Implement Blockwise Op to vectorize existing Ops

Inspired by: aesara-devs/aesara#1215

Co-authored-by: Brandon T. Willard <[email protected]>
Co-authored-by: Purna Chandra Mansingh <[email protected]>
Co-authored-by: Sayam Kumar <[email protected]>
Co-authored-by: Kaustubh <[email protected]>

Author: 5 people

pytensor/tensor/blockwise.py

@@ -22,13 +22,15 @@
 from pytensor.tensor import _get_vector_length, as_tensor_variable
 from pytensor.tensor import elemwise_cgen as cgen
 from pytensor.tensor import get_vector_length
+from pytensor.tensor.blockwise import _vectorize_node, vectorize_not_needed
 from pytensor.tensor.type import (
     TensorType,
     continuous_dtypes,
     discrete_dtypes,
     float_dtypes,
     lvector,
 )
+from pytensor.tensor.utils import import_func_from_string
 from pytensor.tensor.var import TensorVariable
 from pytensor.utils import uniq
 
@@ -228,7 +230,7 @@ def __str__(self):
             return f"Transpose{{axes={self.shuffle}}}"
         return f"DimShuffle{{order=[{','.join(map(str, self.new_order))}]}}"
 
-    def perform(self, node, inp, out, params):
+    def perform(self, node, inp, out, params=None):
         (res,) = inp
         (storage,) = out
 
@@ -662,22 +664,7 @@ def prepare_node(self, node, storage_map, compute_map, impl):
             impl = "c"
 
         if getattr(self, "nfunc_spec", None) and impl != "c":
-            self.nfunc = getattr(np, self.nfunc_spec[0], None)
-            if self.nfunc is None:
-                # Not inside NumPy. So probably another package like scipy.
-                symb = self.nfunc_spec[0].split(".")
-                for idx in range(1, len(self.nfunc_spec[0])):
-                    try:
-                        module = __import__(".".join(symb[:idx]))
-                    except ImportError:
-                        break
-                for sub in symb[1:]:
-                    try:
-                        module = getattr(module, sub)
-                    except AttributeError:
-                        module = None
-                        break
-                self.nfunc = module
+            self.nfunc = import_func_from_string(self.nfunc_spec[0])
 
         if (
             (len(node.inputs) + len(node.outputs)) <= 32
@@ -1759,3 +1746,37 @@ def _get_vector_length_Elemwise(op, var):
         return get_vector_length(var.owner.inputs[0])
 
     raise ValueError(f"Length of {var} cannot be determined")
+
+
+_vectorize_node.register(Elemwise, vectorize_not_needed)
+
+
+@_vectorize_node.register(DimShuffle)
+def vectorize_dimshuffle(op: DimShuffle, node: Apply, x: TensorVariable) -> Apply:
+    batched_ndims = x.type.ndim - node.inputs[0].type.ndim
+    if not batched_ndims:
+        return node.op.make_node(x)
+    input_broadcastable = x.type.broadcastable[:batched_ndims] + op.input_broadcastable
+    # e.g., ds(matrix, order=(1, "x", 0)) -> ds(tensor4, order=(0, 1, 3, "x", 2))
+    # e.g., ds(row, order=(1, "x")) -> ds(tensor4, order=(0, 1, 3, "x"))
+    new_order = list(range(batched_ndims)) + [
+        "x" if (o == "x") else (o + batched_ndims) for o in op.new_order
+    ]
+    return DimShuffle(input_broadcastable, new_order).make_node(x)
+
+
+@_vectorize_node.register(CAReduce)
+def vectorize_careduce(op: CAReduce, node: Apply, x: TensorVariable) -> Apply:
+    batched_ndims = x.type.ndim - node.inputs[0].type.ndim
+    if not batched_ndims:
+        return node.op.make_node(x)
+    axes = op.axis
+    # e.g., sum(matrix, axis=None) -> sum(tensor4, axis=(2, 3))
+    # e.g., sum(matrix, axis=0) -> sum(tensor4, axis=(2,))
+    if axes is None:
+        axes = list(range(node.inputs[0].type.ndim))
+    else:
+        axes = list(axes)
+    new_axes = [axis + batched_ndims for axis in axes]
+    new_op = op.clone(axis=new_axes)
+    return new_op.make_node(x)

pytensor/tensor/elemwise.py

@@ -16,6 +16,7 @@
     get_vector_length,
     infer_static_shape,
 )
+from pytensor.tensor.blockwise import _vectorize_node, vectorize_not_needed
 from pytensor.tensor.random.type import RandomGeneratorType, RandomStateType, RandomType
 from pytensor.tensor.random.utils import normalize_size_param, params_broadcast_shapes
 from pytensor.tensor.shape import shape_tuple
@@ -428,3 +429,8 @@ class DefaultGeneratorMakerOp(AbstractRNGConstructor):
 
 
 default_rng = DefaultGeneratorMakerOp()
+
+
+# RandomVariables are vectorized on the parameters by default.
+# RNG, size and dtype can't be vectorized, but the Op will raise if the wrong input type is passed
+_vectorize_node.register(RandomVariable, vectorize_not_needed)

pytensor/tensor/random/op.py

@@ -2,6 +2,7 @@
 import pytensor.tensor.rewriting.blas
 import pytensor.tensor.rewriting.blas_c
 import pytensor.tensor.rewriting.blas_scipy
+import pytensor.tensor.rewriting.blockwise
 import pytensor.tensor.rewriting.elemwise
 import pytensor.tensor.rewriting.extra_ops
 

pytensor/tensor/rewriting/__init__.py

@@ -0,0 +1,39 @@
+from pytensor.compile.mode import optdb
+from pytensor.graph import node_rewriter
+from pytensor.graph.rewriting.basic import copy_stack_trace, out2in
+from pytensor.tensor.blockwise import Blockwise, vectorize_node
+from pytensor.tensor.rewriting.basic import register_useless
+
+
+@register_useless("fast_compile")
+@node_rewriter([Blockwise])
+def local_useless_blockwise(fgraph, node):
+    # If there is a dispatch implementation that does not require Blockwise, use that instead.
+    # This means a user created a Blockwise manually when there was no need.
+    op = node.op
+    inputs = node.inputs
+    dummy_core_node = op._create_dummy_core_node(node.inputs)
+    vect_node = vectorize_node(dummy_core_node, *inputs)
+    if not isinstance(vect_node.op, Blockwise):
+        return copy_stack_trace(node.outputs, vect_node.outputs)
+
+
+@node_rewriter([Blockwise])
+def local_useless_unbatched_blockwise(fgraph, node):
+    """Remove Blockwise that don't have any batched dims."""
+    op = node.op
+    inputs = node.inputs
+
+    if max(inp.type.ndim - len(sig) for inp, sig in zip(inputs, op.inputs_sig)) == 0:
+        return copy_stack_trace(node.outputs, op.core_op.make_node(*inputs).outputs)
+
+
+# We register this rewrite late, so that other rewrites need only target Blockwise Ops
+optdb.register(
+    "local_useless_unbatched_blockwise",
+    out2in(local_useless_unbatched_blockwise, ignore_newtrees=True),
+    "fast_run",
+    "fast_compile",
+    "blockwise",
+    position=49,
+)

pytensor/tensor/rewriting/blockwise.py

@@ -107,3 +107,27 @@ def as_list(x):
         return list(x)
     except TypeError:
         return [x]
+
+
+def import_func_from_string(func_string: str):  # -> Optional[Callable]:
+    func = getattr(np, func_string, None)
+    if func is not None:
+        return func
+
+    # Not inside NumPy or Scipy. So probably another package like scipy.
+    module = None
+    items = func_string.split(".")
+    for idx in range(1, len(items)):
+        try:
+            module = __import__(".".join(items[:idx]))
+        except ImportError:
+            break
+
+    if module:
+        for sub in items[1:]:
+            try:
+                module = getattr(module, sub)
+            except AttributeError:
+                module = None
+                break
+        return module

pytensor/tensor/utils.py

@@ -0,0 +1,36 @@
+from pytensor import function
+from pytensor.scalar import log as scalar_log
+from pytensor.tensor import matrix, tensor3
+from pytensor.tensor.blockwise import Blockwise
+from pytensor.tensor.elemwise import Elemwise
+from pytensor.tensor.nlinalg import MatrixPinv
+
+
+def test_useless_blockwise_of_elemwise():
+    x = matrix("x")
+    out = Blockwise(Elemwise(scalar_log), signature="()->()")(x)
+
+    assert isinstance(out.owner.op, Blockwise)
+    assert isinstance(out.owner.op.core_op, Elemwise)
+
+    fn = function([x], out, mode="FAST_COMPILE")
+    assert isinstance(fn.maker.fgraph.outputs[0].owner.op, Elemwise)
+
+
+def test_useless_unbatched_blockwise():
+    x = matrix("x")
+    blockwise_op = Blockwise(MatrixPinv(hermitian=False), signature="(m,n)->(n,m)")
+    out = blockwise_op(x)
+
+    assert isinstance(out.owner.op, Blockwise)
+    assert isinstance(out.owner.op.core_op, MatrixPinv)
+
+    fn = function([x], out, mode="FAST_COMPILE")
+    assert isinstance(fn.maker.fgraph.outputs[0].owner.op, MatrixPinv)
+
+    # Test that it's not removed when there are batched dims
+    x = tensor3("x")
+    out = blockwise_op(x)
+    fn = function([x], out, mode="FAST_COMPILE")
+    assert isinstance(fn.maker.fgraph.outputs[0].owner.op, Blockwise)
+    assert isinstance(fn.maker.fgraph.outputs[0].owner.op.core_op, MatrixPinv)