9a8fd0e99c
Benchmarks are neutral to positive. PiperOrigin-RevId: 321611928 Change-Id: I0c7c5535b49b97581c99b1fb77f4ccd7741e3d1c
105 lines
3.6 KiB
C++
105 lines
3.6 KiB
C++
/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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==============================================================================*/
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#include "tensorflow/compiler/xla/service/cpu/cpu_options.h"
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#include "absl/strings/numbers.h"
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#include "absl/strings/str_split.h"
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#include "tensorflow/core/lib/strings/numbers.h"
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namespace {
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const char* const kXlaOptimizeForSizeCpuOption = "xla_cpu_optimize_for_size";
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const char* const kLlvmIrDotTilingFactor = "xla_llvm_dot_tiling_factor";
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const char* const kXlaForceEnableExperimentalLlvmIrGemm =
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"xla_force_enable_experimental_llvm_ir_gemm";
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const char* const kLlvmIrGemmTileSize = "xla_llvm_ir_gemm_tile_size";
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} // namespace
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namespace xla {
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namespace cpu {
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namespace options {
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bool OptimizeForSizeRequested(const HloModuleConfig& config) {
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const auto& extra_options_map =
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config.debug_options().xla_backend_extra_options();
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return extra_options_map.count(kXlaOptimizeForSizeCpuOption) > 0;
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}
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bool VectorizedReduceDisabled(const HloModuleConfig& config) {
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const auto& extra_options_map =
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config.debug_options().xla_backend_extra_options();
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return extra_options_map.count(kXlaOptimizeForSizeCpuOption) > 0;
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}
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absl::optional<int64> LlvmIrGemvTilingFactor(const HloModuleConfig& config) {
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const auto& extra_options_map =
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config.debug_options().xla_backend_extra_options();
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auto it = extra_options_map.find(kLlvmIrDotTilingFactor);
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int64 tiling_factor;
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if (it != extra_options_map.end() &&
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absl::SimpleAtoi(it->second, &tiling_factor)) {
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return tiling_factor;
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}
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return absl::nullopt;
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}
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bool ForceEnableExperimentalLlvmIrGemm(const HloModuleConfig& config) {
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const auto& extra_options_map =
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config.debug_options().xla_backend_extra_options();
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return extra_options_map.count(kXlaForceEnableExperimentalLlvmIrGemm) > 0;
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}
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static absl::string_view RemoveSuffix(absl::string_view str,
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absl::string_view suffix) {
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CHECK_GE(str.size(), suffix.size());
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CHECK_EQ(str.substr(str.size() - suffix.size()), suffix);
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return str.substr(0, str.size() - suffix.size());
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}
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absl::optional<std::tuple<int64, int64, int64>> LlvmIrGemmTileSize(
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const HloModuleConfig& config) {
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const auto& extra_options_map =
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config.debug_options().xla_backend_extra_options();
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auto it = extra_options_map.find(kLlvmIrGemmTileSize);
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if (it == extra_options_map.end()) {
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return absl::nullopt;
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}
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std::vector<string> tile_components = absl::StrSplit(it->second, ':');
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CHECK_EQ(tile_components.size(), 3);
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int64 tile_size_m;
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int64 tile_size_k;
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int64 tile_size_n_in_vector_width;
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CHECK(absl::SimpleAtoi(tile_components[0], &tile_size_m));
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CHECK(absl::SimpleAtoi(tile_components[1], &tile_size_k));
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absl::string_view tile_size_n_in_vector_width_str =
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RemoveSuffix(tile_components[2], "*vectwidth");
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CHECK(absl::SimpleAtoi(tile_size_n_in_vector_width_str,
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&tile_size_n_in_vector_width));
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return std::tuple<int64, int64, int64>(tile_size_m, tile_size_k,
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tile_size_n_in_vector_width);
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}
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} // namespace options
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} // namespace cpu
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} // namespace xla
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