attention_processors.py

import math
from typing import Any
from einops import rearrange
import torch
from diffusers.models.attention_processor import Attention


# flash attention forwards and backwards

# https://arxiv.org/abs/2205.14135

EPSILON = 1e-6


class FlashAttentionFunction(torch.autograd.function.Function):
    @staticmethod
    @torch.no_grad()
    def forward(ctx, q, k, v, mask, causal, q_bucket_size, k_bucket_size):
        """Algorithm 2 in the paper"""

        device = q.device
        dtype = q.dtype
        max_neg_value = -torch.finfo(q.dtype).max
        qk_len_diff = max(k.shape[-2] - q.shape[-2], 0)

        o = torch.zeros_like(q)
        all_row_sums = torch.zeros((*q.shape[:-1], 1), dtype=dtype, device=device)
        all_row_maxes = torch.full(
            (*q.shape[:-1], 1), max_neg_value, dtype=dtype, device=device
        )

        scale = q.shape[-1] ** -0.5

        if mask is None:
            mask = (None,) * math.ceil(q.shape[-2] / q_bucket_size)
        else:
            mask = rearrange(mask, "b n -> b 1 1 n")
            mask = mask.split(q_bucket_size, dim=-1)

        row_splits = zip(
            q.split(q_bucket_size, dim=-2),
            o.split(q_bucket_size, dim=-2),
            mask,
            all_row_sums.split(q_bucket_size, dim=-2),
            all_row_maxes.split(q_bucket_size, dim=-2),
        )

        for ind, (qc, oc, row_mask, row_sums, row_maxes) in enumerate(row_splits):
            q_start_index = ind * q_bucket_size - qk_len_diff

            col_splits = zip(
                k.split(k_bucket_size, dim=-2),
                v.split(k_bucket_size, dim=-2),
            )

            for k_ind, (kc, vc) in enumerate(col_splits):
                k_start_index = k_ind * k_bucket_size

                attn_weights = (
                    torch.einsum("... i d, ... j d -> ... i j", qc, kc) * scale
                )

                if row_mask is not None:
                    attn_weights.masked_fill_(~row_mask, max_neg_value)

                if causal and q_start_index < (k_start_index + k_bucket_size - 1):
                    causal_mask = torch.ones(
                        (qc.shape[-2], kc.shape[-2]), dtype=torch.bool, device=device
                    ).triu(q_start_index - k_start_index + 1)
                    attn_weights.masked_fill_(causal_mask, max_neg_value)

                block_row_maxes = attn_weights.amax(dim=-1, keepdims=True)
                attn_weights -= block_row_maxes
                exp_weights = torch.exp(attn_weights)

                if row_mask is not None:
                    exp_weights.masked_fill_(~row_mask, 0.0)

                block_row_sums = exp_weights.sum(dim=-1, keepdims=True).clamp(
                    min=EPSILON
                )

                new_row_maxes = torch.maximum(block_row_maxes, row_maxes)

                exp_values = torch.einsum(
                    "... i j, ... j d -> ... i d", exp_weights, vc
                )

                exp_row_max_diff = torch.exp(row_maxes - new_row_maxes)
                exp_block_row_max_diff = torch.exp(block_row_maxes - new_row_maxes)

                new_row_sums = (
                    exp_row_max_diff * row_sums
                    + exp_block_row_max_diff * block_row_sums
                )

                oc.mul_((row_sums / new_row_sums) * exp_row_max_diff).add_(
                    (exp_block_row_max_diff / new_row_sums) * exp_values
                )

                row_maxes.copy_(new_row_maxes)
                row_sums.copy_(new_row_sums)

        ctx.args = (causal, scale, mask, q_bucket_size, k_bucket_size)
        ctx.save_for_backward(q, k, v, o, all_row_sums, all_row_maxes)

        return o

    @staticmethod
    @torch.no_grad()
    def backward(ctx, do):
        """Algorithm 4 in the paper"""

        causal, scale, mask, q_bucket_size, k_bucket_size = ctx.args
        q, k, v, o, l, m = ctx.saved_tensors

        device = q.device

        max_neg_value = -torch.finfo(q.dtype).max
        qk_len_diff = max(k.shape[-2] - q.shape[-2], 0)

        dq = torch.zeros_like(q)
        dk = torch.zeros_like(k)
        dv = torch.zeros_like(v)

        row_splits = zip(
            q.split(q_bucket_size, dim=-2),
            o.split(q_bucket_size, dim=-2),
            do.split(q_bucket_size, dim=-2),
            mask,
            l.split(q_bucket_size, dim=-2),
            m.split(q_bucket_size, dim=-2),
            dq.split(q_bucket_size, dim=-2),
        )

        for ind, (qc, oc, doc, row_mask, lc, mc, dqc) in enumerate(row_splits):
            q_start_index = ind * q_bucket_size - qk_len_diff

            col_splits = zip(
                k.split(k_bucket_size, dim=-2),
                v.split(k_bucket_size, dim=-2),
                dk.split(k_bucket_size, dim=-2),
                dv.split(k_bucket_size, dim=-2),
            )

            for k_ind, (kc, vc, dkc, dvc) in enumerate(col_splits):
                k_start_index = k_ind * k_bucket_size

                attn_weights = (
                    torch.einsum("... i d, ... j d -> ... i j", qc, kc) * scale
                )

                if causal and q_start_index < (k_start_index + k_bucket_size - 1):
                    causal_mask = torch.ones(
                        (qc.shape[-2], kc.shape[-2]), dtype=torch.bool, device=device
                    ).triu(q_start_index - k_start_index + 1)
                    attn_weights.masked_fill_(causal_mask, max_neg_value)

                exp_attn_weights = torch.exp(attn_weights - mc)

                if row_mask is not None:
                    exp_attn_weights.masked_fill_(~row_mask, 0.0)

                p = exp_attn_weights / lc

                dv_chunk = torch.einsum("... i j, ... i d -> ... j d", p, doc)
                dp = torch.einsum("... i d, ... j d -> ... i j", doc, vc)

                D = (doc * oc).sum(dim=-1, keepdims=True)
                ds = p * scale * (dp - D)

                dq_chunk = torch.einsum("... i j, ... j d -> ... i d", ds, kc)
                dk_chunk = torch.einsum("... i j, ... i d -> ... j d", ds, qc)

                dqc.add_(dq_chunk)
                dkc.add_(dk_chunk)
                dvc.add_(dv_chunk)

        return dq, dk, dv, None, None, None, None


class FlashAttnProcessor:
    def __call__(
        self,
        attn: Attention,
        hidden_states,
        encoder_hidden_states=None,
        attention_mask=None,
    ) -> Any:
        q_bucket_size = 512
        k_bucket_size = 1024

        h = attn.heads
        q = attn.to_q(hidden_states)

        encoder_hidden_states = (
            encoder_hidden_states
            if encoder_hidden_states is not None
            else hidden_states
        )
        encoder_hidden_states = encoder_hidden_states.to(hidden_states.dtype)

        if hasattr(attn, "hypernetwork") and attn.hypernetwork is not None:
            context_k, context_v = attn.hypernetwork.forward(
                hidden_states, encoder_hidden_states
            )
            context_k = context_k.to(hidden_states.dtype)
            context_v = context_v.to(hidden_states.dtype)
        else:
            context_k = encoder_hidden_states
            context_v = encoder_hidden_states

        k = attn.to_k(context_k)
        v = attn.to_v(context_v)
        del encoder_hidden_states, hidden_states

        q, k, v = map(lambda t: rearrange(t, "b n (h d) -> b h n d", h=h), (q, k, v))

        out = FlashAttentionFunction.apply(
            q, k, v, attention_mask, False, q_bucket_size, k_bucket_size
        )

        out = rearrange(out, "b h n d -> b n (h d)")

        out = attn.to_out[0](out)
        out = attn.to_out[1](out)
        return out