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"""Tests for quantization"""
import numpy as np
import unittest
import shutil
import os
import yaml
import tensorflow as tf

def build_fake_yaml():
    fake_yaml = '''

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from typing import List

class Adaptor:
    def __init__(self) -> None:
        pass
    
    def evaluate(self, model):
        pass
    

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from collections import deque
import time
import os
import subprocess

SLEEP_TIME = 0.5



class Scheduler:

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import torch
class M(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.conv = torch.nn.Conv2d(3, 1, 1)
        self.linear = torch.nn.Linear(224 * 224, 5)

    def forward(self, x):

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# branch yuwenzho/recipe
block_list = self.capability.get('block_info', [])
        
class BlockFallbackTuningSampler(TuningSampler):
    def __init__(self):
        pass
    
    def __iter__(self):
        for op_list in block_list:
            fallback all ops in op_list

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# Copyright (c) 2023 Intel Corporation
#
# 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,

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Neural Solution: Bring the Optimization Power of Intel® Neural Compressor as a Service

Neural Solution, Intel Neural Compressor, Distributed Tuning Deep Learning, Quantization

In today's fast-paced world of deep learning, model compression techniques play a crucial role in enhancing efficiency and reducing computational resources. Intel® Neural Compressor (INC) is a cutting-edge tool that offers a wide range of popular model compression techniques, including quantization, pruning, distillation, and neural architecture search on mainstream frameworks. It supports a wide range of Intel hardware and has been extensively tested. The tool validates thousands of models from popular models by leveraging zero-code optimization solution Neural Coder and automatic accuracy-driven quantization strategies.

In this blog, we are happy

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import torch
from torch.nn import Module, Parameter
from typing import Deque, Set

module = torch.nn.Linear(3, 3)


class PartitionedParameterCoordinator:
    def __all_gather_params(self, params: Set[Parameter], forward: bool, quantize: bool = False) -> None:

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1. Plugs

• Python
• pylance

2. setting

add .vscode/setting.json into current workspace.

{
    "python.languageServer": "Default",
 "python.analysis.inlayHints.callArgumentNames": "off",

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from transformers import AutoModel, AutoTokenizer
import torch

import copy
from torch.ao.quantization.quantize_pt2e import prepare_pt2e, convert_pt2e
import torch.ao.quantization.quantizer.x86_inductor_quantizer as xiq
from torch.ao.quantization.quantizer.x86_inductor_quantizer import X86InductorQuantizer
from torch._export import capture_pre_autograd_graph

model_name_or_path = "bert-base-uncased"