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# modified from https://github.com/lifeiteng/vall-e/blob/main/valle/modules/activation.py
from typing import Optional
from typing import Tuple
import torch
from torch import Tensor
from torch.nn import Linear
from torch.nn import Module
from torch.nn.init import constant_
from torch.nn.init import xavier_normal_
from torch.nn.init import xavier_uniform_
from torch.nn.modules.linear import NonDynamicallyQuantizableLinear
from torch.nn.parameter import Parameter
from torch.nn import functional as F
from AR.modules.patched_mha_with_cache import multi_head_attention_forward_patched
F.multi_head_attention_forward=multi_head_attention_forward_patched
class MultiheadAttention(Module):
r"""Allows the model to jointly attend to information
from different representation subspaces as described in the paper:
`Attention Is All You Need <https://arxiv.org/abs/1706.03762>`_.
Multi-Head Attention is defined as:
.. math::
\text{MultiHead}(Q, K, V) = \text{Concat}(head_1,\dots,head_h)W^O
where :math:`head_i = \text{Attention}(QW_i^Q, KW_i^K, VW_i^V)`.
``forward()`` will use a special optimized implementation if all of the following
conditions are met:
- self attention is being computed (i.e., ``query``, ``key``, and ``value`` are the same tensor. This
restriction will be loosened in the future.)
- Either autograd is disabled (using ``torch.inference_mode`` or ``torch.no_grad``) or no tensor argument ``requires_grad``
- training is disabled (using ``.eval()``)
- dropout is 0
- ``add_bias_kv`` is ``False``
- ``add_zero_attn`` is ``False``
- ``batch_first`` is ``True`` and the input is batched
- ``kdim`` and ``vdim`` are equal to ``embed_dim``
- at most one of ``key_padding_mask`` or ``attn_mask`` is passed
- if a `NestedTensor <https://pytorch.org/docs/stable/nested.html>`_ is passed, neither ``key_padding_mask``
nor ``attn_mask`` is passed
If the optimized implementation is in use, a
`NestedTensor <https://pytorch.org/docs/stable/nested.html>`_ can be passed for
``query``/``key``/``value`` 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.
Args:
embed_dim: Total dimension of the model.
num_heads: Number of parallel attention heads. Note that ``embed_dim`` will be split
across ``num_heads`` (i.e. each head will have dimension ``embed_dim // num_heads``).
dropout: Dropout probability on ``attn_output_weights``. Default: ``0.0`` (no dropout).
bias: If specified, adds bias to input / output projection layers. Default: ``True``.
add_bias_kv: If specified, adds bias to the key and value sequences at dim=0. Default: ``False``.
add_zero_attn: If specified, adds a new batch of zeros to the key and value sequences at dim=1.
Default: ``False``.
kdim: Total number of features for keys. Default: ``None`` (uses ``kdim=embed_dim``).
vdim: Total number of features for values. Default: ``None`` (uses ``vdim=embed_dim``).
batch_first: If ``True``, then the input and output tensors are provided
as (batch, seq, feature). Default: ``False`` (seq, batch, feature).
Examples::
>>> # xdoctest: +SKIP
>>> multihead_attn = nn.MultiheadAttention(embed_dim, num_heads)
>>> attn_output, attn_output_weights = multihead_attn(query, key, value)
"""
__constants__ = ["batch_first"]
bias_k: Optional[torch.Tensor]
bias_v: Optional[torch.Tensor]
def __init__(
self,
embed_dim,
num_heads,
dropout=0.0,
bias=True,
add_bias_kv=False,
add_zero_attn=False,
kdim=None,
vdim=None,
batch_first=False,
linear1_cls=Linear,
linear2_cls=Linear,
device=None,
dtype=None, ) -> None:
factory_kwargs = {"device": device, "dtype": dtype}
super(MultiheadAttention, self).__init__()
self.embed_dim = embed_dim
self.kdim = kdim if kdim is not None else embed_dim
self.vdim = vdim if vdim is not None else embed_dim
self._qkv_same_embed_dim = (self.kdim == embed_dim and
self.vdim == embed_dim)
self.num_heads = num_heads
self.dropout = dropout
self.batch_first = batch_first
self.head_dim = embed_dim // num_heads
assert (self.head_dim * num_heads == self.embed_dim
), "embed_dim must be divisible by num_heads"
if add_bias_kv:
self.bias_k = Parameter(
torch.empty((1, 1, embed_dim), **factory_kwargs))
self.bias_v = Parameter(
torch.empty((1, 1, embed_dim), **factory_kwargs))
else:
self.bias_k = self.bias_v = None
if linear1_cls == Linear:
if not self._qkv_same_embed_dim:
self.q_proj_weight = Parameter(
torch.empty((embed_dim, embed_dim), **factory_kwargs))
self.k_proj_weight = Parameter(
torch.empty((embed_dim, self.kdim), **factory_kwargs))
self.v_proj_weight = Parameter(
torch.empty((embed_dim, self.vdim), **factory_kwargs))
self.register_parameter("in_proj_weight", None)
else:
self.in_proj_weight = Parameter(
torch.empty((3 * embed_dim, embed_dim), **factory_kwargs))
self.register_parameter("q_proj_weight", None)
self.register_parameter("k_proj_weight", None)
self.register_parameter("v_proj_weight", None)
if bias:
self.in_proj_bias = Parameter(
torch.empty(3 * embed_dim, **factory_kwargs))
else:
self.register_parameter("in_proj_bias", None)
self.out_proj = NonDynamicallyQuantizableLinear(
embed_dim, embed_dim, bias=bias, **factory_kwargs)
self._reset_parameters()
else:
if not self._qkv_same_embed_dim:
raise NotImplementedError
else:
self.in_proj_linear = linear1_cls(
embed_dim, 3 * embed_dim, bias=bias, **factory_kwargs)
self.in_proj_weight = self.in_proj_linear.weight
self.register_parameter("q_proj_weight", None)
self.register_parameter("k_proj_weight", None)
self.register_parameter("v_proj_weight", None)
if bias:
self.in_proj_bias = self.in_proj_linear.bias
else:
self.register_parameter("in_proj_bias", None)
self.out_proj = linear2_cls(
embed_dim, embed_dim, bias=bias, **factory_kwargs)
if self.bias_k is not None:
xavier_normal_(self.bias_k)
if self.bias_v is not None:
xavier_normal_(self.bias_v)
self.add_zero_attn = add_zero_attn
def _reset_parameters(self):
if self._qkv_same_embed_dim:
xavier_uniform_(self.in_proj_weight)
else:
xavier_uniform_(self.q_proj_weight)
xavier_uniform_(self.k_proj_weight)
xavier_uniform_(self.v_proj_weight)
if self.in_proj_bias is not None:
constant_(self.in_proj_bias, 0.0)
constant_(self.out_proj.bias, 0.0)
if self.bias_k is not None:
xavier_normal_(self.bias_k)
if self.bias_v is not None:
xavier_normal_(self.bias_v)
def __setstate__(self, state):
# Support loading old MultiheadAttention checkpoints generated by v1.1.0
if "_qkv_same_embed_dim" not in state:
state["_qkv_same_embed_dim"] = True
super(MultiheadAttention, self).__setstate__(state)
def forward(
self,
query: Tensor,
key: Tensor,
value: Tensor,
key_padding_mask: Optional[Tensor]=None,
need_weights: bool=True,
attn_mask: Optional[Tensor]=None,
average_attn_weights: bool=True,cache=None
) -> Tuple[Tensor, Optional[Tensor]]:
r"""
Args:
query: Query embeddings of shape :math:`(L, E_q)` for unbatched input, :math:`(L, N, E_q)` when ``batch_first=False``
or :math:`(N, L, E_q)` when ``batch_first=True``, where :math:`L` is the target sequence length,
:math:`N` is the batch size, and :math:`E_q` is the query embedding dimension ``embed_dim``.
Queries are compared against key-value pairs to produce the output.
See "Attention Is All You Need" for more details.
key: Key embeddings of shape :math:`(S, E_k)` for unbatched input, :math:`(S, N, E_k)` when ``batch_first=False``
or :math:`(N, S, E_k)` when ``batch_first=True``, where :math:`S` is the source sequence length,
:math:`N` is the batch size, and :math:`E_k` is the key embedding dimension ``kdim``.
See "Attention Is All You Need" for more details.
value: Value embeddings of shape :math:`(S, E_v)` for unbatched input, :math:`(S, N, E_v)` when
``batch_first=False`` or :math:`(N, S, E_v)` when ``batch_first=True``, where :math:`S` is the source
sequence length, :math:`N` is the batch size, and :math:`E_v` is the value embedding dimension ``vdim``.
See "Attention Is All You Need" for more details.
key_padding_mask: If specified, a mask of shape :math:`(N, S)` indicating which elements within ``key``
to ignore for the purpose of attention (i.e. treat as "padding"). For unbatched `query`, shape should be :math:`(S)`.
Binary and byte masks are supported.
For a binary mask, a ``True`` value indicates that the corresponding ``key`` value will be ignored for
the purpose of attention. For a float mask, it will be directly added to the corresponding ``key`` value.
need_weights: If specified, returns ``attn_output_weights`` in addition to ``attn_outputs``.
Default: ``True``.
attn_mask: If specified, a 2D or 3D mask preventing attention to certain positions. Must be of shape
:math:`(L, S)` or :math:`(N\cdot\text{num\_heads}, L, S)`, where :math:`N` is the batch size,
:math:`L` is the target sequence length, and :math:`S` is the source sequence length. A 2D mask will be
broadcasted across the batch while a 3D mask allows for a different mask for each entry in the batch.
Binary, byte, and float masks are supported. For a binary mask, a ``True`` value indicates that the
corresponding position is not allowed to attend. For a byte mask, a non-zero value indicates that the
corresponding position is not allowed to attend. For a float mask, the mask values will be added to
the attention weight.
average_attn_weights: If true, indicates that the returned ``attn_weights`` should be averaged across
heads. Otherwise, ``attn_weights`` are provided separately per head. Note that this flag only has an
effect when ``need_weights=True``. Default: ``True`` (i.e. average weights across heads)
Outputs:
- **attn_output** - Attention outputs of shape :math:`(L, E)` when input is unbatched,
:math:`(L, N, E)` when ``batch_first=False`` or :math:`(N, L, E)` when ``batch_first=True``,
where :math:`L` is the target sequence length, :math:`N` is the batch size, and :math:`E` is the
embedding dimension ``embed_dim``.
- **attn_output_weights** - Only returned when ``need_weights=True``. If ``average_attn_weights=True``,
returns attention weights averaged across heads of shape :math:`(L, S)` when input is unbatched or
:math:`(N, L, S)`, where :math:`N` is the batch size, :math:`L` is the target sequence length, and
:math:`S` is the source sequence length. If ``average_attn_weights=False``, returns attention weights per
head of shape :math:`(\text{num\_heads}, L, S)` when input is unbatched or :math:`(N, \text{num\_heads}, L, S)`.
.. note::
`batch_first` argument is ignored for unbatched inputs.
"""
is_batched = query.dim() == 3
if key_padding_mask is not None:
_kpm_dtype = key_padding_mask.dtype
if _kpm_dtype != torch.bool and not torch.is_floating_point(
key_padding_mask):
raise AssertionError(
"only bool and floating types of key_padding_mask are supported"
)
why_not_fast_path = ""
if not is_batched:
why_not_fast_path = f"input not batched; expected query.dim() of 3 but got {query.dim()}"
elif query is not key or key is not value:
# When lifting this restriction, don't forget to either
# enforce that the dtypes all match or test cases where
# they don't!
why_not_fast_path = "non-self attention was used (query, key, and value are not the same Tensor)"
elif (self.in_proj_bias is not None and
query.dtype != self.in_proj_bias.dtype):
why_not_fast_path = f"dtypes of query ({query.dtype}) and self.in_proj_bias ({self.in_proj_bias.dtype}) don't match"
elif (self.in_proj_weight is not None and
query.dtype != self.in_proj_weight.dtype):
# this case will fail anyway, but at least they'll get a useful error message.
why_not_fast_path = f"dtypes of query ({query.dtype}) and self.in_proj_weight ({self.in_proj_weight.dtype}) don't match"
elif self.training:
why_not_fast_path = "training is enabled"
elif not self.batch_first:
why_not_fast_path = "batch_first was not True"
elif self.bias_k is not None:
why_not_fast_path = "self.bias_k was not None"
elif self.bias_v is not None:
why_not_fast_path = "self.bias_v was not None"
elif self.dropout:
why_not_fast_path = f"dropout was {self.dropout}, required zero"
elif self.add_zero_attn:
why_not_fast_path = "add_zero_attn was enabled"
elif not self._qkv_same_embed_dim:
why_not_fast_path = "_qkv_same_embed_dim was not True"
elif attn_mask is not None:
why_not_fast_path = "attn_mask was not None"
elif query.is_nested and key_padding_mask is not None:
why_not_fast_path = (
"key_padding_mask is not supported with NestedTensor input")
elif self.num_heads % 2 == 1:
why_not_fast_path = "num_heads is odd"
elif torch.is_autocast_enabled():
why_not_fast_path = "autocast is enabled"
if not why_not_fast_path:
tensor_args = (query, key, value, self.in_proj_weight,
self.in_proj_bias, self.out_proj.weight,
self.out_proj.bias, )
# We have to use list comprehensions below because TorchScript does not support
# generator expressions.
if torch.overrides.has_torch_function(tensor_args):
why_not_fast_path = "some Tensor argument has_torch_function"
elif not all([(x is None or x.is_cuda or "cpu" in str(x.device))
for x in tensor_args]):
why_not_fast_path = (
"some Tensor argument is neither CUDA nor CPU")
elif torch.is_grad_enabled() and any(
[x is not None and x.requires_grad for x in tensor_args]):
why_not_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_fast_path:
return torch._native_multi_head_attention(
query,
key,
value,
self.embed_dim,
self.num_heads,
self.in_proj_weight,
self.in_proj_bias,
self.out_proj.weight,
self.out_proj.bias,
key_padding_mask
if key_padding_mask is not None else attn_mask,
need_weights,
average_attn_weights,
1 if key_padding_mask is not None else 0
if attn_mask is not None else None, )
any_nested = query.is_nested or key.is_nested or value.is_nested
assert not any_nested, (
"MultiheadAttention does not support NestedTensor outside of its fast path. "
+ f"The fast path was not hit because {why_not_fast_path}")
if self.batch_first and is_batched:
# make sure that the transpose op does not affect the "is" property
if key is value:
if query is key:
query = key = value = query.transpose(1, 0)
else:
query, key = [x.transpose(1, 0) for x in (query, key)]
value = key
else:
query, key, value = [
x.transpose(1, 0) for x in (query, key, value)
]
if not self._qkv_same_embed_dim:
attn_output, attn_output_weights = F.multi_head_attention_forward(
query,
key,
value,
self.embed_dim,
self.num_heads,
self.in_proj_weight,
self.in_proj_bias,
self.bias_k,
self.bias_v,
self.add_zero_attn,
self.dropout,
self.out_proj.weight,
self.out_proj.bias,
training=self.training,
key_padding_mask=key_padding_mask,
need_weights=need_weights,
attn_mask=attn_mask,
use_separate_proj_weight=True,
q_proj_weight=self.q_proj_weight,
k_proj_weight=self.k_proj_weight,
v_proj_weight=self.v_proj_weight,
average_attn_weights=average_attn_weights,cache=cache )
else:
attn_output, attn_output_weights = F.multi_head_attention_forward(
query,
key,
value,
self.embed_dim,
self.num_heads,
self.in_proj_weight,
self.in_proj_bias,
self.bias_k,
self.bias_v,
self.add_zero_attn,
self.dropout,
self.out_proj.weight,
self.out_proj.bias,
training=self.training,
key_padding_mask=key_padding_mask,
need_weights=need_weights,
attn_mask=attn_mask,
average_attn_weights=average_attn_weights,cache=cache )
if self.batch_first and is_batched:
return attn_output.transpose(1, 0), attn_output_weights
else:
return attn_output, attn_output_weights