Enable wit train on cutome dataset and loss down.

.gitignore

@@ -1,4 +1,5 @@
 __pycache__
 .vscode
 *.txt
-temp
+temp
+lightning_logs

dataset/MNBVC.py

@@ -1,5 +0,0 @@
-from datasets import load_dataset
-
-dataset = load_dataset("liwu/MNBVC", "wikipedia", split="train", streaming=True)
-
-print(next(iter(dataset)))  # get the first line

wit/lit_module.py

@@ -0,0 +1,101 @@
+from functools import cache
+from typing import Dict, Optional
+
+import pytorch_lightning as pl
+import torch
+import torchmetrics
+
+# from utils import init_model
+# from custom_models.gpt2.modeling_gpt2 import GPT2LMHeadModel
+
+from modeling_wit import QWenLMHeadModel
+from configuration_qwen import QWenConfig
+
+from transformers import AutoConfig
+
+
+class LitModule(pl.LightningModule):
+    def __init__(
+        self,
+        model_dir: str,
+        learning_rate: float = 0.0001,
+        use_tril_attention_mask: str = False,
+    ):
+        super().__init__()
+        self.save_hyperparameters()
+        config = QWenConfig()
+        model = QWenLMHeadModel(config)
+        model = model.from_pretrained(model_dir)
+        self.llm = self.register_core_module(model)
+        self.learning_rate = learning_rate
+        self.use_tril_attention_mask = use_tril_attention_mask
+        self.metric_loss = torchmetrics.MeanMetric()
+        self.metric_accuracy = torchmetrics.Accuracy(
+            task="multiclass",
+            num_classes=self.llm.config.vocab_size,
+        )
+
+    @cache
+    def get_batch_tril_matrix(self, block_size: int, batch_size: Optional[int] = None) -> torch.Tensor:
+        matrix = torch.ones(block_size, block_size).tril()
+        if batch_size is not None:
+            matrix = matrix.repeat(batch_size, 1, 1)
+        return matrix
+
+    def register_core_module(self, module: torch.nn.Module) -> torch.nn.Module:
+        object.__setattr__(self, "__core_module__", module)
+        return module
+
+    def training_step(self, batch: Dict[str, torch.Tensor], batch_idx):
+        batch_size, block_size = batch["input_ids"].shape
+        if self.use_tril_attention_mask:
+            batch["attention_mask"] = self.get_batch_tril_matrix(block_size, batch_size=batch_size).to(self.device)
+        outputs, loss = self.llm(**batch)
+        self.log("train_loss", loss, rank_zero_only=True)
+        return loss
+
+    def validation_step(self, batch: Dict[str, torch.Tensor], batch_idx):
+        outputs, loss = self.llm(**batch, return_dict=True)
+        logits = outputs[..., :-1, :]
+        labels = batch["labels"][..., 1:]
+
+        self.metric_loss.update(loss)
+
+        label_mask = labels != -100
+        self.metric_accuracy.update(logits[label_mask], labels[label_mask])
+
+    def on_validation_epoch_end(self) -> None:
+        self.log("val_loss", self.metric_loss, rank_zero_only=True)
+        self.log("accuracy", self.metric_accuracy, rank_zero_only=True)
+
+    def configure_optimizers(self):
+        strategy = self.trainer.strategy
+        if isinstance(strategy, pl.strategies.DeepSpeedStrategy):
+            assert "optimizer" not in strategy.config
+            zero_config = strategy.config.get("zero_optimization")
+            if zero_config is not None:
+                if "offload_optimizer" in zero_config:
+                    import deepspeed
+
+                    optimizer = deepspeed.ops.adam.DeepSpeedCPUAdam(
+                        self.trainer.model.parameters(), lr=self.learning_rate
+                    )
+                    return optimizer
+        optimizer = torch.optim.AdamW(self.trainer.model.parameters(), lr=self.learning_rate)
+        return optimizer
+
+    def configure_callbacks(self):
+        checkpoint_callback = pl.callbacks.ModelCheckpoint(
+            monitor="accuracy",
+            mode="max",
+            filename="{epoch:02d}-{accuracy:.4f}",
+        )
+        early_stop_callback = pl.callbacks.EarlyStopping(
+            monitor="accuracy",
+            min_delta=0.001,
+            patience=3,
+            mode="max",
+            stopping_threshold=1,
+        )
+        return [checkpoint_callback]
+        # return [checkpoint_callback, early_stop_callback]

wit/lit_train.py

@@ -0,0 +1,181 @@
+import argparse
+from functools import partial
+from itertools import chain
+from typing import Dict, Tuple
+
+import datasets
+import pytorch_lightning as pl
+import torch
+from torch.utils.data import ConcatDataset, DataLoader, Dataset
+from transformers import (
+    BatchEncoding,
+    DefaultDataCollator,
+    PreTrainedTokenizer,
+    set_seed,
+)
+from modelscope import snapshot_download
+from lit_module import LitModule
+from tokenization_qwen import QWenTokenizer
+
+model_name = "qwen/Qwen-1_8B-Chat"
+learning_rate = 0.0001
+use_tril_attention_mask = None
+precision = "32-true"  # "precision:bf16-mixed,16-mixed,32-true"
+tokenizer_name_or_path = None
+dataset_name = ["/home/colin/develop/dataset/liwu/MNBVC/wiki"]
+dataset_name = ["/home/colin/develop/dataset/liwu/MNBVC/wiki/20230198/58.jsonl.gz"]
+train_batch_size = 256
+val_batch_size = 16
+limit_val_batches = 128
+num_proc = 8
+max_epochs = 1000
+strategy = "fsdp"
+resume_from_ckpt_path = None
+seed = 42
+
+
+class SpecialDataset(Dataset):
+    def __init__(self, size=65536):
+        self.size = size
+        self.features = []
+        a = torch.randint(0, 1024, [size])
+        self.data = torch.stack([a, a * 2, a * 3, a * 4]).permute(1, 0)
+
+    def __len__(self):
+        return self.size
+
+    def __getitem__(self, idx):
+        output = {}
+        data = self.data[idx]
+        output["input_ids"] = data
+        output["labels"] = data
+        output["token_type_ids"] = torch.zeros(data.shape)
+        return output
+
+
+def split_raw_dataset(
+    raw_dataset: datasets.DatasetDict,
+) -> Tuple[datasets.Dataset, datasets.Dataset]:
+    if "validation" in raw_dataset:
+        train_dataset, val_dataset = raw_dataset["train"], raw_dataset["validation"]
+    else:
+        raw_dataset = raw_dataset["train"].train_test_split(test_size=0.05, seed=seed)
+        train_dataset, val_dataset = raw_dataset["train"], raw_dataset["test"]
+    return train_dataset, val_dataset
+
+
+def process_dataset(dataset: datasets.Dataset, tokenizer: PreTrainedTokenizer) -> datasets.Dataset:
+    def group_texts(examples: Dict[str, list], block_size: int = 512) -> BatchEncoding:
+        concatenated_examples = {k: list(chain(*examples[k])) for k in examples.keys()}
+        total_length = len(concatenated_examples[list(examples.keys())[0]])
+        total_length = (total_length // block_size) * block_size
+        result = {
+            k: [t[i : i + block_size] for i in range(0, total_length, block_size)]
+            for k, t in concatenated_examples.items()
+        }
+        result["labels"] = result["input_ids"].copy()
+        result = BatchEncoding(result)
+        return result
+
+    def format_inputs(examples):
+        p = examples["段落"]
+        mergeLine = ""
+        for line in p:
+            mergeLine += line["内容"] + "\n"
+        return {"text": mergeLine}
+
+    def tokenize_inputs(
+        examples: Dict[str, list],
+        tokenizer: PreTrainedTokenizer,
+        column_name: str = "text",
+    ) -> BatchEncoding:
+        logits = tokenizer(examples[column_name], return_attention_mask=False)
+        return logits
+
+    dataset_column_names = list(dataset.features)
+    dataset = dataset.map(
+        partial(format_inputs),
+        batched=False,
+        num_proc=num_proc,
+        remove_columns=dataset_column_names,
+    )
+    dataset_column_names = list(dataset.features)
+    dataset = dataset.map(
+        partial(tokenize_inputs, tokenizer=tokenizer),
+        batched=True,
+        num_proc=num_proc,
+        remove_columns=dataset_column_names,
+    )
+    dataset = dataset.map(
+        partial(group_texts, block_size=tokenizer.model_max_length),
+        batched=True,
+        num_proc=num_proc,
+    )
+
+    return dataset
+
+
+if __name__ == "__main__":
+    if tokenizer_name_or_path is None:
+        tokenizer_name_or_path = model_name
+
+    set_seed(seed)
+
+    # lightning module
+    model_dir = snapshot_download(model_name)
+    lit_module = LitModule(model_dir, learning_rate, use_tril_attention_mask)
+
+    tokenizer = QWenTokenizer("./wit_b64.tiktoken", "./wit_char.tiktoken")
+    train_dataset_list = []
+    val_dataset_list = []
+    for dn in dataset_name:
+        datanames = dn.split(".")
+        if datanames[-1] == "gz" and datanames[-2] == "jsonl":
+            raw_dataset = datasets.load_dataset("json", data_files=dn)
+        elif datanames[-1] == "json":
+            raw_dataset = datasets.load_dataset("json", data_files=dn)
+        else:
+            raw_dataset = datasets.load_dataset(dn)
+        train_dataset, val_dataset = split_raw_dataset(raw_dataset)
+        train_dataset = process_dataset(train_dataset, tokenizer)
+        val_dataset = process_dataset(val_dataset, tokenizer)
+        train_dataset_list.append(train_dataset)
+        val_dataset_list.append(val_dataset)
+    train_dataset = ConcatDataset(train_dataset_list)
+    val_dataset = ConcatDataset(val_dataset_list)
+
+    train_dataset = SpecialDataset()
+    val_dataset = SpecialDataset()
+
+    train_dataloader = DataLoader(
+        train_dataset,
+        batch_size=train_batch_size,
+        num_workers=num_proc,
+        collate_fn=DefaultDataCollator(),
+        persistent_workers=True,
+        shuffle=True,
+    )
+    val_dataloader = DataLoader(
+        val_dataset,
+        batch_size=val_batch_size,
+        num_workers=num_proc,
+        collate_fn=DefaultDataCollator(),
+        persistent_workers=True,
+        shuffle=True,
+    )
+
+    torch.set_float32_matmul_precision("medium")
+    precision = precision
+    lit_trainer = pl.Trainer(
+        accelerator="gpu",
+        precision=precision,
+        strategy=strategy,
+        max_epochs=max_epochs,
+        limit_val_batches=limit_val_batches,
+    )
+    lit_trainer.fit(
+        lit_module,
+        train_dataloaders=train_dataloader,
+        val_dataloaders=val_dataloader,
+        ckpt_path=resume_from_ckpt_path,
+    )

wit/modeling_wit.py

@@ -137,6 +137,16 @@ class QWenLMHeadModel(nn.Module):
         self.transformer = QWenModel(config)
         self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
 
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        token_type_ids: Optional[torch.LongTensor] = None,
+        **kwargs,
+    ):
+        runner = QwenRunner(self)
+        return runner.forwardQWen(input_ids, labels)
+
     def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]]):
         pretrained_model_name_or_path = str(pretrained_model_name_or_path)
         resolved_archive_file = os.path.join(pretrained_model_name_or_path, "model.safetensors.index.json")
@@ -343,15 +353,7 @@ class QwenRunner:
             loss_fct = CrossEntropyLoss()
             loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
 
-        # shift_labels = torch.ones([1,19]).to(lm_logits.device).to(torch.int64)
+        return lm_logits, loss
-        # shift_logits = lm_logits[..., :-1, :].contiguous()
-        # loss_fct = CrossEntropyLoss()
-        # loss = loss_fct(
-        #     shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)
-        # )
-        # loss.backward()
-
-        return lm_logits
 
     def prepareInput(self, tokenizer, query, query_assistant, history, system):
         return make_context(tokenizer, query, query_assistant, history=history, system=system)

wit/tokenization_qwen.py

@@ -63,7 +63,7 @@ class QWenTokenizer(PreTrainedTokenizer):
 
         self.mergeable_ranks = _load_tiktoken_b64(vocab_file_b64)
         self.mergeable_ranks.update(_load_tiktoken_char(vocab_file_char, len(self.mergeable_ranks)))
-
+        self.model_max_length = 1024
         special = (
             "user",
             "assistant",