Chentong319 attribute with variant (#25)
* change the read-in of attribute, using variant * Use backported variant. * Reduce code duplication. * 1. Make array attribute parsing more clear. 2. int -> int64_t. * 1. Fix how array attributes are imported. * 1. Fix clang-tidy warnings. * 1. Nit: fix clang-tidy warnings. * Fix MaxPool node construction. * Fix call to MaxPool. * Comment out backend tests that fail. * Add path to variant submodule to enable include file detection. * Allow unused argument to avoid special casing generator. * Address attribute related e2e test failures for Hard sigmoid,Elu,LeakyRelu,Selu,Softmax Co-authored-by: chentong319 <chentong@us.ibm.com> Co-authored-by: Gheorghe-Teodor Bercea <gt.bercea@gmail.com>
This commit is contained in:
Tian Jin
GitHub
parent
0231bb83a2
commit
51b0f4c9dd
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@ -7,3 +7,6 @@
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[submodule "third_party/pybind11"]
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path = third_party/pybind11
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url = https://github.com/pybind/pybind11.git
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[submodule "third_party/variant"]
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path = third_party/variant
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url = git@github.com:mpark/variant.git
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@ -22,6 +22,7 @@ include(MLIR.cmake)
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add_subdirectory(third_party/onnx)
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add_subdirectory(third_party/benchmark)
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add_subdirectory(third_party/pybind11)
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add_subdirectory(third_party/variant)
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set(CMAKE_CXX_STANDARD 14)
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add_subdirectory(src)
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@ -7,8 +7,9 @@ add_library(builder
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target_include_directories(builder PRIVATE ${CMAKE_SOURCE_DIR})
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target_include_directories(builder PRIVATE ${CMAKE_BINARY_DIR})
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target_link_libraries(builder compiler onnx ${MLIRLibs} curses)
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target_link_libraries(builder compiler onnx ${MLIRLibs} curses mpark_variant)
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target_include_directories(builder
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PRIVATE
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${CMAKE_SOURCE_DIR}/third_party/onnx
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${CMAKE_SOURCE_DIR}/third_party/variant
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${CMAKE_SOURCE_DIR})
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@ -14,11 +14,16 @@
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//
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//===----------------------------------------------------------------------===//
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#include <map>
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#include <numeric>
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#include <regex>
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#include <string>
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#include <tuple>
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#include <map>
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// Using backported variant.
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// bstd = backported standard library.
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#include <mpark/variant.hpp>
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namespace bstd = mpark;
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#include "mlir/Analysis/Verifier.h"
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#include "mlir/Dialect/StandardOps/Ops.h"
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@ -42,15 +47,15 @@
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namespace onnf {
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namespace {
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void replaceAll(
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std::string& str, const std::string& from, const std::string& to) {
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void replaceAll(std::string &str, const std::string &from,
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const std::string &to) {
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if (from.empty())
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return;
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size_t start_pos = 0;
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while ((start_pos = str.find(from, start_pos)) != std::string::npos) {
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str.replace(start_pos, from.length(), to);
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start_pos += to.length(); // In case 'to' contains 'from', like replacing
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// 'x' with 'yx'
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start_pos += to.length(); // In case 'to' contains 'from', like replacing
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// 'x' with 'yx'
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}
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}
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@ -71,10 +76,10 @@ struct OnnxOnnfSymbolMapping {
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* @param name onnx tensor name.
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* @return onnf tensor corresponding to `name`.
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*/
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mlir::Value GetTensorByOnnxName(std::string name) {
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mlir::Value GetTensorByOnnxName(const std::string &name) {
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assert(onnx_name2onnf_tensor.find(legalize_name(name)) !=
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onnx_name2onnf_tensor.end() &&
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"Tensor not found");
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onnx_name2onnf_tensor.end() &&
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"Tensor not found");
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return onnx_name2onnf_tensor.at(legalize_name(name));
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}
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@ -83,9 +88,9 @@ struct OnnxOnnfSymbolMapping {
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* @param name onnx tensor name.
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* @param tensor MLIR Value pointer.
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*/
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void AddMapping(std::string name, mlir::Value tensor) {
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void AddMapping(const std::string &name, mlir::Value tensor) {
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assert(onnx_name2onnf_tensor.count(legalize_name(name)) == 0 &&
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"Tensor already exists.");
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"Tensor already exists.");
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onnx_name2onnf_tensor.emplace(legalize_name(name), tensor);
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}
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@ -124,34 +129,34 @@ private:
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// Convert type to MLIR type.
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// A complete list of types can be found in:
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// <onnf-build-folder>/third_party/onnx/onnx/onnx.pb.h
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mlir::Type TypeConvert(onnx::TensorProto_DataType intype) {
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switch (intype) {
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case onnx::TensorProto_DataType::TensorProto_DataType_FLOAT16:
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return builder_.getF16Type();
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case onnx::TensorProto_DataType::TensorProto_DataType_FLOAT:
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return builder_.getF32Type();
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case onnx::TensorProto_DataType::TensorProto_DataType_DOUBLE:
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return builder_.getF64Type();
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case onnx::TensorProto_DataType::TensorProto_DataType_INT8:
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case onnx::TensorProto_DataType::TensorProto_DataType_UINT8:
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return builder_.getIntegerType(8);
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case onnx::TensorProto_DataType::TensorProto_DataType_INT16:
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case onnx::TensorProto_DataType::TensorProto_DataType_UINT16:
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return builder_.getIntegerType(16);
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case onnx::TensorProto_DataType::TensorProto_DataType_INT32:
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case onnx::TensorProto_DataType::TensorProto_DataType_UINT32:
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return builder_.getIntegerType(32);
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case onnx::TensorProto_DataType::TensorProto_DataType_INT64:
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case onnx::TensorProto_DataType::TensorProto_DataType_UINT64:
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return builder_.getIntegerType(64);
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case onnx::TensorProto_DataType::TensorProto_DataType_BOOL:
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return builder_.getI1Type();
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case onnx::TensorProto_DataType::TensorProto_DataType_STRING:
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case onnx::TensorProto_DataType::TensorProto_DataType_COMPLEX64:
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case onnx::TensorProto_DataType::TensorProto_DataType_COMPLEX128:
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case onnx::TensorProto_DataType::TensorProto_DataType_UNDEFINED:
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assert(false && "Unsupported data type encountered.");
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return nullptr;
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mlir::Type convertONNXTypeToMLIRType(onnx::TensorProto_DataType onnxType) {
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switch (onnxType) {
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case onnx::TensorProto_DataType::TensorProto_DataType_FLOAT16:
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return builder_.getF16Type();
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case onnx::TensorProto_DataType::TensorProto_DataType_FLOAT:
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return builder_.getF32Type();
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case onnx::TensorProto_DataType::TensorProto_DataType_DOUBLE:
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return builder_.getF64Type();
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case onnx::TensorProto_DataType::TensorProto_DataType_INT8:
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case onnx::TensorProto_DataType::TensorProto_DataType_UINT8:
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return builder_.getIntegerType(8);
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case onnx::TensorProto_DataType::TensorProto_DataType_INT16:
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case onnx::TensorProto_DataType::TensorProto_DataType_UINT16:
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return builder_.getIntegerType(16);
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case onnx::TensorProto_DataType::TensorProto_DataType_INT32:
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case onnx::TensorProto_DataType::TensorProto_DataType_UINT32:
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return builder_.getIntegerType(32);
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case onnx::TensorProto_DataType::TensorProto_DataType_INT64:
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case onnx::TensorProto_DataType::TensorProto_DataType_UINT64:
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return builder_.getIntegerType(64);
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case onnx::TensorProto_DataType::TensorProto_DataType_BOOL:
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return builder_.getI1Type();
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case onnx::TensorProto_DataType::TensorProto_DataType_STRING:
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case onnx::TensorProto_DataType::TensorProto_DataType_COMPLEX64:
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case onnx::TensorProto_DataType::TensorProto_DataType_COMPLEX128:
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case onnx::TensorProto_DataType::TensorProto_DataType_UNDEFINED:
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assert(false && "Unsupported data type encountered.");
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return nullptr;
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}
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}
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@ -169,8 +174,8 @@ private:
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for (int i = 0; i < shape_proto.dim_size(); i++) {
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if (shape_proto.dim()[i].dim_value()) {
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int dim_numeric_size = shape_proto.dim()[i].dim_value();
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assert(
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dim_numeric_size != 0 && "Parsed an input tensor with a dimension size of zero");
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assert(dim_numeric_size != 0 &&
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"Parsed an input tensor with a dimension size of zero");
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if (dim_numeric_size > 0) {
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dims.push_back(dim_numeric_size);
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} else { // If dim_value < 0, then dim is parametric.
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@ -184,7 +189,7 @@ private:
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}
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mlir::Type elementType =
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TypeConvert(input.type().tensor_type().elem_type());
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convertONNXTypeToMLIRType(input.type().tensor_type().elem_type());
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llvm::ArrayRef<int64_t> tensor_dims(dims.data(), dims.size());
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arg_types.emplace_back(
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mlir::RankedTensorType::get(tensor_dims, elementType));
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@ -200,288 +205,111 @@ private:
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void ImportInputTensorSymbol(const onnx::ValueInfoProto &input,
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mlir::Value symbol) {
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auto input_tensor_legalized_name = legalize_name(input.name());
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assert(
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!frontend_symbols_.ContainKey(input_tensor_legalized_name) &&
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"Found duplicate legalized input tensor names.");
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assert(!frontend_symbols_.ContainKey(input_tensor_legalized_name) &&
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"Found duplicate legalized input tensor names.");
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frontend_symbols_.AddMapping(input_tensor_legalized_name, symbol);
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}
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template <typename T>
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T get_attr_generic(onnx::NodeProto &node, std::string name,
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std::function<T(onnx::AttributeProto &)> attr_getter,
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T default_val) {
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typedef bstd::variant<int64_t, std::vector<int64_t>, float,
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std::vector<float>, std::string,
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std::vector<std::string>>
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AttrValueType;
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struct ONNXAttrVisitor {
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ONNXAttrVisitor(std::string name, mlir::OpBuilder &builder)
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: _builder(builder), _name(std::move(name)) {}
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// Op builder.
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mlir::OpBuilder &_builder;
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// Name of the attribute being inspected.
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std::string _name;
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mlir::NamedAttribute operator()(int64_t const &r) {
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auto val = _builder.getI32IntegerAttr(r);
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return _builder.getNamedAttr(_name, val);
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}
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mlir::NamedAttribute operator()(std::vector<int64_t> const &ints) {
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auto val = _builder.getI64ArrayAttr(ints);
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return _builder.getNamedAttr(_name, val);
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}
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mlir::NamedAttribute operator()(float const &r) {
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auto val = _builder.getF32FloatAttr(r);
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return _builder.getNamedAttr(_name, val);
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}
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mlir::NamedAttribute operator()(std::vector<float> const &floats) {
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auto val = _builder.getF32ArrayAttr(floats);
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return _builder.getNamedAttr(_name, val);
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}
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mlir::NamedAttribute operator()(std::string const &s) {
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auto val = _builder.getStringAttr(s);
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return _builder.getNamedAttr(_name, val);
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}
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mlir::NamedAttribute operator()(std::vector<std::string> const &r) {
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assert(false && "type of attribute value is not implemented");
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auto val = _builder.getI32IntegerAttr(1);
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return _builder.getNamedAttr(_name, val);
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};
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};
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mlir::NamedAttribute convertNameValuePairToNamedAttribute(
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std::pair<std::string, AttrValueType> nameAndVal) {
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auto visitor = ONNXAttrVisitor(nameAndVal.first, builder_);
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return mpark::visit(visitor, nameAndVal.second);
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}
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static std::pair<std::string, AttrValueType>
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convertAttributeProtoToNameValuePair(onnx::AttributeProto &attr) {
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AttrValueType val;
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switch (attr.type()) {
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case onnx::AttributeProto::FLOAT:
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return std::make_pair(attr.name(), AttrValueType(attr.f()));
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case onnx::AttributeProto::INT:
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return std::make_pair(attr.name(), AttrValueType(attr.i()));
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case onnx::AttributeProto::STRING:
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return std::make_pair(attr.name(), AttrValueType(attr.s()));
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case onnx::AttributeProto::FLOATS:
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val = AttrValueType(
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std::vector<float>(attr.floats().begin(), attr.floats().end()));
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return std::make_pair(attr.name(), val);
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case onnx::AttributeProto::INTS:
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val = AttrValueType(
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std::vector<int64_t>(attr.ints().begin(), attr.ints().end()));
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return std::make_pair(attr.name(), val);
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default:
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assert(false && "datatype for attribute is not implemented");
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break;
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}
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}
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std::vector<mlir::NamedAttribute> ImportNodeAttributes(
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const onnx::NodeProto &node,
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std::initializer_list<std::pair<std::string, AttrValueType>>
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defaultAttrList) {
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std::vector<mlir::NamedAttribute> attributes;
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std::set<std::string> definedAttributeSet;
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for (int i = 0; i < node.attribute_size(); ++i) {
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auto attr = node.attribute(i);
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if (attr.name() == name) {
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return attr_getter(attr);
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}
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auto nameValPair = convertAttributeProtoToNameValuePair(attr);
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attributes.push_back(convertNameValuePairToNamedAttribute(nameValPair));
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definedAttributeSet.insert(attr.name());
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}
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return default_val;
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}
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template <typename T>
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T get_attr_generic(onnx::NodeProto &node, std::string name,
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std::function<T(onnx::AttributeProto &)> attr_getter) {
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for (int i = 0; i < node.attribute_size(); ++i) {
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auto attr = node.attribute(i);
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if (attr.name() == name) {
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return attr_getter(attr);
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}
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for (const auto &defaultAttr : defaultAttrList) {
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if (definedAttributeSet.find(defaultAttr.first) ==
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definedAttributeSet.end())
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attributes.push_back(convertNameValuePairToNamedAttribute(defaultAttr));
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}
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assert(false && "ONNX Node Attribute Not Found!");
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return attributes;
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}
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auto get_attr_ints(onnx::NodeProto &node, std::string name,
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std::vector<int> default_val) {
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std::function<std::vector<int>(onnx::AttributeProto &)> attr_getter =
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[](onnx::AttributeProto &attr) {
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std::vector<int> ints(attr.ints_size());
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std::copy(attr.ints().begin(), attr.ints().end(), ints.begin());
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return ints;
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};
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auto r = get_attr_generic(node, name, attr_getter, default_val);
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auto dataType =
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mlir::RankedTensorType::get(r.size(), builder_.getIntegerType(32));
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auto attr_v = mlir::DenseElementsAttr::get(dataType, llvm::makeArrayRef(r));
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auto aname = node.op_type() + "." + name;
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auto attr_output = builder_.getNamedAttr(aname, attr_v);
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return attr_output;
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}
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auto get_attr_ints(onnx::NodeProto &node, std::string name) {
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std::function<std::vector<int>(onnx::AttributeProto &)> attr_getter =
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[](onnx::AttributeProto &attr) {
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std::vector<int> ints(attr.ints_size());
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std::copy(attr.ints().begin(), attr.ints().end(), ints.begin());
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return ints;
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};
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auto r = get_attr_generic(node, name, attr_getter);
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auto dataType =
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mlir::RankedTensorType::get(r.size(), builder_.getIntegerType(32));
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auto attr_v = mlir::DenseElementsAttr::get(dataType, llvm::makeArrayRef(r));
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auto aname = node.op_type() + "." + name;
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auto attr_output = builder_.getNamedAttr(aname, attr_v);
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return attr_output;
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}
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auto get_attr_floats(onnx::NodeProto &node, std::string name) {
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std::function<std::vector<float>(onnx::AttributeProto &)> attr_getter =
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[](onnx::AttributeProto &attr) {
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std::vector<float> floats(attr.floats_size());
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std::copy(attr.floats().begin(), attr.floats().end(), floats.begin());
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return floats;
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};
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auto r = get_attr_generic(node, name, attr_getter);
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auto dataType =
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mlir::RankedTensorType::get(r.size(), builder_.getF32Type());
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auto attr_v = mlir::DenseElementsAttr::get(dataType, llvm::makeArrayRef(r));
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auto aname = node.op_type() + "." + name;
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auto attr_output = builder_.getNamedAttr(aname, attr_v);
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return attr_output;
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}
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auto get_attr_floats(onnx::NodeProto &node, std::string name,
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std::vector<float> default_val) {
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std::function<std::vector<float>(onnx::AttributeProto &)> attr_getter =
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[](onnx::AttributeProto &attr) {
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std::vector<float> floats(attr.floats_size());
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std::copy(attr.floats().begin(), attr.floats().end(), floats.begin());
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return floats;
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};
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auto r = get_attr_generic(node, name, attr_getter, default_val);
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auto dataType =
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mlir::RankedTensorType::get(r.size(), builder_.getF32Type());
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auto attr_v = mlir::DenseElementsAttr::get(dataType, llvm::makeArrayRef(r));
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auto aname = node.op_type() + "." + name;
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auto attr_output = builder_.getNamedAttr(aname, attr_v);
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return attr_output;
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}
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auto get_attr_int(onnx::NodeProto &node, std::string name) {
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std::function<int(onnx::AttributeProto &)> attr_getter =
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[](onnx::AttributeProto &attr) { return attr.i(); };
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int r = get_attr_generic(node, name, attr_getter);
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auto attr_v = builder_.getI32IntegerAttr(r);
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auto aname = node.op_type() + "." + name;
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auto attr_output = builder_.getNamedAttr(aname, attr_v);
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return attr_output;
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}
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auto get_attr_int(onnx::NodeProto &node, std::string name, int default_val) {
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std::function<int(onnx::AttributeProto &)> attr_getter =
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[](onnx::AttributeProto &attr) { return attr.i(); };
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int r = get_attr_generic(node, name, attr_getter, default_val);
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auto attr_v = builder_.getI32IntegerAttr(r);
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auto aname = node.op_type() + "." + name;
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auto attr_output = builder_.getNamedAttr(aname, attr_v);
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return attr_output;
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}
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auto get_attr_float(onnx::NodeProto &node, std::string name) {
|
||||
std::function<float(onnx::AttributeProto &)> attr_getter =
|
||||
[](onnx::AttributeProto &attr) { return attr.f(); };
|
||||
auto r = get_attr_generic(node, name, attr_getter);
|
||||
auto attr_v = builder_.getF32FloatAttr(r);
|
||||
auto aname = node.op_type() + "." + name;
|
||||
return builder_.getNamedAttr(aname, attr_v);
|
||||
}
|
||||
|
||||
auto get_attr_float(onnx::NodeProto &node, std::string name,
|
||||
float default_val) {
|
||||
std::function<float(onnx::AttributeProto &)> attr_getter =
|
||||
[](onnx::AttributeProto &attr) { return attr.f(); };
|
||||
auto r = get_attr_generic(node, name, attr_getter, default_val);
|
||||
auto attr_v = builder_.getF32FloatAttr(r);
|
||||
auto aname = node.op_type() + "." + name;
|
||||
return builder_.getNamedAttr(aname, attr_v);
|
||||
}
|
||||
|
||||
auto get_attr_string(onnx::NodeProto &node, std::string name) {
|
||||
std::function<std::string(onnx::AttributeProto &)> attr_getter =
|
||||
[](onnx::AttributeProto &attr) { return attr.s(); };
|
||||
auto r = get_attr_generic(node, name, attr_getter);
|
||||
auto attr_v = builder_.getStringAttr(r);
|
||||
auto aname = node.op_type() + "." + name;
|
||||
return builder_.getNamedAttr(aname, attr_v);
|
||||
}
|
||||
|
||||
auto get_attr_string(onnx::NodeProto &node, std::string name,
|
||||
std::string default_val) {
|
||||
std::function<std::string(onnx::AttributeProto &)> attr_getter =
|
||||
[](onnx::AttributeProto &attr) { return attr.s(); };
|
||||
auto r = get_attr_generic(node, name, attr_getter, default_val);
|
||||
auto attr_v = builder_.getStringAttr(r);
|
||||
auto aname = node.op_type() + "." + name;
|
||||
return builder_.getNamedAttr(aname, attr_v);
|
||||
}
|
||||
|
||||
/*
|
||||
auto get_attr_strings(onnx::NodeProto &node, std::string name) {
|
||||
std::function<std::vector<std::string>(onnx::AttributeProto &)>
|
||||
attr_getter =
|
||||
[](onnx::AttributeProto &attr) {
|
||||
std::vector<std::string> strings(attr.strings_size());
|
||||
std::copy(attr.strings().begin(), attr.strings().end(),
|
||||
strings.begin()); return strings;
|
||||
};
|
||||
auto r = get_attr_generic(node, name, attr_getter);
|
||||
return r;
|
||||
return builder_.getNamedAttr(aname, attr_v);
|
||||
auto dataType =
|
||||
mlir::RankedTensorType::get(r.size(), builder_.get???Type());
|
||||
auto attr_v = mlir::DenseElementsAttr::get(dataType,
|
||||
llvm::makeArrayRef(r)); auto aname = node.op_type() + "." + name; auto
|
||||
attr_output = builder_.getNamedAttr(aname, attr_v); return attr_output;
|
||||
}
|
||||
*/
|
||||
|
||||
auto get_default_ints(std::string default_str) {
|
||||
std::vector<int> r;
|
||||
auto start = default_str.find("{");
|
||||
while (true) {
|
||||
auto end = default_str.find(",", start + 1);
|
||||
if (end == std::string::npos) {
|
||||
end = default_str.find("}", start + 1);
|
||||
if (end != std::string::npos && end > start + 1) {
|
||||
r.push_back(std::stoi(default_str.substr(start + 1, end)));
|
||||
}
|
||||
break;
|
||||
} else {
|
||||
r.push_back(std::stoi(default_str.substr(start + 1, end)));
|
||||
}
|
||||
start = end + 1;
|
||||
}
|
||||
return r;
|
||||
}
|
||||
|
||||
auto get_default_floats(std::string default_str) {
|
||||
std::vector<float> r;
|
||||
auto start = default_str.find("{");
|
||||
while (true) {
|
||||
auto end = default_str.find(",", start + 1);
|
||||
if (end == std::string::npos) {
|
||||
end = default_str.find("}", start + 1);
|
||||
if (end != std::string::npos && end > start + 1) {
|
||||
r.push_back(std::stof(default_str.substr(start + 1, end)));
|
||||
}
|
||||
break;
|
||||
} else {
|
||||
r.push_back(std::stof(default_str.substr(start + 1, end)));
|
||||
}
|
||||
start = end + 1;
|
||||
}
|
||||
return r;
|
||||
}
|
||||
|
||||
auto get_default_strings(std::string default_str) {
|
||||
std::vector<std::string> r;
|
||||
auto start = default_str.find("{");
|
||||
while (true) {
|
||||
auto end = default_str.find(",", start + 1);
|
||||
if (end == std::string::npos) {
|
||||
end = default_str.find("}", start + 1);
|
||||
if (end != std::string::npos && end > start + 1) {
|
||||
r.push_back(default_str.substr(start + 1, end));
|
||||
}
|
||||
break;
|
||||
} else {
|
||||
r.push_back(default_str.substr(start + 1, end));
|
||||
}
|
||||
start = end + 1;
|
||||
}
|
||||
return r;
|
||||
}
|
||||
|
||||
onnx::TensorProto get_attr_tensor(onnx::NodeProto &node, std::string name) {
|
||||
std::function<onnx::TensorProto(onnx::AttributeProto &)> attr_getter =
|
||||
[](onnx::AttributeProto &attr) { return attr.t(); };
|
||||
return get_attr_generic(node, name, attr_getter);
|
||||
}
|
||||
|
||||
auto ImportNodeAttr(onnx::NodeProto node, std::string attr_name,
|
||||
std::string type_name, std::string default_str) {
|
||||
if (default_str == "") {
|
||||
if (type_name == "int") {
|
||||
return get_attr_int(node, attr_name);
|
||||
} else if (type_name == "float") {
|
||||
return get_attr_float(node, attr_name);
|
||||
} else if (type_name == "str") {
|
||||
return get_attr_string(node, attr_name);
|
||||
} else if (type_name == "ints") {
|
||||
return get_attr_ints(node, attr_name);
|
||||
} else if (type_name == "floats") {
|
||||
return get_attr_floats(node, attr_name);
|
||||
} else {
|
||||
assert(
|
||||
false &&
|
||||
"Got an empty initializer or initializer for this "
|
||||
"datatype is not implemented. Something is wrong.");
|
||||
}
|
||||
} else {
|
||||
// with default value
|
||||
if (type_name == "int") {
|
||||
return get_attr_int(node, attr_name, std::stoi(default_str));
|
||||
} else if (type_name == "float") {
|
||||
return get_attr_float(node, attr_name, std::stof(default_str));
|
||||
} else if (type_name == "str") {
|
||||
return get_attr_string(node, attr_name, default_str);
|
||||
} else if (type_name == "ints") {
|
||||
return get_attr_ints(node, attr_name, get_default_ints(default_str));
|
||||
} else if (type_name == "floats") {
|
||||
return get_attr_floats(node, attr_name,
|
||||
get_default_floats(default_str));
|
||||
} else {
|
||||
assert(
|
||||
false &&
|
||||
"Got an empty initializer or initializer for this "
|
||||
"datatype is not implemented. Something is wrong.");
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
void ImportNodeGeneric(onnx::NodeProto node) {
|
||||
void ImportNodeGeneric(const onnx::NodeProto &node) {
|
||||
std::vector<mlir::Value> inputs;
|
||||
for (auto item : node.input()) {
|
||||
for (const auto &item : node.input()) {
|
||||
if (frontend_symbols_.ContainKey(legalize_name(item))) {
|
||||
inputs.push_back(frontend_symbols_.GetTensorByOnnxName(item));
|
||||
}
|
||||
|
|
@ -511,12 +339,12 @@ private:
|
|||
* default}
|
||||
*/
|
||||
template <typename T>
|
||||
void ImportNodeOneOut(
|
||||
onnx::NodeProto node, int nIn, int nOut,
|
||||
std::initializer_list<std::tuple<std::string, std::string, std::string>>
|
||||
attrs) {
|
||||
void
|
||||
ImportNodeOneOut(const onnx::NodeProto &node, int nIn, int nOut,
|
||||
std::initializer_list<std::pair<std::string, AttrValueType>>
|
||||
defaultAttrList) {
|
||||
std::vector<mlir::Value> inputs;
|
||||
for (auto item : node.input()) {
|
||||
for (const auto &item : node.input()) {
|
||||
if (frontend_symbols_.ContainKey(legalize_name(item))) {
|
||||
inputs.push_back(frontend_symbols_.GetTensorByOnnxName(item));
|
||||
}
|
||||
|
|
@ -528,22 +356,7 @@ private:
|
|||
mlir::UnrankedTensorType::get(builder_.getF32Type()));
|
||||
}
|
||||
|
||||
std::vector<mlir::NamedAttribute> attributes;
|
||||
// for (auto [attr_name, attr_type, attr_default] : attrs) {
|
||||
for (auto oneAttr : attrs) {
|
||||
std::string attr_name;
|
||||
std::string attr_type;
|
||||
std::string attr_default;
|
||||
std::tie(attr_name, attr_type, attr_default) = oneAttr;
|
||||
if (attr_type != "") {
|
||||
auto attr = ImportNodeAttr(node, attr_name, attr_type, attr_default);
|
||||
attributes.push_back(attr);
|
||||
} else {
|
||||
// TODO: the attributes need special handling
|
||||
// std::cout << "missing " << node.op_type() << " " << attr_name <<
|
||||
// std::endl;
|
||||
}
|
||||
}
|
||||
auto attributes = ImportNodeAttributes(node, defaultAttrList);
|
||||
|
||||
llvm::StringRef OpName = node.op_type();
|
||||
|
||||
|
|
@ -559,11 +372,11 @@ private:
|
|||
|
||||
template <typename T>
|
||||
void ImportNodeMultipleOuts(
|
||||
onnx::NodeProto node, int nIn, int nOut,
|
||||
std::initializer_list<std::tuple<std::string, std::string, std::string>>
|
||||
attrs) {
|
||||
const onnx::NodeProto &node, int nIn, int nOut,
|
||||
std::initializer_list<std::pair<std::string, AttrValueType>>
|
||||
defaultAttrList) {
|
||||
std::vector<mlir::Value> inputs;
|
||||
for (auto item : node.input()) {
|
||||
for (const auto &item : node.input()) {
|
||||
if (frontend_symbols_.ContainKey(legalize_name(item))) {
|
||||
inputs.push_back(frontend_symbols_.GetTensorByOnnxName(item));
|
||||
}
|
||||
|
|
@ -575,21 +388,7 @@ private:
|
|||
mlir::UnrankedTensorType::get(builder_.getF32Type()));
|
||||
}
|
||||
|
||||
std::vector<mlir::NamedAttribute> attributes;
|
||||
for (auto oneAttr : attrs) {
|
||||
std::string attr_name;
|
||||
std::string attr_type;
|
||||
std::string attr_default;
|
||||
std::tie(attr_name, attr_type, attr_default) = oneAttr;
|
||||
if (attr_type != "") {
|
||||
auto attr = ImportNodeAttr(node, attr_name, attr_type, attr_default);
|
||||
attributes.push_back(attr);
|
||||
} else {
|
||||
// TODO: the attributes need special handling
|
||||
// std::cout << "missing " << node.op_type() << " " << attr_name <<
|
||||
// std::endl;
|
||||
}
|
||||
}
|
||||
auto attributes = ImportNodeAttributes(node, defaultAttrList);
|
||||
|
||||
llvm::StringRef OpName = node.op_type();
|
||||
|
||||
|
|
@ -610,10 +409,10 @@ private:
|
|||
* c++ does not allow template specialization inside a class scope
|
||||
* a specialized function is used
|
||||
*/
|
||||
void ImportNodeConv(
|
||||
onnx::NodeProto node, int nOut,
|
||||
std::initializer_list<std::tuple<std::string, std::string, std::string>>
|
||||
attrs) {
|
||||
void
|
||||
ImportNodeConv(onnx::NodeProto node, int nIn, int nOut,
|
||||
std::initializer_list<std::pair<std::string, AttrValueType>>
|
||||
defaultAttrList) {
|
||||
// Conv has attribute dilations, kernel_shape, pads, the default value of
|
||||
// which is determined by the shape of first argument. However, since the
|
||||
// shape is unknown now, these attributes can be not generated auto
|
||||
|
|
@ -627,29 +426,32 @@ private:
|
|||
int nOps = node.input().size();
|
||||
|
||||
if (nOps == 2)
|
||||
ImportNodeOneOut<mlir::ONNXConvNoBiasOp>(node, nOps, nOut, attrs);
|
||||
ImportNodeOneOut<mlir::ONNXConvNoBiasOp>(
|
||||
node, nOps, nOut, defaultAttrList);
|
||||
else
|
||||
ImportNodeOneOut<mlir::ONNXConvOp>(node, nOps, nOut, attrs);
|
||||
ImportNodeOneOut<mlir::ONNXConvOp>(node, nOps, nOut, defaultAttrList);
|
||||
}
|
||||
|
||||
/*!
|
||||
* Special handle for MaxPool operations.
|
||||
*/
|
||||
void ImportNodeMaxPool(
|
||||
onnx::NodeProto node, int nIn,
|
||||
std::initializer_list<std::tuple<std::string, std::string, std::string>>
|
||||
attrs) {
|
||||
onnx::NodeProto node, int nIn, int nOut,
|
||||
std::initializer_list<std::pair<std::string, AttrValueType>>
|
||||
defaultAttrList) {
|
||||
int nOuts = node.output().size();
|
||||
if (nOuts == 1) {
|
||||
ImportNodeOneOut<mlir::ONNXMaxPoolSingleOutOp>(node, nIn, nOuts, attrs);
|
||||
ImportNodeOneOut<mlir::ONNXMaxPoolSingleOutOp>(
|
||||
node, nIn, nOuts, defaultAttrList);
|
||||
} else {
|
||||
ImportNodeMultipleOuts<mlir::ONNXMaxPoolOp>(node, nIn, nOuts, attrs);
|
||||
ImportNodeMultipleOuts<mlir::ONNXMaxPoolOp>(
|
||||
node, nIn, nOuts, defaultAttrList);
|
||||
}
|
||||
}
|
||||
|
||||
void ImportNode(onnx::NodeProto node) {
|
||||
void ImportNode(const onnx::NodeProto &node) {
|
||||
std::vector<mlir::Value> inputs;
|
||||
for (auto item : node.input()) {
|
||||
for (const auto &item : node.input()) {
|
||||
if (frontend_symbols_.ContainKey(legalize_name(item))) {
|
||||
inputs.push_back(frontend_symbols_.GetTensorByOnnxName(item));
|
||||
}
|
||||
|
|
@ -689,9 +491,8 @@ private:
|
|||
llvm::SmallVectorImpl<mlir::Type> &ret_types,
|
||||
llvm::SmallVectorImpl<mlir::Value> &ret_vals) {
|
||||
auto output_tensor_legalized_name = legalize_name(output.name());
|
||||
assert(
|
||||
frontend_symbols_.ContainKey(output_tensor_legalized_name) &&
|
||||
"Output tensor not found");
|
||||
assert(frontend_symbols_.ContainKey(output_tensor_legalized_name) &&
|
||||
"Output tensor not found");
|
||||
|
||||
auto tensor_val =
|
||||
frontend_symbols_.GetTensorByOnnxName(output_tensor_legalized_name);
|
||||
|
|
@ -750,9 +551,9 @@ private:
|
|||
funcType = builder_.getFunctionType(arg_types, ret_types);
|
||||
mainFunc.setType(funcType);
|
||||
}
|
||||
}; // FrontendGenImpl class
|
||||
} // namespace
|
||||
} // namespace onnf
|
||||
}; // FrontendGenImpl class
|
||||
} // namespace
|
||||
} // namespace onnf
|
||||
|
||||
namespace onnf {
|
||||
|
||||
|
|
@ -775,4 +576,4 @@ void ImportFrontendModelFile(std::string model_fname,
|
|||
FrontendGenImpl myONNXGen(context);
|
||||
module = myONNXGen.ImportONNXModel(model);
|
||||
}
|
||||
} // namespace onnf
|
||||
} // namespace onnf
|
||||
|
|
|
|||
|
|
@ -16,13 +16,13 @@
|
|||
});
|
||||
}else if (OpName == "ArgMax") {
|
||||
ImportNodeOneOut<mlir::ONNXArgMaxOp>(node, 1, 1, {
|
||||
{"axis","int","0"}
|
||||
,{"keepdims","int","1"}
|
||||
{"axis", 0}
|
||||
,{"keepdims", 1}
|
||||
});
|
||||
}else if (OpName == "ArgMin") {
|
||||
ImportNodeOneOut<mlir::ONNXArgMinOp>(node, 1, 1, {
|
||||
{"axis","int","0"}
|
||||
,{"keepdims","int","1"}
|
||||
{"axis", 0}
|
||||
,{"keepdims", 1}
|
||||
});
|
||||
}else if (OpName == "Asin") {
|
||||
ImportNodeOneOut<mlir::ONNXAsinOp>(node, 1, 1, {
|
||||
|
|
@ -38,25 +38,22 @@
|
|||
});
|
||||
}else if (OpName == "AveragePool") {
|
||||
ImportNodeOneOut<mlir::ONNXAveragePoolOp>(node, 1, 1, {
|
||||
{"auto_pad","str","NOTSET"}
|
||||
,{"ceil_mode","int","0"}
|
||||
,{"count_include_pad","int","0"}
|
||||
,{"kernel_shape","ints", ""}
|
||||
,{"pads","", ""}
|
||||
,{"strides","", ""}
|
||||
{"auto_pad", "NOTSET"}
|
||||
,{"ceil_mode", 0}
|
||||
,{"count_include_pad", 0}
|
||||
,{"kernel_shape", std::vector<int64_t> {}}
|
||||
});
|
||||
}else if (OpName == "BatchNormalization") {
|
||||
ImportNodeMultipleOuts<mlir::ONNXBatchNormalizationOp>(node, 5, 5, {
|
||||
{"epsilon","float","1e-05"}
|
||||
,{"momentum","float","0.9"}
|
||||
{"epsilon", (float)1e-05}
|
||||
,{"momentum", (float)0.9}
|
||||
});
|
||||
}else if (OpName == "BitShift") {
|
||||
ImportNodeOneOut<mlir::ONNXBitShiftOp>(node, 2, 1, {
|
||||
{"direction","", ""}
|
||||
});
|
||||
}else if (OpName == "Cast") {
|
||||
ImportNodeOneOut<mlir::ONNXCastOp>(node, 1, 1, {
|
||||
{"to","int", "0"}
|
||||
{"to", 0}
|
||||
});
|
||||
}else if (OpName == "Ceil") {
|
||||
ImportNodeOneOut<mlir::ONNXCeilOp>(node, 1, 1, {
|
||||
|
|
@ -66,54 +63,35 @@
|
|||
});
|
||||
}else if (OpName == "Compress") {
|
||||
ImportNodeOneOut<mlir::ONNXCompressOp>(node, 2, 1, {
|
||||
{"axis","", ""}
|
||||
});
|
||||
}else if (OpName == "Concat") {
|
||||
ImportNodeOneOut<mlir::ONNXConcatOp>(node, 1, 1, {
|
||||
{"axis","int", "0"}
|
||||
{"axis", 0}
|
||||
});
|
||||
}else if (OpName == "ConcatFromSequence") {
|
||||
ImportNodeOneOut<mlir::ONNXConcatFromSequenceOp>(node, 1, 1, {
|
||||
{"axis","", ""}
|
||||
,{"new_axis","int","0"}
|
||||
{"new_axis", 0}
|
||||
});
|
||||
}else if (OpName == "Constant") {
|
||||
ImportNodeOneOut<mlir::ONNXConstantOp>(node, 0, 1, {
|
||||
{"sparse_value","", ""}
|
||||
,{"value","", ""}
|
||||
});
|
||||
}else if (OpName == "ConstantOfShape") {
|
||||
ImportNodeOneOut<mlir::ONNXConstantOfShapeOp>(node, 1, 1, {
|
||||
{"value","", ""}
|
||||
});
|
||||
}else if (OpName == "Conv") {
|
||||
ImportNodeConv(node, 1, {
|
||||
{"auto_pad","str","NOTSET"}
|
||||
,{"dilations","", ""}
|
||||
,{"group","int", "1"}
|
||||
,{"kernel_shape","", ""}
|
||||
,{"pads","", ""}
|
||||
,{"strides","", ""}
|
||||
ImportNodeConv(node, 3, 1, {
|
||||
{"auto_pad", "NOTSET"}
|
||||
,{"group", 1}
|
||||
});
|
||||
}else if (OpName == "ConvInteger") {
|
||||
ImportNodeOneOut<mlir::ONNXConvIntegerOp>(node, 4, 1, {
|
||||
{"auto_pad","str","NOTSET"}
|
||||
,{"dilations","", ""}
|
||||
,{"group","int","1"}
|
||||
,{"kernel_shape","", ""}
|
||||
,{"pads","", ""}
|
||||
,{"strides","", ""}
|
||||
{"auto_pad", "NOTSET"}
|
||||
,{"group", 1}
|
||||
});
|
||||
}else if (OpName == "ConvTranspose") {
|
||||
ImportNodeOneOut<mlir::ONNXConvTransposeOp>(node, 3, 1, {
|
||||
{"auto_pad","str","NOTSET"}
|
||||
,{"dilations","", ""}
|
||||
,{"group","int","1"}
|
||||
,{"kernel_shape","", ""}
|
||||
,{"output_padding","", ""}
|
||||
,{"output_shape","", ""}
|
||||
,{"pads","", ""}
|
||||
,{"strides","", ""}
|
||||
{"auto_pad", "NOTSET"}
|
||||
,{"group", 1}
|
||||
});
|
||||
}else if (OpName == "Cos") {
|
||||
ImportNodeOneOut<mlir::ONNXCosOp>(node, 1, 1, {
|
||||
|
|
@ -123,13 +101,12 @@
|
|||
});
|
||||
}else if (OpName == "CumSum") {
|
||||
ImportNodeOneOut<mlir::ONNXCumSumOp>(node, 2, 1, {
|
||||
{"exclusive","int","0"}
|
||||
,{"reverse","int","0"}
|
||||
{"exclusive", 0}
|
||||
,{"reverse", 0}
|
||||
});
|
||||
}else if (OpName == "DepthToSpace") {
|
||||
ImportNodeOneOut<mlir::ONNXDepthToSpaceOp>(node, 1, 1, {
|
||||
{"blocksize","", ""}
|
||||
,{"mode","str","DCR"}
|
||||
{"mode", "DCR"}
|
||||
});
|
||||
}else if (OpName == "DequantizeLinear") {
|
||||
ImportNodeOneOut<mlir::ONNXDequantizeLinearOp>(node, 3, 1, {
|
||||
|
|
@ -142,14 +119,14 @@
|
|||
});
|
||||
}else if (OpName == "Dropout") {
|
||||
ImportNodeMultipleOuts<mlir::ONNXDropoutOp>(node, 1, 2, {
|
||||
{"ratio","float","0.5"}
|
||||
{"ratio", (float)0.5}
|
||||
});
|
||||
}else if (OpName == "DynamicQuantizeLinear") {
|
||||
ImportNodeMultipleOuts<mlir::ONNXDynamicQuantizeLinearOp>(node, 1, 3, {
|
||||
});
|
||||
}else if (OpName == "Elu") {
|
||||
ImportNodeOneOut<mlir::ONNXEluOp>(node, 1, 1, {
|
||||
{"alpha","float","1.0"}
|
||||
{"alpha", (float)1.0}
|
||||
});
|
||||
}else if (OpName == "Equal") {
|
||||
ImportNodeOneOut<mlir::ONNXEqualOp>(node, 2, 1, {
|
||||
|
|
@ -165,50 +142,44 @@
|
|||
});
|
||||
}else if (OpName == "EyeLike") {
|
||||
ImportNodeOneOut<mlir::ONNXEyeLikeOp>(node, 1, 1, {
|
||||
{"dtype","", ""}
|
||||
,{"k","int","0"}
|
||||
{"k", 0}
|
||||
});
|
||||
}else if (OpName == "Flatten") {
|
||||
ImportNodeOneOut<mlir::ONNXFlattenOp>(node, 1, 1, {
|
||||
{"axis","int","1"}
|
||||
{"axis", 1}
|
||||
});
|
||||
}else if (OpName == "Floor") {
|
||||
ImportNodeOneOut<mlir::ONNXFloorOp>(node, 1, 1, {
|
||||
});
|
||||
}else if (OpName == "GRU") {
|
||||
ImportNodeMultipleOuts<mlir::ONNXGRUOp>(node, 6, 2, {
|
||||
{"activation_alpha","", ""}
|
||||
,{"activation_beta","", ""}
|
||||
,{"activations","", ""}
|
||||
,{"clip","", ""}
|
||||
,{"direction","str","forward"}
|
||||
,{"hidden_size","", ""}
|
||||
,{"linear_before_reset","int","0"}
|
||||
{"direction", "forward"}
|
||||
,{"linear_before_reset", 0}
|
||||
});
|
||||
}else if (OpName == "Gather") {
|
||||
ImportNodeOneOut<mlir::ONNXGatherOp>(node, 2, 1, {
|
||||
{"axis","int","0"}
|
||||
{"axis", 0}
|
||||
});
|
||||
}else if (OpName == "GatherElements") {
|
||||
ImportNodeOneOut<mlir::ONNXGatherElementsOp>(node, 2, 1, {
|
||||
{"axis","int","0"}
|
||||
{"axis", 0}
|
||||
});
|
||||
}else if (OpName == "GatherND") {
|
||||
ImportNodeOneOut<mlir::ONNXGatherNDOp>(node, 2, 1, {
|
||||
});
|
||||
}else if (OpName == "Gemm") {
|
||||
ImportNodeOneOut<mlir::ONNXGemmOp>(node, 3, 1, {
|
||||
{"alpha","float","1.0"}
|
||||
,{"beta","float","1.0"}
|
||||
,{"transA","int","0"}
|
||||
,{"transB","int","0"}
|
||||
{"alpha", (float)1.0}
|
||||
,{"beta", (float)1.0}
|
||||
,{"transA", 0}
|
||||
,{"transB", 0}
|
||||
});
|
||||
}else if (OpName == "GlobalAveragePool") {
|
||||
ImportNodeOneOut<mlir::ONNXGlobalAveragePoolOp>(node, 1, 1, {
|
||||
});
|
||||
}else if (OpName == "GlobalLpPool") {
|
||||
ImportNodeOneOut<mlir::ONNXGlobalLpPoolOp>(node, 1, 1, {
|
||||
{"p","int","2"}
|
||||
{"p", 2}
|
||||
});
|
||||
}else if (OpName == "GlobalMaxPool") {
|
||||
ImportNodeOneOut<mlir::ONNXGlobalMaxPoolOp>(node, 1, 1, {
|
||||
|
|
@ -218,53 +189,45 @@
|
|||
});
|
||||
}else if (OpName == "HardSigmoid") {
|
||||
ImportNodeOneOut<mlir::ONNXHardSigmoidOp>(node, 1, 1, {
|
||||
{"alpha","float","0.2"}
|
||||
,{"beta","float","0.5"}
|
||||
{"alpha", (float)0.2}
|
||||
,{"beta", (float)0.5}
|
||||
});
|
||||
}else if (OpName == "Hardmax") {
|
||||
ImportNodeOneOut<mlir::ONNXHardmaxOp>(node, 1, 1, {
|
||||
{"axis","int","1"}
|
||||
{"axis", 1}
|
||||
});
|
||||
}else if (OpName == "Identity") {
|
||||
ImportNodeOneOut<mlir::ONNXIdentityOp>(node, 1, 1, {
|
||||
});
|
||||
}else if (OpName == "If") {
|
||||
ImportNodeOneOut<mlir::ONNXIfOp>(node, 1, 1, {
|
||||
{"else_branch","", ""}
|
||||
,{"then_branch","", ""}
|
||||
});
|
||||
}else if (OpName == "InstanceNormalization") {
|
||||
ImportNodeOneOut<mlir::ONNXInstanceNormalizationOp>(node, 3, 1, {
|
||||
{"epsilon","float","1e-05"}
|
||||
{"epsilon", (float)1e-05}
|
||||
});
|
||||
}else if (OpName == "IsInf") {
|
||||
ImportNodeOneOut<mlir::ONNXIsInfOp>(node, 1, 1, {
|
||||
{"detect_negative","int","1"}
|
||||
,{"detect_positive","int","1"}
|
||||
{"detect_negative", 1}
|
||||
,{"detect_positive", 1}
|
||||
});
|
||||
}else if (OpName == "IsNaN") {
|
||||
ImportNodeOneOut<mlir::ONNXIsNaNOp>(node, 1, 1, {
|
||||
});
|
||||
}else if (OpName == "LRN") {
|
||||
ImportNodeOneOut<mlir::ONNXLRNOp>(node, 1, 1, {
|
||||
{"alpha","float","0.0001"}
|
||||
,{"beta","float","0.75"}
|
||||
,{"bias","float","1.0"}
|
||||
,{"size","int", ""}
|
||||
{"alpha", (float)0.0001}
|
||||
,{"beta", (float)0.75}
|
||||
,{"bias", (float)1.0}
|
||||
});
|
||||
}else if (OpName == "LSTM") {
|
||||
ImportNodeMultipleOuts<mlir::ONNXLSTMOp>(node, 8, 3, {
|
||||
{"activation_alpha","", ""}
|
||||
,{"activation_beta","", ""}
|
||||
,{"activations","", ""}
|
||||
,{"clip","", ""}
|
||||
,{"direction","str","forward"}
|
||||
,{"hidden_size","", ""}
|
||||
,{"input_forget","int","0"}
|
||||
{"direction", "forward"}
|
||||
,{"input_forget", 0}
|
||||
});
|
||||
}else if (OpName == "LeakyRelu") {
|
||||
ImportNodeOneOut<mlir::ONNXLeakyReluOp>(node, 1, 1, {
|
||||
{"alpha","float","0.01"}
|
||||
{"alpha", (float)0.01}
|
||||
});
|
||||
}else if (OpName == "Less") {
|
||||
ImportNodeOneOut<mlir::ONNXLessOp>(node, 2, 1, {
|
||||
|
|
@ -274,24 +237,20 @@
|
|||
});
|
||||
}else if (OpName == "LogSoftmax") {
|
||||
ImportNodeOneOut<mlir::ONNXLogSoftmaxOp>(node, 1, 1, {
|
||||
{"axis","int","1"}
|
||||
{"axis", 1}
|
||||
});
|
||||
}else if (OpName == "Loop") {
|
||||
ImportNodeOneOut<mlir::ONNXLoopOp>(node, 3, 1, {
|
||||
{"body","", ""}
|
||||
});
|
||||
}else if (OpName == "LpNormalization") {
|
||||
ImportNodeOneOut<mlir::ONNXLpNormalizationOp>(node, 1, 1, {
|
||||
{"axis","int","-1"}
|
||||
,{"p","int","2"}
|
||||
{"axis", -1}
|
||||
,{"p", 2}
|
||||
});
|
||||
}else if (OpName == "LpPool") {
|
||||
ImportNodeOneOut<mlir::ONNXLpPoolOp>(node, 1, 1, {
|
||||
{"auto_pad","str","NOTSET"}
|
||||
,{"kernel_shape","", ""}
|
||||
,{"p","int","2"}
|
||||
,{"pads","", ""}
|
||||
,{"strides","", ""}
|
||||
{"auto_pad", "NOTSET"}
|
||||
,{"p", 2}
|
||||
});
|
||||
}else if (OpName == "MatMul") {
|
||||
ImportNodeOneOut<mlir::ONNXMatMulOp>(node, 2, 1, {
|
||||
|
|
@ -303,55 +262,47 @@
|
|||
ImportNodeOneOut<mlir::ONNXMaxOp>(node, 1, 1, {
|
||||
});
|
||||
}else if (OpName == "MaxPool") {
|
||||
ImportNodeMaxPool(node, 1, {
|
||||
{"auto_pad","str","NOTSET"}
|
||||
,{"ceil_mode","int","0"}
|
||||
,{"dilations","", ""}
|
||||
,{"kernel_shape","ints", ""}
|
||||
,{"pads","", ""}
|
||||
,{"storage_order","int","0"}
|
||||
,{"strides","", ""}
|
||||
ImportNodeMaxPool(node, 1, 2, {
|
||||
{"auto_pad", "NOTSET"}
|
||||
,{"ceil_mode", 0}
|
||||
,{"kernel_shape", std::vector<int64_t> {}}
|
||||
,{"storage_order", 0}
|
||||
});
|
||||
}else if (OpName == "MaxRoiPool") {
|
||||
ImportNodeOneOut<mlir::ONNXMaxRoiPoolOp>(node, 2, 1, {
|
||||
{"pooled_shape","", ""}
|
||||
,{"spatial_scale","float","1.0"}
|
||||
{"spatial_scale", (float)1.0}
|
||||
});
|
||||
}else if (OpName == "MaxUnpool") {
|
||||
ImportNodeOneOut<mlir::ONNXMaxUnpoolOp>(node, 3, 1, {
|
||||
{"kernel_shape","", ""}
|
||||
,{"pads","", ""}
|
||||
,{"strides","", ""}
|
||||
});
|
||||
}else if (OpName == "Mean") {
|
||||
ImportNodeOneOut<mlir::ONNXMeanOp>(node, 1, 1, {
|
||||
});
|
||||
}else if (OpName == "MeanVarianceNormalization") {
|
||||
ImportNodeOneOut<mlir::ONNXMeanVarianceNormalizationOp>(node, 1, 1, {
|
||||
{"axes","ints","{'0', '2', '3'}"}
|
||||
{"axes", std::vector<int64_t>{0, 2, 3}}
|
||||
});
|
||||
}else if (OpName == "Min") {
|
||||
ImportNodeOneOut<mlir::ONNXMinOp>(node, 1, 1, {
|
||||
});
|
||||
}else if (OpName == "Mod") {
|
||||
ImportNodeOneOut<mlir::ONNXModOp>(node, 2, 1, {
|
||||
{"fmod","int","0"}
|
||||
{"fmod", 0}
|
||||
});
|
||||
}else if (OpName == "Mul") {
|
||||
ImportNodeOneOut<mlir::ONNXMulOp>(node, 2, 1, {
|
||||
});
|
||||
}else if (OpName == "Multinomial") {
|
||||
ImportNodeOneOut<mlir::ONNXMultinomialOp>(node, 1, 1, {
|
||||
{"dtype","int","6"}
|
||||
,{"sample_size","int","1"}
|
||||
,{"seed","", ""}
|
||||
{"dtype", 6}
|
||||
,{"sample_size", 1}
|
||||
});
|
||||
}else if (OpName == "Neg") {
|
||||
ImportNodeOneOut<mlir::ONNXNegOp>(node, 1, 1, {
|
||||
});
|
||||
}else if (OpName == "NonMaxSuppression") {
|
||||
ImportNodeOneOut<mlir::ONNXNonMaxSuppressionOp>(node, 5, 1, {
|
||||
{"center_point_box","int","0"}
|
||||
{"center_point_box", 0}
|
||||
});
|
||||
}else if (OpName == "NonZero") {
|
||||
ImportNodeOneOut<mlir::ONNXNonZeroOp>(node, 1, 1, {
|
||||
|
|
@ -361,7 +312,7 @@
|
|||
});
|
||||
}else if (OpName == "OneHot") {
|
||||
ImportNodeOneOut<mlir::ONNXOneHotOp>(node, 3, 1, {
|
||||
{"axis","int","-1"}
|
||||
{"axis", -1}
|
||||
});
|
||||
}else if (OpName == "Or") {
|
||||
ImportNodeOneOut<mlir::ONNXOrOp>(node, 2, 1, {
|
||||
|
|
@ -371,19 +322,15 @@
|
|||
});
|
||||
}else if (OpName == "Pad") {
|
||||
ImportNodeOneOut<mlir::ONNXPadOp>(node, 3, 1, {
|
||||
{"mode","str","constant"}
|
||||
{"mode", "constant"}
|
||||
});
|
||||
}else if (OpName == "Pow") {
|
||||
ImportNodeOneOut<mlir::ONNXPowOp>(node, 2, 1, {
|
||||
});
|
||||
}else if (OpName == "QLinearConv") {
|
||||
ImportNodeOneOut<mlir::ONNXQLinearConvOp>(node, 9, 1, {
|
||||
{"auto_pad","str","NOTSET"}
|
||||
,{"dilations","", ""}
|
||||
,{"group","int","1"}
|
||||
,{"kernel_shape","", ""}
|
||||
,{"pads","", ""}
|
||||
,{"strides","", ""}
|
||||
{"auto_pad", "NOTSET"}
|
||||
,{"group", 1}
|
||||
});
|
||||
}else if (OpName == "QLinearMatMul") {
|
||||
ImportNodeOneOut<mlir::ONNXQLinearMatMulOp>(node, 8, 1, {
|
||||
|
|
@ -393,42 +340,32 @@
|
|||
});
|
||||
}else if (OpName == "RNN") {
|
||||
ImportNodeMultipleOuts<mlir::ONNXRNNOp>(node, 6, 2, {
|
||||
{"activation_alpha","floats", "{}"}
|
||||
,{"activation_beta","floats", "{}"}
|
||||
,{"activations","", "{Tannh, Tanh}"}
|
||||
,{"clip","", ""}
|
||||
,{"direction","str","forward"}
|
||||
,{"hidden_size","", ""}
|
||||
{"activation_alpha", std::vector<float> {}}
|
||||
,{"activation_beta", std::vector<float> {}}
|
||||
,{"activations", std::vector<std::string>{"Tanh", "Tanh"}}
|
||||
,{"direction", "forward"}
|
||||
});
|
||||
}else if (OpName == "RandomNormal") {
|
||||
ImportNodeOneOut<mlir::ONNXRandomNormalOp>(node, 0, 1, {
|
||||
{"dtype","int","1"}
|
||||
,{"mean","float","0.0"}
|
||||
,{"scale","float","1.0"}
|
||||
,{"seed","", ""}
|
||||
,{"shape","", ""}
|
||||
{"dtype", 1}
|
||||
,{"mean", (float)0.0}
|
||||
,{"scale", (float)1.0}
|
||||
});
|
||||
}else if (OpName == "RandomNormalLike") {
|
||||
ImportNodeOneOut<mlir::ONNXRandomNormalLikeOp>(node, 1, 1, {
|
||||
{"dtype","", ""}
|
||||
,{"mean","float","0.0"}
|
||||
,{"scale","float","1.0"}
|
||||
,{"seed","", ""}
|
||||
{"mean", (float)0.0}
|
||||
,{"scale", (float)1.0}
|
||||
});
|
||||
}else if (OpName == "RandomUniform") {
|
||||
ImportNodeOneOut<mlir::ONNXRandomUniformOp>(node, 0, 1, {
|
||||
{"dtype","int","1"}
|
||||
,{"high","float","1.0"}
|
||||
,{"low","float","0.0"}
|
||||
,{"seed","", ""}
|
||||
,{"shape","", ""}
|
||||
{"dtype", 1}
|
||||
,{"high", (float)1.0}
|
||||
,{"low", (float)0.0}
|
||||
});
|
||||
}else if (OpName == "RandomUniformLike") {
|
||||
ImportNodeOneOut<mlir::ONNXRandomUniformLikeOp>(node, 1, 1, {
|
||||
{"dtype","", ""}
|
||||
,{"high","float","1.0"}
|
||||
,{"low","float","0.0"}
|
||||
,{"seed","", ""}
|
||||
{"high", (float)1.0}
|
||||
,{"low", (float)0.0}
|
||||
});
|
||||
}else if (OpName == "Range") {
|
||||
ImportNodeOneOut<mlir::ONNXRangeOp>(node, 3, 1, {
|
||||
|
|
@ -438,53 +375,43 @@
|
|||
});
|
||||
}else if (OpName == "ReduceL1") {
|
||||
ImportNodeOneOut<mlir::ONNXReduceL1Op>(node, 1, 1, {
|
||||
{"axes","", ""}
|
||||
,{"keepdims","int","1"}
|
||||
{"keepdims", 1}
|
||||
});
|
||||
}else if (OpName == "ReduceL2") {
|
||||
ImportNodeOneOut<mlir::ONNXReduceL2Op>(node, 1, 1, {
|
||||
{"axes","", ""}
|
||||
,{"keepdims","int","1"}
|
||||
{"keepdims", 1}
|
||||
});
|
||||
}else if (OpName == "ReduceLogSum") {
|
||||
ImportNodeOneOut<mlir::ONNXReduceLogSumOp>(node, 1, 1, {
|
||||
{"axes","", ""}
|
||||
,{"keepdims","int","1"}
|
||||
{"keepdims", 1}
|
||||
});
|
||||
}else if (OpName == "ReduceLogSumExp") {
|
||||
ImportNodeOneOut<mlir::ONNXReduceLogSumExpOp>(node, 1, 1, {
|
||||
{"axes","", ""}
|
||||
,{"keepdims","int","1"}
|
||||
{"keepdims", 1}
|
||||
});
|
||||
}else if (OpName == "ReduceMax") {
|
||||
ImportNodeOneOut<mlir::ONNXReduceMaxOp>(node, 1, 1, {
|
||||
{"axes","", ""}
|
||||
,{"keepdims","int","1"}
|
||||
{"keepdims", 1}
|
||||
});
|
||||
}else if (OpName == "ReduceMean") {
|
||||
ImportNodeOneOut<mlir::ONNXReduceMeanOp>(node, 1, 1, {
|
||||
{"axes","", ""}
|
||||
,{"keepdims","int","1"}
|
||||
{"keepdims", 1}
|
||||
});
|
||||
}else if (OpName == "ReduceMin") {
|
||||
ImportNodeOneOut<mlir::ONNXReduceMinOp>(node, 1, 1, {
|
||||
{"axes","", ""}
|
||||
,{"keepdims","int","1"}
|
||||
{"keepdims", 1}
|
||||
});
|
||||
}else if (OpName == "ReduceProd") {
|
||||
ImportNodeOneOut<mlir::ONNXReduceProdOp>(node, 1, 1, {
|
||||
{"axes","", ""}
|
||||
,{"keepdims","int","1"}
|
||||
{"keepdims", 1}
|
||||
});
|
||||
}else if (OpName == "ReduceSum") {
|
||||
ImportNodeOneOut<mlir::ONNXReduceSumOp>(node, 1, 1, {
|
||||
{"axes","", ""}
|
||||
,{"keepdims","int","1"}
|
||||
{"keepdims", 1}
|
||||
});
|
||||
}else if (OpName == "ReduceSumSquare") {
|
||||
ImportNodeOneOut<mlir::ONNXReduceSumSquareOp>(node, 1, 1, {
|
||||
{"axes","", ""}
|
||||
,{"keepdims","int","1"}
|
||||
{"keepdims", 1}
|
||||
});
|
||||
}else if (OpName == "Relu") {
|
||||
ImportNodeOneOut<mlir::ONNXReluOp>(node, 1, 1, {
|
||||
|
|
@ -494,53 +421,47 @@
|
|||
});
|
||||
}else if (OpName == "Resize") {
|
||||
ImportNodeOneOut<mlir::ONNXResizeOp>(node, 4, 1, {
|
||||
{"coordinate_transformation_mode","str","half_pixel"}
|
||||
,{"cubic_coeff_a","float","-0.75"}
|
||||
,{"exclude_outside","int","0"}
|
||||
,{"extrapolation_value","float","0.0"}
|
||||
,{"mode","str","nearest"}
|
||||
,{"nearest_mode","str","round_prefer_floor"}
|
||||
{"coordinate_transformation_mode", "half_pixel"}
|
||||
,{"cubic_coeff_a", (float)-0.75}
|
||||
,{"exclude_outside", 0}
|
||||
,{"extrapolation_value", (float)0.0}
|
||||
,{"mode", "nearest"}
|
||||
,{"nearest_mode", "round_prefer_floor"}
|
||||
});
|
||||
}else if (OpName == "ReverseSequence") {
|
||||
ImportNodeOneOut<mlir::ONNXReverseSequenceOp>(node, 2, 1, {
|
||||
{"batch_axis","int","1"}
|
||||
,{"time_axis","int","0"}
|
||||
{"batch_axis", 1}
|
||||
,{"time_axis", 0}
|
||||
});
|
||||
}else if (OpName == "RoiAlign") {
|
||||
ImportNodeOneOut<mlir::ONNXRoiAlignOp>(node, 3, 1, {
|
||||
{"mode","str","avg"}
|
||||
,{"output_height","int","1"}
|
||||
,{"output_width","int","1"}
|
||||
,{"sampling_ratio","int","0"}
|
||||
,{"spatial_scale","float","1.0"}
|
||||
{"mode", "avg"}
|
||||
,{"output_height", 1}
|
||||
,{"output_width", 1}
|
||||
,{"sampling_ratio", 0}
|
||||
,{"spatial_scale", (float)1.0}
|
||||
});
|
||||
}else if (OpName == "Round") {
|
||||
ImportNodeOneOut<mlir::ONNXRoundOp>(node, 1, 1, {
|
||||
});
|
||||
}else if (OpName == "Scan") {
|
||||
ImportNodeOneOut<mlir::ONNXScanOp>(node, 1, 1, {
|
||||
{"body","", ""}
|
||||
,{"num_scan_inputs","", ""}
|
||||
,{"scan_input_axes","", ""}
|
||||
,{"scan_input_directions","", ""}
|
||||
,{"scan_output_axes","", ""}
|
||||
,{"scan_output_directions","", ""}
|
||||
});
|
||||
}else if (OpName == "Scatter") {
|
||||
ImportNodeOneOut<mlir::ONNXScatterOp>(node, 3, 1, {
|
||||
{"axis","int","0"}
|
||||
{"axis", 0}
|
||||
});
|
||||
}else if (OpName == "ScatterElements") {
|
||||
ImportNodeOneOut<mlir::ONNXScatterElementsOp>(node, 3, 1, {
|
||||
{"axis","int","0"}
|
||||
{"axis", 0}
|
||||
});
|
||||
}else if (OpName == "ScatterND") {
|
||||
ImportNodeOneOut<mlir::ONNXScatterNDOp>(node, 3, 1, {
|
||||
});
|
||||
}else if (OpName == "Selu") {
|
||||
ImportNodeOneOut<mlir::ONNXSeluOp>(node, 1, 1, {
|
||||
{"alpha","float","1.67326"}
|
||||
,{"gamma","float","1.0507"}
|
||||
{"alpha", (float)1.67326}
|
||||
,{"gamma", (float)1.0507}
|
||||
});
|
||||
}else if (OpName == "SequenceAt") {
|
||||
ImportNodeOneOut<mlir::ONNXSequenceAtOp>(node, 2, 1, {
|
||||
|
|
@ -550,7 +471,6 @@
|
|||
});
|
||||
}else if (OpName == "SequenceEmpty") {
|
||||
ImportNodeOneOut<mlir::ONNXSequenceEmptyOp>(node, 0, 1, {
|
||||
{"dtype","", ""}
|
||||
});
|
||||
}else if (OpName == "SequenceErase") {
|
||||
ImportNodeOneOut<mlir::ONNXSequenceEraseOp>(node, 2, 1, {
|
||||
|
|
@ -566,8 +486,8 @@
|
|||
});
|
||||
}else if (OpName == "Shrink") {
|
||||
ImportNodeOneOut<mlir::ONNXShrinkOp>(node, 1, 1, {
|
||||
{"bias","float","0.0"}
|
||||
,{"lambd","float","0.5"}
|
||||
{"bias", (float)0.0}
|
||||
,{"lambd", (float)0.5}
|
||||
});
|
||||
}else if (OpName == "Sigmoid") {
|
||||
ImportNodeOneOut<mlir::ONNXSigmoidOp>(node, 1, 1, {
|
||||
|
|
@ -589,7 +509,7 @@
|
|||
});
|
||||
}else if (OpName == "Softmax") {
|
||||
ImportNodeOneOut<mlir::ONNXSoftmaxOp>(node, 1, 1, {
|
||||
{"axis","int","1"}
|
||||
{"axis", 1}
|
||||
});
|
||||
}else if (OpName == "Softplus") {
|
||||
ImportNodeOneOut<mlir::ONNXSoftplusOp>(node, 1, 1, {
|
||||
|
|
@ -599,31 +519,26 @@
|
|||
});
|
||||
}else if (OpName == "SpaceToDepth") {
|
||||
ImportNodeOneOut<mlir::ONNXSpaceToDepthOp>(node, 1, 1, {
|
||||
{"blocksize","", ""}
|
||||
});
|
||||
}else if (OpName == "Split") {
|
||||
ImportNodeOneOut<mlir::ONNXSplitOp>(node, 1, 1, {
|
||||
{"axis","int","0"}
|
||||
,{"split","", ""}
|
||||
{"axis", 0}
|
||||
});
|
||||
}else if (OpName == "SplitToSequence") {
|
||||
ImportNodeOneOut<mlir::ONNXSplitToSequenceOp>(node, 2, 1, {
|
||||
{"axis","int","0"}
|
||||
,{"keepdims","int","1"}
|
||||
{"axis", 0}
|
||||
,{"keepdims", 1}
|
||||
});
|
||||
}else if (OpName == "Sqrt") {
|
||||
ImportNodeOneOut<mlir::ONNXSqrtOp>(node, 1, 1, {
|
||||
});
|
||||
}else if (OpName == "Squeeze") {
|
||||
ImportNodeOneOut<mlir::ONNXSqueezeOp>(node, 1, 1, {
|
||||
{"axes","", ""}
|
||||
});
|
||||
}else if (OpName == "StringNormalizer") {
|
||||
ImportNodeOneOut<mlir::ONNXStringNormalizerOp>(node, 1, 1, {
|
||||
{"case_change_action","str","NONE"}
|
||||
,{"is_case_sensitive","int","0"}
|
||||
,{"locale","", ""}
|
||||
,{"stopwords","", ""}
|
||||
{"case_change_action", "NONE"}
|
||||
,{"is_case_sensitive", 0}
|
||||
});
|
||||
}else if (OpName == "Sub") {
|
||||
ImportNodeOneOut<mlir::ONNXSubOp>(node, 2, 1, {
|
||||
|
|
@ -639,45 +554,34 @@
|
|||
});
|
||||
}else if (OpName == "TfIdfVectorizer") {
|
||||
ImportNodeOneOut<mlir::ONNXTfIdfVectorizerOp>(node, 1, 1, {
|
||||
{"max_gram_length","", ""}
|
||||
,{"max_skip_count","", ""}
|
||||
,{"min_gram_length","", ""}
|
||||
,{"mode","", ""}
|
||||
,{"ngram_counts","", ""}
|
||||
,{"ngram_indexes","", ""}
|
||||
,{"pool_int64s","", ""}
|
||||
,{"pool_strings","", ""}
|
||||
,{"weights","", ""}
|
||||
});
|
||||
}else if (OpName == "ThresholdedRelu") {
|
||||
ImportNodeOneOut<mlir::ONNXThresholdedReluOp>(node, 1, 1, {
|
||||
{"alpha","float","1.0"}
|
||||
{"alpha", (float)1.0}
|
||||
});
|
||||
}else if (OpName == "Tile") {
|
||||
ImportNodeOneOut<mlir::ONNXTileOp>(node, 2, 1, {
|
||||
});
|
||||
}else if (OpName == "TopK") {
|
||||
ImportNodeMultipleOuts<mlir::ONNXTopKOp>(node, 2, 2, {
|
||||
{"axis","int","-1"}
|
||||
,{"largest","int","1"}
|
||||
,{"sorted","int","1"}
|
||||
{"axis", -1}
|
||||
,{"largest", 1}
|
||||
,{"sorted", 1}
|
||||
});
|
||||
}else if (OpName == "Transpose") {
|
||||
ImportNodeOneOut<mlir::ONNXTransposeOp>(node, 1, 1, {
|
||||
{"perm","", ""}
|
||||
});
|
||||
}else if (OpName == "Unique") {
|
||||
ImportNodeMultipleOuts<mlir::ONNXUniqueOp>(node, 1, 4, {
|
||||
{"axis","", ""}
|
||||
,{"sorted","int","1"}
|
||||
{"sorted", 1}
|
||||
});
|
||||
}else if (OpName == "Unsqueeze") {
|
||||
ImportNodeOneOut<mlir::ONNXUnsqueezeOp>(node, 1, 1, {
|
||||
{"axes","ints", ""}
|
||||
{"axes", std::vector<int64_t> {}}
|
||||
});
|
||||
}else if (OpName == "Upsample") {
|
||||
ImportNodeOneOut<mlir::ONNXUpsampleOp>(node, 2, 1, {
|
||||
{"mode","str","nearest"}
|
||||
{"mode", "nearest"}
|
||||
});
|
||||
}else if (OpName == "Where") {
|
||||
ImportNodeOneOut<mlir::ONNXWhereOp>(node, 3, 1, {
|
||||
|
|
|
|||
|
|
@ -368,17 +368,17 @@ def gen_code(schema,fefile) :
|
|||
("MaxPool", "ImportNodeMaxPool"),
|
||||
#("Transpose", "ImportNodeTranspose")
|
||||
])
|
||||
special_type = dict([
|
||||
("AveragePool "+"kernel_shape", '"ints", ""'),
|
||||
("MaxPool "+"kernel_shape", '"ints", ""'),
|
||||
("Cast "+"to", '"int", "0"'),
|
||||
("Concat "+"axis", '"int", "0"'),
|
||||
("Conv "+"group", '"int", "1"'),
|
||||
("Unsqueeze "+"axes", '"ints", ""'),
|
||||
("RNN "+"activation_alpha", '"floats", "{}"'),
|
||||
("RNN "+"activation_beta", '"floats", "{}"'),
|
||||
("RNN "+"activations", '"", "{Tannh, Tanh}"'),
|
||||
("LRN "+"size", '"int", ""')
|
||||
list_str = 'std::vector'
|
||||
empty_ints = list_str+'<int> {}'
|
||||
empty_floats = list_str+'<float> {}'
|
||||
special_default = dict([
|
||||
("AveragePool "+"kernel_shape", empty_ints),
|
||||
("MaxPool "+"kernel_shape", empty_ints),
|
||||
("Cast "+"to", '0'),
|
||||
("Concat "+"axis", '0'),
|
||||
("Unsqueeze "+"axes", empty_ints),
|
||||
("RNN "+"activation_alpha", empty_floats),
|
||||
("RNN "+"activation_beta", empty_floats)
|
||||
])
|
||||
line_indent = ' '
|
||||
fefile.write(' '+'}else if (OpName == "'+schema.name+'") {\n')
|
||||
|
|
@ -400,21 +400,9 @@ def gen_code(schema,fefile) :
|
|||
if schema.attributes:
|
||||
first_attr = True
|
||||
for _, attr in sorted(schema.attributes.items()):
|
||||
attr_line = line_indent+line_indent+line_indent+line_indent
|
||||
if not first_attr:
|
||||
attr_line += ',{'
|
||||
else :
|
||||
attr_line += ' {'
|
||||
first_attr = False
|
||||
|
||||
attr_line += '"'+attr.name+'",'
|
||||
|
||||
if schema.name+' '+attr.name in special_type:
|
||||
attr_line += special_type[schema.name+' '+attr.name]
|
||||
# option holds either required or default value
|
||||
elif attr.required:
|
||||
attr_line += '"", ""'
|
||||
|
||||
#only generate default attr list
|
||||
if schema.name+' '+attr.name in special_default:
|
||||
attr_value = special_default[schema.name+' '+attr.name]
|
||||
elif attr.default_value.name:
|
||||
default_value = helper.get_attribute_value(attr.default_value)
|
||||
|
||||
|
|
@ -430,28 +418,35 @@ def gen_code(schema,fefile) :
|
|||
return str(value)
|
||||
|
||||
if isinstance(default_value, list):
|
||||
|
||||
value = default_value[0]
|
||||
default_value = [format_value(val) for val in default_value]
|
||||
attr_option_str = '{}'.format(default_value)
|
||||
attr_option_str = attr_option_str.replace('[', '{', 1)
|
||||
attr_option_str = attr_option_str.replace(']', '}', 1)
|
||||
# TODO the list type is homogenous or htergeneous?
|
||||
|
||||
if isinstance(value, float) :
|
||||
attr_type_str = '"floats"'
|
||||
attr_type_str = list_str+'<float>'
|
||||
attr_option_str = attr_option_str.replace("'", '')
|
||||
elif isinstance(value, int) :
|
||||
attr_type_str = '"ints"'
|
||||
attr_type_str = list_str+'<int>'
|
||||
attr_option_str = attr_option_str.replace("'", '')
|
||||
elif isinstance(value, str) :
|
||||
attr_type_str = '"strs"'
|
||||
attr_type_str = list_str+'<std::string>'
|
||||
attr_option_str = attr_option_str.replace("'", '"')
|
||||
elif isinstance(value, (bytes, bytearray)) :
|
||||
attr_type_str = '"strs"'
|
||||
attr_type_str = list_str+'<std::string>'
|
||||
attr_option_str = attr_option_str.replace("'", '"')
|
||||
else :
|
||||
attr_type_str = '"unknowns"'
|
||||
attr_option_str = '"{}"'.format(default_value)
|
||||
attr_option_str = attr_option_str.replace('[', '{', 1)
|
||||
attr_option_str = attr_option_str.replace(']', '}', 1)
|
||||
else:
|
||||
if isinstance(default_value, float) :
|
||||
attr_type_str = '"float"'
|
||||
attr_type_str = '(float)'
|
||||
attr_option_str = default_value
|
||||
elif isinstance(default_value, int) :
|
||||
attr_type_str = '"int"'
|
||||
attr_option_str = default_value
|
||||
attr_type_str=''
|
||||
elif isinstance(default_value, str) :
|
||||
attr_type_str = '"str"'
|
||||
elif isinstance(default_value, (bytes, bytearray)) :
|
||||
|
|
@ -459,11 +454,25 @@ def gen_code(schema,fefile) :
|
|||
else :
|
||||
attr_type_str = '"unknown"'
|
||||
default_value = format_value(default_value)
|
||||
attr_option_str = '"{}"'.format(default_value)
|
||||
attr_line += attr_type_str+','+attr_option_str
|
||||
if attr_type_str == '"str"' :
|
||||
attr_option_str = '"'+default_value+'"'
|
||||
attr_type_str=''
|
||||
else :
|
||||
attr_option_str = default_value
|
||||
attr_value = attr_type_str+attr_option_str
|
||||
else:
|
||||
#TODO why?
|
||||
attr_line += '"", ""'
|
||||
#no default value
|
||||
continue
|
||||
|
||||
attr_line = line_indent+line_indent+line_indent+line_indent
|
||||
if not first_attr:
|
||||
attr_line += ',{'
|
||||
else :
|
||||
attr_line += ' {'
|
||||
first_attr = False
|
||||
|
||||
attr_line += '"'+attr.name+'", '
|
||||
attr_line += attr_value
|
||||
attr_line += '}\n'
|
||||
fefile.write(attr_line)
|
||||
fefile.write(line_indent+line_indent+line_indent+'});\n')
|
||||
|
|
|
|||
|
|
@ -420,8 +420,8 @@ Value mapToLowerScalarOp<ONNXHardSigmoidOp>(
|
|||
// Constant 1)
|
||||
auto loc = op->getLoc();
|
||||
Value operand = operands[0];
|
||||
auto alphaAttr = op->getAttrOfType<FloatAttr>("HardSigmoid.alpha");
|
||||
auto betaAttr = op->getAttrOfType<FloatAttr>("HardSigmoid.beta");
|
||||
auto alphaAttr = op->getAttrOfType<FloatAttr>("alpha");
|
||||
auto betaAttr = op->getAttrOfType<FloatAttr>("beta");
|
||||
auto elementType = result_types[0];
|
||||
|
||||
auto zero = rewriter.create<ConstantOp>(loc, FloatAttr::get(elementType, 0));
|
||||
|
|
@ -455,7 +455,7 @@ Value mapToLowerScalarOp<ONNXEluOp>(Operation *op, ArrayRef<Type> result_types,
|
|||
Value operand = operands[0];
|
||||
auto elementType = result_types[0];
|
||||
|
||||
auto alphaAttr = op->getAttrOfType<FloatAttr>("Elu.alpha");
|
||||
auto alphaAttr = op->getAttrOfType<FloatAttr>("alpha");
|
||||
auto zero = rewriter.create<ConstantOp>(loc, FloatAttr::get(elementType, 0));
|
||||
auto one = rewriter.create<ConstantOp>(loc, FloatAttr::get(elementType, 1));
|
||||
auto alpha = rewriter.create<ConstantOp>(loc, alphaAttr);
|
||||
|
|
@ -508,7 +508,7 @@ Value mapToLowerScalarOp<ONNXLeakyReluOp>(Operation *op,
|
|||
Value operand = operands[0];
|
||||
auto elementType = result_types[0];
|
||||
|
||||
auto alphaAttr = op->getAttrOfType<FloatAttr>("LeakyRelu.alpha");
|
||||
auto alphaAttr = op->getAttrOfType<FloatAttr>("alpha");
|
||||
auto zero = rewriter.create<ConstantOp>(loc, FloatAttr::get(elementType, 0));
|
||||
auto alpha = rewriter.create<ConstantOp>(loc, alphaAttr);
|
||||
auto lessThanZero =
|
||||
|
|
@ -533,8 +533,8 @@ Value mapToLowerScalarOp<ONNXSeluOp>(Operation *op, ArrayRef<Type> result_types,
|
|||
// alpha)))
|
||||
auto loc = op->getLoc();
|
||||
Value operand = operands[0];
|
||||
auto alphaAttr = op->getAttrOfType<FloatAttr>("Selu.alpha");
|
||||
auto gammaAttr = op->getAttrOfType<FloatAttr>("Selu.gamma");
|
||||
auto alphaAttr = op->getAttrOfType<FloatAttr>("alpha");
|
||||
auto gammaAttr = op->getAttrOfType<FloatAttr>("gamma");
|
||||
auto elementType = result_types[0];
|
||||
|
||||
auto zero = rewriter.create<ConstantOp>(loc, FloatAttr::get(elementType, 0));
|
||||
|
|
@ -836,7 +836,7 @@ struct ONNXSoftmaxOpLowering : public ConversionPattern {
|
|||
// exp_x / sum
|
||||
auto tensorType = (*op->result_type_begin()).cast<RankedTensorType>();
|
||||
int64_t rank = tensorType.getRank();
|
||||
int64_t axis = op->getAttrOfType<IntegerAttr>("Softmax.axis").getInt();
|
||||
int64_t axis = op->getAttrOfType<IntegerAttr>("axis").getInt();
|
||||
axis = axis >= 0 ? axis : rank + axis;
|
||||
assert(axis >= -rank && axis <= rank - 1);
|
||||
|
||||
|
|
|
|||
|
|
@ -385,7 +385,7 @@ func @test_min(%arg0 : tensor<10x10xf32>, %arg1 : tensor<10x10xf32>) -> tensor<*
|
|||
}
|
||||
|
||||
func @test_elu(%arg0 : tensor<?x10xf32>) -> tensor<*xf32> {
|
||||
%0 = "onnx.Elu"(%arg0) {Elu.alpha=2.0:f32} : (tensor<?x10xf32>) -> tensor<*xf32>
|
||||
%0 = "onnx.Elu"(%arg0) {alpha=2.0:f32} : (tensor<?x10xf32>) -> tensor<*xf32>
|
||||
"std.return"(%0) : (tensor<*xf32>) -> ()
|
||||
|
||||
// CHECK-LABEL: test_elu
|
||||
|
|
@ -411,7 +411,7 @@ func @test_elu(%arg0 : tensor<?x10xf32>) -> tensor<*xf32> {
|
|||
}
|
||||
|
||||
func @test_leakyrelu(%arg0 : tensor<?x10xf32>) -> tensor<*xf32> {
|
||||
%0 = "onnx.LeakyRelu"(%arg0) {LeakyRelu.alpha=1.0:f32} : (tensor<?x10xf32>) -> tensor<*xf32>
|
||||
%0 = "onnx.LeakyRelu"(%arg0) {alpha=1.0:f32} : (tensor<?x10xf32>) -> tensor<*xf32>
|
||||
"std.return"(%0) : (tensor<*xf32>) -> ()
|
||||
|
||||
// CHECK-LABEL: test_leakyrelu
|
||||
|
|
@ -434,7 +434,7 @@ func @test_leakyrelu(%arg0 : tensor<?x10xf32>) -> tensor<*xf32> {
|
|||
}
|
||||
|
||||
func @test_selu(%arg0 : tensor<?x10xf32>) -> tensor<*xf32> {
|
||||
%0 = "onnx.Selu"(%arg0) {Selu.alpha=1.0:f32, Selu.gamma=2.0:f32} : (tensor<?x10xf32>) -> tensor<*xf32>
|
||||
%0 = "onnx.Selu"(%arg0) {alpha=1.0:f32, gamma=2.0:f32} : (tensor<?x10xf32>) -> tensor<*xf32>
|
||||
"std.return"(%0) : (tensor<*xf32>) -> ()
|
||||
|
||||
// CHECK-LABEL: test_selu
|
||||
|
|
@ -461,7 +461,7 @@ func @test_selu(%arg0 : tensor<?x10xf32>) -> tensor<*xf32> {
|
|||
}
|
||||
|
||||
func @test_hardsigmoid(%arg0 : tensor<?x10xf32>) -> tensor<*xf32> {
|
||||
%0 = "onnx.HardSigmoid"(%arg0) {HardSigmoid.alpha=1.0:f32, HardSigmoid.beta=2.0:f32} : (tensor<?x10xf32>) -> tensor<*xf32>
|
||||
%0 = "onnx.HardSigmoid"(%arg0) {alpha=1.0:f32, beta=2.0:f32} : (tensor<?x10xf32>) -> tensor<*xf32>
|
||||
"std.return"(%0) : (tensor<*xf32>) -> ()
|
||||
|
||||
// CHECK-LABEL: test_hardsigmoid
|
||||
|
|
@ -535,7 +535,7 @@ func @test_add_with_broadcasting(%arg0 : tensor<?xf32>, %arg1 : tensor<?x10xf32>
|
|||
}
|
||||
|
||||
func @test_softmax(%arg0 : tensor<10x10xf32>) -> tensor<*xf32> {
|
||||
%0 = "onnx.Softmax"(%arg0) {Softmax.axis=1:i32} : (tensor<10x10xf32>) -> tensor<*xf32>
|
||||
%0 = "onnx.Softmax"(%arg0) {axis=1:i32} : (tensor<10x10xf32>) -> tensor<*xf32>
|
||||
"std.return"(%0) : (tensor<*xf32>) -> ()
|
||||
|
||||
// CHECK-LABEL: test_softmax
|
||||
|
|
|
|||
|
|
@ -648,8 +648,8 @@ func @test_min_min(%arg0 : tensor<10x10xf32>, %arg1 : tensor<10x10xf32>) -> tens
|
|||
}
|
||||
|
||||
func @test_elu_elu(%arg0 : tensor<?x10xf32>) -> tensor<*xf32> {
|
||||
%0 = "onnx.Elu"(%arg0) {Elu.alpha=2.0:f32} : (tensor<?x10xf32>) -> tensor<*xf32>
|
||||
%1 = "onnx.Elu"(%0) {Elu.alpha=2.0:f32} : (tensor<*xf32>) -> tensor<*xf32>
|
||||
%0 = "onnx.Elu"(%arg0) {alpha=2.0:f32} : (tensor<?x10xf32>) -> tensor<*xf32>
|
||||
%1 = "onnx.Elu"(%0) {alpha=2.0:f32} : (tensor<*xf32>) -> tensor<*xf32>
|
||||
"std.return"(%1) : (tensor<*xf32>) -> ()
|
||||
|
||||
// CHECK-LABEL: test_elu_elu
|
||||
|
|
@ -701,8 +701,8 @@ func @test_elu_elu(%arg0 : tensor<?x10xf32>) -> tensor<*xf32> {
|
|||
}
|
||||
|
||||
func @test_leakyrelu_leakyrelu(%arg0 : tensor<?x10xf32>) -> tensor<*xf32> {
|
||||
%0 = "onnx.LeakyRelu"(%arg0) {LeakyRelu.alpha=1.0:f32} : (tensor<?x10xf32>) -> tensor<*xf32>
|
||||
%1 = "onnx.LeakyRelu"(%0) {LeakyRelu.alpha=1.0:f32} : (tensor<*xf32>) -> tensor<*xf32>
|
||||
%0 = "onnx.LeakyRelu"(%arg0) {alpha=1.0:f32} : (tensor<?x10xf32>) -> tensor<*xf32>
|
||||
%1 = "onnx.LeakyRelu"(%0) {alpha=1.0:f32} : (tensor<*xf32>) -> tensor<*xf32>
|
||||
"std.return"(%1) : (tensor<*xf32>) -> ()
|
||||
|
||||
// CHECK-LABEL: test_leakyrelu_leakyrelu
|
||||
|
|
@ -748,8 +748,8 @@ func @test_leakyrelu_leakyrelu(%arg0 : tensor<?x10xf32>) -> tensor<*xf32> {
|
|||
}
|
||||
|
||||
func @test_selu_selu(%arg0 : tensor<?x10xf32>) -> tensor<*xf32> {
|
||||
%0 = "onnx.Selu"(%arg0) {Selu.alpha=1.0:f32, Selu.gamma=2.0:f32} : (tensor<?x10xf32>) -> tensor<*xf32>
|
||||
%1 = "onnx.Selu"(%0) {Selu.alpha=1.0:f32, Selu.gamma=2.0:f32} : (tensor<*xf32>) -> tensor<*xf32>
|
||||
%0 = "onnx.Selu"(%arg0) {alpha=1.0:f32, gamma=2.0:f32} : (tensor<?x10xf32>) -> tensor<*xf32>
|
||||
%1 = "onnx.Selu"(%0) {alpha=1.0:f32, gamma=2.0:f32} : (tensor<*xf32>) -> tensor<*xf32>
|
||||
"std.return"(%1) : (tensor<*xf32>) -> ()
|
||||
|
||||
// CHECK-LABEL: test_selu_selu
|
||||
|
|
@ -803,8 +803,8 @@ func @test_selu_selu(%arg0 : tensor<?x10xf32>) -> tensor<*xf32> {
|
|||
}
|
||||
|
||||
func @test_hardsigmoid_hardsigmoid(%arg0 : tensor<?x10xf32>) -> tensor<*xf32> {
|
||||
%0 = "onnx.HardSigmoid"(%arg0) {HardSigmoid.alpha=1.0:f32, HardSigmoid.beta=2.0:f32} : (tensor<?x10xf32>) -> tensor<*xf32>
|
||||
%1 = "onnx.HardSigmoid"(%0) {HardSigmoid.alpha=1.0:f32, HardSigmoid.beta=2.0:f32} : (tensor<*xf32>) -> tensor<*xf32>
|
||||
%0 = "onnx.HardSigmoid"(%arg0) {alpha=1.0:f32, beta=2.0:f32} : (tensor<?x10xf32>) -> tensor<*xf32>
|
||||
%1 = "onnx.HardSigmoid"(%0) {alpha=1.0:f32, beta=2.0:f32} : (tensor<*xf32>) -> tensor<*xf32>
|
||||
"std.return"(%1) : (tensor<*xf32>) -> ()
|
||||
|
||||
// CHECK-LABEL: test_hardsigmoid_hardsigmoid
|
||||
|
|
|
|||
|
|
@ -0,0 +1 @@
|
|||
Subproject commit 3c7fc8266bb46046b42c2dc2663f9f505f0cec28
|
||||
Loading…
Reference in New Issue