mlx.data.Buffer.batch

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mlx.data.Buffer.batch#

Buffer.batch(self: mlx.data._c.Buffer, batch_size: int | List[int], pad: Dict[str, float] = {}, dim: Dict[str, int] = {}) → mlx.data._c.Buffer#

Creates batches from batch_size consecutive samples.

When two samples have arrays that are not the same shape, the batch shape is the smallest shape that contains all samples in each dimension. The places that do not have values are filled with pad values.

When a batch dimension is not provided, the arrays are stacked. If it is provided, the arrays are concatenated along that dimension.

The following example showcases the use the dim argument.

import mlx.data as dx
import numpy as np

dset = dx.buffer_from_vector([{"x": np.random.randn(10, i+1)} for i in range(10)])

print(dset.batch(4)[0]["x"].shape)  # prints (4, 10, 4)
print(dset.batch(4)[1]["x"].shape)  # prints (4, 10, 8)

print(dset.batch(4, dim=dict(x=0))[0]["x"].shape)  # prints (40, 4)
print(dset.batch(4, dim=dict(x=0))[1]["x"].shape)  # prints (40, 8)

print(dset.batch(4, dim=dict(x=1))[0]["x"].shape)  # prints (10, 10)
print(dset.batch(4, dim=dict(x=1))[1]["x"].shape)  # prints (10, 26)
Parameters:

  • batch_size (int) – How many samples to gather in a batch.

  • pad (dict) – The values to use for padding for each key in the samples.

  • dim (dict) – The dimension to concatenate over.

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