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} | |||
], | |||
"source": [ | |||
"import sys\n", | |||
"sys.path.append('..')\n", | |||
"\n", | |||
"from fastNLP import Trainer\n", | |||
"\n", | |||
"trainer = Trainer(\n", | |||
@@ -613,11 +616,11 @@ | |||
{ | |||
"data": { | |||
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"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'acc#acc'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.41</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'total#acc'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">100.0</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'correct#acc'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">41.0</span><span style=\"font-weight: bold\">}</span>\n", | |||
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'acc#acc'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.37</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'total#acc'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">100.0</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'correct#acc'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">37.0</span><span style=\"font-weight: bold\">}</span>\n", | |||
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"\u001b[1m{\u001b[0m\u001b[32m'acc#acc'\u001b[0m: \u001b[1;36m0.41\u001b[0m, \u001b[32m'total#acc'\u001b[0m: \u001b[1;36m100.0\u001b[0m, \u001b[32m'correct#acc'\u001b[0m: \u001b[1;36m41.0\u001b[0m\u001b[1m}\u001b[0m\n" | |||
"\u001b[1m{\u001b[0m\u001b[32m'acc#acc'\u001b[0m: \u001b[1;36m0.37\u001b[0m, \u001b[32m'total#acc'\u001b[0m: \u001b[1;36m100.0\u001b[0m, \u001b[32m'correct#acc'\u001b[0m: \u001b[1;36m37.0\u001b[0m\u001b[1m}\u001b[0m\n" | |||
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"{'acc#acc': 0.41, 'total#acc': 100.0, 'correct#acc': 41.0}" | |||
"{'acc#acc': 0.37, 'total#acc': 100.0, 'correct#acc': 37.0}" | |||
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"{'acc#acc': 0.46, 'total#acc': 100.0, 'correct#acc': 46.0}" | |||
"{'acc#acc': 0.47, 'total#acc': 100.0, 'correct#acc': 47.0}" | |||
] | |||
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"name": "python", | |||
"nbconvert_exporter": "python", | |||
"pygments_lexer": "ipython3", | |||
"version": "3.7.4" | |||
"version": "3.7.13" | |||
}, | |||
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{ | |||
"cells": [ | |||
{ | |||
"cell_type": "markdown", | |||
"metadata": {}, | |||
"source": [ | |||
"# E1. 使用 DistilBert 完成 SST2 分类" | |||
] | |||
}, | |||
{ | |||
"cell_type": "code", | |||
"execution_count": 1, | |||
"metadata": {}, | |||
"outputs": [ | |||
{ | |||
"data": { | |||
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"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n", | |||
"</pre>\n" | |||
], | |||
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"\n" | |||
] | |||
}, | |||
"metadata": {}, | |||
"output_type": "display_data" | |||
}, | |||
{ | |||
"name": "stdout", | |||
"output_type": "stream", | |||
"text": [ | |||
"4.18.0\n" | |||
] | |||
} | |||
], | |||
"source": [ | |||
"import torch\n", | |||
"import torch.nn as nn\n", | |||
"from torch.optim import AdamW\n", | |||
"from torch.utils.data import DataLoader, Dataset\n", | |||
"\n", | |||
"import transformers\n", | |||
"from transformers import AutoTokenizer\n", | |||
"from transformers import AutoModelForSequenceClassification\n", | |||
"\n", | |||
"import sys\n", | |||
"sys.path.append('..')\n", | |||
"\n", | |||
"import fastNLP\n", | |||
"from fastNLP import Trainer\n", | |||
"from fastNLP.core.utils.utils import dataclass_to_dict\n", | |||
"from fastNLP.core.metrics import Accuracy\n", | |||
"\n", | |||
"print(transformers.__version__)" | |||
] | |||
}, | |||
{ | |||
"cell_type": "code", | |||
"execution_count": 2, | |||
"metadata": {}, | |||
"outputs": [], | |||
"source": [ | |||
"GLUE_TASKS = [\"cola\", \"mnli\", \"mnli-mm\", \"mrpc\", \"qnli\", \"qqp\", \"rte\", \"sst2\", \"stsb\", \"wnli\"]\n", | |||
"\n", | |||
"task = \"sst2\"\n", | |||
"model_checkpoint = \"distilbert-base-uncased\"" | |||
] | |||
}, | |||
{ | |||
"cell_type": "code", | |||
"execution_count": 3, | |||
"metadata": { | |||
"scrolled": false | |||
}, | |||
"outputs": [ | |||
{ | |||
"name": "stderr", | |||
"output_type": "stream", | |||
"text": [ | |||
"Using the latest cached version of the module from /remote-home/xrliu/.cache/huggingface/modules/datasets_modules/datasets/glue/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad (last modified on Thu May 26 15:30:15 2022) since it couldn't be found locally at glue., or remotely on the Hugging Face Hub.\n", | |||
"Reusing dataset glue (/remote-home/xrliu/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)\n" | |||
] | |||
}, | |||
{ | |||
"data": { | |||
"application/vnd.jupyter.widget-view+json": { | |||
"model_id": "253d79d7a67e4dc88338448b5bcb3fb9", | |||
"version_major": 2, | |||
"version_minor": 0 | |||
}, | |||
"text/plain": [ | |||
" 0%| | 0/3 [00:00<?, ?it/s]" | |||
] | |||
}, | |||
"metadata": {}, | |||
"output_type": "display_data" | |||
} | |||
], | |||
"source": [ | |||
"from datasets import load_dataset, load_metric\n", | |||
"\n", | |||
"dataset = load_dataset(\"glue\", \"mnli\" if task == \"mnli-mm\" else task)" | |||
] | |||
}, | |||
{ | |||
"cell_type": "code", | |||
"execution_count": 4, | |||
"metadata": {}, | |||
"outputs": [ | |||
{ | |||
"name": "stdout", | |||
"output_type": "stream", | |||
"text": [ | |||
"{'input_ids': [101, 7592, 1010, 2023, 2028, 6251, 999, 102, 1998, 2023, 6251, 3632, 2007, 2009, 1012, 102], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}\n" | |||
] | |||
} | |||
], | |||
"source": [ | |||
"tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, use_fast=True)\n", | |||
"\n", | |||
"print(tokenizer(\"Hello, this one sentence!\", \"And this sentence goes with it.\"))" | |||
] | |||
}, | |||
{ | |||
"cell_type": "code", | |||
"execution_count": 5, | |||
"metadata": {}, | |||
"outputs": [], | |||
"source": [ | |||
"task_to_keys = {\n", | |||
" \"cola\": (\"sentence\", None),\n", | |||
" \"mnli\": (\"premise\", \"hypothesis\"),\n", | |||
" \"mnli-mm\": (\"premise\", \"hypothesis\"),\n", | |||
" \"mrpc\": (\"sentence1\", \"sentence2\"),\n", | |||
" \"qnli\": (\"question\", \"sentence\"),\n", | |||
" \"qqp\": (\"question1\", \"question2\"),\n", | |||
" \"rte\": (\"sentence1\", \"sentence2\"),\n", | |||
" \"sst2\": (\"sentence\", None),\n", | |||
" \"stsb\": (\"sentence1\", \"sentence2\"),\n", | |||
" \"wnli\": (\"sentence1\", \"sentence2\"),\n", | |||
"}\n", | |||
"\n", | |||
"sentence1_key, sentence2_key = task_to_keys[task]" | |||
] | |||
}, | |||
{ | |||
"cell_type": "code", | |||
"execution_count": 6, | |||
"metadata": {}, | |||
"outputs": [ | |||
{ | |||
"name": "stdout", | |||
"output_type": "stream", | |||
"text": [ | |||
"Sentence: hide new secretions from the parental units \n" | |||
] | |||
} | |||
], | |||
"source": [ | |||
"if sentence2_key is None:\n", | |||
" print(f\"Sentence: {dataset['train'][0][sentence1_key]}\")\n", | |||
"else:\n", | |||
" print(f\"Sentence 1: {dataset['train'][0][sentence1_key]}\")\n", | |||
" print(f\"Sentence 2: {dataset['train'][0][sentence2_key]}\")" | |||
] | |||
}, | |||
{ | |||
"cell_type": "code", | |||
"execution_count": 7, | |||
"metadata": {}, | |||
"outputs": [ | |||
{ | |||
"name": "stderr", | |||
"output_type": "stream", | |||
"text": [ | |||
"Loading cached processed dataset at /remote-home/xrliu/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-ca1fbe5e8eb059f3.arrow\n", | |||
"Loading cached processed dataset at /remote-home/xrliu/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-03661263fbf302f5.arrow\n", | |||
"Loading cached processed dataset at /remote-home/xrliu/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-fbe8e7a4e4f18f45.arrow\n" | |||
] | |||
} | |||
], | |||
"source": [ | |||
"def preprocess_function(examples):\n", | |||
" if sentence2_key is None:\n", | |||
" return tokenizer(examples[sentence1_key], truncation=True)\n", | |||
" return tokenizer(examples[sentence1_key], examples[sentence2_key], truncation=True)\n", | |||
"\n", | |||
"encoded_dataset = dataset.map(preprocess_function, batched=True)" | |||
] | |||
}, | |||
{ | |||
"cell_type": "code", | |||
"execution_count": 8, | |||
"metadata": {}, | |||
"outputs": [], | |||
"source": [ | |||
"class ClassModel(nn.Module):\n", | |||
" def __init__(self, num_labels, model_checkpoint):\n", | |||
" nn.Module.__init__(self)\n", | |||
" self.num_labels = num_labels\n", | |||
" self.back_bone = AutoModelForSequenceClassification.from_pretrained(model_checkpoint, \n", | |||
" num_labels=num_labels)\n", | |||
" self.loss_fn = nn.CrossEntropyLoss()\n", | |||
"\n", | |||
" def forward(self, input_ids, attention_mask):\n", | |||
" return self.back_bone(input_ids, attention_mask)\n", | |||
"\n", | |||
" def train_step(self, input_ids, attention_mask, labels):\n", | |||
" pred = self(input_ids, attention_mask).logits\n", | |||
" return {\"loss\": self.loss_fn(pred, labels)}\n", | |||
"\n", | |||
" def evaluate_step(self, input_ids, attention_mask, labels):\n", | |||
" pred = self(input_ids, attention_mask).logits\n", | |||
" pred = torch.max(pred, dim=-1)[1]\n", | |||
" return {\"pred\": pred, \"target\": labels}" | |||
] | |||
}, | |||
{ | |||
"cell_type": "code", | |||
"execution_count": 9, | |||
"metadata": {}, | |||
"outputs": [ | |||
{ | |||
"name": "stderr", | |||
"output_type": "stream", | |||
"text": [ | |||
"Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertForSequenceClassification: ['vocab_projector.weight', 'vocab_layer_norm.bias', 'vocab_transform.bias', 'vocab_projector.bias', 'vocab_layer_norm.weight', 'vocab_transform.weight']\n", | |||
"- This IS expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n", | |||
"- This IS NOT expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n", | |||
"Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['pre_classifier.weight', 'classifier.weight', 'classifier.bias', 'pre_classifier.bias']\n", | |||
"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n" | |||
] | |||
} | |||
], | |||
"source": [ | |||
"num_labels = 3 if task.startswith(\"mnli\") else 1 if task==\"stsb\" else 2\n", | |||
"\n", | |||
"model = ClassModel(num_labels=num_labels, model_checkpoint=model_checkpoint)\n", | |||
"\n", | |||
"optimizers = AdamW(params=model.parameters(), lr=5e-5)" | |||
] | |||
}, | |||
{ | |||
"cell_type": "code", | |||
"execution_count": 10, | |||
"metadata": {}, | |||
"outputs": [], | |||
"source": [ | |||
"class TestDistilBertDataset(Dataset):\n", | |||
" def __init__(self, dataset):\n", | |||
" super(TestDistilBertDataset, self).__init__()\n", | |||
" self.dataset = dataset\n", | |||
"\n", | |||
" def __len__(self):\n", | |||
" return len(self.dataset)\n", | |||
"\n", | |||
" def __getitem__(self, item):\n", | |||
" item = self.dataset[item]\n", | |||
" return item[\"input_ids\"], item[\"attention_mask\"], [item[\"label\"]] " | |||
] | |||
}, | |||
{ | |||
"cell_type": "code", | |||
"execution_count": 11, | |||
"metadata": {}, | |||
"outputs": [], | |||
"source": [ | |||
"def test_bert_collate_fn(batch):\n", | |||
" input_ids, atten_mask, labels = [], [], []\n", | |||
" max_length = [0] * 3\n", | |||
" for each_item in batch:\n", | |||
" input_ids.append(each_item[0])\n", | |||
" max_length[0] = max(max_length[0], len(each_item[0]))\n", | |||
" atten_mask.append(each_item[1])\n", | |||
" max_length[1] = max(max_length[1], len(each_item[1]))\n", | |||
" labels.append(each_item[2])\n", | |||
" max_length[2] = max(max_length[2], len(each_item[2]))\n", | |||
"\n", | |||
" for i in range(3):\n", | |||
" each = (input_ids, atten_mask, labels)[i]\n", | |||
" for item in each:\n", | |||
" item.extend([0] * (max_length[i] - len(item)))\n", | |||
" return {\"input_ids\": torch.cat([torch.tensor([item]) for item in input_ids], dim=0),\n", | |||
" \"attention_mask\": torch.cat([torch.tensor([item]) for item in atten_mask], dim=0),\n", | |||
" \"labels\": torch.cat([torch.tensor(item) for item in labels], dim=0)}" | |||
] | |||
}, | |||
{ | |||
"cell_type": "code", | |||
"execution_count": 12, | |||
"metadata": {}, | |||
"outputs": [], | |||
"source": [ | |||
"dataset_train = TestDistilBertDataset(encoded_dataset[\"train\"])\n", | |||
"dataloader_train = DataLoader(dataset=dataset_train, \n", | |||
" batch_size=32, shuffle=True, collate_fn=test_bert_collate_fn)\n", | |||
"dataset_valid = TestDistilBertDataset(encoded_dataset[\"validation\"])\n", | |||
"dataloader_valid = DataLoader(dataset=dataset_valid, \n", | |||
" batch_size=32, shuffle=False, collate_fn=test_bert_collate_fn)" | |||
] | |||
}, | |||
{ | |||
"cell_type": "code", | |||
"execution_count": 13, | |||
"metadata": {}, | |||
"outputs": [], | |||
"source": [ | |||
"trainer = Trainer(\n", | |||
" model=model,\n", | |||
" driver='torch',\n", | |||
" device='cuda',\n", | |||
" n_epochs=10,\n", | |||
" optimizers=optimizers,\n", | |||
" train_dataloader=dataloader_train,\n", | |||
" evaluate_dataloaders=dataloader_valid,\n", | |||
" metrics={'acc': Accuracy()}\n", | |||
")" | |||
] | |||
}, | |||
{ | |||
"cell_type": "code", | |||
"execution_count": 14, | |||
"metadata": {}, | |||
"outputs": [], | |||
"source": [ | |||
"# help(model.back_bone.forward)" | |||
] | |||
}, | |||
{ | |||
"cell_type": "code", | |||
"execution_count": 15, | |||
"metadata": {}, | |||
"outputs": [ | |||
{ | |||
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"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">----------------------------- Eval. results on Epoch:<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span>, Batch:<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> -----------------------------\n", | |||
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"----------------------------- Eval. results on Epoch:\u001b[1;36m1\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n" | |||
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"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">{</span>\n", | |||
" <span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">\"acc#acc\"</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.871875</span>,\n", | |||
" <span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">\"total#acc\"</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">320.0</span>,\n", | |||
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" <span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">\"acc#acc\"</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.8875</span>,\n", | |||
" <span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">\"total#acc\"</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">320.0</span>,\n", | |||
" <span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">\"correct#acc\"</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">284.0</span>\n", | |||
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"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">---------------------------- Eval. results on Epoch:<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">10</span>, Batch:<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> -----------------------------\n", | |||
"</pre>\n" | |||
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"---------------------------- Eval. results on Epoch:\u001b[1;36m10\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n" | |||
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}, | |||
"metadata": {}, | |||
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"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">{</span>\n", | |||
" <span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">\"acc#acc\"</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.890625</span>,\n", | |||
" <span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">\"total#acc\"</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">320.0</span>,\n", | |||
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] | |||
}, | |||
"metadata": {}, | |||
"output_type": "display_data" | |||
} | |||
], | |||
"source": [ | |||
"trainer.run(num_eval_batch_per_dl=10)" | |||
] | |||
}, | |||
{ | |||
"cell_type": "code", | |||
"execution_count": null, | |||
"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": "3.7.13" | |||
} | |||
}, | |||
"nbformat": 4, | |||
"nbformat_minor": 1 | |||
} |
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{ | |||
"cells": [ | |||
{ | |||
"cell_type": "markdown", | |||
"metadata": {}, | |||
"source": [ | |||
"# E2. 使用 PrefixTuning 完成 SST2 分类" | |||
] | |||
}, | |||
{ | |||
"cell_type": "code", | |||
"execution_count": 1, | |||
"metadata": {}, | |||
"outputs": [ | |||
{ | |||
"data": { | |||
"text/html": [ | |||
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n", | |||
"</pre>\n" | |||
], | |||
"text/plain": [ | |||
"\n" | |||
] | |||
}, | |||
"metadata": {}, | |||
"output_type": "display_data" | |||
}, | |||
{ | |||
"name": "stdout", | |||
"output_type": "stream", | |||
"text": [ | |||
"4.18.0\n" | |||
] | |||
} | |||
], | |||
"source": [ | |||
"import torch\n", | |||
"import torch.nn as nn\n", | |||
"from torch.optim import AdamW\n", | |||
"from torch.utils.data import DataLoader, Dataset\n", | |||
"\n", | |||
"import transformers\n", | |||
"from transformers import AutoTokenizer\n", | |||
"from transformers import AutoModelForSequenceClassification\n", | |||
"\n", | |||
"import sys\n", | |||
"sys.path.append('..')\n", | |||
"\n", | |||
"import fastNLP\n", | |||
"from fastNLP import Trainer\n", | |||
"from fastNLP.core.utils.utils import dataclass_to_dict\n", | |||
"from fastNLP.core.metrics import Accuracy\n", | |||
"\n", | |||
"print(transformers.__version__)" | |||
] | |||
}, | |||
{ | |||
"cell_type": "code", | |||
"execution_count": 2, | |||
"metadata": {}, | |||
"outputs": [], | |||
"source": [ | |||
"GLUE_TASKS = [\"cola\", \"mnli\", \"mnli-mm\", \"mrpc\", \"qnli\", \"qqp\", \"rte\", \"sst2\", \"stsb\", \"wnli\"]\n", | |||
"\n", | |||
"task = \"sst2\"\n", | |||
"model_checkpoint = \"distilbert-base-uncased\"" | |||
] | |||
}, | |||
{ | |||
"cell_type": "code", | |||
"execution_count": 3, | |||
"metadata": { | |||
"scrolled": false | |||
}, | |||
"outputs": [ | |||
{ | |||
"name": "stderr", | |||
"output_type": "stream", | |||
"text": [ | |||
"Using the latest cached version of the module from /remote-home/xrliu/.cache/huggingface/modules/datasets_modules/datasets/glue/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad (last modified on Thu May 26 15:30:15 2022) since it couldn't be found locally at glue., or remotely on the Hugging Face Hub.\n", | |||
"Reusing dataset glue (/remote-home/xrliu/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)\n" | |||
] | |||
}, | |||
{ | |||
"data": { | |||
"application/vnd.jupyter.widget-view+json": { | |||
"model_id": "253d79d7a67e4dc88338448b5bcb3fb9", | |||
"version_major": 2, | |||
"version_minor": 0 | |||
}, | |||
"text/plain": [ | |||
" 0%| | 0/3 [00:00<?, ?it/s]" | |||
] | |||
}, | |||
"metadata": {}, | |||
"output_type": "display_data" | |||
} | |||
], | |||
"source": [ | |||
"from datasets import load_dataset, load_metric\n", | |||
"\n", | |||
"dataset = load_dataset(\"glue\", \"mnli\" if task == \"mnli-mm\" else task)" | |||
] | |||
}, | |||
{ | |||
"cell_type": "code", | |||
"execution_count": 4, | |||
"metadata": {}, | |||
"outputs": [ | |||
{ | |||
"name": "stdout", | |||
"output_type": "stream", | |||
"text": [ | |||
"{'input_ids': [101, 7592, 1010, 2023, 2028, 6251, 999, 102, 1998, 2023, 6251, 3632, 2007, 2009, 1012, 102], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}\n" | |||
] | |||
} | |||
], | |||
"source": [ | |||
"tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, use_fast=True)\n", | |||
"\n", | |||
"print(tokenizer(\"Hello, this one sentence!\", \"And this sentence goes with it.\"))" | |||
] | |||
}, | |||
{ | |||
"cell_type": "code", | |||
"execution_count": 5, | |||
"metadata": {}, | |||
"outputs": [], | |||
"source": [ | |||
"task_to_keys = {\n", | |||
" \"cola\": (\"sentence\", None),\n", | |||
" \"mnli\": (\"premise\", \"hypothesis\"),\n", | |||
" \"mnli-mm\": (\"premise\", \"hypothesis\"),\n", | |||
" \"mrpc\": (\"sentence1\", \"sentence2\"),\n", | |||
" \"qnli\": (\"question\", \"sentence\"),\n", | |||
" \"qqp\": (\"question1\", \"question2\"),\n", | |||
" \"rte\": (\"sentence1\", \"sentence2\"),\n", | |||
" \"sst2\": (\"sentence\", None),\n", | |||
" \"stsb\": (\"sentence1\", \"sentence2\"),\n", | |||
" \"wnli\": (\"sentence1\", \"sentence2\"),\n", | |||
"}\n", | |||
"\n", | |||
"sentence1_key, sentence2_key = task_to_keys[task]" | |||
] | |||
}, | |||
{ | |||
"cell_type": "code", | |||
"execution_count": 6, | |||
"metadata": {}, | |||
"outputs": [ | |||
{ | |||
"name": "stdout", | |||
"output_type": "stream", | |||
"text": [ | |||
"Sentence: hide new secretions from the parental units \n" | |||
] | |||
} | |||
], | |||
"source": [ | |||
"if sentence2_key is None:\n", | |||
" print(f\"Sentence: {dataset['train'][0][sentence1_key]}\")\n", | |||
"else:\n", | |||
" print(f\"Sentence 1: {dataset['train'][0][sentence1_key]}\")\n", | |||
" print(f\"Sentence 2: {dataset['train'][0][sentence2_key]}\")" | |||
] | |||
}, | |||
{ | |||
"cell_type": "code", | |||
"execution_count": 7, | |||
"metadata": {}, | |||
"outputs": [ | |||
{ | |||
"name": "stderr", | |||
"output_type": "stream", | |||
"text": [ | |||
"Loading cached processed dataset at /remote-home/xrliu/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-ca1fbe5e8eb059f3.arrow\n", | |||
"Loading cached processed dataset at /remote-home/xrliu/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-03661263fbf302f5.arrow\n", | |||
"Loading cached processed dataset at /remote-home/xrliu/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-fbe8e7a4e4f18f45.arrow\n" | |||
] | |||
} | |||
], | |||
"source": [ | |||
"def preprocess_function(examples):\n", | |||
" if sentence2_key is None:\n", | |||
" return tokenizer(examples[sentence1_key], truncation=True)\n", | |||
" return tokenizer(examples[sentence1_key], examples[sentence2_key], truncation=True)\n", | |||
"\n", | |||
"encoded_dataset = dataset.map(preprocess_function, batched=True)" | |||
] | |||
}, | |||
{ | |||
"cell_type": "code", | |||
"execution_count": 8, | |||
"metadata": {}, | |||
"outputs": [], | |||
"source": [ | |||
"class ClassModel(nn.Module):\n", | |||
" def __init__(self, num_labels, model_checkpoint):\n", | |||
" nn.Module.__init__(self)\n", | |||
" self.num_labels = num_labels\n", | |||
" self.back_bone = AutoModelForSequenceClassification.from_pretrained(model_checkpoint, \n", | |||
" num_labels=num_labels)\n", | |||
" self.loss_fn = nn.CrossEntropyLoss()\n", | |||
"\n", | |||
" def forward(self, input_ids, attention_mask):\n", | |||
" return self.back_bone(input_ids, attention_mask)\n", | |||
"\n", | |||
" def train_step(self, input_ids, attention_mask, labels):\n", | |||
" pred = self(input_ids, attention_mask).logits\n", | |||
" return {\"loss\": self.loss_fn(pred, labels)}\n", | |||
"\n", | |||
" def evaluate_step(self, input_ids, attention_mask, labels):\n", | |||
" pred = self(input_ids, attention_mask).logits\n", | |||
" pred = torch.max(pred, dim=-1)[1]\n", | |||
" return {\"pred\": pred, \"target\": labels}" | |||
] | |||
}, | |||
{ | |||
"cell_type": "code", | |||
"execution_count": 9, | |||
"metadata": {}, | |||
"outputs": [ | |||
{ | |||
"name": "stderr", | |||
"output_type": "stream", | |||
"text": [ | |||
"Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertForSequenceClassification: ['vocab_projector.weight', 'vocab_layer_norm.bias', 'vocab_transform.bias', 'vocab_projector.bias', 'vocab_layer_norm.weight', 'vocab_transform.weight']\n", | |||
"- This IS expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n", | |||
"- This IS NOT expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n", | |||
"Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['pre_classifier.weight', 'classifier.weight', 'classifier.bias', 'pre_classifier.bias']\n", | |||
"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n" | |||
] | |||
} | |||
], | |||
"source": [ | |||
"num_labels = 3 if task.startswith(\"mnli\") else 1 if task==\"stsb\" else 2\n", | |||
"\n", | |||
"model = ClassModel(num_labels=num_labels, model_checkpoint=model_checkpoint)\n", | |||
"\n", | |||
"optimizers = AdamW(params=model.parameters(), lr=5e-5)" | |||
] | |||
}, | |||
{ | |||
"cell_type": "code", | |||
"execution_count": 10, | |||
"metadata": {}, | |||
"outputs": [], | |||
"source": [ | |||
"class TestDistilBertDataset(Dataset):\n", | |||
" def __init__(self, dataset):\n", | |||
" super(TestDistilBertDataset, self).__init__()\n", | |||
" self.dataset = dataset\n", | |||
"\n", | |||
" def __len__(self):\n", | |||
" return len(self.dataset)\n", | |||
"\n", | |||
" def __getitem__(self, item):\n", | |||
" item = self.dataset[item]\n", | |||
" return item[\"input_ids\"], item[\"attention_mask\"], [item[\"label\"]] " | |||
] | |||
}, | |||
{ | |||
"cell_type": "code", | |||
"execution_count": 11, | |||
"metadata": {}, | |||
"outputs": [], | |||
"source": [ | |||
"def test_bert_collate_fn(batch):\n", | |||
" input_ids, atten_mask, labels = [], [], []\n", | |||
" max_length = [0] * 3\n", | |||
" for each_item in batch:\n", | |||
" input_ids.append(each_item[0])\n", | |||
" max_length[0] = max(max_length[0], len(each_item[0]))\n", | |||
" atten_mask.append(each_item[1])\n", | |||
" max_length[1] = max(max_length[1], len(each_item[1]))\n", | |||
" labels.append(each_item[2])\n", | |||
" max_length[2] = max(max_length[2], len(each_item[2]))\n", | |||
"\n", | |||
" for i in range(3):\n", | |||
" each = (input_ids, atten_mask, labels)[i]\n", | |||
" for item in each:\n", | |||
" item.extend([0] * (max_length[i] - len(item)))\n", | |||
" return {\"input_ids\": torch.cat([torch.tensor([item]) for item in input_ids], dim=0),\n", | |||
" \"attention_mask\": torch.cat([torch.tensor([item]) for item in atten_mask], dim=0),\n", | |||
" \"labels\": torch.cat([torch.tensor(item) for item in labels], dim=0)}" | |||
] | |||
}, | |||
{ | |||
"cell_type": "code", | |||
"execution_count": 12, | |||
"metadata": {}, | |||
"outputs": [], | |||
"source": [ | |||
"dataset_train = TestDistilBertDataset(encoded_dataset[\"train\"])\n", | |||
"dataloader_train = DataLoader(dataset=dataset_train, \n", | |||
" batch_size=32, shuffle=True, collate_fn=test_bert_collate_fn)\n", | |||
"dataset_valid = TestDistilBertDataset(encoded_dataset[\"validation\"])\n", | |||
"dataloader_valid = DataLoader(dataset=dataset_valid, \n", | |||
" batch_size=32, shuffle=False, collate_fn=test_bert_collate_fn)" | |||
] | |||
}, | |||
{ | |||
"cell_type": "code", | |||
"execution_count": 13, | |||
"metadata": {}, | |||
"outputs": [], | |||
"source": [ | |||
"trainer = Trainer(\n", | |||
" model=model,\n", | |||
" driver='torch',\n", | |||
" device='cuda',\n", | |||
" n_epochs=10,\n", | |||
" optimizers=optimizers,\n", | |||
" train_dataloader=dataloader_train,\n", | |||
" evaluate_dataloaders=dataloader_valid,\n", | |||
" metrics={'acc': Accuracy()}\n", | |||
")" | |||
] | |||
}, | |||
{ | |||
"cell_type": "code", | |||
"execution_count": 14, | |||
"metadata": {}, | |||
"outputs": [], | |||
"source": [ | |||
"# help(model.back_bone.forward)" | |||
] | |||
}, | |||
{ | |||
"cell_type": "code", | |||
"execution_count": 15, | |||
"metadata": {}, | |||
"outputs": [ | |||
{ | |||
"data": { | |||
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"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[21:00:11] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO </span> Running evaluator sanity check for <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2</span> batches. <a href=\"file://../fastNLP/core/controllers/trainer.py\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">trainer.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file://../fastNLP/core/controllers/trainer.py#592\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">592</span></a>\n", | |||
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