diff --git a/open/UNM-Roadrunners/measurements/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-base/README.md b/open/UNM-Roadrunners/measurements/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-base/README.md new file mode 100644 index 0000000..4528464 --- /dev/null +++ b/open/UNM-Roadrunners/measurements/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-base/README.md @@ -0,0 +1,3 @@ +| Model | Scenario | Accuracy | Throughput | Latency (in ms) | +|---------------------|------------|-----------------------|--------------|-------------------| +| stable-diffusion-xl | offline | (15.17022, 235.68988) | 0.366 | - | \ No newline at end of file diff --git a/open/UNM-Roadrunners/measurements/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-base/stable-diffusion-xl/offline/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-base.json b/open/UNM-Roadrunners/measurements/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-base/stable-diffusion-xl/offline/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-base.json new file mode 100644 index 0000000..d47b6cc --- /dev/null +++ b/open/UNM-Roadrunners/measurements/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-base/stable-diffusion-xl/offline/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-base.json @@ -0,0 +1,7 @@ +{ + "starting_weights_filename": "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0", + "retraining": "no", + "input_data_types": "fp32", + "weight_data_types": "fp32", + "weight_transformations": "no" +} \ No newline at end of file diff --git a/open/UNM-Roadrunners/measurements/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-base/stable-diffusion-xl/offline/README.md b/open/UNM-Roadrunners/measurements/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-base/stable-diffusion-xl/offline/README.md new file mode 100644 index 0000000..dfceb94 --- /dev/null +++ b/open/UNM-Roadrunners/measurements/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-base/stable-diffusion-xl/offline/README.md @@ -0,0 +1,56 @@ +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-5.14.0-427.37.1.el9_4.x86_64-x86_64-with-glibc2.35 +* CPU version: x86_64 +* Python version: 3.10.12 (main, Sep 11 2024, 15:47:36) [GCC 11.4.0] +* MLCommons CM version: 3.0.1 + +## 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@cm4mlops --checkout=2ef61e7d5c8d89bdf86cf6e5752b8a70f053c1c1 + +cm run script \ + --tags=run-mlperf,inference,_r4.1-dev,_short,_scc24-base \ + --model=sdxl \ + --implementation=reference \ + --framework=pytorch \ + --category=datacenter \ + --scenario=Offline \ + --execution_mode=test \ + --device=cuda \ + --quiet \ + --precision=float16 +``` +*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@cm4mlops without checkout and clean CM cache as follows:* + +```bash +cm rm repo mlcommons@cm4mlops +cm pull repo mlcommons@cm4mlops +cm rm cache -f + +``` + +## Results + +Platform: 0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-base + +Model Precision: fp32 + +### Accuracy Results +`CLIP_SCORE`: `15.17022`, Required accuracy for closed division `>= 31.68632` and `<= 31.81332` +`FID_SCORE`: `235.68988`, Required accuracy for closed division `>= 23.01086` and `<= 23.95008` + +### Performance Results +`Samples per second`: `0.365902` diff --git a/open/UNM-Roadrunners/measurements/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-base/stable-diffusion-xl/offline/accuracy_console.out 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a/open/UNM-Roadrunners/measurements/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-base/stable-diffusion-xl/offline/cpu_info.json b/open/UNM-Roadrunners/measurements/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-base/stable-diffusion-xl/offline/cpu_info.json new file mode 100644 index 0000000..d5934a7 --- /dev/null +++ b/open/UNM-Roadrunners/measurements/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-base/stable-diffusion-xl/offline/cpu_info.json @@ -0,0 +1,26 @@ +{ + "CM_HOST_CPU_WRITE_PROTECT_SUPPORT": "yes", + "CM_HOST_CPU_MICROCODE": "0x21000240", + "CM_HOST_CPU_FPU_SUPPORT": "yes", + "CM_HOST_CPU_FPU_EXCEPTION_SUPPORT": "yes", + "CM_HOST_CPU_BUGS": "spectre_v1 spectre_v2 spec_store_bypass swapgs eibrs_pbrsb", + "CM_HOST_CPU_TLB_SIZE": "Not Found", + "CM_HOST_CPU_CFLUSH_SIZE": "64", + "CM_HOST_CPU_ARCHITECTURE": "x86_64", + "CM_HOST_CPU_TOTAL_CORES": "240", + "CM_HOST_CPU_ON_LINE_CPUS_LIST": "0-239", + "CM_HOST_CPU_VENDOR_ID": "GenuineIntel", + "CM_HOST_CPU_MODEL_NAME": 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b/open/UNM-Roadrunners/measurements/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-base/stable-diffusion-xl/offline/mlperf.conf @@ -0,0 +1,98 @@ +# The format of this config file is 'key = value'. +# The key has the format 'model.scenario.key'. Value is mostly int64_t. +# Model maybe '*' as wildcard. In that case the value applies to all models. +# All times are in milli seconds + +# Set performance_sample_count for each model. +# User can optionally set this to higher values in user.conf. +resnet50.*.performance_sample_count_override = 1024 +ssd-mobilenet.*.performance_sample_count_override = 256 +retinanet.*.performance_sample_count_override = 64 +bert.*.performance_sample_count_override = 10833 +dlrm.*.performance_sample_count_override = 204800 +dlrm-v2.*.performance_sample_count_override = 204800 +rnnt.*.performance_sample_count_override = 2513 +gptj.*.performance_sample_count_override = 13368 +llama2-70b.*.performance_sample_count_override = 24576 +stable-diffusion-xl.*.performance_sample_count_override = 5000 +# set to 0 to let entire sample set to be performance sample +3d-unet.*.performance_sample_count_override = 0 + +# Set seeds. The seeds will be distributed two weeks before the submission. +*.*.qsl_rng_seed = 3066443479025735752 +*.*.sample_index_rng_seed = 10688027786191513374 +*.*.schedule_rng_seed = 14962580496156340209 +# Set seeds for TEST_05. The seeds will be distributed two weeks before the submission. +*.*.test05_qsl_rng_seed = 16799458546791641818 +*.*.test05_sample_index_rng_seed = 5453809927556429288 +*.*.test05_schedule_rng_seed = 5435552105434836064 + + +*.SingleStream.target_latency_percentile = 90 +*.SingleStream.min_duration = 600000 + +*.MultiStream.target_latency_percentile = 99 +*.MultiStream.samples_per_query = 8 +*.MultiStream.min_duration = 600000 +*.MultiStream.min_query_count = 662 +retinanet.MultiStream.target_latency = 528 + +# 3D-UNet uses equal issue mode because it has non-uniform inputs +3d-unet.*.sample_concatenate_permutation = 1 + +# LLM benchmarks have non-uniform inputs and outputs, and use equal issue mode for all latency scenario +gptj.*.sample_concatenate_permutation = 1 +llama2-70b.*.sample_concatenate_permutation = 1 +mixtral-8x7b.*.sample_concatenate_permutation = 1 + +*.Server.target_latency = 10 +*.Server.target_latency_percentile = 99 +*.Server.target_duration = 0 +*.Server.min_duration = 600000 +resnet50.Server.target_latency = 15 +retinanet.Server.target_latency = 100 +bert.Server.target_latency = 130 +dlrm.Server.target_latency = 60 +dlrm-v2.Server.target_latency = 60 +rnnt.Server.target_latency = 1000 +gptj.Server.target_latency = 20000 +stable-diffusion-xl.Server.target_latency = 20000 +# Llama2-70b benchmarks measures token latencies +llama2-70b.*.use_token_latencies = 1 +mixtral-8x7b.*.use_token_latencies = 1 +# gptj benchmark infers token latencies +gptj.*.infer_token_latencies = 1 +gptj.*.token_latency_scaling_factor = 69 +# Only ttft and tpot are tracked for the llama2-70b & mixtral-8x7B benchmark therefore target_latency = 0 +llama2-70b.Server.target_latency = 0 +llama2-70b.Server.ttft_latency = 2000 +llama2-70b.Server.tpot_latency = 200 + +mixtral-8x7b.Server.target_latency = 0 +mixtral-8x7b.Server.ttft_latency = 2000 +mixtral-8x7b.Server.tpot_latency = 200 + +*.Offline.target_latency_percentile = 90 +*.Offline.min_duration = 600000 + +# In Offline scenario, we always have one query. But LoadGen maps this to +# min_sample_count internally in Offline scenario. If the dataset size is larger +# than 24576 we limit the min_query_count to 24576 and otherwise we use +# the dataset size as the limit + +resnet50.Offline.min_query_count = 24576 +retinanet.Offline.min_query_count = 24576 +dlrm-v2.Offline.min_query_count = 24576 +bert.Offline.min_query_count = 10833 +gptj.Offline.min_query_count = 13368 +rnnt.Offline.min_query_count = 2513 +3d-unet.Offline.min_query_count = 43 +stable-diffusion-xl.Offline.min_query_count = 5000 +llama2-70b.Offline.min_query_count = 24576 +mixtral-8x7b.Offline.min_query_count = 15000 + +# These fields should be defined and overridden by user.conf. +*.SingleStream.target_latency = 10 +*.MultiStream.target_latency = 80 +*.Server.target_qps = 1.0 +*.Offline.target_qps = 1.0 diff --git a/open/UNM-Roadrunners/measurements/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-base/stable-diffusion-xl/offline/os_info.json 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"0.44.0", + "zipp": "3.20.2" + } +} \ No newline at end of file diff --git a/open/UNM-Roadrunners/measurements/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-base/stable-diffusion-xl/offline/user.conf b/open/UNM-Roadrunners/measurements/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-base/stable-diffusion-xl/offline/user.conf new file mode 100644 index 0000000..21df3f1 --- /dev/null +++ b/open/UNM-Roadrunners/measurements/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-base/stable-diffusion-xl/offline/user.conf @@ -0,0 +1,5 @@ +stable-diffusion-xl.Offline.target_qps = 0.05 +stable-diffusion-xl.Offline.max_query_count = 10 +stable-diffusion-xl.Offline.min_query_count = 10 +stable-diffusion-xl.Offline.min_duration = 0 +stable-diffusion-xl.Offline.sample_concatenate_permutation = 0 diff --git a/open/UNM-Roadrunners/measurements/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-main/README.md b/open/UNM-Roadrunners/measurements/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-main/README.md new file mode 100644 index 0000000..76a1f4e --- /dev/null +++ b/open/UNM-Roadrunners/measurements/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-main/README.md @@ -0,0 +1,3 @@ +| Model | Scenario | Accuracy | Throughput | Latency (in ms) | +|---------------------|------------|----------------------|--------------|-------------------| +| stable-diffusion-xl | offline | (13.90686, 84.21082) | 0.364 | - | \ No newline at end of file diff --git a/open/UNM-Roadrunners/measurements/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-main/stable-diffusion-xl/offline/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-main.json b/open/UNM-Roadrunners/measurements/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-main/stable-diffusion-xl/offline/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-main.json new file mode 100644 index 0000000..d47b6cc --- /dev/null +++ b/open/UNM-Roadrunners/measurements/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-main/stable-diffusion-xl/offline/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-main.json @@ -0,0 +1,7 @@ +{ + "starting_weights_filename": "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0", + "retraining": "no", + "input_data_types": "fp32", + "weight_data_types": "fp32", + "weight_transformations": "no" +} \ No newline at end of file diff --git a/open/UNM-Roadrunners/measurements/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-main/stable-diffusion-xl/offline/README.md b/open/UNM-Roadrunners/measurements/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-main/stable-diffusion-xl/offline/README.md new file mode 100644 index 0000000..24308db --- /dev/null +++ b/open/UNM-Roadrunners/measurements/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-main/stable-diffusion-xl/offline/README.md @@ -0,0 +1,56 @@ +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-5.14.0-427.37.1.el9_4.x86_64-x86_64-with-glibc2.35 +* CPU version: x86_64 +* Python version: 3.10.12 (main, Sep 11 2024, 15:47:36) [GCC 11.4.0] +* MLCommons CM version: 3.0.1 + +## 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@cm4mlops --checkout=2ef61e7d5c8d89bdf86cf6e5752b8a70f053c1c1 + +cm run script \ + --tags=run-mlperf,inference,_r4.1-dev,_short,_scc24-main \ + --model=sdxl \ + --implementation=reference \ + --framework=pytorch \ + --category=datacenter \ + --scenario=Offline \ + --execution_mode=test \ + --device=cuda \ + --quiet \ + --precision=float16 +``` +*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@cm4mlops without checkout and clean CM cache as follows:* + +```bash +cm rm repo mlcommons@cm4mlops +cm pull repo mlcommons@cm4mlops +cm rm cache -f + +``` + +## Results + +Platform: 0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-main + +Model Precision: fp32 + +### Accuracy Results +`CLIP_SCORE`: `13.90686`, Required accuracy for closed division `>= 31.68632` and `<= 31.81332` +`FID_SCORE`: `84.21082`, Required accuracy for closed division `>= 23.01086` and `<= 23.95008` + +### Performance Results +`Samples per second`: `0.364099` diff --git a/open/UNM-Roadrunners/measurements/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-main/stable-diffusion-xl/offline/accuracy_console.out b/open/UNM-Roadrunners/measurements/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-main/stable-diffusion-xl/offline/accuracy_console.out new file mode 100644 index 0000000..e69de29 diff --git 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b/open/UNM-Roadrunners/measurements/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-main/stable-diffusion-xl/offline/mlperf.conf @@ -0,0 +1,98 @@ +# The format of this config file is 'key = value'. +# The key has the format 'model.scenario.key'. Value is mostly int64_t. +# Model maybe '*' as wildcard. In that case the value applies to all models. +# All times are in milli seconds + +# Set performance_sample_count for each model. +# User can optionally set this to higher values in user.conf. +resnet50.*.performance_sample_count_override = 1024 +ssd-mobilenet.*.performance_sample_count_override = 256 +retinanet.*.performance_sample_count_override = 64 +bert.*.performance_sample_count_override = 10833 +dlrm.*.performance_sample_count_override = 204800 +dlrm-v2.*.performance_sample_count_override = 204800 +rnnt.*.performance_sample_count_override = 2513 +gptj.*.performance_sample_count_override = 13368 +llama2-70b.*.performance_sample_count_override = 24576 +stable-diffusion-xl.*.performance_sample_count_override = 5000 +# set to 0 to let entire sample set to be performance sample +3d-unet.*.performance_sample_count_override = 0 + +# Set seeds. The seeds will be distributed two weeks before the submission. +*.*.qsl_rng_seed = 3066443479025735752 +*.*.sample_index_rng_seed = 10688027786191513374 +*.*.schedule_rng_seed = 14962580496156340209 +# Set seeds for TEST_05. The seeds will be distributed two weeks before the submission. +*.*.test05_qsl_rng_seed = 16799458546791641818 +*.*.test05_sample_index_rng_seed = 5453809927556429288 +*.*.test05_schedule_rng_seed = 5435552105434836064 + + +*.SingleStream.target_latency_percentile = 90 +*.SingleStream.min_duration = 600000 + +*.MultiStream.target_latency_percentile = 99 +*.MultiStream.samples_per_query = 8 +*.MultiStream.min_duration = 600000 +*.MultiStream.min_query_count = 662 +retinanet.MultiStream.target_latency = 528 + +# 3D-UNet uses equal issue mode because it has non-uniform inputs +3d-unet.*.sample_concatenate_permutation = 1 + +# LLM benchmarks have non-uniform inputs and outputs, and use equal issue mode for all latency scenario +gptj.*.sample_concatenate_permutation = 1 +llama2-70b.*.sample_concatenate_permutation = 1 +mixtral-8x7b.*.sample_concatenate_permutation = 1 + +*.Server.target_latency = 10 +*.Server.target_latency_percentile = 99 +*.Server.target_duration = 0 +*.Server.min_duration = 600000 +resnet50.Server.target_latency = 15 +retinanet.Server.target_latency = 100 +bert.Server.target_latency = 130 +dlrm.Server.target_latency = 60 +dlrm-v2.Server.target_latency = 60 +rnnt.Server.target_latency = 1000 +gptj.Server.target_latency = 20000 +stable-diffusion-xl.Server.target_latency = 20000 +# Llama2-70b benchmarks measures token latencies +llama2-70b.*.use_token_latencies = 1 +mixtral-8x7b.*.use_token_latencies = 1 +# gptj benchmark infers token latencies +gptj.*.infer_token_latencies = 1 +gptj.*.token_latency_scaling_factor = 69 +# Only ttft and tpot are tracked for the llama2-70b & mixtral-8x7B benchmark therefore target_latency = 0 +llama2-70b.Server.target_latency = 0 +llama2-70b.Server.ttft_latency = 2000 +llama2-70b.Server.tpot_latency = 200 + +mixtral-8x7b.Server.target_latency = 0 +mixtral-8x7b.Server.ttft_latency = 2000 +mixtral-8x7b.Server.tpot_latency = 200 + +*.Offline.target_latency_percentile = 90 +*.Offline.min_duration = 600000 + +# In Offline scenario, we always have one query. But LoadGen maps this to +# min_sample_count internally in Offline scenario. If the dataset size is larger +# than 24576 we limit the min_query_count to 24576 and otherwise we use +# the dataset size as the limit + +resnet50.Offline.min_query_count = 24576 +retinanet.Offline.min_query_count = 24576 +dlrm-v2.Offline.min_query_count = 24576 +bert.Offline.min_query_count = 10833 +gptj.Offline.min_query_count = 13368 +rnnt.Offline.min_query_count = 2513 +3d-unet.Offline.min_query_count = 43 +stable-diffusion-xl.Offline.min_query_count = 5000 +llama2-70b.Offline.min_query_count = 24576 +mixtral-8x7b.Offline.min_query_count = 15000 + +# These fields should be defined and overridden by user.conf. +*.SingleStream.target_latency = 10 +*.MultiStream.target_latency = 80 +*.Server.target_qps = 1.0 +*.Offline.target_qps = 1.0 diff --git a/open/UNM-Roadrunners/measurements/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-main/stable-diffusion-xl/offline/os_info.json 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b/open/UNM-Roadrunners/results/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-base/stable-diffusion-xl/offline/accuracy/accuracy.txt new file mode 100644 index 0000000..3eb09ae --- /dev/null +++ b/open/UNM-Roadrunners/results/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-base/stable-diffusion-xl/offline/accuracy/accuracy.txt @@ -0,0 +1,2 @@ +Accuracy Results: {'FID_SCORE': 235.68987775810245, 'CLIP_SCORE': 15.170220836997032} +hash=50649708ebcbf30c46fb4bec3954dbff159c490e645607fadc78f16e907925e1 diff --git a/open/UNM-Roadrunners/results/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-base/stable-diffusion-xl/offline/accuracy/images/1.png b/open/UNM-Roadrunners/results/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-base/stable-diffusion-xl/offline/accuracy/images/1.png new file mode 100644 index 0000000..3c43bee Binary files /dev/null and b/open/UNM-Roadrunners/results/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-base/stable-diffusion-xl/offline/accuracy/images/1.png differ diff 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Close up of a red fire hydrant near a sidewalk. +2569 A dark table has a large arrangement of food. +1303 A baby boy standing inside of a wooden crib. +109 People walking toward an airplane to board it. +4509 a couple of horses grazing on some green grass +3009 A horse that is standing with a cart near birds. +2179 A man holds up a Polish sausage on a bun. +1826 Two dogs resting comfortably on a tiled floor. +2094 A cat hiding in a basket of some sort. +3340 Vintage picture of a man and a horse on the farm. diff --git a/open/UNM-Roadrunners/results/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-base/stable-diffusion-xl/offline/accuracy/mlperf_log_accuracy.json b/open/UNM-Roadrunners/results/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-base/stable-diffusion-xl/offline/accuracy/mlperf_log_accuracy.json new file mode 100644 index 0000000..a5fd839 --- /dev/null +++ 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"namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "loadgen.cc", "line_no": 566, "pid": 9813, "tid": 9813}} diff --git a/open/UNM-Roadrunners/results/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-base/stable-diffusion-xl/offline/accuracy/mlperf_log_summary.txt b/open/UNM-Roadrunners/results/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-base/stable-diffusion-xl/offline/accuracy/mlperf_log_summary.txt new file mode 100644 index 0000000..a05d865 --- /dev/null +++ b/open/UNM-Roadrunners/results/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-base/stable-diffusion-xl/offline/accuracy/mlperf_log_summary.txt @@ -0,0 +1,4 @@ + +No warnings encountered during test. + +No errors encountered during test. diff --git a/open/UNM-Roadrunners/results/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-base/stable-diffusion-xl/offline/performance/run_1/mlperf_log_accuracy.json b/open/UNM-Roadrunners/results/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-base/stable-diffusion-xl/offline/performance/run_1/mlperf_log_accuracy.json new file mode 100644 index 0000000..0d4f101 --- /dev/null +++ b/open/UNM-Roadrunners/results/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-base/stable-diffusion-xl/offline/performance/run_1/mlperf_log_accuracy.json @@ -0,0 +1,2 @@ +[ +] diff --git a/open/UNM-Roadrunners/results/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-base/stable-diffusion-xl/offline/performance/run_1/mlperf_log_detail.txt b/open/UNM-Roadrunners/results/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-base/stable-diffusion-xl/offline/performance/run_1/mlperf_log_detail.txt new file mode 100644 index 0000000..1a331b2 --- /dev/null +++ b/open/UNM-Roadrunners/results/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-base/stable-diffusion-xl/offline/performance/run_1/mlperf_log_detail.txt @@ -0,0 +1,87 @@ +:::MLLOG {"key": "loadgen_version", "value": "4.1 @ 41fa8aadd1", "time_ms": 0.003432, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "version.cc", "line_no": 53, "pid": 6458, "tid": 6458}} +:::MLLOG {"key": "loadgen_build_date_local", "value": "2024-10-07T16:36:33.041324", "time_ms": 0.003432, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "version.cc", "line_no": 55, "pid": 6458, "tid": 6458}} +:::MLLOG {"key": "loadgen_build_date_utc", "value": "2024-10-07T16:36:33.041331", "time_ms": 0.003432, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "version.cc", "line_no": 56, "pid": 6458, "tid": 6458}} +:::MLLOG {"key": "loadgen_git_commit_date", "value": "2024-10-01T18:00:17+01:00", "time_ms": 0.003432, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "version.cc", "line_no": 57, "pid": 6458, "tid": 6458}} +:::MLLOG {"key": "loadgen_git_log_message", "value": "41fa8aadd1ba0ecc97f6a519d8b42b04278e5f24 Add format files github action (#1682)\n518b454fd8647bfbd23a074e875e87353f33393e Tflite tpu (#1449)\ne0fdec1c7a75c98cfc194f13d62ac4388d419c8a Fix link in GettingStarted.ipynb (#1512)\n92bd8198d15411d7fb7d7c27f8904bc5a0bcfe7a Fix warning in the submission checker (#1808)\n224cfbf5c0e82cae6d48620025b7e1258ae3666a Fix typo in reference datatype (#1851)", "time_ms": 0.003432, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "version.cc", "line_no": 58, "pid": 6458, "tid": 6458}} +:::MLLOG {"key": "loadgen_git_status_message", "value": "", "time_ms": 0.003432, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "version.cc", "line_no": 60, "pid": 6458, "tid": 6458}} +:::MLLOG {"key": "loadgen_file_sha1", "value": 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{"key": "qsl_name", "value": "PyQSL", "time_ms": 0.011136, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "loadgen.cc", "line_no": 1201, "pid": 6458, "tid": 6458}} +:::MLLOG {"key": "qsl_reported_total_count", "value": 50, "time_ms": 0.011136, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "loadgen.cc", "line_no": 1202, "pid": 6458, "tid": 6458}} +:::MLLOG {"key": "qsl_reported_performance_count", "value": 5000, "time_ms": 0.011136, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "loadgen.cc", "line_no": 1203, "pid": 6458, "tid": 6458}} +:::MLLOG {"key": "requested_scenario", "value": "Offline", "time_ms": 0.015746, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": 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+:::MLLOG {"key": "result_97.00_percentile_latency_ns", "value": 27329742498, "time_ms": 27335.170080, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "results.cc", "line_no": 758, "pid": 6458, "tid": 6458}} +:::MLLOG {"key": "result_99.00_percentile_latency_ns", "value": 27329742498, "time_ms": 27335.170080, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "results.cc", "line_no": 758, "pid": 6458, "tid": 6458}} +:::MLLOG {"key": "result_99.90_percentile_latency_ns", "value": 27329742498, "time_ms": 27335.170080, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "results.cc", "line_no": 758, "pid": 6458, "tid": 6458}} diff --git a/open/UNM-Roadrunners/results/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-base/stable-diffusion-xl/offline/performance/run_1/mlperf_log_summary.txt b/open/UNM-Roadrunners/results/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-base/stable-diffusion-xl/offline/performance/run_1/mlperf_log_summary.txt new file mode 100644 index 0000000..5c23f39 --- /dev/null +++ b/open/UNM-Roadrunners/results/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-base/stable-diffusion-xl/offline/performance/run_1/mlperf_log_summary.txt @@ -0,0 +1,51 @@ +================================================ +MLPerf Results Summary +================================================ +SUT name : PySUT +Scenario : Offline +Mode : PerformanceOnly +Samples per second: 0.365902 +Result is : VALID + Min duration satisfied : Yes + Min queries satisfied : Yes + Early stopping satisfied: Yes + +================================================ +Additional Stats +================================================ +Min latency (ns) : 2953411168 +Max latency (ns) : 27329742498 +Mean latency (ns) : 15122813197 +50.00 percentile latency (ns) : 16469663451 +90.00 percentile latency (ns) : 27329742498 +95.00 percentile latency (ns) : 27329742498 +97.00 percentile latency (ns) : 27329742498 +99.00 percentile latency (ns) : 27329742498 +99.90 percentile latency (ns) : 27329742498 + +================================================ +Test Parameters Used +================================================ +samples_per_query : 10 +target_qps : 0.05 +target_latency (ns): 0 +max_async_queries : 1 +min_duration (ms): 0 +max_duration (ms): 0 +min_query_count : 1 +max_query_count : 10 +qsl_rng_seed : 3066443479025735752 +sample_index_rng_seed : 10688027786191513374 +schedule_rng_seed : 14962580496156340209 +accuracy_log_rng_seed : 0 +accuracy_log_probability : 0 +accuracy_log_sampling_target : 0 +print_timestamps : 0 +performance_issue_unique : 0 +performance_issue_same : 0 +performance_issue_same_index : 0 +performance_sample_count : 5000 + +No warnings encountered during test. + +No errors encountered during test. diff --git a/open/UNM-Roadrunners/results/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-main/stable-diffusion-xl/offline/accuracy/accuracy.txt b/open/UNM-Roadrunners/results/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-main/stable-diffusion-xl/offline/accuracy/accuracy.txt new file mode 100644 index 0000000..f2f2fe1 --- /dev/null +++ b/open/UNM-Roadrunners/results/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-main/stable-diffusion-xl/offline/accuracy/accuracy.txt @@ -0,0 +1,2 @@ +Accuracy Results: {'FID_SCORE': 84.21082273715388, 'CLIP_SCORE': 13.906862080842256} +hash=44fa2d532b0bbd7a6787a7ee90d79e2b973adb835514b69bfca8d4fab790ef69 diff --git a/open/UNM-Roadrunners/results/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-main/stable-diffusion-xl/offline/accuracy/images/109.png 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25570, "tid": 25570}} +:::MLLOG {"key": "power_end", "value": "11-18-2024 19:52:40.870", "time_ms": 1394331.822741, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "loadgen.cc", "line_no": 566, "pid": 25570, "tid": 25570}} diff --git a/open/UNM-Roadrunners/results/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-main/stable-diffusion-xl/offline/accuracy/mlperf_log_summary.txt b/open/UNM-Roadrunners/results/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-main/stable-diffusion-xl/offline/accuracy/mlperf_log_summary.txt new file mode 100644 index 0000000..a05d865 --- /dev/null +++ b/open/UNM-Roadrunners/results/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-main/stable-diffusion-xl/offline/accuracy/mlperf_log_summary.txt @@ -0,0 +1,4 @@ + +No warnings encountered during test. + +No errors encountered during test. diff --git a/open/UNM-Roadrunners/results/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-main/stable-diffusion-xl/offline/performance/run_1/mlperf_log_accuracy.json b/open/UNM-Roadrunners/results/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-main/stable-diffusion-xl/offline/performance/run_1/mlperf_log_accuracy.json new file mode 100644 index 0000000..0d4f101 --- /dev/null +++ b/open/UNM-Roadrunners/results/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-main/stable-diffusion-xl/offline/performance/run_1/mlperf_log_accuracy.json @@ -0,0 +1,2 @@ +[ +] diff --git a/open/UNM-Roadrunners/results/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-main/stable-diffusion-xl/offline/performance/run_1/mlperf_log_detail.txt b/open/UNM-Roadrunners/results/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-main/stable-diffusion-xl/offline/performance/run_1/mlperf_log_detail.txt new file mode 100644 index 0000000..f677033 --- /dev/null +++ b/open/UNM-Roadrunners/results/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-main/stable-diffusion-xl/offline/performance/run_1/mlperf_log_detail.txt @@ -0,0 +1,87 @@ +:::MLLOG {"key": "loadgen_version", "value": "4.1 @ 41fa8aadd1", "time_ms": 0.004427, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "version.cc", "line_no": 53, "pid": 22215, "tid": 22215}} +:::MLLOG {"key": "loadgen_build_date_local", "value": "2024-10-07T16:36:33.041324", "time_ms": 0.004427, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "version.cc", "line_no": 55, "pid": 22215, "tid": 22215}} +:::MLLOG {"key": "loadgen_build_date_utc", "value": "2024-10-07T16:36:33.041331", "time_ms": 0.004427, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "version.cc", "line_no": 56, "pid": 22215, "tid": 22215}} +:::MLLOG {"key": "loadgen_git_commit_date", "value": "2024-10-01T18:00:17+01:00", "time_ms": 0.004427, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "version.cc", "line_no": 57, "pid": 22215, "tid": 22215}} +:::MLLOG {"key": "loadgen_git_log_message", "value": "41fa8aadd1ba0ecc97f6a519d8b42b04278e5f24 Add format files github action (#1682)\n518b454fd8647bfbd23a074e875e87353f33393e Tflite tpu (#1449)\ne0fdec1c7a75c98cfc194f13d62ac4388d419c8a Fix link in GettingStarted.ipynb (#1512)\n92bd8198d15411d7fb7d7c27f8904bc5a0bcfe7a Fix warning in the submission checker (#1808)\n224cfbf5c0e82cae6d48620025b7e1258ae3666a Fix typo in reference datatype (#1851)", "time_ms": 0.004427, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "version.cc", "line_no": 58, "pid": 22215, "tid": 22215}} +:::MLLOG {"key": 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"metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 329, "pid": 22215, "tid": 22215}} +:::MLLOG {"key": "requested_performance_issue_same", "value": false, "time_ms": 0.022692, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 331, "pid": 22215, "tid": 22215}} +:::MLLOG {"key": "requested_performance_issue_same_index", "value": 0, "time_ms": 0.022692, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 333, "pid": 22215, "tid": 22215}} +:::MLLOG {"key": "requested_performance_sample_count_override", "value": 5000, "time_ms": 0.022692, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 335, "pid": 22215, "tid": 22215}} +:::MLLOG {"key": "requested_sample_concatenate_permutation", "value": false, "time_ms": 0.022692, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 337, "pid": 22215, "tid": 22215}} +:::MLLOG {"key": "effective_scenario", "value": "Offline", "time_ms": 0.022843, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 413, "pid": 22215, "tid": 22215}} +:::MLLOG {"key": "effective_test_mode", "value": "PerformanceOnly", "time_ms": 0.022843, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 414, "pid": 22215, "tid": 22215}} +:::MLLOG {"key": "effective_samples_per_query", "value": 10, "time_ms": 0.022843, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 416, "pid": 22215, "tid": 22215}} +:::MLLOG {"key": "effective_target_qps", "value": 0.05, "time_ms": 0.022843, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 417, "pid": 22215, "tid": 22215}} +:::MLLOG {"key": "effective_target_latency_ns", "value": 0, "time_ms": 0.022843, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 418, "pid": 22215, "tid": 22215}} +:::MLLOG {"key": "effective_target_latency_percentile", "value": 0.99, "time_ms": 0.022843, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 419, "pid": 22215, "tid": 22215}} +:::MLLOG {"key": "effective_max_async_queries", "value": 1, "time_ms": 0.022843, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 421, "pid": 22215, "tid": 22215}} +:::MLLOG {"key": "effective_target_duration_ms", "value": 0, "time_ms": 0.022843, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 422, "pid": 22215, "tid": 22215}} +:::MLLOG {"key": "effective_min_duration_ms", "value": 0, "time_ms": 0.022843, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 424, "pid": 22215, "tid": 22215}} +:::MLLOG {"key": "effective_max_duration_ms", "value": 0, "time_ms": 0.022843, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 425, "pid": 22215, "tid": 22215}} +:::MLLOG {"key": "effective_min_query_count", "value": 1, "time_ms": 0.022843, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 426, "pid": 22215, "tid": 22215}} +:::MLLOG {"key": "effective_max_query_count", "value": 10, "time_ms": 0.022843, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 427, "pid": 22215, "tid": 22215}} +:::MLLOG {"key": "effective_min_sample_count", "value": 10, "time_ms": 0.022843, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 428, "pid": 22215, "tid": 22215}} +:::MLLOG {"key": "effective_qsl_rng_seed", "value": 3066443479025735752, "time_ms": 0.022843, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 429, "pid": 22215, "tid": 22215}} +:::MLLOG {"key": "effective_sample_index_rng_seed", "value": 10688027786191513374, "time_ms": 0.022843, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 430, "pid": 22215, "tid": 22215}} +:::MLLOG {"key": "effective_schedule_rng_seed", "value": 14962580496156340209, "time_ms": 0.022843, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 432, "pid": 22215, "tid": 22215}} +:::MLLOG {"key": "effective_accuracy_log_rng_seed", "value": 0, "time_ms": 0.022843, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 433, "pid": 22215, "tid": 22215}} +:::MLLOG {"key": "effective_accuracy_log_probability", "value": 0, "time_ms": 0.022843, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 435, "pid": 22215, "tid": 22215}} +:::MLLOG {"key": "effective_accuracy_log_sampling_target", "value": 0, "time_ms": 0.022843, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 437, "pid": 22215, "tid": 22215}} +:::MLLOG {"key": "effective_print_timestamps", "value": false, "time_ms": 0.022843, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 439, "pid": 22215, "tid": 22215}} +:::MLLOG {"key": "effective_performance_issue_unique", "value": false, "time_ms": 0.022843, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 440, "pid": 22215, "tid": 22215}} +:::MLLOG {"key": "effective_performance_issue_same", "value": false, "time_ms": 0.022843, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 442, "pid": 22215, "tid": 22215}} +:::MLLOG {"key": "effective_performance_issue_same_index", "value": 0, "time_ms": 0.022843, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 444, "pid": 22215, "tid": 22215}} +:::MLLOG {"key": "effective_performance_sample_count", "value": 5000, "time_ms": 0.022843, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 446, "pid": 22215, "tid": 22215}} +:::MLLOG {"key": "effective_sample_concatenate_permutation", "value": false, "time_ms": 0.022843, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 448, "pid": 22215, "tid": 22215}} +:::MLLOG {"key": "generic_message", "value": "Starting performance mode", "time_ms": 0.029327, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "loadgen.cc", "line_no": 841, "pid": 22215, "tid": 22215}} +:::MLLOG {"key": "loaded_qsl_set", "value": 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"time_ms": 0.049420, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "loadgen.cc", "line_no": 613, "pid": 22215, "tid": 22215}} +:::MLLOG {"key": "generated_query_count", "value": 1, "time_ms": 23.851772, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "loadgen.cc", "line_no": 428, "pid": 22215, "tid": 22215}} +:::MLLOG {"key": "generated_samples_per_query", "value": 10, "time_ms": 23.851772, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "loadgen.cc", "line_no": 429, "pid": 22215, "tid": 22215}} +:::MLLOG {"key": "generated_query_duration", "value": 20000000000, "time_ms": 23.851772, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "loadgen.cc", "line_no": 430, "pid": 22215, "tid": 22215}} +:::MLLOG {"key": "generic_message", "value": "Ending naturally: Minimum query count and test duration met.", "time_ms": 13807.936250, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "issue_query_controller.cc", "line_no": 482, "pid": 22215, "tid": 22215}} +:::MLLOG {"key": "logger_swap_request_slots_retry_count", "value": 0, "time_ms": 27496.448859, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "logging.cc", "line_no": 898, "pid": 22215, "tid": 22215}} +:::MLLOG {"key": "logger_swap_request_slots_retry_retry_count", "value": 0, "time_ms": 27496.448859, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "logging.cc", "line_no": 900, "pid": 22215, "tid": 22215}} +:::MLLOG {"key": "logger_swap_request_slots_retry_reencounter_count", "value": 0, "time_ms": 27496.448859, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "logging.cc", "line_no": 902, "pid": 22215, "tid": 22215}} +:::MLLOG {"key": "logger_start_reading_entries_retry_count", "value": 0, "time_ms": 27496.448859, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "logging.cc", "line_no": 904, "pid": 22215, "tid": 22215}} +:::MLLOG {"key": "logger_tls_total_log_cas_fail_count", "value": 0, "time_ms": 27496.448859, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "logging.cc", "line_no": 906, "pid": 22215, "tid": 22215}} +:::MLLOG {"key": "logger_tls_total_swap_buffers_slot_retry_count", "value": 0, "time_ms": 27496.448859, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "logging.cc", "line_no": 908, "pid": 22215, "tid": 22215}} +:::MLLOG {"key": "power_begin", "value": "11-18-2024 19:28:03.228", "time_ms": 27496.449467, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "loadgen.cc", "line_no": 564, "pid": 22215, "tid": 22215}} +:::MLLOG {"key": "power_end", "value": "11-18-2024 19:28:30.694", "time_ms": 27496.449467, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "loadgen.cc", "line_no": 566, "pid": 22215, "tid": 22215}} +:::MLLOG {"key": "result_validity", "value": "VALID", "time_ms": 27496.484772, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "results.cc", "line_no": 655, "pid": 22215, "tid": 22215}} +:::MLLOG {"key": "result_min_duration_met", "value": true, "time_ms": 27496.484772, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "results.cc", "line_no": 660, "pid": 22215, "tid": 22215}} +:::MLLOG {"key": "result_min_queries_met", "value": true, "time_ms": 27496.484772, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "results.cc", "line_no": 661, "pid": 22215, "tid": 22215}} +:::MLLOG {"key": "early_stopping_met", "value": true, "time_ms": 27496.484772, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "results.cc", "line_no": 662, "pid": 22215, "tid": 22215}} +:::MLLOG {"key": "early_stopping_result", "value": "", "time_ms": 27496.484772, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "results.cc", "line_no": 682, "pid": 22215, "tid": 22215}} +:::MLLOG {"key": "result_query_count", "value": 1, "time_ms": 27496.484772, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "results.cc", "line_no": 692, "pid": 22215, "tid": 22215}} +:::MLLOG {"key": "result_samples_per_second", "value": 0.364099, "time_ms": 27496.484772, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "results.cc", "line_no": 748, "pid": 22215, "tid": 22215}} +:::MLLOG {"key": "result_min_latency_ns", "value": 2929119324, "time_ms": 27496.484772, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "results.cc", "line_no": 754, "pid": 22215, "tid": 22215}} +:::MLLOG {"key": "result_max_latency_ns", "value": 27465081782, "time_ms": 27496.484772, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "results.cc", "line_no": 755, "pid": 22215, "tid": 22215}} +:::MLLOG {"key": "result_mean_latency_ns", "value": 15166336738, "time_ms": 27496.484772, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "results.cc", "line_no": 756, "pid": 22215, "tid": 22215}} +:::MLLOG {"key": "result_50.00_percentile_latency_ns", "value": 16507038115, "time_ms": 27496.484772, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "results.cc", "line_no": 758, "pid": 22215, "tid": 22215}} +:::MLLOG {"key": "result_90.00_percentile_latency_ns", "value": 27465081782, "time_ms": 27496.484772, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "results.cc", "line_no": 758, "pid": 22215, "tid": 22215}} +:::MLLOG {"key": "result_95.00_percentile_latency_ns", "value": 27465081782, "time_ms": 27496.484772, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "results.cc", "line_no": 758, "pid": 22215, "tid": 22215}} +:::MLLOG {"key": "result_97.00_percentile_latency_ns", "value": 27465081782, "time_ms": 27496.484772, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "results.cc", "line_no": 758, "pid": 22215, "tid": 22215}} +:::MLLOG {"key": "result_99.00_percentile_latency_ns", "value": 27465081782, "time_ms": 27496.484772, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "results.cc", "line_no": 758, "pid": 22215, "tid": 22215}} +:::MLLOG {"key": "result_99.90_percentile_latency_ns", "value": 27465081782, "time_ms": 27496.484772, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "results.cc", "line_no": 758, "pid": 22215, "tid": 22215}} diff --git a/open/UNM-Roadrunners/results/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-main/stable-diffusion-xl/offline/performance/run_1/mlperf_log_summary.txt b/open/UNM-Roadrunners/results/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-main/stable-diffusion-xl/offline/performance/run_1/mlperf_log_summary.txt new file mode 100644 index 0000000..c8a5898 --- /dev/null +++ b/open/UNM-Roadrunners/results/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-main/stable-diffusion-xl/offline/performance/run_1/mlperf_log_summary.txt @@ -0,0 +1,51 @@ +================================================ +MLPerf Results Summary +================================================ +SUT name : PySUT +Scenario : Offline +Mode : PerformanceOnly +Samples per second: 0.364099 +Result is : VALID + Min duration satisfied : Yes + Min queries satisfied : Yes + Early stopping satisfied: Yes + +================================================ +Additional Stats +================================================ +Min latency (ns) : 2929119324 +Max latency (ns) : 27465081782 +Mean latency (ns) : 15166336738 +50.00 percentile latency (ns) : 16507038115 +90.00 percentile latency (ns) : 27465081782 +95.00 percentile latency (ns) : 27465081782 +97.00 percentile latency (ns) : 27465081782 +99.00 percentile latency (ns) : 27465081782 +99.90 percentile latency (ns) : 27465081782 + +================================================ +Test Parameters Used +================================================ +samples_per_query : 10 +target_qps : 0.05 +target_latency (ns): 0 +max_async_queries : 1 +min_duration (ms): 0 +max_duration (ms): 0 +min_query_count : 1 +max_query_count : 10 +qsl_rng_seed : 3066443479025735752 +sample_index_rng_seed : 10688027786191513374 +schedule_rng_seed : 14962580496156340209 +accuracy_log_rng_seed : 0 +accuracy_log_probability : 0 +accuracy_log_sampling_target : 0 +print_timestamps : 0 +performance_issue_unique : 0 +performance_issue_same : 0 +performance_issue_same_index : 0 +performance_sample_count : 5000 + +No warnings encountered during test. + +No errors encountered during test. diff --git a/open/UNM-Roadrunners/systems/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-base.json b/open/UNM-Roadrunners/systems/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-base.json new file mode 100644 index 0000000..38d9ecf --- /dev/null +++ b/open/UNM-Roadrunners/systems/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-base.json @@ -0,0 +1,37 @@ +{ + "accelerator_frequency": "1785000 MHz", + "accelerator_host_interconnect": "N/A", + "accelerator_interconnect": "N/A", + "accelerator_interconnect_topology": "", + "accelerator_memory_capacity": "93.11541748046875 GB", + "accelerator_memory_configuration": "N/A", + "accelerator_model_name": "NVIDIA H100 NVL", + "accelerator_on-chip_memories": "", + "accelerators_per_node": 3, + "cooling": "air", + "division": "open", + "framework": "pytorch v2.4.1", + "host_memory_capacity": "2.1T", + "host_memory_configuration": "undefined", + "host_network_card_count": "1", + "host_networking": "Gig Ethernet", + "host_networking_topology": "N/A", + "host_processor_caches": "L1d cache: 5.6 MiB (120 instances), L1i cache: 3.8 MiB (120 instances), L2 cache: 240 MiB (120 instances), L3 cache: 600 MiB (2 instances)", + "host_processor_core_count": "60", + "host_processor_frequency": "undefined", + "host_processor_interconnect": "", + "host_processor_model_name": "INTEL(R) XEON(R) PLATINUM 8580", + "host_processors_per_node": "2", + "host_storage_capacity": "17T", + "host_storage_type": "SSD", + "hw_notes": "", + "number_of_nodes": "1", + "operating_system": "Ubuntu 22.04 (linux-5.14.0-427.37.1.el9_4.x86_64-glibc2.35)", + "other_software_stack": "Python: 3.10.12, LLVM-15.0.6", + "status": "available", + "submitter": "UNM-Roadrunners", + "sw_notes": "Automated by MLCommons CM v3.0.1. ", + "system_name": "0b8611a6b9be", + "system_type": "datacenter", + "system_type_detail": "edge server" +} \ No newline at end of file diff --git a/open/UNM-Roadrunners/systems/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-main.json b/open/UNM-Roadrunners/systems/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-main.json new file mode 100644 index 0000000..38d9ecf --- /dev/null +++ b/open/UNM-Roadrunners/systems/0b8611a6b9be-reference-gpu-pytorch_v2.4.1-scc24-main.json @@ -0,0 +1,37 @@ +{ + "accelerator_frequency": "1785000 MHz", + "accelerator_host_interconnect": "N/A", + "accelerator_interconnect": "N/A", + "accelerator_interconnect_topology": "", + "accelerator_memory_capacity": "93.11541748046875 GB", + "accelerator_memory_configuration": "N/A", + "accelerator_model_name": "NVIDIA H100 NVL", + "accelerator_on-chip_memories": "", + "accelerators_per_node": 3, + "cooling": "air", + "division": "open", + "framework": "pytorch v2.4.1", + "host_memory_capacity": "2.1T", + "host_memory_configuration": "undefined", + "host_network_card_count": "1", + "host_networking": "Gig Ethernet", + "host_networking_topology": "N/A", + "host_processor_caches": "L1d cache: 5.6 MiB (120 instances), L1i cache: 3.8 MiB (120 instances), L2 cache: 240 MiB (120 instances), L3 cache: 600 MiB (2 instances)", + "host_processor_core_count": "60", + "host_processor_frequency": "undefined", + "host_processor_interconnect": "", + "host_processor_model_name": "INTEL(R) XEON(R) PLATINUM 8580", + "host_processors_per_node": "2", + "host_storage_capacity": "17T", + "host_storage_type": "SSD", + "hw_notes": "", + "number_of_nodes": "1", + "operating_system": "Ubuntu 22.04 (linux-5.14.0-427.37.1.el9_4.x86_64-glibc2.35)", + "other_software_stack": "Python: 3.10.12, LLVM-15.0.6", + "status": "available", + "submitter": "UNM-Roadrunners", + "sw_notes": "Automated by MLCommons CM v3.0.1. 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