Removes Function Graph, set_test_value/ get_test_value

pytensor/link/numba/dispatch/slinalg.py

@@ -126,13 +126,17 @@
 
         B_is_1d = B.ndim == 1
 
-        if not overwrite_b:
-            B_copy = _copy_to_fortran_order(B)
-        else:
+        if overwrite_b:
             B_copy = B
+        else:
+            if B_is_1d:
+                # _copy_to_fortran_order does nothing with vectors
+                B_copy = np.copy(B)
+            else:
+                B_copy = _copy_to_fortran_order(B)
 
         if B_is_1d:
-            B_copy = np.expand_dims(B, -1)
+            B_copy = np.expand_dims(B_copy, -1)
 
         NRHS = 1 if B_is_1d else int(B_copy.shape[-1])
 

tests/link/jax/test_basic.py

@@ -7,12 +7,12 @@
 from pytensor.compile.builders import OpFromGraph
 from pytensor.compile.function import function
 from pytensor.compile.mode import JAX, Mode
-from pytensor.compile.sharedvalue import SharedVariable, shared
+from pytensor.compile.sharedvalue import shared
 from pytensor.configdefaults import config
 from pytensor.graph import RewriteDatabaseQuery
-from pytensor.graph.basic import Apply
+from pytensor.graph.basic import Apply, Variable
 from pytensor.graph.fg import FunctionGraph
-from pytensor.graph.op import Op, get_test_value
+from pytensor.graph.op import Op
 from pytensor.ifelse import ifelse
 from pytensor.link.jax import JAXLinker
 from pytensor.raise_op import assert_op
@@ -34,25 +34,28 @@ def set_pytensor_flags():
 
 
 def compare_jax_and_py(
-    fgraph: FunctionGraph,
+    graph_inputs: Iterable[Variable],
+    graph_outputs: Variable | Iterable[Variable],
     test_inputs: Iterable,
+    *,
     assert_fn: Callable | None = None,
     must_be_device_array: bool = True,
     jax_mode=jax_mode,
     py_mode=py_mode,
 ):
-    """Function to compare python graph output and jax compiled output for testing equality
+    """Function to compare python function output and jax compiled output for testing equality
 
-    In the tests below computational graphs are defined in PyTensor. These graphs are then passed to
-    this function which then compiles the graphs in both jax and python, runs the calculation
-    in both and checks if the results are the same
+    The inputs and outputs are then passed to this function which then compiles the given function in both
+    jax and python, runs the calculation in both and checks if the results are the same
 
     Parameters
     ----------
-    fgraph: FunctionGraph
-        PyTensor function Graph object
+    graph_inputs:
+        Symbolic inputs to the graph
+    outputs:
+        Symbolic outputs of the graph
     test_inputs: iter
-        Numerical inputs for testing the function graph
+        Numerical inputs for testing the function.
     assert_fn: func, opt
         Assert function used to check for equality between python and jax. If not
         provided uses np.testing.assert_allclose
@@ -68,8 +71,10 @@ def compare_jax_and_py(
     if assert_fn is None:
         assert_fn = partial(np.testing.assert_allclose, rtol=1e-4)
 
-    fn_inputs = [i for i in fgraph.inputs if not isinstance(i, SharedVariable)]
-    pytensor_jax_fn = function(fn_inputs, fgraph.outputs, mode=jax_mode)
+    if any(inp.owner is not None for inp in graph_inputs):
+        raise ValueError("Inputs must be root variables")
+
+    pytensor_jax_fn = function(graph_inputs, graph_outputs, mode=jax_mode)
     jax_res = pytensor_jax_fn(*test_inputs)
 
     if must_be_device_array:
@@ -78,10 +83,10 @@ def compare_jax_and_py(
         else:
             assert isinstance(jax_res, jax.Array)
 
-    pytensor_py_fn = function(fn_inputs, fgraph.outputs, mode=py_mode)
+    pytensor_py_fn = function(graph_inputs, graph_outputs, mode=py_mode)
     py_res = pytensor_py_fn(*test_inputs)
 
-    if len(fgraph.outputs) > 1:
+    if isinstance(graph_outputs, list | tuple):
         for j, p in zip(jax_res, py_res, strict=True):
             assert_fn(j, p)
     else:
@@ -187,16 +192,14 @@ def test_jax_ifelse():
     false_vals = np.r_[-1, -2, -3]
 
     x = ifelse(np.array(True), true_vals, false_vals)
-    x_fg = FunctionGraph([], [x])
 
-    compare_jax_and_py(x_fg, [])
+    compare_jax_and_py([], [x], [])
 
     a = dscalar("a")
-    a.tag.test_value = np.array(0.2, dtype=config.floatX)
+    a_test = np.array(0.2, dtype=config.floatX)
     x = ifelse(a < 0.5, true_vals, false_vals)
-    x_fg = FunctionGraph([a], [x])  # I.e. False
 
-    compare_jax_and_py(x_fg, [get_test_value(i) for i in x_fg.inputs])
+    compare_jax_and_py([a], [x], [a_test])
 
 
 def test_jax_checkandraise():
@@ -209,22 +212,16 @@ def test_jax_checkandraise():
         function((p,), res, mode=jax_mode)
 
 
-def set_test_value(x, v):
-    x.tag.test_value = v
-    return x
-
-
 def test_OpFromGraph():
     x, y, z = matrices("xyz")
     ofg_1 = OpFromGraph([x, y], [x + y], inline=False)
     ofg_2 = OpFromGraph([x, y], [x * y, x - y], inline=False)
 
     o1, o2 = ofg_2(y, z)
     out = ofg_1(x, o1) + o2
-    out_fg = FunctionGraph([x, y, z], [out])
 
     xv = np.ones((2, 2), dtype=config.floatX)
     yv = np.ones((2, 2), dtype=config.floatX) * 3
     zv = np.ones((2, 2), dtype=config.floatX) * 5
 
-    compare_jax_and_py(out_fg, [xv, yv, zv])
+    compare_jax_and_py([x, y, z], [out], [xv, yv, zv])

tests/link/jax/test_blas.py

@@ -4,8 +4,6 @@
 from pytensor.compile.function import function
 from pytensor.compile.mode import Mode
 from pytensor.configdefaults import config
-from pytensor.graph.fg import FunctionGraph
-from pytensor.graph.op import get_test_value
 from pytensor.graph.rewriting.db import RewriteDatabaseQuery
 from pytensor.link.jax import JAXLinker
 from pytensor.tensor import blas as pt_blas
@@ -16,21 +14,20 @@
 def test_jax_BatchedDot():
     # tensor3 . tensor3
     a = tensor3("a")
-    a.tag.test_value = (
+    a_test_value = (
         np.linspace(-1, 1, 10 * 5 * 3).astype(config.floatX).reshape((10, 5, 3))
     )
     b = tensor3("b")
-    b.tag.test_value = (
+    b_test_value = (
         np.linspace(1, -1, 10 * 3 * 2).astype(config.floatX).reshape((10, 3, 2))
     )
     out = pt_blas.BatchedDot()(a, b)
-    fgraph = FunctionGraph([a, b], [out])
-    compare_jax_and_py(fgraph, [get_test_value(i) for i in fgraph.inputs])
+    compare_jax_and_py([a, b], [out], [a_test_value, b_test_value])
 
     # A dimension mismatch should raise a TypeError for compatibility
-    inputs = [get_test_value(a)[:-1], get_test_value(b)]
+    inputs = [a_test_value[:-1], b_test_value]
     opts = RewriteDatabaseQuery(include=[None], exclude=["cxx_only", "BlasOpt"])
     jax_mode = Mode(JAXLinker(), opts)
-    pytensor_jax_fn = function(fgraph.inputs, fgraph.outputs, mode=jax_mode)
+    pytensor_jax_fn = function([a, b], [out], mode=jax_mode)
     with pytest.raises(TypeError):
         pytensor_jax_fn(*inputs)

tests/link/jax/test_blockwise.py

@@ -2,7 +2,6 @@
 import pytest
 
 from pytensor import config
-from pytensor.graph import FunctionGraph
 from pytensor.tensor import tensor
 from pytensor.tensor.blockwise import Blockwise
 from pytensor.tensor.math import Dot, matmul
@@ -32,8 +31,7 @@ def test_matmul(matmul_op):
 
     out = matmul_op(a, b)
     assert isinstance(out.owner.op, Blockwise)
-    fg = FunctionGraph([a, b], [out])
-    fn, _ = compare_jax_and_py(fg, test_values)
+    fn, _ = compare_jax_and_py([a, b], [out], test_values)
 
     # Check we are not adding any unnecessary stuff
     jaxpr = str(jax.make_jaxpr(fn.vm.jit_fn)(*test_values))

tests/link/jax/test_einsum.py

@@ -2,7 +2,6 @@
 import pytest
 
 import pytensor.tensor as pt
-from pytensor.graph import FunctionGraph
 from tests.link.jax.test_basic import compare_jax_and_py
 
 
@@ -22,8 +21,7 @@ def test_jax_einsum():
     }
     x_pt, y_pt, z_pt = (pt.tensor(name, shape=shape) for name, shape in shapes.items())
     out = pt.einsum(subscripts, x_pt, y_pt, z_pt)
-    fg = FunctionGraph([x_pt, y_pt, z_pt], [out])
-    compare_jax_and_py(fg, [x, y, z])
+    compare_jax_and_py([x_pt, y_pt, z_pt], [out], [x, y, z])
 
 
 def test_ellipsis_einsum():
@@ -34,5 +32,4 @@ def test_ellipsis_einsum():
     x_pt = pt.tensor("x", shape=x.shape)
     y_pt = pt.tensor("y", shape=y.shape)
     out = pt.einsum(subscripts, x_pt, y_pt)
-    fg = FunctionGraph([x_pt, y_pt], [out])
-    compare_jax_and_py(fg, [x, y])
+    compare_jax_and_py([x_pt, y_pt], [out], [x, y])

tests/link/jax/test_elemwise.py

@@ -6,8 +6,6 @@
 import pytensor.tensor as pt
 from pytensor.compile import get_mode
 from pytensor.configdefaults import config
-from pytensor.graph.fg import FunctionGraph
-from pytensor.graph.op import get_test_value
 from pytensor.tensor import elemwise as pt_elemwise
 from pytensor.tensor.math import all as pt_all
 from pytensor.tensor.math import prod
@@ -26,87 +24,81 @@ def test_jax_Dimshuffle():
     a_pt = matrix("a")
 
     x = a_pt.T
-    x_fg = FunctionGraph([a_pt], [x])
-    compare_jax_and_py(x_fg, [np.c_[[1.0, 2.0], [3.0, 4.0]].astype(config.floatX)])
+    compare_jax_and_py(
+        [a_pt], [x], [np.c_[[1.0, 2.0], [3.0, 4.0]].astype(config.floatX)]
+    )
 
     x = a_pt.dimshuffle([0, 1, "x"])
-    x_fg = FunctionGraph([a_pt], [x])
-    compare_jax_and_py(x_fg, [np.c_[[1.0, 2.0], [3.0, 4.0]].astype(config.floatX)])
+    compare_jax_and_py(
+        [a_pt], [x], [np.c_[[1.0, 2.0], [3.0, 4.0]].astype(config.floatX)]
+    )
 
     a_pt = tensor(dtype=config.floatX, shape=(None, 1))
     x = a_pt.dimshuffle((0,))
-    x_fg = FunctionGraph([a_pt], [x])
-    compare_jax_and_py(x_fg, [np.c_[[1.0, 2.0, 3.0, 4.0]].astype(config.floatX)])
+    compare_jax_and_py([a_pt], [x], [np.c_[[1.0, 2.0, 3.0, 4.0]].astype(config.floatX)])
 
     a_pt = tensor(dtype=config.floatX, shape=(None, 1))
     x = pt_elemwise.DimShuffle(input_ndim=2, new_order=(0,))(a_pt)
-    x_fg = FunctionGraph([a_pt], [x])
-    compare_jax_and_py(x_fg, [np.c_[[1.0, 2.0, 3.0, 4.0]].astype(config.floatX)])
+    compare_jax_and_py([a_pt], [x], [np.c_[[1.0, 2.0, 3.0, 4.0]].astype(config.floatX)])
 
 
 def test_jax_CAReduce():
     a_pt = vector("a")
     a_pt.tag.test_value = np.r_[1, 2, 3].astype(config.floatX)
 
     x = pt_sum(a_pt, axis=None)
-    x_fg = FunctionGraph([a_pt], [x])
 
-    compare_jax_and_py(x_fg, [np.r_[1, 2, 3].astype(config.floatX)])
+    compare_jax_and_py([a_pt], [x], [np.r_[1, 2, 3].astype(config.floatX)])
 
     a_pt = matrix("a")
     a_pt.tag.test_value = np.c_[[1, 2, 3], [1, 2, 3]].astype(config.floatX)
 
     x = pt_sum(a_pt, axis=0)
-    x_fg = FunctionGraph([a_pt], [x])
 
-    compare_jax_and_py(x_fg, [np.c_[[1, 2, 3], [1, 2, 3]].astype(config.floatX)])
+    compare_jax_and_py([a_pt], [x], [np.c_[[1, 2, 3], [1, 2, 3]].astype(config.floatX)])
 
     x = pt_sum(a_pt, axis=1)
-    x_fg = FunctionGraph([a_pt], [x])
 
-    compare_jax_and_py(x_fg, [np.c_[[1, 2, 3], [1, 2, 3]].astype(config.floatX)])
+    compare_jax_and_py([a_pt], [x], [np.c_[[1, 2, 3], [1, 2, 3]].astype(config.floatX)])
 
     a_pt = matrix("a")
     a_pt.tag.test_value = np.c_[[1, 2, 3], [1, 2, 3]].astype(config.floatX)
 
     x = prod(a_pt, axis=0)
-    x_fg = FunctionGraph([a_pt], [x])
 
-    compare_jax_and_py(x_fg, [np.c_[[1, 2, 3], [1, 2, 3]].astype(config.floatX)])
+    compare_jax_and_py([a_pt], [x], [np.c_[[1, 2, 3], [1, 2, 3]].astype(config.floatX)])
 
     x = pt_all(a_pt)
-    x_fg = FunctionGraph([a_pt], [x])
 
-    compare_jax_and_py(x_fg, [np.c_[[1, 2, 3], [1, 2, 3]].astype(config.floatX)])
+    compare_jax_and_py([a_pt], [x], [np.c_[[1, 2, 3], [1, 2, 3]].astype(config.floatX)])
 
 
 @pytest.mark.parametrize("axis", [None, 0, 1])
 def test_softmax(axis):
     x = matrix("x")
-    x.tag.test_value = np.arange(6, dtype=config.floatX).reshape(2, 3)
+    x_test_value = np.arange(6, dtype=config.floatX).reshape(2, 3)
     out = softmax(x, axis=axis)
-    fgraph = FunctionGraph([x], [out])
-    compare_jax_and_py(fgraph, [get_test_value(i) for i in fgraph.inputs])
+    compare_jax_and_py([x], [out], [x_test_value])
 
 
 @pytest.mark.parametrize("axis", [None, 0, 1])
 def test_logsoftmax(axis):
     x = matrix("x")
-    x.tag.test_value = np.arange(6, dtype=config.floatX).reshape(2, 3)
+    x_test_value = np.arange(6, dtype=config.floatX).reshape(2, 3)
     out = log_softmax(x, axis=axis)
-    fgraph = FunctionGraph([x], [out])
-    compare_jax_and_py(fgraph, [get_test_value(i) for i in fgraph.inputs])
+
+    compare_jax_and_py([x], [out], [x_test_value])
 
 
 @pytest.mark.parametrize("axis", [None, 0, 1])
 def test_softmax_grad(axis):
     dy = matrix("dy")
-    dy.tag.test_value = np.array([[1, 1, 1], [0, 0, 0]], dtype=config.floatX)
+    dy_test_value = np.array([[1, 1, 1], [0, 0, 0]], dtype=config.floatX)
     sm = matrix("sm")
-    sm.tag.test_value = np.arange(6, dtype=config.floatX).reshape(2, 3)
+    sm_test_value = np.arange(6, dtype=config.floatX).reshape(2, 3)
     out = SoftmaxGrad(axis=axis)(dy, sm)
-    fgraph = FunctionGraph([dy, sm], [out])
-    compare_jax_and_py(fgraph, [get_test_value(i) for i in fgraph.inputs])
+
+    compare_jax_and_py([dy, sm], [out], [dy_test_value, sm_test_value])
 
 
 @pytest.mark.parametrize("size", [(10, 10), (1000, 1000)])
@@ -134,6 +126,4 @@ def test_logsumexp_benchmark(size, axis, benchmark):
 def test_multiple_input_multiply():
     x, y, z = vectors("xyz")
     out = pt.mul(x, y, z)
-
-    fg = FunctionGraph(outputs=[out], clone=False)
-    compare_jax_and_py(fg, [[1.5], [2.5], [3.5]])
+    compare_jax_and_py([x, y, z], [out], test_inputs=[[1.5], [2.5], [3.5]])