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[CI] Fix broken ci (#2530)

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vLLM commit https://github.com/vllm-project/vllm/pull/22711 changed the
encode cache entries logic, this PR adapt the same change for vllm
ascend to make CI happy.

Co-Authored-By: zhoux77899 <zhouxiang100@huawei.com>

- vLLM version: v0.10.1.1
- vLLM main:
0ff902f3b4

Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
This commit is contained in:
wangxiyuan
2025-08-26 07:42:24 +08:00
committed by GitHub
parent 99bf25af76
commit 7e494e94a9
5 changed files with 256 additions and 123 deletions
Download Patch File Download Diff File Expand all files Collapse all files

205
tests/ut/core/test_scheduler.py
View File

@@ -295,24 +295,25 @@ class TestAscendScheduler(TestBase):
scheduler.running.append(req)
req.status = RequestStatus.RUNNING
scheduler_output = SchedulerOutput(scheduled_new_reqs=[],
scheduled_cached_reqs=[],
num_scheduled_tokens={
requests[0].request_id: 1,
requests[1].request_id: 2
},
total_num_scheduled_tokens=3,
scheduled_encoder_inputs={},
scheduled_spec_decode_tokens={
requests[0].request_id: [],
requests[1].request_id: [10]
},
num_common_prefix_blocks=0,
finished_req_ids=set(),
free_encoder_input_ids=[],
structured_output_request_ids={},
grammar_bitmask=None)
if vllm_version_is("0.10.1.1"):
scheduler_output = SchedulerOutput(
scheduled_new_reqs=[],
scheduled_cached_reqs=[],
num_scheduled_tokens={
requests[0].request_id: 1,
requests[1].request_id: 2
},
total_num_scheduled_tokens=3,
scheduled_encoder_inputs={},
scheduled_spec_decode_tokens={
requests[0].request_id: [],
requests[1].request_id: [10]
},
num_common_prefix_blocks=0,
finished_req_ids=set(),
free_encoder_input_ids=[],
structured_output_request_ids={},
grammar_bitmask=None)
model_output = ModelRunnerOutput(
req_ids=[req.request_id for req in requests],
req_id_to_index={
@@ -327,6 +328,24 @@ class TestAscendScheduler(TestBase):
prompt_logprobs_dict={},
pooler_output=[])
else:
scheduler_output = SchedulerOutput(
scheduled_new_reqs=[],
scheduled_cached_reqs=[],
num_scheduled_tokens={
requests[0].request_id: 1,
requests[1].request_id: 2
},
total_num_scheduled_tokens=3,
scheduled_encoder_inputs={},
scheduled_spec_decode_tokens={
requests[0].request_id: [],
requests[1].request_id: [10]
},
num_common_prefix_blocks=0,
finished_req_ids=set(),
free_encoder_mm_hashes=[],
structured_output_request_ids={},
grammar_bitmask=None)
model_output = ModelRunnerOutput(
req_ids=[req.request_id for req in requests],
req_id_to_index={
@@ -363,25 +382,25 @@ class TestAscendScheduler(TestBase):
scheduler.running.append(req)
req.status = RequestStatus.RUNNING
scheduler_output = SchedulerOutput(scheduled_new_reqs=[],
scheduled_cached_reqs=[],
num_scheduled_tokens={
requests[0].request_id: 3,
requests[1].request_id: 2
},
total_num_scheduled_tokens=5,
scheduled_encoder_inputs={},
scheduled_spec_decode_tokens={
requests[0].request_id:
[10, 42],
requests[1].request_id: [13]
},
num_common_prefix_blocks=0,
finished_req_ids=set(),
free_encoder_input_ids=[],
structured_output_request_ids={},
grammar_bitmask=None)
if vllm_version_is("0.10.1.1"):
scheduler_output = SchedulerOutput(
scheduled_new_reqs=[],
scheduled_cached_reqs=[],
num_scheduled_tokens={
requests[0].request_id: 3,
requests[1].request_id: 2
},
total_num_scheduled_tokens=5,
scheduled_encoder_inputs={},
scheduled_spec_decode_tokens={
requests[0].request_id: [10, 42],
requests[1].request_id: [13]
},
num_common_prefix_blocks=0,
finished_req_ids=set(),
free_encoder_input_ids=[],
structured_output_request_ids={},
grammar_bitmask=None)
model_output = ModelRunnerOutput(
req_ids=[req.request_id for req in requests],
req_id_to_index={
@@ -395,6 +414,24 @@ class TestAscendScheduler(TestBase):
prompt_logprobs_dict={},
pooler_output=[])
else:
scheduler_output = SchedulerOutput(
scheduled_new_reqs=[],
scheduled_cached_reqs=[],
num_scheduled_tokens={
requests[0].request_id: 3,
requests[1].request_id: 2
},
total_num_scheduled_tokens=5,
scheduled_encoder_inputs={},
scheduled_spec_decode_tokens={
requests[0].request_id: [10, 42],
requests[1].request_id: [13]
},
num_common_prefix_blocks=0,
finished_req_ids=set(),
free_encoder_mm_hashes=[],
structured_output_request_ids={},
grammar_bitmask=None)
model_output = ModelRunnerOutput(
req_ids=[req.request_id for req in requests],
req_id_to_index={
@@ -429,26 +466,25 @@ class TestAscendScheduler(TestBase):
scheduler.running.append(req)
req.status = RequestStatus.RUNNING
scheduler_output = SchedulerOutput(scheduled_new_reqs=[],
scheduled_cached_reqs=[],
num_scheduled_tokens={
requests[0].request_id: 3,
requests[1].request_id: 1
},
total_num_scheduled_tokens=4,
scheduled_encoder_inputs={},
scheduled_spec_decode_tokens={
requests[0].request_id:
[10, 11],
requests[1].request_id: []
},
num_common_prefix_blocks=0,
finished_req_ids=set(),
free_encoder_input_ids=[],
structured_output_request_ids={},
grammar_bitmask=None)
if vllm_version_is("0.10.1.1"):
scheduler_output = SchedulerOutput(
scheduled_new_reqs=[],
scheduled_cached_reqs=[],
num_scheduled_tokens={
requests[0].request_id: 3,
requests[1].request_id: 1
},
total_num_scheduled_tokens=4,
scheduled_encoder_inputs={},
scheduled_spec_decode_tokens={
requests[0].request_id: [10, 11],
requests[1].request_id: []
},
num_common_prefix_blocks=0,
finished_req_ids=set(),
free_encoder_input_ids=[],
structured_output_request_ids={},
grammar_bitmask=None)
model_output = ModelRunnerOutput(
req_ids=[req.request_id for req in requests],
req_id_to_index={
@@ -462,6 +498,24 @@ class TestAscendScheduler(TestBase):
prompt_logprobs_dict={},
pooler_output=[])
else:
scheduler_output = SchedulerOutput(
scheduled_new_reqs=[],
scheduled_cached_reqs=[],
num_scheduled_tokens={
requests[0].request_id: 3,
requests[1].request_id: 1
},
total_num_scheduled_tokens=4,
scheduled_encoder_inputs={},
scheduled_spec_decode_tokens={
requests[0].request_id: [10, 11],
requests[1].request_id: []
},
num_common_prefix_blocks=0,
finished_req_ids=set(),
free_encoder_mm_hashes=[],
structured_output_request_ids={},
grammar_bitmask=None)
model_output = ModelRunnerOutput(
req_ids=[req.request_id for req in requests],
req_id_to_index={
@@ -493,22 +547,21 @@ class TestAscendScheduler(TestBase):
scheduler.requests[requests[0].request_id] = requests[0]
scheduler.running.append(requests[0])
scheduler_output = SchedulerOutput(
scheduled_new_reqs=[],
scheduled_cached_reqs=[],
num_scheduled_tokens={requests[0].request_id: 3},
total_num_scheduled_tokens=3,
scheduled_encoder_inputs={},
scheduled_spec_decode_tokens={
requests[0].request_id: [EOS_TOKEN_ID, 10]
},
num_common_prefix_blocks=0,
finished_req_ids=set(),
free_encoder_input_ids=[],
structured_output_request_ids={},
grammar_bitmask=None)
if vllm_version_is("0.10.1.1"):
scheduler_output = SchedulerOutput(
scheduled_new_reqs=[],
scheduled_cached_reqs=[],
num_scheduled_tokens={requests[0].request_id: 3},
total_num_scheduled_tokens=3,
scheduled_encoder_inputs={},
scheduled_spec_decode_tokens={
requests[0].request_id: [EOS_TOKEN_ID, 10]
},
num_common_prefix_blocks=0,
finished_req_ids=set(),
free_encoder_input_ids=[],
structured_output_request_ids={},
grammar_bitmask=None)
model_output = ModelRunnerOutput(
req_ids=[requests[0].request_id],
req_id_to_index={requests[0].request_id: 0},
@@ -519,6 +572,20 @@ class TestAscendScheduler(TestBase):
pooler_output=[])
else:
scheduler_output = SchedulerOutput(
scheduled_new_reqs=[],
scheduled_cached_reqs=[],
num_scheduled_tokens={requests[0].request_id: 3},
total_num_scheduled_tokens=3,
scheduled_encoder_inputs={},
scheduled_spec_decode_tokens={
requests[0].request_id: [EOS_TOKEN_ID, 10]
},
num_common_prefix_blocks=0,
finished_req_ids=set(),
free_encoder_mm_hashes=[],
structured_output_request_ids={},
grammar_bitmask=None)
model_output = ModelRunnerOutput(
req_ids=[requests[0].request_id],
req_id_to_index={requests[0].request_id: 0},

1
tests/ut/worker/test_input_batch.py
View File

@@ -215,6 +215,7 @@ def _construct_cached_request_state(req_id_suffix: int):
generator=None,
num_computed_tokens=len(output_token_ids),
output_token_ids=output_token_ids,
mm_hashes=None,
)

55
vllm_ascend/core/scheduler.py
View File

@@ -385,23 +385,44 @@ class AscendScheduler(Scheduler):
req_to_new_blocks)
scheduled_cached_reqs = cached_reqs_data
scheduler_output = SchedulerOutput(
scheduled_new_reqs=new_reqs_data,
scheduled_cached_reqs=scheduled_cached_reqs,
num_scheduled_tokens=num_scheduled_tokens,
total_num_scheduled_tokens=total_num_scheduled_tokens,
scheduled_spec_decode_tokens=scheduled_spec_decode_tokens,
scheduled_encoder_inputs={},
num_common_prefix_blocks=num_common_prefix_blocks,
# finished_req_ids is an existing state in the scheduler,
# instead of being newly scheduled in this step.
# It contains the request IDs that are finished in between
# the previous and the current steps.
finished_req_ids=self.finished_req_ids, # type: ignore
free_encoder_input_ids=self.encoder_cache_manager.get_freed_ids(),
structured_output_request_ids={},
grammar_bitmask=None,
)
if vllm_version_is("0.10.1.1"):
scheduler_output = SchedulerOutput(
scheduled_new_reqs=new_reqs_data,
scheduled_cached_reqs=scheduled_cached_reqs,
num_scheduled_tokens=num_scheduled_tokens,
total_num_scheduled_tokens=total_num_scheduled_tokens,
scheduled_spec_decode_tokens=scheduled_spec_decode_tokens,
scheduled_encoder_inputs={},
num_common_prefix_blocks=num_common_prefix_blocks,
# finished_req_ids is an existing state in the scheduler,
# instead of being newly scheduled in this step.
# It contains the request IDs that are finished in between
# the previous and the current steps.
finished_req_ids=self.finished_req_ids, # type: ignore
free_encoder_input_ids=self.encoder_cache_manager.
get_freed_ids(),
structured_output_request_ids={},
grammar_bitmask=None,
)
else:
scheduler_output = SchedulerOutput(
scheduled_new_reqs=new_reqs_data,
scheduled_cached_reqs=scheduled_cached_reqs,
num_scheduled_tokens=num_scheduled_tokens,
total_num_scheduled_tokens=total_num_scheduled_tokens,
scheduled_spec_decode_tokens=scheduled_spec_decode_tokens,
scheduled_encoder_inputs={},
num_common_prefix_blocks=num_common_prefix_blocks,
# finished_req_ids is an existing state in the scheduler,
# instead of being newly scheduled in this step.
# It contains the request IDs that are finished in between
# the previous and the current steps.
finished_req_ids=self.finished_req_ids, # type: ignore
free_encoder_mm_hashes=self.encoder_cache_manager.
get_freed_mm_hashes(),
structured_output_request_ids={},
grammar_bitmask=None,
)
# NOTE(Kuntai): this function is designed for multiple purposes:
# 1. Plan the KV cache store

116
vllm_ascend/worker/model_runner_v1.py
View File

@@ -193,7 +193,9 @@ class NPUModelRunner(LoRAModelRunnerMixin):
# Lazy initialization, these will be set after __init__
self.kv_caches: List[torch.Tensor] = []
self.encoder_cache: Dict[str, Dict[int, torch.Tensor]] = {}
# TODO: remove Dict[str, Dict[int, torch.Tensor]] type after 0.10.1.1
self.encoder_cache: Union[Dict[str, Dict[int, torch.Tensor]],
Dict[str, torch.Tensor]] = {}
self.attn_mask = None
self.attn_state = None
self.requests: Dict[str, CachedRequestState] = {}
@@ -381,7 +383,8 @@ class NPUModelRunner(LoRAModelRunnerMixin):
# Remove finished requests from the cached states.
for req_id in scheduler_output.finished_req_ids:
self.requests.pop(req_id, None)
self.encoder_cache.pop(req_id, None)
if vllm_version_is("0.10.1.1"):
self.encoder_cache.pop(req_id, None)
# Remove the finished requests from the persistent batch.
# NOTE(woosuk): There could be an edge case where finished_req_ids and
# scheduled_req_ids overlap. This happens when a request is aborted and
@@ -390,15 +393,17 @@ class NPUModelRunner(LoRAModelRunnerMixin):
# and handling the second as a new request.
for req_id in scheduler_output.finished_req_ids:
self.input_batch.remove_request(req_id)
# Free the cached encoder outputs.
for req_id, input_id in scheduler_output.free_encoder_input_ids:
encoder_outputs = self.encoder_cache.get(req_id)
if encoder_outputs is not None:
encoder_outputs.pop(input_id, None)
if not encoder_outputs:
self.encoder_cache.pop(req_id, None)
if vllm_version_is("0.10.1.1"):
# Free the cached encoder outputs.
for req_id, input_id in scheduler_output.free_encoder_input_ids:
encoder_outputs = self.encoder_cache.get(req_id)
if encoder_outputs is not None:
encoder_outputs.pop(input_id, None)
if not encoder_outputs:
self.encoder_cache.pop(req_id, None)
else:
for mm_hash in scheduler_output.free_encoder_mm_hashes:
self.encoder_cache.pop(mm_hash, None)
# Remove the unscheduled requests from the persistent batch.
# NOTE(woosuk): The unscheduled requests are either preempted requests
# or running requests that are not scheduled in this step. We remove
@@ -447,6 +452,11 @@ class NPUModelRunner(LoRAModelRunnerMixin):
num_computed_tokens=new_req_data.num_computed_tokens,
output_token_ids=[],
lora_request=new_req_data.lora_request,
**({
"mm_hashes": new_req_data.mm_hashes
} if not vllm_version_is("0.10.1.1") else {
"mm_hashes": None
}),
)
# Only relevant for models using M-RoPE (e.g, Qwen2-VL)
@@ -882,15 +892,25 @@ class NPUModelRunner(LoRAModelRunnerMixin):
# Batch the multi-modal inputs.
mm_kwargs = list[MultiModalKwargsItem]()
req_ids_pos = list[tuple[str, int, PlaceholderRange]]()
if vllm_version_is("0.10.1.1"):
req_ids_pos = list[tuple[str, int, PlaceholderRange]]()
else:
mm_hashes_pos = list[tuple[str, PlaceholderRange]]()
for req_id, encoder_input_ids in scheduled_encoder_inputs.items():
req_state = self.requests[req_id]
for mm_input_id in encoder_input_ids:
mm_kwargs.append(req_state.mm_kwargs[mm_input_id])
req_ids_pos.append(
(req_id, mm_input_id, req_state.mm_positions[mm_input_id]))
if vllm_version_is("0.10.1.1"):
for mm_input_id in encoder_input_ids:
mm_kwargs.append(req_state.mm_kwargs[mm_input_id])
req_ids_pos.append((req_id, mm_input_id,
req_state.mm_positions[mm_input_id]))
else:
for mm_input_id in encoder_input_ids:
# TODO remove this assert after 0.10.1.1
assert req_state.mm_hashes is not None
mm_hash = req_state.mm_hashes[mm_input_id]
mm_kwargs.append(req_state.mm_kwargs[mm_input_id])
mm_hashes_pos.append(
(mm_hash, req_state.mm_positions[mm_input_id]))
# Batch mm inputs as much as we can: if a request in the batch has
# multiple modalities or a different modality than the previous one,
# we process it separately to preserve item order.
@@ -921,19 +941,26 @@ class NPUModelRunner(LoRAModelRunnerMixin):
for output in curr_group_outputs:
encoder_outputs.append(output)
if vllm_version_is("0.10.1.1"):
# Cache the encoder outputs.
for (req_id, input_id, pos_info), output in zip(
req_ids_pos,
encoder_outputs,
):
if req_id not in self.encoder_cache:
self.encoder_cache[req_id] = {}
# Cache the encoder outputs.
for (req_id, input_id, pos_info), output in zip(
req_ids_pos,
encoder_outputs,
):
if req_id not in self.encoder_cache:
self.encoder_cache[req_id] = {}
self.encoder_cache[req_id][input_id] = scatter_mm_placeholders(
output,
is_embed=pos_info.is_embed,
)
self.encoder_cache[req_id][input_id] = scatter_mm_placeholders(
output,
is_embed=pos_info.is_embed,
)
else:
for (mm_hash, pos_info), output in zip(mm_hashes_pos,
encoder_outputs):
self.encoder_cache[mm_hash] = scatter_mm_placeholders(
output,
is_embed=pos_info.is_embed,
)
def _gather_mm_embeddings(
self,
@@ -946,6 +973,8 @@ class NPUModelRunner(LoRAModelRunnerMixin):
req_state = self.requests[req_id]
num_computed_tokens = req_state.num_computed_tokens
mm_positions = req_state.mm_positions
if not vllm_version_is("0.10.1.1"):
mm_hashes = req_state.mm_hashes
for i, pos_info in enumerate(mm_positions):
start_pos = pos_info.offset
num_encoder_tokens = pos_info.length
@@ -963,13 +992,26 @@ class NPUModelRunner(LoRAModelRunnerMixin):
continue
start_idx = max(num_computed_tokens - start_pos, 0)
end_idx = min(
num_computed_tokens - start_pos + num_scheduled_tokens,
num_encoder_tokens)
assert start_idx < end_idx
assert req_id in self.encoder_cache
assert i in self.encoder_cache[req_id]
encoder_output = self.encoder_cache[req_id][i]
if vllm_version_is("0.10.1.1"):
end_idx = min(
num_computed_tokens - start_pos + num_scheduled_tokens,
num_encoder_tokens)
assert start_idx < end_idx
assert req_id in self.encoder_cache
assert i in self.encoder_cache[req_id]
encoder_output = self.encoder_cache[req_id][i]
else:
end_idx = min(
num_computed_tokens - start_pos + num_scheduled_tokens,
num_encoder_tokens,
)
assert start_idx < end_idx
# TODO remove this assert after 0.10.1.1
assert mm_hashes is not None
mm_hash = mm_hashes[i]
encoder_output = self.encoder_cache.get(mm_hash, None)
assert encoder_output is not None,\
f"Encoder cache miss for {mm_hash}."
if (is_embed := pos_info.is_embed) is not None:
is_embed = is_embed[start_idx:end_idx]

2
vllm_ascend/worker/npu_input_batch.py
View File

@@ -47,6 +47,8 @@ class CachedRequestState:
prompt_token_ids: list[int]
mm_kwargs: list[MultiModalKwargsItem]
mm_positions: list[PlaceholderRange]
# TODO: remove Optional after 0.10.1.1
mm_hashes: Optional[list[str]]
sampling_params: Optional[SamplingParams]
pooling_params: Optional[PoolingParams]
generator: Optional[torch.Generator]