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| Original file line number | Diff line number | Diff line change |
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| # Licensed to the Apache Software Foundation (ASF) under one | ||
| # or more contributor license agreements. See the NOTICE file | ||
| # distributed with this work for additional information | ||
| # regarding copyright ownership. The ASF licenses this file | ||
| # to you 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 time | ||
| import gc | ||
| import sys | ||
| import mxnet as mx | ||
| from mxnet.gluon import nn | ||
| from mxnet.contrib import quantization | ||
|
|
||
| #shape, num_hidden: | ||
| sizes = [ | ||
| (( 1, 224), 512), | ||
| (( 1, 224), 4096), | ||
| (( 16, 1024), 1024), | ||
| (( 32, 4096), 1024), | ||
| (( 32, 4096), 4096), | ||
| ((512, 512), 4096)] | ||
|
|
||
| rounds = 1000 | ||
| warmup = 10 | ||
|
|
||
| test_header = "--no_test_header" not in sys.argv | ||
| table_header = "--no_table_header" not in sys.argv | ||
| table_left_colums = "--no_size_column" not in sys.argv | ||
| dump_graph = "--dump_graph" in sys.argv | ||
|
|
||
| def dump_graph_fn(net, postfix): | ||
| if dump_graph: | ||
| net.export("/tmp/fc_add_" + postfix) | ||
|
|
||
| def operator_string(elemwise_add): | ||
| return 'elemwise_add' if elemwise_add else 'npi_add' | ||
|
|
||
| def print_header(header): | ||
| print("\n") | ||
| print(header if test_header else "", "\n") | ||
| if table_header: | ||
| if table_left_colums: | ||
| print("| Shape | Hidden | Mean [ms] |" ) | ||
| print("|------------:|-------:|----------:|" ) | ||
| else: | ||
| print(" Mean [ms] |" ) | ||
| print("----------:|" ) | ||
|
|
||
| def print_value(shape, hidden, mean): | ||
| if table_left_colums: | ||
| print("| ({:4},{:4}) | {:6} | {:9.3f} |".format(shape[0], shape[1], hidden, mean)) | ||
| else: | ||
| print(" {:9.3f} |".format(mean)) | ||
|
|
||
|
|
||
| def measure(net, data0, data1, data2, shape, nhid): | ||
| mx.nd.waitall() | ||
| gc.collect() | ||
| gc.disable() | ||
| for i in range(rounds + warmup): | ||
| if i == warmup: | ||
| start_time = time.time() | ||
| o = net(data0, data1, data2) | ||
| o.wait_to_read() | ||
| end_time = time.time() | ||
| run_time = (end_time - start_time) | ||
| print_value(shape, nhid, 1000 * run_time / rounds) | ||
| gc.enable() | ||
|
|
||
|
|
||
| class FCWithSum(nn.HybridBlock): | ||
| def __init__(self, num_in, num_hidden, elemwise_add, **kwargs): | ||
| super(FCWithSum, self).__init__(**kwargs) | ||
| self.fc0 = nn.Dense(units=num_hidden, in_units=num_in) | ||
| self.fc1 = nn.Dense(units=num_hidden) | ||
| self.elemwise_add = elemwise_add | ||
|
|
||
| def forward(self, data0, data1, data2): | ||
| _fc0 = self.fc0(data0) | ||
| _fc1 = self.fc1(data1) | ||
| if self.elemwise_add: | ||
| _sum0 = mx.nd.elemwise_add(data2.as_nd_ndarray(), _fc0.as_nd_ndarray()).as_np_ndarray() | ||
| _sum1 = mx.nd.elemwise_add(_fc1.as_nd_ndarray(), _sum0.as_nd_ndarray()).as_np_ndarray() | ||
| else: | ||
| _sum0 = data2 + _fc0 | ||
| _sum1 = _fc1 + _sum0 | ||
| return _sum1 | ||
|
|
||
| def benchmark_float(elemwise_add): | ||
| header = operator_string(elemwise_add) + ', float' | ||
| print_header(header) | ||
| for shape, nhid in sizes: | ||
| net = FCWithSum(shape[1], nhid, elemwise_add) | ||
| net.initialize() | ||
| net.hybridize(static_alloc=True, static_shape=True) | ||
| data0 = mx.np.random.uniform(size=shape, low=-1.0, high=1.0) | ||
| data1 = mx.np.random.uniform(size=shape, low=-1.0, high=1.0) | ||
| shape2 = (shape[0], nhid) | ||
| data2 = mx.np.random.uniform(size=shape2, low=-1.0, high=1.0) | ||
| net.optimize_for(data0, data1, data2, backend='ONEDNN') | ||
| measure(net, data0, data1, data2, shape, nhid) | ||
| dump_graph_fn(net, operator_string(elemwise_add) + '_float') | ||
|
|
||
| class CalibIter(mx.io.DataIter): | ||
| def __init__(self, batch, data_shape, batch_size): | ||
| super(CalibIter, self).__init__(batch_size) | ||
| self.label_shape = (batch_size,) | ||
| self.data_shape = data_shape | ||
| if isinstance(data_shape, tuple): | ||
| self.provide_data = [('data', data_shape)] | ||
| else: | ||
| self.provide_data = data_shape | ||
| self.provide_label = [] | ||
| self.batch = batch | ||
| def __iter__(self): | ||
| yield self.batch | ||
|
|
||
| def benchmark_int8(quantize_mode, quantize_granularity, elemwise_add): | ||
| header = operator_string(elemwise_add) + ', mode = ' + quantize_mode + \ | ||
| ', granularity = ' + quantize_granularity | ||
| print_header(header) | ||
| for shape, nhid in sizes: | ||
| net = FCWithSum(shape[1], nhid, elemwise_add) | ||
| net.initialize() | ||
| net.hybridize(static_alloc=True, static_shape=True) | ||
| data0 = mx.np.random.uniform(size=shape, low=-1.0, high=1.0) | ||
| data1 = mx.np.random.uniform(size=shape, low=-1.0, high=1.0) | ||
| shape2 = (shape[0], nhid) | ||
| data2 = mx.np.random.uniform(size=shape2, low=-1.0, high=1.0) | ||
| data = mx.gluon.data.ArrayDataset(data0, data1, data2) | ||
| calib_data = mx.gluon.data.DataLoader(data, batch_size=1) | ||
| net = quantization.quantize_net(net, | ||
| device=mx.cpu(), | ||
| exclude_layers=None, | ||
| exclude_operators=None, | ||
| calib_mode='naive', | ||
| calib_data=calib_data, | ||
| num_calib_batches=1, | ||
| quantize_mode=quantize_mode, | ||
| quantize_granularity=quantize_granularity | ||
| ) | ||
| net.hybridize(static_alloc=True, static_shape=True) | ||
| measure(net, data0, data1, data2, shape, nhid) | ||
| dump_graph_fn(net, operator_string(elemwise_add) + \ | ||
| '_' + str(quantize_mode) + '_' + str(quantize_granularity)) | ||
|
|
||
| for elemwise_add in [True, False]: | ||
| benchmark_float(elemwise_add) | ||
|
|
||
| for quantize_mode in ['smart', 'full']: | ||
| for quantize_granularity in ['tensor-wise', 'channel-wise']: | ||
| for elemwise_add in [True, False]: | ||
| benchmark_int8(quantize_mode, quantize_granularity, elemwise_add) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,54 @@ | ||
| #!/bin/bash | ||
|
|
||
| # Licensed to the Apache Software Foundation (ASF) under one | ||
| # or more contributor license agreements. See the NOTICE file | ||
| # distributed with this work for additional information | ||
| # regarding copyright ownership. The ASF licenses this file | ||
| # to you 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. | ||
|
|
||
| # Script for running python benchmark with properly setting OMP prarameters for it | ||
|
|
||
| check_parametrs() { | ||
| if [ "$#" -eq 0 ] ; then | ||
| echo "Please give python script to run as parameter." | ||
| echo "Optionally you can give number of threads to use and python scripts parameters:" | ||
| echo " `basename "$0"` [num_threads] python_script [python script parameters]" | ||
| exit | ||
| fi | ||
| } | ||
|
|
||
| check_parametrs $@ | ||
|
|
||
| NUM_SOCKET=`lscpu | grep 'Socket(s)' | awk '{print $NF}'` | ||
| CORES_PER_SOCKET=`lscpu | grep 'Core(s) per socket' | awk '{print $NF}'` | ||
| NUM_CORES=$((CORES_PER_SOCKET * NUM_SOCKET)) | ||
|
|
||
| integer_reg='^[0-9]+$' | ||
| if [[ $1 =~ $integer_reg ]] ; then | ||
| if (($1 > $NUM_CORES)); then | ||
| echo >&2 | ||
| echo "WARNING: given number of threads = $1" \ | ||
| " is greater than number of physical cores = $NUM_CORES." >&2 | ||
| echo >&2 | ||
| fi | ||
| NUM_CORES=$1 | ||
| shift | ||
| check_parametrs $@ | ||
| fi | ||
|
|
||
| CORES={0}:${NUM_CORES}:1 | ||
|
|
||
| INSTRUCTION="OMP_NUM_THREADS=${NUM_CORES} OMP_PROC_BIND=TRUE OMP_PLACES=${CORES} python3 -u $@" | ||
| echo $INSTRUCTION >&2 | ||
| eval $INSTRUCTION |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,82 @@ | ||
| #!/bin/bash | ||
|
|
||
| # Licensed to the Apache Software Foundation (ASF) under one | ||
| # or more contributor license agreements. See the NOTICE file | ||
| # distributed with this work for additional information | ||
| # regarding copyright ownership. The ASF licenses this file | ||
| # to you 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. | ||
|
|
||
| # Script for running python benchmark against number of used OMP threads | ||
|
|
||
|
|
||
| help_and_exit() { | ||
| echo "Usage:" | ||
| echo " `basename "$0"` [start_num_threads step_num_threads end_num_threads] python_script [python script parameters]" | ||
| echo "Number of threads range parameters and python script are optional." | ||
| exit | ||
| } | ||
|
|
||
| if [ "$#" -eq 0 ] ; then | ||
| help_and_exit | ||
| fi | ||
|
|
||
| NUM_SOCKET=`lscpu | grep 'Socket(s)' | awk '{print $NF}'` | ||
| CORES_PER_SOCKET=`lscpu | grep 'Core(s) per socket' | awk '{print $NF}'` | ||
| NUM_CORES=$((CORES_PER_SOCKET * NUM_SOCKET)) | ||
|
|
||
| NT_START=1 | ||
| NT_STEP=1 | ||
| NT_END=$NUM_CORES | ||
|
|
||
| integer_reg='^[0-9]+$' | ||
| signed_integer_reg='^[+-]*[0-9]+$' | ||
| if [[ $1 =~ $integer_reg ]] ; then | ||
| if [[ $2 =~ $signed_integer_reg ]] && [[ $3 =~ $integer_reg ]]; then | ||
| NT_START=$1 | ||
| NT_STEP=$2 | ||
| NT_END=$3 | ||
| shift 3 | ||
| if [ "$#" -eq 0 ] ; then | ||
| help_and_exit | ||
| fi | ||
| else | ||
| echo "Provide 3 numbers for threads range: start, step and the end." | ||
| help_and_exit | ||
| fi | ||
| fi | ||
|
|
||
| NT_SEQUENCE=`seq $NT_START $NT_STEP $NT_END` | ||
| if [ -z "$NT_SEQUENCE" ]; then | ||
| echo "Given threads range produce empy sequence." | ||
| help_and_exit | ||
| else | ||
| echo "Start python script $1 for following number of threads:" >&2 | ||
| echo $NT_SEQUENCE >&2 | ||
| fi | ||
|
|
||
| RUN_SCRIPT=`dirname "$0"`/run.sh | ||
| for NT in $NT_SEQUENCE; | ||
| do | ||
| TMP_FILE=/tmp/_result_${NT}.txt | ||
| echo 1>${TMP_FILE} | ||
| if [[ $NT -eq $NT_START ]]; then | ||
| echo "NUM_THREADS = $NT" 1>>${TMP_FILE} | ||
| $RUN_SCRIPT $NT $@ 1>>${TMP_FILE} | ||
| else | ||
| echo " $NT" 1>>${TMP_FILE} | ||
| $RUN_SCRIPT $NT $@ --no_size_column --no_test_header 1>>${TMP_FILE} | ||
| fi | ||
| TMP_FILES+=" ${TMP_FILE}" | ||
| done | ||
| paste -d "" ${TMP_FILES} |
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