commit 6a4027afb74af461106996ab9c2df24ff46b2874
Author: ltcptgeneral <learthurgo@gmail.com>
Date:   Wed Mar 6 21:00:38 2024 -0800

    minimal example of bert idea

diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..8f14cb9
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1 @@
+**/data/*
\ No newline at end of file
diff --git a/test.ipynb b/test.ipynb
new file mode 100644
index 0000000..f97326a
--- /dev/null
+++ b/test.ipynb
@@ -0,0 +1,165 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\art\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
+   "source": [
+    "from torch.utils.data import Dataset\n",
+    "from transformers import BertGenerationEncoder, BertGenerationDecoder, EncoderDecoderModel, BertTokenizer\n",
+    "from tqdm import tqdm as progress_bar\n",
+    "import torch\n",
+    "import matplotlib"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cuda\n"
+     ]
+    }
+   ],
+   "source": [
+    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+    "print(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "You are using a model of type bert to instantiate a model of type bert-generation. This is not supported for all configurations of models and can yield errors.\n",
+      "You are using a model of type bert to instantiate a model of type bert-generation. This is not supported for all configurations of models and can yield errors.\n",
+      "Some weights of BertGenerationDecoder were not initialized from the model checkpoint at google-bert/bert-large-uncased and are newly initialized: ['bert.encoder.layer.0.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.0.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.0.crossattention.output.dense.bias', 'bert.encoder.layer.0.crossattention.output.dense.weight', 'bert.encoder.layer.0.crossattention.self.key.bias', 'bert.encoder.layer.0.crossattention.self.key.weight', 'bert.encoder.layer.0.crossattention.self.query.bias', 'bert.encoder.layer.0.crossattention.self.query.weight', 'bert.encoder.layer.0.crossattention.self.value.bias', 'bert.encoder.layer.0.crossattention.self.value.weight', 'bert.encoder.layer.1.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.1.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.1.crossattention.output.dense.bias', 'bert.encoder.layer.1.crossattention.output.dense.weight', 'bert.encoder.layer.1.crossattention.self.key.bias', 'bert.encoder.layer.1.crossattention.self.key.weight', 'bert.encoder.layer.1.crossattention.self.query.bias', 'bert.encoder.layer.1.crossattention.self.query.weight', 'bert.encoder.layer.1.crossattention.self.value.bias', 'bert.encoder.layer.1.crossattention.self.value.weight', 'bert.encoder.layer.10.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.10.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.10.crossattention.output.dense.bias', 'bert.encoder.layer.10.crossattention.output.dense.weight', 'bert.encoder.layer.10.crossattention.self.key.bias', 'bert.encoder.layer.10.crossattention.self.key.weight', 'bert.encoder.layer.10.crossattention.self.query.bias', 'bert.encoder.layer.10.crossattention.self.query.weight', 'bert.encoder.layer.10.crossattention.self.value.bias', 'bert.encoder.layer.10.crossattention.self.value.weight', 'bert.encoder.layer.11.crossattention.output.LayerNorm.bias', 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'bert.encoder.layer.20.crossattention.output.dense.weight', 'bert.encoder.layer.20.crossattention.self.key.bias', 'bert.encoder.layer.20.crossattention.self.key.weight', 'bert.encoder.layer.20.crossattention.self.query.bias', 'bert.encoder.layer.20.crossattention.self.query.weight', 'bert.encoder.layer.20.crossattention.self.value.bias', 'bert.encoder.layer.20.crossattention.self.value.weight', 'bert.encoder.layer.21.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.21.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.21.crossattention.output.dense.bias', 'bert.encoder.layer.21.crossattention.output.dense.weight', 'bert.encoder.layer.21.crossattention.self.key.bias', 'bert.encoder.layer.21.crossattention.self.key.weight', 'bert.encoder.layer.21.crossattention.self.query.bias', 'bert.encoder.layer.21.crossattention.self.query.weight', 'bert.encoder.layer.21.crossattention.self.value.bias', 'bert.encoder.layer.21.crossattention.self.value.weight', 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'bert.encoder.layer.6.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.6.crossattention.output.dense.bias', 'bert.encoder.layer.6.crossattention.output.dense.weight', 'bert.encoder.layer.6.crossattention.self.key.bias', 'bert.encoder.layer.6.crossattention.self.key.weight', 'bert.encoder.layer.6.crossattention.self.query.bias', 'bert.encoder.layer.6.crossattention.self.query.weight', 'bert.encoder.layer.6.crossattention.self.value.bias', 'bert.encoder.layer.6.crossattention.self.value.weight', 'bert.encoder.layer.7.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.7.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.7.crossattention.output.dense.bias', 'bert.encoder.layer.7.crossattention.output.dense.weight', 'bert.encoder.layer.7.crossattention.self.key.bias', 'bert.encoder.layer.7.crossattention.self.key.weight', 'bert.encoder.layer.7.crossattention.self.query.bias', 'bert.encoder.layer.7.crossattention.self.query.weight', 'bert.encoder.layer.7.crossattention.self.value.bias', 'bert.encoder.layer.7.crossattention.self.value.weight', 'bert.encoder.layer.8.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.8.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.8.crossattention.output.dense.bias', 'bert.encoder.layer.8.crossattention.output.dense.weight', 'bert.encoder.layer.8.crossattention.self.key.bias', 'bert.encoder.layer.8.crossattention.self.key.weight', 'bert.encoder.layer.8.crossattention.self.query.bias', 'bert.encoder.layer.8.crossattention.self.query.weight', 'bert.encoder.layer.8.crossattention.self.value.bias', 'bert.encoder.layer.8.crossattention.self.value.weight', 'bert.encoder.layer.9.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.9.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.9.crossattention.output.dense.bias', 'bert.encoder.layer.9.crossattention.output.dense.weight', 'bert.encoder.layer.9.crossattention.self.key.bias', 'bert.encoder.layer.9.crossattention.self.key.weight', 'bert.encoder.layer.9.crossattention.self.query.bias', 'bert.encoder.layer.9.crossattention.self.query.weight', 'bert.encoder.layer.9.crossattention.self.value.bias', 'bert.encoder.layer.9.crossattention.self.value.weight', 'lm_head.bias', 'lm_head.decoder.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": [
+    "encoder = BertGenerationEncoder.from_pretrained(\"google-bert/bert-large-uncased\", bos_token_id=101, eos_token_id=102)\n",
+    "# add cross attention layers and use BERT's cls token as BOS token and sep token as EOS token\n",
+    "decoder = BertGenerationDecoder.from_pretrained(\"google-bert/bert-large-uncased\", add_cross_attention=True, is_decoder=True, bos_token_id=101, eos_token_id=102)\n",
+    "model = EncoderDecoderModel(encoder=encoder, decoder=decoder)\n",
+    "\n",
+    "# create tokenizer...\n",
+    "tokenizer = BertTokenizer.from_pretrained(\"google-bert/bert-large-uncased\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "\n",
+    "class CodeDataset(Dataset):\n",
+    "    def __init__(self):\n",
+    "        with open(\"data/conala-train.json\") as f:\n",
+    "            self.data = json.load(f)\n",
+    "\n",
+    "    def __len__(self):\n",
+    "        return len(self.data)\n",
+    "\n",
+    "    def __getitem__(self, idx):\n",
+    "        return self.data[idx][\"rewritten_intent\"], self.data[idx][\"snippet\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "optimizer = torch.optim.AdamW(params=model.parameters(), lr=1e-3)\n",
+    "dataloader = CodeDataset()\n",
+    "model = model.to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  0%|          | 0/2379 [00:00<?, ?it/s]C:\\Users\\art\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\transformers\\models\\encoder_decoder\\modeling_encoder_decoder.py:636: FutureWarning: Version v4.12.0 introduces a better way to train encoder-decoder models by computing the loss inside the encoder-decoder framework rather than in the decoder itself. You may observe training discrepancies if fine-tuning a model trained with versions anterior to 4.12.0. The decoder_input_ids are now created based on the labels, no need to pass them yourself anymore.\n",
+      "  warnings.warn(DEPRECATION_WARNING, FutureWarning)\n",
+      "  0%|          | 3/2379 [01:18<16:16:50, 24.67s/it]"
+     ]
+    }
+   ],
+   "source": [
+    "losses = []\n",
+    "epochs = 10\n",
+    "for i in range(epochs):\n",
+    "\n",
+    "    epoch_loss = 0\n",
+    "\n",
+    "    for idx, (question, answer) in progress_bar(enumerate(dataloader), total=len(dataloader)):\n",
+    "\n",
+    "        print(questions)\n",
+    "        input_ids = tokenizer(question, add_special_tokens=False, return_tensors=\"pt\").input_ids.to(device)\n",
+    "        label_ids = tokenizer(answer, return_tensors=\"pt\").input_ids.to(device)\n",
+    "\n",
+    "        loss = model(input_ids=input_ids, decoder_input_ids=label_ids, labels=label_ids).loss\n",
+    "\n",
+    "        optimizer.zero_grad()\n",
+    "        loss.backward()\n",
+    "        optimizer.step()\n",
+    "\n",
+    "        epoch_loss += loss.item()\n",
+    "\n",
+    "    losses.append(epoch_loss)\n",
+    "\n",
+    "plt.plot(losses, color=\"green\", label=\"Training Loss\")\n",
+    "plt.legend(loc = 'upper left')\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.11.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/test.py b/test.py
new file mode 100644
index 0000000..4bc953b
--- /dev/null
+++ b/test.py
@@ -0,0 +1,64 @@
+
+from torch.utils.data import Dataset
+from transformers import BertGenerationEncoder, BertGenerationDecoder, EncoderDecoderModel, BertTokenizer
+from tqdm import tqdm as progress_bar
+import torch
+import matplotlib
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+print(device)
+
+encoder = BertGenerationEncoder.from_pretrained("google-bert/bert-large-uncased", bos_token_id=101, eos_token_id=102)
+# add cross attention layers and use BERT's cls token as BOS token and sep token as EOS token
+decoder = BertGenerationDecoder.from_pretrained("google-bert/bert-large-uncased", add_cross_attention=True, is_decoder=True, bos_token_id=101, eos_token_id=102)
+model = EncoderDecoderModel(encoder=encoder, decoder=decoder)
+
+# create tokenizer...
+tokenizer = BertTokenizer.from_pretrained("google-bert/bert-large-uncased")
+
+import json
+
+class CodeDataset(Dataset):
+    def __init__(self):
+        with open("data/conala-train.json") as f:
+            self.data = json.load(f)
+
+    def __len__(self):
+        return len(self.data)
+
+    def __getitem__(self, idx):
+        intent = self.data[idx]["rewritten_intent"] if self.data[idx]["rewritten_intent"] else self.data[idx]["intent"]
+        return intent, self.data[idx]["snippet"]
+
+
+optimizer = torch.optim.AdamW(params=model.parameters(), lr=1e-3)
+dataloader = CodeDataset()
+model = model.to(device)
+
+losses = []
+epochs = 10
+for i in range(epochs):
+
+    epoch_loss = 0
+
+    for idx, (question, answer) in progress_bar(enumerate(dataloader), total=len(dataloader)):
+
+        print(question)
+        input_ids = tokenizer(question, add_special_tokens=False, return_tensors="pt").input_ids.to(device)
+        label_ids = tokenizer(answer, return_tensors="pt").input_ids.to(device)
+
+        loss = model(input_ids=input_ids, decoder_input_ids=label_ids, labels=label_ids).loss
+
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+
+        epoch_loss += loss.item()
+
+    losses.append(epoch_loss)
+
+plt.plot(losses, color="green", label="Training Loss")
+plt.legend(loc = 'upper left')
+plt.show()
+
+