fairseq/examples/attention_head_selection/src/modules/multihead_functional.py

# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.

from typing import Optional, Tuple
import torch
from torch import Tensor
from torch.nn.functional import (
    linear, softmax, dropout, pad,
    has_torch_function,
    handle_torch_function,
    _in_projection_packed,
)
import math
import warnings


def _scaled_dot_product_attention(
    q: Tensor,
    k: Tensor,
    v: Tensor,
    attn_mask: Optional[Tensor] = None,
    dropout_p: float = 0.0,
    bsz: int = 1,
    subset_heads: Optional[Tensor] = None,
    subset_weights: Optional[Tensor] = None,
) -> Tuple[Tensor, Tensor]:
    B, Nt, E = q.shape
    q = q / math.sqrt(E)
    # B: bsz * total_num_heads
    # (B, Nt, E) x (B, E, Ns) -> (B, Nt, Ns)
    attn = torch.bmm(q, k.transpose(-2, -1))
    if attn_mask is not None:
        attn += attn_mask
    attn = softmax(attn, dim=-1)
    if dropout_p > 0.0:
        attn = dropout(attn, p=dropout_p)
    if subset_heads is None:
        # (B, Nt, Ns) x (B, Ns, E) -> (B, Nt, E)
        output = torch.bmm(attn, v)
    else:
        mixed_output = torch.bmm(attn, v).contiguous().view(bsz, -1, Nt, E)
        output = torch.stack(
            [mixed_output[torch.arange(bsz), subset_heads[:, col], :, :] for col in range(subset_heads.size(1))],
            dim=1
        )
        output = output * subset_weights.unsqueeze(2).unsqueeze(3)
        output = output.contiguous().view(-1, Nt, E)
    if subset_heads is not None:
        _, Nt, Ns = attn.size()
        mixed_attn = attn.view(bsz, -1, Nt, Ns)
        attn = torch.stack(
            [mixed_attn[torch.arange(bsz), subset_heads[:, col], :, :] for col in range(subset_heads.size(1))], dim=1
        )
    return output, attn


def _in_projection(
    q: Tensor,
    k: Tensor,
    v: Tensor,
    w_q: Tensor,
    w_k: Tensor,
    w_v: Tensor,
    b_q: Optional[Tensor] = None,
    b_k: Optional[Tensor] = None,
    b_v: Optional[Tensor] = None,
) -> Tuple[Tensor, Tensor, Tensor]:
    return linear(q, w_q, b_q), linear(k, w_k, b_k), linear(v, w_v, b_v)


def multi_head_attention_forward(
    query: Tensor,
    key: Tensor,
    value: Tensor,
    embed_dim_to_check: int,
    total_num_heads: int,
    num_heads: int,
    in_proj_weight: Tensor,
    in_proj_bias: Optional[Tensor],
    bias_k: Optional[Tensor],
    bias_v: Optional[Tensor],
    add_zero_attn: bool,
    dropout_p: float,
    out_proj_weight: Tensor,
    out_proj_bias: Optional[Tensor],
    training: bool = True,
    key_padding_mask: Optional[Tensor] = None,
    need_weights: bool = True,
    attn_mask: Optional[Tensor] = None,
    use_separate_proj_weight: bool = False,
    q_proj_weight: Optional[Tensor] = None,
    k_proj_weight: Optional[Tensor] = None,
    v_proj_weight: Optional[Tensor] = None,
    static_k: Optional[Tensor] = None,
    static_v: Optional[Tensor] = None,
    subset_heads: Optional[Tensor] = None,
    subset_weights: Optional[Tensor] = None,
):
    tens_ops = (query, key, value, in_proj_weight, in_proj_bias, bias_k, bias_v, out_proj_weight, out_proj_bias)
    if has_torch_function(tens_ops):
        return handle_torch_function(
            multi_head_attention_forward,
            tens_ops,
            query,
            key,
            value,
            embed_dim_to_check,
            total_num_heads,
            num_heads,
            in_proj_weight,
            in_proj_bias,
            bias_k,
            bias_v,
            add_zero_attn,
            dropout_p,
            out_proj_weight,
            out_proj_bias,
            training=training,
            key_padding_mask=key_padding_mask,
            need_weights=need_weights,
            attn_mask=attn_mask,
            use_separate_proj_weight=use_separate_proj_weight,
            q_proj_weight=q_proj_weight,
            k_proj_weight=k_proj_weight,
            v_proj_weight=v_proj_weight,
            static_k=static_k,
            static_v=static_v,
            subset_heads=subset_heads,
            subset_weights=subset_weights
        )

    # set up shape vars
    tgt_len, bsz, embed_dim = query.shape
    src_len, _, _ = key.shape
    assert embed_dim == embed_dim_to_check, \
        f"was expecting embedding dimension of {embed_dim_to_check}, but got {embed_dim}"
    if isinstance(embed_dim, torch.Tensor):
        # embed_dim can be a tensor when JIT tracing
        head_dim = embed_dim.div(num_heads, rounding_mode='trunc')
    else:
        head_dim = embed_dim // num_heads
    assert head_dim * num_heads == embed_dim, f"embed_dim {embed_dim} not divisible by num_heads {num_heads}"
    if use_separate_proj_weight:
        # allow MHA to have different embedding dimensions when separate projection weights are used
        assert key.shape[:2] == value.shape[:2], \
            f"key's sequence and batch dims {key.shape[:2]} do not match value's {value.shape[:2]}"
    else:
        assert key.shape == value.shape, f"key shape {key.shape} does not match value shape {value.shape}"

    #
    # compute in-projection
    #
    if not use_separate_proj_weight:
        q, k, v = _in_projection_packed(query, key, value, in_proj_weight, in_proj_bias)
    else:
        assert q_proj_weight is not None, "use_separate_proj_weight is True but q_proj_weight is None"
        assert k_proj_weight is not None, "use_separate_proj_weight is True but k_proj_weight is None"
        assert v_proj_weight is not None, "use_separate_proj_weight is True but v_proj_weight is None"
        if in_proj_bias is None:
            b_q = b_k = b_v = None
        else:
            b_q, b_k, b_v = in_proj_bias.chunk(3)
        q, k, v = _in_projection(query, key, value, q_proj_weight, k_proj_weight, v_proj_weight, b_q, b_k, b_v)

    # prep attention mask
    if attn_mask is not None:
        if attn_mask.dtype == torch.uint8:
            warnings.warn("Byte tensor for attn_mask in nn.MultiheadAttention is deprecated. Use bool tensor instead.")
            attn_mask = attn_mask.to(torch.bool)
        else:
            assert attn_mask.is_floating_point() or attn_mask.dtype == torch.bool, \
                f"Only float, byte, and bool types are supported for attn_mask, not {attn_mask.dtype}"
        # ensure attn_mask's dim is 3
        if attn_mask.dim() == 2:
            correct_2d_size = (tgt_len, src_len)
            if attn_mask.shape != correct_2d_size:
                raise RuntimeError(f"The shape of the 2D attn_mask is {attn_mask.shape}, but should be {correct_2d_size}.")
            attn_mask = attn_mask.unsqueeze(0)
        elif attn_mask.dim() == 3:
            correct_3d_size = (bsz * total_num_heads, tgt_len, src_len)
            if attn_mask.shape != correct_3d_size:
                raise RuntimeError(f"The shape of the 3D attn_mask is {attn_mask.shape}, but should be {correct_3d_size}.")
        else:
            raise RuntimeError(f"attn_mask's dimension {attn_mask.dim()} is not supported")

    # prep key padding mask
    if key_padding_mask is not None and key_padding_mask.dtype == torch.uint8:
        warnings.warn("Byte tensor for key_padding_mask in nn.MultiheadAttention is deprecated. Use bool tensor instead.")
        key_padding_mask = key_padding_mask.to(torch.bool)

    # add bias along batch dimension (currently second)
    if bias_k is not None and bias_v is not None:
        assert static_k is None, "bias cannot be added to static key."
        assert static_v is None, "bias cannot be added to static value."
        k = torch.cat([k, bias_k.repeat(1, bsz, 1)])
        v = torch.cat([v, bias_v.repeat(1, bsz, 1)])
        if attn_mask is not None:
            attn_mask = pad(attn_mask, (0, 1))
        if key_padding_mask is not None:
            key_padding_mask = pad(key_padding_mask, (0, 1))
    else:
        assert bias_k is None
        assert bias_v is None

    #
    # reshape q, k, v for multihead attention and make em batch first
    #
    q = q.contiguous().view(tgt_len, bsz * total_num_heads, head_dim).transpose(0, 1)
    if static_k is None:
        k = k.contiguous().view(k.shape[0], bsz * total_num_heads, head_dim).transpose(0, 1)
    else:
        # TODO finish disentangling control flow so we don't do in-projections when statics are passed
        assert static_k.size(0) == bsz * total_num_heads, \
            f"expecting static_k.size(0) of {bsz * total_num_heads}, but got {static_k.size(0)}"
        assert static_k.size(2) == head_dim, \
            f"expecting static_k.size(2) of {head_dim}, but got {static_k.size(2)}"
        k = static_k
    if static_v is None:
        v = v.contiguous().view(v.shape[0], bsz * total_num_heads, head_dim).transpose(0, 1)
    else:
        # TODO finish disentangling control flow so we don't do in-projections when statics are passed
        assert static_v.size(0) == bsz * total_num_heads, \
            f"expecting static_v.size(0) of {bsz * total_num_heads}, but got {static_v.size(0)}"
        assert static_v.size(2) == head_dim, \
            f"expecting static_v.size(2) of {head_dim}, but got {static_v.size(2)}"
        v = static_v

    # add zero attention along batch dimension (now first)
    if add_zero_attn:
        zero_attn_shape = (bsz * total_num_heads, 1, head_dim)
        k = torch.cat([k, torch.zeros(zero_attn_shape, dtype=k.dtype, device=k.device)], dim=1)
        v = torch.cat([v, torch.zeros(zero_attn_shape, dtype=v.dtype, device=v.device)], dim=1)
        if attn_mask is not None:
            attn_mask = pad(attn_mask, (0, 1))
        if key_padding_mask is not None:
            key_padding_mask = pad(key_padding_mask, (0, 1))

    # update source sequence length after adjustments
    src_len = k.size(1)

    # merge key padding and attention masks
    if key_padding_mask is not None:
        assert key_padding_mask.shape == (bsz, src_len), \
            f"expecting key_padding_mask shape of {(bsz, src_len)}, but got {key_padding_mask.shape}"
        key_padding_mask = key_padding_mask.view(bsz, 1, 1, src_len).   \
            expand(-1, total_num_heads, -1, -1).reshape(bsz * total_num_heads, 1, src_len)
        if attn_mask is None:
            attn_mask = key_padding_mask
        elif attn_mask.dtype == torch.bool:
            attn_mask = attn_mask.logical_or(key_padding_mask)
        else:
            attn_mask = attn_mask.masked_fill(key_padding_mask, float("-inf"))

    # convert mask to float
    if attn_mask is not None and attn_mask.dtype == torch.bool:
        new_attn_mask = torch.zeros_like(attn_mask, dtype=torch.float)
        new_attn_mask.masked_fill_(attn_mask, float("-inf"))
        attn_mask = new_attn_mask

    # adjust dropout probability
    if not training:
        dropout_p = 0.0

    #
    # (deep breath) calculate attention and out projection
    #
    attn_output, attn_output_weights = _scaled_dot_product_attention(q, k, v, attn_mask, dropout_p, bsz, subset_heads, subset_weights)
    attn_output = attn_output.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim)
    attn_output = linear(attn_output, out_proj_weight, out_proj_bias)

    if need_weights:
        # average attention weights over heads
        attn_output_weights = attn_output_weights.view(bsz, num_heads, tgt_len, src_len)
        return attn_output, attn_output_weights.sum(dim=1) / num_heads
    else:
        return attn_output, None