vLLM/tests/v1/worker/test_gpu_pcp_manager.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from dataclasses import replace

import numpy as np
import pytest
import torch

from vllm.v1.attention.backends.utils import get_dcp_local_seq_lens
from vllm.v1.worker.gpu import cp_utils as gpu_cp_utils
from vllm.v1.worker.gpu import pcp_manager as pcp_manager_module
from vllm.v1.worker.gpu.input_batch import InputBatch, InputBuffers
from vllm.v1.worker.gpu.pcp_manager import PCPManager


def _copy_to_cpu(value, out=None, device=None):
    tensor = torch.from_numpy(value) if isinstance(value, np.ndarray) else value
    if out is not None:
        return out.copy_(tensor)
    return tensor


def test_replicated_decode_piecewise_graph_padding(monkeypatch):
    manager = PCPManager(
        pcp_world_size=2,
        pcp_rank=0,
        device=torch.device("cpu"),
        dcp_world_size=1,
    )
    monkeypatch.setattr(pcp_manager_module, "async_copy_to_gpu", _copy_to_cpu)

    segments_by_rank, per_rank_num_tokens = manager._build_batch_layout(
        num_scheduled_tokens=np.ones(3, dtype=np.int32),
        num_computed_tokens=np.full(3, 16, dtype=np.int32),
        is_prefilling=np.zeros(3, dtype=np.bool_),
        query_start_loc_np=np.arange(4, dtype=np.int32),
        padded_num_tokens=4,
    )

    assert per_rank_num_tokens == [3, 3]
    request_indices = [
        [segment.global_batch_req_idx for segment in rank] for rank in segments_by_rank
    ]
    assert request_indices == [[0, 1, 2], [0, 1, 2]]
    assert torch.equal(manager._hidden_restore_idx, torch.tensor([0, 1, 2]))
    assert torch.equal(
        manager._padded_gather_idx,
        torch.tensor([0, 1, 2, 0, 0, 1, 2, 0]),
    )
    assert torch.equal(
        manager._gathered_kv_write_mask,
        torch.tensor([True, True, True, False, False, False, False, False]),
    )


def test_input_buffers_are_exposed_for_cudagraph_capture():
    manager = PCPManager(
        pcp_world_size=2,
        pcp_rank=0,
        device=torch.device("cpu"),
        max_num_reqs=4,
        max_num_tokens=8,
    )

    assert manager.input_buffers is manager._input_buffers
    assert manager.input_buffers.input_ids.shape == (8,)
    assert manager.input_buffers.positions.shape == (8,)
    assert manager.input_buffers.is_padding.shape == (8,)


@pytest.mark.parametrize(
    ("pcp_world_size", "num_scheduled_tokens", "is_prefilling", "expected"),
    [
        (2, [8], [True], 4),
        (2, [7], [True], 4),
        (2, [3], [False], 3),
        (2, [3, 8], [False, True], 7),
        (4, [2, 9], [False, True], 5),
    ],
)
def test_num_tokens_for_dispatch_uses_largest_pcp_rank(
    pcp_world_size, num_scheduled_tokens, is_prefilling, expected
):
    manager = PCPManager(
        pcp_world_size=pcp_world_size,
        pcp_rank=0,
        device=torch.device("cpu"),
    )

    actual = manager.get_num_tokens_for_dispatch(
        np.asarray(num_scheduled_tokens, dtype=np.int32),
        np.asarray(is_prefilling, dtype=np.bool_),
    )

    assert actual == expected


def test_graph_padding_cannot_be_smaller_than_largest_pcp_rank(monkeypatch):
    manager = PCPManager(
        pcp_world_size=2,
        pcp_rank=0,
        device=torch.device("cpu"),
        dcp_world_size=1,
    )
    monkeypatch.setattr(pcp_manager_module, "async_copy_to_gpu", _copy_to_cpu)

    with pytest.raises(ValueError, match="smaller than the largest rank-local batch"):
        manager._build_batch_layout(
            num_scheduled_tokens=np.ones(3, dtype=np.int32),
            num_computed_tokens=np.full(3, 16, dtype=np.int32),
            is_prefilling=np.zeros(3, dtype=np.bool_),
            query_start_loc_np=np.arange(4, dtype=np.int32),
            padded_num_tokens=2,
        )


def _rank_rows(
    pcp_rank: int,
    pcp_world_size: int,
    num_scheduled_tokens: np.ndarray,
    num_computed_tokens: np.ndarray,
    is_prefilling: np.ndarray,
) -> tuple[np.ndarray, np.ndarray]:
    """One PCP+DCP rank's rows as (global request, extent)."""
    manager = PCPManager(
        pcp_world_size=pcp_world_size,
        pcp_rank=pcp_rank,
        device=torch.device("cpu"),
        dcp_world_size=2,
    )
    query_start_loc_np = np.concatenate([[0], np.cumsum(num_scheduled_tokens)]).astype(
        np.int32
    )
    segments = manager._get_rank_segments(
        pcp_rank, num_scheduled_tokens, is_prefilling, query_start_loc_np
    )
    rows = np.array([segment.global_batch_req_idx for segment in segments])
    extents = (num_computed_tokens + num_scheduled_tokens)[rows]
    return rows, extents


@pytest.mark.parametrize(
    ("num_scheduled_tokens", "num_computed_tokens"),
    [
        ([32, 32], [0, 0]),
        ([32, 32, 32, 32], [0, 0, 0, 0]),
        ([32, 32], [100, 0]),  # one continued, one fresh
        ([64, 32, 48], [0, 512, 0]),  # ragged lengths and mixed contexts
        # A replicated prefill BEHIND a split one. Sorting replicated rows first
        # put request 1 ahead of request 0.
        ([64, 3], [0, 0]),
        ([64, 3, 64], [0, 0, 0]),
    ],
)
@pytest.mark.parametrize("pcp_world_size", [2, 4])
def test_published_row_order_is_identical_on_every_pcp_rank(
    num_scheduled_tokens, num_computed_tokens, pcp_world_size
):
    """Every rank must map its rows to the same global requests, in the same order."""
    num_scheduled_tokens = np.array(num_scheduled_tokens, dtype=np.int32)
    num_computed_tokens = np.array(num_computed_tokens, dtype=np.int32)
    is_prefilling = np.ones(len(num_scheduled_tokens), dtype=np.bool_)

    orders = [
        _rank_rows(
            rank,
            pcp_world_size,
            num_scheduled_tokens,
            num_computed_tokens,
            is_prefilling,
        )[0]
        for rank in range(pcp_world_size)
    ]
    for rank, order in enumerate(orders[1:], start=1):
        assert np.array_equal(orders[0], order), (
            f"rank {rank} rows map to {order.tolist()}, rank 0 to {orders[0].tolist()}"
        )
    # Grouped by request and ascending: what the indexer plan indexes by.
    assert np.all(np.diff(orders[0]) >= 0)


def test_published_row_order_puts_every_decode_before_every_prefill():
    """split_decodes_and_prefills takes the FIRST prefilling row as the boundary."""
    # req 0 is a continued prefill, req 1 is a decode.
    req_idx, _ = _rank_rows(
        pcp_rank=0,
        pcp_world_size=2,
        num_scheduled_tokens=np.array([32, 1], dtype=np.int32),
        num_computed_tokens=np.array([100, 20], dtype=np.int32),
        is_prefilling=np.array([True, False], dtype=np.bool_),
    )
    # The decode (request 1) must be row 0.
    assert req_idx[0] == 1


def test_split_prefill_rows_repeat_the_request_and_its_full_extent():
    """Equal adjacent request indices are what let a backend share a KV region."""
    req_idx, extents = _rank_rows(
        pcp_rank=1,
        pcp_world_size=2,
        num_scheduled_tokens=np.array([64, 3], dtype=np.int32),
        num_computed_tokens=np.array([100, 0], dtype=np.int32),
        is_prefilling=np.ones(2, dtype=np.bool_),
    )
    assert req_idx.tolist() == [0, 0, 1]
    assert extents.tolist() == [164, 164, 3]


@pytest.mark.parametrize("query_len", [1, 2, 3, 5, 6, 9])
def test_dcp_replicates_prefills_too_short_to_split(query_len):
    """A prefill that cannot fill 2*pcp chunks is replicated, not split."""
    pcp_world_size = 2
    num_scheduled_tokens = np.array([query_len], dtype=np.int32)
    is_prefilling = np.ones(1, dtype=np.bool_)

    rows_per_rank = []
    for rank in range(pcp_world_size):
        manager = PCPManager(
            pcp_world_size=pcp_world_size,
            pcp_rank=rank,
            device=torch.device("cpu"),
            dcp_world_size=2,
        )
        assert manager.replicated_requests(num_scheduled_tokens, is_prefilling)[0]
        rows = list(
            manager._iter_rank_chunks(rank, num_scheduled_tokens, is_prefilling)
        )
        assert rows == [(0, 0, query_len)]
        rows_per_rank.append(rows)
    assert rows_per_rank[0] == rows_per_rank[1]


@pytest.mark.parametrize("pcp_world_size", [2, 4, 8])
@pytest.mark.parametrize(
    "query_len", [16, 17, 31, 32, 33, 63, 64, 65, 127, 128, 129, 1000, 4097]
)
def test_pcp_first_chunk_row_is_never_short(pcp_world_size, query_len):
    num_scheduled_tokens = np.array([query_len], dtype=np.int32)
    is_prefilling = np.ones(1, dtype=np.bool_)

    charged_per_rank = []
    for rank in range(pcp_world_size):
        manager = PCPManager(
            pcp_world_size=pcp_world_size,
            pcp_rank=rank,
            device=torch.device("cpu"),
            dcp_world_size=pcp_world_size,
        )
        chunk_lens = [
            chunk_len
            for _, _, chunk_len in manager._iter_rank_chunks(
                rank, num_scheduled_tokens, is_prefilling
            )
        ]
        assert chunk_lens, f"rank {rank} got no rows for {query_len=}"
        assert chunk_lens[0] == max(chunk_lens), (
            f"rank {rank} emitted a short first chunk for {query_len=}: {chunk_lens}"
        )
        charged_per_rank.append(len(chunk_lens) * chunk_lens[0])

    assert len(set(charged_per_rank)) == 1, (
        f"ranks would chunk differently for {query_len=}: {charged_per_rank}"
    )


def _make_global_decode_batch(
    num_computed_tokens: list[int], buffers: InputBuffers, device: torch.device
) -> InputBatch:
    """A replicate-decode global batch as `prepare_inputs` would build it."""
    num_reqs = len(num_computed_tokens)
    num_tokens = num_reqs
    seq_lens_np = np.asarray(num_computed_tokens, dtype=np.int32) + 1

    base = InputBatch.make_dummy(num_reqs, num_tokens, buffers)
    buffers.seq_lens[:num_reqs] = torch.from_numpy(seq_lens_np).to(device)
    buffers.positions[:num_reqs] = torch.tensor(num_computed_tokens, device=device)
    query_start_loc_np = np.arange(num_reqs + 1, dtype=np.int32)
    buffers.query_start_loc[: num_reqs + 1] = torch.from_numpy(query_start_loc_np).to(
        device
    )

    return replace(
        base,
        req_ids=[f"req_{i}" for i in range(num_reqs)],
        num_reqs=num_reqs,
        num_reqs_after_padding=num_reqs,
        idx_mapping=torch.arange(num_reqs, dtype=torch.int32, device=device),
        idx_mapping_np=np.arange(num_reqs, dtype=np.int32),
        num_scheduled_tokens=np.ones(num_reqs, dtype=np.int32),
        num_tokens=num_tokens,
        num_tokens_after_padding=num_tokens,
        num_draft_tokens=0,
        num_draft_tokens_per_req=np.zeros(num_reqs, dtype=np.int32),
        query_start_loc=buffers.query_start_loc[: num_reqs + 1],
        query_start_loc_np=query_start_loc_np,
        seq_lens=buffers.seq_lens[:num_reqs],
        seq_lens_cpu_upper_bound=torch.from_numpy(seq_lens_np),
        dcp_local_seq_lens=None,
        num_computed_tokens_np=np.asarray(num_computed_tokens, dtype=np.int32),
        prefill_len_np=np.zeros(num_reqs, dtype=np.int32),
        num_computed_prefill_tokens_np=np.zeros(num_reqs, dtype=np.int32),
        is_prefilling_np=np.zeros(num_reqs, dtype=np.bool_),
        input_ids=buffers.input_ids[:num_tokens],
        positions=buffers.positions[:num_tokens],
        is_padding=buffers.is_padding[:num_tokens],
        prompt_lens=None,
    )


@pytest.mark.skipif(not torch.cuda.is_available(), reason="needs GPU kernels")
def test_partition_defers_dcp_metadata_to_post_partition_batch():
    """DCP-local lengths must derive from the partitioned batch, not the
    global one: the partition replaces seq_lens, so a pre-partition value is
    stale. partition_batch therefore returns None, and the runtime populates
    the field afterwards from the PCP-owned buffers.
    """
    device = torch.device("cuda:0")
    manager = PCPManager(
        pcp_world_size=2,
        pcp_rank=0,
        device=device,
        max_num_reqs=4,
        max_num_tokens=8,
        dcp_world_size=2,
        dcp_rank=0,
    )

    global_buffers = InputBuffers(4, 8, device)
    global_batch = _make_global_decode_batch([16, 24], global_buffers, device)
    # A leftover from an earlier DCP batch must not survive the partition.
    global_batch.dcp_local_seq_lens = global_buffers.dcp_local_seq_lens[:2]
    global_batch.dcp_local_seq_lens.fill_(-1)

    local_batch = manager.partition_batch(global_batch, padded_num_tokens=2)

    assert local_batch.dcp_local_seq_lens is None
    assert local_batch.seq_lens.tolist() == [17, 25]

    # What execute_model does next: derive DCP metadata from the final batch
    # on the PCP-owned buffers.
    local_batch.dcp_local_seq_lens = gpu_cp_utils.maybe_prepare_dcp_local_seq_lens(
        manager.input_buffers.dcp_local_seq_lens,
        local_batch.seq_lens,
        local_batch.num_reqs,
        dcp_size=2,
        dcp_rank=0,
        cp_interleave=1,
        num_reqs_padded=local_batch.num_reqs_after_padding,
    )
    expected = get_dcp_local_seq_lens(
        torch.tensor([17, 25], dtype=torch.int32), 2, 0, 1
    )
    assert local_batch.dcp_local_seq_lens is not None
    assert torch.equal(local_batch.dcp_local_seq_lens.cpu(), expected)