flame/data.py

# -*- coding: utf-8 -*-

from __future__ import annotations

import copy
import pickle
from copy import deepcopy
from dataclasses import dataclass
from typing import Any, Callable, Dict, Iterable, List, Optional, Union

import datasets
import numpy as np
import torch
from datasets import Dataset, IterableDataset
from datasets.iterable_dataset import ShufflingConfig
from torch.distributed.checkpoint.stateful import Stateful
from torchdata.stateful_dataloader import StatefulDataLoader
from transformers import PreTrainedTokenizer

from torchtitan.tools.logging import logger


class BufferShuffledIterableDataset(IterableDataset):
    def __init__(
        self,
        dataset: Dataset,
        tokenizer: PreTrainedTokenizer,
        seq_len: int = 2048,
        rank: int = 0,
        world_size: int = 1,
        buffer_size: int = 1024,
    ) -> BufferShuffledIterableDataset:
        self.dataset = dataset
        self.tokenizer = tokenizer

        self.data = dataset.shard(world_size, rank)
        self.seq_len = seq_len

        self.rank = rank
        self.world_size = world_size
        self.buffer_size = buffer_size

        if tokenizer.vocab_size < torch.iinfo(torch.int16).max:
            self.dtype = torch.int16
        elif tokenizer.vocab_size < torch.iinfo(torch.int32).max:
            self.dtype = torch.int32
        else:
            self.dtype = torch.int64
        self.states = None
        self.buffer = torch.tensor([], dtype=self.dtype)
        self.tokens = []
        self.rand_id = 0
        self.token_id = 0
        self.rng_state = None
        self._epoch = 0

    def __iter__(self):
        g = torch.Generator()
        g.manual_seed(self._epoch + self.rank)
        if self.rng_state is not None:
            g.set_state(self.rng_state)

        rand_it = self.randint(0, self.buffer_size, g=g)
        if self.states is not None:
            self.data.load_state_dict(self.states)

        # max number of tokens allowed in the chunk buffer
        n_tokens = self.buffer_size * self.seq_len

        while True:
            for sample in self.tokenize(self.data):
                # keep appending the samples to the token buffer
                self.tokens += sample
                # if the token buffer is full, start sampling
                # NOTE: we first convert the token ids to a tensor of shape [n_chunks, seq_len] for efficiency
                if len(self.buffer) == 0 and len(self.tokens) >= n_tokens:
                    self.buffer = torch.tensor(self.tokens[:n_tokens], dtype=self.dtype).view(self.buffer_size, -1)
                    self.tokens = self.tokens[n_tokens:]
                if len(self.buffer) == self.buffer_size:
                    yield from self.sample(rand_it)

            n_chunks = len(self.tokens) // self.seq_len
            # handle the left tokens in the buffer
            if n_chunks > 0:
                n_tokens = n_chunks * self.seq_len
                indices = torch.randperm(n_chunks, generator=g).tolist()
                self.buffer = torch.tensor(self.tokens[:n_tokens], dtype=torch.long).view(n_chunks, -1)
                self.tokens = self.tokens[n_tokens:]
                for i in indices:
                    yield {'input_ids': self.buffer[i]}

    def tokenize(self, data, batch_size: int = 64):
        texts, states = [], []
        for sample in data:
            texts.append(sample['text'])
            states.append(self.data.state_dict())
            if len(texts) == batch_size:
                for s, tokenized in zip(states, self.tokenizer(texts, return_attention_mask=False)['input_ids']):
                    self.states = s
                    yield tokenized
                texts, states = [], []
        if len(texts) > 0:
            for s, tokenized in zip(states, self.tokenizer(texts, return_attention_mask=False)['input_ids']):
                self.states = s
                yield tokenized

    def sample(self, indices):
        n_tokens = (len(self.tokens) // self.seq_len) * self.seq_len
        while self.token_id < n_tokens:
            i = next(indices)
            start, end = self.token_id, self.token_id + self.seq_len
            self.token_id += self.seq_len
            yield {'input_ids': self.buffer[i].to(torch.long)}
            self.buffer[i] = torch.tensor(self.tokens[start:end], dtype=self.dtype)
        self.token_id = 0
        self.tokens = self.tokens[n_tokens:]

    def randint(self, low: int, high: int, buffer_size: int = 1024, g: torch.Generator = torch.Generator()) -> Iterable[int]:
        indices = torch.empty(buffer_size, dtype=torch.long)
        while True:
            # record the generator states before sampling
            self.rng_state = g.get_state()
            indices = torch.randint(low, high, (buffer_size,), out=indices, generator=g)
            for i in indices[self.rand_id:].tolist():
                self.rand_id += 1
                yield i
            self.rand_id = 0

    def set_epoch(self, epoch):
        self._epoch = epoch
        if hasattr(self.dataset, 'set_epoch'):
            self.dataset.set_epoch(epoch)

    def state_dict(self):
        return {
            'states': self.states,
            'buffer': self.buffer.clone(),
            'tokens': deepcopy(self.tokens),
            'rand_id': self.rand_id,
            'token_id': self.token_id,
            'rng_state': self.rng_state,
            'epoch': self._epoch,
        }

    def load_state_dict(self, state_dict):
        self.states = state_dict['states']
        self.buffer = state_dict['buffer'].clone()
        self.tokens = deepcopy(state_dict['tokens'])
        self.rand_id = state_dict['rand_id']
        self.token_id = state_dict['token_id']
        self.rng_state = state_dict['rng_state'].clone() if state_dict['rng_state'] is not None else None
        self._epoch = state_dict['epoch']


class OnlineTokenizedIterableDataset(IterableDataset):
    def __init__(
        self, dataset: Dataset, tokenizer: PreTrainedTokenizer, seq_len: int = 2048, rank: int = 0, world_size: int = 1
    ) -> OnlineTokenizedIterableDataset:
        self.dataset = dataset
        self.tokenizer = tokenizer

        self.data = dataset.shard(world_size, rank)
        self.seq_len = seq_len
        self.rank = rank
        self.world_size = world_size

        self.states = None
        self.tokens = []

    def __iter__(self):
        if self.states is not None:
            self.data.load_state_dict(self.states)

        while True:
            for sample in self.tokenize(self.data):
                # keep appending the samples to the token buffer
                self.tokens += sample

                while len(self.tokens) >= self.seq_len:
                    input_ids = torch.tensor(self.tokens[:self.seq_len], dtype=torch.long)
                    self.tokens = self.tokens[self.seq_len:]
                    yield {'input_ids': input_ids}

    def tokenize(self, data, buffer_size: int = 64):
        buffer, states = [], []
        for sample in data:
            if sample.get('text', None) is not None:
                buffer.append(sample['text'])
            elif sample.get('content', None) is not None:
                buffer.append(sample['content'])
            else:
                raise ValueError(f"No 'text' or 'content' field found in sample:\n{sample}")
            states.append(self.data.state_dict())
            if len(buffer) == buffer_size:
                for s, tokenized in zip(states, self.tokenizer(buffer, return_attention_mask=False)['input_ids']):
                    self.states = s
                    yield tokenized
                buffer, states = [], []
        if len(buffer) > 0:
            for s, tokenized in zip(states, self.tokenizer(buffer, return_attention_mask=False)['input_ids']):
                self.states = s
                yield tokenized

    def state_dict(self):
        return {'states': self.states, 'tokens': deepcopy(self.tokens)}

    def load_state_dict(self, state_dict):
        self.states = state_dict['states']
        self.tokens = deepcopy(state_dict['tokens'])


class BufferShuffledExamplesIterable(datasets.iterable_dataset.BufferShuffledExamplesIterable):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

    def _init_state_dict(self) -> dict:
        self._state_dict = self.ex_iterable._init_state_dict()
        self._state_dict['mem_buffer'] = ([],)
        self._state_dict['bit_generator_state'] = self.generator.bit_generator.state
        self._state_dict['bit_generator_index_offset'] = 0
        self._state_dict['bit_generator_index_offset_shuffle'] = 0
        return self._state_dict

    def __iter__(self):
        buffer_size = self.buffer_size
        rng = deepcopy(self.generator)
        # this is the shuffle buffer that we keep in memory
        mem_buffer = self._state_dict['mem_buffer'][0]
        # this is an infinite iterator that randomly samples the index of the source to pick examples from
        index_offset = self._state_dict['bit_generator_index_offset'] if self._state_dict else 0
        if self._state_dict:
            rng.bit_generator.state = self._state_dict['bit_generator_state']
        indices_iterator = self._iter_random_indices(rng, buffer_size, random_batch_size=buffer_size)
        # skip already consumed ones
        for _ in range(index_offset):
            i = next(indices_iterator)

        for x in self.ex_iterable:
            if len(mem_buffer) < buffer_size:  # if the buffer is not full, keep filling the buffer
                mem_buffer.append(x)
            else:  # otherwise, pick an example from it
                i = next(indices_iterator)
                index_offset = (index_offset + 1) % buffer_size
                if self._state_dict:
                    self._state_dict['bit_generator_index_offset'] = index_offset
                    if index_offset == 0:
                        self._state_dict['bit_generator_state'] = rng.bit_generator.state
                selected = mem_buffer[i]
                mem_buffer[i] = x  # replace the picked example by a new one
                yield selected

        index_offset = self._state_dict['bit_generator_index_offset_shuffle'] if self._state_dict else 0
        if self._state_dict:
            rng.bit_generator.state = self._state_dict['bit_generator_state']

        # when we run out of examples, we shuffle the remaining examples in the buffer and yield them
        for i in rng.permutation(len(mem_buffer))[index_offset:].tolist():
            index_offset = index_offset + 1
            if self._state_dict:
                self._state_dict['bit_generator_index_offset_shuffle'] = index_offset
            yield mem_buffer[i]

    def shuffle_data_sources(self, generator: np.random.Generator) -> BufferShuffledExamplesIterable:
        """Shuffle the wrapped examples iterable as well as the shuffling buffer."""
        return BufferShuffledExamplesIterable(
            self.ex_iterable.shuffle_data_sources(generator), buffer_size=self.buffer_size, generator=generator
        )

    def shard_data_sources(self, num_shards: int, index: int, contiguous=True) -> BufferShuffledExamplesIterable:
        """Keep only the requested shard."""
        return BufferShuffledExamplesIterable(
            self.ex_iterable.shard_data_sources(num_shards, index, contiguous=contiguous),
            buffer_size=self.buffer_size,
            generator=self.generator,
        )

    def load_state_dict(self, state_dict: dict) -> dict:
        def _inner_load_state_dict(state, new_state):
            if new_state is not None and isinstance(state, dict):
                for key in new_state:
                    state[key] = _inner_load_state_dict(state[key], new_state[key])
                return state
            elif new_state is not None and isinstance(state, list):
                for i in range(len(state)):
                    state[i] = _inner_load_state_dict(state[i], new_state[i])
                return state
            return new_state

        return _inner_load_state_dict(self._state_dict, state_dict)


def shuffle(
    dataset: IterableDataset,
    seed: int = 42,
    generator: np.random.Generator = None,
    buffer_size: int = 1024,
):
    generator = np.random.default_rng(seed) if generator is None else deepcopy(generator)
    return IterableDataset(
        ex_iterable=BufferShuffledExamplesIterable(dataset._ex_iterable, buffer_size=buffer_size, generator=generator),
        info=dataset._info.copy(),
        split=dataset._split,
        formatting=dataset._formatting,
        shuffling=ShufflingConfig(generator=generator, _original_seed=seed),
        distributed=copy.deepcopy(dataset._distributed),
        token_per_repo_id=dataset._token_per_repo_id,
    )


@dataclass
class DataCollatorForLanguageModeling:
    """
    Data collator used for language modeling. Inputs are dynamically padded if `varlen=False`.
    If `varlen=True`, sequences are expected to be concatenated, and labels match inputs.

    Args:
        tokenizer ([`PreTrainedTokenizer`] or [`PreTrainedTokenizerFast`]):
            The tokenizer used for encoding the data.
        context_len (`int`, optional):
            When `varlen=True`, sequences longer than this length within a document
            (as determined by `cu_seqlens`) will be further chunked.
        varlen (`bool`):
            Whether to handle variable length concatenated sequences (`True`) or padded batches (`False`).

    Returns:
        A dictionary with the following keys:
        - `input_ids`: Tensor of input IDs. Shape `[batch_size, seq_len]` if `varlen=False`, `[1, total_len]` if `varlen=True`.
        - `labels`: Tensor of labels. Shape matches `input_ids`. Padding positions are masked with -100 if `varlen=False`.
        - `attention_mask`: Tensor indicating non-padding tokens (only if `varlen=False`). Shape matches `input_ids`.
        - `cu_seqlens`: Tensor of cumulative sequence lengths (only if `varlen=True`). Shape `[1, num_sequences + 1]`.

    NOTE: When `varlen=True`, the `batch_size` must be 1.
    """

    tokenizer: PreTrainedTokenizer
    context_len: Optional[int] = None
    varlen: bool = False

    def __call__(self, examples: List[Union[List[int], Dict[str, Any]]]) -> Dict[str, Any]:
        if not isinstance(examples[0], Dict):
            examples = [{'input_ids': example} for example in examples]

        def tensorize(example: Dict[str, Any]) -> Dict[str, Any]:
            tensorized = {}
            for key in ['input_ids', 'cu_seqlens']:
                if key not in example:
                    continue
                if isinstance(example[key], List):
                    tensorized[key] = torch.tensor(example[key], dtype=torch.long)
                elif isinstance(example[key], np.ndarray):
                    tensorized[key] = torch.from_numpy(example[key])
                else:
                    tensorized[key] = example[key]
            return tensorized

        examples = list(map(tensorize, examples))

        if not self.varlen:
            # --- Handling for varlen=False (Batch Padding) ---
            length_of_first = examples[0]['input_ids'].size(0)
            needs_padding = not all(example['input_ids'].size(0) == length_of_first for example in examples)

            if needs_padding:
                # Check for pad token if padding is actually required
                if self.tokenizer.pad_token_id is None:
                    raise ValueError(
                        f'You are attempting to pad samples but the tokenizer you are using '
                        f'({self.tokenizer.__class__.__name__}) does not have a pad token.'
                    )
                # Pad using the tokenizer, ensuring attention_mask is returned
                batch = self.tokenizer.pad(examples, return_tensors='pt', return_attention_mask=True)
            else:
                # No padding needed, stack directly and create a full attention mask
                input_ids = torch.stack([example['input_ids'] for example in examples], dim=0)
                batch = {
                    'input_ids': input_ids,
                    # Create attention mask of all ones
                    'attention_mask': torch.ones_like(input_ids),
                }

            # Create labels by cloning input_ids
            labels = batch['input_ids'].clone()
            # Mask labels only where attention_mask is 0 (padding positions)
            if 'attention_mask' in batch:
                labels[batch['attention_mask'] == 0] = -100
            batch['labels'] = labels

        else:
            # --- Handling for varlen=True (Concatenated Sequences) ---
            if len(examples) > 1:
                raise ValueError('The batch size must be 1 for inputs with variable lengths (varlen=True).')

            batch = {'input_ids': torch.cat([example['input_ids'] for example in examples], dim=0).unsqueeze(0)}

            # --- cu_seqlens calculation logic remains the same ---
            if 'cu_seqlens' in examples[0]:
                batch['cu_seqlens'] = (
                    torch.cat([example['cu_seqlens'] for example in examples], dim=0).unsqueeze(0).to(dtype=torch.int32)
                )  # Ensure int32
            else:
                # determine boundaries by bos/eos positions
                # Check for bos_token_id first
                if self.tokenizer.bos_token_id is not None:
                    cu_seqlens = []
                    # Handle case where the sequence doesn't start with BOS
                    if batch['input_ids'][0, 0] != self.tokenizer.bos_token_id:
                        cu_seqlens.append(torch.tensor([0], device=batch['input_ids'].device))  # Match device
                    # Find all BOS token positions
                    bos_positions = torch.where(batch['input_ids'].eq(self.tokenizer.bos_token_id))[1]
                    # Ensure bos_positions is on the correct device if empty
                    if bos_positions.numel() == 0 and len(cu_seqlens) > 0:
                        cu_seqlens.append(bos_positions.to(cu_seqlens[0].device))
                    elif bos_positions.numel() > 0:
                        cu_seqlens.append(bos_positions)
                    # Add the end of the entire batch
                    cu_seqlens.append(
                        torch.tensor([batch['input_ids'].size(1)], device=batch['input_ids'].device)
                    )  # Match device and use size(1)
                    # Filter out empty tensors before cat
                    cu_seqlens = [t for t in cu_seqlens if t.numel() > 0]
                    if not cu_seqlens:  # Handle case where input is empty or has no BOS
                        batch['cu_seqlens'] = torch.tensor(
                            [0, batch['input_ids'].size(1)], dtype=torch.int32, device=batch['input_ids'].device
                        )
                    else:
                        batch['cu_seqlens'] = torch.cat(cu_seqlens, dim=0).to(dtype=torch.int32)

                # Else, check for eos_token_id
                elif self.tokenizer.eos_token_id is not None:
                    cu_seqlens = [torch.tensor([0], device=batch['input_ids'].device)]  # Match device
                    # Find positions *after* EOS tokens
                    eos_positions = torch.where(batch['input_ids'].eq(self.tokenizer.eos_token_id))[1] + 1
                    # Ensure eos_positions is on the correct device if empty
                    if eos_positions.numel() > 0:
                        cu_seqlens.append(eos_positions)
                    # Handle case where the sequence doesn't end with EOS
                    if batch['input_ids'][0, -1] != self.tokenizer.eos_token_id:
                        # Only add the final length if the last found EOS wasn't already the end
                        if eos_positions.numel() == 0 or eos_positions[-1] != batch['input_ids'].size(1):
                            cu_seqlens.append(
                                torch.tensor([batch['input_ids'].size(1)], device=batch['input_ids'].device)
                            )  # Match device and use size(1)
                    # Filter out empty tensors before cat
                    cu_seqlens = [t for t in cu_seqlens if t.numel() > 0]
                    if not cu_seqlens:  # Handle case where input is empty or has no EOS
                        batch['cu_seqlens'] = torch.tensor(
                            [0, batch['input_ids'].size(1)], dtype=torch.int32, device=batch['input_ids'].device
                        )
                    else:
                        batch['cu_seqlens'] = torch.cat(cu_seqlens, dim=0).to(dtype=torch.int32)
                # Else, neither BOS nor EOS is usable
                else:
                    raise ValueError(
                        'For varlen=True without precomputed cu_seqlens, the tokenizer must have either a bos_token_id '
                        'or an eos_token_id defined to act as sequence separators.'
                    )

                # --- cu_seqlens validation checks remain the same ---
                if batch['cu_seqlens'].numel() < 2:
                    raise ValueError(f'Calculated cu_seqlens must have at least start and end: {batch["cu_seqlens"]}')
                if not torch.all(batch['cu_seqlens'][1:] >= batch['cu_seqlens'][:-1]):
                    raise ValueError(f'Calculated cu_seqlens are not monotonically increasing: {batch["cu_seqlens"]}')
                if batch['cu_seqlens'][0] != 0:
                    raise ValueError(f'Calculated cu_seqlens do not start at 0: {batch["cu_seqlens"]}')
                if batch['cu_seqlens'][-1] != batch['input_ids'].size(1):
                    # Allow empty sequence case where cu_seqlens=[0, 0] and input_ids.size(1)=0
                    if not (batch['cu_seqlens'].tolist() == [0, 0] and batch['input_ids'].size(1) == 0):
                        raise ValueError(
                            f'Calculated cu_seqlens do not end at total length {batch["input_ids"].size(1)}: '
                            f'{batch["cu_seqlens"]}'
                        )

                # --- context_len splitting logic remains the same ---
                if self.context_len is not None:
                    # This logic splits sequences based on context_len *after* initial boundaries are found
                    bos = batch['cu_seqlens'][:-1].tolist()
                    eos = batch['cu_seqlens'][1:].tolist()
                    # Handle empty sequences between boundaries
                    split_boundaries = []
                    for i, j in zip(bos, eos):
                        if i < j:  # Only process non-empty sequences
                            split_boundaries.append(torch.arange(i, j, self.context_len, device=batch['input_ids'].device))
                    # Add the final end point if it wasn't included by arange
                    final_end_point = torch.tensor([batch['input_ids'].size(1)], device=batch['input_ids'].device)
                    # Concatenate all boundaries
                    if not split_boundaries:  # Handle case of completely empty input
                        batch['cu_seqlens'] = torch.tensor([0, 0], dtype=torch.int32, device=batch['input_ids'].device)
                    else:
                        batch['cu_seqlens'] = torch.cat(split_boundaries + [final_end_point]).to(dtype=torch.int32)
                        # Ensure uniqueness and sort, as arange might duplicate the endpoint
                        batch['cu_seqlens'] = torch.unique(batch['cu_seqlens'])

            # Create labels directly from input_ids, NO padding mask needed for varlen
            labels = batch['input_ids'].clone()
            batch['labels'] = labels

        return batch


class ParallelAwareDataLoader(StatefulDataLoader, Stateful):
    """
    A wrapper around the StatefulDataLoader that ensures that the state is stored only once per DP rank.
    """

    def __init__(
        self,
        rank: int,
        dataset: IterableDataset,
        batch_size: int,
        collate_fn: Callable,
        num_workers: int = 0,
        pin_memory: bool = False,
        prefetch_factor: int = 2,
        persistent_workers: bool = False,
        snapshot_every_n_steps: Optional[int] = 1,
    ):
        super().__init__(
            dataset=dataset,
            batch_size=batch_size,
            collate_fn=collate_fn,
            num_workers=num_workers,
            pin_memory=pin_memory,
            prefetch_factor=prefetch_factor,
            persistent_workers=persistent_workers,
            snapshot_every_n_steps=snapshot_every_n_steps,
        )
        self.rank = rank

    def state_dict(self) -> Dict[str, Any]:
        # Store state only for dp rank to avoid replicating the same state across other dimensions
        return {f'rank_{self.rank}': pickle.dumps(super().state_dict())}

    def load_state_dict(self, state_dict: Dict[str, Any]) -> None:
        # State being empty is valid
        if not state_dict:
            return

        if f'rank_{self.rank}' not in state_dict:
            logger.warning(f'DataLoader state is empty for dp rank {self.rank}, expected key rank_{self.rank}')
            return
        super().load_state_dict(pickle.loads(state_dict[f'rank_{self.rank}']))


def build_dataloader(
    dataset: IterableDataset,
    tokenizer: PreTrainedTokenizer,
    rank: int,
    world_size: int,
    batch_size: int,
    seq_len: int,
    context_len: Optional[int] = None,
    varlen: bool = False,
    num_workers: int = 0,
    pin_memory: bool = False,
    persistent_workers: bool = False,
    snapshot_every_n_steps: Optional[int] = 1,
):
    dataset = OnlineTokenizedIterableDataset(
        dataset=dataset, tokenizer=tokenizer, seq_len=seq_len, rank=rank, world_size=world_size
    )
    return ParallelAwareDataLoader(
        rank=rank,
        dataset=dataset,
        batch_size=batch_size,
        collate_fn=DataCollatorForLanguageModeling(tokenizer=tokenizer, context_len=context_len, varlen=varlen),
        num_workers=num_workers,
        pin_memory=pin_memory,
        persistent_workers=persistent_workers,
        snapshot_every_n_steps=snapshot_every_n_steps,
    )