vgoklani

def verify_ddp_weights_equal(model: torch.nn.Module, atol: float = 1e-5) -> None:
    if hasattr(model, "module"):
        model = model.module
    
    world_size = get_world_size()
    for name, param in model.named_parameters():
        gathered_param = gather(param).reshape((world_size, -1))
        absolute_diffs = (gathered_param[None, 0, :] - gathered_param).abs()
        rank_params_eq = (absolute_diffs < atol).all()
        assert rank_params_eq, f"❌ param [{name}] not equal - got max_absolute_diff={absolute_diffs.max()}"

jxmorris12

def verify_ddp_weights_equal(model: torch.nn.Module, atol: float = 1e-5) -> None:
    if hasattr(model, "module"):
        model = model.module
    
    world_size = get_world_size()
    for name, param in model.named_parameters():
        gathered_param = gather(param).reshape((world_size, -1))
        absolute_diffs = (gathered_param[None, 0, :] - gathered_param).abs()
        rank_params_eq = (absolute_diffs < atol).all()
        assert rank_params_eq, f"❌ param [{name}] not equal - got max_absolute_diff={absolute_diffs.max()}"

Birch-san

from transformers import (
  AutoConfig,
  AutoTokenizer,
  BitsAndBytesConfig,
  GenerationConfig,
  AutoModelForCausalLM,
  LlamaTokenizerFast,
  PreTrainedModel,
  TextIteratorStreamer,
  StoppingCriteria,

codekansas

import argparse
import contextlib
import logging
import math
import random
import time
from dataclasses import dataclass
from pathlib import Path
from typing import Callable

Chillee

import torch
import torch._inductor.config
import time

torch._inductor.config.triton.cudagraphs = False
torch.set_float32_matmul_precision('high')

def bench(f, name=None, iters=100, warmup=5, display=True, profile=False):
    for _ in range(warmup):
        f()

sayakpaul

Work	Details
Augmenting convnets with aggregated attention	Tutorial by Aritra
Train a Vision Transformer on small datasets	Tutorial by Aritra
MobileViT	Tutorial by Sayak
Compact Convolutional Transformers	Tutorial by Sayak
Data efficient image transformers	TF implementation, TF pre-trained models, tutorial by Sayak
Class attention image transformers	TF implementation, TF pre-trained models by Sayak
Masked Autoencoders	TF implementation, tutorial by Aritra and Sayak, Contribution to Hugging Face Transformers by Aritra and Sayak
Probing the representation of ViTs

simonster

# Copyright 2022 Google LLC.
# SPDX-License-Identifier: Apache-2.0
# Author: Maithra Raghu <maithra@google.com>


def compute_distance_matrix(patch_size, num_patches, length):
  """Helper function to compute distance matrix."""

  distance_matrix = np.zeros((num_patches, num_patches))

madlag

#!/usr/bin/env python

# Any copyright is dedicated to the Public Domain.
# https://creativecommons.org/publicdomain/zero/1.0/

# Written by Francois Fleuret <francois@fleuret.org>
# Modified by François Lagunas <francois.lagunas@m4x.org>

import time, torch

jstheoriginal

struct SearchBar : View {
    @Binding var searchText: String
    
    var body: some View {
        HStack {
            Image(systemName: "magnifyingglass").foregroundColor(.secondary)
            TextField(
                $searchText,
                placeholder: Text("Search")) {
                    UIApplication.shared.keyWindow?.endEditing(true)

dannguyen

Simple scraper of the ASPX search form for U.S. Congress House financial disclosure results

The following script, given someone's last name, prints a CSV of financial disclosure PDFs (the first 20, for simplicity's sake) as found on the House Financial Disclosure Reports. It's meant to be a proof-of-concept of how to scrape ASPX (and other "stateful" websites) with using plain old requests -- without too much inconvenience -- rather than resorting to something heavy like the selenium websdriver

The search page can be found here: http://clerk.house.gov/public_disc/financial-search.aspx

Here's a screenshot of what it does when you search via web browser: