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.hydra/hydra.yaml

@@ -1,6 +1,6 @@
 hydra:
   run:
-    dir: runs/text_generation/NousResearch/Hermes-3-Llama-3.1-8B/1736822122.5321538
+    dir: runs/text_generation/NousResearch/Hermes-3-Llama-3.1-8B/1736822753.414838
   sweep:
     dir: sweeps/${experiment_name}/${backend.model}/${now:%Y-%m-%d-%H-%M-%S}
     subdir: ${hydra.job.num}
@@ -118,7 +118,7 @@ hydra:
   output_subdir: .hydra
   overrides:
     hydra:
-    - hydra.run.dir=runs/text_generation/NousResearch/Hermes-3-Llama-3.1-8B/1736822122.5321538
+    - hydra.run.dir=runs/text_generation/NousResearch/Hermes-3-Llama-3.1-8B/1736822753.414838
     - hydra.mode=RUN
     task:
     - backend.model=NousResearch/Hermes-3-Llama-3.1-8B
@@ -158,7 +158,7 @@ hydra:
     - path: ''
       schema: structured
       provider: schema
-    output_dir: /app/runs/text_generation/NousResearch/Hermes-3-Llama-3.1-8B/1736822122.5321538
+    output_dir: /app/runs/text_generation/NousResearch/Hermes-3-Llama-3.1-8B/1736822753.414838
     choices:
       benchmark: energy_star
       launcher: process

cli.log

@@ -1,31 +1,17 @@
-[2025-01-14 02:35:25,974][launcher][INFO] - ََAllocating process launcher
-[2025-01-14 02:35:25,974][process][INFO] - 	+ Setting multiprocessing start method to spawn.
-[2025-01-14 02:35:25,996][process][INFO] - 	+ Launched benchmark in isolated process 180.
-[PROC-0][2025-01-14 02:35:29,284][datasets][INFO] - PyTorch version 2.4.0 available.
-[PROC-0][2025-01-14 02:35:30,322][backend][INFO] - َAllocating pytorch backend
-[PROC-0][2025-01-14 02:35:30,322][backend][INFO] - 	+ Setting random seed to 42
-[PROC-0][2025-01-14 02:35:31,909][pytorch][INFO] - 	+ Using AutoModel class AutoModelForCausalLM
-[PROC-0][2025-01-14 02:35:31,909][pytorch][INFO] - 	+ Creating backend temporary directory
-[PROC-0][2025-01-14 02:35:31,909][pytorch][INFO] - 	+ Loading model with random weights
-[PROC-0][2025-01-14 02:35:31,910][pytorch][INFO] - 	+ Creating no weights model
-[PROC-0][2025-01-14 02:35:31,910][pytorch][INFO] - 	+ Creating no weights model directory
-[PROC-0][2025-01-14 02:35:31,910][pytorch][INFO] - 	+ Creating no weights model state dict
-[PROC-0][2025-01-14 02:35:31,936][pytorch][INFO] - 	+ Saving no weights model safetensors
-[PROC-0][2025-01-14 02:35:31,937][pytorch][INFO] - 	+ Saving no weights model pretrained config
-[PROC-0][2025-01-14 02:35:31,938][pytorch][INFO] - 	+ Loading no weights AutoModel
-[PROC-0][2025-01-14 02:35:31,938][pytorch][INFO] - 	+ Loading model directly on device: cuda
-[PROC-0][2025-01-14 02:35:32,676][pytorch][INFO] - 	+ Turning on model's eval mode
-[PROC-0][2025-01-14 02:35:32,683][benchmark][INFO] - Allocating energy_star benchmark
-[PROC-0][2025-01-14 02:35:32,683][energy_star][INFO] - 	+ Loading raw dataset
-[PROC-0][2025-01-14 02:35:33,810][energy_star][INFO] - 	+ Updating Text Generation kwargs with default values
-[PROC-0][2025-01-14 02:35:33,810][energy_star][INFO] - 	+ Initializing Text Generation report
-[PROC-0][2025-01-14 02:35:33,810][energy][INFO] - 	+ Tracking GPU energy on devices [0]
-[PROC-0][2025-01-14 02:35:38,046][energy_star][INFO] - 	+ Preprocessing dataset
-[PROC-0][2025-01-14 02:35:39,018][energy][INFO] - 	+ Saving codecarbon emission data to preprocess_codecarbon.json
-[PROC-0][2025-01-14 02:35:39,018][energy_star][INFO] - 	+ Preparing backend for Inference
-[PROC-0][2025-01-14 02:35:39,018][energy_star][INFO] - 	+ Initialising dataloader
-[PROC-0][2025-01-14 02:35:39,019][energy_star][INFO] - 	+ Warming up backend for Inference
-[PROC-0][2025-01-14 02:35:46,797][energy_star][INFO] - 	+ Additional warmup for Text Generation
-[PROC-0][2025-01-14 02:35:47,959][energy_star][INFO] - 	+ Running Text Generation energy tracking for 10 iterations
-[PROC-0][2025-01-14 02:35:47,959][energy_star][INFO] - 	+ Prefill iteration 1/10
-[2025-01-14 02:40:24,685][experiment][ERROR] - Error during experiment
+[2025-01-14 02:45:56,788][launcher][INFO] - ََAllocating process launcher
+[2025-01-14 02:45:56,789][process][INFO] - 	+ Setting multiprocessing start method to spawn.
+[2025-01-14 02:45:56,807][process][INFO] - 	+ Launched benchmark in isolated process 652.
+[PROC-0][2025-01-14 02:45:59,889][datasets][INFO] - PyTorch version 2.4.0 available.
+[PROC-0][2025-01-14 02:46:00,929][backend][INFO] - َAllocating pytorch backend
+[PROC-0][2025-01-14 02:46:00,929][backend][INFO] - 	+ Setting random seed to 42
+[PROC-0][2025-01-14 02:46:01,644][pytorch][INFO] - 	+ Using AutoModel class AutoModelForCausalLM
+[PROC-0][2025-01-14 02:46:01,644][pytorch][INFO] - 	+ Creating backend temporary directory
+[PROC-0][2025-01-14 02:46:01,645][pytorch][INFO] - 	+ Loading model with random weights
+[PROC-0][2025-01-14 02:46:01,645][pytorch][INFO] - 	+ Creating no weights model
+[PROC-0][2025-01-14 02:46:01,645][pytorch][INFO] - 	+ Creating no weights model directory
+[PROC-0][2025-01-14 02:46:01,645][pytorch][INFO] - 	+ Creating no weights model state dict
+[PROC-0][2025-01-14 02:46:01,664][pytorch][INFO] - 	+ Saving no weights model safetensors
+[PROC-0][2025-01-14 02:46:01,665][pytorch][INFO] - 	+ Saving no weights model pretrained config
+[PROC-0][2025-01-14 02:46:01,666][pytorch][INFO] - 	+ Loading no weights AutoModel
+[PROC-0][2025-01-14 02:46:01,666][pytorch][INFO] - 	+ Loading model directly on device: cuda
+[2025-01-14 02:46:03,321][experiment][ERROR] - Error during experiment

error.log

@@ -1,42 +1,3 @@
-[codecarbon INFO @ 02:35:33] [setup] RAM Tracking...
-[codecarbon INFO @ 02:35:33] [setup] GPU Tracking...
-[codecarbon INFO @ 02:35:33] Tracking Nvidia GPU via pynvml
-[codecarbon DEBUG @ 02:35:33] GPU available. Starting setup
-[codecarbon INFO @ 02:35:33] [setup] CPU Tracking...
-[codecarbon DEBUG @ 02:35:33] Not using PowerGadget, an exception occurred while instantiating IntelPowerGadget : Platform not supported by Intel Power Gadget
-[codecarbon DEBUG @ 02:35:33] Not using the RAPL interface, an exception occurred while instantiating IntelRAPL : Intel RAPL files not found at /sys/class/powercap/intel-rapl on linux
-[codecarbon DEBUG @ 02:35:33] Not using PowerMetrics, an exception occurred while instantiating Powermetrics : Platform not supported by Powermetrics
-[codecarbon WARNING @ 02:35:33] No CPU tracking mode found. Falling back on CPU constant mode.
-[codecarbon DEBUG @ 02:35:35] CPU : We detect a Intel(R) Xeon(R) Platinum 8275CL CPU @ 3.00GHz with a TDP of 240.0 W
-[codecarbon INFO @ 02:35:35] CPU Model on constant consumption mode: Intel(R) Xeon(R) Platinum 8275CL CPU @ 3.00GHz
-[codecarbon INFO @ 02:35:35] >>> Tracker's metadata:
-[codecarbon INFO @ 02:35:35]   Platform system: Linux-5.10.223-212.873.amzn2.x86_64-x86_64-with-glibc2.35
-[codecarbon INFO @ 02:35:35]   Python version: 3.9.21
-[codecarbon INFO @ 02:35:35]   CodeCarbon version: 2.5.1
-[codecarbon INFO @ 02:35:35]   Available RAM : 1121.806 GB
-[codecarbon INFO @ 02:35:35]   CPU count: 96
-[codecarbon INFO @ 02:35:35]   CPU model: Intel(R) Xeon(R) Platinum 8275CL CPU @ 3.00GHz
-[codecarbon INFO @ 02:35:35]   GPU count: 1
-[codecarbon INFO @ 02:35:35]   GPU model: 1 x NVIDIA A100-SXM4-80GB
-[codecarbon DEBUG @ 02:35:36] Not running on AWS
-[codecarbon DEBUG @ 02:35:37] Not running on Azure
-[codecarbon DEBUG @ 02:35:38] Not running on GCP
-[codecarbon INFO @ 02:35:38] Saving emissions data to file /app/runs/text_generation/NousResearch/Hermes-3-Llama-3.1-8B/1736822122.5321538/codecarbon.csv
-[codecarbon DEBUG @ 02:35:38] EmissionsData(timestamp='2025-01-14T02:35:38', project_name='codecarbon', run_id='1b9facce-07dc-48ee-a9da-47fe33191783', duration=0.003597825765609741, emissions=0.0, emissions_rate=0.0, cpu_power=0.0, gpu_power=0.0, ram_power=0.0, cpu_energy=0, gpu_energy=0, ram_energy=0, energy_consumed=0, country_name='United States', country_iso_code='USA', region='virginia', cloud_provider='', cloud_region='', os='Linux-5.10.223-212.873.amzn2.x86_64-x86_64-with-glibc2.35', python_version='3.9.21', codecarbon_version='2.5.1', cpu_count=96, cpu_model='Intel(R) Xeon(R) Platinum 8275CL CPU @ 3.00GHz', gpu_count=1, gpu_model='1 x NVIDIA A100-SXM4-80GB', longitude=-77.4903, latitude=39.0469, ram_total_size=1121.805892944336, tracking_mode='process', on_cloud='N', pue=1.0)
-
-
-[codecarbon INFO @ 02:35:39] Energy consumed for RAM : 0.000000 kWh. RAM Power : 0.3641538619995117 W
-[codecarbon DEBUG @ 02:35:39] RAM : 0.36 W during 0.97 s [measurement time: 0.0013]
-[codecarbon INFO @ 02:35:39] Energy consumed for all GPUs : 0.000020 kWh. Total GPU Power : 74.43200801286302 W
-[codecarbon DEBUG @ 02:35:39] GPU : 74.43 W during 0.97 s [measurement time: 0.0030]
-[codecarbon INFO @ 02:35:39] Energy consumed for all CPUs : 0.000032 kWh. Total CPU Power : 120.0 W
-[codecarbon DEBUG @ 02:35:39] CPU : 120.00 W during 0.97 s [measurement time: 0.0000]
-[codecarbon INFO @ 02:35:39] 0.000052 kWh of electricity used since the beginning.
-[codecarbon DEBUG @ 02:35:39] last_duration=0.966143885627389
-------------------------
-[codecarbon DEBUG @ 02:35:39] EmissionsData(timestamp='2025-01-14T02:35:39', project_name='codecarbon', run_id='1b9facce-07dc-48ee-a9da-47fe33191783', duration=0.9709855187684298, emissions=1.936903211901123e-05, emissions_rate=1.9947807402502106e-05, cpu_power=120.0, gpu_power=74.43200801286302, ram_power=0.3641538619995117, cpu_energy=3.2362359017133716e-05, gpu_energy=2.001140489937825e-05, ram_energy=9.772960757336247e-08, energy_consumed=5.247149352408533e-05, country_name='United States', country_iso_code='USA', region='virginia', cloud_provider='', cloud_region='', os='Linux-5.10.223-212.873.amzn2.x86_64-x86_64-with-glibc2.35', python_version='3.9.21', codecarbon_version='2.5.1', cpu_count=96, cpu_model='Intel(R) Xeon(R) Platinum 8275CL CPU @ 3.00GHz', gpu_count=1, gpu_model='1 x NVIDIA A100-SXM4-80GB', longitude=-77.4903, latitude=39.0469, ram_total_size=1121.805892944336, tracking_mode='process', on_cloud='N', pue=1.0)
-[codecarbon DEBUG @ 02:35:47] EmissionsData(timestamp='2025-01-14T02:35:47', project_name='codecarbon', run_id='1b9facce-07dc-48ee-a9da-47fe33191783', duration=0.004495952278375626, emissions=1.936903211901123e-05, emissions_rate=0.0043081044725877754, cpu_power=120.0, gpu_power=74.43200801286302, ram_power=0.3641538619995117, cpu_energy=3.2362359017133716e-05, gpu_energy=2.001140489937825e-05, ram_energy=9.772960757336247e-08, energy_consumed=5.247149352408533e-05, country_name='United States', country_iso_code='USA', region='virginia', cloud_provider='', cloud_region='', os='Linux-5.10.223-212.873.amzn2.x86_64-x86_64-with-glibc2.35', python_version='3.9.21', codecarbon_version='2.5.1', cpu_count=96, cpu_model='Intel(R) Xeon(R) Platinum 8275CL CPU @ 3.00GHz', gpu_count=1, gpu_model='1 x NVIDIA A100-SXM4-80GB', longitude=-77.4903, latitude=39.0469, ram_total_size=1121.805892944336, tracking_mode='process', on_cloud='N', pue=1.0)
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 Error executing job with overrides: ['backend.model=NousResearch/Hermes-3-Llama-3.1-8B', 'backend.processor=NousResearch/Hermes-3-Llama-3.1-8B']
 Traceback (most recent call last):
   File "/optimum-benchmark/optimum_benchmark/cli.py", line 65, in benchmark_cli
@@ -57,47 +18,33 @@ Traceback (most recent call last):
     fn(i, *args)
   File "/optimum-benchmark/optimum_benchmark/launchers/process/launcher.py", line 63, in entrypoint
     worker_output = worker(*worker_args)
-  File "/optimum-benchmark/optimum_benchmark/experiment.py", line 62, in run
-    benchmark.run(backend)
-  File "/optimum-benchmark/optimum_benchmark/benchmarks/energy_star/benchmark.py", line 174, in run
-    self.run_text_generation_energy_tracking(backend)
-  File "/optimum-benchmark/optimum_benchmark/benchmarks/energy_star/benchmark.py", line 198, in run_text_generation_energy_tracking
-    _ = backend.prefill(inputs, prefill_kwargs)
-  File "/opt/conda/lib/python3.9/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context
+  File "/optimum-benchmark/optimum_benchmark/experiment.py", line 55, in run
+    backend: Backend = backend_factory(backend_config)
+  File "/optimum-benchmark/optimum_benchmark/backends/pytorch/backend.py", line 81, in __init__
+    self.load_model_with_no_weights()
+  File "/optimum-benchmark/optimum_benchmark/backends/pytorch/backend.py", line 246, in load_model_with_no_weights
+    self.load_model_from_pretrained()
+  File "/optimum-benchmark/optimum_benchmark/backends/pytorch/backend.py", line 204, in load_model_from_pretrained
+    self.pretrained_model = self.automodel_class.from_pretrained(
+  File "/opt/conda/lib/python3.9/site-packages/transformers/models/auto/auto_factory.py", line 564, in from_pretrained
+    return model_class.from_pretrained(
+  File "/opt/conda/lib/python3.9/site-packages/transformers/modeling_utils.py", line 3810, in from_pretrained
+    model = cls(config, *model_args, **model_kwargs)
+  File "/opt/conda/lib/python3.9/site-packages/transformers/models/llama/modeling_llama.py", line 1116, in __init__
+    self.model = LlamaModel(config)
+  File "/opt/conda/lib/python3.9/site-packages/transformers/models/llama/modeling_llama.py", line 902, in __init__
+    [LlamaDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+  File "/opt/conda/lib/python3.9/site-packages/transformers/models/llama/modeling_llama.py", line 902, in <listcomp>
+    [LlamaDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+  File "/opt/conda/lib/python3.9/site-packages/transformers/models/llama/modeling_llama.py", line 691, in __init__
+    self.mlp = LlamaMLP(config)
+  File "/opt/conda/lib/python3.9/site-packages/transformers/models/llama/modeling_llama.py", line 288, in __init__
+    self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=config.mlp_bias)
+  File "/opt/conda/lib/python3.9/site-packages/torch/nn/modules/linear.py", line 99, in __init__
+    self.weight = Parameter(torch.empty((out_features, in_features), **factory_kwargs))
+  File "/opt/conda/lib/python3.9/site-packages/torch/utils/_device.py", line 79, in __torch_function__
     return func(*args, **kwargs)
-  File "/optimum-benchmark/optimum_benchmark/backends/pytorch/backend.py", line 350, in prefill
-    return self.pretrained_model.generate(**inputs, **kwargs)
-  File "/opt/conda/lib/python3.9/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context
-    return func(*args, **kwargs)
-  File "/opt/conda/lib/python3.9/site-packages/transformers/generation/utils.py", line 2024, in generate
-    result = self._sample(
-  File "/opt/conda/lib/python3.9/site-packages/transformers/generation/utils.py", line 2982, in _sample
-    outputs = self(**model_inputs, return_dict=True)
-  File "/opt/conda/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
-    return self._call_impl(*args, **kwargs)
-  File "/opt/conda/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl
-    return forward_call(*args, **kwargs)
-  File "/opt/conda/lib/python3.9/site-packages/transformers/models/llama/modeling_llama.py", line 1189, in forward
-    outputs = self.model(
-  File "/opt/conda/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
-    return self._call_impl(*args, **kwargs)
-  File "/opt/conda/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl
-    return forward_call(*args, **kwargs)
-  File "/opt/conda/lib/python3.9/site-packages/transformers/models/llama/modeling_llama.py", line 1001, in forward
-    layer_outputs = decoder_layer(
-  File "/opt/conda/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
-    return self._call_impl(*args, **kwargs)
-  File "/opt/conda/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl
-    return forward_call(*args, **kwargs)
-  File "/opt/conda/lib/python3.9/site-packages/transformers/models/llama/modeling_llama.py", line 750, in forward
-    hidden_states = self.mlp(hidden_states)
-  File "/opt/conda/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
-    return self._call_impl(*args, **kwargs)
-  File "/opt/conda/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl
-    return forward_call(*args, **kwargs)
-  File "/opt/conda/lib/python3.9/site-packages/transformers/models/llama/modeling_llama.py", line 309, in forward
-    down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
-torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 982.00 MiB. GPU 0 has a total capacity of 79.14 GiB of which 527.19 MiB is free. Process 188 has 0 bytes memory in use. Including non-PyTorch memory, this process has 0 bytes memory in use. Of the allocated memory 34.73 GiB is allocated by PyTorch, and 2.38 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 224.00 MiB. GPU 0 has a total capacity of 79.14 GiB of which 100.31 MiB is free. Process 180 has 0 bytes memory in use. Process 188 has 0 bytes memory in use. Including non-PyTorch memory, this process has 0 bytes memory in use. Process 658 has 0 bytes memory in use. Of the allocated memory 4.18 GiB is allocated by PyTorch, and 1.94 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
 
 
 Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.