aparente

---

name: tufte-viz description: | Ideate and critique data visualizations using Edward Tufte's principles from "The Visual Display of Quantitative Information." Use this skill when: (1) Designing new data visualizations or charts (2) Critiquing or improving existing visualizations (3) Reviewing dashboards or reports for graphical integrity (4) Deciding between visualization approaches (5) Reducing chartjunk or improving data-ink ratio (6) Planning small multiples or high-density displays

Birch-san

# the attention layer from this benchmark is from modded-nanogpt, MIT-licensed
# https://github.com/KellerJordan/modded-nanogpt
from typing import Callable, Optional
import math
from pathlib import Path
from dataclasses import dataclass
from functools import partial
import torch
from torch import nn, Tensor, FloatTensor, IntTensor
import torch.nn.functional as F

Chillee

import argparse
import time
from typing import Type

import torch
import torch.nn.functional as F
import torch._inductor.config

torch._inductor.config.triton.multi_kernel = True

ezyang

import torch
from torch import nn
from torch.distributed.tensor.placement_types import Replicate, Shard
from torch.testing._internal.distributed.fake_pg import FakeStore

import torch.distributed as dist
from torch.distributed.device_mesh import init_device_mesh
from torch.distributed.tensor import DTensor, Replicate

world_size = 4

Birch-san

from __future__ import annotations
"""
Fused Attention
===============

This is a Triton implementation of the Flash Attention v2 algorithm from Tri Dao (https://tridao.me/publications/flash2/flash2.pdf)

Credits: OpenAI kernel team
Extra Credits:

bagder

Slop

This collection is limited to only include the reports that were submitted as security vulnerabilities to the curl bug-bounty program on Hackerone.

Several other issues not included here are highly suspcious as well.

Reports

1. [Critical] Curl CVE-2023-38545 vulnerability code changes are disclosed on the internet. #2199174

aksh-at

"""
Proof-of-concept for NAT traversal and low-latency communication over QUIC
between two Modal containers.

In theory this could be used to establish a low-latency p2p connection between a
service running outside Modal and a Modal GPU container, e.g. for real-time
inference on a video stream. Please let us know if you try it!

Usage:
> modal run modal_quic_hole_punch.py

tysam-code

import os
import sys
with open(sys.argv[0]) as f:
    code = f.read() # read the code of this file ASAP, for logging
import uuid
import time
import glob
import subprocess
import contextlib
from dataclasses import dataclass

joey00072

# https://x.com/shxf0072/status/1873038335427658011

import torch
import torch.nn as nn
import torch.nn.functional as F

from dataclasses import dataclass
from collections import OrderedDict

from ohara.modules.norm import RMSNorm

zjlww

from typing import Tuple

import torch
from torch import nn, Tensor
import torch.nn.functional as F
from einops import rearrange
from .modules import HiFiGANEncoder, HiFiGANDecoder, GroupFiniteScalarQuantizer


class AudioCodecModel(nn.Module):