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♻️ refactor: restructure Observer as base class with InferenceObserver

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- Refactor Observer into base class with common enable/disable/reset interface
- Create InferenceObserver subclass for TTFT/TPOT metrics
- Fix TTFT calculation timing: compute after prefill completes instead of
  at decode start (fixes max_tokens=1 returning TTFT=0)
- Integrate InferenceObserver into bench.py and bench_offload.py for
  accurate internal timing metrics vs external wall-clock time
- Add get_summary() and print_summary() methods for structured output

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Co-Authored-By: Claude <noreply@anthropic.com>
Co-Authored-By: Happy <yesreply@happy.engineering>
This commit is contained in:
Zijie Tian
2026-01-28 03:15:33 +08:00
parent f3e4611e3b
commit c16bfcf40f
5 changed files with 156 additions and 37 deletions
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27
nanovllm/engine/llm_engine.py
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@@ -10,7 +10,7 @@ from nanovllm.sampling_params import SamplingParams
from nanovllm.engine.sequence import Sequence
from nanovllm.engine.scheduler import Scheduler
from nanovllm.engine.model_runner import ModelRunner
from nanovllm.utils.observer import Observer
from nanovllm.utils.observer import InferenceObserver
class LLMEngine:
@@ -58,15 +58,18 @@ class LLMEngine:
print(f"[DEBUG LLMEngine.step] Mode={mode}, active_sequences={len(seqs)}")
if not is_prefill:
# The end of the prefill mode. Get TTFT.
if Observer.ttft_start != 0:
Observer.ttft = perf_counter_ns() - Observer.ttft_start
Observer.reset_ttft()
# The start of the decode mode. Get TPOT.
if Observer.tpot_start != 0:
Observer.tpot = perf_counter_ns() - Observer.tpot_start
Observer.tpot_start = perf_counter_ns()
# Decode mode: calculate TPOT from previous decode step
if InferenceObserver.tpot_start != 0:
InferenceObserver.tpot = perf_counter_ns() - InferenceObserver.tpot_start
InferenceObserver.tpot_start = perf_counter_ns()
token_ids = self.model_runner.call("run", seqs, is_prefill)
if is_prefill:
# Calculate TTFT after prefill completes (including chunked prefill)
if InferenceObserver.ttft_start != 0:
InferenceObserver.ttft = perf_counter_ns() - InferenceObserver.ttft_start
InferenceObserver.reset_ttft()
self.scheduler.postprocess(seqs, token_ids)
outputs = [(seq.seq_id, seq.completion_token_ids) for seq in seqs if seq.is_finished]
@@ -91,7 +94,7 @@ class LLMEngine:
log_level = os.environ.get('NANOVLLM_LOG_LEVEL', 'INFO')
debug_enabled = log_level.upper() == 'DEBUG'
Observer.complete_reset()
InferenceObserver.complete_reset()
if use_tqdm:
pbar = tqdm(total=len(prompts), desc="Generating", dynamic_ncols=True)
if not isinstance(sampling_params, list):
@@ -128,8 +131,8 @@ class LLMEngine:
pbar.set_postfix({
"Prefill": f"{int(prefill_throughput)}tok/s",
"Decode": f"{int(decode_throughput)}tok/s",
"ttft": f"{float(Observer.ttft) / 1e6}ms",
"tpot": f"{float(Observer.tpot) / 1e6}ms",
"ttft": f"{float(InferenceObserver.ttft) / 1e6}ms",
"tpot": f"{float(InferenceObserver.tpot) / 1e6}ms",
})
for seq_id, token_ids in output:
outputs[seq_id] = token_ids