Implements shape Ops and MakeVector in PyTorch (#926)

* Implements shape and MakeVector Ops in PyTorch

- Shape
- Shape_i
- Reshape
- SpecifyShape
- Unbroadcast
- MakeVector

pytensor/link/pytorch/dispatch/__init__.py

@@ -6,4 +6,5 @@
 import pytensor.link.pytorch.dispatch.elemwise
 import pytensor.link.pytorch.dispatch.extra_ops
 import pytensor.link.pytorch.dispatch.sort
+import pytensor.link.pytorch.dispatch.shape
 # isort: on

pytensor/link/pytorch/dispatch/basic.py

@@ -1,12 +1,13 @@
 from functools import singledispatch
+from types import NoneType
 
 import torch
 
 from pytensor.compile.ops import DeepCopyOp
 from pytensor.graph.fg import FunctionGraph
 from pytensor.link.utils import fgraph_to_python
 from pytensor.raise_op import CheckAndRaise
-from pytensor.tensor.basic import Alloc, AllocEmpty, ARange, Eye, Join
+from pytensor.tensor.basic import Alloc, AllocEmpty, ARange, Eye, Join, MakeVector
 
 
 @singledispatch
@@ -15,6 +16,11 @@ def pytorch_typify(data, dtype=None, **kwargs):
     return torch.as_tensor(data, dtype=dtype)
 
 
+@pytorch_typify.register(NoneType)
+def pytorch_typify_None(data, **kwargs):
+    return None
+
+
 @singledispatch
 def pytorch_funcify(op, node=None, storage_map=None, **kwargs):
     """Create a PyTorch compatible function from an PyTensor `Op`."""
@@ -116,3 +122,13 @@ def eye(N, M, k):
         return zeros
 
     return eye
+
+
+@pytorch_funcify.register(MakeVector)
+def pytorch_funcify_MakeVector(op, **kwargs):
+    torch_dtype = getattr(torch, op.dtype)
+
+    def makevector(*x):
+        return torch.tensor(x, dtype=torch_dtype)
+
+    return makevector

pytensor/link/pytorch/dispatch/shape.py

@@ -0,0 +1,52 @@
+import torch
+
+from pytensor.link.pytorch.dispatch.basic import pytorch_funcify
+from pytensor.tensor.shape import Reshape, Shape, Shape_i, SpecifyShape, Unbroadcast
+
+
+@pytorch_funcify.register(Reshape)
+def pytorch_funcify_Reshape(op, node, **kwargs):
+    def reshape(x, shape):
+        return torch.reshape(x, tuple(shape))
+
+    return reshape
+
+
+@pytorch_funcify.register(Shape)
+def pytorch_funcify_Shape(op, **kwargs):
+    def shape(x):
+        return x.shape
+
+    return shape
+
+
+@pytorch_funcify.register(Shape_i)
+def pytorch_funcify_Shape_i(op, **kwargs):
+    i = op.i
+
+    def shape_i(x):
+        return torch.tensor(x.shape[i])
+
+    return shape_i
+
+
+@pytorch_funcify.register(SpecifyShape)
+def pytorch_funcify_SpecifyShape(op, node, **kwargs):
+    def specifyshape(x, *shape):
+        assert x.ndim == len(shape)
+        for actual, expected in zip(x.shape, shape):
+            if expected is None:
+                continue
+            if actual != expected:
+                raise ValueError(f"Invalid shape: Expected {shape} but got {x.shape}")
+        return x
+
+    return specifyshape
+
+
+@pytorch_funcify.register(Unbroadcast)
+def pytorch_funcify_Unbroadcast(op, **kwargs):
+    def unbroadcast(x):
+        return x
+
+    return unbroadcast

tests/link/pytorch/test_basic.py

@@ -294,3 +294,10 @@ def test_eye(dtype):
         for _M in range(1, 6):
             for _k in list(range(_M + 2)) + [-x for x in range(1, _N + 2)]:
                 np.testing.assert_array_equal(fn(_N, _M, _k), np.eye(_N, _M, _k))
+
+
+def test_pytorch_MakeVector():
+    x = ptb.make_vector(1, 2, 3)
+    x_fg = FunctionGraph([], [x])
+
+    compare_pytorch_and_py(x_fg, [])

tests/link/pytorch/test_extra_ops.py

@@ -43,18 +43,7 @@ def test_pytorch_CumOp(axis, dtype):
         compare_pytorch_and_py(fgraph, [test_value])
 
 
-@pytest.mark.parametrize(
-    "axis, repeats",
-    [
-        (0, (1, 2, 3)),
-        (1, (3, 3)),
-        pytest.param(
-            None,
-            3,
-            marks=pytest.mark.xfail(reason="Reshape not implemented"),
-        ),
-    ],
-)
+@pytest.mark.parametrize("axis, repeats", [(0, (1, 2, 3)), (1, (3, 3)), (None, 3)])
 def test_pytorch_Repeat(axis, repeats):
     a = pt.matrix("a", dtype="float64")
 

tests/link/pytorch/test_shape.py

@@ -0,0 +1,61 @@
+import numpy as np
+
+import pytensor.tensor as pt
+from pytensor.configdefaults import config
+from pytensor.graph.fg import FunctionGraph
+from pytensor.tensor.shape import Shape, Shape_i, Unbroadcast, reshape
+from pytensor.tensor.type import iscalar, vector
+from tests.link.pytorch.test_basic import compare_pytorch_and_py
+
+
+def test_pytorch_shape_ops():
+    x_np = np.zeros((20, 3))
+    x = Shape()(pt.as_tensor_variable(x_np))
+    x_fg = FunctionGraph([], [x])
+
+    compare_pytorch_and_py(x_fg, [], must_be_device_array=False)
+
+    x = Shape_i(1)(pt.as_tensor_variable(x_np))
+    x_fg = FunctionGraph([], [x])
+
+    compare_pytorch_and_py(x_fg, [], must_be_device_array=False)
+
+
+def test_pytorch_specify_shape():
+    in_pt = pt.matrix("in")
+    x = pt.specify_shape(in_pt, (4, None))
+    x_fg = FunctionGraph([in_pt], [x])
+    compare_pytorch_and_py(x_fg, [np.ones((4, 5)).astype(config.floatX)])
+
+    # When used to assert two arrays have similar shapes
+    in_pt = pt.matrix("in")
+    shape_pt = pt.matrix("shape")
+    x = pt.specify_shape(in_pt, shape_pt.shape)
+    x_fg = FunctionGraph([in_pt, shape_pt], [x])
+    compare_pytorch_and_py(
+        x_fg,
+        [np.ones((4, 5)).astype(config.floatX), np.ones((4, 5)).astype(config.floatX)],
+    )
+
+
+def test_pytorch_Reshape_constant():
+    a = vector("a")
+    x = reshape(a, (2, 2))
+    x_fg = FunctionGraph([a], [x])
+    compare_pytorch_and_py(x_fg, [np.r_[1.0, 2.0, 3.0, 4.0].astype(config.floatX)])
+
+
+def test_pytorch_Reshape_dynamic():
+    a = vector("a")
+    shape_pt = iscalar("b")
+    x = reshape(a, (shape_pt, shape_pt))
+    x_fg = FunctionGraph([a, shape_pt], [x])
+    compare_pytorch_and_py(x_fg, [np.r_[1.0, 2.0, 3.0, 4.0].astype(config.floatX), 2])
+
+
+def test_pytorch_unbroadcast():
+    x_np = np.zeros((20, 1, 1))
+    x = Unbroadcast(0, 2)(pt.as_tensor_variable(x_np))
+    x_fg = FunctionGraph([], [x])
+
+    compare_pytorch_and_py(x_fg, [])

tests/link/pytorch/test_sort.py

@@ -8,16 +8,7 @@
 
 
 @pytest.mark.parametrize("func", (sort, argsort))
-@pytest.mark.parametrize(
-    "axis",
-    [
-        pytest.param(0),
-        pytest.param(1),
-        pytest.param(
-            None, marks=pytest.mark.xfail(reason="Reshape Op not implemented")
-        ),
-    ],
-)
+@pytest.mark.parametrize("axis", [0, 1, None])
 def test_sort(func, axis):
     x = matrix("x", shape=(2, 2), dtype="float64")
     out = func(x, axis=axis)