[mlir][sparse][pytaco] add SDDMM test with two different ways of defining kernel

[aartbik]

typo

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

+# RUN: SUPPORTLIB=%mlir_runner_utils_dir/libmlir_c_runner_utils%shlibext %PYTHON %s | FileCheck %s
+
+import filecmp
+import numpy as np
+import os
+import sys
+import tempfile
+
+_SCRIPT_PATH = os.path.dirname(os.path.abspath(__file__))
+sys.path.append(_SCRIPT_PATH)
+
+from tools import mlir_pytaco_api as pt
+from tools import testing_utils as utils
+
+i, j, k = pt.get_index_vars(3)
+
+# Set up dense matrices.
+A = pt.from_array(np.full((8, 8), 2.0))
+B = pt.from_array(np.full((8, 8), 3.0))
+
+# Set up sparse matrices.
+S = pt.tensor([8, 8], pt.format([pt.compressed, pt.compressed]))
+X = pt.tensor([8, 8], pt.format([pt.compressed, pt.compressed]))
+Y = pt.tensor([8, 8], pt.compressed)  # alternative syntax works too
+
+S.insert([0, 7], 42.0)
+
+# Define the SDDMM kernel. Since this performs the reduction as
+#   sum(k, S[i, j] * A[i, k] * B[k, j])
+# we only compute the intermediate dense matrix product that are actually
+# needed to compute the result, with proper asymptotic complexity.
+X[i, j] = S[i, j] * A[i, k] * B[k, j]
+
+# Alternative way to define SDDMM kernel. Since this performs the reduction as
+#   sum(k, A[i, k] * B[k, j]) * S[i, j]
+# the MLIR lowering results in two separate tensor index expressions that
+# need to be fused properly to guarantee proper asymptotic complexity.
+Y[i, j] = A[i, k] * B[k, j] * S[i, j]
+
+expected = """; extended FROSTT format
+2 1
+8 8
+1 8 2016
+"""
+
+# Force evaluation of the kernels by writing out X and Y.
+with tempfile.TemporaryDirectory() as test_dir:
+  x_file = os.path.join(test_dir, "X.tns")
+  y_file = os.path.join(test_dir, "Y.tns")
+  pt.write(x_file, X)
+  pt.write(y_file, Y)
+  #
+  # CHECK: Compare result True True
+  #
+  x_data = utils.file_as_string(x_file)
+  y_data = utils.file_as_string(y_file)
+  print(f"Compare result {x_data == expected} {y_data == expected}")

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

with open(expected, "r") as expected_f:
       # Compare the two lines of meta data
       if (actual_f.readline() != expected_f.readline() or
           actual_f.readline() != expected_f.readline()):
         return FALSE
 
   actual_data = np.loadtxt(actual, np.float64, skiprows=3)
   expected_data = np.loadtxt(expected, np.float64, skiprows=3)
   return np.allclose(actual_data, expected_data, rtol=rtol)
+
+
+def file_as_string(file: str) -> str:
+  """Returns contents of file as string."""
+  with open(file, "r") as f:
+    return f.read()