remove old

dana/configs/db/Inference/infos/benchmarks.series.json

@@ -1 +0,0 @@
-{"llama_1gpu_0_forward_latency_s_":{"status":{"error":"Unable to find first average","lastBuildId":14055},"description":"\nbenchmark.input_shapes.batch_size: 1\nbackend.torch_dtype: float16\nbenchmark.input_shapes.sequence_length: 200\nbenchmark.new_tokens: 200","state":"similarNeedstriage"},"llama_1gpu_0_forward_throughput_samples_s_":{"status":{"error":"Unable to find first average","lastBuildId":14055},"description":"\nbenchmark.input_shapes.batch_size: 1\nbackend.torch_dtype: float16\nbenchmark.input_shapes.sequence_length: 200\nbenchmark.new_tokens: 200","state":"similarNeedstriage"},"llama_1gpu_0_generate_latency_s_":{"status":{"error":"Unable to find first average","lastBuildId":14055},"description":"\nbenchmark.input_shapes.batch_size: 1\nbackend.torch_dtype: float16\nbenchmark.input_shapes.sequence_length: 200\nbenchmark.new_tokens: 200","state":"similarNeedstriage"},"llama_1gpu_0_generate_throughput_tokens_s_":{"status":{"error":"Unable to find first average","lastBuildId":14055},"description":"\nbenchmark.input_shapes.batch_size: 1\nbackend.torch_dtype: float16\nbenchmark.input_shapes.sequence_length: 200\nbenchmark.new_tokens: 200","state":"similarNeedstriage"},"llama_1gpu_1_forward_latency_s_":{"status":{"error":"Unable to find first average","lastBuildId":14055},"description":"\nbenchmark.input_shapes.batch_size: 1\nbackend.torch_dtype: float32\nbenchmark.input_shapes.sequence_length: 200\nbenchmark.new_tokens: 200","state":"similarNeedstriage"},"llama_1gpu_1_forward_throughput_samples_s_":{"status":{"error":"Unable to find first average","lastBuildId":14055},"description":"\nbenchmark.input_shapes.batch_size: 1\nbackend.torch_dtype: float32\nbenchmark.input_shapes.sequence_length: 200\nbenchmark.new_tokens: 200","state":"similarNeedstriage"},"llama_1gpu_1_generate_latency_s_":{"status":{"error":"Unable to find first average","lastBuildId":14055},"description":"\nbenchmark.input_shapes.batch_size: 1\nbackend.torch_dtype: float32\nbenchmark.input_shapes.sequence_length: 200\nbenchmark.new_tokens: 200","state":"similarNeedstriage"},"llama_1gpu_1_generate_throughput_tokens_s_":{"status":{"error":"Unable to find first average","lastBuildId":14055},"description":"\nbenchmark.input_shapes.batch_size: 1\nbackend.torch_dtype: float32\nbenchmark.input_shapes.sequence_length: 200\nbenchmark.new_tokens: 200","state":"similarNeedstriage"},"llama_1gpu_2_forward_latency_s_":{"status":{"error":"Unable to find first average","lastBuildId":14055},"description":"\nbenchmark.input_shapes.batch_size: 16\nbackend.torch_dtype: float16\nbenchmark.input_shapes.sequence_length: 200\nbenchmark.new_tokens: 200","state":"similarNeedstriage"},"llama_1gpu_2_forward_throughput_samples_s_":{"status":{"error":"Unable to find first average","lastBuildId":14055},"description":"\nbenchmark.input_shapes.batch_size: 16\nbackend.torch_dtype: float16\nbenchmark.input_shapes.sequence_length: 200\nbenchmark.new_tokens: 200","state":"similarNeedstriage"},"llama_1gpu_2_generate_latency_s_":{"status":{"error":"Unable to find first average","lastBuildId":14055},"description":"\nbenchmark.input_shapes.batch_size: 16\nbackend.torch_dtype: float16\nbenchmark.input_shapes.sequence_length: 200\nbenchmark.new_tokens: 200","state":"similarNeedstriage"},"llama_1gpu_2_generate_throughput_tokens_s_":{"status":{"error":"Unable to find first average","lastBuildId":14055},"description":"\nbenchmark.input_shapes.batch_size: 16\nbackend.torch_dtype: float16\nbenchmark.input_shapes.sequence_length: 200\nbenchmark.new_tokens: 200","state":"similarNeedstriage"},"llama_1gpu_3_forward_latency_s_":{"status":{"error":"Unable to find first average","lastBuildId":14055},"description":"\nbenchmark.input_shapes.batch_size: 16\nbackend.torch_dtype: float32\nbenchmark.input_shapes.sequence_length: 200\nbenchmark.new_tokens: 200","state":"similarNeedstriage"},"llama_1gpu_3_forward_throughput_samples_s_":{"status":{"error":"Unable to find first average","lastBuildId":14055},"description":"\nbenchmark.input_shapes.batch_size: 16\nbackend.torch_dtype: float32\nbenchmark.input_shapes.sequence_length: 200\nbenchmark.new_tokens: 200","state":"similarNeedstriage"},"llama_1gpu_3_generate_latency_s_":{"status":{"error":"Unable to find first average","lastBuildId":14055},"description":"\nbenchmark.input_shapes.batch_size: 16\nbackend.torch_dtype: float32\nbenchmark.input_shapes.sequence_length: 200\nbenchmark.new_tokens: 200","state":"similarNeedstriage"},"llama_1gpu_3_generate_throughput_tokens_s_":{"status":{"error":"Unable to find first average","lastBuildId":14055},"description":"\nbenchmark.input_shapes.batch_size: 16\nbackend.torch_dtype: float32\nbenchmark.input_shapes.sequence_length: 200\nbenchmark.new_tokens: 200","state":"similarNeedstriage"}}

dana/configs/db/Inference/infos/benchmarks.statusSeries.json

@@ -1 +0,0 @@
-{"0":{"numSeries":16,"numSeriesSimilar":0,"numSeriesImproved":0,"numSeriesRegression":0,"numSeriesUndefined":16,"time":1695816293533}}

dana/configs/db/Inference/infos/builds.json

@@ -1 +0,0 @@
-{"14054":{"buildId":14054,"infos":{"hash":"153755ee386ac73e04814a94337abcb1208ff5d1","abbrevHash":"153755ee","authorName":"Younes Belkada","authorEmail":"[email protected]","subject":"[`FA` / `tests`] Add use_cache tests for FA models (#26415)","url":null}},"14055":{"buildId":14055,"infos":{"hash":"946bac798caefada3f5f1c9fecdcfd587ed24ac7","abbrevHash":"946bac79","authorName":"statelesshz","authorEmail":"[email protected]","subject":"add bf16 mixed precision support for NPU (#26163)","url":null}}}

dana/configs/db/Inference/series/llama_1gpu/_0_forward/_latency_s/_.json

@@ -1 +0,0 @@
-{"projectId":"Inference","description":"\nbenchmark.input_shapes.batch_size: 1\nbackend.torch_dtype: float16\nbenchmark.input_shapes.sequence_length: 200\nbenchmark.new_tokens: 200","analyse":{"benchmark":{"range":"10%","required":5,"trend":"smaller"}},"assignee":{"compares":{}},"samples":{"14054":0.00239,"14055":0.00312},"state":{"analyse":"similarNeedstriage","compares":{}},"lastBuildId":"14055","analyseResult":{"summary":{"error":"Unable to find first average","lastBuildId":14055}}}

dana/configs/db/Inference/series/llama_1gpu/_0_forward/_throughpu/t_samples_/s_.json

@@ -1 +0,0 @@
-{"projectId":"Inference","description":"\nbenchmark.input_shapes.batch_size: 1\nbackend.torch_dtype: float16\nbenchmark.input_shapes.sequence_length: 200\nbenchmark.new_tokens: 200","analyse":{"benchmark":{"range":"10%","required":5,"trend":"smaller"}},"assignee":{"compares":{}},"samples":{"14054":418,"14055":321},"state":{"analyse":"similarNeedstriage","compares":{}},"lastBuildId":"14055","analyseResult":{"summary":{"error":"Unable to find first average","lastBuildId":14055}}}

dana/configs/db/Inference/series/llama_1gpu/_0_generat/e_latency_/s_.json

@@ -1 +0,0 @@
-{"projectId":"Inference","description":"\nbenchmark.input_shapes.batch_size: 1\nbackend.torch_dtype: float16\nbenchmark.input_shapes.sequence_length: 200\nbenchmark.new_tokens: 200","analyse":{"benchmark":{"range":"10%","required":5,"trend":"smaller"}},"assignee":{"compares":{}},"samples":{"14054":0.491,"14055":0.639},"state":{"analyse":"similarNeedstriage","compares":{}},"lastBuildId":"14055","analyseResult":{"summary":{"error":"Unable to find first average","lastBuildId":14055}}}

dana/configs/db/Inference/series/llama_1gpu/_0_generat/e_throughp/ut_tokens_/s_.json

@@ -1 +0,0 @@
-{"projectId":"Inference","description":"\nbenchmark.input_shapes.batch_size: 1\nbackend.torch_dtype: float16\nbenchmark.input_shapes.sequence_length: 200\nbenchmark.new_tokens: 200","analyse":{"benchmark":{"range":"10%","required":5,"trend":"smaller"}},"assignee":{"compares":{}},"samples":{"14054":407,"14055":313},"state":{"analyse":"similarNeedstriage","compares":{}},"lastBuildId":"14055","analyseResult":{"summary":{"error":"Unable to find first average","lastBuildId":14055}}}

dana/configs/db/Inference/series/llama_1gpu/_1_forward/_latency_s/_.json

@@ -1 +0,0 @@
-{"projectId":"Inference","description":"\nbenchmark.input_shapes.batch_size: 1\nbackend.torch_dtype: float32\nbenchmark.input_shapes.sequence_length: 200\nbenchmark.new_tokens: 200","analyse":{"benchmark":{"range":"10%","required":5,"trend":"smaller"}},"assignee":{"compares":{}},"samples":{"14054":0.00328,"14055":0.00332},"state":{"analyse":"similarNeedstriage","compares":{}},"lastBuildId":"14055","analyseResult":{"summary":{"error":"Unable to find first average","lastBuildId":14055}}}

dana/configs/db/Inference/series/llama_1gpu/_1_forward/_throughpu/t_samples_/s_.json

@@ -1 +0,0 @@
-{"projectId":"Inference","description":"\nbenchmark.input_shapes.batch_size: 1\nbackend.torch_dtype: float32\nbenchmark.input_shapes.sequence_length: 200\nbenchmark.new_tokens: 200","analyse":{"benchmark":{"range":"10%","required":5,"trend":"smaller"}},"assignee":{"compares":{}},"samples":{"14054":305,"14055":301},"state":{"analyse":"similarNeedstriage","compares":{}},"lastBuildId":"14055","analyseResult":{"summary":{"error":"Unable to find first average","lastBuildId":14055}}}

dana/configs/db/Inference/series/llama_1gpu/_1_generat/e_latency_/s_.json

@@ -1 +0,0 @@
-{"projectId":"Inference","description":"\nbenchmark.input_shapes.batch_size: 1\nbackend.torch_dtype: float32\nbenchmark.input_shapes.sequence_length: 200\nbenchmark.new_tokens: 200","analyse":{"benchmark":{"range":"10%","required":5,"trend":"smaller"}},"assignee":{"compares":{}},"samples":{"14054":0.53,"14055":0.537},"state":{"analyse":"similarNeedstriage","compares":{}},"lastBuildId":"14055","analyseResult":{"summary":{"error":"Unable to find first average","lastBuildId":14055}}}

dana/configs/db/Inference/series/llama_1gpu/_1_generat/e_throughp/ut_tokens_/s_.json

@@ -1 +0,0 @@
-{"projectId":"Inference","description":"\nbenchmark.input_shapes.batch_size: 1\nbackend.torch_dtype: float32\nbenchmark.input_shapes.sequence_length: 200\nbenchmark.new_tokens: 200","analyse":{"benchmark":{"range":"10%","required":5,"trend":"smaller"}},"assignee":{"compares":{}},"samples":{"14054":377,"14055":372},"state":{"analyse":"similarNeedstriage","compares":{}},"lastBuildId":"14055","analyseResult":{"summary":{"error":"Unable to find first average","lastBuildId":14055}}}

dana/configs/db/Inference/series/llama_1gpu/_2_forward/_latency_s/_.json

@@ -1 +0,0 @@
-{"projectId":"Inference","description":"\nbenchmark.input_shapes.batch_size: 16\nbackend.torch_dtype: float16\nbenchmark.input_shapes.sequence_length: 200\nbenchmark.new_tokens: 200","analyse":{"benchmark":{"range":"10%","required":5,"trend":"smaller"}},"assignee":{"compares":{}},"samples":{"14054":0.0041,"14055":0.00487},"state":{"analyse":"similarNeedstriage","compares":{}},"lastBuildId":"14055","analyseResult":{"summary":{"error":"Unable to find first average","lastBuildId":14055}}}

dana/configs/db/Inference/series/llama_1gpu/_2_forward/_throughpu/t_samples_/s_.json

@@ -1 +0,0 @@
-{"projectId":"Inference","description":"\nbenchmark.input_shapes.batch_size: 16\nbackend.torch_dtype: float16\nbenchmark.input_shapes.sequence_length: 200\nbenchmark.new_tokens: 200","analyse":{"benchmark":{"range":"10%","required":5,"trend":"smaller"}},"assignee":{"compares":{}},"samples":{"14054":3900,"14055":3290},"state":{"analyse":"similarNeedstriage","compares":{}},"lastBuildId":"14055","analyseResult":{"summary":{"error":"Unable to find first average","lastBuildId":14055}}}

dana/configs/db/Inference/series/llama_1gpu/_2_generat/e_latency_/s_.json

@@ -1 +0,0 @@
-{"projectId":"Inference","description":"\nbenchmark.input_shapes.batch_size: 16\nbackend.torch_dtype: float16\nbenchmark.input_shapes.sequence_length: 200\nbenchmark.new_tokens: 200","analyse":{"benchmark":{"range":"10%","required":5,"trend":"smaller"}},"assignee":{"compares":{}},"samples":{"14054":0.655,"14055":0.765},"state":{"analyse":"similarNeedstriage","compares":{}},"lastBuildId":"14055","analyseResult":{"summary":{"error":"Unable to find first average","lastBuildId":14055}}}

dana/configs/db/Inference/series/llama_1gpu/_2_generat/e_throughp/ut_tokens_/s_.json

@@ -1 +0,0 @@
-{"projectId":"Inference","description":"\nbenchmark.input_shapes.batch_size: 16\nbackend.torch_dtype: float16\nbenchmark.input_shapes.sequence_length: 200\nbenchmark.new_tokens: 200","analyse":{"benchmark":{"range":"10%","required":5,"trend":"smaller"}},"assignee":{"compares":{}},"samples":{"14054":4890,"14055":4180},"state":{"analyse":"similarNeedstriage","compares":{}},"lastBuildId":"14055","analyseResult":{"summary":{"error":"Unable to find first average","lastBuildId":14055}}}

dana/configs/db/Inference/series/llama_1gpu/_3_forward/_latency_s/_.json

@@ -1 +0,0 @@
-{"projectId":"Inference","description":"\nbenchmark.input_shapes.batch_size: 16\nbackend.torch_dtype: float32\nbenchmark.input_shapes.sequence_length: 200\nbenchmark.new_tokens: 200","analyse":{"benchmark":{"range":"10%","required":5,"trend":"smaller"}},"assignee":{"compares":{}},"samples":{"14054":0.00457,"14055":0.00609},"state":{"analyse":"similarNeedstriage","compares":{}},"lastBuildId":"14055","analyseResult":{"summary":{"error":"Unable to find first average","lastBuildId":14055}}}

dana/configs/db/Inference/series/llama_1gpu/_3_forward/_throughpu/t_samples_/s_.json

@@ -1 +0,0 @@
-{"projectId":"Inference","description":"\nbenchmark.input_shapes.batch_size: 16\nbackend.torch_dtype: float32\nbenchmark.input_shapes.sequence_length: 200\nbenchmark.new_tokens: 200","analyse":{"benchmark":{"range":"10%","required":5,"trend":"smaller"}},"assignee":{"compares":{}},"samples":{"14054":3500,"14055":2630},"state":{"analyse":"similarNeedstriage","compares":{}},"lastBuildId":"14055","analyseResult":{"summary":{"error":"Unable to find first average","lastBuildId":14055}}}

dana/configs/db/Inference/series/llama_1gpu/_3_generat/e_latency_/s_.json

@@ -1 +0,0 @@
-{"projectId":"Inference","description":"\nbenchmark.input_shapes.batch_size: 16\nbackend.torch_dtype: float32\nbenchmark.input_shapes.sequence_length: 200\nbenchmark.new_tokens: 200","analyse":{"benchmark":{"range":"10%","required":5,"trend":"smaller"}},"assignee":{"compares":{}},"samples":{"14054":0.539,"14055":0.822},"state":{"analyse":"similarNeedstriage","compares":{}},"lastBuildId":"14055","analyseResult":{"summary":{"error":"Unable to find first average","lastBuildId":14055}}}

dana/configs/db/Inference/series/llama_1gpu/_3_generat/e_throughp/ut_tokens_/s_.json

@@ -1 +0,0 @@
-{"projectId":"Inference","description":"\nbenchmark.input_shapes.batch_size: 16\nbackend.torch_dtype: float32\nbenchmark.input_shapes.sequence_length: 200\nbenchmark.new_tokens: 200","analyse":{"benchmark":{"range":"10%","required":5,"trend":"smaller"}},"assignee":{"compares":{}},"samples":{"14054":5940,"14055":3890},"state":{"analyse":"similarNeedstriage","compares":{}},"lastBuildId":"14055","analyseResult":{"summary":{"error":"Unable to find first average","lastBuildId":14055}}}

dana/configs/db/Training/infos/benchmarks.series.json

@@ -1 +0,0 @@
-{"bert_1gpu_0_training_runtime_s_":{"status":{"error":"Unable to find first average","lastBuildId":14055},"description":"\n+benchmark.training_arguments.per_device_train_batch_size: None\nbackend.torch_dtype: float16\nbenchmark.dataset_shapes.sequence_length: 256","state":"similarNeedstriage"},"bert_1gpu_0_training_throughput_samples_s_":{"status":{"error":"Unable to find first average","lastBuildId":14055},"description":"\n+benchmark.training_arguments.per_device_train_batch_size: None\nbackend.torch_dtype: float16\nbenchmark.dataset_shapes.sequence_length: 256","state":"similarNeedstriage"},"bert_1gpu_1_training_runtime_s_":{"status":{"error":"Unable to find first average","lastBuildId":14055},"description":"\n+benchmark.training_arguments.per_device_train_batch_size: None\nbackend.torch_dtype: float32\nbenchmark.dataset_shapes.sequence_length: 256","state":"similarNeedstriage"},"bert_1gpu_1_training_throughput_samples_s_":{"status":{"error":"Unable to find first average","lastBuildId":14055},"description":"\n+benchmark.training_arguments.per_device_train_batch_size: None\nbackend.torch_dtype: float32\nbenchmark.dataset_shapes.sequence_length: 256","state":"similarNeedstriage"}}

dana/configs/db/Training/infos/benchmarks.statusSeries.json

@@ -1 +0,0 @@
-{"0":{"numSeries":4,"numSeriesSimilar":0,"numSeriesImproved":0,"numSeriesRegression":0,"numSeriesUndefined":4,"time":1695816293579}}

dana/configs/db/Training/infos/builds.json

@@ -1 +0,0 @@
-{"14054":{"buildId":14054,"infos":{"hash":"153755ee386ac73e04814a94337abcb1208ff5d1","abbrevHash":"153755ee","authorName":"Younes Belkada","authorEmail":"[email protected]","subject":"[`FA` / `tests`] Add use_cache tests for FA models (#26415)","url":null}},"14055":{"buildId":14055,"infos":{"hash":"946bac798caefada3f5f1c9fecdcfd587ed24ac7","abbrevHash":"946bac79","authorName":"statelesshz","authorEmail":"[email protected]","subject":"add bf16 mixed precision support for NPU (#26163)","url":null}}}

dana/configs/db/Training/series/bert_1gpu_/0_training/_runtime_s/_.json

@@ -1 +0,0 @@
-{"projectId":"Training","description":"\n+benchmark.training_arguments.per_device_train_batch_size: None\nbackend.torch_dtype: float16\nbenchmark.dataset_shapes.sequence_length: 256","analyse":{"benchmark":{"range":"10%","required":5,"trend":"smaller"}},"assignee":{"compares":{}},"samples":{"14054":37.33880257606506,"14055":32.68557357788086},"state":{"analyse":"similarNeedstriage","compares":{}},"lastBuildId":"14055","analyseResult":{"summary":{"error":"Unable to find first average","lastBuildId":14055}}}

dana/configs/db/Training/series/bert_1gpu_/0_training/_throughpu/t_samples_/s_.json

@@ -1 +0,0 @@
-{"projectId":"Training","description":"\n+benchmark.training_arguments.per_device_train_batch_size: None\nbackend.torch_dtype: float16\nbenchmark.dataset_shapes.sequence_length: 256","analyse":{"benchmark":{"range":"10%","required":5,"trend":"smaller"}},"assignee":{"compares":{}},"samples":{"14054":86.55874792492082,"14055":98.88154455356337},"state":{"analyse":"similarNeedstriage","compares":{}},"lastBuildId":"14055","analyseResult":{"summary":{"error":"Unable to find first average","lastBuildId":14055}}}

dana/configs/db/Training/series/bert_1gpu_/1_training/_runtime_s/_.json

@@ -1 +0,0 @@
-{"projectId":"Training","description":"\n+benchmark.training_arguments.per_device_train_batch_size: None\nbackend.torch_dtype: float32\nbenchmark.dataset_shapes.sequence_length: 256","analyse":{"benchmark":{"range":"10%","required":5,"trend":"smaller"}},"assignee":{"compares":{}},"samples":{"14054":13.53417706489563,"14055":13.473761320114136},"state":{"analyse":"similarNeedstriage","compares":{}},"lastBuildId":"14055","analyseResult":{"summary":{"error":"Unable to find first average","lastBuildId":14055}}}

dana/configs/db/Training/series/bert_1gpu_/1_training/_throughpu/t_samples_/s_.json

@@ -1 +0,0 @@
-{"projectId":"Training","description":"\n+benchmark.training_arguments.per_device_train_batch_size: None\nbackend.torch_dtype: float32\nbenchmark.dataset_shapes.sequence_length: 256","analyse":{"benchmark":{"range":"10%","required":5,"trend":"smaller"}},"assignee":{"compares":{}},"samples":{"14054":238.802845899144,"14055":239.87362720869555},"state":{"analyse":"similarNeedstriage","compares":{}},"lastBuildId":"14055","analyseResult":{"summary":{"error":"Unable to find first average","lastBuildId":14055}}}

dana/configs/db/admin/globalStats.json

@@ -1 +0,0 @@
-{"numSamples":40,"numSeries":20,"projects":{"Inference":{"numSamples":32,"numSeries":16},"Training":{"numSamples":8,"numSeries":4}}}

dana/configs/db/admin/projects.json

@@ -1 +0,0 @@
-{"Inference":{"description":"Benchmarks related to inference","users":"","useBugTracker":false},"Training":{"description":"Benchmarks related to training","users":"","useBugTracker":false}}

raw_results/2023-09-27_10:21:54_153755ee386ac73e04814a94337abcb1208ff5d1/bert_1gpu_training/0/.config/config.yaml

@@ -1,75 +0,0 @@
-backend:
-  name: pytorch
-  version: ${pytorch_version:}
-  _target_: optimum_benchmark.backends.pytorch.backend.PyTorchBackend
-  seed: 42
-  inter_op_num_threads: null
-  intra_op_num_threads: null
-  initial_isolation_check: true
-  continous_isolation_check: true
-  delete_cache: false
-  no_weights: false
-  device_map: null
-  torch_dtype: float16
-  disable_grad: ${is_inference:${benchmark.name}}
-  eval_mode: ${is_inference:${benchmark.name}}
-  amp_autocast: false
-  amp_dtype: null
-  torch_compile: false
-  torch_compile_config: {}
-  bettertransformer: false
-  quantization_scheme: null
-  quantization_config: {}
-  use_ddp: false
-  ddp_config: {}
-  peft_strategy: null
-  peft_config: {}
-benchmark:
-  name: training
-  _target_: optimum_benchmark.benchmarks.training.benchmark.TrainingBenchmark
-  warmup_steps: 40
-  dataset_shapes:
-    dataset_size: 1500
-    sequence_length: 256
-    num_choices: 1
-    feature_size: 80
-    nb_max_frames: 3000
-    audio_sequence_length: 16000
-  training_arguments:
-    skip_memory_metrics: true
-    output_dir: ./trainer_output
-    use_cpu: ${is_cpu:${device}}
-    ddp_find_unused_parameters: false
-    do_train: true
-    do_eval: false
-    do_predict: false
-    report_to: none
-    per_device_train_batch_size: 32
-experiment_name: bert_1gpu_training
-model: bert-base-uncased
-device: cuda
-task: text-classification
-hub_kwargs:
-  revision: main
-  cache_dir: null
-  force_download: false
-  local_files_only: false
-environment:
-  optimum_version: 1.13.1
-  transformers_version: 4.34.0.dev0
-  accelerate_version: 0.23.0
-  diffusers_version: null
-  python_version: 3.10.12
-  system: Linux
-  cpu: ' AMD EPYC 7643 48-Core Processor'
-  cpu_count: 96
-  cpu_ram_mb: 1082028
-  gpus:
-  - Instinct MI210
-  - Instinct MI210
-  - Instinct MI210
-  - Instinct MI210
-  - Instinct MI210
-  - Instinct MI210
-  - Instinct MI210
-  - Instinct MI210

raw_results/2023-09-27_10:21:54_153755ee386ac73e04814a94337abcb1208ff5d1/bert_1gpu_training/0/.config/hydra.yaml

@@ -1,174 +0,0 @@
-hydra:
-  run:
-    dir: runs/${oc.env:COMMIT_DATE_GMT}_${oc.env:COMMIT_SHA}/${experiment_name}
-  sweep:
-    dir: sweeps/${oc.env:COMMIT_DATE_GMT}_${oc.env:COMMIT_SHA}/${experiment_name}
-    subdir: ${hydra.job.num}
-  launcher:
-    _target_: hydra._internal.core_plugins.basic_launcher.BasicLauncher
-  sweeper:
-    _target_: hydra._internal.core_plugins.basic_sweeper.BasicSweeper
-    max_batch_size: null
-    params:
-      +benchmark.training_arguments.per_device_train_batch_size: '32'
-      backend.torch_dtype: float16,float32
-  help:
-    app_name: ${hydra.job.name}
-    header: '${hydra.help.app_name} is powered by Hydra.
-
-      '
-    footer: 'Powered by Hydra (https://hydra.cc)
-
-      Use --hydra-help to view Hydra specific help
-
-      '
-    template: '${hydra.help.header}
-
-      == Configuration groups ==
-
-      Compose your configuration from those groups (group=option)
-
-
-      $APP_CONFIG_GROUPS
-
-
-      == Config ==
-
-      Override anything in the config (foo.bar=value)
-
-
-      $CONFIG
-
-
-      ${hydra.help.footer}
-
-      '
-  hydra_help:
-    template: 'Hydra (${hydra.runtime.version})
-
-      See https://hydra.cc for more info.
-
-
-      == Flags ==
-
-      $FLAGS_HELP
-
-
-      == Configuration groups ==
-
-      Compose your configuration from those groups (For example, append hydra/job_logging=disabled
-      to command line)
-
-
-      $HYDRA_CONFIG_GROUPS
-
-
-      Use ''--cfg hydra'' to Show the Hydra config.
-
-      '
-    hydra_help: ???
-  hydra_logging:
-    version: 1
-    formatters:
-      colorlog:
-        (): colorlog.ColoredFormatter
-        format: '[%(cyan)s%(asctime)s%(reset)s][%(purple)sHYDRA%(reset)s] %(message)s'
-    handlers:
-      console:
-        class: logging.StreamHandler
-        formatter: colorlog
-        stream: ext://sys.stdout
-    root:
-      level: INFO
-      handlers:
-      - console
-    disable_existing_loggers: false
-  job_logging:
-    version: 1
-    formatters:
-      simple:
-        format: '[%(asctime)s][%(name)s][%(levelname)s] - %(message)s'
-      colorlog:
-        (): colorlog.ColoredFormatter
-        format: '[%(cyan)s%(asctime)s%(reset)s][%(blue)s%(name)s%(reset)s][%(log_color)s%(levelname)s%(reset)s]
-          - %(message)s'
-        log_colors:
-          DEBUG: purple
-          INFO: green
-          WARNING: yellow
-          ERROR: red
-          CRITICAL: red
-    handlers:
-      console:
-        class: logging.StreamHandler
-        formatter: colorlog
-        stream: ext://sys.stdout
-      file:
-        class: logging.FileHandler
-        formatter: simple
-        filename: ${hydra.job.name}.log
-    root:
-      level: INFO
-      handlers:
-      - console
-      - file
-    disable_existing_loggers: false
-  env: {}
-  mode: MULTIRUN
-  searchpath: []
-  callbacks: {}
-  output_subdir: .hydra
-  overrides:
-    hydra:
-    - hydra.mode=MULTIRUN
-    task:
-    - +benchmark.training_arguments.per_device_train_batch_size=32
-    - backend.torch_dtype=float16
-  job:
-    name: experiment
-    chdir: true
-    override_dirname: +benchmark.training_arguments.per_device_train_batch_size=32,backend.torch_dtype=float16
-    id: '0'
-    num: 0
-    config_name: bert_1gpu_training
-    env_set: {}
-    env_copy: []
-    config:
-      override_dirname:
-        kv_sep: '='
-        item_sep: ','
-        exclude_keys: []
-  runtime:
-    version: 1.3.2
-    version_base: '1.3'
-    cwd: /home/user/transformers-regression
-    config_sources:
-    - path: hydra.conf
-      schema: pkg
-      provider: hydra
-    - path: optimum_benchmark
-      schema: pkg
-      provider: main
-    - path: hydra_plugins.hydra_colorlog.conf
-      schema: pkg
-      provider: hydra-colorlog
-    - path: /home/user/transformers-regression/configs
-      schema: file
-      provider: command-line
-    - path: ''
-      schema: structured
-      provider: schema
-    output_dir: /home/user/transformers-regression/sweeps/2023-09-27_10:21:54_153755ee386ac73e04814a94337abcb1208ff5d1/bert_1gpu_training/0
-    choices:
-      benchmark: training
-      backend: pytorch
-      hydra/env: default
-      hydra/callbacks: null
-      hydra/job_logging: colorlog
-      hydra/hydra_logging: colorlog
-      hydra/hydra_help: default
-      hydra/help: default
-      hydra/sweeper: basic
-      hydra/launcher: basic
-      hydra/output: default
-  verbose: false

raw_results/2023-09-27_10:21:54_153755ee386ac73e04814a94337abcb1208ff5d1/bert_1gpu_training/0/.config/overrides.yaml

@@ -1,2 +0,0 @@
-- +benchmark.training_arguments.per_device_train_batch_size=32
-- backend.torch_dtype=float16

raw_results/2023-09-27_10:21:54_153755ee386ac73e04814a94337abcb1208ff5d1/bert_1gpu_training/0/experiment.log

@@ -1,17 +0,0 @@
-[2023-09-27 11:57:31,928][experiment][WARNING] - Multiple GPUs detected but CUDA_DEVICE_ORDER is not set. This means that code might allocate resources from the wrong GPUs even if CUDA_VISIBLE_DEVICES is set. Pytorch uses the `FASTEST_FIRST` order by default, which is not guaranteed to be the same as nvidia-smi. `CUDA_DEVICE_ORDER` will be set to `PCI_BUS_ID` to ensure that the GPUs are allocated in the same order as nvidia-smi. 
-[2023-09-27 11:57:34,386][pytorch][INFO] - 	+ Inferred AutoModel class AutoModelForSequenceClassification for task text-classification and model_type bert
-[2023-09-27 11:57:34,386][backend][INFO] - Configuring pytorch backend
-[2023-09-27 11:57:34,389][backend][INFO] - 	+ Checking initial device(s) isolation of CUDA device(s): [0]
-[2023-09-27 11:57:34,517][backend][INFO] - 	+ Checking continuous device(s) isolation of CUDA device(s): [0]
-[2023-09-27 11:57:34,530][pytorch][INFO] - 	+ Loading model on device: cuda
-[2023-09-27 11:57:35,285][benchmark][INFO] - Configuring training benchmark
-[2023-09-27 11:57:35,286][training][INFO] - Running training benchmark
-[2023-09-27 11:57:35,286][dataset_generator][INFO] - Using text-classification task generator
-[2023-09-27 11:57:35,335][pytorch][INFO] - 	+ Setting dataset format to `torch`.
-[2023-09-27 11:57:35,335][pytorch][INFO] - 	+ Wrapping training arguments with transformers.TrainingArguments
-[2023-09-27 11:57:35,337][pytorch][INFO] - 	+ Wrapping model with transformers.Trainer
-[2023-09-27 11:57:35,341][pytorch][INFO] - 	+ Starting training
-[2023-09-27 11:57:55,096][pytorch][INFO] - 	+ Training finished successfully
-[2023-09-27 11:57:55,097][training][INFO] - Saving training results
-[2023-09-27 11:57:55,100][backend][INFO] - Cleaning pytorch backend
-[2023-09-27 11:57:55,100][backend][INFO] - 	+ Deleting pretrained model

raw_results/2023-09-27_10:21:54_153755ee386ac73e04814a94337abcb1208ff5d1/bert_1gpu_training/0/hydra_config.yaml

@@ -1,75 +0,0 @@
-backend:
-  name: pytorch
-  version: 2.1.0+rocm5.6
-  _target_: optimum_benchmark.backends.pytorch.backend.PyTorchBackend
-  seed: 42
-  inter_op_num_threads: null
-  intra_op_num_threads: null
-  initial_isolation_check: true
-  continous_isolation_check: true
-  delete_cache: false
-  no_weights: false
-  device_map: null
-  torch_dtype: float16
-  disable_grad: false
-  eval_mode: false
-  amp_autocast: false
-  amp_dtype: null
-  torch_compile: false
-  torch_compile_config: {}
-  bettertransformer: false
-  quantization_scheme: null
-  quantization_config: {}
-  use_ddp: false
-  ddp_config: {}
-  peft_strategy: null
-  peft_config: {}
-benchmark:
-  name: training
-  _target_: optimum_benchmark.benchmarks.training.benchmark.TrainingBenchmark
-  warmup_steps: 40
-  dataset_shapes:
-    dataset_size: 1500
-    sequence_length: 256
-    num_choices: 1
-    feature_size: 80
-    nb_max_frames: 3000
-    audio_sequence_length: 16000
-  training_arguments:
-    skip_memory_metrics: true
-    output_dir: ./trainer_output
-    use_cpu: false
-    ddp_find_unused_parameters: false
-    do_train: true
-    do_eval: false
-    do_predict: false
-    report_to: none
-    per_device_train_batch_size: 32
-experiment_name: bert_1gpu_training
-model: bert-base-uncased
-device: cuda
-task: text-classification
-hub_kwargs:
-  revision: main
-  cache_dir: null
-  force_download: false
-  local_files_only: false
-environment:
-  optimum_version: 1.13.1
-  transformers_version: 4.34.0.dev0
-  accelerate_version: 0.23.0
-  diffusers_version: null
-  python_version: 3.10.12
-  system: Linux
-  cpu: ' AMD EPYC 7643 48-Core Processor'
-  cpu_count: 96
-  cpu_ram_mb: 1082028
-  gpus:
-  - Instinct MI210
-  - Instinct MI210
-  - Instinct MI210
-  - Instinct MI210
-  - Instinct MI210
-  - Instinct MI210
-  - Instinct MI210
-  - Instinct MI210

raw_results/2023-09-27_10:21:54_153755ee386ac73e04814a94337abcb1208ff5d1/bert_1gpu_training/0/training_results.csv

@@ -1,2 +0,0 @@
-warmup.runtime(s),warmup.throughput(samples/s),training.runtime(s),training.throughput(samples/s),overall_training.runtime(s),overall_training.throughput(samples/s)
-6.112920045852661,209.39256368458834,13.53417706489563,238.802845899144,19.647098064422607,164.50266545228763

raw_results/2023-09-27_10:21:54_153755ee386ac73e04814a94337abcb1208ff5d1/bert_1gpu_training/1/.config/config.yaml

@@ -1,75 +0,0 @@
-backend:
-  name: pytorch
-  version: ${pytorch_version:}
-  _target_: optimum_benchmark.backends.pytorch.backend.PyTorchBackend
-  seed: 42
-  inter_op_num_threads: null
-  intra_op_num_threads: null
-  initial_isolation_check: true
-  continous_isolation_check: true
-  delete_cache: false
-  no_weights: false
-  device_map: null
-  torch_dtype: float32
-  disable_grad: ${is_inference:${benchmark.name}}
-  eval_mode: ${is_inference:${benchmark.name}}
-  amp_autocast: false
-  amp_dtype: null
-  torch_compile: false
-  torch_compile_config: {}
-  bettertransformer: false
-  quantization_scheme: null
-  quantization_config: {}
-  use_ddp: false
-  ddp_config: {}
-  peft_strategy: null
-  peft_config: {}
-benchmark:
-  name: training
-  _target_: optimum_benchmark.benchmarks.training.benchmark.TrainingBenchmark
-  warmup_steps: 40
-  dataset_shapes:
-    dataset_size: 1500
-    sequence_length: 256
-    num_choices: 1
-    feature_size: 80
-    nb_max_frames: 3000
-    audio_sequence_length: 16000
-  training_arguments:
-    skip_memory_metrics: true
-    output_dir: ./trainer_output
-    use_cpu: ${is_cpu:${device}}
-    ddp_find_unused_parameters: false
-    do_train: true
-    do_eval: false
-    do_predict: false
-    report_to: none
-    per_device_train_batch_size: 32
-experiment_name: bert_1gpu_training
-model: bert-base-uncased
-device: cuda
-task: text-classification
-hub_kwargs:
-  revision: main
-  cache_dir: null
-  force_download: false
-  local_files_only: false
-environment:
-  optimum_version: 1.13.1
-  transformers_version: 4.34.0.dev0
-  accelerate_version: 0.23.0
-  diffusers_version: null
-  python_version: 3.10.12
-  system: Linux
-  cpu: ' AMD EPYC 7643 48-Core Processor'
-  cpu_count: 96
-  cpu_ram_mb: 1082028
-  gpus:
-  - Instinct MI210
-  - Instinct MI210
-  - Instinct MI210
-  - Instinct MI210
-  - Instinct MI210
-  - Instinct MI210
-  - Instinct MI210
-  - Instinct MI210

raw_results/2023-09-27_10:21:54_153755ee386ac73e04814a94337abcb1208ff5d1/bert_1gpu_training/1/.config/hydra.yaml

@@ -1,174 +0,0 @@
-hydra:
-  run:
-    dir: runs/${oc.env:COMMIT_DATE_GMT}_${oc.env:COMMIT_SHA}/${experiment_name}
-  sweep:
-    dir: sweeps/${oc.env:COMMIT_DATE_GMT}_${oc.env:COMMIT_SHA}/${experiment_name}
-    subdir: ${hydra.job.num}
-  launcher:
-    _target_: hydra._internal.core_plugins.basic_launcher.BasicLauncher
-  sweeper:
-    _target_: hydra._internal.core_plugins.basic_sweeper.BasicSweeper
-    max_batch_size: null
-    params:
-      +benchmark.training_arguments.per_device_train_batch_size: '32'
-      backend.torch_dtype: float16,float32
-  help:
-    app_name: ${hydra.job.name}
-    header: '${hydra.help.app_name} is powered by Hydra.
-
-      '
-    footer: 'Powered by Hydra (https://hydra.cc)
-
-      Use --hydra-help to view Hydra specific help
-
-      '
-    template: '${hydra.help.header}
-
-      == Configuration groups ==
-
-      Compose your configuration from those groups (group=option)
-
-
-      $APP_CONFIG_GROUPS
-
-
-      == Config ==
-
-      Override anything in the config (foo.bar=value)
-
-
-      $CONFIG
-
-
-      ${hydra.help.footer}
-
-      '
-  hydra_help:
-    template: 'Hydra (${hydra.runtime.version})
-
-      See https://hydra.cc for more info.
-
-
-      == Flags ==
-
-      $FLAGS_HELP
-
-
-      == Configuration groups ==
-
-      Compose your configuration from those groups (For example, append hydra/job_logging=disabled
-      to command line)
-
-
-      $HYDRA_CONFIG_GROUPS
-
-
-      Use ''--cfg hydra'' to Show the Hydra config.
-
-      '
-    hydra_help: ???
-  hydra_logging:
-    version: 1
-    formatters:
-      colorlog:
-        (): colorlog.ColoredFormatter
-        format: '[%(cyan)s%(asctime)s%(reset)s][%(purple)sHYDRA%(reset)s] %(message)s'
-    handlers:
-      console:
-        class: logging.StreamHandler
-        formatter: colorlog
-        stream: ext://sys.stdout
-    root:
-      level: INFO
-      handlers:
-      - console
-    disable_existing_loggers: false
-  job_logging:
-    version: 1
-    formatters:
-      simple:
-        format: '[%(asctime)s][%(name)s][%(levelname)s] - %(message)s'
-      colorlog:
-        (): colorlog.ColoredFormatter
-        format: '[%(cyan)s%(asctime)s%(reset)s][%(blue)s%(name)s%(reset)s][%(log_color)s%(levelname)s%(reset)s]
-          - %(message)s'
-        log_colors:
-          DEBUG: purple
-          INFO: green
-          WARNING: yellow
-          ERROR: red
-          CRITICAL: red
-    handlers:
-      console:
-        class: logging.StreamHandler
-        formatter: colorlog
-        stream: ext://sys.stdout
-      file:
-        class: logging.FileHandler
-        formatter: simple
-        filename: ${hydra.job.name}.log
-    root:
-      level: INFO
-      handlers:
-      - console
-      - file
-    disable_existing_loggers: false
-  env: {}
-  mode: MULTIRUN
-  searchpath: []
-  callbacks: {}
-  output_subdir: .hydra
-  overrides:
-    hydra:
-    - hydra.mode=MULTIRUN
-    task:
-    - +benchmark.training_arguments.per_device_train_batch_size=32
-    - backend.torch_dtype=float32
-  job:
-    name: experiment
-    chdir: true
-    override_dirname: +benchmark.training_arguments.per_device_train_batch_size=32,backend.torch_dtype=float32
-    id: '1'
-    num: 1
-    config_name: bert_1gpu_training
-    env_set: {}
-    env_copy: []
-    config:
-      override_dirname:
-        kv_sep: '='
-        item_sep: ','
-        exclude_keys: []
-  runtime:
-    version: 1.3.2
-    version_base: '1.3'
-    cwd: /home/user/transformers-regression
-    config_sources:
-    - path: hydra.conf
-      schema: pkg
-      provider: hydra
-    - path: optimum_benchmark
-      schema: pkg
-      provider: main
-    - path: hydra_plugins.hydra_colorlog.conf
-      schema: pkg
-      provider: hydra-colorlog
-    - path: /home/user/transformers-regression/configs
-      schema: file
-      provider: command-line
-    - path: ''
-      schema: structured
-      provider: schema
-    output_dir: /home/user/transformers-regression/sweeps/2023-09-27_10:21:54_153755ee386ac73e04814a94337abcb1208ff5d1/bert_1gpu_training/1
-    choices:
-      benchmark: training
-      backend: pytorch
-      hydra/env: default
-      hydra/callbacks: null
-      hydra/job_logging: colorlog
-      hydra/hydra_logging: colorlog
-      hydra/hydra_help: default
-      hydra/help: default
-      hydra/sweeper: basic
-      hydra/launcher: basic
-      hydra/output: default
-  verbose: false

raw_results/2023-09-27_10:21:54_153755ee386ac73e04814a94337abcb1208ff5d1/bert_1gpu_training/1/.config/overrides.yaml

@@ -1,2 +0,0 @@
-- +benchmark.training_arguments.per_device_train_batch_size=32
-- backend.torch_dtype=float32

raw_results/2023-09-27_10:21:54_153755ee386ac73e04814a94337abcb1208ff5d1/bert_1gpu_training/1/experiment.log

@@ -1,16 +0,0 @@
-[2023-09-27 11:57:56,611][pytorch][INFO] - 	+ Inferred AutoModel class AutoModelForSequenceClassification for task text-classification and model_type bert
-[2023-09-27 11:57:56,612][backend][INFO] - Configuring pytorch backend
-[2023-09-27 11:57:56,612][backend][INFO] - 	+ Checking initial device(s) isolation of CUDA device(s): [0]
-[2023-09-27 11:57:56,733][backend][INFO] - 	+ Checking continuous device(s) isolation of CUDA device(s): [0]
-[2023-09-27 11:57:56,749][pytorch][INFO] - 	+ Loading model on device: cuda
-[2023-09-27 11:57:57,432][benchmark][INFO] - Configuring training benchmark
-[2023-09-27 11:57:57,432][training][INFO] - Running training benchmark
-[2023-09-27 11:57:57,433][dataset_generator][INFO] - Using text-classification task generator
-[2023-09-27 11:57:57,467][pytorch][INFO] - 	+ Setting dataset format to `torch`.
-[2023-09-27 11:57:57,468][pytorch][INFO] - 	+ Wrapping training arguments with transformers.TrainingArguments
-[2023-09-27 11:57:57,469][pytorch][INFO] - 	+ Wrapping model with transformers.Trainer
-[2023-09-27 11:57:57,474][pytorch][INFO] - 	+ Starting training
-[2023-09-27 11:58:50,280][pytorch][INFO] - 	+ Training finished successfully
-[2023-09-27 11:58:50,281][training][INFO] - Saving training results
-[2023-09-27 11:58:50,282][backend][INFO] - Cleaning pytorch backend
-[2023-09-27 11:58:50,282][backend][INFO] - 	+ Deleting pretrained model

raw_results/2023-09-27_10:21:54_153755ee386ac73e04814a94337abcb1208ff5d1/bert_1gpu_training/1/hydra_config.yaml

@@ -1,75 +0,0 @@
-backend:
-  name: pytorch
-  version: 2.1.0+rocm5.6
-  _target_: optimum_benchmark.backends.pytorch.backend.PyTorchBackend
-  seed: 42
-  inter_op_num_threads: null
-  intra_op_num_threads: null
-  initial_isolation_check: true
-  continous_isolation_check: true
-  delete_cache: false
-  no_weights: false
-  device_map: null
-  torch_dtype: float32
-  disable_grad: false
-  eval_mode: false
-  amp_autocast: false
-  amp_dtype: null
-  torch_compile: false
-  torch_compile_config: {}
-  bettertransformer: false
-  quantization_scheme: null
-  quantization_config: {}
-  use_ddp: false
-  ddp_config: {}
-  peft_strategy: null
-  peft_config: {}
-benchmark:
-  name: training
-  _target_: optimum_benchmark.benchmarks.training.benchmark.TrainingBenchmark
-  warmup_steps: 40
-  dataset_shapes:
-    dataset_size: 1500
-    sequence_length: 256
-    num_choices: 1
-    feature_size: 80
-    nb_max_frames: 3000
-    audio_sequence_length: 16000
-  training_arguments:
-    skip_memory_metrics: true
-    output_dir: ./trainer_output
-    use_cpu: false
-    ddp_find_unused_parameters: false
-    do_train: true
-    do_eval: false
-    do_predict: false
-    report_to: none
-    per_device_train_batch_size: 32
-experiment_name: bert_1gpu_training
-model: bert-base-uncased
-device: cuda
-task: text-classification
-hub_kwargs:
-  revision: main
-  cache_dir: null
-  force_download: false
-  local_files_only: false
-environment:
-  optimum_version: 1.13.1
-  transformers_version: 4.34.0.dev0
-  accelerate_version: 0.23.0
-  diffusers_version: null
-  python_version: 3.10.12
-  system: Linux
-  cpu: ' AMD EPYC 7643 48-Core Processor'
-  cpu_count: 96
-  cpu_ram_mb: 1082028
-  gpus:
-  - Instinct MI210
-  - Instinct MI210
-  - Instinct MI210
-  - Instinct MI210
-  - Instinct MI210
-  - Instinct MI210
-  - Instinct MI210
-  - Instinct MI210

raw_results/2023-09-27_10:21:54_153755ee386ac73e04814a94337abcb1208ff5d1/bert_1gpu_training/1/training_results.csv

@@ -1,2 +0,0 @@
-warmup.runtime(s),warmup.throughput(samples/s),training.runtime(s),training.throughput(samples/s),overall_training.runtime(s),overall_training.throughput(samples/s)
-15.344141006469727,83.41946280735421,37.33880257606506,86.55874792492082,52.682945013046265,61.34812697353263

raw_results/2023-09-27_10:21:54_153755ee386ac73e04814a94337abcb1208ff5d1/bert_1gpu_training/multirun.yaml

@@ -1,246 +0,0 @@
-hydra:
-  run:
-    dir: runs/${oc.env:COMMIT_DATE_GMT}_${oc.env:COMMIT_SHA}/${experiment_name}
-  sweep:
-    dir: sweeps/${oc.env:COMMIT_DATE_GMT}_${oc.env:COMMIT_SHA}/${experiment_name}
-    subdir: ${hydra.job.num}
-  launcher:
-    _target_: hydra._internal.core_plugins.basic_launcher.BasicLauncher
-  sweeper:
-    _target_: hydra._internal.core_plugins.basic_sweeper.BasicSweeper
-    max_batch_size: null
-    params:
-      +benchmark.training_arguments.per_device_train_batch_size: '32'
-      backend.torch_dtype: float16,float32
-  help:
-    app_name: ${hydra.job.name}
-    header: '${hydra.help.app_name} is powered by Hydra.
-
-      '
-    footer: 'Powered by Hydra (https://hydra.cc)
-
-      Use --hydra-help to view Hydra specific help
-
-      '
-    template: '${hydra.help.header}
-
-      == Configuration groups ==
-
-      Compose your configuration from those groups (group=option)
-
-
-      $APP_CONFIG_GROUPS
-
-
-      == Config ==
-
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raw_results/2023-09-27_10:21:54_153755ee386ac73e04814a94337abcb1208ff5d1/llama_1gpu_inference/0/.config/config.yaml

@@ -1,73 +0,0 @@
-backend:
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-  version: ${pytorch_version:}
-  _target_: optimum_benchmark.backends.pytorch.backend.PyTorchBackend
-  seed: 42
-  inter_op_num_threads: null
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-  torch_dtype: float16
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-  amp_autocast: false
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-  torch_compile: false
-  torch_compile_config: {}
-  bettertransformer: false
-  quantization_scheme: null
-  quantization_config: {}
-  use_ddp: false
-  ddp_config: {}
-  peft_strategy: null
-  peft_config: {}
-benchmark:
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-  _target_: optimum_benchmark.benchmarks.inference.benchmark.InferenceBenchmark
-  duration: 10
-  warmup_runs: 10
-  memory: false
-  energy: false
-  input_shapes:
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-    sequence_length: 200
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-    feature_size: 80
-    nb_max_frames: 3000
-    audio_sequence_length: 16000
-  new_tokens: 200
-  can_diffuse: ${can_diffuse:${task}}
-  can_generate: ${can_generate:${task}}
-  forward_kwargs: {}
-  generate_kwargs: {}
-experiment_name: llama_1gpu_inference
-model: fxmarty/tiny-llama-fast-tokenizer
-device: cuda
-task: ${infer_task:${model}}
-hub_kwargs:
-  revision: main
-  cache_dir: null
-  force_download: false
-  local_files_only: false
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-  transformers_version: 4.34.0.dev0
-  accelerate_version: 0.23.0
-  diffusers_version: null
-  python_version: 3.10.12
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-  cpu: ' AMD EPYC 7643 48-Core Processor'
-  cpu_count: 96
-  cpu_ram_mb: 1082028
-  gpus:
-  - Instinct MI210
-  - Instinct MI210
-  - Instinct MI210
-  - Instinct MI210
-  - Instinct MI210
-  - Instinct MI210
-  - Instinct MI210
-  - Instinct MI210

raw_results/2023-09-27_10:21:54_153755ee386ac73e04814a94337abcb1208ff5d1/llama_1gpu_inference/0/.config/hydra.yaml

@@ -1,174 +0,0 @@
-hydra:
-  run:
-    dir: runs/${oc.env:COMMIT_DATE_GMT}_${oc.env:COMMIT_SHA}/${experiment_name}
-  sweep:
-    dir: sweeps/${oc.env:COMMIT_DATE_GMT}_${oc.env:COMMIT_SHA}/${experiment_name}
-    subdir: ${hydra.job.num}
-  launcher:
-    _target_: hydra._internal.core_plugins.basic_launcher.BasicLauncher
-  sweeper:
-    _target_: hydra._internal.core_plugins.basic_sweeper.BasicSweeper
-    max_batch_size: null
-    params:
-      benchmark.input_shapes.batch_size: 1,16
-      backend.torch_dtype: float16,float32
-  help:
-    app_name: ${hydra.job.name}
-    header: '${hydra.help.app_name} is powered by Hydra.
-
-      '
-    footer: 'Powered by Hydra (https://hydra.cc)
-
-      Use --hydra-help to view Hydra specific help
-
-      '
-    template: '${hydra.help.header}
-
-      == Configuration groups ==
-
-      Compose your configuration from those groups (group=option)
-
-
-      $APP_CONFIG_GROUPS
-
-
-      == Config ==
-
-      Override anything in the config (foo.bar=value)
-
-
-      $CONFIG
-
-
-      ${hydra.help.footer}
-
-      '
-  hydra_help:
-    template: 'Hydra (${hydra.runtime.version})
-
-      See https://hydra.cc for more info.
-
-
-      == Flags ==
-
-      $FLAGS_HELP
-
-
-      == Configuration groups ==
-
-      Compose your configuration from those groups (For example, append hydra/job_logging=disabled
-      to command line)
-
-
-      $HYDRA_CONFIG_GROUPS
-
-
-      Use ''--cfg hydra'' to Show the Hydra config.
-
-      '
-    hydra_help: ???
-  hydra_logging:
-    version: 1
-    formatters:
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-        format: '[%(cyan)s%(asctime)s%(reset)s][%(purple)sHYDRA%(reset)s] %(message)s'
-    handlers:
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-        formatter: colorlog
-        stream: ext://sys.stdout
-    root:
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-      handlers:
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-    disable_existing_loggers: false
-  job_logging:
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-        format: '[%(cyan)s%(asctime)s%(reset)s][%(blue)s%(name)s%(reset)s][%(log_color)s%(levelname)s%(reset)s]
-          - %(message)s'
-        log_colors:
-          DEBUG: purple
-          INFO: green
-          WARNING: yellow
-          ERROR: red
-          CRITICAL: red
-    handlers:
-      console:
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-        formatter: colorlog
-        stream: ext://sys.stdout
-      file:
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-        formatter: simple
-        filename: ${hydra.job.name}.log
-    root:
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-  overrides:
-    hydra:
-    - hydra.mode=MULTIRUN
-    task:
-    - benchmark.input_shapes.batch_size=1
-    - backend.torch_dtype=float16
-  job:
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-    override_dirname: backend.torch_dtype=float16,benchmark.input_shapes.batch_size=1
-    id: '0'
-    num: 0
-    config_name: llama2_1gpu_inference
-    env_set: {}
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-    config:
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-    - path: hydra_plugins.hydra_colorlog.conf
-      schema: pkg
-      provider: hydra-colorlog
-    - path: /home/user/transformers-regression/configs
-      schema: file
-      provider: command-line
-    - path: ''
-      schema: structured
-      provider: schema
-    output_dir: /home/user/transformers-regression/sweeps/2023-09-27_10:21:54_153755ee386ac73e04814a94337abcb1208ff5d1/llama_1gpu_inference/0
-    choices:
-      benchmark: inference
-      backend: pytorch
-      hydra/env: default
-      hydra/callbacks: null
-      hydra/job_logging: colorlog
-      hydra/hydra_logging: colorlog
-      hydra/hydra_help: default
-      hydra/help: default
-      hydra/sweeper: basic
-      hydra/launcher: basic
-      hydra/output: default
-  verbose: false

raw_results/2023-09-27_10:21:54_153755ee386ac73e04814a94337abcb1208ff5d1/llama_1gpu_inference/0/.config/overrides.yaml

@@ -1,2 +0,0 @@
-- benchmark.input_shapes.batch_size=1
-- backend.torch_dtype=float16

raw_results/2023-09-27_10:21:54_153755ee386ac73e04814a94337abcb1208ff5d1/llama_1gpu_inference/0/experiment.log

@@ -1,27 +0,0 @@
-[2023-09-27 11:58:54,429][inference][INFO] - `new_tokens` was set to 200. `max_new_tokens` and `min_new_tokens` will be set to 200.
-[2023-09-27 11:58:54,571][experiment][WARNING] - Multiple GPUs detected but CUDA_DEVICE_ORDER is not set. This means that code might allocate resources from the wrong GPUs even if CUDA_VISIBLE_DEVICES is set. Pytorch uses the `FASTEST_FIRST` order by default, which is not guaranteed to be the same as nvidia-smi. `CUDA_DEVICE_ORDER` will be set to `PCI_BUS_ID` to ensure that the GPUs are allocated in the same order as nvidia-smi. 
-[2023-09-27 11:58:56,806][pytorch][INFO] - 	+ Inferred AutoModel class AutoModelForCausalLM for task text-generation and model_type llama
-[2023-09-27 11:58:56,806][backend][INFO] - Configuring pytorch backend
-[2023-09-27 11:58:56,807][backend][INFO] - 	+ Checking initial device(s) isolation of CUDA device(s): [0]
-[2023-09-27 11:58:56,931][backend][INFO] - 	+ Checking continuous device(s) isolation of CUDA device(s): [0]
-[2023-09-27 11:58:56,946][pytorch][INFO] - 	+ Disabling gradients
-[2023-09-27 11:58:56,947][pytorch][INFO] - 	+ Loading model on device: cuda
-[2023-09-27 11:58:57,576][pytorch][INFO] - 	+ Turning on model's eval mode
-[2023-09-27 11:58:57,583][benchmark][INFO] - Configuring inference benchmark
-[2023-09-27 11:58:57,584][inference][INFO] - Running inference benchmark
-[2023-09-27 11:58:57,584][input_generator][INFO] - Using llama model type generator
-[2023-09-27 11:58:57,606][inference][INFO] - 	+ Preparing input for the forward pass
-[2023-09-27 11:58:57,606][inference][INFO] - 	+ Warming up the forward pass
-[2023-09-27 11:58:57,936][inference][INFO] - 	+ Tracking forward pass latency and throughput
-[2023-09-27 11:58:57,937][latency_tracker][INFO] - Tracked Pytorch devices: [0]
-[2023-09-27 11:59:08,120][inference][INFO] - 	+ Forward pass latency: 3.28e-03 (s)
-[2023-09-27 11:59:08,122][inference][INFO] - 	+ Forward pass throughput: 305.00 (samples/s)
-[2023-09-27 11:59:08,122][inference][INFO] - 	+ Preparing input for the generation pass
-[2023-09-27 11:59:08,122][inference][INFO] - 	+ Warming up the generation pass
-[2023-09-27 11:59:09,154][inference][INFO] - 	+ Tracking generation latency and throughput
-[2023-09-27 11:59:09,154][latency_tracker][INFO] - Tracked Pytorch devices: [0]
-[2023-09-27 11:59:19,765][inference][INFO] - 	+ Generation pass latency: 5.30e-01 (s)
-[2023-09-27 11:59:19,766][inference][INFO] - 	+ Generation pass throughput: 377.00 (tokens/s)
-[2023-09-27 11:59:19,766][inference][INFO] - Saving inference results
-[2023-09-27 11:59:19,773][backend][INFO] - Cleaning pytorch backend
-[2023-09-27 11:59:19,773][backend][INFO] - 	+ Deleting pretrained model

raw_results/2023-09-27_10:21:54_153755ee386ac73e04814a94337abcb1208ff5d1/llama_1gpu_inference/0/hydra_config.yaml

@@ -1,79 +0,0 @@
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-  peft_config: {}
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-  _target_: optimum_benchmark.benchmarks.inference.benchmark.InferenceBenchmark
-  duration: 10
-  warmup_runs: 10
-  memory: false
-  energy: false
-  input_shapes:
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-    sequence_length: 200
-    num_choices: 1
-    feature_size: 80
-    nb_max_frames: 3000
-    audio_sequence_length: 16000
-  new_tokens: 200
-  can_diffuse: false
-  can_generate: true
-  forward_kwargs: {}
-  generate_kwargs:
-    max_new_tokens: 200
-    min_new_tokens: 200
-    do_sample: false
-    use_cache: true
-    pad_token_id: 0
-    num_beams: 1
-experiment_name: llama_1gpu_inference
-model: fxmarty/tiny-llama-fast-tokenizer
-device: cuda
-task: text-generation
-hub_kwargs:
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-  cache_dir: null
-  force_download: false
-  local_files_only: false
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-  cpu: ' AMD EPYC 7643 48-Core Processor'
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-  - Instinct MI210
-  - Instinct MI210
-  - Instinct MI210
-  - Instinct MI210
-  - Instinct MI210
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-  - Instinct MI210

raw_results/2023-09-27_10:21:54_153755ee386ac73e04814a94337abcb1208ff5d1/llama_1gpu_inference/0/inference_results.csv

@@ -1,2 +0,0 @@
-forward.latency(s),forward.throughput(samples/s),generate.latency(s),generate.throughput(tokens/s)
-0.00328,305.0,0.53,377.0

raw_results/2023-09-27_10:21:54_153755ee386ac73e04814a94337abcb1208ff5d1/llama_1gpu_inference/1/.config/config.yaml

@@ -1,73 +0,0 @@
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-  - Instinct MI210
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raw_results/2023-09-27_10:21:54_153755ee386ac73e04814a94337abcb1208ff5d1/llama_1gpu_inference/1/.config/hydra.yaml

@@ -1,174 +0,0 @@
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-    params:
-      benchmark.input_shapes.batch_size: 1,16
-      backend.torch_dtype: float16,float32
-  help:
-    app_name: ${hydra.job.name}
-    header: '${hydra.help.app_name} is powered by Hydra.
-
-      '
-    footer: 'Powered by Hydra (https://hydra.cc)
-
-      Use --hydra-help to view Hydra specific help
-
-      '
-    template: '${hydra.help.header}
-
-      == Configuration groups ==
-
-      Compose your configuration from those groups (group=option)
-
-
-      $APP_CONFIG_GROUPS
-
-
-      == Config ==
-
-      Override anything in the config (foo.bar=value)
-
-
-      $CONFIG
-
-
-      ${hydra.help.footer}
-
-      '
-  hydra_help:
-    template: 'Hydra (${hydra.runtime.version})
-
-      See https://hydra.cc for more info.
-
-
-      == Flags ==
-
-      $FLAGS_HELP
-
-
-      == Configuration groups ==
-
-      Compose your configuration from those groups (For example, append hydra/job_logging=disabled
-      to command line)
-
-
-      $HYDRA_CONFIG_GROUPS
-
-
-      Use ''--cfg hydra'' to Show the Hydra config.
-
-      '
-    hydra_help: ???
-  hydra_logging:
-    version: 1
-    formatters:
-      colorlog:
-        (): colorlog.ColoredFormatter
-        format: '[%(cyan)s%(asctime)s%(reset)s][%(purple)sHYDRA%(reset)s] %(message)s'
-    handlers:
-      console:
-        class: logging.StreamHandler
-        formatter: colorlog
-        stream: ext://sys.stdout
-    root:
-      level: INFO
-      handlers:
-      - console
-    disable_existing_loggers: false
-  job_logging:
-    version: 1
-    formatters:
-      simple:
-        format: '[%(asctime)s][%(name)s][%(levelname)s] - %(message)s'
-      colorlog:
-        (): colorlog.ColoredFormatter
-        format: '[%(cyan)s%(asctime)s%(reset)s][%(blue)s%(name)s%(reset)s][%(log_color)s%(levelname)s%(reset)s]
-          - %(message)s'
-        log_colors:
-          DEBUG: purple
-          INFO: green
-          WARNING: yellow
-          ERROR: red
-          CRITICAL: red
-    handlers:
-      console:
-        class: logging.StreamHandler
-        formatter: colorlog
-        stream: ext://sys.stdout
-      file:
-        class: logging.FileHandler
-        formatter: simple
-        filename: ${hydra.job.name}.log
-    root:
-      level: INFO
-      handlers:
-      - console
-      - file
-    disable_existing_loggers: false
-  env: {}
-  mode: MULTIRUN
-  searchpath: []
-  callbacks: {}
-  output_subdir: .hydra
-  overrides:
-    hydra:
-    - hydra.mode=MULTIRUN
-    task:
-    - benchmark.input_shapes.batch_size=1
-    - backend.torch_dtype=float32
-  job:
-    name: experiment
-    chdir: true
-    override_dirname: backend.torch_dtype=float32,benchmark.input_shapes.batch_size=1
-    id: '1'
-    num: 1
-    config_name: llama2_1gpu_inference
-    env_set: {}
-    env_copy: []
-    config:
-      override_dirname:
-        kv_sep: '='
-        item_sep: ','
-        exclude_keys: []
-  runtime:
-    version: 1.3.2
-    version_base: '1.3'
-    cwd: /home/user/transformers-regression
-    config_sources:
-    - path: hydra.conf
-      schema: pkg
-      provider: hydra
-    - path: optimum_benchmark
-      schema: pkg
-      provider: main
-    - path: hydra_plugins.hydra_colorlog.conf
-      schema: pkg
-      provider: hydra-colorlog
-    - path: /home/user/transformers-regression/configs
-      schema: file
-      provider: command-line
-    - path: ''
-      schema: structured
-      provider: schema
-    output_dir: /home/user/transformers-regression/sweeps/2023-09-27_10:21:54_153755ee386ac73e04814a94337abcb1208ff5d1/llama_1gpu_inference/1
-    choices:
-      benchmark: inference
-      backend: pytorch
-      hydra/env: default
-      hydra/callbacks: null
-      hydra/job_logging: colorlog
-      hydra/hydra_logging: colorlog
-      hydra/hydra_help: default
-      hydra/help: default
-      hydra/sweeper: basic
-      hydra/launcher: basic
-      hydra/output: default
-  verbose: false

raw_results/2023-09-27_10:21:54_153755ee386ac73e04814a94337abcb1208ff5d1/llama_1gpu_inference/1/.config/overrides.yaml

@@ -1,2 +0,0 @@
-- benchmark.input_shapes.batch_size=1
-- backend.torch_dtype=float32