test_shared_prefix_kernels.py

"""
Copyright (c) 2023 by FlashInfer team.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

  http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""

import numpy
import pytest
import torch

import flashinfer


def ceil_div(a, b):
    return (a + b - 1) // b


@pytest.mark.parametrize("batch_size", [12, 17])
@pytest.mark.parametrize("unique_kv_len", [37, 17])
@pytest.mark.parametrize("shared_kv_len", [54, 97, 1979])
@pytest.mark.parametrize("num_heads", [8, 16])
@pytest.mark.parametrize("head_dim", [128, 256])
def test_batch_decode_with_shared_prefix_padded_kv_cache(
    batch_size, unique_kv_len, shared_kv_len, num_heads, head_dim
):
    q = torch.randn(batch_size, num_heads, head_dim).to(0).half()
    k_shared = torch.randn(shared_kv_len, num_heads, head_dim).to(0).half()
    v_shared = torch.randn(shared_kv_len, num_heads, head_dim).to(0).half()
    k_unique = torch.randn(batch_size, unique_kv_len, num_heads, head_dim).to(0).half()
    v_unique = torch.randn(batch_size, unique_kv_len, num_heads, head_dim).to(0).half()

    o = flashinfer.batch_decode_with_shared_prefix_padded_kv_cache(
        q, k_shared, v_shared, k_unique, v_unique
    )

    for i in range(batch_size):
        qi = q[i]
        ki = torch.cat([k_shared, k_unique[i]], dim=0)
        vi = torch.cat([v_shared, v_unique[i]], dim=0)
        o_ref_i = flashinfer.single_decode_with_kv_cache(qi, ki, vi)
        o_i_np = o[i].cpu().numpy()
        o_ref_i_np = o_ref_i.cpu().numpy()
        numpy.testing.assert_allclose(o_i_np, o_ref_i_np, rtol=1e-3, atol=1e-3)


@pytest.mark.parametrize("batch_size", [12, 17])
@pytest.mark.parametrize("unique_kv_len", [37, 17])
@pytest.mark.parametrize("shared_kv_len", [54, 97, 1979])
@pytest.mark.parametrize("num_heads", [8, 16])
@pytest.mark.parametrize("head_dim", [128, 256])
@pytest.mark.parametrize("page_size", [1, 16])
def test_batch_decode_with_shared_prefix_paged_kv_cache(
    batch_size, unique_kv_len, shared_kv_len, num_heads, head_dim, page_size
):
    kv_layout = "NHD"
    q = torch.randn(batch_size, num_heads, head_dim).to(0).half()
    k_shared = torch.randn(shared_kv_len, num_heads, head_dim).to(0).half()
    v_shared = torch.randn(shared_kv_len, num_heads, head_dim).to(0).half()
    k_unique = torch.randn(batch_size, unique_kv_len, num_heads, head_dim).to(0).half()
    v_unique = torch.randn(batch_size, unique_kv_len, num_heads, head_dim).to(0).half()

    kv_data = (
        torch.zeros(
            batch_size * ceil_div(unique_kv_len, page_size),
            2,
            page_size,
            num_heads,
            head_dim,
        )
        .to(0)
        .half()
    )
    kv_indices = (
        torch.arange(0, batch_size * ceil_div(unique_kv_len, page_size)).to(0).int()
    )
    kv_indptr = torch.arange(0, batch_size + 1).to(0).int() * ceil_div(
        unique_kv_len, page_size
    )
    kv_last_page_len = torch.full(
        (batch_size,), (unique_kv_len - 1) % page_size + 1, dtype=torch.int32
    ).to(0)

    workspace_buffer = torch.empty(32 * 1024 * 1024, dtype=torch.int8).to(0)
    wrapper = flashinfer.BatchDecodeWithSharedPrefixPagedKVCacheWrapper(
        workspace_buffer, kv_layout
    )
    wrapper.begin_forward(
        kv_indptr,
        kv_indices,
        kv_last_page_len,
        num_heads,
        num_heads,
        head_dim,
        page_size,
        kv_data.dtype,
    )
    append_indptr = torch.arange(0, batch_size + 1).to(0).int() * unique_kv_len
    flashinfer.append_paged_kv_cache(
        k_unique.view(-1, num_heads, head_dim),
        v_unique.view(-1, num_heads, head_dim),
        append_indptr,
        kv_data,
        kv_indices,
        kv_indptr,
        kv_last_page_len,
        kv_layout,
    )

    o_padded = flashinfer.batch_decode_with_shared_prefix_padded_kv_cache(
        q, k_shared, v_shared, k_unique, v_unique
    )
    o_paged = wrapper.forward(q, k_shared, v_shared, kv_data)
    numpy.testing.assert_allclose(
        o_padded.cpu().numpy(), o_paged.cpu().numpy(), rtol=1e-3, atol=1e-3
    )


@pytest.mark.parametrize("batch_size", [12, 17])
@pytest.mark.parametrize("unique_kv_len", [37, 17])
@pytest.mark.parametrize("shared_kv_len", [128, 512, 2048])
@pytest.mark.parametrize("num_heads", [8, 16])
@pytest.mark.parametrize("causal", [False, True])
@pytest.mark.parametrize("head_dim", [128, 256])
@pytest.mark.parametrize("page_size", [1, 16])
def test_batch_prefill_with_shared_prefix_paged_kv_cache(
    batch_size, unique_kv_len, shared_kv_len, num_heads, causal, head_dim, page_size
):
    assert shared_kv_len % page_size == 0
    kv_layout = "NHD"
    q = torch.randn(batch_size * unique_kv_len, num_heads, head_dim).to(0).half()
    q_indptr = torch.arange(0, batch_size + 1).to(0).int() * unique_kv_len
    k_shared = torch.randn(shared_kv_len, num_heads, head_dim).to(0).half()
    v_shared = torch.randn(shared_kv_len, num_heads, head_dim).to(0).half()
    k_unique = torch.randn(batch_size * unique_kv_len, num_heads, head_dim).to(0).half()
    v_unique = torch.randn(batch_size * unique_kv_len, num_heads, head_dim).to(0).half()

    kv_data = (
        torch.zeros(
            ceil_div(shared_kv_len, page_size)
            + batch_size * ceil_div(unique_kv_len, page_size),
            2,
            page_size,
            num_heads,
            head_dim,
        )
        .to(0)
        .half()
    )
    shared_kv_indices = torch.arange(0, ceil_div(shared_kv_len, page_size)).to(0).int()
    shared_append_indptr = torch.arange(0, 2).to(0).int() * shared_kv_len
    shared_kv_indptr = torch.arange(0, 2).to(0).int() * ceil_div(
        shared_kv_len, page_size
    )
    shared_last_page_len = torch.full(
        (1,), (shared_kv_len - 1) % page_size + 1, dtype=torch.int32
    ).to(0)
    flashinfer.append_paged_kv_cache(
        k_shared,
        v_shared,
        shared_append_indptr,
        kv_data,
        shared_kv_indices,
        shared_kv_indptr,
        shared_last_page_len,
        kv_layout,
    )
    unique_kv_indices = torch.arange(
        0, batch_size * ceil_div(unique_kv_len, page_size)
    ).to(0).int() + ceil_div(shared_kv_len, page_size)
    unique_append_indptr = torch.arange(0, batch_size + 1).to(0).int() * unique_kv_len
    unique_kv_indptr = torch.arange(0, batch_size + 1).to(0).int() * ceil_div(
        unique_kv_len, page_size
    )
    unique_last_page_len = torch.full(
        (batch_size,), (unique_kv_len - 1) % page_size + 1, dtype=torch.int32
    ).to(0)
    flashinfer.append_paged_kv_cache(
        k_unique,
        v_unique,
        unique_append_indptr,
        kv_data,
        unique_kv_indices,
        unique_kv_indptr,
        unique_last_page_len,
        kv_layout,
    )

    baseline_wrapper = flashinfer.BatchPrefillWithPagedKVCacheWrapper(
        torch.empty(32 * 1024 * 1024, dtype=torch.int8).to(0), kv_layout
    )
    cascade_wrapper = flashinfer.BatchPrefillWithSharedPrefixPagedKVCacheWrapper(
        torch.empty(32 * 1024 * 1024, dtype=torch.int8).to(0), kv_layout
    )

    baseline_kv_indices_arr = []
    for i in range(batch_size):
        baseline_kv_indices_arr.append(
            torch.arange(0, ceil_div(shared_kv_len, page_size)).int()
        )
        baseline_kv_indices_arr.append(
            torch.arange(
                i * ceil_div(unique_kv_len, page_size),
                (i + 1) * ceil_div(unique_kv_len, page_size),
            ).int()
            + ceil_div(shared_kv_len, page_size)
        )
    baseline_kv_indices = torch.cat(baseline_kv_indices_arr, dim=0).to(0)
    baseline_kv_indptr = torch.arange(0, batch_size + 1).to(0).int() * (
        ceil_div(shared_kv_len, page_size) + ceil_div(unique_kv_len, page_size)
    )
    baseline_kv_last_page_len = unique_last_page_len
    baseline_wrapper.begin_forward(
        q_indptr,
        baseline_kv_indptr,
        baseline_kv_indices,
        baseline_kv_last_page_len,
        num_heads,
        num_heads,
        head_dim,
    )

    o_baseline = baseline_wrapper.forward(q, kv_data, causal=causal)

    cascade_kv_indices = unique_kv_indices
    cascade_kv_indptr = unique_kv_indptr
    cascade_kv_last_page_len = unique_last_page_len
    cascade_wrapper.begin_forward(
        q_indptr,
        cascade_kv_indptr,
        cascade_kv_indices,
        cascade_kv_last_page_len,
        num_heads,
        num_heads,
        head_dim,
    )

    o_cascade = cascade_wrapper.forward(q, k_shared, v_shared, kv_data, causal=causal)
    numpy.testing.assert_allclose(
        o_baseline.cpu().numpy(), o_cascade.cpu().numpy(), rtol=1e-3, atol=1e-3
    )


if __name__ == "__main__":
    test_batch_decode_with_shared_prefix_padded_kv_cache(12, 37, 54, 8, 128)
    test_batch_decode_with_shared_prefix_paged_kv_cache(12, 37, 54, 8, 128, 16)
    test_batch_prefill_with_shared_prefix_paged_kv_cache(12, 37, 256, 8, True, 128, 16)