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gpt_bigcode

mindnlp.transformers.models.gpt_bigcode.gpt_bigcode

MindNLP gpt_bigcode model

mindnlp.transformers.models.gpt_bigcode.gpt_bigcode.GPTBigCodeAttention

Bases: Module

GPT BigCode Attention

Source code in mindnlp/transformers/models/gpt_bigcode/gpt_bigcode.py 
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class GPTBigCodeAttention(nn.Module):
    """GPT BigCode Attention"""
    def __init__(self, config, is_cross_attention=False, layer_idx=None):
        """
        Initializes the GPTBigCodeAttention class.

        Args:
            self: The instance of the class.
            config: An object containing configuration parameters.
                Must have attributes: multi_query (bool), hidden_size (int), num_attention_heads (int),
                scale_attn_weights (bool), attention_softmax_in_fp32 (bool), scale_attention_softmax_in_fp32 (bool),
                attn_pdrop (float), resid_pdrop (float).
            is_cross_attention: A boolean indicating whether cross-attention is enabled.
            layer_idx: An integer representing the layer index.

        Returns:
            None

        Raises:
            ValueError: If `embed_dim` is not divisible by num_heads.
            NotImplementedError: If cross-attention is enabled and multi-query attention is not supported.
        """
        super().__init__()
        self.mask_value = None

        self.multi_query = config.multi_query
        self.embed_dim = config.hidden_size
        self.num_heads = config.num_attention_heads
        self.head_dim = self.embed_dim // self.num_heads
        self.kv_heads = 1 if self.multi_query else self.num_heads
        self.kv_dim = self.kv_heads * self.head_dim
        self.split_size = self.embed_dim
        if self.head_dim * self.num_heads != self.embed_dim:
            raise ValueError(
                f"`embed_dim` must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
                f" {self.num_heads})."
            )

        self.scale_attn_weights = config.scale_attn_weights
        self.is_cross_attention = is_cross_attention

        self.layer_idx = layer_idx
        self.attention_softmax_in_fp32 = config.attention_softmax_in_fp32
        self.scale_attention_softmax_in_fp32 = (
            config.scale_attention_softmax_in_fp32 and config.attention_softmax_in_fp32
        )

        if self.is_cross_attention:
            if self.multi_query:
                raise NotImplementedError(
                    "Multi-Query Attention not supported for cross_attention")

            self.c_attn = nn.Linear(self.embed_dim, 2 * self.embed_dim)
            self.q_attn = nn.Linear(self.embed_dim, self.embed_dim)
        else:
            self.c_attn = nn.Linear(
                self.embed_dim, self.embed_dim + 2 * self.kv_dim)

        self.c_proj = nn.Linear(self.embed_dim, self.embed_dim)

        self.attn_dropout = nn.Dropout(p=config.attn_pdrop)
        self.resid_dropout = nn.Dropout(p=config.resid_pdrop)

    def _get_mask_value(self, dtype):
        """
        Method _get_mask_value in the class GPTBigCodeAttention.

        Args:
            self (object): The instance of the GPTBigCodeAttention class.
            dtype (str): The data type for the mask value. Should be a valid data type.

        Returns:
            mask_value: Returns the mask value for the specified data type.

        Raises:
            ValueError: If the mask value is None or has a different data type from the specified dtype.
            TypeError: If an invalid data type is provided.
        """
        # torch.where expects a tensor. We use a cache to avoid recreating it every time.
        if self.mask_value is None or self.mask_value.dtype != dtype:
            tmp_value = np.finfo(mindspore.dtype_to_nptype(dtype)).min
            self.mask_value = ops.full([], Tensor(
                tmp_value, dtype=dtype), dtype=dtype)
        return self.mask_value

    def _attn(self, query, key, value, attention_mask=None, head_mask=None):
        """
        This method calculates and applies attention mechanism to the input query, key, and value tensors in the
        GPTBigCodeAttention class.

        Args:
            self: The GPTBigCodeAttention instance.
            query (Tensor): The input query tensor with shape (batch_size, sequence_length, hidden_size)
                if multi_query is False, or (batch_size, sequence_length * num_heads, hidden_size) if multi_query is True.
            key (Tensor): The input key tensor with shape (batch_size * num_heads, hidden_size, sequence_length)
                if multi_query is False, or (batch_size, sequence_length, hidden_size) if multi_query is True.
            value (Tensor): The input value tensor with shape (batch_size * num_heads, sequence_length, hidden_size)
                if multi_query is False, or (batch_size, sequence_length, hidden_size) if multi_query is True.
            attention_mask (Tensor, optional):
                A tensor with shape (batch_size, sequence_length) or
                (batch_size, num_heads, sequence_length, sequence_length) containing attention masks  to be applied to
                the attention scores. Default is None.
            head_mask (Tensor, optional):
                A tensor with shape (batch_size, num_heads, sequence_length) representing the head mask to be applied
                to the attention weights. Default is None.

        Returns:
            Tuple[Tensor, Tensor]:
                A tuple containing the attention output tensor and the attention weights tensor of the specified shapes.

        Raises:
            ValueError: If the shapes of the input tensors are not compatible for the attention calculation.
            TypeError: If the input tensors are not of the expected data type.
            RuntimeError: If an error occurs while performing the attention operation.
        """
        dtype = query.dtype
        softmax_dtype = mindspore.float32 if self.attention_softmax_in_fp32 else dtype
        upcast = dtype != softmax_dtype

        unscale = self.layer_idx + 1 if self.scale_attention_softmax_in_fp32 and upcast else 1
        scale_factor = unscale**-1
        if self.scale_attn_weights:
            scale_factor /= self.head_dim**0.5

        # MQA models: (batch_size, query_length, num_heads * head_dim)
        # MHA models: (batch_size, num_heads, query_length, head_dim)
        query_shape = query.shape
        batch_size = query_shape[0]
        key_length = key.shape[-1]
        if self.multi_query:
            # (batch_size, query_length, num_heads, head_dim) x (batch_size, head_dim, key_length)
            # -> (batch_size, query_length, num_heads, key_length)
            query_length = query_shape[1]
            attn_shape = (batch_size, query_length, self.num_heads, key_length)
            attn_view = (batch_size, query_length * self.num_heads, key_length)
            # No copy needed for MQA 2, or when layer_past is provided.
            query = query.reshape(
                batch_size, query_length * self.num_heads, self.head_dim)
        else:
            # (batch_size, num_heads, query_length, head_dim) x (batch_size, num_heads, head_dim, key_length)
            # -> (batch_size, num_heads, query_length, key_length)
            query_length = query_shape[2]
            attn_shape = (batch_size, self.num_heads, query_length, key_length)
            attn_view = (batch_size * self.num_heads, query_length, key_length)
            # Always copies
            query = query.reshape(
                batch_size * self.num_heads, query_length, self.head_dim)
            # No copy when layer_past is provided.
            key = key.reshape(batch_size * self.num_heads,
                              self.head_dim, key_length)

        attn_weights = mindspore.numpy.empty(
            attn_view, dtype=query.dtype)

        attn_weights = ops.zeros_like(attn_weights)
        beta = 1
        attn_weights = Tensor.baddbmm(
            attn_weights, query, key, beta=beta, alpha=scale_factor).view(attn_shape)

        if upcast:
            if attention_mask is None:
                attn_weights = upcast_softmax(
                    attn_weights, unscale, softmax_dtype)
            else:
                mask_value = self._get_mask_value(softmax_dtype)
                attn_weights = upcast_masked_softmax(
                    attn_weights, attention_mask, mask_value, unscale, softmax_dtype)
        else:
            if attention_mask is not None:
                mask_value = self._get_mask_value(softmax_dtype)

                # The fused kernel is very slow when the key length is not a multiple of 8, so we skip fusion.
                attn_weights = ops.where(
                    Tensor(attention_mask, dtype=mindspore.bool_), attn_weights, mask_value)

            attn_weights = ops.softmax(attn_weights, axis=-1)

        attn_weights = self.attn_dropout(attn_weights)

        # Mask heads if we want to
        if head_mask is not None:
            if self.multi_query:
                head_mask = head_mask.swapaxes(1, 2)
            attn_weights = attn_weights * head_mask

        if self.multi_query:
            attn_output = ops.bmm(attn_weights.view(
                attn_view), value).view(query_shape)
        else:
            attn_output = ops.matmul(attn_weights, value)

        return attn_output, attn_weights

    def forward(
        self,
        hidden_states: mindspore.Tensor,
        layer_past: Optional[mindspore.Tensor] = None,
        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,
        use_cache: Optional[bool] = False,
        output_attentions: Optional[bool] = False,
    ) -> Union[
        Tuple[mindspore.Tensor, Optional[mindspore.Tensor]],
        Tuple[mindspore.Tensor, Optional[mindspore.Tensor],
              Tuple[mindspore.Tensor, ...]],
    ]:
        """
        Construct method in the GPTBigCodeAttention class.

        Args:
            self: The object instance.
            hidden_states (mindspore.Tensor): The input hidden states to the attention mechanism.
            layer_past (Optional[mindspore.Tensor]): Past hidden states for the layer. Default is None.
            attention_mask (Optional[mindspore.Tensor]): Mask to prevent attention to certain positions. Default is None.
            head_mask (Optional[mindspore.Tensor]): Mask for individual attention heads. Default is None.
            encoder_hidden_states (Optional[mindspore.Tensor]): Hidden states from encoder if cross-attention is used.
                Default is None.
            encoder_attention_mask (Optional[mindspore.Tensor]): Mask for encoder attention. Default is None.
            use_cache (Optional[bool]): Whether to cache the key-value pair for future calls. Default is False.
            output_attentions (Optional[bool]): Whether to output the attention weights. Default is False.

        Returns:
            Union[Tuple[mindspore.Tensor, Optional[mindspore.Tensor]], Tuple[mindspore.Tensor, Optional[mindspore.Tensor], Tuple[mindspore.Tensor, ...]]]:
                Tuple containing the attention output tensor and optionally the present key-value pair and attention
                weights.

        Raises:
            ValueError: If 'q_attn' weights are not defined for cross-attention or if class is not instantiated
                with 'is_cross_attention=True'.
        """
        if encoder_hidden_states is not None:
            if not hasattr(self, "q_attn") or not self.is_cross_attention:
                raise ValueError(
                    "If class is used as cross attention, the weights `q_attn` have to be defined. "
                    "Please make sure to instantiate class with `GPTBigCodeAttention(..., is_cross_attention=True)`."
                )

            query = self.q_attn(hidden_states)
            key_value = self.c_attn(encoder_hidden_states)
            attention_mask = encoder_attention_mask.bool()
        elif self.multi_query:
            query, key_value = self.c_attn(hidden_states).split(
                (self.embed_dim, 2 * self.kv_dim), axis=2)
        else:
            # Note: We split as (self.num_heads, 3, self.head_dim) instead of (3, self.num_heads, self.head_dim),
            # i.e., the memory layout is not the same as GPT2.
            # This makes the concatenation with past_key_value more efficient.
            query, key_value = (
                self.c_attn(hidden_states)
                .view(*hidden_states.shape[:2], self.num_heads, 3 * self.head_dim)
                .swapaxes(1, 2)
                .split((self.head_dim, 2 * self.head_dim), axis=3)
            )

        if layer_past is not None:
            key_value = ops.cat((layer_past, key_value), axis=-2)
        present = key_value if use_cache else None

        key, value = key_value.split((self.head_dim, self.head_dim), axis=-1)

        attn_output, attn_weights = self._attn(
            query, key.swapaxes(-1, -2), value, attention_mask, head_mask)

        if not self.multi_query:
            attn_output = attn_output.swapaxes(
                1, 2).reshape(hidden_states.shape)

        attn_output = self.c_proj(attn_output)
        attn_output = self.resid_dropout(attn_output)

        outputs = (attn_output, present)
        if output_attentions:
            if self.multi_query:
                # Transpose to return weights in the usual format (batch_size, num_heads, query_length, key_length)
                attn_weights = attn_weights.swapaxes(1, 2)
            outputs += (attn_weights,)

        return outputs  # a, present, (attentions)

mindnlp.transformers.models.gpt_bigcode.gpt_bigcode.GPTBigCodeAttention.__init__(config, is_cross_attention=False, layer_idx=None)

Initializes the GPTBigCodeAttention class.

PARAMETER DESCRIPTION
self

The instance of the class.
config

An object containing configuration parameters. Must have attributes: multi_query (bool), hidden_size (int), num_attention_heads (int), scale_attn_weights (bool), attention_softmax_in_fp32 (bool), scale_attention_softmax_in_fp32 (bool), attn_pdrop (float), resid_pdrop (float).
is_cross_attention

A boolean indicating whether cross-attention is enabled.

DEFAULT: False

layer_idx

An integer representing the layer index.

DEFAULT: None

RETURNS DESCRIPTION

None
RAISES DESCRIPTION
ValueError

If embed_dim is not divisible by num_heads.
NotImplementedError

If cross-attention is enabled and multi-query attention is not supported.
Source code in mindnlp/transformers/models/gpt_bigcode/gpt_bigcode.py 
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def __init__(self, config, is_cross_attention=False, layer_idx=None):
    """
    Initializes the GPTBigCodeAttention class.

    Args:
        self: The instance of the class.
        config: An object containing configuration parameters.
            Must have attributes: multi_query (bool), hidden_size (int), num_attention_heads (int),
            scale_attn_weights (bool), attention_softmax_in_fp32 (bool), scale_attention_softmax_in_fp32 (bool),
            attn_pdrop (float), resid_pdrop (float).
        is_cross_attention: A boolean indicating whether cross-attention is enabled.
        layer_idx: An integer representing the layer index.

    Returns:
        None

    Raises:
        ValueError: If `embed_dim` is not divisible by num_heads.
        NotImplementedError: If cross-attention is enabled and multi-query attention is not supported.
    """
    super().__init__()
    self.mask_value = None

    self.multi_query = config.multi_query
    self.embed_dim = config.hidden_size
    self.num_heads = config.num_attention_heads
    self.head_dim = self.embed_dim // self.num_heads
    self.kv_heads = 1 if self.multi_query else self.num_heads
    self.kv_dim = self.kv_heads * self.head_dim
    self.split_size = self.embed_dim
    if self.head_dim * self.num_heads != self.embed_dim:
        raise ValueError(
            f"`embed_dim` must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
            f" {self.num_heads})."
        )

    self.scale_attn_weights = config.scale_attn_weights
    self.is_cross_attention = is_cross_attention

    self.layer_idx = layer_idx
    self.attention_softmax_in_fp32 = config.attention_softmax_in_fp32
    self.scale_attention_softmax_in_fp32 = (
        config.scale_attention_softmax_in_fp32 and config.attention_softmax_in_fp32
    )

    if self.is_cross_attention:
        if self.multi_query:
            raise NotImplementedError(
                "Multi-Query Attention not supported for cross_attention")

        self.c_attn = nn.Linear(self.embed_dim, 2 * self.embed_dim)
        self.q_attn = nn.Linear(self.embed_dim, self.embed_dim)
    else:
        self.c_attn = nn.Linear(
            self.embed_dim, self.embed_dim + 2 * self.kv_dim)

    self.c_proj = nn.Linear(self.embed_dim, self.embed_dim)

    self.attn_dropout = nn.Dropout(p=config.attn_pdrop)
    self.resid_dropout = nn.Dropout(p=config.resid_pdrop)

mindnlp.transformers.models.gpt_bigcode.gpt_bigcode.GPTBigCodeAttention.forward(hidden_states, layer_past=None, attention_mask=None, head_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, use_cache=False, output_attentions=False)

Construct method in the GPTBigCodeAttention class.

PARAMETER DESCRIPTION
self

The object instance.
hidden_states

The input hidden states to the attention mechanism.

TYPE: Tensor

layer_past

Past hidden states for the layer. Default is None.

TYPE: Optional[Tensor] DEFAULT: None

attention_mask

Mask to prevent attention to certain positions. Default is None.

TYPE: Optional[Tensor] DEFAULT: None

head_mask

Mask for individual attention heads. Default is None.

TYPE: Optional[Tensor] DEFAULT: None

encoder_hidden_states

Hidden states from encoder if cross-attention is used. Default is None.

TYPE: Optional[Tensor] DEFAULT: None

encoder_attention_mask

Mask for encoder attention. Default is None.

TYPE: Optional[Tensor] DEFAULT: None

use_cache

Whether to cache the key-value pair for future calls. Default is False.

TYPE: Optional[bool] DEFAULT: False

output_attentions

Whether to output the attention weights. Default is False.

TYPE: Optional[bool] DEFAULT: False

RETURNS DESCRIPTION
Union[Tuple[Tensor, Optional[Tensor]], Tuple[Tensor, Optional[Tensor], Tuple[Tensor, ...]]]

Union[Tuple[mindspore.Tensor, Optional[mindspore.Tensor]], Tuple[mindspore.Tensor, Optional[mindspore.Tensor], Tuple[mindspore.Tensor, ...]]]: Tuple containing the attention output tensor and optionally the present key-value pair and attention weights.
RAISES DESCRIPTION
ValueError

If 'q_attn' weights are not defined for cross-attention or if class is not instantiated with 'is_cross_attention=True'.
Source code in mindnlp/transformers/models/gpt_bigcode/gpt_bigcode.py 
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def forward(
    self,
    hidden_states: mindspore.Tensor,
    layer_past: Optional[mindspore.Tensor] = None,
    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,
    use_cache: Optional[bool] = False,
    output_attentions: Optional[bool] = False,
) -> Union[
    Tuple[mindspore.Tensor, Optional[mindspore.Tensor]],
    Tuple[mindspore.Tensor, Optional[mindspore.Tensor],
          Tuple[mindspore.Tensor, ...]],
]:
    """
    Construct method in the GPTBigCodeAttention class.

    Args:
        self: The object instance.
        hidden_states (mindspore.Tensor): The input hidden states to the attention mechanism.
        layer_past (Optional[mindspore.Tensor]): Past hidden states for the layer. Default is None.
        attention_mask (Optional[mindspore.Tensor]): Mask to prevent attention to certain positions. Default is None.
        head_mask (Optional[mindspore.Tensor]): Mask for individual attention heads. Default is None.
        encoder_hidden_states (Optional[mindspore.Tensor]): Hidden states from encoder if cross-attention is used.
            Default is None.
        encoder_attention_mask (Optional[mindspore.Tensor]): Mask for encoder attention. Default is None.
        use_cache (Optional[bool]): Whether to cache the key-value pair for future calls. Default is False.
        output_attentions (Optional[bool]): Whether to output the attention weights. Default is False.

    Returns:
        Union[Tuple[mindspore.Tensor, Optional[mindspore.Tensor]], Tuple[mindspore.Tensor, Optional[mindspore.Tensor], Tuple[mindspore.Tensor, ...]]]:
            Tuple containing the attention output tensor and optionally the present key-value pair and attention
            weights.

    Raises:
        ValueError: If 'q_attn' weights are not defined for cross-attention or if class is not instantiated
            with 'is_cross_attention=True'.
    """
    if encoder_hidden_states is not None:
        if not hasattr(self, "q_attn") or not self.is_cross_attention:
            raise ValueError(
                "If class is used as cross attention, the weights `q_attn` have to be defined. "
                "Please make sure to instantiate class with `GPTBigCodeAttention(..., is_cross_attention=True)`."
            )

        query = self.q_attn(hidden_states)
        key_value = self.c_attn(encoder_hidden_states)
        attention_mask = encoder_attention_mask.bool()
    elif self.multi_query:
        query, key_value = self.c_attn(hidden_states).split(
            (self.embed_dim, 2 * self.kv_dim), axis=2)
    else:
        # Note: We split as (self.num_heads, 3, self.head_dim) instead of (3, self.num_heads, self.head_dim),
        # i.e., the memory layout is not the same as GPT2.
        # This makes the concatenation with past_key_value more efficient.
        query, key_value = (
            self.c_attn(hidden_states)
            .view(*hidden_states.shape[:2], self.num_heads, 3 * self.head_dim)
            .swapaxes(1, 2)
            .split((self.head_dim, 2 * self.head_dim), axis=3)
        )

    if layer_past is not None:
        key_value = ops.cat((layer_past, key_value), axis=-2)
    present = key_value if use_cache else None

    key, value = key_value.split((self.head_dim, self.head_dim), axis=-1)

    attn_output, attn_weights = self._attn(
        query, key.swapaxes(-1, -2), value, attention_mask, head_mask)

    if not self.multi_query:
        attn_output = attn_output.swapaxes(
            1, 2).reshape(hidden_states.shape)

    attn_output = self.c_proj(attn_output)
    attn_output = self.resid_dropout(attn_output)

    outputs = (attn_output, present)
    if output_attentions:
        if self.multi_query:
            # Transpose to return weights in the usual format (batch_size, num_heads, query_length, key_length)
            attn_weights = attn_weights.swapaxes(1, 2)
        outputs += (attn_weights,)

    return outputs  # a, present, (attentions)

mindnlp.transformers.models.gpt_bigcode.gpt_bigcode.GPTBigCodeBlock

Bases: Module

GPT BigCode Block

Source code in mindnlp/transformers/models/gpt_bigcode/gpt_bigcode.py 
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class GPTBigCodeBlock(nn.Module):
    """GPT BigCode Block"""
    def __init__(self, config, layer_idx=None):
        """
        Initializes an instance of the GPTBigCodeBlock class.

        Args:
            self: The object instance.
            config (object): An object containing configuration settings for the GPTBigCodeBlock.
            layer_idx (int, optional): The index of the layer. Defaults to None.

        Returns:
            None

        Raises:
            NotImplementedError: If cross-attention is enabled with multi-query architecture (MQA).

        """
        super().__init__()
        hidden_size = config.hidden_size
        self.inner_dim = config.n_inner if config.n_inner is not None else 4 * hidden_size

        self.ln_1 = nn.LayerNorm(
            [hidden_size], eps=config.layer_norm_epsilon)
        self.attn = GPTBigCodeAttention(config, layer_idx=layer_idx)
        self.ln_2 = nn.LayerNorm(
            [hidden_size], eps=config.layer_norm_epsilon)

        if config.add_cross_attention:
            if config.multi_query:
                raise NotImplementedError(
                    "Cross-attention not implemented for MQA")
            self.crossattention = GPTBigCodeAttention(
                config, is_cross_attention=True, layer_idx=layer_idx)
            self.ln_cross_attn = nn.LayerNorm(
                hidden_size, eps=config.layer_norm_epsilon)

        self.mlp = GPTBigCodeMLP(self.inner_dim, config)

    def forward(
        self,
        hidden_states: Optional[Tuple[mindspore.Tensor]],
        layer_past: Optional[mindspore.Tensor] = None,
        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,
        use_cache: Optional[bool] = False,
        output_attentions: Optional[bool] = False,
    ) -> Union[
        Tuple[mindspore.Tensor], Tuple[mindspore.Tensor,
                                       mindspore.Tensor], Tuple[mindspore.Tensor, mindspore.Tensor, mindspore.Tensor]
    ]:
        """
        This method forwards a GPT (Generative Pre-trained Transformer) big code block.

        Args:
            self: The instance of the class.
            hidden_states (Optional[Tuple[mindspore.Tensor]]): The input hidden states.
            layer_past (Optional[mindspore.Tensor]): The past hidden states of the layer.
            attention_mask (Optional[mindspore.Tensor]): The attention mask to mask some positions in the input.
            head_mask (Optional[mindspore.Tensor]): The mask applied to the heads of the multi-head attention.
            encoder_hidden_states (Optional[mindspore.Tensor]): The hidden states of the encoder.
            encoder_attention_mask (Optional[mindspore.Tensor]): The attention mask for the encoder.
            use_cache (Optional[bool]): Flag to indicate whether to use cache for faster decoding.
            output_attentions (Optional[bool]): Flag to indicate whether to output attentions.

        Returns:
            Union[Tuple[mindspore.Tensor], Tuple[mindspore.Tensor, mindspore.Tensor], Tuple[mindspore.Tensor, mindspore.Tensor, mindspore.Tensor]]:
                The output of the method which may include the hidden states and optionally attention scores.

        Raises:
            ValueError:
                If `encoder_hidden_states` are passed but the cross-attention layers are not instantiated with the
                flag `config.add_cross_attention=True`.
        """
        residual = hidden_states
        hidden_states = self.ln_1(hidden_states)
        attn_outputs = self.attn(
            hidden_states,
            layer_past=layer_past,
            attention_mask=attention_mask,
            head_mask=head_mask,
            use_cache=use_cache,
            output_attentions=output_attentions,
        )
        attn_output = attn_outputs[0]  # output_attn: a, present, (attentions)
        outputs = attn_outputs[1:]
        # residual connection
        hidden_states = attn_output + residual

        if encoder_hidden_states is not None:
            # add one self-attention block for cross-attention
            if not hasattr(self, "crossattention"):
                raise ValueError(
                    f"If `encoder_hidden_states` are passed, {self} has to be instantiated with "
                    "cross-attention layers by setting `config.add_cross_attention=True`"
                )
            residual = hidden_states
            hidden_states = self.ln_cross_attn(hidden_states)
            cross_attn_outputs = self.crossattention(
                hidden_states,
                attention_mask=attention_mask,
                head_mask=head_mask,
                encoder_hidden_states=encoder_hidden_states,
                encoder_attention_mask=encoder_attention_mask,
                output_attentions=output_attentions,
            )
            attn_output = cross_attn_outputs[0]
            # residual connection
            hidden_states = residual + attn_output
            # add cross attentions if we output attention weights
            outputs = outputs + cross_attn_outputs[2:]

        residual = hidden_states
        hidden_states = self.ln_2(hidden_states)
        feed_forward_hidden_states = self.mlp(hidden_states)
        # residual connection
        hidden_states = residual + feed_forward_hidden_states

        if use_cache:
            outputs = (hidden_states,) + outputs
        else:
            outputs = (hidden_states,) + outputs[1:]

        # hidden_states, present, (attentions, cross_attentions)
        return outputs

mindnlp.transformers.models.gpt_bigcode.gpt_bigcode.GPTBigCodeBlock.__init__(config, layer_idx=None)

Initializes an instance of the GPTBigCodeBlock class.

PARAMETER DESCRIPTION
self

The object instance.
config

An object containing configuration settings for the GPTBigCodeBlock.

TYPE: object

layer_idx

The index of the layer. Defaults to None.

TYPE: int DEFAULT: None

RETURNS DESCRIPTION

None
RAISES DESCRIPTION
NotImplementedError

If cross-attention is enabled with multi-query architecture (MQA).
Source code in mindnlp/transformers/models/gpt_bigcode/gpt_bigcode.py 
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def __init__(self, config, layer_idx=None):
    """
    Initializes an instance of the GPTBigCodeBlock class.

    Args:
        self: The object instance.
        config (object): An object containing configuration settings for the GPTBigCodeBlock.
        layer_idx (int, optional): The index of the layer. Defaults to None.

    Returns:
        None

    Raises:
        NotImplementedError: If cross-attention is enabled with multi-query architecture (MQA).

    """
    super().__init__()
    hidden_size = config.hidden_size
    self.inner_dim = config.n_inner if config.n_inner is not None else 4 * hidden_size

    self.ln_1 = nn.LayerNorm(
        [hidden_size], eps=config.layer_norm_epsilon)
    self.attn = GPTBigCodeAttention(config, layer_idx=layer_idx)
    self.ln_2 = nn.LayerNorm(
        [hidden_size], eps=config.layer_norm_epsilon)

    if config.add_cross_attention:
        if config.multi_query:
            raise NotImplementedError(
                "Cross-attention not implemented for MQA")
        self.crossattention = GPTBigCodeAttention(
            config, is_cross_attention=True, layer_idx=layer_idx)
        self.ln_cross_attn = nn.LayerNorm(
            hidden_size, eps=config.layer_norm_epsilon)

    self.mlp = GPTBigCodeMLP(self.inner_dim, config)

mindnlp.transformers.models.gpt_bigcode.gpt_bigcode.GPTBigCodeBlock.forward(hidden_states, layer_past=None, attention_mask=None, head_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, use_cache=False, output_attentions=False)

This method forwards a GPT (Generative Pre-trained Transformer) big code block.

PARAMETER DESCRIPTION
self

The instance of the class.
hidden_states

The input hidden states.

TYPE: Optional[Tuple[Tensor]]

layer_past

The past hidden states of the layer.

TYPE: Optional[Tensor] DEFAULT: None

attention_mask

The attention mask to mask some positions in the input.

TYPE: Optional[Tensor] DEFAULT: None

head_mask

The mask applied to the heads of the multi-head attention.

TYPE: Optional[Tensor] DEFAULT: None

encoder_hidden_states

The hidden states of the encoder.

TYPE: Optional[Tensor] DEFAULT: None

encoder_attention_mask

The attention mask for the encoder.

TYPE: Optional[Tensor] DEFAULT: None

use_cache

Flag to indicate whether to use cache for faster decoding.

TYPE: Optional[bool] DEFAULT: False

output_attentions

Flag to indicate whether to output attentions.

TYPE: Optional[bool] DEFAULT: False

RETURNS DESCRIPTION
Union[Tuple[Tensor], Tuple[Tensor, Tensor], Tuple[Tensor, Tensor, Tensor]]

Union[Tuple[mindspore.Tensor], Tuple[mindspore.Tensor, mindspore.Tensor], Tuple[mindspore.Tensor, mindspore.Tensor, mindspore.Tensor]]: The output of the method which may include the hidden states and optionally attention scores.
RAISES DESCRIPTION
ValueError

If encoder_hidden_states are passed but the cross-attention layers are not instantiated with the flag config.add_cross_attention=True.
Source code in mindnlp/transformers/models/gpt_bigcode/gpt_bigcode.py 
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def forward(
    self,
    hidden_states: Optional[Tuple[mindspore.Tensor]],
    layer_past: Optional[mindspore.Tensor] = None,
    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,
    use_cache: Optional[bool] = False,
    output_attentions: Optional[bool] = False,
) -> Union[
    Tuple[mindspore.Tensor], Tuple[mindspore.Tensor,
                                   mindspore.Tensor], Tuple[mindspore.Tensor, mindspore.Tensor, mindspore.Tensor]
]:
    """
    This method forwards a GPT (Generative Pre-trained Transformer) big code block.

    Args:
        self: The instance of the class.
        hidden_states (Optional[Tuple[mindspore.Tensor]]): The input hidden states.
        layer_past (Optional[mindspore.Tensor]): The past hidden states of the layer.
        attention_mask (Optional[mindspore.Tensor]): The attention mask to mask some positions in the input.
        head_mask (Optional[mindspore.Tensor]): The mask applied to the heads of the multi-head attention.
        encoder_hidden_states (Optional[mindspore.Tensor]): The hidden states of the encoder.
        encoder_attention_mask (Optional[mindspore.Tensor]): The attention mask for the encoder.
        use_cache (Optional[bool]): Flag to indicate whether to use cache for faster decoding.
        output_attentions (Optional[bool]): Flag to indicate whether to output attentions.

    Returns:
        Union[Tuple[mindspore.Tensor], Tuple[mindspore.Tensor, mindspore.Tensor], Tuple[mindspore.Tensor, mindspore.Tensor, mindspore.Tensor]]:
            The output of the method which may include the hidden states and optionally attention scores.

    Raises:
        ValueError:
            If `encoder_hidden_states` are passed but the cross-attention layers are not instantiated with the
            flag `config.add_cross_attention=True`.
    """
    residual = hidden_states
    hidden_states = self.ln_1(hidden_states)
    attn_outputs = self.attn(
        hidden_states,
        layer_past=layer_past,
        attention_mask=attention_mask,
        head_mask=head_mask,
        use_cache=use_cache,
        output_attentions=output_attentions,
    )
    attn_output = attn_outputs[0]  # output_attn: a, present, (attentions)
    outputs = attn_outputs[1:]
    # residual connection
    hidden_states = attn_output + residual

    if encoder_hidden_states is not None:
        # add one self-attention block for cross-attention
        if not hasattr(self, "crossattention"):
            raise ValueError(
                f"If `encoder_hidden_states` are passed, {self} has to be instantiated with "
                "cross-attention layers by setting `config.add_cross_attention=True`"
            )
        residual = hidden_states
        hidden_states = self.ln_cross_attn(hidden_states)
        cross_attn_outputs = self.crossattention(
            hidden_states,
            attention_mask=attention_mask,
            head_mask=head_mask,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            output_attentions=output_attentions,
        )
        attn_output = cross_attn_outputs[0]
        # residual connection
        hidden_states = residual + attn_output
        # add cross attentions if we output attention weights
        outputs = outputs + cross_attn_outputs[2:]

    residual = hidden_states
    hidden_states = self.ln_2(hidden_states)
    feed_forward_hidden_states = self.mlp(hidden_states)
    # residual connection
    hidden_states = residual + feed_forward_hidden_states

    if use_cache:
        outputs = (hidden_states,) + outputs
    else:
        outputs = (hidden_states,) + outputs[1:]

    # hidden_states, present, (attentions, cross_attentions)
    return outputs

mindnlp.transformers.models.gpt_bigcode.gpt_bigcode.GPTBigCodeForCausalLM

Bases: GPTBigCodePreTrainedModel

GPT BigCode for CausalLM

Source code in mindnlp/transformers/models/gpt_bigcode/gpt_bigcode.py 
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class GPTBigCodeForCausalLM(GPTBigCodePreTrainedModel):
    """GPT BigCode for CausalLM"""
    _tied_weights_keys = ["lm_head.weight"]

    def __init__(self, config):
        """
        Initializes the GPTBigCodeForCausalLM class.

        Args:
            self (object): The instance of the class.
            config (object): A configuration object containing settings for the GPTBigCodeForCausalLM model.
                It should include parameters such as n_embd (embedding dimension) and vocab_size (vocabulary size).

        Returns:
            None.

        Raises:
            None.
        """
        super().__init__(config)
        self.transformer = GPTBigCodeModel(config)
        self.lm_head = nn.Linear(
            config.n_embd, config.vocab_size, bias=False)

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

    def get_output_embeddings(self):
        """
        Returns the output embeddings of the GPTBigCodeForCausalLM model.

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

        Returns:
            None.

        Raises:
            None.
        """
        return self.lm_head

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

        Args:
            self (GPTBigCodeForCausalLM): The instance of the GPTBigCodeForCausalLM class.
            new_embeddings (object): The new embeddings to be set as output embeddings for the model.

        Returns:
            None.

        Raises:
            None.
        """
        self.lm_head = new_embeddings

    def prepare_inputs_for_generation(self, input_ids, past_key_values=None, inputs_embeds=None, **kwargs):
        '''
        Prepare inputs for generation.

        Args:
            self (GPTBigCodeForCausalLM): An instance of the GPTBigCodeForCausalLM class.
            input_ids (torch.Tensor): The input tensor of shape [batch_size, sequence_length].
            past_key_values (tuple, optional): The tuple of past key values. Default is None.
            inputs_embeds (torch.Tensor, optional):
                The embedded inputs tensor of shape [batch_size, sequence_length, embedding_size]. Default is None.

        Returns:
            dict: A dictionary containing the model inputs for generation.
                The dictionary may contain the following keys:

                - 'inputs_embeds' (torch.Tensor): The embedded inputs tensor.
                - 'input_ids' (torch.Tensor): The input tensor.
                - 'past_key_values' (tuple): The tuple of past key values.
                - 'use_cache' (bool): Whether to use cache.
                - 'position_ids' (torch.Tensor): The position ids tensor.
                - 'attention_mask' (torch.Tensor): The attention mask tensor.
                - 'token_type_ids' (torch.Tensor): The token type ids tensor.

        Raises:
            None.
        '''
        token_type_ids = kwargs.get("token_type_ids", None)
        # Omit tokens covered by past_key_values
        if past_key_values:
            if self.config.multi_query:
                past_length = past_key_values[0].shape[1]
            else:
                past_length = past_key_values[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:]
            if token_type_ids is not None:
                token_type_ids = token_type_ids[:, -input_ids.shape[1]:]

        attention_mask = kwargs.get("attention_mask", None)
        position_ids = kwargs.get("position_ids", None)

        if attention_mask is not None and position_ids is None:
            # create position_ids on the fly for batch generation
            position_ids = attention_mask.long().cumsum(-1) - 1
            position_ids = position_ids.masked_fill(attention_mask == 0, 1)
            if past_key_values:
                position_ids = position_ids[:, -input_ids.shape[1]:]
        else:
            position_ids = None

        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
        if inputs_embeds is not None and past_key_values is None:
            model_inputs = {"inputs_embeds": inputs_embeds}
        else:
            model_inputs = {"input_ids": input_ids}

        model_inputs.update(
            {
                "past_key_values": past_key_values,
                "use_cache": kwargs.get("use_cache"),
                "position_ids": position_ids,
                "attention_mask": attention_mask,
                "token_type_ids": token_type_ids,
            }
        )
        return model_inputs

    def forward(
        self,
        input_ids: Optional[mindspore.Tensor] = None,
        past_key_values: Optional[Tuple[Tuple[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,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, CausalLMOutputWithCrossAttentions]:
        r"""
        Args:
            labels (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
                Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
                `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
                are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        transformer_outputs = self.transformer(
            input_ids,
            past_key_values=past_key_values,
            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,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        hidden_states = transformer_outputs[0]

        lm_logits = self.lm_head(hidden_states)

        loss = None
        if labels is not None:
            # Shift so that tokens < n predict n
            shift_logits = lm_logits[..., :-1, :]
            shift_labels = labels[..., 1:]
            # Flatten the tokens
            loss_fct = nn.CrossEntropyLoss()
            loss = loss_fct(
                shift_logits.view(-1, shift_logits.shape[-1]), shift_labels.view(-1).to(mindspore.int32))

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

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

    @staticmethod
    def _reorder_cache(
        past_key_values: Tuple[Tuple[mindspore.Tensor]], beam_idx: mindspore.Tensor
    ) -> Tuple[Tuple[mindspore.Tensor]]:
        """
        This function is used to re-order the `past_key_values` cache if [`~PreTrainedModel.beam_search`] or
        [`~PreTrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct
        beam_idx at every generation step.
        """
        return tuple(layer_past.index_select(0, beam_idx) for layer_past in past_key_values)

mindnlp.transformers.models.gpt_bigcode.gpt_bigcode.GPTBigCodeForCausalLM.__init__(config)

Initializes the GPTBigCodeForCausalLM class.

PARAMETER DESCRIPTION
self

The instance of the class.

TYPE: object

config

A configuration object containing settings for the GPTBigCodeForCausalLM model. It should include parameters such as n_embd (embedding dimension) and vocab_size (vocabulary size).

TYPE: object

RETURNS DESCRIPTION

None.
Source code in mindnlp/transformers/models/gpt_bigcode/gpt_bigcode.py 
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def __init__(self, config):
    """
    Initializes the GPTBigCodeForCausalLM class.

    Args:
        self (object): The instance of the class.
        config (object): A configuration object containing settings for the GPTBigCodeForCausalLM model.
            It should include parameters such as n_embd (embedding dimension) and vocab_size (vocabulary size).

    Returns:
        None.

    Raises:
        None.
    """
    super().__init__(config)
    self.transformer = GPTBigCodeModel(config)
    self.lm_head = nn.Linear(
        config.n_embd, config.vocab_size, bias=False)

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

mindnlp.transformers.models.gpt_bigcode.gpt_bigcode.GPTBigCodeForCausalLM.forward(input_ids=None, past_key_values=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, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None)

PARAMETER DESCRIPTION
labels

Labels for language modeling. Note that the labels are shifted inside the model, i.e. you can set labels = input_ids Indices are selected in [-100, 0, ..., config.vocab_size] All labels set to -100 are ignored (masked), the loss is only computed for labels in [0, ..., config.vocab_size]

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

Source code in mindnlp/transformers/models/gpt_bigcode/gpt_bigcode.py 
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def forward(
    self,
    input_ids: Optional[mindspore.Tensor] = None,
    past_key_values: Optional[Tuple[Tuple[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,
    use_cache: Optional[bool] = None,
    output_attentions: Optional[bool] = None,
    output_hidden_states: Optional[bool] = None,
    return_dict: Optional[bool] = None,
) -> Union[Tuple, CausalLMOutputWithCrossAttentions]:
    r"""
    Args:
        labels (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
            Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
            `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
            are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
    """
    return_dict = return_dict if return_dict is not None else self.config.use_return_dict

    transformer_outputs = self.transformer(
        input_ids,
        past_key_values=past_key_values,
        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,
        use_cache=use_cache,
        output_attentions=output_attentions,
        output_hidden_states=output_hidden_states,
        return_dict=return_dict,
    )
    hidden_states = transformer_outputs[0]

    lm_logits = self.lm_head(hidden_states)

    loss = None
    if labels is not None:
        # Shift so that tokens < n predict n
        shift_logits = lm_logits[..., :-1, :]
        shift_labels = labels[..., 1:]
        # Flatten the tokens
        loss_fct = nn.CrossEntropyLoss()
        loss = loss_fct(
            shift_logits.view(-1, shift_logits.shape[-1]), shift_labels.view(-1).to(mindspore.int32))

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

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

mindnlp.transformers.models.gpt_bigcode.gpt_bigcode.GPTBigCodeForCausalLM.get_output_embeddings()

Returns the output embeddings of the GPTBigCodeForCausalLM model.

PARAMETER DESCRIPTION
self

The instance of the GPTBigCodeForCausalLM class.

TYPE: GPTBigCodeForCausalLM

RETURNS DESCRIPTION

None.
Source code in mindnlp/transformers/models/gpt_bigcode/gpt_bigcode.py 
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def get_output_embeddings(self):
    """
    Returns the output embeddings of the GPTBigCodeForCausalLM model.

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

    Returns:
        None.

    Raises:
        None.
    """
    return self.lm_head

mindnlp.transformers.models.gpt_bigcode.gpt_bigcode.GPTBigCodeForCausalLM.prepare_inputs_for_generation(input_ids, past_key_values=None, inputs_embeds=None, **kwargs)

Prepare inputs for generation.

PARAMETER DESCRIPTION
self

An instance of the GPTBigCodeForCausalLM class.

TYPE: GPTBigCodeForCausalLM

input_ids

The input tensor of shape [batch_size, sequence_length].

TYPE: Tensor

past_key_values

The tuple of past key values. Default is None.

TYPE: tuple DEFAULT: None

inputs_embeds

The embedded inputs tensor of shape [batch_size, sequence_length, embedding_size]. Default is None.

TYPE: Tensor DEFAULT: None

RETURNS DESCRIPTION
dict

A dictionary containing the model inputs for generation. The dictionary may contain the following keys:

  • 'inputs_embeds' (torch.Tensor): The embedded inputs tensor.
  • 'input_ids' (torch.Tensor): The input tensor.
  • 'past_key_values' (tuple): The tuple of past key values.
  • 'use_cache' (bool): Whether to use cache.
  • 'position_ids' (torch.Tensor): The position ids tensor.
  • 'attention_mask' (torch.Tensor): The attention mask tensor.
  • 'token_type_ids' (torch.Tensor): The token type ids tensor.
Source code in mindnlp/transformers/models/gpt_bigcode/gpt_bigcode.py 
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def prepare_inputs_for_generation(self, input_ids, past_key_values=None, inputs_embeds=None, **kwargs):
    '''
    Prepare inputs for generation.

    Args:
        self (GPTBigCodeForCausalLM): An instance of the GPTBigCodeForCausalLM class.
        input_ids (torch.Tensor): The input tensor of shape [batch_size, sequence_length].
        past_key_values (tuple, optional): The tuple of past key values. Default is None.
        inputs_embeds (torch.Tensor, optional):
            The embedded inputs tensor of shape [batch_size, sequence_length, embedding_size]. Default is None.

    Returns:
        dict: A dictionary containing the model inputs for generation.
            The dictionary may contain the following keys:

            - 'inputs_embeds' (torch.Tensor): The embedded inputs tensor.
            - 'input_ids' (torch.Tensor): The input tensor.
            - 'past_key_values' (tuple): The tuple of past key values.
            - 'use_cache' (bool): Whether to use cache.
            - 'position_ids' (torch.Tensor): The position ids tensor.
            - 'attention_mask' (torch.Tensor): The attention mask tensor.
            - 'token_type_ids' (torch.Tensor): The token type ids tensor.

    Raises:
        None.
    '''
    token_type_ids = kwargs.get("token_type_ids", None)
    # Omit tokens covered by past_key_values
    if past_key_values:
        if self.config.multi_query:
            past_length = past_key_values[0].shape[1]
        else:
            past_length = past_key_values[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:]
        if token_type_ids is not None:
            token_type_ids = token_type_ids[:, -input_ids.shape[1]:]

    attention_mask = kwargs.get("attention_mask", None)
    position_ids = kwargs.get("position_ids", None)

    if attention_mask is not None and position_ids is None:
        # create position_ids on the fly for batch generation
        position_ids = attention_mask.long().cumsum(-1) - 1
        position_ids = position_ids.masked_fill(attention_mask == 0, 1)
        if past_key_values:
            position_ids = position_ids[:, -input_ids.shape[1]:]
    else:
        position_ids = None

    # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
    if inputs_embeds is not None and past_key_values is None:
        model_inputs = {"inputs_embeds": inputs_embeds}
    else:
        model_inputs = {"input_ids": input_ids}

    model_inputs.update(
        {
            "past_key_values": past_key_values,
            "use_cache": kwargs.get("use_cache"),
            "position_ids": position_ids,
            "attention_mask": attention_mask,
            "token_type_ids": token_type_ids,
        }
    )
    return model_inputs

mindnlp.transformers.models.gpt_bigcode.gpt_bigcode.GPTBigCodeForCausalLM.set_output_embeddings(new_embeddings)

Sets the output embeddings for the GPTBigCodeForCausalLM model.

PARAMETER DESCRIPTION
self

The instance of the GPTBigCodeForCausalLM class.

TYPE: GPTBigCodeForCausalLM

new_embeddings

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

TYPE: object

RETURNS DESCRIPTION

None.
Source code in mindnlp/transformers/models/gpt_bigcode/gpt_bigcode.py 
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def set_output_embeddings(self, new_embeddings):
    """
    Sets the output embeddings for the GPTBigCodeForCausalLM model.

    Args:
        self (GPTBigCodeForCausalLM): The instance of the GPTBigCodeForCausalLM class.
        new_embeddings (object): The new embeddings to be set as output embeddings for the model.

    Returns:
        None.

    Raises:
        None.
    """
    self.lm_head = new_embeddings

mindnlp.transformers.models.gpt_bigcode.gpt_bigcode.GPTBigCodeForSequenceClassification

Bases: GPTBigCodePreTrainedModel

GPT BigCode for Sequence Classification

Source code in mindnlp/transformers/models/gpt_bigcode/gpt_bigcode.py 
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class GPTBigCodeForSequenceClassification(GPTBigCodePreTrainedModel):
    """GPT BigCode for Sequence Classification"""
    def __init__(self, config):
        """
        Initializes a new instance of the GPTBigCodeForSequenceClassification class.

        Args:
            self: The object itself.
            config (GPTBigCodeConfig): The configuration object specifying the model's hyperparameters and settings.

        Returns:
            None

        Raises:
            None
        """
        super().__init__(config)
        self.num_labels = config.num_labels
        self.transformer = GPTBigCodeModel(config)
        self.score = nn.Linear(config.n_embd, self.num_labels, bias=False)

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

    def forward(
        self,
        input_ids: Optional[mindspore.Tensor] = None,
        past_key_values: Optional[Tuple[Tuple[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,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
        r"""
        Args:
            labels (`torch.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

        transformer_outputs = self.transformer(
            input_ids,
            past_key_values=past_key_values,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        hidden_states = transformer_outputs[0]
        logits = self.score(hidden_states)

        if input_ids is not None:
            batch_size, _ = input_ids.shape[:2]
        else:
            batch_size, _ = inputs_embeds.shape[:2]

        assert (
            self.config.pad_token_id is not None or batch_size == 1
        ), "Cannot handle batch sizes > 1 if no padding token is defined."
        if self.config.pad_token_id is None:
            sequence_lengths = -1
        else:
            if input_ids is not None:
                sequence_lengths = ops.ne(
                    input_ids, self.config.pad_token_id).sum(-1) - 1
            else:
                sequence_lengths = -1

        pooled_logits = logits[ops.arange(batch_size), sequence_lengths]

        loss = None
        if labels is not None:

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

        return SequenceClassifierOutputWithPast(
            loss=loss,
            logits=pooled_logits,
            past_key_values=transformer_outputs.past_key_values,
            hidden_states=transformer_outputs.hidden_states,
            attentions=transformer_outputs.attentions,
        )

mindnlp.transformers.models.gpt_bigcode.gpt_bigcode.GPTBigCodeForSequenceClassification.__init__(config)

Initializes a new instance of the GPTBigCodeForSequenceClassification class.

PARAMETER DESCRIPTION
self

The object itself.
config

The configuration object specifying the model's hyperparameters and settings.

TYPE: GPTBigCodeConfig

RETURNS DESCRIPTION

None
Source code in mindnlp/transformers/models/gpt_bigcode/gpt_bigcode.py 
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def __init__(self, config):
    """
    Initializes a new instance of the GPTBigCodeForSequenceClassification class.

    Args:
        self: The object itself.
        config (GPTBigCodeConfig): The configuration object specifying the model's hyperparameters and settings.

    Returns:
        None

    Raises:
        None
    """
    super().__init__(config)
    self.num_labels = config.num_labels
    self.transformer = GPTBigCodeModel(config)
    self.score = nn.Linear(config.n_embd, self.num_labels, bias=False)

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

mindnlp.transformers.models.gpt_bigcode.gpt_bigcode.GPTBigCodeForSequenceClassification.forward(input_ids=None, past_key_values=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, labels=None, use_cache=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: `torch.Tensor` of shape `(batch_size,)`, *optional* DEFAULT: None

Source code in mindnlp/transformers/models/gpt_bigcode/gpt_bigcode.py 
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def forward(
    self,
    input_ids: Optional[mindspore.Tensor] = None,
    past_key_values: Optional[Tuple[Tuple[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,
    use_cache: Optional[bool] = None,
    output_attentions: Optional[bool] = None,
    output_hidden_states: Optional[bool] = None,
    return_dict: Optional[bool] = None,
) -> Union[Tuple, SequenceClassifierOutputWithPast]:
    r"""
    Args:
        labels (`torch.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

    transformer_outputs = self.transformer(
        input_ids,
        past_key_values=past_key_values,
        attention_mask=attention_mask,
        token_type_ids=token_type_ids,
        position_ids=position_ids,
        head_mask=head_mask,
        inputs_embeds=inputs_embeds,
        use_cache=use_cache,
        output_attentions=output_attentions,
        output_hidden_states=output_hidden_states,
        return_dict=return_dict,
    )
    hidden_states = transformer_outputs[0]
    logits = self.score(hidden_states)

    if input_ids is not None:
        batch_size, _ = input_ids.shape[:2]
    else:
        batch_size, _ = inputs_embeds.shape[:2]

    assert (
        self.config.pad_token_id is not None or batch_size == 1
    ), "Cannot handle batch sizes > 1 if no padding token is defined."
    if self.config.pad_token_id is None:
        sequence_lengths = -1
    else:
        if input_ids is not None:
            sequence_lengths = ops.ne(
                input_ids, self.config.pad_token_id).sum(-1) - 1
        else:
            sequence_lengths = -1

    pooled_logits = logits[ops.arange(batch_size), sequence_lengths]

    loss = None
    if labels is not None:

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

    return SequenceClassifierOutputWithPast(
        loss=loss,
        logits=pooled_logits,
        past_key_values=transformer_outputs.past_key_values,
        hidden_states=transformer_outputs.hidden_states,
        attentions=transformer_outputs.attentions,
    )

mindnlp.transformers.models.gpt_bigcode.gpt_bigcode.GPTBigCodeForTokenClassification

Bases: GPTBigCodePreTrainedModel

GPT BigCode for Token Classification

Source code in mindnlp/transformers/models/gpt_bigcode/gpt_bigcode.py 
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class GPTBigCodeForTokenClassification(GPTBigCodePreTrainedModel):
    """GPT BigCode for Token Classification"""
    def __init__(self, config):
        """
        Initializes an instance of the GPTBigCodeForTokenClassification class.

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

                - num_labels: An integer specifying the number of output labels.
                - classifier_dropout: (optional) A float specifying the dropout rate for the classifier layer.
                - hidden_dropout: (optional) A float specifying the dropout rate for hidden layers.

                Note:
                    If both classifier_dropout and hidden_dropout are provided, classifier_dropout takes precedence.

        Returns:
            None.

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

        self.transformer = GPTBigCodeModel(config)
        if hasattr(config, "classifier_dropout") and config.classifier_dropout is not None:
            classifier_dropout = config.classifier_dropout
        elif hasattr(config, "hidden_dropout") and config.hidden_dropout is not None:
            classifier_dropout = config.hidden_dropout
        else:
            classifier_dropout = 0.1
        self.dropout = nn.Dropout(p=classifier_dropout)
        self.classifier = nn.Linear(config.hidden_size, config.num_labels)

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

    def forward(
        self,
        input_ids: Optional[mindspore.Tensor] = None,
        past_key_values: Optional[Tuple[Tuple[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,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, TokenClassifierOutput]:
        r"""
        Args:
            labels (`torch.Tensor` of shape `(batch_size, sequence_length)`, *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

        transformer_outputs = self.transformer(
            input_ids,
            past_key_values=past_key_values,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        hidden_states = transformer_outputs[0]
        hidden_states = self.dropout(hidden_states)
        logits = self.classifier(hidden_states)

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

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

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

mindnlp.transformers.models.gpt_bigcode.gpt_bigcode.GPTBigCodeForTokenClassification.__init__(config)

Initializes an instance of the GPTBigCodeForTokenClassification class.

PARAMETER DESCRIPTION
self

The instance of the class.
config

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

  • num_labels: An integer specifying the number of output labels.
  • classifier_dropout: (optional) A float specifying the dropout rate for the classifier layer.
  • hidden_dropout: (optional) A float specifying the dropout rate for hidden layers.

Note: If both classifier_dropout and hidden_dropout are provided, classifier_dropout takes precedence.
RETURNS DESCRIPTION

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

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

            - num_labels: An integer specifying the number of output labels.
            - classifier_dropout: (optional) A float specifying the dropout rate for the classifier layer.
            - hidden_dropout: (optional) A float specifying the dropout rate for hidden layers.

            Note:
                If both classifier_dropout and hidden_dropout are provided, classifier_dropout takes precedence.

    Returns:
        None.

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

    self.transformer = GPTBigCodeModel(config)
    if hasattr(config, "classifier_dropout") and config.classifier_dropout is not None:
        classifier_dropout = config.classifier_dropout
    elif hasattr(config, "hidden_dropout") and config.hidden_dropout is not None:
        classifier_dropout = config.hidden_dropout
    else:
        classifier_dropout = 0.1
    self.dropout = nn.Dropout(p=classifier_dropout)
    self.classifier = nn.Linear(config.hidden_size, config.num_labels)

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

mindnlp.transformers.models.gpt_bigcode.gpt_bigcode.GPTBigCodeForTokenClassification.forward(input_ids=None, past_key_values=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, labels=None, use_cache=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: `torch.Tensor` of shape `(batch_size, sequence_length)`, *optional* DEFAULT: None

Source code in mindnlp/transformers/models/gpt_bigcode/gpt_bigcode.py 
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def forward(
    self,
    input_ids: Optional[mindspore.Tensor] = None,
    past_key_values: Optional[Tuple[Tuple[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,
    use_cache: Optional[bool] = None,
    output_attentions: Optional[bool] = None,
    output_hidden_states: Optional[bool] = None,
    return_dict: Optional[bool] = None,
) -> Union[Tuple, TokenClassifierOutput]:
    r"""
    Args:
        labels (`torch.Tensor` of shape `(batch_size, sequence_length)`, *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

    transformer_outputs = self.transformer(
        input_ids,
        past_key_values=past_key_values,
        attention_mask=attention_mask,
        token_type_ids=token_type_ids,
        position_ids=position_ids,
        head_mask=head_mask,
        inputs_embeds=inputs_embeds,
        use_cache=use_cache,
        output_attentions=output_attentions,
        output_hidden_states=output_hidden_states,
        return_dict=return_dict,
    )

    hidden_states = transformer_outputs[0]
    hidden_states = self.dropout(hidden_states)
    logits = self.classifier(hidden_states)

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

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

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

mindnlp.transformers.models.gpt_bigcode.gpt_bigcode.GPTBigCodeMLP

Bases: Module

GPT BigCode MLP

Source code in mindnlp/transformers/models/gpt_bigcode/gpt_bigcode.py 
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class GPTBigCodeMLP(nn.Module):
    """GPT BigCode MLP"""
    def __init__(self, intermediate_size, config):
        """
        Initializes an instance of the GPTBigCodeMLP class.

        Args:
            self: The object itself.
            intermediate_size (int): The size of the intermediate layer.
            config (object): The configuration object with various settings for the model.

        Returns:
            None

        Raises:
            None
        """
        super().__init__()
        embed_dim = config.hidden_size
        self.c_fc = nn.Linear(embed_dim, intermediate_size)
        self.c_proj = nn.Linear(intermediate_size, embed_dim)
        self.act = ACT2FN[config.activation_function]
        self.dropout = nn.Dropout(p=config.resid_pdrop)

    def forward(self, hidden_states: Optional[Tuple[mindspore.Tensor]]) -> mindspore.Tensor:
        """
        This method forwards a multi-layer perceptron for the GPT (Generative Pretrained Transformer) model
        using the provided hidden states.

        Args:
            self: The instance of the GPTBigCodeMLP class.
            hidden_states (Optional[Tuple[mindspore.Tensor]]):
                The hidden states to be processed by the multi-layer perceptron.
                It is an optional tuple of mindspore.Tensor containing the input hidden states.
                If not provided, the method will default to None.

        Returns:
            mindspore.Tensor:
                A tensor representing the processed hidden states after passing through the multi-layer perceptron.

        Raises:
            None
        """
        hidden_states = self.c_fc(hidden_states)
        hidden_states = self.act(hidden_states)
        hidden_states = self.c_proj(hidden_states)
        hidden_states = self.dropout(hidden_states)
        return hidden_states

mindnlp.transformers.models.gpt_bigcode.gpt_bigcode.GPTBigCodeMLP.__init__(intermediate_size, config)

Initializes an instance of the GPTBigCodeMLP class.

PARAMETER DESCRIPTION
self

The object itself.
intermediate_size

The size of the intermediate layer.

TYPE: int

config

The configuration object with various settings for the model.

TYPE: object

RETURNS DESCRIPTION

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

    Args:
        self: The object itself.
        intermediate_size (int): The size of the intermediate layer.
        config (object): The configuration object with various settings for the model.

    Returns:
        None

    Raises:
        None
    """
    super().__init__()
    embed_dim = config.hidden_size
    self.c_fc = nn.Linear(embed_dim, intermediate_size)
    self.c_proj = nn.Linear(intermediate_size, embed_dim)
    self.act = ACT2FN[config.activation_function]
    self.dropout = nn.Dropout(p=config.resid_pdrop)

mindnlp.transformers.models.gpt_bigcode.gpt_bigcode.GPTBigCodeMLP.forward(hidden_states)

This method forwards a multi-layer perceptron for the GPT (Generative Pretrained Transformer) model using the provided hidden states.

PARAMETER DESCRIPTION
self

The instance of the GPTBigCodeMLP class.
hidden_states

The hidden states to be processed by the multi-layer perceptron. It is an optional tuple of mindspore.Tensor containing the input hidden states. If not provided, the method will default to None.

TYPE: Optional[Tuple[Tensor]]

RETURNS DESCRIPTION
Tensor

mindspore.Tensor: A tensor representing the processed hidden states after passing through the multi-layer perceptron.
Source code in mindnlp/transformers/models/gpt_bigcode/gpt_bigcode.py 
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def forward(self, hidden_states: Optional[Tuple[mindspore.Tensor]]) -> mindspore.Tensor:
    """
    This method forwards a multi-layer perceptron for the GPT (Generative Pretrained Transformer) model
    using the provided hidden states.

    Args:
        self: The instance of the GPTBigCodeMLP class.
        hidden_states (Optional[Tuple[mindspore.Tensor]]):
            The hidden states to be processed by the multi-layer perceptron.
            It is an optional tuple of mindspore.Tensor containing the input hidden states.
            If not provided, the method will default to None.

    Returns:
        mindspore.Tensor:
            A tensor representing the processed hidden states after passing through the multi-layer perceptron.

    Raises:
        None
    """
    hidden_states = self.c_fc(hidden_states)
    hidden_states = self.act(hidden_states)
    hidden_states = self.c_proj(hidden_states)
    hidden_states = self.dropout(hidden_states)
    return hidden_states

mindnlp.transformers.models.gpt_bigcode.gpt_bigcode.GPTBigCodeModel

Bases: GPTBigCodePreTrainedModel

GPT BigCode Model

Source code in mindnlp/transformers/models/gpt_bigcode/gpt_bigcode.py 
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class GPTBigCodeModel(GPTBigCodePreTrainedModel):
    """GPT BigCode Model"""
    def __init__(self, config):
        """
        __init__

        Initializes the GPTBigCodeModel class.

        Args:
            self(GPTBigCodeModel):
                The instance of the GPTBigCodeModel class.
            config(Config):  An instance of the Config class containing configuration parameters for the model.
                The configuration parameters include:

                - multi_query: bool
                Specifies if the model supports multiple queries.
                - hidden_size: int
                Specifies the dimension of the hidden layers.
                - vocab_size: int
                Specifies the size of the vocabulary.
                - max_position_embeddings: int
                Specifies the maximum number of positions for embeddings.
                - embd_pdrop: float
                Specifies the dropout probability for the embeddings.
                - num_hidden_layers: int
                Specifies the number of hidden layers in the model.
                - layer_norm_epsilon: float
                Specifies the epsilon value for layer normalization.

        Returns:
            None.

        Raises:
            None.
        """
        super().__init__(config)
        self.multi_query = config.multi_query
        self.embed_dim = config.hidden_size

        self.wte = nn.Embedding(config.vocab_size, self.embed_dim)
        self.wpe = nn.Embedding(config.max_position_embeddings, self.embed_dim)

        self.drop = nn.Dropout(p=config.embd_pdrop)
        self.h = nn.ModuleList([GPTBigCodeBlock(config, layer_idx=i)
                              for i in range(config.num_hidden_layers)])
        self.ln_f = nn.LayerNorm(
            [self.embed_dim], eps=config.layer_norm_epsilon)

        self.gradient_checkpointing = False

        max_positions = config.max_position_embeddings
        self.bias = Tensor(
            np.tril(np.ones((max_positions, max_positions))), mindspore.bool_)

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

    def get_input_embeddings(self):
        """
        This method returns the input embeddings for the GPTBigCodeModel.

        Args:
            self: The instance of the GPTBigCodeModel class.

        Returns:
            None: This method returns the input embeddings which are of type None.

        Raises:
            None.
        """
        return self.wte

    def set_input_embeddings(self, new_embeddings):
        """
        Sets the input embeddings for the GPTBigCodeModel.

        Args:
            self (GPTBigCodeModel): The instance of the GPTBigCodeModel class.
            new_embeddings (object): The new input embeddings to be set for the model.
                It can be of any valid type.

        Returns:
            None.

        Raises:
            None.
        """
        self.wte = new_embeddings

    def forward(
        self,
        input_ids: Optional[mindspore.Tensor] = None,
        past_key_values: Optional[List[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,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, BaseModelOutputWithPastAndCrossAttentions]:
        """
        Constructs the GPTBigCodeModel.

        Args:
            self (GPTBigCodeModel): The instance of the GPTBigCodeModel class.
            input_ids (Optional[mindspore.Tensor], optional): The input sequence tensor. Defaults to None.
            past_key_values (Optional[List[mindspore.Tensor]], optional):
                List of tensors containing the past key values of the model. Defaults to None.
            attention_mask (Optional[mindspore.Tensor], optional): The attention mask tensor. Defaults to None.
            token_type_ids (Optional[mindspore.Tensor], optional): The token type ids tensor. Defaults to None.
            position_ids (Optional[mindspore.Tensor], optional): The position ids tensor. Defaults to None.
            head_mask (Optional[mindspore.Tensor], optional): The head mask tensor. Defaults to None.
            inputs_embeds (Optional[mindspore.Tensor], optional): The input embeddings tensor. Defaults to None.
            encoder_hidden_states (Optional[mindspore.Tensor], optional): The hidden states of the encoder.
                Defaults to None.
            encoder_attention_mask (Optional[mindspore.Tensor], optional): The attention mask for the encoder.
                Defaults to None.
            use_cache (Optional[bool], optional): Whether to use cache. Defaults to None.
            output_attentions (Optional[bool], optional): Whether to output attentions. Defaults to None.
            output_hidden_states (Optional[bool], optional): Whether to output hidden states. Defaults to None.
            return_dict (Optional[bool], optional): Whether to return a dictionary. Defaults to None.

        Returns:
            Union[Tuple, BaseModelOutputWithPastAndCrossAttentions]: The output of the GPTBigCodeModel.
                Returns a tuple or a BaseModelOutputWithPastAndCrossAttentions object depending on the value of
                return_dict.

        Raises:
            ValueError: If both input_ids and inputs_embeds are specified.
            ValueError: If neither input_ids nor inputs_embeds are specified.
            ValueError: If batch_size is less than or equal to 0.
            AssertionError: If the encoder_attention_mask has an invalid dimension.

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

        if 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
            input_ids = input_ids.view(-1, input_shape[-1])
            batch_size = input_ids.shape[0]
        elif inputs_embeds is not None:
            input_shape = inputs_embeds.shape[:-1]
            batch_size = inputs_embeds.shape[0]
        else:
            raise ValueError(
                "You have to specify either input_ids or inputs_embeds")

        if batch_size <= 0:
            raise ValueError("batch_size has to be defined and > 0")

        if token_type_ids is not None:
            token_type_ids = token_type_ids.view(-1, input_shape[-1])

        if past_key_values is None:
            past_length = 0
            past_key_values = tuple([None] * len(self.h))
        else:
            past_length = past_key_values[0].shape[-2]

        if attention_mask is not None and len(attention_mask.shape) == 2 and position_ids is None:
            # create position_ids on the fly for batch generation
            position_ids = attention_mask.long().cumsum(-1) - 1
            position_ids = position_ids.masked_fill(attention_mask == 0, 1)
            if past_length > 0:
                position_ids = position_ids[:,
                                            past_length: input_shape[-1] + past_length:]
        elif position_ids is None:
            position_ids = ops.arange(
                past_length, input_shape[-1] + past_length, dtype=mindspore.int64)
            position_ids = position_ids.unsqueeze(0)

        # Self-attention mask.
        query_length = input_shape[-1]
        key_length = past_length + query_length
        self_attention_mask = self.bias[None, key_length - query_length: key_length, :key_length]

        if attention_mask is not None:
            self_attention_mask = self_attention_mask * \
                attention_mask.view(batch_size, 1, -1)
            self_attention_mask = self_attention_mask.bool()

        # MQA models: (batch_size, query_length, n_heads, key_length)
        # MHA models: (batch_size, n_heads, query_length, key_length)
        attention_mask = ops.unsqueeze(
            self_attention_mask, 2 if self.multi_query else 1)

        # 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.add_cross_attention
            and encoder_hidden_states is not None
            and encoder_attention_mask is not None
        ):
            if encoder_attention_mask.dim() == 2:
                encoder_attention_mask.unsqueeze(1)
            assert encoder_attention_mask.dim() == 3
            encoder_attention_mask = encoder_attention_mask.bool(
            ).unsqueeze(2 if self.multi_query else 1)
        else:
            encoder_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
        # head_mask has shape n_layer x batch x n_heads x N x N
        head_mask = self.get_head_mask(head_mask, self.config.n_layer)

        if inputs_embeds is None:
            inputs_embeds = self.wte(input_ids)
        position_embeds = self.wpe(position_ids)
        hidden_states = inputs_embeds + position_embeds

        if token_type_ids is not None:
            token_type_embeds = self.wte(token_type_ids)
            hidden_states = hidden_states + token_type_embeds

        hidden_states = self.drop(hidden_states)
        output_shape = input_shape + (hidden_states.shape[-1],)

        presents = [] if use_cache else None
        all_self_attentions = () if output_attentions else None
        all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
        all_hidden_states = () if output_hidden_states else None
        for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)):
            if output_hidden_states:
                all_hidden_states = all_hidden_states + (hidden_states,)

            outputs = block(
                hidden_states,
                layer_past=layer_past,
                attention_mask=attention_mask,
                head_mask=head_mask[i],
                encoder_hidden_states=encoder_hidden_states,
                encoder_attention_mask=encoder_attention_mask,
                use_cache=use_cache,
                output_attentions=output_attentions,
            )

            hidden_states = outputs[0]
            if use_cache:
                presents.append(outputs[1])

            if output_attentions:
                all_self_attentions = all_self_attentions + \
                    (outputs[2 if use_cache else 1],)
                if self.config.add_cross_attention:
                    all_cross_attentions = all_cross_attentions + \
                        (outputs[3 if use_cache else 2],)

        hidden_states = self.ln_f(hidden_states)

        hidden_states = hidden_states.view(output_shape)
        # Add last hidden state
        if output_hidden_states:
            all_hidden_states = all_hidden_states + (hidden_states,)

        if not return_dict:
            return tuple(
                v
                for v in [hidden_states, presents, all_hidden_states, all_self_attentions, all_cross_attentions]
                if v is not None
            )

        return BaseModelOutputWithPastAndCrossAttentions(
            last_hidden_state=hidden_states,
            past_key_values=presents,
            hidden_states=all_hidden_states,
            attentions=all_self_attentions,
            cross_attentions=all_cross_attentions,
        )

mindnlp.transformers.models.gpt_bigcode.gpt_bigcode.GPTBigCodeModel.__init__(config)

init

Initializes the GPTBigCodeModel class.

PARAMETER DESCRIPTION
self(GPTBigCodeModel)

The instance of the GPTBigCodeModel class.
config(Config)

An instance of the Config class containing configuration parameters for the model. The configuration parameters include:

  • multi_query: bool Specifies if the model supports multiple queries.
  • hidden_size: int Specifies the dimension of the hidden layers.
  • vocab_size: int Specifies the size of the vocabulary.
  • max_position_embeddings: int Specifies the maximum number of positions for embeddings.
  • embd_pdrop: float Specifies the dropout probability for the embeddings.
  • num_hidden_layers: int Specifies the number of hidden layers in the model.
  • layer_norm_epsilon: float Specifies the epsilon value for layer normalization.
RETURNS DESCRIPTION

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

    Initializes the GPTBigCodeModel class.

    Args:
        self(GPTBigCodeModel):
            The instance of the GPTBigCodeModel class.
        config(Config):  An instance of the Config class containing configuration parameters for the model.
            The configuration parameters include:

            - multi_query: bool
            Specifies if the model supports multiple queries.
            - hidden_size: int
            Specifies the dimension of the hidden layers.
            - vocab_size: int
            Specifies the size of the vocabulary.
            - max_position_embeddings: int
            Specifies the maximum number of positions for embeddings.
            - embd_pdrop: float
            Specifies the dropout probability for the embeddings.
            - num_hidden_layers: int
            Specifies the number of hidden layers in the model.
            - layer_norm_epsilon: float
            Specifies the epsilon value for layer normalization.

    Returns:
        None.

    Raises:
        None.
    """
    super().__init__(config)
    self.multi_query = config.multi_query
    self.embed_dim = config.hidden_size

    self.wte = nn.Embedding(config.vocab_size, self.embed_dim)
    self.wpe = nn.Embedding(config.max_position_embeddings, self.embed_dim)

    self.drop = nn.Dropout(p=config.embd_pdrop)
    self.h = nn.ModuleList([GPTBigCodeBlock(config, layer_idx=i)
                          for i in range(config.num_hidden_layers)])
    self.ln_f = nn.LayerNorm(
        [self.embed_dim], eps=config.layer_norm_epsilon)

    self.gradient_checkpointing = False

    max_positions = config.max_position_embeddings
    self.bias = Tensor(
        np.tril(np.ones((max_positions, max_positions))), mindspore.bool_)

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

mindnlp.transformers.models.gpt_bigcode.gpt_bigcode.GPTBigCodeModel.forward(input_ids=None, past_key_values=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, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None)

Constructs the GPTBigCodeModel.

PARAMETER DESCRIPTION
self

The instance of the GPTBigCodeModel class.

TYPE: GPTBigCodeModel

input_ids

The input sequence tensor. Defaults to None.

TYPE: Optional[Tensor] DEFAULT: None

past_key_values

List of tensors containing the past key values of the model. Defaults to None.

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

attention_mask

The attention mask tensor. Defaults to None.

TYPE: Optional[Tensor] DEFAULT: None

token_type_ids

The token type ids tensor. Defaults to None.

TYPE: Optional[Tensor] DEFAULT: None

position_ids

The position ids tensor. Defaults to None.

TYPE: Optional[Tensor] DEFAULT: None

head_mask

The head mask tensor. Defaults to None.

TYPE: Optional[Tensor] DEFAULT: None

inputs_embeds

The input embeddings tensor. Defaults to None.

TYPE: Optional[Tensor] DEFAULT: None

encoder_hidden_states

The hidden states of the encoder. Defaults to None.

TYPE: Optional[Tensor] DEFAULT: None

encoder_attention_mask

The attention mask for the encoder. Defaults to None.

TYPE: Optional[Tensor] DEFAULT: None

use_cache

Whether to use cache. Defaults to None.

TYPE: Optional[bool] DEFAULT: None

output_attentions

Whether to output attentions. Defaults to None.

TYPE: Optional[bool] DEFAULT: None

output_hidden_states

Whether to output hidden states. Defaults to None.

TYPE: Optional[bool] DEFAULT: None

return_dict

Whether to return a dictionary. Defaults to None.

TYPE: Optional[bool] DEFAULT: None

RETURNS DESCRIPTION
Union[Tuple, BaseModelOutputWithPastAndCrossAttentions]

Union[Tuple, BaseModelOutputWithPastAndCrossAttentions]: The output of the GPTBigCodeModel. Returns a tuple or a BaseModelOutputWithPastAndCrossAttentions object depending on the value of return_dict.
RAISES DESCRIPTION
ValueError

If both input_ids and inputs_embeds are specified.
ValueError

If neither input_ids nor inputs_embeds are specified.
ValueError

If batch_size is less than or equal to 0.
AssertionError

If the encoder_attention_mask has an invalid dimension.
Source code in mindnlp/transformers/models/gpt_bigcode/gpt_bigcode.py 
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def forward(
    self,
    input_ids: Optional[mindspore.Tensor] = None,
    past_key_values: Optional[List[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,
    use_cache: Optional[bool] = None,
    output_attentions: Optional[bool] = None,
    output_hidden_states: Optional[bool] = None,
    return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithPastAndCrossAttentions]:
    """
    Constructs the GPTBigCodeModel.

    Args:
        self (GPTBigCodeModel): The instance of the GPTBigCodeModel class.
        input_ids (Optional[mindspore.Tensor], optional): The input sequence tensor. Defaults to None.
        past_key_values (Optional[List[mindspore.Tensor]], optional):
            List of tensors containing the past key values of the model. Defaults to None.
        attention_mask (Optional[mindspore.Tensor], optional): The attention mask tensor. Defaults to None.
        token_type_ids (Optional[mindspore.Tensor], optional): The token type ids tensor. Defaults to None.
        position_ids (Optional[mindspore.Tensor], optional): The position ids tensor. Defaults to None.
        head_mask (Optional[mindspore.Tensor], optional): The head mask tensor. Defaults to None.
        inputs_embeds (Optional[mindspore.Tensor], optional): The input embeddings tensor. Defaults to None.
        encoder_hidden_states (Optional[mindspore.Tensor], optional): The hidden states of the encoder.
            Defaults to None.
        encoder_attention_mask (Optional[mindspore.Tensor], optional): The attention mask for the encoder.
            Defaults to None.
        use_cache (Optional[bool], optional): Whether to use cache. Defaults to None.
        output_attentions (Optional[bool], optional): Whether to output attentions. Defaults to None.
        output_hidden_states (Optional[bool], optional): Whether to output hidden states. Defaults to None.
        return_dict (Optional[bool], optional): Whether to return a dictionary. Defaults to None.

    Returns:
        Union[Tuple, BaseModelOutputWithPastAndCrossAttentions]: The output of the GPTBigCodeModel.
            Returns a tuple or a BaseModelOutputWithPastAndCrossAttentions object depending on the value of
            return_dict.

    Raises:
        ValueError: If both input_ids and inputs_embeds are specified.
        ValueError: If neither input_ids nor inputs_embeds are specified.
        ValueError: If batch_size is less than or equal to 0.
        AssertionError: If the encoder_attention_mask has an invalid dimension.

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

    if 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
        input_ids = input_ids.view(-1, input_shape[-1])
        batch_size = input_ids.shape[0]
    elif inputs_embeds is not None:
        input_shape = inputs_embeds.shape[:-1]
        batch_size = inputs_embeds.shape[0]
    else:
        raise ValueError(
            "You have to specify either input_ids or inputs_embeds")

    if batch_size <= 0:
        raise ValueError("batch_size has to be defined and > 0")

    if token_type_ids is not None:
        token_type_ids = token_type_ids.view(-1, input_shape[-1])

    if past_key_values is None:
        past_length = 0
        past_key_values = tuple([None] * len(self.h))
    else:
        past_length = past_key_values[0].shape[-2]

    if attention_mask is not None and len(attention_mask.shape) == 2 and position_ids is None:
        # create position_ids on the fly for batch generation
        position_ids = attention_mask.long().cumsum(-1) - 1
        position_ids = position_ids.masked_fill(attention_mask == 0, 1)
        if past_length > 0:
            position_ids = position_ids[:,
                                        past_length: input_shape[-1] + past_length:]
    elif position_ids is None:
        position_ids = ops.arange(
            past_length, input_shape[-1] + past_length, dtype=mindspore.int64)
        position_ids = position_ids.unsqueeze(0)

    # Self-attention mask.
    query_length = input_shape[-1]
    key_length = past_length + query_length
    self_attention_mask = self.bias[None, key_length - query_length: key_length, :key_length]

    if attention_mask is not None:
        self_attention_mask = self_attention_mask * \
            attention_mask.view(batch_size, 1, -1)
        self_attention_mask = self_attention_mask.bool()

    # MQA models: (batch_size, query_length, n_heads, key_length)
    # MHA models: (batch_size, n_heads, query_length, key_length)
    attention_mask = ops.unsqueeze(
        self_attention_mask, 2 if self.multi_query else 1)

    # 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.add_cross_attention
        and encoder_hidden_states is not None
        and encoder_attention_mask is not None
    ):
        if encoder_attention_mask.dim() == 2:
            encoder_attention_mask.unsqueeze(1)
        assert encoder_attention_mask.dim() == 3
        encoder_attention_mask = encoder_attention_mask.bool(
        ).unsqueeze(2 if self.multi_query else 1)
    else:
        encoder_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
    # head_mask has shape n_layer x batch x n_heads x N x N
    head_mask = self.get_head_mask(head_mask, self.config.n_layer)

    if inputs_embeds is None:
        inputs_embeds = self.wte(input_ids)
    position_embeds = self.wpe(position_ids)
    hidden_states = inputs_embeds + position_embeds

    if token_type_ids is not None:
        token_type_embeds = self.wte(token_type_ids)
        hidden_states = hidden_states + token_type_embeds

    hidden_states = self.drop(hidden_states)
    output_shape = input_shape + (hidden_states.shape[-1],)

    presents = [] if use_cache else None
    all_self_attentions = () if output_attentions else None
    all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
    all_hidden_states = () if output_hidden_states else None
    for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)):
        if output_hidden_states:
            all_hidden_states = all_hidden_states + (hidden_states,)

        outputs = block(
            hidden_states,
            layer_past=layer_past,
            attention_mask=attention_mask,
            head_mask=head_mask[i],
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            use_cache=use_cache,
            output_attentions=output_attentions,
        )

        hidden_states = outputs[0]
        if use_cache:
            presents.append(outputs[1])

        if output_attentions:
            all_self_attentions = all_self_attentions + \
                (outputs[2 if use_cache else 1],)
            if self.config.add_cross_attention:
                all_cross_attentions = all_cross_attentions + \
                    (outputs[3 if use_cache else 2],)

    hidden_states = self.ln_f(hidden_states)

    hidden_states = hidden_states.view(output_shape)
    # Add last hidden state
    if output_hidden_states:
        all_hidden_states = all_hidden_states + (hidden_states,)

    if not return_dict:
        return tuple(
            v
            for v in [hidden_states, presents, all_hidden_states, all_self_attentions, all_cross_attentions]
            if v is not None
        )

    return BaseModelOutputWithPastAndCrossAttentions(
        last_hidden_state=hidden_states,
        past_key_values=presents,
        hidden_states=all_hidden_states,
        attentions=all_self_attentions,
        cross_attentions=all_cross_attentions,
    )

mindnlp.transformers.models.gpt_bigcode.gpt_bigcode.GPTBigCodeModel.get_input_embeddings()

This method returns the input embeddings for the GPTBigCodeModel.

PARAMETER DESCRIPTION
self

The instance of the GPTBigCodeModel class.
RETURNS DESCRIPTION
None

This method returns the input embeddings which are of type None.
Source code in mindnlp/transformers/models/gpt_bigcode/gpt_bigcode.py 
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def get_input_embeddings(self):
    """
    This method returns the input embeddings for the GPTBigCodeModel.

    Args:
        self: The instance of the GPTBigCodeModel class.

    Returns:
        None: This method returns the input embeddings which are of type None.

    Raises:
        None.
    """
    return self.wte

mindnlp.transformers.models.gpt_bigcode.gpt_bigcode.GPTBigCodeModel.set_input_embeddings(new_embeddings)

Sets the input embeddings for the GPTBigCodeModel.

PARAMETER DESCRIPTION
self

The instance of the GPTBigCodeModel class.

TYPE: GPTBigCodeModel

new_embeddings

The new input embeddings to be set for the model. It can be of any valid type.

TYPE: object

RETURNS DESCRIPTION

None.
Source code in mindnlp/transformers/models/gpt_bigcode/gpt_bigcode.py 
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def set_input_embeddings(self, new_embeddings):
    """
    Sets the input embeddings for the GPTBigCodeModel.

    Args:
        self (GPTBigCodeModel): The instance of the GPTBigCodeModel class.
        new_embeddings (object): The new input embeddings to be set for the model.
            It can be of any valid type.

    Returns:
        None.

    Raises:
        None.
    """
    self.wte = new_embeddings

mindnlp.transformers.models.gpt_bigcode.gpt_bigcode.GPTBigCodePreTrainedModel

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/gpt_bigcode/gpt_bigcode.py 
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class GPTBigCodePreTrainedModel(PreTrainedModel):
    """
    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
    models.
    """
    config_class = GPTBigCodeConfig
    base_model_prefix = "transformer"
    supports_gradient_checkpointing = True
    _no_split_modules = ["GPTBigCodeBlock"]
    _skip_keys_device_placement = "past_key_values"

    def _init_weights(self, cell):
        """Initialize the weights."""
        if isinstance(cell, (GPTBigCodeMLP, GPTBigCodeAttention)):
            sigma = self.config.initializer_range / math.sqrt(2 * self.config.n_layer)
            cell.c_proj.weight.set_data(initializer(Normal(sigma=sigma),cell.c_proj.weight.shape, cell.c_proj.weight.dtype))
        if isinstance(cell, nn.Linear):
            cell.weight.set_data(initializer(Normal(sigma=self.config.initializer_range),
                                             cell.weight.shape, cell.weight.dtype))
            if cell.bias:
                cell.bias.set_data(initializer(
                    'zeros', cell.bias.shape, cell.bias.dtype))
        elif isinstance(cell, nn.Embedding):
            weight = initializer(Normal(sigma=self.config.initializer_range),
                                          cell.weight.shape,
                                          cell.weight.dtype)
            if cell.padding_idx is not None:
                weight[cell.padding_idx] = 0
            cell.weight.set_data(weight)
        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))

    def _set_gradient_checkpointing(self, module, value=False):
        """
        Set the gradient checkpointing for a given module in the GPTBigCodePreTrainedModel.

        Args:
            self (GPTBigCodePreTrainedModel): The instance of the GPTBigCodePreTrainedModel.
            module (object): The module for which the gradient checkpointing needs to be set.
            value (bool): The boolean value indicating whether to enable gradient checkpointing for the module.

        Returns:
            None.

        Raises:
            TypeError: If the module is not an instance of GPTBigCodeModel.
        """
        if isinstance(module, GPTBigCodeModel):
            module.gradient_checkpointing = value

    def _set_gradient_checkpointing(self, module, value=False):
        """
        Sets the gradient checkpointing flag for a specific module in a GPTBigCodePreTrainedModel instance.

        Args:
            self (GPTBigCodePreTrainedModel): The GPTBigCodePreTrainedModel instance.
            module (GPTBigCodeModel): The module for which to set the gradient checkpointing flag.
            value (bool): The value to set the gradient checkpointing flag to. Default is False.

        Returns:
            None.

        Raises:
            None.

        Note:
            Gradient checkpointing is a memory optimization technique used during training of deep neural networks.
            When the gradient checkpointing flag is set to True for a specific module, intermediate activations are not
            stored during the forward pass, which reduces the memory usage at the cost of recomputing those activations
            during the backward pass. This can be useful for models with large memory requirements.
        """
        if isinstance(module, GPTBigCodeModel):
            module.gradient_checkpointing = value

    def _backward_compatibility_gradient_checkpointing(self):
        """
        Support gradient_checkpointing.
        """
        if self.supports_gradient_checkpointing and getattr(self.config, "gradient_checkpointing", False):
            self.gradient_checkpointing_enable()
            # Remove the attribute now that is has been consumed, so it's no saved in the config.
            delattr(self.config, "gradient_checkpointing")

    def gradient_checkpointing_enable(self):
        """
        Activates gradient checkpointing for the current model.
        Note that in other frameworks this feature can be referred to as "activation checkpointing" or "checkpoint
        activations".
        """
        if not self.supports_gradient_checkpointing:
            raise ValueError(
                f"{self.__class__.__name__} does not support gradient checkpointing.")
        self.apply(partial(self._set_gradient_checkpointing, value=True))

mindnlp.transformers.models.gpt_bigcode.gpt_bigcode.GPTBigCodePreTrainedModel.gradient_checkpointing_enable()

Activates gradient checkpointing for the current model. Note that in other frameworks this feature can be referred to as "activation checkpointing" or "checkpoint activations".

Source code in mindnlp/transformers/models/gpt_bigcode/gpt_bigcode.py 
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def gradient_checkpointing_enable(self):
    """
    Activates gradient checkpointing for the current model.
    Note that in other frameworks this feature can be referred to as "activation checkpointing" or "checkpoint
    activations".
    """
    if not self.supports_gradient_checkpointing:
        raise ValueError(
            f"{self.__class__.__name__} does not support gradient checkpointing.")
    self.apply(partial(self._set_gradient_checkpointing, value=True))

mindnlp.transformers.models.gpt_bigcode.gpt_bigcode.masked_softmax(input_x, mask, mask_value)

Fuse kernel for masked softmax.

Source code in mindnlp/transformers/models/gpt_bigcode/gpt_bigcode.py 
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def masked_softmax(input_x: mindspore.Tensor, mask: mindspore.Tensor, mask_value: mindspore.Tensor):
    """Fuse kernel for masked softmax."""
    input_x = ops.where(mask, input_x, mask_value)
    input_x = ops.softmax(input_x, axis=-1)
    return input_x

mindnlp.transformers.models.gpt_bigcode.gpt_bigcode.upcast_masked_softmax(input_x, mask, mask_value, scale, softmax_dtype)

Fuse kernel for upcast masked softmax.

Source code in mindnlp/transformers/models/gpt_bigcode/gpt_bigcode.py 
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def upcast_masked_softmax(
    input_x: mindspore.Tensor, mask: mindspore.Tensor, mask_value: mindspore.Tensor, scale: float, softmax_dtype: mindspore.dtype
):
    """Fuse kernel for upcast masked softmax."""
    input_dtype = input_x.dtype
    input_x = input_x.to(softmax_dtype) * scale
    input_x = ops.where(mask, input_x, mask_value)
    input_x = ops.softmax(input_x, axis=-1).to(input_dtype)
    return input_x

mindnlp.transformers.models.gpt_bigcode.gpt_bigcode.upcast_softmax(input_x, scale, softmax_dtype)

Fuse kernel for upcast softmax.

Source code in mindnlp/transformers/models/gpt_bigcode/gpt_bigcode.py 
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def upcast_softmax(input_x: mindspore.Tensor, scale: float, softmax_dtype: mindspore.dtype):
    """Fuse kernel for upcast softmax."""
    input_dtype = input_x.dtype
    input_x = input_x.to(softmax_dtype) * scale
    input_x = ops.softmax(input_x, axis=-1).to(input_dtype)
    return input_x

mindnlp.transformers.models.gpt_bigcode.gpt_bigcode_config

MindNLP gpt_bigcode config

mindnlp.transformers.models.gpt_bigcode.gpt_bigcode_config.GPTBigCodeConfig

Bases: PretrainedConfig

GPT BigCode config

Source code in mindnlp/transformers/models/gpt_bigcode/gpt_bigcode_config.py 
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class GPTBigCodeConfig(PretrainedConfig):
    r"""
    GPT BigCode config
    """
    model_type = "gpt_bigcode"
    keys_to_ignore_at_inference = ["past_key_values"]
    attribute_map = {
        "hidden_size": "n_embd",
        "max_position_embeddings": "n_positions",
        "num_attention_heads": "n_head",
        "num_hidden_layers": "n_layer",
    }

    def __init__(
        self,
        vocab_size=50257,
        n_positions=1024,
        n_embd=768,
        n_layer=12,
        n_head=12,
        n_inner=None,
        activation_function="gelu_approximate",
        resid_pdrop=0.1,
        embd_pdrop=0.1,
        attn_pdrop=0.1,
        layer_norm_epsilon=1e-5,
        initializer_range=0.02,
        scale_attn_weights=True,
        use_cache=True,
        bos_token_id=50256,
        eos_token_id=50256,
        attention_softmax_in_fp32=True,
        scale_attention_softmax_in_fp32=True,
        multi_query=True,
        **kwargs,
    ):
        """
        __init__

        Initialize a new GPTBigCodeConfig object.

        Args:
            vocab_size (int, optional): The size of the vocabulary. Default is 50257.
            n_positions (int, optional): The maximum sequence length for the model. Default is 1024.
            n_embd (int, optional): The dimension of the embeddings and hidden states. Default is 768.
            n_layer (int, optional): The number of layers in the model. Default is 12.
            n_head (int, optional): The number of attention heads in the model. Default is 12.
            n_inner (int, optional): The inner dimension of the feedforward layers. Default is None.
            activation_function (str, optional): The activation function used in the model. Default is 'gelu_approximate'.
            resid_pdrop (float, optional): The dropout probability for residual connections. Default is 0.1.
            embd_pdrop (float, optional): The dropout probability for embeddings. Default is 0.1.
            attn_pdrop (float, optional): The dropout probability for attention layers. Default is 0.1.
            layer_norm_epsilon (float, optional): The epsilon value for layer normalization. Default is 1e-05.
            initializer_range (float, optional): The range for parameter initializers. Default is 0.02.
            scale_attn_weights (bool, optional): Whether to scale the attention weights. Default is True.
            use_cache (bool, optional): Whether to use caching during inference. Default is True.
            bos_token_id (int, optional): The token id for the beginning of sequence. Default is 50256.
            eos_token_id (int, optional): The token id for the end of sequence. Default is 50256.
            attention_softmax_in_fp32 (bool, optional): Whether to use fp32 for attention softmax. Default is True.
            scale_attention_softmax_in_fp32 (bool, optional): Whether to scale attention softmax in fp32. Default is True.
            multi_query (bool, optional): Whether to use multi-query attention. Default is True.

        Returns:
            None.

        Raises:
            None.
        """
        self.vocab_size = vocab_size
        self.n_positions = n_positions
        self.n_embd = n_embd
        self.n_layer = n_layer
        self.n_head = n_head
        self.n_inner = n_inner
        self.activation_function = activation_function
        self.resid_pdrop = resid_pdrop
        self.embd_pdrop = embd_pdrop
        self.attn_pdrop = attn_pdrop
        self.layer_norm_epsilon = layer_norm_epsilon
        self.initializer_range = initializer_range
        self.scale_attn_weights = scale_attn_weights
        self.use_cache = use_cache
        self.attention_softmax_in_fp32 = attention_softmax_in_fp32
        self.scale_attention_softmax_in_fp32 = scale_attention_softmax_in_fp32
        self.multi_query = multi_query

        self.bos_token_id = bos_token_id
        self.eos_token_id = eos_token_id

        super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)

mindnlp.transformers.models.gpt_bigcode.gpt_bigcode_config.GPTBigCodeConfig.__init__(vocab_size=50257, n_positions=1024, n_embd=768, n_layer=12, n_head=12, n_inner=None, activation_function='gelu_approximate', resid_pdrop=0.1, embd_pdrop=0.1, attn_pdrop=0.1, layer_norm_epsilon=1e-05, initializer_range=0.02, scale_attn_weights=True, use_cache=True, bos_token_id=50256, eos_token_id=50256, attention_softmax_in_fp32=True, scale_attention_softmax_in_fp32=True, multi_query=True, **kwargs)

init

Initialize a new GPTBigCodeConfig object.

PARAMETER DESCRIPTION
vocab_size

The size of the vocabulary. Default is 50257.

TYPE: int DEFAULT: 50257

n_positions

The maximum sequence length for the model. Default is 1024.

TYPE: int DEFAULT: 1024

n_embd

The dimension of the embeddings and hidden states. Default is 768.

TYPE: int DEFAULT: 768

n_layer

The number of layers in the model. Default is 12.

TYPE: int DEFAULT: 12

n_head

The number of attention heads in the model. Default is 12.

TYPE: int DEFAULT: 12

n_inner

The inner dimension of the feedforward layers. Default is None.

TYPE: int DEFAULT: None

activation_function

The activation function used in the model. Default is 'gelu_approximate'.

TYPE: str DEFAULT: 'gelu_approximate'

resid_pdrop

The dropout probability for residual connections. Default is 0.1.

TYPE: float DEFAULT: 0.1

embd_pdrop

The dropout probability for embeddings. Default is 0.1.

TYPE: float DEFAULT: 0.1

attn_pdrop

The dropout probability for attention layers. Default is 0.1.

TYPE: float DEFAULT: 0.1

layer_norm_epsilon

The epsilon value for layer normalization. Default is 1e-05.

TYPE: float DEFAULT: 1e-05

initializer_range

The range for parameter initializers. Default is 0.02.

TYPE: float DEFAULT: 0.02

scale_attn_weights

Whether to scale the attention weights. Default is True.

TYPE: bool DEFAULT: True

use_cache

Whether to use caching during inference. Default is True.

TYPE: bool DEFAULT: True

bos_token_id

The token id for the beginning of sequence. Default is 50256.

TYPE: int DEFAULT: 50256

eos_token_id

The token id for the end of sequence. Default is 50256.

TYPE: int DEFAULT: 50256

attention_softmax_in_fp32

Whether to use fp32 for attention softmax. Default is True.

TYPE: bool DEFAULT: True

scale_attention_softmax_in_fp32

Whether to scale attention softmax in fp32. Default is True.

TYPE: bool DEFAULT: True

multi_query

Whether to use multi-query attention. Default is True.

TYPE: bool DEFAULT: True

RETURNS DESCRIPTION

None.
Source code in mindnlp/transformers/models/gpt_bigcode/gpt_bigcode_config.py 
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def __init__(
    self,
    vocab_size=50257,
    n_positions=1024,
    n_embd=768,
    n_layer=12,
    n_head=12,
    n_inner=None,
    activation_function="gelu_approximate",
    resid_pdrop=0.1,
    embd_pdrop=0.1,
    attn_pdrop=0.1,
    layer_norm_epsilon=1e-5,
    initializer_range=0.02,
    scale_attn_weights=True,
    use_cache=True,
    bos_token_id=50256,
    eos_token_id=50256,
    attention_softmax_in_fp32=True,
    scale_attention_softmax_in_fp32=True,
    multi_query=True,
    **kwargs,
):
    """
    __init__

    Initialize a new GPTBigCodeConfig object.

    Args:
        vocab_size (int, optional): The size of the vocabulary. Default is 50257.
        n_positions (int, optional): The maximum sequence length for the model. Default is 1024.
        n_embd (int, optional): The dimension of the embeddings and hidden states. Default is 768.
        n_layer (int, optional): The number of layers in the model. Default is 12.
        n_head (int, optional): The number of attention heads in the model. Default is 12.
        n_inner (int, optional): The inner dimension of the feedforward layers. Default is None.
        activation_function (str, optional): The activation function used in the model. Default is 'gelu_approximate'.
        resid_pdrop (float, optional): The dropout probability for residual connections. Default is 0.1.
        embd_pdrop (float, optional): The dropout probability for embeddings. Default is 0.1.
        attn_pdrop (float, optional): The dropout probability for attention layers. Default is 0.1.
        layer_norm_epsilon (float, optional): The epsilon value for layer normalization. Default is 1e-05.
        initializer_range (float, optional): The range for parameter initializers. Default is 0.02.
        scale_attn_weights (bool, optional): Whether to scale the attention weights. Default is True.
        use_cache (bool, optional): Whether to use caching during inference. Default is True.
        bos_token_id (int, optional): The token id for the beginning of sequence. Default is 50256.
        eos_token_id (int, optional): The token id for the end of sequence. Default is 50256.
        attention_softmax_in_fp32 (bool, optional): Whether to use fp32 for attention softmax. Default is True.
        scale_attention_softmax_in_fp32 (bool, optional): Whether to scale attention softmax in fp32. Default is True.
        multi_query (bool, optional): Whether to use multi-query attention. Default is True.

    Returns:
        None.

    Raises:
        None.
    """
    self.vocab_size = vocab_size
    self.n_positions = n_positions
    self.n_embd = n_embd
    self.n_layer = n_layer
    self.n_head = n_head
    self.n_inner = n_inner
    self.activation_function = activation_function
    self.resid_pdrop = resid_pdrop
    self.embd_pdrop = embd_pdrop
    self.attn_pdrop = attn_pdrop
    self.layer_norm_epsilon = layer_norm_epsilon
    self.initializer_range = initializer_range
    self.scale_attn_weights = scale_attn_weights
    self.use_cache = use_cache
    self.attention_softmax_in_fp32 = attention_softmax_in_fp32
    self.scale_attention_softmax_in_fp32 = scale_attention_softmax_in_fp32
    self.multi_query = multi_query

    self.bos_token_id = bos_token_id
    self.eos_token_id = eos_token_id

    super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)

mindnlp.transformers.models.gpt_bigcode.gpt_bigcode_tokenizer

GPT2Tokenizer

mindnlp.transformers.models.gpt_bigcode.gpt_bigcode_tokenizer.GPTBigCodeTokenizer

Bases: PreTrainedTokenizer

Tokenizer used for GPT2 text process.

PARAMETER DESCRIPTION
vocab

Vocabulary used to look up words.

TYPE: Vocab

return_token

Whether to return token. If True: return tokens. False: return ids. Default: True.

TYPE: bool

Source code in mindnlp/transformers/models/gpt_bigcode/gpt_bigcode_tokenizer.py 
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class GPTBigCodeTokenizer(PreTrainedTokenizer):
    """
        Tokenizer used for GPT2 text process.

        Args:
            vocab (Vocab): Vocabulary used to look up words.
            return_token (bool): Whether to return token. If True: return tokens. False: return ids. Default: True.

        """
    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES

    def __init__(
        self,
        tokenizer_file=None,
        unk_token="<|endoftext|>",
        bos_token="<|endoftext|>",
        eos_token="<|endoftext|>",
        add_prefix_space=False,
        **kwargs
    ):
        """
        Initializes a new instance of the GPTBigCodeTokenizer class.

        Args:
            self (GPTBigCodeTokenizer): The instance of the class itself.
            tokenizer_file (str): The file path of the tokenizer file to be used. Only string values are supported.
            unk_token (str): The token to represent unknown words. Default is 'endoftext'.
            bos_token (str): The token to represent the beginning of a sentence. Default is 'endoftext'.
            eos_token (str): The token to represent the end of a sentence. Default is 'endoftext'.
            add_prefix_space (bool): Whether to add a prefix space before the input text. Default is False.
            **kwargs: Additional keyword arguments.

        Returns:
            None.

        Raises:
            ValueError: If the tokenizer_file is not of type string.

        """
        super().__init__(
            unk_token=unk_token,
            bos_token=bos_token,
            eos_token=eos_token,
            add_prefix_space=add_prefix_space,
            **kwargs)

        return_token = kwargs.pop('return_token', False)

        if isinstance(tokenizer_file, str):
            self._tokenizer = Tokenizer.from_file(tokenizer_file)
        else:
            raise ValueError(f'only support string, but got {tokenizer_file}')

        self.return_token = return_token
        self.implementation = Implementation.PY

    def execute_py(self, text_input):
        """
        Execute method.
        """
        return self._execute_py(text_input)

    def _execute_py(self, text_input):
        """
        Execute method.
        """
        text_input = self._convert_to_unicode(text_input)
        tokens = self._tokenizer.encode(text_input)
        if self.return_token is True:
            return np.array(tokens.tokens)
        return {"input_ids": Tensor(np.array(tokens.ids)), "attention_mask": Tensor(np.array(tokens.attention_mask))}

    def _convert_to_unicode(self, text_input):
        """Converts `text` to Unicode (if it's not already), assuming utf-8 input."""
        if isinstance(text_input, str):
            return text_input
        if isinstance(text_input, bytes):
            return text_input.decode("utf-8", "ignore")
        if isinstance(text_input, np.ndarray):
            if text_input.dtype.type is np.bytes_:
                text_input = np.char.decode(text_input, "utf-8")
            return str(text_input)
        raise ValueError(
            f"Unsupported string type: {type(text_input)}, {text_input.dtype}")

    def _convert_token_to_id(self, token):
        """
        Converts the given token to its corresponding ID using the GPTBigCodeTokenizer.

        Args:
            self (GPTBigCodeTokenizer): An instance of the GPTBigCodeTokenizer class.
            token (str): The token to be converted to ID.

        Returns:
            int: The ID corresponding to the given token. Returns self.unk_token_id if the token is not found.

        Raises:
            None.
        """
        index = self._tokenizer.token_to_id(token)
        if index is None:
            return self.unk_token_id
        return index

mindnlp.transformers.models.gpt_bigcode.gpt_bigcode_tokenizer.GPTBigCodeTokenizer.__init__(tokenizer_file=None, unk_token='<|endoftext|>', bos_token='<|endoftext|>', eos_token='<|endoftext|>', add_prefix_space=False, **kwargs)

Initializes a new instance of the GPTBigCodeTokenizer class.

PARAMETER DESCRIPTION
self

The instance of the class itself.

TYPE: GPTBigCodeTokenizer

tokenizer_file

The file path of the tokenizer file to be used. Only string values are supported.

TYPE: str DEFAULT: None

unk_token

The token to represent unknown words. Default is 'endoftext'.

TYPE: str DEFAULT: '<|endoftext|>'

bos_token

The token to represent the beginning of a sentence. Default is 'endoftext'.

TYPE: str DEFAULT: '<|endoftext|>'

eos_token

The token to represent the end of a sentence. Default is 'endoftext'.

TYPE: str DEFAULT: '<|endoftext|>'

add_prefix_space

Whether to add a prefix space before the input text. Default is False.

TYPE: bool DEFAULT: False

**kwargs

Additional keyword arguments.

DEFAULT: {}

RETURNS DESCRIPTION

None.
RAISES DESCRIPTION
ValueError

If the tokenizer_file is not of type string.
Source code in mindnlp/transformers/models/gpt_bigcode/gpt_bigcode_tokenizer.py 
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def __init__(
    self,
    tokenizer_file=None,
    unk_token="<|endoftext|>",
    bos_token="<|endoftext|>",
    eos_token="<|endoftext|>",
    add_prefix_space=False,
    **kwargs
):
    """
    Initializes a new instance of the GPTBigCodeTokenizer class.

    Args:
        self (GPTBigCodeTokenizer): The instance of the class itself.
        tokenizer_file (str): The file path of the tokenizer file to be used. Only string values are supported.
        unk_token (str): The token to represent unknown words. Default is 'endoftext'.
        bos_token (str): The token to represent the beginning of a sentence. Default is 'endoftext'.
        eos_token (str): The token to represent the end of a sentence. Default is 'endoftext'.
        add_prefix_space (bool): Whether to add a prefix space before the input text. Default is False.
        **kwargs: Additional keyword arguments.

    Returns:
        None.

    Raises:
        ValueError: If the tokenizer_file is not of type string.

    """
    super().__init__(
        unk_token=unk_token,
        bos_token=bos_token,
        eos_token=eos_token,
        add_prefix_space=add_prefix_space,
        **kwargs)

    return_token = kwargs.pop('return_token', False)

    if isinstance(tokenizer_file, str):
        self._tokenizer = Tokenizer.from_file(tokenizer_file)
    else:
        raise ValueError(f'only support string, but got {tokenizer_file}')

    self.return_token = return_token
    self.implementation = Implementation.PY

mindnlp.transformers.models.gpt_bigcode.gpt_bigcode_tokenizer.GPTBigCodeTokenizer.execute_py(text_input)

Execute method.

Source code in mindnlp/transformers/models/gpt_bigcode/gpt_bigcode_tokenizer.py 
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def execute_py(self, text_input):
    """
    Execute method.
    """
    return self._execute_py(text_input)