wehub-resource-sync
412 lines
15 KiB
Python
412 lines
15 KiB
Python
# copyright (c) 2020 paddlepaddle authors. all rights reserved.
|
|
#
|
|
# 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 argparse
|
|
import logging
|
|
import sys
|
|
import time
|
|
import unittest
|
|
|
|
import numpy as np
|
|
|
|
import paddle
|
|
from paddle import base
|
|
from paddle.inference import Config, create_predictor
|
|
|
|
paddle.enable_static()
|
|
|
|
logging.basicConfig(format='%(asctime)s-%(levelname)s: %(message)s')
|
|
_logger = logging.getLogger(__name__)
|
|
_logger.setLevel(logging.INFO)
|
|
|
|
|
|
def parse_args():
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument('--batch_size', type=int, default=1, help='Batch size.')
|
|
parser.add_argument(
|
|
'--skip_batch_num',
|
|
type=int,
|
|
default=0,
|
|
help='Number of the first minibatches to skip in performance statistics.',
|
|
)
|
|
parser.add_argument(
|
|
'--quant_model', type=str, default='', help='A path to a Quant model.'
|
|
)
|
|
parser.add_argument(
|
|
'--fp32_model',
|
|
type=str,
|
|
default='',
|
|
help='A path to an FP32 model. If empty, the Quant model will be used for FP32 inference.',
|
|
)
|
|
parser.add_argument('--infer_data', type=str, default='', help='Data file.')
|
|
parser.add_argument(
|
|
'--labels', type=str, default='', help='File with labels.'
|
|
)
|
|
parser.add_argument(
|
|
'--batch_num',
|
|
type=int,
|
|
default=0,
|
|
help='Number of batches to process. 0 or less means whole dataset. Default: 0.',
|
|
)
|
|
parser.add_argument(
|
|
'--acc_diff_threshold',
|
|
type=float,
|
|
default=0.01,
|
|
help='Accepted accuracy difference threshold.',
|
|
)
|
|
parser.add_argument(
|
|
'--ops_to_quantize',
|
|
type=str,
|
|
default='',
|
|
help='A comma separated list of operators to quantize. Only quantizable operators are taken into account. If the option is not used, an attempt to quantize all quantizable operators will be made.',
|
|
)
|
|
parser.add_argument(
|
|
'--op_ids_to_skip',
|
|
type=str,
|
|
default='',
|
|
help='A comma separated list of operator ids to skip in quantization.',
|
|
)
|
|
parser.add_argument(
|
|
'--targets',
|
|
type=str,
|
|
default='quant,int8,fp32',
|
|
help='A comma separated list of inference types to run ("int8", "fp32", "quant"). Default: "quant,int8,fp32"',
|
|
)
|
|
parser.add_argument(
|
|
'--debug',
|
|
action='store_true',
|
|
help='If used, the graph of Quant model is drawn.',
|
|
)
|
|
|
|
test_args, args = parser.parse_known_args(namespace=unittest)
|
|
|
|
return test_args, sys.argv[:1] + args
|
|
|
|
|
|
class QuantInt8NLPComparisonTest(unittest.TestCase):
|
|
"""
|
|
Test for accuracy comparison of Quant FP32 and INT8 NLP inference.
|
|
"""
|
|
|
|
def _reader_creator(self, data_file=None, labels_file=None):
|
|
assert data_file, "The dataset file is missing."
|
|
assert labels_file, "The labels file is missing."
|
|
|
|
def reader():
|
|
with (
|
|
open(data_file, 'r') as df,
|
|
open(labels_file, 'r') as lf,
|
|
):
|
|
data_lines = df.readlines()
|
|
labels_lines = lf.readlines()
|
|
assert len(data_lines) == len(labels_lines), (
|
|
"The number of labels does not match the length of the dataset."
|
|
)
|
|
|
|
for i in range(len(data_lines)):
|
|
data_fields = data_lines[i].split(';')
|
|
assert len(data_fields) >= 2, (
|
|
"The number of data fields in the dataset is less than 2"
|
|
)
|
|
buffers = []
|
|
shape = []
|
|
for j in range(2):
|
|
data = data_fields[j].split(':')
|
|
assert len(data) >= 2, (
|
|
"Size of data in the dataset is less than 2"
|
|
)
|
|
# Shape is stored under index 0, while data under 1
|
|
shape = data[0].split()
|
|
shape.pop(0)
|
|
shape_np = np.array(shape).astype("int64")
|
|
buffer_i = data[1].split()
|
|
buffer_np = np.array(buffer_i).astype("int64")
|
|
buffer_np.shape = tuple(shape_np)
|
|
buffers.append(buffer_np)
|
|
yield buffers[0], buffers[1], int(labels_lines[i])
|
|
|
|
return reader
|
|
|
|
def _get_batch_correct(self, batch_output=None, labels=None):
|
|
total = len(batch_output)
|
|
assert total > 0, "The batch output is empty."
|
|
correct = 0
|
|
for n, output in enumerate(batch_output):
|
|
max_idx = np.where(output == output.max())
|
|
if max_idx[0] == labels[n]:
|
|
correct += 1
|
|
return correct
|
|
|
|
def set_config(
|
|
self,
|
|
model_path,
|
|
target='quant',
|
|
):
|
|
config = Config(model_path)
|
|
config.disable_gpu()
|
|
config.switch_specify_input_names(True)
|
|
config.switch_ir_optim(True)
|
|
config.switch_use_feed_fetch_ops(True)
|
|
config.enable_onednn()
|
|
if target == 'int8':
|
|
config.enable_onednn_int8(self._quantized_ops)
|
|
config.delete_pass(
|
|
"constant_folding_pass"
|
|
) # same reason as in analyzer_ernie_int8_tester.cc
|
|
return config
|
|
|
|
def _predict(
|
|
self,
|
|
test_reader=None,
|
|
model_path=None,
|
|
batch_size=1,
|
|
batch_num=1,
|
|
skip_batch_num=0,
|
|
target='fp32',
|
|
):
|
|
assert target in ['quant', 'int8', 'fp32']
|
|
print(f"target: {target}, model path: {model_path}")
|
|
|
|
config = self.set_config(
|
|
model_path,
|
|
target,
|
|
)
|
|
predictor = create_predictor(config)
|
|
|
|
input_names = predictor.get_input_names()
|
|
output_names = predictor.get_output_names()
|
|
|
|
total_correct = 0
|
|
total_samples = 0
|
|
batch_times = []
|
|
ppses = [] # predictions per second
|
|
iters = 0
|
|
infer_start_time = time.time()
|
|
|
|
for data in test_reader():
|
|
if batch_num > 0 and iters >= batch_num:
|
|
break
|
|
if iters == skip_batch_num:
|
|
total_samples = 0
|
|
infer_start_time = time.time()
|
|
# check data
|
|
inputs = []
|
|
inputs.append(np.array([x[0] for x in data]))
|
|
inputs.append(np.array([x[1] for x in data]))
|
|
labels = np.array([x[2] for x in data])
|
|
|
|
for i, name in enumerate(input_names):
|
|
input_tensor = predictor.get_input_handle(name)
|
|
input_tensor.reshape(inputs[i].shape)
|
|
input_tensor.copy_from_cpu(inputs[i].copy())
|
|
|
|
start = time.time()
|
|
predictor.run()
|
|
batch_time = (time.time() - start) * 1000 # in milliseconds
|
|
|
|
out = []
|
|
out = predictor.get_output_handle(output_names[0]).copy_to_cpu()
|
|
batch_times.append(batch_time)
|
|
batch_correct = self._get_batch_correct(out, labels)
|
|
batch_len = len(labels)
|
|
total_samples += batch_len
|
|
total_correct += batch_correct
|
|
batch_acc = float(batch_correct) / float(batch_len)
|
|
pps = batch_len / batch_time * 1000
|
|
ppses.append(pps)
|
|
latency = batch_time / batch_len
|
|
iters += 1
|
|
appx = ' (warm-up)' if iters <= skip_batch_num else ''
|
|
_logger.info(
|
|
f'batch {iters}{appx}, acc: {batch_acc:.4f}, latency: {latency:.4f} ms, predictions per sec: {pps:.2f}'
|
|
)
|
|
# Postprocess benchmark data
|
|
infer_total_time = time.time() - infer_start_time
|
|
batch_latencies = batch_times[skip_batch_num:]
|
|
batch_latency_avg = np.average(batch_latencies)
|
|
latency_avg = batch_latency_avg / batch_size
|
|
ppses = ppses[skip_batch_num:]
|
|
pps_avg = np.average(ppses)
|
|
acc_avg = float(np.sum(total_correct)) / float(total_samples)
|
|
_logger.info(f'Total inference run time: {infer_total_time:.2f} s')
|
|
|
|
return acc_avg, pps_avg, latency_avg
|
|
|
|
def _print_performance(self, title, pps, lat):
|
|
_logger.info(
|
|
f'{title}: avg predictions per sec: {pps:.2f}, avg latency: {lat:.4f} ms'
|
|
)
|
|
|
|
def _print_accuracy(self, title, acc):
|
|
_logger.info(f'{title}: avg accuracy: {acc:.6f}')
|
|
|
|
def _summarize_performance(
|
|
self, quant_pps, quant_lat, int8_pps, int8_lat, fp32_pps, fp32_lat
|
|
):
|
|
_logger.info('--- Performance summary ---')
|
|
self._print_performance('QUANT', quant_pps, quant_lat)
|
|
self._print_performance('INT8', int8_pps, int8_lat)
|
|
if fp32_lat >= 0:
|
|
self._print_performance('FP32', fp32_pps, fp32_lat)
|
|
|
|
def _summarize_accuracy(self, quant_acc, int8_acc, fp32_acc):
|
|
_logger.info('--- Accuracy summary ---')
|
|
self._print_accuracy('Quant', quant_acc)
|
|
self._print_accuracy('INT8', int8_acc)
|
|
if fp32_acc >= 0:
|
|
self._print_accuracy('FP32', fp32_acc)
|
|
|
|
def _compare_accuracy(self, threshold, quant_acc, int8_acc):
|
|
_logger.info(
|
|
f'Accepted accuracy drop threshold: {threshold}. (condition: (Quant_acc - INT8_acc) <= threshold)'
|
|
)
|
|
# Random outputs give accuracy about 0.33, we assume valid accuracy to be at least 0.5
|
|
assert quant_acc > 0.5
|
|
assert int8_acc > 0.5
|
|
assert quant_acc - int8_acc <= threshold
|
|
|
|
def _strings_from_csv(self, string):
|
|
return {s.strip() for s in string.split(',')}
|
|
|
|
def _ints_from_csv(self, string):
|
|
return set(map(int, string.split(',')))
|
|
|
|
def test_graph_transformation(self):
|
|
if not base.core.is_compiled_with_onednn():
|
|
return
|
|
|
|
quant_model_path = test_case_args.quant_model
|
|
assert quant_model_path, (
|
|
'The Quant model path cannot be empty. Please, use the --quant_model option.'
|
|
)
|
|
data_path = test_case_args.infer_data
|
|
assert data_path, (
|
|
'The dataset path cannot be empty. Please, use the --infer_data option.'
|
|
)
|
|
fp32_model_path = test_case_args.fp32_model
|
|
labels_path = test_case_args.labels
|
|
batch_size = test_case_args.batch_size
|
|
batch_num = test_case_args.batch_num
|
|
skip_batch_num = test_case_args.skip_batch_num
|
|
acc_diff_threshold = test_case_args.acc_diff_threshold
|
|
self._debug = test_case_args.debug
|
|
|
|
self._quantized_ops = set()
|
|
if test_case_args.ops_to_quantize:
|
|
self._quantized_ops = self._strings_from_csv(
|
|
test_case_args.ops_to_quantize
|
|
)
|
|
|
|
self._op_ids_to_skip = {-1}
|
|
if test_case_args.op_ids_to_skip:
|
|
self._op_ids_to_skip = self._ints_from_csv(
|
|
test_case_args.op_ids_to_skip
|
|
)
|
|
|
|
self._targets = self._strings_from_csv(test_case_args.targets)
|
|
assert self._targets.intersection({'quant', 'int8', 'fp32'}), (
|
|
'The --targets option, if used, must contain at least one of the targets: "quant", "int8", "fp32".'
|
|
)
|
|
|
|
_logger.info('Quant & INT8 prediction run.')
|
|
_logger.info(f'Quant model: {quant_model_path}')
|
|
if fp32_model_path:
|
|
_logger.info(f'FP32 model: {fp32_model_path}')
|
|
_logger.info(f'Dataset: {data_path}')
|
|
_logger.info(f'Labels: {labels_path}')
|
|
_logger.info(f'Batch size: {batch_size}')
|
|
_logger.info(f'Batch number: {batch_num}')
|
|
_logger.info(f'Accuracy drop threshold: {acc_diff_threshold}.')
|
|
_logger.info(
|
|
'Quantized ops: {}.'.format(
|
|
','.join(self._quantized_ops)
|
|
if self._quantized_ops
|
|
else 'all quantizable'
|
|
)
|
|
)
|
|
_logger.info(
|
|
'Op ids to skip quantization: {}.'.format(
|
|
','.join(map(str, self._op_ids_to_skip))
|
|
if test_case_args.op_ids_to_skip
|
|
else 'none'
|
|
)
|
|
)
|
|
_logger.info('Targets: {}.'.format(','.join(self._targets)))
|
|
|
|
if 'quant' in self._targets:
|
|
_logger.info('--- Quant prediction start ---')
|
|
val_reader = paddle.batch(
|
|
self._reader_creator(data_path, labels_path),
|
|
batch_size=batch_size,
|
|
)
|
|
quant_acc, quant_pps, quant_lat = self._predict(
|
|
val_reader,
|
|
quant_model_path,
|
|
batch_size,
|
|
batch_num,
|
|
skip_batch_num,
|
|
target='quant',
|
|
)
|
|
self._print_performance('Quant', quant_pps, quant_lat)
|
|
self._print_accuracy('Quant', quant_acc)
|
|
|
|
if 'int8' in self._targets:
|
|
_logger.info('--- INT8 prediction start ---')
|
|
val_reader = paddle.batch(
|
|
self._reader_creator(data_path, labels_path),
|
|
batch_size=batch_size,
|
|
)
|
|
int8_acc, int8_pps, int8_lat = self._predict(
|
|
val_reader,
|
|
quant_model_path,
|
|
batch_size,
|
|
batch_num,
|
|
skip_batch_num,
|
|
target='int8',
|
|
)
|
|
self._print_performance('INT8', int8_pps, int8_lat)
|
|
self._print_accuracy('INT8', int8_acc)
|
|
|
|
fp32_acc = fp32_pps = fp32_lat = -1
|
|
if 'fp32' in self._targets and fp32_model_path:
|
|
_logger.info('--- FP32 prediction start ---')
|
|
val_reader = paddle.batch(
|
|
self._reader_creator(data_path, labels_path),
|
|
batch_size=batch_size,
|
|
)
|
|
fp32_acc, fp32_pps, fp32_lat = self._predict(
|
|
val_reader,
|
|
fp32_model_path,
|
|
batch_size,
|
|
batch_num,
|
|
skip_batch_num,
|
|
target='fp32',
|
|
)
|
|
self._print_performance('FP32', fp32_pps, fp32_lat)
|
|
self._print_accuracy('FP32', fp32_acc)
|
|
|
|
if {'int8', 'quant', 'fp32'}.issubset(self._targets):
|
|
self._summarize_performance(
|
|
quant_pps, quant_lat, int8_pps, int8_lat, fp32_pps, fp32_lat
|
|
)
|
|
if {'int8', 'quant'}.issubset(self._targets):
|
|
self._summarize_accuracy(quant_acc, int8_acc, fp32_acc)
|
|
self._compare_accuracy(acc_diff_threshold, quant_acc, int8_acc)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
global test_case_args
|
|
test_case_args, remaining_args = parse_args()
|
|
unittest.main(argv=remaining_args)
|