def gradient_calc(seq_len, input_size, hidden_size, batch_size,
epsilon=None, rand_scale=None, inp_bl=None):
NervanaObject.be.bsz = NervanaObject.be.batch_size = batch_size
input_shape = (input_size, seq_len * batch_size)
# generate input if one is not given
if inp_bl is None:
inp_bl = np.random.randn(*input_shape)
# neon gru instance
gru = GRU(hidden_size, init=Gaussian(), activation=Tanh(), gate_activation=Logistic())
inpa = gru.be.array(np.copy(inp_bl))
# run fprop on the baseline input
gru.configure((input_size, seq_len))
gru.prev_layer = True
gru.allocate()
gru.set_deltas([gru.be.iobuf(gru.in_shape)])
out_bl = gru.fprop(inpa).get()
# random scaling/hash to generate fake loss
if rand_scale is None:
rand_scale = np.random.random(out_bl.shape) * 2.0 - 1.0
# loss function would be:
# loss_bl = np.sum(rand_scale * out_bl)
# run back prop with rand_scale as the errors
# use copy to avoid any interactions
deltas_neon = gru.bprop(gru.be.array(np.copy(rand_scale))).get()
# add a perturbation to each input element
grads_est = np.zeros(inpa.shape)
inp_pert = inp_bl.copy()
for pert_ind in range(inpa.size):
save_val = inp_pert.flat[pert_ind]
inp_pert.flat[pert_ind] = save_val + epsilon
reset_gru(gru)
gru.allocate()
out_pos = gru.fprop(gru.be.array(inp_pert)).get()
inp_pert.flat[pert_ind] = save_val - epsilon
reset_gru(gru)
gru.allocate()
out_neg = gru.fprop(gru.be.array(inp_pert)).get()
# calculate the loss with perturbations
loss_pos = np.sum(rand_scale*out_pos)
loss_neg = np.sum(rand_scale*out_neg)
# compute the gradient estimate
grad = 0.5*(loss_pos-loss_neg)/epsilon
grads_est.flat[pert_ind] = grad
# reset the perturbed input element
inp_pert.flat[pert_ind] = save_val
del gru
return (grads_est, deltas_neon)
def gradient_calc(seq_len, input_size, hidden_size, batch_size,
epsilon=None, rand_scale=None, inp_bl=None):
NervanaObject.be.bsz = NervanaObject.be.batch_size = batch_size
input_shape = (input_size, seq_len * batch_size)
# generate input if one is not given
if inp_bl is None:
inp_bl = np.random.randn(*input_shape)
# neon gru instance
gru = GRU(hidden_size, init=Gaussian(), activation=Tanh(), gate_activation=Logistic())
inpa = gru.be.array(np.copy(inp_bl))
# run fprop on the baseline input
gru.configure((input_size, seq_len))
gru.prev_layer = True
gru.allocate()
gru.set_deltas([gru.be.iobuf(gru.in_shape)])
out_bl = gru.fprop(inpa).get()
# random scaling/hash to generate fake loss
if rand_scale is None:
rand_scale = np.random.random(out_bl.shape) * 2.0 - 1.0
# loss function would be:
# loss_bl = np.sum(rand_scale * out_bl)
# run back prop with rand_scale as the errors
# use copy to avoid any interactions
deltas_neon = gru.bprop(gru.be.array(np.copy(rand_scale))).get()
# add a perturbation to each input element
grads_est = np.zeros(inpa.shape)
inp_pert = inp_bl.copy()
for pert_ind in range(inpa.size):
save_val = inp_pert.flat[pert_ind]
inp_pert.flat[pert_ind] = save_val + epsilon
reset_gru(gru)
gru.allocate()
out_pos = gru.fprop(gru.be.array(inp_pert)).get()
inp_pert.flat[pert_ind] = save_val - epsilon
reset_gru(gru)
gru.allocate()
out_neg = gru.fprop(gru.be.array(inp_pert)).get()
# calculate the loss with perturbations
loss_pos = np.sum(rand_scale * out_pos)
loss_neg = np.sum(rand_scale * out_neg)
# compute the gradient estimate
grad = 0.5 / float(epsilon) * (loss_pos - loss_neg)
grads_est.flat[pert_ind] = grad
# reset the perturbed input element
inp_pert.flat[pert_ind] = save_val
del gru
return (grads_est, deltas_neon)
def check_gru(seq_len, input_size, hidden_size,
batch_size, init_func, inp_moms=[0.0, 1.0]):
# init_func is the initializer for the model params
# inp_moms is the [ mean, std dev] of the random input
input_shape = (input_size, seq_len * batch_size)
output_shape = (hidden_size, seq_len * batch_size)
NervanaObject.be.bsz = NervanaObject.be.batch_size = batch_size
# neon GRU
gru = GRU(hidden_size,
init_func,
activation=Tanh(),
gate_activation=Logistic())
# generate random input tensor
inp = np.random.rand(*input_shape)*inp_moms[1] + inp_moms[0]
inpa = gru.be.array(inp)
# generate random deltas tensor
deltas = np.random.randn(*output_shape)
# run neon fprop
gru.configure((input_size, seq_len))
gru.prev_layer = True
gru.allocate()
gru.set_deltas([gru.be.iobuf(gru.in_shape)])
gru.fprop(inpa)
# reference numpy GRU
gru_ref = RefGRU(input_size, hidden_size)
WGRU = gru_ref.weights
# make ref weights and biases the same with neon model
r_range = range(hidden_size)
z_range = range(hidden_size, hidden_size * 2)
c_range = range(hidden_size * 2, hidden_size * 3)
WGRU[gru_ref.weights_ind_br][:] = gru.b.get()[r_range]
WGRU[gru_ref.weights_ind_bz][:] = gru.b.get()[z_range]
WGRU[gru_ref.weights_ind_bc][:] = gru.b.get()[c_range]
WGRU[gru_ref.weights_ind_Wxr][:] = gru.W_input.get()[r_range]
WGRU[gru_ref.weights_ind_Wxz][:] = gru.W_input.get()[z_range]
WGRU[gru_ref.weights_ind_Wxc][:] = gru.W_input.get()[c_range]
WGRU[gru_ref.weights_ind_Rhr][:] = gru.W_recur.get()[r_range]
WGRU[gru_ref.weights_ind_Rhz][:] = gru.W_recur.get()[z_range]
WGRU[gru_ref.weights_ind_Rhc][:] = gru.W_recur.get()[c_range]
# transpose input X and do fprop
# the reference code expects these shapes:
# input_shape: (seq_len, input_size, batch_size)
# output_shape: (seq_len, hidden_size, batch_size)
inp_ref = inp.copy().T.reshape(
seq_len, batch_size, input_size).swapaxes(1, 2)
deltas_ref = deltas.copy().T.reshape(
seq_len, batch_size, hidden_size).swapaxes(1, 2)
(dWGRU_ref, h_ref_list, dh_ref_list,
dr_ref_list, dz_ref_list, dc_ref_list) = gru_ref.lossFun(inp_ref,
deltas_ref)
print '====Verifying hidden states===='
print allclose_with_out(gru.outputs.get(),
h_ref_list,
rtol=0.0,
atol=1.0e-5)
print 'fprop is verified'
# now test the bprop
print 'Making sure neon GRU match numpy GRU in bprop'
gru.bprop(gru.be.array(deltas))
# grab the delta W from gradient buffer
dWinput_neon = gru.dW_input.get()
dWrecur_neon = gru.dW_recur.get()
db_neon = gru.db.get()
dWxr_neon = dWinput_neon[r_range]
dWxz_neon = dWinput_neon[z_range]
dWxc_neon = dWinput_neon[c_range]
dWrr_neon = dWrecur_neon[r_range]
dWrz_neon = dWrecur_neon[z_range]
dWrc_neon = dWrecur_neon[c_range]
dbr_neon = db_neon[r_range]
dbz_neon = db_neon[z_range]
dbc_neon = db_neon[c_range]
drzc_neon = gru.rzhcan_delta_buffer.get()
dr_neon = drzc_neon[r_range]
dz_neon = drzc_neon[z_range]
dc_neon = drzc_neon[c_range]
dWxr_ref = dWGRU_ref[gru_ref.dW_ind_Wxr]
dWxz_ref = dWGRU_ref[gru_ref.dW_ind_Wxz]
dWxc_ref = dWGRU_ref[gru_ref.dW_ind_Wxc]
dWrr_ref = dWGRU_ref[gru_ref.dW_ind_Rhr]
dWrz_ref = dWGRU_ref[gru_ref.dW_ind_Rhz]
dWrc_ref = dWGRU_ref[gru_ref.dW_ind_Rhc]
dbr_ref = dWGRU_ref[gru_ref.dW_ind_br]
dbz_ref = dWGRU_ref[gru_ref.dW_ind_bz]
dbc_ref = dWGRU_ref[gru_ref.dW_ind_bc]
# print '====Verifying hidden deltas ===='
print '====Verifying r deltas ===='
assert allclose_with_out(dr_neon,
dr_ref_list,
rtol=0.0,
atol=1.0e-5)
print '====Verifying z deltas ===='
assert allclose_with_out(dz_neon,
dz_ref_list,
rtol=0.0,
atol=1.0e-5)
print '====Verifying hcan deltas ===='
assert allclose_with_out(dc_neon,
dc_ref_list,
rtol=0.0,
atol=1.0e-5)
print '====Verifying update on W_input===='
print 'dWxr'
assert allclose_with_out(dWxr_neon,
dWxr_ref,
rtol=0.0,
atol=1.0e-5)
print 'dWxz'
assert allclose_with_out(dWxz_neon,
dWxz_ref,
rtol=0.0,
atol=1.0e-5)
print 'dWxc'
assert allclose_with_out(dWxc_neon,
dWxc_ref,
rtol=0.0,
atol=1.0e-5)
print '====Verifying update on W_recur===='
print 'dWrr'
assert allclose_with_out(dWrr_neon,
dWrr_ref,
rtol=0.0,
atol=1.0e-5)
print 'dWrz'
assert allclose_with_out(dWrz_neon,
dWrz_ref,
rtol=0.0,
atol=1.0e-5)
print 'dWrc'
assert allclose_with_out(dWrc_neon,
dWrc_ref,
rtol=0.0,
atol=1.0e-5)
print '====Verifying update on bias===='
print 'dbr'
assert allclose_with_out(dbr_neon,
dbr_ref,
rtol=0.0,
atol=1.0e-5)
print 'dbz'
assert allclose_with_out(dbz_neon,
dbz_ref,
rtol=0.0,
atol=1.0e-5)
print 'dbc'
assert allclose_with_out(dbc_neon,
dbc_ref,
rtol=0.0,
atol=1.0e-5)
print 'bprop is verified'
return
def check_gru(seq_len,
input_size,
hidden_size,
batch_size,
init_func,
inp_moms=[0.0, 1.0],
add_init_state=False):
# init_func is the initializer for the model params
# inp_moms is the [ mean, std dev] of the random input
input_shape = (input_size, seq_len * batch_size)
output_shape = (hidden_size, seq_len * batch_size)
slice_shape = (hidden_size, batch_size)
NervanaObject.be.bsz = NervanaObject.be.batch_size = batch_size
# neon GRU
gru = GRU(hidden_size,
init_func,
activation=Tanh(),
gate_activation=Logistic())
# generate random input tensor
inp = np.random.rand(*input_shape) * inp_moms[1] + inp_moms[0]
inp_dev = gru.be.array(inp)
# generate random deltas tensor
deltas = np.random.randn(*output_shape)
# run neon fprop
gru.configure((input_size, seq_len))
gru.prev_layer = True
gru.allocate()
test_buffer = DeltasTree()
gru.allocate_deltas(test_buffer)
test_buffer.allocate_buffers()
gru.set_deltas(test_buffer)
if add_init_state:
init_state = np.random.rand(*slice_shape) * inp_moms[1] + inp_moms[0]
init_state_dev = gru.be.array(init_state)
gru.fprop(inp_dev, init_state=init_state_dev)
else:
gru.fprop(inp_dev)
# reference numpy GRU
gru_ref = RefGRU(input_size, hidden_size)
WGRU = gru_ref.weights
# make ref weights and biases the same with neon model
r_range = list(range(hidden_size))
z_range = list(range(hidden_size, hidden_size * 2))
c_range = list(range(hidden_size * 2, hidden_size * 3))
WGRU[gru_ref.weights_ind_br][:] = gru.b.get()[r_range]
WGRU[gru_ref.weights_ind_bz][:] = gru.b.get()[z_range]
WGRU[gru_ref.weights_ind_bc][:] = gru.b.get()[c_range]
WGRU[gru_ref.weights_ind_Wxr][:] = gru.W_input.get()[r_range]
WGRU[gru_ref.weights_ind_Wxz][:] = gru.W_input.get()[z_range]
WGRU[gru_ref.weights_ind_Wxc][:] = gru.W_input.get()[c_range]
WGRU[gru_ref.weights_ind_Rhr][:] = gru.W_recur.get()[r_range]
WGRU[gru_ref.weights_ind_Rhz][:] = gru.W_recur.get()[z_range]
WGRU[gru_ref.weights_ind_Rhc][:] = gru.W_recur.get()[c_range]
# transpose input X and do fprop
# the reference code expects these shapes:
# input_shape: (seq_len, input_size, batch_size)
# output_shape: (seq_len, hidden_size, batch_size)
inp_ref = inp.copy().T.reshape(seq_len, batch_size,
input_size).swapaxes(1, 2)
deltas_ref = deltas.copy().T.reshape(seq_len, batch_size,
hidden_size).swapaxes(1, 2)
if add_init_state:
init_state_ref = init_state.copy()
(dWGRU_ref, h_ref_list, dh_ref_list, dr_ref_list, dz_ref_list,
dc_ref_list) = gru_ref.lossFun(inp_ref, deltas_ref, init_state_ref)
else:
(dWGRU_ref, h_ref_list, dh_ref_list, dr_ref_list, dz_ref_list,
dc_ref_list) = gru_ref.lossFun(inp_ref, deltas_ref)
neon_logger.display('====Verifying hidden states====')
assert allclose_with_out(gru.outputs.get(),
h_ref_list,
rtol=0.0,
atol=1.0e-5)
neon_logger.display('fprop is verified')
# now test the bprop
neon_logger.display('Making sure neon GRU matches numpy GRU in bprop')
gru.bprop(gru.be.array(deltas))
# grab the delta W from gradient buffer
dWinput_neon = gru.dW_input.get()
dWrecur_neon = gru.dW_recur.get()
db_neon = gru.db.get()
dWxr_neon = dWinput_neon[r_range]
dWxz_neon = dWinput_neon[z_range]
dWxc_neon = dWinput_neon[c_range]
dWrr_neon = dWrecur_neon[r_range]
dWrz_neon = dWrecur_neon[z_range]
dWrc_neon = dWrecur_neon[c_range]
dbr_neon = db_neon[r_range]
dbz_neon = db_neon[z_range]
dbc_neon = db_neon[c_range]
drzc_neon = gru.rzhcan_delta_buffer.get()
dr_neon = drzc_neon[r_range]
dz_neon = drzc_neon[z_range]
dc_neon = drzc_neon[c_range]
dWxr_ref = dWGRU_ref[gru_ref.dW_ind_Wxr]
dWxz_ref = dWGRU_ref[gru_ref.dW_ind_Wxz]
dWxc_ref = dWGRU_ref[gru_ref.dW_ind_Wxc]
dWrr_ref = dWGRU_ref[gru_ref.dW_ind_Rhr]
dWrz_ref = dWGRU_ref[gru_ref.dW_ind_Rhz]
dWrc_ref = dWGRU_ref[gru_ref.dW_ind_Rhc]
dbr_ref = dWGRU_ref[gru_ref.dW_ind_br]
dbz_ref = dWGRU_ref[gru_ref.dW_ind_bz]
dbc_ref = dWGRU_ref[gru_ref.dW_ind_bc]
# neon_logger.display '====Verifying hidden deltas ===='
neon_logger.display('====Verifying r deltas ====')
assert allclose_with_out(dr_neon, dr_ref_list, rtol=0.0, atol=1.0e-5)
neon_logger.display('====Verifying z deltas ====')
assert allclose_with_out(dz_neon, dz_ref_list, rtol=0.0, atol=1.0e-5)
neon_logger.display('====Verifying hcan deltas ====')
assert allclose_with_out(dc_neon, dc_ref_list, rtol=0.0, atol=1.0e-5)
neon_logger.display('====Verifying update on W_input====')
neon_logger.display('dWxr')
assert allclose_with_out(dWxr_neon, dWxr_ref, rtol=0.0, atol=1.0e-5)
neon_logger.display('dWxz')
assert allclose_with_out(dWxz_neon, dWxz_ref, rtol=0.0, atol=1.0e-5)
neon_logger.display('dWxc')
assert allclose_with_out(dWxc_neon, dWxc_ref, rtol=0.0, atol=1.0e-5)
neon_logger.display('====Verifying update on W_recur====')
neon_logger.display('dWrr')
assert allclose_with_out(dWrr_neon, dWrr_ref, rtol=0.0, atol=1.0e-5)
neon_logger.display('dWrz')
assert allclose_with_out(dWrz_neon, dWrz_ref, rtol=0.0, atol=1.0e-5)
neon_logger.display('dWrc')
assert allclose_with_out(dWrc_neon, dWrc_ref, rtol=0.0, atol=1.0e-5)
neon_logger.display('====Verifying update on bias====')
neon_logger.display('dbr')
assert allclose_with_out(dbr_neon, dbr_ref, rtol=0.0, atol=1.0e-5)
neon_logger.display('dbz')
assert allclose_with_out(dbz_neon, dbz_ref, rtol=0.0, atol=1.0e-5)
neon_logger.display('dbc')
assert allclose_with_out(dbc_neon, dbc_ref, rtol=0.0, atol=1.0e-5)
neon_logger.display('bprop is verified')
return