Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

fix tune_cfg issue for 3.x static quant #1718

Merged
merged 5 commits into from
Apr 16, 2024
Merged

fix tune_cfg issue for 3.x static quant #1718

Show file tree
Hide file tree
Changes from 2 commits
Commits
Show all changes
5 commits
Select commit Hold shift + click to select a range
935a86c
fix tune_cfg issue for 3.x static quant
violetch24 Apr 10, 2024
bea7d97
refine script
violetch24 Apr 11, 2024
a29addc
minor fix
violetch24 Apr 11, 2024
5401e2d
Merge branch 'master' into zixuan/3x_static
violetch24 Apr 15, 2024
5e32461
remove ipex<1.12
violetch24 Apr 15, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
161 changes: 1 addition & 160 deletions neural_compressor/torch/algorithms/smooth_quant/utility.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -16,20 +16,18 @@
import json
import os
import re
import subprocess
from collections import UserDict

import cpuinfo
import intel_extension_for_pytorch as ipex
import numpy
import psutil
import torch
import tqdm
from packaging.version import Version

from neural_compressor.torch.algorithms.static_quant import (
TransformerBasedModelBlockPatternDetector,
dump_model_op_stats,
generate_activation_observer,
get_quantizable_ops_from_cfgs,
ipex_config_path,
paser_cfgs,
Expand All @@ -42,99 +40,6 @@
ipex_ver = get_ipex_version()


def generate_activation_observer(scheme, algorithm, smooth_quant=False, smooth_quant_enable=False): # pragma: no cover
"""This is a helper method to generate an activation observer.

Args:
scheme (str): Quantization scheme to be used.
algorithm (str): What algorithm for computing the quantization parameters based on.

Returns:
An observer.
"""
kl_activation_observer = {
"name": "HistogramObserver",
"bins": 2048,
"upsample_rate": 128,
"dtype": "torch.quint8",
"qscheme": "torch.per_tensor_affine",
"reduce_range": False,
"quant_min": 0,
"quant_max": 255,
}
minmax_activation_observer = {
"name": "MinMaxObserver",
"dtype": "torch.quint8",
"qscheme": "torch.per_tensor_affine",
"reduce_range": False,
"quant_min": 0,
"quant_max": 255,
}
smoothquant_kl_activation_observer = {
"name": "SmoothQuantActivationObserver",
"smooth_quant_enabled": smooth_quant_enable,
"dtype": "torch.quint8",
"qscheme": "torch.per_tensor_affine",
"reduce_range": False,
"quant_min": 0,
"quant_max": 255,
"alpha": 0.5,
"act_observer": kl_activation_observer,
"act_ic_observer": {
"name": "PerChannelMinMaxObserver",
"ch_axis": -1,
"dtype": "torch.quint8",
"qscheme": "torch.per_channel_affine",
"reduce_range": False,
"quant_min": 0,
"quant_max": 255,
},
}
smoothquant_minmax_activation_observer = {
"name": "SmoothQuantActivationObserver",
"smooth_quant_enabled": smooth_quant_enable,
"dtype": "torch.quint8",
"qscheme": "torch.per_tensor_affine",
"reduce_range": False,
"quant_min": 0,
"quant_max": 255,
"alpha": 0.5,
"act_observer": minmax_activation_observer,
"act_ic_observer": {
"name": "PerChannelMinMaxObserver",
"ch_axis": -1,
"dtype": "torch.quint8",
"qscheme": "torch.per_channel_affine",
"reduce_range": False,
"quant_min": 0,
"quant_max": 255,
},
}
REDUCE_RANGE = False if CpuInfo().vnni else True
if REDUCE_RANGE:
minmax_activation_observer["reduce_range"] = REDUCE_RANGE
kl_activation_observer["reduce_range"] = REDUCE_RANGE
if scheme == "sym":
minmax_activation_observer["qscheme"] = "torch.per_tensor_symmetric"
minmax_activation_observer["dtype"] = "torch.qint8"
minmax_activation_observer["quant_min"] = -128
minmax_activation_observer["quant_max"] = 127
kl_activation_observer["qscheme"] = "torch.per_tensor_symmetric"
kl_activation_observer["dtype"] = "torch.qint8"
kl_activation_observer["quant_min"] = -128
kl_activation_observer["quant_max"] = 127
if smooth_quant and smooth_quant_enable:
if algorithm == "kl":
return smoothquant_kl_activation_observer
if algorithm == "minmax":
return smoothquant_minmax_activation_observer
else:
if algorithm == "kl":
return kl_activation_observer
if algorithm == "minmax":
return minmax_activation_observer


def check_cfg_and_qconfig(
tune_cfg, cfgs, op_infos_from_cfgs, output_tensor_ids_op_name, smooth_quant=False
): # pragma: no cover
Expand Down Expand Up @@ -2275,67 +2180,3 @@ def forward(self, x):
output = self.orig_layer(x)
self.output = output
return output


class CpuInfo(object): # pragma: no cover
"""Get CPU Info."""

def __init__(self):
"""Get whether the cpu numerical format is bf16, the number of sockets, cores and cores per socket."""
self._bf16 = False
self._vnni = False
info = cpuinfo.get_cpu_info()
if "arch" in info and "X86" in info["arch"]:
cpuid = cpuinfo.CPUID()
max_extension_support = cpuid.get_max_extension_support()
if max_extension_support >= 7:
ecx = cpuid._run_asm(
b"\x31\xC9", # xor ecx, ecx
b"\xB8\x07\x00\x00\x00" b"\x0f\xa2" b"\x89\xC8" b"\xC3", # mov eax, 7 # cpuid # mov ax, cx # ret
)
self._vnni = bool(ecx & (1 << 11))
eax = cpuid._run_asm(
b"\xB9\x01\x00\x00\x00", # mov ecx, 1
b"\xB8\x07\x00\x00\x00" b"\x0f\xa2" b"\xC3", # mov eax, 7 # cpuid # ret
)
self._bf16 = bool(eax & (1 << 5))
if "arch" in info and "ARM" in info["arch"]: # pragma: no cover
self._sockets = 1
else:
self._sockets = self.get_number_of_sockets()
self._cores = psutil.cpu_count(logical=False)
self._cores_per_socket = int(self._cores / self._sockets)

@property
def bf16(self):
"""Get whether it is bf16."""
return self._bf16

@property
def vnni(self):
"""Get whether it is vnni."""
return self._vnni

@property
def cores_per_socket(self):
"""Get the cores per socket."""
return self._cores_per_socket

def get_number_of_sockets(self) -> int:
"""Get number of sockets in platform."""
cmd = "cat /proc/cpuinfo | grep 'physical id' | sort -u | wc -l"
if psutil.WINDOWS:
cmd = r'wmic cpu get DeviceID | C:\Windows\System32\find.exe /C "CPU"'

with subprocess.Popen(
args=cmd,
shell=True,
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT,
universal_newlines=False,
) as proc:
proc.wait()
if proc.stdout:
for line in proc.stdout:
return int(line.decode("utf-8", errors="ignore").strip())
return 0
8 changes: 6 additions & 2 deletions neural_compressor/torch/algorithms/static_quant/static_quant.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -51,6 +51,12 @@ def static_quantize(model, tune_cfg, run_fn, example_inputs, inplace=True):
Returns:
A quantized model.
"""
_, cfgs, default_cfgs, fuse_ops, op_infos_from_cfgs, output_tensor_id_op_name = get_quantizable_ops_recursively(
model, example_inputs
)
qscheme = cfg_to_qconfig(
tune_cfg, cfgs, default_cfgs, fuse_ops, op_infos_from_cfgs, output_tensor_id_op_name
) # update json file in ipex_config_path
model.eval()

if ipex_ver.release >= Version("1.12.0").release:
Expand Down Expand Up @@ -80,8 +86,6 @@ def static_quantize(model, tune_cfg, run_fn, example_inputs, inplace=True):

else: # pragma: no cover
# for IPEX version < 1.12
_, cfgs, default_cfgs, fuse_ops = get_quantizable_ops_recursively(model, example_inputs)
qscheme = cfg_to_qconfig(tune_cfg, cfgs, default_cfgs, fuse_ops)
ipex_conf = ipex.quantization.QuantConf(
configure_file=ipex_config_path, qscheme=qscheme
) # pylint: disable=E1101
Expand Down
Loading
Loading