misc: update output token logic

python/sglang/bench_serving.py

@@ -54,6 +54,7 @@ class RequestFuncOutput:
     itl: List[float] = field(default_factory=list)  # List of inter-token latencies
     prompt_len: int = 0
     error: str = ""
+    output_len: int = 0
 
 
 def remove_prefix(text: str, prefix: str) -> str:
@@ -189,6 +190,7 @@ async def async_request_openai_completions(
                     output.generated_text = generated_text
                     output.success = True
                     output.latency = latency
+                    output.output_len = request_func_input.output_len
                 else:
                     output.error = response.reason or ""
                     output.success = False
@@ -451,6 +453,7 @@ def calculate_metrics(
     outputs: List[RequestFuncOutput],
     dur_s: float,
     tokenizer: PreTrainedTokenizerBase,
+    backend: str,
 ) -> Tuple[BenchmarkMetrics, List[int]]:
     actual_output_lens: List[int] = []
     total_input = 0
@@ -460,13 +463,16 @@ def calculate_metrics(
     ttfts: List[float] = []
     for i in range(len(outputs)):
         if outputs[i].success:
-            # We use the tokenizer to count the number of output tokens for all
-            # serving backends instead of looking at len(outputs[i].itl) since
-            # multiple output tokens may be bundled together
-            # Note : this may inflate the output token count slightly
-            output_len = len(
-                tokenizer(outputs[i].generated_text, add_special_tokens=False).input_ids
-            )
+            # We use the tokenizer solely to count output tokens for the TensorRT LLM backend,
+            # as it lacks `ignore_eos` support.
+            if backend == "trt":
+                output_len = len(
+                    tokenizer(
+                        outputs[i].generated_text, add_special_tokens=False
+                    ).input_ids
+                )
+            else:
+                output_len = outputs[i].output_len
             actual_output_lens.append(output_len)
             total_input += input_requests[i][1]
             if output_len > 1:
@@ -571,9 +577,11 @@ async def benchmark(
         outputs=outputs,
         dur_s=benchmark_duration,
         tokenizer=tokenizer,
+        backend=backend,
     )
 
     print("\n{s:{c}^{n}}".format(s=" Serving Benchmark Result ", n=50, c="="))
+    print("{:<40} {:<10}".format("Backend:", backend))
     print("{:<40} {:<10}".format("Traffic request rate:", request_rate))
     print("{:<40} {:<10}".format("Successful requests:", metrics.completed))
     print("{:<40} {:<10.2f}".format("Benchmark duration (s):", benchmark_duration))