def test_nested_irs():
mg1 = pir.Ir().main_graph()
mg2 = pir.Ir().main_graph()
# You cant test graphs with different irs
with pytest.raises(RuntimeError):
with mg1:
with mg2:
pass
def test_tensor_id_conflict_between_Ir():
ir1 = pir.Ir()
with ir1.main_graph():
t1 = pir.variable(1, dtype=pir.float32, name="tensor")
ir2 = pir.Ir()
with ir2.main_graph():
t2 = pir.variable(1, dtype=pir.float32, name="tensor")
assert 2 == len(set([t1, t2])) # test __hash__
assert t1 != t2 # test __eq__
def test_repeat_simple_addition(self, repeat_count: int):
"""Test that a simple x = x + 1 repeated `repeat_count` times will
produce x = `repeat_count`
Args:
repeat_count (int): Number of times to repeat.
"""
ir = pir.Ir()
main = ir.main_graph()
with main:
one = pir.constant(0, pir.dtypes.int32)
add_one = AddOne()
add_one_graph = ir.create_graph(add_one, one)
y = ops.repeat(add_one_graph,
repeat_count,
one,
subgraph_in_to_parent_in={})
d2h = pir.d2h_stream(y.shape, pir.dtypes.int32, name="y_stream")
ops.host_store(d2h, y)
r_y = run_ir(ir, 1, d2h.tensor_id(), {})
assert r_y == repeat_count
def test_repeat_error(self, repeat_count: int):
"""Test an error is thrown with incorrect repeat_count
Args:
repeat_count (int): Number of times to repeat.
"""
ir = pir.Ir()
main = ir.main_graph()
with main:
h2d = pir.h2d_stream((2, 16), pir.dtypes.float32)
x = ops.host_load(h2d, "x")
W = pir.variable(np.random.normal(0, 0.1, (16, 16)), name="W")
b = pir.variable(np.zeros(16), name="b")
linear = Linear()
linear_graph = ir.create_graph(linear, x, out_features=16)
with pytest.raises(ValueError) as e_info:
y = ops.repeat(linear_graph,
repeat_count,
x,
subgraph_in_to_parent_in={
linear.W: W,
linear.b: b
})
assert e_info.value.args[0].startswith(
"Repeat trip count for repeat of")
def test_hook():
ir = pir.Ir()
g = ir.main_graph()
called = False
def hook(_):
nonlocal called
called = True
handle = g.register_op_created_hook(hook)
with g:
x = pir.variable(1)
x = x + 1
assert called
called = False
# Creating this graph will create
# an AddOp on the new graph.
# Ensure this does not trigger the hook.
sg = ir.create_graph(lambda y: y + 1, x)
assert not called
g.remove_op_created_hook(handle)
with g:
x = x + 1
assert not called
def test_get_ir(self):
ir = pir.Ir()
main = ir.main_graph()
with main:
a = pir.variable(1)
assert a.ir() == ir
def test_axes_not_squeezable(self):
ir = pir.Ir()
g = ir.main_graph()
with g:
a = pir.variable(np.ones((1, 2, 3)))
with pytest.raises(ValueError) as excinfo:
c = ops.squeeze(a, axes=[1])
def test_invalid_int(self):
ir = pir.Ir()
g = ir.main_graph()
with g:
t = pir.variable([[1, 2], [3, 4], [5, 6], [7, 8], [9, 10]])
splits = 2
with pytest.raises(ValueError) as excinfo:
c = ops.split(t, splits)
def test_fn(self):
ir = pir.Ir()
g = ir.main_graph()
with g:
a = pir.variable(np.ones((1, 2, 3)))
c = ops.softmax(a, axis=1)
assert len(g.get_tensors()) == 2
assert contains_op_of_type("Softmax", _ir.op.SoftmaxOp, g)
def test_invalid_list(self):
ir = pir.Ir()
g = ir.main_graph()
with g:
t = pir.variable([[1, 2], [3, 4], [5, 6], [7, 8], [9, 10]])
splits = [1, 2]
with pytest.raises(popart_exception) as excinfo:
c = ops.split(t, splits)
def test_flatten(self):
ir = pir.Ir()
g = ir.main_graph()
with g:
a = pir.variable(np.ones((1, 2, 3)))
c = ops.flatten(a)
assert c.shape == (6, )
assert contains_op_of_type("Reshape", _ir.op.ReshapeOp, g)
def test_dunder_scalar_and_slice(self):
ir = pir.Ir()
with ir.main_graph():
t = pir.variable(data)
y = t[0, 3:0:-1, 2]
y_host = run_ir(ir, y)
y_numpy = data[0, 3:0:-1, 2]
assert_array_equal(y_host, y_numpy)
def test_error_lengths(self, inplace):
ir = pir.Ir()
with ir.main_graph():
t = pir.variable(data)
with pytest.raises(ValueError):
if inplace:
y = ops.slice_(t, start=[2], stop=[3, 4], axis=[2, 1])
else:
y = ops.slice(t, start=[2], stop=[3, 4], axis=[2, 1])
def test_fn(self):
ir = pir.Ir()
g = ir.main_graph()
with g:
a = pir.variable(np.ones((1, 2, 3)))
c = ops.gelu(a)
assert len(g.get_tensors()) == 2
assert contains_op_of_type("Gelu", _ir.op.GeluOp, g)
def test_negative(self):
ir = pir.Ir()
g = ir.main_graph()
with g:
a = pir.variable(np.ones((1, 2, 3)))
c = a.reshape((-1, 1))
assert c.shape == (6, 1)
assert contains_op_of_type("Reshape", _ir.op.ReshapeOp, g)
def test_construction1(self, t_class):
"""Test construction of tensors that hold 0-d data at graph creation."""
ir = pir.Ir()
main = ir.main_graph()
with main:
t = ctor_map[t_class](1.0)
assert t.dtype == pir.float32
assert t.shape == ()
assert t.nelms == 1
def test_cmp(self):
ir = pir.Ir()
main = ir.main_graph()
with main:
a = pir.variable(1)
b = pir.variable(1)
assert a != b # test __eq__
assert len(set([a, b])) == 2 # test __hash__
str(a) # test __repr__
def test_cache_env():
os.environ['POPART_CACHE_DIR'] = 'PATH_TO_CACHE'
ir = pir.Ir()
opts = ir._pb_ir.getSessionOptions()
assert opts.enableEngineCaching
assert opts.cachePath == 'PATH_TO_CACHE'
del os.environ['POPART_CACHE_DIR']
def test_fn(self):
ir = pir.Ir()
g = ir.main_graph()
with g:
a = pir.variable(True, pir.bool)
c = ops.logical_not(a)
assert len(g.get_tensors()) == 2
assert len(g.get_variables()) == 1
assert contains_op_of_type("Not", _ir.op.NotOp, g)
def test_get_main_graph():
ir = pir.Ir()
main = ir.main_graph()
with main:
sg = ir.create_empty_graph('sg')
# Main graph does not necessarily need to be top of context
with sg:
assert pir.gmg() == main
def test_squeeze_specified_negative(self):
ir = pir.Ir()
g = ir.main_graph()
with g:
a = pir.variable(np.ones((4, 1, 3, 1, 4, 5, 1)))
c = ops.squeeze(a, axes=[-4, 1])
assert c.shape == (4, 3, 4, 5, 1)
assert len(g.get_tensors()) == 2
assert contains_op_of_type("Reshape", _ir.op.ReshapeOp, g)
def test_tensor_id_conflict():
ir = pir.Ir()
main = ir.main_graph()
with main:
name0 = pir.variable(1, name="tensor").id
name1 = pir.variable(1, name="tensor").id
name2 = pir.constant(1, name="tensor").id
assert name0 == "tensor"
ids = [name0, name1, name2]
assert len(ids) == len(set(ids))
def test_constructor():
"""Test that the `Ir` constructor sets the internal object state
correctly.
"""
ir = pir.Ir()
# The low-level IR should have only one graph - the main graph.
assert len(ir._pb_ir.getAllGraphs()) == 1
main_graph = ir.main_graph()
assert isinstance(main_graph, pir.Graph)
def test_fn(self):
ir = pir.Ir()
g = ir.main_graph()
with g:
seed = pir.variable(np.array([32, 32]), dtype=dtypes.uint32)
x = ops.random_normal(seed, (2, 2))
assert len(g.get_tensors()) == 2
assert len(g.get_variables()) == 1
assert contains_op_of_type("RandomNormal", _ir.op.RandomNormalOp, g)
def test_dunder(self):
ir = pir.Ir()
g = ir.main_graph()
with g:
a = pir.variable(1)
c = -a
assert len(g.get_tensors()) == 2
assert len(g.get_variables()) == 1
assert contains_op_of_type("Neg", _ir.op.NegateOp, g)
def test_fn_with_no_producer(self):
ir = pir.Ir()
g = ir.main_graph()
with g:
a = pir.variable(1)
a_1 = ops.ipu_copy(a, 1, 0)
assert len(g.get_variables()) == 1
assert len(g.get_tensors()) == 2
assert contains_op_of_type("IpuCopy", _ir.op.IpuCopyOp, g)
def test_tensor_method(self):
ir = pir.Ir()
g = ir.main_graph()
with g:
a = pir.variable(np.ones((1, 2, 3)))
c = a.reshape((2, 3, 1))
assert c.shape == (2, 3, 1)
assert len(g.get_tensors()) == 2
assert contains_op_of_type("Reshape", _ir.op.ReshapeOp, g)
def test_squeeze_all(self):
ir = pir.Ir()
g = ir.main_graph()
with g:
a = pir.variable(np.ones((4, 1, 3, 1)))
c = ops.squeeze(a)
assert c.shape == (4, 3)
assert len(g.get_tensors()) == 2
assert contains_op_of_type("Reshape", _ir.op.ReshapeOp, g)
def test_fn(self):
ir = pir.Ir()
g = ir.main_graph()
with g:
a = pir.variable(1)
c = ops.increment_mod(a, 1, 3)
assert len(g.get_tensors()) == 2
assert len(g.get_variables()) == 1
assert contains_op_of_type("IncrementMod", _ir.op.IncrementModOp, g)
def test_needs_casting(self):
ir = pir.Ir()
g = ir.main_graph()
with g:
a = pir.variable(1, pir.int32)
c = ops.logical_not(a)
assert len(g.get_tensors()) == 3
assert len(g.get_variables()) == 1
assert contains_op_of_type("Not", _ir.op.NotOp, g)
