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import math
from typing import Optional
import mlx.core as mx
from mlx.nn.layers.base import Module
class RoPE(Module):
"""Implements the rotary positional encoding.
The traditional implementation rotates consecutive pairs of elements in the
feature dimension while the default implementation rotates pairs with
stride half the feature dimensions for efficiency.
For more details see `RoFormer: Enhanced Transformer with Rotary Position
Embedding <https://arxiv.org/abs/2104.09864>`_.
Args:
dims (int): The feature dimensions to be rotated. If the input feature
is larger than dims then the rest is left unchanged.
traditional (bool, optional): If set to ``True`` choose the traditional
implementation which is slightly less efficient. Default: ``False``.
base (float, optional): The base used to compute angular frequency for
each dimension in the positional encodings. Default: ``10000``.
scale (float, optional): The scale used to scale the positions. Default: ``1.0``.
"""
def __init__(
self,
dims: int,
traditional: bool = False,
base: float = 10000,
scale: float = 1.0,
):
super().__init__()
self.dims = dims
self.traditional = traditional
self.base = base
self.scale = scale
def _extra_repr(self):
return f"{self.dims}, traditional={self.traditional}"
def __call__(self, x, offset: int = 0):
return mx.fast.rope(
x,
self.dims,
traditional=self.traditional,
base=self.base,
scale=self.scale,
offset=offset,
)
class SinusoidalPositionalEncoding(Module):
r"""Implements sinusoidal positional encoding.
For more details see the paper `Attention Is All You Need
<https://arxiv.org/abs/1706.03762>`_.
Args:
dims (int): The dimensionality of the resulting positional embeddings.
min_freq (float, optional): The minimum frequency expected. Default:
``0.0001``.
max_freq (float, optional): The maximum frequency expected. Default:
``1``.
scale (float, optional): A multiplicative scale for the embeddings.
Default: ``sqrt(2/dims)``.
cos_first (bool, optional): If ``True`` embed using ``[cos(x); sin(x)]``
instead of the reverse. Default: ``False``.
full_turns (bool, optional): If ``True`` multiply the frequencies with
:math:`2\pi`. Default: ``False``.
"""
def __init__(
self,
dims: int,
min_freq: float = 0.0001,
max_freq: float = 1,
scale: Optional[float] = None,
cos_first: bool = False,
full_turns: bool = False,
):
super().__init__()
one_zero = 1 - mx.arange(0, dims // 2) / (dims // 2 - 1)
min_freq = math.log(min_freq)
max_freq = math.log(max_freq)
# Start with underscore so it is not included in the parameters
self._sigmas = mx.exp(one_zero * (max_freq - min_freq) + min_freq)
if full_turns:
self._sigmas = self._sigmas * (2 * math.pi)
# Save some constants that define the implementation
self.scale = scale or (2 / dims) ** 0.5
self.cos_first = cos_first
def __call__(self, x):
y = x[..., None] * self._sigmas
cosy = mx.cos(y)
siny = mx.sin(y)
if self.cos_first:
y = mx.concatenate([cosy, siny], axis=-1)
else:
y = mx.concatenate([siny, cosy], axis=-1)
if self.scale != 1:
y = y * self.scale
return y
class ALiBi(Module):
@staticmethod
def create_alibi_matrix(
q_sequence_length: int,
k_sequence_length: int,
num_heads: int,
offset: int,
dtype=mx.float32,
):
x1 = mx.arange(offset, q_sequence_length)
x2 = mx.arange(0, k_sequence_length)
distance_matrix = -mx.abs(
mx.expand_dims(x1[:, None] - x2[None, :], axis=(0, 1))
)
alibi_slope = ALiBi.create_alibi_slope(num_heads=num_heads, dtype=dtype)
alibi_mask = (distance_matrix * alibi_slope).astype(dtype)
return alibi_mask
@staticmethod
def create_alibi_slope(num_heads, dtype):
def get_slopes(n: int):
if math.log2(n).is_integer():
start = 2 ** (-(2 ** -(math.log2(n) - 3)))
return [start * start**i for i in range(n)]
else:
closest_power_of_2 = 2 ** math.floor(math.log2(n))
return (
get_slopes(closest_power_of_2)
+ get_slopes(2 * closest_power_of_2)[0::2][: n - closest_power_of_2]
)
slopes = get_slopes(num_heads)
out = mx.array(slopes, dtype=dtype)
return mx.expand_dims(out, axis=(-1, -2))
def __call__(self, attention_scores, offset=0, mask=None):
alibi_mask = ALiBi.create_alibi_matrix(
q_sequence_length=attention_scores.shape[-2] + offset,
k_sequence_length=attention_scores.shape[-1],
num_heads=attention_scores.shape[1],
offset=offset,
dtype=attention_scores.dtype,
)
if mask is not None:
alibi_mask = alibi_mask + mask
return attention_scores + alibi_mask
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