-large-arxiv")
        >>> model = BigBirdPegasusForCausalLM.from_pretrained(
        ...     "google/bigbird-pegasus-large-arxiv", add_cross_attention=False
        ... )
        >>> assert model.config.is_decoder, f"{model.__class__} has to be configured as a decoder."
        >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
        >>> outputs = model(**inputs)

        >>> logits = outputs.logits
        ```"""

        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
        outputs = self.model.decoder(
            input_ids=input_ids,
            attention_mask=attention_mask,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            head_mask=head_mask,
            cross_attn_head_mask=cross_attn_head_mask,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        logits = self.lm_head(outputs[0])

        loss = None
        if labels is not None:
            loss_fct = CrossEntropyLoss()
            loss = loss_fct(logits.view(-1, self.config.vocab_size), labels.view(-1))

        if not return_dict:
            output = (logits,) + outputs[1:]
            return (loss,) + output if loss is not None else output

        return CausalLMOutputWithCrossAttentions(
            loss=loss,
            logits=logits,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
            cross_attentions=outputs.cross_attentions,
        )

    def prepare_inputs_for_generation(
        self, input_ids, past_key_values=None, attention_mask=None, use_cache=None, **kwargs
    ):
        # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
        if attention_mask is None:
            attention_mask = ops.ones(input_ids.shape, dtype=input_ids.dtype)

        if past_key_values:
            input_ids = input_ids[:, -1:]
        # first step, decoder_cached_states are empty
        return {
            "input_ids": input_ids,  # encoder_outputs is defined. input_ids not needed
            "attention_mask": attention_mask,
            "past_key_values": past_key_values,
            "use_cache": use_cache,
        }

    @staticmethod
    def _reorder_cache(past_key_values, beam_idx):
        reordered_past = ()
        for layer_past in past_key_values:
            reordered_past += (
                tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),
            )
        return reordered_past

mindnlp.transformers.models.bigbird_pegasus.modeling_bigbird_pegasus.BigBirdPegasusForCausalLM.forward(input_ids=None, attention_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, head_mask=None, cross_attn_head_mask=None, past_key_values=None, inputs_embeds=None, labels=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None)

PARAMETER DESCRIPTION
input_ids

Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it.

Indices can be obtained using [AutoTokenizer]. See [PreTrainedTokenizer.encode] and [PreTrainedTokenizer.__call__] for details.

What are input IDs?

TYPE: `mindspore.Tensor` of shape `(batch_size, sequence_length)` DEFAULT: None

attention_mask

Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:

  • 1 for tokens that are not masked,
  • 0 for tokens that are masked.

What are attention masks?

TYPE: `mindspore.Tensor` of shape `(batch_size, sequence_length)`, *optional* DEFAULT: None

encoder_hidden_states

Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder.

TYPE: (`mindspore.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional* DEFAULT: None

encoder_attention_mask

Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in [0, 1]:

TYPE: `mindspore.Tensor` of shape `(batch_size, sequence_length)`, *optional* DEFAULT: None

head_mask

Mask to nullify selected heads of the attention modules. Mask values selected in [0, 1]:

  • 1 indicates the head is not masked,
  • 0 indicates the head is masked.

TYPE: `mindspore.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional* DEFAULT: None

cross_attn_head_mask

Mask to nullify selected heads of the cross-attention modules. Mask values selected in [0, 1]:

  • 1 indicates the head is not masked,
  • 0 indicates the head is masked.

TYPE: `mindspore.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional* DEFAULT: None

past_key_values

Tuple of tuple(mindspore.Tensor) of length config.n_layers, with each tuple having 2 tensors of shape (batch_size, num_heads, sequence_length, embed_size_per_head)) and 2 additional tensors of shape (batch_size, num_heads, encoder_sequence_length, embed_size_per_head). The two additional tensors are only required when the model is used as a decoder in a Sequence to Sequence model.

Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used (see past_key_values input) to speed up sequential decoding.

If past_key_values are used, the user can optionally input only the last decoder_input_ids (those that don't have their past key value states given to this model) of shape (batch_size, 1) instead of all decoder_input_ids of shape (batch_size, sequence_length).

TYPE: `tuple(tuple(mindspore.Tensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True` DEFAULT: None

labels

Labels for computing the masked language modeling loss. Indices should either be in [0, ..., config.vocab_size] or -100 (see input_ids docstring). Tokens with indices set to -100 are ignored (masked), the loss is only computed for the tokens with labels in [0, ..., config.vocab_size].

TYPE: `mindspore.Tensor` of shape `(batch_size, sequence_length)`, *optional* DEFAULT: None

use_cache

If set to True, past_key_values key value states are returned and can be used to speed up decoding (see past_key_values).

  • 1 for tokens that are not masked,
  • 0 for tokens that are masked.

TYPE: `bool`, *optional* DEFAULT: None

output_attentions

Whether or not to return the attentions tensors of all attention layers. See attentions under returned tensors for more detail.

TYPE: `bool`, *optional* DEFAULT: None

output_hidden_states

Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for more detail.

TYPE: `bool`, *optional* DEFAULT: None

return_dict

Whether or not to return a [~utils.ModelOutput] instead of a plain tuple.

TYPE: `bool`, *optional* DEFAULT: None

Example:

>>> from transformers import AutoTokenizer, BigBirdPegasusForCausalLM

>>> tokenizer = AutoTokenizer.from_pretrained("google/bigbird-pegasus-large-arxiv")
>>> model = BigBirdPegasusForCausalLM.from_pretrained(
...     "google/bigbird-pegasus-large-arxiv", add_cross_attention=False
... )
>>> assert model.config.is_decoder, f"{model.__class__} has to be configured as a decoder."
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> outputs = model(**inputs)

>>> logits = outputs.logits
Source code in mindnlp\transformers\models\bigbird_pegasus\modeling_bigbird_pegasus.py 
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def forward(
    self,
    input_ids: mindspore.Tensor = None,
    attention_mask: Optional[mindspore.Tensor] = None,
    encoder_hidden_states: Optional[mindspore.Tensor] = None,
    encoder_attention_mask: Optional[mindspore.Tensor] = None,
    head_mask: Optional[mindspore.Tensor] = None,
    cross_attn_head_mask: Optional[mindspore.Tensor] = None,
    past_key_values: Optional[Tuple[Tuple[mindspore.Tensor]]] = None,
    inputs_embeds: Optional[mindspore.Tensor] = None,
    labels: Optional[mindspore.Tensor] = None,
    use_cache: Optional[bool] = None,
    output_attentions: Optional[bool] = None,
    output_hidden_states: Optional[bool] = None,
    return_dict: Optional[bool] = None,
) -> Union[Tuple, CausalLMOutputWithCrossAttentions]:
    r"""
    Args:
        input_ids (`mindspore.Tensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
            provide it.

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        attention_mask (`mindspore.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

            - 1 for tokens that are **not masked**,
            - 0 for tokens that are **masked**.

            [What are attention masks?](../glossary#attention-mask)
        encoder_hidden_states  (`mindspore.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
            if the model is configured as a decoder.
        encoder_attention_mask (`mindspore.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
            Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used
            in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:
        head_mask (`mindspore.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
            Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:

            - 1 indicates the head is **not masked**,
            - 0 indicates the head is **masked**.

        cross_attn_head_mask (`mindspore.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
            Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`:

            - 1 indicates the head is **not masked**,
            - 0 indicates the head is **masked**.

        past_key_values (`tuple(tuple(mindspore.Tensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
            Tuple of `tuple(mindspore.Tensor)` of length `config.n_layers`, with each tuple having 2 tensors of
            shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of
            shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. The two additional
            tensors are only required when the model is used as a decoder in a Sequence to Sequence model.

            Contains pre-computed hidden-states (key and values in the self-attention blocks and in the
            cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.

            If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
            that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
            all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
        labels (`mindspore.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
        use_cache (`bool`, *optional*):
            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
            (see `past_key_values`).

            - 1 for tokens that are **not masked**,
            - 0 for tokens that are **masked**.
        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under
            returned tensors for more detail.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
            for more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.

    Returns:

    Example:

    ```python
    >>> from transformers import AutoTokenizer, BigBirdPegasusForCausalLM

    >>> tokenizer = AutoTokenizer.from_pretrained("google/bigbird-pegasus-large-arxiv")
    >>> model = BigBirdPegasusForCausalLM.from_pretrained(
    ...     "google/bigbird-pegasus-large-arxiv", add_cross_attention=False
    ... )
    >>> assert model.config.is_decoder, f"{model.__class__} has to be configured as a decoder."
    >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
    >>> outputs = model(**inputs)

    >>> logits = outputs.logits
    ```"""

    output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
    output_hidden_states = (
        output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
    )
    return_dict = return_dict if return_dict is not None else self.config.use_return_dict

    # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
    outputs = self.model.decoder(
        input_ids=input_ids,
        attention_mask=attention_mask,
        encoder_hidden_states=encoder_hidden_states,
        encoder_attention_mask=encoder_attention_mask,
        head_mask=head_mask,
        cross_attn_head_mask=cross_attn_head_mask,
        past_key_values=past_key_values,
        inputs_embeds=inputs_embeds,
        use_cache=use_cache,
        output_attentions=output_attentions,
        output_hidden_states=output_hidden_states,
        return_dict=return_dict,
    )

    logits = self.lm_head(outputs[0])

    loss = None
    if labels is not None:
        loss_fct = CrossEntropyLoss()
        loss = loss_fct(logits.view(-1, self.config.vocab_size), labels.view(-1))

    if not return_dict:
        output = (logits,) + outputs[1:]
        return (loss,) + output if loss is not None else output

    return CausalLMOutputWithCrossAttentions(
        loss=loss,
        logits=logits,
        past_key_values=outputs.past_key_values,
        hidden_states=outputs.hidden_states,
        attentions=outputs.attentions,
        cross_attentions=outputs.cross_attentions,
    )

mindnlp.transformers.models.bigbird_pegasus.modeling_bigbird_pegasus.BigBirdPegasusForConditionalGeneration

Bases: BigBirdPegasusPreTrainedModel

Source code in mindnlp\transformers\models\bigbird_pegasus\modeling_bigbird_pegasus.py 
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class BigBirdPegasusForConditionalGeneration(BigBirdPegasusPreTrainedModel):
    base_model_prefix = "model"
    _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight", "lm_head.weight"]
    _keys_to_ignore_on_load_missing = ["final_logits_bias"]

    def __init__(self, config: BigBirdPegasusConfig):
        super().__init__(config)
        self.model = BigBirdPegasusModel(config)
        self.register_buffer("final_logits_bias", ops.zeros((1, self.model.shared.num_embeddings)))
        self.lm_head = nn.Linear(config.d_model, self.model.shared.num_embeddings, bias=False)

        # Initialize weights and apply final processing
        self.post_init()

    def get_encoder(self):
        return self.model.get_encoder()

    def get_decoder(self):
        return self.model.get_decoder()

    def resize_token_embeddings(self, new_num_tokens: int, pad_to_multiple_of: Optional[int] = None) -> nn.Embedding:
        new_embeddings = super().resize_token_embeddings(new_num_tokens, pad_to_multiple_of)
        self._resize_final_logits_bias(new_embeddings.weight.shape[0])
        return new_embeddings

    def _resize_final_logits_bias(self, new_num_tokens: int) -> None:
        old_num_tokens = self.final_logits_bias.shape[-1]
        if new_num_tokens <= old_num_tokens:
            new_bias = self.final_logits_bias[:, :new_num_tokens]
        else:
            extra_bias = ops.zeros((1, new_num_tokens - old_num_tokens))
            new_bias = ops.cat([self.final_logits_bias, extra_bias], dim=1)
        self.register_buffer("final_logits_bias", new_bias)

    def get_output_embeddings(self):
        return self.lm_head

    def set_output_embeddings(self, new_embeddings):
        self.lm_head = new_embeddings

    def forward(
        self,
        input_ids: mindspore.Tensor = None,
        attention_mask: Optional[mindspore.Tensor] = None,
        decoder_input_ids: Optional[mindspore.Tensor] = None,
        decoder_attention_mask: Optional[mindspore.Tensor] = None,
        head_mask: Optional[mindspore.Tensor] = None,
        decoder_head_mask: Optional[mindspore.Tensor] = None,
        cross_attn_head_mask: Optional[mindspore.Tensor] = None,
        encoder_outputs: Optional[List[mindspore.Tensor]] = None,
        past_key_values: Optional[List[mindspore.Tensor]] = None,
        inputs_embeds: Optional[mindspore.Tensor] = None,
        decoder_inputs_embeds: Optional[mindspore.Tensor] = None,
        labels: Optional[mindspore.Tensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, Seq2SeqLMOutput]:
        r"""
        labels (`mindspore.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.

        Returns:
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        if labels is not None:
            if use_cache:
                logger.warning("The `use_cache` argument is changed to `False` since `labels` is provided.")
            use_cache = False
            if decoder_input_ids is None and decoder_inputs_embeds is None:
                decoder_input_ids = shift_tokens_right(
                    labels, self.config.pad_token_id, self.config.decoder_start_token_id
                )

        outputs = self.model(
            input_ids,
            attention_mask=attention_mask,
            decoder_input_ids=decoder_input_ids,
            encoder_outputs=encoder_outputs,
            decoder_attention_mask=decoder_attention_mask,
            head_mask=head_mask,
            decoder_head_mask=decoder_head_mask,
            cross_attn_head_mask=cross_attn_head_mask,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            decoder_inputs_embeds=decoder_inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        lm_logits = self.lm_head(outputs[0])
        lm_logits = lm_logits + self.final_logits_bias

        masked_lm_loss = None
        if labels is not None:
            loss_fct = CrossEntropyLoss()
            masked_lm_loss = loss_fct(lm_logits.view(-1, self.config.vocab_size), labels.view(-1))

        if not return_dict:
            output = (lm_logits,) + outputs[1:]
            return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output

        return Seq2SeqLMOutput(
            loss=masked_lm_loss,
            logits=lm_logits,
            past_key_values=outputs.past_key_values,
            decoder_hidden_states=outputs.decoder_hidden_states,
            decoder_attentions=outputs.decoder_attentions,
            cross_attentions=outputs.cross_attentions,
            encoder_last_hidden_state=outputs.encoder_last_hidden_state,
            encoder_hidden_states=outputs.encoder_hidden_states,
            encoder_attentions=outputs.encoder_attentions,
        )

    def prepare_inputs_for_generation(
        self,
        decoder_input_ids,
        past_key_values=None,
        attention_mask=None,
        decoder_attention_mask=None,
        head_mask=None,
        decoder_head_mask=None,
        cross_attn_head_mask=None,
        use_cache=None,
        encoder_outputs=None,
        **kwargs,
    ):
        # cut decoder_input_ids if past_key_values is used
        if past_key_values is not None:
            past_length = past_key_values[0][0].shape[2]

            # Some generation methods already pass only the last input ID
            if decoder_input_ids.shape[1] > past_length:
                remove_prefix_length = past_length
            else:
                # Default to old behavior: keep only final ID
                remove_prefix_length = decoder_input_ids.shape[1] - 1

            decoder_input_ids = decoder_input_ids[:, remove_prefix_length:]

        return {
            "input_ids": None,  # encoder_outputs is defined. input_ids not needed
            "encoder_outputs": encoder_outputs,
            "past_key_values": past_key_values,
            "decoder_input_ids": decoder_input_ids,
            "attention_mask": attention_mask,
            "decoder_attention_mask": decoder_attention_mask,
            "head_mask": head_mask,
            "decoder_head_mask": decoder_head_mask,
            "cross_attn_head_mask": cross_attn_head_mask,
            "use_cache": use_cache,  # change this to avoid caching (presumably for debugging)
        }

    def prepare_decoder_input_ids_from_labels(self, labels: mindspore.Tensor):
        return shift_tokens_right(labels, self.config.pad_token_id, self.config.decoder_start_token_id)

    @staticmethod
    def _reorder_cache(past_key_values, beam_idx):
        reordered_past = ()
        for layer_past in past_key_values:
            # cached cross_attention states don't have to be reordered -> they are always the same
            reordered_past += (
                tuple(past_state.index_select(0, beam_idx) for past_state in layer_past[:2])
                + layer_past[2:],
            )
        return reordered_past

mindnlp.transformers.models.bigbird_pegasus.modeling_bigbird_pegasus.BigBirdPegasusForConditionalGeneration.forward(input_ids=None, attention_mask=None, decoder_input_ids=None, decoder_attention_mask=None, head_mask=None, decoder_head_mask=None, cross_attn_head_mask=None, encoder_outputs=None, past_key_values=None, inputs_embeds=None, decoder_inputs_embeds=None, labels=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None)

labels (mindspore.Tensor of shape (batch_size, sequence_length), optional): Labels for computing the masked language modeling loss. Indices should either be in [0, ..., config.vocab_size] or -100 (see input_ids docstring). Tokens with indices set to -100 are ignored (masked), the loss is only computed for the tokens with labels in [0, ..., config.vocab_size].

Returns:

Source code in mindnlp\transformers\models\bigbird_pegasus\modeling_bigbird_pegasus.py 
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def forward(
    self,
    input_ids: mindspore.Tensor = None,
    attention_mask: Optional[mindspore.Tensor] = None,
    decoder_input_ids: Optional[mindspore.Tensor] = None,
    decoder_attention_mask: Optional[mindspore.Tensor] = None,
    head_mask: Optional[mindspore.Tensor] = None,
    decoder_head_mask: Optional[mindspore.Tensor] = None,
    cross_attn_head_mask: Optional[mindspore.Tensor] = None,
    encoder_outputs: Optional[List[mindspore.Tensor]] = None,
    past_key_values: Optional[List[mindspore.Tensor]] = None,
    inputs_embeds: Optional[mindspore.Tensor] = None,
    decoder_inputs_embeds: Optional[mindspore.Tensor] = None,
    labels: Optional[mindspore.Tensor] = None,
    use_cache: Optional[bool] = None,
    output_attentions: Optional[bool] = None,
    output_hidden_states: Optional[bool] = None,
    return_dict: Optional[bool] = None,
) -> Union[Tuple, Seq2SeqLMOutput]:
    r"""
    labels (`mindspore.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
        Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
        config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
        (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.

    Returns:
    """
    return_dict = return_dict if return_dict is not None else self.config.use_return_dict

    if labels is not None:
        if use_cache:
            logger.warning("The `use_cache` argument is changed to `False` since `labels` is provided.")
        use_cache = False
        if decoder_input_ids is None and decoder_inputs_embeds is None:
            decoder_input_ids = shift_tokens_right(
                labels, self.config.pad_token_id, self.config.decoder_start_token_id
            )

    outputs = self.model(
        input_ids,
        attention_mask=attention_mask,
        decoder_input_ids=decoder_input_ids,
        encoder_outputs=encoder_outputs,
        decoder_attention_mask=decoder_attention_mask,
        head_mask=head_mask,
        decoder_head_mask=decoder_head_mask,
        cross_attn_head_mask=cross_attn_head_mask,
        past_key_values=past_key_values,
        inputs_embeds=inputs_embeds,
        decoder_inputs_embeds=decoder_inputs_embeds,
        use_cache=use_cache,
        output_attentions=output_attentions,
        output_hidden_states=output_hidden_states,
        return_dict=return_dict,
    )

    lm_logits = self.lm_head(outputs[0])
    lm_logits = lm_logits + self.final_logits_bias

    masked_lm_loss = None
    if labels is not None:
        loss_fct = CrossEntropyLoss()
        masked_lm_loss = loss_fct(lm_logits.view(-1, self.config.vocab_size), labels.view(-1))

    if not return_dict:
        output = (lm_logits,) + outputs[1:]
        return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output

    return Seq2SeqLMOutput(
        loss=masked_lm_loss,
        logits=lm_logits,
        past_key_values=outputs.past_key_values,
        decoder_hidden_states=outputs.decoder_hidden_states,
        decoder_attentions=outputs.decoder_attentions,
        cross_attentions=outputs.cross_attentions,
        encoder_last_hidden_state=outputs.encoder_last_hidden_state,
        encoder_hidden_states=outputs.encoder_hidden_states,
        encoder_attentions=outputs.encoder_attentions,
    )

mindnlp.transformers.models.bigbird_pegasus.modeling_bigbird_pegasus.BigBirdPegasusForQuestionAnswering

Bases: BigBirdPegasusPreTrainedModel

Source code in mindnlp\transformers\models\bigbird_pegasus\modeling_bigbird_pegasus.py 
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class BigBirdPegasusForQuestionAnswering(BigBirdPegasusPreTrainedModel):
    _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]

    def __init__(self, config):
        super().__init__(config)

        config.num_labels = 2
        self.num_labels = config.num_labels

        self.model = BigBirdPegasusModel(config)
        self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)

        # Initialize weights and apply final processing
        self.post_init()

    # Copied from transformers.models.bart.modeling_bart.BartForQuestionAnswering.forward
    def forward(
        self,
        input_ids: mindspore.Tensor = None,
        attention_mask: Optional[mindspore.Tensor] = None,
        decoder_input_ids: Optional[mindspore.Tensor] = None,
        decoder_attention_mask: Optional[mindspore.Tensor] = None,
        head_mask: Optional[mindspore.Tensor] = None,
        decoder_head_mask: Optional[mindspore.Tensor] = None,
        cross_attn_head_mask: Optional[mindspore.Tensor] = None,
        encoder_outputs: Optional[List[mindspore.Tensor]] = None,
        start_positions: Optional[mindspore.Tensor] = None,
        end_positions: Optional[mindspore.Tensor] = None,
        inputs_embeds: Optional[mindspore.Tensor] = None,
        decoder_inputs_embeds: Optional[mindspore.Tensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, Seq2SeqQuestionAnsweringModelOutput]:
        r"""
        start_positions (`mindspore.Tensor` of shape `(batch_size,)`, *optional*):
            Labels for position (index) of the start of the labelled span for computing the token classification loss.
            Positions are clamped to the length of the sequence (*sequence_length*). Position outside of the sequence
            are not taken into account for computing the loss.
        end_positions (`mindspore.Tensor` of shape `(batch_size,)`, *optional*):
            Labels for position (index) of the end of the labelled span for computing the token classification loss.
            Positions are clamped to the length of the sequence (*sequence_length*). Position outside of the sequence
            are not taken into account for computing the loss.
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        if start_positions is not None and end_positions is not None:
            use_cache = False

        outputs = self.model(
            input_ids,
            attention_mask=attention_mask,
            decoder_input_ids=decoder_input_ids,
            decoder_attention_mask=decoder_attention_mask,
            head_mask=head_mask,
            decoder_head_mask=decoder_head_mask,
            cross_attn_head_mask=cross_attn_head_mask,
            encoder_outputs=encoder_outputs,
            inputs_embeds=inputs_embeds,
            decoder_inputs_embeds=decoder_inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        sequence_output = outputs[0]

        logits = self.qa_outputs(sequence_output)
        start_logits, end_logits = ops.split(logits, 1, dim=-1)
        start_logits = start_logits.squeeze(-1)
        end_logits = end_logits.squeeze(-1)

        total_loss = None
        if start_positions is not None and end_positions is not None:
            # If we are on multi-GPU, split add a dimension
            if len(start_positions.shape) > 1:
                start_positions = start_positions.squeeze(-1)
            if len(end_positions.shape) > 1:
                end_positions = end_positions.squeeze(-1)
            # sometimes the start/end positions are outside our model inputs, we ignore these terms
            ignored_index = start_logits.shape[1]
            start_positions = start_positions.clamp(0, ignored_index)
            end_positions = end_positions.clamp(0, ignored_index)

            loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
            start_loss = loss_fct(start_logits, start_positions)
            end_loss = loss_fct(end_logits, end_positions)
            total_loss = (start_loss + end_loss) / 2

        if not return_dict:
            output = (
                start_logits,
                end_logits,
            ) + outputs[1:]
            return ((total_loss,) + output) if total_loss is not None else output

        return Seq2SeqQuestionAnsweringModelOutput(
            loss=total_loss,
            start_logits=start_logits,
            end_logits=end_logits,
            past_key_values=outputs.past_key_values,
            decoder_hidden_states=outputs.decoder_hidden_states,
            decoder_attentions=outputs.decoder_attentions,
            cross_attentions=outputs.cross_attentions,
            encoder_last_hidden_state=outputs.encoder_last_hidden_state,
            encoder_hidden_states=outputs.encoder_hidden_states,
            encoder_attentions=outputs.encoder_attentions,
        )

mindnlp.transformers.models.bigbird_pegasus.modeling_bigbird_pegasus.BigBirdPegasusForQuestionAnswering.forward(input_ids=None, attention_mask=None, decoder_input_ids=None, decoder_attention_mask=None, head_mask=None, decoder_head_mask=None, cross_attn_head_mask=None, encoder_outputs=None, start_positions=None, end_positions=None, inputs_embeds=None, decoder_inputs_embeds=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None)

start_positions (mindspore.Tensor of shape (batch_size,), optional): Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (sequence_length). Position outside of the sequence are not taken into account for computing the loss. end_positions (mindspore.Tensor of shape (batch_size,), optional): Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (sequence_length). Position outside of the sequence are not taken into account for computing the loss.

Source code in mindnlp\transformers\models\bigbird_pegasus\modeling_bigbird_pegasus.py 
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def forward(
    self,
    input_ids: mindspore.Tensor = None,
    attention_mask: Optional[mindspore.Tensor] = None,
    decoder_input_ids: Optional[mindspore.Tensor] = None,
    decoder_attention_mask: Optional[mindspore.Tensor] = None,
    head_mask: Optional[mindspore.Tensor] = None,
    decoder_head_mask: Optional[mindspore.Tensor] = None,
    cross_attn_head_mask: Optional[mindspore.Tensor] = None,
    encoder_outputs: Optional[List[mindspore.Tensor]] = None,
    start_positions: Optional[mindspore.Tensor] = None,
    end_positions: Optional[mindspore.Tensor] = None,
    inputs_embeds: Optional[mindspore.Tensor] = None,
    decoder_inputs_embeds: Optional[mindspore.Tensor] = None,
    use_cache: Optional[bool] = None,
    output_attentions: Optional[bool] = None,
    output_hidden_states: Optional[bool] = None,
    return_dict: Optional[bool] = None,
) -> Union[Tuple, Seq2SeqQuestionAnsweringModelOutput]:
    r"""
    start_positions (`mindspore.Tensor` of shape `(batch_size,)`, *optional*):
        Labels for position (index) of the start of the labelled span for computing the token classification loss.
        Positions are clamped to the length of the sequence (*sequence_length*). Position outside of the sequence
        are not taken into account for computing the loss.
    end_positions (`mindspore.Tensor` of shape `(batch_size,)`, *optional*):
        Labels for position (index) of the end of the labelled span for computing the token classification loss.
        Positions are clamped to the length of the sequence (*sequence_length*). Position outside of the sequence
        are not taken into account for computing the loss.
    """
    return_dict = return_dict if return_dict is not None else self.config.use_return_dict
    if start_positions is not None and end_positions is not None:
        use_cache = False

    outputs = self.model(
        input_ids,
        attention_mask=attention_mask,
        decoder_input_ids=decoder_input_ids,
        decoder_attention_mask=decoder_attention_mask,
        head_mask=head_mask,
        decoder_head_mask=decoder_head_mask,
        cross_attn_head_mask=cross_attn_head_mask,
        encoder_outputs=encoder_outputs,
        inputs_embeds=inputs_embeds,
        decoder_inputs_embeds=decoder_inputs_embeds,
        use_cache=use_cache,
        output_attentions=output_attentions,
        output_hidden_states=output_hidden_states,
        return_dict=return_dict,
    )

    sequence_output = outputs[0]

    logits = self.qa_outputs(sequence_output)
    start_logits, end_logits = ops.split(logits, 1, dim=-1)
    start_logits = start_logits.squeeze(-1)
    end_logits = end_logits.squeeze(-1)

    total_loss = None
    if start_positions is not None and end_positions is not None:
        # If we are on multi-GPU, split add a dimension
        if len(start_positions.shape) > 1:
            start_positions = start_positions.squeeze(-1)
        if len(end_positions.shape) > 1:
            end_positions = end_positions.squeeze(-1)
        # sometimes the start/end positions are outside our model inputs, we ignore these terms
        ignored_index = start_logits.shape[1]
        start_positions = start_positions.clamp(0, ignored_index)
        end_positions = end_positions.clamp(0, ignored_index)

        loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
        start_loss = loss_fct(start_logits, start_positions)
        end_loss = loss_fct(end_logits, end_positions)
        total_loss = (start_loss + end_loss) / 2

    if not return_dict:
        output = (
            start_logits,
            end_logits,
        ) + outputs[1:]
        return ((total_loss,) + output) if total_loss is not None else output

    return Seq2SeqQuestionAnsweringModelOutput(
        loss=total_loss,
        start_logits=start_logits,
        end_logits=end_logits,
        past_key_values=outputs.past_key_values,
        decoder_hidden_states=outputs.decoder_hidden_states,
        decoder_attentions=outputs.decoder_attentions,
        cross_attentions=outputs.cross_attentions,
        encoder_last_hidden_state=outputs.encoder_last_hidden_state,
        encoder_hidden_states=outputs.encoder_hidden_states,
        encoder_attentions=outputs.encoder_attentions,
    )

mindnlp.transformers.models.bigbird_pegasus.modeling_bigbird_pegasus.BigBirdPegasusForSequenceClassification

Bases: BigBirdPegasusPreTrainedModel

Source code in mindnlp\transformers\models\bigbird_pegasus\modeling_bigbird_pegasus.py 
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class BigBirdPegasusForSequenceClassification(BigBirdPegasusPreTrainedModel):
    _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]

    def __init__(self, config: BigBirdPegasusConfig, **kwargs):
        super().__init__(config, **kwargs)
        self.model = BigBirdPegasusModel(config)
        self.classification_head = BigBirdPegasusClassificationHead(
            config.d_model,
            config.d_model,
            config.num_labels,
            config.classifier_dropout,
        )

        # Initialize weights and apply final processing
        self.post_init()

    # Copied from transformers.models.bart.modeling_bart.BartForSequenceClassification.forward
    def forward(
        self,
        input_ids: mindspore.Tensor = None,
        attention_mask: Optional[mindspore.Tensor] = None,
        decoder_input_ids: Optional[mindspore.Tensor] = None,
        decoder_attention_mask: Optional[mindspore.Tensor] = None,
        head_mask: Optional[mindspore.Tensor] = None,
        decoder_head_mask: Optional[mindspore.Tensor] = None,
        cross_attn_head_mask: Optional[mindspore.Tensor] = None,
        encoder_outputs: Optional[List[mindspore.Tensor]] = None,
        inputs_embeds: Optional[mindspore.Tensor] = None,
        decoder_inputs_embeds: Optional[mindspore.Tensor] = None,
        labels: Optional[mindspore.Tensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, Seq2SeqSequenceClassifierOutput]:
        r"""
        labels (`mindspore.Tensor` of shape `(batch_size,)`, *optional*):
            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
            config.num_labels - 1]`. If `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        if labels is not None:
            use_cache = False

        if input_ids is None and inputs_embeds is not None:
            raise NotImplementedError(
                f"Passing input embeddings is currently not supported for {self.__class__.__name__}"
            )

        outputs = self.model(
            input_ids,
            attention_mask=attention_mask,
            decoder_input_ids=decoder_input_ids,
            decoder_attention_mask=decoder_attention_mask,
            head_mask=head_mask,
            decoder_head_mask=decoder_head_mask,
            cross_attn_head_mask=cross_attn_head_mask,
            encoder_outputs=encoder_outputs,
            inputs_embeds=inputs_embeds,
            decoder_inputs_embeds=decoder_inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        hidden_states = outputs[0]  # last hidden state

        eos_mask = input_ids.eq(self.config.eos_token_id)

        sentence_representation = hidden_states[eos_mask].view(hidden_states.shape[0], -1, hidden_states.shape[-1])[
            :, -1, :
        ]
        logits = self.classification_head(sentence_representation)

        loss = None
        if labels is not None:
            if self.config.problem_type is None:
                if self.config.num_labels == 1:
                    self.config.problem_type = "regression"
                elif self.config.num_labels > 1 and labels.dtype in (mindspore.int64, mindspore.int32):
                    self.config.problem_type = "single_label_classification"
                else:
                    self.config.problem_type = "multi_label_classification"

            if self.config.problem_type == "regression":
                loss_fct = MSELoss()
                if self.config.num_labels == 1:
                    loss = loss_fct(logits.squeeze(), labels.squeeze())
                else:
                    loss = loss_fct(logits, labels)
            elif self.config.problem_type == "single_label_classification":
                loss_fct = CrossEntropyLoss()
                loss = loss_fct(logits.view(-1, self.config.num_labels), labels.view(-1))
            elif self.config.problem_type == "multi_label_classification":
                loss_fct = BCEWithLogitsLoss()
                loss = loss_fct(logits, labels)
        if not return_dict:
            output = (logits,) + outputs[1:]
            return ((loss,) + output) if loss is not None else output

        return Seq2SeqSequenceClassifierOutput(
            loss=loss,
            logits=logits,
            past_key_values=outputs.past_key_values,
            decoder_hidden_states=outputs.decoder_hidden_states,
            decoder_attentions=outputs.decoder_attentions,
            cross_attentions=outputs.cross_attentions,
            encoder_last_hidden_state=outputs.encoder_last_hidden_state,
            encoder_hidden_states=outputs.encoder_hidden_states,
            encoder_attentions=outputs.encoder_attentions,
        )

mindnlp.transformers.models.bigbird_pegasus.modeling_bigbird_pegasus.BigBirdPegasusForSequenceClassification.forward(input_ids=None, attention_mask=None, decoder_input_ids=None, decoder_attention_mask=None, head_mask=None, decoder_head_mask=None, cross_attn_head_mask=None, encoder_outputs=None, inputs_embeds=None, decoder_inputs_embeds=None, labels=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None)

labels (mindspore.Tensor of shape (batch_size,), optional): Labels for computing the sequence classification/regression loss. Indices should be in [0, ..., config.num_labels - 1]. If config.num_labels > 1 a classification loss is computed (Cross-Entropy).

Source code in mindnlp\transformers\models\bigbird_pegasus\modeling_bigbird_pegasus.py 
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def forward(
    self,
    input_ids: mindspore.Tensor = None,
    attention_mask: Optional[mindspore.Tensor] = None,
    decoder_input_ids: Optional[mindspore.Tensor] = None,
    decoder_attention_mask: Optional[mindspore.Tensor] = None,
    head_mask: Optional[mindspore.Tensor] = None,
    decoder_head_mask: Optional[mindspore.Tensor] = None,
    cross_attn_head_mask: Optional[mindspore.Tensor] = None,
    encoder_outputs: Optional[List[mindspore.Tensor]] = None,
    inputs_embeds: Optional[mindspore.Tensor] = None,
    decoder_inputs_embeds: Optional[mindspore.Tensor] = None,
    labels: Optional[mindspore.Tensor] = None,
    use_cache: Optional[bool] = None,
    output_attentions: Optional[bool] = None,
    output_hidden_states: Optional[bool] = None,
    return_dict: Optional[bool] = None,
) -> Union[Tuple, Seq2SeqSequenceClassifierOutput]:
    r"""
    labels (`mindspore.Tensor` of shape `(batch_size,)`, *optional*):
        Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
        config.num_labels - 1]`. If `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
    """
    return_dict = return_dict if return_dict is not None else self.config.use_return_dict
    if labels is not None:
        use_cache = False

    if input_ids is None and inputs_embeds is not None:
        raise NotImplementedError(
            f"Passing input embeddings is currently not supported for {self.__class__.__name__}"
        )

    outputs = self.model(
        input_ids,
        attention_mask=attention_mask,
        decoder_input_ids=decoder_input_ids,
        decoder_attention_mask=decoder_attention_mask,
        head_mask=head_mask,
        decoder_head_mask=decoder_head_mask,
        cross_attn_head_mask=cross_attn_head_mask,
        encoder_outputs=encoder_outputs,
        inputs_embeds=inputs_embeds,
        decoder_inputs_embeds=decoder_inputs_embeds,
        use_cache=use_cache,
        output_attentions=output_attentions,
        output_hidden_states=output_hidden_states,
        return_dict=return_dict,
    )
    hidden_states = outputs[0]  # last hidden state

    eos_mask = input_ids.eq(self.config.eos_token_id)

    sentence_representation = hidden_states[eos_mask].view(hidden_states.shape[0], -1, hidden_states.shape[-1])[
        :, -1, :
    ]
    logits = self.classification_head(sentence_representation)

    loss = None
    if labels is not None:
        if self.config.problem_type is None:
            if self.config.num_labels == 1:
                self.config.problem_type = "regression"
            elif self.config.num_labels > 1 and labels.dtype in (mindspore.int64, mindspore.int32):
                self.config.problem_type = "single_label_classification"
            else:
                self.config.problem_type = "multi_label_classification"

        if self.config.problem_type == "regression":
            loss_fct = MSELoss()
            if self.config.num_labels == 1:
                loss = loss_fct(logits.squeeze(), labels.squeeze())
            else:
                loss = loss_fct(logits, labels)
        elif self.config.problem_type == "single_label_classification":
            loss_fct = CrossEntropyLoss()
            loss = loss_fct(logits.view(-1, self.config.num_labels), labels.view(-1))
        elif self.config.problem_type == "multi_label_classification":
            loss_fct = BCEWithLogitsLoss()
            loss = loss_fct(logits, labels)
    if not return_dict:
        output = (logits,) + outputs[1:]
        return ((loss,) + output) if loss is not None else output

    return Seq2SeqSequenceClassifierOutput(
        loss=loss,
        logits=logits,
        past_key_values=outputs.past_key_values,
        decoder_hidden_states=outputs.decoder_hidden_states,
        decoder_attentions=outputs.decoder_attentions,
        cross_attentions=outputs.cross_attentions,
        encoder_last_hidden_state=outputs.encoder_last_hidden_state,
        encoder_hidden_states=outputs.encoder_hidden_states,
        encoder_attentions=outputs.encoder_attentions,
    )

mindnlp.transformers.models.bigbird_pegasus.modeling_bigbird_pegasus.BigBirdPegasusModel

Bases: BigBirdPegasusPreTrainedModel

Source code in mindnlp\transformers\models\bigbird_pegasus\modeling_bigbird_pegasus.py 
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class BigBirdPegasusModel(BigBirdPegasusPreTrainedModel):
    _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]

    def __init__(self, config: BigBirdPegasusConfig):
        super().__init__(config)

        padding_idx, vocab_size = config.pad_token_id, config.vocab_size
        embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0
        self.shared = BigBirdPegasusScaledWordEmbedding(
            vocab_size, config.d_model, padding_idx, embed_scale=embed_scale
        )

        self.encoder = BigBirdPegasusEncoder(config, self.shared)
        self.decoder = BigBirdPegasusDecoder(config, self.shared)

        # Initialize weights and apply final processing
        self.post_init()

    def get_input_embeddings(self):
        return self.shared

    def set_input_embeddings(self, value):
        self.shared = value
        self.encoder.embed_tokens = self.shared
        self.decoder.embed_tokens = self.shared

    def _tie_weights(self):
        if self.config.tie_word_embeddings:
            self._tie_or_clone_weights(self.encoder.embed_tokens, self.shared)
            self._tie_or_clone_weights(self.decoder.embed_tokens, self.shared)

    def get_encoder(self):
        return self.encoder

    def get_decoder(self):
        return self.decoder

    # Copied from transformers.models.bart.modeling_bart.BartModel.forward with Bart->BigBirdPegasus
    def forward(
        self,
        input_ids: mindspore.Tensor = None,
        attention_mask: Optional[mindspore.Tensor] = None,
        decoder_input_ids: Optional[mindspore.Tensor] = None,
        decoder_attention_mask: Optional[mindspore.Tensor] = None,
        head_mask: Optional[mindspore.Tensor] = None,
        decoder_head_mask: Optional[mindspore.Tensor] = None,
        cross_attn_head_mask: Optional[mindspore.Tensor] = None,
        encoder_outputs: Optional[List[mindspore.Tensor]] = None,
        past_key_values: Optional[List[mindspore.Tensor]] = None,
        inputs_embeds: Optional[mindspore.Tensor] = None,
        decoder_inputs_embeds: Optional[mindspore.Tensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, Seq2SeqModelOutput]:
        # different to other models, BigBirdPegasus automatically creates decoder_input_ids from
        # input_ids if no decoder_input_ids are provided
        if decoder_input_ids is None and decoder_inputs_embeds is None:
            if input_ids is None:
                raise ValueError(
                    "If no `decoder_input_ids` or `decoder_inputs_embeds` are "
                    "passed, `input_ids` cannot be `None`. Please pass either "
                    "`input_ids` or `decoder_input_ids` or `decoder_inputs_embeds`."
                )

            decoder_input_ids = shift_tokens_right(
                input_ids, self.config.pad_token_id, self.config.decoder_start_token_id
            )

        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        use_cache = use_cache if use_cache is not None else self.config.use_cache
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        if encoder_outputs is None:
            encoder_outputs = self.encoder(
                input_ids=input_ids,
                attention_mask=attention_mask,
                head_mask=head_mask,
                inputs_embeds=inputs_embeds,
                output_attentions=output_attentions,
                output_hidden_states=output_hidden_states,
                return_dict=return_dict,
            )
        # If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True
        elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
            encoder_outputs = BaseModelOutput(
                last_hidden_state=encoder_outputs[0],
                hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
                attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
            )

        # decoder outputs consists of (dec_features, past_key_value, dec_hidden, dec_attn)
        decoder_outputs = self.decoder(
            input_ids=decoder_input_ids,
            attention_mask=decoder_attention_mask,
            encoder_hidden_states=encoder_outputs[0],
            encoder_attention_mask=attention_mask,
            head_mask=decoder_head_mask,
            cross_attn_head_mask=cross_attn_head_mask,
            past_key_values=past_key_values,
            inputs_embeds=decoder_inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        if not return_dict:
            return decoder_outputs + encoder_outputs

        return Seq2SeqModelOutput(
            last_hidden_state=decoder_outputs.last_hidden_state,
            past_key_values=decoder_outputs.past_key_values,
            decoder_hidden_states=decoder_outputs.hidden_states,
            decoder_attentions=decoder_outputs.attentions,
            cross_attentions=decoder_outputs.cross_attentions,
            encoder_last_hidden_state=encoder_outputs.last_hidden_state,
            encoder_hidden_states=encoder_outputs.hidden_states,
            encoder_attentions=encoder_outputs.attentions,
        )

mindnlp.transformers.models.bigbird_pegasus.modeling_bigbird_pegasus.BigBirdPegasusPreTrainedModel

Bases: PreTrainedModel

Source code in mindnlp\transformers\models\bigbird_pegasus\modeling_bigbird_pegasus.py 
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class BigBirdPegasusPreTrainedModel(PreTrainedModel):
    config_class = BigBirdPegasusConfig
    base_model_prefix = "model"
    supports_gradient_checkpointing = True
    _no_split_modules = ["BigBirdPegasusEncoderLayer", "BigBirdPegasusDecoderLayer"]
    _skip_keys_device_placement = "past_key_values"
    _supports_param_buffer_assignment = False

    def _init_weights(self, module):
        std = self.config.init_std
        if isinstance(module, nn.Linear):
            nn.init.normal_(module.weight, mean=0.0, std=std)
            if module.bias is not None:
                nn.init.zeros_(module.bias)
        elif isinstance(module, nn.Embedding):
            nn.init.normal_(module.weight, mean=0.0, std=std)
            if module.padding_idx is not None:
                module.weight[module.padding_idx] = 0

    @property
    def dummy_inputs(self):
        pad_token = self.config.pad_token_id
        input_ids = mindspore.tensor([[0, 6, 10, 4, 2], [0, 8, 12, 2, pad_token]])
        dummy_inputs = {
            "attention_mask": input_ids.ne(pad_token),
            "input_ids": input_ids,
        }
        return dummy_inputs