♻️ refactor: restructure Observer as base class with InferenceObserver

- 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

Generated with [Claude Code](https://claude.ai/code)
via [Happy](https://happy.engineering)

Co-Authored-By: Claude <noreply@anthropic.com>
Co-Authored-By: Happy <yesreply@happy.engineering>

bench.py

@@ -2,6 +2,7 @@ import os
 import time
 from random import randint, seed
 from nanovllm import LLM, SamplingParams
+from nanovllm.utils.observer import InferenceObserver
 
 
 def bench_decode(llm, num_seqs, input_len, output_len):
@@ -14,13 +15,17 @@ def bench_decode(llm, num_seqs, input_len, output_len):
     llm.generate(prompt_token_ids, sampling_params, use_tqdm=False)
     t = time.time() - t
 
-    # Calculate metrics
+    # Get metrics from InferenceObserver
-    prefill_tokens = num_seqs * input_len
+    ttft_ms = InferenceObserver.ttft / 1e6
+    tpot_ms = InferenceObserver.tpot / 1e6
+
+    # Calculate throughput from observer metrics
     decode_tokens = num_seqs * output_len
-    decode_throughput = decode_tokens / t
+    decode_throughput = 1000.0 / tpot_ms if tpot_ms > 0 else 0  # tokens/s per sequence
 
     print(f"[Decode] Input: {num_seqs}x{input_len}tok, Output: {decode_tokens}tok, Time: {t:.2f}s")
-    print(f"         Throughput: {decode_throughput:.2f} tok/s (includes prefill overhead)")
+    print(f"         TTFT: {ttft_ms:.2f}ms, TPOT: {tpot_ms:.2f}ms")
+    print(f"         Decode Throughput: {decode_throughput:.2f} tok/s (from observer)")
 
 
 def bench_prefill(llm, num_seqs, input_len):
@@ -33,9 +38,19 @@ def bench_prefill(llm, num_seqs, input_len):
     t = time.time()
     llm.generate(prompt_token_ids, sampling_params, use_tqdm=False)
     t = time.time() - t
+
+    # Get TTFT from InferenceObserver
+    ttft_ms = InferenceObserver.ttft / 1e6
+    ttft_s = ttft_ms / 1000.0
+
     total_input_tokens = num_seqs * input_len
-    throughput = total_input_tokens / t
+    # Use observer TTFT for accurate prefill throughput
-    print(f"[Prefill] Input: {total_input_tokens}tok ({num_seqs}x{input_len}), Time: {t:.2f}s, Throughput: {throughput:.2f}tok/s")
+    throughput_observer = total_input_tokens / ttft_s if ttft_s > 0 else 0
+    throughput_external = total_input_tokens / t
+
+    print(f"[Prefill] Input: {total_input_tokens}tok ({num_seqs}x{input_len})")
+    print(f"          External Time: {t:.2f}s, Throughput: {throughput_external:.2f}tok/s")
+    print(f"          Observer TTFT: {ttft_ms:.2f}ms, Throughput: {throughput_observer:.2f}tok/s")
 
 
 def main():

bench_offload.py

@@ -2,6 +2,7 @@ import os
 import time
 from random import randint, seed
 from nanovllm import LLM, SamplingParams
+from nanovllm.utils.observer import InferenceObserver
 
 
 def bench_decode(llm, num_seqs, input_len, output_len):
@@ -14,16 +15,17 @@ def bench_decode(llm, num_seqs, input_len, output_len):
     llm.generate(prompt_token_ids, sampling_params, use_tqdm=False)
     t = time.time() - t
 
-    # Calculate metrics
+    # Get metrics from InferenceObserver
-    prefill_tokens = num_seqs * input_len
+    ttft_ms = InferenceObserver.ttft / 1e6
-    decode_tokens = num_seqs * output_len
+    tpot_ms = InferenceObserver.tpot / 1e6
 
-    # Approximate: assume prefill takes ~input_len/prefill_speed, rest is decode
+    # Calculate throughput from observer metrics
-    # For more accurate measurement, we'd need internal timing
+    decode_tokens = num_seqs * output_len
-    decode_throughput = decode_tokens / t  # This includes prefill time, so it's a lower bound
+    decode_throughput = 1000.0 / tpot_ms if tpot_ms > 0 else 0  # tokens/s per sequence
 
     print(f"[Decode] Input: {num_seqs}x{input_len}tok, Output: {decode_tokens}tok, Time: {t:.2f}s")
-    print(f"         Throughput: {decode_throughput:.2f} tok/s (includes prefill overhead)")
+    print(f"         TTFT: {ttft_ms:.2f}ms, TPOT: {tpot_ms:.2f}ms")
+    print(f"         Decode Throughput: {decode_throughput:.2f} tok/s (from observer)")
 
 
 def bench_prefill(llm, num_seqs, input_len):
@@ -36,9 +38,19 @@ def bench_prefill(llm, num_seqs, input_len):
     t = time.time()
     llm.generate(prompt_token_ids, sampling_params, use_tqdm=False)
     t = time.time() - t
+
+    # Get TTFT from InferenceObserver
+    ttft_ms = InferenceObserver.ttft / 1e6
+    ttft_s = ttft_ms / 1000.0
+
     total_input_tokens = num_seqs * input_len
-    throughput = total_input_tokens / t
+    # Use observer TTFT for accurate prefill throughput
-    print(f"[Prefill] Input: {total_input_tokens}tok ({num_seqs}x{input_len}), Time: {t:.2f}s, Throughput: {throughput:.2f}tok/s")
+    throughput_observer = total_input_tokens / ttft_s if ttft_s > 0 else 0
+    throughput_external = total_input_tokens / t
+
+    print(f"[Prefill] Input: {total_input_tokens}tok ({num_seqs}x{input_len})")
+    print(f"          External Time: {t:.2f}s, Throughput: {throughput_external:.2f}tok/s")
+    print(f"          Observer TTFT: {ttft_ms:.2f}ms, Throughput: {throughput_observer:.2f}tok/s")
 
 
 def main():

nanovllm/engine/llm_engine.py

@@ -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.
+            # Decode mode: calculate TPOT from previous decode step
-            if Observer.ttft_start != 0:
+            if InferenceObserver.tpot_start != 0:
-                Observer.ttft = perf_counter_ns() - Observer.ttft_start
+                InferenceObserver.tpot = perf_counter_ns() - InferenceObserver.tpot_start
-                Observer.reset_ttft()
+            InferenceObserver.tpot_start = perf_counter_ns()
-            # 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()
         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",
+                    "ttft": f"{float(InferenceObserver.ttft) / 1e6}ms",
-                    "tpot": f"{float(Observer.tpot) / 1e6}ms",
+                    "tpot": f"{float(InferenceObserver.tpot) / 1e6}ms",
                 })
             for seq_id, token_ids in output:
                 outputs[seq_id] = token_ids

nanovllm/engine/scheduler.py

@@ -4,7 +4,7 @@ from typing import TYPE_CHECKING
 
 from nanovllm.config import Config
 from nanovllm.engine.sequence import Sequence, SequenceStatus
-from nanovllm.utils.observer import Observer
+from nanovllm.utils.observer import InferenceObserver
 
 if TYPE_CHECKING:
     from nanovllm.kvcache import KVCacheManager
@@ -32,8 +32,8 @@ class Scheduler:
         num_seqs = 0
         num_batched_tokens = 0
         while self.waiting and num_seqs < self.max_num_seqs:
-            if Observer.ttft_start == 0:
+            if InferenceObserver.ttft_start == 0:
-                Observer.ttft_start = perf_counter_ns()
+                InferenceObserver.ttft_start = perf_counter_ns()
             seq = self.waiting[0]
 
             # Check if sequence is too large

nanovllm/utils/observer.py

@@ -1,17 +1,106 @@
-class Observer():
+"""
-    ttft_start = 0
+Observer 基类和 InferenceObserver 实现。
-    tpot_start = 0
 
-    ttft = 0
+Observer 架构：
-    tpot = 0
+- Observer: 基类，定义通用接口
+- InferenceObserver: 推理性能观测（TTFT/TPOT）
+- MemoryObserver: 内存传输观测（在 memory_observer.py 中定义）
+"""
+
+
+class Observer:
+    """
+    Observer 基类，提供通用的启用/禁用、重置、输出接口。
+
+    所有 Observer 子类应继承此类并实现：
+    - complete_reset(): 重置所有统计数据
+    - get_summary(): 返回统计摘要 dict
+    - print_summary(): 打印人类可读的摘要
+    """
+
+    _enabled: bool = True  # 默认启用
 
     @classmethod
-    def reset_ttft(cls):
+    def enable(cls) -> None:
+        """启用 observer"""
+        cls._enabled = True
+
+    @classmethod
+    def disable(cls) -> None:
+        """禁用 observer"""
+        cls._enabled = False
+
+    @classmethod
+    def is_enabled(cls) -> bool:
+        """检查是否启用"""
+        return cls._enabled
+
+    @classmethod
+    def complete_reset(cls) -> None:
+        """重置所有统计数据（子类实现）"""
+        raise NotImplementedError
+
+    @classmethod
+    def get_summary(cls) -> dict:
+        """返回统计摘要（子类实现）"""
+        raise NotImplementedError
+
+    @classmethod
+    def print_summary(cls) -> None:
+        """打印人类可读的摘要（子类可选覆盖）"""
+        import json
+        print(json.dumps(cls.get_summary(), indent=2))
+
+
+class InferenceObserver(Observer):
+    """
+    推理性能 Observer，统计 TTFT 和 TPOT。
+
+    - TTFT (Time To First Token): 首个 token 生成延迟
+    - TPOT (Time Per Output Token): 每个输出 token 的平均延迟
+
+    统计位置：
+    - TTFT 开始: scheduler.py:35-36 - 第一个 sequence 从 waiting 队列取出时
+    - TTFT 结束: llm_engine.py:69-72 - prefill 完成后（包括 chunked prefill 所有 chunks）
+    - TPOT 开始: llm_engine.py:65 - 每次 decode step 结束时
+    - TPOT 结束: llm_engine.py:62-63 - 下一次 decode step 开始时计算（测量上一次 decode 时间）
+    - 重置: llm_engine.py:97 - generate() 开始时
+
+    注意：TPOT 需要至少 2 个输出 token 才能计算（测量 decode step 间隔）。
+    """
+
+    # 时间戳 (nanoseconds)
+    ttft_start: int = 0
+    tpot_start: int = 0
+
+    # 统计结果 (nanoseconds)
+    ttft: int = 0
+    tpot: int = 0
+
+    @classmethod
+    def reset_ttft(cls) -> None:
+        """重置 TTFT 计时器"""
         cls.ttft_start = 0
 
     @classmethod
-    def complete_reset(cls):
+    def complete_reset(cls) -> None:
+        """重置所有统计数据"""
         cls.ttft_start = 0
         cls.tpot_start = 0
         cls.ttft = 0
         cls.tpot = 0
+
+    @classmethod
+    def get_summary(cls) -> dict:
+        """返回统计摘要"""
+        return {
+            "ttft_ns": cls.ttft,
+            "ttft_ms": cls.ttft / 1e6,
+            "tpot_ns": cls.tpot,
+            "tpot_ms": cls.tpot / 1e6,
+        }
+
+    @classmethod
+    def print_summary(cls) -> None:
+        """打印摘要"""
+        print(f"[InferenceObserver] TTFT: {cls.ttft / 1e6:.2f}ms, TPOT: {cls.tpot / 1e6:.2f}ms")