def test_biRNN_bprop(backend_default, fargs, deltas_buffer):
# basic sanity check with 0 weights random inputs
seq_len, input_size, hidden_size, batch_size = fargs
in_shape = (input_size, seq_len)
NervanaObject.be.bsz = batch_size
# setup the bi-directional rnn
init_glorot = GlorotUniform()
birnn = BiRNN(hidden_size,
activation=Rectlinclip(slope=0),
init=init_glorot)
birnn.configure(in_shape)
birnn.prev_layer = True
birnn.allocate()
birnn.allocate_deltas(deltas_buffer)
deltas_buffer.allocate_buffers()
birnn.set_deltas(deltas_buffer)
# same weight for bi-rnn backward and rnn weights
birnn.W_input_b[:] = birnn.W_input_f
birnn.W_recur_b[:] = birnn.W_recur_f
birnn.b_b[:] = birnn.b_f
birnn.dW[:] = 0
# same weight for bi-directional rnn
init_glorot = GlorotUniform()
rnn = Recurrent(hidden_size,
activation=Rectlinclip(slope=0),
init=init_glorot)
rnn.configure(in_shape)
rnn.prev_layer = True
rnn.allocate()
rnn.allocate_deltas(deltas_buffer)
deltas_buffer.allocate_buffers()
rnn.set_deltas(deltas_buffer)
# inputs and views
lr = np.random.random((input_size, seq_len * batch_size))
lr_rev = list(reversed(get_steps(lr.copy(), in_shape)))
rl = con(lr_rev, axis=1)
# allocate gpu buffers
inp_lr = birnn.be.array(lr)
inp_rl = birnn.be.array(rl)
# outputs
out_lr_g = birnn.fprop(inp_lr)
del_lr = birnn.bprop(out_lr_g).get().copy()
birnn.h_buffer[:] = 0
out_rl_g = birnn.fprop(inp_rl)
del_rl = birnn.bprop(out_rl_g).get().copy()
del_lr_s = get_steps(del_lr, in_shape)
del_rl_s = get_steps(del_rl, in_shape)
for (x, y) in zip(del_lr_s, reversed(del_rl_s)):
assert np.allclose(x, y, rtol=0.0, atol=1.0e-5)
def test_glorot(backend, args):
be = NervanaObject.be
shape_1 = (1, 2)
shape_2 = (1000, 10000)
Wdev_1 = be.empty(shape_1)
Wdev_2 = be.empty(shape_2)
glorot_init = GlorotUniform()
glorot_init.fill(Wdev_1)
glorot_init.fill(Wdev_2)
Whost_1 = Wdev_1.get()
Whost_2 = Wdev_2.get()
mean_1 = np.mean(Whost_1)
mean_2 = np.mean(Whost_2)
assert np.abs(mean_1) > np.abs(mean_2)
return
def test_lookuptable_ones_error(backend_default, basic_linargs):
nin, nout, batch_size, vocab_size = basic_linargs
NervanaObject.be.bsz = batch_size
dtypeu = np.float32
init_glorot = GlorotUniform()
layer = LookupTable(
vocab_size=vocab_size, embedding_dim=nout, init=init_glorot)
inp = np.random.random_integers(0, vocab_size - 1, size=nin * batch_size)
layer.configure(nin)
layer.allocate()
layer.prev_layer = True # Hack to force delta buffer allocation
layer.set_deltas([layer.be.iobuf(nin)])
inputs = layer.be.array(inp.reshape((nin, batch_size)))
out = layer.fprop(inputs).get()
W = layer.W.get()
for i in range(nin * batch_size):
assert np.all(W[inp[i]].T == out[:, i])
err = dtypeu(np.ones((nout, nin * batch_size)))
layer.bprop(layer.be.array(err)).get()
dw = layer.dW.get()
unqidx, count = np.unique(inp, return_counts=True)
dw_exp = np.zeros((1, nout))
for wrd_id, cnt in zip(unqidx, count):
dw_exp = err[:, 0] * cnt
assert np.all(dw_exp == dw[wrd_id, :])
return
def test_lookuptable_zeros_error(backend_default, basic_linargs):
# basic sanity check with 0 weights random inputs
nin, nout, batch_size, vocab_size = basic_linargs
NervanaObject.be.bsz = batch_size
dtypeu = np.float32
init_glorot = GlorotUniform()
layer = LookupTable(
vocab_size=vocab_size, embedding_dim=nout, init=init_glorot)
inp = np.random.random_integers(0, vocab_size - 1, size=nin * batch_size)
layer.configure(nin)
layer.allocate()
layer.prev_layer = True # Hack to force delta buffer allocation
layer.set_deltas([layer.be.iobuf(nin)])
inputs = layer.be.array(inp.reshape((nin, batch_size)))
out = layer.fprop(inputs).get()
W = layer.W.get()
for i in range(nin * batch_size):
assert np.all(W[inp[i]].T == out[:, i])
err = dtypeu(np.zeros((nout, nin * batch_size)))
layer.bprop(layer.be.array(err)).get()
dw = layer.dW.get()
assert np.min(dw) == 0.0 and np.max(dw) == 0.0
return
def test_biLSTM_bprop(backend_default, fargs):
# basic sanity check with 0 weights random inputs
seq_len, input_size, hidden_size, batch_size = fargs
in_shape = (input_size, seq_len)
out_shape = (hidden_size, seq_len)
NervanaObject.be.bsz = batch_size
# setup the bi-directional rnn
init_glorot = GlorotUniform()
bilstm = BiLSTM(hidden_size, gate_activation=Logistic(),
activation=Tanh(), init=init_glorot, reset_cells=True)
bilstm.configure(in_shape)
bilstm.prev_layer = True
bilstm.allocate()
bilstm.set_deltas([bilstm.be.iobuf(bilstm.in_shape)])
# same weight for bi-rnn backward and rnn weights
nout = hidden_size
bilstm.W_input_b[:] = bilstm.W_input_f
bilstm.W_recur_b[:] = bilstm.W_recur_f
bilstm.b_b[:] = bilstm.b_f
bilstm.dW[:] = 0
# inputs and views
lr = np.random.random((input_size, seq_len * batch_size))
lr_rev = list(reversed(get_steps(lr.copy(), in_shape)))
rl = con(lr_rev, axis=1)
# allocate gpu buffers
inp_lr = bilstm.be.array(lr)
inp_rl = bilstm.be.array(rl)
# outputs
out_lr_g = bilstm.fprop(inp_lr)
out_lr = out_lr_g.get().copy()
del_lr = bilstm.bprop(out_lr_g).get().copy()
bilstm.h_buffer[:] = 0
out_rl_g = bilstm.fprop(inp_rl)
out_rl = out_rl_g.get().copy()
del_rl = bilstm.bprop(out_rl_g).get().copy()
# views
out_lr_f_s = get_steps(out_lr[:nout], out_shape)
out_lr_b_s = get_steps(out_lr[nout:], out_shape)
out_rl_f_s = get_steps(out_rl[:nout], out_shape)
out_rl_b_s = get_steps(out_rl[nout:], out_shape)
# asserts
for x_f, x_b, y_f, y_b in zip(out_lr_f_s, out_lr_b_s,
reversed(out_rl_f_s), reversed(out_rl_b_s)):
assert np.allclose(x_f, y_b, rtol=0.0, atol=1.0e-5)
assert np.allclose(x_b, y_f, rtol=0.0, atol=1.0e-5)
del_lr_s = get_steps(del_lr, in_shape)
del_rl_s = get_steps(del_rl, in_shape)
for (x, y) in zip(del_lr_s, reversed(del_rl_s)):
assert np.allclose(x, y, rtol=0.0, atol=1.0e-5)
def test_biLSTM_fprop(backend_default, fargs):
# basic sanity check with 0 weights random inputs
seq_len, input_size, hidden_size, batch_size = fargs
in_shape = (input_size, seq_len)
out_shape = (hidden_size, seq_len)
NervanaObject.be.bsz = batch_size
# setup the bi-directional rnn
init_glorot = GlorotUniform()
bilstm = BiLSTM(hidden_size,
gate_activation=Logistic(),
init=init_glorot,
activation=Tanh(),
reset_cells=True)
bilstm.configure(in_shape)
bilstm.prev_layer = True
bilstm.allocate()
# same weight
nout = hidden_size
bilstm.W_input_b[:] = bilstm.W_input_f
bilstm.W_recur_b[:] = bilstm.W_recur_f
bilstm.b_b[:] = bilstm.b_f
bilstm.dW[:] = 0
# inputs - random and flipped left-to-right inputs
lr = np.random.random((input_size, seq_len * batch_size))
lr_rev = list(reversed(get_steps(lr.copy(), in_shape)))
rl = con(lr_rev, axis=1)
inp_lr = bilstm.be.array(lr)
inp_rl = bilstm.be.array(rl)
# outputs
out_lr = bilstm.fprop(inp_lr).get().copy()
bilstm.h_buffer[:] = 0
out_rl = bilstm.fprop(inp_rl).get().copy()
# views
out_lr_f_s = get_steps(out_lr[:nout], out_shape)
out_lr_b_s = get_steps(out_lr[nout:], out_shape)
out_rl_f_s = get_steps(out_rl[:nout], out_shape)
out_rl_b_s = get_steps(out_rl[nout:], out_shape)
# asserts
for x_f, x_b, y_f, y_b in zip(out_lr_f_s, out_lr_b_s, reversed(out_rl_f_s),
reversed(out_rl_b_s)):
assert allclose_with_out(x_f, y_b, rtol=0.0, atol=1.0e-5)
assert allclose_with_out(x_b, y_f, rtol=0.0, atol=1.0e-5)
def test_biRNN_fprop(backend_default, fargs):
# basic sanity check with 0 weights random inputs
seq_len, input_size, hidden_size, batch_size = fargs
in_shape = (input_size, seq_len)
out_shape = (hidden_size, seq_len)
NervanaObject.be.bsz = batch_size
# setup the bi-directional rnn
init_glorot = GlorotUniform()
birnn = BiRNN(hidden_size,
activation=Rectlinclip(slope=0),
init=init_glorot)
birnn.configure(in_shape)
birnn.prev_layer = True
birnn.allocate()
birnn.set_deltas([birnn.be.iobuf(birnn.in_shape)])
# same weight
nout = hidden_size
birnn.W_input_b[:] = birnn.W_input_f
birnn.W_recur_b[:] = birnn.W_recur_f
birnn.b_b[:] = birnn.b_f
birnn.dW[:] = 0
# inputs - random and flipped left-to-right inputs
lr = np.random.random((input_size, seq_len * batch_size))
lr_rev = list(reversed(get_steps(lr.copy(), in_shape)))
rl = con(lr_rev, axis=1)
inp_lr = birnn.be.array(lr)
inp_rl = birnn.be.array(rl)
# outputs
out_lr = birnn.fprop(inp_lr).get().copy()
birnn.h_buffer[:] = 0
out_rl = birnn.fprop(inp_rl).get().copy()
# views
out_lr_f_s = get_steps(out_lr[:nout], out_shape)
out_lr_b_s = get_steps(out_lr[nout:], out_shape)
out_rl_f_s = get_steps(out_rl[:nout], out_shape)
out_rl_b_s = get_steps(out_rl[nout:], out_shape)
# asserts
for x_f, x_b, y_f, y_b in zip(out_lr_f_s, out_lr_b_s, reversed(out_rl_f_s),
reversed(out_rl_b_s)):
assert np.allclose(x_f, y_b, rtol=0.0, atol=1.0e-5)
assert np.allclose(x_b, y_f, rtol=0.0, atol=1.0e-5)
def test_lookuptable_rand_error(backend_default, basic_linargs, deltas_buffer):
nin, nout, batch_size, vocab_size = basic_linargs
NervanaObject.be.bsz = batch_size
dtypeu = np.float32
init_glorot = GlorotUniform()
layer = LookupTable(vocab_size=vocab_size,
embedding_dim=nout,
init=init_glorot)
inp = np.random.random_integers(0, vocab_size - 1, size=nin * batch_size)
layer.configure(nin)
layer.allocate()
layer.prev_layer = True # Hack to force delta buffer allocation
layer.allocate_deltas(deltas_buffer)
deltas_buffer.allocate_buffers()
layer.set_deltas(deltas_buffer)
inputs = layer.be.array(inp.reshape((nin, batch_size)))
out = layer.fprop(inputs).get()
W = layer.W.get()
for i in range(nin * batch_size):
assert np.all(W[inp[i]].T == out[:, i])
err = dtypeu(np.random.random((nout, nin * batch_size)))
layer.bprop(layer.be.array(err)).get()
dw = layer.dW.get()
unqidx, count = np.unique(inp, return_counts=True)
dw_exp = np.zeros((1, nout))
for wrd_id, cnt in zip(unqidx, count):
dw_exp[:] = 0
cnt_exp = 0
for i, w_id in enumerate(inp):
if w_id == wrd_id:
dw_exp[:] = dw_exp[:] + err[:, i]
cnt_exp += 1
assert np.allclose(dw[wrd_id, :], dw_exp, atol=0, rtol=1e-4)
assert np.allclose(dw_exp, dw[wrd_id, :], atol=0, rtol=1e-4)
assert cnt == cnt_exp
return
def test_bibn(backend_default, fargs):
seq_len, input_size, hidden_size, batch_size = fargs
in_shape = (input_size, seq_len)
NervanaObject.be.bsz = batch_size
# setup the bi-directional rnn
init_glorot = GlorotUniform()
birnn = BiBNRNN(hidden_size, activation=Logistic(), init=init_glorot)
birnn.configure(in_shape)
birnn.prev_layer = True
birnn.allocate()
birnn.set_deltas([birnn.be.iobuf(birnn.in_shape)])
# test fprop
# set the ff buffer
inp_np = np.random.random(birnn.h_ff_buffer.shape)
inp_be = birnn.be.array(inp_np)
birnn.h_ff_buffer[:] = inp_np
# compare the bn output with calling the backend bn
xsum = birnn.be.zeros_like(birnn.xmean)
xvar = birnn.be.zeros_like(birnn.xvar)
gmean = birnn.be.zeros_like(birnn.gmean)
gvar = birnn.be.zeros_like(birnn.gvar)
gamma = birnn.be.ones(birnn.gamma.shape)
beta = birnn.be.zeros_like(birnn.beta)
grad_gamma = birnn.be.zeros_like(gamma)
grad_beta = birnn.be.zeros_like(beta)
out_ref = birnn.be.zeros_like(birnn.h_ff_buffer)
xsum[:] = birnn.be.sum(birnn.h_ff_buffer, axis=1)
birnn.be.compound_fprop_bn(birnn.h_ff_buffer,
xsum,
xvar,
gmean,
gvar,
gamma,
beta,
out_ref,
birnn.eps,
birnn.rho,
accumbeta=0,
relu=False)
# call the bibnrnn layer fprop_bn
out_bn = birnn._fprop_bn(birnn.h_ff_buffer, inference=False)
assert allclose_with_out(out_bn.get(),
out_ref.get(),
rtol=0.0,
atol=1.0e-5)
# test bprop
err_np = np.random.random(birnn.h_ff_buffer.shape)
err_be = birnn.be.array(err_np)
err_out_ref = birnn.be.empty_like(err_be)
birnn.be.compound_bprop_bn(err_out_ref, grad_gamma, grad_beta, err_be,
inp_be, xsum, xvar, gamma, birnn.eps)
err_out_bn = birnn._bprop_bn(err_be, out_bn)
assert allclose_with_out(err_out_bn.get(),
err_out_ref.get(),
rtol=0.0,
atol=1.0e-5)
