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db5c00fad2
Also, add XPU support for Poly example. --------- Signed-off-by: Yao, Matrix <matrix.yao@intel.com>
14777 lines
450 KiB
Plaintext
14777 lines
450 KiB
Plaintext
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"outputs": [
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"env: CUDA_VISIBLE_DEVICES=0 # force using CUDA GPU device 0\n",
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"env: ZE_AFFINITY_MASK=0 # force using Intel XPU device 0\n",
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"env: TOKENIZERS_PARALLELISM=false\n"
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]
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}
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"source": [
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"%env CUDA_VISIBLE_DEVICES=0 # force using CUDA GPU device 0\n",
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"%env ZE_AFFINITY_MASK=0 # force using Intel XPU device 0\n",
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"%env TOKENIZERS_PARALLELISM=false"
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"id": "95b4cfd741795038",
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"libroFormatter": "formatter-string"
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"source": [
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"## Initialize PolyModel"
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]
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"libroFormatter": "formatter-string"
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},
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"outputs": [],
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"source": [
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"import torch\n",
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"from transformers import (\n",
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" AutoModelForSeq2SeqLM,\n",
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" AutoTokenizer,\n",
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" default_data_collator,\n",
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" Seq2SeqTrainingArguments,\n",
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" Seq2SeqTrainer,\n",
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")\n",
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"from datasets import load_dataset, concatenate_datasets\n",
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"from peft import PolyConfig, get_peft_model, TaskType, PeftModel, PeftConfig\n",
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"\n",
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"model_name_or_path = \"google/flan-t5-xl\"\n",
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"\n",
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"r = 8 # rank of lora in poly\n",
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"n_tasks = 4 # number of tasks\n",
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"n_skills = 2 # number of skills (loras)\n",
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"n_splits = 4 # number of heads\n",
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"\n",
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"batch_size = 8\n",
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"lr = 5e-5\n",
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"num_epochs = 8"
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]
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"outputs": [
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"text": [
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"Loading checkpoint shards: 100%|██████████| 2/2 [00:00<00:00, 22.43it/s]\n"
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]
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}
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],
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"source": [
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"tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=True)\n",
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"base_model = AutoModelForSeq2SeqLM.from_pretrained(model_name_or_path, trust_remote_code=True)"
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]
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"metadata": {
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"outputs": [
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"trainable params: 9,441,792 || all params: 2,859,198,976 || trainable%: 0.3302\n"
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]
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}
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],
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"source": [
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||
"peft_config = PolyConfig(\n",
|
||
" task_type=TaskType.SEQ_2_SEQ_LM,\n",
|
||
" poly_type=\"poly\",\n",
|
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" r=r,\n",
|
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" n_tasks=n_tasks,\n",
|
||
" n_skills=n_skills,\n",
|
||
" n_splits=n_splits,\n",
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")\n",
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||
"\n",
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||
"model = get_peft_model(base_model, peft_config)\n",
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"model.print_trainable_parameters()"
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]
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},
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"source": [
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||
"## Prepare datasets\n",
|
||
"\n",
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||
"For this example, we selected four `SuperGLUE` benchmark datasets: `boolq`, `multirc`, `rte`, and `wic`, each with a training set of 1,000 examples and an evaluation set of 100 examples."
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]
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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|
||
]
|
||
},
|
||
"execution": {
|
||
"shell.execute_reply.end": "2023-12-22T03:35:36.853391Z",
|
||
"shell.execute_reply.started": "2023-12-22T03:35:33.398019Z",
|
||
"to_execute": "2023-12-22T03:35:33.384Z"
|
||
},
|
||
"id": "d0b36e7eff50657c",
|
||
"libroFormatter": "formatter-string",
|
||
"outputId": "4198784f-15c6-4812-f96a-c3f62914dbbb",
|
||
"tags": []
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"boolq example: \n",
|
||
"{'input': 'Persian language -- Persian (/ˈpɜːrʒən, -ʃən/), also known by its endonym Farsi (فارسی fārsi (fɒːɾˈsiː) ( listen)), is one of the Western Iranian languages within the Indo-Iranian branch of the Indo-European language family. It is primarily spoken in Iran, Afghanistan (officially known as Dari since 1958), and Tajikistan (officially known as Tajiki since the Soviet era), and some other regions which historically were Persianate societies and considered part of Greater Iran. It is written in the Persian alphabet, a modified variant of the Arabic script, which itself evolved from the Aramaic alphabet.\\nQuestion: do iran and afghanistan speak the same language\\nA. Yes\\nB. No\\nAnswer:', 'output': 'A', 'task_name': 'boolq'}\n",
|
||
"multirc example: \n",
|
||
"{'input': 'While this process moved along, diplomacy continued its rounds. Direct pressure on the Taliban had proved unsuccessful. As one NSC staff note put it, \"Under the Taliban, Afghanistan is not so much a state sponsor of terrorism as it is a state sponsored by terrorists.\" In early 2000, the United States began a high-level effort to persuade Pakistan to use its influence over the Taliban. In January 2000, Assistant Secretary of State Karl Inderfurth and the State Department\\'s counterterrorism coordinator, Michael Sheehan, met with General Musharraf in Islamabad, dangling before him the possibility of a presidential visit in March as a reward for Pakistani cooperation. Such a visit was coveted by Musharraf, partly as a sign of his government\\'s legitimacy. He told the two envoys that he would meet with Mullah Omar and press him on Bin Laden. They left, however, reporting to Washington that Pakistan was unlikely in fact to do anything,\" given what it sees as the benefits of Taliban control of Afghanistan.\" President Clinton was scheduled to travel to India. The State Department felt that he should not visit India without also visiting Pakistan. The Secret Service and the CIA, however, warned in the strongest terms that visiting Pakistan would risk the President\\'s life. Counterterrorism officials also argued that Pakistan had not done enough to merit a presidential visit. But President Clinton insisted on including Pakistan in the itinerary for his trip to South Asia. His one-day stopover on March 25, 2000, was the first time a U.S. president had been there since 1969. At his meeting with Musharraf and others, President Clinton concentrated on tensions between Pakistan and India and the dangers of nuclear proliferation, but also discussed Bin Laden. President Clinton told us that when he pulled Musharraf aside for a brief, one-on-one meeting, he pleaded with the general for help regarding Bin Laden.\" I offered him the moon when I went to see him, in terms of better relations with the United States, if he\\'d help us get Bin Laden and deal with another issue or two.\" The U.S. effort continued. \\nQuestion: What did the high-level effort to persuade Pakistan include?\\nAnswer: Children, Gerd, or Dorian Popa\\nIs it true?\\nA. Yes\\nB. No\\nAnswer:', 'output': 'B', 'task_name': 'multirc'}\n",
|
||
"rte example: \n",
|
||
"{'input': 'No Weapons of Mass Destruction Found in Iraq Yet.\\nWeapons of Mass Destruction Found in Iraq.\\nIs the sentence below entailed by the sentence above?\\nA. Yes\\nB. No\\nAnswer:', 'output': 'B', 'task_name': 'rte'}\n",
|
||
"wic example: \n",
|
||
"{'input': \"Sentence 1: Do you want to come over to my place later?\\nSentence 2: A political system with no place for the less prominent groups.\\nAre 'place' in the above two sentences the same?\\nA. Yes\\nB. No\\nAnswer:\", 'output': 'B', 'task_name': 'wic'}\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# boolq\n",
|
||
"boolq_dataset = (\n",
|
||
" load_dataset(\"super_glue\", \"boolq\")\n",
|
||
" .map(\n",
|
||
" lambda x: {\n",
|
||
" \"input\": f\"{x['passage']}\\nQuestion: {x['question']}\\nA. Yes\\nB. No\\nAnswer:\",\n",
|
||
" # 0 - False\n",
|
||
" # 1 - True\n",
|
||
" \"output\": [\"B\", \"A\"][int(x[\"label\"])],\n",
|
||
" \"task_name\": \"boolq\",\n",
|
||
" }\n",
|
||
" )\n",
|
||
" .select_columns([\"input\", \"output\", \"task_name\"])\n",
|
||
")\n",
|
||
"print(\"boolq example: \")\n",
|
||
"print(boolq_dataset[\"train\"][0])\n",
|
||
"\n",
|
||
"# multirc\n",
|
||
"multirc_dataset = (\n",
|
||
" load_dataset(\"super_glue\", \"multirc\")\n",
|
||
" .map(\n",
|
||
" lambda x: {\n",
|
||
" \"input\": (\n",
|
||
" f\"{x['paragraph']}\\nQuestion: {x['question']}\\nAnswer: {x['answer']}\\nIs it\"\n",
|
||
" \" true?\\nA. Yes\\nB. No\\nAnswer:\"\n",
|
||
" ),\n",
|
||
" # 0 - False\n",
|
||
" # 1 - True\n",
|
||
" \"output\": [\"B\", \"A\"][int(x[\"label\"])],\n",
|
||
" \"task_name\": \"multirc\",\n",
|
||
" }\n",
|
||
" )\n",
|
||
" .select_columns([\"input\", \"output\", \"task_name\"])\n",
|
||
")\n",
|
||
"print(\"multirc example: \")\n",
|
||
"print(multirc_dataset[\"train\"][0])\n",
|
||
"\n",
|
||
"# rte\n",
|
||
"rte_dataset = (\n",
|
||
" load_dataset(\"super_glue\", \"rte\")\n",
|
||
" .map(\n",
|
||
" lambda x: {\n",
|
||
" \"input\": (\n",
|
||
" f\"{x['premise']}\\n{x['hypothesis']}\\nIs the sentence below entailed by the\"\n",
|
||
" \" sentence above?\\nA. Yes\\nB. No\\nAnswer:\"\n",
|
||
" ),\n",
|
||
" # 0 - entailment\n",
|
||
" # 1 - not_entailment\n",
|
||
" \"output\": [\"A\", \"B\"][int(x[\"label\"])],\n",
|
||
" \"task_name\": \"rte\",\n",
|
||
" }\n",
|
||
" )\n",
|
||
" .select_columns([\"input\", \"output\", \"task_name\"])\n",
|
||
")\n",
|
||
"print(\"rte example: \")\n",
|
||
"print(rte_dataset[\"train\"][0])\n",
|
||
"\n",
|
||
"# wic\n",
|
||
"wic_dataset = (\n",
|
||
" load_dataset(\"super_glue\", \"wic\")\n",
|
||
" .map(\n",
|
||
" lambda x: {\n",
|
||
" \"input\": (\n",
|
||
" f\"Sentence 1: {x['sentence1']}\\nSentence 2: {x['sentence2']}\\nAre '{x['word']}'\"\n",
|
||
" \" in the above two sentences the same?\\nA. Yes\\nB. No\\nAnswer:\"\n",
|
||
" ),\n",
|
||
" # 0 - False\n",
|
||
" # 1 - True\n",
|
||
" \"output\": [\"B\", \"A\"][int(x[\"label\"])],\n",
|
||
" \"task_name\": \"wic\",\n",
|
||
" }\n",
|
||
" )\n",
|
||
" .select_columns([\"input\", \"output\", \"task_name\"])\n",
|
||
")\n",
|
||
"print(\"wic example: \")\n",
|
||
"print(wic_dataset[\"train\"][0])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"id": "9fca2225-aaee-47aa-957a-5f8ed3177cdb",
|
||
"metadata": {
|
||
"execution": {
|
||
"shell.execute_reply.end": "2023-12-22T03:35:36.858952Z",
|
||
"shell.execute_reply.started": "2023-12-22T03:35:36.855329Z",
|
||
"to_execute": "2023-12-22T03:35:36.819Z"
|
||
},
|
||
"id": "9fca2225-aaee-47aa-957a-5f8ed3177cdb",
|
||
"libroFormatter": "formatter-string"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# define a task2id map\n",
|
||
"TASK2ID = {\n",
|
||
" \"boolq\": 0,\n",
|
||
" \"multirc\": 1,\n",
|
||
" \"rte\": 2,\n",
|
||
" \"wic\": 3,\n",
|
||
"}\n",
|
||
"\n",
|
||
"\n",
|
||
"def tokenize(examples):\n",
|
||
" inputs, targets = examples[\"input\"], examples[\"output\"]\n",
|
||
" features = tokenizer(inputs, max_length=512, 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",
|
||
" features[\"labels\"] = labels\n",
|
||
" features[\"task_ids\"] = torch.tensor([[TASK2ID[t]] for t in examples[\"task_name\"]]).long()\n",
|
||
" return features"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"id": "0bf6c31c-73cd-4eed-931b-0cad5d7290fb",
|
||
"metadata": {
|
||
"execution": {
|
||
"shell.execute_reply.end": "2023-12-22T03:35:36.929414Z",
|
||
"shell.execute_reply.started": "2023-12-22T03:35:36.860477Z",
|
||
"to_execute": "2023-12-22T03:35:36.849Z"
|
||
},
|
||
"id": "0bf6c31c-73cd-4eed-931b-0cad5d7290fb",
|
||
"libroFormatter": "formatter-string",
|
||
"tags": []
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"def get_superglue_dataset(\n",
|
||
" split=\"train\",\n",
|
||
" n_samples=500,\n",
|
||
"):\n",
|
||
" ds = concatenate_datasets(\n",
|
||
" [\n",
|
||
" boolq_dataset[split].shuffle().select(range(n_samples)),\n",
|
||
" multirc_dataset[split].shuffle().select(range(n_samples)),\n",
|
||
" rte_dataset[split].shuffle().select(range(n_samples)),\n",
|
||
" wic_dataset[split].shuffle().select(range(n_samples)),\n",
|
||
" ]\n",
|
||
" )\n",
|
||
" ds = ds.map(\n",
|
||
" tokenize,\n",
|
||
" batched=True,\n",
|
||
" remove_columns=[\"input\", \"output\", \"task_name\"],\n",
|
||
" load_from_cache_file=False,\n",
|
||
" )\n",
|
||
" return ds"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "oNvh2WGlLo4z",
|
||
"metadata": {
|
||
"id": "oNvh2WGlLo4z",
|
||
"libroFormatter": "formatter-string"
|
||
},
|
||
"source": [
|
||
"As a toy example, we only select 1,000 from each subdataset for training and 100 each for eval."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"id": "1bf88dd1a6aaa6a5",
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"execution": {
|
||
"shell.execute_reply.end": "2023-12-22T03:35:44.953151Z",
|
||
"shell.execute_reply.started": "2023-12-22T03:35:37.023791Z",
|
||
"to_execute": "2023-12-22T03:35:37.009Z"
|
||
},
|
||
"libroFormatter": "formatter-string"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Map: 0%| | 0/4000 [00:00<?, ? examples/s]"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Map: 100%|██████████| 4000/4000 [00:02<00:00, 1880.98 examples/s]\n",
|
||
"Map: 100%|██████████| 400/400 [00:00<00:00, 2124.88 examples/s]\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"superglue_train_dataset = get_superglue_dataset(split=\"train\", n_samples=1000)\n",
|
||
"superglue_eval_dataset = get_superglue_dataset(split=\"test\", n_samples=100)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "550abf92-b8ea-424b-aba0-10d8da941297",
|
||
"metadata": {
|
||
"id": "550abf92-b8ea-424b-aba0-10d8da941297",
|
||
"libroFormatter": "formatter-string"
|
||
},
|
||
"source": [
|
||
"## Train and evaluate"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "b48135d6-0d83-4e8a-b1f0-c292663c84ec",
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 134
|
||
},
|
||
"execution": {
|
||
"shell.execute_reply.end": "",
|
||
"shell.execute_reply.started": "2023-12-22T03:35:45.102182Z",
|
||
"to_execute": "2023-12-22T03:35:44.998Z"
|
||
},
|
||
"id": "b48135d6-0d83-4e8a-b1f0-c292663c84ec",
|
||
"libroFormatter": "formatter-string",
|
||
"outputId": "362dbaae-4a43-423b-d0d1-39839d721177"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# training and evaluation\n",
|
||
"def compute_metrics(eval_preds):\n",
|
||
" preds, labels = eval_preds\n",
|
||
" preds = [[i for i in seq if i != -100] for seq in preds]\n",
|
||
" labels = [[i for i in seq if i != -100] for seq in labels]\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",
|
||
" \"output\",\n",
|
||
" per_device_train_batch_size=batch_size,\n",
|
||
" per_device_eval_batch_size=batch_size,\n",
|
||
" learning_rate=lr,\n",
|
||
" num_train_epochs=num_epochs,\n",
|
||
" eval_strategy=\"epoch\",\n",
|
||
" logging_strategy=\"epoch\",\n",
|
||
" save_strategy=\"no\",\n",
|
||
" report_to=[],\n",
|
||
" predict_with_generate=True,\n",
|
||
" generation_max_length=2,\n",
|
||
" remove_unused_columns=False,\n",
|
||
")\n",
|
||
"trainer = Seq2SeqTrainer(\n",
|
||
" model=model,\n",
|
||
" processing_class=tokenizer,\n",
|
||
" args=training_args,\n",
|
||
" train_dataset=superglue_train_dataset,\n",
|
||
" eval_dataset=superglue_eval_dataset,\n",
|
||
" data_collator=default_data_collator,\n",
|
||
" compute_metrics=compute_metrics,\n",
|
||
")\n",
|
||
"trainer.train()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 15,
|
||
"id": "6bdad1ae-ecf0-4df1-bf27-6474c48c16be",
|
||
"metadata": {
|
||
"execution": {
|
||
"shell.execute_reply.end": "2023-12-22T06:59:00.354149Z",
|
||
"shell.execute_reply.started": "2023-12-22T06:58:59.294716Z",
|
||
"to_execute": "2023-12-22T06:58:59.146Z"
|
||
},
|
||
"id": "6bdad1ae-ecf0-4df1-bf27-6474c48c16be",
|
||
"libroFormatter": "formatter-string"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# saving model\n",
|
||
"model_name_or_path = \"google/flan-t5-xl\"\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": 16,
|
||
"id": "9e539e3d-0944-4f81-bcd6-dec16c66eeea",
|
||
"metadata": {
|
||
"execution": {
|
||
"shell.execute_reply.end": "2023-12-22T06:59:05.542802Z",
|
||
"shell.execute_reply.started": "2023-12-22T06:59:04.397500Z",
|
||
"to_execute": "2023-12-22T06:59:04.248Z"
|
||
},
|
||
"id": "9e539e3d-0944-4f81-bcd6-dec16c66eeea",
|
||
"libroFormatter": "formatter-string"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"total 37M\n",
|
||
"-rw-r--r-- 1 root root 5.1K Aug 4 20:25 README.md\n",
|
||
"-rw-r--r-- 1 root root 381 Aug 4 20:25 adapter_config.json\n",
|
||
"-rw-r--r-- 1 root root 37M Aug 4 20:25 adapter_model.safetensors\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"!ls -lh $peft_model_id"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "52e37b18-d760-4af7-b1ab-40a6880eb92d",
|
||
"metadata": {
|
||
"id": "52e37b18-d760-4af7-b1ab-40a6880eb92d",
|
||
"libroFormatter": "formatter-string"
|
||
},
|
||
"source": [
|
||
"## Load and infer"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 17,
|
||
"id": "8eeb65376093b996",
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"execution": {
|
||
"shell.execute_reply.end": "2023-12-22T06:59:13.768047Z",
|
||
"shell.execute_reply.started": "2023-12-22T06:59:13.765849Z",
|
||
"to_execute": "2023-12-22T06:59:13.627Z"
|
||
},
|
||
"libroFormatter": "formatter-string"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"device_type = torch.accelerator.current_accelerator().type if hasattr(torch, \"accelerator\") else \"cuda\"\n",
|
||
"device = f\"{device_type}:0\" if device_type != \"cpu\" else \"cpu\""
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 18,
|
||
"id": "70490c8e-d301-4e4e-a5a3-de87b53f1942",
|
||
"metadata": {
|
||
"execution": {
|
||
"shell.execute_reply.end": "2023-12-22T07:00:09.333267Z",
|
||
"shell.execute_reply.started": "2023-12-22T06:59:15.706883Z",
|
||
"to_execute": "2023-12-22T06:59:15.561Z"
|
||
},
|
||
"id": "70490c8e-d301-4e4e-a5a3-de87b53f1942",
|
||
"libroFormatter": "formatter-string"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Loading checkpoint shards: 100%|██████████| 2/2 [00:00<00:00, 22.17it/s]\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"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)\n",
|
||
"model = model.to(device)\n",
|
||
"model = model.eval()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 19,
|
||
"id": "80ae512b-545d-470d-874d-969dba42055b",
|
||
"metadata": {
|
||
"execution": {
|
||
"shell.execute_reply.end": "2023-12-22T07:00:17.434446Z",
|
||
"shell.execute_reply.started": "2023-12-22T07:00:17.285745Z",
|
||
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"In 1979, the leaders signed the Egypt-Israel peace treaty on the White House lawn. Both President Begin and Sadat received the Nobel Peace Prize for their work. The two nations have enjoyed peaceful relations to this day.\n",
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"The Israel-Egypt Peace Agreement was signed in 1979.\n",
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"A. Yes\n",
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"B. No\n",
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"Answer:\n",
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"A\n",
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"{'input_ids': tensor([[ 86, 15393, 6, 8, 2440, 3814, 8, 10438, 18, 30387,\n",
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" 3065, 2665, 63, 30, 8, 1945, 1384, 8652, 5, 2867,\n",
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" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], device='xpu:0'), 'task_ids': tensor([2], device='xpu:0')}\n",
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"A\n"
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"\n",
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