{
"cells": [
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"id": "5f93b7d1"
},
"outputs": [],
"source": [
"from transformers import AutoModelForSeq2SeqLM\n",
"import peft\n",
"from peft import get_peft_config, get_peft_model, get_peft_model_state_dict, IA3Config, TaskType\n",
"import torch\n",
"from datasets import load_dataset\n",
"import os\n",
"\n",
"os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n",
"from transformers import AutoTokenizer\n",
"from torch.utils.data import DataLoader\n",
"from transformers import default_data_collator, get_linear_schedule_with_warmup\n",
"from tqdm import tqdm\n",
"from datasets import load_dataset\n",
"\n",
"device = \"cuda\"\n",
"model_name_or_path = \"bigscience/mt0-large\"\n",
"tokenizer_name_or_path = \"bigscience/mt0-large\"\n",
"\n",
"checkpoint_name = \"financial_sentiment_analysis_ia3_v1.pt\"\n",
"text_column = \"sentence\"\n",
"label_column = \"text_label\"\n",
"max_length = 128\n",
"lr = 8e-3\n",
"num_epochs = 3\n",
"batch_size = 8"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "b9e6368c",
"outputId": "fc2888a8-4fe9-4d61-dd2d-753e751e1416"
},
"outputs": [
{
"data": {
"text/plain": [
"<module 'peft' from '/usr/local/lib/python3.10/dist-packages/peft/__init__.py'>"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import importlib\n",
"\n",
"importlib.reload(peft)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"id": "8d0850ac"
},
"outputs": [],
"source": [
"# creating model\n",
"peft_config = IA3Config(task_type=TaskType.SEQ_2_SEQ_LM, inference_mode=False, feedforward_modules=[])\n",
"\n",
"model = AutoModelForSeq2SeqLM.from_pretrained(model_name_or_path)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "e10c3831",
"outputId": "e69c5e07-ae58-446c-8301-e99ac6b85d62"
},
"outputs": [
{
"data": {
"text/plain": [
"MT5ForConditionalGeneration(\n",
" (shared): Embedding(250112, 1024)\n",
" (encoder): MT5Stack(\n",
" (embed_tokens): Embedding(250112, 1024)\n",
" (block): ModuleList(\n",
" (0): MT5Block(\n",
" (layer): ModuleList(\n",
" (0): MT5LayerSelfAttention(\n",
" (SelfAttention): MT5Attention(\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): MT5LayerNorm()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (1): MT5LayerFF(\n",
" (DenseReluDense): MT5DenseGatedActDense(\n",
" (wi_0): Linear(in_features=1024, out_features=2816, bias=False)\n",
" (wi_1): Linear(in_features=1024, out_features=2816, bias=False)\n",
" (wo): Linear(in_features=2816, out_features=1024, bias=False)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" (act): NewGELUActivation()\n",
" )\n",
" (layer_norm): MT5LayerNorm()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" )\n",
" (1-23): 23 x MT5Block(\n",
" (layer): ModuleList(\n",
" (0): MT5LayerSelfAttention(\n",
" (SelfAttention): MT5Attention(\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): MT5LayerNorm()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (1): MT5LayerFF(\n",
" (DenseReluDense): MT5DenseGatedActDense(\n",
" (wi_0): Linear(in_features=1024, out_features=2816, bias=False)\n",
" (wi_1): Linear(in_features=1024, out_features=2816, bias=False)\n",
" (wo): Linear(in_features=2816, out_features=1024, bias=False)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" (act): NewGELUActivation()\n",
" )\n",
" (layer_norm): MT5LayerNorm()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" )\n",
" )\n",
" (final_layer_norm): MT5LayerNorm()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (decoder): MT5Stack(\n",
" (embed_tokens): Embedding(250112, 1024)\n",
" (block): ModuleList(\n",
" (0): MT5Block(\n",
" (layer): ModuleList(\n",
" (0): MT5LayerSelfAttention(\n",
" (SelfAttention): MT5Attention(\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): MT5LayerNorm()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (1): MT5LayerCrossAttention(\n",
" (EncDecAttention): MT5Attention(\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): MT5LayerNorm()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (2): MT5LayerFF(\n",
" (DenseReluDense): MT5DenseGatedActDense(\n",
" (wi_0): Linear(in_features=1024, out_features=2816, bias=False)\n",
" (wi_1): Linear(in_features=1024, out_features=2816, bias=False)\n",
" (wo): Linear(in_features=2816, out_features=1024, bias=False)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" (act): NewGELUActivation()\n",
" )\n",
" (layer_norm): MT5LayerNorm()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" )\n",
" (1-23): 23 x MT5Block(\n",
" (layer): ModuleList(\n",
" (0): MT5LayerSelfAttention(\n",
" (SelfAttention): MT5Attention(\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): MT5LayerNorm()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (1): MT5LayerCrossAttention(\n",
" (EncDecAttention): MT5Attention(\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): MT5LayerNorm()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (2): MT5LayerFF(\n",
" (DenseReluDense): MT5DenseGatedActDense(\n",
" (wi_0): Linear(in_features=1024, out_features=2816, bias=False)\n",
" (wi_1): Linear(in_features=1024, out_features=2816, bias=False)\n",
" (wo): Linear(in_features=2816, out_features=1024, bias=False)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" (act): NewGELUActivation()\n",
" )\n",
" (layer_norm): MT5LayerNorm()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" )\n",
" )\n",
" (final_layer_norm): MT5LayerNorm()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (lm_head): Linear(in_features=1024, out_features=250112, bias=False)\n",
")"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "05978e96",
"outputId": "ea9b7d40-010f-4df0-ec64-a7146a5f8b08"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"trainable params: 282,624 || all params: 1,229,863,936 || trainable%: 0.022980103060766553\n"
]
},
{
"data": {
"text/plain": [
"PeftModelForSeq2SeqLM(\n",
" (base_model): IA3Model(\n",
" (model): MT5ForConditionalGeneration(\n",
" (shared): Embedding(250112, 1024)\n",
" (encoder): MT5Stack(\n",
" (embed_tokens): Embedding(250112, 1024)\n",
" (block): ModuleList(\n",
" (0): MT5Block(\n",
" (layer): ModuleList(\n",
" (0): MT5LayerSelfAttention(\n",
" (SelfAttention): MT5Attention(\n",
" (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
" (k): Linear(\n",
" in_features=1024, out_features=1024, bias=False\n",
" (ia3_l): ParameterDict( (default): Parameter containing: [torch.FloatTensor of size 1024x1])\n",
" )\n",
" (v): Linear(\n",
" in_features=1024, out_features=1024, bias=False\n",
" (ia3_l): ParameterDict( (default): Parameter containing: [torch.FloatTensor of size 1024x1])\n",
" )\n",
" (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
" (relative_attention_bias): Embedding(32, 16)\n",
" )\n",
" (layer_norm): MT5LayerNorm()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (1): MT5LayerFF(\n",
" (DenseReluDense): MT5DenseGatedActDense(\n",
" (wi_0): Linear(in_features=1024, out_features=2816, bias=False)\n",
" (wi_1): Linear(\n",
" in_features=1024, out_features=2816, bias=False\n",
" (ia3_l): ParameterDict( (default): Parameter containing: [torch.FloatTensor of size 2816x1])\n",
" )\n",
" (wo): Linear(in_features=2816, out_features=1024, bias=False)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" (act): NewGELUActivation()\n",
" )\n",
" (layer_norm): MT5LayerNorm()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" )\n",
" (1-23): 23 x MT5Block(\n",
" (layer): ModuleList(\n",
" (0): MT5LayerSelfAttention(\n",
" (SelfAttention): MT5Attention(\n",
" (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
" (k): Linear(\n",
" in_features=1024, out_features=1024, bias=False\n",
" (ia3_l): ParameterDict( (default): Parameter containing: [torch.FloatTensor of size 1024x1])\n",
" )\n",
" (v): Linear(\n",
" in_features=1024, out_features=1024, bias=False\n",
" (ia3_l): ParameterDict( (default): Parameter containing: [torch.FloatTensor of size 1024x1])\n",
" )\n",
" (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
" )\n",
" (layer_norm): MT5LayerNorm()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (1): MT5LayerFF(\n",
" (DenseReluDense): MT5DenseGatedActDense(\n",
" (wi_0): Linear(in_features=1024, out_features=2816, bias=False)\n",
" (wi_1): Linear(\n",
" in_features=1024, out_features=2816, bias=False\n",
" (ia3_l): ParameterDict( (default): Parameter containing: [torch.FloatTensor of size 2816x1])\n",
" )\n",
" (wo): Linear(in_features=2816, out_features=1024, bias=False)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" (act): NewGELUActivation()\n",
" )\n",
" (layer_norm): MT5LayerNorm()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" )\n",
" )\n",
" (final_layer_norm): MT5LayerNorm()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (decoder): MT5Stack(\n",
" (embed_tokens): Embedding(250112, 1024)\n",
" (block): ModuleList(\n",
" (0): MT5Block(\n",
" (layer): ModuleList(\n",
" (0): MT5LayerSelfAttention(\n",
" (SelfAttention): MT5Attention(\n",
" (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
" (k): Linear(\n",
" in_features=1024, out_features=1024, bias=False\n",
" (ia3_l): ParameterDict( (default): Parameter containing: [torch.FloatTensor of size 1024x1])\n",
" )\n",
" (v): Linear(\n",
" in_features=1024, out_features=1024, bias=False\n",
" (ia3_l): ParameterDict( (default): Parameter containing: [torch.FloatTensor of size 1024x1])\n",
" )\n",
" (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
" (relative_attention_bias): Embedding(32, 16)\n",
" )\n",
" (layer_norm): MT5LayerNorm()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (1): MT5LayerCrossAttention(\n",
" (EncDecAttention): MT5Attention(\n",
" (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
" (k): Linear(\n",
" in_features=1024, out_features=1024, bias=False\n",
" (ia3_l): ParameterDict( (default): Parameter containing: [torch.FloatTensor of size 1024x1])\n",
" )\n",
" (v): Linear(\n",
" in_features=1024, out_features=1024, bias=False\n",
" (ia3_l): ParameterDict( (default): Parameter containing: [torch.FloatTensor of size 1024x1])\n",
" )\n",
" (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
" )\n",
" (layer_norm): MT5LayerNorm()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (2): MT5LayerFF(\n",
" (DenseReluDense): MT5DenseGatedActDense(\n",
" (wi_0): Linear(in_features=1024, out_features=2816, bias=False)\n",
" (wi_1): Linear(\n",
" in_features=1024, out_features=2816, bias=False\n",
" (ia3_l): ParameterDict( (default): Parameter containing: [torch.FloatTensor of size 2816x1])\n",
" )\n",
" (wo): Linear(in_features=2816, out_features=1024, bias=False)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" (act): NewGELUActivation()\n",
" )\n",
" (layer_norm): MT5LayerNorm()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" )\n",
" (1-23): 23 x MT5Block(\n",
" (layer): ModuleList(\n",
" (0): MT5LayerSelfAttention(\n",
" (SelfAttention): MT5Attention(\n",
" (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
" (k): Linear(\n",
" in_features=1024, out_features=1024, bias=False\n",
" (ia3_l): ParameterDict( (default): Parameter containing: [torch.FloatTensor of size 1024x1])\n",
" )\n",
" (v): Linear(\n",
" in_features=1024, out_features=1024, bias=False\n",
" (ia3_l): ParameterDict( (default): Parameter containing: [torch.FloatTensor of size 1024x1])\n",
" )\n",
" (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
" )\n",
" (layer_norm): MT5LayerNorm()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (1): MT5LayerCrossAttention(\n",
" (EncDecAttention): MT5Attention(\n",
" (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
" (k): Linear(\n",
" in_features=1024, out_features=1024, bias=False\n",
" (ia3_l): ParameterDict( (default): Parameter containing: [torch.FloatTensor of size 1024x1])\n",
" )\n",
" (v): Linear(\n",
" in_features=1024, out_features=1024, bias=False\n",
" (ia3_l): ParameterDict( (default): Parameter containing: [torch.FloatTensor of size 1024x1])\n",
" )\n",
" (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
" )\n",
" (layer_norm): MT5LayerNorm()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (2): MT5LayerFF(\n",
" (DenseReluDense): MT5DenseGatedActDense(\n",
" (wi_0): Linear(in_features=1024, out_features=2816, bias=False)\n",
" (wi_1): Linear(\n",
" in_features=1024, out_features=2816, bias=False\n",
" (ia3_l): ParameterDict( (default): Parameter containing: [torch.FloatTensor of size 2816x1])\n",
" )\n",
" (wo): Linear(in_features=2816, out_features=1024, bias=False)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" (act): NewGELUActivation()\n",
" )\n",
" (layer_norm): MT5LayerNorm()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" )\n",
" )\n",
" (final_layer_norm): MT5LayerNorm()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (lm_head): Linear(in_features=1024, out_features=250112, bias=False)\n",
" )\n",
" )\n",
")"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model = get_peft_model(model, peft_config)\n",
"model.print_trainable_parameters()\n",
"model"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 140,
"referenced_widgets": [
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},
"id": "4ee2babf",
"outputId": "3c413083-247d-47da-f25c-032764be0beb"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING:datasets.builder:Found cached dataset financial_phrasebank (/root/.cache/huggingface/datasets/financial_phrasebank/sentences_allagree/1.0.0/550bde12e6c30e2674da973a55f57edde5181d53f5a5a34c1531c53f93b7e141)\n"
]
},
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"text/plain": [
" 0%| | 0/1 [00:00<?, ?it/s]"
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{
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"Map: 0%| | 0/2037 [00:00<?, ? examples/s]"
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"model_id": "0c561dab67914ea9b6e1aab803600551",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Map: 0%| | 0/227 [00:00<?, ? examples/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"{'sentence': 'It will be operated by Nokia , and supported by its Nokia NetAct network and service management system .',\n",
" 'label': 1,\n",
" 'text_label': 'neutral'}"
]
},
"execution_count": 17,
"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": 18,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 17,
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"5307864c2b1143f4b44f3f172611113e",
"2e2b6c3f48974ea4aca9b7710a03379e",
"aae78f9bd53348bda45967a38736cb78",
"34db17e0f28d40d6abafb8acd5dda379",
"8361dc2e0a834da6a0ad87f7b0cb4e1b",
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"c25b49b7adaa48a0a3a306aa1e0661b4",
"21f582e1208a4a38ae3c0cdce87e5c14",
"d9d37b8b79f24dbf837327a250a5a346",
"8ba99043c350456d8623ce1d8c98f7a0",
"8bf37c12d5f74f7d8dbba423a9ee3ac3",
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},
"id": "adf9608c",
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{
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"version_major": 2,
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"text/plain": [
"Running tokenizer on dataset: 0%| | 0/2037 [00:00<?, ? examples/s]"
]
},
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{
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"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=3, 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\"]\n",
"eval_dataset = processed_datasets[\"validation\"]\n",
"\n",
"train_dataloader = DataLoader(\n",
" train_dataset, shuffle=True, collate_fn=default_data_collator, batch_size=batch_size, pin_memory=True\n",
")\n",
"eval_dataloader = DataLoader(eval_dataset, collate_fn=default_data_collator, batch_size=batch_size, pin_memory=True)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"id": "f733a3c6"
},
"outputs": [],
"source": [
"# optimizer and lr scheduler\n",
"optimizer = torch.optim.AdamW(model.parameters(), lr=lr)\n",
"lr_scheduler = get_linear_schedule_with_warmup(\n",
" optimizer=optimizer,\n",
" num_warmup_steps=0,\n",
" num_training_steps=(len(train_dataloader) * num_epochs),\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "6b3a4090",
"outputId": "369cfce9-90f2-47a1-8653-ea1168943949"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 255/255 [02:33<00:00, 1.67it/s]\n",
"100%|██████████| 29/29 [00:08<00:00, 3.48it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch=0: train_ppl=tensor(1.4939, device='cuda:0') train_epoch_loss=tensor(0.4014, device='cuda:0') eval_ppl=tensor(1.0514, device='cuda:0') eval_epoch_loss=tensor(0.0501, device='cuda:0')\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 255/255 [02:32<00:00, 1.67it/s]\n",
"100%|██████████| 29/29 [00:08<00:00, 3.43it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch=1: train_ppl=tensor(1.0523, device='cuda:0') train_epoch_loss=tensor(0.0510, device='cuda:0') eval_ppl=tensor(1.0383, device='cuda:0') eval_epoch_loss=tensor(0.0376, device='cuda:0')\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 255/255 [02:32<00:00, 1.68it/s]\n",
"100%|██████████| 29/29 [00:08<00:00, 3.44it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch=2: train_ppl=tensor(1.0397, device='cuda:0') train_epoch_loss=tensor(0.0389, device='cuda:0') eval_ppl=tensor(1.0392, device='cuda:0') eval_epoch_loss=tensor(0.0385, device='cuda:0')\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"# training and evaluation\n",
"model = model.to(device)\n",
"\n",
"for epoch in range(num_epochs):\n",
" model.train()\n",
" total_loss = 0\n",
" for step, batch in enumerate(tqdm(train_dataloader)):\n",
" batch = {k: v.to(device) for k, v in batch.items()}\n",
" outputs = model(**batch)\n",
" loss = outputs.loss\n",
" total_loss += loss.detach().float()\n",
" loss.backward()\n",
" optimizer.step()\n",
" lr_scheduler.step()\n",
" optimizer.zero_grad()\n",
"\n",
" model.eval()\n",
" eval_loss = 0\n",
" eval_preds = []\n",
" for step, batch in enumerate(tqdm(eval_dataloader)):\n",
" batch = {k: v.to(device) for k, v in batch.items()}\n",
" with torch.no_grad():\n",
" outputs = model(**batch)\n",
" loss = outputs.loss\n",
" eval_loss += loss.detach().float()\n",
" eval_preds.extend(\n",
" tokenizer.batch_decode(torch.argmax(outputs.logits, -1).detach().cpu().numpy(), skip_special_tokens=True)\n",
" )\n",
"\n",
" eval_epoch_loss = eval_loss / len(eval_dataloader)\n",
" eval_ppl = torch.exp(eval_epoch_loss)\n",
" train_epoch_loss = total_loss / len(train_dataloader)\n",
" train_ppl = torch.exp(train_epoch_loss)\n",
" print(f\"{epoch=}: {train_ppl=} {train_epoch_loss=} {eval_ppl=} {eval_epoch_loss=}\")"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "6cafa67b",
"outputId": "0db923d2-522c-4cb7-b694-6e2e20beae98"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"accuracy=96.91629955947137 % on the evaluation dataset\n",
"eval_preds[:10]=['neutral', 'neutral', 'neutral', 'neutral', 'positive', 'neutral', 'neutral', 'neutral', 'neutral', 'neutral']\n",
"dataset['validation']['text_label'][:10]=['neutral', 'neutral', 'neutral', 'neutral', 'positive', 'neutral', 'neutral', 'neutral', 'neutral', 'neutral']\n"
]
}
],
"source": [
"# print accuracy\n",
"correct = 0\n",
"total = 0\n",
"for pred, true in zip(eval_preds, dataset[\"validation\"][\"text_label\"]):\n",
" if pred.strip() == true.strip():\n",
" correct += 1\n",
" total += 1\n",
"accuracy = correct / total * 100\n",
"print(f\"{accuracy=} % on the evaluation dataset\")\n",
"print(f\"{eval_preds[:10]=}\")\n",
"print(f\"{dataset['validation']['text_label'][:10]=}\")"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"id": "a8de6005"
},
"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": 23,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "bd20cd4c",
"outputId": "0f25d837-80b1-476f-c897-92c3fef04fb2"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.2M\tbigscience/mt0-large_IA3_SEQ_2_SEQ_LM/adapter_model.bin\n"
]
}
],
"source": [
"ckpt = f\"{peft_model_id}/adapter_model.bin\"\n",
"!du -h $ckpt"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"id": "76c2fc29"
},
"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": 25,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "37d712ce",
"outputId": "4828819a-b640-4f6c-91e3-878b648e9a75"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"25 November 2010 - Finnish paints and coatings company Tikkurila Oyj ( HEL : TIK1V ) said today that Finnish state-owned investment company Solidium Oy sold its 14.7 % stake in the company for a total of EUR98m .\n",
"{'input_ids': tensor([[ 877, 3277, 1068, 259, 264, 515, 143136, 42068, 263,\n",
" 305, 259, 101264, 263, 5835, 22538, 4496, 2697, 20860,\n",
" 385, 274, 76347, 259, 267, 259, 93686, 353, 561,\n",
" 259, 271, 2426, 7883, 533, 515, 143136, 6509, 264,\n",
" 45815, 37624, 5835, 35133, 16558, 20860, 22026, 2476, 5006,\n",
" 487, 1448, 259, 96189, 281, 287, 5835, 332, 259,\n",
" 262, 2725, 304, 2687, 5577, 282, 259, 260, 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, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])}\n",
"tensor([[ 0, 59006, 1]])\n",
"['neutral']\n"
]
}
],
"source": [
"model.eval()\n",
"i = 13\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,
"metadata": {
"id": "66c65ea4"
},
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"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "65e71f78"
},
"outputs": [],
"source": []
}
],
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},
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"name": "python3"
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"language_info": {
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