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interface.py
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# Copyright (c) 2022 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,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import subprocess
import logging
import time
import yaml
import re
from . import globals
if not os.path.exists("neural_coder_workspace"):
os.makedirs("neural_coder_workspace")
def detect_device_(logger):
# device detection
logger.info(f"Device detection started ...")
from .utils.device import detect_device
detect_device()
if globals.device == "cpu_with_amx":
logger.info(f"Device: CPU with AMX")
elif globals.device == "cpu_without_amx":
logger.info(f"Device: CPU without AMX")
elif globals.device == "intel_gpu":
logger.info(f"Device: Intel(R) GPU")
elif globals.device == "cuda":
logger.info(f"Device: CUDA")
elif globals.device == "mutli":
logger.info(f"Device: Multi-Device")
def enable(
code,
features,
target_batch_size=1, # effective for feature "pytorch_change_batch_size"
num_benchmark_iteration=10, # effective for feature "pytorch_benchmark"
generate_patch=True,
overwrite=False,
save_patch_path="",
patch_suffix=".diff",
remove_copy=True,
consider_imports=False,
patch_imports=False,
logging_level="info",
run_bench=False,
entry_code="",
args="",
mode="throughput",
cpu_set_env=True,
ncore_per_instance=-1, # only for "self_defined" mode
ninstances=-1, # only for "self_defined" mode
bench_batch_size=-1, # only for "self_defined" mode
test_code_line=False, # print code line info for debug use
cache_load_transformers=True,
optimum_quant_config="", # only for HF optimum optimizations, yaml or hub path
use_inc=True,
use_modular=False,
modular_item="",
):
"""enable a feature or a couple of features for the code
"""
### Preparation
# set up workspace
ws_path = "neural_coder_workspace/" + \
"enable" + str(time.time()).replace(".","") + "/"
os.makedirs(ws_path)
# user parameters
globals.consider_imports = consider_imports
logging_var = "logging." + logging_level.upper()
globals.logging_level = eval(logging_var)
# set up logging
logger = logging.getLogger(ws_path)
logger.setLevel(globals.logging_level)
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s: - %(message)s',
datefmt='%Y-%m-%d %H:%M:%S')
fh = logging.FileHandler(ws_path + 'enable.log')
fh.setLevel(globals.logging_level)
fh.setFormatter(formatter)
ch = logging.StreamHandler()
ch.setLevel(globals.logging_level)
ch.setFormatter(formatter)
logger.addHandler(fh)
logger.addHandler(ch)
# device detection
detect_device_(logger)
# print key inputs
logger.info(f"Enabling started ...")
logger.info(f"code: {code}")
logger.info(f"features: {features}")
# feature list for reference
'''
feature_list = [
"pytorch_jit_script",
"pytorch_jit_script_ofi",
"pytorch_jit_trace",
"pytorch_jit_trace_ofi",
"pytorch_inc_dynamic_quant",
"pytorch_inc_static_quant_fx",
"pytorch_inc_static_quant_ipex",
"pytorch_inc_bf16",
"pytorch_inc_huggingface_optimum_static",
"pytorch_inc_huggingface_optimum_dynamic",
"pytorch_ipex_fp32",
"pytorch_ipex_bf16",
"pytorch_ipex_int8_static_quant",
"pytorch_ipex_int8_dynamic_quant",
"pytorch_channels_last",
"pytorch_mixed_precision_cpu",
"pytorch_mixed_precision_cuda",
"pytorch_torchdynamo_jit_script",
"pytorch_torchdynamo_jit_script_ofi",
"pytorch_torchdynamo_jit_trace",
"pytorch_torchdynamo_jit_trace_ofi",
"pytorch_torch_profiler",
"pytorch_benchmark",
"pytorch_change_batch_size",
"pytorch_cuda_to_cpu",
"pytorch_lightning_bf16_cpu",
"pytorch_aliblade",
"tensorflow_amp",
"keras_amp",
"tensorflow_inc",
"keras_inc",
"onnx_inc_static_quant_qlinear",
"onnx_inc_static_quant_qdq",
"onnx_inc_dynamic_quant",
"inc_auto",
]
'''
### Enable Benchmark (if run_bench)
if run_bench:
# add "pytorch_change_batch_size" to features
from .utils.cpu_info import get_num_cpu_cores
ncores = get_num_cpu_cores()
if mode == "throughput":
target_batch_size = 2 * ncores
elif mode == "multi_instance":
target_batch_size = 1
elif mode == "latency":
target_batch_size = 1
elif mode == "self_defined":
target_batch_size = bench_batch_size
if "pytorch_change_batch_size" not in features:
features.append("pytorch_change_batch_size")
# add "pytorch_benchmark" to features
if "pytorch_benchmark" not in features:
features.append("pytorch_benchmark")
logger.info(
f"Will perform benchmark on [{mode}] mode with batch size [{target_batch_size}] ...")
#### Feature Enabling
globals.num_benchmark_iteration = str(num_benchmark_iteration + 10) # 10: warmup iteration number
globals.cache_load_transformers = cache_load_transformers
globals.optimum_quant_config = optimum_quant_config
globals.use_modular = use_modular
globals.modular_item = modular_item
# move "pytorch_benchmark" to the last
from .utils.common import move_element_to_last
features = move_element_to_last(features, "pytorch_benchmark")
# not in harness scope
features_outside_harness = [
"pytorch_change_batch_size",
"pytorch_cuda_to_cpu",
"pytorch_lightning_bf16_cpu",
"tensorflow_mixed_precision",
"tensorflow_inc",
"change_trainer_to_nlptrainer",
]
# # features that need creating dummy dataloader (when needed) first
# if "pytorch_inc_static_quant_fx" in features or \
# "pytorch_inc_static_quant_ipex" in features:
# features = ["pytorch_dummy_dataloader"] + features
# features that need reclaiming inputs first (e.g. for "for step, inputs in enumerate(dataloader)")
if "pytorch_jit_trace" in features or \
"pytorch_jit_trace_ofi" in features or \
"pytorch_inc_static_quant_fx" in features or \
"pytorch_inc_static_quant_ipex" in features:
features = ["pytorch_reclaim_inputs"] + features
# intel_extension_for_transformers
if "intel_extension_for_transformers" in features:
features = ["change_trainer_to_nlptrainer"] + features
transformed_list_code_path = []
## Determine Code Domain
# reset globals
globals.reset_globals()
from .utils import handle_user_input
globals.list_code_path, num_user_code_path = handle_user_input.get_all_code_path(code)
from .coders.autoinc import domain
code_domain = domain.determine_domain(globals.list_code_path[0])
if code_domain == "transformers_trainer":
if "pytorch_benchmark" in features:
features = ["pytorch_reclaim_inference_transformers_trainer"] + features
# for BS
args += " --per_device_eval_batch_size " + str(target_batch_size)
globals.batch_size_changed = True
## Feature Transformation
for idx_feature, feature in enumerate(features):
# "inc_auto" auto selection of feature according to fwk
if feature == "inc_auto":
from .coders.autoinc import domain
code_domain = domain.determine_domain(globals.list_code_path[0])
if code_domain == "keras_script":
feature = "keras_inc"
elif code_domain == "tensorflow_keras_model":
feature = "tensorflow_inc"
elif code_domain == "onnx":
feature = "onnx_inc_dynamic_quant"
else:
feature = "pytorch_inc_dynamic_quant"
# reset globals
globals.reset_globals()
from .utils import handle_user_input
globals.list_code_path, num_user_code_path = handle_user_input.get_all_code_path(code)
if len(transformed_list_code_path) > 0:
globals.list_code_path = transformed_list_code_path
# common for all features (transformations),
list_transformed_code = []
# in this list, each item stores the transformed code
# of the corresponding original code
# by the order in code_path
# global behaviors
logger.info(
f"Performing code transformation for feature: [{feature}] ...")
for i in globals.list_code_path:
list_transformed_code.append(open(i, 'r').read())
## 1. Features in Harness Scope
if feature not in features_outside_harness:
from .graphers.code_line import register_code_line
from .graphers.model import register_nnModule_class, register_nnModule_instance_definition
from .graphers.function import register_func_wrap_pair
from .coders.transform import execute_insert_transformation, execute_indent_transformation
# code analysis (call graph, type inference etc)
register_code_line()
register_func_wrap_pair()
register_nnModule_class()
if cache_load_transformers:
preload_file = open(os.path.dirname(__file__) +
"/graphers/preloads/" + "transformers" + ".yaml")
preload_dict = yaml.load(preload_file, Loader=yaml.BaseLoader)
globals.list_class_name += preload_dict["class"]
register_nnModule_instance_definition()
# register transformation
if feature == "pytorch_dummy_dataloader": # is not in harness scope, but needs call graph and type inference
from .coders.pytorch.dummy_dataloader import DummyDataLoader
opt = DummyDataLoader(globals.list_model_def_instance)
opt.register_transformation()
elif feature == "pytorch_reclaim_inputs":
from .coders.pytorch.reclaim_inputs import ReclaimInputs
opt = ReclaimInputs(globals.list_model_def_instance)
opt.register_transformation()
elif feature == "pytorch_reclaim_inference_transformers_trainer":
from .coders.pytorch.reclaim_inference_transformers_trainer import ReclaimInferenceTransformersTrainer
opt = ReclaimInferenceTransformersTrainer(globals.list_model_def_instance)
opt.register_transformation()
elif feature in [
"pytorch_inc_dynamic_quant",
"pytorch_inc_static_quant_fx",
"pytorch_inc_static_quant_ipex",
"pytorch_inc_huggingface_optimum_static",
"pytorch_inc_huggingface_optimum_dynamic",
"onnx_inc_static_quant_qlinear",
"onnx_inc_static_quant_qdq",
"onnx_inc_dynamic_quant",
"intel_extension_for_transformers",
]:
# determine domain
from .coders.autoinc.domain import determine_domain
globals.code_domain = determine_domain(globals.list_code_path[0])
# for transformers code, enable optimum-intel api by default
# if specify use_inc, then still use INC API
if "transformers" in globals.code_domain and not use_inc:
if "static_quant" in feature:
feature = "pytorch_inc_huggingface_optimum_static"
elif "dynamic_quant" in feature:
feature = "pytorch_inc_huggingface_optimum_dynamic"
# optimum-intel quantization config for static and dynamic
if feature == "pytorch_inc_huggingface_optimum_static":
globals.optimum_quant_config = "quantization/quant_config_static"
elif feature == "pytorch_inc_huggingface_optimum_dynamic":
globals.optimum_quant_config = "quantization/quant_config_dynamic"
else:
pass
from .coders.autoinc.autoinc_harness import AutoInc_Harness
from .coders.autoinc.calib_dataloader import Calib_Dataloader
from .coders.autoinc.eval_func import Eval_Func
opt = Calib_Dataloader()
opt.register_transformation()
opt = Eval_Func()
opt.register_transformation()
opt = AutoInc_Harness(backend=feature)
opt.register_transformation()
else:
from .coders.pytorch.harness import Harness
opt = Harness(backend=feature)
opt.register_transformation()
# execute transformation
list_transformed_code = execute_indent_transformation(list_transformed_code)
list_transformed_code = execute_insert_transformation(list_transformed_code)
## 2. Features NOT in Harness Scope
else:
for i in range(len(list_transformed_code)):
# Batch Size
if "pytorch_change_batch_size" in features:
if "batch_size" in list_transformed_code[0]: # entry code has "batch_size"
globals.batch_size_changed = True
from .coders.pytorch.batch_size import BatchSizeCoder
globals.target_batch_size = str(target_batch_size)
list_transformed_code[i] = BatchSizeCoder(list_transformed_code[i]).transform()
# CUDA to CPU
if "pytorch_cuda_to_cpu" in features:
from .coders.pytorch.cuda_to_cpu import CudaToCpu
list_transformed_code[i] = CudaToCpu(list_transformed_code[i]).transform()
# Lightning
if "pytorch_lightning_bf16_cpu" in features:
from .coders.pytorch.lightning import Lightning
list_transformed_code[i] = Lightning(list_transformed_code[i]).transform()
# TF & Keras AMP
if "tensorflow_mixed_precision" in features:
from .coders.tensorflow.amp import TensorFlowKerasAMP
list_transformed_code[i] = TensorFlowKerasAMP(list_transformed_code[i]).transform()
if feature == "tensorflow_inc":
from .coders.tensorflow.inc import TensorFlowKerasINC
list_transformed_code[i] = TensorFlowKerasINC(list_transformed_code[i]).transform()
# Change Trainer to NLPTrainer (only for intel_extension_for_pytorch)
if "change_trainer_to_nlptrainer" in features:
from .coders.pytorch.change_trainer_to_nlptrainer import TrainerToNLPTrainer
list_transformed_code[i] = TrainerToNLPTrainer(list_transformed_code[i]).transform()
logger.info(f"Code transformation for feature: [{feature}] finished.")
for idx_path, path in enumerate(globals.list_code_path):
if path[-14:] == "_nc_enabled.py":
path_transformed = path
else:
path_transformed = path[:-3] + "_nc_enabled.py"
if idx_feature != len(features) - 1:
open(path_transformed, "w").write(list_transformed_code[idx_path])
else:
open(path_transformed, "w").write(list_transformed_code[idx_path].replace(" # [coder-enabled]", ""))
globals.list_code_path[idx_path] = path_transformed
transformed_list_code_path = globals.list_code_path
# test code_line.py
if test_code_line:
# reset globals
globals.reset_globals()
globals.print_code_line_info = True
from .utils import handle_user_input
globals.list_code_path, num_user_code_path = handle_user_input.get_all_code_path(code)
if len(transformed_list_code_path) > 0:
globals.list_code_path = transformed_list_code_path
# common for all features (transformations),
list_transformed_code = []
# in this list, each item stores the transformed code
# of the corresponding original code
# by the order in code_path
for i in globals.list_code_path:
list_transformed_code.append(open(i, 'r').read())
from .graphers.code_line import register_code_line
from .graphers.model import register_nnModule_class, register_nnModule_instance_definition
from .graphers.function import register_func_wrap_pair
from .coders.transform import execute_insert_transformation, execute_indent_transformation
# code analysis (call graph, type inference etc)
register_code_line()
register_func_wrap_pair()
register_nnModule_class()
register_nnModule_instance_definition()
### Output of Enabling
globals.list_code_path, num_user_code_path = handle_user_input.get_all_code_path(code)
if generate_patch:
whole_patch_user_code = ""
for path in globals.list_code_path[0:num_user_code_path]:
path_transformed = path[:-3] + "_nc_enabled.py"
if path_transformed[-25:] == "_nc_enabled_nc_enabled.py":
continue
cmd_gen_patch = "diff -up " + path + " " + path_transformed
sp_gen_patch = subprocess.Popen(
cmd_gen_patch, env=os.environ, shell=True, stdout=subprocess.PIPE) # nosec
sp_gen_patch.wait()
this_patch, _ = sp_gen_patch.communicate()
this_patch = str(this_patch)[2:-1]
whole_patch_user_code += this_patch
if save_patch_path == "":
save_patch_path = ws_path + "neural_coder_patch"
open(save_patch_path + patch_suffix, "w").write(
whole_patch_user_code.replace(r'\n', '\n').replace(r'\t', '\t').replace(r"\'", "\'"))
abs_patch_path = os.path.abspath(
save_patch_path + patch_suffix)
logger.info(f"The patch is saved to: [{abs_patch_path}]")
if overwrite:
sp_overwrite = subprocess.Popen(
"patch -d/ -p0 < " + abs_patch_path, env=os.environ, shell=True, stdout=subprocess.PIPE) # nosec
sp_overwrite.wait()
# os.remove(abs_patch_path) # remove patch after overwrite
if patch_imports:
whole_patch_import_modules = ""
for path in globals.list_code_path[num_user_code_path:]:
path_transformed = path[:-3] + "_nc_enabled.py"
cmd_gen_patch = "diff -up " + path + " " + path_transformed
sp_gen_patch = subprocess.Popen(
cmd_gen_patch, env=os.environ, shell=True, stdout=subprocess.PIPE) # nosec
sp_gen_patch.wait()
this_patch, _ = sp_gen_patch.communicate()
this_patch = str(this_patch)[2:-1]
whole_patch_import_modules += this_patch
if save_patch_path == "":
save_patch_path = ws_path + "neural_coder_patch_import_modules"
open(save_patch_path + patch_suffix, "w").write(
whole_patch_import_modules.replace(r'\n', '\n').replace(r'\t', '\t').replace(r"\'", "\'"))
abs_patch_path = os.path.abspath(
save_patch_path + patch_suffix)
logger.info(
f"The patch for imported modules is saved to: [{abs_patch_path}]")
# remove copy for imports
if remove_copy:
for path in globals.list_code_path:
try:
path_transformed = path[:-3] + "_nc_enabled.py"
os.remove(path_transformed)
except:
pass
### Benchmark
if run_bench:
if "github.com" in code and ".py" in code:
code = globals.list_code_path[0]
entry_code = globals.list_code_path[0]
bench_performance, bench_mode, bench_ws_path = bench(
code=code,
entry_code=entry_code,
args=args,
patch_path=abs_patch_path,
mode=mode,
cpu_set_env=cpu_set_env,
ncore_per_instance=ncore_per_instance, # only for "self_defined" mode
ninstances=ninstances, # only for "self_defined" mode
bench_batch_size=bench_batch_size, # only for "self_defined" mode
)
return bench_performance, bench_mode, bench_ws_path
def bench(
code,
entry_code="",
args="",
patch_path="",
mode="throughput", # throughput, latency, multi_instance or self_defined
logging_level="info",
cpu_set_env=True,
ncore_per_instance=-1, # only for "self_defined" mode
ninstances=-1, # only for "self_defined" mode
bench_batch_size=-1, # only for "self_defined" mode
):
"""benchmark on either "optimized code", or "patch" + "original code"
it does not enable benchmark code lines, or enable change of batch size
all the enabling should be done within enable API
which means the "optimized code" should already have
"pytorch_benchmark" and "pytorch_change_batch_size" enabled
or the "patch" should already have the code modification
for "pytorch_benchmark" and "pytorch_change_batch_size" in it
"""
# set up workspace
ws_path = "neural_coder_workspace/" + "bench" + str(time.time()).replace(".","") + "/"
os.makedirs(ws_path)
# set up logging
logging_var = "logging." + logging_level.upper()
globals.logging_level = eval(logging_var)
logger = logging.getLogger(ws_path)
logger.setLevel(globals.logging_level)
formatter = logging.Formatter(
'%(asctime)s - %(name)s - %(levelname)s: - %(message)s', datefmt='%Y-%m-%d %H:%M:%S')
fh = logging.FileHandler(ws_path + 'bench.log')
fh.setLevel(globals.logging_level)
fh.setFormatter(formatter)
ch = logging.StreamHandler()
ch.setLevel(globals.logging_level)
ch.setFormatter(formatter)
logger.addHandler(ch)
logger.addHandler(fh)
# device detection
detect_device_(logger)
# print key inputs
logger.info(f"Benchmarking started ...")
logger.info(f"code: {code}")
logger.info(f"mode: {mode}")
# entry code
if entry_code == "":
# if not specify entry_code,
# then code has to be a list of one element, or a single string of single path, otherwise quit
if type(code) == list and len(code) == 1:
entry_code = code[0]
elif type(code) == str:
entry_code = code
else:
logger.error(
f"You have to specify an entry_code of your code: [{code}]")
quit()
# patch
if patch_path != "":
sp_patch = subprocess.Popen(
"patch -d/ -p0 < " + patch_path, env=os.environ, shell=True, stdout=subprocess.PIPE) # nosec
sp_patch.wait()
# if mode is "self_defined", user must specify ncpi, nins and bs
if mode == "self_defined":
if ncore_per_instance == -1 or ninstances == -1 or bench_batch_size == -1:
logger.error(
f"You have to specify ncore_per_instance,"
f"ninstances and bench_batch_size for self-defined benchmark mode.")
quit()
# numactl
from .utils import numa_launcher
from .utils.cpu_info import get_num_cpu_cores
ncores = get_num_cpu_cores()
# numactl setup for different modes
if mode == "throughput":
ncore_per_instance = ncores
ninstances = 1
bench_batch_size = 2 * ncores
elif mode == "multi_instance":
ncore_per_instance = 4
ninstances = int(ncores / ncore_per_instance)
bench_batch_size = 1
elif mode == "latency":
ncore_per_instance = 1
ninstances = ncores
bench_batch_size = 1
elif mode == "self_defined":
ncore_per_instance = ncore_per_instance
ninstances = ninstances
bench_batch_size = bench_batch_size
# set cpu env variables
if cpu_set_env:
cmd_env = ''
cmd_env += 'export LD_PRELOAD=${CONDA_PREFIX}/lib/libjemalloc.so'
cmd_env += ' && '
cmd_env += 'export LD_PRELOAD=${LD_PRELOAD}:${CONDA_PREFIX}/lib/libiomp5.so'
cmd_env += ' && '
cmd_env += 'export MALLOC_CONF="oversize_threshold:1,background_thread:true,metadata_thp:auto,'
cmd_env += 'dirty_decay_ms:9000000000,muzzy_decay_ms:9000000000"'
cmd_env += ' && '
cmd_env += 'export KMP_AFFINITY="granularity=fine,compact,1,0"'
cmd_env += ' && '
cmd_env += 'export KMP_BLOCKTIME=1'
cmd_env += ' && '
cmd_env += 'export DNNL_PRIMITIVE_CACHE_CAPACITY=1024'
cmd_env += ' && '
cmd_env += 'export KMP_SETTINGS=1'
sp_set_env = subprocess.Popen(
cmd_env, env=os.environ, shell=True, stdout=subprocess.PIPE) # nosec
sp_set_env.wait()
# benchmark
logger.info(f"Start benchmark on the code ...")
bench_log_path = ws_path + "performance.log"
os.remove(bench_log_path) if os.path.exists(bench_log_path) else 0
args = [args]
numa_launcher.exec_launcher(
ncore_per_instance, ninstances, entry_code, args, bench_log_path)
# get performance (throughput and latency)
bench_log = open(bench_log_path, "r", encoding='unicode_escape').read().split('\n')
IPS = []
MSPI = 0
count_MSPI = 0
P50 = 0
count_P50 = 0
P90 = 0
count_P90 = 0
P99 = 0
count_P99 = 0
acc_delta = 0
for line in bench_log:
if "Neural_Coder_Bench_IPS" in line:
try:
IPS.append(float(line[line.find(":")+3:]))
except ValueError as ve:
pass
if "Neural_Coder_Bench_MSPI" in line:
try:
MSPI += float(line[line.find(":")+3:])
count_MSPI += 1
except ValueError as ve:
pass
if "Neural_Coder_Bench_P50" in line:
try:
P50 += float(line[line.find(":")+3:])
count_P50 += 1
except ValueError as ve:
pass
if "Neural_Coder_Bench_P90" in line:
try:
P90 += float(line[line.find(":")+3:])
count_P90 += 1
except ValueError as ve:
pass
if "Neural_Coder_Bench_P99" in line:
try:
P99 += float(line[line.find(":")+3:])
count_P99 += 1
except ValueError as ve:
pass
if "Accuracy (int8|fp32)" in line:
try:
acc_int8 = float(re.search(r"\d+\.\d+", line).group())
acc_fp32 = float(re.search(r"(?<=\|)\d+\.\d+", line).group())
acc_delta = round((acc_int8 - acc_fp32) / acc_fp32 * 100, 2) # percent of increase/decrease
except ValueError as ve:
pass
if len(IPS) >= 4: # handle extreme values
IPS.sort()
IPS[0] = IPS[1]
IPS[-1] = IPS[-2]
try:
if globals.batch_size_changed: # only times BS if BS has been modified, otherwise times 1
FPS = round(sum(IPS) / len(IPS) * ninstances * bench_batch_size, 3)
else:
FPS = round(sum(IPS) / len(IPS) * ninstances * 1, 3)
except:
FPS = 0
try:
MSPI = round(MSPI / count_MSPI, 3)
except:
MSPI = 0
try:
P50 = round(P50 / count_P50, 3)
except:
P50 = 0
try:
P90 = round(P90 / count_P90, 3)
except:
P90 = 0
try:
P99 = round(P99 / count_P99, 3)
except:
P99 = 0
logger.info(f"Collected throughput on the code is: [{FPS}] (fps)")
logger.info(f"Collected latency on the code is: [{MSPI}] (mspi)")
logger.info(f"Collected latency_p50 on the code is: [{P50}] (mspi)")
logger.info(f"Collected latency_p90 on the code is: [{P90}] (mspi)")
logger.info(f"Collected latency_p99 on the code is: [{P99}] (mspi)")
logger.info(f"Collected accuracy delta on the code is: [{acc_delta}]")
# unpatch
if patch_path != "":
sp_unpatch = subprocess.Popen(
"patch -R -d/ -p0 < " + patch_path, env=os.environ, shell=True, stdout=subprocess.PIPE) # nosec
sp_unpatch.wait()
return [FPS, MSPI, P50, P90, P99, acc_delta], mode, os.path.abspath(ws_path)
def superbench(
code,
entry_code="",
args="",
sweep_objective="feature", # "feature" or "bench_config"
specify_features=[],
bench_feature=[], # only effective when sweep_objective is "bench_config"
mode="throughput",
num_benchmark_iteration=5,
iteration_dynamic_adjust=True,
logging_level="info",
cpu_set_env=True,
ncore_per_instance=-1, # only for "self_defined" mode
ninstances=-1, # only for "self_defined" mode
bench_batch_size=-1, # only for "self_defined" mode
use_inc=True,
auto_quant=False,
):
# set up workspace
ws_path = "neural_coder_workspace/" + \
"superbench" + str(time.time()).replace(".","") + "/"
os.makedirs(ws_path)
# set up logging
logging_var = "logging." + logging_level.upper()
globals.logging_level = eval(logging_var)
logger = logging.getLogger(ws_path)
logger.setLevel(globals.logging_level)
formatter = logging.Formatter(
'%(asctime)s - %(name)s - %(levelname)s: - %(message)s', datefmt='%Y-%m-%d %H:%M:%S')
fh = logging.FileHandler(ws_path+'superbench.log')
fh.setLevel(globals.logging_level)
fh.setFormatter(formatter)
ch = logging.StreamHandler()
ch.setLevel(globals.logging_level)
ch.setFormatter(formatter)
logger.addHandler(ch)
logger.addHandler(fh)
# device detection
detect_device_(logger)
# print key inputs
if auto_quant:
logger.info(f"Auto-Quant started ...")
logger.info(f"Code: {code}")
logger.info(f"Benchmark Mode: {mode} mode")
logger.debug(f"Number of benchmark iterations: {num_benchmark_iteration}")
else:
logger.info(f"SuperBench started ...")
logger.info(f"Code: {code}")
logger.info(f"Benchmark Mode: {mode} mode")
logger.debug(f"Sweep Objective: {sweep_objective}")
logger.debug(f"Number of benchmark iterations: {num_benchmark_iteration}")
# entry code
if entry_code == "":
# if not specify entry_code,
# then code has to be a list of one element,
# or a single string of single path, otherwise quit
if type(code) == list and len(code) == 1:
entry_code = code[0]
elif type(code) == str:
entry_code = code
else:
logger.error(
f"You have to specify an entry_code of your code: [{code}]")
quit()
# detect device compatibility of entry code
from .utils.device import detect_code_device_compatibility
detect_code_device_compatibility(entry_code)
if sweep_objective == "feature":
list_FPS = []
list_accuracy = []
list_features = []
list_mode = []
list_ws_path = []
result = []
if auto_quant:
backends = [
[],
["pytorch_inc_dynamic_quant"],
["pytorch_inc_static_quant_fx"],
["pytorch_inc_static_quant_ipex"],
["pytorch_inc_bf16"],
]
standalones_pool = []
elif len(specify_features) != 0:
backends = [
[],
]
for item in specify_features:
backends.append([item])
standalones_pool = []
else:
# features that is a "backend":
backends = [
"",
"pytorch_ipex_fp32",
"pytorch_ipex_bf16",
"pytorch_inc_static_quant_fx",
"pytorch_inc_static_quant_ipex",
"pytorch_inc_dynamic_quant",
"pytorch_ipex_int8_static_quant",
"pytorch_ipex_int8_dynamic_quant",
]
# features that can be standalone (either use alone or use with "backend"):
standalones_pool = [
"pytorch_mixed_precision_cpu",
"pytorch_channels_last",
]
standalones = []
standalones.append("")
from itertools import combinations
for num_items in range(len(standalones_pool)):
list_comb = list(combinations(standalones_pool, num_items + 1))
for item in list_comb:
jit_feature_count = 0
for i in list(item):
if "jit" in i:
jit_feature_count += 1
if jit_feature_count <= 1:
standalones.append(list(item)) # only appends the item with one JIT feature in it
dry_run = True
for backend in backends:
for standalone in standalones:
features = []
if auto_quant:
features += backend
elif len(specify_features) != 0:
features += backend
else:
features.append(backend)
features += standalone
# exclude conflict features (like jit and jit_ofi)
if "pytorch_ipex_fp32" in features and "pytorch_mixed_precision_cpu" in features:
continue
if "pytorch_ipex_bf16" in features and "pytorch_mixed_precision_cpu" in features:
continue
if "pytorch_ipex_int8_static_quant" in features and "pytorch_mixed_precision_cpu" in features:
continue
if "pytorch_ipex_int8_dynamic_quant" in features and "pytorch_mixed_precision_cpu" in features:
continue
if "pytorch_inc_static_quant_fx" in features and "pytorch_mixed_precision_cpu" in features:
continue
if "pytorch_inc_static_quant_ipex" in features and "pytorch_mixed_precision_cpu" in features:
continue
if "pytorch_inc_dynamic_quant" in features and "pytorch_mixed_precision_cpu" in features:
continue
# device conversion
if "cpu" in globals.device and "cpu" not in globals.list_code_device_compatibility:
features.append("pytorch_cuda_to_cpu")
if features[0] == "" and len(features) > 1:
features = features[1:] # remove ""
if dry_run:
t_start = time.time()
bench_performance, bench_mode, bench_ws_path = enable(
code=code,
entry_code=entry_code,
args=args,
features=features,
mode=mode,
run_bench=True,
num_benchmark_iteration=num_benchmark_iteration,
cpu_set_env=cpu_set_env,
ncore_per_instance=ncore_per_instance,
ninstances=ninstances,
bench_batch_size=bench_batch_size,
use_inc=use_inc,
)
if dry_run:
t_end = time.time()
if iteration_dynamic_adjust:
num_benchmark_iteration = max(int(300 / (t_end - t_start)), 5)
logger.debug(
f"Adjusted number of benchmark iterations after dry-run is {num_benchmark_iteration}")
dry_run = False
def remove_if_have(list, element):
if element in list:
list.remove(element)
return list
features = remove_if_have(features, "pytorch_benchmark")
features = remove_if_have(features, "pytorch_change_batch_size")
features = remove_if_have(features, "pytorch_cuda_to_cpu")
if auto_quant:
# convert feature name to display name for better user experience
if features == ['pytorch_inc_dynamic_quant']:
features_display = "Intel INT8 (Dynamic)"
elif features == ['pytorch_inc_static_quant_fx']:
features_display = "Intel INT8 (Static)"
elif features == ['pytorch_inc_static_quant_ipex']:
features_display = "Intel INT8 (IPEX)"
elif features == ['pytorch_inc_bf16']:
features_display = "Intel BF16"
elif features == []:
features_display = "The Original Model"
logger.info(
f"Benchmark result (performance) of {features_display}"
f" is {bench_performance[0]} (FPS)")
logger.info(
f"Benchmark result (accuracy delta) of {features_display} is {bench_performance[5]} %")
else:
logger.info(
f"Benchmark result (performance) of optimization set [{features}]"
f" is [{bench_performance[0]}] (FPS)")
logger.info(
f"Benchmark result (accuracy delta) of optimization set [{features}]"
f" is [{bench_performance[5]}] %")
d = {} # initialize dict
d["features"] = features
d["FPS"] = bench_performance[0]
d["accuracy"] = bench_performance[5]
d["mode"] = bench_mode
d["workspace_path"] = bench_ws_path
result.append(d)
list_FPS.append(bench_performance[0])
list_accuracy.append(bench_performance[5])
list_features.append(features)
list_mode.append(bench_mode)
list_ws_path.append(bench_ws_path)
# print result
print(f"Superbench result of sweeping [{sweep_objective}] printed below with sorted FPS: ")
print("{:<20} {:<20} {:<20} {:<120}".format(
'Numactl Mode', 'Performance (FPS)', 'Accuracy Delta (%)', 'Features Applied'))
sort_index = sorted(
range(len(list_FPS)),
key=lambda k: list_FPS[k],
reverse=True,
)
for i in sort_index:
if list_FPS[i] != 0:
print(
"{:<20} {:<20} {:<20} {:<120}".format(
str(list_mode[i]),
str(list_FPS[i]),
str(list_accuracy[i]),
str(list_features[i]),