From 9cd41289989f07d08f14d8a67ccc2d6445cc7d43 Mon Sep 17 00:00:00 2001
From: Aart Bik <39774503+aartbik@users.noreply.github.com>
Date: Thu, 18 Jan 2024 07:52:42 -0800
Subject: [PATCH] [mlir][sparse] add a 3-d block and fiber test (#78529)

---
 .../SparseTensor/CPU/sparse_block3d.mlir      | 122 ++++++++++++++++++
 1 file changed, 122 insertions(+)
 create mode 100755 mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_block3d.mlir

diff --git a/mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_block3d.mlir b/mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_block3d.mlir
new file mode 100755
index 000000000000..df12a6e042dd
--- /dev/null
+++ b/mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_block3d.mlir
@@ -0,0 +1,122 @@
+//--------------------------------------------------------------------------------------------------
+// WHEN CREATING A NEW TEST, PLEASE JUST COPY & PASTE WITHOUT EDITS.
+//
+// Set-up that's shared across all tests in this directory. In principle, this
+// config could be moved to lit.local.cfg. However, there are downstream users that
+//  do not use these LIT config files. Hence why this is kept inline.
+//
+// DEFINE: %{sparsifier_opts} = enable-runtime-library=true
+// DEFINE: %{sparsifier_opts_sve} = enable-arm-sve=true %{sparsifier_opts}
+// DEFINE: %{compile} = mlir-opt %s --sparsifier="%{sparsifier_opts}"
+// DEFINE: %{compile_sve} = mlir-opt %s --sparsifier="%{sparsifier_opts_sve}"
+// DEFINE: %{run_libs} = -shared-libs=%mlir_c_runner_utils,%mlir_runner_utils
+// DEFINE: %{run_opts} = -e main -entry-point-result=void
+// DEFINE: %{run} = mlir-cpu-runner %{run_opts} %{run_libs}
+// DEFINE: %{run_sve} = %mcr_aarch64_cmd --march=aarch64 --mattr="+sve" %{run_opts} %{run_libs}
+//
+// DEFINE: %{env} =
+//--------------------------------------------------------------------------------------------------
+
+// RUN: %{compile} | %{run} | FileCheck %s
+//
+// Do the same run, but now with direct IR generation.
+// REDEFINE: %{sparsifier_opts} = enable-runtime-library=false enable-buffer-initialization=true
+// RUN: %{compile} | %{run} | FileCheck %s
+//
+// Do the same run, but now with direct IR generation and vectorization.
+// REDEFINE: %{sparsifier_opts} = enable-runtime-library=false enable-buffer-initialization=true vl=2 reassociate-fp-reductions=true enable-index-optimizations=true
+// RUN: %{compile} | %{run} | FileCheck %s
+//
+// Do the same run, but now with direct IR generation and VLA vectorization.
+// RUN: %if mlir_arm_sve_tests %{ %{compile_sve} | %{run_sve} | FileCheck %s %}
+
+#Sparse1 = #sparse_tensor.encoding<{
+  map = (i, j, k) -> (
+    i : compressed,
+    j : compressed,
+    k : compressed
+  )
+}>
+
+#Sparse2 = #sparse_tensor.encoding<{
+  map = (i, j, k) -> (
+    i floordiv 2 : compressed,
+    j floordiv 2 : compressed,
+    k floordiv 2 : compressed,
+    i mod 2 : dense,
+    j mod 2 : dense,
+    k mod 2 : dense)
+}>
+
+module {
+
+  //
+  // Main driver that tests sparse tensor storage.
+  //
+  func.func @main() {
+    %c0 = arith.constant 0 : index
+    %i0 = arith.constant 0 : i32
+
+    // Setup input dense tensor and convert to two sparse tensors.
+    %d = arith.constant dense <[
+       [ // i=0
+         [ 1, 0, 0, 0 ],
+         [ 0, 0, 0, 0 ],
+         [ 0, 0, 0, 0 ],
+         [ 0, 0, 5, 0 ] ],
+       [ // i=1
+         [ 2, 0, 0, 0 ],
+         [ 0, 0, 0, 0 ],
+         [ 0, 0, 0, 0 ],
+         [ 0, 0, 6, 0 ] ],
+       [ //i=2
+         [ 3, 0, 0, 0 ],
+         [ 0, 0, 0, 0 ],
+         [ 0, 0, 0, 0 ],
+         [ 0, 0, 7, 0 ] ],
+	 //i=3
+       [ [ 4, 0, 0, 0 ],
+         [ 0, 0, 0, 0 ],
+         [ 0, 0, 0, 0 ],
+         [ 0, 0, 8, 0 ] ]
+    ]> : tensor<4x4x4xi32>
+
+    %a = sparse_tensor.convert %d : tensor<4x4x4xi32> to tensor<4x4x4xi32, #Sparse1>
+    %b = sparse_tensor.convert %d : tensor<4x4x4xi32> to tensor<4x4x4xi32, #Sparse2>
+
+    //
+    // If we store the two "fibers" [1,2,3,4] starting at index (0,0,0) and
+    // ending at index (3,0,0) and [5,6,7,8] starting at index (0,3,2) and
+    // ending at index (3,3,2)) with a “DCSR-flavored” along (j,k) with
+    // dense “fibers” in the i-dim, we end up with 8 stored entries.
+    //
+    // CHECK: 8
+    // CHECK-NEXT: ( 1, 5, 2, 6, 3, 7, 4, 8 )
+    //
+    %na = sparse_tensor.number_of_entries %a : tensor<4x4x4xi32, #Sparse1>
+    vector.print %na : index
+    %ma = sparse_tensor.values %a: tensor<4x4x4xi32, #Sparse1> to memref<?xi32>
+    %va = vector.transfer_read %ma[%c0], %i0: memref<?xi32>, vector<8xi32>
+    vector.print %va : vector<8xi32>
+
+    //
+    // If we store full 2x2x2 3-D blocks in the original index order
+    // in a compressed fashion, we end up with 4 blocks to incorporate
+    // all the nonzeros, and thus 32 stored entries.
+    //
+    // CHECK: 32
+    // CHECK-NEXT: ( 1, 0, 0, 0, 2, 0, 0, 0, 0, 0, 5, 0, 0, 0, 6, 0, 3, 0, 0, 0, 4, 0, 0, 0, 0, 0, 7, 0, 0, 0, 8, 0 )
+    //
+    %nb = sparse_tensor.number_of_entries %b : tensor<4x4x4xi32, #Sparse2>
+    vector.print %nb : index
+    %mb = sparse_tensor.values %b: tensor<4x4x4xi32, #Sparse2> to memref<?xi32>
+    %vb = vector.transfer_read %mb[%c0], %i0: memref<?xi32>, vector<32xi32>
+    vector.print %vb : vector<32xi32>
+
+    // Release the resources.
+    bufferization.dealloc_tensor %a : tensor<4x4x4xi32, #Sparse1>
+    bufferization.dealloc_tensor %b : tensor<4x4x4xi32, #Sparse2>
+
+    return
+  }
+}