diff options
| author | Jerry Ge <jerry.ge@arm.com> | 2023-07-19 23:08:16 +0000 |
|---|---|---|
| committer | Jerry Ge <jerry.ge@arm.com> | 2023-07-25 22:49:17 +0000 |
| commit | 9c9c8dafe8f9a32bd70aee268cd537b93865a3ba (patch) | |
| tree | e94fc471261b9f72bef86033fbc76022f55d5de8 | |
| parent | c1e13432b4a218781afd6b0171d4afff11730433 (diff) | |
| download | reference_model-9c9c8dafe8f9a32bd70aee268cd537b93865a3ba.tar.gz | |
Run clang-format and update copyright
- Also added run clang-format to pre-commit runs
Signed-off-by: Jerry Ge <jerry.ge@arm.com>
Change-Id: I4e59ac0afbaa30dce0773aa63d92a1a3b119e2f3
41 files changed, 612 insertions, 588 deletions
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index b253ddd..4a6c9c7 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,4 +1,4 @@ -# Copyright (c) 2021-2022 Arm Limited. +# Copyright (c) 2021-2023 Arm Limited. # SPDX-License-Identifier: Apache-2.0 # See https://pre-commit.com for more information @@ -28,3 +28,13 @@ repos: entry: pytest -m "not postcommit" types: [python] pass_filenames: false + +- repo: local + hooks: + - id: clang-format + name: clang-format + exclude: build|third_party + language: system + entry: clang-format + types: ["c++"] + args: ["-i"]
\ No newline at end of file diff --git a/reference_model/include/debug_types.h b/reference_model/include/debug_types.h index 9ff0098..4b97710 100644 --- a/reference_model/include/debug_types.h +++ b/reference_model/include/debug_types.h @@ -19,7 +19,7 @@ * Defines fundamental debugger datatypes for the functional model */ -#include<stdint.h> +#include <stdint.h> #ifndef DEBUG_TYPES_H_ #define DEBUG_TYPES_H_ diff --git a/reference_model/include/dtype.h b/reference_model/include/dtype.h index 59b7fcd..bde678a 100644 --- a/reference_model/include/dtype.h +++ b/reference_model/include/dtype.h @@ -82,7 +82,7 @@ inline const char* EnumNameTOSAREFTYPE(TOSA_REF_TYPE e) // return corresponding TOSA_REF_TYPE for DType inline TOSA_REF_TYPE ConvertDType(const DType dtype) { - assert(DType_MAX == DType_BF16); // must update whenever DType_MAX changes + assert(DType_MAX == DType_BF16); // must update whenever DType_MAX changes if (g_func_config.precise_mode) { diff --git a/reference_model/include/func_config.h b/reference_model/include/func_config.h index 900ebab..1e93b89 100644 --- a/reference_model/include/func_config.h +++ b/reference_model/include/func_config.h @@ -19,29 +19,31 @@ #include <iostream> #include <stdio.h> -struct tosa_level_t { +struct tosa_level_t +{ int32_t MAX_RANK = 0; int32_t MAX_KERNEL = 0; int32_t MAX_STRIDE = 0; int32_t MAX_SCALE = 0; - bool operator!=(const tosa_level_t &rhs) { - return !(MAX_RANK == rhs.MAX_RANK && MAX_KERNEL == rhs.MAX_KERNEL && - MAX_STRIDE == rhs.MAX_STRIDE && MAX_SCALE == rhs.MAX_SCALE); + bool operator!=(const tosa_level_t& rhs) + { + return !(MAX_RANK == rhs.MAX_RANK && MAX_KERNEL == rhs.MAX_KERNEL && MAX_STRIDE == rhs.MAX_STRIDE && + MAX_SCALE == rhs.MAX_SCALE); } }; struct func_config_t { - std::string operator_fbs = "tosa.fbs"; - std::string test_desc = "desc.json"; - std::string flatbuffer_dir = ""; - std::string output_dir = ""; - std::string tosa_file = ""; - std::string ifm_name = ""; - std::string ifm_file = ""; - std::string ofm_name = ""; - std::string ofm_file = ""; + std::string operator_fbs = "tosa.fbs"; + std::string test_desc = "desc.json"; + std::string flatbuffer_dir = ""; + std::string output_dir = ""; + std::string tosa_file = ""; + std::string ifm_name = ""; + std::string ifm_file = ""; + std::string ofm_name = ""; + std::string ofm_file = ""; uint32_t eval = 1; uint32_t validate_only = 0; uint32_t output_tensors = 1; @@ -49,12 +51,12 @@ struct func_config_t uint32_t dump_intermediates = 0; std::string fp_format = "0.5"; uint32_t precise_mode = 0; - bool abs_mode = 0; // set in main as second run of precise_mode - bool float_is_big_endian = false; // Set in arith_util.h by float_is_big_endian() + bool abs_mode = 0; // set in main as second run of precise_mode + bool float_is_big_endian = false; // Set in arith_util.h by float_is_big_endian() tosa_level_t tosa_level; static constexpr tosa_level_t EIGHTK = { 6, 8192, 8192, 64 }; - static constexpr tosa_level_t NONE = { 0, 0, 0, 0 }; + static constexpr tosa_level_t NONE = { 0, 0, 0, 0 }; }; #endif diff --git a/reference_model/include/func_debug.h b/reference_model/include/func_debug.h index 3794a35..556faf2 100644 --- a/reference_model/include/func_debug.h +++ b/reference_model/include/func_debug.h @@ -60,12 +60,12 @@ void func_enable_signal_handlers(); struct func_debug_t { - uint32_t func_debug_verbosity = 0; // What verbosity level is set? (bitmask) - uint64_t func_debug_mask = 0; // Which units have debugging enabled? (bitmask) - uint64_t func_debug_inst_mask = 0; // Which instances have debugging enabled (bitmask) - uint64_t inst_id = 0; // The instance id for multiple model instances - FILE* func_debug_file = stderr; // Output file - bool is_output_unbuffered = false; // should log files be opened with unbuffered I/O. + uint32_t func_debug_verbosity = 0; // What verbosity level is set? (bitmask) + uint64_t func_debug_mask = 0; // Which units have debugging enabled? (bitmask) + uint64_t func_debug_inst_mask = 0; // Which instances have debugging enabled (bitmask) + uint64_t inst_id = 0; // The instance id for multiple model instances + FILE* func_debug_file = stderr; // Output file + bool is_output_unbuffered = false; // should log files be opened with unbuffered I/O. int init_debug(uint64_t inst_id); int fini_debug(); @@ -115,13 +115,13 @@ struct func_debug_t #endif #ifndef LEVEL_CHECK -#define LEVEL_CHECK(COND, fmt, ...) \ - if (g_func_config.tosa_level != func_config_t::NONE && (!(COND))) \ - { \ - fprintf(g_func_debug.func_debug_file, COL_FATAL("LEVEL_CHECK() fails AT %s:%d %s(): (%s)\n"), __FILE__, __LINE__, \ - __func__, #COND); \ - fprintf(g_func_debug.func_debug_file, COL_FATAL(fmt) "\n", ##__VA_ARGS__); \ - this->parent_sgt->setGraphStatus(GraphStatus::TOSA_UNPREDICTABLE); \ +#define LEVEL_CHECK(COND, fmt, ...) \ + if (g_func_config.tosa_level != func_config_t::NONE && (!(COND))) \ + { \ + fprintf(g_func_debug.func_debug_file, COL_FATAL("LEVEL_CHECK() fails AT %s:%d %s(): (%s)\n"), __FILE__, \ + __LINE__, __func__, #COND); \ + fprintf(g_func_debug.func_debug_file, COL_FATAL(fmt) "\n", ##__VA_ARGS__); \ + this->parent_sgt->setGraphStatus(GraphStatus::TOSA_UNPREDICTABLE); \ } #endif @@ -174,8 +174,6 @@ void func_debug_warning( fprintf(stderr, COL_WARN(fmt) "\n", ##__VA_ARGS__); #endif - - // Is this debug verbosity and unit level enabled? // Provide compiler hints that this is unlikely // Two versions, depending on whether DEBUG_INSTANCE_EXPR is defined in a file or not diff --git a/reference_model/include/graph_status.h b/reference_model/include/graph_status.h index f3be004..91330df 100644 --- a/reference_model/include/graph_status.h +++ b/reference_model/include/graph_status.h @@ -1,4 +1,4 @@ -// Copyright (c) 2022, ARM Limited. +// Copyright (c) 2022-2023, ARM Limited. // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. @@ -22,4 +22,4 @@ enum class GraphStatus : int TOSA_ERROR = 2, }; -#endif // GRAPH_STATUS_H +#endif // GRAPH_STATUS_H diff --git a/reference_model/include/model_runner.h b/reference_model/include/model_runner.h index 86d0056..2c610b8 100644 --- a/reference_model/include/model_runner.h +++ b/reference_model/include/model_runner.h @@ -1,5 +1,5 @@ -// Copyright (c) 2022, ARM Limited. +// Copyright (c) 2022-2023, ARM Limited. // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. @@ -16,8 +16,8 @@ #ifndef MODEL_RUNNER_H_ #define MODEL_RUNNER_H_ -#include "model_common.h" #include "graph_status.h" +#include "model_common.h" #include "tosa_serialization_handler.h" diff --git a/reference_model/include/verify.h b/reference_model/include/verify.h index 0ac35fe..d294388 100644 --- a/reference_model/include/verify.h +++ b/reference_model/include/verify.h @@ -21,50 +21,51 @@ #include <cstddef> #ifdef __cplusplus -extern "C" { +extern "C" +{ #endif /* __cplusplus */ -// Check result -// -// Error is valid only and only if is_valid is true -struct CheckResult -{ - bool is_valid; - double error; -}; + // Check result + // + // Error is valid only and only if is_valid is true + struct CheckResult + { + bool is_valid; + double error; + }; -/// Validate and calculate tensor element error when using an fp32 accumulator -/// -/// \param ref Tensor element calculated using fp64 -/// \param bnd Tensor element calculated using fp64 on abs(input, weights) -/// \param imp Tensor element calculated through the implementation -/// \param KS The kernel size -/// -/// \return Output error -CheckResult tosa_validate_element_accfp32(double ref, double bnd, float imp, size_t KS); + /// Validate and calculate tensor element error when using an fp32 accumulator + /// + /// \param ref Tensor element calculated using fp64 + /// \param bnd Tensor element calculated using fp64 on abs(input, weights) + /// \param imp Tensor element calculated through the implementation + /// \param KS The kernel size + /// + /// \return Output error + CheckResult tosa_validate_element_accfp32(double ref, double bnd, float imp, size_t KS); -/// Validate the accumulated output error -/// -/// \param err_sum Sum of error of all the tensor elements within a tensor -/// \param err_sum_sq Sum of error squares of all the tensor elements within a tensor -/// \param T Number of output (dot-product) elements -/// \param KS The kernel size -/// \param S Test set used as a input/weight generator -/// -/// \return True if the error is within margin else false -bool tosa_validate_output_error(double err_sum, double err_sum_sq, size_t T, size_t KS, int S); + /// Validate the accumulated output error + /// + /// \param err_sum Sum of error of all the tensor elements within a tensor + /// \param err_sum_sq Sum of error squares of all the tensor elements within a tensor + /// \param T Number of output (dot-product) elements + /// \param KS The kernel size + /// \param S Test set used as a input/weight generator + /// + /// \return True if the error is within margin else false + bool tosa_validate_output_error(double err_sum, double err_sum_sq, size_t T, size_t KS, int S); -/// Validate error of whole vector of output data -/// -/// \param ref Output elements calculated using fp64 -/// \param bnd Output elements calculated using fp64 on abs(input, weights) -/// \param imp Output elements calculated using the implementation -/// \param T Number of elements in outputs (need to match) -/// \param KS The kernel size -/// \param S Test set used as a input/weight generator -/// -/// \return True if the error is within margin else false -bool tosa_validate_data_fp32(const double* ref, const double* bnd, const float* imp, size_t T, size_t KS, int S); + /// Validate error of whole vector of output data + /// + /// \param ref Output elements calculated using fp64 + /// \param bnd Output elements calculated using fp64 on abs(input, weights) + /// \param imp Output elements calculated using the implementation + /// \param T Number of elements in outputs (need to match) + /// \param KS The kernel size + /// \param S Test set used as a input/weight generator + /// + /// \return True if the error is within margin else false + bool tosa_validate_data_fp32(const double* ref, const double* bnd, const float* imp, size_t T, size_t KS, int S); #ifdef __cplusplus } diff --git a/reference_model/include/version.h b/reference_model/include/version.h index c81eedd..481a934 100644 --- a/reference_model/include/version.h +++ b/reference_model/include/version.h @@ -1,4 +1,4 @@ -// Copyright (c) 2022, ARM Limited. +// Copyright (c) 2022-2023, ARM Limited. // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. @@ -20,4 +20,4 @@ #define TOSA_REFERENCE_MODEL_VERSION_PATCH 0 #define TOSA_REFERENCE_MODEL_VERSION_DRAFT true -#endif //VERSION_H +#endif //VERSION_H diff --git a/reference_model/samples/model_runner_simple_sample.cpp b/reference_model/samples/model_runner_simple_sample.cpp index 2eebca6..f1c9e96 100644 --- a/reference_model/samples/model_runner_simple_sample.cpp +++ b/reference_model/samples/model_runner_simple_sample.cpp @@ -1,5 +1,5 @@ -// Copyright (c) 2022, ARM Limited. +// Copyright (c) 2022-2023, ARM Limited. // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. @@ -27,24 +27,24 @@ int main() std::string expected_output_file(test_root + "tflite_result.npy"); std::vector<std::string> input_names = { "TosaInput_0", "TosaInput_1" }; - std::string output_name = "TosaOutput_0"; + std::string output_name = "TosaOutput_0"; std::vector<int32_t> input0_shape = { 1, 4, 4, 1 }; std::vector<int32_t> input1_shape = { 1, 4, 4, 4 }; std::vector<int32_t> output_shape = { 1, 4, 4, 4 }; std::vector<std::vector<float>> inputs(input_names.size()); - std::vector<float> expected_outputs = { }; - std::vector<float> actual_outputs = { }; + std::vector<float> expected_outputs = {}; + std::vector<float> actual_outputs = {}; // Read in inputs and expected outputs for sample purposes. - inputs[0] = readFromNpyFile<float>(input0_file.c_str(), input0_shape); - inputs[1] = readFromNpyFile<float>(input1_file.c_str(), input1_shape); + inputs[0] = readFromNpyFile<float>(input0_file.c_str(), input0_shape); + inputs[1] = readFromNpyFile<float>(input1_file.c_str(), input1_shape); expected_outputs = readFromNpyFile<float>(expected_output_file.c_str(), output_shape); tosa::TosaSerializationHandler handler; tosa::tosa_err_t error = handler.LoadFileTosaFlatbuffer(tosa_model_file.c_str()); - if(error != tosa::TOSA_OK) + if (error != tosa::TOSA_OK) { WARNING("An error occurred while loading the model from file."); return 1; @@ -54,7 +54,7 @@ int main() // Initialize the ModelRunner with configurations. IModelRunner runner; status = runner.initialize(handler); - if(status != GraphStatus::TOSA_VALID) + if (status != GraphStatus::TOSA_VALID) { WARNING("An error occurred while initializing."); return 1; @@ -66,7 +66,7 @@ int main() // Run the ModelRunner using test inputs. status = runner.run(); - if(status != GraphStatus::TOSA_VALID) + if (status != GraphStatus::TOSA_VALID) { WARNING("An error occurred when running the model."); return 1; @@ -79,14 +79,14 @@ int main() bool if_accurate = true; for (size_t i = 0; i < expected_outputs.size(); ++i) { - if(actual_outputs[i] != expected_outputs[i]) + if (actual_outputs[i] != expected_outputs[i]) { WARNING("Actual output (%f) doesn't match expected output (%f)."); if_accurate = false; } } - if(!if_accurate) + if (!if_accurate) { WARNING("There were mismatches in actual vs expected output, see above output for more details."); return 1; diff --git a/reference_model/src/arith_util.h b/reference_model/src/arith_util.h index 76f3bb8..cab43ba 100644 --- a/reference_model/src/arith_util.h +++ b/reference_model/src/arith_util.h @@ -218,7 +218,8 @@ inline void float_trunc_bytes(float* src) memcpy(src, &src_as_bytes, sizeof(float)); } -inline void truncateFloatToBFloat(float* src, int64_t size) { +inline void truncateFloatToBFloat(float* src, int64_t size) +{ /* Set the least significant two bytes to zero for each float value in the input src buffer. */ ASSERT_MEM(src); diff --git a/reference_model/src/command_line_utils.h b/reference_model/src/command_line_utils.h index 8fdff01..dcb0564 100644 --- a/reference_model/src/command_line_utils.h +++ b/reference_model/src/command_line_utils.h @@ -19,8 +19,8 @@ #include "func_config.h" #include "func_debug.h" -#include <stdint.h> #include <cxxopts.hpp> +#include <stdint.h> // Read the command line arguments int func_model_parse_cmd_line( @@ -69,30 +69,36 @@ int func_model_parse_cmd_line( // clang-format on auto result = options.parse(argc, argv); - if (result.count("help")) { + if (result.count("help")) + { std::cout << options.help() << std::endl; return 1; } - if (result.count("debugmask")) { + if (result.count("debugmask")) + { auto& v = result["debugmask"].as<std::vector<std::string>>(); for (const std::string& s : v) func_debug.set_mask(s); } - if (result.count("loglevel")) { + if (result.count("loglevel")) + { const std::string& levelstr = result["loglevel"].as<std::string>(); func_debug.set_verbosity(levelstr); } - if (result.count("logfile")) { + if (result.count("logfile")) + { func_debug.set_file(result["logfile"].as<std::string>()); } - if (result.count("input_tensor_file")) { + if (result.count("input_tensor_file")) + { func_config.ifm_name = result["input_tensor_file"].as<std::string>(); } - if (result.count("version")) { + if (result.count("version")) + { std::cout << "Model version " << version << std::endl; } } - catch(const std::exception& e) + catch (const std::exception& e) { std::cerr << e.what() << '\n'; return 1; diff --git a/reference_model/src/func_debug.cc b/reference_model/src/func_debug.cc index 745d076..7979c89 100644 --- a/reference_model/src/func_debug.cc +++ b/reference_model/src/func_debug.cc @@ -1,5 +1,5 @@ -// Copyright (c) 2020, ARM Limited. +// Copyright (c) 2020-2023, ARM Limited. // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. @@ -15,12 +15,12 @@ #include <ctype.h> #include <signal.h> +#include <sstream> #include <stdarg.h> #include <stdio.h> #include <stdlib.h> #include <string.h> #include <sys/types.h> -#include <sstream> #ifndef _MSC_VER #include <execinfo.h> @@ -164,7 +164,7 @@ int func_debug_t::init_debug(uint64_t inst_id) set_verbosity(DEBUG_VERB_NONE); set_inst_mask(DEBUG_INST_ALL); func_debug_file = stderr; - this->inst_id = inst_id; + this->inst_id = inst_id; return 0; } @@ -268,7 +268,7 @@ void func_debug_t::set_inst_mask(const uint64_t mask) } std::vector<std::pair<std::string, int>> debug_str_table = { -#define DEBUG_MODE(NAME, BIT) {#NAME, DEBUG_##NAME}, +#define DEBUG_MODE(NAME, BIT) { #NAME, DEBUG_##NAME }, #include "debug_modes.def" #undef DEBUG_MODE }; @@ -327,7 +327,8 @@ void func_debug_warning( std::string func_debug_t::get_debug_mask_help_string() { std::string rval = "Set debug mask. Valid values are: "; - for (auto& mask : debug_str_table) { + for (auto& mask : debug_str_table) + { rval += mask.first + " "; } rval += "ALL"; diff --git a/reference_model/src/graph_node.h b/reference_model/src/graph_node.h index aafc07f..900f4b8 100644 --- a/reference_model/src/graph_node.h +++ b/reference_model/src/graph_node.h @@ -276,8 +276,8 @@ protected: if (requiredRankMin >= 0 && requiredRankMax >= 0) { ASSERT_MSG(requiredRankMin <= requiredRankMax, - "GraphNode::setRequiredRank: requiredRankMin %d must be <= requiredRankMax %d", requiredRankMin, - requiredRankMax); + "GraphNode::setRequiredRank: requiredRankMin %d must be <= requiredRankMax %d", requiredRankMin, + requiredRankMax); } return 0; diff --git a/reference_model/src/main.cpp b/reference_model/src/main.cpp index 55e6d67..0c86cbd 100644 --- a/reference_model/src/main.cpp +++ b/reference_model/src/main.cpp @@ -16,17 +16,17 @@ #include "model_runner.h" #include "version.h" +#include "arith_util.h" #include "command_line_utils.h" #include "ops/op_factory.h" #include "subgraph_traverser.h" #include "tosa_serialization_handler.h" -#include "arith_util.h" +#include <Eigen/CXX11/Tensor> #include <fstream> #include <iostream> -#include <stdio.h> -#include <Eigen/CXX11/Tensor> #include <nlohmann/json.hpp> +#include <stdio.h> using namespace TosaReference; using namespace tosa; @@ -42,10 +42,8 @@ bool isComplianceModeDotProduct(json& test_desc); int main(int argc, char** argv) { - TosaVersion model_version(TOSA_REFERENCE_MODEL_VERSION_MAJOR, - TOSA_REFERENCE_MODEL_VERSION_MINOR, - TOSA_REFERENCE_MODEL_VERSION_PATCH, - TOSA_REFERENCE_MODEL_VERSION_DRAFT); + TosaVersion model_version(TOSA_REFERENCE_MODEL_VERSION_MAJOR, TOSA_REFERENCE_MODEL_VERSION_MINOR, + TOSA_REFERENCE_MODEL_VERSION_PATCH, TOSA_REFERENCE_MODEL_VERSION_DRAFT); // Initialize configuration and debug subsystems g_func_debug.init_debug(0); @@ -245,7 +243,8 @@ int loadGraph(TosaSerializationHandler& tsh, json& test_desc) { if (tsh.LoadFileSchema(g_func_config.operator_fbs.c_str())) { - FATAL_ERROR("\nJSON file detected. Unable to load TOSA flatbuffer schema from: %s\nCheck --operator_fbs is set correctly", + FATAL_ERROR("\nJSON file detected. Unable to load TOSA flatbuffer schema from: %s\nCheck --operator_fbs " + "is set correctly", g_func_config.operator_fbs.c_str()); } @@ -259,9 +258,8 @@ int loadGraph(TosaSerializationHandler& tsh, json& test_desc) { if (tsh.LoadFileTosaFlatbuffer(graph_fullname)) { - FATAL_ERROR( - "\nError loading TOSA flatbuffer file: %s\n%s%s", - graph_fullname, error_msg1.c_str(), error_msg2.c_str()); + FATAL_ERROR("\nError loading TOSA flatbuffer file: %s\n%s%s", graph_fullname, error_msg1.c_str(), + error_msg2.c_str()); } } diff --git a/reference_model/src/model_runner.cc b/reference_model/src/model_runner.cc index 62d6ce6..0ef44f9 100644 --- a/reference_model/src/model_runner.cc +++ b/reference_model/src/model_runner.cc @@ -1,5 +1,5 @@ -// Copyright (c) 2022, ARM Limited. +// Copyright (c) 2022-2023, ARM Limited. // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. @@ -21,11 +21,11 @@ using namespace TosaReference; func_config_t g_func_config; func_debug_t g_func_debug; -IModelRunner::IModelRunner() : model_runner_impl(new ModelRunnerImpl()) +IModelRunner::IModelRunner() + : model_runner_impl(new ModelRunnerImpl()) {} -IModelRunner::IModelRunner(const func_config_t& func_config, - const func_debug_t& func_debug) +IModelRunner::IModelRunner(const func_config_t& func_config, const func_debug_t& func_debug) : model_runner_impl(new ModelRunnerImpl(func_config, func_debug)) {} diff --git a/reference_model/src/model_runner_impl.cc b/reference_model/src/model_runner_impl.cc index f1b70e7..ce548e9 100644 --- a/reference_model/src/model_runner_impl.cc +++ b/reference_model/src/model_runner_impl.cc @@ -20,11 +20,10 @@ using namespace TosaReference; ModelRunnerImpl::ModelRunnerImpl() {} -ModelRunnerImpl::ModelRunnerImpl(const func_config_t& func_config, - const func_debug_t& func_debug) +ModelRunnerImpl::ModelRunnerImpl(const func_config_t& func_config, const func_debug_t& func_debug) { g_func_config = func_config; - g_func_debug = func_debug; + g_func_debug = func_debug; } ModelRunnerImpl::~ModelRunnerImpl() @@ -339,10 +338,8 @@ GraphStatus ModelRunnerImpl::initialize(TosaSerializationBasicBlock* bb, void ModelRunnerImpl::validateTosaVersion(TosaSerializationHandler& serialization_handler) { - TosaVersion model_version(TOSA_REFERENCE_MODEL_VERSION_MAJOR, - TOSA_REFERENCE_MODEL_VERSION_MINOR, - TOSA_REFERENCE_MODEL_VERSION_PATCH, - TOSA_REFERENCE_MODEL_VERSION_DRAFT); + TosaVersion model_version(TOSA_REFERENCE_MODEL_VERSION_MAJOR, TOSA_REFERENCE_MODEL_VERSION_MINOR, + TOSA_REFERENCE_MODEL_VERSION_PATCH, TOSA_REFERENCE_MODEL_VERSION_DRAFT); TosaVersion::compat_t is_compat = model_version.is_compatible(serialization_handler.GetVersion()); switch (is_compat) diff --git a/reference_model/src/model_runner_impl.h b/reference_model/src/model_runner_impl.h index b43370c..aed8a1e 100644 --- a/reference_model/src/model_runner_impl.h +++ b/reference_model/src/model_runner_impl.h @@ -1,5 +1,5 @@ -// Copyright (c) 2022, ARM Limited. +// Copyright (c) 2022-2023, ARM Limited. // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. @@ -16,8 +16,8 @@ #ifndef MODEL_RUNNER_IMPL_H_ #define MODEL_RUNNER_IMPL_H_ -#include "model_runner.h" #include "graph_status.h" +#include "model_runner.h" #include "version.h" #include "array_proxy.h" diff --git a/reference_model/src/operators.cc b/reference_model/src/operators.cc index b6625d0..f3023dd 100644 --- a/reference_model/src/operators.cc +++ b/reference_model/src/operators.cc @@ -602,7 +602,7 @@ extern "C" TosaNoneAttribute attr; // Create tensors - tosa::TosaSerializationTensor* input = translate_client_tensor(client_input, "input"); + tosa::TosaSerializationTensor* input = translate_client_tensor(client_input, "input"); tosa::TosaSerializationTensor* output = translate_client_tensor(client_output, "output"); // Create operator diff --git a/reference_model/src/ops/activation_funcs.cc b/reference_model/src/ops/activation_funcs.cc index 12d0697..8ca77f7 100644 --- a/reference_model/src/ops/activation_funcs.cc +++ b/reference_model/src/ops/activation_funcs.cc @@ -14,9 +14,9 @@ // limitations under the License. #include "activation_funcs.h" +#include "arith_util.h" #include "quant_util.h" #include "template_types.h" -#include "arith_util.h" #include <cmath> using namespace TosaReference; @@ -34,17 +34,17 @@ int OpClamp<Rank, Dtype>::register_fcn() { case TOSA_REF_TYPE_FP16: case TOSA_REF_TYPE_BF16: - case TOSA_REF_TYPE_FP32: - { + case TOSA_REF_TYPE_FP32: { InEigenType min = (InEigenType)attribute->min_fp(); InEigenType max = (InEigenType)attribute->max_fp(); ERROR_IF(max < min, "OpClamp: max smaller than min"); - this->fcn = [min, max](InEigenType a) -> OutEigenType { return fpTrunc<Dtype>(a <= min ? min : a >= max ? max : a); }; + this->fcn = [min, max](InEigenType a) -> OutEigenType { + return fpTrunc<Dtype>(a <= min ? min : a >= max ? max : a); + }; } break; - case TOSA_REF_TYPE_FP64: - { + case TOSA_REF_TYPE_FP64: { InEigenType min = (InEigenType)attribute->min_fp(); InEigenType max = (InEigenType)attribute->max_fp(); ERROR_IF(max < min, "OpClamp: max smaller than min"); @@ -53,8 +53,7 @@ int OpClamp<Rank, Dtype>::register_fcn() } break; case TOSA_REF_TYPE_INT8: - case TOSA_REF_TYPE_INT16: - { + case TOSA_REF_TYPE_INT16: { InEigenType min = (InEigenType)attribute->min_int(); InEigenType max = (InEigenType)attribute->max_int(); ERROR_IF(max < min, "OpClamp: max smaller than min"); @@ -71,7 +70,8 @@ int OpClamp<Rank, Dtype>::register_fcn() template <int Rank, TOSA_REF_TYPE Dtype> OpClamp<Rank, Dtype>::~OpClamp() { - if (attribute) delete attribute; + if (attribute) + delete attribute; } template <int Rank, TOSA_REF_TYPE Dtype> @@ -86,9 +86,7 @@ int OpSigmoid<Rank, Dtype>::register_fcn() case TOSA_REF_TYPE_FP16: case TOSA_REF_TYPE_BF16: case TOSA_REF_TYPE_FP32: - this->fcn = [](InEigenType a) -> OutEigenType { - return fpTrunc<Dtype>(1.f / (1.f + (expf(-1.f * a)))); - }; + this->fcn = [](InEigenType a) -> OutEigenType { return fpTrunc<Dtype>(1.f / (1.f + (expf(-1.f * a)))); }; break; case TOSA_REF_TYPE_FP64: this->fcn = [](InEigenType a) -> OutEigenType { return (1.L / (1.L + (exp(-1.L * a)))); }; diff --git a/reference_model/src/ops/activation_funcs.h b/reference_model/src/ops/activation_funcs.h index a7e1275..1696668 100644 --- a/reference_model/src/ops/activation_funcs.h +++ b/reference_model/src/ops/activation_funcs.h @@ -86,8 +86,8 @@ public: { register_fcn(); } - using InEigenType = typename GetEigenType<Dtype>::type; - using OutEigenType = typename GetEigenType<Dtype>::type; + using InEigenType = typename GetEigenType<Dtype>::type; + using OutEigenType = typename GetEigenType<Dtype>::type; virtual int register_fcn(); }; diff --git a/reference_model/src/ops/control_flow.cc b/reference_model/src/ops/control_flow.cc index a0e1fc2..0afb7e2 100644 --- a/reference_model/src/ops/control_flow.cc +++ b/reference_model/src/ops/control_flow.cc @@ -95,20 +95,17 @@ int OpControlFlow::evalBlock(TosaSerializationBasicBlock* block, // pass block status back switch (block_sgt.getGraphStatus()) { - case GraphStatus::TOSA_VALID: - { + case GraphStatus::TOSA_VALID: { DEBUG_MED(OP, "Successfully evaluating block %s", block_name.c_str()); break; } - case GraphStatus::TOSA_UNPREDICTABLE: - { + case GraphStatus::TOSA_UNPREDICTABLE: { DEBUG_MED(OP, "Finish evaluating block %s but result is UNPREDICTABLE", block_name.c_str()); DEBUG_MED(OP, "Setting parent graph status to UNPREDICTABLE"); parent_sgt->setGraphStatus(GraphStatus::TOSA_UNPREDICTABLE); break; } - case GraphStatus::TOSA_ERROR: - { + case GraphStatus::TOSA_ERROR: { DEBUG_MED(OP, "Fail evaluating block %s. Result is ERROR", block_name.c_str()); if (parent_sgt->getGraphStatus() != GraphStatus::TOSA_UNPREDICTABLE) { @@ -311,7 +308,8 @@ OpWhileLoop::~OpWhileLoop() int OpWhileLoop::checkTensorAttributes() { - if (!tsh) { + if (!tsh) + { WARNING("OpWhileLoop: tosa serialization handler must not be null"); return 1; } diff --git a/reference_model/src/ops/data_layout.cc b/reference_model/src/ops/data_layout.cc index 86cd752..bc97c89 100644 --- a/reference_model/src/ops/data_layout.cc +++ b/reference_model/src/ops/data_layout.cc @@ -21,9 +21,7 @@ using namespace Eigen; using namespace tosa; template <int Rank, TOSA_REF_TYPE Dtype> -OpConcat<Rank, Dtype>::OpConcat(SubgraphTraverser* sgt_, - TosaAttributeBase* attribute_, - uint64_t id_) +OpConcat<Rank, Dtype>::OpConcat(SubgraphTraverser* sgt_, TosaAttributeBase* attribute_, uint64_t id_) : GraphNode(sgt_, Op_CONCAT, id_) { setRequiredOperands(-1, 1); @@ -125,9 +123,7 @@ int OpConcat<Rank, Dtype>::eval() } template <int Rank, TOSA_REF_TYPE Dtype> -OpPad<Rank, Dtype>::OpPad(SubgraphTraverser* sgt_, - TosaAttributeBase* attribute_, - uint64_t id_) +OpPad<Rank, Dtype>::OpPad(SubgraphTraverser* sgt_, TosaAttributeBase* attribute_, uint64_t id_) : GraphNode(sgt_, Op_PAD, id_) { setRequiredOperands(1, 1); @@ -138,8 +134,7 @@ OpPad<Rank, Dtype>::OpPad(SubgraphTraverser* sgt_, template <int Rank, TOSA_REF_TYPE Dtype> OpPad<Rank, Dtype>::~OpPad() -{ -} +{} template <int Rank, TOSA_REF_TYPE Dtype> int OpPad<Rank, Dtype>::checkTensorAttributes() @@ -215,9 +210,7 @@ int OpPad<Rank, Dtype>::eval() } template <int InRank, int OutRank, TOSA_REF_TYPE Dtype> -OpReshape<InRank, OutRank, Dtype>::OpReshape(SubgraphTraverser* sgt_, - TosaAttributeBase* attribute_, - uint64_t id_) +OpReshape<InRank, OutRank, Dtype>::OpReshape(SubgraphTraverser* sgt_, TosaAttributeBase* attribute_, uint64_t id_) : GraphNode(sgt_, Op_RESHAPE, id_) { setRequiredOperands(1, 1); @@ -251,40 +244,47 @@ int OpReshape<InRank, OutRank, Dtype>::checkTensorAttributes() } // -1 shape inferencing - auto inferred_size = -1; - auto inferred_dim = -1; - auto total_size = getInputs()[0]->getElementCount(); + auto inferred_size = -1; + auto inferred_dim = -1; + auto total_size = getInputs()[0]->getElementCount(); uint32_t accum_size = 1; for (int32_t d = 0; d < OutRank; d++) { auto curr_new_shape = attribute->new_shape()[d]; - if (curr_new_shape != -1) { + if (curr_new_shape != -1) + { accum_size *= curr_new_shape; - } else { + } + else + { ERROR_IF(inferred_dim != -1, "OpReshape: only 1 inferred dimension in output shape is supported"); inferred_dim = d; } } - ERROR_IF((total_size % accum_size) != 0, "OpReshape: shape inference failed, missing dimension would be non-integer"); + ERROR_IF((total_size % accum_size) != 0, + "OpReshape: shape inference failed, missing dimension would be non-integer"); inferred_size = total_size / accum_size; - if (inferred_dim != -1) { + if (inferred_dim != -1) + { getOutputs()[0]->setDimSize(inferred_dim, inferred_size); // Need to also edit the serializedTensor's shape at inferred_dim TosaSerializationTensor* serializedTensor; - for (auto region : parent_sgt->getTsh()->GetRegions()) { - for (auto block : region->GetBlocks()) { - if (block->GetTensorByName(getOutputs()[0]->getName())) { + for (auto region : parent_sgt->getTsh()->GetRegions()) + { + for (auto block : region->GetBlocks()) + { + if (block->GetTensorByName(getOutputs()[0]->getName())) + { serializedTensor = block->GetTensorByName(getOutputs()[0]->getName()); serializedTensor->SetDimSize(inferred_dim, inferred_size); break; } } } - } ERROR_IF(inputs[0]->getElementCount() != outputs[0]->getElementCount(), @@ -294,7 +294,7 @@ int OpReshape<InRank, OutRank, Dtype>::checkTensorAttributes() { auto curr_new_shape = attribute->new_shape()[d]; ERROR_IF(curr_new_shape != -1 && curr_new_shape != outputs[0]->getShape()[d], - "OpReshape: new_shape doesn't match output shape"); + "OpReshape: new_shape doesn't match output shape"); } in = dynamic_cast<TosaReference::TensorTemplate<TIn>*>(inputs[0]); @@ -308,7 +308,7 @@ int OpReshape<InRank, OutRank, Dtype>::eval() { for (int32_t d = 0; d < OutRank; d++) { - array_shape[d] = getOutputs()[0]->getShape()[OutRank - 1 - d]; + array_shape[d] = getOutputs()[0]->getShape()[OutRank - 1 - d]; out_reverser[d] = OutRank - 1 - d; } @@ -347,9 +347,7 @@ int OpReshape<InRank, OutRank, Dtype>::eval() } template <int Rank, TOSA_REF_TYPE Dtype> -OpReverse<Rank, Dtype>::OpReverse(SubgraphTraverser* sgt_, - TosaAttributeBase* attribute_, - uint64_t id_) +OpReverse<Rank, Dtype>::OpReverse(SubgraphTraverser* sgt_, TosaAttributeBase* attribute_, uint64_t id_) : GraphNode(sgt_, Op_REVERSE, id_) { setRequiredOperands(1, 1); @@ -418,9 +416,7 @@ int OpReverse<Rank, Dtype>::eval() } template <int Rank, TOSA_REF_TYPE Dtype> -OpSlice<Rank, Dtype>::OpSlice(SubgraphTraverser* sgt_, - TosaAttributeBase* attribute_, - uint64_t id_) +OpSlice<Rank, Dtype>::OpSlice(SubgraphTraverser* sgt_, TosaAttributeBase* attribute_, uint64_t id_) : GraphNode(sgt_, Op_SLICE, id_) { setRequiredOperands(1, 1); @@ -491,9 +487,7 @@ int OpSlice<Rank, Dtype>::eval() } template <int Rank, TOSA_REF_TYPE Dtype> -OpTileBase<Rank, Dtype>::OpTileBase(SubgraphTraverser* sgt_, - TosaAttributeBase* attribute_, - uint64_t id_) +OpTileBase<Rank, Dtype>::OpTileBase(SubgraphTraverser* sgt_, TosaAttributeBase* attribute_, uint64_t id_) : GraphNode(sgt_, Op_TILE, id_) { setRequiredOperands(1, 1); @@ -693,9 +687,7 @@ int OpTile<6, Dtype>::eval() } template <int Rank, TOSA_REF_TYPE Dtype> -OpTranspose<Rank, Dtype>::OpTranspose(SubgraphTraverser* sgt_, - TosaAttributeBase* attribute_, - uint64_t id_) +OpTranspose<Rank, Dtype>::OpTranspose(SubgraphTraverser* sgt_, TosaAttributeBase* attribute_, uint64_t id_) : GraphNode(sgt_, Op_TRANSPOSE, id_) { setRequiredOperands(1, 1); @@ -707,7 +699,8 @@ OpTranspose<Rank, Dtype>::OpTranspose(SubgraphTraverser* sgt_, template <int Rank, TOSA_REF_TYPE Dtype> OpTranspose<Rank, Dtype>::~OpTranspose() { - if (attribute) delete attribute; + if (attribute) + delete attribute; } template <int Rank, TOSA_REF_TYPE Dtype> diff --git a/reference_model/src/ops/data_nodes.cc b/reference_model/src/ops/data_nodes.cc index b7f987a..07e7e97 100644 --- a/reference_model/src/ops/data_nodes.cc +++ b/reference_model/src/ops/data_nodes.cc @@ -43,9 +43,7 @@ int OpConst::eval() } template <int Rank, TOSA_REF_TYPE Dtype> -OpIdentity<Rank, Dtype>::OpIdentity(SubgraphTraverser* sgt_, - TosaAttributeBase* attribute_, - uint64_t id_) +OpIdentity<Rank, Dtype>::OpIdentity(SubgraphTraverser* sgt_, TosaAttributeBase* attribute_, uint64_t id_) : GraphNode(sgt_, Op_IDENTITY, id_) { setRequiredOperands(1, 1); diff --git a/reference_model/src/ops/ewise_binary.cc b/reference_model/src/ops/ewise_binary.cc index 8578527..3fa4194 100644 --- a/reference_model/src/ops/ewise_binary.cc +++ b/reference_model/src/ops/ewise_binary.cc @@ -23,9 +23,7 @@ using namespace Eigen; using namespace tosa; template <int Rank, TOSA_REF_TYPE InDtype, TOSA_REF_TYPE OutDtype> -BinaryNodeBase<Rank, InDtype, OutDtype>::BinaryNodeBase(SubgraphTraverser* sgt_, - const Op& op_, - uint64_t id_) +BinaryNodeBase<Rank, InDtype, OutDtype>::BinaryNodeBase(SubgraphTraverser* sgt_, const Op& op_, uint64_t id_) : GraphNode(sgt_, op_, id_) { setRequiredOperands(2, 1); @@ -100,11 +98,16 @@ int BinaryNodeBase<Rank, InDtype, OutDtype>::broadcast(std::vector<int>& calcula // calculates the broadcasted output shape calculated_shape = a_shape; - for (size_t i = 0; i < calculated_shape.size(); i++) { - if (calculated_shape[i] == 1) { + for (size_t i = 0; i < calculated_shape.size(); i++) + { + if (calculated_shape[i] == 1) + { calculated_shape[i] = b_shape[i]; - } else { - ERROR_IF(b_shape[i] != 1 && b_shape[i] != calculated_shape[i], "Broadcast_shape failure, input shapes are not compatible"); + } + else + { + ERROR_IF(b_shape[i] != 1 && b_shape[i] != calculated_shape[i], + "Broadcast_shape failure, input shapes are not compatible"); } } @@ -118,7 +121,8 @@ int BinaryNode<Rank, InDtype, OutDtype>::eval() this->broadcast(calculated_shape); auto result_shape = this->result->getShape(); - ERROR_IF(calculated_shape != result_shape, "Broadcast_shape failure, calculated_shape and result_shape don't match"); + ERROR_IF(calculated_shape != result_shape, + "Broadcast_shape failure, calculated_shape and result_shape don't match"); Eigen::array<int, Rank> reshaper; reshaper.fill(1); @@ -210,7 +214,8 @@ int OpArithmeticRightShift<Rank, Dtype>::register_fcn() template <int Rank, TOSA_REF_TYPE Dtype> OpArithmeticRightShift<Rank, Dtype>::~OpArithmeticRightShift() { - if (attribute) delete attribute; + if (attribute) + delete attribute; } template <int Rank, TOSA_REF_TYPE Dtype> @@ -309,21 +314,21 @@ int OpLogicalLeftShift<Rank, Dtype>::register_fcn() case TOSA_REF_TYPE_INT8: this->fcn = [this](InEigenType a, InEigenType b) -> OutEigenType { REQUIRE(b >= 0 && b <= 31, "OpLogicalLeftShift: shift value %d is out of valid range [0, 31]", - (int32_t)b); + (int32_t)b); return static_cast<OutEigenType>(static_cast<int8_t>(a << b)); }; break; case TOSA_REF_TYPE_INT16: this->fcn = [this](InEigenType a, InEigenType b) -> OutEigenType { REQUIRE(b >= 0 && b <= 31, "OpLogicalLeftShift: shift value %d is out of valid range [0, 31]", - (int32_t)b); + (int32_t)b); return static_cast<OutEigenType>(static_cast<int16_t>(a << b)); }; break; case TOSA_REF_TYPE_INT32: this->fcn = [this](InEigenType a, InEigenType b) -> OutEigenType { REQUIRE(b >= 0 && b <= 31, "OpLogicalLeftShift: shift value %d is out of valid range [0, 31]", - (int32_t)b); + (int32_t)b); return static_cast<OutEigenType>(static_cast<int32_t>(a << b)); }; break; @@ -342,21 +347,21 @@ int OpLogicalRightShift<Rank, Dtype>::register_fcn() case TOSA_REF_TYPE_INT8: this->fcn = [this](InEigenType a, InEigenType b) -> OutEigenType { REQUIRE(b >= 0 && b <= 31, "OpLogicalRightShift: shift value %d is out of valid range [0, 31]", - (int32_t)b); + (int32_t)b); return static_cast<OutEigenType>(static_cast<int8_t>(a) >> b); }; break; case TOSA_REF_TYPE_INT16: this->fcn = [this](InEigenType a, InEigenType b) -> OutEigenType { REQUIRE(b >= 0 && b <= 31, "OpLogicalRightShift: shift value %d is out of valid range [0, 31]", - (int32_t)b); + (int32_t)b); return static_cast<OutEigenType>(static_cast<int16_t>(a) >> b); }; break; case TOSA_REF_TYPE_INT32: this->fcn = [this](InEigenType a, InEigenType b) -> OutEigenType { REQUIRE(b >= 0 && b <= 31, "OpLogicalRightShift: shift value %d is out of valid range [0, 31]", - (int32_t)b); + (int32_t)b); return static_cast<OutEigenType>(static_cast<int32_t>(a) >> b); }; break; @@ -494,7 +499,8 @@ int OpMul<Rank, InDtype, OutDtype>::register_fcn() template <int Rank, TOSA_REF_TYPE InDtype, TOSA_REF_TYPE OutDtype> OpMul<Rank, InDtype, OutDtype>::~OpMul() { - if (attribute) delete attribute; + if (attribute) + delete attribute; } template <int Rank, TOSA_REF_TYPE Dtype> @@ -547,9 +553,7 @@ int OpSub<Rank, Dtype>::register_fcn() } template <int Rank, TOSA_REF_TYPE InDtype> -OpTable<Rank, InDtype>::OpTable(SubgraphTraverser* sgt_, - TosaAttributeBase* attribute_, - uint64_t id_) +OpTable<Rank, InDtype>::OpTable(SubgraphTraverser* sgt_, TosaAttributeBase* attribute_, uint64_t id_) : GraphNode(sgt_, Op_TABLE, id_) { setRequiredOperands(1, 1); @@ -561,7 +565,8 @@ OpTable<Rank, InDtype>::OpTable(SubgraphTraverser* sgt_, template <int Rank, TOSA_REF_TYPE InDtype> OpTable<Rank, InDtype>::~OpTable() { - if (attribute) delete attribute; + if (attribute) + delete attribute; } template <int Rank, TOSA_REF_TYPE InDtype> @@ -624,10 +629,11 @@ int OpTable<Rank, InDtype>::eval() int32_t base = table[index]; int32_t next = table[index + 1]; int32_t slope = next - base; - REQUIRE(slope <= std::numeric_limits<int16_t>::max() && slope >= std::numeric_limits<int16_t>::min(), "OpTable: slope out of int16_t range"); + REQUIRE(slope <= std::numeric_limits<int16_t>::max() && slope >= std::numeric_limits<int16_t>::min(), + "OpTable: slope out of int16_t range"); // 4. interpolate, generate 16.7 (23-bit) output - int32_t value = (base << 7) + (slope) * frac; + int32_t value = (base << 7) + (slope)*frac; return value; }); diff --git a/reference_model/src/ops/ewise_binary.h b/reference_model/src/ops/ewise_binary.h index 3a6f24c..1215c93 100644 --- a/reference_model/src/ops/ewise_binary.h +++ b/reference_model/src/ops/ewise_binary.h @@ -137,9 +137,7 @@ template <int Rank, TOSA_REF_TYPE Dtype> class OpArithmeticRightShift : public BinaryNode<Rank, Dtype, Dtype> { public: - OpArithmeticRightShift(SubgraphTraverser* sgt_, - TosaAttributeBase* attribute_, - uint64_t id_) + OpArithmeticRightShift(SubgraphTraverser* sgt_, TosaAttributeBase* attribute_, uint64_t id_) : BinaryNode<Rank, Dtype, Dtype>(sgt_, Op_ARITHMETIC_RIGHT_SHIFT, id_) { INIT_ATTRIBUTE(ArithmeticRightShift); diff --git a/reference_model/src/ops/ewise_ternary.cc b/reference_model/src/ops/ewise_ternary.cc index fd2510f..5861cb2 100644 --- a/reference_model/src/ops/ewise_ternary.cc +++ b/reference_model/src/ops/ewise_ternary.cc @@ -20,9 +20,7 @@ using namespace Eigen; using namespace tosa; template <int Rank, TOSA_REF_TYPE Dtype> -OpSelectBase<Rank, Dtype>::OpSelectBase(SubgraphTraverser* sgt_, - TosaAttributeBase* attribute_, - uint64_t id_) +OpSelectBase<Rank, Dtype>::OpSelectBase(SubgraphTraverser* sgt_, TosaAttributeBase* attribute_, uint64_t id_) : GraphNode(sgt_, Op_SELECT, id_) { setRequiredOperands(3, 1); @@ -83,17 +81,26 @@ int OpSelect<Rank, Dtype>::broadcast(std::vector<int>& calculated_shape) // calculates the broadcasted output shape calculated_shape = cond_shape; - for (size_t i = 0; i < calculated_shape.size(); i++) { - if (calculated_shape[i] == 1) { + for (size_t i = 0; i < calculated_shape.size(); i++) + { + if (calculated_shape[i] == 1) + { calculated_shape[i] = then_shape[i]; - } else { - ERROR_IF(then_shape[i] != 1 && then_shape[i] != calculated_shape[i], "Broadcast_shape failure, input shapes are not compatible"); + } + else + { + ERROR_IF(then_shape[i] != 1 && then_shape[i] != calculated_shape[i], + "Broadcast_shape failure, input shapes are not compatible"); } - if (calculated_shape[i] == 1) { + if (calculated_shape[i] == 1) + { calculated_shape[i] = else_shape[i]; - } else { - ERROR_IF(else_shape[i] != 1 && else_shape[i] != calculated_shape[i], "Broadcast_shape failure, input shapes are not compatible"); + } + else + { + ERROR_IF(else_shape[i] != 1 && else_shape[i] != calculated_shape[i], + "Broadcast_shape failure, input shapes are not compatible"); } } @@ -107,7 +114,8 @@ int OpSelect<Rank, Dtype>::eval() this->broadcast(calculated_shape); auto result_shape = this->out->getShape(); - ERROR_IF(calculated_shape != result_shape, "Broadcast_shape failure, calculated_shape and result_shape don't match"); + ERROR_IF(calculated_shape != result_shape, + "Broadcast_shape failure, calculated_shape and result_shape don't match"); this->out->getTensor() = this->cond->getTensor() .broadcast(this->bcast_cond) diff --git a/reference_model/src/ops/ewise_unary.cc b/reference_model/src/ops/ewise_unary.cc index e6e870e..05c1f4b 100644 --- a/reference_model/src/ops/ewise_unary.cc +++ b/reference_model/src/ops/ewise_unary.cc @@ -237,9 +237,7 @@ int OpLogicalNot<Rank, Dtype>::register_fcn() } template <int Rank, TOSA_REF_TYPE Dtype> -OpNegate<Rank, Dtype>::OpNegate(SubgraphTraverser* sgt_, - TosaAttributeBase* attribute_, - uint64_t id_) +OpNegate<Rank, Dtype>::OpNegate(SubgraphTraverser* sgt_, TosaAttributeBase* attribute_, uint64_t id_) : UnaryNode<Rank, Dtype>(sgt_, Op_NEGATE, id_) { INIT_ATTRIBUTE(Negate); @@ -279,10 +277,11 @@ int OpNegate<Rank, Dtype>::register_fcn() case TOSA_REF_TYPE_INT16: case TOSA_REF_TYPE_INT32: this->fcn = [this](InEigenType a) -> OutEigenType { - int64_t res_in_64 = 0L - a; + int64_t res_in_64 = 0L - a; int64_t i32_max_in_64 = static_cast<int64_t>(std::numeric_limits<int32_t>::max()); int64_t i32_min_in_64 = static_cast<int64_t>(std::numeric_limits<int32_t>::min()); - REQUIRE(res_in_64 <= i32_max_in_64 && res_in_64 >= i32_min_in_64, "OpNegate: result not in acc type range (int32)"); + REQUIRE(res_in_64 <= i32_max_in_64 && res_in_64 >= i32_min_in_64, + "OpNegate: result not in acc type range (int32)"); int64_t max_clip_in_64, min_clip_in_64; if (Dtype == TOSA_REF_TYPE_INT16) @@ -295,17 +294,20 @@ int OpNegate<Rank, Dtype>::register_fcn() max_clip_in_64 = i32_max_in_64; min_clip_in_64 = i32_min_in_64; } - return static_cast<InEigenType>(std::min<int64_t>(max_clip_in_64, std::max<int64_t>(min_clip_in_64, res_in_64))); + return static_cast<InEigenType>( + std::min<int64_t>(max_clip_in_64, std::max<int64_t>(min_clip_in_64, res_in_64))); }; break; case TOSA_REF_TYPE_INT8: this->fcn = [this](InEigenType a) -> OutEigenType { - int64_t res_in_64 = 0 - (a - attribute->input1_zp()); + int64_t res_in_64 = 0 - (a - attribute->input1_zp()); int64_t i32_max_in_64 = static_cast<int64_t>(std::numeric_limits<int32_t>::max()); int64_t i32_min_in_64 = static_cast<int64_t>(std::numeric_limits<int32_t>::min()); - REQUIRE(res_in_64 <= i32_max_in_64 && res_in_64 >= i32_min_in_64, "OpNegate: result not in acc type range (int32)"); + REQUIRE(res_in_64 <= i32_max_in_64 && res_in_64 >= i32_min_in_64, + "OpNegate: result not in acc type range (int32)"); res_in_64 += attribute->output_zp(); - InEigenType result = static_cast<InEigenType>(std::min(std::max(res_in_64, static_cast<int64_t>(QMin)), static_cast<int64_t>(QMax))); + InEigenType result = static_cast<InEigenType>( + std::min(std::max(res_in_64, static_cast<int64_t>(QMin)), static_cast<int64_t>(QMax))); return result; }; break; diff --git a/reference_model/src/ops/image.cc b/reference_model/src/ops/image.cc index 575a500..cf2b60a 100644 --- a/reference_model/src/ops/image.cc +++ b/reference_model/src/ops/image.cc @@ -24,9 +24,7 @@ using namespace Eigen; using namespace tosa; template <TOSA_REF_TYPE InDtype, TOSA_REF_TYPE OutDtype, typename resize_t> -OpResize<InDtype, OutDtype, resize_t>::OpResize(SubgraphTraverser* sgt_, - TosaAttributeBase* attribute_, - uint64_t id_) +OpResize<InDtype, OutDtype, resize_t>::OpResize(SubgraphTraverser* sgt_, TosaAttributeBase* attribute_, uint64_t id_) : GraphNode(sgt_, Op_RESIZE, id_) { setRequiredOperands(1, 1); @@ -134,17 +132,19 @@ int OpResize<InDtype, OutDtype, resize_t>::eval() // Check Tosa Level auto tosa_level = g_func_config.tosa_level; - LEVEL_CHECK(scale_y_n / scale_y_d <= tosa_level.MAX_SCALE, "scale_y_n / scale_y_d should be smaller than or equal to MAX_SCALE"); - LEVEL_CHECK(scale_x_n / scale_x_d <= tosa_level.MAX_SCALE, "scale_x_n / scale_x_d should be smaller than or equal to MAX_SCALE"); + LEVEL_CHECK(scale_y_n / scale_y_d <= tosa_level.MAX_SCALE, + "scale_y_n / scale_y_d should be smaller than or equal to MAX_SCALE"); + LEVEL_CHECK(scale_x_n / scale_x_d <= tosa_level.MAX_SCALE, + "scale_x_n / scale_x_d should be smaller than or equal to MAX_SCALE"); int32_t res_height = 0; - int32_t res_width = 0; + int32_t res_width = 0; if (idiv_check((in_height - 1) * scale_y_n - offset_y + border_y, scale_y_d, res_height)) - return 1; + return 1; if (idiv_check((in_width - 1) * scale_x_n - offset_x + border_x, scale_x_d, res_width)) - return 1; + return 1; ERROR_IF(out_height != res_height + 1, "OpResize: mismatch between output height dimension provided and expected shape"); @@ -247,8 +247,10 @@ int OpResize<InDtype, OutDtype, resize_t>::eval() } acc = in->getTensor()(b, iy, ix, c); } - if ((typeid(resize_t) == typeid(Eigen::bfloat16))) { - ASSERT_MSG(checkValidBFloat(acc), "Resize accumulator float value is not a valid bfloat16 value."); + if ((typeid(resize_t) == typeid(Eigen::bfloat16))) + { + ASSERT_MSG(checkValidBFloat(acc), + "Resize accumulator float value is not a valid bfloat16 value."); } out->getTensor()(b, oy, ox, c) = acc; } diff --git a/reference_model/src/ops/op_factory.h b/reference_model/src/ops/op_factory.h index f276e03..06ef36e 100644 --- a/reference_model/src/ops/op_factory.h +++ b/reference_model/src/ops/op_factory.h @@ -110,7 +110,7 @@ FATAL_ERROR("Can't initialize Tosa" #ATTRIBUTE_NAME "Attribute.\nPre-initialization " \ "of this attribute is required in order to determine the accumulate type."); \ } \ - ConvertDType(accumDType); \ + ConvertDType(accumDType); \ }) #define DEF_FACTORY_TWO_TYPE_RESIZE_INT16(OP, DTYPE1, DTYPE2) \ @@ -192,8 +192,7 @@ { \ switch (inputRank) \ { \ - case 0: \ - { \ + case 0: { \ switch (outputRank) \ { \ DEF_FACTORY_TWO_RANK_ONE_TYPE(OP, 0, 0, DTYPE) \ @@ -205,8 +204,7 @@ DEF_FACTORY_TWO_RANK_ONE_TYPE(OP, 0, 6, DTYPE) \ } \ } \ - case 1: \ - { \ + case 1: { \ switch (outputRank) \ { \ DEF_FACTORY_TWO_RANK_ONE_TYPE(OP, 1, 0, DTYPE) \ @@ -218,8 +216,7 @@ DEF_FACTORY_TWO_RANK_ONE_TYPE(OP, 1, 6, DTYPE) \ } \ } \ - case 2: \ - { \ + case 2: { \ switch (outputRank) \ { \ DEF_FACTORY_TWO_RANK_ONE_TYPE(OP, 2, 0, DTYPE) \ @@ -231,8 +228,7 @@ DEF_FACTORY_TWO_RANK_ONE_TYPE(OP, 2, 6, DTYPE) \ } \ } \ - case 3: \ - { \ + case 3: { \ switch (outputRank) \ { \ DEF_FACTORY_TWO_RANK_ONE_TYPE(OP, 3, 0, DTYPE) \ @@ -244,8 +240,7 @@ DEF_FACTORY_TWO_RANK_ONE_TYPE(OP, 3, 6, DTYPE) \ } \ } \ - case 4: \ - { \ + case 4: { \ switch (outputRank) \ { \ DEF_FACTORY_TWO_RANK_ONE_TYPE(OP, 4, 0, DTYPE) \ @@ -257,8 +252,7 @@ DEF_FACTORY_TWO_RANK_ONE_TYPE(OP, 4, 6, DTYPE) \ } \ } \ - case 5: \ - { \ + case 5: { \ switch (outputRank) \ { \ DEF_FACTORY_TWO_RANK_ONE_TYPE(OP, 5, 0, DTYPE) \ @@ -270,8 +264,7 @@ DEF_FACTORY_TWO_RANK_ONE_TYPE(OP, 5, 6, DTYPE) \ } \ } \ - case 6: \ - { \ + case 6: { \ switch (outputRank) \ { \ DEF_FACTORY_TWO_RANK_ONE_TYPE(OP, 6, 0, DTYPE) \ diff --git a/reference_model/src/ops/reduction.cc b/reference_model/src/ops/reduction.cc index 639da8d..2eb764a 100644 --- a/reference_model/src/ops/reduction.cc +++ b/reference_model/src/ops/reduction.cc @@ -83,27 +83,44 @@ int ReduceNode<Rank, Dtype>::checkTensorAttributes() // These 2 reducers are to overcome a bug introduced in Eigen between 3.3.7 and 3.4.0 // The in-built .any and .all operations now fail on an assert in TensorMorphing.h:150 // which seems to be due to incorrect data being passed internally as m_impl -struct AllReducer { +struct AllReducer +{ static const bool PacketAccess = false; - void reduce(const bool val, bool* accum) { + void reduce(const bool val, bool* accum) + { *accum = *accum && val; } - bool initialize() const { return true; } - bool finalize(const bool accum) const { return accum; } + bool initialize() const + { + return true; + } + bool finalize(const bool accum) const + { + return accum; + } }; -struct AnyReducer { +struct AnyReducer +{ static const bool PacketAccess = false; - void reduce(const bool val, bool* accum) { + void reduce(const bool val, bool* accum) + { *accum = *accum || val; } - bool initialize() const { return false; } - bool finalize(const bool accum) const { return accum; } + bool initialize() const + { + return false; + } + bool finalize(const bool accum) const + { + return accum; + } }; template <int Rank, TOSA_REF_TYPE Dtype> int OpReduceAll<Rank, Dtype>::eval() { - this->out->getTensor() = this->in->getTensor().reduce(this->dims, AllReducer()).reshape(this->out->getTensor().dimensions()); + this->out->getTensor() = + this->in->getTensor().reduce(this->dims, AllReducer()).reshape(this->out->getTensor().dimensions()); return GraphNode::eval(); } @@ -111,7 +128,8 @@ int OpReduceAll<Rank, Dtype>::eval() template <int Rank, TOSA_REF_TYPE Dtype> int OpReduceAny<Rank, Dtype>::eval() { - this->out->getTensor() = this->in->getTensor().reduce(this->dims, AnyReducer()).reshape(this->out->getTensor().dimensions()); + this->out->getTensor() = + this->in->getTensor().reduce(this->dims, AnyReducer()).reshape(this->out->getTensor().dimensions()); return GraphNode::eval(); } @@ -135,14 +153,18 @@ int OpReduceMin<Rank, Dtype>::eval() template <int Rank, TOSA_REF_TYPE Dtype> int OpReduceProduct<Rank, Dtype>::eval() { - switch(Dtype) + switch (Dtype) { case TOSA_REF_TYPE_FP16: case TOSA_REF_TYPE_BF16: - this->out->getTensor() = this->in->getTensor().prod(this->dims).reshape(this->out->getTensor().dimensions()).unaryExpr([](float f){return fpTrunc<Dtype>(f);}); + this->out->getTensor() = this->in->getTensor() + .prod(this->dims) + .reshape(this->out->getTensor().dimensions()) + .unaryExpr([](float f) { return fpTrunc<Dtype>(f); }); break; case TOSA_REF_TYPE_FP32: - this->out->getTensor() = this->in->getTensor().prod(this->dims).reshape(this->out->getTensor().dimensions()); + this->out->getTensor() = + this->in->getTensor().prod(this->dims).reshape(this->out->getTensor().dimensions()); break; default: ERROR_IF(true, "unsupported TOSA_REF_TYPE %s", EnumNameTOSAREFTYPE(Dtype)); @@ -188,7 +210,7 @@ int OpReduceProductDouble<Rank, Dtype>::eval() template <int Rank, TOSA_REF_TYPE Dtype> int OpReduceSum<Rank, Dtype>::eval() { - switch(Dtype) + switch (Dtype) { case TOSA_REF_TYPE_FP16: case TOSA_REF_TYPE_BF16: @@ -208,20 +230,30 @@ int OpReduceSum<Rank, Dtype>::eval() return GraphNode::eval(); } -struct SumRequiresReducer { +struct SumRequiresReducer +{ static const bool PacketAccess = false; - SumRequiresReducer(SubgraphTraverser* parent_sgt) : parent_sgt(parent_sgt) {} - void reduce(const int32_t val, int32_t* accum) { + SumRequiresReducer(SubgraphTraverser* parent_sgt) + : parent_sgt(parent_sgt) + {} + void reduce(const int32_t val, int32_t* accum) + { int64_t res_in_64 = static_cast<int64_t>(*accum) + val; int64_t i32_max_in_64 = static_cast<int64_t>(std::numeric_limits<int32_t>::max()); int64_t i32_min_in_64 = static_cast<int64_t>(std::numeric_limits<int32_t>::min()); REQUIRE(res_in_64 <= i32_max_in_64 && res_in_64 >= i32_min_in_64, "OpReduceSum: result not in i32 range"); *accum = static_cast<int32_t>(res_in_64); } - int32_t initialize() const { return 0; } - int32_t finalize(const int32_t accum) const { return accum; } + int32_t initialize() const + { + return 0; + } + int32_t finalize(const int32_t accum) const + { + return accum; + } - private: +private: SubgraphTraverser* parent_sgt; }; diff --git a/reference_model/src/ops/scatter_gather.cc b/reference_model/src/ops/scatter_gather.cc index 80b6c58..65d61b6 100644 --- a/reference_model/src/ops/scatter_gather.cc +++ b/reference_model/src/ops/scatter_gather.cc @@ -21,9 +21,7 @@ using namespace Eigen; using namespace tosa; template <TOSA_REF_TYPE Dtype> -OpGather<Dtype>::OpGather(SubgraphTraverser* sgt_, - TosaAttributeBase* attribute_, - uint64_t id_) +OpGather<Dtype>::OpGather(SubgraphTraverser* sgt_, TosaAttributeBase* attribute_, uint64_t id_) : GraphNode(sgt_, Op_GATHER, id_) { setRequiredOperands(2, 1); @@ -117,9 +115,7 @@ int OpGather<Dtype>::eval() } template <TOSA_REF_TYPE Dtype> -OpScatter<Dtype>::OpScatter(SubgraphTraverser* sgt_, - TosaAttributeBase* attribute_, - uint64_t id_) +OpScatter<Dtype>::OpScatter(SubgraphTraverser* sgt_, TosaAttributeBase* attribute_, uint64_t id_) : GraphNode(sgt_, Op_SCATTER, id_) { setRequiredOperands(3, 1); diff --git a/reference_model/src/ops/tensor_ops.cc b/reference_model/src/ops/tensor_ops.cc index 5ba7a83..96fd41c 100644 --- a/reference_model/src/ops/tensor_ops.cc +++ b/reference_model/src/ops/tensor_ops.cc @@ -14,9 +14,9 @@ // limitations under the License. #include "tensor_ops.h" +#include "half.hpp" #include "quant_util.h" #include "template_types.h" -#include "half.hpp" using namespace TosaReference; using namespace Eigen; @@ -96,19 +96,16 @@ int check_pool2d_attribute(tosa::TosaPoolAttribute* attribute, int32_t full_H = IH + pad_top + pad_bottom - kernel_y; int32_t full_W = IW + pad_left + pad_right - kernel_x; - if ((full_H % stride_y != 0) || - (full_W % stride_x != 0)) + if ((full_H % stride_y != 0) || (full_W % stride_x != 0)) { msg = "Parameters must yield exact integer output dimensions"; return 1; } - if ((OH != (full_H / stride_y) + 1) || - (OW != (full_W / stride_x) + 1)) + if ((OH != (full_H / stride_y) + 1) || (OW != (full_W / stride_x) + 1)) { msg = "Mismatch between output shape provided and expected output shape (" + - std::to_string((full_H / stride_y) + 1) + "," + - std::to_string((full_W / stride_x) + 1) + ")"; + std::to_string((full_H / stride_y) + 1) + "," + std::to_string((full_W / stride_x) + 1) + ")"; return 1; } @@ -173,19 +170,19 @@ int check_conv_attribute(tosa::TosaConvAttribute* attribute, ASSERT_MSG(conv_dimension == 2 || conv_dimension == 3, "Unsupported convolution dimension") int32_t offset_d = conv_dimension == 3 ? 1 : 0; - int32_t ID = conv_dimension == 3 ? input_shape[1] : 1; - int32_t IH = input_shape[1 + offset_d]; - int32_t IW = input_shape[2 + offset_d]; - int32_t OD = conv_dimension == 3 ? output_shape[1] : 1; - int32_t OH = output_shape[1 + offset_d]; - int32_t OW = output_shape[2 + offset_d]; - - int32_t stride_d = conv_dimension == 3 ? attribute->stride()[0] : 1; - int32_t stride_y = attribute->stride()[0 + offset_d]; - int32_t stride_x = attribute->stride()[1 + offset_d]; - int32_t kernel_d = conv_dimension == 3 ? weights[offset_kernel] : 1; - int32_t kernel_h = weights[offset_kernel + offset_d]; - int32_t kernel_w = weights[offset_kernel + 1 + offset_d]; + int32_t ID = conv_dimension == 3 ? input_shape[1] : 1; + int32_t IH = input_shape[1 + offset_d]; + int32_t IW = input_shape[2 + offset_d]; + int32_t OD = conv_dimension == 3 ? output_shape[1] : 1; + int32_t OH = output_shape[1 + offset_d]; + int32_t OW = output_shape[2 + offset_d]; + + int32_t stride_d = conv_dimension == 3 ? attribute->stride()[0] : 1; + int32_t stride_y = attribute->stride()[0 + offset_d]; + int32_t stride_x = attribute->stride()[1 + offset_d]; + int32_t kernel_d = conv_dimension == 3 ? weights[offset_kernel] : 1; + int32_t kernel_h = weights[offset_kernel + offset_d]; + int32_t kernel_w = weights[offset_kernel + 1 + offset_d]; int32_t dilation_d = conv_dimension == 3 ? attribute->dilation()[0] : 1; int32_t dilation_y = attribute->dilation()[0 + offset_d]; int32_t dilation_x = attribute->dilation()[1 + offset_d]; @@ -202,27 +199,21 @@ int check_conv_attribute(tosa::TosaConvAttribute* attribute, int32_t full_H = IH - 1 + pad_top + pad_bottom - (kernel_h - 1) * dilation_y; int32_t full_W = IW - 1 + pad_left + pad_right - (kernel_w - 1) * dilation_x; - if ((full_H % stride_y != 0) || - (full_W % stride_x != 0) || - (full_D % stride_d != 0)) + if ((full_H % stride_y != 0) || (full_W % stride_x != 0) || (full_D % stride_d != 0)) { msg = "Parameters must yield exact integer output dimensions"; return 1; } - if ((OH != (full_H / stride_y) + 1) || - (OW != (full_W / stride_x) + 1) || - (OD != (full_D / stride_d) + 1)) + if ((OH != (full_H / stride_y) + 1) || (OW != (full_W / stride_x) + 1) || (OD != (full_D / stride_d) + 1)) { std::string msg_d = ""; if (conv_dimension == 3) { msg_d += std::to_string((full_D / stride_d) + 1) + ","; } - msg = "Mismatch between output shape provided and expected output shape (" + - msg_d + - std::to_string((full_H / stride_y) + 1) + "," + - std::to_string((full_W / stride_x) + 1) + ")"; + msg = "Mismatch between output shape provided and expected output shape (" + msg_d + + std::to_string((full_H / stride_y) + 1) + "," + std::to_string((full_W / stride_x) + 1) + ")"; return 1; } @@ -244,12 +235,10 @@ int check_fft_shape(const std::vector<int32_t>& in_real, const std::vector<int32_t>& in_imag, const std::vector<int32_t>& out_real, const std::vector<int32_t>& out_imag, - std::string& msg) { - const bool is_rfft = in_imag.empty(); - auto is_power_of_two = [](int32_t n) -> bool - { - return (n & (n-1)) == 0 && n > 0; - }; + std::string& msg) +{ + const bool is_rfft = in_imag.empty(); + auto is_power_of_two = [](int32_t n) -> bool { return (n & (n - 1)) == 0 && n > 0; }; if (!is_power_of_two(in_real[1]) || !is_power_of_two(in_real[2])) { @@ -309,7 +298,9 @@ int check_fft_shape(const std::vector<int32_t>& in_real, msg = "Output width is expected to match input width / 2 + 1"; return 1; } - } else { + } + else + { if (in_real[2] != out_real[2]) { msg = "Input and output width don't match"; @@ -321,9 +312,7 @@ int check_fft_shape(const std::vector<int32_t>& in_real, } template <int Rank, TOSA_REF_TYPE Dtype> -OpArgMax<Rank, Dtype>::OpArgMax(SubgraphTraverser* sgt_, - TosaAttributeBase* attribute_, - uint64_t id_) +OpArgMax<Rank, Dtype>::OpArgMax(SubgraphTraverser* sgt_, TosaAttributeBase* attribute_, uint64_t id_) : GraphNode(sgt_, Op_ARGMAX, id_) { setRequiredOperands(1, 1); @@ -417,9 +406,7 @@ int OpArgMax<Rank, Dtype>::eval() } template <TOSA_REF_TYPE Dtype, TOSA_REF_TYPE AccDtype> -OpAvgPool2d<Dtype, AccDtype>::OpAvgPool2d(SubgraphTraverser* sgt_, - TosaAttributeBase* attribute_, - uint64_t id_) +OpAvgPool2d<Dtype, AccDtype>::OpAvgPool2d(SubgraphTraverser* sgt_, TosaAttributeBase* attribute_, uint64_t id_) : GraphNode(sgt_, Op_AVG_POOL2D, id_) { setRequiredOperands(1, 1); @@ -485,7 +472,8 @@ ETensor1<int32_t> OpAvgPool2d<Dtype, AccDtype>::calculate_div_map_1d( // adjust divisors on the left side for padding // We start at the leftmost output element, and remove pad_left - (index * stride) elements // until we have no more padding being used - for(int index = 0; (index <= pad_left / stride) && (index < out_size); index++) { + for (int index = 0; (index <= pad_left / stride) && (index < out_size); index++) + { int32_t adjust = pad_left - (index * stride); result(index) -= adjust; } @@ -495,7 +483,8 @@ ETensor1<int32_t> OpAvgPool2d<Dtype, AccDtype>::calculate_div_map_1d( // padding is defined in the initialization of index below. Then we keep moving // to the right, increasing padding until we get to the last output element. int index = std::max(0, ((pad_left + in_size - kernel_size) / stride) + 1); - for (; index < out_size; index++) { + for (; index < out_size; index++) + { int32_t adjust = ((index * stride) + kernel_size) - (pad_left + in_size); result(index) -= adjust; } @@ -524,10 +513,10 @@ int OpAvgPool2d<Dtype, AccDtype>::eval() int pad_bottom = this->attribute->pad()[1]; int pad_left = this->attribute->pad()[2]; int pad_right = this->attribute->pad()[3]; - int kernel_y = this->attribute->kernel()[0]; - int kernel_x = this->attribute->kernel()[1]; - int stride_y = this->attribute->stride()[0]; - int stride_x = this->attribute->stride()[1]; + int kernel_y = this->attribute->kernel()[0]; + int kernel_x = this->attribute->kernel()[1]; + int stride_y = this->attribute->stride()[0]; + int stride_x = this->attribute->stride()[1]; // Check Tosa Level auto tosa_level = g_func_config.tosa_level; @@ -613,12 +602,11 @@ int OpAvgPool2d<Dtype, AccDtype>::eval() Eigen::array<Eigen::IndexPair<Eigen::Index>, 1> contract_dims = { Eigen::IndexPair<Eigen::Index>(1, 0) }; Eigen::array<Eigen::Index, 4> bcast{ out_batch, 1, 1, out_channels }; - ETensor2<int32_t> dm2_w = div_map_w.reshape(Eigen::array<Eigen::Index, 2>{ 1, out_width }); - ETensor2<int32_t> dm2_h = div_map_h.reshape(Eigen::array<Eigen::Index, 2>{ out_height, 1 }); - ETensor4<int32_t> div_map = - dm2_h.contract(dm2_w, contract_dims) - .reshape(Eigen::array<Eigen::Index, 4>{ 1, out_height, out_width, 1 }) - .broadcast(bcast); + ETensor2<int32_t> dm2_w = div_map_w.reshape(Eigen::array<Eigen::Index, 2>{ 1, out_width }); + ETensor2<int32_t> dm2_h = div_map_h.reshape(Eigen::array<Eigen::Index, 2>{ out_height, 1 }); + ETensor4<int32_t> div_map = dm2_h.contract(dm2_w, contract_dims) + .reshape(Eigen::array<Eigen::Index, 4>{ 1, out_height, out_width, 1 }) + .broadcast(bcast); if (Dtype != TOSA_REF_TYPE_FP32 && Dtype != TOSA_REF_TYPE_FP16 && Dtype != TOSA_REF_TYPE_BF16 && Dtype != TOSA_REF_TYPE_FP64) { @@ -649,9 +637,7 @@ int OpAvgPool2d<Dtype, AccDtype>::eval() } template <TOSA_REF_TYPE InDtype, TOSA_REF_TYPE WeightDtype, TOSA_REF_TYPE OutDtype> -OpConv2d<InDtype, WeightDtype, OutDtype>::OpConv2d(SubgraphTraverser* sgt_, - TosaAttributeBase* attribute_, - uint64_t id_) +OpConv2d<InDtype, WeightDtype, OutDtype>::OpConv2d(SubgraphTraverser* sgt_, TosaAttributeBase* attribute_, uint64_t id_) : GraphNode(sgt_, Op_CONV2D, id_) { setRequiredOperands(3, 1); @@ -685,7 +671,7 @@ int OpConv2d<InDtype, WeightDtype, OutDtype>::checkTensorAttributes() } ERROR_IF(outputs[0]->getDtype() != OutDtype, - "OpConv2d: Output data type not supported for this configuration of operator"); + "OpConv2d: Output data type not supported for this configuration of operator"); input = dynamic_cast<TosaReference::TensorTemplate<TIn>*>(inputs[0]); weight = dynamic_cast<TosaReference::TensorTemplate<TWeight>*>(inputs[1]); @@ -694,7 +680,7 @@ int OpConv2d<InDtype, WeightDtype, OutDtype>::checkTensorAttributes() std::string msg; if (check_conv_attribute(attribute, 2 /* conv_dimension */, input->getShape(), output->getShape(), - weight->getShape(), 1 /* offset_kernel */, InDtype, WeightDtype, msg)) + weight->getShape(), 1 /* offset_kernel */, InDtype, WeightDtype, msg)) { msg = "OpConv2d: " + msg; printNodeValidationError(msg.c_str()); @@ -736,15 +722,17 @@ int OpConv2d<InDtype, WeightDtype, OutDtype>::eval() int pad_left = this->attribute->pad()[2]; int pad_right = this->attribute->pad()[3]; - int stride_y = this->attribute->stride()[0]; - int stride_x = this->attribute->stride()[1]; - int dilation_y = this->attribute->dilation()[0]; - int dilation_x = this->attribute->dilation()[1]; + int stride_y = this->attribute->stride()[0]; + int stride_x = this->attribute->stride()[1]; + int dilation_y = this->attribute->dilation()[0]; + int dilation_x = this->attribute->dilation()[1]; // Check Tosa Level auto tosa_level = g_func_config.tosa_level; - LEVEL_CHECK(dilation_y * f_height <= tosa_level.MAX_KERNEL, "dilation_y * KH should be smaller than or equal to MAX_KERNEL"); - LEVEL_CHECK(dilation_x * f_width <= tosa_level.MAX_KERNEL, "dilation_x * KW should be smaller than or equal to MAX_KERNEL"); + LEVEL_CHECK(dilation_y * f_height <= tosa_level.MAX_KERNEL, + "dilation_y * KH should be smaller than or equal to MAX_KERNEL"); + LEVEL_CHECK(dilation_x * f_width <= tosa_level.MAX_KERNEL, + "dilation_x * KW should be smaller than or equal to MAX_KERNEL"); LEVEL_CHECK(pad_top <= tosa_level.MAX_KERNEL, "pad_top should be smaller than or equal to MAX_KERNEL"); LEVEL_CHECK(pad_bottom <= tosa_level.MAX_KERNEL, "pad_bottom should be smaller than or equal to MAX_KERNEL"); LEVEL_CHECK(pad_left <= tosa_level.MAX_KERNEL, "pad_left should be smaller than or equal to MAX_KERNEL"); @@ -756,8 +744,8 @@ int OpConv2d<InDtype, WeightDtype, OutDtype>::eval() "perform OpConv2d, input.shape=[%d,%d,%d,%d], weight.shape=[%d,%d,%d,%d], output.shape=[%d,%d,%d,%d], " "stride=[%d,%d], dilation=[%d,%d], pad=[%d,%d,%d,%d]", in_batch, in_height, in_width, in_channels, f_height, f_width, f_in_channels, f_out_channels, out_batch, - out_height, out_width, out_channels, stride_y, stride_x, dilation_y, dilation_x, pad_top, - pad_bottom, pad_left, pad_right); + out_height, out_width, out_channels, stride_y, stride_x, dilation_y, dilation_x, pad_top, pad_bottom, + pad_left, pad_right); // GEMM-conv2d, left matrix is input, right matrix is weight Eigen::array<Eigen::Index, 2> im2col_input_dims; @@ -827,7 +815,7 @@ int OpConv2d<InDtype, WeightDtype, OutDtype>::eval() // transpose and reshape weight from [OC, H, W, IC] to [H * W * IC, OC] ETensor2<WeightEigenType> im2col_weight = - weight_val.shuffle(Eigen::array<Eigen::Index, 4>({ 1, 2, 3, 0 })).reshape(im2col_weight_dims); + weight_val.shuffle(Eigen::array<Eigen::Index, 4>({ 1, 2, 3, 0 })).reshape(im2col_weight_dims); // don't need to apply bias_multiplier ( * bias_scale and >> bias_shift) since tflite already scale it // and reshaped from [C] to [1, C], and broadcast to [N * H * W, C] @@ -835,8 +823,9 @@ int OpConv2d<InDtype, WeightDtype, OutDtype>::eval() (bias_val.reshape(bias_reshaped_dims).broadcast(bias_bcast_dims)).template cast<OutEigenType>(); // output matrix is [N * H * W, C] - ETensor2<OutEigenType> contracted_result = - (im2col_input.template cast<AccEigenType>().contract(im2col_weight.template cast<AccEigenType>(), contract_dims)).template cast<OutEigenType>(); + ETensor2<OutEigenType> contracted_result = (im2col_input.template cast<AccEigenType>().contract( + im2col_weight.template cast<AccEigenType>(), contract_dims)) + .template cast<OutEigenType>(); // adding bias ETensor2<OutEigenType> biased_output = contracted_result + bias_2d; @@ -854,9 +843,7 @@ int OpConv2d<InDtype, WeightDtype, OutDtype>::eval() } template <TOSA_REF_TYPE InDtype, TOSA_REF_TYPE WeightDtype, TOSA_REF_TYPE OutDtype> -OpConv3d<InDtype, WeightDtype, OutDtype>::OpConv3d(SubgraphTraverser* sgt_, - TosaAttributeBase* attribute_, - uint64_t id_) +OpConv3d<InDtype, WeightDtype, OutDtype>::OpConv3d(SubgraphTraverser* sgt_, TosaAttributeBase* attribute_, uint64_t id_) : GraphNode(sgt_, Op_CONV3D, id_) { setRequiredOperands(3, 1); @@ -890,7 +877,7 @@ int OpConv3d<InDtype, WeightDtype, OutDtype>::checkTensorAttributes() } ERROR_IF(outputs[0]->getDtype() != OutDtype, - "OpConv3d: Output data type not supported for this configuration of operator"); + "OpConv3d: Output data type not supported for this configuration of operator"); input = dynamic_cast<TosaReference::TensorTemplate<TIn>*>(inputs[0]); weight = dynamic_cast<TosaReference::TensorTemplate<TWeight>*>(inputs[1]); @@ -899,7 +886,7 @@ int OpConv3d<InDtype, WeightDtype, OutDtype>::checkTensorAttributes() std::string msg; if (check_conv_attribute(attribute, 3 /* conv_dimension */, input->getShape(), output->getShape(), - weight->getShape(), 1 /* offset_kernel */, InDtype, WeightDtype, msg)) + weight->getShape(), 1 /* offset_kernel */, InDtype, WeightDtype, msg)) { msg = "OpConv3d: " + msg; printNodeValidationError(msg.c_str()); @@ -946,19 +933,22 @@ int OpConv3d<InDtype, WeightDtype, OutDtype>::eval() int pad_left = this->attribute->pad()[4]; int pad_right = this->attribute->pad()[5]; - int stride_d = this->attribute->stride()[0]; - int stride_y = this->attribute->stride()[1]; - int stride_x = this->attribute->stride()[2]; + int stride_d = this->attribute->stride()[0]; + int stride_y = this->attribute->stride()[1]; + int stride_x = this->attribute->stride()[2]; - int dilation_d = this->attribute->dilation()[0]; - int dilation_y = this->attribute->dilation()[1]; - int dilation_x = this->attribute->dilation()[2]; + int dilation_d = this->attribute->dilation()[0]; + int dilation_y = this->attribute->dilation()[1]; + int dilation_x = this->attribute->dilation()[2]; // Check Tosa Level auto tosa_level = g_func_config.tosa_level; - LEVEL_CHECK(dilation_d * f_depth <= tosa_level.MAX_KERNEL, "dilation_d * KD should be smaller than or equal to MAX_KERNEL"); - LEVEL_CHECK(dilation_y * f_height <= tosa_level.MAX_KERNEL, "dilation_y * KH should be smaller than or equal to MAX_KERNEL"); - LEVEL_CHECK(dilation_x * f_width <= tosa_level.MAX_KERNEL, "dilation_x * KW should be smaller than or equal to MAX_KERNEL"); + LEVEL_CHECK(dilation_d * f_depth <= tosa_level.MAX_KERNEL, + "dilation_d * KD should be smaller than or equal to MAX_KERNEL"); + LEVEL_CHECK(dilation_y * f_height <= tosa_level.MAX_KERNEL, + "dilation_y * KH should be smaller than or equal to MAX_KERNEL"); + LEVEL_CHECK(dilation_x * f_width <= tosa_level.MAX_KERNEL, + "dilation_x * KW should be smaller than or equal to MAX_KERNEL"); LEVEL_CHECK(pad_d0 <= tosa_level.MAX_KERNEL, "pad_d0 should be smaller than or equal to MAX_KERNEL"); LEVEL_CHECK(pad_d1 <= tosa_level.MAX_KERNEL, "pad_d1 should be smaller than or equal to MAX_KERNEL"); LEVEL_CHECK(pad_top <= tosa_level.MAX_KERNEL, "pad_top should be smaller than or equal to MAX_KERNEL"); @@ -1103,7 +1093,7 @@ int OpDepthwiseConv2d<InDtype, WeightDtype, OutDtype>::checkTensorAttributes() } ERROR_IF(outputs[0]->getDtype() != OutDtype, - "OpDepthwiseConv2d: Output data type not supported for this configuration of operator"); + "OpDepthwiseConv2d: Output data type not supported for this configuration of operator"); input = dynamic_cast<TosaReference::TensorTemplate<TIn>*>(inputs[0]); weight = dynamic_cast<TosaReference::TensorTemplate<TWeight>*>(inputs[1]); @@ -1112,7 +1102,7 @@ int OpDepthwiseConv2d<InDtype, WeightDtype, OutDtype>::checkTensorAttributes() std::string msg; if (check_conv_attribute(attribute, 2 /* conv_dimension */, input->getShape(), output->getShape(), - weight->getShape(), 0 /* offset_kernel */, InDtype, WeightDtype, msg)) + weight->getShape(), 0 /* offset_kernel */, InDtype, WeightDtype, msg)) { msg = "OpDepthwiseConv2d: " + msg; printNodeValidationError(msg.c_str()); @@ -1155,15 +1145,17 @@ int OpDepthwiseConv2d<InDtype, WeightDtype, OutDtype>::eval() int pad_left = this->attribute->pad()[2]; int pad_right = this->attribute->pad()[3]; - int stride_y = this->attribute->stride()[0]; - int stride_x = this->attribute->stride()[1]; - int dilation_y = this->attribute->dilation()[0]; - int dilation_x = this->attribute->dilation()[1]; + int stride_y = this->attribute->stride()[0]; + int stride_x = this->attribute->stride()[1]; + int dilation_y = this->attribute->dilation()[0]; + int dilation_x = this->attribute->dilation()[1]; // Check Tosa Level auto tosa_level = g_func_config.tosa_level; - LEVEL_CHECK(dilation_y * f_height <= tosa_level.MAX_KERNEL, "dilation_y * KH should be smaller than or equal to MAX_KERNEL"); - LEVEL_CHECK(dilation_x * f_width <= tosa_level.MAX_KERNEL, "dilation_x * KW should be smaller than or equal to MAX_KERNEL"); + LEVEL_CHECK(dilation_y * f_height <= tosa_level.MAX_KERNEL, + "dilation_y * KH should be smaller than or equal to MAX_KERNEL"); + LEVEL_CHECK(dilation_x * f_width <= tosa_level.MAX_KERNEL, + "dilation_x * KW should be smaller than or equal to MAX_KERNEL"); LEVEL_CHECK(pad_top <= tosa_level.MAX_KERNEL, "pad_top should be smaller than or equal to MAX_KERNEL"); LEVEL_CHECK(pad_bottom <= tosa_level.MAX_KERNEL, "pad_bottom should be smaller than or equal to MAX_KERNEL"); LEVEL_CHECK(pad_left <= tosa_level.MAX_KERNEL, "pad_left should be smaller than or equal to MAX_KERNEL"); @@ -1175,8 +1167,8 @@ int OpDepthwiseConv2d<InDtype, WeightDtype, OutDtype>::eval() "perform OpDepthwiseConv2d, input.shape=[%d,%d,%d,%d], weight.shape=[%d,%d,%d,%d], " "output.shape=[%d,%d,%d,%d], stride=[%d,%d], dilation=[%d,%d], pad=[%d,%d,%d,%d]", in_batch, in_height, in_width, in_channels, f_height, f_width, f_in_channels, f_multiplier, out_batch, - out_height, out_width, out_channels, stride_y, stride_x, dilation_y, dilation_x, pad_top, - pad_bottom, pad_left, pad_right); + out_height, out_width, out_channels, stride_y, stride_x, dilation_y, dilation_x, pad_top, pad_bottom, + pad_left, pad_right); Eigen::array<std::pair<int32_t, int32_t>, 4> pad; pad[0] = std::make_pair(0, 0); @@ -1241,9 +1233,9 @@ int OpDepthwiseConv2d<InDtype, WeightDtype, OutDtype>::eval() for (int fw = 0; fw < f_width; fw++) { // Perform multiplication in AccEigenType then cast to OutEigenType - this->output->getTensor()(ob, oh, ow, ic * f_multiplier + cm) += - (OutEigenType)((AccEigenType)input_extract_patches(ob, fh, fw, ow * out_height + oh, ic) * - (AccEigenType)weight_val(fh, fw, ic, cm)); + this->output->getTensor()(ob, oh, ow, ic * f_multiplier + cm) += (OutEigenType)( + (AccEigenType)input_extract_patches(ob, fh, fw, ow * out_height + oh, ic) * + (AccEigenType)weight_val(fh, fw, ic, cm)); } } } @@ -1308,7 +1300,7 @@ int OpFullyConnected<InDtype, WeightDtype, OutDtype>::checkTensorAttributes() } ERROR_IF(outputs[0]->getDtype() != OutDtype, - "OpFullyConnected: Output data type not supported for this configuration of operator"); + "OpFullyConnected: Output data type not supported for this configuration of operator"); output = dynamic_cast<TosaReference::TensorTemplate<TOut>*>(outputs[0]); @@ -1368,9 +1360,7 @@ int OpFullyConnected<InDtype, WeightDtype, OutDtype>::eval() } template <TOSA_REF_TYPE Dtype, TOSA_REF_TYPE OutDtype> -OpMatMul<Dtype, OutDtype>::OpMatMul(SubgraphTraverser* sgt_, - TosaAttributeBase* attribute_, - uint64_t id_) +OpMatMul<Dtype, OutDtype>::OpMatMul(SubgraphTraverser* sgt_, TosaAttributeBase* attribute_, uint64_t id_) : GraphNode(sgt_, Op_MATMUL, id_) { setRequiredOperands(2, 1); @@ -1398,7 +1388,7 @@ int OpMatMul<Dtype, OutDtype>::checkTensorAttributes() } ERROR_IF(outputs[0]->getDtype() != OutDtype, - "OpMatMul: Output data type not supported for this configuration of operator"); + "OpMatMul: Output data type not supported for this configuration of operator"); a = dynamic_cast<TosaReference::TensorTemplate<TIn>*>(inputs[0]); b = dynamic_cast<TosaReference::TensorTemplate<TIn>*>(inputs[1]); @@ -1513,9 +1503,7 @@ int OpMatMul<Dtype, OutDtype>::eval() } template <TOSA_REF_TYPE Dtype> -OpMaxPool2d<Dtype>::OpMaxPool2d(SubgraphTraverser* sgt_, - TosaAttributeBase* attribute_, - uint64_t id_) +OpMaxPool2d<Dtype>::OpMaxPool2d(SubgraphTraverser* sgt_, TosaAttributeBase* attribute_, uint64_t id_) : GraphNode(sgt_, Op_MAX_POOL2D, id_) { setRequiredOperands(1, 1); @@ -1583,10 +1571,10 @@ int OpMaxPool2d<Dtype>::eval() int pad_left = this->attribute->pad()[2]; int pad_right = this->attribute->pad()[3]; - int kernel_y = this->attribute->kernel()[0]; - int kernel_x = this->attribute->kernel()[1]; - int stride_y = this->attribute->stride()[0]; - int stride_x = this->attribute->stride()[1]; + int kernel_y = this->attribute->kernel()[0]; + int kernel_x = this->attribute->kernel()[1]; + int stride_y = this->attribute->stride()[0]; + int stride_x = this->attribute->stride()[1]; // Check Tosa Level auto tosa_level = g_func_config.tosa_level; @@ -1678,14 +1666,13 @@ int OpFFT2d<Dtype>::checkTensorAttributes() if (validateRequiredOperands()) return 1; - if (validateRequiredRank(inputs[0]) || validateRequiredRank(inputs[1]) || - validateRequiredRank(outputs[0]) || validateRequiredRank(outputs[1])) + if (validateRequiredRank(inputs[0]) || validateRequiredRank(inputs[1]) || validateRequiredRank(outputs[0]) || + validateRequiredRank(outputs[1])) { return 1; } - if (inputs[0]->matchType(*outputs[0]) || inputs[1]->matchType(*outputs[1]) || - inputs[0]->matchType(*inputs[1])) + if (inputs[0]->matchType(*outputs[0]) || inputs[1]->matchType(*outputs[1]) || inputs[0]->matchType(*inputs[1])) { printNodeValidationError("OpFFT2d: input and output tensor type mismatch"); return 1; @@ -1699,8 +1686,7 @@ int OpFFT2d<Dtype>::checkTensorAttributes() ASSERT_MEM(in_real && in_imag && out_real && out_imag); std::string msg; - if (check_fft_shape(in_real->getShape(), in_imag->getShape(), - out_real->getShape(), out_imag->getShape(), msg)) + if (check_fft_shape(in_real->getShape(), in_imag->getShape(), out_real->getShape(), out_imag->getShape(), msg)) { msg = "OpFFT2d: " + msg; printNodeValidationError(msg.c_str()); @@ -1713,37 +1699,35 @@ int OpFFT2d<Dtype>::checkTensorAttributes() template <TOSA_REF_TYPE Dtype> int OpFFT2d<Dtype>::eval() { - int in_real_batch = this->in_real->getShape()[0]; + int in_real_batch = this->in_real->getShape()[0]; int in_real_height = this->in_real->getShape()[1]; - int in_real_width = this->in_real->getShape()[2]; + int in_real_width = this->in_real->getShape()[2]; - int in_imag_batch = this->in_imag->getShape()[0]; + int in_imag_batch = this->in_imag->getShape()[0]; int in_imag_height = this->in_imag->getShape()[1]; - int in_imag_width = this->in_imag->getShape()[2]; + int in_imag_width = this->in_imag->getShape()[2]; - int out_real_batch = this->out_real->getShape()[0]; + int out_real_batch = this->out_real->getShape()[0]; int out_real_height = this->out_real->getShape()[1]; - int out_real_width = this->out_real->getShape()[2]; + int out_real_width = this->out_real->getShape()[2]; - int out_imag_batch = this->out_imag->getShape()[0]; + int out_imag_batch = this->out_imag->getShape()[0]; int out_imag_height = this->out_imag->getShape()[1]; - int out_imag_width = this->out_imag->getShape()[2]; + int out_imag_width = this->out_imag->getShape()[2]; // Check Tosa Level auto tosa_level = g_func_config.tosa_level; LEVEL_CHECK(in_real_height <= tosa_level.MAX_KERNEL, "H should be smaller than or equal to MAX_KERNEL"); LEVEL_CHECK(in_real_width <= tosa_level.MAX_KERNEL, "W should be smaller than or equal to MAX_KERNEL"); - DEBUG_INFO(OP, - "perform OpFFT2d, input.shapes=[[%d,%d,%d],[%d,%d,%d]], output.shapes=[[%d,%d,%d],[%d,%d,%d]]", - in_real_batch, in_real_height, in_real_width, - in_imag_batch, in_imag_height, in_imag_width, - out_real_batch, out_real_height, out_real_width, - out_imag_batch, out_imag_height, out_imag_width); + DEBUG_INFO(OP, "perform OpFFT2d, input.shapes=[[%d,%d,%d],[%d,%d,%d]], output.shapes=[[%d,%d,%d],[%d,%d,%d]]", + in_real_batch, in_real_height, in_real_width, in_imag_batch, in_imag_height, in_imag_width, + out_real_batch, out_real_height, out_real_width, out_imag_batch, out_imag_height, out_imag_width); OutEigenType sum_real, sum_imag, a, sign_val = 1.0; - if (attribute->inverse()) { + if (attribute->inverse()) + { sign_val = -1.0; } @@ -1772,7 +1756,8 @@ int OpFFT2d<Dtype>::eval() OutEigenType val_real = in_real_val(n, iy, ix); OutEigenType val_imag = in_imag_val(n, iy, ix); // Use explicit cast to ensure intermmediate calculations are completed using OutEigenType - a = sign_val * 2 * M_PI * ((iy * (OutEigenType)oy) / in_real_height + (ix * (OutEigenType)ox) / in_real_width); + a = sign_val * 2 * M_PI * + ((iy * (OutEigenType)oy) / in_real_height + (ix * (OutEigenType)ox) / in_real_width); sum_real += val_real * cos(a) + val_imag * sin(a); sum_imag += -val_real * sin(a) + val_imag * cos(a); } @@ -1787,9 +1772,7 @@ int OpFFT2d<Dtype>::eval() } template <TOSA_REF_TYPE Dtype> -OpRFFT2d<Dtype>::OpRFFT2d(SubgraphTraverser* sgt_, - TosaAttributeBase* attribute_, - uint64_t id_) +OpRFFT2d<Dtype>::OpRFFT2d(SubgraphTraverser* sgt_, TosaAttributeBase* attribute_, uint64_t id_) : GraphNode(sgt_, Op_RFFT2D, id_) { setRequiredOperands(1, 2); @@ -1797,8 +1780,8 @@ OpRFFT2d<Dtype>::OpRFFT2d(SubgraphTraverser* sgt_, } template <TOSA_REF_TYPE Dtype> -OpRFFT2d<Dtype>::~OpRFFT2d() {} - +OpRFFT2d<Dtype>::~OpRFFT2d() +{} template <TOSA_REF_TYPE Dtype> int OpRFFT2d<Dtype>::checkTensorAttributes() @@ -1806,8 +1789,7 @@ int OpRFFT2d<Dtype>::checkTensorAttributes() if (validateRequiredOperands()) return 1; - if (validateRequiredRank(inputs[0]) || validateRequiredRank(outputs[0]) || - validateRequiredRank(outputs[1])) + if (validateRequiredRank(inputs[0]) || validateRequiredRank(outputs[0]) || validateRequiredRank(outputs[1])) { return 1; } @@ -1818,15 +1800,14 @@ int OpRFFT2d<Dtype>::checkTensorAttributes() return 1; } - in = dynamic_cast<TosaReference::TensorTemplate<TIn>*>(inputs[0]); + in = dynamic_cast<TosaReference::TensorTemplate<TIn>*>(inputs[0]); out_real = dynamic_cast<TosaReference::TensorTemplate<TOut>*>(outputs[0]); out_imag = dynamic_cast<TosaReference::TensorTemplate<TOut>*>(outputs[1]); ASSERT_MEM(in && out_real && out_imag); std::string msg; - if (check_fft_shape(in->getShape(), {}, - out_real->getShape(), out_imag->getShape(), msg)) + if (check_fft_shape(in->getShape(), {}, out_real->getShape(), out_imag->getShape(), msg)) { msg = "OpRFFT2d: " + msg; printNodeValidationError(msg.c_str()); @@ -1839,17 +1820,17 @@ int OpRFFT2d<Dtype>::checkTensorAttributes() template <TOSA_REF_TYPE Dtype> int OpRFFT2d<Dtype>::eval() { - int32_t in_batch = in->getShape()[0]; + int32_t in_batch = in->getShape()[0]; int32_t in_height = in->getShape()[1]; - int32_t in_width = in->getShape()[2]; + int32_t in_width = in->getShape()[2]; - int32_t out_real_batch = out_real->getShape()[0]; + int32_t out_real_batch = out_real->getShape()[0]; int32_t out_real_height = out_real->getShape()[1]; - int32_t out_real_width = out_real->getShape()[2]; + int32_t out_real_width = out_real->getShape()[2]; - int32_t out_imag_batch = out_imag->getShape()[0]; + int32_t out_imag_batch = out_imag->getShape()[0]; int32_t out_imag_height = out_imag->getShape()[1]; - int32_t out_imag_width = out_imag->getShape()[2]; + int32_t out_imag_width = out_imag->getShape()[2]; // Check Tosa Level auto tosa_level = g_func_config.tosa_level; @@ -1859,9 +1840,8 @@ int OpRFFT2d<Dtype>::eval() DEBUG_INFO(OP, "perform OpRFFT2d, input.shape=[%d,%d,%d], output_real.shape=[%d,%d,%d], " "output_imag.shape=[%d,%d,%d]", - in_batch, in_height, in_width, - out_real_batch, out_real_height, out_real_width, - out_imag_batch, out_imag_height, out_imag_width); + in_batch, in_height, in_width, out_real_batch, out_real_height, out_real_width, out_imag_batch, + out_imag_height, out_imag_width); OutEigenType sum_real, sum_imag, a; @@ -1931,7 +1911,7 @@ int OpTransposeConv2d<InDtype, WeightDtype, OutDtype>::checkTensorAttributes() } ERROR_IF(outputs[0]->getDtype() != OutDtype, - "OpTransposeConv2d: Output data type not supported for this configuration of operator"); + "OpTransposeConv2d: Output data type not supported for this configuration of operator"); input = dynamic_cast<TosaReference::TensorTemplate<TIn>*>(inputs[0]); weight = dynamic_cast<TosaReference::TensorTemplate<TWeight>*>(inputs[1]); @@ -1956,8 +1936,6 @@ int OpTransposeConv2d<InDtype, WeightDtype, OutDtype>::checkTensorAttributes() return 1; } - - for (int32_t i : attribute->stride()) { if (i < 1) @@ -1993,7 +1971,8 @@ int OpTransposeConv2d<InDtype, WeightDtype, OutDtype>::checkTensorAttributes() for (size_t i = 0; i < attribute->out_pad().size(); i++) { - ERROR_IF(attribute->out_pad()[i] <= -(weight->getShape()[(i / 2) + 1]), "OpTransposeConv2d: At least one out_pad value is larger than kernel size"); + ERROR_IF(attribute->out_pad()[i] <= -(weight->getShape()[(i / 2) + 1]), + "OpTransposeConv2d: At least one out_pad value is larger than kernel size"); } int32_t H = (IH - 1) * stride_y + out_pad_top + out_pad_bottom + kernel_h; @@ -2002,8 +1981,7 @@ int OpTransposeConv2d<InDtype, WeightDtype, OutDtype>::checkTensorAttributes() if ((OH != H) || (OW != W)) { std::string msg = "OpTransposeConv2d: Mismatch between output shape provided and expected output shape (" + - std::to_string(H) + "," + - std::to_string(W) + ")"; + std::to_string(H) + "," + std::to_string(W) + ")"; printNodeValidationError(msg.c_str()); return 1; } @@ -2057,7 +2035,8 @@ int OpTransposeConv2d<InDtype, WeightDtype, OutDtype>::eval() LEVEL_CHECK(f_height <= tosa_level.MAX_KERNEL, "KH should be smaller than or equal to MAX_KERNEL"); LEVEL_CHECK(f_width <= tosa_level.MAX_KERNEL, "KW should be smaller than or equal to MAX_KERNEL"); LEVEL_CHECK(out_pad_top <= tosa_level.MAX_KERNEL, "out_pad_top should be smaller than or equal to MAX_KERNEL"); - LEVEL_CHECK(out_pad_bottom <= tosa_level.MAX_KERNEL, "out_pad_bottom should be smaller than or equal to MAX_KERNEL"); + LEVEL_CHECK(out_pad_bottom <= tosa_level.MAX_KERNEL, + "out_pad_bottom should be smaller than or equal to MAX_KERNEL"); LEVEL_CHECK(out_pad_left <= tosa_level.MAX_KERNEL, "out_pad_left should be smaller than or equal to MAX_KERNEL"); LEVEL_CHECK(out_pad_right <= tosa_level.MAX_KERNEL, "out_pad_right should be smaller than or equal to MAX_KERNEL"); LEVEL_CHECK(stride_y <= tosa_level.MAX_STRIDE, "stride_y should be smaller than or equal to MAX_STRIDE"); @@ -2066,9 +2045,9 @@ int OpTransposeConv2d<InDtype, WeightDtype, OutDtype>::eval() DEBUG_INFO(OP, "perform OpTransposeConv2d, input.shape=[%d,%d,%d,%d], weight.shape=[%d,%d,%d,%d], " "output.shape=[%d,%d,%d,%d], stride=[%d,%d], out_pad=[%d,%d,%d,%d]", - in_batch, in_height, in_width, in_channels, f_height, f_width, f_out_channels, f_in_channels, - out_batch, out_height, out_width, out_channels, stride_y, stride_x, out_pad_top, - out_pad_bottom, out_pad_left, out_pad_right); + in_batch, in_height, in_width, in_channels, f_height, f_width, f_out_channels, f_in_channels, out_batch, + out_height, out_width, out_channels, stride_y, stride_x, out_pad_top, out_pad_bottom, out_pad_left, + out_pad_right); TIn input_val = this->input->getTensor(); TWeight weight_val = this->weight->getTensor(); @@ -2126,8 +2105,8 @@ int OpTransposeConv2d<InDtype, WeightDtype, OutDtype>::eval() if ((out_x >= 0 && out_x < out_width) && (out_y >= 0 && out_y < out_height)) { this->output->getTensor()(ob, out_y, out_x, oc) += - (OutEigenType) ((AccEigenType)input_val(ob, ih, iw, ic) * - (AccEigenType)weight_val(oc, fh, fw, ic)); + (OutEigenType)((AccEigenType)input_val(ob, ih, iw, ic) * + (AccEigenType)weight_val(oc, fh, fw, ic)); } } } @@ -2162,7 +2141,7 @@ DEF_INSTANTIATE_TWO_TYPE(OpAvgPool2d, INT8, INT32); DEF_INSTANTIATE_TWO_TYPE(OpAvgPool2d, INT16, INT32); DEF_INSTANTIATE_TWO_TYPE(OpAvgPool2d, FP64, FP64); - // [in_t, weight_t, out_t] +// [in_t, weight_t, out_t] DEF_INSTANTIATE_THREE_TYPE(OpConv2d, FP16, FP16, FP16); DEF_INSTANTIATE_THREE_TYPE(OpConv2d, FP16, FP16, FP32); DEF_INSTANTIATE_THREE_TYPE(OpConv2d, BF16, BF16, FP32); diff --git a/reference_model/src/ops/tensor_ops.h b/reference_model/src/ops/tensor_ops.h index df53f2b..f5fcd7f 100644 --- a/reference_model/src/ops/tensor_ops.h +++ b/reference_model/src/ops/tensor_ops.h @@ -55,11 +55,11 @@ public: virtual int checkTensorAttributes(); virtual int eval(); - using InEigenType = typename GetEigenType<Dtype>::type; - using AccEigenType = typename GetAccEigenType<AccDtype>::type; // Note: different from GetEigenType - using OutEigenType = typename GetEigenType<Dtype>::type; - using TIn = Eigen::Tensor<InEigenType, 4>; - using TOut = Eigen::Tensor<OutEigenType, 4>; + using InEigenType = typename GetEigenType<Dtype>::type; + using AccEigenType = typename GetAccEigenType<AccDtype>::type; // Note: different from GetEigenType + using OutEigenType = typename GetEigenType<Dtype>::type; + using TIn = Eigen::Tensor<InEigenType, 4>; + using TOut = Eigen::Tensor<OutEigenType, 4>; static constexpr int64_t QMin = GetQMin<Dtype>::value; static constexpr int64_t QMax = GetQMax<Dtype>::value; @@ -71,7 +71,8 @@ protected: protected: // return a 1D [N] tensor that describes a how many valid elements covered in the input space - ETensor1<int32_t> calculate_div_map_1d(int in_size, int out_size, int kernel_size, int stride, int32_t padding_left, int32_t padding_right); + ETensor1<int32_t> calculate_div_map_1d( + int in_size, int out_size, int kernel_size, int stride, int32_t padding_left, int32_t padding_right); }; template <TOSA_REF_TYPE InDtype, TOSA_REF_TYPE WeightDtype, TOSA_REF_TYPE OutDtype> @@ -86,7 +87,7 @@ public: using InEigenType = typename GetEigenType<InDtype>::type; using WeightEigenType = typename GetEigenType<WeightDtype>::type; - using AccEigenType = typename GetAccEigenType<OutDtype>::type; // Note: different from GetEigenType + using AccEigenType = typename GetAccEigenType<OutDtype>::type; // Note: different from GetEigenType using OutEigenType = typename GetEigenType<OutDtype>::type; using TIn = Eigen::Tensor<InEigenType, 4>; using TWeight = Eigen::Tensor<WeightEigenType, 4>; @@ -116,7 +117,7 @@ public: using InEigenType = typename GetEigenType<InDtype>::type; using WeightEigenType = typename GetEigenType<WeightDtype>::type; - using AccEigenType = typename GetAccEigenType<OutDtype>::type; // Note: different from GetEigenType + using AccEigenType = typename GetAccEigenType<OutDtype>::type; // Note: different from GetEigenType using OutEigenType = typename GetEigenType<OutDtype>::type; using TIn = Eigen::Tensor<InEigenType, 5>; using TWeight = Eigen::Tensor<WeightEigenType, 5>; @@ -146,7 +147,7 @@ public: using InEigenType = typename GetEigenType<InDtype>::type; using WeightEigenType = typename GetEigenType<WeightDtype>::type; - using AccEigenType = typename GetAccEigenType<OutDtype>::type; // Note: different from GetEigenType + using AccEigenType = typename GetAccEigenType<OutDtype>::type; // Note: different from GetEigenType using OutEigenType = typename GetEigenType<OutDtype>::type; using TIn = Eigen::Tensor<InEigenType, 4>; using TWeight = Eigen::Tensor<WeightEigenType, 4>; @@ -174,14 +175,14 @@ public: virtual int checkTensorAttributes() final; virtual int eval() final; - using InEigenType = typename GetEigenType<InDtype>::type; - using WeightEigenType = typename GetEigenType<WeightDtype>::type; - using AccEigenType = typename GetAccEigenType<OutDtype>::type; // Note: different from GetEigenType - using OutEigenType = typename GetEigenType<OutDtype>::type; - using TIn = Eigen::Tensor<InEigenType, 2>; - using TWeight = Eigen::Tensor<WeightEigenType, 2>; - using TBias = Eigen::Tensor<OutEigenType, 1>; - using TOut = Eigen::Tensor<OutEigenType, 2>; + using InEigenType = typename GetEigenType<InDtype>::type; + using WeightEigenType = typename GetEigenType<WeightDtype>::type; + using AccEigenType = typename GetAccEigenType<OutDtype>::type; // Note: different from GetEigenType + using OutEigenType = typename GetEigenType<OutDtype>::type; + using TIn = Eigen::Tensor<InEigenType, 2>; + using TWeight = Eigen::Tensor<WeightEigenType, 2>; + using TBias = Eigen::Tensor<OutEigenType, 1>; + using TOut = Eigen::Tensor<OutEigenType, 2>; static constexpr int64_t AccQMin = GetQMin<OutDtype>::value; static constexpr int64_t AccQMax = GetQMax<OutDtype>::value; @@ -206,7 +207,7 @@ public: virtual int eval() final; using InEigenType = typename GetEigenType<Dtype>::type; - using AccEigenType = typename GetAccEigenType<OutDtype>::type; // Note: different from GetEigenType + using AccEigenType = typename GetAccEigenType<OutDtype>::type; // Note: different from GetEigenType using OutEigenType = typename GetEigenType<OutDtype>::type; using TIn = Eigen::Tensor<InEigenType, 3>; using TOut = Eigen::Tensor<OutEigenType, 3>; @@ -258,10 +259,10 @@ public: virtual int checkTensorAttributes() final; virtual int eval() final; - using InEigenType = typename GetEigenType<Dtype>::type; - using OutEigenType = typename GetEigenType<Dtype>::type; - using TIn = Eigen::Tensor<InEigenType, 3>; - using TOut = Eigen::Tensor<OutEigenType, 3>; + using InEigenType = typename GetEigenType<Dtype>::type; + using OutEigenType = typename GetEigenType<Dtype>::type; + using TIn = Eigen::Tensor<InEigenType, 3>; + using TOut = Eigen::Tensor<OutEigenType, 3>; protected: TosaReference::TensorTemplate<TIn>* in_real; @@ -281,10 +282,10 @@ public: virtual int checkTensorAttributes() final; virtual int eval() final; - using InEigenType = typename GetEigenType<Dtype>::type; - using OutEigenType = typename GetEigenType<Dtype>::type; - using TIn = Eigen::Tensor<InEigenType, 3>; - using TOut = Eigen::Tensor<OutEigenType, 3>; + using InEigenType = typename GetEigenType<Dtype>::type; + using OutEigenType = typename GetEigenType<Dtype>::type; + using TIn = Eigen::Tensor<InEigenType, 3>; + using TOut = Eigen::Tensor<OutEigenType, 3>; protected: TosaReference::TensorTemplate<TIn>* in; @@ -304,7 +305,7 @@ public: using InEigenType = typename GetEigenType<InDtype>::type; using WeightEigenType = typename GetEigenType<WeightDtype>::type; - using AccEigenType = typename GetAccEigenType<OutDtype>::type; // Note: different from GetEigenType + using AccEigenType = typename GetAccEigenType<OutDtype>::type; // Note: different from GetEigenType using OutEigenType = typename GetEigenType<OutDtype>::type; using TIn = Eigen::Tensor<InEigenType, 4>; using TWeight = Eigen::Tensor<WeightEigenType, 4>; diff --git a/reference_model/src/ops/type_conversion.cc b/reference_model/src/ops/type_conversion.cc index fce8e7c..9464fd9 100644 --- a/reference_model/src/ops/type_conversion.cc +++ b/reference_model/src/ops/type_conversion.cc @@ -14,20 +14,18 @@ // limitations under the License. #include "type_conversion.h" -#include "quant_util.h" #include "arith_util.h" +#include "half.hpp" +#include "quant_util.h" #include "template_types.h" #include <cmath> -#include "half.hpp" using namespace TosaReference; using namespace Eigen; using namespace tosa; template <int Rank, TOSA_REF_TYPE InDtype, TOSA_REF_TYPE OutDtype> -OpRescale<Rank, InDtype, OutDtype>::OpRescale(SubgraphTraverser* sgt_, - TosaAttributeBase* attribute_, - uint64_t id_) +OpRescale<Rank, InDtype, OutDtype>::OpRescale(SubgraphTraverser* sgt_, TosaAttributeBase* attribute_, uint64_t id_) : GraphNode(sgt_, Op_RESCALE, id_) { setRequiredOperands(1, 1); @@ -160,12 +158,13 @@ int OpRescale<Rank, InDtype, OutDtype>::eval() else scaled = TosaReference::QuantUtil::apply_scale_16(input_zp_shifted, channel_multiplier, channel_shift); - int64_t res_in_64 = static_cast<int64_t>(scaled) + output_zp; + int64_t res_in_64 = static_cast<int64_t>(scaled) + output_zp; int64_t i32_max_in_64 = static_cast<int64_t>(std::numeric_limits<int32_t>::max()); int64_t i32_min_in_64 = static_cast<int64_t>(std::numeric_limits<int32_t>::min()); if (res_in_64 > i32_max_in_64 || res_in_64 < i32_min_in_64) { - std::string desc = "scaling result [" + std::to_string(scaled) + "] plus output_zp [" + std::to_string(output_zp) + "] not in i32 range"; + std::string desc = "scaling result [" + std::to_string(scaled) + "] plus output_zp [" + + std::to_string(output_zp) + "] not in i32 range"; throw desc; } OutEigenType out_val = static_cast<OutEigenType>(res_in_64); @@ -201,12 +200,13 @@ int OpRescale<Rank, InDtype, OutDtype>::eval() else scaled = TosaReference::QuantUtil::apply_scale_16(input_zp_shifted, tensor_multiplier, tensor_shift); - int64_t res_in_64 = static_cast<int64_t>(scaled) + output_zp; + int64_t res_in_64 = static_cast<int64_t>(scaled) + output_zp; int64_t i32_max_in_64 = static_cast<int64_t>(std::numeric_limits<int32_t>::max()); int64_t i32_min_in_64 = static_cast<int64_t>(std::numeric_limits<int32_t>::min()); if (res_in_64 > i32_max_in_64 || res_in_64 < i32_min_in_64) { - std::string desc = "scaling result [" + std::to_string(scaled) + "] plus output_zp [" + std::to_string(output_zp) + "] not in i32 range"; + std::string desc = "scaling result [" + std::to_string(scaled) + "] plus output_zp [" + + std::to_string(output_zp) + "] not in i32 range"; throw desc; } @@ -234,9 +234,7 @@ int OpRescale<Rank, InDtype, OutDtype>::eval() } template <int Rank, TOSA_REF_TYPE InDtype, TOSA_REF_TYPE OutDtype> -OpCast<Rank, InDtype, OutDtype>::OpCast(SubgraphTraverser* sgt_, - TosaAttributeBase* attribute_, - uint64_t id_) +OpCast<Rank, InDtype, OutDtype>::OpCast(SubgraphTraverser* sgt_, TosaAttributeBase* attribute_, uint64_t id_) : GraphNode(sgt_, Op_CAST, id_) { setRequiredOperands(1, 1); @@ -315,7 +313,7 @@ CastHelper<InDtype, TOSA_REF_TYPE_FP16>::CastHelper() // Integer data converted to fp16 (stored as fp32) fcn = [](InEigenType in) -> float { half_float::half h = half_float::half(in); - float out = half_float::half_cast<float, half_float::half>(h); + float out = half_float::half_cast<float, half_float::half>(h); return out; }; } @@ -354,10 +352,10 @@ CastHelper<TOSA_REF_TYPE_FP16, OutDtype>::CastHelper() fcn = [](float in) -> OutEigenType { // Cast from float representation back to half_float before rounding half_float::half h = half_float::half(in); - h = std::rint(h); - OutEigenType out = half_float::half_cast<OutEigenType, half_float::half>(h); - out = std::max<OutEigenType>(out, OutMin); - out = std::min<OutEigenType>(out, OutMax); + h = std::rint(h); + OutEigenType out = half_float::half_cast<OutEigenType, half_float::half>(h); + out = std::max<OutEigenType>(out, OutMin); + out = std::min<OutEigenType>(out, OutMax); return out; }; } @@ -365,9 +363,7 @@ CastHelper<TOSA_REF_TYPE_FP16, OutDtype>::CastHelper() CastHelper<TOSA_REF_TYPE_FP16, TOSA_REF_TYPE_FP32>::CastHelper() { // No-op since fp16 values treated internally as their fp32 representation - fcn = [](float in) -> OutEigenType { - return in; - }; + fcn = [](float in) -> OutEigenType { return in; }; } template <TOSA_REF_TYPE OutDtype> @@ -385,9 +381,7 @@ CastHelper<TOSA_REF_TYPE_BF16, OutDtype>::CastHelper() CastHelper<TOSA_REF_TYPE_BF16, TOSA_REF_TYPE_FP32>::CastHelper() { // No-op since bf16 values treated as truncated fp32 internally - fcn = [](InEigenType in) -> OutEigenType { - return in; - }; + fcn = [](InEigenType in) -> OutEigenType { return in; }; } template <TOSA_REF_TYPE InDtype> diff --git a/reference_model/src/subgraph_traverser.cc b/reference_model/src/subgraph_traverser.cc index 5f6fd01..a7ef5e9 100644 --- a/reference_model/src/subgraph_traverser.cc +++ b/reference_model/src/subgraph_traverser.cc @@ -14,8 +14,8 @@ // limitations under the License. #include "subgraph_traverser.h" -#include "tosa_model_types.h" #include "arith_util.h" +#include "tosa_model_types.h" #ifndef SUBGRAPH_ERROR_IF #define SUBGRAPH_ERROR_IF(COND, fmt, ...) \ @@ -37,13 +37,15 @@ using namespace TosaReference; using namespace Eigen; using namespace tosa; -SubgraphTraverser::SubgraphTraverser(TosaSerializationBasicBlock* _block, TosaSerializationHandler* _tsh, SubgraphTraverser* _parent_sgt) +SubgraphTraverser::SubgraphTraverser(TosaSerializationBasicBlock* _block, + TosaSerializationHandler* _tsh, + SubgraphTraverser* _parent_sgt) { graph_status = GraphStatus::TOSA_VALID; - block = _block; + block = _block; - tsh = _tsh; + tsh = _tsh; parent_sgt = _parent_sgt; tensors.clear(); nodes.clear(); @@ -151,11 +153,11 @@ int SubgraphTraverser::initializeGraph() TOSA_REF_TYPE input_dtype = TOSA_REF_TYPE_UNKNOWN; TOSA_REF_TYPE output_dtype = TOSA_REF_TYPE_UNKNOWN; TOSA_REF_TYPE weight_dtype = TOSA_REF_TYPE_UNKNOWN; - uint32_t input_rank = 0; - uint32_t output_rank = 0; - uint32_t weight_rank = 0; - int32_t input_index = -1; - int32_t weight_index = -1; + uint32_t input_rank = 0; + uint32_t output_rank = 0; + uint32_t weight_rank = 0; + int32_t input_index = -1; + int32_t weight_index = -1; switch (op->GetOp()) { @@ -185,8 +187,10 @@ int SubgraphTraverser::initializeGraph() std::string input_name = op->GetInputTensorNames()[input_index]; TosaSerializationTensor* input_tensor = nullptr; - for (auto ser_tensor : ser_tensor_vec) { - if (ser_tensor->GetName() == input_name) { + for (auto ser_tensor : ser_tensor_vec) + { + if (ser_tensor->GetName() == input_name) + { input_tensor = ser_tensor; } } @@ -207,8 +211,10 @@ int SubgraphTraverser::initializeGraph() EnumNamesOp()[op->GetOp()], weight_index); std::string weight_name = op->GetInputTensorNames()[weight_index]; TosaSerializationTensor* weight_tensor = nullptr; - for (auto ser_tensor : ser_tensor_vec) { - if (ser_tensor->GetName() == weight_name) { + for (auto ser_tensor : ser_tensor_vec) + { + if (ser_tensor->GetName() == weight_name) + { weight_tensor = ser_tensor; } } @@ -237,14 +243,18 @@ int SubgraphTraverser::initializeGraph() EnumNamesOp()[op->GetOp()], op->GetInputTensorNames().size(), op->GetOutputTensorNames().size()); GraphNode* node = nullptr; - if (this->parent_sgt) { + if (this->parent_sgt) + { node = OpFactory::newOp(this->parent_sgt, tsh, op->GetOp(), op->GetAttribute(), idx, input_dtype, - input_rank, output_dtype, output_rank, weight_dtype, weight_rank); + input_rank, output_dtype, output_rank, weight_dtype, weight_rank); node->setInMainBlock(false); - } else { - node = OpFactory::newOp(this, tsh, op->GetOp(), op->GetAttribute(), idx, input_dtype, - input_rank, output_dtype, output_rank, weight_dtype, weight_rank); - if (node) { + } + else + { + node = OpFactory::newOp(this, tsh, op->GetOp(), op->GetAttribute(), idx, input_dtype, input_rank, + output_dtype, output_rank, weight_dtype, weight_rank); + if (node) + { node->setInMainBlock(true); } } @@ -305,7 +315,9 @@ int SubgraphTraverser::initializeGraph() if (op->GetInputTensorNames().empty() && !node->getOnNextNodeList()) { addToNextNodeList(node); - } else if (!node->getInMainBlock()) { + } + else if (!node->getInMainBlock()) + { non_const_node_vec.push_back(node); } @@ -364,18 +376,21 @@ int SubgraphTraverser::initializeGraph() } // If the node is not in mainblock and not const - for (auto node : non_const_node_vec) { + for (auto node : non_const_node_vec) + { bool all_inputs_from_parent = true; for (std::string& name : node->getInputNames()) { TosaReference::Tensor* t = findTensorByName(name); - if (!t->getIsParentGraphOutput()) { + if (!t->getIsParentGraphOutput()) + { all_inputs_from_parent = false; } } // In the children block, when a node has all its inputs from parent // block, we have to manually add this node to the evaluation list - if (all_inputs_from_parent && !node->getOnNextNodeList()) { + if (all_inputs_from_parent && !node->getOnNextNodeList()) + { addToNextNodeList(node); } } @@ -395,7 +410,8 @@ int SubgraphTraverser::allocateTensor() { if (dim <= 0) { - DEBUG_INFO(GT, "Failed to allocate tensor %s with invalid dimension of %d", ts->GetName().c_str(), dim); + DEBUG_INFO(GT, "Failed to allocate tensor %s with invalid dimension of %d", ts->GetName().c_str(), + dim); this->setGraphStatus(GraphStatus::TOSA_UNPREDICTABLE); return 1; } @@ -591,13 +607,15 @@ int SubgraphTraverser::evaluateNextNode() } // free input tensor if all of its consumers have all of their outputs ready and it's not block's output - if (!currNode->getInMainBlock()) { // we don't free it if the node is in main block and has nested blocks + if (!currNode->getInMainBlock()) + { // we don't free it if the node is in main block and has nested blocks for (auto tensor : currNode->getInputs()) { bool in_use = false; auto tensor_check = findTensorByName(tensor->getName()); - if (tensor_check->getIsParentGraphOutput()) { + if (tensor_check->getIsParentGraphOutput()) + { // if it's parent's block output tensor, we can't free it continue; } @@ -609,7 +627,6 @@ int SubgraphTraverser::evaluateNextNode() { in_use = true; } - } for (auto name : block->GetOutputs()) { diff --git a/reference_model/src/subgraph_traverser.h b/reference_model/src/subgraph_traverser.h index 1cf582e..00989ee 100644 --- a/reference_model/src/subgraph_traverser.h +++ b/reference_model/src/subgraph_traverser.h @@ -16,8 +16,8 @@ #ifndef SUBGRAPH_TRAVERSER_H #define SUBGRAPH_TRAVERSER_H -#include "graph_status.h" #include "graph_node.h" +#include "graph_status.h" #include "model_common.h" #include "ops/op_factory.h" #include "tensor.h" diff --git a/reference_model/src/tensor.cc b/reference_model/src/tensor.cc index c5f5e02..4982c99 100644 --- a/reference_model/src/tensor.cc +++ b/reference_model/src/tensor.cc @@ -30,11 +30,11 @@ TosaReference::Tensor::Tensor(const std::string tensorName_, , shape(shape_) , tensorDtype(ConvertDType(serializationDtype_)) { - producer = nullptr; - isValid = false; + producer = nullptr; + isValid = false; consumers.clear(); - isSubgraphInput = false; - isSubgraphOutput = false; + isSubgraphInput = false; + isSubgraphOutput = false; isParentGraphOutput = false; } @@ -94,16 +94,16 @@ int TosaReference::Tensor::dumpTensorParams(std::ostream& out) const int TosaReference::Tensor::readFromNpyFile(const char* filename) { - uint32_t elements = getElementCount(); + uint32_t elements = getElementCount(); double* f64databuf = nullptr; float* f32databuf = nullptr; half_float::half* f16databuf = nullptr; - int32_t* i32databuf = nullptr; - int64_t* i64databuf = nullptr; - bool* bdatabuf = nullptr; + int32_t* i32databuf = nullptr; + int64_t* i64databuf = nullptr; + bool* bdatabuf = nullptr; NumpyUtilities::NPError nperror; - TOSA_REF_TYPE dtype = getDtype(); - DType serialization_dtype = getSerializationDtype(); + TOSA_REF_TYPE dtype = getDtype(); + DType serialization_dtype = getSerializationDtype(); assert(dtype == ConvertDType(serialization_dtype)); // if dtype is FP64, serialization_dtype must be one of FP32, FP16, BF16 @@ -178,7 +178,8 @@ int TosaReference::Tensor::readFromNpyFile(const char* filename) // Convert from fp16 to fp32 so that fp16 values can be manipulated as float f32databuf = (float*)calloc(sizeof(float), elements); ASSERT_MEM(f32databuf); - for (uint32_t i=0; i < elements; i++) { + for (uint32_t i = 0; i < elements; i++) + { f32databuf[i] = half_float::half_cast<float, half_float::half>(f16databuf[i]); } if (setTensorValueFloat(elements, f32databuf)) @@ -189,12 +190,9 @@ int TosaReference::Tensor::readFromNpyFile(const char* filename) } break; case TOSA_REF_TYPE_BF16: - for (uint32_t i=0; i < elements; i++) + for (uint32_t i = 0; i < elements; i++) { - ASSERT_MSG( - checkValidBFloat(f32databuf[i]), - "Input float value not a valid bfloat16 value." - ); + ASSERT_MSG(checkValidBFloat(f32databuf[i]), "Input float value not a valid bfloat16 value."); } if (setTensorValueFloat(elements, f32databuf)) { @@ -313,15 +311,15 @@ int TosaReference::Tensor::readFromNpyFile(const char* filename) int TosaReference::Tensor::writeToNpyFile(const char* filename) const { - float* f32databuf = nullptr; - double* f64databuf = nullptr; - half_float::half* f16databuf = nullptr; - int32_t* i32databuf = nullptr; - int64_t* i64databuf = nullptr; - bool* bdatabuf = nullptr; + float* f32databuf = nullptr; + double* f64databuf = nullptr; + half_float::half* f16databuf = nullptr; + int32_t* i32databuf = nullptr; + int64_t* i64databuf = nullptr; + bool* bdatabuf = nullptr; NumpyUtilities::NPError nperror = NumpyUtilities::NO_ERROR; - uint32_t elements = getElementCount(); - const TOSA_REF_TYPE dtype = getDtype(); + uint32_t elements = getElementCount(); + const TOSA_REF_TYPE dtype = getDtype(); switch (dtype) { @@ -352,7 +350,8 @@ int TosaReference::Tensor::writeToNpyFile(const char* filename) const return 1; } // Convert fp32 to fp16 so that output file contains valid fp16 data - for (uint32_t i=0; i < elements; i++) { + for (uint32_t i = 0; i < elements; i++) + { f16databuf[i] = half_float::half_cast<half_float::half, float>(f32databuf[i]); } nperror = NumpyUtilities::writeToNpyFile(filename, shape, f16databuf); @@ -592,10 +591,7 @@ int TosaReference::Tensor::readfromVector(const ArrayProxy<float> vals) for (auto v : vals) { - ASSERT_MSG( - checkValidBFloat(v), - "Input float value not a valid bfloat16 value." - ); + ASSERT_MSG(checkValidBFloat(v), "Input float value not a valid bfloat16 value."); } setTensorValueFloat(elements, vals.data()); @@ -625,7 +621,7 @@ int TosaReference::Tensor::readfromVector(const ArrayProxy<half_float::half> val } // Convert from fp16 to fp32 - for (uint32_t i=0; i < elements; i++) + for (uint32_t i = 0; i < elements; i++) { tensor[i] = half_float::half_cast<float, half_float::half>(vals[i]); } @@ -772,10 +768,7 @@ int TosaReference::Tensor::writeToVector(ArrayProxy<float> vals) for (auto v : vals) { - ASSERT_MSG( - checkValidBFloat(v), - "Float value not a valid bfloat16 value." - ); + ASSERT_MSG(checkValidBFloat(v), "Float value not a valid bfloat16 value."); } break; @@ -805,7 +798,7 @@ int TosaReference::Tensor::writeToVector(ArrayProxy<half_float::half> vals) getTensorValueFloat(elements, tensor.data()); // Convert fp32 to fp16 - for (uint32_t i=0; i < elements; i++) + for (uint32_t i = 0; i < elements; i++) { vals[i] = half_float::half_cast<half_float::half, float>(tensor[i]); } diff --git a/reference_model/src/tensor.h b/reference_model/src/tensor.h index d49e004..f59a5e1 100644 --- a/reference_model/src/tensor.h +++ b/reference_model/src/tensor.h @@ -42,7 +42,8 @@ public: int setIsSubgraphOutput(); int setIsParentGraphOutput(); - int getIsParentGraphOutput() const { + int getIsParentGraphOutput() const + { return isParentGraphOutput; } diff --git a/reference_model/src/tosa_model_types.h b/reference_model/src/tosa_model_types.h index 84402ae..fd7ce53 100644 --- a/reference_model/src/tosa_model_types.h +++ b/reference_model/src/tosa_model_types.h @@ -1,4 +1,4 @@ -// Copyright (c) 2022, ARM Limited. +// Copyright (c) 2022-2023, ARM Limited. // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. @@ -19,4 +19,4 @@ inline const uint32_t TOSA_MAX_TENSOR_SIZE = ((1u << 31) - 1); -#endif // TOSA_MODEL_TYPES_H
\ No newline at end of file +#endif // TOSA_MODEL_TYPES_H
\ No newline at end of file diff --git a/reference_model/src/verify.cc b/reference_model/src/verify.cc index 584a418..89d1b67 100644 --- a/reference_model/src/verify.cc +++ b/reference_model/src/verify.cc @@ -100,31 +100,32 @@ CheckResult from_optional(const std::optional<double>& res) } } // namespace -extern "C" { - -CheckResult tosa_validate_element_accfp32(double ref, double bnd, float imp, size_t KS) +extern "C" { - auto err = validate_element<float>(ref, bnd, imp, KS); - return from_optional(err); -} -bool tosa_validate_output_error(double err_sum, double err_sum_sq, size_t T, size_t KS, int S) -{ - if (S != 1 && S != 2) + CheckResult tosa_validate_element_accfp32(double ref, double bnd, float imp, size_t KS) { - // Check error bias magnitude for data sets S which are not positive biased - REQUIRE(abs(err_sum) <= 2 * sqrt(KS * T)); + auto err = validate_element<float>(ref, bnd, imp, KS); + return from_optional(err); } - // Check error variance magnitude - REQUIRE(err_sum_sq <= 0.4 * KS * T); - return true; -} + bool tosa_validate_output_error(double err_sum, double err_sum_sq, size_t T, size_t KS, int S) + { + if (S != 1 && S != 2) + { + // Check error bias magnitude for data sets S which are not positive biased + REQUIRE(abs(err_sum) <= 2 * sqrt(KS * T)); + } + // Check error variance magnitude + REQUIRE(err_sum_sq <= 0.4 * KS * T); + + return true; + } -bool tosa_validate_data_fp32(const double* ref, const double* bnd, const float* imp, size_t T, size_t KS, int S) -{ - return validate_data<float>(ref, bnd, imp, T, KS, S); -} + bool tosa_validate_data_fp32(const double* ref, const double* bnd, const float* imp, size_t T, size_t KS, int S) + { + return validate_data<float>(ref, bnd, imp, T, KS, S); + } -} // extern "C" +} // extern "C" #undef REQUIRE
\ No newline at end of file |