Correctly specify size of sparse_csr tensors in maskedtensor binary ops (#134335)

Pull Request resolved: https://github.com/pytorch/pytorch/pull/134335
Approved by: https://github.com/amjames, https://github.com/cpuhrsch

test/test_maskedtensor.py

@@ -1,9 +1,7 @@
 # Owner(s): ["module: masked operators"]
 
 import torch
-import unittest
 from torch.testing._internal.common_utils import (
-    decorateIf,
     TestCase,
     run_tests,
     make_tensor,
@@ -957,37 +955,6 @@ class TestOperators(TestCase):
 
     @ops(mt_binary_ufuncs, allowed_dtypes=MASKEDTENSOR_FLOAT_TYPES)  # type: ignore[arg-type]
     @parametrize("layout", [torch.strided, torch.sparse_coo, torch.sparse_csr])
-    # FIXME:
-    # Result is just wrong; production logic should be fixed
-    @decorateIf(
-        unittest.expectedFailure,
-        lambda params: (
-            params["op"].name == "add" and
-            params["dtype"] in [torch.float16, torch.float32] and
-            params["device"] == "cpu" and
-            params["layout"] == torch.sparse_csr
-        )
-    )
-    # Result is just wrong; production logic should be fixed
-    @decorateIf(
-        unittest.expectedFailure,
-        lambda params: (
-            params["op"].name == "sub" and
-            params["dtype"] in [torch.float16, torch.float32] and
-            params["device"] == "cpu" and
-            params["layout"] == torch.sparse_csr
-        )
-    )
-    # Result is just wrong; production logic should be fixed
-    @decorateIf(
-        unittest.expectedFailure,
-        lambda params: (
-            params["op"].name == "eq" and
-            params["dtype"] == torch.float64 and
-            params["device"] == "cpu" and
-            params["layout"] == torch.sparse_csr
-        )
-    )
     def test_binary_core(self, device, dtype, op, layout):
         self._test_unary_binary_equality(device, dtype, op, layout)
 

torch/masked/maskedtensor/binary.py

@@ -139,9 +139,10 @@ def _binary_helper(fn, args, kwargs, inplace):
 
         crow = data_args[0].crow_indices()
         col = data_args[0].col_indices()
+        size = data_args[0].size()
         data_args[0] = data_args[0].values()
         v = fn(*data_args)
-        result_data = torch.sparse_csr_tensor(crow, col, v)
+        result_data = torch.sparse_csr_tensor(crow, col, v, size)
 
     else:
         result_data = fn(*data_args)