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fix: Handle special case in Record function #105

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fix: Handle special case in Record function #105

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108 changes: 66 additions & 42 deletions ci/L0_backend_vllm/metrics_test/vllm_metrics_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -49,6 +49,7 @@ def setUp(self):
"The future of AI is",
]
self.sampling_parameters = {"temperature": "0", "top_p": "1"}
self.inference_count = 2

def parse_vllm_metrics(self):
"""
Expand Down Expand Up @@ -82,34 +83,36 @@ def vllm_infer(
user_data = UserData()
number_of_vllm_reqs = len(prompts)

self.triton_client.start_stream(callback=partial(callback, user_data))
for i in range(number_of_vllm_reqs):
request_data = create_vllm_request(
prompts[i],
i,
False,
sampling_parameters,
model_name,
True,
)
self.triton_client.async_stream_infer(
model_name=model_name,
inputs=request_data["inputs"],
request_id=request_data["request_id"],
outputs=request_data["outputs"],
parameters=sampling_parameters,
)

for _ in range(number_of_vllm_reqs):
result = user_data._completed_requests.get()
if type(result) is InferenceServerException:
print(result.message())
self.assertIsNot(type(result), InferenceServerException, str(result))

output = result.as_numpy("text_output")
self.assertIsNotNone(output, "`text_output` should not be None")

self.triton_client.stop_stream()
# Run the inference twice in case metrics are updated but engine crashes.
for _ in range(self.inference_count):
self.triton_client.start_stream(callback=partial(callback, user_data))
for i in range(number_of_vllm_reqs):
request_data = create_vllm_request(
prompts[i],
i,
False,
sampling_parameters,
model_name,
True,
)
self.triton_client.async_stream_infer(
model_name=model_name,
inputs=request_data["inputs"],
request_id=request_data["request_id"],
outputs=request_data["outputs"],
parameters=sampling_parameters,
)

for _ in range(number_of_vllm_reqs):
result = user_data._completed_requests.get()
if type(result) is InferenceServerException:
print(result.message())
self.assertIsNot(type(result), InferenceServerException, str(result))

output = result.as_numpy("text_output")
self.assertIsNotNone(output, "`text_output` should not be None")

self.triton_client.stop_stream()

def test_vllm_metrics(self):
# Test vLLM metrics
Expand All @@ -125,49 +128,70 @@ def test_vllm_metrics(self):
# (2, 133, 144, 2702, 3477, 16)
# (2, 133, 812, 9, 1470, 16)
# (2, 133, 499, 9, 4687, 16)
self.assertEqual(metrics_dict["vllm:prompt_tokens_total"], 18)
self.assertEqual(
metrics_dict["vllm:prompt_tokens_total"], 18 * self.inference_count
)
# vllm:generation_tokens_total
# (5, 65, 14, 16, 144, 533, 7, 28, 848, 30, 10, 512, 4, 50118, 100, 437)
# (5, 812, 9, 5, 1515, 3497, 4, 50118, 50118, 133, 812, 9, 1470, 16, 5, 812)
# (11, 5, 1420, 9, 5, 82, 4, 50118, 50118, 133, 499, 9, 4687, 16, 11, 5)
self.assertEqual(metrics_dict["vllm:generation_tokens_total"], 48)
self.assertEqual(
metrics_dict["vllm:generation_tokens_total"], 48 * self.inference_count
)
# vllm:time_to_first_token_seconds
self.assertEqual(
metrics_dict["vllm:time_to_first_token_seconds_count"], total_prompts
metrics_dict["vllm:time_to_first_token_seconds_count"],
total_prompts * self.inference_count,
)
self.assertGreater(metrics_dict["vllm:time_to_first_token_seconds_sum"], 0)
self.assertEqual(
metrics_dict["vllm:time_to_first_token_seconds_bucket"], total_prompts
metrics_dict["vllm:time_to_first_token_seconds_bucket"],
total_prompts * self.inference_count,
)
# vllm:time_per_output_token_seconds
self.assertEqual(metrics_dict["vllm:time_per_output_token_seconds_count"], 45)
self.assertEqual(
metrics_dict["vllm:time_per_output_token_seconds_count"],
45 * self.inference_count,
)
self.assertGreater(metrics_dict["vllm:time_per_output_token_seconds_sum"], 0)
self.assertEqual(metrics_dict["vllm:time_per_output_token_seconds_bucket"], 45)
self.assertEqual(
metrics_dict["vllm:time_per_output_token_seconds_bucket"],
45 * self.inference_count,
)
# vllm:e2e_request_latency_seconds
self.assertEqual(
metrics_dict["vllm:e2e_request_latency_seconds_count"], total_prompts
metrics_dict["vllm:e2e_request_latency_seconds_count"],
total_prompts * self.inference_count,
)
self.assertGreater(metrics_dict["vllm:e2e_request_latency_seconds_sum"], 0)
self.assertEqual(
metrics_dict["vllm:e2e_request_latency_seconds_bucket"], total_prompts
metrics_dict["vllm:e2e_request_latency_seconds_bucket"],
total_prompts * self.inference_count,
)
# vllm:request_prompt_tokens
self.assertEqual(
metrics_dict["vllm:request_prompt_tokens_count"], total_prompts
metrics_dict["vllm:request_prompt_tokens_count"],
total_prompts * self.inference_count,
)
self.assertEqual(
metrics_dict["vllm:request_prompt_tokens_sum"], 18 * self.inference_count
)
self.assertEqual(metrics_dict["vllm:request_prompt_tokens_sum"], 18)
self.assertEqual(
metrics_dict["vllm:request_prompt_tokens_bucket"], total_prompts
metrics_dict["vllm:request_prompt_tokens_bucket"],
total_prompts * self.inference_count,
)
# vllm:request_generation_tokens
self.assertEqual(
metrics_dict["vllm:request_generation_tokens_count"],
total_prompts,
total_prompts * self.inference_count,
)
self.assertEqual(
metrics_dict["vllm:request_generation_tokens_sum"],
48 * self.inference_count,
)
self.assertEqual(metrics_dict["vllm:request_generation_tokens_sum"], 48)
self.assertEqual(
metrics_dict["vllm:request_generation_tokens_bucket"],
total_prompts,
total_prompts * self.inference_count,
)

# TODO: Revisit this test due to the removal of best_of
Expand Down
2 changes: 2 additions & 0 deletions src/utils/metrics.py
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Original file line number Diff line number Diff line change
Expand Up @@ -240,6 +240,8 @@ def record(
Returns:
None
"""
if iteration_stats is None:
return

# Parse finished request stats into lists
e2e_latency: List[float] = []
Expand Down
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