Fix numba FunctionGraph cache key

pytensor/link/numba/dispatch/basic.py

@@ -9,7 +9,7 @@ from numba import njit as _njit
 from numba.cpython.unsafe.tuple import tuple_setitem  # noqa: F401
 
 from pytensor import config
-from pytensor.graph.basic import Apply, Constant
+from pytensor.graph.basic import Apply, Constant, Variable
 from pytensor.graph.fg import FunctionGraph
 from pytensor.graph.type import Type
 from pytensor.link.numba.cache import compile_numba_function_src, hash_from_pickle_dump
@@ -498,36 +498,46 @@ def numba_funcify_FunctionGraph(
 ):
     # Collect cache keys of every Op/Constant in the FunctionGraph
     # so we can create a global cache key for the whole FunctionGraph
+    fgraph_can_be_cached = [True]
     cache_keys = []
     toposort = fgraph.toposort()
-    clients = fgraph.clients
-    toposort_indices = {node: i for i, node in enumerate(toposort)}
-    # Add dummy output clients which are not included of the toposort
-    toposort_indices |= {
-        clients[out][0][0]: i
-        for i, out in enumerate(fgraph.outputs, start=len(toposort))
+    toposort_coords: dict[Variable, tuple[int, int | str]] = {
+        inp: (0, i) for i, inp in enumerate(fgraph.inputs)
+    }
+    toposort_coords |= {
+        out: (i, j)
+        for i, node in enumerate(toposort, start=1)
+        for j, out in enumerate(node.outputs)
     }
 
-    def op_conversion_and_key_collection(*args, **kwargs):
+    def op_conversion_and_key_collection(op, *args, node, **kwargs):
         # Convert an Op to a funcified function and store the cache_key
 
         # We also Cache each Op so Numba can do less work next time it sees it
-        func, key = numba_funcify_ensure_cache(*args, **kwargs)
-        cache_keys.append(key)
+        func, key = numba_funcify_ensure_cache(op, node=node, *args, **kwargs)
+        if key is None:
+            fgraph_can_be_cached[0] = False
+        else:
+            # Add graph coordinate information (input edges and node location)
+            cache_keys.append(
+                (
+                    tuple(toposort_coords[inp] for inp in node.inputs),
+                    key,
+                )
+            )
         return func
 
     def type_conversion_and_key_collection(value, variable, **kwargs):
         # Convert a constant type to a numba compatible one and compute a cache key for it
 
-        # We need to know where in the graph the constants are used
-        # Otherwise we would hash stack(x, 5.0, 7.0), and stack(5.0, x, 7.0) the same
         # FIXME: It doesn't make sense to call type_conversion on non-constants,
-        #  but that's what fgraph_to_python currently does. We appease it, but don't consider for caching
+        #  but that's what fgraph_to_python currently does.
+        #  We appease it, but don't consider for caching
         if isinstance(variable, Constant):
-            client_indices = tuple(
-                (toposort_indices[node], inp_idx) for node, inp_idx in clients[variable]
-            )
-            cache_keys.append((client_indices, cache_key_for_constant(value)))
+            # Store unique key in toposort_coords. It will be included by whichever nodes make use of the constant
+            constant_cache_key = cache_key_for_constant(value)
+            assert constant_cache_key is not None
+            toposort_coords[variable] = (-1, constant_cache_key)
         return numba_typify(value, variable=variable, **kwargs)
 
     py_func = fgraph_to_python(
@@ -537,12 +547,15 @@ def numba_funcify_FunctionGraph(
         fgraph_name=fgraph_name,
         **kwargs,
     )
-    if any(key is None for key in cache_keys):
+    if not fgraph_can_be_cached[0]:
         # If a single element couldn't be cached, we can't cache the whole FunctionGraph either
         fgraph_key = None
     else:
+        # Add graph coordinate information for fgraph outputs
+        fgraph_output_ancestors = tuple(toposort_coords[out] for out in fgraph.outputs)
+
         # Compose individual cache_keys into a global key for the FunctionGraph
         fgraph_key = sha256(
-            f"({type(fgraph)}, {tuple(cache_keys)}, {len(fgraph.inputs)}, {len(fgraph.outputs)})".encode()
+            f"({type(fgraph)}, {tuple(cache_keys)}, {len(fgraph.inputs)}, {fgraph_output_ancestors})".encode()
         ).hexdigest()
     return numba_njit(py_func), fgraph_key

pytensor/link/utils.py

@@ -735,14 +735,6 @@ def fgraph_to_python(
 
     body_assigns = []
     for node in order:
-        compiled_func = op_conversion_fn(
-            node.op, node=node, storage_map=storage_map, **kwargs
-        )
-
-        # Create a local alias with a unique name
-        local_compiled_func_name = unique_name(compiled_func)
-        global_env[local_compiled_func_name] = compiled_func
-
         node_input_names = []
         for inp in node.inputs:
             local_input_name = unique_name(inp)
@@ -772,6 +764,13 @@ def fgraph_to_python(
 
         node_output_names = [unique_name(v) for v in node.outputs]
 
+        compiled_func = op_conversion_fn(
+            node.op, node=node, storage_map=storage_map, **kwargs
+        )
+        # Create a local alias with a unique name
+        local_compiled_func_name = unique_name(compiled_func)
+        global_env[local_compiled_func_name] = compiled_func
+
         assign_str = f"{', '.join(node_output_names)} = {local_compiled_func_name}({', '.join(node_input_names)})"
         assign_comment_str = f"{indent(str(node), '# ')}"
         assign_block_str = f"{assign_comment_str}\n{assign_str}"

tests/link/numba/test_basic.py

@@ -7,8 +7,7 @@ import numpy as np
 import pytest
 import scipy
 
-from pytensor.compile import SymbolicInput
-from pytensor.tensor.utils import hash_from_ndarray
+from pytensor.tensor import scalar_from_tensor
 
 
 numba = pytest.importorskip("numba")
@@ -16,17 +15,23 @@ numba = pytest.importorskip("numba")
 import pytensor.scalar as ps
 import pytensor.tensor as pt
 from pytensor import config, shared
+from pytensor.compile import SymbolicInput
 from pytensor.compile.function import function
 from pytensor.compile.mode import Mode
 from pytensor.graph.basic import Apply, Variable
+from pytensor.graph.fg import FunctionGraph
 from pytensor.graph.op import Op
 from pytensor.graph.rewriting.db import RewriteDatabaseQuery
 from pytensor.graph.type import Type
 from pytensor.link.numba.dispatch import basic as numba_basic
-from pytensor.link.numba.dispatch.basic import cache_key_for_constant
+from pytensor.link.numba.dispatch.basic import (
+    cache_key_for_constant,
+    numba_funcify_and_cache_key,
+)
 from pytensor.link.numba.linker import NumbaLinker
-from pytensor.scalar.basic import ScalarOp, as_scalar
+from pytensor.scalar.basic import Composite, ScalarOp, as_scalar
 from pytensor.tensor.elemwise import Elemwise
+from pytensor.tensor.utils import hash_from_ndarray
 
 
 if TYPE_CHECKING:
@@ -652,3 +657,77 @@ def test_funcify_dispatch_interop():
         outs[2].owner.op, outs[2].owner
     )
     assert numba.njit(lambda x: fn2_def_cached(x))(test_x) == 2
+
+
+class TestFgraphCacheKey:
+    @staticmethod
+    def generate_and_validate_key(fg):
+        _, key = numba_funcify_and_cache_key(fg)
+        assert key is not None
+        _, key_again = numba_funcify_and_cache_key(fg)
+        assert key == key_again  # Check its stable
+        return key
+
+    def test_node_order(self):
+        x = pt.scalar("x")
+        log_x = pt.log(x)
+        graphs = [
+            pt.exp(x) / log_x,
+            log_x / pt.exp(x),
+            pt.exp(log_x) / x,
+            x / pt.exp(log_x),
+            pt.exp(log_x) / log_x,
+            log_x / pt.exp(log_x),
+        ]
+
+        keys = []
+        for graph in graphs:
+            fg = FunctionGraph([x], [graph], clone=False)
+            keys.append(self.generate_and_validate_key(fg))
+        # Check keys are unique
+        assert len(set(keys)) == len(graphs)
+
+        # Extra unused input should alter the key, because it changes the function signature
+        y = pt.scalar("y")
+        for inputs in [[x, y], [y, x]]:
+            fg = FunctionGraph(inputs, [graphs[0]], clone=False)
+            keys.append(self.generate_and_validate_key(fg))
+        assert len(set(keys)) == len(graphs) + 2
+
+        # Adding an input as an output should also change the key
+        for outputs in [
+            [graphs[0], x],
+            [x, graphs[0]],
+            [x, x, graphs[0]],
+            [x, graphs[0], x],
+            [graphs[0], x, x],
+        ]:
+            fg = FunctionGraph([x], outputs, clone=False)
+            keys.append(self.generate_and_validate_key(fg))
+        assert len(set(keys)) == len(graphs) + 2 + 5
+
+    def test_multi_output(self):
+        x = pt.scalar("x")
+
+        xs = scalar_from_tensor(x)
+        out0, out1 = Elemwise(Composite([xs], [xs * 2, xs - 2]))(x)
+
+        test_outs = [
+            [out0],
+            [out1],
+            [out0, out1],
+            [out1, out0],
+        ]
+        keys = []
+        for test_out in test_outs:
+            fg = FunctionGraph([x], test_out, clone=False)
+            keys.append(self.generate_and_validate_key(fg))
+        assert len(set(keys)) == len(test_outs)
+
+    def test_constant_output(self):
+        fg_pi = FunctionGraph([], [pt.constant(np.pi)])
+        fg_e = FunctionGraph([], [pt.constant(np.e)])
+
+        assert self.generate_and_validate_key(fg_pi) != self.generate_and_validate_key(
+            fg_e
+        )