Merge pull request #331 from eric-wieser/layout-in-type

numba: Change layout to be stored in type itself

clifford/_layout_helpers.py

@@ -8,7 +8,7 @@
 
 """
 
-from typing import TypeVar, Generic, Sequence, Tuple, List
+from typing import TypeVar, Generic, Sequence, Tuple, List, Optional
 import numpy as np
 import functools
 import operator
@@ -252,3 +252,10 @@ def __reduce__(self):
             return __class__, (self._n,)
         else:
             return __class__, (self._n, self._first_index)
+
+
+def layout_short_name(layout) -> Optional[str]:
+    """ helper to get the short name of a layout """
+    if hasattr(layout, '__name__') and '__module__' in layout.__dict__:
+        return "{l.__module__}.{l.__name__}".format(l=layout)
+    return None

clifford/_multivector.py

@@ -7,6 +7,7 @@
 import clifford as cf
 from . import general_exp
 from . import _settings
+from ._layout_helpers import layout_short_name
 
 
 class MultiVector(object):
@@ -507,11 +508,12 @@ def __repr__(self) -> str:
         else:
             dtype_str = None
 
-        if hasattr(self.layout, '__name__') and '__module__' in self.layout.__dict__:
-            fmt = "{l.__module__}.{l.__name__}.MultiVector({v!r}{d})"
+        l_name = layout_short_name(self.layout)
+        args = dict(v=list(self.value), d=dtype_str)
+        if l_name is not None:
+            return "{l}.MultiVector({v!r}{d})".format(l=l_name, **args)
         else:
-            fmt = "{l!r}.MultiVector({v!r}{d})"
-        return fmt.format(l=self.layout, v=list(self.value), d=dtype_str)
+            return "{l!r}.MultiVector({v!r}{d})".format(l=self.layout, **args)
 
     def _repr_pretty_(self, p, cycle):
         if cycle:
@@ -521,8 +523,9 @@ def _repr_pretty_(self, p, cycle):
             p.text(str(self))
             return
 
-        if hasattr(self.layout, '__name__') and '__module__' in self.layout.__dict__:
-            prefix = "{l.__module__}.{l.__name__}.MultiVector(".format(l=self.layout)
+        l_name = layout_short_name(self.layout)
+        if l_name is not None:
+            prefix = "{}.MultiVector(".format(l_name)
             include_layout = False
         else:
             include_layout = True

clifford/numba/_layout.py

@@ -1,58 +1,75 @@
 import numba
 import numba.extending
+from numba.extending import NativeValue
+import llvmlite.ir
 try:
     # module locations as of numba 0.49.0
-    from numba.core import cgutils, types
+    from numba.core import types
     from numba.core.imputils import lower_constant
 except ImportError:
     # module locations prior to numba 0.49.0
-    from numba import cgutils, types
+    from numba import types
     from numba.targets.imputils import lower_constant
 
-from .._layout import Layout
+from .._layout import Layout, _cached_property
+from .._layout_helpers import layout_short_name
+from .._multivector import MultiVector
 
 
-opaque_layout = types.Opaque('Opaque(Layout)')
+# In future we want to store some of the layout in the type (the `order` etc),
+# but store the `names` in the layout instances, so that we can reuse jitted
+# functions across different basis vector names.
 
-
-class LayoutType(types.Type):
-    def __init__(self):
-        super().__init__("LayoutType")
+class LayoutType(types.Dummy):
+    def __init__(self, layout):
+        self.obj = layout
+        # cache of multivector types for this layout
+        self._cache = {}
+        layout_name = layout_short_name(layout)
+        if layout_name is not None:
+            name = "LayoutType({})".format(layout_name)
+        else:
+            name = "LayoutType({!r})".format(layout)
+        super().__init__(name)
 
 
 @numba.extending.register_model(LayoutType)
-class LayoutModel(numba.extending.models.StructModel):
-    def __init__(self, dmm, fe_typ):
-        members = [
-            ('obj', opaque_layout),
-        ]
-        super().__init__(dmm, fe_typ, members)
+class LayoutModel(numba.extending.models.OpaqueModel):
+    pass
 
+# The docs say we should use register a function to determine the numba type
+# with `@numba.extending.typeof_impl.register(LayoutType)`, but this is way
+# too slow (https://github.com/numba/numba/issues/5839). Instead, we use the
+# undocumented `_numba_type_` attribute, and use our own cache.
 
-@numba.extending.typeof_impl.register(Layout)
-def _typeof_Layout(val: Layout, c) -> LayoutType:
-    return LayoutType()
+@_cached_property
+def _numba_type_(self):
+    return LayoutType(self)
 
+Layout._numba_type_ = _numba_type_
 
-# Derived from the `Dispatcher` boxing
 
 @lower_constant(LayoutType)
-def lower_constant_dispatcher(context, builder, typ, pyval):
-    layout = cgutils.create_struct_proxy(typ)(context, builder)
-    layout.obj = context.add_dynamic_addr(builder, id(pyval), info=type(pyval).__name__)
-    return layout._getvalue()
+def lower_constant_Layout(context, builder, typ: LayoutType, pyval: Layout) -> llvmlite.ir.Value:
+    return context.get_dummy_value()
 
 
 @numba.extending.unbox(LayoutType)
-def unbox_Layout(typ, obj, context):
-    layout = cgutils.create_struct_proxy(typ)(context.context, context.builder)
-    layout.obj = obj
-    return numba.extending.NativeValue(layout._getvalue())
+def unbox_Layout(typ: LayoutType, obj: Layout, c) -> NativeValue:
+    return NativeValue(c.context.get_dummy_value())
 
+# Derived from the `Dispatcher` boxing
 
 @numba.extending.box(LayoutType)
-def box_Layout(typ, val, context):
-    val = cgutils.create_struct_proxy(typ)(context.context, context.builder, value=val)
-    obj = val.obj
-    context.pyapi.incref(obj)
+def box_Layout(typ: LayoutType, val: llvmlite.ir.Value, c) -> Layout:
+    obj = c.context.add_dynamic_addr(c.builder, id(typ.obj), info=typ.name)
+    c.pyapi.incref(obj)
     return obj
+
+# methods
+
+@numba.extending.overload_method(LayoutType, 'MultiVector')
+def Layout_MultiVector(self, value):
+    def impl(self, value):
+        return MultiVector(self, value)
+    return impl

clifford/numba/_multivector.py

@@ -3,9 +3,9 @@
 
 For now, this just supports .value wrapping / unwrapping
 """
-import numpy as np
 import numba
 from numba.extending import NativeValue
+import llvmlite.ir
 
 try:
     # module locations as of numba 0.49.0
@@ -26,38 +26,43 @@
 
 
 class MultiVectorType(types.Type):
-    def __init__(self, dtype: np.dtype):
-        assert isinstance(dtype, np.dtype)
-        self.dtype = dtype
-        super().__init__(name='MultiVector[{!r}]'.format(numba.from_dtype(dtype)))
+    def __init__(self, layout: LayoutType, dtype: types.DType):
+        self.layout_type = layout
+        self._scalar_type = dtype
+        super().__init__(name='MultiVector({!r}, {!r})'.format(
+            self.layout_type, self._scalar_type
+        ))
 
     @property
     def key(self):
-        return self.dtype
+        return self.layout_type, self._scalar_type
 
     @property
     def value_type(self):
-        return numba.from_dtype(self.dtype)[:]
-
-    @property
-    def layout_type(self):
-        return LayoutType()
+        return self._scalar_type[:]
 
 
 # The docs say we should use register a function to determine the numba type
 # with `@numba.extending.typeof_impl.register(MultiVector)`, but this is way
 # too slow (https://github.com/numba/numba/issues/5839). Instead, we use the
 # undocumented `_numba_type_` attribute, and use our own cache. In future
 # this may need to be a weak cache, but for now the objects are tiny anyway.
-_cache = {}
 
 @property
 def _numba_type_(self):
+    layout_type = self.layout._numba_type_
+
+    cache = layout_type._cache
     dt = self.value.dtype
+
+    # now use the dtype to key that cache.
     try:
-        return _cache[dt]
+        return cache[dt]
     except KeyError:
-        ret = _cache[dt] = MultiVectorType(dtype=dt)
+        # Computing and hashing `dtype_type` is slow, so we do not use it as a
+        # hash key. The raw numpy dtype is much faster to use as a key.
+        dtype_type = _numpy_support.from_dtype(dt)
+        ret = cache[dt] = MultiVectorType(layout_type, dtype_type)
         return ret
 
 MultiVector._numba_type_ = _numba_type_
@@ -77,7 +82,7 @@ def __init__(self, dmm, fe_type):
 def type_MultiVector(context):
     def typer(layout, value):
         if isinstance(layout, LayoutType) and isinstance(value, types.Array):
-            return MultiVectorType(_numpy_support.as_dtype(value.dtype))
+            return MultiVectorType(layout, value.dtype)
     return typer
 
 
@@ -92,15 +97,11 @@ def impl_MultiVector(context, builder, sig, args):
 
 
 @lower_constant(MultiVectorType)
-def lower_constant_MultiVector(context, builder, typ: MultiVectorType, pyval: MultiVector):
-    value = context.get_constant_generic(builder, typ.value_type, pyval.value)
-    layout = context.get_constant_generic(builder, typ.layout_type, pyval.layout)
-    return impl_ret_borrowed(
-        context,
-        builder,
-        typ,
-        cgutils.pack_struct(builder, (layout, value)),
-    )
+def lower_constant_MultiVector(context, builder, typ: MultiVectorType, pyval: MultiVector) -> llvmlite.ir.Value:
+    mv = cgutils.create_struct_proxy(typ)(context, builder)
+    mv.value = context.get_constant_generic(builder, typ.value_type, pyval.value)
+    mv.layout = context.get_constant_generic(builder, typ.layout_type, pyval.layout)
+    return mv._getvalue()
 
 
 @numba.extending.unbox(MultiVectorType)
@@ -117,7 +118,7 @@ def unbox_MultiVector(typ: MultiVectorType, obj: MultiVector, c) -> NativeValue:
 
 
 @numba.extending.box(MultiVectorType)
-def box_MultiVector(typ: MultiVectorType, val: NativeValue, c) -> MultiVector:
+def box_MultiVector(typ: MultiVectorType, val: llvmlite.ir.Value, c) -> MultiVector:
     mv = cgutils.create_struct_proxy(typ)(c.context, c.builder, value=val)
     mv_obj = c.box(typ.value_type, mv.value)
     layout_obj = c.box(typ.layout_type, mv.layout)

clifford/test/test_numba_extensions.py

@@ -48,3 +48,10 @@ def add_e1(a):
             return cf.MultiVector(a.layout, a.value + e1.value)
 
         assert add_e1(e2) == e1 + e2
+
+    def test_multivector_shorthand(self):
+        @numba.njit
+        def double(a):
+            return a.layout.MultiVector(a.value*2)
+
+        assert double(e2) == 2 * e2