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TensorRT-LLMs/tests/integration/defs/perf/test_perf.py
yufeiwu-nv 52cee573ad
[TRTLLM-8830][test] Overlap scheduler enhancement perf test: Add qwen3_0,8b and llama3.1 test cases (#10114) 
Signed-off-by: yufeiwu-nv <230315618+yufeiwu-nv@users.noreply.github.com>
2025-12-19 17:01:52 +08:00

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# SPDX-FileCopyrightText: Copyright (c) 2022-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
TensorRT LLM perf tests
"""
import os
import re
import shutil
import socket
import sys
from typing import Dict, List, NamedTuple
import pytest
import yaml
from defs.common import get_cpp_benchmark
from defs.trt_test_alternative import (is_linux, is_windows, print_info,
print_warning)
from ..conftest import get_llm_root, llm_models_root, trt_environment
from .open_search_db_utils import (SCENARIO_MATCH_FIELDS, add_id,
get_history_data, get_job_info,
post_new_perf_data, prepare_baseline_data,
prepare_regressive_test_cases,
write_regressive_test_cases)
from .pytorch_model_config import get_model_yaml_config
from .sampler_options_config import get_sampler_options_config
from .utils import (AbstractPerfScriptTestClass, PerfAggrScriptTestCmds,
PerfBenchScriptTestCmds, PerfDisaggScriptTestCmds,
PerfMetricType, PerfMultiNodeDisaggScriptTestCmds,
generate_test_nodes)
if not hasattr(re, "Pattern"):
re.Pattern = type(re.compile(""))
ALLOWED_CONFIGS_CACHE = None # Cache to avoid modifying sys.path many times.
MAP_BY_SOCKET = None
# Model PATH of local dir synced from internal LLM models repo
MODEL_PATH_DICT = {
"llama_v2_7b": "llama-models-v2/llama-v2-7b-hf", # not safetensors repo
"llama_v2_13b": "llama-models-v2/llama-v2-13b-hf", # not safetensors repo
"llama_v2_70b": "llama-models-v2/llama-v2-70b-hf", # not safetensors repo
"llama_v3.1_8b": "llama-3.1-model/Meta-Llama-3.1-8B",
"llama_v3.1_8b_instruct": "llama-3.1-model/Llama-3.1-8B-Instruct",
"llama_v3.1_8b_instruct_fp8": "llama-3.1-model/Llama-3.1-8B-Instruct-FP8",
"llama_v3.1_8b_instruct_fp4":
"modelopt-hf-model-hub/Llama-3.1-8B-Instruct-fp4",
"llama_v3.1_70b": "llama-3.1-model/Meta-Llama-3.1-70B",
"llama_v3.3_70b_instruct": "llama-3.3-models/Llama-3.3-70B-Instruct",
"llama_v3.1_70b_instruct_fp8": "llama-3.1-model/Llama-3.1-70B-Instruct-FP8",
"llama_v3.3_70b_instruct_fp8":
"modelopt-hf-model-hub/Llama-3.3-70B-Instruct-fp8",
"llama_v3.3_70b_instruct_fp4":
"modelopt-hf-model-hub/Llama-3.3-70B-Instruct-fp4",
"llama_v3.1_405b_instruct_fp8":
"llama-3.1-model/Llama-3.1-405B-Instruct-FP8",
"llama_v3.1_405b_instruct_fp4":
"modelopt-hf-model-hub/Llama-3.1-405B-Instruct-fp4",
"llama_v3.1_70b_instruct": "llama-3.1-model/Meta-Llama-3.1-70B-Instruct",
"llama_v3.2_1b": "llama-3.2-models/Llama-3.2-1B",
"llama_v3.1_nemotron_nano_8b": "Llama-3.1-Nemotron-Nano-8B-v1",
"llama_v3.1_nemotron_nano_8b_fp8": "Llama-3.1-Nemotron-Nano-8B-v1-FP8",
"llama_v3.3_nemotron_super_49b":
"nemotron-nas/Llama-3_3-Nemotron-Super-49B-v1",
"llama_v3.3_nemotron_super_49b_fp8":
"nemotron-nas/Llama-3_3-Nemotron-Super-49B-v1-FP8",
"llama_v3.1_nemotron_ultra_253b":
"nemotron-nas/Llama-3_1-Nemotron-Ultra-253B-v1",
"llama_v3.1_nemotron_ultra_253b_fp8":
"nemotron-nas/Llama-3_1-Nemotron-Ultra-253B-v1-FP8",
"llama_v4_scout_17b_16e_instruct":
"llama4-models/Llama-4-Scout-17B-16E-Instruct",
"llama_v4_scout_17b_16e_instruct_fp8":
"llama4-models/Llama-4-Scout-17B-16E-Instruct-FP8",
"llama_v4_scout_17b_16e_instruct_fp4":
"llama4-models/Llama-4-Scout-17B-16E-Instruct-FP4",
"llama_v4_maverick_17b_128e_instruct":
"llama4-models/Llama-4-Maverick-17B-128E-Instruct",
"llama_v4_maverick_17b_128e_instruct_fp8":
"llama4-models/nvidia/Llama-4-Maverick-17B-128E-Instruct-FP8",
"mixtral_8x7b_v0.1": "Mixtral-8x7B-v0.1",
"mixtral_8x7b_v0.1_instruct": "Mixtral-8x7B-Instruct-v0.1",
"mixtral_8x7b_v0.1_instruct_fp8": "Mixtral-8x7B-Instruct-v0.1-fp8",
"mixtral_8x7b_v0.1_instruct_fp4":
"modelopt-hf-model-hub/Mixtral-8x7B-Instruct-v0.1-fp4",
"mistral_nemo_12b_base": "Mistral-Nemo-Base-2407",
"deepseek_r1_distill_qwen_32b": "DeepSeek-R1/DeepSeek-R1-Distill-Qwen-32B",
"mixtral_8x22b_v0.1": "Mixtral-8x22B-v0.1",
"mistral_7b_v0.1": "mistral-7b-v0.1",
"ministral_8b": "Ministral-8B-Instruct-2410",
"ministral_8b_fp8": "Ministral-8B-Instruct-2410-FP8",
"gemma_3_1b_it": "gemma/gemma-3-1b-it",
"deepseek_r1_fp8": "DeepSeek-R1/DeepSeek-R1",
"deepseek_r1_nvfp4": "DeepSeek-R1/DeepSeek-R1-FP4",
"deepseek_r1_0528_fp8": "DeepSeek-R1/DeepSeek-R1-0528/",
"deepseek_r1_0528_fp4": "DeepSeek-R1/DeepSeek-R1-0528-FP4/",
"deepseek_r1_0528_fp4_v2": "DeepSeek-R1/DeepSeek-R1-0528-FP4-v2/",
"deepseek_v3_lite_fp8": "DeepSeek-V3-Lite/fp8",
"deepseek_v3_lite_nvfp4": "DeepSeek-V3-Lite/nvfp4_moe_only",
"qwen2_7b_instruct": "Qwen2-7B-Instruct",
"qwen_14b_chat": "Qwen-14B-Chat",
"qwen3_0.6b": "Qwen3/Qwen3-0.6B",
"qwen3_4b_eagle3": "Qwen3/Qwen3-4B",
"qwen3_235b_a22b_fp8": "Qwen3/saved_models_Qwen3-235B-A22B_fp8_hf",
"qwen3_235b_a22b_fp4": "Qwen3/saved_models_Qwen3-235B-A22B_nvfp4_hf",
"starcoder2_3b": "starcoder2-3b",
"starcoder2_7b": "starcoder2-7b",
"starcoder2_15b": "starcoder2-15b",
"t5": "t5-small", # not supported for trtllm-bench build config
"flan_t5_base":
"flan-t5-small", # not supported for trtllm-bench build config
"flan_t5_large":
"flan-t5-xl", # not supported for trtllm-bench build config
"whisper_large_v3":
"whisper-models/large-v3", # not supported for trtllm-bench tokenizer
"bart_large_cnn": "bart-large-cnn", # not safetensors repo
"mbart_large_50_many_to_one_mmt": "mbart-large-50-many-to-one-mmt",
"mamba_130m": "mamba/mamba-130m-hf",
"mamba_370m": "mamba/mamba-370m-hf",
"mamba_2.8b": "mamba/mamba-2.8b-hf",
"gpt_20b": "gpt-neox-20b",
"gpt_350m_moe": "gpt2-medium",
"phi_4_mini_instruct": "Phi-4-mini-instruct",
"phi_4_multimodal_instruct": "multimodals/Phi-4-multimodal-instruct",
"phi_4_multimodal_instruct_image": "multimodals/Phi-4-multimodal-instruct",
"phi_4_multimodal_instruct_audio": "multimodals/Phi-4-multimodal-instruct",
"phi_4_multimodal_instruct_fp4_image":
"multimodals/Phi-4-multimodal-instruct-FP4",
"phi_4_multimodal_instruct_fp4_audio":
"multimodals/Phi-4-multimodal-instruct-FP4",
"phi_4_multimodal_instruct_fp8_image":
"multimodals/Phi-4-multimodal-instruct-FP8",
"phi_4_multimodal_instruct_fp8_audio":
"multimodals/Phi-4-multimodal-instruct-FP8",
"bielik_11b_v2.2_instruct": "Bielik-11B-v2.2-Instruct",
"bielik_11b_v2.2_instruct_fp8": "Bielik-11B-v2.2-Instruct-FP8",
"mistral_small_v3.1_24b": "Mistral-Small-3.1-24B-Instruct-2503",
"gpt_oss_120b_fp4": "gpt_oss/gpt-oss-120b",
"gpt_oss_20b_fp4": "gpt_oss/gpt-oss-20b",
"nemotron_nano_9b_v2": "NVIDIA-Nemotron-Nano-12B-v2",
"starcoder2_7b": "starcoder2-7b",
}
# Model PATH of HuggingFace
HF_MODEL_PATH = {
"llama_v2_7b_hf": "meta-llama/Llama-2-7b-hf",
"llama_v2_70b_hf": "meta-llama/Llama-2-70b-hf",
"falcon_180b_hf": "tiiuae/falcon-180B",
"gptj_6b_hf": "EleutherAI/gpt-j-6b",
"llama_v3_8b_hf": "meta-llama/Meta-Llama-3-8B",
"llama_v3.1_8b_hf": "meta-llama/Llama-3.1-8B",
"llama_v3.1_8b_instruct_hf": "nvidia/Llama-3.1-8B-Instruct-FP8",
"llama_v3.1_70b_instruct_hf": "meta-llama/Meta-Llama-3.1-70B-Instruct",
"llama_v3_70b_hf": "meta-llama/Meta-Llama-3-70B",
"llama_v3.1_70b_hf": "meta-llama/Llama-3.1-70B",
"llama_v3.1_405b_hf": "meta-llama/Llama-3.1-405B",
"llama_v3.1_nemotron_nano_8b_hf": "nvidia/Llama-3.1-Nemotron-Nano-8B-v1",
"llama_v3.1_nemotron_nano_8b_fp8_hf":
"nvidia/Llama-3.1-Nemotron-Nano-8B-v1-FP8",
"llama_v3.3_nemotron_super_49b_hf":
"nvidia/Llama-3_3-Nemotron-Super-49B-v1",
"llama_v3.3_nemotron_super_49b_fp8_hf":
"nvidia/Llama-3_3-Nemotron-Super-49B-v1-FP8",
"llama_v3.1_nemotron_ultra_253b_fp8_hf":
"nvidia/Llama-3_1-Nemotron-Ultra-253B-v1-FP8",
"mixtral_8x7b_v0.1_hf": "mistralai/Mixtral-8x7B-v0.1",
"mixtral_8x7b_v0.1_instruct_hf": "mistralai/Mixtral-8x7B-Instruct-v0.1",
"mistral_7b_v0.1_hf": "mistralai/Mistral-7B-v0.1",
"ministral_8b_hf": "mistralai/Ministral-8B-Instruct-2410",
"flan_t5_base_hf": "google/flan-t5-small",
"phi_4_mini_instruct_hf": "microsoft/Phi-4-mini-instruct",
"gemma_3_1b_it_hf": "google/gemma-3-1b-it",
}
LORA_MODEL_PATH = {
"llama_v2_13b":
"llama-models-v2/chinese-llama-2-lora-13b",
"mixtral_8x7b_0.1":
"chinese-mixtral-lora",
"llama_v3.1_8b_instruct_fp8":
"lora/llama-3-chinese-8b-instruct-v2-lora/",
"ministral_8b":
"lora/ministral/Ministral-8B-Instruct-2410-Loras-Dummy", # Dummy LoRA for Ministral
"gemma_3_1b_it":
"lora/gemma/gemma-3-1b-it-dummy-lora", # Dummy LoRA for Gemma-3-1B-Instruct
"phi_4_multimodal_instruct_image":
"multimodals/Phi-4-multimodal-instruct/vision-lora",
"phi_4_multimodal_instruct_audio":
"multimodals/Phi-4-multimodal-instruct/speech-lora",
"phi_4_multimodal_instruct_fp4_image":
"multimodals/Phi-4-multimodal-instruct-FP4/vision-lora",
"phi_4_multimodal_instruct_fp4_audio":
"multimodals/Phi-4-multimodal-instruct-FP4/speech-lora",
"phi_4_multimodal_instruct_fp8_image":
"multimodals/Phi-4-multimodal-instruct-FP8/vision-lora",
"phi_4_multimodal_instruct_fp8_audio":
"multimodals/Phi-4-multimodal-instruct-FP8/speech-lora",
}
TIMING_CACHE_DIR = os.environ.get("TIMING_CACHE_DIR", "")
TRUST_REMOTE_CODE_MODELS = { # these models require explicit trust_remote_code=True
"llama_v3.3_nemotron_super_49b",
"llama_v3.3_nemotron_super_49b_fp8",
"llama_v3.1_nemotron_ultra_253b",
"llama_v3.1_nemotron_ultra_253b_fp8",
}
def get_model_dir(model_name: str):
model_dir = ""
if model_name in MODEL_PATH_DICT.keys():
model_dir = os.path.join(llm_models_root(), MODEL_PATH_DICT[model_name])
elif model_name in HF_MODEL_PATH.keys():
model_dir = os.path.join(llm_models_root(),
MODEL_PATH_DICT[model_name.split('_hf')[0]])
return model_dir
def cpu_socket_count_gt_1():
global MAP_BY_SOCKET
if MAP_BY_SOCKET is not None:
return MAP_BY_SOCKET
if is_linux():
with open('/proc/cpuinfo') as f:
cpuinfo = f.read()
physical_id_set = set()
for line in cpuinfo.splitlines():
if line.startswith('physical id'):
_, id_ = line.split(':')
physical_id_set.add(id_.strip())
MAP_BY_SOCKET = len(physical_id_set) > 1
else:
MAP_BY_SOCKET = False
return MAP_BY_SOCKET
# A helper function to import allowed_configs.py.
def import_allowed_perf_config():
if trt_environment:
from llm import allowed_configs
else:
global ALLOWED_CONFIGS_CACHE
if ALLOWED_CONFIGS_CACHE is None:
sys.path.append((os.path.join(get_llm_root(),
"tests/integration/defs/perf")))
import allowed_configs
ALLOWED_CONFIGS_CACHE = allowed_configs
else:
allowed_configs = ALLOWED_CONFIGS_CACHE
return allowed_configs
# Regex commands used to parse the metric result for the metric type.
PERF_METRIC_LOG_QUERIES = {
PerfMetricType.BUILD_TIME:
re.compile(r"Engine generation completed in ([\d\.]+) seconds"),
PerfMetricType.INFERENCE_TIME:
re.compile(r"\[BENCHMARK\].* (?:total_latency|latency)\(ms\) ([\d\.]+)"),
PerfMetricType.FIRST_TOKEN_TIME:
re.compile(r"\[BENCHMARK\].* avg_time_to_first_token\(ms\) ([\d\.]+)"),
PerfMetricType.SEQ_LATENCY:
re.compile(r"\[BENCHMARK\].* avg_sequence_latency\(ms\) ([\d\.]+)"),
PerfMetricType.SEQ_THROUGHPUT:
re.compile(r"\[BENCHMARK\].* seq_throughput\(seq\/sec\) ([\d\.]+)"),
PerfMetricType.TOKEN_THROUGHPUT:
re.compile(
r"\[BENCHMARK\].* (?:token_throughput\(token\/sec\)|tokensPerSec|tokens_per_sec) ([\d\.]+)"
),
PerfMetricType.INFERENCE_PEAK_GPU_MEMORY:
re.compile(r"\[BENCHMARK\].* gpu_peak_mem\(gb\) ([\d\.]+)"),
PerfMetricType.BUILD_PEAK_CPU_MEMORY:
re.compile(
r"Peak memory usage during Engine building and serialization: CPU: ([\d\.]+) .*"
),
PerfMetricType.BUILD_PEAK_GPU_MEMORY:
re.compile(
r"Peak memory usage of TRT CPU/GPU memory allocators: CPU .*, GPU ([\d\.]+) .*"
),
PerfMetricType.ENGINE_SIZE:
re.compile(r".*Total engine size per GPU is ([\d\.]+) MiB.*"),
PerfMetricType.CONTEXT_GPU_MEMORY:
re.compile(r".*Allocated ([\d\.]+) MiB for execution context memory.*"),
PerfMetricType.KV_CACHE_SIZE:
re.compile(r".*Allocated ([\d\.]+) GiB for max tokens in paged KV cache.*"),
PerfMetricType.DISAGG_SERVER_E2EL:
re.compile(r"Median E2EL \(ms\):\s*(\d+\.?\d*)"),
PerfMetricType.DISAGG_SERVER_TTFT:
re.compile(r"Median TTFT \(ms\):\s*(\d+\.?\d*)"),
}
BENCH_PERF_METRIC_LOG_QUERIES = {
PerfMetricType.BUILD_TIME:
re.compile(r"Engine generation completed in ([\d\.]+) seconds"),
PerfMetricType.INFERENCE_TIME:
re.compile(r"Total Latency \(ms\):\s+([\d\.]+)"),
PerfMetricType.TOKEN_THROUGHPUT:
re.compile(r"GPU Output Throughput \(tps\/gpu\):\s+([\d\.]+)"),
PerfMetricType.SEQ_THROUGHPUT:
re.compile(r"Request Throughput \(req\/sec\):\s+([\d\.]+)"),
PerfMetricType.FIRST_TOKEN_TIME:
re.compile(r"Average time-to-first-token \[TTFT\] \(ms\):\s+([\d\.]+)"),
PerfMetricType.OUTPUT_TOKEN_TIME:
re.compile(r"Average time-per-output-token \[TPOT\] \(ms\):\s+([\d\.]+)"),
PerfMetricType.KV_CACHE_SIZE:
re.compile(r".*(?:Allocated ([\d\.]+) GiB for max tokens in paged KV cache|"
r"Final KV cache size after resize: ([\d\.]+) GiB).*"),
}
AGGR_SERVER_PERF_METRIC_LOG_QUERIES = {
PerfMetricType.SEQ_THROUGHPUT:
re.compile(r"Request throughput \(req\/s\):\s+([\d\.]+)"),
PerfMetricType.TOKEN_THROUGHPUT:
re.compile(r"Output token throughput \(tok\/s\):\s+([\d\.]+)"),
PerfMetricType.TOTAL_TOKEN_THROUGHPUT:
re.compile(r"Total Token throughput \(tok\/s\):\s+([\d\.]+)"),
PerfMetricType.USER_THROUGHPUT:
re.compile(r"User throughput \(tok\/s\):\s+([\d\.]+)"),
PerfMetricType.FIRST_TOKEN_TIME:
re.compile(r"Mean TTFT \(ms\):\s+([\d\.]+)"),
PerfMetricType.MEDIAN_FIRST_TOKEN_TIME:
re.compile(r"Median TTFT \(ms\):\s+([\d\.]+)"),
PerfMetricType.P99_FIRST_TOKEN_TIME:
re.compile(r"P99 TTFT \(ms\):\s+([\d\.]+)"),
PerfMetricType.INTER_TOKEN_TIME:
re.compile(r"Mean ITL \(ms\):\s+([\d\.]+)"),
PerfMetricType.MEDIAN_INTER_TOKEN_TIME:
re.compile(r"Median ITL \(ms\):\s+([\d\.]+)"),
PerfMetricType.P99_INTER_TOKEN_TIME:
re.compile(r"P99 ITL \(ms\):\s+([\d\.]+)"),
PerfMetricType.OUTPUT_TOKEN_TIME:
re.compile(r"Mean TPOT \(ms\):\s+([\d\.]+)"),
PerfMetricType.MEDIAN_OUTPUT_TOKEN_TIME:
re.compile(r"Median TPOT \(ms\):\s+([\d\.]+)"),
PerfMetricType.P99_OUTPUT_TOKEN_TIME:
re.compile(r"P99 TPOT \(ms\):\s+([\d\.]+)"),
PerfMetricType.INFERENCE_TIME:
re.compile(r"Mean E2EL \(ms\):\s+([\d\.]+)"),
PerfMetricType.MEDIAN_INFERENCE_TIME:
re.compile(r"Median E2EL \(ms\):\s+([\d\.]+)"),
PerfMetricType.P99_INFERENCE_TIME:
re.compile(r"P99 E2EL \(ms\):\s+([\d\.]+)"),
}
# (Relative threshold, Absolute threshold) for all metric types
PERF_METRIC_THRESHOLD = {
PerfMetricType.BUILD_TIME: (0.1, 30), # Ignore build time regression < 30ms
PerfMetricType.INFERENCE_TIME:
(0.1, 50), # Ignore inference time regression < 50ms
PerfMetricType.MEDIAN_INFERENCE_TIME:
(0.1, 50), # Ignore median inference time regression < 50ms
PerfMetricType.P99_INFERENCE_TIME:
(0.1, 50), # Ignore p99 inference time regression < 50ms
PerfMetricType.FIRST_TOKEN_TIME:
(0.1, 50), # Ignore first token time regression < 50ms
PerfMetricType.MEDIAN_FIRST_TOKEN_TIME:
(0.1, 50), # Ignore median first token time regression < 50ms
PerfMetricType.P99_FIRST_TOKEN_TIME:
(0.1, 50), # Ignore p99 first token time regression < 50ms
PerfMetricType.OUTPUT_TOKEN_TIME:
(0.1, 50), # Ignore per output token time regression < 50ms
PerfMetricType.MEDIAN_OUTPUT_TOKEN_TIME:
(0.1, 50), # Ignore median output token time regression < 50ms
PerfMetricType.P99_OUTPUT_TOKEN_TIME:
(0.1, 50), # Ignore p99 output token time regression < 50ms
PerfMetricType.INTER_TOKEN_TIME:
(0.1, 50), # Ignore inter token time regression < 50ms
PerfMetricType.MEDIAN_INTER_TOKEN_TIME:
(0.1, 50), # Ignore median inter token time regression < 50ms
PerfMetricType.P99_INTER_TOKEN_TIME:
(0.1, 50), # Ignore p99 inter token time regression < 50ms
PerfMetricType.SEQ_LATENCY: (0.1, 50), # Ignore latency regression < 50ms
PerfMetricType.TOKEN_THROUGHPUT: (
-0.1, 10
), # Ignore throughput regression < 10 tokens/s. Negative rel threshold is to indicate that larger is better.
PerfMetricType.TOTAL_TOKEN_THROUGHPUT: (0.1, 10),
PerfMetricType.USER_THROUGHPUT: (0.1, 10),
PerfMetricType.SEQ_THROUGHPUT: (
-0.1, 10
), # Ignore throughput regression < 10 tokens/s. Negative rel threshold is to indicate that larger is better.
PerfMetricType.INFERENCE_PEAK_GPU_MEMORY:
(0.1, 0.1), # Ignore inference peak gpu memory regression < 0.1GiB
PerfMetricType.BUILD_PEAK_CPU_MEMORY:
(0.1, 100), # Ignore build peak cpu memory regression < 100MiB
PerfMetricType.BUILD_PEAK_GPU_MEMORY:
(0.1, 100), # Ignore build peak gpu memory regression < 100MiB
PerfMetricType.ENGINE_SIZE: (0.3,
100), # Ignore engine size regression < 100MiB
PerfMetricType.CONTEXT_GPU_MEMORY:
(0.1, 50), # Ignore context GPU memory < 50MiB
PerfMetricType.KV_CACHE_SIZE: (-0.1, 50), # Ignore value < 50MiB
PerfMetricType.DISAGG_SERVER_E2EL: (0.1,
50), # Ignore E2EL regression < 50ms
PerfMetricType.DISAGG_SERVER_TTFT: (0.1,
50), # Ignore TTFT regression < 50ms
}
PERF_METRIC_STRING = {
PerfMetricType.BUILD_TIME: "build_time",
PerfMetricType.INFERENCE_TIME: "mean_e2el",
PerfMetricType.MEDIAN_INFERENCE_TIME: "median_e2el",
PerfMetricType.P99_INFERENCE_TIME: "p99_e2el",
PerfMetricType.FIRST_TOKEN_TIME: "mean_ttft",
PerfMetricType.MEDIAN_FIRST_TOKEN_TIME: "median_ttft",
PerfMetricType.P99_FIRST_TOKEN_TIME: "p99_ttft",
PerfMetricType.OUTPUT_TOKEN_TIME: "mean_tpot",
PerfMetricType.MEDIAN_OUTPUT_TOKEN_TIME: "median_tpot",
PerfMetricType.P99_OUTPUT_TOKEN_TIME: "p99_tpot",
PerfMetricType.INTER_TOKEN_TIME: "mean_itl",
PerfMetricType.MEDIAN_INTER_TOKEN_TIME: "median_itl",
PerfMetricType.P99_INTER_TOKEN_TIME: "p99_itl",
PerfMetricType.SEQ_LATENCY: "seq_latency",
PerfMetricType.TOKEN_THROUGHPUT: "token_throughput",
PerfMetricType.TOTAL_TOKEN_THROUGHPUT: "total_token_throughput",
PerfMetricType.USER_THROUGHPUT: "user_throughput",
PerfMetricType.SEQ_THROUGHPUT: "seq_throughput",
PerfMetricType.INFERENCE_PEAK_GPU_MEMORY: "inference_peak_gpu_memory",
PerfMetricType.BUILD_PEAK_CPU_MEMORY: "build_peak_cpu_memory",
PerfMetricType.BUILD_PEAK_GPU_MEMORY: "build_peak_gpu_memory",
PerfMetricType.ENGINE_SIZE: "engine_size",
PerfMetricType.CONTEXT_GPU_MEMORY: "context_gpu_memory",
PerfMetricType.KV_CACHE_SIZE: "kv_cache_size",
}
BUILDER_METRICS = [
PerfMetricType.BUILD_TIME, PerfMetricType.BUILD_PEAK_CPU_MEMORY,
PerfMetricType.BUILD_PEAK_GPU_MEMORY, PerfMetricType.ENGINE_SIZE
]
INFERENCE_METRICS = [
PerfMetricType.INFERENCE_TIME,
PerfMetricType.INFERENCE_PEAK_GPU_MEMORY,
PerfMetricType.CONTEXT_GPU_MEMORY,
]
AGGR_SERVER_METRICS = [
PerfMetricType.SEQ_THROUGHPUT,
PerfMetricType.TOKEN_THROUGHPUT,
PerfMetricType.TOTAL_TOKEN_THROUGHPUT,
PerfMetricType.USER_THROUGHPUT,
PerfMetricType.FIRST_TOKEN_TIME,
PerfMetricType.MEDIAN_FIRST_TOKEN_TIME,
PerfMetricType.P99_FIRST_TOKEN_TIME,
PerfMetricType.OUTPUT_TOKEN_TIME,
PerfMetricType.MEDIAN_OUTPUT_TOKEN_TIME,
PerfMetricType.P99_OUTPUT_TOKEN_TIME,
PerfMetricType.INTER_TOKEN_TIME,
PerfMetricType.MEDIAN_INTER_TOKEN_TIME,
PerfMetricType.P99_INTER_TOKEN_TIME,
PerfMetricType.INFERENCE_TIME,
PerfMetricType.MEDIAN_INFERENCE_TIME,
PerfMetricType.P99_INFERENCE_TIME,
]
BENCH_INFERENCE_METRICS = [
PerfMetricType.INFERENCE_TIME,
PerfMetricType.TOKEN_THROUGHPUT,
PerfMetricType.SEQ_THROUGHPUT,
PerfMetricType.KV_CACHE_SIZE,
]
DISAGG_SERVER_METRICS = [
PerfMetricType.DISAGG_SERVER_E2EL,
PerfMetricType.DISAGG_SERVER_TTFT,
]
class PerfTestMetric(NamedTuple):
"""
Configurations of a test metric.
"""
# The original test name used to run the oraginal perf test.
original_test_name: str
# The name for this particular metric.
metric_name: str
# The type of this metric.
metric_type: PerfMetricType
# The regex used to parse this metric.
metric_regex: re.Pattern
# The relative threshold to allow for regressions.
metric_threshold: float
# The absolute threshold to allow for regressions.
metric_abs_threshold: float
# The index of the command of this metric.
# Currently, we run 1 build command plus N benchmark commands.
cmd_idx: int
def to_env_dict(env_vars: str) -> Dict[str, str]:
env = {}
for env_var in env_vars.split():
if "=" in env_var:
key, value = env_var.split("=", 1)
env[key] = value
return env
class ServerConfig:
"""
Configurations of trtllm-server.
"""
def __init__(self, server_config_data: dict, env_vars: str = ""):
# Extract required fields
self.name = server_config_data['name']
self.model_name = server_config_data['model_name']
self.gpus = server_config_data['gpus']
self.model_path = ""
self.env_vars = env_vars
# Extract optional fields with defaults
self.tp = server_config_data.get('tensor_parallel_size', self.gpus)
self.ep = server_config_data.get('moe_expert_parallel_size', 1)
self.pp = server_config_data.get('pipeline_parallel_size', 1)
self.gpus_per_node = server_config_data.get('gpus_per_node', self.gpus)
self.max_num_tokens = server_config_data.get('max_num_tokens', 2048)
self.max_batch_size = server_config_data.get('max_batch_size', 512)
self.max_seq_len = server_config_data.get('max_seq_len', 0)
self.disable_overlap_scheduler = server_config_data.get(
'disable_overlap_scheduler', False)
self.num_postprocess_workers = server_config_data.get(
'num_postprocess_workers', 0)
self.stream_interval = server_config_data.get('stream_interval', 10)
self.attn_backend = server_config_data.get('attn_backend', "TRTLLM")
self.enable_chunked_prefill = server_config_data.get(
'enable_chunked_prefill', False)
self.enable_attention_dp = server_config_data.get(
'enable_attention_dp', False)
self.trust_remote_code = server_config_data.get('trust_remote_code',
False)
# attention_dp_config
attention_dp_config = server_config_data.get('attention_dp_config', {})
self.attention_dp_balance = attention_dp_config.get(
'enable_balance', False)
self.batching_wait_iters = attention_dp_config.get(
'batching_wait_iters', 0)
self.timeout_iters = attention_dp_config.get('timeout_iters', 60)
# moe_config
moe_config = server_config_data.get('moe_config', {})
self.moe_backend = moe_config.get('backend', "")
self.moe_max_num_tokens = moe_config.get('max_num_tokens', 0)
# cuda_graph_config
cuda_graph_config = server_config_data.get('cuda_graph_config', {})
self.enable_cuda_graph = False
if cuda_graph_config:
self.enable_cuda_graph = True
self.enable_padding = cuda_graph_config.get('enable_padding', True)
self.cuda_graph_batch_sizes = cuda_graph_config.get(
'batch_sizes', [])
self.cuda_graph_max_batch_size = cuda_graph_config.get(
'max_batch_size', 0)
else:
self.enable_padding = True
self.cuda_graph_batch_sizes = []
self.cuda_graph_max_batch_size = 0
# kv_cache_config
kv_cache_config = server_config_data.get('kv_cache_config', {})
self.kv_cache_dtype = kv_cache_config.get('dtype', "fp8")
self.enable_block_reuse = kv_cache_config.get('enable_block_reuse',
False)
self.free_gpu_memory_fraction = kv_cache_config.get(
'free_gpu_memory_fraction', 0.8)
# cache_transceiver_config
cache_transceiver_config = server_config_data.get(
'cache_transceiver_config', {})
self.cache_transceiver_backend = cache_transceiver_config.get(
'backend', "")
self.cache_transceiver_max_tokens_in_buffer = cache_transceiver_config.get(
'max_tokens_in_buffer', 0)
# speculative_config
speculative_config = server_config_data.get('speculative_config', {})
self.spec_decoding_type = speculative_config.get('decoding_type', "")
self.num_nextn_predict_layers = speculative_config.get(
'num_nextn_predict_layers', 0)
eagle3_value = speculative_config.get('eagle3_layers_to_capture', [])
if isinstance(eagle3_value, int):
self.eagle3_layers_to_capture = [eagle3_value]
elif isinstance(eagle3_value, list):
self.eagle3_layers_to_capture = eagle3_value
else:
self.eagle3_layers_to_capture = []
self.max_draft_len = speculative_config.get('max_draft_len', 0)
self.speculative_model_dir = speculative_config.get(
'speculative_model_dir', "")
# match_mode: "config" (default, 40+ fields) or "scenario" (benchmark scenario fields for recipe testing)
# When match_mode is "scenario", baselines are matched by scenario identity
# (model, gpu, isl, osl, concurrency, num_gpus) instead of full config fields.
self.match_mode = server_config_data.get('match_mode', "config")
# Store filtered config for extra_llm_api_config (exclude name, model_name, gpus, client_configs)
self.extra_llm_api_config_data = {
k: v
for k, v in server_config_data.items()
if k not in ['name', 'model_name', 'gpus', 'client_configs']
}
def to_cmd(self,
output_dir: str,
numa_bind: bool = False,
disagg_serving_type: str = "") -> List[str]:
model_dir = get_model_dir(self.model_name)
self.model_path = model_dir if os.path.exists(
model_dir) else self.model_name
config_filename = f"extra-llm-api-config.{self.name}.yml"
config_path = os.path.join(output_dir, config_filename)
numa_bind_cmd = []
if numa_bind:
numa_bind_cmd = ["numactl", "-m 0,1"]
cmd = numa_bind_cmd + [
"trtllm-serve", self.model_path, "--backend", "pytorch",
"--extra_llm_api_options", config_path
]
return cmd
def to_env(self) -> Dict[str, str]:
return to_env_dict(self.env_vars)
def to_db_data(self) -> dict:
db_data = {
"s_model_name":
self.model_name.lower(),
"l_gpus":
self.gpus,
"l_tp":
self.tp,
"l_ep":
self.ep,
"l_pp":
self.pp,
"l_gpus_per_node":
self.gpus_per_node,
"l_max_num_tokens":
self.max_num_tokens,
"l_max_batch_size":
self.max_batch_size,
"l_max_seq_len":
self.max_seq_len,
"b_disable_overlap_scheduler":
self.disable_overlap_scheduler,
"l_num_postprocess_workers":
self.num_postprocess_workers,
"l_stream_interval":
self.stream_interval,
"s_attn_backend":
self.attn_backend,
"b_enable_chunked_prefill":
self.enable_chunked_prefill,
"b_enable_attention_dp":
self.enable_attention_dp,
"b_trust_remote_code":
self.trust_remote_code,
# attention_dp_config
"b_attention_dp_balance":
self.attention_dp_balance,
"l_batching_wait_iters":
self.batching_wait_iters,
"l_timeout_iters":
self.timeout_iters,
# moe_config
"s_moe_backend":
self.moe_backend,
"l_moe_max_num_tokens":
self.moe_max_num_tokens,
# cuda_graph_config
"b_enable_cuda_graph":
self.enable_cuda_graph,
"b_enable_padding":
self.enable_padding,
"l_cuda_graph_max_batch_size":
self.cuda_graph_max_batch_size,
"s_cuda_graph_batch_sizes":
",".join(map(str, self.cuda_graph_batch_sizes)),
# kv_cache_config
"s_kv_cache_dtype":
self.kv_cache_dtype,
"b_enable_block_reuse":
self.enable_block_reuse,
"d_free_gpu_memory_fraction":
self.free_gpu_memory_fraction,
# cache_transceiver_config
"s_cache_transceiver_backend":
self.cache_transceiver_backend,
"l_cache_transceiver_max_tokens_in_buffer":
self.cache_transceiver_max_tokens_in_buffer,
# speculative_config
"s_spec_decoding_type":
self.spec_decoding_type,
"l_num_nextn_predict_layers":
self.num_nextn_predict_layers,
"s_eagle3_layers_to_capture":
",".join(map(str, self.eagle3_layers_to_capture)),
"l_max_draft_len":
self.max_draft_len,
"s_speculative_model_dir":
self.speculative_model_dir,
"s_server_log_link":
"",
"s_server_env_var":
self.env_vars,
}
return db_data
def generate_extra_llm_api_config(self) -> str:
"""Generate extra-llm-api-config.yml content"""
# Make a copy to avoid modifying the original
config_data = dict(self.extra_llm_api_config_data)
# Handle speculative_model_dir path conversion if it exists
if 'speculative_config' in config_data and 'speculative_model_dir' in config_data[
'speculative_config']:
spec_model_dir = config_data['speculative_config'][
'speculative_model_dir']
if spec_model_dir:
config_data['speculative_config'][
'speculative_model_dir'] = os.path.join(
llm_models_root(), spec_model_dir)
return yaml.dump(config_data, default_flow_style=False, sort_keys=False)
class ClientConfig:
"""
Configurations of benchmark client.
"""
def __init__(self,
client_config_data: dict,
model_name: str,
env_vars: str = ""):
self.name = client_config_data.get('name', '')
self.model_name = model_name
self.concurrency = client_config_data.get('concurrency', 1)
self.iterations = client_config_data.get('iterations', 1)
self.isl = client_config_data.get('isl', 1024)
self.osl = client_config_data.get('osl', 1024)
self.random_range_ratio = client_config_data.get(
'random_range_ratio', 0.0)
self.backend = client_config_data.get('backend', "")
self.use_chat_template = client_config_data.get('use_chat_template',
False)
self.streaming = client_config_data.get('streaming', True)
self.model_path = ""
self.env_vars = env_vars
def to_cmd(self) -> List[str]:
model_dir = get_model_dir(self.model_name)
self.model_path = model_dir if os.path.exists(
model_dir) else self.model_name
benchmark_cmd = [
"python", "-m", "tensorrt_llm.serve.scripts.benchmark_serving",
"--model", self.model_path, "--dataset-name", "random",
"--random-ids", "--num-prompts",
str(self.concurrency * self.iterations), "--random-input-len",
str(self.isl), "--random-output-len",
str(self.osl), "--random-range-ratio",
str(self.random_range_ratio), "--ignore-eos",
"--percentile-metrics", "ttft,tpot,itl,e2el", "--max-concurrency",
str(self.concurrency)
]
if self.backend:
benchmark_cmd.append("--backend")
benchmark_cmd.append(self.backend)
if self.use_chat_template:
benchmark_cmd.append("--use-chat-template")
if not self.streaming:
benchmark_cmd.append("--non-streaming")
return benchmark_cmd
def to_env(self) -> Dict[str, str]:
return to_env_dict(self.env_vars)
def to_db_data(self) -> dict:
"""Convert ClientConfig to Database data"""
db_data = {
"l_concurrency": self.concurrency,
"l_iterations": self.iterations,
"l_isl": self.isl,
"l_osl": self.osl,
"d_random_range_ratio": self.random_range_ratio,
"s_backend": self.backend,
"b_use_chat_template": self.use_chat_template,
"b_streaming": self.streaming,
"s_client_log_link": "",
"s_client_env_vars": self.env_vars,
}
if self.backend:
db_data["s_backend"] = self.backend
if self.use_chat_template:
db_data["b_use_chat_template"] = self.use_chat_template
return db_data
def parse_select_pattern(select_pattern: str):
"""Parse select pattern like 'r1_fp4_dep4,r1_fp4_tep4:con1_iter1_1024_1024,r1_fp4_tep4:con8_iter1_1024_1024'
Format:
- ',' splits different server configs
- ':' means for this server, we choose specific clients
- If no ':', all clients are chosen for that server
Returns:
- Dict with server name as key and either None (all clients) or set of client names as value
"""
execution_plan = {}
parts = select_pattern.split(',')
for part in parts:
part = part.strip()
if not part: # Skip empty parts
continue
if ':' in part:
# Format: "server_name:client_name"
server_name, client_name = part.split(':', 1)
server_name = server_name.strip()
client_name = client_name.strip()
# Only add if not already set to None (all clients)
if server_name not in execution_plan:
execution_plan[server_name] = set()
if execution_plan[server_name] is not None:
execution_plan[server_name].add(client_name)
else:
# Format: "server_name" - select all clients for this server
server_name = part.strip()
execution_plan[server_name] = None
return execution_plan
def parse_aggr_config_file(config_file_path: str, select_pattern: str = None):
"""Parse YAML configuration file and create ServerConfig and ClientConfig objects for aggregated server
Args:
config_file_path: Path to YAML configuration file
select_pattern: Selection pattern string (e.g., "r1_fp4_dep4,r1_fp4_tep4:con1_iter1_1024_1024")
Returns:
execution_plan: None (all servers/clients) or dict with server names as keys
server_configs: List of ServerConfig objects
server_client_configs: Dict with server id as key and list of ClientConfig as value
"""
# Parse selection pattern
if select_pattern:
execution_plan = parse_select_pattern(select_pattern)
else:
execution_plan = None
# Read YAML config file
with open(config_file_path, 'r') as f:
config = yaml.safe_load(f)
# Read environment config
environment = config.get('environment', {})
if not environment:
environment = {}
# Get environment variables
environment.get('worker_env_var', '')
server_env_var = environment.get('server_env_var', '')
client_env_var = environment.get('client_env_var', '')
server_configs = []
server_client_configs = {}
for server_config_data in config['server_configs']:
server_name = server_config_data['name']
# Check if this server should be included based on execution_plan
if execution_plan is not None and server_name not in execution_plan:
continue
# Create ServerConfig object directly from dict
server_config = ServerConfig(server_config_data, server_env_var)
server_id = len(server_configs)
server_configs.append(server_config)
# Create ClientConfig objects
client_configs = []
selected_client_names = execution_plan.get(
server_name) if execution_plan else None
for client_config_data in server_config_data['client_configs']:
client_name = client_config_data['name']
# Check if this client should be included
# Include if: execution_plan is None OR selected_client_names is None OR client_name in selected_client_names
if execution_plan is not None and selected_client_names is not None:
if client_name not in selected_client_names:
continue
client_config = ClientConfig(client_config_data,
server_config_data['model_name'],
client_env_var)
client_configs.append(client_config)
server_client_configs[server_id] = client_configs
return execution_plan, server_configs, server_client_configs
def parse_multi_node_disagg_config_file(config_file_path: str,
select_pattern: str = None):
disagg_serving_type = os.environ.get("DISAGG_SERVING_TYPE", "BENCHMARK")
# Read YAML config file
with open(config_file_path, 'r') as f:
config = yaml.safe_load(f)
disagg_configs = []
hardware = config.get('hardware', {})
benchmark = config.get('benchmark', {})
environment = config.get('environment', {})
slurm_config = config.get('slurm', {})
worker_config = config.get('worker_config', {})
timeout = slurm_config.get('timeout', 3600)
numa_bind = slurm_config.get('numa_bind', False)
# Get model name from environment
model_name = environment.get('model_name', '')
assert model_name, "model_name is required in environment section"
# Get environment variables
worker_env_var = environment.get('worker_env_var', '')
server_env_var = environment.get('server_env_var', '')
client_env_var = environment.get('client_env_var', '')
# Create ctx_server config data
ctx_server_config_data = {
'name': 'ctx',
'model_name': model_name,
'gpus': hardware.get('gpus_per_ctx_server'),
'gpus_per_node': hardware.get('gpus_per_node'),
**worker_config.get('ctx', {})
}
# Create gen_server config data
gen_server_config_data = {
'name': 'gen',
'model_name': model_name,
'gpus': hardware.get('gpus_per_gen_server'),
'gpus_per_node': hardware.get('gpus_per_node'),
**worker_config.get('gen', {})
}
# Create client config data
concurrency_str = benchmark.get('concurrency_list', '1')
concurrency = int(concurrency_str) if isinstance(concurrency_str,
str) else concurrency_str
client_config_data = {
'name': 'client',
'concurrency': concurrency,
'iterations': benchmark.get('multi_round', 1),
'isl': benchmark.get('input_length', 1024),
'osl': benchmark.get('output_length', 1024),
'random_range_ratio': benchmark.get('benchmark_ratio', 0.0),
'backend': 'openai',
'use_chat_template': False,
'streaming': benchmark.get('streaming', True),
}
# Create disagg_config dict
disagg_config = {
'disagg_serving_type': disagg_serving_type,
'hostname': socket.gethostname(),
'numa_bind': numa_bind,
'timeout': timeout,
'name': 'disagg_config',
'model_name': model_name,
'hardware': hardware,
'ctx_server': ServerConfig(ctx_server_config_data, worker_env_var),
'gen_server': ServerConfig(gen_server_config_data, worker_env_var),
'server_env_var': server_env_var,
'client': ClientConfig(client_config_data, model_name, client_env_var),
}
print_info(f"disagg_config: {disagg_config}")
disagg_configs.append(disagg_config)
return disagg_configs
class PerfTestConfig:
"""
Configurations defining the LLM perf test.
This should hold only the attributes that distinguish different tests.
"""
def __init__(
self,
*,
model_name: str = "",
runtime: str = "python",
static_batching: str = "",
api: str = "",
streaming: str = "",
backend: str = "",
mode: str = "plugin",
data_type: str = "float16",
max_batch_size: int = 512,
max_num_tokens: int = 2048,
gpu_weights_percent: float = -1,
batch_sizes: List[int] = [0],
input_lens: List[int] = [8],
output_lens: List[int] = [1],
num_beams: int = 1,
num_loras: int = 0,
num_reqs: int = 512,
concurrency: int = -1,
quantization: str = "",
kv_cache_free_gpu_mem_fraction: float = 0.9,
kv_cache_dtype: str = "auto",
ep_size: int = None,
tp_size: int = 1,
pp_size: int = 1,
num_gpus: int = 1,
# only for torch-backend currently
extra: bool = False,
# _autodeploy backend specific parameters
ad_compile_backend: str = "torch-opt",
free_mem_ratio: float = 0.9,
extra_runtime: str = "trtllm",
skip_loading_weights: bool = False,
):
# The model name.
self.model_name = model_name
# Python or cpp/cppmanager runtime.
self.runtime = runtime
# static batching for gptManagerBenchmark
self.static_batching = static_batching
# API Type: only executor is allowed
self.api = api
# Backend Type: pytorch or cpp
self.backend = backend
# Streaming responses
self.streaming = streaming
# Plugin or OOTB mode.
self.mode = mode
# Activation dtype.
self.data_type = data_type
# Percentage of weights that resides on GPU.
self.gpu_weights_percent = gpu_weights_percent
# Max Batch Size to build TRT engine with.
self.max_batch_size = max_batch_size
# Max number of tokens to build TRT engine with.
self.max_num_tokens = max_num_tokens
# List of batch sizes to run benchmark with.
self.batch_sizes = batch_sizes
# List of input lens to run benchmark with.
self.input_lens = input_lens
# List of output lens to run benchmark with.
self.output_lens = output_lens
# Number of beams.
self.num_beams = num_beams
# Number of loras.
self.num_loras = num_loras
# Number of requests.
self.num_reqs = num_reqs
# Number of concurrency
self.concurrency = concurrency
# Quantization type.
self.quantization = quantization
# KV cache free gpu mem fraction
self.kv_cache_free_gpu_mem_fraction = kv_cache_free_gpu_mem_fraction
# KV Cache dtype
self.kv_cache_dtype = kv_cache_dtype
# Multiple Profiles
self.multiple_profiles = False
# EP Size
self.ep_size = ep_size
# TP Size
self.tp_size = tp_size
# PP Size
self.pp_size = pp_size
# Number of GPUs.
self.num_gpus = num_gpus
# Extra flag to enable pytorch_model_config reading for TRT backend
self.extra = extra
# _autodeploy backend specific parameters
self.ad_compile_backend = ad_compile_backend
self.free_mem_ratio = free_mem_ratio
self.extra_runtime = extra_runtime
self.skip_loading_weights = skip_loading_weights
# Just build engines
self.build_only = False
# Whether to run disaggregated server perf test.
self.is_disagg_server = False
self.ctx_server_workers = 0
self.gen_server_workers = 0
# Used for perf sanity test
self.upload_to_db = False
self.config_file = None
self.gpu_type = None
self.config_path = None
self.select_pattern = None
# Aggregated mode
self.server_configs = []
self.server_client_configs = {}
# Multi-node disaggregated mode
self.disagg_configs = []
def _to_string_disagg(self, entries: List[str]):
entries.append(f"disagg_server")
if self.ctx_tp_size > 1:
entries.append(f"ctx_tp:{self.ctx_tp_size}")
if self.ctx_dp_size > 1:
entries.append(f"ctx_dp:{self.ctx_dp_size}")
if self.ctx_pp_size > 1:
entries.append(f"ctx_pp:{self.ctx_pp_size}")
if self.gen_tp_size > 1:
entries.append(f"gen_tp:{self.gen_tp_size}")
if self.gen_dp_size > 1:
entries.append(f"gen_dp:{self.gen_dp_size}")
if self.gen_pp_size > 1:
entries.append(f"gen_pp:{self.gen_pp_size}")
return "-".join(entries)
def to_string(self,
custom_server_name: str = None,
custom_client_name: str = None,
custom_bs: int = None,
custom_input_len: int = None,
custom_output_len: int = None,
device_subtype: str = None) -> str:
# Used for perf sanity test
if self.config_file is not None:
entries = ["perf_sanity", self.config_file]
if "disagg" in self.config_file:
# For multi-node disagg, add disagg config name
if custom_server_name is not None:
entries.append(f"disagg:{custom_server_name}")
else:
# For aggr_server
if custom_server_name is not None:
entries.append(f"server:{custom_server_name}")
if custom_client_name is not None:
entries.append(f"client:{custom_client_name}")
return "-".join(entries)
# First, add the model name.
entries = [self.model_name]
# Add device subtype if provided (for autodeploy tests)
if device_subtype:
entries.append(f"subtype:{device_subtype}")
if self.runtime == "cpp": # bertBenchmark runtime
entries.append(f"cpp")
elif self.runtime == "cppmanager": # gptManagerBenchmark runtime
entries.append(f"cppmanager")
if self.api == "exe": # executor
entries.append(f"exe")
if self.streaming == "streaming":
entries.append(f"streaming")
if self.static_batching == "static_batching":
entries.append(f"static_batching")
elif self.runtime == "bench": # trtllm-bench
entries.append(f"bench")
if self.backend == 'pytorch':
entries.append(f"pytorch")
elif self.backend == '_autodeploy':
entries.append(f"_autodeploy")
if self.streaming == "streaming":
entries.append(f"streaming")
elif self.runtime == "disagg_server": # trtllm-serve
entries.append(f"disagg_server")
return self._to_string_disagg(entries)
# Add mode and dtype.
if self.runtime != "bench":
entries.append(self.mode)
entries.append(self.data_type)
if self.gpu_weights_percent != -1:
entries.append(f"gwp:{self.gpu_weights_percent}")
if self.multiple_profiles:
entries.append(f"mp")
# Add Max batch size.
entries.append(f"maxbs:{self.max_batch_size}")
# Add Max number of tokens.
entries.append(f"maxnt:{self.max_num_tokens}")
# Add kv cache free gpu mem fraction.
if self.kv_cache_free_gpu_mem_fraction != 0.9:
entries.append(f"kv_frac:{self.kv_cache_free_gpu_mem_fraction}")
if self.build_only:
entries.append(f"build_only")
if self.batch_sizes[0] > 0:
# Add batch size(s).
if custom_bs is None:
bs_label = "+".join([str(x) for x in self.batch_sizes])
else:
bs_label = str(custom_bs)
entries.append(f"bs:{bs_label}")
# Add input/output lens.
if len(self.output_lens) > 0:
if custom_input_len is None:
io_lens = []
for in_len, out_len in zip(self.input_lens, self.output_lens):
io_lens.append(f"{in_len},{out_len}")
io_len_label = "+".join(io_lens)
else:
assert custom_output_len is not None, \
"custom_output_len must be provided if custom_input_len is specified!"
io_len_label = f"{custom_input_len},{custom_output_len}"
entries.append(f"input_output_len:{io_len_label}")
else:
if custom_input_len is None:
len_label = "+".join([str(x) for x in self.input_lens])
else:
len_label = custom_input_len
entries.append(f"input_len:{len_label}")
# Add number of beams.
if self.num_beams > 1:
entries.append(f"beams:{self.num_beams}")
# Add number of loras.
if self.num_loras > 0:
entries.append(f"loras:{self.num_loras}")
# Add quantization type.
if self.quantization != "":
entries.append(f"quant:{self.quantization}")
# Add kv cache dtype.
if self.kv_cache_dtype != "auto":
entries.append(f"kv_cache_dtype:{self.kv_cache_dtype}")
# Add number of requests.
if self.num_reqs != 512:
entries.append(f"reqs:{self.num_reqs}")
#Add number of concurrency
if self.concurrency != -1:
entries.append(f"con:{self.concurrency}")
#Add EP Size.
if self.ep_size != None:
entries.append(f"ep:{self.ep_size}")
# Add TP Size.
if self.tp_size > 1 and self.tp_size != self.num_gpus:
entries.append(f"tp:{self.tp_size}")
# Add PP Size.
if self.pp_size > 1:
entries.append(f"pp:{self.pp_size}")
# Add number of GPUs.
if self.num_gpus > 1:
entries.append(f"gpus:{self.num_gpus}")
# Add extra flag for llm-api-config.yml.
if self.extra:
entries.append("extra")
# Concatenate labels with "-".
return "-".join(entries)
def __str__(self) -> str:
return self.to_string()
def _load_from_str_disagg(self, labels: List[str]) -> None:
self.ctx_tp_size = 1
self.ctx_dp_size = 1
self.ctx_pp_size = 1
self.gen_tp_size = 1
self.gen_dp_size = 1
self.gen_pp_size = 1
if labels[0].startswith("ctx_tp:"):
self.ctx_tp_size = int(labels.pop(0).replace("ctx_tp:", ""))
elif labels[0].startswith("ctx_dp:"):
self.ctx_dp_size = int(labels.pop(0).replace("ctx_dp:", ""))
elif labels[0].startswith("ctx_pp:"):
self.ctx_pp_size = int(labels.pop(0).replace("ctx_pp:", ""))
else:
raise RuntimeError(f"Wrong label for ctx config: {labels[0]}!")
if labels[0].startswith("gen_tp:"):
self.gen_tp_size = int(labels.pop(0).replace("gen_tp:", ""))
elif labels[0].startswith("gen_dp:"):
self.gen_dp_size = int(labels.pop(0).replace("gen_dp:", ""))
elif labels[0].startswith("gen_pp:"):
self.gen_pp_size = int(labels.pop(0).replace("gen_pp:", ""))
else:
raise RuntimeError(f"Wrong label for gen config: {labels[0]}!")
self.ctx_server_workers = self.ctx_tp_size * self.ctx_dp_size * self.ctx_pp_size
self.gen_server_workers = self.gen_tp_size * self.gen_dp_size * self.gen_pp_size
self.validate()
def load_from_str(self, test_param_labels) -> None:
"""
Populate the config properties given the test param string.
"""
# Extract configs from test param labels.
labels = test_param_labels.split("-")
def get_gpu_type(label: str) -> str:
parts = label.split("_")
if len(parts) < 2 or parts[0] != "l0":
return ""
if parts[1] == "dgx":
if len(parts) >= 3:
gpu_type = f"{parts[1]}_{parts[2]}"
else:
gpu_type = ""
else:
gpu_type = parts[1]
return gpu_type.lower()
# Used for perf sanity test
if "perf_sanity" in labels[0]:
assert len(labels) > 1, "perf_sanity test must have a config file!"
self.upload_to_db = "upload" in labels[0]
self.config_file = labels[1]
if "disagg" in labels[1]:
self.runtime = "multi_node_disagg_server"
else:
self.runtime = "aggr_server"
self.gpu_type = get_gpu_type(labels[1])
config_folder = os.getenv("TRTLLM_CONFIG_FOLDER",
"tests/scripts/perf-sanity")
self.config_path = os.path.join(
config_folder, f"{labels[1]}.yaml"
if not labels[1].endswith(".yaml") else labels[1])
self.select_pattern = labels[2] if len(labels) > 2 else None
return
self.model_name = labels.pop(0)
# Check if device subtype is present (for autodeploy tests)
self.device_subtype = None
if len(labels) > 0 and labels[0].startswith("subtype:"):
self.device_subtype = labels.pop(0).replace("subtype:", "")
assert labels[0] in ["cpp", "cppmanager", "bench", "disagg_server"], \
f"Invalid runtime {labels[0]}!"
self.runtime = labels.pop(0)
if self.runtime == "disagg_server":
return self._load_from_str_disagg(labels)
self.api = labels.pop(0) if labels[0] == "exe" else ""
self.backend = labels.pop(0) if labels[0] in ["pytorch", "_autodeploy"
] else ""
self.streaming = labels.pop(0) if labels[0] == "streaming" else ""
self.static_batching = labels.pop(
0) if labels[0] == "static_batching" else ""
if self.runtime != "bench":
self.mode = labels.pop(0)
self.data_type = labels.pop(0)
if labels[0].startswith("gwp"):
self.gpu_weights_percent = float(labels.pop(0).replace("gwp:", ""))
if labels[0] == "mp":
self.multiple_profiles = True
labels.pop(0)
if labels[0].startswith("maxbs"):
self.max_batch_size = int(labels.pop(0).replace("maxbs:", ""))
if labels[0].startswith("maxnt"):
self.max_num_tokens = int(labels.pop(0).replace("maxnt:", ""))
if labels[0].startswith("kv_frac"):
self.kv_cache_free_gpu_mem_fraction = float(
labels.pop(0).replace("kv_frac:", ""))
if labels[0] == "build_only":
self.build_only = True
labels.pop(0)
if not self.build_only:
if labels[0].startswith("bs:"):
self.batch_sizes = [
int(x) for x in labels.pop(0).replace("bs:", "").split("+")
]
else:
self.batch_sizes = [0]
if labels[0].startswith("input_output_len"):
io_lens = labels.pop(0).replace("input_output_len:",
"").split("+")
self.input_lens = [int(x.split(",")[0]) for x in io_lens]
self.output_lens = [int(x.split(",")[1]) for x in io_lens]
elif labels[0].startswith("input_len"):
self.input_lens = [
int(x)
for x in labels.pop(0).replace("input_len:", "").split("+")
]
self.output_lens = []
else:
raise RuntimeError(
f"Unexpected test name label for seq lens: {labels[0]}!")
if len(labels) > 0:
self.num_beams = 1 if not labels[0].startswith("beams:") else int(
labels.pop(0).replace("beams:", ""))
if len(labels) > 0:
self.num_loras = 0 if not labels[0].startswith("loras:") else int(
labels.pop(0).replace("loras:", ""))
if len(labels) > 0:
self.quantization = "" if not labels[0].startswith(
"quant:") else labels.pop(0).replace("quant:", "")
if len(labels) > 0:
self.kv_cache_dtype = "auto" if not labels[0].startswith(
"kv_cache_dtype:") else labels.pop(0).replace(
"kv_cache_dtype:", "")
if len(labels) > 0:
self.num_reqs = 512 if not labels[0].startswith("reqs:") else int(
labels.pop(0).replace("reqs:", ""))
if len(labels) > 0:
self.concurrency = -1 if not labels[0].startswith("con:") else int(
labels.pop(0).replace("con:", ""))
if len(labels) > 0:
self.ep_size = None if not labels[0].startswith("ep:") else int(
labels.pop(0).replace("ep:", ""))
if len(labels) > 0:
self.tp_size = 1 if not labels[0].startswith("tp:") else int(
labels.pop(0).replace("tp:", ""))
if len(labels) > 0:
self.pp_size = 1 if not labels[0].startswith("pp:") else int(
labels.pop(0).replace("pp:", ""))
if len(labels) > 0:
self.num_gpus = 1 if not labels[0].startswith("gpus:") else int(
labels.pop(0).replace("gpus:", ""))
if len(labels) > 0:
self.extra = True if labels[0] == "extra" else False
if self.extra:
labels.pop(0)
assert len(
labels
) == 0, f"Invalid test name! Some labels cannot be parsed: {labels}"
# Validate the parsed config.
self.validate()
def validate(self):
"""
Validate if the config makes sense.
"""
# Validate model name.
assert len(self.model_name) > 0, "model_name must not be empty!"
assert "-" not in self.model_name, "model_name must not contain '-' character!"
if self.model_name not in MODEL_PATH_DICT.keys(
) and self.model_name not in HF_MODEL_PATH.keys():
allowed_configs = import_allowed_perf_config()
allowed_models = allowed_configs.get_allowed_models()
assert self.model_name in allowed_models, f"model_name {self.model_name} is not in allowed_models!"
# Validate runtime type.
VALID_RUNTIMES = ["cpp", "cppmanager", "bench", "disagg_server"]
assert self.runtime in VALID_RUNTIMES, f"Invalid runtime {self.runtime}!"
if self.runtime == "disagg_server":
# TODO: validate disaggregated server config
return
# Validate plugin mode.
VALID_MODES = ["plugin", "ootb", "ootb_except_mha"]
if self.runtime == "cppmanager":
VALID_MODES += ["plugin_ifb"]
assert self.mode in VALID_MODES, f"Invalid mode {self.mode}!"
# Validate dtype.
VALID_DTYPES = ["float32", "float16", "bfloat16", "float8", "float4"]
assert self.data_type in VALID_DTYPES, f"Invalid data_type {self.data_type}!"
VALID_KV_CACHE_DTYPES = ["auto", "fp8"]
assert self.kv_cache_dtype in VALID_KV_CACHE_DTYPES, f"Invalid kv_cache_dtype {self.kv_cache_dtype}!"
# Validate quantization mode.
if self.model_name in MODEL_PATH_DICT.keys():
VALID_QUANTS = [
"", "nvfp4", "fp8", "int8", "int4_awq", "w4a8_awq", "w4a16_awq",
"int4_wo", "full_prec"
]
else:
VALID_QUANTS = [
"",
"fp8",
"fp8_gemm",
"fp8_kv_cache",
"int8_sq_per_tensor",
"int8_sq_per_token_channel",
"int8_weight_only",
"int4_weight_only",
"int4_weight_only_awq",
"int4_weight_only_gptq",
]
assert self.quantization in VALID_QUANTS, f"Invalid quantization {self.quantization}!"
if self.backend == "pytorch":
assert self.quantization == "", f"Not support passing quantization {self.quantization} for pytorch backend!"
assert self.num_beams >= 1, f"Invalid num_beams: {self.num_beams}!"
assert self.num_loras >= 0, f"Invalid num_loras: {self.num_loras}!"
assert self.num_reqs >= 1, f"Invalid num_reqs: {self.num_reqs}!"
if self.pp_size > 1:
assert self.model_name in MODEL_PATH_DICT.keys(
), f"Invalid model name for pp size {self.pp_size} test"
if self.num_gpus > 1 and self.tp_size == 1 and self.pp_size == 1:
self.tp_size = self.num_gpus
if self.tp_size > 1 or self.pp_size > 1 and self.num_gpus == 1:
self.num_gpus = self.tp_size * self.pp_size
assert self.num_gpus == self.tp_size * self.pp_size, f"Num of GPU shall be equal to TP*PP: {self.num_gpus}, {self.tp_size}, {self.pp_size}"
if self.gpu_weights_percent != -1:
assert 0 <= self.gpu_weights_percent <= 1, f"Invalid gpu_weights_percent: {self.gpu_weights_percent}!"
if not self.build_only:
assert len(self.input_lens) > 0, f"Empty input_lens!"
if self.is_bert_like():
assert len(
self.output_lens
) == 0, f"BERT-like models must not have output_lens!"
else:
assert len(
self.output_lens
) > 0, f"GPT-like models and enc-dec models must have output_lens!"
# BERT with small BS is very unstable. Try to avoid it.
if self.is_bert_like():
if self.runtime == "trtllm-bench":
self.batch_sizes[
0] = self.max_batch_size if self.max_batch_size > 0 else 1
print(f"batch_sizes: {self.batch_sizes}")
assert all(
[b >= 32 for b in self.batch_sizes]
), f"BERT with small BS is very unstable! Please increase to at least 32."
# GPT-350m and Bloom-560m with small BS are very unstable. Only run these small models with larger BS.
if self.model_name in ["gpt_350m", "bloom_560m"]:
assert all(
[b >= 32 for b in self.batch_sizes]
), f"gpt_350m and bloom_560m with small BS are very unstable! Please increase to at least 32."
def set_aggr_server_configs(self, llm_root: str) -> None:
"""
Set the server and client configs.
"""
config_file_path = os.path.join(llm_root, self.config_path)
_, self.server_configs, self.server_client_configs = parse_aggr_config_file(
config_file_path, self.select_pattern)
def set_multi_node_disagg_server_configs(self, llm_root: str) -> None:
"""
Set the multi-node disaggregated server configs.
"""
config_file_path = os.path.join(llm_root, self.config_path)
self.disagg_configs = parse_multi_node_disagg_config_file(
config_file_path, self.select_pattern)
def get_model_family(self) -> str:
"""
Get the model family of the current model.
"""
allowed_configs = import_allowed_perf_config()
allowed_models = allowed_configs.get_allowed_models()
if self.model_name in allowed_models:
return allowed_configs.get_model_family(self.model_name)
else:
return ""
def is_mamba_family(self) -> bool:
"""
Check if the current model family is Mamba.
"""
return self.get_model_family() == 'mamba'
def is_moe_family(self) -> bool:
"""
Check if the current model family is MoE.
"""
allowed_configs = import_allowed_perf_config()
allowed_models = allowed_configs.get_allowed_models()
if self.model_name in allowed_models:
model_config = allowed_configs.get_model_config(self.model_name)
return model_config['moe_num_experts'] > 0 and model_config[
'moe_top_k'] > 0
else:
return False
def get_benchmark_type(self) -> str:
"""
Get the benchmark type of the current model.
"""
allowed_configs = import_allowed_perf_config()
allowed_models = allowed_configs.get_allowed_models()
if self.model_name in allowed_models:
return allowed_configs.get_benchmark_type(self.model_name)
else:
return ""
def is_bert_like(self) -> bool:
"""
Check if the current benchmark is a BERT benchmark.
"""
return self.get_benchmark_type() == "bert"
def is_enc_dec(self) -> bool:
"""
Check if the current benchmark is a EncDec benchmark.
"""
return self.get_benchmark_type() == "enc_dec"
class MultiMetricPerfTest(AbstractPerfScriptTestClass):
"""
Base class for perf tests with multiple metrics.
"""
def __init__(self, full_test_name: str):
# full_test_name is the full test name appearing in test output.
self._full_test_name = full_test_name
# test_domain_name is the part before "::".
self._test_domain_name = "::".join(full_test_name.split("::")[:-1])
# short_test_name is the part after "::".
self._short_test_name = full_test_name.split("::")[-1]
# short_test_name_body is the part before "[" in short_test_name.
self._short_test_name_body = self._short_test_name.split("[")[0]
# test_param_labels is the part inside "[...]".
self._test_param_labels = full_test_name.split("[")[-1][:-1]
# Load test config from test name.
self._config = PerfTestConfig()
self._config.load_from_str(self._test_param_labels)
# This will store the currently running metric.
self._current_metric = None
self.lora_dirs = []
# This will store each test's result
self._test_results = {}
def get_test_name(self) -> str:
return str(self._config)
def set_runtime_configs(self,
llm_root,
working_dir,
output_dir,
perf_cache_fpath,
gpu_clock_lock=None) -> None:
if self._config.runtime == "cpp":
if not self._config.is_bert_like():
raise ValueError(
f"Invalid config: '{self._config.runtime}' is only supported for bert-like models!"
)
benchmark_script = get_cpp_benchmark("bertBenchmark", llm_root)
elif self._config.runtime == "cppmanager":
benchmark_script = get_cpp_benchmark("gptManagerBenchmark",
llm_root)
elif self._config.runtime == "bench":
benchmark_script = "trtllm-bench"
elif self._config.runtime == "aggr_server":
benchmark_script = None
self._config.set_aggr_server_configs(llm_root)
elif self._config.runtime == "disagg_server":
benchmark_script = None
elif self._config.runtime == "multi_node_disagg_server":
benchmark_script = None
self._config.set_multi_node_disagg_server_configs(llm_root)
else:
raise RuntimeError(f"Invalid runtime {self._config.runtime}.")
allowed_configs = import_allowed_perf_config()
allowed_models = allowed_configs.get_allowed_models()
if self._config.runtime == "bench":
build_script = "trtllm-bench"
elif self._config.runtime == "aggr_server":
build_script = None
elif self._config.runtime == "multi_node_disagg_server":
build_script = None
elif self._config.pp_size > 1 or self._config.model_name not in allowed_models:
build_script = "trtllm-build"
else:
# build.py is used to build engines for both python and cpp runtime
build_script = os.path.join(llm_root,
"tests/integration/defs/perf/build.py")
self._build_script = build_script
self._benchmark_script = benchmark_script
self._working_dir = working_dir
self._output_dir = output_dir
self._perf_cache_fpath = perf_cache_fpath
self._llm_root = llm_root
self._gpu_clock_lock = gpu_clock_lock
def get_trtllm_aggr_commands(self, output_dir):
server_cmds = []
server_envs = []
client_cmds = []
client_envs = []
names = []
for server_idx, client_configs in self._config.server_client_configs.items(
):
server_config = self._config.server_configs[server_idx]
server_cmd = server_config.to_cmd(output_dir)
server_env = server_config.to_env()
# Generate extra-llm-api-config.yml
config_content = server_config.generate_extra_llm_api_config()
config_filename = f"extra-llm-api-config.{server_config.name}.yml"
config_path = os.path.join(output_dir, config_filename)
with open(config_path, 'w') as f:
f.write(config_content)
for client_config in client_configs:
server_cmds.append(server_cmd)
server_envs.append(server_env)
client_cmd = client_config.to_cmd()
client_env = client_config.to_env()
client_cmds.append(client_cmd)
client_envs.append(client_env)
names.append(f"{server_config.name}-{client_config.name}")
return server_cmds, server_envs, client_cmds, client_envs, names
def get_trtllm_multi_node_disagg_commands(self, output_dir):
ctx_server_cmds = []
ctx_server_envs = []
gen_server_cmds = []
gen_server_envs = []
disagg_server_cmds = []
disagg_server_envs = []
benchmark_cmds = []
benchmark_envs = []
cmd_idx = 0
for disagg_config in self._config.disagg_configs:
disagg_serving_type = disagg_config['disagg_serving_type']
disagg_config['hostname']
numa_bind = disagg_config['numa_bind']
ctx_server_cmd = None
ctx_server_env = None
gen_server_cmd = None
gen_server_env = None
disagg_server_cmd = None
disagg_server_env = None
benchmark_cmd = None
benchmark_env = None
if "CTX" in disagg_serving_type or "GEN" in disagg_serving_type:
is_ctx = "CTX" in disagg_serving_type
server_config = disagg_config[
'ctx_server'] if is_ctx else disagg_config['gen_server']
server_cmd = server_config.to_cmd(output_dir, numa_bind,
disagg_serving_type)
server_env = server_config.to_env()
if is_ctx:
ctx_server_cmd = server_cmd
ctx_server_env = server_env
else:
gen_server_cmd = server_cmd
gen_server_env = server_env
# Generate extra-llm-api-config.yml
config_content = server_config.generate_extra_llm_api_config()
config_filename = f"extra-llm-api-config.{server_config.name}.yml"
config_path = os.path.join(output_dir, config_filename)
with open(config_path, 'w') as f:
f.write(config_content)
elif "DISAGG_SERVER" in disagg_serving_type:
timeout = disagg_config['timeout']
# Generate DISAGG server command if this is the DISAGG server node
disagg_server_cmd = [
"trtllm-serve", "disaggregated", "-c",
f"{output_dir}/server_config.{cmd_idx}.yaml", "-t",
str(timeout), "-r",
str(timeout)
]
disagg_server_env = to_env_dict(disagg_config['server_env_var'])
elif "BENCHMARK" in disagg_serving_type:
# Generate benchmark command if this is the BENCHMARK server node
benchmark_cmd = disagg_config['client'].to_cmd()
benchmark_env = disagg_config['client'].to_env()
ctx_server_cmds.append(ctx_server_cmd)
ctx_server_envs.append(ctx_server_env)
gen_server_cmds.append(gen_server_cmd)
gen_server_envs.append(gen_server_env)
disagg_server_cmds.append(disagg_server_cmd)
disagg_server_envs.append(disagg_server_env)
benchmark_cmds.append(benchmark_cmd)
benchmark_envs.append(benchmark_env)
cmd_idx += 1
return ctx_server_cmds, ctx_server_envs, gen_server_cmds, gen_server_envs, disagg_server_cmds, disagg_server_envs, benchmark_cmds, benchmark_envs
def get_trtllm_build_command(self, engine_dir, checkpoint_dir) -> list:
build_cmd = [
self._build_script, f"--output_dir={engine_dir}",
f"--checkpoint_dir={checkpoint_dir}",
f"--workers={self._config.tp_size}",
f"--use_paged_context_fmha=enable", f"--monitor_memory",
f"--max_batch_size={self._config.max_batch_size}"
]
# For Multiple Profiles
if self._config.multiple_profiles:
build_cmd.append(f"--multiple_profiles=enable")
else:
build_cmd.append(f"--multiple_profiles=disable")
num_beams = self._config.num_beams
if num_beams > 1:
build_cmd.append(f"--max_beam_width={num_beams}")
gpu_percent = self._config.gpu_weights_percent
if gpu_percent != -1:
build_cmd += [f"--weight_streaming"]
# For engine inspector
build_cmd.append("--profiling_verbosity=layer_names_only")
if self._config.num_loras > 0:
if "mixtral" in self._config.model_name:
build_cmd.append(f"--lora_plugin=auto")
build_cmd.append(f"--moe_plugin=auto")
build_cmd.append(f"--lora_target_modules")
build_cmd.append(f"attn_q")
build_cmd.append(f"attn_k")
build_cmd.append(f"attn_v")
build_cmd.append(f"attn_dense")
build_cmd.append(f"moe_h_to_4h")
build_cmd.append(f"moe_4h_to_h")
build_cmd.append(f"moe_gate")
build_cmd.append(f"moe_router")
elif "llama" in self._config.model_name:
build_cmd.append(f"--lora_plugin=float16")
build_cmd.append(f"--lora_target_modules")
build_cmd.append(f"attn_q")
build_cmd.append(f"attn_k")
build_cmd.append(f"attn_v")
build_cmd.append(f"attn_dense")
build_cmd.append(f"mlp_h_to_4h")
build_cmd.append(f"mlp_4h_to_h")
build_cmd.append(f"mlp_gate")
if TIMING_CACHE_DIR and not self._config.build_only:
timing_cache = os.path.join(TIMING_CACHE_DIR, "model.cache")
build_cmd.append(f"--input_timing_cache={timing_cache}")
build_cmd.append(f"--output_timing_cache={timing_cache}")
return build_cmd
def get_trtllm_bench_model(self):
return get_model_dir(self._config.model_name)
def get_trtllm_bench_build_command(self, engine_dir) -> list:
model_dir = self.get_trtllm_bench_model()
if model_dir == "":
pytest.skip("Model Name is not supported by trtllm-bench")
model_name = self._config.model_name
if not model_name.endswith("_hf"):
model_name = model_name + "_hf"
hf_model_name = HF_MODEL_PATH.get(model_name, "")
build_cmd = [
self._build_script, f"--log_level=info",
f"--workspace={engine_dir}", f"--model={hf_model_name}",
f"--model_path={model_dir}", "build",
f"--tp_size={self._config.tp_size}",
f"--pp_size={self._config.pp_size}"
]
max_seq_len = max(self._config.input_lens) + max(
self._config.output_lens)
build_cmd.append(f"--max_seq_len={max_seq_len}")
# Add max_batch_size and max_num_tokens to ensure build matches runtime configuration
# Note: trtllm-bench requires both to be specified together (option group constraint)
assert self._config.max_batch_size > 0, f"max_batch_size must be > 0, got {self._config.max_batch_size}"
assert self._config.max_num_tokens > 0, f"max_num_tokens must be > 0, got {self._config.max_num_tokens}"
build_cmd.append(f"--max_batch_size={self._config.max_batch_size}")
build_cmd.append(f"--max_num_tokens={self._config.max_num_tokens}")
if self._config.quantization:
build_cmd.append(
f"--quantization={self._config.quantization.upper()}")
if self._config.model_name in TRUST_REMOTE_CODE_MODELS:
build_cmd.append(f"--trust_remote_code=True")
return build_cmd
def get_prepare_data_command(self, engine_dir, input_len,
output_len) -> list:
data_cmd = []
prepare_data_script = os.path.join(self._llm_root, "benchmarks", "cpp",
"prepare_dataset.py")
if self._config.model_name in MODEL_PATH_DICT.keys():
tokenizer_dir = os.path.join(
llm_models_root(), MODEL_PATH_DICT[self._config.model_name])
elif self._config.model_name in HF_MODEL_PATH.keys():
tokenizer_dir = HF_MODEL_PATH[self._config.model_name]
else:
tokenizer_dir = os.path.join(llm_models_root(), "llama-models",
"llama-7b-hf")
if not os.path.exists(engine_dir):
os.makedirs(engine_dir, exist_ok=True)
if self._config.num_loras > 0:
istdev = 16
ostdev = 24
nloras = self._config.num_loras
dataset_path = os.path.join(engine_dir, "synthetic_data.json")
if self._config.model_name in LORA_MODEL_PATH.keys(
) and self._config.backend == "pytorch" and self._config.runtime == "bench":
actual_lora_paths = LORA_MODEL_PATH[self._config.model_name]
if not isinstance(actual_lora_paths, list):
actual_lora_paths = [actual_lora_paths]
for i, actual_lora_path in enumerate(actual_lora_paths):
if not actual_lora_path.startswith("/"):
actual_lora_paths[i] = os.path.join(
llm_models_root(), actual_lora_path)
lora_dir = os.path.join(engine_dir, "loras")
data_cmd += [f"mkdir -p {lora_dir}", ";"]
if len(actual_lora_paths) != nloras:
raise ValueError(
f"Number of LoRA paths ({len(actual_lora_paths)}) does not match requested number of LoRAs ({nloras})"
)
for i, lora_path in enumerate(actual_lora_paths):
self.lora_dirs.append(f"{lora_dir}/{i}")
data_cmd += [f"ln -sf {lora_path} {lora_dir}/{i}", ";"]
data_cmd += [
"trtllm-bench", f"--model={tokenizer_dir}",
"prepare-dataset", "--output", f"{dataset_path}",
f"--rand-task-id 0 {nloras-1}", f"--lora-dir={lora_dir}",
f"token-norm-dist",
f"--num-requests={self._config.num_reqs}",
f"--input-mean={input_len}", f"--output-mean={output_len}",
f"--input-stdev={istdev}", f"--output-stdev={ostdev}"
]
else:
pytest.skip(
f"LoRA config not supported for {self._config.model_name} with the current backend and runtime."
)
else:
istdev = 0
ostdev = 0
dataset_path = os.path.join(engine_dir, "synthetic_data.json")
if self._build_script == 'trtllm-bench':
data_cmd += [
"trtllm-bench", f"--model={tokenizer_dir}",
"prepare-dataset", "--output", f"{dataset_path}",
"token-norm-dist",
f"--num-requests={self._config.num_reqs}",
f"--input-mean={input_len}", f"--output-mean={output_len}",
f"--input-stdev={istdev}", f"--output-stdev={ostdev}"
]
else:
data_cmd += [
"python3", prepare_data_script, f"--output={dataset_path}",
f"--tokenizer={tokenizer_dir}", f"token-norm-dist",
f"--num-requests={self._config.num_reqs}",
f"--input-mean={input_len}", f"--output-mean={output_len}",
f"--input-stdev={istdev}", f"--output-stdev={ostdev}"
]
return data_cmd
def get_trtllm_bench_command(self, engine_dir):
model_dir = self.get_trtllm_bench_model()
model_name = self._config.model_name
dataset_path = os.path.join(engine_dir, "synthetic_data.json")
report_path = os.path.join(engine_dir, "report.json")
if not model_name.endswith("_hf"):
model_name = model_name + "_hf"
hf_model_name = HF_MODEL_PATH.get(model_name, "")
tp_pp_str = f"tp_{self._config.tp_size}_pp_{self._config.pp_size}"
engine_dir = os.path.join(engine_dir, hf_model_name, tp_pp_str)
benchmark_cmd = [
self._benchmark_script,
f"--model={model_name}",
f"--model_path={model_dir}",
"throughput",
f"--dataset={dataset_path}",
f"--max_batch_size={self._config.max_batch_size}",
f"--max_num_tokens={self._config.max_num_tokens}",
f"--report_json={report_path}",
f"--kv_cache_free_gpu_mem_fraction={self._config.kv_cache_free_gpu_mem_fraction}",
]
if self._config.backend == "pytorch":
benchmark_cmd += ["--backend=pytorch"]
elif self._config.backend == "_autodeploy":
benchmark_cmd += ["--backend=_autodeploy"]
else:
benchmark_cmd += [
f"--backend=tensorrt", f"--engine_dir={engine_dir}"
]
if self._config.num_reqs > 0:
benchmark_cmd += [f"--num_requests={self._config.num_reqs}"]
if self._config.concurrency != -1:
benchmark_cmd += [f"--concurrency={self._config.concurrency}"]
if self._config.ep_size != None:
benchmark_cmd += [f"--ep={self._config.ep_size}"]
if self._config.tp_size > 1:
benchmark_cmd += [f"--tp={self._config.tp_size}"]
if self._config.pp_size > 1:
benchmark_cmd += [f"--pp={self._config.pp_size}"]
if self._config.streaming == "streaming":
benchmark_cmd += [f"--streaming"]
if self._config.num_gpus > 1:
benchmark_cmd += [f"--warmup={2 * self._config.num_gpus}"]
#Add extra-llm-api-config.yml for pytorch backend and tensorrt backend with extra flag
if self._config.backend == "pytorch" or (self._config.backend == ""
and self._config.extra):
pytorch_config_path = os.path.join(engine_dir,
"extra-llm-api-config.yml")
if not os.path.exists(pytorch_config_path):
os.makedirs(os.path.dirname(pytorch_config_path), exist_ok=True)
config = get_model_yaml_config(self._config.to_string(),
lora_dirs=self.lora_dirs)
if config:
print_info(f"pytorch/TRT model config: {config}")
with open(pytorch_config_path, 'w') as f:
yaml.dump(config, f, default_flow_style=False)
benchmark_cmd += [
f"--extra_llm_api_options={pytorch_config_path}"
]
# If guided_decoding_backend is set, we need to initialize tokenizer
if config.get('guided_decoding_backend') is not None:
benchmark_cmd += ["--no_skip_tokenizer_init"]
elif self._config.backend == "_autodeploy":
autodeploy_config_path = os.path.join(engine_dir,
"extra_llm_api_options.yaml")
if not os.path.exists(autodeploy_config_path):
os.makedirs(os.path.dirname(autodeploy_config_path),
exist_ok=True)
# Create _autodeploy specific configuration
autodeploy_config = {
'transforms': {
'compile_model': {
'backend': self._config.ad_compile_backend
},
'resize_kv_cache': {
'free_mem_ratio': self._config.free_mem_ratio
},
},
'runtime': self._config.extra_runtime,
'skip_loading_weights': self._config.skip_loading_weights
}
print_info(f"_autodeploy model config: {autodeploy_config}")
with open(autodeploy_config_path, 'w') as f:
yaml.dump(autodeploy_config, f, default_flow_style=False)
benchmark_cmd += [
f"--extra_llm_api_options={autodeploy_config_path}"
]
# for sampler options
sampler_options_path = os.path.join(engine_dir, "sampler_options.yml")
if not os.path.exists(sampler_options_path):
os.makedirs(os.path.dirname(sampler_options_path), exist_ok=True)
sampler_config = get_sampler_options_config(self._config.to_string())
if sampler_config:
print_info(f"sampler options config: {sampler_config}")
with open(sampler_options_path, 'w') as f:
yaml.dump(sampler_config, f, default_flow_style=False)
benchmark_cmd += [f"--sampler_options={sampler_options_path}"]
return benchmark_cmd
def get_commands(self):
# Whether this is python or cpp runtime perf test.
is_python = self._config.runtime == "python"
num_gpus = self._config.num_gpus
is_aggr = self._config.runtime == "aggr_server"
is_disagg = self._config.runtime == "disagg_server"
is_multi_node_disagg = self._config.runtime == "multi_node_disagg_server"
perf_sanity_output_dir = os.path.join(self._output_dir,
self._test_param_labels)
if is_aggr:
if not os.path.exists(perf_sanity_output_dir):
os.makedirs(perf_sanity_output_dir, exist_ok=True)
server_cmds, server_envs, client_cmds, client_envs, names = self.get_trtllm_aggr_commands(
perf_sanity_output_dir)
return PerfAggrScriptTestCmds(server_cmds=server_cmds,
server_envs=server_envs,
client_cmds=client_cmds,
client_envs=client_envs,
names=names,
timeout=3600,
output_dir=perf_sanity_output_dir)
if is_disagg:
ctx_cmd, gen_cmd = self._get_disagg_worker_deploy_command()
server_cmd = self._get_disagg_server_deploy_command()
client_cmd = self._get_disagg_client_command()
benchmark_cmd = self._get_disagg_benchmark_command()
return PerfDisaggScriptTestCmds(ctx_cmd, gen_cmd, server_cmd,
client_cmd, benchmark_cmd)
if is_multi_node_disagg:
if not os.path.exists(perf_sanity_output_dir):
os.makedirs(perf_sanity_output_dir, exist_ok=True)
ctx_server_cmds, ctx_server_envs, gen_server_cmds, gen_server_envs, disagg_server_cmds, disagg_server_envs, benchmark_cmds, benchmark_envs = self.get_trtllm_multi_node_disagg_commands(
perf_sanity_output_dir)
return PerfMultiNodeDisaggScriptTestCmds(
ctx_server_cmds=ctx_server_cmds,
ctx_server_envs=ctx_server_envs,
gen_server_cmds=gen_server_cmds,
gen_server_envs=gen_server_envs,
disagg_server_cmds=disagg_server_cmds,
disagg_server_envs=disagg_server_envs,
benchmark_cmds=benchmark_cmds,
benchmark_envs=benchmark_envs,
timeout=self._config.disagg_configs[0]['timeout'],
hostname=self._config.disagg_configs[0]['hostname'],
disagg_serving_type=self._config.disagg_configs[0]
['disagg_serving_type'],
num_ctx_servers=self._config.disagg_configs[0]['hardware']
['num_ctx_servers'],
num_gen_servers=self._config.disagg_configs[0]['hardware']
['num_gen_servers'],
output_dir=perf_sanity_output_dir)
if is_python and num_gpus > 1:
# TODO: Fix https://nvbugs/4449875
pytest.skip(
"multi-gpu tests with python runtime is skipped because of hanging issue. See https://nvbugs/4449875"
)
if is_windows() and num_gpus > 1:
pytest.skip(
"multi-gpu not supported on Windows yet, skipped for now")
# Construct engine build command.
engine_dir = self._get_engine_dir()
build_cmd = []
if self._config.runtime == "bench":
if self._config.backend in ["pytorch", "_autodeploy"]:
# Skip building process as it is pytorch or _autodeploy backend")
pass
else:
build_cmd = self.get_trtllm_bench_build_command(engine_dir)
else:
pytest.skip("only support trtllm-bench runtime for now")
# Construct prepare synthetic data command
data_cmds = []
# Construct benchmark commands for each bs and seq len combination.
benchmark_cmds = []
for bs in self._config.batch_sizes:
for len_idx, input_len in enumerate(self._config.input_lens):
output_len = None if self._config.is_bert_like(
) else self._config.output_lens[len_idx]
if self._config.runtime == "bench":
benchmark_cmd = self.get_trtllm_bench_command(engine_dir)
else:
pytest.skip("only support trtllm-bench runtime for now")
benchmark_cmds.append(benchmark_cmd)
data_cmd = self.get_prepare_data_command(
engine_dir, input_len, output_len)
data_cmds.append(data_cmd)
# Construct MPI command.
mpi_cmd = []
if num_gpus > 1 and num_gpus <= 8 and not self._config.runtime == "bench":
if cpu_socket_count_gt_1():
mpi_cmd = [
"mpirun", "--map-by", "socket", "-n", f"{num_gpus}",
"--allow-run-as-root"
]
else:
mpi_cmd = ["mpirun", "-n", f"{num_gpus}", "--allow-run-as-root"]
if self._build_script == "trtllm-bench":
return PerfBenchScriptTestCmds(data_cmds, build_cmd, benchmark_cmds,
mpi_cmd, is_python)
else:
pytest.skip("only support trtllm-bench runtime for now")
def get_perf_result(self, outputs: Dict[int, str]) -> float:
"""
Get perf metric result from test output logs.
"""
metric = self._current_metric
cmd_idx = metric.cmd_idx
metric_name = metric.metric_name
num_gpus = self._config.num_gpus
# Make sure we have outputs.
assert cmd_idx in outputs, f"Output log for command {cmd_idx} does not exist!"
# Use all applicable regex patterns to go through the log from the N-th command, where N = cmd_idx.
print_info(
f"Searching for metric {metric_name} from output log of command {cmd_idx} ..."
)
regex_matches = [
metric.metric_regex.search(line)
for line in outputs[cmd_idx].split("\n")
]
metric_values = []
for match in regex_matches:
if match:
# Handle multiple capture groups - use the first non-None group
value = None
for i in range(1, len(match.groups()) + 1):
if match.group(i) is not None:
value = match.group(i)
break
if value is not None:
metric_values.append(float(value))
if len(metric_values) == 0:
if self._build_script == "trtllm-bench" and self._config.num_gpus > 1 and metric.metric_type == PerfMetricType.BUILD_TIME:
print_info("skip building process for multi-gpu test"
) #https://nvbugspro.nvidia.com/bug/5210111
metric_values = [0.0]
else:
raise RuntimeError(
f"Cannot find perf result for {metric_name} from perf script logs!"
)
if metric.metric_type in BUILDER_METRICS and metric.metric_type != PerfMetricType.ENGINE_SIZE:
# For enc-dec models, there are 2 builder perf metrics, we add them up.
if self._config.is_enc_dec():
assert len(
metric_values
) == 2 * num_gpus, f"Enc-Dec models must have num of metrics 2*{num_gpus} but got {len(metric_values)}!"
enc_metrics = metric_values[:num_gpus]
dec_metrics = metric_values[num_gpus:]
gather_function = sum
# Measure BUILD_PEAK_CPU_MEMORY, BUILD_PEAK_GPU_MEMORY by max function
if metric.metric_type in [
PerfMetricType.BUILD_PEAK_CPU_MEMORY,
PerfMetricType.BUILD_PEAK_GPU_MEMORY
]:
gather_function = max
metric_values = [
gather_function([x, y])
for x, y in zip(enc_metrics, dec_metrics)
]
print_info(
f"Combining up enc builder_perf {enc_metrics} and dec builder_perf {dec_metrics} to {metric_values}."
)
# For other models, builder metric should equal # gpus.
elif self._build_script != "trtllm-build" and self._build_script != "trtllm-bench":
assert len(
metric_values
) == num_gpus, f"num of metrics: {len(metric_values)} should match num_gpus: {num_gpus}"
# Use max perf metrics across GPUS
if len(metric_values) > 1:
metric_value = max(metric_values)
print_info(
f"Use max value {metric_value} out of {metric_values} for perf metric {metric_name}."
)
else:
metric_value = metric_values[0]
print_info(
f"Use value {metric_value} for perf metric {metric_name}.")
return metric_value
def get_threshold(self) -> float:
return self._current_metric.metric_threshold
def get_absolute_threshold(self) -> float:
return self._current_metric.metric_abs_threshold
def get_metric_type(self) -> PerfMetricType:
return self._current_metric.metric_type
def run_metrics(self, llm_venv, gpu_clock_lock, session_data_writer,
output_dir):
"""
Run through the commands and parse multiple perf metrics from the logs.
"""
#print info to separate cases
self._current_cmd_idx = 0
metrics = self._get_metrics()
outputs = {}
result_states = {}
errors = []
def add_myelin_time_pass_to(input_env):
time_pass_flag = r" -time_pass=on"
old_myelin_env = input_env.get("__LUNOWUD", "")
if time_pass_flag not in old_myelin_env:
input_env["__LUNOWUD"] = old_myelin_env + time_pass_flag
return old_myelin_env
old_llm_venv = add_myelin_time_pass_to(llm_venv._new_env)
if self._config.runtime == 'bench':
#prepare dataset first for trtllm-bench
print_info(f"Running command for generating dataset")
outputs = self.run_ex("prepare_dataset",
None,
llm_venv,
gpu_clock_lock,
session_data_writer,
output_dir,
outputs=outputs,
original_test_name="prepare_dataset",
cmd_idx=self._current_cmd_idx)
# Save the result state.
result_state = self.get_result_state()
result_states[self._current_cmd_idx] = result_state
if result_state != "valid":
errors.append(self.get_error())
try:
for metric in metrics:
# Make sure that cmd_idx is in ascending order.
assert metric.cmd_idx >= self._current_cmd_idx, "Command indices must be in ascending order!"
self._current_cmd_idx = metric.cmd_idx
self._current_metric = metric
# If the same command has previously failed, do not run it again.
if self._current_cmd_idx in result_states and result_states[
self._current_cmd_idx] == "failed":
print_warning(
f"Skipped running command for {metric.metric_name} since the previous run failed."
)
continue
# If engine build command already failed, do not run benchmark commands.
if 0 in result_states and result_states[0] == "failed":
print_warning(
f"Skipped running command for {metric.metric_name} since the engine building command failed."
)
continue
# Run the command or reuse the existing output logs.
print_info(f"Running command for {metric.metric_name}")
outputs = self.run_ex(
metric.metric_name,
metric.metric_type,
llm_venv,
gpu_clock_lock,
session_data_writer,
output_dir,
outputs=outputs,
original_test_name=metric.original_test_name,
cmd_idx=self._current_cmd_idx)
# Save the result state.
result_state = self.get_result_state()
result_states[self._current_cmd_idx] = result_state
if result_state != "valid":
errors.append(self.get_error())
if self._current_cmd_idx in self._test_results:
del self._test_results[self._current_cmd_idx]
self.upload_test_results_to_database()
finally:
# Clean up engine dir after use.
shutil.rmtree(self._get_engine_dir(), ignore_errors=True)
llm_venv._new_env["__LUNOWUD"] = old_llm_venv
# Check if any commands failed.
if not all([result_states[idx] == "valid" for idx in result_states]):
# If there is only one error, throw it directly.
if len(errors) == 1:
raise errors[0]
# Otherwise, combine all the error messages and re-raise a generic RuntimeError.
msg = "Multiple Errors happened:\n"
for error_idx, e in enumerate(errors):
msg += f"> Error {error_idx+1}/{len(errors)}: {type(e).__name__}: {e}\n"
raise RuntimeError(msg)
def upload_test_results_to_database(self):
"""
Upload the test results and baseline to database.
"""
def prefix_server_config_dict(config_dict: dict,
prefix_name: str) -> dict:
prefixed_dict = {}
for key, value in config_dict.items():
type_prefix = key[0:2] # 'l_', 's_', 'b_', 'd_'
rest = key[2:]
prefixed_dict[f"{type_prefix}{prefix_name}_{rest}"] = value
return prefixed_dict
match_keys = []
# Only aggr_server and multi_node_disagg_server will upload.
if self._config.runtime == "aggr_server":
job_config = get_job_info()
job_config["s_gpu_type"] = self._config.gpu_type
is_post_merge = job_config["b_is_post_merge"]
new_data_dict = {}
cmd_idx = 0
for server_idx, client_configs in self._config.server_client_configs.items(
):
server_config = self._config.server_configs[server_idx]
server_config_dict = server_config.to_db_data()
for client_config in client_configs:
client_config_dict = client_config.to_db_data()
# If cmd_idx not in self._test_results or some metrics missing, skip this cmd_idx
if cmd_idx not in self._test_results or not all(
metric_type in self._test_results[cmd_idx]
for metric_type in AGGR_SERVER_METRICS):
print_info(
f"Skipped posting command {cmd_idx} 's test results since some metrics are missing in test results."
)
cmd_idx += 1
continue
new_data = {
"s_runtime":
"multi_node_aggr_server" if server_config.gpus
!= server_config.gpus_per_node else "aggr_server"
}
new_data.update(job_config)
new_data.update(server_config_dict)
new_data.update(client_config_dict)
for metric_type in AGGR_SERVER_METRICS:
new_data[
f"d_{PERF_METRIC_STRING[metric_type]}"] = self._test_results[
cmd_idx][metric_type]
add_id(new_data)
new_data_dict[cmd_idx] = new_data
cmd_idx += 1
if not match_keys:
if server_config.match_mode == "scenario":
match_keys = SCENARIO_MATCH_FIELDS.copy()
else:
match_keys.append("s_runtime")
match_keys.extend(server_config_dict.keys())
match_keys.extend(client_config_dict.keys())
elif self._config.runtime == "multi_node_disagg_server":
if self._config.disagg_configs[0][
'disagg_serving_type'] != "BENCHMARK":
return
job_config = get_job_info()
job_config["s_gpu_type"] = self._config.gpu_type
is_post_merge = job_config["b_is_post_merge"]
new_data_dict = {}
cmd_idx = 0
for disagg_config in self._config.disagg_configs:
# If cmd_idx not in self._test_results or some metrics missing, skip this cmd_idx
if cmd_idx not in self._test_results or not all(
metric_type in self._test_results[cmd_idx]
for metric_type in AGGR_SERVER_METRICS):
print_info(
f"Skipped posting command {cmd_idx} 's test results since some metrics are missing in test results."
)
cmd_idx += 1
continue
# Get ctx_server and gen_server configs with prefixed keys
ctx_server_config_dict = disagg_config['ctx_server'].to_db_data(
)
gen_server_config_dict = disagg_config['gen_server'].to_db_data(
)
ctx_server_config_dict = prefix_server_config_dict(
ctx_server_config_dict, 'ctx')
gen_server_config_dict = prefix_server_config_dict(
gen_server_config_dict, 'gen')
client_config_dict = disagg_config['client'].to_db_data()
# Build new_data
new_data = {
"s_runtime": "multi_node_disagg_server",
"s_server_env_var": disagg_config['server_env_var']
}
new_data.update(job_config)
new_data.update(ctx_server_config_dict)
new_data.update(gen_server_config_dict)
new_data.update(client_config_dict)
# Add hardware information
hardware = disagg_config.get('hardware', {})
new_data["l_num_ctx_servers"] = hardware.get(
'num_ctx_servers', 0)
new_data["l_num_gen_servers"] = hardware.get(
'num_gen_servers', 0)
# Add metrics from test results
for metric_type in AGGR_SERVER_METRICS:
new_data[
f"d_{PERF_METRIC_STRING[metric_type]}"] = self._test_results[
cmd_idx][metric_type]
add_id(new_data)
new_data_dict[cmd_idx] = new_data
cmd_idx += 1
if not match_keys:
match_keys.extend(
["s_runtime", "l_num_ctx_servers", "l_num_gen_servers"])
match_keys.extend(ctx_server_config_dict.keys())
match_keys.extend(gen_server_config_dict.keys())
match_keys.extend(client_config_dict.keys())
else:
return
# Get history data for each cmd_idx
history_baseline_dict, history_data_dict = get_history_data(
new_data_dict, self._config.gpu_type, match_keys)
# Prepare regressive test cases
regressive_data_list = prepare_regressive_test_cases(
history_baseline_dict, new_data_dict)
if is_post_merge:
# Prepare new baseline data for post-merge
new_baseline_data_dict = prepare_baseline_data(
history_baseline_dict, history_data_dict, new_data_dict)
else:
# Pre-merge does not need to upload baseline data
new_baseline_data_dict = None
if self._config.upload_to_db:
# Upload the new perf data and baseline data to database
post_new_perf_data(new_baseline_data_dict, new_data_dict,
regressive_data_list)
perf_result_output_dir = os.path.join(self._output_dir,
self._test_param_labels)
write_regressive_test_cases(regressive_data_list, new_data_dict,
perf_result_output_dir)
def _get_engine_dir(self) -> str:
"""
Get the engine directory to store the engine.
"""
escaped_label = self._test_param_labels.replace("+", "_").replace(
":", "_").replace(",", "_")
return os.path.join(self._working_dir, "perf_engines", escaped_label)
def _get_metrics(self) -> List[PerfTestMetric]:
"""
Generate all the metric configs for the current test.
"""
metrics = []
if self._config.runtime == "aggr_server":
cmd_idx = 0
for server_idx, client_configs in self._config.server_client_configs.items(
):
server_name = self._config.server_configs[server_idx].name
for client_config in client_configs:
for metric_type in AGGR_SERVER_METRICS:
metrics.append(
PerfTestMetric(
original_test_name=self._full_test_name,
metric_name=self._get_metric_name(
metric_type=metric_type,
server_name=server_name,
client_name=client_config.name),
metric_type=metric_type,
metric_regex=self._get_metric_regex(
metric_type),
metric_threshold=self._get_metric_threshold(
metric_type),
metric_abs_threshold=self.
_get_metric_abs_threshold(metric_type),
cmd_idx=cmd_idx,
))
cmd_idx += 1
return metrics
if self._config.runtime == "disagg_server":
for metric_type in DISAGG_SERVER_METRICS:
metrics.append(
PerfTestMetric(
original_test_name=self._full_test_name,
metric_name=self._get_metric_name(
metric_type=metric_type),
metric_type=metric_type,
metric_regex=self._get_metric_regex(metric_type),
metric_threshold=self._get_metric_threshold(
metric_type),
metric_abs_threshold=self._get_metric_abs_threshold(
metric_type),
cmd_idx=0,
))
return metrics
if self._config.runtime == "multi_node_disagg_server":
cmd_idx = 0
for disagg_config in self._config.disagg_configs:
config_name = disagg_config['name']
for metric_type in AGGR_SERVER_METRICS:
metrics.append(
PerfTestMetric(
original_test_name=self._full_test_name,
metric_name=self._get_metric_name(
metric_type=metric_type,
disagg_config_name=config_name),
metric_type=metric_type,
metric_regex=self._get_metric_regex(metric_type),
metric_threshold=self._get_metric_threshold(
metric_type),
metric_abs_threshold=self._get_metric_abs_threshold(
metric_type),
cmd_idx=cmd_idx,
))
cmd_idx += 1
return metrics
# Build command is the first command.
cmd_idx = 0 if self._config.runtime != "bench" else 1
if self._config.runtime == "bench":
if self._config.backend in ["pytorch", "_autodeploy"]:
print_info(
f"Skip building process for {self._config.model_name} as it is {self._config.backend} backend"
)
builder_metrics = []
else:
builder_metrics = [PerfMetricType.BUILD_TIME]
else:
builder_metrics = BUILDER_METRICS.copy()
# Add all builder_perf metrics
for metric_type in builder_metrics:
metrics.append(
PerfTestMetric(
original_test_name=self._full_test_name,
metric_name=self._get_metric_name(metric_type=metric_type),
metric_type=metric_type,
metric_regex=self._get_metric_regex(metric_type),
metric_threshold=self._get_metric_threshold(metric_type),
metric_abs_threshold=self._get_metric_abs_threshold(
metric_type),
cmd_idx=cmd_idx,
))
if self._config.build_only:
return metrics
# Then, construct inference latency and gpu mem usage metrics, for each
# bs and each seq len.
for bs in self._config.batch_sizes:
for len_idx, input_len in enumerate(self._config.input_lens):
cmd_idx += 1
output_len = None if self._config.is_bert_like(
) else self._config.output_lens[len_idx]
# Get list of metrics depending on config.
if self._config.runtime == "bench":
metric_types = BENCH_INFERENCE_METRICS.copy()
if self._config.streaming == "streaming":
metric_types.append(PerfMetricType.FIRST_TOKEN_TIME)
metric_types.append(PerfMetricType.OUTPUT_TOKEN_TIME)
else:
metric_types = INFERENCE_METRICS.copy()
for metric_type in metric_types:
metrics.append(
PerfTestMetric(
original_test_name=self._full_test_name,
metric_name=self._get_metric_name(
metric_type=metric_type,
bs=bs,
input_len=input_len,
output_len=output_len),
metric_type=metric_type,
metric_regex=self._get_metric_regex(metric_type),
metric_threshold=self._get_metric_threshold(
metric_type),
metric_abs_threshold=self._get_metric_abs_threshold(
metric_type),
cmd_idx=cmd_idx,
))
return metrics
def _get_metric_name(self,
metric_type: PerfMetricType,
bs: int = None,
input_len: int = None,
output_len: int = None,
server_name: str = None,
client_name: str = None,
disagg_config_name: str = None) -> str:
"""
Construct the metric name for given metric_type, bs, input_len, and output_len.
"""
# Get device subtype for autodeploy tests
device_subtype = None
if (hasattr(self, '_gpu_clock_lock') and self._gpu_clock_lock
and self._config.backend == "_autodeploy"):
device_subtype = self._gpu_clock_lock.get_device_subtype()
if metric_type in BUILDER_METRICS:
# We build one engine for all benchmark runs, so add all bs and seq lens to the metric name.
metric_label = self._config.to_string(device_subtype=device_subtype)
elif self._config.runtime == "aggr_server":
metric_label = self._config.to_string(
custom_server_name=server_name,
custom_client_name=client_name,
)
elif self._config.runtime == "multi_node_disagg_server":
metric_label = self._config.to_string(
custom_server_name=disagg_config_name)
else:
# Otherwise, generate per-bs and per-seqlen label.
metric_label = self._config.to_string(
custom_bs=bs,
custom_input_len=input_len,
custom_output_len=output_len,
device_subtype=device_subtype,
)
metric_name = f"test_perf_metric_{metric_type.lower()}"
return self._test_domain_name + "::" + metric_name + "[" + metric_label + "]"
def _get_metric_regex(self, metric_type: PerfMetricType) -> re.Pattern:
"""
Get the regex used to parse the metric result for the metric type.
"""
if self._config.runtime == "bench":
if metric_type not in BENCH_PERF_METRIC_LOG_QUERIES:
raise ValueError(f"Unexpected metric_type: {metric_type}")
return BENCH_PERF_METRIC_LOG_QUERIES[metric_type]
elif self._config.runtime == "aggr_server":
if metric_type not in AGGR_SERVER_PERF_METRIC_LOG_QUERIES:
raise ValueError(f"Unexpected metric_type: {metric_type}")
return AGGR_SERVER_PERF_METRIC_LOG_QUERIES[metric_type]
elif self._config.runtime == "multi_node_disagg_server":
if metric_type not in AGGR_SERVER_PERF_METRIC_LOG_QUERIES:
raise ValueError(f"Unexpected metric_type: {metric_type}")
return AGGR_SERVER_PERF_METRIC_LOG_QUERIES[metric_type]
else:
pytest.skip("only support trtllm-bench runtime for now")
def _get_metric_threshold(self, metric_type: PerfMetricType) -> float:
"""
Get the threshold for the metric type.
"""
if metric_type not in PERF_METRIC_THRESHOLD:
raise ValueError(f"Unexpected metric_type: {metric_type}")
return PERF_METRIC_THRESHOLD[metric_type][0]
def _get_metric_abs_threshold(self, metric_type: PerfMetricType) -> float:
"""
Get the absolute threshold for the metric type.
"""
if metric_type not in PERF_METRIC_THRESHOLD:
raise ValueError(f"Unexpected metric_type: {metric_type}")
return PERF_METRIC_THRESHOLD[metric_type][1]
def _gen_disagg_worker_config(self):
ctx_config = {
'max_batch_size': 32,
'max_num_tokens': 4096,
'max_seq_len': 4096,
'tensor_parallel_size': self._config.ctx_tp_size,
'enable_attention_dp': self._config.ctx_dp_size > 1,
'print_iter_log': True,
'disable_overlap_scheduler': True,
'kv_cache_config': {
'enable_block_reuse': False,
# 'free_gpu_memory_fraction': ctx_free_gpu_memory_fraction,
'free_gpu_memory_fraction': 0.5,
'dtype': 'fp8',
},
'disable_overlap_scheduler': True,
'cache_transceiver_config': {
# 'max_tokens_in_buffer': cache_transceiver_max_num_tokens,
'max_tokens_in_buffer': 4096,
'backend': 'DEFAULT',
},
}
gen_config = {
'tensor_parallel_size': self._config.gen_tp_size,
'enable_attention_dp': self._config.gen_dp_size > 1,
'pipeline_parallel_size': self._config.gen_pp_size,
'max_batch_size': 32,
'max_num_tokens': 4096,
'max_seq_len': 4096,
'cuda_graph_config': {
'enable_padding': True,
'batch_sizes': [1, 2, 4, 8, 16, 32],
},
'print_iter_log': True,
'kv_cache_config': {
'enable_block_reuse': False,
'free_gpu_memory_fraction': 0.5,
'dtype': 'fp8',
},
'cache_transceiver_config': {
'max_tokens_in_buffer': 4096,
'backend': 'DEFAULT',
},
}
return ctx_config, gen_config
def _gen_disagg_server_config(self):
server_config = {
'hostname': 'localhost',
'port': 8000,
'backend': 'pytorch',
'context_servers': {
'num_instances': 1,
'urls': ['localhost:8001']
},
'generation_servers': {
'num_instances': 1,
'urls': ['localhost:8002']
}
}
return server_config
def _get_disagg_worker_deploy_command(self):
ctx_config, gen_config = self._gen_disagg_worker_config()
ctx_config_path = os.path.join(self._working_dir, "ctx_config.yaml")
gen_config_path = os.path.join(self._working_dir, "gen_config.yaml")
with open(ctx_config_path, 'w', encoding='utf-8') as f:
yaml.dump(ctx_config, f)
with open(gen_config_path, 'w', encoding='utf-8') as f:
yaml.dump(gen_config, f)
print_info(f"ctx_server_config: {ctx_config}")
print_info(f"gen_server_config: {gen_config}")
model_path = MODEL_PATH_DICT[self._config.model_name]
model_dir = os.path.join(llm_models_root(), model_path)
ctx_gpu_list = ",".join(
[str(i) for i in range(self._config.ctx_server_workers)])
gen_gpu_list = ",".join([
str(i) for i in range(
self._config.ctx_server_workers,
self._config.ctx_server_workers +
self._config.gen_server_workers)
])
ctx_cmd = f'CUDA_VISIBLE_DEVICES={ctx_gpu_list} trtllm-serve {model_dir} --host localhost --port 8001 --extra_llm_api_options {ctx_config_path}'
gen_cmd = f'CUDA_VISIBLE_DEVICES={gen_gpu_list} trtllm-serve {model_dir} --host localhost --port 8002 --extra_llm_api_options {gen_config_path}'
return ctx_cmd, gen_cmd
def _get_disagg_server_deploy_command(self):
server_config = self._gen_disagg_server_config()
server_config_path = os.path.join(self._working_dir,
"server_config.yaml")
with open(server_config_path, 'w', encoding='utf-8') as f:
yaml.dump(server_config, f)
return f'trtllm-serve disaggregated -c {server_config_path} -t 3600 -r 3600'
def _get_disagg_client_command(self):
client_dir = os.path.join(self._llm_root,
"examples/disaggregated/clients")
client_cmd = [
'python3', f'{client_dir}/disagg_client.py', '-c',
f'{self._working_dir}/server_config.yaml', '-p',
f'{client_dir}/prompts.json', '--ignore-eos',
'--server-start-timeout',
str(3600)
]
return client_cmd
def _get_disagg_benchmark_command(self):
benchmark_script = os.path.join(self._llm_root, "tensorrt_llm", "serve",
"scripts", "benchmark_serving.py")
model_path = MODEL_PATH_DICT[self._config.model_name]
model_dir = os.path.join(llm_models_root(), model_path)
shared_gpt_path = os.path.join(
llm_models_root(), "datasets",
"ShareGPT_V3_unfiltered_cleaned_split.json")
benchmark_cmd = [
'python3',
benchmark_script,
'--model',
model_dir,
'--tokenizer',
model_dir,
'--dataset-name',
'random',
'--dataset-path',
shared_gpt_path,
'--random-input-len',
'1024',
'--random-output-len',
'1024',
'--random-prefix-len',
'0',
'--num-prompts',
'320',
'--max-concurrency',
'32',
'--host',
'localhost',
'--port',
'8000',
'--ignore-eos',
'--no-test-input',
'--percentile-metrics',
'e2el,ttft',
]
return benchmark_cmd
def run_perf_test(perf_case_name, trt_performance_cache_fpath,
trt_gpu_clock_lock, llm_session_data_writer, output_dir,
llm_venv, llm_root):
"""
The actual test definition for TensorRT LLM perf test.
"""
working_dir = llm_venv.get_working_directory()
test_runner = MultiMetricPerfTest(perf_case_name)
test_runner.set_runtime_configs(llm_root, working_dir, output_dir,
trt_performance_cache_fpath,
trt_gpu_clock_lock)
test_runner.run_metrics(llm_venv, trt_gpu_clock_lock,
llm_session_data_writer, output_dir)
def generate_perf_tests(session, config, items):
"""
Generate all the perf tests based on test lists to speed up the test collection time.
"""
print_info(f"Dynamically generating perf tests...")
valid_prefixes = [
"perf/test_perf.py::test_perf[",
# TRT pipeline adds "llm/" prefix, so include it so that TRT-LLM perf tests can run in TRT pipelines.
"llm/perf/test_perf.py::test_perf[",
]
items = generate_test_nodes(session, config, items, valid_prefixes,
run_perf_test)
print_info(f"Completed generating perf tests.")
return items
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