[NFC][Encoding] Outline encodings in lit tests (iree-org#18165)

Outline all the `#iree_encoding.encoding` attributes in lit tests with encodings. This makes the tests much easier to read and update. Signed-off-by: Max Dawkins <[email protected]>
nod-ai · Aug 9, 2024 · 50f18f1 · 50f18f1
1 parent df3d588
commit 50f18f1
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Showing 12 changed files with 892 additions and 703 deletions.
diff --git a/compiler/plugins/target/LLVMCPU/test/materialize_homogeneous_encodings.mlir b/compiler/plugins/target/LLVMCPU/test/materialize_homogeneous_encodings.mlir
@@ -5,11 +5,12 @@
 #map1 = affine_map<(d0, d1, d2) -> (d0, d2)>
 #map2 = affine_map<(d0, d1, d2) -> (d2, d1)>
 #map3 = affine_map<(d0, d1, d2) -> (d0, d1)>
+#encoding = #iree_encoding.encoding<operand_index = 0, op_type = matmul, element_types = [f32, f32, f32], user_indexing_maps = [#map1, #map2, #map3], round_dims_to = array<i64: 16, 16, 16>>
 #device_target_llvm_cpu = #hal.device.target<"llvm-cpu", [#executable_target_embedded_elf_x86_64_]> : !hal.device
 module attributes {hal.device.targets = [#device_target_llvm_cpu]} {
   util.func public @lhs_encoding(%arg0: tensor<?x?xf32>) -> tensor<?x?xf32> {
-    %3 = iree_encoding.set_encoding %arg0 : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, op_type = matmul, element_types = [f32, f32, f32], user_indexing_maps = [#map1, #map2, #map3], round_dims_to = array<i64: 16, 16, 16>>>
-    %4 = iree_encoding.unset_encoding %3 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, op_type = matmul, element_types = [f32, f32, f32], user_indexing_maps = [#map1, #map2, #map3], round_dims_to = array<i64: 16, 16, 16>>> -> tensor<?x?xf32>
+    %3 = iree_encoding.set_encoding %arg0 : tensor<?x?xf32> -> tensor<?x?xf32, #encoding>
+    %4 = iree_encoding.unset_encoding %3 : tensor<?x?xf32, #encoding> -> tensor<?x?xf32>
     util.return %4 : tensor<?x?xf32>
   }
 }

diff --git a/compiler/plugins/target/VulkanSPIRV/test/materialize_homogeneous_encodings.mlir b/compiler/plugins/target/VulkanSPIRV/test/materialize_homogeneous_encodings.mlir
@@ -4,11 +4,12 @@
 #map1 = affine_map<(d0, d1, d2) -> (d0, d2)>
 #map2 = affine_map<(d0, d1, d2) -> (d2, d1)>
 #map3 = affine_map<(d0, d1, d2) -> (d0, d1)>
+#encoding = #iree_encoding.encoding<operand_index = 0, op_type = matmul, element_types = [f32, f32, f32], user_indexing_maps = [#map1, #map2, #map3], round_dims_to = array<i64: 16, 16, 16>>
 #device_target_vulkan = #hal.device.target<"vulkan", [#executable_target_vulkan_spirv_fb]> : !hal.device
 module attributes {hal.device.targets = [#device_target_vulkan]} {
   util.func public @lhs_encoding(%arg0: tensor<?x?xf32>) -> tensor<?x?xf32> {
-    %3 = iree_encoding.set_encoding %arg0 : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, op_type = matmul, element_types = [f32, f32, f32], user_indexing_maps = [#map1, #map2, #map3], round_dims_to = array<i64: 16, 16, 16>>>
-    %4 = iree_encoding.unset_encoding %3 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, op_type = matmul, element_types = [f32, f32, f32], user_indexing_maps = [#map1, #map2, #map3], round_dims_to = array<i64: 16, 16, 16>>> -> tensor<?x?xf32>
+    %3 = iree_encoding.set_encoding %arg0 : tensor<?x?xf32> -> tensor<?x?xf32, #encoding>
+    %4 = iree_encoding.unset_encoding %3 : tensor<?x?xf32, #encoding> -> tensor<?x?xf32>
     util.return %4 : tensor<?x?xf32>
   }
 }
@@ -23,14 +24,15 @@ module attributes {hal.device.targets = [#device_target_vulkan]} {
 #map1 = affine_map<(d0, d1, d2) -> (d0, d2)>
 #map2 = affine_map<(d0, d1, d2) -> (d2, d1)>
 #map3 = affine_map<(d0, d1, d2) -> (d0, d1)>
+#encoding = #iree_encoding.encoding<operand_index = 0, op_type = matmul, element_types = [f32, f32, f32], user_indexing_maps = [#map1, #map2, #map3], round_dims_to = array<i64: 16, 16, 16>>
 #executable_target_embedded_elf_x86_64_ = #hal.executable.target<"llvm-cpu", "embedded-elf-x86_64", {target_triple = "x86_64-none-elf", cpu_features = "+avx512f"}>
 #device_target_llvm_cpu = #hal.device.target<"llvm-cpu", [#executable_target_embedded_elf_x86_64_]> : !hal.device
 #executable_target_vulkan_spirv_fb = #hal.executable.target<"vulkan-spirv", "vulkan-spirv-fb">
 #device_target_vulkan = #hal.device.target<"vulkan", [#executable_target_vulkan_spirv_fb]> : !hal.device
 module attributes {hal.device.targets = [#hal.device.select<[#device_target_vulkan, #device_target_llvm_cpu]> : !hal.device]} {
   util.func public @lhs_encoding(%arg0: tensor<?x?xf32>) -> tensor<?x?xf32> {
-    %3 = iree_encoding.set_encoding %arg0 : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, op_type = matmul, element_types = [f32, f32, f32], user_indexing_maps = [#map1, #map2, #map3], round_dims_to = array<i64: 16, 16, 16>>>
-    %4 = iree_encoding.unset_encoding %3 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, op_type = matmul, element_types = [f32, f32, f32], user_indexing_maps = [#map1, #map2, #map3], round_dims_to = array<i64: 16, 16, 16>>> -> tensor<?x?xf32>
+    %3 = iree_encoding.set_encoding %arg0 : tensor<?x?xf32> -> tensor<?x?xf32, #encoding>
+    %4 = iree_encoding.unset_encoding %3 : tensor<?x?xf32, #encoding> -> tensor<?x?xf32>
     util.return %4 : tensor<?x?xf32>
   }
 }