perf: benchmark decode via real inference engine

- route decode benchmark through InferenceEngine generate path
- add enable_cuda_graph toggle to engine, scheduler, and executor
- make benchmark --cuda-graph/--no-cuda-graph control the toggle
- hoist local time imports to module top

scripts/tools/benchmark.py

@@ -1,4 +1,5 @@
 import json
+import time
 from pathlib import Path
 from typing import Optional, Union
 
@@ -8,9 +9,11 @@ import torch
 from astrai.config import BaseModelConfig, ConfigFactory
 from astrai.extension import ATTN_BACKEND, AttentionBackendFactory, attn_backend
 from astrai.inference.cache import PagePool, TaskCacheManager
+from astrai.inference.engine import InferenceEngine
 from astrai.inference.runtime.graph import CudaGraphContext
 from astrai.inference.workspace import InferenceWorkspace
 from astrai.model import AutoModel, AutoRegressiveLM
+from astrai.tokenize import AutoTokenizer
 
 _DTYPES = ["bfloat16", "float16", "float32"]
 _CACHES = ["contiguous", "paged"]
@@ -58,6 +61,7 @@ class GenerationBenchmark:
         cache_type: str = "contiguous",
         backend: Union[str, ATTN_BACKEND] = ATTN_BACKEND.CUDA,
         cuda_graph: bool = False,
+        tokenizer: Optional[AutoTokenizer] = None,
     ):
         self.device = device
         self.dtype = dtype
@@ -66,6 +70,7 @@ class GenerationBenchmark:
         self.config = config
         self.backend = backend
         self.cuda_graph = cuda_graph
+        self.tokenizer = tokenizer
 
     def _make_pool(self, batch_size: int, max_seq_len: int) -> PagePool:
         if self.cache_type == "contiguous":
@@ -175,8 +180,6 @@ class GenerationBenchmark:
         prompt_length: int = 512,
         num_trials: int = 5,
     ) -> BenchmarkResult:
-        import time
-
         pool = self._make_pool(batch_size, prompt_length)
         workspace = self._make_workspace(pool, self.config)
         task_cache = self._make_task_cache(pool)
@@ -236,12 +239,60 @@ class GenerationBenchmark:
         gen_length: int = 128,
         num_trials: int = 5,
     ) -> BenchmarkResult:
-        if self.cuda_graph and self.backend == "cuda":
-            return self._run_graph_decode_benchmark(
-                batch_size, prompt_length, gen_length, num_trials
-            )
-        return self._run_plain_decode_benchmark(
-            batch_size, prompt_length, gen_length, num_trials
+        if self.tokenizer is None:
+            raise ValueError("Engine decode benchmark requires a tokenizer")
+
+        # Use the real engine so scheduler, executor, sampling, and graph
+        # warmup/replay are included in the measured generation path.
+        phrase = "Benchmark the language model with a realistic generation prompt. "
+        prompt_ids = self.tokenizer.encode(
+            (phrase * (prompt_length // 10 + 2)).strip()
+        )[:prompt_length]
+        prompt = self.tokenizer.decode(prompt_ids, skip_special_tokens=False)
+        prompt_tokens = len(self.tokenizer.encode(prompt))
+        max_seq_len = prompt_tokens + gen_length
+        pool = self._make_pool(batch_size, max_seq_len)
+        engine = InferenceEngine(
+            model=self.model,
+            tokenizer=self.tokenizer,
+            max_batch_size=batch_size,
+            max_seq_len=max_seq_len,
+            cache=pool,
+            enable_cuda_graph=self.cuda_graph,
+        )
+        prompts = [prompt] * batch_size
+
+        try:
+            # Capture graphs and populate the allocator before timing. The
+            # first request also includes model/scheduler startup effects.
+            with attn_backend(self.backend):
+                engine.generate(prompts, max_tokens=gen_length, temperature=0.0)
+            if self.device.startswith("cuda"):
+                torch.cuda.synchronize()
+
+            t0 = time.perf_counter()
+            for _ in range(num_trials):
+                with attn_backend(self.backend):
+                    engine.generate(prompts, max_tokens=gen_length, temperature=0.0)
+            if self.device.startswith("cuda"):
+                torch.cuda.synchronize()
+            elapsed = time.perf_counter() - t0
+        finally:
+            engine.shutdown()
+
+        tokens = batch_size * gen_length * num_trials
+        return BenchmarkResult(
+            name="decode",
+            batch_size=batch_size,
+            seq_len=gen_length,
+            tokens_per_second=tokens / elapsed,
+            latency_ms=elapsed / (gen_length * num_trials) * 1000,
+            metadata={
+                "benchmark_type": "engine_decode",
+                "num_trials": num_trials,
+                "prompt_length": prompt_tokens,
+                "engine": True,
+            },
         )
 
     def _run_graph_decode_benchmark(
@@ -251,8 +302,6 @@ class GenerationBenchmark:
         gen_length: int,
         num_trials: int,
     ) -> BenchmarkResult:
-        import time
-
         max_seq_len = prompt_length + 5 + gen_length * num_trials
         pool = self._make_pool(batch_size, max_seq_len)
         workspace = self._make_workspace(pool, self.config)
@@ -327,8 +376,6 @@ class GenerationBenchmark:
         gen_length: int,
         num_trials: int,
     ) -> BenchmarkResult:
-        import time
-
         max_seq_len = prompt_length + 5 + gen_length * num_trials
         pool = self._make_pool(batch_size, max_seq_len)
         workspace = self._make_workspace(pool, self.config)
@@ -403,9 +450,9 @@ def print_benchmark_result(result: BenchmarkResult) -> None:
 @click.option("--prefill_only", is_flag=True, help="Prefill benchmark only.")
 @click.option("--decode_only", is_flag=True, help="Decode benchmark only.")
 @click.option(
-    "--cuda-graph",
-    is_flag=True,
-    help="Enable CUDA graph capture for decode (cuda backend only).",
+    "--cuda-graph/--no-cuda-graph",
+    default=True,
+    help="Enable or disable CUDA graph capture for engine decode.",
 )
 @click.option(
     "--ckpt",
@@ -464,6 +511,8 @@ def benchmark_command(
             f"({sum(p.numel() for p in model.parameters()) / 1e9:.2f}B params)"
         )
 
+    tokenizer = AutoTokenizer.from_pretrained(ckpt or Path("params"))
+
     model.to(device=device, dtype=dtype_map[dtype])
     model.eval()
 
@@ -478,6 +527,7 @@ def benchmark_command(
             cache_type=cache,
             backend=name,
             cuda_graph=cuda_graph,
+            tokenizer=tokenizer,
         )
 
         click.secho(