Fix slow dot in numba

pytensor/link/numba/dispatch/basic.py

@@ -565,26 +565,27 @@
 def int_to_float_fn(inputs, out_dtype):
     """Create a Numba function that converts integer and boolean ``ndarray``s to floats."""
 
-    if all(
-        input.type.numpy_dtype == np.dtype(out_dtype) for input in inputs
-    ) and isinstance(np.dtype(out_dtype), np.floating):
+    if (
+        all(inp.type.dtype == out_dtype for inp in inputs)
+        and np.dtype(out_dtype).kind == "f"
+    ):
 
-        @numba_njit
+        @numba_njit(inline="always")
         def inputs_cast(x):
             return x
 
-    elif any(i.type.numpy_dtype.kind in "ib" for i in inputs):
+    elif any(i.type.numpy_dtype.kind in "uib" for i in inputs):
         args_dtype = np.dtype(f"f{out_dtype.itemsize}")
 
-        @numba_njit
+        @numba_njit(inline="always")
         def inputs_cast(x):
             return x.astype(args_dtype)
 
     else:
         args_dtype_sz = max(_arg.type.numpy_dtype.itemsize for _arg in inputs)
         args_dtype = np.dtype(f"f{args_dtype_sz}")
 
-        @numba_njit
+        @numba_njit(inline="always")
         def inputs_cast(x):
             return x.astype(args_dtype)
 
@@ -593,17 +594,49 @@
 
 @numba_funcify.register(Dot)
 def numba_funcify_Dot(op, node, **kwargs):
-    # Numba's `np.dot` does not support integer dtypes, so we need to cast to
-    # float.
+    # Numba's `np.dot` does not support integer dtypes, so we need to cast to float.
+    x, y = node.inputs
+    [out] = node.outputs
 
-    out_dtype = node.outputs[0].type.numpy_dtype
-    inputs_cast = int_to_float_fn(node.inputs, out_dtype)
+    x_dtype = x.type.dtype
+    y_dtype = y.type.dtype
+    dot_dtype = f"float{max((32, out.type.numpy_dtype.itemsize * 8))}"
+    out_dtype = out.type.dtype
 
-    @numba_njit
-    def dot(x, y):
-        return np.asarray(np.dot(inputs_cast(x), inputs_cast(y))).astype(out_dtype)
+    if x_dtype == dot_dtype and y_dtype == dot_dtype:
+
+        @numba_njit
+        def dot(x, y):
+            return np.asarray(np.dot(x, y))
+
+    elif x_dtype == dot_dtype and y_dtype != dot_dtype:
+
+        @numba_njit
+        def dot(x, y):
+            return np.asarray(np.dot(x, y.astype(dot_dtype)))
+
+    elif x_dtype != dot_dtype and y_dtype == dot_dtype:
+
+        @numba_njit
+        def dot(x, y):
+            return np.asarray(np.dot(x.astype(dot_dtype), y))
+
+    else:
+
+        @numba_njit()
+        def dot(x, y):
+            return np.asarray(np.dot(x.astype(dot_dtype), y.astype(dot_dtype)))
+
+    if out_dtype == dot_dtype:
+        return dot
+
+    else:
+
+        @numba_njit
+        def dot_with_cast(x, y):
+            return dot(x, y).astype(out_dtype)
 
-    return dot
+    return dot_with_cast
 
 
 @numba_funcify.register(Solve)

tests/link/numba/test_basic.py

@@ -30,7 +30,7 @@
 from pytensor.link.numba.linker import NumbaLinker
 from pytensor.raise_op import assert_op
 from pytensor.scalar.basic import ScalarOp, as_scalar
-from pytensor.tensor import blas
+from pytensor.tensor import blas, tensor
 from pytensor.tensor.elemwise import Elemwise
 from pytensor.tensor.shape import Reshape, Shape, Shape_i, SpecifyShape
 from pytensor.tensor.sort import ArgSortOp, SortOp
@@ -603,43 +603,41 @@ def test_perform_type_convert():
 
 
 @pytest.mark.parametrize(
-    "x, y, exc",
+    "x, y",
     [
         (
             (pt.matrix(), rng.random(size=(3, 2)).astype(config.floatX)),
             (pt.vector(), rng.random(size=(2,)).astype(config.floatX)),
-            None,
         ),
         (
             (pt.matrix(dtype="float64"), rng.random(size=(3, 2)).astype("float64")),
             (pt.vector(dtype="float32"), rng.random(size=(2,)).astype("float32")),
-            None,
         ),
         (
             (pt.lmatrix(), rng.poisson(size=(3, 2))),
             (pt.fvector(), rng.random(size=(2,)).astype("float32")),
-            None,
         ),
         (
             (pt.lvector(), rng.random(size=(2,)).astype(np.int64)),
             (pt.lvector(), rng.random(size=(2,)).astype(np.int64)),
-            None,
+        ),
+        (
+            (pt.vector(dtype="int16"), rng.random(size=(2,)).astype(np.int16)),
+            (pt.vector(dtype="uint8"), rng.random(size=(2,)).astype(np.uint8)),
         ),
     ],
 )
-def test_Dot(x, y, exc):
+def test_Dot(x, y):
     x, x_test_value = x
     y, y_test_value = y
 
     g = ptm.Dot()(x, y)
 
-    cm = contextlib.suppress() if exc is None else pytest.warns(exc)
-    with cm:
-        compare_numba_and_py(
-            [x, y],
-            [g],
-            [x_test_value, y_test_value],
-        )
+    compare_numba_and_py(
+        [x, y],
+        [g],
+        [x_test_value, y_test_value],
+    )
 
 
 @pytest.mark.parametrize(
@@ -937,3 +935,18 @@ def test_Nonzero(input_data):
     compare_numba_and_py(
         graph_inputs=[a], graph_outputs=graph_outputs, test_inputs=[input_data]
     )
+
+
+@pytest.mark.parametrize("dtype", ("float64", "float32", "mixed"))
+def test_mat_vec_dot_performance(dtype, benchmark):
+    A = tensor("A", shape=(512, 512), dtype="float64" if dtype == "mixed" else dtype)
+    x = tensor("x", shape=(512,), dtype="float32" if dtype == "mixed" else dtype)
+    out = ptm.dot(A, x)
+
+    fn = function([A, x], out, mode="NUMBA", trust_input=True)
+
+    rng = np.random.default_rng(948)
+    A_test = rng.standard_normal(size=A.type.shape, dtype=A.type.dtype)
+    x_test = rng.standard_normal(size=x.type.shape, dtype=x.type.dtype)
+    np.testing.assert_allclose(fn(A_test, x_test), np.dot(A_test, x_test), atol=1e-4)
+    benchmark(fn, A_test, x_test)