def test_pick_partial_shape():
x = mx.sym.Variable("x")
index = mx.sym.Variable("index")
y = mx.sym.pick(x, index, axis=1)
# batch size unknown
_, result, _ = y.infer_shape_partial(x=(0, 3, 3), index=(0, 3,))
assert result == [(0, 3)]
with mx.np_compat(True):
_, result, _ = y.infer_shape_partial(x=(-1, 3, 3), index=(-1, 3,))
assert result == [(-1, 3)]
def test_embedding_partial_shape():
# testing embedding with batch size unknown
x = mx.sym.Variable("x")
w = mx.sym.Variable("w")
y = mx.sym.Embedding(data=x, weight=w, input_dim=100, output_dim=10)
_, result_shape, _ = y.infer_shape_partial(x=(0, 5), w=(100, 10))
assert result_shape == [(0, 5, 10)]
with mx.np_compat(True):
_, result_shape, _ = y.infer_shape_partial(x=(-1, 5), w=(100, 10))
assert result_shape == [(-1, 5, 10)]
def test_dot_partial_shape():
x = mx.sym.Variable("x")
y = mx.sym.Variable("y")
z = mx.sym.dot(x, y)
# batch size(first dim) of lhs unknown
_, result_shape, _ = z.infer_shape_partial(x=(0, 3, 4), y=(4, 5))
assert result_shape == [(0, 3, 5)]
with mx.np_compat(True):
_, result_shape, _ = z.infer_shape_partial(x=(-1, 3, 4), y=(4, 5))
assert result_shape == [(-1, 3, 5)]
def test_shape_completely_unknown():
data = mx.sym.var("data")
ret = mx.sym.sin(data)
arg_shapes, out_shapes, _ = ret.infer_shape_partial()
assert arg_shapes[0] == ()
assert out_shapes[0] == ()
with mx.np_compat():
data = mx.sym.var("data")
ret = mx.sym.sin(data)
arg_shapes, out_shapes, _ = ret.infer_shape_partial()
assert arg_shapes[0] is None
assert out_shapes[0] is None
def test_shape_completely_unknown():
data = mx.sym.var("data")
ret = mx.sym.sin(data)
arg_shapes, out_shapes, _ = ret.infer_shape_partial()
assert arg_shapes[0] == ()
assert out_shapes[0] == ()
with mx.np_compat():
data = mx.sym.var("data")
ret = mx.sym.sin(data)
arg_shapes, out_shapes, _ = ret.infer_shape_partial()
assert arg_shapes[0] is None
assert out_shapes[0] is None
def test_transpose_partial_shape():
# test converting tensor shape
# from channels first to channels last
# with batch size unknown
axes = [0, 3, 2, 1]
x = mx.sym.Variable("x")
y = mx.sym.transpose(x, axes=axes)
_, result, _ = y.infer_shape_partial(x=(0, 3, 224, 224))
assert result == [(0, 224, 224, 3)]
with mx.np_compat(True):
_, result, _ = y.infer_shape_partial(x=(-1, 3, 224, 224))
assert result == [(-1, 224, 224, 3)]
def test_where_partial_shape():
x = mx.sym.Variable("x")
y = mx.sym.Variable("y")
cond = mx.sym.Variable("cond")
where_op = mx.sym.where(cond, x, y)
# condition must be fully known to infer shape
_, result, _ = where_op.infer_shape_partial(cond=(0, 2), x=(0, 2), y =(0, 2))
assert result == [()]
_, result, _ = where_op.infer_shape_partial(cond=(0,), x=(2, 2), y =(2, 2))
assert result == [()]
with mx.np_compat(True):
_, result, _ = where_op.infer_shape_partial(cond=(-1, 2), x=(-1, 2), y =(-1, 2))
assert result == [None]
_, result, _ = where_op.infer_shape_partial(cond=(-1,), x=(2, 2), y=(2, 2))
assert result == [None]
def test_batch_dot_partial_shape():
x = mx.sym.Variable("x")
y = mx.sym.Variable("y")
z = mx.sym.batch_dot(x, y)
# lhs and rhs batch size unknown
_, result_shape, _ = z.infer_shape_partial(x=(0, 3, 4), y=(0, 4, 5))
assert result_shape == [(0, 3, 5)]
# rhs second dim unknown
_, result_shape, _ = z.infer_shape_partial(x=(0, 3, 4), y=(0, 0, 5))
assert result_shape == [()]
with mx.np_compat(True):
_, result_shape, _ = z.infer_shape_partial(x=(-1, 3, 4), y=(-1, 4, 5))
assert result_shape == [(-1, 3, 5)]
_, result_shape, _ = z.infer_shape_partial(x=(-1, 3, 4), y=(-1, -1, 5))
assert result_shape == [None]