def template_dot_one_placeholder_and_scalar(row, col, scalar, flex_exceptions,
iters):
arg_array = np.array([i for i in range(col * row)]).reshape(row, col)
ng_placeholder = template_create_placeholder(row, col)
ng_var = ng.placeholder(())
ng_fun = ng.dot(ng_var, ng_placeholder)
flex_exceptions_index = 0
print("Initial scalar: ", scalar)
print("Matrix:\n", arg_array)
with executor(ng_fun, ng_var, ng_placeholder) as m_executor:
for i in range(row):
print("Iteration " + str(i + 1))
ng_op_out = m_executor(scalar, arg_array)
np_op_out = np.dot(scalar, arg_array)
# After each iteration matrix values are updated.
arg_array = ng_op_out
print("Flex dot product result: \n", ng_op_out)
print("Numpy dot product result: \n", np_op_out)
try:
assert_allclose(ng_op_out, np_op_out)
except AssertionError:
print(
"Flex dot product result doesn't match to numpy.\n"
"Try to check if flex result is inside flex exceptions list"
)
print("Flex dot product result: \n", ng_op_out)
print("Current array inside flex exceptions list: \n",
flex_exceptions[flex_exceptions_index])
assert_allclose(ng_op_out,
flex_exceptions[flex_exceptions_index])
# Iterate to the next element of flex exceptions list
flex_exceptions_index += 1
def test_gemm(transformer_factory):
"""
TODO: make this more interesting
"""
n, c = 32, 32
N = ng.make_axis(length=n, name='N')
C = ng.make_axis(length=c)
X = ng.placeholder(axes=[C, N])
Y = ng.placeholder(axes=[N])
W = ng.variable(axes=[C - 1], initial_value=0.1)
Y_hat = ng.dot(W, X)
with executor(Y_hat, X) as ex:
mm_executor = ex
w = np.ones(c) * 0.1
xs = np.ones(n * c).reshape(c, n)
for ii in range(3):
y_hat_val = mm_executor(xs)
# 8.8 fixed point test
# assert np.allclose(np.dot(xs, w) - y_hat_val, 0.075*np.ones(n))
# autoflex test
assert_allclose(np.dot(xs, w), y_hat_val)
def template_dot_two_placeholders(rows_1, col_1, col_2):
ng_placeholder2, ng_placeholder1 = \
template_create_placeholders_for_multiplication(col_2, col_1, rows_1)
ng_fun = ng.dot(ng_placeholder1, ng_placeholder2)
arg_array1 = np.array([i for i in range(col_1 * rows_1)
]).reshape(rows_1, col_1)
arg_array2 = np.array([i for i in range(col_1 * col_2)
]).reshape(col_1, col_2)
print("Matrix 1:\n", arg_array1)
print("Matrix 2:\n", arg_array2)
with executor(ng_fun, ng_placeholder1, ng_placeholder2) as mm_executor:
np_op_out = np.dot(arg_array1, arg_array2)
ng_op_out = mm_executor(arg_array1, arg_array2)
print("Flex dot product result: \n", ng_op_out)
print("Numpy dot product result: \n", np_op_out)
assert_allclose(ng_op_out, np_op_out)
def test_plusconst(transformer_factory):
"""
x + 1.5
"""
x = ng.placeholder(())
x_plus_const = x + 1.5
with executor(x_plus_const, x) as ex:
plusconst_executor = ex
for i in range(5):
# 8.8 fixed point test
# assert plusconst_executor(i) == i + 1.5
# autoflex test
if i == 1:
# expect overflow
assert_allclose(plusconst_executor(i), 1.9999)
else:
assert plusconst_executor(i) == i + 1.5
def execute_calculation(operands, first_operand, const_executor):
iterations = len(operands) != 1
for i in operands:
_operands, expected_result, description = unpack_list(*i)
if description:
print("Description: ", description)
print("Operands: ", _operands)
print("Expected result: ", expected_result)
flex_result = const_executor(*_operands)
try:
print("flex_result: {0:.30}".format(float(flex_result)))
except TypeError:
# exception for arrays
np.set_printoptions(precision=30)
print("flex_result: {}".format(flex_result))
print("difference: ", flex_result - expected_result)
if iterations:
assert_allclose(flex_result, expected_result)
elif not isinstance(first_operand, np.ndarray):
assert flex_result == expected_result
else:
assert np.array_equal(flex_result, expected_result)
def test_sum(transformer_factory, num_units, sequence_length, batch_size):
"""
This tests for a non-deterministic error that arose in ng.sum following
a dot product using the gpu transformer.
"""
shape = (num_units, sequence_length, batch_size)
np_inp = np.random.uniform(-1, 1, shape)
# Use an identity weight matrix on top of it
np_w = np.eye(shape[0])
# Create ngraph versions
inp = ng.constant(np_inp)
reduction_axes = inp.axes[:-2]
other_axes = inp.axes[-2:]
new_axis = ng.make_axis(length=shape[0])
w_axes = ng.make_axes(new_axis) | reduction_axes
w = ng.constant(np_w, axes=w_axes)
# Reshape to do similar dot in numpy
inp_reshape = np.reshape(
np_inp, (np.prod(reduction_axes.lengths), np.prod(other_axes.lengths)))
w_reshape = np.reshape(np_w, (new_axis.length, inp_reshape.shape[0]))
# Reduce dimensions with identity weight matrix
np_x = np.dot(w_reshape, inp_reshape)
x = ng.dot(w, inp)
# Sum over all but the first axis
output_axes = ng.make_axes(x.axes[0])
y = ng.sum(x, out_axes=output_axes)
np_y = np.sum(np_x, axis=1)
with executor([y, x]) as f:
y_val, x_val = f()
assert_allclose(x_val.ravel(), np_x.ravel(), atol=1e-5)
assert_allclose(y_val, np_y, atol=1e-5)
def template_dot_one_placeholder(row, col, const_val, flex_exceptions, iters):
ng_placeholder, ng_variable = template_create_placeholder_and_variable(
col, row, const_val)
ng_fun = ng.dot(ng_variable, ng_placeholder)
arg_array = np.array([i for i in range(row * col)]).reshape(row, col)
arg_array2 = np.copy(arg_array)
flex_exceptions_index = 0
vector = np.ones(row) * const_val
print("Vector: \n", vector)
with executor(ng_fun, ng_placeholder) as mm_executor:
for i in range(iters):
print("Iteration " + str(i + 1))
# After each iteration, matrix values are changed
arg_array = arg_array2 * (i + 1)
print("Matrix: \n", arg_array)
ng_op_out = mm_executor(arg_array)
np_op_out = np.dot(vector, arg_array)
print("Flex dot product result: \n ", ng_op_out)
print("Numpy dot product result: \n", np_op_out)
try:
assert_allclose(ng_op_out, np_op_out)
except AssertionError:
print(
"Flex dot product result doesn't match to numpy.\n"
"Try to check if flex result is inside flex exceptions list"
)
print("Flex dot product result: \n", ng_op_out)
print("Current array inside flex exceptions list: \n",
flex_exceptions[flex_exceptions_index])
assert list(
ng_op_out) == flex_exceptions[flex_exceptions_index]
# Iterate to the next element of flex exceptions list
flex_exceptions_index += 1
