Log if cuda graph is used & extend cuda graph capture to cuda-graph-max-bs

python/sglang/bench_offline_throughput.py

@@ -259,7 +259,9 @@ def throughput_test_once(
         measurement_results["total_input_tokens"]
         + measurement_results["total_output_tokens"]
     ) / latency
-    measurement_results["last_gen_throughput"] = server_info["last_gen_throughput"]
+    measurement_results["last_gen_throughput"] = server_info["internal_states"][0][
+        "last_gen_throughput"
+    ]
 
     return measurement_results
 

python/sglang/bench_one_batch.py

@@ -246,7 +246,7 @@ def extend(reqs, model_runner):
     _maybe_prepare_dp_attn_batch(batch, model_runner)
     model_worker_batch = batch.get_model_worker_batch()
     forward_batch = ForwardBatch.init_new(model_worker_batch, model_runner)
-    logits_output = model_runner.forward(forward_batch)
+    logits_output, _ = model_runner.forward(forward_batch)
     next_token_ids = model_runner.sample(logits_output, forward_batch)
     return next_token_ids, logits_output.next_token_logits, batch
 
@@ -258,7 +258,7 @@ def decode(input_token_ids, batch, model_runner):
     _maybe_prepare_dp_attn_batch(batch, model_runner)
     model_worker_batch = batch.get_model_worker_batch()
     forward_batch = ForwardBatch.init_new(model_worker_batch, model_runner)
-    logits_output = model_runner.forward(forward_batch)
+    logits_output, _ = model_runner.forward(forward_batch)
     next_token_ids = model_runner.sample(logits_output, forward_batch)
     return next_token_ids, logits_output.next_token_logits
 

python/sglang/bench_one_batch_server.py

@@ -25,6 +25,7 @@
 from sglang.srt.entrypoints.http_server import launch_server
 from sglang.srt.server_args import ServerArgs
 from sglang.srt.utils import kill_process_tree
+from sglang.test.test_utils import is_in_ci, write_github_step_summary
 
 
 @dataclasses.dataclass
@@ -33,9 +34,13 @@ class BenchArgs:
     batch_size: Tuple[int] = (1,)
     input_len: Tuple[int] = (1024,)
     output_len: Tuple[int] = (16,)
+    temperature: float = 0.0
+    return_logprob: bool = False
+    input_len_step_percentage: float = 0.0
     result_filename: str = "result.jsonl"
     base_url: str = ""
     skip_warmup: bool = False
+    show_report: bool = False
 
     @staticmethod
     def add_cli_args(parser: argparse.ArgumentParser):
@@ -49,11 +54,19 @@ def add_cli_args(parser: argparse.ArgumentParser):
         parser.add_argument(
             "--output-len", type=int, nargs="+", default=BenchArgs.output_len
         )
+        parser.add_argument("--temperature", type=float, default=BenchArgs.temperature)
+        parser.add_argument("--return-logprob", action="store_true")
+        parser.add_argument(
+            "--input-len-step-percentage",
+            type=float,
+            default=BenchArgs.input_len_step_percentage,
+        )
         parser.add_argument(
             "--result-filename", type=str, default=BenchArgs.result_filename
         )
         parser.add_argument("--base-url", type=str, default=BenchArgs.base_url)
         parser.add_argument("--skip-warmup", action="store_true")
+        parser.add_argument("--show-report", action="store_true")
 
     @classmethod
     def from_cli_args(cls, args: argparse.Namespace):
@@ -99,36 +112,89 @@ def run_one_case(
     batch_size: int,
     input_len: int,
     output_len: int,
+    temperature: float,
+    return_logprob: bool,
+    input_len_step_percentage: float,
     run_name: str,
     result_filename: str,
 ):
+    requests.post(url + "/flush_cache")
+    input_lens = [
+        int(input_len * (1 + (i - (batch_size - 1) / 2) * input_len_step_percentage))
+        for i in range(batch_size)
+    ]
     input_ids = [
-        [int(x) for x in np.random.randint(0, high=16384, size=(input_len,))]
-        for _ in range(batch_size)
+        [int(x) for x in np.random.randint(0, high=16384, size=(input_lens[i],))]
+        for i in range(batch_size)
     ]
 
+    use_structured_outputs = False
+    if use_structured_outputs:
+        texts = []
+        for _ in range(batch_size):
+            texts.append(
+                "Human: What is the capital city of france? can you give as many trivial information as possible about that city? answer in json.\n"
+                * 50
+                + "Assistant:"
+            )
+        json_schema = "$$ANY$$"
+    else:
+        json_schema = None
+
     tic = time.time()
     response = requests.post(
         url + "/generate",
         json={
+            # "text": texts,
             "input_ids": input_ids,
             "sampling_params": {
-                "temperature": 0,
+                "temperature": temperature,
                 "max_new_tokens": output_len,
                 "ignore_eos": True,
+                "json_schema": json_schema,
             },
+            "return_logprob": return_logprob,
+            "stream": True,
         },
+        stream=True,
     )
-    latency = time.time() - tic
 
-    _ = response.json()
-    output_throughput = batch_size * output_len / latency
+    # The TTFT of the last request in the batch
+    ttft = 0.0
+    for chunk in response.iter_lines(decode_unicode=False):
+        chunk = chunk.decode("utf-8")
+        if chunk and chunk.startswith("data:"):
+            if chunk == "data: [DONE]":
+                break
+            data = json.loads(chunk[5:].strip("\n"))
+            if "error" in data:
+                raise RuntimeError(f"Request has failed. {data}.")
+
+            assert (
+                data["meta_info"]["finish_reason"] is None
+                or data["meta_info"]["finish_reason"]["type"] == "length"
+            )
+            if data["meta_info"]["completion_tokens"] == 1:
+                ttft = time.time() - tic
+
+    latency = time.time() - tic
+    input_throughput = batch_size * input_len / ttft
+    output_throughput = batch_size * output_len / (latency - ttft)
     overall_throughput = batch_size * (input_len + output_len) / latency
 
+    server_info = requests.get(url + "/get_server_info").json()
+    acc_length = server_info["internal_states"][0].get("avg_spec_accept_length", None)
+    last_gen_throughput = server_info["internal_states"][0]["last_gen_throughput"]
+
     print(f"batch size: {batch_size}")
+    print(f"input_len: {input_len}")
+    print(f"output_len: {output_len}")
     print(f"latency: {latency:.2f} s")
-    print(f"output throughput: {output_throughput:.2f} token/s")
-    print(f"(input + output) throughput: {overall_throughput:.2f} token/s")
+    print(f"ttft: {ttft:.2f} s")
+    print(f"Last generation throughput: {last_gen_throughput:.2f} tok/s")
+    print(f"Input throughput: {input_throughput:.2f} tok/s")
+    if output_len != 1:
+        print(f"output throughput: {output_throughput:.2f} tok/s")
 
     if result_filename:
         with open(result_filename, "a") as fout:
@@ -140,9 +206,21 @@ def run_one_case(
                 "latency": round(latency, 4),
                 "output_throughput": round(output_throughput, 2),
                 "overall_throughput": round(overall_throughput, 2),
+                "last_gen_throughput": round(last_gen_throughput, 2),
             }
             fout.write(json.dumps(res) + "\n")
 
+    return (
+        batch_size,
+        latency,
+        ttft,
+        input_throughput,
+        output_throughput,
+        overall_throughput,
+        last_gen_throughput,
+        acc_length,
+    )
+
 
 def run_benchmark(server_args: ServerArgs, bench_args: BenchArgs):
     if bench_args.base_url:
@@ -152,34 +230,84 @@ def run_benchmark(server_args: ServerArgs, bench_args: BenchArgs):
 
     # warmup
     if not bench_args.skip_warmup:
+        print("=" * 8 + " Warmup Begin " + "=" * 8)
         run_one_case(
             base_url,
             batch_size=16,
             input_len=1024,
             output_len=16,
+            temperature=bench_args.temperature,
+            return_logprob=bench_args.return_logprob,
+            input_len_step_percentage=bench_args.input_len_step_percentage,
             run_name="",
             result_filename="",
         )
+        print("=" * 8 + " Warmup End   " + "=" * 8 + "\n")
 
     # benchmark
+    result = []
     try:
         for bs, il, ol in itertools.product(
             bench_args.batch_size, bench_args.input_len, bench_args.output_len
         ):
-            run_one_case(
-                base_url,
-                bs,
-                il,
-                ol,
-                bench_args.run_name,
-                bench_args.result_filename,
+            result.append(
+                run_one_case(
+                    base_url,
+                    bs,
+                    il,
+                    ol,
+                    temperature=bench_args.temperature,
+                    return_logprob=bench_args.return_logprob,
+                    input_len_step_percentage=bench_args.input_len_step_percentage,
+                    run_name=bench_args.run_name,
+                    result_filename=bench_args.result_filename,
+                )
             )
     finally:
         if proc:
             kill_process_tree(proc.pid)
 
     print(f"\nResults are saved to {bench_args.result_filename}")
 
+    if not bench_args.show_report:
+        return
+
+    summary = " | batch size | latency (s) | input throughput (tok/s)  | output throughput (tok/s) | acc length | ITL (ms) | input price ($/1M) | output price ($/1M) |\n"
+    summary += "| ---------- | ----------- | ------------------------- | ------------------------- | ---------- | -------- | ------------------ | ------------------- |\n"
+
+    for (
+        batch_size,
+        latency,
+        ttft,
+        input_throughput,
+        output_throughput,
+        overall_throughput,
+        last_gen_throughput,
+        acc_length,
+    ) in result:
+        hourly_cost = 2 * server_args.tp_size  # $2/hour for one H100
+        input_util = 0.7
+        accept_length = round(acc_length, 2) if acc_length is not None else "n/a"
+        line = (
+            f"| {batch_size} | "
+            f"{latency:.2f} | "
+            f"{input_throughput:.2f} | "
+            f"{output_throughput:.2f} | "
+            f"{accept_length} | "
+            f"{1 / (output_throughput/batch_size) * 1000:.2f} | "
+            f"{1e6 / (input_throughput * input_util) / 3600 * hourly_cost:.2f} | "
+            f"{1e6 / output_throughput / 3600 * hourly_cost:.2f} |\n"
+        )
+        summary += line
+
+    # print metrics table
+    print(summary)
+
+    if is_in_ci():
+        write_github_step_summary(
+            f"### Test Nightly Benchmark (bench_one_batch) \n{summary}"
+        )
+
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()

python/sglang/bench_serving.py

@@ -1103,7 +1103,7 @@ async def benchmark(
     lora_names: List[str],
     extra_request_body: Dict[str, Any],
     profile: bool,
-    pd_seperated: bool = False,
+    pd_separated: bool = False,
     flush_cache: bool = False,
     warmup_requests: int = 1,
 ):
@@ -1239,12 +1239,14 @@ async def limited_request_func(request_func_input, pbar):
 
     if "sglang" in backend:
         server_info = requests.get(base_url + "/get_server_info")
-        if pd_seperated:
-            accept_length = server_info.json()["decode"][0].get(
+        if pd_separated:
+            accept_length = server_info.json()["decode"][0]["internal_states"][0].get(
                 "avg_spec_accept_length", None
             )
         else:
-            accept_length = server_info.json().get("avg_spec_accept_length", None)
+            accept_length = server_info.json()["internal_states"][0].get(
+                "avg_spec_accept_length", None
+            )
     else:
         accept_length = None
 
@@ -1541,7 +1543,7 @@ def run_benchmark(args_: argparse.Namespace):
             lora_names=args.lora_name,
             extra_request_body=extra_request_body,
             profile=args.profile,
-            pd_seperated=args.pd_seperated,
+            pd_separated=args.pd_separated,
             flush_cache=args.flush_cache,
         )
     )

python/sglang/srt/constrained/base_grammar_backend.py

@@ -37,6 +37,12 @@ def accept_token(self, token: int) -> None:
         """
         raise NotImplementedError()
 
+    def rollback(self, k: int):
+        raise NotImplementedError()
+
+    def is_terminated(self):
+        raise NotImplementedError()
+
     def allocate_vocab_mask(
         self, vocab_size: int, batch_size: int, device
     ) -> torch.Tensor:

python/sglang/srt/disaggregation/prefill.py

@@ -277,19 +277,17 @@ def process_batch_result_disagg_prefill(
             next_token_ids,
             extend_input_len_per_req,
             extend_logprob_start_len_per_req,
-            bid,
         ) = (
             result.logits_output,
             result.next_token_ids,
             result.extend_input_len_per_req,
             result.extend_logprob_start_len_per_req,
-            result.bid,
         )
 
         # Transfer kv for prefill completed requests and add it into disagg_prefill_infight_queue
         if self.enable_overlap:
             # wait
-            _, next_token_ids = self.tp_worker.resolve_last_batch_result(launch_done)
+            _, next_token_ids, _ = self.tp_worker.resolve_last_batch_result(launch_done)
         else:
             next_token_ids = result.next_token_ids.tolist()
 

python/sglang/srt/entrypoints/engine.py

@@ -330,7 +330,7 @@ def get_server_info(self):
         return {
             **dataclasses.asdict(self.tokenizer_manager.server_args),
             **self.scheduler_info,
-            **internal_states,
+            "internal_states": internal_states,
             "version": __version__,
         }
 

python/sglang/srt/entrypoints/http_server.py

@@ -222,7 +222,7 @@ async def get_server_info():
     return {
         **dataclasses.asdict(_global_state.tokenizer_manager.server_args),
         **_global_state.scheduler_info,
-        **internal_states,
+        "internal_states": internal_states,
         "version": __version__,
     }
 

python/sglang/srt/layers/attention/utils.py

@@ -28,7 +28,8 @@ def create_flashinfer_kv_indices_triton(
 
     num_loop = tl.cdiv(kv_end - kv_start, BLOCK_SIZE)
     for i in range(num_loop):
-        offset = tl.arange(0, BLOCK_SIZE) + i * BLOCK_SIZE
+        # index into req_to_token_ptr needs to be int64
+        offset = tl.arange(0, BLOCK_SIZE).to(tl.int64) + i * BLOCK_SIZE
         mask = offset < kv_end - kv_start
         data = tl.load(
             req_to_token_ptr
@@ -70,8 +71,9 @@ def create_flashmla_kv_indices_triton(
     num_pages_loop = tl.cdiv(kv_end - kv_start, BLOCK_SIZE)
 
     for i in range(num_pages_loop):
+        # index into req_to_token_ptr needs to be int64
         paged_offset = (
-            tl.arange(0, NUM_PAGE_PER_BLOCK) + i * NUM_PAGE_PER_BLOCK
+            tl.arange(0, NUM_PAGE_PER_BLOCK).to(tl.int64) + i * NUM_PAGE_PER_BLOCK
         ) * PAGED_SIZE
         paged_offset_out = tl.arange(0, NUM_PAGE_PER_BLOCK) + i * NUM_PAGE_PER_BLOCK