Make Dot only accept matrix inputs

pytensor/tensor/__init__.py

@@ -107,7 +107,6 @@ from pytensor.gradient import grad, hessian, jacobian
 from pytensor.tensor import (
     blas,
     blas_c,
-    blas_scipy,
     sharedvar,
     xlogx,
 )

pytensor/tensor/basic.py

@@ -1801,8 +1801,7 @@ class Alloc(COp):
                     | pytensor.tensor.blas.Gemv
                     | pytensor.tensor.blas_c.CGemv
                     | pytensor.tensor.blas.Ger
-                    | pytensor.tensor.blas_c.CGer
-                    | pytensor.tensor.blas_scipy.ScipyGer,
+                    | pytensor.tensor.blas_c.CGer,
                 )
             ):
                 # Ops that will work inplace on the Alloc. So if they

pytensor/tensor/blas.py

@@ -83,6 +83,7 @@ import warnings
 from pathlib import Path
 
 import numpy as np
+from scipy.linalg import get_blas_funcs
 
 from pytensor.graph import vectorize_graph
 from pytensor.npy_2_compat import normalize_axis_tuple
@@ -288,18 +289,17 @@ class Ger(Op):
 
         return Apply(self, inputs, [A.type()])
 
-    def perform(self, node, inp, out):
-        cA, calpha, cx, cy = inp
-        (cZ,) = out
-        if self.destructive:
-            A = cA
-        else:
-            A = cA.copy()
-        if calpha != 1:
-            A += calpha * np.outer(cx, cy)
-        else:
-            A += np.outer(cx, cy)
-        cZ[0] = A
+    def perform(self, node, inputs, output_storage):
+        A, alpha, x, y = inputs
+        if A.size:
+            # GER doesn't handle zero-sized inputs
+            ger_func = get_blas_funcs("ger", dtype=A.dtype)
+            if A.flags["C_CONTIGUOUS"]:
+                # Work on transposed system to avoid copying
+                A = ger_func(alpha, y, x, a=A.T, overwrite_a=self.destructive).T
+            else:
+                A = ger_func(alpha, x, y, a=A, overwrite_a=self.destructive)
+        output_storage[0][0] = A
 
     def infer_shape(self, fgraph, node, input_shapes):
         return [input_shapes[0]]
@@ -1128,16 +1128,8 @@ class Dot22(GemmRelated):
         outputs = [tensor(dtype=x.type.dtype, shape=(x.type.shape[0], y.type.shape[1]))]
         return Apply(self, [x, y], outputs)
 
-    def perform(self, node, inp, out):
-        x, y = inp
-        (z,) = out
-        try:
-            z[0] = np.asarray(np.dot(x, y))
-        except ValueError as e:
-            # The error raised by numpy has no shape information, we mean to
-            # add that
-            e.args = (*e.args, x.shape, y.shape)
-            raise
+    def perform(self, node, inputs, output_storage):
+        output_storage[0][0] = np.dot(*inputs)
 
     def infer_shape(self, fgraph, node, input_shapes):
         return [[input_shapes[0][0], input_shapes[1][1]]]

pytensor/tensor/blas_scipy.py

-"""
-Implementations of BLAS Ops based on scipy's BLAS bindings.
-"""
-
-from pytensor.tensor.blas import Ger
-
-
-class ScipyGer(Ger):
-    def perform(self, node, inputs, output_storage):
-        from scipy.linalg.blas import get_blas_funcs
-
-        cA, calpha, cx, cy = inputs
-        (cZ,) = output_storage
-        # N.B. some versions of scipy (e.g. mine) don't actually work
-        # in-place on a, even when I tell it to.
-        A = cA
-        local_ger = get_blas_funcs("ger", dtype=cA.dtype)
-        if A.size == 0:
-            # We don't have to compute anything, A is empty.
-            # We need this special case because Numpy considers it
-            # C-contiguous, which is confusing.
-            if not self.destructive:
-                # Sometimes numpy thinks empty matrices can share memory,
-                # so here to stop DebugMode from complaining.
-                A = A.copy()
-        elif A.flags["C_CONTIGUOUS"]:
-            A = local_ger(calpha, cy, cx, a=A.T, overwrite_a=int(self.destructive)).T
-        else:
-            A = local_ger(calpha, cx, cy, a=A, overwrite_a=int(self.destructive))
-        cZ[0] = A
-
-
-scipy_ger_no_inplace = ScipyGer(False)
-scipy_ger_inplace = ScipyGer(True)

pytensor/tensor/math.py

@@ -40,12 +40,13 @@ from pytensor.tensor.elemwise import (
     get_normalized_batch_axes,
     scalar_elemwise,
 )
-from pytensor.tensor.shape import shape, specify_broadcastable
+from pytensor.tensor.shape import shape, specify_shape
 from pytensor.tensor.type import (
     DenseTensorType,
     complex_dtypes,
     continuous_dtypes,
     discrete_dtypes,
+    float_dtypes,
     int_dtypes,
     tensor,
     uint_dtypes,
@@ -2986,9 +2987,7 @@ pprint.assign(pow, printing.OperatorPrinter("**", 1, "right"))
 
 class Dot(Op):
     """
-    Computes the dot product of two variables. For two matrices, this is
-    equivalent to matrix multiplication. For two vectors, this is the inner
-    product.
+    Computes the dot product of two matrices variables
 
     Notes
     -----
@@ -3001,97 +3000,58 @@ class Dot(Op):
 
     """
 
+    gufunc_signature = "(m,n),(n,p)->(m,p)"
+    gufunc_spec = ("matmul", 2, 1)
     __props__ = ()
 
-    # the rationale for Dot22 is related to getting GEMM Ops into the
-    # graph.  See Dot22 in tensor.blas for details.
-
-    def make_node(self, *inputs):
-        inputs = list(map(as_tensor_variable, inputs))
+    def make_node(self, x, y):
+        x = as_tensor_variable(x)
+        y = as_tensor_variable(y)
 
-        if len(inputs) != 2:
-            raise TypeError(f"Two arguments required, {len(inputs)} given ")
-        if inputs[0].ndim not in (1, 2):
+        if x.type.ndim != 2:
             raise TypeError(
-                "Input 0 (0-indexed) must have ndim of "
-                f"1 or 2, {int(inputs[0].ndim)} given. Consider calling "
-                "pytensor.tensor.dot instead."
+                f"Dot Op expects a 2D tensor as input 0, got {x} with {x.type.ndim} dimensions"
             )
-        if inputs[1].ndim not in (1, 2):
+        if y.type.ndim != 2:
             raise TypeError(
-                "Input 1 (0-indexed) must have ndim of "
-                f"1 or 2, {int(inputs[1].ndim)} given. Consider calling "
-                "pytensor.tensor.dot instead."
+                f"Dot Op expects a 2D tensor as input 1, got {y} with {y.type.ndim} dimensions"
             )
 
-        sx, sy = (input.type.shape for input in inputs)
+        sx, sy = x.type.shape, y.type.shape
         if sx[-1] is not None and sy[0] is not None and sx[-1] != sy[0]:
             raise ValueError(
                 f"Incompatible shared dimension for dot product: {sx}, {sy}"
             )
+        out_shape = (sx[0], sy[1])
+        out_dtype = ps.upcast(x.type.dtype, y.type.dtype)
+        outputs = [tensor(dtype=out_dtype, shape=out_shape)]
+        return Apply(self, [x, y], outputs)
 
-        if len(sy) == 2:
-            sz = sx[:-1] + sy[-1:]
-        elif len(sy) == 1:
-            sz = sx[:-1]
-
-        i_dtypes = [input.type.dtype for input in inputs]
-        outputs = [tensor(dtype=ps.upcast(*i_dtypes), shape=sz)]
-        return Apply(self, inputs, outputs)
-
-    def perform(self, node, inp, out):
-        x, y = inp
-        (z,) = out
-
-        # the asarray is here because dot between two vectors
-        # gives a numpy float object but we need to return a 0d
-        # ndarray
-        z[0] = np.asarray(np.dot(x, y))
+    def perform(self, node, inputs, output_storage):
+        output_storage[0][0] = np.matmul(*inputs)
 
     def grad(self, inp, grads):
         x, y = inp
         (gz,) = grads
-        xdim, ydim, gdim = x.type.ndim, y.type.ndim, gz.type.ndim
-
-        # grad is scalar, so x is vector and y is vector
-        if gdim == 0:
-            xgrad = gz * y
-            ygrad = gz * x
-
-        # x is vector, y is matrix, grad is vector
-        elif xdim == 1 and ydim == 2:
-            xgrad = dot(gz, y.T)
-            ygrad = outer(x.T, gz)
-
-        # x is matrix, y is vector, grad is vector
-        elif xdim == 2 and ydim == 1:
-            xgrad = outer(gz, y.T)
-            ygrad = dot(x.T, gz)
 
-        # x is matrix, y is matrix, grad is matrix
-        elif xdim == ydim == 2:
-            xgrad = dot(gz, y.T)
-            ygrad = dot(x.T, gz)
+        xgrad = self(gz, y.T)
+        ygrad = self(x.T, gz)
 
         # If x or y contain broadcastable dimensions but only one of
         # them know that a matching dimensions is broadcastable, the
         # above code don't always return the right broadcast pattern.
         # This cause problem down the road. See gh-1461.
-        if xgrad.broadcastable != x.broadcastable:
-            xgrad = specify_broadcastable(
-                xgrad, *(ax for (ax, b) in enumerate(x.type.broadcastable) if b)
-            )
-        if ygrad.broadcastable != y.broadcastable:
-            ygrad = specify_broadcastable(
-                ygrad, *(ax for (ax, b) in enumerate(y.type.broadcastable) if b)
-            )
-
-        rval = xgrad, ygrad
+        if xgrad.type.shape != x.type.shape:
+            xgrad = specify_shape(xgrad, x.type.shape)
+        if ygrad.type.shape != y.type.shape:
+            ygrad = specify_shape(ygrad, y.type.shape)
 
-        for elem in rval:
-            assert elem.dtype.find("float") != -1
+        if xgrad.type.dtype not in float_dtypes:
+            raise TypeError("Dot grad x output must be a float type")
+        if ygrad.type.dtype not in float_dtypes:
+            raise TypeError("Dot grad y output must be a float type")
 
-        return rval
+        return xgrad, ygrad
 
     def R_op(self, inputs, eval_points):
         # R_op for a \dot b evaluated at c for a and d for b is
@@ -3116,24 +3076,7 @@ class Dot(Op):
 
     def infer_shape(self, fgraph, node, shapes):
         xshp, yshp = shapes
-        x, y = node.inputs
-
-        # vector / vector
-        if x.ndim == 1 and y.ndim == 1:
-            return [()]
-        # matrix / vector
-        if x.ndim == 2 and y.ndim == 1:
-            return [xshp[:-1]]
-        # vector / matrix
-        if x.ndim == 1 and y.ndim == 2:
-            return [yshp[-1:]]
-        # matrix / matrix
-        if x.ndim == 2 and y.ndim == 2:
-            return [xshp[:-1] + yshp[-1:]]
-        raise NotImplementedError()
-
-    def __str__(self):
-        return "dot"
+        return [[xshp[0], yshp[1]]]
 
 
 _dot = Dot()
@@ -3215,7 +3158,24 @@ def dense_dot(a, b):
     elif a.ndim > 2 or b.ndim > 2:
         return tensordot(a, b, [[a.ndim - 1], [np.maximum(0, b.ndim - 2)]])
     else:
-        return _dot(a, b)
+        row_vector = a.ndim == 1
+        if row_vector:
+            # Promote to row matrix
+            a = a[None]
+
+        col_vector = b.ndim == 1
+        if col_vector:
+            # Promote to column matrix
+            b = b[:, None]
+
+        out = _dot(a, b)
+        if row_vector:
+            # If we promoted a to a row matrix, we need to squeeze the first dimension
+            out = out.squeeze(0)
+        if col_vector:
+            # If we promoted b to a column matrix, we need to squeeze the last dimension
+            out = out.squeeze(-1)
+        return out
 
 
 def tensordot(
@@ -3921,11 +3881,7 @@ def logsumexp(x, axis=None, keepdims=False):
     return log(sum(exp(x), axis=axis, keepdims=keepdims))
 
 
-_matmul = Blockwise(
-    _dot,
-    signature="(m,k),(k,n)->(m,n)",
-    gufunc_spec=("numpy.matmul", 2, 1),
-)
+_matmul = Blockwise(_dot, name="Matmul")
 
 
 def matmul(x1: "ArrayLike", x2: "ArrayLike", dtype: Optional["DTypeLike"] = None):
@@ -3975,7 +3931,7 @@ def matmul(x1: "ArrayLike", x2: "ArrayLike", dtype: Optional["DTypeLike"] = None
     if x1.type.ndim == 0 or x2.type.ndim == 0:
         raise ValueError("matmul operand cannot be scalar")
     if x1.type.ndim == 1 and x2.type.ndim == 1:
-        out = _dot(x1, x2)
+        out = vecdot(x1, x2)
     elif x1.type.ndim == 1:
         out = vecmat(x1, x2)
     elif x2.type.ndim == 1:
@@ -4139,23 +4095,7 @@ def vecmat(
 
 @_vectorize_node.register(Dot)
 def vectorize_node_dot(op, node, batched_x, batched_y):
-    old_x, old_y = node.inputs
-    old_x_ndim = old_x.type.ndim
-    old_y_ndim = old_y.type.ndim
-    match (old_x_ndim, old_y_ndim):
-        case (1, 1):
-            batch_fn = vecdot
-        case (2, 1):
-            batch_fn = matvec
-        case (1, 2):
-            batch_fn = vecmat
-        case (2, 2):
-            batch_fn = matmul
-        case _:
-            raise ValueError(
-                f"Core dot Op should have 1D or 2D inputs, got {old_x_ndim}D and {old_y_ndim}D."
-            )
-    return batch_fn(batched_x, batched_y).owner
+    return matmul(batched_x, batched_y).owner
 
 
 def nan_to_num(x, nan=0.0, posinf=None, neginf=None):

pytensor/tensor/rewriting/__init__.py

 import pytensor.tensor.rewriting.basic
 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.einsum
 import pytensor.tensor.rewriting.elemwise

pytensor/tensor/rewriting/blas.py

@@ -107,7 +107,6 @@ from pytensor.tensor.math import (
 )
 from pytensor.tensor.rewriting.elemwise import local_dimshuffle_lift
 from pytensor.tensor.type import (
-    DenseTensorType,
     TensorType,
     integer_dtypes,
     values_eq_approx_remove_inf_nan,
@@ -580,12 +579,6 @@ class GemmOptimizer(GraphRewriter):
 def local_dot_to_dot22(fgraph, node):
     # This works for tensor.outer too because basic.outer is a macro that
     # produces a dot(dimshuffle,dimshuffle) of form 4 below
-    if not isinstance(node.op, Dot):
-        return
-
-    if any(not isinstance(i.type, DenseTensorType) for i in node.inputs):
-        return False
-
     x, y = node.inputs
     if y.type.dtype != x.type.dtype:
         # TODO: upcast one so the types match
@@ -593,16 +586,7 @@ def local_dot_to_dot22(fgraph, node):
         return
 
     if y.type.dtype in ("float16", "float32", "float64", "complex64", "complex128"):
-        if x.ndim == 2 and y.ndim == 2:
-            new_out = [_dot22(*node.inputs)]
-        elif x.ndim == 2 and y.ndim == 1:
-            new_out = [_dot22(x, y.dimshuffle(0, "x")).dimshuffle(0)]
-        elif x.ndim == 1 and y.ndim == 2:
-            new_out = [_dot22(x.dimshuffle("x", 0), y).dimshuffle(1)]
-        elif x.ndim == 1 and y.ndim == 1:
-            new_out = [_dot22(x.dimshuffle("x", 0), y.dimshuffle(0, "x")).dimshuffle()]
-        else:
-            return
+        new_out = [_dot22(*node.inputs)]
         copy_stack_trace(node.outputs, new_out)
         return new_out
 

pytensor/tensor/rewriting/blas_scipy.py

-from pytensor.graph.rewriting.basic import in2out
-from pytensor.tensor.blas import ger, ger_destructive
-from pytensor.tensor.blas_scipy import scipy_ger_inplace, scipy_ger_no_inplace
-from pytensor.tensor.rewriting.blas import blas_optdb, node_rewriter, optdb
-
-
-@node_rewriter([ger, ger_destructive])
-def use_scipy_ger(fgraph, node):
-    if node.op == ger:
-        return [scipy_ger_no_inplace(*node.inputs)]
-
-
-@node_rewriter([scipy_ger_no_inplace])
-def make_ger_destructive(fgraph, node):
-    if node.op == scipy_ger_no_inplace:
-        return [scipy_ger_inplace(*node.inputs)]
-
-
-use_scipy_blas = in2out(use_scipy_ger)
-make_scipy_blas_destructive = in2out(make_ger_destructive)
-
-
-# scipy_blas is scheduled in the blas_optdb very late, because scipy sortof
-# sucks [citation needed], but it is almost always present.
-# C implementations should be scheduled earlier than this, so that they take
-# precedence. Once the original Ger is replaced, then these optimizations
-# have no effect.
-blas_optdb.register("scipy_blas", use_scipy_blas, "fast_run", position=100)
-
-# this matches the InplaceBlasOpt defined in blas.py
-optdb.register(
-    "make_scipy_blas_destructive",
-    make_scipy_blas_destructive,
-    "fast_run",
-    "inplace",
-    position=50.2,
-)

pytensor/tensor/rewriting/linalg.py

@@ -276,15 +276,7 @@ def cholesky_ldotlt(fgraph, node):
 
     A = node.inputs[0]
     if not (
-        A.owner is not None
-        and (
-            (
-                isinstance(A.owner.op, Dot)
-                # This rewrite only applies to matrix Dot
-                and A.owner.inputs[0].type.ndim == 2
-            )
-            or (A.owner.op == _matmul)
-        )
+        A.owner is not None and (isinstance(A.owner.op, Dot) or (A.owner.op == _matmul))
     ):
         return
 

pytensor/tensor/rewriting/math.py

@@ -19,7 +19,6 @@ from pytensor.graph.rewriting.basic import (
     node_rewriter,
 )
 from pytensor.graph.rewriting.utils import get_clients_at_depth
-from pytensor.raise_op import assert_op
 from pytensor.tensor.basic import (
     Alloc,
     Join,
@@ -34,6 +33,7 @@ from pytensor.tensor.basic import (
     ones_like,
     register_infer_shape,
     switch,
+    zeros,
     zeros_like,
 )
 from pytensor.tensor.elemwise import CAReduce, DimShuffle, Elemwise
@@ -44,12 +44,10 @@ from pytensor.tensor.math import (
     Prod,
     Sum,
     _conj,
-    _dot,
     _matmul,
     add,
     digamma,
     dot,
-    eq,
     erf,
     erfc,
     exp,
@@ -130,16 +128,12 @@ def scalarconsts_rest(inputs, elemwise=True, only_process_constants=False):
     return consts, origconsts, nonconsts
 
 
-@register_canonicalize
-@register_stabilize
+@register_canonicalize("shape_unsafe")
+@register_stabilize("shape_unsafe")
 @node_rewriter([Dot])
 def local_0_dot_x(fgraph, node):
-    if not isinstance(node.op, Dot):
-        return False
-
-    x = node.inputs[0]
-    y = node.inputs[1]
-    replace = (
+    x, y = node.inputs
+    if (
         get_underlying_scalar_constant_value(
             x, only_process_constants=True, raise_not_constant=False
         )
@@ -148,26 +142,12 @@ def local_0_dot_x(fgraph, node):
             y, only_process_constants=True, raise_not_constant=False
         )
         == 0
-    )
-
-    if replace:
-        constant_zero = constant(0, dtype=node.outputs[0].type.dtype)
-        if x.ndim == 2 and y.ndim == 2:
-            constant_zero = assert_op(constant_zero, eq(x.shape[1], y.shape[0]))
-            return [alloc(constant_zero, x.shape[0], y.shape[1])]
-        elif x.ndim == 1 and y.ndim == 2:
-            constant_zero = assert_op(constant_zero, eq(x.shape[0], y.shape[0]))
-            return [alloc(constant_zero, y.shape[1])]
-        elif x.ndim == 2 and y.ndim == 1:
-            constant_zero = assert_op(constant_zero, eq(x.shape[1], y.shape[0]))
-            return [alloc(constant_zero, x.shape[0])]
-        elif x.ndim == 1 and y.ndim == 1:
-            constant_zero = assert_op(constant_zero, eq(x.shape[0], y.shape[0]))
-            return [constant_zero]
+    ):
+        return [zeros((x.shape[0], y.shape[1]), dtype=node.outputs[0].type.dtype)]
 
 
 @register_canonicalize
-@node_rewriter([DimShuffle])
+@node_rewriter([Dot, _matmul])
 def local_lift_transpose_through_dot(fgraph, node):
     r"""Perform the rewrite ``dot(x,y).T -> dot(y.T, x.T)``.
 
@@ -176,22 +156,25 @@ def local_lift_transpose_through_dot(fgraph, node):
     and to later merge consecutive `DimShuffle`\s.
     """
 
-    if not (
-        is_matrix_transpose(node.outputs[0])
-        and node.inputs[0].owner
-        and ((dot_op := node.inputs[0].owner.op) in (_dot, _matmul))
-    ):
-        return False
+    clients = fgraph.clients[node.out]
 
-    x, y = node.inputs[0].owner.inputs
+    if len(clients) != 1:
+        # If the dot is used in more than one place, we don't want to duplicate it
+        return None
 
-    if x.ndim >= y.ndim >= 2:
-        # Output is dot product of transposed inputs in reverse order
-        ret = [dot_op(y.mT, x.mT)]
+    [(client, _)] = clients
 
-        # Copy over stack trace to output from result of dot-product
-        copy_stack_trace(node.inputs[0], ret)
-        return ret
+    if not (isinstance(client.op, DimShuffle) and is_matrix_transpose(client.out)):
+        return None
+
+    x, y = node.inputs
+    # Output is dot product of transposed inputs in reverse order
+    ret = node.op(y.mT, x.mT)
+
+    # Copy over stack trace to output from result of dot-product
+    copy_stack_trace(node.out, ret)
+
+    return {client.out: ret}
 
 
 def _batched_matmul_to_core_matmul(fgraph, node, allow_reshape: bool):
@@ -344,57 +327,34 @@ def local_batched_matmul_to_core_matmul_with_reshape(fgraph, node):
 
 @register_canonicalize
 @register_specialize
-@node_rewriter([_matmul])
-def local_blockwise_dot_to_mul(fgraph, node):
-    """Rewrite blockwise dots that correspond to multiplication without summation.
+@node_rewriter([_matmul, Dot])
+def local_dot_to_mul(fgraph, node):
+    """Rewrite dots that correspond to multiplication without summation.
 
-    We don't touch the regular dot, to not interfere with the BLAS optimizations.
+    We don't touch outer product without batch-dimensions, to allow rewriting into GER,
+    which seems more performant in that case.
+
+    # TODO: Once we blockwise Blas operations we shouldn't do it for outer product with batch-dimensions either
+    # TODO: We may still want to canonicalize outer dot as mul, and detect that for GER.
     """
     a, b = node.inputs
     a_static_shape = a.type.shape
     b_static_shape = b.type.shape
-    core_a_ndim = len(node.op.inputs_sig[0])
-    core_b_ndim = len(node.op.inputs_sig[1])
 
-    if core_a_ndim > 2 or core_b_ndim > 2:
-        # Shouldn't happen, but here just in case
+    # Check if we have matrix-matrix product: (..., m, 1) * (..., 1, n) -> (..., m, n)
+    if not (a_static_shape[-1] == 1 or b_static_shape[-2] == 1):
         return None
 
-    if core_b_ndim == 1:
-        if a_static_shape[-1] == 1 or b_static_shape[-1] == 1:
-            if core_a_ndim == 1:
-                # inner product: (..., 1) * (..., 1) -> (...)
-                # just squeeze the last dimensions of a and b
-                new_a = a.squeeze(-1)
-                new_b = b.squeeze(-1)
-            else:
-                # matrix vector product: (..., m, 1) * (..., 1) -> (..., m)
-                # the last dimension of b is already aligned for the elemwise multiplication
-                # after we squeeze the last dimension of a
-                new_a = a.squeeze(-1)
-                new_b = b
-        else:
-            return None
-
-    else:
-        if a_static_shape[-1] == 1 or b_static_shape[-2] == 1:
-            if core_a_ndim == 1:
-                # vector_matrix product: (..., 1) * (..., 1, n) -> (..., n)
-                # the last dimension of a is already aligned for the elemwise multiplication
-                # after we squeeze the one to last dimension of b
-                new_a = a
-                new_b = b.squeeze(-2)
-            else:
-                # matrix matrix product: (..., m, 1) * (..., 1, n) -> (..., m, n)
-                # the dimensions of a and b are already aligned for the elemwise multiplication
-                new_a = a
-                new_b = b
-        else:
-            return None
+    # If it's a core Dot we only rewrite if there's no outer product
+    # (1, 1) * (1, n) or (m, 1) * (1, 1)
+    # Otherwise we leave as is, so GER can be used instead
+    if isinstance(node.op, Dot) and not (
+        a_static_shape[-2] == 1 or b_static_shape[-1] == 1
+    ):
+        return None
 
-    new_a = copy_stack_trace(a, new_a)
-    new_b = copy_stack_trace(b, new_b)
-    new_out = copy_stack_trace(node.out, mul(new_a, new_b))
+    new_out = mul(a, b)
+    copy_stack_trace(node.out, new_out)
     return [new_out]
 
 

pytensor/tensor/rewriting/subtensor_lift.py

@@ -158,26 +158,11 @@ def local_subtensor_of_dot(fgraph, node):
         a = a.type.clone(shape=a.type.shape[batch_ndim:])()
         b = b.type.clone(shape=b.type.shape[batch_ndim:])()
 
-    a_ndim = a.ndim
-    b_ndim = b.ndim
-    num_a_indices = min(a_ndim - 1, len(idx_list))
-    a_indices = idx_list[:num_a_indices]
-    b_indices = idx_list[num_a_indices:]
-
-    # This is necessary because np.dot sums the last index of a with the second to last of b
-    # so we want to skip the second-to-last index into b.
-    # This wasn't necessary for a, because we just omitted the last index.
-    # We skip this if b.ndim = 1, since then we just want b_sub = b, not b_sub = b[:]
-    # (dot also handles b.ndim < 2 as a special case)
-    if b_ndim > 1 and len(b_indices) >= b_ndim - 1:
-        b_indices = (
-            b_indices[: b_ndim - 2]
-            + (slice(None, None, None),)
-            + b_indices[b_ndim - 2 :]
-        )
+    a_indices = idx_list[:1]
+    b_indices = (slice(None), *idx_list[1:])
 
     a_sub = a[tuple(a_indices)]
-    b_sub = b[tuple(b_indices)] if b_indices else b
+    b_sub = b[tuple(b_indices)]
     r = dot(a_sub, b_sub)
 
     if batch_ndim:

tests/graph/rewriting/test_kanren.py

@@ -37,51 +37,51 @@ def clear_assoccomm():
 
 def test_kanren_basic():
     A_pt = pt.matrix("A")
-    x_pt = pt.vector("x")
+    B_pt = pt.matrix("B")
 
-    y_pt = pt.dot(A_pt, x_pt)
+    y_pt = pt.dot(A_pt, B_pt)
 
     q = var()
-    res = list(run(None, q, eq(y_pt, etuple(_dot, q, x_pt))))
+    res = list(run(None, q, eq(y_pt, etuple(_dot, q, B_pt))))
 
     assert res == [A_pt]
 
 
 def test_KanrenRelationSub_filters():
-    x_pt = pt.vector("x")
-    y_pt = pt.vector("y")
-    z_pt = pt.vector("z")
     A_pt = pt.matrix("A")
+    B_pt = pt.matrix("B")
+    C_pt = pt.matrix("C")
+    D_pt = pt.matrix("D")
 
     fact(commutative, _dot)
     fact(commutative, pt.add)
     fact(associative, pt.add)
 
-    Z_pt = A_pt.dot((x_pt + y_pt) + z_pt)
+    Z_pt = A_pt.dot((B_pt + C_pt) + D_pt)
 
     fgraph = FunctionGraph(outputs=[Z_pt], clone=False)
 
     def distributes(in_lv, out_lv):
-        A_lv, x_lv, y_lv, z_lv = vars(4)
+        A_lv, B_lv, C_lv, D_lv = vars(4)
         return lall(
             # lhs == A * (x + y + z)
             eq_assoccomm(
-                etuple(_dot, A_lv, etuple(pt.add, x_lv, etuple(pt.add, y_lv, z_lv))),
+                etuple(_dot, A_lv, etuple(pt.add, B_lv, etuple(pt.add, C_lv, D_lv))),
                 in_lv,
             ),
             # This relation does nothing but provide us with a means of
             # generating associative-commutative matches in the `kanren`
             # output.
-            eq((A_lv, x_lv, y_lv, z_lv), out_lv),
+            eq((A_lv, B_lv, C_lv, D_lv), out_lv),
         )
 
     def results_filter(results):
         _results = [eval_if_etuple(v) for v in results]
 
         # Make sure that at least a couple permutations are present
-        assert (A_pt, x_pt, y_pt, z_pt) in _results
-        assert (A_pt, y_pt, x_pt, z_pt) in _results
-        assert (A_pt, z_pt, x_pt, y_pt) in _results
+        assert (A_pt, B_pt, C_pt, D_pt) in _results
+        assert (A_pt, C_pt, B_pt, D_pt) in _results
+        assert (A_pt, D_pt, B_pt, C_pt) in _results
 
         return None
 
@@ -121,13 +121,13 @@ def test_KanrenRelationSub_multiout():
 
 def test_KanrenRelationSub_dot():
     """Make sure we can run miniKanren "optimizations" over a graph until a fixed-point/normal-form is reached."""
-    x_pt = pt.vector("x")
-    c_pt = pt.vector("c")
-    d_pt = pt.vector("d")
     A_pt = pt.matrix("A")
     B_pt = pt.matrix("B")
+    C_pt = pt.matrix("C")
+    D_pt = pt.matrix("D")
+    E_pt = pt.matrix("E")
 
-    Z_pt = A_pt.dot(x_pt + B_pt.dot(c_pt + d_pt))
+    Z_pt = A_pt.dot(E_pt + B_pt.dot(C_pt + D_pt))
 
     fgraph = FunctionGraph(outputs=[Z_pt], clone=False)
 
@@ -137,15 +137,15 @@ def test_KanrenRelationSub_dot():
         return lall(
             # lhs == A * (x + b)
             eq(
-                etuple(_dot, var("A"), etuple(pt.add, var("x"), var("b"))),
+                etuple(_dot, var("A"), etuple(pt.add, var("E"), var("B"))),
                 in_lv,
             ),
             # rhs == A * x + A * b
             eq(
                 etuple(
                     pt.add,
-                    etuple(_dot, var("A"), var("x")),
-                    etuple(_dot, var("A"), var("b")),
+                    etuple(_dot, var("A"), var("E")),
+                    etuple(_dot, var("A"), var("B")),
                 ),
                 out_lv,
             ),

tests/link/numba/test_basic.py

@@ -631,7 +631,7 @@ def test_Dot(x, y):
     x, x_test_value = x
     y, y_test_value = y
 
-    g = ptm.Dot()(x, y)
+    g = ptm.dot(x, y)
 
     compare_numba_and_py(
         [x, y],

tests/tensor/rewriting/test_math.py

@@ -4714,14 +4714,15 @@ def test_local_dot_to_mul(batched, a_shape, b_shape):
         == 1
     )
 
-    # For now rewrite only applies to Batched Dots
+    # For now we do not rewrite only the case of unbatched outer
+    core_outer = (not batched) and (a_shape == (3, 1)) and (b_shape == (1, 3))
     rewritten_out = rewrite_graph(out)
     assert rewritten_out.type.shape == out.type.shape
     assert sum(
         isinstance(var.owner.op, (Blockwise | Dot))
         for var in ancestors([rewritten_out])
         if var.owner
-    ) == (0 if batched else 1)
+    ) == (1 if core_outer else 0)
 
     a_test = np.random.normal(size=a.type.shape).astype(a.type.dtype)
     b_test = np.random.normal(size=b.type.shape).astype(b.type.dtype)

tests/tensor/test_blas.py

@@ -9,7 +9,6 @@ from numpy.testing import assert_array_almost_equal
 import pytensor
 import pytensor.scalar as ps
 import pytensor.tensor as pt
-import pytensor.tensor.blas_scipy
 from pytensor.compile.function import function
 from pytensor.compile.io import In
 from pytensor.compile.mode import Mode

tests/tensor/test_blas_c.py

@@ -8,7 +8,6 @@ import pytensor.tensor as pt
 from pytensor.tensor.basic import AllocEmpty
 from pytensor.tensor.blas import Ger
 from pytensor.tensor.blas_c import CGemv, CGer, must_initialize_y_gemv
-from pytensor.tensor.blas_scipy import ScipyGer
 from pytensor.tensor.type import dmatrix, dvector, matrix, scalar, tensor, vector
 from tests import unittest_tools
 from tests.tensor.test_blas import BaseGemv, TestBlasStrides
@@ -68,8 +67,6 @@ class TestCGer(OptimizationTestMixin):
         assert CGer(False) == CGer(False)
         assert CGer(False) != CGer(True)
 
-        assert CGer(True) != ScipyGer(True)
-        assert CGer(False) != ScipyGer(False)
         assert CGer(True) != Ger(True)
         assert CGer(False) != Ger(False)
 

tests/tensor/test_blas_scipy.py

-import pickle
-
-import numpy as np
-
-import pytensor
-from pytensor import tensor as pt
-from pytensor.tensor.blas_scipy import ScipyGer
-from pytensor.tensor.math import outer
-from pytensor.tensor.type import tensor
-from tests.tensor.test_blas import TestBlasStrides, gemm_no_inplace
-from tests.unittest_tools import OptimizationTestMixin
-
-
-class TestScipyGer(OptimizationTestMixin):
-    def setup_method(self):
-        self.mode = pytensor.compile.get_default_mode()
-        self.mode = self.mode.including("fast_run")
-        self.mode = self.mode.excluding("c_blas")  # c_blas trumps scipy Ops
-        dtype = self.dtype = "float64"  # optimization isn't dtype-dependent
-        self.A = tensor(dtype=dtype, shape=(None, None))
-        self.a = tensor(dtype=dtype, shape=())
-        self.x = tensor(dtype=dtype, shape=(None,))
-        self.y = tensor(dtype=dtype, shape=(None,))
-        self.Aval = np.ones((2, 3), dtype=dtype)
-        self.xval = np.asarray([1, 2], dtype=dtype)
-        self.yval = np.asarray([1.5, 2.7, 3.9], dtype=dtype)
-
-    def function(self, inputs, outputs):
-        return pytensor.function(inputs, outputs, self.mode)
-
-    def run_f(self, f):
-        f(self.Aval, self.xval, self.yval)
-        f(self.Aval[::-1, ::-1], self.xval[::-1], self.yval[::-1])
-
-    def b(self, bval):
-        return pt.as_tensor_variable(np.asarray(bval, dtype=self.dtype))
-
-    def test_outer(self):
-        f = self.function([self.x, self.y], outer(self.x, self.y))
-        self.assertFunctionContains(f, ScipyGer(destructive=True))
-
-    def test_A_plus_outer(self):
-        f = self.function([self.A, self.x, self.y], self.A + outer(self.x, self.y))
-        self.assertFunctionContains(f, ScipyGer(destructive=False))
-        self.run_f(f)  # DebugMode tests correctness
-
-    def test_A_plus_scaled_outer(self):
-        f = self.function(
-            [self.A, self.x, self.y], self.A + 0.1 * outer(self.x, self.y)
-        )
-        self.assertFunctionContains(f, ScipyGer(destructive=False))
-        self.run_f(f)  # DebugMode tests correctness
-
-    def test_scaled_A_plus_scaled_outer(self):
-        f = self.function(
-            [self.A, self.x, self.y], 0.2 * self.A + 0.1 * outer(self.x, self.y)
-        )
-        self.assertFunctionContains(f, gemm_no_inplace)
-        self.run_f(f)  # DebugMode tests correctness
-
-    def test_pickle(self):
-        out = ScipyGer(destructive=False)(self.A, self.a, self.x, self.y)
-        f = pytensor.function([self.A, self.a, self.x, self.y], out)
-        new_f = pickle.loads(pickle.dumps(f))
-
-        assert isinstance(new_f.maker.fgraph.toposort()[-1].op, ScipyGer)
-        assert np.allclose(
-            f(self.Aval, 1.0, self.xval, self.yval),
-            new_f(self.Aval, 1.0, self.xval, self.yval),
-        )
-
-
-class TestBlasStridesScipy(TestBlasStrides):
-    mode = pytensor.compile.get_default_mode()
-    mode = mode.including("fast_run").excluding("gpu", "c_blas")

tests/tensor/test_math.py

@@ -1998,50 +1998,20 @@ class TestMean:
         assert mean(ll).eval() == 1
 
 
-def test_dot_numpy_inputs():
-    """Test the `PyTensor.tensor.dot` interface function with NumPy inputs."""
-    a = np.ones(2)
-    b = np.ones(2)
-    res = dot(a, b)
-    assert isinstance(res, Variable)
-    assert isinstance(res.owner.op, Dot)
-
-
 class TestDot:
-    def test_Op_dims(self):
+    def test_valid_ndim(self):
         d0 = scalar()
         d1 = vector()
         d2 = matrix()
         d3 = tensor3()
 
-        with pytest.raises(TypeError):
-            _dot(d0, d0)
-        with pytest.raises(TypeError):
-            _dot(d0, d1)
         with pytest.raises(TypeError):
             _dot(d0, d2)
         with pytest.raises(TypeError):
-            _dot(d0, d3)
-        with pytest.raises(TypeError):
-            _dot(d1, d0)
-        _dot(d1, d1)
-        _dot(d1, d2)
-        with pytest.raises(TypeError):
-            _dot(d1, d3)
-        with pytest.raises(TypeError):
-            _dot(d2, d0)
-        _dot(d2, d1)
-        _dot(d2, d2)
-        with pytest.raises(TypeError):
-            _dot(d2, d3)
-        with pytest.raises(TypeError):
-            _dot(d3, d0)
-        with pytest.raises(TypeError):
-            _dot(d3, d1)
+            _dot(d1, d2)
         with pytest.raises(TypeError):
             _dot(d3, d2)
-        with pytest.raises(TypeError):
-            _dot(d3, d3)
+        _dot(d2, d2)  # Fine
 
     def test_grad(self):
         rng = np.random.default_rng(seed=utt.fetch_seed())
@@ -2089,6 +2059,14 @@ class TestDot:
                             g = grad(z.sum(), y)
                             assert is_super_shape(y, g)
 
+    def test_dot_numpy_inputs(self):
+        """Test the `PyTensor.tensor.dot` interface function with NumPy inputs."""
+        a = np.ones((2, 2))
+        b = np.ones((2, 2))
+        res = dot(a, b)
+        assert isinstance(res, Variable)
+        assert isinstance(res.owner.op, Dot)
+
 
 def test_matrix_vector_ops():
     """Test vecdot, matvec, and vecmat helper functions."""
@@ -2796,7 +2774,7 @@ class TestInferShape(utt.InferShapeTester):
         bdvec_val = random(4, rng=rng)
         self._compile_and_check(
             [advec, bdvec],
-            [Dot()(advec, bdvec)],
+            [dot(advec, bdvec)],
             [advec_val, bdvec_val],
             (Dot, blas.Dot22, blas.Gemv, blas_c.CGemv),
         )
@@ -2808,7 +2786,7 @@ class TestInferShape(utt.InferShapeTester):
         bdmat_val = random(5, 3, rng=rng)
         self._compile_and_check(
             [admat, bdmat],
-            [Dot()(admat, bdmat)],
+            [dot(admat, bdmat)],
             [admat_val, bdmat_val],
             (Dot, blas.Dot22),
         )
@@ -2817,7 +2795,7 @@ class TestInferShape(utt.InferShapeTester):
         bdmat_val = random(4, 5, rng=rng)
         self._compile_and_check(
             [advec, bdmat],
-            [Dot()(advec, bdmat)],
+            [dot(advec, bdmat)],
             [advec_val, bdmat_val],
             (Dot, blas.Dot22, blas.Gemv, blas_c.CGemv),
         )
@@ -2826,7 +2804,7 @@ class TestInferShape(utt.InferShapeTester):
         admat_val = random(5, 4, rng=rng)
         self._compile_and_check(
             [admat, bdvec],
-            [Dot()(admat, bdvec)],
+            [dot(admat, bdvec)],
             [admat_val, bdvec_val],
             (Dot, blas.Dot22, blas.Gemv, blas_c.CGemv),
         )

tests/test_printing.py

@@ -333,7 +333,7 @@ def test_debugprint():
 
 
 def test_debugprint_id_type():
-    a_at = dvector()
+    a_at = dmatrix()
     b_at = dmatrix()
 
     d_at = b_at.dot(a_at)
@@ -344,10 +344,10 @@ def test_debugprint_id_type():
     s = s.getvalue()
 
     exp_res = f"""Add [id {e_at.auto_name}]
- ├─ dot [id {d_at.auto_name}]
+ ├─ Dot [id {d_at.auto_name}]
  │  ├─ <Matrix(float64, shape=(?, ?))> [id {b_at.auto_name}]
- │  └─ <Vector(float64, shape=(?,))> [id {a_at.auto_name}]
- └─ <Vector(float64, shape=(?,))> [id {a_at.auto_name}]
+ │  └─ <Matrix(float64, shape=(?, ?))> [id {a_at.auto_name}]
+ └─ <Matrix(float64, shape=(?, ?))> [id {a_at.auto_name}]
     """
 
     assert [l.strip() for l in s.split("\n")] == [

tests/xtensor/test_math.py

@@ -312,5 +312,7 @@ def test_dot_errors():
     x_test = DataArray(np.ones((2, 3)), dims=("a", "b"))
     y_test = DataArray(np.ones((4, 5)), dims=("b", "c"))
     # Doesn't fail until the rewrite
-    with pytest.raises(ValueError, match="not aligned"):
+    with pytest.raises(
+        ValueError, match="Input operand 1 has a mismatch in its core dimension 0"
+    ):
         fn(x_test, y_test)