[Mlir-commits] [mlir] [mlir][sparse] support sparse dilated convolution. (PR #80470)

[Aart Bik]

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@@ -0,0 +1,148 @@
+//--------------------------------------------------------------------------------------------------
+// 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 entry -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 %}
+
+#CCCC = #sparse_tensor.encoding<{
+  map = (d0, d1, d2, d3) -> (d0 : compressed, d1 : compressed, d2 : compressed, d3 : compressed)
+}>
+
+#CDCC = #sparse_tensor.encoding<{
+  map = (d0, d1, d2, d3) -> (d0 : compressed, d1 : dense, d2 : compressed, d3 : compressed)
+}>
+
+// Creates and returns 4-D buffer of size (%s1, %s2, %s3, %s4) filled with the value %f
+func.func @alloc_4d_filled_f32(%s1 : index, %s2 : index, %s3 : index, %s4 : index, %f : f32) -> tensor<?x?x?x?xf32> {
+  %buf = tensor.empty(%s1, %s2, %s3, %s4) : tensor<?x?x?x?xf32>
+  %ret = linalg.fill ins(%f : f32) outs(%buf : tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32>
+  return %ret : tensor<?x?x?x?xf32>
+}
+
+func.func @conv_2d_nhwc_hwcf(%arg0: tensor<?x?x?x?xf32>, %arg1: tensor<?x?x?x?xf32>, %arg2: tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32> {
+  %ret = linalg.conv_2d_nhwc_hwcf {dilations = dense<2> : tensor<2xi64>,
+                                     strides = dense<1> : tensor<2xi64>}
+     ins (%arg0, %arg1: tensor<?x?x?x?xf32>, tensor<?x?x?x?xf32>)
+    outs (%arg2: tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32>
+  return %ret : tensor<?x?x?x?xf32>
+}
+
+func.func @conv_2d_nhwc_hwcf_CCCC(%arg0: tensor<?x?x?x?xf32, #CCCC>, %arg1: tensor<?x?x?x?xf32>, %arg2: tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32> {
+  %ret = linalg.conv_2d_nhwc_hwcf {dilations = dense<2> : tensor<2xi64>,
+                                     strides = dense<1> : tensor<2xi64>}
+     ins (%arg0, %arg1: tensor<?x?x?x?xf32, #CCCC>, tensor<?x?x?x?xf32>)
+    outs (%arg2: tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32>
+  return %ret : tensor<?x?x?x?xf32>
+}
+
+func.func @conv_2d_nhwc_hwcf_CDCC(%arg0: tensor<?x?x?x?xf32, #CDCC>, %arg1: tensor<?x?x?x?xf32>, %arg2: tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32> {
+  %ret = linalg.conv_2d_nhwc_hwcf {dilations = dense<2> : tensor<2xi64>,
+                                     strides = dense<1> : tensor<2xi64>}
+     ins (%arg0, %arg1: tensor<?x?x?x?xf32, #CDCC>, tensor<?x?x?x?xf32>)
+    outs (%arg2: tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32>
+  return %ret : tensor<?x?x?x?xf32>
+}
+
+func.func @conv_2d_nhwc_hwcf_dual_CDCC(%arg0: tensor<?x?x?x?xf32, #CDCC>, %arg1: tensor<?x?x?x?xf32, #CDCC>, %arg2: tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32> {
+  %ret = linalg.conv_2d_nhwc_hwcf {dilations = dense<2> : tensor<2xi64>,
+                                     strides = dense<1> : tensor<2xi64>}
+     ins (%arg0, %arg1: tensor<?x?x?x?xf32, #CDCC>, tensor<?x?x?x?xf32, #CDCC>)
+    outs (%arg2: tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32>
+  return %ret : tensor<?x?x?x?xf32>
+}
+
+
+func.func @entry() {
+  %c0 = arith.constant 0 : index
+  %c1 = arith.constant 1 : index
+  %c3 = arith.constant 3 : index
+  %c5 = arith.constant 5 : index
+  %c6 = arith.constant 6 : index
+  %c7 = arith.constant 7 : index
+  %f10 = arith.constant 10.00000e+00 : f32
+  %val = arith.constant 2.00000e+00 : f32
+  %zero = arith.constant 0.00000e+00 : f32
+
+  %filter2D_nhwc = call @alloc_4d_filled_f32(%c3, %c3, %c3, %c1, %val) :(index, index, index, index, f32) -> (tensor<?x?x?x?xf32>)
+  %in2D_tmp = call @alloc_4d_filled_f32(%c3, %c7, %c7, %c3, %zero) : (index, index, index, index, f32) -> (tensor<?x?x?x?xf32>)
+  %in2D_nhwc = tensor.insert %f10 into %in2D_tmp[%c0, %c1, %c1, %c0] : tensor<?x?x?x?xf32>
+  %out2D_nhwc = call @alloc_4d_filled_f32(%c3, %c3, %c3, %c1, %zero) : (index, index, index, index, f32) -> (tensor<?x?x?x?xf32>)
+
+  %in2D_nhwc_CCCC = sparse_tensor.convert %in2D_nhwc
+    : tensor<?x?x?x?xf32> to tensor<?x?x?x?xf32, #CCCC>
+  %filter2D_nhwc_CDCC = sparse_tensor.convert %filter2D_nhwc
+    : tensor<?x?x?x?xf32> to tensor<?x?x?x?xf32, #CDCC>
+  %in2D_nhwc_CDCC = sparse_tensor.convert %in2D_nhwc
+    : tensor<?x?x?x?xf32> to tensor<?x?x?x?xf32, #CDCC>
+
+  %dense_ret = call @conv_2d_nhwc_hwcf(%in2D_nhwc, %filter2D_nhwc, %out2D_nhwc) : (tensor<?x?x?x?xf32>, tensor<?x?x?x?xf32>, tensor<?x?x?x?xf32>) -> (tensor<?x?x?x?xf32>)
+  %CCCC_ret = call @conv_2d_nhwc_hwcf_CCCC(%in2D_nhwc_CCCC, %filter2D_nhwc, %out2D_nhwc) : (tensor<?x?x?x?xf32, #CCCC>, tensor<?x?x?x?xf32>, tensor<?x?x?x?xf32>) -> (tensor<?x?x?x?xf32>)
+  %CDCC_ret = call @conv_2d_nhwc_hwcf_CDCC(%in2D_nhwc_CDCC, %filter2D_nhwc, %out2D_nhwc) : (tensor<?x?x?x?xf32, #CDCC>, tensor<?x?x?x?xf32>, tensor<?x?x?x?xf32>) -> (tensor<?x?x?x?xf32>)
+
+  %dual_CDCC_ret = call @conv_2d_nhwc_hwcf_dual_CDCC(%in2D_nhwc_CDCC, %filter2D_nhwc_CDCC, %out2D_nhwc)
+    : (tensor<?x?x?x?xf32, #CDCC>, tensor<?x?x?x?xf32, #CDCC>, tensor<?x?x?x?xf32>) -> (tensor<?x?x?x?xf32>)
+
+  // CHECK:      ( ( ( ( 0 ), ( 0 ), ( 0 ) ), ( ( 0 ), ( 20 ), ( 0 ) ), ( ( 0 ), ( 0 ), ( 0 ) ) ),
----------------
aartbik wrote:

can we make the output have at least one other nonzero? or does this stress test enough

https://github.com/llvm/llvm-project/pull/80470