Add option for passing in hidden ops to Python code generation as a file.
Change: 131133377
This commit is contained in:
Derek Murray
TensorFlower Gardener
parent
2269152197
commit
08ce1a4d2f
@ -311,9 +311,7 @@ py_test(
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tf_gen_op_wrapper_py(
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name = "functional_ops",
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out = "ops/gen_functional_ops.py",
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hidden = [
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"SymbolicGradient",
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],
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hidden_file = "ops/hidden_ops.txt",
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)
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py_library(
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@ -513,51 +511,19 @@ py_test(
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tf_gen_op_wrapper_py(
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name = "array_ops",
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hidden = [
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"BroadcastGradientArgs",
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"ConcatOffset",
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"Concat",
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"Const",
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"EditDistance",
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"MirrorPad",
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"MirrorPadGrad",
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"OneHot",
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"Pack",
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"Pad",
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"Placeholder",
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"RefIdentity",
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"Split",
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"Slice",
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"TileGrad", # Exported through array_grad instead of array_ops.
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"ZerosLike", # TODO(josh11b): Use this instead of the Python version.
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"Unpack",
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],
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hidden_file = "ops/hidden_ops.txt",
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require_shape_functions = True,
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)
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tf_gen_op_wrapper_py(
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name = "candidate_sampling_ops",
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hidden = [
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"AllCandidateSampler",
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"ComputeAccidentalHits",
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"FixedUnigramCandidateSampler",
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"LearnedUnigramCandidateSampler",
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"LogUniformCandidateSampler",
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"ThreadUnsafeUnigramCandidateSampler",
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"UniformCandidateSampler",
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],
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hidden_file = "ops/hidden_ops.txt",
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require_shape_functions = True,
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)
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tf_gen_op_wrapper_py(
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name = "control_flow_ops",
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hidden = [
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"Switch",
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"Merge",
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"RefMerge",
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"Exit",
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"RefExit",
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],
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hidden_file = "ops/hidden_ops.txt",
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require_shape_functions = True,
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deps = [
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"//tensorflow/core:control_flow_ops_op_lib",
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@ -567,263 +533,98 @@ tf_gen_op_wrapper_py(
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tf_gen_op_wrapper_py(
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name = "ctc_ops",
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hidden = [
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"CTCLoss",
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"CTCGreedyDecoder",
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"CTCBeamSearchDecoder",
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],
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hidden_file = "ops/hidden_ops.txt",
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require_shape_functions = True,
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)
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tf_gen_op_wrapper_py(
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name = "data_flow_ops",
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hidden = [
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"Barrier",
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"BarrierClose",
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"BarrierIncompleteSize",
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"BarrierInsertMany",
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"BarrierReadySize",
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"BarrierTakeMany",
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"PriorityQueue",
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"FIFOQueue",
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"HashTable",
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"InitializeTable",
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"InitializeTableFromTextFile",
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"LookupTableExport",
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"LookupTableFind",
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"LookupTableImport",
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"LookupTableInsert",
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"LookupTableSize",
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"MutableHashTable",
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"MutableHashTableOfTensors",
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"Mutex",
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"MutexAcquire",
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"MutexRelease",
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"PaddingFIFOQueue",
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"QueueClose",
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"QueueDequeue",
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"QueueDequeueMany",
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"QueueDequeueUpTo",
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"QueueEnqueue",
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"QueueEnqueueMany",
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"QueueSize",
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"RandomShuffleQueue",
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"Stack",
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"StackPop",
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"StackPush",
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"StackClose",
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"TensorArray",
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"TensorArrayClose",
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"TensorArrayConcat",
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"TensorArrayGrad",
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"TensorArrayRead",
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"TensorArrayPack",
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"TensorArraySize",
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"TensorArraySplit",
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"TensorArrayUnpack",
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"TensorArrayWrite",
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"GetSessionHandle",
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"GetSessionTensor",
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"DeleteSessionTensor",
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],
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hidden_file = "ops/hidden_ops.txt",
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require_shape_functions = True,
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)
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tf_gen_op_wrapper_py(
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name = "image_ops",
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hidden = [
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"RandomCrop",
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"ResizeBilinearGrad",
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"ResizeNearestNeighborGrad",
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"AdjustContrastv2",
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"ScaleImageGrad",
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],
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hidden_file = "ops/hidden_ops.txt",
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require_shape_functions = True,
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)
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tf_gen_op_wrapper_py(
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name = "io_ops",
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hidden = [
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"FixedLengthRecordReader",
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"IdentityReader",
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"ReaderClose",
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"ReaderEnqueueWork",
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"ReaderNumRecordsProduced",
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"ReaderNumWorkUnitsCompleted",
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"ReaderRead",
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"ReaderReadUpTo",
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"ReaderReset",
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"ReaderRestoreState",
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"ReaderSerializeState",
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"ReaderWorkQueueLength",
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"Restore",
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"RestoreSlice",
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"Save",
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"SaveSlices",
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"ShardedFilename",
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"ShardedFilespec",
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"TextLineReader",
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"TFRecordReader",
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"WholeFileReader",
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],
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hidden_file = "ops/hidden_ops.txt",
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require_shape_functions = True,
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)
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tf_gen_op_wrapper_py(
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name = "linalg_ops",
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hidden_file = "ops/hidden_ops.txt",
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require_shape_functions = True,
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)
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tf_gen_op_wrapper_py(
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name = "logging_ops",
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hidden = [
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"Assert",
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"AudioSummary",
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"HistogramAccumulatorSummary",
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"HistogramSummary",
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"ImageSummary",
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"MergeSummary",
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"Print",
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"ScalarSummary",
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"TensorSummary",
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],
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hidden_file = "ops/hidden_ops.txt",
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require_shape_functions = True,
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)
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tf_gen_op_wrapper_py(
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name = "math_ops",
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hidden = [
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"Abs",
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"AddN",
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"All",
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"Any",
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"BatchMatMul",
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"Complex",
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"Max",
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"Mean",
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"Min",
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"Pow",
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"Prod",
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"Range",
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"SparseMatMul",
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"Sum",
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"MatMul",
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"Sigmoid",
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"Tanh",
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"SigmoidGrad",
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"TanhGrad",
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],
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hidden_file = "ops/hidden_ops.txt",
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require_shape_functions = True,
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)
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tf_gen_op_wrapper_py(
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name = "nn_ops",
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hidden = [
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"AvgPoolGrad", # "*Grad" accessible through nn_grad instead of nn_ops.
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"BatchNormWithGlobalNormalization",
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"BatchNormWithGlobalNormalizationGrad",
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"SoftmaxCrossEntropyWithLogits",
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"SparseSoftmaxCrossEntropyWithLogits",
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"LRNGrad",
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"MaxPoolGrad",
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"MaxPoolGradWithArgmax",
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"ReluGrad",
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"Relu6Grad",
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"EluGrad",
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"SoftplusGrad",
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"SoftsignGrad",
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"TopK",
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"TopKV2",
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"BiasAdd",
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"BiasAddV1",
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"Relu6",
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"AvgPool",
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"MaxPool",
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],
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hidden_file = "ops/hidden_ops.txt",
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require_shape_functions = True,
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)
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tf_gen_op_wrapper_py(
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name = "parsing_ops",
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hidden = [
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"ParseExample",
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"ParseSingleSequenceExample",
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],
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hidden_file = "ops/hidden_ops.txt",
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require_shape_functions = True,
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)
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tf_gen_op_wrapper_py(
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name = "random_ops",
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hidden = [
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"RandomGamma",
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"RandomUniform",
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"RandomUniformInt",
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"RandomShuffle",
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"RandomStandardNormal",
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"ParameterizedTruncatedNormal",
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"TruncatedNormal",
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],
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hidden_file = "ops/hidden_ops.txt",
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require_shape_functions = True,
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)
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tf_gen_op_wrapper_py(
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name = "script_ops",
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hidden = [
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"PyFunc",
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"PyFuncStateless",
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],
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hidden_file = "ops/hidden_ops.txt",
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require_shape_functions = True,
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)
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tf_gen_op_wrapper_py(
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name = "state_ops",
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hidden = [
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"Variable",
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"TemporaryVariable",
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"DestroyTemporaryVariable",
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],
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hidden_file = "ops/hidden_ops.txt",
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require_shape_functions = True,
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)
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tf_gen_op_wrapper_py(
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name = "sparse_ops",
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hidden = [
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"DeserializeManySparse",
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"SerializeManySparse",
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"SerializeSparse",
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"SparseAdd",
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"SparseAddGrad",
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"SparseConcat",
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"SparseSplit",
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"SparseSelectLastK",
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"SparseReorder",
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"SparseReshape",
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"SparseToDense",
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"SparseTensorDenseAdd",
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"SparseTensorDenseMatMul",
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],
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hidden_file = "ops/hidden_ops.txt",
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require_shape_functions = True,
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)
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tf_gen_op_wrapper_py(
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name = "string_ops",
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hidden = [
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"StringSplit",
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],
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hidden_file = "ops/hidden_ops.txt",
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require_shape_functions = True,
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)
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tf_gen_op_wrapper_py(
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name = "user_ops",
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hidden = [
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"Fact",
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],
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hidden_file = "ops/hidden_ops.txt",
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require_shape_functions = False,
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)
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tf_gen_op_wrapper_py(
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name = "training_ops",
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out = "training/gen_training_ops.py",
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hidden_file = "ops/hidden_ops.txt",
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require_shape_functions = True,
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)
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@ -646,7 +646,7 @@ void GenerateLowerCaseOpName(const string& str, string* result) {
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} // namespace
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string GetPythonOps(const OpList& ops, const string& hidden_ops,
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string GetPythonOps(const OpList& ops, const std::vector<string>& hidden_ops,
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bool require_shapes) {
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string result;
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// Header
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@ -668,15 +668,13 @@ from tensorflow.python.framework import op_def_library
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)");
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std::vector<string> hidden_vec = str_util::Split(hidden_ops, ',');
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// We'll make a copy of ops that filters out descriptions.
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OpList cleaned_ops;
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auto out = cleaned_ops.mutable_op();
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out->Reserve(ops.op_size());
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for (const auto& op_def : ops.op()) {
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bool is_hidden = false;
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for (const string& hidden : hidden_vec) {
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for (const string& hidden : hidden_ops) {
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if (op_def.name() == hidden) {
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is_hidden = true;
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break;
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@ -726,22 +724,16 @@ _op_def_lib = _InitOpDefLibrary()
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return result;
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}
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void PrintPythonOps(const OpList& ops, const string& hidden_ops,
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void PrintPythonOps(const OpList& ops, const std::vector<string>& hidden_ops,
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bool require_shapes) {
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printf("%s", GetPythonOps(ops, hidden_ops, require_shapes).c_str());
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}
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string GetAllPythonOps(const char* hidden, bool require_shapes) {
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OpList ops;
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OpRegistry::Global()->Export(false, &ops);
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return GetPythonOps(ops, hidden, require_shapes);
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}
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string GetPythonWrappers(const char* op_wrapper_buf, size_t op_wrapper_len) {
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string op_list_str(op_wrapper_buf, op_wrapper_len);
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OpList ops;
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ops.ParseFromString(op_list_str);
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return GetPythonOps(ops, "", false);
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return GetPythonOps(ops, {}, false);
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}
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} // namespace tensorflow
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@ -17,6 +17,7 @@ limitations under the License.
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#define TENSORFLOW_PYTHON_FRAMEWORK_PYTHON_OP_GEN_H_
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#include <string>
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#include <vector>
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#include "tensorflow/core/framework/op_def.pb.h"
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#include "tensorflow/core/platform/types.h"
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@ -26,9 +27,9 @@ namespace tensorflow {
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// list of Op names that should get a leading _ in the output.
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// The Print* version prints the output to stdout, Get* version returns the
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// output as a string.
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void PrintPythonOps(const OpList& ops, const string& hidden_ops,
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void PrintPythonOps(const OpList& ops, const std::vector<string>& hidden_ops,
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bool require_shapes);
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string GetPythonOps(const OpList& ops, const string& hidden_ops,
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string GetPythonOps(const OpList& ops, const std::vector<string>& hidden_ops,
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bool require_shapes);
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// Get the python wrappers for a list of ops in a OpList.
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@ -15,18 +15,73 @@ limitations under the License.
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#include "tensorflow/python/framework/python_op_gen.h"
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#include <memory>
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#include <string>
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#include <vector>
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#include "tensorflow/core/framework/op.h"
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#include "tensorflow/core/framework/op_def.pb.h"
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#include "tensorflow/core/lib/core/errors.h"
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#include "tensorflow/core/lib/io/inputbuffer.h"
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#include "tensorflow/core/lib/strings/scanner.h"
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#include "tensorflow/core/platform/env.h"
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#include "tensorflow/core/platform/init_main.h"
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#include "tensorflow/core/platform/logging.h"
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namespace tensorflow {
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namespace {
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void PrintAllPythonOps(const char* hidden, bool require_shapes) {
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Status ReadHiddenOpsFromFile(const string& filename,
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std::vector<string>* hidden_ops) {
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std::unique_ptr<RandomAccessFile> file;
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TF_CHECK_OK(Env::Default()->NewRandomAccessFile(filename, &file));
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std::unique_ptr<io::InputBuffer> input_buffer(
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new io::InputBuffer(file.get(), 256 << 10));
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string line_contents;
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Status s = input_buffer->ReadLine(&line_contents);
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while (s.ok()) {
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// The parser assumes that the op name is the first string on each
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// line with no preceding whitespace, and ignores lines that do
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// not start with an op name as a comment.
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strings::Scanner scanner{StringPiece(line_contents)};
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StringPiece op_name;
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if (scanner.One(strings::Scanner::LETTER_DIGIT_DOT)
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.Any(strings::Scanner::LETTER_DIGIT_DASH_DOT_SLASH_UNDERSCORE)
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.GetResult(nullptr, &op_name)) {
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hidden_ops->emplace_back(op_name.ToString());
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}
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s = input_buffer->ReadLine(&line_contents);
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}
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if (!errors::IsOutOfRange(s)) return s;
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return Status::OK();
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}
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// The argument parsing is deliberately simplistic to support our only
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// known use cases:
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//
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// 1. Read all op names from a file.
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// 2. Read all op names from the arg as a comma-delimited list.
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//
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// Expected command-line argument syntax:
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// ARG ::= '@' FILENAME
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// | OP_NAME [',' OP_NAME]*
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Status ParseHiddenOpsCommandLine(const char* arg,
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std::vector<string>* hidden_ops) {
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std::vector<string> op_names = str_util::Split(arg, ',');
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if (op_names.size() == 1 && op_names[0].substr(0, 1) == "@") {
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const string filename = op_names[0].substr(1);
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return tensorflow::ReadHiddenOpsFromFile(filename, hidden_ops);
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} else {
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*hidden_ops = std::move(op_names);
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}
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return Status::OK();
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}
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void PrintAllPythonOps(const std::vector<string>& hidden_ops,
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bool require_shapes) {
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OpList ops;
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OpRegistry::Global()->Export(false, &ops);
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PrintPythonOps(ops, hidden, require_shapes);
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PrintPythonOps(ops, hidden_ops, require_shapes);
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}
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} // namespace
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@ -34,10 +89,15 @@ void PrintAllPythonOps(const char* hidden, bool require_shapes) {
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int main(int argc, char* argv[]) {
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tensorflow::port::InitMain(argv[0], &argc, &argv);
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// Usage:
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// gen_main [ @FILENAME | OpName[,OpName]* ] (0 | 1)
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if (argc == 2) {
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tensorflow::PrintAllPythonOps("", std::string(argv[1]) == "1");
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tensorflow::PrintAllPythonOps({}, tensorflow::string(argv[1]) == "1");
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} else if (argc == 3) {
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tensorflow::PrintAllPythonOps(argv[1], std::string(argv[2]) == "1");
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std::vector<tensorflow::string> hidden_ops;
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TF_CHECK_OK(tensorflow::ParseHiddenOpsCommandLine(argv[1], &hidden_ops));
|
||||
tensorflow::PrintAllPythonOps(hidden_ops,
|
||||
tensorflow::string(argv[2]) == "1");
|
||||
} else {
|
||||
return -1;
|
||||
}
|
||||
|
||||
224
tensorflow/python/ops/hidden_ops.txt
Normal file
224
tensorflow/python/ops/hidden_ops.txt
Normal file
@ -0,0 +1,224 @@
|
||||
# array_ops
|
||||
BroadcastGradientArgs
|
||||
ConcatOffset
|
||||
Concat
|
||||
Const
|
||||
EditDistance
|
||||
MirrorPad
|
||||
MirrorPadGrad
|
||||
OneHot
|
||||
Pack
|
||||
Pad
|
||||
Placeholder
|
||||
RefIdentity
|
||||
Split
|
||||
Slice
|
||||
TileGrad # Exported through array_grad instead of array_ops.
|
||||
ZerosLike # TODO(josh11b): Use this instead of the Python version.
|
||||
Unpack
|
||||
|
||||
# candidate_sampling_ops
|
||||
AllCandidateSampler
|
||||
ComputeAccidentalHits
|
||||
FixedUnigramCandidateSampler
|
||||
LearnedUnigramCandidateSampler
|
||||
LogUniformCandidateSampler
|
||||
ThreadUnsafeUnigramCandidateSampler
|
||||
UniformCandidateSampler
|
||||
|
||||
# control_flow_ops
|
||||
Switch
|
||||
Merge
|
||||
RefMerge
|
||||
Exit
|
||||
RefExit
|
||||
|
||||
# ctc_ops
|
||||
CTCLoss
|
||||
CTCGreedyDecoder
|
||||
CTCBeamSearchDecoder
|
||||
|
||||
# data_flow_ops
|
||||
Barrier
|
||||
BarrierClose
|
||||
BarrierIncompleteSize
|
||||
BarrierInsertMany
|
||||
BarrierReadySize
|
||||
BarrierTakeMany
|
||||
PriorityQueue
|
||||
FIFOQueue
|
||||
HashTable
|
||||
InitializeTable
|
||||
InitializeTableFromTextFile
|
||||
LookupTableExport
|
||||
LookupTableFind
|
||||
LookupTableImport
|
||||
LookupTableInsert
|
||||
LookupTableSize
|
||||
MutableHashTable
|
||||
MutableHashTableOfTensors
|
||||
Mutex
|
||||
MutexAcquire
|
||||
MutexRelease
|
||||
PaddingFIFOQueue
|
||||
QueueClose
|
||||
QueueDequeue
|
||||
QueueDequeueMany
|
||||
QueueDequeueUpTo
|
||||
QueueEnqueue
|
||||
QueueEnqueueMany
|
||||
QueueSize
|
||||
RandomShuffleQueue
|
||||
Stack
|
||||
StackPop
|
||||
StackPush
|
||||
StackClose
|
||||
TensorArray
|
||||
TensorArrayClose
|
||||
TensorArrayConcat
|
||||
TensorArrayGrad
|
||||
TensorArrayRead
|
||||
TensorArrayPack
|
||||
TensorArraySize
|
||||
TensorArraySplit
|
||||
TensorArrayUnpack
|
||||
TensorArrayWrite
|
||||
GetSessionHandle
|
||||
GetSessionTensor
|
||||
DeleteSessionTensor
|
||||
|
||||
# functional_ops
|
||||
SymbolicGradient
|
||||
|
||||
# image_ops
|
||||
RandomCrop
|
||||
ResizeBilinearGrad
|
||||
ResizeNearestNeighborGrad
|
||||
AdjustContrastv2
|
||||
ScaleImageGrad
|
||||
|
||||
# io_ops
|
||||
FixedLengthRecordReader
|
||||
IdentityReader
|
||||
ReaderClose
|
||||
ReaderEnqueueWork
|
||||
ReaderNumRecordsProduced
|
||||
ReaderNumWorkUnitsCompleted
|
||||
ReaderRead
|
||||
ReaderReadUpTo
|
||||
ReaderReset
|
||||
ReaderRestoreState
|
||||
ReaderSerializeState
|
||||
ReaderWorkQueueLength
|
||||
Restore
|
||||
RestoreSlice
|
||||
Save
|
||||
SaveSlices
|
||||
ShardedFilename
|
||||
ShardedFilespec
|
||||
TextLineReader
|
||||
TFRecordReader
|
||||
WholeFileReader
|
||||
|
||||
# linalg_ops
|
||||
# (None)
|
||||
|
||||
# logging_ops
|
||||
Assert
|
||||
AudioSummary
|
||||
HistogramAccumulatorSummary
|
||||
HistogramSummary
|
||||
ImageSummary
|
||||
MergeSummary
|
||||
Print
|
||||
ScalarSummary
|
||||
TensorSummary
|
||||
|
||||
# math_ops
|
||||
Abs
|
||||
AddN
|
||||
All
|
||||
Any
|
||||
BatchMatMul
|
||||
Complex
|
||||
Max
|
||||
Mean
|
||||
Min
|
||||
Pow
|
||||
Prod
|
||||
Range
|
||||
SparseMatMul
|
||||
Sum
|
||||
MatMul
|
||||
Sigmoid
|
||||
Tanh
|
||||
SigmoidGrad
|
||||
TanhGrad
|
||||
|
||||
# nn_ops
|
||||
AvgPoolGrad # "*Grad" accessible through nn_grad instead of nn_ops.
|
||||
BatchNormWithGlobalNormalization
|
||||
BatchNormWithGlobalNormalizationGrad
|
||||
SoftmaxCrossEntropyWithLogits
|
||||
SparseSoftmaxCrossEntropyWithLogits
|
||||
LRNGrad
|
||||
MaxPoolGrad
|
||||
MaxPoolGradWithArgmax
|
||||
ReluGrad
|
||||
Relu6Grad
|
||||
EluGrad
|
||||
SoftplusGrad
|
||||
SoftsignGrad
|
||||
TopK
|
||||
TopKV2
|
||||
BiasAdd
|
||||
BiasAddV1
|
||||
Relu6
|
||||
AvgPool
|
||||
MaxPool
|
||||
|
||||
# parsing_ops
|
||||
ParseExample
|
||||
ParseSingleSequenceExample
|
||||
|
||||
# random_ops
|
||||
RandomGamma
|
||||
RandomUniform
|
||||
RandomUniformInt
|
||||
RandomShuffle
|
||||
RandomStandardNormal
|
||||
ParameterizedTruncatedNormal
|
||||
TruncatedNormal
|
||||
|
||||
# script_ops
|
||||
PyFunc
|
||||
PyFuncStateless
|
||||
|
||||
# state_ops
|
||||
Variable
|
||||
TemporaryVariable
|
||||
DestroyTemporaryVariable
|
||||
|
||||
# sparse_ops
|
||||
DeserializeManySparse
|
||||
SerializeManySparse
|
||||
SerializeSparse
|
||||
SparseAdd
|
||||
SparseAddGrad
|
||||
SparseConcat
|
||||
SparseSplit
|
||||
SparseSelectLastK
|
||||
SparseReorder
|
||||
SparseReshape
|
||||
SparseToDense
|
||||
SparseTensorDenseAdd
|
||||
SparseTensorDenseMatMul
|
||||
|
||||
# string_ops
|
||||
StringSplit
|
||||
|
||||
# user_ops
|
||||
Fact
|
||||
|
||||
# training_ops
|
||||
# (None)
|
||||
@ -216,8 +216,8 @@ def tf_gen_op_wrappers_cc(name,
|
||||
alwayslink=1,)
|
||||
|
||||
# Invoke this rule in .../tensorflow/python to build the wrapper library.
|
||||
def tf_gen_op_wrapper_py(name, out=None, hidden=[], visibility=None, deps=[],
|
||||
require_shape_functions=False):
|
||||
def tf_gen_op_wrapper_py(name, out=None, hidden=None, visibility=None, deps=[],
|
||||
require_shape_functions=False, hidden_file=None):
|
||||
# Construct a cc_binary containing the specified ops.
|
||||
tool_name = "gen_" + name + "_py_wrappers_cc"
|
||||
if not deps:
|
||||
@ -236,12 +236,33 @@ def tf_gen_op_wrapper_py(name, out=None, hidden=[], visibility=None, deps=[],
|
||||
if not out:
|
||||
out = "ops/gen_" + name + ".py"
|
||||
|
||||
native.genrule(
|
||||
name=name + "_pygenrule",
|
||||
outs=[out],
|
||||
tools=[tool_name],
|
||||
cmd=("$(location " + tool_name + ") " + ",".join(hidden)
|
||||
+ " " + ("1" if require_shape_functions else "0") + " > $@"))
|
||||
if hidden:
|
||||
# `hidden` is a list of op names to be hidden in the generated module.
|
||||
native.genrule(
|
||||
name=name + "_pygenrule",
|
||||
outs=[out],
|
||||
tools=[tool_name],
|
||||
cmd=("$(location " + tool_name + ") " + ",".join(hidden)
|
||||
+ " " + ("1" if require_shape_functions else "0") + " > $@"))
|
||||
elif hidden_file:
|
||||
# `hidden_file` is file containing a list of op names to be hidden in the
|
||||
# generated module.
|
||||
native.genrule(
|
||||
name=name + "_pygenrule",
|
||||
outs=[out],
|
||||
srcs=[hidden_file],
|
||||
tools=[tool_name],
|
||||
cmd=("$(location " + tool_name + ") @$(location "
|
||||
+ hidden_file + ") " + ("1" if require_shape_functions else "0")
|
||||
+ " > $@"))
|
||||
else:
|
||||
# No ops should be hidden in the generated module.
|
||||
native.genrule(
|
||||
name=name + "_pygenrule",
|
||||
outs=[out],
|
||||
tools=[tool_name],
|
||||
cmd=("$(location " + tool_name + ") "
|
||||
+ ("1" if require_shape_functions else "0") + " > $@"))
|
||||
|
||||
# Make a py_library out of the generated python file.
|
||||
native.py_library(name=name,
|
||||
|
||||
Loading…
Reference in New Issue
Block a user