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

from openvino.runtime import Core, Model, Tensor, PartialShape, Type, Shape, op
from openvino.runtime.op import util as op_util
from openvino.runtime import opset8 as opset
from openvino.runtime.passes import Manager
import numpy as np
import sys, os
import argparse

def pshape2str(partial_shape):

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: