[mlir][linalg][nfc] Move vectorization tests

mlir/test/Dialect/Linalg/vectorize-convolution-flatten.mlir → mlir/test/Dialect/Linalg/vectorization/convolution-with-patterns-flatten.mlir

@@ -1,5 +1,11 @@
 // RUN: mlir-opt -split-input-file -transform-interpreter %s | FileCheck %s
 
+///----------------------------------------------------------------------------------------
+/// Tests for vectorizing depthwise convolutions (with patterns) with the
+/// flattening of the channel dim enabled. This approach is beneficial when the
+/// number of channel dimensions is low.
+///----------------------------------------------------------------------------------------
+
 func.func @depthwise_conv1d_nwc_wc_1x8x3xi8_tensor(%input: tensor<1x8x3xi8>,
                                                    %filter: tensor<1x3xi8>,
                                                    %output: tensor<1x8x3xi8>) -> (tensor<1x8x3xi8>) {

mlir/test/Dialect/Linalg/vectorize-conv-masked-and-scalable.mlir → mlir/test/Dialect/Linalg/vectorization/convolution.mlir

@@ -1,5 +1,15 @@
 // RUN: mlir-opt -split-input-file -transform-interpreter -cse %s | FileCheck %s
 
+///----------------------------------------------------------------------------------------
+/// ATM, all tests in this file require masking. As the support for masking is
+/// limited to depthwise convs, only that variant of convolutions is tested
+/// ATM.
+///
+/// TODO1: Add more types of convolutions (transform.structured.vectorize
+/// usually doesn't require masking when vector sizes are not specified)
+/// TODO2: Add support for masking non-depthwise convs.
+///----------------------------------------------------------------------------------------
+
 func.func @depthwise_conv1d_nwc_wc_1x8x3xi8_tensor(%input: tensor<1x8x?xi8>,
                                                    %filter: tensor<1x?xi8>,
                                                    %output: tensor<1x8x?xi8>) -> (tensor<1x8x?xi8>) {

mlir/test/Dialect/Linalg/vectorization-pad-patterns.mlir → mlir/test/Dialect/Linalg/vectorization/pad-patterns.mlir

@@ -1,5 +1,15 @@
 // RUN: mlir-opt %s -transform-interpreter -split-input-file | FileCheck %s
 
+///----------------------------------------------------------------------------------------
+/// Tests for vectorization patterns for tensor.pad, i.e.
+///   * transform.apply_patterns.linalg.pad_vectorization
+///
+/// These tests are meant os a lower granule than tests in
+///   * pad-with-patterns.mlir.
+/// The goal is to test specific patterns. To this end, some inputs already
+/// contain Vector ops (on top of `tensor.pad`).
+///----------------------------------------------------------------------------------------
+
 ///----------------------------------------------------------------------------------------
 /// [Pattern: PadOpVectorizationWithTransferReadPattern]
 ///----------------------------------------------------------------------------------------

mlir/test/Integration/Dialect/Linalg/CPU/test-padtensor.mlir

@@ -7,6 +7,9 @@
 // RUN:   -shared-libs=%mlir_c_runner_utils,%mlir_runner_utils \
 // RUN: | FileCheck %s
 
+// TODO: Use TD for vectorization and remove `test-linalg-to-vector-patterns`
+// that's otherwise not required.
+
 
 func.func @main() {
   %const = arith.constant dense<[[[1.0, 2.0, 3.0], [2.0, 3.0, 4.0]]]> : tensor<1x2x3xf32>