initial impl of streaming preprocessing

src/axolotl/core/builders/causal.py

@@ -40,6 +40,7 @@ from axolotl.utils.collators import (
     BatchSamplerDataCollatorForSeq2Seq,
     DataCollatorForSeq2Seq,
     MambaDataCollator,
+    StreamingDataCollator,
     V2BatchSamplerDataCollatorForSeq2Seq,
 )
 from axolotl.utils.collators.mm_chat import MultiModalChatDataCollator
@@ -422,6 +423,17 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         is_eval=False,
         **kwargs,
     ):
+        from datasets import IterableDataset
+
+        if isinstance(self.train_dataset, IterableDataset) and not is_eval:
+            LOG.info("Using StreamingDataCollator")
+            return StreamingDataCollator(
+                tokenizer=self.tokenizer,
+                cfg=self.cfg,
+                prompter=None,
+                **kwargs,
+            )
+
         if training_args.pretraining:
             if (
                 self.cfg.pretraining_sample_concatenation is False

src/axolotl/utils/collators/__init__.py

@@ -1,11 +1,19 @@
-"""
+"""Shared axolotl collators for multipack, mamba, multimodal, etc."""
-shared axolotl collators for multipack, mamba, multimodal
-"""
 
-from .batching import (  # noqa: F401
+from .batching import (
     BatchSamplerDataCollatorForSeq2Seq,
     DataCollatorForSeq2Seq,
     PretrainingBatchSamplerDataCollatorForSeq2Seq,
     V2BatchSamplerDataCollatorForSeq2Seq,
 )
-from .mamba import MambaDataCollator  # noqa: F401
+from .mamba import MambaDataCollator
+from .streaming import StreamingDataCollator
+
+__all__ = [
+    "BatchSamplerDataCollatorForSeq2Seq",
+    "DataCollatorForSeq2Seq",
+    "PretrainingBatchSamplerDataCollatorForSeq2Seq",
+    "V2BatchSamplerDataCollatorForSeq2Seq",
+    "MambaDataCollator",
+    "StreamingDataCollator",
+]

src/axolotl/utils/collators/streaming.py

@@ -0,0 +1,146 @@
+from dataclasses import dataclass
+from typing import Any, List
+
+import torch
+from transformers import PreTrainedTokenizerBase, default_data_collator
+from transformers.utils import PaddingStrategy
+
+from axolotl.prompters import Prompter
+from axolotl.utils.dict import DictDefault
+
+
+@dataclass
+class StreamingDataCollator:
+    tokenizer: PreTrainedTokenizerBase
+    cfg: DictDefault
+    prompter: Prompter | None = None
+    padding: bool | str | PaddingStrategy = True
+    max_length: int | None = None
+    pad_to_multiple_of: int | None = None
+    label_pad_token_id: int = -100
+    return_tensors: str = "pt"
+
+    def __post_init__(self):
+        if self.max_length is None:
+            self.max_length = self.cfg.sequence_len
+
+    def __call__(self, raw_batch: List[dict]) -> dict[str, Any]:
+        processed_samples = []
+
+        for raw_sample in raw_batch:
+            formatted_sample = raw_sample
+            if self.prompter:
+                formatted_sample = self._apply_prompt_formatting(raw_sample)
+
+            tokenized_sample = self._tokenize_sample(formatted_sample)
+
+            if len(tokenized_sample["input_ids"]) > self.max_length:
+                tokenized_sample = self._truncate_sample(tokenized_sample)
+
+            if tokenized_sample.get("input_ids"):
+                processed_samples.append(tokenized_sample)
+
+        return self._pad_and_batch(processed_samples)
+
+    def _apply_prompt_formatting(self, raw_sample: dict) -> dict:
+        formatted_text = self.prompter.build_prompt(
+            instruction=raw_sample.get("instruction", ""),
+            input=raw_sample.get("input", ""),
+            output=raw_sample.get("output", ""),
+        )
+        return {"text": formatted_text}
+
+    def _tokenize_sample(self, sample: dict) -> dict:
+        text = sample.get("text", sample.get("content", ""))
+
+        if not text:
+            instruction = sample.get("instruction", "")
+            input_text = sample.get("input", "")
+            output_text = sample.get("output", "")
+
+            parts = []
+            if instruction:
+                parts.append(f"Instruction: {instruction}")
+            if input_text:
+                parts.append(f"Input: {input_text}")
+            if output_text:
+                parts.append(f"Output: {output_text}")
+            text = "\n".join(parts)
+
+        if not text:
+            return {"input_ids": [], "attention_mask": [], "labels": []}
+
+        tokenized = self.tokenizer(
+            text,
+            truncation=False,
+            padding=False,
+            return_tensors=None,
+        )
+
+        tokenized["labels"] = tokenized["input_ids"].copy()
+        return tokenized
+
+    def _truncate_sample(self, tokenized_sample: dict) -> dict:
+        max_len = self.max_length
+        for key in ["input_ids", "attention_mask", "labels"]:
+            if key in tokenized_sample:
+                tokenized_sample[key] = tokenized_sample[key][:max_len]
+        return tokenized_sample
+
+    def _pad_and_batch(self, processed_samples: List[dict]) -> dict[str, Any]:
+        if not processed_samples:
+            processed_samples = [
+                {
+                    "input_ids": [self.tokenizer.eos_token_id],
+                    "attention_mask": [1],
+                    "labels": [self.tokenizer.eos_token_id],
+                }
+            ]
+
+        batch_samples = []
+        for sample in processed_samples:
+            batch_sample = {}
+            for key, value in sample.items():
+                if key in ["input_ids", "attention_mask", "labels"]:
+                    batch_sample[key] = torch.tensor(value, dtype=torch.long)
+            batch_samples.append(batch_sample)
+
+        if self.padding:
+            max_len_in_batch = max(len(sample["input_ids"]) for sample in batch_samples)
+
+            for sample in batch_samples:
+                current_len = len(sample["input_ids"])
+                pad_len = max_len_in_batch - current_len
+
+                if pad_len > 0:
+                    pad_token_id = (
+                        self.tokenizer.pad_token_id or self.tokenizer.eos_token_id
+                    )
+
+                    sample["input_ids"] = torch.cat(
+                        [
+                            sample["input_ids"],
+                            torch.full((pad_len,), pad_token_id, dtype=torch.long),
+                        ]
+                    )
+                    sample["attention_mask"] = torch.cat(
+                        [
+                            sample["attention_mask"],
+                            torch.zeros(pad_len, dtype=torch.long),
+                        ]
+                    )
+                    sample["labels"] = torch.cat(
+                        [
+                            sample["labels"],
+                            torch.full(
+                                (pad_len,), self.label_pad_token_id, dtype=torch.long
+                            ),
+                        ]
+                    )
+
+        batch = {}
+        for key in ["input_ids", "attention_mask", "labels"]:
+            if key in batch_samples[0]:
+                batch[key] = torch.stack([sample[key] for sample in batch_samples])
+
+        return batch

src/axolotl/utils/data/sft.py

@@ -74,18 +74,52 @@ def prepare_datasets(
     Returns:
         Tuple of (train_dataset, eval_dataset, total_steps, prompters).
     """
-    # Determine streaming mode from config
     streaming_mode = _determine_streaming_mode(cfg)
 
-    # Override preprocess_iterable parameter with streaming config
     if streaming_mode:
-        preprocess_iterable = True
+        if cfg.pretraining_dataset:
+            return _prepare_streaming_pretraining_dataset(cfg, tokenizer, processor)
+        else:
+            return _prepare_streaming_sft_dataset(cfg, tokenizer, processor)
+    else:
+        if cfg.pretraining_dataset:
+            return _prepare_pretraining_dataset(
+                cfg, tokenizer, processor, preprocess_iterable=False
+            )
+        else:
+            return _prepare_standard_dataset(
+                cfg, tokenizer, processor, preprocess_iterable=False
+            )
 
-    if cfg.pretraining_dataset:
+
-        return _prepare_pretraining_dataset(
+def _prepare_streaming_sft_dataset(
-            cfg, tokenizer, processor, preprocess_iterable
+    cfg: DictDefault,
+    tokenizer: PreTrainedTokenizer,
+    processor: ProcessorMixin | None,
+) -> tuple[IterableDataset, Dataset | None, int, list[Prompter | None]]:
+    LOG.info("Loading streaming datasets")
+
+    raw_datasets = _load_raw_datasets_for_streaming(cfg, split="train")
+
+    eval_dataset = None
+    if cfg.test_datasets:
+        eval_raw_datasets = _load_raw_datasets_for_streaming(
+            cfg, split="test", dataset_configs=cfg.test_datasets
         )
-    return _prepare_standard_dataset(cfg, tokenizer, processor, preprocess_iterable)
+        eval_dataset = _process_eval_dataset_minimal(
+            eval_raw_datasets, cfg, tokenizer, processor
+        )
+    elif cfg.val_set_size:
+        LOG.info("Validation splits not supported for streaming datasets")
+
+    if not cfg.max_steps:
+        raise ValueError("max_steps must be set when using streaming datasets")
+
+    total_num_steps = cfg.max_steps
+    LOG.info(f"Maximum steps: {total_num_steps}")
+
+    prompters = [None] * len(cfg.datasets) if cfg.datasets else []
+    return raw_datasets, eval_dataset, total_num_steps, prompters
 
 
 def _prepare_standard_dataset(
@@ -138,19 +172,12 @@ def _prepare_standard_dataset(
             )
 
     # Calculate total number of training steps
-    if isinstance(train_dataset, IterableDataset):
+    if cfg.max_steps:
-        # For streaming datasets, we must use max_steps
+        total_num_steps = min(
-        if not cfg.max_steps:
+            calculate_total_num_steps(cfg, train_dataset), cfg.max_steps
-            raise ValueError("max_steps must be set when using streaming datasets")
+        )
-        total_num_steps = cfg.max_steps
     else:
-        # For regular datasets, calculate from dataset size or use max_steps
+        total_num_steps = calculate_total_num_steps(cfg, train_dataset)
-        if cfg.max_steps:
-            total_num_steps = min(
-                calculate_total_num_steps(cfg, train_dataset), cfg.max_steps
-            )
-        else:
-            total_num_steps = calculate_total_num_steps(cfg, train_dataset)
     LOG.info(f"Maximum number of steps set at {total_num_steps}")
     return train_dataset, eval_dataset, total_num_steps, prompters
 
@@ -445,17 +472,14 @@ def _parse_dataset_type(d_type: str) -> tuple[str | None, str | None]:
 
 
 def _handle_train_dataset_split(
-    dataset: Dataset | IterableDataset, cfg: DictDefault
+    dataset: Dataset, cfg: DictDefault
-) -> tuple[Dataset | IterableDataset, Dataset | IterableDataset | None]:
+) -> tuple[Dataset, Dataset | None]:
     """Handle processing for train split, including validation set creation."""
     val_set_size = (
         int(cfg.val_set_size) if cfg.val_set_size > 1 else float(cfg.val_set_size)
     )
 
     if val_set_size:
-        if isinstance(dataset, IterableDataset):
-            LOG.info("Validation splits not supported for streaming datasets, skipping")
-            return dataset, None
         # Create train/validation split
         train_dataset, eval_dataset = create_train_validation_split(
             dataset, cfg, val_set_size
@@ -463,33 +487,27 @@ def _handle_train_dataset_split(
         return train_dataset, eval_dataset
 
     # No validation split - apply deduplication if needed and return as train dataset
-    if cfg.dataset_exact_deduplication and not isinstance(dataset, IterableDataset):
+    if cfg.dataset_exact_deduplication:
         train_dataset, _ = deduplicate_and_log_datasets(dataset=dataset)
     else:
-        if cfg.dataset_exact_deduplication and isinstance(dataset, IterableDataset):
-            LOG.info("Deduplication skipped for streaming datasets (not compatible)")
         train_dataset = dataset
 
     return train_dataset, None
 
 
 def _handle_test_dataset_split(
-    dataset: Dataset | IterableDataset, cfg: DictDefault
+    dataset: Dataset, cfg: DictDefault
-) -> tuple[None, Dataset | IterableDataset | None]:
+) -> tuple[None, Dataset | None]:
     """Handle processing for test split."""
-    if cfg.dataset_exact_deduplication and not isinstance(dataset, IterableDataset):
+    if cfg.dataset_exact_deduplication:
         eval_dataset, _ = deduplicate_and_log_datasets(dataset=dataset)
     else:
-        if cfg.dataset_exact_deduplication and isinstance(dataset, IterableDataset):
-            LOG.info("Deduplication skipped for streaming datasets (not compatible)")
         eval_dataset = dataset
 
     return None, eval_dataset
 
 
-def _apply_dataset_sharding(
+def _apply_dataset_sharding(dataset: Dataset, cfg: DictDefault) -> Dataset:
-    dataset: Dataset | IterableDataset, cfg: DictDefault
-) -> Dataset | IterableDataset:
     """Apply dataset sharding if configured.
 
     Args:
@@ -548,3 +566,78 @@ def _load_and_prepare_datasets(
         train_dataset, eval_dataset = _handle_test_dataset_split(dataset, cfg)
 
     return train_dataset, eval_dataset, prompters
+
+
+def _load_raw_datasets_for_streaming(
+    cfg: DictDefault, split: str = "train", dataset_configs: list | None = None
+) -> IterableDataset:
+    configs = (
+        dataset_configs
+        if dataset_configs is not None
+        else (cfg.datasets if split == "train" else cfg.test_datasets)
+    )
+
+    if not configs:
+        raise ValueError(f"No dataset configurations found for split '{split}'")
+
+    datasets = []
+    for dataset_config in datasets_with_name_generator(configs):
+        raw_dataset = load_dataset_with_config(
+            dataset_config, cfg.hf_use_auth_token, streaming=True
+        )
+
+        if isinstance(raw_dataset, (DatasetDict, IterableDatasetDict)):
+            if dataset_config.split and dataset_config.split in raw_dataset:
+                raw_dataset = raw_dataset[dataset_config.split]
+            elif split in raw_dataset:
+                raw_dataset = raw_dataset[split]
+            else:
+                raise ValueError(
+                    f"no {split} split found for dataset {dataset_config.path}, "
+                    "you may specify a split with 'split: ...'"
+                )
+
+        datasets.append(raw_dataset)
+
+    if len(datasets) == 1:
+        return datasets[0]
+    else:
+        return merge_datasets(datasets, cfg)
+
+
+def _process_eval_dataset_minimal(
+    raw_dataset: IterableDataset,
+    cfg: DictDefault,
+    tokenizer: PreTrainedTokenizer,
+    processor: ProcessorMixin | None,
+) -> Dataset | None:
+    LOG.info("Eval dataset processing skipped for streaming")
+    return None
+
+
+def _prepare_streaming_pretraining_dataset(
+    cfg: DictDefault,
+    tokenizer: PreTrainedTokenizer,
+    processor: ProcessorMixin | None,
+) -> tuple[IterableDataset, Dataset | None, int, list[Prompter | None]]:
+    pretraining_config = _extract_pretraining_config(cfg)
+
+    train_dataset = load_dataset_with_config(
+        pretraining_config, cfg.hf_use_auth_token, streaming=True
+    )
+
+    if isinstance(train_dataset, (DatasetDict, IterableDatasetDict)):
+        if pretraining_config.split and pretraining_config.split in train_dataset:
+            train_dataset = train_dataset[pretraining_config.split]
+        elif "train" in train_dataset:
+            train_dataset = train_dataset["train"]
+        else:
+            raise ValueError("no train split found for pretraining dataset")
+
+    if not cfg.max_steps:
+        raise ValueError("max_steps must be set when using streaming datasets")
+
+    total_num_steps = cfg.max_steps
+    LOG.info(f"Maximum steps: {total_num_steps}")
+
+    return train_dataset, None, total_num_steps, []