. POC named tensors

Author: ricardoV94

pytensor/xtensor/__init__.py

@@ -0,0 +1,8 @@
+import warnings
+import pytensor.xtensor.rewriting
+
+from pytensor.xtensor.variable import XTensorVariable, XTensorConstant, as_xtensor, as_xtensor_variable
+from pytensor.xtensor.type import XTensorType
+
+
+warnings.warn("xtensor module is experimental and full of bugs")

pytensor/xtensor/basic.py

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+from itertools import chain
+
+import pytensor.scalar as ps
+from pytensor.graph import Apply, Op
+import pytensor.xtensor as px
+from pytensor.tensor import TensorType
+
+
+class TensorFromXTensor(Op):
+
+    def make_node(self, x) -> Apply:
+        if not isinstance(x.type, px.XTensorType):
+            raise TypeError(f"x must be have an XTensorType, got {type(x.type)}")
+        output = TensorType(x.type.dtype, shape=x.type.shape)()
+        return Apply(self, [x], [output])
+
+    def perform(self, node, inputs, output_storage) -> None:
+        [x] = inputs
+        output_storage[0][0] = x.copy()
+
+
+tensor_from_xtensor = TensorFromXTensor()
+
+
+class XTensorFromTensor(Op):
+
+    __props__ = ("dims",)
+
+    def __init__(self, dims):
+        super().__init__()
+        self.dims = dims
+
+    def make_node(self, x) -> Apply:
+        if not isinstance(x.type, TensorType):
+            raise TypeError(f"x must be an TensorType type, got {type(x.type)}")
+        output = px.XTensorType(x.type.dtype, dims=self.dims, shape=x.type.shape)()
+        return Apply(self, [x], [output])
+
+    def perform(self, node, inputs, output_storage) -> None:
+        [x] = inputs
+        output_storage[0][0] = x.copy()
+
+
+def xtensor_from_tensor(x, dims):
+    return XTensorFromTensor(dims=dims)(x)
+
+
+class XElemwise(Op):
+
+    __props__ = ("scalar_op",)
+
+    def __init__(self, scalar_op):
+        super().__init__()
+        self.scalar_op = scalar_op
+
+    def make_node(self, *inputs):
+        # TODO: Check dim lengths match
+        inputs = [px.as_xtensor_variable(inp) for inp in inputs]
+        # TODO: This ordering is different than what xarray does
+        unique_dims = sorted(set(chain.from_iterable(inp.type.dims for inp in inputs)))
+        # TODO: Fix dtype
+        output_type = px.XTensorType("float64", dims=unique_dims, shape=(None,) * len(unique_dims))
+        outputs = [output_type() for _ in range(self.scalar_op.nout)]
+        return Apply(self, inputs, outputs)
+
+    def perform(self, *args, **kwargs) -> None:
+        raise NotImplementedError("xtensor operations must be rewritten as tensor operations")
+
+
+add = XElemwise(ps.add)

pytensor/xtensor/rewriting/__init__.py

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+import pytensor.xtensor.rewriting.basic

pytensor/xtensor/rewriting/basic.py

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+from pytensor.graph import node_rewriter
+from pytensor.tensor import expand_dims
+from pytensor.tensor.elemwise import Elemwise
+from pytensor.xtensor.basic import tensor_from_xtensor, XElemwise, xtensor_from_tensor
+from pytensor.xtensor.rewriting.utils import register_xcanonicalize
+
+
+@register_xcanonicalize
+@node_rewriter(tracks=[XElemwise])
+def xelemwise_to_elemwise(fgraph, node):
+    # Convert inputs to TensorVariables and add broadcastable dims
+    output_dims = node.outputs[0].type.dims
+
+    tensor_inputs = []
+    for inp in node.inputs:
+        inp_dims = inp.type.dims
+        axis = [i for i, dim in enumerate(output_dims) if dim not in inp_dims]
+        tensor_inp = tensor_from_xtensor(inp)
+        tensor_inp = expand_dims(tensor_inp, axis)
+        tensor_inputs.append(tensor_inp)
+
+    tensor_outs = Elemwise(scalar_op=node.op.scalar_op)(*tensor_inputs, return_list=True)
+
+    # TODO: copy_stack_trace
+    new_outs = [xtensor_from_tensor(tensor_out, dims=output_dims) for tensor_out in tensor_outs]
+    return new_outs

pytensor/xtensor/rewriting/utils.py

@@ -0,0 +1,33 @@
+from typing import Union
+
+from pytensor.compile import optdb
+from pytensor.graph.rewriting.basic import NodeRewriter
+from pytensor.graph.rewriting.db import RewriteDatabase, EquilibriumDB
+
+
+optdb.register(
+    "xcanonicalize",
+    EquilibriumDB(ignore_newtrees=False),
+    "fast_run",
+    "fast_compile",
+    "xtensor",
+    position=0,
+)
+
+
+def register_xcanonicalize(
+    node_rewriter: Union[RewriteDatabase, NodeRewriter, str], *tags: str, **kwargs
+):
+    if isinstance(node_rewriter, str):
+
+        def register(inner_rewriter: Union[RewriteDatabase, NodeRewriter]):
+            return register_xcanonicalize(inner_rewriter, node_rewriter, *tags, **kwargs)
+
+        return register
+
+    else:
+        name = kwargs.pop("name", None) or node_rewriter.__name__
+        optdb["xtensor"].register(
+            name, node_rewriter, "fast_run", "fast_compile", *tags, **kwargs
+        )
+        return node_rewriter

pytensor/xtensor/type.py

@@ -0,0 +1,147 @@
+from typing import Iterable, Optional, Union, Sequence, TypeVar
+
+import numpy as np
+
+import pytensor
+from pytensor import scalar as aes
+from pytensor.graph.basic import Variable
+from pytensor.graph.type import HasDataType
+from pytensor.tensor.type import TensorType
+
+
+_XTensorTypeType = TypeVar("_XTensorTypeType", bound=TensorType)
+
+
+class XTensorType(TensorType, HasDataType):
+    """A `Type` for sparse tensors.
+
+    Notes
+    -----
+    Currently, sparse tensors can only be matrices (i.e. have two dimensions).
+
+    """
+
+    __props__ = ("dtype", "shape", "dims")
+
+    def __init__(
+        self,
+        dtype: Union[str, np.dtype],
+        *,
+        dims: Sequence[str],
+        shape: Optional[Iterable[Optional[Union[bool, int]]]] = None,
+        name: Optional[str] = None,
+    ):
+        super().__init__(dtype, shape=shape, name=name)
+        if not isinstance(dims, (list, tuple)):
+            raise TypeError("dims must be a list or tuple")
+        dims = tuple(dims)
+        self.dims = dims
+
+    def clone(
+        self,
+        dtype=None,
+        dims=None,
+        shape=None,
+        **kwargs,
+    ):
+        if dtype is None:
+            dtype = self.dtype
+        if dims is None:
+            dims = self.dims
+        if shape is None:
+            shape = self.shape
+        return type(self)(format, dtype, shape=shape, dims=dims, **kwargs)
+
+    def filter(self, value, strict=False, allow_downcast=None):
+        # TODO: Implement this
+        return value
+
+        if isinstance(value, Variable):
+            raise TypeError(
+                "Expected an array-like object, but found a Variable: "
+                "maybe you are trying to call a function on a (possibly "
+                "shared) variable instead of a numeric array?"
+            )
+
+        if (
+            isinstance(value, self.format_cls[self.format])
+            and value.dtype == self.dtype
+        ):
+            return value
+
+        if strict:
+            raise TypeError(
+                f"{value} is not sparse, or not the right dtype (is {value.dtype}, "
+                f"expected {self.dtype})"
+            )
+
+        # The input format could be converted here
+        if allow_downcast:
+            sp = self.format_cls[self.format](value, dtype=self.dtype)
+        else:
+            data = self.format_cls[self.format](value)
+            up_dtype = aes.upcast(self.dtype, data.dtype)
+            if up_dtype != self.dtype:
+                raise TypeError(f"Expected {self.dtype} dtype but got {data.dtype}")
+            sp = data.astype(up_dtype)
+
+        assert sp.format == self.format
+
+        return sp
+
+    def convert_variable(self, var):
+        # TODO: Implement this
+        return var
+        res = super().convert_variable(var)
+
+        if res is None:
+            return res
+
+        if not isinstance(res.type, type(self)):
+            return None
+
+        if res.dims != self.dims:
+            # TODO: Does this make sense?
+            return None
+
+        return res
+
+    def __hash__(self):
+        return super().__hash__() ^ hash(self.dims)
+
+    def __repr__(self):
+        # TODO: Add `?` for unknown shapes like `TensorType` does
+        return f"XTensorType({self.dtype}, {self.dims}, {self.shape})"
+
+    def __eq__(self, other):
+        res = super().__eq__(other)
+
+        if isinstance(res, bool):
+            return res and other.dims == self.dims
+
+        return res
+
+    def is_super(self, otype):
+        # TODO: Implement this
+        return True
+
+        if not super().is_super(otype):
+            return False
+
+        if self.dims == otype.dims:
+            return True
+
+        return False
+
+
+# TODO: Implement creater helper xtensor
+
+pytensor.compile.register_view_op_c_code(
+    XTensorType,
+    """
+    Py_XDECREF(%(oname)s);
+    %(oname)s = %(iname)s;
+    Py_XINCREF(%(oname)s);
+    """,
+    1,
+)