def test_multinomial_0():
# This tests the MultinomialFromUniform Op directly, not going through the
# multinomial() call in GPU random generation.
p = fmatrix()
u = fvector()
m = MultinomialFromUniform("auto")(p, u)
# the m*2 allows the multinomial to reuse output
f = function([p, u], m * 2, allow_input_downcast=True)
# test that both first and second samples can be drawn
utt.assert_allclose(f([[1, 0], [0, 1]], [0.1, 0.1]), [[2, 0], [0, 2]])
# test that both second labels can be drawn
r = f([[0.2, 0.8], [0.3, 0.7]], [0.31, 0.31])
utt.assert_allclose(r, [[0, 2], [0, 2]])
# test that both first labels can be drawn
r = f([[0.2, 0.8], [0.3, 0.7]], [0.21, 0.21])
utt.assert_allclose(r, [[0, 2], [2, 0]])
# change the size to make sure output gets reallocated ok
# and also make sure that the GPU version doesn't screw up the
# transposed-ness
r = f([[0.2, 0.8]], [0.25])
utt.assert_allclose(r, [[0, 2]])
def test_sparseblockgemvF(self):
# Test the fortran order for W (which can happen in the grad for some
# graphs).
b = fmatrix()
W = ftensor4()
h = ftensor3()
iIdx = imatrix()
oIdx = imatrix()
o = self.gemv_op(
b.take(oIdx, axis=0),
DimShuffle((False, False, False, False),
(0, 1, 3, 2))(aet.as_tensor_variable(W)),
h,
iIdx,
oIdx,
)
f = aesara.function([W, h, iIdx, b, oIdx], o, mode=self.mode)
W_val, h_val, iIdx_val, b_val, oIdx_val = self.gemv_data()
th_out = f(np.swapaxes(W_val, 2, 3), h_val, iIdx_val, b_val, oIdx_val)
ref_out = self.gemv_numpy(b_val.take(oIdx_val, axis=0), W_val, h_val,
iIdx_val, oIdx_val)
utt.assert_allclose(ref_out, th_out)
def test_gpu_eigh_opt(self):
A = fmatrix("A")
fn = aesara.function([A], eigh(A), mode=mode_with_gpu)
assert any([
isinstance(node.op, GpuMagmaEigh)
for node in fn.maker.fgraph.toposort()
])
def test_gpu_matrix_inverse_inplace_opt(self):
A = fmatrix("A")
fn = aesara.function([A], matrix_inverse(A), mode=mode_with_gpu)
assert any([
node.op.inplace for node in fn.maker.fgraph.toposort()
if isinstance(node.op, GpuMagmaMatrixInverse)
])
def test_gpu_cholesky_inplace_opt(self):
A = fmatrix("A")
fn = aesara.function([A], GpuMagmaCholesky()(A), mode=mode_with_gpu)
assert any([
node.op.inplace for node in fn.maker.fgraph.toposort()
if isinstance(node.op, GpuMagmaCholesky)
])
def test_select_proportional_to_weight(self):
# Tests that multinomial_wo_replacement selects elements, on average,
# proportional to the their probabilities
th_rng = RandomStream(12345)
p = fmatrix()
n = iscalar()
m = th_rng.choice(size=n, p=p, replace=False)
f = function([p, n], m, allow_input_downcast=True)
n_elements = 100
n_selected = 10
mean_rtol = 0.0005
rng = np.random.default_rng(12345)
pvals = rng.integers(1, 100, (1, n_elements)).astype(config.floatX)
pvals /= pvals.sum(1)
avg_pvals = np.zeros((n_elements, ), dtype=config.floatX)
for rep in range(10000):
res = f(pvals, n_selected)
res = np.squeeze(res)
avg_pvals[res] += 1
avg_pvals /= avg_pvals.sum()
avg_diff = np.mean(abs(avg_pvals - pvals))
assert avg_diff < mean_rtol
def test_Strides2D(self, mode):
np_func = dict(add=np.cumsum, mul=np.cumprod)[mode]
op_class = partial(self.op_class, mode=mode)
x = fmatrix("x")
for axis in [0, 1, None, -1, -2]:
a = np.random.random((42, 30)).astype("float32")
cumop_function = aesara.function(
[x], op_class(axis=axis)(x), mode=self.mode
)
slicings = [
slice(None, None, None), # Normal strides
slice(None, None, 2), # Stepped strides
slice(None, None, -1), # Negative strides
]
# Cartesian product of all slicings to test.
for slicing in product(slicings, repeat=x.ndim):
f = aesara.function(
[x], op_class(axis=axis)(x[slicing]), mode=self.mode
)
assert [
n for n in f.maker.fgraph.toposort() if isinstance(n.op, GpuCumOp)
]
utt.assert_allclose(np_func(a[slicing], axis=axis), f(a))
utt.assert_allclose(
np_func(a[slicing], axis=axis), cumop_function(a[slicing])
)
def test_select_proportional_to_weight(self):
# Tests that ChoiceFromUniform selects elements, on average,
# proportional to the their probabilities
p = fmatrix()
u = fvector()
n = iscalar()
m = multinomial.ChoiceFromUniform(odtype="auto")(p, u, n)
f = function([p, u, n], m, allow_input_downcast=True)
n_elements = 100
n_selected = 10
mean_rtol = 0.0005
rng = np.random.default_rng(12345)
pvals = rng.integers(1, 100, (1, n_elements)).astype(config.floatX)
pvals /= pvals.sum(1)
avg_pvals = np.zeros((n_elements, ), dtype=config.floatX)
for rep in range(10000):
uni = rng.random(n_selected).astype(config.floatX)
res = f(pvals, uni, n_selected)
res = np.squeeze(res)
avg_pvals[res] += 1
avg_pvals /= avg_pvals.sum()
avg_diff = np.mean(abs(avg_pvals - pvals))
assert avg_diff < mean_rtol, avg_diff
def test_hostfromgpu_shape_i():
# Test that the shape is lifted over hostfromgpu
m = mode_with_gpu.including("local_dot_to_dot22",
"local_dot22_to_dot22scalar", "specialize")
a = fmatrix("a")
ca = aesara.gpuarray.type.GpuArrayType("float32", (False, False))()
av = np.asarray(np.random.rand(5, 4), dtype="float32")
cv = gpuarray.asarray(np.random.rand(5, 4),
dtype="float32",
context=get_context(test_ctx_name))
f = aesara.function([a], GpuFromHost(test_ctx_name)(a), mode=m)
assert any(
isinstance(x.op, GpuFromHost) for x in f.maker.fgraph.toposort())
f = aesara.function([a], GpuFromHost(test_ctx_name)(a).shape, mode=m)
topo = f.maker.fgraph.toposort()
assert isinstance(topo[0].op, Shape_i)
assert isinstance(topo[1].op, Shape_i)
assert isinstance(topo[2].op, MakeVector)
assert tuple(f(av)) == (5, 4)
f = aesara.function([ca], host_from_gpu(ca), mode=m)
assert host_from_gpu in [x.op for x in f.maker.fgraph.toposort()]
f = aesara.function([ca], host_from_gpu(ca).shape, mode=m)
topo = f.maker.fgraph.toposort()
assert isinstance(topo[0].op, Shape_i)
assert isinstance(topo[1].op, Shape_i)
assert isinstance(topo[2].op, MakeVector)
assert tuple(f(cv)) == (5, 4)
def test_gpu_qr_incomplete_opt(self):
A = fmatrix("A")
fn = aesara.function([A], qr(A, mode="r"), mode=mode_with_gpu)
assert any([
isinstance(node.op, GpuMagmaQR) and not node.op.complete
for node in fn.maker.fgraph.toposort()
])
def test_softmax_shape_0(self):
x = fmatrix("x")
z = aesara.tensor.nnet.softmax_legacy
f, f_gpu = self._test_softmax(x, x, z, z, self._cmp)
# Aesara can handle that case, but cudnn can't
self._cmp(0, 10, f, f_gpu)
def test_softmax(self):
x = fmatrix("x")
z = aesara.tensor.nnet.softmax_legacy
f, f_gpu = self._test_softmax(x, x, z, z, self._cmp)
self._cmp(2 << 15, 5, f, f_gpu)
def run_gpu_svd(self, A_val, full_matrices=True, compute_uv=True):
A = fmatrix("A")
f = aesara.function(
[A],
gpu_svd(A, full_matrices=full_matrices, compute_uv=compute_uv),
mode=mode_with_gpu,
)
return f(A_val)
def run_gpu_cholesky(self, A_val, lower=True):
A = fmatrix("A")
f = aesara.function(
[A],
GpuMagmaCholesky(lower=lower)(A),
mode=mode_with_gpu.excluding("cusolver"),
)
return f(A_val)
def test_GpuCrossentropySoftmaxArgmax1HotWithBias():
# This is basic test for GpuCrossentropySoftmaxArgmax1HotWithBias
# We check that we loop when their is too much threads
n_in = 1000
batch_size = 4097
n_out = 1250
if not isinstance(mode_with_gpu, aesara.compile.debugmode.DebugMode):
n_in = 4098
n_out = 4099
y = lvector("y")
b = fvector("b")
# we precompute the dot with big shape before to allow the test of
# GpuCrossentropySoftmax1HotWithBiasDx to don't fail with the error
# (the launch timed out and was terminated) on GPU card not
# powerful enough. We need the big shape to check for corner
# case.
dot_result = fmatrix("dot_result")
xx = np.asarray(np.random.rand(batch_size, n_in), dtype=np.float32)
yy = np.ones((batch_size, ), dtype="int32")
b_values = np.zeros((n_out, ), dtype="float32")
W_values = np.asarray(np.random.rand(n_in, n_out), dtype="float32")
dot_value = np.asarray(np.dot(xx, W_values), dtype="float32")
del W_values
p_y_given_x = aesara.tensor.nnet.softmax(dot_result + b)
y_pred = argmax(p_y_given_x, axis=-1)
loss = -mean(log(p_y_given_x)[aet.arange(y.shape[0]), y])
dW = grad(loss, dot_result)
classify = aesara.function(inputs=[y, b, dot_result],
outputs=[loss, y_pred, dW],
mode=mode_without_gpu)
classify_gpu = aesara.function(inputs=[y, b, dot_result],
outputs=[loss, y_pred, dW],
mode=mode_with_gpu)
assert any([
isinstance(node.op,
aesara.tensor.nnet.CrossentropySoftmaxArgmax1HotWithBias)
for node in classify.maker.fgraph.toposort()
])
assert any([
isinstance(node.op, GpuCrossentropySoftmaxArgmax1HotWithBias)
for node in classify_gpu.maker.fgraph.toposort()
])
out = classify(yy, b_values, dot_value)
gout = classify_gpu(yy, b_values, dot_value)
assert len(out) == len(gout) == 3
utt.assert_allclose(out[0], gout[0])
utt.assert_allclose(out[2], gout[2], atol=3e-6)
utt.assert_allclose(out[1], gout[1])
def test_gpu_matrix_inverse(self):
A = fmatrix("A")
fn = aesara.function([A], gpu_matrix_inverse(A), mode=mode_with_gpu)
N = 1000
test_rng = np.random.default_rng(seed=1)
# Copied from tests.tensor.utils.random.
A_val = test_rng.random((N, N)).astype("float32") * 2 - 1
A_val_inv = fn(A_val)
utt.assert_allclose(np.eye(N), np.dot(A_val_inv, A_val), atol=1e-2)
def test_incorrect_type(self):
x = vector("x")
with pytest.raises(TypeError):
# Incorrect shape for test value
x.tag.test_value = np.empty((2, 2))
x = fmatrix("x")
with pytest.raises(TypeError):
# Incorrect dtype (float64) for test value
x.tag.test_value = np.random.random((3, 4))
def test_gemv_infershape(self):
b = fmatrix()
W = ftensor4()
h = ftensor3()
iIdx = imatrix()
oIdx = imatrix()
self._compile_and_check(
[W, h, iIdx, b, oIdx],
[self.gemv_op(b.take(oIdx, axis=0), W, h, iIdx, oIdx)],
self.gemv_data(),
self.gemv_class,
)
def test_cpu_contiguous():
a = fmatrix("a")
i = iscalar("i")
a_val = np.asarray(np.random.rand(4, 5), dtype="float32")
f = aesara.function([a, i], cpu_contiguous(a.reshape((5, 4))[::i]))
topo = f.maker.fgraph.toposort()
assert any([isinstance(node.op, CpuContiguous) for node in topo])
assert f(a_val, 1).flags["C_CONTIGUOUS"]
assert f(a_val, 2).flags["C_CONTIGUOUS"]
assert f(a_val, 3).flags["C_CONTIGUOUS"]
# Test the grad:
utt.verify_grad(cpu_contiguous, [np.random.rand(5, 7, 2)])
def test_dot_infershape(self):
b = fmatrix()
W = ftensor4()
h = ftensor3()
iIdx = imatrix()
oIdx = imatrix()
self._compile_and_check(
[W, h, iIdx, b, oIdx],
[sparse_block_dot(W, h, iIdx, b, oIdx)],
self.gemv_data(),
self.gemv_class,
)
def test_n_samples_1():
p = fmatrix()
u = fvector()
n = iscalar()
m = MultinomialFromUniform("auto")(p, u, n)
f = function([p, u, n], m, allow_input_downcast=True)
np.random.seed(12345)
for i in [1, 5, 10, 100, 1000, 10000]:
uni = np.random.rand(2 * i).astype(config.floatX)
res = f([[1.0, 0.0], [0.0, 1.0]], uni, i)
utt.assert_allclose(res, [[i * 1.0, 0.0], [0.0, i * 1.0]])
def test_transfer_cpu_gpu():
a = fmatrix("a")
g = GpuArrayType(dtype="float32", broadcastable=(False, False))("g")
av = np.asarray(rng.rand(5, 4), dtype="float32")
gv = gpuarray.array(av, context=get_context(test_ctx_name))
f = aesara.function([a], GpuFromHost(test_ctx_name)(a))
fv = f(av)
assert GpuArrayType.values_eq(fv, gv)
f = aesara.function([g], host_from_gpu(g))
fv = f(gv)
assert np.all(fv == av)
def test_complex(self):
rng = np.random.default_rng(2333)
m = fmatrix()
c = at_complex(m[0], m[1])
assert c.type == cvector
r, i = [real(c), imag(c)]
assert r.type == fvector
assert i.type == fvector
f = aesara.function([m], [r, i])
mval = np.asarray(rng.standard_normal((2, 5)), dtype="float32")
rval, ival = f(mval)
assert np.all(rval == mval[0]), (rval, mval[0])
assert np.all(ival == mval[1]), (ival, mval[1])
def test_gpu_singular_values(self):
A = fmatrix("A")
f_cpu = aesara.function([A],
aesara.tensor.nlinalg.svd(A, compute_uv=False),
mode=mode_without_gpu)
f_gpu = aesara.function([A],
gpu_svd(A, compute_uv=False),
mode=mode_with_gpu)
A_val = random(50, 100).astype("float32")
utt.assert_allclose(f_cpu(A_val), f_gpu(A_val))
A_val = random(100, 50).astype("float32")
utt.assert_allclose(f_cpu(A_val), f_gpu(A_val))
def test_gpu_contiguous():
a = fmatrix("a")
i = iscalar("i")
a_val = np.asarray(np.random.rand(4, 5), dtype="float32")
# The reshape is needed otherwise we make the subtensor on the CPU
# to transfer less data.
f = aesara.function([a, i],
gpu_contiguous(a.reshape((5, 4))[::i]),
mode=mode_with_gpu)
topo = f.maker.fgraph.toposort()
assert any([isinstance(node.op, GpuSubtensor) for node in topo])
assert any([isinstance(node.op, GpuContiguous) for node in topo])
assert f(a_val, 1).flags.c_contiguous
assert f(a_val, 2).flags.c_contiguous
assert f(a_val, 2).flags.c_contiguous
def test_blocksparse_inplace_gemv_opt():
b = fmatrix()
W = ftensor4()
h = ftensor3()
iIdx = lmatrix()
oIdx = lmatrix()
o = sparse_block_dot(W, h, iIdx, b, oIdx)
f = aesara.function([W, h, iIdx, b, oIdx], o)
if aesara.config.mode == "FAST_COMPILE":
assert not f.maker.fgraph.toposort()[-1].op.inplace
assert check_stack_trace(f, ops_to_check=[sparse_block_gemv])
else:
assert f.maker.fgraph.toposort()[-1].op.inplace
assert check_stack_trace(f, ops_to_check=[sparse_block_gemv_inplace])
def test_blocksparse_inplace_outer_opt():
b = fmatrix()
W = ftensor4()
h = ftensor3()
iIdx = lmatrix()
oIdx = lmatrix()
o = sparse_block_dot(W, h, iIdx, b, oIdx)
f = aesara.function([W, h, iIdx, b, oIdx],
[o, aesara.gradient.grad(o.sum(), wrt=W)])
if aesara.config.mode == "FAST_COMPILE":
assert not f.maker.fgraph.toposort()[-1].op.inplace
assert check_stack_trace(f, ops_to_check=sparse_block_outer)
else:
assert f.maker.fgraph.toposort()[-1].op.inplace
assert check_stack_trace(f, ops_to_check=sparse_block_outer_inplace)
def test_GpuCrossentropySoftmax1HotWithBiasDx():
# This is basic test for GpuCrossentropySoftmax1HotWithBiasDx
# We check that we loop when their is too much threads
batch_size = 4097
n_out = 1250
if not isinstance(mode_with_gpu, aesara.compile.debugmode.DebugMode):
n_out = 4099
# Seed numpy.random with config.unittests__rseed
utt.seed_rng()
softmax_output_value = np.random.rand(batch_size, n_out).astype("float32")
dnll_value = np.asarray(np.random.rand(batch_size), dtype="float32")
y_idx_value = np.random.randint(low=0, high=5, size=batch_size)
softmax_output = fmatrix()
softmax_output /= softmax_output.sum(axis=1).reshape(
softmax_output.shape[1], 1)
op = crossentropy_softmax_1hot_with_bias_dx(dnll_value, softmax_output,
y_idx_value)
cpu_f = aesara.function([softmax_output], op, mode=mode_without_gpu)
gpu_f = aesara.function([softmax_output], op, mode=mode_with_gpu)
# aesara.printing.debugprint(cpu_f)
# aesara.printing.debugprint(gpu_f)
assert any([
isinstance(node.op,
aesara.tensor.nnet.CrossentropySoftmax1HotWithBiasDx)
for node in cpu_f.maker.fgraph.toposort()
])
assert any([
isinstance(node.op, GpuCrossentropySoftmax1HotWithBiasDx)
for node in gpu_f.maker.fgraph.toposort()
])
cpu_out = cpu_f(softmax_output_value)
gpu_out = gpu_f(softmax_output_value)
rtol = 1e-5
atol = 1e-6
utt.assert_allclose(cpu_out, gpu_out, rtol=rtol, atol=atol)
def test_fail_select_alot(self):
# Tests that multinomial_wo_replacement fails when asked to sample more
# elements than the actual number of elements
th_rng = RandomStream(12345)
p = fmatrix()
n = iscalar()
m = th_rng.multinomial_wo_replacement(pvals=p, n=n)
f = function([p, n], m, allow_input_downcast=True)
n_elements = 100
n_selected = 200
np.random.seed(12345)
pvals = np.random.randint(1, 100, (1, n_elements)).astype(config.floatX)
pvals /= pvals.sum(1)
with pytest.raises(ValueError):
f(pvals, n_selected)
def test_fail_select_alot(self):
# Tests that ChoiceFromUniform fails when asked to sample more
# elements than the actual number of elements
p = fmatrix()
u = fvector()
n = iscalar()
m = multinomial.ChoiceFromUniform(odtype="auto")(p, u, n)
f = function([p, u, n], m, allow_input_downcast=True)
n_elements = 100
n_selected = 200
np.random.seed(12345)
uni = np.random.rand(n_selected).astype(config.floatX)
pvals = np.random.randint(1, 100, (1, n_elements)).astype(config.floatX)
pvals /= pvals.sum(1)
with pytest.raises(ValueError):
f(pvals, uni, n_selected)