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The dataset generation failed
Error code:   DatasetGenerationError
Exception:    ArrowNotImplementedError
Message:      Cannot write struct type 'model_kwargs' with no child field to Parquet. Consider adding a dummy child field.
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2011, in _prepare_split_single
                  writer.write_table(table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 583, in write_table
                  self._build_writer(inferred_schema=pa_table.schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 404, in _build_writer
                  self.pa_writer = self._WRITER_CLASS(self.stream, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pyarrow/parquet/core.py", line 1010, in __init__
                  self.writer = _parquet.ParquetWriter(
                File "pyarrow/_parquet.pyx", line 2157, in pyarrow._parquet.ParquetWriter.__cinit__
                File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
              pyarrow.lib.ArrowNotImplementedError: Cannot write struct type 'model_kwargs' with no child field to Parquet. Consider adding a dummy child field.
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2027, in _prepare_split_single
                  num_examples, num_bytes = writer.finalize()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 602, in finalize
                  self._build_writer(self.schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 404, in _build_writer
                  self.pa_writer = self._WRITER_CLASS(self.stream, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pyarrow/parquet/core.py", line 1010, in __init__
                  self.writer = _parquet.ParquetWriter(
                File "pyarrow/_parquet.pyx", line 2157, in pyarrow._parquet.ParquetWriter.__cinit__
                File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
              pyarrow.lib.ArrowNotImplementedError: Cannot write struct type 'model_kwargs' with no child field to Parquet. Consider adding a dummy child field.
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1529, in compute_config_parquet_and_info_response
                  parquet_operations = convert_to_parquet(builder)
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1154, in convert_to_parquet
                  builder.download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1027, in download_and_prepare
                  self._download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1122, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1882, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2038, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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config
dict
report
dict
{ "name": "pytorch-llama-3.1-8b-Pruned-1-Layer", "backend": { "name": "pytorch", "version": "2.3.1", "_target_": "optimum_benchmark.backends.pytorch.backend.PyTorchBackend", "task": "text-generation", "library": "transformers", "model_type": "llama", "model": "Na0s/Llama-3.1-8b-Pruned-1-Layer", "processor": "Na0s/Llama-3.1-8b-Pruned-1-Layer", "device": "cuda", "device_ids": "0,1", "seed": 42, "inter_op_num_threads": null, "intra_op_num_threads": null, "model_kwargs": {}, "processor_kwargs": {}, "no_weights": false, "device_map": null, "torch_dtype": "bfloat16", "eval_mode": true, "to_bettertransformer": false, "low_cpu_mem_usage": null, "attn_implementation": null, "cache_implementation": null, "autocast_enabled": false, "autocast_dtype": null, "torch_compile": false, "torch_compile_target": "forward", "torch_compile_config": {}, "quantization_scheme": null, "quantization_config": {}, "deepspeed_inference": false, "deepspeed_inference_config": {}, "peft_type": null, "peft_config": {} }, "scenario": { "name": "inference", "_target_": "optimum_benchmark.scenarios.inference.scenario.InferenceScenario", "iterations": 10, "duration": 10, "warmup_runs": 10, "input_shapes": { "batch_size": 1, "num_choices": 2, "sequence_length": 128 }, "new_tokens": null, "memory": true, "latency": true, "energy": false, "forward_kwargs": {}, "generate_kwargs": { "max_new_tokens": 32, "min_new_tokens": 32 }, "call_kwargs": {} }, "launcher": { "name": "process", "_target_": "optimum_benchmark.launchers.process.launcher.ProcessLauncher", "device_isolation": true, "device_isolation_action": "warn", "numactl": false, "numactl_kwargs": {}, "start_method": "spawn" }, "environment": { "cpu": " Intel(R) Xeon(R) Gold 6326 CPU @ 2.90GHz", "cpu_count": 64, "cpu_ram_mb": 540228.182016, "system": "Linux", "machine": "x86_64", "platform": "Linux-5.10.194-release-94a7f6a-81353a4-x86_64-with-glibc2.31", "processor": "x86_64", "python_version": "3.9.5", "gpu": [ "NVIDIA A100 80GB PCIe", "NVIDIA A100-SXM4-80GB", "NVIDIA A100-SXM4-80GB" ], "gpu_count": 3, "gpu_vram_mb": 257698037760, "optimum_benchmark_version": "0.4.0", "optimum_benchmark_commit": null, "transformers_version": "4.43.3", "transformers_commit": null, "accelerate_version": "0.30.1", "accelerate_commit": null, "diffusers_version": null, "diffusers_commit": null, "optimum_version": null, "optimum_commit": null, "timm_version": null, "timm_commit": null, "peft_version": "0.12.0", "peft_commit": null } }
{ "load": { "memory": { "unit": "MB", "max_ram": 13219.991552, "max_global_vram": 17641.177088, "max_process_vram": 15749.61152, "max_reserved": 15315.501056, "max_allocated": 15188.090368 }, "latency": { "unit": "s", "count": 1, "total": 394.300875, "mean": 394.300875, "stdev": 0, "p50": 394.300875, "p90": 394.300875, "p95": 394.300875, "p99": 394.300875, "values": [ 394.300875 ] }, "throughput": null, "energy": null, "efficiency": null }, "prefill": { "memory": { "unit": "MB", "max_ram": 1385.201664, "max_global_vram": 17886.543872, "max_process_vram": 15994.978304, "max_reserved": 15462.301696, "max_allocated": 15312.61696 }, "latency": { "unit": "s", "count": 12, "total": 0.3437252464294434, "mean": 0.028643770535786952, "stdev": 0.003971163270682729, "p50": 0.030289439201354983, "p90": 0.03220915985107422, "p95": 0.032429367446899414, "p99": 0.03263699043273926, "values": [ 0.03209807968139648, 0.02188742446899414, 0.03221702575683594, 0.02173958396911621, 0.027161216735839842, 0.03166655921936035, 0.030915487289428712, 0.02785430335998535, 0.03268889617919922, 0.02966339111328125, 0.02369491195678711, 0.03213836669921875 ] }, "throughput": { "unit": "tokens/s", "value": 4468.685428131026 }, "energy": null, "efficiency": null }, "decode": { "memory": { "unit": "MB", "max_ram": 1434.181632, "max_global_vram": 17888.641024, "max_process_vram": 15997.075456, "max_reserved": 15464.398848, "max_allocated": 15312.618496 }, "latency": { "unit": "s", "count": 12, "total": 10.122860900878907, "mean": 0.8435717417399089, "stdev": 0.0508877924215067, "p50": 0.8532926025390626, "p90": 0.9063542053222656, "p95": 0.9079432525634766, "p99": 0.9094064727783203, "values": [ 0.8565137329101562, 0.8503409423828125, 0.8017994384765625, 0.8974027709960938, 0.7845574340820313, 0.9055210571289063, 0.7872280883789062, 0.90644677734375, 0.780845947265625, 0.9097722778320313, 0.7861881713867187, 0.8562442626953125 ] }, "throughput": { "unit": "tokens/s", "value": 36.748504562351684 }, "energy": null, "efficiency": null }, "per_token": { "memory": null, "latency": { "unit": "s", "count": 372, "total": 10.115577859878533, "mean": 0.027192413601824034, "stdev": 0.004327168519095679, "p50": 0.028446720123291018, "p90": 0.031238963317871092, "p95": 0.03143797702789307, "p99": 0.03180485635757446, "values": [ 0.028653568267822265, 0.026619903564453123, 0.03116543960571289, 0.03080703926086426, 0.026193920135498046, 0.030209024429321288, 0.030867456436157226, 0.027074560165405274, 0.029372415542602538, 0.03152179145812988, 0.028042240142822264, 0.028727296829223634, 0.031926271438598636, 0.028314624786376953, 0.027936767578125, 0.032216064453125, 0.028579839706420897, 0.02798899269104004, 0.032530433654785154, 0.030176256179809572, 0.027122688293457032, 0.032553985595703126, 0.030509056091308592, 0.02693734359741211, 0.031696895599365234, 0.021720064163208007, 0.019160064697265625, 0.0194703369140625, 0.01946419143676758, 0.019392511367797852, 0.019315711975097655, 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