test_cache.py

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

import random

import pytest
import torch

from tests.kernels.utils import DEFAULT_OPCHECK_TEST_UTILS, opcheck
from vllm import _custom_ops as ops
from vllm.model_executor.layers.quantization.utils.quant_utils import scaled_dequantize
from vllm.platforms import current_platform
from vllm.utils.torch_utils import set_random_seed

COPYING_DIRECTION = [("cuda", "cpu"), ("cuda", "cuda"), ("cpu", "cuda")]
DTYPES = [torch.bfloat16, torch.float]
NUM_TOKENS = [42]  # Arbitrary values for testing
NUM_LAYERS = [1]  # Arbitrary values for testing
NUM_HEADS = [8]  # Arbitrary values for testing
HEAD_SIZES = [64, 80, 256]
BLOCK_SIZES = [8, 16, 32]
CACHE_LAYOUTS = ["NHD", "HND"]
KV_SCALE_TYPES = ["tensor", "attn_head"]

# Parameters for MLA tests.
KV_LORA_RANKS = [256, 512]
QK_ROPE_HEAD_DIMS = [64]
NUM_TOKENS_MLA = [42]
BLOCK_SIZES_MLA = [16]
NUM_BLOCKS_MLA = [8]

# Arbitrary values for testing
# don't make it too large. e.g. [1024, 36000] will OOM
NUM_BLOCKS = [1024, 10000]

NUM_MAPPINGS = [256]  # Arbitrary values for testing
SEEDS = [0]
CUDA_DEVICES = [
    f"cuda:{i}" for i in range(1 if torch.accelerator.device_count() == 1 else 2)
]

# We assume fp8 is always enabled for testing.
KV_CACHE_DTYPE = ["auto", "fp8"]

RESHAPE_FLASH_IMPLEMENTATIONS = ["cuda", "triton"]


@pytest.mark.parametrize("num_tokens", NUM_TOKENS)
@pytest.mark.parametrize("num_heads", NUM_HEADS)
@pytest.mark.parametrize("head_size", HEAD_SIZES)
@pytest.mark.parametrize("block_size", BLOCK_SIZES)
@pytest.mark.parametrize("num_blocks", NUM_BLOCKS)
@pytest.mark.parametrize("dtype", DTYPES)
@pytest.mark.parametrize("seed", SEEDS)
@pytest.mark.parametrize("device", CUDA_DEVICES)
@pytest.mark.parametrize("kv_cache_dtype", KV_CACHE_DTYPE)
@torch.inference_mode()
def test_reshape_and_cache(
    kv_cache_factory,
    num_tokens: int,
    num_heads: int,
    head_size: int,
    block_size: int,
    num_blocks: int,
    dtype: torch.dtype,
    seed: int,
    device: str,
    kv_cache_dtype: str,
) -> None:
    if kv_cache_dtype == "fp8" and head_size % 16:
        pytest.skip()
    set_random_seed(seed)
    torch.set_default_device(device)
    torch.accelerator.set_device_index(device)
    # Create a random slot mapping.
    num_slots = block_size * num_blocks
    slot_mapping_lst = random.sample(range(num_slots), num_tokens)
    slot_mapping = torch.tensor(slot_mapping_lst, dtype=torch.long)

    qkv = torch.randn(num_tokens, 3, num_heads, head_size, dtype=dtype)
    _, key, value = qkv.unbind(dim=1)

    # Create the KV caches.
    key_caches, value_caches = kv_cache_factory(
        num_blocks,
        block_size,
        1,
        num_heads,
        head_size,
        kv_cache_dtype,
        dtype,
        seed,
        device,
    )
    key_cache, value_cache = key_caches[0], value_caches[0]

    # Using default kv_scale
    k_scale = (key.amax() / 64.0).to(torch.float32)
    v_scale = (value.amax() / 64.0).to(torch.float32)

    # Clone the KV caches.
    if kv_cache_dtype == "fp8":
        cloned_key_cache = torch.empty_like(key_cache, dtype=torch.float16)
        ops.convert_fp8(cloned_key_cache, key_cache, k_scale.item())
        cloned_value_cache = torch.empty_like(value_cache, dtype=torch.float16)
        ops.convert_fp8(cloned_value_cache, value_cache, v_scale.item())
    else:
        cloned_key_cache = key_cache.clone()
        cloned_value_cache = value_cache.clone()

    # Call the reshape_and_cache kernel.
    opcheck(
        torch.ops._C_cache_ops.reshape_and_cache,
        (
            key,
            value,
            key_cache,
            value_cache,
            slot_mapping,
            kv_cache_dtype,
            k_scale,
            v_scale,
        ),
        cond=(head_size == HEAD_SIZES[0]),
    )
    ops.reshape_and_cache(
        key,
        value,
        key_cache,
        value_cache,
        slot_mapping,
        kv_cache_dtype,
        k_scale,
        v_scale,
    )

    if kv_cache_dtype == "fp8":
        result_key_cache = torch.empty_like(key_cache, dtype=torch.float16)
        ops.convert_fp8(result_key_cache, key_cache, k_scale.item())
        result_value_cache = torch.empty_like(value_cache, dtype=torch.float16)
        ops.convert_fp8(result_value_cache, value_cache, v_scale.item())

    # Run the reference implementation.
    reshaped_key = key.reshape(num_tokens, *key_cache[0, :, :, 0, :].shape)
    block_indices = torch.div(slot_mapping, block_size, rounding_mode="floor")
    block_indices_lst = block_indices.cpu().tolist()
    block_offsets = slot_mapping % block_size
    block_offsets_lst = block_offsets.cpu().tolist()
    for i in range(num_tokens):
        block_idx = block_indices_lst[i]
        block_offset = block_offsets_lst[i]
        cloned_key_cache[block_idx, :, :, block_offset, :] = reshaped_key[i]
        cloned_value_cache[block_idx, :, :, block_offset] = value[i]

    if kv_cache_dtype == "fp8":
        torch.testing.assert_close(
            result_key_cache, cloned_key_cache, atol=0.001, rtol=0.1
        )
        torch.testing.assert_close(
            result_value_cache, cloned_value_cache, atol=0.001, rtol=0.1
        )
    else:
        torch.testing.assert_close(key_cache, cloned_key_cache)
        torch.testing.assert_close(value_cache, cloned_value_cache)


@pytest.mark.parametrize("num_tokens", NUM_TOKENS)
@pytest.mark.parametrize("num_heads", NUM_HEADS)
@pytest.mark.parametrize("head_size", HEAD_SIZES)
@pytest.mark.parametrize("block_size", BLOCK_SIZES)
@pytest.mark.parametrize("num_blocks", NUM_BLOCKS)
@pytest.mark.parametrize("dtype", DTYPES)
@pytest.mark.parametrize("seed", SEEDS)
@pytest.mark.parametrize("device", CUDA_DEVICES)
@pytest.mark.parametrize("kv_cache_dtype", KV_CACHE_DTYPE)
@pytest.mark.parametrize("kv_cache_layout", CACHE_LAYOUTS)
@pytest.mark.parametrize("kv_scale_type", KV_SCALE_TYPES)
@pytest.mark.parametrize("implementation", RESHAPE_FLASH_IMPLEMENTATIONS)
@torch.inference_mode()
def test_reshape_and_cache_flash(
    kv_cache_factory_flashinfer,
    num_tokens: int,
    num_heads: int,
    head_size: int,
    block_size: int,
    num_blocks: int,
    dtype: torch.dtype,
    seed: int,
    device: str,
    kv_cache_dtype: str,
    kv_cache_layout: str,
    kv_scale_type: str,
    implementation: str,
) -> None:
    set_random_seed(seed)
    torch.set_default_device(device)
    torch.accelerator.set_device_index(device)
    assert implementation in ["cuda", "triton"]
    if implementation == "triton" and kv_cache_layout == "HND":
        pytest.skip("Triton implementation only supports NHD layout.")

    if kv_scale_type == "attn_head" and implementation != "cuda":
        pytest.skip("Only CUDA implementation supports attn_head scaling.")

    # fp8 conversion requires continugous memory buffer. Reduce the number of
    # blocks and tokens to consume less memory.
    num_tokens = num_tokens // 2
    num_blocks = num_blocks // 2
    # Create a random slot mapping.
    num_slots = block_size * num_blocks
    slot_mapping_lst = random.sample(range(num_slots), num_tokens)
    slot_mapping = torch.tensor(slot_mapping_lst, dtype=torch.long, device=device)
    qkv = torch.randn(num_tokens, 3, num_heads, head_size, dtype=dtype, device=device)
    _, key, value = qkv.unbind(dim=1)

    # Create the KV caches.
    key_caches, value_caches = kv_cache_factory_flashinfer(
        num_blocks,
        block_size,
        1,
        num_heads,
        head_size,
        kv_cache_dtype,
        dtype,
        device=device,
        cache_layout=kv_cache_layout,
    )
    key_cache, value_cache = key_caches[0], value_caches[0]
    del key_caches
    del value_caches

    if kv_scale_type == "tensor":
        k_scale = (key.amax() / 64.0).to(torch.float32)
        v_scale = (value.amax() / 64.0).to(torch.float32)
    else:  # "attn_head"
        k_scale = (key.amax(dim=(0, 2)) / 64.0).to(torch.float32)
        v_scale = (value.amax(dim=(0, 2)) / 64.0).to(torch.float32)

    def permute_and_compact(x):
        y = x if kv_cache_layout == "NHD" else x.permute(0, 2, 1, 3)
        return y.contiguous()

    key_cache_compact = permute_and_compact(key_cache)
    value_cache_compact = permute_and_compact(value_cache)

    def convert_fp8_local(output, input, scale, kv_dtype):
        fp8_input = input.view(current_platform.fp8_dtype())
        if scale.numel() == 1:  # per-tensor
            result = scaled_dequantize(
                fp8_input.flatten(0, 2), scale, group_shape=None, out_dtype=output.dtype
            ).reshape(*input.shape)
        else:  # per-head: broadcast scale along the head dimension
            # Original code uses dim 2 for NHD, dim 1 for HND
            if kv_cache_layout == "NHD":
                result = fp8_input.to(output.dtype) * scale.view(1, 1, -1, 1)
            else:
                result = fp8_input.to(output.dtype) * scale.view(1, -1, 1, 1)
        output.copy_(result)

    # Clone the KV caches.
    if kv_cache_dtype == "fp8":
        cloned_key_cache = torch.empty_like(key_cache_compact, dtype=torch.float16)
        convert_fp8_local(cloned_key_cache, key_cache_compact, k_scale, kv_cache_dtype)
        cloned_value_cache = torch.empty_like(value_cache_compact, dtype=torch.float16)
        convert_fp8_local(
            cloned_value_cache, value_cache_compact, v_scale, kv_cache_dtype
        )
    else:
        cloned_key_cache = key_cache_compact.clone()
        cloned_value_cache = value_cache_compact.clone()
    # Call the reshape_and_cache kernel.
    if implementation == "cuda":
        opcheck(
            torch.ops._C_cache_ops.reshape_and_cache_flash,
            (
                key,
                value,
                key_cache,
                value_cache,
                slot_mapping,
                kv_cache_dtype,
                k_scale,
                v_scale,
            ),
            cond=(head_size == HEAD_SIZES[0]),
        )
        ops.reshape_and_cache_flash(
            key,
            value,
            key_cache,
            value_cache,
            slot_mapping,
            kv_cache_dtype,
            k_scale,
            v_scale,
        )
    elif implementation == "triton":
        from vllm.v1.attention.ops.triton_reshape_and_cache_flash import (
            triton_reshape_and_cache_flash,
        )

        triton_reshape_and_cache_flash(
            key,
            value,
            key_cache,
            value_cache,
            slot_mapping,
            kv_cache_dtype,
            k_scale,
            v_scale,
        )
    key_cache_compact = permute_and_compact(key_cache)
    value_cache_compact = permute_and_compact(value_cache)

    if kv_cache_dtype == "fp8":
        result_key_cache = torch.empty_like(key_cache_compact, dtype=torch.float16)
        convert_fp8_local(result_key_cache, key_cache_compact, k_scale, kv_cache_dtype)
        result_value_cache = torch.empty_like(value_cache_compact, dtype=torch.float16)
        convert_fp8_local(
            result_value_cache,
            value_cache_compact,
            v_scale,
            kv_cache_dtype,
        )

    # Run the reference implementation.
    block_indices = torch.div(slot_mapping, block_size, rounding_mode="floor")
    block_indices_lst = block_indices.cpu().tolist()
    block_offsets = slot_mapping % block_size
    block_offsets_lst = block_offsets.cpu().tolist()
    for i in range(num_tokens):
        block_idx = block_indices_lst[i]
        block_offset = block_offsets_lst[i]
        if kv_cache_layout == "NHD":
            cloned_key_cache[block_idx, block_offset, :, :] = key[i]
            cloned_value_cache[block_idx, block_offset, :, :] = value[i]
        else:
            cloned_key_cache[block_idx, :, block_offset, :] = key[i]
            cloned_value_cache[block_idx, :, block_offset, :] = value[i]

    if kv_cache_dtype == "fp8":
        torch.testing.assert_close(
            result_key_cache, cloned_key_cache, atol=0.001, rtol=0.1
        )
        torch.testing.assert_close(
            result_value_cache, cloned_value_cache, atol=0.001, rtol=0.1
        )
    else:
        torch.testing.assert_close(key_cache_compact, cloned_key_cache)
        torch.testing.assert_close(value_cache_compact, cloned_value_cache)


@pytest.mark.parametrize("direction", COPYING_DIRECTION)
@pytest.mark.parametrize("num_mappings", NUM_MAPPINGS)
@pytest.mark.parametrize("num_heads", NUM_HEADS)
@pytest.mark.parametrize("head_size", HEAD_SIZES)
@pytest.mark.parametrize("block_size", BLOCK_SIZES)
@pytest.mark.parametrize("num_blocks", NUM_BLOCKS)
@pytest.mark.parametrize("dtype", DTYPES)
@pytest.mark.parametrize("seed", SEEDS)
@pytest.mark.parametrize("device", CUDA_DEVICES)
@pytest.mark.parametrize("kv_cache_dtype", KV_CACHE_DTYPE)
@torch.inference_mode()
def test_swap_blocks(
    kv_cache_factory,
    direction: tuple[str, str],
    num_mappings: int,
    num_heads: int,
    head_size: int,
    block_size: int,
    num_blocks: int,
    dtype: torch.dtype,
    seed: int,
    device: str,
    kv_cache_dtype: str,
) -> None:
    if kv_cache_dtype == "fp8" and "cpu" in direction:
        pytest.skip()
    if kv_cache_dtype == "fp8" and head_size % 16:
        pytest.skip()

    set_random_seed(seed)

    src_device = device if direction[0] == "cuda" else "cpu"
    dst_device = device if direction[1] == "cuda" else "cpu"

    src_blocks = random.sample(range(num_blocks), num_mappings)
    # For the same device, mapping must not overlap
    if src_device == dst_device:
        remaining_blocks = list(set(range(num_blocks)) - set(src_blocks))
        dst_blocks = random.sample(remaining_blocks, num_mappings)
    else:
        dst_blocks = random.sample(range(num_blocks), num_mappings)

    block_mapping = list(zip(src_blocks, dst_blocks))
    block_mapping_tensor = torch.tensor(
        block_mapping, dtype=torch.int64, device="cpu"
    ).view(-1, 2)

    # Create the KV caches on the first device.
    src_key_caches, src_value_caches = kv_cache_factory(
        num_blocks,
        block_size,
        1,
        num_heads,
        head_size,
        kv_cache_dtype,
        dtype,
        seed,
        src_device,
    )

    # Create the KV caches on the second device.
    dist_key_caches, dist_value_caches = kv_cache_factory(
        num_blocks,
        block_size,
        1,
        num_heads,
        head_size,
        kv_cache_dtype,
        dtype,
        seed,
        dst_device,
    )

    src_key_caches_clone = src_key_caches[0].clone()
    src_value_caches_clone = src_value_caches[0].clone()

    # Call the swap_blocks kernel.
    do_opcheck = head_size == HEAD_SIZES[0]
    src_cache = src_key_caches[0]
    block_size_in_bytes = src_cache.element_size() * src_cache.stride(0)
    opcheck(
        torch.ops._C_cache_ops.swap_blocks,
        (
            src_key_caches[0],
            dist_key_caches[0],
            block_size_in_bytes,
            block_mapping_tensor,
        ),
        cond=do_opcheck,
    )
    opcheck(
        torch.ops._C_cache_ops.swap_blocks,
        (
            src_value_caches[0],
            dist_value_caches[0],
            block_size_in_bytes,
            block_mapping_tensor,
        ),
        cond=do_opcheck,
    )

    ops.swap_blocks(
        src_key_caches[0],
        dist_key_caches[0],
        block_size_in_bytes,
        block_mapping_tensor,
    )
    ops.swap_blocks(
        src_value_caches[0],
        dist_value_caches[0],
        block_size_in_bytes,
        block_mapping_tensor,
    )

    for src, dst in block_mapping:
        torch.testing.assert_close(
            src_key_caches_clone[src].cpu(), dist_key_caches[0][dst].cpu()
        )
        torch.testing.assert_close(
            src_value_caches_clone[src].cpu(), dist_value_caches[0][dst].cpu()
        )


@pytest.mark.parametrize("num_heads", NUM_HEADS)
@pytest.mark.parametrize("head_size", HEAD_SIZES)
@pytest.mark.parametrize("block_size", BLOCK_SIZES)
@pytest.mark.parametrize("num_blocks", NUM_BLOCKS)
@pytest.mark.parametrize("dtype", DTYPES)
@pytest.mark.parametrize("seed", SEEDS)
@pytest.mark.parametrize("device", CUDA_DEVICES)
@torch.inference_mode()
def test_fp8_e4m3_conversion(
    num_heads: int,
    head_size: int,
    block_size: int,
    num_blocks: int,
    dtype: torch.dtype,
    seed: int,
    device: str,
) -> None:
    set_random_seed(seed)

    low = -224.0
    high = 224.0
    shape = (num_blocks, num_heads, head_size, block_size)
    cache = torch.empty(shape, dtype=dtype, device=device)
    cache.uniform_(low, high)

    cache_fp8 = torch.empty_like(cache, dtype=torch.uint8)
    ops.convert_fp8(cache_fp8, cache)

    converted_cache = torch.empty_like(cache)
    ops.convert_fp8(converted_cache, cache_fp8)

    torch.testing.assert_close(cache, converted_cache, atol=0.001, rtol=0.1)


def _create_mla_cache(
    num_blocks: int,
    block_size: int,
    entry_size: int,
    dtype: torch.dtype,
    kv_cache_dtype: str,
    device: str,
) -> torch.Tensor:
    cache_dtype = torch.uint8 if kv_cache_dtype == "fp8" else dtype
    return torch.zeros(
        num_blocks, block_size, entry_size, dtype=cache_dtype, device=device
    )


def _fill_mla_cache(cache: torch.Tensor, kv_cache_dtype: str):
    rand_dtype = torch.float16 if kv_cache_dtype == "fp8" else cache.dtype

    vals = torch.randn(*cache.shape, device=cache.device, dtype=rand_dtype)
    if kv_cache_dtype == "fp8":
        temp = torch.zeros_like(cache)
        ops.convert_fp8(temp, vals, 1.0, kv_dtype=kv_cache_dtype)
        vals = temp
    cache.copy_(vals)


@pytest.mark.parametrize("kv_lora_rank", KV_LORA_RANKS)
@pytest.mark.parametrize("qk_rope_head_dim", QK_ROPE_HEAD_DIMS)
@pytest.mark.parametrize("num_tokens", NUM_TOKENS_MLA)
@pytest.mark.parametrize("block_size", BLOCK_SIZES_MLA)
@pytest.mark.parametrize("num_blocks", NUM_BLOCKS_MLA)
@pytest.mark.parametrize("dtype", DTYPES)
@pytest.mark.parametrize("seed", SEEDS)
@pytest.mark.parametrize("device", CUDA_DEVICES)
@pytest.mark.parametrize("kv_cache_dtype", KV_CACHE_DTYPE)
@torch.inference_mode()
def test_concat_and_cache_mla(
    kv_lora_rank: int,
    qk_rope_head_dim: int,
    num_tokens: int,
    block_size: int,
    num_blocks: int,
    dtype: torch.dtype,
    seed: int,
    device: str,
    kv_cache_dtype: str,
) -> None:
    set_random_seed(seed)
    torch.set_default_device(device)
    torch.accelerator.set_device_index(device)

    total_slots = num_blocks * block_size
    slot_mapping_lst = random.sample(range(total_slots), num_tokens)
    slot_mapping = torch.tensor(slot_mapping_lst, dtype=torch.long, device=device)

    kv_c = torch.randn(num_tokens, kv_lora_rank, dtype=dtype, device=device)
    k_pe = torch.randn(num_tokens, qk_rope_head_dim, dtype=dtype, device=device)
    entry_size = kv_lora_rank + qk_rope_head_dim

    scale = torch.tensor(0.1, dtype=torch.float32, device=device)
    kv_cache = _create_mla_cache(
        num_blocks, block_size, entry_size, dtype, kv_cache_dtype, device
    )
    ref_temp = torch.zeros(*kv_cache.shape, dtype=dtype, device=device)

    for i in range(num_tokens):
        slot = slot_mapping[i].item()
        block_idx = slot // block_size
        block_offset = slot % block_size
        ref_temp[block_idx, block_offset, :kv_lora_rank] = kv_c[i]
        ref_temp[block_idx, block_offset, kv_lora_rank:] = k_pe[i]

    if kv_cache_dtype == "fp8":
        ref_kv_cache = torch.empty_like(ref_temp, dtype=kv_cache.dtype)
        ops.convert_fp8(ref_kv_cache, ref_temp, scale.item(), kv_dtype=kv_cache_dtype)
    else:
        ref_kv_cache = ref_temp

    opcheck(
        torch.ops._C_cache_ops.concat_and_cache_mla,
        (kv_c, k_pe, kv_cache, slot_mapping, kv_cache_dtype, scale),
        test_utils=DEFAULT_OPCHECK_TEST_UTILS,
    )

    ops.concat_and_cache_mla(kv_c, k_pe, kv_cache, slot_mapping, kv_cache_dtype, scale)

    if kv_cache_dtype == "fp8":
        result_temp = torch.empty_like(kv_cache, dtype=torch.float16)
        ops.convert_fp8(
            result_temp, kv_cache.contiguous(), scale.item(), kv_dtype=kv_cache_dtype
        )
        expected_temp = torch.empty_like(ref_kv_cache, dtype=torch.float16)
        ops.convert_fp8(
            expected_temp, ref_kv_cache, scale.item(), kv_dtype=kv_cache_dtype
        )
        torch.testing.assert_close(result_temp, expected_temp, atol=0.001, rtol=0.1)
    else:
        torch.testing.assert_close(kv_cache, ref_kv_cache)


@pytest.mark.parametrize("kv_lora_rank", KV_LORA_RANKS)
@pytest.mark.parametrize("qk_rope_head_dim", QK_ROPE_HEAD_DIMS)
@pytest.mark.parametrize("num_tokens", NUM_TOKENS_MLA)
@pytest.mark.parametrize("block_size", BLOCK_SIZES_MLA)
@pytest.mark.parametrize("num_blocks", NUM_BLOCKS_MLA)
@pytest.mark.parametrize("dtype", DTYPES)
@pytest.mark.parametrize("seed", SEEDS)
@pytest.mark.parametrize("device", CUDA_DEVICES)
@torch.inference_mode()
def test_concat_and_cache_ds_mla(
    kv_lora_rank: int,
    qk_rope_head_dim: int,
    num_tokens: int,
    block_size: int,
    num_blocks: int,
    dtype: torch.dtype,
    seed: int,
    device: str,
) -> None:
    if current_platform.is_rocm():
        pytest.skip("concat_and_cache_mla doesn't support fp8_ds_mla on ROCm")
    if dtype.itemsize != 2:
        pytest.skip("ds_mla only supports 16-bit input")
    if kv_lora_rank != 512:
        pytest.skip("fp8_ds_mla requires kv_lora_rank == 512")
    kv_cache_dtype = "fp8_ds_mla"
    set_random_seed(seed)
    torch.set_default_device(device)
    torch.accelerator.set_device_index(device)

    total_slots = num_blocks * block_size
    slot_mapping_lst = random.sample(range(total_slots), num_tokens)
    slot_mapping = torch.tensor(slot_mapping_lst, dtype=torch.long, device=device)

    kv_c = torch.randn(num_tokens, kv_lora_rank, dtype=dtype, device=device)
    k_pe = torch.randn(num_tokens, qk_rope_head_dim, dtype=dtype, device=device)
    entry_size = kv_lora_rank + (4 * 4) + (2 * qk_rope_head_dim)

    scale = torch.tensor(1.0, dtype=torch.float32, device=device)
    kv_cache = _create_mla_cache(
        num_blocks,
        block_size,
        entry_size,
        dtype=torch.uint8,
        kv_cache_dtype=kv_cache_dtype,
        device=device,
    )

    ref_cache = torch.zeros_like(kv_cache, dtype=kv_cache.dtype)
    tile_data = torch.zeros(128, dtype=dtype, device=device)

    for i in range(num_tokens):
        slot = slot_mapping[i].item()
        block_idx = slot // block_size
        block_offset = slot % block_size

        ref_cache_slice = ref_cache[block_idx, block_offset]
        ref_cache_16bit = ref_cache_slice.view(dtype)
        ref_cache_32bit = ref_cache_slice.view(torch.float32)

        kv_c_data = kv_c[i]
        num_tiles = kv_lora_rank // 128
        for tile_idx in range(num_tiles):
            tile_start = tile_idx * 128
            tile_end = (tile_idx + 1) * 128
            tile_data[:] = kv_c_data[tile_start:tile_end]

            # tile_scale = tile_data.amax().to(torch.float32) / 448.
            # NOTE: Using torch's amax() gives different results,
            # so this must be manually computed.
            tile_data_float = tile_data.to(torch.float32)
            manual_max = abs(tile_data_float[0])
            for j in range(1, 128):
                manual_max = max(manual_max, abs(tile_data_float[j]))
            tile_scale = manual_max / 448.0

            ref_cache_32bit[kv_lora_rank // 4 + tile_idx] = tile_scale

            ops.convert_fp8(
                ref_cache_slice[tile_start:tile_end],
                tile_data,
                tile_scale.item(),
                kv_dtype="fp8",
            )

        for j in range(qk_rope_head_dim):
            ref_cache_16bit[kv_lora_rank // 2 + 8 + j] = k_pe[i, j]

    opcheck(
        torch.ops._C_cache_ops.concat_and_cache_mla,
        (kv_c, k_pe, kv_cache, slot_mapping, kv_cache_dtype, scale),
        test_utils=DEFAULT_OPCHECK_TEST_UTILS,
    )

    ops.concat_and_cache_mla(kv_c, k_pe, kv_cache, slot_mapping, kv_cache_dtype, scale)

    for i in range(num_tokens):
        slot = slot_mapping[i].item()
        block_idx = slot // block_size
        block_offset = slot % block_size
        kv_cache_slice = kv_cache[block_idx, block_offset]
        ref_cache_slice = ref_cache[block_idx, block_offset]

        kv_nope = kv_cache_slice[:kv_lora_rank]
        ref_nope = ref_cache_slice[:kv_lora_rank]
        kv_scales = kv_cache_slice.view(torch.float32)[
            kv_lora_rank // 4 : kv_lora_rank // 4 + 4
        ]
        ref_scales = ref_cache_slice.view(torch.float32)[
            kv_lora_rank // 4 : kv_lora_rank // 4 + 4
        ]
        kv_rope = kv_cache_slice.view(dtype)[kv_lora_rank // 2 + 8 :]
        ref_rope = ref_cache_slice.view(dtype)[kv_lora_rank // 2 + 8 :]

        torch.testing.assert_close(kv_nope, ref_nope, atol=0.001, rtol=0.1)
        torch.testing.assert_close(kv_scales, ref_scales, atol=0.001, rtol=0.1)
        torch.testing.assert_close(kv_rope, ref_rope, atol=0.001, rtol=0.1)


@pytest.mark.parametrize("kv_lora_rank", KV_LORA_RANKS)
@pytest.mark.parametrize("qk_rope_head_dim", QK_ROPE_HEAD_DIMS)
@pytest.mark.parametrize("block_size", BLOCK_SIZES_MLA)
@pytest.mark.parametrize("num_blocks", NUM_BLOCKS_MLA)
@pytest.mark.parametrize("dtype", DTYPES)
@pytest.mark.parametrize("seed", SEEDS)
@pytest.mark.parametrize("device", CUDA_DEVICES)
@pytest.mark.parametrize("kv_cache_dtype", KV_CACHE_DTYPE)
@torch.inference_mode()
def test_swap_blocks_mla(
    kv_lora_rank: int,
    qk_rope_head_dim: int,
    block_size: int,
    num_blocks: int,
    dtype: torch.dtype,
    seed: int,
    device: str,
    kv_cache_dtype: str,
) -> None:
    set_random_seed(seed)
    torch.set_default_device(device)
    torch.accelerator.set_device_index(device)

    entry_size = kv_lora_rank + qk_rope_head_dim

    src_cache = _create_mla_cache(
        num_blocks, block_size, entry_size, dtype, kv_cache_dtype, device
    )
    dst_cache = _create_mla_cache(
        num_blocks, block_size, entry_size, dtype, kv_cache_dtype, device
    )

    _fill_mla_cache(src_cache, kv_cache_dtype)
    _fill_mla_cache(dst_cache, kv_cache_dtype)

    src_cache_clone = src_cache.clone()

    num_mappings = min(2, num_blocks // 2)
    src_blocks = random.sample(range(num_blocks), num_mappings)
    remaining_blocks = list(set(range(num_blocks)) - set(src_blocks))
    dst_blocks = random.sample(remaining_blocks, num_mappings)
    block_mapping = list(zip(src_blocks, dst_blocks))
    block_mapping_tensor = torch.tensor(
        block_mapping, dtype=torch.int64, device="cpu"
    ).view(-1, 2)

    block_size_in_bytes = src_cache.element_size() * src_cache.stride(0)
    opcheck(
        torch.ops._C_cache_ops.swap_blocks,
        (src_cache, dst_cache, block_size_in_bytes, block_mapping_tensor),
        test_utils=DEFAULT_OPCHECK_TEST_UTILS,
    )

    ops.swap_blocks(src_cache, dst_cache, block_size_in_bytes, block_mapping_tensor)

    for src, dst in block_mapping:
        torch.testing.assert_close(
            src_cache_clone[src].cpu(),
            dst_cache[dst].cpu(),
            msg=f"Block {src} from src should have been swapped to block "
            f"{dst} in dst_cache.",
        )


@pytest.mark.parametrize("kv_lora_rank", [512])
@pytest.mark.parametrize("qk_rope_head_dim", [64])
@pytest.mark.parametrize("block_size", [16])
@pytest.mark.parametrize("num_blocks", [1024])
@pytest.mark.parametrize("max_seq_len", [512])
@pytest.mark.parametrize("batch_size", [8])
@pytest.mark.parametrize("dtype", [torch.float32])
@pytest.mark.parametrize("kv_cache_dtype", ["auto", "fp8"])
@pytest.mark.parametrize("device", CUDA_DEVICES)
@torch.inference_mode()
def test_gather_and_maybe_dequant_cache_mla(
    kv_lora_rank,
    qk_rope_head_dim,
    block_size,
    num_blocks,
    max_seq_len,
    batch_size,
    dtype,
    kv_cache_dtype,
    device,
):
    entry_size = kv_lora_rank + qk_rope_head_dim
    scale = torch.tensor(0.1, dtype=torch.float32, device=device)
    src_cache = _create_mla_cache(
        num_blocks, block_size, entry_size, dtype, kv_cache_dtype, device
    )
    _fill_mla_cache(src_cache, kv_cache_dtype=kv_cache_dtype)

    seq_len_tensor = torch.randint(
        max_seq_len, max_seq_len + 1, (batch_size,), device=device
    )

    total_tokens = seq_len_tensor.sum()
    cu_seq_lens = torch.empty((batch_size + 1), dtype=torch.int32, device=device)
    cu_seq_lens[0] = 0
    cu_seq_lens[1:] = seq_len_tensor.cumsum(dim=0).to(dtype=torch.int32)
    token_to_seq = torch.arange(0, batch_size, dtype=torch.int32, device=device)
    token_to_seq = torch.repeat_interleave(token_to_seq, seq_len_tensor)
    print("seq_len_tensor", seq_len_tensor)

    tot_blocks_tensor = (seq_len_tensor + block_size - 1) // block_size
    block_table = torch.empty(
        (batch_size, num_blocks), dtype=torch.int32, device=device
    )

    for b in range(batch_size):
        perm = torch.randperm(num_blocks, device=device)
        block_table[b, :] = perm

    dst = torch.zeros((total_tokens, entry_size), dtype=dtype, device=device)

    expected_batches = []
    for b in range(batch_size):
        s = seq_len_tensor[b]
        if s == 0:
            continue
        tot = tot_blocks_tensor[b]
        blocks = block_table[b, :tot].tolist()

        gathered_rows = []
        for i in range(tot - 1):
            block_data = src_cache[blocks[i]]
            if kv_cache_dtype == "fp8":
                dequantized_block = torch.empty_like(block_data, dtype=dtype)
                ops.convert_fp8(dequantized_block, block_data, scale.item())
                gathered_rows.append(dequantized_block)
            else:
                gathered_rows.append(block_data)
        remaining = s - (tot - 1) * block_size
        last_block_data = src_cache[blocks[-1], :remaining, :]
        if kv_cache_dtype == "fp8":
            dequantized_last_block = torch.empty_like(last_block_data, dtype=dtype)
            ops.convert_fp8(dequantized_last_block, last_block_data, scale.item())
            gathered_rows.append(dequantized_last_block)
        else:
            gathered_rows.append(last_block_data)

        batch_expected = torch.cat(gathered_rows, dim=0)
        expected_batches.append(batch_expected)
    expected = torch.cat(expected_batches, dim=0)

    opcheck(
        torch.ops._C_cache_ops.gather_and_maybe_dequant_cache,
        (
            src_cache,
            dst,
            block_table,
            cu_seq_lens,
            token_to_seq,
            total_tokens,
            kv_cache_dtype,
            scale,
            None,
        ),
        test_utils=DEFAULT_OPCHECK_TEST_UTILS,
    )

    ops.gather_and_maybe_dequant_cache(
        src_cache,
        dst,
        block_table,
        cu_seq_lens,
        token_to_seq,
        total_tokens,
        kv_cache_dtype,
        scale,
        None,
    )
    torch.testing.assert_close(dst, expected)


@pytest.mark.parametrize("kv_lora_rank", [512])
@pytest.mark.parametrize("qk_rope_head_dim", [64])
@pytest.mark.parametrize("block_size", [16])
@pytest.mark.parametrize("num_blocks", [1024])
@pytest.mark.parametrize("max_seq_len", [512])
@pytest.mark.parametrize("batch_size", [8])
@pytest.mark.parametrize("dtype", [torch.float32])
@pytest.mark.parametrize(
    "kv_cache_dtype", ["auto"]
)  # You can also test "fp8" if needed.
@pytest.mark.parametrize("device", CUDA_DEVICES)
@torch.inference_mode()
def test_cp_gather_cache_mla(
    kv_lora_rank,
    qk_rope_head_dim,
    block_size,
    num_blocks,
    max_seq_len,
    batch_size,
    dtype,
    kv_cache_dtype,
    device,
):
    entry_size = kv_lora_rank + qk_rope_head_dim
    src_cache = _create_mla_cache(
        num_blocks, block_size, entry_size, dtype, kv_cache_dtype, device
    )
    _fill_mla_cache(src_cache, kv_cache_dtype=kv_cache_dtype)

    seq_len_tensor = torch.randint(0, max_seq_len + 1, (batch_size,), device=device)

    total_tokens = seq_len_tensor.sum()
    cu_seq_lens = torch.empty((batch_size + 1), dtype=torch.int32, device=device)
    cu_seq_lens[0] = 0
    cu_seq_lens[1:] = seq_len_tensor.cumsum(dim=0).to(dtype=torch.int32)
    print("seq_len_tensor", seq_len_tensor)

    tot_blocks_tensor = (seq_len_tensor + block_size - 1) // block_size
    block_table = torch.empty(
        (batch_size, num_blocks), dtype=torch.int32, device=device
    )

    for b in range(batch_size):
        perm = torch.randperm(num_blocks, device=device)
        block_table[b, :] = perm

    dst = torch.zeros((total_tokens, entry_size), dtype=src_cache.dtype, device=device)

    expected_batches = []
    for b in range(batch_size):
        s = seq_len_tensor[b]
        if s == 0:
            continue
        tot = tot_blocks_tensor[b]
        blocks = block_table[b, :tot].tolist()

        gathered_rows = []
        for i in range(tot - 1):
            gathered_rows.append(src_cache[blocks[i]])
        remaining = s - (tot - 1) * block_size
        gathered_rows.append(src_cache[blocks[-1], :remaining, :])

        batch_expected = torch.cat(gathered_rows, dim=0)
        expected_batches.append(batch_expected)
    expected = torch.cat(expected_batches, dim=0)

    opcheck(
        torch.ops._C_cache_ops.cp_gather_cache,
        (src_cache, dst, block_table, cu_seq_lens, batch_size, None),
        test_utils=DEFAULT_OPCHECK_TEST_UTILS,
    )

    ops.cp_gather_cache(src_cache, dst, block_table, cu_seq_lens, batch_size)
    torch.testing.assert_close(dst, expected)


@pytest.mark.parametrize("kv_lora_rank", KV_LORA_RANKS)
@pytest.mark.parametrize("qk_rope_head_dim", QK_ROPE_HEAD_DIMS)
@pytest.mark.parametrize("num_tokens", NUM_TOKENS_MLA)
@pytest.mark.parametrize("block_size", BLOCK_SIZES_MLA)
@pytest.mark.parametrize("num_blocks", NUM_BLOCKS_MLA)
@pytest.mark.parametrize("dtype", DTYPES)
@pytest.mark.parametrize("seed", SEEDS)
@pytest.mark.cpu_model
@pytest.mark.skipif(not current_platform.is_cpu(), reason="CPU only")
@torch.inference_mode()
def test_concat_and_cache_mla_cpu(
    kv_lora_rank: int,
    qk_rope_head_dim: int,
    num_tokens: int,
    block_size: int,
    num_blocks: int,
    dtype: torch.dtype,
    seed: int,
) -> None:
    device = "cpu"
    kv_cache_dtype = "auto"
    set_random_seed(seed)
    torch.set_default_device(device)

    total_slots = num_blocks * block_size
    slot_mapping_lst = random.sample(range(total_slots), num_tokens)
    slot_mapping = torch.tensor(slot_mapping_lst, dtype=torch.long, device=device)

    kv_c = torch.randn(num_tokens, kv_lora_rank, dtype=dtype, device=device)
    k_pe = torch.randn(num_tokens, qk_rope_head_dim, dtype=dtype, device=device)
    entry_size = kv_lora_rank + qk_rope_head_dim

    scale = torch.tensor(0.1, dtype=torch.float32, device=device)
    kv_cache = _create_mla_cache(
        num_blocks, block_size, entry_size, dtype, kv_cache_dtype, device
    )
    ref_temp = torch.zeros(*kv_cache.shape, dtype=dtype, device=device)

    for i in range(num_tokens):
        slot = slot_mapping[i].item()
        block_idx = slot // block_size
        block_offset = slot % block_size
        ref_temp[block_idx, block_offset, :kv_lora_rank] = kv_c[i]
        ref_temp[block_idx, block_offset, kv_lora_rank:] = k_pe[i]

    if kv_cache_dtype == "fp8":
        ref_kv_cache = torch.empty_like(ref_temp, dtype=kv_cache.dtype)
        ops.convert_fp8(ref_kv_cache, ref_temp, scale.item(), kv_dtype=kv_cache_dtype)
    else:
        ref_kv_cache = ref_temp

    opcheck(
        torch.ops._C_cache_ops.concat_and_cache_mla,
        (kv_c, k_pe, kv_cache, slot_mapping, kv_cache_dtype, scale),
        test_utils=DEFAULT_OPCHECK_TEST_UTILS,
    )

    ops.concat_and_cache_mla(kv_c, k_pe, kv_cache, slot_mapping, kv_cache_dtype, scale)
    torch.testing.assert_close(kv_cache, ref_kv_cache)