Merge branch 'main' into ifelse_torch

.pre-commit-config.yaml

@@ -22,7 +22,7 @@ repos:
           )$
       - id: check-merge-conflict
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.5.6
+    rev: v0.6.3
     hooks:
       - id: ruff
         args: ["--fix", "--output-format=full"]

pytensor/compile/mode.py

@@ -471,6 +471,7 @@ def clone(self, link_kwargs=None, optimizer="", **kwargs):
             "BlasOpt",
             "fusion",
             "inplace",
+            "local_uint_constant_indices",
         ],
     ),
 )

pytensor/link/pytorch/dispatch/__init__.py

@@ -7,7 +7,8 @@
 import pytensor.link.pytorch.dispatch.elemwise
 import pytensor.link.pytorch.dispatch.math
 import pytensor.link.pytorch.dispatch.extra_ops
+import pytensor.link.pytorch.dispatch.nlinalg
 import pytensor.link.pytorch.dispatch.shape
 import pytensor.link.pytorch.dispatch.sort
-import pytensor.link.pytorch.dispatch.nlinalg
+import pytensor.link.pytorch.dispatch.subtensor
 # isort: on

pytensor/link/pytorch/dispatch/basic.py

@@ -1,25 +1,41 @@
 from functools import singledispatch
 from types import NoneType
 
+import numpy as np
 import torch
 
 from pytensor.compile.ops import DeepCopyOp
 from pytensor.graph.fg import FunctionGraph
 from pytensor.ifelse import IfElse
 from pytensor.link.utils import fgraph_to_python
 from pytensor.raise_op import CheckAndRaise
-from pytensor.tensor.basic import Alloc, AllocEmpty, ARange, Eye, Join, MakeVector
+from pytensor.tensor.basic import (
+    Alloc,
+    AllocEmpty,
+    ARange,
+    Eye,
+    Join,
+    MakeVector,
+    TensorFromScalar,
+)
 
 
 @singledispatch
-def pytorch_typify(data, dtype=None, **kwargs):
-    r"""Convert instances of PyTensor `Type`\s to PyTorch types."""
+def pytorch_typify(data, **kwargs):
+    raise NotImplementedError(f"pytorch_typify is not implemented for {type(data)}")
+
+
+@pytorch_typify.register(np.ndarray)
+@pytorch_typify.register(torch.Tensor)
+def pytorch_typify_tensor(data, dtype=None, **kwargs):
     return torch.as_tensor(data, dtype=dtype)
 
 
+@pytorch_typify.register(slice)
 @pytorch_typify.register(NoneType)
-def pytorch_typify_None(data, **kwargs):
-    return None
+@pytorch_typify.register(np.number)
+def pytorch_typify_no_conversion_needed(data, **kwargs):
+    return data
 
 
 @singledispatch
@@ -146,3 +162,10 @@ def ifelse(cond, *true_and_false, n_outs=n_outs):
             return torch.stack(true_and_false[n_outs:])
 
     return ifelse
+
+@pytorch_funcify.register(TensorFromScalar)
+def pytorch_funcify_TensorFromScalar(op, **kwargs):
+    def tensorfromscalar(x):
+        return torch.as_tensor(x)
+
+    return tensorfromscalar

pytensor/link/pytorch/dispatch/subtensor.py

@@ -0,0 +1,124 @@
+from pytensor.link.pytorch.dispatch.basic import pytorch_funcify
+from pytensor.tensor.subtensor import (
+    AdvancedIncSubtensor,
+    AdvancedIncSubtensor1,
+    AdvancedSubtensor,
+    AdvancedSubtensor1,
+    IncSubtensor,
+    Subtensor,
+    indices_from_subtensor,
+)
+from pytensor.tensor.type_other import MakeSlice, SliceType
+
+
+def check_negative_steps(indices):
+    for index in indices:
+        if isinstance(index, slice):
+            if index.step is not None and index.step < 0:
+                raise NotImplementedError(
+                    "Negative step sizes are not supported in Pytorch"
+                )
+
+
+@pytorch_funcify.register(Subtensor)
+def pytorch_funcify_Subtensor(op, node, **kwargs):
+    idx_list = op.idx_list
+
+    def subtensor(x, *flattened_indices):
+        indices = indices_from_subtensor(flattened_indices, idx_list)
+        check_negative_steps(indices)
+        return x[indices]
+
+    return subtensor
+
+
+@pytorch_funcify.register(MakeSlice)
+def pytorch_funcify_makeslice(op, **kwargs):
+    def makeslice(*x):
+        return slice(x)
+
+    return makeslice
+
+
+@pytorch_funcify.register(AdvancedSubtensor1)
+@pytorch_funcify.register(AdvancedSubtensor)
+def pytorch_funcify_AdvSubtensor(op, node, **kwargs):
+    def advsubtensor(x, *indices):
+        check_negative_steps(indices)
+        return x[indices]
+
+    return advsubtensor
+
+
+@pytorch_funcify.register(IncSubtensor)
+def pytorch_funcify_IncSubtensor(op, node, **kwargs):
+    idx_list = op.idx_list
+    inplace = op.inplace
+    if op.set_instead_of_inc:
+
+        def set_subtensor(x, y, *flattened_indices):
+            indices = indices_from_subtensor(flattened_indices, idx_list)
+            check_negative_steps(indices)
+            if not inplace:
+                x = x.clone()
+            x[indices] = y
+            return x
+
+        return set_subtensor
+
+    else:
+
+        def inc_subtensor(x, y, *flattened_indices):
+            indices = indices_from_subtensor(flattened_indices, idx_list)
+            check_negative_steps(indices)
+            if not inplace:
+                x = x.clone()
+            x[indices] += y
+            return x
+
+        return inc_subtensor
+
+
+@pytorch_funcify.register(AdvancedIncSubtensor)
+@pytorch_funcify.register(AdvancedIncSubtensor1)
+def pytorch_funcify_AdvancedIncSubtensor(op, node, **kwargs):
+    inplace = op.inplace
+    ignore_duplicates = getattr(op, "ignore_duplicates", False)
+
+    if op.set_instead_of_inc:
+
+        def adv_set_subtensor(x, y, *indices):
+            check_negative_steps(indices)
+            if not inplace:
+                x = x.clone()
+            x[indices] = y.type_as(x)
+            return x
+
+        return adv_set_subtensor
+
+    elif ignore_duplicates:
+
+        def adv_inc_subtensor_no_duplicates(x, y, *indices):
+            check_negative_steps(indices)
+            if not inplace:
+                x = x.clone()
+            x[indices] += y.type_as(x)
+            return x
+
+        return adv_inc_subtensor_no_duplicates
+
+    else:
+        if any(isinstance(idx.type, SliceType) for idx in node.inputs[2:]):
+            raise NotImplementedError(
+                "IncSubtensor with potential duplicates indexes and slice indexing not implemented in PyTorch"
+            )
+
+        def adv_inc_subtensor(x, y, *indices):
+            # Not needed because slices aren't supported
+            # check_negative_steps(indices)
+            if not inplace:
+                x = x.clone()
+            x.index_put_(indices, y.type_as(x), accumulate=True)
+            return x
+
+        return adv_inc_subtensor

pytensor/tensor/basic.py

@@ -3780,15 +3780,16 @@ class AllocDiag(OpFromGraph):
     Wrapper Op for alloc_diag graphs
     """
 
-    __props__ = ("axis1", "axis2")
-
     def __init__(self, *args, axis1, axis2, offset, **kwargs):
         self.axis1 = axis1
         self.axis2 = axis2
         self.offset = offset
 
         super().__init__(*args, **kwargs, strict=True)
 
+    def __str__(self):
+        return f"AllocDiag{{{self.axis1=}, {self.axis2=}, {self.offset=}}}"
+
     @staticmethod
     def is_offset_zero(node) -> bool:
         """

pytensor/tensor/einsum.py

@@ -52,14 +52,15 @@ class Einsum(OpFromGraph):
     desired. We haven't decided whether we want to provide this functionality.
     """
 
-    __props__ = ("subscripts", "path", "optimized")
-
     def __init__(self, *args, subscripts: str, path: PATH, optimized: bool, **kwargs):
         self.subscripts = subscripts
         self.path = path
         self.optimized = optimized
         super().__init__(*args, **kwargs, strict=True)
 
+    def __str__(self):
+        return f"Einsum{{{self.subscripts=}, {self.path=}, {self.optimized=}}}"
+
 
 def _iota(shape: TensorVariable, axis: int) -> TensorVariable:
     """