feat: avoid some copies in torch formatter
State: closed

## perf: reduce copies in TorchFormatter

This PR make changes the torch formatter to avoid unnecessary copies and casts when converting decoded batches to tensors.

Because many arrays are already in a torch-friendly memory layout and dtype, we can do zero‑copy conversions (`torch.from_numpy`) and only fall back to `as_tensor` when a dtype/device change is required. We also consolidate lists of same‑shape tensors with a cheap `stack` only when safe.

Why it helps
- Avoids extra materialization and dtype churn during batched map and indexing.
- Preserves API and outputs; only changes internal conversion logic.


Small benchmark script (based on https://github.com/huggingface/datasets/issues/6104)

```python
import time
from datasets import load_dataset


def main():
    dataset = load_dataset("NightMachinery/hf_datasets_bug1")
    dataset = dataset["train"] if "train" in dataset else dataset
    t0 = time.time()
    dataset.set_format(type="torch")

    # identity map with small batches
    dataset = dataset.map(lambda x: x, batched=True, batch_size=20)

    # force materialization
    data = dataset[:300]
    print(len(data.keys()))

    t1 = time.time()
    print(f"Duration: {t1 - t0:.2f} s")


if __name__ == "__main__":
    main()

```

Without changes

```bash
uv run bench.py
```

```bash
# 303
# Duration: 7.26 s
```

With changes

```bash
uv run bench.py
```

```bash
# 303
# Duration: 4.43 s
```


# Updated reproduction scripts

Below are some simple test cases using `main` and this `refactor-torch-formatter` branch. I've included the two scripts and output when running on a local machine.

```python
# /// script
# requires-python = ">=3.10"
# dependencies = [
#     "torch",
#     "datasets",
#     "pillow",
# ]
#
# [tool.uv.sources]
# datasets = { git = "https://github.com/huggingface/datasets.git" }
# ///
import time
import random
import numpy as np
from PIL import Image
from datasets import Dataset, load_dataset
import torch


def create_mock_images_dataset(num_samples=5000):
    """Create a deterministic mock dataset with PIL images."""
    random.seed(42)
    np.random.seed(42)

    images = []
    labels = []

    for i in range(num_samples):
        # Create deterministic RGB image
        width, height = 64, 64
        rgb_array = np.random.randint(0, 256, (height, width, 3), dtype=np.uint8)
        image = Image.fromarray(rgb_array)
        images.append(image)
        labels.append(i % 10)  # 10 classes

    return Dataset.from_dict({"image": images, "label": labels})


def create_mock_text_dataset(num_samples=5000):
    """Create a deterministic mock dataset with text."""
    random.seed(42)

    words = ["apple", "banana", "cherry", "date", "elderberry", "fig", "grape", "honeydew"]
    texts = []
    labels = []

    for i in range(num_samples):
        # Create deterministic text
        text_length = 5 + (i % 20)  # 5-24 words
        text = " ".join(random.choices(words, k=text_length))
        texts.append(text)
        labels.append(i % 3)  # 3 classes

    return Dataset.from_dict({"text": texts, "label": labels})


def create_mock_ints_dataset(num_samples=5000):
    """Create a deterministic mock dataset with integers."""
    random.seed(42)

    data = []
    labels = []

    for i in range(num_samples):
        # Create deterministic integer arrays
        arr = [random.randint(0, 1000) for _ in range(50)]  # 50 integers each
        data.append(arr)
        labels.append(i % 5)  # 5 classes

    return Dataset.from_dict({"data": data, "label": labels})


def create_mock_floats_dataset(num_samples=5000):
    """Create a deterministic mock dataset with floats."""
    random.seed(42)

    data = []
    labels = []

    for i in range(num_samples):
        # Create deterministic float arrays
        arr = [random.uniform(0.0, 100.0) for _ in range(30)]  # 30 floats each
        data.append(arr)
        labels.append(i % 4)  # 4 classes

    return Dataset.from_dict({"data": data, "label": labels})


def benchmark_dataset(name, dataset, num_samples=1000):
    """Benchmark dataset access speed."""
    print(f"\n=== {name} Dataset Benchmark ===")

    t0 = time.time()
    dataset.set_format(type="torch")

    # identity map with small batches
    dataset = dataset.map(lambda x: x, batched=True, batch_size=20)

    # force materialization
    data = dataset[:num_samples]
    print(f"Keys: {list(data.keys())}")
    print(f"Sample count: {len(data[list(data.keys())[0]])}")

    t1 = time.time()
    print(f"Duration: {t1 - t0:.2f} s")
    print(f"Speed: {num_samples / (t1 - t0):.1f} samples/s")


def main():
    # PIL Images benchmark
    images_dataset = create_mock_images_dataset()
    benchmark_dataset("PIL Images", images_dataset)

    # Text benchmark
    text_dataset = create_mock_text_dataset()
    benchmark_dataset("Text", text_dataset)

    # Integers benchmark
    ints_dataset = create_mock_ints_dataset()
    benchmark_dataset("Integers", ints_dataset)

    # Floats benchmark
    floats_dataset = create_mock_floats_dataset()
    benchmark_dataset("Floats", floats_dataset)


if __name__ == "__main__":
    main()
```

output

```bash
uv run --refresh example1.py
```

```text
=== PIL Images Dataset Benchmark ===
Map:   0%|                                                          | 0/5000 [00:00<?, ? examples/s]/Users/drbh/.cache/uv/environments-v2/example1-2aca1a30e84bdead/lib/python3.10/site-packages/datasets/features/image.py:352: UserWarning: Downcasting array dtype int64 to uint8 to be compatible with 'Pillow'
  warnings.warn(f"Downcasting array dtype {dtype} to {dest_dtype} to be compatible with 'Pillow'")
Map: 100%|█████████████████████████████████████████████| 5000/5000 [00:01<00:00, 3669.15 examples/s]
Keys: ['image', 'label']
Sample count: 1000
Duration: 2.14 s
Speed: 466.5 samples/s

=== Text Dataset Benchmark ===
Map: 100%|███████████████████████████████████████████| 5000/5000 [00:00<00:00, 141327.04 examples/s]
Keys: ['text', 'label']
Sample count: 1000
Duration: 0.04 s
Speed: 27004.3 samples/s

=== Integers Dataset Benchmark ===
Map: 100%|███████████████████████████████████████████| 5000/5000 [00:00<00:00, 112904.90 examples/s]
Keys: ['data', 'label']
Sample count: 1000
Duration: 0.05 s
Speed: 21680.6 samples/s

=== Floats Dataset Benchmark ===
Map: 100%|███████████████████████████████████████████| 5000/5000 [00:00<00:00, 104084.25 examples/s]
Keys: ['data', 'label']
Sample count: 1000
Duration: 0.05 s
Speed: 20215.1 samples/s
```

and this branch specifically

```python
# /// script
# requires-python = ">=3.10"
# dependencies = [
#     "torch",
#     "datasets",
#     "pillow",
# ]
#
# [tool.uv.sources]
# datasets = { git = "https://github.com/huggingface/datasets.git", rev = "refactor-torch-formatter" }
# ///
import time
import random
import numpy as np
from PIL import Image
from datasets import Dataset, load_dataset
import torch


def create_mock_images_dataset(num_samples=5000):
    """Create a deterministic mock dataset with PIL images."""
    random.seed(42)
    np.random.seed(42)

    images = []
    labels = []

    for i in range(num_samples):
        # Create deterministic RGB image
        width, height = 64, 64
        rgb_array = np.random.randint(0, 256, (height, width, 3), dtype=np.uint8)
        image = Image.fromarray(rgb_array)
        images.append(image)
        labels.append(i % 10)  # 10 classes

    return Dataset.from_dict({"image": images, "label": labels})


def create_mock_text_dataset(num_samples=5000):
    """Create a deterministic mock dataset with text."""
    random.seed(42)

    words = [
        "apple",
        "banana",
        "cherry",
        "date",
        "elderberry",
        "fig",
        "grape",
        "honeydew",
    ]
    texts = []
    labels = []

    for i in range(num_samples):
        # Create deterministic text
        text_length = 5 + (i % 20)  # 5-24 words
        text = " ".join(random.choices(words, k=text_length))
        texts.append(text)
        labels.append(i % 3)  # 3 classes

    return Dataset.from_dict({"text": texts, "label": labels})


def create_mock_ints_dataset(num_samples=5000):
    """Create a deterministic mock dataset with integers."""
    random.seed(42)

    data = []
    labels = []

    for i in range(num_samples):
        # Create deterministic integer arrays
        arr = [random.randint(0, 1000) for _ in range(50)]  # 50 integers each
        data.append(arr)
        labels.append(i % 5)  # 5 classes

    return Dataset.from_dict({"data": data, "label": labels})


def create_mock_floats_dataset(num_samples=5000):
    """Create a deterministic mock dataset with floats."""
    random.seed(42)

    data = []
    labels = []

    for i in range(num_samples):
        # Create deterministic float arrays
        arr = [random.uniform(0.0, 100.0) for _ in range(30)]  # 30 floats each
        data.append(arr)
        labels.append(i % 4)  # 4 classes

    return Dataset.from_dict({"data": data, "label": labels})


def benchmark_dataset(name, dataset, num_samples=1000):
    """Benchmark dataset access speed."""
    print(f"\n=== {name} Dataset Benchmark ===")

    t0 = time.time()
    dataset.set_format(type="torch")

    # identity map with small batches
    dataset = dataset.map(lambda x: x, batched=True, batch_size=20)

    # force materialization
    data = dataset[:num_samples]
    print(f"Keys: {list(data.keys())}")
    print(f"Sample count: {len(data[list(data.keys())[0]])}")

    t1 = time.time()
    print(f"Duration: {t1 - t0:.2f} s")
    print(f"Speed: {num_samples / (t1 - t0):.1f} samples/s")


def main():
    # PIL Images benchmark
    images_dataset = create_mock_images_dataset()
    benchmark_dataset("PIL Images", images_dataset)

    # Text benchmark
    text_dataset = create_mock_text_dataset()
    benchmark_dataset("Text", text_dataset)

    # Integers benchmark
    ints_dataset = create_mock_ints_dataset()
    benchmark_dataset("Integers", ints_dataset)

    # Floats benchmark
    floats_dataset = create_mock_floats_dataset()
    benchmark_dataset("Floats", floats_dataset)


if __name__ == "__main__":
    main()

```

```bash
uv run --refresh example2.py
```

```text
    Updated https://github.com/huggingface/datasets.git (2cb64d1b6503afb49d822b20979760efe4519d03)
      Built datasets @ git+https://github.com/huggingface/datasets.git@2cb64d1b6503afb49d822b20979760efe
Uninstalled 1 package in 20ms
Installed 1 package in 5ms

=== PIL Images Dataset Benchmark ===
Map:   0%|                                                          | 0/5000 [00:00<?, ? examples/s]/Users/drbh/.cache/uv/environments-v2/example2-d4af608668b706ec/lib/python3.10/site-packages/datasets/features/image.py:352: UserWarning: Downcasting array dtype int64 to uint8 to be compatible with 'Pillow'
  warnings.warn(f"Downcasting array dtype {dtype} to {dest_dtype} to be compatible with 'Pillow'")
Map: 100%|█████████████████████████████████████████████| 5000/5000 [00:01<00:00, 3645.14 examples/s]
Keys: ['image', 'label']
Sample count: 1000
Duration: 2.04 s
Speed: 491.2 samples/s

=== Text Dataset Benchmark ===
Map: 100%|████████████████████████████████████████████████████| 5000/5000 [00:00<00:00, 169877.28 examples/s]
Keys: ['text', 'label']
Sample count: 1000
Duration: 0.03 s
Speed: 32236.1 samples/s

=== Integers Dataset Benchmark ===
Map: 100%|████████████████████████████████████████████████████| 5000/5000 [00:00<00:00, 131940.33 examples/s]
Keys: ['data', 'label']
Sample count: 1000
Duration: 0.04 s
Speed: 25493.3 samples/s

=== Floats Dataset Benchmark ===
Map: 100%|████████████████████████████████████████████████████| 5000/5000 [00:00<00:00, 120621.64 examples/s]
Keys: ['data', 'label']
Sample count: 1000
Duration: 0.04 s
Speed: 23370.6 samples/s
```

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7787). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

can you re-read your PR please ?