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.hydra/hydra.yaml

@@ -1,6 +1,6 @@
 hydra:
   run:
-    dir: runs/text_generation/NousResearch/Hermes-3-Llama-3.1-8B/1736822753.414838
+    dir: runs/text_generation/NousResearch/Hermes-3-Llama-3.1-8B/1736822753.4816654
   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/1736822753.414838
+    - hydra.run.dir=runs/text_generation/NousResearch/Hermes-3-Llama-3.1-8B/1736822753.4816654
     - 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/1736822753.414838
+    output_dir: /app/runs/text_generation/NousResearch/Hermes-3-Llama-3.1-8B/1736822753.4816654
     choices:
       benchmark: energy_star
       launcher: process

cli.log

@@ -1,17 +1,17 @@
-[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
+[2025-01-14 02:45:57,202][launcher][INFO] - ََAllocating process launcher
+[2025-01-14 02:45:57,202][process][INFO] - 	+ Setting multiprocessing start method to spawn.
+[2025-01-14 02:45:57,220][process][INFO] - 	+ Launched benchmark in isolated process 658.
+[PROC-0][2025-01-14 02:46:00,404][datasets][INFO] - PyTorch version 2.4.0 available.
+[PROC-0][2025-01-14 02:46:01,534][backend][INFO] - َAllocating pytorch backend
+[PROC-0][2025-01-14 02:46:01,534][backend][INFO] - 	+ Setting random seed to 42
+[PROC-0][2025-01-14 02:46:02,148][pytorch][INFO] - 	+ Using AutoModel class AutoModelForCausalLM
+[PROC-0][2025-01-14 02:46:02,148][pytorch][INFO] - 	+ Creating backend temporary directory
+[PROC-0][2025-01-14 02:46:02,148][pytorch][INFO] - 	+ Loading model with random weights
+[PROC-0][2025-01-14 02:46:02,148][pytorch][INFO] - 	+ Creating no weights model
+[PROC-0][2025-01-14 02:46:02,148][pytorch][INFO] - 	+ Creating no weights model directory
+[PROC-0][2025-01-14 02:46:02,148][pytorch][INFO] - 	+ Creating no weights model state dict
+[PROC-0][2025-01-14 02:46:02,151][pytorch][INFO] - 	+ Saving no weights model safetensors
+[PROC-0][2025-01-14 02:46:02,151][pytorch][INFO] - 	+ Saving no weights model pretrained config
+[PROC-0][2025-01-14 02:46:02,152][pytorch][INFO] - 	+ Loading no weights AutoModel
+[PROC-0][2025-01-14 02:46:02,152][pytorch][INFO] - 	+ Loading model directly on device: cuda
+[2025-01-14 02:46:03,353][experiment][ERROR] - Error during experiment

error.log

@@ -28,23 +28,19 @@ Traceback (most recent call last):
     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/transformers/modeling_utils.py", line 3738, in from_pretrained
+    state_dict = load_state_dict(resolved_archive_file)
+  File "/opt/conda/lib/python3.9/site-packages/transformers/modeling_utils.py", line 556, in load_state_dict
+    return safe_load_file(checkpoint_file)
+  File "/opt/conda/lib/python3.9/site-packages/safetensors/torch.py", line 315, in load_file
+    result[k] = f.get_tensor(k)
   File "/opt/conda/lib/python3.9/site-packages/torch/utils/_device.py", line 79, in __torch_function__
     return func(*args, **kwargs)
-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)
+RuntimeError: CUDA error: out of memory
+CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
+For debugging consider passing CUDA_LAUNCH_BLOCKING=1
+Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.
+
 
 
 Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.