Results from GH action on NVIDIA_RTX4090x1

mlcommons · Dec 23, 2024 · 95ab16f · 95ab16f
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diff --git a/closed/MLCommons/code/bert-99/README.md b/closed/MLCommons/code/bert-99/README.md
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+TBD
diff --git a/open/MLCommons/code/bert-99/README.md b/open/MLCommons/code/bert-99/README.md
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+TBD
diff --git a/...-nvidia_original-gpu-tensorrt-vdefault-default_config/bert-99/offline/README.md b/...-nvidia_original-gpu-tensorrt-vdefault-default_config/bert-99/offline/README.md
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+This experiment is generated using the [MLCommons Collective Mind automation framework (CM)](https://github.com/mlcommons/cm4mlops).
+
+*Check [CM MLPerf docs](https://docs.mlcommons.org/inference) for more details.*
+
+## Host platform
+
+* OS version: Linux-6.8.0-49-generic-x86_64-with-glibc2.29
+* CPU version: x86_64
+* Python version: 3.8.10 (default, Nov  7 2024, 13:10:47) 
+[GCC 9.4.0]
+* MLCommons CM version: 3.5.2
+
+## CM Run Command
+
+See [CM installation guide](https://docs.mlcommons.org/inference/install/).
+
+```bash
+pip install -U cmind
+
+cm rm cache -f
+
+cm pull repo mlcommons@mlperf-automations --checkout=225220c7d9bb7e66e5b9a1e1ebfc3e0180fbd094
+
+cm run script \
+	--tags=app,mlperf,inference,generic,_nvidia,_bert-99,_tensorrt,_cuda,_valid,_r4.1-dev_default,_offline \
+	--quiet=true \
+	--env.CM_QUIET=yes \
+	--env.CM_MLPERF_IMPLEMENTATION=nvidia \
+	--env.CM_MLPERF_MODEL=bert-99 \
+	--env.CM_MLPERF_RUN_STYLE=valid \
+	--env.CM_MLPERF_SKIP_SUBMISSION_GENERATION=False \
+	--env.CM_DOCKER_PRIVILEGED_MODE=True \
+	--env.CM_MLPERF_BACKEND=tensorrt \
+	--env.CM_MLPERF_SUBMISSION_SYSTEM_TYPE=datacenter,edge \
+	--env.CM_MLPERF_CLEAN_ALL=True \
+	--env.CM_MLPERF_DEVICE=cuda \
+	--env.CM_MLPERF_SUBMISSION_DIVISION=closed \
+	--env.CM_MLPERF_USE_DOCKER=True \
+	--env.CM_NVIDIA_GPU_NAME=rtx_4090 \
+	--env.CM_HW_NAME=RTX4090x1 \
+	--env.CM_RUN_MLPERF_SUBMISSION_PREPROCESSOR=yes \
+	--env.CM_MLPERF_INFERENCE_PULL_CODE_CHANGES=yes \
+	--env.CM_MLPERF_INFERENCE_PULL_SRC_CHANGES=yes \
+	--env.OUTPUT_BASE_DIR=/home/arjun/gh_action_results \
+	--env.CM_MLPERF_INFERENCE_SUBMISSION_DIR=/home/arjun/gh_action_submissions \
+	--env.CM_MLPERF_SUBMITTER=MLCommons \
+	--env.CM_USE_DATASET_FROM_HOST=yes \
+	--env.CM_USE_MODEL_FROM_HOST=yes \
+	--env.CM_MLPERF_LOADGEN_ALL_SCENARIOS=yes \
+	--env.CM_MLPERF_LOADGEN_COMPLIANCE=yes \
+	--env.CM_MLPERF_SUBMISSION_RUN=yes \
+	--env.CM_RUN_MLPERF_ACCURACY=on \
+	--env.CM_RUN_SUBMISSION_CHECKER=yes \
+	--env.CM_TAR_SUBMISSION_DIR=yes \
+	--env.CM_MLPERF_SUBMISSION_GENERATION_STYLE=full \
+	--env.CM_MLPERF_INFERENCE_VERSION=4.1-dev \
+	--env.CM_RUN_MLPERF_INFERENCE_APP_DEFAULTS=r4.1-dev_default \
+	--env.CM_MLPERF_LOADGEN_ALL_MODES=yes \
+	--env.CM_MLPERF_INFERENCE_SOURCE_VERSION=4.1.23 \
+	--env.CM_MLPERF_LAST_RELEASE=v4.1 \
+	--env.CM_TMP_PIP_VERSION_STRING= \
+	--env.CM_MODEL=bert-99 \
+	--env.CM_MLPERF_CLEAN_SUBMISSION_DIR=yes \
+	--env.CM_RERUN=yes \
+	--env.CM_MLPERF_LOADGEN_EXTRA_OPTIONS= \
+	--env.CM_MLPERF_LOADGEN_MODE=performance \
+	--env.CM_MLPERF_LOADGEN_SCENARIO=Offline \
+	--env.CM_MLPERF_LOADGEN_SCENARIOS,=SingleStream,Offline,Server \
+	--env.CM_MLPERF_LOADGEN_MODES,=performance,accuracy \
+	--env.CM_OUTPUT_FOLDER_NAME=valid_results \
+	--env.CM_DOCKER_REUSE_EXISTING_CONTAINER=yes \
+	--env.CM_DOCKER_DETACHED_MODE=yes \
+	--env.CM_MLPERF_INFERENCE_RESULTS_DIR_=/home/arjun/gh_action_results/valid_results \
+	--env.CM_DOCKER_CONTAINER_ID=bcea82088806 \
+	--env.CM_MLPERF_LOADGEN_COMPLIANCE_TEST=TEST01 \
+	--add_deps_recursive.compiler.tags=gcc \
+	--add_deps_recursive.coco2014-original.tags=_full \
+	--add_deps_recursive.coco2014-preprocessed.tags=_full \
+	--add_deps_recursive.imagenet-original.tags=_full \
+	--add_deps_recursive.imagenet-preprocessed.tags=_full \
+	--add_deps_recursive.openimages-original.tags=_full \
+	--add_deps_recursive.openimages-preprocessed.tags=_full \
+	--add_deps_recursive.openorca-original.tags=_full \
+	--add_deps_recursive.openorca-preprocessed.tags=_full \
+	--add_deps_recursive.coco2014-dataset.tags=_full \
+	--add_deps_recursive.igbh-dataset.tags=_full \
+	--add_deps_recursive.get-mlperf-inference-results-dir.tags=_version.r4_1-dev \
+	--add_deps_recursive.get-mlperf-inference-submission-dir.tags=_version.r4_1-dev \
+	--add_deps_recursive.mlperf-inference-nvidia-scratch-space.tags=_version.r4_1-dev \
+	--adr.compiler.tags=gcc \
+	--adr.coco2014-original.tags=_full \
+	--adr.coco2014-preprocessed.tags=_full \
+	--adr.imagenet-original.tags=_full \
+	--adr.imagenet-preprocessed.tags=_full \
+	--adr.openimages-original.tags=_full \
+	--adr.openimages-preprocessed.tags=_full \
+	--adr.openorca-original.tags=_full \
+	--adr.openorca-preprocessed.tags=_full \
+	--adr.coco2014-dataset.tags=_full \
+	--adr.igbh-dataset.tags=_full \
+	--adr.get-mlperf-inference-results-dir.tags=_version.r4_1-dev \
+	--adr.get-mlperf-inference-submission-dir.tags=_version.r4_1-dev \
+	--adr.mlperf-inference-nvidia-scratch-space.tags=_version.r4_1-dev \
+	--v=False \
+	--print_env=False \
+	--print_deps=False \
+	--dump_version_info=True \
+	--env.OUTPUT_BASE_DIR=/cm-mount/home/arjun/gh_action_results \
+	--env.CM_MLPERF_INFERENCE_SUBMISSION_DIR=/cm-mount/home/arjun/gh_action_submissions \
+	--env.MLPERF_SCRATCH_PATH=/home/cmuser/CM/repos/local/cache/5b2b0cc913a4453a
+```
+*Note that if you want to use the [latest automation recipes](https://docs.mlcommons.org/inference) for MLPerf (CM scripts),
+ you should simply reload mlcommons@mlperf-automations without checkout and clean CM cache as follows:*
+
+```bash
+cm rm repo mlcommons@mlperf-automations
+cm pull repo mlcommons@mlperf-automations
+cm rm cache -f
+
+```
+
+## Results
+
+Platform: RTX4090x1-nvidia_original-gpu-tensorrt-vdefault-default_config
+
+Model Precision: int8
+
+### Accuracy Results 
+`F1`: `90.15674`, Required accuracy for closed division `>= 89.96526`
+
+### Performance Results 
+`Samples per second`: `4124.17`
diff --git a/...onfig/bert-99/offline/RTX4090x1-nvidia_original-gpu-tensorrt-vdefault-default_config.json b/...onfig/bert-99/offline/RTX4090x1-nvidia_original-gpu-tensorrt-vdefault-default_config.json
@@ -0,0 +1,7 @@
+{
+  "starting_weights_filename": "https://zenodo.org/record/3750364/files/bert_large_v1_1_fake_quant.onnx",
+  "retraining": "no",
+  "input_data_types": "int32",
+  "weight_data_types": "int8",
+  "weight_transformations": "quantization, affine fusion"
+}
diff --git a/...nvidia_original-gpu-tensorrt-vdefault-default_config/bert-99/offline/accuracy_console.out b/...nvidia_original-gpu-tensorrt-vdefault-default_config/bert-99/offline/accuracy_console.out
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+[2024-12-22 20:21:07,756 main.py:229 INFO] Detected system ID: KnownSystem.RTX4090x1
+[2024-12-22 20:21:08,172 generate_conf_files.py:107 INFO] Generated measurements/ entries for RTX4090x1_TRT/bert-99/Offline
+[2024-12-22 20:21:08,172 __init__.py:46 INFO] Running command: ./build/bin/harness_bert --logfile_outdir="/cm-mount/home/arjun/gh_action_results/valid_results/RTX4090x1-nvidia_original-gpu-tensorrt-vdefault-default_config/bert-99/offline/accuracy" --logfile_prefix="mlperf_log_" --performance_sample_count=10833 --test_mode="AccuracyOnly" --gpu_batch_size=256 --mlperf_conf_path="/home/cmuser/CM/repos/local/cache/30a4695143b84b89/inference/mlperf.conf" --tensor_path="build/preprocessed_data/squad_tokenized/input_ids.npy,build/preprocessed_data/squad_tokenized/segment_ids.npy,build/preprocessed_data/squad_tokenized/input_mask.npy" --use_graphs=false --user_conf_path="/home/cmuser/CM/repos/mlcommons@mlperf-automations/script/generate-mlperf-inference-user-conf/tmp/403177c9892b404eaf63b19dc57cef7e.conf" --gpu_inference_streams=2 --gpu_copy_streams=2 --gpu_engines="./build/engines/RTX4090x1/bert/Offline/bert-Offline-gpu-int8_S_384_B_256_P_2_vs.custom_k_99_MaxP.plan" --scenario Offline --model bert
+[2024-12-22 20:21:08,172 __init__.py:53 INFO] Overriding Environment
+benchmark : Benchmark.BERT
+buffer_manager_thread_count : 0
+coalesced_tensor : True
+data_dir : /home/cmuser/CM/repos/local/cache/5b2b0cc913a4453a/data
+gpu_batch_size : 256
+gpu_copy_streams : 2
+gpu_inference_streams : 2
+input_dtype : int32
+input_format : linear
+log_dir : /home/cmuser/CM/repos/local/cache/dfbf240f980947f5/repo/closed/NVIDIA/build/logs/2024.12.22-20.21.06
+mlperf_conf_path : /home/cmuser/CM/repos/local/cache/30a4695143b84b89/inference/mlperf.conf
+offline_expected_qps : 0.0
+precision : int8
+preprocessed_data_dir : /home/cmuser/CM/repos/local/cache/5b2b0cc913a4453a/preprocessed_data
+scenario : Scenario.Offline
+system : SystemConfiguration(host_cpu_conf=CPUConfiguration(layout={CPU(name='13th Gen Intel(R) Core(TM) i9-13900K', architecture=<CPUArchitecture.x86_64: AliasedName(name='x86_64', aliases=(), patterns=())>, core_count=24, threads_per_core=1): 1}), host_mem_conf=MemoryConfiguration(host_memory_capacity=Memory(quantity=131.634476, byte_suffix=<ByteSuffix.GB: (1000, 3)>, _num_bytes=131634476000), comparison_tolerance=0.05), accelerator_conf=AcceleratorConfiguration(layout=defaultdict(<class 'int'>, {GPU(name='NVIDIA GeForce RTX 4090', accelerator_type=<AcceleratorType.Discrete: AliasedName(name='Discrete', aliases=(), patterns=())>, vram=Memory(quantity=23.98828125, byte_suffix=<ByteSuffix.GiB: (1024, 3)>, _num_bytes=25757220864), max_power_limit=450.0, pci_id='0x268410DE', compute_sm=89): 1})), numa_conf=None, system_id='RTX4090x1')
+tensor_path : build/preprocessed_data/squad_tokenized/input_ids.npy,build/preprocessed_data/squad_tokenized/segment_ids.npy,build/preprocessed_data/squad_tokenized/input_mask.npy
+test_mode : AccuracyOnly
+use_graphs : False
+user_conf_path : /home/cmuser/CM/repos/mlcommons@mlperf-automations/script/generate-mlperf-inference-user-conf/tmp/403177c9892b404eaf63b19dc57cef7e.conf
+system_id : RTX4090x1
+config_name : RTX4090x1_bert_Offline
+workload_setting : WorkloadSetting(HarnessType.Custom, AccuracyTarget.k_99, PowerSetting.MaxP)
+optimization_level : plugin-enabled
+num_profiles : 2
+config_ver : custom_k_99_MaxP
+accuracy_level : 99%
+inference_server : custom
+skip_file_checks : True
+power_limit : None
+cpu_freq : None
+&&&& RUNNING BERT_HARNESS # ./build/bin/harness_bert
+I1222 20:21:08.208585 19723 main_bert.cc:163] Found 1 GPUs
+I1222 20:21:08.294823 19723 bert_server.cc:147] Engine Path: ./build/engines/RTX4090x1/bert/Offline/bert-Offline-gpu-int8_S_384_B_256_P_2_vs.custom_k_99_MaxP.plan
+[I] [TRT] Loaded engine size: 414 MiB
+[I] [TRT] [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +6, GPU +8, now: CPU 578, GPU 1225 (MiB)
+[I] [TRT] [MemUsageChange] Init cuDNN: CPU +2, GPU +10, now: CPU 580, GPU 1235 (MiB)
+[I] [TRT] [MemUsageChange] TensorRT-managed allocation in engine deserialization: CPU +0, GPU +290, now: CPU 0, GPU 290 (MiB)
+I1222 20:21:08.548007 19723 bert_server.cc:208] Engines Creation Completed
+I1222 20:21:08.560138 19723 bert_core_vs.cc:385] Engine - Device Memory requirements: 704644608
+I1222 20:21:08.560142 19723 bert_core_vs.cc:393] Engine - Number of Optimization Profiles: 2
+I1222 20:21:08.560149 19723 bert_core_vs.cc:415] Engine - Profile 0 maxDims 98304 Bmax=256 Smax=384
+[I] [TRT] [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +0, GPU +8, now: CPU 166, GPU 1901 (MiB)
+[I] [TRT] [MemUsageChange] Init cuDNN: CPU +0, GPU +8, now: CPU 166, GPU 1909 (MiB)
+I1222 20:21:08.603008 19723 bert_core_vs.cc:426] Setting Opt.Prof. to 0
+I1222 20:21:08.603025 19723 bert_core_vs.cc:444] Context creation complete. Max supported batchSize: 256
+[I] [TRT] [MemUsageChange] TensorRT-managed allocation in IExecutionContext creation: CPU +0, GPU +0, now: CPU 0, GPU 290 (MiB)
+I1222 20:21:08.603590 19723 bert_core_vs.cc:476] Setup complete
+I1222 20:21:08.603727 19723 bert_core_vs.cc:385] Engine - Device Memory requirements: 704644608
+I1222 20:21:08.603729 19723 bert_core_vs.cc:393] Engine - Number of Optimization Profiles: 2
+I1222 20:21:08.603731 19723 bert_core_vs.cc:415] Engine - Profile 1 maxDims 98304 Bmax=256 Smax=384
+[I] [TRT] [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +0, GPU +8, now: CPU 289, GPU 2715 (MiB)
+[I] [TRT] [MemUsageChange] Init cuDNN: CPU +0, GPU +10, now: CPU 289, GPU 2725 (MiB)
+I1222 20:21:08.645998 19723 bert_core_vs.cc:426] Setting Opt.Prof. to 1
+[I] [TRT] Could not set default profile 0 for execution context. Profile index must be set explicitly.
+[I] [TRT] [MemUsageChange] TensorRT-managed allocation in IExecutionContext creation: CPU +1, GPU +0, now: CPU 1, GPU 290 (MiB)
+I1222 20:21:08.646291 19723 bert_core_vs.cc:444] Context creation complete. Max supported batchSize: 256
+I1222 20:21:08.646836 19723 bert_core_vs.cc:476] Setup complete
+I1222 20:21:08.876005 19723 main_bert.cc:184] Starting running actual test.
+I1222 20:21:11.529424 19723 main_bert.cc:190] Finished running actual test.
+
+No warnings encountered during test.
+
+No errors encountered during test.
+[2024-12-22 20:21:11,652 run_harness.py:166 INFO] Result: Accuracy run detected.
+[2024-12-22 20:21:11,652 __init__.py:46 INFO] Running command: PYTHONPATH=code/bert/tensorrt/helpers python3 /home/cmuser/CM/repos/local/cache/dfbf240f980947f5/repo/closed/NVIDIA/build/inference/language/bert/accuracy-squad.py --log_file /cm-mount/home/arjun/gh_action_results/valid_results/RTX4090x1-nvidia_original-gpu-tensorrt-vdefault-default_config/bert-99/offline/accuracy/mlperf_log_accuracy.json --vocab_file build/models/bert/vocab.txt --val_data /home/cmuser/CM/repos/local/cache/5b2b0cc913a4453a/data/squad/dev-v1.1.json --out_file /cm-mount/home/arjun/gh_action_results/valid_results/RTX4090x1-nvidia_original-gpu-tensorrt-vdefault-default_config/bert-99/offline/accuracy/predictions.json --output_dtype float16
+{"exact_match": 82.81929990539263, "f1": 90.15673510616978}
+Reading examples...
+Loading cached features from 'eval_features.pickle'...
+Loading LoadGen logs...
+Post-processing predictions...
+Writing predictions to: /cm-mount/home/arjun/gh_action_results/valid_results/RTX4090x1-nvidia_original-gpu-tensorrt-vdefault-default_config/bert-99/offline/accuracy/predictions.json
+Evaluating predictions...
+
+======================== Result summaries: ========================
+