{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "5f93b7d1",
"metadata": {
"ExecuteTime": {
"end_time": "2023-05-30T09:49:56.334329Z",
"start_time": "2023-05-30T09:49:54.494916Z"
}
},
"outputs": [
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtransformers\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m AutoModelForSeq2SeqLM, Seq2SeqTrainingArguments, Seq2SeqTrainer, GenerationConfig\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpeft\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m get_peft_model, PromptTuningInit, PromptTuningConfig, TaskType\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\n",
"File \u001b[0;32m<frozen importlib._bootstrap>:1055\u001b[0m, in \u001b[0;36m_handle_fromlist\u001b[0;34m(module, fromlist, import_, recursive)\u001b[0m\n",
"File \u001b[0;32m~/anaconda3/envs/peft/lib/python3.9/site-packages/transformers/utils/import_utils.py:1076\u001b[0m, in \u001b[0;36m_LazyModule.__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m 1074\u001b[0m value \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_module(name)\n\u001b[1;32m 1075\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m name \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_class_to_module\u001b[38;5;241m.\u001b[39mkeys():\n\u001b[0;32m-> 1076\u001b[0m module \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_module\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_class_to_module\u001b[49m\u001b[43m[\u001b[49m\u001b[43mname\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1077\u001b[0m value \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(module, name)\n\u001b[1;32m 1078\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
"File \u001b[0;32m~/anaconda3/envs/peft/lib/python3.9/site-packages/transformers/utils/import_utils.py:1086\u001b[0m, in \u001b[0;36m_LazyModule._get_module\u001b[0;34m(self, module_name)\u001b[0m\n\u001b[1;32m 1084\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_get_module\u001b[39m(\u001b[38;5;28mself\u001b[39m, module_name: \u001b[38;5;28mstr\u001b[39m):\n\u001b[1;32m 1085\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1086\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mimportlib\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mimport_module\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m.\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mmodule_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;18;43m__name__\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1087\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 1088\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[1;32m 1089\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFailed to import \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmodule_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m because of the following error (look up to see its\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1090\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m traceback):\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00me\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1091\u001b[0m ) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n",
"File \u001b[0;32m~/anaconda3/envs/peft/lib/python3.9/importlib/__init__.py:127\u001b[0m, in \u001b[0;36mimport_module\u001b[0;34m(name, package)\u001b[0m\n\u001b[1;32m 125\u001b[0m \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[1;32m 126\u001b[0m level \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m--> 127\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_bootstrap\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_gcd_import\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m[\u001b[49m\u001b[43mlevel\u001b[49m\u001b[43m:\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpackage\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/anaconda3/envs/peft/lib/python3.9/site-packages/transformers/training_args_seq2seq.py:21\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtyping\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Optional, Union\n\u001b[1;32m 20\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgeneration\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mconfiguration_utils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m GenerationConfig\n\u001b[0;32m---> 21\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtraining_args\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m TrainingArguments\n\u001b[1;32m 22\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m add_start_docstrings\n\u001b[1;32m 25\u001b[0m logger \u001b[38;5;241m=\u001b[39m logging\u001b[38;5;241m.\u001b[39mgetLogger(\u001b[38;5;18m__name__\u001b[39m)\n",
"File \u001b[0;32m~/anaconda3/envs/peft/lib/python3.9/site-packages/transformers/training_args.py:29\u001b[0m\n\u001b[1;32m 25\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtyping\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Any, Dict, List, Optional, Union\n\u001b[1;32m 27\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpackaging\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m version\n\u001b[0;32m---> 29\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdebug_utils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m DebugOption\n\u001b[1;32m 30\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtrainer_utils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m 31\u001b[0m EvaluationStrategy,\n\u001b[1;32m 32\u001b[0m FSDPOption,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 36\u001b[0m ShardedDDPOption,\n\u001b[1;32m 37\u001b[0m )\n\u001b[1;32m 38\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m 39\u001b[0m ExplicitEnum,\n\u001b[1;32m 40\u001b[0m cached_property,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 53\u001b[0m requires_backends,\n\u001b[1;32m 54\u001b[0m )\n",
"File \u001b[0;32m~/anaconda3/envs/peft/lib/python3.9/site-packages/transformers/debug_utils.py:21\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ExplicitEnum, is_torch_available, logging\n\u001b[1;32m 20\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_torch_available():\n\u001b[0;32m---> 21\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\n\u001b[1;32m 24\u001b[0m logger \u001b[38;5;241m=\u001b[39m logging\u001b[38;5;241m.\u001b[39mget_logger(\u001b[38;5;18m__name__\u001b[39m)\n\u001b[1;32m 27\u001b[0m \u001b[38;5;28;01mclass\u001b[39;00m \u001b[38;5;21;01mDebugUnderflowOverflow\u001b[39;00m:\n",
"File \u001b[0;32m~/anaconda3/envs/peft/lib/python3.9/site-packages/torch/__init__.py:1465\u001b[0m\n\u001b[1;32m 1463\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m library\n\u001b[1;32m 1464\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m TYPE_CHECKING:\n\u001b[0;32m-> 1465\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m _meta_registrations\n\u001b[1;32m 1467\u001b[0m \u001b[38;5;66;03m# Enable CUDA Sanitizer\u001b[39;00m\n\u001b[1;32m 1468\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTORCH_CUDA_SANITIZER\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01min\u001b[39;00m os\u001b[38;5;241m.\u001b[39menviron:\n",
"File \u001b[0;32m~/anaconda3/envs/peft/lib/python3.9/site-packages/torch/_meta_registrations.py:7\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_prims_common\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mutils\u001b[39;00m\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Tensor\n\u001b[0;32m----> 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_decomp\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m _add_op_to_registry, global_decomposition_table, meta_table\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_ops\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m OpOverload\n\u001b[1;32m 9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_prims\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m _elementwise_meta, ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND\n",
"File \u001b[0;32m~/anaconda3/envs/peft/lib/python3.9/site-packages/torch/_decomp/__init__.py:169\u001b[0m\n\u001b[1;32m 165\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m decompositions\n\u001b[1;32m 168\u001b[0m \u001b[38;5;66;03m# populate the table\u001b[39;00m\n\u001b[0;32m--> 169\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_decomp\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdecompositions\u001b[39;00m\n\u001b[1;32m 170\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_refs\u001b[39;00m\n\u001b[1;32m 172\u001b[0m \u001b[38;5;66;03m# This list was copied from torch/_inductor/decomposition.py\u001b[39;00m\n\u001b[1;32m 173\u001b[0m \u001b[38;5;66;03m# excluding decompositions that results in prim ops\u001b[39;00m\n\u001b[1;32m 174\u001b[0m \u001b[38;5;66;03m# Resulting opset of decomposition is core aten ops\u001b[39;00m\n",
"File \u001b[0;32m~/anaconda3/envs/peft/lib/python3.9/site-packages/torch/_decomp/decompositions.py:10\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtyping\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Callable, cast, Iterable, List, Optional, Tuple, Union\n\u001b[1;32m 9\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\n\u001b[0;32m---> 10\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_prims\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mprims\u001b[39;00m\n\u001b[1;32m 11\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_prims_common\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mutils\u001b[39;00m\n\u001b[1;32m 12\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mnn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfunctional\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mF\u001b[39;00m\n",
"File \u001b[0;32m~/anaconda3/envs/peft/lib/python3.9/site-packages/torch/_prims/__init__.py:33\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_prims_common\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m 18\u001b[0m check,\n\u001b[1;32m 19\u001b[0m Dim,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 30\u001b[0m type_to_dtype,\n\u001b[1;32m 31\u001b[0m )\n\u001b[1;32m 32\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_prims_common\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mwrappers\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backwards_not_supported\n\u001b[0;32m---> 33\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_subclasses\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfake_tensor\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m FakeTensor, FakeTensorMode\n\u001b[1;32m 34\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01moverrides\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m handle_torch_function, has_torch_function\n\u001b[1;32m 35\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_pytree\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m tree_flatten, tree_map, tree_unflatten\n",
"File \u001b[0;32m~/anaconda3/envs/peft/lib/python3.9/site-packages/torch/_subclasses/__init__.py:3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_subclasses\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfake_tensor\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m 4\u001b[0m DynamicOutputShapeException,\n\u001b[1;32m 5\u001b[0m FakeTensor,\n\u001b[1;32m 6\u001b[0m FakeTensorMode,\n\u001b[1;32m 7\u001b[0m UnsupportedFakeTensorException,\n\u001b[1;32m 8\u001b[0m )\n\u001b[1;32m 10\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_subclasses\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfake_utils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m CrossRefFakeMode\n\u001b[1;32m 12\u001b[0m __all__ \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m 13\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFakeTensor\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 14\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFakeTensorMode\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCrossRefFakeMode\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 18\u001b[0m ]\n",
"File \u001b[0;32m~/anaconda3/envs/peft/lib/python3.9/site-packages/torch/_subclasses/fake_tensor.py:13\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mweakref\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ReferenceType\n\u001b[1;32m 12\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\n\u001b[0;32m---> 13\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_guards\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Source\n\u001b[1;32m 14\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_ops\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m OpOverload\n\u001b[1;32m 15\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_prims_common\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m 16\u001b[0m elementwise_dtypes,\n\u001b[1;32m 17\u001b[0m ELEMENTWISE_TYPE_PROMOTION_KIND,\n\u001b[1;32m 18\u001b[0m is_float_dtype,\n\u001b[1;32m 19\u001b[0m is_integer_dtype,\n\u001b[1;32m 20\u001b[0m )\n",
"File \u001b[0;32m~/anaconda3/envs/peft/lib/python3.9/site-packages/torch/_guards.py:14\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[38;5;66;03m# TODO(voz): Stolen pattern, not sure why this is the case,\u001b[39;00m\n\u001b[1;32m 12\u001b[0m \u001b[38;5;66;03m# but mypy complains.\u001b[39;00m\n\u001b[1;32m 13\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 14\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01msympy\u001b[39;00m \u001b[38;5;66;03m# type: ignore[import]\u001b[39;00m\n\u001b[1;32m 15\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m:\n\u001b[1;32m 16\u001b[0m log\u001b[38;5;241m.\u001b[39mwarning(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNo sympy found\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
"File \u001b[0;32m~/anaconda3/envs/peft/lib/python3.9/site-packages/sympy/__init__.py:74\u001b[0m\n\u001b[1;32m 67\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlogic\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (to_cnf, to_dnf, to_nnf, And, Or, Not, Xor, Nand, Nor,\n\u001b[1;32m 68\u001b[0m Implies, Equivalent, ITE, POSform, SOPform, simplify_logic, bool_map,\n\u001b[1;32m 69\u001b[0m true, false, satisfiable)\n\u001b[1;32m 71\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01massumptions\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (AppliedPredicate, Predicate, AssumptionsContext,\n\u001b[1;32m 72\u001b[0m assuming, Q, ask, register_handler, remove_handler, refine)\n\u001b[0;32m---> 74\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpolys\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (Poly, PurePoly, poly_from_expr, parallel_poly_from_expr,\n\u001b[1;32m 75\u001b[0m degree, total_degree, degree_list, LC, LM, LT, pdiv, prem, pquo,\n\u001b[1;32m 76\u001b[0m pexquo, div, rem, quo, exquo, half_gcdex, gcdex, invert,\n\u001b[1;32m 77\u001b[0m subresultants, resultant, discriminant, cofactors, gcd_list, gcd,\n\u001b[1;32m 78\u001b[0m lcm_list, lcm, terms_gcd, trunc, monic, content, primitive, compose,\n\u001b[1;32m 79\u001b[0m decompose, sturm, gff_list, gff, sqf_norm, sqf_part, sqf_list, sqf,\n\u001b[1;32m 80\u001b[0m factor_list, factor, intervals, refine_root, count_roots, real_roots,\n\u001b[1;32m 81\u001b[0m nroots, ground_roots, nth_power_roots_poly, cancel, reduced, groebner,\n\u001b[1;32m 82\u001b[0m is_zero_dimensional, GroebnerBasis, poly, symmetrize, horner,\n\u001b[1;32m 83\u001b[0m interpolate, rational_interpolate, viete, together,\n\u001b[1;32m 84\u001b[0m BasePolynomialError, ExactQuotientFailed, PolynomialDivisionFailed,\n\u001b[1;32m 85\u001b[0m OperationNotSupported, HeuristicGCDFailed, HomomorphismFailed,\n\u001b[1;32m 86\u001b[0m IsomorphismFailed, ExtraneousFactors, EvaluationFailed,\n\u001b[1;32m 87\u001b[0m RefinementFailed, CoercionFailed, NotInvertible, NotReversible,\n\u001b[1;32m 88\u001b[0m NotAlgebraic, DomainError, PolynomialError, UnificationFailed,\n\u001b[1;32m 89\u001b[0m GeneratorsError, GeneratorsNeeded, ComputationFailed,\n\u001b[1;32m 90\u001b[0m UnivariatePolynomialError, MultivariatePolynomialError,\n\u001b[1;32m 91\u001b[0m PolificationFailed, OptionError, FlagError, minpoly,\n\u001b[1;32m 92\u001b[0m minimal_polynomial, primitive_element, field_isomorphism,\n\u001b[1;32m 93\u001b[0m to_number_field, isolate, round_two, prime_decomp, prime_valuation,\n\u001b[1;32m 94\u001b[0m galois_group, itermonomials, Monomial, lex, grlex,\n\u001b[1;32m 95\u001b[0m grevlex, ilex, igrlex, igrevlex, CRootOf, rootof, RootOf,\n\u001b[1;32m 96\u001b[0m ComplexRootOf, RootSum, roots, Domain, FiniteField, IntegerRing,\n\u001b[1;32m 97\u001b[0m RationalField, RealField, ComplexField, PythonFiniteField,\n\u001b[1;32m 98\u001b[0m GMPYFiniteField, PythonIntegerRing, GMPYIntegerRing, PythonRational,\n\u001b[1;32m 99\u001b[0m GMPYRationalField, AlgebraicField, PolynomialRing, FractionField,\n\u001b[1;32m 100\u001b[0m ExpressionDomain, FF_python, FF_gmpy, ZZ_python, ZZ_gmpy, QQ_python,\n\u001b[1;32m 101\u001b[0m QQ_gmpy, GF, FF, ZZ, QQ, ZZ_I, QQ_I, RR, CC, EX, EXRAW,\n\u001b[1;32m 102\u001b[0m construct_domain, swinnerton_dyer_poly, cyclotomic_poly,\n\u001b[1;32m 103\u001b[0m symmetric_poly, random_poly, interpolating_poly, jacobi_poly,\n\u001b[1;32m 104\u001b[0m chebyshevt_poly, chebyshevu_poly, hermite_poly, hermite_prob_poly,\n\u001b[1;32m 105\u001b[0m legendre_poly, laguerre_poly, apart, apart_list, assemble_partfrac_list,\n\u001b[1;32m 106\u001b[0m Options, ring, xring, vring, sring, field, xfield, vfield, sfield)\n\u001b[1;32m 108\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mseries\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (Order, O, limit, Limit, gruntz, series, approximants,\n\u001b[1;32m 109\u001b[0m residue, EmptySequence, SeqPer, SeqFormula, sequence, SeqAdd, SeqMul,\n\u001b[1;32m 110\u001b[0m fourier_series, fps, difference_delta, limit_seq)\n\u001b[1;32m 112\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfunctions\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (factorial, factorial2, rf, ff, binomial,\n\u001b[1;32m 113\u001b[0m RisingFactorial, FallingFactorial, subfactorial, carmichael,\n\u001b[1;32m 114\u001b[0m fibonacci, lucas, motzkin, tribonacci, harmonic, bernoulli, bell, euler,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 133\u001b[0m Znm, elliptic_k, elliptic_f, elliptic_e, elliptic_pi, beta, mathieus,\n\u001b[1;32m 134\u001b[0m mathieuc, mathieusprime, mathieucprime, riemann_xi, betainc, betainc_regularized)\n",
"File \u001b[0;32m~/anaconda3/envs/peft/lib/python3.9/site-packages/sympy/polys/__init__.py:78\u001b[0m\n\u001b[1;32m 3\u001b[0m __all__ \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m 4\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPoly\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPurePoly\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpoly_from_expr\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mparallel_poly_from_expr\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdegree\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 5\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtotal_degree\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdegree_list\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mLC\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mLM\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mLT\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpdiv\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mprem\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpquo\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 65\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfield\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mxfield\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mvfield\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msfield\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 66\u001b[0m ]\n\u001b[1;32m 68\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpolytools\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (Poly, PurePoly, poly_from_expr,\n\u001b[1;32m 69\u001b[0m parallel_poly_from_expr, degree, total_degree, degree_list, LC, LM,\n\u001b[1;32m 70\u001b[0m LT, pdiv, prem, pquo, pexquo, div, rem, quo, exquo, half_gcdex, gcdex,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 75\u001b[0m count_roots, real_roots, nroots, ground_roots, nth_power_roots_poly,\n\u001b[1;32m 76\u001b[0m cancel, reduced, groebner, is_zero_dimensional, GroebnerBasis, poly)\n\u001b[0;32m---> 78\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpolyfuncs\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (symmetrize, horner, interpolate,\n\u001b[1;32m 79\u001b[0m rational_interpolate, viete)\n\u001b[1;32m 81\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mrationaltools\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m together\n\u001b[1;32m 83\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpolyerrors\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (BasePolynomialError, ExactQuotientFailed,\n\u001b[1;32m 84\u001b[0m PolynomialDivisionFailed, OperationNotSupported, HeuristicGCDFailed,\n\u001b[1;32m 85\u001b[0m HomomorphismFailed, IsomorphismFailed, ExtraneousFactors,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 90\u001b[0m MultivariatePolynomialError, PolificationFailed, OptionError,\n\u001b[1;32m 91\u001b[0m FlagError)\n",
"File \u001b[0;32m~/anaconda3/envs/peft/lib/python3.9/site-packages/sympy/polys/polyfuncs.py:10\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msympy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpolys\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpolyoptions\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m allowed_flags, build_options\n\u001b[1;32m 9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msympy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpolys\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpolytools\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m poly_from_expr, Poly\n\u001b[0;32m---> 10\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msympy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpolys\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mspecialpolys\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m 11\u001b[0m symmetric_poly, interpolating_poly)\n\u001b[1;32m 12\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msympy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpolys\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mrings\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m sring\n\u001b[1;32m 13\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msympy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutilities\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m numbered_symbols, take, public\n",
"File \u001b[0;32m~/anaconda3/envs/peft/lib/python3.9/site-packages/sympy/polys/specialpolys.py:298\u001b[0m\n\u001b[1;32m 294\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m dmp_mul(f, h, n, K), dmp_mul(g, h, n, K), h\n\u001b[1;32m 296\u001b[0m \u001b[38;5;66;03m# A few useful polynomials from Wang's paper ('78).\u001b[39;00m\n\u001b[0;32m--> 298\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msympy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpolys\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mrings\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ring\n\u001b[1;32m 300\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_f_0\u001b[39m():\n\u001b[1;32m 301\u001b[0m R, x, y, z \u001b[38;5;241m=\u001b[39m ring(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx,y,z\u001b[39m\u001b[38;5;124m\"\u001b[39m, ZZ)\n",
"File \u001b[0;32m~/anaconda3/envs/peft/lib/python3.9/site-packages/sympy/polys/rings.py:30\u001b[0m\n\u001b[1;32m 26\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msympy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpolys\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpolyoptions\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (Domain \u001b[38;5;28;01mas\u001b[39;00m DomainOpt,\n\u001b[1;32m 27\u001b[0m Order \u001b[38;5;28;01mas\u001b[39;00m OrderOpt, build_options)\n\u001b[1;32m 28\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msympy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpolys\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpolyutils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (expr_from_dict, _dict_reorder,\n\u001b[1;32m 29\u001b[0m _parallel_dict_from_expr)\n\u001b[0;32m---> 30\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msympy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mprinting\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdefaults\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m DefaultPrinting\n\u001b[1;32m 31\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msympy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutilities\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m public, subsets\n\u001b[1;32m 32\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msympy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutilities\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01miterables\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m is_sequence\n",
"File \u001b[0;32m~/anaconda3/envs/peft/lib/python3.9/site-packages/sympy/printing/__init__.py:5\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;124;03m\"\"\"Printing subsystem\"\"\"\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpretty\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m pager_print, pretty, pretty_print, pprint, pprint_use_unicode, pprint_try_use_unicode\n\u001b[0;32m----> 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlatex\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m latex, print_latex, multiline_latex\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmathml\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m mathml, print_mathml\n\u001b[1;32m 9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m python, print_python\n",
"File \u001b[0;32m~/anaconda3/envs/peft/lib/python3.9/site-packages/sympy/printing/latex.py:18\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msympy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msympify\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m SympifyError\n\u001b[1;32m 17\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msympy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlogic\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mboolalg\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m true, BooleanTrue, BooleanFalse\n\u001b[0;32m---> 18\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msympy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtensor\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01marray\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m NDimArray\n\u001b[1;32m 20\u001b[0m \u001b[38;5;66;03m# sympy.printing imports\u001b[39;00m\n\u001b[1;32m 21\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msympy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mprinting\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mprecedence\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m precedence_traditional\n",
"File \u001b[0;32m~/anaconda3/envs/peft/lib/python3.9/site-packages/sympy/tensor/__init__.py:4\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;124;03m\"\"\"A module to manipulate symbolic objects with indices including tensors\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \n\u001b[1;32m 3\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m----> 4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mindexed\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m IndexedBase, Idx, Indexed\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mindex_methods\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m get_contraction_structure, get_indices\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfunctions\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m shape\n",
"File \u001b[0;32m~/anaconda3/envs/peft/lib/python3.9/site-packages/sympy/tensor/indexed.py:114\u001b[0m\n\u001b[1;32m 112\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msympy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlogic\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m fuzzy_bool, fuzzy_not\n\u001b[1;32m 113\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msympy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msympify\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m _sympify\n\u001b[0;32m--> 114\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msympy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfunctions\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mspecial\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtensor_functions\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m KroneckerDelta\n\u001b[1;32m 115\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msympy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmultipledispatch\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m dispatch\n\u001b[1;32m 116\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msympy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutilities\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01miterables\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m is_sequence, NotIterable\n",
"File \u001b[0;32m~/anaconda3/envs/peft/lib/python3.9/site-packages/sympy/functions/__init__.py:21\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msympy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfunctions\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01melementary\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtrigonometric\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (sin, cos, tan,\n\u001b[1;32m 18\u001b[0m sec, csc, cot, sinc, asin, acos, atan, asec, acsc, acot, atan2)\n\u001b[1;32m 19\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msympy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfunctions\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01melementary\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mexponential\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (exp_polar, exp, log,\n\u001b[1;32m 20\u001b[0m LambertW)\n\u001b[0;32m---> 21\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msympy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfunctions\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01melementary\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mhyperbolic\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (sinh, cosh, tanh, coth,\n\u001b[1;32m 22\u001b[0m sech, csch, asinh, acosh, atanh, acoth, asech, acsch)\n\u001b[1;32m 23\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msympy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfunctions\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01melementary\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mintegers\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m floor, ceiling, frac\n\u001b[1;32m 24\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msympy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfunctions\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01melementary\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpiecewise\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (Piecewise, piecewise_fold,\n\u001b[1;32m 25\u001b[0m piecewise_exclusive)\n",
"File \u001b[0;32m<frozen importlib._bootstrap>:1007\u001b[0m, in \u001b[0;36m_find_and_load\u001b[0;34m(name, import_)\u001b[0m\n",
"File \u001b[0;32m<frozen importlib._bootstrap>:986\u001b[0m, in \u001b[0;36m_find_and_load_unlocked\u001b[0;34m(name, import_)\u001b[0m\n",
"File \u001b[0;32m<frozen importlib._bootstrap>:680\u001b[0m, in \u001b[0;36m_load_unlocked\u001b[0;34m(spec)\u001b[0m\n",
"File \u001b[0;32m<frozen importlib._bootstrap_external>:846\u001b[0m, in \u001b[0;36mexec_module\u001b[0;34m(self, module)\u001b[0m\n",
"File \u001b[0;32m<frozen importlib._bootstrap_external>:978\u001b[0m, in \u001b[0;36mget_code\u001b[0;34m(self, fullname)\u001b[0m\n",
"File \u001b[0;32m<frozen importlib._bootstrap_external>:647\u001b[0m, in \u001b[0;36m_compile_bytecode\u001b[0;34m(data, name, bytecode_path, source_path)\u001b[0m\n",
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
]
}
],
"source": [
"import os\n",
"\n",
"import torch\n",
"from transformers import (\n",
" AutoTokenizer,\n",
" default_data_collator,\n",
" AutoModelForSeq2SeqLM,\n",
" Seq2SeqTrainingArguments,\n",
" Seq2SeqTrainer,\n",
" GenerationConfig,\n",
")\n",
"from peft import get_peft_model, PromptTuningInit, PromptTuningConfig, TaskType\n",
"from datasets import load_dataset\n",
"\n",
"os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n",
"os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n",
"\n",
"device = \"cuda\"\n",
"model_name_or_path = \"t5-large\"\n",
"tokenizer_name_or_path = \"t5-large\"\n",
"\n",
"checkpoint_name = \"financial_sentiment_analysis_prefix_tuning_v1.pt\"\n",
"text_column = \"sentence\"\n",
"label_column = \"text_label\"\n",
"max_length = 8\n",
"lr = 1e0\n",
"num_epochs = 5\n",
"batch_size = 8"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "8d0850ac",
"metadata": {
"ExecuteTime": {
"end_time": "2023-05-30T09:50:04.808527Z",
"start_time": "2023-05-30T09:49:56.953075Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"trainable params: 40960 || all params: 737709056 || trainable%: 0.005552324411210698\n"
]
},
{
"data": {
"text/plain": [
"PeftModelForSeq2SeqLM(\n",
" (base_model): T5ForConditionalGeneration(\n",
" (shared): Embedding(32128, 1024)\n",
" (encoder): T5Stack(\n",
" (embed_tokens): Embedding(32128, 1024)\n",
" (block): ModuleList(\n",
" (0): T5Block(\n",
" (layer): ModuleList(\n",
" (0): T5LayerSelfAttention(\n",
" (SelfAttention): T5Attention(\n",
" (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
" (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
" (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
" (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
" (relative_attention_bias): Embedding(32, 16)\n",
" )\n",
" (layer_norm): T5LayerNorm()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (1): T5LayerFF(\n",
" (DenseReluDense): T5DenseActDense(\n",
" (wi): Linear(in_features=1024, out_features=4096, bias=False)\n",
" (wo): Linear(in_features=4096, out_features=1024, bias=False)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" (act): ReLU()\n",
" )\n",
" (layer_norm): T5LayerNorm()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" )\n",
" (1-23): 23 x T5Block(\n",
" (layer): ModuleList(\n",
" (0): T5LayerSelfAttention(\n",
" (SelfAttention): T5Attention(\n",
" (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
" (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
" (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
" (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
" )\n",
" (layer_norm): T5LayerNorm()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (1): T5LayerFF(\n",
" (DenseReluDense): T5DenseActDense(\n",
" (wi): Linear(in_features=1024, out_features=4096, bias=False)\n",
" (wo): Linear(in_features=4096, out_features=1024, bias=False)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" (act): ReLU()\n",
" )\n",
" (layer_norm): T5LayerNorm()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" )\n",
" )\n",
" (final_layer_norm): T5LayerNorm()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (decoder): T5Stack(\n",
" (embed_tokens): Embedding(32128, 1024)\n",
" (block): ModuleList(\n",
" (0): T5Block(\n",
" (layer): ModuleList(\n",
" (0): T5LayerSelfAttention(\n",
" (SelfAttention): T5Attention(\n",
" (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
" (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
" (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
" (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
" (relative_attention_bias): Embedding(32, 16)\n",
" )\n",
" (layer_norm): T5LayerNorm()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (1): T5LayerCrossAttention(\n",
" (EncDecAttention): T5Attention(\n",
" (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
" (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
" (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
" (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
" )\n",
" (layer_norm): T5LayerNorm()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (2): T5LayerFF(\n",
" (DenseReluDense): T5DenseActDense(\n",
" (wi): Linear(in_features=1024, out_features=4096, bias=False)\n",
" (wo): Linear(in_features=4096, out_features=1024, bias=False)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" (act): ReLU()\n",
" )\n",
" (layer_norm): T5LayerNorm()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" )\n",
" (1-23): 23 x T5Block(\n",
" (layer): ModuleList(\n",
" (0): T5LayerSelfAttention(\n",
" (SelfAttention): T5Attention(\n",
" (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
" (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
" (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
" (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
" )\n",
" (layer_norm): T5LayerNorm()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (1): T5LayerCrossAttention(\n",
" (EncDecAttention): T5Attention(\n",
" (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
" (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
" (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
" (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
" )\n",
" (layer_norm): T5LayerNorm()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (2): T5LayerFF(\n",
" (DenseReluDense): T5DenseActDense(\n",
" (wi): Linear(in_features=1024, out_features=4096, bias=False)\n",
" (wo): Linear(in_features=4096, out_features=1024, bias=False)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" (act): ReLU()\n",
" )\n",
" (layer_norm): T5LayerNorm()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" )\n",
" )\n",
" (final_layer_norm): T5LayerNorm()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (lm_head): Linear(in_features=1024, out_features=32128, bias=False)\n",
" )\n",
" (prompt_encoder): ModuleDict(\n",
" (default): PromptEmbedding(\n",
" (embedding): Embedding(40, 1024)\n",
" )\n",
" )\n",
" (word_embeddings): Embedding(32128, 1024)\n",
")"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# creating model\n",
"peft_config = peft_config = PromptTuningConfig(\n",
" task_type=TaskType.SEQ_2_SEQ_LM,\n",
" prompt_tuning_init=PromptTuningInit.TEXT,\n",
" num_virtual_tokens=20,\n",
" prompt_tuning_init_text=\"What is the sentiment of this article?\\n\",\n",
" inference_mode=False,\n",
" tokenizer_name_or_path=model_name_or_path,\n",
")\n",
"\n",
"model = AutoModelForSeq2SeqLM.from_pretrained(model_name_or_path)\n",
"model = get_peft_model(model, peft_config)\n",
"model.print_trainable_parameters()\n",
"model"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "4ee2babf",
"metadata": {
"ExecuteTime": {
"end_time": "2023-05-30T09:50:09.224782Z",
"start_time": "2023-05-30T09:50:08.172611Z"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Found cached dataset financial_phrasebank (/data/proxem/huggingface/datasets/financial_phrasebank/sentences_allagree/1.0.0/550bde12e6c30e2674da973a55f57edde5181d53f5a5a34c1531c53f93b7e141)\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d3a799c64a2c43258dc6166c90e2e49f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
" 0%| | 0/1 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Map: 0%| | 0/2037 [00:00<?, ? examples/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "",
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"version_minor": 0
},
"text/plain": [
"Map: 0%| | 0/227 [00:00<?, ? examples/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"{'sentence': 'The price of the 10,000 kroon par value bonds was 9663,51 kroons in the primary issue .',\n",
" 'label': 1,\n",
" 'text_label': 'neutral'}"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# loading dataset\n",
"dataset = load_dataset(\"financial_phrasebank\", \"sentences_allagree\")\n",
"dataset = dataset[\"train\"].train_test_split(test_size=0.1)\n",
"dataset[\"validation\"] = dataset[\"test\"]\n",
"del dataset[\"test\"]\n",
"\n",
"classes = dataset[\"train\"].features[\"label\"].names\n",
"dataset = dataset.map(\n",
" lambda x: {\"text_label\": [classes[label] for label in x[\"label\"]]},\n",
" batched=True,\n",
" num_proc=1,\n",
")\n",
"\n",
"dataset[\"train\"][0]"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "adf9608c",
"metadata": {
"ExecuteTime": {
"end_time": "2023-05-30T09:50:12.176663Z",
"start_time": "2023-05-30T09:50:11.421273Z"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/udir/tschilla/anaconda3/envs/peft/lib/python3.9/site-packages/transformers/models/t5/tokenization_t5_fast.py:155: FutureWarning: This tokenizer was incorrectly instantiated with a model max length of 512 which will be corrected in Transformers v5.\n",
"For now, this behavior is kept to avoid breaking backwards compatibility when padding/encoding with `truncation is True`.\n",
"- Be aware that you SHOULD NOT rely on t5-large automatically truncating your input to 512 when padding/encoding.\n",
"- If you want to encode/pad to sequences longer than 512 you can either instantiate this tokenizer with `model_max_length` or pass `max_length` when encoding/padding.\n",
"- To avoid this warning, please instantiate this tokenizer with `model_max_length` set to your preferred value.\n",
" warnings.warn(\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Running tokenizer on dataset: 0%| | 0/2037 [00:00<?, ? examples/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Running tokenizer on dataset: 0%| | 0/227 [00:00<?, ? examples/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# data preprocessing\n",
"tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)\n",
"\n",
"\n",
"def preprocess_function(examples):\n",
" inputs = examples[text_column]\n",
" targets = examples[label_column]\n",
" model_inputs = tokenizer(inputs, max_length=max_length, padding=\"max_length\", truncation=True, return_tensors=\"pt\")\n",
" labels = tokenizer(targets, max_length=2, padding=\"max_length\", truncation=True, return_tensors=\"pt\")\n",
" labels = labels[\"input_ids\"]\n",
" labels[labels == tokenizer.pad_token_id] = -100\n",
" model_inputs[\"labels\"] = labels\n",
" return model_inputs\n",
"\n",
"\n",
"processed_datasets = dataset.map(\n",
" preprocess_function,\n",
" batched=True,\n",
" num_proc=1,\n",
" remove_columns=dataset[\"train\"].column_names,\n",
" load_from_cache_file=False,\n",
" desc=\"Running tokenizer on dataset\",\n",
")\n",
"\n",
"train_dataset = processed_datasets[\"train\"].shuffle()\n",
"eval_dataset = processed_datasets[\"validation\"]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "6b3a4090",
"metadata": {
"ExecuteTime": {
"end_time": "2023-05-30T09:53:10.336984Z",
"start_time": "2023-05-30T09:50:14.780995Z"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/udir/tschilla/anaconda3/envs/peft/lib/python3.9/site-packages/transformers/optimization.py:407: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
" warnings.warn(\n"
]
},
{
"data": {
"text/html": [
"\n",
" <div>\n",
" \n",
" <progress value='1275' max='1275' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [1275/1275 02:52, Epoch 5/5]\n",
" </div>\n",
" <table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>Epoch</th>\n",
" <th>Training Loss</th>\n",
" <th>Validation Loss</th>\n",
" <th>Accuracy</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>4.784800</td>\n",
" <td>0.576933</td>\n",
" <td>0.559471</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>0.648200</td>\n",
" <td>0.437575</td>\n",
" <td>0.577093</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>0.536200</td>\n",
" <td>0.397857</td>\n",
" <td>0.625551</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>0.472200</td>\n",
" <td>0.373160</td>\n",
" <td>0.643172</td>\n",
" </tr>\n",
" <tr>\n",
" <td>5</td>\n",
" <td>0.452500</td>\n",
" <td>0.370234</td>\n",
" <td>0.656388</td>\n",
" </tr>\n",
" </tbody>\n",
"</table><p>"
],
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"<IPython.core.display.HTML object>"
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},
"metadata": {},
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{
"data": {
"text/plain": [
"TrainOutput(global_step=1275, training_loss=1.3787811279296875, metrics={'train_runtime': 173.3699, 'train_samples_per_second': 58.747, 'train_steps_per_second': 7.354, 'total_flos': 344546979840000.0, 'train_loss': 1.3787811279296875, 'epoch': 5.0})"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# training and evaluation\n",
"\n",
"\n",
"def compute_metrics(eval_preds):\n",
" preds, labels = eval_preds\n",
" preds = tokenizer.batch_decode(preds, skip_special_tokens=True)\n",
" labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n",
"\n",
" correct = 0\n",
" total = 0\n",
" for pred, true in zip(preds, labels):\n",
" if pred.strip() == true.strip():\n",
" correct += 1\n",
" total += 1\n",
" accuracy = correct / total\n",
" return {\"accuracy\": accuracy}\n",
"\n",
"\n",
"training_args = Seq2SeqTrainingArguments(\n",
" \"out\",\n",
" per_device_train_batch_size=batch_size,\n",
" learning_rate=lr,\n",
" num_train_epochs=num_epochs,\n",
" evaluation_strategy=\"epoch\",\n",
" logging_strategy=\"epoch\",\n",
" save_strategy=\"no\",\n",
" report_to=[],\n",
" predict_with_generate=True,\n",
" generation_config=GenerationConfig(max_length=max_length),\n",
")\n",
"trainer = Seq2SeqTrainer(\n",
" model=model,\n",
" tokenizer=tokenizer,\n",
" args=training_args,\n",
" train_dataset=train_dataset,\n",
" eval_dataset=eval_dataset,\n",
" data_collator=default_data_collator,\n",
" compute_metrics=compute_metrics,\n",
")\n",
"trainer.train()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "a8de6005",
"metadata": {
"ExecuteTime": {
"end_time": "2023-05-30T09:53:13.045146Z",
"start_time": "2023-05-30T09:53:13.035612Z"
}
},
"outputs": [],
"source": [
"# saving model\n",
"peft_model_id = f\"{model_name_or_path}_{peft_config.peft_type}_{peft_config.task_type}\"\n",
"model.save_pretrained(peft_model_id)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "bd20cd4c",
"metadata": {
"ExecuteTime": {
"end_time": "2023-05-30T09:53:15.240763Z",
"start_time": "2023-05-30T09:53:15.059304Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"164K\tt5-large_PROMPT_TUNING_SEQ_2_SEQ_LM/adapter_model.bin\r\n"
]
}
],
"source": [
"ckpt = f\"{peft_model_id}/adapter_model.bin\"\n",
"!du -h $ckpt"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "76c2fc29",
"metadata": {
"ExecuteTime": {
"end_time": "2023-05-30T09:53:25.055105Z",
"start_time": "2023-05-30T09:53:17.797989Z"
}
},
"outputs": [],
"source": [
"from peft import PeftModel, PeftConfig\n",
"\n",
"peft_model_id = f\"{model_name_or_path}_{peft_config.peft_type}_{peft_config.task_type}\"\n",
"\n",
"config = PeftConfig.from_pretrained(peft_model_id)\n",
"model = AutoModelForSeq2SeqLM.from_pretrained(config.base_model_name_or_path)\n",
"model = PeftModel.from_pretrained(model, peft_model_id)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "d997f1cc",
"metadata": {
"ExecuteTime": {
"end_time": "2023-05-30T09:53:26.777030Z",
"start_time": "2023-05-30T09:53:26.013697Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Aspocomp Group , headquartered in Helsinki , Finland , develops interconnection solutions for the electronics industry .\n",
"{'input_ids': tensor([[ 71, 7990, 7699, 1531, 3, 6, 3, 27630, 16, 29763,\n",
" 3, 6, 16458, 3, 6, 1344, 7, 1413, 28102, 1275,\n",
" 21, 8, 12800, 681, 3, 5, 1]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1]])}\n",
"tensor([[ 0, 7163, 1]])\n",
"['neutral']\n"
]
}
],
"source": [
"model.eval()\n",
"i = 107\n",
"inputs = tokenizer(dataset[\"validation\"][text_column][i], return_tensors=\"pt\")\n",
"print(dataset[\"validation\"][text_column][i])\n",
"print(inputs)\n",
"\n",
"with torch.no_grad():\n",
" outputs = model.generate(input_ids=inputs[\"input_ids\"], max_new_tokens=10)\n",
" print(outputs)\n",
" print(tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fb746c1e",
"metadata": {},
"outputs": [],
"source": []
}
],
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"title_cell": "Table of Contents",
"title_sidebar": "Contents",
"toc_cell": false,
"toc_position": {},
"toc_section_display": true,
"toc_window_display": false
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"r": {
"delete_cmd_postfix": ") ",
"delete_cmd_prefix": "rm(",
"library": "var_list.r",
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