remove metrics and tracing

Signed-off-by: Robert Shaw <robshaw@redhat.com>

.buildkite/test-pipeline.yaml

@@ -208,25 +208,19 @@ steps:
   commands:
   - pytest -v -s distributed/test_eplb_execute.py
 
-- label: Metrics, Tracing Test # 10min
-  mirror_hardwares: [amdexperimental]
-  num_gpus: 2
-  source_file_dependencies:
-  - vllm/
-  - tests/metrics
-  - tests/tracing
-  commands:
-  - pytest -v -s metrics
-  - "pip install \
-      'opentelemetry-sdk>=1.26.0' \
-      'opentelemetry-api>=1.26.0' \
-      'opentelemetry-exporter-otlp>=1.26.0' \
-      'opentelemetry-semantic-conventions-ai>=0.4.1'"
-  - pytest -v -s tracing
-
 ##### fast check tests  #####
 #####  1 GPU test  #####
 
+- label: Regression Test # 5min
+  mirror_hardwares: [amdexperimental]
+  source_file_dependencies:
+  - vllm/
+  - tests/test_regression
+  commands:
+  - pip install modelscope
+  - pytest -v -s test_regression.py
+  working_dir: "/vllm-workspace/tests" # optional
+
 - label: Engine Test # 10min
   mirror_hardwares: [amdexperimental]
   source_file_dependencies:

tests/metrics/test_metrics.py

@@ -1,268 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-import pytest
-import ray
-from prometheus_client import REGISTRY
-
-import vllm.envs as envs
-from vllm import EngineArgs, LLMEngine
-from vllm.engine.arg_utils import AsyncEngineArgs
-from vllm.engine.async_llm_engine import AsyncLLMEngine
-from vllm.engine.metrics import RayPrometheusStatLogger
-from vllm.sampling_params import SamplingParams
-from vllm.test_utils import MODEL_WEIGHTS_S3_BUCKET
-
-
-@pytest.fixture(scope="function", autouse=True)
-def use_v0_only(monkeypatch):
-    """
-    This module tests V0 internals, so set VLLM_USE_V1=0.
-    """
-    monkeypatch.setenv('VLLM_USE_V1', '0')
-
-
-MODELS = [
-    "distilbert/distilgpt2",
-]
-
-
-@pytest.mark.parametrize("model", MODELS)
-@pytest.mark.parametrize("dtype", ["float"])
-@pytest.mark.parametrize("max_tokens", [128])
-def test_metric_counter_prompt_tokens(
-    vllm_runner,
-    example_prompts,
-    model: str,
-    dtype: str,
-    max_tokens: int,
-) -> None:
-    with vllm_runner(model,
-                     dtype=dtype,
-                     disable_log_stats=False,
-                     gpu_memory_utilization=0.4) as vllm_model:
-        tokenizer = vllm_model.llm.get_tokenizer()
-        prompt_token_counts = [
-            len(tokenizer.encode(p)) for p in example_prompts
-        ]
-        # This test needs at least 2 prompts in a batch of different lengths to
-        # verify their token count is correct despite padding.
-        assert len(example_prompts) > 1, "at least 2 prompts are required"
-        assert prompt_token_counts[0] != prompt_token_counts[1], (
-            "prompts of different lengths are required")
-        vllm_prompt_token_count = sum(prompt_token_counts)
-
-        _ = vllm_model.generate_greedy(example_prompts, max_tokens)
-        stat_logger = vllm_model.llm.llm_engine.stat_loggers['prometheus']
-        metric_count = stat_logger.metrics.counter_prompt_tokens.labels(
-            **stat_logger.labels)._value.get()
-
-    assert vllm_prompt_token_count == metric_count, (
-        f"prompt token count: {vllm_prompt_token_count!r}\n"
-        f"metric: {metric_count!r}")
-
-
-@pytest.mark.parametrize("model", MODELS)
-@pytest.mark.parametrize("dtype", ["float"])
-@pytest.mark.parametrize("max_tokens", [128])
-def test_metric_counter_generation_tokens(
-    vllm_runner,
-    example_prompts,
-    model: str,
-    dtype: str,
-    max_tokens: int,
-) -> None:
-    with vllm_runner(model,
-                     dtype=dtype,
-                     disable_log_stats=False,
-                     gpu_memory_utilization=0.4) as vllm_model:
-        vllm_outputs = vllm_model.generate_greedy(example_prompts, max_tokens)
-        tokenizer = vllm_model.llm.get_tokenizer()
-        stat_logger = vllm_model.llm.llm_engine.stat_loggers['prometheus']
-        metric_count = stat_logger.metrics.counter_generation_tokens.labels(
-            **stat_logger.labels)._value.get()
-        vllm_generation_count = 0
-        for i in range(len(example_prompts)):
-            vllm_output_ids, vllm_output_str = vllm_outputs[i]
-            prompt_ids = tokenizer.encode(example_prompts[i])
-            # vllm_output_ids contains both prompt tokens and generation tokens.
-            # We're interested only in the count of the generation tokens.
-            vllm_generation_count += len(vllm_output_ids) - len(prompt_ids)
-
-    assert vllm_generation_count == metric_count, (
-        f"generation token count: {vllm_generation_count!r}\n"
-        f"metric: {metric_count!r}")
-
-
-@pytest.mark.parametrize("model", MODELS)
-@pytest.mark.parametrize("dtype", ["float"])
-@pytest.mark.parametrize(
-    "served_model_name",
-    [None, [], ["ModelName0"], ["ModelName0", "ModelName1", "ModelName2"]])
-def test_metric_set_tag_model_name(vllm_runner, model: str, dtype: str,
-                                   served_model_name: list[str]) -> None:
-    with vllm_runner(model,
-                     dtype=dtype,
-                     disable_log_stats=False,
-                     gpu_memory_utilization=0.3,
-                     served_model_name=served_model_name) as vllm_model:
-        stat_logger = vllm_model.llm.llm_engine.stat_loggers['prometheus']
-        metrics_tag_content = stat_logger.labels["model_name"]
-
-    if envs.VLLM_CI_USE_S3:
-        model = f"{MODEL_WEIGHTS_S3_BUCKET}/{model}"
-    if served_model_name is None or served_model_name == []:
-        assert metrics_tag_content == model, (
-            f"Metrics tag model_name is wrong! expect: {model!r}\n"
-            f"actual: {metrics_tag_content!r}")
-    else:
-        assert metrics_tag_content == served_model_name[0], (
-            f"Metrics tag model_name is wrong! expect: "
-            f"{served_model_name[0]!r}\n"
-            f"actual: {metrics_tag_content!r}")
-
-
-@pytest.mark.parametrize("model", MODELS)
-@pytest.mark.parametrize("dtype", ["half"])
-@pytest.mark.parametrize("max_tokens", [4])
-@pytest.mark.parametrize("disable_log_stats", [True, False])
-@pytest.mark.asyncio
-async def test_async_engine_log_metrics_regression(
-    example_prompts,
-    model: str,
-    dtype: str,
-    max_tokens: int,
-    disable_log_stats: bool,
-) -> None:
-    """
-    Regression test ensuring async engine generates metrics
-    when disable_log_stats=False
-    (see: https://github.com/vllm-project/vllm/pull/4150#pullrequestreview-2008176678)
-    """
-    engine_args = AsyncEngineArgs(
-        model=model,
-        dtype=dtype,
-        disable_log_stats=disable_log_stats,
-    )
-    async_engine = AsyncLLMEngine.from_engine_args(engine_args)
-    for i, prompt in enumerate(example_prompts):
-        results = async_engine.generate(
-            prompt,
-            SamplingParams(max_tokens=max_tokens),
-            f"request-id-{i}",
-        )
-        # Exhaust the async iterator to make the async engine work
-        async for _ in results:
-            pass
-
-    assert_metrics(model, async_engine.engine, disable_log_stats,
-                   len(example_prompts))
-
-
-@pytest.mark.parametrize("model", MODELS)
-@pytest.mark.parametrize("dtype", ["half"])
-@pytest.mark.parametrize("max_tokens", [4])
-@pytest.mark.parametrize("disable_log_stats", [True, False])
-def test_engine_log_metrics_regression(
-    example_prompts,
-    model: str,
-    dtype: str,
-    max_tokens: int,
-    disable_log_stats: bool,
-) -> None:
-    engine_args = EngineArgs(
-        model=model,
-        dtype=dtype,
-        disable_log_stats=disable_log_stats,
-    )
-    engine = LLMEngine.from_engine_args(engine_args)
-    for i, prompt in enumerate(example_prompts):
-        engine.add_request(
-            f"request-id-{i}",
-            prompt,
-            SamplingParams(max_tokens=max_tokens),
-        )
-    while engine.has_unfinished_requests():
-        engine.step()
-
-    if envs.VLLM_CI_USE_S3:
-        model = f"{MODEL_WEIGHTS_S3_BUCKET}/{model}"
-    assert_metrics(model, engine, disable_log_stats, len(example_prompts))
-
-
-def assert_metrics(model: str, engine: LLMEngine, disable_log_stats: bool,
-                   num_requests: int) -> None:
-    if disable_log_stats:
-        with pytest.raises(AttributeError):
-            _ = engine.stat_loggers
-    else:
-        assert (engine.stat_loggers
-                is not None), "engine.stat_loggers should be set"
-        # Ensure the count bucket of request-level histogram metrics matches
-        # the number of requests as a simple sanity check to ensure metrics are
-        # generated
-        labels = {'model_name': model}
-        request_histogram_metrics = [
-            "vllm:e2e_request_latency_seconds",
-            "vllm:request_prompt_tokens",
-            "vllm:request_generation_tokens",
-            "vllm:request_params_n",
-            "vllm:request_params_max_tokens",
-        ]
-        for metric_name in request_histogram_metrics:
-            metric_value = REGISTRY.get_sample_value(f"{metric_name}_count",
-                                                     labels)
-            assert (
-                metric_value == num_requests), "Metrics should be collected"
-
-
-@pytest.mark.parametrize("model", MODELS)
-@pytest.mark.parametrize("dtype", ["half"])
-@pytest.mark.parametrize("max_tokens", [16])
-def test_engine_log_metrics_ray(
-    example_prompts,
-    model: str,
-    dtype: str,
-    max_tokens: int,
-) -> None:
-    # This test is quite weak - it only checks that we can use
-    # RayPrometheusStatLogger without exceptions.
-    # Checking whether the metrics are actually emitted is unfortunately
-    # non-trivial.
-
-    # We have to run in a Ray task for Ray metrics to be emitted correctly
-    @ray.remote(num_gpus=1)
-    def _inner():
-
-        class _RayPrometheusStatLogger(RayPrometheusStatLogger):
-
-            def __init__(self, *args, **kwargs):
-                self._i = 0
-                super().__init__(*args, **kwargs)
-
-            def log(self, *args, **kwargs):
-                self._i += 1
-                return super().log(*args, **kwargs)
-
-        engine_args = EngineArgs(
-            model=model,
-            dtype=dtype,
-            disable_log_stats=False,
-        )
-        engine = LLMEngine.from_engine_args(engine_args)
-        logger = _RayPrometheusStatLogger(
-            local_interval=0.5,
-            labels=dict(model_name=engine.model_config.served_model_name),
-            vllm_config=engine.vllm_config)
-        engine.add_logger("ray", logger)
-        for i, prompt in enumerate(example_prompts):
-            engine.add_request(
-                f"request-id-{i}",
-                prompt,
-                SamplingParams(max_tokens=max_tokens),
-            )
-        while engine.has_unfinished_requests():
-            engine.step()
-        assert logger._i > 0, ".log must be called at least once"
-
-    ray.get(_inner.remote())

tests/test_regression.py

@@ -0,0 +1,78 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Containing tests that check for regressions in vLLM's behavior.
+
+It should include tests that are reported by users and making sure they
+will never happen again.
+
+"""
+import gc
+
+import pytest
+import torch
+
+from vllm import LLM, SamplingParams
+
+
+@pytest.mark.skip(reason="In V1, we reject tokens > max_seq_len")
+def test_duplicated_ignored_sequence_group():
+    """https://github.com/vllm-project/vllm/issues/1655"""
+
+    sampling_params = SamplingParams(temperature=0.01,
+                                     top_p=0.1,
+                                     max_tokens=256)
+    llm = LLM(model="distilbert/distilgpt2",
+              max_num_batched_tokens=4096,
+              tensor_parallel_size=1)
+    prompts = ["This is a short prompt", "This is a very long prompt " * 1000]
+    outputs = llm.generate(prompts, sampling_params=sampling_params)
+
+    assert len(prompts) == len(outputs)
+
+
+def test_max_tokens_none():
+    sampling_params = SamplingParams(temperature=0.01,
+                                     top_p=0.1,
+                                     max_tokens=None)
+    llm = LLM(model="distilbert/distilgpt2",
+              max_num_batched_tokens=4096,
+              tensor_parallel_size=1)
+    prompts = ["Just say hello!"]
+    outputs = llm.generate(prompts, sampling_params=sampling_params)
+
+    assert len(prompts) == len(outputs)
+
+
+def test_gc():
+    llm = LLM(model="distilbert/distilgpt2", enforce_eager=True)
+    del llm
+
+    gc.collect()
+    torch.cuda.empty_cache()
+
+    # The memory allocated for model and KV cache should be released.
+    # The memory allocated for PyTorch and others should be less than 50MB.
+    # Usually, it's around 10MB.
+    allocated = torch.cuda.memory_allocated()
+    assert allocated < 50 * 1024 * 1024
+
+
+def test_model_from_modelscope(monkeypatch: pytest.MonkeyPatch):
+    # model: https://modelscope.cn/models/qwen/Qwen1.5-0.5B-Chat/summary
+    with monkeypatch.context() as m:
+        m.setenv("VLLM_USE_MODELSCOPE", "True")
+        # Don't use HF_TOKEN for ModelScope repos, otherwise it will fail
+        # with 400 Client Error: Bad Request.
+        m.setenv("HF_TOKEN", "")
+        llm = LLM(model="qwen/Qwen1.5-0.5B-Chat")
+
+        prompts = [
+            "Hello, my name is",
+            "The president of the United States is",
+            "The capital of France is",
+            "The future of AI is",
+        ]
+        sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
+
+        outputs = llm.generate(prompts, sampling_params)
+        assert len(outputs) == 4

tests/tracing/test_tracing.py

@@ -1,237 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-# ruff: noqa
-# type: ignore
-from __future__ import annotations
-
-import threading
-from collections.abc import Iterable
-from concurrent import futures
-from typing import Callable, Generator, Literal
-
-import grpc
-import pytest
-from opentelemetry.proto.collector.trace.v1.trace_service_pb2 import (
-    ExportTraceServiceResponse)
-from opentelemetry.proto.collector.trace.v1.trace_service_pb2_grpc import (
-    TraceServiceServicer, add_TraceServiceServicer_to_server)
-from opentelemetry.proto.common.v1.common_pb2 import AnyValue, KeyValue
-from opentelemetry.sdk.environment_variables import (
-    OTEL_EXPORTER_OTLP_TRACES_INSECURE)
-
-from vllm import LLM, SamplingParams
-from vllm.tracing import SpanAttributes
-
-
-@pytest.fixture(scope="function", autouse=True)
-def use_v0_only(monkeypatch: pytest.MonkeyPatch):
-    """
-    Since this module is V0 only, set VLLM_USE_V1=0 for
-    all tests in the module.
-    """
-    with monkeypatch.context() as m:
-        m.setenv('VLLM_USE_V1', '0')
-        yield
-
-
-FAKE_TRACE_SERVER_ADDRESS = "localhost:4317"
-
-FieldName = Literal['bool_value', 'string_value', 'int_value', 'double_value',
-                    'array_value']
-
-
-def decode_value(value: AnyValue):
-    field_decoders: dict[FieldName, Callable] = {
-        "bool_value": (lambda v: v.bool_value),
-        "string_value": (lambda v: v.string_value),
-        "int_value": (lambda v: v.int_value),
-        "double_value": (lambda v: v.double_value),
-        "array_value":
-        (lambda v: [decode_value(item) for item in v.array_value.values]),
-    }
-    for field, decoder in field_decoders.items():
-        if value.HasField(field):
-            return decoder(value)
-    raise ValueError(f"Couldn't decode value: {value}")
-
-
-def decode_attributes(attributes: Iterable[KeyValue]):
-    return {kv.key: decode_value(kv.value) for kv in attributes}
-
-
-class FakeTraceService(TraceServiceServicer):
-
-    def __init__(self):
-        self.request = None
-        self.evt = threading.Event()
-
-    def Export(self, request, context):
-        self.request = request
-        self.evt.set()
-        return ExportTraceServiceResponse()
-
-
-@pytest.fixture
-def trace_service() -> Generator[FakeTraceService, None, None]:
-    """Fixture to set up a fake gRPC trace service"""
-    server = grpc.server(futures.ThreadPoolExecutor(max_workers=1))
-    service = FakeTraceService()
-    add_TraceServiceServicer_to_server(service, server)
-    server.add_insecure_port(FAKE_TRACE_SERVER_ADDRESS)
-    server.start()
-
-    yield service
-
-    server.stop(None)
-
-
-def test_traces(
-    monkeypatch: pytest.MonkeyPatch,
-    trace_service: FakeTraceService,
-):
-    with monkeypatch.context() as m:
-        m.setenv(OTEL_EXPORTER_OTLP_TRACES_INSECURE, "true")
-
-        sampling_params = SamplingParams(
-            temperature=0.01,
-            top_p=0.1,
-            max_tokens=256,
-        )
-        model = "facebook/opt-125m"
-        llm = LLM(
-            model=model,
-            otlp_traces_endpoint=FAKE_TRACE_SERVER_ADDRESS,
-        )
-        prompts = ["This is a short prompt"]
-        outputs = llm.generate(prompts, sampling_params=sampling_params)
-
-        timeout = 5
-        if not trace_service.evt.wait(timeout):
-            raise TimeoutError(
-                f"The fake trace service didn't receive a trace within "
-                f"the {timeout} seconds timeout")
-
-        request = trace_service.request
-        assert len(request.resource_spans) == 1, (
-            f"Expected 1 resource span, "
-            f"but got {len(request.resource_spans)}")
-        assert len(request.resource_spans[0].scope_spans) == 1, (
-            f"Expected 1 scope span, "
-            f"but got {len(request.resource_spans[0].scope_spans)}")
-        assert len(request.resource_spans[0].scope_spans[0].spans) == 1, (
-            f"Expected 1 span, "
-            f"but got {len(request.resource_spans[0].scope_spans[0].spans)}")
-
-        attributes = decode_attributes(
-            request.resource_spans[0].scope_spans[0].spans[0].attributes)
-        assert attributes.get(SpanAttributes.GEN_AI_RESPONSE_MODEL) == model
-        assert attributes.get(
-            SpanAttributes.GEN_AI_REQUEST_ID) == outputs[0].request_id
-        assert attributes.get(SpanAttributes.GEN_AI_REQUEST_TEMPERATURE
-                              ) == sampling_params.temperature
-        assert attributes.get(
-            SpanAttributes.GEN_AI_REQUEST_TOP_P) == sampling_params.top_p
-        assert attributes.get(SpanAttributes.GEN_AI_REQUEST_MAX_TOKENS
-                              ) == sampling_params.max_tokens
-        assert attributes.get(
-            SpanAttributes.GEN_AI_REQUEST_N) == sampling_params.n
-        assert attributes.get(
-            SpanAttributes.GEN_AI_USAGE_PROMPT_TOKENS) == len(
-                outputs[0].prompt_token_ids)
-        completion_tokens = sum(len(o.token_ids) for o in outputs[0].outputs)
-        assert attributes.get(
-            SpanAttributes.GEN_AI_USAGE_COMPLETION_TOKENS) == completion_tokens
-        metrics = outputs[0].metrics
-        assert attributes.get(SpanAttributes.GEN_AI_LATENCY_TIME_IN_QUEUE
-                              ) == metrics.time_in_queue
-        ttft = metrics.first_token_time - metrics.arrival_time
-        assert attributes.get(
-            SpanAttributes.GEN_AI_LATENCY_TIME_TO_FIRST_TOKEN) == ttft
-        e2e_time = metrics.finished_time - metrics.arrival_time
-        assert attributes.get(SpanAttributes.GEN_AI_LATENCY_E2E) == e2e_time
-        assert metrics.scheduler_time > 0
-        assert attributes.get(SpanAttributes.GEN_AI_LATENCY_TIME_IN_SCHEDULER
-                              ) == metrics.scheduler_time
-        # Model forward and model execute should be none, since detailed traces is
-        # not enabled.
-        assert metrics.model_forward_time is None
-        assert metrics.model_execute_time is None
-
-
-def test_traces_with_detailed_steps(
-    monkeypatch: pytest.MonkeyPatch,
-    trace_service: FakeTraceService,
-):
-    with monkeypatch.context() as m:
-        m.setenv(OTEL_EXPORTER_OTLP_TRACES_INSECURE, "true")
-
-        sampling_params = SamplingParams(
-            temperature=0.01,
-            top_p=0.1,
-            max_tokens=256,
-        )
-        model = "facebook/opt-125m"
-        llm = LLM(
-            model=model,
-            otlp_traces_endpoint=FAKE_TRACE_SERVER_ADDRESS,
-            collect_detailed_traces=["all"],
-        )
-        prompts = ["This is a short prompt"]
-        outputs = llm.generate(prompts, sampling_params=sampling_params)
-
-        timeout = 5
-        if not trace_service.evt.wait(timeout):
-            raise TimeoutError(
-                f"The fake trace service didn't receive a trace within "
-                f"the {timeout} seconds timeout")
-
-        request = trace_service.request
-        assert len(request.resource_spans) == 1, (
-            f"Expected 1 resource span, "
-            f"but got {len(request.resource_spans)}")
-        assert len(request.resource_spans[0].scope_spans) == 1, (
-            f"Expected 1 scope span, "
-            f"but got {len(request.resource_spans[0].scope_spans)}")
-        assert len(request.resource_spans[0].scope_spans[0].spans) == 1, (
-            f"Expected 1 span, "
-            f"but got {len(request.resource_spans[0].scope_spans[0].spans)}")
-
-        attributes = decode_attributes(
-            request.resource_spans[0].scope_spans[0].spans[0].attributes)
-        assert attributes.get(SpanAttributes.GEN_AI_RESPONSE_MODEL) == model
-        assert attributes.get(
-            SpanAttributes.GEN_AI_REQUEST_ID) == outputs[0].request_id
-        assert attributes.get(SpanAttributes.GEN_AI_REQUEST_TEMPERATURE
-                              ) == sampling_params.temperature
-        assert attributes.get(
-            SpanAttributes.GEN_AI_REQUEST_TOP_P) == sampling_params.top_p
-        assert attributes.get(SpanAttributes.GEN_AI_REQUEST_MAX_TOKENS
-                              ) == sampling_params.max_tokens
-        assert attributes.get(
-            SpanAttributes.GEN_AI_REQUEST_N) == sampling_params.n
-        assert attributes.get(
-            SpanAttributes.GEN_AI_USAGE_PROMPT_TOKENS) == len(
-                outputs[0].prompt_token_ids)
-        completion_tokens = sum(len(o.token_ids) for o in outputs[0].outputs)
-        assert attributes.get(
-            SpanAttributes.GEN_AI_USAGE_COMPLETION_TOKENS) == completion_tokens
-        metrics = outputs[0].metrics
-        assert attributes.get(SpanAttributes.GEN_AI_LATENCY_TIME_IN_QUEUE
-                              ) == metrics.time_in_queue
-        ttft = metrics.first_token_time - metrics.arrival_time
-        assert attributes.get(
-            SpanAttributes.GEN_AI_LATENCY_TIME_TO_FIRST_TOKEN) == ttft
-        e2e_time = metrics.finished_time - metrics.arrival_time
-        assert attributes.get(SpanAttributes.GEN_AI_LATENCY_E2E) == e2e_time
-        assert metrics.scheduler_time > 0
-        assert attributes.get(SpanAttributes.GEN_AI_LATENCY_TIME_IN_SCHEDULER
-                              ) == metrics.scheduler_time
-        assert metrics.model_forward_time > 0
-        assert attributes.get(
-            SpanAttributes.GEN_AI_LATENCY_TIME_IN_MODEL_FORWARD
-        ) == pytest.approx(metrics.model_forward_time / 1000)
-        assert metrics.model_execute_time > 0
-        assert attributes.get(
-            SpanAttributes.GEN_AI_LATENCY_TIME_IN_MODEL_EXECUTE
-        ) == metrics.model_execute_time
-        assert metrics.model_forward_time < 1000 * metrics.model_execute_time