VTimeLLM/flash-attention/flash_attn/flash_blocksparse_attention.py

import math

import hydra
import torch
import torch.nn as nn
from einops import rearrange

from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input
from flash_attn.flash_blocksparse_attn_interface import (
    convert_blockmask,
    flash_blocksparse_attn_func,
)


class FlashBlocksparseAttention(nn.Module):
    """Implement the scaled dot product attention with softmax.
    Arguments
    ---------
        softmax_temp: The temperature to use for the softmax attention.
                      (default: 1/sqrt(d_keys) where d_keys is computed at
                      runtime)
        attention_dropout: The dropout rate to apply to the attention
                           (default: 0.1)
    """

    def __init__(
        self,
        sparsity_config,
        softmax_temp=None,
        attention_dropout=0.0,
        max_seq_length=2048,
        device=None,
        dtype=None,
    ):
        super().__init__()
        self.sparsity_config = hydra.utils.instantiate(sparsity_config)
        self.softmax_temp = softmax_temp
        self.dropout_p = attention_dropout

        # initialize sparse layout and register as buffer
        max_seq_length = ((max_seq_length + 256 - 1) // 256) * 256
        layout = self.sparsity_config.make_layout(max_seq_length)
        self.register_buffer("layout", layout)
        blockmask_converted = convert_blockmask(self.layout, causal=False)
        self.register_buffer("blockmask_converted", blockmask_converted)
        # logger.info(f'Attention class {self.__class__}: saving={self.layout.float().mean()}')

    def forward(
        self,
        qkv,
        attn_mask=None,
        key_padding_mask=None,
        causal=False,
        cu_seqlens=None,
        max_s=None,
        need_weights=False,
        convert_mask=True,
    ):
        """Implements the multihead softmax attention.
        Arguments
        ---------
            qkv: The tensor containing the query, key, and value. (B, S, 3, H, D) if key_padding_mask is None
            attn_mask: An implementation of BaseMask that encodes where each
                       query can attend to
            key_padding_mask: An implementation of BaseMask that encodes how
                         many query each sequence in the batch consists of
        """
        assert not need_weights
        assert attn_mask is None
        assert qkv.dtype == torch.float16
        assert qkv.is_cuda

        if cu_seqlens is None:
            batch_size = qkv.shape[0]
            seqlen = qkv.shape[1]
            # Convert mask to take a subset
            seqlen_rounded = ((seqlen + 256 - 1) // 256) * 256
            assert seqlen_rounded // 16 <= self.layout.shape[0], (
                seqlen_rounded // 256 <= self.layout.shape[1]
            )
            blockmask = self.layout[: seqlen_rounded // 16, : seqlen_rounded // 256]
            if key_padding_mask is None:
                qkv = rearrange(qkv, "b s ... -> (b s) ...")
                max_s = seqlen
                cu_seqlens = torch.arange(
                    0, (batch_size + 1) * seqlen, step=seqlen, dtype=torch.int32, device=qkv.device
                )
                output = flash_blocksparse_attn_func(
                    qkv,
                    cu_seqlens,
                    blockmask,
                    self.dropout_p if self.training else 0.0,
                    max_s,
                    softmax_scale=self.softmax_temp,
                    causal=causal,
                )
                output = rearrange(output, "(b s) ... -> b s ...", b=batch_size)
            else:
                key_padding_mask_bool = key_padding_mask.bool_matrix
                nheads = qkv.shape[-2]
                x = rearrange(qkv, "b s three h d -> b s (three h d)")
                x_unpad, indices, cu_seqlens, max_s, _ = unpad_input(x, key_padding_mask_bool)
                x_unpad = rearrange(x_unpad, "nnz (three h d) -> nnz three h d", three=3, h=nheads)
                output_unpad = flash_blocksparse_attn_func(
                    x_unpad,
                    cu_seqlens,
                    blockmask,
                    self.dropout_p if self.training else 0.0,
                    max_s,
                    softmax_scale=self.softmax_temp,
                    causal=causal,
                )
                output = rearrange(
                    pad_input(
                        rearrange(output_unpad, "nnz h d -> nnz (h d)"), indices, batch_size, seqlen
                    ),
                    "b s (h d) -> b s h d",
                    h=nheads,
                )
        else:
            assert max_s is not None
            seqlen = max_s
            # Convert mask to take a subset
            seqlen_rounded = ((seqlen + 256 - 1) // 256) * 256
            assert seqlen_rounded // 16 <= self.layout.shape[0], (
                seqlen_rounded // 256 <= self.layout.shape[1]
            )
            blockmask = self.layout[: seqlen_rounded // 16, : seqlen_rounded // 256]
            if convert_mask:
                output = flash_blocksparse_attn_func(
                    qkv,
                    cu_seqlens,
                    blockmask,
                    self.dropout_p if self.training else 0.0,
                    max_s,
                    softmax_scale=self.softmax_temp,
                    causal=causal,
                )
            else:
                output = flash_blocksparse_attn_func(
                    qkv,
                    cu_seqlens,
                    self.blockmask_converted,
                    self.dropout_p if self.training else 0.0,
                    max_s,
                    softmax_scale=self.softmax_temp,
                    causal=causal,
                    convert_mask=False,
                )

        return output, None


class FlashBlocksparseMHA(nn.Module):
    def __init__(
        self,
        embed_dim,
        num_heads,
        sparsity_config,
        bias=True,
        batch_first=True,
        attention_dropout=0.0,
        causal=False,
        max_seq_length=2048,
        device=None,
        dtype=None,
        **kwargs,
    ) -> None:
        assert batch_first
        factory_kwargs = {"device": device, "dtype": dtype}
        super().__init__()
        self.embed_dim = embed_dim
        self.causal = causal

        self.num_heads = num_heads
        assert self.embed_dim % num_heads == 0, "self.kdim must be divisible by num_heads"
        self.head_dim = self.embed_dim // num_heads
        assert self.head_dim in [16, 32, 64], "Only support head_dim == 16, 32, or 64"

        self.Wqkv = nn.Linear(embed_dim, 3 * embed_dim, bias=bias, **factory_kwargs)
        self.inner_attn = FlashBlocksparseAttention(
            sparsity_config,
            attention_dropout=attention_dropout,
            max_seq_length=max_seq_length,
            **factory_kwargs,
        )
        self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias, **factory_kwargs)

    def forward(
        self, x, x_ignored_, x_ignored_1_, attn_mask=None, key_padding_mask=None, need_weights=False
    ):
        qkv = self.Wqkv(x)
        qkv = rearrange(qkv, "b s (three h d) -> b s three h d", three=3, h=self.num_heads)
        context, attn_weights = self.inner_attn(
            qkv, key_padding_mask=key_padding_mask, need_weights=need_weights, causal=self.causal
        )
        return self.out_proj(rearrange(context, "b s h d -> b s (h d)")), attn_weights