<s> """<STR_LIT>""" <EOL> import itertools as itt <EOL> import numpy as np <EOL> from neon import NervanaObject <EOL> from neon . initializers . initializer import Constant , Gaussian <EOL> from neon . layers import GRU <EOL> from neon . transforms import Logistic , Tanh <EOL> from tests . gru_ref import GRU as RefGRU <EOL> from tests . utils import allclose_with_out <EOL> def pytest_generate_tests ( metafunc ) : <EOL> bsz_rng = [ <NUM_LIT:1> ] <EOL> if '<STR_LIT>' in metafunc . fixturenames : <EOL> fargs = [ ] <EOL> if metafunc . config . option . all : <EOL> seq_rng = [ <NUM_LIT:2> , <NUM_LIT:3> , <NUM_LIT:4> ] <EOL> inp_rng = [ <NUM_LIT:3> , <NUM_LIT:5> , <NUM_LIT:10> ] <EOL> out_rng = [ <NUM_LIT:3> , <NUM_LIT:5> , <NUM_LIT:10> ] <EOL> else : <EOL> seq_rng = [ <NUM_LIT:3> ] <EOL> inp_rng = [ <NUM_LIT:5> ] <EOL> out_rng = [ <NUM_LIT:10> ] <EOL> fargs = itt . product ( seq_rng , inp_rng , out_rng , bsz_rng ) <EOL> metafunc . parametrize ( '<STR_LIT>' , fargs ) <EOL> if '<STR_LIT>' in metafunc . fixturenames : <EOL> fargs = [ ] <EOL> if metafunc . config . option . all : <EOL> seq_rng = [ <NUM_LIT:2> , <NUM_LIT:3> ] <EOL> inp_rng = [ <NUM_LIT:5> , <NUM_LIT:10> ] <EOL> out_rng = [ <NUM_LIT:3> , <NUM_LIT:5> , <NUM_LIT:10> ] <EOL> else : <EOL> seq_rng = [ <NUM_LIT:3> ] <EOL> inp_rng = [ <NUM_LIT:5> ] <EOL> out_rng = [ <NUM_LIT:10> ] <EOL> fargs = itt . product ( seq_rng , inp_rng , out_rng , bsz_rng ) <EOL> metafunc . parametrize ( '<STR_LIT>' , fargs ) <EOL> def test_ref_compare_ones ( backend_default , refgruargs ) : <EOL> seq_len , input_size , hidden_size , batch_size = refgruargs <EOL> NervanaObject . be . bsz = NervanaObject . be . batch_size = batch_size <EOL> check_gru ( seq_len , input_size , hidden_size , <EOL> batch_size , Constant ( val = <NUM_LIT:1.0> ) , [ <NUM_LIT:1.0> , <NUM_LIT:0.0> ] ) <EOL> def test_ref_compare_rand ( backend_default , refgruargs ) : <EOL> seq_len , input_size , hidden_size , batch_size = refgruargs <EOL> NervanaObject . be . bsz = NervanaObject . be . batch_size = batch_size <EOL> check_gru ( seq_len , input_size , hidden_size , batch_size , <EOL> Gaussian ( ) ) <EOL> def check_gru ( seq_len , input_size , hidden_size , <EOL> batch_size , init_func , inp_moms = [ <NUM_LIT:0.0> , <NUM_LIT:1.0> ] ) : <EOL> input_shape = ( input_size , seq_len * batch_size ) <EOL> output_shape = ( hidden_size , seq_len * batch_size ) <EOL> NervanaObject . be . bsz = NervanaObject . be . batch_size = batch_size <EOL> gru = GRU ( hidden_size , <EOL> init_func , <EOL> activation = Tanh ( ) , <EOL> gate_activation = Logistic ( ) ) <EOL> inp = np . random . rand ( * input_shape ) * inp_moms [ <NUM_LIT:1> ] + inp_moms [ <NUM_LIT:0> ] <EOL> inpa = gru . be . array ( inp ) <EOL> deltas = np . random . randn ( * output_shape ) <EOL> gru . configure ( ( input_size , seq_len ) ) <EOL> gru . prev_layer = True <EOL> gru . allocate ( ) <EOL> gru . set_deltas ( [ gru . be . iobuf ( gru . in_shape ) ] ) <EOL> gru . fprop ( inpa ) <EOL> gru_ref = RefGRU ( input_size , hidden_size ) <EOL> WGRU = gru_ref . weights <EOL> r_range = range ( hidden_size ) <EOL> z_range = range ( hidden_size , hidden_size * <NUM_LIT:2> ) <EOL> c_range = range ( hidden_size * <NUM_LIT:2> , hidden_size * <NUM_LIT:3> ) <EOL> WGRU [ gru_ref . weights_ind_br ] [ : ] = gru . b . get ( ) [ r_range ] <EOL> WGRU [ gru_ref . weights_ind_bz ] [ : ] = gru . b . get ( ) [ z_range ] <EOL> WGRU [ gru_ref . weights_ind_bc ] [ : ] = gru . b . get ( ) [ c_range ] <EOL> WGRU [ gru_ref . weights_ind_Wxr ] [ : ] = gru . W_input . get ( ) [ r_range ] <EOL> WGRU [ gru_ref . weights_ind_Wxz ] [ : ] = gru . W_input . get ( ) [ z_range ] <EOL> WGRU [ gru_ref . weights_ind_Wxc ] [ : ] = gru . W_input . get ( ) [ c_range ] <EOL> WGRU [ gru_ref . weights_ind_Rhr ] [ : ] = gru . W_recur . get ( ) [ r_range ] <EOL> WGRU [ gru_ref . weights_ind_Rhz ] [ : ] = gru . W_recur . get ( ) [ z_range ] <EOL> WGRU [ gru_ref . weights_ind_Rhc ] [ : ] = gru . W_recur . get ( ) [ c_range ] <EOL> inp_ref = inp . copy ( ) . T . reshape ( <EOL> seq_len , batch_size , input_size ) . swapaxes ( <NUM_LIT:1> , <NUM_LIT:2> ) <EOL> deltas_ref = deltas . copy ( ) . T . reshape ( <EOL> seq_len , batch_size , hidden_size ) . swapaxes ( <NUM_LIT:1> , <NUM_LIT:2> ) <EOL> ( dWGRU_ref , h_ref_list , dh_ref_list , <EOL> dr_ref_list , dz_ref_list , dc_ref_list ) = gru_ref . lossFun ( inp_ref , <EOL> deltas_ref ) <EOL> print '<STR_LIT>' <EOL> print allclose_with_out ( gru . outputs . get ( ) , <EOL> h_ref_list , <EOL> rtol = <NUM_LIT:0.0> , <EOL> atol = <NUM_LIT> ) <EOL> print '<STR_LIT>' <EOL> print '<STR_LIT>' <EOL> gru . bprop ( gru . be . array ( deltas ) ) <EOL> dWinput_neon = gru . dW_input . get ( ) <EOL> dWrecur_neon = gru . dW_recur . get ( ) <EOL> db_neon = gru . db . get ( ) <EOL> dWxr_neon = dWinput_neon [ r_range ] <EOL> dWxz_neon = dWinput_neon [ z_range ] <EOL> dWxc_neon = dWinput_neon [ c_range ] <EOL> dWrr_neon = dWrecur_neon [ r_range ] <EOL> dWrz_neon = dWrecur_neon [ z_range ] <EOL> dWrc_neon = dWrecur_neon [ c_range ] <EOL> dbr_neon = db_neon [ r_range ] <EOL> dbz_neon = db_neon [ z_range ] <EOL> dbc_neon = db_neon [ c_range ] <EOL> drzc_neon = gru . rzhcan_delta_buffer . get ( ) <EOL> dr_neon = drzc_neon [ r_range ] <EOL> dz_neon = drzc_neon [ z_range ] <EOL> dc_neon = drzc_neon [ c_range ] <EOL> dWxr_ref = dWGRU_ref [ gru_ref . dW_ind_Wxr ] <EOL> dWxz_ref = dWGRU_ref [ gru_ref . dW_ind_Wxz ] <EOL> dWxc_ref = dWGRU_ref [ gru_ref . dW_ind_Wxc ] <EOL> dWrr_ref = dWGRU_ref [ gru_ref . dW_ind_Rhr ] <EOL> dWrz_ref = dWGRU_ref [ gru_ref . dW_ind_Rhz ] <EOL> dWrc_ref = dWGRU_ref [ gru_ref . dW_ind_Rhc ] <EOL> dbr_ref = dWGRU_ref [ gru_ref . dW_ind_br ] <EOL> dbz_ref = dWGRU_ref [ gru_ref . dW_ind_bz ] <EOL> dbc_ref = dWGRU_ref [ gru_ref . dW_ind_bc ] <EOL> print '<STR_LIT>' <EOL> assert allclose_with_out ( dr_neon , <EOL> dr_ref_list , <EOL> rtol = <NUM_LIT:0.0> , <EOL> atol = <NUM_LIT> ) <EOL> print '<STR_LIT>' <EOL> assert allclose_with_out ( dz_neon , <EOL> dz_ref_list , <EOL> rtol = <NUM_LIT:0.0> , <EOL> atol = <NUM_LIT> ) <EOL> print '<STR_LIT>' <EOL> assert allclose_with_out ( dc_neon , <EOL> dc_ref_list , <EOL> rtol = <NUM_LIT:0.0> , <EOL> atol = <NUM_LIT> ) <EOL> print '<STR_LIT>' <EOL> print '<STR_LIT>' <EOL> assert allclose_with_out ( dWxr_neon , <EOL> dWxr_ref , <EOL> rtol = <NUM_LIT:0.0> , <EOL> atol = <NUM_LIT> ) <EOL> print '<STR_LIT>' <EOL> assert allclose_with_out ( dWxz_neon , <EOL> dWxz_ref , <EOL> rtol = <NUM_LIT:0.0> , <EOL> atol = <NUM_LIT> ) <EOL> print '<STR_LIT>' <EOL> assert allclose_with_out ( dWxc_neon , <EOL> dWxc_ref , <EOL> rtol = <NUM_LIT:0.0> , <EOL> atol = <NUM_LIT> ) <EOL> print '<STR_LIT>' <EOL> print '<STR_LIT>' <EOL> assert allclose_with_out ( dWrr_neon , <EOL> dWrr_ref , <EOL> rtol = <NUM_LIT:0.0> , <EOL> atol = <NUM_LIT> ) <EOL> print '<STR_LIT>' <EOL> assert allclose_with_out ( dWrz_neon , <EOL> dWrz_ref , <EOL> rtol = <NUM_LIT:0.0> , <EOL> atol = <NUM_LIT> ) <EOL> print '<STR_LIT>' <EOL> assert allclose_with_out ( dWrc_neon , <EOL> dWrc_ref , <EOL> rtol = <NUM_LIT:0.0> , <EOL> atol = <NUM_LIT> ) <EOL> print '<STR_LIT>' <EOL> print '<STR_LIT>' <EOL> assert allclose_with_out ( dbr_neon , <EOL> dbr_ref , <EOL> rtol = <NUM_LIT:0.0> , <EOL> atol = <NUM_LIT> ) <EOL> print '<STR_LIT>' <EOL> assert allclose_with_out ( dbz_neon , <EOL> dbz_ref , <EOL> rtol = <NUM_LIT:0.0> , <EOL> atol = <NUM_LIT> ) <EOL> print '<STR_LIT>' <EOL> assert allclose_with_out ( dbc_neon , <EOL> dbc_ref , <EOL> rtol = <NUM_LIT:0.0> , <EOL> atol = <NUM_LIT> ) <EOL> print '<STR_LIT>' <EOL> return <EOL> def reset_gru ( gru ) : <EOL> gru . x = None <EOL> gru . xs = None <EOL> gru . outputs = None <EOL> return <EOL> def test_gradient_neon_gru ( backend_default , gradgruargs ) : <EOL> seq_len , input_size , hidden_size , batch_size = gradgruargs <EOL> NervanaObject . be . bsz = NervanaObject . be . batch_size = batch_size <EOL> gradient_check ( seq_len , input_size , hidden_size , batch_size ) <EOL> def gradient_check ( seq_len , input_size , hidden_size , batch_size , <EOL> threshold = <NUM_LIT> ) : <EOL> min_max_err = - <NUM_LIT:1.0> <EOL> print '<STR_LIT>' <EOL> for pert_exp in range ( - <NUM_LIT:5> , <NUM_LIT:0> ) : <EOL> input_shape = ( input_size , seq_len * batch_size ) <EOL> output_shape = ( hidden_size , seq_len * batch_size ) <EOL> rand_scale = np . random . random ( output_shape ) * <NUM_LIT> - <NUM_LIT:1.0> <EOL> inp = np . random . randn ( * input_shape ) <EOL> pert_mag = <NUM_LIT> ** pert_exp <EOL> ( grad_est , deltas ) = gradient_calc ( seq_len , <EOL> input_size , <EOL> hidden_size , <EOL> batch_size , <EOL> epsilon = pert_mag , <EOL> rand_scale = rand_scale , <EOL> inp_bl = inp ) <EOL> dd = np . max ( np . abs ( grad_est - deltas ) ) <EOL> print '<STR_LIT>' % ( pert_mag , dd ) <EOL> if min_max_err < <NUM_LIT:0.0> or dd < min_max_err : <EOL> min_max_err = dd <EOL> NervanaObject . be . rng_reset ( ) <EOL> print '<STR_LIT>' % ( min_max_err , pert_mag ) <EOL> print '<STR_LIT>' % ( threshold ) <EOL> assert min_max_err < threshold <EOL> def gradient_calc ( seq_len , input_size , hidden_size , batch_size , <EOL> epsilon = None , rand_scale = None , inp_bl = None ) : <EOL> NervanaObject . be . bsz = NervanaObject . be . batch_size = batch_size <EOL> input_shape = ( input_size , seq_len * batch_size ) <EOL> if inp_bl is None : <EOL> inp_bl = np . random . randn ( * input_shape ) <EOL> gru = GRU ( hidden_size , init = Gaussian ( ) , activation = Tanh ( ) , gate_activation = Logistic ( ) ) <EOL> inpa = gru . be . array ( np . copy ( inp_bl ) ) <EOL> gru . configure ( ( input_size , seq_len ) ) <EOL> gru . prev_layer = True <EOL> gru . allocate ( ) <EOL> gru . set_deltas ( [ gru . be . iobuf ( gru . in_shape ) ] ) <EOL> out_bl = gru . fprop ( inpa ) . get ( ) <EOL> if rand_scale is None : <EOL> rand_scale = np . random . random ( out_bl . shape ) * <NUM_LIT> - <NUM_LIT:1.0> <EOL> deltas_neon = gru . bprop ( gru . be . array ( np . copy ( rand_scale ) ) ) . get ( ) <EOL> grads_est = np . zeros ( inpa . shape ) <EOL> inp_pert = inp_bl . copy ( ) <EOL> for pert_ind in range ( inpa . size ) : <EOL> save_val = inp_pert . flat [ pert_ind ] <EOL> inp_pert . flat [ pert_ind ] = save_val + epsilon <EOL> reset_gru ( gru ) <EOL> gru . allocate ( ) <EOL> out_pos = gru . fprop ( gru . be . array ( inp_pert ) ) . get ( ) <EOL> inp_pert . flat [ pert_ind ] = save_val - epsilon <EOL> reset_gru ( gru ) <EOL> gru . allocate ( ) <EOL> out_neg = gru . fprop ( gru . be . array ( inp_pert ) ) . get ( ) <EOL> loss_pos = np . sum ( rand_scale * out_pos ) <EOL> loss_neg = np . sum ( rand_scale * out_neg ) <EOL> grad = <NUM_LIT:0.5> * ( loss_pos - loss_neg ) / epsilon <EOL> grads_est . flat [ pert_ind ] = grad <EOL> inp_pert . flat [ pert_ind ] = save_val <EOL> del gru <EOL> return ( grads_est , deltas_neon ) </s>
