better

model/supervised_finetuning/custom_datasets/__init__.py

@@ -1,3 +1,4 @@
+import numpy as np
 from datasets import load_dataset
 from sklearn.model_selection import train_test_split
 from torch.utils.data import Dataset, Subset
@@ -5,19 +6,100 @@ from torch.utils.data import Dataset, Subset
 from .prompt_dialogue import PromptGeneratedDataset
 
 QA_SPECIAL_TOKENS = {"Question": "<question>", "Answer": "<answer>"}
+SUMMARIZATION_SPECIAL_TOKENS = {"Text": "", "Summary": "TL;DR:"}
+
+summarization_name_mapping = {
+    "cnn_dailymail": ("article", "highlights"),
+    "samsum": ("dialogue", "summary"),
+    "xsum": ("document", "summary"),
+    "multi_news": ("document", "summary"),
+    "scitldr": ("source", "target"),
+    "billsum": ("text", "summary"),
+    "reddit": ("content", "summary"),
+}
+summarization_config_mapping = {
+    "cnn_dailymail": ("3.0.0",),
+    "samsum": (),
+    "xsum": (),
+    "multi_news": (),
+    "scitldr": ("AIC",),
+    "billsum": (),
+    "reddit": (),
+}
 
 
-class SquadV2Dataset(Dataset):
-    def __init__(self, cache_dir, split):
-        self.dataset = load_dataset("squad_v2", cache_dir=cache_dir, split=split)
+def index_squad_v2(example):
+    return example["title"] + ". " + example["context"] + " " + example["question"], example["answers"]["text"][0]
+
+
+def index_trivia_qa_nocontext(example):
+    # dummy return one randomly
+    return example["question"], example["answer"]["aliases"][np.random.randint(len(example["answer"]["aliases"]))]
+
+
+def index_trivia_qa_context(example):
+    question = example["question"]
+    title = example["title"][np.random.randint(len(example["title"]))]
+    context = example["search_context"][np.random.randint(len(example["search_context"]))]
+    answer = example["answer"]["aliases"][np.random.randint(len(example["answer"]["aliases"]))]
+
+    return title + ". " + context + " " + question, answer
+
+
+def index_adversarial_qa(example):
+    return example["title"] + ". " + example["context"] + " " + example["question"], example["answers"]["text"][0]
+
+
+class QADataset(Dataset):
+    def __init__(self, dataset, cache_dir, split):
+        if dataset == "squad_v2":
+            self.index_fn = index_squad_v2
+            self.dataset = load_dataset("squad_v2", cache_dir=cache_dir, split=split)
+        elif dataset == "trivia_qa_nocontext":
+            self.index_fn = index_trivia_qa_nocontext
+            self.dataset = load_dataset("trivia_qa", "rc.nocontext")
+        elif dataset == "trivia_qa_context":
+            self.index_fn = index_trivia_qa_context
+            self.dataset = load_dataset("trivia_qa", "rc")
+        elif dataset == "adversarial_qa":
+            self.index_fn = index_adversarial_qa
+            self.dataset = load_dataset("adversarial_qa", "adversarialQA")
+        else:
+            raise ValueError("Unknown dataset : " + dataset)
 
     def __len__(self):
         return len(self.dataset)
 
     def __getitem__(self, idx):
         data = self.dataset[idx]
-        # return first answer form list of possible answers
-        return data["title"] + ". " + data["context"] + " " + data["question"], data["answers"]["text"][0]
+        return self.index_fn(data)
+
+
+def index_summary_default(text, summary):
+    return text, summary
+
+
+def index_summary_merge(text, summary):
+    return " ".join(text), " ".join(summary)
+
+
+class SummarizationDataset(Dataset):
+    def __init__(self, dataset, cache_dir, split):
+        self.dataset = load_dataset(dataset, *summarization_config_mapping[dataset], cache_dir=cache_dir, split=split)
+        self.summary_column, self.text_column = summarization_name_mapping[dataset]
+        self.preprocess_fn = index_summary_merge if dataset == "scitdlr" else index_summary_merge
+
+    def __len__(self):
+        return len(self.dataset)
+
+    def __getitem__(self, idx):
+        data = self.dataset[idx]
+        text, summary = data[self.text_column], data[self.summary_column]
+        text, summary = self.preprocess_fn(text, summary)
+
+        return "".join(
+            SUMMARIZATION_SPECIAL_TOKENS["Text"], text, " ", SUMMARIZATION_SPECIAL_TOKENS["Summary"], summary
+        )
 
 
 class WebGPT(Dataset):
@@ -58,9 +140,14 @@ def train_val_dataset(dataset, val_split=0.2):
 def get_one_dataset(conf, dataset_name):
     dataset_name = dataset_name.lower()
 
-    if dataset_name == "squadv2":
-        train = SquadV2Dataset(conf.cache_dir, "train")
-        eval = SquadV2Dataset(conf.cache_dir, "validation")
+    if dataset_name in ["squad_v2", "adversarial_qa", "trivia_qa_context", "trivia_qa_noconext"]:
+        train = QADataset(dataset_name, conf.cache_dir, "train")
+        eval = QADataset(dataset_name, conf.cache_dir, "validation")
+
+    elif dataset_name in ["xsum", "cnn_dailymail", "samsum", "multi_news", "scitldr", "billsum", "reddit"]:
+        train = SummarizationDataset(dataset_name, conf.cache_dir, "train")
+        eval = SummarizationDataset(dataset_name, conf.cache_dir, "validation")
+
     elif dataset_name == "webgpt":
         dataset = WebGPT()
         train, eval = train_val_dataset(dataset, val_split=0.2)

model/supervised_finetuning/losses.py

@@ -1,3 +1,5 @@
+import torch
+import torch.nn.functional as F
 from torch import nn
 
 
@@ -13,3 +15,31 @@ class CrossEntropyLoss(nn.CrossEntropyLoss):
             input = input[mask]
             target = target[mask]
         return super(CrossEntropyLoss, self).forward(input, target)
+
+
+class PolyLoss(nn.Module):
+    def __init__(self, weight=None, size_average=None, ignore_index=-100, reduce=None, reduction="mean", epsilon=1.0):
+        super(PolyLoss, self).__init__()
+        self.weight = torch.tensor(weight)
+        self.ignore_index = ignore_index
+        self.reduction = reduction
+        self.cross_entropy = CrossEntropyLoss(weight, size_average, ignore_index, reduce, "none")
+        self.epsilon = epsilon
+
+    def forward(self, input, target, mask=None):
+        if mask is not None:
+            mask = mask.view(-1).bool()
+            input = input.view(-1, input.size(-1))
+            target = target.view(-1)
+            input = input[mask]
+            target = target[mask]
+
+        onehot_target = F.one_hot(target, num_classes=input.size(-1)).to(device=input.device, dtype=input.dtype)
+        pt = torch.sum(onehot_target * F.softmax(input, -1), -1)
+        CE = self.cross_entropy(input, target)
+        poly1 = CE + self.epsilon * (1 - pt)
+        if self.reduction == "mean":
+            poly1 = poly1.mean()
+        elif self.reduction == "sum":
+            poly1 = poly1.sum()
+        return poly1

model/supervised_finetuning/models/__init__.py

@@ -1,4 +1,4 @@
-from transformers import AutoModelForCausalLM
+import transformers
 
 # from .gptj import get_model as get_gptj_model
 
@@ -25,9 +25,12 @@ def freeze_top_n_layers(model, target_layers):
     return model
 
 
-def get_specific_model(model_name, cache_dir, quantization):
-    return AutoModelForCausalLM.from_pretrained(model_name, cache_dir=cache_dir)
-    # if "gpt-j" in model_name.lower():
-    #     return get_gptj_model(model_name, cache_dir, quantization)
-    # else:
-    #     return AutoModelForCausalLM.from_pretrained(model_name, cache_dir=cache_dir)
+def get_specific_model(model_name, cache_dir, quantization, seq2seqmodel):
+    # encoder-decoder support for Flan-T5 like models
+    # for now, we can use an argument but in the future,
+    # we can automate this
+    if seq2seqmodel:
+        model = transformers.AutoModelForSeq2SeqLM.from_pretrained(model_name, cache_dir=cache_dir)
+    else:
+        model = transformers.AutoModelForCausalLM.from_pretrained(model_name, cache_dir=cache_dir)
+    return model

model/supervised_finetuning/trainer.py

@@ -3,21 +3,22 @@ import os
 from distutils.util import strtobool
 from typing import Any, Dict, List, Optional, Tuple, Union
 
-import bitsandbytes
 import torch
 from torch import nn
 from transformers import PreTrainedModel, Trainer, TrainingArguments
-from transformers.training_args import OptimizerNames
-from utils import get_dataset, get_loss, get_model, get_tokenizer, read_yamls
+from utils import get_dataset, get_loss, get_metrics, get_model, get_tokenizer, read_yamls
 
 os.environ["WANDB_PROJECT"] = "supervised-finetuning"
 
 
-def compute_metrics(eval_pred):
-    pred_ids = eval_pred.predictions
-    labels = eval_pred.label_ids
+def compute_metrics(eval_pred, preprocess_fn, metrics):
+    preds, labels = preprocess_fn(eval_pred)
 
-    return {"accuracy": (pred_ids[labels > 0] == labels[labels > 0]).mean()}
+    out = {}
+    for metric in metrics:
+        out = dict(**out, **metric.compute(predictions=preds, references=labels))
+
+    return out
 
 
 def preprocess_logits_for_metrics(logits, labels):
@@ -31,12 +32,13 @@ class SFTTrainer(Trainer):
         model: Union[PreTrainedModel, nn.Module] = None,
         args: TrainingArguments = None,
         loss_function: str = "CrossEntropyLoss",
+        poly_eps: float = 1.0,
         **kwargs,
     ):
         super().__init__(model, args, **kwargs)
 
         # By default CrossEntropyLoss ignores padding_index -100, but just in case use our own loss_fct
-        self.loss_fct = get_loss(loss_function)
+        self.loss_fct = get_loss(loss_function, poly_eps)
 
     def compute_loss(self, model, inputs, return_outputs=False):
         labels_mask = inputs.pop("label_masks")
@@ -92,6 +94,8 @@ def argument_parsing(notebook=False, notebook_args=None):
     parser.add_argument("--local_rank", type=int, default=-1)
     parser.add_argument("--deepspeed", action="store_true")
     parser.add_argument("--no-deepspeed", dest="deepspeed", action="store_false")
+    parser.add_argument("--poly_eps", type=float, default=1.0)
+    parser.add_argument("--seq2seq_model", action="store_true")
     parser.set_defaults(deepspeed=False)
 
     if notebook:
@@ -131,15 +135,7 @@ if __name__ == "__main__":
     model = get_model(training_conf, tokenizer)
 
     train, evals, collate_fn = get_dataset(training_conf, tokenizer)
-
-    optimizer = OptimizerNames.ADAMW_BNB if training_conf.quantization else None
-
-    if training_conf.quantization:
-        for module in model.modules():
-            if isinstance(module, torch.nn.Embedding):
-                bitsandbytes.optim.GlobalOptimManager.get_instance().register_module_override(
-                    module, "weight", {"optim_bits": 32}
-                )
+    metrics, preprocess_fn = get_metrics(training_conf)
 
     args = TrainingArguments(
         output_dir=f"{training_conf.model_name}-{training_conf.log_dir}-finetuned",
@@ -147,7 +143,6 @@ if __name__ == "__main__":
         warmup_steps=training_conf.warmup_steps,
         learning_rate=float(training_conf.learning_rate),
         deepspeed="configs/zero_config.json" if training_conf.deepspeed else None,
-        optim=optimizer,
         fp16=True,
         local_rank=training_conf.local_rank,
         gradient_checkpointing=training_conf.gradient_checkpointing,

model/supervised_finetuning/utils.py

@@ -1,17 +1,21 @@
+from functools import partial
 from pathlib import Path
 
+import evaluate
+import nltk
+import numpy as np
+import transformers
 import yaml
 from custom_datasets import QA_SPECIAL_TOKENS, get_one_dataset
 from custom_datasets.dialogue_collator import DialogueDataCollator
-from losses import CrossEntropyLoss
+from losses import CrossEntropyLoss, PolyLoss
 from models import freeze_top_n_layers, get_specific_model
 from sklearn.model_selection import train_test_split
 from torch.utils.data import ConcatDataset, Subset
-from transformers import AutoTokenizer
 
 
 def get_tokenizer(conf):
-    tokenizer = AutoTokenizer.from_pretrained(conf.model_name, cache_dir=conf.cache_dir)
+    tokenizer = transformers.AutoTokenizer.from_pretrained(conf.model_name, cache_dir=conf.cache_dir)
 
     if "galactica" in conf.model_name:
         tokenizer.add_special_tokens({"pad_token": "<pad>", "eos_token": "</s>"})
@@ -32,8 +36,51 @@ def get_tokenizer(conf):
     return tokenizer
 
 
+# placeholder for now
+def preprocess_qa(eval_pred):
+    return (eval_pred.predictions, eval_pred.label_ids)
+
+
+def postprocess_summarization(preds, labels):
+    preds = [pred.strip() for pred in preds]
+    labels = [label.strip() for label in labels]
+
+    preds = ["\n".join(nltk.sent_tokenize(pred)) for pred in preds]
+    labels = ["\n".join(nltk.sent_tokenize(label)) for label in labels]
+
+    return preds, labels
+
+
+def preprocess_summarization(eval_pred, tokenizer, ignore_pad_token_for_loss=True):
+    preds, labels = eval_pred
+    decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)
+    if ignore_pad_token_for_loss:
+        labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
+    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)
+
+    decoded_preds, decoded_labels = postprocess_summarization(decoded_preds, decoded_labels)
+    return decoded_preds, decoded_labels
+
+
+def get_metrics(conf, tokenizer):
+    qa_datasets = ["squad_v2", "adversarial_qa", "trivia_qa_context", "trivia_qa_noconext"]
+    summarization_datasets = ["xsum", "cnn_dailymail", "samsum", "multi_news"]
+
+    # the reason behind using a list is that we might want to extend the list of our
+    # metrics in the future for more thorough evaluation
+    if any(dataset in qa_datasets for dataset in conf.datasets):
+        metrics, preprocess_fn = [evaluate.load("squad_v2")], preprocess_qa
+    elif any(dataset in summarization_datasets for dataset in conf.datasets):
+        metrics, preprocess_fn = [evaluate.load("rouge")], partial(
+            preprocess_summarization, tokenizer, ignore_pad_token_for_loss=conf.ignore_pad_token_for_loss
+        )
+    else:
+        raise ValueError("Unknown dataset / task")
+    return metrics, preprocess_fn
+
+
 def get_model(conf, tokenizer):
-    model = get_specific_model(conf.model_name, conf.cache_dir, conf.quantization)
+    model = get_specific_model(conf.model_name, conf.cache_dir, conf.quantization, conf.seq2seqmodel)
 
     if len(tokenizer) != model.get_input_embeddings().num_embeddings:
         assert not conf.freeze_layer, "Cannot change the number of embeddings if the model is frozen."
@@ -65,9 +112,11 @@ def get_dataset(conf, tokenizer):
     return train, evals, collate_fn
 
 
-def get_loss(loss):
+def get_loss(loss, poly_eps):
     if loss == "CrossEntropyLoss":
         return CrossEntropyLoss()
+    elif loss == "Poly":
+        return PolyLoss(epsilon=poly_eps)
     else:
         raise ValueError(f"Loss {loss} not supported")