utils.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import tempfile
from collections import defaultdict
from collections.abc import Callable
from dataclasses import dataclass
from itertools import chain, count
from typing import Any, Literal

import torch

from vllm import SamplingParams
from vllm.config import (
    AttentionConfig,
    CacheConfig,
    DeviceConfig,
    KVTransferConfig,
    ModelConfig,
    SchedulerConfig,
    VllmConfig,
)
from vllm.distributed.kv_transfer.kv_connector.factory import KVConnectorFactory
from vllm.distributed.kv_transfer.kv_connector.v1.base import (
    KVConnectorBase_V1,
    KVConnectorMetadata,
    KVConnectorRole,
)
from vllm.distributed.kv_transfer.kv_connector.v1.example_connector import (  # noqa
    ExampleConnector,
)
from vllm.utils.hashing import sha256
from vllm.v1.core.kv_cache_manager import KVCacheBlocks
from vllm.v1.core.kv_cache_utils import get_request_block_hasher, init_none_hash
from vllm.v1.core.sched.async_scheduler import AsyncScheduler
from vllm.v1.core.sched.scheduler import Scheduler, SchedulerOutput
from vllm.v1.kv_cache_interface import (
    FullAttentionSpec,
    KVCacheConfig,
    KVCacheGroupSpec,
    MambaSpec,
    SlidingWindowSpec,
)
from vllm.v1.outputs import KVConnectorOutput, ModelRunnerOutput
from vllm.v1.request import Request
from vllm.v1.structured_output import StructuredOutputManager

EOS_TOKEN_ID = 50256


def assert_scheduler_empty(scheduler: Scheduler):
    """Confirm the scheduler is "empty" - i.e. no leaks."""
    # Scheduler Metadata.
    assert len(scheduler.requests) == 0
    assert len(scheduler.waiting) == 0
    assert len(scheduler.running) == 0
    assert len(scheduler.finished_req_ids) == 0
    assert len(scheduler.finished_recving_kv_req_ids) == 0

    # EncoderCacheManager.
    assert len(scheduler.encoder_cache_manager.freed) == 0
    assert len(scheduler.encoder_cache_manager.cached) == 0

    # KVCache Manager.
    assert (
        len(
            scheduler.kv_cache_manager.coordinator.single_type_managers[0].req_to_blocks
        )
        == 0
    )
    assert (
        len(
            scheduler.kv_cache_manager.coordinator.single_type_managers[
                0
            ].num_cached_block
        )
        == 0
    )
    num_free_blocks = (
        scheduler.kv_cache_manager.block_pool.free_block_queue.num_free_blocks
    )
    assert num_free_blocks == (scheduler.kv_cache_manager.block_pool.num_gpu_blocks - 1)

    # NOTE(rob): just the ref count on blocks will be 0. The hash
    # value, etc will remain since we lazily evict for prefix cache.
    for block in scheduler.kv_cache_manager.block_pool.blocks:
        assert block.ref_cnt == 0


def create_vllm_config(
    model: str = "facebook/opt-125m",
    max_num_seqs: int = 16,
    max_num_batched_tokens: int = 64,
    block_size: int = 16,
    max_model_len: int = 10000,
    enable_chunked_prefill: bool = True,
    enable_permute_local_kv: bool = False,
    kv_connector_extra_config: dict[str, Any] | None = None,
    dtype: str = "float16",
    cache_dtype: str = "auto",
    hf_overrides: dict[str, Any] | None = None,
    attention_backend: str | None = None,
    kv_load_failure_policy: Literal["recompute", "fail"] = "fail",
    kv_connector: str = "NixlConnector",
    kv_role: str = "kv_both",
) -> VllmConfig:
    """Initialize VllmConfig For Testing."""
    model_config = ModelConfig(
        model=model,
        trust_remote_code=True,
        dtype=dtype,
        seed=42,
        hf_overrides=hf_overrides or {},
    )
    scheduler_config = SchedulerConfig(
        max_num_seqs=max_num_seqs,
        max_num_batched_tokens=max_num_batched_tokens,
        max_model_len=max_model_len,
        enable_chunked_prefill=enable_chunked_prefill,
        is_encoder_decoder=model_config.is_encoder_decoder,
    )
    # Cache config, optionally force APC
    cache_config = CacheConfig(
        block_size=block_size,
        gpu_memory_utilization=0.9,
        cache_dtype=cache_dtype,
        enable_prefix_caching=True,
    )
    kv_transfer_config = KVTransferConfig(
        kv_connector=kv_connector,
        kv_role=kv_role,
        enable_permute_local_kv=enable_permute_local_kv,
        kv_connector_extra_config=kv_connector_extra_config or {},
        kv_load_failure_policy=kv_load_failure_policy,
    )
    attention_config = AttentionConfig(backend=attention_backend)
    return VllmConfig(
        scheduler_config=scheduler_config,
        model_config=model_config,
        cache_config=cache_config,
        kv_transfer_config=kv_transfer_config,
        device_config=DeviceConfig("cpu"),
        attention_config=attention_config,
    )


def create_scheduler(
    vllm_config: VllmConfig,
    num_blocks: int = 10000,
    kv_cache_config: KVCacheConfig | None = None,
) -> Scheduler | AsyncScheduler:
    """Initialize Scheduler For Testing."""
    block_size = vllm_config.cache_config.block_size
    if kv_cache_config is None:
        kv_cache_config = KVCacheConfig(
            num_blocks=num_blocks,  # A large number of blocks to hold all requests
            kv_cache_tensors=[],
            kv_cache_groups=[
                KVCacheGroupSpec(
                    ["layer"],
                    FullAttentionSpec(
                        block_size=block_size,
                        num_kv_heads=1,
                        head_size=1,
                        dtype=torch.float32,
                    ),
                )
            ],
        )
    vllm_config.cache_config.num_gpu_blocks = num_blocks

    scheduler_cls = (
        AsyncScheduler if vllm_config.scheduler_config.async_scheduling else Scheduler
    )
    return scheduler_cls(
        vllm_config=vllm_config,
        kv_cache_config=kv_cache_config,
        log_stats=True,
        structured_output_manager=StructuredOutputManager(vllm_config),
        block_size=block_size,
    )


_request_count = count(1)
_none_hash_initialized = False


def create_request(
    request_id: int | None = None,
    num_tokens: int = 10,
    common_prefix_len=0,
    max_tokens: int = 16,
    do_remote_decode: bool = False,
    do_remote_prefill: bool = False,
    num_remote_blocks: int = 3,
    block_size: int = 16,
    hash_fn: Callable = sha256,
) -> Request:
    """Make dummy request for testing."""
    assert num_tokens >= common_prefix_len >= 0

    if request_id is None:
        request_id = next(_request_count)

    global _none_hash_initialized
    if not _none_hash_initialized:
        init_none_hash(hash_fn)
        _none_hash_initialized = True

    kv_transfer_params: dict[str, Any] | None = None

    if do_remote_decode:
        assert not do_remote_prefill
        kv_transfer_params = dict(do_remote_prefill=False, do_remote_decode=True)
    elif do_remote_prefill:
        kv_transfer_params = dict(
            do_remote_prefill=True,
            do_remote_decode=False,
            remote_engine_id="my-engine-id",
            remote_request_id=f"prefill-{request_id}",
            remote_block_ids=list(range(num_remote_blocks)),
            remote_host="my-host",
            remote_port=1234,
        )

    max_tokens = 1 if do_remote_decode else max_tokens
    sampling_params = SamplingParams(max_tokens=max_tokens)
    sampling_params.update_from_generation_config({}, EOS_TOKEN_ID)

    common_prefix = [1] * common_prefix_len if common_prefix_len > 0 else []
    suffix = [i * request_id for i in range(num_tokens - common_prefix_len)]
    prompt_token_ids = common_prefix + suffix

    req = Request(
        request_id=f"id-{request_id}",
        prompt_token_ids=prompt_token_ids,
        sampling_params=sampling_params,
        pooling_params=None,
        mm_features=None,
        block_hasher=get_request_block_hasher(block_size, hash_fn),
    )
    req.kv_transfer_params = kv_transfer_params
    return req


def create_model_runner_output(
    reqs: list[Request],
    finished_sending: set[str] | None = None,
    finished_recving: set[str] | None = None,
    invalid_block_ids: set[int] | None = None,
    use_eos: bool = False,
    token_id: int = 0,
) -> ModelRunnerOutput:
    """Make dummy model runner output for testing."""

    # Make request data.
    req_ids = [req.request_id for req in reqs]
    req_id_to_index = {req_id: idx for idx, req_id in enumerate(req_ids)}

    # Make sampled tokens.
    sampled_token = EOS_TOKEN_ID if use_eos else token_id
    sampled_token_ids = [[sampled_token] for _ in req_ids]

    kv_connector_output = (
        None
        if (
            finished_sending is None
            and finished_recving is None
            and invalid_block_ids is None
        )
        else KVConnectorOutput(
            finished_sending=finished_sending,
            finished_recving=finished_recving,
            invalid_block_ids=invalid_block_ids or set(),
        )
    )

    # Make output data structure.
    return ModelRunnerOutput(
        req_ids=req_ids,
        req_id_to_index=req_id_to_index,
        sampled_token_ids=sampled_token_ids,
        logprobs=None,
        prompt_logprobs_dict={},
        pooler_output=None,
        kv_connector_output=kv_connector_output,
    )


class TestExampleConnector(ExampleConnector):
    def __init__(self, config: VllmConfig, role, kv_cache_config):
        self.name = config.kv_transfer_config.kv_connector_extra_config["name"]
        self._connector = ExampleConnector(config, role)
        self.call_record: dict[str, int] = defaultdict(int)
        # Use a unique temp file per connector
        self._event_file = (
            tempfile.gettempdir()
            + f"/connector_{self.name}-{self.role.name}_events.log"
        )
        # Start with an empty file
        with open(self._event_file, "w") as _:
            pass

    def __getattribute__(self, name):
        if name in (
            "_connector",
            "call_record",
            "name",
            "_event_file",
            "__class__",
            "__dict__",
            "__getattribute__",
            "__init__",
        ):  # avoid recursion
            return object.__getattribute__(self, name)
        if not hasattr(self._connector, name):
            return object.__getattribute__(self, name)
        attr = getattr(self._connector, name)

        # Intercept calls to the connector interface and write an event
        # for each one to a file, which can be read back in the main test proc.
        if callable(attr):

            def wrapper(*args, **kwargs):
                self.call_record[name] += 1

                # Include args that we're interested in
                to_log = [name]
                for arg in args:
                    if isinstance(arg, int):
                        to_log.append(str(arg))
                    elif isinstance(arg, KVCacheBlocks):
                        to_log.append(f"num_blocks={[len(b) for b in arg.blocks]}")

                # Log the event as a line to the file
                try:
                    with open(self._event_file, "a") as f:
                        f.write(" ".join(to_log) + "\n")
                except Exception as e:
                    print(f"[ERROR] Could not log event {name} for {self.name}: {e}")
                return attr(*args, **kwargs)

            return wrapper
        return attr


@dataclass(frozen=True)
class MockKVConfig:
    matched_tokens: int = 0
    is_async: bool = False


class MockKVConnectorMetadata(KVConnectorMetadata):
    def __init__(self):
        # Scheduler tests check metadata.requests
        self.requests: list = []


class MockKVConnector(KVConnectorBase_V1):
    """Mock KV connector for scheduler tests, supporting both sync and async mode."""

    def __init__(
        self,
        vllm_config: VllmConfig,
        role: KVConnectorRole,
        kv_cache_config: KVCacheConfig | None = None,
    ):
        super().__init__(vllm_config, role, kv_cache_config)
        extra_config = self._kv_transfer_config.kv_connector_extra_config
        self.config = MockKVConfig(
            matched_tokens=extra_config["matched_tokens"],
            is_async=extra_config["is_async"],
        )

    def get_num_new_matched_tokens(
        self,
        request: Request,
        num_computed_tokens: int,
    ) -> tuple[int | None, bool]:
        return (self.config.matched_tokens, self.config.is_async)

    def update_state_after_alloc(
        self,
        request: Request,
        blocks: KVCacheBlocks,
        num_external_tokens: int,
    ):
        pass

    def build_connector_meta(
        self, scheduler_output: SchedulerOutput
    ) -> KVConnectorMetadata:
        metadata = MockKVConnectorMetadata()
        cached_reqs = scheduler_output.scheduled_cached_reqs
        for req_id in chain(
            (req.req_id for req in scheduler_output.scheduled_new_reqs),
            (
                req_id
                for req_id in cached_reqs.req_ids
                if req_id in cached_reqs.resumed_req_ids
            ),
        ):
            metadata.requests.append({"req_id": req_id})
        return metadata

    def start_load_kv(self, kv_caches, finished_req_ids):
        pass

    def wait_for_layer_load(self, layer_name):
        pass

    def save_kv_layer(self, layer_name, kv_layer, attn_metadata, **kwargs):
        pass

    def wait_for_save(self):
        pass


KVConnectorFactory.register_connector(
    "TestExampleConnector", __name__, TestExampleConnector.__name__
)

KVConnectorFactory.register_connector(
    "MockKVConnector", __name__, MockKVConnector.__name__
)


def make_kv_cache_config(
    block_size: int,
    swa_enabled: bool = False,
    mamba_enabled: bool = False,
    sw_size: int = 128,
    num_blocks: int = 100,
) -> KVCacheConfig:
    kv_cache_groups = [
        KVCacheGroupSpec(
            ["layer0", "layer2"],
            FullAttentionSpec(
                block_size=block_size,
                num_kv_heads=4,
                head_size=16,
                dtype=torch.float16,
            ),
        )
    ]
    if swa_enabled:
        kv_cache_groups.append(
            KVCacheGroupSpec(
                ["layer1", "layer3"],
                SlidingWindowSpec(
                    block_size=block_size,
                    num_kv_heads=4,
                    head_size=16,
                    dtype=torch.float16,
                    sliding_window=sw_size,
                ),
            )
        )
    if mamba_enabled:
        kv_cache_groups.append(
            KVCacheGroupSpec(
                ["mamba0", "mamba1"],
                MambaSpec(
                    block_size=block_size,
                    shapes=((16,), (16,)),
                    dtypes=(torch.float16,),
                ),
            )
        )
    return KVCacheConfig(
        num_blocks=num_blocks, kv_cache_tensors=[], kv_cache_groups=kv_cache_groups
    )


def make_nixl_scheduler(has_mamba: bool = False, is_hma_required: bool = False):
    """Create a NixlConnectorScheduler via __new__ (skipping __init__).

    Only sets the two flags needed by the N-1 prefill logic.
    """
    from vllm.distributed.kv_transfer.kv_connector.v1.nixl.scheduler import (
        NixlConnectorScheduler,
    )

    sched = object.__new__(NixlConnectorScheduler)
    sched._has_mamba = has_mamba
    sched._is_hma_required = is_hma_required
    return sched