Update status of WizardLM/WizardLM-7B-V1.0_eval_request_False_float16_Original to FAILED
52573d4
verified
| { | |
| "model": "WizardLM/WizardLM-7B-V1.0", | |
| "base_model": "", | |
| "revision": "main", | |
| "private": false, | |
| "precision": "float16", | |
| "params": 7.0, | |
| "architectures": "LlamaForCausalLM", | |
| "weight_type": "Original", | |
| "status": "FAILED", | |
| "submitted_time": "2024-03-05T16:38:21Z", | |
| "model_type": "💬 : chat models (RLHF, DPO, IFT, ...)", | |
| "source": "leaderboard", | |
| "job_id": 462, | |
| "job_start_time": "2024-04-15T05-18-29.294985", | |
| "main_language": "English", | |
| "error_msg": "CUDA out of memory. Tried to allocate 128.00 MiB. GPU 0 has a total capacity of 31.75 GiB of which 95.75 MiB is free. Process 37689 has 31.65 GiB memory in use. Of the allocated memory 27.41 GiB is allocated by PyTorch, and 3.31 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)", | |
| "traceback": "Traceback (most recent call last):\n File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 198, in wait_download_and_run_request\n run_request(\n File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 64, in run_request\n results = run_eval_on_model(\n ^^^^^^^^^^^^^^^^^^\n File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/run_eval.py\", line 60, in run_eval_on_model\n result = evaluate(\n ^^^^^^^^^\n File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/lm_eval_util.py\", line 145, in evaluate\n results = evaluator.simple_evaluate(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/utils.py\", line 419, in _wrapper\n return fn(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^\n File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/evaluator.py\", line 159, in simple_evaluate\n results = evaluate(\n ^^^^^^^^^\n File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/utils.py\", line 419, in _wrapper\n return fn(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^\n File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/evaluator.py\", line 343, in evaluate\n resps = getattr(lm, reqtype)(cloned_reqs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/models/huggingface.py\", line 1518, in generate_until\n cont = self._model_generate(\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/models/huggingface.py\", line 1063, in _model_generate\n return self.model.generate(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/torch/utils/_contextlib.py\", line 115, in decorate_context\n return func(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/workspace/repos/llm_leaderboard/transformers/src/transformers/generation/utils.py\", line 1572, in generate\n result = self._greedy_search(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/workspace/repos/llm_leaderboard/transformers/src/transformers/generation/utils.py\", line 2477, in _greedy_search\n outputs = self(\n ^^^^^\n File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1511, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1520, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/workspace/repos/llm_leaderboard/transformers/src/transformers/models/llama/modeling_llama.py\", line 1191, in forward\n outputs = self.model(\n ^^^^^^^^^^^\n File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1511, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1520, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/workspace/repos/llm_leaderboard/transformers/src/transformers/models/llama/modeling_llama.py\", line 1018, in forward\n layer_outputs = decoder_layer(\n ^^^^^^^^^^^^^^\n File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1511, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1520, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/workspace/repos/llm_leaderboard/transformers/src/transformers/models/llama/modeling_llama.py\", line 739, in forward\n hidden_states, self_attn_weights, present_key_value = self.self_attn(\n ^^^^^^^^^^^^^^^\n File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1511, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1520, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/workspace/repos/llm_leaderboard/transformers/src/transformers/models/llama/modeling_llama.py\", line 653, in forward\n key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/workspace/repos/llm_leaderboard/transformers/src/transformers/cache_utils.py\", line 146, in update\n self.key_cache[layer_idx] = torch.cat([self.key_cache[layer_idx], key_states], dim=-2)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\ntorch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 128.00 MiB. GPU 0 has a total capacity of 31.75 GiB of which 95.75 MiB is free. Process 37689 has 31.65 GiB memory in use. Of the allocated memory 27.41 GiB is allocated by PyTorch, and 3.31 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)\n" | |
| } |