Revert "Add spdiags sparse matrix initialization (pytorch#78439)"

This reverts commit cfb2034.

Reverted pytorch#78439 on behalf of https://github.com/suo due to broke windows builds, see: https://hud.pytorch.org/pytorch/pytorch/commit/cfb2034b657e8527767f1f74854bc62b4d6d4927

aten/src/ATen/native/native_functions.yaml

@@ -5281,11 +5281,6 @@
     SparseCPU: log_softmax_backward_sparse_cpu
     SparseCUDA: log_softmax_backward_sparse_cuda
 
-- func: _spdiags(Tensor diagonals, Tensor offsets, int[] shape, Layout? layout=None) -> Tensor
-  python_module: sparse
-  dispatch:
-    CPU: spdiags
-
 - func: norm.ScalarOpt_dtype(Tensor self, Scalar? p, *, ScalarType dtype) -> Tensor
   device_check: NoCheck   # TensorIterator
   variants: function, method

build_variables.bzl

@@ -1155,7 +1155,6 @@ aten_native_source_codegen_list = [
     "aten/src/ATen/native/cpu/scaled_modified_bessel_k0.cpp",
     "aten/src/ATen/native/cpu/scaled_modified_bessel_k1.cpp",
     "aten/src/ATen/native/cpu/spherical_bessel_j0.cpp",
-    "aten/src/ATen/native/cpu/SparseFactories.cpp",
     "aten/src/ATen/native/quantized/cpu/kernels/QuantizedOpKernels.cpp",
 ]
 
@@ -1358,7 +1357,6 @@ aten_native_source_non_codegen_list = [
     "aten/src/ATen/native/sparse/SparseTensorMath.cpp",
     "aten/src/ATen/native/sparse/SparseUnaryOps.cpp",
     "aten/src/ATen/native/sparse/SparseCsrTensorMath.cpp",
-    "aten/src/ATen/native/sparse/SparseFactories.cpp",
     "aten/src/ATen/native/transformers/attention.cpp",
     "aten/src/ATen/native/transformers/transformer.cpp",
     "aten/src/ATen/native/utils/Factory.cpp",

docs/source/sparse.rst

@@ -599,7 +599,6 @@ Torch functions specific to sparse Tensors
     smm
     sparse.softmax
     sparse.log_softmax
-    sparse.spdiags
 
 Other functions
 +++++++++++++++

test/test_sparse.py

@@ -8,7 +8,7 @@
 import unittest
 from torch.testing import make_tensor
 from torch.testing._internal.common_utils import TestCase, run_tests, skipIfRocm, do_test_dtypes, \
-    do_test_empty_full, load_tests, TEST_NUMPY, TEST_SCIPY, IS_WINDOWS, gradcheck, coalescedonoff, \
+    do_test_empty_full, load_tests, TEST_NUMPY, IS_WINDOWS, gradcheck, coalescedonoff, \
     DeterministicGuard, first_sample
 from torch.testing._internal.common_cuda import TEST_CUDA, _get_torch_cuda_version
 from numbers import Number
@@ -26,9 +26,6 @@
     floating_and_complex_types_and, integral_types, floating_types_and,
 )
 
-if TEST_SCIPY:
-    import scipy.sparse
-
 # load_tests from torch.testing._internal.common_utils is used to automatically filter tests for
 # sharding on sandcastle. This line silences flake warnings
 load_tests = load_tests
@@ -3561,94 +3558,6 @@ def test(sparse_dims, nnz, with_size, new_size):
         test(4, 6, [7, 3, 1, 3, 1, 3], [7, 3, 1, 3, 2, 3])
         test(4, 6, [7, 3, 1, 3, 2, 1], [7, 3, 1, 3, 2, 3])
 
-    @unittest.skipIf(not TEST_NUMPY, "NumPy is not availible")
-    @onlyCPU
-    @dtypes(*all_types_and_complex_and(torch.bool))
-    def test_sparse_spdiags(self, device, dtype):
-
-        make_diags = functools.partial(make_tensor, dtype=dtype, device=device)
-        make_offsets = functools.partial(torch.tensor, dtype=torch.long, device=device)
-
-        if TEST_SCIPY:
-            def reference(diags, offsets, shape):
-                return scipy.sparse.spdiags(diags, offsets, *shape).toarray()
-
-        else:
-            def reference(diags, offsets, shape):
-                result = torch.zeros(shape, dtype=dtype, device=device)
-                for i, off in enumerate(offsets):
-                    res_view = result.diagonal(off)
-                    data = diags[i]
-                    if off > 0:
-                        data = data[off:]
-
-                    m = min(res_view.shape[0], data.shape[0])
-                    res_view[:m] = data[:m]
-                return result
-
-        def check_valid(diags, offsets, shape, layout=None):
-            ref_out = reference(diags, offsets, shape)
-            out = torch.sparse.spdiags(diags, offsets, shape, layout=layout)
-            if layout is None:
-                ex_layout = torch.sparse_coo
-            else:
-                ex_layout = layout
-            out_dense = out.to_dense()
-            self.assertTrue(out.layout == ex_layout, f"Output layout {out.layout} expected {ex_layout}")
-            self.assertEqual(out_dense, ref_out, f"Result:\n{out_dense} does not match reference:\n{ref_out}")
-
-        def check_invalid(args, error):
-            with self.assertRaisesRegex(RuntimeError, error):
-                torch.sparse.spdiags(*args)
-
-        def valid_cases():
-            # some normal cases
-            yield (make_diags((1, 5)), make_offsets([0]), (5, 5))
-            yield (make_diags((3, 3)), make_offsets([-1, 0, 1]), (4, 4))
-            # noncontigous diags
-            yield (make_diags((5, 4), noncontiguous=True), make_offsets([-1, 1, 0, 2, -2]), (5, 5))
-            # noncontigous offsets
-            yield (make_diags((3, 4)), make_offsets([1, -1, 0, -2, 2])[::2], (5, 5))
-            # noncontigous diags + offsets
-            yield (make_diags((3, 4), noncontiguous=True), make_offsets([1, -1, 0, -2, 2])[::2], (5, 5))
-            # correct dimensionality, 2d, 2d , and shapes match, but the number of diagonals is zero
-            yield (make_diags((0, 3)), make_offsets([]), (3, 3))
-            # forward rotation of upper diagonals
-            yield (make_diags((3, 8)), make_offsets([1, 2, 3]), (4, 4))
-            # rotation exausts input space to read from
-            yield (make_diags((2, 3)), make_offsets([2, 1]), (3, 3))
-            # Simple cases repeated with special output format
-            yield (make_diags((1, 5)), make_offsets([0]), (5, 5), torch.sparse_csc)
-            yield (make_diags((3, 3)), make_offsets([-1, 0, 1]), (4, 4), torch.sparse_csr)
-            # vector diags
-            yield (make_diags((3, )), make_offsets([1]), (4, 4))
-            # Scalar offset
-            yield (make_diags((1, 3)), make_offsets(2), (4, 4))
-            # offsets out of range
-            yield (make_diags((1, 3)), make_offsets([3]), (3, 3))
-            yield (make_diags((1, 3)), make_offsets([-3]), (3, 3))
-
-        for case in valid_cases():
-            check_valid(*case)
-
-        def invalid_cases():
-            yield (make_diags((1, 3)), make_offsets([0]), (3, 2, 3)), "Output shape must be 2d"
-            yield (make_diags((2, 3)), make_offsets([[1, 2], [0, 3]]), (3, 3)), "Offsets must be scalar or vector"
-            yield (make_diags((3, 2, 3)), make_offsets([0, 1, 2]), (4, 4)), "Diagonals must be vector or matrix"
-            yield (make_diags((3, 3)), make_offsets([-1, 0]), (3, 3)),\
-                r"Number of diagonals \(\d\) does not match the number of offsets \(\d\)"
-            yield (make_diags((5,)), make_offsets([0, 1, 2, 3, 4]), (3, 3)),\
-                r"Number of diagonals \(\d\) does not match the number of offsets \(\d\)"
-            yield (make_diags((2, 2)), make_offsets([-1, 0]), (2, 3), torch.strided),\
-                r"Only output layouts \(\w+, \w+, \w+\) are supported, got \w+"
-            yield (make_diags((2, 5)), make_offsets([0, 0]), (5, 5)), "Offset tensor contains duplicate values"
-            yield (make_diags((1, 5)), make_offsets([0]).to(torch.int32), (5, 5)), r"Offset Tensor must have dtype Long but got \w+"
-
-
-        for case, error_regex in invalid_cases():
-            check_invalid(case, error_regex)
-
-
 
 class TestSparseOneOff(TestCase):
     @unittest.skipIf(not TEST_CUDA, 'CUDA not available')