Simplify BatchedDot implementation

The Op now always expects rank 3 inputs, and any dimshuffles are added explicitly by the helper function

Author: ricardoV94

pytensor/link/jax/dispatch/nlinalg.py

@@ -99,9 +99,7 @@ def jax_funcify_BatchedDot(op, **kwargs):
     def batched_dot(a, b):
         if a.shape[0] != b.shape[0]:
             raise TypeError("Shapes must match in the 0-th dimension")
-        if a.ndim == 2 or b.ndim == 2:
-            return jnp.einsum("n...j,nj...->n...", a, b)
-        return jnp.einsum("nij,njk->nik", a, b)
+        return jnp.matmul(a, b)
 
     return batched_dot
 

pytensor/link/numba/dispatch/basic.py

@@ -895,6 +895,8 @@ def numba_funcify_BatchedDot(op, node, **kwargs):
 
     @numba_njit
     def batched_dot(x, y):
+        # Numba does not support 3D matmul
+        # https://github.com/numba/numba/issues/3804
         shape = x.shape[:-1] + y.shape[2:]
         z0 = np.empty(shape, dtype=dtype)
         for i in range(z0.shape[0]):

pytensor/tensor/blas.py

@@ -98,10 +98,11 @@
 from pytensor.printing import FunctionPrinter, pprint
 from pytensor.scalar import bool as bool_t
 from pytensor.tensor import basic as at
+from pytensor.tensor.basic import expand_dims
 from pytensor.tensor.blas_headers import blas_header_text, blas_header_version
 from pytensor.tensor.elemwise import DimShuffle
 from pytensor.tensor.math import add, mul, neg, sub
-from pytensor.tensor.shape import specify_broadcastable
+from pytensor.tensor.shape import shape_padright, specify_broadcastable
 from pytensor.tensor.type import DenseTensorType, TensorType, integer_dtypes, tensor
 from pytensor.utils import memoize
 
@@ -1637,48 +1638,53 @@ def c_code_cache_version(self):
 
 class BatchedDot(COp):
     """
-    Computes the batched dot product of two variables:
+    Computes a batch matrix-matrix dot with tensor3 variables
 
         batched_dot(a, b)[i] = dot(a[i], b[i])
     """
 
     __props__ = ()
+    gufunc_signature = "(b,m,k),(b,k,n)->(b,m,n)"
 
-    def make_node(self, *inputs):
-        inputs = list(map(at.as_tensor_variable, inputs))
+    def make_node(self, x, y):
+        x = at.as_tensor_variable(x)
+        y = at.as_tensor_variable(y)
 
-        if any(not isinstance(i.type, DenseTensorType) for i in inputs):
+        if not (
+            isinstance(x.type, DenseTensorType) and isinstance(y.type, DenseTensorType)
+        ):
             raise NotImplementedError("Only dense tensor types are supported")
 
-        if len(inputs) != 2:
-            raise TypeError(f"Two arguments required, but {len(inputs)} given.")
-        if inputs[0].ndim not in (2, 3):
+        if not (x.type.ndim == 3 and y.type.ndim == 3):
             raise TypeError(
-                "Input 0 (0-indexed)"
-                f" must have ndim of 2 or 3, {int(inputs[0].ndim)} given. Consider"
-                " calling batched_dot instead."
-            )
-        if inputs[1].ndim not in (2, 3):
-            raise TypeError(
-                "Input 1 (0-indexed)"
-                f" must have ndim of 2 or 3, {int(inputs[1].ndim)} given. Consider"
-                " calling batched_dot instead."
+                f"Inputs must have 3 ndim, but got {x.type.ndim} and {y.type.ndim}. "
+                "Consider calling batched_dot instead."
             )
 
-        dtype = pytensor.scalar.upcast(*[input.type.dtype for input in inputs])
-        # upcast inputs to common dtype if needed
-        upcasted_inputs = [at.cast(input, dtype) for input in inputs]
-        out_shape = (
-            (
-                1
-                if inputs[0].type.shape[0] == 1 or inputs[1].type.shape[0] == 1
-                else None,
-            )
-            + inputs[0].type.shape[1:-1]
-            + inputs[1].type.shape[2:]
-        )
-        out_shape = tuple(1 if s == 1 else None for s in out_shape)
-        return Apply(self, upcasted_inputs, [tensor(dtype=dtype, shape=out_shape)])
+        def extract_static_dim(dim_x, dim_y):
+            dims = {dim_x, dim_y} - {None}
+            if len(dims) > 1:
+                # BatchedDot doesn't allow broadcasting
+                raise ValueError(
+                    f"Static dimensions of BatchedDot don't match, got {x.type.shape} and {y.type.shape}"
+                )
+            elif not dims:
+                return None
+            else:
+                return dims.pop()
+
+        x_batch_dim, x_row_dim, x_sum_dim = x.type.shape
+        y_batch_dim, y_sum_dim, y_col_dim = y.type.shape
+        batch_dim = extract_static_dim(x_batch_dim, y_batch_dim)
+        # Raise if static sum dimensions do not match
+        _ = extract_static_dim(x_sum_dim, y_sum_dim)
+        out_shape = (batch_dim, x_row_dim, y_col_dim)
+
+        # Change dtype if needed
+        dtype = pytensor.scalar.upcast(x.type.dtype, y.type.dtype)
+        x, y = at.cast(x, dtype), at.cast(y, dtype)
+        out = tensor(dtype=dtype, shape=out_shape)
+        return Apply(self, [x, y], [out])
 
     def perform(self, node, inp, out):
         x, y = inp
@@ -1690,11 +1696,7 @@ def perform(self, node, inp, out):
                 f" same size in axis 0, but have sizes [{', '.join([str(i.shape[0]) for i in inp])}]."
             )
 
-        shape = self.infer_shape(None, node, [i.shape for i in inp])[0]
-        dtype = node.outputs[0].dtype
-        z0 = z[0] = np.empty(shape, dtype=dtype)
-        for i in range(z0.shape[0]):
-            z0[i] = np.dot(x[i], y[i])
+        z[0] = np.matmul(x, y)
 
     def c_support_code(self, **kwargs):
         batch_gemm_defn = """
@@ -1792,14 +1794,6 @@ def c_lib_dirs(self, **kwargs):
     def c_header_dirs(self, **kwargs):
         return ldflags(libs=False, include_dir=True)
 
-    def c_code_cleanup(self, node, name, inputs, outputs, sub):
-        return """
-        // clean up views
-        Py_XDECREF(xs); xs = 0;
-        Py_XDECREF(ys); ys = 0;
-        Py_XDECREF(zs); zs = 0;
-        """
-
     def c_code(self, node, name, inp, out, sub):
         _x, _y = inp
         (_z,) = out
@@ -1832,12 +1826,11 @@ def contiguous(var, ndim):
         )
 
         # generate code to allocate output based on runtime input shapes
-        z_dims = [f"PyArray_DIMS({_x})[0]"]
-        if x_ndim == 3:
-            z_dims.append(f"PyArray_DIMS({_x})[1]")
-        if y_ndim == 3:
-            z_dims.append(f"PyArray_DIMS({_y})[2]")
-        assert len(z_dims) == z_ndim
+        z_dims = [
+            f"PyArray_DIMS({_x})[0]",
+            f"PyArray_DIMS({_x})[1]",
+            f"PyArray_DIMS({_y})[2]",
+        ]
 
         z_shape_correct = " && ".join(
             "PyArray_DIMS(%s)[%i] == %s" % (_z, i, dim) for i, dim in enumerate(z_dims)
@@ -1880,76 +1873,26 @@ def contiguous(var, ndim):
             )
         contiguate = "\n".join(contiguate)
 
-        def c_dimshuffle(newname, oldname, shape):
-            _fail = fail
-            _shape = ", ".join(
-                "1" if axis is None else "PyArray_DIMS(%s)[%i]" % (oldname, axis)
-                for axis in shape
-            )
-            return (
-                """{
-                npy_intp dims[3] = {%(_shape)s};
-                PyArray_Dims newshape = {dims, 3};
-                %(newname)s = (PyArrayObject*)PyArray_Newshape(%(oldname)s, &newshape, NPY_ANYORDER);
-                if (!%(newname)s)
-                    %(_fail)s
-                // make sure we didn't accidentally copy
-                assert(PyArray_DATA(%(oldname)s) == PyArray_DATA(%(newname)s));
-            }"""
-                % locals()
-            )
-
-        # create tensor3 views for any of x, y, z that are not tensor3, so that
-        # we only need to implement the tensor3-tensor3 batched dot product.
-        # xs, ys and zs will point to these views, or to the original array if
-        # it was already tensor3.
-        # in the latter case, we artificially increase the reference count of
-        # the original array so that the c_code_cleanup method can decref them
-        # all indiscriminately.
-        upcast = []
-        if x_ndim == 3:
-            upcast.append("xs = %(_x)s; Py_XINCREF(xs);")
-        elif x_ndim == 2:
-            upcast.append(c_dimshuffle("xs", _x, (0, None, 1)))
-        if y_ndim == 3:
-            upcast.append("ys = %(_y)s; Py_XINCREF(ys);")
-        elif y_ndim == 2:
-            upcast.append(c_dimshuffle("ys", _y, (0, 1, None)))
-        if z_ndim == 3:
-            upcast.append("zs = %(_z)s; Py_XINCREF(zs);")
-        else:
-            upcast.append(
-                c_dimshuffle(
-                    "zs",
-                    _z,
-                    (0, None if x_ndim == 2 else 1, None if y_ndim == 2 else 1),
-                )
-            )
-        upcast = "\n".join(upcast) % locals()
-
         return (
             """
         int type_num = PyArray_DESCR(%(_x)s)->type_num;
         int type_size = PyArray_DESCR(%(_x)s)->elsize; // in bytes
 
-        // xs, ys, zs will point to views onto %(_x)s, %(_y)s, %(_z)s
-        PyArrayObject *xs = 0, *ys = 0, *zs = 0;
-
-        if (PyArray_NDIM(%(_x)s) != %(x_ndim)s) {
+        if (PyArray_NDIM(%(_x)s) != 3) {
             PyErr_Format(PyExc_NotImplementedError,
-                         "rank(x) != %(x_ndim)s. rank(x) is %%d.",
+                         "rank(x) != 3. rank(x) is %%d.",
                          PyArray_NDIM(%(_x)s));
             %(fail)s;
         }
-        if (PyArray_NDIM(%(_y)s) != %(y_ndim)s) {
+        if (PyArray_NDIM(%(_y)s) != 3) {
             PyErr_Format(PyExc_NotImplementedError,
-                         "rank(y) != %(y_ndim)s. rank(y) is %%d.",
+                         "rank(y) != 3. rank(y) is %%d.",
                          PyArray_NDIM(%(_y)s));
             %(fail)s;
         }
-        if (%(_z)s && PyArray_NDIM(%(_z)s) != %(z_ndim)s) {
+        if (%(_z)s && PyArray_NDIM(%(_z)s) != 3) {
             PyErr_Format(PyExc_NotImplementedError,
-                         "rank(z) != %(z_ndim)s. rank(z) is %%d.",
+                         "rank(z) != 3. rank(z) is %%d.",
                          PyArray_NDIM(%(_z)s));
             %(fail)s;
         }
@@ -1958,36 +1901,32 @@ def c_dimshuffle(newname, oldname, shape):
         %(allocate)s
         // reallocate any noncontiguous arrays or arrays with invalid strides
         %(contiguate)s
-        // add dims to make sure everything is tensor3
-        %(upcast)s
-        // from here on, use xs, ys and zs as they are tensor3 and share memory
-        // with the original %(_x)s, %(_y)s and %(_z)s arrays.
 
-        if ((PyArray_DESCR(xs)->type_num != NPY_DOUBLE)
-            && (PyArray_DESCR(xs)->type_num != NPY_FLOAT))
+        if ((PyArray_DESCR(%(_x)s)->type_num != NPY_DOUBLE)
+            && (PyArray_DESCR(%(_x)s)->type_num != NPY_FLOAT))
         {PyErr_SetString(PyExc_NotImplementedError, "type(x) is not double or float"); %(fail)s;}
 
-        if ((PyArray_DESCR(ys)->type_num != NPY_DOUBLE)
-            && (PyArray_DESCR(ys)->type_num != NPY_FLOAT))
+        if ((PyArray_DESCR(%(_y)s)->type_num != NPY_DOUBLE)
+            && (PyArray_DESCR(%(_y)s)->type_num != NPY_FLOAT))
         {PyErr_SetString(PyExc_NotImplementedError, "type(y) is not double or float"); %(fail)s;}
 
-        if ((PyArray_DESCR(zs)->type_num != NPY_DOUBLE)
-            && (PyArray_DESCR(zs)->type_num != NPY_FLOAT))
+        if ((PyArray_DESCR(%(_z)s)->type_num != NPY_DOUBLE)
+            && (PyArray_DESCR(%(_z)s)->type_num != NPY_FLOAT))
         {PyErr_SetString(PyExc_NotImplementedError, "type(z) is not double or float"); %(fail)s;}
 
-        if ((PyArray_DESCR(xs)->type_num != PyArray_DESCR(ys)->type_num)
-            ||(PyArray_DESCR(xs)->type_num != PyArray_DESCR(zs)->type_num))
+        if ((PyArray_DESCR(%(_x)s)->type_num != PyArray_DESCR(%(_y)s)->type_num)
+            ||(PyArray_DESCR(%(_x)s)->type_num != PyArray_DESCR(%(_z)s)->type_num))
         { PyErr_SetString(PyExc_NotImplementedError, "type(x), type(y), type(z) are not all the same"); %(fail)s; }
 
         switch (type_num)
         {
             case NPY_FLOAT:
-            if (batch_gemm<float>(sgemm_, type_size, xs, ys, zs)) {
+            if (batch_gemm<float>(sgemm_, type_size, %(_x)s, %(_y)s, %(_z)s)) {
                 %(fail)s;
             }
             break;
             case NPY_DOUBLE:
-            if (batch_gemm<double>(dgemm_, type_size, xs, ys, zs)) {
+            if (batch_gemm<double>(dgemm_, type_size, %(_x)s, %(_y)s, %(_z)s)) {
                 %(fail)s;
             }
             break;
@@ -1999,32 +1938,14 @@ def c_dimshuffle(newname, oldname, shape):
     def c_code_cache_version(self):
         from pytensor.tensor.blas_headers import blas_header_version
 
-        return (4, blas_header_version())
+        return (5, blas_header_version())
 
     def grad(self, inp, grads):
         x, y = inp
         (gz,) = grads
-        xdim, ydim, gdim = x.type.ndim, y.type.ndim, gz.type.ndim
 
-        # grad is a vector, so x is a matrix and y is a matrix
-        if gdim == 1:
-            xgrad = gz.dimshuffle(0, "x") * y
-            ygrad = gz.dimshuffle(0, "x") * x
-
-        # x is a matrix, y is a tensor3, grad is a matrix
-        elif xdim == 2 and ydim == 3:
-            xgrad = batched_dot(gz, y.dimshuffle(0, 2, 1))
-            ygrad = x.dimshuffle(0, 1, "x") * gz.dimshuffle(0, "x", 1)
-
-        # x is a tensor3, y is a matrix, grad is a matrix
-        elif xdim == 3 and ydim == 2:
-            xgrad = gz.dimshuffle(0, 1, "x") * y.dimshuffle(0, "x", 1)
-            ygrad = batched_dot(x.dimshuffle(0, 2, 1), gz)
-
-        # x is a tensor3, y is a tensor3, grad is a tensor3
-        elif xdim == ydim == 3:
-            xgrad = batched_dot(gz, y.dimshuffle(0, 2, 1))
-            ygrad = batched_dot(x.dimshuffle(0, 2, 1), gz)
+        xgrad = batched_dot(gz, y.dimshuffle(0, 2, 1))
+        ygrad = batched_dot(x.dimshuffle(0, 2, 1), gz)
 
         # If x or y contain broadcastable dimensions but only one of
         # them know that a matching dimensions is broadcastable, the
@@ -2105,6 +2026,7 @@ def R_op(self, inputs, eval_points):
                         + " to BatchedDot.R_op should have the same shape, but "
                         f"their shapes are {input_values[i].shape} and {eval_point_values[i].shape}, respectively"
                     )
+
         if eval_points[0]:
             t1 = self(eval_points[0], inputs[1])
         if eval_points[1]:
@@ -2118,9 +2040,6 @@ def R_op(self, inputs, eval_points):
             return [t2]
 
     def infer_shape(self, fgraph, node, shapes):
-        for shape_ in shapes:
-            if len(shape_) not in (2, 3):
-                raise NotImplementedError()
         xshp, yshp = shapes
         return [xshp[:-1] + yshp[2:]]
 
@@ -2157,14 +2076,24 @@ def batched_dot(a, b):
     elif b.ndim == 0:
         raise TypeError("b must have at least one (batch) axis")
     elif a.ndim == 1:
-        return a.dimshuffle(*([0] + ["x"] * (b.ndim - 1))) * b
+        return shape_padright(a, (b.ndim - 1)) * b
     elif b.ndim == 1:
-        return a * b.dimshuffle(*([0] + ["x"] * (a.ndim - 1)))
+        return a * shape_padright(b, (a.ndim - 1))
     elif a.ndim > 3 or b.ndim > 3:
         return batched_tensordot(a, b, [[a.ndim - 1], [np.maximum(1, b.ndim - 2)]])
     else:
-        # avoid circular import
-        return _batched_dot(a, b)
+        # If either a or b is a batched vector, expand dims and later squeeze them
+        expanded_axis = []
+        if a.ndim == 2:
+            a = expand_dims(a, axis=1)
+            expanded_axis.append(1)
+        if b.ndim == 2:
+            b = expand_dims(b, axis=2)
+            expanded_axis.append(2)
+        out = _batched_dot(a, b)
+        if expanded_axis:
+            out = out.squeeze(axis=expanded_axis)
+        return out
 
 
 def batched_tensordot(x, y, axes=2):

tests/link/jax/test_nlinalg.py

@@ -43,15 +43,6 @@ def test_jax_BatchedDot():
     with pytest.raises(TypeError):
         pytensor_jax_fn(*inputs)
 
-    # matrix . matrix
-    a = matrix("a")
-    a.tag.test_value = np.linspace(-1, 1, 5 * 3).astype(config.floatX).reshape((5, 3))
-    b = matrix("b")
-    b.tag.test_value = np.linspace(1, -1, 5 * 3).astype(config.floatX).reshape((5, 3))
-    out = at_blas.BatchedDot()(a, b)
-    fgraph = FunctionGraph([a, b], [out])
-    compare_jax_and_py(fgraph, [get_test_value(i) for i in fgraph.inputs])
-
 
 def test_jax_basic_multiout():
     rng = np.random.default_rng(213234)