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# Licensed to the Apache Software Foundation (ASF) under one
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# to you under the Apache License, Version 2.0 (the
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# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
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""" Some of the tests using CUDNN require a special GPU instruction called dp4a.
Ref: http://images.nvidia.com/content/pdf/tesla/184457-Tesla-P4-Datasheet-NV-Final-Letter-Web.pdf
"""
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import os
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import mxnet as mx
import numpy as np
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from mxnet . gluon . model_zoo import vision
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from mxnet . test_utils import assert_almost_equal , assert_exception , rand_ndarray , rand_shape_nd , same , DummyIter
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from common import with_seed
from mxnet . module import Module
from mxnet . io import NDArrayIter
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import unittest
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import operator
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def is_test_for_gpu ( ) :
return mx . current_context ( ) . device_type == ' gpu '
def is_test_for_mkldnn ( ) :
return ( mx . current_context ( ) . device_type == ' cpu '
and os . environ . get ( ' ENABLE_MKLDNN_QUANTIZATION_TEST ' ) == ' 1 ' )
def is_test_for_native_cpu ( ) :
return ( mx . current_context ( ) . device_type == ' cpu '
and os . environ . get ( ' ENABLE_MKLDNN_QUANTIZATION_TEST ' ) == None )
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@with_seed ( )
def test_quantize_float32_to_int8 ( ) :
shape = rand_shape_nd ( 4 )
data = rand_ndarray ( shape , ' default ' , dtype = ' float32 ' )
min_range = mx . nd . min ( data )
max_range = mx . nd . max ( data )
qdata , min_val , max_val = mx . nd . contrib . quantize ( data , min_range , max_range , out_type = ' int8 ' )
data_np = data . asnumpy ( )
min_range = min_range . asscalar ( )
max_range = max_range . asscalar ( )
real_range = np . maximum ( np . abs ( min_range ) , np . abs ( max_range ) )
quantized_range = 127.0
scale = quantized_range / real_range
assert qdata . dtype == np . int8
assert min_val . dtype == np . float32
assert max_val . dtype == np . float32
assert same ( min_val . asscalar ( ) , - real_range )
assert same ( max_val . asscalar ( ) , real_range )
qdata_np = ( np . sign ( data_np ) * np . minimum ( np . abs ( data_np ) * scale + 0.5 , quantized_range ) ) . astype ( np . int8 )
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assert_almost_equal ( qdata . asnumpy ( ) , qdata_np , atol = 1 )
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@with_seed ( )
def test_dequantize_int8_to_float32 ( ) :
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def get_test_data ( real_range , qdata_np ) :
qdata = mx . nd . array ( qdata_np , dtype = np . int8 )
min_range = mx . nd . array ( [ - real_range ] , dtype = np . float32 )
max_range = mx . nd . array ( [ real_range ] , dtype = np . float32 )
return qdata , min_range , max_range
def baseline_dequantization ( qdata , real_range , qdata_np ) :
quantized_range = 127.0
scale = real_range / quantized_range
data_np = qdata_np * scale
return data_np
def test_nd_array_dequantization ( qdata , min_range , max_range , expected_result ) :
data = mx . nd . contrib . dequantize ( qdata , min_range , max_range , out_type = ' float32 ' )
assert data . dtype == np . float32
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assert_almost_equal ( data . asnumpy ( ) , expected_result , atol = 1 )
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def test_symbolic_api_dequantization ( qdata , min_range , max_range , expected_result ) :
sym_data = mx . sym . Variable ( ' data ' )
sym_min_range = mx . sym . Variable ( ' min_range ' )
sym_max_range = mx . sym . Variable ( ' max_range ' )
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dequant = mx . sym . contrib . dequantize ( sym_data , sym_min_range ,
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sym_max_range , out_type = ' float32 ' )
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out = dequant . bind ( ctx = mx . current_context ( ) ,
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args = { ' data ' : qdata , ' min_range ' : min_range , ' max_range ' : max_range } )
data = out . forward ( ) [ 0 ]
assert data . dtype == np . float32
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assert_almost_equal ( data . asnumpy ( ) , expected_result , atol = 1 )
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real_range = 128
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shape = rand_shape_nd ( 4 )
qdata_np = np . random . uniform ( low = - 127 , high = 127 , size = shape ) . astype ( dtype = np . int8 )
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qdata , min_range , max_range = get_test_data ( real_range , qdata_np )
expected_result = baseline_dequantization ( qdata , real_range , qdata_np )
# test nd array implementation.
test_nd_array_dequantization ( qdata , min_range , max_range , expected_result )
# test symbolic api implementaion.
test_symbolic_api_dequantization ( qdata , min_range , max_range , expected_result )
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@with_seed ( )
def test_requantize_int32_to_int8 ( ) :
def quantized_int32_to_float ( qdata , min_range , max_range ) :
assert qdata . dtype == ' int32 '
quantized_range = np . iinfo ( ' int32 ' ) . max
real_range = np . maximum ( np . abs ( min_range ) , np . abs ( max_range ) )
scale = float ( real_range ) / float ( quantized_range )
return qdata . astype ( ' float32 ' ) * scale
def float_to_quantized_int8 ( data , min_range , max_range ) :
assert data . dtype == ' float32 '
real_range = np . maximum ( np . abs ( min_range ) , np . abs ( max_range ) )
quantized_range = np . iinfo ( ' int8 ' ) . max
scale = float ( quantized_range ) / float ( real_range )
return ( np . sign ( data ) * np . minimum ( np . abs ( data ) * scale + 0.5 , quantized_range ) ) . astype ( ' int8 ' )
def requantize ( qdata , min_data , max_data , real_range ) :
data = quantized_int32_to_float ( qdata , min_data , max_data )
output = float_to_quantized_int8 ( data , - real_range , real_range )
return output , - real_range , real_range
def requantize_baseline ( qdata , min_data , max_data , min_calib_range = None , max_calib_range = None ) :
if min_calib_range is not None and max_calib_range is not None :
real_range = np . maximum ( np . abs ( min_calib_range ) , np . abs ( max_calib_range ) )
return requantize ( qdata , min_data , max_data , real_range )
else :
min_range = quantized_int32_to_float ( np . min ( qdata ) , min_data , max_data )
max_range = quantized_int32_to_float ( np . max ( qdata ) , min_data , max_data )
return requantize ( qdata , min_data , max_data , np . maximum ( np . abs ( min_range ) , np . abs ( max_range ) ) )
def check_requantize ( shape , min_calib_range = None , max_calib_range = None ) :
qdata = mx . nd . random . uniform ( low = - 1000.0 , high = 1000.0 , shape = shape ) . astype ( ' int32 ' )
min_range = mx . nd . array ( [ - 1010.0 ] )
max_range = mx . nd . array ( [ 1020.0 ] )
if min_calib_range is None or max_calib_range is None :
qdata_int8 , min_output , max_output = mx . nd . contrib . requantize ( qdata , min_range , max_range )
else :
qdata_int8 , min_output , max_output = mx . nd . contrib . requantize ( qdata , min_range , max_range ,
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min_calib_range = min_calib_range ,
max_calib_range = max_calib_range )
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qdata_int8_np , min_output_np , max_output_np = requantize_baseline ( qdata . asnumpy ( ) , min_range . asscalar ( ) ,
max_range . asscalar ( ) ,
min_calib_range = min_calib_range ,
max_calib_range = max_calib_range )
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assert_almost_equal ( qdata_int8 . asnumpy ( ) , qdata_int8_np , atol = 1 )
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assert_almost_equal ( min_output . asnumpy ( ) , np . array ( [ min_output_np ] ) )
assert_almost_equal ( max_output . asnumpy ( ) , np . array ( [ max_output_np ] ) )
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def check_requantize_with_symbol ( shape , min_calib_range = None , max_calib_range = None ) :
qdata = mx . nd . random . uniform ( low = - 1000.0 , high = 1000.0 , shape = shape ) . astype ( ' int32 ' )
min_range = mx . nd . array ( [ - 1010.0 ] )
max_range = mx . nd . array ( [ 1020.0 ] )
sym_data = mx . sym . Variable ( ' data ' )
sym_min_range = mx . sym . Variable ( ' min_range ' )
sym_max_range = mx . sym . Variable ( ' max_range ' )
if min_calib_range is None or max_calib_range is None :
requant = mx . sym . contrib . requantize ( sym_data , sym_min_range , sym_max_range )
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out = requant . bind ( ctx = mx . current_context ( ) ,
args = { ' data ' : qdata , ' min_range ' : min_range ,
' max_range ' : max_range } )
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qdata_int8 , min_output , max_output = out . forward ( )
else :
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requant = mx . sym . contrib . requantize ( sym_data , sym_min_range , sym_max_range ,
min_calib_range = min_calib_range ,
max_calib_range = max_calib_range )
out = requant . bind ( ctx = mx . current_context ( ) , args = { ' data ' : qdata , ' min_range ' : min_range ,
' max_range ' : max_range } )
qdata_int8 , min_output , max_output = out . forward ( )
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qdata_int8_np , min_output_np , max_output_np = requantize_baseline ( qdata . asnumpy ( ) , min_range . asscalar ( ) ,
max_range . asscalar ( ) ,
min_calib_range = min_calib_range ,
max_calib_range = max_calib_range )
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assert_almost_equal ( qdata_int8 . asnumpy ( ) , qdata_int8_np , atol = 1 )
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assert_almost_equal ( min_output . asnumpy ( ) , np . array ( [ min_output_np ] ) )
assert_almost_equal ( max_output . asnumpy ( ) , np . array ( [ max_output_np ] ) )
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# test with symbol API.
check_requantize_with_symbol ( ( 3 , 4 , 10 , 10 ) )
check_requantize_with_symbol ( ( 32 , 3 , 23 , 23 ) )
check_requantize_with_symbol ( ( 3 , 4 , 10 , 10 ) , min_calib_range = - 1050.0 , max_calib_range = 1040.0 )
check_requantize_with_symbol ( ( 32 , 3 , 23 , 23 ) , min_calib_range = - 134.349 , max_calib_range = 523.43 )
# Test with nd array API
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check_requantize ( ( 3 , 4 , 10 , 10 ) )
check_requantize ( ( 32 , 3 , 23 , 23 ) )
check_requantize ( ( 3 , 4 , 10 , 10 ) , min_calib_range = - 1050.0 , max_calib_range = 1040.0 )
check_requantize ( ( 32 , 3 , 23 , 23 ) , min_calib_range = - 134.349 , max_calib_range = 523.43 )
@with_seed ( )
def test_quantized_conv ( ) :
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def check_quantized_conv ( data_shape , kernel , num_filter , pad , stride , dilate , no_bias , qdtype ) :
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if is_test_for_native_cpu ( ) :
print ( ' skipped testing quantized_conv for native cpu since it is not supported yet ' )
return
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elif is_test_for_mkldnn ( ) :
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# (TODO)Xinyu: https://github.com/apache/mxnet/issues/16830
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print ( ' skipped testing quantized_conv for mkldnn cpu since it is a flaky case ' )
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return
elif qdtype == ' uint8 ' and is_test_for_gpu ( ) :
print ( ' skipped testing quantized_conv for gpu uint8 since it is not supported yet ' )
return
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elif is_test_for_gpu ( ) and len ( data_shape ) != 4 :
print ( ' skipped testing quantized_conv for gpu 5d layout since it is not supported yet ' )
return
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# run fp32 conv
data = mx . sym . Variable ( name = ' data ' , shape = data_shape , dtype = ' float32 ' )
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conv = mx . sym . Convolution ( data = data , kernel = kernel , num_filter = num_filter , pad = pad , stride = stride ,
dilate = dilate , no_bias = no_bias , cudnn_off = False , name = ' conv ' )
arg_shapes , _ , _ = conv . infer_shape ( data = data_shape )
arg_names = conv . list_arguments ( )
conv_exe_fp32 = conv . simple_bind ( ctx = mx . current_context ( ) , grad_req = ' null ' )
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if qdtype == ' uint8 ' :
data_low = 0.0
data_high = 127.0
else :
data_low = - 127.0
data_high = 127.0
conv_exe_fp32 . arg_dict [ arg_names [ 0 ] ] [ : ] = mx . nd . random . uniform ( low = data_low , high = data_high ,
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shape = data_shape ) . astype ( ' int32 ' )
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conv_exe_fp32 . arg_dict [ arg_names [ 1 ] ] [ : ] = mx . nd . random . uniform ( low = - 127.0 , high = 127.0 ,
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shape = arg_shapes [ 1 ] ) . astype ( ' int32 ' )
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if not no_bias :
conv_exe_fp32 . arg_dict [ arg_names [ 2 ] ] [ : ] = mx . nd . random . uniform ( low = - 127.0 , high = 127.0 ,
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shape = arg_shapes [ 2 ] ) . astype ( ' int32 ' )
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output = conv_exe_fp32 . forward ( ) [ 0 ]
# run quantized conv
qdata = mx . sym . Variable ( name = ' qdata ' , shape = data_shape , dtype = qdtype )
qweight = mx . sym . Variable ( name = ' qweight ' , dtype = ' int8 ' )
min_data = mx . sym . Variable ( name = ' min_data ' )
max_data = mx . sym . Variable ( name = ' max_data ' )
min_weight = mx . sym . Variable ( name = ' min_weight ' )
max_weight = mx . sym . Variable ( name = ' max_weight ' )
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quantized_conv = mx . sym . contrib . quantized_conv ( data = qdata , weight = qweight , min_data = min_data ,
max_data = max_data , min_weight = min_weight ,
max_weight = max_weight , kernel = kernel ,
num_filter = num_filter , pad = pad , stride = stride ,
dilate = dilate , no_bias = no_bias )
qarg_names = quantized_conv . list_arguments ( )
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type_dict = None
if not no_bias :
type_dict = { qarg_names [ 2 ] : ' int8 ' }
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conv_exe_int8 = quantized_conv . simple_bind ( ctx = mx . current_context ( ) , type_dict = type_dict , grad_req = ' null ' )
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conv_exe_int8 . arg_dict [ qarg_names [ 0 ] ] [ : ] = conv_exe_fp32 . arg_dict [ arg_names [ 0 ] ] . astype ( qdtype )
conv_exe_int8 . arg_dict [ qarg_names [ 1 ] ] [ : ] = conv_exe_fp32 . arg_dict [ arg_names [ 1 ] ] . astype ( ' int8 ' )
quantized_range = 127.0
if no_bias :
conv_exe_int8 . arg_dict [ qarg_names [ 2 ] ] [ : ] = - quantized_range
conv_exe_int8 . arg_dict [ qarg_names [ 3 ] ] [ : ] = quantized_range
conv_exe_int8 . arg_dict [ qarg_names [ 4 ] ] [ : ] = - quantized_range
conv_exe_int8 . arg_dict [ qarg_names [ 5 ] ] [ : ] = quantized_range
else :
conv_exe_int8 . arg_dict [ qarg_names [ 2 ] ] [ : ] = conv_exe_fp32 . arg_dict [ arg_names [ 2 ] ] . astype ( ' int8 ' )
conv_exe_int8 . arg_dict [ qarg_names [ 3 ] ] [ : ] = - quantized_range
conv_exe_int8 . arg_dict [ qarg_names [ 4 ] ] [ : ] = quantized_range
conv_exe_int8 . arg_dict [ qarg_names [ 5 ] ] [ : ] = - quantized_range
conv_exe_int8 . arg_dict [ qarg_names [ 6 ] ] [ : ] = quantized_range
conv_exe_int8 . arg_dict [ qarg_names [ 7 ] ] [ : ] = - quantized_range
conv_exe_int8 . arg_dict [ qarg_names [ 8 ] ] [ : ] = quantized_range
qoutput , min_range , max_range = conv_exe_int8 . forward ( )
if no_bias :
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assert_almost_equal ( output . asnumpy ( ) , qoutput . asnumpy ( ) , atol = 1 )
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else :
# with adding bias, accuracy loss should not be greater than one
diff = mx . nd . abs ( output - qoutput . astype ( output . dtype ) )
cond = mx . nd . lesser ( 2 , diff ) . sum ( ) . asscalar ( )
assert cond == 0
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for qdtype in [ ' int8 ' , ' uint8 ' ] :
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check_quantized_conv ( ( 3 , 4 , 28 , 28 ) , ( 3 , 3 ) , 128 , ( 1 , 1 ) , ( 1 , 1 ) , ( 1 , 1 ) , True , qdtype )
check_quantized_conv ( ( 3 , 4 , 28 , 28 ) , ( 3 , 3 ) , 128 , ( 1 , 1 ) , ( 1 , 1 ) , ( 1 , 1 ) , False , qdtype )
check_quantized_conv ( ( 1 , 3 , 4 , 28 , 28 ) , ( 1 , 3 , 3 ) , 128 , ( 1 , 1 , 1 ) , ( 1 , 1 , 1 ) , ( 1 , 1 , 1 ) , False , qdtype )
check_quantized_conv ( ( 1 , 3 , 4 , 28 , 28 ) , ( 1 , 3 , 3 ) , 128 , ( 1 , 1 , 1 ) , ( 1 , 1 , 1 ) , ( 1 , 1 , 1 ) , True , qdtype )
check_quantized_conv ( ( 1 , 3 , 4 , 28 , 28 ) , ( 1 , 3 , 3 ) , 128 , ( 1 , 1 , 1 ) , ( 1 , 1 , 1 ) , ( 2 , 2 , 2 ) , False , qdtype )
check_quantized_conv ( ( 1 , 3 , 4 , 28 , 28 ) , ( 1 , 3 , 3 ) , 128 , ( 1 , 1 , 1 ) , ( 1 , 1 , 1 ) , ( 2 , 2 , 2 ) , True , qdtype )
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@with_seed ( )
def test_quantized_elemwise_add ( ) :
def check_quantized_elemwise_add ( data_shape , qtype ) :
if is_test_for_native_cpu ( ) :
print ( ' skipped testing quantized_elemwise_add for native cpu since it is not supported yet ' )
return
elif qtype != ' uint8 ' and qtype != ' int8 ' :
print ( ' skipped testing quantized_elemwise_add for not supported data type ' )
return
elif is_test_for_gpu ( ) :
print ( ' skipped testing quantized_elemwise_add for gpu since it is not supported yet ' )
return
dataA = mx . sym . Variable ( name = ' dataA ' , shape = data_shape , dtype = ' float32 ' )
dataB = mx . sym . Variable ( name = ' dataB ' , shape = data_shape , dtype = ' float32 ' )
elemwise_add_fp32 = mx . sym . elemwise_add ( dataA , dataB )
arg_names = elemwise_add_fp32 . list_arguments ( )
elemwise_add_fp32_exe = elemwise_add_fp32 . simple_bind ( ctx = mx . current_context ( ) , grad_req = ' null ' )
if qtype == ' uint8 ' :
data_low = 0.0
data_high = 255.0
else :
data_low = - 127.0
data_high = 127.0
dataA_val = mx . nd . random . uniform ( low = data_low , high = data_high , shape = data_shape ) . astype ( ' int32 ' )
dataB_val = mx . nd . random . uniform ( low = data_low , high = data_high , shape = data_shape ) . astype ( ' int32 ' )
elemwise_add_fp32_exe . arg_dict [ arg_names [ 0 ] ] [ : ] = dataA_val
elemwise_add_fp32_exe . arg_dict [ arg_names [ 1 ] ] [ : ] = dataB_val
output = elemwise_add_fp32_exe . forward ( ) [ 0 ]
qdataA = mx . sym . Variable ( name = ' qdataA ' , shape = data_shape , dtype = qtype )
qdataB = mx . sym . Variable ( name = ' qdataB ' , shape = data_shape , dtype = qtype )
min_dataA = mx . sym . Variable ( name = ' min_dataA ' )
max_dataA = mx . sym . Variable ( name = ' max_dataA ' )
min_dataB = mx . sym . Variable ( name = ' min_dataB ' )
max_dataB = mx . sym . Variable ( name = ' max_dataB ' )
quantized_elemwise_add = mx . sym . contrib . quantized_elemwise_add ( qdataA , qdataB , min_dataA , max_dataA , min_dataB , max_dataB )
elemwise_add_int8_exe = quantized_elemwise_add . simple_bind ( ctx = mx . current_context ( ) , grad_req = ' null ' )
qarg_names = quantized_elemwise_add . list_arguments ( )
elemwise_add_int8_exe . arg_dict [ qarg_names [ 0 ] ] [ : ] = elemwise_add_fp32_exe . arg_dict [ arg_names [ 0 ] ] . astype ( qtype )
elemwise_add_int8_exe . arg_dict [ qarg_names [ 1 ] ] [ : ] = elemwise_add_fp32_exe . arg_dict [ arg_names [ 1 ] ] . astype ( qtype )
quantized_range = 127.0
elemwise_add_int8_exe . arg_dict [ qarg_names [ 2 ] ] [ : ] = data_low
elemwise_add_int8_exe . arg_dict [ qarg_names [ 3 ] ] [ : ] = data_high
elemwise_add_int8_exe . arg_dict [ qarg_names [ 4 ] ] [ : ] = data_low
elemwise_add_int8_exe . arg_dict [ qarg_names [ 5 ] ] [ : ] = data_high
qoutput , min_range , max_range = elemwise_add_int8_exe . forward ( )
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int8_rslt = qoutput . astype ( output . dtype ) * max_range / 0x7fffffff
diff = mx . nd . abs ( output - int8_rslt )
cond = mx . nd . lesser ( 2 , diff ) . sum ( ) . asscalar ( )
assert cond == 0
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for qtype in [ ' int8 ' , ' uint8 ' ] :
check_quantized_elemwise_add ( ( 4 , 6 ) , qtype )
check_quantized_elemwise_add ( ( 13 , 74 , 52 ) , qtype )
check_quantized_elemwise_add ( ( 3 , 4 , 56 , 56 ) , qtype )
check_quantized_elemwise_add ( ( 32 , 56 , 64 , 11 ) , qtype )
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@with_seed ( )
def test_quantized_elemwise_mul ( ) :
def check_quantized_elemwise_mul ( data_shape , qtype ) :
if is_test_for_native_cpu ( ) :
print ( ' skipped testing quantized_elemwise_mul for native cpu since it is not supported yet ' )
return
elif qtype != ' int8 ' :
print ( ' skipped testing quantized_elemwise_mul for not supported data type ' )
return
elif is_test_for_gpu ( ) :
print ( ' skipped testing quantized_elemwise_mul for gpu since it is not supported yet ' )
return
dataA = mx . sym . Variable ( name = ' dataA ' , shape = data_shape , dtype = ' float32 ' )
dataB = mx . sym . Variable ( name = ' dataB ' , shape = data_shape , dtype = ' float32 ' )
elemwise_mul_fp32 = mx . sym . elemwise_mul ( dataA , dataB )
arg_names = elemwise_mul_fp32 . list_arguments ( )
elemwise_mul_fp32_exe = elemwise_mul_fp32 . simple_bind ( ctx = mx . current_context ( ) , grad_req = ' null ' )
if qtype == ' uint8 ' :
data_low = 0.0
data_high = 255.0
else :
data_low = - 127.0
data_high = 127.0
dataA_val = mx . nd . random . uniform ( low = data_low , high = data_high , shape = data_shape ) . astype ( ' int32 ' )
dataB_val = mx . nd . random . uniform ( low = data_low , high = data_high , shape = data_shape ) . astype ( ' int32 ' )
elemwise_mul_fp32_exe . arg_dict [ arg_names [ 0 ] ] [ : ] = dataA_val
elemwise_mul_fp32_exe . arg_dict [ arg_names [ 1 ] ] [ : ] = dataB_val
output = elemwise_mul_fp32_exe . forward ( ) [ 0 ]
qdataA = mx . sym . Variable ( name = ' qdataA ' , shape = data_shape , dtype = qtype )
qdataB = mx . sym . Variable ( name = ' qdataB ' , shape = data_shape , dtype = qtype )
min_dataA = mx . sym . Variable ( name = ' min_dataA ' )
max_dataA = mx . sym . Variable ( name = ' max_dataA ' )
min_dataB = mx . sym . Variable ( name = ' min_dataB ' )
max_dataB = mx . sym . Variable ( name = ' max_dataB ' )
quantized_elemwise_mul = mx . sym . contrib . quantized_elemwise_mul ( qdataA , qdataB , min_dataA , max_dataA , min_dataB , max_dataB )
elemwise_mul_int8_exe = quantized_elemwise_mul . simple_bind ( ctx = mx . current_context ( ) , grad_req = ' null ' )
qarg_names = quantized_elemwise_mul . list_arguments ( )
elemwise_mul_int8_exe . arg_dict [ qarg_names [ 0 ] ] [ : ] = elemwise_mul_fp32_exe . arg_dict [ arg_names [ 0 ] ] . astype ( qtype )
elemwise_mul_int8_exe . arg_dict [ qarg_names [ 1 ] ] [ : ] = elemwise_mul_fp32_exe . arg_dict [ arg_names [ 1 ] ] . astype ( qtype )
quantized_range = 127.0
elemwise_mul_int8_exe . arg_dict [ qarg_names [ 2 ] ] [ : ] = data_low
elemwise_mul_int8_exe . arg_dict [ qarg_names [ 3 ] ] [ : ] = data_high
elemwise_mul_int8_exe . arg_dict [ qarg_names [ 4 ] ] [ : ] = data_low
elemwise_mul_int8_exe . arg_dict [ qarg_names [ 5 ] ] [ : ] = data_high
qoutput , min_range , max_range = elemwise_mul_int8_exe . forward ( )
fp32_rslt = output . asnumpy ( )
int8_rslt = qoutput . astype ( output . dtype )
assert_almost_equal ( fp32_rslt , int8_rslt , atol = 1e-4 )
for qtype in [ ' int8 ' , ' uint8 ' ] :
check_quantized_elemwise_mul ( ( 4 , 6 ) , qtype )
check_quantized_elemwise_mul ( ( 13 , 74 , 52 ) , qtype )
check_quantized_elemwise_mul ( ( 3 , 4 , 56 , 56 ) , qtype )
check_quantized_elemwise_mul ( ( 32 , 56 , 64 , 11 ) , qtype )
2019-05-01 05:56:04 +08:00
2018-03-26 03:46:32 -07:00
@with_seed ( )
def test_quantized_pooling ( ) :
2018-11-23 11:42:02 +08:00
def check_quantized_pooling ( data_shape , kernel , pool_type , pad , stride , global_pool , qdtype , convention = ' valid ' ) :
2018-06-14 12:58:33 +08:00
if is_test_for_native_cpu ( ) :
print ( ' skipped testing quantized_pooling for native cpu since it is not supported yet ' )
return
elif qdtype == ' uint8 ' and is_test_for_gpu ( ) :
print ( ' skipped testing quantized_pooling for gpu uint8 since it is not supported yet ' )
return
2020-04-18 19:56:38 +08:00
elif is_test_for_gpu ( ) and len ( data_shape ) != 4 :
print ( ' skipped testing quantized_pooling for gpu 5d layout since it is not supported yet ' )
return
2018-06-14 12:58:33 +08:00
data = mx . sym . Variable ( name = ' data ' , shape = data_shape , dtype = ' float32 ' )
pooling_fp32 = mx . sym . Pooling ( data = data , kernel = kernel , pad = pad , stride = stride ,
2018-11-23 11:42:02 +08:00
pool_type = pool_type , global_pool = global_pool , cudnn_off = False ,
pooling_convention = convention )
2018-06-14 12:58:33 +08:00
arg_shapes , _ , _ = pooling_fp32 . infer_shape ( data = data_shape )
arg_names = pooling_fp32 . list_arguments ( )
pooling_fp32_exe = pooling_fp32 . simple_bind ( ctx = mx . current_context ( ) , grad_req = ' null ' )
if qdtype == ' uint8 ' :
data_low = 0.0
data_high = 127.0
else :
data_low = - 127.0
data_high = 127.0
pooling_fp32_exe . arg_dict [ arg_names [ 0 ] ] [ : ] = mx . nd . random . uniform ( low = data_low , high = data_high ,
shape = data_shape ) . astype ( ' int32 ' )
output = pooling_fp32_exe . forward ( ) [ 0 ]
qdata = mx . sym . Variable ( name = ' qdata ' , shape = data_shape , dtype = qdtype )
min_data = mx . sym . Variable ( name = ' min_data ' )
max_data = mx . sym . Variable ( name = ' max_data ' )
quantized_pooling = mx . sym . contrib . quantized_pooling ( data = qdata , min_data = min_data ,
2018-11-23 11:42:02 +08:00
max_data = max_data , kernel = kernel ,
pad = pad , stride = stride , pool_type = pool_type ,
global_pool = global_pool ,
pooling_convention = convention )
2018-06-14 12:58:33 +08:00
pooling_int8_exe = quantized_pooling . simple_bind ( ctx = mx . current_context ( ) , grad_req = ' null ' )
qarg_names = quantized_pooling . list_arguments ( )
pooling_int8_exe . arg_dict [ qarg_names [ 0 ] ] [ : ] = pooling_fp32_exe . arg_dict [ arg_names [ 0 ] ] . astype ( qdtype )
quantized_range = 127.0
pooling_int8_exe . arg_dict [ qarg_names [ 1 ] ] [ : ] = - quantized_range
pooling_int8_exe . arg_dict [ qarg_names [ 2 ] ] [ : ] = quantized_range
qoutput , min_range , max_range = pooling_int8_exe . forward ( )
if pool_type == ' max ' :
assert_almost_equal ( output . asnumpy ( ) , qoutput . asnumpy ( ) )
elif pool_type == ' avg ' : # for avg pooling, fp32 and int8 may be different due to rounding errors
diff = mx . nd . abs ( output - qoutput . astype ( output . dtype ) )
cond = mx . nd . lesser ( 2 , diff ) . sum ( ) . asscalar ( )
assert cond == 0
for qdtype in [ ' int8 ' , ' uint8 ' ] :
check_quantized_pooling ( ( 3 , 4 , 56 , 56 ) , ( 3 , 3 ) , ' max ' , ( 0 , 0 ) , ( 2 , 2 ) , False , qdtype )
check_quantized_pooling ( ( 3 , 4 , 56 , 56 ) , ( 3 , 3 ) , ' max ' , ( 0 , 0 ) , ( 2 , 2 ) , True , qdtype )
check_quantized_pooling ( ( 3 , 512 , 7 , 7 ) , ( 7 , 7 ) , ' avg ' , ( 0 , 0 ) , ( 1 , 1 ) , False , qdtype )
check_quantized_pooling ( ( 3 , 512 , 7 , 7 ) , ( 7 , 7 ) , ' avg ' , ( 0 , 0 ) , ( 1 , 1 ) , True , qdtype )
2020-04-18 19:56:38 +08:00
check_quantized_pooling ( ( 3 , 4 , 3 , 56 , 56 ) , ( 1 , 3 , 3 ) , ' max ' , ( 0 , 0 , 0 ) , ( 1 , 2 , 2 ) , False , qdtype )
check_quantized_pooling ( ( 3 , 4 , 3 , 56 , 56 ) , ( 1 , 3 , 3 ) , ' max ' , ( 0 , 0 , 0 ) , ( 1 , 2 , 2 ) , True , qdtype )
check_quantized_pooling ( ( 3 , 512 , 3 , 7 , 7 ) , ( 1 , 7 , 7 ) , ' avg ' , ( 0 , 0 , 0 ) , ( 1 , 2 , 2 ) , False , qdtype )
check_quantized_pooling ( ( 3 , 512 , 3 , 7 , 7 ) , ( 1 , 7 , 7 ) , ' avg ' , ( 0 , 0 , 0 ) , ( 1 , 2 , 2 ) , True , qdtype )
2018-03-26 03:46:32 -07:00
2018-11-23 11:42:02 +08:00
check_quantized_pooling ( ( 3 , 4 , 56 , 56 ) , ( 3 , 3 ) , ' max ' , ( 0 , 0 ) , ( 2 , 2 ) , False , qdtype , ' full ' )
check_quantized_pooling ( ( 3 , 4 , 56 , 56 ) , ( 3 , 3 ) , ' max ' , ( 0 , 0 ) , ( 2 , 2 ) , True , qdtype , ' full ' )
check_quantized_pooling ( ( 3 , 512 , 7 , 7 ) , ( 7 , 7 ) , ' avg ' , ( 0 , 0 ) , ( 1 , 1 ) , False , qdtype , ' full ' )
check_quantized_pooling ( ( 3 , 512 , 7 , 7 ) , ( 7 , 7 ) , ' avg ' , ( 0 , 0 ) , ( 1 , 1 ) , True , qdtype , ' full ' )
2020-04-18 19:56:38 +08:00
check_quantized_pooling ( ( 3 , 4 , 3 , 56 , 56 ) , ( 1 , 3 , 3 ) , ' max ' , ( 0 , 0 , 0 ) , ( 1 , 2 , 2 ) , False , qdtype , ' full ' )
check_quantized_pooling ( ( 3 , 4 , 3 , 56 , 56 ) , ( 1 , 3 , 3 ) , ' max ' , ( 0 , 0 , 0 ) , ( 1 , 2 , 2 ) , True , qdtype , ' full ' )
check_quantized_pooling ( ( 3 , 512 , 3 , 7 , 7 ) , ( 1 , 7 , 7 ) , ' avg ' , ( 0 , 0 , 0 ) , ( 1 , 2 , 2 ) , False , qdtype , ' full ' )
check_quantized_pooling ( ( 3 , 512 , 3 , 7 , 7 ) , ( 1 , 7 , 7 ) , ' avg ' , ( 0 , 0 , 0 ) , ( 1 , 2 , 2 ) , True , qdtype , ' full ' )
2018-11-23 11:42:02 +08:00
2018-03-26 03:46:32 -07:00
@with_seed ( )
def test_quantized_fc ( ) :
2018-06-14 12:58:33 +08:00
def check_quantized_fc ( data_shape , num_hidden , no_bias , qdtype , flatten = True ) :
2019-03-08 12:35:03 +08:00
if is_test_for_native_cpu ( ) :
Upgrade MKL-DNN dependency to v1.0 (#16555)
* [mkldnn-v1.0] Initiate the transition to MKL-DNN v1.0 (#15706)
* update mkldnn to 1.0.1 release
* change makefile
* change cmake
* update ci build and pip package build
* fix typo in mkldnn.mk
* fix build for USE_BLAS=mkl & bump MKL version
* skip mkldnn unit tests
* remove iomp5 from mx_mkldnn_lib
* ci: skip test_mkldnn_install
* retrigger ci
* retrigger ci
* retrigger ci
* [mkldnn-v1.0] Update MKL-DNN to v1.0.2 (#16012)
* bump mkldnn to v1.0.2
* skip quantization unit test
* add useless build flag
* Fixes openblas installation for static build
* empty commit
* [mkldnn-v1.0] Enable base code with new APIs. (#16064)
* fix comments (#8)
* add base code for mkldnn 1.0
* fix comments
* Update mkldnn.mk
* add base code for mkldnn 1.0
* fix build
* fix lint
* fix lint
* [mkldnn-v1.0] Add MKL-DNN Convolution (#16141)
* add mkldnn conv
* revert unnecessary change
* fix testcase fail for cpu: test_convolution_independent_gradients
* fix failed testcase: test_reshape_transpose_6d&&test_weight_async_reorder
* fix comments
* change variable name from weights to weight in mkldnn_conv
* [mkldnn-v1.0] Add MKL-DNN activation (#16195)
* add mkldnn act; pass lint; pass mnist training
* make bwd as private member
* [mkldnn-v1.0] Add MKL-DNN BN (#16199)
* add mkldnn bn
* add static_cast to transform data type
* change mkldnn_args_map_t
* retrigger CI
* add mkldnn lrn (#16223)
* [mkldnn-v1.0] Add MKL-DNN Transpose (#16250)
* add mkldnn transpose
* using mkldnn_args_map_t instead of std::unordered_map<int, mkldnn::memory>
* [mkldnn-v1.0] Add MKL-DNN softmax (#16246)
* add mkldnn softmax
* trigger CI
* [mkldnn-v1.0] Add MKL-DNN FC (#16221)
* add mkldnn fc; pass lint; pass mnist training
* add TODO info for future debug
* [mkldnn-v1.0] Add MKL-DNN deconv (#16259)
* add mkldnn deconv
* coding style
* trigger CI
* add mkldnn softmax_output (#16222)
* [mkldnn-v1.0] Add MKL-DNN Pooling (#16272)
* add mkldnn pooling
* add workaround for mkldnn v1.0 pooling fwd && bwd workspace mismatch
* code clean
* fix lint error
* trigger CI
* trigger CI
* add extra work_space check and fix some typo
* trigger CI
* [mkldnn-v1.0] Add MKL-DNN reshape&flatten&expand_dims (#16258)
* Add mkldnn 1.0 support for reshape/flatten/expanddims ops
* improve log & modify definition location of args_map_
* fix comments
* rebase code
* trigger CI
* trigger CI
* trigger CI
* trigger CI
* [mkldnn-v1.0] Add MKL-DNN int8 activation&pooling&flatten (#16425)
* Add mkldnn quantized activation/pooling/flatten
* int8 flatten
* [mkldnn-1.0] int8 conv quantize dequantize requantize (#16283)
* int8 conv quantize dequantize requantize
Change-Id: Ibd9df97288a95c61d6d85ec3831fd18b626ca283
* Fix lint
* Fix clang build
Change-Id: I9468774d014c852901e4cc3bffabd8a3d8004519
* add mkldnn sum concat (#16263)
* [mkldnn-1.0] mkldnn int8 elemwise_add (#16454)
* add mkldnn int8 elemwise_add
* add workaround to fix format any issue
* code clean
* upgrade int8 bn to MKLDNN1.0 (#16458)
* [mkldnn-v1.0] Fused RNN Op (#16420)
* [mkldnn-v1.0] Add MKL-DNN int8 fc (#16457)
* Add mkldnn_v1.0 int8 fc
* trigger CI
* trigger CI
* [mkldnn-v1.0] Update enabling flag for MKL dropout (#16433)
* use MSHADOW_USE_MKL to determine whther to use mkl optimized dropout
* rebase code
* [mkldnn-1.0] upgrade int8 concat to MKLDNN1.0 (#16466)
* [mkldnn-1.0] upgrade int8 concat to MKLDNN1.0
* fix lint
* use mkldnn_args_map_t
* update dict usage style
* retrigger CI
* retrigger CI again
* retrigger CI again 2
* [mkldnn-v1.0] Add MKL-DNN slice (#16484)
* change slice to mkldnn v1.0
* fix lint
* [mkldnn-1.0] add mkldnn subgraph fc (#16468)
* add mkldnn subgraph fc
* code clean
* trigger CI
* [mkldnn-v1.0]enable mkldnn concat (#16507)
* enable mkldnn concat
* trigger CI
* trigger CI
* [mkldnn-v1.0] Enable mkldnn cpp-test, copy op, concat op (#16503)
* [mkldnn-v1.0] Enable mkldnn test, copy op, concat op
Exclude gpu topology via MXNET_USE_CUDA
nit
default format
Remove whitespace
* Unix-GPU Tensor-RT build timeout, re-trigger CI
* [mkldnn-1.0] add skipped case for mkldnn_v1.0 (#16470)
* add skipped case for mkldnn_v1.0
* enable mkl quantized testcase
* enable skipped testcase
* trigger CI
* trigger CI
* trigger CI
* trigger CI
* [mkldnn-1.0]enable mkldnn elemwise_sum (#16521)
* enable mkldnn elemwise_sum
* trigger CI
* trigger CI
* trigger CI
* [mkldnn-v1.0] Enable more checks for MXNET_USE_MKLDNN (#16520)
* open USE_MKLDNN check
* trigger ci
* ci
* [mkldnn-v1.0]Minor fix for leakyrelu compile flag (#16519)
* change to MXNET_USE_MKLDNN == 100
* trigger
* remove MKL license (#16534)
* change MXNET_USE_MKLDNN from 100 to 1 (#16551)
* re-enable unit tests (#16565)
* [mkldnn-v1.0] Skip flaky test for unidirectional rnn_relu (#16545)
Skip `test_rnnrelu_sym`, and add some issue tracking message
Add return
Revert test_rnnrelu_sym to origin
* Add some annotations and log strings, rename mem_desc variables (#16609)
* [mkldnn-v1.0]set fc weight layout as mkldnn v0.2x did (#16593)
* set fc weight layout as mkldnn v0.2x did
* fix lint
* [mkldnn-v1.0] Upgrade to MKL-DNN v1.0.4 patch release (#16592)
* upgrade to mkldnn v1.0.3 patch release
* retrigger ci
* mkldnn v1.0.4 patch release
* [mkldnn-1.0]Rebase to master (#16648)
* fixed broken links across multiple files (#16581)
* fix missing docs due to git add issues (#16496)
* Create SECURITY.md (#16573)
* Create SECURITY.md
* Update SECURITY.md
* [Numpy] Support N_D(N>=3) batch_dot (#16586)
* Support N_D(N>=3) batch_dot
* use 1E-4
* fix lint
* remove unnecessary comment
* Update test_numpy_op.py
* Large Vector tests for DGL Ops Part 2 (#16497)
* add hyperbolic, logical, sign and regression tests for large vector
* changed hyperbolic functions into existing trignometric functions
* fix trigo and simple bind needs shape as tuple
* fix logical ops, add with_seed
* fix arcosh in largearray, remove regression from largevector
* [Numpy] Loading numpy-incompatible NDArray in numpy-compatible mode (#16597)
* Make MXIsNumpyShape return enum
* address the comment
* Surpress subgraph log in CI (#16607)
Change-Id: Ia2ed6fdbb1d2cb5cc607a8856ca13ee338e27eac
* Fix dequantize memory corruption (#16606)
Change-Id: I51b62a32987bdbcf96f04b1bc6617e66796f648b
* [MKLDNN]Fix reorder2default (#16602)
* Fix reorder2default
Change-Id: I74c87af9535f6264e6d1ea7eaed089a6480a3358
* fix
Change-Id: I6d07b43b520a47e7c78bd4b4b6390f5fb95e6957
* Fix
Change-Id: Id72f25c34291be4711f55569c6d61467edd6113d
* Fix CI
Change-Id: I8c33a82555d5ace2d0b682c1e3eefa13f3a44768
* Run CI
Change-Id: Ie8a6dab80ef91c0337cafbae4e3db277e0c7ebf7
* second round of fixing broken links in multiple files (#16598)
* Python Docstring Convetion (#16550)
* Docstring convetnion for
* Docstring convention for
* Docstring convention for
* Docstring convention for
* Docstring convention for
* Docstring convention for
* Docstring convention
* Revert removing new line
* Remove white space
* [MXNET-1434] Fix a broken link for basic C++ tutorial (#16461)
* Fix for wrong reqs set after switching from training to inference (#16553)
* Debugging reqs
* Move literal strings to const static members
* Fix lint
* julia/docs: more DRY on page rendering (#16396)
* [mkldnn-v1.0]rebase with master (#16649)
* fixed broken links across multiple files (#16581)
* fix missing docs due to git add issues (#16496)
* Create SECURITY.md (#16573)
* Create SECURITY.md
* Update SECURITY.md
* [Numpy] Support N_D(N>=3) batch_dot (#16586)
* Support N_D(N>=3) batch_dot
* use 1E-4
* fix lint
* remove unnecessary comment
* Update test_numpy_op.py
* Large Vector tests for DGL Ops Part 2 (#16497)
* add hyperbolic, logical, sign and regression tests for large vector
* changed hyperbolic functions into existing trignometric functions
* fix trigo and simple bind needs shape as tuple
* fix logical ops, add with_seed
* fix arcosh in largearray, remove regression from largevector
* [Numpy] Loading numpy-incompatible NDArray in numpy-compatible mode (#16597)
* Make MXIsNumpyShape return enum
* address the comment
* Surpress subgraph log in CI (#16607)
Change-Id: Ia2ed6fdbb1d2cb5cc607a8856ca13ee338e27eac
* Fix dequantize memory corruption (#16606)
Change-Id: I51b62a32987bdbcf96f04b1bc6617e66796f648b
* [MKLDNN]Fix reorder2default (#16602)
* Fix reorder2default
Change-Id: I74c87af9535f6264e6d1ea7eaed089a6480a3358
* fix
Change-Id: I6d07b43b520a47e7c78bd4b4b6390f5fb95e6957
* Fix
Change-Id: Id72f25c34291be4711f55569c6d61467edd6113d
* Fix CI
Change-Id: I8c33a82555d5ace2d0b682c1e3eefa13f3a44768
* Run CI
Change-Id: Ie8a6dab80ef91c0337cafbae4e3db277e0c7ebf7
* second round of fixing broken links in multiple files (#16598)
* Python Docstring Convetion (#16550)
* Docstring convetnion for
* Docstring convention for
* Docstring convention for
* Docstring convention for
* Docstring convention for
* Docstring convention for
* Docstring convention
* Revert removing new line
* Remove white space
* [MXNET-1434] Fix a broken link for basic C++ tutorial (#16461)
* Fix for wrong reqs set after switching from training to inference (#16553)
* Debugging reqs
* Move literal strings to const static members
* Fix lint
* julia/docs: more DRY on page rendering (#16396)
* Disables test_bulking_operator_gpu due to flakiness (#16611)
* C Api for simplebind, fix comment for trigoops, add atol to assert (#16585)
* C Api for simplebind, fix comment for trigoops, add atol to assert
* fix build issues
* fix lint and add regression test
* fix indent
* api doc and function name change
* fix lint and add infer shape test
* Imagenet inference to nightly fix (#16599)
* split to cd and shell
* comment
* lots of prints
* copy binary at correct location
* remove comments
* add mkl lib
* update docker run build function
* set nvidia docker true to run imagenet inference on GPU
* Revert "set nvidia docker true to run imagenet inference on GPU"
This reverts commit 98f8eef2057351d7964f1e9326ea6772c216f0af.
As we don't need GPU for compilation.
* Fix python doc build issue (#16630)
* pin the pip versions
* remove nbconvert comment
* Faster general take (#16615)
* Sped up perf of take op when axis != 0
* Formatting and syntax fixes
* Rename Take to specify axis
* Fix line length lint errors
* [Gluon] Don't serialize shared parameters twice (#16582)
Add deduplicate argument (default of False) to save_parameters.
* Fix index overflow bug in einsum (#16589)
* fix index overflow
* check index overflow
* fix index overflow in einsum path
* fix indent
* reduce NPY_MAXARGS
* safe accumulate
* Move some subgraph verbose to MXNET_SUBGRAPH_VERBOSE=2 (#16622)
* Move subgraph pass log to verbose=2
* Run CI
* add npx reshape (#16640)
* RNNOp only call cuda/cudnn if GPU ctx is requested (#16632)
* fix bad encode (#16641)
* [Perl] - ndarray to native array conversion fix (#16635)
* fixing broken links in multiple files - round 3 (#16634)
* add type switch to weight tensor (#16543)
* numpy doc enhancement (#16637)
* Change NDArray to ndarray for npx ops
Add nonzero
boolean mask supports boolean ndarray
Add argmin op and interoperability test for nonzero
Fix vdot, inner, outter docs
Add nonzero to mx.nd.np
Add docs
Fix
* Fix lint
* Fix
* Fix
* Fix get_constant
* Disable float16 test (#16643)
* Fix GetMKLDNNData for delay alloc (#16618)
* Fix GetMKLDNNData for delay alloc
* Run CI
* Run CI
* Run CI
* Run CI
* Run CI
Change-Id: I7ac2796e0ee8439c92fd2bd7a70a23a359b76b12
* Revert "[mkldnn-1.0]Rebase to master (#16648)"
This reverts commit dea3dd23d1982c913b3af6cfc7f4115c2cfa7244.
* [mkldnn-v1.0] Minor fix of mkldnn-v1.0 transition (#16644)
mk and rm directory in mkldnn.mk
ndarray.cc redundant whitespace
mkldnn_act rename variables of bwd primitives
mkldnn_rnn.cc iterator -> const_iterator
Use != instead of < for iterator in for-loop
Code comment for explaining the reason why excludes the last layer
* [mkldnn-v1.0]rm int8 sum workaround (#16623)
* rm int8 sum workaround due to mkldnn lib update
* simple dims asignments in mkldnn_quantized_elemwise_add.cc
* make MKLDNN macro simple for imperative_utils.h (#16652)
* fix ci jenkins step groovy (#16659)
* Adopt autograd.record() context to RNNOp (#16657)
* Use memcopy instead of set_handle when num_layer=0, direction=1 (#16663)
* fallback mkldnn fc bwd in imperative mode (#16672)
* disable MKLDNN FC backward
* [mkldnn-v1.0] Must reorder and emplace weights for inference primitives (#16682)
* add default parameter for mkldnn rnn
2019-10-31 22:55:13 +08:00
hasMKL = False
2018-12-15 13:35:08 +08:00
for key in os . environ . keys ( ) :
if operator . eq ( key , " BUILD_TAG " ) :
if os . environ [ ' BUILD_TAG ' ] . find ( " MKL " ) != - 1 :
hasMKL = True
break
if hasMKL == False :
print ( ' skipped testing quantized_fc on cpu since s8u8s32 is only supported by MKL BLAS library ' )
return
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elif qdtype == ' uint8 ' and is_test_for_gpu ( ) :
print ( ' skipped testing quantized_fc for gpu uint8 since it is not supported yet ' )
return
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def maxabs ( a , b ) :
return mx . nd . maximum ( mx . nd . abs ( a ) , mx . nd . abs ( b ) )
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data = mx . sym . Variable ( name = ' data ' , shape = data_shape , dtype = ' float32 ' )
fc_fp32 = mx . sym . FullyConnected ( data = data , num_hidden = num_hidden , no_bias = no_bias , flatten = flatten )
arg_shapes , _ , _ = fc_fp32 . infer_shape ( data = data_shape )
arg_names = fc_fp32 . list_arguments ( )
fc_fp32_exe = fc_fp32 . simple_bind ( ctx = mx . current_context ( ) , grad_req = ' null ' )
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int8_range = 127.0
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if qdtype == ' uint8 ' :
data_low = 0.0
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data_high = 63.0
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quantized_range = 255.0
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else :
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data_low = - 63.0
data_high = 63.0
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quantized_range = 127.0
data = mx . nd . random . uniform ( low = data_low , high = data_high ,
shape = data_shape ) . astype ( ' int32 ' )
weight = mx . nd . random . uniform ( low = data_low , high = data_high ,
shape = arg_shapes [ 1 ] ) . astype ( ' int32 ' )
fc_fp32_exe . arg_dict [ arg_names [ 0 ] ] [ : ] = data
fc_fp32_exe . arg_dict [ arg_names [ 1 ] ] [ : ] = weight
data_min = mx . nd . min ( data ) . astype ( ' float32 ' )
data_max = mx . nd . max ( data ) . astype ( ' float32 ' )
weight_min = mx . nd . min ( weight ) . astype ( ' float32 ' )
weight_max = mx . nd . max ( weight ) . astype ( ' float32 ' )
data_range = maxabs ( data_min , data_max )
weight_range = maxabs ( weight_min , weight_max )
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if not no_bias :
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bias = mx . nd . random . uniform ( low = data_low , high = data_high ,
shape = arg_shapes [ 2 ] ) . astype ( ' int32 ' )
bias_min = mx . nd . min ( bias ) . astype ( ' float32 ' )
bias_max = mx . nd . max ( bias ) . astype ( ' float32 ' )
bias_range = maxabs ( bias_min , bias_max )
bias_scale = int8_range / bias_range
data_scale = quantized_range / data_range
weight_scale = int8_range / weight_range
bias_int32_rescale = data_scale * weight_scale / bias_scale
new_bias = mx . nd . cast ( bias , dtype = ' float32 ' ) * bias_int32_rescale
fc_fp32_exe . arg_dict [ arg_names [ 2 ] ] [ : ] = new_bias . astype ( ' int32 ' )
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output = fc_fp32_exe . forward ( ) [ 0 ]
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qdata = mx . sym . Variable ( name = ' qdata ' , shape = data_shape , dtype = qdtype )
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fc_int8 = mx . sym . contrib . quantized_fully_connected ( data = qdata , num_hidden = num_hidden ,
no_bias = no_bias , flatten = flatten )
qarg_names = fc_int8 . list_arguments ( )
type_dict = { qarg_names [ 1 ] : ' int8 ' }
if not no_bias :
type_dict . update ( { qarg_names [ 2 ] : ' int8 ' } )
fc_int8_exe = fc_int8 . simple_bind ( ctx = mx . current_context ( ) , type_dict = type_dict , grad_req = ' null ' )
fc_int8_exe . arg_dict [ qarg_names [ 0 ] ] [ : ] = fc_fp32_exe . arg_dict [ arg_names [ 0 ] ] . astype ( qdtype )
fc_int8_exe . arg_dict [ qarg_names [ 1 ] ] [ : ] = fc_fp32_exe . arg_dict [ arg_names [ 1 ] ] . astype ( ' int8 ' )
if no_bias :
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fc_int8_exe . arg_dict [ qarg_names [ 2 ] ] [ : ] = - data_range
fc_int8_exe . arg_dict [ qarg_names [ 3 ] ] [ : ] = data_range
fc_int8_exe . arg_dict [ qarg_names [ 4 ] ] [ : ] = - weight_range
fc_int8_exe . arg_dict [ qarg_names [ 5 ] ] [ : ] = weight_range
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else :
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fc_int8_exe . arg_dict [ qarg_names [ 2 ] ] [ : ] = bias . astype ( ' int8 ' )
fc_int8_exe . arg_dict [ qarg_names [ 3 ] ] [ : ] = - data_range
fc_int8_exe . arg_dict [ qarg_names [ 4 ] ] [ : ] = data_range
fc_int8_exe . arg_dict [ qarg_names [ 5 ] ] [ : ] = - weight_range
fc_int8_exe . arg_dict [ qarg_names [ 6 ] ] [ : ] = weight_range
fc_int8_exe . arg_dict [ qarg_names [ 7 ] ] [ : ] = - bias_range
fc_int8_exe . arg_dict [ qarg_names [ 8 ] ] [ : ] = bias_range
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qoutput , min_range , max_range = fc_int8_exe . forward ( )
if no_bias :
assert_almost_equal ( output . asnumpy ( ) , qoutput . asnumpy ( ) )
else :
# with adding bias, accuracy loss should not be greater than one
diff = mx . nd . abs ( output - qoutput . astype ( output . dtype ) )
cond = mx . nd . lesser ( 2 , diff ) . sum ( ) . asscalar ( )
assert cond == 0
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for qdtype in [ ' int8 ' , ' uint8 ' ] :
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if is_test_for_mkldnn ( ) :
check_quantized_fc ( ( 32 , 512 , 2 ) , 100 , True , qdtype , flatten = False )
check_quantized_fc ( ( 32 , 512 , 2 ) , 100 , False , qdtype , flatten = False )
check_quantized_fc ( ( 32 , 512 , 2 , 2 ) , 100 , True , qdtype , flatten = False )
check_quantized_fc ( ( 32 , 512 , 2 , 2 ) , 100 , False , qdtype , flatten = False )
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check_quantized_fc ( ( 32 , 512 , 2 , 2 ) , 100 , True , qdtype )
check_quantized_fc ( ( 32 , 111 , 2 , 2 ) , 100 , True , qdtype )
check_quantized_fc ( ( 32 , 512 , 2 , 2 ) , 100 , False , qdtype )
check_quantized_fc ( ( 32 , 111 , 2 , 2 ) , 100 , False , qdtype )
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check_quantized_fc ( ( 256 , 2048 , 2 , 2 ) , 800 , False , qdtype )
check_quantized_fc ( ( 256 , 111 , 2 , 2 ) , 800 , False , qdtype )
check_quantized_fc ( ( 256 , 2048 , 2 , 2 ) , 800 , True , qdtype )
check_quantized_fc ( ( 256 , 111 , 2 , 2 ) , 800 , True , qdtype )
2018-03-26 03:46:32 -07:00
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@with_seed ( )
def test_quantized_rnn ( ) :
def check_quantized_rnn ( num_layers , bidirectional , seq_len , batch_size , input_dim , state_dim ) :
if is_test_for_gpu ( ) :
print ( ' skipped testing test_quantized_rnn for gpu since it is not supported yet ' )
return
if is_test_for_native_cpu ( ) :
print ( ' skipped testing test_quantized_rnn for native cpu since it is not supported yet ' )
return
data_shape = ( seq_len , batch_size , input_dim )
data = mx . sym . Variable ( name = ' data ' , shape = data_shape , dtype = ' float32 ' )
rnn_fp32 = mx . sym . RNN ( data = data ,
num_layers = num_layers ,
bidirectional = bidirectional ,
state_outputs = True ,
state_size = state_dim ,
mode = ' lstm ' ,
name = ' rnn ' )
arg_shapes , _ , _ = rnn_fp32 . infer_shape ( data = data_shape )
arg_names = rnn_fp32 . list_arguments ( )
rnn_fp32_exe = rnn_fp32 . simple_bind ( ctx = mx . current_context ( ) , grad_req = ' null ' )
data = mx . nd . random . uniform ( low = - 1 , high = 1 , shape = arg_shapes [ 0 ] )
weight = mx . nd . random . uniform ( low = - 1 , high = 1 , shape = arg_shapes [ 1 ] )
state = mx . nd . random . uniform ( low = - 1 , high = 1 , shape = arg_shapes [ 2 ] )
cell = mx . nd . random . uniform ( low = - 1 , high = 1 , shape = arg_shapes [ 3 ] )
rnn_fp32_exe . arg_dict [ arg_names [ 0 ] ] [ : ] = data
rnn_fp32_exe . arg_dict [ arg_names [ 1 ] ] [ : ] = weight
rnn_fp32_exe . arg_dict [ arg_names [ 2 ] ] [ : ] = state
rnn_fp32_exe . arg_dict [ arg_names [ 3 ] ] [ : ] = cell
output = rnn_fp32_exe . forward ( ) [ 0 ]
data_min = mx . nd . min ( data )
data_max = mx . nd . max ( data )
qdata = mx . sym . Variable ( name = ' qdata ' , shape = data_shape , dtype = ' uint8 ' )
rnn_int8 = mx . sym . contrib . quantized_rnn ( data = qdata ,
num_layers = num_layers ,
bidirectional = bidirectional ,
state_outputs = True ,
state_size = state_dim ,
mode = ' lstm ' ,
name = ' qrnn ' )
qarg_names = rnn_int8 . list_arguments ( )
rnn_int8_exe = rnn_int8 . simple_bind ( ctx = mx . current_context ( ) , grad_req = ' null ' )
data_scale = 128.0 / ( data_max - data_min )
data_shift = 128.0 - data_max * data_scale
qdata = ( data * data_scale + data_shift + 0.5 ) . astype ( ' uint8 ' )
rnn_int8_exe . arg_dict [ qarg_names [ 0 ] ] [ : ] = qdata
rnn_int8_exe . arg_dict [ qarg_names [ 1 ] ] [ : ] = weight
rnn_int8_exe . arg_dict [ qarg_names [ 2 ] ] [ : ] = state
rnn_int8_exe . arg_dict [ qarg_names [ 3 ] ] [ : ] = cell
rnn_int8_exe . arg_dict [ qarg_names [ 4 ] ] [ : ] = data_scale
rnn_int8_exe . arg_dict [ qarg_names [ 5 ] ] [ : ] = data_shift
qoutput = rnn_int8_exe . forward ( ) [ 0 ]
mse = np . mean ( ( output . asnumpy ( ) - qoutput . asnumpy ( ) ) * * 2 )
assert mse < 0.001
check_quantized_rnn ( 1 , False , 5 , 2 , 16 , 16 )
check_quantized_rnn ( 1 , True , 5 , 2 , 16 , 16 )
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@with_seed ( )
def test_quantized_embedding ( ) :
def check_quantized_embedding ( data_shape , input_dim , output_dim ) :
if is_test_for_gpu ( ) :
print ( ' skipped testing test_quantized_embedding for gpu since it is not supported yet ' )
return
def maxabs ( a , b ) :
return mx . nd . maximum ( mx . nd . abs ( a ) , mx . nd . abs ( b ) )
data0 = mx . sym . Variable ( name = ' data ' , shape = data_shape , dtype = ' int32 ' )
embedding_fp32 = mx . sym . Embedding ( data = data0 , input_dim = input_dim , output_dim = output_dim )
arg_shapes , _ , _ = embedding_fp32 . infer_shape ( data = data_shape )
arg_names = embedding_fp32 . list_arguments ( )
embedding_fp32_exe = embedding_fp32 . simple_bind ( ctx = mx . current_context ( ) , grad_req = ' null ' )
int8_range = 127.0
data = mx . nd . random . uniform ( low = 0 , high = input_dim ,
shape = arg_shapes [ 0 ] ) . astype ( ' int32 ' )
weight = mx . nd . random . uniform ( low = - int8_range , high = int8_range ,
shape = arg_shapes [ 1 ] ) . astype ( ' int32 ' )
embedding_fp32_exe . arg_dict [ arg_names [ 0 ] ] [ : ] = data
embedding_fp32_exe . arg_dict [ arg_names [ 1 ] ] [ : ] = weight
weight_min = mx . nd . min ( weight ) . astype ( ' float32 ' )
weight_max = mx . nd . max ( weight ) . astype ( ' float32 ' )
weight_range = maxabs ( weight_min , weight_max )
output = embedding_fp32_exe . forward ( ) [ 0 ]
embedding_int8 = mx . sym . contrib . quantized_embedding ( data = data0 , input_dim = input_dim , output_dim = output_dim )
qarg_names = embedding_int8 . list_arguments ( )
type_dict = { qarg_names [ 1 ] : ' int8 ' }
embedding_int8_exe = embedding_int8 . simple_bind ( ctx = mx . current_context ( ) , type_dict = type_dict , grad_req = ' null ' )
embedding_int8_exe . arg_dict [ qarg_names [ 0 ] ] [ : ] = embedding_fp32_exe . arg_dict [ arg_names [ 0 ] ]
embedding_int8_exe . arg_dict [ qarg_names [ 1 ] ] [ : ] = embedding_fp32_exe . arg_dict [ arg_names [ 1 ] ] . astype ( ' int8 ' )
embedding_int8_exe . arg_dict [ qarg_names [ 2 ] ] [ : ] = - weight_range
embedding_int8_exe . arg_dict [ qarg_names [ 3 ] ] [ : ] = weight_range
qoutput , min_range , max_range = embedding_int8_exe . forward ( )
assert_almost_equal ( output . asnumpy ( ) , qoutput . asnumpy ( ) )
check_quantized_embedding ( ( 1 , ) , 1000 , 256 )
check_quantized_embedding ( ( 1 , ) , 1024 , 512 )
check_quantized_embedding ( ( 32 , ) , 1000 , 256 )
check_quantized_embedding ( ( 32 , ) , 1024 , 512 )
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@with_seed ( )
def test_quantized_flatten ( ) :
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def check_quantized_flatten ( shape , qdtype ) :
if qdtype == ' uint8 ' :
data_low = 0.0
data_high = 127.0
else :
data_low = - 127.0
data_high = 127.0
qdata = mx . nd . random . uniform ( low = data_low , high = data_high , shape = shape ) . astype ( qdtype )
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min_data = mx . nd . array ( [ - 1023.343 ] , dtype = ' float32 ' )
max_data = mx . nd . array ( [ 2343.324275 ] , dtype = ' float32 ' )
qoutput , min_output , max_output = mx . nd . contrib . quantized_flatten ( qdata , min_data , max_data )
assert qoutput . ndim == 2
assert qoutput . shape [ 0 ] == qdata . shape [ 0 ]
assert qoutput . shape [ 1 ] == np . prod ( qdata . shape [ 1 : ] )
assert same ( qdata . asnumpy ( ) . flatten ( ) , qoutput . asnumpy ( ) . flatten ( ) )
assert same ( min_data . asnumpy ( ) , min_output . asnumpy ( ) )
assert same ( max_data . asnumpy ( ) , max_output . asnumpy ( ) )
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for qdtype in [ ' int8 ' , ' uint8 ' ] :
check_quantized_flatten ( ( 10 , ) , qdtype )
check_quantized_flatten ( ( 10 , 15 ) , qdtype )
check_quantized_flatten ( ( 10 , 15 , 18 ) , qdtype )
check_quantized_flatten ( ( 3 , 4 , 23 , 23 ) , qdtype )
2018-03-26 03:46:32 -07:00
2019-04-18 20:18:45 +08:00
@with_seed ( )
def test_quantized_act ( ) :
def check_quantized_act ( data_shape , qdtype ) :
if is_test_for_native_cpu ( ) :
print ( ' skipped testing quantized_act for native cpu since it is not supported yet ' )
return
elif qdtype == ' int8 ' and is_test_for_mkldnn ( ) :
print ( ' skipped testing quantized_act for mkldnn cpu int8 since it is not supported yet ' )
return
elif is_test_for_gpu ( ) :
print ( ' skipped testing quantized_act for gpu since it is not supported yet ' )
return
data = mx . sym . Variable ( name = ' data ' , shape = data_shape , dtype = ' float32 ' )
act_fp32 = mx . sym . Activation ( data = data , act_type = ' relu ' , name = ' relu ' )
arg_shapes , _ , _ = act_fp32 . infer_shape ( data = data_shape )
arg_names = act_fp32 . list_arguments ( )
act_fp32_exe = act_fp32 . simple_bind ( ctx = mx . current_context ( ) , grad_req = ' null ' )
if qdtype == ' uint8 ' :
data_low = 0.0
data_high = 127.0
else :
data_low = - 127.0
data_high = 127.0
act_fp32_exe . arg_dict [ arg_names [ 0 ] ] [ : ] = mx . nd . random . uniform ( low = data_low ,
high = data_high , shape = data_shape ) . astype ( qdtype )
output = act_fp32_exe . forward ( ) [ 0 ]
qdata = mx . sym . Variable ( name = ' qdata ' , shape = data_shape , dtype = qdtype )
min_data = mx . sym . Variable ( name = ' min_data ' )
max_data = mx . sym . Variable ( name = ' max_data ' )
quantized_act = mx . sym . contrib . quantized_act ( data = qdata , min_data = min_data , max_data = max_data , act_type = ' relu ' )
act_int8_exe = quantized_act . simple_bind ( ctx = mx . current_context ( ) , grad_req = ' null ' )
qarg_names = quantized_act . list_arguments ( )
act_int8_exe . arg_dict [ qarg_names [ 0 ] ] [ : ] = act_fp32_exe . arg_dict [ arg_names [ 0 ] ] . astype ( qdtype )
quantized_range_min = mx . nd . min ( act_int8_exe . arg_dict [ qarg_names [ 0 ] ] [ : ] )
quantized_range_max = mx . nd . max ( act_int8_exe . arg_dict [ qarg_names [ 0 ] ] [ : ] )
act_int8_exe . arg_dict [ qarg_names [ 1 ] ] [ : ] = quantized_range_min . astype ( qdtype )
act_int8_exe . arg_dict [ qarg_names [ 2 ] ] [ : ] = quantized_range_max . astype ( qdtype )
qoutput , min_range , max_range = act_int8_exe . forward ( )
assert_almost_equal ( output . asnumpy ( ) , qoutput . asnumpy ( ) )
assert_almost_equal ( min_range . asscalar ( ) , quantized_range_min . asscalar ( ) )
assert_almost_equal ( max_range . asscalar ( ) , quantized_range_max . asscalar ( ) )
for qdtype in [ ' int8 ' , ' uint8 ' ] :
check_quantized_act ( ( 10 , ) , qdtype )
check_quantized_act ( ( 10 , 15 ) , qdtype )
check_quantized_act ( ( 10 , 15 , 18 ) , qdtype )
check_quantized_act ( ( 3 , 4 , 23 , 23 ) , qdtype )
2018-03-26 03:46:32 -07:00
2019-08-08 18:20:19 +08:00
@with_seed ( )
def test_quantized_bn ( ) :
def get_mean_var ( data ) :
mean = mx . ndarray . mean ( data , axis = 1 , exclude = 1 )
mean_broad = mx . ndarray . expand_dims ( mean , axis = 0 )
mean_broad = mx . ndarray . expand_dims ( mean_broad , axis = 2 )
mean_broad = mx . ndarray . expand_dims ( mean_broad , axis = 3 )
mean_broad = mx . ndarray . broadcast_like ( mean_broad , data )
var = mx . ndarray . multiply ( data - mean_broad , data - mean_broad )
var = mx . ndarray . mean ( var , axis = 1 , exclude = 1 )
return mean , var
def check_quantized_bn ( data_shape , qdtype ) :
2019-08-26 20:56:23 +08:00
if is_test_for_native_cpu ( ) :
2019-08-08 18:20:19 +08:00
print ( ' skipped testing quantize_bn for native cpu since it is not supported yet ' )
return
elif is_test_for_gpu ( ) :
print ( ' skipped testing quantize_bn for gpu since it is not supported yet ' )
return
2019-08-26 20:56:23 +08:00
# qdtype = uint8
if qdtype == ' uint8 ' :
data_low = 0.0
Upgrade MKL-DNN dependency to v1.0 (#16555)
* [mkldnn-v1.0] Initiate the transition to MKL-DNN v1.0 (#15706)
* update mkldnn to 1.0.1 release
* change makefile
* change cmake
* update ci build and pip package build
* fix typo in mkldnn.mk
* fix build for USE_BLAS=mkl & bump MKL version
* skip mkldnn unit tests
* remove iomp5 from mx_mkldnn_lib
* ci: skip test_mkldnn_install
* retrigger ci
* retrigger ci
* retrigger ci
* [mkldnn-v1.0] Update MKL-DNN to v1.0.2 (#16012)
* bump mkldnn to v1.0.2
* skip quantization unit test
* add useless build flag
* Fixes openblas installation for static build
* empty commit
* [mkldnn-v1.0] Enable base code with new APIs. (#16064)
* fix comments (#8)
* add base code for mkldnn 1.0
* fix comments
* Update mkldnn.mk
* add base code for mkldnn 1.0
* fix build
* fix lint
* fix lint
* [mkldnn-v1.0] Add MKL-DNN Convolution (#16141)
* add mkldnn conv
* revert unnecessary change
* fix testcase fail for cpu: test_convolution_independent_gradients
* fix failed testcase: test_reshape_transpose_6d&&test_weight_async_reorder
* fix comments
* change variable name from weights to weight in mkldnn_conv
* [mkldnn-v1.0] Add MKL-DNN activation (#16195)
* add mkldnn act; pass lint; pass mnist training
* make bwd as private member
* [mkldnn-v1.0] Add MKL-DNN BN (#16199)
* add mkldnn bn
* add static_cast to transform data type
* change mkldnn_args_map_t
* retrigger CI
* add mkldnn lrn (#16223)
* [mkldnn-v1.0] Add MKL-DNN Transpose (#16250)
* add mkldnn transpose
* using mkldnn_args_map_t instead of std::unordered_map<int, mkldnn::memory>
* [mkldnn-v1.0] Add MKL-DNN softmax (#16246)
* add mkldnn softmax
* trigger CI
* [mkldnn-v1.0] Add MKL-DNN FC (#16221)
* add mkldnn fc; pass lint; pass mnist training
* add TODO info for future debug
* [mkldnn-v1.0] Add MKL-DNN deconv (#16259)
* add mkldnn deconv
* coding style
* trigger CI
* add mkldnn softmax_output (#16222)
* [mkldnn-v1.0] Add MKL-DNN Pooling (#16272)
* add mkldnn pooling
* add workaround for mkldnn v1.0 pooling fwd && bwd workspace mismatch
* code clean
* fix lint error
* trigger CI
* trigger CI
* add extra work_space check and fix some typo
* trigger CI
* [mkldnn-v1.0] Add MKL-DNN reshape&flatten&expand_dims (#16258)
* Add mkldnn 1.0 support for reshape/flatten/expanddims ops
* improve log & modify definition location of args_map_
* fix comments
* rebase code
* trigger CI
* trigger CI
* trigger CI
* trigger CI
* [mkldnn-v1.0] Add MKL-DNN int8 activation&pooling&flatten (#16425)
* Add mkldnn quantized activation/pooling/flatten
* int8 flatten
* [mkldnn-1.0] int8 conv quantize dequantize requantize (#16283)
* int8 conv quantize dequantize requantize
Change-Id: Ibd9df97288a95c61d6d85ec3831fd18b626ca283
* Fix lint
* Fix clang build
Change-Id: I9468774d014c852901e4cc3bffabd8a3d8004519
* add mkldnn sum concat (#16263)
* [mkldnn-1.0] mkldnn int8 elemwise_add (#16454)
* add mkldnn int8 elemwise_add
* add workaround to fix format any issue
* code clean
* upgrade int8 bn to MKLDNN1.0 (#16458)
* [mkldnn-v1.0] Fused RNN Op (#16420)
* [mkldnn-v1.0] Add MKL-DNN int8 fc (#16457)
* Add mkldnn_v1.0 int8 fc
* trigger CI
* trigger CI
* [mkldnn-v1.0] Update enabling flag for MKL dropout (#16433)
* use MSHADOW_USE_MKL to determine whther to use mkl optimized dropout
* rebase code
* [mkldnn-1.0] upgrade int8 concat to MKLDNN1.0 (#16466)
* [mkldnn-1.0] upgrade int8 concat to MKLDNN1.0
* fix lint
* use mkldnn_args_map_t
* update dict usage style
* retrigger CI
* retrigger CI again
* retrigger CI again 2
* [mkldnn-v1.0] Add MKL-DNN slice (#16484)
* change slice to mkldnn v1.0
* fix lint
* [mkldnn-1.0] add mkldnn subgraph fc (#16468)
* add mkldnn subgraph fc
* code clean
* trigger CI
* [mkldnn-v1.0]enable mkldnn concat (#16507)
* enable mkldnn concat
* trigger CI
* trigger CI
* [mkldnn-v1.0] Enable mkldnn cpp-test, copy op, concat op (#16503)
* [mkldnn-v1.0] Enable mkldnn test, copy op, concat op
Exclude gpu topology via MXNET_USE_CUDA
nit
default format
Remove whitespace
* Unix-GPU Tensor-RT build timeout, re-trigger CI
* [mkldnn-1.0] add skipped case for mkldnn_v1.0 (#16470)
* add skipped case for mkldnn_v1.0
* enable mkl quantized testcase
* enable skipped testcase
* trigger CI
* trigger CI
* trigger CI
* trigger CI
* [mkldnn-1.0]enable mkldnn elemwise_sum (#16521)
* enable mkldnn elemwise_sum
* trigger CI
* trigger CI
* trigger CI
* [mkldnn-v1.0] Enable more checks for MXNET_USE_MKLDNN (#16520)
* open USE_MKLDNN check
* trigger ci
* ci
* [mkldnn-v1.0]Minor fix for leakyrelu compile flag (#16519)
* change to MXNET_USE_MKLDNN == 100
* trigger
* remove MKL license (#16534)
* change MXNET_USE_MKLDNN from 100 to 1 (#16551)
* re-enable unit tests (#16565)
* [mkldnn-v1.0] Skip flaky test for unidirectional rnn_relu (#16545)
Skip `test_rnnrelu_sym`, and add some issue tracking message
Add return
Revert test_rnnrelu_sym to origin
* Add some annotations and log strings, rename mem_desc variables (#16609)
* [mkldnn-v1.0]set fc weight layout as mkldnn v0.2x did (#16593)
* set fc weight layout as mkldnn v0.2x did
* fix lint
* [mkldnn-v1.0] Upgrade to MKL-DNN v1.0.4 patch release (#16592)
* upgrade to mkldnn v1.0.3 patch release
* retrigger ci
* mkldnn v1.0.4 patch release
* [mkldnn-1.0]Rebase to master (#16648)
* fixed broken links across multiple files (#16581)
* fix missing docs due to git add issues (#16496)
* Create SECURITY.md (#16573)
* Create SECURITY.md
* Update SECURITY.md
* [Numpy] Support N_D(N>=3) batch_dot (#16586)
* Support N_D(N>=3) batch_dot
* use 1E-4
* fix lint
* remove unnecessary comment
* Update test_numpy_op.py
* Large Vector tests for DGL Ops Part 2 (#16497)
* add hyperbolic, logical, sign and regression tests for large vector
* changed hyperbolic functions into existing trignometric functions
* fix trigo and simple bind needs shape as tuple
* fix logical ops, add with_seed
* fix arcosh in largearray, remove regression from largevector
* [Numpy] Loading numpy-incompatible NDArray in numpy-compatible mode (#16597)
* Make MXIsNumpyShape return enum
* address the comment
* Surpress subgraph log in CI (#16607)
Change-Id: Ia2ed6fdbb1d2cb5cc607a8856ca13ee338e27eac
* Fix dequantize memory corruption (#16606)
Change-Id: I51b62a32987bdbcf96f04b1bc6617e66796f648b
* [MKLDNN]Fix reorder2default (#16602)
* Fix reorder2default
Change-Id: I74c87af9535f6264e6d1ea7eaed089a6480a3358
* fix
Change-Id: I6d07b43b520a47e7c78bd4b4b6390f5fb95e6957
* Fix
Change-Id: Id72f25c34291be4711f55569c6d61467edd6113d
* Fix CI
Change-Id: I8c33a82555d5ace2d0b682c1e3eefa13f3a44768
* Run CI
Change-Id: Ie8a6dab80ef91c0337cafbae4e3db277e0c7ebf7
* second round of fixing broken links in multiple files (#16598)
* Python Docstring Convetion (#16550)
* Docstring convetnion for
* Docstring convention for
* Docstring convention for
* Docstring convention for
* Docstring convention for
* Docstring convention for
* Docstring convention
* Revert removing new line
* Remove white space
* [MXNET-1434] Fix a broken link for basic C++ tutorial (#16461)
* Fix for wrong reqs set after switching from training to inference (#16553)
* Debugging reqs
* Move literal strings to const static members
* Fix lint
* julia/docs: more DRY on page rendering (#16396)
* [mkldnn-v1.0]rebase with master (#16649)
* fixed broken links across multiple files (#16581)
* fix missing docs due to git add issues (#16496)
* Create SECURITY.md (#16573)
* Create SECURITY.md
* Update SECURITY.md
* [Numpy] Support N_D(N>=3) batch_dot (#16586)
* Support N_D(N>=3) batch_dot
* use 1E-4
* fix lint
* remove unnecessary comment
* Update test_numpy_op.py
* Large Vector tests for DGL Ops Part 2 (#16497)
* add hyperbolic, logical, sign and regression tests for large vector
* changed hyperbolic functions into existing trignometric functions
* fix trigo and simple bind needs shape as tuple
* fix logical ops, add with_seed
* fix arcosh in largearray, remove regression from largevector
* [Numpy] Loading numpy-incompatible NDArray in numpy-compatible mode (#16597)
* Make MXIsNumpyShape return enum
* address the comment
* Surpress subgraph log in CI (#16607)
Change-Id: Ia2ed6fdbb1d2cb5cc607a8856ca13ee338e27eac
* Fix dequantize memory corruption (#16606)
Change-Id: I51b62a32987bdbcf96f04b1bc6617e66796f648b
* [MKLDNN]Fix reorder2default (#16602)
* Fix reorder2default
Change-Id: I74c87af9535f6264e6d1ea7eaed089a6480a3358
* fix
Change-Id: I6d07b43b520a47e7c78bd4b4b6390f5fb95e6957
* Fix
Change-Id: Id72f25c34291be4711f55569c6d61467edd6113d
* Fix CI
Change-Id: I8c33a82555d5ace2d0b682c1e3eefa13f3a44768
* Run CI
Change-Id: Ie8a6dab80ef91c0337cafbae4e3db277e0c7ebf7
* second round of fixing broken links in multiple files (#16598)
* Python Docstring Convetion (#16550)
* Docstring convetnion for
* Docstring convention for
* Docstring convention for
* Docstring convention for
* Docstring convention for
* Docstring convention for
* Docstring convention
* Revert removing new line
* Remove white space
* [MXNET-1434] Fix a broken link for basic C++ tutorial (#16461)
* Fix for wrong reqs set after switching from training to inference (#16553)
* Debugging reqs
* Move literal strings to const static members
* Fix lint
* julia/docs: more DRY on page rendering (#16396)
* Disables test_bulking_operator_gpu due to flakiness (#16611)
* C Api for simplebind, fix comment for trigoops, add atol to assert (#16585)
* C Api for simplebind, fix comment for trigoops, add atol to assert
* fix build issues
* fix lint and add regression test
* fix indent
* api doc and function name change
* fix lint and add infer shape test
* Imagenet inference to nightly fix (#16599)
* split to cd and shell
* comment
* lots of prints
* copy binary at correct location
* remove comments
* add mkl lib
* update docker run build function
* set nvidia docker true to run imagenet inference on GPU
* Revert "set nvidia docker true to run imagenet inference on GPU"
This reverts commit 98f8eef2057351d7964f1e9326ea6772c216f0af.
As we don't need GPU for compilation.
* Fix python doc build issue (#16630)
* pin the pip versions
* remove nbconvert comment
* Faster general take (#16615)
* Sped up perf of take op when axis != 0
* Formatting and syntax fixes
* Rename Take to specify axis
* Fix line length lint errors
* [Gluon] Don't serialize shared parameters twice (#16582)
Add deduplicate argument (default of False) to save_parameters.
* Fix index overflow bug in einsum (#16589)
* fix index overflow
* check index overflow
* fix index overflow in einsum path
* fix indent
* reduce NPY_MAXARGS
* safe accumulate
* Move some subgraph verbose to MXNET_SUBGRAPH_VERBOSE=2 (#16622)
* Move subgraph pass log to verbose=2
* Run CI
* add npx reshape (#16640)
* RNNOp only call cuda/cudnn if GPU ctx is requested (#16632)
* fix bad encode (#16641)
* [Perl] - ndarray to native array conversion fix (#16635)
* fixing broken links in multiple files - round 3 (#16634)
* add type switch to weight tensor (#16543)
* numpy doc enhancement (#16637)
* Change NDArray to ndarray for npx ops
Add nonzero
boolean mask supports boolean ndarray
Add argmin op and interoperability test for nonzero
Fix vdot, inner, outter docs
Add nonzero to mx.nd.np
Add docs
Fix
* Fix lint
* Fix
* Fix
* Fix get_constant
* Disable float16 test (#16643)
* Fix GetMKLDNNData for delay alloc (#16618)
* Fix GetMKLDNNData for delay alloc
* Run CI
* Run CI
* Run CI
* Run CI
* Run CI
Change-Id: I7ac2796e0ee8439c92fd2bd7a70a23a359b76b12
* Revert "[mkldnn-1.0]Rebase to master (#16648)"
This reverts commit dea3dd23d1982c913b3af6cfc7f4115c2cfa7244.
* [mkldnn-v1.0] Minor fix of mkldnn-v1.0 transition (#16644)
mk and rm directory in mkldnn.mk
ndarray.cc redundant whitespace
mkldnn_act rename variables of bwd primitives
mkldnn_rnn.cc iterator -> const_iterator
Use != instead of < for iterator in for-loop
Code comment for explaining the reason why excludes the last layer
* [mkldnn-v1.0]rm int8 sum workaround (#16623)
* rm int8 sum workaround due to mkldnn lib update
* simple dims asignments in mkldnn_quantized_elemwise_add.cc
* make MKLDNN macro simple for imperative_utils.h (#16652)
* fix ci jenkins step groovy (#16659)
* Adopt autograd.record() context to RNNOp (#16657)
* Use memcopy instead of set_handle when num_layer=0, direction=1 (#16663)
* fallback mkldnn fc bwd in imperative mode (#16672)
* disable MKLDNN FC backward
* [mkldnn-v1.0] Must reorder and emplace weights for inference primitives (#16682)
* add default parameter for mkldnn rnn
2019-10-31 22:55:13 +08:00
data_high = 255.0
2019-08-26 20:56:23 +08:00
else :
data_low = - 127.0
data_high = 127.0
Upgrade MKL-DNN dependency to v1.0 (#16555)
* [mkldnn-v1.0] Initiate the transition to MKL-DNN v1.0 (#15706)
* update mkldnn to 1.0.1 release
* change makefile
* change cmake
* update ci build and pip package build
* fix typo in mkldnn.mk
* fix build for USE_BLAS=mkl & bump MKL version
* skip mkldnn unit tests
* remove iomp5 from mx_mkldnn_lib
* ci: skip test_mkldnn_install
* retrigger ci
* retrigger ci
* retrigger ci
* [mkldnn-v1.0] Update MKL-DNN to v1.0.2 (#16012)
* bump mkldnn to v1.0.2
* skip quantization unit test
* add useless build flag
* Fixes openblas installation for static build
* empty commit
* [mkldnn-v1.0] Enable base code with new APIs. (#16064)
* fix comments (#8)
* add base code for mkldnn 1.0
* fix comments
* Update mkldnn.mk
* add base code for mkldnn 1.0
* fix build
* fix lint
* fix lint
* [mkldnn-v1.0] Add MKL-DNN Convolution (#16141)
* add mkldnn conv
* revert unnecessary change
* fix testcase fail for cpu: test_convolution_independent_gradients
* fix failed testcase: test_reshape_transpose_6d&&test_weight_async_reorder
* fix comments
* change variable name from weights to weight in mkldnn_conv
* [mkldnn-v1.0] Add MKL-DNN activation (#16195)
* add mkldnn act; pass lint; pass mnist training
* make bwd as private member
* [mkldnn-v1.0] Add MKL-DNN BN (#16199)
* add mkldnn bn
* add static_cast to transform data type
* change mkldnn_args_map_t
* retrigger CI
* add mkldnn lrn (#16223)
* [mkldnn-v1.0] Add MKL-DNN Transpose (#16250)
* add mkldnn transpose
* using mkldnn_args_map_t instead of std::unordered_map<int, mkldnn::memory>
* [mkldnn-v1.0] Add MKL-DNN softmax (#16246)
* add mkldnn softmax
* trigger CI
* [mkldnn-v1.0] Add MKL-DNN FC (#16221)
* add mkldnn fc; pass lint; pass mnist training
* add TODO info for future debug
* [mkldnn-v1.0] Add MKL-DNN deconv (#16259)
* add mkldnn deconv
* coding style
* trigger CI
* add mkldnn softmax_output (#16222)
* [mkldnn-v1.0] Add MKL-DNN Pooling (#16272)
* add mkldnn pooling
* add workaround for mkldnn v1.0 pooling fwd && bwd workspace mismatch
* code clean
* fix lint error
* trigger CI
* trigger CI
* add extra work_space check and fix some typo
* trigger CI
* [mkldnn-v1.0] Add MKL-DNN reshape&flatten&expand_dims (#16258)
* Add mkldnn 1.0 support for reshape/flatten/expanddims ops
* improve log & modify definition location of args_map_
* fix comments
* rebase code
* trigger CI
* trigger CI
* trigger CI
* trigger CI
* [mkldnn-v1.0] Add MKL-DNN int8 activation&pooling&flatten (#16425)
* Add mkldnn quantized activation/pooling/flatten
* int8 flatten
* [mkldnn-1.0] int8 conv quantize dequantize requantize (#16283)
* int8 conv quantize dequantize requantize
Change-Id: Ibd9df97288a95c61d6d85ec3831fd18b626ca283
* Fix lint
* Fix clang build
Change-Id: I9468774d014c852901e4cc3bffabd8a3d8004519
* add mkldnn sum concat (#16263)
* [mkldnn-1.0] mkldnn int8 elemwise_add (#16454)
* add mkldnn int8 elemwise_add
* add workaround to fix format any issue
* code clean
* upgrade int8 bn to MKLDNN1.0 (#16458)
* [mkldnn-v1.0] Fused RNN Op (#16420)
* [mkldnn-v1.0] Add MKL-DNN int8 fc (#16457)
* Add mkldnn_v1.0 int8 fc
* trigger CI
* trigger CI
* [mkldnn-v1.0] Update enabling flag for MKL dropout (#16433)
* use MSHADOW_USE_MKL to determine whther to use mkl optimized dropout
* rebase code
* [mkldnn-1.0] upgrade int8 concat to MKLDNN1.0 (#16466)
* [mkldnn-1.0] upgrade int8 concat to MKLDNN1.0
* fix lint
* use mkldnn_args_map_t
* update dict usage style
* retrigger CI
* retrigger CI again
* retrigger CI again 2
* [mkldnn-v1.0] Add MKL-DNN slice (#16484)
* change slice to mkldnn v1.0
* fix lint
* [mkldnn-1.0] add mkldnn subgraph fc (#16468)
* add mkldnn subgraph fc
* code clean
* trigger CI
* [mkldnn-v1.0]enable mkldnn concat (#16507)
* enable mkldnn concat
* trigger CI
* trigger CI
* [mkldnn-v1.0] Enable mkldnn cpp-test, copy op, concat op (#16503)
* [mkldnn-v1.0] Enable mkldnn test, copy op, concat op
Exclude gpu topology via MXNET_USE_CUDA
nit
default format
Remove whitespace
* Unix-GPU Tensor-RT build timeout, re-trigger CI
* [mkldnn-1.0] add skipped case for mkldnn_v1.0 (#16470)
* add skipped case for mkldnn_v1.0
* enable mkl quantized testcase
* enable skipped testcase
* trigger CI
* trigger CI
* trigger CI
* trigger CI
* [mkldnn-1.0]enable mkldnn elemwise_sum (#16521)
* enable mkldnn elemwise_sum
* trigger CI
* trigger CI
* trigger CI
* [mkldnn-v1.0] Enable more checks for MXNET_USE_MKLDNN (#16520)
* open USE_MKLDNN check
* trigger ci
* ci
* [mkldnn-v1.0]Minor fix for leakyrelu compile flag (#16519)
* change to MXNET_USE_MKLDNN == 100
* trigger
* remove MKL license (#16534)
* change MXNET_USE_MKLDNN from 100 to 1 (#16551)
* re-enable unit tests (#16565)
* [mkldnn-v1.0] Skip flaky test for unidirectional rnn_relu (#16545)
Skip `test_rnnrelu_sym`, and add some issue tracking message
Add return
Revert test_rnnrelu_sym to origin
* Add some annotations and log strings, rename mem_desc variables (#16609)
* [mkldnn-v1.0]set fc weight layout as mkldnn v0.2x did (#16593)
* set fc weight layout as mkldnn v0.2x did
* fix lint
* [mkldnn-v1.0] Upgrade to MKL-DNN v1.0.4 patch release (#16592)
* upgrade to mkldnn v1.0.3 patch release
* retrigger ci
* mkldnn v1.0.4 patch release
* [mkldnn-1.0]Rebase to master (#16648)
* fixed broken links across multiple files (#16581)
* fix missing docs due to git add issues (#16496)
* Create SECURITY.md (#16573)
* Create SECURITY.md
* Update SECURITY.md
* [Numpy] Support N_D(N>=3) batch_dot (#16586)
* Support N_D(N>=3) batch_dot
* use 1E-4
* fix lint
* remove unnecessary comment
* Update test_numpy_op.py
* Large Vector tests for DGL Ops Part 2 (#16497)
* add hyperbolic, logical, sign and regression tests for large vector
* changed hyperbolic functions into existing trignometric functions
* fix trigo and simple bind needs shape as tuple
* fix logical ops, add with_seed
* fix arcosh in largearray, remove regression from largevector
* [Numpy] Loading numpy-incompatible NDArray in numpy-compatible mode (#16597)
* Make MXIsNumpyShape return enum
* address the comment
* Surpress subgraph log in CI (#16607)
Change-Id: Ia2ed6fdbb1d2cb5cc607a8856ca13ee338e27eac
* Fix dequantize memory corruption (#16606)
Change-Id: I51b62a32987bdbcf96f04b1bc6617e66796f648b
* [MKLDNN]Fix reorder2default (#16602)
* Fix reorder2default
Change-Id: I74c87af9535f6264e6d1ea7eaed089a6480a3358
* fix
Change-Id: I6d07b43b520a47e7c78bd4b4b6390f5fb95e6957
* Fix
Change-Id: Id72f25c34291be4711f55569c6d61467edd6113d
* Fix CI
Change-Id: I8c33a82555d5ace2d0b682c1e3eefa13f3a44768
* Run CI
Change-Id: Ie8a6dab80ef91c0337cafbae4e3db277e0c7ebf7
* second round of fixing broken links in multiple files (#16598)
* Python Docstring Convetion (#16550)
* Docstring convetnion for
* Docstring convention for
* Docstring convention for
* Docstring convention for
* Docstring convention for
* Docstring convention for
* Docstring convention
* Revert removing new line
* Remove white space
* [MXNET-1434] Fix a broken link for basic C++ tutorial (#16461)
* Fix for wrong reqs set after switching from training to inference (#16553)
* Debugging reqs
* Move literal strings to const static members
* Fix lint
* julia/docs: more DRY on page rendering (#16396)
* [mkldnn-v1.0]rebase with master (#16649)
* fixed broken links across multiple files (#16581)
* fix missing docs due to git add issues (#16496)
* Create SECURITY.md (#16573)
* Create SECURITY.md
* Update SECURITY.md
* [Numpy] Support N_D(N>=3) batch_dot (#16586)
* Support N_D(N>=3) batch_dot
* use 1E-4
* fix lint
* remove unnecessary comment
* Update test_numpy_op.py
* Large Vector tests for DGL Ops Part 2 (#16497)
* add hyperbolic, logical, sign and regression tests for large vector
* changed hyperbolic functions into existing trignometric functions
* fix trigo and simple bind needs shape as tuple
* fix logical ops, add with_seed
* fix arcosh in largearray, remove regression from largevector
* [Numpy] Loading numpy-incompatible NDArray in numpy-compatible mode (#16597)
* Make MXIsNumpyShape return enum
* address the comment
* Surpress subgraph log in CI (#16607)
Change-Id: Ia2ed6fdbb1d2cb5cc607a8856ca13ee338e27eac
* Fix dequantize memory corruption (#16606)
Change-Id: I51b62a32987bdbcf96f04b1bc6617e66796f648b
* [MKLDNN]Fix reorder2default (#16602)
* Fix reorder2default
Change-Id: I74c87af9535f6264e6d1ea7eaed089a6480a3358
* fix
Change-Id: I6d07b43b520a47e7c78bd4b4b6390f5fb95e6957
* Fix
Change-Id: Id72f25c34291be4711f55569c6d61467edd6113d
* Fix CI
Change-Id: I8c33a82555d5ace2d0b682c1e3eefa13f3a44768
* Run CI
Change-Id: Ie8a6dab80ef91c0337cafbae4e3db277e0c7ebf7
* second round of fixing broken links in multiple files (#16598)
* Python Docstring Convetion (#16550)
* Docstring convetnion for
* Docstring convention for
* Docstring convention for
* Docstring convention for
* Docstring convention for
* Docstring convention for
* Docstring convention
* Revert removing new line
* Remove white space
* [MXNET-1434] Fix a broken link for basic C++ tutorial (#16461)
* Fix for wrong reqs set after switching from training to inference (#16553)
* Debugging reqs
* Move literal strings to const static members
* Fix lint
* julia/docs: more DRY on page rendering (#16396)
* Disables test_bulking_operator_gpu due to flakiness (#16611)
* C Api for simplebind, fix comment for trigoops, add atol to assert (#16585)
* C Api for simplebind, fix comment for trigoops, add atol to assert
* fix build issues
* fix lint and add regression test
* fix indent
* api doc and function name change
* fix lint and add infer shape test
* Imagenet inference to nightly fix (#16599)
* split to cd and shell
* comment
* lots of prints
* copy binary at correct location
* remove comments
* add mkl lib
* update docker run build function
* set nvidia docker true to run imagenet inference on GPU
* Revert "set nvidia docker true to run imagenet inference on GPU"
This reverts commit 98f8eef2057351d7964f1e9326ea6772c216f0af.
As we don't need GPU for compilation.
* Fix python doc build issue (#16630)
* pin the pip versions
* remove nbconvert comment
* Faster general take (#16615)
* Sped up perf of take op when axis != 0
* Formatting and syntax fixes
* Rename Take to specify axis
* Fix line length lint errors
* [Gluon] Don't serialize shared parameters twice (#16582)
Add deduplicate argument (default of False) to save_parameters.
* Fix index overflow bug in einsum (#16589)
* fix index overflow
* check index overflow
* fix index overflow in einsum path
* fix indent
* reduce NPY_MAXARGS
* safe accumulate
* Move some subgraph verbose to MXNET_SUBGRAPH_VERBOSE=2 (#16622)
* Move subgraph pass log to verbose=2
* Run CI
* add npx reshape (#16640)
* RNNOp only call cuda/cudnn if GPU ctx is requested (#16632)
* fix bad encode (#16641)
* [Perl] - ndarray to native array conversion fix (#16635)
* fixing broken links in multiple files - round 3 (#16634)
* add type switch to weight tensor (#16543)
* numpy doc enhancement (#16637)
* Change NDArray to ndarray for npx ops
Add nonzero
boolean mask supports boolean ndarray
Add argmin op and interoperability test for nonzero
Fix vdot, inner, outter docs
Add nonzero to mx.nd.np
Add docs
Fix
* Fix lint
* Fix
* Fix
* Fix get_constant
* Disable float16 test (#16643)
* Fix GetMKLDNNData for delay alloc (#16618)
* Fix GetMKLDNNData for delay alloc
* Run CI
* Run CI
* Run CI
* Run CI
* Run CI
Change-Id: I7ac2796e0ee8439c92fd2bd7a70a23a359b76b12
* Revert "[mkldnn-1.0]Rebase to master (#16648)"
This reverts commit dea3dd23d1982c913b3af6cfc7f4115c2cfa7244.
* [mkldnn-v1.0] Minor fix of mkldnn-v1.0 transition (#16644)
mk and rm directory in mkldnn.mk
ndarray.cc redundant whitespace
mkldnn_act rename variables of bwd primitives
mkldnn_rnn.cc iterator -> const_iterator
Use != instead of < for iterator in for-loop
Code comment for explaining the reason why excludes the last layer
* [mkldnn-v1.0]rm int8 sum workaround (#16623)
* rm int8 sum workaround due to mkldnn lib update
* simple dims asignments in mkldnn_quantized_elemwise_add.cc
* make MKLDNN macro simple for imperative_utils.h (#16652)
* fix ci jenkins step groovy (#16659)
* Adopt autograd.record() context to RNNOp (#16657)
* Use memcopy instead of set_handle when num_layer=0, direction=1 (#16663)
* fallback mkldnn fc bwd in imperative mode (#16672)
* disable MKLDNN FC backward
* [mkldnn-v1.0] Must reorder and emplace weights for inference primitives (#16682)
* add default parameter for mkldnn rnn
2019-10-31 22:55:13 +08:00
2019-08-08 18:20:19 +08:00
# run fp32 bn
data_sym = mx . sym . Variable ( name = ' data ' , shape = data_shape , dtype = ' float32 ' )
bn_fp32 = mx . sym . BatchNorm ( data = data_sym , name = ' bn ' , use_global_stats = True , fix_gamma = False )
arg_shapes , out_shapes , aux_shapes = bn_fp32 . infer_shape ( data = data_shape )
arg_names = bn_fp32 . list_arguments ( )
aux_names = bn_fp32 . list_auxiliary_states ( )
data = mx . nd . random . uniform ( low = data_low , high = data_high , shape = data_shape )
gamma = mx . nd . random . uniform ( low = data_low , high = data_high , shape = arg_shapes [ 1 ] )
beta = mx . nd . random . uniform ( low = data_low , high = data_high , shape = arg_shapes [ 2 ] )
moving_mean , moving_var = get_mean_var ( data )
bn_fp32_exe = bn_fp32 . simple_bind ( ctx = mx . current_context ( ) , grad_req = ' null ' )
bn_fp32_exe . arg_dict [ arg_names [ 0 ] ] [ : ] = data
bn_fp32_exe . arg_dict [ arg_names [ 1 ] ] [ : ] = gamma
bn_fp32_exe . arg_dict [ arg_names [ 2 ] ] [ : ] = beta
bn_fp32_exe . aux_dict [ aux_names [ 0 ] ] [ : ] = moving_mean
bn_fp32_exe . aux_dict [ aux_names [ 1 ] ] [ : ] = moving_var
output = bn_fp32_exe . forward ( ) [ 0 ]
# generate int8 bn from fp32 bn
arg_params = dict ( )
for k , v in bn_fp32_exe . arg_dict . items ( ) :
if ' data ' in k or ' softmax_label ' in k :
continue
arg_params [ k ] = v
calib_data = NDArrayIter ( data = data , batch_size = data_shape [ 0 ] )
calib_data = DummyIter ( calib_data )
qsym , qarg_params , qaux_params = mx . contrib . quant . quantize_model ( sym = bn_fp32 ,
arg_params = arg_params ,
aux_params = bn_fp32_exe . aux_dict ,
ctx = mx . current_context ( ) ,
Upgrade MKL-DNN dependency to v1.0 (#16555)
* [mkldnn-v1.0] Initiate the transition to MKL-DNN v1.0 (#15706)
* update mkldnn to 1.0.1 release
* change makefile
* change cmake
* update ci build and pip package build
* fix typo in mkldnn.mk
* fix build for USE_BLAS=mkl & bump MKL version
* skip mkldnn unit tests
* remove iomp5 from mx_mkldnn_lib
* ci: skip test_mkldnn_install
* retrigger ci
* retrigger ci
* retrigger ci
* [mkldnn-v1.0] Update MKL-DNN to v1.0.2 (#16012)
* bump mkldnn to v1.0.2
* skip quantization unit test
* add useless build flag
* Fixes openblas installation for static build
* empty commit
* [mkldnn-v1.0] Enable base code with new APIs. (#16064)
* fix comments (#8)
* add base code for mkldnn 1.0
* fix comments
* Update mkldnn.mk
* add base code for mkldnn 1.0
* fix build
* fix lint
* fix lint
* [mkldnn-v1.0] Add MKL-DNN Convolution (#16141)
* add mkldnn conv
* revert unnecessary change
* fix testcase fail for cpu: test_convolution_independent_gradients
* fix failed testcase: test_reshape_transpose_6d&&test_weight_async_reorder
* fix comments
* change variable name from weights to weight in mkldnn_conv
* [mkldnn-v1.0] Add MKL-DNN activation (#16195)
* add mkldnn act; pass lint; pass mnist training
* make bwd as private member
* [mkldnn-v1.0] Add MKL-DNN BN (#16199)
* add mkldnn bn
* add static_cast to transform data type
* change mkldnn_args_map_t
* retrigger CI
* add mkldnn lrn (#16223)
* [mkldnn-v1.0] Add MKL-DNN Transpose (#16250)
* add mkldnn transpose
* using mkldnn_args_map_t instead of std::unordered_map<int, mkldnn::memory>
* [mkldnn-v1.0] Add MKL-DNN softmax (#16246)
* add mkldnn softmax
* trigger CI
* [mkldnn-v1.0] Add MKL-DNN FC (#16221)
* add mkldnn fc; pass lint; pass mnist training
* add TODO info for future debug
* [mkldnn-v1.0] Add MKL-DNN deconv (#16259)
* add mkldnn deconv
* coding style
* trigger CI
* add mkldnn softmax_output (#16222)
* [mkldnn-v1.0] Add MKL-DNN Pooling (#16272)
* add mkldnn pooling
* add workaround for mkldnn v1.0 pooling fwd && bwd workspace mismatch
* code clean
* fix lint error
* trigger CI
* trigger CI
* add extra work_space check and fix some typo
* trigger CI
* [mkldnn-v1.0] Add MKL-DNN reshape&flatten&expand_dims (#16258)
* Add mkldnn 1.0 support for reshape/flatten/expanddims ops
* improve log & modify definition location of args_map_
* fix comments
* rebase code
* trigger CI
* trigger CI
* trigger CI
* trigger CI
* [mkldnn-v1.0] Add MKL-DNN int8 activation&pooling&flatten (#16425)
* Add mkldnn quantized activation/pooling/flatten
* int8 flatten
* [mkldnn-1.0] int8 conv quantize dequantize requantize (#16283)
* int8 conv quantize dequantize requantize
Change-Id: Ibd9df97288a95c61d6d85ec3831fd18b626ca283
* Fix lint
* Fix clang build
Change-Id: I9468774d014c852901e4cc3bffabd8a3d8004519
* add mkldnn sum concat (#16263)
* [mkldnn-1.0] mkldnn int8 elemwise_add (#16454)
* add mkldnn int8 elemwise_add
* add workaround to fix format any issue
* code clean
* upgrade int8 bn to MKLDNN1.0 (#16458)
* [mkldnn-v1.0] Fused RNN Op (#16420)
* [mkldnn-v1.0] Add MKL-DNN int8 fc (#16457)
* Add mkldnn_v1.0 int8 fc
* trigger CI
* trigger CI
* [mkldnn-v1.0] Update enabling flag for MKL dropout (#16433)
* use MSHADOW_USE_MKL to determine whther to use mkl optimized dropout
* rebase code
* [mkldnn-1.0] upgrade int8 concat to MKLDNN1.0 (#16466)
* [mkldnn-1.0] upgrade int8 concat to MKLDNN1.0
* fix lint
* use mkldnn_args_map_t
* update dict usage style
* retrigger CI
* retrigger CI again
* retrigger CI again 2
* [mkldnn-v1.0] Add MKL-DNN slice (#16484)
* change slice to mkldnn v1.0
* fix lint
* [mkldnn-1.0] add mkldnn subgraph fc (#16468)
* add mkldnn subgraph fc
* code clean
* trigger CI
* [mkldnn-v1.0]enable mkldnn concat (#16507)
* enable mkldnn concat
* trigger CI
* trigger CI
* [mkldnn-v1.0] Enable mkldnn cpp-test, copy op, concat op (#16503)
* [mkldnn-v1.0] Enable mkldnn test, copy op, concat op
Exclude gpu topology via MXNET_USE_CUDA
nit
default format
Remove whitespace
* Unix-GPU Tensor-RT build timeout, re-trigger CI
* [mkldnn-1.0] add skipped case for mkldnn_v1.0 (#16470)
* add skipped case for mkldnn_v1.0
* enable mkl quantized testcase
* enable skipped testcase
* trigger CI
* trigger CI
* trigger CI
* trigger CI
* [mkldnn-1.0]enable mkldnn elemwise_sum (#16521)
* enable mkldnn elemwise_sum
* trigger CI
* trigger CI
* trigger CI
* [mkldnn-v1.0] Enable more checks for MXNET_USE_MKLDNN (#16520)
* open USE_MKLDNN check
* trigger ci
* ci
* [mkldnn-v1.0]Minor fix for leakyrelu compile flag (#16519)
* change to MXNET_USE_MKLDNN == 100
* trigger
* remove MKL license (#16534)
* change MXNET_USE_MKLDNN from 100 to 1 (#16551)
* re-enable unit tests (#16565)
* [mkldnn-v1.0] Skip flaky test for unidirectional rnn_relu (#16545)
Skip `test_rnnrelu_sym`, and add some issue tracking message
Add return
Revert test_rnnrelu_sym to origin
* Add some annotations and log strings, rename mem_desc variables (#16609)
* [mkldnn-v1.0]set fc weight layout as mkldnn v0.2x did (#16593)
* set fc weight layout as mkldnn v0.2x did
* fix lint
* [mkldnn-v1.0] Upgrade to MKL-DNN v1.0.4 patch release (#16592)
* upgrade to mkldnn v1.0.3 patch release
* retrigger ci
* mkldnn v1.0.4 patch release
* [mkldnn-1.0]Rebase to master (#16648)
* fixed broken links across multiple files (#16581)
* fix missing docs due to git add issues (#16496)
* Create SECURITY.md (#16573)
* Create SECURITY.md
* Update SECURITY.md
* [Numpy] Support N_D(N>=3) batch_dot (#16586)
* Support N_D(N>=3) batch_dot
* use 1E-4
* fix lint
* remove unnecessary comment
* Update test_numpy_op.py
* Large Vector tests for DGL Ops Part 2 (#16497)
* add hyperbolic, logical, sign and regression tests for large vector
* changed hyperbolic functions into existing trignometric functions
* fix trigo and simple bind needs shape as tuple
* fix logical ops, add with_seed
* fix arcosh in largearray, remove regression from largevector
* [Numpy] Loading numpy-incompatible NDArray in numpy-compatible mode (#16597)
* Make MXIsNumpyShape return enum
* address the comment
* Surpress subgraph log in CI (#16607)
Change-Id: Ia2ed6fdbb1d2cb5cc607a8856ca13ee338e27eac
* Fix dequantize memory corruption (#16606)
Change-Id: I51b62a32987bdbcf96f04b1bc6617e66796f648b
* [MKLDNN]Fix reorder2default (#16602)
* Fix reorder2default
Change-Id: I74c87af9535f6264e6d1ea7eaed089a6480a3358
* fix
Change-Id: I6d07b43b520a47e7c78bd4b4b6390f5fb95e6957
* Fix
Change-Id: Id72f25c34291be4711f55569c6d61467edd6113d
* Fix CI
Change-Id: I8c33a82555d5ace2d0b682c1e3eefa13f3a44768
* Run CI
Change-Id: Ie8a6dab80ef91c0337cafbae4e3db277e0c7ebf7
* second round of fixing broken links in multiple files (#16598)
* Python Docstring Convetion (#16550)
* Docstring convetnion for
* Docstring convention for
* Docstring convention for
* Docstring convention for
* Docstring convention for
* Docstring convention for
* Docstring convention
* Revert removing new line
* Remove white space
* [MXNET-1434] Fix a broken link for basic C++ tutorial (#16461)
* Fix for wrong reqs set after switching from training to inference (#16553)
* Debugging reqs
* Move literal strings to const static members
* Fix lint
* julia/docs: more DRY on page rendering (#16396)
* [mkldnn-v1.0]rebase with master (#16649)
* fixed broken links across multiple files (#16581)
* fix missing docs due to git add issues (#16496)
* Create SECURITY.md (#16573)
* Create SECURITY.md
* Update SECURITY.md
* [Numpy] Support N_D(N>=3) batch_dot (#16586)
* Support N_D(N>=3) batch_dot
* use 1E-4
* fix lint
* remove unnecessary comment
* Update test_numpy_op.py
* Large Vector tests for DGL Ops Part 2 (#16497)
* add hyperbolic, logical, sign and regression tests for large vector
* changed hyperbolic functions into existing trignometric functions
* fix trigo and simple bind needs shape as tuple
* fix logical ops, add with_seed
* fix arcosh in largearray, remove regression from largevector
* [Numpy] Loading numpy-incompatible NDArray in numpy-compatible mode (#16597)
* Make MXIsNumpyShape return enum
* address the comment
* Surpress subgraph log in CI (#16607)
Change-Id: Ia2ed6fdbb1d2cb5cc607a8856ca13ee338e27eac
* Fix dequantize memory corruption (#16606)
Change-Id: I51b62a32987bdbcf96f04b1bc6617e66796f648b
* [MKLDNN]Fix reorder2default (#16602)
* Fix reorder2default
Change-Id: I74c87af9535f6264e6d1ea7eaed089a6480a3358
* fix
Change-Id: I6d07b43b520a47e7c78bd4b4b6390f5fb95e6957
* Fix
Change-Id: Id72f25c34291be4711f55569c6d61467edd6113d
* Fix CI
Change-Id: I8c33a82555d5ace2d0b682c1e3eefa13f3a44768
* Run CI
Change-Id: Ie8a6dab80ef91c0337cafbae4e3db277e0c7ebf7
* second round of fixing broken links in multiple files (#16598)
* Python Docstring Convetion (#16550)
* Docstring convetnion for
* Docstring convention for
* Docstring convention for
* Docstring convention for
* Docstring convention for
* Docstring convention for
* Docstring convention
* Revert removing new line
* Remove white space
* [MXNET-1434] Fix a broken link for basic C++ tutorial (#16461)
* Fix for wrong reqs set after switching from training to inference (#16553)
* Debugging reqs
* Move literal strings to const static members
* Fix lint
* julia/docs: more DRY on page rendering (#16396)
* Disables test_bulking_operator_gpu due to flakiness (#16611)
* C Api for simplebind, fix comment for trigoops, add atol to assert (#16585)
* C Api for simplebind, fix comment for trigoops, add atol to assert
* fix build issues
* fix lint and add regression test
* fix indent
* api doc and function name change
* fix lint and add infer shape test
* Imagenet inference to nightly fix (#16599)
* split to cd and shell
* comment
* lots of prints
* copy binary at correct location
* remove comments
* add mkl lib
* update docker run build function
* set nvidia docker true to run imagenet inference on GPU
* Revert "set nvidia docker true to run imagenet inference on GPU"
This reverts commit 98f8eef2057351d7964f1e9326ea6772c216f0af.
As we don't need GPU for compilation.
* Fix python doc build issue (#16630)
* pin the pip versions
* remove nbconvert comment
* Faster general take (#16615)
* Sped up perf of take op when axis != 0
* Formatting and syntax fixes
* Rename Take to specify axis
* Fix line length lint errors
* [Gluon] Don't serialize shared parameters twice (#16582)
Add deduplicate argument (default of False) to save_parameters.
* Fix index overflow bug in einsum (#16589)
* fix index overflow
* check index overflow
* fix index overflow in einsum path
* fix indent
* reduce NPY_MAXARGS
* safe accumulate
* Move some subgraph verbose to MXNET_SUBGRAPH_VERBOSE=2 (#16622)
* Move subgraph pass log to verbose=2
* Run CI
* add npx reshape (#16640)
* RNNOp only call cuda/cudnn if GPU ctx is requested (#16632)
* fix bad encode (#16641)
* [Perl] - ndarray to native array conversion fix (#16635)
* fixing broken links in multiple files - round 3 (#16634)
* add type switch to weight tensor (#16543)
* numpy doc enhancement (#16637)
* Change NDArray to ndarray for npx ops
Add nonzero
boolean mask supports boolean ndarray
Add argmin op and interoperability test for nonzero
Fix vdot, inner, outter docs
Add nonzero to mx.nd.np
Add docs
Fix
* Fix lint
* Fix
* Fix
* Fix get_constant
* Disable float16 test (#16643)
* Fix GetMKLDNNData for delay alloc (#16618)
* Fix GetMKLDNNData for delay alloc
* Run CI
* Run CI
* Run CI
* Run CI
* Run CI
Change-Id: I7ac2796e0ee8439c92fd2bd7a70a23a359b76b12
* Revert "[mkldnn-1.0]Rebase to master (#16648)"
This reverts commit dea3dd23d1982c913b3af6cfc7f4115c2cfa7244.
* [mkldnn-v1.0] Minor fix of mkldnn-v1.0 transition (#16644)
mk and rm directory in mkldnn.mk
ndarray.cc redundant whitespace
mkldnn_act rename variables of bwd primitives
mkldnn_rnn.cc iterator -> const_iterator
Use != instead of < for iterator in for-loop
Code comment for explaining the reason why excludes the last layer
* [mkldnn-v1.0]rm int8 sum workaround (#16623)
* rm int8 sum workaround due to mkldnn lib update
* simple dims asignments in mkldnn_quantized_elemwise_add.cc
* make MKLDNN macro simple for imperative_utils.h (#16652)
* fix ci jenkins step groovy (#16659)
* Adopt autograd.record() context to RNNOp (#16657)
* Use memcopy instead of set_handle when num_layer=0, direction=1 (#16663)
* fallback mkldnn fc bwd in imperative mode (#16672)
* disable MKLDNN FC backward
* [mkldnn-v1.0] Must reorder and emplace weights for inference primitives (#16682)
* add default parameter for mkldnn rnn
2019-10-31 22:55:13 +08:00
quantized_dtype = qdtype ,
quantize_mode = ' full ' ,
2019-08-08 18:20:19 +08:00
calib_mode = ' naive ' ,
calib_data = calib_data ,
num_calib_examples = 20 )
mod = mx . mod . Module ( symbol = qsym , label_names = None , context = mx . current_context ( ) )
mod . bind ( for_training = False , data_shapes = [ ( ' data ' , data_shape ) ] )
mod . set_params ( qarg_params , qaux_params )
batch = mx . io . DataBatch ( [ data ] , [ ] )
mod . forward ( batch , is_train = False )
2019-08-26 20:56:23 +08:00
output_int8_to_fp32 = mod . get_outputs ( ) [ 0 ]
2019-08-08 18:20:19 +08:00
Upgrade MKL-DNN dependency to v1.0 (#16555)
* [mkldnn-v1.0] Initiate the transition to MKL-DNN v1.0 (#15706)
* update mkldnn to 1.0.1 release
* change makefile
* change cmake
* update ci build and pip package build
* fix typo in mkldnn.mk
* fix build for USE_BLAS=mkl & bump MKL version
* skip mkldnn unit tests
* remove iomp5 from mx_mkldnn_lib
* ci: skip test_mkldnn_install
* retrigger ci
* retrigger ci
* retrigger ci
* [mkldnn-v1.0] Update MKL-DNN to v1.0.2 (#16012)
* bump mkldnn to v1.0.2
* skip quantization unit test
* add useless build flag
* Fixes openblas installation for static build
* empty commit
* [mkldnn-v1.0] Enable base code with new APIs. (#16064)
* fix comments (#8)
* add base code for mkldnn 1.0
* fix comments
* Update mkldnn.mk
* add base code for mkldnn 1.0
* fix build
* fix lint
* fix lint
* [mkldnn-v1.0] Add MKL-DNN Convolution (#16141)
* add mkldnn conv
* revert unnecessary change
* fix testcase fail for cpu: test_convolution_independent_gradients
* fix failed testcase: test_reshape_transpose_6d&&test_weight_async_reorder
* fix comments
* change variable name from weights to weight in mkldnn_conv
* [mkldnn-v1.0] Add MKL-DNN activation (#16195)
* add mkldnn act; pass lint; pass mnist training
* make bwd as private member
* [mkldnn-v1.0] Add MKL-DNN BN (#16199)
* add mkldnn bn
* add static_cast to transform data type
* change mkldnn_args_map_t
* retrigger CI
* add mkldnn lrn (#16223)
* [mkldnn-v1.0] Add MKL-DNN Transpose (#16250)
* add mkldnn transpose
* using mkldnn_args_map_t instead of std::unordered_map<int, mkldnn::memory>
* [mkldnn-v1.0] Add MKL-DNN softmax (#16246)
* add mkldnn softmax
* trigger CI
* [mkldnn-v1.0] Add MKL-DNN FC (#16221)
* add mkldnn fc; pass lint; pass mnist training
* add TODO info for future debug
* [mkldnn-v1.0] Add MKL-DNN deconv (#16259)
* add mkldnn deconv
* coding style
* trigger CI
* add mkldnn softmax_output (#16222)
* [mkldnn-v1.0] Add MKL-DNN Pooling (#16272)
* add mkldnn pooling
* add workaround for mkldnn v1.0 pooling fwd && bwd workspace mismatch
* code clean
* fix lint error
* trigger CI
* trigger CI
* add extra work_space check and fix some typo
* trigger CI
* [mkldnn-v1.0] Add MKL-DNN reshape&flatten&expand_dims (#16258)
* Add mkldnn 1.0 support for reshape/flatten/expanddims ops
* improve log & modify definition location of args_map_
* fix comments
* rebase code
* trigger CI
* trigger CI
* trigger CI
* trigger CI
* [mkldnn-v1.0] Add MKL-DNN int8 activation&pooling&flatten (#16425)
* Add mkldnn quantized activation/pooling/flatten
* int8 flatten
* [mkldnn-1.0] int8 conv quantize dequantize requantize (#16283)
* int8 conv quantize dequantize requantize
Change-Id: Ibd9df97288a95c61d6d85ec3831fd18b626ca283
* Fix lint
* Fix clang build
Change-Id: I9468774d014c852901e4cc3bffabd8a3d8004519
* add mkldnn sum concat (#16263)
* [mkldnn-1.0] mkldnn int8 elemwise_add (#16454)
* add mkldnn int8 elemwise_add
* add workaround to fix format any issue
* code clean
* upgrade int8 bn to MKLDNN1.0 (#16458)
* [mkldnn-v1.0] Fused RNN Op (#16420)
* [mkldnn-v1.0] Add MKL-DNN int8 fc (#16457)
* Add mkldnn_v1.0 int8 fc
* trigger CI
* trigger CI
* [mkldnn-v1.0] Update enabling flag for MKL dropout (#16433)
* use MSHADOW_USE_MKL to determine whther to use mkl optimized dropout
* rebase code
* [mkldnn-1.0] upgrade int8 concat to MKLDNN1.0 (#16466)
* [mkldnn-1.0] upgrade int8 concat to MKLDNN1.0
* fix lint
* use mkldnn_args_map_t
* update dict usage style
* retrigger CI
* retrigger CI again
* retrigger CI again 2
* [mkldnn-v1.0] Add MKL-DNN slice (#16484)
* change slice to mkldnn v1.0
* fix lint
* [mkldnn-1.0] add mkldnn subgraph fc (#16468)
* add mkldnn subgraph fc
* code clean
* trigger CI
* [mkldnn-v1.0]enable mkldnn concat (#16507)
* enable mkldnn concat
* trigger CI
* trigger CI
* [mkldnn-v1.0] Enable mkldnn cpp-test, copy op, concat op (#16503)
* [mkldnn-v1.0] Enable mkldnn test, copy op, concat op
Exclude gpu topology via MXNET_USE_CUDA
nit
default format
Remove whitespace
* Unix-GPU Tensor-RT build timeout, re-trigger CI
* [mkldnn-1.0] add skipped case for mkldnn_v1.0 (#16470)
* add skipped case for mkldnn_v1.0
* enable mkl quantized testcase
* enable skipped testcase
* trigger CI
* trigger CI
* trigger CI
* trigger CI
* [mkldnn-1.0]enable mkldnn elemwise_sum (#16521)
* enable mkldnn elemwise_sum
* trigger CI
* trigger CI
* trigger CI
* [mkldnn-v1.0] Enable more checks for MXNET_USE_MKLDNN (#16520)
* open USE_MKLDNN check
* trigger ci
* ci
* [mkldnn-v1.0]Minor fix for leakyrelu compile flag (#16519)
* change to MXNET_USE_MKLDNN == 100
* trigger
* remove MKL license (#16534)
* change MXNET_USE_MKLDNN from 100 to 1 (#16551)
* re-enable unit tests (#16565)
* [mkldnn-v1.0] Skip flaky test for unidirectional rnn_relu (#16545)
Skip `test_rnnrelu_sym`, and add some issue tracking message
Add return
Revert test_rnnrelu_sym to origin
* Add some annotations and log strings, rename mem_desc variables (#16609)
* [mkldnn-v1.0]set fc weight layout as mkldnn v0.2x did (#16593)
* set fc weight layout as mkldnn v0.2x did
* fix lint
* [mkldnn-v1.0] Upgrade to MKL-DNN v1.0.4 patch release (#16592)
* upgrade to mkldnn v1.0.3 patch release
* retrigger ci
* mkldnn v1.0.4 patch release
* [mkldnn-1.0]Rebase to master (#16648)
* fixed broken links across multiple files (#16581)
* fix missing docs due to git add issues (#16496)
* Create SECURITY.md (#16573)
* Create SECURITY.md
* Update SECURITY.md
* [Numpy] Support N_D(N>=3) batch_dot (#16586)
* Support N_D(N>=3) batch_dot
* use 1E-4
* fix lint
* remove unnecessary comment
* Update test_numpy_op.py
* Large Vector tests for DGL Ops Part 2 (#16497)
* add hyperbolic, logical, sign and regression tests for large vector
* changed hyperbolic functions into existing trignometric functions
* fix trigo and simple bind needs shape as tuple
* fix logical ops, add with_seed
* fix arcosh in largearray, remove regression from largevector
* [Numpy] Loading numpy-incompatible NDArray in numpy-compatible mode (#16597)
* Make MXIsNumpyShape return enum
* address the comment
* Surpress subgraph log in CI (#16607)
Change-Id: Ia2ed6fdbb1d2cb5cc607a8856ca13ee338e27eac
* Fix dequantize memory corruption (#16606)
Change-Id: I51b62a32987bdbcf96f04b1bc6617e66796f648b
* [MKLDNN]Fix reorder2default (#16602)
* Fix reorder2default
Change-Id: I74c87af9535f6264e6d1ea7eaed089a6480a3358
* fix
Change-Id: I6d07b43b520a47e7c78bd4b4b6390f5fb95e6957
* Fix
Change-Id: Id72f25c34291be4711f55569c6d61467edd6113d
* Fix CI
Change-Id: I8c33a82555d5ace2d0b682c1e3eefa13f3a44768
* Run CI
Change-Id: Ie8a6dab80ef91c0337cafbae4e3db277e0c7ebf7
* second round of fixing broken links in multiple files (#16598)
* Python Docstring Convetion (#16550)
* Docstring convetnion for
* Docstring convention for
* Docstring convention for
* Docstring convention for
* Docstring convention for
* Docstring convention for
* Docstring convention
* Revert removing new line
* Remove white space
* [MXNET-1434] Fix a broken link for basic C++ tutorial (#16461)
* Fix for wrong reqs set after switching from training to inference (#16553)
* Debugging reqs
* Move literal strings to const static members
* Fix lint
* julia/docs: more DRY on page rendering (#16396)
* [mkldnn-v1.0]rebase with master (#16649)
* fixed broken links across multiple files (#16581)
* fix missing docs due to git add issues (#16496)
* Create SECURITY.md (#16573)
* Create SECURITY.md
* Update SECURITY.md
* [Numpy] Support N_D(N>=3) batch_dot (#16586)
* Support N_D(N>=3) batch_dot
* use 1E-4
* fix lint
* remove unnecessary comment
* Update test_numpy_op.py
* Large Vector tests for DGL Ops Part 2 (#16497)
* add hyperbolic, logical, sign and regression tests for large vector
* changed hyperbolic functions into existing trignometric functions
* fix trigo and simple bind needs shape as tuple
* fix logical ops, add with_seed
* fix arcosh in largearray, remove regression from largevector
* [Numpy] Loading numpy-incompatible NDArray in numpy-compatible mode (#16597)
* Make MXIsNumpyShape return enum
* address the comment
* Surpress subgraph log in CI (#16607)
Change-Id: Ia2ed6fdbb1d2cb5cc607a8856ca13ee338e27eac
* Fix dequantize memory corruption (#16606)
Change-Id: I51b62a32987bdbcf96f04b1bc6617e66796f648b
* [MKLDNN]Fix reorder2default (#16602)
* Fix reorder2default
Change-Id: I74c87af9535f6264e6d1ea7eaed089a6480a3358
* fix
Change-Id: I6d07b43b520a47e7c78bd4b4b6390f5fb95e6957
* Fix
Change-Id: Id72f25c34291be4711f55569c6d61467edd6113d
* Fix CI
Change-Id: I8c33a82555d5ace2d0b682c1e3eefa13f3a44768
* Run CI
Change-Id: Ie8a6dab80ef91c0337cafbae4e3db277e0c7ebf7
* second round of fixing broken links in multiple files (#16598)
* Python Docstring Convetion (#16550)
* Docstring convetnion for
* Docstring convention for
* Docstring convention for
* Docstring convention for
* Docstring convention for
* Docstring convention for
* Docstring convention
* Revert removing new line
* Remove white space
* [MXNET-1434] Fix a broken link for basic C++ tutorial (#16461)
* Fix for wrong reqs set after switching from training to inference (#16553)
* Debugging reqs
* Move literal strings to const static members
* Fix lint
* julia/docs: more DRY on page rendering (#16396)
* Disables test_bulking_operator_gpu due to flakiness (#16611)
* C Api for simplebind, fix comment for trigoops, add atol to assert (#16585)
* C Api for simplebind, fix comment for trigoops, add atol to assert
* fix build issues
* fix lint and add regression test
* fix indent
* api doc and function name change
* fix lint and add infer shape test
* Imagenet inference to nightly fix (#16599)
* split to cd and shell
* comment
* lots of prints
* copy binary at correct location
* remove comments
* add mkl lib
* update docker run build function
* set nvidia docker true to run imagenet inference on GPU
* Revert "set nvidia docker true to run imagenet inference on GPU"
This reverts commit 98f8eef2057351d7964f1e9326ea6772c216f0af.
As we don't need GPU for compilation.
* Fix python doc build issue (#16630)
* pin the pip versions
* remove nbconvert comment
* Faster general take (#16615)
* Sped up perf of take op when axis != 0
* Formatting and syntax fixes
* Rename Take to specify axis
* Fix line length lint errors
* [Gluon] Don't serialize shared parameters twice (#16582)
Add deduplicate argument (default of False) to save_parameters.
* Fix index overflow bug in einsum (#16589)
* fix index overflow
* check index overflow
* fix index overflow in einsum path
* fix indent
* reduce NPY_MAXARGS
* safe accumulate
* Move some subgraph verbose to MXNET_SUBGRAPH_VERBOSE=2 (#16622)
* Move subgraph pass log to verbose=2
* Run CI
* add npx reshape (#16640)
* RNNOp only call cuda/cudnn if GPU ctx is requested (#16632)
* fix bad encode (#16641)
* [Perl] - ndarray to native array conversion fix (#16635)
* fixing broken links in multiple files - round 3 (#16634)
* add type switch to weight tensor (#16543)
* numpy doc enhancement (#16637)
* Change NDArray to ndarray for npx ops
Add nonzero
boolean mask supports boolean ndarray
Add argmin op and interoperability test for nonzero
Fix vdot, inner, outter docs
Add nonzero to mx.nd.np
Add docs
Fix
* Fix lint
* Fix
* Fix
* Fix get_constant
* Disable float16 test (#16643)
* Fix GetMKLDNNData for delay alloc (#16618)
* Fix GetMKLDNNData for delay alloc
* Run CI
* Run CI
* Run CI
* Run CI
* Run CI
Change-Id: I7ac2796e0ee8439c92fd2bd7a70a23a359b76b12
* Revert "[mkldnn-1.0]Rebase to master (#16648)"
This reverts commit dea3dd23d1982c913b3af6cfc7f4115c2cfa7244.
* [mkldnn-v1.0] Minor fix of mkldnn-v1.0 transition (#16644)
mk and rm directory in mkldnn.mk
ndarray.cc redundant whitespace
mkldnn_act rename variables of bwd primitives
mkldnn_rnn.cc iterator -> const_iterator
Use != instead of < for iterator in for-loop
Code comment for explaining the reason why excludes the last layer
* [mkldnn-v1.0]rm int8 sum workaround (#16623)
* rm int8 sum workaround due to mkldnn lib update
* simple dims asignments in mkldnn_quantized_elemwise_add.cc
* make MKLDNN macro simple for imperative_utils.h (#16652)
* fix ci jenkins step groovy (#16659)
* Adopt autograd.record() context to RNNOp (#16657)
* Use memcopy instead of set_handle when num_layer=0, direction=1 (#16663)
* fallback mkldnn fc bwd in imperative mode (#16672)
* disable MKLDNN FC backward
* [mkldnn-v1.0] Must reorder and emplace weights for inference primitives (#16682)
* add default parameter for mkldnn rnn
2019-10-31 22:55:13 +08:00
assert_almost_equal ( output . asnumpy ( ) , output_int8_to_fp32 . asnumpy ( ) , rtol = 1e-1 , atol = 8 )
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for qdtype in [ ' int8 ' , ' uint8 ' ] :
check_quantized_bn ( ( 32 , 512 , 4 , 4 ) , qdtype )
check_quantized_bn ( ( 32 , 1024 , 8 , 8 ) , qdtype )
check_quantized_bn ( ( 32 , 3 , 224 , 224 ) , qdtype )
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@with_seed ( )
def test_quantize_params ( ) :
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if is_test_for_native_cpu ( ) :
print ( ' skipped testing quantized_params for native cpu since it is not supported yet ' )
return
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data = mx . sym . Variable ( ' data ' )
conv = mx . sym . Convolution ( data , kernel = ( 1 , 1 ) , num_filter = 2048 , name = ' conv ' )
sym = mx . sym . BatchNorm ( data = conv , eps = 2e-05 , fix_gamma = False , momentum = 0.9 , use_global_stats = False , name = ' bn ' )
offline_params = [ name for name in sym . list_arguments ( )
if not name . startswith ( ' data ' ) and not name . endswith ( ' label ' ) ]
params = { }
for name in offline_params :
params [ name ] = mx . nd . uniform ( shape = ( 2 , 2 ) )
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qsym , _ = mx . contrib . quant . _quantize_symbol ( sym , ctx = mx . current_context ( ) ,
offline_params = offline_params , quantize_mode = ' full ' )
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qparams = mx . contrib . quant . _quantize_params ( qsym , params , th_dict = { } )
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param_names = params . keys ( )
qparam_names = qparams . keys ( )
for name in qparam_names :
if name . startswith ( ' bn ' ) :
assert name in param_names
elif name . startswith ( ' conv ' ) :
assert name not in param_names
assert name . find ( ' quantize ' ) != - 1
def get_fp32_sym ( ) :
data = mx . sym . Variable ( ' data ' )
conv = mx . sym . Convolution ( data , kernel = ( 1 , 1 ) , num_filter = 16 , name = ' conv ' )
bn = mx . sym . BatchNorm ( data = conv , eps = 2e-05 , fix_gamma = False , momentum = 0.9 , use_global_stats = False , name = ' bn ' )
act = mx . sym . Activation ( data = bn , act_type = ' relu ' , name = ' relu ' )
pool = mx . sym . Pooling ( act , kernel = ( 4 , 4 ) , pool_type = ' avg ' , name = ' pool ' )
fc = mx . sym . FullyConnected ( pool , num_hidden = 10 , flatten = True , name = ' fc ' )
sym = mx . sym . SoftmaxOutput ( fc , grad_scale = 1 , ignore_label = - 1 , multi_output = False ,
out_grad = False , preserve_shape = False , use_ignore = False , name = ' softmax ' )
return sym
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def get_fp32_residual ( ) :
data = mx . sym . Variable ( ' data ' )
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conv0 = mx . sym . Convolution ( data = data , num_filter = 4 , kernel = ( 1 , 1 ) , pad = ( 0 , 0 ) ,
no_bias = True , name = ' conv0 ' )
bn = mx . sym . BatchNorm ( data = conv0 , fix_gamma = False , eps = 2e-5 , momentum = 0.9 , name = ' bn ' )
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sum0 = mx . sym . elemwise_add ( bn , data , name = ' sum0 ' )
act0 = mx . sym . Activation ( data = sum0 , act_type = ' relu ' , name = ' relu0 ' )
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pool0 = mx . sym . Pooling ( act0 , kernel = ( 4 , 4 ) , pool_type = ' avg ' , name = ' pool0 ' )
conv1 = mx . sym . Convolution ( data = pool0 , num_filter = 4 , kernel = ( 1 , 1 ) , pad = ( 0 , 0 ) ,
no_bias = False , name = ' conv1 ' )
act1 = mx . sym . Activation ( data = conv1 , act_type = ' relu ' , name = ' relu1 ' )
pool1 = mx . sym . Pooling ( act1 , kernel = ( 4 , 4 ) , pool_type = ' avg ' , name = ' pool1 ' )
fc = mx . sym . FullyConnected ( pool1 , num_hidden = 10 , flatten = True , name = ' fc ' )
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sym = mx . sym . SoftmaxOutput ( fc , grad_scale = 1 , ignore_label = - 1 , multi_output = False ,
out_grad = False , preserve_shape = False , use_ignore = False , name = ' softmax ' )
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return sym
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def get_fp32_sym_with_multiple_outputs ( length = 1 ) :
data = mx . sym . Variable ( ' data ' )
inputs = list ( mx . sym . split ( data , axis = 0 , num_outputs = length , squeeze_axis = 1 , name = ' split ' ) )
_conv_outs = [ ]
for i in range ( length ) :
_conv_outs . append ( mx . sym . Convolution ( data = inputs [ i ] , kernel = ( 1 , 1 ) , num_filter = 16 , name = ' conv_ {0} ' . format ( i ) ) )
conv_out = [ mx . sym . expand_dims ( i , axis = 0 ) for i in _conv_outs ]
conv_out = mx . sym . Concat ( * conv_out , dim = 0 , name = ' concat ' )
reshape_out = mx . sym . reshape ( data = conv_out , shape = ( ( length , - 1 ) ) , name = ' reshape ' )
fc_out = mx . sym . FullyConnected ( reshape_out , num_hidden = 10 , flatten = True , name = ' fc ' )
sym = mx . sym . SoftmaxOutput ( fc_out , grad_scale = 1 , ignore_label = - 1 , multi_output = False ,
out_grad = False , preserve_shape = False , use_ignore = False , name = ' softmax ' )
return sym
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@with_seed ( )
def test_quantize_model ( ) :
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def check_quantize_model ( qdtype ) :
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if is_test_for_native_cpu ( ) :
print ( ' skipped testing quantize_model for native cpu since it is not supported yet ' )
return
elif qdtype == ' int8 ' and is_test_for_mkldnn ( ) :
print ( ' skipped testing quantize_model for mkldnn cpu int8 since it is not supported yet ' )
return
elif qdtype == ' uint8 ' and is_test_for_gpu ( ) :
print ( ' skipped testing quantize_model for gpu uint8 since it is not supported yet ' )
return
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def check_params ( params , qparams , qsym = None ) :
if qsym is None :
assert len ( params ) == len ( qparams )
for k , v in params . items ( ) :
assert k in qparams
assert same ( v . asnumpy ( ) , qparams [ k ] . asnumpy ( ) )
else :
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qparams_ground_truth = mx . contrib . quant . _quantize_params ( qsym , params , th_dict = { } )
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assert len ( qparams ) == len ( qparams_ground_truth )
for k , v in qparams_ground_truth . items ( ) :
assert k in qparams
assert same ( v . asnumpy ( ) , qparams [ k ] . asnumpy ( ) )
def check_qsym_calibrated ( qsym ) :
attrs = qsym . attr_dict ( )
for k , v in attrs . items ( ) :
if k . find ( ' requantize_ ' ) != - 1 :
assert ' min_calib_range ' in v
assert ' max_calib_range ' in v
def check_qsym_qdtype ( qsym , qdtype ) :
attrs = qsym . attr_dict ( )
for k , v in attrs . items ( ) :
if k . find ( ' _quantize ' ) != - 1 :
assert ' out_type ' in v
assert v [ ' out_type ' ] == qdtype
sym = get_fp32_sym ( )
batch_size = 4
label_shape = ( batch_size , 10 )
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data_shape = ( batch_size , 4 , 10 , 10 )
length = batch_size # specify num of outputs from split op
msym = get_fp32_sym_with_multiple_outputs ( length )
msym_label_shape = ( length , 10 )
msym_data_shape = ( length , 4 , 4 , 10 , 10 )
for s , dshape , lshape in zip ( ( sym , msym ) , ( data_shape , msym_data_shape ) ,
( label_shape , msym_label_shape ) ) :
mod = Module ( symbol = s )
mod . bind ( data_shapes = [ ( ' data ' , dshape ) ] , label_shapes = [ ( ' softmax_label ' , lshape ) ] )
mod . init_params ( )
arg_params , aux_params = mod . get_params ( )
qsym , qarg_params , qaux_params = mx . contrib . quant . quantize_model ( sym = s ,
arg_params = arg_params ,
aux_params = aux_params ,
ctx = mx . current_context ( ) ,
quantized_dtype = qdtype ,
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calib_mode = ' none ' ,
quantize_mode = ' full ' )
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check_params ( arg_params , qarg_params , qsym )
check_params ( aux_params , qaux_params )
calib_data = mx . nd . random . uniform ( shape = dshape )
calib_data = NDArrayIter ( data = calib_data , batch_size = batch_size )
calib_data = DummyIter ( calib_data )
qsym , qarg_params , qaux_params = mx . contrib . quant . quantize_model ( sym = s ,
arg_params = arg_params ,
aux_params = aux_params ,
ctx = mx . current_context ( ) ,
quantized_dtype = qdtype ,
calib_mode = ' naive ' ,
calib_data = calib_data ,
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num_calib_examples = 20 ,
quantize_mode = ' full ' )
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check_params ( arg_params , qarg_params , qsym )
check_params ( aux_params , qaux_params )
check_qsym_calibrated ( qsym )
check_qsym_qdtype ( qsym , qdtype )
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for qdtype in [ ' int8 ' , ' uint8 ' ] :
check_quantize_model ( qdtype )
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@with_seed ( )
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def test_quantize_model_with_forward ( ) :
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def check_quantize_model ( qdtype ) :
if is_test_for_native_cpu ( ) :
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print ( ' skipped testing test_quantize_model_with_forward for native cpu since it is not supported yet ' )
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return
elif qdtype == ' uint8 ' and is_test_for_gpu ( ) :
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print ( ' skipped testing test_quantize_model_with_forward for gpu uint8 since it is not supported yet ' )
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return
def check_params ( params , qparams , qsym = None ) :
if qsym is None :
assert len ( params ) == len ( qparams )
for k , v in params . items ( ) :
assert k in qparams
assert same ( v . asnumpy ( ) , qparams [ k ] . asnumpy ( ) )
else :
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qparams_ground_truth = mx . contrib . quant . _quantize_params ( qsym , params , th_dict = { } )
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assert len ( qparams ) == len ( qparams_ground_truth )
for k , v in qparams_ground_truth . items ( ) :
assert k in qparams
assert same ( v . asnumpy ( ) , qparams [ k ] . asnumpy ( ) )
def check_qsym_calibrated ( qsym ) :
attrs = qsym . attr_dict ( )
for k , v in attrs . items ( ) :
if k . find ( ' requantize_ ' ) != - 1 :
assert ' min_calib_range ' in v
assert ' max_calib_range ' in v
def check_qsym_qdtype ( qsym , qdtype ) :
attrs = qsym . attr_dict ( )
for k , v in attrs . items ( ) :
if k . find ( ' _quantize ' ) != - 1 :
assert ' out_type ' in v
assert v [ ' out_type ' ] == qdtype
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def check_qsym_forward ( qsym , qarg_params , qaux_params , data_shape , label_shape = None ) :
if label_shape is None :
mod = mx . mod . Module ( symbol = qsym , label_names = None , context = mx . current_context ( ) )
mod . bind ( for_training = False ,
data_shapes = [ ( ' data ' , data_shape ) ] )
else :
mod = mx . mod . Module ( symbol = qsym , context = mx . current_context ( ) )
mod . bind ( for_training = False ,
data_shapes = [ ( ' data ' , data_shape ) ] ,
label_shapes = [ ( ' softmax_label ' , label_shape ) ] )
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mod . set_params ( qarg_params , qaux_params )
data = [ mx . random . uniform ( - 1.0 , 1.0 , shape = shape ) for _ , shape in mod . data_shapes ]
batch = mx . io . DataBatch ( data , [ ] )
mod . forward ( batch , is_train = False )
for output in mod . get_outputs ( ) :
output . wait_to_read ( )
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batch_size = 4
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length = batch_size # specify num of outputs from split op
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sym_list = [ ]
name_list = [ ]
dshape_list = [ ]
lshape_list = [ ]
# sym 1
sym_list . append ( get_fp32_residual ( ) )
name_list . append ( ' sym1 ' )
dshape_list . append ( ( batch_size , 4 , 10 , 10 ) )
lshape_list . append ( ( batch_size , 10 ) )
# sym 2
sym_list . append ( get_fp32_sym_with_multiple_outputs ( length ) )
name_list . append ( ' sym2 ' )
dshape_list . append ( ( length , 4 , 4 , 10 , 10 ) )
lshape_list . append ( ( length , 10 ) )
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data = mx . sym . Variable ( ' data ' )
# sym 3
sym_list . append ( mx . sym . Convolution ( data , kernel = ( 1 , 1 ) , num_filter = 16 , name = ' conv0 ' ) )
name_list . append ( ' sym3 ' )
dshape_list . append ( ( batch_size , 4 , 10 , 10 ) )
lshape_list . append ( None )
# sym 4
cell = mx . rnn . LSTMCell ( num_hidden = 64 )
outputs , _ = cell . unroll ( length , data )
sym_list . append ( mx . sym . Group ( outputs ) )
name_list . append ( ' sym4 ' )
dshape_list . append ( ( batch_size , length , 32 ) )
lshape_list . append ( None )
for s , dshape , lshape , name in zip ( sym_list , dshape_list , lshape_list , name_list ) :
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if qdtype == ' int8 ' and name in [ ' sym1 ' , ' sym2 ' , ' sym3 ' ] :
print ( ' mkldnn_quantized_conv op only supports uint8 as input type, skip test with int8. ' )
continue
if qdtype == ' uint8 ' and name in [ ' sym1 ' ] :
print ( ' mkldnn_quantized_bn doesn \' t support calib_mode=None ' )
continue
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if lshape is None :
mod = Module ( symbol = s , label_names = None )
mod . bind ( for_training = False ,
data_shapes = [ ( ' data ' , dshape ) ] )
else :
mod = Module ( symbol = s )
mod . bind ( for_training = False ,
data_shapes = [ ( ' data ' , dshape ) ] ,
label_shapes = [ ( ' softmax_label ' , lshape ) ] )
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mod . init_params ( )
arg_params , aux_params = mod . get_params ( )
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excluded_sym_names = [ ]
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excluded_op_names = [ ]
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# sym3/sym4 doesn't have such layers
if name not in [ ' sym3 ' , ' sym4 ' ] :
excluded_names = [ ]
if mx . current_context ( ) == mx . cpu ( ) :
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excluded_op_names + = [ ' FullyConnected ' ]
excluded_names + = [ ' conv1 ' ]
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excluded_names + = [ ' concat ' ]
optional_names = [ ' pool0 ' ]
for skip_optional_names in [ False , True ] :
exclude_sym_names = [ ]
if skip_optional_names :
excluded_sym_names = excluded_names
else :
excluded_sym_names = excluded_names + optional_names
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if name == ' sym4 ' :
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excluded_op_names + = [ ' elemwise_add ' , ' elemwise_mul ' ]
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qsym , qarg_params , qaux_params = mx . contrib . quant . quantize_model ( sym = s ,
arg_params = arg_params ,
aux_params = aux_params ,
excluded_sym_names = excluded_sym_names ,
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excluded_op_names = excluded_op_names ,
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ctx = mx . current_context ( ) ,
quantized_dtype = qdtype ,
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calib_mode = ' none ' ,
quantize_mode = ' full ' )
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check_params ( arg_params , qarg_params , qsym )
check_params ( aux_params , qaux_params )
check_qsym_forward ( qsym , qarg_params , qaux_params , dshape , lshape )
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calib_data = mx . nd . random . uniform ( shape = dshape )
calib_data = NDArrayIter ( data = calib_data , batch_size = batch_size )
calib_data = DummyIter ( calib_data )
qsym , qarg_params , qaux_params = mx . contrib . quant . quantize_model ( sym = s ,
arg_params = arg_params ,
aux_params = aux_params ,
excluded_sym_names = excluded_sym_names ,
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excluded_op_names = excluded_op_names ,
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ctx = mx . current_context ( ) ,
quantized_dtype = qdtype ,
calib_mode = ' naive ' ,
calib_data = calib_data ,
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num_calib_examples = 20 ,
quantize_mode = ' full ' )
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check_params ( arg_params , qarg_params , qsym )
check_params ( aux_params , qaux_params )
check_qsym_calibrated ( qsym )
check_qsym_qdtype ( qsym , qdtype )
check_qsym_forward ( qsym , qarg_params , qaux_params , dshape , lshape )
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for qdtype in [ ' int8 ' , ' uint8 ' ] :
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check_quantize_model ( qdtype )
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@with_seed ( )
def test_quantize_gluon_with_forward ( ) :
def check_quantize_net ( qdtype ) :
if is_test_for_native_cpu ( ) :
print ( ' skipped testing test_quantize_model_with_forward for native cpu since it is not supported yet ' )
return
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elif is_test_for_gpu ( ) :
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print ( ' skipped testing test_quantize_model_with_forward for gpu uint8 since it is not supported yet ' )
return
data_shape = ( 32 , 3 , 224 , 224 )
data_shapes = [ mx . io . DataDesc ( name = ' data ' , shape = data_shape ) ]
label_shape = ( 32 , 1 )
batch_size = 1
resnet18_v1 = vision . resnet18_v1 ( pretrained = True )
resnet18_v1 . collect_params ( ) . reset_ctx ( mx . current_context ( ) )
excluded_names_match = [ ]
if mx . current_context ( ) == mx . gpu ( ) :
excluded_names_match + = [ ' activation ' , ' relu ' , ' conv0 ' ]
num_calib_examples = 5
random_data = mx . random . uniform ( shape = data_shape )
random_label = mx . random . uniform ( shape = label_shape )
dataset = mx . gluon . data . dataset . ArrayDataset ( random_data , random_label )
calib_data = mx . gluon . data . DataLoader ( dataset , batch_size = batch_size )
quantized_resnet18_v1 = mx . contrib . quant . quantize_net ( resnet18_v1 , quantized_dtype = qdtype ,
exclude_layers = None ,
exclude_layers_match = excluded_names_match ,
calib_mode = ' none ' ,
data_shapes = data_shapes ,
ctx = mx . current_context ( ) )
quantized_resnet18_v1 . hybridize ( static_alloc = True , static_shape = True )
quantized_resnet18_v1 ( random_data )
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for mode in [ ' naive ' , ' entropy ' ] :
qdtype = qdtype if mode is ' naive ' else ' auto '
quantized_resnet18_v1 = mx . contrib . quant . quantize_net ( resnet18_v1 , quantized_dtype = qdtype ,
exclude_layers = None ,
exclude_layers_match = excluded_names_match ,
calib_data = calib_data ,
calib_mode = mode ,
num_calib_examples = num_calib_examples ,
ctx = mx . current_context ( ) )
quantized_resnet18_v1 . hybridize ( static_alloc = True , static_shape = True )
quantized_resnet18_v1 ( random_data )
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for qdtype in [ ' int8 ' , ' uint8 ' ] :
check_quantize_net ( qdtype )
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@with_seed ( )
def test_quantize_sym_with_calib ( ) :
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if is_test_for_native_cpu ( ) :
print ( ' skipped testing quantized_pooling for native cpu since it is not supported yet ' )
return
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sym = get_fp32_sym ( )
offline_params = [ name for name in sym . list_arguments ( )
if not name . startswith ( ' data ' ) and not name . endswith ( ' label ' ) ]
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qsym , _ = mx . contrib . quant . _quantize_symbol ( sym , ctx = mx . current_context ( ) ,
offline_params = offline_params , quantize_mode = ' full ' )
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requantize_op_names = [ ' requantize_conv ' , ' requantize_fc ' ]
th_dict = { ' conv_output ' : ( np . random . uniform ( low = 100.0 , high = 200.0 ) , np . random . uniform ( low = 100.0 , high = 200.0 ) ) ,
' fc_output ' : ( np . random . uniform ( low = 100.0 , high = 200.0 ) , np . random . uniform ( low = 100.0 , high = 200.0 ) ) }
op_name_to_th_name = { ' requantize_conv ' : ' conv_output ' , ' requantize_fc ' : ' fc_output ' }
cqsym = mx . contrib . quant . _calibrate_quantized_sym ( qsym , th_dict )
attr_dict = cqsym . attr_dict ( )
for name in requantize_op_names :
assert name in attr_dict
lhs = float ( attr_dict [ name ] [ ' min_calib_range ' ] )
rhs = th_dict [ op_name_to_th_name [ name ] ] [ 0 ]
assert_almost_equal ( np . array ( [ lhs ] ) , np . array ( [ rhs ] ) )
lhs = float ( attr_dict [ name ] [ ' max_calib_range ' ] )
rhs = th_dict [ op_name_to_th_name [ name ] ] [ 1 ]
assert_almost_equal ( np . array ( [ lhs ] ) , np . array ( [ rhs ] ) , rtol = 1e-3 , atol = 1e-4 )
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@with_seed ( )
def test_smooth_distribution ( ) :
assert_exception ( lambda : mx . contrib . quant . _smooth_distribution ( np . zeros ( ( 2 , ) ) , eps = 1e-3 ) , ValueError )
dirac_delta = np . zeros ( ( 5 , ) )
dirac_delta [ 2 ] = 1
smooth_dirac_delta = dirac_delta . copy ( )
smooth_dirac_delta + = 1e-3
smooth_dirac_delta [ 2 ] - = 5e-3
assert_almost_equal ( mx . contrib . quant . _smooth_distribution ( dirac_delta , eps = 1e-3 ) , smooth_dirac_delta )
@with_seed ( )
def test_optimal_threshold_adversarial_case ( ) :
# The worst case for the optimal_threshold function is when the values are concentrated
# at one edge: [0, 0, ..., 1000]. (histogram)
# We want to make sure that the optimal threshold in this case is the max.
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hist = [ ]
hist_edges = [ ]
min_val = - 2
max_val = 2
for i in range ( 0 , 998 ) :
hist . append ( 0 )
for i in range ( 0 , 999 ) :
hist_edges . append ( ( max_val - min_val ) / 999 * i + min_val )
hist . append ( 1000 )
hist_edges . append ( max_val )
hist_data = ( hist , hist_edges , min_val , max_val , max_val )
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for dtype in [ ' uint8 ' , ' int8 ' , ' auto ' ] :
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res = mx . contrib . quant . _get_optimal_threshold ( hist_data , dtype , num_quantized_bins = 5 )
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# The threshold should be 2.
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print ( res )
assert abs ( res [ 2 ] - 2 ) < 1e-5
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@with_seed ( )
def test_get_optimal_thresholds ( ) :
# Given an ndarray with elements following a uniform distribution, the optimal threshold
# for quantizing the ndarray should be either abs(min(nd)) or abs(max(nd)).
def get_threshold ( nd ) :
min_nd = mx . nd . min ( nd )
max_nd = mx . nd . max ( nd )
return mx . nd . maximum ( mx . nd . abs ( min_nd ) , mx . nd . abs ( max_nd ) ) . asnumpy ( )
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for dtype in [ ' uint8 ' , ' int8 ' , ' auto ' ] :
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nd = mx . nd . uniform ( low = - 10.532 , high = 11.3432 , shape = ( 8 , 3 , 23 , 23 ) , dtype = np . float64 )
expected_threshold = get_threshold ( nd )
arr = nd . asnumpy ( )
min_range = np . min ( arr )
max_range = np . max ( arr )
th = max ( abs ( min_range ) , abs ( max_range ) )
hist , hist_edges = np . histogram ( arr , bins = 8001 , range = ( - th , th ) )
hist_dict = { ' layer1 ' : ( hist , hist_edges , min_range , max_range , th ) }
th_dict = mx . contrib . quant . _get_optimal_thresholds ( hist_dict , dtype )
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assert ' layer1 ' in th_dict
assert_almost_equal ( np . array ( [ th_dict [ ' layer1 ' ] [ 1 ] ] ) , expected_threshold , rtol = 1e-2 , atol = 1e-4 )
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if __name__ == " __main__ " :
import nose
nose . runmodule ( )
