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fix tune_cfg issue for 3.x static quant #1718

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merged 5 commits into from
Apr 16, 2024
Merged

fix tune_cfg issue for 3.x static quant #1718

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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
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161 changes: 1 addition & 160 deletions neural_compressor/torch/algorithms/smooth_quant/utility.py
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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
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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
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