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{
"cells": [
{
"cell_type": "code",
"execution_count": 32,
"id": "578786b8-092a-4de8-9955-4e87da557639",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: peft in /opt/conda/lib/python3.10/site-packages (0.11.1)\n",
"Requirement already satisfied: numpy>=1.17 in /opt/conda/lib/python3.10/site-packages (from peft) (1.26.3)\n",
"Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from peft) (23.1)\n",
"Requirement already satisfied: psutil in /opt/conda/lib/python3.10/site-packages (from peft) (5.9.0)\n",
"Requirement already satisfied: pyyaml in /opt/conda/lib/python3.10/site-packages (from peft) (6.0.1)\n",
"Requirement already satisfied: torch>=1.13.0 in /opt/conda/lib/python3.10/site-packages (from peft) (2.2.0)\n",
"Requirement already satisfied: transformers in /opt/conda/lib/python3.10/site-packages (from peft) (4.42.3)\n",
"Requirement already satisfied: tqdm in /opt/conda/lib/python3.10/site-packages (from peft) (4.66.4)\n",
"Requirement already satisfied: accelerate>=0.21.0 in /opt/conda/lib/python3.10/site-packages (from peft) (0.32.0)\n",
"Requirement already satisfied: safetensors in /opt/conda/lib/python3.10/site-packages (from peft) (0.4.3)\n",
"Requirement already satisfied: huggingface-hub>=0.17.0 in /opt/conda/lib/python3.10/site-packages (from peft) (0.23.4)\n",
"Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from huggingface-hub>=0.17.0->peft) (3.13.1)\n",
"Requirement already satisfied: fsspec>=2023.5.0 in /opt/conda/lib/python3.10/site-packages (from huggingface-hub>=0.17.0->peft) (2023.12.2)\n",
"Requirement already satisfied: requests in /opt/conda/lib/python3.10/site-packages (from huggingface-hub>=0.17.0->peft) (2.32.3)\n",
"Requirement already satisfied: typing-extensions>=3.7.4.3 in /opt/conda/lib/python3.10/site-packages (from huggingface-hub>=0.17.0->peft) (4.9.0)\n",
"Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch>=1.13.0->peft) (1.12)\n",
"Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch>=1.13.0->peft) (3.1)\n",
"Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch>=1.13.0->peft) (3.1.2)\n",
"Requirement already satisfied: regex!=2019.12.17 in /opt/conda/lib/python3.10/site-packages (from transformers->peft) (2024.5.15)\n",
"Requirement already satisfied: tokenizers<0.20,>=0.19 in /opt/conda/lib/python3.10/site-packages (from transformers->peft) (0.19.1)\n",
"Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch>=1.13.0->peft) (2.1.3)\n",
"Requirement already satisfied: charset-normalizer<4,>=2 in /opt/conda/lib/python3.10/site-packages (from requests->huggingface-hub>=0.17.0->peft) (2.0.4)\n",
"Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests->huggingface-hub>=0.17.0->peft) (3.4)\n",
"Requirement already satisfied: urllib3<3,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests->huggingface-hub>=0.17.0->peft) (1.26.18)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests->huggingface-hub>=0.17.0->peft) (2023.11.17)\n",
"Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch>=1.13.0->peft) (1.3.0)\n",
"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
"\u001b[0m"
]
}
],
"source": [
"#!pip install huggingface_hub torch transformers datasets trl \n",
"#!pip install flash-attn --no-build-isolation\n",
"!pip install --upgrade peft"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "4a74bec4-4bf0-47be-802a-046073da573e",
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"import logging\n",
"\n",
"import datasets\n",
"from datasets import load_dataset\n",
"from peft import LoraConfig\n",
"import torch\n",
"import transformers\n",
"from trl import SFTTrainer, SFTConfig\n",
"from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, BitsAndBytesConfig"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "8a9bc6f8-4a1e-42d8-897d-5225e1b5011a",
"metadata": {},
"outputs": [],
"source": [
"dataset_id = (\"wikitext\", \"wikitext-103-raw-v1\")\n",
"dataset_id = \"HuggingFaceH4/ultrachat_200k\"\n",
"\n",
"dataset = load_dataset(dataset_id)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "f3b226eb-b159-4533-bd33-2746181a80b3",
"metadata": {},
"outputs": [],
"source": [
"training_config = {\n",
" \"bf16\": True,\n",
" \"do_eval\": False,\n",
" \"do_train\": True, # defualts to False, not sure where this fits\n",
" \"learning_rate\": 5.0e-06,\n",
" \"log_level\": \"info\",\n",
" \"logging_steps\": 20,\n",
" \"logging_strategy\": \"steps\",\n",
" \"lr_scheduler_type\": \"cosine\",\n",
" \"num_train_epochs\": 1,\n",
" \"max_steps\": -1,\n",
" \"output_dir\": \"./checkpoint_dir\", # model predictions and checkpoints\n",
" \"overwrite_output_dir\": True,\n",
" \"per_device_eval_batch_size\": 4,\n",
" \"per_device_train_batch_size\": 4,\n",
" \"remove_unused_columns\": True,\n",
" \"save_steps\": 100,\n",
" \"save_total_limit\": 1,\n",
" \"seed\": 0,\n",
" \"gradient_checkpointing\": True,\n",
" \"gradient_checkpointing_kwargs\":{\"use_reentrant\": False},\n",
" \"gradient_accumulation_steps\": 1, # number of steps to accumulate before beckprop\n",
" \"warmup_ratio\": 0.2,\n",
" \"packing\": False,\n",
" \"max_seq_length\": 2048,\n",
" \"dataset_text_field\": \"text\",\n",
" }\n",
"\n",
"peft_config = {\n",
" \"r\": 16, # default values VV\n",
" \"lora_alpha\": 32,\n",
" \"lora_dropout\": 0.05,\n",
" \"bias\": \"none\",\n",
" \"task_type\": \"CAUSAL_LM\",\n",
" \"target_modules\": \"all-linear\",\n",
" \"modules_to_save\": None,\n",
"}\n",
"\n",
"train_conf = SFTConfig(**training_config)\n",
"#train_conf = TrainingArguments(**training_config)\n",
"peft_conf = LoraConfig(**peft_config)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "20c9d834-50fe-4495-b003-7d80495c8439",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "08aed232727444ab814beb2c188090eb",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Loading checkpoint shards: 0%| | 0/2 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Exception ignored in: <bound method IPythonKernel._clean_thread_parent_frames of <ipykernel.ipkernel.IPythonKernel object at 0x76c288d725c0>>\n",
"Traceback (most recent call last):\n",
" File \"/opt/conda/lib/python3.10/site-packages/ipykernel/ipkernel.py\", line 775, in _clean_thread_parent_frames\n",
" def _clean_thread_parent_frames(\n",
"KeyboardInterrupt: \n",
"Exception ignored in: <bound method IPythonKernel._clean_thread_parent_frames of <ipykernel.ipkernel.IPythonKernel object at 0x76c288d725c0>>\n",
"Traceback (most recent call last):\n",
" File \"/opt/conda/lib/python3.10/site-packages/ipykernel/ipkernel.py\", line 775, in _clean_thread_parent_frames\n",
" def _clean_thread_parent_frames(\n",
"KeyboardInterrupt: \n",
"Exception ignored in: <bound method IPythonKernel._clean_thread_parent_frames of <ipykernel.ipkernel.IPythonKernel object at 0x76c288d725c0>>\n",
"Traceback (most recent call last):\n",
" File \"/opt/conda/lib/python3.10/site-packages/ipykernel/ipkernel.py\", line 775, in _clean_thread_parent_frames\n",
" def _clean_thread_parent_frames(\n",
"KeyboardInterrupt: \n",
"Exception ignored in: <bound method IPythonKernel._clean_thread_parent_frames of <ipykernel.ipkernel.IPythonKernel object at 0x76c288d725c0>>\n",
"Traceback (most recent call last):\n",
" File \"/opt/conda/lib/python3.10/site-packages/ipykernel/ipkernel.py\", line 775, in _clean_thread_parent_frames\n",
" def _clean_thread_parent_frames(\n",
"KeyboardInterrupt: \n"
]
}
],
"source": [
"checkpoint_path = \"microsoft/Phi-3-mini-128k-instruct\"\n",
"model_kwargs = dict(\n",
" use_cache=False,\n",
" trust_remote_code=True,\n",
" attn_implementation=\"flash_attention_2\", # loading the model with flash-attenstion support\n",
" torch_dtype=torch.bfloat16,\n",
" device_map=\"auto\"\n",
")\n",
"\n",
"model = AutoModelForCausalLM.from_pretrained(checkpoint_path, **model_kwargs)\n",
"tokenizer = AutoTokenizer.from_pretrained(checkpoint_path, truncation=True)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "d684252c-2151-4601-8ebb-398bd3a63f00",
"metadata": {},
"outputs": [],
"source": [
"tokenizer.model_max_length = 2048\n",
"#tokenizer.pad_token = tokenizer.unk_token # use unk rather than eos token to prevent endless generation\n",
"#tokenizer.pad_token_id = tokenizer.convert_tokens_to_ids(tokenizer.pad_token)\n",
"tokenizer.padding_side = 'right'"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "75869100-99f7-49c7-a9d3-7a3950dd7d72",
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"def preproc(examples, tokenizer):\n",
" messages = examples['messages']\n",
" examples['text'] = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=False) #, return_dict=True)\n",
" return examples\n",
"\n",
"train_dataset = dataset['train_sft']\n",
"test_dataset = dataset['test_sft']\n",
"\n",
"train_dataset = train_dataset.map(preproc,\n",
" fn_kwargs={'tokenizer':tokenizer},\n",
" num_proc=24,\n",
" #batched=True,\n",
" remove_columns=list(train_dataset.features)).select(range(1000))\n",
"\n",
"test_dataset = test_dataset.map(preproc,\n",
" fn_kwargs={'tokenizer':tokenizer},\n",
" num_proc=24,\n",
" #batched=True,\n",
" remove_columns=list(test_dataset.features))#[10000:]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "56cd1b31-6f7e-4c7d-8524-b12cf94b9c9f",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5d79f04152484f9494e389b264fc7176",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Map: 0%| | 0/1000 [00:00<?, ? examples/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using auto half precision backend\n"
]
}
],
"source": [
"trainer = SFTTrainer(\n",
" model=model,\n",
" args=train_conf,\n",
" peft_config=peft_conf,\n",
" train_dataset=train_dataset,\n",
" #eval_dataset=test_dataset,\n",
" # max_seq_length=tokenizer.model_max_length,\n",
" # dataset_text_field=\"text\",\n",
" tokenizer=tokenizer,\n",
" # packing=True\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "d8e6b669-1717-429a-9c43-3c02adb8a3d1",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"***** Running training *****\n",
" Num examples = 1,000\n",
" Num Epochs = 1\n",
" Instantaneous batch size per device = 4\n",
" Total train batch size (w. parallel, distributed & accumulation) = 4\n",
" Gradient Accumulation steps = 1\n",
" Total optimization steps = 250\n",
" Number of trainable parameters = 25,165,824\n"
]
},
{
"data": {
"text/html": [
"\n",
" <div>\n",
" \n",
" <progress value='4' max='250' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [ 4/250 00:04 < 09:17, 0.44 it/s, Epoch 0.01/1]\n",
" </div>\n",
" <table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>Step</th>\n",
" <th>Training Loss</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" </tbody>\n",
"</table><p>"
],
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{
"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[16], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m train_result \u001b[38;5;241m=\u001b[39m \u001b[43mtrainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2\u001b[0m metrics \u001b[38;5;241m=\u001b[39m train_result\u001b[38;5;241m.\u001b[39mmetrics\n\u001b[1;32m 3\u001b[0m trainer\u001b[38;5;241m.\u001b[39msave_state()\n",
"File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/trl/trainer/sft_trainer.py:440\u001b[0m, in \u001b[0;36mSFTTrainer.train\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 437\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mneftune_noise_alpha \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_trainer_supports_neftune:\n\u001b[1;32m 438\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_trl_activate_neftune(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel)\n\u001b[0;32m--> 440\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 442\u001b[0m \u001b[38;5;66;03m# After training we make sure to retrieve back the original forward pass method\u001b[39;00m\n\u001b[1;32m 443\u001b[0m \u001b[38;5;66;03m# for the embedding layer by removing the forward post hook.\u001b[39;00m\n\u001b[1;32m 444\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mneftune_noise_alpha \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_trainer_supports_neftune:\n",
"File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/transformers/trainer.py:1932\u001b[0m, in \u001b[0;36mTrainer.train\u001b[0;34m(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)\u001b[0m\n\u001b[1;32m 1930\u001b[0m hf_hub_utils\u001b[38;5;241m.\u001b[39menable_progress_bars()\n\u001b[1;32m 1931\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1932\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43minner_training_loop\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1933\u001b[0m \u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1934\u001b[0m \u001b[43m \u001b[49m\u001b[43mresume_from_checkpoint\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mresume_from_checkpoint\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1935\u001b[0m \u001b[43m \u001b[49m\u001b[43mtrial\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtrial\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1936\u001b[0m \u001b[43m \u001b[49m\u001b[43mignore_keys_for_eval\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mignore_keys_for_eval\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1937\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/transformers/trainer.py:2268\u001b[0m, in \u001b[0;36mTrainer._inner_training_loop\u001b[0;34m(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)\u001b[0m\n\u001b[1;32m 2265\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcontrol \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcallback_handler\u001b[38;5;241m.\u001b[39mon_step_begin(args, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcontrol)\n\u001b[1;32m 2267\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maccelerator\u001b[38;5;241m.\u001b[39maccumulate(model):\n\u001b[0;32m-> 2268\u001b[0m tr_loss_step \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtraining_step\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2270\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[1;32m 2271\u001b[0m args\u001b[38;5;241m.\u001b[39mlogging_nan_inf_filter\n\u001b[1;32m 2272\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_torch_xla_available()\n\u001b[1;32m 2273\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m (torch\u001b[38;5;241m.\u001b[39misnan(tr_loss_step) \u001b[38;5;129;01mor\u001b[39;00m torch\u001b[38;5;241m.\u001b[39misinf(tr_loss_step))\n\u001b[1;32m 2274\u001b[0m ):\n\u001b[1;32m 2275\u001b[0m \u001b[38;5;66;03m# if loss is nan or inf simply add the average of previous logged losses\u001b[39;00m\n\u001b[1;32m 2276\u001b[0m tr_loss \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m tr_loss \u001b[38;5;241m/\u001b[39m (\u001b[38;5;241m1\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mglobal_step \u001b[38;5;241m-\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_globalstep_last_logged)\n",
"File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/transformers/trainer.py:3324\u001b[0m, in \u001b[0;36mTrainer.training_step\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m 3322\u001b[0m scaled_loss\u001b[38;5;241m.\u001b[39mbackward()\n\u001b[1;32m 3323\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 3324\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43maccelerator\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\u001b[43mloss\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3326\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m loss\u001b[38;5;241m.\u001b[39mdetach() \u001b[38;5;241m/\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39margs\u001b[38;5;241m.\u001b[39mgradient_accumulation_steps\n",
"File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/accelerate/accelerator.py:2151\u001b[0m, in \u001b[0;36mAccelerator.backward\u001b[0;34m(self, loss, **kwargs)\u001b[0m\n\u001b[1;32m 2149\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlomo_backward(loss, learning_rate)\n\u001b[1;32m 2150\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 2151\u001b[0m \u001b[43mloss\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/torch/_tensor.py:522\u001b[0m, in \u001b[0;36mTensor.backward\u001b[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[1;32m 512\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m has_torch_function_unary(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 513\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m handle_torch_function(\n\u001b[1;32m 514\u001b[0m Tensor\u001b[38;5;241m.\u001b[39mbackward,\n\u001b[1;32m 515\u001b[0m (\u001b[38;5;28mself\u001b[39m,),\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 520\u001b[0m inputs\u001b[38;5;241m=\u001b[39minputs,\n\u001b[1;32m 521\u001b[0m )\n\u001b[0;32m--> 522\u001b[0m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mautograd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 523\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgradient\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputs\u001b[49m\n\u001b[1;32m 524\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/torch/autograd/__init__.py:266\u001b[0m, in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[1;32m 261\u001b[0m retain_graph \u001b[38;5;241m=\u001b[39m create_graph\n\u001b[1;32m 263\u001b[0m \u001b[38;5;66;03m# The reason we repeat the same comment below is that\u001b[39;00m\n\u001b[1;32m 264\u001b[0m \u001b[38;5;66;03m# some Python versions print out the first line of a multi-line function\u001b[39;00m\n\u001b[1;32m 265\u001b[0m \u001b[38;5;66;03m# calls in the traceback and some print out the last line\u001b[39;00m\n\u001b[0;32m--> 266\u001b[0m \u001b[43mVariable\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_execution_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_backward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# Calls into the C++ engine to run the backward pass\u001b[39;49;00m\n\u001b[1;32m 267\u001b[0m \u001b[43m \u001b[49m\u001b[43mtensors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 268\u001b[0m \u001b[43m \u001b[49m\u001b[43mgrad_tensors_\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 269\u001b[0m \u001b[43m \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 270\u001b[0m \u001b[43m \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 271\u001b[0m \u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 272\u001b[0m \u001b[43m \u001b[49m\u001b[43mallow_unreachable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 273\u001b[0m \u001b[43m \u001b[49m\u001b[43maccumulate_grad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 274\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
]
}
],
"source": [
"train_result = trainer.train()\n",
"metrics = train_result.metrics\n",
"trainer.save_state()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "4d4207fc-1578-4591-a480-467fd2a5855b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'train_runtime': 506.2204,\n",
" 'train_samples_per_second': 1.975,\n",
" 'train_steps_per_second': 0.494,\n",
" 'total_flos': 4.041582948790272e+16,\n",
" 'train_loss': 1.1037534561157227,\n",
" 'epoch': 1.0}"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"metrics"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f92339ec-0448-40d2-9458-6242e35b9bdc",
"metadata": {},
"outputs": [],
"source": [
"from peft import PeftConfig, PeftModel \n",
"\n",
"checkpoint_path = \"microsoft/Phi-3-mini-128k-instruct\"\n",
"adapter_path = \"./checkpoint_dir/checkpoint-250\"\n",
"\n",
"model_kwargs = dict(\n",
" use_cache=False,\n",
" trust_remote_code=True,\n",
" attn_implementation=\"flash_attention_2\", # loading the model with flash-attenstion support\n",
" torch_dtype=torch.bfloat16,\n",
" device_map=\"auto\"\n",
")\n",
"\n",
"model = AutoModelForCausalLM.from_pretrained(checkpoint_path, **model_kwargs)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "f0cf458d-8b4f-4ff9-bd60-bbe510416cea",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
]
}
],
"source": [
"model = PeftModel.from_pretrained(model, adapter_path)\n",
"tokenizer = AutoTokenizer.from_pretrained(checkpoint_path)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "b5ada882-b7d2-46c5-ba5b-54fab2556832",
"metadata": {},
"outputs": [],
"source": [
"input_text = [\n",
" {'role': 'user', 'content': \"Tell me about cats\"},\n",
"]\n",
"\n",
"input = \"Tell me about cats\"\n",
"\n",
"input = tokenizer(input, return_tensors='pt')\n",
"\n",
"output = model.generate(\n",
" input['input_ids'].to('cuda'),\n",
" max_length=50, # Maximum length of the generated text\n",
" num_return_sequences=1, # Number of sequences to generate\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "139e9973-003a-484f-95f8-42428dd436f5",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tell me about cats.\n",
"\n",
"Chatbot: Cats are fascinating creatures! They are known for their agility, independence, and unique behaviors. They have a keen sense of hearing and can see well in low light\n"
]
}
],
"source": [
"generated_text = tokenizer.decode(output[0], skip_special_tokens=True)\n",
"\n",
"print(generated_text)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6dc4ddb3-3cbf-4d6e-9f57-45acb8acbe25",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.13"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|