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xlm_roberta_xl

mindnlp.transformers.models.xlm_roberta_xl.configuration_xlm_roberta_xl

XLM_ROBERTa_XL configuration

mindnlp.transformers.models.xlm_roberta_xl.configuration_xlm_roberta_xl.XLMRobertaXLConfig

Bases: PretrainedConfig

This is the configuration class to store the configuration of a [XLMRobertaXLModel] or a [TFXLMRobertaXLModel]. It is used to instantiate a XLM_ROBERTA_XL 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 XLM_ROBERTA_XL facebook/xlm-roberta-xl 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 XLM_ROBERTA_XL model. Defines the number of different tokens that can be represented by the inputs_ids passed when calling [XLMRobertaXLModel].

TYPE: `int`, *optional*, defaults to 250880 DEFAULT: 250880

hidden_size

Dimensionality of the encoder layers and the pooler layer.

TYPE: `int`, *optional*, defaults to 2560 DEFAULT: 2560

num_hidden_layers

Number of hidden layers in the Transformer encoder.

TYPE: `int`, *optional*, defaults to 36 DEFAULT: 36

num_attention_heads

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

TYPE: `int`, *optional*, defaults to 32 DEFAULT: 32

intermediate_size

Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.

TYPE: `int`, *optional*, defaults to 10240 DEFAULT: 10240

hidden_act

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 `Callable`, *optional*, defaults to `"gelu"` DEFAULT: 'gelu'

hidden_dropout_prob

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_probs_dropout_prob

The dropout ratio for the attention probabilities.

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

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., 512 or 1024 or 2048).

TYPE: `int`, *optional*, defaults to 514 DEFAULT: 514

type_vocab_size

The vocabulary size of the token_type_ids passed when calling [XLMRobertaXLModel] or [TFXLMRobertaXLModel].

TYPE: `int`, *optional*, defaults to 1 DEFAULT: 1

initializer_range

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

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

layer_norm_eps

The epsilon used by the layer normalization layers.

TYPE: `float`, *optional*, defaults to 1e-5 DEFAULT: 1e-05

position_embedding_type

Type of position embedding. Choose one of "absolute", "relative_key", "relative_key_query". For positional embeddings use "absolute". For more information on "relative_key", please refer to Self-Attention with Relative Position Representations (Shaw et al.). For more information on "relative_key_query", please refer to Method 4 in Improve Transformer Models with Better Relative Position Embeddings (Huang et al.).

TYPE: `str`, *optional*, defaults to `"absolute"` DEFAULT: 'absolute'

use_cache

Whether or not the model should return the last key/values attentions (not used by all models). Only relevant if config.is_decoder=True.

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

classifier_dropout

The dropout ratio for the classification head.

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

Example 
>>> from transformers import XLMRobertaXLConfig, XLMRobertaXLModel
...
>>> # Initializing a XLM_ROBERTA_XL google-bert/bert-base-uncased style configuration
>>> configuration = XLMRobertaXLConfig()
...
>>> # Initializing a model (with random weights) from the google-bert/bert-base-uncased style configuration
>>> model = XLMRobertaXLModel(configuration)
...
>>> # Accessing the model configuration
>>> configuration = model.config
Source code in mindnlp/transformers/models/xlm_roberta_xl/configuration_xlm_roberta_xl.py 
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class XLMRobertaXLConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`XLMRobertaXLModel`] or a [`TFXLMRobertaXLModel`].
    It is used to instantiate a XLM_ROBERTA_XL 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
    XLM_ROBERTA_XL [facebook/xlm-roberta-xl](https://huggingface.co/facebook/xlm-roberta-xl) 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 250880):
            Vocabulary size of the XLM_ROBERTA_XL model. Defines the number of different tokens that can be represented
            by the `inputs_ids` passed when calling [`XLMRobertaXLModel`].
        hidden_size (`int`, *optional*, defaults to 2560):
            Dimensionality of the encoder layers and the pooler layer.
        num_hidden_layers (`int`, *optional*, defaults to 36):
            Number of hidden layers in the Transformer encoder.
        num_attention_heads (`int`, *optional*, defaults to 32):
            Number of attention heads for each attention layer in the Transformer encoder.
        intermediate_size (`int`, *optional*, defaults to 10240):
            Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
        hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`):
            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
            `"relu"`, `"silu"` and `"gelu_new"` are supported.
        hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
        attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
            The dropout ratio for the attention probabilities.
        max_position_embeddings (`int`, *optional*, defaults to 514):
            The maximum sequence length that this model might ever be used with. Typically set this to something large
            just in case (e.g., 512 or 1024 or 2048).
        type_vocab_size (`int`, *optional*, defaults to 1):
            The vocabulary size of the `token_type_ids` passed when calling [`XLMRobertaXLModel`] or
            [`TFXLMRobertaXLModel`].
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        layer_norm_eps (`float`, *optional*, defaults to 1e-5):
            The epsilon used by the layer normalization layers.
        position_embedding_type (`str`, *optional*, defaults to `"absolute"`):
            Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For
            positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to
            [Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155).
            For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models
            with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658).
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models). Only
            relevant if `config.is_decoder=True`.
        classifier_dropout (`float`, *optional*):
            The dropout ratio for the classification head.

    Example:
        ```python
        >>> from transformers import XLMRobertaXLConfig, XLMRobertaXLModel
        ...
        >>> # Initializing a XLM_ROBERTA_XL google-bert/bert-base-uncased style configuration
        >>> configuration = XLMRobertaXLConfig()
        ...
        >>> # Initializing a model (with random weights) from the google-bert/bert-base-uncased style configuration
        >>> model = XLMRobertaXLModel(configuration)
        ...
        >>> # Accessing the model configuration
        >>> configuration = model.config
        ```
    """

    model_type = "xlm-roberta-xl"

    def __init__(
        self,
        vocab_size=250880,
        hidden_size=2560,
        num_hidden_layers=36,
        num_attention_heads=32,
        intermediate_size=10240,
        hidden_act="gelu",
        hidden_dropout_prob=0.1,
        attention_probs_dropout_prob=0.1,
        max_position_embeddings=514,
        type_vocab_size=1,
        initializer_range=0.02,
        layer_norm_eps=1e-05,
        pad_token_id=1,
        bos_token_id=0,
        eos_token_id=2,
        position_embedding_type="absolute",
        use_cache=True,
        classifier_dropout=None,
        **kwargs,
    ):
        super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.hidden_act = hidden_act
        self.intermediate_size = intermediate_size
        self.hidden_dropout_prob = hidden_dropout_prob
        self.attention_probs_dropout_prob = attention_probs_dropout_prob
        self.max_position_embeddings = max_position_embeddings
        self.type_vocab_size = type_vocab_size
        self.initializer_range = initializer_range
        self.layer_norm_eps = layer_norm_eps
        self.position_embedding_type = position_embedding_type
        self.use_cache = use_cache
        self.classifier_dropout = classifier_dropout

mindnlp.transformers.models.xlm_roberta_xl.modeling_xlm_roberta_xl

PyTorch XLM RoBERTa xl,xxl model.

mindnlp.transformers.models.xlm_roberta_xl.modeling_xlm_roberta_xl.XLMRobertaXLClassificationHead

Bases: Module

Head for sentence-level classification tasks.

Source code in mindnlp/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py 
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class XLMRobertaXLClassificationHead(nn.Module):
    """Head for sentence-level classification tasks."""
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        classifier_dropout = (
            config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
        )
        self.dropout = nn.Dropout(p=classifier_dropout)
        self.out_proj = nn.Linear(config.hidden_size, config.num_labels)

    def forward(self, features, **kwargs):
        x = features[:, 0, :]  # take <s> token (equiv. to [CLS])
        x = self.dropout(x)
        x = self.dense(x)
        x = ops.tanh(x)
        x = self.dropout(x)
        x = self.out_proj(x)
        return x

mindnlp.transformers.models.xlm_roberta_xl.modeling_xlm_roberta_xl.XLMRobertaXLEmbeddings

Bases: Module

Same as BertEmbeddings with a tiny tweak for positional embeddings indexing.

Source code in mindnlp/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py 
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class XLMRobertaXLEmbeddings(nn.Module):
    """
    Same as BertEmbeddings with a tiny tweak for positional embeddings indexing.
    """
    # Copied from transformers.models.bert.modeling_bert.BertEmbeddings.__init__
    def __init__(self, config):
        """
        __init__

        Initializes a new instance of the XLMRobertaEmbeddings class.

        Args:
            self: The instance of the XLMRobertaEmbeddings class.
            config: An object containing configuration parameters for the XLMRoberta model.
                It includes the following attributes:

                - vocab_size (int): The size of the vocabulary.
                - hidden_size (int): The dimension of the hidden layers.
                - max_position_embeddings (int): The maximum number of positional embeddings.
                - type_vocab_size (int): The size of the token type vocabulary.
                - layer_norm_eps (float): The epsilon value for layer normalization.
                - hidden_dropout_prob (float): The dropout probability.
                - position_embedding_type (str, optional): The type of position embedding. Defaults to 'absolute'.
                - pad_token_id (int): The id of the padding token.

        Returns:
            None.

        Raises:
            None.
        """
        super().__init__()
        self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
        self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
        self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)

        # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
        # any TensorFlow checkpoint file
        self.dropout = nn.Dropout(p=config.hidden_dropout_prob)
        # position_ids (1, len position emb) is contiguous in memory and exported when serialized
        self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
        self.position_ids = ops.arange(config.max_position_embeddings).broadcast_to((1, -1))
        self.token_type_ids = ops.zeros(self.position_ids.shape, dtype=mindspore.int64)

        # End copy
        self.padding_idx = config.pad_token_id
        self.position_embeddings = nn.Embedding(
            config.max_position_embeddings, config.hidden_size, padding_idx=self.padding_idx
        )

    def forward(
        self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0
    ):
        """
        Class: XLMRobertaEmbeddings

        Method: forward

        This method forwards the embeddings for the XLM-Roberta model.

        Args:
            self: (object) The instance of the class.
            input_ids: (Tensor, optional) The input tensor containing the token ids. Default is None.
            token_type_ids: (Tensor, optional) The input tensor containing the token type ids. Default is None.
            position_ids: (Tensor, optional) The input tensor containing the position ids. Default is None.
            inputs_embeds: (Tensor, optional) The input embeddings tensor. Default is None.
            past_key_values_length: (int) The length of the past key values. Default is 0.

        Returns:
            embeddings: (Tensor) The forwarded embeddings for the XLM-Roberta model.

        Raises:
            ValueError: If both input_ids and inputs_embeds are None,
                or if an unsupported position_embedding_type is provided.
            IndexError: If input_ids or inputs_embeds do not have the expected shape.
            AttributeError: If the 'token_type_ids' attribute is missing in the class.
        """
        if position_ids is None:
            if input_ids is not None:
                # Create the position ids from the input token ids. Any padded tokens remain padded.
                position_ids = create_position_ids_from_input_ids(input_ids, self.padding_idx, past_key_values_length)
            else:
                position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds)

        if input_ids is not None:
            input_shape = input_ids.shape
        else:
            input_shape = inputs_embeds.shape[:-1]

        seq_length = input_shape[1]

        # Setting the token_type_ids to the registered buffer in forwardor where it is all zeros, which usually occurs
        # when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves
        # issue #5664
        if token_type_ids is None:
            if hasattr(self, "token_type_ids"):
                buffered_token_type_ids = self.token_type_ids[:, :seq_length]
                buffered_token_type_ids_expanded = buffered_token_type_ids.expand(input_shape[0], seq_length)
                token_type_ids = buffered_token_type_ids_expanded
            else:
                token_type_ids = ops.zeros(input_shape, dtype=mindspore.int64)

        if inputs_embeds is None:
            inputs_embeds = self.word_embeddings(input_ids)
        token_type_embeddings = self.token_type_embeddings(token_type_ids)

        embeddings = inputs_embeds + token_type_embeddings
        if self.position_embedding_type == "absolute":
            position_embeddings = self.position_embeddings(position_ids)
            embeddings += position_embeddings
        embeddings = self.dropout(embeddings)
        return embeddings

    def create_position_ids_from_inputs_embeds(self, inputs_embeds):
        """
        We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids.

        Args:
            inputs_embeds: mindspore.Tensor

        Returns:
            mindspore.Tensor
        """
        input_shape = inputs_embeds.shape[:-1]
        sequence_length = input_shape[1]

        position_ids = ops.arange(
            self.padding_idx + 1, sequence_length + self.padding_idx + 1, dtype=mindspore.int64)
        return position_ids.unsqueeze(0).broadcast_to(input_shape)

mindnlp.transformers.models.xlm_roberta_xl.modeling_xlm_roberta_xl.XLMRobertaXLEmbeddings.__init__(config)

init

Initializes a new instance of the XLMRobertaEmbeddings class.

PARAMETER DESCRIPTION
self

The instance of the XLMRobertaEmbeddings class.
config

An object containing configuration parameters for the XLMRoberta model. It includes the following attributes:

  • vocab_size (int): The size of the vocabulary.
  • hidden_size (int): The dimension of the hidden layers.
  • max_position_embeddings (int): The maximum number of positional embeddings.
  • type_vocab_size (int): The size of the token type vocabulary.
  • layer_norm_eps (float): The epsilon value for layer normalization.
  • hidden_dropout_prob (float): The dropout probability.
  • position_embedding_type (str, optional): The type of position embedding. Defaults to 'absolute'.
  • pad_token_id (int): The id of the padding token.
RETURNS DESCRIPTION

None.
Source code in mindnlp/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py 
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def __init__(self, config):
    """
    __init__

    Initializes a new instance of the XLMRobertaEmbeddings class.

    Args:
        self: The instance of the XLMRobertaEmbeddings class.
        config: An object containing configuration parameters for the XLMRoberta model.
            It includes the following attributes:

            - vocab_size (int): The size of the vocabulary.
            - hidden_size (int): The dimension of the hidden layers.
            - max_position_embeddings (int): The maximum number of positional embeddings.
            - type_vocab_size (int): The size of the token type vocabulary.
            - layer_norm_eps (float): The epsilon value for layer normalization.
            - hidden_dropout_prob (float): The dropout probability.
            - position_embedding_type (str, optional): The type of position embedding. Defaults to 'absolute'.
            - pad_token_id (int): The id of the padding token.

    Returns:
        None.

    Raises:
        None.
    """
    super().__init__()
    self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
    self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
    self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)

    # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
    # any TensorFlow checkpoint file
    self.dropout = nn.Dropout(p=config.hidden_dropout_prob)
    # position_ids (1, len position emb) is contiguous in memory and exported when serialized
    self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
    self.position_ids = ops.arange(config.max_position_embeddings).broadcast_to((1, -1))
    self.token_type_ids = ops.zeros(self.position_ids.shape, dtype=mindspore.int64)

    # End copy
    self.padding_idx = config.pad_token_id
    self.position_embeddings = nn.Embedding(
        config.max_position_embeddings, config.hidden_size, padding_idx=self.padding_idx
    )

mindnlp.transformers.models.xlm_roberta_xl.modeling_xlm_roberta_xl.XLMRobertaXLEmbeddings.create_position_ids_from_inputs_embeds(inputs_embeds)

We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids.

PARAMETER DESCRIPTION
inputs_embeds

mindspore.Tensor
RETURNS DESCRIPTION

mindspore.Tensor
Source code in mindnlp/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py 
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def create_position_ids_from_inputs_embeds(self, inputs_embeds):
    """
    We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids.

    Args:
        inputs_embeds: mindspore.Tensor

    Returns:
        mindspore.Tensor
    """
    input_shape = inputs_embeds.shape[:-1]
    sequence_length = input_shape[1]

    position_ids = ops.arange(
        self.padding_idx + 1, sequence_length + self.padding_idx + 1, dtype=mindspore.int64)
    return position_ids.unsqueeze(0).broadcast_to(input_shape)

mindnlp.transformers.models.xlm_roberta_xl.modeling_xlm_roberta_xl.XLMRobertaXLEmbeddings.forward(input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0)

Method: forward

This method forwards the embeddings for the XLM-Roberta model.

PARAMETER DESCRIPTION
self

(object) The instance of the class.
input_ids

(Tensor, optional) The input tensor containing the token ids. Default is None.

DEFAULT: None

token_type_ids

(Tensor, optional) The input tensor containing the token type ids. Default is None.

DEFAULT: None

position_ids

(Tensor, optional) The input tensor containing the position ids. Default is None.

DEFAULT: None

inputs_embeds

(Tensor, optional) The input embeddings tensor. Default is None.

DEFAULT: None

past_key_values_length

(int) The length of the past key values. Default is 0.

DEFAULT: 0

RETURNS DESCRIPTION
embeddings

(Tensor) The forwarded embeddings for the XLM-Roberta model.
RAISES DESCRIPTION
ValueError

If both input_ids and inputs_embeds are None, or if an unsupported position_embedding_type is provided.
IndexError

If input_ids or inputs_embeds do not have the expected shape.
AttributeError

If the 'token_type_ids' attribute is missing in the class.
Source code in mindnlp/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py 
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def forward(
    self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0
):
    """
    Class: XLMRobertaEmbeddings

    Method: forward

    This method forwards the embeddings for the XLM-Roberta model.

    Args:
        self: (object) The instance of the class.
        input_ids: (Tensor, optional) The input tensor containing the token ids. Default is None.
        token_type_ids: (Tensor, optional) The input tensor containing the token type ids. Default is None.
        position_ids: (Tensor, optional) The input tensor containing the position ids. Default is None.
        inputs_embeds: (Tensor, optional) The input embeddings tensor. Default is None.
        past_key_values_length: (int) The length of the past key values. Default is 0.

    Returns:
        embeddings: (Tensor) The forwarded embeddings for the XLM-Roberta model.

    Raises:
        ValueError: If both input_ids and inputs_embeds are None,
            or if an unsupported position_embedding_type is provided.
        IndexError: If input_ids or inputs_embeds do not have the expected shape.
        AttributeError: If the 'token_type_ids' attribute is missing in the class.
    """
    if position_ids is None:
        if input_ids is not None:
            # Create the position ids from the input token ids. Any padded tokens remain padded.
            position_ids = create_position_ids_from_input_ids(input_ids, self.padding_idx, past_key_values_length)
        else:
            position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds)

    if input_ids is not None:
        input_shape = input_ids.shape
    else:
        input_shape = inputs_embeds.shape[:-1]

    seq_length = input_shape[1]

    # Setting the token_type_ids to the registered buffer in forwardor where it is all zeros, which usually occurs
    # when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves
    # issue #5664
    if token_type_ids is None:
        if hasattr(self, "token_type_ids"):
            buffered_token_type_ids = self.token_type_ids[:, :seq_length]
            buffered_token_type_ids_expanded = buffered_token_type_ids.expand(input_shape[0], seq_length)
            token_type_ids = buffered_token_type_ids_expanded
        else:
            token_type_ids = ops.zeros(input_shape, dtype=mindspore.int64)

    if inputs_embeds is None:
        inputs_embeds = self.word_embeddings(input_ids)
    token_type_embeddings = self.token_type_embeddings(token_type_ids)

    embeddings = inputs_embeds + token_type_embeddings
    if self.position_embedding_type == "absolute":
        position_embeddings = self.position_embeddings(position_ids)
        embeddings += position_embeddings
    embeddings = self.dropout(embeddings)
    return embeddings

mindnlp.transformers.models.xlm_roberta_xl.modeling_xlm_roberta_xl.XLMRobertaXLForCausalLM

Bases: XLMRobertaXLPreTrainedModel

Source code in mindnlp/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py 
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class XLMRobertaXLForCausalLM(XLMRobertaXLPreTrainedModel):
    _tied_weights_keys = ["lm_head.decoder.weight", "lm_head.decoder.bias"]

    def __init__(self, config):
        """
        Initializes an instance of the XLMRobertaForCausalLM class.

        Args:
            self: The instance of the class.
            config: An object representing the configuration for the XLMRobertaForCausalLM model.

        Returns:
            None.

        Raises:
            None.
        """
        super().__init__(config)

        if not config.is_decoder:
            logger.warning("If you want to use `XLMRobertaLMHeadModel` as a standalone, add `is_decoder=True.`")
        self.roberta = XLMRobertaXLModel(config, add_pooling_layer=False)
        self.lm_head = XLMRobertaXLLMHead(config)

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

    def get_output_embeddings(self):
        """
        Method to retrieve the output embeddings from XLMRobertaForCausalLM model.

        Args:
            self (XLMRobertaForCausalLM): The instance of the XLMRobertaForCausalLM class.
                It is used to access the decoder of the model to get the output embeddings.

        Returns:
            None: This method does not return any value but directly provides access to the output embeddings through
                the decoder.

        Raises:
            None.
        """
        return self.lm_head.decoder

    def set_output_embeddings(self, new_embeddings):
        """
        Sets the output embeddings for the XLMRobertaForCausalLM model.

        Args:
            self (XLMRobertaForCausalLM): The instance of the XLMRobertaForCausalLM class.
            new_embeddings (torch.nn.Module): The new embeddings to be set as the output embeddings for the model.

        Returns:
            None.

        Raises:
            None.

        Note:
            The output embeddings are used in the decoder layer of the XLMRobertaForCausalLM model.
            By setting new embeddings, users can customize the output layer of the model according to their specific
            requirements.

        Example:
            ```python
            >>> model = XLMRobertaForCausalLM.from_pretrained('xlm-roberta-base')
            >>> new_embeddings = torch.nn.Embedding(10, 768)
            >>> model.set_output_embeddings(new_embeddings)
            ```
        """
        self.lm_head.decoder = new_embeddings
        self.lm_head.bias = new_embeddings.bias
    def forward(
        self,
        input_ids: Optional[mindspore.Tensor] = None,
        attention_mask: Optional[mindspore.Tensor] = None,
        token_type_ids: Optional[mindspore.Tensor] = None,
        position_ids: Optional[mindspore.Tensor] = None,
        head_mask: Optional[mindspore.Tensor] = None,
        inputs_embeds: Optional[mindspore.Tensor] = None,
        encoder_hidden_states: Optional[mindspore.Tensor] = None,
        encoder_attention_mask: Optional[mindspore.Tensor] = None,
        labels: Optional[mindspore.Tensor] = None,
        past_key_values: Tuple[Tuple[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[mindspore.Tensor], CausalLMOutputWithCrossAttentions]:
        r"""
        Args:
            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]`:

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

            labels (`mindspore.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
                Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in
                `[-100, 0, ..., config.vocab_size]` (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]`
            past_key_values (`tuple(tuple(mindspore.Tensor))` of length `config.n_layers` with each tuple having 4 tensors of
                shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
                Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up 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)`.
            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`).

        Returns:
            `Union[Tuple[mindspore.Tensor], CausalLMOutputWithCrossAttentions]`

        Example:
            ```python
            >>> from transformers import AutoTokenizer, XLMRobertaForCausalLM, AutoConfig
            ...
            >>> tokenizer = AutoTokenizer.from_pretrained("roberta-base")
            >>> config = AutoConfig.from_pretrained("roberta-base")
            >>> config.is_decoder = True
            >>> model = XLMRobertaForCausalLM.from_pretrained("roberta-base", config=config)
            ...
            >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
            >>> outputs = model(**inputs)
            ...
            >>> prediction_logits = outputs.logits
            ```
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        if labels is not None:
            use_cache = False

        outputs = self.roberta(
            input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            past_key_values=past_key_values,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        sequence_output = outputs[0]
        prediction_scores = self.lm_head(sequence_output)

        lm_loss = None
        if labels is not None:
            # we are doing next-token prediction; shift prediction scores and input ids by one
            shifted_prediction_scores = prediction_scores[:, :-1, :]
            labels = labels[:, 1:]
            lm_loss = ops.cross_entropy(shifted_prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))

        if not return_dict:
            output = (prediction_scores,) + outputs[2:]
            return ((lm_loss,) + output) if lm_loss is not None else output

        return CausalLMOutputWithCrossAttentions(
            loss=lm_loss,
            logits=prediction_scores,
            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, **model_kwargs):
        input_shape = input_ids.shape
        # 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 = input_ids.new_ones(input_shape)

        # 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 input_ids.shape[1] > past_length:
                remove_prefix_length = past_length
            else:
                # Default to old behavior: keep only final ID
                remove_prefix_length = input_ids.shape[1] - 1

            input_ids = input_ids[:, remove_prefix_length:]

        return {"input_ids": input_ids, "attention_mask": attention_mask, "past_key_values": past_key_values}

    def _reorder_cache(self, past_key_values, beam_idx):
        """
        Reorders the cache of past key values based on the provided beam index.

        Args:
            self (XLMRobertaForCausalLM): The instance of XLMRobertaForCausalLM.
            past_key_values (tuple): A tuple of past key values for each layer.
            beam_idx (torch.Tensor): A tensor containing the beam indices.

        Returns:
            None.

        Raises:
            IndexError: If the beam index is out of range for the past_key_values.
            TypeError: If the input types are incorrect or incompatible.
        """
        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.xlm_roberta_xl.modeling_xlm_roberta_xl.XLMRobertaXLForCausalLM.__init__(config)

Initializes an instance of the XLMRobertaForCausalLM class.

PARAMETER DESCRIPTION
self

The instance of the class.
config

An object representing the configuration for the XLMRobertaForCausalLM model.
RETURNS DESCRIPTION

None.
Source code in mindnlp/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py 
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def __init__(self, config):
    """
    Initializes an instance of the XLMRobertaForCausalLM class.

    Args:
        self: The instance of the class.
        config: An object representing the configuration for the XLMRobertaForCausalLM model.

    Returns:
        None.

    Raises:
        None.
    """
    super().__init__(config)

    if not config.is_decoder:
        logger.warning("If you want to use `XLMRobertaLMHeadModel` as a standalone, add `is_decoder=True.`")
    self.roberta = XLMRobertaXLModel(config, add_pooling_layer=False)
    self.lm_head = XLMRobertaXLLMHead(config)

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

mindnlp.transformers.models.xlm_roberta_xl.modeling_xlm_roberta_xl.XLMRobertaXLForCausalLM.forward(input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, encoder_hidden_states=None, encoder_attention_mask=None, labels=None, past_key_values=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None)

PARAMETER DESCRIPTION
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]:

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

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

labels

Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in [-100, 0, ..., config.vocab_size] (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).

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

RETURNS DESCRIPTION
Union[Tuple[Tensor], CausalLMOutputWithCrossAttentions]

Union[Tuple[mindspore.Tensor], CausalLMOutputWithCrossAttentions]
Example 
>>> from transformers import AutoTokenizer, XLMRobertaForCausalLM, AutoConfig
...
>>> tokenizer = AutoTokenizer.from_pretrained("roberta-base")
>>> config = AutoConfig.from_pretrained("roberta-base")
>>> config.is_decoder = True
>>> model = XLMRobertaForCausalLM.from_pretrained("roberta-base", config=config)
...
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> outputs = model(**inputs)
...
>>> prediction_logits = outputs.logits
Source code in mindnlp/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py 
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def forward(
    self,
    input_ids: Optional[mindspore.Tensor] = None,
    attention_mask: Optional[mindspore.Tensor] = None,
    token_type_ids: Optional[mindspore.Tensor] = None,
    position_ids: Optional[mindspore.Tensor] = None,
    head_mask: Optional[mindspore.Tensor] = None,
    inputs_embeds: Optional[mindspore.Tensor] = None,
    encoder_hidden_states: Optional[mindspore.Tensor] = None,
    encoder_attention_mask: Optional[mindspore.Tensor] = None,
    labels: Optional[mindspore.Tensor] = None,
    past_key_values: Tuple[Tuple[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[mindspore.Tensor], CausalLMOutputWithCrossAttentions]:
    r"""
    Args:
        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]`:

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

        labels (`mindspore.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
            Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in
            `[-100, 0, ..., config.vocab_size]` (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]`
        past_key_values (`tuple(tuple(mindspore.Tensor))` of length `config.n_layers` with each tuple having 4 tensors of
            shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
            Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up 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)`.
        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`).

    Returns:
        `Union[Tuple[mindspore.Tensor], CausalLMOutputWithCrossAttentions]`

    Example:
        ```python
        >>> from transformers import AutoTokenizer, XLMRobertaForCausalLM, AutoConfig
        ...
        >>> tokenizer = AutoTokenizer.from_pretrained("roberta-base")
        >>> config = AutoConfig.from_pretrained("roberta-base")
        >>> config.is_decoder = True
        >>> model = XLMRobertaForCausalLM.from_pretrained("roberta-base", config=config)
        ...
        >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
        >>> outputs = model(**inputs)
        ...
        >>> prediction_logits = outputs.logits
        ```
    """
    return_dict = return_dict if return_dict is not None else self.config.use_return_dict
    if labels is not None:
        use_cache = False

    outputs = self.roberta(
        input_ids,
        attention_mask=attention_mask,
        token_type_ids=token_type_ids,
        position_ids=position_ids,
        head_mask=head_mask,
        inputs_embeds=inputs_embeds,
        encoder_hidden_states=encoder_hidden_states,
        encoder_attention_mask=encoder_attention_mask,
        past_key_values=past_key_values,
        use_cache=use_cache,
        output_attentions=output_attentions,
        output_hidden_states=output_hidden_states,
        return_dict=return_dict,
    )

    sequence_output = outputs[0]
    prediction_scores = self.lm_head(sequence_output)

    lm_loss = None
    if labels is not None:
        # we are doing next-token prediction; shift prediction scores and input ids by one
        shifted_prediction_scores = prediction_scores[:, :-1, :]
        labels = labels[:, 1:]
        lm_loss = ops.cross_entropy(shifted_prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))

    if not return_dict:
        output = (prediction_scores,) + outputs[2:]
        return ((lm_loss,) + output) if lm_loss is not None else output

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

mindnlp.transformers.models.xlm_roberta_xl.modeling_xlm_roberta_xl.XLMRobertaXLForCausalLM.get_output_embeddings()

Method to retrieve the output embeddings from XLMRobertaForCausalLM model.

PARAMETER DESCRIPTION
self

The instance of the XLMRobertaForCausalLM class. It is used to access the decoder of the model to get the output embeddings.

TYPE: XLMRobertaForCausalLM

RETURNS DESCRIPTION
None

This method does not return any value but directly provides access to the output embeddings through the decoder.
Source code in mindnlp/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py 
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def get_output_embeddings(self):
    """
    Method to retrieve the output embeddings from XLMRobertaForCausalLM model.

    Args:
        self (XLMRobertaForCausalLM): The instance of the XLMRobertaForCausalLM class.
            It is used to access the decoder of the model to get the output embeddings.

    Returns:
        None: This method does not return any value but directly provides access to the output embeddings through
            the decoder.

    Raises:
        None.
    """
    return self.lm_head.decoder

mindnlp.transformers.models.xlm_roberta_xl.modeling_xlm_roberta_xl.XLMRobertaXLForCausalLM.set_output_embeddings(new_embeddings)

Sets the output embeddings for the XLMRobertaForCausalLM model.

PARAMETER DESCRIPTION
self

The instance of the XLMRobertaForCausalLM class.

TYPE: XLMRobertaForCausalLM

new_embeddings

The new embeddings to be set as the output embeddings for the model.

TYPE: Module

RETURNS DESCRIPTION

None.
Note

The output embeddings are used in the decoder layer of the XLMRobertaForCausalLM model. By setting new embeddings, users can customize the output layer of the model according to their specific requirements.

Example 
>>> model = XLMRobertaForCausalLM.from_pretrained('xlm-roberta-base')
>>> new_embeddings = torch.nn.Embedding(10, 768)
>>> model.set_output_embeddings(new_embeddings)
Source code in mindnlp/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py 
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def set_output_embeddings(self, new_embeddings):
    """
    Sets the output embeddings for the XLMRobertaForCausalLM model.

    Args:
        self (XLMRobertaForCausalLM): The instance of the XLMRobertaForCausalLM class.
        new_embeddings (torch.nn.Module): The new embeddings to be set as the output embeddings for the model.

    Returns:
        None.

    Raises:
        None.

    Note:
        The output embeddings are used in the decoder layer of the XLMRobertaForCausalLM model.
        By setting new embeddings, users can customize the output layer of the model according to their specific
        requirements.

    Example:
        ```python
        >>> model = XLMRobertaForCausalLM.from_pretrained('xlm-roberta-base')
        >>> new_embeddings = torch.nn.Embedding(10, 768)
        >>> model.set_output_embeddings(new_embeddings)
        ```
    """
    self.lm_head.decoder = new_embeddings
    self.lm_head.bias = new_embeddings.bias

mindnlp.transformers.models.xlm_roberta_xl.modeling_xlm_roberta_xl.XLMRobertaXLForMaskedLM

Bases: XLMRobertaXLPreTrainedModel

XLMRobertaForMaskedLM

Source code in mindnlp/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py 
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class XLMRobertaXLForMaskedLM(XLMRobertaXLPreTrainedModel):
    """XLMRobertaForMaskedLM"""
    _tied_weights_keys = ["lm_head.decoder.weight", "lm_head.decoder.bias"]

    def __init__(self, config):
        """
        Initializes an instance of XLMRobertaForMaskedLM.

        Args:
            self: The instance of the class.
            config (object): The configuration object containing the settings for the model.
                It should have attributes like 'is_decoder' to control the behavior of the model.
                If 'is_decoder' is set to True, a warning message will be logged.
                Ensure that 'is_decoder' is set to False for bi-directional self-attention.

        Returns:
            None.

        Raises:
            None.
        """
        super().__init__(config)

        if config.is_decoder:
            logger.warning(
                "If you want to use `XLMRobertaForMaskedLM` make sure `config.is_decoder=False` for "
                "bi-directional self-attention."
            )

        self.roberta = XLMRobertaXLModel(config, add_pooling_layer=False)
        self.lm_head = XLMRobertaXLLMHead(config)

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

    def get_output_embeddings(self):
        """Get the output embeddings for the XLM-Roberta model.

        Args:
            self (XLMRobertaForMaskedLM): The instance of the XLMRobertaForMaskedLM class.

        Returns:
            None.

        Raises:
            None.
        """
        return self.lm_head.decoder

    def set_output_embeddings(self, new_embeddings):
        """
        This method sets the output embeddings for the XLMRobertaForMaskedLM model.

        Args:
            self (XLMRobertaForMaskedLM): The instance of the XLMRobertaForMaskedLM class.
            new_embeddings (torch.nn.Module): The new embeddings to be set as the output embeddings for the model. 
                It should be an instance of torch.nn.Module representing the new embeddings.

        Returns:
            None.

        Raises:
            TypeError: If the new_embeddings parameter is not an instance of torch.nn.Module.
            AttributeError: If the lm_head.decoder attribute does not exist or is not accessible within the
                XLMRobertaForMaskedLM instance.
        """
        self.lm_head.decoder = new_embeddings
        self.lm_head.bias = new_embeddings.bias

    def forward(
        self,
        input_ids: Optional[mindspore.Tensor] = None,
        attention_mask: Optional[mindspore.Tensor] = None,
        token_type_ids: Optional[mindspore.Tensor] = None,
        position_ids: Optional[mindspore.Tensor] = None,
        head_mask: Optional[mindspore.Tensor] = None,
        inputs_embeds: Optional[mindspore.Tensor] = None,
        encoder_hidden_states: Optional[mindspore.Tensor] = None,
        encoder_attention_mask: Optional[mindspore.Tensor] = None,
        labels: Optional[mindspore.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple[mindspore.Tensor], MaskedLMOutput]:
        r"""
        Args:
            labels (`mindspore.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
                Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
                config.vocab_size]` (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]`
            kwargs (`Dict[str, any]`, optional, defaults to *{}*):
                Used to hide legacy arguments that have been deprecated.
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        outputs = self.roberta(
            input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        sequence_output = outputs[0]
        prediction_scores = self.lm_head(sequence_output)

        masked_lm_loss = None
        if labels is not None:
            # move labels to correct device to enable model parallelism
            masked_lm_loss = ops.cross_entropy(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))

        if not return_dict:
            output = (prediction_scores,) + outputs[2:]
            return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output

        return MaskedLMOutput(
            loss=masked_lm_loss,
            logits=prediction_scores,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )

mindnlp.transformers.models.xlm_roberta_xl.modeling_xlm_roberta_xl.XLMRobertaXLForMaskedLM.__init__(config)

Initializes an instance of XLMRobertaForMaskedLM.

PARAMETER DESCRIPTION
self

The instance of the class.
config

The configuration object containing the settings for the model. It should have attributes like 'is_decoder' to control the behavior of the model. If 'is_decoder' is set to True, a warning message will be logged. Ensure that 'is_decoder' is set to False for bi-directional self-attention.

TYPE: object

RETURNS DESCRIPTION

None.
Source code in mindnlp/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py 
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def __init__(self, config):
    """
    Initializes an instance of XLMRobertaForMaskedLM.

    Args:
        self: The instance of the class.
        config (object): The configuration object containing the settings for the model.
            It should have attributes like 'is_decoder' to control the behavior of the model.
            If 'is_decoder' is set to True, a warning message will be logged.
            Ensure that 'is_decoder' is set to False for bi-directional self-attention.

    Returns:
        None.

    Raises:
        None.
    """
    super().__init__(config)

    if config.is_decoder:
        logger.warning(
            "If you want to use `XLMRobertaForMaskedLM` make sure `config.is_decoder=False` for "
            "bi-directional self-attention."
        )

    self.roberta = XLMRobertaXLModel(config, add_pooling_layer=False)
    self.lm_head = XLMRobertaXLLMHead(config)

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

mindnlp.transformers.models.xlm_roberta_xl.modeling_xlm_roberta_xl.XLMRobertaXLForMaskedLM.forward(input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, encoder_hidden_states=None, encoder_attention_mask=None, labels=None, output_attentions=None, output_hidden_states=None, return_dict=None)

PARAMETER DESCRIPTION
labels

Labels for computing the masked language modeling loss. Indices should be in [-100, 0, ..., config.vocab_size] (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

kwargs

Used to hide legacy arguments that have been deprecated.

TYPE: `Dict[str, any]`, optional, defaults to *{}*

Source code in mindnlp/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py 
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def forward(
    self,
    input_ids: Optional[mindspore.Tensor] = None,
    attention_mask: Optional[mindspore.Tensor] = None,
    token_type_ids: Optional[mindspore.Tensor] = None,
    position_ids: Optional[mindspore.Tensor] = None,
    head_mask: Optional[mindspore.Tensor] = None,
    inputs_embeds: Optional[mindspore.Tensor] = None,
    encoder_hidden_states: Optional[mindspore.Tensor] = None,
    encoder_attention_mask: Optional[mindspore.Tensor] = None,
    labels: Optional[mindspore.Tensor] = None,
    output_attentions: Optional[bool] = None,
    output_hidden_states: Optional[bool] = None,
    return_dict: Optional[bool] = None,
) -> Union[Tuple[mindspore.Tensor], MaskedLMOutput]:
    r"""
    Args:
        labels (`mindspore.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
            Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
            config.vocab_size]` (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]`
        kwargs (`Dict[str, any]`, optional, defaults to *{}*):
            Used to hide legacy arguments that have been deprecated.
    """
    return_dict = return_dict if return_dict is not None else self.config.use_return_dict

    outputs = self.roberta(
        input_ids,
        attention_mask=attention_mask,
        token_type_ids=token_type_ids,
        position_ids=position_ids,
        head_mask=head_mask,
        inputs_embeds=inputs_embeds,
        encoder_hidden_states=encoder_hidden_states,
        encoder_attention_mask=encoder_attention_mask,
        output_attentions=output_attentions,
        output_hidden_states=output_hidden_states,
        return_dict=return_dict,
    )
    sequence_output = outputs[0]
    prediction_scores = self.lm_head(sequence_output)

    masked_lm_loss = None
    if labels is not None:
        # move labels to correct device to enable model parallelism
        masked_lm_loss = ops.cross_entropy(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))

    if not return_dict:
        output = (prediction_scores,) + outputs[2:]
        return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output

    return MaskedLMOutput(
        loss=masked_lm_loss,
        logits=prediction_scores,
        hidden_states=outputs.hidden_states,
        attentions=outputs.attentions,
    )

mindnlp.transformers.models.xlm_roberta_xl.modeling_xlm_roberta_xl.XLMRobertaXLForMaskedLM.get_output_embeddings()

Get the output embeddings for the XLM-Roberta model.

PARAMETER DESCRIPTION
self

The instance of the XLMRobertaForMaskedLM class.

TYPE: XLMRobertaForMaskedLM

RETURNS DESCRIPTION

None.
Source code in mindnlp/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py 
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def get_output_embeddings(self):
    """Get the output embeddings for the XLM-Roberta model.

    Args:
        self (XLMRobertaForMaskedLM): The instance of the XLMRobertaForMaskedLM class.

    Returns:
        None.

    Raises:
        None.
    """
    return self.lm_head.decoder

mindnlp.transformers.models.xlm_roberta_xl.modeling_xlm_roberta_xl.XLMRobertaXLForMaskedLM.set_output_embeddings(new_embeddings)

This method sets the output embeddings for the XLMRobertaForMaskedLM model.

PARAMETER DESCRIPTION
self

The instance of the XLMRobertaForMaskedLM class.

TYPE: XLMRobertaForMaskedLM

new_embeddings

The new embeddings to be set as the output embeddings for the model. It should be an instance of torch.nn.Module representing the new embeddings.

TYPE: Module

RETURNS DESCRIPTION

None.
RAISES DESCRIPTION
TypeError

If the new_embeddings parameter is not an instance of torch.nn.Module.
AttributeError

If the lm_head.decoder attribute does not exist or is not accessible within the XLMRobertaForMaskedLM instance.
Source code in mindnlp/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py 
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def set_output_embeddings(self, new_embeddings):
    """
    This method sets the output embeddings for the XLMRobertaForMaskedLM model.

    Args:
        self (XLMRobertaForMaskedLM): The instance of the XLMRobertaForMaskedLM class.
        new_embeddings (torch.nn.Module): The new embeddings to be set as the output embeddings for the model. 
            It should be an instance of torch.nn.Module representing the new embeddings.

    Returns:
        None.

    Raises:
        TypeError: If the new_embeddings parameter is not an instance of torch.nn.Module.
        AttributeError: If the lm_head.decoder attribute does not exist or is not accessible within the
            XLMRobertaForMaskedLM instance.
    """
    self.lm_head.decoder = new_embeddings
    self.lm_head.bias = new_embeddings.bias

mindnlp.transformers.models.xlm_roberta_xl.modeling_xlm_roberta_xl.XLMRobertaXLForMultipleChoice

Bases: XLMRobertaXLPreTrainedModel

XLMRobertaForMultipleChoice

Source code in mindnlp/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py 
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class XLMRobertaXLForMultipleChoice(XLMRobertaXLPreTrainedModel):
    """XLMRobertaForMultipleChoice"""
    def __init__(self, config):
        """
        __init__

        Initialize the XLMRobertaForMultipleChoice model.

        Args:
            self: The instance of the class.
            config: An instance of the configuration class containing the model configuration.
                It is used to initialize the XLMRobertaModel, dropout, and classifier.
                It should be of type XLMRobertaConfig.

        Returns:
            None.

        Raises:
            None.
        """
        super().__init__(config)

        self.roberta = XLMRobertaXLModel(config)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
        self.classifier = nn.Linear(config.hidden_size, 1)

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

    def forward(
        self,
        input_ids: Optional[mindspore.Tensor] = None,
        token_type_ids: Optional[mindspore.Tensor] = None,
        attention_mask: Optional[mindspore.Tensor] = None,
        labels: Optional[mindspore.Tensor] = None,
        position_ids: Optional[mindspore.Tensor] = None,
        head_mask: Optional[mindspore.Tensor] = None,
        inputs_embeds: Optional[mindspore.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple[mindspore.Tensor], MultipleChoiceModelOutput]:
        r"""
        Args:
            labels (`mindspore.Tensor` of shape `(batch_size,)`, *optional*):
                Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
                num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See
                `input_ids` above)
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]

        flat_input_ids = input_ids.view(-1, input_ids.shape[-1]) if input_ids is not None else None
        flat_position_ids = position_ids.view(-1, position_ids.shape[-1]) if position_ids is not None else None
        flat_token_type_ids = token_type_ids.view(-1, token_type_ids.shape[-1]) if token_type_ids is not None else None
        flat_attention_mask = attention_mask.view(-1, attention_mask.shape[-1]) if attention_mask is not None else None
        flat_inputs_embeds = (
            inputs_embeds.view(-1, inputs_embeds.shape[-2], inputs_embeds.shape[-1])
            if inputs_embeds is not None
            else None
        )

        outputs = self.roberta(
            flat_input_ids,
            position_ids=flat_position_ids,
            token_type_ids=flat_token_type_ids,
            attention_mask=flat_attention_mask,
            head_mask=head_mask,
            inputs_embeds=flat_inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        pooled_output = outputs[1]

        pooled_output = self.dropout(pooled_output)
        logits = self.classifier(pooled_output)
        reshaped_logits = logits.view(-1, num_choices)

        loss = None
        if labels is not None:
            # move labels to correct device to enable model parallelism
            loss = ops.cross_entropy(reshaped_logits, labels)

        if not return_dict:
            output = (reshaped_logits,) + outputs[2:]
            return ((loss,) + output) if loss is not None else output

        return MultipleChoiceModelOutput(
            loss=loss,
            logits=reshaped_logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )

mindnlp.transformers.models.xlm_roberta_xl.modeling_xlm_roberta_xl.XLMRobertaXLForMultipleChoice.__init__(config)

init

Initialize the XLMRobertaForMultipleChoice model.

PARAMETER DESCRIPTION
self

The instance of the class.
config

An instance of the configuration class containing the model configuration. It is used to initialize the XLMRobertaModel, dropout, and classifier. It should be of type XLMRobertaConfig.
RETURNS DESCRIPTION

None.
Source code in mindnlp/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py 
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def __init__(self, config):
    """
    __init__

    Initialize the XLMRobertaForMultipleChoice model.

    Args:
        self: The instance of the class.
        config: An instance of the configuration class containing the model configuration.
            It is used to initialize the XLMRobertaModel, dropout, and classifier.
            It should be of type XLMRobertaConfig.

    Returns:
        None.

    Raises:
        None.
    """
    super().__init__(config)

    self.roberta = XLMRobertaXLModel(config)
    self.dropout = nn.Dropout(config.hidden_dropout_prob)
    self.classifier = nn.Linear(config.hidden_size, 1)

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

mindnlp.transformers.models.xlm_roberta_xl.modeling_xlm_roberta_xl.XLMRobertaXLForMultipleChoice.forward(input_ids=None, token_type_ids=None, attention_mask=None, labels=None, position_ids=None, head_mask=None, inputs_embeds=None, output_attentions=None, output_hidden_states=None, return_dict=None)

PARAMETER DESCRIPTION
labels

Labels for computing the multiple choice classification loss. Indices should be in [0, ..., num_choices-1] where num_choices is the size of the second dimension of the input tensors. (See input_ids above)

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

Source code in mindnlp/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py 
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def forward(
    self,
    input_ids: Optional[mindspore.Tensor] = None,
    token_type_ids: Optional[mindspore.Tensor] = None,
    attention_mask: Optional[mindspore.Tensor] = None,
    labels: Optional[mindspore.Tensor] = None,
    position_ids: Optional[mindspore.Tensor] = None,
    head_mask: Optional[mindspore.Tensor] = None,
    inputs_embeds: Optional[mindspore.Tensor] = None,
    output_attentions: Optional[bool] = None,
    output_hidden_states: Optional[bool] = None,
    return_dict: Optional[bool] = None,
) -> Union[Tuple[mindspore.Tensor], MultipleChoiceModelOutput]:
    r"""
    Args:
        labels (`mindspore.Tensor` of shape `(batch_size,)`, *optional*):
            Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
            num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See
            `input_ids` above)
    """
    return_dict = return_dict if return_dict is not None else self.config.use_return_dict
    num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]

    flat_input_ids = input_ids.view(-1, input_ids.shape[-1]) if input_ids is not None else None
    flat_position_ids = position_ids.view(-1, position_ids.shape[-1]) if position_ids is not None else None
    flat_token_type_ids = token_type_ids.view(-1, token_type_ids.shape[-1]) if token_type_ids is not None else None
    flat_attention_mask = attention_mask.view(-1, attention_mask.shape[-1]) if attention_mask is not None else None
    flat_inputs_embeds = (
        inputs_embeds.view(-1, inputs_embeds.shape[-2], inputs_embeds.shape[-1])
        if inputs_embeds is not None
        else None
    )

    outputs = self.roberta(
        flat_input_ids,
        position_ids=flat_position_ids,
        token_type_ids=flat_token_type_ids,
        attention_mask=flat_attention_mask,
        head_mask=head_mask,
        inputs_embeds=flat_inputs_embeds,
        output_attentions=output_attentions,
        output_hidden_states=output_hidden_states,
        return_dict=return_dict,
    )
    pooled_output = outputs[1]

    pooled_output = self.dropout(pooled_output)
    logits = self.classifier(pooled_output)
    reshaped_logits = logits.view(-1, num_choices)

    loss = None
    if labels is not None:
        # move labels to correct device to enable model parallelism
        loss = ops.cross_entropy(reshaped_logits, labels)

    if not return_dict:
        output = (reshaped_logits,) + outputs[2:]
        return ((loss,) + output) if loss is not None else output

    return MultipleChoiceModelOutput(
        loss=loss,
        logits=reshaped_logits,
        hidden_states=outputs.hidden_states,
        attentions=outputs.attentions,
    )

mindnlp.transformers.models.xlm_roberta_xl.modeling_xlm_roberta_xl.XLMRobertaXLForQuestionAnswering

Bases: XLMRobertaXLPreTrainedModel

XLMRobertaForQuestionAnswering

Source code in mindnlp/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py 
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class XLMRobertaXLForQuestionAnswering(XLMRobertaXLPreTrainedModel):
    """XLMRobertaForQuestionAnswering"""
    def __init__(self, config):
        """
        Initializes the XLMRobertaForQuestionAnswering class.

        Args:
            self (XLMRobertaForQuestionAnswering): The instance of the XLMRobertaForQuestionAnswering class.
            config (XLMRobertaConfig): The configuration object for the XLM-RoBERTa model. It contains various parameters 
                for model initialization, such as num_labels, hidden_size, and more.

        Returns:
            None.

        Raises:
            TypeError: If the provided config is not of type XLMRobertaConfig.
            ValueError: If the number of labels in the config is not a positive integer.
        """
        super().__init__(config)
        self.num_labels = config.num_labels

        self.roberta = XLMRobertaXLModel(config, add_pooling_layer=False)
        self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)

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

    def forward(
        self,
        input_ids: Optional[mindspore.Tensor] = None,
        attention_mask: Optional[mindspore.Tensor] = None,
        token_type_ids: Optional[mindspore.Tensor] = None,
        position_ids: Optional[mindspore.Tensor] = None,
        head_mask: Optional[mindspore.Tensor] = None,
        inputs_embeds: Optional[mindspore.Tensor] = None,
        start_positions: Optional[mindspore.Tensor] = None,
        end_positions: Optional[mindspore.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple[mindspore.Tensor], QuestionAnsweringModelOutput]:
        r"""
        Args:
            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

        outputs = self.roberta(
            input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            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 = logits.split(1, axis=-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)

            start_loss = ops.cross_entropy(start_logits, start_positions, ignore_index=ignored_index)
            end_loss = ops.cross_entropy(end_logits, end_positions, ignore_index=ignored_index)
            total_loss = (start_loss + end_loss) / 2

        if not return_dict:
            output = (start_logits, end_logits) + outputs[2:]
            return ((total_loss,) + output) if total_loss is not None else output

        return QuestionAnsweringModelOutput(
            loss=total_loss,
            start_logits=start_logits,
            end_logits=end_logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )

mindnlp.transformers.models.xlm_roberta_xl.modeling_xlm_roberta_xl.XLMRobertaXLForQuestionAnswering.__init__(config)

Initializes the XLMRobertaForQuestionAnswering class.

PARAMETER DESCRIPTION
self

The instance of the XLMRobertaForQuestionAnswering class.

TYPE: XLMRobertaForQuestionAnswering

config

The configuration object for the XLM-RoBERTa model. It contains various parameters for model initialization, such as num_labels, hidden_size, and more.

TYPE: XLMRobertaConfig

RETURNS DESCRIPTION

None.
RAISES DESCRIPTION
TypeError

If the provided config is not of type XLMRobertaConfig.
ValueError

If the number of labels in the config is not a positive integer.
Source code in mindnlp/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py 
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def __init__(self, config):
    """
    Initializes the XLMRobertaForQuestionAnswering class.

    Args:
        self (XLMRobertaForQuestionAnswering): The instance of the XLMRobertaForQuestionAnswering class.
        config (XLMRobertaConfig): The configuration object for the XLM-RoBERTa model. It contains various parameters 
            for model initialization, such as num_labels, hidden_size, and more.

    Returns:
        None.

    Raises:
        TypeError: If the provided config is not of type XLMRobertaConfig.
        ValueError: If the number of labels in the config is not a positive integer.
    """
    super().__init__(config)
    self.num_labels = config.num_labels

    self.roberta = XLMRobertaXLModel(config, add_pooling_layer=False)
    self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)

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

mindnlp.transformers.models.xlm_roberta_xl.modeling_xlm_roberta_xl.XLMRobertaXLForQuestionAnswering.forward(input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, start_positions=None, end_positions=None, output_attentions=None, output_hidden_states=None, return_dict=None)

PARAMETER DESCRIPTION
start_positions

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.

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

end_positions

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.

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

Source code in mindnlp/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py 
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def forward(
    self,
    input_ids: Optional[mindspore.Tensor] = None,
    attention_mask: Optional[mindspore.Tensor] = None,
    token_type_ids: Optional[mindspore.Tensor] = None,
    position_ids: Optional[mindspore.Tensor] = None,
    head_mask: Optional[mindspore.Tensor] = None,
    inputs_embeds: Optional[mindspore.Tensor] = None,
    start_positions: Optional[mindspore.Tensor] = None,
    end_positions: Optional[mindspore.Tensor] = None,
    output_attentions: Optional[bool] = None,
    output_hidden_states: Optional[bool] = None,
    return_dict: Optional[bool] = None,
) -> Union[Tuple[mindspore.Tensor], QuestionAnsweringModelOutput]:
    r"""
    Args:
        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

    outputs = self.roberta(
        input_ids,
        attention_mask=attention_mask,
        token_type_ids=token_type_ids,
        position_ids=position_ids,
        head_mask=head_mask,
        inputs_embeds=inputs_embeds,
        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 = logits.split(1, axis=-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)

        start_loss = ops.cross_entropy(start_logits, start_positions, ignore_index=ignored_index)
        end_loss = ops.cross_entropy(end_logits, end_positions, ignore_index=ignored_index)
        total_loss = (start_loss + end_loss) / 2

    if not return_dict:
        output = (start_logits, end_logits) + outputs[2:]
        return ((total_loss,) + output) if total_loss is not None else output

    return QuestionAnsweringModelOutput(
        loss=total_loss,
        start_logits=start_logits,
        end_logits=end_logits,
        hidden_states=outputs.hidden_states,
        attentions=outputs.attentions,
    )

mindnlp.transformers.models.xlm_roberta_xl.modeling_xlm_roberta_xl.XLMRobertaXLForSequenceClassification

Bases: XLMRobertaXLPreTrainedModel

Source code in mindnlp/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py 
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class XLMRobertaXLForSequenceClassification(XLMRobertaXLPreTrainedModel):
    def __init__(self, config):
        """
        Args:
            self (object): The instance of the class.
            config (object):
                The configuration object containing the model hyperparameters and settings.

                - Type: XLMRobertaConfig
                - Purpose: Specifies the configuration for the XLM-Roberta model.
                - Restrictions: Must be a valid instance of XLMRobertaConfig.

        Returns:
            None.

        Raises:
            None.
        """
        super().__init__(config)
        self.num_labels = config.num_labels
        self.config = config

        self.roberta = XLMRobertaXLModel(config, add_pooling_layer=False)
        self.classifier = XLMRobertaXLClassificationHead(config)

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

    def forward(
        self,
        input_ids: Optional[mindspore.Tensor] = None,
        attention_mask: Optional[mindspore.Tensor] = None,
        token_type_ids: Optional[mindspore.Tensor] = None,
        position_ids: Optional[mindspore.Tensor] = None,
        head_mask: Optional[mindspore.Tensor] = None,
        inputs_embeds: Optional[mindspore.Tensor] = None,
        labels: Optional[mindspore.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple[mindspore.Tensor], SequenceClassifierOutput]:
        r"""
        Args:
            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 regression loss is computed (Mean-Square loss), 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

        outputs = self.roberta(
            input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        sequence_output = outputs[0]
        logits = self.classifier(sequence_output)

        loss = None
        if labels is not None:
            # move labels to correct device to enable model parallelism
            if self.config.problem_type is None:
                if self.num_labels == 1:
                    self.config.problem_type = "regression"
                elif self.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":
                if self.num_labels == 1:
                    loss = ops.mse_loss(logits.squeeze(), labels.squeeze())
                else:
                    loss = ops.mse_loss(logits, labels)
            elif self.config.problem_type == "single_label_classification":
                loss = ops.cross_entropy(logits.view(-1, self.num_labels), labels.view(-1))
            elif self.config.problem_type == "multi_label_classification":
                loss = ops.binary_cross_entropy_with_logits(logits, labels)

        if not return_dict:
            output = (logits,) + outputs[2:]
            return ((loss,) + output) if loss is not None else output

        return SequenceClassifierOutput(
            loss=loss,
            logits=logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )

mindnlp.transformers.models.xlm_roberta_xl.modeling_xlm_roberta_xl.XLMRobertaXLForSequenceClassification.__init__(config)

PARAMETER DESCRIPTION
self

The instance of the class.

TYPE: object

config

The configuration object containing the model hyperparameters and settings.

  • Type: XLMRobertaConfig
  • Purpose: Specifies the configuration for the XLM-Roberta model.
  • Restrictions: Must be a valid instance of XLMRobertaConfig.

TYPE: object

RETURNS DESCRIPTION

None.
Source code in mindnlp/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py 
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def __init__(self, config):
    """
    Args:
        self (object): The instance of the class.
        config (object):
            The configuration object containing the model hyperparameters and settings.

            - Type: XLMRobertaConfig
            - Purpose: Specifies the configuration for the XLM-Roberta model.
            - Restrictions: Must be a valid instance of XLMRobertaConfig.

    Returns:
        None.

    Raises:
        None.
    """
    super().__init__(config)
    self.num_labels = config.num_labels
    self.config = config

    self.roberta = XLMRobertaXLModel(config, add_pooling_layer=False)
    self.classifier = XLMRobertaXLClassificationHead(config)

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

mindnlp.transformers.models.xlm_roberta_xl.modeling_xlm_roberta_xl.XLMRobertaXLForSequenceClassification.forward(input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, labels=None, output_attentions=None, output_hidden_states=None, return_dict=None)

PARAMETER DESCRIPTION
labels

Labels for computing the sequence classification/regression loss. Indices should be in [0, ..., config.num_labels - 1]. If config.num_labels == 1 a regression loss is computed (Mean-Square loss), If config.num_labels > 1 a classification loss is computed (Cross-Entropy).

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

Source code in mindnlp/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py 
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def forward(
    self,
    input_ids: Optional[mindspore.Tensor] = None,
    attention_mask: Optional[mindspore.Tensor] = None,
    token_type_ids: Optional[mindspore.Tensor] = None,
    position_ids: Optional[mindspore.Tensor] = None,
    head_mask: Optional[mindspore.Tensor] = None,
    inputs_embeds: Optional[mindspore.Tensor] = None,
    labels: Optional[mindspore.Tensor] = None,
    output_attentions: Optional[bool] = None,
    output_hidden_states: Optional[bool] = None,
    return_dict: Optional[bool] = None,
) -> Union[Tuple[mindspore.Tensor], SequenceClassifierOutput]:
    r"""
    Args:
        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 regression loss is computed (Mean-Square loss), 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

    outputs = self.roberta(
        input_ids,
        attention_mask=attention_mask,
        token_type_ids=token_type_ids,
        position_ids=position_ids,
        head_mask=head_mask,
        inputs_embeds=inputs_embeds,
        output_attentions=output_attentions,
        output_hidden_states=output_hidden_states,
        return_dict=return_dict,
    )
    sequence_output = outputs[0]
    logits = self.classifier(sequence_output)

    loss = None
    if labels is not None:
        # move labels to correct device to enable model parallelism
        if self.config.problem_type is None:
            if self.num_labels == 1:
                self.config.problem_type = "regression"
            elif self.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":
            if self.num_labels == 1:
                loss = ops.mse_loss(logits.squeeze(), labels.squeeze())
            else:
                loss = ops.mse_loss(logits, labels)
        elif self.config.problem_type == "single_label_classification":
            loss = ops.cross_entropy(logits.view(-1, self.num_labels), labels.view(-1))
        elif self.config.problem_type == "multi_label_classification":
            loss = ops.binary_cross_entropy_with_logits(logits, labels)

    if not return_dict:
        output = (logits,) + outputs[2:]
        return ((loss,) + output) if loss is not None else output

    return SequenceClassifierOutput(
        loss=loss,
        logits=logits,
        hidden_states=outputs.hidden_states,
        attentions=outputs.attentions,
    )

mindnlp.transformers.models.xlm_roberta_xl.modeling_xlm_roberta_xl.XLMRobertaXLForTokenClassification

Bases: XLMRobertaXLPreTrainedModel

XLMRobertaForTokenClassification

Source code in mindnlp/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py 
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class XLMRobertaXLForTokenClassification(XLMRobertaXLPreTrainedModel):
    """XLMRobertaForTokenClassification"""
    def __init__(self, config):
        """
        Initializes the XLMRobertaForTokenClassification model.

        Args:
            self: The instance of the XLMRobertaForTokenClassification class.
            config: An object containing configuration settings for the model.
                It must provide the following attributes:

                - num_labels (int): The number of labels for token classification.
                - classifier_dropout (float, optional): The dropout probability for the classifier layer.
                If not specified, it defaults to the hidden dropout probability specified in the configuration.
                - hidden_dropout_prob (float): The dropout probability for hidden layers.
                - hidden_size (int): The size of the hidden layers in the model.

        Returns:
            None.

        Raises:
            TypeError: If config is not provided or is not an instance of the expected configuration object.
            ValueError: If the required attributes (num_labels, hidden_dropout_prob, hidden_size) are missing from
                the config.
        """
        super().__init__(config)
        self.num_labels = config.num_labels

        self.roberta = XLMRobertaXLModel(config, add_pooling_layer=False)
        classifier_dropout = (
            config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
        )
        self.dropout = nn.Dropout(p=classifier_dropout)
        self.classifier = nn.Linear(config.hidden_size, config.num_labels)

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

    def forward(
        self,
        input_ids: Optional[mindspore.Tensor] = None,
        attention_mask: Optional[mindspore.Tensor] = None,
        token_type_ids: Optional[mindspore.Tensor] = None,
        position_ids: Optional[mindspore.Tensor] = None,
        head_mask: Optional[mindspore.Tensor] = None,
        inputs_embeds: Optional[mindspore.Tensor] = None,
        labels: Optional[mindspore.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple[mindspore.Tensor], TokenClassifierOutput]:
        r"""
        Args:
            labels (`mindspore.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
                Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        outputs = self.roberta(
            input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        sequence_output = outputs[0]

        sequence_output = self.dropout(sequence_output)
        logits = self.classifier(sequence_output)

        loss = None
        if labels is not None:
            # Only keep active parts of the loss
            if attention_mask is not None:
                active_loss = attention_mask.view(-1) == 1
                active_logits = logits.view(-1, self.num_labels)
                active_labels = ops.where(
                    active_loss, labels.view(-1), mindspore.Tensor(-100).to(dtype=labels.dtype)
                )
                loss = ops.cross_entropy(active_logits, active_labels)
            else:
                loss = ops.cross_entropy(logits.view(-1, self.num_labels), labels.view(-1))

        if not return_dict:
            output = (logits,) + outputs[2:]
            return ((loss,) + output) if loss is not None else output

        return TokenClassifierOutput(
            loss=loss,
            logits=logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )

mindnlp.transformers.models.xlm_roberta_xl.modeling_xlm_roberta_xl.XLMRobertaXLForTokenClassification.__init__(config)

Initializes the XLMRobertaForTokenClassification model.

PARAMETER DESCRIPTION
self

The instance of the XLMRobertaForTokenClassification class.
config

An object containing configuration settings for the model. It must provide the following attributes:

  • num_labels (int): The number of labels for token classification.
  • classifier_dropout (float, optional): The dropout probability for the classifier layer. If not specified, it defaults to the hidden dropout probability specified in the configuration.
  • hidden_dropout_prob (float): The dropout probability for hidden layers.
  • hidden_size (int): The size of the hidden layers in the model.
RETURNS DESCRIPTION

None.
RAISES DESCRIPTION
TypeError

If config is not provided or is not an instance of the expected configuration object.
ValueError

If the required attributes (num_labels, hidden_dropout_prob, hidden_size) are missing from the config.
Source code in mindnlp/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py 
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def __init__(self, config):
    """
    Initializes the XLMRobertaForTokenClassification model.

    Args:
        self: The instance of the XLMRobertaForTokenClassification class.
        config: An object containing configuration settings for the model.
            It must provide the following attributes:

            - num_labels (int): The number of labels for token classification.
            - classifier_dropout (float, optional): The dropout probability for the classifier layer.
            If not specified, it defaults to the hidden dropout probability specified in the configuration.
            - hidden_dropout_prob (float): The dropout probability for hidden layers.
            - hidden_size (int): The size of the hidden layers in the model.

    Returns:
        None.

    Raises:
        TypeError: If config is not provided or is not an instance of the expected configuration object.
        ValueError: If the required attributes (num_labels, hidden_dropout_prob, hidden_size) are missing from
            the config.
    """
    super().__init__(config)
    self.num_labels = config.num_labels

    self.roberta = XLMRobertaXLModel(config, add_pooling_layer=False)
    classifier_dropout = (
        config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
    )
    self.dropout = nn.Dropout(p=classifier_dropout)
    self.classifier = nn.Linear(config.hidden_size, config.num_labels)

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

mindnlp.transformers.models.xlm_roberta_xl.modeling_xlm_roberta_xl.XLMRobertaXLForTokenClassification.forward(input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, labels=None, output_attentions=None, output_hidden_states=None, return_dict=None)

PARAMETER DESCRIPTION
labels

Labels for computing the token classification loss. Indices should be in [0, ..., config.num_labels - 1].

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

Source code in mindnlp/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py 
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def forward(
    self,
    input_ids: Optional[mindspore.Tensor] = None,
    attention_mask: Optional[mindspore.Tensor] = None,
    token_type_ids: Optional[mindspore.Tensor] = None,
    position_ids: Optional[mindspore.Tensor] = None,
    head_mask: Optional[mindspore.Tensor] = None,
    inputs_embeds: Optional[mindspore.Tensor] = None,
    labels: Optional[mindspore.Tensor] = None,
    output_attentions: Optional[bool] = None,
    output_hidden_states: Optional[bool] = None,
    return_dict: Optional[bool] = None,
) -> Union[Tuple[mindspore.Tensor], TokenClassifierOutput]:
    r"""
    Args:
        labels (`mindspore.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
            Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
    """
    return_dict = return_dict if return_dict is not None else self.config.use_return_dict

    outputs = self.roberta(
        input_ids,
        attention_mask=attention_mask,
        token_type_ids=token_type_ids,
        position_ids=position_ids,
        head_mask=head_mask,
        inputs_embeds=inputs_embeds,
        output_attentions=output_attentions,
        output_hidden_states=output_hidden_states,
        return_dict=return_dict,
    )

    sequence_output = outputs[0]

    sequence_output = self.dropout(sequence_output)
    logits = self.classifier(sequence_output)

    loss = None
    if labels is not None:
        # Only keep active parts of the loss
        if attention_mask is not None:
            active_loss = attention_mask.view(-1) == 1
            active_logits = logits.view(-1, self.num_labels)
            active_labels = ops.where(
                active_loss, labels.view(-1), mindspore.Tensor(-100).to(dtype=labels.dtype)
            )
            loss = ops.cross_entropy(active_logits, active_labels)
        else:
            loss = ops.cross_entropy(logits.view(-1, self.num_labels), labels.view(-1))

    if not return_dict:
        output = (logits,) + outputs[2:]
        return ((loss,) + output) if loss is not None else output

    return TokenClassifierOutput(
        loss=loss,
        logits=logits,
        hidden_states=outputs.hidden_states,
        attentions=outputs.attentions,
    )

mindnlp.transformers.models.xlm_roberta_xl.modeling_xlm_roberta_xl.XLMRobertaXLModel

Bases: XLMRobertaXLPreTrainedModel

The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of cross-attention is added between the self-attention layers, following the architecture described in Attention is all you need*_ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin. To behave as an decoder the model needs to be initialized with the is_decoder argument of the configuration set to True. To be used in a Seq2Seq model, the model needs to initialized with both is_decoder argument and add_cross_attention set to True; an encoder_hidden_states is then expected as an input to the forward pass. .. _*Attention is all you need: https://arxiv.org/abs/1706.03762

Source code in mindnlp/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py 
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class XLMRobertaXLModel(XLMRobertaXLPreTrainedModel):
    """
    The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
    cross-attention is added between the self-attention layers, following the architecture described in *Attention is
    all you need*_ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz
    Kaiser and Illia Polosukhin. To behave as an decoder the model needs to be initialized with the `is_decoder`
    argument of the configuration set to `True`. To be used in a Seq2Seq model, the model needs to initialized with
    both `is_decoder` argument and `add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as
    an input to the forward pass. .. _*Attention is all you need*: https://arxiv.org/abs/1706.03762
    """

    # Copied from transformers.models.clap.modeling_clap.ClapTextModel.__init__ with ClapText->XLMRobertaXL
    def __init__(self, config, add_pooling_layer=True):
        super().__init__(config)
        self.config = config

        self.embeddings = XLMRobertaXLEmbeddings(config)
        self.encoder = XLMRobertaXLEncoder(config)

        self.pooler = XLMRobertaXLPooler(config) if add_pooling_layer else None

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

    def get_input_embeddings(self):
        return self.embeddings.word_embeddings

    def set_input_embeddings(self, value):
        self.embeddings.word_embeddings = value

    def _prune_heads(self, heads_to_prune):
        """
        Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
        class PreTrainedModel
        """
        for layer, heads in heads_to_prune.items():
            self.encoder.layer[layer].attention.prune_heads(heads)

    def forward(
        self,
        input_ids: Optional[mindspore.Tensor] = None,
        attention_mask: Optional[mindspore.Tensor] = None,
        token_type_ids: Optional[mindspore.Tensor] = None,
        position_ids: Optional[mindspore.Tensor] = None,
        head_mask: Optional[mindspore.Tensor] = None,
        inputs_embeds: Optional[mindspore.Tensor] = None,
        encoder_hidden_states: Optional[mindspore.Tensor] = None,
        encoder_attention_mask: Optional[mindspore.Tensor] = None,
        past_key_values: Optional[List[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[mindspore.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]:
        r"""
        Args:
            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]`:

                - 1 for tokens that are **not masked**,
                - 0 for tokens that are **masked**.
            past_key_values (`tuple(tuple(mindspore.Tensor))` of length `config.n_layers` with each tuple having 4 tensors
                of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
                Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up 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)`.
            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`).
        """
        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

        if self.config.is_decoder:
            use_cache = use_cache if use_cache is not None else self.config.use_cache
        else:
            use_cache = False

        if input_ids is not None and inputs_embeds is not None:
            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
        if input_ids is not None:
            self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
            input_shape = input_ids.shape
        elif inputs_embeds is not None:
            input_shape = inputs_embeds.shape[:-1]
        else:
            raise ValueError("You have to specify either input_ids or inputs_embeds")

        batch_size, seq_length = input_shape
        # past_key_values_length
        past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0

        if attention_mask is None:
            attention_mask = ops.ones(((batch_size, seq_length + past_key_values_length)))
        if token_type_ids is None:
            if hasattr(self.embeddings, "token_type_ids"):
                buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length]
                buffered_token_type_ids_expanded = buffered_token_type_ids.broadcast_to((batch_size, seq_length))
                token_type_ids = buffered_token_type_ids_expanded
            else:
                token_type_ids = ops.zeros(input_shape, dtype=mindspore.int64)

        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
        # ourselves in which case we just need to make it broadcastable to all heads.
        extended_attention_mask: mindspore.Tensor = self.get_extended_attention_mask(attention_mask, input_shape)

        # If a 2D or 3D attention mask is provided for the cross-attention
        # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
        if self.config.is_decoder and encoder_hidden_states is not None:
            encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.shape
            encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
            if encoder_attention_mask is None:
                encoder_attention_mask = ops.ones(encoder_hidden_shape)
            encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
        else:
            encoder_extended_attention_mask = None

        # Prepare head mask if needed
        # 1.0 in head_mask indicate we keep the head
        # attention_probs has shape bsz x n_heads x N x N
        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)

        embedding_output = self.embeddings(
            input_ids=input_ids,
            position_ids=position_ids,
            token_type_ids=token_type_ids,
            inputs_embeds=inputs_embeds,
            past_key_values_length=past_key_values_length,
        )
        encoder_outputs = self.encoder(
            embedding_output,
            attention_mask=extended_attention_mask,
            head_mask=head_mask,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_extended_attention_mask,
            past_key_values=past_key_values,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        sequence_output = encoder_outputs[0]
        pooled_output = self.pooler(sequence_output) if self.pooler is not None else None

        if not return_dict:
            return (sequence_output, pooled_output) + encoder_outputs[1:]

        return BaseModelOutputWithPoolingAndCrossAttentions(
            last_hidden_state=sequence_output,
            pooler_output=pooled_output,
            past_key_values=encoder_outputs.past_key_values,
            hidden_states=encoder_outputs.hidden_states,
            attentions=encoder_outputs.attentions,
            cross_attentions=encoder_outputs.cross_attentions,
        )

mindnlp.transformers.models.xlm_roberta_xl.modeling_xlm_roberta_xl.XLMRobertaXLModel.forward(input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, encoder_hidden_states=None, encoder_attention_mask=None, past_key_values=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None)

PARAMETER DESCRIPTION
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]:

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

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).

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

Source code in mindnlp/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py 
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def forward(
    self,
    input_ids: Optional[mindspore.Tensor] = None,
    attention_mask: Optional[mindspore.Tensor] = None,
    token_type_ids: Optional[mindspore.Tensor] = None,
    position_ids: Optional[mindspore.Tensor] = None,
    head_mask: Optional[mindspore.Tensor] = None,
    inputs_embeds: Optional[mindspore.Tensor] = None,
    encoder_hidden_states: Optional[mindspore.Tensor] = None,
    encoder_attention_mask: Optional[mindspore.Tensor] = None,
    past_key_values: Optional[List[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[mindspore.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]:
    r"""
    Args:
        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]`:

            - 1 for tokens that are **not masked**,
            - 0 for tokens that are **masked**.
        past_key_values (`tuple(tuple(mindspore.Tensor))` of length `config.n_layers` with each tuple having 4 tensors
            of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
            Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up 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)`.
        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`).
    """
    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

    if self.config.is_decoder:
        use_cache = use_cache if use_cache is not None else self.config.use_cache
    else:
        use_cache = False

    if input_ids is not None and inputs_embeds is not None:
        raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
    if input_ids is not None:
        self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
        input_shape = input_ids.shape
    elif inputs_embeds is not None:
        input_shape = inputs_embeds.shape[:-1]
    else:
        raise ValueError("You have to specify either input_ids or inputs_embeds")

    batch_size, seq_length = input_shape
    # past_key_values_length
    past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0

    if attention_mask is None:
        attention_mask = ops.ones(((batch_size, seq_length + past_key_values_length)))
    if token_type_ids is None:
        if hasattr(self.embeddings, "token_type_ids"):
            buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length]
            buffered_token_type_ids_expanded = buffered_token_type_ids.broadcast_to((batch_size, seq_length))
            token_type_ids = buffered_token_type_ids_expanded
        else:
            token_type_ids = ops.zeros(input_shape, dtype=mindspore.int64)

    # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
    # ourselves in which case we just need to make it broadcastable to all heads.
    extended_attention_mask: mindspore.Tensor = self.get_extended_attention_mask(attention_mask, input_shape)

    # If a 2D or 3D attention mask is provided for the cross-attention
    # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
    if self.config.is_decoder and encoder_hidden_states is not None:
        encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.shape
        encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
        if encoder_attention_mask is None:
            encoder_attention_mask = ops.ones(encoder_hidden_shape)
        encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
    else:
        encoder_extended_attention_mask = None

    # Prepare head mask if needed
    # 1.0 in head_mask indicate we keep the head
    # attention_probs has shape bsz x n_heads x N x N
    # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
    # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
    head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)

    embedding_output = self.embeddings(
        input_ids=input_ids,
        position_ids=position_ids,
        token_type_ids=token_type_ids,
        inputs_embeds=inputs_embeds,
        past_key_values_length=past_key_values_length,
    )
    encoder_outputs = self.encoder(
        embedding_output,
        attention_mask=extended_attention_mask,
        head_mask=head_mask,
        encoder_hidden_states=encoder_hidden_states,
        encoder_attention_mask=encoder_extended_attention_mask,
        past_key_values=past_key_values,
        use_cache=use_cache,
        output_attentions=output_attentions,
        output_hidden_states=output_hidden_states,
        return_dict=return_dict,
    )
    sequence_output = encoder_outputs[0]
    pooled_output = self.pooler(sequence_output) if self.pooler is not None else None

    if not return_dict:
        return (sequence_output, pooled_output) + encoder_outputs[1:]

    return BaseModelOutputWithPoolingAndCrossAttentions(
        last_hidden_state=sequence_output,
        pooler_output=pooled_output,
        past_key_values=encoder_outputs.past_key_values,
        hidden_states=encoder_outputs.hidden_states,
        attentions=encoder_outputs.attentions,
        cross_attentions=encoder_outputs.cross_attentions,
    )

mindnlp.transformers.models.xlm_roberta_xl.modeling_xlm_roberta_xl.XLMRobertaXLPreTrainedModel

Bases: PreTrainedModel

An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models.

Source code in mindnlp/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py 
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class XLMRobertaXLPreTrainedModel(PreTrainedModel):
    """
    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
    models.
    """
    config_class = XLMRobertaXLConfig
    base_model_prefix = "roberta"

    # Copied from transformers.models.bert.modeling_bert.BertPreTrainedModel._init_weights
    def _init_weights(self, cell):
        """Initialize the weights"""
        if isinstance(cell, nn.Linear):
            # Slightly different from the TF version which uses truncated_normal for initialization
            # cf https://github.com/pytorch/pytorch/pull/5617
            cell.weight.set_data(initializer(Normal(self.config.initializer_range),
                                                    cell.weight.shape, cell.weight.dtype))
            if cell.bias is not None:
                cell.bias.set_data(initializer('zeros', cell.bias.shape, cell.bias.dtype))
        elif isinstance(cell, nn.Embedding):
            weight = np.random.normal(0.0, self.config.initializer_range, cell.weight.shape)
            if cell.padding_idx:
                weight[cell.padding_idx] = 0

            cell.weight.set_data(Tensor(weight, cell.weight.dtype))
        elif isinstance(cell, nn.LayerNorm):
            cell.weight.set_data(initializer('ones', cell.weight.shape, cell.weight.dtype))
            cell.bias.set_data(initializer('zeros', cell.bias.shape, cell.bias.dtype))

mindnlp.transformers.models.xlm_roberta_xl.modeling_xlm_roberta_xl.XLMRobertaXLSelfAttention

Bases: Module

Source code in mindnlp/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py 
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class XLMRobertaXLSelfAttention(nn.Module):
    def __init__(self, config, position_embedding_type=None):
        super().__init__()
        if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
            raise ValueError(
                f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
                f"heads ({config.num_attention_heads})"
            )

        self.num_attention_heads = config.num_attention_heads
        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
        self.all_head_size = self.num_attention_heads * self.attention_head_size

        self.query = nn.Linear(config.hidden_size, self.all_head_size)
        self.key = nn.Linear(config.hidden_size, self.all_head_size)
        self.value = nn.Linear(config.hidden_size, self.all_head_size)

        self.dropout = nn.Dropout(p=config.attention_probs_dropout_prob)
        self.position_embedding_type = position_embedding_type or getattr(
            config, "position_embedding_type", "absolute"
        )
        if self.position_embedding_type in ('relative_key', 'relative_key_query'):
            self.max_position_embeddings = config.max_position_embeddings
            self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size)

        self.is_decoder = config.is_decoder

    def transpose_for_scores(self, x: mindspore.Tensor) -> mindspore.Tensor:
        """transpose_for_scores"""
        new_x_shape = x.shape[:-1] + (self.num_attention_heads, self.attention_head_size)
        x = x.view(new_x_shape)
        return x.permute(0, 2, 1, 3)

    def forward(
        self,
        hidden_states: mindspore.Tensor,
        attention_mask: Optional[mindspore.Tensor] = None,
        head_mask: Optional[mindspore.Tensor] = None,
        encoder_hidden_states: Optional[mindspore.Tensor] = None,
        encoder_attention_mask: Optional[mindspore.Tensor] = None,
        past_key_value: Optional[Tuple[Tuple[mindspore.Tensor]]] = None,
        output_attentions: Optional[bool] = False,
    ) -> Tuple[mindspore.Tensor]:
        """
        Constructs the self-attention mechanism for the XLMRoberta model.

        Args:
            self: An instance of the XLMRobertaSelfAttention class.
            hidden_states (mindspore.Tensor): The input hidden states. Shape (batch_size, seq_length, hidden_size).
            attention_mask (Optional[mindspore.Tensor]): The attention mask tensor.
                Shape (batch_size, seq_length, seq_length). Defaults to None.
            head_mask (Optional[mindspore.Tensor]): The head mask tensor.
                Shape (num_attention_heads, seq_length, seq_length). Defaults to None.
            encoder_hidden_states (Optional[mindspore.Tensor]): The hidden states from the encoder.
                Shape (batch_size, seq_length, hidden_size). Defaults to None.
            encoder_attention_mask (Optional[mindspore.Tensor]): The attention mask for the encoder hidden states.
                Shape (batch_size, seq_length, seq_length). Defaults to None.
            past_key_value (Optional[Tuple[Tuple[mindspore.Tensor]]]):
                The past key-value pairs for each layer in the encoder. Defaults to None.
            output_attentions (Optional[bool]): Whether to output attention probabilities. Defaults to False.

        Returns:
            Tuple[mindspore.Tensor]: A tuple containing the context layer tensor.
                Shape (batch_size, seq_length, hidden_size).

                - If output_attentions is True, the tuple also contains attention probabilities tensor.
                Shape (batch_size, num_attention_heads, seq_length, seq_length).
                - If the model is a decoder, the tuple also contains the past key-value pairs.

        Raises:
            None.
        """
        mixed_query_layer = self.query(hidden_states)

        # If this is instantiated as a cross-attention module, the keys
        # and values come from an encoder; the attention mask needs to be
        # such that the encoder's padding tokens are not attended to.
        is_cross_attention = encoder_hidden_states is not None

        if is_cross_attention and past_key_value is not None:
            # reuse k,v, cross_attentions
            key_layer = past_key_value[0]
            value_layer = past_key_value[1]
            attention_mask = encoder_attention_mask
        elif is_cross_attention:
            key_layer = self.transpose_for_scores(self.key(encoder_hidden_states))
            value_layer = self.transpose_for_scores(self.value(encoder_hidden_states))
            attention_mask = encoder_attention_mask
        elif past_key_value is not None:
            key_layer = self.transpose_for_scores(self.key(hidden_states))
            value_layer = self.transpose_for_scores(self.value(hidden_states))
            key_layer = ops.cat([past_key_value[0], key_layer], axis=2)
            value_layer = ops.cat([past_key_value[1], value_layer], axis=2)
        else:
            key_layer = self.transpose_for_scores(self.key(hidden_states))
            value_layer = self.transpose_for_scores(self.value(hidden_states))

        query_layer = self.transpose_for_scores(mixed_query_layer)

        use_cache = past_key_value is not None
        if self.is_decoder:
            # if cross_attention save Tuple(mindspore.Tensor, mindspore.Tensor) of all cross attention key/value_states.
            # Further calls to cross_attention layer can then reuse all cross-attention
            # key/value_states (first "if" case)
            # if uni-directional self-attention (decoder) save Tuple(mindspore.Tensor, mindspore.Tensor) of
            # all previous decoder key/value_states. Further calls to uni-directional self-attention
            # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
            # if encoder bi-directional self-attention `past_key_value` is always `None`
            past_key_value = (key_layer, value_layer)
        # Take the dot product between "query" and "key" to get the raw attention scores.
        attention_scores = ops.matmul(query_layer, key_layer.swapaxes(-1, -2))
        if self.position_embedding_type in ('relative_key', 'relative_key_query'):
            query_length, key_length = query_layer.shape[2], key_layer.shape[2]
            if use_cache:
                position_ids_l = mindspore.Tensor(key_length - 1, dtype=mindspore.int64).view(-1, 1)
            else:
                position_ids_l = ops.arange(query_length, dtype=mindspore.int64).view(-1, 1)
            position_ids_r = ops.arange(key_length, dtype=mindspore.int64).view(1, -1)
            distance = position_ids_l - position_ids_r

            positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
            positional_embedding = positional_embedding.to(dtype=query_layer.dtype)  # fp16 compatibility

            if self.position_embedding_type == "relative_key":
                relative_position_scores = ops.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
                attention_scores = attention_scores + relative_position_scores
            elif self.position_embedding_type == "relative_key_query":
                relative_position_scores_query = ops.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
                relative_position_scores_key = ops.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding)
                attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key
        attention_scores = attention_scores / math.sqrt(self.attention_head_size)
        if attention_mask is not None:
            # Apply the attention mask is (precomputed for all layers in XLMRobertaModel forward() function)
            attention_scores = attention_scores + attention_mask
        # Normalize the attention scores to probabilities.
        attention_probs = ops.softmax(attention_scores, axis=-1)
        # This is actually dropping out entire tokens to attend to, which might
        # seem a bit unusual, but is taken from the original Transformer paper.
        attention_probs = self.dropout(attention_probs)

        # Mask heads if we want to
        if head_mask is not None:
            attention_probs = attention_probs * head_mask

        context_layer = ops.matmul(attention_probs, value_layer)
        context_layer = context_layer.permute(0, 2, 1, 3)
        new_context_layer_shape = context_layer.shape[:-2] + (self.all_head_size,)
        context_layer = context_layer.view(new_context_layer_shape)

        outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)

        if self.is_decoder:
            outputs = outputs + (past_key_value,)
        return outputs

mindnlp.transformers.models.xlm_roberta_xl.modeling_xlm_roberta_xl.XLMRobertaXLSelfAttention.forward(hidden_states, attention_mask=None, head_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, past_key_value=None, output_attentions=False)

Constructs the self-attention mechanism for the XLMRoberta model.

PARAMETER DESCRIPTION
self

An instance of the XLMRobertaSelfAttention class.
hidden_states

The input hidden states. Shape (batch_size, seq_length, hidden_size).

TYPE: Tensor

attention_mask

The attention mask tensor. Shape (batch_size, seq_length, seq_length). Defaults to None.

TYPE: Optional[Tensor] DEFAULT: None

head_mask

The head mask tensor. Shape (num_attention_heads, seq_length, seq_length). Defaults to None.

TYPE: Optional[Tensor] DEFAULT: None

encoder_hidden_states

The hidden states from the encoder. Shape (batch_size, seq_length, hidden_size). Defaults to None.

TYPE: Optional[Tensor] DEFAULT: None

encoder_attention_mask

The attention mask for the encoder hidden states. Shape (batch_size, seq_length, seq_length). Defaults to None.

TYPE: Optional[Tensor] DEFAULT: None

past_key_value

The past key-value pairs for each layer in the encoder. Defaults to None.

TYPE: Optional[Tuple[Tuple[Tensor]]] DEFAULT: None

output_attentions

Whether to output attention probabilities. Defaults to False.

TYPE: Optional[bool] DEFAULT: False

RETURNS DESCRIPTION
Tuple[Tensor]

Tuple[mindspore.Tensor]: A tuple containing the context layer tensor. Shape (batch_size, seq_length, hidden_size).

  • If output_attentions is True, the tuple also contains attention probabilities tensor. Shape (batch_size, num_attention_heads, seq_length, seq_length).
  • If the model is a decoder, the tuple also contains the past key-value pairs.
Source code in mindnlp/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py 
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def forward(
    self,
    hidden_states: mindspore.Tensor,
    attention_mask: Optional[mindspore.Tensor] = None,
    head_mask: Optional[mindspore.Tensor] = None,
    encoder_hidden_states: Optional[mindspore.Tensor] = None,
    encoder_attention_mask: Optional[mindspore.Tensor] = None,
    past_key_value: Optional[Tuple[Tuple[mindspore.Tensor]]] = None,
    output_attentions: Optional[bool] = False,
) -> Tuple[mindspore.Tensor]:
    """
    Constructs the self-attention mechanism for the XLMRoberta model.

    Args:
        self: An instance of the XLMRobertaSelfAttention class.
        hidden_states (mindspore.Tensor): The input hidden states. Shape (batch_size, seq_length, hidden_size).
        attention_mask (Optional[mindspore.Tensor]): The attention mask tensor.
            Shape (batch_size, seq_length, seq_length). Defaults to None.
        head_mask (Optional[mindspore.Tensor]): The head mask tensor.
            Shape (num_attention_heads, seq_length, seq_length). Defaults to None.
        encoder_hidden_states (Optional[mindspore.Tensor]): The hidden states from the encoder.
            Shape (batch_size, seq_length, hidden_size). Defaults to None.
        encoder_attention_mask (Optional[mindspore.Tensor]): The attention mask for the encoder hidden states.
            Shape (batch_size, seq_length, seq_length). Defaults to None.
        past_key_value (Optional[Tuple[Tuple[mindspore.Tensor]]]):
            The past key-value pairs for each layer in the encoder. Defaults to None.
        output_attentions (Optional[bool]): Whether to output attention probabilities. Defaults to False.

    Returns:
        Tuple[mindspore.Tensor]: A tuple containing the context layer tensor.
            Shape (batch_size, seq_length, hidden_size).

            - If output_attentions is True, the tuple also contains attention probabilities tensor.
            Shape (batch_size, num_attention_heads, seq_length, seq_length).
            - If the model is a decoder, the tuple also contains the past key-value pairs.

    Raises:
        None.
    """
    mixed_query_layer = self.query(hidden_states)

    # If this is instantiated as a cross-attention module, the keys
    # and values come from an encoder; the attention mask needs to be
    # such that the encoder's padding tokens are not attended to.
    is_cross_attention = encoder_hidden_states is not None

    if is_cross_attention and past_key_value is not None:
        # reuse k,v, cross_attentions
        key_layer = past_key_value[0]
        value_layer = past_key_value[1]
        attention_mask = encoder_attention_mask
    elif is_cross_attention:
        key_layer = self.transpose_for_scores(self.key(encoder_hidden_states))
        value_layer = self.transpose_for_scores(self.value(encoder_hidden_states))
        attention_mask = encoder_attention_mask
    elif past_key_value is not None:
        key_layer = self.transpose_for_scores(self.key(hidden_states))
        value_layer = self.transpose_for_scores(self.value(hidden_states))
        key_layer = ops.cat([past_key_value[0], key_layer], axis=2)
        value_layer = ops.cat([past_key_value[1], value_layer], axis=2)
    else:
        key_layer = self.transpose_for_scores(self.key(hidden_states))
        value_layer = self.transpose_for_scores(self.value(hidden_states))

    query_layer = self.transpose_for_scores(mixed_query_layer)

    use_cache = past_key_value is not None
    if self.is_decoder:
        # if cross_attention save Tuple(mindspore.Tensor, mindspore.Tensor) of all cross attention key/value_states.
        # Further calls to cross_attention layer can then reuse all cross-attention
        # key/value_states (first "if" case)
        # if uni-directional self-attention (decoder) save Tuple(mindspore.Tensor, mindspore.Tensor) of
        # all previous decoder key/value_states. Further calls to uni-directional self-attention
        # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
        # if encoder bi-directional self-attention `past_key_value` is always `None`
        past_key_value = (key_layer, value_layer)
    # Take the dot product between "query" and "key" to get the raw attention scores.
    attention_scores = ops.matmul(query_layer, key_layer.swapaxes(-1, -2))
    if self.position_embedding_type in ('relative_key', 'relative_key_query'):
        query_length, key_length = query_layer.shape[2], key_layer.shape[2]
        if use_cache:
            position_ids_l = mindspore.Tensor(key_length - 1, dtype=mindspore.int64).view(-1, 1)
        else:
            position_ids_l = ops.arange(query_length, dtype=mindspore.int64).view(-1, 1)
        position_ids_r = ops.arange(key_length, dtype=mindspore.int64).view(1, -1)
        distance = position_ids_l - position_ids_r

        positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
        positional_embedding = positional_embedding.to(dtype=query_layer.dtype)  # fp16 compatibility

        if self.position_embedding_type == "relative_key":
            relative_position_scores = ops.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
            attention_scores = attention_scores + relative_position_scores
        elif self.position_embedding_type == "relative_key_query":
            relative_position_scores_query = ops.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
            relative_position_scores_key = ops.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding)
            attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key
    attention_scores = attention_scores / math.sqrt(self.attention_head_size)
    if attention_mask is not None:
        # Apply the attention mask is (precomputed for all layers in XLMRobertaModel forward() function)
        attention_scores = attention_scores + attention_mask
    # Normalize the attention scores to probabilities.
    attention_probs = ops.softmax(attention_scores, axis=-1)
    # This is actually dropping out entire tokens to attend to, which might
    # seem a bit unusual, but is taken from the original Transformer paper.
    attention_probs = self.dropout(attention_probs)

    # Mask heads if we want to
    if head_mask is not None:
        attention_probs = attention_probs * head_mask

    context_layer = ops.matmul(attention_probs, value_layer)
    context_layer = context_layer.permute(0, 2, 1, 3)
    new_context_layer_shape = context_layer.shape[:-2] + (self.all_head_size,)
    context_layer = context_layer.view(new_context_layer_shape)

    outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)

    if self.is_decoder:
        outputs = outputs + (past_key_value,)
    return outputs

mindnlp.transformers.models.xlm_roberta_xl.modeling_xlm_roberta_xl.XLMRobertaXLSelfAttention.transpose_for_scores(x)

transpose_for_scores

Source code in mindnlp/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py 
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def transpose_for_scores(self, x: mindspore.Tensor) -> mindspore.Tensor:
    """transpose_for_scores"""
    new_x_shape = x.shape[:-1] + (self.num_attention_heads, self.attention_head_size)
    x = x.view(new_x_shape)
    return x.permute(0, 2, 1, 3)

mindnlp.transformers.models.xlm_roberta_xl.modeling_xlm_roberta_xl.create_position_ids_from_input_ids(input_ids, padding_idx, past_key_values_length=0)

Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols are ignored. This is modified from fairseq's utils.make_positions.

PARAMETER DESCRIPTION
x

mindspore.Tensor x:
RETURNS DESCRIPTION

mindspore.Tensor
Source code in mindnlp/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py 
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def create_position_ids_from_input_ids(input_ids, padding_idx, past_key_values_length=0):
    """
    Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols
    are ignored. This is modified from fairseq's `utils.make_positions`.

    Args:
        x: mindspore.Tensor x:

    Returns:
        mindspore.Tensor
    """
    # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA.
    mask = input_ids.ne(padding_idx).int()
    incremental_indices = (ops.cumsum(mask, axis=1).astype(mask.dtype) + past_key_values_length) * mask
    return incremental_indices.long() + padding_idx