test_attention_splitting.py

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

import pytest
import torch

from tests.v1.attention.test_attention_backends import BATCH_SPECS
from tests.v1.attention.utils import BatchSpec, create_common_attn_metadata
from vllm.v1.attention.backends.utils import (
    UBatchSlice,
    _make_metadata_with_slice,
    slice_query_start_locs,
    split_attn_metadata,
    split_decodes_and_prefills,
)
from vllm.v1.worker.ubatch_utils import maybe_create_ubatch_slices


@pytest.fixture
def sample_query_start_loc():
    """Sample query_start_loc tensor for testing"""
    return torch.tensor([0, 5, 12, 20, 35, 50])


def test_basic_slice_middle(sample_query_start_loc):
    """Test slicing from middle of tensor"""
    req_slice = slice(1, 3)  # slice from index 1 to 3
    result = slice_query_start_locs(sample_query_start_loc, req_slice)

    expected = torch.tensor([0, 7, 15])
    assert torch.equal(result, expected)


def test_slice_from_beginning(sample_query_start_loc):
    """Test slicing from the beginning of tensor"""
    req_slice = slice(0, 2)  # slice from index 0 to 2
    result = slice_query_start_locs(sample_query_start_loc, req_slice)

    expected = torch.tensor([0, 5, 12])
    assert torch.equal(result, expected)


def test_slice_to_end(sample_query_start_loc):
    """Test slicing to the end of tensor"""
    req_slice = slice(3, 5)  # slice from index 3 to 5 (last index)
    result = slice_query_start_locs(sample_query_start_loc, req_slice)

    expected = torch.tensor([0, 15, 30])
    assert torch.equal(result, expected)


def test_single_element_slice(sample_query_start_loc):
    """Test slice that results in single element"""
    req_slice = slice(2, 3)  # slice from index 2 to 3
    result = slice_query_start_locs(sample_query_start_loc, req_slice)

    expected = torch.tensor([0, 8])
    assert torch.equal(result, expected)


def test_full_tensor_slice(sample_query_start_loc):
    """Test slicing the entire tensor"""
    req_slice = slice(0, 5)  # slice entire tensor
    result = slice_query_start_locs(sample_query_start_loc, req_slice)

    expected = torch.tensor([0, 5, 12, 20, 35, 50])
    assert torch.equal(result, expected)


def test_slice_bounds_edge_cases(sample_query_start_loc):
    # Test slice that goes exactly to the last element
    req_slice = slice(4, 5)  # Last index
    result = slice_query_start_locs(sample_query_start_loc, req_slice)

    expected = torch.tensor([0, 15])
    assert torch.equal(result, expected)


@pytest.fixture
def small_decode_metadata():
    """Create metadata for small decode batch"""
    batch_spec = BATCH_SPECS["small_decode"]
    device = torch.device("cpu")
    return create_common_attn_metadata(batch_spec, block_size=16, device=device)


@pytest.fixture
def large_decode_metadata():
    """Create metadata for small decode batch"""
    batch_spec = BATCH_SPECS["large_decode"]
    device = torch.device("cpu")
    return create_common_attn_metadata(batch_spec, block_size=16, device=device)


@pytest.fixture
def mixed_small_metadata():
    """Create metadata for mixed small batch"""
    batch_spec = BATCH_SPECS["mixed_small"]
    device = torch.device("cpu")
    return create_common_attn_metadata(batch_spec, block_size=16, device=device)


# Tests for _make_metadata_with_slice
def test_make_metadata_with_slice_decode_batch(small_decode_metadata):
    """Test slicing decode batch metadata"""
    # Split first request only
    ubatch_slice = UBatchSlice(slice(0, 1), slice(0, 1))

    result = _make_metadata_with_slice(ubatch_slice, small_decode_metadata)

    # Check sliced results
    assert result.num_reqs == 1  # slice(0, 1) gives 1 requests
    assert result.num_actual_tokens == 1  # slice(0, 1) gives 1 token
    assert result.max_query_len == 1
    assert torch.equal(result.query_start_loc, torch.tensor([0, 1]))
    assert torch.equal(result.seq_lens, torch.tensor([32]))


def test_make_metadata_with_slice_mixed_batch(mixed_small_metadata):
    """Test slicing mixed batch metadata"""
    ubatch_slice = UBatchSlice(slice(1, 3), slice(1, 7))  # Requests 1-3, tokens 1-7

    result = _make_metadata_with_slice(ubatch_slice, mixed_small_metadata)

    assert result.num_reqs == 2  # slice(1, 3) gives 2 requests
    assert result.num_actual_tokens == 6  # slice(1, 7) gives 6 tokens
    assert result.max_query_len == 5
    assert torch.equal(result.query_start_loc, torch.tensor([0, 1, 6]))
    assert torch.equal(result.seq_lens, torch.tensor([40, 48]))


def test_split_attn_metadata_decode_batch(large_decode_metadata):
    """Test splitting decode batch into two equal parts"""
    num_tokens = large_decode_metadata.num_reqs
    mid_point = num_tokens // 2
    ubatch_slices = [
        UBatchSlice(slice(0, mid_point), slice(0, mid_point)),
        UBatchSlice(slice(mid_point, num_tokens), slice(mid_point, num_tokens)),
    ]

    results = split_attn_metadata(ubatch_slices, large_decode_metadata)

    assert len(results) == 2

    # Check first split
    assert results[0].num_reqs == mid_point
    assert results[0].num_actual_tokens == mid_point
    assert torch.equal(results[0].seq_lens, torch.tensor([2048] * mid_point))

    # Check second split
    assert results[1].num_reqs == mid_point
    assert results[1].num_actual_tokens == mid_point
    assert torch.equal(results[1].seq_lens, torch.tensor([2048] * mid_point))


def apply_split_decodes_and_prefills(
    query_lens: list[int],
    decode_threshold: int,
    require_uniform: bool,
    padded_num_tokens: int | None = None,
):
    """Helper function to apply split_decodes_and_prefills and return
    the results."""
    device = torch.device("cpu")
    seq_lens = [10 * (i + 1) for i in range(len(query_lens))]
    common_metadata = create_common_attn_metadata(
        BatchSpec(seq_lens=seq_lens, query_lens=query_lens),
        block_size=16,
        device=device,
    )

    if padded_num_tokens is not None:
        common_metadata.num_actual_tokens = padded_num_tokens

    return split_decodes_and_prefills(
        common_metadata,
        decode_threshold=decode_threshold,
        require_uniform=require_uniform,
    )


def test_split_decodes_and_prefills_nonuniform_all_ones():
    query_lens = [1, 1, 1]
    num_decodes, num_prefills, num_decode_tokens, num_prefill_tokens = (
        apply_split_decodes_and_prefills(query_lens, 1, False)
    )
    assert num_decodes == 3
    assert num_prefills == 0
    assert num_decode_tokens == 3
    assert num_prefill_tokens == 0


def test_split_decodes_and_prefills_nonuniform_all_short_decodes():
    query_lens = [1, 2, 1, 3, 2, 1, 2]
    num_decodes, num_prefills, num_decode_tokens, num_prefill_tokens = (
        apply_split_decodes_and_prefills(query_lens, 3, False)
    )
    assert num_decodes == 7
    assert num_prefills == 0
    assert num_decode_tokens == sum(query_lens)
    assert num_prefill_tokens == 0


def test_split_decodes_and_prefills_nonuniform_all_prefills():
    query_lens = [4, 5, 6, 7]
    num_decodes, num_prefills, num_decode_tokens, num_prefill_tokens = (
        apply_split_decodes_and_prefills(query_lens, 3, False)
    )
    assert num_decodes == 0
    assert num_prefills == 4
    assert num_decode_tokens == 0
    assert num_prefill_tokens == sum(query_lens)


def test_split_decodes_and_prefills_nonuniform_mixed_batch():
    query_lens = [2, 1, 3, 4, 5, 6, 7, 8]
    num_decodes, num_prefills, num_decode_tokens, num_prefill_tokens = (
        apply_split_decodes_and_prefills(query_lens, 4, False)
    )
    assert num_decodes == 4  # 2, 1, 3, 4 are all <= 4
    assert num_prefills == 4  # 5, 6, 7, 8 are all > 4
    assert num_decode_tokens == 10  # 2 + 1 + 3 + 4
    assert num_prefill_tokens == 26  # 5 + 6 + 7 + 8


def test_split_decodes_and_prefills_uniform_all_ones():
    query_lens = [1, 1, 1]
    num_decodes, num_prefills, num_decode_tokens, num_prefill_tokens = (
        apply_split_decodes_and_prefills(query_lens, 1, True)
    )
    assert num_decodes == 3
    assert num_prefills == 0
    assert num_decode_tokens == 3
    assert num_prefill_tokens == 0


def test_split_decodes_and_prefills_uniform_all_short_decodes():
    query_lens = [2, 2, 1, 3, 2, 1, 2]
    num_decodes, num_prefills, num_decode_tokens, num_prefill_tokens = (
        apply_split_decodes_and_prefills(query_lens, 3, True)
    )
    assert num_decodes == 2
    assert num_prefills == 5
    assert num_decode_tokens == 4
    assert num_prefill_tokens == (1 + 3 + 2 + 1 + 2)


def test_split_decodes_and_prefills_uniform_all_prefills():
    query_lens = [4, 5, 6, 7]
    num_decodes, num_prefills, num_decode_tokens, num_prefill_tokens = (
        apply_split_decodes_and_prefills(query_lens, 3, True)
    )
    assert num_decodes == 0
    assert num_prefills == 4
    assert num_decode_tokens == 0
    assert num_prefill_tokens == sum(query_lens)


def test_split_decodes_and_prefills_uniform_mixed_batch_all_uniform_decodes():
    query_lens = [2, 2, 2, 4, 5, 6, 7, 8]
    num_decodes, num_prefills, num_decode_tokens, num_prefill_tokens = (
        apply_split_decodes_and_prefills(query_lens, 4, True)
    )
    assert num_decodes == 3  # 2, 2, 2 are all <= 4 and uniform
    assert num_prefills == 5  # 4, 5, 6, 7, 8 are all > 4
    assert num_decode_tokens == 6  # 2 + 2 + 2
    assert num_prefill_tokens == 30  # 4 + 5 + 6 + 7 + 8


def test_split_decodes_and_prefills_uniform_mixed_batch_non_uniform_decodes():
    query_lens = [2, 1, 2, 4, 5, 6, 7, 8]
    num_decodes, num_prefills, num_decode_tokens, num_prefill_tokens = (
        apply_split_decodes_and_prefills(query_lens, 4, True)
    )
    assert num_decodes == 1  # only the first 2 is taken as decode
    assert num_prefills == 7  # 1, 2, 4, 5, 6, 7, 8 are all > 4 or non-uniform
    assert num_decode_tokens == 2  # only the first 2
    assert num_prefill_tokens == (sum(query_lens) - 2)  # rest of the tokens


def test_split_decodes_and_prefills_uniform_padded_batch_all_same():
    """uniform batch where all query lengths are identical with 0 length padded reqs."""
    # All query lengths are 2, with decode_threshold=3 (so 2 <= 3)
    # This triggers the padded uniform path at line 891
    query_lens = [2, 2, 2, 0]
    padded_num_tokens = 8
    num_decodes, num_prefills, num_decode_tokens, num_prefill_tokens = (
        apply_split_decodes_and_prefills(query_lens, 3, True, padded_num_tokens)
    )
    # With uniform batch, all requests are treated as decodes
    assert num_decodes == 4
    assert num_prefills == 0
    assert num_decode_tokens == padded_num_tokens
    assert num_prefill_tokens == 0


@pytest.mark.parametrize(
    "seq_lens,query_lens,split_point,expected_first_reqs,expected_second_reqs",
    [
        # Split in the middle of request 1
        ([32, 40], [8, 8], 12, 2, 1),
        # Split inside the first request
        ([32, 40], [8, 8], 4, 1, 2),
    ],
)
def test_prefill_split_across_ubatches(
    seq_lens, query_lens, split_point, expected_first_reqs, expected_second_reqs
):
    """Test splitting a prefill across ubatches"""
    import numpy as np

    device = torch.device("cpu")
    batch_spec = BatchSpec(seq_lens=seq_lens, query_lens=query_lens)
    common = create_common_attn_metadata(batch_spec, block_size=16, device=device)

    num_scheduled_tokens = np.array(query_lens, dtype=np.int32)
    qsl_np = common.query_start_loc_cpu.numpy()
    num_tokens = common.num_actual_tokens

    ubatch_slices, _ = maybe_create_ubatch_slices(
        True,
        num_scheduled_tokens,
        num_tokens,
        batch_spec.batch_size,
        split_point=split_point,
    )
    assert ubatch_slices is not None and len(ubatch_slices) == 2

    first_meta = _make_metadata_with_slice(ubatch_slices[0], common)
    second_meta = _make_metadata_with_slice(ubatch_slices[1], common)

    # Token counts match the split
    assert first_meta.num_actual_tokens == split_point
    assert second_meta.num_actual_tokens == num_tokens - split_point

    # Number of requests per ubatch
    assert first_meta.num_reqs == expected_first_reqs
    assert second_meta.num_reqs == expected_second_reqs

    # Identify which request is split and how many tokens are in the first chunk
    split_req_idx = int(np.searchsorted(qsl_np, split_point, side="right") - 1)
    tokens_in_first_chunk = split_point - int(qsl_np[split_req_idx])
    orig_q_lens = common.query_start_loc_cpu[1:] - common.query_start_loc_cpu[:-1]

    # Check query length continuity: first-chunk + second-chunk == original qlen
    # First ubatch last request query length
    qlen_first_last = int(
        first_meta.query_start_loc_cpu[-1] - first_meta.query_start_loc_cpu[-2]
    )
    # Second ubatch first request query length
    qlen_second_first = int(
        second_meta.query_start_loc_cpu[1] - second_meta.query_start_loc_cpu[0]
    )
    assert qlen_first_last == tokens_in_first_chunk
    assert qlen_first_last + qlen_second_first == int(orig_q_lens[split_req_idx])

    # Check seq_lens adjustments
    # Context lengths per original request
    context_lens = [s - q for s, q in zip(seq_lens, query_lens)]

    # First ubatch: last request's seq_len should be
    #  context + tokens_in_first_chunk
    expected_seqlen = context_lens[split_req_idx] + tokens_in_first_chunk
    assert int(first_meta.seq_lens[-1]) == expected_seqlen

    # For full preceding requests in first ubatch, seq_lens should match
    #  originals
    for i in range(first_meta.num_reqs - 1):
        assert int(first_meta.seq_lens[i]) == seq_lens[i]

    # Second ubatch: first request (continuation) seq_len should be full
    #  original
    assert int(second_meta.seq_lens[0]) == seq_lens[split_req_idx]
    # Any following full requests in second ubatch should match originals
    for j in range(1, second_meta.num_reqs):
        # Map to original request index
        orig_idx = split_req_idx + j
        assert int(second_meta.seq_lens[j]) == seq_lens[orig_idx]