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[3/N] Refactor scheduler for chunked prefill scheduling #3550
[3/N] Refactor scheduler for chunked prefill scheduling #3550
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before: Throughput: 2.01 requests/s, 972.94 tokens/s Benchmark result. I'd say it is just the same |
@simon-mo Updated (plz take a look one more time);
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vllm/core/scheduler.py
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self.running.extend([s.seq_group for s in prefills.seq_groups]) | ||
self.running.extend([s.seq_group for s in decodes.seq_groups]) | ||
self.running.extend([s.seq_group for s in swapped_in.seq_groups]) |
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can you help me understand what clears self.running
each step?
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it is popped out when it is scheduled from each func!
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seems not
Lines 281 to 287 in 810c56d
seq_group = self.running[0] | |
new_token_size = ( | |
seq_group.num_seqs(status=SequenceStatus.RUNNING) * | |
self.num_decoding_tokens_per_seq) | |
if num_batched_tokens + new_token_size > token_budget: | |
break |
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+1 The logic here is confusing. Some of the requests in self.running
will be poped out in _schedule_decodes
. But the lines here give people a feeling that self.running
is a queue that keep extending to infinity.
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This is basically the same behavior as the master;
Line 342 in d8658c8
self.running = running |
It is cleared up when the model output is processed
vllm/vllm/engine/llm_engine.py
Line 600 in 563c1d7
self.scheduler.free_finished_seq_groups() |
I will comment it here
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Thanks for the changes! The code looks better than the last version. My main concern on this PR is that self.running
seems to be a leaking abstraction that is used everywhere. Can we somehow make this interface a bit more clean?
vllm/core/scheduler.py
Outdated
self.running.extend([s.seq_group for s in prefills.seq_groups]) | ||
self.running.extend([s.seq_group for s in decodes.seq_groups]) | ||
self.running.extend([s.seq_group for s in swapped_in.seq_groups]) |
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+1 The logic here is confusing. Some of the requests in self.running
will be poped out in _schedule_decodes
. But the lines here give people a feeling that self.running
is a queue that keep extending to infinity.
As we discussed offline, I updated code based on the proposal I made.
|
vllm/core/scheduler.py
Outdated
self.running.extend([s.seq_group for s in prefills.seq_groups]) | ||
self.running.extend([s.seq_group for s in decodes.seq_groups]) | ||
self.running.extend([s.seq_group for s in swapped_in.seq_groups]) |
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This is basically the same behavior as the master;
Line 342 in d8658c8
self.running = running |
It is cleared up when the model output is processed
vllm/vllm/engine/llm_engine.py
Line 600 in 563c1d7
self.scheduler.free_finished_seq_groups() |
I will comment it here
@@ -573,17 +791,13 @@ def _preempt_by_recompute( | |||
seq.status = SequenceStatus.WAITING | |||
self.free_seq(seq) | |||
seq.reset_state_for_recompute() | |||
# NOTE: For FCFS, we insert the preempted sequence group to the front | |||
# of the waiting queue. | |||
self.waiting.appendleft(seq_group) |
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updated within _schedule now
sampler test failure seems unrelated |
lora test failure unrelated |
Refactor the current scheduler to make it easy to understand with chunked prefill later.
This simply moves logic for prefill scheduling and decoding scheudling to a dedicated function. The purpose of doing this is we want the different scheduling policy for chunked prefill (by default, we do prefill -> decoding. But when chunked prefill is enabled, we want decoding -> prefill to reduce ITL impact).
The functionality must be exactly the same except that I made it use lora_enabled instead of directly checking if lora config is None.
Related: #3130
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