Summary: 166 instances, 142 unique

Text	Count
// TODO: convert first/last into inequality check (<=, >=), so they can work with boundaries	1
if (currentFunction.GetCurrentRegion() == nullptr) // TODO: put this check in GetCurrentFunction()	1
// TODO: for each dimension, loop over minimum of input and output interval. Then we don't have to check if the value is out-of-bounds	2
// TODO: inputTypes, outputTypes, extraArgs	2
// TODO: fix TransformInputBlock to take ranges instead of tileSize/filterSize/blockSize	1
// TODO: if we're not separable, we want to reduce first and accumulate a single output channel (== filter index)	1
// TODO: reset output layout (incl. transpose info)	1
Sparsity GetWeightsSparsity(const WeightsType& weights) // TODO: add layout	1
// TODO: in the "trainFiltersIndependently" case, we should	1
// TODO: log error	2
// TODO: fix this so that GetNonPointerType call isn't needed	1
// TODO: remove this eventually	1
// TODO: add implicit padding	1
// TODO: deal with merge points -- need to skip parallel area and return something before path split.	1
// TODO: use the entries of dataOrder to compute the indices	1
auto twiddleFactorsUnwrappedVar = module.ConstantArray(std::string("twiddles_") + std::to_string(halfN), twiddleFactorsUnwrapped); // TODO: encode type name in variable name	1
// TODO: if FunctionType was a function that took a vector of inputs, then we could dispense with this `if constexpr` block	1
// TODO: rename this function to something without "train" or "predictor" in its name	1
// TODO: combine this and IsCompleteAncestor so it's not O(N^2) (via set-intersect?)	1
// TODO: assert (channelStart + filterRange.size * numFilterChannels) < numChannels) --- make sure it doesn't wrap around while processing a block	1
// TODO: reverse order of args?	1
// TODO : make this a template specialization of Define(), currently lambdas and std::functions aren't	1
// TODO: need to know if we're going to invoke any kernels after the inner loops, and remove them from the valid kernel groups	1
std::vector<std::string> categoryNames; // TODO: read this in from a file	1
// TODO : Generalize to machine characteristics and move out of CachingStrategies	1
with DriveTest(model=model,  # TODO - no labels, no expected	1
// TODO: block-vectorize this:	1
# TODO - only chose the convolution method based on perf at this point. Need other strategy.	1
// TODO: this might not be needed in the high level api	1
// TODO: in both the "independent channel" and "spatial filter" cases, we really are optimizing to find a scalar result.	1
// TODO: add a comment describing the logic behind setting channelIndex (it allows us to unify depthwise-separabale and non-separable logic)	1
// TODO: assert we're idle (until we can handle multiple task arrays to be active)	1
// TODO: check version number and read this format if in back-compat mode	1
// TODO:	1
// TODO : determine if a vectorized approach is worthwhile here	1
// TODO: move `GetLoopRage` somewhere else	1
auto channelIndex = inputRange.channels.begin; // #### TODO: verify we don't really want inputRange.channels.index here	1
// TODO: rename this to avoid clashes with other PointerOffset()	1
// TODO: throw if we read bad values or hit EOF too soon	1
// TODO: record info about both phases (sparsify and reoptimize)	1
// TODO: assert back of _objectInfo == objInfo	1
// TODO: We want to only fire on a loop involving a leaf child of the index	1
# TODO - drivetest.py does not support "orangepi0"	1
// TODO: add a reorder node here that adds padding to the output, if necessary	1
// TODO: rename 'output' param here --- it's the input to the convolutional layer	1
# TODO - change this order requirement to make it more flexible	1
// TODO: deal with padding	1
# TODO: This logic is very fragile, we may want to have a model	1
# TODO: get rid of this, but keep the filter-transform part	1
archiver["filterSize"] << _filterSize; // TODO: get this from weights layout	1
## TODO:	1
// TODO: re-enable this branch when scalar port bug is fixed	1
// TODO: each boundary index needs its own "placement" value (e.g., you could have a kernel that runs when j==0 and k==N-1)	1
// TODO: Put this back once we're using the transposed output layout	2
// TODO: What does "runtime" variable mean? Stack/heap? This is only called in 1 place, from MapCompiler::AllocatePortFunctionArgument	1
// TODO: rename this function to imply we're running an optimization	1
// TODO: deal with eventually not having an emit-time-constant range here	1
// TODO: Fix this to deal with convParams.stride != 1	1
<!-- TODO: import this text via <xsl:import href=.../> -->	1
// TODO: add index, testVal to AND list, later return a conjunction of equality predicates	1
// TODO: restore state of variables	2
// TODO: put the above IREmitter call in the IRFunctionEmitter constructor	3
raise RuntimeError("FIXME: Traced RNNs don't support backward")	1
// TODO: this is really paddedHeight * filterSize * batchSize * stackSize - inputPadding * filterSize * batchSize	1
// TODO: check this carefully to make sure it's valid for stackSize != all and stackSize != 1	1
// TODO: should this be 64 bits for 64-bit systems?	1
// TODO: also add std::array of archivable variant types	1
// TODO: something about regions	1
// TODO: make these be TransformDataWithSubmodel	1
bit_map = 1  # TODO: need a way to get this from the model	2
// TODO: write out as a table?	1
llvm::StructType* IRThreadPoolTaskQueue::GetTaskQueueDataType(IRModuleEmitter& module) const // TODO: come up with a naming convention for "class" structs like this	1
bool needsDereference = valType->isPointerTy(); // TODO: Maybe this should be `isPtrOrPtrVectorTy()` or even `isPtrOrPtrVectorTy() || isArrayTy()`	1
// TODO: fix MatrixVectorMultiplyNode so it exposes the transpose options supported by BLAS GEMV functions.	1
// TODO: interleave load/compress more tightly to eliminate need for a scratch variable to hold a whole row	1
// TODO: allocate a set of temporaries per thread and parallelize the big outer loop	1
// TODO: Move this out to the API surface	2
// TODO: combine precompiled-IR case with use-own-function case	1
// TODO: rename 'output' parameter -- it's where we graft the new node	2
// TODO: fix up logic for deciding how many tasks to use.	1
math::MultiplyScaleAddUpdate(1.0, GetLabelEmbeddings(), similarityToPrototypes, 0.0, labels); // TODO due to the zero, there is a more appropriate operation	1
// TODO: Change this so that IDs are the responsibility of the EmitterContext	1
// TODO: Make this the basis of an iterator for MemoryLayout	2
// TODO: interleave load/compress more tightly to eliminate need for a scratch variable to hold the whole row	1
// TODO: turn this into a real For() loop	1
# TODO - here we ignore the global compiler options.	1
// TODO: add order (row-maj / channel-maj) parameter	1
// TODO: just copy the node and modify its layer	1
if (IsFullyConnectedLayerNode(&node)) // TODO: replace with submodel-matcher	1
// TODO: fix this to work with depthwise-separable convolutions	1
// TODO: add to _functions list	1
// TODO : Support buffer alignment in CppEmitterContext	1
throw 0; // TODO: throw a real exception (of type value::Exception::DebugTrapException, perhaps)	1
// TODO: need to allow using non-"dimension" indices as well (for non-innermost kernels)	1
auto fn = reinterpret_cast<void (*)()>(jitter.GetFunctionAddress(_moduleName + "_PrintNodeProfilingInfo")); // TODO: hide this reinterpret_cast in a templated method of IRExecutionEngine	1
// TODO: can we use an array type here?	1
// TODO (kerha): _computeContext isn't copied right now. Not sure if it should be. [2019-08-23]	1
// TODO: set initial value of index variable (at least in loop-nest-printing case)	1
// TODO: pass in original layer's output shape, for verification? (or, compare output of this function with original layer output shape)	1
// TODO: assert index1 and index2 are both in this dimension	1
# TODO: check package versions (currently we just check names)	1
llvm::StructType* IRThreadPoolTaskArray::GetTaskArrayDataType(IRModuleEmitter& module) // TODO: come up with a naming convention for "class" structs like this	1
// TODO: find a way to tell the differerence between a port that doesn't have a mapping because	1
// TODO: set a flag indicating that this function is done	1
// TODO: add this function to the _functions list??	6
// TODO: find a better (more general) way to indicate what the solution is, rather than with a "isSpatialConvolution" flag	1
// TODO: add options for normalizing and/or mean-subtracting	1
// TODO: get input of submodel to retrain	1
# TODO - only optimize based on convolution method at this point	1
// TODO: figure out what to do with the "where" parameter	2
## TODO: emit just a single loop for 1D convolutions	1
// TODO: add alignment directive	1
// TODO: have a more specific check to see if the variable is mapped to a port, rather than if it's a function input/output	1
# TODO: make this recursive to deal with multi-level directories	1
// TODO: make new function that emits the contents of the task function (which is also the stuff emitted in the serial case), and call this from	1
config[option[i][2:]] = True  # TODO - assuming flag without argument sets to True	1
archiver["inputLayout"] << _inputMemoryLayout; // TODO: get rid of this	1
// TODO: get types in a way that doesn't require emitting these variables	1
// TODO: fix	1
// TODO: assert(bitcount(length) == 1)  (i.e., length is a power of 2)	3
// TODO : make this a template specialization of Define(), currently lambdas and std::functions aren't	1
// TODO: I think now the outputTile is always contiguous, so we can just iterate over it continuously	1
// TODO: put this in a function that preprocesses the kernel predicates when adding the kernels to the schedule	1
// TODO: return nullptr if out of bounds (this is device-side code, and we may not be able to throw exceptions)	4
// TODO: add a reorder node here that makes the input be a contiguous vector, if necessary	1
// TODO: eventually, pass this in to ComputeTransformedOutput, instead of just assuming a layout there	1
// TODO move to Array slice code and generalize	1
// TODO: explicitly check for empty loop?	1
// TODO: later we may normalize the loops, in which case indexScale here will be the loop increment	1
model::PortMemoryLayout _inputMemoryLayout; // TODO: get rid of this by using a ReinterpretLayoutNode if necessary	1
else if (IsConvolutionalLayerNode(&node)) // TODO: replace with submodel-matcher	1
// TODO: get rid of const_cast	2
# TODO: combine MatrixLiteral with MatrixExpr	1
// TODO: Rename this function to make it clear we're adding nodes to the model (if normalizeInputs is true)	1
# TODO: only set this property (and omit ${LANGUAGE_LIBRARIES} from the swig_link_libraries call) if we're	1
// TODO: Interpolate if there is a sample, and currentTime > sampleTime	1
// TODO: let's make a popcount function that does the right thing	1
// TODO: See about removing the "% numChannels" if we can know that it's unnecessary at compile-time (e.g., if numFilterChannels == N*numChannels) for N > 0	1
// TODO : replace memory offsets with absolute offset support	1
// TODO: bounds-checking	1
// TODO: rename these to something without "train" and "predictor" in the name	1
// const auto& outputLayout = this->GetOutputMemoryLayout().ReorderedCopy({2,0,1}); // TODO: reorder from r,c,d -> d,r,c	2