test_flash_attn.py

import math

import pytest
import torch
import torch.nn.functional as F
from einops import rearrange, repeat
from flash_attn import (
    flash_attn_func,
    flash_attn_kvpacked_func,
    flash_attn_qkvpacked_func,
    flash_attn_varlen_func,
    flash_attn_varlen_kvpacked_func,
    flash_attn_varlen_qkvpacked_func,
    flash_attn_with_kvcache,
)
from flash_attn.bert_padding import pad_input, unpad_input
from flash_attn.flash_attn_interface import _get_block_size_n
from flash_attn.layers.rotary import apply_rotary_emb

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 attn_bias_from_alibi_slopes(
    slopes, seqlen_q, seqlen_k, query_padding_mask=None, key_padding_mask=None, causal=False
):
    batch, nheads = slopes.shape
    device = slopes.device
    slopes = rearrange(slopes, "b h -> b h 1 1")
    if causal:
        return torch.arange(-seqlen_k + 1, 1, device=device, dtype=torch.float32) * slopes
    else:
        row_idx = rearrange(torch.arange(seqlen_q, device=device, dtype=torch.long), "s -> s 1")
        col_idx = torch.arange(seqlen_k, device=device, dtype=torch.long)
        sk = (
            seqlen_k
            if key_padding_mask is None
            else rearrange(key_padding_mask.sum(-1), "b -> b 1 1 1")
        )
        sq = (
            seqlen_q
            if query_padding_mask is None
            else rearrange(query_padding_mask.sum(-1), "b -> b 1 1 1")
        )
        relative_pos = torch.abs(row_idx + sk - sq - col_idx)
        return -slopes * relative_pos.to(dtype=slopes.dtype)


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 + 1, (batch_size, 1), device=device
        )
    elif mode == "third":
        lengths = torch.randint(max_seqlen // 3, max_seqlen + 1, (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 construct_local_mask(
    seqlen_q,
    seqlen_k,
    window_size=(-1, -1),  # -1 means infinite window size
    query_padding_mask=None,
    key_padding_mask=None,
    device=None,
    key_leftpad=None,
):
    row_idx = rearrange(torch.arange(seqlen_q, device=device, dtype=torch.long), "s -> s 1")
    col_idx = torch.arange(seqlen_k, device=device, dtype=torch.long)
    if key_leftpad is not None:
        key_leftpad = rearrange(key_leftpad, "b -> b 1 1 1")
        col_idx = repeat(col_idx, "s -> b 1 1 s", b=key_leftpad.shape[0])
        col_idx = torch.where(col_idx >= key_leftpad, col_idx - key_leftpad, 2**32)
    sk = (
        seqlen_k
        if key_padding_mask is None
        else rearrange(key_padding_mask.sum(-1), "b -> b 1 1 1")
    )
    sq = (
        seqlen_q
        if query_padding_mask is None
        else rearrange(query_padding_mask.sum(-1), "b -> b 1 1 1")
    )
    if window_size[0] < 0:
        return col_idx > row_idx + sk - sq + window_size[1]
    else:
        sk = torch.full_like(col_idx, seqlen_k) if key_padding_mask is None else sk
        return torch.logical_or(
            col_idx > torch.minimum(row_idx + sk - sq + window_size[1], sk),
            col_idx < row_idx + sk - sq - window_size[0],
        )


def attention_ref(
    q,
    k,
    v,
    query_padding_mask=None,
    key_padding_mask=None,
    attn_bias=None,
    dropout_p=0.0,
    dropout_mask=None,
    causal=False,
    window_size=(-1, -1),  # -1 means infinite window size
    softcap=0.0,
    upcast=True,
    reorder_ops=False,
    key_leftpad=None,
):
    """
    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)
        attn_bias: broadcastable to (batch_size, nheads, seqlen_q, seqlen_k)
        dropout_p: float
        dropout_mask: (batch_size, nheads, seqlen_q, seqlen_k)
        causal: whether to apply causal masking
        window_size: (int, int), left and right window size
        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 q, 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
    """
    if causal:
        window_size = (window_size[0], 0)
    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 softcap > 0:
        scores /= softcap
        scores = scores.tanh()
        scores *= softcap
    if key_padding_mask is not None:
        scores.masked_fill_(rearrange(~key_padding_mask, "b s -> b 1 1 s"), float("-inf"))
    if window_size[0] >= 0 or window_size[1] >= 0:
        local_mask = construct_local_mask(
            seqlen_q,
            seqlen_k,
            window_size,
            query_padding_mask,
            key_padding_mask,
            q.device,
            key_leftpad=key_leftpad,
        )
        scores.masked_fill_(local_mask, float("-inf"))
    if attn_bias is not None:
        scores = scores + attn_bias
    attention = torch.softmax(scores, dim=-1).to(v.dtype)
    # Some rows might be completely masked out so we fill them with zero instead of NaN
    if window_size[0] >= 0 or window_size[1] >= 0:
        attention = attention.masked_fill(torch.all(local_mask, dim=-1, keepdim=True), 0.0)
    # We want to mask here so that the attention matrix doesn't have any NaNs
    # Otherwise we'll get NaN in dV
    if query_padding_mask is not None:
        attention = attention.masked_fill(rearrange(~query_padding_mask, "b s -> b 1 s 1"), 0.0)
    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)
    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,
    attn_bias=None,
    dropout_p=0.0,
    dropout_mask=None,
    causal=False,
    window_size=(-1, -1),  # -1 means infinite window size
    softcap=0.0,
    upcast=True,
    reorder_ops=False,
    key_leftpad=None,
):
    return attention_ref(
        q,
        kv[:, :, 0],
        kv[:, :, 1],
        query_padding_mask,
        key_padding_mask,
        attn_bias,
        dropout_p,
        dropout_mask,
        upcast=upcast,
        causal=causal,
        window_size=window_size,
        softcap=softcap,
        reorder_ops=reorder_ops,
        key_leftpad=key_leftpad,
    )


def attention_qkvpacked_ref(
    qkv,
    key_padding_mask=None,
    attn_bias=None,
    dropout_p=0.0,
    dropout_mask=None,
    causal=False,
    window_size=(-1, -1),  # -1 means infinite window size
    softcap=0.0,
    upcast=True,
    reorder_ops=False,
):
    return attention_ref(
        qkv[:, :, 0],
        qkv[:, :, 1],
        qkv[:, :, 2],
        key_padding_mask,
        key_padding_mask,
        attn_bias,
        dropout_p,
        dropout_mask,
        upcast=upcast,
        causal=causal,
        window_size=window_size,
        softcap=softcap,
        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,
    seqlen_q,
    seqlen_k,
    query_padding_mask,
    key_padding_mask,
    head_dim,
    is_dropout,
    causal=False,
    window_size=(-1, -1),  # -1 means infinite window size
):
    """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_rounded)
        key_padding_mask: (batch_size, seqlen_k_rounded)
    """
    if causal:
        window_size = (window_size[0], 0)
    seqlen_q_rounded, seqlen_k_rounded = S.shape[-2:]
    S_converted = S
    if window_size[0] >= 0 or window_size[1] >= 0:
        local_mask = construct_local_mask(
            seqlen_q,
            seqlen_k,
            window_size,
            query_padding_mask,
            key_padding_mask,
            S.device,
        )
        local_mask = F.pad(
            local_mask,
            (0, seqlen_k_rounded - seqlen_k, 0, seqlen_q_rounded - seqlen_q),
            value=True,
        )
        S_converted = S_converted.masked_fill(local_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_rounded
    )
    if query_padding_mask is not None:
        query_padding_mask = F.pad(query_padding_mask, (0, seqlen_q_rounded - seqlen_q_og))
        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:
        key_padding_mask = F.pad(key_padding_mask, (0, seqlen_k_rounded - seqlen_k_og))
        S_converted = S_converted.masked_fill(rearrange(~key_padding_mask, "b s -> b 1 1 s"), 0.0)
    S_converted = F.pad(S_converted, (0, 0, 0, seqlen_q_og - seqlen_q_rounded))
    S_converted = F.pad(S_converted, (0, seqlen_k_og - seqlen_k_rounded))
    return S_converted[:, :, :seqlen_q, :seqlen_k]


def normalize_flash_attn_S(
    attn_unnorm,
    q,
    k,
    v,
    query_padding_mask=None,
    key_padding_mask=None,
    attn_bias=None,
    is_dropout=False,
    causal=False,
    window_size=(-1, -1),  # -1 means infinite window size
):
    """
    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)
        attn_bias: broadcastable to (batch_size, nheads, seqlen_q, seqlen_k)
    Output:
        softmax_lse: (batch_size, nheads, seqlen_q)
        softmax_max: (batch_size, nheads, seqlen_q)
    """
    if causal:
        window_size = (window_size[0], 0)
    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 window_size[0] >= 0 or window_size[1] >= 0:
        local_mask = construct_local_mask(
            seqlen_q,
            seqlen_k,
            window_size,
            query_padding_mask,
            key_padding_mask,
            q.device,
        )
        scores.masked_fill_(local_mask, float("-inf"))
    if attn_bias is not None:
        scores = scores + attn_bias.to(dtype=scores.dtype)
    block_size_n = _get_block_size_n(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)
    # lse could be -inf (i.e. all values in scores are -inf), and we want to set those to inf
    # so that when we do torch.exp(m - lse), we get 0.0 instead of NaN.
    lse[lse == float("-inf")] = float("inf")
    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(m - lse), "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,
    window_size=(-1, -1),  # -1 means infinite window size
):
    """
    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)
    """
    if causal:
        window_size = (window_size[0], 0)
    batch_size, nheads, seqlen_q, seqlen_k = dropout_mask.shape
    dropped = ~dropout_mask
    valid = torch.ones_like(dropout_mask)
    if query_padding_mask is not None:
        dropped.masked_fill_(rearrange(~query_padding_mask, "b s -> b 1 s 1"), False)
        valid.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)
        valid.masked_fill_(rearrange(~key_padding_mask, "b s -> b 1 1 s"), False)
    if window_size[0] >= 0 or window_size[1] >= 0:
        local_mask = construct_local_mask(
            seqlen_q,
            seqlen_k,
            window_size,
            query_padding_mask,
            key_padding_mask,
            dropout_mask.device,
        )
        dropped.masked_fill_(local_mask, False)
        valid.masked_fill_(local_mask, False)
    dropped_total = dropped.sum()
    return dropped.sum() / valid.sum()


@pytest.mark.parametrize("dtype", ([torch.float16] if is_sm75 else [torch.float16, torch.bfloat16]))
# @pytest.mark.parametrize("dtype", [torch.float16])
@pytest.mark.parametrize("deterministic", [False, True])
# @pytest.mark.parametrize("deterministic", [False])
@pytest.mark.parametrize("alibi", [False, True])
# @pytest.mark.parametrize("alibi", [False])
@pytest.mark.parametrize("local", [False, True])
# @pytest.mark.parametrize("local", [False])
@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, 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, 384, 768, 1024, 1025, 2048])
# @pytest.mark.parametrize("seqlen", [512])
@pytest.mark.parametrize("dropout_p", [0.0, 0.17])
# @pytest.mark.parametrize("dropout_p", [0.0])
def test_flash_attn_qkvpacked(seqlen, d, dropout_p, causal, local, alibi, deterministic, 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 = 4
    nheads = 9
    window_size = (-1, -1) if not local else torch.randint(0, seqlen, (2,))
    qkv = torch.randn(
        batch_size, seqlen, 3, nheads, d, device=device, dtype=dtype, requires_grad=True
    )
    if alibi:
        alibi_slopes = torch.rand(batch_size, nheads, device=device, dtype=torch.float32) * 0.3
        attn_bias = attn_bias_from_alibi_slopes(alibi_slopes, seqlen, seqlen, causal=causal)
    else:
        alibi_slopes, attn_bias = None, None
    out, lse, S_dmask = flash_attn_qkvpacked_func(
        qkv,
        dropout_p,
        causal=causal,
        window_size=window_size,
        alibi_slopes=alibi_slopes,
        deterministic=deterministic,
        return_attn_probs=True,
    )
    if dropout_p > 0.0:
        S_dmask_converted = convert_flash_attn_S_to_softmax(
            S_dmask,
            seqlen,
            seqlen,
            None,
            None,
            d,
            dropout_p > 0.0,
            causal=causal,
            window_size=window_size,
        )
        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,
            attn_bias,
            dropout_p > 0.0,
            causal=causal,
            window_size=window_size,
        )
        dropout_fraction = get_dropout_fraction(
            dropout_mask, None, None, causal=causal, window_size=window_size
        ).item()
        print(f"Actual dropout fraction: {dropout_fraction}")
    else:
        dropout_mask = None

    out_ref, attn_ref = attention_qkvpacked_ref(
        qkv, None, attn_bias, dropout_p, dropout_mask, causal=causal, window_size=window_size
    )
    out_pt, attn_pt = attention_qkvpacked_ref(
        qkv,
        None,
        attn_bias,
        dropout_p,
        dropout_mask,
        causal=causal,
        window_size=window_size,
        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 (d > 224 and dropout_p == 0)) 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()
        # With alibi, many of the prob values are 0.0 & -0.0 so dropout_fraction isn't accurate
        if not alibi:
            assert abs(dropout_fraction - dropout_p) <= (0.01 if not local else 0.025)

    if (d <= MAX_HEADDIM_SM8x or (d > 224 and dropout_p == 0)) 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("deterministic", [False, True])
# @pytest.mark.parametrize("deterministic", [True])
@pytest.mark.parametrize("alibi", [False, True])
# @pytest.mark.parametrize("alibi", [True])
@pytest.mark.parametrize("local", [False, True])
# @pytest.mark.parametrize("local", [True])
@pytest.mark.parametrize("causal", [False, True])
# @pytest.mark.parametrize('causal', [False])
@pytest.mark.parametrize("d", [32, 59, 64, 80, 96, 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", [97, 128, 200, 257, 384, 512, 768, 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, local, alibi, deterministic, 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
    window_size = (-1, -1) if not local else torch.randint(0, seqlen, (2,))
    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')
    if alibi:
        alibi_slopes = torch.rand(batch_size, nheads, device=device, dtype=torch.float32) * 0.3
        attn_bias = attn_bias_from_alibi_slopes(
            alibi_slopes, seqlen, seqlen, key_padding_mask, key_padding_mask, causal=causal
        )
    else:
        alibi_slopes, attn_bias = None, None

    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,
        causal=causal,
        window_size=window_size,
        alibi_slopes=alibi_slopes,
        deterministic=deterministic,
        return_attn_probs=True,
    )
    out = output_pad_fn(out_unpad)
    if dropout_p > 0.0:
        S_dmask_converted = convert_flash_attn_S_to_softmax(
            S_dmask,
            seqlen,
            seqlen,
            key_padding_mask,
            key_padding_mask,
            d,
            dropout_p > 0.0,
            causal=causal,
            window_size=window_size,
        )
        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,
            attn_bias,
            dropout_p > 0.0,
            causal=causal,
            window_size=window_size,
        )
        dropout_fraction = get_dropout_fraction(
            dropout_mask, key_padding_mask, key_padding_mask, causal=causal, window_size=window_size
        ).item()
        print(f"Actual dropout fraction: {dropout_fraction}")
    else:
        dropout_mask = None

    out_ref, attn_ref = attention_qkvpacked_ref(
        qkv,
        key_padding_mask,
        attn_bias,
        dropout_p,
        dropout_mask,
        causal=causal,
        window_size=window_size,
    )
    out_pt, attn_pt = attention_qkvpacked_ref(
        qkv,
        key_padding_mask,
        attn_bias,
        dropout_p,
        dropout_mask,
        causal=causal,
        window_size=window_size,
        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 (d > 224 and dropout_p == 0)) 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()
        # With alibi, many of the prob values are 0.0 & -0.0 so dropout_fraction isn't accurate
        if not alibi:
            assert abs(dropout_fraction - dropout_p) <= (0.01 if not local else 0.025)

    if (d <= MAX_HEADDIM_SM8x or (d > 224 and dropout_p == 0)) 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("deterministic", [False, True])
# @pytest.mark.parametrize("deterministic", [True])
@pytest.mark.parametrize("alibi", [False, True])
# @pytest.mark.parametrize("alibi", [True])
@pytest.mark.parametrize("local", [False, True])
# @pytest.mark.parametrize("local", [True])
@pytest.mark.parametrize("causal", [False, True])
# @pytest.mark.parametrize("causal", [True])
@pytest.mark.parametrize("d", [32, 40, 59, 64, 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', [(256, 128)])
@pytest.mark.parametrize("dropout_p", [0.0, 0.17])
# @pytest.mark.parametrize("dropout_p", [0.17])
@pytest.mark.parametrize("softcap", [0.0, 50.0])
def test_flash_attn_output(
    seqlen_q, seqlen_k, d, dropout_p, causal, local, alibi, deterministic, mha_type, dtype, kvpacked, softcap
):
    if (
        max(seqlen_q, seqlen_k) >= 2048
        and torch.cuda.get_device_properties("cuda").total_memory <= 16 * 2**30
    ):
        pytest.skip()  # Reference implementation OOM
    if softcap > 0.0 and dropout_p > 0.0:
        pytest.skip("Softcap and dropout not supported together")
    device = "cuda"
    # set seed
    torch.random.manual_seed(0)
    batch_size = 4
    nheads = 6 if softcap == 0.0 else 4  # softcap reference impl takes more memory
    nheads_k = nheads if mha_type == "mha" else (1 if mha_type == "mqa" else 2)
    assert nheads % nheads_k == 0
    window_size = (-1, -1) if not local else torch.randint(0, seqlen_k, (2,))
    q = torch.randn(batch_size, seqlen_q, nheads, d, device=device, dtype=dtype, requires_grad=True)
    if softcap > 0:
        # Ensure the values of qk are at least within softcap range.
        q = q * softcap
    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 alibi:
        alibi_slopes = torch.rand(batch_size, nheads, device=device, dtype=torch.float32) * 0.3
        attn_bias = attn_bias_from_alibi_slopes(alibi_slopes, seqlen_q, seqlen_k, causal=causal)
    else:
        alibi_slopes, attn_bias = None, None

    if kvpacked:
        out, lse, S_dmask = flash_attn_kvpacked_func(
            q,
            kv,
            dropout_p,
            causal=causal,
            window_size=window_size,
            softcap=softcap,
            alibi_slopes=alibi_slopes,
            deterministic=deterministic,
            return_attn_probs=True,
        )
    else:
        out, lse, S_dmask = flash_attn_func(
            q,
            k,
            v,
            dropout_p,
            causal=causal,
            window_size=window_size,
            softcap=softcap,
            alibi_slopes=alibi_slopes,
            deterministic=deterministic,
            return_attn_probs=True,
        )
    if dropout_p > 0.0:
        S_dmask_converted = convert_flash_attn_S_to_softmax(
            S_dmask,
            seqlen_q,
            seqlen_k,
            None,
            None,
            d,
            dropout_p > 0.0,
            causal=causal,
            window_size=window_size,
        )
        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,
            attn_bias,
            dropout_p > 0.0,
            causal=causal,
            window_size=window_size,
        )
        dropout_fraction = get_dropout_fraction(
            dropout_mask, None, None, causal=causal, window_size=window_size
        ).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,
            attn_bias,
            dropout_p,
            dropout_mask,
            causal=causal,
            window_size=window_size,
            softcap=softcap,
        )
        out_pt, attn_pt = attention_kvpacked_ref(
            q,
            kv,
            None,
            None,
            attn_bias,
            dropout_p,
            dropout_mask,
            causal=causal,
            window_size=window_size,
            softcap=softcap,
            upcast=False,
            reorder_ops=True,
        )
    else:
        out_ref, attn_ref = attention_ref(
            q,
            k,
            v,
            None,
            None,
            attn_bias,
            dropout_p,
            dropout_mask,
            causal=causal,
            window_size=window_size,
            softcap=softcap,
        )
        out_pt, attn_pt = attention_ref(
            q,
            k,
            v,
            None,
            None,
            attn_bias,
            dropout_p,
            dropout_mask,
            causal=causal,
            window_size=window_size,
            softcap=softcap,
            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 (d > 224 and dropout_p == 0)) or (is_sm80 or is_sm90)) and softcap == 0.0:
        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()
        # With alibi, many of the prob values are 0.0 & -0.0 so dropout_fraction isn't accurate
        if not alibi:
            assert abs(dropout_fraction - dropout_p) <= (0.01 if not local else 0.025)

    if ((d <= MAX_HEADDIM_SM8x or (d > 224 and dropout_p == 0)) or (is_sm80 or is_sm90)) and softcap == 0.0:
        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("deterministic", [False, True])
# @pytest.mark.parametrize("deterministic", [True])
@pytest.mark.parametrize("alibi", [False, True])
# @pytest.mark.parametrize("alibi", [True])
@pytest.mark.parametrize("local", [False, True])
# @pytest.mark.parametrize("local", [True])
@pytest.mark.parametrize("causal", [False, True])
# @pytest.mark.parametrize('causal', [True])
@pytest.mark.parametrize("d", [32, 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",
    [
        (1, 147),
        (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("softcap", [0.0, 50.0])
# @pytest.mark.parametrize('dropout_p', [0.0])
def test_flash_attn_varlen_output(
    seqlen_q, seqlen_k, d, dropout_p, causal, local, alibi, deterministic, mha_type, dtype, kvpacked, softcap
):
    if (
        max(seqlen_q, seqlen_k) >= 2048
        and torch.cuda.get_device_properties("cuda").total_memory <= 16 * 2**30
    ):
        pytest.skip()  # Reference implementation OOM
    if softcap > 0.0 and dropout_p > 0.0:
        pytest.skip("Softcap and dropout not supported together")
    device = "cuda"
    # set seed
    torch.random.manual_seed(0)
    batch_size = 4
    nheads = 6 if softcap == 0.0 else 4  # softcap reference impl takes more memory
    nheads_k = nheads if mha_type == "mha" else (1 if mha_type == "mqa" else 2)
    assert nheads % nheads_k == 0
    window_size = (-1, -1) if not local else torch.randint(0, seqlen_k, (2,))
    q = torch.randn(batch_size, seqlen_q, nheads, d, device=device, dtype=dtype, requires_grad=True)
    if softcap > 0:
        # Ensure the values of qk are at least within softcap range.
        q = q * softcap

    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 alibi:
        alibi_slopes = torch.rand(batch_size, nheads, device=device, dtype=torch.float32) * 0.3
        attn_bias = attn_bias_from_alibi_slopes(
            alibi_slopes, seqlen_q, seqlen_k, query_padding_mask, key_padding_mask, causal=causal
        )
    else:
        alibi_slopes, attn_bias = None, None

    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,
            causal=causal,
            window_size=window_size,
            softcap=softcap,
            alibi_slopes=alibi_slopes,
            deterministic=deterministic,
            return_attn_probs=True,
        )
    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,
            causal=causal,
            window_size=window_size,
            softcap=softcap,
            alibi_slopes=alibi_slopes,
            deterministic=deterministic,
            return_attn_probs=True,
        )
    out = output_pad_fn(out_unpad)
    if dropout_p > 0.0:
        S_dmask_converted = convert_flash_attn_S_to_softmax(
            S_dmask,
            seqlen_q,
            seqlen_k,
            query_padding_mask,
            key_padding_mask,
            d,
            dropout_p > 0.0,
            causal=causal,
            window_size=window_size,
        )
        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,
            attn_bias,
            dropout_p > 0.0,
            causal=causal,
            window_size=window_size,
        )
        dropout_fraction = get_dropout_fraction(
            dropout_mask,
            query_padding_mask,
            key_padding_mask,
            causal=causal,
            window_size=window_size,
        ).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,
            attn_bias,
            dropout_p,
            dropout_mask,
            causal=causal,
            window_size=window_size,
            softcap=softcap,
        )
        out_pt, attn_pt = attention_kvpacked_ref(
            q,
            kv,
            query_padding_mask,
            key_padding_mask,
            attn_bias,
            dropout_p,
            dropout_mask,
            causal=causal,
            window_size=window_size,
            softcap=softcap,
            upcast=False,
            reorder_ops=True,
        )
    else:
        out_ref, attn_ref = attention_ref(
            q,
            k,
            v,
            query_padding_mask,
            key_padding_mask,
            attn_bias,
            dropout_p,
            dropout_mask,
            causal=causal,
            window_size=window_size,
            softcap=softcap,
        )
        out_pt, attn_pt = attention_ref(
            q,
            k,
            v,
            query_padding_mask,
            key_padding_mask,
            attn_bias,
            dropout_p,
            dropout_mask,
            causal=causal,
            window_size=window_size,
            softcap=softcap,
            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 (d > 224 and dropout_p == 0)) or (is_sm80 or is_sm90)) and softcap == 0.0:
        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()
        # With alibi, many of the prob values are 0.0 & -0.0 so dropout_fraction isn't accurate
        if not alibi:
            assert abs(dropout_fraction - dropout_p) <= (0.01 if not local else 0.04)

    if ((d <= MAX_HEADDIM_SM8x or (d > 224 and dropout_p == 0)) or (is_sm80 or is_sm90)) and softcap == 0.0:
        assert (dq - dq_ref).abs().max().item() <= 3 * (dq_pt - dq_ref).abs().max().item()
        assert (dk - dk_ref).abs().max().item() <= 3 * (dk_pt - dk_ref).abs().max().item()
        assert (dv - dv_ref).abs().max().item() <= 3 * (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.bfloat16])
@pytest.mark.parametrize("local", [False, True])
# @pytest.mark.parametrize("local", [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, 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, 128])
@pytest.mark.parametrize("swap_sq_sk", [False, True])
# @pytest.mark.parametrize("swap_sq_sk", [True])
@pytest.mark.parametrize(
    "seqlen_q,seqlen_k",
    [
        (1, 239),
        (3, 799),
        (127, 512),
        (127, 513),
        (113, 203),
        (128, 217),
        (113, 211),
        (108, 256),
        (256, 512),
        (1023, 1024),
    ],
)
# @pytest.mark.parametrize('seqlen_q,seqlen_k', [(256, 128)])
def test_flash_attn_causal(seqlen_q, seqlen_k, swap_sq_sk, d, local, dtype):
    if (
        max(seqlen_q, seqlen_k) >= 2048
        and torch.cuda.get_device_properties("cuda").total_memory <= 16 * 2**30
    ):
        pytest.skip()  # Reference implementation OOM
    if swap_sq_sk:
        seqlen_q, seqlen_k = seqlen_k, seqlen_q
    device = "cuda"
    causal = True
    # set seed
    torch.random.manual_seed(0)
    batch_size = 8
    nheads = 9
    window_size = (-1, -1) if not local else torch.randint(0, seqlen_k, (2,))
    q = torch.randn(batch_size, seqlen_q, nheads, d, device=device, dtype=dtype, requires_grad=True)
    k = torch.randn(batch_size, seqlen_k, nheads, d, device=device, dtype=dtype, requires_grad=True)
    v = torch.randn(batch_size, seqlen_k, nheads, d, device=device, dtype=dtype, requires_grad=True)
    out = flash_attn_func(q, k, v, 0.0, causal=causal, window_size=window_size)
    out_ref, attn_ref = attention_ref(
        q, k, v, None, None, None, 0.0, None, causal=causal, window_size=window_size
    )
    out_pt, attn_pt = attention_ref(
        q,
        k,
        v,
        None,
        None,
        None,
        0.0,
        None,
        causal=causal,
        window_size=window_size,
        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()}")

    g = torch.randn_like(out)
    do_o = (g.float() * out.float()).sum(-1)
    if (d <= MAX_HEADDIM_SM8x or d > 224) or (is_sm80 or is_sm90):
        (
            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() + 1e-5

    if (d <= MAX_HEADDIM_SM8x or d > 224) or (is_sm80 or is_sm90):
        assert (dq - dq_ref).abs().max().item() <= 2 * (dq_pt - dq_ref).abs().max().item() + 1e-5
        assert (dk - dk_ref).abs().max().item() <= 2 * (dk_pt - dk_ref).abs().max().item() + 1e-5
        assert (dv - dv_ref).abs().max().item() <= 2 * (dv_pt - dv_ref).abs().max().item() + 1e-5


@pytest.mark.parametrize("dtype", ([torch.float16] if is_sm75 else [torch.float16, torch.bfloat16]))
# @pytest.mark.parametrize("dtype", [torch.bfloat16])
@pytest.mark.parametrize("local", [False, True])
# @pytest.mark.parametrize("local", [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, 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("swap_sq_sk", [False, True])
# @pytest.mark.parametrize("swap_sq_sk", [True])
@pytest.mark.parametrize(
    "seqlen_q,seqlen_k",
    [
        (1, 239),
        (3, 799),
        (127, 512),
        (127, 513),
        (113, 203),
        (128, 217),
        (113, 211),
        (108, 256),
        (256, 512),
        (1023, 1024),
    ],
)
# TODO: add smaller page sizes when https://github.com/Dao-AILab/flash-attention/pull/824 is merged
@pytest.mark.parametrize("paged_kv_block_size", [None, 256, 512])
# @pytest.mark.parametrize("seqlen_q,seqlen_k", [(256, 128)])
def test_flash_attn_varlen_causal(
    seqlen_q, seqlen_k, swap_sq_sk, d, local, paged_kv_block_size, dtype
):
    if (
        max(seqlen_q, seqlen_k) >= 2048
        and torch.cuda.get_device_properties("cuda").total_memory <= 16 * 2**30
    ):
        pytest.skip()  # Reference implementation OOM
    if swap_sq_sk:
        seqlen_q, seqlen_k = seqlen_k, seqlen_q
    device = "cuda"
    causal = True
    # set seed
    torch.random.manual_seed(0)
    batch_size = 8
    nheads = 9
    window_size = (-1, -1) if not local else torch.randint(0, seqlen_k, (2,))
    q = torch.randn(batch_size, seqlen_q, nheads, d, device=device, dtype=dtype, requires_grad=True)

    if paged_kv_block_size is None:
        k = torch.randn(
            batch_size, seqlen_k, nheads, d, device=device, dtype=dtype, requires_grad=True
        )
        v = torch.randn(
            batch_size, seqlen_k, nheads, d, device=device, dtype=dtype, requires_grad=True
        )
        block_table = None
    else:
        k, v, block_table, k_cache_paged, v_cache_paged, num_blocks = _generate_block_kvcache(
            seqlen_k, paged_kv_block_size, batch_size, nheads, d, device, dtype
        )
    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")
    (
        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 = flash_attn_varlen_func(
        q_unpad,
        k_unpad if paged_kv_block_size is None else k_cache_paged,
        v_unpad if paged_kv_block_size is None else v_cache_paged,
        cu_seqlens_q,
        cu_seqlens_k,
        max_seqlen_q,
        max_seqlen_k,
        0.0,
        causal=causal,
        window_size=window_size,
        block_table=block_table,
    )
    out = output_pad_fn(out_unpad)
    out_ref, attn_ref = attention_ref(
        q,
        k,
        v,
        query_padding_mask,
        key_padding_mask,
        None,
        0.0,
        None,
        causal=causal,
        window_size=window_size,
    )
    out_pt, attn_pt = attention_ref(
        q,
        k,
        v,
        query_padding_mask,
        key_padding_mask,
        None,
        0.0,
        None,
        causal=causal,
        window_size=window_size,
        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()}")

    g = torch.randn_like(out)
    do_o = (g.float() * out.float()).sum(-1)
    test_backward = (d <= MAX_HEADDIM_SM8x or d > 224 or is_sm80 or is_sm90) and block_table is None
    if test_backward:
        (
            dq_unpad,
            dk_unpad,
            dv_unpad,
        ) = torch.autograd.grad(out, (q_unpad, k_unpad, v_unpad), g)
        dq = dq_pad_fn(dq_unpad)
        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)
        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() + 1e-5

    if test_backward:
        assert (dq - dq_ref).abs().max().item() <= 2 * (dq_pt - dq_ref).abs().max().item() + 1e-5
        assert (dk - dk_ref).abs().max().item() <= 2 * (dk_pt - dk_ref).abs().max().item() + 1e-5
        assert (dv - dv_ref).abs().max().item() <= 2 * (dv_pt - dv_ref).abs().max().item() + 1e-5


@pytest.mark.parametrize("dtype", ([torch.float16] if is_sm75 else [torch.float16, torch.bfloat16]))
# @pytest.mark.parametrize("dtype", [torch.float16])
@pytest.mark.parametrize("deterministic", [False, True])
# @pytest.mark.parametrize("deterministic", [True])
@pytest.mark.parametrize("alibi", [False, True])
# @pytest.mark.parametrize("alibi", [True])
@pytest.mark.parametrize("local", [False, True])
# @pytest.mark.parametrize("local", [False])
@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, 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("swap_sq_sk", [False, True])
# @pytest.mark.parametrize("swap_sq_sk", [False])
@pytest.mark.parametrize(
    "seqlen_q,seqlen_k",
    [
        (3, 1024),
        (1, 339),
        (64, 800),
        (3, 799),
        (64, 2048),
        (16, 20000),
        (16, 100000),
        (128, 128),
        (256, 256),
    ],
)
# @pytest.mark.parametrize('seqlen_q,seqlen_k', [(256, 128)])
def test_flash_attn_splitkv(
    seqlen_q, seqlen_k, swap_sq_sk, d, causal, local, alibi, deterministic, dtype
):
    if swap_sq_sk:
        seqlen_q, seqlen_k = seqlen_k, seqlen_q
    device = "cuda"
    # set seed
    torch.random.manual_seed(0)
    batch_size = 1
    nheads = 12
    window_size = (-1, -1) if not local else torch.randint(0, seqlen_k, (2,))
    q = torch.randn(batch_size, seqlen_q, nheads, d, device=device, dtype=dtype, requires_grad=True)
    k = torch.randn(batch_size, seqlen_k, nheads, d, device=device, dtype=dtype, requires_grad=True)
    v = torch.randn(batch_size, seqlen_k, nheads, d, device=device, dtype=dtype, requires_grad=True)
    if alibi:
        alibi_slopes = torch.rand(batch_size, nheads, device=device, dtype=torch.float32) * 0.3
        attn_bias = attn_bias_from_alibi_slopes(alibi_slopes, seqlen_q, seqlen_k, causal=causal)
    else:
        alibi_slopes, attn_bias = None, None
    out, lse, _ = flash_attn_func(
        q,
        k,
        v,
        0.0,
        causal=causal,
        window_size=window_size,
        alibi_slopes=alibi_slopes,
        deterministic=deterministic,
        return_attn_probs=True,
    )
    out_ref, attn_ref = attention_ref(
        q, k, v, None, None, attn_bias, 0.0, None, causal=causal, window_size=window_size
    )
    out_pt, attn_pt = attention_ref(
        q,
        k,
        v,
        None,
        None,
        attn_bias,
        0.0,
        None,
        causal=causal,
        window_size=window_size,
        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()}")

    g = torch.randn_like(out)
    do_o = (g.float() * out.float()).sum(-1)
    if (d <= MAX_HEADDIM_SM8x or d > 224) or (is_sm80 or is_sm90):
        (
            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() + 1e-5

    mult = 2 if not alibi else 8
    if (d <= MAX_HEADDIM_SM8x or d > 224) or (is_sm80 or is_sm90):
        assert (dq - dq_ref).abs().max().item() <= mult * (dq_pt - dq_ref).abs().max().item() + 2e-4
        assert (dk - dk_ref).abs().max().item() <= mult * (dk_pt - dk_ref).abs().max().item() + 2e-4
        assert (dv - dv_ref).abs().max().item() <= mult * (dv_pt - dv_ref).abs().max().item() + 2e-4


# @pytest.mark.parametrize("dtype", ([torch.float16] if is_sm75 else [torch.float16, torch.bfloat16]))
@pytest.mark.parametrize("dtype", [torch.float16])
@pytest.mark.parametrize("num_splits", [1, 0])
# @pytest.mark.parametrize("num_splits", [1])
@pytest.mark.parametrize("mha_type", ["mha", "mqa", "gqa"])
# @pytest.mark.parametrize("mha_type", ["mha"])
@pytest.mark.parametrize("new_kv", [False, True])
# @pytest.mark.parametrize("new_kv", [False])
@pytest.mark.parametrize("alibi", [False, True])
# @pytest.mark.parametrize("alibi", [False])
@pytest.mark.parametrize("local", [False, True])
# @pytest.mark.parametrize("local", [False])
@pytest.mark.parametrize("causal", [False, True])
# @pytest.mark.parametrize("causal", [False])
@pytest.mark.parametrize("seqlen_new_eq_seqlen_q", [True, False])
# @pytest.mark.parametrize("seqlen_new_eq_seqlen_q", [True])
@pytest.mark.parametrize("rotary_interleaved", [False, True])
# @pytest.mark.parametrize("rotary_interleaved", [False])
@pytest.mark.parametrize("rotary_fraction", [0.0, 0.5, 1.0])
# @pytest.mark.parametrize("rotary_fraction", [0.0])
@pytest.mark.parametrize("paged_kv_block_size", [None, 256])
# @pytest.mark.parametrize("paged_kv_block_size", [256, 512])
# @pytest.mark.parametrize("paged_kv_block_size", [None])
@pytest.mark.parametrize("has_leftpad", [False, True])
# @pytest.mark.parametrize("has_leftpad", [True])
# @pytest.mark.parametrize("has_batch_idx", [False, True])
@pytest.mark.parametrize("has_batch_idx", [False])
@pytest.mark.parametrize("d", [32, 59, 64, 80, 128, 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', [56, 80])
# @pytest.mark.parametrize("d", [128])
@pytest.mark.parametrize(
    "seqlen_q,seqlen_k",
    [
        (1, 128),
        (1, 339),
        (3, 1024),
        (64, 800),
        (64, 256),
        (3, 799),
        (64, 2048),
        (16, 20000),
        (1, 128 * 1024),
        (16, 128 * 1024),
        (128, 128),
    ],
)
# @pytest.mark.parametrize('seqlen_q,seqlen_k', [(256, 128)])
def test_flash_attn_kvcache(
    seqlen_q,
    seqlen_k,
    d,
    has_batch_idx,
    has_leftpad,
    paged_kv_block_size,
    rotary_fraction,
    rotary_interleaved,
    seqlen_new_eq_seqlen_q,
    causal,
    local,
    alibi,
    new_kv,
    mha_type,
    num_splits,
    dtype,
):
    if seqlen_q > seqlen_k and new_kv:
        pytest.skip()
    if not new_kv and rotary_fraction > 0.0:
        pytest.skip()
    if has_batch_idx and paged_kv_block_size is not None:
        pytest.skip()
    if has_leftpad and paged_kv_block_size is not None:
        pytest.skip()
    device = "cuda"
    # set seed
    torch.random.manual_seed(0)
    batch_size = 2
    batch_size_cache = batch_size if not has_batch_idx else batch_size * 2
    nheads = 6
    # rotary_dim must be a multiple of 16, and must be <= d
    rotary_dim = math.floor(int(rotary_fraction * d) / 16) * 16
    nheads_k = nheads if mha_type == "mha" else (1 if mha_type == "mqa" else 3)
    assert nheads % nheads_k == 0
    window_size = (-1, -1) if not local else torch.randint(0, seqlen_k, (2,))
    q = torch.randn(batch_size, seqlen_q, nheads, d, device=device, dtype=dtype)
    seqlen_new = seqlen_q if seqlen_new_eq_seqlen_q else torch.randint(1, seqlen_q + 1, (1,)).item()
    if new_kv:
        k = torch.randn(batch_size, seqlen_new, nheads_k, d, device=device, dtype=dtype)
        v = torch.randn(batch_size, seqlen_new, nheads_k, d, device=device, dtype=dtype)
    else:
        k, v = None, None
    if paged_kv_block_size is None:
        k_cache = torch.randn(batch_size_cache, seqlen_k, nheads_k, d, device=device, dtype=dtype)
        v_cache = torch.randn(batch_size_cache, seqlen_k, nheads_k, d, device=device, dtype=dtype)
        block_table = None
    else:
        (
            k_cache,
            v_cache,
            block_table,
            k_cache_paged,
            v_cache_paged,
            num_blocks,
        ) = _generate_block_kvcache(
            seqlen_k, paged_kv_block_size, batch_size, nheads_k, d, device, dtype
        )
    cache_seqlens = torch.randint(
        0 if new_kv else 1,
        # If we don't use seqlen_q in the case of causal and rotary, cos/sin won't be long enough
        (
            (seqlen_k - (seqlen_q if (causal or local) and rotary_dim > 1 else seqlen_new) + 1)
            if new_kv
            else (seqlen_k + 1)
        ),
        (batch_size,),
        dtype=torch.int32,
        device=device,
    )
    if has_leftpad:
        cache_leftpad = torch.cat([torch.randint(0, cache_seqlens[i].item(), (1,), dtype=torch.int32, device=device)
                                   if cache_seqlens[i].item() > 0 else torch.zeros(1, dtype=torch.int32, device=device)
                                   for i in range(batch_size)])
    else:
        cache_leftpad = None
    arange = rearrange(torch.arange(seqlen_k, device=device), "s -> 1 s")
    cache_seqlens_expanded = rearrange(cache_seqlens, "b -> b 1")
    key_padding_mask = arange < cache_seqlens_expanded + (seqlen_new if new_kv else 0)
    if has_leftpad:
        key_padding_mask = torch.logical_and(
            key_padding_mask, arange >= cache_leftpad.unsqueeze(-1).expand(-1, seqlen_k)
        )
    if has_batch_idx:
        cache_batch_idx = torch.randperm(batch_size_cache, dtype=torch.int32, device=device)[
            :batch_size
        ]
    else:
        cache_batch_idx = None
    if alibi:
        alibi_slopes = torch.rand(batch_size, nheads, device=device, dtype=torch.float32) * 0.3
        attn_bias = attn_bias_from_alibi_slopes(
            alibi_slopes, seqlen_q, seqlen_k, None, key_padding_mask, causal=causal
        )
    else:
        alibi_slopes, attn_bias = None, None
    # cache_seqlens = torch.tensor([64], dtype=torch.int32, device=device)
    if rotary_dim > 0:
        angle = (
            torch.rand(
                seqlen_k if paged_kv_block_size is None else num_blocks * paged_kv_block_size,
                rotary_dim // 2,
                device=device,
            )
            * 2
            * math.pi
        )
        cos = torch.cos(angle).to(dtype=dtype)
        sin = torch.sin(angle).to(dtype=dtype)
        if causal or local:
            q_ro = apply_rotary_emb(
                q, cos, sin, seqlen_offsets=cache_seqlens, interleaved=rotary_interleaved
            )
        else:
            q_ro = rearrange(
                apply_rotary_emb(
                    rearrange(q, "b s h d -> b 1 (s h) d"),
                    cos,
                    sin,
                    seqlen_offsets=cache_seqlens,
                    interleaved=rotary_interleaved,
                ),
                "b 1 (s h) d -> b s h d",
                s=seqlen_q,
            )
        # q_ro = q
        k_ro = apply_rotary_emb(
            k, cos, sin, seqlen_offsets=cache_seqlens, interleaved=rotary_interleaved
        )
    else:
        cos, sin = None, None
        q_ro, k_ro = q, k
    # k_cache[:, 64:] = -1
    k_cache_ref = (
        k_cache if not has_batch_idx else k_cache[cache_batch_idx.to(dtype=torch.long)]
    ).clone()
    v_cache_ref = (
        v_cache if not has_batch_idx else v_cache[cache_batch_idx.to(dtype=torch.long)]
    ).clone()
    if new_kv:
        update_mask = torch.logical_and(
            cache_seqlens_expanded <= arange, arange < cache_seqlens_expanded + seqlen_new
        )
        k_cache_ref[update_mask] = rearrange(k_ro, "b s ... -> (b s) ...")
        v_cache_ref[update_mask] = rearrange(v, "b s ... -> (b s) ...")
    k_cache_rep = repeat(k_cache_ref, "b s h d -> b s (h g) d", g=nheads // nheads_k)
    v_cache_rep = repeat(v_cache_ref, "b s h d -> b s (h g) d", g=nheads // nheads_k)
    out = flash_attn_with_kvcache(
        q,
        k_cache if paged_kv_block_size is None else k_cache_paged,
        v_cache if paged_kv_block_size is None else v_cache_paged,
        k,
        v,
        rotary_cos=cos,
        rotary_sin=sin,
        cache_seqlens=cache_seqlens,
        cache_batch_idx=cache_batch_idx,
        cache_leftpad=cache_leftpad,
        block_table=block_table,
        causal=causal,
        window_size=window_size,
        rotary_interleaved=rotary_interleaved,
        alibi_slopes=alibi_slopes,
        num_splits=num_splits,
    )
    # out = flash_attn_with_kvcache(
    #     q, k_cache, v_cache, cache_seqlens=cache_seqlens, causal=causal, window_size=window_size
    # )
    # out = flash_attn_with_kvcache(q, k_cache, v_cache, causal=causal, window_size=window_size)
    # qk = torch.einsum("bqhd,bkhd->bhqk", q, k_cache_ref)
    # m = qk.amax(-1, keepdim=True)
    # s_tmp = torch.exp((qk - m) / math.sqrt(d))
    # o1 = torch.einsum('bhst,bthd->bshd', s_tmp, v_cache_ref)
    # lse_ref = torch.logsumexp(qk / math.sqrt(d), -1)
    # probs = torch.softmax(qk, dim=-1)
    out_ref, _ = attention_ref(
        q_ro,
        k_cache_rep,
        v_cache_rep,
        None,
        key_padding_mask,
        attn_bias,
        0.0,
        None,
        causal=causal,
        window_size=window_size,
        key_leftpad=cache_leftpad,
    )
    out_pt, _ = attention_ref(
        q_ro,
        k_cache_rep,
        v_cache_rep,
        None,
        key_padding_mask,
        attn_bias,
        0.0,
        None,
        causal=causal,
        window_size=window_size,
        upcast=False,
        reorder_ops=True,
        key_leftpad=cache_leftpad,
    )
    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()}")

    # Check that FlashAttention's numerical error is at most twice the numerical error
    # of a Pytorch implementation.
    if new_kv:
        if paged_kv_block_size is None:
            k_cache_select = (
                k_cache if not has_batch_idx else k_cache[cache_batch_idx.to(dtype=torch.long)]
            )
            v_cache_select = (
                v_cache if not has_batch_idx else v_cache[cache_batch_idx.to(dtype=torch.long)]
            )
        else:
            k_cache_select = rearrange(
                k_cache_paged[block_table.to(dtype=torch.long).flatten()],
                "(b nblocks) block_size ... -> b (nblocks block_size) ...",
                b=batch_size,
            )[:, :seqlen_k]
            v_cache_select = rearrange(
                v_cache_paged[block_table.to(dtype=torch.long).flatten()],
                "(b nblocks) block_size ... -> b (nblocks block_size) ...",
                b=batch_size,
            )[:, :seqlen_k]
        assert torch.allclose(k_cache_select, k_cache_ref, rtol=1e-3, atol=1e-3)
        assert torch.equal(v_cache_select, v_cache_ref)
    mult = 3 if not alibi else 5
    assert (out - out_ref).abs().max().item() <= mult * (out_pt - out_ref).abs().max().item() + 1e-5


def _generate_block_kvcache(seqlen_k, paged_kv_block_size, batch_size, nheads_k, d, device, dtype):
    num_blocks = math.ceil(seqlen_k / paged_kv_block_size) * batch_size * 3
    k_cache_paged = torch.randn(
        num_blocks, paged_kv_block_size, nheads_k, d, device=device, dtype=dtype
    )
    v_cache_paged = torch.randn(
        num_blocks, paged_kv_block_size, nheads_k, d, device=device, dtype=dtype
    )
    block_table = rearrange(
        torch.randperm(num_blocks, dtype=torch.int32, device=device),
        "(b nblocks) -> b nblocks",
        b=batch_size,
    )
    k_cache = rearrange(
        # pytorch 1.12 doesn't have indexing with int32
        k_cache_paged[block_table.to(dtype=torch.long).flatten()],
        "(b nblocks) block_size ... -> b (nblocks block_size) ...",
        b=batch_size,
    )[:, :seqlen_k]
    v_cache = rearrange(
        v_cache_paged[block_table.to(dtype=torch.long).flatten()],
        "(b nblocks) block_size ... -> b (nblocks block_size) ...",
        b=batch_size,
    )[:, :seqlen_k]
    return k_cache, v_cache, block_table, k_cache_paged, v_cache_paged, num_blocks


# @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, 56, 64, 80, 96, 128])
# @pytest.mark.parametrize("d", [32, 64, 96, 128, 160, 192])
# @pytest.mark.parametrize('d', [128])
@pytest.mark.parametrize(
    "seqlen_q,seqlen_k",
    [
        (1, 239),
        (239, 1),
        (3, 799),
        (799, 3),
        (1024, 128),
        (97, 97),
        (128, 128),
        (200, 200),
        (256, 256),
        (257, 257),
        (384, 384),
        (512, 512),
        (768, 768),
        (1024, 1024),
    ],
)
@pytest.mark.parametrize("dropout_p", [0.0, 0.17])
# @pytest.mark.parametrize("dropout_p", [0.0])
def test_flash_attn_race_condition(seqlen_q, seqlen_k, 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
    q = torch.randn(batch_size, seqlen_q, nheads, d, device=device, dtype=dtype, requires_grad=True)
    k = torch.randn(batch_size, seqlen_k, nheads, d, device=device, dtype=dtype, requires_grad=True)
    v = torch.randn(batch_size, seqlen_k, nheads, d, device=device, dtype=dtype, requires_grad=True)
    torch.random.manual_seed(42)
    out0, lse0, _ = flash_attn_func(q, k, v, dropout_p, causal=causal, return_attn_probs=True)
    g = torch.randn_like(out0)
    if (d <= MAX_HEADDIM_SM8x or (d > 224 and dropout_p == 0)) or (is_sm80 or is_sm90):
        (
            dq0,
            dk0,
            dv0,
        ) = torch.autograd.grad(out0, (q, k, v), g)
        # Numerical error if we just do any arithmetic on dq
        dq_atol = 2 * ((dq0 + 0.3 - 0.3) - dq0).abs().max().item()

    for i in range(250):
        torch.random.manual_seed(42)
        out, lse, _ = flash_attn_func(q, k, v, dropout_p, causal=causal, return_attn_probs=True)
        assert torch.equal(out, out0)
        assert torch.equal(lse, lse0)

        if (d <= MAX_HEADDIM_SM8x or (d > 224 and dropout_p == 0)) or (is_sm80 or is_sm90):
            (
                dq,
                dk,
                dv,
            ) = torch.autograd.grad(out, (q, k, v), g)
            dq_equal = torch.allclose(dq, dq0, atol=dq_atol)
            if not dq_equal:
                print(f"Iter {i}, {dq_atol = }, dQ max diff: {(dq - dq0).abs().max().item()}")
            assert torch.equal(dv, dv0)
            assert torch.equal(dk, dk0)
            assert dq_equal


@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()


@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])
def test_flash_attn_bwd_varlen_overflow(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 or varlen.
    """
    device = "cuda"
    # set seed
    torch.random.manual_seed(0)
    nheads = 5
    q_cuseqlen = torch.tensor([0, 76, 110, 256], device=device, dtype=torch.int32)
    k_cuseqlen = torch.tensor([0, 1, 2, 3], device=device, dtype=torch.int32)
    Mq = 256
    Mk = 3

    q = torch.randn([Mq, nheads, d], dtype=dtype, device=device) * 3
    k, v = [torch.randn([Mk, nheads, d], dtype=dtype, device=device) * 3 for _ in range(2)]
    q.requires_grad_(True)
    k.requires_grad_(True)
    v.requires_grad_(True)

    out = flash_attn_varlen_func(q, k, v, q_cuseqlen, k_cuseqlen, Mq, Mk, causal=causal)
    g = torch.randn_like(out)
    out.backward(g)

    assert not q.grad.isnan().any()
    assert not k.grad.isnan().any()
    assert not v.grad.isnan().any()


@pytest.mark.parametrize("dtype", ([torch.float16] if is_sm75 else [torch.float16, torch.bfloat16]))
# @pytest.mark.parametrize("dtype", [torch.bfloat16])
@pytest.mark.parametrize("local", [False, True])
# @pytest.mark.parametrize("local", [True])
@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, 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("swap_sq_sk", [False, True])
# @pytest.mark.parametrize("swap_sq_sk", [False])
@pytest.mark.parametrize(
    "seqlen_q,seqlen_k",
    [
        (1, 239),
        (3, 799),
        (127, 512),
        (127, 513),
        (113, 203),
        (128, 217),
        (113, 211),
        (108, 256),
        (256, 512),
        (1023, 1024),
    ],
)
# @pytest.mark.parametrize('seqlen_q,seqlen_k', [(256, 128)])
def test_flash_attn_deterministic(seqlen_q, seqlen_k, swap_sq_sk, d, causal, local, dtype):
    if (
        max(seqlen_q, seqlen_k) >= 2048
        and torch.cuda.get_device_properties("cuda").total_memory <= 16 * 2**30
    ):
        pytest.skip()  # Reference implementation OOM
    if swap_sq_sk:
        seqlen_q, seqlen_k = seqlen_k, seqlen_q
    device = "cuda"
    # set seed
    torch.random.manual_seed(0)
    batch_size = 4
    nheads = 9
    window_size = (-1, -1) if not local else torch.randint(0, seqlen_k, (2,))
    q = torch.randn(batch_size, seqlen_q, nheads, d, device=device, dtype=dtype, requires_grad=True)
    k = torch.randn(batch_size, seqlen_k, nheads, d, device=device, dtype=dtype, requires_grad=True)
    v = torch.randn(batch_size, seqlen_k, nheads, d, device=device, dtype=dtype, requires_grad=True)
    out = flash_attn_func(q, k, v, 0.0, causal=causal, window_size=window_size, deterministic=True)

    g = torch.randn_like(out)
    if (d <= MAX_HEADDIM_SM8x or d > 224) or (is_sm80 or is_sm90):
        dq0, dk0, dv0 = torch.autograd.grad(out, (q, k, v), g, retain_graph=True)
        for _ in range(50):
            dq, dk, dv = torch.autograd.grad(out, (q, k, v), g, retain_graph=True)
            assert torch.equal(dv, dv0)
            assert torch.equal(dk, dk0)
            assert torch.equal(dq, dq0)


@pytest.mark.parametrize("dtype", ([torch.float16] if is_sm75 else [torch.float16, torch.bfloat16]))
# @pytest.mark.parametrize("dtype", [torch.bfloat16])
@pytest.mark.parametrize("local", [False, True])
# @pytest.mark.parametrize("local", [True])
@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, 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("swap_sq_sk", [False, True])
# @pytest.mark.parametrize("swap_sq_sk", [True])
@pytest.mark.parametrize(
    "seqlen_q,seqlen_k",
    [
        (1, 239),
        (3, 799),
        (127, 512),
        (127, 513),
        (113, 203),
        (128, 217),
        (113, 211),
        (108, 256),
        (256, 512),
        (1023, 1024),
    ],
)
# @pytest.mark.parametrize("seqlen_q,seqlen_k", [(256, 128)])
def test_flash_attn_varlen_deterministic(seqlen_q, seqlen_k, swap_sq_sk, d, causal, local, dtype):
    if (
        max(seqlen_q, seqlen_k) >= 2048
        and torch.cuda.get_device_properties("cuda").total_memory <= 16 * 2**30
    ):
        pytest.skip()  # Reference implementation OOM
    if swap_sq_sk:
        seqlen_q, seqlen_k = seqlen_k, seqlen_q
    device = "cuda"
    # set seed
    torch.random.manual_seed(0)
    batch_size = 2
    nheads = 9
    window_size = (-1, -1) if not local else torch.randint(0, seqlen_k, (2,))
    q = torch.randn(batch_size, seqlen_q, nheads, d, device=device, dtype=dtype, requires_grad=True)
    k = torch.randn(batch_size, seqlen_k, nheads, d, device=device, dtype=dtype, requires_grad=True)
    v = torch.randn(batch_size, seqlen_k, nheads, 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")
    (
        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 = flash_attn_varlen_func(
        q_unpad,
        k_unpad,
        v_unpad,
        cu_seqlens_q,
        cu_seqlens_k,
        max_seqlen_q,
        max_seqlen_k,
        0.0,
        causal=causal,
        window_size=window_size,
        deterministic=True,
    )

    g = torch.randn_like(out)
    if (d <= MAX_HEADDIM_SM8x or d > 224) or (is_sm80 or is_sm90):
        dq0, dk0, dv0 = torch.autograd.grad(out, (q_unpad, k_unpad, v_unpad), g, retain_graph=True)
        for _ in range(50):
            dq, dk, dv = torch.autograd.grad(out, (q_unpad, k_unpad, v_unpad), g, retain_graph=True)
            assert torch.equal(dv, dv0)
            assert torch.equal(dk, dk0)
            assert torch.equal(dq, dq0)