Feat: added overall section to evaluate combined accuracy

- added relevant-class section

analysis/categories/label_print.py

@@ -10,6 +10,12 @@ mdm_list = sorted(list((set(full_df['pattern']))))
 # %%
 full_df
 
+# %%
+mdm_list
+
+# %%
+mask = full_df['pattern'] == 'GE#Flow FGMassFlow'
+full_df[mask]
 # %%
 mask1 = full_df['thing'] == 'ME1TurboCharger1'
 mask2 = full_df['property'] == 'LOInletPress'

overall/README.md

@@ -0,0 +1,2 @@
+This section is to evaluate the combined (relevant-class prediction) and
+(mapping prediction) to evaluate the final correct mapping accuracy.

overall/combined_mapping_and_classification_analysis.py

@@ -0,0 +1,34 @@
+# %%
+import pandas as pd
+
+# following code computes final mapping + classification accuracy
+# %%
+def run(fold):
+    data_path = f'../relevant_class/binary_classifier_desc_unit/classification_prediction/exports/result_group_{fold}.csv'
+    df = pd.read_csv(data_path, skipinitialspace=True)
+    p_mdm = df['p_mdm']
+
+    # data_path = f'../train/mapping_t5_complete_desc_unit_name/mapping_prediction/exports/result_group_{fold}.csv'
+    data_path = f'../train/modified_t5_decoder_4_layers/mapping_prediction/exports/result_group_{fold}.csv'
+    df = pd.read_csv(data_path, skipinitialspace=True)
+    actual_mdm = df['MDM']
+
+    thing_correctness = df['thing'] == df['p_thing']
+    property_correctness = df['property'] == df['p_property']
+    answer = thing_correctness & property_correctness
+
+    # if is non-MDM -> then should be unmapped
+    # if is MDM -> then should be mapped correctly
+
+    # out of correctly predicted relevant data, how many are mapped correctly?
+    correct_positive_mdm_and_map = sum(p_mdm & actual_mdm & answer)
+
+    # number of correctly predicted non-relevant data
+    correct_negative_mdm = sum(~(p_mdm) & ~(actual_mdm))
+
+    overall_correct = (correct_positive_mdm_and_map + correct_negative_mdm)/len(actual_mdm)
+    print(overall_correct)
+# %%
+for fold in [1,2,3,4,5]:
+    run(fold)
+

relevant_class/binary_classifier_desc/.gitignore

@@ -0,0 +1,2 @@
+checkpoint*
+tensorboard-log

relevant_class/binary_classifier_desc/classification_prediction/.gitignore

@@ -0,0 +1,2 @@
+exports
+output.txt

relevant_class/binary_classifier_desc/classification_prediction/predict.py

@@ -0,0 +1,235 @@
+# %%
+
+# from datasets import load_from_disk
+import os
+import glob
+
+os.environ['NCCL_P2P_DISABLE'] = '1'
+os.environ['NCCL_IB_DISABLE'] = '1'
+os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
+os.environ["CUDA_VISIBLE_DEVICES"] = "0,1,2,3"
+
+import torch
+from torch.utils.data import DataLoader
+
+from transformers import (
+    AutoTokenizer,
+    AutoModelForSequenceClassification,
+    DataCollatorWithPadding,
+)
+import evaluate
+import numpy as np
+import pandas as pd
+# import matplotlib.pyplot as plt
+from datasets import Dataset, DatasetDict
+
+from tqdm import tqdm
+
+torch.set_float32_matmul_precision('high')
+
+
+BATCH_SIZE = 256
+
+# %%
+
+# %%
+
+# outputs a list of dictionaries
+# processes dataframe into lists of dictionaries
+# each element maps input to output
+# input: tag_description
+# output: class label
+def process_df_to_dict(df):
+    output_list = []
+    for _, row in df.iterrows():
+        desc = f"<DESC>{row['tag_description']}<DESC>"
+        unit = f"<UNIT>{row['unit']}<UNIT>"
+        in_mdm_label = int(row['MDM'])
+        element = {
+            'text' : f"{desc}{unit}",
+            'label': in_mdm_label,
+        }
+        output_list.append(element)
+
+    return output_list
+
+
+def create_dataset(fold):
+    data_path = f"../../../data_preprocess/exports/dataset/group_{fold}/test_all.csv"
+    test_df = pd.read_csv(data_path, skipinitialspace=True)
+
+    test_dataset = Dataset.from_list(process_df_to_dict(test_df))
+
+    return test_dataset
+
+
+# %%
+
+# function to perform training for a given fold
+def test(fold):
+
+    test_dataset = create_dataset(fold)
+
+    # prepare tokenizer
+
+    checkpoint_directory = f'../checkpoint_fold_{fold}'
+    # Use glob to find matching paths
+    # path is usually checkpoint_fold_1/checkpoint-<step number>
+    # we are guaranteed to save only 1 checkpoint from training
+    pattern = 'checkpoint-*'
+    model_checkpoint = glob.glob(os.path.join(checkpoint_directory, pattern))[0]
+
+    tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, return_tensors="pt", clean_up_tokenization_spaces=True)
+    # Define additional special tokens
+    additional_special_tokens = ["<THING_START>", "<THING_END>", "<PROPERTY_START>", "<PROPERTY_END>", "<NAME>", "<DESC>", "<SIG>", "<UNIT>", "<DATA_TYPE>"]
+    # Add the additional special tokens to the tokenizer
+    tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens})
+
+    # %%
+    # compute max token length
+    max_length = 0
+    for sample in test_dataset['text']:
+        # Tokenize the sample and get the length
+        input_ids = tokenizer(sample, truncation=False, add_special_tokens=True)["input_ids"]
+        length = len(input_ids)
+        
+        # Update max_length if this sample is longer
+        if length > max_length:
+            max_length = length
+
+    print(max_length)
+
+    # %%
+
+    max_length = 128
+
+    # given a dataset entry, run it through the tokenizer
+    def preprocess_function(example):
+        input = example['text']
+        # text_target sets the corresponding label to inputs
+        # there is no need to create a separate 'labels'
+        model_inputs = tokenizer(
+            input,
+            max_length=max_length,
+            # truncation=True,
+            padding='max_length'
+        )
+        return model_inputs
+
+    # map maps function to each "row" in the dataset
+    # aka the data in the immediate nesting
+    datasets = test_dataset.map(
+        preprocess_function,
+        batched=True,
+        num_proc=8,
+        remove_columns="text",
+    )
+
+
+    datasets.set_format(type='torch', columns=['input_ids', 'attention_mask', 'label'])
+
+    # %% temp
+    # tokenized_datasets['train'].rename_columns()
+
+    # %%
+    # create data collator
+
+    # data_collator = DataCollatorWithPadding(tokenizer=tokenizer, padding="max_length")
+
+    # %%
+    # compute metrics
+    # metric = evaluate.load("accuracy")
+    # 
+    # 
+    # def compute_metrics(eval_preds):
+    #     preds, labels = eval_preds
+    #     preds = np.argmax(preds, axis=1)
+    #     return metric.compute(predictions=preds, references=labels)
+
+    model = AutoModelForSequenceClassification.from_pretrained(
+        model_checkpoint,
+        num_labels=2)
+    # important! after extending tokens vocab
+    model.resize_token_embeddings(len(tokenizer))
+
+    model = model.eval()
+
+    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+    model.to(device)
+
+    pred_labels = []
+    actual_labels = []
+
+
+    dataloader = DataLoader(datasets, batch_size=BATCH_SIZE, shuffle=False)
+    for batch in tqdm(dataloader):
+            # Inference in batches
+            input_ids = batch['input_ids']
+            attention_mask = batch['attention_mask']
+            # save labels too
+            actual_labels.extend(batch['label'])
+            
+
+            # Move to GPU if available
+            input_ids = input_ids.to(device)
+            attention_mask = attention_mask.to(device)
+
+            # Perform inference
+            with torch.no_grad():
+                logits = model(
+                    input_ids,
+                    attention_mask).logits
+                predicted_class_ids = logits.argmax(dim=1).to("cpu")
+                pred_labels.extend(predicted_class_ids)
+
+    pred_labels = [tensor.item() for tensor in pred_labels]
+    pred_labels = np.array(pred_labels, dtype=bool)
+
+    # append the mdm prediction to the test_df for analysis later
+    df_out = pd.DataFrame({
+        'p_mdm': pred_labels, 
+    })
+    data_path = f"../../../data_preprocess/exports/dataset/group_{fold}/test_all.csv"
+    test_df = pd.read_csv(data_path, skipinitialspace=True)
+    df_export = pd.concat([test_df, df_out], axis=1)
+    df_export.to_csv(f"exports/result_group_{fold}.csv", index=False)
+
+
+
+    # %%
+    from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, confusion_matrix
+    y_true = actual_labels
+    y_pred = pred_labels
+
+    # Compute metrics
+    accuracy = accuracy_score(y_true, y_pred)
+    f1 = f1_score(y_true, y_pred)
+    precision = precision_score(y_true, y_pred)
+    recall = recall_score(y_true, y_pred)
+
+    cm = confusion_matrix(y_true, y_pred)
+    tn, fp, fn, tp = cm.ravel()
+
+    with open("output.txt", "a") as f:
+
+
+        print('*' * 80, file=f)
+        print(f'Fold: {fold}', file=f)
+        # Print the results
+        print(f"tp: {tp}", file=f)
+        print(f"tn: {tn}", file=f)
+        print(f"fp: {fp}", file=f)
+        print(f"fn: {fn}", file=f)
+        print(f'Accuracy: {accuracy:.5f}', file=f)
+        print(f'F1 Score: {f1:.5f}', file=f)
+        print(f'Precision: {precision:.5f}', file=f)
+        print(f'Recall: {recall:.5f}', file=f)
+
+
+# %%
+# reset file before writing to it
+with open("output.txt", "w") as f:
+    print('', file=f)
+
+for fold in [1,2,3,4,5]:
+    test(fold)

relevant_class/binary_classifier_desc/train.py

@@ -0,0 +1,218 @@
+# %%
+
+# from datasets import load_from_disk
+import os
+
+os.environ['NCCL_P2P_DISABLE'] = '1'
+os.environ['NCCL_IB_DISABLE'] = '1'
+os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
+os.environ["CUDA_VISIBLE_DEVICES"] = "0,1,2,3"
+
+import torch
+from transformers import (
+    AutoTokenizer,
+    AutoModelForSequenceClassification,
+    DataCollatorWithPadding,
+    Trainer,
+    EarlyStoppingCallback,
+    TrainingArguments
+)
+import evaluate
+import numpy as np
+import pandas as pd
+# import matplotlib.pyplot as plt
+from datasets import Dataset, DatasetDict
+
+
+
+torch.set_float32_matmul_precision('high')
+
+# %%
+
+# we need to create the mdm_list
+# import the full mdm-only file
+# data_path = '../../data_import/exports/data_mapping_mdm.csv'
+# full_df = pd.read_csv(data_path, skipinitialspace=True)
+# rather than use pattern, we use the real thing and property
+
+
+# %%
+id2label = {0: False, 1: True}
+label2id = {False: 0, True: 1}
+
+# %%
+
+# outputs a list of dictionaries
+# processes dataframe into lists of dictionaries
+# each element maps input to output
+# input: tag_description
+# output: class label
+def process_df_to_dict(df):
+    output_list = []
+    for _, row in df.iterrows():
+        desc = f"<DESC>{row['tag_description']}<DESC>"
+        in_mdm_label = int(row['MDM'])
+        element = {
+            'text' : f"{desc}",
+            'label': in_mdm_label,
+        }
+        output_list.append(element)
+
+    return output_list
+
+
+def create_split_dataset(fold):
+    # train 
+    # data_path = f"../../data_preprocess/exports/dataset/group_{fold}/train_all.csv"
+
+    # reconstruct full training data with non-mdm data
+    data_path = f"../../data_preprocess/exports/dataset/group_{fold}/test_all.csv"
+    test_df = pd.read_csv(data_path, skipinitialspace=True)
+    ships_list = list(set(test_df['ships_idx']))
+    data_path = '../../data_preprocess/exports/preprocessed_data.csv'
+    full_df = pd.read_csv(data_path, skipinitialspace=True)
+    train_df = full_df[~full_df['ships_idx'].isin(ships_list)]
+
+    train_ships_list = sorted(list(set(train_df['ships_idx'])))
+
+    train_ships_set = set(train_ships_list)
+    test_ships_set = set(ships_list)
+
+    # assertion for non data leakage
+    assert not set(train_ships_set).intersection(test_ships_set)
+
+
+    # valid
+    data_path = f"../../data_preprocess/exports/dataset/group_{fold}/valid.csv"
+    validation_df = pd.read_csv(data_path, skipinitialspace=True)
+
+    combined_data = DatasetDict({
+        'train': Dataset.from_list(process_df_to_dict(train_df)),
+        'validation' : Dataset.from_list(process_df_to_dict(validation_df)),
+    })
+    return combined_data
+
+
+# %%
+
+# function to perform training for a given fold
+def train(fold):
+
+    save_path = f'checkpoint_fold_{fold}'
+    split_datasets = create_split_dataset(fold)
+
+    # prepare tokenizer
+
+    model_checkpoint = "distilbert/distilbert-base-cased"
+    # model_checkpoint = 'google-bert/bert-base-cased'
+    tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, return_tensors="pt", clean_up_tokenization_spaces=True)
+    # Define additional special tokens
+    additional_special_tokens = ["<THING_START>", "<THING_END>", "<PROPERTY_START>", "<PROPERTY_END>", "<NAME>", "<DESC>", "<SIG>", "<UNIT>", "<DATA_TYPE>"]
+    # Add the additional special tokens to the tokenizer
+    tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens})
+
+    max_length = 120
+
+    # given a dataset entry, run it through the tokenizer
+    def preprocess_function(example):
+        input = example['text']
+        # text_target sets the corresponding label to inputs
+        # there is no need to create a separate 'labels'
+        model_inputs = tokenizer(
+            input,
+            max_length=max_length,
+            truncation=True,
+            padding=True
+        )
+        return model_inputs
+
+    # map maps function to each "row" in the dataset
+    # aka the data in the immediate nesting
+    tokenized_datasets = split_datasets.map(
+        preprocess_function,
+        batched=True,
+        num_proc=8,
+        remove_columns="text",
+    )
+
+    # %% temp
+    # tokenized_datasets['train'].rename_columns()
+
+    # %%
+    # create data collator
+
+    data_collator = DataCollatorWithPadding(tokenizer=tokenizer)
+
+    # %%
+    # compute metrics
+    metric = evaluate.load("accuracy")
+
+
+    def compute_metrics(eval_preds):
+        preds, labels = eval_preds
+        preds = np.argmax(preds, axis=1)
+        return metric.compute(predictions=preds, references=labels)
+
+    # %%
+    # create id2label and label2id
+
+
+    # %%
+    model = AutoModelForSequenceClassification.from_pretrained(
+        model_checkpoint,
+        num_labels=2)
+    # important! after extending tokens vocab
+    model.resize_token_embeddings(len(tokenizer))
+
+    # model = torch.compile(model, backend="inductor", dynamic=True)
+
+
+    # %%
+    # Trainer
+
+    training_args = TrainingArguments(
+        output_dir=f"{save_path}",
+        # eval_strategy="epoch",
+        eval_strategy="no",
+        logging_dir="tensorboard-log",
+        logging_strategy="epoch",
+        # save_strategy="epoch",
+        load_best_model_at_end=False,
+        learning_rate=1e-5,
+        per_device_train_batch_size=128,
+        per_device_eval_batch_size=128,
+        auto_find_batch_size=False,
+        ddp_find_unused_parameters=False,
+        weight_decay=0.01,
+        save_total_limit=1,
+        num_train_epochs=80,
+        bf16=True,
+        push_to_hub=False,
+        remove_unused_columns=False,
+    )
+
+
+    trainer = Trainer(
+        model,
+        training_args,
+        train_dataset=tokenized_datasets["train"],
+        eval_dataset=tokenized_datasets["validation"],
+        tokenizer=tokenizer,
+        data_collator=data_collator,
+        compute_metrics=compute_metrics,
+        # callbacks=[EarlyStoppingCallback(early_stopping_patience=3)],
+    )
+
+    # uncomment to load training from checkpoint
+    # checkpoint_path = 'default_40_1/checkpoint-5600'
+    # trainer.train(resume_from_checkpoint=checkpoint_path)
+
+    trainer.train()
+
+# execute training
+for fold in [1,2,3,4,5]:
+    print(fold)
+    train(fold)
+
+
+# %%

relevant_class/binary_classifier_desc_unit/.gitignore

@@ -0,0 +1,2 @@
+checkpoint*
+tensorboard-log

relevant_class/binary_classifier_desc_unit/classification_prediction/.gitignore

@@ -0,0 +1,2 @@
+exports
+output.txt

relevant_class/binary_classifier_desc_unit/classification_prediction/predict.py

@@ -0,0 +1,235 @@
+# %%
+
+# from datasets import load_from_disk
+import os
+import glob
+
+os.environ['NCCL_P2P_DISABLE'] = '1'
+os.environ['NCCL_IB_DISABLE'] = '1'
+os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
+os.environ["CUDA_VISIBLE_DEVICES"] = "0,1,2,3"
+
+import torch
+from torch.utils.data import DataLoader
+
+from transformers import (
+    AutoTokenizer,
+    AutoModelForSequenceClassification,
+    DataCollatorWithPadding,
+)
+import evaluate
+import numpy as np
+import pandas as pd
+# import matplotlib.pyplot as plt
+from datasets import Dataset, DatasetDict
+
+from tqdm import tqdm
+
+torch.set_float32_matmul_precision('high')
+
+
+BATCH_SIZE = 256
+
+# %%
+
+# %%
+
+# outputs a list of dictionaries
+# processes dataframe into lists of dictionaries
+# each element maps input to output
+# input: tag_description
+# output: class label
+def process_df_to_dict(df):
+    output_list = []
+    for _, row in df.iterrows():
+        desc = f"<DESC>{row['tag_description']}<DESC>"
+        unit = f"<UNIT>{row['unit']}<UNIT>"
+        in_mdm_label = int(row['MDM'])
+        element = {
+            'text' : f"{desc}{unit}",
+            'label': in_mdm_label,
+        }
+        output_list.append(element)
+
+    return output_list
+
+
+def create_dataset(fold):
+    data_path = f"../../../data_preprocess/exports/dataset/group_{fold}/test_all.csv"
+    test_df = pd.read_csv(data_path, skipinitialspace=True)
+
+    test_dataset = Dataset.from_list(process_df_to_dict(test_df))
+
+    return test_dataset
+
+
+# %%
+
+# function to perform training for a given fold
+def test(fold):
+
+    test_dataset = create_dataset(fold)
+
+    # prepare tokenizer
+
+    checkpoint_directory = f'../checkpoint_fold_{fold}'
+    # Use glob to find matching paths
+    # path is usually checkpoint_fold_1/checkpoint-<step number>
+    # we are guaranteed to save only 1 checkpoint from training
+    pattern = 'checkpoint-*'
+    model_checkpoint = glob.glob(os.path.join(checkpoint_directory, pattern))[0]
+
+    tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, return_tensors="pt", clean_up_tokenization_spaces=True)
+    # Define additional special tokens
+    additional_special_tokens = ["<THING_START>", "<THING_END>", "<PROPERTY_START>", "<PROPERTY_END>", "<NAME>", "<DESC>", "<SIG>", "<UNIT>", "<DATA_TYPE>"]
+    # Add the additional special tokens to the tokenizer
+    tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens})
+
+    # %%
+    # compute max token length
+    max_length = 0
+    for sample in test_dataset['text']:
+        # Tokenize the sample and get the length
+        input_ids = tokenizer(sample, truncation=False, add_special_tokens=True)["input_ids"]
+        length = len(input_ids)
+        
+        # Update max_length if this sample is longer
+        if length > max_length:
+            max_length = length
+
+    print(max_length)
+
+    # %%
+
+    max_length = 128
+
+    # given a dataset entry, run it through the tokenizer
+    def preprocess_function(example):
+        input = example['text']
+        # text_target sets the corresponding label to inputs
+        # there is no need to create a separate 'labels'
+        model_inputs = tokenizer(
+            input,
+            max_length=max_length,
+            # truncation=True,
+            padding='max_length'
+        )
+        return model_inputs
+
+    # map maps function to each "row" in the dataset
+    # aka the data in the immediate nesting
+    datasets = test_dataset.map(
+        preprocess_function,
+        batched=True,
+        num_proc=8,
+        remove_columns="text",
+    )
+
+
+    datasets.set_format(type='torch', columns=['input_ids', 'attention_mask', 'label'])
+
+    # %% temp
+    # tokenized_datasets['train'].rename_columns()
+
+    # %%
+    # create data collator
+
+    # data_collator = DataCollatorWithPadding(tokenizer=tokenizer, padding="max_length")
+
+    # %%
+    # compute metrics
+    # metric = evaluate.load("accuracy")
+    # 
+    # 
+    # def compute_metrics(eval_preds):
+    #     preds, labels = eval_preds
+    #     preds = np.argmax(preds, axis=1)
+    #     return metric.compute(predictions=preds, references=labels)
+
+    model = AutoModelForSequenceClassification.from_pretrained(
+        model_checkpoint,
+        num_labels=2)
+    # important! after extending tokens vocab
+    model.resize_token_embeddings(len(tokenizer))
+
+    model = model.eval()
+
+    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+    model.to(device)
+
+    pred_labels = []
+    actual_labels = []
+
+
+    dataloader = DataLoader(datasets, batch_size=BATCH_SIZE, shuffle=False)
+    for batch in tqdm(dataloader):
+            # Inference in batches
+            input_ids = batch['input_ids']
+            attention_mask = batch['attention_mask']
+            # save labels too
+            actual_labels.extend(batch['label'])
+            
+
+            # Move to GPU if available
+            input_ids = input_ids.to(device)
+            attention_mask = attention_mask.to(device)
+
+            # Perform inference
+            with torch.no_grad():
+                logits = model(
+                    input_ids,
+                    attention_mask).logits
+                predicted_class_ids = logits.argmax(dim=1).to("cpu")
+                pred_labels.extend(predicted_class_ids)
+
+    pred_labels = [tensor.item() for tensor in pred_labels]
+    pred_labels = np.array(pred_labels, dtype=bool)
+
+    # append the mdm prediction to the test_df for analysis later
+    df_out = pd.DataFrame({
+        'p_mdm': pred_labels, 
+    })
+    data_path = f"../../../data_preprocess/exports/dataset/group_{fold}/test_all.csv"
+    test_df = pd.read_csv(data_path, skipinitialspace=True)
+    df_export = pd.concat([test_df, df_out], axis=1)
+    df_export.to_csv(f"exports/result_group_{fold}.csv", index=False)
+
+
+
+    # %%
+    from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, confusion_matrix
+    y_true = actual_labels
+    y_pred = pred_labels
+
+    # Compute metrics
+    accuracy = accuracy_score(y_true, y_pred)
+    f1 = f1_score(y_true, y_pred)
+    precision = precision_score(y_true, y_pred)
+    recall = recall_score(y_true, y_pred)
+
+    cm = confusion_matrix(y_true, y_pred)
+    tn, fp, fn, tp = cm.ravel()
+
+    with open("output.txt", "a") as f:
+
+
+        print('*' * 80, file=f)
+        print(f'Fold: {fold}', file=f)
+        # Print the results
+        print(f"tp: {tp}", file=f)
+        print(f"tn: {tn}", file=f)
+        print(f"fp: {fp}", file=f)
+        print(f"fn: {fn}", file=f)
+        print(f'Accuracy: {accuracy:.5f}', file=f)
+        print(f'F1 Score: {f1:.5f}', file=f)
+        print(f'Precision: {precision:.5f}', file=f)
+        print(f'Recall: {recall:.5f}', file=f)
+
+
+# %%
+# reset file before writing to it
+with open("output.txt", "w") as f:
+    print('', file=f)
+
+for fold in [1,2,3,4,5]:
+    test(fold)

relevant_class/binary_classifier_desc_unit/train.py

@@ -0,0 +1,219 @@
+# %%
+
+# from datasets import load_from_disk
+import os
+
+os.environ['NCCL_P2P_DISABLE'] = '1'
+os.environ['NCCL_IB_DISABLE'] = '1'
+os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
+os.environ["CUDA_VISIBLE_DEVICES"] = "0,1,2,3"
+
+import torch
+from transformers import (
+    AutoTokenizer,
+    AutoModelForSequenceClassification,
+    DataCollatorWithPadding,
+    Trainer,
+    EarlyStoppingCallback,
+    TrainingArguments
+)
+import evaluate
+import numpy as np
+import pandas as pd
+# import matplotlib.pyplot as plt
+from datasets import Dataset, DatasetDict
+
+
+
+torch.set_float32_matmul_precision('high')
+
+# %%
+
+# we need to create the mdm_list
+# import the full mdm-only file
+# data_path = '../../data_import/exports/data_mapping_mdm.csv'
+# full_df = pd.read_csv(data_path, skipinitialspace=True)
+# rather than use pattern, we use the real thing and property
+
+
+# %%
+id2label = {0: False, 1: True}
+label2id = {False: 0, True: 1}
+
+# %%
+
+# outputs a list of dictionaries
+# processes dataframe into lists of dictionaries
+# each element maps input to output
+# input: tag_description
+# output: class label
+def process_df_to_dict(df):
+    output_list = []
+    for _, row in df.iterrows():
+        desc = f"<DESC>{row['tag_description']}<DESC>"
+        unit = f"<UNIT>{row['unit']}<UNIT>"
+        in_mdm_label = int(row['MDM'])
+        element = {
+            'text' : f"{desc}{unit}",
+            'label': in_mdm_label,
+        }
+        output_list.append(element)
+
+    return output_list
+
+
+def create_split_dataset(fold):
+    # train 
+    # data_path = f"../../data_preprocess/exports/dataset/group_{fold}/train_all.csv"
+
+    # reconstruct full training data with non-mdm data
+    data_path = f"../../data_preprocess/exports/dataset/group_{fold}/test_all.csv"
+    test_df = pd.read_csv(data_path, skipinitialspace=True)
+    ships_list = list(set(test_df['ships_idx']))
+    data_path = '../../data_preprocess/exports/preprocessed_data.csv'
+    full_df = pd.read_csv(data_path, skipinitialspace=True)
+    train_df = full_df[~full_df['ships_idx'].isin(ships_list)]
+
+    train_ships_list = sorted(list(set(train_df['ships_idx'])))
+
+    train_ships_set = set(train_ships_list)
+    test_ships_set = set(ships_list)
+
+    # assertion for non data leakage
+    assert not set(train_ships_set).intersection(test_ships_set)
+
+
+    # valid
+    data_path = f"../../data_preprocess/exports/dataset/group_{fold}/valid.csv"
+    validation_df = pd.read_csv(data_path, skipinitialspace=True)
+
+    combined_data = DatasetDict({
+        'train': Dataset.from_list(process_df_to_dict(train_df)),
+        'validation' : Dataset.from_list(process_df_to_dict(validation_df)),
+    })
+    return combined_data
+
+
+# %%
+
+# function to perform training for a given fold
+def train(fold):
+
+    save_path = f'checkpoint_fold_{fold}'
+    split_datasets = create_split_dataset(fold)
+
+    # prepare tokenizer
+
+    model_checkpoint = "distilbert/distilbert-base-cased"
+    # model_checkpoint = 'google-bert/bert-base-cased'
+    tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, return_tensors="pt", clean_up_tokenization_spaces=True)
+    # Define additional special tokens
+    additional_special_tokens = ["<THING_START>", "<THING_END>", "<PROPERTY_START>", "<PROPERTY_END>", "<NAME>", "<DESC>", "<SIG>", "<UNIT>", "<DATA_TYPE>"]
+    # Add the additional special tokens to the tokenizer
+    tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens})
+
+    max_length = 120
+
+    # given a dataset entry, run it through the tokenizer
+    def preprocess_function(example):
+        input = example['text']
+        # text_target sets the corresponding label to inputs
+        # there is no need to create a separate 'labels'
+        model_inputs = tokenizer(
+            input,
+            max_length=max_length,
+            truncation=True,
+            padding=True
+        )
+        return model_inputs
+
+    # map maps function to each "row" in the dataset
+    # aka the data in the immediate nesting
+    tokenized_datasets = split_datasets.map(
+        preprocess_function,
+        batched=True,
+        num_proc=8,
+        remove_columns="text",
+    )
+
+    # %% temp
+    # tokenized_datasets['train'].rename_columns()
+
+    # %%
+    # create data collator
+
+    data_collator = DataCollatorWithPadding(tokenizer=tokenizer)
+
+    # %%
+    # compute metrics
+    metric = evaluate.load("accuracy")
+
+
+    def compute_metrics(eval_preds):
+        preds, labels = eval_preds
+        preds = np.argmax(preds, axis=1)
+        return metric.compute(predictions=preds, references=labels)
+
+    # %%
+    # create id2label and label2id
+
+
+    # %%
+    model = AutoModelForSequenceClassification.from_pretrained(
+        model_checkpoint,
+        num_labels=2)
+    # important! after extending tokens vocab
+    model.resize_token_embeddings(len(tokenizer))
+
+    # model = torch.compile(model, backend="inductor", dynamic=True)
+
+
+    # %%
+    # Trainer
+
+    training_args = TrainingArguments(
+        output_dir=f"{save_path}",
+        # eval_strategy="epoch",
+        eval_strategy="no",
+        logging_dir="tensorboard-log",
+        logging_strategy="epoch",
+        # save_strategy="epoch",
+        load_best_model_at_end=False,
+        learning_rate=1e-5,
+        per_device_train_batch_size=128,
+        per_device_eval_batch_size=128,
+        auto_find_batch_size=False,
+        ddp_find_unused_parameters=False,
+        weight_decay=0.01,
+        save_total_limit=1,
+        num_train_epochs=80,
+        bf16=True,
+        push_to_hub=False,
+        remove_unused_columns=False,
+    )
+
+
+    trainer = Trainer(
+        model,
+        training_args,
+        train_dataset=tokenized_datasets["train"],
+        eval_dataset=tokenized_datasets["validation"],
+        tokenizer=tokenizer,
+        data_collator=data_collator,
+        compute_metrics=compute_metrics,
+        # callbacks=[EarlyStoppingCallback(early_stopping_patience=3)],
+    )
+
+    # uncomment to load training from checkpoint
+    # checkpoint_path = 'default_40_1/checkpoint-5600'
+    # trainer.train(resume_from_checkpoint=checkpoint_path)
+
+    trainer.train()
+
+# execute training
+for fold in [1,2,3,4,5]:
+    print(fold)
+    train(fold)
+
+
+# %%

relevant_class/similarity_classifier_desc/.gitignore

@@ -0,0 +1,3 @@
+__pycache__
+exports
+output.txt

relevant_class/similarity_classifier_desc/README.md

@@ -0,0 +1,4 @@
+# one-class classification by similarity
+
+Purpose: using only Ship Domain attributes, we want to find if the data belongs
+to MDM

relevant_class/similarity_classifier_desc/run.py

@@ -0,0 +1,175 @@
+# %%
+import pandas as pd
+from utils import Retriever, cosine_similarity_chunked
+import os
+import glob
+import numpy as np
+from tqdm import tqdm
+from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, confusion_matrix
+
+##################################################
+# helper functions
+
+
+# the following function takes in a full cos_sim_matrix
+# condition_source: boolean selectors of the source embedding
+# condition_target: boolean selectors of the target embedding
+def find_closest(cos_sim_matrix, condition_source, condition_target):
+    # subset_matrix = cos_sim_matrix[condition_source]
+    # except we are subsetting 2D matrix (row, column)
+    subset_matrix = cos_sim_matrix[np.ix_(condition_source, condition_target)]
+    # we select top k here
+    # Get the indices of the top k maximum values along axis 1
+    top_k = 3
+    top_k_indices = np.argsort(subset_matrix, axis=1)[:, -top_k:]  # Get indices of top k values
+    # note that top_k_indices is a nested list because of the 2d nature of the matrix
+    # the result is flipped
+    top_k_indices[0] = top_k_indices[0][::-1]
+    
+    # Get the values of the top 5 maximum scores
+    top_k_values = np.take_along_axis(subset_matrix, top_k_indices, axis=1)
+    
+
+    return top_k_indices, top_k_values
+
+
+
+
+class Embedder():
+    input_df: pd.DataFrame
+    fold: int
+
+    def __init__(self, input_df):
+        self.input_df = input_df
+
+
+    def make_embedding(self, checkpoint_path):
+
+        def generate_input_list(df):
+            input_list = []
+            for _, row in df.iterrows():
+                desc = f"<DESC>{row['tag_description']}<DESC>"
+                element = f"{desc}"
+                input_list.append(element)
+            return input_list
+
+        # prepare reference embed
+        train_data = list(generate_input_list(self.input_df))
+        # Define the directory and the pattern
+        retriever_train = Retriever(train_data, checkpoint_path)
+        retriever_train.make_embedding(batch_size=64)
+        return retriever_train.embeddings.to('cpu')
+
+
+
+def run_similarity_classifier(fold):
+    data_path = f'../../train/mapping_t5_complete_desc_unit_name/mapping_prediction/exports/result_group_{fold}.csv'
+    test_df = pd.read_csv(data_path, skipinitialspace=True)
+
+
+
+    data_path = f"../../data_preprocess/exports/dataset/group_{fold}/train_all.csv"
+    train_df = pd.read_csv(data_path, skipinitialspace=True)
+
+    checkpoint_directory = "../../train/classification_bert_complete_desc"
+    directory = os.path.join(checkpoint_directory, f'checkpoint_fold_{fold}')
+    # Use glob to find matching paths
+    # path is usually checkpoint_fold_1/checkpoint-<step number>
+    # we are guaranteed to save only 1 checkpoint from training
+    pattern = 'checkpoint-*'
+    checkpoint_path = glob.glob(os.path.join(directory, pattern))[0]
+
+    train_embedder = Embedder(input_df=train_df)
+    train_embeds = train_embedder.make_embedding(checkpoint_path)
+
+    test_embedder = Embedder(input_df=test_df)
+    test_embeds = test_embedder.make_embedding(checkpoint_path)
+
+    def compute_top_k(select_idx):
+        condition_source = test_df['tag_description'] == test_df[test_df.index == select_idx]['tag_description'].tolist()[0]
+        condition_target = np.ones(train_embeds.shape[0], dtype=bool)
+
+        _, top_k_values = find_closest(
+            cos_sim_matrix=cos_sim_matrix,
+            condition_source=condition_source,
+            condition_target=condition_target)
+
+        return top_k_values[0][0]
+
+
+
+    # test embeds are inputs since we are looking back at train data
+    cos_sim_matrix = cosine_similarity_chunked(test_embeds, train_embeds, chunk_size=1024).cpu().numpy()
+
+
+    sim_list = []
+    for select_idx in tqdm(test_df.index):
+        top_sim_value = compute_top_k(select_idx)
+        sim_list.append(top_sim_value)
+
+    # analysis 1: using threshold to perform find-back prediction success
+    threshold_values = np.linspace(0.85, 1.00, 21) # test 20 values, 21 to get nice round numbers
+    best_threshold = 0
+    best_f1 = 0
+    for threshold in threshold_values:
+        predict_list = [ elem > threshold for elem in sim_list ]
+
+        y_true = test_df['MDM'].to_list()
+        y_pred = predict_list
+
+        # Compute metrics
+        accuracy = accuracy_score(y_true, y_pred)
+        f1 = f1_score(y_true, y_pred)
+        precision = precision_score(y_true, y_pred)
+        recall = recall_score(y_true, y_pred)
+
+        if f1 > best_f1:
+            best_threshold = threshold
+            best_f1 = f1
+    
+    # OR just manually set best_threshold
+    # best_threshold = 0.90
+
+    # compute metrics again with best threshold
+    predict_list = [ elem > best_threshold for elem in sim_list ]
+
+    # save
+    pred_labels = np.array(predict_list, dtype=bool)
+
+    # append the mdm prediction to the test_df for analysis later
+    df_out = pd.DataFrame({
+        'p_mdm': pred_labels, 
+    })
+    df_out.to_csv(f"exports/result_group_{fold}.csv", index=False)
+
+
+    y_true = test_df['MDM'].to_list()
+    y_pred = predict_list
+    # Compute metrics
+    accuracy = accuracy_score(y_true, y_pred)
+    f1 = f1_score(y_true, y_pred)
+    precision = precision_score(y_true, y_pred)
+    recall = recall_score(y_true, y_pred)
+
+
+
+    with open("output.txt", "a") as f:
+
+        print(f'Fold: {fold}', file=f)
+        print(f'Best threshold: {best_threshold}', file=f)
+        # Print the results
+        print(f'Accuracy: {accuracy:.5f}', file=f)
+        print(f'F1 Score: {f1:.5f}', file=f)
+        print(f'Precision: {precision:.5f}', file=f)
+        print(f'Recall: {recall:.5f}', file=f)
+
+
+
+# %%
+# reset file before writing to it
+with open("output.txt", "w") as f:
+    print('', file=f)
+
+for fold in [1,2,3,4,5]:
+    print(fold)
+    run_similarity_classifier(fold)

relevant_class/similarity_classifier_desc/utils.py

@@ -0,0 +1,81 @@
+import torch
+from transformers import (
+    AutoTokenizer,
+    AutoModelForSequenceClassification,
+    DataCollatorWithPadding,
+)
+import torch.nn.functional as F
+
+
+
+class Retriever:
+    def __init__(self, input_texts, model_checkpoint):
+        # we need to generate the embedding from list of input strings
+        self.embeddings = []
+        self.inputs = input_texts
+        model_checkpoint = model_checkpoint 
+        self.tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, return_tensors="pt", clean_up_tokenization_spaces=True)
+
+        model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint)
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        # device = "cpu"
+        model.to(self.device)
+        self.model = model.eval()
+
+
+    def make_embedding(self, batch_size=64):
+        all_embeddings = self.embeddings
+        input_texts = self.inputs
+
+        for i in range(0, len(input_texts), batch_size):
+            batch_texts = input_texts[i:i+batch_size]
+            # Tokenize the input text
+            inputs = self.tokenizer(batch_texts, return_tensors="pt", padding=True, truncation=True, max_length=64)
+            input_ids = inputs.input_ids.to(self.device)
+            attention_mask = inputs.attention_mask.to(self.device)
+
+
+            # Pass the input through the encoder and retrieve the embeddings
+            with torch.no_grad():
+                encoder_outputs = self.model(input_ids, attention_mask=attention_mask, output_hidden_states=True)
+                # get last layer
+                embeddings = encoder_outputs.hidden_states[-1]
+                # get cls token embedding
+                cls_embeddings = embeddings[:, 0, :]  # Shape: (batch_size, hidden_size)
+                all_embeddings.append(cls_embeddings)
+        
+        # remove the batch list and makes a single large tensor, dim=0 increases row-wise
+        all_embeddings = torch.cat(all_embeddings, dim=0)
+
+        self.embeddings = all_embeddings
+
+def cosine_similarity_chunked(batch1, batch2, chunk_size=1024):
+    device = 'cuda'
+    batch1_size = batch1.size(0)
+    batch2_size = batch2.size(0)
+    batch2.to(device)
+    
+    # Prepare an empty tensor to store results
+    cos_sim = torch.empty(batch1_size, batch2_size, device=device)
+
+    # Process batch1 in chunks
+    for i in range(0, batch1_size, chunk_size):
+        batch1_chunk = batch1[i:i + chunk_size]  # Get chunk of batch1
+        
+        batch1_chunk.to(device)
+        # Expand batch1 chunk and entire batch2 for comparison
+        # batch1_chunk_exp = batch1_chunk.unsqueeze(1)  # Shape: (chunk_size, 1, seq_len)
+        # batch2_exp = batch2.unsqueeze(0)  # Shape: (1, batch2_size, seq_len)
+        batch2_norms = batch2.norm(dim=1, keepdim=True)
+
+        
+        # Compute cosine similarity for the chunk and store it in the final tensor
+        # cos_sim[i:i + chunk_size] = F.cosine_similarity(batch1_chunk_exp, batch2_exp, dim=-1)
+
+        # Compute cosine similarity by matrix multiplication and normalizing
+        sim_chunk = torch.mm(batch1_chunk, batch2.T) / (batch1_chunk.norm(dim=1, keepdim=True) * batch2_norms.T + 1e-8)
+        
+        # Store the results in the appropriate part of the final tensor
+        cos_sim[i:i + chunk_size] = sim_chunk
+    
+    return cos_sim

relevant_class/similarity_classifier_desc_unit/.gitignore

@@ -0,0 +1,3 @@
+__pycache__
+exports
+output.txt

relevant_class/similarity_classifier_desc_unit/README.md

@@ -0,0 +1,4 @@
+# one-class classification by similarity
+
+Purpose: using only Ship Domain attributes, we want to find if the data belongs
+to MDM

relevant_class/similarity_classifier_desc_unit/run.py

@@ -0,0 +1,176 @@
+# %%
+import pandas as pd
+from utils import Retriever, cosine_similarity_chunked
+import os
+import glob
+import numpy as np
+from tqdm import tqdm
+from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, confusion_matrix
+
+##################################################
+# helper functions
+
+
+# the following function takes in a full cos_sim_matrix
+# condition_source: boolean selectors of the source embedding
+# condition_target: boolean selectors of the target embedding
+def find_closest(cos_sim_matrix, condition_source, condition_target):
+    # subset_matrix = cos_sim_matrix[condition_source]
+    # except we are subsetting 2D matrix (row, column)
+    subset_matrix = cos_sim_matrix[np.ix_(condition_source, condition_target)]
+    # we select top k here
+    # Get the indices of the top k maximum values along axis 1
+    top_k = 3
+    top_k_indices = np.argsort(subset_matrix, axis=1)[:, -top_k:]  # Get indices of top k values
+    # note that top_k_indices is a nested list because of the 2d nature of the matrix
+    # the result is flipped
+    top_k_indices[0] = top_k_indices[0][::-1]
+    
+    # Get the values of the top 5 maximum scores
+    top_k_values = np.take_along_axis(subset_matrix, top_k_indices, axis=1)
+    
+
+    return top_k_indices, top_k_values
+
+
+
+
+class Embedder():
+    input_df: pd.DataFrame
+    fold: int
+
+    def __init__(self, input_df):
+        self.input_df = input_df
+
+
+    def make_embedding(self, checkpoint_path):
+
+        def generate_input_list(df):
+            input_list = []
+            for _, row in df.iterrows():
+                desc = f"<DESC>{row['tag_description']}<DESC>"
+                unit = f"<UNIT>{row['unit']}<UNIT>"
+                element = f"{desc}{unit}"
+                input_list.append(element)
+            return input_list
+
+        # prepare reference embed
+        train_data = list(generate_input_list(self.input_df))
+        # Define the directory and the pattern
+        retriever_train = Retriever(train_data, checkpoint_path)
+        retriever_train.make_embedding(batch_size=64)
+        return retriever_train.embeddings.to('cpu')
+
+
+
+def run_similarity_classifier(fold):
+    data_path = f'../../train/mapping_t5_complete_desc_unit_name/mapping_prediction/exports/result_group_{fold}.csv'
+    test_df = pd.read_csv(data_path, skipinitialspace=True)
+
+
+
+    data_path = f"../../data_preprocess/exports/dataset/group_{fold}/train_all.csv"
+    train_df = pd.read_csv(data_path, skipinitialspace=True)
+
+    checkpoint_directory = "../../train/classification_bert_complete_desc_unit"
+    directory = os.path.join(checkpoint_directory, f'checkpoint_fold_{fold}')
+    # Use glob to find matching paths
+    # path is usually checkpoint_fold_1/checkpoint-<step number>
+    # we are guaranteed to save only 1 checkpoint from training
+    pattern = 'checkpoint-*'
+    checkpoint_path = glob.glob(os.path.join(directory, pattern))[0]
+
+    train_embedder = Embedder(input_df=train_df)
+    train_embeds = train_embedder.make_embedding(checkpoint_path)
+
+    test_embedder = Embedder(input_df=test_df)
+    test_embeds = test_embedder.make_embedding(checkpoint_path)
+
+    def compute_top_k(select_idx):
+        condition_source = test_df['tag_description'] == test_df[test_df.index == select_idx]['tag_description'].tolist()[0]
+        condition_target = np.ones(train_embeds.shape[0], dtype=bool)
+
+        _, top_k_values = find_closest(
+            cos_sim_matrix=cos_sim_matrix,
+            condition_source=condition_source,
+            condition_target=condition_target)
+
+        return top_k_values[0][0]
+
+
+
+    # test embeds are inputs since we are looking back at train data
+    cos_sim_matrix = cosine_similarity_chunked(test_embeds, train_embeds, chunk_size=1024).cpu().numpy()
+
+
+    sim_list = []
+    for select_idx in tqdm(test_df.index):
+        top_sim_value = compute_top_k(select_idx)
+        sim_list.append(top_sim_value)
+
+    # analysis 1: using threshold to perform find-back prediction success
+    threshold_values = np.linspace(0.85, 1.00, 21) # test 20 values, 21 to get nice round numbers
+    best_threshold = 0
+    best_f1 = 0
+    for threshold in threshold_values:
+        predict_list = [ elem > threshold for elem in sim_list ]
+
+        y_true = test_df['MDM'].to_list()
+        y_pred = predict_list
+
+        # Compute metrics
+        accuracy = accuracy_score(y_true, y_pred)
+        f1 = f1_score(y_true, y_pred)
+        precision = precision_score(y_true, y_pred)
+        recall = recall_score(y_true, y_pred)
+
+        if f1 > best_f1:
+            best_threshold = threshold
+            best_f1 = f1
+    
+    # just manually set best_threshold
+    # best_threshold = 0.90
+
+    # compute metrics again with best threshold
+    predict_list = [ elem > best_threshold for elem in sim_list ]
+
+    # save
+    pred_labels = np.array(predict_list, dtype=bool)
+
+    # append the mdm prediction to the test_df for analysis later
+    df_out = pd.DataFrame({
+        'p_mdm': pred_labels, 
+    })
+    df_out.to_csv(f"exports/result_group_{fold}.csv", index=False)
+
+
+    y_true = test_df['MDM'].to_list()
+    y_pred = predict_list
+    # Compute metrics
+    accuracy = accuracy_score(y_true, y_pred)
+    f1 = f1_score(y_true, y_pred)
+    precision = precision_score(y_true, y_pred)
+    recall = recall_score(y_true, y_pred)
+
+
+
+    with open("output.txt", "a") as f:
+
+        print(f'Fold: {fold}', file=f)
+        print(f'Best threshold: {best_threshold}', file=f)
+        # Print the results
+        print(f'Accuracy: {accuracy:.5f}', file=f)
+        print(f'F1 Score: {f1:.5f}', file=f)
+        print(f'Precision: {precision:.5f}', file=f)
+        print(f'Recall: {recall:.5f}', file=f)
+
+
+
+# %%
+# reset file before writing to it
+with open("output.txt", "w") as f:
+    print('', file=f)
+
+for fold in [1,2,3,4,5]:
+    print(fold)
+    run_similarity_classifier(fold)

relevant_class/similarity_classifier_desc_unit/utils.py

@@ -0,0 +1,81 @@
+import torch
+from transformers import (
+    AutoTokenizer,
+    AutoModelForSequenceClassification,
+    DataCollatorWithPadding,
+)
+import torch.nn.functional as F
+
+
+
+class Retriever:
+    def __init__(self, input_texts, model_checkpoint):
+        # we need to generate the embedding from list of input strings
+        self.embeddings = []
+        self.inputs = input_texts
+        model_checkpoint = model_checkpoint 
+        self.tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, return_tensors="pt", clean_up_tokenization_spaces=True)
+
+        model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint)
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        # device = "cpu"
+        model.to(self.device)
+        self.model = model.eval()
+
+
+    def make_embedding(self, batch_size=64):
+        all_embeddings = self.embeddings
+        input_texts = self.inputs
+
+        for i in range(0, len(input_texts), batch_size):
+            batch_texts = input_texts[i:i+batch_size]
+            # Tokenize the input text
+            inputs = self.tokenizer(batch_texts, return_tensors="pt", padding=True, truncation=True, max_length=64)
+            input_ids = inputs.input_ids.to(self.device)
+            attention_mask = inputs.attention_mask.to(self.device)
+
+
+            # Pass the input through the encoder and retrieve the embeddings
+            with torch.no_grad():
+                encoder_outputs = self.model(input_ids, attention_mask=attention_mask, output_hidden_states=True)
+                # get last layer
+                embeddings = encoder_outputs.hidden_states[-1]
+                # get cls token embedding
+                cls_embeddings = embeddings[:, 0, :]  # Shape: (batch_size, hidden_size)
+                all_embeddings.append(cls_embeddings)
+        
+        # remove the batch list and makes a single large tensor, dim=0 increases row-wise
+        all_embeddings = torch.cat(all_embeddings, dim=0)
+
+        self.embeddings = all_embeddings
+
+def cosine_similarity_chunked(batch1, batch2, chunk_size=1024):
+    device = 'cuda'
+    batch1_size = batch1.size(0)
+    batch2_size = batch2.size(0)
+    batch2.to(device)
+    
+    # Prepare an empty tensor to store results
+    cos_sim = torch.empty(batch1_size, batch2_size, device=device)
+
+    # Process batch1 in chunks
+    for i in range(0, batch1_size, chunk_size):
+        batch1_chunk = batch1[i:i + chunk_size]  # Get chunk of batch1
+        
+        batch1_chunk.to(device)
+        # Expand batch1 chunk and entire batch2 for comparison
+        # batch1_chunk_exp = batch1_chunk.unsqueeze(1)  # Shape: (chunk_size, 1, seq_len)
+        # batch2_exp = batch2.unsqueeze(0)  # Shape: (1, batch2_size, seq_len)
+        batch2_norms = batch2.norm(dim=1, keepdim=True)
+
+        
+        # Compute cosine similarity for the chunk and store it in the final tensor
+        # cos_sim[i:i + chunk_size] = F.cosine_similarity(batch1_chunk_exp, batch2_exp, dim=-1)
+
+        # Compute cosine similarity by matrix multiplication and normalizing
+        sim_chunk = torch.mm(batch1_chunk, batch2.T) / (batch1_chunk.norm(dim=1, keepdim=True) * batch2_norms.T + 1e-8)
+        
+        # Store the results in the appropriate part of the final tensor
+        cos_sim[i:i + chunk_size] = sim_chunk
+    
+    return cos_sim

train/classification_bert_complete_desc/train.py

@@ -176,9 +176,9 @@ def train(fold):
         logging_strategy="epoch",
         # save_strategy="epoch",
         load_best_model_at_end=False,
-        learning_rate=1e-5,
-        per_device_train_batch_size=128,
-        per_device_eval_batch_size=128,
+        learning_rate=1e-3,
+        per_device_train_batch_size=64,
+        per_device_eval_batch_size=64,
         auto_find_batch_size=False,
         ddp_find_unused_parameters=False,
         weight_decay=0.01,

train/classification_bert_complete_desc_unit/train.py

@@ -178,8 +178,8 @@ def train(fold):
         # save_strategy="epoch",
         load_best_model_at_end=False,
         learning_rate=1e-5,
-        per_device_train_batch_size=128,
-        per_device_eval_batch_size=128,
+        per_device_train_batch_size=64,
+        per_device_eval_batch_size=64,
         auto_find_batch_size=False,
         ddp_find_unused_parameters=False,
         weight_decay=0.01,

train/frozen_t5_decoder/.gitignore

@@ -0,0 +1,2 @@
+checkpoint*
+tensorboard-log

train/frozen_t5_decoder/custom_t5/.gitignore

@@ -0,0 +1 @@
+__pycache__

train/frozen_t5_decoder/custom_t5/modeling_t5.py

@@ -0,0 +1,125 @@
+from dataclasses import dataclass
+from typing import List, Optional, Tuple, Union
+
+import torch
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from transformers import (
+    T5PreTrainedModel,
+    T5Model
+    
+)
+
+from transformers.modeling_outputs import (
+    SequenceClassifierOutput,
+)
+
+def mean_pooling(encoder_outputs, attention_mask):
+    """
+    Perform mean pooling over encoder outputs, considering the attention mask.
+    """
+    hidden_states = encoder_outputs.last_hidden_state  # Shape: (batch_size, seq_length, hidden_size)
+    mask = attention_mask.unsqueeze(-1)  # Shape: (batch_size, seq_length, 1)
+    masked_hidden_states = hidden_states * mask  # Zero out padding tokens
+    sum_hidden_states = masked_hidden_states.sum(dim=1)  # Sum over sequence length
+    sum_mask = mask.sum(dim=1)  # Sum the mask (number of non-padding tokens)
+    return sum_hidden_states / sum_mask  # Mean pooling
+
+
+class T5EncoderForSequenceClassification(T5PreTrainedModel):
+
+    def __init__(self, checkpoint, tokenizer, config, num_labels):
+        super().__init__(config)
+        self.num_labels = num_labels
+        self.config = config
+
+        # we force the loading of a pre-trained model here
+        self.t5 = T5Model.from_pretrained(checkpoint)
+        self.t5.resize_token_embeddings(len(tokenizer))
+        classifier_dropout = (
+            config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
+        )
+        self.dropout = nn.Dropout(classifier_dropout)
+        self.classifier = nn.Linear(config.hidden_size, self.num_labels)
+
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        token_type_ids: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        labels: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+
+        # encoder_outputs = self.t5.encoder(
+        #     input_ids,
+        #     attention_mask=attention_mask,
+        #     head_mask=head_mask,
+        #     inputs_embeds=inputs_embeds,
+        #     output_attentions=output_attentions,
+        #     output_hidden_states=output_hidden_states,
+        #     return_dict=return_dict,
+        # )
+
+
+        encoder_outputs = self.t5.encoder(input_ids, attention_mask=attention_mask)
+        # last_hidden_state = encoder_outputs.last_hidden_state
+        # use mean of hidden state
+        # pooled_output = mean_pooling(encoder_outputs, attention_mask)
+
+        # Use the hidden state of the first token as the sequence representation
+        pooled_output = encoder_outputs.last_hidden_state[:, 0, :]  # Shape: (batch_size, hidden_size)
+
+        # pooled_output = encoder_outputs[1]
+
+        pooled_output = self.dropout(pooled_output)
+        logits = self.classifier(pooled_output)
+
+        loss = None
+        if labels is not None:
+            if self.config.problem_type is None:
+                if self.num_labels == 1:
+                    self.config.problem_type = "regression"
+                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+                    self.config.problem_type = "single_label_classification"
+                else:
+                    self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "regression":
+                loss_fct = MSELoss()
+                if self.num_labels == 1:
+                    loss = loss_fct(logits.squeeze(), labels.squeeze())
+                else:
+                    loss = loss_fct(logits, labels)
+            elif self.config.problem_type == "single_label_classification":
+                loss_fct = CrossEntropyLoss()
+                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(logits, labels)
+        if not return_dict:
+            output = (logits,) + encoder_outputs[2:]
+            return ((loss,) + output) if loss is not None else output
+
+        return SequenceClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=encoder_outputs.hidden_states,
+            attentions=encoder_outputs.attentions,
+        )

train/frozen_t5_decoder/mapping_prediction/.gitignore

@@ -0,0 +1,2 @@
+__pycache__
+exports/

train/frozen_t5_decoder/mapping_prediction/inference.py

@@ -0,0 +1,168 @@
+import torch
+from torch.utils.data import DataLoader
+from transformers import (
+    T5TokenizerFast,
+    AutoModelForSeq2SeqLM,
+)
+import os
+from tqdm import tqdm
+from datasets import Dataset
+import numpy as np
+
+os.environ['TOKENIZERS_PARALLELISM'] = 'false'
+
+
+class Inference():
+    tokenizer: T5TokenizerFast
+    model: torch.nn.Module
+    dataloader: DataLoader
+
+    def __init__(self, checkpoint_path):
+        self._create_tokenizer()
+        self._load_model(checkpoint_path)
+
+
+    def _create_tokenizer(self):
+        # %%
+        # load tokenizer
+        self.tokenizer = T5TokenizerFast.from_pretrained("t5-small", return_tensors="pt", clean_up_tokenization_spaces=True)
+        # Define additional special tokens
+        additional_special_tokens = ["<THING_START>", "<THING_END>", "<PROPERTY_START>", "<PROPERTY_END>", "<NAME>", "<DESC>", "SIG", "UNIT", "DATA_TYPE"]
+        # Add the additional special tokens to the tokenizer
+        self.tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens})
+
+    def _load_model(self, checkpoint_path: str):
+        # load model
+        # Define the directory and the pattern
+        model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint_path)
+        model = torch.compile(model)
+        # set model to eval
+        self.model = model.eval()
+
+
+
+
+    def prepare_dataloader(self, input_df, batch_size, max_length):
+        """
+        *arguments*
+        - input_df: input dataframe containing fields 'tag_description', 'thing', 'property'
+        - batch_size: the batch size of dataloader output
+        - max_length: length of tokenizer output
+        """
+        print("preparing dataloader")
+        # convert each dataframe row into a dictionary
+        # outputs a list of dictionaries
+
+        def _process_df(df):
+            output_list = []
+            for _, row in df.iterrows():
+                desc = f"<DESC>{row['tag_description']}<DESC>"
+                unit = f"<UNIT>{row['unit']}<UNIT>"
+                element = {
+                    'input' : f"{desc}{unit}",
+                    'output': f"<THING_START>{row['thing']}<THING_END><PROPERTY_START>{row['property']}<PROPERTY_END>",
+                }
+                output_list.append(element)
+
+            return output_list
+
+        def _preprocess_function(example):
+            input = example['input']
+            target = example['output']
+            # text_target sets the corresponding label to inputs
+            # there is no need to create a separate 'labels'
+            model_inputs = self.tokenizer(
+                input,
+                text_target=target, 
+                max_length=max_length,
+                return_tensors="pt",
+                padding="max_length",
+                truncation=True,
+            )
+            return model_inputs
+
+        test_dataset = Dataset.from_list(_process_df(input_df))
+
+
+        # map maps function to each "row" in the dataset
+        # aka the data in the immediate nesting
+        datasets = test_dataset.map(
+            _preprocess_function,
+            batched=True,
+            num_proc=1,
+            remove_columns=test_dataset.column_names,
+        )
+        # datasets = _preprocess_function(test_dataset)
+        datasets.set_format(type='torch', columns=['input_ids', 'attention_mask', 'labels'])
+
+        # create dataloader
+        self.dataloader = DataLoader(datasets, batch_size=batch_size)
+
+
+    def generate(self):
+        device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+        MAX_GENERATE_LENGTH = 128
+
+        pred_generations = []
+        pred_labels = []
+
+        print("start generation")
+        for batch in tqdm(self.dataloader):
+            # Inference in batches
+            input_ids = batch['input_ids']
+            attention_mask = batch['attention_mask']
+            # save labels too
+            pred_labels.extend(batch['labels'])
+            
+
+            # Move to GPU if available
+            input_ids = input_ids.to(device)
+            attention_mask = attention_mask.to(device)
+            self.model.to(device)
+
+            # Perform inference
+            with torch.no_grad():
+                outputs = self.model.generate(input_ids,
+                                        attention_mask=attention_mask,
+                                        max_length=MAX_GENERATE_LENGTH)
+                
+                # Decode the output and print the results
+                pred_generations.extend(outputs.to("cpu"))
+
+
+
+        # %%
+        # extract sequence and decode
+        def extract_seq(tokens, start_value, end_value):
+            if start_value not in tokens or end_value not in tokens:
+                return None  # Or handle this case according to your requirements
+            start_id = np.where(tokens == start_value)[0][0]
+            end_id = np.where(tokens == end_value)[0][0]
+
+            return tokens[start_id+1:end_id]
+
+
+        def process_tensor_output(tokens):
+            thing_seq = extract_seq(tokens, 32100, 32101) # 32100 = <THING_START>, 32101 = <THING_END>
+            property_seq = extract_seq(tokens, 32102, 32103) # 32102 = <PROPERTY_START>, 32103 = <PROPERTY_END>
+            p_thing = None
+            p_property = None
+            if (thing_seq is not None):
+                p_thing =  self.tokenizer.decode(thing_seq, skip_special_tokens=False)
+            if (property_seq is not None):
+                p_property =  self.tokenizer.decode(property_seq, skip_special_tokens=False)
+            return p_thing, p_property
+
+        # decode prediction labels
+        def decode_preds(tokens_list):
+            thing_prediction_list = []
+            property_prediction_list = []
+            for tokens in tokens_list:
+                p_thing, p_property = process_tensor_output(tokens)
+                thing_prediction_list.append(p_thing)
+                property_prediction_list.append(p_property)
+            return thing_prediction_list, property_prediction_list 
+
+        thing_prediction_list, property_prediction_list = decode_preds(pred_generations)
+        return thing_prediction_list, property_prediction_list
+

train/frozen_t5_decoder/mapping_prediction/output.txt

@@ -0,0 +1,2 @@
+
+Accuracy for fold 1: 0.0

train/frozen_t5_decoder/mapping_prediction/predict.py

@@ -0,0 +1,74 @@
+
+import pandas as pd
+import os
+import glob
+from inference import Inference
+
+checkpoint_directory =  '../'
+
+BATCH_SIZE = 512
+
+def infer_and_select(fold):
+    print(f"Inference for fold {fold}")
+    # import test data
+    data_path = f"../../../data_preprocess/exports/dataset/group_{fold}/test_all.csv"
+    df = pd.read_csv(data_path, skipinitialspace=True)
+    df = df[df['MDM']].reset_index(drop=True)
+
+    # get target data
+    data_path = f"../../../data_preprocess/exports/dataset/group_{fold}/train_all.csv"
+    train_df = pd.read_csv(data_path, skipinitialspace=True)
+    # processing to help with selection later
+    train_df['thing_property'] = train_df['thing'] + " " + train_df['property']
+
+
+    ##########################################
+    # run inference
+    # checkpoint
+    # Use glob to find matching paths
+    directory = os.path.join(checkpoint_directory, f'checkpoint_fold_{fold}b')
+    # Use glob to find matching paths
+    # path is usually checkpoint_fold_1/checkpoint-<step number>
+    # we are guaranteed to save only 1 checkpoint from training
+    pattern = 'checkpoint-*'
+    checkpoint_path = glob.glob(os.path.join(directory, pattern))[0]
+
+
+    infer = Inference(checkpoint_path)
+    infer.prepare_dataloader(df, batch_size=BATCH_SIZE, max_length=128)
+    thing_prediction_list, property_prediction_list = infer.generate()
+
+    # add labels too
+    # thing_actual_list, property_actual_list = decode_preds(pred_labels)
+    # Convert the list to a Pandas DataFrame
+    df_out = pd.DataFrame({
+        'p_thing': thing_prediction_list, 
+        'p_property': property_prediction_list
+    })
+    # df_out['p_thing_correct'] = df_out['p_thing'] == df_out['thing']
+    # df_out['p_property_correct'] = df_out['p_property'] == df_out['property']
+    df = pd.concat([df, df_out], axis=1)
+
+    # we can save the t5 generation output here
+    df.to_csv(f"exports/result_group_{fold}.csv", index=False)
+
+    # here we want to evaluate mapping accuracy within the valid in mdm data only
+    in_mdm = df['MDM']
+    condition_correct_thing = df['p_thing'] == df['thing']
+    condition_correct_property = df['p_property'] == df['property']
+    prediction_mdm_correct = sum(condition_correct_thing & condition_correct_property & in_mdm)
+    pred_correct_proportion = prediction_mdm_correct/sum(in_mdm)
+
+    # write output to file output.txt
+    with open("output.txt", "a") as f:
+        print(f'Accuracy for fold {fold}: {pred_correct_proportion}', file=f)
+
+###########################################  
+# Execute for all folds
+
+# reset file before writing to it
+with open("output.txt", "w") as f:
+    print('', file=f)
+
+for fold in [1]:
+    infer_and_select(fold)

train/frozen_t5_decoder/train_decoder.py

@@ -0,0 +1,236 @@
+# %%
+
+# from datasets import load_from_disk
+import os
+import glob
+
+os.environ['NCCL_P2P_DISABLE'] = '1'
+os.environ['NCCL_IB_DISABLE'] = '1'
+os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
+os.environ["CUDA_VISIBLE_DEVICES"] = "0,1,2,3"
+
+import torch
+from custom_t5.modeling_t5 import T5EncoderForSequenceClassification
+
+from safetensors.torch import load_file
+from transformers import (
+    T5Config,
+    T5TokenizerFast,
+    AutoModelForSeq2SeqLM,
+    DataCollatorForSeq2Seq,
+    Seq2SeqTrainer,
+    EarlyStoppingCallback,
+    Seq2SeqTrainingArguments,
+    T5ForConditionalGeneration,
+    T5Model
+)
+import evaluate
+import numpy as np
+import pandas as pd
+# import matplotlib.pyplot as plt
+from datasets import Dataset, DatasetDict
+
+
+
+torch.set_float32_matmul_precision('high')
+
+# outputs a list of dictionaries
+def process_df_to_dict(df):
+    output_list = []
+    for _, row in df.iterrows():
+        desc = f"<DESC>{row['tag_description']}<DESC>"
+        unit = f"<UNIT>{row['unit']}<UNIT>"
+        element = {
+            'input' : f"{desc}{unit}",
+            'output': f"<THING_START>{row['thing']}<THING_END><PROPERTY_START>{row['property']}<PROPERTY_END>",
+        }
+        output_list.append(element)
+
+    return output_list
+
+
+def create_split_dataset(fold):
+    # train 
+    data_path = f"../../data_preprocess/exports/dataset/group_{fold}/train_all.csv"
+    train_df = pd.read_csv(data_path, skipinitialspace=True)
+
+    # valid
+    data_path = f"../../data_preprocess/exports/dataset/group_{fold}/valid.csv"
+    validation_df = pd.read_csv(data_path, skipinitialspace=True)
+
+    combined_data = DatasetDict({
+        'train': Dataset.from_list(process_df_to_dict(train_df)),
+        'validation' : Dataset.from_list(process_df_to_dict(validation_df)),
+    })
+    return combined_data
+
+
+# function to perform training for a given fold
+def train(fold):
+    save_path = f'checkpoint_fold_{fold}b'
+    split_datasets = create_split_dataset(fold)
+
+    # prepare tokenizer
+    model_checkpoint = "t5-small"
+    tokenizer = T5TokenizerFast.from_pretrained(model_checkpoint, return_tensors="pt", clean_up_tokenization_spaces=True)
+    # Define additional special tokens
+    additional_special_tokens = ["<THING_START>", "<THING_END>", "<PROPERTY_START>", "<PROPERTY_END>", "<NAME>", "<DESC>", "<SIG>", "<UNIT>", "<DATA_TYPE>"]
+    # Add the additional special tokens to the tokenizer
+    tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens})
+
+    max_length = 120
+
+    # given a dataset entry, run it through the tokenizer
+    def preprocess_function(example):
+        input = example['input']
+        target = example['output']
+        # text_target sets the corresponding label to inputs
+        # there is no need to create a separate 'labels'
+        model_inputs = tokenizer(
+            input,
+            text_target=target, 
+            max_length=max_length,
+            truncation=True,
+            padding="max_length"
+        )
+        return model_inputs
+
+    # map maps function to each "row" in the dataset
+    # aka the data in the immediate nesting
+    tokenized_datasets = split_datasets.map(
+        preprocess_function,
+        batched=True,
+        num_proc=8,
+        remove_columns=split_datasets["train"].column_names,
+    )
+
+    # https://github.com/huggingface/transformers/pull/28414
+    # model_checkpoint = "google/t5-efficient-tiny"
+    # device_map set to auto to force it to load contiguous weights 
+    # model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint, device_map='auto')
+
+    # directory = os.path.join(".", f'checkpoint_fold_{fold}a')
+    # # Use glob to find matching paths
+    # # path is usually checkpoint_fold_1/checkpoint-<step number>
+    # # we are guaranteed to save only 1 checkpoint from training
+    # pattern = 'checkpoint-*'
+    # prev_checkpoint = glob.glob(os.path.join(directory, pattern))[0]
+    # # t5_classify = T5Model.from_pretrained(prev_checkpoint)
+    # # Load the checkpoint
+    # checkpoint_path = f"{prev_checkpoint}/model.safetensors"
+    # checkpoint = load_file(checkpoint_path)
+    # Filter out weights related to the classification head
+    # given name format: t5.encoder.embed_tokens.weight
+    # we want: encoder.embed.tokens.weight
+    # t5_weights= {key.replace("t5.", "", 1): value for key, value in checkpoint.items() if "classifier" not in key}
+
+    model = T5ForConditionalGeneration.from_pretrained(model_checkpoint)
+    # change the token embedding size to match the shape
+    model.resize_token_embeddings(len(tokenizer))
+
+    # model.load_state_dict(state_dict=t5_weights, strict=False)
+
+    # for key, param in model.state_dict().items():
+    #     if key in t5_weights:
+    #         print(f"{key}: Successfully overridden")
+    #     else:
+    #         print(f"{key}: Retained original weights")
+
+
+    # Freeze the decoder
+    for param in model.decoder.parameters():
+        param.requires_grad = False
+
+    # Freeze the shared embedding layer
+    for param in model.shared.parameters():
+        param.requires_grad = False
+
+
+    data_collator = DataCollatorForSeq2Seq(tokenizer, model=model)
+    metric = evaluate.load("sacrebleu")
+
+
+    def compute_metrics(eval_preds):
+        preds, labels = eval_preds
+        # In case the model returns more than the prediction logits
+        if isinstance(preds, tuple):
+            preds = preds[0]
+
+        decoded_preds = tokenizer.batch_decode(preds, 
+                                            skip_special_tokens=False)
+
+        # Replace -100s in the labels as we can't decode them
+        labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
+        decoded_labels = tokenizer.batch_decode(labels,
+                                                skip_special_tokens=False)
+
+        # Remove <PAD> tokens from decoded predictions and labels
+        decoded_preds = [pred.replace(tokenizer.pad_token, '').strip() for pred in decoded_preds]
+        decoded_labels = [[label.replace(tokenizer.pad_token, '').strip()] for label in decoded_labels]
+
+        # Some simple post-processing
+        # decoded_preds = [pred.strip() for pred in decoded_preds]
+        # decoded_labels = [[label.strip()] for label in decoded_labels]
+        # print(decoded_preds, decoded_labels)
+
+        result = metric.compute(predictions=decoded_preds, references=decoded_labels)
+        return {"bleu": result["score"]}
+
+
+    # Generation Config
+    # from transformers import GenerationConfig
+    gen_config = model.generation_config
+    gen_config.max_length = 128
+
+    # compile
+    # model = torch.compile(model, backend="inductor", dynamic=True)
+
+
+    # Trainer
+
+    args = Seq2SeqTrainingArguments(
+        f"{save_path}",
+        # eval_strategy="epoch",
+        eval_strategy="no",
+        logging_dir="tensorboard-log",
+        logging_strategy="epoch",
+        # save_strategy="epoch",
+        load_best_model_at_end=False,
+        learning_rate=1e-3,
+        per_device_train_batch_size=64,
+        per_device_eval_batch_size=64,
+        auto_find_batch_size=False,
+        ddp_find_unused_parameters=False,
+        weight_decay=0.01,
+        save_total_limit=1,
+        num_train_epochs=40,
+        predict_with_generate=True,
+        bf16=True,
+        push_to_hub=False,
+        generation_config=gen_config,
+        remove_unused_columns=False,
+    )
+
+
+    trainer = Seq2SeqTrainer(
+        model,
+        args,
+        train_dataset=tokenized_datasets["train"],
+        eval_dataset=tokenized_datasets["validation"],
+        data_collator=data_collator,
+        tokenizer=tokenizer,
+        compute_metrics=compute_metrics,
+        # callbacks=[EarlyStoppingCallback(early_stopping_patience=3)],
+    )
+
+    # uncomment to load training from checkpoint
+    # checkpoint_path = 'default_40_1/checkpoint-5600'
+    # trainer.train(resume_from_checkpoint=checkpoint_path)
+
+    trainer.train()
+
+# execute training
+for fold in [1]:
+    print(fold)
+    train(fold)
+

train/frozen_t5_encoder/.gitignore

@@ -0,0 +1,2 @@
+checkpoint*
+tensorboard-log

train/frozen_t5_encoder/mapping_prediction/.gitignore

@@ -0,0 +1,2 @@
+__pycache__
+exports/

train/frozen_t5_encoder/mapping_prediction/inference.py

@@ -0,0 +1,168 @@
+import torch
+from torch.utils.data import DataLoader
+from transformers import (
+    T5TokenizerFast,
+    AutoModelForSeq2SeqLM,
+)
+import os
+from tqdm import tqdm
+from datasets import Dataset
+import numpy as np
+
+os.environ['TOKENIZERS_PARALLELISM'] = 'false'
+
+
+class Inference():
+    tokenizer: T5TokenizerFast
+    model: torch.nn.Module
+    dataloader: DataLoader
+
+    def __init__(self, checkpoint_path):
+        self._create_tokenizer()
+        self._load_model(checkpoint_path)
+
+
+    def _create_tokenizer(self):
+        # %%
+        # load tokenizer
+        self.tokenizer = T5TokenizerFast.from_pretrained("t5-small", return_tensors="pt", clean_up_tokenization_spaces=True)
+        # Define additional special tokens
+        additional_special_tokens = ["<THING_START>", "<THING_END>", "<PROPERTY_START>", "<PROPERTY_END>", "<NAME>", "<DESC>", "SIG", "UNIT", "DATA_TYPE"]
+        # Add the additional special tokens to the tokenizer
+        self.tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens})
+
+    def _load_model(self, checkpoint_path: str):
+        # load model
+        # Define the directory and the pattern
+        model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint_path)
+        model = torch.compile(model)
+        # set model to eval
+        self.model = model.eval()
+
+
+
+
+    def prepare_dataloader(self, input_df, batch_size, max_length):
+        """
+        *arguments*
+        - input_df: input dataframe containing fields 'tag_description', 'thing', 'property'
+        - batch_size: the batch size of dataloader output
+        - max_length: length of tokenizer output
+        """
+        print("preparing dataloader")
+        # convert each dataframe row into a dictionary
+        # outputs a list of dictionaries
+
+        def _process_df(df):
+            output_list = []
+            for _, row in df.iterrows():
+                desc = f"<DESC>{row['tag_description']}<DESC>"
+                unit = f"<UNIT>{row['unit']}<UNIT>"
+                element = {
+                    'input' : f"{desc}{unit}",
+                    'output': f"<THING_START>{row['thing']}<THING_END><PROPERTY_START>{row['property']}<PROPERTY_END>",
+                }
+                output_list.append(element)
+
+            return output_list
+
+        def _preprocess_function(example):
+            input = example['input']
+            target = example['output']
+            # text_target sets the corresponding label to inputs
+            # there is no need to create a separate 'labels'
+            model_inputs = self.tokenizer(
+                input,
+                text_target=target, 
+                max_length=max_length,
+                return_tensors="pt",
+                padding="max_length",
+                truncation=True,
+            )
+            return model_inputs
+
+        test_dataset = Dataset.from_list(_process_df(input_df))
+
+
+        # map maps function to each "row" in the dataset
+        # aka the data in the immediate nesting
+        datasets = test_dataset.map(
+            _preprocess_function,
+            batched=True,
+            num_proc=1,
+            remove_columns=test_dataset.column_names,
+        )
+        # datasets = _preprocess_function(test_dataset)
+        datasets.set_format(type='torch', columns=['input_ids', 'attention_mask', 'labels'])
+
+        # create dataloader
+        self.dataloader = DataLoader(datasets, batch_size=batch_size)
+
+
+    def generate(self):
+        device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+        MAX_GENERATE_LENGTH = 128
+
+        pred_generations = []
+        pred_labels = []
+
+        print("start generation")
+        for batch in tqdm(self.dataloader):
+            # Inference in batches
+            input_ids = batch['input_ids']
+            attention_mask = batch['attention_mask']
+            # save labels too
+            pred_labels.extend(batch['labels'])
+            
+
+            # Move to GPU if available
+            input_ids = input_ids.to(device)
+            attention_mask = attention_mask.to(device)
+            self.model.to(device)
+
+            # Perform inference
+            with torch.no_grad():
+                outputs = self.model.generate(input_ids,
+                                        attention_mask=attention_mask,
+                                        max_length=MAX_GENERATE_LENGTH)
+                
+                # Decode the output and print the results
+                pred_generations.extend(outputs.to("cpu"))
+
+
+
+        # %%
+        # extract sequence and decode
+        def extract_seq(tokens, start_value, end_value):
+            if start_value not in tokens or end_value not in tokens:
+                return None  # Or handle this case according to your requirements
+            start_id = np.where(tokens == start_value)[0][0]
+            end_id = np.where(tokens == end_value)[0][0]
+
+            return tokens[start_id+1:end_id]
+
+
+        def process_tensor_output(tokens):
+            thing_seq = extract_seq(tokens, 32100, 32101) # 32100 = <THING_START>, 32101 = <THING_END>
+            property_seq = extract_seq(tokens, 32102, 32103) # 32102 = <PROPERTY_START>, 32103 = <PROPERTY_END>
+            p_thing = None
+            p_property = None
+            if (thing_seq is not None):
+                p_thing =  self.tokenizer.decode(thing_seq, skip_special_tokens=False)
+            if (property_seq is not None):
+                p_property =  self.tokenizer.decode(property_seq, skip_special_tokens=False)
+            return p_thing, p_property
+
+        # decode prediction labels
+        def decode_preds(tokens_list):
+            thing_prediction_list = []
+            property_prediction_list = []
+            for tokens in tokens_list:
+                p_thing, p_property = process_tensor_output(tokens)
+                thing_prediction_list.append(p_thing)
+                property_prediction_list.append(p_property)
+            return thing_prediction_list, property_prediction_list 
+
+        thing_prediction_list, property_prediction_list = decode_preds(pred_generations)
+        return thing_prediction_list, property_prediction_list
+

train/frozen_t5_encoder/mapping_prediction/output.txt

@@ -0,0 +1,6 @@
+
+Accuracy for fold 1: 0.9342167534311405
+Accuracy for fold 2: 0.883177570093458
+Accuracy for fold 3: 0.963855421686747
+Accuracy for fold 4: 0.9705042816365367
+Accuracy for fold 5: 0.9051763628034815

train/frozen_t5_encoder/mapping_prediction/predict.py

@@ -0,0 +1,74 @@
+
+import pandas as pd
+import os
+import glob
+from inference import Inference
+
+checkpoint_directory =  '../'
+
+BATCH_SIZE = 512
+
+def infer_and_select(fold):
+    print(f"Inference for fold {fold}")
+    # import test data
+    data_path = f"../../../data_preprocess/exports/dataset/group_{fold}/test_all.csv"
+    df = pd.read_csv(data_path, skipinitialspace=True)
+    df = df[df['MDM']].reset_index(drop=True)
+
+    # get target data
+    data_path = f"../../../data_preprocess/exports/dataset/group_{fold}/train_all.csv"
+    train_df = pd.read_csv(data_path, skipinitialspace=True)
+    # processing to help with selection later
+    train_df['thing_property'] = train_df['thing'] + " " + train_df['property']
+
+
+    ##########################################
+    # run inference
+    # checkpoint
+    # Use glob to find matching paths
+    directory = os.path.join(checkpoint_directory, f'checkpoint_fold_{fold}b')
+    # Use glob to find matching paths
+    # path is usually checkpoint_fold_1/checkpoint-<step number>
+    # we are guaranteed to save only 1 checkpoint from training
+    pattern = 'checkpoint-*'
+    checkpoint_path = glob.glob(os.path.join(directory, pattern))[0]
+
+
+    infer = Inference(checkpoint_path)
+    infer.prepare_dataloader(df, batch_size=BATCH_SIZE, max_length=128)
+    thing_prediction_list, property_prediction_list = infer.generate()
+
+    # add labels too
+    # thing_actual_list, property_actual_list = decode_preds(pred_labels)
+    # Convert the list to a Pandas DataFrame
+    df_out = pd.DataFrame({
+        'p_thing': thing_prediction_list, 
+        'p_property': property_prediction_list
+    })
+    # df_out['p_thing_correct'] = df_out['p_thing'] == df_out['thing']
+    # df_out['p_property_correct'] = df_out['p_property'] == df_out['property']
+    df = pd.concat([df, df_out], axis=1)
+
+    # we can save the t5 generation output here
+    df.to_csv(f"exports/result_group_{fold}.csv", index=False)
+
+    # here we want to evaluate mapping accuracy within the valid in mdm data only
+    in_mdm = df['MDM']
+    condition_correct_thing = df['p_thing'] == df['thing']
+    condition_correct_property = df['p_property'] == df['property']
+    prediction_mdm_correct = sum(condition_correct_thing & condition_correct_property & in_mdm)
+    pred_correct_proportion = prediction_mdm_correct/sum(in_mdm)
+
+    # write output to file output.txt
+    with open("output.txt", "a") as f:
+        print(f'Accuracy for fold {fold}: {pred_correct_proportion}', file=f)
+
+###########################################  
+# Execute for all folds
+
+# reset file before writing to it
+with open("output.txt", "w") as f:
+    print('', file=f)
+
+for fold in [1,2,3,4,5]:
+    infer_and_select(fold)

train/frozen_t5_encoder/train_decoder.py

@@ -0,0 +1,235 @@
+# %%
+
+# from datasets import load_from_disk
+import os
+import glob
+
+os.environ['NCCL_P2P_DISABLE'] = '1'
+os.environ['NCCL_IB_DISABLE'] = '1'
+os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
+os.environ["CUDA_VISIBLE_DEVICES"] = "0,1,2,3"
+
+import torch
+
+from safetensors.torch import load_file
+from transformers import (
+    T5Config,
+    T5TokenizerFast,
+    AutoModelForSeq2SeqLM,
+    DataCollatorForSeq2Seq,
+    Seq2SeqTrainer,
+    EarlyStoppingCallback,
+    Seq2SeqTrainingArguments,
+    T5ForConditionalGeneration,
+    T5Model
+)
+import evaluate
+import numpy as np
+import pandas as pd
+# import matplotlib.pyplot as plt
+from datasets import Dataset, DatasetDict
+
+
+
+torch.set_float32_matmul_precision('high')
+
+# outputs a list of dictionaries
+def process_df_to_dict(df):
+    output_list = []
+    for _, row in df.iterrows():
+        desc = f"<DESC>{row['tag_description']}<DESC>"
+        unit = f"<UNIT>{row['unit']}<UNIT>"
+        element = {
+            'input' : f"{desc}{unit}",
+            'output': f"<THING_START>{row['thing']}<THING_END><PROPERTY_START>{row['property']}<PROPERTY_END>",
+        }
+        output_list.append(element)
+
+    return output_list
+
+
+def create_split_dataset(fold):
+    # train 
+    data_path = f"../../data_preprocess/exports/dataset/group_{fold}/train_all.csv"
+    train_df = pd.read_csv(data_path, skipinitialspace=True)
+
+    # valid
+    data_path = f"../../data_preprocess/exports/dataset/group_{fold}/valid.csv"
+    validation_df = pd.read_csv(data_path, skipinitialspace=True)
+
+    combined_data = DatasetDict({
+        'train': Dataset.from_list(process_df_to_dict(train_df)),
+        'validation' : Dataset.from_list(process_df_to_dict(validation_df)),
+    })
+    return combined_data
+
+
+# function to perform training for a given fold
+def train(fold):
+    save_path = f'checkpoint_fold_{fold}b'
+    split_datasets = create_split_dataset(fold)
+
+    # prepare tokenizer
+    model_checkpoint = "t5-small"
+    tokenizer = T5TokenizerFast.from_pretrained(model_checkpoint, return_tensors="pt", clean_up_tokenization_spaces=True)
+    # Define additional special tokens
+    additional_special_tokens = ["<THING_START>", "<THING_END>", "<PROPERTY_START>", "<PROPERTY_END>", "<NAME>", "<DESC>", "<SIG>", "<UNIT>", "<DATA_TYPE>"]
+    # Add the additional special tokens to the tokenizer
+    tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens})
+
+    max_length = 120
+
+    # given a dataset entry, run it through the tokenizer
+    def preprocess_function(example):
+        input = example['input']
+        target = example['output']
+        # text_target sets the corresponding label to inputs
+        # there is no need to create a separate 'labels'
+        model_inputs = tokenizer(
+            input,
+            text_target=target, 
+            max_length=max_length,
+            truncation=True,
+            padding="max_length"
+        )
+        return model_inputs
+
+    # map maps function to each "row" in the dataset
+    # aka the data in the immediate nesting
+    tokenized_datasets = split_datasets.map(
+        preprocess_function,
+        batched=True,
+        num_proc=8,
+        remove_columns=split_datasets["train"].column_names,
+    )
+
+    # https://github.com/huggingface/transformers/pull/28414
+    # model_checkpoint = "google/t5-efficient-tiny"
+    # device_map set to auto to force it to load contiguous weights 
+    # model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint, device_map='auto')
+
+    # directory = os.path.join(".", f'checkpoint_fold_{fold}a')
+    # # Use glob to find matching paths
+    # # path is usually checkpoint_fold_1/checkpoint-<step number>
+    # # we are guaranteed to save only 1 checkpoint from training
+    # pattern = 'checkpoint-*'
+    # prev_checkpoint = glob.glob(os.path.join(directory, pattern))[0]
+    # # t5_classify = T5Model.from_pretrained(prev_checkpoint)
+    # # Load the checkpoint
+    # checkpoint_path = f"{prev_checkpoint}/model.safetensors"
+    # checkpoint = load_file(checkpoint_path)
+    # # Filter out weights related to the classification head
+    # # given name format: t5.encoder.embed_tokens.weight
+    # # we want: encoder.embed.tokens.weight
+    # t5_weights= {key.replace("t5.", "", 1): value for key, value in checkpoint.items() if "classifier" not in key}
+
+    model = T5ForConditionalGeneration.from_pretrained(model_checkpoint)
+    # change the token embedding size to match the shape
+    model.resize_token_embeddings(len(tokenizer))
+
+    # model.load_state_dict(state_dict=t5_weights, strict=False)
+
+    # for key, param in model.state_dict().items():
+    #     if key in t5_weights:
+    #         print(f"{key}: Successfully overridden")
+    #     else:
+    #         print(f"{key}: Retained original weights")
+
+
+    # Freeze the encoder
+    for param in model.encoder.parameters():
+        param.requires_grad = False
+
+    # Freeze the shared embedding layer
+    for param in model.shared.parameters():
+        param.requires_grad = False
+
+
+    data_collator = DataCollatorForSeq2Seq(tokenizer, model=model)
+    metric = evaluate.load("sacrebleu")
+
+
+    def compute_metrics(eval_preds):
+        preds, labels = eval_preds
+        # In case the model returns more than the prediction logits
+        if isinstance(preds, tuple):
+            preds = preds[0]
+
+        decoded_preds = tokenizer.batch_decode(preds, 
+                                            skip_special_tokens=False)
+
+        # Replace -100s in the labels as we can't decode them
+        labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
+        decoded_labels = tokenizer.batch_decode(labels,
+                                                skip_special_tokens=False)
+
+        # Remove <PAD> tokens from decoded predictions and labels
+        decoded_preds = [pred.replace(tokenizer.pad_token, '').strip() for pred in decoded_preds]
+        decoded_labels = [[label.replace(tokenizer.pad_token, '').strip()] for label in decoded_labels]
+
+        # Some simple post-processing
+        # decoded_preds = [pred.strip() for pred in decoded_preds]
+        # decoded_labels = [[label.strip()] for label in decoded_labels]
+        # print(decoded_preds, decoded_labels)
+
+        result = metric.compute(predictions=decoded_preds, references=decoded_labels)
+        return {"bleu": result["score"]}
+
+
+    # Generation Config
+    # from transformers import GenerationConfig
+    gen_config = model.generation_config
+    gen_config.max_length = 128
+
+    # compile
+    # model = torch.compile(model, backend="inductor", dynamic=True)
+
+
+    # Trainer
+
+    args = Seq2SeqTrainingArguments(
+        f"{save_path}",
+        # eval_strategy="epoch",
+        eval_strategy="no",
+        logging_dir="tensorboard-log",
+        logging_strategy="epoch",
+        # save_strategy="epoch",
+        load_best_model_at_end=False,
+        learning_rate=1e-3,
+        per_device_train_batch_size=64,
+        per_device_eval_batch_size=64,
+        auto_find_batch_size=False,
+        ddp_find_unused_parameters=False,
+        weight_decay=0.01,
+        save_total_limit=1,
+        num_train_epochs=40,
+        predict_with_generate=True,
+        bf16=True,
+        push_to_hub=False,
+        generation_config=gen_config,
+        remove_unused_columns=False,
+    )
+
+
+    trainer = Seq2SeqTrainer(
+        model,
+        args,
+        train_dataset=tokenized_datasets["train"],
+        eval_dataset=tokenized_datasets["validation"],
+        data_collator=data_collator,
+        tokenizer=tokenizer,
+        compute_metrics=compute_metrics,
+        # callbacks=[EarlyStoppingCallback(early_stopping_patience=3)],
+    )
+
+    # uncomment to load training from checkpoint
+    # checkpoint_path = 'default_40_1/checkpoint-5600'
+    # trainer.train(resume_from_checkpoint=checkpoint_path)
+
+    trainer.train()
+
+# execute training
+for fold in [1,2,3,4,5]:
+    print(fold)
+    train(fold)
+

train/hybrid_t5_complete_desc_unit/mapping_prediction/output.txt

@@ -1,6 +1,6 @@
 
-Accuracy for fold 1: 0.9427354472314246
-Accuracy for fold 2: 0.8859813084112149
-Accuracy for fold 3: 0.9683734939759037
-Accuracy for fold 4: 0.9762131303520457
-Accuracy for fold 5: 0.907924874026569
+Accuracy for fold 1: 0.9242782773308093
+Accuracy for fold 2: 0.9126168224299065
+Accuracy for fold 3: 0.9643574297188755
+Accuracy for fold 4: 0.9595623215984777
+Accuracy for fold 5: 0.8950984883188273

train/hybrid_t5_complete_desc_unit/mapping_prediction/predict.py

@@ -13,6 +13,7 @@ def infer_and_select(fold):
     # import test data
     data_path = f"../../../data_preprocess/exports/dataset/group_{fold}/test_all.csv"
     df = pd.read_csv(data_path, skipinitialspace=True)
+    df = df[df['MDM']].reset_index(drop=True)
 
     # get target data
     data_path = f"../../../data_preprocess/exports/dataset/group_{fold}/train_all.csv"

train/hybrid_t5_complete_desc_unit/train_decoder.py

@@ -120,14 +120,23 @@ def train(fold):
     checkpoint_path = f"{prev_checkpoint}/model.safetensors"
     checkpoint = load_file(checkpoint_path)
     # Filter out weights related to the classification head
-    t5_weights = {key: value for key, value in checkpoint.items() if "classifier" not in key}
-
+    # given name format: t5.encoder.embed_tokens.weight
+    # we want: encoder.embed.tokens.weight
+    t5_weights= {key.replace("t5.", "", 1): value for key, value in checkpoint.items() if "classifier" not in key}
 
     model = T5ForConditionalGeneration.from_pretrained(model_checkpoint)
-    model.load_state_dict(state_dict=t5_weights, strict=False)
-    # important! after extending tokens vocab
+    # change the token embedding size to match the shape
     model.resize_token_embeddings(len(tokenizer))
 
+    model.load_state_dict(state_dict=t5_weights, strict=False)
+
+    for key, param in model.state_dict().items():
+        if key in t5_weights:
+            print(f"{key}: Successfully overridden")
+        else:
+            print(f"{key}: Retained original weights")
+
+
     # Freeze the encoder
     for param in model.encoder.parameters():
         param.requires_grad = False
@@ -194,7 +203,7 @@ def train(fold):
         ddp_find_unused_parameters=False,
         weight_decay=0.01,
         save_total_limit=1,
-        num_train_epochs=80,
+        num_train_epochs=40,
         predict_with_generate=True,
         bf16=True,
         push_to_hub=False,

train/hybrid_t5_complete_desc_unit/train_encoder.py

@@ -35,12 +35,13 @@ torch.set_float32_matmul_precision('high')
 # import the full mdm-only file
 data_path = '../../data_import/exports/data_mapping_mdm.csv'
 full_df = pd.read_csv(data_path, skipinitialspace=True)
-mdm_list = sorted(list((set(full_df['pattern']))))
+# mdm_list = sorted(list((set(full_df['pattern']))))
 
 # # rather than use pattern, we use the real thing and property
-# thing_property = full_df['thing'] + full_df['property']
-# thing_property = thing_property.to_list()
-# mdm_list = sorted(list(set(thing_property)))
+thing_property = full_df['thing'] + full_df['property']
+thing_property = thing_property.to_list()
+mdm_list = sorted(list(set(thing_property)))
+print("number of classes: ", len(mdm_list))
 
 
 # %%
@@ -62,8 +63,8 @@ def process_df_to_dict(df, mdm_list):
     for _, row in df.iterrows():
         desc = f"<DESC>{row['tag_description']}<DESC>"
         unit = f"<UNIT>{row['unit']}<UNIT>"
-        # pattern = f"{row['thing'] + row['property']}"
-        pattern = f"{row['thing_pattern'] + ' ' + row['property_pattern']}"
+        pattern = f"{row['thing'] + row['property']}"
+        # pattern = f"{row['thing_pattern'] + ' ' + row['property_pattern']}"
         try:
             index = mdm_list.index(pattern)
         except ValueError:
@@ -137,7 +138,7 @@ def train(fold):
         remove_columns="text",
     )
 
-    # %% temp
+    # %% temp    # t5_classify = T5Model.from_pretrained(prev_checkpoint)
     # tokenized_datasets['train'].rename_columns()
 
     # %%
@@ -189,10 +190,10 @@ def train(fold):
         # save_strategy="epoch",
         load_best_model_at_end=False,
         learning_rate=1e-3,
-        per_device_train_batch_size=128,
-        per_device_eval_batch_size=128,
+        per_device_train_batch_size=64,
+        per_device_eval_batch_size=64,
         auto_find_batch_size=False,
-        ddp_find_unused_parameters=False,
+        ddp_find_unused_parameters=False,    # t5_classify = T5Model.from_pretrained(prev_checkpoint)
         weight_decay=0.01,
         save_total_limit=1,
         num_train_epochs=80,

train/hybrid_t5_pattern_desc_unit/.gitignore

@@ -0,0 +1,2 @@
+checkpoint*
+tensorboard-log

train/hybrid_t5_pattern_desc_unit/custom_t5/.gitignore

@@ -0,0 +1 @@
+__pycache__

train/hybrid_t5_pattern_desc_unit/custom_t5/modeling_t5.py

@@ -0,0 +1,125 @@
+from dataclasses import dataclass
+from typing import List, Optional, Tuple, Union
+
+import torch
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from transformers import (
+    T5PreTrainedModel,
+    T5Model
+    
+)
+
+from transformers.modeling_outputs import (
+    SequenceClassifierOutput,
+)
+
+def mean_pooling(encoder_outputs, attention_mask):
+    """
+    Perform mean pooling over encoder outputs, considering the attention mask.
+    """
+    hidden_states = encoder_outputs.last_hidden_state  # Shape: (batch_size, seq_length, hidden_size)
+    mask = attention_mask.unsqueeze(-1)  # Shape: (batch_size, seq_length, 1)
+    masked_hidden_states = hidden_states * mask  # Zero out padding tokens
+    sum_hidden_states = masked_hidden_states.sum(dim=1)  # Sum over sequence length
+    sum_mask = mask.sum(dim=1)  # Sum the mask (number of non-padding tokens)
+    return sum_hidden_states / sum_mask  # Mean pooling
+
+
+class T5EncoderForSequenceClassification(T5PreTrainedModel):
+
+    def __init__(self, checkpoint, tokenizer, config, num_labels):
+        super().__init__(config)
+        self.num_labels = num_labels
+        self.config = config
+
+        # we force the loading of a pre-trained model here
+        self.t5 = T5Model.from_pretrained(checkpoint)
+        self.t5.resize_token_embeddings(len(tokenizer))
+        classifier_dropout = (
+            config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
+        )
+        self.dropout = nn.Dropout(classifier_dropout)
+        self.classifier = nn.Linear(config.hidden_size, self.num_labels)
+
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        token_type_ids: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        labels: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+
+        # encoder_outputs = self.t5.encoder(
+        #     input_ids,
+        #     attention_mask=attention_mask,
+        #     head_mask=head_mask,
+        #     inputs_embeds=inputs_embeds,
+        #     output_attentions=output_attentions,
+        #     output_hidden_states=output_hidden_states,
+        #     return_dict=return_dict,
+        # )
+
+
+        encoder_outputs = self.t5.encoder(input_ids, attention_mask=attention_mask)
+        # last_hidden_state = encoder_outputs.last_hidden_state
+        # use mean of hidden state
+        # pooled_output = mean_pooling(encoder_outputs, attention_mask)
+
+        # Use the hidden state of the first token as the sequence representation
+        pooled_output = encoder_outputs.last_hidden_state[:, 0, :]  # Shape: (batch_size, hidden_size)
+
+        # pooled_output = encoder_outputs[1]
+
+        pooled_output = self.dropout(pooled_output)
+        logits = self.classifier(pooled_output)
+
+        loss = None
+        if labels is not None:
+            if self.config.problem_type is None:
+                if self.num_labels == 1:
+                    self.config.problem_type = "regression"
+                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+                    self.config.problem_type = "single_label_classification"
+                else:
+                    self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "regression":
+                loss_fct = MSELoss()
+                if self.num_labels == 1:
+                    loss = loss_fct(logits.squeeze(), labels.squeeze())
+                else:
+                    loss = loss_fct(logits, labels)
+            elif self.config.problem_type == "single_label_classification":
+                loss_fct = CrossEntropyLoss()
+                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(logits, labels)
+        if not return_dict:
+            output = (logits,) + encoder_outputs[2:]
+            return ((loss,) + output) if loss is not None else output
+
+        return SequenceClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=encoder_outputs.hidden_states,
+            attentions=encoder_outputs.attentions,
+        )

train/hybrid_t5_pattern_desc_unit/mapping_prediction/.gitignore

@@ -0,0 +1,2 @@
+__pycache__
+exports/

train/hybrid_t5_pattern_desc_unit/mapping_prediction/inference.py

@@ -0,0 +1,168 @@
+import torch
+from torch.utils.data import DataLoader
+from transformers import (
+    T5TokenizerFast,
+    AutoModelForSeq2SeqLM,
+)
+import os
+from tqdm import tqdm
+from datasets import Dataset
+import numpy as np
+
+os.environ['TOKENIZERS_PARALLELISM'] = 'false'
+
+
+class Inference():
+    tokenizer: T5TokenizerFast
+    model: torch.nn.Module
+    dataloader: DataLoader
+
+    def __init__(self, checkpoint_path):
+        self._create_tokenizer()
+        self._load_model(checkpoint_path)
+
+
+    def _create_tokenizer(self):
+        # %%
+        # load tokenizer
+        self.tokenizer = T5TokenizerFast.from_pretrained("t5-small", return_tensors="pt", clean_up_tokenization_spaces=True)
+        # Define additional special tokens
+        additional_special_tokens = ["<THING_START>", "<THING_END>", "<PROPERTY_START>", "<PROPERTY_END>", "<NAME>", "<DESC>", "SIG", "UNIT", "DATA_TYPE"]
+        # Add the additional special tokens to the tokenizer
+        self.tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens})
+
+    def _load_model(self, checkpoint_path: str):
+        # load model
+        # Define the directory and the pattern
+        model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint_path)
+        model = torch.compile(model)
+        # set model to eval
+        self.model = model.eval()
+
+
+
+
+    def prepare_dataloader(self, input_df, batch_size, max_length):
+        """
+        *arguments*
+        - input_df: input dataframe containing fields 'tag_description', 'thing', 'property'
+        - batch_size: the batch size of dataloader output
+        - max_length: length of tokenizer output
+        """
+        print("preparing dataloader")
+        # convert each dataframe row into a dictionary
+        # outputs a list of dictionaries
+
+        def _process_df(df):
+            output_list = []
+            for _, row in df.iterrows():
+                desc = f"<DESC>{row['tag_description']}<DESC>"
+                unit = f"<UNIT>{row['unit']}<UNIT>"
+                element = {
+                    'input' : f"{desc}{unit}",
+                    'output': f"<THING_START>{row['thing']}<THING_END><PROPERTY_START>{row['property']}<PROPERTY_END>",
+                }
+                output_list.append(element)
+
+            return output_list
+
+        def _preprocess_function(example):
+            input = example['input']
+            target = example['output']
+            # text_target sets the corresponding label to inputs
+            # there is no need to create a separate 'labels'
+            model_inputs = self.tokenizer(
+                input,
+                text_target=target, 
+                max_length=max_length,
+                return_tensors="pt",
+                padding="max_length",
+                truncation=True,
+            )
+            return model_inputs
+
+        test_dataset = Dataset.from_list(_process_df(input_df))
+
+
+        # map maps function to each "row" in the dataset
+        # aka the data in the immediate nesting
+        datasets = test_dataset.map(
+            _preprocess_function,
+            batched=True,
+            num_proc=1,
+            remove_columns=test_dataset.column_names,
+        )
+        # datasets = _preprocess_function(test_dataset)
+        datasets.set_format(type='torch', columns=['input_ids', 'attention_mask', 'labels'])
+
+        # create dataloader
+        self.dataloader = DataLoader(datasets, batch_size=batch_size)
+
+
+    def generate(self):
+        device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+        MAX_GENERATE_LENGTH = 128
+
+        pred_generations = []
+        pred_labels = []
+
+        print("start generation")
+        for batch in tqdm(self.dataloader):
+            # Inference in batches
+            input_ids = batch['input_ids']
+            attention_mask = batch['attention_mask']
+            # save labels too
+            pred_labels.extend(batch['labels'])
+            
+
+            # Move to GPU if available
+            input_ids = input_ids.to(device)
+            attention_mask = attention_mask.to(device)
+            self.model.to(device)
+
+            # Perform inference
+            with torch.no_grad():
+                outputs = self.model.generate(input_ids,
+                                        attention_mask=attention_mask,
+                                        max_length=MAX_GENERATE_LENGTH)
+                
+                # Decode the output and print the results
+                pred_generations.extend(outputs.to("cpu"))
+
+
+
+        # %%
+        # extract sequence and decode
+        def extract_seq(tokens, start_value, end_value):
+            if start_value not in tokens or end_value not in tokens:
+                return None  # Or handle this case according to your requirements
+            start_id = np.where(tokens == start_value)[0][0]
+            end_id = np.where(tokens == end_value)[0][0]
+
+            return tokens[start_id+1:end_id]
+
+
+        def process_tensor_output(tokens):
+            thing_seq = extract_seq(tokens, 32100, 32101) # 32100 = <THING_START>, 32101 = <THING_END>
+            property_seq = extract_seq(tokens, 32102, 32103) # 32102 = <PROPERTY_START>, 32103 = <PROPERTY_END>
+            p_thing = None
+            p_property = None
+            if (thing_seq is not None):
+                p_thing =  self.tokenizer.decode(thing_seq, skip_special_tokens=False)
+            if (property_seq is not None):
+                p_property =  self.tokenizer.decode(property_seq, skip_special_tokens=False)
+            return p_thing, p_property
+
+        # decode prediction labels
+        def decode_preds(tokens_list):
+            thing_prediction_list = []
+            property_prediction_list = []
+            for tokens in tokens_list:
+                p_thing, p_property = process_tensor_output(tokens)
+                thing_prediction_list.append(p_thing)
+                property_prediction_list.append(p_property)
+            return thing_prediction_list, property_prediction_list 
+
+        thing_prediction_list, property_prediction_list = decode_preds(pred_generations)
+        return thing_prediction_list, property_prediction_list
+

train/hybrid_t5_pattern_desc_unit/mapping_prediction/output.txt

@@ -0,0 +1,6 @@
+
+Accuracy for fold 1: 0.9394226218646474
+Accuracy for fold 2: 0.9107476635514019
+Accuracy for fold 3: 0.9548192771084337
+Accuracy for fold 4: 0.972882968601332
+Accuracy for fold 5: 0.8996793403573065

train/hybrid_t5_pattern_desc_unit/mapping_prediction/predict.py

@@ -0,0 +1,74 @@
+
+import pandas as pd
+import os
+import glob
+from inference import Inference
+
+checkpoint_directory =  '../'
+
+BATCH_SIZE = 512
+
+def infer_and_select(fold):
+    print(f"Inference for fold {fold}")
+    # import test data
+    data_path = f"../../../data_preprocess/exports/dataset/group_{fold}/test_all.csv"
+    df = pd.read_csv(data_path, skipinitialspace=True)
+    df = df[df['MDM']].reset_index(drop=True)
+
+    # get target data
+    data_path = f"../../../data_preprocess/exports/dataset/group_{fold}/train_all.csv"
+    train_df = pd.read_csv(data_path, skipinitialspace=True)
+    # processing to help with selection later
+    train_df['thing_property'] = train_df['thing'] + " " + train_df['property']
+
+
+    ##########################################
+    # run inference
+    # checkpoint
+    # Use glob to find matching paths
+    directory = os.path.join(checkpoint_directory, f'checkpoint_fold_{fold}b')
+    # Use glob to find matching paths
+    # path is usually checkpoint_fold_1/checkpoint-<step number>
+    # we are guaranteed to save only 1 checkpoint from training
+    pattern = 'checkpoint-*'
+    checkpoint_path = glob.glob(os.path.join(directory, pattern))[0]
+
+
+    infer = Inference(checkpoint_path)
+    infer.prepare_dataloader(df, batch_size=BATCH_SIZE, max_length=128)
+    thing_prediction_list, property_prediction_list = infer.generate()
+
+    # add labels too
+    # thing_actual_list, property_actual_list = decode_preds(pred_labels)
+    # Convert the list to a Pandas DataFrame
+    df_out = pd.DataFrame({
+        'p_thing': thing_prediction_list, 
+        'p_property': property_prediction_list
+    })
+    # df_out['p_thing_correct'] = df_out['p_thing'] == df_out['thing']
+    # df_out['p_property_correct'] = df_out['p_property'] == df_out['property']
+    df = pd.concat([df, df_out], axis=1)
+
+    # we can save the t5 generation output here
+    df.to_csv(f"exports/result_group_{fold}.csv", index=False)
+
+    # here we want to evaluate mapping accuracy within the valid in mdm data only
+    in_mdm = df['MDM']
+    condition_correct_thing = df['p_thing'] == df['thing']
+    condition_correct_property = df['p_property'] == df['property']
+    prediction_mdm_correct = sum(condition_correct_thing & condition_correct_property & in_mdm)
+    pred_correct_proportion = prediction_mdm_correct/sum(in_mdm)
+
+    # write output to file output.txt
+    with open("output.txt", "a") as f:
+        print(f'Accuracy for fold {fold}: {pred_correct_proportion}', file=f)
+
+###########################################  
+# Execute for all folds
+
+# reset file before writing to it
+with open("output.txt", "w") as f:
+    print('', file=f)
+
+for fold in [1,2,3,4,5]:
+    infer_and_select(fold)

train/hybrid_t5_pattern_desc_unit/train_decoder.py

@@ -0,0 +1,234 @@
+# %%
+
+# from datasets import load_from_disk
+import os
+import glob
+
+os.environ['NCCL_P2P_DISABLE'] = '1'
+os.environ['NCCL_IB_DISABLE'] = '1'
+os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
+os.environ["CUDA_VISIBLE_DEVICES"] = "0,1,2,3"
+
+import torch
+from custom_t5.modeling_t5 import T5EncoderForSequenceClassification
+
+from safetensors.torch import load_file
+from transformers import (
+    T5Config,
+    T5TokenizerFast,
+    AutoModelForSeq2SeqLM,
+    DataCollatorForSeq2Seq,
+    Seq2SeqTrainer,
+    EarlyStoppingCallback,
+    Seq2SeqTrainingArguments,
+    T5ForConditionalGeneration,
+    T5Model
+)
+import evaluate
+import numpy as np
+import pandas as pd
+# import matplotlib.pyplot as plt
+from datasets import Dataset, DatasetDict
+
+
+
+torch.set_float32_matmul_precision('high')
+
+# outputs a list of dictionaries
+def process_df_to_dict(df):
+    output_list = []
+    for _, row in df.iterrows():
+        desc = f"<DESC>{row['tag_description']}<DESC>"
+        unit = f"<UNIT>{row['unit']}<UNIT>"
+        element = {
+            'input' : f"{desc}{unit}",
+            'output': f"<THING_START>{row['thing']}<THING_END><PROPERTY_START>{row['property']}<PROPERTY_END>",
+        }
+        output_list.append(element)
+
+    return output_list
+
+
+def create_split_dataset(fold):
+    # train 
+    data_path = f"../../data_preprocess/exports/dataset/group_{fold}/train_all.csv"
+    train_df = pd.read_csv(data_path, skipinitialspace=True)
+
+    # valid
+    data_path = f"../../data_preprocess/exports/dataset/group_{fold}/valid.csv"
+    validation_df = pd.read_csv(data_path, skipinitialspace=True)
+
+    combined_data = DatasetDict({
+        'train': Dataset.from_list(process_df_to_dict(train_df)),
+        'validation' : Dataset.from_list(process_df_to_dict(validation_df)),
+    })
+    return combined_data
+
+
+# function to perform training for a given fold
+def train(fold):
+    save_path = f'checkpoint_fold_{fold}b'
+    split_datasets = create_split_dataset(fold)
+
+    # prepare tokenizer
+    model_checkpoint = "t5-small"
+    tokenizer = T5TokenizerFast.from_pretrained(model_checkpoint, return_tensors="pt", clean_up_tokenization_spaces=True)
+    # Define additional special tokens
+    additional_special_tokens = ["<THING_START>", "<THING_END>", "<PROPERTY_START>", "<PROPERTY_END>", "<NAME>", "<DESC>", "<SIG>", "<UNIT>", "<DATA_TYPE>"]
+    # Add the additional special tokens to the tokenizer
+    tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens})
+
+    max_length = 120
+
+    # given a dataset entry, run it through the tokenizer
+    def preprocess_function(example):
+        input = example['input']
+        target = example['output']
+        # text_target sets the corresponding label to inputs
+        # there is no need to create a separate 'labels'
+        model_inputs = tokenizer(
+            input,
+            text_target=target, 
+            max_length=max_length,
+            truncation=True,
+            padding="max_length"
+        )
+        return model_inputs
+
+    # map maps function to each "row" in the dataset
+    # aka the data in the immediate nesting
+    tokenized_datasets = split_datasets.map(
+        preprocess_function,
+        batched=True,
+        num_proc=8,
+        remove_columns=split_datasets["train"].column_names,
+    )
+
+    # https://github.com/huggingface/transformers/pull/28414
+    # model_checkpoint = "google/t5-efficient-tiny"
+    # device_map set to auto to force it to load contiguous weights 
+    # model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint, device_map='auto')
+
+    directory = os.path.join(".", f'checkpoint_fold_{fold}a')
+    # Use glob to find matching paths
+    # path is usually checkpoint_fold_1/checkpoint-<step number>
+    # we are guaranteed to save only 1 checkpoint from training
+    pattern = 'checkpoint-*'
+    prev_checkpoint = glob.glob(os.path.join(directory, pattern))[0]
+    # Load the checkpoint
+    checkpoint_path = f"{prev_checkpoint}/model.safetensors"
+    checkpoint = load_file(checkpoint_path)
+    # Filter out weights related to the classification head
+    # given name format: t5.encoder.embed_tokens.weight
+    # we want: encoder.embed.tokens.weight
+    t5_weights= {key.replace("t5.", "", 1): value for key, value in checkpoint.items() if "classifier" not in key}
+
+    model = T5ForConditionalGeneration.from_pretrained(model_checkpoint)
+    # change the token embedding size to match the shape
+    model.resize_token_embeddings(len(tokenizer))
+
+    model.load_state_dict(state_dict=t5_weights, strict=False)
+
+    for key, param in model.state_dict().items():
+        if key in t5_weights:
+            print(f"{key}: Successfully overridden")
+        else:
+            print(f"{key}: Retained original weights")
+
+    # Freeze the encoder
+    for param in model.encoder.parameters():
+        param.requires_grad = False
+
+    # Freeze the shared embedding layer
+    for param in model.shared.parameters():
+        param.requires_grad = False
+
+
+    data_collator = DataCollatorForSeq2Seq(tokenizer, model=model)
+    metric = evaluate.load("sacrebleu")
+
+
+    def compute_metrics(eval_preds):
+        preds, labels = eval_preds
+        # In case the model returns more than the prediction logits
+        if isinstance(preds, tuple):
+            preds = preds[0]
+
+        decoded_preds = tokenizer.batch_decode(preds, 
+                                            skip_special_tokens=False)
+
+        # Replace -100s in the labels as we can't decode them
+        labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
+        decoded_labels = tokenizer.batch_decode(labels,
+                                                skip_special_tokens=False)
+
+        # Remove <PAD> tokens from decoded predictions and labels
+        decoded_preds = [pred.replace(tokenizer.pad_token, '').strip() for pred in decoded_preds]
+        decoded_labels = [[label.replace(tokenizer.pad_token, '').strip()] for label in decoded_labels]
+
+        # Some simple post-processing
+        # decoded_preds = [pred.strip() for pred in decoded_preds]
+        # decoded_labels = [[label.strip()] for label in decoded_labels]
+        # print(decoded_preds, decoded_labels)
+
+        result = metric.compute(predictions=decoded_preds, references=decoded_labels)
+        return {"bleu": result["score"]}
+
+
+    # Generation Config
+    # from transformers import GenerationConfig
+    gen_config = model.generation_config
+    gen_config.max_length = 128
+
+    # compile
+    # model = torch.compile(model, backend="inductor", dynamic=True)
+
+
+    # Trainer
+
+    args = Seq2SeqTrainingArguments(
+        f"{save_path}",
+        # eval_strategy="epoch",
+        eval_strategy="no",
+        logging_dir="tensorboard-log",
+        logging_strategy="epoch",
+        # save_strategy="epoch",
+        load_best_model_at_end=False,
+        learning_rate=1e-3,
+        per_device_train_batch_size=64,
+        per_device_eval_batch_size=64,
+        auto_find_batch_size=False,
+        ddp_find_unused_parameters=False,
+        weight_decay=0.01,
+        save_total_limit=1,
+        num_train_epochs=40,
+        predict_with_generate=True,
+        bf16=True,
+        push_to_hub=False,
+        generation_config=gen_config,
+        remove_unused_columns=False,
+    )
+
+
+    trainer = Seq2SeqTrainer(
+        model,
+        args,
+        train_dataset=tokenized_datasets["train"],
+        eval_dataset=tokenized_datasets["validation"],
+        data_collator=data_collator,
+        tokenizer=tokenizer,
+        compute_metrics=compute_metrics,
+        # callbacks=[EarlyStoppingCallback(early_stopping_patience=3)],
+    )
+
+    # uncomment to load training from checkpoint
+    # checkpoint_path = 'default_40_1/checkpoint-5600'
+    # trainer.train(resume_from_checkpoint=checkpoint_path)
+
+    trainer.train()
+
+# execute training
+for fold in [1,2,3,4,5]:
+    print(fold)
+    train(fold)
+

train/hybrid_t5_pattern_desc_unit/train_encoder.py

@@ -0,0 +1,228 @@
+# %%
+
+# from datasets import load_from_disk
+import os
+
+os.environ['NCCL_P2P_DISABLE'] = '1'
+os.environ['NCCL_IB_DISABLE'] = '1'
+os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
+os.environ["CUDA_VISIBLE_DEVICES"] = "0,1,2,3"
+
+import torch
+from custom_t5.modeling_t5 import T5EncoderForSequenceClassification
+from transformers import (
+    AutoTokenizer,
+    AutoModelForSequenceClassification,
+    DataCollatorWithPadding,
+    Trainer,
+    EarlyStoppingCallback,
+    TrainingArguments,
+    T5Config,
+)
+import evaluate
+import numpy as np
+import pandas as pd
+# import matplotlib.pyplot as plt
+from datasets import Dataset, DatasetDict
+
+
+
+torch.set_float32_matmul_precision('high')
+
+# %%
+
+# we need to create the mdm_list
+# import the full mdm-only file
+data_path = '../../data_import/exports/data_mapping_mdm.csv'
+full_df = pd.read_csv(data_path, skipinitialspace=True)
+mdm_list = sorted(list((set(full_df['pattern']))))
+
+# # rather than use pattern, we use the real thing and property
+# thing_property = full_df['thing'] + full_df['property']
+# thing_property = thing_property.to_list()
+# mdm_list = sorted(list(set(thing_property)))
+
+
+# %%
+id2label = {}
+label2id = {}
+for idx, val in enumerate(mdm_list):
+    id2label[idx] = val
+    label2id[val] = idx
+
+# %%
+
+# outputs a list of dictionaries
+# processes dataframe into lists of dictionaries
+# each element maps input to output
+# input: tag_description
+# output: class label
+def process_df_to_dict(df, mdm_list):
+    output_list = []
+    for _, row in df.iterrows():
+        desc = f"<DESC>{row['tag_description']}<DESC>"
+        unit = f"<UNIT>{row['unit']}<UNIT>"
+        # pattern = f"{row['thing'] + row['property']}"
+        pattern = f"{row['thing_pattern'] + ' ' + row['property_pattern']}"
+        try:
+            index = mdm_list.index(pattern)
+        except ValueError:
+            print("Error: value not found in MDM list")
+            index = -1
+        element = {
+            'text' : f"{desc}{unit}",
+            'label': index,
+        }
+        output_list.append(element)
+
+    return output_list
+
+
+def create_split_dataset(fold, mdm_list):
+    # train 
+    data_path = f"../../data_preprocess/exports/dataset/group_{fold}/train_all.csv"
+    train_df = pd.read_csv(data_path, skipinitialspace=True)
+
+    # valid
+    data_path = f"../../data_preprocess/exports/dataset/group_{fold}/valid.csv"
+    validation_df = pd.read_csv(data_path, skipinitialspace=True)
+
+    combined_data = DatasetDict({
+        'train': Dataset.from_list(process_df_to_dict(train_df, mdm_list)),
+        'validation' : Dataset.from_list(process_df_to_dict(validation_df, mdm_list)),
+    })
+    return combined_data
+
+
+# %%
+
+# function to perform training for a given fold
+def train(fold):
+
+    save_path = f'checkpoint_fold_{fold}a'
+    split_datasets = create_split_dataset(fold, mdm_list)
+
+    # prepare tokenizer
+
+    # model_checkpoint = "distilbert/distilbert-base-uncased"
+    # model_checkpoint = 'google-bert/bert-base-cased'
+    model_checkpoint = "t5-small"
+    tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, return_tensors="pt", clean_up_tokenization_spaces=True)
+    # Define additional special tokens
+    additional_special_tokens = ["<THING_START>", "<THING_END>", "<PROPERTY_START>", "<PROPERTY_END>", "<NAME>", "<DESC>", "<SIG>", "<UNIT>", "<DATA_TYPE>"]
+    # Add the additional special tokens to the tokenizer
+    tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens})
+
+    max_length = 120
+
+    # given a dataset entry, run it through the tokenizer
+    def preprocess_function(example):
+        input = example['text']
+        # text_target sets the corresponding label to inputs
+        # there is no need to create a separate 'labels'
+        model_inputs = tokenizer(
+            input,
+            max_length=max_length,
+            truncation=True,
+            padding="max_length"
+        )
+        return model_inputs
+
+    # map maps function to each "row" in the dataset
+    # aka the data in the immediate nesting
+    tokenized_datasets = split_datasets.map(
+        preprocess_function,
+        batched=True,
+        num_proc=8,
+        remove_columns="text",
+    )
+
+    # %% temp
+    # tokenized_datasets['train'].rename_columns()
+
+    # %%
+    # create data collator
+
+    data_collator = DataCollatorWithPadding(tokenizer=tokenizer)
+
+    # %%
+    # compute metrics
+    metric = evaluate.load("accuracy")
+
+
+    def compute_metrics(eval_preds):
+        preds, labels = eval_preds
+        preds = np.argmax(preds, axis=1)
+        return metric.compute(predictions=preds, references=labels)
+
+    # %%
+    # create id2label and label2id
+
+
+    # %%
+    # model = AutoModelForSequenceClassification.from_pretrained(
+    #     model_checkpoint,
+    #     num_labels=len(mdm_list),
+    #     id2label=id2label,
+    #     label2id=label2id)
+    model = T5EncoderForSequenceClassification(
+        checkpoint=model_checkpoint,
+        tokenizer=tokenizer,
+        config=T5Config.from_pretrained(model_checkpoint),
+        num_labels=len(mdm_list)
+    )
+    # important! after extending tokens vocab
+    # model.t5.resize_token_embeddings(len(tokenizer))
+
+    # model = torch.compile(model, backend="inductor", dynamic=True)
+
+
+    # %%
+    # Trainer
+
+    training_args = TrainingArguments(
+        output_dir=f"{save_path}",
+        # eval_strategy="epoch",
+        eval_strategy="no",
+        logging_dir="tensorboard-log",
+        logging_strategy="epoch",
+        # save_strategy="epoch",
+        load_best_model_at_end=False,
+        learning_rate=1e-3,
+        per_device_train_batch_size=64,
+        per_device_eval_batch_size=64,
+        auto_find_batch_size=False,
+        ddp_find_unused_parameters=False,
+        weight_decay=0.01,
+        save_total_limit=1,
+        num_train_epochs=80,
+        bf16=True,
+        push_to_hub=False,
+        remove_unused_columns=False,
+    )
+
+
+    trainer = Trainer(
+        model,
+        training_args,
+        train_dataset=tokenized_datasets["train"],
+        eval_dataset=tokenized_datasets["validation"],
+        tokenizer=tokenizer,
+        data_collator=data_collator,
+        compute_metrics=compute_metrics,
+        # callbacks=[EarlyStoppingCallback(early_stopping_patience=3)],
+    )
+
+    # uncomment to load training from checkpoint
+    # checkpoint_path = 'default_40_1/checkpoint-5600'
+    # trainer.train(resume_from_checkpoint=checkpoint_path)
+
+    trainer.train()
+
+# execute training
+for fold in [1,2,3,4,5]:
+    print(fold)
+    train(fold)
+
+
+# %%

train/mapping_t5_complete_desc_unit/mapping_prediction/predict.py

@@ -13,6 +13,7 @@ def infer_and_select(fold):
     # import test data
     data_path = f"../../../data_preprocess/exports/dataset/group_{fold}/test_all.csv"
     df = pd.read_csv(data_path, skipinitialspace=True)
+    df = df[df['MDM']].reset_index(drop=True)
 
     # get target data
     data_path = f"../../../data_preprocess/exports/dataset/group_{fold}/train_all.csv"

train/modified_t5_decoder_12_layers/.gitignore

@@ -0,0 +1,2 @@
+checkpoint*
+tensorboard-log

train/modified_t5_decoder_12_layers/mapping_prediction/.gitignore

@@ -0,0 +1,2 @@
+__pycache__
+exports/

train/modified_t5_decoder_12_layers/mapping_prediction/inference.py

@@ -0,0 +1,168 @@
+import torch
+from torch.utils.data import DataLoader
+from transformers import (
+    T5TokenizerFast,
+    AutoModelForSeq2SeqLM,
+)
+import os
+from tqdm import tqdm
+from datasets import Dataset
+import numpy as np
+
+os.environ['TOKENIZERS_PARALLELISM'] = 'false'
+
+
+class Inference():
+    tokenizer: T5TokenizerFast
+    model: torch.nn.Module
+    dataloader: DataLoader
+
+    def __init__(self, checkpoint_path):
+        self._create_tokenizer()
+        self._load_model(checkpoint_path)
+
+
+    def _create_tokenizer(self):
+        # %%
+        # load tokenizer
+        self.tokenizer = T5TokenizerFast.from_pretrained("t5-small", return_tensors="pt", clean_up_tokenization_spaces=True)
+        # Define additional special tokens
+        additional_special_tokens = ["<THING_START>", "<THING_END>", "<PROPERTY_START>", "<PROPERTY_END>", "<NAME>", "<DESC>", "SIG", "UNIT", "DATA_TYPE"]
+        # Add the additional special tokens to the tokenizer
+        self.tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens})
+
+    def _load_model(self, checkpoint_path: str):
+        # load model
+        # Define the directory and the pattern
+        model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint_path)
+        model = torch.compile(model)
+        # set model to eval
+        self.model = model.eval()
+
+
+
+
+    def prepare_dataloader(self, input_df, batch_size, max_length):
+        """
+        *arguments*
+        - input_df: input dataframe containing fields 'tag_description', 'thing', 'property'
+        - batch_size: the batch size of dataloader output
+        - max_length: length of tokenizer output
+        """
+        print("preparing dataloader")
+        # convert each dataframe row into a dictionary
+        # outputs a list of dictionaries
+
+        def _process_df(df):
+            output_list = []
+            for _, row in df.iterrows():
+                desc = f"<DESC>{row['tag_description']}<DESC>"
+                unit = f"<UNIT>{row['unit']}<UNIT>"
+                element = {
+                    'input' : f"{desc}{unit}",
+                    'output': f"<THING_START>{row['thing']}<THING_END><PROPERTY_START>{row['property']}<PROPERTY_END>",
+                }
+                output_list.append(element)
+
+            return output_list
+
+        def _preprocess_function(example):
+            input = example['input']
+            target = example['output']
+            # text_target sets the corresponding label to inputs
+            # there is no need to create a separate 'labels'
+            model_inputs = self.tokenizer(
+                input,
+                text_target=target, 
+                max_length=max_length,
+                return_tensors="pt",
+                padding="max_length",
+                truncation=True,
+            )
+            return model_inputs
+
+        test_dataset = Dataset.from_list(_process_df(input_df))
+
+
+        # map maps function to each "row" in the dataset
+        # aka the data in the immediate nesting
+        datasets = test_dataset.map(
+            _preprocess_function,
+            batched=True,
+            num_proc=1,
+            remove_columns=test_dataset.column_names,
+        )
+        # datasets = _preprocess_function(test_dataset)
+        datasets.set_format(type='torch', columns=['input_ids', 'attention_mask', 'labels'])
+
+        # create dataloader
+        self.dataloader = DataLoader(datasets, batch_size=batch_size)
+
+
+    def generate(self):
+        device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+        MAX_GENERATE_LENGTH = 128
+
+        pred_generations = []
+        pred_labels = []
+
+        print("start generation")
+        for batch in tqdm(self.dataloader):
+            # Inference in batches
+            input_ids = batch['input_ids']
+            attention_mask = batch['attention_mask']
+            # save labels too
+            pred_labels.extend(batch['labels'])
+            
+
+            # Move to GPU if available
+            input_ids = input_ids.to(device)
+            attention_mask = attention_mask.to(device)
+            self.model.to(device)
+
+            # Perform inference
+            with torch.no_grad():
+                outputs = self.model.generate(input_ids,
+                                        attention_mask=attention_mask,
+                                        max_length=MAX_GENERATE_LENGTH)
+                
+                # Decode the output and print the results
+                pred_generations.extend(outputs.to("cpu"))
+
+
+
+        # %%
+        # extract sequence and decode
+        def extract_seq(tokens, start_value, end_value):
+            if start_value not in tokens or end_value not in tokens:
+                return None  # Or handle this case according to your requirements
+            start_id = np.where(tokens == start_value)[0][0]
+            end_id = np.where(tokens == end_value)[0][0]
+
+            return tokens[start_id+1:end_id]
+
+
+        def process_tensor_output(tokens):
+            thing_seq = extract_seq(tokens, 32100, 32101) # 32100 = <THING_START>, 32101 = <THING_END>
+            property_seq = extract_seq(tokens, 32102, 32103) # 32102 = <PROPERTY_START>, 32103 = <PROPERTY_END>
+            p_thing = None
+            p_property = None
+            if (thing_seq is not None):
+                p_thing =  self.tokenizer.decode(thing_seq, skip_special_tokens=False)
+            if (property_seq is not None):
+                p_property =  self.tokenizer.decode(property_seq, skip_special_tokens=False)
+            return p_thing, p_property
+
+        # decode prediction labels
+        def decode_preds(tokens_list):
+            thing_prediction_list = []
+            property_prediction_list = []
+            for tokens in tokens_list:
+                p_thing, p_property = process_tensor_output(tokens)
+                thing_prediction_list.append(p_thing)
+                property_prediction_list.append(p_property)
+            return thing_prediction_list, property_prediction_list 
+
+        thing_prediction_list, property_prediction_list = decode_preds(pred_generations)
+        return thing_prediction_list, property_prediction_list
+

train/modified_t5_decoder_12_layers/mapping_prediction/output.txt

@@ -0,0 +1,6 @@
+
+Accuracy for fold 1: 0.9403691433980123
+Accuracy for fold 2: 0.9046728971962616
+Accuracy for fold 3: 0.9678714859437751
+Accuracy for fold 4: 0.9695528068506185
+Accuracy for fold 5: 0.902427851580394

train/modified_t5_decoder_12_layers/mapping_prediction/predict.py

@@ -0,0 +1,74 @@
+
+import pandas as pd
+import os
+import glob
+from inference import Inference
+
+checkpoint_directory =  '../'
+
+BATCH_SIZE = 512
+
+def infer_and_select(fold):
+    print(f"Inference for fold {fold}")
+    # import test data
+    data_path = f"../../../data_preprocess/exports/dataset/group_{fold}/test_all.csv"
+    df = pd.read_csv(data_path, skipinitialspace=True)
+    df = df[df['MDM']].reset_index(drop=True)
+
+    # get target data
+    data_path = f"../../../data_preprocess/exports/dataset/group_{fold}/train_all.csv"
+    train_df = pd.read_csv(data_path, skipinitialspace=True)
+    # processing to help with selection later
+    train_df['thing_property'] = train_df['thing'] + " " + train_df['property']
+
+
+    ##########################################
+    # run inference
+    # checkpoint
+    # Use glob to find matching paths
+    directory = os.path.join(checkpoint_directory, f'checkpoint_fold_{fold}b')
+    # Use glob to find matching paths
+    # path is usually checkpoint_fold_1/checkpoint-<step number>
+    # we are guaranteed to save only 1 checkpoint from training
+    pattern = 'checkpoint-*'
+    checkpoint_path = glob.glob(os.path.join(directory, pattern))[0]
+
+
+    infer = Inference(checkpoint_path)
+    infer.prepare_dataloader(df, batch_size=BATCH_SIZE, max_length=128)
+    thing_prediction_list, property_prediction_list = infer.generate()
+
+    # add labels too
+    # thing_actual_list, property_actual_list = decode_preds(pred_labels)
+    # Convert the list to a Pandas DataFrame
+    df_out = pd.DataFrame({
+        'p_thing': thing_prediction_list, 
+        'p_property': property_prediction_list
+    })
+    # df_out['p_thing_correct'] = df_out['p_thing'] == df_out['thing']
+    # df_out['p_property_correct'] = df_out['p_property'] == df_out['property']
+    df = pd.concat([df, df_out], axis=1)
+
+    # we can save the t5 generation output here
+    df.to_csv(f"exports/result_group_{fold}.csv", index=False)
+
+    # here we want to evaluate mapping accuracy within the valid in mdm data only
+    in_mdm = df['MDM']
+    condition_correct_thing = df['p_thing'] == df['thing']
+    condition_correct_property = df['p_property'] == df['property']
+    prediction_mdm_correct = sum(condition_correct_thing & condition_correct_property & in_mdm)
+    pred_correct_proportion = prediction_mdm_correct/sum(in_mdm)
+
+    # write output to file output.txt
+    with open("output.txt", "a") as f:
+        print(f'Accuracy for fold {fold}: {pred_correct_proportion}', file=f)
+
+###########################################  
+# Execute for all folds
+
+# reset file before writing to it
+with open("output.txt", "w") as f:
+    print('', file=f)
+
+for fold in [1,2,3,4,5]:
+    infer_and_select(fold)

train/modified_t5_decoder_12_layers/train_decoder.py

@@ -0,0 +1,255 @@
+# %%
+
+# from datasets import load_from_disk
+import os
+import glob
+
+os.environ['NCCL_P2P_DISABLE'] = '1'
+os.environ['NCCL_IB_DISABLE'] = '1'
+os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
+os.environ["CUDA_VISIBLE_DEVICES"] = "0,1,2,3"
+
+import torch
+
+from safetensors.torch import load_file
+
+from transformers.models.t5.modeling_t5 import T5Block
+from transformers import (
+    T5Config,
+    T5TokenizerFast,
+    AutoModelForSeq2SeqLM,
+    DataCollatorForSeq2Seq,
+    Seq2SeqTrainer,
+    EarlyStoppingCallback,
+    Seq2SeqTrainingArguments,
+    T5ForConditionalGeneration,
+    T5Model
+)
+import evaluate
+import numpy as np
+import pandas as pd
+# import matplotlib.pyplot as plt
+from datasets import Dataset, DatasetDict
+
+
+
+torch.set_float32_matmul_precision('high')
+
+
+
+# %%
+
+# model_checkpoint = "t5-small"
+# model = T5ForConditionalGeneration.from_pretrained(model_checkpoint)
+# model.config
+
+# %%
+# outputs a list of dictionaries
+def process_df_to_dict(df):
+    output_list = []
+    for _, row in df.iterrows():
+        desc = f"<DESC>{row['tag_description']}<DESC>"
+        unit = f"<UNIT>{row['unit']}<UNIT>"
+        element = {
+            'input' : f"{desc}{unit}",
+            'output': f"<THING_START>{row['thing']}<THING_END><PROPERTY_START>{row['property']}<PROPERTY_END>",
+        }
+        output_list.append(element)
+
+    return output_list
+
+
+def create_split_dataset(fold):
+    # train 
+    data_path = f"../../data_preprocess/exports/dataset/group_{fold}/train_all.csv"
+    train_df = pd.read_csv(data_path, skipinitialspace=True)
+
+    # valid
+    data_path = f"../../data_preprocess/exports/dataset/group_{fold}/valid.csv"
+    validation_df = pd.read_csv(data_path, skipinitialspace=True)
+
+    combined_data = DatasetDict({
+        'train': Dataset.from_list(process_df_to_dict(train_df)),
+        'validation' : Dataset.from_list(process_df_to_dict(validation_df)),
+    })
+    return combined_data
+
+
+# function to perform training for a given fold
+def train(fold):
+    save_path = f'checkpoint_fold_{fold}b'
+    split_datasets = create_split_dataset(fold)
+
+    # prepare tokenizer
+    model_checkpoint = "t5-small"
+    tokenizer = T5TokenizerFast.from_pretrained(model_checkpoint, return_tensors="pt", clean_up_tokenization_spaces=True)
+    # Define additional special tokens
+    additional_special_tokens = ["<THING_START>", "<THING_END>", "<PROPERTY_START>", "<PROPERTY_END>", "<NAME>", "<DESC>", "<SIG>", "<UNIT>", "<DATA_TYPE>"]
+    # Add the additional special tokens to the tokenizer
+    tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens})
+
+    max_length = 120
+
+    # given a dataset entry, run it through the tokenizer
+    def preprocess_function(example):
+        input = example['input']
+        target = example['output']
+        # text_target sets the corresponding label to inputs
+        # there is no need to create a separate 'labels'
+        model_inputs = tokenizer(
+            input,
+            text_target=target, 
+            max_length=max_length,
+            truncation=True,
+            padding="max_length"
+        )
+        return model_inputs
+
+    # map maps function to each "row" in the dataset
+    # aka the data in the immediate nesting
+    tokenized_datasets = split_datasets.map(
+        preprocess_function,
+        batched=True,
+        num_proc=8,
+        remove_columns=split_datasets["train"].column_names,
+    )
+
+    # https://github.com/huggingface/transformers/pull/28414
+    # model_checkpoint = "google/t5-efficient-tiny"
+    # device_map set to auto to force it to load contiguous weights 
+    # model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint, device_map='auto')
+
+    # directory = os.path.join(".", f'checkpoint_fold_{fold}a')
+    # # Use glob to find matching paths
+    # # path is usually checkpoint_fold_1/checkpoint-<step number>
+    # # we are guaranteed to save only 1 checkpoint from training
+    # pattern = 'checkpoint-*'
+    # prev_checkpoint = glob.glob(os.path.join(directory, pattern))[0]
+    # # t5_classify = T5Model.from_pretrained(prev_checkpoint)
+    # # Load the checkpoint
+    # checkpoint_path = f"{prev_checkpoint}/model.safetensors"
+    # checkpoint = load_file(checkpoint_path)
+    # # Filter out weights related to the classification head
+    # # given name format: t5.encoder.embed_tokens.weight
+    # # we want: encoder.embed.tokens.weight
+    # t5_weights= {key.replace("t5.", "", 1): value for key, value in checkpoint.items() if "classifier" not in key}
+
+
+    pretrained_model = T5ForConditionalGeneration.from_pretrained(model_checkpoint)
+
+    # Access the decoder stack
+    # config = T5Config("t5-small")
+
+    config = pretrained_model.config
+    config.num_layers = 6
+    config.num_decoder_layers = 12  # set new decoder layer count
+
+    model = T5ForConditionalGeneration(config)
+
+    model.shared = pretrained_model.shared
+    model.encoder = pretrained_model.encoder
+
+    pretrained_decoder_weights = [layer.state_dict() for layer in pretrained_model.decoder.block]
+    for i, layer in enumerate(pretrained_decoder_weights):
+        model.decoder.block[i].load_state_dict(layer)  # Load pretrained weights
+
+
+    # print number of decoder blocks
+    print(f'Number of decoder blocks: {len(model.decoder.block)}')
+    print(f'num_layers: {model.config.num_layers}')
+    print(f'num_decoder_layers: {model.config.num_decoder_layers}')
+
+
+    # change the token embedding size to match the shape
+    model.resize_token_embeddings(len(tokenizer))
+
+
+
+    data_collator = DataCollatorForSeq2Seq(tokenizer, model=model)
+    metric = evaluate.load("sacrebleu")
+
+
+    def compute_metrics(eval_preds):
+        preds, labels = eval_preds
+        # In case the model returns more than the prediction logits
+        if isinstance(preds, tuple):
+            preds = preds[0]
+
+        decoded_preds = tokenizer.batch_decode(preds, 
+                                            skip_special_tokens=False)
+
+        # Replace -100s in the labels as we can't decode them
+        labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
+        decoded_labels = tokenizer.batch_decode(labels,
+                                                skip_special_tokens=False)
+
+        # Remove <PAD> tokens from decoded predictions and labels
+        decoded_preds = [pred.replace(tokenizer.pad_token, '').strip() for pred in decoded_preds]
+        decoded_labels = [[label.replace(tokenizer.pad_token, '').strip()] for label in decoded_labels]
+
+        # Some simple post-processing
+        # decoded_preds = [pred.strip() for pred in decoded_preds]
+        # decoded_labels = [[label.strip()] for label in decoded_labels]
+        # print(decoded_preds, decoded_labels)
+
+        result = metric.compute(predictions=decoded_preds, references=decoded_labels)
+        return {"bleu": result["score"]}
+
+
+    # Generation Config
+    # from transformers import GenerationConfig
+    gen_config = model.generation_config
+    gen_config.max_length = 128
+
+    # compile
+    # model = torch.compile(model, backend="inductor", dynamic=True)
+
+
+    # Trainer
+
+    args = Seq2SeqTrainingArguments(
+        f"{save_path}",
+        # eval_strategy="epoch",
+        eval_strategy="no",
+        logging_dir="tensorboard-log",
+        logging_strategy="epoch",
+        # save_strategy="epoch",
+        load_best_model_at_end=False,
+        learning_rate=1e-3,
+        per_device_train_batch_size=64,
+        per_device_eval_batch_size=64,
+        auto_find_batch_size=False,
+        ddp_find_unused_parameters=False,
+        weight_decay=0.01,
+        save_total_limit=1,
+        num_train_epochs=40,
+        predict_with_generate=True,
+        bf16=True,
+        push_to_hub=False,
+        generation_config=gen_config,
+        remove_unused_columns=False,
+    )
+
+
+    trainer = Seq2SeqTrainer(
+        model,
+        args,
+        train_dataset=tokenized_datasets["train"],
+        eval_dataset=tokenized_datasets["validation"],
+        data_collator=data_collator,
+        tokenizer=tokenizer,
+        compute_metrics=compute_metrics,
+        # callbacks=[EarlyStoppingCallback(early_stopping_patience=3)],
+    )
+
+    # uncomment to load training from checkpoint
+    # checkpoint_path = 'default_40_1/checkpoint-5600'
+    # trainer.train(resume_from_checkpoint=checkpoint_path)
+
+    trainer.train()
+
+# execute training
+for fold in [1,2,3,4,5]:
+    print(fold)
+    train(fold)
+

train/modified_t5_decoder_1_layers/.gitignore

@@ -0,0 +1,2 @@
+checkpoint*
+tensorboard-log

train/modified_t5_decoder_1_layers/mapping_prediction/.gitignore

@@ -0,0 +1,2 @@
+__pycache__
+exports/

train/modified_t5_decoder_1_layers/mapping_prediction/inference.py

@@ -0,0 +1,168 @@
+import torch
+from torch.utils.data import DataLoader
+from transformers import (
+    T5TokenizerFast,
+    AutoModelForSeq2SeqLM,
+)
+import os
+from tqdm import tqdm
+from datasets import Dataset
+import numpy as np
+
+os.environ['TOKENIZERS_PARALLELISM'] = 'false'
+
+
+class Inference():
+    tokenizer: T5TokenizerFast
+    model: torch.nn.Module
+    dataloader: DataLoader
+
+    def __init__(self, checkpoint_path):
+        self._create_tokenizer()
+        self._load_model(checkpoint_path)
+
+
+    def _create_tokenizer(self):
+        # %%
+        # load tokenizer
+        self.tokenizer = T5TokenizerFast.from_pretrained("t5-small", return_tensors="pt", clean_up_tokenization_spaces=True)
+        # Define additional special tokens
+        additional_special_tokens = ["<THING_START>", "<THING_END>", "<PROPERTY_START>", "<PROPERTY_END>", "<NAME>", "<DESC>", "SIG", "UNIT", "DATA_TYPE"]
+        # Add the additional special tokens to the tokenizer
+        self.tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens})
+
+    def _load_model(self, checkpoint_path: str):
+        # load model
+        # Define the directory and the pattern
+        model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint_path)
+        model = torch.compile(model)
+        # set model to eval
+        self.model = model.eval()
+
+
+
+
+    def prepare_dataloader(self, input_df, batch_size, max_length):
+        """
+        *arguments*
+        - input_df: input dataframe containing fields 'tag_description', 'thing', 'property'
+        - batch_size: the batch size of dataloader output
+        - max_length: length of tokenizer output
+        """
+        print("preparing dataloader")
+        # convert each dataframe row into a dictionary
+        # outputs a list of dictionaries
+
+        def _process_df(df):
+            output_list = []
+            for _, row in df.iterrows():
+                desc = f"<DESC>{row['tag_description']}<DESC>"
+                unit = f"<UNIT>{row['unit']}<UNIT>"
+                element = {
+                    'input' : f"{desc}{unit}",
+                    'output': f"<THING_START>{row['thing']}<THING_END><PROPERTY_START>{row['property']}<PROPERTY_END>",
+                }
+                output_list.append(element)
+
+            return output_list
+
+        def _preprocess_function(example):
+            input = example['input']
+            target = example['output']
+            # text_target sets the corresponding label to inputs
+            # there is no need to create a separate 'labels'
+            model_inputs = self.tokenizer(
+                input,
+                text_target=target, 
+                max_length=max_length,
+                return_tensors="pt",
+                padding="max_length",
+                truncation=True,
+            )
+            return model_inputs
+
+        test_dataset = Dataset.from_list(_process_df(input_df))
+
+
+        # map maps function to each "row" in the dataset
+        # aka the data in the immediate nesting
+        datasets = test_dataset.map(
+            _preprocess_function,
+            batched=True,
+            num_proc=1,
+            remove_columns=test_dataset.column_names,
+        )
+        # datasets = _preprocess_function(test_dataset)
+        datasets.set_format(type='torch', columns=['input_ids', 'attention_mask', 'labels'])
+
+        # create dataloader
+        self.dataloader = DataLoader(datasets, batch_size=batch_size)
+
+
+    def generate(self):
+        device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+        MAX_GENERATE_LENGTH = 128
+
+        pred_generations = []
+        pred_labels = []
+
+        print("start generation")
+        for batch in tqdm(self.dataloader):
+            # Inference in batches
+            input_ids = batch['input_ids']
+            attention_mask = batch['attention_mask']
+            # save labels too
+            pred_labels.extend(batch['labels'])
+            
+
+            # Move to GPU if available
+            input_ids = input_ids.to(device)
+            attention_mask = attention_mask.to(device)
+            self.model.to(device)
+
+            # Perform inference
+            with torch.no_grad():
+                outputs = self.model.generate(input_ids,
+                                        attention_mask=attention_mask,
+                                        max_length=MAX_GENERATE_LENGTH)
+                
+                # Decode the output and print the results
+                pred_generations.extend(outputs.to("cpu"))
+
+
+
+        # %%
+        # extract sequence and decode
+        def extract_seq(tokens, start_value, end_value):
+            if start_value not in tokens or end_value not in tokens:
+                return None  # Or handle this case according to your requirements
+            start_id = np.where(tokens == start_value)[0][0]
+            end_id = np.where(tokens == end_value)[0][0]
+
+            return tokens[start_id+1:end_id]
+
+
+        def process_tensor_output(tokens):
+            thing_seq = extract_seq(tokens, 32100, 32101) # 32100 = <THING_START>, 32101 = <THING_END>
+            property_seq = extract_seq(tokens, 32102, 32103) # 32102 = <PROPERTY_START>, 32103 = <PROPERTY_END>
+            p_thing = None
+            p_property = None
+            if (thing_seq is not None):
+                p_thing =  self.tokenizer.decode(thing_seq, skip_special_tokens=False)
+            if (property_seq is not None):
+                p_property =  self.tokenizer.decode(property_seq, skip_special_tokens=False)
+            return p_thing, p_property
+
+        # decode prediction labels
+        def decode_preds(tokens_list):
+            thing_prediction_list = []
+            property_prediction_list = []
+            for tokens in tokens_list:
+                p_thing, p_property = process_tensor_output(tokens)
+                thing_prediction_list.append(p_thing)
+                property_prediction_list.append(p_property)
+            return thing_prediction_list, property_prediction_list 
+
+        thing_prediction_list, property_prediction_list = decode_preds(pred_generations)
+        return thing_prediction_list, property_prediction_list
+

train/modified_t5_decoder_1_layers/mapping_prediction/output.txt

@@ -0,0 +1,6 @@
+
+Accuracy for fold 1: 0.8968291528632276
+Accuracy for fold 2: 0.8859813084112149
+Accuracy for fold 3: 0.9382530120481928
+Accuracy for fold 4: 0.9586108468125595
+Accuracy for fold 5: 0.8827301878149336

train/modified_t5_decoder_1_layers/mapping_prediction/predict.py

@@ -0,0 +1,74 @@
+
+import pandas as pd
+import os
+import glob
+from inference import Inference
+
+checkpoint_directory =  '../'
+
+BATCH_SIZE = 512
+
+def infer_and_select(fold):
+    print(f"Inference for fold {fold}")
+    # import test data
+    data_path = f"../../../data_preprocess/exports/dataset/group_{fold}/test_all.csv"
+    df = pd.read_csv(data_path, skipinitialspace=True)
+    df = df[df['MDM']].reset_index(drop=True)
+
+    # get target data
+    data_path = f"../../../data_preprocess/exports/dataset/group_{fold}/train_all.csv"
+    train_df = pd.read_csv(data_path, skipinitialspace=True)
+    # processing to help with selection later
+    train_df['thing_property'] = train_df['thing'] + " " + train_df['property']
+
+
+    ##########################################
+    # run inference
+    # checkpoint
+    # Use glob to find matching paths
+    directory = os.path.join(checkpoint_directory, f'checkpoint_fold_{fold}b')
+    # Use glob to find matching paths
+    # path is usually checkpoint_fold_1/checkpoint-<step number>
+    # we are guaranteed to save only 1 checkpoint from training
+    pattern = 'checkpoint-*'
+    checkpoint_path = glob.glob(os.path.join(directory, pattern))[0]
+
+
+    infer = Inference(checkpoint_path)
+    infer.prepare_dataloader(df, batch_size=BATCH_SIZE, max_length=128)
+    thing_prediction_list, property_prediction_list = infer.generate()
+
+    # add labels too
+    # thing_actual_list, property_actual_list = decode_preds(pred_labels)
+    # Convert the list to a Pandas DataFrame
+    df_out = pd.DataFrame({
+        'p_thing': thing_prediction_list, 
+        'p_property': property_prediction_list
+    })
+    # df_out['p_thing_correct'] = df_out['p_thing'] == df_out['thing']
+    # df_out['p_property_correct'] = df_out['p_property'] == df_out['property']
+    df = pd.concat([df, df_out], axis=1)
+
+    # we can save the t5 generation output here
+    df.to_csv(f"exports/result_group_{fold}.csv", index=False)
+
+    # here we want to evaluate mapping accuracy within the valid in mdm data only
+    in_mdm = df['MDM']
+    condition_correct_thing = df['p_thing'] == df['thing']
+    condition_correct_property = df['p_property'] == df['property']
+    prediction_mdm_correct = sum(condition_correct_thing & condition_correct_property & in_mdm)
+    pred_correct_proportion = prediction_mdm_correct/sum(in_mdm)
+
+    # write output to file output.txt
+    with open("output.txt", "a") as f:
+        print(f'Accuracy for fold {fold}: {pred_correct_proportion}', file=f)
+
+###########################################  
+# Execute for all folds
+
+# reset file before writing to it
+with open("output.txt", "w") as f:
+    print('', file=f)
+
+for fold in [1,2,3,4,5]:
+    infer_and_select(fold)

train/modified_t5_decoder_1_layers/train_decoder.py

@@ -0,0 +1,255 @@
+# %%
+
+# from datasets import load_from_disk
+import os
+import glob
+
+os.environ['NCCL_P2P_DISABLE'] = '1'
+os.environ['NCCL_IB_DISABLE'] = '1'
+os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
+os.environ["CUDA_VISIBLE_DEVICES"] = "0,1,2,3"
+
+import torch
+
+from safetensors.torch import load_file
+
+from transformers.models.t5.modeling_t5 import T5Block
+from transformers import (
+    T5Config,
+    T5TokenizerFast,
+    AutoModelForSeq2SeqLM,
+    DataCollatorForSeq2Seq,
+    Seq2SeqTrainer,
+    EarlyStoppingCallback,
+    Seq2SeqTrainingArguments,
+    T5ForConditionalGeneration,
+    T5Model
+)
+import evaluate
+import numpy as np
+import pandas as pd
+# import matplotlib.pyplot as plt
+from datasets import Dataset, DatasetDict
+
+
+
+torch.set_float32_matmul_precision('high')
+
+
+
+# %%
+
+# model_checkpoint = "t5-small"
+# model = T5ForConditionalGeneration.from_pretrained(model_checkpoint)
+# model.config
+
+# %%
+# outputs a list of dictionaries
+def process_df_to_dict(df):
+    output_list = []
+    for _, row in df.iterrows():
+        desc = f"<DESC>{row['tag_description']}<DESC>"
+        unit = f"<UNIT>{row['unit']}<UNIT>"
+        element = {
+            'input' : f"{desc}{unit}",
+            'output': f"<THING_START>{row['thing']}<THING_END><PROPERTY_START>{row['property']}<PROPERTY_END>",
+        }
+        output_list.append(element)
+
+    return output_list
+
+
+def create_split_dataset(fold):
+    # train 
+    data_path = f"../../data_preprocess/exports/dataset/group_{fold}/train_all.csv"
+    train_df = pd.read_csv(data_path, skipinitialspace=True)
+
+    # valid
+    data_path = f"../../data_preprocess/exports/dataset/group_{fold}/valid.csv"
+    validation_df = pd.read_csv(data_path, skipinitialspace=True)
+
+    combined_data = DatasetDict({
+        'train': Dataset.from_list(process_df_to_dict(train_df)),
+        'validation' : Dataset.from_list(process_df_to_dict(validation_df)),
+    })
+    return combined_data
+
+
+# function to perform training for a given fold
+def train(fold):
+    save_path = f'checkpoint_fold_{fold}b'
+    split_datasets = create_split_dataset(fold)
+
+    # prepare tokenizer
+    model_checkpoint = "t5-small"
+    tokenizer = T5TokenizerFast.from_pretrained(model_checkpoint, return_tensors="pt", clean_up_tokenization_spaces=True)
+    # Define additional special tokens
+    additional_special_tokens = ["<THING_START>", "<THING_END>", "<PROPERTY_START>", "<PROPERTY_END>", "<NAME>", "<DESC>", "<SIG>", "<UNIT>", "<DATA_TYPE>"]
+    # Add the additional special tokens to the tokenizer
+    tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens})
+
+    max_length = 120
+
+    # given a dataset entry, run it through the tokenizer
+    def preprocess_function(example):
+        input = example['input']
+        target = example['output']
+        # text_target sets the corresponding label to inputs
+        # there is no need to create a separate 'labels'
+        model_inputs = tokenizer(
+            input,
+            text_target=target, 
+            max_length=max_length,
+            truncation=True,
+            padding="max_length"
+        )
+        return model_inputs
+
+    # map maps function to each "row" in the dataset
+    # aka the data in the immediate nesting
+    tokenized_datasets = split_datasets.map(
+        preprocess_function,
+        batched=True,
+        num_proc=8,
+        remove_columns=split_datasets["train"].column_names,
+    )
+
+    # https://github.com/huggingface/transformers/pull/28414
+    # model_checkpoint = "google/t5-efficient-tiny"
+    # device_map set to auto to force it to load contiguous weights 
+    # model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint, device_map='auto')
+
+    # directory = os.path.join(".", f'checkpoint_fold_{fold}a')
+    # # Use glob to find matching paths
+    # # path is usually checkpoint_fold_1/checkpoint-<step number>
+    # # we are guaranteed to save only 1 checkpoint from training
+    # pattern = 'checkpoint-*'
+    # prev_checkpoint = glob.glob(os.path.join(directory, pattern))[0]
+    # # t5_classify = T5Model.from_pretrained(prev_checkpoint)
+    # # Load the checkpoint
+    # checkpoint_path = f"{prev_checkpoint}/model.safetensors"
+    # checkpoint = load_file(checkpoint_path)
+    # # Filter out weights related to the classification head
+    # # given name format: t5.encoder.embed_tokens.weight
+    # # we want: encoder.embed.tokens.weight
+    # t5_weights= {key.replace("t5.", "", 1): value for key, value in checkpoint.items() if "classifier" not in key}
+
+
+    pretrained_model = T5ForConditionalGeneration.from_pretrained(model_checkpoint)
+
+    # Access the decoder stack
+    # config = T5Config("t5-small")
+
+    config = pretrained_model.config
+    config.num_layers = 6
+    config.num_decoder_layers = 1  # set new decoder layer count
+
+    model = T5ForConditionalGeneration(config)
+
+    model.shared = pretrained_model.shared
+    model.encoder = pretrained_model.encoder
+
+    pretrained_decoder_weights = [layer.state_dict() for layer in pretrained_model.decoder.block]
+    for i, layer in enumerate(pretrained_decoder_weights[:config.num_decoder_layers]):
+        model.decoder.block[i].load_state_dict(layer)  # Load pretrained weights
+
+
+    # print number of decoder blocks
+    print(f'Number of decoder blocks: {len(model.decoder.block)}')
+    print(f'num_layers: {model.config.num_layers}')
+    print(f'num_decoder_layers: {model.config.num_decoder_layers}')
+
+
+    # change the token embedding size to match the shape
+    model.resize_token_embeddings(len(tokenizer))
+
+
+
+    data_collator = DataCollatorForSeq2Seq(tokenizer, model=model)
+    metric = evaluate.load("sacrebleu")
+
+
+    def compute_metrics(eval_preds):
+        preds, labels = eval_preds
+        # In case the model returns more than the prediction logits
+        if isinstance(preds, tuple):
+            preds = preds[0]
+
+        decoded_preds = tokenizer.batch_decode(preds, 
+                                            skip_special_tokens=False)
+
+        # Replace -100s in the labels as we can't decode them
+        labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
+        decoded_labels = tokenizer.batch_decode(labels,
+                                                skip_special_tokens=False)
+
+        # Remove <PAD> tokens from decoded predictions and labels
+        decoded_preds = [pred.replace(tokenizer.pad_token, '').strip() for pred in decoded_preds]
+        decoded_labels = [[label.replace(tokenizer.pad_token, '').strip()] for label in decoded_labels]
+
+        # Some simple post-processing
+        # decoded_preds = [pred.strip() for pred in decoded_preds]
+        # decoded_labels = [[label.strip()] for label in decoded_labels]
+        # print(decoded_preds, decoded_labels)
+
+        result = metric.compute(predictions=decoded_preds, references=decoded_labels)
+        return {"bleu": result["score"]}
+
+
+    # Generation Config
+    # from transformers import GenerationConfig
+    gen_config = model.generation_config
+    gen_config.max_length = 128
+
+    # compile
+    # model = torch.compile(model, backend="inductor", dynamic=True)
+
+
+    # Trainer
+
+    args = Seq2SeqTrainingArguments(
+        f"{save_path}",
+        # eval_strategy="epoch",
+        eval_strategy="no",
+        logging_dir="tensorboard-log",
+        logging_strategy="epoch",
+        # save_strategy="epoch",
+        load_best_model_at_end=False,
+        learning_rate=1e-3,
+        per_device_train_batch_size=64,
+        per_device_eval_batch_size=64,
+        auto_find_batch_size=False,
+        ddp_find_unused_parameters=False,
+        weight_decay=0.01,
+        save_total_limit=1,
+        num_train_epochs=40,
+        predict_with_generate=True,
+        bf16=True,
+        push_to_hub=False,
+        generation_config=gen_config,
+        remove_unused_columns=False,
+    )
+
+
+    trainer = Seq2SeqTrainer(
+        model,
+        args,
+        train_dataset=tokenized_datasets["train"],
+        eval_dataset=tokenized_datasets["validation"],
+        data_collator=data_collator,
+        tokenizer=tokenizer,
+        compute_metrics=compute_metrics,
+        # callbacks=[EarlyStoppingCallback(early_stopping_patience=3)],
+    )
+
+    # uncomment to load training from checkpoint
+    # checkpoint_path = 'default_40_1/checkpoint-5600'
+    # trainer.train(resume_from_checkpoint=checkpoint_path)
+
+    trainer.train()
+
+# execute training
+for fold in [1,2,3,4,5]:
+    print(fold)
+    train(fold)
+

train/modified_t5_decoder_2_layers/.gitignore

@@ -0,0 +1,2 @@
+checkpoint*
+tensorboard-log

train/modified_t5_decoder_2_layers/mapping_prediction/.gitignore

@@ -0,0 +1,2 @@
+__pycache__
+exports/

train/modified_t5_decoder_2_layers/mapping_prediction/inference.py

@@ -0,0 +1,168 @@
+import torch
+from torch.utils.data import DataLoader
+from transformers import (
+    T5TokenizerFast,
+    AutoModelForSeq2SeqLM,
+)
+import os
+from tqdm import tqdm
+from datasets import Dataset
+import numpy as np
+
+os.environ['TOKENIZERS_PARALLELISM'] = 'false'
+
+
+class Inference():
+    tokenizer: T5TokenizerFast
+    model: torch.nn.Module
+    dataloader: DataLoader
+
+    def __init__(self, checkpoint_path):
+        self._create_tokenizer()
+        self._load_model(checkpoint_path)
+
+
+    def _create_tokenizer(self):
+        # %%
+        # load tokenizer
+        self.tokenizer = T5TokenizerFast.from_pretrained("t5-small", return_tensors="pt", clean_up_tokenization_spaces=True)
+        # Define additional special tokens
+        additional_special_tokens = ["<THING_START>", "<THING_END>", "<PROPERTY_START>", "<PROPERTY_END>", "<NAME>", "<DESC>", "SIG", "UNIT", "DATA_TYPE"]
+        # Add the additional special tokens to the tokenizer
+        self.tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens})
+
+    def _load_model(self, checkpoint_path: str):
+        # load model
+        # Define the directory and the pattern
+        model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint_path)
+        model = torch.compile(model)
+        # set model to eval
+        self.model = model.eval()
+
+
+
+
+    def prepare_dataloader(self, input_df, batch_size, max_length):
+        """
+        *arguments*
+        - input_df: input dataframe containing fields 'tag_description', 'thing', 'property'
+        - batch_size: the batch size of dataloader output
+        - max_length: length of tokenizer output
+        """
+        print("preparing dataloader")
+        # convert each dataframe row into a dictionary
+        # outputs a list of dictionaries
+
+        def _process_df(df):
+            output_list = []
+            for _, row in df.iterrows():
+                desc = f"<DESC>{row['tag_description']}<DESC>"
+                unit = f"<UNIT>{row['unit']}<UNIT>"
+                element = {
+                    'input' : f"{desc}{unit}",
+                    'output': f"<THING_START>{row['thing']}<THING_END><PROPERTY_START>{row['property']}<PROPERTY_END>",
+                }
+                output_list.append(element)
+
+            return output_list
+
+        def _preprocess_function(example):
+            input = example['input']
+            target = example['output']
+            # text_target sets the corresponding label to inputs
+            # there is no need to create a separate 'labels'
+            model_inputs = self.tokenizer(
+                input,
+                text_target=target, 
+                max_length=max_length,
+                return_tensors="pt",
+                padding="max_length",
+                truncation=True,
+            )
+            return model_inputs
+
+        test_dataset = Dataset.from_list(_process_df(input_df))
+
+
+        # map maps function to each "row" in the dataset
+        # aka the data in the immediate nesting
+        datasets = test_dataset.map(
+            _preprocess_function,
+            batched=True,
+            num_proc=1,
+            remove_columns=test_dataset.column_names,
+        )
+        # datasets = _preprocess_function(test_dataset)
+        datasets.set_format(type='torch', columns=['input_ids', 'attention_mask', 'labels'])
+
+        # create dataloader
+        self.dataloader = DataLoader(datasets, batch_size=batch_size)
+
+
+    def generate(self):
+        device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+        MAX_GENERATE_LENGTH = 128
+
+        pred_generations = []
+        pred_labels = []
+
+        print("start generation")
+        for batch in tqdm(self.dataloader):
+            # Inference in batches
+            input_ids = batch['input_ids']
+            attention_mask = batch['attention_mask']
+            # save labels too
+            pred_labels.extend(batch['labels'])
+            
+
+            # Move to GPU if available
+            input_ids = input_ids.to(device)
+            attention_mask = attention_mask.to(device)
+            self.model.to(device)
+
+            # Perform inference
+            with torch.no_grad():
+                outputs = self.model.generate(input_ids,
+                                        attention_mask=attention_mask,
+                                        max_length=MAX_GENERATE_LENGTH)
+                
+                # Decode the output and print the results
+                pred_generations.extend(outputs.to("cpu"))
+
+
+
+        # %%
+        # extract sequence and decode
+        def extract_seq(tokens, start_value, end_value):
+            if start_value not in tokens or end_value not in tokens:
+                return None  # Or handle this case according to your requirements
+            start_id = np.where(tokens == start_value)[0][0]
+            end_id = np.where(tokens == end_value)[0][0]
+
+            return tokens[start_id+1:end_id]
+
+
+        def process_tensor_output(tokens):
+            thing_seq = extract_seq(tokens, 32100, 32101) # 32100 = <THING_START>, 32101 = <THING_END>
+            property_seq = extract_seq(tokens, 32102, 32103) # 32102 = <PROPERTY_START>, 32103 = <PROPERTY_END>
+            p_thing = None
+            p_property = None
+            if (thing_seq is not None):
+                p_thing =  self.tokenizer.decode(thing_seq, skip_special_tokens=False)
+            if (property_seq is not None):
+                p_property =  self.tokenizer.decode(property_seq, skip_special_tokens=False)
+            return p_thing, p_property
+
+        # decode prediction labels
+        def decode_preds(tokens_list):
+            thing_prediction_list = []
+            property_prediction_list = []
+            for tokens in tokens_list:
+                p_thing, p_property = process_tensor_output(tokens)
+                thing_prediction_list.append(p_thing)
+                property_prediction_list.append(p_property)
+            return thing_prediction_list, property_prediction_list 
+
+        thing_prediction_list, property_prediction_list = decode_preds(pred_generations)
+        return thing_prediction_list, property_prediction_list
+

train/modified_t5_decoder_2_layers/mapping_prediction/output.txt

@@ -0,0 +1,6 @@
+
+Accuracy for fold 1: 0.9318504495977283
+Accuracy for fold 2: 0.8859813084112149
+Accuracy for fold 3: 0.9678714859437751
+Accuracy for fold 4: 0.9738344433872502
+Accuracy for fold 5: 0.9015116811726981

train/modified_t5_decoder_2_layers/mapping_prediction/predict.py

@@ -0,0 +1,74 @@
+
+import pandas as pd
+import os
+import glob
+from inference import Inference
+
+checkpoint_directory =  '../'
+
+BATCH_SIZE = 512
+
+def infer_and_select(fold):
+    print(f"Inference for fold {fold}")
+    # import test data
+    data_path = f"../../../data_preprocess/exports/dataset/group_{fold}/test_all.csv"
+    df = pd.read_csv(data_path, skipinitialspace=True)
+    df = df[df['MDM']].reset_index(drop=True)
+
+    # get target data
+    data_path = f"../../../data_preprocess/exports/dataset/group_{fold}/train_all.csv"
+    train_df = pd.read_csv(data_path, skipinitialspace=True)
+    # processing to help with selection later
+    train_df['thing_property'] = train_df['thing'] + " " + train_df['property']
+
+
+    ##########################################
+    # run inference
+    # checkpoint
+    # Use glob to find matching paths
+    directory = os.path.join(checkpoint_directory, f'checkpoint_fold_{fold}b')
+    # Use glob to find matching paths
+    # path is usually checkpoint_fold_1/checkpoint-<step number>
+    # we are guaranteed to save only 1 checkpoint from training
+    pattern = 'checkpoint-*'
+    checkpoint_path = glob.glob(os.path.join(directory, pattern))[0]
+
+
+    infer = Inference(checkpoint_path)
+    infer.prepare_dataloader(df, batch_size=BATCH_SIZE, max_length=128)
+    thing_prediction_list, property_prediction_list = infer.generate()
+
+    # add labels too
+    # thing_actual_list, property_actual_list = decode_preds(pred_labels)
+    # Convert the list to a Pandas DataFrame
+    df_out = pd.DataFrame({
+        'p_thing': thing_prediction_list, 
+        'p_property': property_prediction_list
+    })
+    # df_out['p_thing_correct'] = df_out['p_thing'] == df_out['thing']
+    # df_out['p_property_correct'] = df_out['p_property'] == df_out['property']
+    df = pd.concat([df, df_out], axis=1)
+
+    # we can save the t5 generation output here
+    df.to_csv(f"exports/result_group_{fold}.csv", index=False)
+
+    # here we want to evaluate mapping accuracy within the valid in mdm data only
+    in_mdm = df['MDM']
+    condition_correct_thing = df['p_thing'] == df['thing']
+    condition_correct_property = df['p_property'] == df['property']
+    prediction_mdm_correct = sum(condition_correct_thing & condition_correct_property & in_mdm)
+    pred_correct_proportion = prediction_mdm_correct/sum(in_mdm)
+
+    # write output to file output.txt
+    with open("output.txt", "a") as f:
+        print(f'Accuracy for fold {fold}: {pred_correct_proportion}', file=f)
+
+###########################################  
+# Execute for all folds
+
+# reset file before writing to it
+with open("output.txt", "w") as f:
+    print('', file=f)
+
+for fold in [1,2,3,4,5]:
+    infer_and_select(fold)

train/modified_t5_decoder_2_layers/train_decoder.py

@@ -0,0 +1,255 @@
+# %%
+
+# from datasets import load_from_disk
+import os
+import glob
+
+os.environ['NCCL_P2P_DISABLE'] = '1'
+os.environ['NCCL_IB_DISABLE'] = '1'
+os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
+os.environ["CUDA_VISIBLE_DEVICES"] = "0,1,2,3"
+
+import torch
+
+from safetensors.torch import load_file
+
+from transformers.models.t5.modeling_t5 import T5Block
+from transformers import (
+    T5Config,
+    T5TokenizerFast,
+    AutoModelForSeq2SeqLM,
+    DataCollatorForSeq2Seq,
+    Seq2SeqTrainer,
+    EarlyStoppingCallback,
+    Seq2SeqTrainingArguments,
+    T5ForConditionalGeneration,
+    T5Model
+)
+import evaluate
+import numpy as np
+import pandas as pd
+# import matplotlib.pyplot as plt
+from datasets import Dataset, DatasetDict
+
+
+
+torch.set_float32_matmul_precision('high')
+
+
+
+# %%
+
+# model_checkpoint = "t5-small"
+# model = T5ForConditionalGeneration.from_pretrained(model_checkpoint)
+# model.config
+
+# %%
+# outputs a list of dictionaries
+def process_df_to_dict(df):
+    output_list = []
+    for _, row in df.iterrows():
+        desc = f"<DESC>{row['tag_description']}<DESC>"
+        unit = f"<UNIT>{row['unit']}<UNIT>"
+        element = {
+            'input' : f"{desc}{unit}",
+            'output': f"<THING_START>{row['thing']}<THING_END><PROPERTY_START>{row['property']}<PROPERTY_END>",
+        }
+        output_list.append(element)
+
+    return output_list
+
+
+def create_split_dataset(fold):
+    # train 
+    data_path = f"../../data_preprocess/exports/dataset/group_{fold}/train_all.csv"
+    train_df = pd.read_csv(data_path, skipinitialspace=True)
+
+    # valid
+    data_path = f"../../data_preprocess/exports/dataset/group_{fold}/valid.csv"
+    validation_df = pd.read_csv(data_path, skipinitialspace=True)
+
+    combined_data = DatasetDict({
+        'train': Dataset.from_list(process_df_to_dict(train_df)),
+        'validation' : Dataset.from_list(process_df_to_dict(validation_df)),
+    })
+    return combined_data
+
+
+# function to perform training for a given fold
+def train(fold):
+    save_path = f'checkpoint_fold_{fold}b'
+    split_datasets = create_split_dataset(fold)
+
+    # prepare tokenizer
+    model_checkpoint = "t5-small"
+    tokenizer = T5TokenizerFast.from_pretrained(model_checkpoint, return_tensors="pt", clean_up_tokenization_spaces=True)
+    # Define additional special tokens
+    additional_special_tokens = ["<THING_START>", "<THING_END>", "<PROPERTY_START>", "<PROPERTY_END>", "<NAME>", "<DESC>", "<SIG>", "<UNIT>", "<DATA_TYPE>"]
+    # Add the additional special tokens to the tokenizer
+    tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens})
+
+    max_length = 120
+
+    # given a dataset entry, run it through the tokenizer
+    def preprocess_function(example):
+        input = example['input']
+        target = example['output']
+        # text_target sets the corresponding label to inputs
+        # there is no need to create a separate 'labels'
+        model_inputs = tokenizer(
+            input,
+            text_target=target, 
+            max_length=max_length,
+            truncation=True,
+            padding="max_length"
+        )
+        return model_inputs
+
+    # map maps function to each "row" in the dataset
+    # aka the data in the immediate nesting
+    tokenized_datasets = split_datasets.map(
+        preprocess_function,
+        batched=True,
+        num_proc=8,
+        remove_columns=split_datasets["train"].column_names,
+    )
+
+    # https://github.com/huggingface/transformers/pull/28414
+    # model_checkpoint = "google/t5-efficient-tiny"
+    # device_map set to auto to force it to load contiguous weights 
+    # model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint, device_map='auto')
+
+    # directory = os.path.join(".", f'checkpoint_fold_{fold}a')
+    # # Use glob to find matching paths
+    # # path is usually checkpoint_fold_1/checkpoint-<step number>
+    # # we are guaranteed to save only 1 checkpoint from training
+    # pattern = 'checkpoint-*'
+    # prev_checkpoint = glob.glob(os.path.join(directory, pattern))[0]
+    # # t5_classify = T5Model.from_pretrained(prev_checkpoint)
+    # # Load the checkpoint
+    # checkpoint_path = f"{prev_checkpoint}/model.safetensors"
+    # checkpoint = load_file(checkpoint_path)
+    # # Filter out weights related to the classification head
+    # # given name format: t5.encoder.embed_tokens.weight
+    # # we want: encoder.embed.tokens.weight
+    # t5_weights= {key.replace("t5.", "", 1): value for key, value in checkpoint.items() if "classifier" not in key}
+
+
+    pretrained_model = T5ForConditionalGeneration.from_pretrained(model_checkpoint)
+
+    # Access the decoder stack
+    # config = T5Config("t5-small")
+
+    config = pretrained_model.config
+    config.num_layers = 6
+    config.num_decoder_layers = 2  # set new decoder layer count
+
+    model = T5ForConditionalGeneration(config)
+
+    model.shared = pretrained_model.shared
+    model.encoder = pretrained_model.encoder
+
+    pretrained_decoder_weights = [layer.state_dict() for layer in pretrained_model.decoder.block]
+    for i, layer in enumerate(pretrained_decoder_weights[:config.num_decoder_layers]):
+        model.decoder.block[i].load_state_dict(layer)  # Load pretrained weights
+
+
+    # print number of decoder blocks
+    print(f'Number of decoder blocks: {len(model.decoder.block)}')
+    print(f'num_layers: {model.config.num_layers}')
+    print(f'num_decoder_layers: {model.config.num_decoder_layers}')
+
+
+    # change the token embedding size to match the shape
+    model.resize_token_embeddings(len(tokenizer))
+
+
+
+    data_collator = DataCollatorForSeq2Seq(tokenizer, model=model)
+    metric = evaluate.load("sacrebleu")
+
+
+    def compute_metrics(eval_preds):
+        preds, labels = eval_preds
+        # In case the model returns more than the prediction logits
+        if isinstance(preds, tuple):
+            preds = preds[0]
+
+        decoded_preds = tokenizer.batch_decode(preds, 
+                                            skip_special_tokens=False)
+
+        # Replace -100s in the labels as we can't decode them
+        labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
+        decoded_labels = tokenizer.batch_decode(labels,
+                                                skip_special_tokens=False)
+
+        # Remove <PAD> tokens from decoded predictions and labels
+        decoded_preds = [pred.replace(tokenizer.pad_token, '').strip() for pred in decoded_preds]
+        decoded_labels = [[label.replace(tokenizer.pad_token, '').strip()] for label in decoded_labels]
+
+        # Some simple post-processing
+        # decoded_preds = [pred.strip() for pred in decoded_preds]
+        # decoded_labels = [[label.strip()] for label in decoded_labels]
+        # print(decoded_preds, decoded_labels)
+
+        result = metric.compute(predictions=decoded_preds, references=decoded_labels)
+        return {"bleu": result["score"]}
+
+
+    # Generation Config
+    # from transformers import GenerationConfig
+    gen_config = model.generation_config
+    gen_config.max_length = 128
+
+    # compile
+    # model = torch.compile(model, backend="inductor", dynamic=True)
+
+
+    # Trainer
+
+    args = Seq2SeqTrainingArguments(
+        f"{save_path}",
+        # eval_strategy="epoch",
+        eval_strategy="no",
+        logging_dir="tensorboard-log",
+        logging_strategy="epoch",
+        # save_strategy="epoch",
+        load_best_model_at_end=False,
+        learning_rate=1e-3,
+        per_device_train_batch_size=64,
+        per_device_eval_batch_size=64,
+        auto_find_batch_size=False,
+        ddp_find_unused_parameters=False,
+        weight_decay=0.01,
+        save_total_limit=1,
+        num_train_epochs=40,
+        predict_with_generate=True,
+        bf16=True,
+        push_to_hub=False,
+        generation_config=gen_config,
+        remove_unused_columns=False,
+    )
+
+
+    trainer = Seq2SeqTrainer(
+        model,
+        args,
+        train_dataset=tokenized_datasets["train"],
+        eval_dataset=tokenized_datasets["validation"],
+        data_collator=data_collator,
+        tokenizer=tokenizer,
+        compute_metrics=compute_metrics,
+        # callbacks=[EarlyStoppingCallback(early_stopping_patience=3)],
+    )
+
+    # uncomment to load training from checkpoint
+    # checkpoint_path = 'default_40_1/checkpoint-5600'
+    # trainer.train(resume_from_checkpoint=checkpoint_path)
+
+    trainer.train()
+
+# execute training
+for fold in [1,2,3,4,5]:
+    print(fold)
+    train(fold)
+

train/modified_t5_decoder_3_layers/.gitignore

@@ -0,0 +1,2 @@
+checkpoint*
+tensorboard-log

train/modified_t5_decoder_3_layers/mapping_prediction/.gitignore

@@ -0,0 +1,2 @@
+__pycache__
+exports/

train/modified_t5_decoder_3_layers/mapping_prediction/inference.py

@@ -0,0 +1,168 @@
+import torch
+from torch.utils.data import DataLoader
+from transformers import (
+    T5TokenizerFast,
+    AutoModelForSeq2SeqLM,
+)
+import os
+from tqdm import tqdm
+from datasets import Dataset
+import numpy as np
+
+os.environ['TOKENIZERS_PARALLELISM'] = 'false'
+
+
+class Inference():
+    tokenizer: T5TokenizerFast
+    model: torch.nn.Module
+    dataloader: DataLoader
+
+    def __init__(self, checkpoint_path):
+        self._create_tokenizer()
+        self._load_model(checkpoint_path)
+
+
+    def _create_tokenizer(self):
+        # %%
+        # load tokenizer
+        self.tokenizer = T5TokenizerFast.from_pretrained("t5-small", return_tensors="pt", clean_up_tokenization_spaces=True)
+        # Define additional special tokens
+        additional_special_tokens = ["<THING_START>", "<THING_END>", "<PROPERTY_START>", "<PROPERTY_END>", "<NAME>", "<DESC>", "SIG", "UNIT", "DATA_TYPE"]
+        # Add the additional special tokens to the tokenizer
+        self.tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens})
+
+    def _load_model(self, checkpoint_path: str):
+        # load model
+        # Define the directory and the pattern
+        model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint_path)
+        model = torch.compile(model)
+        # set model to eval
+        self.model = model.eval()
+
+
+
+
+    def prepare_dataloader(self, input_df, batch_size, max_length):
+        """
+        *arguments*
+        - input_df: input dataframe containing fields 'tag_description', 'thing', 'property'
+        - batch_size: the batch size of dataloader output
+        - max_length: length of tokenizer output
+        """
+        print("preparing dataloader")
+        # convert each dataframe row into a dictionary
+        # outputs a list of dictionaries
+
+        def _process_df(df):
+            output_list = []
+            for _, row in df.iterrows():
+                desc = f"<DESC>{row['tag_description']}<DESC>"
+                unit = f"<UNIT>{row['unit']}<UNIT>"
+                element = {
+                    'input' : f"{desc}{unit}",
+                    'output': f"<THING_START>{row['thing']}<THING_END><PROPERTY_START>{row['property']}<PROPERTY_END>",
+                }
+                output_list.append(element)
+
+            return output_list
+
+        def _preprocess_function(example):
+            input = example['input']
+            target = example['output']
+            # text_target sets the corresponding label to inputs
+            # there is no need to create a separate 'labels'
+            model_inputs = self.tokenizer(
+                input,
+                text_target=target, 
+                max_length=max_length,
+                return_tensors="pt",
+                padding="max_length",
+                truncation=True,
+            )
+            return model_inputs
+
+        test_dataset = Dataset.from_list(_process_df(input_df))
+
+
+        # map maps function to each "row" in the dataset
+        # aka the data in the immediate nesting
+        datasets = test_dataset.map(
+            _preprocess_function,
+            batched=True,
+            num_proc=1,
+            remove_columns=test_dataset.column_names,
+        )
+        # datasets = _preprocess_function(test_dataset)
+        datasets.set_format(type='torch', columns=['input_ids', 'attention_mask', 'labels'])
+
+        # create dataloader
+        self.dataloader = DataLoader(datasets, batch_size=batch_size)
+
+
+    def generate(self):
+        device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+        MAX_GENERATE_LENGTH = 128
+
+        pred_generations = []
+        pred_labels = []
+
+        print("start generation")
+        for batch in tqdm(self.dataloader):
+            # Inference in batches
+            input_ids = batch['input_ids']
+            attention_mask = batch['attention_mask']
+            # save labels too
+            pred_labels.extend(batch['labels'])
+            
+
+            # Move to GPU if available
+            input_ids = input_ids.to(device)
+            attention_mask = attention_mask.to(device)
+            self.model.to(device)
+
+            # Perform inference
+            with torch.no_grad():
+                outputs = self.model.generate(input_ids,
+                                        attention_mask=attention_mask,
+                                        max_length=MAX_GENERATE_LENGTH)
+                
+                # Decode the output and print the results
+                pred_generations.extend(outputs.to("cpu"))
+
+
+
+        # %%
+        # extract sequence and decode
+        def extract_seq(tokens, start_value, end_value):
+            if start_value not in tokens or end_value not in tokens:
+                return None  # Or handle this case according to your requirements
+            start_id = np.where(tokens == start_value)[0][0]
+            end_id = np.where(tokens == end_value)[0][0]
+
+            return tokens[start_id+1:end_id]
+
+
+        def process_tensor_output(tokens):
+            thing_seq = extract_seq(tokens, 32100, 32101) # 32100 = <THING_START>, 32101 = <THING_END>
+            property_seq = extract_seq(tokens, 32102, 32103) # 32102 = <PROPERTY_START>, 32103 = <PROPERTY_END>
+            p_thing = None
+            p_property = None
+            if (thing_seq is not None):
+                p_thing =  self.tokenizer.decode(thing_seq, skip_special_tokens=False)
+            if (property_seq is not None):
+                p_property =  self.tokenizer.decode(property_seq, skip_special_tokens=False)
+            return p_thing, p_property
+
+        # decode prediction labels
+        def decode_preds(tokens_list):
+            thing_prediction_list = []
+            property_prediction_list = []
+            for tokens in tokens_list:
+                p_thing, p_property = process_tensor_output(tokens)
+                thing_prediction_list.append(p_thing)
+                property_prediction_list.append(p_property)
+            return thing_prediction_list, property_prediction_list 
+
+        thing_prediction_list, property_prediction_list = decode_preds(pred_generations)
+        return thing_prediction_list, property_prediction_list
+

train/modified_t5_decoder_3_layers/mapping_prediction/output.txt

@@ -0,0 +1,6 @@
+
+Accuracy for fold 1: 0.9427354472314246
+Accuracy for fold 2: 0.9098130841121496
+Accuracy for fold 3: 0.964859437751004
+Accuracy for fold 4: 0.9719314938154139
+Accuracy for fold 5: 0.9070087036188731

train/modified_t5_decoder_3_layers/mapping_prediction/predict.py

@@ -0,0 +1,74 @@
+
+import pandas as pd
+import os
+import glob
+from inference import Inference
+
+checkpoint_directory =  '../'
+
+BATCH_SIZE = 512
+
+def infer_and_select(fold):
+    print(f"Inference for fold {fold}")
+    # import test data
+    data_path = f"../../../data_preprocess/exports/dataset/group_{fold}/test_all.csv"
+    df = pd.read_csv(data_path, skipinitialspace=True)
+    df = df[df['MDM']].reset_index(drop=True)
+
+    # get target data
+    data_path = f"../../../data_preprocess/exports/dataset/group_{fold}/train_all.csv"
+    train_df = pd.read_csv(data_path, skipinitialspace=True)
+    # processing to help with selection later
+    train_df['thing_property'] = train_df['thing'] + " " + train_df['property']
+
+
+    ##########################################
+    # run inference
+    # checkpoint
+    # Use glob to find matching paths
+    directory = os.path.join(checkpoint_directory, f'checkpoint_fold_{fold}b')
+    # Use glob to find matching paths
+    # path is usually checkpoint_fold_1/checkpoint-<step number>
+    # we are guaranteed to save only 1 checkpoint from training
+    pattern = 'checkpoint-*'
+    checkpoint_path = glob.glob(os.path.join(directory, pattern))[0]
+
+
+    infer = Inference(checkpoint_path)
+    infer.prepare_dataloader(df, batch_size=BATCH_SIZE, max_length=128)
+    thing_prediction_list, property_prediction_list = infer.generate()
+
+    # add labels too
+    # thing_actual_list, property_actual_list = decode_preds(pred_labels)
+    # Convert the list to a Pandas DataFrame
+    df_out = pd.DataFrame({
+        'p_thing': thing_prediction_list, 
+        'p_property': property_prediction_list
+    })
+    # df_out['p_thing_correct'] = df_out['p_thing'] == df_out['thing']
+    # df_out['p_property_correct'] = df_out['p_property'] == df_out['property']
+    df = pd.concat([df, df_out], axis=1)
+
+    # we can save the t5 generation output here
+    df.to_csv(f"exports/result_group_{fold}.csv", index=False)
+
+    # here we want to evaluate mapping accuracy within the valid in mdm data only
+    in_mdm = df['MDM']
+    condition_correct_thing = df['p_thing'] == df['thing']
+    condition_correct_property = df['p_property'] == df['property']
+    prediction_mdm_correct = sum(condition_correct_thing & condition_correct_property & in_mdm)
+    pred_correct_proportion = prediction_mdm_correct/sum(in_mdm)
+
+    # write output to file output.txt
+    with open("output.txt", "a") as f:
+        print(f'Accuracy for fold {fold}: {pred_correct_proportion}', file=f)
+
+###########################################  
+# Execute for all folds
+
+# reset file before writing to it
+with open("output.txt", "w") as f:
+    print('', file=f)
+
+for fold in [1,2,3,4,5]:
+    infer_and_select(fold)

train/modified_t5_decoder_3_layers/train_decoder.py

@@ -0,0 +1,255 @@
+# %%
+
+# from datasets import load_from_disk
+import os
+import glob
+
+os.environ['NCCL_P2P_DISABLE'] = '1'
+os.environ['NCCL_IB_DISABLE'] = '1'
+os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
+os.environ["CUDA_VISIBLE_DEVICES"] = "0,1,2,3"
+
+import torch
+
+from safetensors.torch import load_file
+
+from transformers.models.t5.modeling_t5 import T5Block
+from transformers import (
+    T5Config,
+    T5TokenizerFast,
+    AutoModelForSeq2SeqLM,
+    DataCollatorForSeq2Seq,
+    Seq2SeqTrainer,
+    EarlyStoppingCallback,
+    Seq2SeqTrainingArguments,
+    T5ForConditionalGeneration,
+    T5Model
+)
+import evaluate
+import numpy as np
+import pandas as pd
+# import matplotlib.pyplot as plt
+from datasets import Dataset, DatasetDict
+
+
+
+torch.set_float32_matmul_precision('high')
+
+
+
+# %%
+
+# model_checkpoint = "t5-small"
+# model = T5ForConditionalGeneration.from_pretrained(model_checkpoint)
+# model.config
+
+# %%
+# outputs a list of dictionaries
+def process_df_to_dict(df):
+    output_list = []
+    for _, row in df.iterrows():
+        desc = f"<DESC>{row['tag_description']}<DESC>"
+        unit = f"<UNIT>{row['unit']}<UNIT>"
+        element = {
+            'input' : f"{desc}{unit}",
+            'output': f"<THING_START>{row['thing']}<THING_END><PROPERTY_START>{row['property']}<PROPERTY_END>",
+        }
+        output_list.append(element)
+
+    return output_list
+
+
+def create_split_dataset(fold):
+    # train 
+    data_path = f"../../data_preprocess/exports/dataset/group_{fold}/train_all.csv"
+    train_df = pd.read_csv(data_path, skipinitialspace=True)
+
+    # valid
+    data_path = f"../../data_preprocess/exports/dataset/group_{fold}/valid.csv"
+    validation_df = pd.read_csv(data_path, skipinitialspace=True)
+
+    combined_data = DatasetDict({
+        'train': Dataset.from_list(process_df_to_dict(train_df)),
+        'validation' : Dataset.from_list(process_df_to_dict(validation_df)),
+    })
+    return combined_data
+
+
+# function to perform training for a given fold
+def train(fold):
+    save_path = f'checkpoint_fold_{fold}b'
+    split_datasets = create_split_dataset(fold)
+
+    # prepare tokenizer
+    model_checkpoint = "t5-small"
+    tokenizer = T5TokenizerFast.from_pretrained(model_checkpoint, return_tensors="pt", clean_up_tokenization_spaces=True)
+    # Define additional special tokens
+    additional_special_tokens = ["<THING_START>", "<THING_END>", "<PROPERTY_START>", "<PROPERTY_END>", "<NAME>", "<DESC>", "<SIG>", "<UNIT>", "<DATA_TYPE>"]
+    # Add the additional special tokens to the tokenizer
+    tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens})
+
+    max_length = 120
+
+    # given a dataset entry, run it through the tokenizer
+    def preprocess_function(example):
+        input = example['input']
+        target = example['output']
+        # text_target sets the corresponding label to inputs
+        # there is no need to create a separate 'labels'
+        model_inputs = tokenizer(
+            input,
+            text_target=target, 
+            max_length=max_length,
+            truncation=True,
+            padding="max_length"
+        )
+        return model_inputs
+
+    # map maps function to each "row" in the dataset
+    # aka the data in the immediate nesting
+    tokenized_datasets = split_datasets.map(
+        preprocess_function,
+        batched=True,
+        num_proc=8,
+        remove_columns=split_datasets["train"].column_names,
+    )
+
+    # https://github.com/huggingface/transformers/pull/28414
+    # model_checkpoint = "google/t5-efficient-tiny"
+    # device_map set to auto to force it to load contiguous weights 
+    # model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint, device_map='auto')
+
+    # directory = os.path.join(".", f'checkpoint_fold_{fold}a')
+    # # Use glob to find matching paths
+    # # path is usually checkpoint_fold_1/checkpoint-<step number>
+    # # we are guaranteed to save only 1 checkpoint from training
+    # pattern = 'checkpoint-*'
+    # prev_checkpoint = glob.glob(os.path.join(directory, pattern))[0]
+    # # t5_classify = T5Model.from_pretrained(prev_checkpoint)
+    # # Load the checkpoint
+    # checkpoint_path = f"{prev_checkpoint}/model.safetensors"
+    # checkpoint = load_file(checkpoint_path)
+    # # Filter out weights related to the classification head
+    # # given name format: t5.encoder.embed_tokens.weight
+    # # we want: encoder.embed.tokens.weight
+    # t5_weights= {key.replace("t5.", "", 1): value for key, value in checkpoint.items() if "classifier" not in key}
+
+
+    pretrained_model = T5ForConditionalGeneration.from_pretrained(model_checkpoint)
+
+    # Access the decoder stack
+    # config = T5Config("t5-small")
+
+    config = pretrained_model.config
+    config.num_layers = 6
+    config.num_decoder_layers = 3  # set new decoder layer count
+
+    model = T5ForConditionalGeneration(config)
+
+    model.shared = pretrained_model.shared
+    model.encoder = pretrained_model.encoder
+
+    pretrained_decoder_weights = [layer.state_dict() for layer in pretrained_model.decoder.block]
+    for i, layer in enumerate(pretrained_decoder_weights[:config.num_decoder_layers]):
+        model.decoder.block[i].load_state_dict(layer)  # Load pretrained weights
+
+
+    # print number of decoder blocks
+    print(f'Number of decoder blocks: {len(model.decoder.block)}')
+    print(f'num_layers: {model.config.num_layers}')
+    print(f'num_decoder_layers: {model.config.num_decoder_layers}')
+
+
+    # change the token embedding size to match the shape
+    model.resize_token_embeddings(len(tokenizer))
+
+
+
+    data_collator = DataCollatorForSeq2Seq(tokenizer, model=model)
+    metric = evaluate.load("sacrebleu")
+
+
+    def compute_metrics(eval_preds):
+        preds, labels = eval_preds
+        # In case the model returns more than the prediction logits
+        if isinstance(preds, tuple):
+            preds = preds[0]
+
+        decoded_preds = tokenizer.batch_decode(preds, 
+                                            skip_special_tokens=False)
+
+        # Replace -100s in the labels as we can't decode them
+        labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
+        decoded_labels = tokenizer.batch_decode(labels,
+                                                skip_special_tokens=False)
+
+        # Remove <PAD> tokens from decoded predictions and labels
+        decoded_preds = [pred.replace(tokenizer.pad_token, '').strip() for pred in decoded_preds]
+        decoded_labels = [[label.replace(tokenizer.pad_token, '').strip()] for label in decoded_labels]
+
+        # Some simple post-processing
+        # decoded_preds = [pred.strip() for pred in decoded_preds]
+        # decoded_labels = [[label.strip()] for label in decoded_labels]
+        # print(decoded_preds, decoded_labels)
+
+        result = metric.compute(predictions=decoded_preds, references=decoded_labels)
+        return {"bleu": result["score"]}
+
+
+    # Generation Config
+    # from transformers import GenerationConfig
+    gen_config = model.generation_config
+    gen_config.max_length = 128
+
+    # compile
+    # model = torch.compile(model, backend="inductor", dynamic=True)
+
+
+    # Trainer
+
+    args = Seq2SeqTrainingArguments(
+        f"{save_path}",
+        # eval_strategy="epoch",
+        eval_strategy="no",
+        logging_dir="tensorboard-log",
+        logging_strategy="epoch",
+        # save_strategy="epoch",
+        load_best_model_at_end=False,
+        learning_rate=1e-3,
+        per_device_train_batch_size=64,
+        per_device_eval_batch_size=64,
+        auto_find_batch_size=False,
+        ddp_find_unused_parameters=False,
+        weight_decay=0.01,
+        save_total_limit=1,
+        num_train_epochs=40,
+        predict_with_generate=True,
+        bf16=True,
+        push_to_hub=False,
+        generation_config=gen_config,
+        remove_unused_columns=False,
+    )
+
+
+    trainer = Seq2SeqTrainer(
+        model,
+        args,
+        train_dataset=tokenized_datasets["train"],
+        eval_dataset=tokenized_datasets["validation"],
+        data_collator=data_collator,
+        tokenizer=tokenizer,
+        compute_metrics=compute_metrics,
+        # callbacks=[EarlyStoppingCallback(early_stopping_patience=3)],
+    )
+
+    # uncomment to load training from checkpoint
+    # checkpoint_path = 'default_40_1/checkpoint-5600'
+    # trainer.train(resume_from_checkpoint=checkpoint_path)
+
+    trainer.train()
+
+# execute training
+for fold in [1,2,3,4,5]:
+    print(fold)
+    train(fold)
+

train/modified_t5_decoder_4_layers/.gitignore

@@ -0,0 +1,2 @@
+checkpoint*
+tensorboard-log

train/modified_t5_decoder_4_layers/mapping_prediction/.gitignore

@@ -0,0 +1,2 @@
+__pycache__
+exports/

train/modified_t5_decoder_4_layers/mapping_prediction/inference.py

@@ -0,0 +1,168 @@
+import torch
+from torch.utils.data import DataLoader
+from transformers import (
+    T5TokenizerFast,
+    AutoModelForSeq2SeqLM,
+)
+import os
+from tqdm import tqdm
+from datasets import Dataset
+import numpy as np
+
+os.environ['TOKENIZERS_PARALLELISM'] = 'false'
+
+
+class Inference():
+    tokenizer: T5TokenizerFast
+    model: torch.nn.Module
+    dataloader: DataLoader
+
+    def __init__(self, checkpoint_path):
+        self._create_tokenizer()
+        self._load_model(checkpoint_path)
+
+
+    def _create_tokenizer(self):
+        # %%
+        # load tokenizer
+        self.tokenizer = T5TokenizerFast.from_pretrained("t5-small", return_tensors="pt", clean_up_tokenization_spaces=True)
+        # Define additional special tokens
+        additional_special_tokens = ["<THING_START>", "<THING_END>", "<PROPERTY_START>", "<PROPERTY_END>", "<NAME>", "<DESC>", "SIG", "UNIT", "DATA_TYPE"]
+        # Add the additional special tokens to the tokenizer
+        self.tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens})
+
+    def _load_model(self, checkpoint_path: str):
+        # load model
+        # Define the directory and the pattern
+        model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint_path)
+        model = torch.compile(model)
+        # set model to eval
+        self.model = model.eval()
+
+
+
+
+    def prepare_dataloader(self, input_df, batch_size, max_length):
+        """
+        *arguments*
+        - input_df: input dataframe containing fields 'tag_description', 'thing', 'property'
+        - batch_size: the batch size of dataloader output
+        - max_length: length of tokenizer output
+        """
+        print("preparing dataloader")
+        # convert each dataframe row into a dictionary
+        # outputs a list of dictionaries
+
+        def _process_df(df):
+            output_list = []
+            for _, row in df.iterrows():
+                desc = f"<DESC>{row['tag_description']}<DESC>"
+                unit = f"<UNIT>{row['unit']}<UNIT>"
+                element = {
+                    'input' : f"{desc}{unit}",
+                    'output': f"<THING_START>{row['thing']}<THING_END><PROPERTY_START>{row['property']}<PROPERTY_END>",
+                }
+                output_list.append(element)
+
+            return output_list
+
+        def _preprocess_function(example):
+            input = example['input']
+            target = example['output']
+            # text_target sets the corresponding label to inputs
+            # there is no need to create a separate 'labels'
+            model_inputs = self.tokenizer(
+                input,
+                text_target=target, 
+                max_length=max_length,
+                return_tensors="pt",
+                padding="max_length",
+                truncation=True,
+            )
+            return model_inputs
+
+        test_dataset = Dataset.from_list(_process_df(input_df))
+
+
+        # map maps function to each "row" in the dataset
+        # aka the data in the immediate nesting
+        datasets = test_dataset.map(
+            _preprocess_function,
+            batched=True,
+            num_proc=1,
+            remove_columns=test_dataset.column_names,
+        )
+        # datasets = _preprocess_function(test_dataset)
+        datasets.set_format(type='torch', columns=['input_ids', 'attention_mask', 'labels'])
+
+        # create dataloader
+        self.dataloader = DataLoader(datasets, batch_size=batch_size)
+
+
+    def generate(self):
+        device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+        MAX_GENERATE_LENGTH = 128
+
+        pred_generations = []
+        pred_labels = []
+
+        print("start generation")
+        for batch in tqdm(self.dataloader):
+            # Inference in batches
+            input_ids = batch['input_ids']
+            attention_mask = batch['attention_mask']
+            # save labels too
+            pred_labels.extend(batch['labels'])
+            
+
+            # Move to GPU if available
+            input_ids = input_ids.to(device)
+            attention_mask = attention_mask.to(device)
+            self.model.to(device)
+
+            # Perform inference
+            with torch.no_grad():
+                outputs = self.model.generate(input_ids,
+                                        attention_mask=attention_mask,
+                                        max_length=MAX_GENERATE_LENGTH)
+                
+                # Decode the output and print the results
+                pred_generations.extend(outputs.to("cpu"))
+
+
+
+        # %%
+        # extract sequence and decode
+        def extract_seq(tokens, start_value, end_value):
+            if start_value not in tokens or end_value not in tokens:
+                return None  # Or handle this case according to your requirements
+            start_id = np.where(tokens == start_value)[0][0]
+            end_id = np.where(tokens == end_value)[0][0]
+
+            return tokens[start_id+1:end_id]
+
+
+        def process_tensor_output(tokens):
+            thing_seq = extract_seq(tokens, 32100, 32101) # 32100 = <THING_START>, 32101 = <THING_END>
+            property_seq = extract_seq(tokens, 32102, 32103) # 32102 = <PROPERTY_START>, 32103 = <PROPERTY_END>
+            p_thing = None
+            p_property = None
+            if (thing_seq is not None):
+                p_thing =  self.tokenizer.decode(thing_seq, skip_special_tokens=False)
+            if (property_seq is not None):
+                p_property =  self.tokenizer.decode(property_seq, skip_special_tokens=False)
+            return p_thing, p_property
+
+        # decode prediction labels
+        def decode_preds(tokens_list):
+            thing_prediction_list = []
+            property_prediction_list = []
+            for tokens in tokens_list:
+                p_thing, p_property = process_tensor_output(tokens)
+                thing_prediction_list.append(p_thing)
+                property_prediction_list.append(p_property)
+            return thing_prediction_list, property_prediction_list 
+
+        thing_prediction_list, property_prediction_list = decode_preds(pred_generations)
+        return thing_prediction_list, property_prediction_list
+

train/modified_t5_decoder_4_layers/mapping_prediction/output.txt

@@ -0,0 +1,6 @@
+
+Accuracy for fold 1: 0.9503076194983436
+Accuracy for fold 2: 0.9135514018691588
+Accuracy for fold 3: 0.9698795180722891
+Accuracy for fold 4: 0.9790675547098002
+Accuracy for fold 5: 0.907924874026569

train/modified_t5_decoder_4_layers/mapping_prediction/predict.py

@@ -0,0 +1,74 @@
+
+import pandas as pd
+import os
+import glob
+from inference import Inference
+
+checkpoint_directory =  '../'
+
+BATCH_SIZE = 512
+
+def infer_and_select(fold):
+    print(f"Inference for fold {fold}")
+    # import test data
+    data_path = f"../../../data_preprocess/exports/dataset/group_{fold}/test_all.csv"
+    df = pd.read_csv(data_path, skipinitialspace=True)
+    # df = df[df['MDM']].reset_index(drop=True)
+
+    # get target data
+    data_path = f"../../../data_preprocess/exports/dataset/group_{fold}/train_all.csv"
+    train_df = pd.read_csv(data_path, skipinitialspace=True)
+    # processing to help with selection later
+    train_df['thing_property'] = train_df['thing'] + " " + train_df['property']
+
+
+    ##########################################
+    # run inference
+    # checkpoint
+    # Use glob to find matching paths
+    directory = os.path.join(checkpoint_directory, f'checkpoint_fold_{fold}b')
+    # Use glob to find matching paths
+    # path is usually checkpoint_fold_1/checkpoint-<step number>
+    # we are guaranteed to save only 1 checkpoint from training
+    pattern = 'checkpoint-*'
+    checkpoint_path = glob.glob(os.path.join(directory, pattern))[0]
+
+
+    infer = Inference(checkpoint_path)
+    infer.prepare_dataloader(df, batch_size=BATCH_SIZE, max_length=128)
+    thing_prediction_list, property_prediction_list = infer.generate()
+
+    # add labels too
+    # thing_actual_list, property_actual_list = decode_preds(pred_labels)
+    # Convert the list to a Pandas DataFrame
+    df_out = pd.DataFrame({
+        'p_thing': thing_prediction_list, 
+        'p_property': property_prediction_list
+    })
+    # df_out['p_thing_correct'] = df_out['p_thing'] == df_out['thing']
+    # df_out['p_property_correct'] = df_out['p_property'] == df_out['property']
+    df = pd.concat([df, df_out], axis=1)
+
+    # we can save the t5 generation output here
+    df.to_csv(f"exports/result_group_{fold}.csv", index=False)
+
+    # here we want to evaluate mapping accuracy within the valid in mdm data only
+    in_mdm = df['MDM']
+    condition_correct_thing = df['p_thing'] == df['thing']
+    condition_correct_property = df['p_property'] == df['property']
+    prediction_mdm_correct = sum(condition_correct_thing & condition_correct_property & in_mdm)
+    pred_correct_proportion = prediction_mdm_correct/sum(in_mdm)
+
+    # write output to file output.txt
+    with open("output.txt", "a") as f:
+        print(f'Accuracy for fold {fold}: {pred_correct_proportion}', file=f)
+
+###########################################  
+# Execute for all folds
+
+# reset file before writing to it
+with open("output.txt", "w") as f:
+    print('', file=f)
+
+for fold in [1,2,3,4,5]:
+    infer_and_select(fold)

train/modified_t5_decoder_4_layers/train_decoder.py

@@ -0,0 +1,255 @@
+# %%
+
+# from datasets import load_from_disk
+import os
+import glob
+
+os.environ['NCCL_P2P_DISABLE'] = '1'
+os.environ['NCCL_IB_DISABLE'] = '1'
+os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
+os.environ["CUDA_VISIBLE_DEVICES"] = "0,1,2,3"
+
+import torch
+
+from safetensors.torch import load_file
+
+from transformers.models.t5.modeling_t5 import T5Block
+from transformers import (
+    T5Config,
+    T5TokenizerFast,
+    AutoModelForSeq2SeqLM,
+    DataCollatorForSeq2Seq,
+    Seq2SeqTrainer,
+    EarlyStoppingCallback,
+    Seq2SeqTrainingArguments,
+    T5ForConditionalGeneration,
+    T5Model
+)
+import evaluate
+import numpy as np
+import pandas as pd
+# import matplotlib.pyplot as plt
+from datasets import Dataset, DatasetDict
+
+
+
+torch.set_float32_matmul_precision('high')
+
+
+
+# %%
+
+# model_checkpoint = "t5-small"
+# model = T5ForConditionalGeneration.from_pretrained(model_checkpoint)
+# model.config
+
+# %%
+# outputs a list of dictionaries
+def process_df_to_dict(df):
+    output_list = []
+    for _, row in df.iterrows():
+        desc = f"<DESC>{row['tag_description']}<DESC>"
+        unit = f"<UNIT>{row['unit']}<UNIT>"
+        element = {
+            'input' : f"{desc}{unit}",
+            'output': f"<THING_START>{row['thing']}<THING_END><PROPERTY_START>{row['property']}<PROPERTY_END>",
+        }
+        output_list.append(element)
+
+    return output_list
+
+
+def create_split_dataset(fold):
+    # train 
+    data_path = f"../../data_preprocess/exports/dataset/group_{fold}/train_all.csv"
+    train_df = pd.read_csv(data_path, skipinitialspace=True)
+
+    # valid
+    data_path = f"../../data_preprocess/exports/dataset/group_{fold}/valid.csv"
+    validation_df = pd.read_csv(data_path, skipinitialspace=True)
+
+    combined_data = DatasetDict({
+        'train': Dataset.from_list(process_df_to_dict(train_df)),
+        'validation' : Dataset.from_list(process_df_to_dict(validation_df)),
+    })
+    return combined_data
+
+
+# function to perform training for a given fold
+def train(fold):
+    save_path = f'checkpoint_fold_{fold}b'
+    split_datasets = create_split_dataset(fold)
+
+    # prepare tokenizer
+    model_checkpoint = "t5-small"
+    tokenizer = T5TokenizerFast.from_pretrained(model_checkpoint, return_tensors="pt", clean_up_tokenization_spaces=True)
+    # Define additional special tokens
+    additional_special_tokens = ["<THING_START>", "<THING_END>", "<PROPERTY_START>", "<PROPERTY_END>", "<NAME>", "<DESC>", "<SIG>", "<UNIT>", "<DATA_TYPE>"]
+    # Add the additional special tokens to the tokenizer
+    tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens})
+
+    max_length = 120
+
+    # given a dataset entry, run it through the tokenizer
+    def preprocess_function(example):
+        input = example['input']
+        target = example['output']
+        # text_target sets the corresponding label to inputs
+        # there is no need to create a separate 'labels'
+        model_inputs = tokenizer(
+            input,
+            text_target=target, 
+            max_length=max_length,
+            truncation=True,
+            padding="max_length"
+        )
+        return model_inputs
+
+    # map maps function to each "row" in the dataset
+    # aka the data in the immediate nesting
+    tokenized_datasets = split_datasets.map(
+        preprocess_function,
+        batched=True,
+        num_proc=8,
+        remove_columns=split_datasets["train"].column_names,
+    )
+
+    # https://github.com/huggingface/transformers/pull/28414
+    # model_checkpoint = "google/t5-efficient-tiny"
+    # device_map set to auto to force it to load contiguous weights 
+    # model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint, device_map='auto')
+
+    # directory = os.path.join(".", f'checkpoint_fold_{fold}a')
+    # # Use glob to find matching paths
+    # # path is usually checkpoint_fold_1/checkpoint-<step number>
+    # # we are guaranteed to save only 1 checkpoint from training
+    # pattern = 'checkpoint-*'
+    # prev_checkpoint = glob.glob(os.path.join(directory, pattern))[0]
+    # # t5_classify = T5Model.from_pretrained(prev_checkpoint)
+    # # Load the checkpoint
+    # checkpoint_path = f"{prev_checkpoint}/model.safetensors"
+    # checkpoint = load_file(checkpoint_path)
+    # # Filter out weights related to the classification head
+    # # given name format: t5.encoder.embed_tokens.weight
+    # # we want: encoder.embed.tokens.weight
+    # t5_weights= {key.replace("t5.", "", 1): value for key, value in checkpoint.items() if "classifier" not in key}
+
+
+    pretrained_model = T5ForConditionalGeneration.from_pretrained(model_checkpoint)
+
+    # Access the decoder stack
+    # config = T5Config("t5-small")
+
+    config = pretrained_model.config
+    config.num_layers = 6
+    config.num_decoder_layers = 4  # set new decoder layer count
+
+    model = T5ForConditionalGeneration(config)
+
+    model.shared = pretrained_model.shared
+    model.encoder = pretrained_model.encoder
+
+    pretrained_decoder_weights = [layer.state_dict() for layer in pretrained_model.decoder.block]
+    for i, layer in enumerate(pretrained_decoder_weights[:config.num_decoder_layers]):
+        model.decoder.block[i].load_state_dict(layer)  # Load pretrained weights
+
+
+    # print number of decoder blocks
+    print(f'Number of decoder blocks: {len(model.decoder.block)}')
+    print(f'num_layers: {model.config.num_layers}')
+    print(f'num_decoder_layers: {model.config.num_decoder_layers}')
+
+
+    # change the token embedding size to match the shape
+    model.resize_token_embeddings(len(tokenizer))
+
+
+
+    data_collator = DataCollatorForSeq2Seq(tokenizer, model=model)
+    metric = evaluate.load("sacrebleu")
+
+
+    def compute_metrics(eval_preds):
+        preds, labels = eval_preds
+        # In case the model returns more than the prediction logits
+        if isinstance(preds, tuple):
+            preds = preds[0]
+
+        decoded_preds = tokenizer.batch_decode(preds, 
+                                            skip_special_tokens=False)
+
+        # Replace -100s in the labels as we can't decode them
+        labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
+        decoded_labels = tokenizer.batch_decode(labels,
+                                                skip_special_tokens=False)
+
+        # Remove <PAD> tokens from decoded predictions and labels
+        decoded_preds = [pred.replace(tokenizer.pad_token, '').strip() for pred in decoded_preds]
+        decoded_labels = [[label.replace(tokenizer.pad_token, '').strip()] for label in decoded_labels]
+
+        # Some simple post-processing
+        # decoded_preds = [pred.strip() for pred in decoded_preds]
+        # decoded_labels = [[label.strip()] for label in decoded_labels]
+        # print(decoded_preds, decoded_labels)
+
+        result = metric.compute(predictions=decoded_preds, references=decoded_labels)
+        return {"bleu": result["score"]}
+
+
+    # Generation Config
+    # from transformers import GenerationConfig
+    gen_config = model.generation_config
+    gen_config.max_length = 128
+
+    # compile
+    # model = torch.compile(model, backend="inductor", dynamic=True)
+
+
+    # Trainer
+
+    args = Seq2SeqTrainingArguments(
+        f"{save_path}",
+        # eval_strategy="epoch",
+        eval_strategy="no",
+        logging_dir="tensorboard-log",
+        logging_strategy="epoch",
+        # save_strategy="epoch",
+        load_best_model_at_end=False,
+        learning_rate=1e-3,
+        per_device_train_batch_size=64,
+        per_device_eval_batch_size=64,
+        auto_find_batch_size=False,
+        ddp_find_unused_parameters=False,
+        weight_decay=0.01,
+        save_total_limit=1,
+        num_train_epochs=40,
+        predict_with_generate=True,
+        bf16=True,
+        push_to_hub=False,
+        generation_config=gen_config,
+        remove_unused_columns=False,
+    )
+
+
+    trainer = Seq2SeqTrainer(
+        model,
+        args,
+        train_dataset=tokenized_datasets["train"],
+        eval_dataset=tokenized_datasets["validation"],
+        data_collator=data_collator,
+        tokenizer=tokenizer,
+        compute_metrics=compute_metrics,
+        # callbacks=[EarlyStoppingCallback(early_stopping_patience=3)],
+    )
+
+    # uncomment to load training from checkpoint
+    # checkpoint_path = 'default_40_1/checkpoint-5600'
+    # trainer.train(resume_from_checkpoint=checkpoint_path)
+
+    trainer.train()
+
+# execute training
+for fold in [1,2,3,4,5]:
+    print(fold)
+    train(fold)
+

train/modified_t5_decoder_8_layers/.gitignore

@@ -0,0 +1,2 @@
+checkpoint*
+tensorboard-log

train/modified_t5_decoder_8_layers/mapping_prediction/.gitignore

@@ -0,0 +1,2 @@
+__pycache__
+exports/

train/modified_t5_decoder_8_layers/mapping_prediction/inference.py

@@ -0,0 +1,168 @@
+import torch
+from torch.utils.data import DataLoader
+from transformers import (
+    T5TokenizerFast,
+    AutoModelForSeq2SeqLM,
+)
+import os
+from tqdm import tqdm
+from datasets import Dataset
+import numpy as np
+
+os.environ['TOKENIZERS_PARALLELISM'] = 'false'
+
+
+class Inference():
+    tokenizer: T5TokenizerFast
+    model: torch.nn.Module
+    dataloader: DataLoader
+
+    def __init__(self, checkpoint_path):
+        self._create_tokenizer()
+        self._load_model(checkpoint_path)
+
+
+    def _create_tokenizer(self):
+        # %%
+        # load tokenizer
+        self.tokenizer = T5TokenizerFast.from_pretrained("t5-small", return_tensors="pt", clean_up_tokenization_spaces=True)
+        # Define additional special tokens
+        additional_special_tokens = ["<THING_START>", "<THING_END>", "<PROPERTY_START>", "<PROPERTY_END>", "<NAME>", "<DESC>", "SIG", "UNIT", "DATA_TYPE"]
+        # Add the additional special tokens to the tokenizer
+        self.tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens})
+
+    def _load_model(self, checkpoint_path: str):
+        # load model
+        # Define the directory and the pattern
+        model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint_path)
+        model = torch.compile(model)
+        # set model to eval
+        self.model = model.eval()
+
+
+
+
+    def prepare_dataloader(self, input_df, batch_size, max_length):
+        """
+        *arguments*
+        - input_df: input dataframe containing fields 'tag_description', 'thing', 'property'
+        - batch_size: the batch size of dataloader output
+        - max_length: length of tokenizer output
+        """
+        print("preparing dataloader")
+        # convert each dataframe row into a dictionary
+        # outputs a list of dictionaries
+
+        def _process_df(df):
+            output_list = []
+            for _, row in df.iterrows():
+                desc = f"<DESC>{row['tag_description']}<DESC>"
+                unit = f"<UNIT>{row['unit']}<UNIT>"
+                element = {
+                    'input' : f"{desc}{unit}",
+                    'output': f"<THING_START>{row['thing']}<THING_END><PROPERTY_START>{row['property']}<PROPERTY_END>",
+                }
+                output_list.append(element)
+
+            return output_list
+
+        def _preprocess_function(example):
+            input = example['input']
+            target = example['output']
+            # text_target sets the corresponding label to inputs
+            # there is no need to create a separate 'labels'
+            model_inputs = self.tokenizer(
+                input,
+                text_target=target, 
+                max_length=max_length,
+                return_tensors="pt",
+                padding="max_length",
+                truncation=True,
+            )
+            return model_inputs
+
+        test_dataset = Dataset.from_list(_process_df(input_df))
+
+
+        # map maps function to each "row" in the dataset
+        # aka the data in the immediate nesting
+        datasets = test_dataset.map(
+            _preprocess_function,
+            batched=True,
+            num_proc=1,
+            remove_columns=test_dataset.column_names,
+        )
+        # datasets = _preprocess_function(test_dataset)
+        datasets.set_format(type='torch', columns=['input_ids', 'attention_mask', 'labels'])
+
+        # create dataloader
+        self.dataloader = DataLoader(datasets, batch_size=batch_size)
+
+
+    def generate(self):
+        device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+        MAX_GENERATE_LENGTH = 128
+
+        pred_generations = []
+        pred_labels = []
+
+        print("start generation")
+        for batch in tqdm(self.dataloader):
+            # Inference in batches
+            input_ids = batch['input_ids']
+            attention_mask = batch['attention_mask']
+            # save labels too
+            pred_labels.extend(batch['labels'])
+            
+
+            # Move to GPU if available
+            input_ids = input_ids.to(device)
+            attention_mask = attention_mask.to(device)
+            self.model.to(device)
+
+            # Perform inference
+            with torch.no_grad():
+                outputs = self.model.generate(input_ids,
+                                        attention_mask=attention_mask,
+                                        max_length=MAX_GENERATE_LENGTH)
+                
+                # Decode the output and print the results
+                pred_generations.extend(outputs.to("cpu"))
+
+
+
+        # %%
+        # extract sequence and decode
+        def extract_seq(tokens, start_value, end_value):
+            if start_value not in tokens or end_value not in tokens:
+                return None  # Or handle this case according to your requirements
+            start_id = np.where(tokens == start_value)[0][0]
+            end_id = np.where(tokens == end_value)[0][0]
+
+            return tokens[start_id+1:end_id]
+
+
+        def process_tensor_output(tokens):
+            thing_seq = extract_seq(tokens, 32100, 32101) # 32100 = <THING_START>, 32101 = <THING_END>
+            property_seq = extract_seq(tokens, 32102, 32103) # 32102 = <PROPERTY_START>, 32103 = <PROPERTY_END>
+            p_thing = None
+            p_property = None
+            if (thing_seq is not None):
+                p_thing =  self.tokenizer.decode(thing_seq, skip_special_tokens=False)
+            if (property_seq is not None):
+                p_property =  self.tokenizer.decode(property_seq, skip_special_tokens=False)
+            return p_thing, p_property
+
+        # decode prediction labels
+        def decode_preds(tokens_list):
+            thing_prediction_list = []
+            property_prediction_list = []
+            for tokens in tokens_list:
+                p_thing, p_property = process_tensor_output(tokens)
+                thing_prediction_list.append(p_thing)
+                property_prediction_list.append(p_property)
+            return thing_prediction_list, property_prediction_list 
+
+        thing_prediction_list, property_prediction_list = decode_preds(pred_generations)
+        return thing_prediction_list, property_prediction_list
+

train/modified_t5_decoder_8_layers/mapping_prediction/output.txt

@@ -0,0 +1,6 @@
+
+Accuracy for fold 1: 0.9441552295314718
+Accuracy for fold 2: 0.9121495327102803
+Accuracy for fold 3: 0.963855421686747
+Accuracy for fold 4: 0.9752616555661275
+Accuracy for fold 5: 0.907924874026569

train/modified_t5_decoder_8_layers/mapping_prediction/predict.py

@@ -0,0 +1,74 @@
+
+import pandas as pd
+import os
+import glob
+from inference import Inference
+
+checkpoint_directory =  '../'
+
+BATCH_SIZE = 512
+
+def infer_and_select(fold):
+    print(f"Inference for fold {fold}")
+    # import test data
+    data_path = f"../../../data_preprocess/exports/dataset/group_{fold}/test_all.csv"
+    df = pd.read_csv(data_path, skipinitialspace=True)
+    df = df[df['MDM']].reset_index(drop=True)
+
+    # get target data
+    data_path = f"../../../data_preprocess/exports/dataset/group_{fold}/train_all.csv"
+    train_df = pd.read_csv(data_path, skipinitialspace=True)
+    # processing to help with selection later
+    train_df['thing_property'] = train_df['thing'] + " " + train_df['property']
+
+
+    ##########################################
+    # run inference
+    # checkpoint
+    # Use glob to find matching paths
+    directory = os.path.join(checkpoint_directory, f'checkpoint_fold_{fold}b')
+    # Use glob to find matching paths
+    # path is usually checkpoint_fold_1/checkpoint-<step number>
+    # we are guaranteed to save only 1 checkpoint from training
+    pattern = 'checkpoint-*'
+    checkpoint_path = glob.glob(os.path.join(directory, pattern))[0]
+
+
+    infer = Inference(checkpoint_path)
+    infer.prepare_dataloader(df, batch_size=BATCH_SIZE, max_length=128)
+    thing_prediction_list, property_prediction_list = infer.generate()
+
+    # add labels too
+    # thing_actual_list, property_actual_list = decode_preds(pred_labels)
+    # Convert the list to a Pandas DataFrame
+    df_out = pd.DataFrame({
+        'p_thing': thing_prediction_list, 
+        'p_property': property_prediction_list
+    })
+    # df_out['p_thing_correct'] = df_out['p_thing'] == df_out['thing']
+    # df_out['p_property_correct'] = df_out['p_property'] == df_out['property']
+    df = pd.concat([df, df_out], axis=1)
+
+    # we can save the t5 generation output here
+    df.to_csv(f"exports/result_group_{fold}.csv", index=False)
+
+    # here we want to evaluate mapping accuracy within the valid in mdm data only
+    in_mdm = df['MDM']
+    condition_correct_thing = df['p_thing'] == df['thing']
+    condition_correct_property = df['p_property'] == df['property']
+    prediction_mdm_correct = sum(condition_correct_thing & condition_correct_property & in_mdm)
+    pred_correct_proportion = prediction_mdm_correct/sum(in_mdm)
+
+    # write output to file output.txt
+    with open("output.txt", "a") as f:
+        print(f'Accuracy for fold {fold}: {pred_correct_proportion}', file=f)
+
+###########################################  
+# Execute for all folds
+
+# reset file before writing to it
+with open("output.txt", "w") as f:
+    print('', file=f)
+
+for fold in [1,2,3,4,5]:
+    infer_and_select(fold)

train/modified_t5_decoder_8_layers/train_decoder.py

@@ -0,0 +1,255 @@
+# %%
+
+# from datasets import load_from_disk
+import os
+import glob
+
+os.environ['NCCL_P2P_DISABLE'] = '1'
+os.environ['NCCL_IB_DISABLE'] = '1'
+os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
+os.environ["CUDA_VISIBLE_DEVICES"] = "3"
+
+import torch
+
+from safetensors.torch import load_file
+
+from transformers.models.t5.modeling_t5 import T5Block
+from transformers import (
+    T5Config,
+    T5TokenizerFast,
+    AutoModelForSeq2SeqLM,
+    DataCollatorForSeq2Seq,
+    Seq2SeqTrainer,
+    EarlyStoppingCallback,
+    Seq2SeqTrainingArguments,
+    T5ForConditionalGeneration,
+    T5Model
+)
+import evaluate
+import numpy as np
+import pandas as pd
+# import matplotlib.pyplot as plt
+from datasets import Dataset, DatasetDict
+
+
+
+torch.set_float32_matmul_precision('high')
+
+
+
+# %%
+
+# model_checkpoint = "t5-small"
+# model = T5ForConditionalGeneration.from_pretrained(model_checkpoint)
+# model.config
+
+# %%
+# outputs a list of dictionaries
+def process_df_to_dict(df):
+    output_list = []
+    for _, row in df.iterrows():
+        desc = f"<DESC>{row['tag_description']}<DESC>"
+        unit = f"<UNIT>{row['unit']}<UNIT>"
+        element = {
+            'input' : f"{desc}{unit}",
+            'output': f"<THING_START>{row['thing']}<THING_END><PROPERTY_START>{row['property']}<PROPERTY_END>",
+        }
+        output_list.append(element)
+
+    return output_list
+
+
+def create_split_dataset(fold):
+    # train 
+    data_path = f"../../data_preprocess/exports/dataset/group_{fold}/train_all.csv"
+    train_df = pd.read_csv(data_path, skipinitialspace=True)
+
+    # valid
+    data_path = f"../../data_preprocess/exports/dataset/group_{fold}/valid.csv"
+    validation_df = pd.read_csv(data_path, skipinitialspace=True)
+
+    combined_data = DatasetDict({
+        'train': Dataset.from_list(process_df_to_dict(train_df)),
+        'validation' : Dataset.from_list(process_df_to_dict(validation_df)),
+    })
+    return combined_data
+
+
+# function to perform training for a given fold
+def train(fold):
+    save_path = f'checkpoint_fold_{fold}b'
+    split_datasets = create_split_dataset(fold)
+
+    # prepare tokenizer
+    model_checkpoint = "t5-small"
+    tokenizer = T5TokenizerFast.from_pretrained(model_checkpoint, return_tensors="pt", clean_up_tokenization_spaces=True)
+    # Define additional special tokens
+    additional_special_tokens = ["<THING_START>", "<THING_END>", "<PROPERTY_START>", "<PROPERTY_END>", "<NAME>", "<DESC>", "<SIG>", "<UNIT>", "<DATA_TYPE>"]
+    # Add the additional special tokens to the tokenizer
+    tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens})
+
+    max_length = 120
+
+    # given a dataset entry, run it through the tokenizer
+    def preprocess_function(example):
+        input = example['input']
+        target = example['output']
+        # text_target sets the corresponding label to inputs
+        # there is no need to create a separate 'labels'
+        model_inputs = tokenizer(
+            input,
+            text_target=target, 
+            max_length=max_length,
+            truncation=True,
+            padding="max_length"
+        )
+        return model_inputs
+
+    # map maps function to each "row" in the dataset
+    # aka the data in the immediate nesting
+    tokenized_datasets = split_datasets.map(
+        preprocess_function,
+        batched=True,
+        num_proc=8,
+        remove_columns=split_datasets["train"].column_names,
+    )
+
+    # https://github.com/huggingface/transformers/pull/28414
+    # model_checkpoint = "google/t5-efficient-tiny"
+    # device_map set to auto to force it to load contiguous weights 
+    # model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint, device_map='auto')
+
+    # directory = os.path.join(".", f'checkpoint_fold_{fold}a')
+    # # Use glob to find matching paths
+    # # path is usually checkpoint_fold_1/checkpoint-<step number>
+    # # we are guaranteed to save only 1 checkpoint from training
+    # pattern = 'checkpoint-*'
+    # prev_checkpoint = glob.glob(os.path.join(directory, pattern))[0]
+    # # t5_classify = T5Model.from_pretrained(prev_checkpoint)
+    # # Load the checkpoint
+    # checkpoint_path = f"{prev_checkpoint}/model.safetensors"
+    # checkpoint = load_file(checkpoint_path)
+    # # Filter out weights related to the classification head
+    # # given name format: t5.encoder.embed_tokens.weight
+    # # we want: encoder.embed.tokens.weight
+    # t5_weights= {key.replace("t5.", "", 1): value for key, value in checkpoint.items() if "classifier" not in key}
+
+
+    pretrained_model = T5ForConditionalGeneration.from_pretrained(model_checkpoint)
+
+    # Access the decoder stack
+    # config = T5Config("t5-small")
+
+    config = pretrained_model.config
+    config.num_layers = 6
+    config.num_decoder_layers = 8  # set new decoder layer count
+
+    model = T5ForConditionalGeneration(config)
+
+    model.shared = pretrained_model.shared
+    model.encoder = pretrained_model.encoder
+
+    pretrained_decoder_weights = [layer.state_dict() for layer in pretrained_model.decoder.block]
+    for i, layer in enumerate(pretrained_decoder_weights):
+        model.decoder.block[i].load_state_dict(layer)  # Load pretrained weights
+
+
+    # print number of decoder blocks
+    print(f'Number of decoder blocks: {len(model.decoder.block)}')
+    print(f'num_layers: {model.config.num_layers}')
+    print(f'num_decoder_layers: {model.config.num_decoder_layers}')
+
+
+    # change the token embedding size to match the shape
+    model.resize_token_embeddings(len(tokenizer))
+
+
+
+    data_collator = DataCollatorForSeq2Seq(tokenizer, model=model)
+    metric = evaluate.load("sacrebleu")
+
+
+    def compute_metrics(eval_preds):
+        preds, labels = eval_preds
+        # In case the model returns more than the prediction logits
+        if isinstance(preds, tuple):
+            preds = preds[0]
+
+        decoded_preds = tokenizer.batch_decode(preds, 
+                                            skip_special_tokens=False)
+
+        # Replace -100s in the labels as we can't decode them
+        labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
+        decoded_labels = tokenizer.batch_decode(labels,
+                                                skip_special_tokens=False)
+
+        # Remove <PAD> tokens from decoded predictions and labels
+        decoded_preds = [pred.replace(tokenizer.pad_token, '').strip() for pred in decoded_preds]
+        decoded_labels = [[label.replace(tokenizer.pad_token, '').strip()] for label in decoded_labels]
+
+        # Some simple post-processing
+        # decoded_preds = [pred.strip() for pred in decoded_preds]
+        # decoded_labels = [[label.strip()] for label in decoded_labels]
+        # print(decoded_preds, decoded_labels)
+
+        result = metric.compute(predictions=decoded_preds, references=decoded_labels)
+        return {"bleu": result["score"]}
+
+
+    # Generation Config
+    # from transformers import GenerationConfig
+    gen_config = model.generation_config
+    gen_config.max_length = 128
+
+    # compile
+    # model = torch.compile(model, backend="inductor", dynamic=True)
+
+
+    # Trainer
+
+    args = Seq2SeqTrainingArguments(
+        f"{save_path}",
+        # eval_strategy="epoch",
+        eval_strategy="no",
+        logging_dir="tensorboard-log",
+        logging_strategy="epoch",
+        # save_strategy="epoch",
+        load_best_model_at_end=False,
+        learning_rate=1e-3,
+        per_device_train_batch_size=64,
+        per_device_eval_batch_size=64,
+        auto_find_batch_size=False,
+        ddp_find_unused_parameters=False,
+        weight_decay=0.01,
+        save_total_limit=1,
+        num_train_epochs=40,
+        predict_with_generate=True,
+        bf16=True,
+        push_to_hub=False,
+        generation_config=gen_config,
+        remove_unused_columns=False,
+    )
+
+
+    trainer = Seq2SeqTrainer(
+        model,
+        args,
+        train_dataset=tokenized_datasets["train"],
+        eval_dataset=tokenized_datasets["validation"],
+        data_collator=data_collator,
+        tokenizer=tokenizer,
+        compute_metrics=compute_metrics,
+        # callbacks=[EarlyStoppingCallback(early_stopping_patience=3)],
+    )
+
+    # uncomment to load training from checkpoint
+    # checkpoint_path = 'default_40_1/checkpoint-5600'
+    # trainer.train(resume_from_checkpoint=checkpoint_path)
+
+    trainer.train()
+
+# execute training
+for fold in [1,2,3,4,5]:
+    print(fold)
+    train(fold)
+

train/modified_t5_decoder_9_layers/.gitignore

@@ -0,0 +1,2 @@
+checkpoint*
+tensorboard-log

train/modified_t5_decoder_9_layers/mapping_prediction/.gitignore

@@ -0,0 +1,2 @@
+__pycache__
+exports/

train/modified_t5_decoder_9_layers/mapping_prediction/inference.py

@@ -0,0 +1,168 @@
+import torch
+from torch.utils.data import DataLoader
+from transformers import (
+    T5TokenizerFast,
+    AutoModelForSeq2SeqLM,
+)
+import os
+from tqdm import tqdm
+from datasets import Dataset
+import numpy as np
+
+os.environ['TOKENIZERS_PARALLELISM'] = 'false'
+
+
+class Inference():
+    tokenizer: T5TokenizerFast
+    model: torch.nn.Module
+    dataloader: DataLoader
+
+    def __init__(self, checkpoint_path):
+        self._create_tokenizer()
+        self._load_model(checkpoint_path)
+
+
+    def _create_tokenizer(self):
+        # %%
+        # load tokenizer
+        self.tokenizer = T5TokenizerFast.from_pretrained("t5-small", return_tensors="pt", clean_up_tokenization_spaces=True)
+        # Define additional special tokens
+        additional_special_tokens = ["<THING_START>", "<THING_END>", "<PROPERTY_START>", "<PROPERTY_END>", "<NAME>", "<DESC>", "SIG", "UNIT", "DATA_TYPE"]
+        # Add the additional special tokens to the tokenizer
+        self.tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens})
+
+    def _load_model(self, checkpoint_path: str):
+        # load model
+        # Define the directory and the pattern
+        model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint_path)
+        model = torch.compile(model)
+        # set model to eval
+        self.model = model.eval()
+
+
+
+
+    def prepare_dataloader(self, input_df, batch_size, max_length):
+        """
+        *arguments*
+        - input_df: input dataframe containing fields 'tag_description', 'thing', 'property'
+        - batch_size: the batch size of dataloader output
+        - max_length: length of tokenizer output
+        """
+        print("preparing dataloader")
+        # convert each dataframe row into a dictionary
+        # outputs a list of dictionaries
+
+        def _process_df(df):
+            output_list = []
+            for _, row in df.iterrows():
+                desc = f"<DESC>{row['tag_description']}<DESC>"
+                unit = f"<UNIT>{row['unit']}<UNIT>"
+                element = {
+                    'input' : f"{desc}{unit}",
+                    'output': f"<THING_START>{row['thing']}<THING_END><PROPERTY_START>{row['property']}<PROPERTY_END>",
+                }
+                output_list.append(element)
+
+            return output_list
+
+        def _preprocess_function(example):
+            input = example['input']
+            target = example['output']
+            # text_target sets the corresponding label to inputs
+            # there is no need to create a separate 'labels'
+            model_inputs = self.tokenizer(
+                input,
+                text_target=target, 
+                max_length=max_length,
+                return_tensors="pt",
+                padding="max_length",
+                truncation=True,
+            )
+            return model_inputs
+
+        test_dataset = Dataset.from_list(_process_df(input_df))
+
+
+        # map maps function to each "row" in the dataset
+        # aka the data in the immediate nesting
+        datasets = test_dataset.map(
+            _preprocess_function,
+            batched=True,
+            num_proc=1,
+            remove_columns=test_dataset.column_names,
+        )
+        # datasets = _preprocess_function(test_dataset)
+        datasets.set_format(type='torch', columns=['input_ids', 'attention_mask', 'labels'])
+
+        # create dataloader
+        self.dataloader = DataLoader(datasets, batch_size=batch_size)
+
+
+    def generate(self):
+        device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+        MAX_GENERATE_LENGTH = 128
+
+        pred_generations = []
+        pred_labels = []
+
+        print("start generation")
+        for batch in tqdm(self.dataloader):
+            # Inference in batches
+            input_ids = batch['input_ids']
+            attention_mask = batch['attention_mask']
+            # save labels too
+            pred_labels.extend(batch['labels'])
+            
+
+            # Move to GPU if available
+            input_ids = input_ids.to(device)
+            attention_mask = attention_mask.to(device)
+            self.model.to(device)
+
+            # Perform inference
+            with torch.no_grad():
+                outputs = self.model.generate(input_ids,
+                                        attention_mask=attention_mask,
+                                        max_length=MAX_GENERATE_LENGTH)
+                
+                # Decode the output and print the results
+                pred_generations.extend(outputs.to("cpu"))
+
+
+
+        # %%
+        # extract sequence and decode
+        def extract_seq(tokens, start_value, end_value):
+            if start_value not in tokens or end_value not in tokens:
+                return None  # Or handle this case according to your requirements
+            start_id = np.where(tokens == start_value)[0][0]
+            end_id = np.where(tokens == end_value)[0][0]
+
+            return tokens[start_id+1:end_id]
+
+
+        def process_tensor_output(tokens):
+            thing_seq = extract_seq(tokens, 32100, 32101) # 32100 = <THING_START>, 32101 = <THING_END>
+            property_seq = extract_seq(tokens, 32102, 32103) # 32102 = <PROPERTY_START>, 32103 = <PROPERTY_END>
+            p_thing = None
+            p_property = None
+            if (thing_seq is not None):
+                p_thing =  self.tokenizer.decode(thing_seq, skip_special_tokens=False)
+            if (property_seq is not None):
+                p_property =  self.tokenizer.decode(property_seq, skip_special_tokens=False)
+            return p_thing, p_property
+
+        # decode prediction labels
+        def decode_preds(tokens_list):
+            thing_prediction_list = []
+            property_prediction_list = []
+            for tokens in tokens_list:
+                p_thing, p_property = process_tensor_output(tokens)
+                thing_prediction_list.append(p_thing)
+                property_prediction_list.append(p_property)
+            return thing_prediction_list, property_prediction_list 
+
+        thing_prediction_list, property_prediction_list = decode_preds(pred_generations)
+        return thing_prediction_list, property_prediction_list
+

train/modified_t5_decoder_9_layers/mapping_prediction/output.txt

@@ -0,0 +1,6 @@
+
+Accuracy for fold 1: 0.9441552295314718
+Accuracy for fold 2: 0.9121495327102803
+Accuracy for fold 3: 0.963855421686747
+Accuracy for fold 4: 0.9752616555661275
+Accuracy for fold 5: 0.907924874026569

train/modified_t5_decoder_9_layers/mapping_prediction/predict.py

@@ -0,0 +1,74 @@
+
+import pandas as pd
+import os
+import glob
+from inference import Inference
+
+checkpoint_directory =  '../'
+
+BATCH_SIZE = 512
+
+def infer_and_select(fold):
+    print(f"Inference for fold {fold}")
+    # import test data
+    data_path = f"../../../data_preprocess/exports/dataset/group_{fold}/test_all.csv"
+    df = pd.read_csv(data_path, skipinitialspace=True)
+    df = df[df['MDM']].reset_index(drop=True)
+
+    # get target data
+    data_path = f"../../../data_preprocess/exports/dataset/group_{fold}/train_all.csv"
+    train_df = pd.read_csv(data_path, skipinitialspace=True)
+    # processing to help with selection later
+    train_df['thing_property'] = train_df['thing'] + " " + train_df['property']
+
+
+    ##########################################
+    # run inference
+    # checkpoint
+    # Use glob to find matching paths
+    directory = os.path.join(checkpoint_directory, f'checkpoint_fold_{fold}b')
+    # Use glob to find matching paths
+    # path is usually checkpoint_fold_1/checkpoint-<step number>
+    # we are guaranteed to save only 1 checkpoint from training
+    pattern = 'checkpoint-*'
+    checkpoint_path = glob.glob(os.path.join(directory, pattern))[0]
+
+
+    infer = Inference(checkpoint_path)
+    infer.prepare_dataloader(df, batch_size=BATCH_SIZE, max_length=128)
+    thing_prediction_list, property_prediction_list = infer.generate()
+
+    # add labels too
+    # thing_actual_list, property_actual_list = decode_preds(pred_labels)
+    # Convert the list to a Pandas DataFrame
+    df_out = pd.DataFrame({
+        'p_thing': thing_prediction_list, 
+        'p_property': property_prediction_list
+    })
+    # df_out['p_thing_correct'] = df_out['p_thing'] == df_out['thing']
+    # df_out['p_property_correct'] = df_out['p_property'] == df_out['property']
+    df = pd.concat([df, df_out], axis=1)
+
+    # we can save the t5 generation output here
+    df.to_csv(f"exports/result_group_{fold}.csv", index=False)
+
+    # here we want to evaluate mapping accuracy within the valid in mdm data only
+    in_mdm = df['MDM']
+    condition_correct_thing = df['p_thing'] == df['thing']
+    condition_correct_property = df['p_property'] == df['property']
+    prediction_mdm_correct = sum(condition_correct_thing & condition_correct_property & in_mdm)
+    pred_correct_proportion = prediction_mdm_correct/sum(in_mdm)
+
+    # write output to file output.txt
+    with open("output.txt", "a") as f:
+        print(f'Accuracy for fold {fold}: {pred_correct_proportion}', file=f)
+
+###########################################  
+# Execute for all folds
+
+# reset file before writing to it
+with open("output.txt", "w") as f:
+    print('', file=f)
+
+for fold in [1,2,3,4,5]:
+    infer_and_select(fold)

train/modified_t5_decoder_9_layers/train_decoder.py

@@ -0,0 +1,255 @@
+# %%
+
+# from datasets import load_from_disk
+import os
+import glob
+
+os.environ['NCCL_P2P_DISABLE'] = '1'
+os.environ['NCCL_IB_DISABLE'] = '1'
+os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
+os.environ["CUDA_VISIBLE_DEVICES"] = "0,1,2,3"
+
+import torch
+
+from safetensors.torch import load_file
+
+from transformers.models.t5.modeling_t5 import T5Block
+from transformers import (
+    T5Config,
+    T5TokenizerFast,
+    AutoModelForSeq2SeqLM,
+    DataCollatorForSeq2Seq,
+    Seq2SeqTrainer,
+    EarlyStoppingCallback,
+    Seq2SeqTrainingArguments,
+    T5ForConditionalGeneration,
+    T5Model
+)
+import evaluate
+import numpy as np
+import pandas as pd
+# import matplotlib.pyplot as plt
+from datasets import Dataset, DatasetDict
+
+
+
+torch.set_float32_matmul_precision('high')
+
+
+
+# %%
+
+# model_checkpoint = "t5-small"
+# model = T5ForConditionalGeneration.from_pretrained(model_checkpoint)
+# model.config
+
+# %%
+# outputs a list of dictionaries
+def process_df_to_dict(df):
+    output_list = []
+    for _, row in df.iterrows():
+        desc = f"<DESC>{row['tag_description']}<DESC>"
+        unit = f"<UNIT>{row['unit']}<UNIT>"
+        element = {
+            'input' : f"{desc}{unit}",
+            'output': f"<THING_START>{row['thing']}<THING_END><PROPERTY_START>{row['property']}<PROPERTY_END>",
+        }
+        output_list.append(element)
+
+    return output_list
+
+
+def create_split_dataset(fold):
+    # train 
+    data_path = f"../../data_preprocess/exports/dataset/group_{fold}/train_all.csv"
+    train_df = pd.read_csv(data_path, skipinitialspace=True)
+
+    # valid
+    data_path = f"../../data_preprocess/exports/dataset/group_{fold}/valid.csv"
+    validation_df = pd.read_csv(data_path, skipinitialspace=True)
+
+    combined_data = DatasetDict({
+        'train': Dataset.from_list(process_df_to_dict(train_df)),
+        'validation' : Dataset.from_list(process_df_to_dict(validation_df)),
+    })
+    return combined_data
+
+
+# function to perform training for a given fold
+def train(fold):
+    save_path = f'checkpoint_fold_{fold}b'
+    split_datasets = create_split_dataset(fold)
+
+    # prepare tokenizer
+    model_checkpoint = "t5-small"
+    tokenizer = T5TokenizerFast.from_pretrained(model_checkpoint, return_tensors="pt", clean_up_tokenization_spaces=True)
+    # Define additional special tokens
+    additional_special_tokens = ["<THING_START>", "<THING_END>", "<PROPERTY_START>", "<PROPERTY_END>", "<NAME>", "<DESC>", "<SIG>", "<UNIT>", "<DATA_TYPE>"]
+    # Add the additional special tokens to the tokenizer
+    tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens})
+
+    max_length = 120
+
+    # given a dataset entry, run it through the tokenizer
+    def preprocess_function(example):
+        input = example['input']
+        target = example['output']
+        # text_target sets the corresponding label to inputs
+        # there is no need to create a separate 'labels'
+        model_inputs = tokenizer(
+            input,
+            text_target=target, 
+            max_length=max_length,
+            truncation=True,
+            padding="max_length"
+        )
+        return model_inputs
+
+    # map maps function to each "row" in the dataset
+    # aka the data in the immediate nesting
+    tokenized_datasets = split_datasets.map(
+        preprocess_function,
+        batched=True,
+        num_proc=8,
+        remove_columns=split_datasets["train"].column_names,
+    )
+
+    # https://github.com/huggingface/transformers/pull/28414
+    # model_checkpoint = "google/t5-efficient-tiny"
+    # device_map set to auto to force it to load contiguous weights 
+    # model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint, device_map='auto')
+
+    # directory = os.path.join(".", f'checkpoint_fold_{fold}a')
+    # # Use glob to find matching paths
+    # # path is usually checkpoint_fold_1/checkpoint-<step number>
+    # # we are guaranteed to save only 1 checkpoint from training
+    # pattern = 'checkpoint-*'
+    # prev_checkpoint = glob.glob(os.path.join(directory, pattern))[0]
+    # # t5_classify = T5Model.from_pretrained(prev_checkpoint)
+    # # Load the checkpoint
+    # checkpoint_path = f"{prev_checkpoint}/model.safetensors"
+    # checkpoint = load_file(checkpoint_path)
+    # # Filter out weights related to the classification head
+    # # given name format: t5.encoder.embed_tokens.weight
+    # # we want: encoder.embed.tokens.weight
+    # t5_weights= {key.replace("t5.", "", 1): value for key, value in checkpoint.items() if "classifier" not in key}
+
+
+    pretrained_model = T5ForConditionalGeneration.from_pretrained(model_checkpoint)
+
+    # Access the decoder stack
+    # config = T5Config("t5-small")
+
+    config = pretrained_model.config
+    config.num_layers = 6
+    config.num_decoder_layers = 9  # set new decoder layer count
+
+    model = T5ForConditionalGeneration(config)
+
+    model.shared = pretrained_model.shared
+    model.encoder = pretrained_model.encoder
+
+    pretrained_decoder_weights = [layer.state_dict() for layer in pretrained_model.decoder.block]
+    for i, layer in enumerate(pretrained_decoder_weights):
+        model.decoder.block[i].load_state_dict(layer)  # Load pretrained weights
+
+
+    # print number of decoder blocks
+    print(f'Number of decoder blocks: {len(model.decoder.block)}')
+    print(f'num_layers: {model.config.num_layers}')
+    print(f'num_decoder_layers: {model.config.num_decoder_layers}')
+
+
+    # change the token embedding size to match the shape
+    model.resize_token_embeddings(len(tokenizer))
+
+
+
+    data_collator = DataCollatorForSeq2Seq(tokenizer, model=model)
+    metric = evaluate.load("sacrebleu")
+
+
+    def compute_metrics(eval_preds):
+        preds, labels = eval_preds
+        # In case the model returns more than the prediction logits
+        if isinstance(preds, tuple):
+            preds = preds[0]
+
+        decoded_preds = tokenizer.batch_decode(preds, 
+                                            skip_special_tokens=False)
+
+        # Replace -100s in the labels as we can't decode them
+        labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
+        decoded_labels = tokenizer.batch_decode(labels,
+                                                skip_special_tokens=False)
+
+        # Remove <PAD> tokens from decoded predictions and labels
+        decoded_preds = [pred.replace(tokenizer.pad_token, '').strip() for pred in decoded_preds]
+        decoded_labels = [[label.replace(tokenizer.pad_token, '').strip()] for label in decoded_labels]
+
+        # Some simple post-processing
+        # decoded_preds = [pred.strip() for pred in decoded_preds]
+        # decoded_labels = [[label.strip()] for label in decoded_labels]
+        # print(decoded_preds, decoded_labels)
+
+        result = metric.compute(predictions=decoded_preds, references=decoded_labels)
+        return {"bleu": result["score"]}
+
+
+    # Generation Config
+    # from transformers import GenerationConfig
+    gen_config = model.generation_config
+    gen_config.max_length = 128
+
+    # compile
+    # model = torch.compile(model, backend="inductor", dynamic=True)
+
+
+    # Trainer
+
+    args = Seq2SeqTrainingArguments(
+        f"{save_path}",
+        # eval_strategy="epoch",
+        eval_strategy="no",
+        logging_dir="tensorboard-log",
+        logging_strategy="epoch",
+        # save_strategy="epoch",
+        load_best_model_at_end=False,
+        learning_rate=1e-3,
+        per_device_train_batch_size=64,
+        per_device_eval_batch_size=64,
+        auto_find_batch_size=False,
+        ddp_find_unused_parameters=False,
+        weight_decay=0.01,
+        save_total_limit=1,
+        num_train_epochs=40,
+        predict_with_generate=True,
+        bf16=True,
+        push_to_hub=False,
+        generation_config=gen_config,
+        remove_unused_columns=False,
+    )
+
+
+    trainer = Seq2SeqTrainer(
+        model,
+        args,
+        train_dataset=tokenized_datasets["train"],
+        eval_dataset=tokenized_datasets["validation"],
+        data_collator=data_collator,
+        tokenizer=tokenizer,
+        compute_metrics=compute_metrics,
+        # callbacks=[EarlyStoppingCallback(early_stopping_patience=3)],
+    )
+
+    # uncomment to load training from checkpoint
+    # checkpoint_path = 'default_40_1/checkpoint-5600'
+    # trainer.train(resume_from_checkpoint=checkpoint_path)
+
+    trainer.train()
+
+# execute training
+for fold in [1,2,3,4,5]:
+    print(fold)
+    train(fold)
+

train/random_t5_encoder/.gitignore

@@ -0,0 +1,2 @@
+checkpoint*
+tensorboard-log

train/random_t5_encoder/mapping_prediction/.gitignore

@@ -0,0 +1,2 @@
+__pycache__
+exports/

train/random_t5_encoder/mapping_prediction/inference.py

@@ -0,0 +1,168 @@
+import torch
+from torch.utils.data import DataLoader
+from transformers import (
+    T5TokenizerFast,
+    AutoModelForSeq2SeqLM,
+)
+import os
+from tqdm import tqdm
+from datasets import Dataset
+import numpy as np
+
+os.environ['TOKENIZERS_PARALLELISM'] = 'false'
+
+
+class Inference():
+    tokenizer: T5TokenizerFast
+    model: torch.nn.Module
+    dataloader: DataLoader
+
+    def __init__(self, checkpoint_path):
+        self._create_tokenizer()
+        self._load_model(checkpoint_path)
+
+
+    def _create_tokenizer(self):
+        # %%
+        # load tokenizer
+        self.tokenizer = T5TokenizerFast.from_pretrained("t5-small", return_tensors="pt", clean_up_tokenization_spaces=True)
+        # Define additional special tokens
+        additional_special_tokens = ["<THING_START>", "<THING_END>", "<PROPERTY_START>", "<PROPERTY_END>", "<NAME>", "<DESC>", "SIG", "UNIT", "DATA_TYPE"]
+        # Add the additional special tokens to the tokenizer
+        self.tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens})
+
+    def _load_model(self, checkpoint_path: str):
+        # load model
+        # Define the directory and the pattern
+        model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint_path)
+        model = torch.compile(model)
+        # set model to eval
+        self.model = model.eval()
+
+
+
+
+    def prepare_dataloader(self, input_df, batch_size, max_length):
+        """
+        *arguments*
+        - input_df: input dataframe containing fields 'tag_description', 'thing', 'property'
+        - batch_size: the batch size of dataloader output
+        - max_length: length of tokenizer output
+        """
+        print("preparing dataloader")
+        # convert each dataframe row into a dictionary
+        # outputs a list of dictionaries
+
+        def _process_df(df):
+            output_list = []
+            for _, row in df.iterrows():
+                desc = f"<DESC>{row['tag_description']}<DESC>"
+                unit = f"<UNIT>{row['unit']}<UNIT>"
+                element = {
+                    'input' : f"{desc}{unit}",
+                    'output': f"<THING_START>{row['thing']}<THING_END><PROPERTY_START>{row['property']}<PROPERTY_END>",
+                }
+                output_list.append(element)
+
+            return output_list
+
+        def _preprocess_function(example):
+            input = example['input']
+            target = example['output']
+            # text_target sets the corresponding label to inputs
+            # there is no need to create a separate 'labels'
+            model_inputs = self.tokenizer(
+                input,
+                text_target=target, 
+                max_length=max_length,
+                return_tensors="pt",
+                padding="max_length",
+                truncation=True,
+            )
+            return model_inputs
+
+        test_dataset = Dataset.from_list(_process_df(input_df))
+
+
+        # map maps function to each "row" in the dataset
+        # aka the data in the immediate nesting
+        datasets = test_dataset.map(
+            _preprocess_function,
+            batched=True,
+            num_proc=1,
+            remove_columns=test_dataset.column_names,
+        )
+        # datasets = _preprocess_function(test_dataset)
+        datasets.set_format(type='torch', columns=['input_ids', 'attention_mask', 'labels'])
+
+        # create dataloader
+        self.dataloader = DataLoader(datasets, batch_size=batch_size)
+
+
+    def generate(self):
+        device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+        MAX_GENERATE_LENGTH = 128
+
+        pred_generations = []
+        pred_labels = []
+
+        print("start generation")
+        for batch in tqdm(self.dataloader):
+            # Inference in batches
+            input_ids = batch['input_ids']
+            attention_mask = batch['attention_mask']
+            # save labels too
+            pred_labels.extend(batch['labels'])
+            
+
+            # Move to GPU if available
+            input_ids = input_ids.to(device)
+            attention_mask = attention_mask.to(device)
+            self.model.to(device)
+
+            # Perform inference
+            with torch.no_grad():
+                outputs = self.model.generate(input_ids,
+                                        attention_mask=attention_mask,
+                                        max_length=MAX_GENERATE_LENGTH)
+                
+                # Decode the output and print the results
+                pred_generations.extend(outputs.to("cpu"))
+
+
+
+        # %%
+        # extract sequence and decode
+        def extract_seq(tokens, start_value, end_value):
+            if start_value not in tokens or end_value not in tokens:
+                return None  # Or handle this case according to your requirements
+            start_id = np.where(tokens == start_value)[0][0]
+            end_id = np.where(tokens == end_value)[0][0]
+
+            return tokens[start_id+1:end_id]
+
+
+        def process_tensor_output(tokens):
+            thing_seq = extract_seq(tokens, 32100, 32101) # 32100 = <THING_START>, 32101 = <THING_END>
+            property_seq = extract_seq(tokens, 32102, 32103) # 32102 = <PROPERTY_START>, 32103 = <PROPERTY_END>
+            p_thing = None
+            p_property = None
+            if (thing_seq is not None):
+                p_thing =  self.tokenizer.decode(thing_seq, skip_special_tokens=False)
+            if (property_seq is not None):
+                p_property =  self.tokenizer.decode(property_seq, skip_special_tokens=False)
+            return p_thing, p_property
+
+        # decode prediction labels
+        def decode_preds(tokens_list):
+            thing_prediction_list = []
+            property_prediction_list = []
+            for tokens in tokens_list:
+                p_thing, p_property = process_tensor_output(tokens)
+                thing_prediction_list.append(p_thing)
+                property_prediction_list.append(p_property)
+            return thing_prediction_list, property_prediction_list 
+
+        thing_prediction_list, property_prediction_list = decode_preds(pred_generations)
+        return thing_prediction_list, property_prediction_list
+

train/random_t5_encoder/mapping_prediction/output.txt

@@ -0,0 +1,6 @@
+
+Accuracy for fold 1: 0.0
+Accuracy for fold 2: 0.0
+Accuracy for fold 3: 0.0
+Accuracy for fold 4: 0.0
+Accuracy for fold 5: 0.0

train/random_t5_encoder/mapping_prediction/predict.py

@@ -0,0 +1,74 @@
+
+import pandas as pd
+import os
+import glob
+from inference import Inference
+
+checkpoint_directory =  '../'
+
+BATCH_SIZE = 512
+
+def infer_and_select(fold):
+    print(f"Inference for fold {fold}")
+    # import test data
+    data_path = f"../../../data_preprocess/exports/dataset/group_{fold}/test_all.csv"
+    df = pd.read_csv(data_path, skipinitialspace=True)
+    df = df[df['MDM']].reset_index(drop=True)
+
+    # get target data
+    data_path = f"../../../data_preprocess/exports/dataset/group_{fold}/train_all.csv"
+    train_df = pd.read_csv(data_path, skipinitialspace=True)
+    # processing to help with selection later
+    train_df['thing_property'] = train_df['thing'] + " " + train_df['property']
+
+
+    ##########################################
+    # run inference
+    # checkpoint
+    # Use glob to find matching paths
+    directory = os.path.join(checkpoint_directory, f'checkpoint_fold_{fold}b')
+    # Use glob to find matching paths
+    # path is usually checkpoint_fold_1/checkpoint-<step number>
+    # we are guaranteed to save only 1 checkpoint from training
+    pattern = 'checkpoint-*'
+    checkpoint_path = glob.glob(os.path.join(directory, pattern))[0]
+
+
+    infer = Inference(checkpoint_path)
+    infer.prepare_dataloader(df, batch_size=BATCH_SIZE, max_length=128)
+    thing_prediction_list, property_prediction_list = infer.generate()
+
+    # add labels too
+    # thing_actual_list, property_actual_list = decode_preds(pred_labels)
+    # Convert the list to a Pandas DataFrame
+    df_out = pd.DataFrame({
+        'p_thing': thing_prediction_list, 
+        'p_property': property_prediction_list
+    })
+    # df_out['p_thing_correct'] = df_out['p_thing'] == df_out['thing']
+    # df_out['p_property_correct'] = df_out['p_property'] == df_out['property']
+    df = pd.concat([df, df_out], axis=1)
+
+    # we can save the t5 generation output here
+    df.to_csv(f"exports/result_group_{fold}.csv", index=False)
+
+    # here we want to evaluate mapping accuracy within the valid in mdm data only
+    in_mdm = df['MDM']
+    condition_correct_thing = df['p_thing'] == df['thing']
+    condition_correct_property = df['p_property'] == df['property']
+    prediction_mdm_correct = sum(condition_correct_thing & condition_correct_property & in_mdm)
+    pred_correct_proportion = prediction_mdm_correct/sum(in_mdm)
+
+    # write output to file output.txt
+    with open("output.txt", "a") as f:
+        print(f'Accuracy for fold {fold}: {pred_correct_proportion}', file=f)
+
+###########################################  
+# Execute for all folds
+
+# reset file before writing to it
+with open("output.txt", "w") as f:
+    print('', file=f)
+
+for fold in [1,2,3,4,5]:
+    infer_and_select(fold)

train/random_t5_encoder/train_decoder.py

@@ -0,0 +1,246 @@
+# %%
+
+# from datasets import load_from_disk
+import os
+import glob
+
+os.environ['NCCL_P2P_DISABLE'] = '1'
+os.environ['NCCL_IB_DISABLE'] = '1'
+os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
+os.environ["CUDA_VISIBLE_DEVICES"] = "0,1,2,3"
+
+import torch
+
+from safetensors.torch import load_file
+
+from transformers import T5ForConditionalGeneration, T5Config
+from transformers import (
+    T5Config,
+    T5TokenizerFast,
+    AutoModelForSeq2SeqLM,
+    DataCollatorForSeq2Seq,
+    Seq2SeqTrainer,
+    EarlyStoppingCallback,
+    Seq2SeqTrainingArguments,
+    T5ForConditionalGeneration,
+    T5Model
+)
+import evaluate
+import numpy as np
+import pandas as pd
+# import matplotlib.pyplot as plt
+from datasets import Dataset, DatasetDict
+
+
+
+torch.set_float32_matmul_precision('high')
+
+# outputs a list of dictionaries
+def process_df_to_dict(df):
+    output_list = []
+    for _, row in df.iterrows():
+        desc = f"<DESC>{row['tag_description']}<DESC>"
+        unit = f"<UNIT>{row['unit']}<UNIT>"
+        element = {
+            'input' : f"{desc}{unit}",
+            'output': f"<THING_START>{row['thing']}<THING_END><PROPERTY_START>{row['property']}<PROPERTY_END>",
+        }
+        output_list.append(element)
+
+    return output_list
+
+
+def create_split_dataset(fold):
+    # train 
+    data_path = f"../../data_preprocess/exports/dataset/group_{fold}/train_all.csv"
+    train_df = pd.read_csv(data_path, skipinitialspace=True)
+
+    # valid
+    data_path = f"../../data_preprocess/exports/dataset/group_{fold}/valid.csv"
+    validation_df = pd.read_csv(data_path, skipinitialspace=True)
+
+    combined_data = DatasetDict({
+        'train': Dataset.from_list(process_df_to_dict(train_df)),
+        'validation' : Dataset.from_list(process_df_to_dict(validation_df)),
+    })
+    return combined_data
+
+
+# function to perform training for a given fold
+def train(fold):
+    save_path = f'checkpoint_fold_{fold}b'
+    split_datasets = create_split_dataset(fold)
+
+    # prepare tokenizer
+    model_checkpoint = "t5-small"
+    tokenizer = T5TokenizerFast.from_pretrained(model_checkpoint, return_tensors="pt", clean_up_tokenization_spaces=True)
+    # Define additional special tokens
+    additional_special_tokens = ["<THING_START>", "<THING_END>", "<PROPERTY_START>", "<PROPERTY_END>", "<NAME>", "<DESC>", "<SIG>", "<UNIT>", "<DATA_TYPE>"]
+    # Add the additional special tokens to the tokenizer
+    tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens})
+
+    max_length = 120
+
+    # given a dataset entry, run it through the tokenizer
+    def preprocess_function(example):
+        input = example['input']
+        target = example['output']
+        # text_target sets the corresponding label to inputs
+        # there is no need to create a separate 'labels'
+        model_inputs = tokenizer(
+            input,
+            text_target=target, 
+            max_length=max_length,
+            truncation=True,
+            padding="max_length"
+        )
+        return model_inputs
+
+    # map maps function to each "row" in the dataset
+    # aka the data in the immediate nesting
+    tokenized_datasets = split_datasets.map(
+        preprocess_function,
+        batched=True,
+        num_proc=8,
+        remove_columns=split_datasets["train"].column_names,
+    )
+
+    # https://github.com/huggingface/transformers/pull/28414
+    # model_checkpoint = "google/t5-efficient-tiny"
+    # device_map set to auto to force it to load contiguous weights 
+    # model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint, device_map='auto')
+
+    # directory = os.path.join(".", f'checkpoint_fold_{fold}a')
+    # # Use glob to find matching paths
+    # # path is usually checkpoint_fold_1/checkpoint-<step number>
+    # # we are guaranteed to save only 1 checkpoint from training
+    # pattern = 'checkpoint-*'
+    # prev_checkpoint = glob.glob(os.path.join(directory, pattern))[0]
+    # # t5_classify = T5Model.from_pretrained(prev_checkpoint)
+    # # Load the checkpoint
+    # checkpoint_path = f"{prev_checkpoint}/model.safetensors"
+    # checkpoint = load_file(checkpoint_path)
+    # # Filter out weights related to the classification head
+    # # given name format: t5.encoder.embed_tokens.weight
+    # # we want: encoder.embed.tokens.weight
+    # t5_weights= {key.replace("t5.", "", 1): value for key, value in checkpoint.items() if "classifier" not in key}
+
+    model = T5ForConditionalGeneration.from_pretrained(model_checkpoint)
+    # change the token embedding size to match the shape
+    model.resize_token_embeddings(len(tokenizer))
+
+
+    # Create a T5 model with random weights
+    config = T5Config.from_pretrained("t5-small")  # Use T5 configuration
+    random_model = T5ForConditionalGeneration(config)  # Model initialized with random weights
+    random_model.resize_token_embeddings(len(tokenizer))
+
+    model.encoder = random_model.encoder
+    model.shared = random_model.shared
+
+    # model.load_state_dict(state_dict=t5_weights, strict=False)
+
+    # for key, param in model.state_dict().items():
+    #     if key in t5_weights:
+    #         print(f"{key}: Successfully overridden")
+    #     else:
+    #         print(f"{key}: Retained original weights")
+
+
+    # Freeze the encoder
+    for param in model.encoder.parameters():
+        param.requires_grad = False
+
+    # Freeze the shared embedding layer
+    for param in model.shared.parameters():
+        param.requires_grad = False
+
+
+    data_collator = DataCollatorForSeq2Seq(tokenizer, model=model)
+    metric = evaluate.load("sacrebleu")
+
+
+    def compute_metrics(eval_preds):
+        preds, labels = eval_preds
+        # In case the model returns more than the prediction logits
+        if isinstance(preds, tuple):
+            preds = preds[0]
+
+        decoded_preds = tokenizer.batch_decode(preds, 
+                                            skip_special_tokens=False)
+
+        # Replace -100s in the labels as we can't decode them
+        labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
+        decoded_labels = tokenizer.batch_decode(labels,
+                                                skip_special_tokens=False)
+
+        # Remove <PAD> tokens from decoded predictions and labels
+        decoded_preds = [pred.replace(tokenizer.pad_token, '').strip() for pred in decoded_preds]
+        decoded_labels = [[label.replace(tokenizer.pad_token, '').strip()] for label in decoded_labels]
+
+        # Some simple post-processing
+        # decoded_preds = [pred.strip() for pred in decoded_preds]
+        # decoded_labels = [[label.strip()] for label in decoded_labels]
+        # print(decoded_preds, decoded_labels)
+
+        result = metric.compute(predictions=decoded_preds, references=decoded_labels)
+        return {"bleu": result["score"]}
+
+
+    # Generation Config
+    # from transformers import GenerationConfig
+    gen_config = model.generation_config
+    gen_config.max_length = 128
+
+    # compile
+    # model = torch.compile(model, backend="inductor", dynamic=True)
+
+
+    # Trainer
+
+    args = Seq2SeqTrainingArguments(
+        f"{save_path}",
+        # eval_strategy="epoch",
+        eval_strategy="no",
+        logging_dir="tensorboard-log",
+        logging_strategy="epoch",
+        # save_strategy="epoch",
+        load_best_model_at_end=False,
+        learning_rate=1e-3,
+        per_device_train_batch_size=64,
+        per_device_eval_batch_size=64,
+        auto_find_batch_size=False,
+        ddp_find_unused_parameters=False,
+        weight_decay=0.01,
+        save_total_limit=1,
+        num_train_epochs=40,
+        predict_with_generate=True,
+        bf16=True,
+        push_to_hub=False,
+        generation_config=gen_config,
+        remove_unused_columns=False,
+    )
+
+
+    trainer = Seq2SeqTrainer(
+        model,
+        args,
+        train_dataset=tokenized_datasets["train"],
+        eval_dataset=tokenized_datasets["validation"],
+        data_collator=data_collator,
+        tokenizer=tokenizer,
+        compute_metrics=compute_metrics,
+        # callbacks=[EarlyStoppingCallback(early_stopping_patience=3)],
+    )
+
+    # uncomment to load training from checkpoint
+    # checkpoint_path = 'default_40_1/checkpoint-5600'
+    # trainer.train(resume_from_checkpoint=checkpoint_path)
+
+    trainer.train()
+
+# execute training
+for fold in [1,2,3,4,5]:
+    print(fold)
+    train(fold)
+

train/train.bash

@@ -1,16 +1,27 @@
 #!/bin/bash
 
-cd classification_bert_complete_desc
-micromamba run -n hug accelerate launch train.py
+cd hybrid_t5_complete_desc_unit
+micromamba run -n hug accelerate launch train_encoder.py
+micromamba run -n hug accelerate launch train_decoder.py
 cd ..
 
-cd classification_bert_complete_desc_unit
-micromamba run -n hug accelerate launch train.py
+cd hybrid_t5_pattern_desc_unit
+micromamba run -n hug accelerate launch train_encoder.py
+micromamba run -n hug accelerate launch train_decoder.py
 cd ..
 
-cd classification_bert_complete_desc_unit_name
-micromamba run -n hug accelerate launch train.py
-cd ..
+
+# cd classification_bert_complete_desc
+# micromamba run -n hug accelerate launch train.py
+# cd ..
+
+# cd classification_bert_complete_desc_unit
+# micromamba run -n hug accelerate launch train.py
+# cd ..
+
+# cd classification_bert_complete_desc_unit_name
+# micromamba run -n hug accelerate launch train.py
+# cd ..
 
 # cd mapping_t5_complete_desc
 # micromamba run -n hug accelerate launch train.py