FM / ATen

/usr/local/lib64/python3.6/site-packages/torch/include/ATen/

/usr/local/lib64/python3.6/site-packages/torch/include/ATen

Name	Size	Mode	Actions
core/	-	0755	rm
cpu/	-	0755	rm
cuda/	-	0755	rm
cudnn/	-	0755	rm
detail/	-	0755	rm
hip/	-	0755	rm
native/	-	0755	rm
quantized/	-	0755	rm
AccumulateType.h	4438	0644	edit dl rm
ArrayRef.h	44	0644	edit dl rm
ATen.h	998	0644	edit dl rm
autocast_mode.h	6716	0644	edit dl rm
Backend.h	43	0644	edit dl rm
Backtrace.h	46	0644	edit dl rm
BatchedFallback.h	965	0644	edit dl rm
BatchedTensorImpl.h	5383	0644	edit dl rm
CompositeExplicitAutogradFunctions.h	1622	0644	edit dl rm
CompositeExplicitAutogradFunctions_inl.h	54075	0644	edit dl rm
CompositeImplicitAutogradFunctions.h	1622	0644	edit dl rm
CompositeImplicitAutogradFunctions_inl.h	142082	0644	edit dl rm
Config.h	734	0644	edit dl rm
Context.h	12767	0644	edit dl rm
cpp_custom_type_hack.h	5326	0644	edit dl rm
CPUApplyUtils.h	12582	0644	edit dl rm
CPUFixedAllocator.h	830	0644	edit dl rm
CPUFunctions.h	1600	0644	edit dl rm
CPUFunctions_inl.h	171924	0644	edit dl rm
CPUGeneratorImpl.h	1431	0644	edit dl rm
CUDAFunctions.h	1601	0644	edit dl rm
CUDAFunctions_inl.h	185696	0644	edit dl rm
CUDAGeneratorImpl.h	4695	0644	edit dl rm
Device.h	42	0644	edit dl rm
DeviceGuard.h	1134	0644	edit dl rm
Dimname.h	31	0644	edit dl rm
DimVector.h	46	0644	edit dl rm
Dispatch.h	52137	0644	edit dl rm
div_rtn.h	204	0644	edit dl rm
DLConvertor.h	576	0644	edit dl rm
dlpack.h	5244	0644	edit dl rm
DynamicLibrary.h	369	0644	edit dl rm
ExpandUtils.h	14506	0644	edit dl rm
Formatting.h	34	0644	edit dl rm
Functions.h	846326	0644	edit dl rm
Generator.h	46	0644	edit dl rm
InferSize.h	2143	0644	edit dl rm
InitialTensorOptions.h	445	0644	edit dl rm
Layout.h	42	0644	edit dl rm
MapAllocator.h	2999	0644	edit dl rm
MatrixRef.h	3016	0644	edit dl rm
MemoryOverlap.h	1117	0644	edit dl rm
MetaFunctions.h	1601	0644	edit dl rm
MetaFunctions_inl.h	84006	0644	edit dl rm
NamedTensor.h	35	0644	edit dl rm
NamedTensorUtils.h	5747	0644	edit dl rm
NativeFunctions.h	354651	0644	edit dl rm
NativeMetaFunctions.h	35445	0644	edit dl rm
NumericUtils.h	2787	0644	edit dl rm
OpaqueTensorImpl.h	6080	0644	edit dl rm
Operators.h	1707199	0644	edit dl rm
OpMathType.h	460	0644	edit dl rm
Parallel.h	4875	0644	edit dl rm
ParallelNative.h	2443	0644	edit dl rm
ParallelNativeTBB.h	2934	0644	edit dl rm
ParallelOpenMP.h	3049	0644	edit dl rm
PTThreadPool.h	394	0644	edit dl rm
record_function.h	24044	0644	edit dl rm
RedispatchFunctions.h	1112886	0644	edit dl rm
RegistrationDeclarations.h	545777	0644	edit dl rm
SavedTensorHooks.h	328	0644	edit dl rm
Scalar.h	44	0644	edit dl rm
ScalarOps.h	2272	0644	edit dl rm
ScalarType.h	129	0644	edit dl rm
SequenceNumber.h	373	0644	edit dl rm
SmallVector.h	47	0644	edit dl rm
SparseCsrTensorImpl.h	2045	0644	edit dl rm
SparseCsrTensorUtils.h	523	0644	edit dl rm
SparseTensorImpl.h	12417	0644	edit dl rm
SparseTensorUtils.h	4219	0644	edit dl rm
Storage.h	43	0644	edit dl rm
Tensor.h	48	0644	edit dl rm
TensorAccessor.h	51	0644	edit dl rm
TensorGeometry.h	1855	0644	edit dl rm
TensorIndexing.h	21923	0644	edit dl rm
TensorIterator.h	29962	0644	edit dl rm
TensorIteratorInternal.h	1862	0644	edit dl rm
TensorMeta.h	2917	0644	edit dl rm
TensorNames.h	2519	0644	edit dl rm
TensorOperators.h	3275	0644	edit dl rm
TensorOptions.h	49	0644	edit dl rm
TensorUtils.h	5687	0644	edit dl rm
ThreadLocalState.h	3289	0644	edit dl rm
TracerMode.h	5576	0644	edit dl rm
TypeDefault.h	680	0644	edit dl rm
Utils.h	5993	0644	edit dl rm
Version.h	340	0644	edit dl rm
VmapMode.h	952	0644	edit dl rm
VmapTransforms.h	7654	0644	edit dl rm
WrapDimUtils.h	3438	0644	edit dl rm
WrapDimUtilsMulti.h	768	0644	edit dl rm

Edit: /usr/local/lib64/python3.6/site-packages/torch/include/ATen/BatchedTensorImpl.h (5383B)

#pragma once

#include <bitset>

#include <ATen/ArrayRef.h>
#include <ATen/SmallVector.h>
#include <ATen/Tensor.h>

namespace at {

// We assume this in a few other places in the codebase,
// but there isn't a centralized definition.
constexpr int64_t kVmapMaxTensorDims = 64;

// The valid vmap levels range from [0, 64). This effectively means that we
// support a maximum of 64 nested vmaps.
constexpr int64_t kVmapNumLevels = 64;

// Store this number of elements of BatchDims on the stack. Most people will
// probably use <= 5 nested vmaps, but adjust this number as necessary.
constexpr int64_t kBatchDimsStackSize = 5;

// a BatchDim represents a "private" dimension on a Tensor created inside of
// vmap. It is a (level, dim) tuple, with the `dim` indicating which dimension
// is being vmap'ed over and the `level` being an identifier for which vmap
// said dimension was created inside. The `dim` corresponds to a "physical
// dim" - it is a dimension index on the underlying physical tensor that is being
// vmapped over.
struct BatchDim {
  BatchDim(int64_t level, int64_t dim) : dim_(dim), level_(level) {}
  int64_t dim() const {
    return dim_;
  }
  int64_t level() const {
    return level_;
  }
 private:
  int64_t dim_;
  int64_t level_;
};

using BatchDims = SmallVector<BatchDim, kBatchDimsStackSize>;
using BatchDimsRef = ArrayRef<BatchDim>;

// A BatchedTensorImpl holds an underlying Tensor and a list of BatchDim
// NB: We use the term "BatchedTensor" to mean a Tensor that is backed with a
// BatchedTensorImpl.
//
// The batch dimensions are treated as being "private"; they are not user-visible.
// For example, in the following Tensor,
//    bt = BatchedTensorImpl(ones(2, 3, 5, 7), [(lvl=1, dim=0), (lvl=2, dim=1)])
// dimensions 0 and 1 are batch dimensions.
//
// bt.sizes() returns (5, 7); bt.sum(0) performs a reduction over the (public)
// dim 0, which is equivalent to dim 3 in the underlying ones(2, 3, 5, 7) tensor.
struct TORCH_API BatchedTensorImpl : public c10::TensorImpl {
  explicit BatchedTensorImpl(Tensor value, BatchDims bdims);

// Returns a reference to BatchDims that represent which dimensions of this
  // tensor are private.
  BatchDimsRef bdims() const { return bdims_; }

// BatchedTensorImpl wraps a Tensor
  const Tensor& value() const { return value_; };

// Given a public dimension index, return the dimension index in the underlying
  // value() tensor.
  // For example, if we have
  //    bt = BatchedTensorImpl(ones(2, 3, 5, 7), [(lvl=1, dim=0), (lvl=2, dim=2)])
  // bt.actualDim(0) -> 1
  // bt.actualDim(1) -> 3
  // bt.actualDim(2) -> Error
  int64_t actualDim(int64_t dim, bool wrap_dim = true) const;

// Override a bunch of methods inherited from TensorImpl to return error messages.
  bool is_contiguous_custom(at::MemoryFormat memory_format) const override;
  void set_size(int64_t dim, int64_t new_size) override;
  void set_stride(int64_t dim, int64_t new_stride) override;
  void set_storage_offset(int64_t storage_offset) override;
#ifdef DEBUG
  bool has_storage() const override;
#endif

private:
  // see NOTE: [BatchedTensorImpl levels invariant]
  void checkInvariants() const;
  const char* tensorimpl_type_name() const override;

Tensor value_;

// Note: [BatchedTensorImpl levels invariant]
  // There is an invariant that the BatchDims must be stored in increasing `level`
  // order. That is, for i < j, bdims_[i].level must be less than bdims_[j].level.
  BatchDims bdims_;
};

// NB: We use the term "BatchedTensor" to mean a Tensor that is backed with a
// BatchedTensorImpl.
inline bool isBatchedTensor(const Tensor& tensor) {
  return tensor.unsafeGetTensorImpl()->key_set().has(DispatchKey::Batched);
}

// It is unsafe to call this on a Tensor that is not backed by a
// BatchedTensorImpl. Please use `maybeGetBatchedImpl` whenever possible.
inline BatchedTensorImpl* unsafeGetBatchedImpl(Tensor tensor) {
  return static_cast<BatchedTensorImpl*>(tensor.unsafeGetTensorImpl());
}

inline BatchedTensorImpl* maybeGetBatchedImpl(Tensor tensor) {
  if (!isBatchedTensor(tensor)) {
    return nullptr;
  }
  return unsafeGetBatchedImpl(tensor);
}

// Returns a bitset. If bit i is set, then that means dim i is a batchdim.
inline std::bitset<kVmapMaxTensorDims> createBatchDimBitset(BatchDimsRef bdims) {
  std::bitset<kVmapMaxTensorDims> is_bdim;
  for (const auto& bdim : bdims) {
    is_bdim.set(bdim.dim());
  }
  return is_bdim;
}

// Creates a bitset for all of the levels present in `bdims`
inline std::bitset<kVmapNumLevels> createVmapLevelsBitset(BatchDimsRef bdims) {
  std::bitset<kVmapNumLevels> result;
  for (const auto& bdim : bdims) {
    result.set(bdim.level());
  }
  return result;
}

inline std::ostream& operator<<(std::ostream& out, const BatchDim& bdim) {
  out << "(lvl=" << bdim.level() << ", dim=" << bdim.dim() << ")";
  return out;
}

// Use this to construct a BatchedTensor from a regular Tensor
TORCH_API Tensor makeBatched(const Tensor& tensor, BatchDims bdims);

// Adds a batch dim to `tensor`, returning a BatchedTensor
TORCH_API Tensor addBatchDim(const Tensor& tensor, int64_t level, int64_t dim);

// Checks if an inplace operation on self and other is "vmap compatible".
// See NOTE: [vmap-incompatible in-place operations] for the definition of this.
TORCH_API bool inplaceIsVmapCompatible(const Tensor& self, const Tensor& other);

}

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