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
values |
|---|---|---|---|---|---|---|
null | pytorch-main/torch/csrc/dynamo/cpython_defs.h | #pragma once
#include <Python.h>
#include <torch/csrc/utils/python_compat.h>
// Functions that need to be copied from the CPython source
// should go in cpython_defs.c. Copying is required when, e.g.,
// we need to call internal CPython functions that are not exposed.
#if IS_PYTHON_3_11_PLUS
#include <internal/pyco... | 559 | 23.347826 | 67 | h |
null | pytorch-main/torch/csrc/inductor/aot_inductor_interface.h | #pragma once
#include <stddef.h>
#include <stdint.h>
#ifdef __GNUC__
#define AOT_INDUCTOR_EXPORT __attribute__((__visibility__("default")))
#else // !__GNUC__
#ifdef _WIN32
#define AOT_INDUCTOR_EXPORT __declspec(dllexport)
#else // !_WIN32
#define AOT_INDUCTOR_EXPORT
#endif // _WIN32
#endif // __GNUC__
enum class AO... | 3,485 | 32.84466 | 80 | h |
null | pytorch-main/torch/csrc/inductor/aot_inductor_model.h | #pragma once
#include <stdexcept>
#include <string>
#include <vector>
#include <ATen/ATen.h>
#include <c10/cuda/CUDAGuard.h>
#define AOT_VECTOR_SIZE_CHECK(vec, expected_size) \
{ \
auto actual_size = vec.size(); \
TORCH_CHECK( ... | 5,156 | 27.491713 | 76 | h |
null | pytorch-main/torch/csrc/inductor/aot_inductor_model_container.h | #pragma once
#include <future>
#include <mutex>
#include <shared_mutex>
#include <torch/csrc/inductor/aot_inductor_model.h>
namespace torch {
namespace aot_inductor {
class AOTInductorModelContainer {
public:
AOTInductorModelContainer(size_t num_models) {
LOG(INFO) << "Constructing an AOTInductorModelContain... | 5,562 | 30.429379 | 80 | h |
null | pytorch-main/torch/csrc/jit/jit_log.h | #pragma once
#include <torch/csrc/Export.h>
#include <memory>
#include <ostream>
#include <string>
#include <unordered_map>
// `TorchScript` offers a simple logging facility that can enabled by setting an
// environment variable `PYTORCH_JIT_LOG_LEVEL`.
// Logging is enabled on a per file basis. To enable logging in
... | 4,798 | 36.20155 | 80 | h |
null | pytorch-main/torch/csrc/jit/jit_opt_limit.h | #pragma once
#include <torch/csrc/Export.h>
#include <string>
#include <unordered_map>
// `TorchScript` offers a simple optimization limit checker
// that can be configured through environment variable `PYTORCH_JIT_OPT_LIMIT`.
// The purpose is to limit how many optimization you can make per pass.
// This is useful fo... | 1,405 | 34.15 | 95 | h |
null | pytorch-main/torch/csrc/jit/api/compilation_unit.h | #pragma once
#include <ATen/core/function.h>
#include <c10/util/Exception.h>
#include <torch/csrc/jit/api/function_impl.h>
#include <torch/csrc/jit/frontend/name_mangler.h>
#include <torch/csrc/jit/frontend/source_range.h>
#include <torch/csrc/jit/ir/ir.h>
#include <torch/csrc/jit/runtime/graph_executor.h>
#include <t... | 11,773 | 32.166197 | 81 | h |
null | pytorch-main/torch/csrc/jit/api/function_impl.h | #pragma once
#include <ATen/core/function.h>
#include <torch/csrc/jit/ir/ir.h>
#include <torch/csrc/jit/runtime/graph_executor.h>
#include <torch/csrc/utils/memory.h>
namespace torch {
namespace jit {
struct TORCH_API GraphFunction : public Function {
// NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
Gra... | 6,167 | 30.151515 | 106 | h |
null | pytorch-main/torch/csrc/jit/api/method.h | #pragma once
#include <ATen/core/function.h>
#include <ATen/core/ivalue.h>
#include <ATen/core/stack.h>
#include <torch/csrc/api/include/torch/imethod.h>
#include <torch/csrc/jit/api/function_impl.h>
namespace torch {
namespace jit {
using ObjectPtr = c10::intrusive_ptr<c10::ivalue::Object>;
// A method in a module... | 2,261 | 25.928571 | 78 | h |
null | pytorch-main/torch/csrc/jit/api/module.h | #pragma once
#include <c10/util/Exception.h>
#include <torch/csrc/autograd/variable.h>
#include <torch/csrc/jit/api/object.h>
#include <torch/csrc/jit/frontend/source_range.h>
#include <torch/csrc/jit/ir/ir.h>
#include <torch/csrc/jit/ir/named_value.h>
#include <torch/csrc/jit/runtime/argument_spec.h>
#include <torch/c... | 23,166 | 33.119293 | 102 | h |
null | pytorch-main/torch/csrc/jit/api/object.h | #pragma once
#include <ATen/core/functional.h>
#include <ATen/core/ivalue.h>
#include <c10/util/Optional.h>
#include <torch/csrc/jit/api/method.h>
#include <utility>
namespace torch {
namespace jit {
struct Resolver;
using ResolverPtr = std::shared_ptr<Resolver>;
using ObjectPtr = c10::intrusive_ptr<c10::ivalue::O... | 6,016 | 29.236181 | 80 | h |
null | pytorch-main/torch/csrc/jit/backends/backend.h | #pragma once
#include <ATen/core/builtin_function.h>
#include <ATen/core/stack.h>
#include <torch/csrc/jit/backends/backend_interface.h>
#include <torch/custom_class.h>
namespace torch {
namespace jit {
namespace {
// NOLINTNEXTLINE(clang-diagnostic-unneeded-internal-declaration)
inline c10::FunctionSchema getIsAvail... | 4,060 | 32.841667 | 83 | h |
null | pytorch-main/torch/csrc/jit/backends/backend_debug_handler.h | #pragma once
#include <ATen/core/ivalue.h>
#include <torch/csrc/jit/backends/backend_detail.h>
#include <torch/csrc/jit/ir/ir.h>
#include <torch/csrc/jit/ir/scope.h>
#include <atomic>
namespace torch {
namespace jit {
/*
* BackendDebugHandleManager is responsible for issuing debug handles to
* backends. Debug h... | 6,332 | 43.914894 | 80 | h |
null | pytorch-main/torch/csrc/jit/backends/backend_debug_info.h | #pragma once
#ifndef BUILD_LITE_INTERPRETER
#include <torch/csrc/jit/backends/backend_debug_handler.h>
#endif
#include <torch/custom_class.h>
namespace torch {
namespace jit {
constexpr static auto kBackendUtilsNamespace = "backendutils";
constexpr static auto kBackendDebugInfoClass = "BackendDebugInfo";
#ifndef BU... | 2,338 | 34.439394 | 80 | h |
null | pytorch-main/torch/csrc/jit/backends/backend_detail.h | #pragma once
#include <torch/csrc/jit/api/module.h>
#include <ATen/core/jit_type.h>
#include <functional>
namespace torch {
namespace jit {
using DebugHandleType = int64_t;
using NodeToDebugHandle = std::unordered_map<Node*, DebugHandleType>;
using BackendDebugHandleGenerator =
std::function<NodeToDebugHandl... | 1,104 | 25.309524 | 80 | h |
null | pytorch-main/torch/csrc/jit/backends/backend_exception.h | #pragma once
#include <c10/util/Exception.h>
namespace c10 {
class TORCH_API BackendRuntimeException : public c10::Error {
public:
// Use debug_handle to throw exception
BackendRuntimeException(
SourceLocation loc,
std::string msg,
int64_t debug_handle)
: c10::Error(loc, msg) {
debug_h... | 2,085 | 36.927273 | 79 | h |
null | pytorch-main/torch/csrc/jit/backends/backend_interface.h | #pragma once
#include <torch/custom_class.h>
namespace torch {
namespace jit {
// Interface for a JIT backend.
class TORCH_API PyTorchBackendInterface : public torch::CustomClassHolder {
public:
PyTorchBackendInterface() noexcept;
~PyTorchBackendInterface() override;
// Returns true if the backend is availab... | 1,184 | 32.857143 | 79 | h |
null | pytorch-main/torch/csrc/jit/backends/coreml/objc/PTMCoreMLTensorSpec.h | #include <c10/core/ScalarType.h>
#import <nlohmann/json.hpp>
#include <string>
namespace torch {
namespace jit {
namespace mobile {
namespace coreml {
struct TensorSpec {
std::string name = "";
c10::ScalarType dtype = c10::ScalarType::Float;
};
static inline c10::ScalarType scalar_type(const std::string& type_s... | 728 | 21.090909 | 75 | h |
null | pytorch-main/torch/csrc/jit/backends/xnnpack/xnnpack_graph_builder.h | // Copyright (c) Meta Platforms, Inc. and affiliates.
//
// This source code is licensed under the BSD-style license found in the
// LICENSE file in the root directory of this source tree.
#include <ATen/Functions.h>
#include <ATen/Utils.h>
#include <torch/torch.h>
#include <xnnpack.h>
#include <unordered_set>
#includ... | 3,235 | 32.020408 | 78 | h |
null | pytorch-main/torch/csrc/jit/backends/xnnpack/compiler/xnn_compiler.h | // Copyright (c) Meta Platforms, Inc. and affiliates.
//
// This source code is licensed under the BSD-style license found in the
// LICENSE file in the root directory of this source tree.
#include <caffe2/torch/csrc/jit/backends/xnnpack/executor/xnn_executor.h>
#include <xnnpack.h>
#include <memory>
#include <vector>... | 857 | 26.677419 | 76 | h |
null | pytorch-main/torch/csrc/jit/backends/xnnpack/executor/xnn_executor.h | // Copyright (c) Meta Platforms, Inc. and affiliates.
//
// This source code is licensed under the BSD-style license found in the
// LICENSE file in the root directory of this source tree.
#pragma once
#include <xnnpack.h>
#include <memory>
#include <vector>
namespace torch {
namespace jit {
namespace xnnpack {
names... | 1,642 | 20.906667 | 80 | h |
null | pytorch-main/torch/csrc/jit/backends/xnnpack/serialization/serializer.h | // Copyright (c) Meta Platforms, Inc. and affiliates.
//
// This source code is licensed under the BSD-style license found in the
// LICENSE file in the root directory of this source tree.
#include <torch/csrc/jit/backends/xnnpack/serialization/schema_generated.h>
#include <cstddef>
#include <cstdint>
#include <string... | 2,779 | 29.888889 | 80 | h |
null | pytorch-main/torch/csrc/jit/codegen/cuda/interface.h | #pragma once
#include <c10/macros/Export.h>
#include <torch/csrc/jit/ir/ir.h>
#include <torch/csrc/jit/passes/pass_manager.h>
#include <torch/csrc/jit/runtime/profiling_record.h>
/*
* This file contains APIs for cuda fuser;
*
* We use an empty static struct to hold the function pointers, which are
* registered se... | 1,929 | 31.711864 | 77 | h |
null | pytorch-main/torch/csrc/jit/codegen/fuser/arg_spec.h | #pragma once
#include <ATen/ATen.h>
#include <ATen/core/functional.h> // fmap
#include <c10/util/hash.h>
#include <torch/csrc/Export.h>
#include <torch/csrc/jit/codegen/fuser/tensor_desc.h>
#include <cstdint>
#include <vector>
namespace torch {
namespace jit {
namespace fuser {
// Describes the (runtime) arguments t... | 1,463 | 23 | 73 | h |
null | pytorch-main/torch/csrc/jit/codegen/fuser/codegen.h | #pragma once
#include <torch/csrc/Export.h>
#include <torch/csrc/jit/codegen/fuser/arg_spec.h>
#include <torch/csrc/jit/codegen/fuser/partition_desc.h>
#include <torch/csrc/jit/codegen/fuser/tensor_desc.h>
#include <torch/csrc/jit/ir/ir.h>
#include <iostream>
#include <string>
#include <tuple>
#include <vector>
name... | 832 | 25.870968 | 80 | h |
null | pytorch-main/torch/csrc/jit/codegen/fuser/compiler.h | #pragma once
#include <ATen/core/stack.h>
#include <torch/csrc/Export.h>
#include <torch/csrc/jit/codegen/fuser/arg_spec.h>
#include <torch/csrc/jit/codegen/fuser/fused_kernel.h>
#include <torch/csrc/jit/codegen/fuser/interface.h>
#include <torch/csrc/jit/codegen/fuser/kernel_spec.h>
#include <torch/csrc/jit/ir/ir.h>
... | 1,862 | 29.540984 | 79 | h |
null | pytorch-main/torch/csrc/jit/codegen/fuser/executor.h | #pragma once
#include <ATen/core/stack.h>
#include <torch/csrc/Export.h>
#include <torch/csrc/jit/codegen/fuser/fused_kernel.h>
#include <torch/csrc/jit/codegen/fuser/kernel_spec.h>
#include <cstdint>
namespace torch {
namespace jit {
namespace fuser {
// Runs the fusion associated with the key (see registerFusion(... | 550 | 21.958333 | 80 | h |
null | pytorch-main/torch/csrc/jit/codegen/fuser/fused_kernel.h | #pragma once
#include <ATen/ATen.h>
#include <ATen/Utils.h>
#include <torch/csrc/jit/codegen/fuser/partition_desc.h>
#include <torch/csrc/jit/codegen/fuser/tensor_desc.h>
#include <cstdint>
#include <string>
#include <vector>
namespace torch {
namespace jit {
namespace fuser {
struct FusedKernel {
AT_DISALLOW_COP... | 3,415 | 31.846154 | 79 | h |
null | pytorch-main/torch/csrc/jit/codegen/fuser/interface.h | #pragma once
#include <ATen/ATen.h>
#include <ATen/core/stack.h>
#include <torch/csrc/Export.h>
#include <torch/csrc/jit/ir/ir.h>
#include <cstdint>
#include <memory>
#include <vector>
namespace torch {
namespace jit {
constexpr int kCPUDevice = -1;
// Assigns a "key" to the given fusion_group that it can use to r... | 1,747 | 29.666667 | 75 | h |
null | pytorch-main/torch/csrc/jit/codegen/fuser/kernel_cache.h | #pragma once
#include <c10/util/Optional.h>
#include <torch/csrc/Export.h>
#include <torch/csrc/jit/codegen/fuser/kernel_spec.h>
#include <torch/csrc/jit/ir/ir.h>
#include <cstdint>
#include <functional>
namespace torch {
namespace jit {
namespace fuser {
// A thread-safe cache interface.
// Normalizes the graph b... | 1,041 | 27.162162 | 78 | h |
null | pytorch-main/torch/csrc/jit/codegen/fuser/kernel_spec.h | #pragma once
#include <ATen/ATen.h>
#include <ATen/core/stack.h>
#include <c10/util/Optional.h>
#include <torch/csrc/Export.h>
#include <torch/csrc/jit/codegen/fuser/arg_spec.h>
#include <torch/csrc/jit/codegen/fuser/fused_kernel.h>
#include <torch/csrc/jit/codegen/fuser/interface.h>
#include <torch/csrc/jit/ir/ir.h>
... | 4,633 | 29.486842 | 80 | h |
null | pytorch-main/torch/csrc/jit/codegen/fuser/partition_desc.h | #pragma once
#include <c10/util/Exception.h>
#include <torch/csrc/Export.h>
#include <torch/csrc/jit/codegen/fuser/tensor_desc.h>
#include <cstdint>
#include <memory>
#include <vector>
namespace torch {
namespace jit {
namespace fuser {
// Descriptor for chunk-ing an input tensor into subtensors
// OR concat-ing an... | 1,897 | 28.2 | 78 | h |
null | pytorch-main/torch/csrc/jit/codegen/fuser/tensor_desc.h | #pragma once
#include <ATen/ATen.h>
#include <ATen/core/jit_type.h>
#include <c10/util/Exception.h>
#include <c10/util/hash.h>
#include <torch/csrc/Export.h>
#include <algorithm>
#include <iostream>
#include <vector>
namespace torch {
namespace jit {
namespace fuser {
// type information needed by the compiler for ... | 3,146 | 27.87156 | 78 | h |
null | pytorch-main/torch/csrc/jit/codegen/fuser/tensor_info.h | #pragma once
#include <torch/csrc/Export.h>
#include <cstdint>
namespace torch {
namespace jit {
namespace fuser {
// Host-side view of TensorInfo
// Note dims[0] - we need to dynamically allocate the dims.
struct TORCH_API TensorInfo {
uint32_t* sizes(size_t nDim) {
return &sizes_strides[0];
}
uint32_t* s... | 638 | 20.3 | 78 | h |
null | pytorch-main/torch/csrc/jit/codegen/fuser/cpu/fused_kernel.h | #pragma once
#include <ATen/ATen.h>
#include <torch/csrc/Export.h>
#include <torch/csrc/jit/codegen/fuser/fused_kernel.h>
#include <cstdint>
#include <memory>
#include <string>
// Forward declare DynamicLibrary
namespace at {
struct DynamicLibrary;
}
namespace torch {
namespace jit {
namespace fuser {
namespace cpu... | 1,116 | 21.34 | 72 | h |
null | pytorch-main/torch/csrc/jit/codegen/fuser/cpu/resource_strings.h | #pragma once
#include <ATen/code_template.h>
namespace torch {
namespace jit {
namespace fuser {
namespace cpu {
/*with type_as not checking type of its input, a fusion group can have non-fp32
tensor as input. Correct code for this case is generated, however, nvrtc does
not know how to handle int*_t integer types, s... | 2,353 | 20.796296 | 79 | h |
null | pytorch-main/torch/csrc/jit/codegen/fuser/cuda/fused_kernel.h | #pragma once
#include <ATen/ATen.h>
#include <torch/csrc/Export.h>
#include <torch/csrc/jit/codegen/fuser/fused_kernel.h>
#include <cuda.h>
#include <cuda_runtime.h>
#include <nvrtc.h>
#include <cstdint>
#include <string>
#include <vector>
namespace torch {
namespace jit {
namespace fuser {
namespace cuda {
// que... | 1,601 | 22.910448 | 78 | h |
null | pytorch-main/torch/csrc/jit/codegen/fuser/cuda/resource_strings.h | #pragma once
#include <ATen/code_template.h>
#include <torch/csrc/Export.h>
namespace torch {
namespace jit {
namespace fuser {
namespace cuda {
/*with type_as not checking type of its input, a fusion group can have non-fp32
tensor as input. Correct code for this case is generated, however, nvrtc does
not know how t... | 10,777 | 25.223844 | 80 | h |
null | pytorch-main/torch/csrc/jit/codegen/onednn/LlgaTensorImpl.h | #pragma once
#include <ATen/ATen.h>
#include <ATen/Config.h>
#include <oneapi/dnnl/dnnl_graph.hpp>
#include <torch/csrc/jit/ir/ir.h>
namespace torch {
namespace jit {
namespace fuser {
namespace onednn {
// Engine represents a device and its context. From the device kind, the engine
// knows how to generate code fo... | 7,689 | 26.761733 | 80 | h |
null | pytorch-main/torch/csrc/jit/codegen/onednn/graph_fuser.h | #pragma once
#include <torch/csrc/jit/codegen/onednn/graph_helper.h>
#include <torch/csrc/jit/ir/ir.h>
namespace torch {
namespace jit {
namespace fuser {
namespace onednn {
struct WorkBlock : public std::pair<Node*, Node*> {
using pair::pair;
Node* begin() {
return this->first;
}
Node* end() {
retu... | 1,291 | 22.925926 | 77 | h |
null | pytorch-main/torch/csrc/jit/codegen/onednn/graph_helper.h | #pragma once
#include <oneapi/dnnl/dnnl_graph.hpp>
#include <torch/csrc/jit/codegen/onednn/operator.h>
#include <torch/csrc/jit/ir/alias_analysis.h>
#include <torch/csrc/jit/ir/ir.h>
namespace torch {
namespace jit {
namespace fuser {
namespace onednn {
#define STRIDED_LAYOUT 0
#define OPAQUE_LAYOUT 1
struct OpPart... | 2,539 | 23.190476 | 77 | h |
null | pytorch-main/torch/csrc/jit/codegen/onednn/interface.h | #pragma once
#include <ATen/Config.h>
#include <torch/csrc/jit/ir/ir.h>
#include <torch/csrc/jit/passes/pass_manager.h>
namespace torch {
namespace jit {
namespace fuser {
namespace onednn {
static std::atomic<bool> onednn_enabled{false};
static std::atomic<bool>& getLlgaEnabled() {
return onednn_enabled;
}
C10_E... | 1,448 | 22 | 76 | h |
null | pytorch-main/torch/csrc/jit/codegen/onednn/kernel.h | #pragma once
#include <unordered_map>
#include <oneapi/dnnl/dnnl_graph.hpp>
#include <torch/csrc/jit/codegen/onednn/LlgaTensorImpl.h>
#include <torch/csrc/jit/codegen/onednn/graph_helper.h>
#include <torch/csrc/jit/ir/ir.h>
#include <torch/csrc/jit/runtime/interpreter.h>
#include <c10/util/CallOnce.h>
namespace tor... | 2,769 | 27.854167 | 78 | h |
null | pytorch-main/torch/csrc/jit/codegen/onednn/operator.h | #pragma once
#include <oneapi/dnnl/dnnl_graph.hpp>
#include <torch/csrc/jit/codegen/onednn/LlgaTensorImpl.h>
#include <torch/csrc/jit/ir/ir.h>
namespace torch {
namespace jit {
namespace fuser {
namespace onednn {
class Operator {
public:
Operator(const Node* node, dnnl::graph::op::kind kind)
: n(node), o(g... | 3,977 | 25 | 78 | h |
null | pytorch-main/torch/csrc/jit/codegen/onednn/prepare_binary.h | #pragma once
#include <torch/csrc/jit/ir/ir.h>
namespace torch {
namespace jit {
namespace fuser {
namespace onednn {
// Prepare binary ops for LLGA
//
// The pass does the following:
//
// - Convert scalar input of aten::add and aten::mul into Float tensor with
// dimension [1]
//
// - Decompose fused add into at... | 574 | 20.296296 | 75 | h |
null | pytorch-main/torch/csrc/jit/cuda/cuda.h | #include <ATen/cuda/CUDAEvent.h>
#include <c10/core/Device.h>
#include <c10/cuda/CUDAStream.h>
#include <torch/custom_class.h>
namespace torch {
namespace jit {
class CUDAEvent;
// This class is a wrapper around c10::cuda::CUDAStream.
// It is needed because TorchBind does not support all of the argument types
// for... | 5,363 | 27.83871 | 80 | h |
null | pytorch-main/torch/csrc/jit/frontend/concrete_module_type.h | #pragma once
#include <ATen/core/ivalue.h>
#include <torch/csrc/jit/api/module.h>
#include <torch/csrc/jit/python/pybind_utils.h>
#include <memory>
#include <string>
#include <vector>
namespace torch {
namespace jit {
enum class IterableModuleKind { NONE, LIST, DICT, PARAMLIST, PARAMDICT };
class ConcreteModuleType;... | 9,062 | 36.450413 | 80 | h |
null | pytorch-main/torch/csrc/jit/frontend/error_report.h | #pragma once
#include <c10/util/Optional.h>
#include <torch/csrc/jit/frontend/tree.h>
namespace torch {
namespace jit {
struct Call {
std::string fn_name;
SourceRange caller_range;
};
struct TORCH_API ErrorReport : public std::exception {
ErrorReport(const ErrorReport& e);
explicit ErrorReport(SourceRange ... | 1,485 | 26.018182 | 80 | h |
null | pytorch-main/torch/csrc/jit/frontend/ir_emitter.h | #pragma once
#include <functional>
#include <memory>
#include <string>
#include <torch/csrc/jit/api/module.h>
#include <torch/csrc/jit/frontend/error_report.h>
#include <torch/csrc/jit/frontend/resolver.h>
#include <torch/csrc/jit/frontend/sugared_value.h>
#include <torch/csrc/jit/frontend/tree_views.h>
#include <torc... | 541 | 23.636364 | 66 | h |
null | pytorch-main/torch/csrc/jit/frontend/lexer.h | #pragma once
#include <c10/macros/Macros.h>
#include <c10/util/C++17.h>
#include <c10/util/Exception.h>
#include <torch/csrc/Export.h>
#include <torch/csrc/jit/frontend/parser_constants.h>
#include <torch/csrc/jit/frontend/source_range.h>
#include <torch/csrc/jit/frontend/strtod.h>
#include <algorithm>
#include <clocal... | 19,302 | 32.396194 | 80 | h |
null | pytorch-main/torch/csrc/jit/frontend/mini_environment.h | #pragma once
#include <ATen/core/jit_type.h>
#include <torch/csrc/jit/ir/ir.h>
namespace torch {
namespace jit {
// Simple data structure for containing a type T in nested control blocks
// Should only be used after initial compilation where type checking and
// loads and stores are emitted
template <typename T>
st... | 1,400 | 23.155172 | 78 | h |
null | pytorch-main/torch/csrc/jit/frontend/name_mangler.h | #pragma once
#include <ATen/core/qualified_name.h>
#include <torch/csrc/Export.h>
namespace torch {
namespace jit {
/**
* class NameMangler
*
* Utility to mangle qualified names in order to make them unique. We use this
* in various places where we to de-duplicate qualified names.
*/
class TORCH_API NameMangler... | 655 | 22.428571 | 78 | h |
null | pytorch-main/torch/csrc/jit/frontend/parse_string_literal.h | #pragma once
#include <c10/util/Optional.h>
#include <torch/csrc/jit/frontend/error_report.h>
#include <torch/csrc/jit/frontend/lexer.h>
namespace torch {
namespace jit {
inline bool isCharCount(char c, const std::string& str, size_t start, int len) {
// count checks from [start, start + len)
return start + len <... | 2,295 | 25.090909 | 80 | h |
null | pytorch-main/torch/csrc/jit/frontend/resolver.h | #pragma once
#include <ATen/core/jit_type.h>
#include <ATen/core/qualified_name.h>
#include <torch/csrc/jit/frontend/sugared_value.h>
namespace torch {
namespace jit {
struct Resolver;
using ResolverPtr = std::shared_ptr<Resolver>;
/**
* class Resolver
*
* Represents an "outer environment" in which we an look up... | 1,982 | 27.73913 | 80 | h |
null | pytorch-main/torch/csrc/jit/frontend/schema_matching.h | #pragma once
#include <torch/csrc/Export.h>
#include <torch/csrc/jit/ir/ir.h>
#include <torch/csrc/jit/ir/named_value.h>
#include <ATen/core/function_schema.h>
namespace torch {
namespace jit {
// Try to match a list of inputs and keyword 'attributes' to this
// schema. Return the flat list of positional inputs to t... | 2,133 | 29.056338 | 77 | h |
null | pytorch-main/torch/csrc/jit/frontend/schema_type_parser.h | #pragma once
#include <ATen/core/alias_info.h>
#include <ATen/core/jit_type.h>
#include <c10/macros/Macros.h>
#include <c10/util/FunctionRef.h>
#include <torch/csrc/jit/frontend/lexer.h>
namespace torch {
namespace jit {
using TypePtr = c10::TypePtr;
struct TORCH_API SchemaTypeParser {
TypePtr parseBaseType();
... | 1,113 | 26.170732 | 79 | h |
null | pytorch-main/torch/csrc/jit/frontend/script_type_parser.h | #pragma once
#include <ATen/core/jit_type.h>
#include <torch/csrc/Export.h>
#include <torch/csrc/jit/frontend/resolver.h>
#include <torch/csrc/jit/frontend/tree_views.h>
namespace torch {
namespace jit {
/**
* class ScriptTypeParser
*
* Parses expressions in our typed AST format (TreeView) into types and
* typena... | 1,605 | 27.678571 | 76 | h |
null | pytorch-main/torch/csrc/jit/frontend/source_range.h | #pragma once
#include <c10/util/Exception.h>
#include <c10/util/Optional.h>
#include <algorithm>
#include <iostream>
#include <iterator>
#include <memory>
#include <numeric>
#include <unordered_map>
namespace torch {
namespace jit {
class SourceRangeUnpickler;
struct SourceRange;
// A stringlike class backed by a v... | 12,679 | 26.868132 | 80 | h |
null | pytorch-main/torch/csrc/jit/frontend/source_ref.h | #pragma once
#include <functional>
#include <memory>
#include <ATen/core/ivalue.h>
#include <c10/macros/Export.h>
#include <torch/csrc/jit/frontend/source_range.h>
namespace torch {
namespace jit {
/**
* SourceRef does two things:
* 1. Owns a Source object.
* 2. Serves as lookup key to the owned Source in as... | 1,319 | 26.5 | 79 | h |
null | pytorch-main/torch/csrc/jit/frontend/tracer.h | #pragma once
#include <ATen/core/Dimname.h>
#include <ATen/core/class_type.h>
#include <ATen/core/jit_type.h>
#include <ATen/core/stack.h>
#include <ATen/core/symbol.h>
#include <c10/util/Exception.h>
#include <torch/csrc/Export.h>
#include <torch/csrc/jit/frontend/source_range.h>
#include <torch/csrc/utils/variadic.... | 12,858 | 29.911058 | 82 | h |
null | pytorch-main/torch/csrc/jit/frontend/tree.h | #pragma once
#include <functional>
#include <memory>
#include <unordered_map>
#include <vector>
#include <c10/util/SmallVector.h>
#include <c10/util/intrusive_ptr.h>
#include <torch/csrc/jit/frontend/lexer.h>
namespace torch {
namespace jit {
// Trees are used to represent all forms of TC IR, pre- and post-typechec... | 6,629 | 29 | 80 | h |
null | pytorch-main/torch/csrc/jit/frontend/versioned_symbols.h | #pragma once
#include <caffe2/serialize/versions.h>
#include <torch/csrc/Export.h>
#include <torch/csrc/jit/api/module.h>
#include <cstdint>
namespace torch {
namespace jit {
// Maps the given symbol into an implementation of its behavior at the
// given version.
// See note [Versioned Symbols]
TORCH_API Symbol
get_... | 620 | 27.227273 | 70 | h |
null | pytorch-main/torch/csrc/jit/ir/alias_analysis.h | #pragma once
#include <ATen/core/alias_info.h>
#include <c10/util/flat_hash_map.h>
#include <torch/csrc/jit/ir/ir.h>
#include <torch/csrc/jit/ir/type_hashing.h>
#include <torch/csrc/jit/passes/create_functional_graphs.h>
#include <torch/csrc/jit/passes/utils/memory_dag.h>
namespace torch {
namespace jit {
/**
* Ali... | 12,850 | 38.786378 | 80 | h |
null | pytorch-main/torch/csrc/jit/ir/attributes.h | #pragma once
#include <ATen/core/Tensor.h>
#include <string>
#include <vector>
#include <ATen/core/jit_type_base.h>
#include <ATen/core/symbol.h>
#include <torch/csrc/Export.h>
namespace torch {
namespace jit {
using ::c10::Symbol;
constexpr int max_tensor_display_size = 10;
enum class AttributeKind {
f,
fs,
... | 4,929 | 25.648649 | 78 | h |
null | pytorch-main/torch/csrc/jit/ir/constants.h | #pragma once
#include <ATen/core/ivalue.h>
#include <ATen/core/jit_type.h>
#include <torch/csrc/Export.h>
#include <torch/csrc/jit/frontend/source_range.h>
#include <torch/csrc/jit/ir/scope.h>
// helpers for handling constants in the IR
// - create constant nodes from ints, floats, complex, intlist, Tensors, and
// ot... | 2,032 | 31.790323 | 80 | h |
null | pytorch-main/torch/csrc/jit/ir/graph_node_list.h | #pragma once
#include <c10/util/Exception.h>
namespace torch {
namespace jit {
// Intrusive doubly linked lists with sane reverse iterators.
// The header file is named generic_graph_node_list.h because it is ONLY
// used for Graph's Node lists, and if you want to use it for other
// things, you will have to do some... | 6,360 | 30.490099 | 80 | h |
null | pytorch-main/torch/csrc/jit/ir/ir_views.h | #pragma once
#include <c10/util/irange.h>
#include <torch/csrc/jit/ir/ir.h>
namespace torch {
namespace jit {
struct IfView {
explicit IfView(Node* node) : node_(node) {
AT_ASSERT(node->kind() == ::c10::prim::If);
}
Value* cond() const {
return node_->input(0);
}
Block* thenBlock() const {
retu... | 4,648 | 27.175758 | 80 | h |
null | pytorch-main/torch/csrc/jit/ir/irparser.h | #pragma once
#include <torch/csrc/Export.h>
#include <string>
#include <unordered_map>
#include <c10/util/Optional.h>
#include <torch/csrc/Export.h>
namespace torch {
namespace jit {
struct Graph;
struct Value;
// \brief Parse IR from \p STR constructing the corresponding IR in\ GRAPH.
// if parse_tensor_constants... | 1,163 | 27.390244 | 79 | h |
null | pytorch-main/torch/csrc/jit/ir/named_value.h | #pragma once
#include <ATen/core/ivalue.h>
#include <torch/csrc/jit/frontend/source_range.h>
#include <torch/csrc/jit/ir/constants.h>
#include <torch/csrc/utils/variadic.h>
namespace torch {
namespace jit {
struct Value;
/**
* A value with optional extra name and location information. Used during
* schema matching... | 2,434 | 27.647059 | 80 | h |
null | pytorch-main/torch/csrc/jit/ir/scope.h | #pragma once
#include <ATen/core/jit_type.h>
#include <ATen/core/symbol.h>
#include <c10/util/Optional.h>
#include <c10/util/intrusive_ptr.h>
#include <torch/csrc/Export.h>
#include <torch/csrc/jit/frontend/source_range.h>
#include <unordered_map>
namespace torch {
namespace jit {
struct ModuleInstanceInfo;
constexpr ... | 6,801 | 31.545455 | 79 | h |
null | pytorch-main/torch/csrc/jit/ir/subgraph_matcher.h | #pragma once
#include <torch/csrc/jit/ir/ir.h>
#include <unordered_map>
#include <vector>
namespace torch {
namespace jit {
/**
* \brief A structure describing a match of a pattern in a graph.
*
* The structure contains an anchor node, from which the match was found, and
* match-maps for nodes and values. A mat... | 3,151 | 41.026667 | 80 | h |
null | pytorch-main/torch/csrc/jit/mobile/code.h | #pragma once
#include <vector>
#include <ATen/core/ivalue.h>
#include <ATen/core/operator_name.h>
#include <torch/csrc/jit/runtime/instruction.h>
namespace torch {
namespace jit {
namespace mobile {
using Stack = std::vector<c10::IValue>;
using DebugHandle = int64_t;
class Function;
// NOLINTNEXTLINE(cppcoreguide... | 1,186 | 28.675 | 79 | h |
null | pytorch-main/torch/csrc/jit/mobile/debug_info.h | #pragma once
#include <c10/util/flat_hash_map.h>
#include <caffe2/serialize/inline_container.h>
#include <torch/csrc/jit/api/compilation_unit.h>
#include <torch/csrc/jit/ir/scope.h>
#include <torch/csrc/jit/serialization/source_range_serialization.h>
namespace torch {
namespace jit {
/*
* MobileDebugTable:
* Deseria... | 2,230 | 37.465517 | 80 | h |
null | pytorch-main/torch/csrc/jit/mobile/file_format.h | #pragma once
#include <array>
#include <cerrno>
#include <cstddef>
#include <cstring>
#include <fstream>
#include <istream>
#include <memory>
#include <c10/core/CPUAllocator.h>
#include <c10/core/impl/alloc_cpu.h>
#include <caffe2/serialize/read_adapter_interface.h>
#if defined(HAVE_MMAP)
#include <fcntl.h>
#include... | 6,632 | 32.670051 | 80 | h |
null | pytorch-main/torch/csrc/jit/mobile/flatbuffer_loader.h | #pragma once
#include <istream>
#include <memory>
#include <string>
#include <unordered_map>
#include <vector>
#include <ATen/core/ivalue.h>
#include <c10/core/Device.h>
#include <c10/macros/Macros.h>
#include <c10/util/Optional.h>
#include <torch/csrc/jit/mobile/module.h>
/**
* Defines the public API for loading f... | 4,999 | 35.49635 | 80 | h |
null | pytorch-main/torch/csrc/jit/mobile/function.h | #pragma once
#include <vector>
#include <ATen/core/function.h>
#include <ATen/core/function_schema.h>
#include <ATen/core/ivalue.h>
#include <torch/csrc/jit/mobile/code.h>
namespace torch {
namespace jit {
enum OpCode : uint8_t;
struct Instruction;
struct OperatorString;
namespace mobile {
class TORCH_API Function... | 2,900 | 32.344828 | 76 | h |
null | pytorch-main/torch/csrc/jit/mobile/import.h | #pragma once
#include <torch/csrc/jit/mobile/module.h>
#include <torch/csrc/jit/mobile/parse_operators.h>
#include <istream>
#include <memory>
#include <caffe2/serialize/file_adapter.h>
namespace torch {
namespace jit {
using caffe2::serialize::FileAdapter;
using caffe2::serialize::IStreamAdapter;
using caffe2::seri... | 3,930 | 33.787611 | 73 | h |
null | pytorch-main/torch/csrc/jit/mobile/import_data.h | #pragma once
#include <ATen/core/TensorBase.h>
#include <c10/core/Device.h>
#include <c10/util/Optional.h>
#include <torch/csrc/jit/mobile/module.h>
#include <istream>
#include <map>
#include <string>
namespace torch {
namespace jit {
/**
* Loads named parameters from the serialized data in @p in.
*
* Calls #TOR... | 1,031 | 25.461538 | 76 | h |
null | pytorch-main/torch/csrc/jit/mobile/import_export_common.h | #pragma once
/**
* @file
* Declarations shared between import_data.cpp and export_data.cpp
*/
namespace torch {
namespace jit {
namespace mobile {
namespace internal {
/**
* The name of the mobile::Module attribute which contains saved parameters, as
* a Dict of names to Tensors. Only used for Flatbuffer serial... | 554 | 22.125 | 79 | h |
null | pytorch-main/torch/csrc/jit/mobile/interpreter.h | #pragma once
#include <vector>
#include <torch/csrc/jit/mobile/code.h>
#include <torch/csrc/jit/mobile/frame.h>
namespace torch {
namespace jit {
namespace mobile {
struct InterpreterState {
TORCH_API explicit InterpreterState(const Code& code);
TORCH_API bool run(Stack& stack);
private:
void enterFrame(con... | 688 | 21.225806 | 71 | h |
null | pytorch-main/torch/csrc/jit/mobile/module.h | #pragma once
#include <ATen/core/jit_type.h>
#include <torch/csrc/jit/mobile/debug_info.h>
#include <torch/csrc/jit/mobile/function.h>
#include <torch/csrc/jit/mobile/method.h>
#include <torch/csrc/jit/mobile/quantization.h>
namespace torch {
namespace jit {
namespace mobile {
using Stack = std::vector<c10::IValue>;
... | 5,946 | 29.341837 | 80 | h |
null | pytorch-main/torch/csrc/jit/mobile/observer.h | #pragma once
#include <c10/util/ThreadLocalDebugInfo.h>
#include <string>
#include <unordered_map>
#include <vector>
namespace torch {
class MobileDebugInfo : public c10::DebugInfoBase {
public:
const std::string& getModelName() {
return model_name_;
}
void setModelName(const std::string& model_name) {
... | 3,637 | 31.774775 | 80 | h |
null | pytorch-main/torch/csrc/jit/mobile/parse_bytecode.h | #pragma once
#include <torch/csrc/jit/mobile/function.h>
namespace torch {
namespace jit {
namespace mobile {
using c10::IValue;
TORCH_API void parseInstructions(
const std::string& function_name,
c10::ivalue::TupleElements&& ins_list,
c10::ivalue::TupleElements& debug_handles_m_tuple,
mobile::Function... | 790 | 29.423077 | 75 | h |
null | pytorch-main/torch/csrc/jit/mobile/parse_operators.h | #pragma once
#include <torch/csrc/jit/mobile/function.h>
namespace torch {
namespace jit {
using c10::IValue;
enum MobileModuleLoadOptions {
OPERATOR_CHECK = 1,
// PARSE_ALL_EXTRA_FILE_MAPS is used to gate for ExtraFileMaps to pull all
// files automatically without explicit entries mapping. Refer to PR for a
... | 734 | 25.25 | 76 | h |
null | pytorch-main/torch/csrc/jit/mobile/profiler_edge.h | #pragma once
#include <torch/csrc/autograd/profiler_kineto.h>
#include <torch/csrc/jit/mobile/module.h>
namespace torch {
namespace jit {
namespace mobile {
// If we dont have kineto available then edge profiler does not
// work since it relies on Kineto
#ifdef USE_KINETO
class TORCH_API KinetoEdgeCPUProfiler {
publ... | 4,538 | 36.825 | 80 | h |
null | pytorch-main/torch/csrc/jit/mobile/quantization.h | #pragma once
#include <c10/macros/Export.h>
#include <string>
namespace torch {
namespace jit {
namespace mobile {
class Module;
namespace quantization {
/*
* Device side PTQ API.
* Once the model has been prepared for quantization on server side, such model
* is sent to device. On device side the model is further... | 1,288 | 32.051282 | 80 | h |
null | pytorch-main/torch/csrc/jit/mobile/register_ops_common_utils.h | #pragma once
#include <ATen/Context.h>
#include <ATen/NativeFunctions.h>
#include <ATen/core/ivalue.h>
#include <ATen/core/stack.h>
#include <torch/csrc/jit/runtime/jit_exception.h>
#include <torch/csrc/jit/runtime/vararg_functions.h>
#include <iostream>
namespace torch {
namespace jit {
inline void noop(Stack& n) ... | 1,704 | 28.396552 | 79 | h |
null | pytorch-main/torch/csrc/jit/mobile/type_parser.h | #pragma once
#include <ATen/core/dynamic_type.h>
#include <ATen/core/jit_type.h>
#include <unordered_set>
namespace c10 {
class TORCH_API TypeParser {
public:
explicit TypeParser(std::string pythonStr);
explicit TypeParser(std::vector<std::string>& pythonStrs);
TypePtr parse();
std::vector<TypePtr> parseLi... | 1,443 | 25.254545 | 78 | h |
null | pytorch-main/torch/csrc/jit/mobile/upgrader_mobile.h | #pragma once
// #include <ATen/core/ivalue.h>
#include <ATen/core/ivalue_inl.h>
#include <torch/csrc/jit/mobile/code.h>
#include <torch/csrc/jit/mobile/function.h>
#include <torch/csrc/jit/serialization/import_export_functions.h>
#include <memory>
#include <string>
#include <unordered_map>
#include <vector>
namespac... | 981 | 21.318182 | 70 | h |
null | pytorch-main/torch/csrc/jit/mobile/compatibility/backport.h | #pragma once
#include <c10/macros/Export.h>
#include <istream>
#include <memory>
namespace caffe2 {
namespace serialize {
class ReadAdapterInterface;
class PyTorchStreamWriter;
} // namespace serialize
} // namespace caffe2
namespace torch {
namespace jit {
TORCH_API bool _backport_for_mobile(
std::istream& in,... | 826 | 20.205128 | 39 | h |
null | pytorch-main/torch/csrc/jit/mobile/compatibility/backport_manager.h | #pragma once
#include <functional>
#include <memory>
#include <unordered_map>
namespace c10 {
struct IValue;
}
namespace caffe2 {
namespace serialize {
class IStreamAdapter;
class ReadAdapterInterface;
class PyTorchStreamWriter;
class PyTorchStreamReader;
} // namespace serialize
} // namespace caffe2
namespace tor... | 1,286 | 21.982143 | 79 | h |
null | pytorch-main/torch/csrc/jit/mobile/compatibility/model_compatibility.h | #pragma once
#include <c10/macros/Export.h>
#include <torch/csrc/jit/mobile/compatibility/runtime_compatibility.h>
#include <istream>
#include <memory>
#include <unordered_map>
namespace caffe2 {
namespace serialize {
class PyTorchStreamReader;
class ReadAdapterInterface;
} // namespace serialize
} // namespace caff... | 3,591 | 32.570093 | 80 | h |
null | pytorch-main/torch/csrc/jit/mobile/compatibility/runtime_compatibility.h | #pragma once
#include <c10/macros/Export.h>
#include <c10/util/Optional.h>
#include <memory>
#include <unordered_map>
#include <unordered_set>
namespace torch {
namespace jit {
// Struct storing metadata of an operator that can be useful for versioning
struct OperatorInfo {
// The number of arguments within the s... | 1,233 | 26.422222 | 81 | h |
null | pytorch-main/torch/csrc/jit/mobile/model_tracer/BuildFeatureTracer.h | #pragma once
#include <ATen/record_function.h>
#include <c10/util/Synchronized.h>
#include <map>
#include <set>
#include <string>
namespace torch {
namespace jit {
namespace mobile {
/* The BuildFeatureTracer class handles the attachment and removal of a
* recording callback that traces the invocation of code that ... | 1,011 | 23.095238 | 79 | h |
null | pytorch-main/torch/csrc/jit/mobile/model_tracer/CustomClassTracer.h | #pragma once
#include <ATen/record_function.h>
#include <c10/util/Synchronized.h>
#include <map>
#include <set>
#include <string>
namespace torch {
namespace jit {
namespace mobile {
/* The CustomClassTracer class handles the attachment and removal of a recording
* callback that traces the invocation of code that h... | 1,009 | 23.047619 | 80 | h |
null | pytorch-main/torch/csrc/jit/mobile/model_tracer/KernelDTypeTracer.h | #pragma once
#include <ATen/record_function.h>
#include <c10/util/Synchronized.h>
#include <map>
#include <set>
#include <string>
namespace torch {
namespace jit {
namespace mobile {
/* The KernelDTypeTracer class handles the attachment and removal of a recording
* callback that traces the invocation of code that ha... | 1,262 | 29.071429 | 80 | h |
null | pytorch-main/torch/csrc/jit/mobile/model_tracer/MobileModelRunner.h | #pragma once
#include <iostream>
#include <mutex>
#include <sstream>
#include <torch/csrc/autograd/grad_mode.h>
#include <torch/csrc/jit/mobile/import.h>
#include <torch/csrc/jit/mobile/module.h>
#include <torch/csrc/jit/serialization/export.h>
#include <torch/script.h>
namespace torch {
namespace jit {
namespace mo... | 5,156 | 32.927632 | 80 | h |
null | pytorch-main/torch/csrc/jit/mobile/model_tracer/OperatorCallTracer.h | #pragma once
#include <ATen/record_function.h>
#include <c10/util/Synchronized.h>
namespace torch {
namespace jit {
namespace mobile {
/* The OperatorCallTracer class handles the attachment and removal of a
* recording callback that traces invocation of ATen (and other) PyTorch
* operators that get called via the D... | 968 | 25.189189 | 76 | h |
null | pytorch-main/torch/csrc/jit/mobile/model_tracer/TracerRunner.h | #pragma once
#include <set>
#include <string>
#include <vector>
#include <ATen/core/ivalue.h>
#include <torch/csrc/jit/mobile/model_tracer/BuildFeatureTracer.h>
#include <torch/csrc/jit/mobile/model_tracer/CustomClassTracer.h>
#include <torch/csrc/jit/mobile/model_tracer/KernelDTypeTracer.h>
namespace torch {
namesp... | 1,157 | 25.318182 | 75 | h |
null | pytorch-main/torch/csrc/jit/mobile/nnc/aot_compiler.h | #pragma once
#include <torch/csrc/Export.h>
#include <torch/csrc/jit/ir/ir.h>
#include <torch/csrc/jit/mobile/nnc/context.h>
namespace torch {
namespace jit {
namespace mobile {
namespace nnc {
// Performs Ahead Of Time compilation of a given method in a model
// returning the compiled function and LLVM assembly cod... | 705 | 27.24 | 77 | h |
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