import math
from functools import partial

import torch
import torch.nn.functional as F

import pytest

from einops import rearrange, repeat

from flash_attn.flash_attn_interface import flash_attn_func, flash_attn_unpadded_qkvpacked_func, _get_block_size, flash_attn_unpadded_kvpacked_func, flash_attn_unpadded_func
from flash_attn.flash_attn_interface import flash_attn_unpadded_qkvpacked_split_func
from flash_attn.bert_padding import unpad_input, pad_input, index_first_axis


is_sm75 = torch.cuda.get_device_capability('cuda') == (7, 5)
is_sm80 = torch.cuda.get_device_capability('cuda') == (8, 0)


def generate_random_padding_mask(max_seqlen, batch_size, device, mode='random'):
    assert mode in ['full', 'random', 'third', 'split']
    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)
    elif mode == 'split':
        lengths0 = torch.randint(min(128, max_seqlen), max_seqlen + 1,
                                 (batch_size // 4 * 3, 1), device=device)
        lengths1 = torch.randint(min(max(1, max_seqlen - 20), 128), min(max_seqlen, 128) + 1,
                                 (batch_size - batch_size // 4 * 3, 1), device=device)
        lengths = torch.cat([lengths0, lengths1], dim=0)
    padding_mask = repeat(torch.arange(max_seqlen, device=device), 's -> b s', b=batch_size) < lengths
    return padding_mask


def generate_qkv(x, Wqkv, nheads, query_padding_mask=None, key_padding_mask=None,
                 kvpacked=False, qkvpacked=False):
    """
    Arguments:
        x: (batch_size, seqlen, nheads * d)
        Wqkv: nn.Linear(nheads * d, 3 * nheads * d)
        query_padding_mask: (batch_size, seqlen), bool
        key_padding_mask: (batch_size, seqlen), bool
    """
    assert not (kvpacked and qkvpacked)
    batch_size, seqlen, dim = x.shape
    q, k, v = Wqkv(x).chunk(3, dim=-1)

    if query_padding_mask is not None:
        q_unpad, indices_q, cu_seqlens_q, max_seqlen_q = unpad_input(q, query_padding_mask)
        q_unpad = rearrange(q_unpad, 'nnz (h d) -> nnz h d', h=nheads)
        output_pad_fn = lambda output_unpad: rearrange(
            pad_input(rearrange(output_unpad, 'nnz h d -> nnz (h d)'), indices_q, batch_size, seqlen),
            'b s (h d) -> b s h d', h=nheads
        )
    else:
        q_unpad = rearrange(q, 'b s (h d) -> (b s) h d', h=nheads)
        cu_seqlens_q = torch.arange(0, (batch_size + 1) * seqlen, step=seqlen, dtype=torch.int32,
                                    device=q_unpad.device)
        max_seqlen_q = seqlen
        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)
        k_unpad = rearrange(k_unpad, 'nnz (h d) -> nnz h d', h=nheads)
        v_unpad, _, _, _ = unpad_input(v, key_padding_mask)
        v_unpad = rearrange(v_unpad, 'nnz (h d) -> nnz h d', h=nheads)
    else:
        k_unpad = rearrange(k, 'b s (h d) -> (b s) h d', h=nheads)
        v_unpad = rearrange(v, 'b s (h d) -> (b s) h d', h=nheads)
        cu_seqlens_k = torch.arange(0, (batch_size + 1) * seqlen, step=seqlen, dtype=torch.int32,
                                    device=q_unpad.device)
        max_seqlen_k = seqlen

    if qkvpacked:
        assert (query_padding_mask == key_padding_mask).all()
        qkv_unpad = torch.stack([q_unpad, k_unpad, v_unpad], dim=1)
        qkv = rearrange(torch.stack([q, k, v], dim=2), 'b s t (h d) -> b s t h d', h=nheads)
        if query_padding_mask is not None:
            dqkv_pad_fn = lambda dqkv_unpad: rearrange(
                pad_input(rearrange(dqkv_unpad, 'nnz t h d -> nnz (t h d)'), indices_q, batch_size, seqlen),
                'b s (t h d) -> b s t h d', t=3, h=nheads
            )
        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)
        q = rearrange(q, 'b s (h d) -> b s h d', h=nheads)
        kv = rearrange(torch.stack([k, v], dim=2), 'b s t (h d) -> b s t h d', h=nheads)
        dq_pad_fn = output_pad_fn
        if key_padding_mask is not None:
            dkv_pad_fn = lambda dkv_unpad: rearrange(
                pad_input(rearrange(dkv_unpad, 'nnz t h d -> nnz (t h d)'), indices_k, batch_size, seqlen),
                'b s (t h d) -> b s t h d', t=2, h=nheads
            )
        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:
        q, k, v = [rearrange(z, 'b s (h d) -> b s h d', h=nheads).detach().requires_grad_()
                   for z in [q, k, v]]
        dq_pad_fn = output_pad_fn
        if key_padding_mask is not None:
            dk_pad_fn = lambda dk_unpad: rearrange(
                pad_input(rearrange(dk_unpad, 'nnz h d -> nnz (h d)'), indices_k, batch_size, seqlen),
                'b s (h d) -> b s h d', h=nheads
            )
        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, k, v,
                output_pad_fn, dq_pad_fn, dk_pad_fn)


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


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


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


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


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


def convert_flash_attn_S_to_softmax(S, query_padding_mask, key_padding_mask, head_dim, is_dropout,
                                    causal=False):
    """FlashAttention stores the S matrix in a different way.
    Arguments:
        S: (batch_size, nheads, seqlen_q, seqlen_k)
        query_padding_mask: (batch_size, seqlen_q)
        key_padding_mask: (batch_size, seqlen_k)
    """
    S_flat = rearrange(S, 'b h t s -> b h (t s)')
    seqlen_q, seqlen_k = S.shape[-2:]
    block_size = _get_block_size(S.device, head_dim, is_dropout)
    loop_steps = (seqlen_k + block_size - 1) // block_size
    warps_n = 4
    mmas_n = (seqlen_k // warps_n // 16) if seqlen_k <= block_size else (block_size // warps_n // 16)
    S_converted = rearrange(S_flat, 'b h (loop nsteps mmas_n warps_n eight t r c0 c1) -> b h (nsteps r eight) (loop mmas_n warps_n c0 t c1)',
                            loop=loop_steps, nsteps=seqlen_q // 16, mmas_n=mmas_n, warps_n=warps_n, eight=8, t=4,
                            r=2, c0=2, c1=2)

    # 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 seqlen_q_og < seqlen_q:
        query_padding_mask = F.pad(query_padding_mask, (0, seqlen_q - seqlen_q_og))
    else:
        query_padding_mask = query_padding_mask[:, :seqlen_q]
    S_converted = S_converted.masked_fill(rearrange(~query_padding_mask, 'b s -> b 1 s 1'), 0.0)
    seqlen_k_og = key_padding_mask.shape[-1]
    if seqlen_k_og < seqlen_k:
        key_padding_mask = F.pad(key_padding_mask, (0, seqlen_k - seqlen_k_og))
    else:
        key_padding_mask = key_padding_mask[:, :seqlen_k]
    S_converted = S_converted.masked_fill(rearrange(~key_padding_mask, 'b s -> b 1 1 s'), 0.0)
    if causal:
        causal_mask = torch.triu(torch.ones(seqlen_q, seqlen_k, dtype=torch.bool, device=S.device), 1)
        S_converted.masked_fill_(causal_mask, 0.0)
    if seqlen_q_og < seqlen_q:
        S_converted = S_converted[:, :, :seqlen_q_og, :]
    else:
        S_converted = F.pad(S_converted, (0, 0, 0, seqlen_q_og - seqlen_q))
    if seqlen_k_og < seqlen_k:
        S_converted = S_converted[:, :, :, :seqlen_k_og]
    else:
        S_converted = F.pad(S_converted, (0, seqlen_k_og - seqlen_k))
    return S_converted


def normalize_flash_attn_S(attn_unnorm, q, k, v, query_padding_mask=None, key_padding_mask=None,
                           is_dropout=False, causal=False):
    """
    Arguments:
        q: (batch_size, seqlen_q, nheads, head_dim)
        k, v: (batch_size, seqlen_k, nheads, head_dim)
        key_padding_mask: (batch_size, seqlen_q)
    Output:
        softmax_lse: (batch_size, nheads, seqlen_q)
        softmax_max: (batch_size, nheads, seqlen_q)
    """
    q, k, v = q.float(), k.float(), v.float()
    _, seqlen_q, _, head_dim = q.shape
    seqlen_k = k.shape[1]
    scores = torch.einsum('bthd,bshd->bhts', q / math.sqrt(head_dim), k)
    if key_padding_mask is not None:
        scores.masked_fill_(rearrange(~key_padding_mask, 'b s -> b 1 1 s'), float('-inf'))
    if causal:
        causal_mask = torch.triu(torch.ones(seqlen_q, seqlen_k, dtype=torch.bool, device=q.device), 1)
        scores.masked_fill_(causal_mask, float('-inf'))
    block_size = _get_block_size(scores.device, head_dim, is_dropout)
    scores_block = scores.split(block_size, dim=-1)
    lse_block = torch.stack([torch.logsumexp(s, dim=-1) for s in scores_block], dim=-1)
    lcse_block = torch.logcumsumexp(lse_block, dim=-1).unbind(dim=-1)
    scores_max_block = ([torch.amax(scores_block[0], dim=-1)]
                        + [torch.maximum(torch.amax(s, dim=-1), lcse)
                           for s, lcse in zip(scores_block[1:], lcse_block[:-1])])
    attn_unnorm_block = attn_unnorm.split(block_size, dim=-1)
    attn_norm = torch.cat([a / rearrange(torch.exp(lcse_block[-1] - m), 'b h s -> b h s 1')
                           for a, m in zip(attn_unnorm_block, scores_max_block)], dim=-1)
    if query_padding_mask is not None:
        attn_norm.masked_fill_(rearrange(~query_padding_mask, 'b s -> b 1 s 1'), 0.0)
    return attn_norm.to(dtype=attn_unnorm.dtype)


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


@pytest.mark.parametrize('dtype', ([torch.float16] if is_sm75 else [torch.float16, torch.bfloat16]))
# @pytest.mark.parametrize('dtype', [torch.float16])
@pytest.mark.parametrize('causal', [False, True])
# @pytest.mark.parametrize('causal', [False])
@pytest.mark.parametrize('d', [128, 64, 80, 40, 32, 16])
# @pytest.mark.parametrize('d', [64])
@pytest.mark.parametrize('seqlen', [97, 128, 200, 256, 257, 384, 512, 768, 1024, 1025, 2048])
# @pytest.mark.parametrize('seqlen', [128])
@pytest.mark.parametrize('dropout_p', [0.0, 0.17])
# @pytest.mark.parametrize('dropout_p', [0.0])
def test_flash_attn_unpadded_qkvpacked(seqlen, d, dropout_p, causal, dtype):
    if seqlen >= 2048 and torch.cuda.get_device_properties('cuda').total_memory <= 16 * 2**30:
        pytest.skip()  # Reference implementation OOM
    device = 'cuda'
    # if dtype == torch.float16:
    #     rtol, atol = (1e-3, 3e-4) if not causal else (1e-3, 1e-3)
    # else:  # torch.bfloat16
    #     rtol, atol = (3e-3, 3e-3) if not causal else (1e-3, 1e-3)
    # set seed
    torch.random.manual_seed(0)
    # Set smaller batch size so it would trigger num_splits > 1
    batch_size = 8
    nheads = 4
    x = torch.randn(batch_size, seqlen, nheads * d, device=device, dtype=dtype, requires_grad=True)
    Wqkv = torch.nn.Linear(nheads * d, 3 * nheads * d, device=device, dtype=dtype)

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

    qkv_unpad, cu_seqlens, max_seqlen, qkv, output_pad_fn, dqkv_pad_fn = generate_qkv(
        x, Wqkv, nheads, key_padding_mask, key_padding_mask, qkvpacked=True
    )

    output_unpad, sm_lse, S_dmask = flash_attn_unpadded_qkvpacked_func(
        qkv_unpad, cu_seqlens, max_seqlen, dropout_p, return_attn_probs=True, causal=causal
    )
    output = output_pad_fn(output_unpad)
    S_dmask_converted = convert_flash_attn_S_to_softmax(
        S_dmask, key_padding_mask, key_padding_mask, d, dropout_p > 0.0, causal=causal
    )
    dropout_mask = S_dmask_converted >= 0
    attn_unnorm = S_dmask_converted.abs()
    attn = normalize_flash_attn_S(attn_unnorm, qkv[:, :, 0], qkv[:, :, 1], qkv[:, :, 2],
                                  key_padding_mask, key_padding_mask, dropout_p > 0.0, causal=causal)
    dropout_fraction = get_dropout_fraction(dropout_mask, key_padding_mask, key_padding_mask,
                                            causal=causal).item()

    output_ref, attn_ref = attention_qkvpacked_ref(qkv, key_padding_mask, dropout_p, dropout_mask,
                                                   causal=causal)
    output_pt, attn_pt = attention_qkvpacked_ref(qkv, key_padding_mask, dropout_p, dropout_mask,
                                                 causal=causal, upcast=False, reorder_ops=True)
    print(f'Actual dropout fraction: {dropout_fraction}')
    print(f'Output max diff: {(output - output_ref).abs().max().item()}')
    print(f'Output mean diff: {(output - output_ref).abs().mean().item()}')
    print(f'Pytorch max diff: {(output_pt - output_ref).abs().max().item()}')
    print(f'Pytorch mean diff: {(output_pt - output_ref).abs().mean().item()}')
    print(f'Attention max diff: {(attn - attn_ref).abs().max().item()}')
    print(f'Attention Pytorch max diff: {(attn_pt - attn_ref).abs().max().item()}')

    if is_sm80 or d <= 64:  # Only run backward for d=128 on A100
        g = torch.randn_like(output)
        dqkv_unpad, = torch.autograd.grad(output, qkv_unpad, g)
        dqkv = dqkv_pad_fn(dqkv_unpad)
        dqkv_ref, = torch.autograd.grad(output_ref, qkv, g)
        dqkv_pt, = torch.autograd.grad(output_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 (output - output_ref).abs().max().item() <= 2 * (output_pt - output_ref).abs().max().item()
    # assert torch.allclose(output, output_ref, rtol=rtol, atol=atol)
    assert (attn - attn_ref).abs().max().item() <= 2 * (attn_pt - attn_ref).abs().max().item()
    # assert torch.allclose(attn, attn_ref, rtol=rtol, atol=atol)
    if dropout_p == 0.0:
        assert dropout_mask.all()
    else:
        assert 0.98 <= dropout_fraction / dropout_p <= 1.02

    if is_sm80 or d <= 64:  # Only run backward for d=128 on A100
        # Error for dK and dV could be a bit higher if we're splitting along seqlen_q dimension
        assert (dqkv - dqkv_ref).abs().max().item() <= 4 * (dqkv_pt - dqkv_ref).abs().max().item()
        # assert torch.allclose(dqkv, dqkv_ref, rtol=rtol, atol=atol)


@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('d', [128, 64, 80, 40, 32, 16])
# @pytest.mark.parametrize('d', [64])
@pytest.mark.parametrize('seqlen', [97, 128, 200, 256, 257, 384, 512, 768, 1024, 1025, 2048])
# @pytest.mark.parametrize('seqlen', [128])
@pytest.mark.parametrize('dropout_p', [0.0, 0.17])
# @pytest.mark.parametrize('dropout_p', [0.0])
def test_flash_attn_unpadded_kvpacked(seqlen, d, dropout_p, causal, dtype):
    if seqlen >= 2048 and torch.cuda.get_device_properties('cuda').total_memory <= 16 * 2**30:
        pytest.skip()  # Reference implementation OOM
    device = 'cuda'
    # if dtype == torch.float16:
    #     rtol, atol = (1e-3, 3e-4) if not causal else (1e-3, 1e-3)
    # else:  # torch.bfloat16
    #     rtol, atol = (3e-3, 3e-3) if not causal else (1e-3, 1e-3)
    # set seed
    torch.random.manual_seed(0)
    batch_size = 32
    nheads = 4
    x = torch.randn(batch_size, seqlen, nheads * d, device=device, dtype=dtype, requires_grad=True)
    Wqkv = torch.nn.Linear(nheads * d, 3 * nheads * d, device=device, dtype=dtype)

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

    (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(
         x, Wqkv, nheads, query_padding_mask, key_padding_mask, kvpacked=True
     )

    output_unpad, sm_lse, S_dmask = flash_attn_unpadded_kvpacked_func(
        q_unpad, kv_unpad, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k,
        dropout_p, return_attn_probs=True, causal=causal
    )
    output = output_pad_fn(output_unpad)
    S_dmask_converted = convert_flash_attn_S_to_softmax(
        S_dmask, query_padding_mask, key_padding_mask, d, dropout_p > 0.0, causal=causal
    )
    dropout_mask = S_dmask_converted >= 0
    attn_unnorm = S_dmask_converted.abs()
    attn = normalize_flash_attn_S(attn_unnorm, q, kv[:, :, 0], kv[:, :, 1],
                                  query_padding_mask, key_padding_mask, dropout_p > 0.0, causal=causal)
    dropout_fraction = get_dropout_fraction(dropout_mask, query_padding_mask, key_padding_mask,
                                            causal=causal)

    output_ref, attn_ref = attention_kvpacked_ref(q, kv, query_padding_mask, key_padding_mask,
                                                  dropout_p, dropout_mask, causal=causal)
    output_pt, attn_pt = attention_kvpacked_ref(q, kv, query_padding_mask, key_padding_mask,
                                                dropout_p, dropout_mask, causal=causal,
                                                upcast=False, reorder_ops=True)
    print(f'Actual dropout fraction: {dropout_fraction}')
    print(f'Output max diff: {(output - output_ref).abs().max().item()}')
    print(f'Output mean diff: {(output - output_ref).abs().mean().item()}')
    print(f'Pytorch max diff: {(output_pt - output_ref).abs().max().item()}')
    print(f'Pytorch mean diff: {(output_pt - output_ref).abs().mean().item()}')
    print(f'Attention max diff: {(attn - attn_ref).abs().max().item()}')
    print(f'Attention Pytorch max diff: {(attn_pt - attn_ref).abs().max().item()}')

    if is_sm80 or d <= 64:  # Only run backward for d=128 on A100
        g = torch.randn_like(output)
        dq_unpad, dkv_unpad, = torch.autograd.grad(output, (q_unpad, kv_unpad), g)
        dq = dq_pad_fn(dq_unpad)
        dkv = dkv_pad_fn(dkv_unpad)
        dq_ref, dkv_ref, = torch.autograd.grad(output_ref, (q, kv), g)
        dq_pt, dkv_pt = torch.autograd.grad(output_pt, (q, kv), g)
        print(f'dQ max diff: {(dq - dq_ref).abs().max().item()}')
        print(f'dK max diff: {(dkv[:, :, 0] - dkv_ref[:, :, 0]).abs().max().item()}')
        print(f'dV max diff: {(dkv[:, :, 1] - dkv_ref[:, :, 1]).abs().max().item()}')
        print(f'dQ Pytorch max diff: {(dq_pt - dq_ref).abs().max().item()}')
        print(f'dK Pytorch max diff: {(dkv_pt[:, :, 0] - dkv_ref[:, :, 0]).abs().max().item()}')
        print(f'dV Pytorch max diff: {(dkv_pt[:, :, 1] - dkv_ref[:, :, 1]).abs().max().item()}')

    # Check that FlashAttention's numerical error is at most twice the numerical error
    # of a Pytorch implementation.
    assert (output - output_ref).abs().max().item() <= 2 * (output_pt - output_ref).abs().max().item()
    # assert torch.allclose(output, output_ref, rtol=rtol, atol=atol)
    assert (attn - attn_ref).abs().max().item() <= 2 * (attn_pt - attn_ref).abs().max().item()
    # assert torch.allclose(attn, attn_ref, rtol=rtol, atol=atol)
    if dropout_p == 0.0:
        assert dropout_mask.all()
    else:
        assert 0.99 <= dropout_fraction / dropout_p <= 1.01

    if is_sm80 or d <= 64:  # Only run backward for d=128 on A100
        assert (dq - dq_ref).abs().max().item() <= 2 * (dq_pt - dq_ref).abs().max().item()
        assert (dkv - dkv_ref).abs().max().item() <= 2 * (dkv_pt - dkv_ref).abs().max().item()
        # assert torch.allclose(dq, dq_ref, rtol=rtol, atol=atol)
        # assert torch.allclose(dkv, dkv_ref, rtol=rtol, atol=atol)


@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('d', [128, 64, 80, 40, 32, 16])
# @pytest.mark.parametrize('d', [64])
@pytest.mark.parametrize('seqlen', [97, 128, 200, 256, 257, 384, 512, 768, 1024, 1025, 2048])
# @pytest.mark.parametrize('seqlen', [128])
@pytest.mark.parametrize('dropout_p', [0.0, 0.17])
# @pytest.mark.parametrize('dropout_p', [0.0])
def test_flash_attn_unpadded(seqlen, d, dropout_p, causal, dtype):
    if seqlen >= 2048 and torch.cuda.get_device_properties('cuda').total_memory <= 16 * 2**30:
        pytest.skip()  # Reference implementation OOM
    device = 'cuda'
    # if dtype == torch.float16:
    #     rtol, atol = (1e-3, 3e-4) if not causal else (1e-3, 1e-3)
    # else:  # torch.bfloat16
    #     rtol, atol = (3e-3, 3e-3) if not causal else (1e-3, 1e-3)
    # set seed
    torch.random.manual_seed(0)
    batch_size = 32
    nheads = 4
    x = torch.randn(batch_size, seqlen, nheads * d, device=device, dtype=dtype, requires_grad=True)
    Wqkv = torch.nn.Linear(nheads * d, 3 * nheads * d, device=device, dtype=dtype)

    query_padding_mask = generate_random_padding_mask(seqlen, batch_size, device, mode='random')
    key_padding_mask = generate_random_padding_mask(seqlen, 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(
         x, Wqkv, nheads, query_padding_mask, key_padding_mask
     )

    output_unpad, sm_lse, S_dmask = flash_attn_unpadded_func(
        q_unpad, k_unpad, v_unpad, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k,
        dropout_p, return_attn_probs=True, causal=causal
    )
    output = output_pad_fn(output_unpad)
    S_dmask_converted = convert_flash_attn_S_to_softmax(
        S_dmask, query_padding_mask, key_padding_mask, d, dropout_p > 0.0, causal=causal
    )
    dropout_mask = S_dmask_converted >= 0
    attn_unnorm = S_dmask_converted.abs()
    attn = normalize_flash_attn_S(attn_unnorm, q, k, v, query_padding_mask, key_padding_mask,
                                  dropout_p > 0.0, causal=causal)
    dropout_fraction = get_dropout_fraction(dropout_mask, query_padding_mask, key_padding_mask,
                                            causal=causal)

    output_ref, attn_ref = attention_ref(q, k, v, query_padding_mask, key_padding_mask,
                                         dropout_p, dropout_mask, causal=causal)
    output_pt, attn_pt = attention_ref(q, k, v, query_padding_mask, key_padding_mask,
                                       dropout_p, dropout_mask, causal=causal,
                                       upcast=False, reorder_ops=True)
    print(f'Actual dropout fraction: {dropout_fraction}')
    print(f'Output max diff: {(output - output_ref).abs().max().item()}')
    print(f'Output mean diff: {(output - output_ref).abs().mean().item()}')
    print(f'Pytorch max diff: {(output_pt - output_ref).abs().max().item()}')
    print(f'Pytorch mean diff: {(output_pt - output_ref).abs().mean().item()}')
    print(f'Attention max diff: {(attn - attn_ref).abs().max().item()}')
    print(f'Attention Pytorch max diff: {(attn_pt - attn_ref).abs().max().item()}')

    if is_sm80 or d <= 64:  # Only run backward for d=128 on A100
        g = torch.randn_like(output)
        dq_unpad, dk_unpad, dv_unpad, = torch.autograd.grad(output, (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(output_ref, (q, k, v), g)
        dq_pt, dk_pt, dv_pt, = torch.autograd.grad(output_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 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()}')

    # Check that FlashAttention's numerical error is at most twice the numerical error
    # of a Pytorch implementation.
    assert (output - output_ref).abs().max().item() <= 2 * (output_pt - output_ref).abs().max().item()
    # assert torch.allclose(output, output_ref, rtol=rtol, atol=atol)
    assert (attn - attn_ref).abs().max().item() <= 2 * (attn_pt - attn_ref).abs().max().item()
    # assert torch.allclose(attn, attn_ref, rtol=rtol, atol=atol)
    if dropout_p == 0.0:
        assert dropout_mask.all()
    else:
        assert 0.99 <= dropout_fraction / dropout_p <= 1.01

    if is_sm80 or d <= 64:  # Only run backward for d=128 on A100
        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()
        # assert torch.allclose(dq, dq_ref, rtol=rtol, atol=atol)
        # assert torch.allclose(dk, dk_ref, rtol=rtol, atol=atol)
        # assert torch.allclose(dv, dv_ref, rtol=rtol, atol=atol)


@pytest.mark.parametrize('dtype', ([torch.float16] if is_sm75 else [torch.float16, torch.bfloat16]))
# @pytest.mark.parametrize('dtype', [torch.float16])
@pytest.mark.parametrize('causal', [False, True])
# @pytest.mark.parametrize('causal', [False])
@pytest.mark.parametrize('d', [128, 64, 80, 40, 32, 16])
# @pytest.mark.parametrize('d', [64])
@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_split(seqlen, d, dropout_p, causal, dtype):
    if seqlen >= 2048 and torch.cuda.get_device_properties('cuda').total_memory <= 16 * 2**30:
        pytest.skip()  # Reference implementation OOM
    device = 'cuda'
    # if dtype == torch.float16:
    #     rtol, atol = (1e-3, 3e-4) if not causal else (1e-3, 1e-3)
    # else:  # torch.bfloat16
    #     rtol, atol = (3e-3, 3e-3) if not causal else (1e-3, 1e-3)
    # set seed
    torch.random.manual_seed(0)
    batch_size = 32
    nheads = 4
    x = torch.randn(batch_size, seqlen, nheads * d, device=device, dtype=dtype, requires_grad=True)
    Wqkv = torch.nn.Linear(nheads * d, 3 * nheads * d, device=device, dtype=dtype)

    key_padding_mask = generate_random_padding_mask(seqlen, batch_size, device, mode='split')
    batch_size0 = batch_size // 4 * 3  # this must match what's in generate_random_padding_mask
    # key_padding_mask = generate_random_padding_mask(seqlen, batch_size, device, mode='full')

    qkv_unpad, cu_seqlens, max_seqlen0, qkv, output_pad_fn, dqkv_pad_fn = generate_qkv(
        x, Wqkv, nheads, key_padding_mask, key_padding_mask, qkvpacked=True
    )
    max_seqlen1 = 128

    output_unpad, sm_lse, S_dmask0, S_dmask1 = flash_attn_unpadded_qkvpacked_split_func(
        qkv_unpad, cu_seqlens, max_seqlen0, max_seqlen1, batch_size0, dropout_p,
        return_attn_probs=True, causal=causal
    )
    output = output_pad_fn(output_unpad)
    S_dmask0_converted = convert_flash_attn_S_to_softmax(
        S_dmask0, key_padding_mask[:batch_size0], key_padding_mask[:batch_size0], d, dropout_p > 0.0, causal=causal
    )
    S_dmask1_converted = convert_flash_attn_S_to_softmax(
        S_dmask1, key_padding_mask[batch_size0:, :max_seqlen1], key_padding_mask[batch_size0:, :max_seqlen1], d, dropout_p > 0.0, causal=causal
    )
    padding = (S_dmask0_converted.shape[-1] - S_dmask1_converted.shape[-1],
               S_dmask0_converted.shape[-2] - S_dmask1_converted.shape[-2])
    S_dmask_converted = torch.cat([S_dmask0_converted,
                                   F.pad(S_dmask1_converted, (0, padding[0], 0, padding[1]))], dim=0)
    dropout_mask = S_dmask_converted >= 0
    attn_unnorm = S_dmask_converted.abs()
    attn = normalize_flash_attn_S(attn_unnorm, qkv[:, :, 0], qkv[:, :, 1], qkv[:, :, 2],
                                  key_padding_mask, key_padding_mask, dropout_p > 0.0, causal=causal)
    dropout_fraction = get_dropout_fraction(dropout_mask, key_padding_mask, key_padding_mask,
                                            causal=causal).item()

    output_ref, attn_ref = attention_qkvpacked_ref(qkv, key_padding_mask, dropout_p, dropout_mask,
                                                   causal=causal)
    output_pt, attn_pt = attention_qkvpacked_ref(qkv, key_padding_mask, dropout_p, dropout_mask,
                                                 causal=causal, upcast=False, reorder_ops=True)
    print(f'Actual dropout fraction: {dropout_fraction}')
    print(f'Output max diff: {(output - output_ref).abs().max().item()}')
    print(f'Output mean diff: {(output - output_ref).abs().mean().item()}')
    print(f'Pytorch max diff: {(output_pt - output_ref).abs().max().item()}')
    print(f'Pytorch mean diff: {(output_pt - output_ref).abs().mean().item()}')
    print(f'Attention max diff: {(attn - attn_ref).abs().max().item()}')
    print(f'Attention Pytorch max diff: {(attn_pt - attn_ref).abs().max().item()}')

    if is_sm80 or d <= 64:  # Only run backward for d=128 on A100
        g = torch.randn_like(output)
        dqkv_unpad, = torch.autograd.grad(output, qkv_unpad, g)
        dqkv = dqkv_pad_fn(dqkv_unpad)
        dqkv_ref, = torch.autograd.grad(output_ref, qkv, g)
        dqkv_pt, = torch.autograd.grad(output_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 (output - output_ref).abs().max().item() <= 2 * (output_pt - output_ref).abs().max().item()
    # assert torch.allclose(output, output_ref, rtol=rtol, atol=atol)
    assert (attn - attn_ref).abs().max().item() <= 2 * (attn_pt - attn_ref).abs().max().item()
    # assert torch.allclose(attn, attn_ref, rtol=rtol, atol=atol)
    if dropout_p == 0.0:
        assert dropout_mask.all()
    else:
        assert 0.99 <= dropout_fraction / dropout_p <= 1.01

    if is_sm80 or d <= 64:  # Only run backward for d=128 on A100
        assert (dqkv - dqkv_ref).abs().max().item() <= 2 * (dqkv_pt - dqkv_ref).abs().max().item()
        # assert torch.allclose(dqkv, dqkv_ref, rtol=rtol, atol=atol)


@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('d', [128, 64, 80, 40, 32, 16])
# @pytest.mark.parametrize('d', [64])
@pytest.mark.parametrize('seqlen', [97, 128, 200, 256, 257, 384, 512, 768, 1024, 1025, 2048])
# @pytest.mark.parametrize('seqlen', [128])
@pytest.mark.parametrize('dropout_p', [0.0, 0.17])
# @pytest.mark.parametrize('dropout_p', [0.0])
def test_flash_attn_race_condition(seqlen, d, dropout_p, causal, dtype):
    if seqlen >= 2048 and torch.cuda.get_device_properties('cuda').total_memory <= 16 * 2**30:
        pytest.skip()  # Reference implementation OOM
    device = 'cuda'
    # set seed
    torch.random.manual_seed(0)
    batch_size = 32
    nheads = 4
    x = torch.randn(batch_size, seqlen, nheads * d, device=device, dtype=dtype, requires_grad=True)
    Wqkv = torch.nn.Linear(nheads * d, 3 * nheads * d, device=device, dtype=dtype)

    query_padding_mask = generate_random_padding_mask(seqlen, batch_size, device, mode='random')
    key_padding_mask = generate_random_padding_mask(seqlen, 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(
         x, Wqkv, nheads, query_padding_mask, key_padding_mask
     )

    torch.random.manual_seed(0)
    output_unpad_0, sm_lse_0, S_dmask_0 = flash_attn_unpadded_func(
        q_unpad, k_unpad, v_unpad, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k,
        dropout_p, return_attn_probs=True, causal=causal
    )
    S_dmask_converted_0 = convert_flash_attn_S_to_softmax(
        S_dmask_0, query_padding_mask, key_padding_mask, d, dropout_p > 0.0, causal=causal
    )

    if is_sm80 or d <= 64:  # Only run backward for d=128 on A100
        g = torch.randn_like(output_unpad_0)
        dq_unpad_0, dk_unpad_0, dv_unpad_0, = torch.autograd.grad(output_unpad_0,
                                                                  (q_unpad, k_unpad, v_unpad), g)

    for _ in range(10):
        torch.random.manual_seed(0)
        output_unpad, sm_lse, S_dmask = flash_attn_unpadded_func(
            q_unpad, k_unpad, v_unpad, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k,
            dropout_p, return_attn_probs=True, causal=causal
        )
        S_dmask_converted = convert_flash_attn_S_to_softmax(
            S_dmask, query_padding_mask, key_padding_mask, d, dropout_p > 0.0, causal=causal
        )
        assert torch.equal(output_unpad, output_unpad_0)
        # sm_lse has some parts that are uninitialized from torch.empty
        # assert torch.equal(sm_lse, sm_lse_0)
        assert torch.equal(S_dmask_converted, S_dmask_converted_0)

        if is_sm80 or d <= 64:  # Only run backward for d=128 on A100
            dq_unpad, dk_unpad, dv_unpad, = torch.autograd.grad(output_unpad,
                                                                (q_unpad, k_unpad, v_unpad), g)
            assert torch.equal(dq_unpad, dq_unpad_0)
            assert torch.equal(dk_unpad, dk_unpad_0)
            assert torch.equal(dv_unpad, dv_unpad_0)


@pytest.mark.skipif(torch.cuda.device_count() < 2, reason='requires multiple GPUs')
def test_flash_attn_multigpu():
    seqlen = 256
    d = 64
    dropout_p = 0.0
    causal = False
    dtype = torch.float16
    device = 'cuda:1'
    torch.random.manual_seed(0)
    batch_size = 32
    nheads = 4
    x = torch.randn(batch_size, seqlen, nheads * d, device=device, dtype=dtype, requires_grad=True)
    Wqkv = torch.nn.Linear(nheads * d, 3 * nheads * d, device=device, dtype=dtype)

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

    qkv_unpad, cu_seqlens, max_seqlen, qkv, output_pad_fn, dqkv_pad_fn = generate_qkv(
        x, Wqkv, nheads, key_padding_mask, key_padding_mask, qkvpacked=True
    )

    output_unpad, sm_lse, S_dmask = flash_attn_unpadded_qkvpacked_func(
        qkv_unpad, cu_seqlens, max_seqlen, dropout_p, return_attn_probs=True, causal=causal
    )
    output = output_pad_fn(output_unpad)
    S_dmask_converted = convert_flash_attn_S_to_softmax(
        S_dmask, key_padding_mask, key_padding_mask, d, dropout_p > 0.0, causal=causal
    )
    dropout_mask = S_dmask_converted >= 0
    attn_unnorm = S_dmask_converted.abs()
    attn = normalize_flash_attn_S(attn_unnorm, qkv[:, :, 0], qkv[:, :, 1], qkv[:, :, 2],
                                  key_padding_mask, key_padding_mask, dropout_p > 0.0, causal=causal)
    dropout_fraction = get_dropout_fraction(dropout_mask, key_padding_mask, key_padding_mask,
                                            causal=causal).item()

    output_ref, attn_ref = attention_qkvpacked_ref(qkv, key_padding_mask, dropout_p, dropout_mask,
                                                   causal=causal)
    output_pt, attn_pt = attention_qkvpacked_ref(qkv, key_padding_mask, dropout_p, dropout_mask,
                                                 causal=causal, upcast=False, reorder_ops=True)
    print(f'Actual dropout fraction: {dropout_fraction}')
    print(f'Output max diff: {(output - output_ref).abs().max().item()}')
    print(f'Output mean diff: {(output - output_ref).abs().mean().item()}')
    print(f'Pytorch max diff: {(output_pt - output_ref).abs().max().item()}')
    print(f'Pytorch mean diff: {(output_pt - output_ref).abs().mean().item()}')
    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(output)
    dqkv_unpad, = torch.autograd.grad(output, qkv_unpad, g)
    dqkv = dqkv_pad_fn(dqkv_unpad)
    dqkv_ref, = torch.autograd.grad(output_ref, qkv, g)
    dqkv_pt, = torch.autograd.grad(output_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 (output - output_ref).abs().max().item() <= 2 * (output_pt - output_ref).abs().max().item()
    # assert torch.allclose(output, output_ref, rtol=rtol, atol=atol)
    assert (attn - attn_ref).abs().max().item() <= 2 * (attn_pt - attn_ref).abs().max().item()
    # assert torch.allclose(attn, attn_ref, rtol=rtol, atol=atol)
    if dropout_p == 0.0:
        assert dropout_mask.all()
    else:
        assert 0.99 <= dropout_fraction / dropout_p <= 1.01

    assert (dqkv - dqkv_ref).abs().max().item() <= 2 * (dqkv_pt - dqkv_ref).abs().max().item()


from flash_attn.flash_attn_triton import flash_attn_func


@pytest.mark.skipif(not is_sm80, reason='Triton version is only tested on A100')
@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', [40, 48, 64, 128, 80, 88, 96])
# @pytest.mark.parametrize('d', [64])
# @pytest.mark.parametrize('seqlen', [97, 128, 200, 256, 257, 384, 512, 768, 1024, 1025, 2048])
@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('bias_shape', ([None, '1h1k', '1hqk', 'b11k', 'b1qk']))
# @pytest.mark.parametrize('bias_shape', (['1h1k']))
def test_flash_attn_triton_output(seqlen_q, seqlen_k, d, causal, dtype, bias_shape):
    if seqlen_q >= 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 = 32
    nheads = 4
    q = torch.randn(batch_size, seqlen_q, nheads, d, device=device, dtype=dtype)
    k, v = torch.randn(batch_size, seqlen_k, 2, nheads, d, device=device, dtype=dtype).unbind(dim=2)
    if bias_shape == '1h1k':
        bias = torch.randn(1, nheads, 1, seqlen_k, dtype=torch.float, device=device)
    elif bias_shape == '1hqk':
        bias = torch.randn(1, nheads, seqlen_q, seqlen_k, dtype=torch.float, device=device)
    elif bias_shape == 'b11k':
        bias = torch.randn(batch_size, 1, 1, seqlen_k, dtype=torch.float, device=device)
    elif bias_shape == 'b1qk':
        bias = torch.randn(batch_size, 1, seqlen_q, seqlen_k, dtype=torch.float, device=device)
    else:
        bias = None

    q, k, v = [x.detach().requires_grad_() for x in [q, k, v]]
    output = flash_attn_func(q, k, v, bias, causal)

    output_ref, attn_ref = attention_ref(q, k, v, bias=bias, causal=causal)
    output_pt, attn_pt = attention_ref(q, k, v, bias=bias, causal=causal, upcast=False,
                                       reorder_ops=True)
    print(f'Output max diff: {(output - output_ref).abs().max().item()}')
    print(f'Output mean diff: {(output - output_ref).abs().mean().item()}')
    print(f'Pytorch max diff: {(output_pt - output_ref).abs().max().item()}')
    print(f'Pytorch mean diff: {(output_pt - output_ref).abs().mean().item()}')

    g = torch.randn_like(output)
    dq, dk, dv = torch.autograd.grad(output, (q, k, v), g)
    dq_ref, dk_ref, dv_ref, = torch.autograd.grad(output_ref, (q, k, v), g)
    dq_pt, dk_pt, dv_pt, = torch.autograd.grad(output_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 (output - output_ref).abs().max().item() <= 2 * (output_pt - output_ref).abs().max().item()
    # assert torch.allclose(output, output_ref, rtol=rtol, atol=atol)

    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.skipif(not is_sm80, reason='Triton version is only tested on A100')
@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', [40, 48, 64, 128, 80, 88, 96])
# @pytest.mark.parametrize('d', [96])
# @pytest.mark.parametrize('seqlen', [97, 128, 200, 256, 257, 384, 512, 768, 1024, 1025, 2048])
@pytest.mark.parametrize('seqlen_q,seqlen_k', [(113, 203), (128, 217), (91, 211), (108, 256), (256, 512), (512, 256), (1024, 1024), (1023, 1024), (1024, 1023), (2048, 2048)])
# @pytest.mark.parametrize('seqlen_q,seqlen_k', [(256, 512)])
@pytest.mark.parametrize('bias_shape', ([None, '1h1k', '1hqk', 'b11k', 'b1qk']))
def test_flash_attn_triton_race_condition(seqlen_q, seqlen_k, d, causal, dtype, bias_shape):
    if seqlen_q >= 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 = 32
    nheads = 4
    q = torch.randn(batch_size, seqlen_q, nheads, d, device=device, dtype=dtype)
    k, v = torch.randn(batch_size, seqlen_k, 2, nheads, d, device=device, dtype=dtype).unbind(dim=2)
    if bias_shape == '1h1k':
        bias = torch.randn(1, nheads, 1, seqlen_k, dtype=torch.float, device=device)
    elif bias_shape == '1hqk':
        bias = torch.randn(1, nheads, seqlen_q, seqlen_k, dtype=torch.float, device=device)
    elif bias_shape == 'b11k':
        bias = torch.randn(batch_size, 1, 1, seqlen_k, dtype=torch.float, device=device)
    elif bias_shape == 'b1qk':
        bias = torch.randn(batch_size, 1, seqlen_q, seqlen_k, dtype=torch.float, device=device)
    else:
        bias = None

    q, k, v = [x.detach().requires_grad_() for x in [q, k, v]]
    output_0 = flash_attn_func(q, k, v, bias, causal)

    g = torch.randn_like(output_0)
    dq_0, dk_0, dv_0 = torch.autograd.grad(output_0, (q, k, v), g)

    # The SEQUENCE_PARALLEL option for the bwd to makes dq non-deterministic
    deterministic_dq = False
    # Numerical error if we just do any arithmetic on dq
    dq_atol = ((dq_0 + 0.3 - 0.3) - dq_0).abs().max().item()
    equal_fn = torch.equal if deterministic_dq else partial(torch.allclose, atol=dq_atol)
    # Run 10000 times and check that the results don't change
    for i in range(10000):
        output = flash_attn_func(q, k, v, bias, causal)
        output_equal = torch.equal(output, output_0)
        if not output_equal:  # Printing / computing diff sometimes makes the race condition disappear
            print(f'Output max diff: {(output - output_0).abs().max().item()}')
        assert torch.equal(output, output_0)
        dq, dk, dv = torch.autograd.grad(output, (q, k, v), g)
        dq_equal = equal_fn(dq, dq_0)
        dk_equal = torch.equal(dk, dk_0)
        dv_equal = torch.equal(dv, dv_0)
        if not (dq_equal and dk_equal and dv_equal):
            print(f'{i = }')
            print(f'dQ max diff: {(dq - dq_0).abs().max().item()}')
            print(f'dK max diff: {(dk - dk_0).abs().max().item()}')
            print(f'dV max diff: {(dv - dv_0).abs().max().item()}')
        assert equal_fn(dq, dq_0)
        assert torch.equal(dk, dk_0)
        assert torch.equal(dv, dv_0)
