Fixed some bugs in the adapter, and the in the notebook.

FLEXible-FL · Jan 24, 2024 · b39504a · b39504a
1 parent 03d42c0
commit b39504a
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Showing 3 changed files with 6 additions and 37 deletions.
diff --git a/flexnlp/notebooks/Federated SS with SentenceTransformers using FLEXible.ipynb b/flexnlp/notebooks/Federated SS with SentenceTransformers using FLEXible.ipynb
@@ -209,24 +209,6 @@
    "source": [
     "from flexnlp.utils.adapters import ss_triplet_input_adapter\n",
     "\n",
-    "def create_input_examples_for_training(X_data_as_list, X_test_as_list):\n",
-    "    \"\"\"Function to create a DataLoader to train/finetune the model at client level.\n",
-    "    This function also create a dev_example \n",
-    "    Args:\n",
-    "        X_data_as_list (list): List containing the examples. Each example is a dict\n",
-    "        with the following keys: query, pos, neg.\n",
-    "        X_test_as_list (list): List containing the test/validation examples. Each\n",
-    "        example is a dict with the following keys: query, pos, neg.\n",
-    "    Returns:\n",
-    "        train_dataloader (DataLoader): A DataLoader for training the model, batched and shuffled.\n",
-    "        dev_examples (InputExamples): An InputExample object from SentenceTransformers so it can be\n",
-    "        used on the a TripleEvaluator.\n",
-    "\n",
-    "    \"\"\"\n",
-    "    train_examples = [InputExample(texts=[example['query'], example['pos'][0], example['neg'][0]]) for example in X_data_as_list]\n",
-    "    dev_examples = [InputExample(texts=[example['query'], example['pos'][0], example['neg'][0]]) for example in X_test_as_list]\n",
-    "    return DataLoader(train_examples, shuffle=True, batch_size=16), dev_examples\n",
-    "\n",
     "def train(client_flex_model: FlexModel, client_data):\n",
     "    print(\"Training client\")\n",
     "    model = client_flex_model['model']\n",
@@ -236,15 +218,14 @@
     "    X_data = client_data.X_data.tolist()\n",
     "    tam_train = int(len(X_data) * 0.75)\n",
     "    X_data, X_test = X_data[:tam_train], X_data[tam_train:]\n",
-    "    train_dataloader, dev_examples = create_input_examples_for_training(X_data_as_list=X_data, X_test_as_list=X_test)\n",
     "    train_dataloader, _ = ss_triplet_input_adapter(X_data, X_test)\n",
     "    train_loss = losses.TripletLoss(model=model)\n",
     "    # evaluator = TripletEvaluator.from_input_examples(dev_examples)\n",
     "    warmup_steps = int(len(train_dataloader) * 1 * 0.1) #10% of train data\n",
     "    model.fit(train_objectives=[(train_dataloader, train_loss)],\n",
     "        epochs=1,\n",
     "        warmup_steps=warmup_steps,\n",
-    "        evaluator=evaluator,\n",
+    "        # evaluator=evaluator,\n",
     "        evaluation_steps=1000,\n",
     "    )\n",
     "    # model.evaluate(evaluator, 'model_evaluation')\n",
@@ -332,23 +313,11 @@
     "from flex.pool import evaluate_server_model\n",
     "from flex.data import Dataset\n",
     "\n",
-    "def create_input_examples_for_testing(X_test_as_list):\n",
-    "    \"\"\"Function to create an InputExample to evaluate the model at server level\n",
-    "\n",
-    "    Args:\n",
-    "        X_test_as_list (list): List containing the examples. Each example is a dict\n",
-    "        with the following keys: query, pos, neg.\n",
-    "    Returns:\n",
-    "        test_examples (List[InputExample]): A list with the input formatted to be used\n",
-    "        for the TripletEvaluator.\n",
-    "    \"\"\"\n",
-    "    return [InputExample(texts=[example['query'], example['pos'][0], example['neg'][0]]) for example in X_test_as_list]\n",
-    "\n",
     "test_dataset = Dataset.from_huggingface_dataset(test_dataset, X_columns=['set'])\n",
     "\n",
     "@evaluate_server_model\n",
     "def evaluate_global_model(server_flex_model: FlexModel, test_data=None):\n",
-    "    X_test = create_input_examples_for_testing(X_test_as_list=test_dataset.X_data.tolist())\n",
+    "    _, X_test = ss_triplet_input_adapter(X_test_as_list=test_dataset.X_data.tolist(), train=False)\n",
     "    model = server_flex_model[\"model\"]\n",
     "    evaluator = TripletEvaluator.from_input_examples(X_test)\n",
     "    model.evaluate(evaluator, 'server_evaluation')\n",
@@ -434,7 +403,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.11.3"
   },
   "orig_nbformat": 4
  },

diff --git a/flexnlp/notebooks/FederatedSS.py b/flexnlp/notebooks/FederatedSS.py
@@ -117,7 +117,7 @@ def train(client_flex_model: FlexModel, client_data: Dataset):
     X_data = client_data.X_data.tolist()
     tam_train = int(len(X_data) * 0.75)
     X_data, X_test = X_data[:tam_train], X_data[tam_train:]
-    train_dataloader, dev_examples = ss_triplet_input_adapter(X_data_as_list=X_data, X_test_as_list=X_test)
+    train_dataloader, dev_examples = ss_triplet_input_adapter(X_train_as_list=X_data, X_test_as_list=X_test)
     train_loss = losses.TripletLoss(model=model)
     evaluator = TripletEvaluator.from_input_examples(dev_examples)
     warmup_steps = int(len(train_dataloader) * 1 * 0.1) #10% of train data
@@ -153,7 +153,7 @@ def create_input_examples_for_testing(X_test_as_list):
 
 @evaluate_server_model
 def evaluate_global_model(server_flex_model: FlexModel, test_data=None):
-    X_test = ss_triplet_input_adapter(X_test_as_list=test_dataset.X_data.tolist(), train=False)
+    _, X_test = ss_triplet_input_adapter(X_test_as_list=test_dataset.X_data.tolist(), train=False)
     model = server_flex_model["model"]
     evaluator = TripletEvaluator.from_input_examples(X_test)
     model.evaluate(evaluator, 'server_evaluation')

diff --git a/flexnlp/utils/adapters/ss_adapters.py b/flexnlp/utils/adapters/ss_adapters.py
@@ -25,7 +25,7 @@ def ss_triplet_input_adapter(X_train_as_list: list = None, X_test_as_list: list
     train_examples = None
     dev_examples = None
     if train and len(X_train_as_list) > 1:
-        train_examples = [InputExample(texts=[example['query'], example['pos'][0], example['neg'][0]]) for example in X_data_as_list]
+        train_examples = [InputExample(texts=[example['query'], example['pos'][0], example['neg'][0]]) for example in X_train_as_list]
         train_examples = DataLoader(train_examples, shuffle=shuffle, batch_size=batch_size)
     if test and len(X_test_as_list) > 1:
         dev_examples = [InputExample(texts=[example['query'], example['pos'][0], example['neg'][0]]) for example in X_test_as_list]