def testSameDim(self):
A = CuMatrix()
B = CuMatrix()
self.assertTrue(same_dim_cu_matrix(A, B))
A = CuMatrix.new_from_size(10, 10)
B = CuMatrix.new_from_size(10, 9)
self.assertFalse(same_dim_cu_matrix(A, B))
def testSwap(self):
for i in range(10):
dim = (10 * i, 4 * i)
M = Matrix(np.random.random(dim))
A = CuMatrix.new_from_matrix(M)
B = CuMatrix.new_from_size(A.num_rows(), A.num_cols())
B.Swap(A)
self.assertAlmostEqual(A.sum(), B.sum(), places = 4) #Kaldi's precision is aweful
self.assertAlmostEqual(M.sum(), B.sum(), places = 4) #Kaldi's precision is aweful
C = CuMatrix.new_from_size(M.shape[0], M.shape[1])
C.SwapWithMatrix(M)
self.assertAlmostEqual(B.sum(), C.sum(), places = 4) #Kaldi's precision is aweful
def testcopy_from_mat(self):
for i in range(10):
rows, cols = 10 * i, 5 * i
A = Matrix(rows, cols)
A.set_randn_()
B = CuMatrix.new_from_size(*A.shape)
B.copy_from_mat(A)
self.assertAlmostEqual(A.sum(), B.sum(), places=4)
A = CuMatrix.new_from_size(rows, cols)
A.set_randn()
B = CuMatrix.new_from_size(rows, cols)
B.copy_from_cu_mat(A)
self.assertAlmostEqual(A.sum(), B.sum(), places=4)
def testNew(self):
A = CuMatrix()
self.assertIsNotNone(A)
self.assertEqual(0, A.num_rows())
self.assertEqual(0, A.num_cols())
dim = A.dim()
self.assertEqual(0, dim.rows)
self.assertEqual(0, dim.cols)
A = CuMatrix.new_from_size(10, 10)
self.assertIsNotNone(A)
self.assertEqual(10, A.num_rows())
self.assertEqual(10, A.num_cols())
dim = A.dim()
self.assertEqual(10, dim.rows)
self.assertEqual(10, dim.cols)
A = CuMatrix.new_from_matrix(Matrix([[2, 3], [5, 7]]))
self.assertIsNotNone(A)
self.assertEqual(2, A.num_rows())
self.assertEqual(2, A.num_cols())
B = CuMatrix.new_from_other(A)
self.assertIsNotNone(B)
self.assertEqual(2, B.num_rows())
self.assertEqual(2, B.num_cols())
def test_nnet_compute(self):
gen_config = NnetGenerationOptions()
test_collapse_model = random.choice([True, False])
configs = generate_config_sequence(gen_config)
nnet = Nnet()
for j, config in enumerate(configs):
print("Input config[{}]:".format(j))
print(config)
istrm = istringstream.from_str(config)
nnet.read_config(istrm)
request = ComputationRequest()
inputs = compute_example_computation_request_simple(nnet, request)
if test_collapse_model:
set_batchnorm_test_mode(True, nnet)
set_dropout_test_mode(True, nnet)
compiler = Compiler(request, nnet)
opts = CompilerOptions()
computation = compiler.create_computation(opts)
nnet_collapsed = Nnet.new_from_other(nnet)
if test_collapse_model:
collapse_config = CollapseModelConfig()
collapse_model(collapse_config, nnet_collapsed)
compiler_collapsed = Compiler(request, nnet_collapsed)
computation_collapsed = compiler_collapsed.create_computation(opts)
computation_collapsed.compute_cuda_indexes()
ostrm = ostringstream()
computation.print_computation(ostrm, nnet)
print("Generated computation:")
print(ostrm.to_str())
check_config = CheckComputationOptions()
check_config.check_rewrite = True
checker = ComputationChecker(check_config, nnet, computation)
checker.check()
if random.choice([True, False]):
opt_config = NnetOptimizeOptions()
optimize(opt_config, nnet, max_output_time_in_request(request),
computation)
ostrm = ostringstream()
computation.print_computation(ostrm, nnet)
print("Optimized computation:")
print(ostrm.to_str())
compute_opts = NnetComputeOptions()
compute_opts.debug = random.choice([True, False])
computation.compute_cuda_indexes()
computer = NnetComputer(compute_opts, computation, nnet, nnet)
for i, ispec in enumerate(request.inputs):
temp = CuMatrix.new_from_matrix(inputs[i])
print("Input sum:", temp.sum())
computer.accept_input(ispec.name, temp)
computer.run()
output = computer.get_output_destructive("output")
print("Output sum:", output.sum())
if test_collapse_model:
computer_collapsed = NnetComputer(compute_opts,
computation_collapsed,
nnet_collapsed, nnet_collapsed)
for i, ispec in enumerate(request.inputs):
temp = CuMatrix.new_from_matrix(inputs[i])
print("Input sum:", temp.sum())
computer_collapsed.accept_input(ispec.name, temp)
computer_collapsed.run()
output_collapsed = computer_collapsed.get_output_destructive("output")
print("Output sum [collapsed]:", output_collapsed.sum())
self.assertTrue(approx_equal_cu_matrix(output, output_collapsed),
"Regular and collapsed computation outputs differ.")
output_deriv = CuMatrix.new_from_size(output.num_rows(),
output.num_cols())
output_deriv.set_randn()
if request.outputs[0].has_deriv:
computer.accept_input("output", output_deriv)
computer.run()
for i, ispec in enumerate(request.inputs):
if ispec.has_deriv:
in_deriv = computer.get_output_destructive(ispec.name)
print("Input-deriv sum for input {} is:".format(ispec.name),
in_deriv.sum())