| Name | ml-dtypes JSON |
| Version |
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| Summary | ml_dtypes is a stand-alone implementation of several NumPy dtype extensions used in machine learning. |
| upload_time | 2025-07-29 18:39:19 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | None |
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# ml_dtypes
[](https://github.com/jax-ml/ml_dtypes/actions/workflows/test.yml)
[](https://github.com/jax-ml/ml_dtypes/actions/workflows/wheels.yml)
[](https://badge.fury.io/py/ml_dtypes)
`ml_dtypes` is a stand-alone implementation of several NumPy dtype extensions used in machine learning libraries, including:
- [`bfloat16`](https://en.wikipedia.org/wiki/Bfloat16_floating-point_format):
an alternative to the standard [`float16`](https://en.wikipedia.org/wiki/Half-precision_floating-point_format) format
- 8-bit floating point representations, parameterized by number of exponent and
mantissa bits, as well as the bias (if any) and representability of infinity,
NaN, and signed zero.
* `float8_e3m4`
* `float8_e4m3`
* `float8_e4m3b11fnuz`
* `float8_e4m3fn`
* `float8_e4m3fnuz`
* `float8_e5m2`
* `float8_e5m2fnuz`
* `float8_e8m0fnu`
- Microscaling (MX) sub-byte floating point representations:
* `float4_e2m1fn`
* `float6_e2m3fn`
* `float6_e3m2fn`
- Narrow integer encodings:
* `int2`
* `int4`
* `uint2`
* `uint4`
See below for specifications of these number formats.
## Installation
The `ml_dtypes` package is tested with Python versions 3.9-3.12, and can be installed
with the following command:
```
pip install ml_dtypes
```
To test your installation, you can run the following:
```
pip install absl-py pytest
pytest --pyargs ml_dtypes
```
To build from source, clone the repository and run:
```
git submodule init
git submodule update
pip install .
```
## Example Usage
```python
>>> from ml_dtypes import bfloat16
>>> import numpy as np
>>> np.zeros(4, dtype=bfloat16)
array([0, 0, 0, 0], dtype=bfloat16)
```
Importing `ml_dtypes` also registers the data types with numpy, so that they may
be referred to by their string name:
```python
>>> np.dtype('bfloat16')
dtype(bfloat16)
>>> np.dtype('float8_e5m2')
dtype(float8_e5m2)
```
## Specifications of implemented floating point formats
### `bfloat16`
A `bfloat16` number is a single-precision float truncated at 16 bits.
Exponent: 8, Mantissa: 7, exponent bias: 127. IEEE 754, with NaN and inf.
### `float4_e2m1fn`
Exponent: 2, Mantissa: 1, bias: 1.
Extended range: no inf, no NaN.
Microscaling format, 4 bits (encoding: `0bSEEM`) using byte storage (higher 4
bits are unused). NaN representation is undefined.
Possible absolute values: [`0`, `0.5`, `1`, `1.5`, `2`, `3`, `4`, `6`]
### `float6_e2m3fn`
Exponent: 2, Mantissa: 3, bias: 1.
Extended range: no inf, no NaN.
Microscaling format, 6 bits (encoding: `0bSEEMMM`) using byte storage (higher 2
bits are unused). NaN representation is undefined.
Possible values range: [`-7.5`; `7.5`]
### `float6_e3m2fn`
Exponent: 3, Mantissa: 2, bias: 3.
Extended range: no inf, no NaN.
Microscaling format, 4 bits (encoding: `0bSEEEMM`) using byte storage (higher 2
bits are unused). NaN representation is undefined.
Possible values range: [`-28`; `28`]
### `float8_e3m4`
Exponent: 3, Mantissa: 4, bias: 3. IEEE 754, with NaN and inf.
### `float8_e4m3`
Exponent: 4, Mantissa: 3, bias: 7. IEEE 754, with NaN and inf.
### `float8_e4m3b11fnuz`
Exponent: 4, Mantissa: 3, bias: 11.
Extended range: no inf, NaN represented by 0b1000'0000.
### `float8_e4m3fn`
Exponent: 4, Mantissa: 3, bias: 7.
Extended range: no inf, NaN represented by 0bS111'1111.
The `fn` suffix is for consistency with the corresponding LLVM/MLIR type, signaling this type is not consistent with IEEE-754. The `f` indicates it is finite values only. The `n` indicates it includes NaNs, but only at the outer range.
### `float8_e4m3fnuz`
8-bit floating point with 3 bit mantissa.
An 8-bit floating point type with 1 sign bit, 4 bits exponent and 3 bits mantissa. The suffix `fnuz` is consistent with LLVM/MLIR naming and is derived from the differences to IEEE floating point conventions. `F` is for "finite" (no infinities), `N` for with special NaN encoding, `UZ` for unsigned zero.
This type has the following characteristics:
* bit encoding: S1E4M3 - `0bSEEEEMMM`
* exponent bias: 8
* infinities: Not supported
* NaNs: Supported with sign bit set to 1, exponent bits and mantissa bits set to all 0s - `0b10000000`
* denormals when exponent is 0
### `float8_e5m2`
Exponent: 5, Mantissa: 2, bias: 15. IEEE 754, with NaN and inf.
### `float8_e5m2fnuz`
8-bit floating point with 2 bit mantissa.
An 8-bit floating point type with 1 sign bit, 5 bits exponent and 2 bits mantissa. The suffix `fnuz` is consistent with LLVM/MLIR naming and is derived from the differences to IEEE floating point conventions. `F` is for "finite" (no infinities), `N` for with special NaN encoding, `UZ` for unsigned zero.
This type has the following characteristics:
* bit encoding: S1E5M2 - `0bSEEEEEMM`
* exponent bias: 16
* infinities: Not supported
* NaNs: Supported with sign bit set to 1, exponent bits and mantissa bits set to all 0s - `0b10000000`
* denormals when exponent is 0
### `float8_e8m0fnu`
[OpenCompute MX](https://www.opencompute.org/documents/ocp-microscaling-formats-mx-v1-0-spec-final-pdf)
scale format E8M0, which has the following properties:
* Unsigned format
* 8 exponent bits
* Exponent range from -127 to 127
* No zero and infinity
* Single NaN value (0xFF).
## `int2`, `int4`, `uint2` and `uint4`
2 and 4-bit integer types, where each element is represented unpacked (i.e.,
padded up to a byte in memory).
NumPy does not support types smaller than a single byte: for example, the
distance between adjacent elements in an array (`.strides`) is expressed as
an integer number of bytes. Relaxing this restriction would be a considerable
engineering project. These types therefore use an unpacked representation, where
each element of the array is padded up to a byte in memory. The lower two or four
bits of each byte contain the representation of the number, whereas the remaining
upper bits are ignored.
## Quirks of low-precision Arithmetic
If you're exploring the use of low-precision dtypes in your code, you should be
careful to anticipate when the precision loss might lead to surprising results.
One example is the behavior of aggregations like `sum`; consider this `bfloat16`
summation in NumPy (run with version 1.24.2):
```python
>>> from ml_dtypes import bfloat16
>>> import numpy as np
>>> rng = np.random.default_rng(seed=0)
>>> vals = rng.uniform(size=10000).astype(bfloat16)
>>> vals.sum()
256
```
The true sum should be close to 5000, but numpy returns exactly 256: this is
because `bfloat16` does not have the precision to increment `256` by values less than
`1`:
```python
>>> bfloat16(256) + bfloat16(1)
256
```
After 256, the next representable value in bfloat16 is 258:
```python
>>> np.nextafter(bfloat16(256), bfloat16(np.inf))
258
```
For better results you can specify that the accumulation should happen in a
higher-precision type like `float32`:
```python
>>> vals.sum(dtype='float32').astype(bfloat16)
4992
```
In contrast to NumPy, projects like [JAX](http://jax.readthedocs.io/) which support
low-precision arithmetic more natively will often do these kinds of higher-precision
accumulations automatically:
```python
>>> import jax.numpy as jnp
>>> jnp.array(vals).sum()
Array(4992, dtype=bfloat16)
```
## License
*This is not an officially supported Google product.*
The `ml_dtypes` source code is licensed under the Apache 2.0 license
(see [LICENSE](LICENSE)). Pre-compiled wheels are built with the
[EIGEN](https://eigen.tuxfamily.org/) project, which is released under the
MPL 2.0 license (see [LICENSE.eigen](LICENSE.eigen)).
Raw data
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"description": "# ml_dtypes\n\n[](https://github.com/jax-ml/ml_dtypes/actions/workflows/test.yml)\n[](https://github.com/jax-ml/ml_dtypes/actions/workflows/wheels.yml)\n[](https://badge.fury.io/py/ml_dtypes)\n\n`ml_dtypes` is a stand-alone implementation of several NumPy dtype extensions used in machine learning libraries, including:\n\n- [`bfloat16`](https://en.wikipedia.org/wiki/Bfloat16_floating-point_format):\n an alternative to the standard [`float16`](https://en.wikipedia.org/wiki/Half-precision_floating-point_format) format\n- 8-bit floating point representations, parameterized by number of exponent and\n mantissa bits, as well as the bias (if any) and representability of infinity,\n NaN, and signed zero.\n * `float8_e3m4`\n * `float8_e4m3`\n * `float8_e4m3b11fnuz`\n * `float8_e4m3fn`\n * `float8_e4m3fnuz`\n * `float8_e5m2`\n * `float8_e5m2fnuz`\n * `float8_e8m0fnu`\n- Microscaling (MX) sub-byte floating point representations:\n * `float4_e2m1fn`\n * `float6_e2m3fn`\n * `float6_e3m2fn`\n- Narrow integer encodings:\n * `int2`\n * `int4`\n * `uint2`\n * `uint4`\n\nSee below for specifications of these number formats.\n\n## Installation\n\nThe `ml_dtypes` package is tested with Python versions 3.9-3.12, and can be installed\nwith the following command:\n```\npip install ml_dtypes\n```\nTo test your installation, you can run the following:\n```\npip install absl-py pytest\npytest --pyargs ml_dtypes\n```\nTo build from source, clone the repository and run:\n```\ngit submodule init\ngit submodule update\npip install .\n```\n\n## Example Usage\n\n```python\n>>> from ml_dtypes import bfloat16\n>>> import numpy as np\n>>> np.zeros(4, dtype=bfloat16)\narray([0, 0, 0, 0], dtype=bfloat16)\n```\nImporting `ml_dtypes` also registers the data types with numpy, so that they may\nbe referred to by their string name:\n\n```python\n>>> np.dtype('bfloat16')\ndtype(bfloat16)\n>>> np.dtype('float8_e5m2')\ndtype(float8_e5m2)\n```\n\n## Specifications of implemented floating point formats\n\n### `bfloat16`\n\nA `bfloat16` number is a single-precision float truncated at 16 bits.\n\nExponent: 8, Mantissa: 7, exponent bias: 127. IEEE 754, with NaN and inf.\n\n### `float4_e2m1fn`\n\nExponent: 2, Mantissa: 1, bias: 1.\n\nExtended range: no inf, no NaN.\n\nMicroscaling format, 4 bits (encoding: `0bSEEM`) using byte storage (higher 4\nbits are unused). NaN representation is undefined.\n\nPossible absolute values: [`0`, `0.5`, `1`, `1.5`, `2`, `3`, `4`, `6`]\n\n### `float6_e2m3fn`\n\nExponent: 2, Mantissa: 3, bias: 1.\n\nExtended range: no inf, no NaN.\n\nMicroscaling format, 6 bits (encoding: `0bSEEMMM`) using byte storage (higher 2\nbits are unused). NaN representation is undefined.\n\nPossible values range: [`-7.5`; `7.5`]\n\n### `float6_e3m2fn`\n\nExponent: 3, Mantissa: 2, bias: 3.\n\nExtended range: no inf, no NaN.\n\nMicroscaling format, 4 bits (encoding: `0bSEEEMM`) using byte storage (higher 2\nbits are unused). NaN representation is undefined.\n\nPossible values range: [`-28`; `28`]\n\n### `float8_e3m4`\n\nExponent: 3, Mantissa: 4, bias: 3. 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The suffix `fnuz` is consistent with LLVM/MLIR naming and is derived from the differences to IEEE floating point conventions. `F` is for \"finite\" (no infinities), `N` for with special NaN encoding, `UZ` for unsigned zero.\n\nThis type has the following characteristics:\n * bit encoding: S1E4M3 - `0bSEEEEMMM`\n * exponent bias: 8\n * infinities: Not supported\n * NaNs: Supported with sign bit set to 1, exponent bits and mantissa bits set to all 0s - `0b10000000`\n * denormals when exponent is 0\n\n### `float8_e5m2`\n\nExponent: 5, Mantissa: 2, bias: 15. IEEE 754, with NaN and inf.\n\n### `float8_e5m2fnuz`\n\n8-bit floating point with 2 bit mantissa.\n\nAn 8-bit floating point type with 1 sign bit, 5 bits exponent and 2 bits mantissa. The suffix `fnuz` is consistent with LLVM/MLIR naming and is derived from the differences to IEEE floating point conventions. `F` is for \"finite\" (no infinities), `N` for with special NaN encoding, `UZ` for unsigned zero.\n\nThis type has the following characteristics:\n * bit encoding: S1E5M2 - `0bSEEEEEMM`\n * exponent bias: 16\n * infinities: Not supported\n * NaNs: Supported with sign bit set to 1, exponent bits and mantissa bits set to all 0s - `0b10000000`\n * denormals when exponent is 0\n\n### `float8_e8m0fnu`\n\n[OpenCompute MX](https://www.opencompute.org/documents/ocp-microscaling-formats-mx-v1-0-spec-final-pdf)\nscale format E8M0, which has the following properties:\n * Unsigned format\n * 8 exponent bits\n * Exponent range from -127 to 127\n * No zero and infinity\n * Single NaN value (0xFF).\n\n## `int2`, `int4`, `uint2` and `uint4`\n\n2 and 4-bit integer types, where each element is represented unpacked (i.e.,\npadded up to a byte in memory).\n\nNumPy does not support types smaller than a single byte: for example, the\ndistance between adjacent elements in an array (`.strides`) is expressed as\nan integer number of bytes. Relaxing this restriction would be a considerable\nengineering project. These types therefore use an unpacked representation, where\neach element of the array is padded up to a byte in memory. 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Pre-compiled wheels are built with the\n[EIGEN](https://eigen.tuxfamily.org/) project, which is released under the\nMPL 2.0 license (see [LICENSE.eigen](LICENSE.eigen)).\n",
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