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[Misc] Remove in ModelRunnerOutput

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Signed-off-by: Woosuk Kwon <woosuk.kwon@berkeley.edu>
This commit is contained in:
Woosuk Kwon 2025-08-23 21:09:20 -07:00
parent 65197a5fb3
commit efd4bc967d
7 changed files with 4 additions and 77 deletions
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4
tests/v1/core/test_async_scheduler.py
View File

@ -17,10 +17,6 @@ def _make_model_runner_output(
req_ids = list(scheduler_output.num_scheduled_tokens.keys()) req_ids = list(scheduler_output.num_scheduled_tokens.keys())
return ModelRunnerOutput( return ModelRunnerOutput(
req_ids=req_ids, req_ids=req_ids,
req_id_to_index={
req_id: i
for i, req_id in enumerate(req_ids)
},
sampled_token_ids=[[i] for i in range(len(req_ids))], sampled_token_ids=[[i] for i in range(len(req_ids))],
logprobs=None, logprobs=None,
prompt_logprobs_dict={}, prompt_logprobs_dict={},

65
tests/v1/core/test_scheduler.py
View File

@ -148,13 +148,8 @@ def test_schedule_partial_requests():
# The third request is also scheduled partially. # The third request is also scheduled partially.
# The <img> tokens are not scheduled because of the encoder budget. # The <img> tokens are not scheduled because of the encoder budget.
assert output.num_scheduled_tokens[requests[2].request_id] == 100 assert output.num_scheduled_tokens[requests[2].request_id] == 100
req_to_index = {
request.request_id: i
for i, request in enumerate(requests)
}
model_runner_output = ModelRunnerOutput( model_runner_output = ModelRunnerOutput(
req_ids=[request.request_id for request in requests], req_ids=[request.request_id for request in requests],
req_id_to_index=req_to_index,
# Only the first request has a sampled token id because # Only the first request has a sampled token id because
# the rest requests are still being prefilled. # the rest requests are still being prefilled.
sampled_token_ids=[[0], [], []], sampled_token_ids=[[0], [], []],
@ -200,13 +195,8 @@ def test_no_mm_input_chunking():
# We want to only see the 400 text tokens at the start scheduled # We want to only see the 400 text tokens at the start scheduled
assert output.num_scheduled_tokens[requests[0].request_id] == 400 assert output.num_scheduled_tokens[requests[0].request_id] == 400
req_to_index = {
request.request_id: i
for i, request in enumerate(requests)
}
model_runner_output = ModelRunnerOutput( model_runner_output = ModelRunnerOutput(
req_ids=[request.request_id for request in requests], req_ids=[request.request_id for request in requests],
req_id_to_index=req_to_index,
sampled_token_ids=[[] for _ in range(len(requests))], sampled_token_ids=[[] for _ in range(len(requests))],
logprobs=None, logprobs=None,
prompt_logprobs_dict={}, prompt_logprobs_dict={},
@ -263,13 +253,8 @@ def test_schedule_concurrent_partial_requests(enable_prefix_caching: bool):
assert output.num_scheduled_tokens[requests[1].request_id] == 400 assert output.num_scheduled_tokens[requests[1].request_id] == 400
# The third request is also scheduled partially - 1024 - 400 - 400 = 224. # The third request is also scheduled partially - 1024 - 400 - 400 = 224.
assert output.num_scheduled_tokens[requests[2].request_id] == 224 assert output.num_scheduled_tokens[requests[2].request_id] == 224
req_to_index = {
request.request_id: i
for i, request in enumerate(requests)
}
model_runner_output = ModelRunnerOutput( model_runner_output = ModelRunnerOutput(
req_ids=[request.request_id for request in requests], req_ids=[request.request_id for request in requests],
req_id_to_index=req_to_index,
sampled_token_ids=[[] for _ in range(len(requests))], sampled_token_ids=[[] for _ in range(len(requests))],
logprobs=None, logprobs=None,
prompt_logprobs_dict={}, prompt_logprobs_dict={},
@ -293,7 +278,6 @@ def test_schedule_concurrent_partial_requests(enable_prefix_caching: bool):
# All the remaining tokens in the third request are processed. # All the remaining tokens in the third request are processed.
model_runner_output = ModelRunnerOutput( model_runner_output = ModelRunnerOutput(
req_ids=[request.request_id for request in requests], req_ids=[request.request_id for request in requests],
req_id_to_index=req_to_index,
sampled_token_ids=[[0], [0]] + [[] for _ in range(len(requests) - 2)], sampled_token_ids=[[0], [0]] + [[] for _ in range(len(requests) - 2)],
logprobs=None, logprobs=None,
prompt_logprobs_dict={}, prompt_logprobs_dict={},
@ -344,10 +328,6 @@ def test_stop_via_update_from_output():
model_output = ModelRunnerOutput( model_output = ModelRunnerOutput(
req_ids=[req.request_id for req in requests], req_ids=[req.request_id for req in requests],
req_id_to_index={
req.request_id: i
for i, req in enumerate(requests)
},
sampled_token_ids=[[EOS_TOKEN_ID], sampled_token_ids=[[EOS_TOKEN_ID],
[10, [10,
11]], # First request hits EOS, second continues 11]], # First request hits EOS, second continues
@ -398,10 +378,6 @@ def test_stop_via_update_from_output():
model_output = ModelRunnerOutput( model_output = ModelRunnerOutput(
req_ids=[req.request_id for req in requests], req_ids=[req.request_id for req in requests],
req_id_to_index={
req.request_id: i
for i, req in enumerate(requests)
},
sampled_token_ids=[[10, 42, 12], sampled_token_ids=[[10, 42, 12],
[13, 14]], # First request hits stop token [13, 14]], # First request hits stop token
logprobs=None, logprobs=None,
@ -450,10 +426,6 @@ def test_stop_via_update_from_output():
model_output = ModelRunnerOutput( model_output = ModelRunnerOutput(
req_ids=[req.request_id for req in requests], req_ids=[req.request_id for req in requests],
req_id_to_index={
req.request_id: i
for i, req in enumerate(requests)
},
sampled_token_ids=[[10, 11, 12], sampled_token_ids=[[10, 11, 12],
[13]], # First request exceeds max_tokens [13]], # First request exceeds max_tokens
logprobs=None, logprobs=None,
@ -496,11 +468,11 @@ def test_stop_via_update_from_output():
model_output = ModelRunnerOutput( model_output = ModelRunnerOutput(
req_ids=[requests[0].request_id], req_ids=[requests[0].request_id],
req_id_to_index={requests[0].request_id: 0},
sampled_token_ids=[[EOS_TOKEN_ID, 10, 11]], sampled_token_ids=[[EOS_TOKEN_ID, 10, 11]],
logprobs=None, logprobs=None,
prompt_logprobs_dict={}, prompt_logprobs_dict={},
pooler_output=[]) pooler_output=[],
)
scheduler.update_from_output(scheduler_output, model_output) scheduler.update_from_output(scheduler_output, model_output)
@ -544,7 +516,6 @@ def test_schedule_concurrent_batches(enable_prefix_caching: Optional[bool],
# Model output of the first request. # Model output of the first request.
model_runner_output = ModelRunnerOutput( model_runner_output = ModelRunnerOutput(
req_ids=[requests[0].request_id], req_ids=[requests[0].request_id],
req_id_to_index={requests[0].request_id: 0},
sampled_token_ids=[[0]], sampled_token_ids=[[0]],
logprobs=None, logprobs=None,
prompt_logprobs_dict={}, prompt_logprobs_dict={},
@ -561,7 +532,6 @@ def test_schedule_concurrent_batches(enable_prefix_caching: Optional[bool],
# Model output of the second request. # Model output of the second request.
model_runner_output = ModelRunnerOutput( model_runner_output = ModelRunnerOutput(
req_ids=[requests[1].request_id], req_ids=[requests[1].request_id],
req_id_to_index={requests[1].request_id: 0},
sampled_token_ids=[[0]], sampled_token_ids=[[0]],
logprobs=None, logprobs=None,
prompt_logprobs_dict={}, prompt_logprobs_dict={},
@ -596,7 +566,6 @@ def test_preempt_during_execution():
# Get the output of the first request. # Get the output of the first request.
model_runner_output0 = ModelRunnerOutput( model_runner_output0 = ModelRunnerOutput(
req_ids=[requests[0].request_id], req_ids=[requests[0].request_id],
req_id_to_index={requests[0].request_id: 0},
sampled_token_ids=[[0]], sampled_token_ids=[[0]],
logprobs=None, logprobs=None,
prompt_logprobs_dict={}, prompt_logprobs_dict={},
@ -613,7 +582,6 @@ def test_preempt_during_execution():
model_runner_output1 = ModelRunnerOutput( model_runner_output1 = ModelRunnerOutput(
req_ids=[requests[1].request_id], req_ids=[requests[1].request_id],
req_id_to_index={requests[1].request_id: 0},
sampled_token_ids=[[42]], sampled_token_ids=[[42]],
logprobs=None, logprobs=None,
prompt_logprobs_dict={}, prompt_logprobs_dict={},
@ -651,11 +619,9 @@ def test_schedule_spec_decoding_stats(spec_tokens, output_tokens, expected):
scheduler = create_scheduler(num_speculative_tokens=num_spec_tokens) scheduler = create_scheduler(num_speculative_tokens=num_spec_tokens)
requests = create_requests(num_requests=len(spec_tokens), num_tokens=1) requests = create_requests(num_requests=len(spec_tokens), num_tokens=1)
req_ids = [] req_ids = []
req_to_index = {}
for i, request in enumerate(requests): for i, request in enumerate(requests):
scheduler.add_request(request) scheduler.add_request(request)
req_ids.append(request.request_id) req_ids.append(request.request_id)
req_to_index[request.request_id] = i
# Schedule a decode, which will also draft speculative tokens # Schedule a decode, which will also draft speculative tokens
output = scheduler.schedule() output = scheduler.schedule()
@ -668,7 +634,6 @@ def test_schedule_spec_decoding_stats(spec_tokens, output_tokens, expected):
model_runner_output = ModelRunnerOutput( model_runner_output = ModelRunnerOutput(
req_ids=req_ids, req_ids=req_ids,
req_id_to_index=req_to_index,
sampled_token_ids=[[0] for _ in range(len(requests))], sampled_token_ids=[[0] for _ in range(len(requests))],
logprobs=None, logprobs=None,
prompt_logprobs_dict={}, prompt_logprobs_dict={},
@ -709,7 +674,6 @@ def test_schedule_spec_decoding_stats(spec_tokens, output_tokens, expected):
model_runner_output = ModelRunnerOutput( model_runner_output = ModelRunnerOutput(
req_ids=req_ids, req_ids=req_ids,
req_id_to_index=req_to_index,
sampled_token_ids=output_tokens, sampled_token_ids=output_tokens,
logprobs=None, logprobs=None,
prompt_logprobs_dict={}, prompt_logprobs_dict={},
@ -829,15 +793,12 @@ def test_kv_connector_basic():
max_tokens=MAX_TOKENS, max_tokens=MAX_TOKENS,
block_size=BLOCK_SIZE) block_size=BLOCK_SIZE)
req_ids = [] req_ids = []
req_to_index = {}
for i, request in enumerate(requests): for i, request in enumerate(requests):
scheduler.add_request(request) scheduler.add_request(request)
req_ids.append(request.request_id) req_ids.append(request.request_id)
req_to_index[request.request_id] = i
MODEL_RUNNER_OUTPUT = ModelRunnerOutput( MODEL_RUNNER_OUTPUT = ModelRunnerOutput(
req_ids=req_ids, req_ids=req_ids,
req_id_to_index=req_to_index,
sampled_token_ids=[[1000]] * len(req_ids), sampled_token_ids=[[1000]] * len(req_ids),
logprobs=None, logprobs=None,
prompt_logprobs_dict={}, prompt_logprobs_dict={},
@ -875,15 +836,12 @@ def test_kv_connector_basic():
max_tokens=MAX_TOKENS, max_tokens=MAX_TOKENS,
block_size=BLOCK_SIZE) block_size=BLOCK_SIZE)
req_ids = [] req_ids = []
req_to_index = {}
for i, request in enumerate(requests): for i, request in enumerate(requests):
scheduler.add_request(request) scheduler.add_request(request)
req_ids.append(request.request_id) req_ids.append(request.request_id)
req_to_index[request.request_id] = i
MODEL_RUNNER_OUTPUT = ModelRunnerOutput( MODEL_RUNNER_OUTPUT = ModelRunnerOutput(
req_ids=req_ids, req_ids=req_ids,
req_id_to_index=req_to_index,
sampled_token_ids=[[1000]] * len(req_ids), sampled_token_ids=[[1000]] * len(req_ids),
logprobs=None, logprobs=None,
prompt_logprobs_dict={}, prompt_logprobs_dict={},
@ -942,15 +900,12 @@ def test_kv_connector_unable_to_allocate():
max_tokens=MAX_TOKENS, max_tokens=MAX_TOKENS,
block_size=BLOCK_SIZE) block_size=BLOCK_SIZE)
req_ids = [] req_ids = []
req_to_index = {}
for i, request in enumerate(requests): for i, request in enumerate(requests):
scheduler.add_request(request) scheduler.add_request(request)
req_ids.append(request.request_id) req_ids.append(request.request_id)
req_to_index[request.request_id] = i
MODEL_RUNNER_OUTPUT = ModelRunnerOutput( MODEL_RUNNER_OUTPUT = ModelRunnerOutput(
req_ids=req_ids, req_ids=req_ids,
req_id_to_index=req_to_index,
sampled_token_ids=[[1000]] * len(req_ids), sampled_token_ids=[[1000]] * len(req_ids),
logprobs=None, logprobs=None,
prompt_logprobs_dict={}, prompt_logprobs_dict={},
@ -1023,15 +978,12 @@ def test_kv_connector_handles_preemption():
max_tokens=MAX_TOKENS, max_tokens=MAX_TOKENS,
block_size=BLOCK_SIZE) block_size=BLOCK_SIZE)
req_ids = [] req_ids = []
req_to_index = {}
for i, request in enumerate(requests): for i, request in enumerate(requests):
scheduler.add_request(request) scheduler.add_request(request)
req_ids.append(request.request_id) req_ids.append(request.request_id)
req_to_index[request.request_id] = i
MODEL_RUNNER_OUTPUT = ModelRunnerOutput( MODEL_RUNNER_OUTPUT = ModelRunnerOutput(
req_ids=req_ids, req_ids=req_ids,
req_id_to_index=req_to_index,
sampled_token_ids=[[1000]] * len(req_ids), sampled_token_ids=[[1000]] * len(req_ids),
logprobs=None, logprobs=None,
prompt_logprobs_dict={}, prompt_logprobs_dict={},
@ -1121,10 +1073,6 @@ def test_kv_connector_handles_preemption():
def make_output(scheduler: Scheduler): def make_output(scheduler: Scheduler):
return ModelRunnerOutput( return ModelRunnerOutput(
req_ids=[req.request_id for req in scheduler.running], req_ids=[req.request_id for req in scheduler.running],
req_id_to_index={
req.request_id: i
for i, req in enumerate(scheduler.running)
},
sampled_token_ids=[[1000]] * len(scheduler.running), sampled_token_ids=[[1000]] * len(scheduler.running),
logprobs=None, logprobs=None,
prompt_logprobs_dict={}, prompt_logprobs_dict={},
@ -1446,10 +1394,6 @@ def test_priority_scheduling_preemption():
# Simulate model execution to move requests to running state # Simulate model execution to move requests to running state
model_output = ModelRunnerOutput( model_output = ModelRunnerOutput(
req_ids=[req.request_id for req in low_priority_requests], req_ids=[req.request_id for req in low_priority_requests],
req_id_to_index={
req.request_id: i
for i, req in enumerate(low_priority_requests)
},
sampled_token_ids=[[100] for _ in low_priority_requests], sampled_token_ids=[[100] for _ in low_priority_requests],
logprobs=None, logprobs=None,
prompt_logprobs_dict={}, prompt_logprobs_dict={},
@ -1518,10 +1462,6 @@ def test_priority_scheduling_no_preemption_when_space_available():
output = scheduler.schedule() output = scheduler.schedule()
model_output = ModelRunnerOutput( model_output = ModelRunnerOutput(
req_ids=[req.request_id for req in low_priority_requests], req_ids=[req.request_id for req in low_priority_requests],
req_id_to_index={
req.request_id: i
for i, req in enumerate(low_priority_requests)
},
sampled_token_ids=[[100] for _ in low_priority_requests], sampled_token_ids=[[100] for _ in low_priority_requests],
logprobs=None, logprobs=None,
prompt_logprobs_dict={}, prompt_logprobs_dict={},
@ -1762,7 +1702,6 @@ def test_priority_scheduling_heap_property():
# Simulate completion to make room for next request # Simulate completion to make room for next request
model_output = ModelRunnerOutput( model_output = ModelRunnerOutput(
req_ids=[req.req_id], req_ids=[req.req_id],
req_id_to_index={req.req_id: 0},
sampled_token_ids=[[100]], sampled_token_ids=[[100]],
logprobs=None, logprobs=None,
prompt_logprobs_dict={}, prompt_logprobs_dict={},

2
tests/v1/kv_connector/unit/utils.py
View File

@ -182,7 +182,6 @@ def create_model_runner_output(
# Make request data. # Make request data.
req_ids = [req.request_id for req in reqs] 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. # Make sampled tokens.
sampled_token = EOS_TOKEN_ID if use_eos else 0 sampled_token = EOS_TOKEN_ID if use_eos else 0
@ -198,7 +197,6 @@ def create_model_runner_output(
# Make output data structure. # Make output data structure.
return ModelRunnerOutput( return ModelRunnerOutput(
req_ids=req_ids, req_ids=req_ids,
req_id_to_index=req_id_to_index,
sampled_token_ids=sampled_token_ids, sampled_token_ids=sampled_token_ids,
logprobs=None, logprobs=None,
prompt_logprobs_dict={}, prompt_logprobs_dict={},

4
vllm/v1/core/sched/scheduler.py
View File

@ -784,7 +784,8 @@ class Scheduler(SchedulerInterface):
# to avoid expensive operations inside the loop. # to avoid expensive operations inside the loop.
stopped_running_reqs: set[Request] = set() stopped_running_reqs: set[Request] = set()
stopped_preempted_reqs: set[Request] = set() stopped_preempted_reqs: set[Request] = set()
for req_id, num_tokens_scheduled in num_scheduled_tokens.items(): for req_index, req_id in enumerate(model_runner_output.req_ids):
num_tokens_scheduled = num_scheduled_tokens[req_id]
assert num_tokens_scheduled > 0 assert num_tokens_scheduled > 0
request = self.requests.get(req_id) request = self.requests.get(req_id)
if request is None: if request is None:
@ -793,7 +794,6 @@ class Scheduler(SchedulerInterface):
# in pipeline parallelism). # in pipeline parallelism).
continue continue
req_index = model_runner_output.req_id_to_index[req_id]
generated_token_ids = sampled_token_ids[ generated_token_ids = sampled_token_ids[
req_index] if sampled_token_ids else [] req_index] if sampled_token_ids else []

3
vllm/v1/outputs.py
View File

@ -85,8 +85,6 @@ class ModelRunnerOutput:
# [num_reqs] # [num_reqs]
req_ids: list[str] req_ids: list[str]
# req_id -> index
req_id_to_index: dict[str, int]
# num_reqs x num_generated_tokens # num_reqs x num_generated_tokens
# num_generated_tokens is the number of tokens # num_generated_tokens is the number of tokens
@ -124,7 +122,6 @@ class DraftTokenIds:
EMPTY_MODEL_RUNNER_OUTPUT = ModelRunnerOutput(req_ids=[], EMPTY_MODEL_RUNNER_OUTPUT = ModelRunnerOutput(req_ids=[],
req_id_to_index={},
sampled_token_ids=[], sampled_token_ids=[],
logprobs=None, logprobs=None,
prompt_logprobs_dict={}, prompt_logprobs_dict={},

2
vllm/v1/worker/gpu_model_runner.py
View File

@ -1494,7 +1494,6 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
return ModelRunnerOutput( return ModelRunnerOutput(
req_ids=self.input_batch.req_ids, req_ids=self.input_batch.req_ids,
req_id_to_index=self.input_batch.req_id_to_index,
sampled_token_ids=[], sampled_token_ids=[],
logprobs=None, logprobs=None,
prompt_logprobs_dict={}, prompt_logprobs_dict={},
@ -1785,7 +1784,6 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
return ModelRunnerOutput( return ModelRunnerOutput(
req_ids=self.input_batch.req_ids, req_ids=self.input_batch.req_ids,
req_id_to_index=self.input_batch.req_id_to_index,
sampled_token_ids=valid_sampled_token_ids, sampled_token_ids=valid_sampled_token_ids,
logprobs=logprobs_lists, logprobs=logprobs_lists,
prompt_logprobs_dict=prompt_logprobs_dict, prompt_logprobs_dict=prompt_logprobs_dict,

1
vllm/v1/worker/tpu_model_runner.py
View File

@ -1145,7 +1145,6 @@ class TPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
model_runner_output = ModelRunnerOutput( model_runner_output = ModelRunnerOutput(
req_ids=req_ids, req_ids=req_ids,
req_id_to_index=self.input_batch.req_id_to_index,
sampled_token_ids=valid_sampled_token_ids, sampled_token_ids=valid_sampled_token_ids,
logprobs=logprobs_lists, logprobs=logprobs_lists,
prompt_logprobs_dict=prompt_logprobs_dict, prompt_logprobs_dict=prompt_logprobs_dict,