awni

import torch
import torch.autograd as autograd
import numpy as np

np.random.seed(11)

for size in range(1, 2000, 1):
    a = np.random.randint(0, 2, size).astype(np.uint8)
    av = autograd.Variable(torch.ByteTensor(a))

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import time
import torch
import torch.nn as nn
from torch.autograd import Variable

def attend_bmm(eh, dhx):
    dhx = dhx.unsqueeze(1)
    pax = torch.bmm(eh, dhx.transpose(1,2)).squeeze(dim=2)
    ax = nn.functional.softmax(pax)

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"""
Author: Awni Hannun

This is an example CTC decoder written in Python. The code is
intended to be a simple example and is not designed to be
especially efficient.

The algorithm is a prefix beam search for a model trained
with the CTC loss function.

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import numpy as np

def fleiss_kappa(ratings):
    """
    Args:
        ratings: An N x R numpy array. N is the number of
            samples and R is the number of reviewers. Each
            entry (n, r) is the category assigned to example
            n by reviewer r.
    Returns:

awni

awni

import time

import mlx.core as mx
import mlx.nn as nn

from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union

@dataclass
class ModelArgs:

awni

awni

Ops with Data Dependent Shapes

This is a short article on a common type of not-yet-supported operation in MLX: ops where the output shape depends on the input data. Here's an outline:

1. An introduction to these operations, followed by an explanation of why they are challenging to implement efficiently.
2. A discussion on when and how to work-around these missing operations with a couple of examples.

The Ops

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MLX LM with the OpenAI Python Package

1. Install

Install MLX LM and openai:

pip install mlx-lm openai

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from typing import Callable, Tuple

import operator
from functools import reduce
from itertools import product
import mlx.core as mx

def _interpolate(
    x: mx.array, scale_factor: Tuple, indices_fn: Callable, align_corners: bool = False
):