AutoAndroidController/py/venv/Lib/site-packages/torch/nn/modules/transformer.py

# mypy: allow-untyped-defs
import copy
import warnings
from typing import Any, Callable, Optional, Union

import torch
import torch.nn.functional as F
from torch import Tensor
from torch.nn.init import xavier_uniform_

from .activation import MultiheadAttention
from .container import ModuleList
from .dropout import Dropout
from .linear import Linear
from .module import Module
from .normalization import LayerNorm


__all__ = [
    "Transformer",
    "TransformerEncoder",
    "TransformerDecoder",
    "TransformerEncoderLayer",
    "TransformerDecoderLayer",
]


def _generate_square_subsequent_mask(
    sz: int,
    device: Optional[torch.device] = None,
    dtype: Optional[torch.dtype] = None,
) -> Tensor:
    r"""Generate a square causal mask for the sequence.

    The masked positions are filled with float('-inf'). Unmasked positions are filled with float(0.0).
    """
    return torch.triu(
        torch.full((sz, sz), float("-inf"), dtype=dtype, device=device),
        diagonal=1,
    )


def _get_seq_len(src: Tensor, batch_first: bool) -> Optional[int]:
    if src.is_nested:
        return None
    else:
        src_size = src.size()
        if len(src_size) == 2:
            # unbatched: S, E
            return src_size[0]
        else:
            # batched: B, S, E if batch_first else S, B, E
            seq_len_pos = 1 if batch_first else 0
            return src_size[seq_len_pos]


class Transformer(Module):
    r"""A basic transformer layer.


    This Transformer layer implements the original Transformer architecture described
    in the `Attention Is All You Need <https://arxiv.org/abs/1706.03762>`_ paper. The
    intent of this layer is as a reference implementation for foundational understanding
    and thus it contains only limited features relative to newer Transformer architectures.
    Given the fast pace of innovation in transformer-like architectures, we recommend
    exploring this `tutorial <https://pytorch.org/tutorials/intermediate/transformer_building_blocks.html>`_
    to build an efficient transformer layer from building blocks in core or using higher
    level libraries from the `PyTorch Ecosystem <https://landscape.pytorch.org/>`_.

    Args:
        d_model: the number of expected features in the encoder/decoder inputs (default=512).
        nhead: the number of heads in the multiheadattention models (default=8).
        num_encoder_layers: the number of sub-encoder-layers in the encoder (default=6).
        num_decoder_layers: the number of sub-decoder-layers in the decoder (default=6).
        dim_feedforward: the dimension of the feedforward network model (default=2048).
        dropout: the dropout value (default=0.1).
        activation: the activation function of encoder/decoder intermediate layer, can be a string
            ("relu" or "gelu") or a unary callable. Default: relu
        custom_encoder: custom encoder (default=None).
        custom_decoder: custom decoder (default=None).
        layer_norm_eps: the eps value in layer normalization components (default=1e-5).
        batch_first: If ``True``, then the input and output tensors are provided
            as (batch, seq, feature). Default: ``False`` (seq, batch, feature).
        norm_first: if ``True``, encoder and decoder layers will perform LayerNorms before
            other attention and feedforward operations, otherwise after. Default: ``False`` (after).
        bias: If set to ``False``, ``Linear`` and ``LayerNorm`` layers will not learn an additive
            bias. Default: ``True``.

    Examples:
        >>> transformer_model = nn.Transformer(nhead=16, num_encoder_layers=12)
        >>> src = torch.rand((10, 32, 512))
        >>> tgt = torch.rand((20, 32, 512))
        >>> out = transformer_model(src, tgt)

    Note: A full example to apply nn.Transformer module for the word language model is available in
    https://github.com/pytorch/examples/tree/master/word_language_model
    """

    def __init__(
        self,
        d_model: int = 512,
        nhead: int = 8,
        num_encoder_layers: int = 6,
        num_decoder_layers: int = 6,
        dim_feedforward: int = 2048,
        dropout: float = 0.1,
        activation: Union[str, Callable[[Tensor], Tensor]] = F.relu,
        custom_encoder: Optional[Any] = None,
        custom_decoder: Optional[Any] = None,
        layer_norm_eps: float = 1e-5,
        batch_first: bool = False,
        norm_first: bool = False,
        bias: bool = True,
        device=None,
        dtype=None,
    ) -> None:
        factory_kwargs = {"device": device, "dtype": dtype}
        super().__init__()
        torch._C._log_api_usage_once(f"torch.nn.modules.{self.__class__.__name__}")

        if custom_encoder is not None:
            self.encoder = custom_encoder
        else:
            encoder_layer = TransformerEncoderLayer(
                d_model,
                nhead,
                dim_feedforward,
                dropout,
                activation,
                layer_norm_eps,
                batch_first,
                norm_first,
                bias,
                **factory_kwargs,
            )
            encoder_norm = LayerNorm(
                d_model, eps=layer_norm_eps, bias=bias, **factory_kwargs
            )
            self.encoder = TransformerEncoder(
                encoder_layer, num_encoder_layers, encoder_norm
            )

        if custom_decoder is not None:
            self.decoder = custom_decoder
        else:
            decoder_layer = TransformerDecoderLayer(
                d_model,
                nhead,
                dim_feedforward,
                dropout,
                activation,
                layer_norm_eps,
                batch_first,
                norm_first,
                bias,
                **factory_kwargs,
            )
            decoder_norm = LayerNorm(
                d_model, eps=layer_norm_eps, bias=bias, **factory_kwargs
            )
            self.decoder = TransformerDecoder(
                decoder_layer, num_decoder_layers, decoder_norm
            )

        self._reset_parameters()

        self.d_model = d_model
        self.nhead = nhead

        self.batch_first = batch_first

    def forward(
        self,
        src: Tensor,
        tgt: Tensor,
        src_mask: Optional[Tensor] = None,
        tgt_mask: Optional[Tensor] = None,
        memory_mask: Optional[Tensor] = None,
        src_key_padding_mask: Optional[Tensor] = None,
        tgt_key_padding_mask: Optional[Tensor] = None,
        memory_key_padding_mask: Optional[Tensor] = None,
        src_is_causal: Optional[bool] = None,
        tgt_is_causal: Optional[bool] = None,
        memory_is_causal: bool = False,
    ) -> Tensor:
        r"""Take in and process masked source/target sequences.

        .. note::

            If a boolean tensor is provided for any of the [src/tgt/memory]_mask arguments, positions with a ``True`` value are
            not allowed to participate in the attention,
            which is the opposite of the definition for :attr:`attn_mask`
            in :func:`torch.nn.functional.scaled_dot_product_attention`.

        Args:
            src: the sequence to the encoder (required).
            tgt: the sequence to the decoder (required).
            src_mask: the additive mask for the src sequence (optional).
            tgt_mask: the additive mask for the tgt sequence (optional).
            memory_mask: the additive mask for the encoder output (optional).
            src_key_padding_mask: the Tensor mask for src keys per batch (optional).
            tgt_key_padding_mask: the Tensor mask for tgt keys per batch (optional).
            memory_key_padding_mask: the Tensor mask for memory keys per batch (optional).
            src_is_causal: If specified, applies a causal mask as ``src_mask``.
                Default: ``None``; try to detect a causal mask.
                Warning:
                ``src_is_causal`` provides a hint that ``src_mask`` is
                the causal mask. Providing incorrect hints can result in
                incorrect execution, including forward and backward
                compatibility.
            tgt_is_causal: If specified, applies a causal mask as ``tgt_mask``.
                Default: ``None``; try to detect a causal mask.
                Warning:
                ``tgt_is_causal`` provides a hint that ``tgt_mask`` is
                the causal mask. Providing incorrect hints can result in
                incorrect execution, including forward and backward
                compatibility.
            memory_is_causal: If specified, applies a causal mask as
                ``memory_mask``.
                Default: ``False``.
                Warning:
                ``memory_is_causal`` provides a hint that
                ``memory_mask`` is the causal mask. Providing incorrect
                hints can result in incorrect execution, including
                forward and backward compatibility.

        Shape:
            - src: :math:`(S, E)` for unbatched input, :math:`(S, N, E)` if `batch_first=False` or
              `(N, S, E)` if `batch_first=True`.
            - tgt: :math:`(T, E)` for unbatched input, :math:`(T, N, E)` if `batch_first=False` or
              `(N, T, E)` if `batch_first=True`.
            - src_mask: :math:`(S, S)` or :math:`(N\cdot\text{num\_heads}, S, S)`.
            - tgt_mask: :math:`(T, T)` or :math:`(N\cdot\text{num\_heads}, T, T)`.
            - memory_mask: :math:`(T, S)`.
            - src_key_padding_mask: :math:`(S)` for unbatched input otherwise :math:`(N, S)`.
            - tgt_key_padding_mask: :math:`(T)` for unbatched input otherwise :math:`(N, T)`.
            - memory_key_padding_mask: :math:`(S)` for unbatched input otherwise :math:`(N, S)`.

            Note: [src/tgt/memory]_mask ensures that position :math:`i` is allowed to attend the unmasked
            positions. If a BoolTensor is provided, positions with ``True``
            are not allowed to attend while ``False`` values will be unchanged. If a FloatTensor
            is provided, it will be added to the attention weight.
            [src/tgt/memory]_key_padding_mask provides specified elements in the key to be ignored by
            the attention. If a BoolTensor is provided, the positions with the
            value of ``True`` will be ignored while the position with the value of ``False`` will be unchanged.

            - output: :math:`(T, E)` for unbatched input, :math:`(T, N, E)` if `batch_first=False` or
              `(N, T, E)` if `batch_first=True`.

            Note: Due to the multi-head attention architecture in the transformer model,
            the output sequence length of a transformer is same as the input sequence
            (i.e. target) length of the decoder.

            where :math:`S` is the source sequence length, :math:`T` is the target sequence length, :math:`N` is the
            batch size, :math:`E` is the feature number

        Examples:
            >>> # xdoctest: +SKIP
            >>> output = transformer_model(
            ...     src, tgt, src_mask=src_mask, tgt_mask=tgt_mask
            ... )
        """
        is_batched = src.dim() == 3
        if not self.batch_first and src.size(1) != tgt.size(1) and is_batched:
            raise RuntimeError("the batch number of src and tgt must be equal")
        elif self.batch_first and src.size(0) != tgt.size(0) and is_batched:
            raise RuntimeError("the batch number of src and tgt must be equal")

        if src.size(-1) != self.d_model or tgt.size(-1) != self.d_model:
            raise RuntimeError(
                "the feature number of src and tgt must be equal to d_model"
            )

        memory = self.encoder(
            src,
            mask=src_mask,
            src_key_padding_mask=src_key_padding_mask,
            is_causal=src_is_causal,
        )
        output = self.decoder(
            tgt,
            memory,
            tgt_mask=tgt_mask,
            memory_mask=memory_mask,
            tgt_key_padding_mask=tgt_key_padding_mask,
            memory_key_padding_mask=memory_key_padding_mask,
            tgt_is_causal=tgt_is_causal,
            memory_is_causal=memory_is_causal,
        )
        return output

    @staticmethod
    def generate_square_subsequent_mask(
        sz: int,
        device: Optional[torch.device] = None,
        dtype: Optional[torch.dtype] = None,
    ) -> Tensor:
        r"""Generate a square causal mask for the sequence.

        The masked positions are filled with float('-inf'). Unmasked positions are filled with float(0.0).
        """
        return _generate_square_subsequent_mask(sz, dtype=dtype, device=device)

    def _reset_parameters(self) -> None:
        r"""Initiate parameters in the transformer model."""
        for p in self.parameters():
            if p.dim() > 1:
                xavier_uniform_(p)


class TransformerEncoder(Module):
    r"""TransformerEncoder is a stack of N encoder layers.

    This TransformerEncoder layer implements the original architecture described
    in the `Attention Is All You Need <https://arxiv.org/abs/1706.03762>`_ paper. The
    intent of this layer is as a reference implementation for foundational understanding
    and thus it contains only limited features relative to newer Transformer architectures.
    Given the fast pace of innovation in transformer-like architectures, we recommend
    exploring this `tutorial <https://pytorch.org/tutorials/intermediate/transformer_building_blocks.html>`_
    to build efficient layers from building blocks in core or using higher
    level libraries from the `PyTorch Ecosystem <https://landscape.pytorch.org/>`_.

    .. warning::
        All layers in the TransformerEncoder are initialized with the same parameters.
        It is recommended to manually initialize the layers after creating the TransformerEncoder instance.

    Args:
        encoder_layer: an instance of the TransformerEncoderLayer() class (required).
        num_layers: the number of sub-encoder-layers in the encoder (required).
        norm: the layer normalization component (optional).
        enable_nested_tensor: if True, input will automatically convert to nested tensor
            (and convert back on output). This will improve the overall performance of
            TransformerEncoder when padding rate is high. Default: ``True`` (enabled).

    Examples:
        >>> encoder_layer = nn.TransformerEncoderLayer(d_model=512, nhead=8)
        >>> transformer_encoder = nn.TransformerEncoder(encoder_layer, num_layers=6)
        >>> src = torch.rand(10, 32, 512)
        >>> out = transformer_encoder(src)
    """

    __constants__ = ["norm"]

    def __init__(
        self,
        encoder_layer: "TransformerEncoderLayer",
        num_layers: int,
        norm: Optional[Module] = None,
        enable_nested_tensor: bool = True,
        mask_check: bool = True,
    ) -> None:
        super().__init__()
        torch._C._log_api_usage_once(f"torch.nn.modules.{self.__class__.__name__}")
        self.layers = _get_clones(encoder_layer, num_layers)
        self.num_layers = num_layers
        self.norm = norm
        # this attribute saves the value providedat object construction
        self.enable_nested_tensor = enable_nested_tensor
        # this attribute controls whether nested tensors are used
        self.use_nested_tensor = enable_nested_tensor
        self.mask_check = mask_check

        enc_layer = "encoder_layer"
        why_not_sparsity_fast_path = ""
        if not isinstance(encoder_layer, torch.nn.TransformerEncoderLayer):
            why_not_sparsity_fast_path = f"{enc_layer} was not TransformerEncoderLayer"
        elif encoder_layer.norm_first:
            why_not_sparsity_fast_path = f"{enc_layer}.norm_first was True"
        elif not encoder_layer.self_attn.batch_first:
            why_not_sparsity_fast_path = (
                f"{enc_layer}.self_attn.batch_first was not True"
                + "(use batch_first for better inference performance)"
            )
        elif not encoder_layer.self_attn._qkv_same_embed_dim:
            why_not_sparsity_fast_path = (
                f"{enc_layer}.self_attn._qkv_same_embed_dim was not True"
            )
        elif encoder_layer.self_attn.in_proj_bias is None:
            why_not_sparsity_fast_path = f"{enc_layer}.self_attn was passed bias=False"
        elif not encoder_layer.activation_relu_or_gelu:
            why_not_sparsity_fast_path = (
                f"{enc_layer}.activation_relu_or_gelu was not True"
            )
        elif not (encoder_layer.norm1.eps == encoder_layer.norm2.eps):
            why_not_sparsity_fast_path = (
                f"{enc_layer}.norm1.eps was not equal to {enc_layer}.norm2.eps"
            )
        elif encoder_layer.self_attn.num_heads % 2 == 1:
            why_not_sparsity_fast_path = f"{enc_layer}.self_attn.num_heads is odd"

        if enable_nested_tensor and why_not_sparsity_fast_path:
            warnings.warn(
                f"enable_nested_tensor is True, but self.use_nested_tensor is False because {why_not_sparsity_fast_path}"
            )
            self.use_nested_tensor = False

    def forward(
        self,
        src: Tensor,
        mask: Optional[Tensor] = None,
        src_key_padding_mask: Optional[Tensor] = None,
        is_causal: Optional[bool] = None,
    ) -> Tensor:
        r"""Pass the input through the encoder layers in turn.

        Args:
            src: the sequence to the encoder (required).
            mask: the mask for the src sequence (optional).
            src_key_padding_mask: the mask for the src keys per batch (optional).
            is_causal: If specified, applies a causal mask as ``mask``.
                Default: ``None``; try to detect a causal mask.
                Warning:
                ``is_causal`` provides a hint that ``mask`` is the
                causal mask. Providing incorrect hints can result in
                incorrect execution, including forward and backward
                compatibility.

        Shape:
            see the docs in :class:`~torch.nn.Transformer`.
        """
        src_key_padding_mask = F._canonical_mask(
            mask=src_key_padding_mask,
            mask_name="src_key_padding_mask",
            other_type=F._none_or_dtype(mask),
            other_name="mask",
            target_type=src.dtype,
        )

        mask = F._canonical_mask(
            mask=mask,
            mask_name="mask",
            other_type=None,
            other_name="",
            target_type=src.dtype,
            check_other=False,
        )

        output = src
        convert_to_nested = False
        first_layer = self.layers[0]
        src_key_padding_mask_for_layers = src_key_padding_mask
        why_not_sparsity_fast_path = ""
        str_first_layer = "self.layers[0]"
        batch_first = first_layer.self_attn.batch_first
        is_fastpath_enabled = torch.backends.mha.get_fastpath_enabled()

        if not is_fastpath_enabled:
            why_not_sparsity_fast_path = (
                "torch.backends.mha.get_fastpath_enabled() was not True"
            )
        elif not hasattr(self, "use_nested_tensor"):
            why_not_sparsity_fast_path = "use_nested_tensor attribute not present"
        elif not self.use_nested_tensor:
            why_not_sparsity_fast_path = (
                "self.use_nested_tensor (set in init) was not True"
            )
        elif first_layer.training:
            why_not_sparsity_fast_path = f"{str_first_layer} was in training mode"
        elif not src.dim() == 3:
            why_not_sparsity_fast_path = (
                f"input not batched; expected src.dim() of 3 but got {src.dim()}"
            )
        elif src_key_padding_mask is None:
            why_not_sparsity_fast_path = "src_key_padding_mask was None"
        elif (
            (not hasattr(self, "mask_check")) or self.mask_check
        ) and not torch._nested_tensor_from_mask_left_aligned(
            src, src_key_padding_mask.logical_not()
        ):
            why_not_sparsity_fast_path = "mask_check enabled, and src and src_key_padding_mask was not left aligned"
        elif output.is_nested:
            why_not_sparsity_fast_path = "NestedTensor input is not supported"
        elif mask is not None:
            why_not_sparsity_fast_path = (
                "src_key_padding_mask and mask were both supplied"
            )
        elif torch.is_autocast_enabled():
            why_not_sparsity_fast_path = "autocast is enabled"

        if not why_not_sparsity_fast_path:
            tensor_args = (
                src,
                first_layer.self_attn.in_proj_weight,
                first_layer.self_attn.in_proj_bias,
                first_layer.self_attn.out_proj.weight,
                first_layer.self_attn.out_proj.bias,
                first_layer.norm1.weight,
                first_layer.norm1.bias,
                first_layer.norm2.weight,
                first_layer.norm2.bias,
                first_layer.linear1.weight,
                first_layer.linear1.bias,
                first_layer.linear2.weight,
                first_layer.linear2.bias,
            )
            _supported_device_type = [
                "cpu",
                "cuda",
                torch.utils.backend_registration._privateuse1_backend_name,
            ]
            if torch.overrides.has_torch_function(tensor_args):
                why_not_sparsity_fast_path = "some Tensor argument has_torch_function"
            elif src.device.type not in _supported_device_type:
                why_not_sparsity_fast_path = (
                    f"src device is neither one of {_supported_device_type}"
                )
            elif torch.is_grad_enabled() and any(x.requires_grad for x in tensor_args):
                why_not_sparsity_fast_path = (
                    "grad is enabled and at least one of query or the "
                    "input/output projection weights or biases requires_grad"
                )

            if (not why_not_sparsity_fast_path) and (src_key_padding_mask is not None):
                convert_to_nested = True
                output = torch._nested_tensor_from_mask(
                    output, src_key_padding_mask.logical_not(), mask_check=False
                )
                src_key_padding_mask_for_layers = None

        seq_len = _get_seq_len(src, batch_first)
        is_causal = _detect_is_causal_mask(mask, is_causal, seq_len)

        for mod in self.layers:
            output = mod(
                output,
                src_mask=mask,
                is_causal=is_causal,
                src_key_padding_mask=src_key_padding_mask_for_layers,
            )

        if convert_to_nested:
            output = output.to_padded_tensor(0.0, src.size())

        if self.norm is not None:
            output = self.norm(output)

        return output


class TransformerDecoder(Module):
    r"""TransformerDecoder is a stack of N decoder layers.

    This TransformerDecoder layer implements the original architecture described
    in the `Attention Is All You Need <https://arxiv.org/abs/1706.03762>`_ paper. The
    intent of this layer is as a reference implementation for foundational understanding
    and thus it contains only limited features relative to newer Transformer architectures.
    Given the fast pace of innovation in transformer-like architectures, we recommend
    exploring this `tutorial <https://pytorch.org/tutorials/intermediate/transformer_building_blocks.html>`_
    to build efficient layers from building blocks in core or using higher
    level libraries from the `PyTorch Ecosystem <https://landscape.pytorch.org/>`_.

    .. warning::
        All layers in the TransformerDecoder are initialized with the same parameters.
        It is recommended to manually initialize the layers after creating the TransformerDecoder instance.

    Args:
        decoder_layer: an instance of the TransformerDecoderLayer() class (required).
        num_layers: the number of sub-decoder-layers in the decoder (required).
        norm: the layer normalization component (optional).

    Examples:
        >>> decoder_layer = nn.TransformerDecoderLayer(d_model=512, nhead=8)
        >>> transformer_decoder = nn.TransformerDecoder(decoder_layer, num_layers=6)
        >>> memory = torch.rand(10, 32, 512)
        >>> tgt = torch.rand(20, 32, 512)
        >>> out = transformer_decoder(tgt, memory)
    """

    __constants__ = ["norm"]

    def __init__(
        self,
        decoder_layer: "TransformerDecoderLayer",
        num_layers: int,
        norm: Optional[Module] = None,
    ) -> None:
        super().__init__()
        torch._C._log_api_usage_once(f"torch.nn.modules.{self.__class__.__name__}")
        self.layers = _get_clones(decoder_layer, num_layers)
        self.num_layers = num_layers
        self.norm = norm

    def forward(
        self,
        tgt: Tensor,
        memory: Tensor,
        tgt_mask: Optional[Tensor] = None,
        memory_mask: Optional[Tensor] = None,
        tgt_key_padding_mask: Optional[Tensor] = None,
        memory_key_padding_mask: Optional[Tensor] = None,
        tgt_is_causal: Optional[bool] = None,
        memory_is_causal: bool = False,
    ) -> Tensor:
        r"""Pass the inputs (and mask) through the decoder layer in turn.

        Args:
            tgt: the sequence to the decoder (required).
            memory: the sequence from the last layer of the encoder (required).
            tgt_mask: the mask for the tgt sequence (optional).
            memory_mask: the mask for the memory sequence (optional).
            tgt_key_padding_mask: the mask for the tgt keys per batch (optional).
            memory_key_padding_mask: the mask for the memory keys per batch (optional).
            tgt_is_causal: If specified, applies a causal mask as ``tgt mask``.
                Default: ``None``; try to detect a causal mask.
                Warning:
                ``tgt_is_causal`` provides a hint that ``tgt_mask`` is
                the causal mask. Providing incorrect hints can result in
                incorrect execution, including forward and backward
                compatibility.
            memory_is_causal: If specified, applies a causal mask as
                ``memory mask``.
                Default: ``False``.
                Warning:
                ``memory_is_causal`` provides a hint that
                ``memory_mask`` is the causal mask. Providing incorrect
                hints can result in incorrect execution, including
                forward and backward compatibility.

        Shape:
            see the docs in :class:`~torch.nn.Transformer`.
        """
        output = tgt

        seq_len = _get_seq_len(tgt, self.layers[0].self_attn.batch_first)
        tgt_is_causal = _detect_is_causal_mask(tgt_mask, tgt_is_causal, seq_len)

        for mod in self.layers:
            output = mod(
                output,
                memory,
                tgt_mask=tgt_mask,
                memory_mask=memory_mask,
                tgt_key_padding_mask=tgt_key_padding_mask,
                memory_key_padding_mask=memory_key_padding_mask,
                tgt_is_causal=tgt_is_causal,
                memory_is_causal=memory_is_causal,
            )

        if self.norm is not None:
            output = self.norm(output)

        return output


class TransformerEncoderLayer(Module):
    r"""TransformerEncoderLayer is made up of self-attn and feedforward network.

    This TransformerEncoderLayer implements the original architecture described
    in the `Attention Is All You Need <https://arxiv.org/abs/1706.03762>`_ paper. The
    intent of this layer is as a reference implementation for foundational understanding
    and thus it contains only limited features relative to newer Transformer architectures.
    Given the fast pace of innovation in transformer-like architectures, we recommend
    exploring this `tutorial <https://pytorch.org/tutorials/intermediate/transformer_building_blocks.html>`_
    to build efficient layers from building blocks in core or using higher
    level libraries from the `PyTorch Ecosystem <https://landscape.pytorch.org/>`_.

    TransformerEncoderLayer can handle either traditional torch.tensor inputs,
    or Nested Tensor inputs.  Derived classes are expected to similarly accept
    both input formats.  (Not all combinations of inputs are currently
    supported by TransformerEncoderLayer while Nested Tensor is in prototype
    state.)

    If you are implementing a custom layer, you may derive it either from
    the Module or TransformerEncoderLayer class.  If your custom layer
    supports both torch.Tensors and Nested Tensors inputs, make its
    implementation a derived class of TransformerEncoderLayer. If your custom
    Layer supports only torch.Tensor inputs, derive its implementation from
    Module.

    Args:
        d_model: the number of expected features in the input (required).
        nhead: the number of heads in the multiheadattention models (required).
        dim_feedforward: the dimension of the feedforward network model (default=2048).
        dropout: the dropout value (default=0.1).
        activation: the activation function of the intermediate layer, can be a string
            ("relu" or "gelu") or a unary callable. Default: relu
        layer_norm_eps: the eps value in layer normalization components (default=1e-5).
        batch_first: If ``True``, then the input and output tensors are provided
            as (batch, seq, feature). Default: ``False`` (seq, batch, feature).
        norm_first: if ``True``, layer norm is done prior to attention and feedforward
            operations, respectively. Otherwise it's done after. Default: ``False`` (after).
        bias: If set to ``False``, ``Linear`` and ``LayerNorm`` layers will not learn an additive
            bias. Default: ``True``.

    Examples:
        >>> encoder_layer = nn.TransformerEncoderLayer(d_model=512, nhead=8)
        >>> src = torch.rand(10, 32, 512)
        >>> out = encoder_layer(src)

    Alternatively, when ``batch_first`` is ``True``:
        >>> encoder_layer = nn.TransformerEncoderLayer(
        ...     d_model=512, nhead=8, batch_first=True
        ... )
        >>> src = torch.rand(32, 10, 512)
        >>> out = encoder_layer(src)

    Fast path:
        forward() will use a special optimized implementation described in
        `FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness`_ if all of the following
        conditions are met:

        - Either autograd is disabled (using ``torch.inference_mode`` or ``torch.no_grad``) or no tensor
          argument ``requires_grad``
        - training is disabled (using ``.eval()``)
        - batch_first is ``True`` and the input is batched (i.e., ``src.dim() == 3``)
        - activation is one of: ``"relu"``, ``"gelu"``, ``torch.functional.relu``, or ``torch.functional.gelu``
        - at most one of ``src_mask`` and ``src_key_padding_mask`` is passed
        - if src is a `NestedTensor <https://pytorch.org/docs/stable/nested.html>`_, neither ``src_mask``
          nor ``src_key_padding_mask`` is passed
        - the two ``LayerNorm`` instances have a consistent ``eps`` value (this will naturally be the case
          unless the caller has manually modified one without modifying the other)

        If the optimized implementation is in use, a
        `NestedTensor <https://pytorch.org/docs/stable/nested.html>`_ can be
        passed for ``src`` to represent padding more efficiently than using a padding
        mask. In this case, a `NestedTensor <https://pytorch.org/docs/stable/nested.html>`_ will be
        returned, and an additional speedup proportional to the fraction of the input that
        is padding can be expected.

        .. _`FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness`:
         https://arxiv.org/abs/2205.14135

    """

    __constants__ = ["norm_first"]

    def __init__(
        self,
        d_model: int,
        nhead: int,
        dim_feedforward: int = 2048,
        dropout: float = 0.1,
        activation: Union[str, Callable[[Tensor], Tensor]] = F.relu,
        layer_norm_eps: float = 1e-5,
        batch_first: bool = False,
        norm_first: bool = False,
        bias: bool = True,
        device=None,
        dtype=None,
    ) -> None:
        factory_kwargs = {"device": device, "dtype": dtype}
        super().__init__()
        self.self_attn = MultiheadAttention(
            d_model,
            nhead,
            dropout=dropout,
            bias=bias,
            batch_first=batch_first,
            **factory_kwargs,
        )
        # Implementation of Feedforward model
        self.linear1 = Linear(d_model, dim_feedforward, bias=bias, **factory_kwargs)
        self.dropout = Dropout(dropout)
        self.linear2 = Linear(dim_feedforward, d_model, bias=bias, **factory_kwargs)

        self.norm_first = norm_first
        self.norm1 = LayerNorm(d_model, eps=layer_norm_eps, bias=bias, **factory_kwargs)
        self.norm2 = LayerNorm(d_model, eps=layer_norm_eps, bias=bias, **factory_kwargs)
        self.dropout1 = Dropout(dropout)
        self.dropout2 = Dropout(dropout)

        # Legacy string support for activation function.
        if isinstance(activation, str):
            activation = _get_activation_fn(activation)

        # We can't test self.activation in forward() in TorchScript,
        # so stash some information about it instead.
        if activation is F.relu or isinstance(activation, torch.nn.ReLU):
            self.activation_relu_or_gelu = 1
        elif activation is F.gelu or isinstance(activation, torch.nn.GELU):
            self.activation_relu_or_gelu = 2
        else:
            self.activation_relu_or_gelu = 0
        self.activation = activation

    def __setstate__(self, state):
        super().__setstate__(state)
        if not hasattr(self, "activation"):
            self.activation = F.relu

    def forward(
        self,
        src: Tensor,
        src_mask: Optional[Tensor] = None,
        src_key_padding_mask: Optional[Tensor] = None,
        is_causal: bool = False,
    ) -> Tensor:
        r"""Pass the input through the encoder layer.

        Args:
            src: the sequence to the encoder layer (required).
            src_mask: the mask for the src sequence (optional).
            src_key_padding_mask: the mask for the src keys per batch (optional).
            is_causal: If specified, applies a causal mask as ``src mask``.
                Default: ``False``.
                Warning:
                ``is_causal`` provides a hint that ``src_mask`` is the
                causal mask. Providing incorrect hints can result in
                incorrect execution, including forward and backward
                compatibility.

        Shape:
            see the docs in :class:`~torch.nn.Transformer`.
        """
        src_key_padding_mask = F._canonical_mask(
            mask=src_key_padding_mask,
            mask_name="src_key_padding_mask",
            other_type=F._none_or_dtype(src_mask),
            other_name="src_mask",
            target_type=src.dtype,
        )

        src_mask = F._canonical_mask(
            mask=src_mask,
            mask_name="src_mask",
            other_type=None,
            other_name="",
            target_type=src.dtype,
            check_other=False,
        )

        is_fastpath_enabled = torch.backends.mha.get_fastpath_enabled()

        why_not_sparsity_fast_path = ""
        if not is_fastpath_enabled:
            why_not_sparsity_fast_path = (
                "torch.backends.mha.get_fastpath_enabled() was not True"
            )
        elif not src.dim() == 3:
            why_not_sparsity_fast_path = (
                f"input not batched; expected src.dim() of 3 but got {src.dim()}"
            )
        elif self.training:
            why_not_sparsity_fast_path = "training is enabled"
        elif not self.self_attn.batch_first:
            why_not_sparsity_fast_path = "self_attn.batch_first was not True"
        elif self.self_attn.in_proj_bias is None:
            why_not_sparsity_fast_path = "self_attn was passed bias=False"
        elif not self.self_attn._qkv_same_embed_dim:
            why_not_sparsity_fast_path = "self_attn._qkv_same_embed_dim was not True"
        elif not self.activation_relu_or_gelu:
            why_not_sparsity_fast_path = "activation_relu_or_gelu was not True"
        elif not (self.norm1.eps == self.norm2.eps):
            why_not_sparsity_fast_path = "norm1.eps is not equal to norm2.eps"
        elif src.is_nested and (
            src_key_padding_mask is not None or src_mask is not None
        ):
            why_not_sparsity_fast_path = "neither src_key_padding_mask nor src_mask are not supported with NestedTensor input"
        elif self.self_attn.num_heads % 2 == 1:
            why_not_sparsity_fast_path = "num_head is odd"
        elif torch.is_autocast_enabled():
            why_not_sparsity_fast_path = "autocast is enabled"
        elif any(
            len(getattr(m, "_forward_hooks", {}))
            + len(getattr(m, "_forward_pre_hooks", {}))
            for m in self.modules()
        ):
            why_not_sparsity_fast_path = "forward pre-/hooks are attached to the module"
        if not why_not_sparsity_fast_path:
            tensor_args = (
                src,
                self.self_attn.in_proj_weight,
                self.self_attn.in_proj_bias,
                self.self_attn.out_proj.weight,
                self.self_attn.out_proj.bias,
                self.norm1.weight,
                self.norm1.bias,
                self.norm2.weight,
                self.norm2.bias,
                self.linear1.weight,
                self.linear1.bias,
                self.linear2.weight,
                self.linear2.bias,
            )

            # We have to use list comprehensions below because TorchScript does not support
            # generator expressions.
            _supported_device_type = [
                "cpu",
                "cuda",
                torch.utils.backend_registration._privateuse1_backend_name,
            ]
            if torch.overrides.has_torch_function(tensor_args):
                why_not_sparsity_fast_path = "some Tensor argument has_torch_function"
            elif not all(
                (x.device.type in _supported_device_type) for x in tensor_args
            ):
                why_not_sparsity_fast_path = (
                    "some Tensor argument's device is neither one of "
                    f"{_supported_device_type}"
                )
            elif torch.is_grad_enabled() and any(x.requires_grad for x in tensor_args):
                why_not_sparsity_fast_path = (
                    "grad is enabled and at least one of query or the "
                    "input/output projection weights or biases requires_grad"
                )

            if not why_not_sparsity_fast_path:
                merged_mask, mask_type = self.self_attn.merge_masks(
                    src_mask, src_key_padding_mask, src
                )
                return torch._transformer_encoder_layer_fwd(
                    src,
                    self.self_attn.embed_dim,
                    self.self_attn.num_heads,
                    self.self_attn.in_proj_weight,
                    self.self_attn.in_proj_bias,
                    self.self_attn.out_proj.weight,
                    self.self_attn.out_proj.bias,
                    self.activation_relu_or_gelu == 2,
                    self.norm_first,
                    self.norm1.eps,
                    self.norm1.weight,
                    self.norm1.bias,
                    self.norm2.weight,
                    self.norm2.bias,
                    self.linear1.weight,
                    self.linear1.bias,
                    self.linear2.weight,
                    self.linear2.bias,
                    merged_mask,
                    mask_type,
                )

        # see Fig. 1 of https://arxiv.org/pdf/2002.04745v1.pdf
        x = src
        if self.norm_first:
            x = x + self._sa_block(
                self.norm1(x), src_mask, src_key_padding_mask, is_causal=is_causal
            )
            x = x + self._ff_block(self.norm2(x))
        else:
            x = self.norm1(
                x
                + self._sa_block(x, src_mask, src_key_padding_mask, is_causal=is_causal)
            )
            x = self.norm2(x + self._ff_block(x))

        return x

    # self-attention block
    def _sa_block(
        self,
        x: Tensor,
        attn_mask: Optional[Tensor],
        key_padding_mask: Optional[Tensor],
        is_causal: bool = False,
    ) -> Tensor:
        x = self.self_attn(
            x,
            x,
            x,
            attn_mask=attn_mask,
            key_padding_mask=key_padding_mask,
            need_weights=False,
            is_causal=is_causal,
        )[0]
        return self.dropout1(x)

    # feed forward block
    def _ff_block(self, x: Tensor) -> Tensor:
        x = self.linear2(self.dropout(self.activation(self.linear1(x))))
        return self.dropout2(x)


class TransformerDecoderLayer(Module):
    r"""TransformerDecoderLayer is made up of self-attn, multi-head-attn and feedforward network.

    This TransformerDecoderLayer implements the original architecture described
    in the `Attention Is All You Need <https://arxiv.org/abs/1706.03762>`_ paper. The
    intent of this layer is as a reference implementation for foundational understanding
    and thus it contains only limited features relative to newer Transformer architectures.
    Given the fast pace of innovation in transformer-like architectures, we recommend
    exploring this `tutorial <https://pytorch.org/tutorials/intermediate/transformer_building_blocks.html>`_
    to build efficient layers from building blocks in core or using higher
    level libraries from the `PyTorch Ecosystem <https://landscape.pytorch.org/>`_.

    Args:
        d_model: the number of expected features in the input (required).
        nhead: the number of heads in the multiheadattention models (required).
        dim_feedforward: the dimension of the feedforward network model (default=2048).
        dropout: the dropout value (default=0.1).
        activation: the activation function of the intermediate layer, can be a string
            ("relu" or "gelu") or a unary callable. Default: relu
        layer_norm_eps: the eps value in layer normalization components (default=1e-5).
        batch_first: If ``True``, then the input and output tensors are provided
            as (batch, seq, feature). Default: ``False`` (seq, batch, feature).
        norm_first: if ``True``, layer norm is done prior to self attention, multihead
            attention and feedforward operations, respectively. Otherwise it's done after.
            Default: ``False`` (after).
        bias: If set to ``False``, ``Linear`` and ``LayerNorm`` layers will not learn an additive
            bias. Default: ``True``.

    Examples:
        >>> decoder_layer = nn.TransformerDecoderLayer(d_model=512, nhead=8)
        >>> memory = torch.rand(10, 32, 512)
        >>> tgt = torch.rand(20, 32, 512)
        >>> out = decoder_layer(tgt, memory)

    Alternatively, when ``batch_first`` is ``True``:
        >>> decoder_layer = nn.TransformerDecoderLayer(
        ...     d_model=512, nhead=8, batch_first=True
        ... )
        >>> memory = torch.rand(32, 10, 512)
        >>> tgt = torch.rand(32, 20, 512)
        >>> out = decoder_layer(tgt, memory)
    """

    __constants__ = ["norm_first"]

    def __init__(
        self,
        d_model: int,
        nhead: int,
        dim_feedforward: int = 2048,
        dropout: float = 0.1,
        activation: Union[str, Callable[[Tensor], Tensor]] = F.relu,
        layer_norm_eps: float = 1e-5,
        batch_first: bool = False,
        norm_first: bool = False,
        bias: bool = True,
        device=None,
        dtype=None,
    ) -> None:
        factory_kwargs = {"device": device, "dtype": dtype}
        super().__init__()
        self.self_attn = MultiheadAttention(
            d_model,
            nhead,
            dropout=dropout,
            batch_first=batch_first,
            bias=bias,
            **factory_kwargs,
        )
        self.multihead_attn = MultiheadAttention(
            d_model,
            nhead,
            dropout=dropout,
            batch_first=batch_first,
            bias=bias,
            **factory_kwargs,
        )
        # Implementation of Feedforward model
        self.linear1 = Linear(d_model, dim_feedforward, bias=bias, **factory_kwargs)
        self.dropout = Dropout(dropout)
        self.linear2 = Linear(dim_feedforward, d_model, bias=bias, **factory_kwargs)

        self.norm_first = norm_first
        self.norm1 = LayerNorm(d_model, eps=layer_norm_eps, bias=bias, **factory_kwargs)
        self.norm2 = LayerNorm(d_model, eps=layer_norm_eps, bias=bias, **factory_kwargs)
        self.norm3 = LayerNorm(d_model, eps=layer_norm_eps, bias=bias, **factory_kwargs)
        self.dropout1 = Dropout(dropout)
        self.dropout2 = Dropout(dropout)
        self.dropout3 = Dropout(dropout)

        # Legacy string support for activation function.
        if isinstance(activation, str):
            self.activation = _get_activation_fn(activation)
        else:
            self.activation = activation

    def __setstate__(self, state):
        if "activation" not in state:
            state["activation"] = F.relu
        super().__setstate__(state)

    def forward(
        self,
        tgt: Tensor,
        memory: Tensor,
        tgt_mask: Optional[Tensor] = None,
        memory_mask: Optional[Tensor] = None,
        tgt_key_padding_mask: Optional[Tensor] = None,
        memory_key_padding_mask: Optional[Tensor] = None,
        tgt_is_causal: bool = False,
        memory_is_causal: bool = False,
    ) -> Tensor:
        r"""Pass the inputs (and mask) through the decoder layer.

        Args:
            tgt: the sequence to the decoder layer (required).
            memory: the sequence from the last layer of the encoder (required).
            tgt_mask: the mask for the tgt sequence (optional).
            memory_mask: the mask for the memory sequence (optional).
            tgt_key_padding_mask: the mask for the tgt keys per batch (optional).
            memory_key_padding_mask: the mask for the memory keys per batch (optional).
            tgt_is_causal: If specified, applies a causal mask as ``tgt mask``.
                Default: ``False``.
                Warning:
                ``tgt_is_causal`` provides a hint that ``tgt_mask`` is
                the causal mask. Providing incorrect hints can result in
                incorrect execution, including forward and backward
                compatibility.
            memory_is_causal: If specified, applies a causal mask as
                ``memory mask``.
                Default: ``False``.
                Warning:
                ``memory_is_causal`` provides a hint that
                ``memory_mask`` is the causal mask. Providing incorrect
                hints can result in incorrect execution, including
                forward and backward compatibility.

        Shape:
            see the docs in :class:`~torch.nn.Transformer`.
        """
        # see Fig. 1 of https://arxiv.org/pdf/2002.04745v1.pdf

        x = tgt
        if self.norm_first:
            x = x + self._sa_block(
                self.norm1(x), tgt_mask, tgt_key_padding_mask, tgt_is_causal
            )
            x = x + self._mha_block(
                self.norm2(x),
                memory,
                memory_mask,
                memory_key_padding_mask,
                memory_is_causal,
            )
            x = x + self._ff_block(self.norm3(x))
        else:
            x = self.norm1(
                x + self._sa_block(x, tgt_mask, tgt_key_padding_mask, tgt_is_causal)
            )
            x = self.norm2(
                x
                + self._mha_block(
                    x, memory, memory_mask, memory_key_padding_mask, memory_is_causal
                )
            )
            x = self.norm3(x + self._ff_block(x))

        return x

    # self-attention block
    def _sa_block(
        self,
        x: Tensor,
        attn_mask: Optional[Tensor],
        key_padding_mask: Optional[Tensor],
        is_causal: bool = False,
    ) -> Tensor:
        x = self.self_attn(
            x,
            x,
            x,
            attn_mask=attn_mask,
            key_padding_mask=key_padding_mask,
            is_causal=is_causal,
            need_weights=False,
        )[0]
        return self.dropout1(x)

    # multihead attention block
    def _mha_block(
        self,
        x: Tensor,
        mem: Tensor,
        attn_mask: Optional[Tensor],
        key_padding_mask: Optional[Tensor],
        is_causal: bool = False,
    ) -> Tensor:
        x = self.multihead_attn(
            x,
            mem,
            mem,
            attn_mask=attn_mask,
            key_padding_mask=key_padding_mask,
            is_causal=is_causal,
            need_weights=False,
        )[0]
        return self.dropout2(x)

    # feed forward block
    def _ff_block(self, x: Tensor) -> Tensor:
        x = self.linear2(self.dropout(self.activation(self.linear1(x))))
        return self.dropout3(x)


def _get_clones(module, N):
    # FIXME: copy.deepcopy() is not defined on nn.module
    return ModuleList([copy.deepcopy(module) for i in range(N)])


def _get_activation_fn(activation: str) -> Callable[[Tensor], Tensor]:
    if activation == "relu":
        return F.relu
    elif activation == "gelu":
        return F.gelu

    raise RuntimeError(f"activation should be relu/gelu, not {activation}")


def _detect_is_causal_mask(
    mask: Optional[Tensor],
    is_causal: Optional[bool] = None,
    size: Optional[int] = None,
) -> bool:
    """Return whether the given attention mask is causal.

    Warning:
    If ``is_causal`` is not ``None``, its value will be returned as is.  If a
    user supplies an incorrect ``is_causal`` hint,

    ``is_causal=False`` when the mask is in fact a causal attention.mask
       may lead to reduced performance relative to what would be achievable
       with ``is_causal=True``;
    ``is_causal=True`` when the mask is in fact not a causal attention.mask
       may lead to incorrect and unpredictable execution - in some scenarios,
       a causal mask may be applied based on the hint, in other execution
       scenarios the specified mask may be used.  The choice may not appear
       to be deterministic, in that a number of factors like alignment,
       hardware SKU, etc influence the decision whether to use a mask or
       rely on the hint.
    ``size`` if not None, check whether the mask is a causal mask of the provided size
       Otherwise, checks for any causal mask.
    """
    # Prevent type refinement
    make_causal = is_causal is True

    if is_causal is None and mask is not None:
        sz = size if size is not None else mask.size(-2)
        causal_comparison = _generate_square_subsequent_mask(
            sz, device=mask.device, dtype=mask.dtype
        )

        # Do not use `torch.equal` so we handle batched masks by
        # broadcasting the comparison.
        if mask.size() == causal_comparison.size():
            make_causal = bool((mask == causal_comparison).all())
        else:
            make_causal = False

    return make_causal