[NFC] Delete outdated e2e encoding tests. (#19487)
It was introduced for GPU matmul data-tiling when the multi_mma
code-generation was not ready yet. There is an e2e path in IREE (see
tests/e2e/matmul/) now, so we no longer need the test suite.
Signed-off-by: hanhanW <hanhan0912@gmail.com>
diff --git a/tests/e2e/rocm_specific/BUILD.bazel b/tests/e2e/rocm_specific/BUILD.bazel
deleted file mode 100644
index 438fb07..0000000
--- a/tests/e2e/rocm_specific/BUILD.bazel
+++ /dev/null
@@ -1,24 +0,0 @@
-# Copyright 2024 The IREE Authors
-#
-# Licensed under the Apache License v2.0 with LLVM Exceptions.
-# See https://llvm.org/LICENSE.txt for license information.
-# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
-
-# Tests for end-to-end IREE support specific to the vulkan-spirv lowering.
-
-load("//build_tools/bazel:iree_check_test.bzl", "iree_check_single_backend_test_suite")
-
-package(
- features = ["layering_check"],
- licenses = ["notice"], # Apache 2.0
-)
-
-iree_check_single_backend_test_suite(
- name = "check_rocm_hip",
- srcs = ["encoding.mlir"],
- compiler_flags = [
- "--iree-global-opt-experimental-rocm-data-tiling",
- ],
- driver = "hip",
- target_backend = "rocm",
-)
diff --git a/tests/e2e/rocm_specific/CMakeLists.txt b/tests/e2e/rocm_specific/CMakeLists.txt
deleted file mode 100644
index c428b12..0000000
--- a/tests/e2e/rocm_specific/CMakeLists.txt
+++ /dev/null
@@ -1,26 +0,0 @@
-################################################################################
-# Autogenerated by build_tools/bazel_to_cmake/bazel_to_cmake.py from #
-# tests/e2e/rocm_specific/BUILD.bazel #
-# #
-# Use iree_cmake_extra_content from iree/build_defs.oss.bzl to add arbitrary #
-# CMake-only content. #
-# #
-# To disable autogeneration for this file entirely, delete this header. #
-################################################################################
-
-iree_add_all_subdirs()
-
-iree_check_single_backend_test_suite(
- NAME
- check_rocm_hip
- SRCS
- "encoding.mlir"
- TARGET_BACKEND
- "rocm"
- DRIVER
- "hip"
- COMPILER_FLAGS
- "--iree-global-opt-experimental-rocm-data-tiling"
-)
-
-### BAZEL_TO_CMAKE_PRESERVES_ALL_CONTENT_BELOW_THIS_LINE ###
diff --git a/tests/e2e/rocm_specific/encoding.mlir b/tests/e2e/rocm_specific/encoding.mlir
deleted file mode 100644
index 2718f9d..0000000
--- a/tests/e2e/rocm_specific/encoding.mlir
+++ /dev/null
@@ -1,232 +0,0 @@
-//===----------------------------------------------------------------------===//
-// Utility Methods
-//===----------------------------------------------------------------------===//
-
-func.func private @generate_2D_source_f16(%height : index, %width : index) -> tensor<?x?xf16> {
- %init_source = tensor.empty(%height, %width) : tensor<?x?xf16>
- %source = linalg.generic {
- indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>],
- iterator_types = ["parallel", "parallel"]}
- outs(%init_source : tensor<?x?xf16>) {
- ^bb0(%b0 : f16):
- %outer = linalg.index 0 : index
- %inner = linalg.index 1 : index
- %strided = arith.muli %outer, %width : index
- %linearized = arith.addi %inner, %strided : index
- %linearized_i16 = arith.index_cast %linearized : index to i16
- %linearized_f16 = arith.sitofp %linearized_i16 : i16 to f16
- linalg.yield %linearized_f16 : f16
- } -> tensor<?x?xf16>
- // This blocks the fusion for inputs and testing ops.
- %0 = util.optimization_barrier %source : tensor<?x?xf16>
- %1 = flow.tensor.tie_shape %0 : tensor<?x?xf16>{%height, %width}
- return %1 : tensor<?x?xf16>
-}
-
-func.func private @generate_2D_source_f32(%height : index, %width : index) -> tensor<?x?xf32> {
- %init_source = tensor.empty(%height, %width) : tensor<?x?xf32>
- %source = linalg.generic {
- indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>],
- iterator_types = ["parallel", "parallel"]}
- outs(%init_source : tensor<?x?xf32>) {
- ^bb0(%b0 : f32):
- %outer = linalg.index 0 : index
- %inner = linalg.index 1 : index
- %strided = arith.muli %outer, %width : index
- %linearized = arith.addi %inner, %strided : index
- %linearized_i32 = arith.index_cast %linearized : index to i32
- %linearized_f32 = arith.sitofp %linearized_i32 : i32 to f32
- linalg.yield %linearized_f32 : f32
- } -> tensor<?x?xf32>
- // This blocks the fusion for inputs and testing ops.
- %0 = util.optimization_barrier %source : tensor<?x?xf32>
- %1 = flow.tensor.tie_shape %0 : tensor<?x?xf32>{%height, %width}
- return %1 : tensor<?x?xf32>
-}
-
-func.func private @generate_2D_source_i8(%height : index, %width : index) -> tensor<?x?xi8> {
- %c255 = arith.constant 255 : index
- %init_source = tensor.empty(%height, %width) : tensor<?x?xi8>
- %source = linalg.generic {
- indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>],
- iterator_types = ["parallel", "parallel"]}
- outs(%init_source : tensor<?x?xi8>) {
- ^bb0(%b0 : i8):
- %outer = linalg.index 0 : index
- %inner = linalg.index 1 : index
- %strided = arith.muli %outer, %width : index
- %linearized = arith.addi %inner, %strided : index
- %linearized_rem = arith.remsi %linearized, %c255 : index
- %linearized_i8 = arith.index_cast %linearized_rem : index to i8
- linalg.yield %linearized_i8 : i8
- } -> tensor<?x?xi8>
- // This blocks the fusion for inputs and testing ops.
- %0 = util.optimization_barrier %source : tensor<?x?xi8>
- %1 = flow.tensor.tie_shape %0 : tensor<?x?xi8>{%height, %width}
- return %1 : tensor<?x?xi8>
-}
-
-func.func private @generate_2D_source_i32(%height : index, %width : index) -> tensor<?x?xi32> {
- %init_source = tensor.empty(%height, %width) : tensor<?x?xi32>
- %source = linalg.generic {
- indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>],
- iterator_types = ["parallel", "parallel"]}
- outs(%init_source : tensor<?x?xi32>) {
- ^bb0(%b0 : i32):
- %outer = linalg.index 0 : index
- %inner = linalg.index 1 : index
- %strided = arith.muli %outer, %width : index
- %linearized = arith.addi %inner, %strided : index
- %linearized_i32 = arith.index_cast %linearized : index to i32
- linalg.yield %linearized_i32 : i32
- } -> tensor<?x?xi32>
- // This blocks the fusion for inputs and testing ops.
- %0 = util.optimization_barrier %source : tensor<?x?xi32>
- %1 = flow.tensor.tie_shape %0 : tensor<?x?xi32>{%height, %width}
- return %1 : tensor<?x?xi32>
-}
-
-//===----------------------------------------------------------------------===//
-// f32.f32.f32 variants
-//===----------------------------------------------------------------------===//
-
-#map = affine_map<(d0, d1, d2) -> (d0, d2)>
-#map1 = affine_map<(d0, d1, d2) -> (d2, d1)>
-#map2 = affine_map<(d0, d1, d2) -> (d0, d1)>
-#encoding_f32f32f32_lhs = #iree_encoding.encoding<operand_index = 0, op_type = matmul, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2], round_dims_to = array<i64: 32, 32, 32>>
-#encoding_f32f32f32_rhs = #iree_encoding.encoding<operand_index = 1, op_type = matmul, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2], round_dims_to = array<i64: 32, 32, 32>>
-#encoding_f32f32f32_acc = #iree_encoding.encoding<operand_index = 2, op_type = matmul, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2], round_dims_to = array<i64: 32, 32, 32>>
-
-func.func @set_encoding_f32f32f32_lhs() {
- %height = arith.constant 129 : index
- %width = arith.constant 255 : index
- %0 = call @generate_2D_source_f32(%height, %width) : (index, index) -> tensor<?x?xf32>
- %source = tensor.cast %0 : tensor<?x?xf32> to tensor<129x255xf32>
-
- %1 = iree_encoding.set_encoding %source : tensor<129x255xf32> -> tensor<129x255xf32, #encoding_f32f32f32_lhs>
- %barrire = util.optimization_barrier %1 : tensor<129x255xf32, #encoding_f32f32f32_lhs>
- %2 = iree_encoding.unset_encoding %1 : tensor<129x255xf32, #encoding_f32f32f32_lhs> -> tensor<129x255xf32>
- check.expect_almost_eq(%2, %source) : tensor<129x255xf32>
- return
-}
-
-func.func @set_encoding_f32f32f32_rhs() {
- %height = arith.constant 129 : index
- %width = arith.constant 255 : index
- %0 = call @generate_2D_source_f32(%height, %width) : (index, index) -> tensor<?x?xf32>
- %source = tensor.cast %0 : tensor<?x?xf32> to tensor<129x255xf32>
-
- %1 = iree_encoding.set_encoding %source : tensor<129x255xf32> -> tensor<129x255xf32, #encoding_f32f32f32_rhs>
- %barrire = util.optimization_barrier %1 : tensor<129x255xf32, #encoding_f32f32f32_rhs>
- %2 = iree_encoding.unset_encoding %1 : tensor<129x255xf32, #encoding_f32f32f32_rhs> -> tensor<129x255xf32>
- check.expect_almost_eq(%2, %source) : tensor<129x255xf32>
- return
-}
-
-func.func @set_encoding_f32f32f32_acc() {
- %height = arith.constant 129 : index
- %width = arith.constant 255 : index
- %0 = call @generate_2D_source_f32(%height, %width) : (index, index) -> tensor<?x?xf32>
- %source = tensor.cast %0 : tensor<?x?xf32> to tensor<129x255xf32>
-
- %1 = iree_encoding.set_encoding %source : tensor<129x255xf32> -> tensor<129x255xf32, #encoding_f32f32f32_acc>
- %barrire = util.optimization_barrier %1 : tensor<129x255xf32, #encoding_f32f32f32_acc>
- %2 = iree_encoding.unset_encoding %1 : tensor<129x255xf32, #encoding_f32f32f32_acc> -> tensor<129x255xf32>
- check.expect_almost_eq(%2, %source) : tensor<129x255xf32>
- return
-}
-
-//===----------------------------------------------------------------------===//
-// i8.i8.i32 variants
-//===----------------------------------------------------------------------===//
-
-#encoding_i8i8i32_lhs = #iree_encoding.encoding<operand_index = 0, op_type = matmul, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2], round_dims_to = array<i64: 32, 32, 32>>
-#encoding_i8i8i32_rhs = #iree_encoding.encoding<operand_index = 1, op_type = matmul, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2], round_dims_to = array<i64: 32, 32, 32>>
-#encoding_i8i8i32_acc = #iree_encoding.encoding<operand_index = 2, op_type = matmul, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2], round_dims_to = array<i64: 32, 32, 32>>
-
-func.func @set_encoding_i8i8i32_lhs() {
- %height = arith.constant 129 : index
- %width = arith.constant 255 : index
- %0 = call @generate_2D_source_i8(%height, %width) : (index, index) -> tensor<?x?xi8>
- %source = tensor.cast %0 : tensor<?x?xi8> to tensor<129x255xi8>
-
- %1 = iree_encoding.set_encoding %source : tensor<129x255xi8> -> tensor<129x255xi8, #encoding_i8i8i32_lhs>
- %barrire = util.optimization_barrier %1 : tensor<129x255xi8, #encoding_i8i8i32_lhs>
- %2 = iree_encoding.unset_encoding %1 : tensor<129x255xi8, #encoding_i8i8i32_lhs> -> tensor<129x255xi8>
- check.expect_eq(%2, %source) : tensor<129x255xi8>
- return
-}
-
-func.func @set_encoding_i8i8i32_rhs() {
- %height = arith.constant 129 : index
- %width = arith.constant 255 : index
- %0 = call @generate_2D_source_i8(%height, %width) : (index, index) -> tensor<?x?xi8>
- %source = tensor.cast %0 : tensor<?x?xi8> to tensor<129x255xi8>
-
- %1 = iree_encoding.set_encoding %source : tensor<129x255xi8> -> tensor<129x255xi8, #encoding_i8i8i32_rhs>
- %barrire = util.optimization_barrier %1 : tensor<129x255xi8, #encoding_i8i8i32_rhs>
- %2 = iree_encoding.unset_encoding %1 : tensor<129x255xi8, #encoding_i8i8i32_rhs> -> tensor<129x255xi8>
- check.expect_eq(%2, %source) : tensor<129x255xi8>
- return
-}
-
-func.func @set_encoding_i8i8i32_acc() {
- %height = arith.constant 129 : index
- %width = arith.constant 255 : index
- %0 = call @generate_2D_source_i32(%height, %width) : (index, index) -> tensor<?x?xi32>
- %source = tensor.cast %0 : tensor<?x?xi32> to tensor<129x255xi32>
-
- %1 = iree_encoding.set_encoding %source : tensor<129x255xi32> -> tensor<129x255xi32, #encoding_i8i8i32_acc>
- %barrire = util.optimization_barrier %1 : tensor<129x255xi32, #encoding_i8i8i32_acc>
- %2 = iree_encoding.unset_encoding %1 : tensor<129x255xi32, #encoding_i8i8i32_acc> -> tensor<129x255xi32>
- check.expect_eq(%2, %source) : tensor<129x255xi32>
- return
-}
-
-
-//===----------------------------------------------------------------------===//
-// f16.f16.f32 variants
-//===----------------------------------------------------------------------===//
-
-#encoding_f16f16f32_lhs = #iree_encoding.encoding<operand_index = 0, op_type = matmul, element_types = [f16, f16, f32], user_indexing_maps = [#map, #map1, #map2], round_dims_to = array<i64: 32, 32, 32>>
-#encoding_f16f16f32_rhs = #iree_encoding.encoding<operand_index = 1, op_type = matmul, element_types = [f16, f16, f32], user_indexing_maps = [#map, #map1, #map2], round_dims_to = array<i64: 32, 32, 32>>
-#encoding_f16f16f32_acc = #iree_encoding.encoding<operand_index = 2, op_type = matmul, element_types = [f16, f16, f32], user_indexing_maps = [#map, #map1, #map2], round_dims_to = array<i64: 32, 32, 32>>
-
-func.func @set_encoding_f16f16f32_lhs() {
- %height = arith.constant 129 : index
- %width = arith.constant 255 : index
- %0 = call @generate_2D_source_f16(%height, %width) : (index, index) -> tensor<?x?xf16>
- %source = tensor.cast %0 : tensor<?x?xf16> to tensor<129x255xf16>
-
- %1 = iree_encoding.set_encoding %source : tensor<129x255xf16> -> tensor<129x255xf16, #encoding_f16f16f32_lhs>
- %barrire = util.optimization_barrier %1 : tensor<129x255xf16, #encoding_f16f16f32_lhs>
- %2 = iree_encoding.unset_encoding %1 : tensor<129x255xf16, #encoding_f16f16f32_lhs> -> tensor<129x255xf16>
- check.expect_eq(%2, %source) : tensor<129x255xf16>
- return
-}
-
-func.func @set_encoding_f16f16f32_rhs() {
- %height = arith.constant 129 : index
- %width = arith.constant 255 : index
- %0 = call @generate_2D_source_f16(%height, %width) : (index, index) -> tensor<?x?xf16>
- %source = tensor.cast %0 : tensor<?x?xf16> to tensor<129x255xf16>
-
- %1 = iree_encoding.set_encoding %source : tensor<129x255xf16> -> tensor<129x255xf16, #encoding_f16f16f32_rhs>
- %barrire = util.optimization_barrier %1 : tensor<129x255xf16, #encoding_f16f16f32_rhs>
- %2 = iree_encoding.unset_encoding %1 : tensor<129x255xf16, #encoding_f16f16f32_rhs> -> tensor<129x255xf16>
- check.expect_eq(%2, %source) : tensor<129x255xf16>
- return
-}
-
-func.func @set_encoding_f16f16f32_acc() {
- %height = arith.constant 129 : index
- %width = arith.constant 255 : index
- %0 = call @generate_2D_source_f32(%height, %width) : (index, index) -> tensor<?x?xf32>
- %source = tensor.cast %0 : tensor<?x?xf32> to tensor<129x255xf32>
-
- %1 = iree_encoding.set_encoding %source : tensor<129x255xf32> -> tensor<129x255xf32, #encoding_f16f16f32_acc>
- %barrire = util.optimization_barrier %1 : tensor<129x255xf32, #encoding_f16f16f32_acc>
- %2 = iree_encoding.unset_encoding %1 : tensor<129x255xf32, #encoding_f16f16f32_acc> -> tensor<129x255xf32>
- check.expect_eq(%2, %source) : tensor<129x255xf32>
- return
-}
