709 lines
28 KiB
Python
709 lines
28 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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from typing import Optional
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import pytest
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import torch
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from vllm.config import CacheConfig, ModelConfig, SchedulerConfig, VllmConfig
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from vllm.multimodal.inputs import MultiModalKwargs, PlaceholderRange
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from vllm.sampling_params import SamplingParams
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from vllm.v1.core.sched.output import SchedulerOutput
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from vllm.v1.core.sched.scheduler import Scheduler
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from vllm.v1.kv_cache_interface import (FullAttentionSpec, KVCacheConfig,
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KVCacheGroupSpec)
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from vllm.v1.outputs import ModelRunnerOutput
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from vllm.v1.request import Request, RequestStatus
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from vllm.v1.structured_output import StructuredOutputManager
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EOS_TOKEN_ID = 50256
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def create_scheduler(
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model: str = "facebook/opt-125m",
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max_num_seqs: int = 16,
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max_num_batched_tokens: int = 8192,
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enable_prefix_caching: Optional[bool] = None,
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long_prefill_token_threshold: int = 0,
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) -> Scheduler:
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'''Create scheduler under test.
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Args:
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model: model under test
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max_num_seqs: max sequences to schedule
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max_num_batch_tokens: max num tokens to batch
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enable_prefix_caching: optionally force APC config
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(True/False) or use default
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(None)
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Returns:
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:class:`Scheduler` instance
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'''
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scheduler_config = SchedulerConfig(
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max_num_seqs=max_num_seqs,
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max_num_batched_tokens=max_num_batched_tokens,
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max_model_len=max_num_batched_tokens,
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long_prefill_token_threshold=long_prefill_token_threshold,
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)
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model_config = ModelConfig(
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model=model,
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task="auto",
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tokenizer=model,
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tokenizer_mode="auto",
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trust_remote_code=True,
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dtype="float16",
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seed=42,
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)
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# Cache config, optionally force APC
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kwargs_cache = ({} if enable_prefix_caching is None else {
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'enable_prefix_caching': enable_prefix_caching
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})
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cache_config = CacheConfig(
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block_size=16,
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gpu_memory_utilization=0.9,
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swap_space=0,
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cache_dtype="auto",
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**kwargs_cache,
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)
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vllm_config = VllmConfig(
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scheduler_config=scheduler_config,
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model_config=model_config,
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cache_config=cache_config,
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)
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kv_cache_config = KVCacheConfig(
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num_blocks=10000, # A large number of blocks to hold all requests
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tensors={},
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kv_cache_groups=[
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KVCacheGroupSpec(['layer'],
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FullAttentionSpec(16, 1, 1, torch.float32, False))
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],
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)
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cache_config.num_gpu_blocks = 10000
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return Scheduler(
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scheduler_config,
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model_config,
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cache_config,
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lora_config=None,
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kv_cache_config=kv_cache_config,
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log_stats=True,
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structured_output_manager=StructuredOutputManager(vllm_config),
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)
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def create_requests(num_requests: int,
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num_tokens: int = 10,
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mm_positions: Optional[list[PlaceholderRange]] = None,
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max_tokens: int = 16,
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stop_token_ids: Optional[list[int]] = None,
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prompt_logprobs: Optional[int] = None):
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sampling_params = SamplingParams(ignore_eos=False,
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max_tokens=max_tokens,
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stop_token_ids=stop_token_ids,
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prompt_logprobs=prompt_logprobs)
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requests = []
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for i in range(num_requests):
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if mm_positions is not None:
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mm_position = mm_positions[i]
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mm_inputs = [MultiModalKwargs({})] * len(mm_position)
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else:
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mm_position = None
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mm_inputs = None
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request = Request(
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request_id=f"{i}",
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prompt=None,
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prompt_token_ids=[i] * num_tokens,
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sampling_params=sampling_params,
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multi_modal_inputs=mm_inputs,
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multi_modal_placeholders=mm_position,
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multi_modal_hashes=None,
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eos_token_id=EOS_TOKEN_ID,
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arrival_time=0,
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)
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requests.append(request)
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return requests
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def test_add_requests():
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scheduler = create_scheduler()
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requests = create_requests(num_requests=10)
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for i, request in enumerate(requests):
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scheduler.add_request(request)
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assert request.request_id in scheduler.requests
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assert len(scheduler.waiting) == i + 1
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def test_finish_request():
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scheduler = create_scheduler()
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requests = create_requests(num_requests=10)
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for request in requests:
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scheduler.add_request(request)
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for i, request in enumerate(requests):
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scheduler.finish_requests(request.request_id,
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RequestStatus.FINISHED_ABORTED)
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assert request.request_id not in scheduler.requests
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assert len(scheduler.waiting) == 9 - i
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def test_get_num_unfinished_requests():
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scheduler = create_scheduler()
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requests = create_requests(num_requests=10)
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for request in requests:
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scheduler.add_request(request)
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for i, request in enumerate(requests):
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scheduler.finish_requests(request.request_id,
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RequestStatus.FINISHED_STOPPED)
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assert scheduler.get_num_unfinished_requests() == len(requests) - i - 1
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@pytest.mark.parametrize("enable_prefix_caching, prompt_logprobs", [
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(None, None),
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(True, 5),
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])
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def test_schedule(enable_prefix_caching: Optional[bool],
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prompt_logprobs: Optional[int]):
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'''Test scheduling.
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Two cases: default APC/no prompt logprobs; APC=True + prompt logprobs
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'''
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scheduler = create_scheduler(enable_prefix_caching=enable_prefix_caching)
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requests = create_requests(num_requests=10,
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prompt_logprobs=prompt_logprobs)
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for request in requests:
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scheduler.add_request(request)
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# Test initial scheduling
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output = scheduler.schedule()
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assert len(output.scheduled_new_reqs) == len(requests)
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assert len(output.scheduled_cached_reqs) == 0
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assert len(output.finished_req_ids) == 0
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# Verify all requests are scheduled.
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for req_id, num_tokens in output.num_scheduled_tokens.items():
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assert num_tokens == len(requests[int(req_id)].prompt_token_ids)
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# Verify requests moved from waiting to running
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assert len(scheduler.waiting) == 0
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assert len(scheduler.running) == len(requests)
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for i, request in enumerate(requests):
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assert scheduler.running[i] == request
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def test_schedule_multimodal_requests():
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scheduler = create_scheduler(model="llava-hf/llava-1.5-7b-hf")
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mm_positions = [[PlaceholderRange(offset=i, length=100)]
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for i in range(10)]
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requests = create_requests(
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num_requests=10,
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num_tokens=200,
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mm_positions=mm_positions,
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)
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for request in requests:
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scheduler.add_request(request)
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output = scheduler.schedule()
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assert len(output.scheduled_new_reqs) == len(requests)
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assert len(output.scheduled_cached_reqs) == 0
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assert len(output.finished_req_ids) == 0
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for req_id, num_tokens in output.num_scheduled_tokens.items():
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assert num_tokens == len(requests[int(req_id)].prompt_token_ids)
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assert len(output.scheduled_encoder_inputs) == 10
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for req_id, encoder_input in output.scheduled_encoder_inputs.items():
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assert len(encoder_input) == 1
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def test_schedule_partial_requests():
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"""Test scheduling behavior with partial requests.
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This test verifies that:
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1. The scheduler can handle multiple partial requests in a single step when
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constrained by encoder budget.
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2. A request in RUNNING state may be unscheduled in subsequent steps if
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there is insufficient encoder budget.
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"""
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scheduler = create_scheduler(
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model="llava-hf/llava-1.5-7b-hf",
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max_num_batched_tokens=1024,
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)
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mm_positions = [[PlaceholderRange(offset=100, length=600)]
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for _ in range(3)]
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requests = create_requests(
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num_requests=3,
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num_tokens=800,
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mm_positions=mm_positions,
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)
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for request in requests:
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scheduler.add_request(request)
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output = scheduler.schedule()
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assert len(output.scheduled_new_reqs) == 3
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assert len(output.scheduled_cached_reqs) == 0
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assert len(output.finished_req_ids) == 0
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assert scheduler.max_num_encoder_input_tokens == 1024
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# The first request is scheduled fully.
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assert output.num_scheduled_tokens[requests[0].request_id] == 800
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# The second request is scheduled partially.
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# The <img> tokens are not scheduled because of the encoder budget.
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assert output.num_scheduled_tokens[requests[1].request_id] == 100
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# The third request is also scheduled partially.
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# The <img> tokens are not scheduled because of the encoder budget.
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assert output.num_scheduled_tokens[requests[2].request_id] == 100
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req_to_index = {
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request.request_id: i
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for i, request in enumerate(requests)
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}
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model_runner_output = ModelRunnerOutput(
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req_ids=[request.request_id for request in requests],
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req_id_to_index=req_to_index,
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# Only the first request has a sampled token id because
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# the rest requests are still being prefilled.
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sampled_token_ids=[[0], [], []],
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spec_token_ids=None,
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logprobs=None,
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prompt_logprobs_dict={},
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)
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scheduler.update_from_output(output, model_runner_output)
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# Schedule the next step.
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# Only the first and second requests are scheduled.
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# The third request is in the RUNNING state but not scheduled in this step
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# because of the encoder budget.
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output = scheduler.schedule()
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assert len(scheduler.running) == 3
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assert len(output.scheduled_new_reqs) == 0
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assert len(output.scheduled_cached_reqs) == 2
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assert len(output.finished_req_ids) == 0
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assert output.num_scheduled_tokens[requests[0].request_id] == 1
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assert output.num_scheduled_tokens[requests[1].request_id] == 700
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assert requests[2].request_id not in output.num_scheduled_tokens
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@pytest.mark.parametrize("enable_prefix_caching", [True, False])
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def test_schedule_concurrent_partial_requests(enable_prefix_caching: bool):
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"""Test scheduling behavior with concurrent partial requests.
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This test verifies that: there are multiple long prefill requests in the
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RUNNING state, and we can schedule them together.
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"""
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scheduler = create_scheduler(
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model="facebook/opt-125m",
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max_num_batched_tokens=1024,
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long_prefill_token_threshold=400,
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enable_prefix_caching=enable_prefix_caching,
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)
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requests = create_requests(
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num_requests=3,
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num_tokens=800,
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)
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for request in requests:
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scheduler.add_request(request)
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output = scheduler.schedule()
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assert len(output.scheduled_new_reqs) == 3
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assert len(output.scheduled_cached_reqs) == 0
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assert len(output.finished_req_ids) == 0
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# The first request is scheduled partially - 400.
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assert output.num_scheduled_tokens[requests[0].request_id] == 400
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# The second request is scheduled partially - 400.
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assert output.num_scheduled_tokens[requests[1].request_id] == 400
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# The third request is also scheduled partially - 1024 - 400 - 400 = 224.
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assert output.num_scheduled_tokens[requests[2].request_id] == 224
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req_to_index = {
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request.request_id: i
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for i, request in enumerate(requests)
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}
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model_runner_output = ModelRunnerOutput(
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req_ids=[request.request_id for request in requests],
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req_id_to_index=req_to_index,
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sampled_token_ids=[[] for _ in range(len(requests))],
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spec_token_ids=None,
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logprobs=None,
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prompt_logprobs_dict={},
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)
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scheduler.update_from_output(output, model_runner_output)
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# Schedule the next step. All three requests are running.
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# Processed the remaining prefills of the first and second requests.
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output1 = scheduler.schedule()
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assert len(scheduler.running) == 3
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assert len(output1.scheduled_new_reqs) == 0
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assert len(output1.scheduled_cached_reqs) == 3
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assert len(output1.finished_req_ids) == 0
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assert output1.num_scheduled_tokens[requests[0].request_id] == 400
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assert output1.num_scheduled_tokens[requests[1].request_id] == 400
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assert output1.num_scheduled_tokens[requests[2].request_id] == 224
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# Schedule the third step. All three requests are running.
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# First and second requests are in the decode stage.
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# All the remaining tokens in the third request are processed.
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model_runner_output = ModelRunnerOutput(
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req_ids=[request.request_id for request in requests],
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req_id_to_index=req_to_index,
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sampled_token_ids=[[0], [0]] + [[] for _ in range(len(requests) - 2)],
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spec_token_ids=None,
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logprobs=None,
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prompt_logprobs_dict={},
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)
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scheduler.update_from_output(output1, model_runner_output)
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output2 = scheduler.schedule()
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assert len(scheduler.running) == 3
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assert len(output2.scheduled_new_reqs) == 0
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assert len(output2.scheduled_cached_reqs) == 3
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assert len(output2.finished_req_ids) == 0
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assert output2.num_scheduled_tokens[requests[0].request_id] == 1
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assert output2.num_scheduled_tokens[requests[1].request_id] == 1
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assert output2.num_scheduled_tokens[
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requests[2].request_id] == 800 - 224 - 224
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def test_stop_via_update_from_output():
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"""Test stopping behavior through update_from_output"""
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scheduler = create_scheduler()
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# Test case 1: Stop on EOS token
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requests = create_requests(num_requests=2, max_tokens=10)
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for req in requests:
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req.num_computed_tokens = req.num_tokens
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scheduler.requests[req.request_id] = req
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scheduler.running.append(req)
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scheduler.scheduled_req_ids.add(req.request_id)
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scheduler_output = SchedulerOutput(scheduled_new_reqs=[],
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scheduled_cached_reqs=[],
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num_scheduled_tokens={
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requests[0].request_id: 1,
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requests[1].request_id: 2
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},
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total_num_scheduled_tokens=3,
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scheduled_encoder_inputs={},
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scheduled_spec_decode_tokens={
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requests[0].request_id: [],
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requests[1].request_id: [10]
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},
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num_common_prefix_blocks=0,
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finished_req_ids=set(),
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free_encoder_input_ids=[],
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structured_output_request_ids={},
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grammar_bitmask=None)
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model_output = ModelRunnerOutput(
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req_ids=[req.request_id for req in requests],
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req_id_to_index={
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req.request_id: i
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for i, req in enumerate(requests)
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},
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sampled_token_ids=[[EOS_TOKEN_ID],
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[10,
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11]], # First request hits EOS, second continues
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spec_token_ids=None,
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logprobs=None,
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prompt_logprobs_dict={})
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scheduler.update_from_output(scheduler_output, model_output)
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# Verify first request stopped, second continues
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assert len(scheduler.running) == 1
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assert scheduler.running[0].request_id == requests[1].request_id
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assert requests[0].status == RequestStatus.FINISHED_STOPPED
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assert requests[0].request_id in scheduler.finished_req_ids
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assert list(requests[0].output_token_ids) == [EOS_TOKEN_ID]
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assert list(requests[1].output_token_ids) == [10, 11]
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# Test case 2: Stop on custom stop token
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scheduler = create_scheduler()
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requests = create_requests(num_requests=2,
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max_tokens=10,
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stop_token_ids=[42, 43])
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for req in requests:
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req.num_computed_tokens = req.num_tokens
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scheduler.requests[req.request_id] = req
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scheduler.running.append(req)
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scheduler.scheduled_req_ids.add(req.request_id)
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scheduler_output = SchedulerOutput(scheduled_new_reqs=[],
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scheduled_cached_reqs=[],
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num_scheduled_tokens={
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requests[0].request_id: 3,
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requests[1].request_id: 2
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},
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total_num_scheduled_tokens=5,
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scheduled_encoder_inputs={},
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scheduled_spec_decode_tokens={
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requests[0].request_id: [10, 42],
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requests[1].request_id: [13]
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},
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num_common_prefix_blocks=0,
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finished_req_ids=set(),
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free_encoder_input_ids=[],
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structured_output_request_ids={},
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grammar_bitmask=None)
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model_output = ModelRunnerOutput(
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req_ids=[req.request_id for req in requests],
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req_id_to_index={
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req.request_id: i
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for i, req in enumerate(requests)
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},
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sampled_token_ids=[[10, 42, 12],
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[13, 14]], # First request hits stop token
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spec_token_ids=None,
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logprobs=None,
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prompt_logprobs_dict={})
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scheduler.update_from_output(scheduler_output, model_output)
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# Verify first request stopped on custom token
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assert len(scheduler.running) == 1
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assert scheduler.running[0].request_id == requests[1].request_id
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assert requests[0].status == RequestStatus.FINISHED_STOPPED
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assert requests[0].stop_reason == 42
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assert requests[0].request_id in scheduler.finished_req_ids
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assert list(requests[0].output_token_ids) == [10, 42]
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assert list(requests[1].output_token_ids) == [13, 14]
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# Test case 3: Stop on max tokens
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scheduler = create_scheduler()
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requests = create_requests(num_requests=2, max_tokens=2)
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for req in requests:
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req.num_computed_tokens = req.num_tokens
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scheduler.requests[req.request_id] = req
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scheduler.running.append(req)
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scheduler.scheduled_req_ids.add(req.request_id)
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scheduler_output = SchedulerOutput(scheduled_new_reqs=[],
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scheduled_cached_reqs=[],
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num_scheduled_tokens={
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requests[0].request_id: 3,
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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={
|
|
req.request_id: i
|
|
for i, req in enumerate(requests)
|
|
},
|
|
sampled_token_ids=[[10, 11, 12],
|
|
[13]], # First request exceeds max_tokens
|
|
spec_token_ids=None,
|
|
logprobs=None,
|
|
prompt_logprobs_dict={})
|
|
|
|
scheduler.update_from_output(scheduler_output, model_output)
|
|
|
|
# Verify first request stopped due to length
|
|
assert len(scheduler.running) == 1
|
|
assert scheduler.running[0].request_id == requests[1].request_id
|
|
assert requests[0].status == RequestStatus.FINISHED_LENGTH_CAPPED
|
|
assert requests[0].request_id in scheduler.finished_req_ids
|
|
assert list(requests[0].output_token_ids) == [10, 11
|
|
] # Truncated to max_tokens
|
|
assert list(requests[1].output_token_ids) == [13]
|
|
|
|
# Test case 4: Ignore EOS flag
|
|
scheduler = create_scheduler()
|
|
requests = create_requests(num_requests=1, max_tokens=10)
|
|
requests[0].sampling_params.ignore_eos = True
|
|
requests[0].num_computed_tokens = requests[0].num_tokens
|
|
scheduler.requests[requests[0].request_id] = requests[0]
|
|
scheduler.running.append(requests[0])
|
|
scheduler.scheduled_req_ids.add(requests[0].request_id)
|
|
|
|
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},
|
|
sampled_token_ids=[[EOS_TOKEN_ID, 10, 11]],
|
|
spec_token_ids=None,
|
|
logprobs=None,
|
|
prompt_logprobs_dict={})
|
|
|
|
scheduler.update_from_output(scheduler_output, model_output)
|
|
|
|
# Verify request continues past EOS
|
|
assert len(scheduler.running) == 1
|
|
assert not requests[0].is_finished()
|
|
assert list(requests[0].output_token_ids) == [EOS_TOKEN_ID, 10, 11]
|
|
|
|
|
|
@pytest.mark.parametrize("enable_prefix_caching, prompt_logprobs", [
|
|
(None, None),
|
|
(True, 5),
|
|
])
|
|
def test_schedule_concurrent_batches(enable_prefix_caching: Optional[bool],
|
|
prompt_logprobs: Optional[int]):
|
|
scheduler = create_scheduler(
|
|
max_num_batched_tokens=1024,
|
|
max_num_seqs=2,
|
|
enable_prefix_caching=enable_prefix_caching,
|
|
)
|
|
requests = create_requests(
|
|
num_requests=2,
|
|
num_tokens=512,
|
|
prompt_logprobs=prompt_logprobs,
|
|
)
|
|
|
|
# Schedule the first request.
|
|
scheduler.add_request(requests[0])
|
|
scheduler_output0 = scheduler.schedule()
|
|
assert len(scheduler_output0.scheduled_new_reqs) == 1
|
|
assert scheduler_output0.num_scheduled_tokens[
|
|
requests[0].request_id] == 512
|
|
|
|
# The first request is still running, so only schedule the second request.
|
|
scheduler.add_request(requests[1])
|
|
scheduler_output1 = scheduler.schedule()
|
|
assert len(scheduler_output1.scheduled_new_reqs) == 1
|
|
assert scheduler_output1.num_scheduled_tokens[
|
|
requests[1].request_id] == 512
|
|
|
|
# Model output of the first request.
|
|
model_runner_output = ModelRunnerOutput(
|
|
req_ids=[requests[0].request_id],
|
|
req_id_to_index={requests[0].request_id: 0},
|
|
sampled_token_ids=[[0]],
|
|
spec_token_ids=None,
|
|
logprobs=None,
|
|
prompt_logprobs_dict={},
|
|
)
|
|
scheduler.update_from_output(scheduler_output0, model_runner_output)
|
|
|
|
# Schedule the next step.
|
|
# The first request can be scheduled again while the second
|
|
# request is still running.
|
|
scheduler_output2 = scheduler.schedule()
|
|
assert scheduler_output2.num_scheduled_tokens[requests[0].request_id] == 1
|
|
|
|
# Model output of the second request.
|
|
model_runner_output = ModelRunnerOutput(
|
|
req_ids=[requests[1].request_id],
|
|
req_id_to_index={requests[1].request_id: 0},
|
|
sampled_token_ids=[[0]],
|
|
spec_token_ids=None,
|
|
logprobs=None,
|
|
prompt_logprobs_dict={},
|
|
)
|
|
scheduler.update_from_output(scheduler_output1, model_runner_output)
|
|
|
|
|
|
# Note - these test cases mirror some of those in test_rejection_sampler.py
|
|
@pytest.mark.parametrize(
|
|
"spec_tokens,output_tokens,expected",
|
|
[
|
|
([[1, 2, 3]], [[1, 2, 3, 4]], (3, 3)), # perfect match
|
|
([[1, 2, 3]], [[1, 5]], (3, 1)), # early mismatch
|
|
([[1, 2], [3]], [[1, 2, 5], [3, 4]], (3, 3)), # multiple sequences
|
|
([[1]], [[1, 2]], (1, 1)), # single token sequence
|
|
([[]], [[5]], (0, 0)), # empty sequence
|
|
([[1, 2, 3], [4, 5, 6]], [[1, 2, 7], [4, 8]],
|
|
(6, 3)), # multiple mismatches
|
|
])
|
|
def test_schedule_spec_decoding_stats(spec_tokens, output_tokens, expected):
|
|
"""Test scheduling behavior with speculative decoding.
|
|
|
|
This test verifies that:
|
|
1. Speculated tokens get scheduled correctly
|
|
2. Spec decoding stats properly count number of draft and accepted tokens
|
|
"""
|
|
scheduler = create_scheduler()
|
|
requests = create_requests(num_requests=len(spec_tokens), num_tokens=1)
|
|
req_ids = []
|
|
req_to_index = {}
|
|
for i, request in enumerate(requests):
|
|
scheduler.add_request(request)
|
|
req_ids.append(request.request_id)
|
|
req_to_index[request.request_id] = i
|
|
|
|
# Schedule a decode, which will also draft speculative tokens
|
|
output = scheduler.schedule()
|
|
assert len(output.scheduled_new_reqs) == len(requests)
|
|
assert output.total_num_scheduled_tokens == len(requests)
|
|
for i in range(len(requests)):
|
|
req_id = requests[i].request_id
|
|
assert output.num_scheduled_tokens[req_id] == 1
|
|
assert req_id not in output.scheduled_spec_decode_tokens
|
|
|
|
model_runner_output = ModelRunnerOutput(
|
|
req_ids=req_ids,
|
|
req_id_to_index=req_to_index,
|
|
sampled_token_ids=[[0] for _ in range(len(requests))],
|
|
spec_token_ids=spec_tokens,
|
|
logprobs=None,
|
|
prompt_logprobs_dict={},
|
|
)
|
|
engine_core_outputs = scheduler.update_from_output(output,
|
|
model_runner_output)
|
|
|
|
for i in range(len(requests)):
|
|
running_req = scheduler.running[i]
|
|
# The prompt token
|
|
assert running_req.num_computed_tokens == 1
|
|
# The prompt token and the sampled token
|
|
assert running_req.num_tokens == 2
|
|
# The prompt token, the sampled token, and the speculated tokens
|
|
assert running_req.num_tokens_with_spec == 2 + len(spec_tokens[i])
|
|
|
|
# No draft or accepted tokens counted yet
|
|
assert engine_core_outputs.scheduler_stats.spec_decoding_stats is not None
|
|
stats = engine_core_outputs.scheduler_stats.spec_decoding_stats
|
|
assert stats.num_draft_tokens == 0
|
|
assert stats.num_accepted_tokens == 0
|
|
|
|
# Schedule the speculated tokens for validation
|
|
output = scheduler.schedule()
|
|
assert len(output.scheduled_new_reqs) == 0
|
|
# The sampled token and speculated tokens
|
|
assert output.total_num_scheduled_tokens == \
|
|
len(requests) + sum(len(ids) for ids in spec_tokens)
|
|
for i in range(len(requests)):
|
|
req_id = requests[i].request_id
|
|
assert output.num_scheduled_tokens[req_id] == 1 + len(spec_tokens[i])
|
|
if spec_tokens[i]:
|
|
assert len(output.scheduled_spec_decode_tokens[req_id]) == \
|
|
len(spec_tokens[i])
|
|
else:
|
|
assert req_id not in output.scheduled_spec_decode_tokens
|
|
|
|
model_runner_output = ModelRunnerOutput(
|
|
req_ids=req_ids,
|
|
req_id_to_index=req_to_index,
|
|
sampled_token_ids=output_tokens,
|
|
spec_token_ids=None,
|
|
logprobs=None,
|
|
prompt_logprobs_dict={},
|
|
)
|
|
engine_core_outputs = scheduler.update_from_output(output,
|
|
model_runner_output)
|
|
|
|
assert engine_core_outputs.scheduler_stats.spec_decoding_stats is not None
|
|
stats = engine_core_outputs.scheduler_stats.spec_decoding_stats
|
|
assert stats.num_draft_tokens == expected[0]
|
|
assert stats.num_accepted_tokens == expected[1]
|