def test_lookuptable_zeros_error(backend_default, basic_linargs, deltas_buffer):
# 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.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.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_lookuptable_zeros_error(backend_default, basic_linargs,
deltas_buffer):
# 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.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.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_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_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
