test_flash_attention_4.py

# Adapted from https://github.com/Dao-AILab/flash-attention/blob/b31ae1e4cd22cf5f820a2995b74b7cd3bd54355a/tests/cute/test_flash_attn.py

# Copyright (c) 2025, Jay Shah, Ganesh Bikshandi, Ying Zhang, Vijay Thakkar, Pradeep Ramani, Tri Dao.

import itertools
import math
from functools import partial

import pytest
import torch
import torch.nn.functional as F
from einops import rearrange, repeat
from sgl_kernel.flash_attn import flash_attn_varlen_func
from utils import is_hopper

flash_attn_varlen_func = partial(flash_attn_varlen_func, ver=4)


def unpad_input(hidden_states, attention_mask, unused_mask=None):
    """
    Arguments:
        hidden_states: (batch, seqlen, ...)
        attention_mask: (batch, seqlen), bool / int, 1 means valid and 0 means not valid.
        unused_mask: (batch, seqlen), bool / int, 1 means the element is allocated but unused.
    Return:
        hidden_states: (total_nnz, ...), where total_nnz = number of tokens selected in attention_mask + unused_mask.
        indices: (total_nnz), the indices of masked tokens from the flattened input sequence.
        cu_seqlens: (batch + 1), the cumulative sequence lengths, used to index into hidden_states.
        max_seqlen_in_batch: int
        seqused: (batch), returns the number of tokens selected in attention_mask + unused_mask.
    """
    all_masks = (
        (attention_mask + unused_mask) if unused_mask is not None else attention_mask
    )
    seqlens_in_batch = all_masks.sum(dim=-1, dtype=torch.int32)
    used_seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
    indices = torch.nonzero(all_masks.flatten(), as_tuple=False).flatten()
    max_seqlen_in_batch = seqlens_in_batch.max().item()
    cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0))
    # TD [2022-03-04] We don't want to index with a bool mask, because Pytorch will expand the
    # bool mask, then call nonzero to get the indices, then index with those. The indices is @dim
    # times larger than it needs to be, wasting memory. It's faster and more memory-efficient to
    # index with integer indices.
    return (
        rearrange(hidden_states, "b s ... -> (b s) ...")[indices],
        indices,
        cu_seqlens,
        max_seqlen_in_batch,
        used_seqlens_in_batch,
    )


def pad_input(hidden_states, indices, batch, seqlen):
    """
    Arguments:
        hidden_states: (total_nnz, ...), where total_nnz = number of tokens in selected in attention_mask.
        indices: (total_nnz), the indices that represent the non-masked tokens of the original padded input sequence.
        batch: int, batch size for the padded sequence.
        seqlen: int, maximum sequence length for the padded sequence.
    Return:
        hidden_states: (batch, seqlen, ...)
    """
    dim = hidden_states.shape[1:]
    output = torch.zeros(
        (batch * seqlen), *dim, device=hidden_states.device, dtype=hidden_states.dtype
    )
    output[indices] = hidden_states
    return rearrange(output, "(b s) ... -> b s ...", b=batch)


def generate_random_padding_mask(
    max_seqlen, batch_size, device, mode="random", zero_lengths=False
):
    assert mode in ["full", "random", "third"]
    if mode == "full":
        lengths = torch.full(
            (batch_size, 1), max_seqlen, device=device, dtype=torch.int32
        )
    elif mode == "random":
        lengths = torch.randint(
            max(0 if zero_lengths else 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
        )

    if zero_lengths:
        # Generate zero-lengths every 5 batches and the last batch.
        for i in range(batch_size):
            if i % 5 == 0:
                lengths[i] = 0
        lengths[-1] = 0
    padding_mask = (
        repeat(torch.arange(max_seqlen, device=device), "s -> b s", b=batch_size)
        < lengths
    )
    return padding_mask


def generate_qkv(
    q,
    k,
    v,
    query_padding_mask=None,
    key_padding_mask=None,
    qv=None,
    kvpacked=False,
    qkvpacked=False,
    query_unused_mask=None,
    key_unused_mask=None,
):
    """
    Arguments:
        q: (batch_size, seqlen_q, nheads, d)
        k: (batch_size, seqlen_k, nheads_k, d)
        v: (batch_size, seqlen_k, nheads_k, d_v)
        query_padding_mask: (batch_size, seqlen), bool
        key_padding_mask: (batch_size, seqlen), bool
    """
    assert not (kvpacked and qkvpacked)
    batch_size, seqlen_q, nheads, d = q.shape
    d_v = v.shape[-1]
    _, seqlen_k, nheads_k, _ = k.shape
    assert k.shape == (batch_size, seqlen_k, nheads_k, d)
    assert v.shape == (batch_size, seqlen_k, nheads_k, d_v)
    if query_unused_mask is not None or key_unused_mask is not None:
        assert not kvpacked
        assert not qkvpacked

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

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

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


def construct_local_mask(
    seqlen_q,
    seqlen_k,
    window_size=(None, None),
    sink_token_length=0,
    query_padding_mask=None,
    key_padding_mask=None,
    key_leftpad=None,
    device=None,
):
    row_idx = rearrange(
        torch.arange(seqlen_q, device=device, dtype=torch.long), "s -> s 1"
    )
    col_idx = torch.arange(seqlen_k, device=device, dtype=torch.long)
    if key_leftpad is not None:
        key_leftpad = rearrange(key_leftpad, "b -> b 1 1 1")
        col_idx = repeat(col_idx, "s -> b 1 1 s", b=key_leftpad.shape[0])
        col_idx = torch.where(col_idx >= key_leftpad, col_idx - key_leftpad, 2**32)
    sk = (
        seqlen_k
        if key_padding_mask is None
        else rearrange(key_padding_mask.sum(-1), "b -> b 1 1 1")
    )
    sq = (
        seqlen_q
        if query_padding_mask is None
        else rearrange(query_padding_mask.sum(-1), "b -> b 1 1 1")
    )
    if window_size[0] is None:
        return col_idx > row_idx + sk - sq + window_size[1]
    else:
        sk = torch.full_like(col_idx, seqlen_k) if key_padding_mask is None else sk
        return torch.logical_or(
            col_idx > torch.minimum(row_idx + sk - sq + window_size[1], sk),
            torch.logical_and(
                col_idx < row_idx + sk - sq - window_size[0],
                col_idx >= sink_token_length,
            ),
        )


def construct_chunk_mask(
    seqlen_q,
    seqlen_k,
    attention_chunk,
    query_padding_mask=None,
    key_padding_mask=None,
    key_leftpad=None,
    device=None,
):
    row_idx = rearrange(
        torch.arange(seqlen_q, device=device, dtype=torch.long), "s -> s 1"
    )
    col_idx = torch.arange(seqlen_k, device=device, dtype=torch.long)
    if key_leftpad is not None:
        key_leftpad = rearrange(key_leftpad, "b -> b 1 1 1")
        col_idx = repeat(col_idx, "s -> b 1 1 s", b=key_leftpad.shape[0])
        col_idx = torch.where(col_idx >= key_leftpad, col_idx - key_leftpad, 2**32)
    sk = (
        seqlen_k
        if key_padding_mask is None
        else rearrange(key_padding_mask.sum(-1), "b -> b 1 1 1")
    )
    sq = (
        seqlen_q
        if query_padding_mask is None
        else rearrange(query_padding_mask.sum(-1), "b -> b 1 1 1")
    )
    sk = torch.full_like(col_idx, seqlen_k) if key_padding_mask is None else sk
    # Subtract remainder instead of divide and then multiply to take care of negative values
    col_limit_left_chunk = row_idx + sk - sq - (row_idx + sk - sq) % attention_chunk
    return torch.logical_or(
        col_idx < col_limit_left_chunk,
        col_idx >= col_limit_left_chunk + attention_chunk,
    )


def attention_ref(
    q,
    k,
    v,
    query_padding_mask=None,
    key_padding_mask=None,
    key_leftpad=None,
    attn_bias=None,
    dropout_p=0.0,
    dropout_mask=None,
    causal=False,
    qv=None,
    q_descale=None,
    k_descale=None,
    v_descale=None,
    window_size=(None, None),
    attention_chunk=0,
    sink_token_length=0,
    learnable_sink=None,
    softcap=0.0,
    upcast=True,
    reorder_ops=False,
    intermediate_dtype=None,
):
    """
    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_v)
        qv: (batch_size, seqlen_q, nheads, head_dim_v)
        query_padding_mask: (batch_size, seqlen_q)
        key_padding_mask: (batch_size, seqlen_k)
        attn_bias: broadcastable to (batch_size, nheads, seqlen_q, seqlen_k)
        dropout_p: float
        dropout_mask: (batch_size, nheads, seqlen_q, seqlen_k)
        causal: whether to apply causal masking
        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_v)
        attention: (batch_size, nheads, seqlen_q, seqlen_k), softmax after dropout
    """
    if causal:
        window_size = (window_size[0], 0)
    dtype_og = q.dtype
    if upcast:
        q, k, v = q.float(), k.float(), v.float()
        qv = qv.float() if qv is not None else None
    if q_descale is not None:
        q_descale = repeat(q_descale, "b h -> b 1 (h g) 1", g=q.shape[2] // k.shape[2])
        q = (q.float() * q_descale).to(q.dtype)
        qv = (qv.float() * q_descale).to(qv.dtype) if qv is not None else None
    if k_descale is not None:
        k = (k.float() * rearrange(k_descale, "b h -> b 1 h 1")).to(dtype=k.dtype)
    if v_descale is not None:
        v = (v.float() * rearrange(v_descale, "b h -> b 1 h 1")).to(dtype=v.dtype)
    seqlen_q, seqlen_k = q.shape[1], k.shape[1]
    k = repeat(k, "b s h d -> b s (h g) d", g=q.shape[2] // k.shape[2])
    v = repeat(v, "b s h d -> b s (h g) d", g=q.shape[2] // v.shape[2])
    d = q.shape[-1]
    dv = v.shape[-1]
    softmax_scale = 1.0 / math.sqrt(d if qv is None else d + dv)
    if not reorder_ops:
        scores = torch.einsum("bthd,bshd->bhts", q * softmax_scale, k)
    else:
        scores = torch.einsum("bthd,bshd->bhts", q, k * softmax_scale)
    if qv is not None:
        scores = scores + torch.einsum("bthd,bshd->bhts", qv * softmax_scale, v)
    if softcap > 0:
        scores = torch.tanh(scores / softcap) * softcap
    if key_padding_mask is not None:
        scores.masked_fill_(
            rearrange(~key_padding_mask, "b s -> b 1 1 s"), float("-inf")
        )
    local_mask = None
    if window_size[0] is not None or window_size[1] is not None:
        local_mask = construct_local_mask(
            seqlen_q,
            seqlen_k,
            window_size,
            sink_token_length,
            query_padding_mask,
            key_padding_mask,
            key_leftpad=key_leftpad,
            device=q.device,
        )
    if attention_chunk > 0:
        chunk_mask = construct_chunk_mask(
            seqlen_q,
            seqlen_k,
            attention_chunk,
            query_padding_mask,
            key_padding_mask,
            key_leftpad=key_leftpad,
            device=q.device,
        )
        local_mask = (
            torch.logical_or(local_mask, chunk_mask)
            if local_mask is not None
            else chunk_mask
        )
    if local_mask is not None:
        scores.masked_fill_(local_mask, float("-inf"))
    if attn_bias is not None:
        scores = scores + attn_bias
    if learnable_sink is None:
        attention = torch.softmax(scores, dim=-1).to(v.dtype)
    else:
        scores_fp32 = scores.to(torch.float32)
        logits_max = torch.amax(scores_fp32, dim=-1, keepdim=True)
        learnable_sink = rearrange(learnable_sink, "h -> h 1 1")
        logits_or_sinks_max = torch.maximum(learnable_sink, logits_max)
        unnormalized_scores = torch.exp(scores_fp32 - logits_or_sinks_max)
        normalizer = unnormalized_scores.sum(dim=-1, keepdim=True) + torch.exp(
            learnable_sink - logits_or_sinks_max
        )
        attention = (unnormalized_scores / normalizer).to(v.dtype)
    # We want to mask here so that the attention matrix doesn't have any NaNs
    # Otherwise we'll get NaN in dV
    if query_padding_mask is not None:
        attention = attention.masked_fill(
            rearrange(~query_padding_mask, "b s -> b 1 s 1"), 0.0
        )
    # Without this we might get NaN in dv
    if key_padding_mask is not None:
        attention = attention.masked_fill(
            rearrange(~key_padding_mask, "b s -> b 1 1 s"), 0.0
        )
    # Some rows might be completely masked out so we fill them with zero instead of NaN
    if local_mask is not None:
        attention = attention.masked_fill(
            torch.all(local_mask, dim=-1, keepdim=True), 0.0
        )
    dropout_scaling = 1.0 / (1 - dropout_p)
    # attention_drop = attention.masked_fill(~dropout_mask, 0.0) * dropout_scaling
    # output = torch.einsum('bhts,bshd->bthd', attention_drop , v)
    if dropout_mask is not None:
        attention_drop = attention.masked_fill(~dropout_mask, 0.0)
    else:
        attention_drop = attention
    if intermediate_dtype is not None:
        attention_drop = attention_drop.to(intermediate_dtype).to(attention_drop.dtype)
    output = torch.einsum("bhts,bshd->bthd", attention_drop, v * dropout_scaling)
    if query_padding_mask is not None:
        output.masked_fill_(rearrange(~query_padding_mask, "b s -> b s 1 1"), 0.0)
    return output.to(dtype=dtype_og), attention.to(dtype=dtype_og)


@pytest.mark.skipif(
    is_hopper(),
    reason="skip on hopper",
)
# @pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16, torch.float8_e4m3fn])
@pytest.mark.parametrize("dtype", [torch.bfloat16])
@pytest.mark.parametrize("mha_type", ["mha", "mqa", "gqa"])
# @pytest.mark.parametrize("mha_type", ["mqa"])
@pytest.mark.parametrize("has_learnable_sink", [False, True])
# @pytest.mark.parametrize("has_learnable_sink", [False])
# @pytest.mark.parametrize("has_qv", [False, True])
@pytest.mark.parametrize("has_qv", [False])
# @pytest.mark.parametrize("deterministic", [False, True])
@pytest.mark.parametrize("deterministic", [False])
# @pytest.mark.parametrize("softcap", [0.0, 15.0])
@pytest.mark.parametrize("softcap", [0.0])
@pytest.mark.parametrize("local", [False, True])
# @pytest.mark.parametrize("local", [False])
@pytest.mark.parametrize("causal", [False, True])
# @pytest.mark.parametrize("causal", [False])
# @pytest.mark.parametrize("add_unused_qkv", [False, True])
@pytest.mark.parametrize("add_unused_qkv", [False])
# @pytest.mark.parametrize("d", [32, 64, 96, 128, 160, 192, 224, 256])
# @pytest.mark.parametrize('d', [32, 40, 64, 80, 96, 128, 160, 192, 256])
# @pytest.mark.parametrize('d', [32, 64, 96, 128, 160, 192])
# @pytest.mark.parametrize('d', [56, 80])
# @pytest.mark.parametrize('d', [32, 40, 64, 80, 96, 128])
# @pytest.mark.parametrize("d", [64, 96, 128])
@pytest.mark.parametrize("d", [128, 192])
# @pytest.mark.parametrize("d", [192])
@pytest.mark.parametrize(
    "seqlen_q,seqlen_k",
    [
        # (1, 1),
        # (1, 3),
        # (2, 1),
        (511, 1),
        (3, 513),
        (64, 128),
        (128, 128),
        (256, 256),
        (113, 203),
        (128, 217),
        (113, 211),
        (108, 256),
        (256, 512),
        (307, 256),
        (640, 128),
        (512, 256),
        (1024, 1024),
        (1023, 1024),
        (1024, 1023),
        (2048, 2048),
    ],
)
def test_flash_attn_varlen_output(
    seqlen_q,
    seqlen_k,
    d,
    add_unused_qkv,
    causal,
    local,
    softcap,
    deterministic,
    has_qv,
    has_learnable_sink,
    mha_type,
    dtype,
):
    if (
        causal or local
    ):  # Right now we only support causal attention with seqlen_k == seqlen_q
        seqlen_k = seqlen_q
    device = "cuda"
    # set seed
    torch.random.manual_seed(seqlen_q + seqlen_k + d + int(causal) * 2 + int(local))
    batch_size = 49 if seqlen_q <= 1024 else 7
    nheads = 6
    # batch_size = 1
    # nheads = 1
    nheads_kv = nheads if mha_type == "mha" else (3 if mha_type == "gqa" else 1)
    dtype_ref = torch.bfloat16 if dtype == torch.float8_e4m3fn else dtype
    # dv_vals = [128, d] if d > 128 and d <= 192 else ([256, 512, d] if d <= 64 else [d])
    dv_vals = [128] if d == 192 else ([d] if d != 128 else [64, d])
    if dtype == torch.float8_e4m3fn:
        dv_vals = [d]
    # attention_chunk_vals = [torch.randint(1, seqlen_k * 2, (1,)).item(), 0] if seqlen_q <= seqlen_k else [0]
    attention_chunk_vals = [0]
    for dv, attention_chunk in itertools.product(dv_vals, attention_chunk_vals):
        q_ref = torch.randn(
            batch_size, seqlen_q, nheads, d, device=device, dtype=dtype_ref
        )
        if softcap > 0.0:
            # Ensure the values of qk are at least within softcap range.
            q_ref = (q_ref * softcap / 4).detach().requires_grad_()
        q_ref = q_ref.to(dtype).to(dtype_ref).requires_grad_()
        k_ref = (
            torch.randn(
                batch_size, seqlen_k, nheads_kv, d, device=device, dtype=dtype_ref
            )
            .to(dtype)
            .to(dtype_ref)
            .requires_grad_()
        )
        v_ref = (
            torch.randn(
                batch_size, seqlen_k, nheads_kv, dv, device=device, dtype=dtype_ref
            )
            .to(dtype)
            .to(dtype_ref)
            .requires_grad_()
        )
        if has_qv:
            qv_ref = (
                torch.randn(
                    batch_size, seqlen_q, nheads, dv, device=device, dtype=dtype_ref
                )
                .to(dtype)
                .to(dtype_ref)
            )
        else:
            qv_ref = None
        # Put window_size after QKV randn so that window_size changes from test to test
        window_size = (
            (None, None) if not local else torch.randint(0, seqlen_k, (2,)).tolist()
        )
        if has_learnable_sink:
            learnable_sink = torch.randn(nheads, dtype=torch.bfloat16, device=device)
        else:
            learnable_sink = None
        if dtype == torch.float8_e4m3fn:
            q_descale, k_descale, v_descale = [
                torch.rand(batch_size, nheads_kv, device=device, dtype=torch.float32)
                * 2
                for _ in range(3)
            ]
        else:
            q_descale, k_descale, v_descale = None, None, None
        q, k, v = [x.detach().requires_grad_() for x in (q_ref, k_ref, v_ref)]
        qv = qv_ref.detach() if has_qv else None
        query_padding_mask = generate_random_padding_mask(
            seqlen_q, batch_size, device, mode="random", zero_lengths=False
        )
        # TODO: test zero_lengths
        key_padding_mask = generate_random_padding_mask(
            # seqlen_k, batch_size, device, mode="random", zero_lengths=True
            seqlen_k,
            batch_size,
            device,
            mode="random",
            zero_lengths=False,
        )

        def _gen_unused_masks(padding_mask, add_unused, max_seq_len, bs, device):
            if add_unused:
                another_mask = generate_random_padding_mask(max_seq_len, bs, device)
                attn_mask = torch.logical_and(padding_mask, another_mask)
                unused_mask = torch.logical_xor(
                    torch.logical_or(padding_mask, another_mask), attn_mask
                )
            else:
                attn_mask = padding_mask
                unused_mask = None
            return attn_mask, unused_mask

        query_padding_mask, query_unused_mask = _gen_unused_masks(
            query_padding_mask, add_unused_qkv, seqlen_q, batch_size, q.device
        )
        # query_padding_mask[:] = True
        # query_unused_mask = None
        key_padding_mask, key_unused_mask = _gen_unused_masks(
            key_padding_mask, add_unused_qkv, seqlen_k, batch_size, k.device
        )

        if causal or local:
            key_padding_mask = query_padding_mask

        (
            q_unpad,
            k_unpad,
            v_unpad,
            qv_unpad,
            cu_seqlens_q,
            cu_seqlens_k,
            seqused_q,
            seqused_k,
            max_seqlen_q,
            max_seqlen_k,
            q,
            k,
            v,
            qv,
            output_pad_fn,
            dq_pad_fn,
            dk_pad_fn,
        ) = generate_qkv(
            q,
            k,
            v,
            query_padding_mask,
            key_padding_mask,
            qv=qv,
            kvpacked=False,
            query_unused_mask=query_unused_mask,
            key_unused_mask=key_unused_mask,
        )
        q_unpad, k_unpad, v_unpad = [
            x.detach().to(dtype).requires_grad_() for x in (q_unpad, k_unpad, v_unpad)
        ]
        out_ref, attn_ref = attention_ref(
            q_ref,
            k_ref,
            v_ref,
            query_padding_mask,
            key_padding_mask,
            causal=causal,
            qv=qv_ref,
            q_descale=q_descale,
            k_descale=k_descale,
            v_descale=v_descale,
            window_size=window_size,
            attention_chunk=attention_chunk,
            learnable_sink=learnable_sink,
            softcap=softcap,
        )
        out_pt, attn_pt = attention_ref(
            q_ref,
            k_ref,
            v_ref,
            query_padding_mask,
            key_padding_mask,
            causal=causal,
            qv=qv_ref,
            q_descale=q_descale,
            k_descale=k_descale,
            v_descale=v_descale,
            window_size=window_size,
            attention_chunk=attention_chunk,
            learnable_sink=learnable_sink,
            softcap=softcap,
            upcast=False,
            reorder_ops=True,
            intermediate_dtype=dtype if dtype == torch.float8_e4m3fn else None,
        )

        print(f"Pytorch max diff: {(out_pt - out_ref).abs().max().item()}")
        print(f"Pytorch mean diff: {(out_pt - out_ref).abs().mean().item()}")

        if query_unused_mask is not None:
            q_zero_masking = rearrange(query_unused_mask, "b s -> b s 1 1")

        # Numerical error if we just do any arithmetic on out_ref
        fwd_atol = 2 * (out_ref + 0.3 - 0.3 - out_ref).abs().max().item()
        rtol = 2 if softcap == 0.0 else 3

        pack_gqa_vals = [False, True, None]
        # num_splits_vals = [1, 3]
        num_splits_vals = [1]
        for pack_gqa, num_splits in itertools.product(pack_gqa_vals, num_splits_vals):
            out_unpad, lse = flash_attn_varlen_func(
                q_unpad,
                k_unpad,
                v_unpad,
                cu_seqlens_q=cu_seqlens_q,
                cu_seqlens_k=cu_seqlens_k,
                max_seqlen_q=None,
                max_seqlen_k=None,
                # seqused_q=seqused_q,
                # seqused_k=seqused_k,
                causal=causal,
                # qv=qv_unpad,
                # q_descale=q_descale,
                # k_descale=k_descale, v_descale=v_descale,
                window_size=window_size,
                # attention_chunk=attention_chunk,
                sinks=learnable_sink,
                softcap=softcap,
                pack_gqa=pack_gqa,
                return_softmax_lse=True,
            )
            out = output_pad_fn(out_unpad)
            if query_unused_mask is not None:
                out.masked_fill_(q_zero_masking, 0.0)
            print(f"Output max diff: {(out - out_ref).abs().max().item()}")
            print(f"Output mean diff: {(out - out_ref).abs().mean().item()}")
            # if not causal:
            #     print(f"LSE max diff: {(lse - lse_ref).abs().max().item()}")
            # breakpoint()

            # Check that FlashAttention's numerical error is at most 3x the numerical error
            # of a Pytorch implementation.
            assert (out - out_ref).abs().max().item() <= rtol * (
                out_pt - out_ref
            ).abs().max().item() + fwd_atol

        if (
            dtype != torch.float8_e4m3fn
            and not has_qv
            and not dv > 256
            and not attention_chunk != 0
            and dv == d
            and not has_learnable_sink
            and False
        ):
            g_unpad = torch.randn_like(out_unpad)
            do_o = ((g_unpad.float() * out_unpad.float()).sum(-1)).transpose(-1, -2)
            # import flash_attn_3_cuda
            # dq_unpad, dk_unpad, dv_unpad, softmax_d, dq_accum, lse_log2 = flash_attn_3_cuda.bwd_varlen(
            #     g_unpad,
            #     q_unpad,
            #     k_unpad,
            #     v_unpad,
            #     out_unpad,
            #     lse,
            #     None,
            #     None,
            #     None,
            #     cu_seqlens_q,
            #     cu_seqlens_k,
            #     None, None,
            #     max_seqlen_q,
            #     max_seqlen_k,
            #     d ** (-0.5),
            #     causal,
            #     window_size[0], window_size[1],
            #     softcap,
            #     deterministic,
            #     0,  # sm_margin
            # )
            dq_unpad, dk_unpad, dv_unpad = torch.autograd.grad(
                out_unpad, (q_unpad, k_unpad, v_unpad), g_unpad
            )
            dq = dq_pad_fn(dq_unpad)
            dk = dk_pad_fn(dk_unpad)
            dv = dk_pad_fn(dv_unpad)
            if key_unused_mask is not None:
                k_zero_masking = rearrange(key_unused_mask, "b s -> b s 1 1")
                dk.masked_fill_(k_zero_masking, 0.0)
                dv.masked_fill_(k_zero_masking, 0.0)
            if query_unused_mask is not None:
                dq.masked_fill_(q_zero_masking, 0.0)
            # print(f"dO_O max diff: {(softmax_d - do_o).abs().max().item()}")
            # assert (softmax_d - do_o).abs().max().item() <= 1e-5
            # assert dq_accum.abs().max().item() == 0.0
            g = output_pad_fn(g_unpad)

            # qk = torch.einsum('bthd,bshd->bhts', q / (d ** 0.5), k).float()
            # qk = torch.masked_fill(qk, rearrange(~key_padding_mask, "b s -> b 1 1 s"), float("-inf"))
            # dS = torch.einsum('bthd,bshd->bhts', g.float(), v.float())
            # P = torch.softmax(qk, -1)
            # dP = P * (dS - (g.float() * out.float()).sum(-1).transpose(1, 2).unsqueeze(-1))
            # dQ = torch.einsum('bhts,bshd->bthd', dP, k.float())
            # dV = torch.einsum('bhts,bthd->bshd', P, g.float())
            # dK = torch.einsum('bhts,bthd->bshd', dP, q.float())

            # dq, dk, dv = torch.autograd.grad(out, (q, k, v), g)
            dq_ref, dk_ref, dv_ref = torch.autograd.grad(
                out_ref, (q_ref, k_ref, v_ref), g
            )
            dq_pt, dk_pt, dv_pt = torch.autograd.grad(out_pt, (q_ref, k_ref, v_ref), 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()}")
            # breakpoint()
            dq_atol = 2 * (dq_ref + 0.3 - 0.3 - dq_ref).abs().max().item() + (
                0 if softcap == 0 else 3e-4
            )
            assert (dq - dq_ref).abs().max().item() <= rtol * (
                dq_pt - dq_ref
            ).abs().max().item() + dq_atol
            dk_atol = 2 * (dk_ref + 0.3 - 0.3 - dk_ref).abs().max().item() + (
                0 if softcap == 0 else 3e-4
            )
            assert (dk - dk_ref).abs().max().item() <= rtol * (
                dk_pt - dk_ref
            ).abs().max().item() + dk_atol
            dv_atol = 2 * (dv_ref + 0.3 - 0.3 - dv_ref).abs().max().item() + (
                0 if softcap == 0 else 3e-4
            )
            assert (dv - dv_ref).abs().max().item() <= rtol * (
                dv_pt - dv_ref
            ).abs().max().item() + dv_atol


if __name__ == "__main__":
    pytest.main([__file__])