younesbelkada

import transformers
import transformers.models as models

models_that_support_accelerate = []

for model in dir(models):
    if model[0] != '_':
        model_module = getattr(models, model)
        if hasattr(model_module, "modeling_" + model):
            modeling_module = getattr(model_module, "modeling_" + model)        

younesbelkada

# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software

younesbelkada

import numpy as np
import csv
import argparse
from tqdm import tqdm
import torch

import evaluate
from datasets import load_dataset
from transformers import AutoModelForCausalLM, AutoTokenizer

younesbelkada

import tensorflow as tf
import torch

import torch.nn.functional as F
    

# adapted from: https://discuss.pytorch.org/t/tf-extract-image-patches-in-pytorch/43837/8
def torch_extract_patches(
    x, patch_height, patch_width, padding=None
):

younesbelkada

import tensorflow as tf
import torch
import math

import torch.nn.functional as F
    

# adapted from: https://discuss.pytorch.org/t/tf-extract-image-patches-in-pytorch/43837/8
def torch_extract_patches(
    x, patch_height, patch_width, padding=None

younesbelkada

import os
import json 
import evaluate
import torch
import torchvision
import numpy as np
from tqdm import tqdm
from PIL import Image
from transformers import DetrFeatureExtractor, DetrForObjectDetection, DetrImageProcessor

younesbelkada

import os
import evaluate
import torch
import torchvision
from tqdm import tqdm
from transformers import DetrFeatureExtractor, DetrForObjectDetection

COCO_DIR = os.path.join(os.getcwd(), "data")
path_img = os.path.join(COCO_DIR, "val2017")
path_anno = os.path.join(COCO_DIR, "annotations/instances_val2017.json")

younesbelkada

from t5x import checkpoints
from flax.traverse_util import flatten_dict, unflatten_dict

def rename_keys(key):
    if "kernel" in key:
        key = key.replace("kernel", "weight")
    return key

flax_checkpoint_path = "/home/younes_huggingface_co/code/pix2struct/pix2struct_base"

younesbelkada

# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,

younesbelkada

bert-base-uncased - A100 - half - cuda

batch_size	seq_len	pad_percentage	HF_time	BT_time	Speedup
8	64	0	0.012947250976562501	0.007366779174804688	1.7575185395598296
8	64	0.1	0.012887490234375	0.0072092669677734375	1.7876283805252486
8	64	0.2	0.012887449951171874	0.007392645263671876	1.7432799074645124
8	64	0.5	0.012756295166015626	0.007139061889648438	1.7868307297506574
8	64	0.75	0.0132109521484375	0.007268699951171876	1.8175123800931694
8	128	0	0.012552437744140625	0.0075138049316406256	1.6705833939449668