def test_bprop(self):
r = []
for i in xrange(self.N):
matrices = []
nrows = self.rng.random_integers(1, 3000)
ncols = [0]
col_slices = []
device_ids = []
for _ in xrange(self.rng.random_integers(1, 10)):
_ncols = self.rng.random_integers(1, 2000)
ncols.append(ncols[-1] + _ncols)
if self.rng.choice([True, False]):
device_ids.append(0)
col_slices.append((ncols[-2], ncols[-1]))
else:
device_ids.append(None)
matrices.append(self.rng.rand(nrows, _ncols).astype(np.float32))
true_labels = self.rng.randint(ncols[-1], size=(nrows, 1)).astype(np.int32)
if not col_slices:
r.append(True)
continue
output = {}
for processor_type in ['gpu', 'cpu']:
quagga.processor_type = processor_type
qmatrices = [Connector(Matrix.from_npa(m), d_id) for m, d_id in izip(matrices, device_ids)]
qtrue_labels = Connector(Matrix.from_npa(true_labels))
hstack_block = HorizontalStackBlock(*qmatrices)
sce_block = SoftmaxCeBlock(hstack_block.output, qtrue_labels)
for m in qmatrices:
m.fprop()
qtrue_labels.fprop()
hstack_block.fprop()
sce_block.fprop()
sce_block.bprop()
hstack_block.bprop()
output[processor_type] = [m.backward_matrix.to_host()
for m in qmatrices if m.bpropagable]
for dL_dm_gpu, dL_dm_cpu in izip(output['gpu'], output['cpu']):
if not np.allclose(dL_dm_gpu, dL_dm_cpu):
r.append(False)
break
else:
r.append(True)
self.assertEqual(sum(r), self.N)
def test_numpy_fprop(self):
r = []
quagga.processor_type = 'gpu'
for _ in xrange(self.N):
matrices = []
nrows = self.rng.random_integers(1, 5000)
for _ in xrange(self.rng.random_integers(1, 10)):
ncols = self.rng.random_integers(1, 5000)
matrices.append(self.rng.rand(nrows, ncols).astype(np.float32))
numpy_output = np.hstack([m for m in matrices])
matrices = [Connector(Matrix.from_npa(m)) for m in matrices]
hstack_block = HorizontalStackBlock(*matrices)
for m in matrices:
m.fprop()
hstack_block.fprop()
quagga_output = hstack_block.output.to_host()
r.append(np.allclose(numpy_output, quagga_output))
self.assertEqual(sum(r), self.N)
def test_numpy_fprop(self):
r = []
quagga.processor_type = 'gpu'
for _ in xrange(self.N):
matrices = []
nrows = self.rng.random_integers(1, 5000)
for _ in xrange(self.rng.random_integers(1, 10)):
ncols = self.rng.random_integers(1, 5000)
matrices.append(self.rng.rand(nrows, ncols).astype(np.float32))
numpy_output = np.hstack([m for m in matrices])
matrices = [Connector(Matrix.from_npa(m)) for m in matrices]
hstack_block = HorizontalStackBlock(*matrices)
for m in matrices:
m.fprop()
hstack_block.fprop()
quagga_output = hstack_block.output.to_host()
r.append(np.allclose(numpy_output, quagga_output))
self.assertEqual(sum(r), self.N)
def test_fprop(self):
r = []
for i in xrange(self.N):
matrices = []
nrows = self.rng.random_integers(1, 5000)
for _ in xrange(self.rng.random_integers(1, 10)):
ncols = self.rng.random_integers(1, 5000)
matrices.append(self.rng.rand(nrows, ncols).astype(np.float32))
output = {}
for processor_type in ['gpu', 'cpu']:
quagga.processor_type = processor_type
qmatrices = [Connector(Matrix.from_npa(m)) for m in matrices]
for m in qmatrices:
m.fprop()
hstack_block = HorizontalStackBlock(*qmatrices)
hstack_block.fprop()
output[processor_type] = hstack_block.output.to_host()
r.append(np.allclose(output['gpu'], output['cpu']))
self.assertEqual(sum(r), self.N)
def test_fprop(self):
r = []
for i in xrange(self.N):
matrices = []
nrows = self.rng.random_integers(1, 5000)
for _ in xrange(self.rng.random_integers(1, 10)):
ncols = self.rng.random_integers(1, 5000)
matrices.append(self.rng.rand(nrows, ncols).astype(np.float32))
output = {}
for processor_type in ['gpu', 'cpu']:
quagga.processor_type = processor_type
qmatrices = [Connector(Matrix.from_npa(m)) for m in matrices]
for m in qmatrices:
m.fprop()
hstack_block = HorizontalStackBlock(*qmatrices)
hstack_block.fprop()
output[processor_type] = hstack_block.output.to_host()
r.append(np.allclose(output['gpu'], output['cpu']))
self.assertEqual(sum(r), self.N)
def test_bprop(self):
r = []
for i in xrange(self.N):
matrices = []
nrows = self.rng.random_integers(1, 3000)
ncols = [0]
col_slices = []
device_ids = []
for _ in xrange(self.rng.random_integers(1, 10)):
_ncols = self.rng.random_integers(1, 2000)
ncols.append(ncols[-1] + _ncols)
if self.rng.choice([True, False]):
device_ids.append(0)
col_slices.append((ncols[-2], ncols[-1]))
else:
device_ids.append(None)
matrices.append(
self.rng.rand(nrows, _ncols).astype(np.float32))
true_labels = self.rng.randint(ncols[-1],
size=(nrows, 1)).astype(np.int32)
if not col_slices:
r.append(True)
continue
output = {}
for processor_type in ['gpu', 'cpu']:
quagga.processor_type = processor_type
qmatrices = [
Connector(Matrix.from_npa(m), d_id)
for m, d_id in izip(matrices, device_ids)
]
qtrue_labels = Connector(Matrix.from_npa(true_labels))
hstack_block = HorizontalStackBlock(*qmatrices)
sce_block = SoftmaxCeBlock(hstack_block.output, qtrue_labels)
for m in qmatrices:
m.fprop()
qtrue_labels.fprop()
hstack_block.fprop()
sce_block.fprop()
sce_block.bprop()
hstack_block.bprop()
output[processor_type] = [
m.backward_matrix.to_host() for m in qmatrices
if m.bpropagable
]
for dL_dm_gpu, dL_dm_cpu in izip(output['gpu'], output['cpu']):
if not np.allclose(dL_dm_gpu, dL_dm_cpu):
r.append(False)
break
else:
r.append(True)
self.assertEqual(sum(r), self.N)
