# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project


import pytest

from vllm.platforms import current_platform
from vllm.utils.torch_utils import set_random_seed

try:
    import flashinfer
except ImportError:
    if current_platform.is_rocm():
        pytest.skip(
            "flashinfer is not supported for vLLM on ROCm.", allow_module_level=True
        )

import torch

NUM_HEADS = [(32, 8), (6, 1)]
HEAD_SIZES = [128, 256]
BLOCK_SIZES = [16, 32]
DTYPES = [torch.bfloat16]
NUM_BLOCKS = 32768  # Large enough to test overflow in index calculation.
SOFT_CAPS = [None, 30.0]
SLIDING_WINDOWS = [None, 64]


def ref_paged_attn(
    query: torch.Tensor,
    key_cache: torch.Tensor,
    value_cache: torch.Tensor,
    query_lens: list[int],
    kv_lens: list[int],
    block_tables: torch.Tensor,
    scale: float,
    sliding_window: int | None = None,
    soft_cap: float | None = None,
) -> torch.Tensor:
    num_seqs = len(query_lens)
    block_tables = block_tables.cpu().numpy()
    _, block_size, num_kv_heads, head_size = key_cache.shape

    outputs: list[torch.Tensor] = []
    start_idx = 0
    for i in range(num_seqs):
        query_len = query_lens[i]
        kv_len = kv_lens[i]
        q = query[start_idx : start_idx + query_len]
        q *= scale

        num_kv_blocks = (kv_len + block_size - 1) // block_size
        block_indices = block_tables[i, :num_kv_blocks]

        k = key_cache[block_indices].view(-1, num_kv_heads, head_size)
        k = k[:kv_len]
        v = value_cache[block_indices].view(-1, num_kv_heads, head_size)
        v = v[:kv_len]

        if q.shape[1] != k.shape[1]:
            k = torch.repeat_interleave(k, q.shape[1] // k.shape[1], dim=1)
            v = torch.repeat_interleave(v, q.shape[1] // v.shape[1], dim=1)
        attn = torch.einsum("qhd,khd->hqk", q, k).float()
        empty_mask = torch.ones(query_len, kv_len)
        mask = torch.triu(empty_mask, diagonal=kv_len - query_len + 1).bool()
        if sliding_window is not None:
            sliding_window_mask = (
                torch.triu(
                    empty_mask, diagonal=kv_len - (query_len + sliding_window) + 1
                )
                .bool()
                .logical_not()
            )
            mask |= sliding_window_mask
        if soft_cap is not None:
            attn = soft_cap * torch.tanh(attn / soft_cap)
        attn.masked_fill_(mask, float("-inf"))
        attn = torch.softmax(attn, dim=-1).to(v.dtype)
        out = torch.einsum("hqk,khd->qhd", attn, v)

        outputs.append(out)
        start_idx += query_len

    return torch.cat(outputs, dim=0)


def _make_paged_kv_metadata(
    kv_lens: list[int],
    block_size: int,
    num_blocks: int,
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
    """Build paged-KV metadata tensors for fast_plan_decode tests.

    Returns:
        kv_indptr          – CPU int32, shape [num_seqs + 1]
        kv_indices         – CUDA int32, shape [total_blocks]
        kv_last_page_lens  – CPU int32, shape [num_seqs]
        block_tables       – CUDA int32, shape [num_seqs, max_blocks_per_seq]
    """
    num_seqs = len(kv_lens)
    max_blocks = (max(kv_lens) + block_size - 1) // block_size
    block_tables = torch.randint(
        0, num_blocks, (num_seqs, max_blocks), dtype=torch.int32, device="cuda"
    )

    indptr_list = [0]
    indices_list: list[int] = []
    last_lens_list: list[int] = []
    for i, seq_len in enumerate(kv_lens):
        n = (seq_len + block_size - 1) // block_size
        indices_list.extend(block_tables[i, :n].cpu().tolist())
        indptr_list.append(indptr_list[-1] + n)
        last_lens_list.append(seq_len % block_size or block_size)

    return (
        torch.tensor(indptr_list, dtype=torch.int32, device="cpu"),
        torch.tensor(indices_list, dtype=torch.int32, device="cuda"),
        torch.tensor(last_lens_list, dtype=torch.int32, device="cpu"),
        block_tables,
    )


def _make_cg_decode_wrapper(
    num_seqs: int,
    kv_indices_buffer: torch.Tensor,
    workspace_buffer: torch.Tensor,
    use_tensor_cores: bool = True,
) -> "flashinfer.BatchDecodeWithPagedKVCacheWrapper":
    """Create a cudagraph-enabled BatchDecodeWithPagedKVCacheWrapper.

    *kv_indices_buffer* is shared with the caller so that fast_plan_decode
    can avoid the device-to-device index copy on subsequent (cudagraph) calls.
    """
    return flashinfer.BatchDecodeWithPagedKVCacheWrapper(
        workspace_buffer,
        "NHD",
        use_cuda_graph=True,
        paged_kv_indptr_buffer=torch.zeros(
            num_seqs + 1, dtype=torch.int32, device="cuda"
        ),
        paged_kv_indices_buffer=kv_indices_buffer,
        paged_kv_last_page_len_buffer=torch.zeros(
            num_seqs, dtype=torch.int32, device="cuda"
        ),
        use_tensor_cores=use_tensor_cores,
    )


def test_fast_decode_plan_importable() -> None:
    """fast_decode_plan must be importable from flashinfer.decode.

    This is a forward-compatibility smoke test: if FlashInfer reorganises its
    public API the import will fail before any other test does.
    """
    from flashinfer.decode import fast_decode_plan  # noqa: F401

    assert callable(fast_decode_plan)


@pytest.mark.parametrize("dtype", DTYPES)
@torch.inference_mode
def test_fast_plan_decode_warmup_uses_full_plan(dtype: torch.dtype) -> None:
    """On the first call fast_plan_decode must route through self.plan() and
    flip vllm_first_call to False on the wrapper object."""
    from unittest.mock import patch

    from vllm.v1.attention.backends.flashinfer import fast_plan_decode

    torch.set_default_device("cuda")
    set_random_seed(0)

    kv_lens = [128, 64]
    block_size = 16
    num_seqs = len(kv_lens)
    num_query_heads, num_kv_heads = 8, 2
    head_size = 128

    kv_indptr, kv_indices, kv_last_page_lens, _ = _make_paged_kv_metadata(
        kv_lens, block_size, NUM_BLOCKS
    )

    workspace = torch.empty(128 * 1024 * 1024, dtype=torch.int8)
    wrapper = _make_cg_decode_wrapper(num_seqs, kv_indices.clone(), workspace)

    assert getattr(wrapper, "vllm_first_call", True) is True

    with patch.object(wrapper, "plan", wraps=wrapper.plan) as mock_plan:
        fast_plan_decode(
            wrapper,
            indptr_cpu=kv_indptr,
            indices=kv_indices,
            last_page_len_cpu=kv_last_page_lens,
            num_qo_heads=num_query_heads,
            num_kv_heads=num_kv_heads,
            head_dim=head_size,
            page_size=block_size,
            q_data_type=dtype,
            kv_data_type=dtype,
        )
        mock_plan.assert_called_once()

    assert wrapper.vllm_first_call is False, (
        "vllm_first_call should be False after the first fast_plan_decode call"
    )


@pytest.mark.parametrize("kv_lens", [[1328, 18, 463], [1, 54, 293, 70]])
@pytest.mark.parametrize("num_heads", NUM_HEADS)
@pytest.mark.parametrize("head_size", HEAD_SIZES)
@pytest.mark.parametrize("block_size", BLOCK_SIZES)
@pytest.mark.parametrize("dtype", DTYPES)
@torch.inference_mode
def test_fast_plan_decode_matches_full_plan(
    kv_lens: list[int],
    num_heads: tuple[int, int],
    head_size: int,
    block_size: int,
    dtype: torch.dtype,
) -> None:
    """fast_plan_decode's cudagraph path (delegating to FlashInfer's
    fast_decode_plan) must produce attention output numerically identical to
    a standard plan() call.

    Both the warmup call (self.plan) and the subsequent fast call
    (fast_decode_plan) are verified against the same reference.
    """
    from vllm.v1.attention.backends.flashinfer import fast_plan_decode

    torch.set_default_device("cuda")
    set_random_seed(0)
    num_seqs = len(kv_lens)
    num_query_heads, num_kv_heads = num_heads

    query = torch.randn(num_seqs, num_query_heads, head_size, dtype=dtype)
    key_value_cache = torch.randn(
        NUM_BLOCKS, 2, block_size, num_kv_heads, head_size, dtype=dtype
    )

    kv_indptr, kv_indices, kv_last_page_lens, _ = _make_paged_kv_metadata(
        kv_lens, block_size, NUM_BLOCKS
    )

    # Reference output via the standard plan()
    workspace_ref = torch.empty(128 * 1024 * 1024, dtype=torch.int8)
    ref_wrapper = flashinfer.BatchDecodeWithPagedKVCacheWrapper(
        workspace_ref, "NHD", use_tensor_cores=True
    )
    ref_wrapper.plan(
        kv_indptr,
        kv_indices,
        kv_last_page_lens,
        num_query_heads,
        num_kv_heads,
        head_size,
        block_size,
        "NONE",
        q_data_type=dtype,
        kv_data_type=dtype,
    )
    ref_output = ref_wrapper.run(query, key_value_cache)

    # CUDAGraph wrapper exercised through fast_plan_decode
    kv_indices_buf = kv_indices.clone()
    workspace_cg = torch.empty(128 * 1024 * 1024, dtype=torch.int8)
    cg_wrapper = _make_cg_decode_wrapper(num_seqs, kv_indices_buf, workspace_cg)

    plan_kwargs: dict = dict(
        indptr_cpu=kv_indptr,
        indices=kv_indices_buf,
        last_page_len_cpu=kv_last_page_lens,
        num_qo_heads=num_query_heads,
        num_kv_heads=num_kv_heads,
        head_dim=head_size,
        page_size=block_size,
        q_data_type=dtype,
        kv_data_type=dtype,
    )

    # First call – warmup path (routes through self.plan)
    fast_plan_decode(cg_wrapper, **plan_kwargs)
    warmup_output = cg_wrapper.run(query, key_value_cache)
    torch.testing.assert_close(warmup_output, ref_output, atol=1e-2, rtol=1e-2)

    # Second call – fast path (routes through fast_decode_plan from FlashInfer)
    fast_plan_decode(cg_wrapper, **plan_kwargs)
    fast_output = cg_wrapper.run(query, key_value_cache)
    torch.testing.assert_close(fast_output, ref_output, atol=1e-2, rtol=1e-2)


@pytest.mark.parametrize("kv_lens", [[1328, 18, 463], [1, 54, 293, 70]])
@pytest.mark.parametrize("num_heads", NUM_HEADS)
@pytest.mark.parametrize("head_size", HEAD_SIZES)
@pytest.mark.parametrize("block_size", BLOCK_SIZES)
@pytest.mark.parametrize("dtype", DTYPES)
@pytest.mark.parametrize("soft_cap", SOFT_CAPS)
@pytest.mark.parametrize("sliding_window", SLIDING_WINDOWS)
@torch.inference_mode
def test_flashinfer_decode_with_paged_kv(
    kv_lens: list[int],
    num_heads: tuple[int, int],
    head_size: int,
    dtype: torch.dtype,
    block_size: int,
    soft_cap: float | None,
    sliding_window: int | None,
) -> None:
    torch.set_default_device("cuda")
    set_random_seed(0)
    num_seqs = len(kv_lens)
    num_query_heads = num_heads[0]
    num_kv_heads = num_heads[1]
    assert num_query_heads % num_kv_heads == 0
    max_kv_len = max(kv_lens)
    scale = head_size**-0.5

    query = torch.randn(num_seqs, num_query_heads, head_size, dtype=dtype)

    key_value_cache = torch.randn(
        NUM_BLOCKS, 2, block_size, num_kv_heads, head_size, dtype=dtype
    )
    key_cache = key_value_cache[:, 0, :, :, :].squeeze(1)
    value_cache = key_value_cache[:, 1, :, :, :].squeeze(1)

    max_num_blocks_per_seq = (max_kv_len + block_size - 1) // block_size
    block_tables = torch.randint(
        0, NUM_BLOCKS, (num_seqs, max_num_blocks_per_seq), dtype=torch.int32
    )

    kv_indptr = [0]
    kv_indices = []
    kv_last_page_lens = []
    for i in range(num_seqs):
        seq_len = kv_lens[i]
        assert seq_len > 0
        num_blocks = (seq_len + block_size - 1) // block_size
        kv_indices.extend(block_tables[i, :num_blocks])
        kv_indptr.append(kv_indptr[-1] + num_blocks)
        kv_last_page_len = seq_len % block_size
        if kv_last_page_len == 0:
            kv_last_page_len = block_size
        kv_last_page_lens.append(kv_last_page_len)

    kv_indptr = torch.tensor(kv_indptr, dtype=torch.int32)
    kv_indices = torch.tensor(kv_indices, dtype=torch.int32)
    kv_last_page_lens = torch.tensor(kv_last_page_lens, dtype=torch.int32)

    workspace_buffer = torch.empty(128 * 1024 * 1024, dtype=torch.int8)
    wrapper = flashinfer.BatchDecodeWithPagedKVCacheWrapper(
        workspace_buffer, "NHD", use_tensor_cores=True
    )
    wrapper.plan(
        kv_indptr,
        kv_indices,
        kv_last_page_lens,
        num_query_heads,
        num_kv_heads,
        head_size,
        block_size,
        "NONE",
        window_left=sliding_window - 1 if sliding_window is not None else -1,
        q_data_type=dtype,
        kv_data_type=dtype,
        logits_soft_cap=soft_cap,
    )

    output = wrapper.run(query, key_value_cache)

    ref_output = ref_paged_attn(
        query=query,
        key_cache=key_cache,
        value_cache=value_cache,
        query_lens=[1] * num_seqs,
        kv_lens=kv_lens,
        block_tables=block_tables,
        scale=scale,
        soft_cap=soft_cap,
        sliding_window=sliding_window,
    )
    (
        torch.testing.assert_close(output, ref_output, atol=1e-2, rtol=1e-2),
        f"{torch.max(torch.abs(output - ref_output))}",
    )


@pytest.mark.parametrize("seq_lens", [[(1, 1328), (5, 18), (129, 463)]])
@pytest.mark.parametrize("num_heads", NUM_HEADS)
@pytest.mark.parametrize("head_size", HEAD_SIZES)
@pytest.mark.parametrize("block_size", BLOCK_SIZES)
@pytest.mark.parametrize("dtype", DTYPES)
@pytest.mark.parametrize("soft_cap", SOFT_CAPS)
@pytest.mark.parametrize("sliding_window", SLIDING_WINDOWS)
@torch.inference_mode
def test_flashinfer_prefill_with_paged_kv(
    seq_lens: list[tuple[int, int]],
    num_heads: tuple[int, int],
    head_size: int,
    dtype: torch.dtype,
    block_size: int,
    soft_cap: float | None,
    sliding_window: int | None,
) -> None:
    torch.set_default_device("cuda")
    set_random_seed(0)
    num_seqs = len(seq_lens)
    query_lens = [x[0] for x in seq_lens]
    kv_lens = [x[1] for x in seq_lens]
    num_query_heads = num_heads[0]
    num_kv_heads = num_heads[1]
    assert num_query_heads % num_kv_heads == 0
    max_kv_len = max(kv_lens)
    scale = head_size**-0.5

    query = torch.randn(sum(query_lens), num_query_heads, head_size, dtype=dtype)
    key_value_cache = torch.randn(
        NUM_BLOCKS, 2, block_size, num_kv_heads, head_size, dtype=dtype
    )
    key_cache = key_value_cache[:, 0, :, :, :].squeeze(1)
    value_cache = key_value_cache[:, 1, :, :, :].squeeze(1)

    # Normalize the scale of the key and value caches to mitigate
    # numerical instability.
    key_cache /= head_size**0.5
    value_cache /= head_size**0.5

    max_num_blocks_per_seq = (max_kv_len + block_size - 1) // block_size
    block_tables = torch.randint(
        0, NUM_BLOCKS, (num_seqs, max_num_blocks_per_seq), dtype=torch.int32
    )

    qo_indptr = [0]
    kv_indptr = [0]
    kv_indices = []
    kv_last_page_lens = []
    for i in range(num_seqs):
        seq_len = kv_lens[i]
        assert seq_len > 0
        num_blocks = (seq_len + block_size - 1) // block_size
        kv_indices.extend(block_tables[i, :num_blocks])
        kv_indptr.append(kv_indptr[-1] + num_blocks)
        kv_last_page_len = seq_len % block_size
        if kv_last_page_len == 0:
            kv_last_page_len = block_size
        kv_last_page_lens.append(kv_last_page_len)
        qo_indptr.append(qo_indptr[-1] + query_lens[i])

    qo_indptr = torch.tensor(qo_indptr, dtype=torch.int32)
    kv_indptr = torch.tensor(kv_indptr, dtype=torch.int32)
    kv_indices = torch.tensor(kv_indices, dtype=torch.int32)
    kv_last_page_lens = torch.tensor(kv_last_page_lens, dtype=torch.int32)

    workspace_buffer = torch.empty(128 * 1024 * 1024, dtype=torch.int8)
    wrapper = flashinfer.BatchPrefillWithPagedKVCacheWrapper(workspace_buffer, "NHD")
    wrapper.plan(
        qo_indptr,
        kv_indptr,
        kv_indices,
        kv_last_page_lens,
        num_query_heads,
        num_kv_heads,
        head_size,
        block_size,
        window_left=sliding_window - 1 if sliding_window is not None else -1,
        q_data_type=dtype,
        kv_data_type=dtype,
        logits_soft_cap=soft_cap,
    )

    output = wrapper.run(
        query,
        key_value_cache,
    )

    ref_output = ref_paged_attn(
        query=query,
        key_cache=key_cache,
        value_cache=value_cache,
        query_lens=query_lens,
        kv_lens=kv_lens,
        block_tables=block_tables,
        scale=scale,
        soft_cap=soft_cap,
        sliding_window=sliding_window,
    )
    (
        torch.testing.assert_close(output, ref_output, atol=5e-2, rtol=1e-2),
        f"{torch.max(torch.abs(output - ref_output))}",
    )


@pytest.mark.parametrize("seq_lens", [[(1, 132), (5, 18)]])
@pytest.mark.parametrize("num_heads", NUM_HEADS)
@pytest.mark.parametrize("head_size", HEAD_SIZES)
@pytest.mark.parametrize("block_size", BLOCK_SIZES)
@pytest.mark.parametrize("dtype", DTYPES)
@pytest.mark.parametrize("soft_cap", SOFT_CAPS)
def test_flashinfer_prefill_with_paged_fp8_kv(
    seq_lens: list[tuple[int, int]],
    num_heads: tuple[int, int],
    head_size: int,
    dtype: torch.dtype,
    block_size: int,
    soft_cap: float | None,
) -> None:
    pytest.skip("TODO: fix the accuracy issue")
    torch.set_default_device("cuda")
    set_random_seed(0)
    num_seqs = len(seq_lens)
    query_lens = [x[0] for x in seq_lens]
    kv_lens = [x[1] for x in seq_lens]
    num_query_heads = num_heads[0]
    num_kv_heads = num_heads[1]
    assert num_query_heads % num_kv_heads == 0
    max_kv_len = max(kv_lens)
    scale = head_size**-0.5

    kv_cache_dtype = torch.float8_e4m3fn

    query = torch.randn(sum(query_lens), num_query_heads, head_size, dtype=dtype)
    NUM_BLOCKS_FP8 = 2048
    key_value_cache = torch.randn(
        NUM_BLOCKS_FP8, 2, block_size, num_kv_heads, head_size, dtype=dtype
    )
    key_cache, value_cache = torch.chunk(key_value_cache, 2, dim=1)
    key_cache /= head_size**0.5
    value_cache /= head_size**0.5

    k_scale = key_cache.amax().item() / 448.0
    v_scale = value_cache.amax().item() / 448.0

    kv_cache_fp8 = torch.cat([key_cache / k_scale, value_cache / v_scale], dim=1).to(
        kv_cache_dtype
    )

    assert kv_cache_fp8.shape == key_value_cache.shape
    max_num_blocks_per_seq = (max_kv_len + block_size - 1) // block_size
    block_tables = torch.randint(
        0, NUM_BLOCKS_FP8, (num_seqs, max_num_blocks_per_seq), dtype=torch.int32
    )

    qo_indptr = [0]
    kv_indptr = [0]
    kv_indices = []
    kv_last_page_lens = []
    for i in range(num_seqs):
        seq_len = kv_lens[i]
        assert seq_len > 0
        num_blocks = (seq_len + block_size - 1) // block_size
        kv_indices.extend(block_tables[i, :num_blocks])
        kv_indptr.append(kv_indptr[-1] + num_blocks)
        kv_last_page_len = seq_len % block_size
        if kv_last_page_len == 0:
            kv_last_page_len = block_size
        kv_last_page_lens.append(kv_last_page_len)
        qo_indptr.append(qo_indptr[-1] + query_lens[i])

    qo_indptr = torch.tensor(qo_indptr, dtype=torch.int32)
    kv_indptr = torch.tensor(kv_indptr, dtype=torch.int32)
    kv_indices = torch.tensor(kv_indices, dtype=torch.int32)
    kv_last_page_lens = torch.tensor(kv_last_page_lens, dtype=torch.int32)

    workspace_buffer = torch.empty(128 * 1024 * 1024, dtype=torch.int8)
    wrapper = flashinfer.BatchPrefillWithPagedKVCacheWrapper(workspace_buffer, "NHD")
    wrapper.plan(
        qo_indptr,
        kv_indptr,
        kv_indices,
        kv_last_page_lens,
        num_query_heads,
        num_kv_heads,
        head_size,
        block_size,
        q_data_type=dtype,
        kv_data_type=kv_cache_dtype,
        logits_soft_cap=soft_cap,
    )

    output = wrapper.run(query, kv_cache_fp8, k_scale=k_scale, v_scale=v_scale)

    ref_output = ref_paged_attn(
        query=query,
        key_cache=key_cache.squeeze(1),
        value_cache=value_cache.squeeze(1),
        query_lens=query_lens,
        kv_lens=kv_lens,
        block_tables=block_tables,
        scale=scale,
        soft_cap=soft_cap,
    )
    del query
    del block_tables
    # verify prefill fp8
    (
        torch.testing.assert_close(output, ref_output, atol=5e-2, rtol=1e-2),
        f"{torch.max(torch.abs(output - ref_output))}",
    )


@pytest.mark.parametrize("kv_lens", [[1328, 18, 463], [1, 54, 293, 70]])
@pytest.mark.parametrize("num_heads", NUM_HEADS)
@pytest.mark.parametrize("head_size", HEAD_SIZES)
@pytest.mark.parametrize("block_size", BLOCK_SIZES)
@pytest.mark.parametrize("dtype", DTYPES)
@pytest.mark.parametrize("soft_cap", SOFT_CAPS)
@pytest.mark.skip(reason="TODO: fix the accuracy issue")
@torch.inference_mode
def test_flashinfer_decode_with_paged_fp8_kv(
    kv_lens: list[int],
    num_heads: tuple[int, int],
    head_size: int,
    dtype: torch.dtype,
    block_size: int,
    soft_cap: float | None,
) -> None:
    # test doesn't work for num_heads = (16,16)
    torch.set_default_device("cuda")
    set_random_seed(0)
    num_seqs = len(kv_lens)
    num_query_heads = num_heads[0]
    num_kv_heads = num_heads[1]
    assert num_query_heads % num_kv_heads == 0
    max_kv_len = max(kv_lens)
    scale = head_size**-0.5
    use_tensor_cores = True
    kv_cache_dtype = torch.float8_e4m3fn

    query = torch.randn(num_seqs, num_query_heads, head_size, dtype=dtype)
    NUM_BLOCKS_FP8 = 2048
    key_value_cache = torch.randn(
        NUM_BLOCKS_FP8, 2, block_size, num_kv_heads, head_size, dtype=dtype
    )
    key_cache, value_cache = torch.chunk(key_value_cache, 2, dim=1)
    key_cache /= head_size**0.5
    value_cache /= head_size**0.5

    k_scale = key_cache.amax().item() / 448.0
    v_scale = value_cache.amax().item() / 448.0

    key_cache_fp8 = (key_cache / k_scale).to(kv_cache_dtype)
    value_cache_fp8 = (value_cache / v_scale).to(kv_cache_dtype)
    assert key_cache_fp8.shape[1] == 1 and value_cache_fp8.shape[1] == 1
    kv_cache_fp8 = torch.cat([key_cache_fp8, value_cache_fp8], dim=1)

    max_num_blocks_per_seq = (max_kv_len + block_size - 1) // block_size
    block_tables = torch.randint(
        0, NUM_BLOCKS_FP8, (num_seqs, max_num_blocks_per_seq), dtype=torch.int32
    )

    kv_indptr = [0]
    kv_indices = []
    kv_last_page_lens = []
    for i in range(num_seqs):
        seq_len = kv_lens[i]
        assert seq_len > 0
        num_blocks = (seq_len + block_size - 1) // block_size
        kv_indices.extend(block_tables[i, :num_blocks])
        kv_indptr.append(kv_indptr[-1] + num_blocks)
        kv_last_page_len = seq_len % block_size
        if kv_last_page_len == 0:
            kv_last_page_len = block_size
        kv_last_page_lens.append(kv_last_page_len)

    kv_indptr = torch.tensor(kv_indptr, dtype=torch.int32)
    kv_indices = torch.tensor(kv_indices, dtype=torch.int32)
    kv_last_page_lens = torch.tensor(kv_last_page_lens, dtype=torch.int32)

    workspace_buffer = torch.empty(128 * 1024 * 1024, dtype=torch.int8)
    wrapper = flashinfer.BatchDecodeWithPagedKVCacheWrapper(
        workspace_buffer, "NHD", use_tensor_cores=use_tensor_cores
    )
    wrapper.plan(
        kv_indptr,
        kv_indices,
        kv_last_page_lens,
        num_query_heads,
        num_kv_heads,
        head_size,
        block_size,
        "NONE",
        q_data_type=dtype,
        kv_data_type=kv_cache_dtype,
        logits_soft_cap=soft_cap,
    )
    output = wrapper.run(query, kv_cache_fp8, k_scale=k_scale, v_scale=v_scale)
    key_cache = key_value_cache[:, 0, :, :, :].squeeze(1)
    value_cache = key_value_cache[:, 1, :, :, :].squeeze(1)

    ref_output = ref_paged_attn(
        query=query,
        key_cache=key_cache,
        value_cache=value_cache,
        query_lens=[1] * num_seqs,
        kv_lens=kv_lens,
        block_tables=block_tables,
        scale=scale,
        soft_cap=soft_cap,
    )
    # Temporary fix: Increasing the tolerance. Seems like a flashinfer issue
    (
        torch.testing.assert_close(output, ref_output, atol=2e-2, rtol=1e-2),
        f"{torch.max(torch.abs(output - ref_output))}",
    )