VTimeLLM/flash-attention/tests/test_flash_attn_ck.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 test_flash_attn import (
    attn_bias_from_alibi_slopes,
    convert_flash_attn_S_to_softmax,
    generate_qkv,
    generate_random_padding_mask,
    _generate_block_kvcache,
    attention_ref,
    attention_kvpacked_ref,
    attention_qkvpacked_ref,
)

from flash_attn.layers.rotary import apply_rotary_emb

def is_bwd_hdim_supported(d):
    return d <= 256


def ck_randval_to_dropout_mask(randval, p):
    # If p = 0.3, randval in 255 * (0.7, 1.0] will be dropout
    # randval in 255 * [0, 0.7] will be kept
    # If return dropout_mask >=0, value will be kept
    return math.floor(255.0 * (1 - p)) - randval.to(torch.float32)


def pad_rearrange_dropout_mask_hts_to_bhss(S_dmask, cu_seqlens_q, seqlen_q_rounded, seqlen_k_rounded):
    """ pad + rearrange [nheads, total_q, max_seqlen_k] into [b, nheads, seqlen_q_rounded, seqlen_k_rounded]
    Arguments:
        S_dmask: (nheads, total_q, max_seqlen_k)
        cu_seqlens_q: (b + 1)
    Output:
        S_dmask: (b, nheads, seqlen_q_rounded, seqlen_k_rounded)
    """
    batch_size = cu_seqlens_q.numel() - 1
    seqlens_q = torch.roll(cu_seqlens_q, shifts = -1) - cu_seqlens_q
    seqlens_q = seqlens_q[0:batch_size].tolist()
    S_dmask = torch.split(S_dmask, seqlens_q, dim=1)
    # [(nheads, seqlen_q0, max_seqlen_k), (nheads, seqlen_q1, max_seqlen_k), ..., (nheads, seqlen_qb, max_seqlen_k)]
    masks = ()
    for mask in S_dmask:
        # (nheads, seqlen_qi, max_seqlen_k) -> (nheads, seqlen_q_rounded, seqlen_k_rounded)
        mask = F.pad(mask, (0, seqlen_k_rounded - mask.shape[2], 0, seqlen_q_rounded - mask.shape[1], 0, 0)).unsqueeze(1)
        masks = masks + (mask, )
    S_dmask = torch.cat(masks, dim=1)

    S_dmask = S_dmask.transpose(0, 1)
    return S_dmask


@pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16])
@pytest.mark.parametrize("deterministic", [False, True])
@pytest.mark.parametrize("alibi", [False, True])
@pytest.mark.parametrize("local", [False, True])
@pytest.mark.parametrize("causal", [False, True])
@pytest.mark.parametrize("d", [32, 40, 59, 64, 80, 96, 111, 128, 160, 192, 224, 256])
@pytest.mark.parametrize("seqlen", [97, 128, 200, 384, 768, 1024, 1025, 2048])
@pytest.mark.parametrize("dropout_p", [0.0, 0.17])
def test_flash_attn_qkvpacked(seqlen, d, dropout_p, causal, local, alibi, deterministic, dtype):
    if d > 256:
        pytest.skip()

    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:
        # TODO - move to c++ mha_varlen_fwd()
        S_dmask = ck_randval_to_dropout_mask(S_dmask, dropout_p)
        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
        # CK does not return P. Hence, we don't test the attn here.
    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,
    )

    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.
    assert (out - out_ref).abs().max().item() <= 2 * (out_pt - out_ref).abs().max().item()

    g = torch.randn_like(out)
    if is_bwd_hdim_supported(d):
        (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()}")

        # TODO - use 10 times to check, wait for ck to fix bwd precision issue
        assert (dqkv - dqkv_ref).abs().max().item() <= 10 * (dqkv_pt - dqkv_ref).abs().max().item()


@pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16])
@pytest.mark.parametrize("deterministic", [False, True])
@pytest.mark.parametrize("alibi", [False, True])
@pytest.mark.parametrize("local", [False, True])
@pytest.mark.parametrize("causal", [False, True])
@pytest.mark.parametrize("d", [32, 59, 64, 80, 96, 128, 160, 192, 224, 256])
@pytest.mark.parametrize("seqlen", [97, 128, 200, 257, 384, 512, 768, 1025, 2048])
@pytest.mark.parametrize("dropout_p", [0, 0.17])
def test_flash_attn_varlen_qkvpacked(seqlen, d, dropout_p, causal, local, alibi, deterministic, dtype):
    if d > 256:
        pytest.skip()

    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:
        # TODO - move to c++ mha_varlen_fwd()
        S_dmask = ck_randval_to_dropout_mask(S_dmask, dropout_p)
        S_dmask = pad_rearrange_dropout_mask_hts_to_bhss(S_dmask, cu_seqlens, seqlen, seqlen)

        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
        # CK does not return P. Hence, we don't test the attn here.
    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()}")

    # 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()

    g = torch.randn_like(out)
    if is_bwd_hdim_supported(d):
        (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()}")

        # TODO - use 10 times to check, wait for ck to fix bwd precision issue
        assert (dqkv - dqkv_ref).abs().max().item() <= 10 * (dqkv_pt - dqkv_ref).abs().max().item()


@pytest.mark.parametrize("kvpacked", [True, False])
@pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16])
@pytest.mark.parametrize("mha_type", ["mha", "mqa", "gqa"])
@pytest.mark.parametrize("deterministic", [False, True])
@pytest.mark.parametrize("alibi", [False, True])
@pytest.mark.parametrize("local", [False, True])
@pytest.mark.parametrize("causal", [False, True])
@pytest.mark.parametrize("d", [32, 40, 59, 64, 96, 111, 128, 160, 192, 224, 256])
@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("dropout_p", [0.0, 0.17])
def test_flash_attn_output(
    seqlen_q, seqlen_k, d, dropout_p, causal, local, alibi, deterministic, mha_type, dtype, kvpacked
):
    device = "cuda"
    # set seed
    torch.random.manual_seed(0)
    batch_size = 4
    nheads = 9
    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, requires_grad=True)
    if kvpacked:
        kv = torch.randn(
            batch_size, seqlen_k, 2, nheads_k, d, device=device, dtype=dtype, requires_grad=True
        )
    else:
        k = torch.randn(
            batch_size, seqlen_k, nheads_k, d, device=device, dtype=dtype, requires_grad=True
        )
        v = torch.randn(
            batch_size, seqlen_k, nheads_k, d, device=device, dtype=dtype, requires_grad=True
        )
    if 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,
            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,
            alibi_slopes=alibi_slopes,
            deterministic=deterministic,
            return_attn_probs=True,
        )
    if dropout_p > 0.0:
        # TODO - move to c++ mha_varlen_fwd()
        S_dmask = ck_randval_to_dropout_mask(S_dmask, dropout_p)
        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
        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)
        # CK does not return P. Hence, we don't test the attn here.
    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,
        )
        out_pt, attn_pt = attention_kvpacked_ref(
            q,
            kv,
            None,
            None,
            attn_bias,
            dropout_p,
            dropout_mask,
            causal=causal,
            window_size=window_size,
            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,
        )
        out_pt, attn_pt = attention_ref(
            q,
            k,
            v,
            None,
            None,
            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()}")

    # 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()

    g = torch.randn_like(out)
    if is_bwd_hdim_supported(d):
        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()}")

        # TODO - use 10 times to check, wait for ck to fix bwd precision issue
        assert (dq - dq_ref).abs().max().item() <= 10 * (dq_pt - dq_ref).abs().max().item()
        assert (dk - dk_ref).abs().max().item() <= 10 * (dk_pt - dk_ref).abs().max().item()
        assert (dv - dv_ref).abs().max().item() <= 10 * (dv_pt - dv_ref).abs().max().item()


@pytest.mark.parametrize("kvpacked", [True, False])
@pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16])
@pytest.mark.parametrize("mha_type", ["mha", "mqa", "gqa"])
@pytest.mark.parametrize("deterministic", [False, True])
@pytest.mark.parametrize("alibi", [False, True])
@pytest.mark.parametrize("local", [False, True])
@pytest.mark.parametrize("causal", [False, True])
@pytest.mark.parametrize("d", [32, 59, 64, 80, 96, 111, 128, 160, 192, 224, 256])
@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("dropout_p", [0.0, 0.17])
def test_flash_attn_varlen_output(
    seqlen_q, seqlen_k, d, dropout_p, causal, local, alibi, deterministic, mha_type, dtype, kvpacked
):
    device = "cuda"
    # set seed
    torch.random.manual_seed(0)
    batch_size = 4
    nheads = 9
    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, requires_grad=True)
    if kvpacked:
        kv = torch.randn(
            batch_size, seqlen_k, 2, nheads_k, d, device=device, dtype=dtype, requires_grad=True
        )
    else:
        k = torch.randn(
            batch_size, seqlen_k, nheads_k, d, device=device, dtype=dtype, requires_grad=True
        )
        v = torch.randn(
            batch_size, seqlen_k, nheads_k, d, device=device, dtype=dtype, requires_grad=True
        )

    query_padding_mask = generate_random_padding_mask(seqlen_q, batch_size, device, mode="random")
    key_padding_mask = generate_random_padding_mask(seqlen_k, batch_size, device, mode="random")
    # key_padding_mask = generate_random_padding_mask(seqlen_k, batch_size, device, mode='full')
    if 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,
            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,
            alibi_slopes=alibi_slopes,
            deterministic=deterministic,
            return_attn_probs=True,
        )
    out = output_pad_fn(out_unpad)
    if dropout_p > 0.0:
        # TODO - move to c++ mha_varlen_fwd()
        S_dmask = ck_randval_to_dropout_mask(S_dmask, dropout_p)
        S_dmask = pad_rearrange_dropout_mask_hts_to_bhss(S_dmask, cu_seqlens_q, seqlen_q, seqlen_k)
        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
        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)
        # CK does not return P. Hence, we don't test the attn here.
    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,
        )
        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,
            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,
        )
        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,
            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()}")

    # Check that FlashAttention's numerical error is at most 4 times the numerical error
    # of a Pytorch implementation.
    assert (out - out_ref).abs().max().item() <= 4 * (out_pt - out_ref).abs().max().item()

    g = torch.randn_like(out)
    if is_bwd_hdim_supported(d):
        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()}")

        # TODO - use 10 times to check, wait for ck to fix bwd precision issue
        assert (dq - dq_ref).abs().max().item() <= 10 * (dq_pt - dq_ref).abs().max().item()
        assert (dk - dk_ref).abs().max().item() <= 10 * (dk_pt - dk_ref).abs().max().item()
        assert (dv - dv_ref).abs().max().item() <= 10 * (dv_pt - dv_ref).abs().max().item()


@pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16])
@pytest.mark.parametrize("local", [False, True])
@pytest.mark.parametrize("d", [32, 40, 59, 64, 80, 96, 111, 128, 160, 192, 224, 256])
@pytest.mark.parametrize("swap_sq_sk", [False, 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),
    ],
)
def test_flash_attn_causal(seqlen_q, seqlen_k, swap_sq_sk, d, local, dtype):
    if max(seqlen_q, seqlen_k) >= 2048:
        pytest.skip()
    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()}")

    # Check that FlashAttention's numerical error is at most 4 times the numerical error
    # of a Pytorch implementation.
    assert (out - out_ref).abs().max().item() <= 4 * (out_pt - out_ref).abs().max().item() + 1e-5

    g = torch.randn_like(out)
    if is_bwd_hdim_supported(d):
        do_o = (g.float() * out.float()).sum(-1)
        (
            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()}")

    # TODO - use 10 times to check, wait for ck to fix bwd precision issue
    assert (dq - dq_ref).abs().max().item() <= 10 * (dq_pt - dq_ref).abs().max().item() + 1e-4
    assert (dk - dk_ref).abs().max().item() <= 10 * (dk_pt - dk_ref).abs().max().item() + 1e-4
    assert (dv - dv_ref).abs().max().item() <= 10 * (dv_pt - dv_ref).abs().max().item() + 1e-4


@pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16])
@pytest.mark.parametrize("local", [False, True])
@pytest.mark.parametrize("d", [32, 40, 59, 64, 80, 96, 111, 128, 160, 192, 224, 256])
@pytest.mark.parametrize("swap_sq_sk", [False, 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("paged_kv_block_size", [None, 256, 512])
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:
        pytest.skip()
    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()}")

    # 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

    g = torch.randn_like(out)
    if is_bwd_hdim_supported(d):
        do_o = (g.float() * out.float()).sum(-1)
        test_backward = 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()}")

        if test_backward:
            # TODO - use 10 times to check, wait for ck to fix bwd precision issue
            assert (dq - dq_ref).abs().max().item() <= 10 * (dq_pt - dq_ref).abs().max().item() + 1e-5
            assert (dk - dk_ref).abs().max().item() <= 10 * (dk_pt - dk_ref).abs().max().item() + 1e-5
            assert (dv - dv_ref).abs().max().item() <= 10 * (dv_pt - dv_ref).abs().max().item() + 1e-5


# TODO - support splitkv
# def test_flash_attn_splitkv


# TODO - Support has_leftpad
@pytest.mark.parametrize("dtype", [torch.float16])
@pytest.mark.parametrize("num_splits", [1, 0])
@pytest.mark.parametrize("mha_type", ["mha", "mqa", "gqa"])
@pytest.mark.parametrize("new_kv", [False, True])
@pytest.mark.parametrize("alibi", [False, True])
@pytest.mark.parametrize("local", [False, True])
@pytest.mark.parametrize("causal", [False, True])
@pytest.mark.parametrize("seqlen_new_eq_seqlen_q", [True, False])
@pytest.mark.parametrize("rotary_interleaved", [False, True])
@pytest.mark.parametrize("rotary_fraction", [0.0, 0.5, 1.0])
@pytest.mark.parametrize("paged_kv_block_size", [None, 256])
@pytest.mark.parametrize("has_leftpad", [False])
@pytest.mark.parametrize("has_batch_idx", [False, True])
@pytest.mark.parametrize("d", [32, 59, 64, 80, 128, 256])
@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),
    ],
)
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 = 1
    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, key_leftpad=cache_leftpad
        )
    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 f16, bf16 need 4
    mult = 4 if not alibi else 5
    assert (out - out_ref).abs().max().item() <= mult * (out_pt - out_ref).abs().max().item() + 1e-5



@pytest.mark.parametrize("dtype", [torch.float16])
@pytest.mark.parametrize("causal", [False, True])
@pytest.mark.parametrize("d", [32, 40, 59, 64, 80, 96, 111, 128, 160, 192, 224, 256])
@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])
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 dropout_p == 0 and is_bwd_hdim_supported(d):
        (
            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 dropout_p == 0:
            (
                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("d", [16, 32, 64])
@pytest.mark.parametrize("seqlen", [1, 2, 5, 17, 128])
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.
    """

    # TODO - 1 or 2 might fail, need to check
    if seqlen == 1 or seqlen == 2:
        pytest.skip()

    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() <= 7 * (
        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, torch.bfloat16])
@pytest.mark.parametrize("causal", [False, True])
@pytest.mark.parametrize("d", [64, 128])
@pytest.mark.parametrize("seqlen", [97, 128, 200, 256])
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("d", [16, 32, 64])
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, torch.bfloat16])
@pytest.mark.parametrize("local", [False, True])
@pytest.mark.parametrize("causal", [False, True])
@pytest.mark.parametrize("d", [32, 40, 59, 64, 80, 96, 111, 128, 160, 192, 224, 256])
@pytest.mark.parametrize("swap_sq_sk", [False, 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),
    ],
)
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)
    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, torch.bfloat16])
@pytest.mark.parametrize("local", [False, True])
@pytest.mark.parametrize("causal", [False, True])
@pytest.mark.parametrize("d", [32, 40, 59, 64, 80, 96, 111, 128, 160, 192, 224, 256])
@pytest.mark.parametrize("swap_sq_sk", [False, 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),
    ],
)
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)
    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)