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| author | Kevin Cheng <kevin.cheng@arm.com> | 2021-03-03 11:21:43 -0800 |
|---|---|---|
| committer | Kevin Cheng <kevin.cheng@arm.com> | 2021-04-27 16:01:59 -0700 |
| commit | 550ccc52de231621c0bf0c05ae2a398eec37ff51 (patch) | |
| tree | d4a5bd8d24560135784208c0fe35615b1d043249 | |
| parent | cf6224e6e8ba4fc2984de3e542538c38e27c9f57 (diff) | |
| download | reference_model-550ccc52de231621c0bf0c05ae2a398eec37ff51.tar.gz | |
Replace serialization/ and verif/ with MLPlatform's serialization_lib submodule
- Remove Usage and Format
- Run black on verif/*.py scripts
Signed-off-by: Kevin Cheng <kevin.cheng@arm.com>
Change-Id: Ie81515891eb0039540f614894f4b6b0e0e78ba74
63 files changed, 1735 insertions, 9384 deletions
diff --git a/.gitmodules b/.gitmodules index 9a6276e..c112866 100644 --- a/.gitmodules +++ b/.gitmodules @@ -1,6 +1,6 @@ [submodule "thirdparty/eigen"] path = thirdparty/eigen url = https://gitlab.com/libeigen/eigen.git -[submodule "thirdparty/flatbuffers"] - path = thirdparty/flatbuffers - url = https://github.com/google/flatbuffers +[submodule "thirdparty/serialization_lib"] + path = thirdparty/serialization_lib + url = https://review.mlplatform.org/tosa/serialization_lib diff --git a/CMakeLists.txt b/CMakeLists.txt index 19c5824..04141aa 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -3,15 +3,10 @@ cmake_minimum_required (VERSION 3.4) set(CMAKE_INSTALL_PREFIX ".") project(tosa_tools LANGUAGES CXX) -option(TOSA_TOOLS_BUILD_SERIALIZATION "Enable building of Tosa Serialization Library" ON) option(TOSA_TOOLS_BUILD_REFERENCE_MODEL "Enable building of Tosa Reference Model" ON) add_subdirectory(thirdparty) -if(TOSA_TOOLS_BUILD_SERIALIZATION) - add_subdirectory(serialization) -endif() - if(TOSA_TOOLS_BUILD_REFERENCE_MODEL) add_subdirectory(reference_model) endif() @@ -29,8 +29,8 @@ tools: * GCC (tested with 7.5.0) or Clang C++ compiler (tested with clang-9) with C++17 support -The model includes the TOSA Serialization Library, Eigen 3.3.7, and -FlatBuffers 1.11.0 as git submodules. The model is written using +The model includes the TOSA Serialization Library and Eigen 3.3.7 +as git submodules. The model is written using C++17 and has been primarily tested on Ubuntu x86_64 18.04 LTS Linux systems. @@ -42,8 +42,7 @@ The testing infrastructure requires: Check out the required git submodules with: ``` bash -$ git submodule init -$ git submodule update +$ git submodule update --init --recursive ``` # Compilation diff --git a/reference_model/CMakeLists.txt b/reference_model/CMakeLists.txt index 0ba8afb..153a5bd 100644 --- a/reference_model/CMakeLists.txt +++ b/reference_model/CMakeLists.txt @@ -26,8 +26,8 @@ else() set(CMAKE_CXX_FLAGS "-Wall -Wno-ignored-attributes") endif() -set(FLATBUFFERS_DIR "../thirdparty/flatbuffers/") -set(SERIALIZATION_DIR "../serialization") +set(FLATBUFFERS_DIR "../thirdparty/serialization_lib/third_party/flatbuffers/") +set(SERIALIZATION_DIR "../thirdparty/serialization_lib/") set (CXX_SOURCE src/main.cpp @@ -64,13 +64,13 @@ target_include_directories(tosa_reference_model ${FLATBUFFERS_DIR}/include ../thirdparty/eigen/ ../thirdparty/eigen/unsupported/ - ${SERIALIZATION_DIR} + ${SERIALIZATION_DIR}/include ) target_link_libraries(tosa_reference_model PRIVATE + tosa_serialization_lib flatbuffers - tosa_serialization ) install (TARGETS tosa_reference_model DESTINATION bin) diff --git a/reference_model/src/main.cpp b/reference_model/src/main.cpp index ec2fdc9..240d913 100644 --- a/reference_model/src/main.cpp +++ b/reference_model/src/main.cpp @@ -15,8 +15,6 @@ #include <stdio.h> -#include "flatbuffers/idl.h" -#include "flatbuffers/util.h" #include "model_common.h" #include "ops/op_factory.h" #include "subgraph_traverser.h" diff --git a/reference_model/src/ops/control_flow.cc b/reference_model/src/ops/control_flow.cc index 9d5db40..827e01f 100644 --- a/reference_model/src/ops/control_flow.cc +++ b/reference_model/src/ops/control_flow.cc @@ -292,9 +292,8 @@ int OpWhileLoop::checkTensorAttributes() int OpWhileLoop::eval() { - TosaReference::Tensor0<bool> cond_output_ctensor( - std::string("cond_output"), DType_BOOL, std::vector<Usage>({ Usage_ACTIVATION }), - std::vector<Format>({ Format_UNKNOWN }), std::vector<int32_t>({}), false); + TosaReference::Tensor0<bool> cond_output_ctensor(std::string("cond_output"), DType_BOOL, + std::vector<int32_t>({})); cond_output_ctensor.allocate(); std::vector<TosaReference::Tensor*> cond_block_outputs; diff --git a/reference_model/src/ops/tensor_ops.cc b/reference_model/src/ops/tensor_ops.cc index d6cd1cd..b8c7ade 100644 --- a/reference_model/src/ops/tensor_ops.cc +++ b/reference_model/src/ops/tensor_ops.cc @@ -103,12 +103,6 @@ int OpAvgPool2d<Dtype>::checkTensorAttributes() in = dynamic_cast<TosaReference::TensorTemplate<TIn>*>(inputs[0]); out = dynamic_cast<TosaReference::TensorTemplate<TOut>*>(outputs[0]); - if (!in->hasFormat(Format_NHWC)) - { - printNodeValidationError("OpAvgPool2d: unsupported tensor format"); - return 1; - } - if (attribute->padding().size() != 4) { printNodeValidationError("OpAvgPool2d: illegal size for attribute padding"); @@ -321,28 +315,11 @@ int OpConv2d<InDtype, WeightDtype>::checkTensorAttributes() printNodeValidationError("OpConv2d: bias tensor must be rank 1"); } - if (inputs[1]->getIsConst() == 0) - { - printNodeValidationError("OpConv2d: weight tensor is not const typed"); - } - input = dynamic_cast<TosaReference::TensorTemplate<TIn>*>(inputs[0]); weight = dynamic_cast<TosaReference::TensorTemplate<TWeight>*>(inputs[1]); bias = dynamic_cast<TosaReference::TensorTemplate<TBias>*>(inputs[2]); output = dynamic_cast<TosaReference::TensorTemplate<TAcc>*>(outputs[0]); - if (!input->hasFormat(Format_NHWC)) - { - printNodeValidationError("OpConv2d: unsupported input tensor format"); - return 1; - } - - if (!weight->hasFormat(Format_OHWI)) - { - printNodeValidationError("OpConv2d: unsupported weight tensor format"); - return 1; - } - if (attribute->padding().size() != 4) { printNodeValidationError("OpConv2d: illegal size for attribute padding"); @@ -530,28 +507,11 @@ int OpDepthwiseConv2d<InDtype, WeightDtype>::checkTensorAttributes() printNodeValidationError("OpDepthwiseConv2d: bias tensor must be rank 1"); } - if (inputs[1]->getIsConst() == 0) - { - printNodeValidationError("OpDepthwiseConv2d: weight tensor is not const typed"); - } - input = dynamic_cast<TosaReference::TensorTemplate<TIn>*>(inputs[0]); weight = dynamic_cast<TosaReference::TensorTemplate<TWeight>*>(inputs[1]); bias = dynamic_cast<TosaReference::TensorTemplate<TBias>*>(inputs[2]); output = dynamic_cast<TosaReference::TensorTemplate<TAcc>*>(outputs[0]); - if (!input->hasFormat(Format_NHWC)) - { - printNodeValidationError("OpDepthwiseConv2d: unsupported input tensor format"); - return 1; - } - - if (!weight->hasFormat(Format_HWIM)) - { - printNodeValidationError("OpDepthwiseConv2d: unsupported weight tensor format"); - return 1; - } - if (attribute->padding().size() != 4) { printNodeValidationError("OpDepthwiseConv2d: illegal size for attribute padding"); @@ -881,12 +841,6 @@ int OpMaxPool2d<Dtype>::checkTensorAttributes() in = dynamic_cast<TosaReference::TensorTemplate<TIn>*>(inputs[0]); out = dynamic_cast<TosaReference::TensorTemplate<TOut>*>(outputs[0]); - if (!in->hasFormat(Format_NHWC)) - { - printNodeValidationError("OpMaxPool2d: unsupported tensor format"); - return 1; - } - if (attribute->padding().size() != 4) { printNodeValidationError("OpMaxPool2d: illegal size for attribute padding"); @@ -1021,28 +975,11 @@ int OpTransposeConv2d<InDtype, OutDtype>::checkTensorAttributes() return 1; } - if (inputs[1]->getIsConst() == 0) - { - printNodeValidationError("OpTransposeConv2d: weight tensor is not const typed"); - } - input = dynamic_cast<TosaReference::TensorTemplate<TIn>*>(inputs[0]); weight = dynamic_cast<TosaReference::TensorTemplate<TWeight>*>(inputs[1]); bias = dynamic_cast<TosaReference::TensorTemplate<TBias>*>(inputs[2]); output = dynamic_cast<TosaReference::TensorTemplate<TAcc>*>(outputs[0]); - if (!input->hasFormat(Format_NHWC)) - { - printNodeValidationError("OpTransposeConv2d: unsupported input tensor format"); - return 1; - } - - if (!weight->hasFormat(Format_OHWI)) - { - printNodeValidationError("OpTransposeConv2d: unsupported weight tensor format"); - return 1; - } - if (attribute->outpad().size() != 2) { printNodeValidationError("OpTransposeConv2d: illegal size for attribute outpad"); diff --git a/reference_model/src/subgraph_traverser.cc b/reference_model/src/subgraph_traverser.cc index 082f802..5096ffa 100644 --- a/reference_model/src/subgraph_traverser.cc +++ b/reference_model/src/subgraph_traverser.cc @@ -103,110 +103,118 @@ int SubgraphTraverser::initializeGraph() for (auto op : block->GetOperators()) { // translated TosaSerializationOperator to GraphNode - DType in_dtype = DType_UNKNOWN, out_dtype = DType_UNKNOWN, weight_dtype = DType_UNKNOWN; - uint32_t in_rank = 0, out_rank = 0, weight_rank = 0; - for (auto name : op->GetInputTensorNames()) - { - - TosaSerializationTensor* ts = block->GetTensorByName(name); - ASSERT_MSG(ts, "SubgraphTraverser: fail to get tensor %s from TosaSerializationHandler", name.c_str()); - - if (ts->HasUsage(Usage_WEIGHT)) - { - weight_dtype = ts->GetDtype(); - weight_rank = ts->GetShape().size(); - } - else if (ts->HasUsage(Usage_INDEX)) - { - // do nothing, but this will prevent tensor's dtype/rank being wrongly used as template argument when initializing this op - } - else if (ts->HasUsage(Usage_ACTIVATION)) - { - if (ts->GetShape().size() >= in_rank) - { - in_dtype = ts->GetDtype(); - in_rank = ts->GetShape().size(); - } - } - } - - // if dtype/rank still not initialized with above pass, we initialize without Usage check - if (in_dtype == DType_UNKNOWN && in_rank == 0) - { - for (auto name : op->GetInputTensorNames()) - { - TosaSerializationTensor* ts = block->GetTensorByName(name); - ASSERT_MSG(ts, "SubgraphTraverser: fail to get tensor %s from TosaSerializationHandler", name.c_str()); - - if (ts->GetShape().size() >= in_rank) - { - in_dtype = ts->GetDtype(); - in_rank = ts->GetShape().size(); - } - } - } - - for (auto name : op->GetOutputTensorNames()) - { - - TosaSerializationTensor* ts = block->GetTensorByName(name); - ASSERT_MSG(ts, "SubgraphTraverser: fail to get tensor %s from TosaSerializationHandler", name.c_str()); - - out_dtype = ts->GetDtype(); - out_rank = ts->GetShape().size(); - } + DType input_dtype = DType_UNKNOWN; + DType output_dtype = DType_UNKNOWN; + DType weight_dtype = DType_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; + + switch (op->GetOp()) + { + case Op_CONV2D: + case Op_DEPTHWISE_CONV2D: + case Op_TRANSPOSE_CONV2D: + case Op_FULLY_CONNECTED: + input_index = 0; + weight_index = 1; + break; + case Op_SELECT: + input_index = 1; + break; + default: + if (!op->GetInputTensorNames().empty()) + input_index = 0; + break; + } + + if (input_index != -1) + { + ASSERT_MSG((size_t)input_index < op->GetInputTensorNames().size(), + "Op=%s, input_index %d must be within [0, num_input - 1]", EnumNamesOp()[op->GetOp()], + input_index); + + std::string input_name = op->GetInputTensorNames()[input_index]; + TosaSerializationTensor* input_tensor = block->GetTensorByName(input_name); + ASSERT_MSG(input_tensor, "SubgraphTraverser: fail to get input tensor %s from TosaSerializationHandler", + input_name.c_str()); + input_dtype = input_tensor->GetDtype(); + input_rank = input_tensor->GetShape().size(); + } + + if (weight_index != -1) + { + ASSERT_MSG((size_t)weight_index < op->GetInputTensorNames().size(), + "Op=%s, weight_index %d must be within [0, num_input - 1]", EnumNamesOp()[op->GetOp()], + weight_index); + std::string weight_name = op->GetInputTensorNames()[weight_index]; + TosaSerializationTensor* weight_tensor = block->GetTensorByName(weight_name); + ASSERT_MSG(weight_tensor, "SubgraphTraverser: fail to get weight tensor %s from TosaSerializationHandler", + weight_name.c_str()); + weight_dtype = weight_tensor->GetDtype(); + weight_rank = weight_tensor->GetShape().size(); + } + + std::string output_name = op->GetOutputTensorNames()[0]; + TosaSerializationTensor* output_tensor = block->GetTensorByName(output_name); + ASSERT_MSG(output_tensor, "SubgraphTraverser: fail to get output tensor %s from TosaSerializationHandler", + output_name.c_str()); + output_dtype = output_tensor->GetDtype(); + output_rank = output_tensor->GetShape().size(); DEBUG_INFO(GT, "Creating operator id_%03u, %8s, %lu input tensors, %lu output tensors", idx, EnumNamesOp()[op->GetOp()], op->GetInputTensorNames().size(), op->GetOutputTensorNames().size()); - GraphNode* cn = OpFactory::newOp(tsh, op->GetOp(), op->GetAttribute(), op->GetQInfo(), idx, in_dtype, in_rank, - out_dtype, out_rank, weight_dtype, weight_rank); - if (!cn) + GraphNode* node = OpFactory::newOp(tsh, op->GetOp(), op->GetAttribute(), op->GetQInfo(), idx, input_dtype, + input_rank, output_dtype, output_rank, weight_dtype, weight_rank); + if (!node) { - if (weight_dtype == DType_UNKNOWN && weight_rank == 0) + if (weight_index == -1) { fprintf(g_func_debug.func_debug_file, "OpFactory could not allocate op %8s input=(%s rank %d) -> (%s rank %d)", - EnumNamesOp()[op->GetOp()], EnumNamesDType()[in_dtype], in_rank, EnumNamesDType()[out_dtype], - out_rank); + EnumNamesOp()[op->GetOp()], EnumNamesDType()[input_dtype], input_rank, + EnumNamesDType()[output_dtype], output_rank); } else { fprintf(g_func_debug.func_debug_file, "OpFactory could not allocate op %8s input=(%s rank %d), weight=(%s rank %d) -> (%s rank %d)", - EnumNamesOp()[op->GetOp()], EnumNamesDType()[in_dtype], in_rank, EnumNamesDType()[weight_dtype], - weight_rank, EnumNamesDType()[out_dtype], out_rank); + EnumNamesOp()[op->GetOp()], EnumNamesDType()[input_dtype], input_rank, + EnumNamesDType()[weight_dtype], weight_rank, EnumNamesDType()[output_dtype], output_rank); } - for (auto ts : op->GetInputTensors()) + for (auto& ts : op->GetInputTensorNames()) { - fprintf(g_func_debug.func_debug_file, "Input: %s\n", ts->GetName().c_str()); + fprintf(g_func_debug.func_debug_file, "Input: %s\n", ts.c_str()); } - for (auto ts : op->GetOutputTensors()) + for (auto& ts : op->GetOutputTensorNames()) { - fprintf(g_func_debug.func_debug_file, "Output: %s\n", ts->GetName().c_str()); + fprintf(g_func_debug.func_debug_file, "Output: %s\n", ts.c_str()); } FATAL_ERROR("Unsupported operation type or rank."); } - for (auto name : op->GetInputTensorNames()) + for (auto& name : op->GetInputTensorNames()) { - cn->addInputName(name); + node->addInputName(name); } for (auto name : op->GetOutputTensorNames()) { - cn->addOutputName(name); + node->addOutputName(name); } - addNode(cn); + addNode(node); // if node doesn't have any inputs (i.e. CONST) // it should be ready for evaluation - if (op->GetInputTensorNames().empty() && !cn->getOnNextNodeList()) + if (op->GetInputTensorNames().empty() && !node->getOnNextNodeList()) { - addToNextNodeList(cn); + addToNextNodeList(node); } idx++; @@ -215,47 +223,40 @@ int SubgraphTraverser::initializeGraph() for (auto ts : block->GetTensors()) { - bool is_const = false; - if (ts->HasUsage(Usage_WEIGHT)) - { - is_const = true; - } - DEBUG_INFO(GT, "Creating tensor %s", ts->GetName().c_str()); - TosaReference::Tensor* ct = - TensorFactory::newTensor(ts->GetName(), ts->GetDtype(), ts->GetUsage(), ts->GetFormat(), ts->GetShape(), - is_const, ts->GetShape().size()); + TosaReference::Tensor* tensor = + TensorFactory::newTensor(ts->GetName(), ts->GetDtype(), ts->GetShape(), ts->GetShape().size()); - if (ts->GetNpyFilePtr()) + if (!ts->GetNpyFilePtr().empty()) { - if (ct->allocate()) + if (tensor->allocate()) { - FATAL_ERROR("Fail to allocate Eigen tensor %s", ct->getName().c_str()); + FATAL_ERROR("Fail to allocate Eigen tensor %s", tensor->getName().c_str()); } bzero(tensor_fullname, sizeof(tensor_fullname)); snprintf(tensor_fullname, sizeof(tensor_fullname), "%s/%s", g_func_config.subgraph_dir, - ts->GetNpyFilePtr()->c_str()); - if (ct->readFromNpyFile(tensor_fullname)) + ts->GetNpyFilePtr().c_str()); + if (tensor->readFromNpyFile(tensor_fullname)) { - FATAL_ERROR("Cannot read input data into graph tensor %s from block %s", ct->getName().c_str(), + FATAL_ERROR("Cannot read input data into graph tensor %s from block %s", tensor->getName().c_str(), block->GetName().c_str()); } } // update this->tensors - addTensor(ct); + addTensor(tensor); } DEBUG_INFO(GT, "Enumerating block %s graph inputs", block->GetName().c_str()); for (auto& input_name : block->GetInputs()) { - TosaReference::Tensor* ct = findTensorByName(input_name); + TosaReference::Tensor* tensor = findTensorByName(input_name); DEBUG_INFO(GT, "input tensor name=%s", input_name.c_str()); - if (ct) + if (tensor) { - ct->setIsSubgraphInput(); - inputTensors.push_back(ct); + tensor->setIsSubgraphInput(); + inputTensors.push_back(tensor); } else { @@ -266,12 +267,12 @@ int SubgraphTraverser::initializeGraph() DEBUG_INFO(GT, "Enumerating block %s graph outputs", block->GetName().c_str()); for (auto& output_name : block->GetOutputs()) { - TosaReference::Tensor* ct = findTensorByName(output_name); + TosaReference::Tensor* tensor = findTensorByName(output_name); DEBUG_INFO(GT, "output tensor name=%s\n", output_name.c_str()); - if (ct) + if (tensor) { - ct->setIsSubgraphOutput(); - outputTensors.push_back(ct); + tensor->setIsSubgraphOutput(); + outputTensors.push_back(tensor); } else { @@ -333,12 +334,12 @@ int SubgraphTraverser::evaluateNextNode() WARNING("Node %lu has been evaluated %d times. Loop suspected.", currNode->getID(), currNode->getEvalCount()); } - for (auto ct : currNode->getOutputs()) + for (auto tensor : currNode->getOutputs()) { - if (!ct->is_allocated()) - if (ct->allocate()) + if (!tensor->is_allocated()) + if (tensor->allocate()) { - FATAL_ERROR("Fail to allocate Eigen tensor %s", ct->getName().c_str()); + FATAL_ERROR("Fail to allocate Eigen tensor %s", tensor->getName().c_str()); } } @@ -348,26 +349,26 @@ int SubgraphTraverser::evaluateNextNode() } // free input tensor if all of its consumers have all of their outputs ready and it's not block's output - for (auto ct : currNode->getInputs()) + for (auto tensor : currNode->getInputs()) { bool in_use = false; - for (auto cn : ct->getConsumers()) + for (auto node : tensor->getConsumers()) { - if (!cn->hasAllOutputsReady()) + if (!node->hasAllOutputsReady()) { in_use = true; } } for (auto name : block->GetOutputs()) { - if (name == ct->getName()) + if (name == tensor->getName()) { in_use = true; } } if (!in_use) { - ct->deallocate(); + tensor->deallocate(); } } @@ -433,29 +434,29 @@ int SubgraphTraverser::clearAllNodeMarkings() return false; } -int SubgraphTraverser::addTensor(TosaReference::Tensor* ct) +int SubgraphTraverser::addTensor(TosaReference::Tensor* tensor) { // Enforce no duplicate tensors/tensor names // O(N), but the number of tensors is small for (TosaReference::Tensor* currTensor : tensors) { - if (ct == currTensor || currTensor->getName() == ct->getName()) + if (tensor == currTensor || currTensor->getName() == tensor->getName()) { - FATAL_ERROR("Error: Duplicate tensor or tensor name being added to graph: %s\n", ct->getName().c_str()); + FATAL_ERROR("Error: Duplicate tensor or tensor name being added to graph: %s\n", tensor->getName().c_str()); return 1; } } - tensors.push_back(ct); + tensors.push_back(tensor); - if (ct->getIsSubgraphInput()) + if (tensor->getIsSubgraphInput()) { - inputTensors.push_back(ct); + inputTensors.push_back(tensor); } - if (ct->getIsSubgraphOutput()) + if (tensor->getIsSubgraphOutput()) { - outputTensors.push_back(ct); + outputTensors.push_back(tensor); } return 0; diff --git a/reference_model/src/tensor.cc b/reference_model/src/tensor.cc index da81bcd..1efebe3 100644 --- a/reference_model/src/tensor.cc +++ b/reference_model/src/tensor.cc @@ -22,17 +22,11 @@ using namespace tosa; TosaReference::Tensor::Tensor(std::string tensorName_, DType tensorDtype_, - const std::vector<Usage>& tensorUsage_, - const std::vector<Format>& tensorFormat_, - std::vector<int> shape_, - int isConst_) + std::vector<int> shape_) { tensorName = std::string(tensorName_); tensorDtype = tensorDtype_; - tensorUsage = std::vector<Usage>(tensorUsage_); - tensorFormat = std::vector<Format>(tensorFormat_); shape = std::vector<int>(shape_); - isConst = isConst_; producer = nullptr; isValid = false; consumers.clear(); @@ -74,17 +68,16 @@ int TosaReference::Tensor::addConsumer(GraphNode* node) int TosaReference::Tensor::dumpTensorParams(FILE* out) const { - fprintf(out, "Name: %s DType=%s Usage=%s isValid=%d Rank=%d Shape=%s\n", tensorName.c_str(), - EnumNamesDType()[getDtype()], getUsageAsString().c_str(), getIsValid(), getRank(), - getShapeAsString().c_str()); + fprintf(out, "Name: %s DType=%s isValid=%d Rank=%d Shape=%s\n", tensorName.c_str(), EnumNamesDType()[getDtype()], + getIsValid(), getRank(), getShapeAsString().c_str()); return 0; } int TosaReference::Tensor::dumpTensorParams(std::ostream& out) const { - out << "Name: " << getName() << " DType=" << EnumNamesDType()[getDtype()] << " Usage=" << getUsageAsString() - << " isValid=" << getIsValid() << " Rank=" << getRank() << " Shape=" << getShapeAsString() << "\n"; + out << "Name: " << getName() << " DType=" << EnumNamesDType()[getDtype()] << " isValid=" << getIsValid() + << " Rank=" << getRank() << " Shape=" << getShapeAsString() << "\n"; return 0; } diff --git a/reference_model/src/tensor.h b/reference_model/src/tensor.h index 4f77cfc..d39cc7c 100644 --- a/reference_model/src/tensor.h +++ b/reference_model/src/tensor.h @@ -35,10 +35,7 @@ class Tensor public: Tensor(std::string tensorName_, DType tensorDtype__, - const std::vector<Usage>& tensorUsage_, - const std::vector<Format>& tensorFormat_, - std::vector<int> shape_, - int isConst_); + std::vector<int> shape_); virtual ~Tensor(); @@ -75,11 +72,6 @@ public: return isValid; } - int getIsConst() const - { - return isConst; - } - GraphNode* getProducer() { return producer; @@ -111,62 +103,6 @@ public: return shape_str; } - const std::vector<Usage>& getUsage() const - { - return tensorUsage; - } - - bool hasUsage(Usage usage) const - { - for (auto& usg : tensorUsage) - { - if (usg == usage) - { - return true; - } - } - return false; - } - - std::string getUsageAsString() const - { - std::string usage_str("["); - for (auto& usg : tensorUsage) - { - usage_str += (std::string(EnumNamesUsage()[usg]) + ", "); - } - usage_str.append("]"); - return usage_str; - } - - const std::vector<Format>& getFormat() const - { - return tensorFormat; - } - - bool hasFormat(Format format) const - { - for (auto& fmt : tensorFormat) - { - if (fmt == format) - { - return true; - } - } - return false; - } - - std::string getFormatAsString() const - { - std::string format_str("["); - for (auto& fmt : tensorFormat) - { - format_str += (std::string(EnumNamesFormat()[fmt]) + ", "); - } - format_str.append("]"); - return format_str; - } - const uint32_t getElementCount() const { uint32_t elements = 1; @@ -282,9 +218,6 @@ public: protected: std::string tensorName; DType tensorDtype; - std::vector<Usage> tensorUsage; - std::vector<Format> tensorFormat; - int isConst; int isValid; std::vector<int> shape; int isSubgraphInput; @@ -309,11 +242,8 @@ class TensorTemplate : public Tensor public: TensorTemplate(std::string tensorName_, DType tensorDtype_, - const std::vector<Usage>& tensorUsage_, - const std::vector<Format>& tensorFormat_, - std::vector<int> shape_, - int isConst_) - : Tensor(tensorName_, tensorDtype_, tensorUsage_, tensorFormat_, shape_, isConst_) + std::vector<int> shape_) + : Tensor(tensorName_, tensorDtype_, shape_) { tensor = nullptr; } @@ -678,10 +608,7 @@ class TensorFactory public: static Tensor* newTensor(std::string tensorName_, DType tensorDtype_, - const std::vector<Usage>& tensorUsage_, - const std::vector<Format>& tensorFormat_, std::vector<int> shape_, - int isConst_, const uint32_t rank) { switch (tensorDtype_) @@ -690,26 +617,19 @@ public: switch (rank) { case 0: - return new Tensor0<float>(tensorName_, tensorDtype_, tensorUsage_, tensorFormat_, shape_, - isConst_); + return new Tensor0<float>(tensorName_, tensorDtype_, shape_); case 1: - return new Tensor1<float>(tensorName_, tensorDtype_, tensorUsage_, tensorFormat_, shape_, - isConst_); + return new Tensor1<float>(tensorName_, tensorDtype_, shape_); case 2: - return new Tensor2<float>(tensorName_, tensorDtype_, tensorUsage_, tensorFormat_, shape_, - isConst_); + return new Tensor2<float>(tensorName_, tensorDtype_, shape_); case 3: - return new Tensor3<float>(tensorName_, tensorDtype_, tensorUsage_, tensorFormat_, shape_, - isConst_); + return new Tensor3<float>(tensorName_, tensorDtype_, shape_); case 4: - return new Tensor4<float>(tensorName_, tensorDtype_, tensorUsage_, tensorFormat_, shape_, - isConst_); + return new Tensor4<float>(tensorName_, tensorDtype_, shape_); case 5: - return new Tensor5<float>(tensorName_, tensorDtype_, tensorUsage_, tensorFormat_, shape_, - isConst_); + return new Tensor5<float>(tensorName_, tensorDtype_, shape_); case 6: - return new Tensor6<float>(tensorName_, tensorDtype_, tensorUsage_, tensorFormat_, shape_, - isConst_); + return new Tensor6<float>(tensorName_, tensorDtype_, shape_); default: goto done; } @@ -721,26 +641,19 @@ public: switch (rank) { case 0: - return new Tensor0<int32_t>(tensorName_, tensorDtype_, tensorUsage_, tensorFormat_, shape_, - isConst_); + return new Tensor0<int32_t>(tensorName_, tensorDtype_, shape_); case 1: - return new Tensor1<int32_t>(tensorName_, tensorDtype_, tensorUsage_, tensorFormat_, shape_, - isConst_); + return new Tensor1<int32_t>(tensorName_, tensorDtype_, shape_); case 2: - return new Tensor2<int32_t>(tensorName_, tensorDtype_, tensorUsage_, tensorFormat_, shape_, - isConst_); + return new Tensor2<int32_t>(tensorName_, tensorDtype_, shape_); case 3: - return new Tensor3<int32_t>(tensorName_, tensorDtype_, tensorUsage_, tensorFormat_, shape_, - isConst_); + return new Tensor3<int32_t>(tensorName_, tensorDtype_, shape_); case 4: - return new Tensor4<int32_t>(tensorName_, tensorDtype_, tensorUsage_, tensorFormat_, shape_, - isConst_); + return new Tensor4<int32_t>(tensorName_, tensorDtype_, shape_); case 5: - return new Tensor5<int32_t>(tensorName_, tensorDtype_, tensorUsage_, tensorFormat_, shape_, - isConst_); + return new Tensor5<int32_t>(tensorName_, tensorDtype_, shape_); case 6: - return new Tensor6<int32_t>(tensorName_, tensorDtype_, tensorUsage_, tensorFormat_, shape_, - isConst_); + return new Tensor6<int32_t>(tensorName_, tensorDtype_, shape_); default: goto done; } @@ -748,26 +661,19 @@ public: switch (rank) { case 0: - return new Tensor0<int64_t>(tensorName_, tensorDtype_, tensorUsage_, tensorFormat_, shape_, - isConst_); + return new Tensor0<int64_t>(tensorName_, tensorDtype_, shape_); case 1: - return new Tensor1<int64_t>(tensorName_, tensorDtype_, tensorUsage_, tensorFormat_, shape_, - isConst_); + return new Tensor1<int64_t>(tensorName_, tensorDtype_, shape_); case 2: - return new Tensor2<int64_t>(tensorName_, tensorDtype_, tensorUsage_, tensorFormat_, shape_, - isConst_); + return new Tensor2<int64_t>(tensorName_, tensorDtype_, shape_); case 3: - return new Tensor3<int64_t>(tensorName_, tensorDtype_, tensorUsage_, tensorFormat_, shape_, - isConst_); + return new Tensor3<int64_t>(tensorName_, tensorDtype_, shape_); case 4: - return new Tensor4<int64_t>(tensorName_, tensorDtype_, tensorUsage_, tensorFormat_, shape_, - isConst_); + return new Tensor4<int64_t>(tensorName_, tensorDtype_, shape_); case 5: - return new Tensor5<int64_t>(tensorName_, tensorDtype_, tensorUsage_, tensorFormat_, shape_, - isConst_); + return new Tensor5<int64_t>(tensorName_, tensorDtype_, shape_); case 6: - return new Tensor6<int64_t>(tensorName_, tensorDtype_, tensorUsage_, tensorFormat_, shape_, - isConst_); + return new Tensor6<int64_t>(tensorName_, tensorDtype_, shape_); default: goto done; } @@ -775,26 +681,19 @@ public: switch (rank) { case 0: - return new Tensor0<bool>(tensorName_, tensorDtype_, tensorUsage_, tensorFormat_, shape_, - isConst_); + return new Tensor0<bool>(tensorName_, tensorDtype_, shape_); case 1: - return new Tensor1<bool>(tensorName_, tensorDtype_, tensorUsage_, tensorFormat_, shape_, - isConst_); + return new Tensor1<bool>(tensorName_, tensorDtype_, shape_); case 2: - return new Tensor2<bool>(tensorName_, tensorDtype_, tensorUsage_, tensorFormat_, shape_, - isConst_); + return new Tensor2<bool>(tensorName_, tensorDtype_, shape_); case 3: - return new Tensor3<bool>(tensorName_, tensorDtype_, tensorUsage_, tensorFormat_, shape_, - isConst_); + return new Tensor3<bool>(tensorName_, tensorDtype_, shape_); case 4: - return new Tensor4<bool>(tensorName_, tensorDtype_, tensorUsage_, tensorFormat_, shape_, - isConst_); + return new Tensor4<bool>(tensorName_, tensorDtype_, shape_); case 5: - return new Tensor5<bool>(tensorName_, tensorDtype_, tensorUsage_, tensorFormat_, shape_, - isConst_); + return new Tensor5<bool>(tensorName_, tensorDtype_, shape_); case 6: - return new Tensor6<bool>(tensorName_, tensorDtype_, tensorUsage_, tensorFormat_, shape_, - isConst_); + return new Tensor6<bool>(tensorName_, tensorDtype_, shape_); default: goto done; } diff --git a/serialization/CMakeLists.txt b/serialization/CMakeLists.txt deleted file mode 100644 index 7bca824..0000000 --- a/serialization/CMakeLists.txt +++ /dev/null @@ -1,32 +0,0 @@ -cmake_minimum_required (VERSION 3.4) - -# Copyright (c) 2020, ARM Limited. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -project (tosa) - -set (CMAKE_CXX_STANDARD 11) -set (CMAKE_CXX_FLAGS "-g -Wall") -set (FLATBUFFERS_SRC_DIR "../thirdparty/flatbuffers") - -set (SOURCE - tosa_serialization_handler.cpp -) - -add_library(tosa_serialization STATIC ${SOURCE}) - -include_directories("./") - -target_link_libraries(tosa_serialization PRIVATE flatbuffers) diff --git a/serialization/attribute.def b/serialization/attribute.def deleted file mode 100644 index a146f47..0000000 --- a/serialization/attribute.def +++ /dev/null @@ -1,98 +0,0 @@ - -// Copyright (c) 2020, ARM Limited. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// http://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -/* - Syntax: - DEF_ATTRIBUTE(ATTRIBUTE_NAME, NUM_ARGS_IN_ATTRIBUTES, ARG0_TYPE, ARG0_SCALAR_OR_VECTOR, ARGS0_NAME, ...) - - Description: - ATTRIBUTE_NAME: corresponding attribute name, must match corresponding "table XXXAttribute" in tosa.fbs - NUM_ARGS_IN_ATTRIBUTES: number of arguments in this attribute - ARG0_TYPE: data type of arg0 in attribute - ARG0_SCALAR_OR_VECTOR: is arg0 a scalar(S) or a vector(V) - ARG0_NAME: name of arg0 - ...: variadic variables for more arguments, depending on NUM_ARGS_IN_ATTRIBUTES -*/ - -DEF_ATTRIBUTE(Pool2d, 3, - int32_t, V, padding, - int32_t, V, kernel, - int32_t, V, stride) - -DEF_ATTRIBUTE(Conv2d, 3, - int32_t, V, padding, - int32_t, V, stride, - int32_t, V, dilation) - -DEF_ATTRIBUTE(TransposeConv2d, 4, - int32_t, V, outpad, - int32_t, V, stride, - int32_t, V, dilation, - int32_t, V, output_shape) - -DEF_ATTRIBUTE(ReluN, 2, - int32_t, S, max_int, - float, S, max_fp) - -DEF_ATTRIBUTE(Axis, 1, - int32_t, S, axis) - -DEF_ATTRIBUTE(Reshape, 1, - int32_t, V, shape) - -DEF_ATTRIBUTE(Slice, 2, - int32_t, V, begin, - int32_t, V, size) - -DEF_ATTRIBUTE(Tile, 1, - int32_t, V, multiples) - -DEF_ATTRIBUTE(Resize, 7, - int32_t, V, output_size, - int32_t, V, stride, - int32_t, V, offset, - int32_t, S, shift, - float, V, stride_fp, - float, V, offset_fp, - ResizeMode, S, mode) - -DEF_ATTRIBUTE(Clamp, 4, - int32_t, S, min_int, - int32_t, S, max_int, - float, S, min_fp, - float, S, max_fp) - -DEF_ATTRIBUTE(Rescale, 7, - int32_t, S, input_zp, - int32_t, S, output_zp, - int32_t, V, multiplier, - int32_t, V, shift, - bool, S, scale32, - bool, S, double_round, - bool, S, per_channel) - -DEF_ATTRIBUTE(Mul, 1, - int32_t, S, shift) - -DEF_ATTRIBUTE(ArithmeticRightShift, 1, - bool, S, round) - -DEF_ATTRIBUTE(CondIf, 2, - string, S, then_branch, - string, S, else_branch) - -DEF_ATTRIBUTE(WhileLoop, 2, - string, S, cond_branch, - string, S, body_branch) diff --git a/serialization/attribute.h b/serialization/attribute.h deleted file mode 100644 index 2a33a8f..0000000 --- a/serialization/attribute.h +++ /dev/null @@ -1,181 +0,0 @@ - -// Copyright (c) 2020, ARM Limited. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// http://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#ifndef _TOSA_SERIALIZATION_ATTRIBUTE_H -#define _TOSA_SERIALIZATION_ATTRIBUTE_H -#include "flatbuffers/idl.h" -#include "flatbuffers/util.h" -#include "tosa_generated.h" - -using std::string; - -namespace tosa -{ - -class TosaAttributeBase -{ -public: - virtual ~TosaAttributeBase() - {} -}; - -class TosaNoneAttribute : public TosaAttributeBase -{ -public: - TosaNoneAttribute() - {} - TosaNoneAttribute(TosaNoneAttribute* p) - {} -}; - -#define DEF_ARGS_VER0_S_STR(V) _##V = p->V()->str(); -#define DEF_ARGS_VER0_S_DEFAULT(V) _##V = p->V(); - -#define DEF_ARGS_VER0_S_int32_t(V) DEF_ARGS_VER0_S_DEFAULT(V) -#define DEF_ARGS_VER0_S_float(V) DEF_ARGS_VER0_S_DEFAULT(V) -#define DEF_ARGS_VER0_S_bool(V) DEF_ARGS_VER0_S_DEFAULT(V) -#define DEF_ARGS_VER0_S_ResizeMode(V) DEF_ARGS_VER0_S_DEFAULT(V) -#define DEF_ARGS_VER0_S_string(V) DEF_ARGS_VER0_S_STR(V) - -#define DEF_ARGS_VER0_S(T, V) DEF_ARGS_VER0_S_##T(V) -#define DEF_ARGS_VER0_V(T, V) _##V = std::vector<T>(p->V()->begin(), p->V()->end()); - -#define DEF_ARGS_VER1_S(T, V) const T& V -#define DEF_ARGS_VER1_V(T, V) const std::vector<T>& V -#define DEF_ARGS_VER2_S(T, V) _##V = V; -#define DEF_ARGS_VER2_V(T, V) _##V = V; -#define DEF_ARGS_VER3_S(T, V) \ - T V() const \ - { \ - return _##V; \ - } -#define DEF_ARGS_VER3_V(T, V) \ - std::vector<T> V() const \ - { \ - return _##V; \ - } -#define DEF_ARGS_VER4_S(T, V) T _##V; -#define DEF_ARGS_VER4_V(T, V) std::vector<T> _##V; - -// another level of preprocessor indirection to handle ", " as function's input argument -#define DEF_ARGS_VER1_TRUE(T, F, V) DEF_ARGS_VER1_##F(T, V) -#define DEF_ARGS_VER1_FALSE(T, F, V) , DEF_ARGS_VER1_##F(T, V) - -#define DEF_ARGS_VER0(FIRST, T, F, V) DEF_ARGS_VER0_##F(T, V) -#define DEF_ARGS_VER1(FIRST, T, F, V) DEF_ARGS_VER1_##FIRST(T, F, V) -#define DEF_ARGS_VER2(FIRST, T, F, V) DEF_ARGS_VER2_##F(T, V) -#define DEF_ARGS_VER3(FIRST, T, F, V) DEF_ARGS_VER3_##F(T, V) -#define DEF_ARGS_VER4(FIRST, T, F, V) DEF_ARGS_VER4_##F(T, V) - -#define DEF_ARGS_0(VER, ...) -#define DEF_ARGS_1(VER, T0, F0, V0) DEF_ARGS_##VER(TRUE, T0, F0, V0) -#define DEF_ARGS_2(VER, T0, F0, V0, T1, F1, V1) DEF_ARGS_##VER(TRUE, T0, F0, V0) DEF_ARGS_##VER(FALSE, T1, F1, V1) -#define DEF_ARGS_3(VER, T0, F0, V0, T1, F1, V1, T2, F2, V2) \ - DEF_ARGS_##VER(TRUE, T0, F0, V0) DEF_ARGS_##VER(FALSE, T1, F1, V1) DEF_ARGS_##VER(FALSE, T2, F2, V2) -#define DEF_ARGS_4(VER, T0, F0, V0, T1, F1, V1, T2, F2, V2, T3, F3, V3) \ - DEF_ARGS_##VER(TRUE, T0, F0, V0) DEF_ARGS_##VER(FALSE, T1, F1, V1) DEF_ARGS_##VER(FALSE, T2, F2, V2) \ - DEF_ARGS_##VER(FALSE, T3, F3, V3) -#define DEF_ARGS_5(VER, T0, F0, V0, T1, F1, V1, T2, F2, V2, T3, F3, V3, T4, F4, V4) \ - DEF_ARGS_##VER(TRUE, T0, F0, V0) DEF_ARGS_##VER(FALSE, T1, F1, V1) DEF_ARGS_##VER(FALSE, T2, F2, V2) \ - DEF_ARGS_##VER(FALSE, T3, F3, V3) DEF_ARGS_##VER(FALSE, T4, F4, V4) - -#define DEF_ARGS_6(VER, T0, F0, V0, T1, F1, V1, T2, F2, V2, T3, F3, V3, T4, F4, V4, T5, F5, V5) \ - DEF_ARGS_##VER(TRUE, T0, F0, V0) DEF_ARGS_##VER(FALSE, T1, F1, V1) DEF_ARGS_##VER(FALSE, T2, F2, V2) \ - DEF_ARGS_##VER(FALSE, T3, F3, V3) DEF_ARGS_##VER(FALSE, T4, F4, V4) DEF_ARGS_##VER(FALSE, T5, F5, V5) - -#define DEF_ARGS_7(VER, T0, F0, V0, T1, F1, V1, T2, F2, V2, T3, F3, V3, T4, F4, V4, T5, F5, V5, T6, F6, V6) \ - DEF_ARGS_##VER(TRUE, T0, F0, V0) DEF_ARGS_##VER(FALSE, T1, F1, V1) DEF_ARGS_##VER(FALSE, T2, F2, V2) \ - DEF_ARGS_##VER(FALSE, T3, F3, V3) DEF_ARGS_##VER(FALSE, T4, F4, V4) DEF_ARGS_##VER(FALSE, T5, F5, V5) \ - DEF_ARGS_##VER(FALSE, T6, F6, V6) - -#define DEF_VER0_VAR_DECL_PTR(NAME) const NAME* p = static_cast<const NAME*>(options); -#define DEF_VER0_VAR_0(NAME) -#define DEF_VER0_VAR_1(NAME) DEF_VER0_VAR_DECL_PTR(NAME) -#define DEF_VER0_VAR_2(NAME) DEF_VER0_VAR_DECL_PTR(NAME) -#define DEF_VER0_VAR_3(NAME) DEF_VER0_VAR_DECL_PTR(NAME) -#define DEF_VER0_VAR_4(NAME) DEF_VER0_VAR_DECL_PTR(NAME) -#define DEF_VER0_VAR_5(NAME) DEF_VER0_VAR_DECL_PTR(NAME) -#define DEF_VER0_VAR_6(NAME) DEF_VER0_VAR_DECL_PTR(NAME) -#define DEF_VER0_VAR_7(NAME) DEF_VER0_VAR_DECL_PTR(NAME) - -#define DEF_ATTRIBUTE(NAME, NUM_ARGS, ...) \ - class Tosa##NAME##Attribute : public TosaAttributeBase \ - { \ - public: \ - Tosa##NAME##Attribute(const TosaAttributeBase* options) \ - { \ - const Tosa##NAME##Attribute* p = reinterpret_cast<const Tosa##NAME##Attribute*>(options); \ - *this = *p; \ - } \ - Tosa##NAME##Attribute(const Tosa##NAME##Attribute* p) \ - { \ - *this = *p; \ - } \ - Tosa##NAME##Attribute(const void* options){ DEF_VER0_VAR_##NUM_ARGS(NAME##Attribute) \ - DEF_ARGS_##NUM_ARGS(VER0, __VA_ARGS__) } Tosa##NAME \ - ##Attribute(DEF_ARGS_##NUM_ARGS(VER1, __VA_ARGS__)) \ - { \ - DEF_ARGS_##NUM_ARGS(VER2, __VA_ARGS__) \ - } \ - virtual ~Tosa##NAME##Attribute() \ - {} \ - DEF_ARGS_##NUM_ARGS(VER3, __VA_ARGS__) private : DEF_ARGS_##NUM_ARGS(VER4, __VA_ARGS__) \ - }; - -#include "attribute.def" -#undef DEF_ATTRIBUTE -#undef DEF_ARGS_0 -#undef DEF_ARGS_1 -#undef DEF_ARGS_2 -#undef DEF_ARGS_3 -#undef DEF_ARGS_4 -#undef DEF_ARGS_5 -#undef DEF_ARGS_6 -#undef DEF_ARGS_7 -#undef DEF_ARGS_VER0 -#undef DEF_ARGS_VER1 -#undef DEF_ARGS_VER2 -#undef DEF_ARGS_VER3 -#undef DEF_ARGS_VER4 -#undef DEF_ARGS_VER0_S_int32_t -#undef DEF_ARGS_VER0_S_float -#undef DEF_ARGS_VER0_S_bool -#undef DEF_ARGS_VER0_S_ResizeMode -#undef DEF_ARGS_VER0_S_string -#undef DEF_ARGS_VER0_S_STR -#undef DEF_ARGS_VER0_S_DEFAULT -#undef DEF_ARGS_VER1_TRUE -#undef DEF_ARGS_VER1_FALSE -#undef DEF_ARGS_VER0_S -#undef DEF_ARGS_VER0_V -#undef DEF_ARGS_VER1_S -#undef DEF_ARGS_VER1_V -#undef DEF_ARGS_VER2_S -#undef DEF_ARGS_VER2_V -#undef DEF_ARGS_VER3_S -#undef DEF_ARGS_VER3_V -#undef DEF_ARGS_VER4_S -#undef DEF_ARGS_VER4_V -#undef DEF_VER0_VAR_0 -#undef DEF_VER0_VAR_1 -#undef DEF_VER0_VAR_2 -#undef DEF_VER0_VAR_3 -#undef DEF_VER0_VAR_4 -#undef DEF_VER0_VAR_5 -#undef DEF_VER0_VAR_DECL_PTR - -} // namespace tosa - -#endif // _TOSA_SERIALIZATION_ATTRIBUTE_H diff --git a/serialization/operator.def b/serialization/operator.def deleted file mode 100644 index 9a93b70..0000000 --- a/serialization/operator.def +++ /dev/null @@ -1,124 +0,0 @@ - -// Copyright (c) 2020, ARM Limited. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// http://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -/* - Syntax: - DEF_OPERATOR(MLIR_NAME, SCHEMA_NAME, REF_IMPL_NAME, OPTIONS, QUANT_INFO) - - Description: - MLIR_NAME: the symbolic string of this op, must match tosa_ops.td - SCHEMA_NAME: corresponding operator name, must match "enum Op" in serialization/tosa.fbs - REF_IMPL_NAME: name used internally in tosa reference implementation - OPTIONS: compile time constant options of this op, corresponding to operator_option.def - QUANT_INFO: quantization infomation of this op, corresponding to quant_info.def -*/ - - -/* tensor operators */ -DEF_OPERATOR(argmax, ARGMAX, ArgMax, Axis, None) -DEF_OPERATOR(avg_pool2d, AVG_POOL2D, AvgPool2d, Pool2d, Unary) -DEF_OPERATOR(conv2d, CONV2D, Conv2d, Conv2d, Conv) -DEF_OPERATOR(conv3d, CONV3D, Conv3d, None, None) -DEF_OPERATOR(depthwise_conv2d, DEPTHWISE_CONV2D, DepthwiseConv2d, Conv2d, Conv) -DEF_OPERATOR(fully_connected, FULLY_CONNECTED, FullyConnected, None, Conv) -DEF_OPERATOR(matmul, MATMUL, MatMul, None, MatMul) -DEF_OPERATOR(max_pool2d, MAX_POOL2D, MaxPool2d, Pool2d, None) -DEF_OPERATOR(transpose_conv2d, TRANSPOSE_CONV2D, TransposeConv2d, TransposeConv2d, Conv) - -/* activation */ -DEF_OPERATOR(clamp, CLAMP, Clamp, Clamp, None) -DEF_OPERATOR(reluN, RELUN, ReluN, ReluN, None) -DEF_OPERATOR(sigmoid, SIGMOID, Sigmoid, None, None) -DEF_OPERATOR(tanh, TANH, Tanh, None, None) - -/* elementwise - binary */ -DEF_OPERATOR(add, ADD, Add, None, None) -DEF_OPERATOR(arithmetic_right_shift, ARITHMETIC_RIGHT_SHIFT, ArithmeticRightShift, ArithmeticRightShift, None) -DEF_OPERATOR(bitwise_and, BITWISE_AND, BitwiseAnd, None, None) -DEF_OPERATOR(bitwise_or, BITWISE_OR, BitwiseOr, None, None) -DEF_OPERATOR(bitwise_xor, BITWISE_XOR, BitwiseXor, None, None) -DEF_OPERATOR(logical_and, LOGICAL_AND, LogicalAnd, None, None) -DEF_OPERATOR(logical_left_shift, LOGICAL_LEFT_SHIFT, LogicalLeftShift, None, None) -DEF_OPERATOR(logical_right_shift, LOGICAL_RIGHT_SHIFT, LogicalRightShift, None, None) -DEF_OPERATOR(logical_or, LOGICAL_OR, LogicalOr, None, None) -DEF_OPERATOR(logical_xor, LOGICAL_XOR, LogicalXor, None, None) -DEF_OPERATOR(maximum, MAXIMUM, Maximum, None, None) -DEF_OPERATOR(minimum, MINIMUM, Minimum, None, None) -DEF_OPERATOR(mul, MUL, Mul, Mul, None) -DEF_OPERATOR(pow, POW, Pow, None, None) -DEF_OPERATOR(sub, SUB, Sub, None, None) -DEF_OPERATOR(table, TABLE, Table, None, None) - -/* elementwise - unary */ -DEF_OPERATOR(abs, ABS, Abs, None, None) -DEF_OPERATOR(bitwise_not, BITWISE_NOT, BitwiseNot, None, None) -DEF_OPERATOR(ceil, CEIL, Ceil, None, None) -DEF_OPERATOR(clz, CLZ, Clz, None, None) -DEF_OPERATOR(exp, EXP, Exp, None, None) -DEF_OPERATOR(floor, FLOOR, Floor, None, None) -DEF_OPERATOR(log, LOG, Log, None, None) -DEF_OPERATOR(logical_not, LOGICAL_NOT, LogicalNot, None, None) -DEF_OPERATOR(negate, NEGATE, Negate, None, Unary) -DEF_OPERATOR(reciprocal, RECIPROCAL, Reciprocal, None, None) -DEF_OPERATOR(rsqrt, RSQRT, Rsqrt, None, None) - -/* elementwise - ternary */ -DEF_OPERATOR(select, SELECT, Select, None, None) - -/* logical */ -DEF_OPERATOR(equal, EQUAL, Equal, None, None) -DEF_OPERATOR(greater, GREATER, Greater, None, None) -DEF_OPERATOR(greater_equal, GREATER_EQUAL, GreaterEqual, None, None) - -/* reduction */ -DEF_OPERATOR(reduce_any, REDUCE_ANY, ReduceAny, Reduce, None) -DEF_OPERATOR(reduce_all, REDUCE_ALL, ReduceAll, Reduce, None) -DEF_OPERATOR(reduce_max, REDUCE_MAX, ReduceMax, Reduce, None) -DEF_OPERATOR(reduce_min, REDUCE_MIN, ReduceMin, Reduce, None) -DEF_OPERATOR(reduce_prod, REDUCE_PRODUCT, ReduceProduct, Reduce, None) -DEF_OPERATOR(reduce_sum, REDUCE_SUM, ReduceSum, Reduce, None) - -/* memory operation */ -DEF_OPERATOR(concat, CONCAT, Concat, Axis, None) -DEF_OPERATOR(pad, PAD, Pad, None, Pad) -DEF_OPERATOR(reshape, RESHAPE, Reshape, Reshape, None) -DEF_OPERATOR(reverse, REVERSE, Reverse, Reverse, None) -DEF_OPERATOR(slice, SLICE, Slice, Slice, None) -DEF_OPERATOR(tile, TILE, Tile, Tile, None) -DEF_OPERATOR(transpose, TRANSPOSE, Transpose, None, None) - -/* gather/scatter */ -DEF_OPERATOR(gather, GATHER, Gather, None, None) -DEF_OPERATOR(scatter, SCATTER, Scatter, None, None) - -/* image */ -DEF_OPERATOR(resize, RESIZE, Resize, Resize, None) - -/* quantization */ -DEF_OPERATOR(cast, CAST, Cast, None, None) -DEF_OPERATOR(rescale, RESCALE, Rescale, Rescale, None) - -/* data nodes */ -DEF_OPERATOR(const, CONST, Const, None, None) -DEF_OPERATOR(placeholder, PLACEHOLDER, Placeholder, None, None) -DEF_OPERATOR(identity, IDENTITY, Identity, None, None) -DEF_OPERATOR(identityn, IDENTITYN, IdentityN, None, None) - -/* custom operations */ -DEF_OPERATOR(custom, CUSTOM, Custom, None, None) - -/* control flow operators */ -DEF_OPERATOR(cond_if, COND_IF, CondIf, CondIf, None) -DEF_OPERATOR(while_loop, WHILE_LOOP, WhileLoop, WhileLoop, None) diff --git a/serialization/quant_info.def b/serialization/quant_info.def deleted file mode 100644 index 39dc101..0000000 --- a/serialization/quant_info.def +++ /dev/null @@ -1,43 +0,0 @@ - -// Copyright (c) 2020, ARM Limited. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// http://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -/* - Syntax: - DEF_QUANTIZATION_INFO(NAME, NUM_ARGS_IN_OPTIONS, ARG0_TYPE, ARG0_SCALAR_OR_VECTOR, ARGS0_NAME, ...) - - Description: - NAME: corresponding quantization info name, must match corresponding "table XXXQuantInfo" in tosa.fbs - NUM_ARGS_IN_QINFO: number of arguments in this quantization info - ARG0_TYPE: data type of arg0 - ARG0_SCALAR_OR_VECTOR: is arg0 a scalar (S) or a vector (V) - ARG0_NAME: name of arg0 - ...: variadic variables for more arguments, depending on NUM_ARGS_IN_QINFO -*/ - - -DEF_QUANTIZATION_INFO(Unary, 2, - int32_t, S, input_zp, - int32_t, S, output_zp) - -DEF_QUANTIZATION_INFO(Conv, 2, - int32_t, S, input_zp, - int32_t, S, weight_zp) - -DEF_QUANTIZATION_INFO(MatMul, 2, - int32_t, S, a_zp, - int32_t, S, b_zp) - -DEF_QUANTIZATION_INFO(Pad, 1, - int32_t, S, input_zp) diff --git a/serialization/quant_info.h b/serialization/quant_info.h deleted file mode 100644 index 03dcab9..0000000 --- a/serialization/quant_info.h +++ /dev/null @@ -1,164 +0,0 @@ - -// Copyright (c) 2020, ARM Limited. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// http://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#ifndef _TOSA_SERIALIZATION_QUANT_INFO_H -#define _TOSA_SERIALIZATION_QUANT_INFO_H -#include "flatbuffers/idl.h" -#include "flatbuffers/util.h" -#include "tosa_generated.h" - -namespace tosa -{ - -class TosaQuantInfoBase -{ -public: - virtual ~TosaQuantInfoBase() - {} -}; - -class TosaNoneQuantInfo : public TosaQuantInfoBase -{ -public: - TosaNoneQuantInfo() - {} - TosaNoneQuantInfo(TosaNoneQuantInfo* p) - {} -}; - -#define DEF_ARGS_VER0_S(T, V) _##V = p->V(); -#define DEF_ARGS_VER0_V(T, V) _##V = std::vector<T>(p->V()->begin(), p->V()->end()); -#define DEF_ARGS_VER1_S(T, V) const T& V -#define DEF_ARGS_VER1_V(T, V) const std::vector<T>& V -#define DEF_ARGS_VER2_S(T, V) _##V = V; -#define DEF_ARGS_VER2_V(T, V) _##V = V; -#define DEF_ARGS_VER3_S(T, V) \ - T V() const \ - { \ - return _##V; \ - } -#define DEF_ARGS_VER3_V(T, V) \ - std::vector<T> V() const \ - { \ - return _##V; \ - } -#define DEF_ARGS_VER4_S(T, V) T _##V; -#define DEF_ARGS_VER4_V(T, V) std::vector<T> _##V; - -// another level of preprocessor indirection to handle ", " as function's input argument -#define DEF_ARGS_VER1_TRUE(T, F, V) DEF_ARGS_VER1_##F(T, V) -#define DEF_ARGS_VER1_FALSE(T, F, V) , DEF_ARGS_VER1_##F(T, V) - -#define DEF_ARGS_VER0(FIRST, T, F, V) DEF_ARGS_VER0_##F(T, V) -#define DEF_ARGS_VER1(FIRST, T, F, V) DEF_ARGS_VER1_##FIRST(T, F, V) -#define DEF_ARGS_VER2(FIRST, T, F, V) DEF_ARGS_VER2_##F(T, V) -#define DEF_ARGS_VER3(FIRST, T, F, V) DEF_ARGS_VER3_##F(T, V) -#define DEF_ARGS_VER4(FIRST, T, F, V) DEF_ARGS_VER4_##F(T, V) - -#define DEF_ARGS_1(VER, T0, F0, V0) DEF_ARGS_##VER(TRUE, T0, F0, V0) -#define DEF_ARGS_2(VER, T0, F0, V0, T1, F1, V1) DEF_ARGS_##VER(TRUE, T0, F0, V0) DEF_ARGS_##VER(FALSE, T1, F1, V1) -#define DEF_ARGS_3(VER, T0, F0, V0, T1, F1, V1, T2, F2, V2) \ - DEF_ARGS_##VER(TRUE, T0, F0, V0) DEF_ARGS_##VER(FALSE, T1, F1, V1) DEF_ARGS_##VER(FALSE, T2, F2, V2) -#define DEF_ARGS_4(VER, T0, F0, V0, T1, F1, V1, T2, F2, V2, T3, F3, V3) \ - DEF_ARGS_##VER(TRUE, T0, F0, V0) DEF_ARGS_##VER(FALSE, T1, F1, V1) DEF_ARGS_##VER(FALSE, T2, F2, V2) \ - DEF_ARGS_##VER(FALSE, T3, F3, V3) -#define DEF_ARGS_5(VER, T0, F0, V0, T1, F1, V1, T2, F2, V2, T3, F3, V3, T4, F4, V4) \ - DEF_ARGS_##VER(TRUE, T0, F0, V0) DEF_ARGS_##VER(FALSE, T1, F1, V1) DEF_ARGS_##VER(FALSE, T2, F2, V2) \ - DEF_ARGS_##VER(FALSE, T3, F3, V3) DEF_ARGS_##VER(FALSE, T4, F4, V4) -#define DEF_ARGS_6(VER, T0, F0, V0, T1, F1, V1, T2, F2, V2, T3, F3, V3, T4, F4, V4, T5, F5, V5) \ - DEF_ARGS_##VER(TRUE, T0, F0, V0) DEF_ARGS_##VER(FALSE, T1, F1, V1) DEF_ARGS_##VER(FALSE, T2, F2, V2) \ - DEF_ARGS_##VER(FALSE, T3, F3, V3) DEF_ARGS_##VER(FALSE, T4, F4, V4) DEF_ARGS_##VER(FALSE, T5, F5, V5) -#define DEF_ARGS_7(VER, T0, F0, V0, T1, F1, V1, T2, F2, V2, T3, F3, V3, T4, F4, V4, T5, F5, V5, T6, F6, V6) \ - DEF_ARGS_##VER(TRUE, T0, F0, V0) DEF_ARGS_##VER(FALSE, T1, F1, V1) DEF_ARGS_##VER(FALSE, T2, F2, V2) \ - DEF_ARGS_##VER(FALSE, T3, F3, V3) DEF_ARGS_##VER(FALSE, T4, F4, V4) DEF_ARGS_##VER(FALSE, T5, F5, V5) \ - DEF_ARGS_##VER(FALSE, T6, F6, V6) -#define DEF_ARGS_8(VER, T0, F0, V0, T1, F1, V1, T2, F2, V2, T3, F3, V3, T4, F4, V4, T5, F5, V5, T6, F6, V6, T7, F7, \ - V7) \ - DEF_ARGS_##VER(TRUE, T0, F0, V0) DEF_ARGS_##VER(FALSE, T1, F1, V1) DEF_ARGS_##VER(FALSE, T2, F2, V2) \ - DEF_ARGS_##VER(FALSE, T3, F3, V3) DEF_ARGS_##VER(FALSE, T4, F4, V4) DEF_ARGS_##VER(FALSE, T5, F5, V5) \ - DEF_ARGS_##VER(FALSE, T6, F6, V6) DEF_ARGS_##VER(FALSE, T7, F7, V7) -#define DEF_ARGS_9(VER, T0, F0, V0, T1, F1, V1, T2, F2, V2, T3, F3, V3, T4, F4, V4, T5, F5, V5, T6, F6, V6, T7, F7, \ - V7, T8, F8, V8) \ - DEF_ARGS_##VER(TRUE, T0, F0, V0) DEF_ARGS_##VER(FALSE, T1, F1, V1) DEF_ARGS_##VER(FALSE, T2, F2, V2) \ - DEF_ARGS_##VER(FALSE, T3, F3, V3) DEF_ARGS_##VER(FALSE, T4, F4, V4) DEF_ARGS_##VER(FALSE, T5, F5, V5) \ - DEF_ARGS_##VER(FALSE, T6, F6, V6) DEF_ARGS_##VER(FALSE, T7, F7, V7) DEF_ARGS_##VER(FALSE, T8, F8, V8) -#define DEF_ARGS_10(VER, T0, F0, V0, T1, F1, V1, T2, F2, V2, T3, F3, V3, T4, F4, V4, T5, F5, V5, T6, F6, V6, T7, F7, \ - V7, T8, F8, V8, T9, F9, V9) \ - DEF_ARGS_##VER(TRUE, T0, F0, V0) DEF_ARGS_##VER(FALSE, T1, F1, V1) DEF_ARGS_##VER(FALSE, T2, F2, V2) \ - DEF_ARGS_##VER(FALSE, T3, F3, V3) DEF_ARGS_##VER(FALSE, T4, F4, V4) DEF_ARGS_##VER(FALSE, T5, F5, V5) \ - DEF_ARGS_##VER(FALSE, T6, F6, V6) DEF_ARGS_##VER(FALSE, T7, F7, V7) DEF_ARGS_##VER(FALSE, T8, F8, V8) \ - DEF_ARGS_##VER(FALSE, T9, F9, V9) - -#define DEF_QUANTIZATION_INFO(NAME, NUM_ARGS, ...) \ - class Tosa##NAME##QuantInfo : public TosaQuantInfoBase \ - { \ - public: \ - Tosa##NAME##QuantInfo(const TosaQuantInfoBase* qinfo) \ - { \ - const Tosa##NAME##QuantInfo* p = dynamic_cast<const Tosa##NAME##QuantInfo*>(qinfo); \ - assert(p); \ - *this = *p; \ - } \ - Tosa##NAME##QuantInfo(const Tosa##NAME##QuantInfo* p) \ - { \ - *this = *p; \ - } \ - Tosa##NAME##QuantInfo(const void* qinfo) \ - { \ - const NAME##QuantInfo* p = static_cast<const NAME##QuantInfo*>(qinfo); \ - DEF_ARGS_##NUM_ARGS(VER0, __VA_ARGS__) \ - } \ - Tosa##NAME##QuantInfo(DEF_ARGS_##NUM_ARGS(VER1, __VA_ARGS__)) \ - { \ - DEF_ARGS_##NUM_ARGS(VER2, __VA_ARGS__) \ - } \ - virtual ~Tosa##NAME##QuantInfo() \ - {} \ - DEF_ARGS_##NUM_ARGS(VER3, __VA_ARGS__) private : DEF_ARGS_##NUM_ARGS(VER4, __VA_ARGS__) \ - }; - -#include "quant_info.def" -#undef DEF_QUANTIZATION_INFO -#undef DEF_ARGS_1 -#undef DEF_ARGS_2 -#undef DEF_ARGS_3 -#undef DEF_ARGS_4 -#undef DEF_ARGS_5 -#undef DEF_ARGS_6 -#undef DEF_ARGS_7 -#undef DEF_ARGS_8 -#undef DEF_ARGS_9 -#undef DEF_ARGS_10 -#undef DEF_ARGS_VER0 -#undef DEF_ARGS_VER1 -#undef DEF_ARGS_VER2 -#undef DEF_ARGS_VER3 -#undef DEF_ARGS_VER4 -#undef DEF_ARGS_VER1_TRUE -#undef DEF_ARGS_VER1_FALSE -#undef DEF_ARGS_VER0_S -#undef DEF_ARGS_VER0_V -#undef DEF_ARGS_VER1_S -#undef DEF_ARGS_VER1_V -#undef DEF_ARGS_VER2_S -#undef DEF_ARGS_VER2_V -#undef DEF_ARGS_VER3_S -#undef DEF_ARGS_VER3_V -#undef DEF_ARGS_VER4_S -#undef DEF_ARGS_VER4_V - -} // namespace tosa - -#endif diff --git a/serialization/tosa.fbs b/serialization/tosa.fbs deleted file mode 100644 index e30c3cf..0000000 --- a/serialization/tosa.fbs +++ /dev/null @@ -1,325 +0,0 @@ - -// Copyright (c) 2020-2021, ARM Limited. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// http://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -namespace tosa; - -// This corresponds to the version. -file_identifier "TOSA"; -// File extension of any written files. -file_extension "tosa"; - -enum DType:uint32 { - UNKNOWN = 0, - BOOL, - UINT8, - INT4, - INT8, - INT16, - INT32, - INT48, - FLOAT, -} - -enum Format:uint32 { - UNKNOWN = 0, - NHWC, - NDHWC, - OHWI, - HWIM, - DOHWI, -} - -enum Usage:uint32 { - UNKNOWN = 0, - ACTIVATION, - WEIGHT, - INDEX, -} - -enum ResizeMode:uint32 { - UNKNOWN = 0, - NEAREST, - BILINEAR, -} - -enum Op:uint32 { - UNKNOWN = 0, - - // Tensor Operator - ARGMAX, - AVG_POOL2D, - CONV2D, - CONV3D, - DEPTHWISE_CONV2D, - FULLY_CONNECTED, - MATMUL, - MAX_POOL2D, - TRANSPOSE_CONV2D, - - // Activation - CLAMP, - RELUN, - SIGMOID, - TANH, - - // Elementwise-Binary - ADD, - ARITHMETIC_RIGHT_SHIFT, - BITWISE_AND, - BITWISE_OR, - BITWISE_XOR, - LOGICAL_AND, - LOGICAL_LEFT_SHIFT, - LOGICAL_RIGHT_SHIFT, - LOGICAL_OR, - LOGICAL_XOR, - MAXIMUM, - MINIMUM, - MUL, - POW, - SUB, - TABLE, - - // Elementwise-Unary - ABS, - BITWISE_NOT, - CEIL, - CLZ, - EXP, - FLOOR, - LOG, - LOGICAL_NOT, - NEGATE, - RECIPROCAL, - RSQRT, - - // Elementwise-Ternary - SELECT, - - // Logical - EQUAL, - GREATER, - GREATER_EQUAL, - - // Reduction - REDUCE_ANY, - REDUCE_ALL, - REDUCE_MAX, - REDUCE_MIN, - REDUCE_PRODUCT, - REDUCE_SUM, - - // Data layout operation - CONCAT, - PAD, - RESHAPE, - REVERSE, - SLICE, - TILE, - TRANSPOSE, - - // Gather/scatter operation - GATHER, - SCATTER, - - // Image - RESIZE, - - // Type conversion - CAST, - RESCALE, - - // Data Nodes - CONST, - PLACEHOLDER, - IDENTITY, - IDENTITYN, - - // Custom operations - CUSTOM, - - // Control flow operators - COND_IF, - WHILE_LOOP, -} - -union Attribute { - Pool2dAttribute, - Conv2dAttribute, - TransposeConv2dAttribute, - ReluNAttribute, - AxisAttribute, - ReshapeAttribute, - SliceAttribute, - TileAttribute, - ResizeAttribute, - ClampAttribute, - RescaleAttribute, - MulAttribute, - ArithmeticRightShiftAttribute, - CondIfAttribute, - WhileLoopAttribute, -} - -table Pool2dAttribute { - padding: [int32]; - kernel: [int32]; - stride: [int32]; -} - -table Conv2dAttribute { - padding: [int32]; - stride: [int32]; - dilation: [int32]; -} - -table TransposeConv2dAttribute { - outpad: [int32]; - stride: [int32]; - dilation: [int32]; - output_shape: [int32]; -} - -table ReluNAttribute { - max_int: int32; - max_fp: float; -} - -table AxisAttribute { - axis: int32; -} - -table ReshapeAttribute { - shape: [int32]; -} - -table SliceAttribute { - begin: [int32]; - size: [int32]; -} - -table TileAttribute { - multiples: [int32]; -} - -table ResizeAttribute { - output_size: [int32]; - stride: [int32]; - offset: [int32]; - shift: int32; - stride_fp: [float]; - offset_fp: [float]; - mode: ResizeMode; -} - -table ClampAttribute { - min_int: int32; - max_int: int32; - min_fp: float; - max_fp: float; -} - -table RescaleAttribute { - input_zp: int32; - output_zp: int32; - multiplier: [int32]; - shift: [int32]; - scale32: bool; - double_round: bool; - per_channel: bool; -} - -table MulAttribute { - shift: int32; -} - -table ArithmeticRightShiftAttribute { - round: bool; -} - -table CondIfAttribute { - then_branch: string; - else_branch: string; -} - -table WhileLoopAttribute { - cond_branch: string; - body_branch: string; -} - -union QuantInfo { - UnaryQuantInfo, - ConvQuantInfo, - MatMulQuantInfo, - PadQuantInfo, -} - -table UnaryQuantInfo { - input_zp: int32; - output_zp: int32; -} - -table ConvQuantInfo { - input_zp: int32; - weight_zp: int32; -} - -table MatMulQuantInfo { - a_zp: int32; - b_zp: int32; -} - -table PadQuantInfo { - input_zp: int32; -} - -table Version { - _major: int32 = 0; - _minor: int32 = 21; - _patch: int32 = 0; - _experimental: bool = false; -} - -table TosaTensor { - name:string; // name of the tensor, used for solving dependency - shape:[int32]; // shape of the tensor - type:DType; // data type of the tensor - usage:[Usage]; // vector of possible usages. for the convenience of debugging only. - format:[Format]; // vector of possible formats. for the convenience of debugging only. - npy_filename: string; // numpy array filename -} - -table TosaOperator { - op:Op; // operator enum - attribute: Attribute; // union structure. operator attribute - inputs:[string]; // list of input tensor names - outputs:[string]; // list of output tensor names - quant_info: QuantInfo; // op-based quantization information -} - -table TosaBasicBlock { - name:string; // basic block name - operators:[TosaOperator]; // operators array - tensors:[TosaTensor]; // tensors array - inputs:[string]; // name of graph inputs - outputs:[string]; // name of graph outputs -} - -table TosaGraph { - version: Version; - blocks:[TosaBasicBlock]; // basic blocks array -} - -root_type TosaGraph; diff --git a/serialization/tosa_generated.h b/serialization/tosa_generated.h deleted file mode 100644 index f2796af..0000000 --- a/serialization/tosa_generated.h +++ /dev/null @@ -1,2683 +0,0 @@ - -// Copyright (c) 2020-2021, ARM Limited. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// http://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -// automatically generated by the FlatBuffers compiler, do not modify - - -#ifndef FLATBUFFERS_GENERATED_TOSA_TOSA_H_ -#define FLATBUFFERS_GENERATED_TOSA_TOSA_H_ - -#include "flatbuffers/flatbuffers.h" - -namespace tosa { - -struct Pool2dAttribute; - -struct Conv2dAttribute; - -struct TransposeConv2dAttribute; - -struct ReluNAttribute; - -struct AxisAttribute; - -struct ReshapeAttribute; - -struct SliceAttribute; - -struct TileAttribute; - -struct ResizeAttribute; - -struct ClampAttribute; - -struct RescaleAttribute; - -struct MulAttribute; - -struct ArithmeticRightShiftAttribute; - -struct CondIfAttribute; - -struct WhileLoopAttribute; - -struct UnaryQuantInfo; - -struct ConvQuantInfo; - -struct MatMulQuantInfo; - -struct PadQuantInfo; - -struct Version; - -struct TosaTensor; - -struct TosaOperator; - -struct TosaBasicBlock; - -struct TosaGraph; - -enum DType { - DType_UNKNOWN = 0, - DType_BOOL = 1, - DType_UINT8 = 2, - DType_INT4 = 3, - DType_INT8 = 4, - DType_INT16 = 5, - DType_INT32 = 6, - DType_INT48 = 7, - DType_FLOAT = 8, - DType_MIN = DType_UNKNOWN, - DType_MAX = DType_FLOAT -}; - -inline const DType (&EnumValuesDType())[9] { - static const DType values[] = { - DType_UNKNOWN, - DType_BOOL, - DType_UINT8, - DType_INT4, - DType_INT8, - DType_INT16, - DType_INT32, - DType_INT48, - DType_FLOAT - }; - return values; -} - -inline const char * const *EnumNamesDType() { - static const char * const names[] = { - "UNKNOWN", - "BOOL", - "UINT8", - "INT4", - "INT8", - "INT16", - "INT32", - "INT48", - "FLOAT", - nullptr - }; - return names; -} - -inline const char *EnumNameDType(DType e) { - if (e < DType_UNKNOWN || e > DType_FLOAT) return ""; - const size_t index = static_cast<size_t>(e); - return EnumNamesDType()[index]; -} - -enum Format { - Format_UNKNOWN = 0, - Format_NHWC = 1, - Format_NDHWC = 2, - Format_OHWI = 3, - Format_HWIM = 4, - Format_DOHWI = 5, - Format_MIN = Format_UNKNOWN, - Format_MAX = Format_DOHWI -}; - -inline const Format (&EnumValuesFormat())[6] { - static const Format values[] = { - Format_UNKNOWN, - Format_NHWC, - Format_NDHWC, - Format_OHWI, - Format_HWIM, - Format_DOHWI - }; - return values; -} - -inline const char * const *EnumNamesFormat() { - static const char * const names[] = { - "UNKNOWN", - "NHWC", - "NDHWC", - "OHWI", - "HWIM", - "DOHWI", - nullptr - }; - return names; -} - -inline const char *EnumNameFormat(Format e) { - if (e < Format_UNKNOWN || e > Format_DOHWI) return ""; - const size_t index = static_cast<size_t>(e); - return EnumNamesFormat()[index]; -} - -enum Usage { - Usage_UNKNOWN = 0, - Usage_ACTIVATION = 1, - Usage_WEIGHT = 2, - Usage_INDEX = 3, - Usage_MIN = Usage_UNKNOWN, - Usage_MAX = Usage_INDEX -}; - -inline const Usage (&EnumValuesUsage())[4] { - static const Usage values[] = { - Usage_UNKNOWN, - Usage_ACTIVATION, - Usage_WEIGHT, - Usage_INDEX - }; - return values; -} - -inline const char * const *EnumNamesUsage() { - static const char * const names[] = { - "UNKNOWN", - "ACTIVATION", - "WEIGHT", - "INDEX", - nullptr - }; - return names; -} - -inline const char *EnumNameUsage(Usage e) { - if (e < Usage_UNKNOWN || e > Usage_INDEX) return ""; - const size_t index = static_cast<size_t>(e); - return EnumNamesUsage()[index]; -} - -enum ResizeMode { - ResizeMode_UNKNOWN = 0, - ResizeMode_NEAREST = 1, - ResizeMode_BILINEAR = 2, - ResizeMode_MIN = ResizeMode_UNKNOWN, - ResizeMode_MAX = ResizeMode_BILINEAR -}; - -inline const ResizeMode (&EnumValuesResizeMode())[3] { - static const ResizeMode values[] = { - ResizeMode_UNKNOWN, - ResizeMode_NEAREST, - ResizeMode_BILINEAR - }; - return values; -} - -inline const char * const *EnumNamesResizeMode() { - static const char * const names[] = { - "UNKNOWN", - "NEAREST", - "BILINEAR", - nullptr - }; - return names; -} - -inline const char *EnumNameResizeMode(ResizeMode e) { - if (e < ResizeMode_UNKNOWN || e > ResizeMode_BILINEAR) return ""; - const size_t index = static_cast<size_t>(e); - return EnumNamesResizeMode()[index]; -} - -enum Op { - Op_UNKNOWN = 0, - Op_ARGMAX = 1, - Op_AVG_POOL2D = 2, - Op_CONV2D = 3, - Op_CONV3D = 4, - Op_DEPTHWISE_CONV2D = 5, - Op_FULLY_CONNECTED = 6, - Op_MATMUL = 7, - Op_MAX_POOL2D = 8, - Op_TRANSPOSE_CONV2D = 9, - Op_CLAMP = 10, - Op_RELUN = 11, - Op_SIGMOID = 12, - Op_TANH = 13, - Op_ADD = 14, - Op_ARITHMETIC_RIGHT_SHIFT = 15, - Op_BITWISE_AND = 16, - Op_BITWISE_OR = 17, - Op_BITWISE_XOR = 18, - Op_LOGICAL_AND = 19, - Op_LOGICAL_LEFT_SHIFT = 20, - Op_LOGICAL_RIGHT_SHIFT = 21, - Op_LOGICAL_OR = 22, - Op_LOGICAL_XOR = 23, - Op_MAXIMUM = 24, - Op_MINIMUM = 25, - Op_MUL = 26, - Op_POW = 27, - Op_SUB = 28, - Op_TABLE = 29, - Op_ABS = 30, - Op_BITWISE_NOT = 31, - Op_CEIL = 32, - Op_CLZ = 33, - Op_EXP = 34, - Op_FLOOR = 35, - Op_LOG = 36, - Op_LOGICAL_NOT = 37, - Op_NEGATE = 38, - Op_RECIPROCAL = 39, - Op_RSQRT = 40, - Op_SELECT = 41, - Op_EQUAL = 42, - Op_GREATER = 43, - Op_GREATER_EQUAL = 44, - Op_REDUCE_ANY = 45, - Op_REDUCE_ALL = 46, - Op_REDUCE_MAX = 47, - Op_REDUCE_MIN = 48, - Op_REDUCE_PRODUCT = 49, - Op_REDUCE_SUM = 50, - Op_CONCAT = 51, - Op_PAD = 52, - Op_RESHAPE = 53, - Op_REVERSE = 54, - Op_SLICE = 55, - Op_TILE = 56, - Op_TRANSPOSE = 57, - Op_GATHER = 58, - Op_SCATTER = 59, - Op_RESIZE = 60, - Op_CAST = 61, - Op_RESCALE = 62, - Op_CONST = 63, - Op_PLACEHOLDER = 64, - Op_IDENTITY = 65, - Op_IDENTITYN = 66, - Op_CUSTOM = 67, - Op_COND_IF = 68, - Op_WHILE_LOOP = 69, - Op_MIN = Op_UNKNOWN, - Op_MAX = Op_WHILE_LOOP -}; - -inline const Op (&EnumValuesOp())[70] { - static const Op values[] = { - Op_UNKNOWN, - Op_ARGMAX, - Op_AVG_POOL2D, - Op_CONV2D, - Op_CONV3D, - Op_DEPTHWISE_CONV2D, - Op_FULLY_CONNECTED, - Op_MATMUL, - Op_MAX_POOL2D, - Op_TRANSPOSE_CONV2D, - Op_CLAMP, - Op_RELUN, - Op_SIGMOID, - Op_TANH, - Op_ADD, - Op_ARITHMETIC_RIGHT_SHIFT, - Op_BITWISE_AND, - Op_BITWISE_OR, - Op_BITWISE_XOR, - Op_LOGICAL_AND, - Op_LOGICAL_LEFT_SHIFT, - Op_LOGICAL_RIGHT_SHIFT, - Op_LOGICAL_OR, - Op_LOGICAL_XOR, - Op_MAXIMUM, - Op_MINIMUM, - Op_MUL, - Op_POW, - Op_SUB, - Op_TABLE, - Op_ABS, - Op_BITWISE_NOT, - Op_CEIL, - Op_CLZ, - Op_EXP, - Op_FLOOR, - Op_LOG, - Op_LOGICAL_NOT, - Op_NEGATE, - Op_RECIPROCAL, - Op_RSQRT, - Op_SELECT, - Op_EQUAL, - Op_GREATER, - Op_GREATER_EQUAL, - Op_REDUCE_ANY, - Op_REDUCE_ALL, - Op_REDUCE_MAX, - Op_REDUCE_MIN, - Op_REDUCE_PRODUCT, - Op_REDUCE_SUM, - Op_CONCAT, - Op_PAD, - Op_RESHAPE, - Op_REVERSE, - Op_SLICE, - Op_TILE, - Op_TRANSPOSE, - Op_GATHER, - Op_SCATTER, - Op_RESIZE, - Op_CAST, - Op_RESCALE, - Op_CONST, - Op_PLACEHOLDER, - Op_IDENTITY, - Op_IDENTITYN, - Op_CUSTOM, - Op_COND_IF, - Op_WHILE_LOOP - }; - return values; -} - -inline const char * const *EnumNamesOp() { - static const char * const names[] = { - "UNKNOWN", - "ARGMAX", - "AVG_POOL2D", - "CONV2D", - "CONV3D", - "DEPTHWISE_CONV2D", - "FULLY_CONNECTED", - "MATMUL", - "MAX_POOL2D", - "TRANSPOSE_CONV2D", - "CLAMP", - "RELUN", - "SIGMOID", - "TANH", - "ADD", - "ARITHMETIC_RIGHT_SHIFT", - "BITWISE_AND", - "BITWISE_OR", - "BITWISE_XOR", - "LOGICAL_AND", - "LOGICAL_LEFT_SHIFT", - "LOGICAL_RIGHT_SHIFT", - "LOGICAL_OR", - "LOGICAL_XOR", - "MAXIMUM", - "MINIMUM", - "MUL", - "POW", - "SUB", - "TABLE", - "ABS", - "BITWISE_NOT", - "CEIL", - "CLZ", - "EXP", - "FLOOR", - "LOG", - "LOGICAL_NOT", - "NEGATE", - "RECIPROCAL", - "RSQRT", - "SELECT", - "EQUAL", - "GREATER", - "GREATER_EQUAL", - "REDUCE_ANY", - "REDUCE_ALL", - "REDUCE_MAX", - "REDUCE_MIN", - "REDUCE_PRODUCT", - "REDUCE_SUM", - "CONCAT", - "PAD", - "RESHAPE", - "REVERSE", - "SLICE", - "TILE", - "TRANSPOSE", - "GATHER", - "SCATTER", - "RESIZE", - "CAST", - "RESCALE", - "CONST", - "PLACEHOLDER", - "IDENTITY", - "IDENTITYN", - "CUSTOM", - "COND_IF", - "WHILE_LOOP", - nullptr - }; - return names; -} - -inline const char *EnumNameOp(Op e) { - if (e < Op_UNKNOWN || e > Op_WHILE_LOOP) return ""; - const size_t index = static_cast<size_t>(e); - return EnumNamesOp()[index]; -} - -enum Attribute { - Attribute_NONE = 0, - Attribute_Pool2dAttribute = 1, - Attribute_Conv2dAttribute = 2, - Attribute_TransposeConv2dAttribute = 3, - Attribute_ReluNAttribute = 4, - Attribute_AxisAttribute = 5, - Attribute_ReshapeAttribute = 6, - Attribute_SliceAttribute = 7, - Attribute_TileAttribute = 8, - Attribute_ResizeAttribute = 9, - Attribute_ClampAttribute = 10, - Attribute_RescaleAttribute = 11, - Attribute_MulAttribute = 12, - Attribute_ArithmeticRightShiftAttribute = 13, - Attribute_CondIfAttribute = 14, - Attribute_WhileLoopAttribute = 15, - Attribute_MIN = Attribute_NONE, - Attribute_MAX = Attribute_WhileLoopAttribute -}; - -inline const Attribute (&EnumValuesAttribute())[16] { - static const Attribute values[] = { - Attribute_NONE, - Attribute_Pool2dAttribute, - Attribute_Conv2dAttribute, - Attribute_TransposeConv2dAttribute, - Attribute_ReluNAttribute, - Attribute_AxisAttribute, - Attribute_ReshapeAttribute, - Attribute_SliceAttribute, - Attribute_TileAttribute, - Attribute_ResizeAttribute, - Attribute_ClampAttribute, - Attribute_RescaleAttribute, - Attribute_MulAttribute, - Attribute_ArithmeticRightShiftAttribute, - Attribute_CondIfAttribute, - Attribute_WhileLoopAttribute - }; - return values; -} - -inline const char * const *EnumNamesAttribute() { - static const char * const names[] = { - "NONE", - "Pool2dAttribute", - "Conv2dAttribute", - "TransposeConv2dAttribute", - "ReluNAttribute", - "AxisAttribute", - "ReshapeAttribute", - "SliceAttribute", - "TileAttribute", - "ResizeAttribute", - "ClampAttribute", - "RescaleAttribute", - "MulAttribute", - "ArithmeticRightShiftAttribute", - "CondIfAttribute", - "WhileLoopAttribute", - nullptr - }; - return names; -} - -inline const char *EnumNameAttribute(Attribute e) { - if (e < Attribute_NONE || e > Attribute_WhileLoopAttribute) return ""; - const size_t index = static_cast<size_t>(e); - return EnumNamesAttribute()[index]; -} - -template<typename T> struct AttributeTraits { - static const Attribute enum_value = Attribute_NONE; -}; - -template<> struct AttributeTraits<Pool2dAttribute> { - static const Attribute enum_value = Attribute_Pool2dAttribute; -}; - -template<> struct AttributeTraits<Conv2dAttribute> { - static const Attribute enum_value = Attribute_Conv2dAttribute; -}; - -template<> struct AttributeTraits<TransposeConv2dAttribute> { - static const Attribute enum_value = Attribute_TransposeConv2dAttribute; -}; - -template<> struct AttributeTraits<ReluNAttribute> { - static const Attribute enum_value = Attribute_ReluNAttribute; -}; - -template<> struct AttributeTraits<AxisAttribute> { - static const Attribute enum_value = Attribute_AxisAttribute; -}; - -template<> struct AttributeTraits<ReshapeAttribute> { - static const Attribute enum_value = Attribute_ReshapeAttribute; -}; - -template<> struct AttributeTraits<SliceAttribute> { - static const Attribute enum_value = Attribute_SliceAttribute; -}; - -template<> struct AttributeTraits<TileAttribute> { - static const Attribute enum_value = Attribute_TileAttribute; -}; - -template<> struct AttributeTraits<ResizeAttribute> { - static const Attribute enum_value = Attribute_ResizeAttribute; -}; - -template<> struct AttributeTraits<ClampAttribute> { - static const Attribute enum_value = Attribute_ClampAttribute; -}; - -template<> struct AttributeTraits<RescaleAttribute> { - static const Attribute enum_value = Attribute_RescaleAttribute; -}; - -template<> struct AttributeTraits<MulAttribute> { - static const Attribute enum_value = Attribute_MulAttribute; -}; - -template<> struct AttributeTraits<ArithmeticRightShiftAttribute> { - static const Attribute enum_value = Attribute_ArithmeticRightShiftAttribute; -}; - -template<> struct AttributeTraits<CondIfAttribute> { - static const Attribute enum_value = Attribute_CondIfAttribute; -}; - -template<> struct AttributeTraits<WhileLoopAttribute> { - static const Attribute enum_value = Attribute_WhileLoopAttribute; -}; - -bool VerifyAttribute(flatbuffers::Verifier &verifier, const void *obj, Attribute type); -bool VerifyAttributeVector(flatbuffers::Verifier &verifier, const flatbuffers::Vector<flatbuffers::Offset<void>> *values, const flatbuffers::Vector<uint8_t> *types); - -enum QuantInfo { - QuantInfo_NONE = 0, - QuantInfo_UnaryQuantInfo = 1, - QuantInfo_ConvQuantInfo = 2, - QuantInfo_MatMulQuantInfo = 3, - QuantInfo_PadQuantInfo = 4, - QuantInfo_MIN = QuantInfo_NONE, - QuantInfo_MAX = QuantInfo_PadQuantInfo -}; - -inline const QuantInfo (&EnumValuesQuantInfo())[5] { - static const QuantInfo values[] = { - QuantInfo_NONE, - QuantInfo_UnaryQuantInfo, - QuantInfo_ConvQuantInfo, - QuantInfo_MatMulQuantInfo, - QuantInfo_PadQuantInfo - }; - return values; -} - -inline const char * const *EnumNamesQuantInfo() { - static const char * const names[] = { - "NONE", - "UnaryQuantInfo", - "ConvQuantInfo", - "MatMulQuantInfo", - "PadQuantInfo", - nullptr - }; - return names; -} - -inline const char *EnumNameQuantInfo(QuantInfo e) { - if (e < QuantInfo_NONE || e > QuantInfo_PadQuantInfo) return ""; - const size_t index = static_cast<size_t>(e); - return EnumNamesQuantInfo()[index]; -} - -template<typename T> struct QuantInfoTraits { - static const QuantInfo enum_value = QuantInfo_NONE; -}; - -template<> struct QuantInfoTraits<UnaryQuantInfo> { - static const QuantInfo enum_value = QuantInfo_UnaryQuantInfo; -}; - -template<> struct QuantInfoTraits<ConvQuantInfo> { - static const QuantInfo enum_value = QuantInfo_ConvQuantInfo; -}; - -template<> struct QuantInfoTraits<MatMulQuantInfo> { - static const QuantInfo enum_value = QuantInfo_MatMulQuantInfo; -}; - -template<> struct QuantInfoTraits<PadQuantInfo> { - static const QuantInfo enum_value = QuantInfo_PadQuantInfo; -}; - -bool VerifyQuantInfo(flatbuffers::Verifier &verifier, const void *obj, QuantInfo type); -bool VerifyQuantInfoVector(flatbuffers::Verifier &verifier, const flatbuffers::Vector<flatbuffers::Offset<void>> *values, const flatbuffers::Vector<uint8_t> *types); - -struct Pool2dAttribute FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_PADDING = 4, - VT_KERNEL = 6, - VT_STRIDE = 8 - }; - const flatbuffers::Vector<int32_t> *padding() const { - return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_PADDING); - } - const flatbuffers::Vector<int32_t> *kernel() const { - return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_KERNEL); - } - const flatbuffers::Vector<int32_t> *stride() const { - return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_STRIDE); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyOffset(verifier, VT_PADDING) && - verifier.VerifyVector(padding()) && - VerifyOffset(verifier, VT_KERNEL) && - verifier.VerifyVector(kernel()) && - VerifyOffset(verifier, VT_STRIDE) && - verifier.VerifyVector(stride()) && - verifier.EndTable(); - } -}; - -struct Pool2dAttributeBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_padding(flatbuffers::Offset<flatbuffers::Vector<int32_t>> padding) { - fbb_.AddOffset(Pool2dAttribute::VT_PADDING, padding); - } - void add_kernel(flatbuffers::Offset<flatbuffers::Vector<int32_t>> kernel) { - fbb_.AddOffset(Pool2dAttribute::VT_KERNEL, kernel); - } - void add_stride(flatbuffers::Offset<flatbuffers::Vector<int32_t>> stride) { - fbb_.AddOffset(Pool2dAttribute::VT_STRIDE, stride); - } - explicit Pool2dAttributeBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - Pool2dAttributeBuilder &operator=(const Pool2dAttributeBuilder &); - flatbuffers::Offset<Pool2dAttribute> Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<Pool2dAttribute>(end); - return o; - } -}; - -inline flatbuffers::Offset<Pool2dAttribute> CreatePool2dAttribute( - flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset<flatbuffers::Vector<int32_t>> padding = 0, - flatbuffers::Offset<flatbuffers::Vector<int32_t>> kernel = 0, - flatbuffers::Offset<flatbuffers::Vector<int32_t>> stride = 0) { - Pool2dAttributeBuilder builder_(_fbb); - builder_.add_stride(stride); - builder_.add_kernel(kernel); - builder_.add_padding(padding); - return builder_.Finish(); -} - -inline flatbuffers::Offset<Pool2dAttribute> CreatePool2dAttributeDirect( - flatbuffers::FlatBufferBuilder &_fbb, - const std::vector<int32_t> *padding = nullptr, - const std::vector<int32_t> *kernel = nullptr, - const std::vector<int32_t> *stride = nullptr) { - auto padding__ = padding ? _fbb.CreateVector<int32_t>(*padding) : 0; - auto kernel__ = kernel ? _fbb.CreateVector<int32_t>(*kernel) : 0; - auto stride__ = stride ? _fbb.CreateVector<int32_t>(*stride) : 0; - return tosa::CreatePool2dAttribute( - _fbb, - padding__, - kernel__, - stride__); -} - -struct Conv2dAttribute FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_PADDING = 4, - VT_STRIDE = 6, - VT_DILATION = 8 - }; - const flatbuffers::Vector<int32_t> *padding() const { - return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_PADDING); - } - const flatbuffers::Vector<int32_t> *stride() const { - return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_STRIDE); - } - const flatbuffers::Vector<int32_t> *dilation() const { - return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_DILATION); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyOffset(verifier, VT_PADDING) && - verifier.VerifyVector(padding()) && - VerifyOffset(verifier, VT_STRIDE) && - verifier.VerifyVector(stride()) && - VerifyOffset(verifier, VT_DILATION) && - verifier.VerifyVector(dilation()) && - verifier.EndTable(); - } -}; - -struct Conv2dAttributeBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_padding(flatbuffers::Offset<flatbuffers::Vector<int32_t>> padding) { - fbb_.AddOffset(Conv2dAttribute::VT_PADDING, padding); - } - void add_stride(flatbuffers::Offset<flatbuffers::Vector<int32_t>> stride) { - fbb_.AddOffset(Conv2dAttribute::VT_STRIDE, stride); - } - void add_dilation(flatbuffers::Offset<flatbuffers::Vector<int32_t>> dilation) { - fbb_.AddOffset(Conv2dAttribute::VT_DILATION, dilation); - } - explicit Conv2dAttributeBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - Conv2dAttributeBuilder &operator=(const Conv2dAttributeBuilder &); - flatbuffers::Offset<Conv2dAttribute> Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<Conv2dAttribute>(end); - return o; - } -}; - -inline flatbuffers::Offset<Conv2dAttribute> CreateConv2dAttribute( - flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset<flatbuffers::Vector<int32_t>> padding = 0, - flatbuffers::Offset<flatbuffers::Vector<int32_t>> stride = 0, - flatbuffers::Offset<flatbuffers::Vector<int32_t>> dilation = 0) { - Conv2dAttributeBuilder builder_(_fbb); - builder_.add_dilation(dilation); - builder_.add_stride(stride); - builder_.add_padding(padding); - return builder_.Finish(); -} - -inline flatbuffers::Offset<Conv2dAttribute> CreateConv2dAttributeDirect( - flatbuffers::FlatBufferBuilder &_fbb, - const std::vector<int32_t> *padding = nullptr, - const std::vector<int32_t> *stride = nullptr, - const std::vector<int32_t> *dilation = nullptr) { - auto padding__ = padding ? _fbb.CreateVector<int32_t>(*padding) : 0; - auto stride__ = stride ? _fbb.CreateVector<int32_t>(*stride) : 0; - auto dilation__ = dilation ? _fbb.CreateVector<int32_t>(*dilation) : 0; - return tosa::CreateConv2dAttribute( - _fbb, - padding__, - stride__, - dilation__); -} - -struct TransposeConv2dAttribute FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_OUTPAD = 4, - VT_STRIDE = 6, - VT_DILATION = 8, - VT_OUTPUT_SHAPE = 10 - }; - const flatbuffers::Vector<int32_t> *outpad() const { - return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_OUTPAD); - } - const flatbuffers::Vector<int32_t> *stride() const { - return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_STRIDE); - } - const flatbuffers::Vector<int32_t> *dilation() const { - return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_DILATION); - } - const flatbuffers::Vector<int32_t> *output_shape() const { - return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_OUTPUT_SHAPE); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyOffset(verifier, VT_OUTPAD) && - verifier.VerifyVector(outpad()) && - VerifyOffset(verifier, VT_STRIDE) && - verifier.VerifyVector(stride()) && - VerifyOffset(verifier, VT_DILATION) && - verifier.VerifyVector(dilation()) && - VerifyOffset(verifier, VT_OUTPUT_SHAPE) && - verifier.VerifyVector(output_shape()) && - verifier.EndTable(); - } -}; - -struct TransposeConv2dAttributeBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_outpad(flatbuffers::Offset<flatbuffers::Vector<int32_t>> outpad) { - fbb_.AddOffset(TransposeConv2dAttribute::VT_OUTPAD, outpad); - } - void add_stride(flatbuffers::Offset<flatbuffers::Vector<int32_t>> stride) { - fbb_.AddOffset(TransposeConv2dAttribute::VT_STRIDE, stride); - } - void add_dilation(flatbuffers::Offset<flatbuffers::Vector<int32_t>> dilation) { - fbb_.AddOffset(TransposeConv2dAttribute::VT_DILATION, dilation); - } - void add_output_shape(flatbuffers::Offset<flatbuffers::Vector<int32_t>> output_shape) { - fbb_.AddOffset(TransposeConv2dAttribute::VT_OUTPUT_SHAPE, output_shape); - } - explicit TransposeConv2dAttributeBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - TransposeConv2dAttributeBuilder &operator=(const TransposeConv2dAttributeBuilder &); - flatbuffers::Offset<TransposeConv2dAttribute> Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<TransposeConv2dAttribute>(end); - return o; - } -}; - -inline flatbuffers::Offset<TransposeConv2dAttribute> CreateTransposeConv2dAttribute( - flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset<flatbuffers::Vector<int32_t>> outpad = 0, - flatbuffers::Offset<flatbuffers::Vector<int32_t>> stride = 0, - flatbuffers::Offset<flatbuffers::Vector<int32_t>> dilation = 0, - flatbuffers::Offset<flatbuffers::Vector<int32_t>> output_shape = 0) { - TransposeConv2dAttributeBuilder builder_(_fbb); - builder_.add_output_shape(output_shape); - builder_.add_dilation(dilation); - builder_.add_stride(stride); - builder_.add_outpad(outpad); - return builder_.Finish(); -} - -inline flatbuffers::Offset<TransposeConv2dAttribute> CreateTransposeConv2dAttributeDirect( - flatbuffers::FlatBufferBuilder &_fbb, - const std::vector<int32_t> *outpad = nullptr, - const std::vector<int32_t> *stride = nullptr, - const std::vector<int32_t> *dilation = nullptr, - const std::vector<int32_t> *output_shape = nullptr) { - auto outpad__ = outpad ? _fbb.CreateVector<int32_t>(*outpad) : 0; - auto stride__ = stride ? _fbb.CreateVector<int32_t>(*stride) : 0; - auto dilation__ = dilation ? _fbb.CreateVector<int32_t>(*dilation) : 0; - auto output_shape__ = output_shape ? _fbb.CreateVector<int32_t>(*output_shape) : 0; - return tosa::CreateTransposeConv2dAttribute( - _fbb, - outpad__, - stride__, - dilation__, - output_shape__); -} - -struct ReluNAttribute FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_MAX_INT = 4, - VT_MAX_FP = 6 - }; - int32_t max_int() const { - return GetField<int32_t>(VT_MAX_INT, 0); - } - float max_fp() const { - return GetField<float>(VT_MAX_FP, 0.0f); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField<int32_t>(verifier, VT_MAX_INT) && - VerifyField<float>(verifier, VT_MAX_FP) && - verifier.EndTable(); - } -}; - -struct ReluNAttributeBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_max_int(int32_t max_int) { - fbb_.AddElement<int32_t>(ReluNAttribute::VT_MAX_INT, max_int, 0); - } - void add_max_fp(float max_fp) { - fbb_.AddElement<float>(ReluNAttribute::VT_MAX_FP, max_fp, 0.0f); - } - explicit ReluNAttributeBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - ReluNAttributeBuilder &operator=(const ReluNAttributeBuilder &); - flatbuffers::Offset<ReluNAttribute> Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<ReluNAttribute>(end); - return o; - } -}; - -inline flatbuffers::Offset<ReluNAttribute> CreateReluNAttribute( - flatbuffers::FlatBufferBuilder &_fbb, - int32_t max_int = 0, - float max_fp = 0.0f) { - ReluNAttributeBuilder builder_(_fbb); - builder_.add_max_fp(max_fp); - builder_.add_max_int(max_int); - return builder_.Finish(); -} - -struct AxisAttribute FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_AXIS = 4 - }; - int32_t axis() const { - return GetField<int32_t>(VT_AXIS, 0); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField<int32_t>(verifier, VT_AXIS) && - verifier.EndTable(); - } -}; - -struct AxisAttributeBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_axis(int32_t axis) { - fbb_.AddElement<int32_t>(AxisAttribute::VT_AXIS, axis, 0); - } - explicit AxisAttributeBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - AxisAttributeBuilder &operator=(const AxisAttributeBuilder &); - flatbuffers::Offset<AxisAttribute> Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<AxisAttribute>(end); - return o; - } -}; - -inline flatbuffers::Offset<AxisAttribute> CreateAxisAttribute( - flatbuffers::FlatBufferBuilder &_fbb, - int32_t axis = 0) { - AxisAttributeBuilder builder_(_fbb); - builder_.add_axis(axis); - return builder_.Finish(); -} - -struct ReshapeAttribute FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_SHAPE = 4 - }; - const flatbuffers::Vector<int32_t> *shape() const { - return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_SHAPE); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyOffset(verifier, VT_SHAPE) && - verifier.VerifyVector(shape()) && - verifier.EndTable(); - } -}; - -struct ReshapeAttributeBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_shape(flatbuffers::Offset<flatbuffers::Vector<int32_t>> shape) { - fbb_.AddOffset(ReshapeAttribute::VT_SHAPE, shape); - } - explicit ReshapeAttributeBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - ReshapeAttributeBuilder &operator=(const ReshapeAttributeBuilder &); - flatbuffers::Offset<ReshapeAttribute> Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<ReshapeAttribute>(end); - return o; - } -}; - -inline flatbuffers::Offset<ReshapeAttribute> CreateReshapeAttribute( - flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset<flatbuffers::Vector<int32_t>> shape = 0) { - ReshapeAttributeBuilder builder_(_fbb); - builder_.add_shape(shape); - return builder_.Finish(); -} - -inline flatbuffers::Offset<ReshapeAttribute> CreateReshapeAttributeDirect( - flatbuffers::FlatBufferBuilder &_fbb, - const std::vector<int32_t> *shape = nullptr) { - auto shape__ = shape ? _fbb.CreateVector<int32_t>(*shape) : 0; - return tosa::CreateReshapeAttribute( - _fbb, - shape__); -} - -struct SliceAttribute FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_BEGIN = 4, - VT_SIZE = 6 - }; - const flatbuffers::Vector<int32_t> *begin() const { - return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_BEGIN); - } - const flatbuffers::Vector<int32_t> *size() const { - return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_SIZE); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyOffset(verifier, VT_BEGIN) && - verifier.VerifyVector(begin()) && - VerifyOffset(verifier, VT_SIZE) && - verifier.VerifyVector(size()) && - verifier.EndTable(); - } -}; - -struct SliceAttributeBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_begin(flatbuffers::Offset<flatbuffers::Vector<int32_t>> begin) { - fbb_.AddOffset(SliceAttribute::VT_BEGIN, begin); - } - void add_size(flatbuffers::Offset<flatbuffers::Vector<int32_t>> size) { - fbb_.AddOffset(SliceAttribute::VT_SIZE, size); - } - explicit SliceAttributeBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - SliceAttributeBuilder &operator=(const SliceAttributeBuilder &); - flatbuffers::Offset<SliceAttribute> Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<SliceAttribute>(end); - return o; - } -}; - -inline flatbuffers::Offset<SliceAttribute> CreateSliceAttribute( - flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset<flatbuffers::Vector<int32_t>> begin = 0, - flatbuffers::Offset<flatbuffers::Vector<int32_t>> size = 0) { - SliceAttributeBuilder builder_(_fbb); - builder_.add_size(size); - builder_.add_begin(begin); - return builder_.Finish(); -} - -inline flatbuffers::Offset<SliceAttribute> CreateSliceAttributeDirect( - flatbuffers::FlatBufferBuilder &_fbb, - const std::vector<int32_t> *begin = nullptr, - const std::vector<int32_t> *size = nullptr) { - auto begin__ = begin ? _fbb.CreateVector<int32_t>(*begin) : 0; - auto size__ = size ? _fbb.CreateVector<int32_t>(*size) : 0; - return tosa::CreateSliceAttribute( - _fbb, - begin__, - size__); -} - -struct TileAttribute FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_MULTIPLES = 4 - }; - const flatbuffers::Vector<int32_t> *multiples() const { - return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_MULTIPLES); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyOffset(verifier, VT_MULTIPLES) && - verifier.VerifyVector(multiples()) && - verifier.EndTable(); - } -}; - -struct TileAttributeBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_multiples(flatbuffers::Offset<flatbuffers::Vector<int32_t>> multiples) { - fbb_.AddOffset(TileAttribute::VT_MULTIPLES, multiples); - } - explicit TileAttributeBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - TileAttributeBuilder &operator=(const TileAttributeBuilder &); - flatbuffers::Offset<TileAttribute> Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<TileAttribute>(end); - return o; - } -}; - -inline flatbuffers::Offset<TileAttribute> CreateTileAttribute( - flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset<flatbuffers::Vector<int32_t>> multiples = 0) { - TileAttributeBuilder builder_(_fbb); - builder_.add_multiples(multiples); - return builder_.Finish(); -} - -inline flatbuffers::Offset<TileAttribute> CreateTileAttributeDirect( - flatbuffers::FlatBufferBuilder &_fbb, - const std::vector<int32_t> *multiples = nullptr) { - auto multiples__ = multiples ? _fbb.CreateVector<int32_t>(*multiples) : 0; - return tosa::CreateTileAttribute( - _fbb, - multiples__); -} - -struct ResizeAttribute FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_OUTPUT_SIZE = 4, - VT_STRIDE = 6, - VT_OFFSET = 8, - VT_SHIFT = 10, - VT_STRIDE_FP = 12, - VT_OFFSET_FP = 14, - VT_MODE = 16 - }; - const flatbuffers::Vector<int32_t> *output_size() const { - return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_OUTPUT_SIZE); - } - const flatbuffers::Vector<int32_t> *stride() const { - return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_STRIDE); - } - const flatbuffers::Vector<int32_t> *offset() const { - return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_OFFSET); - } - int32_t shift() const { - return GetField<int32_t>(VT_SHIFT, 0); - } - const flatbuffers::Vector<float> *stride_fp() const { - return GetPointer<const flatbuffers::Vector<float> *>(VT_STRIDE_FP); - } - const flatbuffers::Vector<float> *offset_fp() const { - return GetPointer<const flatbuffers::Vector<float> *>(VT_OFFSET_FP); - } - ResizeMode mode() const { - return static_cast<ResizeMode>(GetField<uint32_t>(VT_MODE, 0)); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyOffset(verifier, VT_OUTPUT_SIZE) && - verifier.VerifyVector(output_size()) && - VerifyOffset(verifier, VT_STRIDE) && - verifier.VerifyVector(stride()) && - VerifyOffset(verifier, VT_OFFSET) && - verifier.VerifyVector(offset()) && - VerifyField<int32_t>(verifier, VT_SHIFT) && - VerifyOffset(verifier, VT_STRIDE_FP) && - verifier.VerifyVector(stride_fp()) && - VerifyOffset(verifier, VT_OFFSET_FP) && - verifier.VerifyVector(offset_fp()) && - VerifyField<uint32_t>(verifier, VT_MODE) && - verifier.EndTable(); - } -}; - -struct ResizeAttributeBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_output_size(flatbuffers::Offset<flatbuffers::Vector<int32_t>> output_size) { - fbb_.AddOffset(ResizeAttribute::VT_OUTPUT_SIZE, output_size); - } - void add_stride(flatbuffers::Offset<flatbuffers::Vector<int32_t>> stride) { - fbb_.AddOffset(ResizeAttribute::VT_STRIDE, stride); - } - void add_offset(flatbuffers::Offset<flatbuffers::Vector<int32_t>> offset) { - fbb_.AddOffset(ResizeAttribute::VT_OFFSET, offset); - } - void add_shift(int32_t shift) { - fbb_.AddElement<int32_t>(ResizeAttribute::VT_SHIFT, shift, 0); - } - void add_stride_fp(flatbuffers::Offset<flatbuffers::Vector<float>> stride_fp) { - fbb_.AddOffset(ResizeAttribute::VT_STRIDE_FP, stride_fp); - } - void add_offset_fp(flatbuffers::Offset<flatbuffers::Vector<float>> offset_fp) { - fbb_.AddOffset(ResizeAttribute::VT_OFFSET_FP, offset_fp); - } - void add_mode(ResizeMode mode) { - fbb_.AddElement<uint32_t>(ResizeAttribute::VT_MODE, static_cast<uint32_t>(mode), 0); - } - explicit ResizeAttributeBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - ResizeAttributeBuilder &operator=(const ResizeAttributeBuilder &); - flatbuffers::Offset<ResizeAttribute> Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<ResizeAttribute>(end); - return o; - } -}; - -inline flatbuffers::Offset<ResizeAttribute> CreateResizeAttribute( - flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset<flatbuffers::Vector<int32_t>> output_size = 0, - flatbuffers::Offset<flatbuffers::Vector<int32_t>> stride = 0, - flatbuffers::Offset<flatbuffers::Vector<int32_t>> offset = 0, - int32_t shift = 0, - flatbuffers::Offset<flatbuffers::Vector<float>> stride_fp = 0, - flatbuffers::Offset<flatbuffers::Vector<float>> offset_fp = 0, - ResizeMode mode = ResizeMode_UNKNOWN) { - ResizeAttributeBuilder builder_(_fbb); - builder_.add_mode(mode); - builder_.add_offset_fp(offset_fp); - builder_.add_stride_fp(stride_fp); - builder_.add_shift(shift); - builder_.add_offset(offset); - builder_.add_stride(stride); - builder_.add_output_size(output_size); - return builder_.Finish(); -} - -inline flatbuffers::Offset<ResizeAttribute> CreateResizeAttributeDirect( - flatbuffers::FlatBufferBuilder &_fbb, - const std::vector<int32_t> *output_size = nullptr, - const std::vector<int32_t> *stride = nullptr, - const std::vector<int32_t> *offset = nullptr, - int32_t shift = 0, - const std::vector<float> *stride_fp = nullptr, - const std::vector<float> *offset_fp = nullptr, - ResizeMode mode = ResizeMode_UNKNOWN) { - auto output_size__ = output_size ? _fbb.CreateVector<int32_t>(*output_size) : 0; - auto stride__ = stride ? _fbb.CreateVector<int32_t>(*stride) : 0; - auto offset__ = offset ? _fbb.CreateVector<int32_t>(*offset) : 0; - auto stride_fp__ = stride_fp ? _fbb.CreateVector<float>(*stride_fp) : 0; - auto offset_fp__ = offset_fp ? _fbb.CreateVector<float>(*offset_fp) : 0; - return tosa::CreateResizeAttribute( - _fbb, - output_size__, - stride__, - offset__, - shift, - stride_fp__, - offset_fp__, - mode); -} - -struct ClampAttribute FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_MIN_INT = 4, - VT_MAX_INT = 6, - VT_MIN_FP = 8, - VT_MAX_FP = 10 - }; - int32_t min_int() const { - return GetField<int32_t>(VT_MIN_INT, 0); - } - int32_t max_int() const { - return GetField<int32_t>(VT_MAX_INT, 0); - } - float min_fp() const { - return GetField<float>(VT_MIN_FP, 0.0f); - } - float max_fp() const { - return GetField<float>(VT_MAX_FP, 0.0f); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField<int32_t>(verifier, VT_MIN_INT) && - VerifyField<int32_t>(verifier, VT_MAX_INT) && - VerifyField<float>(verifier, VT_MIN_FP) && - VerifyField<float>(verifier, VT_MAX_FP) && - verifier.EndTable(); - } -}; - -struct ClampAttributeBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_min_int(int32_t min_int) { - fbb_.AddElement<int32_t>(ClampAttribute::VT_MIN_INT, min_int, 0); - } - void add_max_int(int32_t max_int) { - fbb_.AddElement<int32_t>(ClampAttribute::VT_MAX_INT, max_int, 0); - } - void add_min_fp(float min_fp) { - fbb_.AddElement<float>(ClampAttribute::VT_MIN_FP, min_fp, 0.0f); - } - void add_max_fp(float max_fp) { - fbb_.AddElement<float>(ClampAttribute::VT_MAX_FP, max_fp, 0.0f); - } - explicit ClampAttributeBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - ClampAttributeBuilder &operator=(const ClampAttributeBuilder &); - flatbuffers::Offset<ClampAttribute> Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<ClampAttribute>(end); - return o; - } -}; - -inline flatbuffers::Offset<ClampAttribute> CreateClampAttribute( - flatbuffers::FlatBufferBuilder &_fbb, - int32_t min_int = 0, - int32_t max_int = 0, - float min_fp = 0.0f, - float max_fp = 0.0f) { - ClampAttributeBuilder builder_(_fbb); - builder_.add_max_fp(max_fp); - builder_.add_min_fp(min_fp); - builder_.add_max_int(max_int); - builder_.add_min_int(min_int); - return builder_.Finish(); -} - -struct RescaleAttribute FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_INPUT_ZP = 4, - VT_OUTPUT_ZP = 6, - VT_MULTIPLIER = 8, - VT_SHIFT = 10, - VT_SCALE32 = 12, - VT_DOUBLE_ROUND = 14, - VT_PER_CHANNEL = 16 - }; - int32_t input_zp() const { - return GetField<int32_t>(VT_INPUT_ZP, 0); - } - int32_t output_zp() const { - return GetField<int32_t>(VT_OUTPUT_ZP, 0); - } - const flatbuffers::Vector<int32_t> *multiplier() const { - return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_MULTIPLIER); - } - const flatbuffers::Vector<int32_t> *shift() const { - return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_SHIFT); - } - bool scale32() const { - return GetField<uint8_t>(VT_SCALE32, 0) != 0; - } - bool double_round() const { - return GetField<uint8_t>(VT_DOUBLE_ROUND, 0) != 0; - } - bool per_channel() const { - return GetField<uint8_t>(VT_PER_CHANNEL, 0) != 0; - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField<int32_t>(verifier, VT_INPUT_ZP) && - VerifyField<int32_t>(verifier, VT_OUTPUT_ZP) && - VerifyOffset(verifier, VT_MULTIPLIER) && - verifier.VerifyVector(multiplier()) && - VerifyOffset(verifier, VT_SHIFT) && - verifier.VerifyVector(shift()) && - VerifyField<uint8_t>(verifier, VT_SCALE32) && - VerifyField<uint8_t>(verifier, VT_DOUBLE_ROUND) && - VerifyField<uint8_t>(verifier, VT_PER_CHANNEL) && - verifier.EndTable(); - } -}; - -struct RescaleAttributeBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_input_zp(int32_t input_zp) { - fbb_.AddElement<int32_t>(RescaleAttribute::VT_INPUT_ZP, input_zp, 0); - } - void add_output_zp(int32_t output_zp) { - fbb_.AddElement<int32_t>(RescaleAttribute::VT_OUTPUT_ZP, output_zp, 0); - } - void add_multiplier(flatbuffers::Offset<flatbuffers::Vector<int32_t>> multiplier) { - fbb_.AddOffset(RescaleAttribute::VT_MULTIPLIER, multiplier); - } - void add_shift(flatbuffers::Offset<flatbuffers::Vector<int32_t>> shift) { - fbb_.AddOffset(RescaleAttribute::VT_SHIFT, shift); - } - void add_scale32(bool scale32) { - fbb_.AddElement<uint8_t>(RescaleAttribute::VT_SCALE32, static_cast<uint8_t>(scale32), 0); - } - void add_double_round(bool double_round) { - fbb_.AddElement<uint8_t>(RescaleAttribute::VT_DOUBLE_ROUND, static_cast<uint8_t>(double_round), 0); - } - void add_per_channel(bool per_channel) { - fbb_.AddElement<uint8_t>(RescaleAttribute::VT_PER_CHANNEL, static_cast<uint8_t>(per_channel), 0); - } - explicit RescaleAttributeBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - RescaleAttributeBuilder &operator=(const RescaleAttributeBuilder &); - flatbuffers::Offset<RescaleAttribute> Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<RescaleAttribute>(end); - return o; - } -}; - -inline flatbuffers::Offset<RescaleAttribute> CreateRescaleAttribute( - flatbuffers::FlatBufferBuilder &_fbb, - int32_t input_zp = 0, - int32_t output_zp = 0, - flatbuffers::Offset<flatbuffers::Vector<int32_t>> multiplier = 0, - flatbuffers::Offset<flatbuffers::Vector<int32_t>> shift = 0, - bool scale32 = false, - bool double_round = false, - bool per_channel = false) { - RescaleAttributeBuilder builder_(_fbb); - builder_.add_shift(shift); - builder_.add_multiplier(multiplier); - builder_.add_output_zp(output_zp); - builder_.add_input_zp(input_zp); - builder_.add_per_channel(per_channel); - builder_.add_double_round(double_round); - builder_.add_scale32(scale32); - return builder_.Finish(); -} - -inline flatbuffers::Offset<RescaleAttribute> CreateRescaleAttributeDirect( - flatbuffers::FlatBufferBuilder &_fbb, - int32_t input_zp = 0, - int32_t output_zp = 0, - const std::vector<int32_t> *multiplier = nullptr, - const std::vector<int32_t> *shift = nullptr, - bool scale32 = false, - bool double_round = false, - bool per_channel = false) { - auto multiplier__ = multiplier ? _fbb.CreateVector<int32_t>(*multiplier) : 0; - auto shift__ = shift ? _fbb.CreateVector<int32_t>(*shift) : 0; - return tosa::CreateRescaleAttribute( - _fbb, - input_zp, - output_zp, - multiplier__, - shift__, - scale32, - double_round, - per_channel); -} - -struct MulAttribute FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_SHIFT = 4 - }; - int32_t shift() const { - return GetField<int32_t>(VT_SHIFT, 0); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField<int32_t>(verifier, VT_SHIFT) && - verifier.EndTable(); - } -}; - -struct MulAttributeBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_shift(int32_t shift) { - fbb_.AddElement<int32_t>(MulAttribute::VT_SHIFT, shift, 0); - } - explicit MulAttributeBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - MulAttributeBuilder &operator=(const MulAttributeBuilder &); - flatbuffers::Offset<MulAttribute> Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<MulAttribute>(end); - return o; - } -}; - -inline flatbuffers::Offset<MulAttribute> CreateMulAttribute( - flatbuffers::FlatBufferBuilder &_fbb, - int32_t shift = 0) { - MulAttributeBuilder builder_(_fbb); - builder_.add_shift(shift); - return builder_.Finish(); -} - -struct ArithmeticRightShiftAttribute FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_ROUND = 4 - }; - bool round() const { - return GetField<uint8_t>(VT_ROUND, 0) != 0; - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField<uint8_t>(verifier, VT_ROUND) && - verifier.EndTable(); - } -}; - -struct ArithmeticRightShiftAttributeBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_round(bool round) { - fbb_.AddElement<uint8_t>(ArithmeticRightShiftAttribute::VT_ROUND, static_cast<uint8_t>(round), 0); - } - explicit ArithmeticRightShiftAttributeBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - ArithmeticRightShiftAttributeBuilder &operator=(const ArithmeticRightShiftAttributeBuilder &); - flatbuffers::Offset<ArithmeticRightShiftAttribute> Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<ArithmeticRightShiftAttribute>(end); - return o; - } -}; - -inline flatbuffers::Offset<ArithmeticRightShiftAttribute> CreateArithmeticRightShiftAttribute( - flatbuffers::FlatBufferBuilder &_fbb, - bool round = false) { - ArithmeticRightShiftAttributeBuilder builder_(_fbb); - builder_.add_round(round); - return builder_.Finish(); -} - -struct CondIfAttribute FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_THEN_BRANCH = 4, - VT_ELSE_BRANCH = 6 - }; - const flatbuffers::String *then_branch() const { - return GetPointer<const flatbuffers::String *>(VT_THEN_BRANCH); - } - const flatbuffers::String *else_branch() const { - return GetPointer<const flatbuffers::String *>(VT_ELSE_BRANCH); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyOffset(verifier, VT_THEN_BRANCH) && - verifier.VerifyString(then_branch()) && - VerifyOffset(verifier, VT_ELSE_BRANCH) && - verifier.VerifyString(else_branch()) && - verifier.EndTable(); - } -}; - -struct CondIfAttributeBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_then_branch(flatbuffers::Offset<flatbuffers::String> then_branch) { - fbb_.AddOffset(CondIfAttribute::VT_THEN_BRANCH, then_branch); - } - void add_else_branch(flatbuffers::Offset<flatbuffers::String> else_branch) { - fbb_.AddOffset(CondIfAttribute::VT_ELSE_BRANCH, else_branch); - } - explicit CondIfAttributeBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - CondIfAttributeBuilder &operator=(const CondIfAttributeBuilder &); - flatbuffers::Offset<CondIfAttribute> Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<CondIfAttribute>(end); - return o; - } -}; - -inline flatbuffers::Offset<CondIfAttribute> CreateCondIfAttribute( - flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset<flatbuffers::String> then_branch = 0, - flatbuffers::Offset<flatbuffers::String> else_branch = 0) { - CondIfAttributeBuilder builder_(_fbb); - builder_.add_else_branch(else_branch); - builder_.add_then_branch(then_branch); - return builder_.Finish(); -} - -inline flatbuffers::Offset<CondIfAttribute> CreateCondIfAttributeDirect( - flatbuffers::FlatBufferBuilder &_fbb, - const char *then_branch = nullptr, - const char *else_branch = nullptr) { - auto then_branch__ = then_branch ? _fbb.CreateString(then_branch) : 0; - auto else_branch__ = else_branch ? _fbb.CreateString(else_branch) : 0; - return tosa::CreateCondIfAttribute( - _fbb, - then_branch__, - else_branch__); -} - -struct WhileLoopAttribute FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_COND_BRANCH = 4, - VT_BODY_BRANCH = 6 - }; - const flatbuffers::String *cond_branch() const { - return GetPointer<const flatbuffers::String *>(VT_COND_BRANCH); - } - const flatbuffers::String *body_branch() const { - return GetPointer<const flatbuffers::String *>(VT_BODY_BRANCH); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyOffset(verifier, VT_COND_BRANCH) && - verifier.VerifyString(cond_branch()) && - VerifyOffset(verifier, VT_BODY_BRANCH) && - verifier.VerifyString(body_branch()) && - verifier.EndTable(); - } -}; - -struct WhileLoopAttributeBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_cond_branch(flatbuffers::Offset<flatbuffers::String> cond_branch) { - fbb_.AddOffset(WhileLoopAttribute::VT_COND_BRANCH, cond_branch); - } - void add_body_branch(flatbuffers::Offset<flatbuffers::String> body_branch) { - fbb_.AddOffset(WhileLoopAttribute::VT_BODY_BRANCH, body_branch); - } - explicit WhileLoopAttributeBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - WhileLoopAttributeBuilder &operator=(const WhileLoopAttributeBuilder &); - flatbuffers::Offset<WhileLoopAttribute> Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<WhileLoopAttribute>(end); - return o; - } -}; - -inline flatbuffers::Offset<WhileLoopAttribute> CreateWhileLoopAttribute( - flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset<flatbuffers::String> cond_branch = 0, - flatbuffers::Offset<flatbuffers::String> body_branch = 0) { - WhileLoopAttributeBuilder builder_(_fbb); - builder_.add_body_branch(body_branch); - builder_.add_cond_branch(cond_branch); - return builder_.Finish(); -} - -inline flatbuffers::Offset<WhileLoopAttribute> CreateWhileLoopAttributeDirect( - flatbuffers::FlatBufferBuilder &_fbb, - const char *cond_branch = nullptr, - const char *body_branch = nullptr) { - auto cond_branch__ = cond_branch ? _fbb.CreateString(cond_branch) : 0; - auto body_branch__ = body_branch ? _fbb.CreateString(body_branch) : 0; - return tosa::CreateWhileLoopAttribute( - _fbb, - cond_branch__, - body_branch__); -} - -struct UnaryQuantInfo FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_INPUT_ZP = 4, - VT_OUTPUT_ZP = 6 - }; - int32_t input_zp() const { - return GetField<int32_t>(VT_INPUT_ZP, 0); - } - int32_t output_zp() const { - return GetField<int32_t>(VT_OUTPUT_ZP, 0); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField<int32_t>(verifier, VT_INPUT_ZP) && - VerifyField<int32_t>(verifier, VT_OUTPUT_ZP) && - verifier.EndTable(); - } -}; - -struct UnaryQuantInfoBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_input_zp(int32_t input_zp) { - fbb_.AddElement<int32_t>(UnaryQuantInfo::VT_INPUT_ZP, input_zp, 0); - } - void add_output_zp(int32_t output_zp) { - fbb_.AddElement<int32_t>(UnaryQuantInfo::VT_OUTPUT_ZP, output_zp, 0); - } - explicit UnaryQuantInfoBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - UnaryQuantInfoBuilder &operator=(const UnaryQuantInfoBuilder &); - flatbuffers::Offset<UnaryQuantInfo> Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<UnaryQuantInfo>(end); - return o; - } -}; - -inline flatbuffers::Offset<UnaryQuantInfo> CreateUnaryQuantInfo( - flatbuffers::FlatBufferBuilder &_fbb, - int32_t input_zp = 0, - int32_t output_zp = 0) { - UnaryQuantInfoBuilder builder_(_fbb); - builder_.add_output_zp(output_zp); - builder_.add_input_zp(input_zp); - return builder_.Finish(); -} - -struct ConvQuantInfo FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_INPUT_ZP = 4, - VT_WEIGHT_ZP = 6 - }; - int32_t input_zp() const { - return GetField<int32_t>(VT_INPUT_ZP, 0); - } - int32_t weight_zp() const { - return GetField<int32_t>(VT_WEIGHT_ZP, 0); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField<int32_t>(verifier, VT_INPUT_ZP) && - VerifyField<int32_t>(verifier, VT_WEIGHT_ZP) && - verifier.EndTable(); - } -}; - -struct ConvQuantInfoBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_input_zp(int32_t input_zp) { - fbb_.AddElement<int32_t>(ConvQuantInfo::VT_INPUT_ZP, input_zp, 0); - } - void add_weight_zp(int32_t weight_zp) { - fbb_.AddElement<int32_t>(ConvQuantInfo::VT_WEIGHT_ZP, weight_zp, 0); - } - explicit ConvQuantInfoBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - ConvQuantInfoBuilder &operator=(const ConvQuantInfoBuilder &); - flatbuffers::Offset<ConvQuantInfo> Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<ConvQuantInfo>(end); - return o; - } -}; - -inline flatbuffers::Offset<ConvQuantInfo> CreateConvQuantInfo( - flatbuffers::FlatBufferBuilder &_fbb, - int32_t input_zp = 0, - int32_t weight_zp = 0) { - ConvQuantInfoBuilder builder_(_fbb); - builder_.add_weight_zp(weight_zp); - builder_.add_input_zp(input_zp); - return builder_.Finish(); -} - -struct MatMulQuantInfo FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_A_ZP = 4, - VT_B_ZP = 6 - }; - int32_t a_zp() const { - return GetField<int32_t>(VT_A_ZP, 0); - } - int32_t b_zp() const { - return GetField<int32_t>(VT_B_ZP, 0); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField<int32_t>(verifier, VT_A_ZP) && - VerifyField<int32_t>(verifier, VT_B_ZP) && - verifier.EndTable(); - } -}; - -struct MatMulQuantInfoBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_a_zp(int32_t a_zp) { - fbb_.AddElement<int32_t>(MatMulQuantInfo::VT_A_ZP, a_zp, 0); - } - void add_b_zp(int32_t b_zp) { - fbb_.AddElement<int32_t>(MatMulQuantInfo::VT_B_ZP, b_zp, 0); - } - explicit MatMulQuantInfoBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - MatMulQuantInfoBuilder &operator=(const MatMulQuantInfoBuilder &); - flatbuffers::Offset<MatMulQuantInfo> Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<MatMulQuantInfo>(end); - return o; - } -}; - -inline flatbuffers::Offset<MatMulQuantInfo> CreateMatMulQuantInfo( - flatbuffers::FlatBufferBuilder &_fbb, - int32_t a_zp = 0, - int32_t b_zp = 0) { - MatMulQuantInfoBuilder builder_(_fbb); - builder_.add_b_zp(b_zp); - builder_.add_a_zp(a_zp); - return builder_.Finish(); -} - -struct PadQuantInfo FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_INPUT_ZP = 4 - }; - int32_t input_zp() const { - return GetField<int32_t>(VT_INPUT_ZP, 0); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField<int32_t>(verifier, VT_INPUT_ZP) && - verifier.EndTable(); - } -}; - -struct PadQuantInfoBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_input_zp(int32_t input_zp) { - fbb_.AddElement<int32_t>(PadQuantInfo::VT_INPUT_ZP, input_zp, 0); - } - explicit PadQuantInfoBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - PadQuantInfoBuilder &operator=(const PadQuantInfoBuilder &); - flatbuffers::Offset<PadQuantInfo> Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<PadQuantInfo>(end); - return o; - } -}; - -inline flatbuffers::Offset<PadQuantInfo> CreatePadQuantInfo( - flatbuffers::FlatBufferBuilder &_fbb, - int32_t input_zp = 0) { - PadQuantInfoBuilder builder_(_fbb); - builder_.add_input_zp(input_zp); - return builder_.Finish(); -} - -struct Version FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT__MAJOR = 4, - VT__MINOR = 6, - VT__PATCH = 8, - VT__EXPERIMENTAL = 10 - }; - int32_t _major() const { - return GetField<int32_t>(VT__MAJOR, 0); - } - int32_t _minor() const { - return GetField<int32_t>(VT__MINOR, 21); - } - int32_t _patch() const { - return GetField<int32_t>(VT__PATCH, 0); - } - bool _experimental() const { - return GetField<uint8_t>(VT__EXPERIMENTAL, 0) != 0; - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField<int32_t>(verifier, VT__MAJOR) && - VerifyField<int32_t>(verifier, VT__MINOR) && - VerifyField<int32_t>(verifier, VT__PATCH) && - VerifyField<uint8_t>(verifier, VT__EXPERIMENTAL) && - verifier.EndTable(); - } -}; - -struct VersionBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add__major(int32_t _major) { - fbb_.AddElement<int32_t>(Version::VT__MAJOR, _major, 0); - } - void add__minor(int32_t _minor) { - fbb_.AddElement<int32_t>(Version::VT__MINOR, _minor, 21); - } - void add__patch(int32_t _patch) { - fbb_.AddElement<int32_t>(Version::VT__PATCH, _patch, 0); - } - void add__experimental(bool _experimental) { - fbb_.AddElement<uint8_t>(Version::VT__EXPERIMENTAL, static_cast<uint8_t>(_experimental), 0); - } - explicit VersionBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - VersionBuilder &operator=(const VersionBuilder &); - flatbuffers::Offset<Version> Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<Version>(end); - return o; - } -}; - -inline flatbuffers::Offset<Version> CreateVersion( - flatbuffers::FlatBufferBuilder &_fbb, - int32_t _major = 0, - int32_t _minor = 21, - int32_t _patch = 0, - bool _experimental = false) { - VersionBuilder builder_(_fbb); - builder_.add__patch(_patch); - builder_.add__minor(_minor); - builder_.add__major(_major); - builder_.add__experimental(_experimental); - return builder_.Finish(); -} - -struct TosaTensor FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_NAME = 4, - VT_SHAPE = 6, - VT_TYPE = 8, - VT_USAGE = 10, - VT_FORMAT = 12, - VT_NPY_FILENAME = 14 - }; - const flatbuffers::String *name() const { - return GetPointer<const flatbuffers::String *>(VT_NAME); - } - const flatbuffers::Vector<int32_t> *shape() const { - return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_SHAPE); - } - DType type() const { - return static_cast<DType>(GetField<uint32_t>(VT_TYPE, 0)); - } - const flatbuffers::Vector<uint32_t> *usage() const { - return GetPointer<const flatbuffers::Vector<uint32_t> *>(VT_USAGE); - } - const flatbuffers::Vector<uint32_t> *format() const { - return GetPointer<const flatbuffers::Vector<uint32_t> *>(VT_FORMAT); - } - const flatbuffers::String *npy_filename() const { - return GetPointer<const flatbuffers::String *>(VT_NPY_FILENAME); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyOffset(verifier, VT_NAME) && - verifier.VerifyString(name()) && - VerifyOffset(verifier, VT_SHAPE) && - verifier.VerifyVector(shape()) && - VerifyField<uint32_t>(verifier, VT_TYPE) && - VerifyOffset(verifier, VT_USAGE) && - verifier.VerifyVector(usage()) && - VerifyOffset(verifier, VT_FORMAT) && - verifier.VerifyVector(format()) && - VerifyOffset(verifier, VT_NPY_FILENAME) && - verifier.VerifyString(npy_filename()) && - verifier.EndTable(); - } -}; - -struct TosaTensorBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_name(flatbuffers::Offset<flatbuffers::String> name) { - fbb_.AddOffset(TosaTensor::VT_NAME, name); - } - void add_shape(flatbuffers::Offset<flatbuffers::Vector<int32_t>> shape) { - fbb_.AddOffset(TosaTensor::VT_SHAPE, shape); - } - void add_type(DType type) { - fbb_.AddElement<uint32_t>(TosaTensor::VT_TYPE, static_cast<uint32_t>(type), 0); - } - void add_usage(flatbuffers::Offset<flatbuffers::Vector<uint32_t>> usage) { - fbb_.AddOffset(TosaTensor::VT_USAGE, usage); - } - void add_format(flatbuffers::Offset<flatbuffers::Vector<uint32_t>> format) { - fbb_.AddOffset(TosaTensor::VT_FORMAT, format); - } - void add_npy_filename(flatbuffers::Offset<flatbuffers::String> npy_filename) { - fbb_.AddOffset(TosaTensor::VT_NPY_FILENAME, npy_filename); - } - explicit TosaTensorBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - TosaTensorBuilder &operator=(const TosaTensorBuilder &); - flatbuffers::Offset<TosaTensor> Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<TosaTensor>(end); - return o; - } -}; - -inline flatbuffers::Offset<TosaTensor> CreateTosaTensor( - flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset<flatbuffers::String> name = 0, - flatbuffers::Offset<flatbuffers::Vector<int32_t>> shape = 0, - DType type = DType_UNKNOWN, - flatbuffers::Offset<flatbuffers::Vector<uint32_t>> usage = 0, - flatbuffers::Offset<flatbuffers::Vector<uint32_t>> format = 0, - flatbuffers::Offset<flatbuffers::String> npy_filename = 0) { - TosaTensorBuilder builder_(_fbb); - builder_.add_npy_filename(npy_filename); - builder_.add_format(format); - builder_.add_usage(usage); - builder_.add_type(type); - builder_.add_shape(shape); - builder_.add_name(name); - return builder_.Finish(); -} - -inline flatbuffers::Offset<TosaTensor> CreateTosaTensorDirect( - flatbuffers::FlatBufferBuilder &_fbb, - const char *name = nullptr, - const std::vector<int32_t> *shape = nullptr, - DType type = DType_UNKNOWN, - const std::vector<uint32_t> *usage = nullptr, - const std::vector<uint32_t> *format = nullptr, - const char *npy_filename = nullptr) { - auto name__ = name ? _fbb.CreateString(name) : 0; - auto shape__ = shape ? _fbb.CreateVector<int32_t>(*shape) : 0; - auto usage__ = usage ? _fbb.CreateVector<uint32_t>(*usage) : 0; - auto format__ = format ? _fbb.CreateVector<uint32_t>(*format) : 0; - auto npy_filename__ = npy_filename ? _fbb.CreateString(npy_filename) : 0; - return tosa::CreateTosaTensor( - _fbb, - name__, - shape__, - type, - usage__, - format__, - npy_filename__); -} - -struct TosaOperator FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_OP = 4, - VT_ATTRIBUTE_TYPE = 6, - VT_ATTRIBUTE = 8, - VT_INPUTS = 10, - VT_OUTPUTS = 12, - VT_QUANT_INFO_TYPE = 14, - VT_QUANT_INFO = 16 - }; - Op op() const { - return static_cast<Op>(GetField<uint32_t>(VT_OP, 0)); - } - Attribute attribute_type() const { - return static_cast<Attribute>(GetField<uint8_t>(VT_ATTRIBUTE_TYPE, 0)); - } - const void *attribute() const { - return GetPointer<const void *>(VT_ATTRIBUTE); - } - template<typename T> const T *attribute_as() const; - const Pool2dAttribute *attribute_as_Pool2dAttribute() const { - return attribute_type() == Attribute_Pool2dAttribute ? static_cast<const Pool2dAttribute *>(attribute()) : nullptr; - } - const Conv2dAttribute *attribute_as_Conv2dAttribute() const { - return attribute_type() == Attribute_Conv2dAttribute ? static_cast<const Conv2dAttribute *>(attribute()) : nullptr; - } - const TransposeConv2dAttribute *attribute_as_TransposeConv2dAttribute() const { - return attribute_type() == Attribute_TransposeConv2dAttribute ? static_cast<const TransposeConv2dAttribute *>(attribute()) : nullptr; - } - const ReluNAttribute *attribute_as_ReluNAttribute() const { - return attribute_type() == Attribute_ReluNAttribute ? static_cast<const ReluNAttribute *>(attribute()) : nullptr; - } - const AxisAttribute *attribute_as_AxisAttribute() const { - return attribute_type() == Attribute_AxisAttribute ? static_cast<const AxisAttribute *>(attribute()) : nullptr; - } - const ReshapeAttribute *attribute_as_ReshapeAttribute() const { - return attribute_type() == Attribute_ReshapeAttribute ? static_cast<const ReshapeAttribute *>(attribute()) : nullptr; - } - const SliceAttribute *attribute_as_SliceAttribute() const { - return attribute_type() == Attribute_SliceAttribute ? static_cast<const SliceAttribute *>(attribute()) : nullptr; - } - const TileAttribute *attribute_as_TileAttribute() const { - return attribute_type() == Attribute_TileAttribute ? static_cast<const TileAttribute *>(attribute()) : nullptr; - } - const ResizeAttribute *attribute_as_ResizeAttribute() const { - return attribute_type() == Attribute_ResizeAttribute ? static_cast<const ResizeAttribute *>(attribute()) : nullptr; - } - const ClampAttribute *attribute_as_ClampAttribute() const { - return attribute_type() == Attribute_ClampAttribute ? static_cast<const ClampAttribute *>(attribute()) : nullptr; - } - const RescaleAttribute *attribute_as_RescaleAttribute() const { - return attribute_type() == Attribute_RescaleAttribute ? static_cast<const RescaleAttribute *>(attribute()) : nullptr; - } - const MulAttribute *attribute_as_MulAttribute() const { - return attribute_type() == Attribute_MulAttribute ? static_cast<const MulAttribute *>(attribute()) : nullptr; - } - const ArithmeticRightShiftAttribute *attribute_as_ArithmeticRightShiftAttribute() const { - return attribute_type() == Attribute_ArithmeticRightShiftAttribute ? static_cast<const ArithmeticRightShiftAttribute *>(attribute()) : nullptr; - } - const CondIfAttribute *attribute_as_CondIfAttribute() const { - return attribute_type() == Attribute_CondIfAttribute ? static_cast<const CondIfAttribute *>(attribute()) : nullptr; - } - const WhileLoopAttribute *attribute_as_WhileLoopAttribute() const { - return attribute_type() == Attribute_WhileLoopAttribute ? static_cast<const WhileLoopAttribute *>(attribute()) : nullptr; - } - const flatbuffers::Vector<flatbuffers::Offset<flatbuffers::String>> *inputs() const { - return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<flatbuffers::String>> *>(VT_INPUTS); - } - const flatbuffers::Vector<flatbuffers::Offset<flatbuffers::String>> *outputs() const { - return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<flatbuffers::String>> *>(VT_OUTPUTS); - } - QuantInfo quant_info_type() const { - return static_cast<QuantInfo>(GetField<uint8_t>(VT_QUANT_INFO_TYPE, 0)); - } - const void *quant_info() const { - return GetPointer<const void *>(VT_QUANT_INFO); - } - template<typename T> const T *quant_info_as() const; - const UnaryQuantInfo *quant_info_as_UnaryQuantInfo() const { - return quant_info_type() == QuantInfo_UnaryQuantInfo ? static_cast<const UnaryQuantInfo *>(quant_info()) : nullptr; - } - const ConvQuantInfo *quant_info_as_ConvQuantInfo() const { - return quant_info_type() == QuantInfo_ConvQuantInfo ? static_cast<const ConvQuantInfo *>(quant_info()) : nullptr; - } - const MatMulQuantInfo *quant_info_as_MatMulQuantInfo() const { - return quant_info_type() == QuantInfo_MatMulQuantInfo ? static_cast<const MatMulQuantInfo *>(quant_info()) : nullptr; - } - const PadQuantInfo *quant_info_as_PadQuantInfo() const { - return quant_info_type() == QuantInfo_PadQuantInfo ? static_cast<const PadQuantInfo *>(quant_info()) : nullptr; - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField<uint32_t>(verifier, VT_OP) && - VerifyField<uint8_t>(verifier, VT_ATTRIBUTE_TYPE) && - VerifyOffset(verifier, VT_ATTRIBUTE) && - VerifyAttribute(verifier, attribute(), attribute_type()) && - VerifyOffset(verifier, VT_INPUTS) && - verifier.VerifyVector(inputs()) && - verifier.VerifyVectorOfStrings(inputs()) && - VerifyOffset(verifier, VT_OUTPUTS) && - verifier.VerifyVector(outputs()) && - verifier.VerifyVectorOfStrings(outputs()) && - VerifyField<uint8_t>(verifier, VT_QUANT_INFO_TYPE) && - VerifyOffset(verifier, VT_QUANT_INFO) && - VerifyQuantInfo(verifier, quant_info(), quant_info_type()) && - verifier.EndTable(); - } -}; - -template<> inline const Pool2dAttribute *TosaOperator::attribute_as<Pool2dAttribute>() const { - return attribute_as_Pool2dAttribute(); -} - -template<> inline const Conv2dAttribute *TosaOperator::attribute_as<Conv2dAttribute>() const { - return attribute_as_Conv2dAttribute(); -} - -template<> inline const TransposeConv2dAttribute *TosaOperator::attribute_as<TransposeConv2dAttribute>() const { - return attribute_as_TransposeConv2dAttribute(); -} - -template<> inline const ReluNAttribute *TosaOperator::attribute_as<ReluNAttribute>() const { - return attribute_as_ReluNAttribute(); -} - -template<> inline const AxisAttribute *TosaOperator::attribute_as<AxisAttribute>() const { - return attribute_as_AxisAttribute(); -} - -template<> inline const ReshapeAttribute *TosaOperator::attribute_as<ReshapeAttribute>() const { - return attribute_as_ReshapeAttribute(); -} - -template<> inline const SliceAttribute *TosaOperator::attribute_as<SliceAttribute>() const { - return attribute_as_SliceAttribute(); -} - -template<> inline const TileAttribute *TosaOperator::attribute_as<TileAttribute>() const { - return attribute_as_TileAttribute(); -} - -template<> inline const ResizeAttribute *TosaOperator::attribute_as<ResizeAttribute>() const { - return attribute_as_ResizeAttribute(); -} - -template<> inline const ClampAttribute *TosaOperator::attribute_as<ClampAttribute>() const { - return attribute_as_ClampAttribute(); -} - -template<> inline const RescaleAttribute *TosaOperator::attribute_as<RescaleAttribute>() const { - return attribute_as_RescaleAttribute(); -} - -template<> inline const MulAttribute *TosaOperator::attribute_as<MulAttribute>() const { - return attribute_as_MulAttribute(); -} - -template<> inline const ArithmeticRightShiftAttribute *TosaOperator::attribute_as<ArithmeticRightShiftAttribute>() const { - return attribute_as_ArithmeticRightShiftAttribute(); -} - -template<> inline const CondIfAttribute *TosaOperator::attribute_as<CondIfAttribute>() const { - return attribute_as_CondIfAttribute(); -} - -template<> inline const WhileLoopAttribute *TosaOperator::attribute_as<WhileLoopAttribute>() const { - return attribute_as_WhileLoopAttribute(); -} - -template<> inline const UnaryQuantInfo *TosaOperator::quant_info_as<UnaryQuantInfo>() const { - return quant_info_as_UnaryQuantInfo(); -} - -template<> inline const ConvQuantInfo *TosaOperator::quant_info_as<ConvQuantInfo>() const { - return quant_info_as_ConvQuantInfo(); -} - -template<> inline const MatMulQuantInfo *TosaOperator::quant_info_as<MatMulQuantInfo>() const { - return quant_info_as_MatMulQuantInfo(); -} - -template<> inline const PadQuantInfo *TosaOperator::quant_info_as<PadQuantInfo>() const { - return quant_info_as_PadQuantInfo(); -} - -struct TosaOperatorBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_op(Op op) { - fbb_.AddElement<uint32_t>(TosaOperator::VT_OP, static_cast<uint32_t>(op), 0); - } - void add_attribute_type(Attribute attribute_type) { - fbb_.AddElement<uint8_t>(TosaOperator::VT_ATTRIBUTE_TYPE, static_cast<uint8_t>(attribute_type), 0); - } - void add_attribute(flatbuffers::Offset<void> attribute) { - fbb_.AddOffset(TosaOperator::VT_ATTRIBUTE, attribute); - } - void add_inputs(flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<flatbuffers::String>>> inputs) { - fbb_.AddOffset(TosaOperator::VT_INPUTS, inputs); - } - void add_outputs(flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<flatbuffers::String>>> outputs) { - fbb_.AddOffset(TosaOperator::VT_OUTPUTS, outputs); - } - void add_quant_info_type(QuantInfo quant_info_type) { - fbb_.AddElement<uint8_t>(TosaOperator::VT_QUANT_INFO_TYPE, static_cast<uint8_t>(quant_info_type), 0); - } - void add_quant_info(flatbuffers::Offset<void> quant_info) { - fbb_.AddOffset(TosaOperator::VT_QUANT_INFO, quant_info); - } - explicit TosaOperatorBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - TosaOperatorBuilder &operator=(const TosaOperatorBuilder &); - flatbuffers::Offset<TosaOperator> Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<TosaOperator>(end); - return o; - } -}; - -inline flatbuffers::Offset<TosaOperator> CreateTosaOperator( - flatbuffers::FlatBufferBuilder &_fbb, - Op op = Op_UNKNOWN, - Attribute attribute_type = Attribute_NONE, - flatbuffers::Offset<void> attribute = 0, - flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<flatbuffers::String>>> inputs = 0, - flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<flatbuffers::String>>> outputs = 0, - QuantInfo quant_info_type = QuantInfo_NONE, - flatbuffers::Offset<void> quant_info = 0) { - TosaOperatorBuilder builder_(_fbb); - builder_.add_quant_info(quant_info); - builder_.add_outputs(outputs); - builder_.add_inputs(inputs); - builder_.add_attribute(attribute); - builder_.add_op(op); - builder_.add_quant_info_type(quant_info_type); - builder_.add_attribute_type(attribute_type); - return builder_.Finish(); -} - -inline flatbuffers::Offset<TosaOperator> CreateTosaOperatorDirect( - flatbuffers::FlatBufferBuilder &_fbb, - Op op = Op_UNKNOWN, - Attribute attribute_type = Attribute_NONE, - flatbuffers::Offset<void> attribute = 0, - const std::vector<flatbuffers::Offset<flatbuffers::String>> *inputs = nullptr, - const std::vector<flatbuffers::Offset<flatbuffers::String>> *outputs = nullptr, - QuantInfo quant_info_type = QuantInfo_NONE, - flatbuffers::Offset<void> quant_info = 0) { - auto inputs__ = inputs ? _fbb.CreateVector<flatbuffers::Offset<flatbuffers::String>>(*inputs) : 0; - auto outputs__ = outputs ? _fbb.CreateVector<flatbuffers::Offset<flatbuffers::String>>(*outputs) : 0; - return tosa::CreateTosaOperator( - _fbb, - op, - attribute_type, - attribute, - inputs__, - outputs__, - quant_info_type, - quant_info); -} - -struct TosaBasicBlock FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_NAME = 4, - VT_OPERATORS = 6, - VT_TENSORS = 8, - VT_INPUTS = 10, - VT_OUTPUTS = 12 - }; - const flatbuffers::String *name() const { - return GetPointer<const flatbuffers::String *>(VT_NAME); - } - const flatbuffers::Vector<flatbuffers::Offset<TosaOperator>> *operators() const { - return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<TosaOperator>> *>(VT_OPERATORS); - } - const flatbuffers::Vector<flatbuffers::Offset<TosaTensor>> *tensors() const { - return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<TosaTensor>> *>(VT_TENSORS); - } - const flatbuffers::Vector<flatbuffers::Offset<flatbuffers::String>> *inputs() const { - return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<flatbuffers::String>> *>(VT_INPUTS); - } - const flatbuffers::Vector<flatbuffers::Offset<flatbuffers::String>> *outputs() const { - return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<flatbuffers::String>> *>(VT_OUTPUTS); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyOffset(verifier, VT_NAME) && - verifier.VerifyString(name()) && - VerifyOffset(verifier, VT_OPERATORS) && - verifier.VerifyVector(operators()) && - verifier.VerifyVectorOfTables(operators()) && - VerifyOffset(verifier, VT_TENSORS) && - verifier.VerifyVector(tensors()) && - verifier.VerifyVectorOfTables(tensors()) && - VerifyOffset(verifier, VT_INPUTS) && - verifier.VerifyVector(inputs()) && - verifier.VerifyVectorOfStrings(inputs()) && - VerifyOffset(verifier, VT_OUTPUTS) && - verifier.VerifyVector(outputs()) && - verifier.VerifyVectorOfStrings(outputs()) && - verifier.EndTable(); - } -}; - -struct TosaBasicBlockBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_name(flatbuffers::Offset<flatbuffers::String> name) { - fbb_.AddOffset(TosaBasicBlock::VT_NAME, name); - } - void add_operators(flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<TosaOperator>>> operators) { - fbb_.AddOffset(TosaBasicBlock::VT_OPERATORS, operators); - } - void add_tensors(flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<TosaTensor>>> tensors) { - fbb_.AddOffset(TosaBasicBlock::VT_TENSORS, tensors); - } - void add_inputs(flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<flatbuffers::String>>> inputs) { - fbb_.AddOffset(TosaBasicBlock::VT_INPUTS, inputs); - } - void add_outputs(flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<flatbuffers::String>>> outputs) { - fbb_.AddOffset(TosaBasicBlock::VT_OUTPUTS, outputs); - } - explicit TosaBasicBlockBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - TosaBasicBlockBuilder &operator=(const TosaBasicBlockBuilder &); - flatbuffers::Offset<TosaBasicBlock> Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<TosaBasicBlock>(end); - return o; - } -}; - -inline flatbuffers::Offset<TosaBasicBlock> CreateTosaBasicBlock( - flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset<flatbuffers::String> name = 0, - flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<TosaOperator>>> operators = 0, - flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<TosaTensor>>> tensors = 0, - flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<flatbuffers::String>>> inputs = 0, - flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<flatbuffers::String>>> outputs = 0) { - TosaBasicBlockBuilder builder_(_fbb); - builder_.add_outputs(outputs); - builder_.add_inputs(inputs); - builder_.add_tensors(tensors); - builder_.add_operators(operators); - builder_.add_name(name); - return builder_.Finish(); -} - -inline flatbuffers::Offset<TosaBasicBlock> CreateTosaBasicBlockDirect( - flatbuffers::FlatBufferBuilder &_fbb, - const char *name = nullptr, - const std::vector<flatbuffers::Offset<TosaOperator>> *operators = nullptr, - const std::vector<flatbuffers::Offset<TosaTensor>> *tensors = nullptr, - const std::vector<flatbuffers::Offset<flatbuffers::String>> *inputs = nullptr, - const std::vector<flatbuffers::Offset<flatbuffers::String>> *outputs = nullptr) { - auto name__ = name ? _fbb.CreateString(name) : 0; - auto operators__ = operators ? _fbb.CreateVector<flatbuffers::Offset<TosaOperator>>(*operators) : 0; - auto tensors__ = tensors ? _fbb.CreateVector<flatbuffers::Offset<TosaTensor>>(*tensors) : 0; - auto inputs__ = inputs ? _fbb.CreateVector<flatbuffers::Offset<flatbuffers::String>>(*inputs) : 0; - auto outputs__ = outputs ? _fbb.CreateVector<flatbuffers::Offset<flatbuffers::String>>(*outputs) : 0; - return tosa::CreateTosaBasicBlock( - _fbb, - name__, - operators__, - tensors__, - inputs__, - outputs__); -} - -struct TosaGraph FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_VERSION = 4, - VT_BLOCKS = 6 - }; - const Version *version() const { - return GetPointer<const Version *>(VT_VERSION); - } - const flatbuffers::Vector<flatbuffers::Offset<TosaBasicBlock>> *blocks() const { - return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<TosaBasicBlock>> *>(VT_BLOCKS); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyOffset(verifier, VT_VERSION) && - verifier.VerifyTable(version()) && - VerifyOffset(verifier, VT_BLOCKS) && - verifier.VerifyVector(blocks()) && - verifier.VerifyVectorOfTables(blocks()) && - verifier.EndTable(); - } -}; - -struct TosaGraphBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_version(flatbuffers::Offset<Version> version) { - fbb_.AddOffset(TosaGraph::VT_VERSION, version); - } - void add_blocks(flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<TosaBasicBlock>>> blocks) { - fbb_.AddOffset(TosaGraph::VT_BLOCKS, blocks); - } - explicit TosaGraphBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - TosaGraphBuilder &operator=(const TosaGraphBuilder &); - flatbuffers::Offset<TosaGraph> Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<TosaGraph>(end); - return o; - } -}; - -inline flatbuffers::Offset<TosaGraph> CreateTosaGraph( - flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset<Version> version = 0, - flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<TosaBasicBlock>>> blocks = 0) { - TosaGraphBuilder builder_(_fbb); - builder_.add_blocks(blocks); - builder_.add_version(version); - return builder_.Finish(); -} - -inline flatbuffers::Offset<TosaGraph> CreateTosaGraphDirect( - flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset<Version> version = 0, - const std::vector<flatbuffers::Offset<TosaBasicBlock>> *blocks = nullptr) { - auto blocks__ = blocks ? _fbb.CreateVector<flatbuffers::Offset<TosaBasicBlock>>(*blocks) : 0; - return tosa::CreateTosaGraph( - _fbb, - version, - blocks__); -} - -inline bool VerifyAttribute(flatbuffers::Verifier &verifier, const void *obj, Attribute type) { - switch (type) { - case Attribute_NONE: { - return true; - } - case Attribute_Pool2dAttribute: { - auto ptr = reinterpret_cast<const Pool2dAttribute *>(obj); - return verifier.VerifyTable(ptr); - } - case Attribute_Conv2dAttribute: { - auto ptr = reinterpret_cast<const Conv2dAttribute *>(obj); - return verifier.VerifyTable(ptr); - } - case Attribute_TransposeConv2dAttribute: { - auto ptr = reinterpret_cast<const TransposeConv2dAttribute *>(obj); - return verifier.VerifyTable(ptr); - } - case Attribute_ReluNAttribute: { - auto ptr = reinterpret_cast<const ReluNAttribute *>(obj); - return verifier.VerifyTable(ptr); - } - case Attribute_AxisAttribute: { - auto ptr = reinterpret_cast<const AxisAttribute *>(obj); - return verifier.VerifyTable(ptr); - } - case Attribute_ReshapeAttribute: { - auto ptr = reinterpret_cast<const ReshapeAttribute *>(obj); - return verifier.VerifyTable(ptr); - } - case Attribute_SliceAttribute: { - auto ptr = reinterpret_cast<const SliceAttribute *>(obj); - return verifier.VerifyTable(ptr); - } - case Attribute_TileAttribute: { - auto ptr = reinterpret_cast<const TileAttribute *>(obj); - return verifier.VerifyTable(ptr); - } - case Attribute_ResizeAttribute: { - auto ptr = reinterpret_cast<const ResizeAttribute *>(obj); - return verifier.VerifyTable(ptr); - } - case Attribute_ClampAttribute: { - auto ptr = reinterpret_cast<const ClampAttribute *>(obj); - return verifier.VerifyTable(ptr); - } - case Attribute_RescaleAttribute: { - auto ptr = reinterpret_cast<const RescaleAttribute *>(obj); - return verifier.VerifyTable(ptr); - } - case Attribute_MulAttribute: { - auto ptr = reinterpret_cast<const MulAttribute *>(obj); - return verifier.VerifyTable(ptr); - } - case Attribute_ArithmeticRightShiftAttribute: { - auto ptr = reinterpret_cast<const ArithmeticRightShiftAttribute *>(obj); - return verifier.VerifyTable(ptr); - } - case Attribute_CondIfAttribute: { - auto ptr = reinterpret_cast<const CondIfAttribute *>(obj); - return verifier.VerifyTable(ptr); - } - case Attribute_WhileLoopAttribute: { - auto ptr = reinterpret_cast<const WhileLoopAttribute *>(obj); - return verifier.VerifyTable(ptr); - } - default: return false; - } -} - -inline bool VerifyAttributeVector(flatbuffers::Verifier &verifier, const flatbuffers::Vector<flatbuffers::Offset<void>> *values, const flatbuffers::Vector<uint8_t> *types) { - if (!values || !types) return !values && !types; - if (values->size() != types->size()) return false; - for (flatbuffers::uoffset_t i = 0; i < values->size(); ++i) { - if (!VerifyAttribute( - verifier, values->Get(i), types->GetEnum<Attribute>(i))) { - return false; - } - } - return true; -} - -inline bool VerifyQuantInfo(flatbuffers::Verifier &verifier, const void *obj, QuantInfo type) { - switch (type) { - case QuantInfo_NONE: { - return true; - } - case QuantInfo_UnaryQuantInfo: { - auto ptr = reinterpret_cast<const UnaryQuantInfo *>(obj); - return verifier.VerifyTable(ptr); - } - case QuantInfo_ConvQuantInfo: { - auto ptr = reinterpret_cast<const ConvQuantInfo *>(obj); - return verifier.VerifyTable(ptr); - } - case QuantInfo_MatMulQuantInfo: { - auto ptr = reinterpret_cast<const MatMulQuantInfo *>(obj); - return verifier.VerifyTable(ptr); - } - case QuantInfo_PadQuantInfo: { - auto ptr = reinterpret_cast<const PadQuantInfo *>(obj); - return verifier.VerifyTable(ptr); - } - default: return false; - } -} - -inline bool VerifyQuantInfoVector(flatbuffers::Verifier &verifier, const flatbuffers::Vector<flatbuffers::Offset<void>> *values, const flatbuffers::Vector<uint8_t> *types) { - if (!values || !types) return !values && !types; - if (values->size() != types->size()) return false; - for (flatbuffers::uoffset_t i = 0; i < values->size(); ++i) { - if (!VerifyQuantInfo( - verifier, values->Get(i), types->GetEnum<QuantInfo>(i))) { - return false; - } - } - return true; -} - -inline const tosa::TosaGraph *GetTosaGraph(const void *buf) { - return flatbuffers::GetRoot<tosa::TosaGraph>(buf); -} - -inline const tosa::TosaGraph *GetSizePrefixedTosaGraph(const void *buf) { - return flatbuffers::GetSizePrefixedRoot<tosa::TosaGraph>(buf); -} - -inline const char *TosaGraphIdentifier() { - return "TOSA"; -} - -inline bool TosaGraphBufferHasIdentifier(const void *buf) { - return flatbuffers::BufferHasIdentifier( - buf, TosaGraphIdentifier()); -} - -inline bool VerifyTosaGraphBuffer( - flatbuffers::Verifier &verifier) { - return verifier.VerifyBuffer<tosa::TosaGraph>(TosaGraphIdentifier()); -} - -inline bool VerifySizePrefixedTosaGraphBuffer( - flatbuffers::Verifier &verifier) { - return verifier.VerifySizePrefixedBuffer<tosa::TosaGraph>(TosaGraphIdentifier()); -} - -inline const char *TosaGraphExtension() { - return "tosa"; -} - -inline void FinishTosaGraphBuffer( - flatbuffers::FlatBufferBuilder &fbb, - flatbuffers::Offset<tosa::TosaGraph> root) { - fbb.Finish(root, TosaGraphIdentifier()); -} - -inline void FinishSizePrefixedTosaGraphBuffer( - flatbuffers::FlatBufferBuilder &fbb, - flatbuffers::Offset<tosa::TosaGraph> root) { - fbb.FinishSizePrefixed(root, TosaGraphIdentifier()); -} - -} // namespace tosa - -#endif // FLATBUFFERS_GENERATED_TOSA_TOSA_H_ diff --git a/serialization/tosa_serialization_handler.cpp b/serialization/tosa_serialization_handler.cpp deleted file mode 100644 index ad33d23..0000000 --- a/serialization/tosa_serialization_handler.cpp +++ /dev/null @@ -1,1532 +0,0 @@ - -// Copyright (c) 2020, ARM Limited. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// http://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#include "tosa_serialization_handler.h" - -#include <iostream> -using namespace tosa; - -TosaSerializationTensor::TosaSerializationTensor(const flatbuffers::String* name, - const flatbuffers::Vector<uint32_t>& usage, - const flatbuffers::Vector<int32_t>& shape, - DType dtype, - const flatbuffers::Vector<uint32_t>& format, - const flatbuffers::String* npy_filename) -{ - _dtype = dtype; - - _usage = new std::vector<Usage>(usage.size()); - for (uint32_t us : usage) - { - _usage->push_back((Usage)us); - } - assert(_usage); - - _format = new std::vector<Format>(format.size()); - for (uint32_t fm : format) - { - _format->push_back((Format)fm); - } - assert(_format); - - _shape = new std::vector<int32_t>(shape.begin(), shape.end()); - - _shape = new std::vector<int32_t>(shape.begin(), shape.end()); - assert(_shape); - - assert(name); - _name = new std::string(name->str()); - assert(_name); - - if (npy_filename) - { - _npy_filename = new std::string(npy_filename->str()); - assert(_npy_filename); - } - else - { - _npy_filename = nullptr; - } -} - -TosaSerializationTensor::TosaSerializationTensor(std::string name, - const std::vector<Usage>& usage, - const std::vector<int32_t>& shape, - DType dtype, - const std::vector<Format>& format, - const std::string* npy_filename) -{ - - _dtype = dtype; - - _usage = new std::vector<Usage>(usage); - assert(_usage); - - _format = new std::vector<Format>(format); - assert(_format); - - _shape = new std::vector<int32_t>(shape); - assert(_shape); - - _name = new std::string(name); - assert(_name); - - if (npy_filename) - { - _npy_filename = new std::string(*npy_filename); - assert(_npy_filename); - } - else - { - _npy_filename = nullptr; - } -} - -TosaSerializationTensor::TosaSerializationTensor() -{ - _dtype = DType_UNKNOWN; - - _usage = new std::vector<Usage>(); - _format = new std::vector<Format>(); - _shape = new std::vector<int32_t>(); - _name = new std::string("UNKNOWN"); - assert(_usage && _format && _shape && _name); - - _npy_filename = nullptr; -} - -TosaSerializationTensor::TosaSerializationTensor(const TosaSerializationTensor& rhs) -{ - _dtype = rhs._dtype; - - assert(rhs._usage); - _usage = new std::vector<Usage>(*rhs._usage); - assert(_usage); - - assert(rhs._format); - _format = new std::vector<Format>(*rhs._format); - assert(_format); - - assert(rhs._shape); - _shape = new std::vector<int32_t>(*rhs._shape); - assert(_shape); - - assert(rhs._name); - _name = new std::string(*rhs._name); - assert(_name); - - if (rhs._npy_filename) - { - _npy_filename = new std::string(*rhs._npy_filename); - assert(_npy_filename); - } - else - { - _npy_filename = nullptr; - } -} - -TosaSerializationTensor& TosaSerializationTensor::operator=(const TosaSerializationTensor& rhs) -{ - _dtype = rhs._dtype; - - delete _usage; - assert(rhs._usage); - _usage = new std::vector<Usage>(*rhs._usage); - assert(_usage); - - delete _format; - assert(rhs._format); - _format = new std::vector<Format>(*rhs._format); - assert(_format); - - delete _shape; - assert(rhs._shape); - _shape = new std::vector<int32_t>(*rhs._shape); - assert(_shape); - - delete _name; - assert(rhs._name); - _name = new std::string(*rhs._name); - assert(_name); - - if (_npy_filename) - delete _npy_filename; - - if (rhs._npy_filename) - { - _npy_filename = new std::string(*rhs._npy_filename); - } - else - { - _npy_filename = nullptr; - } - return *this; -} - -TosaSerializationTensor::TosaSerializationTensor(TosaSerializationTensor&& rhs) -{ - _dtype = rhs._dtype; - std::swap(_format, rhs._format); - std::swap(_usage, rhs._usage); - std::swap(_shape, rhs._shape); - std::swap(_name, rhs._name); - std::swap(_npy_filename, rhs._npy_filename); -} - -TosaSerializationTensor& TosaSerializationTensor::operator=(TosaSerializationTensor&& rhs) -{ - _dtype = rhs._dtype; - std::swap(_format, rhs._format); - std::swap(_usage, rhs._usage); - std::swap(_shape, rhs._shape); - std::swap(_name, rhs._name); - std::swap(_npy_filename, rhs._npy_filename); - return *this; -} - -TosaSerializationTensor::~TosaSerializationTensor() -{ - delete _usage; - delete _format; - delete _shape; - delete _name; - if (_npy_filename) - delete _npy_filename; -} - -TosaSerializationOperator::TosaSerializationOperator(Op op, - Attribute attribute_type, - const TosaAttributeBase* attribute, - QuantInfo qinfo_type, - const TosaQuantInfoBase* qinfo, - std::vector<std::string> input_tensor_names, - std::vector<std::string> output_tensor_names) -{ - _op = op; - _attribute_type = attribute_type; - - switch (attribute_type) - { - case Attribute_NONE: - _attribute = new TosaNoneAttribute(); - break; -#define DEF_ATTRIBUTE(NAME, ...) \ - case Attribute_##NAME##Attribute: \ - _attribute = new Tosa##NAME##Attribute(attribute); \ - break; -#include "attribute.def" -#undef DEF_ATTRIBUTE - default: - printf("TosaSerializationOperator::TosaSerializationOperator(): Attribute %s not implemented yet\n", - EnumNamesAttribute()[attribute_type]); - assert(0); - } - - _qinfo_type = qinfo_type; - switch (qinfo_type) - { - case QuantInfo_NONE: - _qinfo = new TosaNoneQuantInfo(); - break; -#define DEF_QUANTIZATION_INFO(NAME, ...) \ - case QuantInfo_##NAME##QuantInfo: \ - _qinfo = new Tosa##NAME##QuantInfo(qinfo); \ - break; -#include "quant_info.def" -#undef DEF_QUANTIZATION_INFO - default: - printf("TosaSerializationOperator::TosaSerializationOperator(): QuantInfo %s not implemented yet\n", - EnumNamesQuantInfo()[qinfo_type]); - assert(0); - } - - assert(_attribute && _qinfo); - - _input_tensor_names = new std::vector<std::string>(input_tensor_names); - _output_tensor_names = new std::vector<std::string>(output_tensor_names); - - assert(_input_tensor_names && _output_tensor_names); - - _input_tensors = new std::vector<TosaSerializationTensor*>(); - _output_tensors = new std::vector<TosaSerializationTensor*>(); - - assert(_input_tensors && _output_tensors); -} - -TosaSerializationOperator::~TosaSerializationOperator() -{ - delete _attribute; - delete _qinfo; - delete _input_tensor_names; - delete _output_tensor_names; - // TosaSerializationTensor should be free'd in TosaSerializationSerializationHandler destructor - delete _input_tensors; - delete _output_tensors; -} - -TosaSerializationBasicBlock::TosaSerializationBasicBlock(std::string name, - std::vector<TosaSerializationOperator*> operators, - std::vector<TosaSerializationTensor*> tensors, - std::vector<std::string> inputs, - std::vector<std::string> outputs) -{ - - _name = new std::string(name); - assert(_name); - - _operators = new std::vector<TosaSerializationOperator*>(operators); - assert(_operators); - - _tensors = new std::vector<TosaSerializationTensor*>(tensors); - assert(_tensors); - - _inputs = new std::vector<std::string>(inputs); - assert(_inputs); - - _outputs = new std::vector<std::string>(outputs); - assert(_outputs); -} - -TosaSerializationBasicBlock::~TosaSerializationBasicBlock() -{ - delete _name; - - // deallocate all operators - for (auto op : GetOperators()) - { - delete op; // ~TosaSerializationOperator() - } - delete _operators; - - // deallocate all tensors - for (auto ts : GetTensors()) - { - delete ts; // ~TosaSerializationTensor() - } - _tensors->clear(); - - delete _inputs; - delete _outputs; -} - -TosaSerializationHandler::TosaSerializationHandler() -{ - _schemaLoaded = false; - _builder = new flatbuffers::FlatBufferBuilder(); - _parser = new flatbuffers::Parser(); - _blocks = new std::vector<TosaSerializationBasicBlock*>(); - - assert(_builder && _parser && _blocks); - - SetTosaVersion(); -} - -TosaSerializationHandler::~TosaSerializationHandler() -{ - if (_version) - delete _version; - delete _builder; - delete _parser; - - Clear(); // deallocate all basic blocks - - delete _blocks; -} - -tosa_err_t TosaSerializationHandler::SetTosaVersion() -{ - // version is specified within .fbs - // and it's encoded as defaulted value of CreateTosaVersion() - // need to write out one object to read that value out - // TODO: very costly now. is there any better way to encode constant in .fbs? - auto fboffset_version = CreateVersion(*_builder); - auto fboffset_tosa_graph = CreateTosaGraphDirect(*_builder, fboffset_version, nullptr); - _builder->Finish(fboffset_tosa_graph); - std::string jsongen; - uint8_t* buf = _builder->GetBufferPointer(); - auto fb_tosa_graph = GetTosaGraph(buf); - auto fb_tosa_version = fb_tosa_graph->version(); - - _version = new TosaVersion(fb_tosa_version->_major(), fb_tosa_version->_minor(), fb_tosa_version->_patch(), - fb_tosa_version->_experimental()); - - assert(_version); - return TOSA_OK; -} - -tosa_err_t TosaSerializationHandler::LoadFileSchema(const char* schema_filename) -{ - std::string schema; - bool ok; - - ok = flatbuffers::LoadFile(schema_filename, false, &schema); - if (!ok) - { - printf("Error loading schema file: %s\n", schema_filename); - return TOSA_FILE_ERROR; - } - - ok = _parser->Parse(schema.c_str()); - if (!ok) - { - printf("Error parsing ISA schema file: %s\n", schema_filename); - return TOSA_FILE_ERROR; - } - _schemaLoaded = true; - - return TOSA_OK; -} - -tosa_err_t TosaSerializationHandler::LoadFileJson(const char* filename) -{ - std::string jsonfile; - bool ok; - tosa_err_t err; - - if (!_schemaLoaded) - { - return TOSA_SCHEMA_MISSING; - } - - ok = flatbuffers::LoadFile(filename, false, &jsonfile); - if (!ok) - { - printf("Error loading json file: %s\n", filename); - return TOSA_FILE_ERROR; - } - - ok = _parser->Parse(jsonfile.c_str()); - if (!ok) - { - printf("Error parsing json file: %s\n", filename); - return TOSA_FILE_ERROR; - } - - uint8_t* buf = _parser->builder_.GetBufferPointer(); - - err = InitWithBuf(buf); - if (err != TOSA_OK) - { - return err; - } - - return TOSA_OK; -} - -tosa_err_t TosaSerializationHandler::SaveFileJson(const char* filename) -{ - std::string jsongen; - tosa_err_t err; - - if (!_schemaLoaded) - { - return TOSA_SCHEMA_MISSING; - } - - err = FreezeBuilder(); - if (err != TOSA_OK) - { - return err; - } - - uint8_t* buf = _builder->GetBufferPointer(); - - if (!GenerateText(*_parser, buf, &jsongen)) - { - printf("Couldn't serialize parsed data to JSON!\n"); - return TOSA_FILE_ERROR; - } - - FILE* file = fopen(filename, "wb"); - - if (!file) - { - printf("Couldn't open output file: %s\n", filename); - return TOSA_FILE_ERROR; - } - - if (fwrite(jsongen.c_str(), sizeof(char), jsongen.size(), file) != jsongen.size()) - { - printf("Error writing to json output file: %s\n", filename); - fclose(file); - return TOSA_FILE_ERROR; - } - - if (file) - fclose(file); - - return TOSA_OK; -} - -tosa_err_t TosaSerializationHandler::LoadFileTosaFlatbuffer(const char* filename) -{ - std::string read_buffer; - tosa_err_t err; - uint8_t* buf; - bool ok; - - ok = flatbuffers::LoadFile(filename, false, &read_buffer); - if (!ok) - { - printf("Error loading flatbuffer file: %s\n", filename); - return TOSA_FILE_ERROR; - } - - buf = (uint8_t*)read_buffer.data(); - - err = InitWithBuf(buf); - if (err != TOSA_OK) - { - return err; - } - - return TOSA_OK; -} - -tosa_err_t TosaSerializationHandler::SaveFileTosaFlatbuffer(const char* filename) -{ - tosa_err_t err; - - err = FreezeBuilder(); - if (err != TOSA_OK) - { - return err; - } - - uint8_t* buf = _builder->GetBufferPointer(); - - bool ok = flatbuffers::SaveFile(filename, (const char*)buf, _builder->GetSize(), false); - if (!ok) - { - printf("Error saving floatbuffer file: %s\n", filename); - return TOSA_FILE_ERROR; - } - - return TOSA_OK; -} - -tosa_err_t TosaSerializationHandler::Clear() -{ - // deallocate all basic blocks - for (auto bb : GetBlocks()) - { - delete bb; - } - _blocks->clear(); - - return TOSA_OK; -} - -tosa_err_t TosaSerializationHandler::CheckTosaVersion(const TosaVersion& read_version) -{ - if ((*_version) != read_version) - { - printf("WARNING: read tosa version: %s != schema tosa version %s\n", read_version.to_string().c_str(), - this->_version->to_string().c_str()); - return TOSA_VERSION_MISMATCH; - } - - return TOSA_OK; -} - -tosa_err_t TosaSerializationHandler::InitWithBuf(const uint8_t* buf) -{ - auto fb_tosa_graph = GetTosaGraph(buf); - auto fb_tosa_version = fb_tosa_graph->version(); - auto fb_tosa_blocks = fb_tosa_graph->blocks(); - - std::vector<std::string> operator_inputs_container; - std::vector<std::string> operator_outputs_container; - - std::vector<TosaSerializationOperator*> block_operators_container; - std::vector<TosaSerializationTensor*> block_tensors_container; - std::vector<std::string> block_inputs_container; - std::vector<std::string> block_outputs_container; - - TosaAttributeBase* typed_attribute = NULL; - TosaQuantInfoBase* typed_qinfo = NULL; - TosaSerializationOperator* new_operator = NULL; - TosaSerializationBasicBlock* new_block = NULL; - TosaSerializationTensor* new_tensor = NULL; - - // erase container - Clear(); - - TosaVersion read_version(fb_tosa_version->_major(), fb_tosa_version->_minor(), fb_tosa_version->_patch(), - fb_tosa_version->_experimental()); - tosa_err_t err = CheckTosaVersion(read_version); - - if (err != TOSA_OK) - return err; - - for (size_t i = 0; i < fb_tosa_blocks->size(); i++) - { - auto curr_block = fb_tosa_blocks->Get(i); - - auto block_name = curr_block->name()->str(); - - auto fb_tosa_operators = curr_block->operators(); - block_operators_container.clear(); - for (size_t j = 0; j < fb_tosa_operators->size(); j++) - { - auto curr_operator = fb_tosa_operators->Get(j); - - auto operator_op = curr_operator->op(); - auto attribute_type = curr_operator->attribute_type(); - auto attribute = curr_operator->attribute(); - auto operator_qinfo_type = curr_operator->quant_info_type(); - auto operator_qinfo = curr_operator->quant_info(); - - // input tensors - auto operator_inputs = curr_operator->inputs(); - operator_inputs_container.clear(); - if (operator_inputs) - { - for (size_t k = 0; k < operator_inputs->size(); k++) - { - auto curr_input = operator_inputs->Get(k); - operator_inputs_container.push_back(curr_input->str()); - } - } - - // output tensors - auto operator_outputs = curr_operator->outputs(); - operator_outputs_container.clear(); - if (operator_outputs) - { - for (size_t k = 0; k < operator_outputs->size(); k++) - { - auto curr_output = operator_outputs->Get(k); - operator_outputs_container.push_back(curr_output->str()); - } - } - - switch (attribute_type) - { - case Attribute_NONE: - typed_attribute = new TosaNoneAttribute(); - break; -#define DEF_ATTRIBUTE(NAME, ...) \ - case Attribute_##NAME##Attribute: \ - typed_attribute = new Tosa##NAME##Attribute(attribute); \ - break; -#include "attribute.def" -#undef DEF_ATTRIBUTE - default: - printf("TosaSerializationHandler::InitWithBuf(): Attribute %s not implemented yet\n", - EnumNamesAttribute()[attribute_type]); - return TOSA_INTERNAL_ERROR; - } - - switch (operator_qinfo_type) - { - case QuantInfo_NONE: - typed_qinfo = new TosaNoneQuantInfo(); - break; -#define DEF_QUANTIZATION_INFO(NAME, ...) \ - case QuantInfo_##NAME##QuantInfo: \ - typed_qinfo = new Tosa##NAME##QuantInfo(operator_qinfo); \ - break; - -#include "quant_info.def" -#undef DEF_QUANTIZATION_INFO - default: - printf("TosaSerializationHandler::InitWithBuf(): QuantInfo %s not implemented yet\n", - EnumNamesQuantInfo()[operator_qinfo_type]); - return TOSA_INTERNAL_ERROR; - } - - new_operator = - new TosaSerializationOperator(operator_op, attribute_type, typed_attribute, operator_qinfo_type, - typed_qinfo, operator_inputs_container, operator_outputs_container); - if (new_operator) - { - block_operators_container.push_back(new_operator); - } - else - { - return TOSA_MEMORY_ERROR; - } - - if (typed_attribute) - delete typed_attribute; - if (typed_qinfo) - delete typed_qinfo; - } - - auto fb_tosa_tensors = curr_block->tensors(); - block_tensors_container.clear(); - for (size_t j = 0; j < fb_tosa_tensors->size(); j++) - { - auto curr_tensor = fb_tosa_tensors->Get(j); - - auto tensor_name = curr_tensor->name(); - auto tensor_usage = curr_tensor->usage(); - auto tensor_shape = curr_tensor->shape(); - auto tensor_type = curr_tensor->type(); - auto tensor_format = curr_tensor->format(); - auto tensor_npy_filename = curr_tensor->npy_filename(); - - new_tensor = new TosaSerializationTensor(tensor_name, *tensor_usage, *tensor_shape, tensor_type, - *tensor_format, tensor_npy_filename); - if (new_tensor) - { - block_tensors_container.push_back(new_tensor); - } - else - { - return TOSA_MEMORY_ERROR; - } - } - - auto block_inputs = curr_block->inputs(); - auto block_outputs = curr_block->outputs(); - - block_inputs_container.clear(); - block_outputs_container.clear(); - - for (size_t j = 0; j < block_inputs->size(); j++) - { - auto curr_block_input = block_inputs->Get(j); - block_inputs_container.push_back(curr_block_input->str()); - } - for (size_t j = 0; j < block_outputs->size(); j++) - { - auto curr_block_output = block_outputs->Get(j); - block_outputs_container.push_back(curr_block_output->str()); - } - - new_block = new TosaSerializationBasicBlock(block_name, block_operators_container, block_tensors_container, - block_inputs_container, block_outputs_container); - if (new_block) - { - this->GetBlocks().push_back(new_block); - } - else - { - return TOSA_MEMORY_ERROR; - } - } - - return TOSA_OK; -} - -tosa_err_t TosaSerializationHandler::FreezeBuilder() -{ - std::vector<flatbuffers::Offset<TosaBasicBlock>> fboffset_blocks; - - std::vector<flatbuffers::Offset<TosaOperator>> fboffset_block_operators; - std::vector<flatbuffers::Offset<TosaTensor>> fboffset_block_tensors; - std::vector<flatbuffers::Offset<flatbuffers::String>> fboffset_block_inputs; - std::vector<flatbuffers::Offset<flatbuffers::String>> fboffset_block_outputs; - - std::vector<flatbuffers::Offset<flatbuffers::String>> fboffset_operator_inputs; - std::vector<flatbuffers::Offset<flatbuffers::String>> fboffset_operator_outputs; - - // translate TosaFlatbufferOperator to flatbuffers::Offset<TosaOperator> - for (auto block : GetBlocks()) - { - fboffset_block_operators.clear(); - fboffset_block_tensors.clear(); - fboffset_block_inputs.clear(); - fboffset_block_outputs.clear(); - - auto block_name = _builder->CreateString(block->GetName().c_str()); - - for (auto tensor_str : block->GetInputs()) - { - auto tensor_name = _builder->CreateString(tensor_str.c_str()); - fboffset_block_inputs.push_back(tensor_name); - } - - for (auto tensor_str : block->GetOutputs()) - { - auto tensor_name = _builder->CreateString(tensor_str.c_str()); - fboffset_block_outputs.push_back(tensor_name); - } - - auto fb_block_inputs = _builder->CreateVector(fboffset_block_inputs); - auto fb_block_outputs = _builder->CreateVector(fboffset_block_outputs); - - for (auto op : block->GetOperators()) - { - fboffset_operator_inputs.clear(); - fboffset_operator_outputs.clear(); - - auto operator_op = op->GetOp(); - auto attribute_type = op->GetAttributeType(); - - for (auto tensor_str : op->GetInputTensorNames()) - { - auto tensor_name = _builder->CreateString(tensor_str.c_str()); - fboffset_operator_inputs.push_back(tensor_name); - } - - for (auto tensor_str : op->GetOutputTensorNames()) - { - auto tensor_name = _builder->CreateString(tensor_str.c_str()); - fboffset_operator_outputs.push_back(tensor_name); - } - - auto fb_operator_inputs = _builder->CreateVector(fboffset_operator_inputs); - auto fb_operator_outputs = _builder->CreateVector(fboffset_operator_outputs); - - flatbuffers::Offset<void> fb_attribute; - switch (attribute_type) - { - case Attribute_NONE: - fb_attribute = 0; - break; - -#define DEF_ARGS_S_STR(NAME, V) , _builder->CreateString(reinterpret_cast<Tosa##NAME*>(op->GetAttribute())->V().c_str()) -#define DEF_ARGS_S_DEFAULT(NAME, V) , reinterpret_cast<Tosa##NAME*>(op->GetAttribute())->V() - -#define DEF_ARGS_S_int32_t(NAME, V) DEF_ARGS_S_DEFAULT(NAME, V) -#define DEF_ARGS_S_float(NAME, V) DEF_ARGS_S_DEFAULT(NAME, V) -#define DEF_ARGS_S_bool(NAME, V) DEF_ARGS_S_DEFAULT(NAME, V) -#define DEF_ARGS_S_ResizeMode(NAME, V) DEF_ARGS_S_DEFAULT(NAME, V) -#define DEF_ARGS_S_string(NAME, V) DEF_ARGS_S_STR(NAME, V) - -#define DEF_ARGS_S(NAME, T, V) DEF_ARGS_S_##T(NAME, V) -#define DEF_ARGS_V(NAME, T, V) , _builder->CreateVector<T>(reinterpret_cast<Tosa##NAME*>(op->GetAttribute())->V()) - -#define DEF_ARGS_1(NAME, T0, F0, V0) DEF_ARGS_##F0(NAME, T0, V0) -#define DEF_ARGS_2(NAME, T0, F0, V0, T1, F1, V1) DEF_ARGS_##F0(NAME, T0, V0) DEF_ARGS_##F1(NAME, T1, V1) -#define DEF_ARGS_3(NAME, T0, F0, V0, T1, F1, V1, T2, F2, V2) \ - DEF_ARGS_##F0(NAME, T0, V0) DEF_ARGS_##F1(NAME, T1, V1) DEF_ARGS_##F2(NAME, T2, V2) -#define DEF_ARGS_4(NAME, T0, F0, V0, T1, F1, V1, T2, F2, V2, T3, F3, V3) \ - DEF_ARGS_##F0(NAME, T0, V0) DEF_ARGS_##F1(NAME, T1, V1) DEF_ARGS_##F2(NAME, T2, V2) DEF_ARGS_##F3(NAME, T3, V3) -#define DEF_ARGS_5(NAME, T0, F0, V0, T1, F1, V1, T2, F2, V2, T3, F3, V3, T4, F4, V4) \ - DEF_ARGS_##F0(NAME, T0, V0) DEF_ARGS_##F1(NAME, T1, V1) DEF_ARGS_##F2(NAME, T2, V2) DEF_ARGS_##F3(NAME, T3, V3) \ - DEF_ARGS_##F4(NAME, T4, V4) -#define DEF_ARGS_6(NAME, T0, F0, V0, T1, F1, V1, T2, F2, V2, T3, F3, V3, T4, F4, V4, T5, F5, V5) \ - DEF_ARGS_##F0(NAME, T0, V0) DEF_ARGS_##F1(NAME, T1, V1) DEF_ARGS_##F2(NAME, T2, V2) DEF_ARGS_##F3(NAME, T3, V3) \ - DEF_ARGS_##F4(NAME, T4, V4) DEF_ARGS_##F5(NAME, T5, V5) -#define DEF_ARGS_7(NAME, T0, F0, V0, T1, F1, V1, T2, F2, V2, T3, F3, V3, T4, F4, V4, T5, F5, V5, T6, F6, V6) \ - DEF_ARGS_##F0(NAME, T0, V0) DEF_ARGS_##F1(NAME, T1, V1) DEF_ARGS_##F2(NAME, T2, V2) DEF_ARGS_##F3(NAME, T3, V3) \ - DEF_ARGS_##F4(NAME, T4, V4) DEF_ARGS_##F5(NAME, T5, V5) DEF_ARGS_##F6(NAME, T6, V6) -#define DEF_ATTRIBUTE(NAME, NUM_ARGS, ...) \ - case Attribute_##NAME##Attribute: \ - fb_attribute = Create##NAME##Attribute(*_builder DEF_ARGS_##NUM_ARGS(NAME##Attribute, __VA_ARGS__)).Union(); \ - break; - -#include "attribute.def" -#undef DEF_ATTRIBUTE -#undef DEF_ARGS_1 -#undef DEF_ARGS_2 -#undef DEF_ARGS_3 -#undef DEF_ARGS_4 -#undef DEF_ARGS_5 -#undef DEF_ARGS_6 -#undef DEF_ARGS_7 -#undef DEF_ARGS_S -#undef DEF_ARGS_V -#undef DEF_ARGS_S_int32_t -#undef DEF_ARGS_S_float -#undef DEF_ARGS_S_bool -#undef DEF_ARGS_S_ResizeMode -#undef DEF_ARGS_S_string -#undef DEF_ARGS_S_STR -#undef DEF_ARGS_S_DEFAULT - default: - printf("TosaSerializationHandler::FreezeBuilder(): Attribute %s not implemented yet\n", - EnumNamesAttribute()[attribute_type]); - return TOSA_INTERNAL_ERROR; - } - - auto qinfo_type = op->GetQInfoType(); - flatbuffers::Offset<void> fb_operator_qinfo; - switch (qinfo_type) - { - case QuantInfo_NONE: - fb_operator_qinfo = 0; - break; -#define DEF_ARGS_S(NAME, T, V) , reinterpret_cast<Tosa##NAME*>(op->GetQInfo())->V() -#define DEF_ARGS_V(NAME, T, V) , _builder->CreateVector<T>(reinterpret_cast<Tosa##NAME*>(op->GetQInfo())->V()) - -#define DEF_ARGS_1(NAME, T0, F0, V0) DEF_ARGS_##F0(NAME, T0, V0) -#define DEF_ARGS_2(NAME, T0, F0, V0, T1, F1, V1) DEF_ARGS_##F0(NAME, T0, V0) DEF_ARGS_##F1(NAME, T1, V1) -#define DEF_ARGS_3(NAME, T0, F0, V0, T1, F1, V1, T2, F2, V2) \ - DEF_ARGS_##F0(NAME, T0, V0) DEF_ARGS_##F1(NAME, T1, V1) DEF_ARGS_##F2(NAME, T2, V2) -#define DEF_ARGS_4(NAME, T0, F0, V0, T1, F1, V1, T2, F2, V2, T3, F3, V3) \ - DEF_ARGS_##F0(NAME, T0, V0) DEF_ARGS_##F1(NAME, T1, V1) DEF_ARGS_##F2(NAME, T2, V2) DEF_ARGS_##F3(NAME, T3, V3) -#define DEF_ARGS_5(NAME, T0, F0, V0, T1, F1, V1, T2, F2, V2, T3, F3, V3, T4, F4, V4) \ - DEF_ARGS_##F0(NAME, T0, V0) DEF_ARGS_##F1(NAME, T1, V1) DEF_ARGS_##F2(NAME, T2, V2) DEF_ARGS_##F3(NAME, T3, V3) \ - DEF_ARGS_##F4(NAME, T4, V4) -#define DEF_ARGS_6(NAME, T0, F0, V0, T1, F1, V1, T2, F2, V2, T3, F3, V3, T4, F4, V4, T5, F5, V5) \ - DEF_ARGS_##F0(NAME, T0, V0) DEF_ARGS_##F1(NAME, T1, V1) DEF_ARGS_##F2(NAME, T2, V2) DEF_ARGS_##F3(NAME, T3, V3) \ - DEF_ARGS_##F4(NAME, T4, V4) DEF_ARGS_##F5(NAME, T5, V5) -#define DEF_ARGS_7(NAME, T0, F0, V0, T1, F1, V1, T2, F2, V2, T3, F3, V3, T4, F4, V4, T5, F5, V5, T6, F6, V6) \ - DEF_ARGS_##F0(NAME, T0, V0) DEF_ARGS_##F1(NAME, T1, V1) DEF_ARGS_##F2(NAME, T2, V2) DEF_ARGS_##F3(NAME, T3, V3) \ - DEF_ARGS_##F4(NAME, T4, V4) DEF_ARGS_##F5(NAME, T5, V5) DEF_ARGS_##F6(NAME, T6, V6) -#define DEF_ARGS_8(NAME, T0, F0, V0, T1, F1, V1, T2, F2, V2, T3, F3, V3, T4, F4, V4, T5, F5, V5, T6, F6, V6, T7, F7, \ - V7) \ - DEF_ARGS_##F0(NAME, T0, V0) DEF_ARGS_##F1(NAME, T1, V1) DEF_ARGS_##F2(NAME, T2, V2) DEF_ARGS_##F3(NAME, T3, V3) \ - DEF_ARGS_##F4(NAME, T4, V4) DEF_ARGS_##F5(NAME, T5, V5) DEF_ARGS_##F6(NAME, T6, V6) \ - DEF_ARGS_##F7(NAME, T7, V7) -#define DEF_ARGS_9(NAME, T0, F0, V0, T1, F1, V1, T2, F2, V2, T3, F3, V3, T4, F4, V4, T5, F5, V5, T6, F6, V6, T7, F7, \ - V7, T8, F8, V8) \ - DEF_ARGS_##F0(NAME, T0, V0) DEF_ARGS_##F1(NAME, T1, V1) DEF_ARGS_##F2(NAME, T2, V2) DEF_ARGS_##F3(NAME, T3, V3) \ - DEF_ARGS_##F4(NAME, T4, V4) DEF_ARGS_##F5(NAME, T5, V5) DEF_ARGS_##F6(NAME, T6, V6) \ - DEF_ARGS_##F7(NAME, T7, V7) DEF_ARGS_##F8(NAME, T8, V8) -#define DEF_ARGS_10(NAME, T0, F0, V0, T1, F1, V1, T2, F2, V2, T3, F3, V3, T4, F4, V4, T5, F5, V5, T6, F6, V6, T7, F7, \ - V7, T8, F8, V8, T9, F9, V9) \ - DEF_ARGS_##F0(NAME, T0, V0) DEF_ARGS_##F1(NAME, T1, V1) DEF_ARGS_##F2(NAME, T2, V2) DEF_ARGS_##F3(NAME, T3, V3) \ - DEF_ARGS_##F4(NAME, T4, V4) DEF_ARGS_##F5(NAME, T5, V5) DEF_ARGS_##F6(NAME, T6, V6) \ - DEF_ARGS_##F7(NAME, T7, V7) DEF_ARGS_##F8(NAME, T8, V8) DEF_ARGS_##F9(NAME, T9, V9) -#define DEF_QUANTIZATION_INFO(NAME, NUM_ARGS, ...) \ - case QuantInfo_##NAME##QuantInfo: \ - fb_operator_qinfo = \ - Create##NAME##QuantInfo(*_builder DEF_ARGS_##NUM_ARGS(NAME##QuantInfo, __VA_ARGS__)).Union(); \ - break; - -#include "quant_info.def" -#undef DEF_QUANTIZATION_INFO -#undef DEF_ARGS_1 -#undef DEF_ARGS_2 -#undef DEF_ARGS_3 -#undef DEF_ARGS_4 -#undef DEF_ARGS_5 -#undef DEF_ARGS_6 -#undef DEF_ARGS_7 -#undef DEF_ARGS_8 -#undef DEF_ARGS_9 -#undef DEF_ARGS_10 -#undef DEF_ARGS_S -#undef DEF_ARGS_V - default: - printf("TosaSerializationHandler::FreezeBuilder(): Attribute %s not implemented yet\n", - EnumNamesAttribute()[attribute_type]); - return TOSA_INTERNAL_ERROR; - } - - auto fboffset_operator = - CreateTosaOperator(*_builder, operator_op, attribute_type, fb_attribute, fb_operator_inputs, - fb_operator_outputs, qinfo_type, fb_operator_qinfo); - fboffset_block_operators.push_back(fboffset_operator); - } - - auto fb_block_operators = _builder->CreateVector(fboffset_block_operators); - - for (auto tensor : block->GetTensors()) - { - - auto tensor_name = _builder->CreateString(tensor->GetName().c_str()); - auto tensor_usage = - _builder->CreateVector(std::vector<uint32_t>(tensor->GetUsage().begin(), tensor->GetUsage().end())); - auto tensor_shape = _builder->CreateVector(tensor->GetShape()); - auto tensor_dtype = tensor->GetDtype(); - auto tensor_format = - _builder->CreateVector(std::vector<uint32_t>(tensor->GetFormat().begin(), tensor->GetFormat().end())); - flatbuffers::Offset<flatbuffers::String> tensor_npy_filename = 0; - if (tensor->GetNpyFilePtr()) - tensor_npy_filename = _builder->CreateString(tensor->GetNpyFilePtr()->c_str()); - - auto fboffset_tensor = CreateTosaTensor(*_builder, tensor_name, tensor_shape, tensor_dtype, tensor_usage, - tensor_format, tensor_npy_filename); - fboffset_block_tensors.push_back(fboffset_tensor); - } - - auto fb_block_tensors = _builder->CreateVector(fboffset_block_tensors); - - auto fboffset_block = CreateTosaBasicBlock(*_builder, block_name, fb_block_operators, fb_block_tensors, - fb_block_inputs, fb_block_outputs); - fboffset_blocks.push_back(fboffset_block); - } - - auto fb_blocks = _builder->CreateVector(fboffset_blocks); - - auto fb_version = CreateVersion(*_builder, GetTosaVersion()->_major, GetTosaVersion()->_minor, - GetTosaVersion()->_patch, GetTosaVersion()->_experimental); - - auto fb_graph = CreateTosaGraph(*_builder, fb_version, fb_blocks); - _builder->Finish(fb_graph); - - return TOSA_OK; -} - -// Magic NUMPY header -static const char NUMPY_HEADER_STR[] = "\x93NUMPY\x1\x0\x76\x0{"; -static const int NUMPY_HEADER_SZ = 128; - -NumpyUtilities::NPError NumpyUtilities::readFromNpyFile(const char* filename, const uint32_t elems, bool* databuf) -{ - const char dtype_str[] = "'|b1'"; - FILE* infile = nullptr; - NPError rc = NO_ERROR; - - assert(filename); - assert(databuf); - - infile = fopen(filename, "rb"); - if (!infile) - { - rc = FILE_NOT_FOUND; - goto done; - } - - rc = checkNpyHeader(infile, elems, dtype_str); - if (rc != NO_ERROR) - { - goto done; - } - - // Read in the data from numpy byte array to native bool - // array format - for (uint32_t i = 0; i < elems; i++) - { - int val = fgetc(infile); - - if (val == EOF) - { - rc = FILE_IO_ERROR; - goto done; - } - - databuf[i] = val; - } - -done: - - if (infile) - fclose(infile); - - return rc; -} - -NumpyUtilities::NPError NumpyUtilities::readFromNpyFile(const char* filename, const uint32_t elems, int32_t* databuf) -{ - const char dtype_str[] = "'<i4'"; - FILE* infile = nullptr; - NPError rc = NO_ERROR; - - assert(filename); - assert(databuf); - - infile = fopen(filename, "rb"); - if (!infile) - { - rc = FILE_NOT_FOUND; - goto done; - } - - rc = checkNpyHeader(infile, elems, dtype_str); - if (rc != NO_ERROR) - { - goto done; - } - - // Now we are at the beginning of the data - // Parse based on the datatype and number of dimensions - if (fread(databuf, sizeof(int32_t), elems, infile) != elems) - { - rc = FILE_IO_ERROR; - goto done; - } - -done: - - if (infile) - fclose(infile); - - return rc; -} - -NumpyUtilities::NPError NumpyUtilities::readFromNpyFile(const char* filename, const uint32_t elems, int64_t* databuf) -{ - const char dtype_str[] = "'<i8'"; - FILE* infile = nullptr; - NPError rc = NO_ERROR; - - assert(filename); - assert(databuf); - - infile = fopen(filename, "rb"); - if (!infile) - { - rc = FILE_NOT_FOUND; - goto done; - } - - rc = checkNpyHeader(infile, elems, dtype_str); - if (rc != NO_ERROR) - { - goto done; - } - - // Now we are at the beginning of the data - // Parse based on the datatype and number of dimensions - if (fread(databuf, sizeof(int64_t), elems, infile) != elems) - { - rc = FILE_IO_ERROR; - goto done; - } - -done: - - if (infile) - fclose(infile); - - return rc; -} - -NumpyUtilities::NPError NumpyUtilities::readFromNpyFile(const char* filename, const uint32_t elems, float* databuf) -{ - const char dtype_str[] = "'<f4'"; - FILE* infile = nullptr; - NPError rc = NO_ERROR; - - assert(filename); - assert(databuf); - - infile = fopen(filename, "rb"); - if (!infile) - { - rc = FILE_NOT_FOUND; - goto done; - } - - rc = checkNpyHeader(infile, elems, dtype_str); - if (rc != NO_ERROR) - { - goto done; - } - - // Now we are at the beginning of the data - // Parse based on the datatype and number of dimensions - if (fread(databuf, sizeof(float), elems, infile) != elems) - { - rc = FILE_IO_ERROR; - goto done; - } - -done: - - if (infile) - fclose(infile); - - return rc; -} - -NumpyUtilities::NPError NumpyUtilities::checkNpyHeader(FILE* infile, const uint32_t elems, const char* dtype_str) -{ - char buf[NUMPY_HEADER_SZ + 1]; - char* ptr = nullptr; - NPError rc = NO_ERROR; - bool foundFormat = false; - bool foundOrder = false; - bool foundShape = false; - bool fortranOrder = false; - std::vector<int> shape; - uint32_t totalElems = 1; - char* outer_end = NULL; - - assert(infile); - assert(elems > 0); - - if (fread(buf, NUMPY_HEADER_SZ, 1, infile) != 1) - { - rc = HEADER_PARSE_ERROR; - goto done; - } - - if (memcmp(buf, NUMPY_HEADER_STR, sizeof(NUMPY_HEADER_STR) - 1)) - { - rc = HEADER_PARSE_ERROR; - goto done; - } - - ptr = strtok_r(buf + sizeof(NUMPY_HEADER_STR) - 1, ":", &outer_end); - - // Read in the data type, order, and shape - while (ptr && (!foundFormat || !foundOrder || !foundShape)) - { - - // End of string? - if (!ptr) - break; - - // Skip whitespace - while (isspace(*ptr)) - ptr++; - - // Parse the dictionary field name - if (!strcmp(ptr, "'descr'")) - { - ptr = strtok_r(NULL, ",", &outer_end); - if (!ptr) - break; - - while (isspace(*ptr)) - ptr++; - - if (strcmp(ptr, dtype_str)) - { - rc = FILE_TYPE_MISMATCH; - goto done; - } - - foundFormat = true; - } - else if (!strcmp(ptr, "'fortran_order'")) - { - ptr = strtok_r(NULL, ",", &outer_end); - if (!ptr) - break; - - while (isspace(*ptr)) - ptr++; - - if (!strcmp(ptr, "False")) - { - fortranOrder = false; - } - else - { - rc = FILE_TYPE_MISMATCH; - goto done; - } - - foundOrder = true; - } - else if (!strcmp(ptr, "'shape'")) - { - - ptr = strtok_r(NULL, "(", &outer_end); - if (!ptr) - break; - ptr = strtok_r(NULL, ")", &outer_end); - if (!ptr) - break; - - while (isspace(*ptr)) - ptr++; - - // The shape contains N comma-separated integers. Read up to 4. - char* end = NULL; - - ptr = strtok_r(ptr, ",", &end); - for (int i = 0; i < 4; i++) - { - // Out of dimensions - if (!ptr) - break; - - int dim = atoi(ptr); - - // Dimension is 0 - if (dim == 0) - break; - - shape.push_back(dim); - totalElems *= dim; - ptr = strtok_r(NULL, ",", &end); - } - - foundShape = true; - } - else - { - rc = HEADER_PARSE_ERROR; - goto done; - } - - if (!ptr) - break; - - ptr = strtok_r(NULL, ":", &outer_end); - } - - if (!foundShape || !foundFormat || !foundOrder) - { - rc = HEADER_PARSE_ERROR; - goto done; - } - - // Validate header - if (fortranOrder != false) - { - rc = FILE_TYPE_MISMATCH; - goto done; - } - - if (totalElems != elems) - { - rc = BUFFER_SIZE_MISMATCH; - goto done; - } - - // Go back to the begininng and read until the end of the header dictionary - rewind(infile); - int val; - - do - { - val = fgetc(infile); - } while (val != EOF && val != '\n'); - -done: - - return rc; -} - -NumpyUtilities::NPError NumpyUtilities::writeToNpyFile(const char* filename, const uint32_t elems, const bool* databuf) -{ - std::vector<int32_t> shape = { (int32_t)elems }; - return writeToNpyFile(filename, shape, databuf); -} - -NumpyUtilities::NPError - NumpyUtilities::writeToNpyFile(const char* filename, const std::vector<int32_t>& shape, const bool* databuf) -{ - const char dtype_str[] = "'|b1'"; - FILE* outfile = nullptr; - NPError rc = NO_ERROR; - uint32_t totalElems = 1; - - assert(filename); - assert(shape.size() >= 0); - assert(databuf); - - outfile = fopen(filename, "wb"); - - if (!outfile) - { - rc = FILE_NOT_FOUND; - goto done; - } - - for (uint32_t i = 0; i < shape.size(); i++) - { - totalElems *= shape[i]; - } - - rc = writeNpyHeader(outfile, shape, dtype_str); - - // Numpy save format stores booleans as a byte array - // with one byte per boolean. This somewhat inefficiently - // remaps from system bool[] to this format. - for (uint32_t i = 0; i < totalElems; i++) - { - int val = databuf[i] ? 1 : 0; - if (fputc(val, outfile) == EOF) - { - rc = FILE_IO_ERROR; - goto done; - } - } - -done: - - if (outfile) - fclose(outfile); - - return rc; -} - -NumpyUtilities::NPError - NumpyUtilities::writeToNpyFile(const char* filename, const uint32_t elems, const int32_t* databuf) -{ - std::vector<int32_t> shape = { (int32_t)elems }; - return writeToNpyFile(filename, shape, databuf); -} - -NumpyUtilities::NPError - NumpyUtilities::writeToNpyFile(const char* filename, const std::vector<int32_t>& shape, const int32_t* databuf) -{ - const char dtype_str[] = "'<i4'"; - FILE* outfile = nullptr; - NPError rc = NO_ERROR; - uint32_t totalElems = 1; - - assert(filename); - assert(shape.size() >= 0); - assert(databuf); - - outfile = fopen(filename, "wb"); - - if (!outfile) - { - rc = FILE_NOT_FOUND; - goto done; - } - - for (uint32_t i = 0; i < shape.size(); i++) - { - totalElems *= shape[i]; - } - - rc = writeNpyHeader(outfile, shape, dtype_str); - - if (fwrite(databuf, sizeof(int32_t), totalElems, outfile) != totalElems) - { - rc = FILE_IO_ERROR; - goto done; - } - -done: - - if (outfile) - fclose(outfile); - - return rc; -} - -NumpyUtilities::NPError - NumpyUtilities::writeToNpyFile(const char* filename, const uint32_t elems, const int64_t* databuf) -{ - std::vector<int32_t> shape = { (int32_t)elems }; - return writeToNpyFile(filename, shape, databuf); -} - -NumpyUtilities::NPError - NumpyUtilities::writeToNpyFile(const char* filename, const std::vector<int32_t>& shape, const int64_t* databuf) -{ - const char dtype_str[] = "'<i8'"; - FILE* outfile = nullptr; - NPError rc = NO_ERROR; - uint32_t totalElems = 1; - - assert(filename); - assert(shape.size() >= 0); - assert(databuf); - - outfile = fopen(filename, "wb"); - - if (!outfile) - { - rc = FILE_NOT_FOUND; - goto done; - } - - for (uint32_t i = 0; i < shape.size(); i++) - { - totalElems *= shape[i]; - } - - rc = writeNpyHeader(outfile, shape, dtype_str); - - if (fwrite(databuf, sizeof(int64_t), totalElems, outfile) != totalElems) - { - rc = FILE_IO_ERROR; - goto done; - } - -done: - - if (outfile) - fclose(outfile); - - return rc; -} - -NumpyUtilities::NPError NumpyUtilities::writeToNpyFile(const char* filename, const uint32_t elems, const float* databuf) -{ - std::vector<int32_t> shape = { (int32_t)elems }; - return writeToNpyFile(filename, shape, databuf); -} - -NumpyUtilities::NPError - NumpyUtilities::writeToNpyFile(const char* filename, const std::vector<int32_t>& shape, const float* databuf) -{ - const char dtype_str[] = "'<f4'"; - FILE* outfile = nullptr; - NPError rc = NO_ERROR; - uint32_t totalElems = 1; - - assert(filename); - assert(shape.size() >= 0); - assert(databuf); - - outfile = fopen(filename, "wb"); - - if (!outfile) - { - rc = FILE_NOT_FOUND; - goto done; - } - - for (uint32_t i = 0; i < shape.size(); i++) - { - totalElems *= shape[i]; - } - - rc = writeNpyHeader(outfile, shape, dtype_str); - - if (fwrite(databuf, sizeof(float), totalElems, outfile) != totalElems) - { - rc = FILE_IO_ERROR; - goto done; - } - -done: - - if (outfile) - fclose(outfile); - - return rc; -} - -NumpyUtilities::NPError - NumpyUtilities::writeNpyHeader(FILE* outfile, const std::vector<int32_t>& shape, const char* dtype_str) -{ - NPError rc = NO_ERROR; - uint32_t i; - char header[NUMPY_HEADER_SZ + 1]; - int headerPos = 0; - - assert(outfile); - assert(shape.size() >= 0); - - // Space-fill the header and end with a newline to start per numpy spec - memset(header, 0x20, NUMPY_HEADER_SZ); - header[NUMPY_HEADER_SZ - 1] = '\n'; - header[NUMPY_HEADER_SZ] = 0; - - // Write out the hard-coded header. We only support a 128-byte 1.0 header - // for now, which should be sufficient for simple tensor types of any - // reasonable rank. - memcpy(header, NUMPY_HEADER_STR, sizeof(NUMPY_HEADER_STR) - 1); - headerPos += sizeof(NUMPY_HEADER_STR) - 1; - - // Output the format dictionary - // Hard-coded for I32 for now - headerPos += - snprintf(header + headerPos, NUMPY_HEADER_SZ - headerPos, "'descr': %s, 'fortran_order': False, 'shape': (%d,", - dtype_str, shape.size() > 0 ? shape[0] : 1); - - // Remainder of shape array - for (i = 1; i < shape.size(); i++) - { - headerPos += snprintf(header + headerPos, NUMPY_HEADER_SZ - headerPos, " %d,", shape[i]); - } - - // Close off the dictionary - headerPos += snprintf(header + headerPos, NUMPY_HEADER_SZ - headerPos, "), }"); - - // snprintf leaves a NULL at the end. Replace with a space - header[headerPos] = 0x20; - - if (fwrite(header, NUMPY_HEADER_SZ, 1, outfile) != 1) - { - rc = FILE_IO_ERROR; - goto done; - } - -done: - - return rc; -} diff --git a/serialization/tosa_serialization_handler.h b/serialization/tosa_serialization_handler.h deleted file mode 100644 index 124b8e0..0000000 --- a/serialization/tosa_serialization_handler.h +++ /dev/null @@ -1,423 +0,0 @@ - -// Copyright (c) 2020, ARM Limited. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// http://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#ifndef _TOSA_SERIALIZATION_HANDLER_H -#define _TOSA_SERIALIZATION_HANDLER_H -#include "attribute.h" -#include "flatbuffers/idl.h" -#include "flatbuffers/util.h" -#include "quant_info.h" -#include "tosa_generated.h" -#include <cstdint> -#include <memory> -#include <string> -#include <vector> - -namespace tosa -{ - -enum tosa_err_t -{ - TOSA_OK, - TOSA_USER_ERROR, - TOSA_FILE_ERROR, - TOSA_MEMORY_ERROR, - TOSA_SCHEMA_MISSING, - TOSA_INTERNAL_ERROR, - TOSA_VERSION_MISMATCH, - NUM_TOSA_ERROR -}; - -struct TosaVersion -{ - int32_t _major; - int32_t _minor; - int32_t _patch; - bool _experimental; - - TosaVersion() = delete; - TosaVersion(int32_t major, int32_t minor, int32_t patch, bool experimental) - { - _major = major; - _minor = minor; - _patch = patch; - _experimental = experimental; - } - - std::string to_string() const - { - std::string str; - str += std::to_string(_major) + "."; - str += std::to_string(_minor) + "."; - str += std::to_string(_patch); - if (_experimental) - str += "(experimental)"; - return str; - }; - - bool operator==(const TosaVersion& rhs) - { - if (rhs._major == _major && rhs._minor == _minor && rhs._patch == _patch && rhs._experimental == _experimental) - { - return true; - } - return false; - } - - bool operator!=(const TosaVersion& rhs) - { - return !((*this) == rhs); - } -}; - -class TosaSerializationHandler; - -class TosaSerializationTensor -{ -public: - // constructor and destructor - TosaSerializationTensor(const flatbuffers::String* name, - const flatbuffers::Vector<uint32_t>& usage, - const flatbuffers::Vector<int32_t>& shape, - DType dtype, - const flatbuffers::Vector<uint32_t>& format, - const flatbuffers::String* npy_filename); - TosaSerializationTensor(std::string name, - const std::vector<Usage>& usage, - const std::vector<int32_t>& shape, - DType dtype, - const std::vector<Format>& format, - const std::string* npy_filename); - TosaSerializationTensor(); - ~TosaSerializationTensor(); - - // copy constructor/assignment - TosaSerializationTensor(const TosaSerializationTensor& rhs); - TosaSerializationTensor& operator=(const TosaSerializationTensor& rhs); - - // move constructor/assignment - TosaSerializationTensor(TosaSerializationTensor&& rhs); - TosaSerializationTensor& operator=(TosaSerializationTensor&& rhs); - - // accessor - std::string GetName() const - { - return *_name; - } - const std::vector<int32_t>& GetShape() const - { - return *_shape; - } - DType GetDtype() - { - return _dtype; - } - bool HasFormat(Format format) - { - for (Format us : *_format) - { - if (us == format) - return true; - } - return false; - } - std::vector<Format>& GetFormat() - { - return *_format; - } - bool HasUsage(Usage usage) - { - for (Usage us : *_usage) - { - if (us == usage) - return true; - } - return false; - } - std::vector<Usage>& GetUsage() - { - return *_usage; - } - std::string* GetNpyFilePtr() const - { - return _npy_filename; - } - - // modifier - void SetDtype(DType dtype) - { - _dtype = dtype; - } - void SetName(std::string name) - { - *_name = name; - } - -private: - DType _dtype; /* data type enumeration, see tosa_isa_generated.h */ - std::vector<Format>* _format; /* list of possible tensor format */ - std::vector<Usage>* _usage; /* list of possible tensor usage */ - std::vector<int32_t>* _shape; /* shape of the tensor */ - std::string* _name; /* name of the tensor, used for solving dependency */ - std::string* _npy_filename; /* numpy array filename if not null. so null is the distinguisher */ -}; - -class TosaSerializationOperator -{ -public: - // use default copy, void constructor - // constructor and destructor - TosaSerializationOperator(Op op_name, - Attribute attribute_type, - const TosaAttributeBase* attribute, - QuantInfo qinfo_type, - const TosaQuantInfoBase* qinfo, - std::vector<std::string> input_tensor_names, - std::vector<std::string> output_tensor_names); - ~TosaSerializationOperator(); - - // accessor - Op GetOp() const - { - return _op; - } - Attribute GetAttributeType() const - { - return _attribute_type; - } - TosaAttributeBase* GetAttribute() const - { - return _attribute; - } - QuantInfo GetQInfoType() const - { - return _qinfo_type; - } - TosaQuantInfoBase* GetQInfo() const - { - return _qinfo; - } - std::vector<std::string>& GetInputTensorNames() const - { - return *_input_tensor_names; - } - std::vector<std::string>& GetOutputTensorNames() const - { - return *_output_tensor_names; - } - std::vector<TosaSerializationTensor*>& GetInputTensors() const - { - return *_input_tensors; - } - std::vector<TosaSerializationTensor*>& GetOutputTensors() const - { - return *_output_tensors; - } - -private: - Op _op; /* operator enum, see tosa_isa_generated.h for enumeration table */ - Attribute _attribute_type; /* operator attribute enum, used for dynamic casting TosaAttributeBase class */ - TosaAttributeBase* _attribute; /* real attribute class goes here */ - QuantInfo _qinfo_type; /* QuantInfo enum */ - TosaQuantInfoBase* _qinfo; /* base class pointer of QuantInfo */ - std::vector<std::string>* _input_tensor_names; /* array of input tensor names */ - std::vector<std::string>* _output_tensor_names; /* array of output tensor names */ - - std::vector<TosaSerializationTensor*>* _input_tensors; /* array of input TosaSerializationTensor */ - std::vector<TosaSerializationTensor*>* _output_tensors; /* array of output TosaSerializationTensor */ -}; - -class TosaSerializationBasicBlock -{ -public: - // constructor and destructor - TosaSerializationBasicBlock(std::string name, - std::vector<TosaSerializationOperator*> operators, - std::vector<TosaSerializationTensor*> tensors, - std::vector<std::string> inputs, - std::vector<std::string> outputs); - ~TosaSerializationBasicBlock(); - - // accessor - std::string GetName() const - { - return *_name; - } - std::vector<TosaSerializationOperator*>& GetOperators() - { - return *_operators; - } - std::vector<TosaSerializationTensor*>& GetTensors() - { - return *_tensors; - } - - TosaSerializationTensor* GetTensorByName(std::string name) - { - TosaSerializationTensor* result = nullptr; - for (auto tensor : GetTensors()) - { - if (tensor->GetName() == name) - { - result = tensor; - break; - } - } - return result; - } - - std::vector<std::string>& GetInputs() - { - return *_inputs; - } - std::vector<std::string>& GetOutputs() - { - return *_outputs; - } - -private: - std::string* _name; /* name of basic block */ - std::vector<TosaSerializationOperator*>* _operators; /* TosaSerializationOperator list */ - std::vector<TosaSerializationTensor*>* _tensors; /* TosaSerializationTensor list */ - std::vector<std::string>* _inputs; /* array of string to specify block inputs */ - std::vector<std::string>* _outputs; /* array of string to specify block outputs */ -}; - -/* - * this is a helper class for writing/reading Tosa ISA - * supported format: .tosa (flatbuffer), .json - * and provide high-level std::vector-like interface - * to access internal data structure - */ -class TosaSerializationHandler -{ -public: - // constructor and destructor - TosaSerializationHandler(); - ~TosaSerializationHandler(); - - // file io - tosa_err_t LoadFileJson(const char* filename); - tosa_err_t LoadFileTosaFlatbuffer(const char* filename); - tosa_err_t SaveFileJson(const char* filename); - tosa_err_t SaveFileTosaFlatbuffer(const char* filename); - tosa_err_t LoadFileSchema(const char* filename); - - // version - TosaVersion* GetTosaVersion() const - { - return _version; - } - - // accessor - std::vector<TosaSerializationBasicBlock*>& GetBlocks() - { - return *_blocks; - } - - TosaSerializationBasicBlock* GetBlockByName(std::string name) - { - TosaSerializationBasicBlock* result = nullptr; - for (auto block : GetBlocks()) - { - if (block->GetName() == name) - { - result = block; - break; - } - } - return result; - } - TosaSerializationBasicBlock* GetMainBlock() - { - TosaSerializationBasicBlock* main_block = GetBlockByName(std::string("main")); - assert(main_block); - return main_block; - } - - std::vector<std::string>& GetInputs() - { - return GetMainBlock()->GetInputs(); - } - std::vector<std::string>& GetOutputs() - { - return GetMainBlock()->GetOutputs(); - } - - bool GetSchemaLoaded() const - { - return _schemaLoaded; - } - -protected: - tosa_err_t Clear(); - tosa_err_t InitWithBuf(const uint8_t* buf); - tosa_err_t FreezeBuilder(); - tosa_err_t SetTosaVersion(); - tosa_err_t CheckTosaVersion(const TosaVersion& read_version); - -private: - TosaVersion* _version; /* tosa version */ - flatbuffers::FlatBufferBuilder* _builder; /* flatbuffer builder */ - flatbuffers::Parser* _parser; /* flatbuffer parser, used for json parsing */ - std::vector<TosaSerializationBasicBlock*>* _blocks; /* array structure to store all TosaSerializationBasicBlock */ - bool _schemaLoaded; /* is the schema properly loaded? */ -}; - -class NumpyUtilities -{ -public: - enum NPError - { - NO_ERROR = 0, - FILE_NOT_FOUND, - FILE_IO_ERROR, - FILE_TYPE_MISMATCH, - HEADER_PARSE_ERROR, - BUFFER_SIZE_MISMATCH, - }; - - static NPError readFromNpyFile(const char* filename, const uint32_t elems, float* buf); - - static NPError readFromNpyFile(const char* filename, const uint32_t elems, int32_t* buf); - - static NPError readFromNpyFile(const char* filename, const uint32_t elems, int64_t* buf); - - static NPError readFromNpyFile(const char* filename, const uint32_t elems, bool* buf); - - static NPError writeToNpyFile(const char* filename, const std::vector<int32_t>& shape, const bool* buf); - - static NPError writeToNpyFile(const char* filename, const uint32_t elems, const bool* buf); - - static NPError writeToNpyFile(const char* filename, const std::vector<int32_t>& shape, const int32_t* buf); - - static NPError writeToNpyFile(const char* filename, const uint32_t elems, const int32_t* buf); - - static NPError writeToNpyFile(const char* filename, const std::vector<int32_t>& shape, const int64_t* buf); - - static NPError writeToNpyFile(const char* filename, const uint32_t elems, const int64_t* buf); - - static NPError writeToNpyFile(const char* filename, const std::vector<int32_t>& shape, const float* buf); - - static NPError writeToNpyFile(const char* filename, const uint32_t elems, const float* buf); - -private: - static NPError checkNpyHeader(FILE* infile, const uint32_t elems, const char* dtype_str); - static NPError writeNpyHeader(FILE* infile, const std::vector<int32_t>& shape, const char* dtype_str); -}; - -} // namespace tosa - -#endif // _TOSA_SERIALIZATION_HANDLER_H diff --git a/thirdparty/CMakeLists.txt b/thirdparty/CMakeLists.txt index 8c7bee3..66b72b9 100644 --- a/thirdparty/CMakeLists.txt +++ b/thirdparty/CMakeLists.txt @@ -4,7 +4,4 @@ set(CMAKE_INSTALL_PREFIX "./thirdparty" CACHE PATH "..." FORCE) project(thirdparty LANGUAGES CXX) -# Flatbuffers tests are not needed -set(FLATBUFFERS_BUILD_TESTS OFF) - -add_subdirectory(flatbuffers) +add_subdirectory(serialization_lib) diff --git a/thirdparty/flatbuffers b/thirdparty/flatbuffers deleted file mode 160000 -Subproject bf9eb67ab9371755c6bcece13cadc7693bcbf26 diff --git a/thirdparty/serialization_lib b/thirdparty/serialization_lib new file mode 160000 +Subproject 2364dcd7241d730021bf68e000e5a6411b9f09d diff --git a/verif/tosa/ArithmeticRightShiftAttribute.py b/verif/tosa/ArithmeticRightShiftAttribute.py deleted file mode 100644 index eaa52ab..0000000 --- a/verif/tosa/ArithmeticRightShiftAttribute.py +++ /dev/null @@ -1,45 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# Copyright (c) 2020, ARM Limited. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -# namespace: tosa - -import flatbuffers - -class ArithmeticRightShiftAttribute(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsArithmeticRightShiftAttribute(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = ArithmeticRightShiftAttribute() - x.Init(buf, n + offset) - return x - - # ArithmeticRightShiftAttribute - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # ArithmeticRightShiftAttribute - def Round(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos)) - return False - -def ArithmeticRightShiftAttributeStart(builder): builder.StartObject(1) -def ArithmeticRightShiftAttributeAddRound(builder, round): builder.PrependBoolSlot(0, round, 0) -def ArithmeticRightShiftAttributeEnd(builder): return builder.EndObject() diff --git a/verif/tosa/Attribute.py b/verif/tosa/Attribute.py deleted file mode 100644 index 5d79a08..0000000 --- a/verif/tosa/Attribute.py +++ /dev/null @@ -1,37 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# Copyright (c) 2020, ARM Limited. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -# namespace: tosa - -class Attribute(object): - NONE = 0 - Pool2dAttribute = 1 - Conv2dAttribute = 2 - TransposeConv2dAttribute = 3 - ReluNAttribute = 4 - AxisAttribute = 5 - ReshapeAttribute = 6 - SliceAttribute = 7 - TileAttribute = 8 - ResizeAttribute = 9 - ClampAttribute = 10 - RescaleAttribute = 11 - MulAttribute = 12 - ArithmeticRightShiftAttribute = 13 - CondIfAttribute = 14 - WhileLoopAttribute = 15 - diff --git a/verif/tosa/AxisAttribute.py b/verif/tosa/AxisAttribute.py deleted file mode 100644 index d47eb81..0000000 --- a/verif/tosa/AxisAttribute.py +++ /dev/null @@ -1,45 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# Copyright (c) 2020, ARM Limited. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -# namespace: tosa - -import flatbuffers - -class AxisAttribute(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsAxisAttribute(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = AxisAttribute() - x.Init(buf, n + offset) - return x - - # AxisAttribute - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # AxisAttribute - def Axis(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) - return 0 - -def AxisAttributeStart(builder): builder.StartObject(1) -def AxisAttributeAddAxis(builder, axis): builder.PrependInt32Slot(0, axis, 0) -def AxisAttributeEnd(builder): return builder.EndObject() diff --git a/verif/tosa/ClampAttribute.py b/verif/tosa/ClampAttribute.py deleted file mode 100644 index ddc95cf..0000000 --- a/verif/tosa/ClampAttribute.py +++ /dev/null @@ -1,69 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# Copyright (c) 2020, ARM Limited. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -# namespace: tosa - -import flatbuffers - -class ClampAttribute(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsClampAttribute(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = ClampAttribute() - x.Init(buf, n + offset) - return x - - # ClampAttribute - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # ClampAttribute - def MinInt(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) - return 0 - - # ClampAttribute - def MaxInt(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) - return 0 - - # ClampAttribute - def MinFp(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Float32Flags, o + self._tab.Pos) - return 0.0 - - # ClampAttribute - def MaxFp(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Float32Flags, o + self._tab.Pos) - return 0.0 - -def ClampAttributeStart(builder): builder.StartObject(4) -def ClampAttributeAddMinInt(builder, minInt): builder.PrependInt32Slot(0, minInt, 0) -def ClampAttributeAddMaxInt(builder, maxInt): builder.PrependInt32Slot(1, maxInt, 0) -def ClampAttributeAddMinFp(builder, minFp): builder.PrependFloat32Slot(2, minFp, 0.0) -def ClampAttributeAddMaxFp(builder, maxFp): builder.PrependFloat32Slot(3, maxFp, 0.0) -def ClampAttributeEnd(builder): return builder.EndObject() diff --git a/verif/tosa/CondIfAttribute.py b/verif/tosa/CondIfAttribute.py deleted file mode 100644 index 0bf4566..0000000 --- a/verif/tosa/CondIfAttribute.py +++ /dev/null @@ -1,53 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# Copyright (c) 2020, ARM Limited. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -# namespace: tosa - -import flatbuffers - -class CondIfAttribute(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsCondIfAttribute(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = CondIfAttribute() - x.Init(buf, n + offset) - return x - - # CondIfAttribute - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # CondIfAttribute - def ThenBranch(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.String(o + self._tab.Pos) - return None - - # CondIfAttribute - def ElseBranch(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.String(o + self._tab.Pos) - return None - -def CondIfAttributeStart(builder): builder.StartObject(2) -def CondIfAttributeAddThenBranch(builder, thenBranch): builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(thenBranch), 0) -def CondIfAttributeAddElseBranch(builder, elseBranch): builder.PrependUOffsetTRelativeSlot(1, flatbuffers.number_types.UOffsetTFlags.py_type(elseBranch), 0) -def CondIfAttributeEnd(builder): return builder.EndObject() diff --git a/verif/tosa/Conv2dAttribute.py b/verif/tosa/Conv2dAttribute.py deleted file mode 100644 index c7861a5..0000000 --- a/verif/tosa/Conv2dAttribute.py +++ /dev/null @@ -1,109 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# Copyright (c) 2020, ARM Limited. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -# namespace: tosa - -import flatbuffers - -class Conv2dAttribute(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsConv2dAttribute(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = Conv2dAttribute() - x.Init(buf, n + offset) - return x - - # Conv2dAttribute - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # Conv2dAttribute - def Padding(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - a = self._tab.Vector(o) - return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) - return 0 - - # Conv2dAttribute - def PaddingAsNumpy(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o) - return 0 - - # Conv2dAttribute - def PaddingLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - - # Conv2dAttribute - def Stride(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - a = self._tab.Vector(o) - return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) - return 0 - - # Conv2dAttribute - def StrideAsNumpy(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o) - return 0 - - # Conv2dAttribute - def StrideLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - - # Conv2dAttribute - def Dilation(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) - if o != 0: - a = self._tab.Vector(o) - return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) - return 0 - - # Conv2dAttribute - def DilationAsNumpy(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) - if o != 0: - return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o) - return 0 - - # Conv2dAttribute - def DilationLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - -def Conv2dAttributeStart(builder): builder.StartObject(3) -def Conv2dAttributeAddPadding(builder, padding): builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(padding), 0) -def Conv2dAttributeStartPaddingVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def Conv2dAttributeAddStride(builder, stride): builder.PrependUOffsetTRelativeSlot(1, flatbuffers.number_types.UOffsetTFlags.py_type(stride), 0) -def Conv2dAttributeStartStrideVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def Conv2dAttributeAddDilation(builder, dilation): builder.PrependUOffsetTRelativeSlot(2, flatbuffers.number_types.UOffsetTFlags.py_type(dilation), 0) -def Conv2dAttributeStartDilationVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def Conv2dAttributeEnd(builder): return builder.EndObject() diff --git a/verif/tosa/ConvQuantInfo.py b/verif/tosa/ConvQuantInfo.py deleted file mode 100644 index a88bfa6..0000000 --- a/verif/tosa/ConvQuantInfo.py +++ /dev/null @@ -1,53 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# Copyright (c) 2020, ARM Limited. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -# namespace: tosa - -import flatbuffers - -class ConvQuantInfo(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsConvQuantInfo(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = ConvQuantInfo() - x.Init(buf, n + offset) - return x - - # ConvQuantInfo - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # ConvQuantInfo - def InputZp(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) - return 0 - - # ConvQuantInfo - def WeightZp(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) - return 0 - -def ConvQuantInfoStart(builder): builder.StartObject(2) -def ConvQuantInfoAddInputZp(builder, inputZp): builder.PrependInt32Slot(0, inputZp, 0) -def ConvQuantInfoAddWeightZp(builder, weightZp): builder.PrependInt32Slot(1, weightZp, 0) -def ConvQuantInfoEnd(builder): return builder.EndObject() diff --git a/verif/tosa/CustomAttribute.py b/verif/tosa/CustomAttribute.py deleted file mode 100644 index 25f6759..0000000 --- a/verif/tosa/CustomAttribute.py +++ /dev/null @@ -1,45 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# Copyright (c) 2020, ARM Limited. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -# namespace: tosa - -import flatbuffers - -class CustomAttribute(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsCustomAttribute(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = CustomAttribute() - x.Init(buf, n + offset) - return x - - # CustomAttribute - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # CustomAttribute - def Identifier(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.String(o + self._tab.Pos) - return None - -def CustomAttributeStart(builder): builder.StartObject(1) -def CustomAttributeAddIdentifier(builder, identifier): builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(identifier), 0) -def CustomAttributeEnd(builder): return builder.EndObject() diff --git a/verif/tosa/DType.py b/verif/tosa/DType.py deleted file mode 100644 index 2e30531..0000000 --- a/verif/tosa/DType.py +++ /dev/null @@ -1,30 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# Copyright (c) 2020-2021, ARM Limited. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -# namespace: tosa - -class DType(object): - UNKNOWN = 0 - BOOL = 1 - UINT8 = 2 - INT4 = 3 - INT8 = 4 - INT16 = 5 - INT32 = 6 - INT48 = 7 - FLOAT = 8 - diff --git a/verif/tosa/Format.py b/verif/tosa/Format.py deleted file mode 100644 index 5db4f27..0000000 --- a/verif/tosa/Format.py +++ /dev/null @@ -1,27 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# Copyright (c) 2020, ARM Limited. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -# namespace: tosa - -class Format(object): - UNKNOWN = 0 - NHWC = 1 - NDHWC = 2 - OHWI = 3 - HWIM = 4 - DOHWI = 5 - diff --git a/verif/tosa/MatMulQuantInfo.py b/verif/tosa/MatMulQuantInfo.py deleted file mode 100644 index b8390a9..0000000 --- a/verif/tosa/MatMulQuantInfo.py +++ /dev/null @@ -1,53 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# Copyright (c) 2020, ARM Limited. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -# namespace: tosa - -import flatbuffers - -class MatMulQuantInfo(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsMatMulQuantInfo(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = MatMulQuantInfo() - x.Init(buf, n + offset) - return x - - # MatMulQuantInfo - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # MatMulQuantInfo - def AZp(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) - return 0 - - # MatMulQuantInfo - def BZp(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) - return 0 - -def MatMulQuantInfoStart(builder): builder.StartObject(2) -def MatMulQuantInfoAddAZp(builder, aZp): builder.PrependInt32Slot(0, aZp, 0) -def MatMulQuantInfoAddBZp(builder, bZp): builder.PrependInt32Slot(1, bZp, 0) -def MatMulQuantInfoEnd(builder): return builder.EndObject() diff --git a/verif/tosa/MulAttribute.py b/verif/tosa/MulAttribute.py deleted file mode 100644 index f45b285..0000000 --- a/verif/tosa/MulAttribute.py +++ /dev/null @@ -1,45 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# Copyright (c) 2020, ARM Limited. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -# namespace: tosa - -import flatbuffers - -class MulAttribute(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsMulAttribute(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = MulAttribute() - x.Init(buf, n + offset) - return x - - # MulAttribute - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # MulAttribute - def Shift(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) - return 0 - -def MulAttributeStart(builder): builder.StartObject(1) -def MulAttributeAddShift(builder, shift): builder.PrependInt32Slot(0, shift, 0) -def MulAttributeEnd(builder): return builder.EndObject() diff --git a/verif/tosa/Op.py b/verif/tosa/Op.py deleted file mode 100644 index ea9cdfe..0000000 --- a/verif/tosa/Op.py +++ /dev/null @@ -1,91 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# Copyright (c) 2020, ARM Limited. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -# namespace: tosa - -class Op(object): - UNKNOWN = 0 - ARGMAX = 1 - AVG_POOL2D = 2 - CONV2D = 3 - CONV3D = 4 - DEPTHWISE_CONV2D = 5 - FULLY_CONNECTED = 6 - MATMUL = 7 - MAX_POOL2D = 8 - TRANSPOSE_CONV2D = 9 - CLAMP = 10 - RELUN = 11 - SIGMOID = 12 - TANH = 13 - ADD = 14 - ARITHMETIC_RIGHT_SHIFT = 15 - BITWISE_AND = 16 - BITWISE_OR = 17 - BITWISE_XOR = 18 - LOGICAL_AND = 19 - LOGICAL_LEFT_SHIFT = 20 - LOGICAL_RIGHT_SHIFT = 21 - LOGICAL_OR = 22 - LOGICAL_XOR = 23 - MAXIMUM = 24 - MINIMUM = 25 - MUL = 26 - POW = 27 - SUB = 28 - TABLE = 29 - ABS = 30 - BITWISE_NOT = 31 - CEIL = 32 - CLZ = 33 - EXP = 34 - FLOOR = 35 - LOG = 36 - LOGICAL_NOT = 37 - NEGATE = 38 - RECIPROCAL = 39 - RSQRT = 40 - SELECT = 41 - EQUAL = 42 - GREATER = 43 - GREATER_EQUAL = 44 - REDUCE_ANY = 45 - REDUCE_ALL = 46 - REDUCE_MAX = 47 - REDUCE_MIN = 48 - REDUCE_PRODUCT = 49 - REDUCE_SUM = 50 - CONCAT = 51 - PAD = 52 - RESHAPE = 53 - REVERSE = 54 - SLICE = 55 - TILE = 56 - TRANSPOSE = 57 - GATHER = 58 - SCATTER = 59 - RESIZE = 60 - CAST = 61 - RESCALE = 62 - CONST = 63 - PLACEHOLDER = 64 - IDENTITY = 65 - IDENTITYN = 66 - CUSTOM = 67 - COND_IF = 68 - WHILE_LOOP = 69 - diff --git a/verif/tosa/PadQuantInfo.py b/verif/tosa/PadQuantInfo.py deleted file mode 100644 index df61926..0000000 --- a/verif/tosa/PadQuantInfo.py +++ /dev/null @@ -1,45 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# Copyright (c) 2020, ARM Limited. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -# namespace: tosa - -import flatbuffers - -class PadQuantInfo(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsPadQuantInfo(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = PadQuantInfo() - x.Init(buf, n + offset) - return x - - # PadQuantInfo - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # PadQuantInfo - def InputZp(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) - return 0 - -def PadQuantInfoStart(builder): builder.StartObject(1) -def PadQuantInfoAddInputZp(builder, inputZp): builder.PrependInt32Slot(0, inputZp, 0) -def PadQuantInfoEnd(builder): return builder.EndObject() diff --git a/verif/tosa/Pool2dAttribute.py b/verif/tosa/Pool2dAttribute.py deleted file mode 100644 index 1520de2..0000000 --- a/verif/tosa/Pool2dAttribute.py +++ /dev/null @@ -1,109 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# Copyright (c) 2020, ARM Limited. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -# namespace: tosa - -import flatbuffers - -class Pool2dAttribute(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsPool2dAttribute(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = Pool2dAttribute() - x.Init(buf, n + offset) - return x - - # Pool2dAttribute - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # Pool2dAttribute - def Padding(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - a = self._tab.Vector(o) - return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) - return 0 - - # Pool2dAttribute - def PaddingAsNumpy(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o) - return 0 - - # Pool2dAttribute - def PaddingLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - - # Pool2dAttribute - def Kernel(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - a = self._tab.Vector(o) - return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) - return 0 - - # Pool2dAttribute - def KernelAsNumpy(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o) - return 0 - - # Pool2dAttribute - def KernelLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - - # Pool2dAttribute - def Stride(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) - if o != 0: - a = self._tab.Vector(o) - return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) - return 0 - - # Pool2dAttribute - def StrideAsNumpy(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) - if o != 0: - return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o) - return 0 - - # Pool2dAttribute - def StrideLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - -def Pool2dAttributeStart(builder): builder.StartObject(3) -def Pool2dAttributeAddPadding(builder, padding): builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(padding), 0) -def Pool2dAttributeStartPaddingVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def Pool2dAttributeAddKernel(builder, kernel): builder.PrependUOffsetTRelativeSlot(1, flatbuffers.number_types.UOffsetTFlags.py_type(kernel), 0) -def Pool2dAttributeStartKernelVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def Pool2dAttributeAddStride(builder, stride): builder.PrependUOffsetTRelativeSlot(2, flatbuffers.number_types.UOffsetTFlags.py_type(stride), 0) -def Pool2dAttributeStartStrideVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def Pool2dAttributeEnd(builder): return builder.EndObject() diff --git a/verif/tosa/QuantInfo.py b/verif/tosa/QuantInfo.py deleted file mode 100644 index 0544cce..0000000 --- a/verif/tosa/QuantInfo.py +++ /dev/null @@ -1,26 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# Copyright (c) 2020, ARM Limited. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -# namespace: tosa - -class QuantInfo(object): - NONE = 0 - UnaryQuantInfo = 1 - ConvQuantInfo = 2 - MatMulQuantInfo = 3 - PadQuantInfo = 4 - diff --git a/verif/tosa/README.md b/verif/tosa/README.md deleted file mode 100644 index de8c1f9..0000000 --- a/verif/tosa/README.md +++ /dev/null @@ -1,14 +0,0 @@ -TOSA FlatBuffers python serialization library -============================================= - -Files in this directory are automatically generated by running: - -``` bash -../build/thirdparty/flatbuffers/flatc --python ../serialization/tosa.fbs -``` - -From the ``verif/`` directory. Flatc is compiled along with the *TOSA -Reference Model*. - -*Because they are automatically generated, please do not edit the -python files in this directory by hand.* diff --git a/verif/tosa/ReluNAttribute.py b/verif/tosa/ReluNAttribute.py deleted file mode 100644 index e446c03..0000000 --- a/verif/tosa/ReluNAttribute.py +++ /dev/null @@ -1,53 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# Copyright (c) 2020, ARM Limited. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -# namespace: tosa - -import flatbuffers - -class ReluNAttribute(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsReluNAttribute(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = ReluNAttribute() - x.Init(buf, n + offset) - return x - - # ReluNAttribute - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # ReluNAttribute - def MaxInt(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) - return 0 - - # ReluNAttribute - def MaxFp(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Float32Flags, o + self._tab.Pos) - return 0.0 - -def ReluNAttributeStart(builder): builder.StartObject(2) -def ReluNAttributeAddMaxInt(builder, maxInt): builder.PrependInt32Slot(0, maxInt, 0) -def ReluNAttributeAddMaxFp(builder, maxFp): builder.PrependFloat32Slot(1, maxFp, 0.0) -def ReluNAttributeEnd(builder): return builder.EndObject() diff --git a/verif/tosa/RescaleAttribute.py b/verif/tosa/RescaleAttribute.py deleted file mode 100644 index 0ec8c2b..0000000 --- a/verif/tosa/RescaleAttribute.py +++ /dev/null @@ -1,125 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# Copyright (c) 2020, ARM Limited. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -# namespace: tosa - -import flatbuffers - -class RescaleAttribute(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsRescaleAttribute(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = RescaleAttribute() - x.Init(buf, n + offset) - return x - - # RescaleAttribute - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # RescaleAttribute - def InputZp(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) - return 0 - - # RescaleAttribute - def OutputZp(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) - return 0 - - # RescaleAttribute - def Multiplier(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) - if o != 0: - a = self._tab.Vector(o) - return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) - return 0 - - # RescaleAttribute - def MultiplierAsNumpy(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) - if o != 0: - return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o) - return 0 - - # RescaleAttribute - def MultiplierLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - - # RescaleAttribute - def Shift(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) - if o != 0: - a = self._tab.Vector(o) - return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) - return 0 - - # RescaleAttribute - def ShiftAsNumpy(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) - if o != 0: - return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o) - return 0 - - # RescaleAttribute - def ShiftLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - - # RescaleAttribute - def Scale32(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12)) - if o != 0: - return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos)) - return False - - # RescaleAttribute - def DoubleRound(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14)) - if o != 0: - return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos)) - return False - - # RescaleAttribute - def PerChannel(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(16)) - if o != 0: - return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos)) - return False - -def RescaleAttributeStart(builder): builder.StartObject(7) -def RescaleAttributeAddInputZp(builder, inputZp): builder.PrependInt32Slot(0, inputZp, 0) -def RescaleAttributeAddOutputZp(builder, outputZp): builder.PrependInt32Slot(1, outputZp, 0) -def RescaleAttributeAddMultiplier(builder, multiplier): builder.PrependUOffsetTRelativeSlot(2, flatbuffers.number_types.UOffsetTFlags.py_type(multiplier), 0) -def RescaleAttributeStartMultiplierVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def RescaleAttributeAddShift(builder, shift): builder.PrependUOffsetTRelativeSlot(3, flatbuffers.number_types.UOffsetTFlags.py_type(shift), 0) -def RescaleAttributeStartShiftVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def RescaleAttributeAddScale32(builder, scale32): builder.PrependBoolSlot(4, scale32, 0) -def RescaleAttributeAddDoubleRound(builder, doubleRound): builder.PrependBoolSlot(5, doubleRound, 0) -def RescaleAttributeAddPerChannel(builder, perChannel): builder.PrependBoolSlot(6, perChannel, 0) -def RescaleAttributeEnd(builder): return builder.EndObject() diff --git a/verif/tosa/ReshapeAttribute.py b/verif/tosa/ReshapeAttribute.py deleted file mode 100644 index 2c50cef..0000000 --- a/verif/tosa/ReshapeAttribute.py +++ /dev/null @@ -1,61 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# Copyright (c) 2020, ARM Limited. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -# namespace: tosa - -import flatbuffers - -class ReshapeAttribute(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsReshapeAttribute(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = ReshapeAttribute() - x.Init(buf, n + offset) - return x - - # ReshapeAttribute - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # ReshapeAttribute - def Shape(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - a = self._tab.Vector(o) - return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) - return 0 - - # ReshapeAttribute - def ShapeAsNumpy(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o) - return 0 - - # ReshapeAttribute - def ShapeLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - -def ReshapeAttributeStart(builder): builder.StartObject(1) -def ReshapeAttributeAddShape(builder, shape): builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(shape), 0) -def ReshapeAttributeStartShapeVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def ReshapeAttributeEnd(builder): return builder.EndObject() diff --git a/verif/tosa/ResizeAttribute.py b/verif/tosa/ResizeAttribute.py deleted file mode 100644 index 35be73a..0000000 --- a/verif/tosa/ResizeAttribute.py +++ /dev/null @@ -1,173 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# Copyright (c) 2020, ARM Limited. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -# namespace: tosa - -import flatbuffers - -class ResizeAttribute(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsResizeAttribute(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = ResizeAttribute() - x.Init(buf, n + offset) - return x - - # ResizeAttribute - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # ResizeAttribute - def OutputSize(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - a = self._tab.Vector(o) - return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) - return 0 - - # ResizeAttribute - def OutputSizeAsNumpy(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o) - return 0 - - # ResizeAttribute - def OutputSizeLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - - # ResizeAttribute - def Stride(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - a = self._tab.Vector(o) - return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) - return 0 - - # ResizeAttribute - def StrideAsNumpy(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o) - return 0 - - # ResizeAttribute - def StrideLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - - # ResizeAttribute - def Offset(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) - if o != 0: - a = self._tab.Vector(o) - return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) - return 0 - - # ResizeAttribute - def OffsetAsNumpy(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) - if o != 0: - return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o) - return 0 - - # ResizeAttribute - def OffsetLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - - # ResizeAttribute - def Shift(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) - return 0 - - # ResizeAttribute - def StrideFp(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12)) - if o != 0: - a = self._tab.Vector(o) - return self._tab.Get(flatbuffers.number_types.Float32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) - return 0 - - # ResizeAttribute - def StrideFpAsNumpy(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12)) - if o != 0: - return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Float32Flags, o) - return 0 - - # ResizeAttribute - def StrideFpLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - - # ResizeAttribute - def OffsetFp(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14)) - if o != 0: - a = self._tab.Vector(o) - return self._tab.Get(flatbuffers.number_types.Float32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) - return 0 - - # ResizeAttribute - def OffsetFpAsNumpy(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14)) - if o != 0: - return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Float32Flags, o) - return 0 - - # ResizeAttribute - def OffsetFpLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - - # ResizeAttribute - def Mode(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(16)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Uint32Flags, o + self._tab.Pos) - return 0 - -def ResizeAttributeStart(builder): builder.StartObject(7) -def ResizeAttributeAddOutputSize(builder, outputSize): builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(outputSize), 0) -def ResizeAttributeStartOutputSizeVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def ResizeAttributeAddStride(builder, stride): builder.PrependUOffsetTRelativeSlot(1, flatbuffers.number_types.UOffsetTFlags.py_type(stride), 0) -def ResizeAttributeStartStrideVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def ResizeAttributeAddOffset(builder, offset): builder.PrependUOffsetTRelativeSlot(2, flatbuffers.number_types.UOffsetTFlags.py_type(offset), 0) -def ResizeAttributeStartOffsetVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def ResizeAttributeAddShift(builder, shift): builder.PrependInt32Slot(3, shift, 0) -def ResizeAttributeAddStrideFp(builder, strideFp): builder.PrependUOffsetTRelativeSlot(4, flatbuffers.number_types.UOffsetTFlags.py_type(strideFp), 0) -def ResizeAttributeStartStrideFpVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def ResizeAttributeAddOffsetFp(builder, offsetFp): builder.PrependUOffsetTRelativeSlot(5, flatbuffers.number_types.UOffsetTFlags.py_type(offsetFp), 0) -def ResizeAttributeStartOffsetFpVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def ResizeAttributeAddMode(builder, mode): builder.PrependUint32Slot(6, mode, 0) -def ResizeAttributeEnd(builder): return builder.EndObject() diff --git a/verif/tosa/ResizeMode.py b/verif/tosa/ResizeMode.py deleted file mode 100644 index 02bed51..0000000 --- a/verif/tosa/ResizeMode.py +++ /dev/null @@ -1,24 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# Copyright (c) 2020, ARM Limited. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -# namespace: tosa - -class ResizeMode(object): - UNKNOWN = 0 - NEAREST = 1 - BILINEAR = 2 - diff --git a/verif/tosa/SliceAttribute.py b/verif/tosa/SliceAttribute.py deleted file mode 100644 index d156a4a..0000000 --- a/verif/tosa/SliceAttribute.py +++ /dev/null @@ -1,85 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# Copyright (c) 2020, ARM Limited. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -# namespace: tosa - -import flatbuffers - -class SliceAttribute(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsSliceAttribute(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = SliceAttribute() - x.Init(buf, n + offset) - return x - - # SliceAttribute - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # SliceAttribute - def Begin(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - a = self._tab.Vector(o) - return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) - return 0 - - # SliceAttribute - def BeginAsNumpy(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o) - return 0 - - # SliceAttribute - def BeginLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - - # SliceAttribute - def Size(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - a = self._tab.Vector(o) - return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) - return 0 - - # SliceAttribute - def SizeAsNumpy(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o) - return 0 - - # SliceAttribute - def SizeLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - -def SliceAttributeStart(builder): builder.StartObject(2) -def SliceAttributeAddBegin(builder, begin): builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(begin), 0) -def SliceAttributeStartBeginVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def SliceAttributeAddSize(builder, size): builder.PrependUOffsetTRelativeSlot(1, flatbuffers.number_types.UOffsetTFlags.py_type(size), 0) -def SliceAttributeStartSizeVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def SliceAttributeEnd(builder): return builder.EndObject() diff --git a/verif/tosa/TileAttribute.py b/verif/tosa/TileAttribute.py deleted file mode 100644 index 6385edd..0000000 --- a/verif/tosa/TileAttribute.py +++ /dev/null @@ -1,61 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# Copyright (c) 2020, ARM Limited. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -# namespace: tosa - -import flatbuffers - -class TileAttribute(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsTileAttribute(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = TileAttribute() - x.Init(buf, n + offset) - return x - - # TileAttribute - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # TileAttribute - def Multiples(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - a = self._tab.Vector(o) - return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) - return 0 - - # TileAttribute - def MultiplesAsNumpy(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o) - return 0 - - # TileAttribute - def MultiplesLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - -def TileAttributeStart(builder): builder.StartObject(1) -def TileAttributeAddMultiples(builder, multiples): builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(multiples), 0) -def TileAttributeStartMultiplesVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def TileAttributeEnd(builder): return builder.EndObject() diff --git a/verif/tosa/TosaBasicBlock.py b/verif/tosa/TosaBasicBlock.py deleted file mode 100644 index 42a7379..0000000 --- a/verif/tosa/TosaBasicBlock.py +++ /dev/null @@ -1,123 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# Copyright (c) 2020, ARM Limited. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -# namespace: tosa - -import flatbuffers - -class TosaBasicBlock(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsTosaBasicBlock(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = TosaBasicBlock() - x.Init(buf, n + offset) - return x - - # TosaBasicBlock - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # TosaBasicBlock - def Name(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.String(o + self._tab.Pos) - return None - - # TosaBasicBlock - def Operators(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - x = self._tab.Vector(o) - x += flatbuffers.number_types.UOffsetTFlags.py_type(j) * 4 - x = self._tab.Indirect(x) - from .TosaOperator import TosaOperator - obj = TosaOperator() - obj.Init(self._tab.Bytes, x) - return obj - return None - - # TosaBasicBlock - def OperatorsLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - - # TosaBasicBlock - def Tensors(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) - if o != 0: - x = self._tab.Vector(o) - x += flatbuffers.number_types.UOffsetTFlags.py_type(j) * 4 - x = self._tab.Indirect(x) - from .TosaTensor import TosaTensor - obj = TosaTensor() - obj.Init(self._tab.Bytes, x) - return obj - return None - - # TosaBasicBlock - def TensorsLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - - # TosaBasicBlock - def Inputs(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) - if o != 0: - a = self._tab.Vector(o) - return self._tab.String(a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) - return "" - - # TosaBasicBlock - def InputsLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - - # TosaBasicBlock - def Outputs(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12)) - if o != 0: - a = self._tab.Vector(o) - return self._tab.String(a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) - return "" - - # TosaBasicBlock - def OutputsLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - -def TosaBasicBlockStart(builder): builder.StartObject(5) -def TosaBasicBlockAddName(builder, name): builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(name), 0) -def TosaBasicBlockAddOperators(builder, operators): builder.PrependUOffsetTRelativeSlot(1, flatbuffers.number_types.UOffsetTFlags.py_type(operators), 0) -def TosaBasicBlockStartOperatorsVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def TosaBasicBlockAddTensors(builder, tensors): builder.PrependUOffsetTRelativeSlot(2, flatbuffers.number_types.UOffsetTFlags.py_type(tensors), 0) -def TosaBasicBlockStartTensorsVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def TosaBasicBlockAddInputs(builder, inputs): builder.PrependUOffsetTRelativeSlot(3, flatbuffers.number_types.UOffsetTFlags.py_type(inputs), 0) -def TosaBasicBlockStartInputsVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def TosaBasicBlockAddOutputs(builder, outputs): builder.PrependUOffsetTRelativeSlot(4, flatbuffers.number_types.UOffsetTFlags.py_type(outputs), 0) -def TosaBasicBlockStartOutputsVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def TosaBasicBlockEnd(builder): return builder.EndObject() diff --git a/verif/tosa/TosaGraph.py b/verif/tosa/TosaGraph.py deleted file mode 100644 index 92568b9..0000000 --- a/verif/tosa/TosaGraph.py +++ /dev/null @@ -1,71 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# Copyright (c) 2020, ARM Limited. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -# namespace: tosa - -import flatbuffers - -class TosaGraph(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsTosaGraph(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = TosaGraph() - x.Init(buf, n + offset) - return x - - # TosaGraph - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # TosaGraph - def Version(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - x = self._tab.Indirect(o + self._tab.Pos) - from .Version import Version - obj = Version() - obj.Init(self._tab.Bytes, x) - return obj - return None - - # TosaGraph - def Blocks(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - x = self._tab.Vector(o) - x += flatbuffers.number_types.UOffsetTFlags.py_type(j) * 4 - x = self._tab.Indirect(x) - from .TosaBasicBlock import TosaBasicBlock - obj = TosaBasicBlock() - obj.Init(self._tab.Bytes, x) - return obj - return None - - # TosaGraph - def BlocksLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - -def TosaGraphStart(builder): builder.StartObject(2) -def TosaGraphAddVersion(builder, version): builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(version), 0) -def TosaGraphAddBlocks(builder, blocks): builder.PrependUOffsetTRelativeSlot(1, flatbuffers.number_types.UOffsetTFlags.py_type(blocks), 0) -def TosaGraphStartBlocksVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def TosaGraphEnd(builder): return builder.EndObject() diff --git a/verif/tosa/TosaOperator.py b/verif/tosa/TosaOperator.py deleted file mode 100644 index ab4a160..0000000 --- a/verif/tosa/TosaOperator.py +++ /dev/null @@ -1,117 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# Copyright (c) 2020, ARM Limited. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -# namespace: tosa - -import flatbuffers - -class TosaOperator(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsTosaOperator(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = TosaOperator() - x.Init(buf, n + offset) - return x - - # TosaOperator - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # TosaOperator - def Op(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Uint32Flags, o + self._tab.Pos) - return 0 - - # TosaOperator - def AttributeType(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Uint8Flags, o + self._tab.Pos) - return 0 - - # TosaOperator - def Attribute(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) - if o != 0: - from flatbuffers.table import Table - obj = Table(bytearray(), 0) - self._tab.Union(obj, o) - return obj - return None - - # TosaOperator - def Inputs(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) - if o != 0: - a = self._tab.Vector(o) - return self._tab.String(a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) - return "" - - # TosaOperator - def InputsLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - - # TosaOperator - def Outputs(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12)) - if o != 0: - a = self._tab.Vector(o) - return self._tab.String(a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) - return "" - - # TosaOperator - def OutputsLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - - # TosaOperator - def QuantInfoType(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Uint8Flags, o + self._tab.Pos) - return 0 - - # TosaOperator - def QuantInfo(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(16)) - if o != 0: - from flatbuffers.table import Table - obj = Table(bytearray(), 0) - self._tab.Union(obj, o) - return obj - return None - -def TosaOperatorStart(builder): builder.StartObject(7) -def TosaOperatorAddOp(builder, op): builder.PrependUint32Slot(0, op, 0) -def TosaOperatorAddAttributeType(builder, attributeType): builder.PrependUint8Slot(1, attributeType, 0) -def TosaOperatorAddAttribute(builder, attribute): builder.PrependUOffsetTRelativeSlot(2, flatbuffers.number_types.UOffsetTFlags.py_type(attribute), 0) -def TosaOperatorAddInputs(builder, inputs): builder.PrependUOffsetTRelativeSlot(3, flatbuffers.number_types.UOffsetTFlags.py_type(inputs), 0) -def TosaOperatorStartInputsVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def TosaOperatorAddOutputs(builder, outputs): builder.PrependUOffsetTRelativeSlot(4, flatbuffers.number_types.UOffsetTFlags.py_type(outputs), 0) -def TosaOperatorStartOutputsVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def TosaOperatorAddQuantInfoType(builder, quantInfoType): builder.PrependUint8Slot(5, quantInfoType, 0) -def TosaOperatorAddQuantInfo(builder, quantInfo): builder.PrependUOffsetTRelativeSlot(6, flatbuffers.number_types.UOffsetTFlags.py_type(quantInfo), 0) -def TosaOperatorEnd(builder): return builder.EndObject() diff --git a/verif/tosa/TosaTensor.py b/verif/tosa/TosaTensor.py deleted file mode 100644 index 0b30266..0000000 --- a/verif/tosa/TosaTensor.py +++ /dev/null @@ -1,133 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# Copyright (c) 2020, ARM Limited. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -# namespace: tosa - -import flatbuffers - -class TosaTensor(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsTosaTensor(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = TosaTensor() - x.Init(buf, n + offset) - return x - - # TosaTensor - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # TosaTensor - def Name(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.String(o + self._tab.Pos) - return None - - # TosaTensor - def Shape(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - a = self._tab.Vector(o) - return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) - return 0 - - # TosaTensor - def ShapeAsNumpy(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o) - return 0 - - # TosaTensor - def ShapeLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - - # TosaTensor - def Type(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Uint32Flags, o + self._tab.Pos) - return 0 - - # TosaTensor - def Usage(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) - if o != 0: - a = self._tab.Vector(o) - return self._tab.Get(flatbuffers.number_types.Uint32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) - return 0 - - # TosaTensor - def UsageAsNumpy(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) - if o != 0: - return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Uint32Flags, o) - return 0 - - # TosaTensor - def UsageLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - - # TosaTensor - def Format(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12)) - if o != 0: - a = self._tab.Vector(o) - return self._tab.Get(flatbuffers.number_types.Uint32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) - return 0 - - # TosaTensor - def FormatAsNumpy(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12)) - if o != 0: - return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Uint32Flags, o) - return 0 - - # TosaTensor - def FormatLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - - # TosaTensor - def NpyFilename(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14)) - if o != 0: - return self._tab.String(o + self._tab.Pos) - return None - -def TosaTensorStart(builder): builder.StartObject(6) -def TosaTensorAddName(builder, name): builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(name), 0) -def TosaTensorAddShape(builder, shape): builder.PrependUOffsetTRelativeSlot(1, flatbuffers.number_types.UOffsetTFlags.py_type(shape), 0) -def TosaTensorStartShapeVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def TosaTensorAddType(builder, type): builder.PrependUint32Slot(2, type, 0) -def TosaTensorAddUsage(builder, usage): builder.PrependUOffsetTRelativeSlot(3, flatbuffers.number_types.UOffsetTFlags.py_type(usage), 0) -def TosaTensorStartUsageVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def TosaTensorAddFormat(builder, format): builder.PrependUOffsetTRelativeSlot(4, flatbuffers.number_types.UOffsetTFlags.py_type(format), 0) -def TosaTensorStartFormatVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def TosaTensorAddNpyFilename(builder, npyFilename): builder.PrependUOffsetTRelativeSlot(5, flatbuffers.number_types.UOffsetTFlags.py_type(npyFilename), 0) -def TosaTensorEnd(builder): return builder.EndObject() diff --git a/verif/tosa/TransposeConv2dAttribute.py b/verif/tosa/TransposeConv2dAttribute.py deleted file mode 100644 index 043d8e8..0000000 --- a/verif/tosa/TransposeConv2dAttribute.py +++ /dev/null @@ -1,133 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# Copyright (c) 2020, ARM Limited. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -# namespace: tosa - -import flatbuffers - -class TransposeConv2dAttribute(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsTransposeConv2dAttribute(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = TransposeConv2dAttribute() - x.Init(buf, n + offset) - return x - - # TransposeConv2dAttribute - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # TransposeConv2dAttribute - def Outpad(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - a = self._tab.Vector(o) - return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) - return 0 - - # TransposeConv2dAttribute - def OutpadAsNumpy(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o) - return 0 - - # TransposeConv2dAttribute - def OutpadLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - - # TransposeConv2dAttribute - def Stride(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - a = self._tab.Vector(o) - return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) - return 0 - - # TransposeConv2dAttribute - def StrideAsNumpy(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o) - return 0 - - # TransposeConv2dAttribute - def StrideLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - - # TransposeConv2dAttribute - def Dilation(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) - if o != 0: - a = self._tab.Vector(o) - return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) - return 0 - - # TransposeConv2dAttribute - def DilationAsNumpy(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) - if o != 0: - return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o) - return 0 - - # TransposeConv2dAttribute - def DilationLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - - # TransposeConv2dAttribute - def OutputShape(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) - if o != 0: - a = self._tab.Vector(o) - return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) - return 0 - - # TransposeConv2dAttribute - def OutputShapeAsNumpy(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) - if o != 0: - return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o) - return 0 - - # TransposeConv2dAttribute - def OutputShapeLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - -def TransposeConv2dAttributeStart(builder): builder.StartObject(4) -def TransposeConv2dAttributeAddOutpad(builder, outpad): builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(outpad), 0) -def TransposeConv2dAttributeStartOutpadVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def TransposeConv2dAttributeAddStride(builder, stride): builder.PrependUOffsetTRelativeSlot(1, flatbuffers.number_types.UOffsetTFlags.py_type(stride), 0) -def TransposeConv2dAttributeStartStrideVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def TransposeConv2dAttributeAddDilation(builder, dilation): builder.PrependUOffsetTRelativeSlot(2, flatbuffers.number_types.UOffsetTFlags.py_type(dilation), 0) -def TransposeConv2dAttributeStartDilationVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def TransposeConv2dAttributeAddOutputShape(builder, outputShape): builder.PrependUOffsetTRelativeSlot(3, flatbuffers.number_types.UOffsetTFlags.py_type(outputShape), 0) -def TransposeConv2dAttributeStartOutputShapeVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def TransposeConv2dAttributeEnd(builder): return builder.EndObject() diff --git a/verif/tosa/UnaryQuantInfo.py b/verif/tosa/UnaryQuantInfo.py deleted file mode 100644 index 9ae0214..0000000 --- a/verif/tosa/UnaryQuantInfo.py +++ /dev/null @@ -1,53 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# Copyright (c) 2020, ARM Limited. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -# namespace: tosa - -import flatbuffers - -class UnaryQuantInfo(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsUnaryQuantInfo(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = UnaryQuantInfo() - x.Init(buf, n + offset) - return x - - # UnaryQuantInfo - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # UnaryQuantInfo - def InputZp(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) - return 0 - - # UnaryQuantInfo - def OutputZp(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) - return 0 - -def UnaryQuantInfoStart(builder): builder.StartObject(2) -def UnaryQuantInfoAddInputZp(builder, inputZp): builder.PrependInt32Slot(0, inputZp, 0) -def UnaryQuantInfoAddOutputZp(builder, outputZp): builder.PrependInt32Slot(1, outputZp, 0) -def UnaryQuantInfoEnd(builder): return builder.EndObject() diff --git a/verif/tosa/Usage.py b/verif/tosa/Usage.py deleted file mode 100644 index 4c42daa..0000000 --- a/verif/tosa/Usage.py +++ /dev/null @@ -1,25 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# Copyright (c) 2020, ARM Limited. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -# namespace: tosa - -class Usage(object): - UNKNOWN = 0 - ACTIVATION = 1 - WEIGHT = 2 - INDEX = 3 - diff --git a/verif/tosa/Version.py b/verif/tosa/Version.py deleted file mode 100644 index e327507..0000000 --- a/verif/tosa/Version.py +++ /dev/null @@ -1,69 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# Copyright (c) 2020, ARM Limited. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -# namespace: tosa - -import flatbuffers - -class Version(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsVersion(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = Version() - x.Init(buf, n + offset) - return x - - # Version - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # Version - def _major(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) - return 0 - - # Version - def _minor(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) - return 21 - - # Version - def _patch(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) - return 0 - - # Version - def _experimental(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) - if o != 0: - return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos)) - return False - -def VersionStart(builder): builder.StartObject(4) -def VersionAdd_major(builder, Major): builder.PrependInt32Slot(0, Major, 0) -def VersionAdd_minor(builder, Minor): builder.PrependInt32Slot(1, Minor, 21) -def VersionAdd_patch(builder, Patch): builder.PrependInt32Slot(2, Patch, 0) -def VersionAdd_experimental(builder, Experimental): builder.PrependBoolSlot(3, Experimental, 0) -def VersionEnd(builder): return builder.EndObject() diff --git a/verif/tosa/WhileLoopAttribute.py b/verif/tosa/WhileLoopAttribute.py deleted file mode 100644 index c37977f..0000000 --- a/verif/tosa/WhileLoopAttribute.py +++ /dev/null @@ -1,53 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# Copyright (c) 2020, ARM Limited. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -# namespace: tosa - -import flatbuffers - -class WhileLoopAttribute(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsWhileLoopAttribute(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = WhileLoopAttribute() - x.Init(buf, n + offset) - return x - - # WhileLoopAttribute - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # WhileLoopAttribute - def CondBranch(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.String(o + self._tab.Pos) - return None - - # WhileLoopAttribute - def BodyBranch(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.String(o + self._tab.Pos) - return None - -def WhileLoopAttributeStart(builder): builder.StartObject(2) -def WhileLoopAttributeAddCondBranch(builder, condBranch): builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(condBranch), 0) -def WhileLoopAttributeAddBodyBranch(builder, bodyBranch): builder.PrependUOffsetTRelativeSlot(1, flatbuffers.number_types.UOffsetTFlags.py_type(bodyBranch), 0) -def WhileLoopAttributeEnd(builder): return builder.EndObject() diff --git a/verif/tosa/__init__.py b/verif/tosa/__init__.py deleted file mode 100644 index ee1ab30..0000000 --- a/verif/tosa/__init__.py +++ /dev/null @@ -1,15 +0,0 @@ - -# Copyright (c) 2020, ARM Limited. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - diff --git a/verif/tosa_ref_run.py b/verif/tosa_ref_run.py index 2035147..26c64da 100644 --- a/verif/tosa_ref_run.py +++ b/verif/tosa_ref_run.py @@ -19,6 +19,7 @@ import shlex import subprocess from tosa_test_runner import TosaTestRunner, run_sh_command + class TosaRefRunner(TosaTestRunner): def __init__(self, args, runnerArgs, testDir): super().__init__(args, runnerArgs, testDir) @@ -28,28 +29,33 @@ class TosaRefRunner(TosaTestRunner): # Uses arguments from the argParser args, not the runnerArgs args = self.args - ref_cmd = [ args.ref_model_path, - '-Csubgraph_file={}'.format(self.testDesc['tosa_file']), - '-Csubgraph_dir={}'.format(self.testDir), - '-Cinput_dir={}'.format(self.testDir), - '-Coutput_dir={}'.format(self.testDir), - '-Coutput_tensor_prefix=ref-', # Naming agreement with TosaSerializer - ] + ref_cmd = [ + args.ref_model_path, + "-Csubgraph_file={}".format(self.testDesc["tosa_file"]), + "-Csubgraph_dir={}".format(self.testDir), + "-Cinput_dir={}".format(self.testDir), + "-Coutput_dir={}".format(self.testDir), + "-Coutput_tensor_prefix=ref-", # Naming agreement with TosaSerializer + ] # Build up input tensor_name/filename list inputTensors = [] - for i in range(len(self.testDesc['ifm_placeholder'])): - inputTensors.append('{}:{}'.format(self.testDesc['ifm_placeholder'][i], self.testDesc['ifm_file'][i])) + for i in range(len(self.testDesc["ifm_placeholder"])): + inputTensors.append( + "{}:{}".format( + self.testDesc["ifm_placeholder"][i], self.testDesc["ifm_file"][i] + ) + ) - ref_cmd.append('-Cinput_tensor={}'.format(','.join(inputTensors))) + ref_cmd.append("-Cinput_tensor={}".format(",".join(inputTensors))) if args.ref_debug: - ref_cmd.extend(['-dALL', '-l{}'.format(args.ref_debug)]) + ref_cmd.extend(["-dALL", "-l{}".format(args.ref_debug)]) if args.ref_intermediates: - ref_cmd.extend(['-Ddump_intermediates=1']) + ref_cmd.extend(["-Ddump_intermediates=1"]) - expectedFailure = self.testDesc['expected_failure'] + expectedFailure = self.testDesc["expected_failure"] try: run_sh_command(self.args, ref_cmd) diff --git a/verif/tosa_serializer.py b/verif/tosa_serializer.py index 136f7aa..fa1fdcb 100644 --- a/verif/tosa_serializer.py +++ b/verif/tosa_serializer.py @@ -1,5 +1,3 @@ - - # Copyright (c) 2020-2021, ARM Limited. # # Licensed under the Apache License, Version 2.0 (the "License"); @@ -16,37 +14,57 @@ #!/usr/bin/env python3 +import os +import sys +import json import flatbuffers import numpy as np from enum import Enum, IntEnum, unique -from tosa import TosaGraph, TosaBasicBlock, TosaTensor, TosaOperator, DType, Format, Usage, Op, ResizeMode, Version +from tosa import ( + TosaGraph, + TosaBasicBlock, + TosaTensor, + TosaOperator, + DType, + Op, + ResizeMode, + Version, +) + +# Include the ../thirdparty/serialization_lib/python directory in PYTHONPATH +parent_dir = os.path.dirname(os.path.realpath(__file__)) +sys.path.append( + os.path.join(parent_dir, "..", "thirdparty", "serialization_lib", "python") +) import tosa -import os -import json # With the way flatc generates its python types, there is no programatic way # to get string names for the integer types. Manually maintain a string table # here. -DTypeNames = [ 'UNKNOWN', - 'BOOL', - 'UINT8', - 'INT4', - 'INT8', - 'INT16', - 'INT32', - 'INT48', - 'FLOAT' ] +DTypeNames = [ + "UNKNOWN", + "BOOL", + "UINT8", + "INT4", + "INT8", + "INT16", + "INT32", + "INT48", + "FLOAT", +] + def dtype_str_to_val(name): for i in range(len(DTypeNames)): if name.casefold() == DTypeNames[i].casefold(): return i - raise Exception('Unable to parse DType name {}'.format(name)) + raise Exception("Unable to parse DType name {}".format(name)) class TosaSerializerUnion: - '''This class handles encapsulating and serializing union types into flatbuffers''' + """This class handles encapsulating and serializing union types into flatbuffers""" + def __init__(self): # A tuple of the start and end functions. Set by the options constructors below @@ -105,8 +123,9 @@ class TosaSerializerUnion: return endFcn(builder) + class TosaSerializerAttribute(TosaSerializerUnion): - '''This class handles encapsulating all of the enumerated types for attributes''' + """This class handles encapsulating all of the enumerated types for attributes""" def __init__(self): super().__init__() @@ -117,12 +136,9 @@ class TosaSerializerAttribute(TosaSerializerUnion): self.utype = Attribute.Attribute().Pool2dAttribute self.optFcns = (a.Pool2dAttributeStart, a.Pool2dAttributeEnd) - self.intvecs.append((a.Pool2dAttributeAddPadding, - padding)) - self.intvecs.append((a.Pool2dAttributeAddKernel, - kernel)) - self.intvecs.append((a.Pool2dAttributeAddStride, - stride)) + self.intvecs.append((a.Pool2dAttributeAddPadding, padding)) + self.intvecs.append((a.Pool2dAttributeAddKernel, kernel)) + self.intvecs.append((a.Pool2dAttributeAddStride, stride)) def Conv2dAttribute(self, padding, stride, dilation): from tosa import Conv2dAttribute as a, Attribute @@ -130,12 +146,9 @@ class TosaSerializerAttribute(TosaSerializerUnion): self.utype = Attribute.Attribute().Conv2dAttribute self.optFcns = (a.Conv2dAttributeStart, a.Conv2dAttributeEnd) - self.intvecs.append((a.Conv2dAttributeAddPadding, - padding)) - self.intvecs.append((a.Conv2dAttributeAddStride, - stride)) - self.intvecs.append((a.Conv2dAttributeAddDilation, - dilation)) + self.intvecs.append((a.Conv2dAttributeAddPadding, padding)) + self.intvecs.append((a.Conv2dAttributeAddStride, stride)) + self.intvecs.append((a.Conv2dAttributeAddDilation, dilation)) def TransposeConv2DAttribute(self, outpad, stride, dilation, output_shape): from tosa import TransposeConv2dAttribute as a, Attribute @@ -143,14 +156,10 @@ class TosaSerializerAttribute(TosaSerializerUnion): self.utype = Attribute.Attribute().TransposeConv2dAttribute self.optFcns = (a.TransposeConv2dAttributeStart, a.TransposeConv2dAttributeEnd) - self.intvecs.append((a.TransposeConv2dAttributeAddOutpad, - outpad)) - self.intvecs.append((a.TransposeConv2dAttributeAddStride, - stride)) - self.intvecs.append((a.TransposeConv2dAttributeAddDilation, - dilation)) - self.intvecs.append((a.TransposeConv2dAttributeAddOutputShape, - output_shape)) + self.intvecs.append((a.TransposeConv2dAttributeAddOutpad, outpad)) + self.intvecs.append((a.TransposeConv2dAttributeAddStride, stride)) + self.intvecs.append((a.TransposeConv2dAttributeAddDilation, dilation)) + self.intvecs.append((a.TransposeConv2dAttributeAddOutputShape, output_shape)) def ReluNAttribute(self, maxint, maxfp): from tosa import ReluNAttribute as a, Attribute @@ -161,15 +170,13 @@ class TosaSerializerAttribute(TosaSerializerUnion): self.ints.append((a.ReluNAttributeAddMaxInt, maxint)) self.ints.append((a.ReluNAttributeAddMaxFp, maxfp)) - def AxisAttribute(self, axis): from tosa import AxisAttribute as a, Attribute self.utype = Attribute.Attribute().AxisAttribute self.optFcns = (a.AxisAttributeStart, a.AxisAttributeEnd) - self.ints.append((a.AxisAttributeAddAxis, - axis)) + self.ints.append((a.AxisAttributeAddAxis, axis)) def ReshapeAttribute(self, shape): from tosa import ReshapeAttribute as a, Attribute @@ -177,8 +184,7 @@ class TosaSerializerAttribute(TosaSerializerUnion): self.utype = Attribute.Attribute().ReshapeAttribute self.optFcns = (a.ReshapeAttributeStart, a.ReshapeAttributeEnd) - self.intvecs.append((a.ReshapeAttributeAddShape, - shape)) + self.intvecs.append((a.ReshapeAttributeAddShape, shape)) def SliceAttribute(self, begin, size): from tosa import SliceAttribute as a, Attribute @@ -186,10 +192,8 @@ class TosaSerializerAttribute(TosaSerializerUnion): self.utype = Attribute.Attribute().SliceAttribute self.optFcns = (a.SliceAttributeStart, a.SliceAttributeEnd) - self.intvecs.append((a.SliceAttributeAddBegin, - begin)) - self.intvecs.append((a.SliceAttributeAddSize, - size)) + self.intvecs.append((a.SliceAttributeAddBegin, begin)) + self.intvecs.append((a.SliceAttributeAddSize, size)) def TileAttribute(self, multiples): from tosa import TileAttribute as a, Attribute @@ -197,29 +201,23 @@ class TosaSerializerAttribute(TosaSerializerUnion): self.utype = Attribute.Attribute().TileAttribute self.optFcns = (a.TileAttributeStart, a.TileAttributeEnd) - self.intvecs.append((a.TileAttributeAddMultiples, - multiples)) + self.intvecs.append((a.TileAttributeAddMultiples, multiples)) - def ResizeAttribute(self, output_size, stride, offset, shift, stride_fp, offset_fp, mode): + def ResizeAttribute( + self, output_size, stride, offset, shift, stride_fp, offset_fp, mode + ): from tosa import ResizeAttribute as a, Attribute self.utype = Attribute.Attribute().ResizeAttribute self.optFcns = (a.ResizeAttributeStart, a.ResizeAttributeEnd) - self.intvecs.append((a.ResizeAttributeAddOutputSize, - output_size)) - self.intvecs.append((a.ResizeAttributeAddStride, - stride)) - self.intvecs.append((a.ResizeAttributeAddOffset, - offset)) - self.ints.append((a.ResizeAttributeAddShift, - shift)) - self.fpvecs.append((a.ResizeAttributeAddStrideFp, - stride_fp)) - self.fpvecs.append((a.ResizeAttributeAddOffsetFp, - offset_fp)) - self.ints.append((a.ResizeAttributeAddMode, - mode)) + self.intvecs.append((a.ResizeAttributeAddOutputSize, output_size)) + self.intvecs.append((a.ResizeAttributeAddStride, stride)) + self.intvecs.append((a.ResizeAttributeAddOffset, offset)) + self.ints.append((a.ResizeAttributeAddShift, shift)) + self.fpvecs.append((a.ResizeAttributeAddStrideFp, stride_fp)) + self.fpvecs.append((a.ResizeAttributeAddOffsetFp, offset_fp)) + self.ints.append((a.ResizeAttributeAddMode, mode)) def ClampAttribute(self, minint, maxint, minfp, maxfp): from tosa import ClampAttribute as a, Attribute @@ -227,36 +225,27 @@ class TosaSerializerAttribute(TosaSerializerUnion): self.utype = Attribute.Attribute().ClampAttribute self.optFcns = (a.ClampAttributeStart, a.ClampAttributeEnd) - self.ints.append((a.ClampAttributeAddMinInt, - minint)) - self.ints.append((a.ClampAttributeAddMaxInt, - maxint)) + self.ints.append((a.ClampAttributeAddMinInt, minint)) + self.ints.append((a.ClampAttributeAddMaxInt, maxint)) - self.ints.append((a.ClampAttributeAddMinFp, - minfp)) - self.ints.append((a.ClampAttributeAddMaxFp, - maxfp)) + self.ints.append((a.ClampAttributeAddMinFp, minfp)) + self.ints.append((a.ClampAttributeAddMaxFp, maxfp)) - def RescaleAttribute(self, input_zp, output_zp, multiplier, shift, scale32, double_round, per_channel): + def RescaleAttribute( + self, input_zp, output_zp, multiplier, shift, scale32, double_round, per_channel + ): from tosa import RescaleAttribute as a, Attribute self.utype = Attribute.Attribute().RescaleAttribute self.optFcns = (a.RescaleAttributeStart, a.RescaleAttributeEnd) - self.ints.append((a.RescaleAttributeAddInputZp, - input_zp)) - self.ints.append((a.RescaleAttributeAddOutputZp, - output_zp)) - self.intvecs.append((a.RescaleAttributeAddMultiplier, - multiplier)) - self.intvecs.append((a.RescaleAttributeAddShift, - shift)) - self.bools.append((a.RescaleAttributeAddScale32, - scale32)) - self.bools.append((a.RescaleAttributeAddDoubleRound, - double_round)) - self.bools.append((a.RescaleAttributeAddPerChannel, - per_channel)) + self.ints.append((a.RescaleAttributeAddInputZp, input_zp)) + self.ints.append((a.RescaleAttributeAddOutputZp, output_zp)) + self.intvecs.append((a.RescaleAttributeAddMultiplier, multiplier)) + self.intvecs.append((a.RescaleAttributeAddShift, shift)) + self.bools.append((a.RescaleAttributeAddScale32, scale32)) + self.bools.append((a.RescaleAttributeAddDoubleRound, double_round)) + self.bools.append((a.RescaleAttributeAddPerChannel, per_channel)) def MulAttribute(self, shift): from tosa import MulAttribute as a, Attribute @@ -264,17 +253,18 @@ class TosaSerializerAttribute(TosaSerializerUnion): self.utype = Attribute.Attribute().MulAttribute self.optFcns = (a.MulAttributeStart, a.MulAttributeEnd) - self.ints.append((a.MulAttributeAddShift, - shift)) + self.ints.append((a.MulAttributeAddShift, shift)) def ArithmeticRightShiftAttribute(self, round): from tosa import ArithmeticRightShiftAttribute as a, Attribute self.utype = Attribute.Attribute().ArithmeticRightShiftAttribute - self.optFcns = (a.ArithmeticRightShiftAttributeStart, a.ArithmeticRightShiftAttributeEnd) + self.optFcns = ( + a.ArithmeticRightShiftAttributeStart, + a.ArithmeticRightShiftAttributeEnd, + ) - self.bools.append((a.ArithmeticRightShiftAttributeAddRound, - round)) + self.bools.append((a.ArithmeticRightShiftAttributeAddRound, round)) def CustomAttribute(self, identifier): from tosa import CustomAttribute as a, Attribute @@ -282,8 +272,7 @@ class TosaSerializerAttribute(TosaSerializerUnion): self.utype = Attribute.Attribute().CustomAttribute self.optFcns = (a.CustomAttributeStart, a.CustomAttributeEnd) - self.strings.append((a.CustomAttributeAddIdentifier, - identifier)) + self.strings.append((a.CustomAttributeAddIdentifier, identifier)) def CondIfAttribute(self, then_branch, else_branch): from tosa import CondIfAttribute as a, Attribute @@ -291,10 +280,8 @@ class TosaSerializerAttribute(TosaSerializerUnion): self.utype = Attribute.Attribute().CondIfAttribute self.optFcns = (a.CondIfAttributeStart, a.CondIfAttributeEnd) - self.strings.append((a.CondIfAttributeAddThenBranch, - then_branch)) - self.strings.append((a.CondIfAttributeAddElseBranch, - else_branch)) + self.strings.append((a.CondIfAttributeAddThenBranch, then_branch)) + self.strings.append((a.CondIfAttributeAddElseBranch, else_branch)) def WhileLoopAttribute(self, cond_branch, body_branch): from tosa import WhileLoopAttribute as a, Attribute @@ -302,13 +289,13 @@ class TosaSerializerAttribute(TosaSerializerUnion): self.utype = Attribute.Attribute().WhileLoopAttribute self.optFcns = (a.WhileLoopAttributeStart, a.WhileLoopAttributeEnd) - self.strings.append((a.WhileLoopAttributeAddCondBranch, - cond_branch)) - self.strings.append((a.WhileLoopAttributeAddBodyBranch, - body_branch)) + self.strings.append((a.WhileLoopAttributeAddCondBranch, cond_branch)) + self.strings.append((a.WhileLoopAttributeAddBodyBranch, body_branch)) + class TosaSerializerQuantInfo(TosaSerializerUnion): - '''This class handles encapsulating all of the enumerated types for quantinfo types''' + """This class handles encapsulating all of the enumerated types for quantinfo types""" + def __init__(self): super().__init__() @@ -343,8 +330,16 @@ class TosaSerializerQuantInfo(TosaSerializerUnion): self.optFcns = (q.PadQuantInfoStart, q.PadQuantInfoEnd) self.ints.append((q.PadQuantInfoAddInputZp, input_zp)) + class TosaSerializerTensor: - def __init__(self, name, shape, dtype, usage, dformat, filename = None, placeholderFilename = None): + def __init__( + self, + name, + shape, + dtype, + filename=None, + placeholderFilename=None, + ): self.name = name if isinstance(shape, np.ndarray): @@ -353,8 +348,6 @@ class TosaSerializerTensor: self.shape = shape self.dtype = dtype - self.usage = TosaSerializer.toList(usage) - self.dformat = TosaSerializer.toList(dformat) # Filename for const tensors. This gets written to the .tosa serialization self.filename = filename @@ -366,58 +359,35 @@ class TosaSerializerTensor: self.placeholderFilename = placeholderFilename def __str__(self): - str = 'TosaSerializerTensor name: {} shape: {} dtype: {} Usage: {} format {} filename: {}'.format( - self.name, self.shape, DTypeNames[self.dtype], self.usage, self.dformat, self.filename) + str = "TosaSerializerTensor name: {} shape: {} dtype: {} filename: {}".format( + self.name, + self.shape, + DTypeNames[self.dtype], + self.filename, + ) return str - def addUsage(self, usage): - self.usage.append(usage) - - def addFormat(self, format): - self.dformat.append(format) - def setDtype(self, dtype): self.dtype = dtype - def merge(self, name, shape, dtype, usage, dformat, filename = None): - # Merge in additional usage/formats to the list - found = 0 - for i in self.usage: - if i == usage: - found = 1 - break - if not found: - self.usage.append(usage) - - found = 0 - for i in self.dformat: - if i == dformat: - found = 1 - break - if not found: - self.dformat.append(dformat) - def serialize(self, builder): fb_name = builder.CreateString(self.name) if self.filename: fb_filename = builder.CreateString(self.filename) fb_shapes = TosaSerializer.serializeInt32Vec(builder, self.shape) - fb_usage = TosaSerializer.serializeInt32Vec(builder, self.usage) - fb_dformat = TosaSerializer.serializeInt32Vec(builder, self.dformat) TosaTensor.TosaTensorStart(builder) TosaTensor.TosaTensorAddName(builder, fb_name) TosaTensor.TosaTensorAddShape(builder, fb_shapes) TosaTensor.TosaTensorAddType(builder, self.dtype) - TosaTensor.TosaTensorAddUsage(builder, fb_usage) - TosaTensor.TosaTensorAddFormat(builder, fb_dformat) if self.filename: TosaTensor.TosaTensorAddNpyFilename(builder, fb_filename) return TosaTensor.TosaTensorEnd(builder) + class TosaSerializerOperator: - def __init__(self, op, inputs, outputs, attributes = None, quantInfo = None): + def __init__(self, op, inputs, outputs, attributes=None, quantInfo=None): self.op = op self.attributes = attributes self.inputs = TosaSerializer.toList(inputs) @@ -425,18 +395,22 @@ class TosaSerializerOperator: self.quantInfo = quantInfo def __str__(self): - str = 'Op {}\n----\n'.format(self.op) + str = "Op {}\n----\n".format(self.op) for i in self.inputs: - str = str + ' Input: {}\n'.format(i) + str = str + " Input: {}\n".format(i) for o in self.outputs: - str = str + ' Output: {}\n'.format(o) + str = str + " Output: {}\n".format(o) return str def serialize(self, builder): - fb_inputs = TosaSerializer.serializeStrVec(builder, self.inputs, TosaOperator.TosaOperatorStartInputsVector) - fb_outputs = TosaSerializer.serializeStrVec(builder, self.outputs, TosaOperator.TosaOperatorStartOutputsVector) + fb_inputs = TosaSerializer.serializeStrVec( + builder, self.inputs, TosaOperator.TosaOperatorStartInputsVector + ) + fb_outputs = TosaSerializer.serializeStrVec( + builder, self.outputs, TosaOperator.TosaOperatorStartOutputsVector + ) # Need to serialize quant_info and attributes enums still if self.attributes is not None: fb_attributes = self.attributes.serialize(builder) @@ -457,6 +431,7 @@ class TosaSerializerOperator: return TosaOperator.TosaOperatorEnd(builder) + class TosaSerializerBasicBlock: def __init__(self, name): self.name = name @@ -468,14 +443,21 @@ class TosaSerializerBasicBlock: self.inputs = [] self.outputs = [] - def addTensor(self, name, shape, dtype, usage, dformat, filename = None, placeholderFilename = None): + def addTensor( + self, + name, + shape, + dtype, + filename=None, + placeholderFilename=None, + ): try: # Someone already added this tensor. - # We may have to add more usages and formats tens = self.tensors[name] - filename = tens.merge(name, shape, dtype, usage, dformat, filename) except KeyError: - self.tensors[name] = TosaSerializerTensor(name, shape, dtype, usage, dformat, filename, placeholderFilename) + self.tensors[name] = TosaSerializerTensor( + name, shape, dtype, filename, placeholderFilename + ) return self.tensors[name] @@ -485,15 +467,27 @@ class TosaSerializerBasicBlock: def addOutput(self, name): self.outputs.append(name) - def addOperator(self, op, inputs, outputs, attributes = None, quant_info = None): - self.operators.append(TosaSerializerOperator(op, inputs, outputs, attributes, quant_info)) + def addOperator(self, op, inputs, outputs, attributes=None, quant_info=None): + self.operators.append( + TosaSerializerOperator(op, inputs, outputs, attributes, quant_info) + ) def serialize(self, builder): fb_name = builder.CreateString(self.name) - fbv_inputs = TosaSerializer.serializeStrVec(builder, list(self.inputs), TosaBasicBlock.TosaBasicBlockStartInputsVector) - fbv_outputs = TosaSerializer.serializeStrVec(builder, list(self.outputs), TosaBasicBlock.TosaBasicBlockStartOutputsVector) - fbv_tensors = TosaSerializer.serializeObjVec(builder, list(self.tensors.values()), TosaBasicBlock.TosaBasicBlockStartTensorsVector) - fbv_operators = TosaSerializer.serializeObjVec(builder, self.operators, TosaBasicBlock.TosaBasicBlockStartOperatorsVector) + fbv_inputs = TosaSerializer.serializeStrVec( + builder, list(self.inputs), TosaBasicBlock.TosaBasicBlockStartInputsVector + ) + fbv_outputs = TosaSerializer.serializeStrVec( + builder, list(self.outputs), TosaBasicBlock.TosaBasicBlockStartOutputsVector + ) + fbv_tensors = TosaSerializer.serializeObjVec( + builder, + list(self.tensors.values()), + TosaBasicBlock.TosaBasicBlockStartTensorsVector, + ) + fbv_operators = TosaSerializer.serializeObjVec( + builder, self.operators, TosaBasicBlock.TosaBasicBlockStartOperatorsVector + ) TosaBasicBlock.TosaBasicBlockStart(builder) TosaBasicBlock.TosaBasicBlockAddName(builder, fb_name) @@ -503,6 +497,7 @@ class TosaSerializerBasicBlock: TosaBasicBlock.TosaBasicBlockAddOperators(builder, fbv_operators) return TosaBasicBlock.TosaBasicBlockEnd(builder) + @unique class TensorDir(IntEnum): PLACEHOLDER = 0 @@ -510,6 +505,7 @@ class TensorDir(IntEnum): INTERMEDIATE = 2 RESULT = 3 + class TosaSerializer: def __init__(self, pathPrefix): @@ -522,7 +518,7 @@ class TosaSerializer: self.builder = flatbuffers.Builder(0) self.basicBlocks = [] - self.startBasicBlock('main') + self.startBasicBlock("main") self.pathPrefix = pathPrefix # Indicies used for adding/naming tensors @@ -533,23 +529,23 @@ class TosaSerializer: # Is this an illegal test that is expected to fail? self.expectedFailure = False - self.expectedFailureDesc = '' + self.expectedFailureDesc = "" def __str__(self): - str = '' + str = "" for bb in self.basicBlocks: str = str + bb.__str__() return str - def addPlaceholder(self, shape, dtype, usage, dformat, vals): + def addPlaceholder(self, shape, dtype, vals): if not self.currBasicBlock: - raise Exception('addTensor called without valid basic block') + raise Exception("addTensor called without valid basic block") - name = 'input-{}'.format(self.currInputIdx) - filename = '{}.npy'.format(name) + name = "input-{}".format(self.currInputIdx) + filename = "{}.npy".format(name) self.currInputIdx = self.currInputIdx + 1 - tens = self.currBasicBlock.addTensor(name, shape, dtype, usage, dformat, None, filename) + tens = self.currBasicBlock.addTensor(name, shape, dtype, None, filename) # This is always an input to the block self.currBasicBlock.addInput(name) # Add the operator now @@ -560,15 +556,15 @@ class TosaSerializer: return tens - def addConst(self, shape, dtype, usage, dformat, vals): + def addConst(self, shape, dtype, vals): if not self.currBasicBlock: - raise Exception('addTensor called without valid basic block') + raise Exception("addTensor called without valid basic block") - name = 'const-{}'.format(self.currInputIdx) - filename = '{}.npy'.format(name) + name = "const-{}".format(self.currInputIdx) + filename = "{}.npy".format(name) self.currInputIdx = self.currInputIdx + 1 - tens = self.currBasicBlock.addTensor(name, shape, dtype, usage, dformat, filename) + tens = self.currBasicBlock.addTensor(name, shape, dtype, filename) # Add the operator now self.currBasicBlock.addOperator(tosa.Op.Op().CONST, [], name) @@ -576,51 +572,54 @@ class TosaSerializer: np.save(os.path.join(self.pathPrefix, filename), vals, False) return tens - def addIntermediate(self, shape, dtype, usage, dformat): + def addIntermediate(self, shape, dtype): if not self.currBasicBlock: - raise Exception('addTensor called without valid basic block') + raise Exception("addTensor called without valid basic block") - name = 'layer-{}'.format(self.currLayerIdx) - filename = None # No file, so no filename + name = "layer-{}".format(self.currLayerIdx) + filename = None # No file, so no filename self.currLayerIdx = self.currLayerIdx + 1 - tens = self.currBasicBlock.addTensor(name, shape, dtype, usage, dformat, filename) + tens = self.currBasicBlock.addTensor(name, shape, dtype, filename) return tens def addInputTensor(self, tensor): self.currBasicBlock.addOperator(tosa.Op.Op().PLACEHOLDER, [], tensor.name) - self.currBasicBlock.addTensor(tensor.name, tensor.shape, tensor.dtype, tensor.usage, tensor.dformat) + self.currBasicBlock.addTensor(tensor.name, tensor.shape, tensor.dtype) self.currBasicBlock.addInput(tensor.name) def addOutputTensor(self, tensor): self.currBasicBlock.addOutput(tensor.name) - def addOutput(self, shape, dtype, usage, dformat): + def addOutput(self, shape, dtype): if not self.currBasicBlock: - raise Exception('addTensor called without valid basic block') + raise Exception("addTensor called without valid basic block") - name = 'result-{}'.format(self.currResultIdx) + name = "result-{}".format(self.currResultIdx) self.currResultIdx = self.currResultIdx + 1 - tens = self.currBasicBlock.addTensor(name, shape, dtype, usage, dformat, None) + tens = self.currBasicBlock.addTensor(name, shape, dtype, None) self.currBasicBlock.addOutput(name) return tens - def addOperator(self, op, inputs, outputs, attributes = None, quant_info = None): + def addOperator(self, op, inputs, outputs, attributes=None, quant_info=None): - if op == tosa.Op.Op().PLACEHOLDER or \ - op == tosa.Op.Op().CONST: - raise Exception('Use addPlaceholderTensor() or addConstTensor() to add PLACEHOLDER and CONST ops') + if op == tosa.Op.Op().PLACEHOLDER or op == tosa.Op.Op().CONST: + raise Exception( + "Use addPlaceholderTensor() or addConstTensor() to add PLACEHOLDER and CONST ops" + ) - return self.currBasicBlock.addOperator(op, inputs, outputs, attributes, quant_info) + return self.currBasicBlock.addOperator( + op, inputs, outputs, attributes, quant_info + ) - def setExpectedFailure(self, desc='', val=True): + def setExpectedFailure(self, desc="", val=True): self.expectedFailure = val self.expectedFailureDesc = desc - def setExpectedFailure(self, desc='', val=True): + def setExpectedFailure(self, desc="", val=True): self.expectedFailure = val self.expectedFailureDesc = desc @@ -635,7 +634,9 @@ class TosaSerializer: Version.VersionAdd_experimental(builder, TOSA_VERSION[3]) version = Version.VersionEnd(builder) - fbv_bb = TosaSerializer.serializeObjVec(builder, self.basicBlocks, TosaGraph.TosaGraphStartBlocksVector) + fbv_bb = TosaSerializer.serializeObjVec( + builder, self.basicBlocks, TosaGraph.TosaGraphStartBlocksVector + ) TosaGraph.TosaGraphStart(builder) TosaGraph.TosaGraphAddVersion(builder, version) @@ -646,11 +647,11 @@ class TosaSerializer: return self.builder.Output() def writeJson(self, tosa_filename): - '''Write a json test file so that it is fairly easy to pick up the test - and generate commands for third party tool''' + """Write a json test file so that it is fairly easy to pick up the test + and generate commands for third party tool""" test_desc = dict() - test_desc['tosa_file'] = tosa_filename + test_desc["tosa_file"] = tosa_filename ifm_name = [] ifm_shape = [] ifm_file = [] @@ -659,7 +660,7 @@ class TosaSerializer: ofm_shape = [] for b in self.basicBlocks: - if b.name == 'main': + if b.name == "main": for i in b.inputs: ifm_name.append(i) ifm_shape.append(b.tensors[i].shape) @@ -669,19 +670,19 @@ class TosaSerializer: ofm_shape.append(b.tensors[o].shape) # Make up an OFM filename here. One isn't generated until the reference tool is # run, so any name is a good name - ofm_file.append('ref-{}.npy'.format(o)) - - test_desc['ifm_placeholder'] = ifm_name - test_desc['ifm_file'] = ifm_file - test_desc['ifm_shape'] = ifm_shape - test_desc['ofm_name'] = ofm_name - test_desc['ofm_shape'] = ofm_shape - test_desc['ofm_file'] = ofm_file - test_desc['expected_failure'] = self.expectedFailure + ofm_file.append("ref-{}.npy".format(o)) + + test_desc["ifm_placeholder"] = ifm_name + test_desc["ifm_file"] = ifm_file + test_desc["ifm_shape"] = ifm_shape + test_desc["ofm_name"] = ofm_name + test_desc["ofm_shape"] = ofm_shape + test_desc["ofm_file"] = ofm_file + test_desc["expected_failure"] = self.expectedFailure if self.expectedFailureDesc: - test_desc['expected_failure_desc'] = self.expectedFailureDesc + test_desc["expected_failure_desc"] = self.expectedFailureDesc - return json.dumps(test_desc, indent=' ') + return json.dumps(test_desc, indent=" ") def startBasicBlock(self, name): self.currBasicBlock = TosaSerializerBasicBlock(name) @@ -748,7 +749,9 @@ class TosaSerializer: # Store the version as a global variable so that it only needs to be # generated once per process. global TOSA_VERSION - TOSA_VERSION = [root.Version()._major(), - root.Version()._minor(), - root.Version()._patch(), - root.Version()._experimental() ] + TOSA_VERSION = [ + root.Version()._major(), + root.Version()._minor(), + root.Version()._patch(), + root.Version()._experimental(), + ] diff --git a/verif/tosa_test_gen.py b/verif/tosa_test_gen.py index ae1a5c6..b059ef5 100644 --- a/verif/tosa_test_gen.py +++ b/verif/tosa_test_gen.py @@ -32,19 +32,24 @@ import math from enum import IntEnum, Enum, unique +# Include the ../thirdparty/serialization_lib/python directory in PYTHONPATH +parent_dir = os.path.dirname(os.path.realpath(__file__)) +sys.path.append( + os.path.join(parent_dir, "..", "thirdparty", "serialization_lib", "python") +) import tosa_serializer as ts from tosa_serializer import * import tosa # Convenience variables to the flatc-generated types that should be enums, but aren't DType = tosa.DType.DType() -Usage = tosa.Usage.Usage() -Format = tosa.Format.Format() -Op = tosa.Op.Op() +Op = tosa.Op.Op() ResizeMode = tosa.ResizeMode.ResizeMode() + class TosaQuantGen: - '''QuantizedInfo random generator helper functions. Specify with 'qgen': in the operator defintion''' + """QuantizedInfo random generator helper functions. Specify with 'qgen': in the operator defintion""" + def __init__(self): pass @@ -107,30 +112,31 @@ class TosaQuantGen: m = -m multiplier = round(m * (1 << scaleBits)) - assert(multiplier <= (1 << scaleBits)) + assert multiplier <= (1 << scaleBits) if multiplier == (1 << scaleBits): multiplier = multiplier // 2 shift = shift + 1 shift = (-shift) + scaleBits - #print('scalefp {} scaleBits {} m {} mult {} shift {}'.format(scaleFp, scaleBits, m, multiplier, shift)) + # print('scalefp {} scaleBits {} m {} mult {} shift {}'.format(scaleFp, scaleBits, m, multiplier, shift)) - assert(multiplier <= (1 << scaleBits)) - assert(shift >= 0 and shift <= 63) + assert multiplier <= (1 << scaleBits) + assert shift >= 0 and shift <= 63 return multiplier, shift -class TosaTensorGen(): - ''' Tensor generators create a shape list for the placeholder and const tensor - data operands for the operator. The actual random data is generated separately for each test.''' +class TosaTensorGen: + """Tensor generators create a shape list for the placeholder and const tensor + data operands for the operator. The actual random data is generated separately for each test.""" + def __init__(self): pass @staticmethod def tgBasic(testGen, opName, rank): - pl, const = opName['operands'] + pl, const = opName["operands"] shape = testGen.makeShape(rank) shape_list = [] @@ -141,9 +147,9 @@ class TosaTensorGen(): @staticmethod def tgNHWC(testGen, opName, rank): - pl, const = opName['operands'] + pl, const = opName["operands"] - assert(rank == 4) + assert rank == 4 shape = testGen.makeShape(rank) @@ -159,11 +165,11 @@ class TosaTensorGen(): @staticmethod def tgScatter(testGen, opName, rank): - pl, const = opName['operands'] + pl, const = opName["operands"] - assert(pl == 2) - assert(const == 0) - assert(rank == 3) + assert pl == 2 + assert const == 0 + assert rank == 3 values_in_shape = testGen.makeShape(rank) @@ -171,7 +177,9 @@ class TosaTensorGen(): if testGen.args.max_batch_size: values_in_shape[0] = (values_in_shape[0] % testGen.args.max_batch_size) + 1 - W = testGen.randInt(testGen.args.tensor_shape_range[0], testGen.args.tensor_shape_range[1]) + W = testGen.randInt( + testGen.args.tensor_shape_range[0], testGen.args.tensor_shape_range[1] + ) input_shape = [values_in_shape[0], W, values_in_shape[2]] shape_list = [] @@ -184,7 +192,7 @@ class TosaTensorGen(): def tgBroadcastFuzz(testGen, op, rank): shape = testGen.makeShape(rank) - pl, const = op['operands'] + pl, const = op["operands"] shape_list = [] @@ -204,9 +212,9 @@ class TosaTensorGen(): @staticmethod def tgConv2D(testGen, op, rank): - pl, const = op['operands'] + pl, const = op["operands"] - assert(rank == 4) + assert rank == 4 # IFM dimensions are NHWC ifm_shape = testGen.makeShape(rank) @@ -216,7 +224,7 @@ class TosaTensorGen(): ifm_shape[0] = (ifm_shape[0] % testGen.args.max_batch_size) + 1 # Get the filter height/width from the operator parameters - filter_hw = op['filter'] + filter_hw = op["filter"] # Generate a random OFM depth ofm_depth = testGen.makeShape(1)[0] @@ -231,9 +239,9 @@ class TosaTensorGen(): @staticmethod def tgTransposeConv2D(testGen, op, rank): - pl, const = op['operands'] + pl, const = op["operands"] - assert(rank == 4) + assert rank == 4 # IFM dimensions are NHWC ifm_shape = testGen.makeShape(rank) @@ -243,7 +251,7 @@ class TosaTensorGen(): ifm_shape[0] = (ifm_shape[0] % testGen.args.max_batch_size) + 1 # Get the filter height/width from the operator parameters - filter_hw = op['filter'] + filter_hw = op["filter"] # Generate a random OFM depth ofm_depth = testGen.makeShape(1)[0] @@ -255,10 +263,10 @@ class TosaTensorGen(): @staticmethod def tgDepthwiseConv2D(testGen, op, rank): - pl, const = op['operands'] + pl, const = op["operands"] - assert(rank == 4) - assert(pl == 1 and const == 2) + assert rank == 4 + assert pl == 1 and const == 2 # IFM dimensions are NHWC ifm_shape = testGen.makeShape(rank) @@ -269,11 +277,13 @@ class TosaTensorGen(): # Get the filter height/width from the operator parameters # Filter is KH, HW, C, M - filter_hw = op['filter'] + filter_hw = op["filter"] # Generate a random OFM depth, but don't let it get too big because # the output depth is M * C - filter_m = (testGen.makeShape(1)[0] % (testGen.args.tensor_shape_range[1] // 4)) + 1 + filter_m = ( + testGen.makeShape(1)[0] % (testGen.args.tensor_shape_range[1] // 4) + ) + 1 # The filter dimensions are HWCM filter_shape = np.asarray([filter_hw[0], filter_hw[1], ifm_shape[3], filter_m]) @@ -285,10 +295,10 @@ class TosaTensorGen(): @staticmethod def tgFullyConnected(testGen, op, rank): - pl, const = op['operands'] + pl, const = op["operands"] - assert(rank == 2) - assert(pl == 2 and const == 0) + assert rank == 2 + assert pl == 2 and const == 0 input_shape = testGen.makeShape(rank) filter_oc = testGen.makeShape(1)[0] @@ -300,10 +310,10 @@ class TosaTensorGen(): @staticmethod def tgMatmul(testGen, op, rank): - pl, const = op['operands'] + pl, const = op["operands"] - assert(rank == 2) - assert(pl == 2 and const == 0) + assert rank == 2 + assert pl == 2 and const == 0 a_shape = testGen.makeShape(rank) b_oc = testGen.makeShape(1)[0] @@ -311,29 +321,31 @@ class TosaTensorGen(): return [a_shape, b_shape] + class TosaArgGen: - '''Argument generators create exhaustive or random lists of attributes for operators that take - attributes or other parameters. The return value is a list of (descriptive_name, [arglist]) - tuples where the descriptive_name is appended to the test name and the arglist is expanded - as arguments to the operator build function.''' + """Argument generators create exhaustive or random lists of attributes for operators that take + attributes or other parameters. The return value is a list of (descriptive_name, [arglist]) + tuples where the descriptive_name is appended to the test name and the arglist is expanded + as arguments to the operator build function.""" + def __init__(self): pass @staticmethod def agNone(testGen, opName, shapeList, dtype): - '''A trivial argument generator for operators that don't take any - non-tensor arguments''' - return [('', [])] + """A trivial argument generator for operators that don't take any + non-tensor arguments""" + return [("", [])] @staticmethod def agAxis(testGen, opName, shapeList, dtype): - '''Build the axis argument for operators that take a single axis''' + """Build the axis argument for operators that take a single axis""" axes = [] shape = shapeList[0] for a in range(0, len(shape)): - axes.append(('axis_{}'.format(a), [a])) + axes.append(("axis_{}".format(a), [a])) return axes @staticmethod @@ -344,8 +356,8 @@ class TosaArgGen: filter_shape = shapeList[1] # Must be rank 4 - assert(len(ifm_shape) == 4) - assert(len(filter_shape) == 4) + assert len(ifm_shape) == 4 + assert len(filter_shape) == 4 maxStride = testGen.args.max_conv_stride maxPadding = testGen.args.max_conv_padding + 1 @@ -356,20 +368,24 @@ class TosaArgGen: for padding in range(0, (maxPadding) ** 4): for dilation in range(0, maxDilation ** 2): - s = [stride // maxStride + 1, - stride % maxStride + 1] - p = [(padding // (maxPadding * 4)) % maxPadding, - (padding // (maxPadding * 2)) % maxPadding, - (padding // (maxPadding * 1)) % maxPadding, - padding % maxPadding] - d = [ dilation // maxDilation + 1, - dilation % maxDilation + 1] + s = [stride // maxStride + 1, stride % maxStride + 1] + p = [ + (padding // (maxPadding * 4)) % maxPadding, + (padding // (maxPadding * 2)) % maxPadding, + (padding // (maxPadding * 1)) % maxPadding, + padding % maxPadding, + ] + d = [dilation // maxDilation + 1, dilation % maxDilation + 1] # 4 padding parameters for regular conv2d - arg_list.append(('st{}{}_pad{}{}{}{}_dilat{}{}'.format(s[0], s[1], - p[0], p[1], p[2], p[3], - d[0], d[1]), - [ s, p, d ])) + arg_list.append( + ( + "st{}{}_pad{}{}{}{}_dilat{}{}".format( + s[0], s[1], p[0], p[1], p[2], p[3], d[0], d[1] + ), + [s, p, d], + ) + ) return arg_list @staticmethod @@ -380,8 +396,8 @@ class TosaArgGen: filter_shape = shapeList[1] # Must be rank 4 - assert(len(ifm_shape) == 4) - assert(len(filter_shape) == 4) + assert len(ifm_shape) == 4 + assert len(filter_shape) == 4 maxStride = testGen.args.max_conv_stride maxPadding = testGen.args.max_conv_padding + 1 @@ -392,27 +408,47 @@ class TosaArgGen: for out_padding in range(0, (maxPadding) ** 2): for dilation in range(0, maxDilation ** 2): - s = [stride // maxStride + 1, - stride % maxStride + 1] - p = [(out_padding // (maxPadding * 1)) % maxPadding, - out_padding % maxPadding] - d = [ dilation // maxDilation + 1, - dilation % maxDilation + 1] - - oh = (ifm_shape[1] - filter_shape[1] - (filter_shape[1] - 1) * (d[0] - 1) + \ - 2 * p[0]) // s[0] + 1 - - ow = (ifm_shape[2] - filter_shape[2] - (filter_shape[2] - 1) * (d[1] - 1) + \ - 2 * p[1]) // s[1] + 1 + s = [stride // maxStride + 1, stride % maxStride + 1] + p = [ + (out_padding // (maxPadding * 1)) % maxPadding, + out_padding % maxPadding, + ] + d = [dilation // maxDilation + 1, dilation % maxDilation + 1] + + oh = ( + ifm_shape[1] + - filter_shape[1] + - (filter_shape[1] - 1) * (d[0] - 1) + + 2 * p[0] + ) // s[0] + 1 + + ow = ( + ifm_shape[2] + - filter_shape[2] + - (filter_shape[2] - 1) * (d[1] - 1) + + 2 * p[1] + ) // s[1] + 1 # Output shape - os = [ ifm_shape[0], oh, ow, filter_shape[0] ] - - arg_list.append(('st{}{}_outpad{}{}_dilat{}{}_os{}x{}x{}x{}'.format(s[0], s[1], - p[0], p[1], - d[0], d[1], - os[0], os[1], os[2], os[3]), - [ s, p, d, os ])) + os = [ifm_shape[0], oh, ow, filter_shape[0]] + + arg_list.append( + ( + "st{}{}_outpad{}{}_dilat{}{}_os{}x{}x{}x{}".format( + s[0], + s[1], + p[0], + p[1], + d[0], + d[1], + os[0], + os[1], + os[2], + os[3], + ), + [s, p, d, os], + ) + ) return arg_list @@ -430,14 +466,14 @@ class TosaArgGen: paddings = np.zeros((rank * 2), dtype=np.int32) # Fill in the 1's - for r in (range(rank * 2)): + for r in range(rank * 2): if (v >> r) & 1: paddings[r] = 1 # Reshape back to a 2D array paddings = paddings.reshape((rank, 2)) - arg_list.append(('pad{0:b}'.format(v), [ paddings ])) + arg_list.append(("pad{0:b}".format(v), [paddings])) return arg_list @@ -446,7 +482,7 @@ class TosaArgGen: arg_list = [] shape = shapeList[0] - assert(len(shape) == 4) + assert len(shape) == 4 maxStride = testGen.args.max_pooling_stride maxKernel = testGen.args.max_pooling_kernel @@ -455,19 +491,23 @@ class TosaArgGen: for kernel in range(0, maxKernel ** 2): for stride in range(0, maxStride ** 2): for padding in range(0, maxPadding ** 4): - s = [stride // maxStride + 1, - stride % maxStride + 1] - k = [(kernel // maxKernel) + 2, - (kernel % maxKernel) + 2] - p = [(padding // (maxPadding * 4)) % maxPadding, - (padding // (maxPadding * 2)) % maxPadding, - (padding // (maxPadding * 1)) % maxPadding, - padding % maxPadding] - - arg_list.append(('st{}{}_kern{}{}_pad{}{}{}{}'.format(s[0], s[1], - k[0], k[1], - p[0], p[1], p[2], p[3]), - [k, s, p])) + s = [stride // maxStride + 1, stride % maxStride + 1] + k = [(kernel // maxKernel) + 2, (kernel % maxKernel) + 2] + p = [ + (padding // (maxPadding * 4)) % maxPadding, + (padding // (maxPadding * 2)) % maxPadding, + (padding // (maxPadding * 1)) % maxPadding, + padding % maxPadding, + ] + + arg_list.append( + ( + "st{}{}_kern{}{}_pad{}{}{}{}".format( + s[0], s[1], k[0], k[1], p[0], p[1], p[2], p[3] + ), + [k, s, p], + ) + ) return arg_list @staticmethod @@ -476,20 +516,20 @@ class TosaArgGen: # Enumerate the output types here if inDtype == DType.INT8: - dtypeList = [ DType.BOOL, DType.INT16, DType.INT32, DType.FLOAT ] + dtypeList = [DType.BOOL, DType.INT16, DType.INT32, DType.FLOAT] elif inDtype == DType.INT16: - dtypeList = [ DType.BOOL, DType.INT8, DType.INT32, DType.FLOAT ] + dtypeList = [DType.BOOL, DType.INT8, DType.INT32, DType.FLOAT] elif inDtype == DType.INT32: - dtypeList = [ DType.BOOL, DType.INT8, DType.INT16, DType.FLOAT ] + dtypeList = [DType.BOOL, DType.INT8, DType.INT16, DType.FLOAT] elif inDtype == DType.BOOL: - dtypeList = [ DType.INT8, DType.INT16, DType.INT32 ] + dtypeList = [DType.INT8, DType.INT16, DType.INT32] elif inDtype == DType.FLOAT: - dtypeList = [ DType.INT8, DType.INT16, DType.INT32 ] + dtypeList = [DType.INT8, DType.INT16, DType.INT32] else: - raise Exception('Unexpected input dtype: {}'.format(inDtype)) + raise Exception("Unexpected input dtype: {}".format(inDtype)) for dtype in dtypeList: - arg_list.append(('out{}'.format(DTypeNames[dtype]), [dtype])) + arg_list.append(("out{}".format(DTypeNames[dtype]), [dtype])) return arg_list @@ -498,17 +538,26 @@ class TosaArgGen: arg_list = [] # Enumerate the output types here - for dtype in [ DType.INT8, DType.INT16, DType.INT32 ]: - for scale32 in [ False, True ]: - for double_round in [ False, True ]: - for per_channel in [ False, True ]: + for dtype in [DType.INT8, DType.INT16, DType.INT32]: + for scale32 in [False, True]: + for double_round in [False, True]: + for per_channel in [False, True]: if inDtype == DType.INT48 and scale32: # Illegal condition. Must be scale32=False continue - arg_list.append(('out{}_sc{}_dr{}_pc{}'.format(DTypeNames[dtype], int(scale32), int(double_round), int(per_channel)), - [dtype, scale32, double_round, per_channel])) + arg_list.append( + ( + "out{}_sc{}_dr{}_pc{}".format( + DTypeNames[dtype], + int(scale32), + int(double_round), + int(per_channel), + ), + [dtype, scale32, double_round, per_channel], + ) + ) return arg_list @@ -521,9 +570,9 @@ class TosaArgGen: shift = testGen.randInt(0, 32) - arg_list.append(('perm{}_shift{}'.format(p, shift), [shift])) + arg_list.append(("perm{}_shift{}".format(p, shift), [shift])) else: - arg_list.append(('shift0', [0])) + arg_list.append(("shift0", [0])) return arg_list @@ -531,8 +580,8 @@ class TosaArgGen: def agArithmeticRightShift(testGen, opName, shapeList, dtype): arg_list = [] - arg_list.append(('roundTrue', [True])) - arg_list.append(('roundFalse', [False])) + arg_list.append(("roundTrue", [True])) + arg_list.append(("roundFalse", [False])) return arg_list @@ -563,7 +612,7 @@ class TosaArgGen: for p in range(testGen.args.num_rand_permutations): newRank = testGen.randInt(1, 6) newShape = [] - if (len(factors) < newRank): + if len(factors) < newRank: continue remainingElements = totalElements @@ -572,7 +621,9 @@ class TosaArgGen: # pick rank-1 factors newShape.append(shuffledFactors[0]) remainingElements = remainingElements // shuffledFactors[0] - shuffledFactors = testGen.rng.permutation(TosaArgGen.getFactors(remainingElements)) + shuffledFactors = testGen.rng.permutation( + TosaArgGen.getFactors(remainingElements) + ) newShape.append(remainingElements) # Toss in a -1 sometimes @@ -580,11 +631,10 @@ class TosaArgGen: if minusOne < newRank: newShape[minusOne] = -1 - arg_list.append(('perm{}_rank{}'.format(p, newRank), [newShape])) + arg_list.append(("perm{}_rank{}".format(p, newRank), [newShape])) return arg_list - @staticmethod def agTranspose(testGen, opName, shapeList, dtype): arg_list = [] @@ -603,7 +653,7 @@ class TosaArgGen: break if not found: - arg_list.append(('perm{}'.format(p), [perms])) + arg_list.append(("perm{}".format(p), [perms])) return arg_list @@ -618,7 +668,7 @@ class TosaArgGen: begin = [] size = [] - valid=True + valid = True for i in range(rank): if ifm_shape[i] > 1: @@ -633,7 +683,7 @@ class TosaArgGen: size.append(1) if valid: - arg_list.append(('perm{}'.format(p), [begin, size])) + arg_list.append(("perm{}".format(p), [begin, size])) return arg_list @staticmethod @@ -652,7 +702,7 @@ class TosaArgGen: for i in range(rank): multiples.append(testGen.randInt(1, 4)) - arg_list.append(('perm{}'.format(p), [multiples])) + arg_list.append(("perm{}".format(p), [multiples])) return arg_list @@ -666,15 +716,15 @@ class TosaArgGen: # Exclude illegal {mode, type} configurations. Pick legal output types if m == ResizeMode.NEAREST and dtype == DType.INT8: - outputDTypeList = [ DType.INT32 ] + outputDTypeList = [DType.INT32] elif m == ResizeMode.NEAREST and dtype == DType.INT16: - outputDTypeList = [ DType.INT16 ] + outputDTypeList = [DType.INT16] elif m == ResizeMode.BILINEAR and dtype == DType.INT8: - outputDTypeList = [ DType.INT8 ] + outputDTypeList = [DType.INT8] elif m == ResizeMode.BILINEAR and dtype == DType.INT16: - outputDTypeList = [ DType.INT48 ] + outputDTypeList = [DType.INT48] elif dtype == DType.FLOAT: - outputDTypeList = [ DType.FLOAT ] + outputDTypeList = [DType.FLOAT] else: continue @@ -683,7 +733,7 @@ class TosaArgGen: # Randomly generate legal output dimensions and shift # and then compute the stride and offset based on them - output_dims = [ testGen.randInt(1), testGen.randInt(1) ] + output_dims = [testGen.randInt(1), testGen.randInt(1)] in_center_h = (ifm_shape[1] - 1) / 2.0 in_center_w = (ifm_shape[2] - 1) / 2.0 out_center_h = (output_dims[0] - 1) / 2.0 @@ -698,12 +748,33 @@ class TosaArgGen: shift = 0 stride = [0, 0] offset = [0, 0] - stride_fp = [ fp_stride_y, fp_stride_x] - offset_fp = [ fp_offset_y, fp_offset_x] - arg_list.append(('mode{}_odim{}x{}_out{}_st{:.2f}x{:.2f}_off{:.2f}x{:.2f}'.format(m, output_dims[0], output_dims[1], - testGen.typeStr(outputDType), stride_fp[0], stride_fp[1], - offset_fp[0], offset_fp[1]), - [m, stride, offset, shift, stride_fp, offset_fp, output_dims, dtype, outputDType])) + stride_fp = [fp_stride_y, fp_stride_x] + offset_fp = [fp_offset_y, fp_offset_x] + arg_list.append( + ( + "mode{}_odim{}x{}_out{}_st{:.2f}x{:.2f}_off{:.2f}x{:.2f}".format( + m, + output_dims[0], + output_dims[1], + testGen.typeStr(outputDType), + stride_fp[0], + stride_fp[1], + offset_fp[0], + offset_fp[1], + ), + [ + m, + stride, + offset, + shift, + stride_fp, + offset_fp, + output_dims, + dtype, + outputDType, + ], + ) + ) else: shift = 11 unit = float(1 << shift) @@ -712,7 +783,14 @@ class TosaArgGen: offset_y = int(round(fp_offset_y * unit)) offset_x = int(round(fp_offset_x * unit)) - while (stride_y >= 32768 or stride_x >= 32768 or offset_y >= 32768 or offset_x >= 32768 or offset_y < -32768 or offset_x < -32768): + while ( + stride_y >= 32768 + or stride_x >= 32768 + or offset_y >= 32768 + or offset_x >= 32768 + or offset_y < -32768 + or offset_x < -32768 + ): shift = shift - 1 unit = float(1 << shift) stride_y = int(round(fp_stride_y * unit)) @@ -720,16 +798,38 @@ class TosaArgGen: offset_y = int(round(fp_offset_y * unit)) offset_x = int(round(fp_offset_x * unit)) - stride = [ stride_y, stride_x] - offset = [ offset_y, offset_x] + stride = [stride_y, stride_x] + offset = [offset_y, offset_x] stride_fp = [0.0, 0.0] offset_fp = [0.0, 0.0] - arg_list.append(('mode{}_shift{}_odim{}x{}_out{}_st{}x{}_off{}x{}'.format(m, shift, output_dims[0], output_dims[1], - testGen.typeStr(outputDType), stride[0], stride[1], - offset[0], offset[1]), - [m, stride, offset, shift, stride_fp, offset_fp, output_dims, dtype, outputDType])) + arg_list.append( + ( + "mode{}_shift{}_odim{}x{}_out{}_st{}x{}_off{}x{}".format( + m, + shift, + output_dims[0], + output_dims[1], + testGen.typeStr(outputDType), + stride[0], + stride[1], + offset[0], + offset[1], + ), + [ + m, + stride, + offset, + shift, + stride_fp, + offset_fp, + output_dims, + dtype, + outputDType, + ], + ) + ) return arg_list @@ -740,7 +840,7 @@ class TosaArgGen: arg_list = [] for c in [False, True]: - arg_list.append(('cond{}'.format(int(c)), [ c ])) + arg_list.append(("cond{}".format(int(c)), [c])) return arg_list @@ -749,10 +849,11 @@ class TosaArgGen: arg_list = [] for iter in [0, 1, 4]: - arg_list.append(('iter{}'.format(iter), [ iter ])) + arg_list.append(("iter{}".format(iter), [iter])) return arg_list + class TosaTestGen: def __init__(self, args): self.args = args @@ -777,11 +878,13 @@ class TosaTestGen: return self.ser def serialize(self, testName): - with open(os.path.join(self.basePath, self.testPath, '{}.tosa'.format(testName)), 'wb') as fd: + with open( + os.path.join(self.basePath, self.testPath, "{}.tosa".format(testName)), "wb" + ) as fd: fd.write(self.ser.serialize()) - with open(os.path.join(self.basePath, self.testPath, 'desc.json'), 'w') as fd: - fd.write(self.ser.writeJson('{}.tosa'.format(testName))) + with open(os.path.join(self.basePath, self.testPath, "desc.json"), "w") as fd: + fd.write(self.ser.writeJson("{}.tosa".format(testName))) def getRandTensor(self, shape, dtype): RAND_SHIFT_FACTOR = 0.5 @@ -797,20 +900,26 @@ class TosaTestGen: elif dtype == DType.INT16: return np.int32(self.rng.integers(low=-32768, high=32768, size=shape)) elif dtype == DType.INT32: - return np.int32(self.rng.integers(low=-(1 << 31), high=(1 << 31), size=shape)) + return np.int32( + self.rng.integers(low=-(1 << 31), high=(1 << 31), size=shape) + ) elif dtype == DType.INT48: - return np.int64(self.rng.integers(low=-(1 << 47), high=(1 << 47), size=shape)) + return np.int64( + self.rng.integers(low=-(1 << 47), high=(1 << 47), size=shape) + ) elif dtype == DType.FLOAT: - return np.float32(self.rng.random(size=shape) - RAND_SHIFT_FACTOR * RAND_SCALE_FACTOR) + return np.float32( + self.rng.random(size=shape) - RAND_SHIFT_FACTOR * RAND_SCALE_FACTOR + ) else: - raise Exception('Unrecognized Dtype: {}'.format(dtype)) + raise Exception("Unrecognized Dtype: {}".format(dtype)) def buildPlaceholderTensors(self, shape_list, dtype): placeholders = [] for shape in shape_list: arr = self.getRandTensor(shape, dtype) - placeholders.append(self.ser.addPlaceholder(shape, dtype, Usage.ACTIVATION, [], arr)) + placeholders.append(self.ser.addPlaceholder(shape, dtype, arr)) return placeholders @@ -819,16 +928,20 @@ class TosaTestGen: for shape in shape_list: arr = self.getRandTensor(shape, dtype) - consts.append(self.ser.addConst(shape, dtype, Usage.ACTIVATION, [], arr)) + consts.append(self.ser.addConst(shape, dtype, arr)) return consts def makeShape(self, rank): if self.targetted_shape: return np.int32(self.targetted_shape) - return np.int32(self.rng.integers(low=self.args.tensor_shape_range[0], - high=self.args.tensor_shape_range[1], - size=rank)) + return np.int32( + self.rng.integers( + low=self.args.tensor_shape_range[0], + high=self.args.tensor_shape_range[1], + size=rank, + ) + ) def setTargetShape(self, shape): self.targetted_shape = shape @@ -848,13 +961,13 @@ class TosaTestGen: elif dtype == DType.INT16: low, high = (-32768, 32768) elif dtype == DType.INT32: - low, high = (-(1<<31), (1<<31)) + low, high = (-(1 << 31), (1 << 31)) elif dtype == DType.INT48: - low, high = (-(1<<47), (1<<47)) + low, high = (-(1 << 47), (1 << 47)) # Special size return np.int64(self.rng.integers(low, high, size=1))[0] else: - raise Exception('Unknown dtype: {}'.format(dtype)) + raise Exception("Unknown dtype: {}".format(dtype)) return np.int32(self.rng.integers(low, high, size=1))[0] @@ -865,30 +978,30 @@ class TosaTestGen: for i in shape: sStr.append(str(i)) - return 'x'.join(sStr) + return "x".join(sStr) def typeStr(self, t): if t == DType.BOOL: - return 'b' + return "b" elif t == DType.INT4: - return 'i4' + return "i4" elif t == DType.INT8: - return 'i8' + return "i8" elif t == DType.UINT8: - return 'u8' + return "u8" elif t == DType.INT16: - return 'i16' + return "i16" elif t == DType.INT32: - return 'i32' + return "i32" elif t == DType.INT48: - return 'i48' + return "i48" elif t == DType.FLOAT: - return 'float' + return "float" else: - raise Exception('Unknown dtype, cannot convert to string: {}'.format(t)) + raise Exception("Unknown dtype, cannot convert to string: {}".format(t)) def typeWidth(self, t): - ''' Get the datatype width for integer types''' + """ Get the datatype width for integer types""" if t == DType.INT4: return 4 elif t == DType.INT8: @@ -902,7 +1015,7 @@ class TosaTestGen: elif t == DType.INT48: return 48 else: - raise Exception('Unknown dtype, cannot convert to string: {}'.format(t)) + raise Exception("Unknown dtype, cannot convert to string: {}".format(t)) # Argument generators # Returns a list of tuples (stringDescriptor, [build_fcn_arg_list]) @@ -910,8 +1023,7 @@ class TosaTestGen: # The build_fcn_arg_list is expanded and passed to the operator test # build function - - def build_unary(self, op, a, qinfo = None): + def build_unary(self, op, a, qinfo=None): result_tens = OutputShaper.unaryOp(self.ser, a) self.ser.addOperator(op, [a.name], [result_tens.name], None, qinfo) return result_tens @@ -952,7 +1064,7 @@ class TosaTestGen: def build_table(self, op, a): # Constant size, random values table_arr = self.getRandTensor([513], DType.INT16) - table_tens = self.ser.addConst(table_arr.shape, DType.INT16, Usage.INDEX, [], table_arr) + table_tens = self.ser.addConst(table_arr.shape, DType.INT16, table_arr) result_tens = OutputShaper.tableOp(self.ser, a, table_tens) self.ser.addOperator(op, [a.name, table_tens.name], [result_tens.name], None) @@ -985,43 +1097,38 @@ class TosaTestGen: self.ser.addOperator(op, [a.name], [result_tens.name], attr) return result_tens - def build_pool2d(self, op, input, kernel, stride, pad, qinfo = None): + def build_pool2d(self, op, input, kernel, stride, pad, qinfo=None): result_tens = OutputShaper.pool2dOp(self.ser, input, kernel, stride, pad) attr = ts.TosaSerializerAttribute() attr.Pool2dAttribute(kernel, stride, pad) - input.addFormat(Format.NHWC) self.ser.addOperator(op, [input.name], [result_tens.name], attr, qinfo) return result_tens def build_conv2d(self, op, ifm, filter, bias, strides, padding, dilations, qinfo): - assert(len(padding) == 4) - result_tens = OutputShaper.conv2dOp(self.ser, ifm, filter, strides, padding, dilations) + assert len(padding) == 4 + result_tens = OutputShaper.conv2dOp( + self.ser, ifm, filter, strides, padding, dilations + ) attr = ts.TosaSerializerAttribute() attr.Conv2dAttribute(padding, strides, dilations) - ifm.addFormat(Format.NHWC) - # Update the filter ordering - filter.addUsage(Usage.WEIGHT) - filter.addFormat(Format.OHWI) - - self.ser.addOperator(op, [ifm.name, filter.name, bias.name], [result_tens.name], attr, qinfo) + self.ser.addOperator( + op, [ifm.name, filter.name, bias.name], [result_tens.name], attr, qinfo + ) return result_tens - def build_transpose_conv2d(self, op, ifm, filter, stride, outpad, dilation, output_shape, qinfo): - assert(len(outpad) == 2) + def build_transpose_conv2d( + self, op, ifm, filter, stride, outpad, dilation, output_shape, qinfo + ): + assert len(outpad) == 2 result_tens = OutputShaper.transposeConv2DOp(self.ser, ifm, output_shape) attr = ts.TosaSerializerAttribute() attr.TransposeConv2DAttribute(outpad, stride, dilation, output_shape) - ifm.addFormat(Format.NHWC) - # Update the filter ordering - filter.addUsage(Usage.WEIGHT) - filter.addFormat(Format.OHWI) - # Create bias here since the acc_t depends on (but isn't the same as) the input dtype # The bias is OC if ifm.dtype == DType.INT8: @@ -1031,32 +1138,39 @@ class TosaTestGen: elif ifm.dtype == DType.FLOAT: bias_type = DType.FLOAT else: - raise Exception('Unsupported dtype for transpose_conv2d: {}'.format(ifm.dtype)) + raise Exception( + "Unsupported dtype for transpose_conv2d: {}".format(ifm.dtype) + ) bias_arr = self.getRandTensor([filter.shape[0]], bias_type) - bias_tens = self.ser.addConst([filter.shape[0]], bias_type, [], [], bias_arr) + bias_tens = self.ser.addConst([filter.shape[0]], bias_type, bias_arr) - self.ser.addOperator(op, [ifm.name, filter.name, bias_tens.name], [result_tens.name], attr, qinfo) + self.ser.addOperator( + op, [ifm.name, filter.name, bias_tens.name], [result_tens.name], attr, qinfo + ) return result_tens - def build_depthwise_conv2d(self, op, ifm, filter, bias, strides, padding, dilations, qinfo): - result_tens = OutputShaper.depthwiseConv2dOp(self.ser, ifm, filter, strides, padding, dilations) + def build_depthwise_conv2d( + self, op, ifm, filter, bias, strides, padding, dilations, qinfo + ): + result_tens = OutputShaper.depthwiseConv2dOp( + self.ser, ifm, filter, strides, padding, dilations + ) attr = ts.TosaSerializerAttribute() attr.Conv2dAttribute(padding, strides, dilations) - ifm.addFormat(Format.NHWC) - filter.addUsage(Usage.WEIGHT) - filter.addFormat(Format.HWIM) - - self.ser.addOperator(op, [ifm.name, filter.name, bias.name], [result_tens.name], attr, qinfo) + self.ser.addOperator( + op, [ifm.name, filter.name, bias.name], [result_tens.name], attr, qinfo + ) return result_tens def build_fully_connected(self, op, ifm, filter, bias, qinfo): result_tens = OutputShaper.fullyConnectedOp(self.ser, ifm, filter) - filter.addUsage(Usage.WEIGHT) - self.ser.addOperator(op, [ifm.name, filter.name, bias.name], [result_tens.name], None, qinfo) + self.ser.addOperator( + op, [ifm.name, filter.name, bias.name], [result_tens.name], None, qinfo + ) return result_tens def build_matmul(self, op, a, b, qinfo): @@ -1142,9 +1256,11 @@ class TosaTestGen: # Need to turn the padding array into a TOSA tensor here. # This is one of the few tensor operands that does not get # randomly generated - padding_tens = self.ser.addConst(padding.shape, DType.INT32, [], [], padding) + padding_tens = self.ser.addConst(padding.shape, DType.INT32, padding) - self.ser.addOperator(op, [a.name, padding_tens.name], [result_tens.name], None, qinfo) + self.ser.addOperator( + op, [a.name, padding_tens.name], [result_tens.name], None, qinfo + ) def build_reshape(self, op, a, newShape): result_tens = OutputShaper.reshapeOp(self.ser, a, newShape) @@ -1167,7 +1283,7 @@ class TosaTestGen: def build_transpose(self, op, a, perms): result_tens = OutputShaper.transposeOp(self.ser, a, perms) - perms_tens = self.ser.addConst([len(perms)], DType.INT32, Usage.ACTIVATION, [], np.int32(perms)) + perms_tens = self.ser.addConst([len(perms)], DType.INT32, np.int32(perms)) self.ser.addOperator(op, [a.name, perms_tens.name], [result_tens.name]) return result_tens @@ -1190,16 +1306,19 @@ class TosaTestGen: self.ser.addOperator(op, [a.name], [result_tens.name], attr) return result_tens - def build_gather(self, op, values): # Create a new indicies tensor # here with data that doesn't exceed the dimensions of the values tensor - K = values.shape[1] # K - W = self.randInt(self.args.tensor_shape_range[0], self.args.tensor_shape_range[1]) # W - indicies_arr = np.int32(self.rng.integers(low=0, high=K, size=[values.shape[0], W])) # (N, W) - indicies = self.ser.addConst(indicies_arr.shape, DType.INT32, Usage.INDEX, [], indicies_arr) + K = values.shape[1] # K + W = self.randInt( + self.args.tensor_shape_range[0], self.args.tensor_shape_range[1] + ) # W + indicies_arr = np.int32( + self.rng.integers(low=0, high=K, size=[values.shape[0], W]) + ) # (N, W) + indicies = self.ser.addConst(indicies_arr.shape, DType.INT32, indicies_arr) result_tens = OutputShaper.gatherOp(self.ser, values, indicies) @@ -1212,32 +1331,65 @@ class TosaTestGen: # Create a new indicies tensor # here with data that doesn't exceed the dimensions of the values_in tensor - K = values_in.shape[1] # K - W = input.shape[1] # W - indicies_arr = np.int32(self.rng.integers(low=0, high=K, size=[values_in.shape[0], W])) # (N, W) - indicies = self.ser.addConst(indicies_arr.shape, DType.INT32, Usage.INDEX, [], indicies_arr) + K = values_in.shape[1] # K + W = input.shape[1] # W + indicies_arr = np.int32( + self.rng.integers(low=0, high=K, size=[values_in.shape[0], W]) + ) # (N, W) + indicies = self.ser.addConst(indicies_arr.shape, DType.INT32, indicies_arr) result_tens = OutputShaper.scatterOp(self.ser, values_in, indicies, input) - self.ser.addOperator(op, [values_in.name, indicies.name, input.name], [result_tens.name]) + self.ser.addOperator( + op, [values_in.name, indicies.name, input.name], [result_tens.name] + ) return result_tens - def build_resize(self, op, input, mode, stride, offset, shift, stride_fp, offset_fp, output_dims, input_dtype, output_dtype): - result_tens = OutputShaper.resizeOp(self.ser, input, mode, stride, offset, shift, stride_fp, offset_fp, output_dims, input_dtype, output_dtype) + def build_resize( + self, + op, + input, + mode, + stride, + offset, + shift, + stride_fp, + offset_fp, + output_dims, + input_dtype, + output_dtype, + ): + result_tens = OutputShaper.resizeOp( + self.ser, + input, + mode, + stride, + offset, + shift, + stride_fp, + offset_fp, + output_dims, + input_dtype, + output_dtype, + ) attr = ts.TosaSerializerAttribute() - attr.ResizeAttribute(output_dims, stride, offset, shift, stride_fp, offset_fp, mode) + attr.ResizeAttribute( + output_dims, stride, offset, shift, stride_fp, offset_fp, mode + ) self.ser.addOperator(op, [input.name], [result_tens.name], attr) return result_tens def build_identityn(self, op, val, val2): - result_tens = OutputShaper.unaryOp(self.ser, val) + result_tens = OutputShaper.unaryOp(self.ser, val) result_tens2 = OutputShaper.unaryOp(self.ser, val2) - self.ser.addOperator(op, [val.name, val2.name], [result_tens.name, result_tens2.name]) + self.ser.addOperator( + op, [val.name, val2.name], [result_tens.name, result_tens2.name] + ) return result_tens def build_placeholder(self, op, val): @@ -1287,27 +1439,30 @@ class TosaTestGen: # Cap the scaling at 2^15 - 1 for scale16 scale_arr = np.clip(scale_arr, 1.0 / (1 << 31), 32767.0) - #print('{} {} -> {}'.format(out_type_width, in_type_width, scale_arr)) + # print('{} {} -> {}'.format(out_type_width, in_type_width, scale_arr)) multiplier_arr = np.int32(np.zeros(shape=[nc])) shift_arr = np.int32(np.zeros(shape=[nc])) for i in range(nc): - multiplier_arr[i], shift_arr[i] = TosaQuantGen.computeMultiplierAndShift(scale_arr[i], scale32) + multiplier_arr[i], shift_arr[i] = TosaQuantGen.computeMultiplierAndShift( + scale_arr[i], scale32 + ) if shift_arr[i] < 2 or shift_arr[i] > 62: - self.ser.setExpectedFailure(True, 'OpRescale: invalid shift value') + self.ser.setExpectedFailure(True, "OpRescale: invalid shift value") - #print('multiplier {} shift {} inzp {} outzp {}'.format(multiplier_arr, shift_arr, input_zp, output_zp)) + # print('multiplier {} shift {} inzp {} outzp {}'.format(multiplier_arr, shift_arr, input_zp, output_zp)) attr = ts.TosaSerializerAttribute() - attr.RescaleAttribute(input_zp, - output_zp, - multiplier_arr, - shift_arr, - scale32, - double_round, - - per_channel) + attr.RescaleAttribute( + input_zp, + output_zp, + multiplier_arr, + shift_arr, + scale32, + double_round, + per_channel, + ) self.ser.addOperator(op, [val.name], [result_tens.name], attr) return result_tens @@ -1318,7 +1473,7 @@ class TosaTestGen: # and fill them with const nodes for the body. # Condition tensor - cond_tens = self.ser.addConst([], DType.BOOL, Usage.ACTIVATION, [], [cond]) + cond_tens = self.ser.addConst([], DType.BOOL, [cond]) # Make then/else tensors out_shape = then_tens.shape @@ -1326,11 +1481,11 @@ class TosaTestGen: else_arr = np.int32(self.rng.integers(0, 255, size=out_shape)) # And the result tensor based on any of the outputs - result_tens = self.ser.addOutput(out_shape, DType.INT32, Usage.ACTIVATION, []) + result_tens = self.ser.addOutput(out_shape, DType.INT32) # Create the attribute with the names of the then/else blocks - then_block = 'THEN_BLOCK' - else_block = 'ELSE_BLOCK' + then_block = "THEN_BLOCK" + else_block = "ELSE_BLOCK" attr = ts.TosaSerializerAttribute() attr.CondIfAttribute(then_block, else_block) @@ -1339,11 +1494,11 @@ class TosaTestGen: self.ser.startBasicBlock(then_block) # Build the actual then/else tensors inside their blocks - then_tens = self.ser.addConst(out_shape, DType.INT32, Usage.ACTIVATION, [], then_arr) + then_tens = self.ser.addConst(out_shape, DType.INT32, then_arr) self.ser.addOutputTensor(then_tens) self.ser.startBasicBlock(else_block) - else_tens = self.ser.addConst(out_shape, DType.INT32, Usage.ACTIVATION, [], else_arr) + else_tens = self.ser.addConst(out_shape, DType.INT32, else_arr) self.ser.addOutputTensor(else_tens) return result_tens @@ -1353,67 +1508,71 @@ class TosaTestGen: # alternately add or subtract them based on the condition # Condition tensor - cond_tens = self.ser.addConst([], DType.BOOL, Usage.ACTIVATION, [], [cond]) + cond_tens = self.ser.addConst([], DType.BOOL, [cond]) - result_tens = self.ser.addOutput(a.shape, a.dtype, Usage.ACTIVATION, []) + result_tens = self.ser.addOutput(a.shape, a.dtype) self.ser.currBasicBlock.addOutput(result_tens.name) # Create the attribute with the names of the then/else blocks - then_block = 'THEN_BLOCK' - else_block = 'ELSE_BLOCK' + then_block = "THEN_BLOCK" + else_block = "ELSE_BLOCK" attr = ts.TosaSerializerAttribute() attr.CondIfAttribute(then_block, else_block) # Finally, build the op and the two blocks - self.ser.addOperator(op, [cond_tens.name, a.name, b.name], [result_tens.name], attr) + self.ser.addOperator( + op, [cond_tens.name, a.name, b.name], [result_tens.name], attr + ) self.ser.startBasicBlock(then_block) self.ser.addInputTensor(a) self.ser.addInputTensor(b) - then_tens = self.ser.addOutput(a.shape, a.dtype, a.usage, a.dformat) + then_tens = self.ser.addOutput(a.shape, a.dtype) self.ser.addOperator(Op.ADD, [a.name, b.name], [then_tens.name]) self.ser.startBasicBlock(else_block) self.ser.addInputTensor(a) self.ser.addInputTensor(b) - else_tens = self.ser.addOutput(a.shape, a.dtype, a.usage, a.dformat) + else_tens = self.ser.addOutput(a.shape, a.dtype) self.ser.addOperator(Op.SUB, [a.name, b.name], [else_tens.name]) return result_tens def build_while_loop(self, op, a, iter_val): - iter = self.ser.addPlaceholder([], DType.INT32, Usage.ACTIVATION, [], [np.int32(iter_val)]) + iter = self.ser.addPlaceholder([], DType.INT32, [np.int32(iter_val)]) - cond_block = 'COND_BLOCK' - body_block = 'BODY_BLOCK' + cond_block = "COND_BLOCK" + body_block = "BODY_BLOCK" attr = ts.TosaSerializerAttribute() attr.WhileLoopAttribute(cond_block, body_block) # Accumulator tensor - #acc = self.ser.addOutput(a.shape, a.dtype, a.usage, a.dformat) + # acc = self.ser.addOutput(a.shape, a.dtype) acc_init_val = np.int32(np.zeros(a.shape)) - acc = self.ser.addPlaceholder(a.shape, a.dtype, a.usage, a.dformat, acc_init_val) + acc = self.ser.addPlaceholder(a.shape, a.dtype, acc_init_val) # Intermediate/output tensors for everything going through the loop - iter_out = self.ser.addIntermediate(iter.shape, iter.dtype, iter.usage, iter.dformat) - a_out = self.ser.addIntermediate(a.shape, a.dtype, a.usage, a.dformat) - acc_out = self.ser.addIntermediate(acc.shape, acc.dtype, acc.usage, acc.dformat) + iter_out = self.ser.addIntermediate(iter.shape, iter.dtype) + a_out = self.ser.addIntermediate(a.shape, a.dtype) + acc_out = self.ser.addIntermediate(acc.shape, acc.dtype) # While_loop operator - self.ser.addOperator(op, - [iter.name, a.name, acc.name], - [iter_out.name, a_out.name, acc_out.name], attr) + self.ser.addOperator( + op, + [iter.name, a.name, acc.name], + [iter_out.name, a_out.name, acc_out.name], + attr, + ) # COND block (input: iter, output: cond_tens ) self.ser.startBasicBlock(cond_block) self.ser.addInputTensor(iter) self.ser.addInputTensor(a) self.ser.addInputTensor(acc) - zero_tens = self.ser.addConst([], DType.INT32, [], [], [np.int32(0)]) - cond_tens = self.ser.addOutput([], DType.BOOL, [], []) - self.ser.addOperator(Op.GREATER, [iter.name, zero_tens.name], - [cond_tens.name]) + zero_tens = self.ser.addConst([], DType.INT32, [np.int32(0)]) + cond_tens = self.ser.addOutput([], DType.BOOL) + self.ser.addOperator(Op.GREATER, [iter.name, zero_tens.name], [cond_tens.name]) # BODY block (input: a, acc, iter, output: a, acc, iter) # Note that local intermediate tensors need to be declared here for the outputs @@ -1421,9 +1580,9 @@ class TosaTestGen: self.ser.addInputTensor(iter) self.ser.addInputTensor(a) self.ser.addInputTensor(acc) - one_tens = self.ser.addConst([], DType.INT32, [], [], [np.int32(1)]) - iter_body_out = self.ser.addIntermediate(iter.shape, iter.dtype, iter.usage, iter.dformat) - acc_body_out = self.ser.addIntermediate(acc.shape, acc.dtype, acc.usage, acc.dformat) + one_tens = self.ser.addConst([], DType.INT32, [np.int32(1)]) + iter_body_out = self.ser.addIntermediate(iter.shape, iter.dtype) + acc_body_out = self.ser.addIntermediate(acc.shape, acc.dtype) self.ser.addOperator(Op.ADD, [a.name, acc.name], [acc_body_out.name]) self.ser.addOperator(Op.SUB, [iter.name, one_tens.name], [iter_body_out.name]) self.ser.addOutputTensor(iter_body_out) @@ -1432,21 +1591,22 @@ class TosaTestGen: return acc_out - - def genOpTestList(self, opName, shapeFilter=[None], rankFilter=None, dtypeFilter=None): + def genOpTestList( + self, opName, shapeFilter=[None], rankFilter=None, dtypeFilter=None + ): try: op = self.TOSA_OP_LIST[opName] except KeyError as e: - raise Exception('Cannot find op with name {}'.format(opName)) + raise Exception("Cannot find op with name {}".format(opName)) # Initialize a new random number generator self.rng = np.random.default_rng(self.random_seed) - build_fcn, tgen_fcn, agen_fcn = op['build_fcn'] + build_fcn, tgen_fcn, agen_fcn = op["build_fcn"] # Generate the lists of arguments - rmin, rmax = op['rank'] + rmin, rmax = op["rank"] # Test list consists of a tuple of: # (opName, testNameStr, dtype, shapeList, argumentsList) @@ -1461,7 +1621,7 @@ class TosaTestGen: if rankFilter is not None and r not in rankFilter: continue - for t in op['types']: + for t in op["types"]: # Filter tests based on dtype? if dtypeFilter is not None: @@ -1487,13 +1647,15 @@ class TosaTestGen: if agen_fcn: argList = agen_fcn(self, opName, shapeList, t) else: - argList = [('', [])] + argList = [("", [])] for argStr, args in argList: if argStr: - testStr = '{}_{}_{}_{}'.format(opName, shapeStr, typeStr, argStr) + testStr = "{}_{}_{}_{}".format( + opName, shapeStr, typeStr, argStr + ) else: - testStr = '{}_{}_{}'.format(opName, shapeStr, typeStr) + testStr = "{}_{}_{}".format(opName, shapeStr, typeStr) testList.append((opName, testStr, t, shapeList, args)) @@ -1503,16 +1665,16 @@ class TosaTestGen: try: op = self.TOSA_OP_LIST[opName] except KeyError as e: - raise Exception('Cannot find op with name {}'.format(opName)) + raise Exception("Cannot find op with name {}".format(opName)) # Create a serializer self.createSerializer(opName, testStr) - build_fcn, tgen_fcn, agen_fcn = op['build_fcn'] - pCount, cCount = op['operands'] + build_fcn, tgen_fcn, agen_fcn = op["build_fcn"] + pCount, cCount = op["operands"] try: - qgen = op['qgen'] + qgen = op["qgen"] except KeyError: qgen = None @@ -1520,8 +1682,10 @@ class TosaTestGen: tens = [] # If test is ArithmeticRightShift, force value of operand[1] to be within [0, num_bits] - if op['op'] == Op.ARITHMETIC_RIGHT_SHIFT: - assert pCount == 2 and cCount == 0, 'Op.ArithmeticRightShift must have 2 placeholders, 0 consts' + if op["op"] == Op.ARITHMETIC_RIGHT_SHIFT: + assert ( + pCount == 2 and cCount == 0 + ), "Op.ArithmeticRightShift must have 2 placeholders, 0 consts" placeholders = [] for idx, shape in enumerate(shapeList[:]): @@ -1533,10 +1697,10 @@ class TosaTestGen: elif dtype == DType.INT32: arr = np.int32(self.rng.integers(low=0, high=32, size=shape)) else: - raise Exception('OpArithmeticRightShift: invalid input dtype') + raise Exception("OpArithmeticRightShift: invalid input dtype") else: arr = self.getRandTensor(shapeList[0], dtype) - placeholders.append(self.ser.addPlaceholder(shape, dtype, Usage.ACTIVATION, [], arr)) + placeholders.append(self.ser.addPlaceholder(shape, dtype, arr)) tens.extend(placeholders) else: @@ -1550,36 +1714,44 @@ class TosaTestGen: try: if qinfo is not None: - resultName = build_fcn(self, op['op'], *tens, *testArgs, qinfo) + resultName = build_fcn(self, op["op"], *tens, *testArgs, qinfo) else: - resultName = build_fcn(self, op['op'], *tens, *testArgs) + resultName = build_fcn(self, op["op"], *tens, *testArgs) except TypeError as e: - print('build_fcn: {}\nTensors: {}\nArgs: {}\n'.format(build_fcn, tens, testArgs)) + print( + "build_fcn: {}\nTensors: {}\nArgs: {}\n".format( + build_fcn, tens, testArgs + ) + ) raise e # Save the serialized test - self.serialize('test') + self.serialize("test") def createDynamicOpLists(self): # Dynamically create op lists for convolutions with a list of kernel sizes - KERNELS = [ [1, 1], [2, 2], [3, 3], [5, 5], [3, 1], [1, 3] ] + KERNELS = [[1, 1], [2, 2], [3, 3], [5, 5], [3, 1], [1, 3]] for k in KERNELS: - testName = 'conv2d_{}x{}'.format(k[0], k[1]) - self.TOSA_OP_LIST[testName] = self.TOSA_OP_LIST['conv2d_TEMPLATE'].copy() - self.TOSA_OP_LIST[testName]['filter'] = k - self.TOSA_OP_LIST[testName]['template'] = False - - testName = 'depthwise_conv2d_{}x{}'.format(k[0], k[1]) - self.TOSA_OP_LIST[testName] = self.TOSA_OP_LIST['depthwise_conv2d_TEMPLATE'].copy() - self.TOSA_OP_LIST[testName]['filter'] = k - self.TOSA_OP_LIST[testName]['template'] = False - - testName = 'transpose_conv2d_{}x{}'.format(k[0], k[1]) - self.TOSA_OP_LIST[testName] = self.TOSA_OP_LIST['transpose_conv2d_TEMPLATE'].copy() - self.TOSA_OP_LIST[testName]['filter'] = k - self.TOSA_OP_LIST[testName]['template'] = False + testName = "conv2d_{}x{}".format(k[0], k[1]) + self.TOSA_OP_LIST[testName] = self.TOSA_OP_LIST["conv2d_TEMPLATE"].copy() + self.TOSA_OP_LIST[testName]["filter"] = k + self.TOSA_OP_LIST[testName]["template"] = False + + testName = "depthwise_conv2d_{}x{}".format(k[0], k[1]) + self.TOSA_OP_LIST[testName] = self.TOSA_OP_LIST[ + "depthwise_conv2d_TEMPLATE" + ].copy() + self.TOSA_OP_LIST[testName]["filter"] = k + self.TOSA_OP_LIST[testName]["template"] = False + + testName = "transpose_conv2d_{}x{}".format(k[0], k[1]) + self.TOSA_OP_LIST[testName] = self.TOSA_OP_LIST[ + "transpose_conv2d_TEMPLATE" + ].copy() + self.TOSA_OP_LIST[testName]["filter"] = k + self.TOSA_OP_LIST[testName]["template"] = False # Delete any templates after having created any dynamic ops # This is a two-pass operation because it's bad practice to delete @@ -1587,7 +1759,7 @@ class TosaTestGen: keyList = [] for k in self.TOSA_OP_LIST: try: - if self.TOSA_OP_LIST[k]['template'] == True: + if self.TOSA_OP_LIST[k]["template"] == True: keyList.append(k) continue except KeyError: @@ -1597,36 +1769,46 @@ class TosaTestGen: del self.TOSA_OP_LIST[k] def initOpListDefaults(self): - '''Fill in default fields for ops if they aren't already specified. - Look for missing required fields (datastructure linting).''' + """Fill in default fields for ops if they aren't already specified. + Look for missing required fields (datastructure linting).""" for op in self.TOSA_OP_LIST: # Required fields try: - pl, c = self.TOSA_OP_LIST[op]['operands'] + pl, c = self.TOSA_OP_LIST[op]["operands"] except (KeyError, ValueError, TypeError): - raise Exception('Op {} is missing a valid operand tuple in TOSA_OP_LIST'.format(op)) + raise Exception( + "Op {} is missing a valid operand tuple in TOSA_OP_LIST".format(op) + ) try: - fcn, tgen, arggen = self.TOSA_OP_LIST[op]['build_fcn'] + fcn, tgen, arggen = self.TOSA_OP_LIST[op]["build_fcn"] except (KeyError, ValueError, TypeError): - raise Exception('Op {} is missing a valid build_fcn tuple in TOSA_OP_LIST'.format(op)) + raise Exception( + "Op {} is missing a valid build_fcn tuple in TOSA_OP_LIST".format( + op + ) + ) try: - types = self.TOSA_OP_LIST[op]['types'] + types = self.TOSA_OP_LIST[op]["types"] except KeyError as e: - raise Exception('Op {} is missing a valid type list in TOSA_OP_LIST'.format(op)) + raise Exception( + "Op {} is missing a valid type list in TOSA_OP_LIST".format(op) + ) try: - opcode = self.TOSA_OP_LIST[op]['op'] + opcode = self.TOSA_OP_LIST[op]["op"] except KeyError as e: - raise Exception('Op {} is missing the Op field in TOSA_OP_LIST'.format(op)) + raise Exception( + "Op {} is missing the Op field in TOSA_OP_LIST".format(op) + ) # Put in default rank range, if missing try: - rank = self.TOSA_OP_LIST[op]['rank'] + rank = self.TOSA_OP_LIST[op]["rank"] except KeyError: - self.TOSA_OP_LIST[op]['rank'] = self.DEFAULT_RANK_RANGE + self.TOSA_OP_LIST[op]["rank"] = self.DEFAULT_RANK_RANGE # Tensor operator list # 'op': op name @@ -1635,495 +1817,517 @@ class TosaTestGen: # if not specified, defaults to (1, 4) # 'build_fcn': tuple of the function to (build_operator(), TensorGen function, ArgGen enum) # 'types': array of datatypes to be tested - TYPE_FP = [ DType.FLOAT ] + TYPE_FP = [DType.FLOAT] - TYPE_INT = [ DType.INT8, DType.INT16, DType.INT32 ] # Excludes INT4 - TYPE_INT_FP = [ DType.INT8, DType.INT16, DType.INT32, DType.FLOAT ] # Excludes INT4 + TYPE_INT = [DType.INT8, DType.INT16, DType.INT32] # Excludes INT4 + TYPE_INT_FP = [DType.INT8, DType.INT16, DType.INT32, DType.FLOAT] # Excludes INT4 - TYPE_BOOL = [ DType.BOOL ] - TYPE_FI32 = [ DType.FLOAT, DType.INT32 ] - TYPE_FIB = [ DType.FLOAT, DType.INT8, DType.INT16, DType.INT32, DType.BOOL ] - TYPE_FI16 = [ DType.FLOAT, DType.INT16 ] + TYPE_BOOL = [DType.BOOL] + TYPE_FI32 = [DType.FLOAT, DType.INT32] + TYPE_FIB = [DType.FLOAT, DType.INT8, DType.INT16, DType.INT32, DType.BOOL] + TYPE_FI16 = [DType.FLOAT, DType.INT16] - TYPE_NARROW_INT_FP = [ DType.INT8, DType.INT16, DType.FLOAT ] + TYPE_NARROW_INT_FP = [DType.INT8, DType.INT16, DType.FLOAT] DEFAULT_RANK_RANGE = (1, 4) TOSA_OP_LIST = { # Binary ops - 'add': - { 'op': Op.ADD, - 'operands': (2, 0), - 'build_fcn': (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), - 'types': TYPE_FI32 }, - - 'arithmetic_right_shift': - { 'op': Op.ARITHMETIC_RIGHT_SHIFT, - 'operands': (2, 0), - 'build_fcn': (build_arithmetic_right_shift, TosaTensorGen.tgBroadcastFuzz, TosaArgGen.agArithmeticRightShift), - 'types': TYPE_INT }, - - 'bitwise_and': - { 'op': Op.BITWISE_AND, - 'operands': (2, 0), - 'build_fcn': (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), - 'types': TYPE_INT }, - - 'bitwise_or': - { 'op': Op.BITWISE_OR, - 'operands': (2, 0), - 'build_fcn': (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), - 'types': TYPE_INT }, - - 'bitwise_xor': - { 'op': Op.BITWISE_XOR, - 'operands': (2, 0), - 'build_fcn': (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), - 'types': TYPE_INT }, - - 'logical_and': - { 'op': Op.LOGICAL_AND, - 'operands': (2, 0), - 'build_fcn': (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), - 'types': TYPE_BOOL }, - - 'logical_left_shift': - { 'op': Op.LOGICAL_LEFT_SHIFT, - 'operands': (2, 0), - 'build_fcn': (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), - 'types': TYPE_INT }, - - 'logical_right_shift': - { 'op': Op.LOGICAL_RIGHT_SHIFT, - 'operands': (2, 0), - 'build_fcn': (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), - 'types': TYPE_INT }, - - 'logical_or': - { 'op': Op.LOGICAL_OR, - 'operands': (2, 0), - 'build_fcn': (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), - 'types': TYPE_BOOL }, - - 'logical_xor': - { 'op': Op.LOGICAL_XOR, - 'operands': (2, 0), - 'build_fcn': (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), - 'types': TYPE_BOOL }, - - 'max': - { 'op': Op.MAXIMUM, - 'operands': (2, 0), - 'build_fcn': (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), - 'types': TYPE_FI32 }, - - 'min': - { 'op': Op.MINIMUM, - 'operands': (2, 0), - 'build_fcn': (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), - 'types': TYPE_FI32 }, - - 'mul': - { 'op': Op.MUL, - 'operands': (2, 0), - 'build_fcn': (build_mul, TosaTensorGen.tgBroadcastFuzz, TosaArgGen.agMul), - 'types': TYPE_INT_FP }, - - 'pow': - { 'op': Op.POW, - 'operands': (2, 0), - 'build_fcn': (build_binary_broadcast, TosaTensorGen.tgBasic, None), - 'types': TYPE_FP }, - - 'sub': - { 'op': Op.SUB, - 'operands': (2, 0), - 'build_fcn': (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), - 'types': TYPE_FI32 }, - - 'table': - { 'op': Op.TABLE, - # Use the automatic generation functions to create the input array - # but create the table tensor in the build function, as it may be - # a different type from the input - 'operands': (1, 0), - 'build_fcn': (build_table, TosaTensorGen.tgBasic, None), - 'types': [ DType.INT16 ] }, - - 'argmax': - { 'op': Op.ARGMAX, - 'operands': (1, 0), - 'build_fcn': (build_argmax, TosaTensorGen.tgBasic, TosaArgGen.agAxis), - 'types': TYPE_NARROW_INT_FP }, - + "add": { + "op": Op.ADD, + "operands": (2, 0), + "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), + "types": TYPE_FI32, + }, + "arithmetic_right_shift": { + "op": Op.ARITHMETIC_RIGHT_SHIFT, + "operands": (2, 0), + "build_fcn": ( + build_arithmetic_right_shift, + TosaTensorGen.tgBroadcastFuzz, + TosaArgGen.agArithmeticRightShift, + ), + "types": TYPE_INT, + }, + "bitwise_and": { + "op": Op.BITWISE_AND, + "operands": (2, 0), + "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), + "types": TYPE_INT, + }, + "bitwise_or": { + "op": Op.BITWISE_OR, + "operands": (2, 0), + "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), + "types": TYPE_INT, + }, + "bitwise_xor": { + "op": Op.BITWISE_XOR, + "operands": (2, 0), + "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), + "types": TYPE_INT, + }, + "logical_and": { + "op": Op.LOGICAL_AND, + "operands": (2, 0), + "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), + "types": TYPE_BOOL, + }, + "logical_left_shift": { + "op": Op.LOGICAL_LEFT_SHIFT, + "operands": (2, 0), + "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), + "types": TYPE_INT, + }, + "logical_right_shift": { + "op": Op.LOGICAL_RIGHT_SHIFT, + "operands": (2, 0), + "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), + "types": TYPE_INT, + }, + "logical_or": { + "op": Op.LOGICAL_OR, + "operands": (2, 0), + "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), + "types": TYPE_BOOL, + }, + "logical_xor": { + "op": Op.LOGICAL_XOR, + "operands": (2, 0), + "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), + "types": TYPE_BOOL, + }, + "max": { + "op": Op.MAXIMUM, + "operands": (2, 0), + "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), + "types": TYPE_FI32, + }, + "min": { + "op": Op.MINIMUM, + "operands": (2, 0), + "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), + "types": TYPE_FI32, + }, + "mul": { + "op": Op.MUL, + "operands": (2, 0), + "build_fcn": (build_mul, TosaTensorGen.tgBroadcastFuzz, TosaArgGen.agMul), + "types": TYPE_INT_FP, + }, + "pow": { + "op": Op.POW, + "operands": (2, 0), + "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBasic, None), + "types": TYPE_FP, + }, + "sub": { + "op": Op.SUB, + "operands": (2, 0), + "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), + "types": TYPE_FI32, + }, + "table": { + "op": Op.TABLE, + # Use the automatic generation functions to create the input array + # but create the table tensor in the build function, as it may be + # a different type from the input + "operands": (1, 0), + "build_fcn": (build_table, TosaTensorGen.tgBasic, None), + "types": [DType.INT16], + }, + "argmax": { + "op": Op.ARGMAX, + "operands": (1, 0), + "build_fcn": (build_argmax, TosaTensorGen.tgBasic, TosaArgGen.agAxis), + "types": TYPE_NARROW_INT_FP, + }, # Templated operator. Filled in by createDynamicOpLists - 'conv2d_TEMPLATE': - { 'op': Op.CONV2D, - 'operands': (1, 2), - 'rank': (4, 4), - 'build_fcn': (build_conv2d, TosaTensorGen.tgConv2D, TosaArgGen.agConv2D), - 'qgen': TosaQuantGen.qgConv, - 'types': TYPE_NARROW_INT_FP, - 'template': True }, - + "conv2d_TEMPLATE": { + "op": Op.CONV2D, + "operands": (1, 2), + "rank": (4, 4), + "build_fcn": (build_conv2d, TosaTensorGen.tgConv2D, TosaArgGen.agConv2D), + "qgen": TosaQuantGen.qgConv, + "types": TYPE_NARROW_INT_FP, + "template": True, + }, # Templated operator. Filled in by createDynamicOpLists - 'depthwise_conv2d_TEMPLATE': - { 'op': Op.DEPTHWISE_CONV2D, - 'operands': (1, 2), - 'filter': [1, 1], - 'rank': (4, 4), - 'build_fcn': (build_depthwise_conv2d, TosaTensorGen.tgDepthwiseConv2D, TosaArgGen.agConv2D), - 'qgen': TosaQuantGen.qgConv, - 'types': TYPE_NARROW_INT_FP, - 'template': True }, - + "depthwise_conv2d_TEMPLATE": { + "op": Op.DEPTHWISE_CONV2D, + "operands": (1, 2), + "filter": [1, 1], + "rank": (4, 4), + "build_fcn": ( + build_depthwise_conv2d, + TosaTensorGen.tgDepthwiseConv2D, + TosaArgGen.agConv2D, + ), + "qgen": TosaQuantGen.qgConv, + "types": TYPE_NARROW_INT_FP, + "template": True, + }, # Templated operator. Filled in by createDynamicOpLists - 'transpose_conv2d_TEMPLATE': - { 'op': Op.TRANSPOSE_CONV2D, - 'operands': (1, 1), - 'rank': (4, 4), - 'build_fcn': (build_transpose_conv2d, TosaTensorGen.tgTransposeConv2D, TosaArgGen.agTransposeConv2D), - 'qgen': TosaQuantGen.qgConv, - 'types': TYPE_NARROW_INT_FP, - 'template': True }, - - 'fully_connected': - { 'op': Op.FULLY_CONNECTED, - 'operands': (2, 0), - 'rank': (2, 2), - 'build_fcn': (build_fully_connected, TosaTensorGen.tgFullyConnected, None), - 'qgen': TosaQuantGen.qgConv, - 'types': TYPE_NARROW_INT_FP }, - - 'matmul': - { 'op': Op.MATMUL, - 'operands': (2, 0), - 'rank': (2, 2), - 'build_fcn': (build_matmul, TosaTensorGen.tgMatmul, None), - 'qgen': TosaQuantGen.qgMatmul, - 'types': TYPE_NARROW_INT_FP }, - + "transpose_conv2d_TEMPLATE": { + "op": Op.TRANSPOSE_CONV2D, + "operands": (1, 1), + "rank": (4, 4), + "build_fcn": ( + build_transpose_conv2d, + TosaTensorGen.tgTransposeConv2D, + TosaArgGen.agTransposeConv2D, + ), + "qgen": TosaQuantGen.qgConv, + "types": TYPE_FP, + "template": True, + }, + "fully_connected": { + "op": Op.FULLY_CONNECTED, + "operands": (2, 0), + "rank": (2, 2), + "build_fcn": (build_fully_connected, TosaTensorGen.tgFullyConnected, None), + "qgen": TosaQuantGen.qgConv, + "types": TYPE_NARROW_INT_FP, + }, + "matmul": { + "op": Op.MATMUL, + "operands": (2, 0), + "rank": (2, 2), + "build_fcn": (build_matmul, TosaTensorGen.tgMatmul, None), + "qgen": TosaQuantGen.qgMatmul, + "types": TYPE_NARROW_INT_FP, + }, # Unary operators - 'abs': - { 'op': Op.ABS, - 'operands': (1, 0), - 'build_fcn': (build_unary, TosaTensorGen.tgBasic, None), - 'types': TYPE_FI32 }, - - 'bitwise_not': - { 'op': Op.BITWISE_NOT, - 'operands': (1, 0), - 'build_fcn': (build_unary, TosaTensorGen.tgBasic, None), - 'types': TYPE_INT }, - - 'ceil': - { 'op': Op.CEIL, - 'operands': (1, 0), - 'build_fcn': (build_unary, TosaTensorGen.tgBasic, None), - 'types': TYPE_FP }, - - 'clz': - { 'op': Op.CLZ, - 'operands': (1, 0), - 'build_fcn': (build_unary, TosaTensorGen.tgBasic, None), - 'types': [ DType.INT32 ] }, - - 'exp': - { 'op': Op.EXP, - 'operands': (1, 0), - 'build_fcn': (build_unary, TosaTensorGen.tgBasic, None), - 'types': TYPE_FP }, - - 'floor': - { 'op': Op.FLOOR, - 'operands': (1, 0), - 'build_fcn': (build_unary, TosaTensorGen.tgBasic, None), - 'types': TYPE_FP }, - - 'log': - { 'op': Op.LOG, - 'operands': (1, 0), - 'build_fcn': (build_unary, TosaTensorGen.tgBasic, None), - 'types': TYPE_FP }, - - 'floor': - { 'op': Op.FLOOR, - 'operands': (1, 0), - 'build_fcn': (build_unary, TosaTensorGen.tgBasic, None), - 'types': TYPE_FP }, - - 'logical_not': - { 'op': Op.LOGICAL_NOT, - 'operands': (1, 0), - 'build_fcn': (build_unary, TosaTensorGen.tgBasic, None), - 'types': TYPE_BOOL }, - - 'negate': - { 'op': Op.NEGATE, - 'operands': (1, 0), - 'build_fcn': (build_unary, TosaTensorGen.tgBasic, None), - 'qgen': TosaQuantGen.qgUnary, - 'types': TYPE_INT_FP }, - - 'reciprocal': - { 'op': Op.RECIPROCAL, - 'operands': (1, 0), - 'build_fcn': (build_unary, TosaTensorGen.tgBasic, None), - 'types': TYPE_FP }, - - 'rsqrt': - { 'op': Op.RSQRT, - 'operands': (1, 0), - 'build_fcn': (build_unary, TosaTensorGen.tgBasic, None), - 'types': TYPE_FP }, - + "abs": { + "op": Op.ABS, + "operands": (1, 0), + "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), + "types": TYPE_FI32, + }, + "bitwise_not": { + "op": Op.BITWISE_NOT, + "operands": (1, 0), + "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), + "types": TYPE_INT, + }, + "ceil": { + "op": Op.CEIL, + "operands": (1, 0), + "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), + "types": TYPE_FP, + }, + "clz": { + "op": Op.CLZ, + "operands": (1, 0), + "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), + "types": [DType.INT32], + }, + "exp": { + "op": Op.EXP, + "operands": (1, 0), + "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), + "types": TYPE_FP, + }, + "floor": { + "op": Op.FLOOR, + "operands": (1, 0), + "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), + "types": TYPE_FP, + }, + "log": { + "op": Op.LOG, + "operands": (1, 0), + "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), + "types": TYPE_FP, + }, + "floor": { + "op": Op.FLOOR, + "operands": (1, 0), + "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), + "types": TYPE_FP, + }, + "logical_not": { + "op": Op.LOGICAL_NOT, + "operands": (1, 0), + "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), + "types": TYPE_BOOL, + }, + "negate": { + "op": Op.NEGATE, + "operands": (1, 0), + "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), + "qgen": TosaQuantGen.qgUnary, + "types": TYPE_INT_FP, + }, + "reciprocal": { + "op": Op.RECIPROCAL, + "operands": (1, 0), + "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), + "types": TYPE_FP, + }, + "rsqrt": { + "op": Op.RSQRT, + "operands": (1, 0), + "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), + "types": TYPE_FP, + }, # Ternary operators - 'select': - { 'op': Op.SELECT, - 'operands': (3, 0), - 'build_fcn': (build_select, TosaTensorGen.tgBroadcastFuzz, None), - 'types': TYPE_FIB }, - + "select": { + "op": Op.SELECT, + "operands": (3, 0), + "build_fcn": (build_select, TosaTensorGen.tgBroadcastFuzz, None), + "types": TYPE_FIB, + }, # Comparison operators - 'equal': - { 'op': Op.EQUAL, - 'operands': (2, 0), - 'build_fcn': (build_comparison, TosaTensorGen.tgBroadcastFuzz, None), - 'types': TYPE_FI32 }, - - 'greater_equal': - { 'op': Op.GREATER_EQUAL, - 'operands': (2, 0), - 'build_fcn': (build_comparison, TosaTensorGen.tgBroadcastFuzz, None), - 'types': TYPE_FI32 }, - - 'greater': - { 'op': Op.GREATER, - 'operands': (2, 0), - 'build_fcn': (build_comparison, TosaTensorGen.tgBroadcastFuzz, None), - 'types': TYPE_FI32 }, - + "equal": { + "op": Op.EQUAL, + "operands": (2, 0), + "build_fcn": (build_comparison, TosaTensorGen.tgBroadcastFuzz, None), + "types": TYPE_FI32, + }, + "greater_equal": { + "op": Op.GREATER_EQUAL, + "operands": (2, 0), + "build_fcn": (build_comparison, TosaTensorGen.tgBroadcastFuzz, None), + "types": TYPE_FI32, + }, + "greater": { + "op": Op.GREATER, + "operands": (2, 0), + "build_fcn": (build_comparison, TosaTensorGen.tgBroadcastFuzz, None), + "types": TYPE_FI32, + }, # Pooling operators - 'avg_pool2d': - { 'op': Op.AVG_POOL2D, - 'operands': (1, 0), - 'rank': (4, 4), - 'build_fcn': (build_pool2d, TosaTensorGen.tgNHWC, TosaArgGen.agPooling), - 'qgen': TosaQuantGen.qgUnary, - 'types': TYPE_NARROW_INT_FP }, - - - 'max_pool2d': - { 'op': Op.MAX_POOL2D, - 'operands': (1, 0), - 'rank': (4, 4), - 'build_fcn': (build_pool2d, TosaTensorGen.tgNHWC, TosaArgGen.agPooling), - 'types': TYPE_NARROW_INT_FP }, - + "avg_pool2d": { + "op": Op.AVG_POOL2D, + "operands": (1, 0), + "rank": (4, 4), + "build_fcn": (build_pool2d, TosaTensorGen.tgNHWC, TosaArgGen.agPooling), + "qgen": TosaQuantGen.qgUnary, + "types": TYPE_NARROW_INT_FP, + }, + "max_pool2d": { + "op": Op.MAX_POOL2D, + "operands": (1, 0), + "rank": (4, 4), + "build_fcn": (build_pool2d, TosaTensorGen.tgNHWC, TosaArgGen.agPooling), + "types": TYPE_NARROW_INT_FP, + }, # Reduce operators - 'reduce_any': - { 'op': Op.REDUCE_ANY, - 'operands': (1, 0), - 'build_fcn': (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), - 'types': TYPE_BOOL }, - - 'reduce_all': - { 'op': Op.REDUCE_ALL, - 'operands': (1, 0), - 'build_fcn': (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), - 'types': TYPE_BOOL }, - - 'reduce_max': - { 'op': Op.REDUCE_MAX, - 'operands': (1, 0), - 'build_fcn': (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), - 'types': TYPE_INT_FP }, - - 'reduce_min': - { 'op': Op.REDUCE_MAX, - 'operands': (1, 0), - 'build_fcn': (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), - 'types': TYPE_INT_FP }, - - 'reduce_product': - { 'op': Op.REDUCE_PRODUCT, - 'operands': (1, 0), - 'build_fcn': (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), - 'types': TYPE_FP }, - - 'reduce_sum': - { 'op': Op.REDUCE_SUM, - 'operands': (1, 0), - 'build_fcn': (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), - 'types': TYPE_FI32 }, - + "reduce_any": { + "op": Op.REDUCE_ANY, + "operands": (1, 0), + "build_fcn": (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), + "types": TYPE_BOOL, + }, + "reduce_all": { + "op": Op.REDUCE_ALL, + "operands": (1, 0), + "build_fcn": (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), + "types": TYPE_BOOL, + }, + "reduce_max": { + "op": Op.REDUCE_MAX, + "operands": (1, 0), + "build_fcn": (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), + "types": TYPE_INT_FP, + }, + "reduce_min": { + "op": Op.REDUCE_MAX, + "operands": (1, 0), + "build_fcn": (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), + "types": TYPE_INT_FP, + }, + "reduce_product": { + "op": Op.REDUCE_PRODUCT, + "operands": (1, 0), + "build_fcn": (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), + "types": TYPE_FP, + }, + "reduce_sum": { + "op": Op.REDUCE_SUM, + "operands": (1, 0), + "build_fcn": (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), + "types": TYPE_FI32, + }, # Activation functions - 'clamp': - { 'op': Op.CLAMP, - 'operands': (1, 0), - 'build_fcn': (build_clamp, TosaTensorGen.tgBasic, None), - 'types': TYPE_NARROW_INT_FP }, - - 'relun': - { 'op': Op.RELUN, - 'operands': (1, 0), - 'build_fcn': (build_relun, TosaTensorGen.tgBasic, None), - 'types': TYPE_FI32 }, - - 'sigmoid': - { 'op': Op.SIGMOID, - 'operands': (1, 0), - 'build_fcn': (build_sigmoid, TosaTensorGen.tgBasic, None), - 'types': TYPE_FP }, - - 'tanh': - { 'op': Op.TANH, - 'operands': (1, 0), - 'build_fcn': (build_tanh, TosaTensorGen.tgBasic, None), - 'types': TYPE_FP }, - + "clamp": { + "op": Op.CLAMP, + "operands": (1, 0), + "build_fcn": (build_clamp, TosaTensorGen.tgBasic, None), + "types": TYPE_NARROW_INT_FP, + }, + "relun": { + "op": Op.RELUN, + "operands": (1, 0), + "build_fcn": (build_relun, TosaTensorGen.tgBasic, None), + "types": TYPE_FI32, + }, + "sigmoid": { + "op": Op.SIGMOID, + "operands": (1, 0), + "build_fcn": (build_sigmoid, TosaTensorGen.tgBasic, None), + "types": TYPE_FP, + }, + "tanh": { + "op": Op.TANH, + "operands": (1, 0), + "build_fcn": (build_tanh, TosaTensorGen.tgBasic, None), + "types": TYPE_FP, + }, # Data layout operators - 'concat': - { 'op': Op.CONCAT, - 'operands': (2, 0), - 'build_fcn': (build_concat, TosaTensorGen.tgBasic, TosaArgGen.agAxis), - 'types': TYPE_FIB }, - - 'pad': - { 'op': Op.PAD, - 'operands': (1, 0), - 'build_fcn': (build_pad, TosaTensorGen.tgBasic, TosaArgGen.agPad), - 'qgen': TosaQuantGen.qgPad, - 'types': TYPE_FIB }, - - 'reshape': - { 'op': Op.RESHAPE, - 'operands': (1, 0), - 'build_fcn': (build_reshape, TosaTensorGen.tgBasic, TosaArgGen.agReshape), - 'types': TYPE_FIB }, - - 'reverse': - { 'op': Op.REVERSE, - 'operands': (1, 0), - 'build_fcn': (build_reverse, TosaTensorGen.tgBasic, TosaArgGen.agAxis), - 'types': TYPE_FIB }, - - 'slice': - { 'op': Op.SLICE, - 'operands': (1, 0), - 'build_fcn': (build_slice, TosaTensorGen.tgBasic, TosaArgGen.agSlice), - 'types': TYPE_FIB }, - - 'tile': - { 'op': Op.TILE, - 'operands': (1, 0), - 'build_fcn': (build_tile, TosaTensorGen.tgBasic, TosaArgGen.agTile), - 'types': TYPE_FIB }, - - 'transpose': - { 'op': Op.TRANSPOSE, - 'operands': (1, 0), - 'rank': (2, 4), # Do not allow tranpose on rank=1 - 'build_fcn': (build_transpose, TosaTensorGen.tgBasic, TosaArgGen.agTranspose), - 'types': TYPE_FIB }, - + "concat": { + "op": Op.CONCAT, + "operands": (2, 0), + "build_fcn": (build_concat, TosaTensorGen.tgBasic, TosaArgGen.agAxis), + "types": TYPE_FIB, + }, + "pad": { + "op": Op.PAD, + "operands": (1, 0), + "build_fcn": (build_pad, TosaTensorGen.tgBasic, TosaArgGen.agPad), + "qgen": TosaQuantGen.qgPad, + "types": TYPE_FIB, + }, + "reshape": { + "op": Op.RESHAPE, + "operands": (1, 0), + "build_fcn": (build_reshape, TosaTensorGen.tgBasic, TosaArgGen.agReshape), + "types": TYPE_FIB, + }, + "reverse": { + "op": Op.REVERSE, + "operands": (1, 0), + "build_fcn": (build_reverse, TosaTensorGen.tgBasic, TosaArgGen.agAxis), + "types": TYPE_FIB, + }, + "slice": { + "op": Op.SLICE, + "operands": (1, 0), + "build_fcn": (build_slice, TosaTensorGen.tgBasic, TosaArgGen.agSlice), + "types": TYPE_FIB, + }, + "tile": { + "op": Op.TILE, + "operands": (1, 0), + "build_fcn": (build_tile, TosaTensorGen.tgBasic, TosaArgGen.agTile), + "types": TYPE_FIB, + }, + "transpose": { + "op": Op.TRANSPOSE, + "operands": (1, 0), + "rank": (2, 4), # Do not allow tranpose on rank=1 + "build_fcn": ( + build_transpose, + TosaTensorGen.tgBasic, + TosaArgGen.agTranspose, + ), + "types": TYPE_FIB, + }, # Scatter/Gather - 'gather': - { 'op': Op.GATHER, - # Only specify 'values' tensor here. 'indices' is generated in op building stage - 'operands': (1, 0), - 'rank': (3, 3), - 'build_fcn': (build_gather, TosaTensorGen.tgBasic, None), - 'types': TYPE_INT_FP }, - - 'scatter': - { 'op': Op.SCATTER, - # Only specify 'values_in' tensor here. - #'indices' and 'input' are generated in op building stage - 'operands': (2, 0), - 'rank': (3, 3), - 'build_fcn': (build_scatter, TosaTensorGen.tgScatter, None), - 'types': TYPE_INT_FP }, - + "gather": { + "op": Op.GATHER, + # Only specify 'values' tensor here. 'indices' is generated in op building stage + "operands": (1, 0), + "rank": (3, 3), + "build_fcn": (build_gather, TosaTensorGen.tgBasic, None), + "types": TYPE_INT_FP, + }, + "scatter": { + "op": Op.SCATTER, + # Only specify 'values_in' tensor here. + #'indices' and 'input' are generated in op building stage + "operands": (2, 0), + "rank": (3, 3), + "build_fcn": (build_scatter, TosaTensorGen.tgScatter, None), + "types": TYPE_INT_FP, + }, # Image operations - 'resize': - { 'op': Op.RESIZE, - 'operands': (1, 0), - 'rank': (4, 4), - 'build_fcn': ( build_resize, TosaTensorGen.tgNHWC, TosaArgGen.agResize), - 'types': [ DType.INT8, DType.INT16, DType.FLOAT ] }, - - + "resize": { + "op": Op.RESIZE, + "operands": (1, 0), + "rank": (4, 4), + "build_fcn": (build_resize, TosaTensorGen.tgNHWC, TosaArgGen.agResize), + "types": [DType.INT8, DType.INT16, DType.FLOAT], + }, # Data nodes - 'placeholder': - { 'op': Op.PLACEHOLDER, - 'operands': (1, 0), - 'build_fcn': ( build_placeholder, TosaTensorGen.tgBasic, None), - 'types': TYPE_FIB }, - - 'const': - { 'op': Op.CONST, - 'operands': (1, 0), - 'build_fcn': ( build_placeholder, TosaTensorGen.tgBasic, None), - 'types': TYPE_FIB }, - - - 'identity': - { 'op': Op.IDENTITY, - 'operands': (1, 0), - 'build_fcn': ( build_unary, TosaTensorGen.tgBasic, None), - 'types': TYPE_FIB }, - - - 'identityn': - { 'op': Op.IDENTITYN, - 'operands': (2, 0), - 'build_fcn': ( build_identityn, TosaTensorGen.tgBasic, None), - 'types': TYPE_FIB }, - + "placeholder": { + "op": Op.PLACEHOLDER, + "operands": (1, 0), + "build_fcn": (build_placeholder, TosaTensorGen.tgBasic, None), + "types": TYPE_FIB, + }, + "const": { + "op": Op.CONST, + "operands": (1, 0), + "build_fcn": (build_placeholder, TosaTensorGen.tgBasic, None), + "types": TYPE_FIB, + }, + "identity": { + "op": Op.IDENTITY, + "operands": (1, 0), + "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), + "types": TYPE_FIB, + }, + "identityn": { + "op": Op.IDENTITYN, + "operands": (2, 0), + "build_fcn": (build_identityn, TosaTensorGen.tgBasic, None), + "types": TYPE_FIB, + }, # Type conversion - 'cast': - { 'op': Op.CAST, - 'operands': (1, 0), - 'build_fcn': ( build_cast, TosaTensorGen.tgBasic, TosaArgGen.agCast ), - 'types': [ DType.FLOAT, DType.INT8, DType.INT16, DType.INT32, DType.BOOL ] }, - - 'rescale': - { 'op': Op.RESCALE, - 'operands': (1, 0), - 'build_fcn': ( build_rescale, TosaTensorGen.tgBasic, TosaArgGen.agRescale ), - 'types': [ DType.INT8, DType.INT16, DType.INT32, DType.INT48 ] }, - + "cast": { + "op": Op.CAST, + "operands": (1, 0), + "build_fcn": (build_cast, TosaTensorGen.tgBasic, TosaArgGen.agCast), + "types": [DType.FLOAT, DType.INT8, DType.INT16, DType.INT32, DType.BOOL], + }, + "rescale": { + "op": Op.RESCALE, + "operands": (1, 0), + "build_fcn": (build_rescale, TosaTensorGen.tgBasic, TosaArgGen.agRescale), + "types": [DType.INT8, DType.INT16, DType.INT32, DType.INT48], + }, # Custom # Not implemented. - # Control flow - # Two varients of cond_if, one that generates one of two constant tensors (no # inputs to the basic blocks, one output) and another that either adds or subtracts two tensors # (two inputs to the basic blocks, one output) - 'cond_if_const': - { 'op': Op.COND_IF, - 'operands': (0, 2), - 'build_fcn': ( build_cond_if_const, TosaTensorGen.tgBasic, TosaArgGen.agCondIf ), - 'types': [ DType.BOOL ] }, - - 'cond_if_binary': - { 'op': Op.COND_IF, - 'operands': (2, 0), - 'build_fcn': ( build_cond_if_binary, TosaTensorGen.tgBasic, TosaArgGen.agCondIf ), - 'types': TYPE_FI32 }, - + "cond_if_const": { + "op": Op.COND_IF, + "operands": (0, 2), + "build_fcn": ( + build_cond_if_const, + TosaTensorGen.tgBasic, + TosaArgGen.agCondIf, + ), + "types": [DType.BOOL], + }, + "cond_if_binary": { + "op": Op.COND_IF, + "operands": (2, 0), + "build_fcn": ( + build_cond_if_binary, + TosaTensorGen.tgBasic, + TosaArgGen.agCondIf, + ), + "types": TYPE_FI32, + }, # while_loop - 'while_loop': - { 'op': Op.WHILE_LOOP, - 'operands': (0, 1), - 'build_fcn': ( build_while_loop, TosaTensorGen.tgBasic, TosaArgGen.agWhileLoop ), - 'types': [DType.INT32] }, - - + "while_loop": { + "op": Op.WHILE_LOOP, + "operands": (0, 1), + "build_fcn": ( + build_while_loop, + TosaTensorGen.tgBasic, + TosaArgGen.agWhileLoop, + ), + "types": [DType.INT32], + }, } + class OutputShaper: # Methods in this class compute the expected output shape and datatype # for common classes of operations @@ -2134,8 +2338,8 @@ class OutputShaper: # creating a new output tensor @staticmethod def binaryBroadcastOp(ser, a, b): - assert(len(a.shape) == len(b.shape)) - assert(a.dtype == b.dtype) + assert len(a.shape) == len(b.shape) + assert a.dtype == b.dtype shape = [] for i in range(len(a.shape)): @@ -2144,39 +2348,39 @@ class OutputShaper: else: shape.append(a.shape[i]) - return ser.addOutput(shape, a.dtype, a.usage, a.dformat) + return ser.addOutput(shape, a.dtype) @staticmethod def binaryNonBroadcastOp(ser, a, b): - assert(len(a.shape) == len(b.shape)) - assert(a.dtype == b.dtype) + assert len(a.shape) == len(b.shape) + assert a.dtype == b.dtype shape = [] for i in range(len(a.shape)): - assert(a.shape[i] == b.shape[i]) + assert a.shape[i] == b.shape[i] shape.append(a.shape[i]) - return ser.addOutput(shape, a.dtype, a.usage, a.dformat) + return ser.addOutput(shape, a.dtype) @staticmethod def unaryOp(ser, a): - return ser.addOutput(a.shape, a.dtype, a.usage, a.dformat) + return ser.addOutput(a.shape, a.dtype) @staticmethod def selectOp(ser, cond, a, b): - assert(len(a.shape) == len(b.shape) and len(a.shape) == len(cond.shape)) - assert(a.dtype == b.dtype) + assert len(a.shape) == len(b.shape) and len(a.shape) == len(cond.shape) + assert a.dtype == b.dtype shape = [] for i in range(len(a.shape)): shape.append(max(cond.shape[i], a.shape[i], b.shape[i])) - return ser.addOutput(shape, a.dtype, a.usage, a.dformat) + return ser.addOutput(shape, a.dtype) @staticmethod def binaryComparisonOp(ser, a, b): - assert(len(a.shape) == len(b.shape)) - assert(a.dtype == b.dtype) + assert len(a.shape) == len(b.shape) + assert a.dtype == b.dtype # Do broadcast shape = [] @@ -2187,7 +2391,7 @@ class OutputShaper: shape.append(a.shape[i]) # Force the output type to bool - return ser.addOutput(shape, DType.BOOL, a.usage, a.dformat) + return ser.addOutput(shape, DType.BOOL) @staticmethod def reduceOp(ser, a, axis): @@ -2196,13 +2400,13 @@ class OutputShaper: shape[axis] = 1 - return ser.addOutput(shape, a.dtype, a.usage, a.dformat) + return ser.addOutput(shape, a.dtype) @staticmethod def argmaxOp(ser, a, axis): shape = a.shape.copy() del shape[axis] - return ser.addOutput(shape, DType.INT32, a.usage, a.dformat) + return ser.addOutput(shape, DType.INT32) @staticmethod def conv2dOp(ser, ifm, filter, strides, padding, dilations): @@ -2216,17 +2420,27 @@ class OutputShaper: # From H,W to T,B,L,R padding = [padding[0], padding[0], padding[1], padding[1]] - h = (ifm.shape[1] - filter.shape[1] - (filter.shape[1] - 1) * (dilations[0] - 1) + \ - padding[0] + padding[1]) // strides[0] + 1 - - w = (ifm.shape[2] - filter.shape[2] - (filter.shape[2] - 1) * (dilations[1] - 1) + \ - padding[2] + padding[3]) // strides[1] + 1 + h = ( + ifm.shape[1] + - filter.shape[1] + - (filter.shape[1] - 1) * (dilations[0] - 1) + + padding[0] + + padding[1] + ) // strides[0] + 1 + + w = ( + ifm.shape[2] + - filter.shape[2] + - (filter.shape[2] - 1) * (dilations[1] - 1) + + padding[2] + + padding[3] + ) // strides[1] + 1 if h <= 0 or w <= 0: # Invalid test parameters? h = 0 w = 0 - ser.setExpectedFailure(True, 'Invalid combination of conv2d parameters') + ser.setExpectedFailure(True, "Invalid combination of conv2d parameters") ofm_shape = [ifm.shape[0], h, w, filter.shape[0]] @@ -2237,29 +2451,39 @@ class OutputShaper: elif ifm.dtype == DType.FLOAT: out_dtype = DType.FLOAT else: - raise Exception('Unsupported input dtype: {}'.format(ifm.dtype)) + raise Exception("Unsupported input dtype: {}".format(ifm.dtype)) if ifm.dtype == DType.INT16: ser.setExpectedFailure(True, "INT16 support is in progress") - return ser.addOutput(ofm_shape, out_dtype, ifm.usage, ifm.dformat) + return ser.addOutput(ofm_shape, out_dtype) @staticmethod def depthwiseConv2dOp(ser, ifm, filter, strides, padding, dilations): # IFM: NHWC # Filter: HWCM # OFM: NHW C*M - h = (ifm.shape[1] - filter.shape[0] - (filter.shape[0] - 1) * (dilations[0] - 1) + \ - padding[0] + padding[1]) // strides[0] + 1 - - w = (ifm.shape[2] - filter.shape[1] - (filter.shape[1] - 1) * (dilations[1] - 1) + \ - padding[2] + padding[3]) // strides[1] + 1 + h = ( + ifm.shape[1] + - filter.shape[0] + - (filter.shape[0] - 1) * (dilations[0] - 1) + + padding[0] + + padding[1] + ) // strides[0] + 1 + + w = ( + ifm.shape[2] + - filter.shape[1] + - (filter.shape[1] - 1) * (dilations[1] - 1) + + padding[2] + + padding[3] + ) // strides[1] + 1 if h <= 0 or w <= 0: # Invalid test parameters? h = 0 w = 0 - ser.setExpectedFailure(True, 'Invalid combination of conv2d parameters') + ser.setExpectedFailure(True, "Invalid combination of conv2d parameters") ofm_shape = [ifm.shape[0], h, w, filter.shape[2] * filter.shape[3]] @@ -2270,13 +2494,12 @@ class OutputShaper: elif ifm.dtype == DType.FLOAT: out_dtype = DType.FLOAT else: - raise Exception('Unsupported input dtype: {}'.format(ifm.dtype)) + raise Exception("Unsupported input dtype: {}".format(ifm.dtype)) if ifm.dtype == DType.INT16: ser.setExpectedFailure(True, "INT16 support is in progress") - return ser.addOutput(ofm_shape, out_dtype, ifm.usage, ifm.dformat) - + return ser.addOutput(ofm_shape, out_dtype) @staticmethod def pool2dOp(ser, ifm, kernel, stride, pad): @@ -2288,10 +2511,10 @@ class OutputShaper: # Invalid test parameters? h = 0 w = 0 - ser.setExpectedFailure(True, 'Invalid combination of pooling parameters') + ser.setExpectedFailure(True, "Invalid combination of pooling parameters") ofm_shape = [ifm.shape[0], h, w, ifm.shape[3]] - return ser.addOutput(ofm_shape, ifm.dtype, ifm.usage, ifm.dformat) + return ser.addOutput(ofm_shape, ifm.dtype) @staticmethod def fullyConnectedOp(ser, input, filter): @@ -2308,12 +2531,12 @@ class OutputShaper: elif input.dtype == DType.FLOAT: out_dtype = DType.FLOAT else: - raise Exception('Unsupported input dtype: {}'.format(input.dtype)) + raise Exception("Unsupported input dtype: {}".format(input.dtype)) if input.dtype == DType.INT16: ser.setExpectedFailure(True, "INT16 support is in progress") - return ser.addOutput(output_shape, out_dtype, input.usage, input.dformat) + return ser.addOutput(output_shape, out_dtype) @staticmethod def matmulOp(ser, a, b): @@ -2330,9 +2553,9 @@ class OutputShaper: elif a.dtype == DType.FLOAT: out_dtype = DType.FLOAT else: - raise Exception('UNsupported input dtype for matmul: {}'.format(a.dtype)) + raise Exception("UNsupported input dtype for matmul: {}".format(a.dtype)) - return ser.addOutput(output_shape, out_dtype, a.usage, a.dformat) + return ser.addOutput(output_shape, out_dtype) @staticmethod def concatOp(ser, a, b, axis): @@ -2340,7 +2563,7 @@ class OutputShaper: output_shape = a.shape.copy() output_shape[axis] = a.shape[axis] + b.shape[axis] - return ser.addOutput(output_shape, a.dtype, a.usage, a.dformat) + return ser.addOutput(output_shape, a.dtype) @staticmethod def padOp(ser, a, padding): @@ -2350,7 +2573,7 @@ class OutputShaper: for i in range(len(output_shape)): output_shape[i] = padding[i][0] + padding[i][1] + output_shape[i] - return ser.addOutput(output_shape, a.dtype, a.usage, a.dformat) + return ser.addOutput(output_shape, a.dtype) @staticmethod def reshapeOp(ser, a, shape): @@ -2371,34 +2594,34 @@ class OutputShaper: if output_shape[i] == -1: output_shape[i] = totalElements // totalOutputElements - return ser.addOutput(output_shape, a.dtype, a.usage, a.dformat) + return ser.addOutput(output_shape, a.dtype) @staticmethod def sliceOp(ser, a, begin, size): output_shape = size.copy() - return ser.addOutput(output_shape, a.dtype, a.usage, a.dformat) + return ser.addOutput(output_shape, a.dtype) @staticmethod def tileOp(ser, a, multiples): output_shape = a.shape.copy() - assert(len(multiples) == len(output_shape)) + assert len(multiples) == len(output_shape) for i in range(len(output_shape)): output_shape[i] = a.shape[i] * multiples[i] - return ser.addOutput(output_shape, a.dtype, a.usage, a.dformat) + return ser.addOutput(output_shape, a.dtype) @staticmethod def transposeOp(ser, a, perms): output_shape = a.shape.copy() - assert(len(perms) == len(output_shape)) + assert len(perms) == len(output_shape) for i in range(len(output_shape)): output_shape[i] = a.shape[perms[i]] - return ser.addOutput(output_shape, a.dtype, a.usage, a.dformat) + return ser.addOutput(output_shape, a.dtype) @staticmethod def gatherOp(ser, values, indices): @@ -2408,72 +2631,84 @@ class OutputShaper: output_shape = [values.shape[0], indices.shape[1], values.shape[2]] - return ser.addOutput(output_shape, values.dtype, values.usage, values.dformat) + return ser.addOutput(output_shape, values.dtype) @staticmethod def scatterOp(ser, values_in, indices, input): assert len(values_in.shape) == 3 assert len(indices.shape) == 2 assert len(input.shape) == 3 - assert values_in.shape[0] == indices.shape[0] # N - assert input.shape[1] == indices.shape[1] # W - assert values_in.shape[2] == input.shape[2] # C + assert values_in.shape[0] == indices.shape[0] # N + assert input.shape[1] == indices.shape[1] # W + assert values_in.shape[2] == input.shape[2] # C output_shape = values_in.shape - return ser.addOutput(output_shape, values_in.dtype, values_in.usage, values_in.dformat) + return ser.addOutput(output_shape, values_in.dtype) @staticmethod def tableOp(ser, input, table): # Same shape as the input, but with the type of the table. - return ser.addOutput(input.shape, DType.INT32, input.usage, input.dformat) + return ser.addOutput(input.shape, DType.INT32) @staticmethod - def resizeOp(ser, input, mode, stride, offset, shift, stride_fp, offset_fp, output_dims, input_dtype, output_dtype): + def resizeOp( + ser, + input, + mode, + stride, + offset, + shift, + stride_fp, + offset_fp, + output_dims, + input_dtype, + output_dtype, + ): output_dims = [input.shape[0], output_dims[0], output_dims[1], input.shape[3]] if input_dtype == DType.FLOAT: if stride_fp[0] <= 0 or stride_fp[1] <= 0: - ser.setExpectedFailure(True, 'Negative or zero stride') + ser.setExpectedFailure(True, "Negative or zero stride") else: if stride[0] <= 0 or stride[1] <= 0: - ser.setExpectedFailure(True, 'Negative or zero stride') + ser.setExpectedFailure(True, "Negative or zero stride") if mode == ResizeMode.BILINEAR: if input_dtype == DType.INT8: if output_dtype != DType.INT32: - ser.setExpectedFailure(True, 'Invalid output data type') + ser.setExpectedFailure(True, "Invalid output data type") elif input_dtype == DType.INT16: if output_dtype != DType.INT48: - ser.setexpectedfailure(true, 'Invalid output data type') + ser.setexpectedfailure(true, "Invalid output data type") elif input_dtype == DType.FLOAT: if output_dtype != DType.FLOAT: - ser.setexpectedfailure(true, 'Invalid output data type') + ser.setexpectedfailure(true, "Invalid output data type") else: - ser.setexpectedfailure(true, 'Invalid input data type') + ser.setexpectedfailure(true, "Invalid input data type") elif mode == ResizeMode.NEAREST: if input_dtype == DType.INT8: if output_dtype != DType.INT8: - ser.setExpectedFailure(True, 'Invalid output data type') + ser.setExpectedFailure(True, "Invalid output data type") elif input_dtype == DType.INT16: if output_dtype != DType.INT16: - ser.setexpectedfailure(true, 'Invalid output data type') + ser.setexpectedfailure(true, "Invalid output data type") elif input_dtype == DType.FLOAT: if output_dtype != DType.FLOAT: - ser.setexpectedfailure(true, 'Invalid output data type') + ser.setexpectedfailure(true, "Invalid output data type") else: - ser.setexpectedfailure(true, 'Invalid input data type') + ser.setexpectedfailure(true, "Invalid input data type") else: - ser.setexpectedfailure(true, 'Invalid resize mode') + ser.setexpectedfailure(true, "Invalid resize mode") - return ser.addOutput(output_dims, output_dtype, input.usage, input.dformat) + return ser.addOutput(output_dims, output_dtype) @staticmethod def typeConversionOp(ser, val, out_dtype): - return ser.addOutput(val.shape, out_dtype, val.usage, val.dformat) + return ser.addOutput(val.shape, out_dtype) @staticmethod def transposeConv2DOp(ser, ifm, output_shape): @@ -2484,12 +2719,12 @@ class OutputShaper: elif ifm.dtype == DType.FLOAT: out_dtype = DType.FLOAT else: - raise Exception('Unsupported input dtype: {}'.format(ifm.dtype)) + raise Exception("Unsupported input dtype: {}".format(ifm.dtype)) if output_shape[1] <= 0 or output_shape[2] <= 0: - ser.setExpectedFailure(True, 'Negative output shape') + ser.setExpectedFailure(True, "Negative output shape") if ifm.dtype == DType.INT16: ser.setExpectedFailure(True, "INT16 support is in progress") - return ser.addOutput(output_shape, out_dtype, ifm.usage, ifm.dformat) + return ser.addOutput(output_shape, out_dtype) diff --git a/verif/tosa_test_runner.py b/verif/tosa_test_runner.py index 6549192..82d447e 100644 --- a/verif/tosa_test_runner.py +++ b/verif/tosa_test_runner.py @@ -19,29 +19,34 @@ import shlex import subprocess from enum import IntEnum, unique + def run_sh_command(args, full_cmd, capture_output=False): - '''Utility function to run an external command. Optionally return captured stdout/stderr''' + """Utility function to run an external command. Optionally return captured stdout/stderr""" # Quote the command line for printing - full_cmd_esc = [ shlex.quote(x) for x in full_cmd ] + full_cmd_esc = [shlex.quote(x) for x in full_cmd] if args.verbose: - print('### Running {}'.format(' '.join(full_cmd_esc))) + print("### Running {}".format(" ".join(full_cmd_esc))) if capture_output: rc = subprocess.run(full_cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE) if rc.returncode != 0: - print(rc.stdout.decode('utf-8')) - print(rc.stderr.decode('utf-8')) - raise Exception('Error running command: {}.\n{}'.format(' '.join(full_cmd_esc), rc.stderr.decode('utf-8'))) + print(rc.stdout.decode("utf-8")) + print(rc.stderr.decode("utf-8")) + raise Exception( + "Error running command: {}.\n{}".format( + " ".join(full_cmd_esc), rc.stderr.decode("utf-8") + ) + ) return (rc.stdout, rc.stderr) else: rc = subprocess.run(full_cmd) if rc.returncode != 0: - raise Exception('Error running command: {}'.format(' '.join(full_cmd_esc))) + raise Exception("Error running command: {}".format(" ".join(full_cmd_esc))) -class TosaTestRunner: +class TosaTestRunner: def __init__(self, args, runnerArgs, testDir): self.args = args @@ -49,7 +54,7 @@ class TosaTestRunner: self.testDir = testDir # Load the json test file - with open(os.path.join(testDir, 'desc.json'), 'r') as fd: + with open(os.path.join(testDir, "desc.json"), "r") as fd: self.testDesc = json.load(fd) def runModel(self): diff --git a/verif/tosa_verif_build_tests.py b/verif/tosa_verif_build_tests.py index b8a24dd..15482e6 100755 --- a/verif/tosa_verif_build_tests.py +++ b/verif/tosa_verif_build_tests.py @@ -32,11 +32,18 @@ import traceback from enum import IntEnum, Enum, unique from datetime import datetime -# Include the ../shared directory in PYTHONPATH +# Include the ../scripts and ../scripts/xunit directory in PYTHONPATH parent_dir = os.path.dirname(os.path.realpath(__file__)) -sys.path.append(os.path.join(parent_dir, '..', 'scripts')) -sys.path.append(os.path.join(parent_dir, '..', 'scripts', 'xunit')) +sys.path.append(os.path.join(parent_dir, "..", "scripts")) +sys.path.append(os.path.join(parent_dir, "..", "scripts", "xunit")) + import xunit + +# Include the ../thirdparty/serialization_lib/python directory in PYTHONPATH +parent_dir = os.path.dirname(os.path.realpath(__file__)) +sys.path.append( + os.path.join(parent_dir, "..", "thirdparty", "serialization_lib", "python") +) from tosa_serializer import * from tosa_test_gen import TosaTestGen import tosa @@ -44,77 +51,153 @@ import tosa # Used for parsing a comma-separated list of integers in a string # to an actual list of integers def str_to_list(in_s): - '''Converts a comma-separated list of string integers to a python list of ints''' - lst = in_s.split(',') + """Converts a comma-separated list of string integers to a python list of ints""" + lst = in_s.split(",") out_list = [] for i in lst: out_list.append(int(i)) return out_list + def auto_int(x): - '''Converts hex/dec argument values to an int''' + """Converts hex/dec argument values to an int""" return int(x, 0) + def parseArgs(): parser = argparse.ArgumentParser() - parser.add_argument('-o', dest='output_dir', type=str, default='vtest', - help='Test output directory') - - parser.add_argument('--seed', dest='random_seed', default=42, type=int, - help='Random seed for test generation') - - parser.add_argument('--filter', dest='filter', default='', type=str, - help='Filter operator test names by this expression') - - parser.add_argument('-v', '--verbose', dest='verbose', action='count', - help='Verbose operation') + parser.add_argument( + "-o", dest="output_dir", type=str, default="vtest", help="Test output directory" + ) + + parser.add_argument( + "--seed", + dest="random_seed", + default=42, + type=int, + help="Random seed for test generation", + ) + + parser.add_argument( + "--filter", + dest="filter", + default="", + type=str, + help="Filter operator test names by this expression", + ) + + parser.add_argument( + "-v", "--verbose", dest="verbose", action="count", help="Verbose operation" + ) # Constraints on tests - parser.add_argument('--tensor-dim-range', dest='tensor_shape_range', default='1,64', - type=lambda x: str_to_list(x), - help='Min,Max range of tensor shapes') - - parser.add_argument('--max-batch-size', dest='max_batch_size', default=1, type=int, - help='Maximum batch size for NHWC tests') - - parser.add_argument('--max-conv-padding', dest='max_conv_padding', default=1, type=int, - help='Maximum padding for Conv tests') - - parser.add_argument('--max-conv-dilation', dest='max_conv_dilation', default=2, type=int, - help='Maximum dilation for Conv tests') - - parser.add_argument('--max-conv-stride', dest='max_conv_stride', default=2, type=int, - help='Maximum stride for Conv tests') - - parser.add_argument('--max-pooling-padding', dest='max_pooling_padding', default=1, type=int, - help='Maximum padding for pooling tests') - - parser.add_argument('--max-pooling-stride', dest='max_pooling_stride', default=2, type=int, - help='Maximum stride for pooling tests') - - parser.add_argument('--max-pooling-kernel', dest='max_pooling_kernel', default=2, type=int, - help='Maximum padding for pooling tests') - - parser.add_argument('--num-rand-permutations', dest='num_rand_permutations', default=6, type=int, - help='Number of random permutations for a given shape/rank for randomly-sampled parameter spaces') + parser.add_argument( + "--tensor-dim-range", + dest="tensor_shape_range", + default="1,64", + type=lambda x: str_to_list(x), + help="Min,Max range of tensor shapes", + ) + + parser.add_argument( + "--max-batch-size", + dest="max_batch_size", + default=1, + type=int, + help="Maximum batch size for NHWC tests", + ) + + parser.add_argument( + "--max-conv-padding", + dest="max_conv_padding", + default=1, + type=int, + help="Maximum padding for Conv tests", + ) + + parser.add_argument( + "--max-conv-dilation", + dest="max_conv_dilation", + default=2, + type=int, + help="Maximum dilation for Conv tests", + ) + + parser.add_argument( + "--max-conv-stride", + dest="max_conv_stride", + default=2, + type=int, + help="Maximum stride for Conv tests", + ) + + parser.add_argument( + "--max-pooling-padding", + dest="max_pooling_padding", + default=1, + type=int, + help="Maximum padding for pooling tests", + ) + + parser.add_argument( + "--max-pooling-stride", + dest="max_pooling_stride", + default=2, + type=int, + help="Maximum stride for pooling tests", + ) + + parser.add_argument( + "--max-pooling-kernel", + dest="max_pooling_kernel", + default=2, + type=int, + help="Maximum padding for pooling tests", + ) + + parser.add_argument( + "--num-rand-permutations", + dest="num_rand_permutations", + default=6, + type=int, + help="Number of random permutations for a given shape/rank for randomly-sampled parameter spaces", + ) # Targetting a specific shape/rank/dtype - parser.add_argument('--target-shape', dest='target_shapes', action='append', default=[], type=lambda x: str_to_list(x), - help='Create tests with a particular input tensor shape, e.g., 1,4,4,8 (may be repeated for tests that require multiple input shapes)') - - parser.add_argument('--target-rank', dest='target_ranks', action='append', default=None, type=lambda x: auto_int(x), - help='Create tests with a particular input tensor rank') - - parser.add_argument('--target-dtype', dest='target_dtypes', action='append', default=None, type=lambda x: dtype_str_to_val(x), - help='Create test with a particular DType (may be repeated)') + parser.add_argument( + "--target-shape", + dest="target_shapes", + action="append", + default=[], + type=lambda x: str_to_list(x), + help="Create tests with a particular input tensor shape, e.g., 1,4,4,8 (may be repeated for tests that require multiple input shapes)", + ) + + parser.add_argument( + "--target-rank", + dest="target_ranks", + action="append", + default=None, + type=lambda x: auto_int(x), + help="Create tests with a particular input tensor rank", + ) + + parser.add_argument( + "--target-dtype", + dest="target_dtypes", + action="append", + default=None, + type=lambda x: dtype_str_to_val(x), + help="Create test with a particular DType (may be repeated)", + ) args = parser.parse_args() return args -def main(): +def main(): args = parseArgs() @@ -122,16 +205,23 @@ def main(): testList = [] for op in ttg.TOSA_OP_LIST: - if re.match(args.filter + '.*', op): - testList.extend(ttg.genOpTestList(op, shapeFilter=args.target_shapes, rankFilter=args.target_ranks, dtypeFilter=args.target_dtypes)) - - print('{} matching tests'.format(len(testList))) + if re.match(args.filter + ".*", op): + testList.extend( + ttg.genOpTestList( + op, + shapeFilter=args.target_shapes, + rankFilter=args.target_ranks, + dtypeFilter=args.target_dtypes, + ) + ) + + print("{} matching tests".format(len(testList))) for opName, testStr, dtype, shapeList, testArgs in testList: if args.verbose: print(testStr) ttg.serializeTest(opName, testStr, dtype, shapeList, testArgs) - print('Done creating {} tests'.format(len(testList))) + print("Done creating {} tests".format(len(testList))) -if __name__ == '__main__': +if __name__ == "__main__": exit(main()) diff --git a/verif/tosa_verif_run_ref.py b/verif/tosa_verif_run_ref.py index 2284e35..e3926ea 100755 --- a/verif/tosa_verif_run_ref.py +++ b/verif/tosa_verif_run_ref.py @@ -33,42 +33,90 @@ import importlib from enum import IntEnum, Enum, unique from datetime import datetime -# Include the ../shared directory in PYTHONPATH +# Include the ../scripts and ../scripts/xunit directory in PYTHONPATH parent_dir = os.path.dirname(os.path.realpath(__file__)) -sys.path.append(os.path.join(parent_dir, '..', 'scripts')) -sys.path.append(os.path.join(parent_dir, '..', 'scripts', 'xunit')) +sys.path.append(os.path.join(parent_dir, "..", "scripts")) +sys.path.append(os.path.join(parent_dir, "..", "scripts", "xunit")) + import xunit + +# Include the ../thirdparty/serialization_lib/python directory in PYTHONPATH +parent_dir = os.path.dirname(os.path.realpath(__file__)) +sys.path.append( + os.path.join(parent_dir, "..", "thirdparty", "serialization_lib", "python") +) import tosa from tosa_test_gen import TosaTestGen from tosa_test_runner import TosaTestRunner no_color_printing = False -#from run_tf_unit_test import LogColors, print_color, run_sh_command +# from run_tf_unit_test import LogColors, print_color, run_sh_command + def parseArgs(): parser = argparse.ArgumentParser() - parser.add_argument('-t', '--test', dest='test', type=str, nargs='+', - help='Test(s) to run') - parser.add_argument('--seed', dest='random_seed', default=42, type=int, - help='Random seed for test generation') - parser.add_argument('--ref-model-path', dest='ref_model_path', - default='build/reference_model/tosa_reference_model', type=str, - help='Path to reference model executable') - parser.add_argument('--ref-debug', dest='ref_debug', default='', type=str, - help='Reference debug flag (low, med, high)') - parser.add_argument('--ref-intermediates', dest='ref_intermediates', default=0, type=int, - help='Reference model dumps intermediate tensors') - parser.add_argument('-v', '--verbose', dest='verbose', action='count', - help='Verbose operation') - parser.add_argument('-j', '--jobs', dest='jobs', type=int, default=1, - help='Number of parallel jobs') - parser.add_argument('--sut-module', '-s', dest='sut_module', type=str, nargs='+', default=['tosa_ref_run'], - help='System under test module to load (derives from TosaTestRunner). May be repeated') - parser.add_argument('--sut-module-args', dest='sut_module_args', type=str, nargs='+', default=[], - help='System under test module arguments. Use sutmodulename:argvalue to pass an argument. May be repeated.') - parser.add_argument('--xunit-file', dest='xunit_file', type=str, default='result.xml', - help='XUnit output file') + parser.add_argument( + "-t", "--test", dest="test", type=str, nargs="+", help="Test(s) to run" + ) + parser.add_argument( + "--seed", + dest="random_seed", + default=42, + type=int, + help="Random seed for test generation", + ) + parser.add_argument( + "--ref-model-path", + dest="ref_model_path", + default="build/reference_model/tosa_reference_model", + type=str, + help="Path to reference model executable", + ) + parser.add_argument( + "--ref-debug", + dest="ref_debug", + default="", + type=str, + help="Reference debug flag (low, med, high)", + ) + parser.add_argument( + "--ref-intermediates", + dest="ref_intermediates", + default=0, + type=int, + help="Reference model dumps intermediate tensors", + ) + parser.add_argument( + "-v", "--verbose", dest="verbose", action="count", help="Verbose operation" + ) + parser.add_argument( + "-j", "--jobs", dest="jobs", type=int, default=1, help="Number of parallel jobs" + ) + parser.add_argument( + "--sut-module", + "-s", + dest="sut_module", + type=str, + nargs="+", + default=["tosa_ref_run"], + help="System under test module to load (derives from TosaTestRunner). May be repeated", + ) + parser.add_argument( + "--sut-module-args", + dest="sut_module_args", + type=str, + nargs="+", + default=[], + help="System under test module arguments. Use sutmodulename:argvalue to pass an argument. May be repeated.", + ) + parser.add_argument( + "--xunit-file", + dest="xunit_file", + type=str, + default="result.xml", + help="XUnit output file", + ) args = parser.parse_args() @@ -78,6 +126,7 @@ def parseArgs(): return args + def workerThread(task_queue, runnerList, args, result_queue): while True: try: @@ -88,21 +137,31 @@ def workerThread(task_queue, runnerList, args, result_queue): if test is None: break - msg = '' + msg = "" start_time = datetime.now() try: for runnerModule, runnerArgs in runnerList: if args.verbose: - print('Running runner {} with test {}'.format(runnerModule.__name__, test)) + print( + "Running runner {} with test {}".format( + runnerModule.__name__, test + ) + ) runner = runnerModule.TosaRefRunner(args, runnerArgs, test) try: rc = runner.runModel() except Exception as e: rc = TosaTestRunner.Result.INTERNAL_ERROR except Exception as e: - print('Internal regression error: {}'.format(e)) - print(''.join(traceback.format_exception(etype=type(e), value=e, tb=e.__traceback__))) + print("Internal regression error: {}".format(e)) + print( + "".join( + traceback.format_exception( + etype=type(e), value=e, tb=e.__traceback__ + ) + ) + ) rc = TosaTestRunner.Result.INTERNAL_ERROR end_time = datetime.now() @@ -112,25 +171,27 @@ def workerThread(task_queue, runnerList, args, result_queue): return True + def loadRefModules(args): # Returns a tuple of (runner_module, [argument list]) runnerList = [] for r in args.sut_module: if args.verbose: - print('Loading module {}'.format(r)) + print("Loading module {}".format(r)) runner = importlib.import_module(r) # Look for arguments associated with this runner - runnerArgPrefix = '{}:'.format(r) + runnerArgPrefix = "{}:".format(r) runnerArgList = [] for a in args.sut_module_args: if a.startswith(runnerArgPrefix): - runnerArgList.append(a[len(runnerArgPrefix):]) + runnerArgList.append(a[len(runnerArgPrefix) :]) runnerList.append((runner, runnerArgList)) return runnerList + def main(): args = parseArgs() @@ -143,10 +204,12 @@ def main(): for t in args.test: taskQueue.put((t)) - print('Running {} tests '.format(taskQueue.qsize())) + print("Running {} tests ".format(taskQueue.qsize())) for i in range(args.jobs): - t = threading.Thread(target=workerThread, args=(taskQueue, runnerList, args, resultQueue)) + t = threading.Thread( + target=workerThread, args=(taskQueue, runnerList, args, resultQueue) + ) t.setDaemon(True) t.start() threads.append(t) @@ -165,34 +228,40 @@ def main(): resultList.append((test, rc, msg, time_delta)) results[rc] = results[rc] + 1 - xunit_result = xunit.xunit_results('Regressions') - xunit_suite = xunit_result.create_suite('Unit tests') + xunit_result = xunit.xunit_results("Regressions") + xunit_suite = xunit_result.create_suite("Unit tests") # Sort by test name for test, rc, msg, time_delta in sorted(resultList, key=lambda tup: tup[0]): test_name = test - xt = xunit.xunit_test(test_name, 'reference') + xt = xunit.xunit_test(test_name, "reference") - xt.time = str(float(time_delta.seconds) + (float(time_delta.microseconds) * 1e-6)) + xt.time = str( + float(time_delta.seconds) + (float(time_delta.microseconds) * 1e-6) + ) - if rc == TosaTestRunner.Result.EXPECTED_PASS or rc == TosaTestRunner.Result.EXPECTED_FAILURE: + if ( + rc == TosaTestRunner.Result.EXPECTED_PASS + or rc == TosaTestRunner.Result.EXPECTED_FAILURE + ): if args.verbose: - print('{} {}'.format(rc.name, test_name)) + print("{} {}".format(rc.name, test_name)) else: xt.failed(msg) - print('{} {}'.format(rc.name, test_name)) + print("{} {}".format(rc.name, test_name)) xunit_suite.tests.append(xt) resultQueue.task_done() xunit_result.write_results(args.xunit_file) - print('Totals: ', end='') + print("Totals: ", end="") for result in TosaTestRunner.Result: - print('{} {}, '.format(results[result], result.name.lower()), end ='') + print("{} {}, ".format(results[result], result.name.lower()), end="") print() return 0 -if __name__ == '__main__': + +if __name__ == "__main__": exit(main()) |