Files
vllm-omni/tests/diffusion/diffusion_kv/test_metadata.py
T
102ba716bf [Feat] Add native Mooncake KV transfer from AR to DiT (#7166)
Signed-off-by: asukaqaq-s <1311722138@qq.com>
Signed-off-by: Acerak01-fy <wfy2003324@163.com>
Co-authored-by: Acerak01-fy <wfy2003324@163.com>
Co-authored-by: amy-why-3459 <wuhaiyan17@huawei.com>
2026-09-20 14:44:55 +00:00

188 lines
6.0 KiB
Python

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import pytest
from vllm_omni.diffusion.diffusion_kv.metadata import (
DiffusionKVContextMetadata,
DiffusionKVMetadata,
DiffusionKVSequenceMetadata,
)
from vllm_omni.diffusion.diffusion_kv.request import DiffusionKVRequest
from vllm_omni.diffusion.request import OmniDiffusionRequest
from vllm_omni.diffusion.sched.base_scheduler import BaseScheduler
from vllm_omni.diffusion.sched.interface import CachedRequestData, DiffusionSchedulerOutput
from vllm_omni.inputs.data import OmniDiffusionSamplingParams
pytestmark = [pytest.mark.diffusion, pytest.mark.core_model, pytest.mark.cpu]
def test_native_request_metadata_round_trips_and_stays_optional() -> None:
params = {
"transfer_id": "xfer-req-0",
"do_remote_decode": True,
"do_remote_prefill": False,
"remote_engine_id": "dit-engine-0",
}
kv_request = DiffusionKVRequest(
"req-0/diffusion-kv/0",
sequence_id=0,
prefix_len=4,
target_len=2,
seq_len=6,
kv_transfer_params=params,
prompt_token_ids=[0] * 6,
)
public_request = OmniDiffusionRequest(
prompt="prompt",
sampling_params=OmniDiffusionSamplingParams(num_inference_steps=1),
request_id="req-0",
kv_transfer_params=params,
)
scheduler_output = DiffusionSchedulerOutput(
step_id=0,
scheduled_new_reqs=[],
scheduled_cached_reqs=CachedRequestData.make_empty(),
finished_req_ids=set(),
num_running_reqs=0,
num_waiting_reqs=0,
)
assert kv_request.kv_transfer_params is params
assert kv_request.prompt_token_ids == [0] * 6
assert public_request.kv_transfer_params is params
assert scheduler_output.kv_connector_metadata is None
class _MetadataScheduler(BaseScheduler):
def update_from_output(self, sched_output, output) -> set[str]:
del sched_output, output
return set()
def test_scheduler_copies_opaque_params_to_sequence_requests() -> None:
params = {"transfer_id": "xfer-req-0", "do_remote_prefill": True, "num_transfer_tokens": 3}
sequence = DiffusionKVRequest(
"req-0/diffusion-kv/0",
sequence_id=0,
prefix_len=4,
target_len=2,
seq_len=6,
prompt_token_ids=[10, 11, 12, 13],
)
request = OmniDiffusionRequest(
prompt="prompt",
sampling_params=OmniDiffusionSamplingParams(num_inference_steps=1),
request_id="req-0",
diffusion_kv_requests=(sequence,),
kv_transfer_params=params,
)
scheduler = _MetadataScheduler()
scheduler._diffusion_kv_manager = object()
state = scheduler._make_request_state("req-0", request)
assert state.diffusion_kv_requests[0].kv_transfer_params == params
assert state.diffusion_kv_requests[0].kv_transfer_params is not params
assert sequence.prompt_token_ids == [10, 11, 12]
assert sequence.num_prompt_tokens == 3
assert sequence.seq_len == 6
def test_diffusion_kv_metadata_uses_native_cache_group_block_ids() -> None:
context = DiffusionKVContextMetadata(
context_id="text",
cache_role="cross_attention",
num_tokens=3,
block_ids=([7], [11]),
)
sequence = DiffusionKVSequenceMetadata(
sequence_id=1,
prefix_len=4,
target_len=2,
seq_len=8,
block_ids=([1, 2], [5, 6]),
context_ids=(context.context_id,),
)
metadata = DiffusionKVMetadata(
request_id="req-0",
allocation_generation=3,
sequences=(sequence,),
contexts=(context,),
)
assert metadata.sequences[0].block_ids == ([1, 2], [5, 6])
assert metadata.contexts[0].block_ids == ([7], [11])
def test_cfg_sequences_can_share_one_request_context() -> None:
shared_context = DiffusionKVContextMetadata(
context_id="shared-text",
cache_role="cross_attention",
num_tokens=3,
block_ids=([7], [11]),
)
sequences = tuple(
DiffusionKVSequenceMetadata(
sequence_id=sequence_id,
prefix_len=4,
target_len=2,
seq_len=8,
block_ids=([sequence_id + 1],),
context_ids=(shared_context.context_id,),
)
for sequence_id in range(2)
)
metadata = DiffusionKVMetadata(
request_id="req-shared-context",
allocation_generation=1,
sequences=sequences,
contexts=(shared_context,),
)
assert metadata.contexts == (shared_context,)
assert metadata.sequences[0].context_ids == (shared_context.context_id,)
assert metadata.sequences[1].context_ids == (shared_context.context_id,)
def test_cfg_sequences_can_reference_branch_specific_contexts() -> None:
conditional_context = DiffusionKVContextMetadata(
context_id="conditional-text",
cache_role="cross_attention",
num_tokens=3,
block_ids=([7], [11]),
)
unconditional_context = DiffusionKVContextMetadata(
context_id="unconditional-text",
cache_role="cross_attention",
num_tokens=3,
block_ids=([8], [12]),
)
metadata = DiffusionKVMetadata(
request_id="req-branch-contexts",
allocation_generation=1,
sequences=(
DiffusionKVSequenceMetadata(
sequence_id=0,
prefix_len=4,
target_len=2,
seq_len=8,
block_ids=([1],),
context_ids=(conditional_context.context_id,),
),
DiffusionKVSequenceMetadata(
sequence_id=1,
prefix_len=4,
target_len=2,
seq_len=8,
block_ids=([2],),
context_ids=(unconditional_context.context_id,),
),
),
contexts=(conditional_context, unconditional_context),
)
assert metadata.sequences[0].context_ids == (conditional_context.context_id,)
assert metadata.sequences[1].context_ids == (unconditional_context.context_id,)