test_comm_ops.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""Test the communication operators.

Run `pytest tests/distributed/test_comm_ops.py`.
"""

from collections.abc import Callable
from typing import Any

import pytest
import ray
import torch

from vllm.distributed import (
    broadcast_tensor_dict,
    get_pp_group,
    tensor_model_parallel_all_gather,
    tensor_model_parallel_all_reduce,
    tensor_model_parallel_reduce_scatter,
)
from vllm.distributed.parallel_state import GroupCoordinator, TensorMetadata
from vllm.v1.worker.gpu_worker import AsyncIntermediateTensors

from ..utils import (
    init_test_distributed_environment,
    multi_gpu_test,
    multi_process_parallel,
)


@ray.remote(num_gpus=1, max_calls=1)
def all_reduce_test_worker(
    monkeypatch: pytest.MonkeyPatch,
    tp_size: int,
    pp_size: int,
    rank: int,
    distributed_init_port: str,
):
    # it is important to delete the CUDA_VISIBLE_DEVICES environment variable
    # so that each worker can see all the GPUs
    # they will be able to set the device to the correct GPU
    monkeypatch.delenv("CUDA_VISIBLE_DEVICES", raising=False)

    device = torch.device(f"cuda:{rank}")
    torch.cuda.set_device(device)
    init_test_distributed_environment(tp_size, pp_size, rank, distributed_init_port)
    num_elements = 8
    all_tensors = [
        torch.arange(num_elements, dtype=torch.float32, device="cuda") * (r + 1)
        for r in range(tp_size)
    ]
    expected = torch.sum(torch.stack(all_tensors, dim=0), dim=0)
    t = all_tensors[rank % tp_size]
    t = tensor_model_parallel_all_reduce(t)
    torch.testing.assert_close(t, expected)


@ray.remote(num_gpus=1, max_calls=1)
def reduce_scatter_test_worker(
    monkeypatch: pytest.MonkeyPatch,
    tp_size: int,
    pp_size: int,
    rank: int,
    distributed_init_port: str,
):
    # it is important to delete the CUDA_VISIBLE_DEVICES environment variable
    # so that each worker can see all the GPUs
    # they will be able to set the device to the correct GPU
    monkeypatch.delenv("CUDA_VISIBLE_DEVICES", raising=False)
    device = torch.device(f"cuda:{rank}")
    torch.cuda.set_device(device)
    init_test_distributed_environment(tp_size, pp_size, rank, distributed_init_port)

    num_elements = 8
    all_tensors = [
        torch.arange(num_elements, dtype=torch.float32, device="cuda") * (r + 1)
        for r in range(tp_size)
    ]

    index = rank % tp_size
    partition_size = num_elements // tp_size
    all_reduce = torch.sum(torch.stack(all_tensors, dim=0), dim=0)
    expected = all_reduce[index * partition_size : (index + 1) * partition_size]
    t = all_tensors[index]
    t = tensor_model_parallel_reduce_scatter(t, 0)
    torch.testing.assert_close(t, expected)


@ray.remote(num_gpus=1, max_calls=1)
def all_gather_test_worker(
    monkeypatch: pytest.MonkeyPatch,
    tp_size: int,
    pp_size: int,
    rank: int,
    distributed_init_port: str,
):
    # it is important to delete the CUDA_VISIBLE_DEVICES environment variable
    # so that each worker can see all the GPUs
    # they will be able to set the device to the correct GPU
    monkeypatch.delenv("CUDA_VISIBLE_DEVICES", raising=False)
    device = torch.device(f"cuda:{rank}")
    torch.cuda.set_device(device)
    init_test_distributed_environment(tp_size, pp_size, rank, distributed_init_port)
    num_dimensions = 3
    tensor_size = list(range(2, num_dimensions + 2))
    total_size = 1
    for s in tensor_size:
        total_size *= s
    for all_gather_dimension in range(num_dimensions):
        all_tensors = [
            torch.arange(total_size, dtype=torch.float32, device="cuda").reshape(
                tensor_size
            )
            * (r + 1)
            for r in range(tp_size)
        ]
        expected = torch.cat(all_tensors, dim=all_gather_dimension)
        t = all_tensors[rank % tp_size]
        t = tensor_model_parallel_all_gather(t, all_gather_dimension)
        torch.testing.assert_close(t, expected)


@ray.remote(num_gpus=1, max_calls=1)
def broadcast_tensor_dict_test_worker(
    monkeypatch: pytest.MonkeyPatch,
    tp_size: int,
    pp_size: int,
    rank: int,
    distributed_init_port: str,
):
    # it is important to delete the CUDA_VISIBLE_DEVICES environment variable
    # so that each worker can see all the GPUs
    # they will be able to set the device to the correct GPU
    monkeypatch.delenv("CUDA_VISIBLE_DEVICES", raising=False)
    device = torch.device(f"cuda:{rank}")
    torch.cuda.set_device(device)
    init_test_distributed_environment(tp_size, pp_size, rank, distributed_init_port)
    test_dict = {
        # device tensor
        "a": torch.arange(8, dtype=torch.float32, device="cuda"),
        # CPU tensor
        "b": torch.arange(16, dtype=torch.int8, device="cpu"),
        "c": "test",
        "d": [1, 2, 3],
        "e": {"a": 1, "b": 2},
        # empty tensor
        "f": torch.tensor([], dtype=torch.float32, device="cuda"),
    }

    if (rank % tp_size) == 0:
        broadcast_tensor_dict(test_dict, src=0)
    else:
        recv_dict = broadcast_tensor_dict(src=0)
        assert len(recv_dict) == len(test_dict)
        torch.testing.assert_close(recv_dict["a"], test_dict["a"])
        torch.testing.assert_close(recv_dict["b"], test_dict["b"])
        assert recv_dict["c"] == test_dict["c"]
        assert recv_dict["d"] == test_dict["d"]
        assert recv_dict["e"] == test_dict["e"]
        torch.testing.assert_close(recv_dict["f"], test_dict["f"])


@ray.remote(num_gpus=1, max_calls=1)
def send_recv_tensor_dict_test_worker(
    monkeypatch: pytest.MonkeyPatch,
    tp_size: int,
    pp_size: int,
    rank: int,
    distributed_init_port: str,
):
    monkeypatch.delenv("CUDA_VISIBLE_DEVICES", raising=False)
    device = torch.device(f"cuda:{rank}")
    torch.cuda.set_device(device)
    init_test_distributed_environment(tp_size, pp_size, rank, distributed_init_port)

    test_dict = {
        # device tensor
        "a": torch.arange(8, dtype=torch.float32, device="cuda"),
        # CPU tensor
        "b": torch.arange(16, dtype=torch.int8, device="cpu"),
        "c": "test",
        "d": [1, 2, 3],
        "e": {"a": 1, "b": 2},
        # empty tensor
        "f": torch.tensor([], dtype=torch.float32, device="cuda"),
    }

    if not get_pp_group().is_first_rank:
        recv_dict = get_pp_group().recv_tensor_dict()

    if not get_pp_group().is_last_rank:
        get_pp_group().send_tensor_dict(test_dict)

    if not get_pp_group().is_first_rank:
        assert len(recv_dict) == len(test_dict)
        torch.testing.assert_close(recv_dict["a"], test_dict["a"])
        torch.testing.assert_close(recv_dict["b"], test_dict["b"])
        assert recv_dict["c"] == test_dict["c"]
        assert recv_dict["d"] == test_dict["d"]
        assert recv_dict["e"] == test_dict["e"]
        torch.testing.assert_close(recv_dict["f"], test_dict["f"])


class _DummyWork:
    def __init__(self) -> None:
        self.wait_calls = 0

    def wait(self) -> None:
        self.wait_calls += 1


class _DummyAllGatherGroup:
    def __init__(self, world_size: int, rank_in_group: int) -> None:
        self.world_size = world_size
        self.rank_in_group = rank_in_group

    def all_gather(self, t: torch.Tensor, dim: int = 0) -> torch.Tensor:
        # duplicate local slice across ranks.
        assert dim == 0
        return torch.cat([t for _ in range(self.world_size)], dim=0)


def _make_group_for_unit_test(
    rank_in_group: int = 0, world_size: int = 2
) -> GroupCoordinator:
    # avoid running GroupCoordinator.__init__ (it wires up real process groups).
    g = GroupCoordinator.__new__(GroupCoordinator)
    g.world_size = world_size
    g.rank_in_group = rank_in_group
    g.ranks = list(range(world_size))
    g.use_cpu_custom_send_recv = False
    g.device_group = None
    g.cpu_group = None
    return g


def test_irecv_tensor_dict_send_allgather_postprocess_binds_keys(
    monkeypatch: pytest.MonkeyPatch,
) -> None:
    def fake_irecv(t: torch.Tensor, *args: Any, **kwargs: Any) -> _DummyWork:
        t.fill_(1)
        return _DummyWork()

    monkeypatch.setattr(torch.distributed, "is_initialized", lambda: True)
    monkeypatch.setattr(torch.distributed, "irecv", fake_irecv)

    g = _make_group_for_unit_test(rank_in_group=0, world_size=2)
    # 2 tensors so we can catch late-binding bugs in postprocess closures.
    metadata_list = [
        ("a", TensorMetadata("cpu", torch.int32, torch.Size([4]))),
        ("b", TensorMetadata("cpu", torch.int32, torch.Size([4]))),
    ]
    g.recv_object = lambda src=None: metadata_list  # type: ignore[method-assign]

    ag = _DummyAllGatherGroup(world_size=2, rank_in_group=0)
    td, handles, postprocess = g.irecv_tensor_dict(all_gather_group=ag)

    assert td is not None
    assert len(handles) == 2
    assert len(postprocess) == 2

    # before postprocess, dict holds the TP slice (shape 2).
    assert td["a"].shape == torch.Size([2])
    assert td["b"].shape == torch.Size([2])

    # simulate worker-side "defer wait": wait + postprocess later.
    for handle in handles:
        handle.wait()
    for fn in postprocess:
        fn()

    # after postprocess, dict values are reconstructed to full shape (shape 4),
    # and each key should be updated independently
    assert td["a"].shape == torch.Size([4])
    assert td["b"].shape == torch.Size([4])
    torch.testing.assert_close(td["a"], torch.ones(4, dtype=torch.int32))
    torch.testing.assert_close(td["b"], torch.ones(4, dtype=torch.int32))


def test_async_intermediate_tensors_lazy_wait() -> None:
    work = _DummyWork()
    post_calls = {"n": 0}

    def post() -> None:
        post_calls["n"] += 1

    it = AsyncIntermediateTensors(
        {"x": torch.tensor([1])},
        comm_handles=[work],
        comm_postprocess=[post],
    )

    # accessing non-tensor attributes should not trigger wait.
    assert it.kv_connector_output is None
    assert work.wait_calls == 0
    assert post_calls["n"] == 0

    # first access of `.tensors` triggers wait + postprocess.
    _ = it.tensors
    assert work.wait_calls == 1
    assert post_calls["n"] == 1

    # subsequent access should not re-wait.
    _ = it.tensors
    assert work.wait_calls == 1
    assert post_calls["n"] == 1


@ray.remote(num_gpus=1, max_calls=1)
def send_recv_test_worker(
    monkeypatch: pytest.MonkeyPatch,
    tp_size: int,
    pp_size: int,
    rank: int,
    distributed_init_port: str,
):
    monkeypatch.delenv("CUDA_VISIBLE_DEVICES", raising=False)
    device = torch.device(f"cuda:{rank}")
    torch.cuda.set_device(device)
    init_test_distributed_environment(tp_size, pp_size, rank, distributed_init_port)

    size = 64
    test_tensor = torch.arange(64, dtype=torch.float32, device="cuda")

    if not get_pp_group().is_first_rank:
        recv_tensor = get_pp_group().recv(size, dtype=torch.float32)

    if not get_pp_group().is_last_rank:
        get_pp_group().send(test_tensor)

    if not get_pp_group().is_first_rank:
        torch.testing.assert_close(test_tensor, recv_tensor)


@multi_gpu_test(num_gpus=2)
@pytest.mark.parametrize("tp_size", [2])
@pytest.mark.parametrize(
    "test_target",
    [all_reduce_test_worker, all_gather_test_worker, broadcast_tensor_dict_test_worker],
)
def test_multi_process_tensor_parallel(
    monkeypatch: pytest.MonkeyPatch,
    tp_size: int,
    test_target: Callable[..., Any],
):
    multi_process_parallel(monkeypatch, tp_size, 1, test_target)


@multi_gpu_test(num_gpus=2)
@pytest.mark.parametrize("pp_size", [2])
@pytest.mark.parametrize(
    "test_target", [send_recv_test_worker, send_recv_tensor_dict_test_worker]
)
def test_multi_process_pipeline_parallel(
    monkeypatch: pytest.MonkeyPatch,
    pp_size: int,
    test_target: Callable[..., Any],
):
    multi_process_parallel(monkeypatch, 1, pp_size, test_target)


@multi_gpu_test(num_gpus=4)
@pytest.mark.parametrize("tp_size", [2])
@pytest.mark.parametrize("pp_size", [2])
@pytest.mark.parametrize(
    "test_target",
    [
        send_recv_test_worker,
        send_recv_tensor_dict_test_worker,
        all_reduce_test_worker,
        all_gather_test_worker,
        broadcast_tensor_dict_test_worker,
    ],
)
def test_multi_process_tensor_parallel_pipeline_parallel(
    tp_size: int,
    pp_size: int,
    test_target: Callable[..., Any],
    monkeypatch: pytest.MonkeyPatch,
):
    multi_process_parallel(monkeypatch, tp_size, pp_size, test_target)