flash-attention/tests/test_flash_attn.py

import math

import torch
import torch.nn.functional as F

import pytest

from einops import rearrange, repeat

from flash_attn import flash_attn_func, flash_attn_kvpacked_func, flash_attn_qkvpacked_func
from flash_attn import flash_attn_varlen_qkvpacked_func, flash_attn_varlen_kvpacked_func
from flash_attn import flash_attn_varlen_func
from flash_attn.flash_attn_interface import _get_block_size
from flash_attn.bert_padding import unpad_input, pad_input, index_first_axis


MAX_HEADDIM_SM8x = 192


is_sm75 = torch.cuda.get_device_capability('cuda') == (7, 5)
is_sm8x = torch.cuda.get_device_capability('cuda')[0] == 8
is_sm80 = torch.cuda.get_device_capability('cuda') == (8, 0)
is_sm90 = torch.cuda.get_device_capability('cuda') == (9, 0)


def generate_random_padding_mask(max_seqlen, batch_size, device, mode='random'):
    assert mode in ['full', 'random', 'third']
    if mode == 'full':
        lengths = torch.full((batch_size, 1), max_seqlen, device=device, dtype=torch.int32)
    elif mode == 'random':
        lengths = torch.randint(max(1, max_seqlen - 20), max_seqlen, (batch_size, 1), device=device)
    elif mode == 'third':
        lengths = torch.randint(max_seqlen // 3, max_seqlen, (batch_size, 1), device=device)
    padding_mask = repeat(torch.arange(max_seqlen, device=device), 's -> b s', b=batch_size) < lengths
    return padding_mask


def generate_qkv(q, k, v, query_padding_mask=None, key_padding_mask=None,
                 kvpacked=False, qkvpacked=False):
    """
    Arguments:
        q: (batch_size, seqlen_q, nheads, d)
        k: (batch_size, seqlen_k, nheads_k, d)
        v: (batch_size, seqlen_k, nheads_k, d)
        query_padding_mask: (batch_size, seqlen), bool
        key_padding_mask: (batch_size, seqlen), bool
    """
    assert not (kvpacked and qkvpacked)
    batch_size, seqlen_q, nheads, d = q.shape
    _, seqlen_k, nheads_k, _ = k.shape
    assert k.shape == (batch_size, seqlen_k, nheads_k, d)
    assert v.shape == (batch_size, seqlen_k, nheads_k, d)

    if query_padding_mask is not None:
        q_unpad, indices_q, cu_seqlens_q, max_seqlen_q = unpad_input(q, query_padding_mask)
        output_pad_fn = lambda output_unpad: pad_input(output_unpad, indices_q, batch_size, seqlen_q)
    else:
        q_unpad = rearrange(q, 'b s h d -> (b s) h d')
        cu_seqlens_q = torch.arange(0, (batch_size + 1) * seqlen_q, step=seqlen_q, dtype=torch.int32,
                                    device=q_unpad.device)
        max_seqlen_q = seqlen_q
        output_pad_fn = lambda output_unpad: rearrange(output_unpad, '(b s) h d -> b s h d', b=batch_size)

    if key_padding_mask is not None:
        k_unpad, indices_k, cu_seqlens_k, max_seqlen_k = unpad_input(k, key_padding_mask)
        v_unpad, _, _, _ = unpad_input(v, key_padding_mask)
    else:
        k_unpad = rearrange(k, 'b s h d -> (b s) h d')
        v_unpad = rearrange(v, 'b s h d -> (b s) h d')
        cu_seqlens_k = torch.arange(0, (batch_size + 1) * seqlen_k, step=seqlen_k, dtype=torch.int32,
                                    device=k_unpad.device)
        max_seqlen_k = seqlen_k

    if qkvpacked:
        assert (query_padding_mask == key_padding_mask).all()
        assert nheads == nheads_k
        qkv_unpad = torch.stack([q_unpad, k_unpad, v_unpad], dim=1)
        qkv = torch.stack([q, k, v], dim=2)
        if query_padding_mask is not None:
            dqkv_pad_fn = lambda dqkv_unpad: pad_input(dqkv_unpad, indices_q, batch_size, seqlen_q)
        else:
            dqkv_pad_fn = lambda dqkv_unpad: rearrange(dqkv_unpad, '(b s) t h d -> b s t h d', b=batch_size)
        return (qkv_unpad.detach().requires_grad_(), cu_seqlens_q, max_seqlen_q,
                qkv.detach().requires_grad_(), output_pad_fn, dqkv_pad_fn)
    elif kvpacked:
        kv_unpad = torch.stack([k_unpad, v_unpad], dim=1)
        kv = torch.stack([k, v], dim=2)
        dq_pad_fn = output_pad_fn
        if key_padding_mask is not None:
            dkv_pad_fn = lambda dkv_unpad: pad_input(dkv_unpad, indices_k, batch_size, seqlen_k)
        else:
            dkv_pad_fn = lambda dkv_unpad: rearrange(dkv_unpad, '(b s) t h d -> b s t h d', b=batch_size)
        return (q_unpad.detach().requires_grad_(), kv_unpad.detach().requires_grad_(),
                cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k,
                q.detach().requires_grad_(), kv.detach().requires_grad_(),
                output_pad_fn, dq_pad_fn, dkv_pad_fn)
    else:
        dq_pad_fn = output_pad_fn
        if key_padding_mask is not None:
            dk_pad_fn = lambda dk_unpad: pad_input(dk_unpad, indices_k, batch_size, seqlen_k)
        else:
            dk_pad_fn = lambda dk_unpad: rearrange(dk_unpad, '(b s) h d -> b s h d', b=batch_size)
        return (q_unpad.detach().requires_grad_(), k_unpad.detach().requires_grad_(),
                v_unpad.detach().requires_grad_(),
                cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k,
                q.detach().requires_grad_(), k.detach().requires_grad_(),
                v.detach().requires_grad_(),
                output_pad_fn, dq_pad_fn, dk_pad_fn)


def attention_ref(q, k, v, query_padding_mask=None, key_padding_mask=None, dropout_p=0.0,
                  dropout_mask=None, causal=False, upcast=True, reorder_ops=False):
    """
    Arguments:
        q: (batch_size, seqlen_q, nheads, head_dim)
        k: (batch_size, seqlen_k, nheads_k, head_dim)
        v: (batch_size, seqlen_k, nheads_k, head_dim)
        query_padding_mask: (batch_size, seqlen_q)
        key_padding_mask: (batch_size, seqlen_k)
        dropout_p: float
        dropout_mask: (batch_size, nheads, seqlen_q, seqlen_k)
        upcast: whether to cast all inputs to fp32, do all computation in fp32, then cast
            output back to fp16/bf16.
        reorder_ops: whether to change the order of operations (scaling k instead of scaling k, etc.)
            without changing the math. This is to estimate the numerical error from operation
            reordering.
    Output:
        output: (batch_size, seqlen_q, nheads, head_dim)
        attention: (batch_size, nheads, seqlen_q, seqlen_k), softmax after dropout
    """
    dtype_og = q.dtype
    if upcast:
        q, k, v = q.float(), k.float(), v.float()
    seqlen_q, seqlen_k = q.shape[1], k.shape[1]
    k = repeat(k, "b s h d -> b s (h g) d", g=q.shape[2] // k.shape[2])
    v = repeat(v, "b s h d -> b s (h g) d", g=q.shape[2] // v.shape[2])
    d = q.shape[-1]
    if not reorder_ops:
        scores = torch.einsum('bthd,bshd->bhts', q / math.sqrt(d), k)
    else:
        scores = torch.einsum('bthd,bshd->bhts', q, k / math.sqrt(d))
    if key_padding_mask is not None:
        scores.masked_fill_(rearrange(~key_padding_mask, 'b s -> b 1 1 s'), float('-inf'))
    if causal:
        causal_mask = torch.triu(torch.ones(seqlen_q, seqlen_k, dtype=torch.bool, device=q.device), 1)
        scores.masked_fill_(causal_mask, float('-inf'))
    attention = torch.softmax(scores, dim=-1)
    dropout_scaling = 1.0 / (1 - dropout_p)
    # attention_drop = attention.masked_fill(~dropout_mask, 0.0) * dropout_scaling
    # output = torch.einsum('bhts,bshd->bthd', attention_drop , v)
    if dropout_mask is not None:
        attention_drop = attention.masked_fill(~dropout_mask, 0.0)
    else:
        attention_drop = attention
    output = torch.einsum('bhts,bshd->bthd', attention_drop, v * dropout_scaling)
    if query_padding_mask is not None:
        output.masked_fill_(rearrange(~query_padding_mask, 'b s -> b s 1 1'), 0.0)
        attention = attention.masked_fill(rearrange(~query_padding_mask, 'b s -> b 1 s 1'), 0.0)
    return output.to(dtype=dtype_og), attention.to(dtype=dtype_og)


def attention_kvpacked_ref(q, kv, query_padding_mask=None, key_padding_mask=None, dropout_p=0.0,
                           dropout_mask=None, causal=False, upcast=True, reorder_ops=False):
    return attention_ref(q, kv[:, :, 0], kv[:, :, 1], query_padding_mask,
                         key_padding_mask, dropout_p, dropout_mask, upcast=upcast, causal=causal,
                         reorder_ops=reorder_ops)


def attention_qkvpacked_ref(qkv, key_padding_mask=None, dropout_p=0.0,
                            dropout_mask=None, causal=False, upcast=True, reorder_ops=False):
    return attention_ref(qkv[:, :, 0], qkv[:, :, 1], qkv[:, :, 2], key_padding_mask,
                         key_padding_mask, dropout_p, dropout_mask, upcast=upcast, causal=causal,
                         reorder_ops=reorder_ops)


def generate_sparsity_mask(seqlen, sparsity=0.3):
    repeats = seqlen // 16 // 2
    # mask = torch.stack([torch.tensor([1, 0] * repeats, dtype=torch.bool, device='cuda'),
    #                     torch.tensor([0, 1] * repeats, dtype=torch.bool, device='cuda')], dim=-1)
    # mask = torch.stack([torch.tensor([1, 1] * repeats, dtype=torch.bool, device='cuda'),
    #                     torch.tensor([1, 1] * repeats, dtype=torch.bool, device='cuda')], dim=-1)
    # mask = torch.stack([torch.tensor([1, 1] * repeats, dtype=torch.bool, device='cuda')], dim=-1)
    # mask = torch.stack([torch.tensor([1, 0] * repeats, dtype=torch.bool, device='cuda')], dim=-1)
    nrow, ncol = seqlen // 16, seqlen // 256
    mask = torch.rand(nrow, ncol, device='cuda') < sparsity
    return mask


def attention_blocksparse_ref(qkv, blockmask, attn_mask, dropout_p, dropout_mask):
    """
    Arguments:
        qkv: (batch_size, seqlen, 3, nheads, head_dim)
        blockmask: (seqlen / 16, seqlen / 256)
        attn_mask: (batch_size, seqlen)
        dropout_p: float
        dropout_mask: (batch_size, nheads, seqlen, seqlen)
    Output:
        output: (batch_size, seqlen, nheads, head_dim)
        attention: softmax after dropout
    """
    q, k, v = qkv.float().unbind(dim=2)
    d = qkv.shape[-1]
    seqlen = qkv.shape[1]
    scores = torch.einsum('bthd,bshd->bhts', q / math.sqrt(d), k)
    scores.masked_fill_(rearrange(~attn_mask, 'b s -> b 1 1 s'), float('-inf'))
    blockmask = repeat(blockmask, 's_16 s_256 -> (s_16 16) (s_256 256)')
    blockmask = blockmask[:seqlen, :seqlen]
    scores.masked_fill_(rearrange(~blockmask, 't s -> 1 1 t s'), float('-inf'))
    attention = torch.softmax(scores, dim=-1)
    attention = attention.masked_fill(rearrange(~attn_mask, 'b s -> b 1 s 1'), 0.0)
    attention = attention.masked_fill_(rearrange(~blockmask, 't s -> 1 1 t s'), 0.0)
    attention_drop = attention.masked_fill(~dropout_mask, 0.0) / (1 - dropout_p)
    output = torch.einsum('bhts,bshd->bthd', attention_drop , v)
    output.masked_fill_(rearrange(~attn_mask, 'b s -> b s 1 1'), 0)
    return output.to(dtype=qkv.dtype), attention.to(dtype=qkv.dtype)


def convert_flash_attn_S_to_softmax(S, query_padding_mask, key_padding_mask, head_dim, is_dropout,
                                    causal=False):
    """FlashAttention stores the S matrix in a different way.
    Arguments:
        S: (batch_size, nheads, seqlen_q_rounded, seqlen_k_rounded)
        query_padding_mask: (batch_size, seqlen_q)
        key_padding_mask: (batch_size, seqlen_k)
    """
    seqlen_q, seqlen_k = S.shape[-2:]
    warps_n = 4
    blocksize_m, blocksize_n = _get_block_size(S.device, head_dim, is_dropout, causal)
    nblocks_n = (seqlen_k + blocksize_n - 1) // blocksize_n
    nblocks_m = (seqlen_q + blocksize_m - 1) // blocksize_m
    mmas_n = (blocksize_n + 16 - 1) // 16
    S_flat = rearrange(S, 'b h (nblocks_m blocksize_m) (nblocks_n blocksize_n) -> b h nblocks_m nblocks_n (blocksize_m blocksize_n)',
                       blocksize_m=blocksize_m, blocksize_n=blocksize_n)
    S_converted = rearrange(S_flat, 'b h nblocks_m nblocks_n (mmas_n mmas_m warps_n eight four c2 c1 c0) -> b h (nblocks_m mmas_m warps_n c1 eight) (nblocks_n mmas_n c2 four c0)',
                            mmas_n=mmas_n, warps_n=warps_n, eight=8, c0=2, c1=2, c2=2, four=4)
    if causal:
        causal_mask = torch.triu(torch.ones(seqlen_q, seqlen_k, dtype=torch.bool, device=S.device), 1)
        S_converted.masked_fill_(causal_mask, 0.0)

    # Need to zero out things not in attention_mask in case S was initialized with random values
    # and some of those values aren't overwritten.
    seqlen_q_og = query_padding_mask.shape[-1] if query_padding_mask is not None else seqlen_q
    if query_padding_mask is not None:
        if seqlen_q_og < seqlen_q:
            query_padding_mask = F.pad(query_padding_mask, (0, seqlen_q - seqlen_q_og))
        else:
            query_padding_mask = query_padding_mask[:, :seqlen_q]
        S_converted = S_converted.masked_fill(rearrange(~query_padding_mask, 'b s -> b 1 s 1'), 0.0)
    seqlen_k_og = key_padding_mask.shape[-1] if key_padding_mask is not None else seqlen_k
    if key_padding_mask is not None:
        if seqlen_k_og < seqlen_k:
            key_padding_mask = F.pad(key_padding_mask, (0, seqlen_k - seqlen_k_og))
        else:
            key_padding_mask = key_padding_mask[:, :seqlen_k]
        S_converted = S_converted.masked_fill(rearrange(~key_padding_mask, 'b s -> b 1 1 s'), 0.0)
    if seqlen_q_og < seqlen_q:
        S_converted = S_converted[:, :, :seqlen_q_og, :]
    else:
        S_converted = F.pad(S_converted, (0, 0, 0, seqlen_q_og - seqlen_q))
    if seqlen_k_og < seqlen_k:
        S_converted = S_converted[:, :, :, :seqlen_k_og]
    else:
        S_converted = F.pad(S_converted, (0, seqlen_k_og - seqlen_k))
    return S_converted


def normalize_flash_attn_S(attn_unnorm, q, k, v, query_padding_mask=None, key_padding_mask=None,
                           is_dropout=False, causal=False):
    """
    Arguments:
        q: (batch_size, seqlen_q, nheads, head_dim)
        k, v: (batch_size, seqlen_k, nheads, head_dim)
        key_padding_mask: (batch_size, seqlen_q)
    Output:
        softmax_lse: (batch_size, nheads, seqlen_q)
        softmax_max: (batch_size, nheads, seqlen_q)
    """
    q, k, v = q.float(), k.float(), v.float()
    _, seqlen_q, _, head_dim = q.shape
    seqlen_k = k.shape[1]
    scores = torch.einsum('bthd,bshd->bhts', q / math.sqrt(head_dim), k)
    if key_padding_mask is not None:
        scores.masked_fill_(rearrange(~key_padding_mask, 'b s -> b 1 1 s'), float('-inf'))
    if causal:
        causal_mask = torch.triu(torch.ones(seqlen_q, seqlen_k, dtype=torch.bool, device=q.device), 1)
        scores.masked_fill_(causal_mask, float('-inf'))
    _, block_size_n = _get_block_size(scores.device, head_dim, is_dropout, causal)
    scores_block = scores.split(block_size_n, dim=-1)
    lse_block = torch.stack([torch.logsumexp(s, dim=-1) for s in scores_block], dim=-1)
    lse = torch.logsumexp(lse_block, dim=-1)
    scores_max_block = torch.stack([torch.amax(s, dim=-1) for s in scores_block], dim=-1)
    cummax_block = torch.cummax(scores_max_block.flip(-1), dim=-1).values.flip(-1).unbind(dim=-1)
    attn_unnorm_block = attn_unnorm.split(block_size_n, dim=-1)
    attn_norm = torch.cat([a / rearrange(torch.exp(lse - m), 'b h s -> b h s 1')
                           for a, m in zip(attn_unnorm_block, cummax_block)], dim=-1)
    if query_padding_mask is not None:
        attn_norm.masked_fill_(rearrange(~query_padding_mask, 'b s -> b 1 s 1'), 0.0)
    return attn_norm.to(dtype=attn_unnorm.dtype)


def get_dropout_fraction(dropout_mask, query_padding_mask=None, key_padding_mask=None, causal=False):
    """
    dropout_mask: (batch_size, nheads, seqlen_q, seqlen_k), bool. True means keep, False means drop.
    query_padding_mask: (batch_size, seqlen_q)
    key_padding_mask: (batch_size, seqlen_k)
    """
    batch_size, nheads, seqlen_q, seqlen_k = dropout_mask.shape
    dropped = ~dropout_mask
    if query_padding_mask is not None:
        dropped.masked_fill_(rearrange(~query_padding_mask, 'b s -> b 1 s 1'), False)
    if key_padding_mask is not None:
        dropped.masked_fill_(rearrange(~key_padding_mask, 'b s -> b 1 1 s'), False)
    if causal:
        causal_mask = torch.triu(torch.ones(seqlen_q, seqlen_k, dtype=torch.bool,
                                            device=dropout_mask.device), 1)
        dropped.masked_fill_(causal_mask, False)
    dropped_total = dropped.sum()
    query_lengths = (query_padding_mask.sum(dim=-1) if query_padding_mask is not None
                     else torch.full((batch_size,), seqlen_q, device=dropout_mask.device))
    key_lengths = (key_padding_mask.sum(dim=-1) if key_padding_mask is not None
                   else torch.full((batch_size,), seqlen_k, device=dropout_mask.device))
    if not causal:
        numel_per_batch = query_lengths * key_lengths
    else:
        numel_per_batch = torch.where(
            query_lengths <= key_lengths,
            query_lengths * (query_lengths + 1) / 2,
            query_lengths * key_lengths - (key_lengths * (key_lengths - 1) / 2)
        )
    return dropped_total / (numel_per_batch.sum() * nheads)


@pytest.mark.parametrize('dtype', ([torch.float16] if is_sm75 else [torch.float16, torch.bfloat16]))
# @pytest.mark.parametrize('dtype', [torch.float16])
@pytest.mark.parametrize('causal', [False, True])
# @pytest.mark.parametrize('causal', [True])
@pytest.mark.parametrize('d', [32, 40, 59, 64, 80, 96, 111, 128, 160, 192, 224, 256])
# @pytest.mark.parametrize('d', [32, 64, 96, 128, 160, 192, 224, 256])
# @pytest.mark.parametrize('d', [32, 64, 96, 128])
# @pytest.mark.parametrize('d', [64])
# @pytest.mark.parametrize('seqlen', [128, 256, 384, 512, 768, 1024, 2048])
@pytest.mark.parametrize('seqlen', [97, 128, 200, 256, 257, 384, 512, 768, 1024, 1025, 2048])
# @pytest.mark.parametrize('seqlen', [97])
@pytest.mark.parametrize('dropout_p', [0.0, 0.17])
# @pytest.mark.parametrize('dropout_p', [0.17])
def test_flash_attn_qkvpacked(seqlen, d, dropout_p, causal, dtype):
    if seqlen >= 2048 and torch.cuda.get_device_properties('cuda').total_memory <= 16 * 2**30:
        pytest.skip()  # Reference implementation OOM
    device = 'cuda'
    # set seed
    torch.random.manual_seed(0)
    batch_size = 16
    nheads = 9
    qkv = torch.randn(batch_size, seqlen, 3, nheads, d, device=device, dtype=dtype,
                      requires_grad=True)
    out, lse, S_dmask = flash_attn_qkvpacked_func(
        qkv, dropout_p, return_attn_probs=True, causal=causal
    )
    if dropout_p > 0.0:
        S_dmask_converted = convert_flash_attn_S_to_softmax(
            S_dmask, None, None, d, dropout_p > 0.0, causal=causal
        )[:, :, :seqlen, :seqlen]
        dropout_mask = S_dmask_converted >= 0
        attn_unnorm = S_dmask_converted.abs()
        attn = normalize_flash_attn_S(attn_unnorm, qkv[:, :, 0], qkv[:, :, 1], qkv[:, :, 2],
                                      None, None, dropout_p > 0.0, causal=causal)
        dropout_fraction = get_dropout_fraction(dropout_mask, None, None, causal=causal).item()
        print(f'Actual dropout fraction: {dropout_fraction}')
    else:
        dropout_mask = None

    out_ref, attn_ref = attention_qkvpacked_ref(qkv, None, dropout_p, dropout_mask, causal=causal)
    out_pt, attn_pt = attention_qkvpacked_ref(qkv, None, dropout_p, dropout_mask, causal=causal,
                                              upcast=False, reorder_ops=True)
    # v = qkv[:, :, 2].float()
    # qk = torch.einsum('bshd,bthd->bhst', qkv[:, :, 0], qkv[:, :, 1]).float()
    # if causal:
    #     causal_mask = torch.triu(torch.ones(seqlen, seqlen, dtype=torch.bool, device=qkv.device), 1)
    #     qk.masked_fill_(causal_mask, float('-inf'))
    # m = qk.amax(-1, keepdim=True)
    # s_tmp = torch.exp((qk - m) / math.sqrt(d))
    # p_tmp = torch.softmax(qk / math.sqrt(d), -1)
    # p_dropped = p_tmp if dropout_mask is None else p_tmp.masked_fill(~dropout_mask, 0)
    # lse_ref = torch.logsumexp(qk / math.sqrt(d), -1)
    # qk_max1 = torch.max(qk[:, :, 128:, 192:], -1, keepdim=True).values
    # qk_max2 = torch.max(qk[:, :, 128:, 128:], -1, keepdim=True).values
    # qk_max3 = torch.max(qk[:, :, 128:, 64:], -1, keepdim=True).values
    # qk_max4 = torch.max(qk[:, :, 128:, :], -1, keepdim=True).values
    # o1 = torch.einsum('bhst,bthd->bshd', torch.exp((qk[:, :, 128:, 192:] - qk_max1) / math.sqrt(d)), v[:, 192:])
    # o2 = torch.einsum('bhst,bthd->bshd', torch.exp((qk[:, :, 128:, 128:] - qk_max2) / math.sqrt(d)), v[:, 128:])
    # o3 = torch.einsum('bhst,bthd->bshd', torch.exp((qk[:, :, 128:, 64:] - qk_max3) / math.sqrt(d)), v[:, 64:])
    # o4 = torch.einsum('bhst,bthd->bshd', torch.exp((qk[:, :, 128:, :] - qk_max4) / math.sqrt(d)), v[:, :])
    print(f'Output max diff: {(out - out_ref).abs().max().item()}')
    print(f'Output mean diff: {(out - out_ref).abs().mean().item()}')
    print(f'Pytorch max diff: {(out_pt - out_ref).abs().max().item()}')
    print(f'Pytorch mean diff: {(out_pt - out_ref).abs().mean().item()}')
    if dropout_p > 0.0:
        print(f'Attention max diff: {(attn - attn_ref).abs().max().item()}')
        print(f'Attention Pytorch max diff: {(attn_pt - attn_ref).abs().max().item()}')

    g = torch.randn_like(out)
    # do_o = (g.float() * out.float()).sum(-1)
    # dv_tmp = torch.einsum('bhts,bthd->bshd', attn_pt[:, :, :64], g[:, :64])
    # dv_tmp1 = torch.einsum('bhts,bthd->bshd', attn_pt[:, :, 64:], g[:, 64:])
    if d <= MAX_HEADDIM_SM8x or (is_sm80 or is_sm90):
        dqkv, = torch.autograd.grad(out, qkv, g)
        dqkv_ref, = torch.autograd.grad(out_ref, qkv, g)
        dqkv_pt, = torch.autograd.grad(out_pt, qkv, g)
        print(f'dQ max diff: {(dqkv[:, :, 0] - dqkv_ref[:, :, 0]).abs().max().item()}')
        print(f'dK max diff: {(dqkv[:, :, 1] - dqkv_ref[:, :, 1]).abs().max().item()}')
        print(f'dV max diff: {(dqkv[:, :, 2] - dqkv_ref[:, :, 2]).abs().max().item()}')
        print(f'dQKV mean diff: {(dqkv - dqkv_ref).abs().mean().item()}')
        print(f'dQ Pytorch max diff: {(dqkv_pt[:, :, 0] - dqkv_ref[:, :, 0]).abs().max().item()}')
        print(f'dK Pytorch max diff: {(dqkv_pt[:, :, 1] - dqkv_ref[:, :, 1]).abs().max().item()}')
        print(f'dV Pytorch max diff: {(dqkv_pt[:, :, 2] - dqkv_ref[:, :, 2]).abs().max().item()}')
        print(f'dQKV Pytorch mean diff: {(dqkv_pt - dqkv_ref).abs().mean().item()}')

    # Check that FlashAttention's numerical error is at most twice the numerical error
    # of a Pytorch implementation.
    assert (out - out_ref).abs().max().item() <= 2 * (out_pt - out_ref).abs().max().item()

    if dropout_p > 0.0:
        assert (attn - attn_ref).abs().max().item() <= 2 * (attn_pt - attn_ref).abs().max().item()
        assert abs(dropout_fraction - dropout_p) <= 0.01

    if d <= MAX_HEADDIM_SM8x or (is_sm80 or is_sm90):
        assert (dqkv - dqkv_ref).abs().max().item() <= 2 * (dqkv_pt - dqkv_ref).abs().max().item()



@pytest.mark.parametrize('dtype', ([torch.float16] if is_sm75 else [torch.float16, torch.bfloat16]))
# @pytest.mark.parametrize('dtype', [torch.float16])
@pytest.mark.parametrize('causal', [False, True])
# @pytest.mark.parametrize('causal', [False])
@pytest.mark.parametrize('d', [32, 40, 59, 64, 80, 96, 111, 128, 160, 192, 224, 256])
# @pytest.mark.parametrize('d', [64])
@pytest.mark.parametrize('seqlen', [97, 128, 200, 256, 257, 384, 512, 768, 1024, 1025, 2048])
# @pytest.mark.parametrize('seqlen', [128])
@pytest.mark.parametrize('dropout_p', [0.0, 0.17])
# @pytest.mark.parametrize('dropout_p', [0.0])
def test_flash_attn_varlen_qkvpacked(seqlen, d, dropout_p, causal, dtype):
    if seqlen >= 2048 and torch.cuda.get_device_properties('cuda').total_memory <= 16 * 2**30:
        pytest.skip()  # Reference implementation OOM
    device = 'cuda'
    # set seed
    torch.random.manual_seed(0)
    batch_size = 5
    nheads = 6
    qkv = torch.randn(batch_size, seqlen, 3, nheads, d, device=device, dtype=dtype,
                      requires_grad=True)

    key_padding_mask = generate_random_padding_mask(seqlen, batch_size, device, mode='random')
    # key_padding_mask = generate_random_padding_mask(seqlen, batch_size, device, mode='full')

    qkv_unpad, cu_seqlens, max_seqlen, qkv, output_pad_fn, dqkv_pad_fn = generate_qkv(
        *qkv.unbind(dim=2), key_padding_mask, key_padding_mask, qkvpacked=True
    )

    out_unpad, sm_lse, S_dmask = flash_attn_varlen_qkvpacked_func(
        qkv_unpad, cu_seqlens, max_seqlen, dropout_p, return_attn_probs=True, causal=causal
    )
    out = output_pad_fn(out_unpad)
    if dropout_p > 0.0:
        S_dmask_converted = convert_flash_attn_S_to_softmax(
            S_dmask, key_padding_mask, key_padding_mask, d, dropout_p > 0.0, causal=causal
        )[:, :, :seqlen, :seqlen]
        dropout_mask = S_dmask_converted >= 0
        attn_unnorm = S_dmask_converted.abs()
        attn = normalize_flash_attn_S(attn_unnorm, qkv[:, :, 0], qkv[:, :, 1], qkv[:, :, 2],
                                      key_padding_mask, key_padding_mask, dropout_p > 0.0,
                                      causal=causal)
        dropout_fraction = get_dropout_fraction(dropout_mask, key_padding_mask, key_padding_mask,
                                                causal=causal).item()
        print(f'Actual dropout fraction: {dropout_fraction}')
    else:
        dropout_mask = None

    out_ref, attn_ref = attention_qkvpacked_ref(qkv, key_padding_mask, dropout_p, dropout_mask,
                                                causal=causal)
    out_pt, attn_pt = attention_qkvpacked_ref(qkv, key_padding_mask, dropout_p, dropout_mask,
                                              causal=causal, upcast=False, reorder_ops=True)
    print(f'Output max diff: {(out - out_ref).abs().max().item()}')
    print(f'Output mean diff: {(out - out_ref).abs().mean().item()}')
    print(f'Pytorch max diff: {(out_pt - out_ref).abs().max().item()}')
    print(f'Pytorch mean diff: {(out_pt - out_ref).abs().mean().item()}')
    if dropout_p > 0.0:
        print(f'Attention max diff: {(attn - attn_ref).abs().max().item()}')
        print(f'Attention Pytorch max diff: {(attn_pt - attn_ref).abs().max().item()}')

    g = torch.randn_like(out)
    if d <= MAX_HEADDIM_SM8x or (is_sm80 or is_sm90):
        dqkv_unpad, = torch.autograd.grad(out, qkv_unpad, g)
        dqkv = dqkv_pad_fn(dqkv_unpad)
        dqkv_ref, = torch.autograd.grad(out_ref, qkv, g)
        dqkv_pt, = torch.autograd.grad(out_pt, qkv, g)
        print(f'dQ max diff: {(dqkv[:, :, 0] - dqkv_ref[:, :, 0]).abs().max().item()}')
        print(f'dK max diff: {(dqkv[:, :, 1] - dqkv_ref[:, :, 1]).abs().max().item()}')
        print(f'dV max diff: {(dqkv[:, :, 2] - dqkv_ref[:, :, 2]).abs().max().item()}')
        print(f'dQKV mean diff: {(dqkv - dqkv_ref).abs().mean().item()}')
        print(f'dQ Pytorch max diff: {(dqkv_pt[:, :, 0] - dqkv_ref[:, :, 0]).abs().max().item()}')
        print(f'dK Pytorch max diff: {(dqkv_pt[:, :, 1] - dqkv_ref[:, :, 1]).abs().max().item()}')
        print(f'dV Pytorch max diff: {(dqkv_pt[:, :, 2] - dqkv_ref[:, :, 2]).abs().max().item()}')
        print(f'dQKV Pytorch mean diff: {(dqkv_pt - dqkv_ref).abs().mean().item()}')

    # Check that FlashAttention's numerical error is at most twice the numerical error
    # of a Pytorch implementation.
    assert (out - out_ref).abs().max().item() <= 2 * (out_pt - out_ref).abs().max().item()

    if dropout_p > 0.0:
        assert (attn - attn_ref).abs().max().item() <= 2 * (attn_pt - attn_ref).abs().max().item()
        assert abs(dropout_fraction - dropout_p) <= 0.01

    if d <= MAX_HEADDIM_SM8x or (is_sm80 or is_sm90):
        assert (dqkv - dqkv_ref).abs().max().item() <= 2 * (dqkv_pt - dqkv_ref).abs().max().item()


@pytest.mark.parametrize('kvpacked', [True, False])
# @pytest.mark.parametrize('kvpacked', [False])
@pytest.mark.parametrize('dtype', ([torch.float16] if is_sm75 else [torch.float16, torch.bfloat16]))
# @pytest.mark.parametrize('dtype', [torch.bfloat16])
@pytest.mark.parametrize('mha_type', ["mha", "mqa", "gqa"])
# @pytest.mark.parametrize('mha_type', ["mha"])
@pytest.mark.parametrize('causal', [False, True])
# @pytest.mark.parametrize('causal', [False])
@pytest.mark.parametrize('d', [32, 40, 59, 64, 80, 96, 111, 128, 160, 192, 224, 256])
# @pytest.mark.parametrize('d', [32, 64, 96, 128, 160, 192, 224, 256])
# @pytest.mark.parametrize('d', [32, 40, 64, 80, 96, 128, 160, 192])
# @pytest.mark.parametrize('d', [32, 64, 96, 128, 160, 192])
# @pytest.mark.parametrize('d', [56, 80])
# @pytest.mark.parametrize('d', [64])
@pytest.mark.parametrize('seqlen_q,seqlen_k', [(113, 203), (128, 217), (113, 211), (108, 256), (256, 512), (512, 256), (1024, 1024), (1023, 1024), (1024, 1023), (2048, 2048)])
# @pytest.mark.parametrize('seqlen_q,seqlen_k', [(128, 128)])
@pytest.mark.parametrize('dropout_p', [0.0, 0.17])
# @pytest.mark.parametrize('dropout_p', [0.0])
def test_flash_attn_output(seqlen_q, seqlen_k, d, dropout_p, causal, mha_type, dtype, kvpacked):
    if max(seqlen_q, seqlen_k) >= 2048 and torch.cuda.get_device_properties('cuda').total_memory <= 16 * 2**30:
        pytest.skip()  # Reference implementation OOM
    device = 'cuda'
    # set seed
    torch.random.manual_seed(0)
    batch_size = 16
    nheads = 9
    nheads_k = nheads if mha_type == "mha" else (1 if mha_type == "mqa" else 3)
    assert nheads % nheads_k == 0
    q = torch.randn(batch_size, seqlen_q, nheads, d, device=device, dtype=dtype, requires_grad=True)
    if kvpacked:
        kv = torch.randn(batch_size, seqlen_k, 2, nheads_k, d, device=device, dtype=dtype,
                         requires_grad=True)
    else:
        k = torch.randn(batch_size, seqlen_k, nheads_k, d, device=device, dtype=dtype,
                        requires_grad=True)
        v = torch.randn(batch_size, seqlen_k, nheads_k, d, device=device, dtype=dtype,
                        requires_grad=True)

    if kvpacked:
        out, lse, S_dmask = flash_attn_kvpacked_func(
            q, kv, dropout_p, return_attn_probs=True, causal=causal
        )
    else:
        out, lse, S_dmask = flash_attn_func(
            q, k, v, dropout_p, return_attn_probs=True, causal=causal
        )
    if dropout_p > 0.0:
        S_dmask_converted = convert_flash_attn_S_to_softmax(
            S_dmask, None, None, d, dropout_p > 0.0, causal=causal
        )[:, :, :seqlen_q, :seqlen_k]
        dropout_mask = S_dmask_converted >= 0
        attn_unnorm = S_dmask_converted.abs()
        if kvpacked:
            kv_rep = repeat(kv, "b s two h d -> b s two (h g) d", g=nheads // nheads_k)
            k_rep, v_rep = kv_rep.unbind(dim=2)
        else:
            k_rep = repeat(k, "b s h d -> b s (h g) d", g=nheads // nheads_k)
            v_rep = repeat(v, "b s h d -> b s (h g) d", g=nheads // nheads_k)
        attn = normalize_flash_attn_S(attn_unnorm, q, k_rep, v_rep,
                                      None, None, dropout_p > 0.0, causal=causal)
        dropout_fraction = get_dropout_fraction(dropout_mask, None, None, causal=causal).item()
        print(f'Actual dropout fraction: {dropout_fraction}')
    else:
        dropout_mask = None

    if kvpacked:
        out_ref, attn_ref = attention_kvpacked_ref(q, kv, None, None, dropout_p, dropout_mask,
                                                causal=causal)
        out_pt, attn_pt = attention_kvpacked_ref(q, kv, None, None, dropout_p, dropout_mask,
                                                causal=causal, upcast=False, reorder_ops=True)
    else:
        out_ref, attn_ref = attention_ref(q, k, v, None, None, dropout_p, dropout_mask,
                                          causal=causal)
        out_pt, attn_pt = attention_ref(q, k, v, None, None, dropout_p, dropout_mask,
                                        causal=causal, upcast=False, reorder_ops=True)

    print(f'Output max diff: {(out - out_ref).abs().max().item()}')
    print(f'Output mean diff: {(out - out_ref).abs().mean().item()}')
    print(f'Pytorch max diff: {(out_pt - out_ref).abs().max().item()}')
    print(f'Pytorch mean diff: {(out_pt - out_ref).abs().mean().item()}')
    if dropout_p > 0.0:
        print(f'Attention max diff: {(attn - attn_ref).abs().max().item()}')
        print(f'Attention Pytorch max diff: {(attn_pt - attn_ref).abs().max().item()}')

    g = torch.randn_like(out)
    do_o = (g.float() * out.float()).sum(-1)
    if d <= MAX_HEADDIM_SM8x or (is_sm80 or is_sm90):
        if kvpacked:
            dq, dkv, = torch.autograd.grad(out, (q, kv), g)
            dk, dv = dkv.unbind(2)
            dq_ref, dkv_ref, = torch.autograd.grad(out_ref, (q, kv), g)
            dk_ref, dv_ref = dkv_ref.unbind(2)
            dq_pt, dkv_pt, = torch.autograd.grad(out_pt, (q, kv), g)
            dk_pt, dv_pt = dkv_pt.unbind(2)
        else:
            dq, dk, dv, = torch.autograd.grad(out, (q, k, v), g)
            dq_ref, dk_ref, dv_ref, = torch.autograd.grad(out_ref, (q, k, v), g)
            dq_pt, dk_pt, dv_pt, = torch.autograd.grad(out_pt, (q, k, v), g)
        print(f'dQ max diff: {(dq - dq_ref).abs().max().item()}')
        print(f'dK max diff: {(dk - dk_ref).abs().max().item()}')
        print(f'dV max diff: {(dv - dv_ref).abs().max().item()}')
        print(f'dQ mean diff: {(dq - dq_ref).abs().mean().item()}')
        print(f'dK mean diff: {(dk - dk_ref).abs().mean().item()}')
        print(f'dV mean diff: {(dv - dv_ref).abs().mean().item()}')
        print(f'dQ Pytorch max diff: {(dq_pt - dq_ref).abs().max().item()}')
        print(f'dK Pytorch max diff: {(dk_pt - dk_ref).abs().max().item()}')
        print(f'dV Pytorch max diff: {(dv_pt - dv_ref).abs().max().item()}')
        print(f'dQ Pytorch mean diff: {(dq_pt - dq_ref).abs().mean().item()}')
        print(f'dK Pytorch mean diff: {(dk_pt - dk_ref).abs().mean().item()}')
        print(f'dV Pytorch mean diff: {(dv_pt - dv_ref).abs().mean().item()}')

    # Check that FlashAttention's numerical error is at most twice the numerical error
    # of a Pytorch implementation.
    assert (out - out_ref).abs().max().item() <= 2 * (out_pt - out_ref).abs().max().item()

    if dropout_p > 0.0:
        assert (attn - attn_ref).abs().max().item() <= 2 * (attn_pt - attn_ref).abs().max().item()
        assert abs(dropout_fraction - dropout_p) <= 0.01

    if d <= MAX_HEADDIM_SM8x or (is_sm80 or is_sm90):
        assert (dq - dq_ref).abs().max().item() <= 2 * (dq_pt - dq_ref).abs().max().item()
        assert (dk - dk_ref).abs().max().item() <= 2 * (dk_pt - dk_ref).abs().max().item()
        assert (dv - dv_ref).abs().max().item() <= 2 * (dv_pt - dv_ref).abs().max().item()


@pytest.mark.parametrize('kvpacked', [True, False])
# @pytest.mark.parametrize('kvpacked', [False])
@pytest.mark.parametrize('dtype', ([torch.float16] if is_sm75 else [torch.float16, torch.bfloat16]))
# @pytest.mark.parametrize('dtype', [torch.float16])
@pytest.mark.parametrize('mha_type', ["mha", "mqa", "gqa"])
# @pytest.mark.parametrize('mha_type', ["mqa"])
@pytest.mark.parametrize('causal', [False, True])
# @pytest.mark.parametrize('causal', [True])
@pytest.mark.parametrize('d', [32, 40, 59, 64, 80, 96, 111, 128, 160, 192, 224, 256])
# @pytest.mark.parametrize('d', [32, 64, 96, 128, 160, 192, 224, 256])
# @pytest.mark.parametrize('d', [64])
@pytest.mark.parametrize('seqlen_q,seqlen_k', [(113, 203), (128, 217), (113, 211), (108, 256), (256, 512), (512, 256), (1024, 1024), (1023, 1024), (1024, 1023), (2048, 2048)])
# @pytest.mark.parametrize('seqlen_q,seqlen_k', [(128, 128)])
@pytest.mark.parametrize('dropout_p', [0.0, 0.17])
# @pytest.mark.parametrize('dropout_p', [0.0])
def test_flash_attn_varlen_output(seqlen_q, seqlen_k, d, dropout_p, causal, mha_type, dtype,
                                  kvpacked):
    if max(seqlen_q, seqlen_k) >= 2048 and torch.cuda.get_device_properties('cuda').total_memory <= 16 * 2**30:
        pytest.skip()  # Reference implementation OOM
    device = 'cuda'
    # set seed
    torch.random.manual_seed(0)
    batch_size = 16
    nheads = 9
    nheads_k = nheads if mha_type == "mha" else (1 if mha_type == "mqa" else 3)
    assert nheads % nheads_k == 0
    q = torch.randn(batch_size, seqlen_q, nheads, d, device=device, dtype=dtype, requires_grad=True)
    if kvpacked:
        kv = torch.randn(batch_size, seqlen_k, 2, nheads_k, d, device=device, dtype=dtype,
                         requires_grad=True)
    else:
        k = torch.randn(batch_size, seqlen_k, nheads_k, d, device=device, dtype=dtype,
                        requires_grad=True)
        v = torch.randn(batch_size, seqlen_k, nheads_k, d, device=device, dtype=dtype,
                        requires_grad=True)

    query_padding_mask = generate_random_padding_mask(seqlen_q, batch_size, device, mode='random')
    key_padding_mask = generate_random_padding_mask(seqlen_k, batch_size, device, mode='random')
    # key_padding_mask = generate_random_padding_mask(seqlen_k, batch_size, device, mode='full')

    if kvpacked:
        (q_unpad, kv_unpad, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k, q, kv,
        output_pad_fn, dq_pad_fn, dkv_pad_fn) = generate_qkv(
            q, *kv.unbind(dim=2), query_padding_mask, key_padding_mask, kvpacked=True
        )
        out_unpad, sm_lse, S_dmask = flash_attn_varlen_kvpacked_func(
            q_unpad, kv_unpad, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k,
            dropout_p, return_attn_probs=True, causal=causal
        )
    else:
        (q_unpad, k_unpad, v_unpad, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k, q, k, v,
        output_pad_fn, dq_pad_fn, dk_pad_fn) = generate_qkv(
            q, k, v, query_padding_mask, key_padding_mask, kvpacked=False
        )
        out_unpad, sm_lse, S_dmask = flash_attn_varlen_func(
            q_unpad, k_unpad, v_unpad, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k,
            dropout_p, return_attn_probs=True, causal=causal
        )
    out = output_pad_fn(out_unpad)
    if dropout_p > 0.0:
        S_dmask_converted = convert_flash_attn_S_to_softmax(
            S_dmask, query_padding_mask, key_padding_mask, d, dropout_p > 0.0, causal=causal
        )[:, :, :seqlen_q, :seqlen_k]
        dropout_mask = S_dmask_converted >= 0
        attn_unnorm = S_dmask_converted.abs()
        if kvpacked:
            kv_rep = repeat(kv, "b s two h d -> b s two (h g) d", g=nheads // nheads_k)
            k_rep, v_rep = kv_rep.unbind(dim=2)
        else:
            k_rep = repeat(k, "b s h d -> b s (h g) d", g=nheads // nheads_k)
            v_rep = repeat(v, "b s h d -> b s (h g) d", g=nheads // nheads_k)
        attn = normalize_flash_attn_S(attn_unnorm, q, k_rep, v_rep,
                                      query_padding_mask, key_padding_mask,
                                      dropout_p > 0.0, causal=causal)
        dropout_fraction = get_dropout_fraction(dropout_mask, query_padding_mask,
                                                key_padding_mask, causal=causal).item()
        print(f'Actual dropout fraction: {dropout_fraction}')
    else:
        dropout_mask = None

    if kvpacked:
        out_ref, attn_ref = attention_kvpacked_ref(q, kv, query_padding_mask, key_padding_mask,
                                                dropout_p, dropout_mask, causal=causal)
        out_pt, attn_pt = attention_kvpacked_ref(q, kv, query_padding_mask, key_padding_mask,
                                                dropout_p, dropout_mask,
                                                causal=causal, upcast=False, reorder_ops=True)
    else:
        out_ref, attn_ref = attention_ref(q, k, v, query_padding_mask, key_padding_mask,
                                          dropout_p, dropout_mask, causal=causal)
        out_pt, attn_pt = attention_ref(q, k, v, query_padding_mask, key_padding_mask,
                                        dropout_p, dropout_mask,
                                        causal=causal, upcast=False, reorder_ops=True)

    print(f'Output max diff: {(out - out_ref).abs().max().item()}')
    print(f'Output mean diff: {(out - out_ref).abs().mean().item()}')
    print(f'Pytorch max diff: {(out_pt - out_ref).abs().max().item()}')
    print(f'Pytorch mean diff: {(out_pt - out_ref).abs().mean().item()}')
    if dropout_p > 0.0:
        print(f'Attention max diff: {(attn - attn_ref).abs().max().item()}')
        print(f'Attention Pytorch max diff: {(attn_pt - attn_ref).abs().max().item()}')

    g = torch.randn_like(out)
    if d <= MAX_HEADDIM_SM8x or (is_sm80 or is_sm90):
        if kvpacked:
            dq_unpad, dkv_unpad, = torch.autograd.grad(out, (q_unpad, kv_unpad), g)
            dk, dv = dkv_pad_fn(dkv_unpad).unbind(2)
            dq_ref, dkv_ref, = torch.autograd.grad(out_ref, (q, kv), g)
            dk_ref, dv_ref = dkv_ref.unbind(2)
            dq_pt, dkv_pt, = torch.autograd.grad(out_pt, (q, kv), g)
            dk_pt, dv_pt = dkv_pt.unbind(2)
        else:
            dq_unpad, dk_unpad, dv_unpad, = torch.autograd.grad(out, (q_unpad, k_unpad, v_unpad), g)
            dk = dk_pad_fn(dk_unpad)
            dv = dk_pad_fn(dv_unpad)
            dq_ref, dk_ref, dv_ref, = torch.autograd.grad(out_ref, (q, k, v), g)
            dq_pt, dk_pt, dv_pt, = torch.autograd.grad(out_pt, (q, k, v), g)
        dq = dq_pad_fn(dq_unpad)
        print(f'dQ max diff: {(dq - dq_ref).abs().max().item()}')
        print(f'dK max diff: {(dk - dk_ref).abs().max().item()}')
        print(f'dV max diff: {(dv - dv_ref).abs().max().item()}')
        print(f'dQ mean diff: {(dq - dq_ref).abs().mean().item()}')
        print(f'dK mean diff: {(dk - dk_ref).abs().mean().item()}')
        print(f'dV mean diff: {(dv - dv_ref).abs().mean().item()}')
        print(f'dQ Pytorch max diff: {(dq_pt - dq_ref).abs().max().item()}')
        print(f'dK Pytorch max diff: {(dk_pt - dk_ref).abs().max().item()}')
        print(f'dV Pytorch max diff: {(dv_pt - dv_ref).abs().max().item()}')
        print(f'dQ Pytorch mean diff: {(dq_pt - dq_ref).abs().mean().item()}')
        print(f'dK Pytorch mean diff: {(dk_pt - dk_ref).abs().mean().item()}')
        print(f'dV Pytorch mean diff: {(dv_pt - dv_ref).abs().mean().item()}')

    # Check that FlashAttention's numerical error is at most twice the numerical error
    # of a Pytorch implementation.
    assert (out - out_ref).abs().max().item() <= 2 * (out_pt - out_ref).abs().max().item()

    if dropout_p > 0.0:
        assert (attn - attn_ref).abs().max().item() <= 2 * (attn_pt - attn_ref).abs().max().item()
        assert abs(dropout_fraction - dropout_p) <= 0.01

    if d <= MAX_HEADDIM_SM8x or (is_sm80 or is_sm90):
        assert (dq - dq_ref).abs().max().item() <= 2 * (dq_pt - dq_ref).abs().max().item()
        assert (dk - dk_ref).abs().max().item() <= 2 * (dk_pt - dk_ref).abs().max().item()
        assert (dv - dv_ref).abs().max().item() <= 2 * (dv_pt - dv_ref).abs().max().item()


@pytest.mark.parametrize('dtype', ([torch.float16] if is_sm75 else [torch.float16, torch.bfloat16]))
# @pytest.mark.parametrize('dtype', [torch.float16])
@pytest.mark.parametrize('causal', [False, True])
# @pytest.mark.parametrize('causal', [False])
# @pytest.mark.parametrize('d', [32, 56, 64, 80, 96, 128])
@pytest.mark.parametrize('d', [32, 64, 96, 128, 160, 192])
# @pytest.mark.parametrize('d', [128])
# @pytest.mark.parametrize('seqlen', [97, 128, 200, 256, 257, 384, 512, 768, 1024, 1025, 2048])
# @pytest.mark.parametrize('seqlen', [128, 256, 384, 512, 768, 1024, 2048])
@pytest.mark.parametrize('seqlen', [128])
# @pytest.mark.parametrize('dropout_p', [0.0, 0.17])
@pytest.mark.parametrize('dropout_p', [0.0])
def test_flash_attn_race_condition(seqlen, d, dropout_p, causal, dtype):
    device = 'cuda'
    # set seed
    torch.random.manual_seed(0)
    batch_size = 60  # Sometimes we need large batch size for the race conditions to trigger
    nheads = 4
    qkv = torch.randn(batch_size, seqlen, 3, nheads, d, device=device, dtype=dtype,
                      requires_grad=True)
    out0, lse0, _ = flash_attn_qkvpacked_func(
        qkv, dropout_p, return_attn_probs=True, causal=causal
    )
    g = torch.randn_like(out0)
    if d <= MAX_HEADDIM_SM8x or (is_sm80 or is_sm90):
        dqkv0, = torch.autograd.grad(out0, qkv, g)
        # Numerical error if we just do any arithmetic on dq
        dq_atol = 2 * ((dqkv0[:, :, 0] + 0.3 - 0.3) - dqkv0[:, :, 0]).abs().max().item()

    for i in range(200):
        torch.random.manual_seed(0)
        out, lse, S_dmask = flash_attn_qkvpacked_func(
            qkv, dropout_p, return_attn_probs=True, causal=causal
        )
        assert torch.equal(out, out0)
        assert torch.equal(lse, lse0)

        if d <= MAX_HEADDIM_SM8x or (is_sm80 or is_sm90):
            dqkv, = torch.autograd.grad(out, qkv, g)
            dq_equal = torch.allclose(dqkv[:, :, 0], dqkv0[:, :, 0], atol=dq_atol)
            if not dq_equal:
                dq0 = dqkv0[:, :, 0]
                dq = dqkv[:, :, 0]
                print(f'Iter {i}, {dq_atol = }, dQ max diff: {(dqkv[:, :, 0] - dqkv0[:, :, 0]).abs().max().item()}')
            assert dq_equal
            assert torch.equal(dqkv[:, :, 1], dqkv0[:, :, 1])
            assert torch.equal(dqkv[:, :, 2], dqkv0[:, :, 2])


@pytest.mark.parametrize('dtype', [torch.float16])
@pytest.mark.parametrize('causal', [False, True])
# @pytest.mark.parametrize('causal', [False])
@pytest.mark.parametrize('d', [16, 32, 64])
# @pytest.mark.parametrize('d', [16])
@pytest.mark.parametrize('seqlen', [1, 2, 5, 17, 128])
# @pytest.mark.parametrize('seqlen', [2])
def test_flash_attn_bwd_overflow(seqlen, d, causal, dtype):
    """ We previously had a bug where not masking elements beyond seqlen_k caused NaN in dQ,
    in the case where seqlen % 128 != 0.
    """
    device = 'cuda'
    # set seed
    torch.random.manual_seed(0)
    batch_size = 2
    nheads = 5
    q = torch.randn([batch_size, seqlen, nheads, d], dtype=dtype, device="cuda") * 5
    k, v = [torch.randn([batch_size, seqlen, nheads, d], dtype=dtype, device="cuda") * 3 for _ in range(2)]
    q.requires_grad_(True)
    k.requires_grad_(True)
    v.requires_grad_(True)
    out = flash_attn_func(q, k, v, causal=causal)
    g = torch.randn_like(out)
    out.backward(g)
    q_pt = q.detach().clone().requires_grad_(True)
    k_pt = k.detach().clone().requires_grad_(True)
    v_pt = v.detach().clone().requires_grad_(True)
    out_pt, _ = attention_ref(q_pt, k_pt, v_pt, causal=causal, upcast=False, reorder_ops=True)
    out_pt.backward(g)
    q_ref = q.detach().clone().requires_grad_(True)
    k_ref = k.detach().clone().requires_grad_(True)
    v_ref = v.detach().clone().requires_grad_(True)
    out_ref, attn_ref = attention_ref(q_ref, k_ref, v_ref, causal=causal)
    out_ref.backward(g)
    print(f'dQ max diff: {(q.grad - q_ref.grad).abs().max().item()}')
    print(f'dK max diff: {(k.grad - k_ref.grad).abs().max().item()}')
    print(f'dV max diff: {(v.grad - v_ref.grad).abs().max().item()}')
    print(f'dQ Pytorch max diff: {(q_pt.grad - q_ref.grad).abs().max().item()}')
    print(f'dK Pytorch max diff: {(k_pt.grad - k_ref.grad).abs().max().item()}')
    print(f'dV Pytorch max diff: {(v_pt.grad - v_ref.grad).abs().max().item()}')
    assert (out - out_ref).abs().max().item() <= 2 * (out_pt - out_ref).abs().max().item()
    assert (q.grad - q_ref.grad).abs().max().item() <= 5 * (q_pt.grad - q_ref.grad).abs().max().item() + 1e-3
    assert (k.grad - k_ref.grad).abs().max().item() <= 5 * (k_pt.grad - k_ref.grad).abs().max().item() + 1e-3
    assert (v.grad - v_ref.grad).abs().max().item() <= 5 * (v_pt.grad - v_ref.grad).abs().max().item() + 1e-3


@pytest.mark.parametrize('dtype', ([torch.float16] if is_sm75 else [torch.float16, torch.bfloat16]))
# @pytest.mark.parametrize('dtype', [torch.bfloat16])
@pytest.mark.parametrize('causal', [False, True])
# @pytest.mark.parametrize('causal', [False])
@pytest.mark.parametrize('d', [64, 128])
# @pytest.mark.parametrize('d', [64])
@pytest.mark.parametrize('seqlen', [97, 128, 200, 256])
# @pytest.mark.parametrize('seqlen', [128])
def test_flash_attn_bwd_transpose(seqlen, d, causal, dtype):
    """ We previously had a bug where we were using the wrong strides of dout, which shows up
    when dout is not contiguous.
    """
    device = 'cuda'
    # set seed
    torch.random.manual_seed(0)
    batch_size = 5
    nheads = 2
    q, k, v = [torch.randn([batch_size, seqlen, nheads, d], dtype=dtype, device="cuda",
                           requires_grad=True)
               for _ in range(3)]
    out = rearrange(flash_attn_func(q, k, v, causal=causal), "b s ... -> s b ...")
    # So g is not contiguous
    g = torch.randn(seqlen, 2 * batch_size, nheads, d, dtype=dtype, device="cuda")[:, ::2]
    out.backward(g)
    q_pt = q.detach().clone().requires_grad_(True)
    k_pt = k.detach().clone().requires_grad_(True)
    v_pt = v.detach().clone().requires_grad_(True)
    out_pt, attn_pt = attention_ref(q_pt, k_pt, v_pt, causal=causal, upcast=False, reorder_ops=True)
    out_pt = rearrange(out_pt, "b s ... -> s b ...")
    out_pt.backward(g)
    q_ref = q.detach().clone().requires_grad_(True)
    k_ref = k.detach().clone().requires_grad_(True)
    v_ref = v.detach().clone().requires_grad_(True)
    out_ref, attn_ref = attention_ref(q_ref, k_ref, v_ref, causal=causal)
    out_ref = rearrange(out_ref, "b s ... -> s b ...")
    out_ref.backward(g)
    print(f'dQ max diff: {(q.grad - q_ref.grad).abs().max().item()}')
    print(f'dK max diff: {(k.grad - k_ref.grad).abs().max().item()}')
    print(f'dV max diff: {(v.grad - v_ref.grad).abs().max().item()}')
    print(f'dQ Pytorch max diff: {(q_pt.grad - q_ref.grad).abs().max().item()}')
    print(f'dK Pytorch max diff: {(k_pt.grad - k_ref.grad).abs().max().item()}')
    print(f'dV Pytorch max diff: {(v_pt.grad - v_ref.grad).abs().max().item()}')
    assert (out - out_ref).abs().max().item() <= 2 * (out_pt - out_ref).abs().max().item()
    assert (q.grad - q_ref.grad).abs().max().item() <= 2 * (q_pt.grad - q_ref.grad).abs().max().item()
    assert (k.grad - k_ref.grad).abs().max().item() <= 2 * (k_pt.grad - k_ref.grad).abs().max().item()
    assert (v.grad - v_ref.grad).abs().max().item() <= 2 * (v_pt.grad - v_ref.grad).abs().max().item()