Remove uses of unique_name_generator in numba dispatch

It's more readable and avoids potential bugs when force_unique is not set to True

Author: ricardoV94

pytensor/link/numba/dispatch/scalar.py

@@ -14,7 +14,6 @@
 from pytensor.link.numba.dispatch.cython_support import wrap_cython_function
 from pytensor.link.utils import (
     get_name_for_object,
-    unique_name_generator,
 )
 from pytensor.scalar.basic import (
     Add,
@@ -81,14 +80,11 @@ def numba_funcify_ScalarOp(op, node, **kwargs):
         scalar_func_numba = generate_fallback_impl(op, node, **kwargs)
 
     scalar_op_fn_name = get_name_for_object(scalar_func_numba)
-
+    prefix = "x" if scalar_func_name != "x" else "y"
+    input_names = [f"{prefix}{i}" for i in range(len(node.inputs))]
     global_env = {"scalar_func_numba": scalar_func_numba}
 
     if input_inner_dtypes is None and output_inner_dtype is None:
-        unique_names = unique_name_generator(
-            [scalar_op_fn_name, "scalar_func_numba"], suffix_sep="_"
-        )
-        input_names = ", ".join(unique_names(v, force_unique=True) for v in node.inputs)
         if not has_pyx_skip_dispatch:
             scalar_op_src = f"""
 def {scalar_op_fn_name}({input_names}):
@@ -108,13 +104,6 @@ def {scalar_op_fn_name}({input_names}):
             for i, i_dtype in enumerate(input_inner_dtypes)
         }
         global_env.update(input_tmp_dtype_names)
-
-        unique_names = unique_name_generator(
-            [scalar_op_fn_name, "scalar_func_numba", *global_env.keys()],
-            suffix_sep="_",
-        )
-
-        input_names = [unique_names(v, force_unique=True) for v in node.inputs]
         converted_call_args = ", ".join(
             f"direct_cast({i_name}, {i_tmp_dtype_name})"
             for i_name, i_tmp_dtype_name in zip(
@@ -135,7 +124,7 @@ def {scalar_op_fn_name}({", ".join(input_names)}):
     scalar_op_fn = compile_numba_function_src(
         scalar_op_src,
         scalar_op_fn_name,
-        {**globals(), **global_env},
+        globals() | global_env,
     )
 
     # Functions that call a function pointer can't be cached
@@ -157,8 +146,8 @@ def switch(condition, x, y):
 
 def binary_to_nary_func(inputs: list[Variable], binary_op_name: str, binary_op: str):
     """Create a Numba-compatible N-ary function from a binary function."""
-    unique_names = unique_name_generator(["binary_op_name"], suffix_sep="_")
-    input_names = [unique_names(v, force_unique=True) for v in inputs]
+    var_prefix = "x" if binary_op_name != "x" else "y"
+    input_names = [f"{var_prefix}{i}" for i in range(len(inputs))]
     input_signature = ", ".join(input_names)
     output_expr = binary_op.join(input_names)
 

pytensor/link/numba/dispatch/tensor_basic.py

@@ -10,7 +10,6 @@
     register_funcify_and_cache_key,
     register_funcify_default_op_cache_key,
 )
-from pytensor.link.utils import unique_name_generator
 from pytensor.tensor.basic import (
     Alloc,
     AllocEmpty,
@@ -28,15 +27,7 @@
 
 @register_funcify_default_op_cache_key(AllocEmpty)
 def numba_funcify_AllocEmpty(op, node, **kwargs):
-    global_env = {
-        "np": np,
-        "dtype": np.dtype(op.dtype),
-    }
-
-    unique_names = unique_name_generator(
-        ["np", "dtype", "allocempty", "scalar_shape"], suffix_sep="_"
-    )
-    shape_var_names = [unique_names(v, force_unique=True) for v in node.inputs]
+    shape_var_names = [f"sh{i}" for i in range(len(node.inputs))]
     shape_var_item_names = [f"{name}_item" for name in shape_var_names]
     shapes_to_items_src = indent(
         "\n".join(
@@ -56,21 +47,15 @@ def allocempty({", ".join(shape_var_names)}):
     """
 
     alloc_fn = compile_numba_function_src(
-        alloc_def_src, "allocempty", {**globals(), **global_env}
+        alloc_def_src, "allocempty", globals() | {"np": np, "dtype": np.dtype(op.dtype)}
     )
 
     return numba_basic.numba_njit(alloc_fn)
 
 
 @register_funcify_and_cache_key(Alloc)
 def numba_funcify_Alloc(op, node, **kwargs):
-    global_env = {"np": np}
-
-    unique_names = unique_name_generator(
-        ["np", "alloc", "val_np", "val", "scalar_shape", "res"],
-        suffix_sep="_",
-    )
-    shape_var_names = [unique_names(v, force_unique=True) for v in node.inputs[1:]]
+    shape_var_names = [f"sh{i}" for i in range(len(node.inputs) - 1)]
     shape_var_item_names = [f"{name}_item" for name in shape_var_names]
     shapes_to_items_src = indent(
         "\n".join(
@@ -102,7 +87,7 @@ def alloc(val, {", ".join(shape_var_names)}):
     alloc_fn = compile_numba_function_src(
         alloc_def_src,
         "alloc",
-        {**globals(), **global_env},
+        globals() | {"np": np},
     )
 
     cache_key = sha256(
@@ -207,14 +192,7 @@ def eye(N, M, k):
 @register_funcify_default_op_cache_key(MakeVector)
 def numba_funcify_MakeVector(op, node, **kwargs):
     dtype = np.dtype(op.dtype)
-
-    global_env = {"np": np, "dtype": dtype}
-
-    unique_names = unique_name_generator(
-        ["np"],
-        suffix_sep="_",
-    )
-    input_names = [unique_names(v, force_unique=True) for v in node.inputs]
+    input_names = [f"x{i}" for i in range(len(node.inputs))]
 
     def create_list_string(x):
         args = ", ".join([f"{i}.item()" for i in x] + ([""] if len(x) == 1 else []))
@@ -228,7 +206,7 @@ def makevector({", ".join(input_names)}):
     makevector_fn = compile_numba_function_src(
         makevector_def_src,
         "makevector",
-        {**globals(), **global_env},
+        globals() | {"np": np, "dtype": dtype},
     )
 
     return numba_basic.numba_njit(makevector_fn)