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[https://nvbugs/6115832][fix] Fix SSE stream parsing in benchmark client to handle split chunks #13686

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Merged
longlee0622 merged 1 commit into from
May 9, 2026
+111 −89
Merged

[https://nvbugs/6115832][fix] Fix SSE stream parsing in benchmark client to handle split chunks #13686

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160 changes: 80 additions & 80 deletions tensorrt_llm/serve/scripts/backend_request_func.py
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Original file line number Diff line number Diff line change
Expand Up @@ -18,6 +18,27 @@
AIOHTTP_TIMEOUT = aiohttp.ClientTimeout(total=6 * 60 * 60)


async def _iter_sse_data(response_content):
"""Yield SSE data payloads from a byte stream.

Buffers incoming bytes on newline boundaries, decodes UTF-8, strips
non-data lines and the ``data:`` prefix, and skips ``[DONE]`` sentinels.
Callers receive only JSON-ready strings.
"""
buf = b""
async for chunk in response_content:
buf += chunk
while b"\n" in buf:
line_bytes, buf = buf.split(b"\n", 1)
line = line_bytes.decode("utf-8").strip()
if not line or not line.startswith("data:"):
continue
payload = line.removeprefix("data:").lstrip()
if payload == "[DONE]":
continue
yield payload
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@dataclass
class RequestFuncInput:
prompt: str
Expand Down Expand Up @@ -88,14 +109,7 @@ async def async_request_trt_llm(
if response.status == 200:
output.success = True
if streaming:
async for chunk_bytes in response.content:
chunk_bytes = chunk_bytes.strip()
if not chunk_bytes:
continue

chunk = chunk_bytes.decode("utf-8").removeprefix(
"data:")

async for chunk in _iter_sse_data(response.content):
data = json.loads(chunk)
output.generated_text += data["text_output"]
timestamp = time.perf_counter()
Expand Down Expand Up @@ -197,45 +211,38 @@ async def async_request_openai_completions(
if response.status == 200:
if streaming:
first_chunk_received = False
async for chunk_bytes in response.content:
chunk_bytes = chunk_bytes.strip()
if not chunk_bytes:
continue

chunk = chunk_bytes.decode("utf-8").removeprefix(
"data: ")
if chunk != "[DONE]":
data = json.loads(chunk)

# NOTE: Some completion API might have a last
# usage summary response without a token so we
# want to check a token was generated
if choices := data.get("choices"):
# Note that text could be empty here
# e.g. for special tokens
text = choices[0].get("text")
timestamp = time.perf_counter()
# First token
if not first_chunk_received:
first_chunk_received = True
ttft = time.perf_counter() - st
output.ttft = ttft

# Decoding phase
else:
output.itl.append(timestamp -
most_recent_timestamp)

most_recent_timestamp = timestamp
generated_text += text or ""

# Extract avg_decoded_tokens_per_iter from streaming response
if "avg_decoded_tokens_per_iter" in choices[0]:
output.avg_decoded_tokens_per_iter = choices[
0]["avg_decoded_tokens_per_iter"]
elif usage := data.get("usage"):
output.output_tokens = usage.get(
"completion_tokens")
async for chunk in _iter_sse_data(response.content):
data = json.loads(chunk)

# NOTE: Some completion API might have a last
# usage summary response without a token so we
# want to check a token was generated
if choices := data.get("choices"):
# Note that text could be empty here
# e.g. for special tokens
text = choices[0].get("text")
timestamp = time.perf_counter()
# First token
if not first_chunk_received:
first_chunk_received = True
ttft = time.perf_counter() - st
output.ttft = ttft

# Decoding phase
else:
output.itl.append(timestamp -
most_recent_timestamp)

most_recent_timestamp = timestamp
generated_text += text or ""

# Extract avg_decoded_tokens_per_iter from streaming response
if "avg_decoded_tokens_per_iter" in choices[0]:
output.avg_decoded_tokens_per_iter = choices[0][
"avg_decoded_tokens_per_iter"]
elif usage := data.get("usage"):
output.output_tokens = usage.get(
"completion_tokens")
if first_chunk_received:
output.success = True
else:
Expand Down Expand Up @@ -342,40 +349,33 @@ async def async_request_openai_chat_completions(
if response.status == 200:
output.success = True
if streaming:
async for chunk_bytes in response.content:
chunk_bytes = chunk_bytes.strip()
if not chunk_bytes:
continue

chunk = chunk_bytes.decode("utf-8").removeprefix(
"data: ")
if chunk != "[DONE]":
timestamp = time.perf_counter()
data = json.loads(chunk)

if choices := data.get("choices"):
content = choices[0]["delta"].get("content")
# First token
if ttft == 0.0:
ttft = timestamp - st
output.ttft = ttft

# Decoding phase
else:
output.itl.append(timestamp -
most_recent_timestamp)

generated_text += content or ""

# Extract avg_decoded_tokens_per_iter from streaming chat response
if "avg_decoded_tokens_per_iter" in choices[0]:
output.avg_decoded_tokens_per_iter = choices[
0]["avg_decoded_tokens_per_iter"]
elif usage := data.get("usage"):
output.output_tokens = usage.get(
"completion_tokens")
async for chunk in _iter_sse_data(response.content):
timestamp = time.perf_counter()
data = json.loads(chunk)

most_recent_timestamp = timestamp
if choices := data.get("choices"):
content = choices[0]["delta"].get("content")
# First token
if ttft == 0.0:
ttft = timestamp - st
output.ttft = ttft

# Decoding phase
else:
output.itl.append(timestamp -
most_recent_timestamp)

generated_text += content or ""

# Extract avg_decoded_tokens_per_iter from streaming chat response
if "avg_decoded_tokens_per_iter" in choices[0]:
output.avg_decoded_tokens_per_iter = choices[0][
"avg_decoded_tokens_per_iter"]
elif usage := data.get("usage"):
output.output_tokens = usage.get(
"completion_tokens")

most_recent_timestamp = timestamp

output.generated_text = generated_text
output.latency = most_recent_timestamp - st
Expand Down
40 changes: 31 additions & 9 deletions tests/integration/defs/perf/test_perf_sanity.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1178,7 +1178,9 @@ def get_gpu_type() -> str:
except (subprocess.CalledProcessError, FileNotFoundError, IndexError):
raise RuntimeError("Failed to get GPU type")

self.upload_to_db = "upload" in test_case_name.split("-")[0]
self.upload_to_db = "upload" in test_case_name.split("-")[0] and bool(
os.environ.get("OPEN_SEARCH_DB_BASE_URL", "")
)
self.gpu_type = get_gpu_type()

# Parse test case name to get config_base_name, select_pattern, runtime, benchmark_mode
Expand Down Expand Up @@ -1526,15 +1528,32 @@ def _check_benchmark_errors(self, output: str) -> None:
if not output:
return

# Tolerance: allow up to 1% failed requests for high-concurrency benchmarks
# where transient network issues can cause rare individual request failures.
max_failure_rate = 0.01

# Check for non-zero failed requests (default benchmark)
failed_requests_match = re.search(r"Failed requests:\s+(\d+)", output)
if failed_requests_match:
failed_count = int(failed_requests_match.group(1))
if failed_count > 0:
error_msg = f"Benchmark output contains {failed_count} failed requests."
raise RuntimeError(error_msg)
total_match = re.search(r"Successful requests:\s+(\d+)", output)
total_requests = (
int(total_match.group(1)) + failed_count if total_match else failed_count
)
failure_rate = failed_count / total_requests if total_requests > 0 else 1.0
if failure_rate > max_failure_rate:
error_msg = (
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f"Benchmark output contains {failed_count} failed requests "
f"({failure_rate:.2%} failure rate exceeds {max_failure_rate:.0%} threshold)."
)
raise RuntimeError(error_msg)
return

# Check for explicit failure markers (default benchmark)
# Check for explicit failure markers (default benchmark) only when
# the numeric "Failed requests:" line was not found (the markers are
# always printed together with the numeric count, so this avoids
# double-counting when the failure rate is within tolerance).
if "!FAILED REQUESTS!" in output or "!CHECK LOG FOR ERRORS!" in output:
error_msg = "Benchmark output contains failure markers."
raise RuntimeError(error_msg)
Expand All @@ -1550,11 +1569,14 @@ def _check_benchmark_errors(self, output: str) -> None:
num_prompts = int(num_prompts_match.group(1))
failed_count = num_prompts - successful_count
if failed_count > 0:
error_msg = (
f"SA benchmark: {failed_count} of {num_prompts} requests failed "
f"({successful_count} successful)."
)
raise RuntimeError(error_msg)
failure_rate = failed_count / num_prompts if num_prompts > 0 else 1.0
if failure_rate > max_failure_rate:
error_msg = (
f"SA benchmark: {failed_count} of {num_prompts} requests failed "
f"({successful_count} successful, "
f"{failure_rate:.2%} exceeds {max_failure_rate:.0%} threshold)."
)
raise RuntimeError(error_msg)

def run_ex(self, commands) -> Dict[int, List[str]]:
"""Run commands and collect outputs."""
Expand Down
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