chu-tianxiang

# Adapted from the triton implementation of flash-attention v2
# https://github.com/openai/triton/blob/main/python/tutorials/06-fused-attention.py
import time
import torch
import torch.utils.benchmark as benchmark
import triton
import triton.language as tl


@triton.jit

hankcs

#!/bin/bash

# This script downloads and compiles the Ontonotes 2012 data in a helpful format
# for co-reference resolution. It generates 3 files: {train, dev, test}.english.v4_gold_conll,
# as well as a directory 'conll-2012' which contains the raw extracted data.
# The script downloads and runs some python scripts which require python 2.X.

ONTONOTES_PATH=$1
LANGUAGE=$2

bbrighttaer

# Author: bbrighttaer
# Date: 5/24/19
# Time: 12:27 AM
# File: math.py


from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals

ferrine

import torch.nn
import collections

class Builder(object):
    def __init__(self, *namespaces):
        self._namespace = collections.ChainMap(*namespaces)

    def __call__(self, name, *args, **kwargs):
        try:
            return self._namespace[name](*args, **kwargs)

thomwolf

##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## Created by: Hang Zhang, Rutgers University, Email: [email protected]
## Modified by Thomas Wolf, HuggingFace Inc., Email: [email protected]
## Copyright (c) 2017-2018
##
## This source code is licensed under the MIT-style license found in the
## LICENSE file in the root directory of this source tree
##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

"""Encoding Data Parallel"""

thomwolf

model.zero_grad()                                   # Reset gradients tensors
for i, (inputs, labels) in enumerate(training_set):
    predictions = model(inputs)                     # Forward pass
    loss = loss_function(predictions, labels)       # Compute loss function
    loss = loss / accumulation_steps                # Normalize our loss (if averaged)
    loss.backward()                                 # Backward pass
    if (i+1) % accumulation_steps == 0:             # Wait for several backward steps
        optimizer.step()                            # Now we can do an optimizer step
        model.zero_grad()                           # Reset gradients tensors
        if (i+1) % evaluation_steps == 0:           # Evaluate the model when we...

jeasinema

#!/usr/bin/env python
# -*- coding:UTF-8 -*-

import torch
import torch.nn as nn
import torch.nn.init as init


def weight_init(m):
    '''

goldsborough

# Instructions for installing GCC 4.9 on various platforms.
# The commands show instructions for GCC 4.9, but any higher version will also work!

# Ubuntu (https://askubuntu.com/questions/466651/how-do-i-use-the-latest-gcc-on-ubuntu/581497#581497)
sudo apt-get install software-properties-common
sudo add-apt-repository ppa:ubuntu-toolchain-r/test
sudo apt-get update
sudo apt-get install gcc-4.9 g++-4.9
sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-4.9 60 --slave /usr/bin/g++ g++ /usr/bin/g++-4.9

alexdevero

Windows Registry Editor Version 5.00

[HKEY_CLASSES_ROOT\.md]
@="markdown"

[HKEY_CLASSES_ROOT\.md\ShellNew]
"NullFile"=""

[HKEY_CLASSES_ROOT\markdown]
@="Blank Markdown file"

mblondel

# Author: Mathieu Blondel
# License: BSD 3 clause

import numpy as np


def projection_simplex(V, z=1, axis=None):
    """
    Projection of x onto the simplex, scaled by z:
        P(x; z) = argmin_{y >= 0, sum(y) = z} ||y - x||^2