[mlir][sparse][taco] Use np.array_equal to compare integer values.

[bixia]

Summary

Fix MLIR-PyTACO and some tests to use np.array_equal to compare integer
values.

mlir/test/Integration/Dialect/SparseTensor/taco/test_simple_tensor_algebra.py

 A.insert([1, 2], 40)
 B.insert([0, 0], 20)
 B.insert([1, 2], 30)
 C.insert([0, 1], 5)
 C.insert([1, 2], 7)
 D[i, j] = A[i, j] + B[i, j] - C[i, j]
 
 indices, values = D.get_coordinates_and_values()
-passed = np.allclose(indices, [[0, 0], [0, 1], [1, 2]])
+passed = np.array_equal(indices, [[0, 0], [0, 1], [1, 2]])
 passed += np.allclose(values, [20.0, 5.0, 63.0])
 
 # CHECK: Number of passed: 2
 print("Number of passed:", passed)

mlir/test/Integration/Dialect/SparseTensor/taco/tools/mlir_pytaco.py

def unpack(self) -> None:
     if self.is_dense() or self.is_unpacked():
       return
 
     # Use the output MLIR sparse tensor pointer to retrieve the COO-flavored
     # values and verify the values.
     rank, nse, shape, values, indices = utils.sparse_tensor_to_coo_tensor(
         self._packed_sparse_value, self._dtype.value)
     assert rank == self.order
-    assert np.allclose(self.shape, shape)
+    assert np.array_equal(self.shape, shape)
     assert nse == len(values)
     self._coords = indices
     self._values = values
     self._sparse_value_location = _SparseValueInfo._UNPACKED
 
   def __repr__(self) -> str:
     self._sync_value()
     self.unpack()

mlir/test/Integration/Dialect/SparseTensor/taco/unit_test_tensor_io.py

with open(file_name, "w") as file:
       file.write(_get_mtx_data("general"))
     a = mlir_pytaco_io.read(file_name, _FORMAT)
   passed = 0
   # The value of a is stored as an MLIR sparse tensor.
   passed += (not a.is_unpacked())
   a.unpack()
   passed += (a.is_unpacked())
   coords, values = a.get_coordinates_and_values()
-  passed += np.allclose(coords, [[0, 1], [2, 0], [2, 1]])
+  passed += np.array_equal(coords, [[0, 1], [2, 0], [2, 1]])
   passed += np.allclose(values, [2.0, 3.0, 4.0])
   # CHECK: 4
   print(passed)
 
 
 # CHECK-LABEL: test_read_mtx_matrix_symmetry
 @testing_utils.run_test
 def test_read_mtx_matrix_symmetry():
   with tempfile.TemporaryDirectory() as test_dir:
     file_name = os.path.join(test_dir, "data.mtx")
     with open(file_name, "w") as file:
       file.write(_get_mtx_data("symmetric"))
     a = mlir_pytaco_io.read(file_name, _FORMAT)
   passed = 0
   # The value of a is stored as an MLIR sparse tensor.
   passed += (not a.is_unpacked())
   a.unpack()
   passed += (a.is_unpacked())
   coords, values = a.get_coordinates_and_values()
   print(coords)
   print(values)
-  passed += np.allclose(coords,
+  passed += np.array_equal(coords,
                            [[0, 1], [0, 2], [1, 0], [1, 2], [2, 0], [2, 1]])
   passed += np.allclose(values, [2.0, 3.0, 2.0, 4.0, 3.0, 4.0])
   # CHECK: 4
   print(passed)
 
 
 _TNS_DATA = """2 3
 3 2
 3 1 3
with open(file_name, "w") as file:
       file.write(_TNS_DATA)
     a = mlir_pytaco_io.read(file_name, _FORMAT)
   passed = 0
   # The value of a is stored as an MLIR sparse tensor.
   passed += (not a.is_unpacked())
   a.unpack()
   passed += (a.is_unpacked())
   coords, values = a.get_coordinates_and_values()
-  passed += np.allclose(coords, [[0, 1], [2, 0], [2, 1]])
+  passed += np.array_equal(coords, [[0, 1], [2, 0], [2, 1]])
   passed += np.allclose(values, [2.0, 3.0, 4.0])
   # CHECK: 4
   print(passed)
 
 
 # CHECK-LABEL: test_write_unpacked_tns
 @testing_utils.run_test
 def test_write_unpacked_tns():

mlir/test/Integration/Dialect/SparseTensor/taco/unit_test_tensor_utils.py

with tempfile.TemporaryDirectory() as test_dir:
 
     # Read the data from the file and construct an MLIR sparse tensor.
     sparse_tensor, o_shape = pytaco_utils.create_sparse_tensor(
         file_name, sparsity_codes, "f64")
 
   passed = 0
 
   # Verify the output shape for the tensor.
-  if np.allclose(o_shape, t.shape):
+  if np.array_equal(o_shape, t.shape):
     passed += 1
 
   # Use the output MLIR sparse tensor pointer to retrieve the COO-flavored
   # values and verify the values.
   o_rank, o_nse, o_shape, o_values, o_indices = (
       pytaco_utils.sparse_tensor_to_coo_tensor(sparse_tensor, np.float64))
-  if o_rank == t.rank and o_nse == t.nse and np.allclose(
-      o_shape, t.shape) and np.allclose(o_values, t.values) and np.allclose(
+  if o_rank == t.rank and o_nse == t.nse and np.array_equal(
+      o_shape, t.shape) and np.allclose(o_values, t.values) and np.array_equal(
           o_indices, t.indices):
     passed += 1
 
   return passed
 
 
 # A 2D sparse tensor data in COO-flavored format.
 _rank = 2