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提交 344ff6be authored 作者: Brandon T. Willard's avatar Brandon T. Willard 提交者： Brandon T. Willard
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Fix seeding issues in tests.tensor.nnet.test_basic

上级 04aecbee
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正在显示 包含 87 行增加 和 70 行删除
tests/tensor/nnet/test_basic.py
浏览文件 @ 344ff6be
......@@ -108,7 +108,8 @@ class TestSoftmax(utt.InferShapeTester):
@pytest.mark.parametrize("axis", [None, 0, 1, 2, 3, -1, -2])
def test_perform(self, axis):
x = tensor4("x")
xv = np.random.standard_normal((2, 3, 4, 5)).astype(config.floatX)
rng = np.random.default_rng(utt.fetch_seed())
xv = rng.standard_normal((2, 3, 4, 5)).astype(config.floatX)
f = aesara.function([x], softmax(x, axis=axis))
assert np.allclose(f(xv), sp.softmax(xv, axis=axis))
......@@ -119,11 +120,13 @@ class TestSoftmax(utt.InferShapeTester):
def f(a):
return softmax(a, axis=axis)[:, column]
utt.verify_grad(f, [np.random.random((3, 4, 2))])
rng = np.random.default_rng(utt.fetch_seed())
utt.verify_grad(f, [rng.random((3, 4, 2))])
def test_infer_shape(self):
admat = matrix()
admat_val = np.random.random((3, 4)).astype(config.floatX)
rng = np.random.default_rng(utt.fetch_seed())
admat_val = rng.random((3, 4)).astype(config.floatX)
self._compile_and_check(
[admat], [Softmax(axis=-1)(admat)], [admat_val], Softmax
)
......@@ -132,14 +135,16 @@ class TestSoftmax(utt.InferShapeTester):
x = vector()
f = aesara.function([x], softmax(x, axis=None))
xv = np.random.standard_normal((6,)).astype(config.floatX)
rng = np.random.default_rng(utt.fetch_seed())
xv = rng.standard_normal((6,)).astype(config.floatX)
assert np.allclose(f(xv), sp.softmax(xv))
def test_vector_grad(self):
def f(a):
return softmax(a, axis=None)
utt.verify_grad(f, [np.random.random((4))])
rng = np.random.default_rng(utt.fetch_seed())
utt.verify_grad(f, [rng.random((4))])
def test_valid_axis(self):
valid_axis_tester(Softmax)
......@@ -150,22 +155,24 @@ class TestSoftmaxWithBias(utt.InferShapeTester):
def f(a, b):
return softmax_with_bias(a, b)[:, 0]
utt.verify_grad(f, [np.random.random((3, 4)), np.random.random((4))])
rng = np.random.default_rng(utt.fetch_seed())
utt.verify_grad(f, [rng.random((3, 4)), rng.random((4))])
def f(a, b):
return softmax_with_bias(a, b)[:, 1]
utt.verify_grad(f, [np.random.random((3, 4)), np.random.random((4))])
utt.verify_grad(f, [rng.random((3, 4)), rng.random((4))])
def f(a, b):
return softmax_with_bias(a, b)[:, 2]
utt.verify_grad(f, [np.random.random((3, 4)), np.random.random((4))])
utt.verify_grad(f, [rng.random((3, 4)), rng.random((4))])
def f(a, b):
return softmax_with_bias(a, b)[:, 3]
utt.verify_grad(f, [np.random.random((3, 4)), np.random.random((4))])
utt.verify_grad(f, [rng.random((3, 4)), rng.random((4))])
def test_broadcast(self):
# test that we don't raise an error during optimization for no good
......@@ -187,8 +194,9 @@ class TestSoftmaxWithBias(utt.InferShapeTester):
# print f.maker.fgraph.toposort()
def test_softmax_with_bias_trace(self):
a = aesara.shared(np.random.standard_normal((3,)).astype(config.floatX))
b = aesara.shared(np.float32(np.random.standard_normal()))
rng = np.random.default_rng(utt.fetch_seed())
a = aesara.shared(rng.standard_normal((3,)).astype(config.floatX))
b = aesara.shared(np.float32(rng.standard_normal()))
sm = softmax(a + b)
f = aesara.function([], sm)
assert check_stack_trace(f, ops_to_check="last")
......@@ -196,8 +204,9 @@ class TestSoftmaxWithBias(utt.InferShapeTester):
def test_infer_shape(self):
admat = matrix()
advec = vector()
admat_val = np.random.random((3, 4)).astype(config.floatX)
advec_val = np.random.random((4)).astype(config.floatX)
rng = np.random.default_rng(utt.fetch_seed())
admat_val = rng.random((3, 4)).astype(config.floatX)
advec_val = rng.random((4)).astype(config.floatX)
self._compile_and_check(
[admat, advec],
[SoftmaxWithBias()(admat, advec)],
......@@ -213,20 +222,23 @@ class TestLogSoftmax(utt.InferShapeTester):
def f(a):
return logsoftmax(a, axis=axis)[:, column]
utt.verify_grad(f, [np.random.random((3, 4))])
rng = np.random.default_rng(utt.fetch_seed())
utt.verify_grad(f, [rng.random((3, 4))])
def test_vector_perform(self):
x = vector()
f = aesara.function([x], logsoftmax(x, axis=None))
xv = np.random.standard_normal((6,)).astype(config.floatX)
rng = np.random.default_rng(utt.fetch_seed())
xv = rng.standard_normal((6,)).astype(config.floatX)
assert np.allclose(f(xv), sp.log_softmax(xv))
def test_vector_grad(self):
def f(a):
return logsoftmax(a, axis=None)
utt.verify_grad(f, [np.random.random((4))])
rng = np.random.default_rng(utt.fetch_seed())
utt.verify_grad(f, [rng.random((4,))])
def test_matrix_perform_and_opt(self):
m = config.mode
......@@ -243,9 +255,9 @@ class TestLogSoftmax(utt.InferShapeTester):
cm2 = -at_sum(y * logsm, axis=1)
grad_node = grad(cm2.mean(), x)
# create some inputs into a softmax that are large and labels
a = np.exp(10 * np.random.random((5, 10)).astype(config.floatX))
# create some one-hot coded labels
rng = np.random.default_rng(utt.fetch_seed())
a = np.exp(10 * rng.random((5, 10)).astype(config.floatX))
b = np.eye(5, 10).astype(config.floatX)
# show equivalence of softmax and exponentiated numerically stable
......@@ -294,7 +306,7 @@ class TestLogSoftmax(utt.InferShapeTester):
m.check_isfinite = False
# some inputs that are large to make the gradient explode in the non
# optimized case
rng = np.random.default_rng(98324)
rng = np.random.default_rng(utt.fetch_seed())
a = np.exp(10 * rng.random((5, 10)).astype(config.floatX))
def myfunc(x):
......@@ -340,8 +352,9 @@ class TestSoftmaxGrad(utt.InferShapeTester):
def test_infer_shape(self):
admat = matrix()
bdmat = matrix()
admat_val = np.random.random((3, 4)).astype(config.floatX)
bdmat_val = np.random.random((3, 4)).astype(config.floatX)
rng = np.random.default_rng(utt.fetch_seed())
admat_val = rng.random((3, 4)).astype(config.floatX)
bdmat_val = rng.random((3, 4)).astype(config.floatX)
self._compile_and_check(
[admat, bdmat],
[SoftmaxGrad(axis=-1)(admat, bdmat)],
......@@ -360,14 +373,16 @@ class TestCrossEntropySoftmax1Hot:
def f(a, b):
return crossentropy_softmax_1hot_with_bias(a, b, y_idx)[0]
utt.verify_grad(f, [np.random.random((3, 4)), np.random.random((4))])
rng = np.random.default_rng(utt.fetch_seed())
utt.verify_grad(f, [rng.random((3, 4)), rng.random((4))])
y_idx = [0, 1, 3]
def f(a):
return crossentropy_softmax_1hot(a, y_idx)[0]
utt.verify_grad(f, [np.random.random((3, 4))])
utt.verify_grad(f, [rng.random((3, 4))])
def test_vector(self):
y_idx = [3]
......@@ -375,7 +390,8 @@ class TestCrossEntropySoftmax1Hot:
def f(a):
return crossentropy_softmax_1hot(shape_padleft(a), y_idx)[0]
utt.verify_grad(f, [np.random.random((4))])
rng = np.random.default_rng(utt.fetch_seed())
utt.verify_grad(f, [rng.random((4,))])
def test_vectors(self):
y_idx = [3]
......@@ -383,21 +399,20 @@ class TestCrossEntropySoftmax1Hot:
def f(a, b):
return crossentropy_softmax_1hot(shape_padleft(a) + b, y_idx)[0]
utt.verify_grad(f, [np.random.random((4)), np.random.random((4))])
rng = np.random.default_rng(utt.fetch_seed())
utt.verify_grad(f, [rng.random((4,)), rng.random((4))])
class TestCrossEntropySoftmax1HotWithBiasDx(utt.InferShapeTester):
def test_basic(self):
rng = np.random.default_rng(utt.fetch_seed())
def ff(class_dtype):
def f(sm):
# Class indices
y = (
np.random.default_rng()
.integers(low=0, high=5, size=10)
.astype(class_dtype)
)
y = rng.integers(low=0, high=5, size=10).astype(class_dtype)
return crossentropy_softmax_1hot_with_bias_dx(
np.random.random((10)),
rng.random((10)),
sm,
y, # Gradient w.r.t. NLL. # Softmax output.
)
......@@ -405,7 +420,7 @@ class TestCrossEntropySoftmax1HotWithBiasDx(utt.InferShapeTester):
return f
# Build a random softmax output whose rows sum to 1.
softmax_output = np.random.random((10, 5))
softmax_output = rng.random((10, 5))
softmax_output /= softmax_output.sum(axis=1).reshape(10, 1)
for dtype in ["uint8", "int8", "uint64", "int64"]:
utt.verify_grad(ff(dtype), [softmax_output])
......@@ -463,13 +478,13 @@ class TestCrossEntropySoftmaxArgmax1HotWithBias(utt.InferShapeTester):
n_classes = 5
n_samples = 3
rng = np.random.default_rng(utt.fetch_seed())
# First test gradient when getting a gradient on the NLL output.
def grad_on_nll_dtype(dtype):
def grad_on_nll(x, b):
y_idx = (
np.random.default_rng()
.integers(low=0, high=n_classes, size=n_samples)
.astype(dtype)
y_idx = rng.integers(low=0, high=n_classes, size=n_samples).astype(
dtype
)
return self.op(x, b, y_idx=y_idx)[0]
......@@ -479,8 +494,8 @@ class TestCrossEntropySoftmaxArgmax1HotWithBias(utt.InferShapeTester):
utt.verify_grad(
grad_on_nll_dtype(dtype),
[
np.random.random((n_samples, n_classes)),
np.random.random((n_classes)),
rng.random((n_samples, n_classes)),
rng.random((n_classes)),
],
)
......@@ -489,14 +504,12 @@ class TestCrossEntropySoftmaxArgmax1HotWithBias(utt.InferShapeTester):
return self.op(
x,
b,
y_idx=np.random.default_rng().integers(
low=0, high=n_classes, size=n_samples
),
y_idx=rng.integers(low=0, high=n_classes, size=n_samples),
)[1]
utt.verify_grad(
grad_on_softmax,
[np.random.random((n_samples, n_classes)), np.random.random((n_classes))],
[rng.random((n_samples, n_classes)), rng.random((n_classes))],
)
def test_infer_shape(self):
......@@ -534,7 +547,8 @@ class TestPrepend(utt.InferShapeTester):
x = matrix("x")
y = Prepend_scalar_constant_to_each_row(4.0)(x)
f = aesara.function([x], y)
m = np.random.random((3, 5)).astype(config.floatX)
rng = np.random.default_rng(utt.fetch_seed())
m = rng.random((3, 5)).astype(config.floatX)
my = f(m)
assert my.shape == (3, 6)
assert np.all(my[:, 0] == 4.0)
......@@ -608,7 +622,8 @@ class TestCrossEntropyCategorical1Hot(utt.InferShapeTester):
def oplike(x):
return op(x, [0, 1])
utt.verify_grad(oplike, [x_val], rng=np.random)
rng = np.random.default_rng(utt.fetch_seed())
utt.verify_grad(oplike, [x_val], rng=rng)
def test_infer_shape(self):
admat = matrix()
......@@ -1023,7 +1038,6 @@ class TestSoftmaxOpt:
#
def setup_method(self):
self.rng = np.random.default_rng(utt.fetch_seed())
self.mode = aesara.compile.mode.get_default_mode()
self.mode = self.mode.including("canonicalize")
......@@ -1049,7 +1063,8 @@ class TestSoftmaxOpt:
assert len(f_ops) == 1
assert isinstance(f_ops[0], Softmax)
c_val = self.rng.random((3, 4)).astype(config.floatX)
rng = np.random.default_rng(utt.fetch_seed())
c_val = rng.random((3, 4)).astype(config.floatX)
assert np.allclose(f(c_val), sp.softmax(c_val, axis=axis))
@pytest.mark.parametrize("axis", [None, 0, 1, 2, -1, -2, -3, (0, 1, 2)])
......@@ -1067,7 +1082,8 @@ class TestSoftmaxOpt:
assert len(f_ops) == 1
assert isinstance(f_ops[0], Softmax)
c_val = self.rng.random((3, 4, 5)).astype(config.floatX)
rng = np.random.default_rng(utt.fetch_seed())
c_val = rng.random((3, 4, 5)).astype(config.floatX)
assert np.allclose(f(c_val), sp.softmax(c_val, axis=axis))
@pytest.mark.skip(reason="Optimization not enabled for the moment")
......@@ -1086,7 +1102,8 @@ class TestSoftmaxOpt:
assert isinstance(g_ops[0], Softmax)
assert isinstance(g_ops[1], SoftmaxGrad)
g(self.rng.random((3, 4)), self.rng.uniform(0.5, 1, (3, 4)))
rng = np.random.default_rng(utt.fetch_seed())
g(rng.random((3, 4)), rng.uniform(0.5, 1, (3, 4)))
def test_transpose_basic(self):
# this should be a transposed softmax
......@@ -1196,14 +1213,13 @@ def test_stabilize_log_softmax():
# call the function so debug mode can verify the optimized
# version matches the unoptimized version
rng = np.random.default_rng([2012, 8, 22])
rng = np.random.default_rng(utt.fetch_seed())
f(np.cast[config.floatX](rng.random((2, 3))))
def test_relu():
x = matrix("x")
seed = utt.fetch_seed()
rng = np.random.default_rng(seed)
rng = np.random.default_rng(utt.fetch_seed())
X = rng.standard_normal((20, 30)).astype(config.floatX)
# test the base case, without custom alpha value
......@@ -1223,11 +1239,12 @@ def test_relu():
)
y = relu(x, alpha).eval({x: X, alpha: A})
assert np.allclose(y, np.where(X > 0, X, A * X), rtol=3e-5)
# test that for alpha of ndarray don't cause upcast.
x = matrix("x", dtype="float32")
rng = np.random.default_rng(seed)
X = rng.standard_normal((20, 30)).astype("float32")
alpha = np.asarray(0.123, dtype="float32")
y = relu(x, alpha).eval({x: X})
assert np.allclose(y, np.where(X > 0, X, alpha * X))
assert y.dtype == "float32"
......@@ -1243,9 +1260,11 @@ def test_h_softmax():
h_softmax_level2_size = 3
output_size = h_softmax_level1_size * h_softmax_level2_size
rng = np.random.default_rng(utt.fetch_seed())
# First level of h_softmax
W1 = np.asarray(
np.random.normal(size=(input_size, h_softmax_level1_size)), dtype=config.floatX
rng.normal(size=(input_size, h_softmax_level1_size)), dtype=config.floatX
)
W1 = aesara.shared(W1)
b1 = aesara.shared(
......@@ -1254,9 +1273,7 @@ def test_h_softmax():
# Second level of h_softmax
W2 = np.asarray(
np.random.normal(
size=(h_softmax_level1_size, input_size, h_softmax_level2_size)
),
rng.normal(size=(h_softmax_level1_size, input_size, h_softmax_level2_size)),
dtype=config.floatX,
)
W2 = aesara.shared(W2)
......@@ -1300,8 +1317,8 @@ def test_h_softmax():
fun_output_tg = aesara.function([x, y], y_hat_tg)
fun_output = aesara.function([x], y_hat_all)
x_mat = np.random.normal(size=(batch_size, input_size)).astype(config.floatX)
y_mat = np.random.default_rng().integers(0, output_size, batch_size).astype("int32")
x_mat = rng.normal(size=(batch_size, input_size)).astype(config.floatX)
y_mat = rng.integers(0, output_size, batch_size).astype("int32")
tg_output = fun_output_tg(x_mat, y_mat)
all_outputs = fun_output(x_mat)
......@@ -1315,8 +1332,7 @@ def test_h_softmax():
def test_elu():
x = matrix("x")
seed = utt.fetch_seed()
rng = np.random.default_rng(seed)
rng = np.random.default_rng(utt.fetch_seed())
X = rng.standard_normal((20, 30)).astype(config.floatX)
# test the base case, without custom alpha value
......@@ -1334,8 +1350,7 @@ def test_selu():
scale = 1.0507009873554804934193349852946
x = matrix("x")
seed = utt.fetch_seed()
rng = np.random.default_rng(seed)
rng = np.random.default_rng(utt.fetch_seed())
X = rng.standard_normal((20, 30)).astype(config.floatX)
y = selu(x).eval({x: X})
......@@ -1371,11 +1386,6 @@ TestSoftsign = makeBroadcastTester(
class TestSigmoidBinaryCrossentropy:
def _get_test_inputs(self, n=50):
pred, target = np.random.standard_normal((2, n)).astype(config.floatX)
# apply sigmoid to target, but not pred
return [pred, 1 / (1 + np.exp(-target))]
def test_matches_binary_crossentropy(self):
# Test sigmoid_binary_crossentropy(p, t) ==
# binary_crossentropy(sigmoid(p), t).
......@@ -1388,11 +1398,18 @@ class TestSigmoidBinaryCrossentropy:
test_val = sigmoid_binary_crossentropy(pred, target)
f_test = aesara.function(inputs, test_val)
test_inputs = self._get_test_inputs()
rng = np.random.default_rng(utt.fetch_seed())
pred, target = rng.standard_normal((2, 50)).astype(config.floatX)
test_inputs = [pred, 1 / (1 + np.exp(-target))]
utt.assert_allclose(f_reference(*test_inputs), f_test(*test_inputs))
def test_grad(self):
utt.verify_grad(sigmoid_binary_crossentropy, self._get_test_inputs())
rng = np.random.default_rng(utt.fetch_seed())
pred, target = rng.standard_normal((2, 50)).astype(config.floatX)
test_inputs = [pred, 1 / (1 + np.exp(-target))]
utt.verify_grad(sigmoid_binary_crossentropy, test_inputs)
def test_confusion_matrix():
......
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