usstq

from transformers import AutoTokenizer, AutoModelForCausalLM
from optimum.intel import OVModelForCausalLM
import time
import argparse
import os, sys
import numpy as np
import torch
import hashlib
import itertools

usstq

# SPDX-License-Identifier: Apache-2.0

# A library and utility for drawing ONNX nets. Most of this implementation has
# been borrowed from the caffe2 implementation
# https://github.com/caffe2/caffe2/blob/master/caffe2/python/net_drawer.py
#
# The script takes two required arguments:
#   -input: a path to a serialized ModelProto .pb file.
#   -output: a path to write a dot file representation of the graph
#

usstq

#!/usr/bin/python3
import onnx
import onnx.numpy_helper
import sys

def get_value_info(m, name):
    if (name.startswith('%')):
        name = name[1:]
    for a in m.graph.value_info:
        if a.name == name:

usstq

/*
Description:
Code2 is consistantly faster than Code because SSE2 instructions (like movdqu)
is not cooperating well with AVX registers. use AVX (vmovdqu) as much as possible.

------------------------------------------------------------------------------
Install vtune & make a symbolic link
    /opt/intel/vtune_amplifier -> /home/xxx/intel/oneapi/vtune/2021.5.0/

How to compile: add following rules to Makefile: