progress/SpecForge/specforge/layers/ring/ring_flash_attn.py

import torch
from yunchang.kernels import AttnType, select_flash_attn_impl

from .utils import RingComm, update_out_and_lse


def ring_flash_attn_forward(
    process_group,
    q: torch.Tensor,
    k: torch.Tensor,
    v: torch.Tensor,
    softmax_scale,
    dropout_p=0,
    causal=True,
    window_size=(-1, -1),
    softcap=0.0,
    alibi_slopes=None,
    deterministic=False,
    attn_type: AttnType = AttnType.FA,
    attn_processor=None,
):
    comm = RingComm(process_group)

    out = None
    lse = None

    next_k, next_v = None, None

    for step in range(comm.world_size):
        if step + 1 != comm.world_size:
            next_k: torch.Tensor = comm.send_recv(k)
            next_v: torch.Tensor = comm.send_recv(v)
            comm.commit()

        if not causal or step <= comm.rank:
            fn = select_flash_attn_impl(
                attn_type, stage="fwd-only", attn_processor=attn_processor
            )
            block_out, block_lse = fn(
                q,
                k,
                v,
                dropout_p=dropout_p,
                softmax_scale=softmax_scale,
                causal=causal and step == 0,
                window_size=window_size,
                softcap=softcap,
                alibi_slopes=alibi_slopes,
                return_softmax=True and dropout_p > 0,
            )
            if attn_type == AttnType.SPARSE_SAGE:
                out, lse = block_out, block_lse
            else:
                out, lse = update_out_and_lse(out, lse, block_out, block_lse)

        if step + 1 != comm.world_size:
            comm.wait()
            k = next_k
            v = next_v

    out = out.to(q.dtype)
    if attn_type != AttnType.SPARSE_SAGE:
        lse = lse.squeeze(dim=-1).transpose(1, 2)
    return out, lse


def ring_flash_attn_backward(
    process_group,
    dout,
    q,
    k,
    v,
    out,
    softmax_lse,
    softmax_scale,
    dropout_p=0,
    causal=True,
    window_size=(-1, -1),
    softcap=0.0,
    alibi_slopes=None,
    deterministic=False,
    attn_type: AttnType = AttnType.FA,
):
    kv_comm = RingComm(process_group)
    d_kv_comm = RingComm(process_group)
    dq, dk, dv = None, None, None
    next_dk, next_dv = None, None

    block_dq_buffer = torch.empty(q.shape, dtype=q.dtype, device=q.device)
    block_dk_buffer = torch.empty(k.shape, dtype=k.dtype, device=k.device)
    block_dv_buffer = torch.empty(v.shape, dtype=v.dtype, device=v.device)

    next_dk, next_dv = None, None
    next_k, next_v = None, None

    for step in range(kv_comm.world_size):
        if step + 1 != kv_comm.world_size:
            next_k = kv_comm.send_recv(k)
            next_v = kv_comm.send_recv(v)
            kv_comm.commit()
        if step <= kv_comm.rank or not causal:
            bwd_causal = causal and step == 0
            fn = select_flash_attn_impl(attn_type, stage="bwd-only")
            fn(
                dout,
                q,
                k,
                v,
                out,
                softmax_lse,
                block_dq_buffer,
                block_dk_buffer,
                block_dv_buffer,
                dropout_p,
                softmax_scale,
                bwd_causal,
                window_size,
                softcap,
                alibi_slopes,
                deterministic,
                rng_state=None,
            )

            if dq is None:
                dq = block_dq_buffer.to(torch.float32)
                dk = block_dk_buffer.to(torch.float32)
                dv = block_dv_buffer.to(torch.float32)
            else:
                dq += block_dq_buffer
                d_kv_comm.wait()
                dk = block_dk_buffer + next_dk
                dv = block_dv_buffer + next_dv
        elif step != 0:
            d_kv_comm.wait()
            dk = next_dk
            dv = next_dv

        if step + 1 != kv_comm.world_size:
            kv_comm.wait()
            k = next_k
            v = next_v

        next_dk = d_kv_comm.send_recv(dk)
        next_dv = d_kv_comm.send_recv(dv)
        d_kv_comm.commit()

    d_kv_comm.wait()

    return dq.to(torch.bfloat16), next_dk.to(q.dtype), next_dv.to(q.dtype)


class RingFlashAttnFunc(torch.autograd.Function):
    @staticmethod
    def forward(
        ctx,
        q,
        k,
        v,
        dropout_p,
        softmax_scale,
        causal,
        window_size,
        softcap,
        alibi_slopes,
        deterministic,
        return_softmax,
        group,
        attn_type,
        attn_processor,
    ):
        if softmax_scale is None:
            softmax_scale = q.shape[-1] ** (-0.5)

        assert alibi_slopes is None
        k = k.contiguous()
        v = v.contiguous()
        out, softmax_lse = ring_flash_attn_forward(
            group,
            q,
            k,
            v,
            softmax_scale=softmax_scale,
            dropout_p=dropout_p,
            causal=causal,
            window_size=window_size,
            softcap=softcap,
            alibi_slopes=alibi_slopes,
            deterministic=False,
            attn_type=attn_type,
            attn_processor=attn_processor,
        )
        # this should be out_padded
        ctx.save_for_backward(q, k, v, out, softmax_lse)
        ctx.dropout_p = dropout_p
        ctx.softmax_scale = softmax_scale
        ctx.causal = causal
        ctx.window_size = window_size
        ctx.softcap = softcap
        ctx.alibi_slopes = alibi_slopes
        ctx.deterministic = deterministic
        ctx.group = group
        ctx.attn_type = attn_type
        ctx.attn_processor = attn_processor
        return out if not return_softmax else (out, softmax_lse, None)

    @staticmethod
    def backward(ctx, dout, *args):
        q, k, v, out, softmax_lse = ctx.saved_tensors
        dq, dk, dv = ring_flash_attn_backward(
            ctx.group,
            dout,
            q,
            k,
            v,
            out,
            softmax_lse,
            softmax_scale=ctx.softmax_scale,
            dropout_p=ctx.dropout_p,
            causal=ctx.causal,
            window_size=ctx.window_size,
            softcap=ctx.softcap,
            alibi_slopes=ctx.alibi_slopes,
            deterministic=ctx.deterministic,
            attn_type=ctx.attn_type,
        )
        return (
            dq,
            dk,
            dv,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
        )


def ring_flash_attn_qkvpacked_func(
    qkv,
    dropout_p=0.0,
    softmax_scale=None,
    causal=False,
    window_size=(-1, -1),
    softcap=0.0,
    alibi_slopes=None,
    deterministic=False,
    return_attn_probs=False,
    group=None,
    attn_type: AttnType = AttnType.FA,
):
    return RingFlashAttnFunc.apply(
        qkv[:, :, 0],
        qkv[:, :, 1],
        qkv[:, :, 2],
        dropout_p,
        softmax_scale,
        causal,
        window_size,
        softcap,
        alibi_slopes,
        deterministic,
        return_attn_probs,
        group,
        attn_type,
    )


def ring_flash_attn_kvpacked_func(
    q,
    kv,
    dropout_p=0.0,
    softmax_scale=None,
    causal=False,
    window_size=(-1, -1),
    softcap=0.0,
    alibi_slopes=None,
    deterministic=False,
    return_attn_probs=False,
    group=None,
    attn_type: AttnType = AttnType.FA,
):
    return RingFlashAttnFunc.apply(
        q,
        kv[:, :, 0],
        kv[:, :, 1],
        dropout_p,
        softmax_scale,
        causal,
        window_size,
        softcap,
        alibi_slopes,
        deterministic,
        return_attn_probs,
        group,
        attn_type,
    )


def ring_flash_attn_func(
    q,
    k,
    v,
    dropout_p=0.0,
    softmax_scale=None,
    causal=False,
    window_size=(-1, -1),
    softcap=0.0,
    alibi_slopes=None,
    deterministic=False,
    return_attn_probs=False,
    group=None,
    attn_type: AttnType = AttnType.FA,
    attn_processor=None,
):
    return RingFlashAttnFunc.apply(
        q,
        k,
        v,
        dropout_p,
        softmax_scale,
        causal,
        window_size,
        softcap,
        alibi_slopes,
        deterministic,
        return_attn_probs,
        group,
        attn_type,
        attn_processor,
    )