repo stringlengths 1 152 ⌀ | file stringlengths 14 221 | code stringlengths 501 25k | file_length int64 501 25k | avg_line_length float64 20 99.5 | max_line_length int64 21 134 | extension_type stringclasses 2
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|---|---|---|---|---|---|---|
null | pytorch-main/torch/csrc/jit/runtime/symbolic_script.h | #pragma once
// This file is temporary until native_functions.yaml and derivatives.yaml are
// merged. Ideally this should all go into native_functions.yaml
#include <c10/util/Optional.h>
#include <c10/util/StringUtil.h>
#include <torch/csrc/jit/api/module.h>
namespace torch {
namespace jit {
struct GradientPair {
... | 598 | 27.52381 | 78 | h |
null | pytorch-main/torch/csrc/jit/runtime/symbolic_shape_registry.h | #pragma once
// This file is temporary until native_functions.yaml and derivatives.yaml are
// merged. Ideally this should all go into native_functions.yaml
#include <torch/csrc/Export.h>
#include <torch/csrc/jit/ir/ir.h>
namespace torch {
namespace jit {
/*
ADDING A NEW SHAPE GRAPH:
- For one node schema, there is ... | 2,820 | 38.180556 | 80 | h |
null | pytorch-main/torch/csrc/jit/runtime/vararg_functions.h | #pragma once
#include <ATen/core/List.h>
#include <ATen/core/functional.h>
#include <ATen/core/ivalue.h>
#include <ATen/core/jit_type.h>
#include <ATen/core/stack.h>
namespace torch {
namespace jit {
void tupleUnpack(Stack& stack);
void format(Stack& stack, size_t num_inputs);
void einsum(Stack& stack, size_t num_i... | 1,172 | 25.659091 | 80 | h |
null | pytorch-main/torch/csrc/jit/runtime/variable_tensor_list.h | #pragma once
#include <ATen/core/Tensor.h>
namespace torch {
namespace jit {
// a wrapper to mark places where we expect all the at::Tensors to be
// variables
struct variable_tensor_list : public std::vector<at::Tensor> {
variable_tensor_list() = default;
template <class InputIt>
variable_tensor_list(InputIt f... | 552 | 26.65 | 69 | h |
null | pytorch-main/torch/csrc/jit/runtime/interpreter/can_emit_inline.h | #pragma once
#include <memory>
#include <torch/csrc/jit/ir/ir.h>
namespace torch {
namespace jit {
namespace interpreter {
/*
This is an optimization that reduces the number of store/load/move nodes needed
by recognizing that parts of the graph are simple trees like a*x + b*y. When
this happens it is possible to wor... | 4,004 | 35.081081 | 80 | h |
null | pytorch-main/torch/csrc/jit/runtime/interpreter/frame.h | #pragma once
#include <atomic>
#include <memory>
#include <torch/csrc/jit/runtime/interpreter/code_impl.h>
#include <torch/csrc/jit/runtime/profiling_record.h>
namespace torch {
namespace jit {
namespace interpreter {
// A Frame captures function's state
// (e.g. `pc` and `base_pointer`)
// Each Frame corresponds t... | 1,160 | 24.8 | 64 | h |
null | pytorch-main/torch/csrc/jit/runtime/static/ProcessedNodeInputs.h | #pragma once
#include <cstddef>
#include <cstdint>
#include <memory>
#include <c10/macros/Macros.h>
#include <c10/util/Logging.h>
/**
* Packed representation of input indices for ProcessedNode.
*/
class ProcessedNodeInputs {
private:
// This keeps the size usage for inputs + outputs down to 16 bytes;
// we u... | 6,340 | 25.420833 | 76 | h |
null | pytorch-main/torch/csrc/jit/runtime/static/memory_planner.h | #pragma once
#include <torch/csrc/jit/runtime/static/impl.h>
namespace torch {
namespace jit {
// A StorageGroup represents a collection of tensors that share backing storage.
class StorageGroup {
public:
// Every storage group must contain at least one tensor.
explicit StorageGroup(at::Tensor* tensor) : group_... | 9,942 | 32.033223 | 80 | h |
null | pytorch-main/torch/csrc/jit/runtime/static/ops.h | #pragma once
#include <ATen/Utils.h>
#include <torch/csrc/jit/ir/ir.h>
#include <torch/csrc/jit/runtime/static/impl.h>
namespace at {
namespace native {
at::Tensor& reshape_copy_out(
at::Tensor& out,
const at::Tensor& self,
const at::DimVector& proposed_shape,
bool infer_size = true);
at::Tensor& to_c... | 5,549 | 28.057592 | 80 | h |
null | pytorch-main/torch/csrc/jit/runtime/static/passes.h | #include <torch/csrc/jit/ir/ir.h>
namespace torch {
namespace jit {
TORCH_API void FuseInferenceOpsForSparseNN(
std::shared_ptr<torch::jit::Graph>& graph);
TORCH_API void EliminateTrivialEquallySplit(
std::shared_ptr<torch::jit::Graph>& graph);
TORCH_API void FuseListUnpack(std::shared_ptr<torch::jit::Graph... | 3,655 | 38.73913 | 80 | h |
null | pytorch-main/torch/csrc/jit/runtime/static/processed_node_wrapper.h | #pragma once
#include <ATen/ATen.h>
#include <torch/csrc/jit/runtime/static/impl.h>
namespace torch {
namespace jit {
// The following class facilitates code reuse between ProcessedNodeInputWrapper
// and ProcessedNodeOutputWrapper via CRTP
template <typename DerivedWrapper>
class ProcessedNodeWrapperBase {
public:... | 6,620 | 29.939252 | 80 | h |
null | pytorch-main/torch/csrc/jit/runtime/static/static_method.h | #pragma once
#include <torch/csrc/api/include/torch/imethod.h>
#include <torch/csrc/jit/runtime/static/impl.h>
namespace torch {
namespace jit {
class StaticMethod : public torch::IMethod {
public:
StaticMethod(
std::shared_ptr<StaticModule> static_module,
std::string method_name)
: static_modul... | 1,349 | 25.470588 | 76 | h |
null | pytorch-main/torch/csrc/jit/runtime/static/te_wrapper.h | #pragma once
#include <torch/csrc/jit/tensorexpr/codegen.h>
#include <torch/csrc/jit/tensorexpr/ir.h>
#include <torch/csrc/jit/tensorexpr/ir_simplifier.h>
#include <torch/csrc/jit/tensorexpr/llvm_codegen.h>
#include <torch/csrc/jit/tensorexpr/loopnest.h>
namespace torch {
namespace jit {
class TEWrapper {
public:
... | 1,170 | 23.914894 | 64 | h |
null | pytorch-main/torch/csrc/jit/serialization/callstack_debug_info_serialization.h | #pragma once
#include <c10/core/Allocator.h>
#include <torch/csrc/jit/frontend/source_range.h>
#include <torch/csrc/jit/ir/scope.h>
#include <ATen/core/ivalue.h>
#include <vector>
#include <c10/util/flat_hash_map.h>
namespace c10 {
struct IValue;
}
namespace torch {
namespace jit {
class Pickler;
class InlinedCa... | 2,624 | 27.532609 | 80 | h |
null | pytorch-main/torch/csrc/jit/serialization/export.h | #pragma once
#include <caffe2/serialize/inline_container.h>
#include <torch/csrc/jit/api/module.h>
#include <torch/csrc/jit/ir/ir.h>
#include <torch/csrc/jit/serialization/export_bytecode.h>
#include <torch/csrc/jit/serialization/flatbuffer_serializer.h>
#include <torch/csrc/jit/serialization/pickler.h>
#include <torc... | 11,530 | 40.035587 | 80 | h |
null | pytorch-main/torch/csrc/jit/serialization/export_bytecode.h | #pragma once
#include <tuple>
#include <unordered_map>
#include <ATen/core/function_schema.h>
#include <ATen/core/ivalue.h>
#include <ATen/core/jit_type.h>
#include <ATen/core/qualified_name.h>
#include <torch/csrc/jit/backends/backend_debug_handler.h>
#include <torch/csrc/jit/mobile/function.h>
#include <torch/csrc/... | 1,436 | 29.574468 | 79 | h |
null | pytorch-main/torch/csrc/jit/serialization/flatbuffer_serializer.h | #pragma once
#include <functional>
#include <memory>
#include <string>
#include <unordered_map>
#include <vector>
#include <ATen/core/ivalue.h>
#include <c10/macros/Macros.h>
#include <torch/csrc/jit/mobile/module.h>
/**
* Defines the public API for serializing mobile modules to flatbuffer.
* Note that this header... | 3,068 | 31.305263 | 79 | h |
null | pytorch-main/torch/csrc/jit/serialization/import.h | #pragma once
#include <ATen/core/ivalue.h>
#include <caffe2/serialize/inline_container.h>
#include <torch/csrc/jit/api/module.h>
#include <torch/csrc/jit/ir/ir.h>
#include <istream>
namespace caffe2 {
namespace serialize {
class ReadAdapterInterface;
} // namespace serialize
} // namespace caffe2
namespace torch {
... | 4,918 | 30.941558 | 79 | h |
null | pytorch-main/torch/csrc/jit/serialization/import_export_constants.h | #pragma once
#include <cstddef>
namespace torch {
namespace jit {
constexpr size_t BYTECODE_INDEX_INSTRUCTION = 0;
constexpr size_t BYTECODE_INDEX_OPERATOR = 1;
constexpr size_t BYTECODE_INDEX_CONSTANT = 2;
constexpr size_t BYTECODE_INDEX_TYPE = 3;
constexpr size_t BYTECODE_INDEX_REGISTER_SIZE = 4;
constexpr size_t B... | 670 | 29.5 | 59 | h |
null | pytorch-main/torch/csrc/jit/serialization/import_export_helpers.h | #pragma once
#include <memory>
#include <string>
namespace caffe2 {
namespace serialize {
class PyTorchStreamReader;
}
} // namespace caffe2
namespace torch {
namespace jit {
struct Source;
// Convert a class type's qualifier name to the corresponding path the source
// file it should be written to.
//
// Qualifie... | 709 | 20.515152 | 77 | h |
null | pytorch-main/torch/csrc/jit/serialization/import_read.h | #pragma once
#include <torch/csrc/jit/serialization/unpickler.h>
#include <memory>
namespace caffe2 {
namespace serialize {
class PyTorchStreamReader;
} // namespace serialize
} // namespace caffe2
namespace torch {
namespace jit {
TORCH_API IValue readArchiveAndTensors(
const std::string& archive_name,
con... | 875 | 26.375 | 78 | h |
null | pytorch-main/torch/csrc/jit/serialization/import_source.h | #pragma once
#include <ATen/core/ivalue_inl.h>
#include <ATen/core/qualified_name.h>
#include <c10/util/Optional.h>
#include <torch/csrc/jit/api/module.h>
#include <torch/csrc/jit/frontend/parser.h>
#include <torch/csrc/jit/frontend/resolver.h>
#include <torch/csrc/jit/frontend/script_type_parser.h>
#include <torch/cs... | 3,477 | 32.442308 | 80 | h |
null | pytorch-main/torch/csrc/jit/serialization/pickle.h | #pragma once
#include <ATen/core/ivalue.h>
#include <c10/util/ArrayRef.h>
#include <caffe2/serialize/inline_container.h>
#include <torch/csrc/Export.h>
#include <torch/csrc/jit/serialization/pickler.h>
#include <torch/csrc/jit/serialization/unpickler.h>
namespace torch {
namespace jit {
/// Pickle an IValue by calli... | 3,257 | 34.413043 | 80 | h |
null | pytorch-main/torch/csrc/jit/serialization/pickler.h | #pragma once
#include <ATen/core/qualified_name.h>
#include <string>
#include <utility>
#include <vector>
#include <ATen/Utils.h>
#include <ATen/core/ivalue.h>
#include <ATen/core/jit_type.h>
#include <c10/util/ArrayRef.h>
#include <c10/util/FbcodeMaps.h>
#include <c10/util/intrusive_ptr.h>
#include <c10/util/string_... | 13,832 | 31.169767 | 80 | h |
null | pytorch-main/torch/csrc/jit/serialization/python_print.h | #pragma once
#include <torch/csrc/Export.h>
#include <torch/csrc/jit/api/module.h>
#include <torch/csrc/jit/ir/ir.h>
#include <iostream>
#include <vector>
namespace torch {
namespace jit {
struct Method;
struct Module;
struct PythonPrintImpl;
struct PrintDepsTable {
void add(const c10::NamedTypePtr& type);
size... | 1,373 | 21.16129 | 59 | h |
null | pytorch-main/torch/csrc/jit/serialization/source_range_serialization.h | #pragma once
#include <c10/core/Allocator.h>
#include <torch/csrc/jit/frontend/source_range.h>
#include <ATen/core/ivalue.h>
#include <unordered_map>
#include <vector>
namespace c10 {
struct IValue;
}
namespace torch {
namespace jit {
class Pickler;
class SourceRangeSerializer;
static constexpr size_t kByteOffset... | 1,684 | 23.42029 | 79 | h |
null | pytorch-main/torch/csrc/jit/serialization/source_range_serialization_impl.h | #pragma once
#include <torch/csrc/jit/serialization/source_range_serialization.h>
namespace torch {
namespace jit {
// Do this clownyness with virtual functions because of the split
// between ATen core and torch
class ConcreteSourceRangeUnpickler : public SourceRangeUnpickler {
public:
ConcreteSourceRangeUnpick... | 705 | 21.774194 | 68 | h |
null | pytorch-main/torch/csrc/jit/serialization/storage_context.h | #pragma once
#include <ATen/core/ivalue.h>
namespace torch {
namespace jit {
// Used in torch.package and TorchScript serialization to coordinate
// sharing of storages between models. Also used to create deterministic
// naming for storages.
class TORCH_API SerializationStorageContext {
public:
explicit Serializ... | 2,484 | 27.895349 | 79 | h |
null | pytorch-main/torch/csrc/jit/serialization/type_name_uniquer.h | #pragma once
#include <torch/csrc/jit/frontend/name_mangler.h>
#include <torch/csrc/jit/ir/type_hashing.h>
namespace torch {
namespace jit {
/**
* class TypeNameUniquer
*
* Generates a unique name for every type `t` passed in. Types that compare
* equal with EqualType will receive the same unique name.
*
* Thi... | 779 | 21.941176 | 77 | h |
null | pytorch-main/torch/csrc/jit/serialization/unpickler.h | #pragma once
#include <ATen/core/ivalue.h>
#include <c10/util/ArrayRef.h>
#include <caffe2/serialize/inline_container.h>
#include <torch/csrc/Export.h>
#include <torch/csrc/jit/frontend/script_type_parser.h>
#include <torch/csrc/jit/serialization/pickler.h>
namespace torch {
namespace jit {
using TypeResolver =
... | 7,001 | 36.047619 | 80 | h |
null | pytorch-main/torch/csrc/jit/tensorexpr/analysis.h | #pragma once
#include <torch/csrc/jit/tensorexpr/ir.h>
#include <torch/csrc/jit/tensorexpr/ir_visitor.h>
#include <torch/csrc/jit/tensorexpr/stmt.h>
#include <torch/csrc/jit/tensorexpr/tensor.h>
#include <utility>
namespace torch {
namespace jit {
namespace tensorexpr {
class HasRand : public IRVisitor {
public:
... | 9,180 | 21.55774 | 79 | h |
null | pytorch-main/torch/csrc/jit/tensorexpr/block_codegen.h | #pragma once
#include <string>
#include <unordered_map>
#include <unordered_set>
#include <utility>
#include <ATen/ATen.h>
#include <torch/csrc/jit/resource_guard.h>
#include <torch/csrc/jit/tensorexpr/analysis.h>
#include <torch/csrc/jit/tensorexpr/codegen.h>
#include <torch/csrc/jit/tensorexpr/ir.h>
#include <torch... | 4,241 | 27.092715 | 80 | h |
null | pytorch-main/torch/csrc/jit/tensorexpr/bounds_inference.h | #pragma once
#include <map>
#include <unordered_map>
#include <vector>
#include <torch/csrc/Export.h>
#include <torch/csrc/jit/tensorexpr/mem_dependency_checker.h>
namespace torch {
namespace jit {
namespace tensorexpr {
class Expr;
class Buf;
class Stmt;
enum C10_API_ENUM TensorAccessKind { kLoad, kStore, kMutate... | 2,216 | 26.37037 | 79 | h |
null | pytorch-main/torch/csrc/jit/tensorexpr/bounds_overlap.h | #pragma once
#include <torch/csrc/jit/tensorexpr/expr.h>
#include <torch/csrc/jit/tensorexpr/ir.h>
#include <deque>
#include <utility>
#include <vector>
namespace torch {
namespace jit {
namespace tensorexpr {
namespace analysis {
// A simple class containing the start and end of a range in a single dimension.
stru... | 4,526 | 34.093023 | 80 | h |
null | pytorch-main/torch/csrc/jit/tensorexpr/codegen.h | #pragma once
#include <ATen/ATen.h>
#include <torch/csrc/jit/tensorexpr/ir.h>
#include <torch/csrc/jit/tensorexpr/tensor.h>
#include <utility>
namespace torch {
namespace jit {
namespace tensorexpr {
template <typename T>
class PaddedBuffer;
class TORCH_API CodeGen {
public:
class BufferArg;
class CallArg;
... | 8,136 | 27.651408 | 97 | h |
null | pytorch-main/torch/csrc/jit/tensorexpr/cpp_codegen.h | #pragma once
#include <torch/csrc/jit/tensorexpr/codegen.h>
#include <torch/csrc/jit/tensorexpr/ir_printer.h>
namespace torch {
namespace jit {
namespace tensorexpr {
class CppVarNameRewriter;
// Generates C++ code from the IR.
//
// Vector operations are unrolled.
// For example:
// C[Ramp(0, 1, 3)] = A[Ramp(0, 2,... | 2,278 | 21.126214 | 66 | h |
null | pytorch-main/torch/csrc/jit/tensorexpr/cuda_codegen.h | #pragma once
#include <unordered_map>
#include <unordered_set>
#include <ATen/ATen.h>
#include <ATen/cuda/CUDAContext.h>
#include <ATen/cuda/nvrtc_stub/ATenNVRTC.h>
#include <c10/cuda/CUDACachingAllocator.h>
#include <c10/cuda/CUDAGuard.h>
#include <torch/csrc/jit/resource_guard.h>
#include <torch/csrc/jit/tensorexpr... | 8,368 | 27.273649 | 80 | h |
null | pytorch-main/torch/csrc/jit/tensorexpr/cuda_random.h | #pragma once
namespace torch {
namespace jit {
namespace tensorexpr {
constexpr auto philox_random_string = R"(
class Philox {
public:
__device__ inline Philox(unsigned long long seed,
unsigned long long subsequence,
unsigned long long offset) {
key.x = (un... | 2,642 | 24.171429 | 69 | h |
null | pytorch-main/torch/csrc/jit/tensorexpr/eval.h | #pragma once
#include <cmath>
#include <cstring>
#include <type_traits>
#include <unordered_map>
#include <utility>
#include <vector>
#include <c10/macros/Macros.h>
#include <c10/util/Logging.h>
#include <c10/util/math_compat.h>
#include <c10/util/string_utils.h>
#include <torch/csrc/jit/tensorexpr/codegen.h>
#includ... | 11,078 | 30.836207 | 79 | h |
null | pytorch-main/torch/csrc/jit/tensorexpr/exceptions.h | #pragma once
#include <torch/csrc/Export.h>
#include <torch/csrc/jit/tensorexpr/fwd_decls.h>
#include <sstream>
#include <stdexcept>
// Forward declarations of types
namespace torch {
namespace jit {
namespace tensorexpr {
class Expr;
class Stmt;
} // namespace tensorexpr
} // namespace jit
} // namespace torch
// ... | 3,239 | 34.217391 | 80 | h |
null | pytorch-main/torch/csrc/jit/tensorexpr/expr.h | /**
* This file implements the core classes for Tensor Expressions.
*
* The structure of the expressions is inspired by Halide/TVM IR.
*/
#pragma once
#include <c10/core/MemoryFormat.h>
#include <c10/util/Optional.h>
#include <torch/csrc/jit/tensorexpr/fwd_decls.h>
#include <torch/csrc/jit/tensorexpr/ir_mutator.h>... | 14,343 | 27.688 | 80 | h |
null | pytorch-main/torch/csrc/jit/tensorexpr/external_functions.h | #pragma once
#include <ATen/Config.h>
#include <ATen/Functions.h>
#include <c10/macros/Macros.h>
#include <torch/csrc/Export.h>
#include <cstdint>
#include <vector>
#define FOR_ALL_EXTERNAL_FUNCTIONS(_) \
_(nnc_aten_adaptive_avg_pool2d) \
_(nnc_aten_addmm) \
_(nnc_aten_conv2d) ... | 3,485 | 29.051724 | 69 | h |
null | pytorch-main/torch/csrc/jit/tensorexpr/external_functions_registry.h | #pragma once
#include <torch/csrc/Export.h>
#include <cstdint>
#include <string>
#include <unordered_map>
namespace torch {
namespace jit {
namespace tensorexpr {
// The external functions that could be called from NNC must have the same
// signature defined by `NNCExternalFunction`.
//
// Why this signature?
// It ... | 2,356 | 37.016129 | 80 | h |
null | pytorch-main/torch/csrc/jit/tensorexpr/fwd_decls.h | #pragma once
#include <c10/core/ScalarType.h>
#include <memory>
namespace torch {
namespace jit {
namespace tensorexpr {
template <typename Node>
using NodePtr = std::shared_ptr<Node>;
template <typename To, typename From>
NodePtr<To> to(NodePtr<From> x) {
return std::dynamic_pointer_cast<To>(x);
}
template <type... | 3,034 | 22.346154 | 64 | h |
null | pytorch-main/torch/csrc/jit/tensorexpr/graph_opt.h | #pragma once
#include <torch/csrc/jit/ir/ir.h>
namespace torch {
namespace jit {
namespace tensorexpr {
// Optimize aten::cat ops in the given subgraph.
//
// Moving users of cat to its inputs.
// Cat ops get lowered into multiple loops, one per input. When the result
// of cat is used by some other op, it res... | 4,484 | 37.663793 | 80 | h |
null | pytorch-main/torch/csrc/jit/tensorexpr/half_support.h | #pragma once
#include <torch/csrc/jit/tensorexpr/codegen.h>
#include <torch/csrc/jit/tensorexpr/ir.h>
#include <torch/csrc/jit/tensorexpr/ir_visitor.h>
#include <torch/csrc/jit/tensorexpr/tensor.h>
namespace torch {
namespace jit {
namespace tensorexpr {
// Walk the Statement looking for Half size loads/stores.
clas... | 5,836 | 25.775229 | 79 | h |
null | pytorch-main/torch/csrc/jit/tensorexpr/hash_provider.h | #pragma once
#include <torch/csrc/jit/tensorexpr/ir.h>
#include <torch/csrc/jit/tensorexpr/ir_printer.h>
#include <torch/csrc/jit/tensorexpr/ir_visitor.h>
#include <torch/csrc/jit/tensorexpr/tensor.h>
#include <utility>
namespace torch {
namespace jit {
namespace tensorexpr {
struct TORCH_API SimplifierHashType {
... | 7,983 | 25.177049 | 80 | h |
null | pytorch-main/torch/csrc/jit/tensorexpr/ir.h | #pragma once
#include <string>
#include <utility>
#include <vector>
#include <c10/util/string_utils.h>
#include <torch/csrc/jit/tensorexpr/exceptions.h>
#include <torch/csrc/jit/tensorexpr/expr.h>
#include <torch/csrc/jit/tensorexpr/fwd_decls.h>
#include <torch/csrc/jit/tensorexpr/stmt.h>
#include <ATen/core/ivalue.... | 23,598 | 24.239572 | 80 | h |
null | pytorch-main/torch/csrc/jit/tensorexpr/ir_cloner.h | #pragma once
#include <c10/core/ScalarType.h>
#include <torch/csrc/Export.h>
#include <vector>
#include <torch/csrc/jit/tensorexpr/ir_mutator.h>
namespace torch {
namespace jit {
namespace tensorexpr {
class TORCH_API IRCloner : public IRMutator {
public:
~IRCloner() override = default;
ExprPtr mutate(AddPtr v)... | 2,110 | 30.984848 | 79 | h |
null | pytorch-main/torch/csrc/jit/tensorexpr/ir_mutator.h | #pragma once
#include <c10/core/ScalarType.h>
#include <torch/csrc/Export.h>
#include <torch/csrc/jit/tensorexpr/fwd_decls.h>
#include <vector>
namespace torch {
namespace jit {
namespace tensorexpr {
class TORCH_API IRMutator {
public:
virtual ~IRMutator() = default;
virtual ExprPtr mutate(AddPtr v);
virtual ... | 2,140 | 30.955224 | 78 | h |
null | pytorch-main/torch/csrc/jit/tensorexpr/ir_printer.h | #pragma once
#include <iostream>
#include <torch/csrc/jit/tensorexpr/fwd_decls.h>
#include <torch/csrc/jit/tensorexpr/ir.h>
#include <torch/csrc/jit/tensorexpr/ir_visitor.h>
#include <torch/csrc/jit/tensorexpr/unique_name_manager.h>
namespace torch {
namespace jit {
namespace tensorexpr {
class Tensor;
class TORCH... | 3,861 | 28.480916 | 78 | h |
null | pytorch-main/torch/csrc/jit/tensorexpr/ir_simplifier.h | #pragma once
#include <torch/csrc/jit/tensorexpr/bounds_overlap.h>
#include <torch/csrc/jit/tensorexpr/eval.h>
#include <torch/csrc/jit/tensorexpr/hash_provider.h>
#include <torch/csrc/jit/tensorexpr/ir.h>
#include <torch/csrc/jit/tensorexpr/ir_mutator.h>
#include <torch/csrc/jit/tensorexpr/ir_visitor.h>
#include <tor... | 15,520 | 26.965766 | 80 | h |
null | pytorch-main/torch/csrc/jit/tensorexpr/ir_visitor.h | #pragma once
#include <c10/core/ScalarType.h>
#include <torch/csrc/Export.h>
#include <torch/csrc/jit/tensorexpr/fwd_decls.h>
namespace torch {
namespace jit {
namespace tensorexpr {
class TORCH_API IRVisitor {
public:
virtual ~IRVisitor() = default;
virtual void visit(AddPtr v);
virtual void visit(SubPtr v);
... | 1,943 | 28.907692 | 71 | h |
null | pytorch-main/torch/csrc/jit/tensorexpr/kernel.h | #pragma once
#include <c10/util/variant.h>
#include <torch/csrc/jit/ir/ir.h>
#include <torch/csrc/jit/passes/symbolic_shape_runtime_fusion.h>
#include <torch/csrc/jit/passes/utils/subgraph_utils.h>
#include <torch/csrc/jit/runtime/interpreter.h>
#include <torch/csrc/jit/tensorexpr/analysis.h>
#include <torch/csrc/jit/... | 13,311 | 33.666667 | 80 | h |
null | pytorch-main/torch/csrc/jit/tensorexpr/llvm_codegen.h | #pragma once
#ifdef TORCH_ENABLE_LLVM
#include <torch/csrc/Export.h>
#include <torch/csrc/jit/tensorexpr/codegen.h>
#include <torch/csrc/jit/tensorexpr/ir.h>
#include <torch/csrc/jit/tensorexpr/ir_visitor.h>
#include <c10/util/Optional.h>
#include <unordered_map>
#include <vector>
namespace torch {
namespace jit {... | 3,850 | 25.743056 | 79 | h |
null | pytorch-main/torch/csrc/jit/tensorexpr/llvm_jit.h | #pragma once
#ifdef TORCH_ENABLE_LLVM
#include <c10/macros/Macros.h>
#include <c10/util/Exception.h>
#include <c10/util/Optional.h>
#include <torch/csrc/Export.h>
C10_DIAGNOSTIC_PUSH_AND_IGNORED_IF_DEFINED("-Wsuggest-override")
#include <llvm/ExecutionEngine/JITSymbol.h>
C10_DIAGNOSTIC_POP()
#include <llvm/ExecutionE... | 1,951 | 24.025641 | 77 | h |
null | pytorch-main/torch/csrc/jit/tensorexpr/loopnest.h | #pragma once
#include <string>
#include <unordered_map>
#include <unordered_set>
#include <vector>
#include <torch/csrc/Export.h>
#include <torch/csrc/jit/tensorexpr/fwd_decls.h>
namespace torch {
namespace jit {
namespace tensorexpr {
class Expr;
class Var;
class Buf;
class Tensor;
class Function;
class Stmt;
clas... | 21,347 | 34.169687 | 80 | h |
null | pytorch-main/torch/csrc/jit/tensorexpr/lowerings.h | // This file defines classes for registering standard lowerings from JIT to TE
// IR.
#pragma once
#include <c10/util/variant.h>
#include <torch/csrc/jit/ir/ir.h>
#include <torch/csrc/jit/runtime/interpreter.h>
#include <torch/csrc/jit/tensorexpr/analysis.h>
#include <torch/csrc/jit/tensorexpr/codegen.h>
#include <tor... | 1,353 | 25.54902 | 78 | h |
null | pytorch-main/torch/csrc/jit/tensorexpr/mem_dependency_checker.h | #pragma once
#include <c10/core/ScalarType.h>
#include <torch/csrc/Export.h>
#include <utility>
#include <vector>
#include <torch/csrc/jit/tensorexpr/bounds_overlap.h>
#include <torch/csrc/jit/tensorexpr/ir_mutator.h>
#include <torch/csrc/jit/tensorexpr/ir_simplifier.h>
#include <torch/csrc/jit/tensorexpr/ir_visitor.h... | 13,239 | 30.826923 | 80 | h |
null | pytorch-main/torch/csrc/jit/tensorexpr/reduction.h | #pragma once
#include <torch/csrc/jit/tensorexpr/expr.h>
#include <torch/csrc/jit/tensorexpr/ir.h>
#include <torch/csrc/jit/tensorexpr/ir_printer.h>
#include <torch/csrc/jit/tensorexpr/types.h>
#include <functional>
#include <utility>
#include <vector>
namespace torch {
namespace jit {
namespace tensorexpr {
using ... | 8,685 | 27.385621 | 80 | h |
null | pytorch-main/torch/csrc/jit/tensorexpr/registerizer.h | #pragma once
#include <c10/core/ScalarType.h>
#include <c10/util/irange.h>
#include <torch/csrc/Export.h>
#include <torch/csrc/jit/tensorexpr/hash_provider.h>
#include <torch/csrc/jit/tensorexpr/ir_mutator.h>
#include <torch/csrc/jit/tensorexpr/ir_simplifier.h>
#include <torch/csrc/jit/tensorexpr/ir_visitor.h>
#inclu... | 12,540 | 27.896313 | 80 | h |
null | pytorch-main/torch/csrc/jit/tensorexpr/stmt.h | #pragma once
#include <algorithm>
#include <list>
#include <string>
#include <unordered_set>
#include <utility>
#include <vector>
#include <torch/csrc/jit/tensorexpr/expr.h>
namespace torch {
namespace jit {
namespace tensorexpr {
// The common base between all statement node.
class TORCH_API Stmt : public std::enab... | 24,321 | 22.72878 | 80 | h |
null | pytorch-main/torch/csrc/jit/tensorexpr/tensor.h | #pragma once
#include <torch/csrc/Export.h>
#include <functional>
#include <utility>
#include <vector>
#include <torch/csrc/jit/tensorexpr/expr.h>
#include <torch/csrc/jit/tensorexpr/reduction.h>
namespace torch {
namespace jit {
namespace tensorexpr {
class TORCH_API Tensor {
public:
// NOLINTNEXTLINE(cppcoregu... | 10,793 | 31.709091 | 80 | h |
null | pytorch-main/torch/csrc/jit/tensorexpr/types.h | #pragma once
#include <cstdint>
#include <iostream>
#include <c10/core/ScalarType.h>
#include <c10/util/Logging.h>
#include <torch/csrc/Export.h>
#include <torch/csrc/jit/tensorexpr/exceptions.h>
namespace torch {
namespace jit {
namespace tensorexpr {
using int32 = std::int32_t;
class Dtype;
TORCH_API std::ostre... | 4,312 | 25.460123 | 77 | h |
null | pytorch-main/torch/csrc/jit/tensorexpr/unique_name_manager.h | #pragma once
#include <string>
#include <unordered_map>
#include <unordered_set>
#include <torch/csrc/Export.h>
#include <torch/csrc/jit/tensorexpr/fwd_decls.h>
namespace torch {
namespace jit {
namespace tensorexpr {
class VarHandle;
class Var;
using VarNameMap = std::unordered_map<VarPtr, std::string>;
// A man... | 926 | 22.769231 | 78 | h |
null | pytorch-main/torch/csrc/jit/tensorexpr/var_substitutor.h | #pragma once
#include <unordered_map>
#include <utility>
#include <vector>
#include <torch/csrc/jit/tensorexpr/analysis.h>
#include <torch/csrc/jit/tensorexpr/ir.h>
#include <torch/csrc/jit/tensorexpr/ir_mutator.h>
#include <torch/csrc/jit/tensorexpr/ir_visitor.h>
#include <torch/csrc/jit/tensorexpr/reduction.h>
nam... | 1,812 | 25.661765 | 74 | h |
null | pytorch-main/torch/csrc/jit/tensorexpr/operators/conv2d.h | #pragma once
#include <torch/csrc/jit/tensorexpr/operators/misc.h>
#include <torch/csrc/jit/tensorexpr/tensor.h>
namespace torch {
namespace jit {
namespace tensorexpr {
// An API to compute 2D depthwise convolutions with bias.
TORCH_API Tensor conv2d_depthwise(
BufHandle input,
BufHandle weight,
BufHand... | 2,943 | 26.773585 | 60 | h |
null | pytorch-main/torch/csrc/jit/tensorexpr/operators/matmul.h | #pragma once
#include <torch/csrc/jit/tensorexpr/kernel.h>
namespace torch {
namespace jit {
namespace tensorexpr {
Tensor computeMatmul(
const std::vector<ArgValue>& inputs,
const std::vector<ExprHandle>& outputShape,
const std::vector<ExprHandle>& outputStrides,
const c10::optional<ScalarType>& out... | 653 | 25.16 | 49 | h |
null | pytorch-main/torch/csrc/jit/tensorexpr/operators/misc.h | #pragma once
#include <torch/csrc/jit/tensorexpr/fwd_decls.h>
#include <torch/csrc/jit/tensorexpr/lowerings.h>
#include <torch/csrc/jit/tensorexpr/tensor.h>
namespace torch {
namespace jit {
namespace tensorexpr {
struct TensorInfo {
std::vector<int64_t> dims;
c10::ScalarType dtype;
};
c10::optional<TensorInfo> ... | 3,324 | 32.585859 | 71 | h |
null | pytorch-main/torch/csrc/jit/tensorexpr/operators/pointwise.h | #pragma once
#include <torch/csrc/jit/tensorexpr/kernel.h>
namespace torch {
namespace jit {
namespace tensorexpr {
TORCH_API Tensor computeSign(
const std::vector<ArgValue>& inputs,
const std::vector<ExprHandle>& outputShape,
c10::optional<std::vector<ExprHandle>> outputStrides = c10::nullopt);
Tensor ... | 3,202 | 35.816092 | 77 | h |
null | pytorch-main/torch/csrc/jit/tensorexpr/operators/quantization.h | #pragma once
#include <torch/csrc/jit/tensorexpr/kernel.h>
namespace torch {
namespace jit {
namespace tensorexpr {
TORCH_API ExprHandle quantizePerTensorQParamFromArg(ArgValue arg);
TORCH_API double immQScale(const BufHandle& qx);
TORCH_API int64_t immQZero(const BufHandle& qx);
TORCH_API ScalarType immQDType(co... | 5,582 | 33.677019 | 66 | h |
null | pytorch-main/torch/csrc/jit/tensorexpr/operators/reduction.h | #pragma once
#include <torch/csrc/jit/tensorexpr/kernel.h>
namespace torch {
namespace jit {
namespace tensorexpr {
TORCH_API Tensor computeSum(
const std::vector<ArgValue>& inputs,
const std::vector<ExprHandle>& outputShape,
const std::vector<ExprHandle>& outputStrides,
const c10::optional<ScalarTyp... | 1,155 | 30.243243 | 49 | h |
null | pytorch-main/torch/csrc/jit/testing/file_check.h | #pragma once
#include <torch/csrc/Export.h>
#include <memory>
#include <string>
namespace torch {
namespace jit {
struct Graph;
namespace testing {
struct FileCheckImpl;
struct FileCheck {
public:
TORCH_API explicit FileCheck();
TORCH_API ~FileCheck();
// Run FileCheck against test string
TORCH_API void... | 2,407 | 29.871795 | 80 | h |
null | pytorch-main/torch/csrc/jit/testing/hooks_for_testing.h | #pragma once
#include <torch/csrc/Export.h>
#include <torch/csrc/jit/api/compilation_unit.h>
#include <functional>
#include <memory>
namespace torch {
namespace jit {
struct Module;
using ModuleHook = std::function<void(Module module)>;
using FunctionHook = std::function<void(StrongFunctionPtr function)>;
TORCH_API ... | 603 | 26.454545 | 72 | h |
null | pytorch-main/torch/csrc/lazy/backend/backend_device.h | #pragma once
#include <memory>
#include <ostream>
#include <string>
#include <ATen/Tensor.h>
#include <c10/macros/Export.h>
#include <c10/util/Deprecated.h>
#include <c10/util/Optional.h>
namespace c10 {
struct Device;
}
namespace torch {
namespace lazy {
// Backend should extend it and define their own supported ... | 2,876 | 27.485149 | 78 | h |
null | pytorch-main/torch/csrc/lazy/backend/backend_interface.h | #pragma once
#include <ATen/Tensor.h>
#include <torch/csrc/lazy/backend/backend_data.h>
#include <torch/csrc/lazy/backend/backend_device.h>
#include <torch/csrc/lazy/backend/lowering_context.h>
#include <torch/csrc/lazy/core/lazy_graph_executor.h>
#include <torch/csrc/lazy/core/shape.h>
#include <torch/csrc/lazy/core/... | 4,846 | 29.484277 | 79 | h |
null | pytorch-main/torch/csrc/lazy/backend/lowering_context.h | #pragma once
#include <memory>
#include <string>
#include <unordered_map>
#include <utility>
#include <vector>
#include <torch/csrc/lazy/backend/backend_data.h>
#include <torch/csrc/lazy/backend/backend_device.h>
#include <torch/csrc/lazy/core/ir.h>
#include <torch/csrc/lazy/core/ir_util.h>
namespace torch {
namespa... | 3,308 | 27.773913 | 80 | h |
null | pytorch-main/torch/csrc/lazy/core/cache.h | /**
* Cache utils in this file is adapted from PyTorch/XLA
* https://github.com/pytorch/xla/blob/master/third_party/xla_client/cache.h
*/
#pragma once
#include <functional>
#include <list>
#include <memory>
#include <mutex>
#include <unordered_map>
#include <utility>
namespace torch {
namespace lazy {
// Generic... | 3,406 | 25.207692 | 80 | h |
null | pytorch-main/torch/csrc/lazy/core/config.h | #pragma once
#include <c10/macros/Export.h>
#include <c10/util/Flags.h>
C10_DECLARE_bool(torch_lazy_ir_debug);
C10_DECLARE_bool(torch_lazy_handle_special_scalars);
C10_DECLARE_bool(torch_lazy_all_numbers_special_scalars);
C10_DECLARE_bool(torch_lazy_param_aliasing);
C10_DECLARE_bool(torch_lazy_reuse_ir);
C10_DECLARE_b... | 858 | 29.678571 | 57 | h |
null | pytorch-main/torch/csrc/lazy/core/debug_util.h | #pragma once
#include <iostream>
#include <string>
#include <vector>
#include <torch/csrc/lazy/core/tensor.h>
namespace torch {
namespace lazy {
TORCH_API std::function<std::vector<SourceLocation>()>&
GetPythonFramesFunction();
TORCH_API std::string GetFirstUserFrameInPython();
class TORCH_API DebugUtil {
public... | 1,319 | 25.938776 | 80 | h |
null | pytorch-main/torch/csrc/lazy/core/dynamic_ir.h | #pragma once
#include <ATen/core/symbol.h>
#include <functional>
#include <memory>
#include <set>
#include <string>
#include <unordered_map>
#include <unordered_set>
#include <utility>
#include <vector>
#include <c10/core/ScalarType.h>
#include <c10/util/Flags.h>
#include <torch/csrc/lazy/core/hash.h>
#include <torc... | 1,586 | 25.45 | 71 | h |
null | pytorch-main/torch/csrc/lazy/core/hash.h | /**
* Hash utils in this file is adapted from PyTorch/XLA
* https://github.com/pytorch/xla/blob/e0e5f937a0ba8d904f9608137dc8c51ba439df2d/third_party/xla_client/util.h
*/
#pragma once
#include <ATen/Tensor.h>
#include <c10/core/Scalar.h>
#include <c10/util/int128.h>
#include <torch/csrc/Export.h>
#include <cstring>
... | 7,190 | 29.730769 | 109 | h |
null | pytorch-main/torch/csrc/lazy/core/helpers.h | #pragma once
#include <c10/core/Scalar.h>
#include <c10/util/BFloat16.h>
#include <c10/util/Half.h>
#include <c10/util/Optional.h>
#include <torch/csrc/lazy/core/permutation_util.h>
#include <torch/csrc/lazy/core/shape.h>
#include <torch/csrc/lazy/core/util.h>
#include <complex>
#include <functional>
#include <tuple>... | 2,259 | 29.958904 | 79 | h |
null | pytorch-main/torch/csrc/lazy/core/ir.h | #pragma once
#include <ATen/core/symbol.h>
#include <functional>
#include <memory>
#include <set>
#include <string>
#include <unordered_map>
#include <unordered_set>
#include <utility>
#include <vector>
#include <c10/core/ScalarType.h>
#include <c10/util/ArrayRef.h>
#include <c10/util/Flags.h>
#include <torch/csrc/l... | 8,007 | 25.782609 | 80 | h |
null | pytorch-main/torch/csrc/lazy/core/ir_builder.h | #pragma once
#include <c10/core/ScalarType.h>
#include <c10/util/Optional.h>
#include <torch/csrc/lazy/backend/backend_interface.h>
#include <torch/csrc/lazy/core/config.h>
#include <torch/csrc/lazy/core/ir.h>
#include <torch/csrc/lazy/core/tensor.h>
#include <torch/csrc/lazy/core/trie.h>
#include <vector>
// This fi... | 4,721 | 30.271523 | 80 | h |
null | pytorch-main/torch/csrc/lazy/core/ir_metadata.h | #pragma once
#include <c10/util/Optional.h>
#include <string>
#include <vector>
namespace torch {
namespace lazy {
struct SourceLocation {
std::string file;
std::string function;
int line = -1;
};
TORCH_API void EmitShortFrameInfo(
std::ostream& stream,
const std::vector<SourceLocation>& frames);
TOR... | 1,154 | 22.1 | 80 | h |
null | pytorch-main/torch/csrc/lazy/core/ir_util.h | #pragma once
#include <unordered_map>
#include <vector>
#include <torch/csrc/lazy/core/ir.h>
namespace torch {
namespace lazy {
class TORCH_API Util {
public:
// Tracks the emission status of the nodes during the post-order generation.
// It helps tracking loops within the computation graphs.
enum EmitStatus... | 1,391 | 28 | 80 | h |
null | pytorch-main/torch/csrc/lazy/core/lazy_graph_executor.h | #pragma once
#include <c10/util/ArrayRef.h>
#include <torch/csrc/lazy/backend/lowering_context.h>
#include <torch/csrc/lazy/core/cache.h>
#include <torch/csrc/lazy/core/ir_util.h>
#include <torch/csrc/lazy/core/multi_wait.h>
#include <torch/csrc/lazy/core/tensor.h>
#include <torch/csrc/lazy/core/util.h>
namespace tor... | 14,859 | 33.800937 | 80 | h |
null | pytorch-main/torch/csrc/lazy/core/metrics.h | /**
* This file is adapted from PyTorch/XLA
* https://github.com/pytorch/xla/blob/master/third_party/xla_client/metrics.h
*/
#pragma once
#include <atomic>
#include <functional>
#include <map>
#include <memory>
#include <mutex>
#include <string>
#include <vector>
#include <c10/macros/Export.h>
namespace torch {
... | 7,882 | 26.855124 | 80 | h |
null | pytorch-main/torch/csrc/lazy/core/multi_wait.h | /**
* This file is adapted from PyTorch/XLA
* https://github.com/pytorch/xla/blob/master/third_party/xla_client/multi_wait.h
*/
#pragma once
#include <condition_variable>
#include <exception>
#include <functional>
#include <memory>
#include <mutex>
#include <c10/macros/Export.h>
namespace torch {
namespace lazy ... | 1,740 | 26.634921 | 81 | h |
null | pytorch-main/torch/csrc/lazy/core/permutation_util.h | #pragma once
#include <c10/util/ArrayRef.h>
#include <c10/util/Exception.h>
#include <c10/util/irange.h>
#include <vector>
namespace torch {
namespace lazy {
TORCH_API std::vector<int64_t> InversePermutation(
c10::ArrayRef<int64_t> input_permutation);
TORCH_API bool IsPermutation(c10::ArrayRef<int64_t> permuta... | 1,277 | 28.045455 | 82 | h |
null | pytorch-main/torch/csrc/lazy/core/shape.h | #pragma once
#include <ostream>
#include <vector>
#include <c10/core/Scalar.h>
#include <torch/csrc/jit/passes/symbolic_shape_analysis.h>
#include <torch/csrc/lazy/core/hash.h>
C10_DECLARE_bool(ltc_enable_symbolic_shapes);
namespace torch {
namespace lazy {
class TORCH_API Shape {
public:
Shape() = default;
... | 2,020 | 23.950617 | 74 | h |
null | pytorch-main/torch/csrc/lazy/core/tensor.h | #pragma once
#include <c10/core/SymNodeImpl.h>
#include <c10/util/intrusive_ptr.h>
#include <torch/csrc/lazy/backend/backend_data.h>
#include <torch/csrc/lazy/backend/backend_device.h>
#include <torch/csrc/lazy/core/ir.h>
#include <torch/csrc/lazy/core/util.h>
namespace torch {
namespace lazy {
class TORCH_API SymNo... | 9,349 | 35.24031 | 80 | h |
null | pytorch-main/torch/csrc/lazy/core/tensor_impl.h | #pragma once
#include <ATen/Tensor.h>
#include <c10/core/SymIntArrayRef.h>
#include <c10/core/TensorImpl.h>
#include <torch/csrc/lazy/core/tensor.h>
namespace torch {
namespace lazy {
// Tensor implementation class used to be fed to the at::Tensor.
// Its scope is just to handle an LazyTensor.
class TORCH_API LTCTe... | 1,910 | 29.333333 | 78 | h |
null | pytorch-main/torch/csrc/lazy/core/tensor_util.h | #pragma once
#include <torch/csrc/lazy/backend/backend_interface.h>
#include <torch/csrc/lazy/core/shape.h>
#include <ATen/FunctionalTensorWrapper.h>
#include <string>
#include <vector>
namespace torch {
namespace lazy {
TORCH_API std::vector<int64_t> ComputeArrayStrides(
c10::ArrayRef<int64_t> sizes);
TORCH_... | 2,561 | 31.43038 | 80 | h |
null | pytorch-main/torch/csrc/lazy/core/trie.h | #pragma once
#include <atomic>
#include <list>
#include <c10/core/ScalarType.h>
#include <torch/csrc/lazy/core/ir.h>
#include <torch/csrc/lazy/core/metrics.h>
namespace torch {
namespace lazy {
struct TORCH_API TrieNode {
static size_t GetNextUniqueId() {
static thread_local size_t id_generator = 0;
retur... | 2,217 | 26.725 | 80 | h |
null | pytorch-main/torch/csrc/lazy/core/util.h | /**
* Most of the utils in this file is adapted from PyTorch/XLA
* https://github.com/pytorch/xla/blob/master/third_party/xla_client/util.h
*/
#pragma once
#include <exception>
#include <functional>
#include <vector>
#include <c10/util/Optional.h>
#include <c10/util/OptionalArrayRef.h>
namespace torch {
namespac... | 2,830 | 21.291339 | 79 | h |
null | pytorch-main/torch/csrc/lazy/core/internal_ops/ltc_ops.h | #pragma once
#include <torch/csrc/lazy/core/ir.h>
#include <c10/util/CallOnce.h>
#include <mutex>
#include <string>
namespace torch {
namespace lazy {
class TORCH_API OpKindWrapper {
public:
explicit OpKindWrapper(const char* name) : name_(name) {}
const OpKind& operator*() const {
return get();
}
o... | 1,492 | 27.169811 | 79 | h |
null | pytorch-main/torch/csrc/lazy/core/ops/utils.h | #include <vector>
#include <torch/csrc/lazy/core/tensor_util.h>
#include <torch/csrc/lazy/core/util.h>
namespace torch {
namespace lazy {
TORCH_API bool StrideIsSupported(c10::ArrayRef<int64_t> stride);
TORCH_API std::vector<int64_t> GetArrayStridePermutation(
c10::ArrayRef<int64_t> stride);
TORCH_API Shape Ma... | 1,010 | 23.071429 | 80 | h |
null | pytorch-main/torch/csrc/lazy/ts_backend/dynamic_ir.h | #pragma once
#include <ATen/core/symbol.h>
#include <functional>
#include <memory>
#include <set>
#include <string>
#include <unordered_map>
#include <unordered_set>
#include <utility>
#include <vector>
#include <c10/core/ScalarType.h>
#include <c10/util/Flags.h>
#include <torch/csrc/lazy/core/dynamic_ir.h>
#include... | 2,503 | 28.116279 | 71 | h |
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