Merge pull request #776 from pydata/reshape-func

ENH: Implement `reshape` function

sparse/mlir_backend/__init__.py

@@ -15,10 +15,12 @@
 )
 from ._ops import (
     add,
+    reshape,
 )
 
 __all__ = [
     "add",
     "asarray",
     "asdtype",
+    "reshape",
 ]

sparse/mlir_backend/_constructors.py

@@ -239,7 +239,7 @@ def from_sps(cls, arr: np.ndarray) -> "Dense":
 
             return dense_instance
 
-        def to_sps(self, shape: tuple[int, ...]) -> sps.csr_array:
+        def to_sps(self, shape: tuple[int, ...]) -> np.ndarray:
             data = ranked_memref_to_numpy(self.data)
             return data.reshape(shape)
 

sparse/mlir_backend/_ops.py

@@ -5,10 +5,12 @@
 from mlir import ir
 from mlir.dialects import arith, func, linalg, sparse_tensor, tensor
 
+import numpy as np
+
 from ._common import fn_cache
-from ._constructors import Tensor
+from ._constructors import Tensor, numpy_to_ranked_memref
 from ._core import CWD, DEBUG, MLIR_C_RUNNER_UTILS, ctx, pm
-from ._dtypes import DType, FloatingDType
+from ._dtypes import DType, FloatingDType, Index
 
 
 @fn_cache
@@ -68,11 +70,35 @@ def add(a, b):
     return mlir.execution_engine.ExecutionEngine(module, opt_level=2, shared_libs=[MLIR_C_RUNNER_UTILS])
 
 
+@fn_cache
+def get_reshape_module(
+    a_tensor_type: ir.RankedTensorType,
+    shape_tensor_type: ir.RankedTensorType,
+    out_tensor_type: ir.RankedTensorType,
+) -> ir.Module:
+    with ir.Location.unknown(ctx):
+        module = ir.Module.create()
+
+        with ir.InsertionPoint(module.body):
+
+            @func.FuncOp.from_py_func(a_tensor_type, shape_tensor_type)
+            def reshape(a, shape):
+                return tensor.reshape(out_tensor_type, a, shape)
+
+            reshape.func_op.attributes["llvm.emit_c_interface"] = ir.UnitAttr.get()
+            if DEBUG:
+                (CWD / "reshape_module.mlir").write_text(str(module))
+            pm.run(module.operation)
+            if DEBUG:
+                (CWD / "reshape_module_opt.mlir").write_text(str(module))
+
+    return mlir.execution_engine.ExecutionEngine(module, opt_level=2, shared_libs=[MLIR_C_RUNNER_UTILS])
+
+
 def add(x1: Tensor, x2: Tensor) -> Tensor:
     ret_obj = x1._format_class()
     out_tensor_type = x1._obj.get_tensor_definition(x1.shape)
 
-    # TODO: Add proper caching
     # TODO: Decide what will be the output tensor_type
     add_module = get_add_module(
         x1._obj.get_tensor_definition(x1.shape),
@@ -88,3 +114,24 @@ def add(x1: Tensor, x2: Tensor) -> Tensor:
         *x2._obj.to_module_arg(),
     )
     return Tensor(ret_obj, shape=out_tensor_type.shape)
+
+
+def reshape(x: Tensor, /, shape: tuple[int, ...]) -> Tensor:
+    ret_obj = x._format_class()
+    x_tensor_type = x._obj.get_tensor_definition(x.shape)
+    out_tensor_type = x._obj.get_tensor_definition(shape)
+
+    with ir.Location.unknown(ctx):
+        shape_tensor_type = ir.RankedTensorType.get([len(shape)], Index.get_mlir_type())
+
+    reshape_module = get_reshape_module(x_tensor_type, shape_tensor_type, out_tensor_type)
+
+    shape = np.array(shape)
+    reshape_module.invoke(
+        "reshape",
+        ctypes.pointer(ctypes.pointer(ret_obj)),
+        *x._obj.to_module_arg(),
+        ctypes.pointer(ctypes.pointer(numpy_to_ranked_memref(shape))),
+    )
+
+    return Tensor(ret_obj, shape=out_tensor_type.shape)

sparse/mlir_backend/tests/test_simple.py

@@ -75,6 +75,15 @@ def sampler_real_floating(size: tuple[int, ...]):
     raise NotImplementedError(f"{dtype=} not yet supported.")
 
 
+def get_exampe_csf_arrays(dtype: np.dtype) -> tuple:
+    pos_1 = np.array([0, 1, 3], dtype=np.int64)
+    crd_1 = np.array([1, 0, 1], dtype=np.int64)
+    pos_2 = np.array([0, 3, 5, 7], dtype=np.int64)
+    crd_2 = np.array([0, 1, 3, 0, 3, 0, 1], dtype=np.int64)
+    data = np.array([1, 2, 3, 4, 5, 6, 7], dtype=dtype)
+    return pos_1, crd_1, pos_2, crd_2, data
+
+
 @parametrize_dtypes
 @pytest.mark.parametrize("shape", [(100,), (10, 200), (5, 10, 20)])
 def test_dense_format(dtype, shape):
@@ -176,11 +185,7 @@ def test_add(rng, dtype):
 @parametrize_dtypes
 def test_csf_format(dtype):
     SHAPE = (2, 2, 4)
-    pos_1 = np.array([0, 1, 3], dtype=np.int64)
-    crd_1 = np.array([1, 0, 1], dtype=np.int64)
-    pos_2 = np.array([0, 3, 5, 7], dtype=np.int64)
-    crd_2 = np.array([0, 1, 3, 0, 3, 0, 1], dtype=np.int64)
-    data = np.array([1, 2, 3, 4, 5, 6, 7], dtype=dtype)
+    pos_1, crd_1, pos_2, crd_2, data = get_exampe_csf_arrays(dtype)
     csf = [pos_1, crd_1, pos_2, crd_2, data]
 
     csf_tensor = sparse.asarray(csf, shape=SHAPE, dtype=sparse.asdtype(dtype), format="csf")
@@ -192,3 +197,70 @@ def test_csf_format(dtype):
     csf_2 = [pos_1, crd_1, pos_2, crd_2, data * 2]
     for actual, expected in zip(res_tensor, csf_2, strict=False):
         np.testing.assert_array_equal(actual, expected)
+
+
+@parametrize_dtypes
+def test_reshape(rng, dtype):
+    DENSITY = 0.5
+    sampler = generate_sampler(dtype, rng)
+
+    # CSR, CSC, COO
+    for shape, new_shape in [((100, 50), (25, 200)), ((80, 1), (8, 10))]:
+        for format in ["csr", "csc", "coo"]:
+            if format == "coo":
+                # NOTE: Blocked by https://github.com/llvm/llvm-project/pull/109135
+                continue
+            if format == "csc":
+                # NOTE: Blocked by https://github.com/llvm/llvm-project/issues/109641
+                continue
+
+            arr = sps.random_array(
+                shape, density=DENSITY, format=format, dtype=dtype, random_state=rng, data_sampler=sampler
+            )
+            if format == "coo":
+                arr.sum_duplicates()
+
+            tensor = sparse.asarray(arr)
+
+            actual = sparse.reshape(tensor, shape=new_shape).to_scipy_sparse()
+            expected = arr.todense().reshape(new_shape)
+
+            np.testing.assert_array_equal(actual.todense(), expected)
+
+    # CSF
+    csf_shape = (2, 2, 4)
+    for shape, new_shape, expected_arrs in [
+        (
+            csf_shape,
+            (4, 4, 1),
+            [
+                np.array([0, 0, 3, 5, 7]),
+                np.array([0, 1, 3, 0, 3, 0, 1]),
+                np.array([0, 1, 2, 3, 4, 5, 6, 7]),
+                np.array([0, 0, 0, 0, 0, 0, 0]),
+                np.array([1, 2, 3, 4, 5, 6, 7]),
+            ],
+        ),
+        (
+            csf_shape,
+            (2, 1, 8),
+            [
+                np.array([0, 1, 2]),
+                np.array([0, 0]),
+                np.array([0, 3, 7]),
+                np.array([4, 5, 7, 0, 3, 4, 5]),
+                np.array([1, 2, 3, 4, 5, 6, 7]),
+            ],
+        ),
+    ]:
+        csf = get_exampe_csf_arrays(dtype)
+        csf_tensor = sparse.asarray(csf, shape=shape, dtype=sparse.asdtype(dtype), format="csf")
+
+        result = sparse.reshape(csf_tensor, shape=new_shape).to_scipy_sparse()
+
+        for actual, expected in zip(result, expected_arrs, strict=False):
+            np.testing.assert_array_equal(actual, expected)
+
+    # DENSE
+    # NOTE: dense reshape is probably broken in MLIR
+    # dense = np.arange(math.prod(SHAPE), dtype=dtype).reshape(SHAPE)