Add Array.asformat method and add back reshape function.

pixi.toml

@@ -27,7 +27,7 @@ mkdocs-jupyter = "*"
 
 [feature.tests.tasks]
 test = "pytest --pyargs sparse -n auto"
-test-mlir = { cmd = "pytest --pyargs sparse/mlir_backend -v" }
+test-mlir = { cmd = "pytest --pyargs sparse.mlir_backend -v" }
 test-finch = { cmd = "pytest --pyargs sparse/tests -n auto -v", depends-on = ["precompile"] }
 
 [feature.tests.dependencies]
@@ -55,17 +55,20 @@ finch-tensor = ">=0.1.31"
 SPARSE_BACKEND = "Finch"
 
 [feature.finch.target.osx-arm64.activation.env]
+SPARSE_BACKEND = "Finch"
 PYTHONFAULTHANDLER = "${HOME}/faulthandler.log"
 
 [feature.mlir.dependencies]
 scipy = ">=0.19"
-mlir-python-bindings = "19.*"
+
+[feature.mlir.target.osx-arm64.pypi-dependencies]
+finch-mlir = ">=0.0.2"
 
 [feature.mlir.activation.env]
 SPARSE_BACKEND = "MLIR"
 
 [environments]
 tests = ["tests", "extras"]
 docs = ["docs", "extras"]
-mlir-dev = ["tests", "mlir"]
-finch-dev = ["tests", "finch"]
+mlir-dev = {features = ["tests", "mlir"], no-default-feature = true}
+finch-dev = {features = ["tests", "finch"], no-default-feature = true}

sparse/mlir_backend/__init__.py

@@ -27,7 +27,7 @@
     uint32,
     uint64,
 )
-from ._ops import add
+from ._ops import add, reshape
 
 __all__ = [
     "add",
@@ -36,6 +36,7 @@
     "to_numpy",
     "to_scipy",
     "levels",
+    "reshape",
     "from_constituent_arrays",
     "int8",
     "int16",

sparse/mlir_backend/_array.py

@@ -41,5 +41,10 @@ def copy(self) -> "Array":
         arrs = tuple(arr.copy() for arr in self.get_constituent_arrays())
         return from_constituent_arrays(format=self.format, arrays=arrs, shape=self.shape)
 
+    def asformat(self, format: StorageFormat) -> "Array":
+        from ._ops import asformat
+
+        return asformat(self, format=format)
+
     def get_constituent_arrays(self) -> tuple[np.ndarray, ...]:
         return self._storage.get_constituent_arrays()

sparse/mlir_backend/_common.py

@@ -1,6 +1,7 @@
 import ctypes
 import functools
 import weakref
+from collections.abc import Iterable
 
 import mlir_finch.runtime as rt
 
@@ -52,3 +53,13 @@
         ctypes.pythonapi.Py_DecRef(ptr)
 
     weakref.finalize(owner, finalizer, ptr)
+
+
+def as_shape(x) -> tuple[int]:
+    if not isinstance(x, Iterable):
+        x = (x,)
+
+    if not all(isinstance(xi, int) for xi in x):
+        raise TypeError("Shape must be an `int` or tuple of `int`s.")
+
+    return tuple(int(xi) for xi in x)

sparse/mlir_backend/_core.py

@@ -28,6 +28,12 @@
 libc.free.argtypes = [ctypes.c_void_p]
 libc.free.restype = None
 
+SHARED_LIBS = []
+if DEBUG:
+    SHARED_LIBS.append(MLIR_C_RUNNER_UTILS)
+
+OPT_LEVEL = 0 if DEBUG else 2
+
 # TODO: remove global state
 ctx = Context()
 

sparse/mlir_backend/_dtypes.py

@@ -76,10 +76,10 @@ def np_dtype(self) -> np.dtype:
         return np.dtype(getattr(np, f"uint{self.bit_width}"))
 
 
-int8 = UnsignedIntegerDType(bit_width=8)
-int16 = UnsignedIntegerDType(bit_width=16)
-int32 = UnsignedIntegerDType(bit_width=32)
-int64 = UnsignedIntegerDType(bit_width=64)
+uint8 = UnsignedIntegerDType(bit_width=8)
+uint16 = UnsignedIntegerDType(bit_width=16)
+uint32 = UnsignedIntegerDType(bit_width=32)
+uint64 = UnsignedIntegerDType(bit_width=64)
 
 
 @dataclasses.dataclass(eq=True, frozen=True, kw_only=True)
@@ -89,10 +89,10 @@ def np_dtype(self) -> np.dtype:
         return np.dtype(getattr(np, f"int{self.bit_width}"))
 
 
-uint8 = SignedIntegerDType(bit_width=8)
-uint16 = SignedIntegerDType(bit_width=16)
-uint32 = SignedIntegerDType(bit_width=32)
-uint64 = SignedIntegerDType(bit_width=64)
+int8 = SignedIntegerDType(bit_width=8)
+int16 = SignedIntegerDType(bit_width=16)
+int32 = SignedIntegerDType(bit_width=32)
+int64 = SignedIntegerDType(bit_width=64)
 
 
 intp: SignedIntegerDType = locals()[f"int{_PTR_WIDTH}"]

sparse/mlir_backend/_ops.py

@@ -1,14 +1,18 @@
 import ctypes
+import math
 
 import mlir_finch.execution_engine
 import mlir_finch.passmanager
 from mlir_finch import ir
 from mlir_finch.dialects import arith, complex, func, linalg, sparse_tensor, tensor
 
+import numpy as np
+
 from ._array import Array
-from ._common import fn_cache
-from ._core import CWD, DEBUG, SHARED_LIBS, ctx, pm
+from ._common import as_shape, fn_cache
+from ._core import CWD, DEBUG, OPT_LEVEL, SHARED_LIBS, ctx, pm
 from ._dtypes import DType, IeeeComplexFloatingDType, IeeeRealFloatingDType, IntegerDType
+from .levels import StorageFormat, _determine_format
 
 
 @fn_cache
@@ -17,7 +21,6 @@
     b_tensor_type: ir.RankedTensorType,
     out_tensor_type: ir.RankedTensorType,
     dtype: DType,
-    rank: int,
 ) -> ir.Module:
     with ir.Location.unknown(ctx):
         module = ir.Module.create()
@@ -31,7 +34,7 @@
             raise RuntimeError(f"Can not add {dtype=}.")
 
         dtype = dtype._get_mlir_type()
-        ordering = ir.AffineMap.get_permutation(range(rank))
+        max_rank = out_tensor_type.rank
 
         with ir.InsertionPoint(module.body):
 
@@ -42,8 +45,13 @@
                     [out_tensor_type],
                     [a, b],
                     [out],
-                    ir.ArrayAttr.get([ir.AffineMapAttr.get(p) for p in (ordering,) * 3]),
-                    ir.ArrayAttr.get([ir.Attribute.parse("#linalg.iterator_type<parallel>")] * rank),
+                    ir.ArrayAttr.get(
+                        [
+                            ir.AffineMapAttr.get(ir.AffineMap.get_minor_identity(max_rank, t.rank))
+                            for t in (a_tensor_type, b_tensor_type, out_tensor_type)
+                        ]
+                    ),
+                    ir.ArrayAttr.get([ir.Attribute.parse("#linalg.iterator_type<parallel>")] * max_rank),
                 )
                 block = generic_op.regions[0].blocks.append(dtype, dtype, dtype)
                 with ir.InsertionPoint(block):
@@ -72,7 +80,7 @@
         if DEBUG:
             (CWD / "add_module_opt.mlir").write_text(str(module))
 
-    return mlir_finch.execution_engine.ExecutionEngine(module, opt_level=2, shared_libs=SHARED_LIBS)
+    return mlir_finch.execution_engine.ExecutionEngine(module, opt_level=OPT_LEVEL, shared_libs=SHARED_LIBS)
 
 
 @fn_cache
@@ -97,7 +105,7 @@
             if DEBUG:
                 (CWD / "reshape_module_opt.mlir").write_text(str(module))
 
-    return mlir_finch.execution_engine.ExecutionEngine(module, opt_level=2, shared_libs=SHARED_LIBS)
+    return mlir_finch.execution_engine.ExecutionEngine(module, opt_level=OPT_LEVEL, shared_libs=SHARED_LIBS)
 
 
 @fn_cache
@@ -125,26 +133,94 @@
             if DEBUG:
                 (CWD / "broadcast_to_module_opt.mlir").write_text(str(module))
 
-    return mlir_finch.execution_engine.ExecutionEngine(module, opt_level=2, shared_libs=SHARED_LIBS)
+    return mlir_finch.execution_engine.ExecutionEngine(module, opt_level=OPT_LEVEL, shared_libs=SHARED_LIBS)
+
+
+@fn_cache
+def get_convert_module(
+    in_tensor_type: ir.RankedTensorType,
+    out_tensor_type: ir.RankedTensorType,
+):
+    with ir.Location.unknown(ctx):
+        module = ir.Module.create()
+
+        with ir.InsertionPoint(module.body):
 
+            @func.FuncOp.from_py_func(in_tensor_type)
+            def convert(in_tensor):
+                return sparse_tensor.convert(out_tensor_type, in_tensor)
 
-def add(x1: Array, x2: Array) -> Array:
-    ret_storage_format = x1.format
+            convert.func_op.attributes["llvm.emit_c_interface"] = ir.UnitAttr.get()
+            if DEBUG:
+                (CWD / "convert_module.mlir").write_text(str(module))
+            pm.run(module.operation)
+            if DEBUG:
+                (CWD / "convert_module.mlir").write_text(str(module))
+
+    return mlir_finch.execution_engine.ExecutionEngine(module, opt_level=OPT_LEVEL, shared_libs=SHARED_LIBS)
+
+
+def add(x1: Array, x2: Array, /) -> Array:
+    # TODO: Determine output format via autoscheduler
+    ret_storage_format = _determine_format(x1.format, x2.format, dtype=x1.dtype, union=True)
     ret_storage = ret_storage_format._get_ctypes_type(owns_memory=True)()
-    out_tensor_type = ret_storage_format._get_mlir_type(shape=x1.shape)
+    out_tensor_type = ret_storage_format._get_mlir_type(shape=np.broadcast_shapes(x1.shape, x2.shape))
 
-    # TODO: Decide what will be the output tensor_type
     add_module = get_add_module(
         x1._get_mlir_type(),
         x2._get_mlir_type(),
         out_tensor_type=out_tensor_type,
         dtype=x1.dtype,
-        rank=x1.ndim,
     )
     add_module.invoke(
         "add",
         ctypes.pointer(ctypes.pointer(ret_storage)),
         *x1._to_module_arg(),
         *x2._to_module_arg(),
     )
-    return Array(storage=ret_storage, shape=out_tensor_type.shape)
+    return Array(storage=ret_storage, shape=tuple(out_tensor_type.shape))
+
+
+def asformat(x: Array, /, format: StorageFormat) -> Array:
+    if x.format == format:
+        return x
+
+    out_tensor_type = format._get_mlir_type(shape=x.shape)
+    ret_storage = format._get_ctypes_type(owns_memory=True)()
+
+    convert_module = get_convert_module(
+        x._get_mlir_type(),
+        out_tensor_type,
+    )
+
+    convert_module.invoke(
+        "convert",
+        ctypes.pointer(ctypes.pointer(ret_storage)),
+        *x._to_module_arg(),
+    )
+
+    return Array(storage=ret_storage, shape=x.shape)
+
+
+def reshape(x: Array, /, shape: tuple[int, ...]) -> Array:
+    from ._conversions import _from_numpy
+
+    shape = as_shape(shape)
+    if math.prod(x.shape) != math.prod(shape):
+        raise ValueError(f"`math.prod(x.shape) != math.prod(shape)`, {x.shape=}, {shape=}")
+
+    ret_storage_format = _determine_format(x.format, dtype=x.dtype, union=len(shape) > x.ndim, out_ndim=len(shape))
+    shape_array = _from_numpy(np.asarray(shape, dtype=np.uint64))
+    out_tensor_type = ret_storage_format._get_mlir_type(shape=shape)
+    ret_storage = ret_storage_format._get_ctypes_type(owns_memory=True)()
+
+    reshape_module = get_reshape_module(x._get_mlir_type(), shape_array._get_mlir_type(), out_tensor_type)
+
+    reshape_module.invoke(
+        "reshape",
+        ctypes.pointer(ctypes.pointer(ret_storage)),
+        *x._to_module_arg(),
+        *shape_array._to_module_arg(),
+    )
+
+    return Array(storage=ret_storage, shape=shape)