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/caffe2/utils/threadpool/ThreadPoolCommon.h | #ifndef CAFFE2_UTILS_THREADPOOL_COMMON_H_
#define CAFFE2_UTILS_THREADPOOL_COMMON_H_
#ifdef __APPLE__
#include <TargetConditionals.h>
#endif
// caffe2 depends upon NNPACK, which depends upon this threadpool, so
// unfortunately we can't reference core/common.h here
// This is copied from core/common.h's definition of... | 677 | 31.285714 | 76 | h |
null | pytorch-main/caffe2/utils/threadpool/WorkersPool.h | #pragma once
#include <atomic>
#include <condition_variable>
#include <thread>
#include "c10/util/thread_name.h"
#include <c10/util/irange.h>
#include "caffe2/core/common.h"
#include "caffe2/core/logging.h"
#if defined(_MSC_VER)
#include <intrin.h>
#endif
namespace caffe2 {
// Uses code derived from gemmlowp,
// ht... | 11,631 | 30.101604 | 112 | h |
null | pytorch-main/caffe2/utils/threadpool/pthreadpool-cpp.h | #pragma once
#ifdef USE_PTHREADPOOL
#ifdef USE_INTERNAL_PTHREADPOOL_IMPL
#include <caffe2/utils/threadpool/pthreadpool.h>
#else
#include <pthreadpool.h>
#endif
#include <functional>
#include <memory>
#include <mutex>
namespace caffe2 {
class PThreadPool final {
public:
explicit PThreadPool(size_t thread_count);... | 1,417 | 24.781818 | 79 | h |
null | pytorch-main/caffe2/utils/threadpool/pthreadpool.h | // pthreadpool header from https://github.com/Maratyszcza/pthreadpool
// for NNPACK
#ifndef CAFFE2_UTILS_PTHREADPOOL_H_
#define CAFFE2_UTILS_PTHREADPOOL_H_
#include "ThreadPoolCommon.h"
#include <stddef.h> // for size_t
#include <stdint.h> // for uint32_t
#if defined(USE_PTHREADPOOL)
// This is a hack.
// Mainly int... | 6,344 | 31.706186 | 119 | h |
null | pytorch-main/caffe2/utils/threadpool/thread_pool_guard.h | #pragma once
#include <c10/macros/Macros.h>
namespace caffe2 {
// A RAII, thread local (!) guard that enables or disables grad mode upon
// construction, and sets it back to the original value upon destruction.
struct TORCH_API _NoPThreadPoolGuard {
static bool is_enabled();
static void set_enabled(bool enabled)... | 567 | 22.666667 | 73 | h |
null | pytorch-main/caffe2/video/optical_flow.h | #ifndef CAFFE2_VIDEO_OPTICAL_FLOW_H_
#define CAFFE2_VIDEO_OPTICAL_FLOW_H_
#include <opencv2/core.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/opencv.hpp>
#include <opencv2/video.hpp>
#include <caffe2/core/logging.h>
namespace caffe2 {
// Four different types of optical flow algorithms supported;
// BroxOpt... | 1,306 | 24.627451 | 68 | h |
null | pytorch-main/caffe2/video/video_decoder.h | #ifndef CAFFE2_VIDEO_VIDEO_DECODER_H_
#define CAFFE2_VIDEO_VIDEO_DECODER_H_
#include <caffe2/core/logging.h>
#include <stdio.h>
#include <memory>
#include <string>
#include <vector>
extern "C" {
#include <libavcodec/avcodec.h>
#include <libavformat/avformat.h>
#include <libavformat/avio.h>
#include <libavutil/log.h>
... | 13,882 | 25.393536 | 80 | h |
null | pytorch-main/caffe2/video/video_io.h | #ifndef CAFFE2_VIDEO_VIDEO_IO_H_
#define CAFFE2_VIDEO_VIDEO_IO_H_
#include <caffe2/core/common.h>
#include <caffe2/video/optical_flow.h>
#include <caffe2/video/video_decoder.h>
#include <opencv2/opencv.hpp>
#include <random>
#include <istream>
#include <ostream>
namespace caffe2 {
TORCH_API void ClipTransformRGB(
... | 1,296 | 23.942308 | 42 | h |
null | pytorch-main/functorch/csrc/dim/arena.h | // Copyright (c) Facebook, Inc. and its affiliates.
// All rights reserved.
//
// 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 <ATen/ATen.h>
#include "minpybind.h"
#ifdef _WIN32
#include <intrin.h>
// https://sta... | 9,186 | 26.588589 | 111 | h |
null | pytorch-main/functorch/csrc/dim/python_variable_simple.h | // Copyright (c) Facebook, Inc. and its affiliates.
// All rights reserved.
//
// 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
// note: pytorch's python variable simple includes pybind which conflicts with minpybind
// so ... | 1,526 | 29.54 | 95 | h |
null | pytorch-main/modules/detectron/group_spatial_softmax_op.h | /**
* Copyright (c) 2016-present, Facebook, Inc.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable ... | 2,354 | 29.584416 | 79 | h |
null | pytorch-main/modules/detectron/ps_roi_pool_op.h | /**
* Copyright (c) 2016-present, Facebook, Inc.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable ... | 2,702 | 27.755319 | 77 | h |
null | pytorch-main/modules/detectron/roi_pool_f_op.h | /**
* Copyright (c) 2016-present, Facebook, Inc.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable ... | 2,470 | 29.134146 | 77 | h |
null | pytorch-main/modules/detectron/sample_as_op.h | /**
* Copyright (c) 2016-present, Facebook, Inc.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable ... | 1,551 | 26.714286 | 75 | h |
null | pytorch-main/modules/detectron/select_smooth_l1_loss_op.h | /**
* Copyright (c) 2016-present, Facebook, Inc.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable ... | 2,515 | 31.25641 | 80 | h |
null | pytorch-main/modules/detectron/sigmoid_cross_entropy_loss_op.h | /**
* Copyright (c) 2016-present, Facebook, Inc.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable ... | 2,402 | 29.417722 | 75 | h |
null | pytorch-main/modules/detectron/sigmoid_focal_loss_op.h | /**
* Copyright (c) 2016-present, Facebook, Inc.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable ... | 2,624 | 30.25 | 79 | h |
null | pytorch-main/modules/detectron/smooth_l1_loss_op.h | /**
* Copyright (c) 2016-present, Facebook, Inc.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable ... | 2,372 | 30.223684 | 80 | h |
null | pytorch-main/modules/detectron/softmax_focal_loss_op.h | /**
* Copyright (c) 2016-present, Facebook, Inc.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable ... | 2,930 | 30.858696 | 79 | h |
null | pytorch-main/modules/detectron/spatial_narrow_as_op.h | /**
* Copyright (c) 2016-present, Facebook, Inc.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable ... | 1,745 | 26.28125 | 75 | h |
null | pytorch-main/modules/detectron/upsample_nearest_op.h | /**
* Copyright (c) 2016-present, Facebook, Inc.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable ... | 2,829 | 25.448598 | 75 | h |
null | pytorch-main/modules/observers/net_observer_reporter.h | #pragma once
#include <map>
#include "caffe2/core/common.h"
#include "caffe2/core/net.h"
#include "observers/macros.h"
namespace caffe2 {
struct PerformanceInformation {
// Analytic
int64_t flops = 0;
int64_t bytes_written = 0;
int64_t bytes_read = 0;
std::vector<TensorShape> tensor_shapes = {};
std::ve... | 927 | 22.794872 | 70 | h |
null | pytorch-main/modules/observers/observer_config.h | #pragma once
#include "observers/macros.h"
#include "observers/net_observer_reporter.h"
#include "caffe2/core/common.h"
namespace caffe2 {
/*
netInitSampleRate_ == 1 && operatorNetSampleRatio_ == 1 :
Log operator metrics in every iteration
netInitSampleRate_ == 1 && operatorNetSampleRatio_ == 0 :
Lo... | 3,351 | 32.52 | 77 | h |
null | pytorch-main/modules/observers/perf_observer.h | #pragma once
#include "caffe2/core/common.h"
#include "caffe2/core/net.h"
#include "caffe2/core/observer.h"
#include "caffe2/core/timer.h"
#include "observers/macros.h"
#include <unordered_map>
namespace caffe2 {
double getClockTimeMilliseconds();
class CAFFE2_OBSERVER_API PerfNetObserver : public NetObserver {
p... | 1,784 | 25.641791 | 79 | h |
null | pytorch-main/test/cpp/api/support.h | #pragma once
#include <test/cpp/common/support.h>
#include <gtest/gtest.h>
#include <ATen/TensorIndexing.h>
#include <c10/util/Exception.h>
#include <torch/nn/cloneable.h>
#include <torch/types.h>
#include <torch/utils.h>
#include <string>
#include <utility>
namespace torch {
namespace test {
// Lets you use a co... | 5,772 | 28.304569 | 80 | h |
null | pytorch-main/test/cpp/common/support.h | #pragma once
#include <c10/util/Exception.h>
#include <gtest/gtest.h>
#include <stdexcept>
#include <string>
namespace torch {
namespace test {
#define ASSERT_THROWS_WITH(statement, substring) \
{ \
std::string assert_t... | 1,526 | 43.911765 | 73 | h |
null | pytorch-main/test/cpp/jit/test_custom_class_registrations.h | #include <torch/custom_class.h>
#include <torch/script.h>
namespace torch {
namespace jit {
struct ScalarTypeClass : public torch::CustomClassHolder {
ScalarTypeClass(at::ScalarType s) : scalar_type_(s) {}
at::ScalarType scalar_type_;
};
template <class T>
struct MyStackClass : torch::CustomClassHolder {
std::... | 923 | 21 | 73 | h |
null | pytorch-main/test/cpp/jit/test_utils.h | #pragma once
#include <torch/csrc/jit/ir/irparser.h>
#include <torch/csrc/jit/runtime/autodiff.h>
#include <torch/csrc/jit/runtime/interpreter.h>
#include <torch/csrc/jit/testing/file_check.h>
namespace {
static inline void trim(std::string& s) {
s.erase(s.begin(), std::find_if(s.begin(), s.end(), [](unsigned char ... | 3,586 | 33.161905 | 77 | h |
null | pytorch-main/test/cpp/lazy/test_lazy_ops_util.h | #pragma once
#include <gtest/gtest.h>
#include <torch/csrc/lazy/backend/backend_device.h>
#include <torch/csrc/lazy/core/debug_util.h>
#include <torch/csrc/lazy/core/ir.h>
#include <torch/csrc/lazy/core/tensor.h>
#include <torch/torch.h>
#include <cmath>
#include <functional>
#include <string>
#include <unordered_set... | 2,306 | 27.134146 | 80 | h |
null | pytorch-main/test/cpp/rpc/e2e_test_base.h | #include <gtest/gtest.h>
#include <torch/csrc/distributed/autograd/context/container.h>
#include <torch/csrc/distributed/autograd/context/context.h>
#include <torch/csrc/distributed/autograd/engine/dist_engine.h>
#include <torch/csrc/distributed/autograd/utils.h>
#include <torch/csrc/distributed/c10d/TCPStore.hpp>
#in... | 5,578 | 31.248555 | 80 | h |
null | pytorch-main/test/cpp/tensorexpr/gtest_assert_float_eq.h | #pragma once
#include <cmath>
// Copyright 2005, Google Inc.
// All rights reserved.
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are
// met:
//
// * Redistributions of source code must retain the above copyright
// n... | 4,741 | 38.516667 | 93 | h |
null | pytorch-main/test/cpp/tensorexpr/padded_buffer.h | #pragma once
#include <string>
#include <vector>
#include <c10/util/irange.h>
#include "torch/csrc/jit/tensorexpr/eval.h"
namespace torch {
namespace jit {
namespace tensorexpr {
template <typename T>
struct DefaultPaddedValue;
template <>
struct DefaultPaddedValue<int> {
static const int kValue = static_cast<in... | 7,034 | 27.950617 | 82 | h |
null | pytorch-main/test/cpp/tensorexpr/test_base.h | #pragma once
#if defined(USE_GTEST)
#include <gtest/gtest.h>
#include <test/cpp/common/support.h>
#else
#include <cmath>
#include "c10/util/Exception.h"
#include "test/cpp/tensorexpr/gtest_assert_float_eq.h"
#define ASSERT_EQ(x, y, ...) TORCH_INTERNAL_ASSERT((x) == (y), __VA_ARGS__)
#define ASSERT_FLOAT_EQ(x, y, ...) ... | 2,853 | 31.431818 | 80 | h |
null | pytorch-main/test/cpp/tensorexpr/test_utils.h | #pragma once
#include <memory>
#include <vector>
#include <test/cpp/tensorexpr/test_base.h>
#include <torch/csrc/jit/tensorexpr/fwd_decls.h>
#include <torch/csrc/jit/testing/file_check.h>
namespace torch {
namespace jit {
using namespace torch::jit::tensorexpr;
#define IS_NODE(T, node) \
{ ... | 2,614 | 32.101266 | 66 | h |
null | pytorch-main/test/custom_backend/custom_backend.h | #include <torch/csrc/jit/backends/backend.h>
#include <torch/csrc/jit/backends/backend_detail.h>
#include <torch/csrc/jit/api/module.h>
namespace torch {
namespace custom_backend {
// This custom JIT backend is intended to do the minimal amount of work
// necessary to test that the JIT backend registration endpoints a... | 2,927 | 30.826087 | 80 | h |
null | pytorch-main/test/custom_operator/op.h | #include <torch/script.h>
#include <cstddef>
#include <vector>
#include <string>
// clang-format off
# if defined(_WIN32)
# if defined(custom_ops_EXPORTS)
# define CUSTOM_OP_API __declspec(dllexport)
# else
# define CUSTOM_OP_API __declspec(dllimport)
# endif
# else
# define CUSTOM_OP_API
# e... | 530 | 20.24 | 65 | h |
null | pytorch-main/test/edge/Evalue.h | #pragma once
#include <ATen/ATen.h>
/**
* WARNING: EValue is a class used by Executorch, for its boxed operators. It
* contains similar logic as `IValue` in PyTorch, by providing APIs to convert
* boxed values to unboxed values.
*
* It's mirroring a fbcode internal source file
* [`EValue.h`](https://www.internal... | 13,932 | 28.027083 | 96 | h |
null | pytorch-main/test/edge/kernel_runtime_context.h | #pragma once
namespace torch {
namespace executor {
/**
* Bucket type abstraction that contains many elements of runtime state that
* a kernel author may want available, but would otherwise be unable to access.
*
* Forwarded along to all operators when running in lean mode. NOTE: Will not be
* forwarded to opera... | 757 | 33.454545 | 80 | h |
null | pytorch-main/test/edge/templates/Functions.h | // clang-format off
#pragma once
#include <ATen/Context.h>
#include <ATen/DeviceGuard.h>
#include <ATen/TensorUtils.h>
#include <ATen/TracerMode.h>
#include <ATen/core/Generator.h>
#include <ATen/core/Reduction.h>
#include <ATen/core/Tensor.h>
#include <c10/core/Scalar.h>
#include <c10/core/Storage.h>
#include <c10/cor... | 574 | 21.115385 | 35 | h |
null | pytorch-main/test/edge/templates/NativeFunctions.h | #pragma once
// ${generated_comment}
#ifdef TORCH_ASSERT_NO_OPERATORS
#error This change adds a dependency on native_functions.yaml, \
meaning the file will need to be re-compiled every time an operator \
is changed or added. Consider if your change would be better placed in \
another file, or i... | 1,131 | 34.375 | 83 | h |
null | pytorch-main/third_party/miniz-2.1.0/examples/example1.c | // example1.c - Demonstrates miniz.c's compress() and uncompress() functions (same as zlib's).
// Public domain, May 15 2011, Rich Geldreich, richgel99@gmail.com. See "unlicense" statement at the end of tinfl.c.
#include <stdio.h>
#include "miniz.h"
typedef unsigned char uint8;
typedef unsigned short uint16;
typedef un... | 3,109 | 28.339623 | 116 | c |
null | pytorch-main/third_party/miniz-2.1.0/examples/example3.c | // example3.c - Demonstrates how to use miniz.c's deflate() and inflate() functions for simple file compression.
// Public domain, May 15 2011, Rich Geldreich, richgel99@gmail.com. See "unlicense" statement at the end of tinfl.c.
// For simplicity, this example is limited to files smaller than 4GB, but this is not a li... | 6,726 | 23.914815 | 122 | c |
null | pytorch-main/third_party/miniz-2.1.0/examples/example4.c | // example4.c - Uses tinfl.c to decompress a zlib stream in memory to an output file
// Public domain, May 15 2011, Rich Geldreich, richgel99@gmail.com. See "unlicense" statement at the end of tinfl.c.
#include "miniz_tinfl.h"
#include <stdio.h>
#include <limits.h>
typedef unsigned char uint8;
typedef unsigned short u... | 2,681 | 25.038835 | 129 | c |
null | pytorch-main/third_party/nvfuser/benchmark/utils.h | #pragma once
#include <torch/csrc/jit/codegen/cuda/executor.h>
#include <torch/csrc/jit/codegen/cuda/fusion.h>
#include <torch/csrc/jit/codegen/cuda/ir_all_nodes.h>
#include <torch/csrc/jit/codegen/cuda/ir_utils.h>
#include <torch/csrc/jit/codegen/cuda/kernel_cache.h>
#include <torch/csrc/jit/codegen/cuda/lower2device... | 7,615 | 36.15122 | 80 | h |
null | pytorch-main/third_party/nvfuser/csrc/arith.h | #pragma once
#include <c10/macros/Export.h>
#include <ir_interface_nodes.h>
#include <type.h>
#include <type_promotion.h>
class Val;
/*
* The operations defined in this header is intended as user facing functions.
* Generally users should not directly instantiate temporary TensorViews they
* should instead use t... | 24,505 | 35.197932 | 80 | h |
null | pytorch-main/third_party/nvfuser/csrc/compute_at.h | #pragma once
#include <inlining.h>
#include <root_domain_map.h>
#include <transform_replay.h>
#include <c10/macros/Export.h>
#include <c10/util/Exception.h>
#include <deque>
#include <unordered_map>
#include <unordered_set>
#include <vector>
namespace torch {
namespace jit {
namespace fuser {
namespace cuda {
clas... | 1,021 | 21.217391 | 77 | h |
null | pytorch-main/third_party/nvfuser/csrc/compute_at_map.h | #pragma once
#include <disjoint_set.h>
#include <ir_all_nodes.h>
#include <kernel_ir.h>
#include <lower_trivial_reductions.h>
#include <deque>
#include <unordered_map>
namespace torch {
namespace jit {
namespace fuser {
namespace cuda {
// There's three modes of these iter domain mappings all uniquely important in
... | 10,018 | 36.807547 | 80 | h |
null | pytorch-main/third_party/nvfuser/csrc/contiguity.h | #pragma once
#include <c10/macros/Export.h>
#include <compute_at_map.h>
#include <disjoint_set.h>
#include <ir_all_nodes.h>
#include <lower_shift.h>
#include <lower_trivial_broadcast.h>
namespace torch {
namespace jit {
namespace fuser {
namespace cuda {
// Goes through the transformations associated with a series ... | 12,057 | 37.647436 | 80 | h |
null | pytorch-main/third_party/nvfuser/csrc/disjoint_set.h | #pragma once
#include <c10/util/Exception.h>
#include <algorithm>
#include <initializer_list>
#include <unordered_map>
#include <unordered_set>
#include <vector>
// For printing of the set when using a Statement as the type for the set
#include <ir_base_nodes.h>
namespace torch {
namespace jit {
namespace fuser {
n... | 9,139 | 26.613293 | 83 | h |
null | pytorch-main/third_party/nvfuser/csrc/dispatch.h | #pragma once
#include <c10/macros/Export.h>
#include <c10/util/Exception.h>
#include <utils.h>
#include <unordered_map>
// dispatch.h prevents the need from adding manual dispatch in every class that
// wants to define how to process a series of nodes. dispatch.h provides 4
// classes that can be inherited providin... | 12,148 | 31.055409 | 80 | h |
null | pytorch-main/third_party/nvfuser/csrc/dynamic_type.h | #pragma once
#include <c10/macros/Export.h>
#include <c10/util/Exception.h>
#include <c10/util/variant.h>
#include <cmath>
#include <iostream>
namespace torch {
namespace jit {
namespace fuser {
namespace cuda {
class TORCH_CUDA_CU_API IntOrDouble {
c10::variant<double, int64_t> value_;
public:
IntOrDouble(int... | 10,968 | 34.044728 | 81 | h |
null | pytorch-main/third_party/nvfuser/csrc/evaluator_common.h | #pragma once
#include <dynamic_type.h>
#include <executor_kernel_arg.h>
#include <executor_launch_params.h>
#include <fusion.h>
#include <ir_all_nodes.h>
#include <lower2device.h>
#include <c10/core/DeviceType.h>
namespace torch {
namespace jit {
namespace fuser {
namespace cuda {
//! This is the common space for ex... | 10,558 | 29.694767 | 79 | h |
null | pytorch-main/third_party/nvfuser/csrc/executor.h | #pragma once
#include <executor_launch_params.h>
#include <executor_utils.h>
#include <fusion.h>
#include <ir_all_nodes.h>
#include <ir_cloner.h>
#include <ir_printer.h>
#include <kernel_expr_evaluator.h>
#include <lower2device.h>
#include <utils.h>
#include <c10/core/DeviceType.h>
namespace torch {
namespace jit {
n... | 11,047 | 32.377644 | 87 | h |
null | pytorch-main/third_party/nvfuser/csrc/executor_kernel_arg.h | #pragma once
#include <ATen/core/ivalue.h>
#include <ATen/cuda/CUDAGeneratorImpl.h>
#include <c10/util/Exception.h>
#include <type.h>
#include <torch/csrc/jit/ir/ir.h>
#include <array>
namespace torch {
namespace jit {
namespace fuser {
namespace cuda {
// This should match the tensor used in the code generation (al... | 10,829 | 26.211055 | 80 | h |
null | pytorch-main/third_party/nvfuser/csrc/executor_launch_params.h | #pragma once
#include <type.h>
namespace torch {
namespace jit {
namespace fuser {
namespace cuda {
class TORCH_CUDA_CU_API LaunchParams {
public:
static constexpr int64_t UNINITIALIZED_VAL = -1;
LaunchParams(
int64_t gdimx = UNINITIALIZED_VAL,
int64_t gdimy = UNINITIALIZED_VAL,
int64_t gdimz ... | 3,378 | 23.664234 | 79 | h |
null | pytorch-main/third_party/nvfuser/csrc/executor_utils.h | #pragma once
#include <ATen/core/ivalue.h>
#include <c10/core/DeviceType.h>
#include <c10/util/Exception.h>
#include <cuda.h>
#include <torch/csrc/jit/ir/ir.h>
#include <executor_kernel_arg.h>
#include <expr_evaluator.h>
#include <fusion.h>
#include <ir_all_nodes.h>
#include <kernel.h>
#include <kernel_expr_evalua... | 10,456 | 32.196825 | 79 | h |
null | pytorch-main/third_party/nvfuser/csrc/expr_evaluator.h | #pragma once
#include <c10/macros/Export.h>
#include <dynamic_type.h>
#include <ir_interface_nodes.h>
#include <iter_visitor.h>
#include <c10/util/Optional.h>
#include <string>
#include <unordered_map>
namespace torch {
namespace jit {
namespace fuser {
namespace cuda {
class FusionPrecomputedValues;
//! Calculat... | 1,739 | 24.217391 | 75 | h |
null | pytorch-main/third_party/nvfuser/csrc/fusion.h | #pragma once
#include <ATen/core/ivalue.h>
#include <c10/macros/Export.h>
#include <c10/util/Exception.h>
#include <ir_base_nodes.h>
#include <ir_container.h>
#include <iter_visitor.h>
#include <unordered_map>
#include <unordered_set>
#include <vector>
namespace torch {
namespace jit {
namespace fuser {
namespace c... | 9,564 | 32.096886 | 80 | h |
null | pytorch-main/third_party/nvfuser/csrc/fusion_segmenter.h | #pragma once
#include <fusion.h>
#include <ir_base_nodes.h>
#include <kernel_cache.h>
#include <scheduler/all_schedulers.h>
#include <scheduler/registry.h>
#include <utils.h>
#include <deque>
#include <list>
#include <unordered_set>
#include <vector>
namespace torch {
namespace jit {
namespace fuser {
namespace cuda... | 20,721 | 31.944356 | 80 | h |
null | pytorch-main/third_party/nvfuser/csrc/grouped_reduction.h | #pragma once
#include <ir_all_nodes.h>
namespace torch {
namespace jit {
namespace fuser {
namespace cuda {
//! Horizontally fuse multiple reductions.
//!
//! Given a list of tensors produced by ReductionOp, create a new
//! GroupedReductionOp expression that takes the input tensors of the
//! original reductions an... | 1,389 | 32.095238 | 69 | h |
null | pytorch-main/third_party/nvfuser/csrc/index_compute.h | #pragma once
#include <iter_visitor.h>
#include <root_domain_map.h>
#include <unordered_map>
#include <unordered_set>
#include <vector>
/*
* Index compute takes in a list of indices typically generated from the
* surrounding for loop nest. The number of indicies are intended to match the
* number of dimensions of... | 16,388 | 35.582589 | 80 | h |
null | pytorch-main/third_party/nvfuser/csrc/inlining.h | #pragma once
#include <ir_interface_nodes.h>
#include <maxinfo_propagator.h>
#include <transform_replay.h>
#include <memory>
#include <unordered_set>
namespace torch {
namespace jit {
namespace fuser {
namespace cuda {
class MaxPosCalculator {
// Root domains in producer that's unmappable to any of its consumers
... | 3,386 | 32.534653 | 80 | h |
null | pytorch-main/third_party/nvfuser/csrc/instrumentation.h | #pragma once
#include <utils.h>
#include <nvToolsExt.h>
// NOLINTNEXTLINE(modernize-deprecated-headers)
#include <stdio.h>
#include <chrono>
#include <cstdio>
namespace torch {
namespace jit {
namespace fuser {
namespace cuda {
namespace inst {
//! An optional record of selected timestamped operations, events and ... | 2,676 | 24.254717 | 79 | h |
null | pytorch-main/third_party/nvfuser/csrc/ir_base_nodes.h | #pragma once
#include <c10/core/ScalarType.h>
#include <c10/macros/Export.h>
#include <c10/util/Exception.h>
#include <c10/util/Optional.h>
#include <type.h>
#include <utils.h>
#include <cstdint>
#include <iostream>
#include <limits>
#include <memory>
#include <stdexcept>
#include <unordered_map>
#include <vector>
... | 16,319 | 30.085714 | 80 | h |
null | pytorch-main/third_party/nvfuser/csrc/ir_builder.h | #pragma once
#include <fusion.h>
#include <ir_all_nodes.h>
#include <ir_container.h>
namespace torch {
namespace jit {
namespace fuser {
namespace cuda {
namespace kir {
class Kernel;
}
class IrCloner;
// Passkey for builder to register properties with statements, and to call
// functions in IrContainer
class TORC... | 4,495 | 30.886525 | 78 | h |
null | pytorch-main/third_party/nvfuser/csrc/ir_cloner.h | #pragma once
#include <c10/macros/Export.h>
#include <dispatch.h>
#include <ir_builder.h>
#include <unordered_map>
#include <vector>
namespace torch {
namespace jit {
namespace fuser {
namespace cuda {
class IrContainer;
//! Clones nodes from an exiting Fusion
//!
//! \warning IrCloner machinery is a specialized h... | 3,832 | 27.819549 | 80 | h |
null | pytorch-main/third_party/nvfuser/csrc/ir_container.h | #pragma once
#include <c10/macros/Export.h>
#include <ir_base_nodes.h>
#include <utils.h>
#include <deque>
#include <unordered_map>
#include <unordered_set>
namespace torch {
namespace jit {
namespace fuser {
namespace cuda {
class IrBuilderPasskey;
class ExprPasskey;
class OptOutMutator;
class Int;
class Bool;
c... | 5,280 | 29.177143 | 79 | h |
null | pytorch-main/third_party/nvfuser/csrc/ir_graphviz.h | #pragma once
#include <c10/macros/Export.h>
#include <dispatch.h>
#include <sstream>
#include <string>
#include <unordered_map>
#include <unordered_set>
#include <vector>
namespace torch {
namespace jit {
namespace fuser {
namespace cuda {
// Generates a DOT (https://www.graphviz.org) graph
// representation of a f... | 3,844 | 28.351145 | 77 | h |
null | pytorch-main/third_party/nvfuser/csrc/ir_interface_nodes.h | #pragma once
#include <c10/macros/Export.h>
#include <fusion.h>
#include <ir_base_nodes.h>
#include <ir_internal_nodes.h>
#include <mma_type.h>
#include <torch/csrc/jit/ir/ir.h>
//! Nodes in here are intended to be "user facing" users in this sense being
//! those that want to be able to generate CUDA code.
namesp... | 20,123 | 32.484193 | 80 | h |
null | pytorch-main/third_party/nvfuser/csrc/ir_iostream.h | #pragma once
#include <c10/macros/Export.h>
#include <dispatch.h>
#include <c10/util/irange.h>
#include <iostream>
namespace torch {
namespace jit {
namespace fuser {
namespace cuda {
class Fusion;
namespace kir {
class Kernel;
class Scope;
} // namespace kir
//! Define pretty printing functions for IR nodes
//!... | 4,421 | 26.128834 | 72 | h |
null | pytorch-main/third_party/nvfuser/csrc/ir_printer.h | #pragma once
#include <c10/macros/Export.h>
#include <ir_iostream.h>
#include <iter_visitor.h>
#include <iostream>
namespace torch {
namespace jit {
namespace fuser {
namespace cuda {
//! Prints computation Fusion IR nodes
//!
//! IrMathPrinter and IrTransformPrinter allow the splitting up of fusion print
//! func... | 1,615 | 25.933333 | 80 | h |
null | pytorch-main/third_party/nvfuser/csrc/ir_utils.h | #pragma once
#include <ir_all_nodes.h>
#include <type.h>
#include <iterator>
#include <unordered_map>
namespace torch {
namespace jit {
namespace fuser {
namespace cuda {
namespace ir_utils {
// Replace values in fusion using ValReplacementMutator
void replaceValue(
Fusion*,
const std::unordered_map<Val*, ... | 11,988 | 34.055556 | 80 | h |
null | pytorch-main/third_party/nvfuser/csrc/iter_visitor.h | #pragma once
#include <c10/macros/Export.h>
#include <dispatch.h>
#include <type.h>
#include <deque>
#include <unordered_set>
#include <vector>
namespace torch {
namespace jit {
namespace fuser {
namespace cuda {
class Fusion;
class Statement;
class Expr;
class Val;
/*
* IterVisitor starts from leaf nodes, fusio... | 14,030 | 38.974359 | 88 | h |
null | pytorch-main/third_party/nvfuser/csrc/kernel.h | #pragma once
#include <c10/macros/Export.h>
#include <fusion.h>
#include <ir_base_nodes.h>
#include <ir_builder.h>
#include <lower_sync_information.h>
#include <lower_warp_reduce.h>
#include <parallel_dimension_map.h>
#include <utils.h>
#include <vectorization_info.h>
#include <memory>
#include <unordered_map>
#incl... | 7,358 | 27.523256 | 80 | h |
null | pytorch-main/third_party/nvfuser/csrc/kernel_cache.h | #pragma once
#include <evaluator_common.h>
#include <executor.h>
#include <fusion.h>
#include <fusion_segmenter.h>
#include <scheduler/all_schedulers.h>
#include <scheduler/registry.h>
#include <c10/macros/Export.h>
#include <c10/util/ArrayRef.h>
#include <mutex>
#include <type_traits>
#include <unordered_map>
name... | 16,677 | 34.409766 | 97 | h |
null | pytorch-main/third_party/nvfuser/csrc/kernel_expr_evaluator.h |
#pragma once
#include <c10/macros/Export.h>
#include <dispatch.h>
#include <dynamic_type.h>
#include <evaluator_common.h>
#include <kernel_ir.h>
#include <c10/util/Optional.h>
#include <unordered_map>
namespace torch {
namespace jit {
namespace fuser {
namespace cuda {
class GpuLower;
namespace kir {
//! Calcu... | 1,994 | 23.9375 | 74 | h |
null | pytorch-main/third_party/nvfuser/csrc/kernel_ir.h | #pragma once
#include <ir_all_nodes.h>
#include <ir_base_nodes.h>
#include <parallel_type_bitmap.h>
#include <type.h>
#include <utils.h>
#include <c10/macros/Export.h>
#include <c10/util/Optional.h>
#include <cstdint>
#include <string>
#include <unordered_map>
#include <vector>
namespace torch {
namespace jit {
nam... | 24,348 | 23.324675 | 80 | h |
null | pytorch-main/third_party/nvfuser/csrc/kernel_ir_dispatch.h | #pragma once
#include <dispatch.h>
namespace torch {
namespace jit {
namespace fuser {
namespace cuda {
class Expr;
namespace kir {
class Predicate;
class TensorIndex;
class ForLoop;
class IfThenElse;
class Scope;
// Base visitor class that visits all nodes in provided vector<Expr*>.
//
// Includes visiting throug... | 4,500 | 30.041379 | 80 | h |
null | pytorch-main/third_party/nvfuser/csrc/lower2device.h | #pragma once
#include <c10/macros/Export.h>
#include <compute_at_map.h>
#include <ir_all_nodes.h>
#include <kernel.h>
#include <kernel_ir.h>
#include <lower_allocation.h>
#include <lower_double_buffer.h>
#include <lower_fused_reduction.h>
#include <lower_index_hoist.h>
#include <lower_predicate.h>
#include <lower_pre... | 6,832 | 28.076596 | 80 | h |
null | pytorch-main/third_party/nvfuser/csrc/lower_alias_memory.h | #pragma once
#include <c10/macros/Export.h>
#include <dispatch.h>
#include <ir_all_nodes.h>
#include <vector>
namespace torch {
namespace jit {
namespace fuser {
namespace cuda {
//! Reuse Allocation nodes via pointer aliasing
//!
//! First pass finds candidate TensorViews
//! A candidate TensorView is anything in... | 925 | 24.027027 | 75 | h |
null | pytorch-main/third_party/nvfuser/csrc/lower_allocation.h | #pragma once
#include <c10/macros/Export.h>
#include <ir_all_nodes.h>
#include <kernel_ir.h>
#include <vector>
namespace torch {
namespace jit {
namespace fuser {
namespace cuda {
//! Buffer allocation information to store in GPU lower to avoid
//! logic duplication
struct LocalAllocationInfo {
kir::Allocate* a... | 705 | 20.393939 | 77 | h |
null | pytorch-main/third_party/nvfuser/csrc/lower_bank_conflict.h | #pragma once
#include <dynamic_type.h>
#include <executor_launch_params.h>
#include <ir_base_nodes.h>
#include <kernel.h>
#include <unordered_map>
#include <utility>
namespace torch {
namespace jit {
namespace fuser {
namespace cuda {
// for more info on shared memory access see page 54-72 of:
// https://on-demand.... | 1,723 | 35.680851 | 117 | h |
null | pytorch-main/third_party/nvfuser/csrc/lower_divisible_split.h | #pragma once
#include <c10/macros/Export.h>
#include <compute_at_map.h>
#include <fusion.h>
#include <ir_all_nodes.h>
namespace torch {
namespace jit {
namespace fuser {
namespace cuda {
// Looks through all transformations assocaited with view, or enforced divisible
// vectorization splits and gathers all splits t... | 827 | 26.6 | 80 | h |
null | pytorch-main/third_party/nvfuser/csrc/lower_double_buffer.h | #pragma once
#include <c10/macros/Export.h>
#include <ir_all_nodes.h>
#include <kernel_ir.h>
#include <kernel_ir_dispatch.h>
// Double buffering a tensor doubles its allocation size and uses two
// buffers to facilitate computation and memory access
// overlapping. The basic form of code looks like as follows:
//
//... | 9,456 | 36.828 | 80 | h |
null | pytorch-main/third_party/nvfuser/csrc/lower_fused_reduction.h | #pragma once
#include <ir_all_nodes.h>
namespace torch {
namespace jit {
namespace fuser {
namespace cuda {
//! Keep track of certain patterns of reductions.
//!
//! - Allreduce IterDomain: reduced and broadcast domain.
class FusedReductionInfo {
public:
void markAsAllreduce(IterDomain* id);
bool isAllreduce(I... | 863 | 23.685714 | 70 | h |
null | pytorch-main/third_party/nvfuser/csrc/lower_fusion_simplifier.h | #pragma once
#include <c10/macros/Export.h>
#include <dispatch.h>
#include <fusion.h>
#include <ir_all_nodes.h>
#include <lower_trivial_reductions.h>
#include <vector>
namespace torch {
namespace jit {
namespace fuser {
namespace cuda {
// Replaces trivial reductions with Unary Set Ops
void trivialReductionReplace... | 580 | 20.518519 | 71 | h |
null | pytorch-main/third_party/nvfuser/csrc/lower_index.h | #pragma once
#include <c10/macros/Export.h>
#include <instrumentation.h>
#include <kernel_ir.h>
#include <kernel_ir_dispatch.h>
#include <root_domain_map.h>
#include <vector>
namespace torch {
namespace jit {
namespace fuser {
namespace cuda {
// TODO: Replace with mutator as IndexLowering is replacing expr's with... | 4,787 | 32.25 | 80 | h |
null | pytorch-main/third_party/nvfuser/csrc/lower_index_compute.h | #pragma once
#include <fusion.h>
#include <index_compute.h>
namespace torch {
namespace jit {
namespace fuser {
namespace cuda {
// Struct to hold useful information from an index pass on iterdomain graph.
// Used to return the IndexCompute structure back to the indexing calls in
// index_compute.cpp. Other structur... | 7,821 | 39.95288 | 80 | h |
null | pytorch-main/third_party/nvfuser/csrc/lower_index_hoist.h | #pragma once
#include <ir_all_nodes.h>
#include <functional>
#include <unordered_map>
#include <vector>
// Hoisting common index subexpressions
//
// Class CommonIndexMap is updated during the lowering as new indices
// are inserted. An index is uniquely identified with CommonIndexKey,
// which consists of the concr... | 5,270 | 32.788462 | 78 | h |
null | pytorch-main/third_party/nvfuser/csrc/lower_insert_syncs.h | #pragma once
#include <c10/macros/Export.h>
#include <ir_all_nodes.h>
#include <kernel_ir.h>
#include <vector>
namespace torch {
namespace jit {
namespace fuser {
namespace cuda {
//! Insert sync at end of for-loops to prevent write-after-read race condition.
//!
//! WAR race condition occurs when the next iterati... | 882 | 26.59375 | 79 | h |
null | pytorch-main/third_party/nvfuser/csrc/lower_instrument.h | #pragma once
#include <ir_all_nodes.h>
namespace torch {
namespace jit {
namespace fuser {
namespace cuda {
//! Set up KernelPerformanceProfile of GpuLower when enabled, which
//! keeps track of expressions to profile. A new TensorView is added
//! for storing profiling results. The expression list is prepended
//! ... | 753 | 30.416667 | 70 | h |
null | pytorch-main/third_party/nvfuser/csrc/lower_loops.h |
#pragma once
#include <c10/macros/Export.h>
#include <compute_at_map.h>
#include <instrumentation.h>
#include <ir_all_nodes.h>
#include <kernel_ir.h>
#include <lower_thread_predicate.h>
namespace torch {
namespace jit {
namespace fuser {
namespace cuda {
//! Loop nest generator pass will get IR that looks somethin... | 1,844 | 26.132353 | 79 | h |
null | pytorch-main/third_party/nvfuser/csrc/lower_magic_zero.h | #pragma once
#include <ir_all_nodes.h>
#include <kernel_ir.h>
#include <vector>
namespace torch {
namespace jit {
namespace fuser {
namespace cuda {
struct IndexFromIdGraph;
//! Insert magic zero definition at the begining of the kernel. Insert magic
//! zero update after every (outer most) loop nest with a compil... | 2,580 | 31.670886 | 79 | h |
null | pytorch-main/third_party/nvfuser/csrc/lower_misaligned_vectorization.h | #pragma once
#include <c10/macros/Export.h>
#include <ir_all_nodes.h>
#include <vector>
namespace torch {
namespace jit {
namespace fuser {
namespace cuda {
//! Transform for-loop structure to handle misaligned addresses
//!
//! Sections of misaligned addresses are handled sequentially
//! while aligned addresses u... | 2,830 | 22.991525 | 79 | h |
null | pytorch-main/third_party/nvfuser/csrc/lower_predicate_elimination.h | #pragma once
#include <c10/macros/Export.h>
#include <ir_all_nodes.h>
#include <kernel_ir.h>
#include <vector>
namespace torch {
namespace jit {
namespace fuser {
namespace cuda {
class TORCH_CUDA_CU_API PredicateElimination : public IterVisitor {
public:
void build(Fusion* fusion);
//! True if expr does not ... | 1,888 | 28.061538 | 73 | h |
null | pytorch-main/third_party/nvfuser/csrc/lower_replace_size.h | #pragma once
#include <c10/macros/Export.h>
#include <dispatch.h>
#include <fusion.h>
#include <ir_all_nodes.h>
namespace torch {
namespace jit {
namespace fuser {
namespace cuda {
// TensorViews are all based on symbolic sizes. When we first initialize them
// we don't know if they're inputs or outputs which would... | 756 | 28.115385 | 78 | h |
null | pytorch-main/third_party/nvfuser/csrc/lower_shift.h | #pragma once
#include <c10/macros/Export.h>
#include <dispatch.h>
#include <ir_all_nodes.h>
#include <kernel_ir.h>
#include <vector>
namespace torch {
namespace jit {
namespace fuser {
namespace cuda {
class LoopIndexing;
//! Auxiliary class to represent information about halo of an axis
class AxisHaloInfo {
pub... | 8,349 | 33.937238 | 75 | h |
null | pytorch-main/third_party/nvfuser/csrc/lower_sync_information.h | #pragma once
#include <ir_all_nodes.h>
#include <parallel_type_bitmap.h>
#include <unordered_map>
namespace torch {
namespace jit {
namespace fuser {
namespace cuda {
class SyncMap {
public:
std::string toString() const;
//! Validates all tensors are consistently parallelized. Basically,
//! when a producer... | 1,195 | 25 | 80 | h |
null | pytorch-main/third_party/nvfuser/csrc/lower_thread_predicate.h |
#pragma once
#include <c10/macros/Export.h>
#include <ir_all_nodes.h>
#include <lower_utils.h>
#include <parallel_type_bitmap.h>
#include <unordered_map>
#include <unordered_set>
#include <utility>
namespace torch {
namespace jit {
namespace fuser {
namespace cuda {
//! Maps TensorViews to a { ParallelTypeBitmap,... | 5,449 | 36.328767 | 78 | h |
null | pytorch-main/third_party/nvfuser/csrc/lower_trivial_broadcast.h | #pragma once
#include <ir_all_nodes.h>
#include <root_domain_map.h>
#include <c10/macros/Export.h>
namespace torch {
namespace jit {
namespace fuser {
namespace cuda {
//! Traverse and collect all concretized broadcast domains.
//!
//! The traversal first initializes the origin map with broadcast
//! domains in inp... | 2,164 | 29.928571 | 75 | h |
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